type of graphical presentation

Graphical Representation of Data

Graphical representation of data is an attractive method of showcasing numerical data that help in analyzing and representing quantitative data visually. A graph is a kind of a chart where data are plotted as variables across the coordinate. It became easy to analyze the extent of change of one variable based on the change of other variables. Graphical representation of data is done through different mediums such as lines, plots, diagrams, etc. Let us learn more about this interesting concept of graphical representation of data, the different types, and solve a few examples.

Definition of Graphical Representation of Data

A graphical representation is a visual representation of data statistics-based results using graphs, plots, and charts. This kind of representation is more effective in understanding and comparing data than seen in a tabular form. Graphical representation helps to qualify, sort, and present data in a method that is simple to understand for a larger audience. Graphs enable in studying the cause and effect relationship between two variables through both time series and frequency distribution. The data that is obtained from different surveying is infused into a graphical representation by the use of some symbols, such as lines on a line graph, bars on a bar chart, or slices of a pie chart. This visual representation helps in clarity, comparison, and understanding of numerical data.

Representation of Data

The word data is from the Latin word Datum, which means something given. The numerical figures collected through a survey are called data and can be represented in two forms - tabular form and visual form through graphs. Once the data is collected through constant observations, it is arranged, summarized, and classified to finally represented in the form of a graph. There are two kinds of data - quantitative and qualitative. Quantitative data is more structured, continuous, and discrete with statistical data whereas qualitative is unstructured where the data cannot be analyzed.

Principles of Graphical Representation of Data

The principles of graphical representation are algebraic. In a graph, there are two lines known as Axis or Coordinate axis. These are the X-axis and Y-axis. The horizontal axis is the X-axis and the vertical axis is the Y-axis. They are perpendicular to each other and intersect at O or point of Origin. On the right side of the Origin, the Xaxis has a positive value and on the left side, it has a negative value. In the same way, the upper side of the Origin Y-axis has a positive value where the down one is with a negative value. When -axis and y-axis intersect each other at the origin it divides the plane into four parts which are called Quadrant I, Quadrant II, Quadrant III, Quadrant IV. This form of representation is seen in a frequency distribution that is represented in four methods, namely Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon.

Advantages and Disadvantages of Graphical Representation of Data

Listed below are some advantages and disadvantages of using a graphical representation of data:

• It improves the way of analyzing and learning as the graphical representation makes the data easy to understand.
• It can be used in almost all fields from mathematics to physics to psychology and so on.
• It is easy to understand for its visual impacts.
• It shows the whole and huge data in an instance.
• It is mainly used in statistics to determine the mean, median, and mode for different data

The main disadvantage of graphical representation of data is that it takes a lot of effort as well as resources to find the most appropriate data and then represent it graphically.

Rules of Graphical Representation of Data

While presenting data graphically, there are certain rules that need to be followed. They are listed below:

• Suitable Title: The title of the graph should be appropriate that indicate the subject of the presentation.
• Measurement Unit: The measurement unit in the graph should be mentioned.
• Proper Scale: A proper scale needs to be chosen to represent the data accurately.
• Index: For better understanding, index the appropriate colors, shades, lines, designs in the graphs.
• Data Sources: Data should be included wherever it is necessary at the bottom of the graph.
• Simple: The construction of a graph should be easily understood.
• Neat: The graph should be visually neat in terms of size and font to read the data accurately.

Uses of Graphical Representation of Data

The main use of a graphical representation of data is understanding and identifying the trends and patterns of the data. It helps in analyzing large quantities, comparing two or more data, making predictions, and building a firm decision. The visual display of data also helps in avoiding confusion and overlapping of any information. Graphs like line graphs and bar graphs, display two or more data clearly for easy comparison. This is important in communicating our findings to others and our understanding and analysis of the data.

Types of Graphical Representation of Data

Data is represented in different types of graphs such as plots, pies, diagrams, etc. They are as follows,

Related Topics

Listed below are a few interesting topics that are related to the graphical representation of data, take a look.

• x and y graph
• Frequency Polygon
• Cumulative Frequency

Examples on Graphical Representation of Data

Example 1 : A pie chart is divided into 3 parts with the angles measuring as 2x, 8x, and 10x respectively. Find the value of x in degrees.

We know, the sum of all angles in a pie chart would give 360º as result. ⇒ 2x + 8x + 10x = 360º ⇒ 20 x = 360º ⇒ x = 360º/20 ⇒ x = 18º Therefore, the value of x is 18º.

Example 2: Ben is trying to read the plot given below. His teacher has given him stem and leaf plot worksheets. Can you help him answer the questions? i) What is the mode of the plot? ii) What is the mean of the plot? iii) Find the range.

Solution: i) Mode is the number that appears often in the data. Leaf 4 occurs twice on the plot against stem 5.

Hence, mode = 54

ii) The sum of all data values is 12 + 14 + 21 + 25 + 28 + 32 + 34 + 36 + 50 + 53 + 54 + 54 + 62 + 65 + 67 + 83 + 88 + 89 + 91 = 958

To find the mean, we have to divide the sum by the total number of values.

Mean = Sum of all data values ÷ 19 = 958 ÷ 19 = 50.42

iii) Range = the highest value - the lowest value = 91 - 12 = 79

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Practice Questions on Graphical Representation of Data

Faqs on graphical representation of data, what is graphical representation.

Graphical representation is a form of visually displaying data through various methods like graphs, diagrams, charts, and plots. It helps in sorting, visualizing, and presenting data in a clear manner through different types of graphs. Statistics mainly use graphical representation to show data.

What are the Different Types of Graphical Representation?

The different types of graphical representation of data are:

• Stem and leaf plot
• Scatter diagrams
• Frequency Distribution

Is the Graphical Representation of Numerical Data?

Yes, these graphical representations are numerical data that has been accumulated through various surveys and observations. The method of presenting these numerical data is called a chart. There are different kinds of charts such as a pie chart, bar graph, line graph, etc, that help in clearly showcasing the data.

What is the Use of Graphical Representation of Data?

Graphical representation of data is useful in clarifying, interpreting, and analyzing data plotting points and drawing line segments , surfaces, and other geometric forms or symbols.

What are the Ways to Represent Data?

Tables, charts, and graphs are all ways of representing data, and they can be used for two broad purposes. The first is to support the collection, organization, and analysis of data as part of the process of a scientific study.

What is the Objective of Graphical Representation of Data?

The main objective of representing data graphically is to display information visually that helps in understanding the information efficiently, clearly, and accurately. This is important to communicate the findings as well as analyze the data.

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Graphical Representation of Data

Graphical Representation of Data: Graphical Representation of Data,” where numbers and facts become lively pictures and colorful diagrams . Instead of staring at boring lists of numbers, we use fun charts, cool graphs, and interesting visuals to understand information better. In this exciting concept of data visualization, we’ll learn about different kinds of graphs, charts, and pictures that help us see patterns and stories hidden in data.

There is an entire branch in mathematics dedicated to dealing with collecting, analyzing, interpreting, and presenting numerical data in visual form in such a way that it becomes easy to understand and the data becomes easy to compare as well, the branch is known as Statistics .

The branch is widely spread and has a plethora of real-life applications such as Business Analytics, demography, Astro statistics, and so on . In this article, we have provided everything about the graphical representation of data, including its types, rules, advantages, etc.

Table of Content

What is Graphical Representation

Types of graphical representations, line graphs, histograms , stem and leaf plot , box and whisker plot .

• Graphical Representations used in Maths

Value-Based or Time Series Graphs 

Frequency based, principles of graphical representations, advantages and disadvantages of using graphical system, general rules for graphical representation of data, frequency polygon, solved examples on graphical representation of data.

Graphics Representation is a way of representing any data in picturized form . It helps a reader to understand the large set of data very easily as it gives us various data patterns in visualized form.

There are two ways of representing data,

• Pictorial Representation through graphs.

They say, “A picture is worth a thousand words”.  It’s always better to represent data in a graphical format. Even in Practical Evidence and Surveys, scientists have found that the restoration and understanding of any information is better when it is available in the form of visuals as Human beings process data better in visual form than any other form.

Does it increase the ability 2 times or 3 times? The answer is it increases the Power of understanding 60,000 times for a normal Human being, the fact is amusing and true at the same time.

Check: Graph and its representations

Comparison between different items is best shown with graphs, it becomes easier to compare the crux of the data about different items. Let’s look at all the different types of graphical representations briefly: 

A line graph is used to show how the value of a particular variable changes with time. We plot this graph by connecting the points at different values of the variable. It can be useful for analyzing the trends in the data and predicting further trends. 

A bar graph is a type of graphical representation of the data in which bars of uniform width are drawn with equal spacing between them on one axis (x-axis usually), depicting the variable. The values of the variables are represented by the height of the bars. 

This is similar to bar graphs, but it is based frequency of numerical values rather than their actual values. The data is organized into intervals and the bars represent the frequency of the values in that range. That is, it counts how many values of the data lie in a particular range. 

It is a plot that displays data as points and checkmarks above a number line, showing the frequency of the point.  

This is a type of plot in which each value is split into a “leaf”(in most cases, it is the last digit) and “stem”(the other remaining digits). For example: the number 42 is split into leaf (2) and stem (4).  

These plots divide the data into four parts to show their summary. They are more concerned about the spread, average, and median of the data. 

It is a type of graph which represents the data in form of a circular graph. The circle is divided such that each portion represents a proportion of the whole. 

Graphical Representations used in Math’s

Graphs in Math are used to study the relationships between two or more variables that are changing. Statistical data can be summarized in a better way using graphs. There are basically two lines of thoughts of making graphs in maths: 

• Value-Based or Time Series Graphs

These graphs allow us to study the change of a variable with respect to another variable within a given interval of time. The variables can be anything. Time Series graphs study the change of variable with time. They study the trends, periodic behavior, and patterns in the series. We are more concerned with the values of the variables here rather than the frequency of those values. 

Example: Line Graph

These kinds of graphs are more concerned with the distribution of data. How many values lie between a particular range of the variables, and which range has the maximum frequency of the values. They are used to judge a spread and average and sometimes median of a variable under study.

Also read: Types of Statistical Data

• All types of graphical representations follow algebraic principles.
• When plotting a graph, there’s an origin and two axes.
• The x-axis is horizontal, and the y-axis is vertical.
• The axes divide the plane into four quadrants.
• The origin is where the axes intersect.
• Positive x-values are to the right of the origin; negative x-values are to the left.
• Positive y-values are above the x-axis; negative y-values are below.

• It gives us a summary of the data which is easier to look at and analyze.
• It saves time.
• We can compare and study more than one variable at a time.

Disadvantages

• It usually takes only one aspect of the data and ignores the other. For example, A bar graph does not represent the mean, median, and other statistics of the data. 
• Interpretation of graphs can vary based on individual perspectives, leading to subjective conclusions.
• Poorly constructed or misleading visuals can distort data interpretation and lead to incorrect conclusions.

Check : Diagrammatic and Graphic Presentation of Data

We should keep in mind some things while plotting and designing these graphs. The goal should be a better and clear picture of the data. Following things should be kept in mind while plotting the above graphs: 

• Whenever possible, the data source must be mentioned for the viewer.
• Always choose the proper colors and font sizes. They should be chosen to keep in mind that the graphs should look neat.
• The measurement Unit should be mentioned in the top right corner of the graph.
• The proper scale should be chosen while making the graph, it should be chosen such that the graph looks accurate.
• Last but not the least, a suitable title should be chosen.

A frequency polygon is a graph that is constructed by joining the midpoint of the intervals. The height of the interval or the bin represents the frequency of the values that lie in that interval. 

Question 1: What are different types of frequency-based plots? 

Types of frequency-based plots:  Histogram Frequency Polygon Box Plots

Question 2: A company with an advertising budget of Rs 10,00,00,000 has planned the following expenditure in the different advertising channels such as TV Advertisement, Radio, Facebook, Instagram, and Printed media. The table represents the money spent on different channels. 

Draw a bar graph for the following data. 

• Put each of the channels on the x-axis
• The height of the bars is decided by the value of each channel.

Question 3: Draw a line plot for the following data 

• Put each of the x-axis row value on the x-axis
• joint the value corresponding to the each value of the x-axis.

Question 4: Make a frequency plot of the following data: 

• Draw the class intervals on the x-axis and frequencies on the y-axis.
• Calculate the midpoint of each class interval.

Now join the mid points of the intervals and their corresponding frequencies on the graph. 

This graph shows both the histogram and frequency polygon for the given distribution.

Related Article:

Graphical Representation of Data| Practical Work in Geography Class 12 What are the different ways of Data Representation What are the different ways of Data Representation? Charts and Graphs for Data Visualization

Conclusion of Graphical Representation

Graphical representation is a powerful tool for understanding data, but it’s essential to be aware of its limitations. While graphs and charts can make information easier to grasp, they can also be subjective, complex, and potentially misleading . By using graphical representations wisely and critically, we can extract valuable insights from data, empowering us to make informed decisions with confidence.

Graphical Representation of Data – FAQs

What are the advantages of using graphs to represent data.

Graphs offer visualization, clarity, and easy comparison of data, aiding in outlier identification and predictive analysis.

What are the common types of graphs used for data representation?

Common graph types include bar, line, pie, histogram, and scatter plots , each suited for different data representations and analysis purposes.

How do you choose the most appropriate type of graph for your data?

Select a graph type based on data type, analysis objective, and audience familiarity to effectively convey information and insights.

How do you create effective labels and titles for graphs?

Use descriptive titles, clear axis labels with units, and legends to ensure the graph communicates information clearly and concisely.

How do you interpret graphs to extract meaningful insights from data?

Interpret graphs by examining trends, identifying outliers, comparing data across categories, and considering the broader context to draw meaningful insights and conclusions.

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Guide On Graphical Representation of Data – Types, Importance, Rules, Principles And Advantages

What are Graphs and Graphical Representation?

Graphs, in the context of data visualization, are visual representations of data using various graphical elements such as charts, graphs, and diagrams. Graphical representation of data , often referred to as graphical presentation or simply graphs which plays a crucial role in conveying information effectively.

Principles of Graphical Representation

Effective graphical representation follows certain fundamental principles that ensure clarity, accuracy, and usability:Clarity : The primary goal of any graph is to convey information clearly and concisely. Graphs should be designed in a way that allows the audience to quickly grasp the key points without confusion.

• Simplicity: Simplicity is key to effective data visualization. Extraneous details and unnecessary complexity should be avoided to prevent confusion and distraction.
• Relevance: Include only relevant information that contributes to the understanding of the data. Irrelevant or redundant elements can clutter the graph.
• Visualization: Select a graph type that is appropriate for the supplied data. Different graph formats, like bar charts, line graphs, and scatter plots, are appropriate for various sorts of data and relationships.

Rules for Graphical Representation of Data

Creating effective graphical representations of data requires adherence to certain rules:

• Select the Right Graph: Choosing the appropriate type of graph is essential. For example, bar charts are suitable for comparing categories, while line charts are better for showing trends over time.
• Label Axes Clearly: Axis labels should be descriptive and include units of measurement where applicable. Clear labeling ensures the audience understands the data’s context.
• Use Appropriate Colors: Colors can enhance understanding but should be used judiciously. Avoid overly complex color schemes and ensure that color choices are accessible to all viewers.
• Avoid Misleading Scaling: Scale axes appropriately to prevent exaggeration or distortion of data. Misleading scaling can lead to incorrect interpretations.
• Include Data Sources: Always provide the source of your data. This enhances transparency and credibility.

Importance of Graphical Representation of Data

Graphical representation of data in statistics is of paramount importance for several reasons:

• Enhances Understanding: Graphs simplify complex data, making it more accessible and understandable to a broad audience, regardless of their statistical expertise.
• Helps Decision-Making: Visual representations of data enable informed decision-making. Decision-makers can easily grasp trends and insights, leading to better choices.
• Engages the Audience: Graphs capture the audience’s attention more effectively than raw data. This engagement is particularly valuable when presenting findings or reports.
• Universal Language: Graphs serve as a universal language that transcends linguistic barriers. They can convey information to a global audience without the need for translation.

Advantages of Graphical Representation

The advantages of graphical representation of data extend to various aspects of communication and analysis:

• Clarity: Data is presented visually, improving clarity and reducing the likelihood of misinterpretation.
• Efficiency: Graphs enable the quick absorption of information. Key insights can be found in seconds, saving time and effort.
• Memorability: Visuals are more memorable than raw data. Audiences are more likely to retain information presented graphically.
• Problem-Solving: Graphs help in identifying and solving problems by revealing trends, correlations, and outliers that may require further investigation.

Use of Graphical Representations

Graphical representations find applications in a multitude of fields:

• Business: In the business world, graphs are used to illustrate financial data, track performance metrics, and present market trends. They are invaluable tools for strategic decision-making.
• Science: Scientists employ graphs to visualize experimental results, depict scientific phenomena, and communicate research findings to both colleagues and the general public.
• Education: Educators utilize graphs to teach students about data analysis, statistics, and scientific concepts. Graphs make learning more engaging and memorable.
• Journalism: Journalists rely on graphs to support their stories with data-driven evidence. Graphs make news articles more informative and impactful.

Types of Graphical Representation

There exists a diverse array of graphical representations, each suited to different data types and purposes. Common types include:

1.Bar Charts:

Used to compare categories or discrete data points, often side by side.

2. Line Charts:

Ideal for showing trends and changes over time, such as stock market performance or temperature fluctuations.

3. Pie Charts:

Display parts of a whole, useful for illustrating proportions or percentages.

4. Scatter Plots:

Reveal relationships between two variables and help identify correlations.

5. Histograms:

Depict the distribution of data, especially in the context of continuous variables.

In conclusion, the graphical representation of data is an indispensable tool for simplifying complex information, aiding in decision-making, and enhancing communication across diverse fields. By following the principles and rules of effective data visualization, individuals and organizations can harness the power of graphs to convey their messages, support their arguments, and drive informed actions.

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FAQs on Graphical Representation of Data

What is the purpose of graphical representation.

Graphical representation serves the purpose of simplifying complex data, making it more accessible and understandable through visual means.

Why are graphs and diagrams important?

Graphs and diagrams are crucial because they provide visual clarity, aiding in the comprehension and retention of information.

How do graphs help learning?

Graphs engage learners by presenting information visually, which enhances understanding and retention, particularly in educational settings.

Who uses graphs?

Professionals in various fields, including scientists, analysts, educators, and business leaders, use graphs to convey data effectively and support decision-making.

Where are graphs used in real life?

Graphs are used in real-life scenarios such as business reports, scientific research, news articles, and educational materials to make data more accessible and meaningful.

Why are graphs important in business?

In business, graphs are vital for analyzing financial data, tracking performance metrics, and making informed decisions, contributing to success.

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Home » Graphical Methods – Types, Examples and Guide

Graphical Methods – Types, Examples and Guide

Table of Contents

Graphical Methods

Definition:

Graphical methods refer to techniques used to visually represent data, relationships, or processes using charts, graphs, diagrams, or other graphical formats. These methods are widely used in various fields such as science, engineering, business, and social sciences, among others, to analyze, interpret and communicate complex information in a concise and understandable way.

Types of Graphical Methods

Here are some of the most common types of graphical methods for data analysis and visual presentation:

Line Graphs

These are commonly used to show trends over time, such as the stock prices of a particular company or the temperature over a certain period. They consist of a series of data points connected by a line that shows the trend of the data over time. Line graphs are useful for identifying patterns in data, such as seasonal changes or long-term trends.

These are commonly used to compare values of different categories, such as sales figures for different products or the number of students in different grade levels. Bar charts use bars that are either horizontal or vertical and represent the data values. They are useful for comparing data visually and identifying differences between categories.

These are used to show how a whole is divided into parts, such as the percentage of students in a school who are enrolled in different programs. Pie charts use a circle that is divided into sectors, with each sector representing a portion of the whole. They are useful for showing proportions and identifying which parts of a whole are larger or smaller.

Scatter Plots

These are used to visualize the relationship between two variables, such as the correlation between a person’s height and weight. Scatter plots consist of a series of data points that are plotted on a graph and connected by a line or curve. They are useful for identifying trends and relationships between variables.

These are used to show the distribution of data across a two-dimensional plane, such as a map of a city showing the density of population in different areas. Heat maps use color-coded cells to represent different levels of data, with darker colors indicating higher values. They are useful for identifying areas of high or low density and for highlighting patterns in data.

These are used to show the distribution of data in a single variable, such as the distribution of ages of a group of people. Histograms use bars that represent the frequency of each data value, with taller bars indicating a higher frequency. They are useful for identifying the shape of a distribution and for identifying outliers or unusual data values.

Network Diagrams

These are used to show the relationships between different entities or nodes, such as the relationships between people in a social network. Network diagrams consist of nodes that are connected by lines that represent the relationship. They are useful for identifying patterns in complex data and for understanding the structure of a network.

Box plots, also known as box-and-whisker plots, are a type of graphical method used to show the distribution of data in a single variable. They consist of a box with whiskers extending from the top and bottom of the box. The box represents the middle 50% of the data, with the median value indicated by a line inside the box. The whiskers represent the range of the data, with any data points outside the whiskers indicated as outliers. Box plots are useful for identifying the spread and shape of a distribution and for identifying outliers or unusual data values.

Applications of Graphical Methods

Graphical methods have a wide range of applications in various fields, including:

• Business : Graphical methods are commonly used in business to analyze sales data, financial data, and other types of data. They are useful for identifying trends, patterns, and outliers, as well as for presenting data in a clear and concise manner to stakeholders.
• Science and engineering: Graphical methods are used extensively in scientific and engineering fields to analyze data and to present research findings. They are useful for visualizing complex data sets and for identifying relationships between variables.
• Social sciences: Graphical methods are used in social sciences to analyze and present data related to human behavior, such as demographics, survey results, and statistical analyses. They are useful for identifying trends and patterns in large data sets and for communicating findings to a broader audience.
• Education : Graphical methods are used in education to present information to students and to help them understand complex concepts. They are useful for visualizing data and for presenting information in a way that is easy to understand.
• Healthcare : Graphical methods are used in healthcare to analyze patient data, to track disease outbreaks, and to present medical information to patients. They are useful for identifying patterns and trends in patient data and for communicating medical information in a clear and concise manner.
• Sports : Graphical methods are used in sports to analyze and present data related to player performance, team statistics, and game outcomes. They are useful for identifying trends and patterns in player and team data and for communicating this information to coaches, players, and fans.

Examples of Graphical Methods

Here are some examples of real-time applications of graphical methods:

• Stock Market: Line graphs, candlestick charts, and bar charts are widely used in real-time trading systems to display stock prices and trends over time. Traders use these charts to analyze historical data and make informed decisions about buying and selling stocks in real-time.
• Weather Forecasting : Heat maps and radar maps are commonly used in weather forecasting to display current weather conditions and to predict future weather patterns. These maps are useful for tracking the movement of storms, identifying areas of high and low pressure, and predicting the likelihood of severe weather events.
• Social Media Analytics: Scatter plots and network diagrams are commonly used in social media analytics to track the spread of information across social networks. Analysts use these graphs to identify patterns in user behavior, to track the popularity of specific topics or hashtags, and to monitor the influence of key opinion leaders.
• Traffic Analysis: Heat maps and network diagrams are used in traffic analysis to visualize traffic flow patterns and to identify areas of congestion or accidents. These graphs are useful for predicting traffic patterns, optimizing traffic flow, and improving transportation infrastructure.
• Medical Diagnostics: Box plots and histograms are commonly used in medical diagnostics to display the distribution of patient data, such as blood pressure, heart rate, or blood sugar levels. These graphs are useful for identifying patterns in patient data, diagnosing medical conditions, and monitoring the effectiveness of treatments in real-time.
• Cybersecurity: Heat maps and network diagrams are used in cybersecurity to visualize network traffic patterns and to identify potential security threats. These graphs are useful for identifying anomalies in network traffic, detecting and mitigating cyber attacks, and improving network security protocols.

How to use Graphical Methods

Here are some general steps to follow when using graphical methods to analyze and present data:

• Identify the research question: Before creating any graphs, it’s important to identify the research question or hypothesis you want to explore. This will help you select the appropriate type of graph and ensure that the data you collect is relevant to your research question.
• Collect and organize the data: Collect the data you need to answer your research question and organize it in a way that makes it easy to work with. This may involve sorting, filtering, or cleaning the data to ensure that it is accurate and relevant.
• Select the appropriate graph : There are many different types of graphs available, each with its own strengths and weaknesses. Select the appropriate graph based on the type of data you have and the research question you are exploring. For example, a scatterplot may be appropriate for exploring the relationship between two continuous variables, while a bar chart may be appropriate for comparing categorical data.
• Create the graph: Once you have selected the appropriate graph, create it using software or a tool that allows you to customize the graph based on your needs. Be sure to include appropriate labels and titles, and ensure that the graph is clearly legible.
• Analyze the graph: Once you have created the graph, analyze it to identify patterns, trends, and relationships in the data. Look for outliers or other anomalies that may require further investigation.
• Draw conclusions: Based on your analysis of the graph, draw conclusions about the research question you are exploring. Use the graph to support your conclusions and to communicate your findings to others.
• Iterate and refine: Finally, refine your graph or create additional graphs as needed to further explore your research question. Iteratively refining and revising your graphs can help to ensure that you are accurately representing the data and that you are drawing the appropriate conclusions.

When to use Graphical Methods

Graphical methods can be used in a variety of situations to help analyze, interpret, and communicate data. Here are some general guidelines on when to use graphical methods:

• To identify patterns and trends: Graphical methods are useful for identifying patterns and trends in data, which may be difficult to see in raw data tables or spreadsheets. Graphs can reveal trends that may not be immediately apparent in the data, making it easier to draw conclusions and make predictions.
• To compare data: Graphs can be used to compare data from different sources or over different time periods. Graphical comparisons can make it easier to identify differences or similarities in the data, which can be useful for making decisions and taking action.
• To summarize data : Graphs can be used to summarize large amounts of data in a single visual display. This can be particularly useful when presenting data to a broad audience, as it can help to simplify complex data sets and make them more accessible.
• To communicate data: Graphs can be used to communicate data and findings to a variety of audiences, including stakeholders, colleagues, and the general public. Graphs can be particularly useful in situations where data needs to be presented quickly and in a way that is easy to understand.
• To identify outliers: Graphical methods are useful for identifying outliers or anomalies in the data. Outliers can be indicative of errors or unusual events, and may warrant further investigation.

Purpose of Graphical Methods

The purpose of graphical methods is to help people analyze, interpret, and communicate data in a way that is both accurate and understandable. Graphical methods provide visual representations of data that can be easier to interpret than tables of numbers or raw data sets. Graphical methods help to reveal patterns and trends that may not be immediately apparent in the data, making it easier to draw conclusions and make predictions. They can also help to identify outliers or unusual data points that may warrant further investigation.

In addition to helping people analyze and interpret data, graphical methods also serve an important communication function. Graphs can be used to present data to a wide range of audiences, including stakeholders, colleagues, and the general public. Graphs can help to simplify complex data sets, making them more accessible and easier to understand. By presenting data in a clear and concise way, graphical methods can help people make informed decisions and take action based on the data.

Overall, the purpose of graphical methods is to provide a powerful tool for analyzing, interpreting, and communicating data. Graphical methods help people to better understand the data they are working with, to identify patterns and trends, and to make informed decisions based on the data.

Characteristics of Graphical Methods

Here are some characteristics of graphical methods:

• Visual Representation: Graphical methods provide a visual representation of data, which can be easier to interpret than tables of numbers or raw data sets. Graphs can help to reveal patterns and trends that may not be immediately apparent in the data.
• Simplicity : Graphical methods simplify complex data sets, making them more accessible and easier to understand. By presenting data in a clear and concise way, graphical methods can help people make informed decisions and take action based on the data.
• Comparability : Graphical methods can be used to compare data from different sources or over different time periods. This can help to identify differences or similarities in the data, which can be useful for making decisions and taking action.
• Flexibility : Graphical methods can be adapted to different types of data, including continuous, categorical, and ordinal data. Different types of graphs can be used to display different types of data, depending on the characteristics of the data and the research question.
• Accuracy : Graphical methods should accurately represent the data being analyzed. Graphs should be properly scaled and labeled to avoid distorting the data or misleading viewers.
• Clarity : Graphical methods should be clear and easy to read. Graphs should be designed with the viewer in mind, using appropriate colors, labels, and titles to ensure that the message of the graph is conveyed effectively.

Advantages of Graphical Methods

Graphical methods offer several advantages for analyzing and presenting data, including:

• Clear visualization: Graphical methods provide a clear and intuitive visual representation of data that can help people understand complex relationships, trends, and patterns in the data. This can be particularly useful when dealing with large and complex data sets.
• Efficient communication: Graphical methods can help to communicate complex data sets in an efficient and accessible way. Visual representations can be easier to understand than numerical data alone, and can help to convey key messages quickly.
• Effective comparison: Graphical methods allow for easy comparison between different data sets, making it easier to identify trends, patterns, and differences. This can help in making decisions, identifying areas for improvement, or developing new insights.
• Improved decision-making: Graphical methods can help to inform decision-making by presenting data in a clear and easy-to-understand format. They can also help to identify key areas of focus, enabling individuals or teams to make more informed decisions.
• Increased engagement: Graphical methods can help to engage audiences by presenting data in an engaging and interactive way. This can be particularly useful in presentations or reports, where visual representations can help to maintain audience attention and interest.
• Better understanding: Graphical methods can help individuals to better understand the data they are working with, by providing a clear and intuitive visual representation of the data. This can lead to improved insights and decision-making, as well as better understanding of the implications of the data.

Limitations of Graphical Methods

Here are a few limitations to consider:

• Misleading representation: Graphical methods can potentially misrepresent data if they are not designed properly. For example, inappropriate scaling or labeling of the axes or the use of certain types of graphs can create a distorted view of the data.
• Limited scope: Graphical methods can only display a limited amount of data, which can make it difficult to capture the full complexity of a data set. Additionally, some types of data may be difficult to represent visually.
• Time-consuming : Creating graphs can be a time-consuming process, particularly if multiple graphs need to be created and analyzed. This can be a limitation in situations where time is limited or resources are scarce.
• Technical skills: Some graphical methods require technical skills to create and interpret. For example, certain types of graphs may require knowledge of specialized software or programming languages.
• Interpretation : Interpreting graphs can be subjective, and the same graph can be interpreted in different ways by different people. This can lead to confusion or disagreements when using graphs to communicate data.
• Accessibility : Some graphical methods may not be accessible to all audiences, particularly those with visual impairments. Additionally, some types of graphs may not be accessible to those with limited literacy or numeracy skills.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Data Presentation

Josée Dupuis, PhD, Professor of Biostatistics, Boston University School of Public Health

Wayne LaMorte, MD, PhD, MPH, Professor of Epidemiology, Boston University School of Public Health

Introduction

While graphical summaries of data can certainly be powerful ways of communicating results clearly and unambiguously in a way that facilitates our ability to think about the information, poorly designed graphical displays can be ambiguous, confusing, and downright misleading. The keys to excellence in graphical design and communication are much like the keys to good writing. Adhere to fundamental principles of style and communicate as logically, accurately, and clearly as possible. Excellence in writing is generally achieved by avoiding unnecessary words and paragraphs; it is efficient. In a similar fashion, excellence in graphical presentation is generally achieved by efficient designs that avoid unnecessary ink.

Excellence in graphical presentation depends on:

• Choosing the best medium for presenting the information
• Designing the components of the graph in a way that communicates the information as clearly and accurately as possible.

Table or Graph?

• Tables are generally best if you want to be able to look up specific information or if the values must be reported precisely.
• Graphics are best for illustrating trends and making comparisons

The side by side illustrations below show the same information, first in table form and then in graphical form. While the information in the table is precise, the real goal is to compare a series of clinical outcomes in subjects taking either a drug or a placebo. The graphical presentation on the right makes it possible to quickly see that for each of the outcomes evaluated, the drug produced relief in a great proportion of subjects. Moreover, the viewer gets a clear sense of the magnitude of improvement, and the error bars provided a sense of the uncertainty in the data.

Principles for Table Display

• Sort table rows in a meaningful way
• Avoid alphabetical listing!
• Use rates, proportions or ratios in addition (or instead of) totals
• Show more than two time points if available
• Multiple time points may be better presented in a Figure
• Similar data should go down columns
• Highlight important comparisons
• Show the source of the data

Consider the data in the table below from http://www.cancer.gov/cancertopics/types/commoncancers

Our ability to quickly understand the relative frequency of these cancers is hampered by presenting them in alphabetical order. It is much easier for the reader to grasp the relative frequency by listing them from most frequent to least frequent as in the next table.

However, the same information might be presented more effectively with a dot plot, as shown below.

Data from http://www.cancer.gov/cancertopics/types/commoncancers

Principles of Graphical Excellence from E.R. Tufte

Pattern Perception

Pattern perception is done by

• Detection: recognition of geometry encoding physical values
• Assembly: grouping of detected symbol elements; discerning overall patterns in data
• Estimation: assessment of relative magnitudes of two physical values

Geographic Variation in Cancer

As an example, Tufte offers a series of maps that summarize the age-adjusted mortality rates for various types of cancer in the 3,056 counties in the United States. The maps showing the geographic variation in stomach cancer are shown below.

These maps summarize an enormous amount of information and present it efficiently, coherently, and effectively.in a way that invites the viewer to make comparisons and to think about the substance of the findings. Consider, for example, that the region to the west of the Great Lakes was settled largely by immigrants from Germany and Scand anavia, where traditional methods of preserving food included pickling and curing of fish by smoking. Could these methods be associated with an increased risk of stomach cancer?

John Snow's Spot Map of Cholera Cases

Consider also the spot map that John Snow presented after the cholera outbreak in the Broad Street section of London in September 1854. Snow ascertained the place of residence or work of the victims and represented them on a map of the area using a small black disk to represent each victim and stacking them when more than one occurred at a particular location. Snow reasoned that cholera was probably caused by something that was ingested, because of the intense diarrhea and vomiting of the victims, and he noted that the vast majority of cholera deaths occurred in people who lived or worked in the immediate vicinity of the broad street pump (shown with a red dot that we added for clarity). He further ascertained that most of the victims drank water from the Broad Street pump, and it was this evidence that persuaded the authorities to remove the handle from the pump in order to prevent more deaths.

Humans can readily perceive differences like this when presented effectively as in the two previous examples. However, humans are not good at estimating differences without directly seeing them (especially for steep curves), and we are particularly bad at perceiving relative angles (the principal perception task used in a pie chart).

The use of pie charts is generally discouraged. Consider the pie chart on the left below. It is difficult to accurately assess the relative size of the components in the pie chart, because the human eye has difficulty judging angles. The dot plot on the right shows the same data, but it is much easier to quickly assess the relative size of the components and how they changed from Fiscal Year 2000 to Fiscal Year 2007.

Consider the information in the two pie charts below (showing the same information).The 3-dimensional pie chart on the left distorts the relative proportions. In contrast the 2-dimensional pie chart on the right makes it much easier to compare the relative size of the varies components..

More Principles of Graphical Excellence

Exclude Unneeded Dimensions

These 3-dimensional techniques distort the data and actually interfere with our ability to make accurate comparisons. The distortion caused by 3-dimensional elements can be particularly severe when the graphic is slanted at an angle or when the viewer tends to compare ends up unwittingly comparing the areas of the ink rather than the heights of the bars.

It is much easier to make comparisons with a chart like the one below.

Source: Huang, C, Guo C, Nichols C, Chen S, Martorell R. Elevated levels of protein in urine in adulthood after exposure to

the Chinese famine of 1959–61 during gestation and the early postnatal period. Int. J. Epidemiol. (2014) 43 (6): 1806-1814 .

Omit "Chart Junk"

Consider these two examples.

Here is a simple enumeration of the number of pets in a neighborhood. There is absolutely no reason to connect these counts with lines. This is, in fact, confusing and inappropriate and nothing more than "chart junk."

Source: http://www.go-education.com/free-graph-maker.html

Moiré Vibration

Moiré effects are sometimes used in modern art to produce the appearance of vibration and movement. However, when these effects are applied to statistical presentations, they are distracting and add clutter because the visual noise interferes with the interpretation of the data.

Tufte presents the example shown below from Instituto de Expansao Commercial, Brasil, Graphicos Estatisticas (Rio de Janeiro, 1929, p. 15).

 While the intention is to present quantitative information about the textile industry, the moiré effects do not add anything, and they are distracting, if not visually annoying.

Present Data to Facilitate Comparisons

Here is an attempt to compare catches of cod fish and crab across regions and to relate the variation to changes in water temperature. The problem here is that the Y-axes are vastly different, making it hard to sort out what's really going on. Even the Y-axes for temperature are vastly different.

http://seananderson.ca/courses/11-multipanel/multipanel.pdf1

The ability to make comparisons is greatly facilitated by using the same scales for axes, as illustrated below.

Data source: Dawber TR, Meadors GF, Moore FE Jr. Epidemiological approaches to heart disease:

the Framingham Study. Am J Public Health Nations Health. 1951;41(3):279-81. PMID: 14819398

It is also important to avoid distorting the X-axis. Note in the example below that the space between 0.05 to 0.1 is the same as space between 0.1 and 0.2.

Source: Park JH, Gail MH, Weinberg CR, et al. Distribution of allele frequencies and effect sizes and

their interrelationships for common genetic susceptibility variants. Proc Natl Acad Sci U S A. 2011; 108:18026-31.

Consider the range of the Y-axis. In the examples below there is no relevant information below $40,000, so it is not necessary to begin the Y-axis at 0. The graph on the right makes more sense.

Also, consider using a log scale. this can be particularly useful when presenting ratios as in the example below.

Source: Broman KW, Murray JC, Sheffield VC, White RL, Weber JL (1998) Comprehensive human genetic maps:

Individual and sex-specific variation in recombination. American Journal of Human Genetics 63:861-869, Figure 1

We noted earlier that pie charts make it difficult to see differences within a single pie chart, but this is particularly difficult when data is presented with multiple pie charts, as in the example below.

Source: Bell ML, et al. (2007) Spatial and temporal variation in PM2.5 chemical composition in the United States

for health effects studies. Environmental Health Perspectives 115:989-995, Figure 3

When multiple comparisons are being made, it is essential to use colors and symbols in a consistent way, as in this example.

Source: Manning AK, LaValley M, Liu CT, et al.  Meta-Analysis of Gene-Environment Interaction:

Joint Estimation of SNP and SNP x Environment Regression Coefficients.  Genet Epidemiol 2011, 35(1):11-8.

Avoid putting too many lines on the same chart. In the example below, the only thing that is readily apparent is that 1980 was a very hot summer.

Data from National Weather Service Weather Forecast Office at

http://www.srh.noaa.gov/tsa/?n=climo_tulyeartemp

Make Efficient Use of Space

Reduce the Ratio of Ink to Information

This isn't efficient, because this graphic is totally uninformative.

Source: Mykland P, Tierney L, Yu B (1995) Regeneration in Markov chain samplers.  Journal of the American Statistical Association 90:233-241, Figure 1

Bar graphs add ink without conveying any additional information, and they are distracting. The graph below on the left inappropriately uses bars which clutter the graph without adding anything. The graph on the right displays the same data, by does so more clearly and with less clutter.

Multiple Types of Information on the Same Figure

Choosing the Best Graph Type

Bar Charts, Error Bars and Dot Plots

As noted previously, bar charts can be problematic. Here is another one presenting means and error bars, but the error bars are misleading because they only extend in one direction. A better alternative would have been to to use full error bars with a scatter plot, as illustrated previously (right).

Consider the four graphs below presenting the incidence of cancer by type. The upper left graph unnecessary uses bars, which take up a lot of ink. This layout also ends up making the fonts for the types of cancer too small. Small font is also a problem for the dot plot at the upper right, and this one also has unnecessary grid lines across the entire width.

The graph at the lower left has more readable labels and uses a simple dot plot, but the rank order is difficult to figure out.

The graph at the lower right is clearly the best, since the labels are readable, the magnitude of incidence is shown clearly by the dot plots, and the cancers are sorted by frequency.

Single Continuous Numeric Variable

In this situation a cumulative distribution function conveys the most information and requires no grouping of the variable. A box plot will show selected quantiles effectively, and box plots are especially useful when stratifying by multiple categories of another variable.

Histograms are also possible. Consider the examples below.

Two Variables

 The two graphs below summarize BMI (Body Mass Index) measurements in four categories, i.e., younger and older men and women. The graph on the left shows the means and 95% confidence interval for the mean in each of the four groups. This is easy to interpret, but the viewer cannot see that the data is actually quite skewed. The graph on the right shows the same information presented as a box plot. With this presentation method one gets a better understanding of the skewed distribution and how the groups compare.

The next example is a scatter plot with a superimposed smoothed line of prediction. The shaded region embracing the blue line is a representation of the 95% confidence limits for the estimated prediction. This was created using "ggplot" in the R programming language.

Source: Frank E. Harrell Jr. on graphics:  http://biostat.mc.vanderbilt.edu/twiki/pub/Main/StatGraphCourse/graphscourse.pdf (page 121)

Multivariate Data

The example below shows the use of multiple panels.

Source: Cleveland S. The Elements of Graphing Data. Hobart Press, Summit, NJ, 1994.

Displaying Uncertainty

• Error bars showing confidence limits
• Confidence bands drawn using two lines
• Shaded confidence bands
• Bayesian credible intervals
• Bayesian posterior densities

Confidence Limits

Shaded Confidence Bands

Source: Frank E. Harrell Jr. on graphics:  http://biostat.mc.vanderbilt.edu/twiki/pub/Main/StatGraphCourse/graphscourse.pdf

Source: Tweedie RL and Mengersen KL. (1992) Br. J. Cancer 66: 700-705

Forest Plot

This is a Forest plot summarizing 26 studies of cigarette smoke exposure on risk of lung cancer. The sizes of the black boxes indicating the estimated odds ratio are proportional to the sample size in each study.

Data from Tweedie RL and Mengersen KL. (1992) Br. J. Cancer 66: 700-705

Summary Recommendations

• In general, avoid bar plots
• Avoid chart junk and the use of too much ink relative to the information you are displaying. Keep it simple and clear.
• Avoid pie charts, because humans have difficulty perceiving relative angles.
• Pay attention to scale, and make scales consistent.
• Explore several ways to display the data!

12 Tips on How to Display Data Badly

Adapted from Wainer H.  How to Display Data Badly.  The American Statistician 1984; 38: 137-147. 

• Show as few data as possible
• Hide what data you do show; minimize the data-ink ratio
• Ignore the visual metaphor altogether
• Only order matters
• Graph data out of context
• Change scales in mid-axis
• Emphasize the trivial;  ignore the important
• Jiggle the baseline
• Alphabetize everything.
• Make your labels illegible, incomplete, incorrect, and ambiguous.
• More is murkier: use a lot of decimal places and make your graphs three dimensional whenever possible.
• If it has been done well in the past, think of another way to do it

Additional Resources

• Stephen Few: Designing Effective Tables and Graphs. http://www.perceptualedge.com/images/Effective_Chart_Design.pdf
• Gary Klaas: Presenting Data: Tabular and graphic display of social indicators. Illinois State University, 2002. http://lilt.ilstu.edu/gmklass/pos138/datadisplay/sections/goodcharts.htm (Note: The web site will be discontinued to be replaced by the Just Plain Data Analysis site).

Graphical Representation

Graphical representation definition.

Graphical representation refers to the use of charts and graphs to visually display, analyze, clarify, and interpret numerical data, functions, and other qualitative structures. ‍

What is Graphical Representation?

Graphical representation refers to the use of intuitive charts to clearly visualize and simplify data sets. Data is ingested into graphical representation of data software and then represented by a variety of symbols, such as lines on a line chart, bars on a bar chart, or slices on a pie chart, from which users can gain greater insight than by numerical analysis alone. 

Representational graphics can quickly illustrate general behavior and highlight phenomenons, anomalies, and relationships between data points that may otherwise be overlooked, and may contribute to predictions and better, data-driven decisions. The types of representational graphics used will depend on the type of data being explored.

Types of Graphical Representation

Data charts are available in a wide variety of maps, diagrams, and graphs that typically include textual titles and legends to denote the purpose, measurement units, and variables of the chart. Choosing the most appropriate chart depends on a variety of different factors -- the nature of the data, the purpose of the chart, and whether a graphical representation of qualitative data or a graphical representation of quantitative data is being depicted. There are dozens of different formats for graphical representation of data. Some of the most popular charts include:

• Bar Graph -- contains a vertical axis and horizontal axis and displays data as rectangular bars with lengths proportional to the values that they represent; a useful visual aid for marketing purposes
• Choropleth -- thematic map in which an aggregate summary of a geographic characteristic within an area is represented by patterns of shading proportionate to a statistical variable
• Flow Chart -- diagram that depicts a workflow graphical representation with the use of arrows and geometric shapes; a useful visual aid for business and finance purposes
• Heatmap -- a colored, two-dimensional matrix of cells in which each cell represents a grouping of data and each cell’s color indicates its relative value
• Histogram – frequency distribution and graphical representation uses adjacent vertical bars erected over discrete intervals to represent the data frequency within a given interval; a useful visual aid for meteorology and environment purposes
• Line Graph – displays continuous data; ideal for predicting future events over time;  a useful visual aid for marketing purposes
• Pie Chart -- shows percentage values as a slice of pie; a useful visual aid for marketing purposes
• Pointmap -- CAD & GIS contract mapping and drafting solution that visualizes the location of data on a map by plotting geographic latitude and longitude data
• Scatter plot -- a diagram that shows the relationship between two sets of data, where each dot represents individual pieces of data and each axis represents a quantitative measure
• Stacked Bar Graph -- a graph in which each bar is segmented into parts, with the entire bar representing the whole, and each segment representing different categories of that whole; a useful visual aid for political science and sociology purposes
• Timeline Chart -- a long bar labelled with dates paralleling it that display a list of events in chronological order, a useful visual aid for history charting purposes
• Tree Diagram -- a hierarchical genealogical tree that illustrates a family structure; a useful visual aid for history charting purposes
• Venn Diagram -- consists of multiple overlapping usually circles, each representing a set; the default inner join graphical representation

Proprietary and open source software for graphical representation of data is available in a wide variety of programming languages. Software packages often provide spreadsheets equipped with built-in charting functions.

Advantages and Disadvantages of Graphical Representation of Data

Tabular and graphical representation of data are a vital component in analyzing and understanding large quantities of numerical data and the relationship between data points. Data visualization is one of the most fundamental approaches to data analysis, providing an intuitive and universal means to visualize, abstract, and share complex data patterns. The primary advantages of graphical representation of data are:

• Facilitates and improves learning: graphics make data easy to understand and eliminate language and literacy barriers
• Understanding content: visuals are more effective than text in human understanding
• Flexibility of use: graphical representation can be leveraged in nearly every field involving data
• Increases structured thinking: users can make quick, data-driven decisions at a glance with visual aids
• Supports creative, personalized reports for more engaging and stimulating visual  presentations 
• Improves communication: analyzing graphs that highlight relevant themes is significantly faster than reading through a descriptive report line by line
• Shows the whole picture: an instantaneous, full view of all variables, time frames, data behavior and relationships

Disadvantages of graphical representation of data typically concern the cost of human effort and resources, the process of selecting the most appropriate graphical and tabular representation of data, greater design complexity of visualizing data, and the potential for human bias.

Why Graphical Representation of Data is Important

Graphic visual representation of information is a crucial component in understanding and identifying patterns and trends in the ever increasing flow of data. Graphical representation enables the quick analysis of large amounts of data at one time and can aid in making predictions and informed decisions. Data visualizations also make collaboration significantly more efficient by using familiar visual metaphors to illustrate relationships and highlight meaning, eliminating complex, long-winded explanations of an otherwise chaotic-looking array of figures. 

Data only has value once its significance has been revealed and consumed, and its consumption is best facilitated with graphical representation tools that are designed with human cognition and perception in mind. Human visual processing is very efficient at detecting relationships and changes between sizes, shapes, colors, and quantities. Attempting to gain insight from numerical data alone, especially in big data instances in which there may be billions of rows of data, is exceedingly cumbersome and inefficient.

Does HEAVY.AI Offer a Graphical Representation Solution?

HEAVY.AI's visual analytics platform is an interactive data visualization client that works seamlessly with server-side technologies HEAVY.AIDB and Render to enable data science analysts to easily visualize and instantly interact with massive datasets. Analysts can interact with conventional charts and data tables, as well as big data graphical representations such as massive-scale scatterplots and geo charts. Data visualization contributes to a broad range of use cases, including performance analysis in business and guiding research in academia.

PowerPoint Charts, Graphs, & Tables Made Easy | Tips & Tricks

In today's digital world, effective communication is key, especially in presentations. After all, in a world saturated with information, the power to express your message clearly and impactfully can make all the difference.

We know that conveying complex information can be challenging, but guess what? It doesn't have to be! After discussing this with our 200+ expert presentation designers , I've gathered their best practices and strategies to create this comprehensive guide.

Below, you will find expert tips and tricks for making, customizing, and presenting PowerPoint charts, graphs, and tables. Stay with us!

Today, we'll explore the following topics:

• PowerPoint Charts and Graphs 

Tables in PowerPoint

Free powerpoint charts, graphs, and tables templates, ready to enhance your presentations our team at 24slides is here to help, powerpoint charts and graphs.

If you are thinking of adding tables to your PowerPoint presentation, let me first show you two other great options: charts and graphs.

Charts and graphs stand out for making complex information easy to read at a glance. They’re ideal for identifying trends, representing patterns, and making decisions easier. In addition, charts and graphs capture the audience's attention.

You have many types to choose from, and we'll go over the most important ones later. In the meantime, here are some examples:

Undoubtedly, one of the best ways to take your presentations to the next level.

But you may have a question in mind: What is the difference between a chart and a graph in PowerPoint? Charts refer to any visual representation of data, whether graphical or non-graphical (such as tables). Graphs, on the other hand, refer specifically to the graphical representation of data (such as bar charts).

In other words, all graphs are charts, but not all charts are graphs.

People often confuse these terms in PowerPoint, but they actually refer to different visual elements.

How to Make a Chart in PowerPoint?

First, go to the Insert tab. Then, click on Chart and select your favorite chart type. Finally, enter your data or copy it from somewhere else. Simple!

Here you have the detailed step-by-step instructions:

• Select the slide where you want to add the chart. Choose the Insert tab, then select the Illustrations group's Chart option.

• A dialog box for inserting charts will appear. Choose a category on the left, then double-click the chart you want on the right.

• When inserted, the chart appears alongside a spreadsheet. Here, you have to replace the placeholder data with your own details. 

To edit your chart's content, use the selection handles in the spreadsheet to add or remove data.

• When inserting a chart, you will see small buttons on the upper right side of the chart. 

Format using the Chart Elements button. Click on “+” to tweak the chart title, data labels, and more. Use the Chart Styles button (brush) to change the chart's color or style. Finally, the Chart Filters button (funnel) will show or hide data from your chart.

Customizing Charts in PowerPoint

We already know about the power of PowerPoint charts, but we still have one more step to take: customizing them.

• Edit data: You can modify data directly in PowerPoint. Just double-click on the chart to open the associated Excel spreadsheet. Here, you can add, delete, or edit data. If you want to do it like a pro, check out how to Link or Embed an Excel File in PowerPoint. 
• Change the design: Go to the design tab. Here, you can add or remove elements such as titles, captions, labels, etc.
• Change color and style: Select the format tab. In this section, you will find options to change the chart's color and style. You can even make individual changes.
• Add shape effects: Go to the format tab and unleash your creativity. You can add shadows, reflections, and 3D effects.

And there you have it; now you know how to customize your PowerPoint Chart. If you are looking for more inspiration, take a look at our detailed Flowchart and Gantt Chart articles.

Chart vs table

Is a chart better than a table?

We already know the importance of using tables in PowerPoint presentations. However, you may have a question in mind: are charts better than tables? The short answer is: it depends.

First off, think about what type of data you are dealing with and, most importantly, what message you are trying to get across.

Charts are great for showing trends, making comparisons, and connecting data points. They’re also visually appealing. Conversely, tables could be your perfect selection for numerical data and comprehensive details.

The most important types of charts in PPT and which one is best for you

We have checked out why adding visuals is a game-changer for your presentations. However, which one is best for your needs? 

Based on our more than 10 years of expertise and creating around 17,500 slides per month, these are the charts most requested by our customers. Let's explore each one!

“Columns, bars, lines, and pie charts are top picks for clients because they're more descriptive and easier to get for the audience.” Briana/ Design Manager

Column Chart

Ideal for making comparisons. You can represent data in an attractive and clear way. It’s also a great option for showing changes over time. Here, you can emphasize the difference in quantities.

Imagine you're tracking sales for a store. If you have many categories of sales data and need to compare them, a column chart could be just what you need.

Download our Free Column Chart Template here.

Like the column chart, the bar chart can simplify complex information quickly , especially when comparing data. But, the horizontal layout might influence how people see things, potentially altering how they understand your data. Keep this in mind!

When you have long category labels or many categories, choose a bar chart instead of a column chart. Horizontal bars are easier to read and take up less space in the presentation.

Download our Free Bar Chart Template here.

The top choice for showing trends over time. You can even combine it with other charts. For example, you can add them to a column chart to display different data at a glance. This makes it easier for viewers to understand complex information.

But how to make a line graph in PowerPoint? First, click on the Insert tab. Then, click on Graph and select Line Graph. That's it—it's as simple as that.

Download our Free Line Chart Template here .

The best for showing proportions. Not only is it easy to understand, but you will also be able to illustrate percentages or parts of a whole.

Pie charts are easy to create, you need to figure out the percentages or proportions of each data category. But remember, keep the chart to six or fewer sections. This maintains data impact, avoiding confusion.

Download our Free Pie Chart Template here .

How to Use Charts and Graphs Effectively?

We already know how to use PowerPoint charts, graphs, and tables, but we want to go one step further. Here are the best tips for making effective PowerPoint presentations.

• Choose the right type of chart. Choose graphics that best suit your data. For example, use column or bar charts to compare categories, line charts to show trends over time, and pie charts to display parts of a whole.
• Be selective. Avoid using too much information, eliminate irrelevant details, and keep it simple. By focusing on the most important data points, you enhance the clarity of the information for your audience.
• Pay attention to color. When presenting data , keep in mind the consistency of the colors and make sure essential information stands out. Avoid using too many colors here, as this can be distracting.
• Add context. Make your titles clear and descriptive. Labels should also serve as a guide for viewers to understand everything easily. This could mean explaining trends, defining terms, or just describing where the data comes from.
• Consistency. Use the same style and format for your graphics and data. Ensure brand consistency in a presentation is key. This creates a professional and polished visual presentation.
• Be creative. Try unique ways to showcase your data, like infographics or custom graphics. For example, you can use a bar chart to compare categories and a line chart to show the trend over time.

Pro Tip: Creating a PowerPoint infographic is one of the most creative ways to present data. They provide a visually engaging and easy-to-follow format for presenting complex information. Briana/ Design Manager

PowerPoint tables help organize and display data in a structured way for presentations. They’re made up of rows and columns containing text, numerical data, or other information.

Tables are awesome for showing comparisons, summarizing information, sharing research findings, and planning. Because of all that, they are a top choice for visualizing financial or statistical data. They’re incredibly versatile and practical!

All you need to do is put the right labels on, and reading should be a breeze. Believe us, your audience will appreciate it. Do you want to present data in detail and make comparisons? Then, this is your best option.

People have been using PowerPoint tables for a long time. Why? That's simple: they’re easy to read.

Here's an example:

Download our Free Table Template here .

How to Make a Table in PowerPoint?

Inserting tables in PowerPoint is quite simple. Just click on Insert and then on Table . Next, just drag the mouse down to choose the number of rows and columns you need.

Should you require a bigger table? You can manually select the values for the columns and rows. 

Customizing tables in PowerPoint

Now that we know how to create a table in PowerPoint, let's customize it. But first, let's learn how to add rows and columns in PowerPoint.

• How to add a row to a table in PowerPoint?

Click on a cell in the existing table. Go to the Layout tab in the ribbon and select Insert . Select Insert Rows Above or Insert Rows Below , depending on where you want to add the new row.

• How to add a column to a table in PowerPoint?

Click on an adjacent cell in the table. Go to the Layout tab in the ribbon and then select Insert . Choose either Insert Columns Left or Insert Columns Right , depending on where you want to add the new column.

Now that you have the structure of your table ready, let's give it some styling:

• Applying style in your table presentation

To edit your tables, first select a cell. Then, click on the Design tab to pick the style you like best. Finally, click on the drop-down arrow to see the complete Table Styles gallery .

That's it. Now you know how to use tables in PowerPoint.

How to Use Tables Effectively?

Tables are powerful tools for presenting data in a structured format. They can enhance clarity, facilitate comparisons, and convey complex information.

However, when you don't use them correctly, they can have the opposite effect, making the information flat and boring. So here are golden rules to help you:

Keep it simple

Don't overload your table with too much information. Focus on the most important information to keep it clear and easy to read. Remember, the powerful presentation of data is in simplicity.

Consider whether gridlines are necessary for your table. Removing them can make your board look cleaner and more professional.

Although many don't mention it, choosing the right words is vital. The more you can say of the same idea in fewer words, the better. Avoid using words or connectors that add nothing to the message.

Highlight key data points

Make your table pop using bold, italics, or fun colors to highlight important data or headings. This will make the table easier to read.

Consider adding shades for alternate rows to make your table easier to read. Make the shadow subtle, to avoid distraction from the data itself.

You can use color to emphasize backgrounds or text. No matter which method you opt for to add contrast, remember that “less is more” when creating an effective table.

Consistency

Consistency is crucial in tables, as it is in graphics. Ensure that the font style, size, and color are the same across the entire table. This helps maintain visual harmony.

Align your text and numbers properly so they're easier to read and give your table a polished look. If you will use decimals, think about aligning them to facilitate comparisons.

In this article, we have explored the benefits of incorporating visuals like charts, graphs, and presentation tables in PowerPoint. We also know how to add them and ensure they look good. 

Just remember to pick the right chart and keep your presentations consistent.

And as I said at the beginning, conveying complex information doesn't have to be challenging! Our Templates by 24Slides platform has hundreds of free PowerPoint charts, graphs, and table templates. 

You can download and combine different templates to create a shiny PowerPoint Presentation. All the examples in this article are fully customizable, allowing you to insert your data without worrying about design. Enjoy them!

Knowing how to use PowerPoint charts, graphs, and tables can make the difference between a successful presentation and a failed one. However, mastering the art of presenting data takes more time and effort. 

The good news? You can always trust professionals to do the heavy work, allowing you to focus on improving your product or service — what really matters to your business.

With an average satisfaction score of 4.8 out of 5 from over 1.3 million redesigned slides, it's safe to say we're incredibly proud of the product we deliver.

We're the world's largest presentation design company.

Not only will you receive an attractive presentation, but we will create one that fits your brand's visual guidelines. Most importantly, it will help emphasize your message and engage your audience.

Ready to elevate your PowerPoint presentations? Explore this content!

• PowerPoint 101: The Ultimate Guide for Beginners
• Mastering the Art of Presenting Data in PowerPoint
• The Ultimate Brand Identity Presentation Guide [FREE PPT Template]
• 7 Essential Storytelling Techniques for your Business Presentation
• The Cost of PowerPoint Presentations: Discover the hidden expenses you might overlook!

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• Graphical Representation

What is a Graph

In mathematics, a graph is a diagrammatic illustration that is used to represent data values in a systematic, organized and understandable manner.  It is indeed a very tedious task to analyze lots of data. However, when the same numerical data is represented in a pictorial form, it becomes easy to understand the relationship between the provided data objects and the concepts represented. It is often said that a picture is worth a thousand words. Therefore, graphs are particularly useful when it comes to displaying and analyzing data. 

The data have shown on the graph usually represents a relationship between various things for comparison among them. It could also help us to understand the changing trends over some time. With the help of graphs, it becomes easier to comprehend information.

Types of Graphical Representation 

To represent various kinds of data, different kinds of graphs are used. Some of the commonly used graphs are as follows: 

In a line graph, a line shows trends in data. It can also be used to predict the changing trends of the displayed data objects in the future. 

A bar graph is used when data has been categorized or sorted. It is the best kind of graph for comparing data. In this, solid bars are used to represent different categories or data values.

A histogram is similar to a bar graph. However, instead of making comparisons, it groups the numerical data into ranges. It is most commonly used to show frequency distributions. 

Pie or Circle Graph

In a pie chart, a circle represents statistical graphics. It is divided into many slices or pies to represent the proportion of numbers. The length of the arc of each pipe corresponds to the quantity represented by it.

Stem and Leaf Graph

A stem and leaf plot is a special type of table in which the data values are divided into a stem, which represents the initial digit or digits, and a leaf, which usually represents the last digit. 

How to plot the Data Accurately on Graphs?

It is of utmost importance that the information which is being represented graphically should be accurate and easy to understand. The various points that should be kept in mind are: 

The scale chosen to plot the graph should be according to the data values that have to be represented.

The index makes it easier for the reader to read and interpret the data represented by various colours, patterns, designs, etc.

The Source of Data

As and when necessary, the source of data can be mentioned at the bottom of the graph. 

The purpose of making the graph is defeated if the representation does not look tidy. Hence, it must be ensured that the data so represented is neat and visually appealing. 

There is no need to unnecessarily complicate the graph. The simpler, the better.

Basics of Graphical Representation

A graph usually consists of two lines called the coordinate axes. The horizontal line is called the x-axis, and the vertical line is called the y axis. The intersection of the two axes is the point of origin. The values on the x-axis towards the right of the origin are considered positive, and towards the left are negative. Similarly, on the y-axis, the values above the origin will be positive and the values below the origin will be negative. 

 Benefits of using Graphs 

Graphs save time. If the same information is written down, it becomes a period process to spot the trends and be able to analyze the data properly. 

A graph can be used to represent information neatly and also takes less space.

It is easy to understand.

Analysing a graphical representation of data does not take much and helps in making quick decisions. 

Graphs give you a summarized version of a long report that contains a large amount of data. 

Graphs and tables are less likely to have any errors and mistakes. 

Graphical representation of two or more data sets will allow you to compare the information and take preventive measures to avoid mistakes in the future. 

By making the data easy to understand, graphs eliminate the literacy barriers so that anyone can analyse and interpret the presented data. 

With just a glance at the graphical representation, a person can make quick and informed decisions.  

Some Rules for Graphical Representation of Data 

Like any other mathematical concept, graphical representation also has some rules you must follow. These rules will help you present the information on a graph effectively. Below are the rules for graphical representation of data: 

When you are making a graph, you should give it an appropriate title that highlights the subject of the given data.

While making a graph, do not forget to mention the measurement unit. 

Make an index using colours, designs, shades, lines, etc. to make the graphical representation easier to understand.  

You have to choose an appropriate scale to represent the given set of data. 

Construct the graph as simple as possible so that everyone can easily understand the presented data.

Whether you are making a pie chart or a bar graph, it should look neat and clean so that the teacher can easily read the figures. 

Importance of Graphical Representation 

Graphical representation gives you a visual presentation of the given data to make it easier to understand. Graphs help you identify different patterns over a short and long period. It assists you in the interpretation of data and comparison of two or more data sets. Here are reasons why graphical representation is important: 

Graphs are widely accepted in the corporate world as it summarises the data into an understandable format and avoids wastage of time. 

When you want to compare two or more different data sets, graphs are your best choice. A graphical representation of all the data sets will allow you to quickly analyze the information and help you in making quick decisions. 

Through descriptive reports and information, it becomes difficult to make decisions. However, with graphs, the management can analyse the situation more clearly and make the right decisions. 

With tables and graphs, the information can be presented in an organised and logical manner, making it easier to understand for anyone. 

Graphical representation of data does not demand much of your time, improving the overall efficiency. You can quickly make the graphs within minutes and focus on other important work. 

Qualitative representation might include many grammatical errors and other mistakes that can mislead the person reading it. Since graphs involve numerical representation of data, there are fewer chances of errors and mistakes. 

Graphs give you the entire summary of a large amount of data.    

FAQs on Graphical Representation

1. What is a frequency polygon graph?

A frequency polygon graph can be used to represent the same set of data which is represented by a histogram. In this type of graph, lines are used to connect the midpoints of each interval. The frequencies of the data interval are represented by the height at which the midpoints are plotted in the graph. A frequency polygon can be created using the already drawn histogram, or by calculating the midpoint from the intervals of the frequency distribution table. To calculate the midpoint, we need to find the average of the upper and the lower values of the interval/range. 

Frequency polygon gives us an idea regarding the shape of the data and the trends that it follows during a particular duration of time. 

Steps to draw a frequency polygon: 

Calculate the classmark for each interval, which is equal to (upper limit + lower limit)/2. 

Represent the class marks on the x-axis and their corresponding frequencies on the y-axis. 

For every class mark on the x-axis, plot the frequencies of the y-axis.  

Join all the obtained points to get a curve.

The figure obtained is called a frequency polygon. 

2. What is the difference between a Bar Graph and a Histogram?

The most commonly visible difference between a bar graph and a histogram is that, in a bar graph, the bars have spaces between them, whereas, in a histogram, the bars are drawn adjacent to each other, without leaving any spaces. 

As they both make use of bars to represent the data, it becomes slightly difficult to understand the fundamental difference between the two. A histogram is a graphical representation that uses bars to demonstrate the frequency of numerical data. In a histogram, elements are grouped, so they can be considered as ranges.

A bar graph is a diagrammatic representation that uses bars for the comparison of different categories of data.  The plotted elements are treated as individual entities, and not as a range. The bars can be drawn horizontally or vertically. The height of the bar corresponds to the size of the data object.

3. From which platform can I learn Graphical Representation?

Vedantu is the best e-learning platform from where you can learn Graphical Representation. To start studying the concept of graphical representations, you can visit our official website or download our mobile app from the app store or play store. Our learning platform is available to all students across the globe for absolutely free. Apart from the Graphical Representation, you will find plenty of study material for different topics of Maths. From the website, you can learn concepts, such as Number System, Area of Triangle, Factorisation, and much more.    

4. What are the advantages of a Bar Graph?

A bar graph is the most widely used method of graphical representation. Below are some of the advantages of a bar graph: 

A bar graph shows every category from the given frequency distribution. 

Bar graphs summarize a large chunk of data into a simple, understandable, and interpretable form. 

With a bar graph, you can easily compare two or more different data sets. 

You can study the varying patterns in a bar graph over a long period. 

A bar graph makes the trends easier to highlight than other types of graphical representation.  

5. How to decide which graph is suitable for a situation?

Sometimes, the question does not specify which type of graph you have to use. In these cases, you will have to analyze the given data and decide which graph will be more suitable. When you have to compare two different categories of data sets, you should use a bar graph as it makes the data easy to interpret. If you have to find the trends and progress over a short period, you can use line graphs. Moreover, when you have to represent a whole graphically, a pie chart is the best option.   

• Math Article

Graphical Representation

Graphical Representation is a way of analysing numerical data. It exhibits the relation between data, ideas, information and concepts in a diagram. It is easy to understand and it is one of the most important learning strategies. It always depends on the type of information in a particular domain. There are different types of graphical representation. Some of them are as follows:

• Line Graphs – Line graph or the linear graph is used to display the continuous data and it is useful for predicting future events over time.
• Bar Graphs – Bar Graph is used to display the category of data and it compares the data using solid bars to represent the quantities.
• Histograms – The graph that uses bars to represent the frequency of numerical data that are organised into intervals. Since all the intervals are equal and continuous, all the bars have the same width.
• Line Plot – It shows the frequency of data on a given number line. ‘ x ‘ is placed above a number line each time when that data occurs again.
• Frequency Table – The table shows the number of pieces of data that falls within the given interval.
• Circle Graph – Also known as the pie chart that shows the relationships of the parts of the whole. The circle is considered with 100% and the categories occupied is represented with that specific percentage like 15%, 56%, etc.
• Stem and Leaf Plot – In the stem and leaf plot, the data are organised from least value to the greatest value. The digits of the least place values from the leaves and the next place value digit forms the stems.
• Box and Whisker Plot – The plot diagram summarises the data by dividing into four parts. Box and whisker show the range (spread) and the middle ( median) of the data.

General Rules for Graphical Representation of Data

There are certain rules to effectively present the information in the graphical representation. They are:

• Suitable Title: Make sure that the appropriate title is given to the graph which indicates the subject of the presentation.
• Measurement Unit: Mention the measurement unit in the graph.
• Proper Scale: To represent the data in an accurate manner, choose a proper scale.
• Index: Index the appropriate colours, shades, lines, design in the graphs for better understanding.
• Data Sources: Include the source of information wherever it is necessary at the bottom of the graph.
• Keep it Simple: Construct a graph in an easy way that everyone can understand.
• Neat: Choose the correct size, fonts, colours etc in such a way that the graph should be a visual aid for the presentation of information.

Graphical Representation in Maths

In Mathematics, a graph is defined as a chart with statistical data, which are represented in the form of curves or lines drawn across the coordinate point plotted on its surface. It helps to study the relationship between two variables where it helps to measure the change in the variable amount with respect to another variable within a given interval of time. It helps to study the series distribution and frequency distribution for a given problem.  There are two types of graphs to visually depict the information. They are:

• Time Series Graphs – Example: Line Graph
• Frequency Distribution Graphs – Example: Frequency Polygon Graph

Principles of Graphical Representation

Algebraic principles are applied to all types of graphical representation of data. In graphs, it is represented using two lines called coordinate axes. The horizontal axis is denoted as the x-axis and the vertical axis is denoted as the y-axis. The point at which two lines intersect is called an origin ‘O’. Consider x-axis, the distance from the origin to the right side will take a positive value and the distance from the origin to the left side will take a negative value. Similarly, for the y-axis, the points above the origin will take a positive value, and the points below the origin will a negative value.

Generally, the frequency distribution is represented in four methods, namely

• Smoothed frequency graph
• Pie diagram
• Cumulative or ogive frequency graph
• Frequency Polygon

Merits of Using Graphs

Some of the merits of using graphs are as follows:

• The graph is easily understood by everyone without any prior knowledge.
• It saves time
• It allows us to relate and compare the data for different time periods
• It is used in statistics to determine the mean, median and mode for different data, as well as in the interpolation and the extrapolation of data.

Example for Frequency polygonGraph

Here are the steps to follow to find the frequency distribution of a frequency polygon and it is represented in a graphical way.

• Obtain the frequency distribution and find the midpoints of each class interval.
• Represent the midpoints along x-axis and frequencies along the y-axis.
• Plot the points corresponding to the frequency at each midpoint.
• Join these points, using lines in order.
• To complete the polygon, join the point at each end immediately to the lower or higher class marks on the x-axis.

Draw the frequency polygon for the following data

Mark the class interval along x-axis and frequencies along the y-axis.

Let assume that class interval 0-10 with frequency zero and 90-100 with frequency zero.

Now calculate the midpoint of the class interval.

Using the midpoint and the frequency value from the above table, plot the points A (5, 0), B (15, 4), C (25, 6), D (35, 8), E (45, 10), F (55, 12), G (65, 14), H (75, 7), I (85, 5) and J (95, 0).

To obtain the frequency polygon ABCDEFGHIJ, draw the line segments AB, BC, CD, DE, EF, FG, GH, HI, IJ, and connect all the points.

Frequently Asked Questions

What are the different types of graphical representation.

Some of the various types of graphical representation include:

• Line Graphs
• Frequency Table
• Circle Graph, etc.

Read More:  Types of Graphs

What are the Advantages of Graphical Method?

Some of the advantages of graphical representation are:

• It makes data more easily understandable.
• It saves time.
• It makes the comparison of data more efficient.

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Very useful for understand the basic concepts in simple and easy way. Its very useful to all students whether they are school students or college sudents

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Home Blog Design How to Make a Presentation Graph

How to Make a Presentation Graph

Visuals are a core element of effective communication, and regardless of the niche, graphs facilitate understanding data and trends. Data visualization techniques aim to make data engaging, easy to recall and contextualize while posing as a medium to simplify complex concepts .

In this article, we’ll guide you through the process of creating a presentation graph, briefly covering the types of graphs you can use in presentations, and how to customize them for maximum effectiveness. Additionally, you can find references on how to narrate your graphs while delivering a presentation.

Table of Contents

What is a Presentation Graph?

Types of graphs commonly used in presentations, how to select a presentation graph type, design principles for effective presentation graphs, working with presentation graph templates, integrating the graph into your presentation, common mistakes to avoid when making a presentation graph, final words.

A presentation graph is a visual representation of data, crafted in either 2D or 3D format, designed to illustrate relationships among two or more variables. Its primary purpose is to facilitate understanding of complex information, trends, and patterns, making it easier for an audience to grasp insights during a presentation. 

By visually encoding data, presentation graphs help highlight correlations, distributions, and anomalies within the dataset, thereby supporting more informed decision-making and discussion. 

Various types of graphs are commonly used in presentations. Each type serves specific purposes, allowing presenters to choose the most suitable format for conveying their data accurately. Here, we’ll discuss some common examples of presentation graphs.

Check our guide for more information about the differences between charts vs. graphs .

A bar chart is a visual tool that represents data using horizontal bars, where the length of each bar correlates with the data value it represents. This type of chart is used to compare discrete categories or groups, highlighting differences in quantities or frequencies across these categories. 

For more information check our collection of bar chart PowerPoint templates .

Column Graphs

Column graphs are a variation of bar charts. They display data through vertical columns, allowing for comparing values across different categories or over time. Each column’s height indicates the data value, making it straightforward to observe differences and trends.

Line Graphs

Line graphs depict information as a series of data points connected by straight lines. They are primarily used to show trends over time or continuous data, with the x-axis typically representing time intervals and the y-axis representing the measured values. Line graphs highlight the rate of change between the data points, indicating trends and fluctuations.

For more information check our collection of line chart PowerPoint templates .

Circle Graphs

Circle graphs, commonly known as pie charts or donut charts, present the data distribution as fractions of an entity. They provide a quick understanding of the relative sizes of each component within a dataset. Pie charts are particularly effective when the goal is to highlight the contribution of each part to the whole data.

For more information check our collection of circle diagram templates .

Area Graphs

Area graphs are similar to line graphs, but the space below the line is filled in, emphasizing the volume beneath the curve. They represent cumulative totals over time through the use of sequential data points, making it easier to see total values and the relative significance of different parts of the data.

For more information check our collection of area chart PowerPoint templates .

Cone, Cylinder, and Pyramid Graphs

Three-dimensional graphs, such as cones, cylinders, and pyramids, create a dynamic visual impact on presentations. While not as common as the other types, they are used for their ability to add depth and dimension to data representation. These graphs create a visually engaging experience for the audience, although sometimes they sacrifice accuracy for the sake of visuals.

For more information check our collection of pyramid diagram PowerPoint templates .

As a presenter, you must be aware of both the topic’s requirements to discuss and your audience’s needs. Different graphs fulfill distinct purposes, and selecting the right one is critical for effective communication.

Line Graphs for Trends Over Time

A line graph is effective when you want to present trends or changes over a continuous period, like sales performance over months. Each point on the line represents a specific time, offering a clear visual representation of the data’s progression.

Bar Graphs for Comparing Quantities

If your goal is to compare quantities or values across different categories, such as sales figures for various products, a bar graph is suitable. The varying lengths of bars make it easy to compare the magnitudes of different categories.

Pie Charts for Showing Proportions

Use pie charts when you want to illustrate parts of a whole. For example, to represent the percentage distribution of expenses in a budget, a pie chart divides the total into segments, each corresponding to a category.

Follow these guidelines to create your presentation graph for the data you intend to represent. 

How to Make a Presentation Graph in PowerPoint

Start by opening your presentation slide deck. For this tutorial’s purpose, we’ll work with a blank slide.

Switch to the Insert tab and click on Chart . 

A new dialogue window will open, where you have to select the chart type and the specific representation type—i.e., for area charts, you can choose from 2D or 3D area charts and their distribution method.

If you hover over the selected chart, it will zoom in to check the details. Double-click to insert the chosen graph into the slide.

As we can see, a spreadsheet to edit the data is now available. If you accidentally close it, go to Chart Design > Edit Data.

Replace the data in the numbers to reflect the data you need to showcase. The columns’ titles indicate the text the legend shows for each series. Then, we can close the spreadsheet and continue customizing it.

By clicking on the paintbrush, we access the Style options for the graph. We can change the background color, layout style, and more.

If we switch to the Color tab inside of Style , we can modify the color scheme for the presentation graph. And as simple as that is how to make a graph in PowerPoint.

How to Make a Presentation Graph in Google Slides

Now, let’s see how to create a graph in Google Slides. We start once again from a blank slide.

Go to Insert > Chart . Select your desired presentation graph option. In our case, we will work with a Pie Chart.

To change the placeholder data, click on Edit Data .

If you missed the emergent tab, you can go to the three points in the graph, click on them, and select Open Source .

The graph will most likely cover the data spreadsheet, so move it to one side to see the entire data range. In this case, the auto-generated graph is wrong as the sum gives 110%. We’ll correct that now.

And this is how it looks with the corrected data.

Next, we click on the three dots on the chart and select Edit the Chart . This shall open all customization options.

At the Setup tab, we can change the chart style and select from various options. 

The data will refresh in that case and adapt its representation to the new style.

If we switch to the Customize tab (it says Customise, as the selected language is UK English), we can fine-tune our presentation graph starting from the background color.

Activate the 3D checkbox to change to a 3D pie chart (applicable to any graph).

We can find tailored settings for the Pie Chart to convert it to a donut chart, with settings like the donut hole size.

The Pie Slice section helps us change the color scheme for each one of the slices.

We can change the title and axis titles in the Chart and axis titles section.

Finally, the Legend section offers many customization options to alter the legend’s format.

Once the customization process is completed, close the Google Spreadsheets tab, go to your presentation chart, and click Update .

Google Slides will refresh the data for your created presentation graph with the last synced data.

Adhering to certain design principles is imperative for creating graphs and communicating information effectively.

Simplicity and Clarity

A graph should be clean and free from unnecessary details. Clear graphs have visible data points and helpful short texts for better understanding. Even if it looks simple, it can still show important information. To make it easy to understand, avoid adding distortions, shading, weird perspectives, too many colors, unnecessary decorations, or 3D effects [2]. It is also essential to ensure the plotted data points are clear, not hidden or covered.

Use of Color and Contrast

Thoughtful use of color and contrast enhances visual appeal and distinguishes different elements within the graph. Colors can effectively improve the chart presentation in three ways: highlighting specific data, grouping items, and encoding quantitative values. However, do not use fancy or varying colors in the background. We suggest resisting decorating graphs excessively, as it can hinder clear data presentation [4]. Only use different colors when they highlight important differences in the data.

Labeling and Legends

Accurate labeling is crucial to provide context and understanding. While designing graphs, we don’t expect the viewer to guess. Instead, we clearly label titles and axes.  Clear labeling means displaying both axes on your graph, including measurement units if needed. Identify symbols and patterns in a legend or caption [3]. Legends explain symbols and patterns in a graph.

Scale and Proportion

For more clarity, we keep the measurement scales consistent and avoid distortions for accuracy. This ensures the exact difference between all the values. It will present data relationships and prevent misinterpretation due to skewed visual perceptions.

Tips for Customizing Graphs

PowerPoint provides various customization options—Right-click on elements like axes, data points, or legends to format them. You can also change colors, fonts, and styles to match your presentation’s look.

Coloring Your Data

When you want to make different parts of your chart stand out, click on a bar or line. Then, right-click and choose “Format Data Series.” Here, you can pick a color that helps each set of data pop. Do this for each part of your chart to make it visually appealing.

Changing the Chart Background

If you want to change the background color around your chart, right-click on the white space. Choose “Format Chart Area” and change the background color to something that complements your data.

Customizing Line Styles

Change the appearance of your lines for a unique look. Click on a line in your chart, right-click, and select “Format Data Series.” Experiment with different line styles, such as solid, dashed, or dotted.

Fine-tuning Axis Appearance

To make your chart axes look polished, right-click on the X or Y axis and choose “Format Axis.” Adjust properties like line color, tick marks, and label font to suit your design.

Perfecting Legends

Legends can be tweaked for a more integrated look. Right-click on the legend, select “Format Legend,” and adjust options like placement, font size, and background color to enhance the overall appearance.

Creating graphs in PowerPoint or Google Slides from scratch can be time-consuming, and ultimately, it won’t yield the same results as professional-made designs. We invite you to discover some cool designs for presentation graphs PPT templates made by SlideModel.

1. Dashboard Presentation Graph for PowerPoint & Google Slides

Don’t worry about how to make a graph in PowerPoint – let’s us bring the resources in the shape of a cool dashboard layout. Ideal for any kind of e-commerce business, you can track expenses or income, evaluate metrics, and much more.

Use This Template

2. Infographic Donut Chart Presentation Template

Explain concepts in different hierarchy levels, or processes that require a set of sequential steps by implementing this donut chart PPT template. Each segment has a bubble callout to expand further information for the areas required.

3. Presentation Graph Slide Deck PPT Template

All that’s required to create a data-driven presentation is here. Customize donut charts, funnels, histograms, point & figure charts, and more to create professionally-designed presentation slides.

4. PowerPoint Charts Slide Deck

If you’re looking for clean layouts for column graphs, area charts, line graphs and donut charts, this is the template you need in your toolbox. Perfect for marketing, financial and academic reports.

Consider its relevance to the content when incorporating your graph into the presentation. Insert the graph in a slide where it logically fits within the flow of information.

Positioning the Graph Appropriately in the Presentation

Deciding where to put your graph in the presentation is essential. You want it to be where everyone can see it easily and where it makes sense. Usually, you place the graph on a slide that talks about the data or topic related to the graph. This way, people can look at the graph simultaneously when you talk about it. Make sure it is not too small. If needed, you can make it bigger or smaller to fit nicely on the slide. The goal is to position the graph so that it helps your audience understand your information better.

Ensuring Consistency with the Overall Design of the Presentation

Align the graph with the overall design of your presentation to maintain a cohesive visual appeal. You can use consistent colors, fonts, and styles to integrate the graph seamlessly. The graph must complement the theme and tone of your slides. Therefore, you should avoid flashy or distracting elements that may deviate from the established design. The goal is to create a harmonious and professional presentation where the graph blends naturally without causing visual disruptions. However, we recommend you use bar chart templates already available for presentation.

Narrating Your Graph

When explaining your graph during the presentation, start by providing context. Clearly state what the graph illustrates and its significance to the audience. Use simple and direct language, avoiding unnecessary jargon. It is important to walk through the axes, data points, and any trends you want to highlight. Speaking moderately allows the audience to absorb the information without feeling rushed. You can take pause when needed to emphasize crucial points or transitions.

You can learn more about creative techniques to narrate your graph in our data storytelling guide.

Overloading with Information

One common mistake is presenting too much information on a single graph. Avoid filling the graph with excessive data points or unnecessary details.

Misleading Scales or Axes

Scale mistakes, such as uneven intervals or a bar chart with zero baselines, are common graphical mistakes [5]. Misleading scales can distort the interpretation of the graph and lead to incorrect conclusions. Scales should accurately present the data without exaggerating certain aspects.

Inappropriate Graph Types for the Data

Selecting an inappropriate graph type for your data is a mistake to avoid. Choose a graph type that effectively communicates the nature of your data. For instance, a pie chart for time-based trends might not be the most suitable choice. Match the graph type to the data characteristics to convey information accurately.

Working with presentation graphs may feel challenging for a beginner in presentation design software. Still, practice makes the master. Start by clearly stating your objectives in terms of data representation—this will make the presentation graph-type selection process much easier. Customize the graph by working with appropriate color combinations (you can learn more about this in our color theory guide), as this can also help highlight relevant data sections that may influence an informed decision.

Everything depends on your creative skills and how you want to showcase information. As a final piece of advice, we highly recommend working with one graph per slide, unless you opted for a dashboard layout. Ideally, graphs should be seen from a distance, and working with reduced sizes may hinder accurate data representation.

[1] https://uogqueensmcf.com/wp-content/uploads/2020/BA Modules/Medical Laboratory/Medical Laboratory Courses PPT/Year III Sem II/Biostatistics/lecture 1.pdf (Accessed: 06 March 2024).

[2] Five Principles of Good Graphs. https://scc.ms.unimelb.edu.au/resources/data-visualisation-and-exploration/data-visualisation

[3} Guide to fairly good graphs. Statistics LibreTexts. https://stats.libretexts.org/Bookshelves/Applied_Statistics/Biological_Statistics_(McDonald)/07%3A_Miscellany/7.02%3A_Guide_to_Fairly_Good_Graphs

[4] Practical rules for using color in charts. https://nbisweden.github.io/Rcourse/files/rules_for_using_color.pdf

[5] https://iase-web.org/islp/documents/Media/How%20To%20Avoid.pdf [6] Duquia, R.P. et al. (2014) Presenting data in tables and charts , Anais brasileiros de dermatologia . 10.1590/abd1806-4841.20143388

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• Graphical Presentation of Data

Table of Contents

Graphical presentation of data is an essential tool for researchers and decision-makers to convey complex information in a clear and concise manner. It involves using different types of charts, graphs, and diagrams to represent numerical data visually. In this blog, we will explore the different types of graphical representation and their applications in research.

Types of Graphs and Charts

• Bar Graphs: Used to compare discrete values, such as sales figures for different products.
• Line Graphs: Used to show trends over time, such as stock prices over a period.
• Pie Charts: Used to represent parts of a whole, such as the percentage of revenue by product category.
• Scatter Plots: Used to show the relationship between two variables, such as the correlation between temperature and ice cream sales.
• Heat Maps: Used to show the density of data, such as the concentration of customer complaints by region.

Choosing the Right Graphical Representation

The choice of graphical representation depends on the nature of the data and the purpose of the analysis. Some factors to consider include:

• Data type (discrete or continuous)
• Data distribution (normal or skewed)
• Number of variables
• Audience preferences

Best Practices for Graphical Presentation of Data

• Keep it simple and uncluttered
• Use appropriate scales and axes labels
• Use colors and patterns judiciously
• Avoid 3D effects and unnecessary embellishments
• Provide clear titles and captions
• Use appropriate fonts and font sizes
• Ensure readability for colorblind individuals

Graphical presentation of data is a powerful tool for visualizing complex information and communicating insights effectively. By selecting the appropriate chart or graph for the data and following best practices for presentation, researchers and decision-makers can make informed decisions and gain a deeper understanding of their data.

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Research Methodology for Management Decisions

1 Research Methodology: An Overview

• Meaning of Research
• Research Methodology
• Research Method
• Business Research Method
• Types of Research
• Importance of business research
• Role of research in important areas

2 Steps for Research Process

• Research process
• Define research problems
• Research Problem as Hypothesis Testing
• Extensive literature review in research
• Development of working hypothesis
• Preparing the research design
• Collecting the data
• Analysis of data
• Preparation of the report or the thesis

3 Research Designs

• Functions and Goals of Research Design
• Characteristics of a Good Design
• Different Types of Research Designs
• Exploratory Research Design
• Descriptive Research Design
• Experimental Research Design
• Types of Experimental Designs

4 Methods and Techniques of Data Collection

• Primary and Secondary Data
• Methods of Collecting Primary Data
• Merits and Demerits of Different Methods of Collecting Primary Data
• Designing a Questionnaire
• Pretesting a Questionnaire
• Editing of Primary Data
• Technique of Interview
• Collection of Secondary Data
• Scrutiny of Secondary Data

5 Attitude Measurement and Scales

• Attitudes, Attributes and Beliefs
• Issues in Attitude Measurement
• Scaling of Attitudes
• Deterministic Attitude Measurement Models: The Guttman Scale
• Thurstone’s Equal-Appearing Interval Scale
• The Semantic Differential Scale
• Summative Models: The Likert Scale
• The Q-Sort Technique
• Multidimensional Scaling
• Selection of an Appropriate Attitude Measurement Scale
• Limitations of Attitude Measurement Scales

6 Questionnaire Designing

• Introductory decisions
• Contents of the questionnaire
• Format of the questionnaire
• Steps involved in the questionnaire
• Structure and Design of Questionnaire
• Management of Fieldwork
• Ambiguities in the Questionnaire Methods

7 Sampling and Sampling Design

• Advantage of Sampling Over Census
• Simple Random Sampling
• Sampling Frame
• Probabilistic As pects of Sampling
• Stratified Random Sampling
• Other Methods of Sampling
• Sampling Design
• Non-Probability Sampling Methods

8 Data Processing

• Editing of Data
• Coding of Data
• Classification of Data
• Statistical Series
• Tables as Data Presentation Devices

9 Statistical Analysis and Interpretation of Data: Nonparametric Tests

• One Sample Tests
• Two Sample Tests
• K Sample Tests

10 Multivariate Analysis of Data

• Regression Analysis
• Discriminant Analysis
• Factor Analysis

11 Ethics in Research

• Principles of research ethics
• Advantages of research ethics
• Limitations of the research ethics
• Steps involved in ethics
• What are research misconducts?

12 Substance of Reports

• Research Proposal
• Categories of Report
• Reviewing the Draft

13 Formats of Reports

• Parts of a Report
• Cover and Title Page
• Introductory Pages
• Reference Section
• Typing Instructions
• Copy Reading
• Proof Reading

14 Presentation of a Report

• Communication Dimensions
• Presentation Package
• Audio-Visual Aids
• Presenter’s Poise

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Blog Data Visualization 10 Data Presentation Examples For Strategic Communication

10 Data Presentation Examples For Strategic Communication

Written by: Krystle Wong Sep 28, 2023

Knowing how to present data is like having a superpower. 

Data presentation today is no longer just about numbers on a screen; it’s storytelling with a purpose. It’s about captivating your audience, making complex stuff look simple and inspiring action. 

To help turn your data into stories that stick, influence decisions and make an impact, check out Venngage’s free chart maker or follow me on a tour into the world of data storytelling along with data presentation templates that work across different fields, from business boardrooms to the classroom and beyond. Keep scrolling to learn more! 

Click to jump ahead:

10 Essential data presentation examples + methods you should know

What should be included in a data presentation, what are some common mistakes to avoid when presenting data, faqs on data presentation examples, transform your message with impactful data storytelling.

Data presentation is a vital skill in today’s information-driven world. Whether you’re in business, academia, or simply want to convey information effectively, knowing the different ways of presenting data is crucial. For impactful data storytelling, consider these essential data presentation methods:

1. Bar graph

Ideal for comparing data across categories or showing trends over time.

Bar graphs, also known as bar charts are workhorses of data presentation. They’re like the Swiss Army knives of visualization methods because they can be used to compare data in different categories or display data changes over time. 

In a bar chart, categories are displayed on the x-axis and the corresponding values are represented by the height of the bars on the y-axis. 

It’s a straightforward and effective way to showcase raw data, making it a staple in business reports, academic presentations and beyond.

Make sure your bar charts are concise with easy-to-read labels. Whether your bars go up or sideways, keep it simple by not overloading with too many categories.

2. Line graph

Great for displaying trends and variations in data points over time or continuous variables.

Line charts or line graphs are your go-to when you want to visualize trends and variations in data sets over time.

One of the best quantitative data presentation examples, they work exceptionally well for showing continuous data, such as sales projections over the last couple of years or supply and demand fluctuations. 

The x-axis represents time or a continuous variable and the y-axis represents the data values. By connecting the data points with lines, you can easily spot trends and fluctuations.

A tip when presenting data with line charts is to minimize the lines and not make it too crowded. Highlight the big changes, put on some labels and give it a catchy title.

3. Pie chart

Useful for illustrating parts of a whole, such as percentages or proportions.

Pie charts are perfect for showing how a whole is divided into parts. They’re commonly used to represent percentages or proportions and are great for presenting survey results that involve demographic data. 

Each “slice” of the pie represents a portion of the whole and the size of each slice corresponds to its share of the total. 

While pie charts are handy for illustrating simple distributions, they can become confusing when dealing with too many categories or when the differences in proportions are subtle.

Don’t get too carried away with slices — label those slices with percentages or values so people know what’s what and consider using a legend for more categories.

4. Scatter plot

Effective for showing the relationship between two variables and identifying correlations.

Scatter plots are all about exploring relationships between two variables. They’re great for uncovering correlations, trends or patterns in data. 

In a scatter plot, every data point appears as a dot on the chart, with one variable marked on the horizontal x-axis and the other on the vertical y-axis.

By examining the scatter of points, you can discern the nature of the relationship between the variables, whether it’s positive, negative or no correlation at all.

If you’re using scatter plots to reveal relationships between two variables, be sure to add trendlines or regression analysis when appropriate to clarify patterns. Label data points selectively or provide tooltips for detailed information.

5. Histogram

Best for visualizing the distribution and frequency of a single variable.

Histograms are your choice when you want to understand the distribution and frequency of a single variable. 

They divide the data into “bins” or intervals and the height of each bar represents the frequency or count of data points falling into that interval. 

Histograms are excellent for helping to identify trends in data distributions, such as peaks, gaps or skewness.

Here’s something to take note of — ensure that your histogram bins are appropriately sized to capture meaningful data patterns. Using clear axis labels and titles can also help explain the distribution of the data effectively.

6. Stacked bar chart

Useful for showing how different components contribute to a whole over multiple categories.

Stacked bar charts are a handy choice when you want to illustrate how different components contribute to a whole across multiple categories. 

Each bar represents a category and the bars are divided into segments to show the contribution of various components within each category. 

This method is ideal for highlighting both the individual and collective significance of each component, making it a valuable tool for comparative analysis.

Stacked bar charts are like data sandwiches—label each layer so people know what’s what. Keep the order logical and don’t forget the paintbrush for snazzy colors. Here’s a data analysis presentation example on writers’ productivity using stacked bar charts:

7. Area chart

Similar to line charts but with the area below the lines filled, making them suitable for showing cumulative data.

Area charts are close cousins of line charts but come with a twist. 

Imagine plotting the sales of a product over several months. In an area chart, the space between the line and the x-axis is filled, providing a visual representation of the cumulative total. 

This makes it easy to see how values stack up over time, making area charts a valuable tool for tracking trends in data.

For area charts, use them to visualize cumulative data and trends, but avoid overcrowding the chart. Add labels, especially at significant points and make sure the area under the lines is filled with a visually appealing color gradient.

8. Tabular presentation

Presenting data in rows and columns, often used for precise data values and comparisons.

Tabular data presentation is all about clarity and precision. Think of it as presenting numerical data in a structured grid, with rows and columns clearly displaying individual data points. 

A table is invaluable for showcasing detailed data, facilitating comparisons and presenting numerical information that needs to be exact. They’re commonly used in reports, spreadsheets and academic papers.

When presenting tabular data, organize it neatly with clear headers and appropriate column widths. Highlight important data points or patterns using shading or font formatting for better readability.

9. Textual data

Utilizing written or descriptive content to explain or complement data, such as annotations or explanatory text.

Textual data presentation may not involve charts or graphs, but it’s one of the most used qualitative data presentation examples. 

It involves using written content to provide context, explanations or annotations alongside data visuals. Think of it as the narrative that guides your audience through the data. 

Well-crafted textual data can make complex information more accessible and help your audience understand the significance of the numbers and visuals.

Textual data is your chance to tell a story. Break down complex information into bullet points or short paragraphs and use headings to guide the reader’s attention.

10. Pictogram

Using simple icons or images to represent data is especially useful for conveying information in a visually intuitive manner.

Pictograms are all about harnessing the power of images to convey data in an easy-to-understand way. 

Instead of using numbers or complex graphs, you use simple icons or images to represent data points. 

For instance, you could use a thumbs up emoji to illustrate customer satisfaction levels, where each face represents a different level of satisfaction. 

Pictograms are great for conveying data visually, so choose symbols that are easy to interpret and relevant to the data. Use consistent scaling and a legend to explain the symbols’ meanings, ensuring clarity in your presentation.

Looking for more data presentation ideas? Use the Venngage graph maker or browse through our gallery of chart templates to pick a template and get started! 

A comprehensive data presentation should include several key elements to effectively convey information and insights to your audience. Here’s a list of what should be included in a data presentation:

1. Title and objective

• Begin with a clear and informative title that sets the context for your presentation.
• State the primary objective or purpose of the presentation to provide a clear focus.

2. Key data points

• Present the most essential data points or findings that align with your objective.
• Use charts, graphical presentations or visuals to illustrate these key points for better comprehension.

3. Context and significance

• Provide a brief overview of the context in which the data was collected and why it’s significant.
• Explain how the data relates to the larger picture or the problem you’re addressing.

4. Key takeaways

• Summarize the main insights or conclusions that can be drawn from the data.
• Highlight the key takeaways that the audience should remember.

5. Visuals and charts

• Use clear and appropriate visual aids to complement the data.
• Ensure that visuals are easy to understand and support your narrative.

6. Implications or actions

• Discuss the practical implications of the data or any recommended actions.
• If applicable, outline next steps or decisions that should be taken based on the data.

7. Q&A and discussion

• Allocate time for questions and open discussion to engage the audience.
• Address queries and provide additional insights or context as needed.

Presenting data is a crucial skill in various professional fields, from business to academia and beyond. To ensure your data presentations hit the mark, here are some common mistakes that you should steer clear of:

Overloading with data

Presenting too much data at once can overwhelm your audience. Focus on the key points and relevant information to keep the presentation concise and focused. Here are some free data visualization tools you can use to convey data in an engaging and impactful way. 

Assuming everyone’s on the same page

It’s easy to assume that your audience understands as much about the topic as you do. But this can lead to either dumbing things down too much or diving into a bunch of jargon that leaves folks scratching their heads. Take a beat to figure out where your audience is coming from and tailor your presentation accordingly.

Misleading visuals

Using misleading visuals, such as distorted scales or inappropriate chart types can distort the data’s meaning. Pick the right data infographics and understandable charts to ensure that your visual representations accurately reflect the data.

Not providing context

Data without context is like a puzzle piece with no picture on it. Without proper context, data may be meaningless or misinterpreted. Explain the background, methodology and significance of the data.

Not citing sources properly

Neglecting to cite sources and provide citations for your data can erode its credibility. Always attribute data to its source and utilize reliable sources for your presentation.

Not telling a story

Avoid simply presenting numbers. If your presentation lacks a clear, engaging story that takes your audience on a journey from the beginning (setting the scene) through the middle (data analysis) to the end (the big insights and recommendations), you’re likely to lose their interest.

Infographics are great for storytelling because they mix cool visuals with short and sweet text to explain complicated stuff in a fun and easy way. Create one with Venngage’s free infographic maker to create a memorable story that your audience will remember.

Ignoring data quality

Presenting data without first checking its quality and accuracy can lead to misinformation. Validate and clean your data before presenting it.

Simplify your visuals

Fancy charts might look cool, but if they confuse people, what’s the point? Go for the simplest visual that gets your message across. Having a dilemma between presenting data with infographics v.s data design? This article on the difference between data design and infographics might help you out. 

Missing the emotional connection

Data isn’t just about numbers; it’s about people and real-life situations. Don’t forget to sprinkle in some human touch, whether it’s through relatable stories, examples or showing how the data impacts real lives.

Skipping the actionable insights

At the end of the day, your audience wants to know what they should do with all the data. If you don’t wrap up with clear, actionable insights or recommendations, you’re leaving them hanging. Always finish up with practical takeaways and the next steps.

Can you provide some data presentation examples for business reports?

Business reports often benefit from data presentation through bar charts showing sales trends over time, pie charts displaying market share,or tables presenting financial performance metrics like revenue and profit margins.

What are some creative data presentation examples for academic presentations?

Creative data presentation ideas for academic presentations include using statistical infographics to illustrate research findings and statistical data, incorporating storytelling techniques to engage the audience or utilizing heat maps to visualize data patterns.

What are the key considerations when choosing the right data presentation format?

When choosing a chart format , consider factors like data complexity, audience expertise and the message you want to convey. Options include charts (e.g., bar, line, pie), tables, heat maps, data visualization infographics and interactive dashboards.

Knowing the type of data visualization that best serves your data is just half the battle. Here are some best practices for data visualization to make sure that the final output is optimized. 

How can I choose the right data presentation method for my data?

To select the right data presentation method, start by defining your presentation’s purpose and audience. Then, match your data type (e.g., quantitative, qualitative) with suitable visualization techniques (e.g., histograms, word clouds) and choose an appropriate presentation format (e.g., slide deck, report, live demo).

For more presentation ideas , check out this guide on how to make a good presentation or use a presentation software to simplify the process.  

How can I make my data presentations more engaging and informative?

To enhance data presentations, use compelling narratives, relatable examples and fun data infographics that simplify complex data. Encourage audience interaction, offer actionable insights and incorporate storytelling elements to engage and inform effectively.

The opening of your presentation holds immense power in setting the stage for your audience. To design a presentation and convey your data in an engaging and informative, try out Venngage’s free presentation maker to pick the right presentation design for your audience and topic. 

What is the difference between data visualization and data presentation?

Data presentation typically involves conveying data reports and insights to an audience, often using visuals like charts and graphs. Data visualization , on the other hand, focuses on creating those visual representations of data to facilitate understanding and analysis. 

Now that you’ve learned a thing or two about how to use these methods of data presentation to tell a compelling data story , it’s time to take these strategies and make them your own. 

But here’s the deal: these aren’t just one-size-fits-all solutions. Remember that each example we’ve uncovered here is not a rigid template but a source of inspiration. It’s all about making your audience go, “Wow, I get it now!”

Think of your data presentations as your canvas – it’s where you paint your story, convey meaningful insights and make real change happen. 

So, go forth, present your data with confidence and purpose and watch as your strategic influence grows, one compelling presentation at a time.

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Presentation ideas • Tips and Tricks

15 Creative Ways to Use Charts and Graphs in Presentations

Emily Bryce

12 December 2022

In today’s data-driven world, presentations are no longer just about presenting ideas and concepts, but also about presenting data in an engaging and easy-to-understand manner. This is where charts and graphs come in. They help to visualize data, making it easier for the audience to grasp and retain information. In this blog post, we will explore creative ways to use charts and graphs in presentations.

1. Use charts and graphs to compare data

One of the most common uses of charts and graphs is to compare data. Whether you are comparing sales figures, market trends or customer feedback, charts and graphs can help you present the information in a visually compelling way. Use bar charts, line graphs, pie charts, and scatter plots to showcase the data in a way that makes it easy to understand and compare.

2. Use charts and graphs to show trends

Another way to use charts and graphs in presentations is to show trends over time. For example, if you are presenting the growth of your business over the last five years, use a line graph to show the upward trend. If you want to show the fluctuations in your business over a period of time, use a scatter plot to highlight the highs and lows.

3. Use charts and graphs to show relationships

Charts and graphs can also be used to show the relationship between different sets of data. For example, if you are presenting the correlation between customer satisfaction and sales, use a scatter plot to show the relationship between the two variables. You can also use bubble charts to show the relationship between three different variables.

4. Use charts and graphs to show distribution

If you are presenting data that is distributed across a range, such as the ages of your customers, use a histogram to show the distribution. Histograms are great for showing the frequency distribution of data, and they can help you identify patterns and trends in the data.

5. Use charts and graphs to show proportions

Pie charts are a great way to show proportions. Use pie charts to show the proportion of sales for different products or the proportion of the budget allocated to different departments. Make sure to keep the number of categories to a minimum to ensure that the chart is easy to read.

6. Use creative chart and graph designs

Charts and graphs don’t have to be boring. Use creative designs and colors to make your charts and graphs stand out. For example, you can use a bar chart with a gradient background to make it more visually appealing. You can also use icons and images to make your charts and graphs more engaging.

7. Use charts and graphs to tell a story

Finally, use charts and graphs to tell a story. Don’t just present the data, but use it to support your message. Use a combination of charts and graphs to create a narrative that engages your audience and leaves them with a clear understanding of your message.

In conclusion, charts and graphs are a powerful tool for presenting data in an engaging and easy-to-understand manner. Use them creatively to showcase data, tell a story, and leave a lasting impression on your audience. With the right use of charts and graphs, you can take your presentations to the next level.

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Graphical Representation: Advantages, Types & Examples

Graphical Representation: A graph is a categorised representation of data. It helps us understand the data easily. Data is a collection of numerical figures collected through surveying. The word data came from the Latin word ‘Datum’, which means ‘something given’. After developing a research question, data is being collected constantly through observation. Then the data collected is arranged, summarised, classified, and finally represented graphically. This is the concept of graphical representation of data.

Let’s study different kinds of graphical representations with examples, the types of graphical representation, and graphical representation of data in statistics, in this article.

What Are Graphical Representations?

Graphical representation refers to the use of intuitive charts to visualise clearly and simplify data sets. Data obtained from surveying is ingested into a graphical representation of data software. Then it is represented by some symbols, such as lines on a line graph, bars on a bar chart, or slices of a pie chart. In this way, users can achieve much more clarity and understanding than by numerical study alone. 

Advantages of Graphical Representation

Some of the advantages of using graphs are listed below:

• The graph helps us understand the data or information even when we have no idea about it.
• It saves time.
• It makes it easier for us to compare the data for different time periods or different kinds.
• It is mainly used in statistics to determine the mean, median and mode for different data and interpolation and extrapolation of data.

Use of Graphical Representations

The main agenda of presenting scientific data into graphs is to provide information efficiently to utilise the power of visual display while avoiding confusion or deception. This is important in communicating our findings to others and our understanding and analysis of the data.

Graphical data representation is crucial in understanding and identifying trends and patterns in the ever-increasing data flow. Graphical representation helps in quick analysis of large quantities and can support making predictions and informed decisions.

General Rules for Graphical Representation of Data

The following are a few rules to present the information in the graphical representation:

• Suitable title:  The title of the graph should be appropriate that indicates the subject of the presentation.
• Measurement unit:  The measurement unit in the graph should be mentioned.
• Proper scale:   Choose a proper scale to represent the data accurately.
• Index:  For better understanding, index the appropriate colours, shades, lines, and designs in the graphs. 
• Data sources:  Data should be included wherever it is necessary at the bottom of the graph.
• Keep it simple:  The construction of a graph should be such a way that it is effortlessly understood.
• Neat:  The correct size, fonts, colours etc., should be chosen so that the graph should be a visual aid for presenting the information.

Types of Graphical Representation

1. Line graph 2. Histogram 3. Bar graph 4. Pie chart 5. Frequency polygon 6. Ogives or Cumulative frequency graphs

1. Line Graph

A line graph is a chart used to show information that changes over time. We plot line graphs by connecting several points with straight lines.  Another name is a line chart. The line graph contains two axes: \(x-\)axis and \(y-\)axis.

• The horizontal axis is the \(x-\)axis.
• The vertical axis is the \(y-\)axis.

Example: The following graph shows the number of motorbikes sold on different days of the week.

2. Histogram

Continuous data represented on the two-dimensional graph is called a histogram. In the histogram, the bars are placed continuously side by side without a gap between consecutive bars. In other words, rectangles are erected on the class intervals of the distribution. The areas of the rectangles formed by bars are proportional to the frequencies.

Example: Following is an example of a histogram showing the average pass percentage of students.

3. Bar Graph

Bar graphs can be of two types – horizontal bar graphs and vertical bar graphs. While a horizontal bar graph is applied for qualitative data or data varying over space, the vertical bar graph is associated with quantitative data or time-series data.

Bars are rectangles of varying lengths and of equal width usually are drawn either horizontally or vertically. We consider multiple or grouped bar graphs to compare related series. Component or sub-divided bar diagrams are applied for representing data divided into several components. 

Example:  The following graph is an example of a bar graph representing the money spent month-wise

4. Pie Chart

The sector of a circle represents various observations or components, and the whole circle represents the sum of the value of all the components. The total central angle of a circle is \({360^{\rm{o}}}\) and is divided according to the values of the components.

The central angle of a component\( = \frac{{{\rm{ value}}\,{\rm{of}}\,{\rm{the}}\,{\rm{component }}}}{{{\rm{total}}\,{\rm{value}}}} \times {360^{\rm{o}}}\)

Sometimes, the value of the components is expressed in percentages. In such cases, The central angle of a component\( = \frac{{{\rm{ percentage}}\,{\rm{value}}\,{\rm{of}}\,{\rm{the}}\,{\rm{component }}}}{{100}} \times {360^{\rm{o}}}\)

Example:  The following figure represents a pie-chart

5. Frequency Polygon

A frequency polygon is another way of representing frequency distribution graphically. Follow the steps below to make a frequency polygon:

(i) Calculate and obtain the frequency distribution and the mid-points of each class interval. (ii) Represent the mid-points along the \(x-\)axis and the frequencies along the \(y-\)axis. (iii) Mark the points corresponding to the frequency at each midpoint. (iv) Now join these points in straight lines. (v) To finish the frequency polygon, join the consecutive points at each end (as the case may be at zero frequency) on the \(x-\)axis.

Example: The following graph is the frequency polygon showing the road race results.

6. Ogives or Cumulative Frequency Graphs

By plotting cumulative frequency against the respective class intervals, we obtain ogives. There are two ogives – less than type ogives and more than type.

Less than type ogives is obtained by taking less than cumulative frequency on the vertical axis. We can obtain more than type ogives by plotting more than type cumulative frequency on the vertical axis and joining the plotted points successively by line segments.

Example: The below graph represents the less than and more than ogives for the entrance examination scores of \(60\) students.

Solved Examples – Basic Graphical Representation

Q.1. The wildlife population in the following years, \(2013, 2014, 2015, 2016, 2017, 2018,\) and \(2019\) were \(300, 200, 400, 600, 500, 400\) and \(500,\) respectively. Represent these data using a line graph. Ans: We can represent the population for seven consecutive years by drawing a line diagram as given below. Let us consider years on the horizontal axis and population on the vertical axis.

For the year \(2013,\) the population was \(300.\) It can be written as a point \((2013, 300)\) Similarly, we can write the points for the succeeding years as follows: \((2014, 200), (2015, 400), (2016, 600), (2017, 500), (2018, 400)\) and \((2019, 500)\)

We can obtain the line graph by plotting all these points and joining them using a ruler. The following line diagram shows the population of wildlife from \(2013\) to \(2019.\)

Q.2. Draw a histogram for the following data that represents the marks scored by \(120\) students in an examination:

Ans: The class intervals are of an equal length of \(20\) marks. Let us indicate the class intervals along the \(x-\)axis and the number of students along the \(y-\)axis, with the appropriate scale. The histogram is given below.

Q.3. The total number of scoops of vanilla ice cream in the different months of a year is given below:

For the above data, draw a bar graph. Ans: The following graph represents the number of vanilla ice cream scoops sold from March to July. The month is indicated along the \(x-\)axis, and the number of scoops sold is represented along the \(y-\)axis.

Q.4. The number of hours spent by a working woman on various activities on a working day is given below. Using the angle measurement, draw a pie chart.

Ans: The central angle of a component\( = \frac{{{\rm{ value}}\,{\rm{of}}\,{\rm{the}}\,{\rm{component }}}}{{{\rm{total}}\,{\rm{value}}}} \times {360^{\rm{o}}}\). We may calculate the central angles for various components as follow:

By knowing the central angle, a pie chart is drawn,

Q.5. Draw a frequency polygon for the following data using a histogram.

Ans: To draw a frequency polygon, we take the imagined classes \(135-140\) at the beginning and \(175-180\) at the end, each with frequency zero. The following is the frequency table tabulated for the given data

Let’s mark the class intervals along the \(x-\)axis and the frequency along the \(y-\)axis.

Using the above table, plot the points on the histogram: \((137.5, 0), (142.5, 35), (147.5, 40), (152.5, 55), (157.5, 50), (162.5, 40),\) \((167.5, 35), (172.5, 20)\) and \((177.5, 0).\)

We join these points one after the other to obtain the required frequency polygon.

In this article, we have studied the details of the graphical representation of data. We learnt the meaning, uses, and advantages of using graphs . Then we studied the different types of graphs with examples. Lastly, we solved examples to help students understand the concept in a better way.

Frequently Asked Questions (FAQs) on Basic Graphical Representation

Q.1: What are graphical representations? Ans: Graphical representations represent given data using charts or graphs numerically and then visually analyse and interpret the information.

Q.2: What are the 6 types of graphs used? Ans: The following are the types of graphs we use commonly: 1. Line graph 2. Histogram 3. Bar graph 4. Pie chart 5. Frequency polygon 6. Ogives or cumulative frequency graphs

Q.3: What are the advantages of the graphical method? Ans: The advantages of using a graphical method are: 1. Facilitates improved learning 2. Knowing the content 3. Usage of flexibility 4. Increases thinking 5. Supports creative, personalised reports for more engaging and stimulating visual presentations 6. Better communication 7. It shows the whole picture

Q.4: What is the graphical representation of an idea? Ans: The graphical representations exhibit relationships between ideas, data, information and concepts in a visual graph or map. Graphical representations are effortless to acknowledge.

Q.5: How do you do frequency polygon? Ans: Frequency distribution is first obtained, and the midpoints of each class interval are found. Mark the midpoints along the \(x-\)axis and frequencies along the \(y-\)axis. Plot the points corresponding to the frequency. Join the points, using line segments in order.

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8 Types of Presentations and Examples of When You Can Use Them

Presentations help you communicate ideas in a simple way that sticks with your target audience. here’s what you need to know to have success with all types of presentations..

For your presentation to be effective, you need to choose the right format and recognize the nuances of each one. Here’s a look at eight types of presentations you can use to share your knowledge.

8 Types of Presentations

1. Providing Information

The primary purpose of any type of presentation is to provide information to an audience. The difference between this method and others is that there are many elements you have to consider in order to be effective. That includes slide design , talking points, and usually, a time limit.

2. Teaching

When you’re educating, use several examples to illustrate your points. If your audience doesn’t understand something you’re talking about, give them specific examples so they can see for themselves what you mean.

Repetition is key when you teach a new concept. It’s important to include a variety examples throughout your slide deck to reinforce your information. This helps combat your audience getting bored or tired from hearing the same thing over and over again.

3. Reporting

You can use presentations when reporting by showing research findings and conclusions. The most important thing to remember is that you need to design your slides to highlight your most critical data. That way, your audience will walk away understanding its high points.

It’s important to know your audience before you jump into your presentation and start selling. Research must be the first step of the process, so you can design a presentation that speaks to your people.

Also, be sure to not overwhelm yourself or others by packing too much information into one slide.

5. Problem-Solving

While it’s a less common use case, you can also use presentations to sort out problems. This is especially useful when you’re working with a team. It acts as a simple way to get everyone on the same page before making a decision.

6. Decision Making

Once you come to an agreement that something is an issue and discover some ways to solve it, there are still choices you need to make. You can use presentations to explore and explain different options before you finalize your next step forward.

7. Entertaining

Creating a presentation with entertainment in mind is a nice way to break up any potential monotony and deliver important information, at the same time.

The entertainment factor doesn’t necessarily have to be goofy or fun, but it should be compelling for the audience and capture their attention. Visuals are particularly important here.

8. Motivational

Stories are good tools for bringing any message home. Use personal anecdotes and examples that illustrate points. This will help people remember your message when they need it most, and it also makes it easier for the audience to connect with you.

3 Presentation Use Cases

Want to take your information and put it in presentation format for your audience? Before you start, use these examples to gain inspiration.

1. Business Presentation Examples

Business presentations don’t have to be boring. Take these tips to wow your colleagues and your audience. 

Conferences

There are many different companies and ideas competing for attention at conferences. Use storytelling and bold design choices to stand out.

Raising Awareness

Getting a new initiative going in an organization is no easy feat. Use a presentation to fill in stakeholders on what you want to do and get their approval.

Sales Decks

Selling has a direct impact on revenue goals, so it’s critical for your presentation to support that. Include questions, pain points, and supporting data to let your potential customers know you “get” them.

2. Presentation Ideas for Kids and Students

Education requires a lot of listening and absorbing information. Help kids and students show what they know with these presentation formats.

All About Them

For younger or new students, this is an easy presentation idea. They can create slides that explain details about themselves to learn the art of public speaking. It also helps their peers get to know them better.

Charts and Graphics

Facts and data play a key role in understanding a concept. However, keeping track of them all can be intimidating. Take them through the process of communicating complex ideas visually, with this presentation idea for students.

Storytelling

Stories are an important part of early learning but, eventually, we all learn there’s a place for stories outside of a book. Students and kids can create presentations that focus on this skill.

3. Virtual Presentation Ideas

Virtual presentations are more prevalent than ever, but engaging an audience when you aren’t in the same room isn’t easy.

If you’re sharing ideas with a group, make it interactive by giving a workshop-style presentation. Be sure to leave room to ask and answer questions, as well as save space for group discussions.

Ask Me Anything

The question and answer format is a popular presentation type, but you can add even more interest with slides. Use images, fonts , and colors that are on brand and increase engagement. 

Information and Gamification

Gamification results in 14% higher scores on skill-based assessments. To amplify people’s understanding of the concepts you present, use gamification throughout your slide deck.

How to Put Together Presentation Ideas without PowerPoint

If you’re looking for creative presentation ideas without PowerPoint , Shutterstock Create’s slideshow presentation maker is easy to use. Our designer-crafted templates are super-simple to customize and make your own in just a few clicks. 

We have thousands of graphics in a multitude of styles, shapes, and sizes you can use to create designs that others will notice. We also offer gorgeous stock photos to help you communicate exactly what you need to with each visual. Everyone has something to teach, now it’s your turn. Use these ideas to create all types of presentations and communicate effectively.

Need some more presentation inspo? We’ve got you covered:

• How to Make a Professional Video Presentation
• 10 Fun “Presentation Night” Ideas
• Google Slides vs. PowerPoint: Which Is Best to Make a Slideshow?

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• Graphic Presentation of Data

Apart from diagrams, Graphic presentation is another way of the presentation of data and information. Usually, graphs are used to present time series and frequency distributions. In this article, we will look at the graphic presentation of data and information along with its merits, limitations , and types.

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Construction of a graph.

The graphic presentation of data and information offers a quick and simple way of understanding the features and drawing comparisons. Further, it is an effective analytical tool and a graph can help us in finding the mode, median, etc.

We can locate a point in a plane using two mutually perpendicular lines – the X-axis (the horizontal line) and the Y-axis (the vertical line). Their point of intersection is the Origin .

We can locate the position of a point in terms of its distance from both these axes. For example, if a point P is 3 units away from the Y-axis and 5 units away from the X-axis, then its location is as follows:

Browse more Topics under Descriptive Statistics

• Definition and Characteristics of Statistics
• Stages of Statistical Enquiry
• Importance and Functions of Statistics
• Nature of Statistics – Science or Art?
• Application of Statistics
• Law of Statistics and Distrust of Statistics
• Meaning and Types of Data
• Methods of Collecting Data
• Sample Investigation
• Classification of Data
• Tabulation of Data
• Frequency Distribution of Data
• Diagrammatic Presentation of Data
• Measures of Central Tendency
• Mean Median Mode
• Measures of Dispersion
• Standard Deviation
• Variance Analysis

Some points to remember:

• We measure the distance of the point from the Y-axis along the X-axis. Similarly, we measure the distance of the point from the X-axis along the Y-axis. Therefore, to measure 3 units from the Y-axis, we move 3 units along the X-axis and likewise for the other coordinate .
• We then draw perpendicular lines from these two points.
• The point where the perpendiculars intersect is the position of the point P.
• We denote it as follows (3,5) or (abscissa, ordinate). Together, they are the coordinates of the point P.
• The four parts of the plane are Quadrants.
• Also, we can plot different points for a different pair of values.

General Rules for Graphic Presentation of Data and Information

There are certain guidelines for an attractive and effective graphic presentation of data and information. These are as follows:

• Suitable Title – Ensure that you give a suitable title to the graph which clearly indicates the subject for which you are presenting it.
• Unit of Measurement – Clearly state the unit of measurement below the title.
• Suitable Scale – Choose a suitable scale so that you can represent the entire data in an accurate manner.
• Index – Include a brief index which explains the different colors and shades, lines and designs that you have used in the graph. Also, include a scale of interpretation for better understanding.
• Data Sources – Wherever possible, include the sources of information at the bottom of the graph.
• Keep it Simple – You should construct a graph which even a layman (without any exposure in the areas of statistics or mathematics) can understand.
• Neat – A graph is a visual aid for the presentation of data and information. Therefore, you must keep it neat and attractive. Choose the right size, right lettering, and appropriate lines, colors, dashes, etc.

Merits of a Graph

• The graph presents data in a manner which is easier to understand.
• It allows us to present statistical data in an attractive manner as compared to tables. Users can understand the main features, trends, and fluctuations of the data at a glance.
• A graph saves time.
• It allows the viewer to compare data relating to two different time-periods or regions.
• The viewer does not require prior knowledge of mathematics or statistics to understand a graph.
• We can use a graph to locate the mode, median, and mean values of the data.
• It is useful in forecasting, interpolation, and extrapolation of data.

Limitations of a Graph

• A graph lacks complete accuracy of facts.
• It depicts only a few selected characteristics of the data.
• We cannot use a graph in support of a statement.
• A graph is not a substitute for tables.
• Usually, laymen find it difficult to understand and interpret a graph.
• Typically, a graph shows the unreasonable tendency of the data and the actual values are not clear.

Types of Graphs

Graphs are of two types:

• Time Series graphs
• Frequency Distribution graphs

Time Series Graphs

A time series graph or a “ histogram ” is a graph which depicts the value of a variable over a different point of time. In a time series graph, time is the most important factor and the variable is related to time. It helps in the understanding and analysis of the changes in the variable at a different point of time. Many statisticians and businessmen use these graphs because they are easy to understand and also because they offer complex information in a simple manner.

Further, constructing a time series graph does not require a user with technical skills. Here are some major steps in the construction of a time series graph:

• Represent time on the X-axis and the value of the variable on the Y-axis.
• Start the Y-value with zero and devise a suitable scale which helps you present the whole data in the given space.
• Plot the values of the variable and join different point with a straight line.
• You can plot multiple variables through different lines.

You can use a line graph to summarize how two pieces of information are related and how they vary with each other.

• You can compare multiple continuous data-sets easily
• You can infer the interim data from the graph line

Disadvantages

• It is only used with continuous data.

Use of a false Base Line

Usually, in a graph, the vertical line starts from the Origin. However, in some cases, a false Base Line is used for a better representation of the data. There are two scenarios where you should use a false Base Line:

• To magnify the minor fluctuation in the time series data
• To economize the space

Net Balance Graph

If you have to show the net balance of income and expenditure or revenue and costs or imports and exports, etc., then you must use a net balance graph. You can use different colors or shades for positive and negative differences.

Frequency Distribution Graphs

Let’s look at the different types of frequency distribution graphs.

A histogram is a graph of a grouped frequency distribution. In a histogram, we plot the class intervals on the X-axis and their respective frequencies on the Y-axis. Further, we create a rectangle on each class interval with its height proportional to the frequency density of the class.

Frequency Polygon or Histograph

A frequency polygon or a Histograph is another way of representing a frequency distribution on a graph. You draw a frequency polygon by joining the midpoints of the upper widths of the adjacent rectangles of the histogram with straight lines.

Frequency Curve

When you join the verticals of a polygon using a smooth curve, then the resulting figure is a Frequency Curve. As the number of observations increase, we need to accommodate more classes. Therefore, the width of each class reduces. In such a scenario, the variable tends to become continuous and the frequency polygon starts taking the shape of a frequency curve.

Cumulative Frequency Curve or Ogive

A cumulative frequency curve or Ogive is the graphical representation of a cumulative frequency distribution. Since a cumulative frequency is either of a ‘less than’ or a ‘more than’ type, Ogives are of two types too – ‘less than ogive’ and ‘more than ogive’.

Scatter Diagram

A scatter diagram or a dot chart enables us to find the nature of the relationship between the variables. If the plotted points are scattered a lot, then the relationship between the two variables is lesser.

Solved Question

Q1. What are the general rules for the graphic presentation of data and information?

Answer: The general rules for the graphic presentation of data are:

• Use a suitable title
• Clearly specify the unit of measurement
• Ensure that you choose a suitable scale
• Provide an index specifying the colors, lines, and designs used in the graph
• If possible, provide the sources of information at the bottom of the graph
• Keep the graph simple and neat.

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Descriptive Statistics

• Nature of Statistics – Science or Art?

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10 Superb Data Presentation Examples To Learn From

The best way to learn how to present data effectively is to see data presentation examples from the professionals in the field.

We collected superb examples of graphical presentation and visualization of data in statistics, research, sales, marketing, business management, and other areas.

On this page:

How to present data effectively? Clever tips.

• 10 Real-life examples of data presentation with interpretation.

Download the above infographic in PDF

Your audience should be able to walk through the graphs and visualizations easily while enjoy and respond to the story.

[bctt tweet=”Your reports and graphical presentations should not just deliver statistics, numbers, and data. Instead, they must tell a story, illustrate a situation, provide proofs, win arguments, and even change minds.” username=””]

Before going to data presentation examples let’s see some essential tips to help you build powerful data presentations.

1. Keep it simple and clear

The presentation should be focused on your key message and you need to illustrate it very briefly.

Graphs and charts should communicate your core message, not distract from it. A complicated and overloaded chart can distract and confuse. Eliminate anything repetitive or decorative.

2. Pick up the right visuals for the job

A vast number of types of graphs and charts are available at your disposal – pie charts, line and bar graphs, scatter plot , Venn diagram , etc.

Choosing the right type of chart can be a tricky business. Practically, the choice depends on 2 major things: on the kind of analysis you want to present and on the data types you have.

Commonly, when we aim to facilitate a comparison, we use a bar chart or radar chart. When we want to show trends over time, we use a line chart or an area chart and etc.

3. Break the complex concepts into multiple graphics

It’s can be very hard for a public to understand a complicated graphical visualization. Don’t present it as a huge amount of visual data.

Instead, break the graphics into pieces and illustrate how each piece corresponds to the previous one.

4. Carefully choose the colors

Colors provoke different emotions and associations that affect the way your brand or story is perceived. Sometimes color choices can make or break your visuals.

It is no need to be a designer to make the right color selections. Some golden rules are to stick to 3 or 4 colors avoiding full-on rainbow look and to borrow ideas from relevant chart designs.

Another tip is to consider the brand attributes and your audience profile. You will see appropriate color use in the below data presentation examples.

5. Don’t leave a lot of room for words

The key point in graphical data presentation is to tell the story using visuals and images, not words. Give your audience visual facts, not text.

However, that doesn’t mean words have no importance.

A great advice here is to think that every letter is critical, and there’s no room for wasted and empty words. Also, don’t create generic titles and headlines, build them around the core message.

6. Use good templates and software tools

Building data presentation with AI nowadays means using some kind of software programs and templates. There are many available options – from free graphing software solutions to advanced data visualization tools.

Choosing a good software gives you the power to create good and high-quality visualizations. Make sure you are using templates that provides characteristics like colors, fonts, and chart styles.

A small investment of time to research the software options prevents a large loss of productivity and efficiency at the end.

10 Superb data presentation examples 

Here we collected some of the best examples of data presentation made by one of the biggest names in the graphical data visualization software and information research.

These brands put a lot of money and efforts to investigate how professional graphs and charts should look.

1. Sales Stage History  Funnel Chart 

Data is beautiful and this sales stage funnel chart by Zoho Reports prove this. The above funnel chart represents the different stages in a sales process (Qualification, Need Analysis, Initial Offer, etc.) and shows the potential revenue for each stage for the last and this quarter.

The potential revenue for each sales stage is displayed by a different color and sized according to the amount. The chart is very colorful, eye-catching, and intriguing.

2. Facebook Ads Data Presentation Examples

These are other data presentation examples from Zoho Reports. The first one is a stacked bar chart that displays the impressions breakdown by months and types of Facebook campaigns.

Impressions are one of the vital KPI examples in digital marketing intelligence and business. The first graph is designed to help you compare and notice sharp differences at the Facebook campaigns that have the most influence on impression movements.

The second one is an area chart that shows the changes in the costs for the same Facebook campaigns over the months.

The 2 examples illustrate how multiple and complicated data can be presented clearly and simply in a visually appealing way.

3. Sales Opportunity Data Presentation

These two bar charts (stacked and horizontal bar charts) by Microsoft Power Bi are created to track sales opportunities and revenue by region and sales stage.

The stacked bar graph shows the revenue probability in percentage determined by the current sales stage (Lead, Quality, Solution…) over the months. The horizontal bar chart represents the size of the sales opportunity (Small, Medium, Large) according to regions (East, Central, West).

Both graphs are impressive ways for a sales manager to introduce the upcoming opportunity to C-level managers and stakeholders. The color combination is rich but easy to digest.

4. Power 100 Data Visualization 

Want to show hierarchical data? Treemaps can be perfect for the job. This is a stunning treemap example by Infogram.com that shows you who are the most influential industries. As you see the Government is on the top.

This treemap is a very compact and space-efficient visualization option for presenting hierarchies, that gives you a quick overview of the structure of the most powerful industries.

So beautiful way to compare the proportions between things via their area size.

When it comes to best research data presentation examples in statistics, Nielsen information company is an undoubted leader. The above professional looking line graph by Nielsen represent the slowing alcoholic grow of 4 alcohol categories (Beer, Wine, Spirits, CPG) for the period of 12 months.

The chart is an ideal example of a data visualization that incorporates all the necessary elements of an effective and engaging graph. It uses color to let you easily differentiate trends and allows you to get a global sense of the data. Additionally, it is incredibly simple to understand.

6. Digital Health Research Data Visualization Example

Digital health is a very hot topic nowadays and this stunning donut chart by IQVIA shows the proportion of different mobile health apps by therapy area (Mental Health, Diabetes, Kidney Disease, and etc.). 100% = 1749 unique apps.

This is a wonderful example of research data presentation that provides evidence of Digital Health’s accelerating innovation and app expansion.

Besides good-looking, this donut chart is very space-efficient because the blank space inside it is used to display information too.

7. Disease Research Data Visualization Examples

Presenting relationships among different variables is hard to understand and confusing -especially when there is a huge number of them. But using the appropriate visuals and colors, the IQVIA did a great job simplifying this data into a clear and digestible format.

The above stacked bar charts by IQVIA represents the distribution of oncology medicine spendings by years and product segments (Protected Brand Price, Protected Brand Volume, New Brands, etc.).

The chart allows you to clearly see the changes in spendings and where they occurred – a great example of telling a deeper story in a simple way.

8. Textual and Qualitative Data Presentation Example

When it comes to easy to understand and good looking textual and qualitative data visualization, pyramid graph has a top place. To know what is qualitative data see our post quantitative vs qualitative data .

9. Product Metrics Graph Example

If you are searching for excel data presentation examples, this stylish template from Smartsheet can give you good ideas for professional looking design.

The above stacked bar chart represents product revenue breakdown by months and product items. It reveals patterns and trends over the first half of the year that can be a good basis for data-driven decision-making .

10. Supply Chain Data Visualization Example 

This bar chart created by ClicData  is an excellent example of how trends over time can be effectively and professionally communicated through the use of well-presented visualization.

It shows the dynamics of pricing through the months based on units sold, units shipped, and current inventory. This type of graph pack a whole lot of information into a simple visual. In addition, the chart is connected to real data and is fully interactive.

The above data presentation examples aim to help you learn how to present data effectively and professionally.

About The Author

Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

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August 15th, 2024

29 Best Types of Charts and Graphs for Data Visualization

By: Alysha Gullion · 8 min read

Selecting the right chart is crucial for effective data presentation. The choice depends on your data type, audience, and intended message. For example, line charts work well for time trends, while pie charts show proportions. Complex visualizations like correlation heat maps may not suit audiences unfamiliar with data science. This article will outline various graph types and their typical uses, noting that some graphs may fit multiple categories but will be mentioned only once for simplicity. By understanding these options, you can choose the most impactful way to present your data.

How to Find Data for Graphs and Charts

Trying to find high-quality, interesting data for creating charts and graphs is always difficult. We used the following open-source repo of datasets for all of the graphs and charts in this post: vincentarelbundock.github.io . Other options for finding datasets include Kaggle , which is a prominent data science community and data repository, or the UC Irvine Machine Learning Repository .

How to Create Charts and Graphs

Various tools cater to different needs in chart and graph creation. Excel is widely used in business for its simplicity. Tableau is favored by data analysts for interactive visualizations. Researchers often use SPSS for complex statistical graphs, while data scientists prefer R for its programming flexibility. For those seeking a more intuitive approach, Julius offers a unique alternative. Supporting both Python and R, Julius allows users to generate graphs using plain language descriptions, making it accessible to both beginners and experienced users. When choosing a tool, consider your technical skills and visualization requirements.

Comparison Charts

Comparison charts or graphs are used to compare quantities across different categories. Their purpose is to highlight the differences and similarities within data sets, making it easier for viewers to draw conclusions about the variations amongst various groups.

You can find the code associated with these charts by visiting our community forum . 

1. Bar/Column charts

Bar and column charts provide clear comparisons between discrete categories (i.e., car models) based on a quantitative measure (e.g., miles per gallon, MPG). They are widely used as they offer a quick and effective way to visualize differences amongst categorical variables. The difference between bar and column charts is based on their orientation: bar charts display their bars horizontally, while column charts display them vertically.

The data used in this visualization can be accessed here . This data frame consists of 32 observations on 11 numeric variables and was collected in 1974 from Motor Trend US magazine. It details fuel consumption of 10 different motor vehicles. We will create a bar chart to compare miles per gallon between each car model. 

Python Example

The images above compare the fuel efficiency of each car model. The graph shows that the Mercedes-Benz 240D outperforms its counterparts in terms of miles per gallon.

2. Grouped/Clustered Bar Chart

Grouped or clustered bar charts are used to compare frequencies, counts, or other measures across multiple categories and groups. 

For this visualization, we will be using a dataset from the College Scorecard, which contains college-by-year data on how students are doing after graduation, available here . This data frame contains 48,445 rows and 8 variables. We will create a grouped bar chart to compare the counts of working vs. not working for five institutions in the year 2007.

In the images above, we can see that graduates from ASA college tended to have a substantially higher count of ‘working’ individuals compared to the other institutions.

3. Dumbbell Plot

Often mistaken for a type of bar chart, the dumbbell plot differs by displaying two values for each category rather than one. It shows two points connected by a line, which displays the minimum and maximum values of data points for each category. Dumbbell plots are useful for displaying variability, distributions, and confidence intervals within categories. 

For this visualization, we will be using a dataset that contains daily temperatures (minimum and maximum) for Clemson, South Carolina from January 1st, 1930 to December 31st, 2020 (33,148 observations). The dataset can be accessed here .

For simplicity, we will focus on the year 1930 and 2020, which contains 365 observations each. We will plot the average minimum and maximum temperature for each month in the year 1930 and 2020.

Overall, the trend suggests that 2020 experienced higher temperatures compared to 1930. For yearly averages, 2020 had a higher average minimum temperature (52.43°F vs 48.68°F in 1930) but a slightly lower average maximum temperature (72.77°F vs 73.90°F in 1930).

4. Radar Chart

Radar charts are useful for displaying multivariate data in a way that is easy to compare across different variables. However, some users may find this chart difficult to interpret depending on the information and message presented. 

For this example, we are going to plot the fitness scores of five individuals. The assessed fitness components included: cardiovascular endurance, muscle strength, flexibility, body composition, balance and nutrition. Each component was ranked from a scale of 1 to 10, with 10 being the highest and 1 being the worst. The dataset can be accessed here .

These radar charts show how each individual's fitness varies across the six components, providing an overall comparison on a single plot.

5. Dot Plot

Dot plots show one or more qualitative values for each category, allowing for comparison across multiple values within and between categories. They provide an informative visualization, effectively condensing information in an easy to read format. 

For this visualization, we will use a dataset containing the stats of starter Pokémon and from Generations I through VI (19 entries). This dataset can be accessed here .

In the images above, we can see the different stats for the starters from generations I through VI. Who will you choose? I always choose Mudkip, he is my favourite. 

Correlation Charts

Correlation graphs are used to visualize relationships between variables, showing how one variable changes in relation to another. They show the strength and direction of these relationships, which is important in fields like statistics, economics, and data science.

6. Heatmap & Correlation Matrices

Heatmaps and correlation matrices are great visualizations that are simple for readers to understand. They use a colour gradient to represent the value of variables in a two-dimensional space. They are good tools for identifying patterns, variable-variable relationships, and anomalies in complex datasets. 

For this visualization, we will use a dataset called ‘cerebellum_gene_expression2, accessible here . We will randomly choose 20 genes and create a correlation matrix to visualize gene expression rates via a heatmap. 

The original dataset can be accessed through this file , which is an example dataset provided by the tissueGeneExpression package from the genomicsclass GitHub repository. It contains 500 genes, randomly selected from a dataset of 22,215 entries. 

The image above displays the correlation matrix for 20 randomly selected genes. In the matrix, yellow indicates a strong positive correlation (both variables increase or decrease together), while dark blue indicates a strong negative correlation (as one increases the other decreases). Green represents a weak correlation or no correlation.

7. Bubble Chart

A bubble chart is a data visualization technique that displays multiple dimensions of data within a two-dimensional plot. The ‘bubbles’ represent data points, with their positions determined by two variables, and the size representing the third variable. 

The dataset used to create this graph was from the 2000 US census, and can be accessed here . It contains 437 entries and 28 columns representing various demographic measurements. We will visualize the relationship between education level, poverty, total population and population density in the top 15 counties from Illinois.

The R and Python graphs follow the same formatting. Each bubble represents one of the top 15 counties in Illinois. The size of the bubble corresponds to the total population density of the county, the colour indicates the population density (with lighter colours representing higher density). Each bubble is labeled with the county abbreviation. 

8. Scatter Plot

A scatter plot is a type of data visualization technique that displays values for two variables for a set of data points. It shows how one variable is affected by another, which can reveal relationships between them. Each point on the plot represents an individual data point, with its position along the x-axis representing one variable and its position on the y-axis indicating another variable. 

For this visualization, we are using a dataset called ‘insurance’, which can be accessed here . This dataset includes data on monthly quotes and television advertising expenditure from a US-based insurance company, collected from January 2002 to April 2002. This dataset contains 40 entries and 3 columns. The visualization will examine the relationship between TV advertisements and quotes given. A trendline will be added to help visualize the relationship. 

Python Example 

A positive relationship was observed between increases in TV advertisement and quotes given, as displayed by the increasing trendline.

9. Hexagonal binning

Hexagonal binning is a technique used for large, complex datasets with continuous numerical data in two dimensions. It displays the distribution and density of points, which is particularly useful when over-plotting occurs.

For this visualization, we will use a dataset containing daily observations made for the S&P 500 stock market from 1950 to 2018. The dataset includes 17,346 observations and 7 variables. It can be accessed here . The visualization will be plotting the volume by closing price.  

The yellow hexagon at the lower left corner indicates a clustering of points (high density of points here) that represents low closing price and trading volume. Here, the closing price was equal to $44.64 per share, and the volume of trade is ≤ 2.5 million shares. This specific point makes up ~8.0% of the total dataset.

10. Contour plot + Surface Plot

This is another technique that is used for visualizing data distributions and densities within a two dimensional field. It is oftentimes used to create topographic maps of data. For simplicity, we are going to plot the function Z = sin(sqrt(X^2 + Y^2)).

You can manipulate the surface plot directly within Julius itself to examine different angles, allowing for an in-depth exploration of the plotted points.

Part-to-Whole & Hierarchical Charts

Part-to-Whole visualizations show how individual portions contribute to the whole. Hierarchical graphs represent data in a tree-like structure, displaying relationships between different levels of data.

11. Stacked Bar Graphs

Stacked bar graphs show the composition of different categories within a dataset. Each bar represents the total amount, with segments within the bar representing the categories and their proportion to the total. 

For this example, we will use data from a 2020 Financial Independence (FI) Survey conducted on Reddit. This dataset examined people’s finances and the changes experienced during the pandemic. The full dataset can be accessed here , which contains 1998 rows and 65 variables. We will be using a cleaned version of the full dataset, that contains the same number of rows but only 3 variables. This dataset can be accessed here . 

The visualization focuses on the columns pan_inc_chg (pandemic income change), pan_exp_chg (pandemic expense change), and pan_fi_chg (pandemic financial independence change), as they contain multiple categories relevant to the analysis.

The results show that the pandemic had varying effects on income, leading to reductions in expenses for many individuals. The combination of stable or increased income, along with decreased expenses, may have contributed to a slight improvement in the financial independence for some people.

12. Dendrogram

Dendrograms are tree-like diagrams that show the arrangement of clusters formed by a hierarchical structure. They are commonly used in fields such as biology, bioinformatics, and machine learning to visualize the relationships between data points. 

For this visualization, we will use a dataset called ‘cerebellum_gene_expression2’, which can be accessed here . We are only going to plot the first 20 genes for this visualization. 

The original dataset can be accessed through this file . This example dataset, provided by the ‘tissueGeneExpression’ package from the genomicsclass GitHub repository, includes 500 genes randomly selected from a larger dataset containing 22,215 entries.

Genes grouped together at lower heights in this dendrogram have more similar expression patterns across samples. Additionally, the higher the branching point between two pairs of genes or clusters, the more dissimilar they are. For example, x.MAML1 and x.FIBP are clustered closely together, suggesting similar expression patterns.

13. Pie Chart

A pie chart is a circular statistical graph divided into slices to show the relative proportions of different categories within a dataset. Each slice represents a category, and the size of the slice corresponds to the proportion of that category in relation to the whole. 

For this visualization, we will use a dataset from a 2010 poll on whether airports should use full-body scanners. The poll collected a total of 1137 responses and included two factors. The dataset can be accessed here .

Both visualizations show group responses regarding body scanner use in airports for security purposes, with an overall trend suggesting that people approve of their use.

14. Donut Chart

Donut charts are similar to pie charts, but they have a hole in the center of the circle, giving them their name. This inner circle’s removal allows for the additional information to be shown in the chart. The length of each arc corresponds to the proportion of the category it represents. 

For this visualization, we will use a dataset detailing the chemical composition (Aluminum, Iron, Magnesium, Calcium, and Sodium) found at four different archaeological sites in Great Britain (26 entries). We will compare the different chemical composition of pottery amongst the four sites. The dataset can be accessed here .

Across all four different sites, we can observe variations in the chemical composition of the pottery. Aluminum, the primary chemical compound, constitutes the highest percentage in composition of each pottery sample, but its percentages vary amongst sites.  

15. Population Pyramid

Also known as age-sex pyramids, population pyramids are visualizations that display the gender distribution of a population. They are typically presented as a bar chart, with age cohorts displayed horizontally to the left or right. One side represents males, while the other side shows females. 

For this visualization, we will use a dataset containing male and female birth rates in London from 1962 to 1710 (82 rows; 7 variables). For simplicity, we will only plot male and female data for the first 20 years. The dataset can be accessed here . 

The population distribution between males and females appears steady amongst the years, showing a slight decrease in births for both sexes from 1641 to 1648. 

Data Over Time (Temporal) Charts

Temporal charts are used to display data over time, revealing trends, patterns, and changes. They are essential for time series analysis and can be presented in multiple different forms depending on the type of data and the message intended to be conveyed.

You can find the code associated with these charts by visiting our community forum .

16. Area Chart

Area charts are a type of data visualizations used to represent quantitative data and show how values change over a period of time. They plot a continuous variable and are great at showing the magnitude of change over time or visualizing cumulative effects. 

We will be using the London dataset (82 rows; 7 variables) to visualize the mortality rate and plague deaths over time. The dataset can be accessed here . 

These charts visualize the impact of the plague on mortality rates. We can see a peak between 1660 and 1670, during which the majority of deaths were due to plague.

17. Line chart

Line charts are among the most commonly used types of charts worldwide. They are great at showing overall trends or progress over time. The x-axis typically represents the continuous variables (usually time), while the y-axis displays the dependent variable, showing how its value changes.

For this visualization, we will use a dataset called ‘trump_tweet’, which tracks the number of tweets by Mr. Trump from 2009 to 2017. The full dataset can be accessed here (20,761 rows; 8 variables), while the condensed dataset used for this visualization is available here (9 rows; one variable).

This line chart displays the number of tweets made by Mr. Trump over an eight year period. The lowest number of tweets was recorded in 2009 (~43 tweets/year), while his highest was in 2013 (~5,616 tweets/year). 

18. Candlestick Chart

A candlestick chart is a financial visualization used to analyze price movements of an asset, derivative, or currency. It is commonly used in technical analysis to predict market trends. The chart displays the high, low, opening, and closing prices of a product within a specific time frame. 

For this chart, we will use the S&P 500 stock market dataset. This dataset includes daily observations from 1950 to 2018, with a total of 17,346 entries and 7 variables. The original dataset can be accessed here , while the one we are using for the visualization is here . For this chart, we are only focusing on a short timeframe, specifically March 1974 high, low, opening, closing prices and volume. 

The green candlesticks indicate the days when the closing price was higher than the opening price, suggesting buyer pressure. Red candlesticks indicate days where the closing price was lower than the opening price, suggesting selling pressure. Candlesticks with small bodies, where the opening and closing prices are close together, suggest market indecision. 

Overall, this chart shows that the market started positively (as indicated by many green candlesticks), experienced a brief mid-month dip (indicated by the red candlesticks), and then recovered slightly, as shown by some green candlesticks.

19. Stream graph

A stream graph displays changes in the magnitude of categorical data over time. It is a variation of the stacked area bar graph, where the baseline is not anchored to a singular point but rather moves up or down, allowing the to display a natural flow. 

For this visualization, we will use a dataset that measures air pollutants in Leeds (UK) from 1994 to 1998 (Heffernan and Tawn, 2004). The winter dataset includes measurements between November to February of the various air pollutants (532 rows with 5 variables). The dataset can be accessed here .

The images shows how the composition of the pollutants change over time, with peaks and dips of pollutants illustrated throughout the season.

20. Gantt chart

A Gantt chart is a visual tool used in project management to plan and track the progress of tasks. It displays individual tasks or activities along a timeline, highlighting their scheduled start and end dates. Gantt charts are a great way for visualizing sequences of tasks, duration, and the dependencies between tasks. 

For this visualization, we will use a dataset showing task allocation between start and end dates of my Master’s program. The dataset can be accessed here (contains 17 rows, with 4 columns).

R Example 

Distribution Charts

Distribution charts are meant to show the spread of data across various categories or values. They help readers understand the frequency, range, and the overall shape of the data’s distribution. In addition, it can help readers understand the patterns, central tendency, and variations within their dataset.

21. Density plot

A density plot measures the probability distribution of a continuous variable. By providing a smooth curve that represents the distribution of data points over a range, it helps readers to identify patterns, trends, and the overall shape of the distribution. Density plots are useful for visualizing the distribution, identifying modes, and comparing distributions between multiple groups.

For this visualization, we will use the “iris” dataset (151 rows, 5 columns). This is a common dataset that contains information on petal width, petal length, sepal width and sepal length of three different iris species (Setosa, Versicolour, and Virginica). It is often used as an introductory model for clustering algorithms in machine learning. For this visualization, we will be using it to compare how flower features differ between species. The dataset can be accessed by simply asking Julius to retrieve it in Python or R, or it can be accessed here . 

The density plot reveals the following observations: For Setosa, the distribution of petal width and length is generally on the lower end compared to the other species of iris’s, suggesting that Setosa would be easily distinguished by its smaller petal dimensions. 

Versicolor shows some overlap with Virginica regarding sepal length and width, but exhibits less variation and tends to concentrate around 5.5cm (sepal length) and 3.0cm (sepal width).Vericolor can be identified by its intermediate petal size – larger than Setosa but smaller than Virginica. Virginica, on the other hand, displays the largest petal length and width, though it does show some high variability due to the spread of points along the x-axis.

22. Histogram

A histogram is used to display the distribution of a dataset by dividing it into intervals, or bins, and counting the data points that fall into each bin. The height of each bar represents the frequency of data points falling into that specific interval. Histograms are commonly used to display frequency distribution of a continuous variable.  

For this visualization, we will use a dataset comparing thermometer readings between Mr. Trump and Mr. Obama (3,081 rows, 3 columns). We will visualize the frequencies of scores between Mr. Trump and Mr. Obama. The dataset can be found here .

The dataset shows a non-normal distribution, as evident by the multiple peaks observed in the trendline.

23. Jitter Plot

A jitter plot is similar to scatter plot but introduces intentional random dispersions of points – referred to as ‘jittering’ – along one axis to prevent overlapping. This technique reveals the density and distribution of data points that would otherwise overlap. This is useful when your data points may have the same values or relatively close values across categories.    

For this visualization, we will use a dataset comparing dried plant weight yields (30 observations) under three different conditions (control, treatment 1, and treatment 2). The dataset can be accessed here .

Both images demonstrate how a jitter plot effectively prevents overlapping between points with identical or nearly identical values.

24. Beeswarm Chart

A beeswarm chart visualizes data points along a single axis, with dots representing each individual datapoint. This method does slightly rearrange the points to avoid overlapping.  

We will use the same plant growth dataset from the jitter plot visualization to illustrate how the data points appear in comparison to the jitter plot. The dataset can be accessed here .

The beeswarm plot is more appealing with a larger sample size, but this example provides a general idea of its format. Unlike the jitter plot, data points in a beeswarm plot are positioned in a vertical line, with slight dispersion when multiple points overlap. Although some beeswarm plots do not include boxplot and box-and-whiskers plot, adding these can help visualize interquartile ranges. 

From a general observation, treatment 2 appears to have a slightly higher overall weight compared to the control and treatment 1. However, it is important to note that outliers in treatment 1 and the control can skew this range.

25. Boxplot (Box-and-whisker plot)

A boxplot, or box-and-whiskers plot, is a standardized method for displaying the distribution of a dataset. It highlights five key aspects: the minimum value, the first quartile (Q1), median, third quartile (Q3), and the maximum value. This allows the reader to examine the spread of the data, central tendency, and identify potential outliers, making it a great tool for exploratory data analysis. 

For this visualization, we will use a dataset from Baumann & Jones, as reported by Moore & McCabe (1993). The dataset examines whether three different teaching methods – traditional (Basal), innovative 1 (DRTA), and innovative 2 (Strat) – affected reading comprehension in students. The data frame has 66 rows with 6 columns: group, pretest.1, pretest.2, post.test.1, post.test.2, post.test.3. The dataset can be accessed here .

The visualization was created by averaging the scores between the two pre-tests and three post-tests by teaching methods, and then plotting them.

From quick observation, there appears to be differences in test performance associated with teaching methods. The Basal method seems to show the lowest median test score in comparison to the DRTA and Strat. However, these initial observations should be confirmed through further statistical testing.

Geospatial & Other

Geospatial visualizations are designed to represent data with geographic information, such as coordinates, GPS, longitude, and latitude. Their purpose is to communicate spatial patterns and relationships. Also included in this section are flow charts and network diagrams, which show how ideas or concepts are related to one another.

26. Geographic Heat Map

A geographic heat map shows where points are most concentrated within a specific geographic location by using colours to represent density. This type of map is useful for highlighting patterns, trends, and hotspots in spatial data. 

For this visualization, we will use a dataset that includes the locations of 1000 seismic events near Fiji since 1964. This dataset, part of the Harvard PRIM-H project dataset, was obtained by Dr. John Woodhouse from the department of Geophysics. This dataset can be accessed here . 

27. Choropleth map

A choropleth map is a thematic map where areas are shaded (or patterned) based on the values of a variable, such as population density, income level, or election results. Colours are used to represent different densities or magnitudes, which provides a comparative visual between spatial data distributions. 

For this visualization, we will use data from the 2017 American Census Society. It has 3221 entries, with 37 columns detailing various demographic information. This dataset can be accessed here .

28. Network diagram

A network diagram is a visualization tool used to show connections between multiple different elements, illustrating how different entities (nodes) are connected to one another. 

For this visualization, we will use a document that outlines the sequence of tasks in a project. It defines the nodes (tasks), dependencies, and gives a short description of the dependencies. This document can be accessed here and the google sheet can be accessed here . 

Network diagrams are great ways to organize your thoughts and visualize how events are connected to one another.

29. Flowchart

A flowchart is a visual representation of a process, workflow, or system. It uses symbols and arrows to signify a sequence of steps, decisions, or actions. Flowcharts are similar to network diagrams, as they clearly illustrate how different activities or steps are connected, making it easy to understand the flow of activities involved in the process. 

For this example, we will create a flowchart outlining the process of online purchases. The Google document can be accessed here , which contains all the information you need to create the flowchart. You can simply copy and paste the text into the chat box. 

This article has served as a visual guide to 29 diverse chart and graph types, each designed to address specific data presentation needs. From simple bar charts to complex network diagrams, we've explored a range of visualization options to help you choose the right tool for your data story. Understanding these different graph types empowers you to communicate your insights more effectively, regardless of your audience or data complexity.

Throughout this journey, we've used Julius to generate our examples, showcasing how it seamlessly supports both R and Python users. Julius's ability to create these visualizations through simple, natural language commands demonstrates how data visualization tools are evolving to become more accessible. As you continue to explore and apply these chart types in your own work, consider how platforms like Julius can streamline your process, allowing you to focus on the story your data tells rather than the technicalities of graph creation.

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The pits and falls of graphical presentation

Graphics are powerful tools to communicate research results and to gain information from data. However, researchers should be careful when deciding which data to plot and the type of graphic to use, as well as other details. The consequence of bad decisions in these features varies from making research results unclear to distortions of these results, through the creation of “chartjunk” with useless information. This paper is not another tutorial about “good graphics” and “bad graphics”. Instead, it presents guidelines for graphic presentation of research results and some uncommon, but useful examples to communicate basic and complex data types, especially multivariate model results, which are commonly presented only by tables. By the end, there are no answers here, just ideas meant to inspire others on how to create their own graphics.

Introduction

Graphical presentations are powerful instruments for the communication of research results. However, they are also prone to misunderstanding and manipulation. Since statistical graphics are aimed to search patterns and information on empirical data ( 1 ), every aspects of graphic design (scales, colours, shapes, etc.) can influence how the results are interpreted. A worldwide famous case of graphical manipulation was broadcasted recently by the government-run television station VTV, from Venezuela. Figure 1 reproduces the results of the 2013 presidential election after 80% of votes counted. All three graphics present the same data, but do they communicate the same information? According to Mills, “if you torture data long enough, it will say whatever you want it to” ( 2 ).

Three ways to present the same data that may lead to different interpretations. Data are from Venezuela’s presidential election counting of votes in 2013. On the left, the way results were broadcasted on VTV channel. In the middle, same result presented with scale adjusted to data. On the right, scale was kept to show the wider possible interval (0–100).

With this in mind, this paper aims to review some important pitfalls when designing and interpreting statistical graphics from research papers. Additionally, some common and not so common types of graphical presentations will be shown, giving examples of when and how to use them.

Some basic rules

Most of the work has already been done. You had an idea, designed your research, collected the data and even the scary statistical analysis is now complete. It’s time to present your results. What is the best way to do it?

The first question to answer is whether you will use text, tables or graphics. Clearly, graphics will make your paper look more beautiful. However, you have to keep in mind that the purpose of your paper should always be to accurately and clearly communicate your results and, for this, the simpler, the better. Moreover, most scientific journals have limitations regarding the number of figures and tables one can include in a paper. So, if you have some secondary data that can be presented as simple text, do it. For instance, the age of the research subjects when this information serves simply to characterize your sample.

What about the main results? Before you decide between tables and graphics (text is never good to communicate main quantitative results), you must decide what kind of information you want to communicate. While tables are better to show specific information, graphics are better in communicating trends and comparisons ( 3 ), which are usually more related to practice ( 4 ). Statisticians always like tables more than graphics, because they do not fear numbers and with tables it is possible to do the maths again, checking results. However, in general, people have difficulties in perceiving trends, patterns, and the magnitude of differences from numbers alone. They will understand the results better if looking at lines and bars, using the great ability of the human eye to detect patterns from visual stimuli ( 3 ). We usually work better with qualitative information (“treatment A is more efficient than treatment B”) than with quantitative ones (“group one presented 75 ± 27 kg and group two presented 90 ± 35 kg of body mass”).

If you decided to present your data in a graphic, of whatever kind, you must follow some basic rules. They are so basic, and so simple, that it is easy to forget them. Most of these omissions, fortunately, do not pass the peer review process. Nevertheless, this will cause you some unnecessary waste of time and frustration. So, let us see three of these basic rules: correctly identify each component of your graphic, pay attention to the scales, and do not waste space with unnecessary details.

First, your graphic is designed to present data to others. Do not expect everyone understand your data as you (supposedly) do. This means that you need to label every axis in the plot, preferably providing the units (years, cm, mlO 2 .kg −1 .min −1 , etc.). In addition, it is important to provide legends to data when more than one series of data are plotted. This is very important because, as stated before, people will look for trends and patterns in your graphics. How would they know what it means unless they correctly identify which variables are being plotted?

The second important rule is to be careful with scales. There are many cases where the best, or more appropriate, choice is not so clear. Although anyone could say, looking at Figure 1 , that the first plot presents an inadequate, biased, scale, the choice between second and third plots is not so trivial. In addition, there is no direct answer. As a rule of thumb, we must remember the purpose of the graphic. Look at your graphic and ask yourself if it is telling you the “truth”, or, in another words if data is accurately presented. Scales should show great differences only when they really exists. In Figure 1 , specifically, it would be preferable to use the second plot, because it allows a good view of the difference without distorting it, and there are not much blank spaces in the graph. However, again, there is no “right” answer.

The third rule is the more important one for the design of a good and informative graphic and is also the one most violated in published papers: “save the ink!”. This statement was presented by Connor ( 4 ), based on ideas of Tufte ( 5 ). All parts and components of a statistical graphic must to be designed to transmit important information to the reader. To Tufte, “graphical excellence” is to show more ideas in the shortest time with the least ink ( 5 ).

It is rare to see published graphics without axis identification or without legends. They do not pass the peer-review process, as said before. However, it is not unusual to see coloured figures, full of shapes, lines, extra dimensions and other components and attributes that are completely meaningless. An idea from Few ( 6 ) is that if someone start to use random ATTRIBUTES when writing text , the reader would immediately think that something was wrong. Don’t you agree? But it is OK to use random attributes when plotting data? Each colour, each form, even the size, must be used to show aspect feature of the data or not used at all. For instance, it is not unusual to see graphics like Figure 2 , where different shades of gray are meaningless, since all bars describe the same variable, and consequently it serves only to distract the reader.

Number of papers published about physical training using the word “Functional” at title, abstract or keywords in the search.

With these three basic rules, the next tough question to answer is: which graphic model should you choose to present your data? Two types of graphics will be presented here: basic and advanced. In this paper, basic graphics are those usually found on the majority of papers, like bar plot, line plot, histograms, etc. This kind of graphic presentation will fit well to almost all research designs and can easily be constructed using common software with some “clicks”. On the other hand, advanced types here will focus specially on the presentation of multivariate model results and other relatively unusual graphics. It is impossible to cover all the types and just some very interesting ideas will be approached that can be used directly or as an idea to even more elaborated graphics that will fit to your particular data.

Basic graphic types

Line and bar plots are some of the most basic and most useful statistical graphics. They are simple, direct and clear. When should you use one and not the other? If you have longitudinal data (like a time series), you should prefer line plots, given the continuity of the line. And this is also the exactly argument to not use line plots with data of independent observations or variables. For instance, see Figure 3 and observe how line induces you to perceive continuity.

Leprosy prevalence at Brazilian States in the year 2000 (data available at ( 18 )).

As previously said, graphics are good to communicate trends. Line plots show trends by the slope of their lines. Nevertheless, for independent or categorical variables, lines will transmit wrong information. Does continuity make sense in figure 3 ?

An unusual line plot is presented in figure 4 . This graphic describes simulated data from a very common research design where a group is assessed before and after a treatment. Since you have a small sample size, why not present all data instead of just means and standard deviations? Here, the slopes will show the trend to an effect, which can be confirmed by a statistical test.

Line plot presenting simulated data from ten individuals before and after physical training. Slopes of the lines suggest a trend to increase maximal oxygen consumption (VO 2 ) response after training.

Of course, this kind of plots can only work under especial conditions that include the already cited small sample size and a reasonable uniform dispersion of data. Otherwise, data superposition would prevent visualization.

Other very popular graphic model is the bar plot. It can be used with both continuous (representing means) and categorical (representing frequencies) variables. Although anyone knows what a bar plot is, there are three very frequent mistakes in its use in scientific papers. The first one is the use of 3D bars, usually together with grid lines ( Figure 5a ). Remember to keep your graph as simple as possible. The use of 3D bars will just make the understanding harder, while the use of grid lines will not make the task more amenable, serving only to distract the reader. In addition, you should avoid clustering a lot of information on the same graph ( Figure 5b ). An option to present this kind of data will be described in advanced types below. Preferably, when categories have no natural order, plot them in a descendent order of frequency. Another important tip when using bar plot to present means is to always show standard deviations ( Figure 5c ).

Three examples of bar plots. A: what do 3D view and grid lines add to the graphic, beside confusion? B: so many information in just one graphic is very confusing (see section about advanced models as a suggestion on how to deal with this). C: a good example of the use of bar plots with means and standard errors.

A different form of bar plot that is also very useful is the histogram. The difference between a bar plot and a histogram is that histograms are used to present frequencies (or density) of continuous variables. Histograms are used to describe continuous variables distributions that can be presented both in absolute (frequency) or relative (density) scales. Each bar will describe the frequency of observations between two contiguous intervals, in contrast with bar plots, where each bar describes the frequency of a single category (or value). The additional plot of a line representing a theoretical probability distribution (like the Normal distribution in Figure 6 ) will help readers to judge the adherence between data and a theoretical distribution.

Histogram with probability distribution. Simulated data. On the left, data fits well to normal distribution. On the right, it seems more like a uniform distribution.

Another way to represent data distribution is using box plot or strip plot. Both plots are very similar, since both present data distribution. Box plots ( Figure 7 ) present a box which limits comprise the central 50% of data, the inferior limit of the box indicates the position of the first quartile, which means that 25% of data are equal to or less than that value, and the upper limit of the box describes the third quartile, which means that 75% of data are equal to or less than that value. The line inside the box marks the median value, or the second quartile, indicating that 50% of data are equal to or less than that value. The lines/whiskers outside the box usually indicate one and a half times the range between the third and first quartiles from the box limits. Points outside these limits show extreme values. These lines/whiskers can also indicate either the extreme values (minimum and maximum) or the limits of some confidence interval (e.g., 95% CI). Of course, it is important to indicate what these lines represent in the figure’s legend.

On the left, a schematic representation of a box plot, indicating the position of the first, second (median) and third quartiles. On the right, physical activity (PA) level (in METs-minute/wk x 100) from a group of 135 women presented by each domain from International Physical Activity Questionaire (IPAQ) instrument and total. OCP - PA during work time (occupational); HH - PA during household activities; LTPA - leisure time PA; TRPA - transportation PA. Total is the sum of all domains (unpublished data). Each domain is represented by an individual box plot, similar of that on the left figure.

If we look at the right side of Figure 7 , we will see that the occupational domain presents a highly asymmetrical distribution, with 50% of data equal to zero and the other 50% varying from zero to more than 1000 METs-minute/wk. A MET is a metabolic equivalent measure used to estimate caloric expenditure of physical activity. More information on MET can be found in an article by Ainsworth et al . ( 7 ). The total domain, on the other hand, is more symmetrical. It is worth of note that we can, with this side-by-side boxplots, compare the distribution of five different variables at the same time on a very compact graphic, which would be impossible with, for instance, histograms.

Strip plots present all data points in the graph. If two data points present the same value, they can be plotted side by side. It is much more interesting when you use both box plot and strip plot combined. We can see ( Figure 8 ), for instance, that only one individual with more than 60 years of age presented high level of physical activity. This information would not be easily identified if using only one of the plots alone.

Combination of box and strip plot showing age distribution according to the qualitative (low, moderate and high) physical activity level of 135 women. Note that the majority of individuals above 60 years-old presents low level of physical activity.

Another very common way to represent categorical data distribution is by using pie charts. It is also a good way to miscommunicate your data. Unless the difference among categories is big enough, human eye cannot distinguish among different sizes of pie pieces. Moreover, the problem increases with the number of categories, becoming difficult to distinguish even the categories themselves, especially when the use of colours are not allowed. You can use numbers to identify quantities in a pie chart, but if you need to rely on numbers, why should you use graphics at all? Every author who has written about graphical presentations will not recommend the use of pie charts. It is better to try something different, like a bar plot, for instance.

The last common type of graphic is one of the most useful ones: scatter plots. A scatter plot provides the best way to identify relationships between two continuous variables and is the main graphical representation to be used during exploratory data analysis. Its use will be explored in the following section.

Advanced graphic types

The incredible development of microcomputers has allowed the construction of an almost unlimited number of graphical representations in an easy way. This is good, because the big data era demands more and more ability to present data. Nowadays, everyone is able to plot data into a map easily. Maps, by the way, are resources still under-used in scientific papers and that can offer great assistance, especially in epidemiological studies. In figure 9 , for instance, we can see leprosy prevalence in Brazil presented in maps. While it is clear that leprosy prevalence decreased between the years 2000 to 2009, only in maps it is possible to see a geospatial relationship. Leprosy is known to be a disease strongly related to socioeconomic factors. Since the South and Southeast regions are the most developed in Brazil, as expected, the leprosy prevalence is the lowest in these areas.

Maps presenting prevalence of leprosy in Brazil in the years 2000 (left) and 2009 (right). Maps allow us to see not just the decrease on prevalence rates, but also an association between prevalence and geographical regions ( 18 ).

However, the use of maps only makes sense if the geospatial information is important and if the graphical resolution allows a good visualization. If, for instance, instead of states, cities were plotted, it could be very difficult to clearly identify the information on the map.

One of the greatest difficulties when presenting results is showing complex multivariate models. Usually, multivariate models are presented only as tables, highlighting coefficients, its standard errors, confidence intervals, P values, and alike. One problem with this approach is exemplified by the classical Anscombe Quartet ( 8 ). Simple linear regression models fitted to four datasets result in the same equation: y = 3 + 0.5x. They all present the same R 2 = 0.667 and the same standard error of β 1 = 0.118. Now, let us take a look at the four plots ( Figure 10 ).

Graphic presentation of Anscombe Quartet ( 8 ). Although all can be described by the same equation, with the same coefficient of determination, four datasets are not the same.

It is now clear that describing the statistics related to the model is not enough. But how should multivariate data be represented? This is probably the most complex task in graphic presentation. Some examples will be provided here, but you will need eventually to find your own when fitting it to your data set.

The first suggestion is to create profiles based on the model’s results. For instance, let us refer to Correa et al . ( 9 ). The authors present the results of 124 patients submitted to salvage abdominoperineal resection for anal cancer. It was a survival analysis research that found three variables related to survival time: nodal disease, resection margin, and lymphovascular invasion. Since they are all binary (yes/no) variables, it was possible to create 2 3 =8 different profiles from the combination of variables ( Figure 11a ). Each profile represents one particular survival probability (up to 5-years, i.e. 60 months) and can be plotted on a graph. Clearly, eight lines in just one plot is not the best choice. It was even difficult to choose eight different types of line. The authors proposed a pathological risk score related to the number of positive variables presented by an individual, reducing it to four lines ( Figure 11b ). It is obvious that this data presentation is much more informative than a table with coefficients and P values.

Profiles created from a multiple survival model applied to cancer patients. A: eight profiles created by the combination of three significant variables. B: pathological score based on the number of positive variables ( 9 ).

The second suggestion for representing results from multivariate models is widely used by Professor Hans Rosling, one of the most prominent names in data visualization nowadays. His videos on TED project ( 10 ) and others that can be found on the web are certainly worth exploring. Figure 12 presents data on the population size, continent, income, and life expectancy of about 200 countries in the year 2010. Incomes are presented in the x-axis, while life expectancies (dependent variable) are presented in the y-axis. Notice that all the “ink” on the graph is used to communicate data. See that geometrical forms represent continents, form sizes directly reflect population sizes.

Representation of a relationship among income (Gross Domestic Product per capita) and life expectancy. Each point represents a country. The shape of the points describes the continent. The size of the points is related to the population size.

Although Figure 12 presents only year 2010 data, original available data begins at 1810. A video showing the trend, from 1810 to 2010, can be found at website cited in reference 11 ( 11 ). Data are available at the Gapminder website ( 12 ). With this type of graphic, you must choose one “main” independent variable to be represented in the x-axis.

The third suggestion requires, again, that you have a main independent variable and it was proposed by Paffenbarger et al . ( 13 ) in their seminal paper about the relationship between physical activity and cardiovascular health. In Figure 13 , it is clear that individuals smoking at least 20 cigarettes/day, but spending at least 2,000 kcal/wk with physical activity present less risk of coronary arterial disease (CAD) than non-smoking sedentary individuals. It is, actually, just a 3D bar plot. However, is an unusual way to represent odd ratios. More than numbers, this kind of plot makes this relationship clearer.

Relationship between cigarette smoking and physical activity on the risk of coronary arterial disease (CAD). Non-smoking sedentary individuals (first column on the left) present greater risk than highly active individuals smoking 20 or more cigarettes per day (third column on the right). Data adapted from ( 13 ).

Another common challenge when presenting data is the representation of questionnaire results, particularly those related to Likert scale survey questions. How to describe 20 or more questions, sometimes with more than one group of individuals, without being boring? Generally, authors opt to using several bar plots. Although several bar plots are better than several pie charts, it is difficult to create a whole picture of the data if looking at one question at a time. The best option here is probably the use of a diverging stacked bar chart, a suggestion proposed by Robbins and Heiberger ( 14 ). Figure 14 presents simulated Likert data. Each bar is centered with neutral category (“no opinion”) equally divided between “positive” and “negative” sides. The purpose here is just to see if there is a positive (“strongly agree” or “agree”) or negative (“strongly disagree” or “disagree”) trend in each question. It would be difficult to differentiate between subcategories inside each question.

Example of diverging stacked bar chart presenting the same data of the middle graphic from Figure 5 . Although it is difficult to compare independent levels among questions, it is easier to compare the level of “agreement” (“agree” + “strongly agree”) or the level of “disagreement” (“disagree” + “strongly disagree”) among questions.

The last idea of graphical presentation that will be shown here is not necessarily related to multivariate models but to an emerging field of study, social networks. Social networks are a powerful tool used in different fields of science, from epidemiology to economics ( 15 ). In addition, social network studies rely mostly on graph theory. Visually, a graph is a map of nodes linked by edges. Each node represents an “individual” and the edges represent the “relationship” between individuals. Figures of graphs are not easy to draw. A simple representation of 50 individuals can be a mess ( Figure 15 ) and the use of special software like Gephi ( 16 ) may be necessary.

Simulated network structure of 50 individuals. Each circle (node) represents an individual and each line (edge) represents a relationship. It is possible to see that the simulated disease is dependent on the network structure (e.g., an infectious disease).

To try a graph application, anyone with a Facebook account can use Touch Graph application ( 17 ), which will generate a graph of your own Facebook network.

As we reach the end of this paper, you are probably thinking about which graphic to use, after so many examples, and, most importantly, how to do it. The first question is the harder one and will depend on your data. First, considering your data, think about the type of graphic you want and what it must show, and only then begin to think about how to do it. Do not allow that software limitations determine which type of graphic will be used. If your software cannot build your desired graphic type, change the software, not the type of graphic. It is important to construct your graphical presentation with even more care and attention than you construct text, because graphics are meant to communicate results, the most important part of the research. Finally, it must be said: do not be afraid to try something new. It is a good practice to look at published papers to see how they did it, but it is important to keep an open mind about how to represent your results.

Acknowledgments

Author would like to thanks to Dr. Arianne Reis and Dr. Marcelo Ribeiro-Alves for the valuable manuscript revision and suggestions. Author is the recipient of a postdoctoral scholarship from Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).

Potential conflict of interest

None declared.

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10 Methods of Data Presentation That Really Work in 2024

Leah Nguyen • 20 August, 2024 • 13 min read

Have you ever presented a data report to your boss/coworkers/teachers thinking it was super dope like you’re some cyber hacker living in the Matrix, but all they saw was a pile of static numbers that seemed pointless and didn't make sense to them?

Understanding digits is rigid . Making people from non-analytical backgrounds understand those digits is even more challenging.

How can you clear up those confusing numbers and make your presentation as clear as the day? Let's check out these best ways to present data. 💎

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Get any of the above examples as templates. Sign up for free and take what you want from the template library!

Data Presentation - What Is It?

The term ’data presentation’ relates to the way you present data in a way that makes even the most clueless person in the room understand. 

Some say it’s witchcraft (you’re manipulating the numbers in some ways), but we’ll just say it’s the power of turning dry, hard numbers or digits into a visual showcase that is easy for people to digest.

Presenting data correctly can help your audience understand complicated processes, identify trends, and instantly pinpoint whatever is going on without exhausting their brains.

Good data presentation helps…

• Make informed decisions and arrive at positive outcomes . If you see the sales of your product steadily increase throughout the years, it’s best to keep milking it or start turning it into a bunch of spin-offs (shoutout to Star Wars👀).
• Reduce the time spent processing data . Humans can digest information graphically 60,000 times faster than in the form of text. Grant them the power of skimming through a decade of data in minutes with some extra spicy graphs and charts.
• Communicate the results clearly . Data does not lie. They’re based on factual evidence and therefore if anyone keeps whining that you might be wrong, slap them with some hard data to keep their mouths shut.
• Add to or expand the current research . You can see what areas need improvement, as well as what details often go unnoticed while surfing through those little lines, dots or icons that appear on the data board.

Methods of Data Presentation and Examples

Imagine you have a delicious pepperoni, extra-cheese pizza. You can decide to cut it into the classic 8 triangle slices, the party style 12 square slices, or get creative and abstract on those slices. 

There are various ways to cut a pizza and you get the same variety with how you present your data. In this section, we will bring you the 10 ways to slice a pizza - we mean to present your data - that will make your company’s most important asset as clear as day. Let's dive into 10 ways to present data efficiently.

#1 - Tabular 

Among various types of data presentation, tabular is the most fundamental method, with data presented in rows and columns. Excel or Google Sheets would qualify for the job. Nothing fancy.

This is an example of a tabular presentation of data on Google Sheets. Each row and column has an attribute (year, region, revenue, etc.), and you can do a custom format to see the change in revenue throughout the year.

When presenting data as text, all you do is write your findings down in paragraphs and bullet points, and that’s it. A piece of cake to you, a tough nut to crack for whoever has to go through all of the reading to get to the point.

• 65% of email users worldwide access their email via a mobile device.
• Emails that are optimised for mobile generate 15% higher click-through rates.
• 56% of brands using emojis in their email subject lines had a higher open rate.

(Source: CustomerThermometer )

All the above quotes present statistical information in textual form. Since not many people like going through a wall of texts, you’ll have to figure out another route when deciding to use this method, such as breaking the data down into short, clear statements, or even as catchy puns if you’ve got the time to think of them.

#3 - Pie chart

A pie chart (or a ‘donut chart’ if you stick a hole in the middle of it) is a circle divided into slices that show the relative sizes of data within a whole. If you’re using it to show percentages, make sure all the slices add up to 100%.

The pie chart is a familiar face at every party and is usually recognised by most people. However, one setback of using this method is our eyes sometimes can’t identify the differences in slices of a circle, and it’s nearly impossible to compare similar slices from two different pie charts, making them the villains in the eyes of data analysts.

#4 - Bar chart

The bar chart is a chart that presents a bunch of items from the same category, usually in the form of rectangular bars that are placed at an equal distance from each other. Their heights or lengths depict the values they represent.

They can be as simple as this:

Or more complex and detailed like this example of data presentation. Contributing to an effective statistic presentation, this one is a grouped bar chart that not only allows you to compare categories but also the groups within them as well.

#5 - Histogram

Similar in appearance to the bar chart but the rectangular bars in histograms don’t often have the gap like their counterparts.

Instead of measuring categories like weather preferences or favourite films as a bar chart does, a histogram only measures things that can be put into numbers.

Teachers can use presentation graphs like a histogram to see which score group most of the students fall into, like in this example above.

#6 - Line graph

Recordings to ways of displaying data, we shouldn't overlook the effectiveness of line graphs. Line graphs are represented by a group of data points joined together by a straight line. There can be one or more lines to compare how several related things change over time. 

On a line chart’s horizontal axis, you usually have text labels, dates or years, while the vertical axis usually represents the quantity (e.g.: budget, temperature or percentage).

#7 - Pictogram graph

A pictogram graph uses pictures or icons relating to the main topic to visualise a small dataset. The fun combination of colours and illustrations makes it a frequent use at schools.

Pictograms are a breath of fresh air if you want to stay away from the monotonous line chart or bar chart for a while. However, they can present a very limited amount of data and sometimes they are only there for displays and do not represent real statistics.

#8 - Radar chart

If presenting five or more variables in the form of a bar chart is too stuffy then you should try using a radar chart, which is one of the most creative ways to present data.

Radar charts show data in terms of how they compare to each other starting from the same point. Some also call them ‘spider charts’ because each aspect combined looks like a spider web.

Radar charts can be a great use for parents who’d like to compare their child’s grades with their peers to lower their self-esteem. You can see that each angular represents a subject with a score value ranging from 0 to 100. Each student’s score across 5 subjects is highlighted in a different colour.

If you think that this method of data presentation somehow feels familiar, then you’ve probably encountered one while playing Pokémon .

#9 - Heat map

A heat map represents data density in colours. The bigger the number, the more colour intensity that data will be represented.

Most US citizens would be familiar with this data presentation method in geography. For elections, many news outlets assign a specific colour code to a state, with blue representing one candidate and red representing the other. The shade of either blue or red in each state shows the strength of the overall vote in that state.

Another great thing you can use a heat map for is to map what visitors to your site click on. The more a particular section is clicked the ‘hotter’ the colour will turn, from blue to bright yellow to red.

#10 - Scatter plot

If you present your data in dots instead of chunky bars, you’ll have a scatter plot. 

A scatter plot is a grid with several inputs showing the relationship between two variables. It’s good at collecting seemingly random data and revealing some telling trends.

For example, in this graph, each dot shows the average daily temperature versus the number of beach visitors across several days. You can see that the dots get higher as the temperature increases, so it’s likely that hotter weather leads to more visitors.

5 Data Presentation Mistakes to Avoid

#1 - assume your audience understands what the numbers represent.

You may know all the behind-the-scenes of your data since you’ve worked with them for weeks, but your audience doesn’t.

Showing without telling only invites more and more questions from your audience, as they have to constantly make sense of your data, wasting the time of both sides as a result.

While showing your data presentations, you should tell them what the data are about before hitting them with waves of numbers first. You can use interactive activities such as polls , word clouds , online quizzes and Q&A sections , combined with icebreaker games , to assess their understanding of the data and address any confusion beforehand.

#2 - Use the wrong type of chart

Charts such as pie charts must have a total of 100% so if your numbers accumulate to 193% like this example below, you’re definitely doing it wrong.

Before making a chart, ask yourself: what do I want to accomplish with my data? Do you want to see the relationship between the data sets, show the up and down trends of your data, or see how segments of one thing make up a whole?

Remember, clarity always comes first. Some data visualisations may look cool, but if they don’t fit your data, steer clear of them. 

#3 - Make it 3D

3D is a fascinating graphical presentation example. The third dimension is cool, but full of risks.

Can you see what’s behind those red bars? Because we can’t either. You may think that 3D charts add more depth to the design, but they can create false perceptions as our eyes see 3D objects closer and bigger than they appear, not to mention they cannot be seen from multiple angles.

#4 - Use different types of charts to compare contents in the same category

This is like comparing a fish to a monkey. Your audience won’t be able to identify the differences and make an appropriate correlation between the two data sets. 

Next time, stick to one type of data presentation only. Avoid the temptation of trying various data visualisation methods in one go and make your data as accessible as possible.

#5 - Bombard the audience with too much information

The goal of data presentation is to make complex topics much easier to understand, and if you’re bringing too much information to the table, you’re missing the point.

The more information you give, the more time it will take for your audience to process it all. If you want to make your data understandable and give your audience a chance to remember it, keep the information within it to an absolute minimum. You should end your session with open-ended questions to see what your participants really think.

What are the Best Methods of Data Presentation?

Finally, which is the best way to present data?

The answer is…

There is none! Each type of presentation has its own strengths and weaknesses and the one you choose greatly depends on what you’re trying to do. 

For example:

• Go for a scatter plot if you’re exploring the relationship between different data values, like seeing whether the sales of ice cream go up because of the temperature or because people are just getting more hungry and greedy each day?
• Go for a line graph if you want to mark a trend over time. 
• Go for a heat map if you like some fancy visualisation of the changes in a geographical location, or to see your visitors' behaviour on your website.
• Go for a pie chart (especially in 3D) if you want to be shunned by others because it was never a good idea👇

Frequently Asked Questions

What is a chart presentation.

A chart presentation is a way of presenting data or information using visual aids such as charts, graphs, and diagrams. The purpose of a chart presentation is to make complex information more accessible and understandable for the audience.

When can I use charts for the presentation?

Charts can be used to compare data, show trends over time, highlight patterns, and simplify complex information.

Why should you use charts for presentation?

You should use charts to ensure your contents and visuals look clean, as they are the visual representative, provide clarity, simplicity, comparison, contrast and super time-saving!

What are the 4 graphical methods of presenting data?

Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon.

Leah Nguyen

Words that convert, stories that stick. I turn complex ideas into engaging narratives - helping audiences learn, remember, and take action.

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W.H.O. Declares Global Emergency Over New Mpox Outbreak

The epidemic is concentrated in the Democratic Republic of Congo, but the virus has now appeared in a dozen other African countries.

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By Apoorva Mandavilli

The rapid spread of mpox, formerly called monkeypox, in African countries constitutes a global health emergency, the World Health Organization declared on Wednesday.

This is the second time in three years that the W.H.O. has designated an mpox epidemic as a global emergency. It previously did so in July 2022. That outbreak went on to affect nearly 100,000 people , primarily gay and bisexual men, in 116 countries, and killed about 200 people.

The threat this time is deadlier. Since the beginning of this year, the Democratic Republic of Congo alone has reported 15,600 mpox cases and 537 deaths. Those most at risk include women and children under 15.

“The detection and rapid spread of a new clade of mpox in eastern D.R.C., its detection in neighboring countries that had not previously reported mpox, and the potential for further spread within Africa and beyond is very worrying,” said Dr. Tedros Adhanom Ghebreyesus, the W.H.O.’s director general.

The outbreak has spread through 13 countries in Africa, including a few that had never reported mpox cases before. On Tuesday, the Africa Centers for Disease Control and Prevention declared a “public health emergency of continental security,” the first time the organization has taken that step since the African Union granted it the power to do so last year.

“It’s in the interests of the countries, of the continent and of the world to get our arms around this and stop transmission as soon as we can,” said Dr. Nicole Lurie, the executive director for preparedness and response at the Coalition for Epidemic Preparedness Innovations, a nonprofit that finances vaccine development.

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