what is graphical representation of data with example

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.

• 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

Thanks very much for the information

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What Is Data Visualization: Brief Theory, Useful Tips and Awesome Examples

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By Al Boicheva

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Updated: June 23, 2022

To create data visualization in order to present your data is no longer just a nice to have skill. Now, the skill to effectively sort and communicate your data through charts is a must-have for any business in any field that deals with data. Data visualization helps businesses quickly make sense of complex data and start making decisions based on that data. This is why today we’ll talk about what is data visualization. We’ll discuss how and why does it work, what type of charts to choose in what cases, how to create effective charts, and, of course, end with beautiful examples.

So let’s jump right in. As usual, don’t hesitate to fast-travel to a particular section of your interest.

Article overview: 1. What Does Data Visualization Mean? 2. How Does it Work? 3. When to Use it? 4. Why Use it? 5. Types of Data Visualization 6. Data Visualization VS Infographics: 5 Main Differences 7. How to Create Effective Data Visualization?: 5 Useful Tips 8. Examples of Data Visualization

1. What is Data Visualization?

Data Visualization is a graphic representation of data that aims to communicate numerous heavy data in an efficient way that is easier to grasp and understand . In a way, data visualization is the mapping between the original data and graphic elements that determine how the attributes of these elements vary. The visualization is usually made by the use of charts, lines, or points, bars, and maps.

• Data Viz is a branch of Descriptive statistics but it requires both design, computer, and statistical skills.
• Aesthetics and functionality go hand in hand to communicate complex statistics in an intuitive way.
• Data Viz tools and technologies are essential for making data-driven decisions.
• It’s a fine balance between form and functionality.
• Every STEM field benefits from understanding data.

2. How Does it Work?

If we can see it, our brains can internalize and reflect on it. This is why it’s much easier and more effective to make sense of a chart and see trends than to read a massive document that would take a lot of time and focus to rationalize. We wouldn’t want to repeat the cliche that humans are visual creatures, but it’s a fact that visualization is much more effective and comprehensive.

In a way, we can say that data Viz is a form of storytelling with the purpose to help us make decisions based on data. Such data might include:

• Tracking sales
• Identifying trends
• Identifying changes
• Monitoring goals
• Monitoring results
• Combining data

3. When to Use it?

Data visualization is useful for companies that deal with lots of data on a daily basis. It’s essential to have your data and trends instantly visible. Better than scrolling through colossal spreadsheets. When the trends stand out instantly this also helps your clients or viewers to understand them instead of getting lost in the clutter of numbers.

With that being said, Data Viz is suitable for:

• Annual reports
• Presentations
• Social media micronarratives
• Informational brochures
• Trend-trafficking
• Candlestick chart for financial analysis
• Determining routes

Common cases when data visualization sees use are in sales, marketing, healthcare, science, finances, politics, and logistics.

4. Why Use it?

Short answer: decision making. Data Visualization comes with the undeniable benefits of quickly recognizing patterns and interpret data. More specifically, it is an invaluable tool to determine the following cases.

• Identifying correlations between the relationship of variables.
• Getting market insights about audience behavior.
• Determining value vs risk metrics.
• Monitoring trends over time.
• Examining rates and potential through frequency.
• Ability to react to changes.

5. Types of Data Visualization

As you probably already guessed, Data Viz is much more than simple pie charts and graphs styled in a visually appealing way. The methods that this branch uses to visualize statistics include a series of effective types.

Map visualization is a great method to analyze and display geographically related information and present it accurately via maps. This intuitive way aims to distribute data by region. Since maps can be 2D or 3D, static or dynamic, there are numerous combinations one can use in order to create a Data Viz map.

COVID-19 Spending Data Visualization POGO by George Railean

The most common ones, however, are:

• Regional Maps: Classic maps that display countries, cities, or districts. They often represent data in different colors for different characteristics in each region.
• Line Maps: They usually contain space and time and are ideal for routing, especially for driving or taxi routes in the area due to their analysis of specific scenes.
• Point Maps: These maps distribute data of geographic information. They are ideal for businesses to pinpoint the exact locations of their buildings in a region.
• Heat Maps: They indicate the weight of a geographical area based on a specific property. For example, a heat map may distribute the saturation of infected people by area.

Charts present data in the form of graphs, diagrams, and tables. They are often confused with graphs since graphs are indeed a subcategory of charts. However, there is a small difference: graphs show the mathematical relationship between groups of data and is only one of the chart methods to represent data.

Infographic Data Visualization by Madeline VanRemmen

With that out of the way, let’s talk about the most basic types of charts in data visualization.

They use a series of bars that illustrate data development.  They are ideal for lighter data and follow trends of no more than three variables or else, the bars become cluttered and hard to comprehend. Ideal for year-on-year comparisons and monthly breakdowns.

These familiar circular graphs divide data into portions. The bigger the slice, the bigger the portion. They are ideal for depicting sections of a whole and their sum must always be 100%. Avoid pie charts when you need to show data development over time or lack a value for any of the portions. Doughnut charts have the same use as pie charts.

They use a line or more than one lines that show development over time. It allows tracking multiple variables at the same time. A great example is tracking product sales by a brand over the years. Area charts have the same use as line charts.

Scatter Plot

These charts allow you to see patterns through data visualization. They have an x-axis and a y-axis for two different values. For example, if your x-axis contains information about car prices while the y-axis is about salaries, the positive or negative relationship will tell you about what a person’s car tells about their salary.

Unlike the charts we just discussed, tables show data in almost a raw format. They are ideal when your data is hard to present visually and aim to show specific numerical data that one is supposed to read rather than visualize.

Data Visualisation | To bee or not to bee by Aishwarya Anand Singh

For example, charts are perfect to display data about a particular illness over a time period in a particular area, but a table comes to better use when you also need to understand specifics such as causes, outcomes, relapses, a period of treatment, and so on.

6. Data Visualization VS Infographics

5 main differences.

They are not that different as both visually represent data. It is often you search for infographics and find images titled Data Visualization and the other way around. In many cases, however, these titles aren’t misleading. Why is that?

• Data visualization is made of just one element. It could be a map, a chart, or a table. Infographics , on the other hand, often include multiple Data Viz elements.
• Unlike data visualizations that can be simple or extremely complex and heavy, infographics are simple and target wider audiences. The latter is usually comprehensible even to people outside of the field of research the infographic represents.
• Interestingly enough, data Viz doesn’t offer narratives and conclusions, it’s a tool and basis for reaching those. While infographics, in most cases offer a story and a narrative. For example, a data visualization map may have the title “Air pollution saturation by region”, while an infographic with the same data would go “Areas A and B are the most polluted in Country C”.
• Data visualizations can be made in Excel or use other tools that automatically generate the design unless they are set for presentation or publishing. The aesthetics of infographics , however, are of great importance and the designs must be appealing to wider audiences.
• In terms of interaction, data visualizations often offer interactive charts, especially in an online form. Infographics, on the other hand, rarely have interaction and are usually static images.

While on topic, you could also be interested to check out these 50 engaging infographic examples that make complex data look great.

7. Tips to Create Effective Data Visualization

The process is naturally similar to creating Infographics and it revolves around understanding your data and audience. To be more precise, these are the main steps and best practices when it comes to preparing an effective visualization of data for your viewers to instantly understand.

1. Do Your Homework

Preparation is half the work already done. Before you even start visualizing data, you have to be sure you understand that data to the last detail.

Knowing your audience is undeniable another important part of the homework, as different audiences process information differently. Who are the people you’re visualizing data for? How do they process visual data? Is it enough to hand them a single pie chart or you’ll need a more in-depth visual report?

The third part of preparing is to determine exactly what you want to communicate to the audience. What kind of information you’re visualizing and does it reflect your goal?

And last, think about how much data you’ll be working with and take it into account.

2. Choose the Right Type of Chart

In a previous section, we listed the basic chart types that find use in data visualization. To determine best which one suits your work, there are a few things to consider.

• How many variables will you have in a chart?
• How many items will you place for each of your variables?
• What will be the relation between the values (time period, comparison, distributions, etc.)

With that being said, a pie chart would be ideal if you need to present what portions of a whole takes each item. For example, you can use it to showcase what percent of the market share takes a particular product. Pie charts, however, are unsuitable for distributions, comparisons, and following trends through time periods. Bar graphs, scatter plots,s and line graphs are much more effective in those cases.

Another example is how to use time in your charts. It’s way more accurate to use a horizontal axis because time should run left to right. It’s way more visually intuitive.

3. Sort your Data

Start with removing every piece of data that does not add value and is basically excess for the chart. Sometimes, you have to work with a huge amount of data which will inevitably make your chart pretty complex and hard to read. Don’t hesitate to split your information into two or more charts. If that won’t work for you, you could use highlights or change the entire type of chart with something that would fit better.

Tip: When you use bar charts and columns for comparison, sort the information in an ascending or a descending way by value instead of alphabetical order.

4. Use Colors to Your Advantage

In every form of visualization, colors are your best friend and the most powerful tool. They create contrasts, accents, and emphasis and lead the eye intuitively. Even here, color theory is important.

When you design your chart, make sure you don’t use more than 5 or 6 colors. Anything more than that will make your graph overwhelming and hard to read for your viewers. However, color intensity is a different thing that you can use to your advantage. For example, when you compare the same concept in different periods of time, you could sort your data from the lightest shade of your chosen color to its darker one. It creates a strong visual progression, proper to your timeline.

Things to consider when you choose colors:

• Different colors for different categories.
• A consistent color palette for all charts in a series that you will later compare.
• It’s appropriate to use color blind-friendly palettes.

5. Get Inspired

Always put your inspiration to work when you want to be at the top of your game. Look through examples, infographics, and other people’s work and see what works best for each type of data you need to implement.

This Twitter account Data Visualization Society is a great way to start. In the meantime, we’ll also handpick some amazing examples that will get you in the mood to start creating the visuals for your data.

8. Examples for Data Visualization

As another art form, Data Viz is a fertile ground for some amazing well-designed graphs that prove that data is beautiful. Now let’s check out some.

Dark Souls III Experience Data

We start with Meng Hsiao Wei’s personal project presenting his experience with playing Dark Souls 3. It’s a perfect example that infographics and data visualization are tools for personal designs as well. The research is pretty massive yet very professionally sorted into different types of charts for the different concepts. All data visualizations are made with the same color palette and look great in infographics.

My dark souls 3 playing data by Meng Hsiao Wei

Greatest Movies of all Time

Katie Silver has compiled a list of the 100 greatest movies of all time based on critics and crowd reviews. The visualization shows key data points for every movie such as year of release, oscar nominations and wins, budget, gross, IMDB score, genre, filming location, setting of the film, and production studio. All movies are ordered by the release date.

100 Greatest Movies Data Visualization by Katie Silver

The Most Violent Cities

Federica Fragapane shows data for the 50 most violent cities in the world in 2017. The items are arranged on a vertical axis based on population and ordered along the horizontal axis according to the homicide rate.

The Most Violent Cities by Federica Fragapane

Family Businesses as Data

These data visualizations and illustrations were made by Valerio Pellegrini for Perspectives Magazine. They show a pie chart with sector breakdown as well as a scatter plot for contribution for employment.

PERSPECTIVES MAGAZINE – Family Businesses by Valerio Pellegrini

Orbit Map of the Solar System

The map shows data on the orbits of more than 18000 asteroids in the solar system. Each asteroid is shown at its position on New Years’ Eve 1999, colored by type of asteroid.

An Orbit Map of the Solar System by Eleanor Lutz

The Semantics Of Headlines

Katja Flükiger has a take on how headlines tell the story. The data visualization aims to communicate how much is the selling influencing the telling. The project was completed at Maryland Institute College of Art to visualize references to immigration and color-coding the value judgments implied by word choice and context.

The Semantics of Headlines by Katja Flükiger

Moon and Earthquakes

This data visualization works on answering whether the moon is responsible for earthquakes. The chart features the time and intensity of earthquakes in response to the phase and orbit location of the moon.

Moon and Earthquakes by Aishwarya Anand Singh

Dawn of the Nanosats

The visualization shows the satellites launched from 2003 to 2015. The graph represents the type of institutions focused on projects as well as the nations that financed them. On the left, it is shown the number of launches per year and satellite applications.

WIRED UK – Dawn of the by Nanosats by Valerio Pellegrini

Final Words

Data visualization is not only a form of science but also a form of art. Its purpose is to help businesses in any field quickly make sense of complex data and start making decisions based on that data. To make your graphs efficient and easy to read, it’s all about knowing your data and audience. This way you’ll be able to choose the right type of chart and use visual techniques to your advantage.

You may also be interested in some of these related articles:

• Infographics for Marketing: How to Grab and Hold the Attention
• 12 Animated Infographics That Will Engage Your Mind from Start to Finish
• 50 Engaging Infographic Examples That Make Complex Ideas Look Great
• Good Color Combinations That Go Beyond Trends: Inspirational Examples and Ideas

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Al Boicheva

Al is an illustrator at GraphicMama with out-of-the-box thinking and a passion for anything creative. In her free time, you will see her drooling over tattoo art, Manga, and horror movies.

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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.

21 Best Data Visualization Types: Examples of Graphs and Charts Uses

Those who master different data visualization types and techniques (such as graphs, charts, diagrams, and maps) are gaining the most value from data.

Why? Because they can analyze data and make the best-informed decisions.

Whether you work in business, marketing, sales, statistics, or anything else, you need data visualization techniques and skills.

Graphs and charts make data much more understandable for the human brain.

On this page:

• What are data visualization techniques? Definition, benefits, and importance.
• 21 top data visualization types. Examples of graphs and charts with an explanation.
• When to use different data visualization graphs, charts, diagrams, and maps?
• How to create effective data visualization?
• 10 best data visualization tools for creating compelling graphs and charts.

What Are Data V isualization T echniques? Definition And Benefits.

Data visualization techniques are visual elements (like a line graph, bar chart, pie chart, etc.) that are used to represent information and data.

Big data hides a story (like a trend and pattern).

By using different types of graphs and charts, you can easily see and understand trends, outliers, and patterns in data.

They allow you to get the meaning behind figures and numbers and make important decisions or conclusions.

Data visualization techniques can benefit you in several ways to improve decision making.

Key benefits:

• Data is processed faster Visualized data is processed faster than text and table reports. Our brains can easily recognize images and make sense of them.
• Better analysis Help you analyze better reports in sales, marketing, product management, etc. Thus, you can focus on the areas that require attention such as areas for improvement, errors or high-performing spots.
• Faster decision making Businesses who can understand and quickly act on their data will gain more competitive advantages because they can make informed decisions sooner than the competitors.
• You can easily identify relationships, trends, patterns Visuals are especially helpful when you’re trying to find trends, patterns or relationships among hundreds or thousands of variables. Data is presented in ways that are easy to consume while allowing exploration. Therefore, people across all levels in your company can dive deeper into data and use the insights for faster and smarter decisions.
• No need for coding or data science skills There are many advanced tools that allow you to create beautiful charts and graphs without the need for data scientist skills . Thereby, a broad range of business users can create, visually explore, and discover important insights into data.

How Do Data Visualization Techniques work?

Data visualization techniques convert tons of data into meaningful visuals using software tools.

The tools can operate various types of data and present them in visual elements like charts, diagrams, and maps.

They allow you to easily analyze massive amounts of information, discover trends and patterns in data and then make data-driven decisions .

Why data visualization is very important for any job?

Each professional industry benefits from making data easier to understand. Government, marketing, finance, sales, science, consumer goods, education, sports, and so on.

As all types of organizations become more and more data-driven, the ability to work with data isn’t a good plus, it’s essential.

Whether you’re in sales and need to present your products to prospects or a manager trying to optimize employee performance – everything is measurable and needs to be scored against different KPI s.

We need to constantly analyze and share data with our team or customers.

Having data visualization skills will allow you to understand what is happening in your company and to make the right decisions for the good of the organization.

Before start using visuals, you must know…

Data visualization is one of the most important skills for the modern-day worker.

However, it’s not enough to see your data in easily digestible visuals to get real insights and make the right decisions.

• First : to define the information you need to present
• Second: to find the best possible visual to show that information

Don’t start with “I need a bar chart/pie chart/map here. Let’s make one that looks cool” . This is how you can end up with misleading visualizations that, while beautiful, don’t help for smart decision making.

Regardless of the type of data visualization, its purpose is to help you see a pattern or trend in the data being analyzed.

The goal is not to come up with complex descriptions such as: “ A’s sales were more than B by 5.8% in 2018, and despite a sales growth of 30% in 2019, A’s sales became less than B by 6.2% in 2019. ”

A good data visualization summarizes and presents information in a way that enables you to focus on the most important points.

Let’s go through 21 data visualization types with examples, outline their features, and explain how and when to use them for the best results.

21 Best Types Of Data Visualization With Examples And Uses

1. Line Graph

The line graph is the most popular type of graph with many business applications because they show an overall trend clearly and concisely.

What is a line graph?

A line graph (also known as a line chart) is a graph used to visualize the values of something over a specified period of time.

For example, your sales department may plot the change in the number of sales your company has on hand over time.

Data points that display the values are connected by straight lines.

When to use line graphs?

• When you want to display trends.
• When you want to represent trends for different categories over the same period of time and thus to show comparison.

For example, the above line graph shows the total units of a company sales of Product A, Product B, and Product C from 2012 to 2019.

Here, you can see at a glance that the top-performing product over the years is product C, followed by Product B.

2. Bar Chart

At some point or another, you’ve interacted with a bar chart before. Bar charts are very popular data visualization types as they allow you to easily scan them for valuable insights.

And they are great for comparing several different categories of data.

What is a bar chart?

A bar chart (also called bar graph) is a chart that represents data using bars of different heights.

The bars can be two types – vertical or horizontal. It doesn’t matter which type you use.

The bar chart can easily compare the data for each variable at each moment in time.

For example, a bar chart could compare your company’s sales from this year to last year.

When to use a bar chart?

• When you need to compare several different categories.
• When you need to show how large data changes over time.

The above bar graph visualizes revenue by age group for three different product lines – A, B, and C.

You can see more granular differences between revenue for each product within each age group.

As different product lines are groups by age group, you can easily see that the group of 34-45-year-old buyers are the most valuable to your business as they are your biggest customers.

3. Column Chart

If you want to make side-by-side comparisons of different values, the column chart is your answer.

What is a column chart?

A column chart is a type of bar chart that uses vertical bars to show a comparison between categories.

If something can be counted, it can be displayed in a column chart.

Column charts work best for showing the situation at a point in time (for example, the number of products sold on a website).

Their main purpose is to draw attention to total numbers rather than the trend (trends are more suitable for a line chart).

When to use a column chart?

• When you need to show a side-by-side comparison of different values.
• When you want to emphasize the difference between values.
• When you want to highlight the total figures rather than the trends.

For example, the column chart above shows the traffic sources of a website. It illustrates direct traffic vs search traffic vs social media traffic on a series of dates.

The numbers don’t change much from day to day, so a line graph isn’t appropriate as it wouldn’t reveal anything important in terms of trends.

The important information here is the concrete number of visitors coming from different sources to the website each day.

4. Pie Chart

Pie charts are attractive data visualization types. At a high-level, they’re easy to read and used for representing relative sizes.

What is a pie chart?

A Pie Chart is a circular graph that uses “pie slices” to display relative sizes of data.

A pie chart is a perfect choice for visualizing percentages because it shows each element as part of a whole.

The entire pie represents 100 percent of a whole. The pie slices represent portions of the whole.

When to use a pie chart?

• When you want to represent the share each value has of the whole.
• When you want to show how a group is broken down into smaller pieces.

The above pie chart shows which traffic sources bring in the biggest share of total visitors.

You see that Searches is the most effective source, followed by Social Media, and then Links.

At a glance, your marketing team can spot what’s working best, helping them to concentrate their efforts to maximize the number of visitors.

5. Area Chart 

If you need to present data that depicts a time-series relationship, an area chart is a great option.

What is an area chart?

An area chart is a type of chart that represents the change in one or more quantities over time. It is similar to a line graph.

In both area charts and line graphs, data points are connected by a line to show the value of a quantity at different times. They are both good for showing trends.

However, the area chart is different from the line graph, because the area between the x-axis and the line is filled in with color. Thus, area charts give a sense of the overall volume.

Area charts emphasize a trend over time. They aren’t so focused on showing exact values.

Also, area charts are perfect for indicating the change among different data groups.

When to use an area chart?

• When you want to use multiple lines to make a comparison between groups (aka series).
• When you want to track not only the whole value but also want to understand the breakdown of that total by groups.

In the area chart above, you can see how much revenue is overlapped by cost.

Moreover, you see at once where the pink sliver of profit is at its thinnest.

Thus, you can spot where cash flow really is tightest, rather than where in the year your company simply has the most cash.

Area charts can help you with things like resource planning, financial management, defining appropriate storage space, and more.

6. Scatter Plot

The scatter plot is also among the popular data visualization types and has other names such as a scatter diagram, scatter graph, and correlation chart.

Scatter plot helps in many areas of today’s world – business, biology, social statistics, data science and etc.

What is a Scatter plot?

Scatter plot is a graph that represents a relationship between two variables . The purpose is to show how much one variable affects another.

Usually, when there is a relationship between 2 variables, the first one is called independent. The second variable is called dependent because its values depend on the first variable.

But it is also possible to have no relationship between 2 variables at all.

When to use a Scatter plot?

• When you need to observe and show relationships between two numeric variables.
• When just want to visualize the correlation between 2 large datasets without regard to time.

The above scatter plot illustrates the relationship between monthly e-commerce sales and online advertising costs of a company.

At a glance, you can see that online advertising costs affect monthly e-commerce sales.

When online advertising costs increase, e-commerce sales also increase.

Scatter plots also show if there are unexpected gaps in the data or if there are any outlier points.

7. Bubble chart

If you want to display 3 related dimensions of data in one elegant visualization, a bubble chart will help you.

What is a bubble chart?

A bubble chart is like an extension of the scatter plot used to display relationships between three variables.

The variables’ values for each point are shown by horizontal position, vertical position, and dot size.

In a bubble chart, we can make three different pairwise comparisons (X vs. Y, Y vs. Z, X vs. Z).

When to use a bubble chart?

• When you want to depict and show relationships between three variables.

The bubble chart above illustrates the relationship between 3 dimensions of data:

• Cost (X-Axis)
• Profit (Y-Axis)
• Probability of Success (%) (Bubble Size).

Bubbles are proportional to the third dimension – the probability of success. The larger the bubble, the greater the probability of success.

It is obvious that Product A has the highest probability of success.

8. Pyramid Graph

Pyramid graphs are very interesting and visually appealing graphs. Moreover, they are one of the most easy-to-read data visualization types and techniques.

What is a pyramid graph?

It is a graph in the shape of a triangle or pyramid. It is best used when you want to show some kind of hierarchy. The pyramid levels display some kind of progressive order, such as:

• More important to least important. For example, CEOs at the top and temporary employees on the bottom level.
• Specific to least specific. For example, expert fields at the top, general fields at the bottom.
• Older to newer.

When to use a pyramid graph?

• When you need to illustrate some kind of hierarchy or progressive order

Image Source: Conceptdraw

The above is a 5 Level Pyramid of information system types that is based on the hierarchy in an organization.

It shows progressive order from tacit knowledge to more basic knowledge. Executive information system at the top and transaction processing system on the bottom level.

The levels are displayed in different colors. It’s very easy to read and understand.

9. Treemaps

Treemaps also show a hierarchical structure like the pyramid graph, however in a completely different way.

What is a treemap?

Treemap is a type of data visualization technique that is used to display a hierarchical structure using nested rectangles.

Data is organized as branches and sub-branches. Treemaps display quantities for each category and sub-category via a rectangle area size.

Treemaps are a compact and space-efficient option for showing hierarchies.

They are also great at comparing the proportions between categories via their area size. Thus, they provide an instant sense of which data categories are the most important overall.

When to use a treemap?

• When you want to illustrate hierarchies and comparative value between categories and subcategories.

Image source: Power BI

For example, let’s say you work in a company that sells clothing categories: Urban, Rural, Youth, and Mix.

The above treemap depicts the sales of different clothing categories, which are then broken down by clothing manufacturers.

You see at a glance that Urban is your most successful clothing category, but that the Quibus is your most valuable clothing manufacturer, across all categories.

10. Funnel chart

Funnel charts are used to illustrate optimizations, specifically to see which stages most impact drop-off.

Illustrating the drop-offs helps to show the importance of each stage.

What is a funnel chart?

A funnel chart is a popular data visualization type that shows the flow of users through a sales or other business process.

It looks like a funnel that starts from a large head and ends in a smaller neck. The number of users at each step of the process is visualized from the funnel width as it narrows.

A funnel chart is very useful for identifying potential problem areas in the sales process.

When to use a funnel chart?

• When you need to represent stages in a sales or other business process and show the amount of revenue for each stage.

Image Source: DevExpress

This funnel chart shows the conversion rate of a website.

The conversion rate shows what percentage of all visitors completed a specific desired action (such as subscription or purchase).

The chart starts with the people that visited the website and goes through every touchpoint until the final desired action – renewal of the subscription.

You can see easily where visitors are dropping out of the process.

11. Venn Diagram 

Venn diagrams are great data visualization types for representing relationships between items and highlighting how the items are similar and different.

What is a Venn diagram?

A Venn Diagram is an illustration that shows logical relationships between two or more data groups. Typically, the Venn diagram uses circles (both overlapping and nonoverlapping).

Venn diagrams can clearly show how given items are similar and different.

Venn diagram with 2 and 3 circles are the most common types. Diagrams with a larger number of circles (5,6,7,8,10…) become extremely complicated.

When to use a Venn diagram?

• When you want to compare two or more options and see what they have in common.
• When you need to show how given items are similar or different.
• To display logical relationships from various datasets.

The above Venn chart clearly shows the core customers of a product – the people who like eating fast foods but don’t want to gain weight.

The Venn chart gives you an instant understanding of who you will need to sell.

Then, you can plan how to attract the target segment with advertising and promotions.

12. Decision Tree

As graphical representations of complex or simple problems and questions, decision trees have an important role in business, finance, marketing, and in any other areas.

What is a decision tree?

A decision tree is a diagram that shows possible solutions to a decision.

It displays different outcomes from a set of decisions. The diagram is a widely used decision-making tool for analysis and planning.

The diagram starts with a box (or root), which branches off into several solutions. That’s why it is called a decision tree.

Decision trees are helpful for a variety of reasons. Not only they are easy-to-understand diagrams that support you ‘see’ your thoughts, but also because they provide a framework for estimating all possible alternatives.

When to use a decision tree?

• When you need help in making decisions and want to display several possible solutions.

Imagine you are an IT project manager and you need to decide whether to start a particular project or not.

You need to take into account important possible outcomes and consequences.

The decision tree, in this case, might look like the diagram above.

13. Fishbone Diagram

Fishbone diagram is a key tool for root cause analysis that has important uses in almost any business area.

It is recognized as one of the best graphical methods to understand and solve problems because it takes into consideration all the possible causes.

What is a fishbone diagram?

A fishbone diagram (also known as a cause and effect diagram, Ishikawa diagram or herringbone diagram) is a data visualization technique for categorizing the potential causes of a problem.

The main purpose is to find the root cause.

It combines brainstorming with a kind of mind mapping and makes you think about all potential causes of a given problem, rather than just the one or two.

It also helps you see the relationships between the causes in an easy to understand way.

When to use a fishbone diagram?

• When you want to display all the possible causes of a problem in a simple, easy to read graphical way.

Let’s say you are an online marketing specialist working for a company witch experience low website traffic.

You have the task to find the main reasons. Above is a fishbone diagram example that displays the possible reasons and can help you resolve the situation.

14. Process Flow Diagram

If you need to visualize a specific process, the process flow diagram will help you a lot.

What is the process flow diagram?

As the name suggests, it is a graphical way of describing a process, its elements (steps), and their sequence.

Process flow diagrams show how a large complex process is broken down into smaller steps or tasks and how these go together.

As a data visualization technique, it can help your team see the bigger picture while illustrating the stages of a process.

When to use a process flow diagram?

• When you need to display steps in a process and want to show their sequences clearly.

The above process flow diagram shows clearly the relationship between tasks in a customer ordering process.

The large ordering process is broken down into smaller functions and steps.

15. Spider/Radar Chart

Imagine, you need to rank your favorite beer on 8 aspects (Bitterness, Sweetness, Sourness, Saltiness, Hop, Malt, Yeast, and Special Grain) and then show them graphically. You can use a radar chart.

What is a radar chart?

Radar chart (also called spider, web, and polar bar) is a popular data visualization technique that displays multivariate data.

In can compare several items with many metrics of characteristics.

To be effective and clear, the radar chart should have more than 2 but no more than 6 items that are judged.

When to use a radar chart?

• When you need to compare several items with more than 5 metrics of characteristics.

The above radar chart compares employee’s performance with a scale of 1-5 on skills such as Communications, Problem-solving, Meeting deadlines, Technical knowledge, Teamwork.

A point that is closer to the center on an axis shows a lower value and a worse performance.

It is obvious that Mary has a better performance than Linda.

16. Mind Map

Mind maps are beautiful data visuals that represent complex relationships in a very digestible way.

What is a mind map?

A mind map is a popular diagram that represents ideas and concepts.

It can help you structure your information and analyze, recall, and generate new ideas.

It is called a mind map because it is structured in a way that resembles how the human brain works.

And, best of all, it is a fun and artistic data visualization technique that engages your brain in a much richer way.

When to use a mind map?

• When you want to visualize and connect ideas in an easy to digest way.
• When you want to capture your thoughts/ideas and bring them to life in visual form.

Image source: Lucidchart

The above example of a mind map illustrates the key elements for running a successful digital marketing campaign.

It can help you prepare and organize your marketing efforts more effectively.

17. Gantt Chart

A well-structured Gantt chart aids you to manage your project successfully against time.

What is a Gantt chart?

Gantt charts are data visualization types used to schedule projects by splitting them into tasks and subtasks and putting them on a timeline.

Each task is listed on one side of the chart. This task also has a horizontal line opposite it representing the length of the task.

By displaying tasks with the Gantt chart, you can see how long each task will take and which tasks will overlap.

Gantt charts are super useful for scheduling and planning projects.

They help you estimate how long a project should take and determine the resources needed.

They also help you plan the order in which you’ll complete tasks and manage the dependencies between tasks.

When to use a Gantt chart?

• When you need to plan and track the tasks in project schedules.

Image Source: Aha.io

The above example is a portfolio planning Gantt Chart Template that illustrates very well how Gantt Charts work.

It visualizes the release timeline for multiple products for an entire year.

It shows also dependencies between releases.

You can use it to help team members understand the release schedule for the upcoming year, the duration of each release, and the time for delivering.

This helps you in resource planning and allows teams to coordinate implementation plans.

18. Organizational Charts

Organizational charts are data visualization types widely used for management and planning.

What is an organizational chart?

An organizational chart (also called an org chart) is a diagram that illustrates a relationship hierarchy.

The most common application of an org chart is to display the structure of a business or other organization.

Org charts are very useful for showing work responsibilities and reporting relationships.

They help leaders effectively manage growth or change.

Moreover, they show employees how their work fits into the company’s overall structure.

When to use the org chart?

• When you want to display a hierarchical structure of a department, company or other types of organization.

Image Source: Organimi

The above hierarchical org chart illustrates the chain of command that goes from the top (e.g., the CEOs) down (e.g., entry-level and low-level employees) and each person has a supervisor.

It clearly shows levels of authority and responsibility and who each person reports to.

It also shows employees the career paths and chances for promotion.

19. Area Map

Most business data has a location. Revenue, sales, customers, or population are often displayed with a dimensional variable on a map.

What is an area map?

It is a map that visualizes location data.

They allow you to see immediately which geographical locations are most important to your brand and business.

Image Source: Infogram

The map above depicts sales by location and the color indicates the level of sales (the darker the blue, the higher the sales).

These data visualization types are very useful as they show where in the world most of your sales are from and where your most valuable sales are from.

Insights like these illustrate weaknesses in a sales and marketing strategy in seconds.

20. Infographics

In recent years, the use of infographics has exploded in almost every industry.

From sales and marketing to science and healthcare, infographics are applied everywhere to present information in a visually appealing way.

What is an infographic?

Infographics are specific data visualization types that combine images, charts, graphs, and text. The purpose is to represent an easy-to-understand overview of a topic.

However, the main goal of an infographic is not only to provide information but also to make the viewing experience fun and engaging for readers.

It makes data beautiful—and easy to digest.

When you want to represent and share information, there are many data visualization types to do that – spreadsheets, graphs, charts, emails, etc.

But when you need to show data in a visually impactful way, the infographic is the most effective choice.

When to use infographics?

• When you need to present complex data in a concise, highly visually-pleasing way.

Image Source: Venngage

The above statistical infographic represents an overview of Social Buzz’s biggest social platforms by age and geography.

For example, we see that 75% of active Facebook users are 18-29 years old and 48% of active users live in North America.

21. T-Chart

If you want to compare and contrast items in a table form, T-Chart can be your solution.

What is a T-Chart?

A T-Chart is a type of graphic organizer in the shape of the English letter “T”. It is used for comparison by separating information into two or more columns.

You can use T-Chart to compare ideas, concepts or solutions clearly and effectively.

T-Charts are often used for comparison of pros and cons, facts and opinions.

By using T-Chart, you can list points side by side, achieve a quick, at-a-glance overview of the facts, and arrive at conclusions quickly and easily.

When to use a T-Chart?

• When you need to compare and contrast two or more items.
• When you want to evaluate the pros and cons of a decision.

The above T-Chart example clearly outlines the cons and pros of hiring a social media manager in a company.

10 Best Data Visualization Tools

There is a broad range of data visualization tools that allow you to make fascinating graphs, charts, diagrams, maps, and dashboards in no time.

They vary from BI (Business Intelligence) tools with robust features and comprehensive dashboards to more simple software for just creating graphs and charts.

Here we’ve collected some of the most popular solutions. They can help you present your data in a way that facilitates understanding and decision making.

1. Visme is a data presentation and visualization tool that allows you to create stunning data reports. It provides a great variety of presentation tools and templates for a unique design.

2. Infogram is a chart software tool that provides robust diagram-making capabilities. It comes with an intuitive drag-and-drop editor and ready-made templates for reports. You can also add images for your reports, icons, GIFs, photos, etc.

3. Venngage is an infographic maker. But it also is a great chart software for small businesses because of its ease of use, intuitive design, and great templates.

4. SmartDraw is best for those that have someone graphic design skills. It has a slightly more advanced design and complexity than Venngage, Visme, and Infogram, … so having some design skills is an advantage. It’s a drawing tool with a wide range of charts, diagrams, maps, and well-designed templates.

5. Creately is a dynamic diagramming tool that offers the best free version. It can be deployed from the cloud or on the desktop and allows you to create your graphs, charts, diagrams, and maps without any tech skills.

6. Edraw Max is an all-in-one diagramming software tool that allows you to create different data visualization types at a high speed. These include process flow charts, line graphs, org charts, mind maps, infographics, floor plans, network diagrams, and many others. Edraw Max has a wide selection of templates and symbols, letting you to rapidly produce the visuals you need for any purpose.

7. Chartio is an efficient business intelligence tool that can help you make sense of your company data. Chartio is simple to use and allows you to explore all sorts of information in real-time.

8. Sisense – a business intelligence platform with a full range of data visualizations. You can create dashboards and graphical representations with a drag and drop user interface.

9. Tableau – a business intelligence system that lets you quickly create, connect, visualize, and share data seamlessly.

10. Domo is a cloud business intelligence platform that helps you examine data using graphs and charts. You can conduct advanced analysis and create great interactive visualization.

Data visualization techniques are vital components of data analysis, as they can summarize large amounts of data effectively in an easy to understand graphical form.

There are countless data visualization types, each with different pros, cons, and use cases.

The trickiest part is to choose the right visual to represent your data.

Your choice depends on several factors – the kind of conclusion you want to draw, your audience, the key metrics, etc.

I hope the above article helps you understand better the basic graphs and their uses.

When you create your graph or diagram, always remember this:

A good graph is the one reduced to its simplest and most elegant form without sacrificing what matters most – the purpose of the visual.

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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• Last Modified 22-06-2023

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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17 Data Visualization Techniques All Professionals Should Know

• 17 Sep 2019

There’s a growing demand for business analytics and data expertise in the workforce. But you don’t need to be a professional analyst to benefit from data-related skills.

Becoming skilled at common data visualization techniques can help you reap the rewards of data-driven decision-making , including increased confidence and potential cost savings. Learning how to effectively visualize data could be the first step toward using data analytics and data science to your advantage to add value to your organization.

Several data visualization techniques can help you become more effective in your role. Here are 17 essential data visualization techniques all professionals should know, as well as tips to help you effectively present your data.

Access your free e-book today.

What Is Data Visualization?

Data visualization is the process of creating graphical representations of information. This process helps the presenter communicate data in a way that’s easy for the viewer to interpret and draw conclusions.

There are many different techniques and tools you can leverage to visualize data, so you want to know which ones to use and when. Here are some of the most important data visualization techniques all professionals should know.

Data Visualization Techniques

The type of data visualization technique you leverage will vary based on the type of data you’re working with, in addition to the story you’re telling with your data .

Here are some important data visualization techniques to know:

• Gantt Chart
• Box and Whisker Plot
• Waterfall Chart
• Scatter Plot
• Pictogram Chart
• Highlight Table
• Bullet Graph
• Choropleth Map
• Network Diagram
• Correlation Matrices

1. Pie Chart

Pie charts are one of the most common and basic data visualization techniques, used across a wide range of applications. Pie charts are ideal for illustrating proportions, or part-to-whole comparisons.

Because pie charts are relatively simple and easy to read, they’re best suited for audiences who might be unfamiliar with the information or are only interested in the key takeaways. For viewers who require a more thorough explanation of the data, pie charts fall short in their ability to display complex information.

2. Bar Chart

The classic bar chart , or bar graph, is another common and easy-to-use method of data visualization. In this type of visualization, one axis of the chart shows the categories being compared, and the other, a measured value. The length of the bar indicates how each group measures according to the value.

One drawback is that labeling and clarity can become problematic when there are too many categories included. Like pie charts, they can also be too simple for more complex data sets.

3. Histogram

Unlike bar charts, histograms illustrate the distribution of data over a continuous interval or defined period. These visualizations are helpful in identifying where values are concentrated, as well as where there are gaps or unusual values.

Histograms are especially useful for showing the frequency of a particular occurrence. For instance, if you’d like to show how many clicks your website received each day over the last week, you can use a histogram. From this visualization, you can quickly determine which days your website saw the greatest and fewest number of clicks.

4. Gantt Chart

Gantt charts are particularly common in project management, as they’re useful in illustrating a project timeline or progression of tasks. In this type of chart, tasks to be performed are listed on the vertical axis and time intervals on the horizontal axis. Horizontal bars in the body of the chart represent the duration of each activity.

Utilizing Gantt charts to display timelines can be incredibly helpful, and enable team members to keep track of every aspect of a project. Even if you’re not a project management professional, familiarizing yourself with Gantt charts can help you stay organized.

5. Heat Map

A heat map is a type of visualization used to show differences in data through variations in color. These charts use color to communicate values in a way that makes it easy for the viewer to quickly identify trends. Having a clear legend is necessary in order for a user to successfully read and interpret a heatmap.

There are many possible applications of heat maps. For example, if you want to analyze which time of day a retail store makes the most sales, you can use a heat map that shows the day of the week on the vertical axis and time of day on the horizontal axis. Then, by shading in the matrix with colors that correspond to the number of sales at each time of day, you can identify trends in the data that allow you to determine the exact times your store experiences the most sales.

6. A Box and Whisker Plot

A box and whisker plot , or box plot, provides a visual summary of data through its quartiles. First, a box is drawn from the first quartile to the third of the data set. A line within the box represents the median. “Whiskers,” or lines, are then drawn extending from the box to the minimum (lower extreme) and maximum (upper extreme). Outliers are represented by individual points that are in-line with the whiskers.

This type of chart is helpful in quickly identifying whether or not the data is symmetrical or skewed, as well as providing a visual summary of the data set that can be easily interpreted.

7. Waterfall Chart

A waterfall chart is a visual representation that illustrates how a value changes as it’s influenced by different factors, such as time. The main goal of this chart is to show the viewer how a value has grown or declined over a defined period. For example, waterfall charts are popular for showing spending or earnings over time.

8. Area Chart

An area chart , or area graph, is a variation on a basic line graph in which the area underneath the line is shaded to represent the total value of each data point. When several data series must be compared on the same graph, stacked area charts are used.

This method of data visualization is useful for showing changes in one or more quantities over time, as well as showing how each quantity combines to make up the whole. Stacked area charts are effective in showing part-to-whole comparisons.

9. Scatter Plot

Another technique commonly used to display data is a scatter plot . A scatter plot displays data for two variables as represented by points plotted against the horizontal and vertical axis. This type of data visualization is useful in illustrating the relationships that exist between variables and can be used to identify trends or correlations in data.

Scatter plots are most effective for fairly large data sets, since it’s often easier to identify trends when there are more data points present. Additionally, the closer the data points are grouped together, the stronger the correlation or trend tends to be.

10. Pictogram Chart

Pictogram charts , or pictograph charts, are particularly useful for presenting simple data in a more visual and engaging way. These charts use icons to visualize data, with each icon representing a different value or category. For example, data about time might be represented by icons of clocks or watches. Each icon can correspond to either a single unit or a set number of units (for example, each icon represents 100 units).

In addition to making the data more engaging, pictogram charts are helpful in situations where language or cultural differences might be a barrier to the audience’s understanding of the data.

11. Timeline

Timelines are the most effective way to visualize a sequence of events in chronological order. They’re typically linear, with key events outlined along the axis. Timelines are used to communicate time-related information and display historical data.

Timelines allow you to highlight the most important events that occurred, or need to occur in the future, and make it easy for the viewer to identify any patterns appearing within the selected time period. While timelines are often relatively simple linear visualizations, they can be made more visually appealing by adding images, colors, fonts, and decorative shapes.

12. Highlight Table

A highlight table is a more engaging alternative to traditional tables. By highlighting cells in the table with color, you can make it easier for viewers to quickly spot trends and patterns in the data. These visualizations are useful for comparing categorical data.

Depending on the data visualization tool you’re using, you may be able to add conditional formatting rules to the table that automatically color cells that meet specified conditions. For instance, when using a highlight table to visualize a company’s sales data, you may color cells red if the sales data is below the goal, or green if sales were above the goal. Unlike a heat map, the colors in a highlight table are discrete and represent a single meaning or value.

13. Bullet Graph

A bullet graph is a variation of a bar graph that can act as an alternative to dashboard gauges to represent performance data. The main use for a bullet graph is to inform the viewer of how a business is performing in comparison to benchmarks that are in place for key business metrics.

In a bullet graph, the darker horizontal bar in the middle of the chart represents the actual value, while the vertical line represents a comparative value, or target. If the horizontal bar passes the vertical line, the target for that metric has been surpassed. Additionally, the segmented colored sections behind the horizontal bar represent range scores, such as “poor,” “fair,” or “good.”

14. Choropleth Maps

A choropleth map uses color, shading, and other patterns to visualize numerical values across geographic regions. These visualizations use a progression of color (or shading) on a spectrum to distinguish high values from low.

Choropleth maps allow viewers to see how a variable changes from one region to the next. A potential downside to this type of visualization is that the exact numerical values aren’t easily accessible because the colors represent a range of values. Some data visualization tools, however, allow you to add interactivity to your map so the exact values are accessible.

15. Word Cloud

A word cloud , or tag cloud, is a visual representation of text data in which the size of the word is proportional to its frequency. The more often a specific word appears in a dataset, the larger it appears in the visualization. In addition to size, words often appear bolder or follow a specific color scheme depending on their frequency.

Word clouds are often used on websites and blogs to identify significant keywords and compare differences in textual data between two sources. They are also useful when analyzing qualitative datasets, such as the specific words consumers used to describe a product.

16. Network Diagram

Network diagrams are a type of data visualization that represent relationships between qualitative data points. These visualizations are composed of nodes and links, also called edges. Nodes are singular data points that are connected to other nodes through edges, which show the relationship between multiple nodes.

There are many use cases for network diagrams, including depicting social networks, highlighting the relationships between employees at an organization, or visualizing product sales across geographic regions.

17. Correlation Matrix

A correlation matrix is a table that shows correlation coefficients between variables. Each cell represents the relationship between two variables, and a color scale is used to communicate whether the variables are correlated and to what extent.

Correlation matrices are useful to summarize and find patterns in large data sets. In business, a correlation matrix might be used to analyze how different data points about a specific product might be related, such as price, advertising spend, launch date, etc.

Other Data Visualization Options

While the examples listed above are some of the most commonly used techniques, there are many other ways you can visualize data to become a more effective communicator. Some other data visualization options include:

• Bubble clouds
• Circle views
• Dendrograms
• Dot distribution maps
• Open-high-low-close charts
• Polar areas
• Radial trees
• Ring Charts
• Sankey diagram
• Span charts
• Streamgraphs
• Wedge stack graphs
• Violin plots

Tips For Creating Effective Visualizations

Creating effective data visualizations requires more than just knowing how to choose the best technique for your needs. There are several considerations you should take into account to maximize your effectiveness when it comes to presenting data.

Related : What to Keep in Mind When Creating Data Visualizations in Excel

One of the most important steps is to evaluate your audience. For example, if you’re presenting financial data to a team that works in an unrelated department, you’ll want to choose a fairly simple illustration. On the other hand, if you’re presenting financial data to a team of finance experts, it’s likely you can safely include more complex information.

Another helpful tip is to avoid unnecessary distractions. Although visual elements like animation can be a great way to add interest, they can also distract from the key points the illustration is trying to convey and hinder the viewer’s ability to quickly understand the information.

Finally, be mindful of the colors you utilize, as well as your overall design. While it’s important that your graphs or charts are visually appealing, there are more practical reasons you might choose one color palette over another. For instance, using low contrast colors can make it difficult for your audience to discern differences between data points. Using colors that are too bold, however, can make the illustration overwhelming or distracting for the viewer.

Related : Bad Data Visualization: 5 Examples of Misleading Data

Visuals to Interpret and Share Information

No matter your role or title within an organization, data visualization is a skill that’s important for all professionals. Being able to effectively present complex data through easy-to-understand visual representations is invaluable when it comes to communicating information with members both inside and outside your business.

There’s no shortage in how data visualization can be applied in the real world. Data is playing an increasingly important role in the marketplace today, and data literacy is the first step in understanding how analytics can be used in business.

Are you interested in improving your analytical skills? Learn more about Business Analytics , our eight-week online course that can help you use data to generate insights and tackle business decisions.

This post was updated on January 20, 2022. It was originally published on September 17, 2019.

About the Author

10 Good and Bad Examples of Data Visualization in 2024

Make sure you avoid these common mistakes when visualizing data as well as some best practices to follow in 2024

As someone who’s been doing data visualization for over 10 years, I’ve come across so many mistakes people make. Make sure you avoid these common mistakes when visualizing data as well as some best practices to follow in 2024. The most common errors include:

• Using the wrong graphs/charts for their particular purpose
• Not making the best use out of colors.
• Creating misleading graphs/charts
• Trying to incorporate too much information in one graph

Whether you're building a dashboard , presentation, or more, it's important to be wise with your data visualization choices.

Here are some examples of each so you can learn to avoid them.

Using the Wrong Graphs and Carts

“Which graphs should I be using?”

As a general rule of thumb:

• Bar charts are for showing the relationship between 1 categorical variable (e.g. color, car model, gender) against 1 numerical variable (height, test scores, IQ and other measurements).
• Pie charts are for the same thing, but aren’t very good for data that contains more than 2-3 categories. E.g. it’s fine for gender since it only has male, female and other, but it’s terrible for listing all car models.
• Scatterplots are for finding correlations between 2 numerical variables.
• Time series are for showing changes over time ( time vs. numerical variable ).

There’s many more than this, but those are the main ones. To learn about this in greater detail, read our post about data visualization techniques .

Bad Data Visualization Example #1: Presenting Qualitative Data

Not all data can be visualized into graphs or charts. For instance, data pertaining to employee details: including first & last name, email address, ethnicity, job title etc.

The biggest mistake would be to present the raw data like this:

Just because a dataset contains a bunch of qualitative data like "name" and "email address" doesn't mean it can't be visualized.

There are two ways to visualize it:

• Gallery View

Card view is good for visualizing raw data:

Gallery view is good for visualizing data with images (for instance: employee headshot photos). An example of gallery view is FlixGem .

Both of these visualizations aren't just to make things "look nicer." But they allow you to easily filter through the data with interactive tags. This is important for both data analysis and presentation.

How to create visualizations for qualitative data:

It might look complicated to create, but existing tools make your job dead simple:

• Upload your data to Polymer Search .
• Launch Polymer App
• Choose the desired layout: Grid view, card view or gallery view

Bad Data Visualization Example #2: Pie chart with too many categories

Pie charts are best used when there are 2-3 items that make up a whole. Any more than that, and it’s difficult for the human eye to distinguish between the parts of a circle.

Notice how it’s hard to distinguish the size of these parts. 

Is “China” bigger than “Other”? 

It’s hard for our eyes to tell the difference. Instead, replace this with a bar chart:

Good example: Proper Bar Chart

Notice how “China” and “Other” are far apart, but we can easily distinguish that one is larger than the other? That’s because our eyes are more sensitive to length of bars than parts of a circle. 

Bar charts will be your go-to chart for data visualization.

Bad Data Visualization Example #3: Multi-colored bar charts

It might look pretty, and you might be wondering “what’s wrong with it?”

The more colors you use, the less comprehensible the visualization will be. More colors = more categories the brain must process. 

On top of that, there’s a better way to handle colors:

Good example: Proper color design

Colors allow us to highlight whatever information we want. 

If we wanted to highlight the country with the biggest CO2 emissions, we can use red vs. grey:

Notice how China immediately sticks out and we get the point across.

Other times, it’s a good idea to use multiple shades of the same color.

Another good example: Proper pie chart  

Bad Data Visualization Example #4: Horizontal bar charts

Horizontal bar charts suffer from the same issue as pie charts: once there are too many categories, you run out of space to include text and it becomes hard to digest:

Instead, it’s better to use vertical bar charts (by switching the axes around):

Good example: Vertical bar charts

This gives unlimited space for including text and is easier for the brain to digest.

Bad Data Visualization Example #5: Too much information

Here’s an example of someone trying to include too much information on one chart: 

Including too much information ruins the point of data visualization in the first place. The purpose of data visualization is to allow the audience to easily digest the information and this graph does the opposite of that.

Instead, take the time to rearrange your data and create multiple graphs to convey your point.

Bad Data Visualization Example #6: 3D graphs

Studies have shown that 3D rendering can negatively affect graph comprehension. It might be tempting to be creative and ‘3D’ your graphs, but there are better ways to get creative .

Bad Data Visualization Example #7: Charts that don’t start at zero (misleading)

Sometimes it’s okay to break this rule, but in general:

• Bar charts should always start at 0, because our eyes are very sensitive to the size of bars.
• Scatterplots and time series should almost always start at 0.
• Line graphs can sometimes break this rule.

Since the y-axis doesn’t start at 0, it’s easy to fool someone that product 2 is failing, but in actuality:

The same applies to other graphs like time series:

Since the y-axis doesn’t start at 0, it’s easy to fool someone that the price of something is exponentially rising where in actuality, the increase is only about 10-20%.

Bad Data Visualization Example #8: Tables With no Context

Spreadsheets and pivot tables with no context are meaningless.

Look at this pivot table:

It's a pivot table showing which product line and gender are generating the most income. Even though it's ordered from highest to lowest income generated, what exactly do these numbers mean? How high is $1580?

These numbers are meaningless without context.

Good Example: Table with Context

Instead of just giving a raw number, it's highly recommended to provide a mean deviation, that is, how far a number is from the average:

Now we can look and go "Oh $1580 is 23% above the mean."

Creating these might be off-putting to some people since it takes more time and effort, but a tool like Polymer Search does all of this automatically for you - and creates pivot tables faster than Excel .

Bad Data Visualization Example #9: Too much graphics, no structure

The aesthetic aspect of data visualizations is undoubtedly important. But it should not come at the expense of digestibility. 

Take the graphic below, for example. 

There's no doubt that the artist spent a lot of time creating this piece. But there are a handful of issues that leave much to be desired.

For one, the visualization follows no organizational structure or order whatsoever. 

At first look, users may think that the headers in each "slice" are the names of the apps that produced the data. In turn, some people might think there's an app out there called "#LOVE" or "Americans." 

Some slices don't even have a header at all. Only upon close inspection, which takes a couple of minutes of the audience's precious time, will they realize that these headers aren't what they seem. 

The visualization also uses some questionable color pairs (just take a look at the "Airbnb" and "Twitter" sections). 

However, out of all these issues, the biggest problem is that the creators decided to build a single graphic for multiple, inconsistent data types. 

Remember, popular data visualization formats like pie charts, graphs, and tables exist for a reason — and that is to help readers comprehend data faster and more effectively. For that to work, you need to start with a consistent, clean dataset that comes together to tell a cohesive story.

If the graphic above is meant to bombard the user with a mash of large numbers, you can say that the creators succeeded. 

Bad Data Visualization Example #10: Uncalibrated Y-Axis

In data visualizations, a single setting can substantially change the way users interpret the data. 

Charts that don't start at zero are a great example, and they are often used to mislead the audience. 

Another example is the chart below:

You might think that interest rates soared from 2008 to 2012. After all, the bar for 2012 is several times higher compared to the one for 2008. 

But if you read the scale, you'll know that interest rates actually only increased by a tiny 0.012% in four years. 

Good example: Chart with a reasonable Y-Axis range

The bar chart above is created to highlight the importance of the Y-Axis. By using an extremely minuscule range, the differences between the numbers are greatly exaggerated. 

Here's the same data with a more reasonable Y-Axis range:

Bad Data Visualization Example #11: Unproportionate pie slices

Pie charts with too many categories are bad, but at least they aren't deliberately misleading. 

Charts that flat-out misrepresent the numbers with unproportionate visuals, however, are a serious offense. 

Take a look at this chart, for example:

The visual may look cute, but the numbers don't make sense. 

For instance, the 38.5% slice is roughly twice the size of the 31.0% part. It's also misleading that the 31%, 17.1%, and 7.2% slices are very similar in size. 

If we were to guess, the creator may have built the data visualization manually and failed to use proportional sizes for the data. 

Good example: Tool-based chart with accurate proportions

With a data visualization tool, you don't have to second-guess the graphical proportions of your data. You simply choose a data visualization type, plug in the numbers, and watch the software render the graphic for you.

Granted, this version doesn't have bees and fancy shading. But it does a much better job of communicating the accurate and proportionate composition of honey.

Not to mention that it took less than five minutes to create this graphic using a data visualization tool. 

Another good example: Auto-generated pie chart

Here's another good example, which is an automatically generated pie chart using pre-loaded values on Polymer:

This time, the graphic only took a few seconds to create. That's because Polymer's drag-and-drop visualization tool instantly creates anything — from column charts to pivot tables — using values from a connected data source.

Bad Data Visualization Example #12: Truncated Y-Axis

You've already seen Y-Axes that don't start at 0 and use uncalibrated ranges. 

The chart below uses a different tactic that can skew how viewers interpret the data. 

First off, notice that the "government funding" bar is smaller than the "revenue" bar in both years. That's despite the fact that the government funding in both years exceeded $1.2 billion, whereas the revenue bars are only supposed to represent $490 million and $573 million.

Misleading, right? 

Technically, the visualization didn't lie. The problem is, the Y-Axis scale is truncated from $700 million all the way to $1.7 billion (that's $1 billion jump). 

While the chart itself is correct within the scale, the gap between $700 million and $1.7 billion made the "government funding" bar a lot smaller than it is. 

Now, we're in no position to claim whether this is intentional or not. But, if you're creating charts, never use a truncated Y-Axis to prevent viewers from misinterpreting your data.

Good example: Clean bar chart with a consistent scale

Whether you're creating a horizontal or vertical bar chart, never mess with your scale. 

In the example below, we used Polymer to visualize the total number of leads generated per keyword in a Pay Per Click (PPC) campaign:

Notice how the X-Axis scale starts at 0 and uses a consistent, reasonable range. This gives a much clearer picture of how much better "bubble inventory download" is than other keywords in terms of generating leads. 

Bad Data Visualization Example #13: Unique for no reason

This next data visualization is pretty interesting — and not in a good way. 

As you can see, the graphic is meant to visualize the frequency of consumers doing gardening work. 

There's absolutely no reason for this not to be a bar or pie chart. But instead of using a popular, more readable type of visualization, the creator decided to use a unique graphic for no good reason. 

Another problem is the header "less often." 

Less often than what? 

Bear in mind that the visualization is based on a survey. That means people either had to respond with "less often" or something else more meaningful, like "less than once a month." 

If it's the latter, why didn't the visualization just say that?

Good example: Keeping it simple

If you think about it, the story behind the visualization above is rather straightforward. It's just a survey on how often consumers do gardening work — and the job is simply to visualize the results and make the data easier to digest. 

Here's an efficient way to do this: 

Apart from using a crystal-clear bar chart, the label "less often" is also replaced with "less than once a month." This eliminates any ambiguity from the visualization's message.

Bad Data Visualization Example #14: Needlessly 3D chart

3D and data simply don't mix — as mentioned in #6 above. 

But the NYTimes back in 2008 decided that 3D rendering doesn't make a chart confusing enough. 

Can you guess what type of chart is actually being used here? 

If you guessed "bar chart," congratulations — you're just like most people. 

But most people guessed wrong. 

In fact, the chart above is a list of several pie charts. 

Look closely and notice how the charts bend inwards in the middle. That's the center point of the pie chart. 

Here's the thing: the data can be adequately explained with either a bar chart or a collection of pie charts. Even a simple bulleted list would've effectively conveyed the message. 

The problem is, they used 3D pie charts and tried to arrange them like a typical bar graph. It's one bad design choice on top of another. 

Good example: Clean, readable, and interactive table

This Polymer-powered table offers the fastest and most efficient way to convey the information above:

Not only is the visualization clean and readable, but it's also interactive. 

Users can filter out professions they're not interested in reviewing. Additionally, the data can be sorted alphabetically or based on their prestige rating.

Another good example: Interactive bar graph

Here's the same data represented as an interactive bar graph:

Unlike the original chart from the NYTimes, this graph features plain, flat bars with a value at the center. Everything is the same shade, but the graph still does an immensely better job of conveying information.

Bad Data Visualization Example #15: What's going on here?

Before you look at the next visualization, take a deep breath and prepare for a little headache.

So, what do you think is going on here?

The funny thing is, data visualizations are tools to ease the interpretation of data. They're not supposed to be brain teasers that leave viewers with more questions than when they started. 

It might take you a while to piece this puzzle together. To save you the trouble, here's what's happening in the graphic above. 

The full green bar visualizes the "business should take responsibility" value. That's the percentage of respondents who believe the companies should be responsible for the items listed on the left (create jobs, drive innovation, support local communities, etc.).   

The blue line in the middle marks the actual performance of the business in each expected responsibility. For example, when it comes to creating jobs, 50% of businesses are actually doing well. 

Lastly, the shaded part of the bar to the right is the performance gap. In simple terms, that's the difference between the performance of businesses and the expectations of the respondents. 

Good example: Comparing data side-by-side

Ultimately, the goal of the visualization above is to compare the public's expectations with the actual performance of businesses when it comes to specific issues. 

Rather than stacking both metrics in one bar, just create two bars for each. 

That's why this version is far superior to the mess above:

Bad Data Visualization Example #16: Cumulative charts

A cumulative chart has its uses, but it can be misleading when measuring certain values. 

For example, check out this cumulative annual revenue chart:

The line is going up so the business must be doing well, right? 

Keep in mind that cumulative charts can only go up. If you look really closely, you'll notice an almost imperceptible slope that indicates a slowdown. 

Good example: Year-Over-Year (YOY) charts

Instead of cumulative data, this chart tracks the YOY revenue of the business:

If you're the business owner, this visualization is scary — the complete opposite of what the cumulative chart makes you feel. 

Regardless, the YOY is the visualization you need to see. It shows the reality that your business could be well on its way to shutting down, giving you the opportunity to diagnose the problem and possibly turn things around. 

Bad Data Visualization Example #17: Unadjusted data

You might think that using raw, unadjusted data leads to accurate analyses.

While it's true in some cases, especially with smaller datasets, "unadjusted" means you're completely ignoring the importance of data quality.

In other words, you might end up with a chart like this: 

The chart above is meant to represent yearly recorded temperatures. But since the creator carpet-bombed the plot area with blue dots, it's impossible to use the data for insights. 

Good example: Temperature anomalies by season

There's a reason why data cleansing is important. 

Biases, errors, outliers, and incomplete data can all lead to faulty readings. For one, the chart didn't organize the data by season — a huge factor that affects temperature records.

How can you tell if the dots above 82 degrees aren't due to heat waves (which are, by definition, abnormal)? Should viewers just assume those values were logged during summertime? 

That's why this chart, which segments the data by season, is a lot better in helping the audience understand what the data is trying to say. 

Bad Data Visualization Example #18: Just use the right data visualization type

A lot of data visualization mishaps could've been avoided if the creator only used the appropriate chart type. 

Flexible data visualization and Business Intelligence (BI) tools like Polymer are available. There's no need to forcefully use a sankey chart for a dataset that's best visualized as a table, bar graph, or even a set of pie charts. 

To give you an idea, here's a quick look at the data visualizations you can add to your Polymer dashboard. 

Every visualization tool you need for just about any dataset is here. 

You have zero reason to experiment with an incompatible chart and end up with something awkward or borderline unreadable.

Good Data Visualization Examples

Data visualization can be incredibly powerful when done correctly. Not only does it make complex data more understandable, but it can also reveal patterns, correlations, and insights that might not be visible otherwise. Here are a few examples of good data visualization practices:

• Interactive Dashboards : With advancements in technology, it's now possible to create dynamic and interactive dashboards. These allow users to drill down into specific data points, zoom into regions of interest, or toggle between different data sets. This gives users a more immersive experience and helps them understand the data at a deeper level.
• Use of Color : Color can be a powerful tool when visualizing data. For instance, a heat map that uses a gradient of colors to show the density of data points can quickly highlight areas of interest or concern. But remember, always consider those who are color-blind and choose palettes that are universally understandable.
• Storytelling with Data : Instead of simply presenting numbers and charts, some visualizations tell a story. This can be a sequence of events, a comparison of scenarios, or even a progression over time. When data tells a story, it becomes much more memorable and impactful.

Bad Data Visualization Examples

While there are many ways to effectively visualize data, there are also common pitfalls that can render a visualization confusing or even misleading. Here are some examples of what to avoid:

• 3D Pie Charts : While they might look fancy, 3D pie charts can distort the perception of data. The angles and perspective can make some sections look larger than they actually are, leading to misinterpretations.
• Overloading with Data : It's tempting to include as much data as possible in a single visualization, but this can lead to clutter and confusion. It's crucial to be selective and only include the most relevant data points.
• Ignoring Scale : Not starting the y-axis at zero or using inconsistent scales can make differences seem more pronounced than they actually are. This can be misleading and should be avoided.

In the context of the current content, it's also essential to emphasize the importance of choosing the right type of chart or graph for the data. For example, while bar charts and pie charts have their place, they aren't always the best choice. It's crucial to understand the nature of the data and the message you want to convey before selecting a visualization method.

Once you learn the many data visualization techniques, know when to use each graph and become aware of all the good and bad practices, you’ll be a pro data analyst in no time!

There are many ways to enhance your visualization skills - with Polymer Search you’ll be able to instantly generate interactive graphs/charts/pivot tables in a matter of seconds. You simply upload your data and the AI will automatically turn it into an interactive spreadsheet and provide quick and easy data visualization tools that are available in no other tool.

You’ll also be able to create your own web app in a couple of minutes with no coding experience required. Simply upload your dataset and Polymer will automatically transform it into a web application where you can share all your visualizations. Unleash Polymer's AI  to unlock more insights in your data.

It’s braindead easy to use!

Sign up to Polymer Search today and get a free trial.

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• Graphical Representation of Data

Overview to Graphical Representation of Data

Mathematics is a field that deals with the gathering, analysis, interpretation, and presentation of numerical information in a very approach that's straightforward to know and compare. Business statistics, demographics,  statistics, and in other spheres of work graphical representation is used.

Tables and graphs show the area of information. People capture more information as soon as it is presented in a more attractive way than in any other format. Graphs are an effective way for showing comparisons between things completely as it has always been straightforward to explore the full information associated with different things.

The use of accurate charts to properly visualize and modify information sets is called image processing. The information is redirected to a computer image code and is represented by a variety of symbols, such as lines on a line chart, bar charts, or chart items, wherever users will gain more insight than numbers. analysis only.

Standalone images will help, predict and create advanced data-driven options by quickly depicting common behaviours and simple, unusual events, and interactions between information objects that cannot be marked. The categories of graphic images used are determined by the type of information being investigated.

Data charts are available in a variety of formats, as well as maps, diagrams, and graphs, and often contain written articles and fables to show the purpose of the chart, units of measurement, and variations, type of information, target chart and whether or not a general image in choosing the most effective chart.

Different Formats

1. Line Graphs - A line graph could be a visual illustration of how the worth of variables changes over time. Points with completely different variable values are coupled to create this graph. It may help evaluate information trends and predict future trends.

2. Graphs Bars - A bar chart could be a form of illustration of knowledge during which bars of a similar dimension are drawn on one axis (usually x-axis) with an equal area between them, showing dynamics. The length of the bars represents the variable values.

3. Histograms - this is often just like bar graphs, except that it supports the numbers' frequency instead of their actual values. The info is broken into intervals, and also the bars represent the frequency vary at intervals. That is, it calculates what percentage information values fall at intervals a given distance.

4. Pie Chart - A kind of graph during which information is pictured as a circular graph. A circle is split into sections, each representing a share of the full.

5. Heatmap - A heatmap could be a two-dimensional, matrix-coloured matrix during which every cell represents a group of knowledge and also the colour of every cell indicates its relative importance.

6. Purpose Map - Point map could be a contract answer for CAD and GIS for writing and an answer that edits the world and line of longitude inform variables to check information mapping.

Benefits of Graphics Illustration

The ability to investigate and perceive giant amounts of numerical information and also the relationship between information points needs table usage and graphical illustration of knowledge. One of the foremost vital ways to investigate information is to check information, providing a straightforward and comprehensive way to represent, visualize, and discuss advanced information patterns.

1. Graphics build information easier to interpret and clear language and learning barriers, simplifying and rising learning.

2. Content comprehension is easily done by human understanding.

3. Performance flexibility: Image displays may be employed in nearly any data-related field.

4. Increase organized thinking: visual aids enable users to create quicker, data-driven selections at a look.

5. Supports engaging and fun visual displays by permitting inventive, relevant reports.

6. Improves communication: reading graphics that emphasize key themes is quicker than reading a close line-by-line report.

7. Shows the full picture: all dynamics, time frames, information behaviour, and relationships are displayed in real-time.

Understanding and distinguishing patterns and trends within the ever-increasing flow of knowledge need a transparent visual illustration of the info. The employment of image displays permits speedy synchronous testing of massive information, which might facilitate the formation of foreseeable predictions and knowledgeable selections.

Graphical representation is a method of numerical data analysis. It shows a diagram of the relationship between knowledge, ideas, information, and concepts. It is easy to understand and one of the key learning strategies. The knowledge in a particular domain always depends on the type of information.

The visual representation forms are distinct. Some of the following are:

1. Line Graphs: Linear graphs display the continuous data and are useful for the prediction of future events over time.

2. Bar Graphs: Bar Graph is used for displaying the classification of details and compares data to the amounts by using solid bars.

3. Histograms: This chart, which uses bars to represent the frequency of numerical data, which are grouped in intervals, has the same width. Since all intervals are similar and continuous.

4. Line Plot: It shows the data frequency on a given line. 

5. Frequency Table: The table shows the number of data pieces within the interval given.

6. Circle Graph: Circle graph is a diagram which shows the relationships of the entire component. The circle shall be 100% and the categories occupied shall be represented by a certain percentage, such as 15%, 56%, etc.

7. Stem and Leaf Plot: Data from the lowest value to the highest value are arranged in the stem and leaf plot. The pictures of the lowest places in the sheets and the next places are the numbers.

8. Box and Whisker Plot: The diagram sums up the data in four sections. The graph is shown. Box and whisker indicate the range of information (distribution) and the medium data range.

General Rules for Graphical Representation of Data

There are some rules to display the data and information effectively in the graphical picture. They are as stated below:

Suitable Title: Ensure that the chart showing the topic of the presentation is given the appropriate title.

Measurement Unit: Make sure to mention the unit of measurement in the graph.

Proper Scale: Choose a proper scale to represent the data in an accurate manner.

Index: Index the corresponding colours, shades, rows, graphs format to better understand.

Data Sources: Include the information source at the bottom of the graph wherever necessary.

Keep it Simple: Construct a graph in an easy way that everyone can understand.

Neat: Choose the correct size, lettering, colours, etc. so that the chart is a visual aid to the screen.

Graphical Representation in Maths:

For mathematics, a diagram is a graph with statistical data represented by curves or lines across the coordinate point on its surface. It helps to research the relation between two variables whereby the change of the variable amount in respect of another variable can be calculated within a certain time interval. The distribution of the sequence and the frequency distribution can be analysed for a particular problem.

The data can be visually represented with two types of graphs. As listed below, they are as follows:

Time Series Graphs

Example: Line Graph.

Frequency Distribution Graphs

Example: Frequency Polygon Graph.

Principles of Graphical Representation:

All forms of graphical data representation are governed by algebraic principles. For diagrams, the co-ordinate axis is represented with two rows. The X-axis is a horizontal axis, while the Y-axis is indicated on the vertical axis. The intersecting point of two lines is called ‘O’. Take x-axis into account that the distance between origin and right is good and the distance between the source and left is good. The distance above the origin is also positive for the y-axis, and the distance below the origin is negative.

Generally, frequency distribution is represented in the following methods, namely:

Smoothed frequency graph.

Pie diagram.

Cumulative or ogive frequency graph.

Frequency Polygon.

Merits of Using Graphs.

Advantages of Graphical Representation of Data

The visual depiction of documents has different advantages that are as follows: 

This report is suitable for busy people because it emphasizes the subject of the report comfortably. It helps to avoid wasting time.

Data can be contrasted in terms of graphic representation. This kind of comparative analysis helps to understand and focus easily.

It takes a lot of time to correctly present concise data.

Corporate managers study the diagrams and very easily decide about the feasibility of the document.

A logical sequence is developed to clarify the public definition when tables, models, and graphs are used for data.

Poorly trained or illiterate people can easily understand graphics because a line-by-line diagram does not require a concise text.

Tables need less effort and less time for modelling, graphs, and pictures. This approach is always easy to understand the details.

Errors are reliable, insightful or descriptive. Since graphic figures, tablets and diagrams show less error and error usually.

The viewer gets a simple, complete idea from this depiction. There can be no place to judge 100 words.

Disadvantages of Graphical Representation of Data:

Document graphic representation is not unrestricted. The graphical representation problems of data or reports are as follows: 

The reports of graphical representation are costly because of the images, and colours. Combining content with human effort is costly in terms of visual layout.

It takes less time to represent a normal file, but the representation of the graph takes time since graphs and figures rely on more time.

Inconsistencies are all likely to occur due to the sophistication of the graphical representations. It leads to community awareness problems.

Graphs show the complete view of data that can keep anything from being kept secret.

Sample Example for Frequency polygon:

Here are the steps to be followed in order to find the frequency distribution of a polygon and it is graphically represented.

Get the frequency distribution and find the intervals of each group.

Mark the middle points along with the X-axis and y-axis frequencies.

At each mid-point, draw the points that are the same as the frequency.

Using lines in order to incorporate these details.

To complete the polygon, attach the point to the bottom or high-class points in the X-axis immediately at each end.

NCERT Study Material

• School Guide
• Mathematics
• Number System and Arithmetic
• Trigonometry
• Probability
• Mensuration
• Maths Formulas
• Class 8 Maths Notes
• Class 9 Maths Notes
• Class 10 Maths Notes
• Class 11 Maths Notes
• Class 12 Maths Notes

Histogram – Definition, Types, Graph, and Examples

Histogram: A histogram is a graphical representation used in statistics to show the distribution of numerical data. It looks somewhat like a bar chart, but with key differences that make it suitable for showing how data is distributed across continuous intervals or specific categories that are considered “bins”.

A histogram is similar to a bar graph. The basic difference between the two is that bar charts correlate a value with a single category or discrete variable, whereas histograms visualize frequencies for continuous variables.

In this article, we have provided every detail about Histograms, their definition, types, examples, how the histogram looks, etc.

Table of Content

What is Histogram?

Histogram meaning, parts of a histogram, types of histogram.

Uniform Histogram

Bimodal Histogram

Symmetric Histogram

Right-Skewed Histogram

Left-Skewed Histogram

Frequency Histogram

Relative frequency histogram, cumulative frequency histogram, cumulative relative frequency histogram, histogram examples, 1. normal distribution histogram, 2. skewed distribution histogram, histogram graph, how to draw histogram, how to interpret a histogram, when to use histogram, advantages of histogram, disadvantages of histogram, applications of histogram, difference between bar graph and histogram, histogram solved examples, practice problems of histogram.

Histogram is a graphical representation that condenses data series into easy-to-understand numerical data by grouping them into logical ranges of varying heights, often known as bins. Essentially, it summarises discrete or continuous data. Histogram is a tool for visualising the distribution of data across a continuous interval or period.

In a histogram data is grouped into continuous number ranges and each range corresponds to a vertical bar.

• Horizontal axis displays the number range.
• Vertical axis (frequency) represents the amount of data present in each range.

It allows us to assess where the values are concentrated, what the extremes are, and whether there are any gaps or anomalous values. A Histogram is similar to a vertical bar graph, however, the distinction is that the Histogram has no space between the bars, but a bar graph has.

A histogram is a graph that shows the distribution of data. It resembles a sequence of interconnected bars. Each bar represents a range of values, and its height indicates how many data points are inside that range.

A histogram is a graph that represents the distribution of data. Here are the essential components, presented in simple terms:

Title: This is similar to the name of the histogram. It explains what the histogram is about and what data it displays.

X-axis: Visualize the X-axis as a horizontal line at the bottom of the histogram. It displays the many categories or groups that the data is sorted into. For example, if you’re measuring people’s heights, the X-axis may indicate several height ranges such as “5-6 feet” or “6-7 feet.”

Y-axis: The Y-axis appears as a vertical line on the side of the histogram. It displays the number of times something occurs in each category or group shown on the X-axis. So, if you’re measuring heights, the Y-axis may display how many individuals are in each height range.

Bars: Bars are like the histogram’s building blocks. They are the vertical rectangles you see on the chart. Each bar on the X-axis represents a category or group, and its height indicates how many times something occurs inside that category. So, higher bars indicate more occurrences, whereas shorter bars indicate fewer occurrences. The bar’s width remains constant, but it indicates the range covered by each category on the X-axis. If all of the bars have the same width, it indicates that the categories are all the same size.

The following are some typical forms of histograms and their characteristics:

A uniform distribution means that the data is uniformly distributed among the classes, with each having a same number of elements. It may display many peaks, suggesting varying degrees of incidence.

A histogram is called bimodal if it has two distinct peaks. This implies that the data consists of observations from two distinct groups or categories, with notable variations between them.

Symmetric Histogram is also known as a bell-shaped histogram, it has perfect symmetry when divided vertically down the centre, with both sides matching each other in size and shape. The balance reflects a steady distribution pattern.

A right-skewed histogram shows bars leaning towards the right side. This signifies that the majority of the data points are on the left side, with a few outliers reaching to the right. Consider a histogram showing the distribution of family earnings. A right-skewed histogram occurs when the majority of families are in lower income groups, but a small number of highly rich households skew the average income.

A left-skewed histogram shows bars that lean towards the left side. This means that the majority of the data points are on the right side, with a few exceptionally low values extending to the left. Consider a histogram reflecting the distribution of test scores in a classroom. A left-skewed histogram occurs when the majority of students receive excellent grades but a few do badly, resulting in an average that is dragged to the left.

A frequency histogram visually displays how often specific values appear in data. Each bar represents a range of values, with its height indicating the frequency of occurrences. For instance, if we’re tracking study hours, the histogram shows how many students fall into each study time range, offering insights into study habits across the student population.

Relative Frequency Histogram displays proportions instead of exact counts for each interval. For example, in a class of 20 students, it might show that 25% scored between 70 and 80%. Relative frequency histograms offer insights into the occurrence of distinct values and distribution patterns within a dataset.

A cumulative frequency histogram is a graph that depicts the total number of values up to a specific point. Instead of displaying the frequency of each individual number, it shows the cumulative frequency, which increases as you walk down the graph.

For example, if you’re graphing test scores, the height of each bar shows the total number of points received to that moment. So, if the first bar displays a frequency of 10 for scores 0-10 and the second bar shows a frequency of 20 for scores 0-20, it indicates that 20 students scored 20 or below.

A Cumulative Relative Frequency Histogram is a histogram that depicts the percentage of data points in a dataset that fall below a specific number. Each bar indicates the sum of relative frequencies up to a certain point.

For example, if we have test results ranging from 0 to 100, the histogram will show the percentage of students that scored below each level. If 30% of students scored less than 70, the bar for 70 would be 30% higher.

• The histogram will likely be bell-shaped, indicating a normal distribution.
• The highest bar (or bars) will be around the middle score range, with the height of bars decreasing symmetrically as you move away from the middle.
• The histogram will have a long tail to the right, indicating a right-skewed distribution.
• Most of the bars will be taller on the left side of the histogram (representing younger employees), with the height of bars decreasing as you move to the right (older age groups).

A histogram is like a bar graph that shows data in columns along the x-axis. Each column represents a range of outcomes, and the height of the column on the y-axis shows how many times that outcome occurred in the data. It’s a simple and effective way to see how data is spread out or distributed. A histogram chart is added below,

Histogram is the basic toll of representing data and we can easily draw histogram by following the steps added below:

Step 1: Collect the data you wish to display in the histogram. This might range from test results to population distribution. For example: Assume you get the following test scores: 14, 20, 12, 26, 8, 7, 2, 28, 30, 16, 18, 23. First arrange it in ascending order. Exam results: 2, 7, 8, 12, 14, 16, 18, 19, 23, 26, 28 and 30.

Step 2: Determine the number of intervals, or “bins,” you wish to split your data into. This is determined by the scope and distribution of your data, as well as the amount of information you choose to display. Assume we wish to divide the scores into 5 bins.

Step 3: Determine the limits of each bin. These bounds should encompass the complete range of your data and be regularly spaced. 0-5 – 10 – 15 – 20 – 25 – 30.

Step 4: Count the number of data points that belong in each bin.

Step 5: On a graph, show the bin borders on the x-axis and the frequency of data points in each bin on the y-axis. Create bars for each bin, with the height of each bar representing the frequency of data points in that bin.

In this histogram, the x-axis depicts the bins, while the y-axis indicates the frequency of data points falling within each bin. The bars represent the sample data’s distribution across the given bins.

A histogram is a type of bar graph that displays the distribution of data. Assume you have a collection of numbers, such as test results or people’s heights. A histogram divides these numbers into ranges known as “bins,” and then illustrates how many data points fall into each bin by creating bars. The higher the bar, the more data points are contained inside that range.

So, when you look at a histogram, you can immediately observe where the majority of the data is, whether it is grouped in one location or spread out, and whether there are any strange patterns, such as gaps or outliers.

Histogram graphs are utilized under various scenarios and some of them are,

• When you have numbers as data.
• To understand how your data is distributed, especially whether it follows a typical pattern.
• To determine if a process satisfies consumer needs.
• Analyze the results of a supplier’s procedure.
• Compare changes in a process over time.
• To compare the results of several processes.
• When you want to quickly and clearly show people how your data is distributed.

Histograms provide various advantages for data analysis and visualization:

• Histograms provide an easy-to-understand visual representation, allowing for fast estimate of key statistical measures such as the mean and median based solely on the shape and central tendency of the graph.
• Histograms can give insights into probable future data events by highlighting patterns and trends in the current dataset, which can help with forecasting and decision-making.
• Histograms are a simple way to exhibit data of variable frequencies and kinds, allowing for clear communication and analysis across several domains and disciplines.
• Statisticians respect histograms for their consistency since they organize data into intervals with uniform distribution, guaranteeing precision and dependability when showing data distributions.

Histograms has advantages as well as disadvantages for data analysis and visualization:

• While histograms provide a simple visual representation, they might oversimplify complicated data patterns, potentially leading to misconceptions or missing subtleties in the information.
• Histogram interpretation can be subjective since people perceive patterns differently, resulting in various judgements regarding the data.
• The bin sizes and intervals used in a histogram can influence the visual look and perception of the data. Poorly designed bins might hide key details or generate fake patterns.
• In rare situations, histograms can be visually deceiving, particularly if the scale or axes are modified to emphasize some traits or downplay others, resulting in inaccurate data interpretation.
• While histograms are useful for visualizing a single dataset, they may not allow for straightforward comparison of many datasets, particularly those with differing sizes or distributions.
• In the Classroom : Imagine a teacher curious about how well her students understood the latest math test. She plots their scores on a histogram, and it’s like seeing the class’s academic portrait. She can quickly spot if most kids nailed it (scores clustered in the high end), if it was a mixed bag, or if she needs to revisit some topics (scores spread out or leaning towards the lower end).
• Health Checks : Think of a doctor looking over the ages of patients getting flu shots. A histogram helps the doc see at a glance which age groups are rolling up their sleeves the most and which might need a nudge. It’s like getting a health pulse on the community with just a peek.
• Nature’s Patterns : A biologist tracking the growth of plants in different environments uses a histogram to visualize growth patterns. It’s as if the plants are telling their stories, showing which conditions make them thrive or barely hang on, all through the height and spread of those bars.
• Market Trends : Imagine a business owner gauging when their store is busiest by plotting customer visits across the day. The histogram becomes a daily schedule, highlighting rush hours when the store should be fully staffed and quiet times perfect for restocking.

A histogram is one of the most frequent graphs used to represent frequency distribution. The histogram appears more like a bar graph, but there is a distinction between the two. The differences between the bar graph and the histogram are as follows:

People Also View:

Interpretations of Histogram Histogram-Definition, Types, Steps to Draw Definition, Types, Examples | How To Make Bar Graphs? Bar graphs and Histograms

Example 1: Present the following information as a histogram:

We take the Marks on the graph’s horizontal axis and, based on the first column of the data, set the scale to 1 unit = 10. We pick number of students on the vertical axis of the graph and use the second column of the table to determine the scale: 1 unit = 10. Now we’ll create the relevant histogram.

Example 2: Present the following information as a histogram:

We take the Marks on the graph’s horizontal axis and, based on the first column of the data, set the scale to 1 unit = 10. We pick number of students on the vertical axis of the graph and use the second column of the table to determine the scale: 1 unit = 5. Now we’ll create the relevant histogram.

1) Present the following information as a histogram:

2) Present the following information as a histogram:

Histogram – FAQs

What is histogram and how is it different from a bar graph.

A histogram is a graphical depiction of frequency distributions in continuous data, whereas a bar chart is used in categorical data. A histogram, unlike a bar chart, depicts frequencies for continuous variables with no gaps between the bars.

What are key elements of a histogram?

A histogram is made up of a title, an X-axis (which represents categories or groups), a Y-axis (which shows frequency), and bars (which show frequency distribution).

What are many forms of histograms and what do they represent?

Histograms can be uniform, bimodal, symmetric (bell-shaped), right-skewed, or left-skewed. Each class represents distinct patterns in data distribution.

When should you use a histogram?

Histograms are important for analyzing numerical data distributions, evaluating process changes over time, determining if data meets customer demands, and visualising data distribution patterns quickly and effectively.

What are advantage of utilizing histograms?

Histograms simplify complicated data representation, make statistical measurements easier to read, provide insights into future data patterns, improve data communication, and assure consistent and trustworthy data visualization.

What is a right-skewed histogram, and when does it appear?

A right-skewed histogram means that the majority of data points are on the left side, with outliers stretching to the right. It happens when a dataset has a small number of high-value outliers.

What is a left-skewed histogram, and when does it appear?

A left-skewed histogram depicts the majority of data points on the right side, with outliers to the left. It happens when there are just a few low-value outliers in a dataset.

What is difference between a bar graph and a histogram?

Bar graphs show categorical data, whereas histograms display numerical data. Furthermore, bar graphs depict independent variables on the x-axis, whereas histograms show a continuous scale on the x-axis that represents ranges of data values.

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Title: cear: automatic construction of a knowledge graph of chemical entities and roles from scientific literature.

Abstract: Ontologies are formal representations of knowledge in specific domains that provide a structured framework for organizing and understanding complex information. Creating ontologies, however, is a complex and time-consuming endeavor. ChEBI is a well-known ontology in the field of chemistry, which provides a comprehensive resource for defining chemical entities and their properties. However, it covers only a small fraction of the rapidly growing knowledge in chemistry and does not provide references to the scientific literature. To address this, we propose a methodology that involves augmenting existing annotated text corpora with knowledge from Chebi and fine-tuning a large language model (LLM) to recognize chemical entities and their roles in scientific text. Our experiments demonstrate the effectiveness of our approach. By combining ontological knowledge and the language understanding capabilities of LLMs, we achieve high precision and recall rates in identifying both the chemical entities and roles in scientific literature. Furthermore, we extract them from a set of 8,000 ChemRxiv articles, and apply a second LLM to create a knowledge graph (KG) of chemical entities and roles (CEAR), which provides complementary information to ChEBI, and can help to extend it.

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