When dealing with numbers in statistics, incorporating data visualization is integral to creating a readable and understandable summary of a dataset. It doesn’t matter if it’s a large or small dataset, visualizing data using graphs and charts will contribute largely to your audience understanding the message.

That said, with so many graph and chart types in data visualization, selecting the best one for your business or data can be tricky. Each type has its unique strengths and weaknesses, which determine its effectiveness in various situations.

You may need to visualize the outcome of scientific research, a sales report, an industry infographic, or a pitch deck demographic. Graphs and charts are easy ways of showcasing each of these content.

What is a Graph?

A graph is a pictorial representation of data in an organized manner. Graphs are usually formed from various data points, which represent the relationship between two or more things.

A picture says a thousand words, they say. A graph, on the other hand, not only says a thousand words but also tells a million stories.

Each point, stroke, color, or shape on a graph has a different meaning that helps in interpreting a graph. They are of different types and vary in structure, with some having just points, others have points joined together by lines, and so on.

Graphs and Charts – What is the Difference?

Although sometimes used interchangeably, it is important to note that there is a difference between graphs and charts. Summarily, we can say that all graphs are charts but not all charts are graphs.

A graph is a mathematical diagram that depicts the relationship between two or more sets of numerical data over a period of time. Basic data is mainly 2-dimensional with a focus on raw data represented through lines, curves, etc.

Charts, on the other hand, is a representation of datasets with the intent of making the user understand the information in a better manner. Graphs are a good example of charts used for data visualization.

Types of Graphs and Charts

There are various types of graphs and charts used in data visualization. However, in this article, we’ll be covering the top 11 types that are used to visualize business data.

Bar Chart/Graph

A bar chart is a graph represented by spaced rectangular bars that describe the data points in a set of data. It is usually used to plot discrete and categorical data.

The horizontal axis of the chart represents categorical data while the vertical axis of the chart defines discrete data. Although the rectangular bars in a bar chart are mostly placed vertically, they can also be horizontal.

For horizontally placed rectangular bars, the categorical data is defined on the vertical axis while the horizontal axis defines the discrete data.

Types of Bar Chart

• Grouped Bar Chart

Grouped bar charts are used when the datasets have subgroups that need to be visualized on the graph. Each subgroup is usually differentiated from the other by shading them with distinct colors.

• Stacked Bar Chart

The stacked bar graphs are also used to show subgroups in a dataset. But in this case, the rectangular bars defining each group are stacked on top of each other.

• Segmented Bar Chart

This is the type of stacked bar chart where each stacked bar shows the percentage of its discrete value from the total value. The total percentage is 100%

Advantages of a Bar Chart

• Summarizes a large amount of data in an understandable form.
• Easily accessible to a wide audience.

Disadvantages of a Bar Chart

• It does not reveal key assumptions like causes, effects, patterns, etc.
• May require further explanation.

Pie Chart

A pie chart is a circular graph used to illustrate numerical proportions in a dataset. This graph is usually divided into various sectors, where each sector represents the proportion of a particular numerical element in the set.

Like slicing a pizza into portions, a pie chart separates each sector to show the proportion of an element in the dataset. The degree of the sector and its percentage area relative to the circle’s total area define this proportion.

Types of Pie Chart

• Simple Pie Chart

This is the most basic type of pie chart and can also be simply called a pie chart.

• Exploded Pie Chart

An exploded pie chart separates (or explodes) one sector from the circle to highlight a specific element in the dataset.

• Pie of Pie

As the name suggests, a pie of pie is a chart that generates an entirely new (usually small) pie chart from the existing one. It can be used to reduce clutteredness and lay emphasis on a particular group of elements.

• Bar of Pie

This is similar to the pie of pie, with the main difference being that a bar chart is what is generated in this case rather than a pie chart.

• 3D Pie Chart

This is a type of pie chart that is represented in a 3-dimensional space.

Uses of Pie Chart

• It summarizes data into a visually appealing form.
• It is quite simple compared to many graph types.

Disadvantages of Pie Chart

• It is inapplicable for large datasets.
• It cannot visualize groups of data.

Line Graph or Chart 

Line graphs are represented by a group of data points joined together by a straight line. Each of these data points describes the relationship between the horizontal and the vertical axis on the graph.

The graph may ascend, descend, or do both depending on what kind of data is being visualized. When studying the relationship between price and supply, it goes down and for peace and demand, it goes up.

When constructing a line chart, you may decide to include the data points or not.

Types of Line Graph

• Simple Line Graph

In a simple line graph, only one line is plotted on the graph. One of the axes defines the independent variables while the other axis contains dependent variables.

• Multiple Line Graph

Multiple line graphs contain two or more lines representing more than one variable in a dataset. This type of graph can be used to study two or more variables over the same period of time.

• Compound Line Graph

A compound line graph extends the simple line graph, used for handling different groups of data within a larger dataset. Each line in the compound line graph shades downward to the x-axis.

In a compound line graph, each data group, shown by a simple line graph, is stacked above the next.

Uses of a Line Graph

• It helps in studying data trends over a period of time.
• They are easy to read and plot.

Disadvantages of a Line Graph

• It can only be used to visualize data over a short period of time.
• It is not convenient to plot when dealing with fractions and decimals

Histogram Chart

Histogram chart visualizes the frequency of discrete and continuous data in a dataset using joined rectangular bars. Each rectangular bar defines the number of elements that fall into a predefined class interval.

Types of Histogram Chart

The histogram chart is classified into different parts depending on their distribution

• Normal Distribution

A normally distributed histogram chart is usually bell-shaped. As the name suggests, this distribution is normal and is the standard for how a normal histogram chart should look like.

• Bimodal Distribution

In a bimodally distributed histogram chart, we have two groups of histogram charts that are of normal distribution. It is formed as a result of combining two processes in a dataset.

• Skewed Distribution

This is an asymmetric graph with an off-center pick usually tending towards the end of the graph. A histogram chart can be said to be right or left-skewed depending on the direction where the peak tends towards.

• Random Distribution

This type of histogram chart does not have a regular pattern. It produces multiple peaks and can also be called a multimodal distribution.

• Edge Peak Distribution

This distribution has a structure that is similar to that of a normal distribution with a large peak at one of its edges being the distinguishing factor.

• Comb Distribution

The comb distribution has a “comb-like” structure, where the rectangular bars alternate between tall and short.

Uses of Histogram Chart

• It helps in visualizing large amounts of data.
• It reveals the variation, centering, and distribution of the data.

Cons

• It does not visualize the exact values in a dataset.
• It only visualizes continuous data.

Area Chart 

Area charts visualize data trends over time by filling the space between the line segment and the x-axis with color. In simpler terms, an area chart is an extension of the line chart.

Types of Area Chart

• Simple area Chart

In a simple area chart, the colored segments overlap each other in the chart area. They are placed above each other such that they intersect.

• Stacked Area Chart

A stacked area chart arranges the colored segments on top of each other, preventing any intersection.

• 100% Stacked area Chart

This type of stacked area chart measures the area occupied by each data group as a percentage of its total amount. The vertical axis usually totals a hundred percent.

• 3-D Area Chart

This area chart measures data on a 3-dimensional space.

Uses of Area Chart

• It is visually appealing.
• It gives a clear comparison of different groups of data.

Cons

• It may be difficult to read when compared to other data types.

Dot Graph or Plot

A dot plot uses vertical dot-like markers to represent data points. Like a histogram and bar chart, it shows the height of marker groups to reflect the frequency of elements in a specific class interval.

Types of Dot Plot

• The Wilkinson Dot Plot

This type of dot plot uses the local displacement to prevent the dots on the plot from overlapping. This dot lot was created by Leland Wilkinson.

• Cleaveland Dot Plot

This is a scatterplot-like chart that displays data vertically in one dimension. It was developed by William Cleaveland.

Pros

• They are usually very colorful and visually appealing.

Cons

• Consequently, large datasets cause the data points to clutter and become unreadable.
• It is usually difficult to read frequency from the chart

Scatter Plot

Scatter plots visualize random variables using dot-like markers that represent each data point. These markers typically scatter across the chart area of the plot.

Types of Scatter Plot

Scatter plots are categorized into different types based on the correlation between the data points. The following sections highlight these correlation types:

• Positive Correlation

A positive correlation exists between two data groups on a scatter plot if one group increases as the other increases. A scatter plot can exhibit high or low positive correlation.

• Negative Correlation

A scatter plot demonstrates negative correlation when an increase in one data group results in a decrease in the other. The scatter plot can display a high or low negative correlation.

• No Correlation

A scatter plot shows two data groups as having no correlation when they lack a clear relationship.

Pros 

• It clearly shows data spread
• Ir is usually colorful and visually appealing

Cons

• It cannot give the exact extent of correlation.
• It serves only to study the relationship between two variables.

Bubble Chart

A bubble chart is a multivariable graph that uses bubbles to represent data points in 3 dimensions. It represents data points with bubbles, with the first two variables determining the location of the bubble on the x and y-axis while the 3rd variable determines the size of the bubble.

Types of Bubble Chart

The structure of bubble charts,are divide into different parts that depends on the dataset’s variables, data type, and graph dimensions.

• Simple Bubble Chart

The simple bubble chart is the most basic type of bubble chart and is equivalent to the normal bubble chart.

• Labeled Bubble Chart

Labeled bubble charts mark the bubbles for easy identification, particularly when dealing with various data groups.

• Multivariable Bubble Chart

In a multivariable bubble chart, where the dataset typically contains more than three variables (especially four), the fourth variable is often distinguished by color.

• Map Bubble Chart

A map bubble chart visualizes data directly on a map.

• 3D Bubble Chart

This bubble chart visualizes data in a 3-dimensional space. The bubbles on a 3D bubble Chart are spherical.

Uses of Bubble Chart

• It can visualize up to 4-dimensional data.
• It is visually appealing

Cons

• It is not good for visualizing large data.
• It may be difficult to ascertain specific values at a glance.

Pictogram Graph

A pictogram graph uses pictures or icons to visualize a small dataset of discrete data. In a pictogram, the icon represents a predefined unit and describes the frequency of the variables in the dataset.

Pros

• It is great for finding weight.

Cons

• The data between the points remains undetermined.

Radar Chart

A radar chart is a graphical method used for displaying multivariate data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point. (Source: Wikipedia). It is also called the spider graph.

Types of Radar Chart

• Simple Radar Chart

This is the most basic type of the radar chart and is equivalent to the normal radar chart. It consists of a sequence of radii drawn from the center point and joined together.

• Radar Chart With Markers

Radar charts with markers highlight each data point on the spider graph.

• Filled Radar Chart

Filled radar charts color the space between the lines and the center of the spider web.

Uses of Radar Chart

• Use it to compare multiple unrelated variables in a dataset.
• The outliers are easily noticeable.

Cons

• It is not good for large datasets.
• It has limited uses in data analysis.

Spline Chart

A spline chart connects data points with a smooth curve, creating a visually appealing line chart.

Pros

• You can draw them easily.
• It can visualize quadratic functions and polynomials

Cons

• Plotting becomes challenging when working with fractions and decimals.

How to Use Collect Online Data for Graphs & Charts with Formplus

Follow these simple steps to collect online data and create graphs and charts using Formplus.

Create an online form

To create a new survey on Formplus, go to Forms in the top menu, then click on the Create Form button. Alternatively, you can go to your  Dashboard, then click on the Create new form button.

Collect Data For Your Graph

After creating your survey, choose any of the available form fields in the left sidebar of the Form builder. Select the form field required to collect information for your graph by clicking or dragging and dropping it on the live preview section.

Customize Your Online Form

Once you’ve added all necessary form fields, click the Save button in the top right corner. This will automatically take you to the Customize page. On this page, you can beautify your survey by adding a logo, color, font, background image, etc. using the built-in Formplus features. Alternatively, you can add your own custom CSS.

Share & Start Collecting Graph data

After beautifying the survey to fit your taste, you can now share with respondents and collect the necessary data for your graph. With Formplus’ multiple sharing options, you can share your online survey via email, social media, QR code, etc, or even embed it on your website.

Uses of Graphs & Chart 

• In Mathematics and Statistics

Analytical geometry uses graphs and charts to map functions of multiple variables on a Cartesian coordinate system. They also help determine correlations and regressions within statistical datasets.

• In Data Interpretation

Charts and graphs are mainly used in data interpretation to make sense of a dataset. By summarizing the information in a dataset, the charts simplify the data interpretation process.

Conclusion 

There are different types of charts used in data visualization, with each of these charts being used in different situations. The best chart for a situation depends on its specific strengths and weaknesses.

For this reason, we may use bar charts in some instances and radar charts in others. Data analysts select the most suitable chart types by considering the chart’s strengths, weaknesses, and the needs of the intended audience.