Types of Charts and Graphs: How to Choose the Right One for Your Data

Key takeaways

• Bar charts, scatter plots, line graphs, and other types of charts and graphs are data visualization tools used to reveal meaningful insights and draw conclusions. 

• Different types of charts and graphs are used to tell compelling stories, highlight insights, and reveal scale, comparisons, and trends.

• You can choose the right chart or graph by determining whether you want to show comparisons, trends, or relationships between data sets.

Learn more about the different types of charts and graphs, and discover how to visualize and present your data findings in the proper format. Then, consider enrolling in the Google Data Analytics Professional Certificate on Coursera. You’ll have the opportunity to learn key tools necessary for a career in data analytics in as little as six months. By the end, you’ll have gained an immersive understanding of the daily practices and skills of a data analyst.

What are charts and graphs?

Charts and graphs are useful data visualization tools that concisely present quantitative and qualitative data, helping you interpret and analyze it more effectively. You can use them to assess a variety of different data sets. Charts and graphs can reveal patterns and trends that may not have been obvious from the raw data, allowing you to draw new conclusions.

Charts and graphs provide a clearer interpretation of data that is not only visually appealing but also easier to understand. By visualizing abstract data, you can tell compelling stories, highlight important insights, and reveal scale, comparisons, and trends over time.

Why use charts and graphs? 

Charts or graphs can help simplify complex data, making it easier for you and your audience to understand the patterns, trends, or insights of a data set. These data visualization techniques include several formatting options to illustrate your data effectively. When presenting a data set, the right chart or graph can clearly communicate your message and make your insights more valuable. 

What is the difference between charts and graphs? 

Charts are visual representations of data, such as tables, diagrams, and graphs. Graphs are a specific type of chart, which more often show how data changes over time. Both can help you visualize information and turn your raw data into actionable insights [1]

What are the types of charts and graphs?

When choosing among different types of charts and graphs, it is helpful to understand what each type represents and how it can visualize your data. Different visualizations are meant to highlight different insights, so selecting the appropriate chart ensures that your data is not only accurate but also easy to interpret. Explore different types of charts and graphs to determine the right one for displaying your data [2]. 

• Bar charts

• Line charts

• Pie charts

• Scatter plot

• Network graph

What is a bar chart, and when should you use one?

Bar charts are vertical or horizontal graphical displays that use bars of varying lengths or heights to represent categorical data. The height or length of a bar is proportional to the value it represents, allowing you to quickly make visual comparisons across categories. Bar charts are useful when you need to demonstrate a relationship between dependent and independent variables, and are helpful when you want to demonstrate significant changes over time.

What is a line chart, and when should you use one?

Line charts are best for demonstrating smaller changes over time and for comparing trends among multiple groups across the same period. They consist of points connected by lines, with each point representing a specific value at a specific time. Line charts can show continuity and are optimal when your data set has one time variable and one numeric variable, though you can also plot multiple lines on a line graph to compare multiple dependent variables. 

What is a pie chart, and when should you use one?

Pie charts illustrate relative proportions, helping you visualize the relationship between several parts and the whole. A pie chart is a circular diagram divided into sections, with each “slice” of the pie representing a category’s share of the whole. You should only use pie charts when the parts of your data set are mutually exclusive and add up to a whole, as they are intended to show a “big picture” relationship. 

What is a scatter plot, and when should you use one?

Scatter plots display the correlation between two variables by plotting data points on an x-axis and a y-axis. Each point represents a single observation, and unlike line charts, the points do not connect. A scatter plot can illustrate exponential increases or decreases in your data sets, though it is important not to have too few or too many data points. Too few points may demonstrate trends entirely by chance, whereas too many may overlap and warp your audience’s interpretation of the full data set.

What is a network graph, and when should you use one?

Graphs show complex relationships among your data points, using vertices to represent them and edges between vertices to represent connections. An x-y line graph displays related values that demonstrate a change in one variable as a function of another across two axes. Network graphs have weighted edges with numerical values that specify quantities, such as distance or time.

How to choose the right chart or graph

When visualizing your information with a chart or graph, first determine whether you want to show comparisons, trends, or relationships between data sets. Each type of chart and graph serves a different purpose, so your choice should align with your overall goal [3].

Comparison charts

Comparison charts are a great option when you need to compare features of data points. You can use them to show differences and similarities, or to highlight advantages and disadvantages of variables. Because of their versatility, comparison charts can help you perform data-driven analyses and draw important conclusions.

Trend charts

Trend charts are visualizations that show how data changes over time. They can help you identify patterns within your data set, such as growth or decline, cycles, or maximum and minimum values. The most common type of trend chart is a line chart, which is a great way to show subtle changes in your data over equal time intervals. However, line charts work best when you have multiple data series; if you are only charting one set, consider using a scatter plot.

Distribution charts

Distribution charts illustrate the data's distribution across a range of values. You can use them to show distribution analysis and identify the mean, median, range, and outliers in your data set. Distribution charts can help you track data, such as the frequency of a certain event across a population or survey responders by age. Histograms are one type of distribution chart.

Relationship charts

You can use relationship charts to determine how two or more variables relate to one another. Relationship charts are also helpful for identifying trends and outliers in clusters of data. Scatter graphs are a type of relationship chart that you can use to demonstrate correlations between variables. Bubble charts are helpful for showing three or more dimensions of data.

Composition charts

Composition charts compare parts to a whole, with all the sections adding up to 100 percent. Pie charts are a type of composition chart that can clearly demarcate differences between your variables through sections or “slices” of a pie. You can use composition charts for small or large data sets, though it's best to make demarcations clear between your sections with color coding. Tree maps and stacked bar charts are additional types of composition charts.

Learn more: Google Sheets Dynamic Charts: How to Build Visuals That Update Automatically

When should you use a bar chart vs. a line chart?

You should use a bar chart when comparing two variables from distinct groups and a line chart when comparing changes in variables over a period of time. A line chart is optimal for smaller or incremental changes over time and can show continuity in a way that bar charts cannot. 

What are the best practices when choosing charts?

When choosing or creating a chart or graph to represent your data, it is important to follow these guidelines to accurately illustrate your information and achieve your intended goals with a data set.

• Choose the right chart for the data: First, make sure you select the correct visual for your data. For example, you should not demonstrate comparative data with multiple pie charts, as they can be difficult for your audience to interpret.

• Create pleasant visuals: Bright colors and lots of graphics may seem exciting, but they can obstruct the information you're trying to present. Use colors that are easy to distinguish from one another, and try not to use more than six. You shouldn’t necessarily use a complex chart to represent your data; sometimes, a simple bar chart is all you need.

• Use labeled and relevant data: Charts can be difficult to interpret if they lack clear labels or include unnecessary information. Use data labels to guide your reader and indicate which points you are representing with the chart.

• Ensure accuracy and freedom from errors: Converting raw data to a graphical format is a complex process that requires some trial and error to prevent inaccuracies from slipping through. Double-check the chart and ensure the graph accurately represents the information and that you are highlighting your selected data rather than manipulating it. 

How do you get started with data visualization?

To get started with data visualization, you can begin by practicing with accessible online tools, such as Google Sheets, Microsoft Excel, and Canva, which allow you to create charts and graphs using your data. To build your skills further, consider enrolling in certificate programs or online courses to gain hands-on experience and learn industry-level data visualization tools like Microsoft Power BI or Tableau.

If data visualization and analysis are something you wish to pursue further, you can pursue a degree in data science, statistics, or business analytics. Higher education programs can provide a deeper foundation in data analysis and help you explore a career as a data scientist, business intelligence analyst, or data visualization engineer.

Explore more data and career-building resources

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• Watch on YouTube: Histogram vs. Bar Graph: Understanding the Key Differences

• Read our Career Chat issue: Data Analyst Career Spotlight: What it is and How to Get Started

• Follow a tutorial: How to Use Data Validation in Google Sheets

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