The Ultimate Guide to Choosing the Right Chart for Your Data

Data visualization is a powerful way to explore data, convey insights, and tell compelling stories. The human brain processes visual information much faster than text or numbers. In fact, studies show that we process images 60,000 times faster than text. Effective charts and graphs can help you quickly identify patterns, outliers, and trends in your data that would be hard to discern from looking at raw numbers alone.

However, with so many different chart types to choose from, it can be tricky to determine which one will best showcase your data and get your message across. Should you use a bar chart or a line graph? A pie chart or a treemap? The type of visualization you select can significantly impact how your audience understands and interprets your data.

In this guide, we‘ll break down the various categories of charts, explore which types work best for different scenarios, and share tips for creating clear and compelling data visualizations. By the end, you‘ll be equipped with the knowledge to choose the perfect chart to bring your data to life.

The Five Main Categories of Charts

While there are dozens of different chart types, most fall into one of five main categories based on the kind of information they show:

1. Comparison charts
2. Distribution charts
3. Composition charts
4. Relationship charts
5. Trend charts

Let‘s take a closer look at each category and the most common chart types within them.

Comparison Charts

Comparison charts are used to compare values, such as the performance of different entities, products, or groups. They make it easy to see which values are the highest and lowest and the differences between them. The most frequently used types of comparison charts include:

• Bar charts
• Column charts
• Line charts
• Dot plots
• Radar charts

Bar charts and column charts display the lengths of rectangular bars to compare values across categories. They are ideal for comparing a small to moderate number of discrete categories, especially when the category names are long. Bar charts display the categories on the Y axis and the values on the X axis, while column charts do the opposite.

Line charts use data points connected by line segments to compare values or show the relationship between two continuous variables. They are great for comparing many values over a period of time to see overall trends. When comparing multiple groups over time, each group gets its own line.

Dot plots consist of data points plotted as dots on a simple scale. They are useful for comparing the distribution or pattern of several sets of data to find similarities and differences. Unlike line charts, the data points are not connected by line segments.

Radar charts, also known as spider charts or star plots, plot values for multiple quantitative variables along separate axes that all start from the same point. They can show which variables have similar values or are scoring high vs. low relative to the other variables. However, radar charts can be tricky to read and are best used for ordinal ratings rather than true quantitative data.

Distribution Charts

Distribution charts visualize how quantitative values are distributed across a data set. They answer the question "How many values lie within a certain numerical range?" and can reveal features like the central tendency, variance, and shape of a data set‘s distribution. Popular distribution charts include:

• Histograms
• Box plots
• Violin plots
• Density plots

Histograms show the distribution of a numerical variable by dividing the range of values into bins and plotting the number or frequency of observations that fall into each bin as the height of a bar. The bars in a histogram are always adjacent with no gaps, unlike a bar chart.

Box plots display the distribution of values in a data set using the five-number summary of minimum, maximum, median, first quartile, and third quartile. The box spans the interquartile range with lines extending to the min and max points. Box plots are useful for comparing distributions between several groups.

Violin plots are similar to box plots but add a kernel density plot on each side of the box to show the entire distribution of values. They look a bit like back-to-back histograms or a violin and can show more nuance than a box plot.

Density plots show the continuous distribution of a numeric value as a smoothed line that gives a rough estimate of the probability density function. They are ideal for showing "peaks and valleys" in a distribution. Multiple density lines can be overlaid to compare distributions between groups.

Composition Charts

Composition charts show how individual parts make up a whole or how a whole is divided into distinct parts. They visualize the relative sizes or proportions of categories as a fraction of a whole. Common composition chart types are:

• Pie charts
• Stacked bar/column charts
• Waterfall charts
• Treemaps

Pie charts divide a circle into slices sized proportionally to represent relative sizes of categories as a percentage of a whole. They are most effective when comparing a small number of categories. However, they are often criticized for being difficult to read accurately, especially when comparing similar-sized slices.

Stacked bar charts and stacked column charts use divided bars or columns to show both the total size and the relative proportions of categorical subdivisions that make up that total. Unlike a pie chart, they can stack many categories and compare compositions across multiple groups.

Waterfall charts illustrate how positive and negative values contribute to a total amount. They show a running total as values are added or subtracted sequentially, which is useful for understanding financial statements and other scenarios of incremental changes leading to a final outcome.

Treemaps display hierarchical data as a set of nested rectangles. Each branch is given a rectangle sized proportionally to a specified value. They can be used to visualize the drive capacity or file size of components in a computer directory or as a space-constrained alternative to pie charts.

Relationship Charts

Relationship charts are used to understand the correlation or connection between two or more variables. They uncover how changes in one variable impact the values of other variables or how different entities in a system are interconnected. Popular relationship charts include:

• Scatter plots
• Bubble charts
• Heatmaps
• Network diagrams

Scatter plots plot data points using Cartesian coordinates to visualize the relationship between two quantitative variables. If two variables are correlated, the data points will fall along a diagonal line or curve. Scatterplots can also incorporate a third variable using color, size, or shape.

Bubble charts are scatter plots where a third dimension of the data is represented through the size of the circles. Like a scatter plot, they show the relationship between two quantitative variables but use the bubble size to visualize a third quantitative variable. They can also incorporate a fourth dimension using bubble color.

Heatmaps encode values as colors in a matrix to visualize the relationship between two categorical variables. Higher values are usually represented by darker colors. They are useful for showing a general pattern in a large amount of multidimensional data, such as the correlation between features in a machine learning model.

Network diagrams use nodes connected by lines or arrows to represent the relationships between entities in a complex interconnected system. They can show directional or non-directional connections and incorporate weighting using line thickness or color. Network charts are common in biology, transportation systems, and social network analysis.

Trend Charts

Trend charts show how values change over a continuous variable, most often a time period. They emphasize patterns of increases, decreases, cycles, and exceptions in values. The most frequently used trend charts are:

• Line charts
• Area charts
• Stream graphs
• Candlestick charts

We already discussed line charts as one of the most versatile chart types for comparing values or showing trends over time. They are go-to chart for time series data.

Area charts are essentially line charts where the area between the line and the axis is filled with color or shading. Stacked area charts are used to visualize the trend in the individual parts as well as the composition of the whole over time.

Stream graphs are a variation of a stacked area chart where the baseline is shifted to minimize the change in wiggle between layers, centering the layers on top of each other. While aesthetically pleasing, they can be challenging to read values from accurately.

Candlestick charts are used to show trends in the price movement of a financial instrument over time, often in combination with transaction volume. Each "candlestick" shows the opening, closing, high, and low prices for a particular time interval.

How to Choose the Right Chart

Now that you understand the different categories of charts and common types within them, how do you go about selecting the best one for your data? Here are some key questions to ask when choosing a chart or graph:

1. What is the main message you want to communicate?
2. What type of data do you have (quantitative, categorical, time series, etc.)?
3. How many data points and variables do you need to show?
4. Who is your target audience and what is their level of graphicacy?

The type of message you are trying to get across should be the primary driver behind your chart selection. Is your goal to compare values, show the distribution of data, understand the composition of categories, analyze the relationship between variables, or visualize a trend over time? Let your communication objective guide you to the appropriate chart category.

Next, consider what type of data you are working with. Quantitative data that is continuous can be displayed using scatter plots, line charts, or histograms, while categorical data is better suited for bar charts and pie charts. When you have three or more quantitative variables, scatter plots and bubble charts are useful options.

The number of data points and variables is also an important factor. Pie charts quickly become cluttered when comparing more than about five categories. Heatmaps may be a better alternative for larger data sets with two categorical variables. Line charts with multiple series can become difficult to read with over four or five groups.

Finally, keep your audience in mind. What is their level of data literacy and familiarity with different chart types? Engaging graphics are of little use if your audience can‘t quickly and accurately interpret them. When in doubt, lean towards simpler, more common chart types like bar charts and line charts. Make sure to use clear labels, legends, and annotations to explain exactly what the viewer is looking at.

Data Visualization Best Practices and Common Pitfalls

Creating effective data visualizations that inform and influence is both an art and a science. Simply picking the right chart type is not enough – you also need to style and format your chart following data visualization best practices:

• Always include a title that summarizes the main takeaway
• Label your axes and include the units of measurement
• Start the Y-axis at zero to accurately represent values
• Avoid using 3D charts, which can skew perception of values
• Use a legend if you have multiple data series
• Annotate outliers and important data points
• Leverage preattentive attributes like color, size, and shape
• Make sure there is sufficient contrast between colors
• Order categories intuitively, such as by value or alphabetically

Some common pitfalls to watch out for are using the wrong chart type for your data, displaying too much data in a single chart, using misleading axis scales, and putting style over substance with excessive chart junk and decoration. Remember the goal is to communicate information as clearly and accurately as possible.

Tools for Creating Great Data Visualizations

There are countless software tools available for building compelling charts, graphs, and data visualizations. Some popular options include:

• Microsoft Excel
• Tableau
• Qlik
• Google Charts
• Plot.ly
• Infogram
• Datawrapper
• D3.js

Many business intelligence and analytics platforms like Tableau, Qlik, and Microsoft Power BI offer drag-and-drop interfaces for quickly creating interactive dashboards with common chart types. Spreadsheet tools like Excel and Google Sheets also provide a variety of basic charts and graphs.

For those who want more flexibility and customization, there are also many open-source charting libraries in R, Python, and JavaScript such as Plot.ly, D3.js, Chart.js, and Matplotlib. These allow you to generate highly custom static and interactive charts for embedding in webpages and applications.

Data Visualization Examples and Inspiration

There are many great examples of beautiful, insightful data visualizations across business, journalism, and academia. A few exceptional visualizations that illustrate what‘s possible with charts and graphs:

• The Upshot‘s interactive charts on income mobility across generations
• R2D3‘s visual introduction to machine learning
• Bloomberg‘s animated line charts showing what factors are warming the planet
• Flowing Data‘s compilation of 15 ways to visualize the same dataset

For more chart inspiration and examples of data visualization done right, check out the Information is Beautiful Awards, the /r/dataisbeautiful subreddit, and the FiveThirtyEight‘s data visualization tag.

Conclusion

We have covered a lot of ground in this guide to choosing the right chart for your data. We learned about the importance of data visualization, the five main categories of charts, how to match your message and data to the optimal chart type, and best practices for designing compelling visualizations.

As you can see, a lot of thought and care goes into creating charts and graphs that enlighten and persuade. But the payoff in terms of quickly conveying insights and emotionally engaging your audience is well worth the effort. Effective data visualization is an essential skill for data scientists, analysts, and really anyone who works with data.

The next time you need to communicate information to others, don‘t just present a dry table of numbers – bring your data story to life with the right visualization. Figure out the message you want to get across, pick a chart type that aligns with your goals and data, and design and format it following data visualization best practices.

If you do that consistently, you‘ll be able to harness the power of data visualization to explore, explain, and influence using data.