Guess what? You have completed the whole course – well done you! You now have the tools you need to create meaningful and beautiful data visualizations.
Let's do one more summary.
In this course, we talked about four data visualization goals: distribution, composition (parts of the whole), change over time, and relationships between variables. We learned the charts that are best suited to each goal:
- Distribution: We discovered that bar charts visualize the distribution of categorical variables and histograms visualize the distribution of numerical variables.
- Composition: We discussed pie charts (and why they are controversial), bar charts, and 100% stacked bar charts.
- Change over time: We learned how and when to use bar charts and line charts.
- Relationships between variables: We examined scatter plots for visualizing the relationship between two numerical variables and mosaic plots for visualizing the relationship between two categorical variables.
We also outlined a number of good practices that you can use for almost any chart type:
- Always describe your chart:
- Choose a descriptive chart title.
- Create meaningful axis titles.
- Set proper tick labels on each axis.
- Include source information.
- Add a legend that describes the variables – and give the legend a descriptive title, too.
- Make the data stand out. Use color to focus attention on the data. Avoid overly bright hues; choose shades that are visually pleasing and that work in harmony with the story you're telling.
- Avoid clutter. Remove all unnecessary elements from your chart, including redundant grids and unneeded axes.
- If exact values are important to your story, add labels to the pertinent data points. Limit yourself to two decimal places to avoid overcrowding!
Awesome job! Now go forth and use your data visualization skills to enlighten the world! Don’t forget to check our Introduction to R course, where you can master the basics of the R language. Also, take a look at our Tidyverse and Lists and Functions in R courses! 😉