Visualization Best Practices in R
Learn to effectively convey your data with an overview of common charts, alternative visualization types, and perception-driven style enhancements.
- 4 hours
- 13 Videos
- 49 Exercises
- 2,285 Participants
- 4,200 XP
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Course Description
This course will help you take your data visualization skills beyond the basics and hone them into a powerful member of your data science toolkit. Over the lessons we will use two interesting open datasets to cover different types of data (proportions, point-data, single distributions, and multiple distributions) and discuss the pros and cons of the most common visualizations. In addition, we will cover some less common alternatives visualizations for the data types and how to tweak default ggplot settings to most efficiently and effectively get your message across.
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1
Proportions of a whole
FreeIn this chapter, we focus on visualizing proportions of a whole; we see that pie charts really aren't so bad, along with discussing the waffle chart and stacked bars for comparing multiple proportions.
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Single distributions
We now move on to visualizing distributional data, we expose the fragility of histograms, discuss when it is better to shift to a kernel density plots, and how to make both plots work best for your data.
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Point data
We shift our focus now to single-observation or point data and go over when bar charts are appropriate and when they are not, what to use when they are not, and general perception-based enhancements for your charts.
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Comparing distributions
Finishing off we take a look at comparing multiple distributions to each other. We see why the traditional box plots are very dangerous and how to easily improve them, along with investigating when you should use more advanced alternatives like the beeswarm plot and violin plots.
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1
Proportions of a whole
FreeIn this chapter, we focus on visualizing proportions of a whole; we see that pie charts really aren't so bad, along with discussing the waffle chart and stacked bars for comparing multiple proportions.
-
Point data
We shift our focus now to single-observation or point data and go over when bar charts are appropriate and when they are not, what to use when they are not, and general perception-based enhancements for your charts.
-
Single distributions
We now move on to visualizing distributional data, we expose the fragility of histograms, discuss when it is better to shift to a kernel density plots, and how to make both plots work best for your data.
-
Comparing distributions
Finishing off we take a look at comparing multiple distributions to each other. We see why the traditional box plots are very dangerous and how to easily improve them, along with investigating when you should use more advanced alternatives like the beeswarm plot and violin plots.
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Nick Strayer
PhD Candidate, Biostatistics; Vanderbilt University
I am currently biostatistician and data scientist at Vanderbilt University. My research focuses on the fusion of machine learning and data visualization to explore and explain electronic health records data. I have worked as a data journalist in the graphics department at the New York Times, a data scientist at Johns Hopkins University Data Science Lab, and as a 'Data Artist in Residence' at data visualization startup Conduce. I am active on Twitter (nicholasstrayer) and blog about data science and visualization with fellow DataCamp instructor Lucy D'Agostino McGowan at livefreeordichotomize.com.
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Collaborators
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Chester Ismay
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David Campos
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Shon Inouye