7 Visualizations You Should Learn in R

7 Visualizations You Should Learn in R

This blog was originally posted here

 With ever increasing volume of data, it is impossible to tell stories without visualizations. Data visualization is an art of how to turn numbers into useful knowledge.

R Programming lets you learn this art by offering a set of inbuilt functions and libraries to build visualizations and present data. Before the technical implementations of the visualization, let’s see first how to select the right chart type.

Selecting the Right Chart Type

There are four basic presentation types:

  1. Comparison
  2. Composition
  3. Distribution
  4. Relationship

To determine which amongst these is best suited for your data, I suggest you should answer a few questions like,

  • How many variables do you want to show in a single chart?
  • How many data points will you display for each variable?
  • Will you display values over a period of time, or among items or groups?

Below is a great explanation on selecting a right chart type by Dr. Andrew Abela.

In your day-to-day activities, you’ll come across the below listed 7 charts most of the time.

  1. Scatter Plot
  2. Histogram
  3. Bar & Stack Bar Chart
  4. Box Plot
  5. Area Chart
  6. Heat Map
  7. Correlogram

To learn about the 7 charts listed above, click here. For more articles about R, click here.



Link: 7 Visualizations You Should Learn in R