7 Visualizations You Should Learn in R

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

     

     

     

     

     

     

     

     

     

 

 

 

 

Some opinions expressed in this article may be those of a guest author and not necessarily Analytikus. Staff authors are listed https://www.datasciencecentral.com/profiles/blogs/7-visualizations-you-should-learn-in-r

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