# 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

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Comment by Vishal Kapur on December 27, 2018 at 7:34am

I liked it.. Can you please share more such articles and links on missing data and imputation techniques...

Comment by R Bohn on May 25, 2018 at 10:54am

This is useful. More on categorical variables?

Box plot and its extensions: Violin plot, notched box plot.

Mosaic plot

Or, are there just too many possibilities so there is no point in trying to cover a broad spectrum?

Comment by Thiago Buselato Maurício on January 23, 2018 at 12:41pm

Thanks Emmanuelle Rieuf, it's always better when we have some material that summarize knowledge.

Comment by Peter Myers on January 22, 2018 at 8:55am

I saw that post recently and liked it too.

Type Category Variables Number Variables Short Description Description summary(dataset) 0 0 Easy way to start Check all descriptive statistics Correlogram 0 All Easy way to start Correlation/relationship of all variables Scatter Plot 0-1 2 Numeric variable focus A natural next step after using correlogram Histogram 0 1 Distributions Distribution of one variable using counting Box Plot 0-1 1 T-Tests Check many descriptive statistics split by 0-1 category variables. Very good with two sample T-Tests/Z-Tests Bar Chart 1 1 Category focused summation Get an summation by one category variable Heat Map 2 1 Category focused summation Get an summation by two category variables Area Chart 0-1 1 Time series
Comment by Claude Cundiff on January 20, 2018 at 11:08pm

Excellent post.  I was brainstorming over this topic tonight and saw a post on Twitter from Dr Borne referencing your post. Nice coincidence!