7 Visualizations You Should Learn in R

This article was posted by Dikesh Jariwala on R Bloggers.

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:

Comparison
Composition
Distribution
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.

Scatter Plot
Histogram
Bar & Stack Bar Chart
Box Plot
Area Chart
Heat Map
Correlogram

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

Top DSC Resources

Article: What is Data Science? 24 Fundamental Articles Answering This Question
Article: Hitchhiker's Guide to Data Science, Machine Learning, R, Python
Tutorial: Data Science Cheat Sheet
Tutorial: How to Become a Data Scientist - On Your Own
Categories: Data Science - Machine Learning - AI - IoT - Deep Learning
Tools: Hadoop - DataViZ - Python - R - SQL - Excel
Techniques: Clustering - Regression - SVM - Neural Nets - Ensembles - Decision Trees
Links: Cheat Sheets - Books - Events - Webinars - Tutorials - Training - News - Jobs
Links: Announcements - Salary Surveys - Data Sets - Certification - RSS Feeds - About Us
Newsletter: Sign-up - Past Editions - Members-Only Section - Content Search - For Bloggers
DSC on: Ning - Twitter - LinkedIn - Facebook - GooglePlus

14 members like this

Comment

You need to be a member of Data Science Central to add comments!

Join Data Science Central

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!