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
Follow us on Twitter: @DataScienceCtrl | @AnalyticBridge
Comment
-
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 plotOr, 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!
Follow Us
Top Content
Announcements
Videos
Resources
© 2018 Data Science Central ®
Powered by
Badges | Report an Issue | Privacy Policy | Terms of Service
You need to be a member of Data Science Central to add comments!
Join Data Science Central