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.
To determine which amongst these is best suited for your data, I suggest you should answer a few questions like,
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.
To learn about the 7 charts listed above, click here. For more articles about R, click here.
Top DSC Resources
Follow us on Twitter: @DataScienceCtrl | @AnalyticBridge
Comment
I liked it.. Can you please share more such articles and links on missing data and imputation techniques...
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?
Thanks Emmanuelle Rieuf, it's always better when we have some material that summarize knowledge.
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 |
Excellent post. I was brainstorming over this topic tonight and saw a post on Twitter from Dr Borne referencing your post. Nice coincidence!
© 2020 Data Science Central ® Powered by
Badges | Report an Issue | Privacy Policy | Terms of Service
Most Popular Content on DSC
To not miss this type of content in the future, subscribe to our newsletter.
Other popular resources
Archives: 2008-2014 | 2015-2016 | 2017-2019 | Book 1 | Book 2 | More
Most popular articles
You need to be a member of Data Science Central to add comments!
Join Data Science Central