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Data visualisation in Python and R

Amanda Miotto, QCIF’s eResearch Analyst at Griffith University, shares her favourite website for data visualisation, and a 'cheat sheet' on graphical parameters in R.

Last year, I was introduced to Brisbane data analyst Yan Holtz’s great website about how to choose the right visualisation for your data.
 
From Data to Viz” has become my absolute favourite website to tell people about! It is a classification of chart types based on input data format. It comes in the form of a decision tree leading to a set of potentially appropriate visualisations to represent the data set.
 
Along with guidance of which graph will suit your data, “From Data to Viz” includes links to the Python and R code available to make your graph.
 
The website also includes articles on the most common mistakes and there are helpful discussions on common design decisions.
 
For further information, you can also check out Yan’s graph galleries, R Graph Gallery and Python Graph Gallery.
 
I have also recently found what has become my favourite ‘cheat sheet’ to date, as I have definitely struggled with making graphs with statistical program R.
 
Gaston Sanchez, a statistician, data scientist and lecturer in the Department of Statistics at the University of California, Berkeley, has created and publicly shared a visual cheat sheet for some graphical plot parameters in R. 
 
The cheat sheet really helps to understand how par ( ) works. 
 
Gaston has included tips for axes, symbol styles, figure arrangements, text and labels. 
 
Check out his cheat sheet here.
 
Any issues, talk to your local eResearch support staff for help.