Here is a quite extensive list of useful links for learning R and Python. Most of them are free, but you will also find good books that I have found useful. For sure, R and Python are both valuable programming languages for scientists. The R language has its focus on statistical computing whereas Python is a more general purpose language.
The Comprehensive R Archive Network – Here you will find a lot of information and pre-compiled binaries for most operation systems.
Rstudio is the IDE (Integrated development environment) that I find most useful.
Free R books and guides
Introduction to R
An Introduction to R (PDF)
The R guide (PDF)
Advanced R – companion site for the book. Although it is the companion site for the book you can find a lot of information on R. Useful.
Advanced R (Chapman & Hall/CRC The R Series) – not free
Integrated Development Environments (IDE)
Spyder – a very useful Python IDE. It offers similar functionalities as RStudio. It is a Scientific PYthon Development EnviRonment!
Rodeo – An IDE that is very similar to RStudio.
See my post RStudio-like Python IDEs – Rodeo & Spyder for more information on both Rodeo and Spyder.
Experiment building software
PsychoPy – Free and Open Source Experiment builder written in Python. With PsychoPy you can create experiments by writing your own Python scripts or using its drag-and-drop graphical interface.
Expyriment is more like a library for making generation of experiments easier than using plain Python.
OpenSesame supports, much like PsychoPy, both Python scripting and a graphical interface for creating experiments. OpenSesame lets you choose which back-end you want use. Therefore, the application offer the same functionality as PsychoPy (back-end “Psycho”), Expyriment (back-end Xpyriment) and more (e.g.m OpenGL).
If you are interested in more about all the above mentioned applications (and more) see my posts: Python apps and libraries for creating experiments and Free & Useful Software – PsychoPy.
Free Python books and guides
Think Bayes – free introduction to Bayesian statistics using Python.
Think Stats: Probability and Statistics for Programmers – free introduction to Probability and Statistics. Note that, both Think Bayes and Think Stats can be bought (click on the links).
- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
(Not free; see my review)
- Learn how to use Pandas in my Pandas Daframe Tutorial. It covers everything from how to read and write Excel files (i.e., CSV and .xlsx) to a dataframe, subsetting, slicing, and much more manipulation of the dataframe. Check it out!
- Learn how to randomly select rows from a dataframe in the Pandas Sample Tutorial