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.
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
Pandas is a great package that makes data manipulation, descriptive statistics, data visualization, amongst other things very easy to perform, Below are some Pandas dataframe tutorials, how to read and write data (e.g., SPSS, Excel, CSV, HTML tables, and JSON files).
- 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