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.

### R programming

##### Software

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)

Notes on the use of R for psychology experiments and questionnaires (PDF)

Practical Regression and Anova Using R (PDF)

Using R for psychological research

The R guide (PDF)

Quick-R: Accessing the power of R

R Excel Tutorial: How to Read and Write xlsx files in R

##### Advanced

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.

##### Courses

#### Books

Introduction to Scientific Programming and Simulation Using R (Chapman & Hall/CRC The R Series)

Advanced R (Chapman & Hall/CRC The R Series)

### Python

#### Software/Packages

##### Integrated Development Environments (IDE)

Scrapy – An easy-to-use open-source (and of course free) tool for extracting data from the web (see my post)

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

###### Statistics

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 Resources:

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