As a user of Anaconda python I have been receiving (Ubuntu) system warnings of low free space in my home directory. Investigating what was causing this I found out that Anaconda python had several versions of each package. The overall size of the
pkgs directory was 14+ GB. After cleaning it is about 3GB.
The second largest directory was my mail in Thunderbird.
So it would be wise, especially if you are limited in disk space, to clean Anaconda. The commands I used are as follows:
conda clean --all
conda update conda
# just to make sure nothing is broken and
# your environment is updated
source activate <your-environment>
conda update --all
conda clean --all
last updated: 2015-08-29
References & Most helpful commands
- Short R Reference Card (PDF) R commands
- Knitr Reference Card
- Advanced R (wiki), Hadley Wickham Programming in R, UC Riverside
- Introduction to R, A First Course in R (PDF), University of Notre Dame
- MATLAB commands in numerical Pythom (NumPY): as well as Octave & R (PDF)
- Numerical Analysis & Statistics: MATLAB, R, NumPy – a side-by-side reference sheet
- data.table intro
- data.table faq
- Exploratory Data Analysis with data.table (videos)
- data.table cheat sheet
- Cheatsheets (Data Wrangling with dplyr & tidyr, R Markdown, Shiny)
- R Documentation
- Resources to help you learn and use R, Institute of Digital Research and Education (idre), UCLA
Tutorials & Handy packages
Hands-on dplyr tutorial for faster data manipulation in R Interactive Visualizations From R Using Rcharts rMaps – Interactive Maps from R (github repo) (requires “devtools” from cran)
Using R for Psychological Research – Personality Project, William Revelle
Try R by Code School (on codeschool)
Introduction to R, Leada
see Assorted links – Data Visualization (to be published later)
Tidy Data, Hadley Wickham [PDF]
Big Data & Society – Open-access journal
Hacks for better productivity
Sublime and R
Using Sublime Text 2 for R Using R in Sublime Text 3
Video (training) courses
Introduction to Data Science with R, Garrett Grolemund, O’Reilly Media
Lists of Resources by others
Scraping Twitter and Web Data Using R – Pablo Barbera
see Assorted links – Data sources (To be published later)
If you’d like to contribute to this list, please leave them in the comments below.
The open-source world keeps surprising me. It is really amazing how internationally distributed individuals meet and collaborate on open-source projects and develop amazing products that exceed commercially available products. One such example is the development of the R statistical programming language.
Watch the video below and observe how its development since 1997 is similar to the work of ants and bees constructing colonies and hives.
Below is a nice map created by Ali Rebaie of universities offering degrees in Data Science based on data from this github repo. Contribute using this Google spreadsheet.
[via Ali Rebaie]