---+!! Learn Biocomputing %TOC% ---++ General Resources *Software Carpentry* %BR% https://software-carpentry.org/lessons/ *Code Academy* %BR% https://www.codecademy.com/ ---++ Learning R The programming language R was originally designed for statistical analysis, but researchers now use it for a wide variety of data analysis and visualization tasks. %BR% Download R here: https://www.r-project.org %BR% Use the RStudio IDE to write and run R code. Download here: https://rstudio.com *Primers on RStudio Cloud* %BR% https://rstudio.cloud/learn/primers %BR% These are short interactive lessons that let you write R code in your web browser. They use the "tidyverse" style of R coding, which we recommend for all new users. The first 4 lessons: "Basics", "Work with Data", "Visualize Data", and "Tidy your Data" give a strong foundation for writing R code to explore your data. ---++Git / GitHub *Interactive Git lesson* %BR% https://github.com/jlord/git-it-electron ---++ Recommended Books *Practical Computing for Biologists* by Steven Haddock and Casey Dunn %BR% [[https://www.amazon.com/dp/0878933913/ref=cm_sw_r_tw_dp_U_x_5WVPEbFH8JJ6V][Buy on Amazon]] %BR% This book provides an exceptionally clean and well written introduction for biologists to begin working with computational data. The beginning focus on regular expressions and working with text files will help anyone starting out with coding. %BR% %BR% *Bioinformatics Data Skills: Reproducible and Robust Research with Open Source Tools* by Vince Buffalo %BR% [[https://www.amazon.com/dp/0878933913/ref=cm_sw_r_tw_dp_U_x_5WVPEbFH8JJ6V][Buy on Amazon]] %BR% This book is one of the few available "intermediate" level bioinformatic texts. Once you have mastered some basics in UNIX or Python/R programming, this books introduces more advanced concepts like workflow management and dealing with specific biological data.
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Topic revision: r2 - 2020-04-27 - SeanLeonard