Bioinformatics for Pharmacy

What is R

R is both a programming language and software environment for statistical computing, which is free and open-source (https://www.r-project.org/about.html).

The was initiated by Robert Gentleman and Ross Ihaka (University of Auckland) in the early 1990s as a different implementation of the S language, which was developed at Bell Laboratories.

Since 1997, R has been developed by the .

R is platform independent and can run on Microsoft Windows, Mac OS and Unix/Linux systems.

Popularity: https://www.tiobe.com/tiobe-index/

Resources and Disclaimer

The material is largely based on: * Introduction to R for the Université Côte d’Azur: https://github.com/mattiaguerini/slides-intro-to-R

• Applied Statistics for Bioinformatics using R by Wim P. Krijnen, 2009: https://faculty.ucr.edu/~tgirke/Documents/R_BioCond/documentation/Krijnen-IntroBioInfStatistics.pdf

• Rstudio4edu: https://github.com/dcossyleon/basic-course-website

• Applied Statistics using R: https://github.com/daviddalpiaz/appliedstats/

• Boston University Course: https://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/R/R2_SummaryStats-Graphs/R2_SummaryStats-Graphs_print.html

• Software Carpentry: https://github.com/swcarpentry/r-novice-inflammation and https://github.com/swcarpentry/r-novice-gapminder

• A Quick Intro to Chemical Informatics in R: https://www.cureffi.org/2013/09/23/a-quick-intro-to-chemical-informatics-in-r/

Getting Started

To get started, you’ll need to install two pieces of software:

• R, the actual programming language. https://cran.r-project.org

• RStudio, an excellent IDE for working with R. https://www.rstudio.com

Why RStudio?

• Easier to use (everything is in one space)

• Many useful integrations (e.g. R-projects, R-markdown, shiny …)

• Plenty of shortcuts (alt + shift + k)

• Plenty of cheatsheets (see top panel)

Screenshot of RConsole

RConsole

Screenshot of RStudio

RStudio

Glossary

• command: user input (text or numbers) that R understands

• script: a sequence of commands collected in a text file, each separated by a new line

• environment: a list of named variables that we have generated/imported by means of a series of commands

• history: the list of past commands thaty we have used

• help: a documentation of all the functions available in R (the user manual)

• package: a collection of additional functions and dataset