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Getting started with R and RStudio

Mikhail Dozmorov

Virginia Commonwealth University

09-22-2020

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R history

  • Elegant, object-oriented programming language.

    • Started by Robert Gentleman and Ross Ihaka (hence R) in 1995 as a free, independent, open-source implementation of the S programming language
    • Currently, maintained by the R Core development team

  • An extremely powerful language for statistical modeling, machine learning, data manipulation, and visualization.

    • Efficient data analysis
    • Optimized operations on vectors, matrices lists
    • Very sophisticated graphs and data displays

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More R history

  • 1992: R development begins as a research project in Auckland, NZ by Robert Gentleman and Ross Ihaka
  • 1993: First binary versions of R published at Statlib
  • 1995: R first distributed as open-source software, under GPL2 license
  • 1997: R core group formed
  • 1997: CRAN founded (by Kurt Jornik and Fritz Leisch)
  • 1999: The R website, r-project.org, founded
  • 1999: First in-person meeting of R Core team, at inaugural Directions in Statistical Computing conference, Vienna
  • 2000: R 1.0.0 released (February 29)
  • 2000: John Chambers, recipient of the 1998 ACM Software Systems Award for the S language, joins R Core
  • 2001: R News founded (later to become the R Journal)
  • 2003: R Foundation founded
  • 2004: First UseR! conference (in Vienna)
  • 2004: R 2.0.0 released
  • 2009: First edition of the R Journal
  • 2013: R 3.0.0 released
  • 2015: R Consortium founded, with R Foundation participation
  • 2016: New R logo adopted
  • 2020: R 4.0.0 released

https://blog.revolutionanalytics.com/2017/10/updated-history-of-r.html

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Organizations behind R

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Bioconductor

"Is an open source and open development software project to provide tools for the analysis and comprehension of genomic data."

Goals

  • To provide widespread access to a broad range of powerful statistical and graphical methods for the analysis of genomic data

  • To provide a common software platform that enables the rapid development and deployment of extensible, scalable, and interoperable software

  • To further scientific understanding by producing high-quality documentation and reproducible research

  • To train researchers on computational and statistical methods for the analysis of genomic data

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Advantages and disadvantages of R

  • Free and easy to install

  • Highly extensible through packages, collections of functions, data, and documentation

  • Active developer community

  • Anyone can contribute

  • Some learning curve

  • No centralized support

  • Less consistency in function/variable naming and structure

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Install/update R

  • Installation for Windows, Mac, and Linux is available

  • New version of R is released every 6 month, typically around April/October

    • Bioconductor updates follow shortly thereafter

  • Use installr R package to cleanly update R on Windows

https://cran.r-project.org/, https://CRAN.R-project.org/package=installr

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Run R

  • Command line. Type R and enter the R prompt

    • Type R CMD to see the list of available commands
    • Type Rscript to see how you can run R commands and scripts from the command line. E.g., Rscript -e 'print("Hello, world!")'

  • R console, installs with R

  • Rcommander. GUI for better interactions with R

  • RStudio. Integrated development environment for basic and advanced R operations

RStudio

  • RStudio is a program that allows you to run R in a more user-friendly environment

    • RStudio offers some GUI (graphical user interface) features for R

  • RStudio is a very flexible IDE (Integrated development environment)

    • The same R program is running underneath the interface

  • RStudio is open-source software (and therefore is free)

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Why RStudio

  • Project-centric work - scripts and data are organized in one folder (project), easily accessible

  • Single workspace with four (rearrangeable, zoomable) panels

  • Work on multiple projects simultaneously in several instances of RStudio

  • Work on multiple (types of) scripts

  • See all variables in R environment, easily visualize them

  • Easy access to help, plots, packages

  • Simple integration with Git version control system

  • After you install R and RStudio, you only need to run RStudio

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RStudio main window

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R Console

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Create/open new script or other file

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Open an existing script:

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Rearrangeable tabbed interface

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History of commands

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Environment tab

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Plots tab

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RStudio keyboard shortcuts

  • Command (Ctrl) + Enter - Run current line/selection, go to the next line
  • Alt + Enter - Run current line/selection, stay on the current line
  • Ctrl + 1 - Move cursor to source
  • Ctrl + 2 - Move cursor to console
  • Ctrl + L - Clear console
  • Ctrl + Alt + I - Create new code chunk
  • Ctrl + Alt + c - Run current code chunk
  • All the usual - Ctrl + Z, C, X, V, S, O

See more via menu "Help -> Keyboard Shortcuts Help", "Help -> Cheatsheets"

https://support.rstudio.com/hc/en-us/articles/200711853-Keyboard-Shortcuts

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RStudio is more than IDE

RStudio PBC develops many now-gold-standard R packages

  • tidyverse – R packages for data science, including ggplot2, dplyr, tidyr, and purrr

  • shiny – An interactive web technology

  • rmarkdown – Insert R code into markdown documents

  • knitr – Dynamic reports combining R, TeX, Markdown & HTML

  • tensorflow - open-source software library for Machine Intelligence

  • reticulate - provides a comprehensive set of

  • devtools – Package development tool

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RSstudio-inspired analogs

Rodeo - A Native Python IDE for Data Science

https://rodeo.yhat.com/

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RStudio cloud

Getting started locally

  • Install R
  • Install RStudio
  • Install packages (rmarkdown, tidyverse)
  • Load packages
  • Install Git ...

Getting started online

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RStudio cloud

  • An online, cloud-based instance of RStudio IDE, accessible via a web browser
  • No hardware requirements or software setup requirements from the user's side
  • Share projects with specific people or anyone
  • Import projects from GitHub
  • Free plan is always available (up to 15 projects, 1 shared space, 15 project-hours per month, up to 1Gb RAM and 1 CPU per project)

https://rstudio.cloud/

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R history

  • Elegant, object-oriented programming language.

    • Started by Robert Gentleman and Ross Ihaka (hence R) in 1995 as a free, independent, open-source implementation of the S programming language
    • Currently, maintained by the R Core development team

  • An extremely powerful language for statistical modeling, machine learning, data manipulation, and visualization.

    • Efficient data analysis
    • Optimized operations on vectors, matrices lists
    • Very sophisticated graphs and data displays

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