Before we start

We will be using:

• R (of course), and you can get it from https://cloud.r-project.org/

• RStudio (highly recommended), an IDE for R, and you can get if from https://www.rstudio.com/products/rstudio/

• Materials for this presentation (including the presentation itself) can be downloaded from https://github.com/USCbiostats/intro2r-hcp-edition

What is R

The programming language

• Free software oriented for statistical computing and graphics (although people use for everything now a days)

• Is maintain mostly by the R Core Team (about 20 members worldwide), with the support of The R Foundation

• Has a thriving community of users and developers worldwide:

  • Institutional support from: rOpenSci, R Consortium, etc.

  • About 13,000 packages(libraries) on CRAN (The Comprehensive R Archive Network)

  • More than 1,000 attendees in useR!2017 conference

A little bit of history

• R (R Core Team 2018) is an implementation of the S (Statistics) programming language, which was created in 1976 by John Chambers while at Bell Labs.

• R itself was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand. First release in 1995.

• Currently developed by the R Development Core Team, of which Chambers is a member.

(Source wiki)

The first lesson: Getting help

in R

In R, if you want to:

• Know about the sqrt function? ?sqrt, or help("sqrt")

• Know about the makeCluster function in the R package parallel? ?parallel::makeCluster, or help("makeCluster", package="parallel")

• Know about the Regular Expressions? ??"Regular Expressions", or help.search("Regular Expressions")

• See a full list of the functions and help files available in the package boot: help(package="boot").

• Look at more in deph information about the Matrix package? vignette(package="Matrix")

On the web

• Checkout the CRAN Task Views

• Take a look at the rstats tag on StackOverflow.com

• Visit the r-bloggers.com website

• Read one of the dozens of online free books about R created with the R package bookdown at bookdown.org

• Ask a question on twitter using the #rstats hashtag.

Books that I recommend

• The art of R programming https://nostarch.com/artofr.htm (Matloff 2011)

• Advanced R http://adv-r.had.co.nz/ (Wickham 2015)

• R Programming for Data Science https://bookdown.org/rdpeng/rprogdatascience/ (Peng 2012)

• R for Data Science http://r4ds.had.co.nz/ (Wickham and Grolemund 2016)

• Scientific Programming and Simulation using R (Jones, Maillardet, and Robinson 2009)

The first lesson: Getting help (How to read it?)

The first lesson: Getting help (a mental model)

Questions

1. Using the stats package, How can you estimate a generalized linear model in R?

2. What is the command to transpose a matrix in R? What about the command for inverting a matrix?

3. Looking at CRAN task Views, what R packages are available for convex optimization? What about stochastic optimization?

4. Create a list of R packages that provide wrappers for working with Slurm.

5. What does return the function for fitting nonlinear least squares in the stats package?