All attendees are expected to come to the workshop with R, RStudio, and necessary R packages already installed!

STEP 1:  Install R

Ensure you have the latest version of R installed on your computer.  You should install R first, then RStudio (see below). We anticipate all participants are familiar with R.

1. Click here to get to the download page

2. At the top of the page, select the version of R corresponding to your operating system (Mac, Linux, Windows)

3. Follow directions for download and installation.

STEP 2:  Install RStudio

1. Go to the RStudio download page 

2. Go to the second step to download RStudio.  If you have already installed R, you do not have to do it again. 

3. Follow the instructions of the Installer.

STEP 3:  Install R Packages 

1. Open RStudio to start R

2. Install synthpop.  The synthpop package imports (but does not automatically load) several additional package: lattice, MASS, methods, nnet, ggplot2, graphics, stats, utils, rpart, party, foreign, plyr, proto, polspline, randomForest,ranger, classInt, mipfp, survival, stringr, rmutil, broman.  Students do not need to install the imported packages by hand.  They will be downloaded and installed when they install synthpop.

3. Install the following additional packages that are listed in blue.  The ones in red have already been installed by synthpop.  Make sure you have a good internet connection.  You may need to install these one at a time.  

• caret

• caretEnsemble

• gam 

• gbm

• gtsummary

• dplyr

• ISLR2

• kableExtra

• pacman

• pdp 

• pROC

• rattle

• splines

• tidyr

• tidyverse

• tidymodels

• epi

• bshazard

• muhaz

• ggsurvfit

• lubridate

• survminer

• broom

• flexsurv

• randomForestSRC

• readxl

• tibbles

• ggplot2

• MASS

• randomForest

• rpart

• survival

• synthpop

STEP 4:  Download recommended texts

We recommend the following textbooks as a companion to the workshop.  Those who need some familiarity with R should consult Introduction to Data Science, Part I.

Introduction to Data Science, Part I 

Data Wrangling and Visualization with R

Introduction to Data Science, Part II

Statistics and Prediction Algorithms

Introduction to Statistical Learning (ISLR), edition 2