1. Getting Started

This chapter gets the local setup out of the way before the rest of the book starts using packages, notebooks, and datasets.

1.1. Choose an environment

You may work through this book in any of these ways.

Use the Docker image described in the preface if you want a prebuilt JupyterLab environment.
Install R locally and work in RStudio if you prefer a desktop IDE.
Install R, IRkernel, and Jupyter if you want local notebooks without Docker.

The examples are standard R code, so the book does not depend on one editor.

1.2. Install packages

Some chapters use contributed packages rather than only base R. Install them once with install.packages.

[ ]:

install.packages(c(
  'tidyverse',
  'purrr',
  'ggplot2',
  'randomForest',
  'e1071',
  'pROC',
  'mice',
  'Amelia',
  'missForest',
  'Hmisc',
  'mi'
))

1.3. Load packages

Load packages at the top of a notebook or script before using their functions.

[ ]:

library(tidyverse)
library(ggplot2)
library(randomForest)
library(e1071)

1.4. Get help

Use the built-in help system frequently while reading the book.

[ ]:

?mean
help('lm')
example('plot')

1.5. Keep moving

Once your environment is working and packages install cleanly, continue into the core language chapters. You can come back to this page whenever you need a reminder on package installation or help lookup.