Everyday-R: Practical R for Data Science
Note: this book is a work in progress. All source code for this project are available on my GitHub, which is linked in 1.4.
This book serves as a collection of R Markdown files helps users in learning the practical syntax and usage of R for data science. Mainly, code snippets and workflow aimed at tackling everyday tasks in data science will be covered, which includes data cleaning, data wrangling, iterations, machine learning with
caret, data visualization, and web app design using
Shiny. Each broad topic will be split into chapters, though there will be some overlap.
This book assumes readers have a basic grasp of R programming language. There are tons of useful resources that provides the first few steps into programming with R and I hope this book can serve as a useful complement to using R - a pocket reference of sorts!
It depends on who you ask, but many users emphasize the importance of learning the syntax of base R before diving into commonly used packages like
data.table. It is definitely a good idea to get the hang of base R if you’re developing an app or a package for example - this would lessen the number of dependencies your program has!
However, similarly to
pandas in Python, the popularity of
dplyr and associated packages within the
tidyverse suite has soared and you wouldn’t be surprised to see
tidyverse solutions as the top answers in forums like Stack Overflow (this can be frustrating if you’re a base R purist). Using
tidyverse for data science can definitely make your life easy - I find their syntax more pretty intuitive - but I’d like to sit on the fence on the base R vs.
tidyverse debate; you should know both! For that reason, in this book I will try to use both interchangeably.
Code chunks will be presented in a typical Markdown format as such, with the code output below:
##  85.26307 96.82261 94.98865 64.74789 62.91580 59.41495 53.36960 ##  40.26686 91.77510 58.91136 58.16438 27.29287 11.23847 58.15263 ##  16.19651 76.12419 47.41721 68.03331 47.56923 48.88864
When using commands outside of base R, the loading of the parent package will be explicitly shown to avoid confusion:
## Warning in microbenchmark::microbenchmark(runif(n = 20, min = 0, ## max = 100)): less accurate nanosecond times to avoid potential ## integer overflows
## Unit: nanoseconds ## expr min lq mean median uq ## runif(n = 20, min = 0, max = 100) 984 1066 1219.34 1107 1148 ## max neval ## 7872 100
Typically in longer chains of code, I will use
magrittr as a pipe. This is usually standard practice in code using packages from the
tidyverse so it’s a good habit to start using it. However, keep in mind - as of the recent R version (shown below), there is a native R pipe
|> which behave almost - but not always - in a similar fashion.
Finally, here is the R version I am currently using:
## _ ## platform aarch64-apple-darwin20 ## arch aarch64 ## os darwin20 ## system aarch64, darwin20 ## status ## major 4 ## minor 3.1 ## year 2023 ## month 06 ## day 16 ## svn rev 84548 ## language R ## version.string R version 4.3.1 (2023-06-16) ## nickname Beagle Scouts
tidyverse: a collection of packages for data science, including
caret: package for implementation of machine learning models, with support for algorithms such as
mlbench: package for benchmarks and datasets in machine learning.
broom: package for summarizing of model estimates.
ggpubr: package for publication-ready data visualizations.
Shiny: package for implementation and designing of interactive web apps.
R packages found in this book are available on CRAN and thus can be installed simply by running
install.packages(). For packages not on CRAN (or if you want to download developmental versions of a package), you can install packages straight from a GitHub repository by running
All code used to compile this book as well as the individual markdown files are available on my repository here