Everyday-R: Practical R for Data Science
Chapter 1 Introduction
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 that aims to assist users in learning the practical syntax and usage of R. Mainly, code snippets and workflow aimed at tackling everyday tasks in data science will be covered, including 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.
1.1 R syntax in this book
Code chunks will be presented in a typical Markdown format as such, with the code output below:
## [1] 61.730378 55.742246 93.733909 73.541553
## [5] 33.601481 58.174088 98.087900 69.854770
## [9] 2.479445 33.739732 32.691337 18.190343
## [13] 42.149178 12.052854 81.348673 56.093152
## [17] 49.868675 76.773096 13.215394 57.569796
When using commands outside of base R, the loading of the parent package will be explicitly shown to avoid confusion:
## Unit: microseconds
## expr min lq
## runif(n = 20, min = 0, max = 100) 1.417 1.501
## mean median uq max neval
## 1.69272 1.584 1.709 8.125 100
Typically in longer chains of code, I will use %>%
from 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.
Finally, here is the R version I am currently using:
## _
## platform x86_64-apple-darwin17.0
## arch x86_64
## os darwin17.0
## system x86_64, darwin17.0
## status
## major 4
## minor 0.5
## year 2021
## month 03
## day 31
## svn rev 80133
## language R
## version.string R version 4.0.5 (2021-03-31)
## nickname Shake and Throw
1.2 R packages commonly used in this book
tidyverse
: a collection of packages for data science, includingdplyr
,purrr
,stringr
,forcats
,readr
, andggplot
.caret
: package for implementation of machine learning models, with support for algorithms such asranger
,rpart
,xgbTree
, andsvmLinear
.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.
1.3 Installing R packages
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 devtools::install_github()
.
1.4 Code availability
All code used to compile this book as well as the individual markdown files are available on my repository here