R is a programming language used to analyze data. That’s all it’s good for, and it does this one job very, very well. Any analysis you can think of can be done with R. Because of these features, R has become a very popular tool for data science.
So what’s “data science”? Long story short, it’s a buzzword used to describe the relatively new field of applied data analysis. Our data often has valuable information to tell us. Data science is the “science” of extracting these worthwhile insights from our data. This tutorial aims to teach the basics of data science: loading data, performing statistics, and conveying this information in a useful manner.
At the end of this tutorial, you’ll know how to:
dplyr and purrr.ggplot2.Before you start, make sure you have both R and RStudio installed and ready to go.
You may also wish to install the tidyverse packages with install.packages("tidyverse") beforehand. We’ll be using them a lot.
Giving credit where credit is due, these materials borrow liberally from Software Carpentry’s R for Reproducible Science workshop as well as a lot of concepts from Hadley Wickham’s “Advanced R” book.
© Jeff Stafford // https://jstaf.github.io/r-data-science/