R Tidyverse Applications

Course details

Are you an R programmer looking to take your R tidyverse knowledge to the next level? In this course, Megan Silvey aims to provide R programmers with a better overall understanding of the R tidyverse and how to utilize its packages. Megan uses practical, real-world examples that showcase concepts that you can use in your own work. Using a dataset from KinetEco’s sales, customer, and product data, Megan shows you how you can use the R tidyverse packages to load, clean, analyze, and visualize this data in an efficient manner with these packages.

Concepts

Introduction

Introduction to R
What is RStudio

Load Data

Introducing the R tidyverse
The R tidyverse packages
Introducing KinetEco
Understanding KinetEco Data
What is a tibble
Loading data with the tidyverse
Load and write data with readr
Load data with readxl and write with writexl
Solution - Load data

Manipulate Data

Dealing with missing values with tidyr
Reshaping data with tidyr
Mutate strings with stringr
Manipulate strings with stringr
Join and split strings with stringr
Understanding dates with lubridate
Edit dates with lubridate
Solution - Manipulate data

Factor and Map Data

Factor categorical data with forcats
Inspect and mutate factors with forcats
Adjust factor levels with forcats
Map functions with purrr
Other functions with purrr
Solution - Factor data

Analyze and Visualize Data

Summarize data with dplyr
Group data with dplyr
Mutate data with dplyr
Filter data with dplyr
Join data with dplyr
Plot one variable visualizations with ggplot2
Plot two variable visualizations with ggplot2
Solution - Perform data analysis

Conclusion

Next steps