Tidyverse Learnings
1
Introduction
1.1
Aim
1.2
Motivation
2
Get Started with
tidyverse
2.1
The R Language
2.2
R Packages
3
What is Tidyverse?
3.1
Why use the Tidyverse?
3.2
Strengths of Tidyverse
3.2.1
Data import
3.2.2
Data wrangling
3.3
Maintain the Tidyverse
4
Being Tidy with RStudio Projects
4.1
Why should we use projects in RStudio?
4.1.1
Easier import
4.1.2
Improved Reproducibility
4.1.3
Improved Collaboration
4.2
Create a new Project
5
Introducing the %>% Operator
5.1
What is the %>% Operator
5.2
Significance of %>%
6
Importing, Modifying, and Filtering Data
6.1
Separate raw and clean data folders
6.2
Import .xlsx files with readxl in R
6.3
Import .csv files with readr into R
6.4
Is it a data frame or tibble?
6.5
Select and Filter Data
6.6
Convert strings to dates with mutate
6.7
Separating columns into multiple columns
6.8
Filter out NA values
6.9
Export .csv files with readr
6.10
Export .rdata objects for later use
7
Summarizing and Tabulating Data in
tidyverse
8
Wide and Long Data
References
Appendices
About Author
Copyright © Can Aytöre
Tidyverse Learnings
Chapter 8
Wide and Long Data