8  Welcome to the Tidyverse!

9 Overview of the Tidyverse & tidy data

9.1 The tidy ecosystem

9.2 Tidy data principles

9.3 Core packages in the Tidyverse and general functions

9.4 Terminology

10 Importing and exporting with readr

10.1 Overview

10.1.1 Tabular data - what counts?

10.1.1.1 File types

10.1.1.2 R objects, including tibbles

10.1.2 Reading, writing, rereading (intermediate datasets)

10.2 Reading data with read_* functions

10.3 Writing data with write_* functions

10.4 Other packages

10.4.1 readxl for Excel files

10.4.2 haven for SPSS, SAS, and Stata files

10.4.3 googlesheets4 for Google Sheets

10.4.4 jsonlite for JSON files

10.4.5 DBI and dbplyr for databases

10.5 Handling common import/export issues

11 Data manipulation with dplyr

11.1 Introduction to dplyr

11.1.1 the point of pipelines (highly readable, but verbose)

11.1.2 The pipe operator (%>%) and magrittr

11.1.3 Chaining operations with pipes

11.2 Selecting data

11.2.1 select, rename

11.2.2 filter

11.2.3 arrange

11.3 Manipulating data

11.3.1 mutate

11.3.2 summarize

11.3.3 group_by

11.3.4 distinct

11.3.5 count

11.4 Guided Exercise: dplyr practice

12 Data tidying with tidyr

12.1 What counts as “tidying” data?

12.1.1 Remember what “tidy” means in the tidyverse

12.1.2 Tidying is reshaping and systematically cleaning data

12.2 Reshape data

12.2.1 pivot_longer

12.2.2 pivot_wider

12.2.3 cast, melt, gather, spread, etc.

12.3 Combine and split cells

12.3.1 unite

12.3.2 separate functions

12.4 Expand tables

12.4.1 expand

12.4.2 complete

12.5 Handle missing values

12.5.1 drop_na

12.5.2 fill

12.5.3 replace_na

12.6 Advanced: Nested data

12.6.1 What is nested data and why might you use it?

12.6.2 tidyr & nested data

We’re not going to cover this in D2MR, but tidyr has functions for creating, reshaping, and transforming nested data.