Learning the R Tidyverse (2017)

Course details

R is an incredibly powerful and widely used programming language for statistical analysis and data science. The "tidyverse" collects some of the most versatile R packages: ggplot2, dplyr, tidyr, readr, purrr, and tibble. The packages work in harmony to clean, process, model, and visualize data.

This course introduces the core concepts of the tidyverse as compared to the traditional base R. It focuses on the novice user and those unfamiliar with the pipe (%>%) operator. After covering these R basics, instructor Martin Hadley progresses to importing and filtering data from Excel, CSV, and SPSS files, and summarizing and tabulating data in the tidyverse. Then learn how to identify if data is too wide or long and convert it if necessary, and conduct nonstandard evaluation. By the end of the course, you should be able to integrate the tidyverse into your R workflow and leverage a variety of new tools for importing, filtering, visualizing, and modeling research and statistical data.

Learning objectives
Understanding the pipe (%>%) operator
Importing .xlsx and .csv files
Filtering and summarizing data sets
Using tidyr to convert wide and long data sets
Non-standard evaluation and programming with the tidyverse

Concepts

0. Introduction

01 - Welcome
02 - What you should know
03 - Exercise files

1. Getting Started with the tidyverse

04 - What is the tidyverse
05 - Why use the tidyverse
06 - Strengths of the tidyverse
07 - Set up R and RStudio for the tidyverse
08 - Maintain the tidyverse
09 - Prevent issues with plyr and dplyr

2. Being Tidy with RStudio Projects

10 - Why should you use projects in RStudio
11 - Disable auto-saving of RData for reproducibility
12 - Create a new project

3. Introducing the Operator

13 - What is the operator
14 - Identify where to use
15 - Signficance of
16 - Alternate options to

4. Importing, Modifying, and Filtering Data

17 - Separate raw and clean data folders
18 - Import .xlsx files with readxl in R
19 - Import .csv files with readr into R
20 - Is it a data frame or a tibble
21 - Select and filter data
22 - Convert strings to dates with mutate
23 - Separating columns into multiple columns
24 - Filter out NA values
25 - Export .csv files with readr
26 - Export .rdata objects for later

5. Summarizing and Tabulating Data in the tidyverse

27 - Sample data and cross-validation with dplyr
28 - Categorizing data with group by
29 - Count members of subgroups within groups with n()
30 - Cumulative sums and more - cumsum, cumall, and cumany
31 - Create group summaries
32 - Remember to ungroup before moving on

6. Wide and Long Data

33 - Identify if data is wide or long
34 - The benefits of long (or tidy) data
35 - Convert data from wide to long
36 - Convert data from long to wide

7. select(), select (), , and Non-Standard Evaluation

37 - Non-standard evaluation and programming with the tidyverse
38 - Compare group by and group by
39 - Tidy evaluation, quo, and

Conclusion

40 - Next steps