Learning the R Tidyverse

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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 like ggplot2 and forcats, which are all designed around the concepts of tidy data, a framework for problem-solving and writing R code for everything—from data wrangling and analysis to visualization and modeling.

This course introduces the core concepts of the tidyverse for data wrangling, cleaning and tidying. It focuses on the novice user and shows you why and how to make use of the two pipes (%>% and |>). Join instructor Charlie Hadley as she progresses through the basics of importing and filtering data from Excel, CSV, and SPSS files, as well as summarizing and tabulating data using pivot_*() and across() functions and the power of nested tibbles. By the end of this course, you’ll be equipped with practical new skills for wrangling realistic datasets, including mismatched dates, poorly parsed numerical columns, multiple-choice survey questions, and more.

01 - Introduction

01 - Getting started in the R tidyverse

02 - How to use the exercise files

02 - 1. Tidyverse Fundamentals

01 - What is the tidyverse

02 - Installing, loading, and working with the tidyverse packages

03 - Introducing data.frame and tibbles

04 - What are %% and for in the tidyverse

05 - Using the %% pipe in your code

06 - Using the pipe in your code

07 - Datasets built into the tidyverse packages

08 - Using the select() function to obtain columns from data

09 - Using the filter() function to filter data by conditions

10 - Using the mutate() function to modify and add columns

11 - Challenge Rewrite this code to use the pipe of your choice

12 - Solution Rewrite this code to use the pipe of your choice

03 - 2. Tidy Data The Fundamental Idea behind the Tidyverse

01 - What is tidy data

02 - Why does ggplot2 want tidy data

03 - Using pivot longer() to tidy data into a long format

04 - Cleaning column names with the janitor package

05 - Tidying columns containing multiple values with separate ()

06 - List columns and nested tibbles

04 - 3. Reading Data In and Out of the Tidyverse

01 - Using projects to simplify file paths

02 - Using read csv() to read CSV files

03 - Using read excel() to read data from Excel files

04 - Using haven to import from SPSS and other formats

05 - 4. Grouping and Summarizing Data with the Tidyverse

01 - Grouping and summarizing data by column or row

02 - Cross tabulations with count()

03 - Column-wise groups group by() and mutate()

04 - Column-wise groups group by() and summarize()

05 - Column-wise groups group by() and reframe()

06 - Column-wise groups Using the .by argument instead of group by()

07 - Row-wise groups rowwise() and c across()

08 - Remember to ungroup()

09 - Challenge Find maximum penguin dimension by island

10 - Solution Find maximum penguin dimension by island

06 - 5. Important Packages and Functions in the Tidyverse

01 - ggplot2 for beautiful data storytelling

02 - stringr for friendly string manipulation

03 - lubridate for manipulating dates and times

04 - forcats for manipulating factors

05 - purrr for doing many things like iteration

07 - 6. Working Smart with the Tidyverse

01 - Handling NAs in the tidyverse with drop na() and replace na()

02 - Use case when() instead of nested if or ifelse()

03 - Use tidy-select functions to work with many columns at once

04 - Using across() in mutate() to modify multiple columns at once

05 - Filtering many columns at once with if any() and if all()

06 - Understanding how the tidyverse evolves and deprecates

07 - Challenge Find all love songs remaining below position 80 in the top 10

08 - Solution Find all love songs remaining below position 80 in the top 10

08 - Conclusion

01 - Next steps

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تولید کننده: LinkedIn Learning (Lynda)