Learning the R Tidyverse

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Charlie Joey Hadley

3:18:51

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01 - Introduction

01 - Getting started in the R tidyverse.mp4

00:56

02 - How to use the exercise files.mp4

01:56

02 - 1. Tidyverse Fundamentals

01 - What is the tidyverse.mp4

02:22

02 - Installing, loading, and working with the tidyverse packages.mp4

05:01

03 - Introducing data.frame and tibbles.mp4

08:34

04 - What are %% and for in the tidyverse.mp4

03:46

05 - Using the %% pipe in your code.mp4

05:00

06 - Using the pipe in your code.mp4

06:18

07 - Datasets built into the tidyverse packages.mp4

03:15

08 - Using the select() function to obtain columns from data.mp4

04:23

09 - Using the filter() function to filter data by conditions.mp4

06:10

10 - Using the mutate() function to modify and add columns.mp4

04:00

11 - Challenge Rewrite this code to use the pipe of your choice.mp4

02:38

12 - Solution Rewrite this code to use the pipe of your choice.mp4

03:19

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

01 - What is tidy data.mp4

03:02

02 - Why does ggplot2 want tidy data.mp4

04:22

03 - Using pivot longer() to tidy data into a long format.mp4

04:26

04 - Cleaning column names with the janitor package.mp4

03:21

05 - Tidying columns containing multiple values with separate ().mp4

04:44

06 - List columns and nested tibbles.mp4

05:03

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

01 - Using projects to simplify file paths.mp4

04:57

02 - Using read csv() to read CSV files.mp4

04:52

03 - Using read excel() to read data from Excel files.mp4

02:15

04 - Using haven to import from SPSS and other formats.mp4

04:56

05 - 4. Grouping and Summarizing Data with the Tidyverse

01 - Grouping and summarizing data by column or row.mp4

02:22

02 - Cross tabulations with count().mp4

03:21

03 - Column-wise groups group by() and mutate().mp4

03:38

04 - Column-wise groups group by() and summarize().mp4

03:10

05 - Column-wise groups group by() and reframe().mp4

03:08

06 - Column-wise groups Using the .by argument instead of group by().mp4

02:40

07 - Row-wise groups rowwise() and c across().mp4

04:12

08 - Remember to ungroup().mp4

01:50

09 - Challenge Find maximum penguin dimension by island.mp4

01:03

10 - Solution Find maximum penguin dimension by island.mp4

03:40

06 - 5. Important Packages and Functions in the Tidyverse

01 - ggplot2 for beautiful data storytelling.mp4

06:57

02 - stringr for friendly string manipulation.mp4

08:02

03 - lubridate for manipulating dates and times.mp4

04:28

04 - forcats for manipulating factors.mp4

07:27

05 - purrr for doing many things like iteration.mp4

08:41

07 - 6. Working Smart with the Tidyverse

01 - Handling NAs in the tidyverse with drop na() and replace na().mp4

04:12

02 - Use case when() instead of nested if or ifelse().mp4

06:36

03 - Use tidy-select functions to work with many columns at once.mp4

04:04

04 - Using across() in mutate() to modify multiple columns at once.mp4

06:52

05 - Filtering many columns at once with if any() and if all().mp4

03:37

06 - Understanding how the tidyverse evolves and deprecates.mp4

03:00

07 - Challenge Find all love songs remaining below position 80 in the top 10.mp4

01:03

08 - Solution Find all love songs remaining below position 80 in the top 10.mp4

03:38

08 - Conclusion

01 - Next steps.mp4

01:34

Description

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.

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R Programming

Charlie Joey Hadley

Instructor's Courses

I'm extremely pleased to split my time between being a Data Coach at Admiral Group and as an independent data science training consultant. I've been working in data science and developing data curricula in some capacity since 2012, from redesigning Wolfram's programming training courses to building a data visualisation service at University of Oxford and now as the very first Data Coach in Admiral's UK Data Academy. I'm also a Visiting Lecturer at BCU delivering their Data Science for Healthcare Applications Masters' module.

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.