Data Wrangling in R

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Mike Chapple

2:51:15

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[1] Introduction

[1] Preparing for data wrangling .mp4

00:58

[2] What you need to know.mp4

00:44

[3] Exercise files.mp4

01:11

[2] 1. Tidy Data

[1] What is tidy data.mp4

03:36

[2] Variables, observations, and values.mp4

04:22

[3] Common data problems.mp4

07:39

[4] Using the tidyverse.mp4

04:37

[3] 2. Working with Tibbles

[1] Building and printing tibbles.mp4

05:04

[2] Subsetting tibbles.mp4

01:56

[3] Filtering tibbles.mp4

03:02

[4] 3. Importing Data into R

[1] What are CSV files.mp4

02:53

[2] Importing CSV files into R.mp4

06:39

[3] What are TSV files.mp4

01:29

[4] Importing TSV files into R.mp4

06:03

[5] Importing delimited files into R.mp4

03:33

[6] Importing fixed-width files into R.mp4

03:38

[7] Importing Excel files into R.mp4

05:44

[8] Reading data from databases and the web.mp4

02:20

[5] 4. Data Transformation

[1] Wide vs. long datasets.mp4

03:11

[2] Making wide datasets long with pivot longer().mp4

03:54

[3] Making long datasets wide with pivot wider().mp4

03:29

[4] Converting data types in R.mp4

07:03

[5] Working with dates and times in R.mp4

04:51

[6] 5. Data Cleaning

[1] Detecting outliers.mp4

08:19

[2] Missing and special values in R.mp4

05:21

[3] Breaking apart columns with separate().mp4

03:48

[4] Combining columns with unite().mp4

02:29

[5] Manipulating strings in R with stringr.mp4

09:15

[7] 6. Data Wrangling Case Study Coal Consumption

[1] Understanding the coal dataset.mp4

01:59

[2] Reading in the coal dataset.mp4

02:39

[3] Converting the coal dataset from long to wide.mp4

02:53

[4] Segmenting the coal dataset.mp4

03:32

[5] Visualizing the coal dataset.mp4

02:29

[8] 7. Data Wrangling Case Study Water Quality

[1] Understanding the water quality dataset.mp4

01:32

[2] Reading in the water quality dataset.mp4

01:35

[3] Filtering the water quality dataset.mp4

05:17

[4] Water quality data types.mp4

03:02

[5] Correcting data entry errors.mp4

02:43

[6] Identifying and removing outliers.mp4

03:42

[7] Converting temperature from Fahrenheit to Celsius.mp4

02:20

[8] Widening the water quality dataset.mp4

04:33

[9] 8. Data Wrangling Case Study Social Security Disability

[1] Understanding the social security disability dataset.mp4

02:28

[2] Importing the social security disability dataset.mp4

01:22

[3] Making the social security disability dataset long.mp4

01:34

[4] Formatting dates in the social security disability dataset.mp4

04:06

[5] Fiscal years in the social security disability dataset.mp4

02:21

[6] Widening the social security disability dataset.mp4

01:46

[7] Visualizing the social security disability dataset.mp4

01:25

[10] Conclusion

[1] Next steps.mp4

00:49

Description

The tidy format provides a standardized way of organizing data values within a dataset. By leveraging tidy data principles, statisticians, analysts, and data scientists can spend less time cleaning data and more time tackling the more compelling aspects of data analysis. In this course, learn about the principles of tidy data, discover how to create and manipulate data tibbles, and find out how to use the tibbles in importing, transforming, and cleaning your data. Instructor Mike Chapple uses R and the tidyverse packages to teach the concept of data wrangling—the data cleaning and data transformation tasks that consume a substantial portion of analysts' time. He wraps up with three hands-on case studies that reinforce the data wrangling principles and tactics covered in this course.

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R Programming

Mike Chapple

Instructor's Courses

Cybersecurity and analytics educator and leader with over 20 years of experience in government, the private sector and higher education. Author of over 30 books, including best-selling study guides from Wiley covering the CISSP, Security+, CISM, CySA+, CIPP/US, and PenTest+ exams. Creator of over 100 cybersecurity and business analytics video courses on LinkedIn Learning.

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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.