Data Wrangling in R (2017)

Learn about the principles of tidy data and discover how to import, transform, clean, and wrangle data using the R programming language.

Introduction

• Preparing for data wrangling
• What you need to know
• Exercise files

1. Tidy Data

• What is tidy data?
• Variables, observations, and values
• Common data problems
• Using the tidyverse

2. Working with Tibbles

• Building and printing tibbles
• Subsetting tibbles
• Filtering tibbles

3. Importing Data into R

• What are CSV files?
• Importing CSV files into R
• What are TSV files?
• Importing TSV files into R
• Importing delimited files into R
• Importing fixed-width files into R
• Importing Excel files into R
• Reading data from databases and the web

4. Data Transformation

• Wide vs. long datasets
• Making wide datasets long with pivot_longer()
• Making long datasets wide with pivot_wider()
• Converting data types in R
• Working with dates and times in R

5. Data Cleaning

• Detecting outliers
• Missing and special values in R
• Breaking apart columns with separate()
• Combining columns with unite()
• Manipulating strings in R with stringr

6. Data Wrangling Case Study: Coal Consumption

• Understanding the coal dataset
• Reading in the coal dataset
• Converting the coal dataset from wide to long
• Segmenting the coal dataset
• Visualizing the coal dataset

7. Data Wrangling Case Study: Water Quality

• Understanding the water quality dataset
• Reading in the water quality dataset
• Filtering the water quality dataset
• Water quality data types
• Correcting data entry errors
• Identifying and removing outliers
• Converting temperature from Fahrenheit to Celsius
• Widening the water quality dataset

8. Data Wrangling Case Study: Social Security Disability

• Understanding the social security disability dataset
• Importing the social security disability dataset
• Making the social security disability dataset long
• Formatting dates in the social security disability dataset
• Fiscal years in the social security disability dataset
• Widening the social security disability dataset
• Visualizing the social security disability dataset

Conclusion

• Next steps