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Data Wrangling in R

Video Introducing this tutorial

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 long to wide
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