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LinkedIn Learning

Data Wrangling in R

via LinkedIn Learning

Overview

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

Syllabus

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

Taught by

Mike Chapple

Reviews

4.8 rating at LinkedIn Learning based on 80 ratings

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