Introduction
Welcome
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What you need to know
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Using the exercise files
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1. Tidy Data
What is tidy data?
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Variables, observations, and values
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Common data problems
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Using the tidyverse
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2. Working with Tibbles
Building and printing tibbles
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Subsetting tibbles
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Filtering tibbles
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3. Importing Data into R
What are CSV files?
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Importing CSV files into R
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What are TSV files?
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Importing TSV files into R
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Importing delimited files into R
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Importing fixed-width files into R
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Importing Excel files into R
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Reading data from databases and the web
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4. Data Transformation
Wide vs. long datasets
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Making wide datasets long with gather()
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Making long datasets wide with spread()
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Converting data types in R
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Working with dates and times in R
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5. Data Cleaning
Detecting outliers
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Missing and special values in R
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Breaking apart columns with separate()
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Combining columns with unite()
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Manipulating strings in R with stringr
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6. Data Wrangling Case Study: Coal Consumption
Understanding the coal dataset
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Reading in the coal dataset
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Converting the coal dataset from long to wide
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Segmenting the coal dataset
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Visualizing the coal dataset
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7. Data Wrangling Case Study: Water Quality
Understanding the water quality dataset
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Reading in the water quality dataset
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Filtering the water quality dataset
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Water quality data types
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Correcting data entry errors
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Identifying and removing outliers
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Converting temperature from Fahrenheit to Celsius
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Widening the water quality dataset
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8. Data Wrangling Case Study: Social Security Disability Claims
Understanding the Social Security Disability dataset
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Importing the Social Security Disability dataset
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Making the Social Security Disability dataset long
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Formatting dates in the Social Security Disability dataset
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Handling fiscal years in the Social Security Disability dataset
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Widening the Social Security Disability dataset
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Visualizing the Social Security Disability dataset
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Ex_Files_Data_Wrangling_R.zip
(604 KB)