Data Wrangling in R
- Offered byLinkedin Learning
Data Wrangling in R at Linkedin Learning Overview
Duration | 3 hours |
Total fee | ₹899 |
Mode of learning | Online |
Difficulty level | Intermediate |
Credential | Certificate |
Data Wrangling in R at Linkedin Learning Highlights
- Earn a sharable certificate
Data Wrangling in R at Linkedin Learning Course details
- 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
Data Wrangling in R at Linkedin Learning Curriculum
Introduction
Preparing for data wrangling
What you need to know
Exercise files
Tidy Data
What is tidy data?
Variables, observations, and values
Common data problems
Using the tidyverse
Working with Tibbles
Building and printing tibbles
Subsetting tibbles
Filtering tibbles
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
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
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
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
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
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
Data Wrangling in R at Linkedin Learning Faculty details
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