Data Tidying and Importing with R
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Data Tidying and Importing with R
This course is part of Data Science with R Specialization
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What you'll learn
Apply tidy data principles to manipulate and restructure data (e.g., subsetting, adding columns, and transforming data between wide and long formats)
Develop and implement code to join data sets and perform basic web scraping to collect data
Apply data structures such as wide and long formats, using code to convert between these formats as part of data preparation and analysis
Skills you'll gain
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There are 3 modules in this course
Build confidence working with messy, real-world data. In this course, you’ll learn how to import, clean, and organize data in R so that it’s ready for analysis, visualization, or modeling.
Using dplyr, tidyr, and other Tidyverse tools, you’ll practice joining datasets, reshaping data, and creating efficient data pipelines that support reproducible work. You’ll also explore how to responsibly collect and scrape data from online sources, including ethical and legal considerations. By the end of this course, you’ll know how to transform raw datasets into structured, tidy formats and you’ll understand how responsible data handling and documentation are essential to high-quality, ethical data science.
Tidy datasets have a specific structure: each variable is a column, and each observation is a row. In this module, we use functional verbs from the dplyr package in R to transform data into a ready-to-use tidy data format. Additionally, we use functional verbs to manipulate data frames.
What's included
6 videos12 readings1 assignment2 discussion prompts1 plugin
6 videos•Total 73 minutes
- Welcome•2 minutes
- Tidy Data•4 minutes
- Tidying Data•7 minutes
- Code Along :: Country Populations Over Time•24 minutes
- Joining Data•10 minutes
- Code Along :: Continent Populations•25 minutes
12 readings•Total 115 minutes
- Course Overview•10 minutes
- Meet Your Instructors•10 minutes
- Get Ready to Compute with R and RStudio!•10 minutes
- Discussion Guidelines•10 minutes
- Report a problem with the course•5 minutes
- JSS :: Tidy Data•10 minutes
- R4DS :: Chp 5 - Data Tidying (Sections 5.3 and 5.4)•10 minutes
- Code Along :: Country Populations Over Time - Companion•10 minutes
- Code Along :: Country Populations Over Time - Companion (Complete)•10 minutes
- R4DS :: Chp 19.1 - 19.4 - Joins•10 minutes
- Code Along :: Continent Populations - Companion•10 minutes
- Code Along :: Continent Populations - Companion (Complete)•10 minutes
1 assignment•Total 60 minutes
- Tidy Data Quiz•60 minutes
2 discussion prompts•Total 20 minutes
- Course Introductions•10 minutes
- Tidy Basketball Reflection (Optional)•10 minutes
1 plugin•Total 15 minutes
- Tidy Basketball•15 minutes
A column in our data set can be stored as many different types, such as numbers or characters. These different data types inform how R treats the data, and whether certain functions are compatible to use with certain types of data. In this module, we discuss more in detail, the different data types classified by R, data classes, as well as how to recode variables in a data set to be different types, classes, or take on different values.
What's included
6 videos13 readings1 assignment1 discussion prompt1 plugin
6 videos•Total 77 minutes
- Data Types•11 minutes
- Code Along :: That's My Type•8 minutes
- Data Classes•7 minutes
- Code Along :: Halving CO2 Emissions•19 minutes
- Importing Data•16 minutes
- Code Along :: Importing and Recoding•14 minutes
13 readings•Total 130 minutes
- R4DS :: Chp 12.1 - 12.4 - Logical Vectors•10 minutes
- R4DS :: Chp 13 - Numbers•10 minutes
- R4DS :: Chp 14.1 - 14.3 - Strings•10 minutes
- Code Along :: That's My Type - Companion•10 minutes
- Code Along :: That's My Type - Companion (Complete)•10 minutes
- R4DS :: Chp 16 - Factors•10 minutes
- R4DS :: Chp 17 - Dates and Times•10 minutes
- Code Along :: Halving CO2 Emissions - Companion•10 minutes
- Code Along :: Halving CO2 Emissions - Companion (Complete)•10 minutes
- R4DS :: Chp 7 - Data Import•10 minutes
- R4DS :: Chp 20 - Spreadsheets•10 minutes
- Code Along :: Importing and Recoding - Companion•10 minutes
- Code Along :: Importing and Recoding - Companion (Complete)•10 minutes
1 assignment•Total 60 minutes
- Importing + Recoding Data Quiz•60 minutes
1 discussion prompt•Total 10 minutes
- Nobel Prize Winners & Sales Data Reflection (Optional)•10 minutes
1 plugin•Total 15 minutes
- Nobel Prize Winners & Sales Data•15 minutes
Web scraping is the process of extracting this information automatically and transforming it into a structured dataset. In this module, we go over how to perform basic web scraping in R to make an abundance of data online more easily accessible.
What's included
4 videos6 readings1 assignment2 discussion prompts1 plugin
4 videos•Total 55 minutes
- Web Scraping•10 minutes
- Web Scraping Considerations•5 minutes
- Code Along :: Scraping an eCommerce Page•20 minutes
- Code Along :: Scraping many eCommerce Pages•20 minutes
6 readings•Total 60 minutes
- Code Along :: Scraping an eCommerce Page - Companion (Complete)•10 minutes
- R4DS :: Chp 25.1 - 25.2 - Functions•10 minutes
- R4DS :: Chp 26 - Iteration (Optional)•10 minutes
- Code Along :: Scraping Many eCommerce Pages - Companion (Complete)•10 minutes
- Final Course Project (Optional)•10 minutes
- Share your learning experience•10 minutes
1 assignment•Total 60 minutes
- Web Scraping and Programming Quiz•60 minutes
2 discussion prompts•Total 20 minutes
- IMDB + Web Scraping Reflection (Optional)•10 minutes
- Final Project Reflection (Optional)•10 minutes
1 plugin•Total 15 minutes
- IMDB + Web Scraping•15 minutes
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