Data Tidying and Importing with R

Data Tidying and Importing with R

This course is part of Data Science with R Specialization

Instructors: Dr. Elijah Meyer

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

11 reviews

Beginner level

No prior experience required

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

11 reviews

Beginner level

No prior experience required

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

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

Tools you'll learn

R Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

3 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the Data Science with R Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

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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Instructors

Dr. Elijah Meyer

Duke University

4 Courses 2,557 learners

Offered by

Duke University

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When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

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