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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

Instructors: Dr. Elijah Meyer

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

12 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.

12 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

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

3 assignments

Taught in English

91%

of learners achieved a positive career outcome

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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.

Module details

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 videosTotal 73 minutes

Welcome2 minutes
Tidy Data4 minutes
Tidying Data7 minutes
Code Along :: Country Populations Over Time24 minutes
Joining Data10 minutes
Code Along :: Continent Populations25 minutes

12 readingsTotal 115 minutes

Course Overview10 minutes
Meet Your Instructors10 minutes
Get Ready to Compute with R and RStudio!10 minutes
Discussion Guidelines10 minutes
Report a problem with the course5 minutes
JSS :: Tidy Data10 minutes
R4DS :: Chp 5 - Data Tidying (Sections 5.3 and 5.4)10 minutes
Code Along :: Country Populations Over Time - Companion10 minutes
Code Along :: Country Populations Over Time - Companion (Complete)10 minutes
R4DS :: Chp 19.1 - 19.4 - Joins10 minutes
Code Along :: Continent Populations - Companion10 minutes
Code Along :: Continent Populations - Companion (Complete)10 minutes

1 assignmentTotal 60 minutes

Tidy Data Quiz60 minutes

2 discussion promptsTotal 20 minutes

Course Introductions10 minutes
Tidy Basketball Reflection (Optional)10 minutes

1 pluginTotal 15 minutes

Tidy Basketball15 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 videosTotal 77 minutes

Data Types11 minutes
Code Along :: That's My Type8 minutes
Data Classes7 minutes
Code Along :: Halving CO2 Emissions19 minutes
Importing Data16 minutes
Code Along :: Importing and Recoding14 minutes

13 readingsTotal 130 minutes

R4DS :: Chp 12.1 - 12.4 - Logical Vectors10 minutes
R4DS :: Chp 13 - Numbers10 minutes
R4DS :: Chp 14.1 - 14.3 - Strings10 minutes
Code Along :: That's My Type - Companion10 minutes
Code Along :: That's My Type - Companion (Complete)10 minutes
R4DS :: Chp 16 - Factors10 minutes
R4DS :: Chp 17 - Dates and Times10 minutes
Code Along :: Halving CO2 Emissions - Companion10 minutes
Code Along :: Halving CO2 Emissions - Companion (Complete)10 minutes
R4DS :: Chp 7 - Data Import10 minutes
R4DS :: Chp 20 - Spreadsheets10 minutes
Code Along :: Importing and Recoding - Companion10 minutes
Code Along :: Importing and Recoding - Companion (Complete)10 minutes

1 assignmentTotal 60 minutes

Importing + Recoding Data Quiz60 minutes

1 discussion promptTotal 10 minutes

Nobel Prize Winners & Sales Data Reflection (Optional)10 minutes

1 pluginTotal 15 minutes

Nobel Prize Winners & Sales Data15 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.