Duke University
Data Tidying and Importing with R
Duke University

Data Tidying and Importing with R

Dr. Elijah Meyer
Mine Çetinkaya-Rundel

Instructors: Dr. Elijah Meyer

Included with Coursera Plus

Gain insight into a topic and learn the fundamentals.
Beginner level
No prior experience required
12 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level
No prior experience required
12 hours to complete
3 weeks at 4 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

Details to know

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Recently updated!

September 2024

Assessments

3 assignments

Taught in English

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There are 3 modules in this course

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 videos11 readings1 assignment3 discussion prompts1 plugin

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

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 videos5 readings1 assignment2 discussion prompts1 plugin

Instructors

Dr. Elijah Meyer
Duke University
2 Courses257 learners

Offered by

Duke University

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