Duke University

Data Tidying and Importing with R

Duke University

Data Tidying and Importing with R

This course is part of Data Science with R Specialization

Dr. Elijah Meyer
Mine Çetinkaya-Rundel

Instructors: Dr. Elijah Meyer

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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
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

Details to know

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Assessments

3 assignments

Taught in English

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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

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

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 videos6 readings1 assignment2 discussion prompts1 plugin

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Instructors

Dr. Elijah Meyer
Duke University
4 Courses 2,477 learners

Offered by

Duke University

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