This course aims to better develop your statistical toolkit in the world of statistics and data science. You will learn how to collect, manipulate, and transform data in R into a more readily usable format using tidyverse data pipelines, primarily using verbs from the dplyr and tidyr packages. The topics covered provide you with the tools necessary to convert data to be better suited for data visualization (Course 1) and modeling; which is to come in this certificate program in a future course. Additionally, we discuss the topics of web scraping and the considerations one must take prior to scraping data from the web.
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
Sponsored by Coursera Learning Team
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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Assessments
3 assignments
Taught in English
Recently updated!
September 2024
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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.
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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 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 videos5 readings1 assignment2 discussion prompts1 plugin
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