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
Shareable Certificate
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Develop Your Specialized Knowledge
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.
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.
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.