To carry out data science, you need to gather, filter, transform, and explore data sets. In this course, you'll explore examples of data wrangling in R.
Learning Objectives
Data Wrangling in R
- start the course
- recognize common tasks and libraries for data wrangling in R
The dplyr Library
- identify the features of the dplyr library for data wrangling in R
- use dplyr and related functions to explore data frames
- examine subsets of data using dplyr's filtering functions
- use dplyr's pipe operator "%>%" to compose functions
- mutate tabular data with dplyr to compute new columns
- use dplyr's summary functions
- use dplyr's select function and its features
- combine data sets using dplyr's join functions
- apply set operations to tables using dplyr
- order rows in tabular data with dplyr's arrange function
The tidyr Library
- identify the features of the tidyr library for data wrangling in R
- use tidyr's gather function
- use tidyr's separate function
Rectangular Data Extraction
- use the readr library to extract csv data
- use the readxl library to extract Excel data
Practice: Using the dplyr Library
- manipulate a data set using multiple dplyr verbs