Data Wrangling in R Overview/Description Target Audience Prerequisites Expected Duration Lesson Objectives Course Number Expertise Level Overview/Description
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.
Target Audience
Individuals with some R and data science experience working toward a wider degree of knowledge in using R for data science
Prerequisites
None
Expected Duration (hours)
1.5
Lesson Objectives Data Wrangling in R
start the course
recognize common tasks and libraries for data wrangling in R
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
identify the features of the tidyr library for data wrangling in R
use tidyr's gather function
use tidyr's separate function
use the readr library to extract csv data
use the readxl library to extract Excel data
manipulate a data set using multiple dplyr verbs
Course Number: df_dsur_a01_it_enus
Expertise Level
Intermediate