• Online, Self-Paced
Course 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.

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

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Data Administration
  • Systems Analysis