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Data Wrangling in R


Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number


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