R Workshop #4: Data Manipulation with Tidyverse
Data Manipulation with Tidyverse
Jarrod Griffin, Junior Marketing Scientist at the Center for Customer Insights and Digital Marketing, explains the concepts of Data Wrangling, the Tidyverse Package, Tidy data and tibbles while also demonstrating how to convert various forms of data into tidy data. He also demonstrates the use of key Dplyr verbs as well as the piping operator.
If you haven't installed R or RStudio, click here to learn how!
Learning Outcomes
1.Describe the concept of Data Wrangling
2.Descibe how tibbles are different from data frames
3.Explain how to convert wide or long data to "Tidy" data
4. Be familiar with key dplyr verbs and use them to transform data
5. Zoom in on data using the piping operator
6. Compare subgroups with group_by in conjunction with piping and dplyer verbs
Check below to view the video!
Relevant Links and Code:
Workshop 4 GitHub: https://github.com/jsgriffin96/r_workshop_4
Book: https://r4ds.had.co.nz/index.html
wide_pop <- read_csv("https://raw.githubusercontent.com/jsgriffin96/r_workshop_4/master/data/wide-population.csv")
long_states <- read_csv("https://raw.githubusercontent.com/jsgriffin96/r_workshop_4/master/data/long-population.csv")
state_rate <- read_csv("https://raw.githubusercontent.com/jsgriffin96/r_workshop_4/master/data/state_rate.csv")
Supplementary Video on How to Link RStudio and Github: https://www.youtube.com/watch?v=ssEYd8T07y4&feature=youtu.be
