Data Wrangling with R

Table of contents

• 1 Overview
  • 1.1 Learning outcome
  • 1.2 Textbook chapters covered
• 2 Introduction to data wrangling
  • 2.1 loading packages
• 3 what is Tidyverse
  • 3.1 intitial data prep and exploration
• 4 data wrangling with dplyr

Note

note: this codebook will follow the structure and content of the published codebook of M05-1-Data wrangling with Tidyvverse in R

1 Overview

1.1 Learning outcome

By the end of this module, you should be able to:

• Describe the concept of Data Wrangling.
• Describe how Tibbles are different from data frames
• Explain how to convert wide or long data to “Tidy” data
• Explain how to merge relational data sets using join functions.
• Explain how to use grouped mutates and filter together.
• Be familiar with major dplyr functions for transforming data.
• Create a new variable with mutate() and case_when().
• Use the pipe operator to shape the data to prepare for analysis and visualization

1.2 Textbook chapters covered

• Ch3: Data Transformation
• Ch5: Data Tidying
• Ch13: Numbers

2 Introduction to data wrangling

2.1 loading packages

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.0     ✔ readr     2.1.6
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.2     ✔ tibble    3.3.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.2
✔ purrr     1.2.1     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

3 what is Tidyverse

The tidyverse is a collection of R packages that share a common design philosophy and are designed to work together seamlessly. The tidyverse includes packages for data manipulation, visualization, and modeling, among other tasks.

Some of the core packages in the tidyverse include:

• ggplot2: for data visualization
• dplyr: for data manipulation
• tidyr: for data tidying
• readr: for data import
• purrr: for functional programming
• tibble: for data frames
• stringr: for string manipulation
• forcats: for working with categorical variables

3.1 intitial data prep and exploration

#install.packages("nycflights13")
library(nycflights13)
data()

4 data wrangling with dplyr

Note

For MacOS, use shift + option + I for multi cursor activation

Data sets in package ‘nycflights13’:

• airlines Airline names.
• airports Airport metadata
• flights Flights data
• planes Plane metadata.
• weather Hourly weather data

flights <- flights

flights |>
  count(year, month) |>
  arrange(desc(n)) |>
  mutate(month = as_factor(month)) |>
  mutate(month = fct_reorder(month,n)) |>
  ggplot(aes(month, n, fill = year)) +
  geom_col(fill = "#FF007F", show.legend = FALSE) +
  coord_flip() +
  labs(x = "Month", 
      y = "Number of flights",
      title = " Number of Daily flights by month during 2013 at New york"
      )