03_tutorial_class_activity.ipynb

Kernel: R

library(tidyverse)
passengers <- read_csv("data/AirPassengers.csv")

── Attaching packages ─────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.2.0     ✔ purrr   0.3.2
✔ tibble  2.1.3     ✔ dplyr   0.8.3
✔ tidyr   0.8.3     ✔ stringr 1.4.0
✔ readr   1.3.1     ✔ forcats 0.4.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
Parsed with column specification:
cols(
  Year = col_double(),
  Jan = col_double(),
  Feb = col_double(),
  Mar = col_double(),
  Apr = col_double(),
  May = col_double(),
  Jun = col_double(),
  Jul = col_double(),
  Aug = col_double(),
  Sep = col_double(),
  Oct = col_double(),
  Nov = col_double(),
  Dec = col_double()
)

Class activity 1:

The airpassenger data set contains the monthly totals of international airline passengers from 1949 to 1960. If we are interested in asking whether year or month has a relationship with the number of monthly international airline passengers would we consider the data below tidy? True or false?

head(passengers)

Class activity 2:

Use the tidyverse functions to make this data set ________.

# answer goes here

Class activity 3:

Use the tidyverse function spread to transform the data set to its original state:

# answer goes here