MPA 5830 - Module 03 Exercises

Spring 2020

Exercise 1

Using the cmhflights data from last week, create a column that unites the three columns Year, Month, and DayofMonth into a single column that we will name date_of_flight. This column should separate the three fields by “-”.

Hint: You will have to use load(...) with here(...).

library(tidyverse)
library(tidylog)
library(here)

load(
  here("data", "cmhflights_01092017.RData")
  )

cmhflights %>%
  unite(
    col = "date_of_flight", # name of the new column 
    c(Year, Month, DayofMonth), # columns to be united
    sep = "-", # the separator to be used 
    remove = FALSE # keep the original column
  ) %>%
  select(c(1, 2, 4, 5)) # only show these columns in the output 

## # A tibble: 35,993 x 4
##    date_of_flight  Year Month DayofMonth
##    <chr>          <int> <int>      <int>
##  1 2017-1-1        2017     1          1
##  2 2017-1-2        2017     1          2
##  3 2017-1-3        2017     1          3
##  4 2017-1-4        2017     1          4
##  5 2017-1-5        2017     1          5
##  6 2017-1-6        2017     1          6
##  7 2017-1-7        2017     1          7
##  8 2017-1-8        2017     1          8
##  9 2017-1-9        2017     1          9
## 10 2017-1-11       2017     1         11
## # … with 35,983 more rows

Exercise 2

Sticking with cmhflights, separate OriginCityName into two new columns, origin_city and origin_state. Do the same for DestCityname, calling the new columns destination_city and destination_state, respectively. Both city columns should only display the name of the city, while both state columns should only display the abbreviated state name (for example, “CA”, “OH”, etc.)

cmhflights %>%
  separate(
    col = OriginCityName, # column to be separated 
    into = c("origin_city", "origin_state"), # names of new columns
    sep = ', ', # the separator 
    remove = FALSE # keep original column
  ) %>%
  separate(
    col = DestCityName, # column to be separated 
    into = c("destination_city", "destination_state"), # names of new columns
    sep = ', ', # the separator 
    remove = FALSE # keep original column
  ) %>%
  select(origin_city, origin_state, destination_city, destination_state) # display these columns

## # A tibble: 35,993 x 4
##    origin_city origin_state destination_city destination_state
##    <chr>       <chr>        <chr>            <chr>            
##  1 Los Angeles CA           Columbus         OH               
##  2 Los Angeles CA           Columbus         OH               
##  3 Los Angeles CA           Columbus         OH               
##  4 Los Angeles CA           Columbus         OH               
##  5 Los Angeles CA           Columbus         OH               
##  6 Los Angeles CA           Columbus         OH               
##  7 Los Angeles CA           Columbus         OH               
##  8 Los Angeles CA           Columbus         OH               
##  9 Los Angeles CA           Columbus         OH               
## 10 Los Angeles CA           Columbus         OH               
## # … with 35,983 more rows

Exercise 3

Tidy the weather data such that the resulting data-set, called wdf, has the days (the d1-d31 columns) as rows and TMIN and TMAX as columns. The end result should be as shown below:

read.delim(
 file = "http://stat405.had.co.nz/data/weather.txt",
 stringsAsFactors = FALSE
 ) -> weather

id	year	month	days	TMAX	TMIN
MX000017004	2010	1	d1	NA	NA
MX000017004	2010	1	d2	NA	NA
MX000017004	2010	1	d3	NA	NA
MX000017004	2010	1	d4	NA	NA
MX000017004	2010	1	d5	NA	NA
MX000017004	2010	1	d6	NA	NA
MX000017004	2010	1	d7	NA	NA
MX000017004	2010	1	d8	NA	NA
MX000017004	2010	1	d9	NA	NA
MX000017004	2010	1	d10	NA	NA

weather %>%
  group_by(id, year, month, element) %>%
  pivot_longer(
    names_to = "variable", # name of the new column where names will be stored 
    values_to = "value", # name of the new column where values will be stored
    5:35 # the columns to be flipped into rows
  ) %>%
  pivot_wider(
    names_from = element, # column whose values are used to make new columns 
    values_from = value # where to get the values for the new columns 
  )

## # A tibble: 341 x 6
## # Groups:   id, year, month [11]
##    id           year month variable  TMAX  TMIN
##    <chr>       <int> <int> <chr>    <int> <int>
##  1 MX000017004  2010     1 d1          NA    NA
##  2 MX000017004  2010     1 d2          NA    NA
##  3 MX000017004  2010     1 d3          NA    NA
##  4 MX000017004  2010     1 d4          NA    NA
##  5 MX000017004  2010     1 d5          NA    NA
##  6 MX000017004  2010     1 d6          NA    NA
##  7 MX000017004  2010     1 d7          NA    NA
##  8 MX000017004  2010     1 d8          NA    NA
##  9 MX000017004  2010     1 d9          NA    NA
## 10 MX000017004  2010     1 d10         NA    NA
## # … with 331 more rows