AE 06: Data types and classes

Application exercise

Packages

We will use the following two packages in this application exercise.

tidyverse: For data import, wrangling, and visualization.
skimr: For summarizing the entire data frame at once.
scales: For better axis labels.

library(tidyverse)
library(skimr)
library(scales)

Type coercion

Demo: Determine the type of the following vector. And then, change the type to numeric.
```
x <- c("1", "2", "3")
typeof(x)
```
```
[1] "character"
```
```
as.numeric(x)
```
```
[1] 1 2 3
```
Demo: Once again, determine the type of the following vector. And then, change the type to numeric. What’s different than the previous exercise?
```
y <- c("a", "b", "c")
typeof(y)
```
```
[1] "character"
```
```
as.numeric(y)
```
```
Warning: NAs introduced by coercion
```
```
[1] NA NA NA
```
Demo: Once again, determine the type of the following vector. And then, change the type to numeric. What’s different than the previous exercise?
```
z <- c("1", "2", "three")
typeof(z)
```
```
[1] "character"
```
```
as.numeric(z)
```
```
Warning: NAs introduced by coercion
```
```
[1]  1  2 NA
```
Demo: Suppose you conducted a survey where you asked people how many cars their household owns collectively. And the answers are as follows:
```
survey_results <- tibble(cars = c(1, 2, "three"))
survey_results
```
```
# A tibble: 3 × 1
  cars 
  <chr>
1 1    
2 2    
3 three
```
This is annoying because of that third survey taker who just had to go and type out the number instead of providing as a numeric value. So now you need to update the cars variable to be numeric. You do the following
```
survey_results |>
  mutate(cars = as.numeric(cars))
```
```
Warning in mask$eval_all_mutate(quo): NAs introduced by coercion
```
```
# A tibble: 3 × 1
   cars
  <dbl>
1     1
2     2
3    NA
```
And now things are even more annoying because you get a warning NAs introduced by coercion that happened while computing cars = as.numeric(cars) and the response from the third survey taker is now an NA (you lost their data). Fix your mutate() call to avoid this warning.
```
survey_results |>
  mutate(
    cars = if_else(cars == "three", "3", cars),
    cars = as.numeric(cars)
    )
```
```
# A tibble: 3 × 1
   cars
  <dbl>
1     1
2     2
3     3
```

Your turn: First, guess the type of the vector. Then, check if you guessed right. I’ve done the first one for you, you’ll see that it’s helpful to check the type of each element of the vector first.

c(1, 1L, "C")

v1 <- c(1, 1L, "C")

# to help you guess
typeof(1)

[1] "double"

typeof(1L)

[1] "integer"

typeof("C")

[1] "character"

# to check after you guess
typeof(v1)

[1] "character"

c(1L / 0, "A")

v2 <- c(1L / 0, "A")

# to help you guess
typeof(1L)

[1] "integer"

typeof(0)

[1] "double"

typeof(1L / 0)

[1] "double"

typeof("A")

[1] "character"

# to check after you guess
typeof(v2)

[1] "character"

c(1:3, 5)

v3 <- c(1:3, 5)

# to help you guess
typeof(1:3)

[1] "integer"

typeof(5)

[1] "double"

# to check after you guess
typeof(v3)

[1] "double"

c(3, "3+")

v4 <- c(3, "3+")

# to help you guess
typeof(3)

[1] "double"

typeof("3+")

[1] "character"

# to check after you guess
typeof(v4)

[1] "character"

c(NA, TRUE)

v5 <- c(NA, TRUE)

# to help you guess
typeof(NA)

[1] "logical"

typeof(TRUE)

[1] "logical"

# to check after you guess
typeof(v5)

[1] "logical"

Hotel bookings

# From TidyTuesday: https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-02-11/readme.md

hotels <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-11/hotels.csv")

Question: Take a look at the the following visualization. How are the months ordered? What would be a better order?

Demo: Reorder the months on the x-axis (levels of arrival_date_month) in a way that makes more sense. You will want to use a function from the forcats package, see https://forcats.tidyverse.org/reference/index.html for inspiration and help.

hotels |>
  group_by(hotel, arrival_date_month) |>   # group by hotel type and arrival month
  summarise(mean_adr = mean(adr), .groups = "drop") |>       # calculate mean adr for each group
  ggplot(aes(
    x = arrival_date_month,                 # x-axis = arrival_date_month
    y = mean_adr,                           # y-axis = mean_adr calculated above
    group = hotel,                          # group lines by hotel type
    color = hotel)                          # and color by hotel type
  ) +
  geom_line() +                             # use lines to represent data
  theme_minimal() +                         # use a minimal theme
  labs(
    x = "Arrival month",                 # customize labels
    y = "Mean ADR (average daily rate)",
    title = "Comparison of resort and city hotel prices across months",
    subtitle = "Resort hotel prices soar in the summer while ciry hotel prices remain relatively constant throughout the year",
    color = "Hotel type"
  )

Stretch goal: If you finish the above task before time is up, change the y-axis label so the values are shown with dollar signs, e.g. $80 instead of 80. You will want to use a function from the scales package, see https://scales.r-lib.org/reference/index.html for inspiration and help.

hotels |>
  group_by(hotel, arrival_date_month) |>   # group by hotel type and arrival month
  summarise(mean_adr = mean(adr), .groups = "drop") |>       # calculate mean adr for each group
  ggplot(aes(
    x = arrival_date_month,                 # x-axis = arrival_date_month
    y = mean_adr,                           # y-axis = mean_adr calculated above
    group = hotel,                          # group lines by hotel type
    color = hotel)                          # and color by hotel type
  ) +
  geom_line() +                             # use lines to represent data
  theme_minimal() +                         # use a minimal theme
  labs(
    x = "Arrival month",                 # customize labels
    y = "Mean ADR (average daily rate)",
    title = "Comparison of resort and city hotel prices across months",
    subtitle = "Resort hotel prices soar in the summer while ciry hotel prices remain relatively constant throughout the year",
    color = "Hotel type"
  ) +
  scale_y_continuous(labels = label_dollar())