13 Setup: Types

Purpose: Vectors can hold data of only one type. While this isn’t a course on computer science, there are some type “gotchas” to look out for when doing data science. This exercise will help us get ahead of those issues.

Reading: Types

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──

## ✔ ggplot2 3.4.0      ✔ purrr   1.0.1 
## ✔ tibble  3.1.8      ✔ dplyr   1.0.10
## ✔ tidyr   1.2.1      ✔ stringr 1.5.0 
## ✔ readr   2.1.3      ✔ forcats 0.5.2

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

13.1 Objects vs Strings

13.1.1 q1 Describe what is wrong with the code below.

## TASK: Describe what went wrong here
## Set our airport
airport <- "BOS"

## Check our airport value
airport == ATL

Observations:

ATL (without quotes) is trying to refer to an object (variable); we would need to write "ATL" (with quotes) to produce a string.

13.2 Casting

Sometimes our data will not be in the form we want; in this case we may need to cast the data to another format.

as.integer(x) converts to integer
as.numeric(x) converts to real (floating point)
as.character(x) converts to character (string)
as.logical(x) converts to logical (boolean)

13.2.1 q2 Cast the following vector `v_string` to integers.

v_string <- c("00", "45", "90")
v_integer <- as.integer(v_string)

Use the following test to check your work.

## NOTE: No need to change this!
assertthat::assert_that(
  assertthat::are_equal(
                v_integer,
                c(0L, 45L, 90L)
  )
)

## [1] TRUE

print("Great job!")

## [1] "Great job!"