12 Setup: Vector Basics

Purpose: Vectors are the most important object we’ll work with when doing data science. To that end, let’s learn some basics about vectors.

Reading: Programming Basics. Topics: vectors Reading Time: ~10 minutes

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()

12.0.1 q1 What single-letter `R` function do you use to create vectors with specific entries? Use that function to create a vector with the numbers `1, 2, 3` below.

vec_q1 <- c(1, 2, 3)

vec_q1

## [1] 1 2 3

Use the following tests to check your work:

## NOTE: No need to change this
assertthat::assert_that(length(vec_q1) == 3)

## [1] TRUE

assertthat::assert_that(mean(vec_q1) == 2)

## [1] TRUE

print("Nice!")

## [1] "Nice!"

12.0.2 q2 Did you know that you can use `c()` to extend a vector as well? Use this to add the extra entry `4` to `vec_q1`.

vec_q2 <- c(vec_q1, 4)

vec_q2

## [1] 1 2 3 4

Use the following tests to check your work:

## NOTE: No need to change this
assertthat::assert_that(length(vec_q2) == 4)

## [1] TRUE

assertthat::assert_that(mean(vec_q2) == 2.5)

## [1] TRUE

print("Well done!")

## [1] "Well done!"