13 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: (None, this is the reading)

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.4     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Remember the c() function? Let’s use it to work with vectors!

13.0.1 q1 Create a vector

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!"

13.0.2 q2 Extend a vector

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!"