Functions: Indexing Vectors

Question 1

subset()

Answer 1

function in R is a convenient way to filter rows and select specific columns of a data frame (or similar objects like matrices). It’s particularly useful when you want a clean and readable way to subset your data without using complex indexing or logical expressions.

subset(x, subset, select, drop = FALSE)

Question 2

types of indexing

numerical indexing

Answer 2

What is the first boat name?

With numerical indexing, you enter a vector of integers corresponding to the values in the vector you want to access in the form a[index], where a is the vector, and index is a vector of index values. For example, let’s use numerical indexing to get values from our boat vectors.

boat.names[1]
##[1] “a”

boat.colors[1:5]
##[1] “black” “green” “pink” “blue” “blue”

boat.ages[seq(1, 5, by = 2)]
##[1] 143 356 647

Question 3

indexing objects

Answer 3

when you define an object (vector or scalar) that is meant to be used as an indexing argument

my.index <- 3:5
boat.names[my.index]
##[1] “c” “d” “e”

Question 4

types of indexing

logical indexing

Answer 4

A logical vector is a vector that only contains TRUE and FALSE values

a logical vector, combined with the brackets [ ], acts as a filter for the vector it is indexing

a <- c(1, 2, 3, 4, 5)
a[c(TRUE, FALSE, TRUE, FALSE, TRUE)]
##[1] 1 3 5

Question 5

logical operators

Answer 5

Question 6

examples of logical vectors

Answer 6

Which ages are > 100?

boat.ages > 100
##[1] TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE

boat.ages == 23
##[1] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE

boat.names == “c”
##[1] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

Question 7

comparing vectors with other vectors using logic

Answer 7

Which boats had a higher price than cost?

boat.prices > boat.costs
##[1] TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE

boat.prices < boat.costs
##[1] FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE

Question 8

using logical vectors as indexing arguments

Answer 8

What were the prices of boats older than 100?

boat.prices[boat.ages > 100]
##[1] 53 54 264 532

Question 9

“&” (and) and “|” (or) operators

Answer 9

In addition to using single comparison operators, you can combine multiple logical vectors using the OR (which looks like | and AND & commands. The OR | operation will return TRUE if any of the logical vectors is TRUE, while the AND & operation will only return TRUE if all of the values in the logical vectors is TRUE

Question 10

examples of & and | vectors

Answer 10

Which boats had prices greater than 200 OR less than 100?

boat.prices > 200 | boat.prices < 100
##[1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE

boat.names[boat.prices > 200 | boat.prices < 100]
##[1] “a” “b” “c” “d” “e” “f” “g” “h” “i”

You can combine as many logical vectors as you want (as long as they all have the same length!

Question 11

%in% operator

Answer 11

The %in% operation helps you to easily create multiple OR arguments.Imagine you have a vector of categorical data that can take on many different values. For example, you could have a vector x indicating people’s favorite letters.

x == “a” | x == “b” | x == “c” | x == “d”
##[1] TRUE FALSE TRUE TRUE FALSE

^ This is the same as the following shorter and easier code:

x %in% c(“a”, “b”, “c”, “d”)
##[1] TRUE FALSE TRUE TRUE FALSE