Vectors

Vectors Basics

• Remember that all values in R have a type
• A vector is a sequence of values that all have the same type
• Create using the c() function, which stands for “combine”

states <- c("FL", "FL", "GA", "SC")

• Using the str function we learned last time shows that this is a vector of 4 character strings

str(states)

• Select pieces of a vector by slicing the vector (like slicing a pizza)
• Use square brackets []
• In general [] in R means, “give me a piece of something”
• states[1] gives us the first value in the vector
• states[1:3] gives us the first through the third values
• 1:3 works by makeing a vector of the whole numbers 1 through 3.
• So, this is the same as states[1:3] is the same as states[c(1, 2, 3)]

• You can use a vector to get any subset or order you want states[c(4, 1, 3)]

• Many functions in R take a vector as input and return a value
• This includes the function length which determines how many items are in a vector

length(states)

• We can also calculate common summary statistics
• For example, if we have a vector of population counts

count <- c(9, 16, 3, 10)
mean(count)
max(count)
min(count)
sum(count)

Do Exercise 6 - Basic Vectors.

Null values

• So far we’ve worked with vectors that contain no missing values
• But most real world data has values that are missing for a variety of reasons
• For example, kangaroo rats don’t like being caught by humans and are pretty good at escaping before you’ve finished measuring them
• Missing values, known as “null” values, are written in R as NA with no quotes, which is short for “not available”
• So a vector of 4 population counts with the third value missing would look like

count_na <- c(9, 16, NA, 10)

• If we try to take the mean of this vector we get NA?

mean(count_na)

• Hard to say what a calculation including NA should be
• So most calculations return NA when NA is in the data
• Can tell many functions to remove the NA before calculating
• Do this using an optional argument, which is an argument that we don’t have to include unless we want to modify the default behavior of the function
• Add optional arguments by providing their name (na.rm), =, and the value that we want those arguments to take (TRUE)

mean(count_na, na.rm = TRUE)

Do Exercise 7 - Nulls in Vectors.

Working with multiple vectors

• Build on example where we have information on states and population counts by adding areas

states <- c("FL", "FL", "GA", "SC")
count <- c(9, 16, 3, 10)
area <- c(3, 5, 1.9, 2.7)

Vector math

• We can divide the count vector by the area vector to get a vector of the density of individuals in that area

density <- count / area

• This works because when we divide vectors, R divides the first value in the first vector by the first value in the second vector, then divides the second values in each vector, and so on
• Element-wise: operating on one element at a time

Filtering

• Subsetting or “filtering” is done using []
• Like with slicing, the [] say “give me a piece of something”
• Selects parts of vectors based on “conditions” not position
• Get the density values in site a

density[states == 'FL']

• == is how we indicate “equal to” in most programming languages.

• Not =. = is used for assignment.

• Can also do “not equal to”

density[states != 'FL']

• Numerical comparisons like greater or less than
• Select states that meet with some restrictions on density

states[density > 3]
states[density < 3]
states[density <= 3]

• Can subset a vector based on itself
• If we want to look at the densities greater than 3
• density is both the vector being subset and part of the condition

density[density > 3]

• Multiple vectors can be used together to perform element-wise math, where we do the same calculation for each position in the vectors
• We can also filter the values in vector based on the values in another vector or itself

Do Exercise 8 - Shrub Volume Vectors.