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”

sites <- c("a", "a", "b", "c")

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

str(sites)

• 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”
• sites[1] gives us the first value in the vector
• sites[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 sites[1:3] is the same as sites[c(1, 2, 3)]

• You can use a vector to get any subset or order you want sites[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(sites)

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

density <- c(2.8, 3.2, 1.5, 3.8)
mean(density)
max(density)
min(density)
sum(density)

Do 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
• So a vector of 4 population densities with the third value missing would look like

density <- c(2.8, 3.2, NA, 3.8)

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

mean(density)

• 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

mean(density, na.rm = TRUE)

• Null values are how we indicate missing data in R
• We write nulls using NA
• Can remove them in many calculations using the optional argument na.rm = TRUE

Do Nulls in Vectors.

Working with multiple vectors

• Build on example where we have information on sites and population densities by adding areas

sites <- c("a", "a", "b", "c")
density <- c(2.8, 3.2, 1.5, 3.8)
area <- c(3, 5, 1.9, 2.7)

Vector math

• Vector math combines values in the same position

• Element-wise: operating on one element at a time

• We can multiple the density and area vectors together to get a vector of the number of individuals we expect in that area

number <- density * area

• This works because when we multiply vectors, R multiples the first values in each vector, then multiplies the second values in each vector, and so on

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[sites == 'a']

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

• Not =. = is used for assignment.

• Can also do “not equal to”

density[sites != 'a']

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

sites[density > 3]
sites[density < 3]
sites[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 Shrub Volume Vectors 1-3.