Vectors

• A sequence of values with the same type
• Create using c(), which stands for “combine”

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

• str(sites)

• Slicing:
  • Use []
  • In general [] in R means, “give me a piece of something”
  • sites[1]
  • sites[1:3]
    • 1:3 makes a vector. So, this is the same as
  • sites[c(1, 2, 3)]
  • sites[c(4, 1, 3)]
    • You can use a vector to get any subset or order you want
• Math functions:

length(sites)
density_ha <- c(2.8, 3.2, 1.5, 3.8)
mean(density_ha)
max(density_ha)
min(density_ha)
sum(density_ha)

Do Bird Banding 1-4.

Null values

• So far we’ve worked with data with no missing values
• How many of you have missing values in your data?

density_ha <- c(2.8, 3.2, 1.5, NA)
mean(density_ha)

• Why did we get 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

mean(density_ha, na.rm = TRUE)

Working with multiple vectors

• Vector math combines values in the same position
• Element-wise: operating on one element at a time

density_ha <- c(2.8, 3.2, 1.5, 3.8)
area_ha <- c(3, 5, 1.9, 2.7)
total_number <- density_ha * area_ha

• Subsetting is done using [], like slicing

area[sites == 'a']

• == means “equal to” in most languages.

• Not =. = is used for assignment.

• Can also do “not equal to”

area[sites != 'a']

• Greater or less than

sites[area_ha > 3]
sites[area_ha >= 3]
sites[area_ha < 3]

• And we can subset a vector based on itself

sites[sites != 'a']

Do Shrub Volume Vectors 1-3.

Data frames

• A list of equal length vectors grouped together

• data.frame()

surveys <- data.frame(sites, density_ha, area_ha)

• Useful commands:
  • str(surveys)
  • length(surveys)
  • nrow(surveys), ncol(surveys)
• Subsetting:
  • [row, column]
  • surveys[1, 2]
  • surveys[1:2, 2:3]
  • surveys[, 3]
  • surveys[“area_ha”]
  • surveys[c(“area_ha”, “sites”)]
  • surveys$area_ha
  • surveys[[“area_ha”]]

Reading in external data

• read.csv()
  • Main argument is the location of the data - url or path on computer
  • Go to Datasets page on site and copy Shrub dimensions url

shrub_data <- read.csv('https://datacarpentry.org/semester-biology/data/shrub-dimensions-labeled.csv')

Factors

str(shrub_data)

• The shrubID column has type Factor
• Special data type in R for categorical data
• Useful for statistics, but can mess up some aspects of computation
• Can eliminate during imports with stringsAsFactors

shrub_data <- read.csv('https://datacarpentry.org/semester-biology/data/shrub-dimensions-labeled.csv', stringsAsFactors = FALSE)
str(shrub_data)

Start Shrub Volume Data Frame, but just use the url instead of downloading the file.