Data Science with R for the Life Sciences

Data Structures

Notes

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.