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data-structures-R.md

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Data Structures
R

Vectors

  • A sequence of values with the same type
  • Create using c(), which stands for "combine"
sites <- c("a", "a", "b", “c”)

  • Functions:

    • str(sites)
    • length(sites)

  • Slicing:

    • 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:

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]({{ site.baseurl }}/exercises/Vectors-shrub-volume-vectors-R/).

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:
    • total_number[sites == 'a']
      • == means "equal to" in most languages.
      • Not =. = is used for assignment.
      • !=, <, >

Do [Shrub Volume Vectors]({{ site.baseurl }}/exercises/Vectors-shrub-volume-vectors-R/).

Matrices (if linear algebra folks)

  • A two-dimensional set of values with a single data type
x <- matrix(1:6, 2)
y <- matrix(1:3, ncol = 1)
x %*% y

Data frames

  • A list of equal length vectors grouped together

  • data.frame()

surveys <- data.frame(sites, density_ha, area_ha)
  • 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')
  • Useful commands:
    • str(surveys)
    • length(surveys)
    • nrow(surveys), ncol(surveys)
  • Subsetting columns:
    • surveys[“area_ha”]
    • surveys[c(“area_ha”, “sites”)]
    • surveys$area_ha
    • surveys[[“area_ha”]]

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]({{ site.baseurl }}/exercises/Shrub-volume-data-frame-R/), but just use the url instead of downloading the file.