Lesson 4 of 17 · Week 02 · Thu Oct 1

Data Structures

Start with a small table

Here's a small data frame about garden plants. Type it into your script and run it; we'll use it all lesson:

plants <- data.frame(species   = c("fern", "basil", "cactus"),
                     height_cm = c(32.5, 21.0, 14.2),
                     flowering = c(FALSE, TRUE, TRUE))
plants
plants$height_cm            # one column, with $
plants$height_cm * 10       # in millimetres
paste("My", plants$species)

Each column of a data frame is a vector, and every element of a vector has the same type. These two ideas explain most of R's surprising behaviour, so they're the subject of this lesson.

Adapted from Software Carpentry's “Data Structures” episode (CC BY 4.0, The Carpentries). Text, examples and exercises have been rewritten for this course.

Data types

R has five basic (“atomic”) types. typeof() tells you which one you have:

typeof(3.14)      # "double"     numbers with decimals (the default for numbers)
typeof(2L)        # "integer"    whole numbers; the L makes it an integer
typeof(TRUE)      # "logical"    TRUE / FALSE
typeof("fern")    # "character"  text
typeof(1 + 2i)    # "complex"    rarely needed

class() answers a related question: what kind of object is this (e.g. "numeric", "data.frame", "factor")? str() shows both the structure and a preview.

Vectors and coercion

Make vectors with c(), sequences with : or seq(), and empty ones with vector("numeric", length = 3). Since a vector holds one type only, mixing types forces R to coerce everything to the most flexible type present:

logical < integer < double < complex < character
c(2, "three")      # "2" "three"       character wins
c(TRUE, 5)         # 1 5               TRUE becomes 1

This matters when data is messy: one stray non-number in a column of a CSV (say, "2.3 or 2.4") turns the whole column into text. You convert deliberately with as.numeric(), as.character(), as.logical(). Anything that can't be converted becomes NA (with a warning).

Other vector helpers: length(), head(), tail(), append(), and seq(10, 100, by = 10).

Names

fares <- c(adult = 2.50, student = 1.25)
names(fares)                       # "adult" "student"
fares["student"]                   # look up by name
names(fares)[2] <- "child"         # change one name
names(fares) <- c("A", "C")        # replace them all

R also has built-in constants you can use for names: LETTERS, letters, month.name and month.abb.

Factors

A factor stores categorical data: a fixed set of levels, with each value pointing at one.

size <- factor(c("small", "large", "small", "medium"))
levels(size)             # sorted alphabetically by default
size <- factor(size, levels = c("small", "medium", "large"))   # set the order
table(size)

Lists

A list is a vector whose elements can be anything, including different types and lengths:

trip <- list(place = "Monteverde", days = 3, species_seen = c("quetzal", "sloth"))
trip$species_seen
trip[["days"]]

A data frame is actually a list of equal-length vectors (its columns), which explains the ways of pulling pieces out of it:

• plants$species, plants[["species"]], plants[[1]]: the column itself, a vector.
• plants["species"], plants[1]: a smaller data frame with just that column.
• plants[2, 1]: row 2, column 1, a single value. plants[, 1]: all rows of column 1 (a vector). plants[2, ]: row 2, all columns (a one-row data frame).

Matrices

A matrix is a vector with rows and columns: every element has the same type.

m <- matrix(1:6, nrow = 2, ncol = 3)   # filled column by column
m
dim(m); nrow(m); ncol(m)
matrix(1:6, nrow = 2, byrow = TRUE)    # filled row by row

Exercises

Each exercise gives you up to 10 checked attempts. Use Run as many times as you like to experiment first, then click Check answer when you're ready. Your exercises use numbers/subsets unique to you, so answers won't match a classmate's exactly.