23 data-structures

Hadley Wickham
Stat405Data structures
Thursday, 11 November 2010

Assessment
• Final: there is no ﬁnal
• Two groups still haven’t sent me
electronic versions of their project
• Many of you STILL HAVEN’T returned
your team evaluations

1. Basic data types
2. Vectors, matrices & arrays
3. Lists & data.frames

Vector
Matrix
Array
List
Data frame
1d
2d
nd
Same types Different types

character
numeric
logical
mode()
length() A scalar is a vector of length 1
as.character(c(T, F))
as.character(seq_len(5))
as.logical(c(0, 1, 100))
as.logical(c("T", "F", "a"))
as.numeric(c("A", "100"))
as.numeric(c(T, F))
When vectors of
different types occur
in an expression,
they will be
automatically
coerced to the same
type: character >
numeric > logical
names() Optional, but useful
Technically, these are all atomic vectors

Your turn
Experiment with automatic coercion.
What is happening in the following cases?
104 & 2 < 4
mean(diamonds$cut == "Good")
c(T, F, T, T, "F")
c(1, 2, 3, 4, F)

Matrix (2d)
Array (>2d)
a <- seq_len(12)
dim(a) <- c(1, 12)
dim(a) <- c(4, 3)
dim(a) <- c(2, 6)
dim(a) <- c(3, 2, 2)
1 5 9
2 6 10
3 7 11
4 8 12
1 2 3 4 5 6 7 8 9 10 11 12
1 3 5 7 9 11
2 4 6 8 10 12
(1, 12)
(4, 3) (2, 6)Just like a vector.
Has mode() and
length().
Create with matrix
() or array(), or
from a vector by
setting dim()
as.vector()
converts back to a
vector

List
Is also a vector (so has
mode, length and names),
but is different in that it can
store any other vector inside
it (including lists).
Use unlist() to convert to
a vector. Use as.list() to
convert a vector to a list.
c(1, 2, c(3, 4))
list(1, 2, list(3, 4))
c("a", T, 1:3)
list("a", T, 1:3)
a <- list(1:3, 1:5)
unlist(a)
as.list(a)
b <- list(1:3, "a", "b")
unlist(b)
Technically a recursive vector

Data frame
List of vectors, each of the
same length. (Cross
between list and matrix)
Different to matrix in that
each column can have a
different type

# How do you convert a matrix to a data frame?
# How do you convert a data frame to a matrix?
# What is different?
# What does these subsetting operations do?
# Why do they work? (Remember to use str)
diamonds[1]
diamonds[[1]]
diamonds[["cut"]]

x <- sample(12)
# What's the difference between a & b?
a <- matrix(x, 4, 3)
b <- array(x, c(4, 3))
# What's the difference between x & y
y <- matrix(x, 12)
# How are these subsetting operations different?
a[, 1]
a[, 1, drop = FALSE]
a[1, ]
a[1, , drop = FALSE]

1d names() length() c()
2d
colnames()
rownames()
ncol()
nrow()
cbind()
rbind()
nd dimnames() dim() abind()
(special package)

b <- seq_len(10)
a <- letters[b]
# What sort of matrix does this create?
rbind(a, b)
cbind(a, b)
# Why would you want to use a data frame here?
# How would you create it?

load(url("http://had.co.nz/stat405/data/quiz.rdata"))
# What is a? What is b?
# How are they different? How are they similar?
# How can you turn a in to b?
# How can you turn b in to a?
# What are c, d, and e?
# How are they different? How are they similar?
# How can you turn one into another?
# What is f?
# How can you extract the first element?
# How can you extract the first value in the first
# element?

# a is numeric vector, containing the numbers 1 to 10
# b is a list of numeric scalars
# they contain the same values, but in a different format
identical(a[1], b[[1]])
identical(a, unlist(b))
identical(b, as.list(a))
# c is a named list
# d is a data.frame
# e is a numeric matrix
# From most to least general: c, d, e
identical(c, as.list(d))
identical(d, as.data.frame(c))
identical(e, data.matrix(d))

# f is a list of matrices of different dimensions
f[[1]]
f[[1]][1, 2]

# What does these subsetting operations do?
# Why do they work? (Remember to use str)
diamonds[1]
diamonds[[1]]
diamonds[["cut"]]
diamonds[["cut"]][1:10]
diamonds$cut[1:10]

Vectors x[1:4] —
Matrices
Arrays
x[1:4, ]
x[, 2:3, ]
x[1:4, ,
drop = F]
Lists
x[[1]]
x$name
x[1]

# What's the difference between a & b?
a <- matrix(x, 4, 3)
b <- array(x, c(4, 3))
# What's the difference between x & y
y <- matrix(x, 12)
# How are these subsetting operations different?
a[, 1]
a[, 1, drop = FALSE]
a[1, ]
a[1, , drop = FALSE]

1d c()
2d
matrix()
data.frame()
t()
nd array() aperm()

23 data-structures

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