11 Data Structures

Stat405 Data structures

Hadley Wickham
Wednesday, 30 September 2009

1. Outline for next couple of weeks
2. Basic data types
3. Vectors, matrices & arrays
4. Lists & data.frames


Outline
More details about the underlying data
structures we’ve been using so far.
Then pulling apart those data structures
(particularly data frames), operating on
each piece and putting them back
together again.
An alternative to for loops when each
piece is independent.


1d Vector List

2d Matrix Data frame

nd Array

Same types Different types


When vectors of
character as.character(c(T, F))
different types occur
as.character(seq_len(5)) in an expression,
as.logical(c(0, 1, 100)) they will be
numeric automatically
as.logical(c("T", "F", "a"))
as.numeric(c("A", "100")) coerced to the same
logical as.numeric(c(T, F))
type: character >
numeric > logical

mode()
names() Optional, but useful
length() A scalar is a vector of length 1

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) (1, 12)

Array (>2d) 1 2 3 4 5 6 7 8 9 10 11 12

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

List
Is also a vector (so has c(1, 2, c(3, 4))
mode, length and names), list(1, 2, list(3, 4))
but is different in that it can
store any other vector inside c("a", T, 1:3)
it (including lists). list("a", T, 1:3)

Use unlist() to convert to a <- list(1:3, 1:5)
a vector. Use as.list() to unlist(a)
convert a vector to a list. 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()

colnames() ncol() cbind()
2d
rownames() nrow() rbind()

abind()
nd dimnames() dim() (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 x[1:4, ] x[1:4, ,
Arrays x[, 2:3, ] drop = F]

x[[1]]
Lists 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()

matrix()
2d t()
data.frame()

nd array() aperm()


11 Data Structures

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

En vedette

En vedette (7)

Similaire à 11 Data Structures

Similaire à 11 Data Structures (20)

Plus de Hadley Wickham

Plus de Hadley Wickham (20)

Dernier

Dernier (20)

11 Data Structures