1.
Stat405 Data structures
Hadley Wickham
Wednesday, 30 September 2009
2.
1. Outline for next couple of weeks
2. Basic data types
3. Vectors, matrices & arrays
4. Lists & data.frames
Wednesday, 30 September 2009
3.
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.
Wednesday, 30 September 2009
4.
1d Vector List
2d Matrix Data frame
nd Array
Same types Different types
Wednesday, 30 September 2009
5.
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
Wednesday, 30 September 2009
6.
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)
Wednesday, 30 September 2009
7.
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
Wednesday, 30 September 2009
8.
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
Wednesday, 30 September 2009
9.
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
Wednesday, 30 September 2009
10.
# 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"]]
Wednesday, 30 September 2009
11.
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]
Wednesday, 30 September 2009
13.
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?
Wednesday, 30 September 2009
14.
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?
Wednesday, 30 September 2009
15.
# 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))
Wednesday, 30 September 2009
16.
# f is a list of matrices of different dimensions
f[[1]]
f[[1]][1, 2]
Wednesday, 30 September 2009
17.
# 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]
Wednesday, 30 September 2009
19.
# 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]
Wednesday, 30 September 2009
21.
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?
Wednesday, 30 September 2009
Views
Actions
Embeds 0
Report content