The document discusses different data structures in R, including vectors, factors, matrices, lists, and data frames. It focuses on vectors, noting there are four main types: logical, numeric, character, and factors. Vectors are one-dimensional objects that contain elements of the same type. Logical vectors are useful for indexing, while numeric vectors can be created with functions like seq and rep. Character vectors can store names and be manipulated with functions such as grep and gsub. Factors represent categorical variables as a vector of codes with attribute levels.

1. © 2012 Heather Turner
Part I
Writing R Code
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2. © 2012 Heather Turner
Data Structures
Data structures are the building blocks of code. In R
there are four main types of structure:
• vectors and factors
• matrices and arrays
• lists
• data frames
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3. © 2012 Heather Turner
Vectors
Vectors are one dimensional data objects
> c(1, 2, 3)
[1] 1 2 3
The elements must all be of the same type: logical,
numeric or character, and will be coerced if necessary:
> c(NA, FALSE, 1)
[1] NA 0 1
> c(NA, TRUE, 1, "a")
[1] NA "TRUE" "1" "a"
The functions as.logical, as.numeric and as.character
will coerce to the corresponding type, producing NAs
when coercion fails.
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4. © 2012 Heather Turner
Logical Vectors
Logical vectors are commonly used for indexing
> x <- c(1, 1, 2, 2)
> x[x > 1]
[1] 2 2
The logical binary operators are >, <, ==, <=, >= and !=,
whilst ! is a unary operator. Some functions also return
logical vectors, e.g. duplicated:
> duplicated(x)
[1] FALSE TRUE FALSE TRUE
> !duplicated(x)
[1] TRUE FALSE TRUE FALSE
Single element logical vectors (scalars) are useful for
flow control, see later.
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5. © 2012 Heather Turner
Numeric Vectors
The are several convenience function for creating
numeric vectors, the most common being seq and rep.
As they are so useful there are fast shortcuts for
particular cases
> x <- 3:5
> seq_len(3)
[1] 1 2 3
> seq_along(x)
[1] 1 2 3
> rep.int(1:2, times = c(2, 3))
[1] 1 1 2 2 2
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6. © 2012 Heather Turner
Character Vectors
Character vectors may be used for creating names
> names(x) <- paste(LETTERS[1:3], 1229:1231, sep = "")
> x
A1229 B1230 C1231
3 4 5
Useful functions to extract information from character
vectors include nchar, substr, strsplit, grep and gsub.
The last two are for pattern matching and replacement
> grep("123", names(x), fixed = TRUE)
[1] 2 3
> as.numeric(gsub("[A-C]", "", names(x)))
[1] 1229 1230 1231
The latter uses a regular expression, see references.
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7. © 2012 Heather Turner
Factors
Factors are a special type of vector representing a
categorical variable as a vector of numeric codes with
an attribute giving the levels
> colour <- factor(rep(c("red", "green", "blue"), 2))
> str(colour)
Factor w/ 3 levels "blue","green",..: 3 2 1 3 2 1
The function gl is useful for creating factors
> gl(3, 1, length = 6, labels = c("blue", "green", "red"))
[1] blue green red blue green red
Levels: blue green red
> gl(3, 2, length = 6, labels = c("red", "green", "blue"))
[1] red red green green blue blue
Levels: red green blue
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