R Programming week1-Data Type

Objects

R has five basic or “atomic” classes of objects:

character

numeric (real numbers)

integer

complex

logical (True/False)

The most basic object is a vector

A vector can only contain objects of the same class

BUT: The one exception is a list, which is represented as a vector but can contain objects of

different classes (indeed, that’s usually why we use them)

Empty vectors can be created with the vector() function.

Numbers

Numbers in R a generally treated as numeric objects (i.e. double precision real numbers)

If you explicitly want an integer, you need to specify the L suffix

Ex: Entering 1 gives you a numeric object; entering 1L explicitly gives you an integer.

There is also a special number Inf which represents infinity; e.g. 1 / 0; Inf can be used in

ordinary calculations; e.g. 1 / Inf is 0

The value NaN represents an undefined value (“not a number”); e.g. 0 / 0; NaN can also be

thought of as a missing value (more on that later)

Attributes

R objects can have attributes

names, dimnames

dimensions (e.g. matrices, arrays)

class

length

Attributes of an object can be accessed using the attributes() function.

Creating Vectors

The c() function can be used to create vectors of objects.

Using the vector() function

> x <- vector("numeric", length = 10)

> x

 0 0 0 0 0 0 0 0 0 0

Mixing Objects Mixing Objects

> y <- c(1.7, "a") ## character

> y <- c(TRUE, 2) ## numeric

> y <- c("a", TRUE) ## character

When different objects are mixed in a vector, coercion occurs so that every element in the vector is

of the same class.

Explicit Coercion

Objects can be explicitly coerced from one class to another using the as.* functions, if available.

> x <- 0:6

> class(x)

 "integer"

> as.numeric(x)

 0 1 2 3 4 5 6

> as.logical(x)

 FALSE TRUE TRUE TRUE TRUE TRUE TRUE

> as.character(x)

 "0" "1" "2" "3" "4" "5" "6"

Nonsensical coercion results in NAs.

> x <- c("a", "b", "c")

> as.numeric(x)

 NA NA NA

Warning message:

NAs introduced by coercion

> as.logical(x)

 NA NA NA

> as.complex(x)

 0+0i 1+0i 2+0i 3+0i 4+0i 5+0i 6+0i

Lists

Lists are a special type of vector that can contain elements of different classes. Lists are a very

important data type in R and you should get to know them well.

> x <- list(1, "a", TRUE, 1 + 4i)

> x

[]

 1

[]

 "a"

[]

 TRUE

[]

 1+4i

Matrices Matrices

Matrices are vectors with a dimension attribute. The dimension attribute is itself an integer vector of length 2 (nrow, ncol)

> m <- matrix(nrow = 2, ncol = 3)

> m

[,1] [,2] [,3]

[1,] NA NA NA

[2,] NA NA NA

> dim(m)

 2 3

> attributes(m)

\$dim

 2 3

Matrices (cont’d)

Matrices are constructed column-wise, so entries can be thought of starting in the “upper left” corner and running down the columns.

> m <- matrix(1:6, nrow = 2, ncol = 3)

> m

[,1] [,2] [,3]

[1,] 1 3 5

[2,] 2 4 6

Matrices can also be created directly from vectors by adding a dimension attribute.

> m <- 1:10

> m

 1 2 3 4 5 6 7 8 9 10

> dim(m) <- c(2, 5)

> m

[,1] [,2] [,3] [,4] [,5]

[1,] 1 3 5 7 9

[2,] 2 4 6 8 10

cbind-ing and rbind-ing cbind-ing and rbind-ing

Matrices can be created by column-binding or row-binding with cbind() and rbind().

> x <- 1:3

> y <- 10:12

> cbind(x, y)

x y

[1,] 1 10

[2,] 2 11

[3,] 3 12

> rbind(x, y)

[,1] [,2] [,3]

x 1 2 3

y 10 11 12

Factors

Factors are used to represent categorical data. Factors can be unordered or ordered. One can think

of a factor as an integer vector where each integer has a label.

Factors are treated specially by modelling functions like lm() and glm()

Using factors with labels is better than using integers because factors are self-describing; having

a variable that has values “Male” and “Female” is better than a variable that has values 1 and 2.

> x <- factor(c("yes", "yes", "no", "yes", "no"))

> x

 yes yes no yes no

Levels: no yes

> table(x)

x

no yes

2 3

> unclass(x)

 2 2 1 2 1

attr(,"levels")

 "no" "yes"

The order of the levels can be set using the levels argument to factor(). This can be important

in linear modelling because the first level is used as the baseline level.

> x <- factor(c("yes", "yes", "no", "yes", "no"),

levels = c("yes", "no"))

> x

 yes yes no yes no

Levels: yes no

Missing Values Missing Values

Missing values are denoted by NA or NaN for undefined mathematical operations.

is.na() is used to test objects if they are NA

is.nan() is used to test for NaN

NA values have a class also, so there are integer NA, character NA, etc.

A NaN value is also NA but the converse is not true

> x <- c(1, 2, NA, 10, 3)

> is.na(x)

 FALSE FALSE TRUE FALSE FALSE

> is.nan(x)

 FALSE FALSE FALSE FALSE FALSE

> x <- c(1, 2, NaN, NA, 4)

> is.na(x)

 FALSE FALSE TRUE TRUE FALSE

> is.nan(x)

 FALSE FALSE TRUE FALSE FALSE

Data Frames

Data frames are used to store tabular data

They are represented as a special type of list where every element of the list has to have the

same length

Each element of the list can be thought of as a column and the length of each element of the list

is the number of rows

Unlike matrices, data frames can store different classes of objects in each column (just like lists);

matrices must have every element be the same class

Data frames also have a special attribute called row.names

Can be converted to a matrix by calling data.matrix()

> x <- data.frame(foo = 1:4, bar = c(T, T, F, F))

> x

foo bar

1 1 TRUE

2 2 TRUE

3 3 FALSE

4 4 FALSE

> nrow(x)

 4

> ncol(x)

 2

Names

R objects can also have names, which is very useful for writing readable code and self-describing

objects.

> x <- 1:3

> names(x)

NULL

> names(x) <- c("foo", "bar", "norf")

> x

foo bar norf

1 2 3

> names(x)

 "foo" "bar" "norf"

Summary

Data Types

atomic classes: numeric, logical, character, integer, complex \

vectors, lists

factors

missing values

data frames

names