Objects
R has five basic or “atomic” classes ofobjects:
character
numeric (real numbers)
integer
complex
logical (True/False)
The most basic object is a vector
A vector can only contain objects of thesame class
BUT: The one exception is a list, which isrepresented as a vector but can contain objects of
different classes (indeed, that’s usuallywhy we use them)
Empty vectors can be created with thevector() function.
Numbers
Numbers in R a generally treated as numericobjects (i.e. double precision real numbers)
If you explicitly want an integer, you needto 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 whichrepresents 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 thatlater)
Attributes
R objects can have attributes
names, dimnames
dimensions (e.g. matrices, arrays)
class
length
other user-defined attributes/metadata
Attributes of an object can be accessedusing the attributes() function.
Creating Vectors
The c() function can be used to createvectors of objects.
Using the vector() function
> x <- vector("numeric",length = 10)
> x
[1]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 avector, coercion occurs so that every element in the vector is
of the same class.
Explicit Coercion
Objects can be explicitly coerced from oneclass to another using the as.* functions, if available.
> x <- 0:6
> class(x)
[1] "integer"
> as.numeric(x)
[1] 0 1 2 3 4 5 6
> as.logical(x)
[1] FALSE TRUE TRUE TRUE TRUE TRUE TRUE
> as.character(x)
[1] "0" "1""2" "3" "4" "5" "6"
Nonsensical coercion results in NAs.
> x <- c("a","b", "c")
> as.numeric(x)
[1] NA NA NA
Warning message:
NAs introduced by coercion
> as.logical(x)
[1] NA NA NA
> as.complex(x)
[1] 0 0i 1 0i 2 0i 3 0i 4 0i 5 0i 6 0i
Lists
Lists are a special type of vector that cancontain elements of different classes. Lists are a very
important data type in R and you should getto know them well.
> x <- list(1, "a", TRUE, 1 4i)
> x
[[1]]
[1] 1
[[2]]
[1] "a"
[[3]]
[1] TRUE
[[4]]
[1] 1 4i
Matrices
Matrices are vectors with a dimensionattribute. 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)
[1] 2 3
> attributes(m)
$dim
[1] 2 3
Matrices (cont’d)
Matrices are constructed column-wise, soentries can be thought of starting in the “upper left” corner and running downthe 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 fromvectors by adding a dimension attribute.
> m <- 1:10
> m
[1] 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 andrbind-ing
Matrices can be created by column-bindingor 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 categoricaldata. Factors can be unordered or ordered. One can think
of a factor as an integer vector where eachinteger has a label.
Factors are treated specially by modellingfunctions like lm() and glm()
Using factors with labels is better thanusing 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
[1] yes yes no yes no
Levels: no yes
> table(x)
x
no yes
2 3
> unclass(x)
[1] 2 2 1 2 1
attr(,"levels")
[1] "no" "yes"
The order of the levels can be set usingthe levels argument to factor(). This can be important
in linear modelling because the first levelis used as the baseline level.
> x <- factor(c("yes","yes", "no", "yes", "no"),
levels = c("yes", "no"))
> x
[1] yes yes no yes no
Levels: yes no
Missing Values
Missing values are denoted by NA or NaN for undefined mathematical operations.
is.na() is used to test objects if they areNA
is.nan() is used to test for NaN
NA values have a class also, so there areinteger NA, character NA, etc.
A NaNvalue is also NA but the converse is not true
> x <- c(1, 2, NA, 10, 3)
> is.na(x)
[1] FALSE FALSE TRUE FALSE FALSE
> is.nan(x)
[1] FALSE FALSE FALSE FALSE FALSE
> x <- c(1, 2, NaN,NA, 4)
> is.na(x)
[1] FALSE FALSE TRUE TRUE FALSE
> is.nan(x)
[1] FALSE FALSE TRUE FALSE FALSE
Data Frames
Data frames are used to store tabular data
They are represented as a special type oflist where every element of the list has to have the
same length
Each element of the list can be thought ofas a column and the length of each element of the list
is the number of rows
Unlike matrices, data frames can storedifferent classes of objects in each column (just like lists);
matrices must have every element be thesame class
Data frames also have a special attributecalled row.names
Data frames are usually created by callingread.table() or read.csv()
Can be converted to a matrix by callingdata.matrix()
> x <- data.frame(foo = 1:4, bar =c(T, T, F, F))
> x
foobar
1 1 TRUE
2 2 TRUE
3 3 FALSE
4 4 FALSE
> nrow(x)
[1] 4
> ncol(x)
[1] 2
Names
R objects can also have names, which isvery 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 23
> names(x)
[1] "foo" "bar""norf"
Summary
Data Types
atomic classes: numeric, logical,character, integer, complex
vectors, lists
factors
missing values
data frames
names
