Overview
Teaching: 20 min
Exercises: 10 minQuestions
How do I use and maneuver within other data classes available in R?
How do I detect and manage missing and incorrect data?
How can I create a class that contains multiple data types?
Objectives
Explore additional data types in R including Lists and Matrices
Learn about missing data and other special values
Messy Data
Not all real world data is as clean as the data we have used in these lessons. Sometimes they contain missing information, incorrect values or are just generally messy. How can we identify and handle these situations>
R supports missing data in vectors. They are represented as NA (Not Available)
and can be used for all the vector types covered in this lesson:
x <- c(0.5, NA, 0.7)
x <- c(TRUE, FALSE, NA)
x <- c("a", NA, "c", "d", "e")
x <- c(1+5i, 2-3i, NA)
The function is.na() indicates the elements of the vectors that represent
missing data, and the function anyNA() returns TRUE if the vector contains
any missing values:
x <- c("a", NA, "c", "d", NA)
y <- c("a", "b", "c", "d", "e")
is.na(x)
[1] FALSE TRUE FALSE FALSE TRUE
is.na(y)
[1] FALSE FALSE FALSE FALSE FALSE
anyNA(x)
[1] TRUE
anyNA(y)
[1] FALSE
Inf is infinity. You can have either positive or negative infinity.
1/0
[1] Inf
NaN means Not a Number. It’s an undefined value.
0/0
[1] NaN
In R matrices are an extension of the numeric or character vectors. They are not a separate type of object but simply an atomic vector with dimensions; the number of rows and columns.
m <- matrix(nrow = 2, ncol = 2)
m
[,1] [,2]
[1,] NA NA
[2,] NA NA
dim(m)
[1] 2 2
Matrices in R are filled column-wise.
m <- matrix(1:6, nrow = 2, ncol = 3)
Other ways to construct a matrix
m <- 1:10
dim(m) <- c(2, 5)
This takes a vector and transform into a matrix with 2 rows and 5 columns.
Another way is to bind columns or rows using 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
You can also use the byrow argument to specify how the matrix is filled. From R’s own documentation:
mdat <- matrix(c(1,2,3, 11,12,13), nrow = 2, ncol = 3, byrow = TRUE)
mdat
[,1] [,2] [,3]
[1,] 1 2 3
[2,] 11 12 13
In R lists act as containers. Unlike atomic vectors, the contents of a list are not restricted to a single mode and can encompass any mixture of data types. Lists are sometimes called generic vectors, because the elements of a list can by of any type of R object, even lists containing further lists. This property makes them fundamentally different from atomic vectors.
A list is a special type of vector. Each element can be a different type.
Create lists using list() or coerce other objects using as.list(). An empty
list of the required length can be created using vector()
x <- list(1, "a", TRUE, 1+4i)
x
[[1]]
[1] 1
[[2]]
[1] "a"
[[3]]
[1] TRUE
[[4]]
[1] 1+4i
x <- vector("list", length = 5) empty list
length(x)
[1] 5
x[[1]]
NULL
x <- 1:10
x <- as.list(x)
length(x)
[1] 10
x[1]?x[[1]]?xlist <- list(a = "Karthik Ram", b = 1:10, data = head(iris))
xlist
$a
[1] "Karthik Ram"
$b
[1] 1 2 3 4 5 6 7 8 9 10
$data
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
Lists can be extremely useful inside functions. You can “staple” together lots of different kinds of results into a single object that a function can return.
A list does not print to the console like a vector. Instead, each element of the list starts on a new line.
Elements are indexed by double brackets. Single brackets will still return a(nother) list.
Key Points
R uses special values to indicate missing or incorrect data
A matrix is a three dimensional vector
A list is a container that can contain objects of different data types.