R matrix, factor, and operations on missing value and other objects (2024)

Examples of Matrices

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

m <- matrix(nrow = 2, ncol = 3) # create matrix with 2 row and 3 columnm

## [,1] [,2] [,3]## [1,] NA NA NA## [2,] NA NA NA

dim(m) # get dimentions

## [1] 2 3

attributes(m) # get attribute of m

## $dim## [1] 2 3

m <- matrix(1:6, nrow = 2, ncol = 3) # create matrix with valuesm

## [,1] [,2] [,3]## [1,] 1 3 5## [2,] 2 4 6

m[1,2] # get the element at first row and second column

## [1] 3

m <- 1:10 # get vectorsm

## [1] 1 2 3 4 5 6 7 8 9 10

dim(m)<- c(2,5) # create matrix directly from vectors by adding dimension attributem

## [,1] [,2] [,3] [,4] [,5]## [1,] 1 3 5 7 9## [2,] 2 4 6 8 10

x <- 1:3y <- 10:12cbind(x,y) # create matrix by column binding

## x y## [1,] 1 10## [2,] 2 11## [3,] 3 12

rbind(x,y) # create matrix by row binding

## [,1] [,2] [,3]## x 1 2 3## y 10 11 12

Examples of Factors

Factors are used to represent categorical data and can be unordered or ordered. One can think of a factor as an integer vector where each integer has a label. Factors are important in statistical modeling and 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

## [1] yes yes no yes no ## Levels: no yes

table(x)

## x## no yes ## 2 3

unclass(x) # See the underlying representation of factor 

## [1] 2 2 1 2 1## attr(,"levels")## [1] "no" "yes"

x <- factor(c("yes", "yes", "no", "yes", "no"), levels <- c("yes", "no")) # The order of the levels of a factor can be set using the levels argument to factor()x

## [1] yes yes no yes no ## Levels: yes no

Examples of Missing values

Missing values are denoted by NA or NaN for q undefined mathematical operations. is.na() is used to test objects if they are NA is.nan() is used to test for NaN A NaN is also NA but the converse is not true

x <- c(1, 2, NA, 10, 3) ## Create a vector with NAs in itis.na(x)

## [1] FALSE FALSE TRUE FALSE FALSE

is.nan(x)

## [1] FALSE FALSE FALSE FALSE FALSE

x <- c(1, 2, NaN, NA, 4) ## Now create a vector with both NA and NaN valuesis.na(x)

## [1] FALSE FALSE TRUE TRUE FALSE

is.nan(x)

## [1] FALSE FALSE TRUE FALSE FALSE

Examples of Data Frames

Data frames are used to store tabular data in R. Data frames 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. Data frames have a special attribute called row.names which indicate information about each row of the data frame.

x <- data.frame(foo = 1:4, bar = c(T, T, F, F)) # create a data framex

## foo bar## 1 1 TRUE## 2 2 TRUE## 3 3 FALSE## 4 4 FALSE

nrow(x) 

## [1] 4

ncol(x)

## [1] 2

data.matrix(x) # convert data frame to a matrix

## foo bar## [1,] 1 1## [2,] 2 1## [3,] 3 0## [4,] 4 0

Examples of names

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

x <- 1:3names(x)

## NULL

names(x) <- c("New York", "Seattle", "Los Angeles") # set the names for vector xx

## New York Seattle Los Angeles ## 1 2 3

x <- list("Los Angeles" = 1, Boston = 2, London = 3) # list can also have names x

## $`Los Angeles`## [1] 1## ## $Boston## [1] 2## ## $London## [1] 3

m <- matrix(1:4, nrow = 2, ncol = 2)dimnames(m) <- list(c("a", "b"), c("c", "d")) # Matrices can have both column and row names.m

## c d## a 1 3## b 2 4

colnames(m) <- c("h", "f") # set column names rownames(m) <- c("x", "z") # set row namesm

## h f## x 1 3## z 2 4

Examples of subsetting operation

There are three operators that can be used to extract subsets of R objects. • The [ operator always returns an object of the same class as the original. It can be used to select multiple elements of an object. • The [[ operator is used to extract elements of a list or a data frame. It can only be used to extract a single element and the class of the returned object will not necessarily be a list or data frame. • The $operator is used to extract elements of a list or data frame by literal name. Its semantics are similar to that of [[.

x <- c("a", "b", "c", "c", "d", "a") x[1] ## Extract the first element

## [1] "a"

x[2] ## Extract the second element 

## [1] "b"

x[1:4] ## extract multiple elements

## [1] "a" "b" "c" "c"

x[c(1, 3, 4)]

## [1] "a" "c" "c"

u <- x> "a"x[u] ## extract elements of a vector that satisfy a given condition.

## [1] "b" "c" "c" "d"

x[x>"a"]

## [1] "b" "c" "c" "d"

x <- matrix(1:6, 2, 3) x

## [,1] [,2] [,3]## [1,] 1 3 5## [2,] 2 4 6

x[1, 2] ## get row 1 column 2 element in matrix x

## [1] 3

x[2, 1]

## [1] 2

x[1, ] ## Extract the first row 

## [1] 1 3 5

x[, 2] ## Extract the second column 

## [1] 3 4

x[1, 2, drop = FALSE] ## turn off the default returning vector

## [,1]## [1,] 3

x[1, ]

## [1] 1 3 5

x[1, , drop = FALSE] 

## [,1] [,2] [,3]## [1,] 1 3 5

x <- list(foo = 1:4, bar = 0.6) x

## $foo## [1] 1 2 3 4## ## $bar## [1] 0.6

x[[1]] ## get the first element in list use [[]]

## [1] 1 2 3 4

x[["bar"]] ## get the element bar

## [1] 0.6

x$bar 

## [1] 0.6

x <- list(foo = 1:4, bar = 0.6, baz = "hello") # create a listname <- "foo"x[[name]] ## computed index for "foo" 

## [1] 1 2 3 4

x$foo ## get the element with name foo

## [1] 1 2 3 4

x <- list(a = list(10, 12, 14), b = c(3.14, 2.81)) # create a nested listx[[c(1, 3)]] ## Get the 3rd element of the 1st element

## [1] 14

x[[1]][[3]] ## same as above

## [1] 14

x[[c(2, 1)]]## 1st element of the 2nd element 

## [1] 3.14

x <- list(aardvark = 1:5) ## create a new listx$a ## partial matching of a list element name

## [1] 1 2 3 4 5

x[["a"]] ## by default, exact matching of a list element name

## NULL

x[["a", exact = FALSE]] ## partial matching of a list element name

## [1] 1 2 3 4 5

Examples of removing NA values

A common task in data analysis is removing missing values (NAs).

x <- c(1, 2, NA, 4, NA, 5)bad <- is.na(x)print(bad)

## [1] FALSE FALSE TRUE FALSE TRUE FALSE

x[!bad] ## removing the NA values

## [1] 1 2 4 5

## creating two vectors with missing values, now we want to take subset with no missing values in both vectorsx <- c(1, NA,3, 4, NA, 5) y <- c("a", "b", NA, "d", NA, "f")good <- complete.cases(x, y)good

## [1] TRUE FALSE FALSE TRUE FALSE TRUE

x[good] # good cases in x

## [1] 1 4 5

y[good] # good cases in y

## [1] "a" "d" "f"

Examples of vectorized operations

Many operations in R are vectorized, meaning that operations occur in parallel in certain R objects. This allows you to write code that is efficient, concise, and easier to read than in non-vectorized languages.

x <- 1:4 y <- 6:9z <- x + y z

## [1] 7 9 11 13

x >= 2

## [1] FALSE TRUE TRUE TRUE

x-y

## [1] -5 -5 -5 -5

x*y

## [1] 6 14 24 36

x <- matrix(1:4, 2, 2)x

## [,1] [,2]## [1,] 1 3## [2,] 2 4

y <- matrix(rep(10, 4), 2, 2) x

## [,1] [,2]## [1,] 1 3## [2,] 2 4

## element-wise multiplicationx*y

## [,1] [,2]## [1,] 10 30## [2,] 20 40

## element-wise divisionx/y

## [,1] [,2]## [1,] 0.1 0.3## [2,] 0.2 0.4

## true matrix multiplicationx %*% y

## [,1] [,2]## [1,] 40 40## [2,] 60 60