Module 2
Data Structures (Part II)
We continue exploring different data structures in R programming language. In this module we will discuss matrices and data frames.
Matrices
Matrices are R objects in which elements are arranged in a two-dimensional rectangular layout (columns and rows). A column is a vertical representation of data, while a row is a horizontal representation of data. Like vectors, matrices work with homogeneous data only.
Creating Matrices
We use matrix() function to create a matrix. It has the following arguments (input values):
matrix(data, nrow, ncol, byrow, dimnames)
- data is the input vector which becomes the data elements of the matrix
- nrow is the number of rows to be created
- ncol is the number of columns to be created
-
byrow is a logical argument. If
TRUEthen the input vector elements are arranged by row - dimname is the names assigned to the rows and columns
let’s create a matrix containing c(1:12) elements:
# Elements are arranged sequentially by row
matrix_1 <- matrix(c(1:12), nrow = 4, byrow = TRUE)
print(matrix_1)
#> [,1] [,2] [,3]
#> [1,] 1 2 3
#> [2,] 4 5 6
#> [3,] 7 8 9
#> [4,] 10 11 12
# Elements are arranged sequentially by column
matrix_2 <- matrix(c(1:12), nrow = 4, byrow = FALSE)
print(matrix_2)
#> [,1] [,2] [,3]
#> [1,] 1 5 9
#> [2,] 2 6 10
#> [3,] 3 7 11
#> [4,] 4 8 12
# Specifying the number of columns instead
matrix_3 <- matrix(c(1:12), ncol = 4, byrow = TRUE)
print(matrix_3)
#> [,1] [,2] [,3] [,4]
#> [1,] 1 2 3 4
#> [2,] 5 6 7 8
#> [3,] 9 10 11 12
# Defining names of columns and rows in a matrix
rows <- c("row1", "row2", "row3", "row4")
cols <- c("col1", "col2", "col3")
matrix_4 <- matrix(c(1:12), nrow = 4, byrow = TRUE, dimnames = list(rows, cols))
print(matrix_4)
#> col1 col2 col3
#> row1 1 2 3
#> row2 4 5 6
#> row3 7 8 9
#> row4 10 11 12Accessing Matrix Elements
Elements of a matrix can be accessed by using the column and row index of the element and square brackets:
# Accessing the element in 3rd columns and 1st row
matrix_1[1, 3]
#> [1] 3
# Accessing the element in 2nd column and 4th row
matrix_1[4, 2]
#> [1] 11
# Accessing only the 2nd row
matrix_1[2, ]
#> [1] 4 5 6
# Accessing only the 3rd column
matrix_1[, 3]
#> [1] 3 6 9 12
# Accessing only the 2nd and 3rd rows
matrix_1[c(2,3), ]
#> [,1] [,2] [,3]
#> [1,] 4 5 6
#> [2,] 7 8 9Adding and Removing Rows & Columns
Use cbind() function to add additional columns into a matrix:
print(matrix_1)
#> [,1] [,2] [,3]
#> [1,] 1 2 3
#> [2,] 4 5 6
#> [3,] 7 8 9
#> [4,] 10 11 12
matrix_5 <- cbind(matrix_1, c(10, 20, 30, 40))
print(matrix_5)
#> [,1] [,2] [,3] [,4]
#> [1,] 1 2 3 10
#> [2,] 4 5 6 20
#> [3,] 7 8 9 30
#> [4,] 10 11 12 40Use rbind() function to add additional rows into a matrix:
print(matrix_1)
#> [,1] [,2] [,3]
#> [1,] 1 2 3
#> [2,] 4 5 6
#> [3,] 7 8 9
#> [4,] 10 11 12
matrix_6 <- rbind(matrix_1, c(10, 20, 30))
print(matrix_6)
#> [,1] [,2] [,3]
#> [1,] 1 2 3
#> [2,] 4 5 6
#> [3,] 7 8 9
#> [4,] 10 11 12
#> [5,] 10 20 30Use c() function and negative indexes to remove rows and columns from a matrix:
Assigning Values to Matrix Elements
# Assigning a single value
print(matrix_1)
#> [,1] [,2] [,3]
#> [1,] 1 2 3
#> [2,] 4 5 6
#> [3,] 7 8 9
#> [4,] 10 11 12
matrix_1[1, 1] <- 100
print(matrix_1)
#> [,1] [,2] [,3]
#> [1,] 100 2 3
#> [2,] 4 5 6
#> [3,] 7 8 9
#> [4,] 10 11 12
# Assigning a row
matrix_1[2, ] <- c(11, 22, 33)
print(matrix_1)
#> [,1] [,2] [,3]
#> [1,] 100 2 3
#> [2,] 11 22 33
#> [3,] 7 8 9
#> [4,] 10 11 12
# Replace elements that are equal to 8 with 0
matrix_1[matrix_1 == 8] <- 0
print(matrix_1)
#> [,1] [,2] [,3]
#> [1,] 100 2 3
#> [2,] 11 22 33
#> [3,] 7 0 9
#> [4,] 10 11 12Matrix Operations
matrix_8 <- matrix(1:8, nrow = 4, byrow = T)
print(matrix_8)
#> [,1] [,2]
#> [1,] 1 2
#> [2,] 3 4
#> [3,] 5 6
#> [4,] 7 8
matrix_9 <- matrix(1:8, nrow = 4, byrow = T)
print(matrix_9)
#> [,1] [,2]
#> [1,] 1 2
#> [2,] 3 4
#> [3,] 5 6
#> [4,] 7 8
# Elementwise Addition
print(matrix_8 + matrix_9)
#> [,1] [,2]
#> [1,] 2 4
#> [2,] 6 8
#> [3,] 10 12
#> [4,] 14 16
# Elementwise Subtraction
print(matrix_8 - matrix_9)
#> [,1] [,2]
#> [1,] 0 0
#> [2,] 0 0
#> [3,] 0 0
#> [4,] 0 0
# Elementwise Multiplication
print(matrix_8 * matrix_9)
#> [,1] [,2]
#> [1,] 1 4
#> [2,] 9 16
#> [3,] 25 36
#> [4,] 49 64
# Elementwise Division
print(matrix_8 / matrix_9)
#> [,1] [,2]
#> [1,] 1 1
#> [2,] 1 1
#> [3,] 1 1
#> [4,] 1 1
# Multiplication by Constant
print(2 * matrix_8)
#> [,1] [,2]
#> [1,] 2 4
#> [2,] 6 8
#> [3,] 10 12
#> [4,] 14 16
# Matrix Multiplication
matrix_10 <- matrix(1:8, nrow = 2, byrow = T)
print(matrix_10)
#> [,1] [,2] [,3] [,4]
#> [1,] 1 2 3 4
#> [2,] 5 6 7 8
print(matrix_8 %*% matrix_10)
#> [,1] [,2] [,3] [,4]
#> [1,] 11 14 17 20
#> [2,] 23 30 37 44
#> [3,] 35 46 57 68
#> [4,] 47 62 77 92
# Matrix Transpose
print(t(matrix_8))
#> [,1] [,2] [,3] [,4]
#> [1,] 1 3 5 7
#> [2,] 2 4 6 8Matrix Dimensions
Use dim() function to check matrix dimensions:
print(matrix_1)
#> [,1] [,2] [,3]
#> [1,] 100 2 3
#> [2,] 11 22 33
#> [3,] 7 0 9
#> [4,] 10 11 12
dim(matrix_1)
#> [1] 4 3length() function, when applied to a matrix, shows how many elements are stored in the matrix:
length(matrix_1)
#> [1] 12Data Frames
A data frame is a table in which each column contains values of one variable and each row contains one set of values from each column. In other words, it is a data structure that represents cases in which there are observations (rows) and measurements (columns). Following are the characteristics of a data frame:
- The column names should be non-empty
- The data stored in a data frame can be of numeric, factor (discussed later), or character type
- Each column should contain same number of data items
Creating a Data Frame
We can use a data.frame() function to create a data frame from a set of vectors:
Name <- c("James", "Linda", "Stacy", "Mary", "Tom", "Anna", "Bob", "Jeniffer", "Lucas", "Amy")
Age <- c(22, 56, 34, 48, 19, 31, 68, 72, 39, 52)
State <- c("California", "New York", "New York", "Michigan", "Texas", "Ohio", "Arizona", "Florida", "Nebraska", "Indiana")
Salary <- c(30000, 96500, 72000, 54300, 25000, 61000, 74700, 40000, 83000, 92400)
df <- data.frame(Name, Age, State, Salary)
print(df)
#> Name Age State Salary
#> 1 James 22 California 30000
#> 2 Linda 56 New York 96500
#> 3 Stacy 34 New York 72000
#> 4 Mary 48 Michigan 54300
#> 5 Tom 19 Texas 25000
#> 6 Anna 31 Ohio 61000
#> 7 Bob 68 Arizona 74700
#> 8 Jeniffer 72 Florida 40000
#> 9 Lucas 39 Nebraska 83000
#> 10 Amy 52 Indiana 92400We can use srt() function to check a structure of the data frame and summary() to get a quick summary on the variables stored in this data frame:
# Data frame structure
str(df)
#> 'data.frame': 10 obs. of 4 variables:
#> $ Name : chr "James" "Linda" "Stacy" "Mary" ...
#> $ Age : num 22 56 34 48 19 31 68 72 39 52
#> $ State : chr "California" "New York" "New York" "Michigan" ...
#> $ Salary: num 30000 96500 72000 54300 25000 61000 74700 40000 83000 92400
# Summary of variables stored in a data frame
summary(df)
#> Name Age State
#> Length:10 Min. :19.00 Length:10
#> Class :character 1st Qu.:31.75 Class :character
#> Mode :character Median :43.50 Mode :character
#> Mean :44.10
#> 3rd Qu.:55.00
#> Max. :72.00
#> Salary
#> Min. :25000
#> 1st Qu.:43575
#> Median :66500
#> Mean :62890
#> 3rd Qu.:80925
#> Max. :96500head() and tail() functions allow us to view the first and last 6 rows of a data frame, respectively (number of rows can be specified as an argument):
head(df)
#> Name Age State Salary
#> 1 James 22 California 30000
#> 2 Linda 56 New York 96500
#> 3 Stacy 34 New York 72000
#> 4 Mary 48 Michigan 54300
#> 5 Tom 19 Texas 25000
#> 6 Anna 31 Ohio 61000
tail(df)
#> Name Age State Salary
#> 5 Tom 19 Texas 25000
#> 6 Anna 31 Ohio 61000
#> 7 Bob 68 Arizona 74700
#> 8 Jeniffer 72 Florida 40000
#> 9 Lucas 39 Nebraska 83000
#> 10 Amy 52 Indiana 92400
head(df, 3)
#> Name Age State Salary
#> 1 James 22 California 30000
#> 2 Linda 56 New York 96500
#> 3 Stacy 34 New York 72000Use colnames() function to get a list of column names or to change the names of columns in a data frame:
colnames(df)
#> [1] "Name" "Age" "State" "Salary"
colnames(df)[1] <- "NAME"
print(df)
#> NAME Age State Salary
#> 1 James 22 California 30000
#> 2 Linda 56 New York 96500
#> 3 Stacy 34 New York 72000
#> 4 Mary 48 Michigan 54300
#> 5 Tom 19 Texas 25000
#> 6 Anna 31 Ohio 61000
#> 7 Bob 68 Arizona 74700
#> 8 Jeniffer 72 Florida 40000
#> 9 Lucas 39 Nebraska 83000
#> 10 Amy 52 Indiana 92400
colnames(df) <- c("NAME", "AGE", "STATE", "SALARY")
print(df)
#> NAME AGE STATE SALARY
#> 1 James 22 California 30000
#> 2 Linda 56 New York 96500
#> 3 Stacy 34 New York 72000
#> 4 Mary 48 Michigan 54300
#> 5 Tom 19 Texas 25000
#> 6 Anna 31 Ohio 61000
#> 7 Bob 68 Arizona 74700
#> 8 Jeniffer 72 Florida 40000
#> 9 Lucas 39 Nebraska 83000
#> 10 Amy 52 Indiana 92400Subsetting/Extracting Elements/Columns/Rows from a Data Frame
We can use [], $, [[]] to extract elements, columns, or rows from a data frame (uses the same rule as matrices):
# Extracting the first column using its position; output: data frame
df[1]
#> NAME
#> 1 James
#> 2 Linda
#> 3 Stacy
#> 4 Mary
#> 5 Tom
#> 6 Anna
#> 7 Bob
#> 8 Jeniffer
#> 9 Lucas
#> 10 Amy
# Extracting the first column using its position; output: vector
df[[1]]
#> [1] "James" "Linda" "Stacy" "Mary" "Tom"
#> [6] "Anna" "Bob" "Jeniffer" "Lucas" "Amy"
# Extracting a column using its name; output: data frame
df["AGE"]
#> AGE
#> 1 22
#> 2 56
#> 3 34
#> 4 48
#> 5 19
#> 6 31
#> 7 68
#> 8 72
#> 9 39
#> 10 52
df[c("AGE", "STATE")]
#> AGE STATE
#> 1 22 California
#> 2 56 New York
#> 3 34 New York
#> 4 48 Michigan
#> 5 19 Texas
#> 6 31 Ohio
#> 7 68 Arizona
#> 8 72 Florida
#> 9 39 Nebraska
#> 10 52 Indiana
# Extracting a column using its name; output: vector
df[["AGE"]]
#> [1] 22 56 34 48 19 31 68 72 39 52
# Extracting a column using its name and '$' operator; output: vector
df$AGE
#> [1] 22 56 34 48 19 31 68 72 39 52
# Extracting a single element
df[1, 2]
#> [1] 22
# Extracting multiple elements
df[1:4, c(1, 3)]
#> NAME STATE
#> 1 James California
#> 2 Linda New York
#> 3 Stacy New York
#> 4 Mary Michigan
# Extracting the first row only
df[1, ]
#> NAME AGE STATE SALARY
#> 1 James 22 California 30000
# Extracting the third column only
df[, 3]
#> [1] "California" "New York" "New York" "Michigan"
#> [5] "Texas" "Ohio" "Arizona" "Florida"
#> [9] "Nebraska" "Indiana"
# Extracting all elements except the second column
df[, -2]
#> NAME STATE SALARY
#> 1 James California 30000
#> 2 Linda New York 96500
#> 3 Stacy New York 72000
#> 4 Mary Michigan 54300
#> 5 Tom Texas 25000
#> 6 Anna Ohio 61000
#> 7 Bob Arizona 74700
#> 8 Jeniffer Florida 40000
#> 9 Lucas Nebraska 83000
#> 10 Amy Indiana 92400
# Extracting elements using logical operator
df[df$AGE < 50, ]
#> NAME AGE STATE SALARY
#> 1 James 22 California 30000
#> 3 Stacy 34 New York 72000
#> 4 Mary 48 Michigan 54300
#> 5 Tom 19 Texas 25000
#> 6 Anna 31 Ohio 61000
#> 9 Lucas 39 Nebraska 83000Assigning New Value to Data Frame Elements
Like in matrices, we can use <- operator to assign values:
df[1, 4] <- 100000
print(df)
#> NAME AGE STATE SALARY
#> 1 James 22 California 100000
#> 2 Linda 56 New York 96500
#> 3 Stacy 34 New York 72000
#> 4 Mary 48 Michigan 54300
#> 5 Tom 19 Texas 25000
#> 6 Anna 31 Ohio 61000
#> 7 Bob 68 Arizona 74700
#> 8 Jeniffer 72 Florida 40000
#> 9 Lucas 39 Nebraska 83000
#> 10 Amy 52 Indiana 92400Adding/Removing Columns/Rows
# Adding a new column using `$` operator
df$EXPERIENCE = c(2, 30, 10, 22, 1, 12, 40, 55, 15, 22)
print(df)
#> NAME AGE STATE SALARY EXPERIENCE
#> 1 James 22 California 100000 2
#> 2 Linda 56 New York 96500 30
#> 3 Stacy 34 New York 72000 10
#> 4 Mary 48 Michigan 54300 22
#> 5 Tom 19 Texas 25000 1
#> 6 Anna 31 Ohio 61000 12
#> 7 Bob 68 Arizona 74700 40
#> 8 Jeniffer 72 Florida 40000 55
#> 9 Lucas 39 Nebraska 83000 15
#> 10 Amy 52 Indiana 92400 22
# Adding a new column using `cbind()` function
Sex <- c("Male", "Female", "Female", "Female", "Male", "Female", "Male", "Female", "Male", "Female")
df_2 <- cbind(df, Sex)
print(df_2)
#> NAME AGE STATE SALARY EXPERIENCE Sex
#> 1 James 22 California 100000 2 Male
#> 2 Linda 56 New York 96500 30 Female
#> 3 Stacy 34 New York 72000 10 Female
#> 4 Mary 48 Michigan 54300 22 Female
#> 5 Tom 19 Texas 25000 1 Male
#> 6 Anna 31 Ohio 61000 12 Female
#> 7 Bob 68 Arizona 74700 40 Male
#> 8 Jeniffer 72 Florida 40000 55 Female
#> 9 Lucas 39 Nebraska 83000 15 Male
#> 10 Amy 52 Indiana 92400 22 Female
# Adding a new row using `rbind()` function
new_obs <- data.frame(NAME = "Jack", AGE = 41, STATE = "Texas", SALARY = 150000, EXPERIENCE = 20)
df_3 <- rbind(df, new_obs)
print(df_3)
#> NAME AGE STATE SALARY EXPERIENCE
#> 1 James 22 California 100000 2
#> 2 Linda 56 New York 96500 30
#> 3 Stacy 34 New York 72000 10
#> 4 Mary 48 Michigan 54300 22
#> 5 Tom 19 Texas 25000 1
#> 6 Anna 31 Ohio 61000 12
#> 7 Bob 68 Arizona 74700 40
#> 8 Jeniffer 72 Florida 40000 55
#> 9 Lucas 39 Nebraska 83000 15
#> 10 Amy 52 Indiana 92400 22
#> 11 Jack 41 Texas 150000 20
# Removing columns from a data frame using a `c()` function
df_4 <- df_3[, -4]
print(df_4)
#> NAME AGE STATE EXPERIENCE
#> 1 James 22 California 2
#> 2 Linda 56 New York 30
#> 3 Stacy 34 New York 10
#> 4 Mary 48 Michigan 22
#> 5 Tom 19 Texas 1
#> 6 Anna 31 Ohio 12
#> 7 Bob 68 Arizona 40
#> 8 Jeniffer 72 Florida 55
#> 9 Lucas 39 Nebraska 15
#> 10 Amy 52 Indiana 22
#> 11 Jack 41 Texas 20
# Removing columns by assigning a `NULL` value
df_3[c("AGE", "SALARY")] <- NULL
print(df_3)
#> NAME STATE EXPERIENCE
#> 1 James California 2
#> 2 Linda New York 30
#> 3 Stacy New York 10
#> 4 Mary Michigan 22
#> 5 Tom Texas 1
#> 6 Anna Ohio 12
#> 7 Bob Arizona 40
#> 8 Jeniffer Florida 55
#> 9 Lucas Nebraska 15
#> 10 Amy Indiana 22
#> 11 Jack Texas 20
# Removing rows from a data frame
df_3[-11, ]
#> NAME STATE EXPERIENCE
#> 1 James California 2
#> 2 Linda New York 30
#> 3 Stacy New York 10
#> 4 Mary Michigan 22
#> 5 Tom Texas 1
#> 6 Anna Ohio 12
#> 7 Bob Arizona 40
#> 8 Jeniffer Florida 55
#> 9 Lucas Nebraska 15
#> 10 Amy Indiana 22Re-ordering Columns/Rows in a Data Frame
In order to re-order columns in a data frame, pass a vector with the desired order of columns using [] operator:
# Re-ordering columns in a data frame
df_3[, c("EXPERIENCE", "NAME", "STATE")]
#> EXPERIENCE NAME STATE
#> 1 2 James California
#> 2 30 Linda New York
#> 3 10 Stacy New York
#> 4 22 Mary Michigan
#> 5 1 Tom Texas
#> 6 12 Anna Ohio
#> 7 40 Bob Arizona
#> 8 55 Jeniffer Florida
#> 9 15 Lucas Nebraska
#> 10 22 Amy Indiana
#> 11 20 Jack Texas
# Re-ordering rows in a data frame
df_3[order(df_3$EXPERIENCE), ] # Ascending order
#> NAME STATE EXPERIENCE
#> 5 Tom Texas 1
#> 1 James California 2
#> 3 Stacy New York 10
#> 6 Anna Ohio 12
#> 9 Lucas Nebraska 15
#> 11 Jack Texas 20
#> 4 Mary Michigan 22
#> 10 Amy Indiana 22
#> 2 Linda New York 30
#> 7 Bob Arizona 40
#> 8 Jeniffer Florida 55
df_3[order(df_3$EXPERIENCE, decreasing = TRUE), ] # Descending order
#> NAME STATE EXPERIENCE
#> 8 Jeniffer Florida 55
#> 7 Bob Arizona 40
#> 2 Linda New York 30
#> 4 Mary Michigan 22
#> 10 Amy Indiana 22
#> 11 Jack Texas 20
#> 9 Lucas Nebraska 15
#> 6 Anna Ohio 12
#> 3 Stacy New York 10
#> 1 James California 2
#> 5 Tom Texas 1Factors
Factor is a special type of vectors used to categorize data and store it as levels. It can store both character and integer types of data. They are useful in the columns which have a limited number of unique values. For instance, Female or Male, TRUE or FALSE etc.
Creating Factors
Use factor() function in R to create a factor:
vec1 <- c("YES", "NO", "YES", "YES", "YES", "NO", "NO", "YES")
print(vec1)
#> [1] "YES" "NO" "YES" "YES" "YES" "NO" "NO" "YES"
is.factor(vec1)
#> [1] FALSE
fac1 <- factor(vec1)
print(fac1)
#> [1] YES NO YES YES YES NO NO YES
#> Levels: NO YES
is.factor(fac1)
#> [1] TRUE
#############
vec2 <- c("Jazz", "Rock", "Classic", "Pop", "Classic", "Jazz", "Jazz", "Rock")
fac2 <- factor(vec2)
print(fac2)
#> [1] Jazz Rock Classic Pop Classic Jazz Jazz
#> [8] Rock
#> Levels: Classic Jazz Pop Rock
#############
vec3 <- c(1, 2, 3, 2, 2, 3, 1, 3, 2, 3, 1, 1)
fac3 <- factor(vec3)
print(fac3)
#> [1] 1 2 3 2 2 3 1 3 2 3 1 1
#> Levels: 1 2 3To create an ordered factor, use order argument inside a factor() function:
fac4 <- factor(vec3, ordered = TRUE)
print(fac4)
#> [1] 1 2 3 2 2 3 1 3 2 3 1 1
#> Levels: 1 < 2 < 3You can convert a numeric variable into a factor by dividing it into intervals (segments) using a cut() function:
age_factored <- cut(df_4$AGE, 3)
print(age_factored)
#> [1] (18.9,36.7] (54.3,72.1] (18.9,36.7] (36.7,54.3]
#> [5] (18.9,36.7] (18.9,36.7] (54.3,72.1] (54.3,72.1]
#> [9] (36.7,54.3] (36.7,54.3] (36.7,54.3]
#> Levels: (18.9,36.7] (36.7,54.3] (54.3,72.1]You can even add labels for these groups:
Factor Levels
Use levels() function to see what levels a factor variable has:
levels(fac1)
#> [1] "NO" "YES"
levels(fac2)
#> [1] "Classic" "Jazz" "Pop" "Rock"
levels(fac3)
#> [1] "1" "2" "3"
levels(fac4)
#> [1] "1" "2" "3"You can also set the levels by adding the levels argument inside the factor() function:
vec2 <- c("Jazz", "Rock", "Classic", "Pop", "Classic", "Jazz", "Jazz", "Rock")
fac5 <- factor(vec2, levels = c("Classic", "Jazz", "Pop", "Rock", "Other"))
print(fac5)
#> [1] Jazz Rock Classic Pop Classic Jazz Jazz
#> [8] Rock
#> Levels: Classic Jazz Pop Rock OtherYou can change the order of levels by passing the level argument to a factor function with the desired order of levels:
Accessing Factor Elements
You can access elements stored in a factor the same way as it was done with vectors, using [].
Assigning/Changing Values to/of Factor Elements
You can assign a new value to factor elements using the assignment sign <-:
print(fac2)
#> [1] Jazz Rock Classic Pop Classic Jazz Jazz
#> [8] Rock
#> Levels: Classic Jazz Pop Rock
fac2[1] <- "Classic"
print(fac2)
#> [1] Classic Rock Classic Pop Classic Jazz Jazz
#> [8] Rock
#> Levels: Classic Jazz Pop RockBut what happens when you assign a new value that is not already specified in the factor levels?
print(fac2)
#> [1] Classic Rock Classic Pop Classic Jazz Jazz
#> [8] Rock
#> Levels: Classic Jazz Pop Rock
fac2[1] <- "Opera"
#> Warning in `[<-.factor`(`*tmp*`, 1, value = "Opera"):
#> invalid factor level, NA generated
print(fac2)
#> [1] <NA> Rock Classic Pop Classic Jazz Jazz
#> [8] Rock
#> Levels: Classic Jazz Pop RockIt will generate NA, because there is no Opera option among the levels specified in the variable. To add this value, we first need to add it to the levels:
Generating Factors
We can generate factor levels by using the gl() function. It takes two integers as input which indicates how many levels and how many times each level.
Converting Numeric Data into Factors and Back
# Converting numeric data into factors using as.factor() function
vec3 <- c(0, 1, 2, 2, 1, 0, 2, 1, 0)
fac9 <- as.factor(vec3)
print(fac9)
#> [1] 0 1 2 2 1 0 2 1 0
#> Levels: 0 1 2
# Converting factors back into numeric data using as.numeric() function (part I)
as.numeric(fac9)
#> [1] 1 2 3 3 2 1 3 2 1
# Converting factors back into numeric data using as.numeric() function (part II)
as.numeric(levels(fac9)[fac9])
#> [1] 0 1 2 2 1 0 2 1 0