Arrays are the R data objects which can store data in more than two dimensions. For example − If we create an array of dimension (2, 3, 4) then it creates 4 rectangular matrices each with 2 rows and 3 columns. Arrays can store only data type.

An array is created using the array() function. It takes vectors as input and uses the values in the dim parameter to create an array.

Example

The following example creates an array of two 3×3 matrices each with 3 rows and 3 columns.

# Create two vectors of different lengths.
vector1 <- c(5,9,3)
vector2 <- c(10,11,12,13,14,15)
# Take these vectors as input to the array.
result <- array(c(vector1,vector2),dim = c(3,3,2))
print(result)
When we execute the above code, it produces the following result −
, , 1

     [,1] [,2] [,3]

[1,]    5   10   13

[2,]    9   11   14

[3,]    3   12   15
, , 2

     [,1] [,2] [,3]

[1,]    5   10   13

[2,]    9   11   14

[3,]    3   12   15

Naming Columns and Rows

We can give names to the rows, columns and matrices in the array by using the dimnames parameter.

# Create two vectors of different lengths.
vector1 <- c(5,9,3)
vector2 <- c(10,11,12,13,14,15)
column.names <- c("COL1","COL2","COL3")
row.names <- c("ROW1","ROW2","ROW3")
matrix.names <- c("Matrix1","Matrix2")

# Take these vectors as input to the array.
result <- array(c(vector1,vector2),dim = c(3,3,2),dimnames = list(row.names,column.names,
   matrix.names))
print(result)
When we execute the above code, it produces the following result −
, , Matrix1

     COL1 COL2 COL3

ROW1    5   10   13

ROW2    9   11   14

ROW3    3   12   15

, , Matrix2

     COL1 COL2 COL3

ROW1    5   10   13

ROW2    9   11   14

ROW3    3   12   15

Accessing Array Elements

# Create two vectors of different lengths.
vector1 <- c(5,9,3)
vector2 <- c(10,11,12,13,14,15)
column.names <- c("COL1","COL2","COL3")
row.names <- c("ROW1","ROW2","ROW3")
matrix.names <- c("Matrix1","Matrix2")

# Take these vectors as input to the array.
result <- array(c(vector1,vector2),dim = c(3,3,2),dimnames = list(row.names,
   column.names, matrix.names))
# Print the third row of the second matrix of the array.
print(result[3,,2])
# Print the element in the 1st row and 3rd column of the 1st matrix.
print(result[1,3,1])

# Print the 2nd Matrix.
print(result[,,2])
When we execute the above code, it produces the following result −
COL1 COL2 COL3

   3   12   15

[1] 13

     COL1 COL2 COL3

ROW1    5   10   13

ROW2    9   11   14

ROW3    3   12   15

Manipulating Array Elements

As array is made up matrices in multiple dimensions, the operations on elements of array are carried out by accessing elements of the matrices.

# Create two vectors of different lengths.
vector1 <- c(5,9,3)
vector2 <- c(10,11,12,13,14,15)

# Take these vectors as input to the array.
array1 <- array(c(vector1,vector2),dim = c(3,3,2))

# Create two vectors of different lengths.
vector3 <- c(9,1,0)
vector4 <- c(6,0,11,3,14,1,2,6,9)
array2 <- array(c(vector1,vector2),dim = c(3,3,2))
# create matrices from these arrays.
matrix1 <- array1[,,2]
matrix2 <- array2[,,2]

# Add the matrices.
result <- matrix1+matrix2
print(result)

When we execute the above code, it produces the following result −

     [,1] [,2] [,3]

[1,]   10   20   26

[2,]   18   22   28

[3,]    6   24   30

Calculations Across Array Elements

We can do calculations across the elements in an array using the apply() function.

Syntax

apply(x, margin, fun)

Following is the description of the parameters used −

x is an array.
margin is the name of the data set used.
fun is the function to be applied across the elements of the array.

Example

We use the apply() function below to calculate the sum of the elements in the rows of an array across all the matrices.

# Create two vectors of different lengths.
vector1 <- c(5,9,3)
vector2 <- c(10,11,12,13,14,15)
# Take these vectors as input to the array.
new.array <- array(c(vector1,vector2),dim = c(3,3,2))
print(new.array)
# Use apply to calculate the sum of the rows across all the matrices.
result <- apply(new.array, c(1), sum)
print(result)
When we execute the above code, it produces the following result −
, , 1

     [,1] [,2] [,3]

[1,]    5   10   13

[2,]    9   11   14

[3,]    3   12   15

, , 2

     [,1] [,2] [,3]

[1,]    5   10   13

[2,]    9   11   14

[3,]    3   12   15

[1] 56 68 60

R – Factors

Factors are the data objects which are used to categorize the data and store it as levels. They can store both strings and integers. They are useful in the columns which have a limited number of unique values. Like “Male, “Female” and True, False etc. They are useful in data analysis for statistical modeling.

Factors are created using the factor () function by taking a vector as input.

Example

# Create a vector as input.
data <- c("East","West","East","North","North","East","West","West","West","East","North")
print(data)
print(is.factor(data))
# Apply the factor function.
factor_data <- factor(data)
print(factor_data)
print(is.factor(factor_data))
When we execute the above code, it produces the following result −

[1] "East"  "West"  "East"  "North" "North" "East"  "West"  "West"  "West"  "East" "North"

[1] FALSE

[1] East  West  East  North North East  West  West  West  East  North

Levels: East North West

[1] TRUE

Factors in Data Frame

On creating any data frame with a column of text data, R treats the text column as categorical data and creates factors on it.

# Create the vectors for data frame.
height <- c(132,151,162,139,166,147,122)
weight <- c(48,49,66,53,67,52,40)
gender <- c("male","male","female","female","male","female","male")

# Create the data frame.
input_data <- data.frame(height,weight,gender)
print(input_data)
# Test if the gender column is a factor.
print(is.factor(input_data$gender))
# Print the gender column so see the levels.
print(input_data$gender)
When we execute the above code, it produces the following result −

  height weight gender

1    132     48   male

2    151     49   male

3    162     66 female

4    139     53 female

5    166     67   male

6    147     52 female

7    122     40   male

[1] TRUE

[1] male   male   female female male   female male 

Levels: female male

Changing the Order of Levels

The order of the levels in a factor can be changed by applying the factor function again with new order of the levels.

data <- c("East","West","East","North","North","East","West",
   "West","West","East","North")
# Create the factors
factor_data <- factor(data)
print(factor_data)
# Apply the factor function with required order of the level.
new_order_data <- factor(factor_data,levels = c("East","West","North"))
print(new_order_data)
When we execute the above code, it produces the following result −

 [1] East  West  East  North North East  West  West  West  East  North
Levels: East North West

 [1] East  West  East  North North East  West  West  West  East  North

Levels: East West North

Generating Factor Levels

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.

Syntax

gl(n, k, labels)

Following is the description of the parameters used −

n is a integer giving the number of levels.
k is a integer giving the number of replications.
labels is a vector of labels for the resulting factor levels.

Example

v <- gl(3, 4, labels = c("Tampa", "Seattle","Boston"))
print(v)
When we execute the above code, it produces the following result −
Tampa   Tampa   Tampa   Tampa   Seattle Seattle Seattle Seattle Boston
[10] Boston  Boston  Boston
Levels: Tampa Seattle Boston

So, this brings us to the end of blog. This Tecklearn ‘Arrays and Factors in R Language’ blog helps you with commonly asked questions if you are looking out for a job in Data Science. If you wish to learn R Language and build a career in Data Science domain, then check out our interactive, Data Science using R Language Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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