Generally, while doing programming in any programming language, you need to use various variables to store various information. Variables are nothing but reserved memory locations to store values. This means that, when you create a variable you reserve some space in memory.

You may like to store information of various data types like character, wide character, integer, floating point, double floating point, Boolean etc. Based on the data type of a variable, the operating system allocates memory and decides what can be stored in the reserved memory.

In contrast to other programming languages like C and java in R, the variables are not declared as some data type. The variables are assigned with R-Objects and the data type of the R-object becomes the data type of the variable. There are many types of R-objects. The frequently used ones are −

Vectors
Lists
Matrices
Arrays
Factors
Data Frames

The simplest of these objects is the vector object and there are six data types of these atomic vectors, also termed as six classes of vectors. The other R-Objects are built upon the atomic vectors.

Data Type	Example	Verify
Logical	TRUE, FALSE	v <- TRUE print(class(v)) it produces the following result − [1] “logical”
Numeric	12.3, 5, 999	v <- 23.5 print(class(v)) it produces the following result − [1] “numeric”
Integer	2L, 34L, 0L	v <- 2L print(class(v)) it produces the following result − [1] “integer”
Complex	3 + 2i	v <- 2+5i print(class(v)) it produces the following result − [1] “complex”
Character	‘a’ , ‘”good”, “TRUE”, ‘23.4’	v <- “TRUE” print(class(v)) it produces the following result − [1] “character”
Raw	“Hello” is stored as 48 65 6c 6c 6f	v <- charToRaw(“Hello”) print(class(v)) it produces the following result − [1] “raw”

In R programming, the very basic data types are the R-objects called vectors which hold elements of different classes as shown above. Please note in R the number of classes is not confined to only the above six types. For example, we can use many atomic vectors and create an array whose class will become array.

Vectors

When you want to create vector with more than one element, you should use c() function which means to combine the elements into a vector.

# Create a vector.

apple <- c('red','green',"yellow")

print(apple)

# Get the class of the vector.

print(class(apple))

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

[1] "red"    "green"  "yellow"

[1] "character"

Lists

A list is an R-object which can contain many different types of elements inside it like vectors, functions and even another list inside it.

# Create a list.
list1 <- list(c(2,5,3),21.3,sin)
# Print the list.
print(list1)
When we execute the above code, it produces the following result −
[[1]]
[1] 2 5 3
[[2]]
[1] 21.3
[[3]]

function (x)  .Primitive("sin")

Matrices

A matrix is a two-dimensional rectangular data set. It can be created using a vector input to the matrix function.

# Create a matrix.
M = matrix( c('a','a','b','c','b','a'), nrow = 2, ncol = 3, byrow = TRUE)
print(M)
When we execute the above code, it produces the following result −
[,1] [,2] [,3]
[1,] "a"  "a"  "b"
[2,] "c"  "b"  "a"

Arrays

While matrices are confined to two dimensions, arrays can be of any number of dimensions. The array function takes a dim attribute which creates the required number of dimension. In the below example we create an array with two elements which are 3×3 matrices each.

# Create an array.

a <- array(c('green','yellow'),dim = c(3,3,2))
print(a)
When we execute the above code, it produces the following result −
, , 1

[,1]     [,2]     [,3]
[1,] "green"  "yellow" "green"
[2,] "yellow" "green"  "yellow"
[3,] "green"  "yellow" "green"
, , 2

[,1]     [,2]     [,3]
[1,] "yellow" "green"  "yellow"
[2,] "green"  "yellow" "green"
[3,] "yellow" "green"  "yellow"

Factors

Factors are the r-objects which are created using a vector. It stores the vector along with the distinct values of the elements in the vector as labels. The labels are always character irrespective of whether it is numeric or character or Boolean etc. in the input vector. They are useful in statistical modeling.

Factors are created using the factor() function. The nlevels functions gives the count of levels.

# Create a vector.
apple_colors <- c('green','green','yellow','red','red','red','green')

# Create a factor object.
factor_apple <- factor(apple_colors)

# Print the factor.
print(factor_apple)
print(nlevels(factor_apple))
When we execute the above code, it produces the following result −
[1] green  green  yellow red    red    red    green
Levels: green red yellow
[1] 3

Data Frames

Data frames are tabular data objects. Unlike a matrix in data frame each column can contain different modes of data. The first column can be numeric while the second column can be character and third column can be logical. It is a list of vectors of equal length.

Data Frames are created using the data.frame() function.

# Create the data frame.
BMI <-    data.frame(
gender = c("Male", "Male","Female"),
height = c(152, 171.5, 165),
weight = c(81,93, 78),
Age = c(42,38,26)
)

print(BMI)

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

gender height weight Age
1   Male  152.0     81  42
2   Male  171.5     93  38
3 Female  165.0     78  26

So, this brings us to the end of blog. This Tecklearn ‘Data Types 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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What you will Learn in this Course?

Introduction to Data Science

Need for Data Science
What is Data Science
Life Cycle of Data Science
Applications of Data Science
Introduction to Big Data
Introduction to Machine Learning
Introduction to Deep Learning
Introduction to R&R-Studio
Project Based Data Science

Introduction to R

Introduction to R
Data Exploration
Operators in R
Inbuilt Functions in R
Flow Control Statements & User Defined Functions
Data Structures in R

Data Manipulation

Need for Data Manipulation
Introduction to dplyr package
Select (), filter(), mutate(), sample_n(), sample_frac() & count() functions
Getting summarized results with the summarise() function,
Combining different functions with the pipe operator
Implementing sql like operations with sqldf()

Visualization of Data

Loading different types of datasets in R
Arranging the data
Plotting the graphs

Introduction to Statistics

Types of Data
Probability
Correlation and Co-variance
Hypothesis Testing
Standardization and Normalization

Introduction to Machine Learning

What is Machine Learning?
Machine Learning Use-Cases
Machine Learning Process Flow
Machine Learning Categories
Supervised Learning algorithm: Linear Regression and Logistic Regression

Logistic Regression

Intro to Logistic Regression
Simple Logistic Regression in R
Multiple Logistic Regression in R
Confusion Matrix
ROC Curve

Classification Techniques

What are classification and its use cases?
What is Decision Tree?
Algorithm for Decision Tree Induction
Creating a Perfect Decision Tree
Confusion Matrix
What is Random Forest?
What is Naive Bayes?
Support Vector Machine: Classification

Decision Tree

Decision Tree in R
Information Gain
Gini Index
Pruning

Recommender Engines

What is Association Rules & its use cases?
What is Recommendation Engine & it’s working?
Types of Recommendations
User-Based Recommendation
Item-Based Recommendation
Difference: User-Based and Item-Based Recommendation
Recommendation use cases

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What is Time Series data?
Time Series variables
Different components of Time Series data
Visualize the data to identify Time Series Components
Implement ARIMA model for forecasting
Exponential smoothing models
Identifying different time series scenario based on which different Exponential Smoothing model can be applied

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