- R Tutorial
- R - Home
- R - Overview
- R - Environment Setup
- R - Basic Syntax
- R - Data Types
- R - Variables
- R - Operators
- R - Decision Making
- R - Loops
- R - Functions
- R - Strings
- R - Vectors
- R - Lists
- R - Matrices
- R - Arrays
- R - Factors
- R - Data Frames
- R - Packages
- R - Data Reshaping

- R Data Interfaces
- R - CSV Files
- R - Excel Files
- R - Binary Files
- R - XML Files
- R - JSON Files
- R - Web Data
- R - Database

- R Charts & Graphs
- R - Pie Charts
- R - Bar Charts
- R - Boxplots
- R - Histograms
- R - Line Graphs
- R - Scatterplots

- R Statistics Examples
- R - Mean, Median & Mode
- R - Linear Regression
- R - Multiple Regression
- R - Logistic Regression
- R - Normal Distribution
- R - Binomial Distribution
- R - Poisson Regression
- R - Analysis of Covariance
- R - Time Series Analysis
- R - Nonlinear Least Square
- R - Decision Tree
- R - Random Forest
- R - Survival Analysis
- R - Chi Square Tests

- R Useful Resources
- R - Interview Questions
- R - Quick Guide
- R - Useful Resources
- R - Discussion

# R - Data Types

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 3x3 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