R – Overview

R is a programming language and software environment for statistical analysis, graphics representation and reporting. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team.

The core of R is an interpreted computer language which allows branching and looping as well as modular programming using functions. R allows integration with the procedures written in the C, C++, .Net, Python or FORTRAN languages for efficiency.

R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows and Mac.

R is free software distributed under a GNU-style copy left, and an official part of the GNU project called GNU S.

Evolution of R

R was initially written by Ross Ihaka and Robert Gentleman at the Department of Statistics of the University of Auckland in Auckland, New Zealand. R made its first appearance in 1993.

• A large group of individuals has contributed to R by sending code and bug reports.
• Since mid-1997 there has been a core group (the “R Core Team”) who can modify the R source code archive.

Features of R

As stated earlier, R is a programming language and software environment for statistical analysis, graphics representation and reporting. The following are the important features of R −

• R is a well-developed, simple and effective programming language which includes conditionals, loops, user defined recursive functions and input and output facilities.
• R has an effective data handling and storage facility,
• R provides a suite of operators for calculations on arrays, lists, vectors and matrices.
• R provides a large, coherent and integrated collection of tools for data analysis.
• R provides graphical facilities for data analysis and display either directly at the computer or printing at the papers.

As a conclusion, R is world’s most widely used statistics programming language. It’s the # 1 choice of data scientists and supported by a vibrant and talented community of contributors. R is taught in universities and deployed in mission critical business applications. This tutorial will teach you R programming along with suitable examples in simple and easy steps.

R – Environment Setup

Try it Option Online

You really do not need to set up your own environment to start learning R programming language. Reason is very simple, we already have set up R Programming environment online, so that you can compile and execute all the available examples online at the same time when you are doing your theory work. This gives you confidence in what you are reading and to check the result with different options. Feel free to modify any example and execute it online.

Try the following example using Try it option at the website available at the top right corner of the below sample code box −

# Print Hello World. 
print("Hello World") 
 
# Add two numbers. 
print(23.9 + 11.6)

For most of the examples given in this tutorial, you will find Try itoption at the website, so just make use of it and enjoy your learning.

Local Environment Setup

If you are still willing to set up your environment for R, you can follow the steps given below.

Windows Installation

You can download the Windows installer version of R from R-3.2.2 for Windows (32/64 bit) and save it in a local directory.

As it is a Windows installer (.exe) with a name “R-version-win.exe”. You can just double click and run the installer accepting the default settings. If your Windows is 32-bit version, it installs the 32-bit version. But if your windows is 64-bit, then it installs both the 32-bit and 64-bit versions.

After installation you can locate the icon to run the Program in a directory structure “R\R3.2.2\bin\i386\Rgui.exe” under the Windows Program Files. Clicking this icon brings up the R-GUI which is the R console to do R Programming.

Linux Installation

R is available as a binary for many versions of Linux at the location R Binaries.

The instruction to install Linux varies from flavor to flavor. These steps are mentioned under each type of Linux version in the mentioned link. However, if you are in a hurry, then you can use yum command to install R as follows −

$ yum install R

Above command will install core functionality of R programming along with standard packages, still you need additional package, then you can launch R prompt as follows −

$ R

R version 3.2.0 (2015-04-16) -- "Full of  Ingredients"          
Copyright (C) 2015 The R Foundation for Statistical Computing
Platform: x86_64-redhat-linux-gnu (64-bit)
        
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
            
R is a collaborative project with many  contributors.                    
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
       
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

>  

Now you can use install command at R prompt to install the required package. For example, the following command will install plotrix package which is required for 3D charts.

> install("plotrix")

R – Basic Syntax

As a convention, we will start learning R programming by writing a “Hello, World!” program. Depending on the needs, you can program either at R command prompt or you can use an R script file to write your program. Let’s check both one by one.

R Command Prompt

Once you have R environment setup, then it’s easy to start your R command prompt by just typing the following command at your command prompt −

$ R

This will launch R interpreter and you will get a prompt > where you can start typing your program as follows −

> myString <- "Hello, World!"
> print ( myString)
[1] "Hello, World!"

Here first statement defines a string variable myString, where we assign a string “Hello, World!” and then next statement print() is being used to print the value stored in variable myString.

R Script File

Usually, you will do your programming by writing your programs in script files and then you execute those scripts at your command prompt with the help of R interpreter called Rscript. So let’s start with writing following code in a text file called test.R as under −

# My first program in R Programming
myString <- "Hello, World!"

print ( myString)

Save the above code in a file test.R and execute it at Linux command prompt as given below. Even if you are using Windows or other system, syntax will remain same.

$ Rscript test.R 

When we run the above program, it produces the following result.

[1] "Hello, World!"

Comments

Comments are like helping text in your R program and they are ignored by the interpreter while executing your actual program. Single comment is written using # in the beginning of the statement as follows −

# My first program in R Programming

R does not support multi-line comments but you can perform a trick which is something as follows −

if(FALSE) {
   "This is a demo for multi-line comments and it should be put inside either a single
      OR double quote"
}

myString <- "Hello, World!"
print ( myString)

Though above comments will be executed by R interpreter, they will not interfere with your actual program. You should put such comments inside, either single or double quote.

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.

v <- TRUE 
print(class(v))

[1] "logical" 

v <- 23.5
print(class(v))

[1] "numeric"

v <- 2L
print(class(v))

[1] "integer"

v <- 2+5i
print(class(v))

[1] "complex"

v <- "TRUE"
print(class(v))

[1] "character"

v <- charToRaw("Hello")
print(class(v))

[1] "raw" 

In R programming, the very basic data types are the R-objects called vectorswhich 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 usec() 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    yellow green 
Levels: green red yellow
# applying the nlevels function we can know the number of distinct values
[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  

R – Variables

A variable provides us with named storage that our programs can manipulate. A variable in R can store an atomic vector, group of atomic vectors or a combination of many Robjects. A valid variable name consists of letters, numbers and the dot or underline characters. The variable name starts with a letter or the dot not followed by a number.

Variable Assignment

The variables can be assigned values using leftward, rightward and equal to operator. The values of the variables can be printed using print() orcat()function. The cat() function combines multiple items into a continuous print output.

# Assignment using equal operator.
var.1 = c(0,1,2,3)           

# Assignment using leftward operator.
var.2 <- c("learn","R")   

# Assignment using rightward operator.   
c(TRUE,1) -> var.3           

print(var.1)
cat ("var.1 is ", var.1 ,"\n")
cat ("var.2 is ", var.2 ,"\n")
cat ("var.3 is ", var.3 ,"\n")

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

[1] 0 1 2 3
var.1 is  0 1 2 3 
var.2 is  learn R 
var.3 is  1 1 

Note − The vector c(TRUE,1) has a mix of logical and numeric class. So logical class is coerced to numeric class making TRUE as 1.

Data Type of a Variable

In R, a variable itself is not declared of any data type, rather it gets the data type of the R – object assigned to it. So R is called a dynamically typed language, which means that we can change a variable’s data type of the same variable again and again when using it in a program.

var_x <- "Hello"
cat("The class of var_x is ",class(var_x),"\n")

var_x <- 34.5
cat("  Now the class of var_x is ",class(var_x),"\n")

var_x <- 27L
cat("   Next the class of var_x becomes ",class(var_x),"\n")

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

The class of var_x is  character 
   Now the class of var_x is  numeric 
      Next the class of var_x becomes  integer

Finding Variables

To know all the variables currently available in the workspace we use the ls()function. Also the ls() function can use patterns to match the variable names.

print(ls())

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

[1] "my var"     "my_new_var" "my_var"     "var.1"      
[5] "var.2"      "var.3"      "var.name"   "var_name2."
[9] "var_x"      "varname" 

Note − It is a sample output depending on what variables are declared in your environment.

The ls() function can use patterns to match the variable names.

# List the variables starting with the pattern "var".
print(ls(pattern = "var"))

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

[1] "my var"     "my_new_var" "my_var"     "var.1"      
[5] "var.2"      "var.3"      "var.name"   "var_name2."
[9] "var_x"      "varname"    

The variables starting with dot(.) are hidden, they can be listed using “all.names = TRUE” argument to ls() function.

print(ls(all.name = TRUE))

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

[1] ".cars"        ".Random.seed" ".var_name"    ".varname"     ".varname2"   
[6] "my var"       "my_new_var"   "my_var"       "var.1"        "var.2"        
[11]"var.3"        "var.name"     "var_name2."   "var_x"  

Deleting Variables

Variables can be deleted by using the rm() function. Below we delete the variable var.3. On printing the value of the variable error is thrown.

rm(var.3)
print(var.3)

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

[1] "var.3"
Error in print(var.3) : object 'var.3' not found

All the variables can be deleted by using the rm() and ls() function together.

rm(list = ls())
print(ls())

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

character(0)

R – Operators

An operator is a symbol that tells the compiler to perform specific mathematical or logical manipulations. R language is rich in built-in operators and provides following types of operators.

Types of Operators

We have the following types of operators in R programming −

• Arithmetic Operators
• Relational Operators
• Logical Operators
• Assignment Operators
• Miscellaneous Operators

Arithmetic Operators

Following table shows the arithmetic operators supported by R language. The operators act on each element of the vector.

v <- c( 2,5.5,6)
t <- c(8, 3, 4)
print(v+t)

[1] 10.0  8.5  10.0

v <- c( 2,5.5,6)
t <- c(8, 3, 4)
print(v-t)

[1] -6.0  2.5  2.0

v <- c( 2,5.5,6)
t <- c(8, 3, 4)
print(v*t)

[1] 16.0 16.5 24.0

v <- c( 2,5.5,6)
t <- c(8, 3, 4)
print(v/t)

[1] 0.250000 1.833333 1.500000

v <- c( 2,5.5,6)
t <- c(8, 3, 4)
print(v%%t)

[1] 2.0 2.5 2.0

v <- c( 2,5.5,6)
t <- c(8, 3, 4)
print(v%/%t)

[1] 0 1 1

v <- c( 2,5.5,6)
t <- c(8, 3, 4)
print(v^t)

[1]  256.000  166.375 1296.000

Relational Operators

Following table shows the relational operators supported by R language. Each element of the first vector is compared with the corresponding element of the second vector. The result of comparison is a Boolean value.

v <- c(2,5.5,6,9)
t <- c(8,2.5,14,9)
print(v>t)

[1] FALSE  TRUE FALSE FALSE

v <- c(2,5.5,6,9)
t <- c(8,2.5,14,9)
print(v < t)

[1]  TRUE FALSE  TRUE FALSE

v <- c(2,5.5,6,9)
t <- c(8,2.5,14,9)
print(v == t)

[1] FALSE FALSE FALSE  TRUE

v <- c(2,5.5,6,9)
t <- c(8,2.5,14,9)
print(v<=t)

[1]  TRUE FALSE  TRUE  TRUE

v <- c(2,5.5,6,9)
t <- c(8,2.5,14,9)
print(v>=t)

[1] FALSE  TRUE FALSE  TRUE

v <- c(2,5.5,6,9)
t <- c(8,2.5,14,9)
print(v!=t)

[1]  TRUE  TRUE  TRUE FALSE

Logical Operators

Following table shows the logical operators supported by R language. It is applicable only to vectors of type logical, numeric or complex. All numbers greater than 1 are considered as logical value TRUE.

Each element of the first vector is compared with the corresponding element of the second vector. The result of comparison is a Boolean value.

v <- c(3,1,TRUE,2+3i)
t <- c(4,1,FALSE,2+3i)
print(v&t)

[1]  TRUE  TRUE FALSE  TRUE

v <- c(3,0,TRUE,2+2i)
t <- c(4,0,FALSE,2+3i)
print(v|t)

[1]  TRUE FALSE  TRUE  TRUE

v <- c(3,0,TRUE,2+2i)
print(!v)

[1] FALSE  TRUE FALSE FALSE

The logical operator && and || considers only the first element of the vectors and give a vector of single element as output.

v <- c(3,0,TRUE,2+2i)
t <- c(1,3,TRUE,2+3i)
print(v&&t)

[1] TRUE

v <- c(0,0,TRUE,2+2i)
t <- c(0,3,TRUE,2+3i)
print(v||t)

[1] FALSE

Assignment Operators

These operators are used to assign values to vectors.

v1 <- c(3,1,TRUE,2+3i)
v2 <<- c(3,1,TRUE,2+3i)
v3 = c(3,1,TRUE,2+3i)
print(v1)
print(v2)
print(v3)

[1] 3+0i 1+0i 1+0i 2+3i
[1] 3+0i 1+0i 1+0i 2+3i
[1] 3+0i 1+0i 1+0i 2+3i

c(3,1,TRUE,2+3i) -> v1
c(3,1,TRUE,2+3i) ->> v2 
print(v1)
print(v2)

[1] 3+0i 1+0i 1+0i 2+3i
[1] 3+0i 1+0i 1+0i 2+3i

Miscellaneous Operators

These operators are used to for specific purpose and not general mathematical or logical computation.

v <- 2:8
print(v)

[1] 2 3 4 5 6 7 8

v1 <- 8
v2 <- 12
t <- 1:10
print(v1 %in% t) 
print(v2 %in% t)

[1] TRUE
[1] FALSE

M = matrix( c(2,6,5,1,10,4), nrow = 2,ncol = 3,byrow = TRUE)
t = M %*% t(M)
print(t)

      [,1] [,2]
[1,]   65   82
[2,]   82  117

R – Decision making

Decision making structures require the programmer to specify one or more conditions to be evaluated or tested by the program, along with a statement or statements to be executed if the condition is determined to be true, and optionally, other statements to be executed if the condition is determined to befalse.

Following is the general form of a typical decision making structure found in most of the programming languages −

R provides the following types of decision making statements. Click the following links to check their detail.

R – Loops

There may be a situation when you need to execute a block of code several number of times. In general, statements are executed sequentially. The first statement in a function is executed first, followed by the second, and so on.

Programming languages provide various control structures that allow for more complicated execution paths.

A loop statement allows us to execute a statement or group of statements multiple times and the following is the general form of a loop statement in most of the programming languages −

R programming language provides the following kinds of loop to handle looping requirements. Click the following links to check their detail.

Loop Control Statements

Loop control statements change execution from its normal sequence. When execution leaves a scope, all automatic objects that were created in that scope are destroyed.

R supports the following control statements. Click the following links to check their detail.

R – Functions

A function is a set of statements organized together to perform a specific task. R has a large number of in-built functions and the user can create their own functions.

In R, a function is an object so the R interpreter is able to pass control to the function, along with arguments that may be necessary for the function to accomplish the actions.

The function in turn performs its task and returns control to the interpreter as well as any result which may be stored in other objects.

Function Definition

An R function is created by using the keyword function. The basic syntax of an R function definition is as follows −

function_name <- function(arg_1, arg_2, ...) {
   Function body 
}

Function Components

The different parts of a function are −

• Function Name − This is the actual name of the function. It is stored in R environment as an object with this name.
• Arguments − An argument is a placeholder. When a function is invoked, you pass a value to the argument. Arguments are optional; that is, a function may contain no arguments. Also arguments can have default values.
• Function Body − The function body contains a collection of statements that defines what the function does.
• Return Value − The return value of a function is the last expression in the function body to be evaluated.

R has many in-built functions which can be directly called in the program without defining them first. We can also create and use our own functions referred as user defined functions.

Built-in Function

Simple examples of in-built functions are seq(), mean(), max(), sum(x) andpaste(…) etc. They are directly called by user written programs. You can refermost widely used R functions.

# Create a sequence of numbers from 32 to 44.
print(seq(32,44))

# Find mean of numbers from 25 to 82.
print(mean(25:82))

# Find sum of numbers frm 41 to 68.
print(sum(41:68))

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

[1] 32 33 34 35 36 37 38 39 40 41 42 43 44
[1] 53.5
[1] 1526

User-defined Function

We can create user-defined functions in R. They are specific to what a user wants and once created they can be used like the built-in functions. Below is an example of how a function is created and used.

# Create a function to print squares of numbers in sequence.
new.function <- function(a) {
   for(i in 1:a) {
      b <- i^2
      print(b)
   }
}

Calling a Function

# Create a function to print squares of numbers in sequence.
new.function <- function(a) {
   for(i in 1:a) {
      b <- i^2
      print(b)
   }
}

# Call the function new.function supplying 6 as an argument.
new.function(6)

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

[1] 1
[1] 4
[1] 9
[1] 16
[1] 25
[1] 36

Calling a Function without an Argument

# Create a function without an argument.
new.function <- function() {
   for(i in 1:5) {
      print(i^2)
   }
}	

# Call the function without supplying an argument.
new.function()

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

[1] 1
[1] 4
[1] 9
[1] 16
[1] 25

Calling a Function with Argument Values (by position and by name)

The arguments to a function call can be supplied in the same sequence as defined in the function or they can be supplied in a different sequence but assigned to the names of the arguments.

# Create a function with arguments.
new.function <- function(a,b,c) {
   result <- a * b + c
   print(result)
}

# Call the function by position of arguments.
new.function(5,3,11)

# Call the function by names of the arguments.
new.function(a = 11, b = 5, c = 3)

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

[1] 26
[1] 58

Calling a Function with Default Argument

We can define the value of the arguments in the function definition and call the function without supplying any argument to get the default result. But we can also call such functions by supplying new values of the argument and get non default result.

# Create a function with arguments.
new.function <- function(a = 3, b = 6) {
   result <- a * b
   print(result)
}

# Call the function without giving any argument.
new.function()

# Call the function with giving new values of the argument.
new.function(9,5)

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

[1] 18
[1] 45

Lazy Evaluation of Function

Arguments to functions are evaluated lazily, which means so they are evaluated only when needed by the function body.

# Create a function with arguments.
new.function <- function(a, b) {
   print(a^2)
   print(a)
   print(b)
}

# Evaluate the function without supplying one of the arguments.
new.function(6)

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

[1] 36
[1] 6
Error in print(b) : argument "b" is missing, with no default

R – Strings

Any value written within a pair of single quote or double quotes in R is treated as a string. Internally R stores every string within double quotes, even when you create them with single quote.

Rules Applied in String Construction

• The quotes at the beginning and end of a string should be both double quotes or both single quote. They can not be mixed.
• Double quotes can be inserted into a string starting and ending with single quote.
• Single quote can be inserted into a string starting and ending with double quotes.
• Double quotes can not be inserted into a string starting and ending with double quotes.
• Single quote can not be inserted into a string starting and ending with single quote.

Examples of Valid Strings

Following examples clarify the rules about creating a string in R.

a <- 'Start and end with single quote'
print(a)

b <- "Start and end with double quotes"
print(b)

c <- "single quote ' in between double quotes"
print(c)

d <- 'Double quotes " in between single quote'
print(d)

When the above code is run we get the following output −

[1] "Start and end with single quote"
[1] "Start and end with double quotes"
[1] "single quote ' in between double quote"
[1] "Double quote \" in between single quote"

Examples of Invalid Strings

e <- 'Mixed quotes" 
print(e)

f <- 'Single quote ' inside single quote'
print(f)

g <- "Double quotes " inside double quotes"
print(g)

When we run the script it fails giving below results.

...: unexpected INCOMPLETE_STRING

.... unexpected symbol 
1: f <- 'Single quote ' inside

unexpected symbol
1: g <- "Double quotes " inside

String Manipulation

Concatenating Strings – paste() function

Many strings in R are combined using the paste() function. It can take any number of arguments to be combined together.

Syntax

The basic syntax for paste function is −

paste(..., sep = " ", collapse = NULL)

Following is the description of the parameters used −

• … represents any number of arguments to be combined.
• sep represents any separator between the arguments. It is optional.
• collapse is used to eliminate the space in between two strings. But not the space within two words of one string.

Example

a <- "Hello"
b <- 'How'
c <- "are you? "

print(paste(a,b,c))

print(paste(a,b,c, sep = "-"))

print(paste(a,b,c, sep = "", collapse = ""))

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

[1] "Hello How are you? "
[1] "Hello-How-are you? "
[1] "HelloHoware you? "

Formatting numbers & strings – format() function

Numbers and strings can be formatted to a specific style using format()function.

Syntax

The basic syntax for format function is −

format(x, digits, nsmall, scientific, width, justify = c("left", "right", "centre", "none")) 

Following is the description of the parameters used −

• x is the vector input.
• digits is the total number of digits displayed.
• nsmall is the minimum number of digits to the right of the decimal point.
• scientific is set to TRUE to display scientific notation.
• width indicates the minimum width to be displayed by padding blanks in the beginning.
• justify is the display of the string to left, right or center.

Example

# Total number of digits displayed. Last digit rounded off.
result <- format(23.123456789, digits = 9)
print(result)

# Display numbers in scientific notation.
result <- format(c(6, 13.14521), scientific = TRUE)
print(result)

# The minimum number of digits to the right of the decimal point.
result <- format(23.47, nsmall = 5)
print(result)

# Format treats everything as a string.
result <- format(6)
print(result)

# Numbers are padded with blank in the beginning for width.
result <- format(13.7, width = 6)
print(result)

# Left justify strings.
result <- format("Hello", width = 8, justify = "l")
print(result)

# Justfy string with center.
result <- format("Hello", width = 8, justify = "c")
print(result)

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

[1] "23.1234568"
[1] "6.000000e+00" "1.314521e+01"
[1] "23.47000"
[1] "6"
[1] "  13.7"
[1] "Hello   "
[1] " Hello  "

Counting number of characters in a string – nchar() function

This function counts the number of characters including spaces in a string.

Syntax

The basic syntax for nchar() function is −

nchar(x)

Following is the description of the parameters used −

• x is the vector input.

Example

result <- nchar("Count the number of characters")
print(result)

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

[1] 30

Changing the case – toupper() & tolower() functions

These functions change the case of characters of a string.

Syntax

The basic syntax for toupper() & tolower() function is −

toupper(x)
tolower(x)

Following is the description of the parameters used −

• x is the vector input.

Example

# Changing to Upper case.
result <- toupper("Changing To Upper")
print(result)

# Changing to lower case.
result <- tolower("Changing To Lower")
print(result)

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

[1] "CHANGING TO UPPER"
[1] "changing to lower"

Extracting parts of a string – substring() function

This function extracts parts of a String.

Syntax

The basic syntax for substring() function is −

substring(x,first,last)

Following is the description of the parameters used −

• x is the character vector input.
• first is the position of the first character to be extracted.
• last is the position of the last character to be extracted.

Example

# Extract characters from 5th to 7th position.
result <- substring("Extract", 5, 7)
print(result)

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

[1] "act"

R – Vectors

Vectors are the most basic R data objects and there are six types of atomic vectors. They are logical, integer, double, complex, character and raw.

Vector Creation

Single Element Vector

Even when you write just one value in R, it becomes a vector of length 1 and belongs to one of the above vector types.

# Atomic vector of type character.
print("abc");

# Atomic vector of type double.
print(12.5)

# Atomic vector of type integer.
print(63L)

# Atomic vector of type logical.
print(TRUE)

# Atomic vector of type complex.
print(2+3i)

# Atomic vector of type raw.
print(charToRaw('hello'))

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

[1] "abc"
[1] 12.5
[1] 63
[1] TRUE
[1] 2+3i
[1] 68 65 6c 6c 6f

Multiple Elements Vector

Using colon operator with numeric data

# Creating a sequence from 5 to 13.
v <- 5:13
print(v)

# Creating a sequence from 6.6 to 12.6.
v <- 6.6:12.6
print(v)

# If the final element specified does not belong to the sequence then it is discarded.
v <- 3.8:11.4
print(v)

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

[1]  5  6  7  8  9 10 11 12 13
[1]  6.6  7.6  8.6  9.6 10.6 11.6 12.6
[1]  3.8  4.8  5.8  6.8  7.8  8.8  9.8 10.8

Using sequence (Seq.) operator

# Create vector with elements from 5 to 9 incrementing by 0.4.
print(seq(5, 9, by = 0.4))

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

[1] 5.0 5.4 5.8 6.2 6.6 7.0 7.4 7.8 8.2 8.6 9.0

Using the c() function

The non-character values are coerced to character type if one of the elements is a character.

# The logical and numeric values are converted to characters.
s <- c('apple','red',5,TRUE)
print(s)

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

[1] "apple" "red"   "5"     "TRUE" 

Accessing Vector Elements

Elements of a Vector are accessed using indexing. The [ ] brackets are used for indexing. Indexing starts with position 1. Giving a negative value in the index drops that element from result.TRUE, FALSE or 0 and 1 can also be used for indexing.

# Accessing vector elements using position.
t <- c("Sun","Mon","Tue","Wed","Thurs","Fri","Sat")
u <- t[c(2,3,6)]
print(u)

# Accessing vector elements using logical indexing.
v <- t[c(TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE)]
print(v)

# Accessing vector elements using negative indexing.
x <- t[c(-2,-5)]
print(x)

# Accessing vector elements using 0/1 indexing.
y <- t[c(0,0,0,0,0,0,1)]
print(y)

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

[1] "Mon" "Tue" "Fri"
[1] "Sun" "Fri"
[1] "Sun" "Tue" "Wed" "Fri" "Sat"
[1] "Sun"

Vector Manipulation

Vector arithmetic

Two vectors of same length can be added, subtracted, multiplied or divided giving the result as a vector output.

# Create two vectors.
v1 <- c(3,8,4,5,0,11)
v2 <- c(4,11,0,8,1,2)

# Vector addition.
add.result <- v1+v2
print(add.result)

# Vector substraction.
sub.result <- v1-v2
print(sub.result)

# Vector multiplication.
multi.result <- v1*v2
print(multi.result)

# Vector division.
divi.result <- v1/v2
print(divi.result)

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

[1]  7 19  4 13  1 13
[1] -1 -3  4 -3 -1  9
[1] 12 88  0 40  0 22
[1] 0.7500000 0.7272727       Inf 0.6250000 0.0000000 5.5000000

Vector element recycling

If we apply arithmetic operations to two vectors of unequal length, then the elements of the shorter vector are recycled to complete the operations.

v1 <- c(3,8,4,5,0,11)
v2 <- c(4,11)
# V2 becomes c(4,11,4,11,4,11)

add.result <- v1+v2
print(add.result)

sub.result <- v1-v2
print(sub.result)

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

[1]  7 19  8 16  4 22
[1] -1 -3  0 -6 -4  0

Vector Element Sorting

Elements in a vector can be sorted using the sort() function.

v <- c(3,8,4,5,0,11, -9, 304)

# Sort the elements of the vector.
sort.result <- sort(v)
print(sort.result)

# Sort the elements in the reverse order.
revsort.result <- sort(v, decreasing = TRUE)
print(revsort.result)

# Sorting character vectors.
v <- c("Red","Blue","yellow","violet")
sort.result <- sort(v)
print(sort.result)

# Sorting character vectors in reverse order.
revsort.result <- sort(v, decreasing = TRUE)
print(revsort.result)

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

[1]  -9   0   3   4   5   8  11 304
[1] 304  11   8   5   4   3   0  -9
[1] "Blue"   "Red"    "violet" "yellow"
[1] "yellow" "violet" "Red"    "Blue" 

Lists are the R objects which contain elements of different types like − numbers, strings, vectors and another list inside it. A list can also contain a matrix or a function as its elements. List is created using list() function.

Creating a List

Following is an example to create a list containing strings, numbers, vectors and a logical values

# Create a list containing strings, numbers, vectors and a logical values.
list_data <- list("Red", "Green", c(21,32,11), TRUE, 51.23, 119.1)
print(list_data)

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

[[1]]
[1] "Red"

[[2]]
[1] "Green"

[[3]]
[1] 21 32 11

[[4]]
[1] TRUE

[[5]]
[1] 51.23

[[6]]
[1] 119.1

Naming List Elements

The list elements can be given names and they can be accessed using these names.

# Create a list containing a vector, a matrix and a list.
list_data <- list(c("Jan","Feb","Mar"), matrix(c(3,9,5,1,-2,8), nrow = 2),
   list("green",12.3))

# Give names to the elements in the list.
names(list_data) <- c("1st Quarter", "A_Matrix", "A Inner list")

# Show the list.
print(list_data)

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

$`1st_Quarter`
[1] "Jan" "Feb" "Mar"

$A_Matrix
     [,1] [,2] [,3]
[1,]    3    5   -2
[2,]    9    1    8

$A_Inner_list
$A_Inner_list[[1]]
[1] "green"

$A_Inner_list[[2]]
[1] 12.3

Accessing List Elements

Elements of the list can be accessed by the index of the element in the list. In case of named lists it can also be accessed using the names.

We continue to use the list in the above example −

# Create a list containing a vector, a matrix and a list.
list_data <- list(c("Jan","Feb","Mar"), matrix(c(3,9,5,1,-2,8), nrow = 2),
   list("green",12.3))

# Give names to the elements in the list.
names(list_data) <- c("1st Quarter", "A_Matrix", "A Inner list")

# Access the first element of the list.
print(list_data[1])

# Access the thrid element. As it is also a list, all its elements will be printed.
print(list_data[3])

# Access the list element using the name of the element.
print(list_data$A_Matrix)

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

$`1st_Quarter`
[1] "Jan" "Feb" "Mar"

$A_Inner_list
$A_Inner_list[[1]]
[1] "green"

$A_Inner_list[[2]]
[1] 12.3

     [,1] [,2] [,3]
[1,]    3    5   -2
[2,]    9    1    8

Manipulating List Elements

We can add, delete and update list elements as shown below. We can add and delete elements only at the end of a list. But we can update any element.

# Create a list containing a vector, a matrix and a list.
list_data <- list(c("Jan","Feb","Mar"), matrix(c(3,9,5,1,-2,8), nrow = 2),
   list("green",12.3))

# Give names to the elements in the list.
names(list_data) <- c("1st Quarter", "A_Matrix", "A Inner list")

# Add element at the end of the list.
list_data[4] <- "New element"
print(list_data[4])

# Remove the last element.
list_data[4] <- NULL

# Print the 4th Element.
print(list_data[4])

# Update the 3rd Element.
list_data[3] <- "updated element"
print(list_data[3])

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

[[1]]
[1] "New element"

$
NULL

$`A Inner list`
[1] "updated element"

Merging Lists

You can merge many lists into one list by placing all the lists inside one list() function.

# Create two lists.
list1 <- list(1,2,3)
list2 <- list("Sun","Mon","Tue")

# Merge the two lists.
merged.list <- c(list1,list2)

# Print the merged list.
print(merged.list)

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

[[1]]
[1] 1

[[2]]
[1] 2

[[3]]
[1] 3

[[4]]
[1] "Sun"

[[5]]
[1] "Mon"

[[6]]
[1] "Tue"

Converting List to Vector

A list can be converted to a vector so that the elements of the vector can be used for further manipulation. All the arithmetic operations on vectors can be applied after the list is converted into vectors. To do this conversion, we use theunlist() function. It takes the list as input and produces a vector.

# Create lists.
list1 <- list(1:5)
print(list1)

list2 <-list(10:14)
print(list2)

# Convert the lists to vectors.
v1 <- unlist(list1)
v2 <- unlist(list2)

print(v1)
print(v2)

# Now add the vectors
result <- v1+v2
print(result)

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

[[1]]
[1] 1 2 3 4 5

[[1]]
[1] 10 11 12 13 14

[1] 1 2 3 4 5
[1] 10 11 12 13 14
[1] 11 13 15 17 19

R – Matrices

Matrices are the R objects in which the elements are arranged in a two-dimensional rectangular layout. They contain elements of the same atomic types. Though we can create a matrix containing only characters or only logical values, they are not of much use. We use matrices containing numeric elements to be used in mathematical calculations.

A Matrix is created using the matrix() function.

Syntax

The basic syntax for creating a matrix in R is −

matrix(data, nrow, ncol, byrow, dimnames)

Following is the description of the parameters used −

• 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 clue. If TRUE then the input vector elements are arranged by row.
• dimname is the names assigned to the rows and columns.

Example

Create a matrix taking a vector of numbers as input

# Elements are arranged sequentially by row.
M <- matrix(c(3:14), nrow = 4, byrow = TRUE)
print(M)

# Elements are arranged sequentially by column.
N <- matrix(c(3:14), nrow = 4, byrow = FALSE)
print(N)

# Define the column and row names.
rownames = c("row1", "row2", "row3", "row4")
colnames = c("col1", "col2", "col3")

P <- matrix(c(3:14), nrow = 4, byrow = TRUE, dimnames = list(rownames, colnames))
print(P)

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

     [,1] [,2] [,3]
[1,]    3    4    5
[2,]    6    7    8
[3,]    9   10   11
[4,]   12   13   14
     [,1] [,2] [,3]
[1,]    3    7   11
[2,]    4    8   12
[3,]    5    9   13
[4,]    6   10   14
     col1 col2 col3
row1    3    4    5
row2    6    7    8
row3    9   10   11
row4   12   13   14

Accessing Elements of a Matrix

Elements of a matrix can be accessed by using the column and row index of the element. We consider the matrix P above to find the specific elements below.

# Define the column and row names.
rownames = c("row1", "row2", "row3", "row4")
colnames = c("col1", "col2", "col3")

# Create the matrix.
P <- matrix(c(3:14), nrow = 4, byrow = TRUE, dimnames = list(rownames, colnames))

# Access the element at 3rd column and 1st row.
print(P[1,3])

# Access the element at 2nd column and 4th row.
print(P[4,2])

# Access only the  2nd row.
print(P[2,])

# Access only the 3rd column.
print(P[,3])

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

[1] 5
[1] 13
col1 col2 col3 
   6    7    8 
row1 row2 row3 row4 
   5    8   11   14 

Matrix Computations

Various mathematical operations are performed on the matrices using the R operators. The result of the operation is also a matrix.

The dimensions (number of rows and columns) should be same for the matrices involved in the operation.

Matrix Addition & Subtraction

# Create two 2x3 matrices.
matrix1 <- matrix(c(3, 9, -1, 4, 2, 6), nrow = 2)
print(matrix1)

matrix2 <- matrix(c(5, 2, 0, 9, 3, 4), nrow = 2)
print(matrix2)

# Add the matrices.
result <- matrix1 + matrix2
cat("Result of addition","\n")
print(result)

# Subtract the matrices
result <- matrix1 - matrix2
cat("Result of subtraction","\n")
print(result)

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

     [,1] [,2] [,3]
[1,]    3   -1    2
[2,]    9    4    6
     [,1] [,2] [,3]
[1,]    5    0    3
[2,]    2    9    4
Result of addition 
     [,1] [,2] [,3]
[1,]    8   -1    5
[2,]   11   13   10
Result of subtraction 
     [,1] [,2] [,3]
[1,]   -2   -1   -1
[2,]    7   -5    2

Matrix Multiplication & Division

# Create two 2x3 matrices.
matrix1 <- matrix(c(3, 9, -1, 4, 2, 6), nrow = 2)
print(matrix1)

matrix2 <- matrix(c(5, 2, 0, 9, 3, 4), nrow = 2)
print(matrix2)

# Multiply the matrices.
result <- matrix1 * matrix2
cat("Result of multiplication","\n")
print(result)

# Divide the matrices
result <- matrix1 / matrix2
cat("Result of division","\n")
print(result)

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

     [,1] [,2] [,3]
[1,]    3   -1    2
[2,]    9    4    6
     [,1] [,2] [,3]
[1,]    5    0    3
[2,]    2    9    4
Result of multiplication 
     [,1] [,2] [,3]
[1,]   15    0    6
[2,]   18   36   24
Result of division 
     [,1]      [,2]      [,3]
[1,]  0.6      -Inf 0.6666667
[2,]  4.5 0.4444444 1.5000000

R – Arrays

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 thedimnames 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(column.names,row.names,
   matrix.names))
print(result)

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

, , Matrix1

     ROW1 ROW2 ROW3
COL1    5   10   13
COL2    9   11   14
COL3    3   12   15

, , Matrix2

     ROW1 ROW2 ROW3
COL1    5   10   13
COL2    9   11   14
COL3    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(column.names,
   row.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 −

ROW1 ROW2 ROW3 
   3   12   15 
[1] 13
     ROW1 ROW2 ROW3
COL1    5   10   13
COL2    9   11   14
COL3    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

R – Data Frames

A data frame is a table or a two-dimensional array-like structure in which each column contains values of one variable and each row contains one set of values from each column.

Following are the characteristics of a data frame.

• The column names should be non-empty.
• The row names should be unique.
• The data stored in a data frame can be of numeric, factor or character type.
• Each column should contain same number of data items.

Create Data Frame

# Create the data frame.
emp.data <- data.frame(
   emp_id = c (1:5), 
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25), 
   
   start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11",
      "2015-03-27")),
   stringsAsFactors = FALSE
)
# Print the data frame.			
print(emp.data)

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

 emp_id    emp_name     salary     start_date
1     1     Rick        623.30     2012-01-01
2     2     Dan         515.20     2013-09-23
3     3     Michelle    611.00     2014-11-15
4     4     Ryan        729.00     2014-05-11
5     5     Gary        843.25     2015-03-27

Get the Structure of the Data Frame

The structure of the data frame can be seen by using str() function.

# Create the data frame.
emp.data <- data.frame(
   emp_id = c (1:5), 
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25), 
   
   start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11",
      "2015-03-27")),
   stringsAsFactors = FALSE
)
# Get the structure of the data frame.
str(emp.data)

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

'data.frame':   5 obs. of  4 variables:
 $ emp_id    : int  1 2 3 4 5
 $ emp_name  : chr  "Rick" "Dan" "Michelle" "Ryan" ...
 $ salary    : num  623 515 611 729 843
 $ start_date: Date, format: "2012-01-01" "2013-09-23" "2014-11-15" "2014-05-11" ...

Summary of Data in Data Frame

The statistical summary and nature of the data can be obtained by applyingsummary() function.

# Create the data frame.
emp.data <- data.frame(
   emp_id = c (1:5), 
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25), 
   
   start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11",
      "2015-03-27")),
   stringsAsFactors = FALSE
)
# Print the summary.
print(summary(emp.data))

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

     emp_id    emp_name             salary        start_date        
 Min.   :1   Length:5           Min.   :515.2   Min.   :2012-01-01  
 1st Qu.:2   Class :character   1st Qu.:611.0   1st Qu.:2013-09-23  
 Median :3   Mode  :character   Median :623.3   Median :2014-05-11  
 Mean   :3                      Mean   :664.4   Mean   :2014-01-14  
 3rd Qu.:4                      3rd Qu.:729.0   3rd Qu.:2014-11-15  
 Max.   :5                      Max.   :843.2   Max.   :2015-03-27 

Extract Data from Data Frame

Extract specific column from a data frame using column name.

# Create the data frame.
emp.data <- data.frame(
   emp_id = c (1:5),
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25),
   
   start_date = as.Date(c("2012-01-01","2013-09-23","2014-11-15","2014-05-11",
      "2015-03-27")),
   stringsAsFactors = FALSE
)
# Extract Specific columns.
result <- data.frame(emp.data$emp_name,emp.data$salary)
print(result)

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

  emp.data.emp_name emp.data.salary
1              Rick          623.30
2               Dan          515.20
3          Michelle          611.00
4              Ryan          729.00
5              Gary          843.25

Extract the first two rows and then all columns

# Create the data frame.
emp.data <- data.frame(
   emp_id = c (1:5),
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25),
   
   start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11",
      "2015-03-27")),
   stringsAsFactors = FALSE
)
# Extract first two rows.
result <- emp.data[1:2,]
print(result)

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

  emp_id    emp_name   salary    start_date
1      1     Rick      623.3     2012-01-01
2      2     Dan       515.2     2013-09-23

Extract 3^rd and 5^th row with 2^nd and 4^th column

# Create the data frame.
emp.data <- data.frame(
   emp_id = c (1:5), 
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25), 
   
	start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11",
      "2015-03-27")),
   stringsAsFactors = FALSE
)

# Extract 3rd and 5th row with 2nd and 4th column.
result <- emp.data[c(3,5),c(2,4)]
print(result)

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

  emp_name start_date
3 Michelle 2014-11-15
5     Gary 2015-03-27

Expand Data Frame

A data frame can be expanded by adding columns and rows.

Add Column

Just add the column vector using a new column name.

# Create the data frame.
emp.data <- data.frame(
   emp_id = c (1:5), 
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25), 
   
   start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11",
      "2015-03-27")),
   stringsAsFactors = FALSE
)

# Add the "dept" coulmn.
emp.data$dept <- c("IT","Operations","IT","HR","Finance")
v <- emp.data
print(v)

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

  emp_id   emp_name    salary    start_date       dept
1     1    Rick        623.30    2012-01-01       IT
2     2    Dan         515.20    2013-09-23       Operations
3     3    Michelle    611.00    2014-11-15       IT
4     4    Ryan        729.00    2014-05-11       HR
5     5    Gary        843.25    2015-03-27       Finance

Add Row

To add more rows permanently to an existing data frame, we need to bring in the new rows in the same structure as the existing data frame and use therbind() function.

In the example below we create a data frame with new rows and merge it with the existing data frame to create the final data frame.

# Create the first data frame.
emp.data <- data.frame(
   emp_id = c (1:5), 
   emp_name = c("Rick","Dan","Michelle","Ryan","Gary"),
   salary = c(623.3,515.2,611.0,729.0,843.25), 
   
   start_date = as.Date(c("2012-01-01", "2013-09-23", "2014-11-15", "2014-05-11",
      "2015-03-27")),
   dept = c("IT","Operations","IT","HR","Finance"),
   stringsAsFactors = FALSE
)

# Create the second data frame
emp.newdata <- 	data.frame(
   emp_id = c (6:8), 
   emp_name = c("Rasmi","Pranab","Tusar"),
   salary = c(578.0,722.5,632.8), 
   start_date = as.Date(c("2013-05-21","2013-07-30","2014-06-17")),
   dept = c("IT","Operations","Fianance"),
   stringsAsFactors = FALSE
)

# Bind the two data frames.
emp.finaldata <- rbind(emp.data,emp.newdata)
print(emp.finaldata)

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

  emp_id     emp_name    salary     start_date       dept
1      1     Rick        623.30     2012-01-01       IT
2      2     Dan         515.20     2013-09-23       Operations
3      3     Michelle    611.00     2014-11-15       IT
4      4     Ryan        729.00     2014-05-11       HR
5      5     Gary        843.25     2015-03-27       Finance
6      6     Rasmi       578.00     2013-05-21       IT
7      7     Pranab      722.50     2013-07-30       Operations
8      8     Tusar       632.80     2014-06-17       Fianance

R – Packages

R packages are a collection of R functions, complied code and sample data. They are stored under a directory called “library” in the R environment. By default, R installs a set of packages during installation. More packages are added later, when they are needed for some specific purpose. When we start the R console, only the default packages are available by default. Other packages which are already installed have to be loaded explicitly to be used by the R program that is going to use them.

All the packages available in R language are listed at R Packages.

Below is a list of commands to be used to check, verify and use the R packages.

Check Available R Packages

Get library locations containing R packages

.libPaths()

When we execute the above code, it produces the following result. It may vary depending on the local settings of your pc.

[2] "C:/Program Files/R/R-3.2.2/library"

Get the list of all the packages installed

library()

When we execute the above code, it produces the following result. It may vary depending on the local settings of your pc.

Packages in library ‘C:/Program Files/R/R-3.2.2/library’:

base                    The R Base Package
boot                    Bootstrap Functions (Originally by Angelo Canty
                        for S)
class                   Functions for Classification
cluster                 "Finding Groups in Data": Cluster Analysis
                        Extended Rousseeuw et al.
codetools               Code Analysis Tools for R
compiler                The R Compiler Package

Get all packages currently loaded in the R environment

search()

When we execute the above code, it produces the following result. It may vary depending on the local settings of your pc.

[1] ".GlobalEnv"        "package:stats"     "package:graphics" 
[4] "package:grDevices" "package:utils"     "package:datasets" 
[7] "package:methods"   "Autoloads"         "package:base" 

Install a New Package

There are two ways to add new R packages. One is installing directly from the CRAN directory and another is downloading the package to your local system and installing it manually.

Install directly from CRAN

The following command gets the packages directly from CRAN webpage and installs the package in the R environment. You may be prompted to choose a nearest mirror. Choose the one appropriate to your location.

 install.packages("Package Name")
 
# Install the package named "XML".
 install.packages("XML")

Install package manually

Go to the link R Packages to download the package needed. Save the package as a .zip file in a suitable location in the local system.

Now you can run the following command to install this package in the R environment.

install.packages(file_name_with_path, repos = NULL, type = "source")

# Install the package named "XML"
install.packages("E:/XML_3.98-1.3.zip", repos = NULL, type = "source")

Load Package to Library

Before a package can be used in the code, it must be loaded to the current R environment. You also need to load a package that is already installed previously but not available in the current environment.

A package is loaded using the following command −

library("package Name", lib.loc = "path to library")

# Load the package named "XML"
install.packages("E:/XML_3.98-1.3.zip", repos = NULL, type = "source")

R – Data Reshaping

Data Reshaping in R is about changing the way data is organized into rows and columns. Most of the time data processing in R is done by taking the input data as a data frame. It is easy to extract data from the rows and columns of a data frame but there are situations when we need the data frame in a format that is different from format in which we received it. R has many functions to split, merge and change the rows to columns and vice-versa in a data frame.

Joining Columns and Rows in a Data Frame

We can join multiple vectors to create a data frame using the cbind()function. Also we can merge two data frames using rbind() function.

# Create vector objects.
city <- c("Tampa","Seattle","Hartford","Denver")
state <- c("FL","WA","CT","CO")
zipcode <- c(33602,98104,06161,80294)

# Combine above three vectors into one data frame.
addresses <- cbind(city,state,zipcode)

# Print a header.
cat("# # # # The First data frame\n") 

# Print the data frame.
print(addresses)

# Create another data frame with similar columns
new.address <- data.frame(
   city = c("Lowry","Charlotte"),
   state = c("CO","FL"),
   zipcode = c("80230","33949"),
   stringsAsFactors = FALSE
)

# Print a header.
cat("# # # The Second data frame\n") 

# Print the data frame.
print(new.address)

# Combine rows form both the data frames.
all.addresses <- rbind(addresses,new.address)

# Print a header.
cat("# # # The combined data frame\n") 

# Print the result.
print(all.addresses)

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

# # # # The First data frame
     city       state zipcode
[1,] "Tampa"    "FL"  "33602"
[2,] "Seattle"  "WA"  "98104"
[3,] "Hartford" "CT"   "6161" 
[4,] "Denver"   "CO"  "80294"

# # # The Second data frame
       city       state   zipcode
1      Lowry      CO      80230
2      Charlotte  FL      33949

# # # The combined data frame
       city      state zipcode
1      Tampa     FL    33602
2      Seattle   WA    98104
3      Hartford  CT     6161
4      Denver    CO    80294
5      Lowry     CO    80230
6     Charlotte  FL    33949

Merging Data Frames

We can merge two data frames by using the merge() function. The data frames must have same column names on which the merging happens.

In the example below, we consider the data sets about Diabetes in Pima Indian Women available in the library names “MASS”. we merge the two data sets based on the values of blood pressure(“bp”) and body mass index(“bmi”). On choosing these two columns for merging, the records where values of these two variables match in both data sets are combined together to form a single data frame.

library(MASS)
merged.Pima <- merge(x = Pima.te, y = Pima.tr,
   by.x = c("bp", "bmi"),
   by.y = c("bp", "bmi")
)
print(merged.Pima)
nrow(merged.Pima)

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

   bp  bmi npreg.x glu.x skin.x ped.x age.x type.x npreg.y glu.y skin.y ped.y
1  60 33.8       1   117     23 0.466    27     No       2   125     20 0.088
2  64 29.7       2    75     24 0.370    33     No       2   100     23 0.368
3  64 31.2       5   189     33 0.583    29    Yes       3   158     13 0.295
4  64 33.2       4   117     27 0.230    24     No       1    96     27 0.289
5  66 38.1       3   115     39 0.150    28     No       1   114     36 0.289
6  68 38.5       2   100     25 0.324    26     No       7   129     49 0.439
7  70 27.4       1   116     28 0.204    21     No       0   124     20 0.254
8  70 33.1       4    91     32 0.446    22     No       9   123     44 0.374
9  70 35.4       9   124     33 0.282    34     No       6   134     23 0.542
10 72 25.6       1   157     21 0.123    24     No       4    99     17 0.294
11 72 37.7       5    95     33 0.370    27     No       6   103     32 0.324
12 74 25.9       9   134     33 0.460    81     No       8   126     38 0.162
13 74 25.9       1    95     21 0.673    36     No       8   126     38 0.162
14 78 27.6       5    88     30 0.258    37     No       6   125     31 0.565
15 78 27.6      10   122     31 0.512    45     No       6   125     31 0.565
16 78 39.4       2   112     50 0.175    24     No       4   112     40 0.236
17 88 34.5       1   117     24 0.403    40    Yes       4   127     11 0.598
   age.y type.y
1     31     No
2     21     No
3     24     No
4     21     No
5     21     No
6     43    Yes
7     36    Yes
8     40     No
9     29    Yes
10    28     No
11    55     No
12    39     No
13    39     No
14    49    Yes
15    49    Yes
16    38     No
17    28     No
[1] 17

Melting and Casting

One of the most interesting aspects of R programming is about changing the shape of the data in multiple steps to get a desired shape. The functions used to do this are called melt() and cast().

We consider the dataset called ships present in the library called “MASS”.

library(MASS)
print(ships)

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

     type year   period   service   incidents
1     A   60     60        127         0
2     A   60     75         63         0
3     A   65     60       1095         3
4     A   65     75       1095         4
5     A   70     60       1512         6
.............
.............
8     A   75     75       2244         11
9     B   60     60      44882         39
10    B   60     75      17176         29
11    B   65     60      28609         58
............
............
17    C   60     60      1179          1
18    C   60     75       552          1
19    C   65     60       781          0
............
............

Melt the Data

Now we melt the data to organize it, converting all columns other than type and year into multiple rows.

molten.ships <- melt(ships, id = c("type","year"))
print(molten.ships)

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

      type year  variable  value
1      A   60    period      60
2      A   60    period      75
3      A   65    period      60
4      A   65    period      75
............
............
9      B   60    period      60
10     B   60    period      75
11     B   65    period      60
12     B   65    period      75
13     B   70    period      60
...........
...........
41     A   60    service    127
42     A   60    service     63
43     A   65    service   1095
...........
...........
70     D   70    service   1208
71     D   75    service      0
72     D   75    service   2051
73     E   60    service     45
74     E   60    service      0
75     E   65    service    789
...........
...........
101    C   70    incidents    6
102    C   70    incidents    2
103    C   75    incidents    0
104    C   75    incidents    1
105    D   60    incidents    0
106    D   60    incidents    0
...........
...........

Cast the Molten Data

We can cast the molten data into a new form where the aggregate of each type of ship for each year is created. It is done using the cast() function.

recasted.ship <- cast(molten.ships, type+year~variable,sum)
print(recasted.ship)

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

     type year  period  service  incidents
1     A   60    135       190      0
2     A   65    135      2190      7
3     A   70    135      4865     24
4     A   75    135      2244     11
5     B   60    135     62058     68
6     B   65    135     48979    111
7     B   70    135     20163     56
8     B   75    135      7117     18
9     C   60    135      1731      2
10    C   65    135      1457      1
11    C   70    135      2731      8
12    C   75    135       274      1
13    D   60    135       356      0
14    D   65    135       480      0
15    D   70    135      1557     13
16    D   75    135      2051      4
17    E   60    135        45      0
18    E   65    135      1226     14
19    E   70    135      3318     17
20    E   75    135       542      1

R – CSV Files

In R, we can read data from files stored outside the R environment. We can also write data into files which will be stored and accessed by the operating system. R can read and write into various file formats like csv, excel, xml etc.

In this chapter we will learn to read data from a csv file and then write data into a csv file. The file should be present in current working directory so that R can read it. Of course we can also set our own directory and read files from there.

Getting and Setting the Working Directory

You can check which directory the R workspace is pointing to using thegetwd() function. You can also set a new working directory usingsetwd()function.

# Get and print current working directory.
print(getwd())

# Set current working directory.
setwd("/web/com")

# Get and print current working directory.
print(getwd())

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

[1] "/web/com/1441086124_2016"
[1] "/web/com"

This result depends on your OS and your current directory where you are working.

Input as CSV File

The csv file is a text file in which the values in the columns are separated by a comma. Let’s consider the following data present in the file named input.csv.

You can create this file using windows notepad by copying and pasting this data. Save the file as input.csv using the save As All files(*.*) option in notepad.

id,name,salary,start_date,dept
1,Rick,623.3,2012-01-01,IT
2,Dan,515.2,2013-09-23,Operations
3,Michelle,611,2014-11-15,IT
4,Ryan,729,2014-05-11,HR
 ,Gary,843.25,2015-03-27,Finance
6,Nina,578,2013-05-21,IT
7,Simon,632.8,2013-07-30,Operations
8,Guru,722.5,2014-06-17,Finance

Reading a CSV File

Following is a simple example of read.csv() function to read a CSV file available in your current working directory −

data <- read.csv("input.csv")
print(data)

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

      id,   name,    salary,   start_date,     dept
1      1    Rick     623.30    2012-01-01      IT
2      2    Dan      515.20    2013-09-23      Operations
3      3    Michelle 611.00    2014-11-15      IT
4      4    Ryan     729.00    2014-05-11      HR
5     NA    Gary     843.25    2015-03-27      Finance
6      6    Nina     578.00    2013-05-21      IT
7      7    Simon    632.80    2013-07-30      Operations
8      8    Guru     722.50    2014-06-17      Finance

Analyzing the CSV File

By default the read.csv() function gives the output as a data frame. This can be easily checked as follows. Also we can check the number of columns and rows.

data <- read.csv("input.csv")

print(is.data.frame(data))
print(ncol(data))
print(nrow(data))

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

[1] TRUE
[1] 5
[1] 8

Once we read data in a data frame, we can apply all the functions applicable to data frames as explained in subsequent section.

Get the maximum salary

# Create a data frame.
data <- read.csv("input.csv")

# Get the max salary from data frame.
sal <- max(data$salary)
print(sal)

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

[1] 843.25

Get the details of the person with max salary

We can fetch rows meeting specific filter criteria similar to a SQL where clause.

# Create a data frame.
data <- read.csv("input.csv")

# Get the max salary from data frame.
sal <- max(data$salary)

# Get the person detail having max salary.
retval <- subset(data, salary == max(salary))
print(retval)

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

      id    name  salary  start_date    dept
5     NA    Gary  843.25  2015-03-27    Finance

Get all the people working in IT department

# Create a data frame.
data <- read.csv("input.csv")

retval <- subset( data, dept == "IT")
print(retval)

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

       id   name      salary   start_date   dept
1      1    Rick      623.3    2012-01-01   IT
3      3    Michelle  611.0    2014-11-15   IT
6      6    Nina      578.0    2013-05-21   IT

Get the persons in IT department whose salary is greater than 600

# Create a data frame.
data <- read.csv("input.csv")

info <- subset(data, salary > 600 & dept == "IT")
print(info)

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

       id   name      salary   start_date   dept
1      1    Rick      623.3    2012-01-01   IT
3      3    Michelle  611.0    2014-11-15   IT

Get the people who joined on or after 2014

# Create a data frame.
data <- read.csv("input.csv")

retval <- subset(data, as.Date(start_date) > as.Date("2014-01-01"))
print(retval)

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

       id   name     salary   start_date    dept
3      3    Michelle 611.00   2014-11-15    IT
4      4    Ryan     729.00   2014-05-11    HR
5     NA    Gary     843.25   2015-03-27    Finance
8      8    Guru     722.50   2014-06-17    Finance

Writing into a CSV File

R can create csv file form existing data frame. The write.csv() function is used to create the csv file. This file gets created in the working directory.

# Create a data frame.
data <- read.csv("input.csv")
retval <- subset(data, as.Date(start_date) > as.Date("2014-01-01"))

# Write filtered data into a new file.
write.csv(retval,"output.csv")
newdata <- read.csv("output.csv")
print(newdata)

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

  X      id   name      salary   start_date    dept
1 3      3    Michelle  611.00   2014-11-15    IT
2 4      4    Ryan      729.00   2014-05-11    HR
3 5     NA    Gary      843.25   2015-03-27    Finance
4 8      8    Guru      722.50   2014-06-17    Finance

Here the column X comes from the data set newper. This can be dropped using additional parameters while writing the file.

# Create a data frame.
data <- read.csv("input.csv")
retval <- subset(data, as.Date(start_date) > as.Date("2014-01-01"))

# Write filtered data into a new file.
write.csv(retval,"output.csv", row.names = FALSE)
newdata <- read.csv("output.csv")
print(newdata)

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

      id    name      salary   start_date    dept
1      3    Michelle  611.00   2014-11-15    IT
2      4    Ryan      729.00   2014-05-11    HR
3     NA    Gary      843.25   2015-03-27    Finance
4      8    Guru      722.50   2014-06-17    Finance

R – Excel File

Microsoft Excel is the most widely used spreadsheet program which stores data in the .xls or .xlsx format. R can read directly from these files using some excel specific packages. Few such packages are – XLConnect, xlsx, gdata etc. We will be using xlsx package. R can also write into excel file using this package.

Install xlsx Package

You can use the following command in the R console to install the “xlsx” package. It may ask to install some additional packages on which this package is dependent. Follow the same command with required package name to install the additional packages.

install.packages("xlsx")

Verify and Load the “xlsx” Package

Use the following command to verify and load the “xlsx” package.

# Verify the package is installed.
any(grepl("xlsx",installed.packages()))

# Load the library into R workspace.
library("xlsx")

When the script is run we get the following output.

[1] TRUE
Loading required package: rJava
Loading required package: methods
Loading required package: xlsxjars

Input as xlsx File

Open Microsoft excel. Copy and paste the following data in the work sheet named as sheet1.

id	name      salary    start_date	dept
1	Rick	  623.3	    1/1/2012	IT
2	Dan       515.2     9/23/2013   Operations
3	Michelle  611	    11/15/2014	IT
4	Ryan	  729	    5/11/2014	HR
5	Gary	  843.25    3/27/2015	Finance
6	Nina	  578       5/21/2013	IT
7	Simon	  632.8	    7/30/2013	Operations
8	Guru	  722.5	    6/17/2014	Finance

Also copy and paste the following data to another worksheet and rename this worksheet to “city”.

name	 city
Rick	 Seattle
Dan      Tampa
Michelle Chicago
Ryan	 Seattle
Gary	 Houston
Nina	 Boston
Simon	 Mumbai
Guru	 Dallas

Save the Excel file as “input.xlsx”. You should save it in the current working directory of the R workspace.

Reading the Excel File

The input.xlsx is read by using the read.xlsx() function as shown below. The result is stored as a data frame in the R environment.

# Read the first worksheet in the file input.xlsx.
data <- read.xlsx("input.xlsx", sheetIndex = 1)
print(data)

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

      id,   name,    salary,   start_date,     dept
1      1    Rick     623.30    2012-01-01      IT
2      2    Dan      515.20    2013-09-23      Operations
3      3    Michelle 611.00    2014-11-15      IT
4      4    Ryan     729.00    2014-05-11      HR
5     NA    Gary     843.25    2015-03-27      Finance
6      6    Nina     578.00    2013-05-21      IT
7      7    Simon    632.80    2013-07-30      Operations
8      8    Guru     722.50    2014-06-17      Finance

R – Binary Files

A binary file is a file that contains information stored only in form of bits and bytes.(0’s and 1’s). They are not human readable as the bytes in it translate to characters and symbols which contain many other non-printable characters. Attempting to read a binary file using any text editor will show characters like Ø and ð.

The binary file has to be read by specific programs to be useable. For example, the binary file of a Microsoft Word program can be read to a human readable form only by the Word program. Which indicates that, besides the human readable text, there is a lot more information like formatting of characters and page numbers etc., which are also stored along with alphanumeric characters. And finally a binary file is a continuous sequence of bytes. The line break we see in a text file is a character joining first line to the next.

Sometimes, the data generated by other programs are required to be processed by R as a binary file. Also R is required to create binary files which can be shared with other programs.

R has two functions WriteBin() and readBin() to create and read binary files.

Syntax

writeBin(object, con)
readBin(con, what, n )

Following is the description of the parameters used −

• con is the connection object to read or write the binary file.
• object is the binary file which to be written.
• what is the mode like character, integer etc. representing the bytes to be read.
• n is the number of bytes to read from the binary file.

Example

We consider the R inbuilt data “mtcars”. First we create a csv file from it and convert it to a binary file and store it as a OS file. Next we read this binary file created into R.

Writing the Binary File

We read the data frame “mtcars” as a csv file and then write it as a binary file to the OS.

# Read the "mtcars" data frame as a csv file and store only the columns 
   "cyl", "am" and "gear".
write.table(mtcars, file = "mtcars.csv",row.names = FALSE, na = "", 
   col.names = TRUE, sep = ",")

# Store 5 records from the csv file as a new data frame.
new.mtcars <- read.table("mtcars.csv",sep = ",",header = TRUE,nrows = 5)

# Create a connection object to write the binary file using mode "wb".
write.filename = file("/web/com/binmtcars.dat", "wb")

# Write the column names of the data frame to the connection object.
writeBin(colnames(new.mtcars), write.filename)

# Write the records in each of the column to the file.
writeBin(c(new.mtcars$cyl,new.mtcars$am,new.mtcars$gear), write.filename)

# Close the file for writing so that it can be read by other program.
close(write.filename)

Reading the Binary File

The binary file created above stores all the data as continuous bytes. So we will read it by choosing appropriate values of column names as well as the column values.

# Create a connection object to read the file in binary mode using "rb".
read.filename <- file("/web/com/binmtcars.dat", "rb")

# First read the column names. n = 3 as we have 3 columns.
column.names <- readBin(read.filename, character(),  n = 3)

# Next read the column values. n = 18 as we have 3 column names and 15 values.
read.filename <- file("/web/com/binmtcars.dat", "rb")
bindata <- readBin(read.filename, integer(),  n = 18)

# Print the data.
print(bindata)

# Read the values from 4th byte to 8th byte which represents "cyl".
cyldata = bindata[4:8]
print(cyldata)

# Read the values form 9th byte to 13th byte which represents "am".
amdata = bindata[9:13]
print(amdata)

# Read the values form 9th byte to 13th byte which represents "gear".
geardata = bindata[14:18]
print(geardata)

# Combine all the read values to a dat frame.
finaldata = cbind(cyldata, amdata, geardata)
colnames(finaldata) = column.names
print(finaldata)

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

 [1]    7108963 1728081249    7496037          6          6          4
 [7]          6          8          1          1          1          0
[13]          0          4          4          4          3          3

[1] 6 6 4 6 8

[1] 1 1 1 0 0

[1] 4 4 4 3 3

     cyl am gear
[1,]   6  1    4
[2,]   6  1    4
[3,]   4  1    4
[4,]   6  0    3
[5,]   8  0    3

As we can see, we got the original data back by reading the binary file in R.

R – XML Files

XML is a file format which shares both the file format and the data on the World Wide Web, intranets, and elsewhere using standard ASCII text. It stands for Extensible Markup Language (XML). Similar to HTML it contains markup tags. But unlike HTML where the markup tag describes structure of the page, in xml the markup tags describe the meaning of the data contained into he file.

You can read a xml file in R using the “XML” package. This package can be installed using following command.

install.packages("XML")

Input Data

Create a XMl file by copying the below data into a text editor like notepad. Save the file with a .xml extension and choosing the file type as all files(*.*).

<RECORDS>
   <EMPLOYEE>
      <ID>1</ID>
      <NAME>Rick</NAME>
      <SALARY>623.3</SALARY>
      <STARTDATE>1/1/2012</STARTDATE>
      <DEPT>IT</DEPT>
   </EMPLOYEE>
	
   <EMPLOYEE>
      <ID>2</ID>
      <NAME>Dan</NAME>
      <SALARY>515.2</SALARY>
      <STARTDATE>9/23/2013</STARTDATE>
      <DEPT>Operations</DEPT>
   </EMPLOYEE>
   
   <EMPLOYEE>
      <ID>3</ID>
      <NAME>Michelle</NAME>
      <SALARY>611</SALARY>
      <STARTDATE>11/15/2014</STARTDATE>
      <DEPT>IT</DEPT>
   </EMPLOYEE>
   
   <EMPLOYEE>
      <ID>4</ID>
      <NAME>Ryan</NAME>
      <SALARY>729</SALARY>
      <STARTDATE>5/11/2014</STARTDATE>
      <DEPT>HR</DEPT>
   </EMPLOYEE>
   
   <EMPLOYEE>
      <ID>5</ID>
      <NAME>Gary</NAME>
      <SALARY>843.25</SALARY>
      <STARTDATE>3/27/2015</STARTDATE>
      <DEPT>Finance</DEPT>
   </EMPLOYEE>
   
   <EMPLOYEE>
      <ID>6</ID>
      <NAME>Nina</NAME>
      <SALARY>578</SALARY>
      <STARTDATE>5/21/2013</STARTDATE>
      <DEPT>IT</DEPT>
   </EMPLOYEE>
   
   <EMPLOYEE>
      <ID>7</ID>
      <NAME>Simon</NAME>
      <SALARY>632.8</SALARY>
      <STARTDATE>7/30/2013</STARTDATE>
      <DEPT>Operations</DEPT>
   </EMPLOYEE>
   
   <EMPLOYEE>
      <ID>8</ID>
      <NAME>Guru</NAME>
      <SALARY>722.5</SALARY>
      <STARTDATE>6/17/2014</STARTDATE>
      <DEPT>Finance</DEPT>
   </EMPLOYEE>
	
</RECORDS>

Reading XML File

The xml file is read by R using the function xmlParse(). It is stored as a list in R.

# Load the package required to read XML files.
library("XML")

# Also load the other required package.
library("methods")

# Give the input file name to the function.
result <- xmlParse(file = "input.xml")

# Print the result.
print(result)

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

1
    Rick
    623.3
    1/1/2012
    IT
  
  
    2
    Dan
    515.2
    9/23/2013
    Operations
  
  
    3
    Michelle
    611
    11/15/2014
    IT
  
  
    4
    Ryan
    729
    5/11/2014
    HR
  
  
    5
    Gary
    843.25
    3/27/2015
    Finance
  
  
    6
    Nina
    578
    5/21/2013
    IT
  
  
    7
    Simon
    632.8
    7/30/2013
    Operations
  
  
    8
    Guru
    722.5
    6/17/2014
    Finance

Get Number of Nodes Present in XML File

# Load the packages required to read XML files.
library("XML")
library("methods")

# Give the input file name to the function.
result <- xmlParse(file = "input.xml")

# Exract the root node form the xml file.
rootnode <- xmlRoot(result)

# Find number of nodes in the root.
rootsize <- xmlSize(rootnode)

# Print the result.
print(rootsize)

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

output
[1] 8

Details of the First Node

Let’s look at the first record of the parsed file. It will give us an idea of the various elements present in the top level node.

# Load the packages required to read XML files.
library("XML")
library("methods")

# Give the input file name to the function.
result <- xmlParse(file = "input.xml")

# Exract the root node form the xml file.
rootnode <- xmlRoot(result)

# Print the result.
print(rootnode[1])

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

$EMPLOYEE
  1
  Rick
  623.3
  1/1/2012
  IT
 

attr(,"class")
[1] "XMLInternalNodeList" "XMLNodeList" 

Get Different Elements of a Node

# Load the packages required to read XML files.
library("XML")
library("methods")

# Give the input file name to the function.
result <- xmlParse(file = "input.xml")

# Exract the root node form the xml file.
rootnode <- xmlRoot(result)

# Get the first element of the first node.
print(rootnode[[1]][[1]])

# Get the fifth element of the first node.
print(rootnode[[1]][[5]])

# Get the second element of the third node.
print(rootnode[[3]][[2]])

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

1 
IT 
Michelle 

XML to Data Frame

To handle the data effectively in large files we read the data in the xml file as a data frame. Then process the data frame for data analysis.

# Load the packages required to read XML files.
library("XML")
library("methods")

# Convert the input xml file to a data frame.
xmldataframe <- xmlToDataFrame("input.xml")
print(xmldataframe)

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

      ID    NAME     SALARY    STARTDATE       DEPT
1      1    Rick     623.30    2012-01-01      IT
2      2    Dan      515.20    2013-09-23      Operations
3      3    Michelle 611.00    2014-11-15      IT
4      4    Ryan     729.00    2014-05-11      HR
5     NA    Gary     843.25    2015-03-27      Finance
6      6    Nina     578.00    2013-05-21      IT
7      7    Simon    632.80    2013-07-30      Operations
8      8    Guru     722.50    2014-06-17      Finance

As the data is now available as a dataframe we can use data frame related function to read and manipulate the file.

R – JSON Files

JSON file stores data as text in human-readable format. Json stands for JavaScript Object Notation. R can read JSON files using the rjson package.

Install rjson Package

In the R console, you can issue the following command to install the rjson package.

install.packages("rjson")

Input Data

Create a JSON file by copying the below data into a text editor like notepad. Save the file with a .json extension and choosing the file type as all files(*.*).

{ 
   "ID":["1","2","3","4","5","6","7","8" ],
   "Name":["Rick","Dan","Michelle","Ryan","Gary","Nina","Simon","Guru" ],
   "Salary":["623.3","515.2","611","729","843.25","578","632.8","722.5" ],
   
   "StartDate":[ "1/1/2012","9/23/2013","11/15/2014","5/11/2014","3/27/2015","5/21/2013",
      "7/30/2013","6/17/2014"],
   "Dept":[ "IT","Operations","IT","HR","Finance","IT","Operations","Finance"]
}

Read the JSON File

The JSON file is read by R using the function from JSON(). It is stored as a list in R.

# Load the package required to read JSON files.
library("rjson")

# Give the input file name to the function.
result <- fromJSON(file = "input.json")

# Print the result.
print(result)

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

$ID
[1] "1"   "2"   "3"   "4"   "5"   "6"   "7"   "8"

$Name
[1] "Rick"     "Dan"      "Michelle" "Ryan"     "Gary"     "Nina"     "Simon"    "Guru"

$Salary
[1] "623.3"  "515.2"  "611"    "729"    "843.25" "578"    "632.8"  "722.5"

$StartDate
[1] "1/1/2012"   "9/23/2013"  "11/15/2014" "5/11/2014"  "3/27/2015"  "5/21/2013"
   "7/30/2013"  "6/17/2014"

$Dept
[1] "IT"         "Operations" "IT"         "HR"         "Finance"    "IT"
   "Operations" "Finance"

Convert JSON to a Data Frame

We can convert the extracted data above to a R data frame for further analysis using the as.data.frame() function.

# Load the package required to read JSON files.
library("rjson")

# Give the input file name to the function.
result <- fromJSON(file = "input.json")

# Convert JSON file to a data frame.
json_data_frame <- as.data.frame(result)

print(json_data_frame)

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

      id,   name,    salary,   start_date,     dept
1      1    Rick     623.30    2012-01-01      IT
2      2    Dan      515.20    2013-09-23      Operations
3      3    Michelle 611.00    2014-11-15      IT
4      4    Ryan     729.00    2014-05-11      HR
5     NA    Gary     843.25    2015-03-27      Finance
6      6    Nina     578.00    2013-05-21      IT
7      7    Simon    632.80    2013-07-30      Operations
8      8    Guru     722.50    2014-06-17      Finance

R – Web Data

Many websites provide data for consumption by its users. For example the World Health Organization(WHO) provides reports on health and medical information in the form of CSV, txt and XML files. Using R programs, we can programmatically extract specific data from such websites. Some packages in R which are used to scrap data form the web are − “RCurl”,XML”, and “stringr”. They are used to connect to the URL’s, identify required links for the files and download them to the local environment.

Install R Packages

The following packages are required for processing the URL’s and links to the files. If they are not available in your R Environment, you can install them using following commands.

install.packages("RCurl")
install.packages("XML")
install.packages("stringr")
install.packages("pylr")

Input Data

We will visit the URL weather data and download the CSV files using R for the year 2015.

Example

We will use the function getHTMLLinks() to gather the URLs of the files. Then we will use the function downlaod.file() to save the files to the local system. As we will be applying the same code again and again for multiple files, we will create a function to be called multiple times. The filenames are passed as parameters in form of a R list object to this function.

# Read the URL.
url <- "http://www.geos.ed.ac.uk/~weather/jcmb_ws/"

# Gather the html links present in the webpage.
links <- getHTMLLinks(url)

# Identify only the links which point to the JCMB 2015 files. 
filenames <- links[str_detect(links, "JCMB_2015")]

# Store the file names as a list.
filenames_list <- as.list(filenames)

# Create a function to download the files by passing the URL and filename list.
downloadcsv <- function (mainurl,filename) {
   filedetails <- str_c(mainurl,filename)
   download.file(filedetails,filename)
}

# Now apply the l_ply function and save the files into the current R working directory.
l_ply(filenames,downloadcsv,mainurl = "http://www.geos.ed.ac.uk/~weather/jcmb_ws/")

Verify the File Download

After running the above code, you can locate the following files in the current R working directory.

"JCMB_2015.csv" "JCMB_2015_Apr.csv" "JCMB_2015_Feb.csv" "JCMB_2015_Jan.csv"
   "JCMB_2015_Mar.csv"

R – Databases

The data is Relational database systems are stored in a normalized format. So, to carry out statistical computing we will need very advanced and complex Sql queries. But R can connect easily to many relational databases like MySql, Oracle, Sql server etc. and fetch records from them as a data frame. Once the data is available in the R environment, it becomes a normal R data set and can be manipulated or analyzed using all the powerful packages and functions.

In this tutorial we will be using MySql as our reference database for connecting to R.

RMySQL Package

R has a built-in package named “RMySQL” which provides native connectivity between with MySql database. You can install this package in the R environment using the following command.

install.packages("RMySQL")

Connecting R to MySql

Once the package is installed we create a connection object in R to connect to the database. It takes the username, password, database name and host name as input.

# Create a connection Object to MySQL database.
# We will connect to the sampel database named "sakila" that comes with MySql installation.
mysqlconnection = dbConnect(MySQL(), user = 'root', password = '', dbname = 'sakila',
   host = 'localhost')

# List the tables available in this database.
 dbListTables(mysqlconnection)

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

 [1] "actor"                      "actor_info"                
 [3] "address"                    "category"                  
 [5] "city"                       "country"                   
 [7] "customer"                   "customer_list"             
 [9] "film"                       "film_actor"                
[11] "film_category"              "film_list"                 
[13] "film_text"                  "inventory"                 
[15] "language"                   "nicer_but_slower_film_list"
[17] "payment"                    "rental"                    
[19] "sales_by_film_category"     "sales_by_store"            
[21] "staff"                      "staff_list"                
[23] "store"                     

Querying the Tables

We can query the database tables in MySql using the function dbSendQuery(). The query gets executed in MySql and the result set is returned using the Rfetch() function. Finally it is stored as a data frame in R.

# Query the "actor" tables to get all the rows.
result = dbSendQuery(mysqlconnection, "select * from actor")

# Store the result in a R data frame object. n = 5 is used to fetch first 5 rows.
data.frame = fetch(result, n = 5)
print(data.fame)

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

        actor_id   first_name    last_name         last_update
1        1         PENELOPE      GUINESS           2006-02-15 04:34:33
2        2         NICK          WAHLBERG          2006-02-15 04:34:33
3        3         ED            CHASE             2006-02-15 04:34:33
4        4         JENNIFER      DAVIS             2006-02-15 04:34:33
5        5         JOHNNY        LOLLOBRIGIDA      2006-02-15 04:34:33

Query with Filter Clause

We can pass any valid select query to get the result.

result = dbSendQuery(mysqlconnection, "select * from actor where last_name = 'TORN'")

# Fetch all the records(with n = -1) and store it as a data frame.
data.frame = fetch(result, n = -1)
print(data)

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

        actor_id    first_name     last_name         last_update
1        18         DAN            TORN              2006-02-15 04:34:33
2        94         KENNETH        TORN              2006-02-15 04:34:33
3       102         WALTER         TORN              2006-02-15 04:34:33

Updating Rows in the Tables

We can update the rows in a Mysql table by passing the update query to the dbSendQuery() function.

dbSendQuery(mysqlconnection, "update mtcars set disp = 168.5 where hp = 110")

After executing the above code we can see the table updated in the MySql Environment.

Inserting Data into the Tables

dbSendQuery(mysqlconnection,
   "insert into mtcars(row_names, mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb)
   values('New Mazda RX4 Wag', 21, 6, 168.5, 110, 3.9, 2.875, 17.02, 0, 1, 4, 4)"
)

After executing the above code we can see the row inserted into the table in the MySql Environment.

Creating Tables in MySql

We can create tables in the MySql using the function dbWriteTable(). It overwrites the table if it already exists and takes a data frame as input.

# Create the connection object to the database where we want to create the table.
mysqlconnection = dbConnect(MySQL(), user = 'root', password = '', dbname = 'sakila', 
   host = 'localhost')

# Use the R data frame "mtcars" to create the table in MySql.
# All the rows of mtcars are taken inot MySql.
dbWriteTable(mysqlconnection, "mtcars", mtcars[, ], overwrite = TRUE)

After executing the above code we can see the table created in the MySql Environment.

Dropping Tables in MySql

We can drop the tables in MySql database passing the drop table statement into the dbSendQuery() in the same way we used it for querying data from tables.

dbSendQuery(mysqlconnection, 'drop table if exists mtcars')

After executing the above code we can see the table is dropped in the MySql Environment.

R – Pie Charts

R Programming language has numerous libraries to create charts and graphs. A pie-chart is a representation of values as slices of a circle with different colors. The slices are labeled and the numbers corresponding to each slice is also represented in the chart.

In R the pie chart is created using the pie() function which takes positive numbers as a vector input. The additional parameters are used to control labels, color, title etc.

Syntax

The basic syntax for creating a pie-chart using the R is −

pie(x, labels, radius, main, col, clockwise)

Following is the description of the parameters used −

• x is a vector containing the numeric values used in the pie chart.
• labels is used to give description to the slices.
• radius indicates the radius of the circle of the pie chart.(value between −1 and +1).
• main indicates the title of the chart.
• col indicates the color palette.
• clockwise is a logical value indicating if the slices are drawn clockwise or anti clockwise.

Example

A very simple pie-chart is created using just the input vector and labels. The below script will create and save the pie chart in the current R working directory.

# Create data for the graph.
x <- c(21, 62, 10, 53)
labels <- c("London", "New York", "Singapore", "Mumbai")

# Give the chart file a name.
png(file = "city.jpg")

# Plot the chart.
pie(x,labels)

# Save the file.
dev.off()

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

Pie Chart Title and Colors

We can expand the features of the chart by adding more parameters to the function. We will use parameter main to add a title to the chart and another parameter is col which will make use of rainbow colour pallet while drawing the chart. The length of the pallet should be same as the number of values we have for the chart. Hence we use length(x).

Example

The below script will create and save the pie chart in the current R working directory.

# Create data for the graph.
x <- c(21, 62, 10, 53)
labels <- c("London", "New York", "Singapore", "Mumbai")

# Give the chart file a name.
png(file = "city_title_colours.jpg")

# Plot the chart with title and rainbow color pallet.
pie(x, labels, main = "City pie chart", col = rainbow(length(x)))

# Save the file.
dev.off()

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

Slice Percentages and Chart Legend

We can add slice percentage and a chart legend by creating additional chart variables.

# Create data for the graph.
x <-  c(21, 62, 10,53)
labels <-  c("London","New York","Singapore","Mumbai")

piepercent<- round(100*x/sum(x), 1)

# Give the chart file a name.
png(file = "city_percentage_legends.jpg")

# Plot the chart.
pie(x, labels = piepercent, main = "City pie chart",col = rainbow(length(x)))
legend("topright", c("London","New York","Singapore","Mumbai"), cex = 0.8,
   fill = rainbow(length(x)))

# Save the file.
dev.off()

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

3D Pie Chart

A pie chart with 3 dimensions can be drawn using additional packages. The package plotrix has a function called pie3D() that is used for this.

# Get the library.
library(plotrix)

# Create data for the graph.
x <-  c(21, 62, 10,53)
lbl <-  c("London","New York","Singapore","Mumbai")

# Give the chart file a name.
png(file = "3d_pie_chart.jpg")

# Plot the chart.
pie3D(x,labels = lbl,explode = 0.1, main = "Pie Chart of Countries ")

# Save the file.
dev.off()

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

R – Bar Charts

A bar chart represents data in rectangular bars with length of the bar proportional to the value of the variable.

R uses the function barplot() to create bar charts. R can draw both vertical and Horizontal bars in the bar chart. In bar chart each of the bars can be given different colors.

Syntax

The basic syntax to create a bar-chart in R is:

barplot(H,xlab,ylab,main, names.arg,col)

Following is the description of the parameters used :

• H is a vector or matrix containing numeric values used in bar chart.
• xlab is the label for x axis.
• ylab is the label for y axis.
• main is the title of the bar chart.
• names.arg is a vector of names appearing under each bar.
• col is used to give colors to the bars in the graph.

Example

A simple bar chart is created using just the input vector and the name of each bar.

The below script will create and save the bar chart in the current R working directory.

# Create the data for the chart
H <- c(7,12,28,3,41)

# Give the chart file a name
png(file = "barchart.png")

# Plot the bar chart 
barplot(H)

# Save the file
dev.off()

When we execute above code, it produces following result −

Bar Chart Labels, Title and Colors

The features of the bar chart can be expanded by adding more parameters. Themain parameter is used to add title. The col parameter is used to add colors to the bars. The args.name is a vector having same number of values as the input vector to describe the meaning of each bar.

Example

The below script will create and save the bar chart in the current R working directory.

# Create the data for the chart
H <- c(7,12,28,3,41)
M <- c("Mar","Apr","May","Jun","Jul")

# Give the chart file a name
png(file = "barchart_months_revenue.png")

# Plot the bar chart 
barplot(H,names.arg=M,xlab="Month",ylab="Revenue",col="blue",
main="Revenue chart",border="red")

# Save the file
dev.off()

When we execute above code, it produces following result −

Group Bar Chart and Stacked Bar Chart

We can create bar chart with groups of bars and stacks in each bar by using a matrix as input values.

More than two variables are represented as a matrix which is used to create the group bar chart and stacked bar chart.

#Create the input vectors.
colors=c("green","orange","brown")
months <- c("Mar","Apr","May","Jun","Jul")
regions <- c("East","West","North")

#Create the matrix of the values.
Values <- matrix(c(2,9,3,11,9,4,8,7,3,12,5,2,8,10,11),nrow=3,ncol=5,byrow=TRUE)

# Give the chart file a name
png(file = "barchart_stacked.png")

#Create the bar chart
barplot(Values, main = "total revenue", names.arg = months, xlab = "month", ylab = "revenue", col = colours)

# Add the legend to the chart
legend("topleft", regions, cex = 1.3, fill = colours)

# Save the file
dev.off()

R – Boxplots

Boxplots are a measure of how well distributed is the data in a data set. It divides the data set into three quartiles. This graph represents the minimum, maximum, median, first quartile and third quartile in the data set. It is also useful in comparing the distribution of data across data sets by drawing boxplots for each of them.

Boxplots are created in R by using the boxplot() function.

Syntax

The basic syntax to create a boxplot in R is −

boxplot(x, data, notch, varwidth, names, main)

Following is the description of the parameters used −

• x is a vector or a formula.
• data is the data frame.
• notch is a logical value. Set as TRUE to draw a notch.
• varwidth is a logical value. Set as true to draw width of the box proportionate to the sample size.
• names are the group labels which will be printed under each boxplot.
• main is used to give a title to the graph.

Example

We use the data set “mtcars” available in the R environment to create a basic boxplot. Let’s look at the columns “mpg” and “cyl” in mtcars.

input <- mtcars[,c('mpg','cyl')]
print(head(input))

When we execute above code, it produces following result −

                   mpg  cyl
Mazda RX4         21.0   6
Mazda RX4 Wag     21.0   6
Datsun 710        22.8   4
Hornet 4 Drive    21.4   6
Hornet Sportabout 18.7   8
Valiant           18.1   6

Creating the Boxplot

The below script will create a boxplot graph for the relation between mpg (miles per gallon) and cyl (number of cylinders).

# Give the chart file a name.
png(file = "boxplot.png")

# Plot the chart.
boxplot(mpg ~ cyl, data = mtcars, xlab = "Number of Cylinders",
   ylab = "Miles Per Gallon", main = "Mileage Data")

# Save the file.
dev.off()

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

Boxplot with Notch

We can draw boxplot with notch to find out how the medians of different data groups match with each other.

The below script will create a boxplot graph with notch for each of the data group.

# Give the chart file a name.
png(file = "boxplot_with_notch.png")

# Plot the chart.
boxplot(mpg ~ cyl, data = mtcars, 
   xlab = "Number of Cylinders",
   ylab = "Miles Per Gallon", 
   main = "Mileage Data",
   notch = TRUE, 
   varwidth = TRUE, 
   col = c("green","yellow","purple"),
   names = c("High","Medium","Low")
)
# Save the file.
dev.off()