R is a programming language that supports several data types. Here are some common data types in R:
1. Numeric: Represents real numbers (e.g., 3.14, -2.5).
R
x <- 3.14
y<-5
2. Integer: Represents integer numbers (e.g., 1, -5).
R
y <- 5L # L indicates an integer
3. Character: Represents text or strings.
R
z <- "Hello, World!"
4. Logical: Represents Boolean values (TRUE or FALSE).
R
is_r_programming_fun <- TRUE
5. Factor: Represents categorical data with predefined levels.
R
gender <- c("Male", "Female", "Male", "Female")
To check the data type of a variable, you can use the class() function or the typeof() function:
R
# Using class()
class(x) # Returns the class of x
# Using typeof()
typeof(y) # Returns the type of y
For example, if you want to check the data type of a variable x, you can use class(x) or typeof(x). Both functions will give you information about the data type of the variable.
R
# Example
x <- 3.14
class(x) # Returns "numeric"
typeof(x) # Returns "double"
5 ^3
Operator Name Example
+ Addition x + y
- Subtraction x - y
* Multiplication x * y
/ Division x / y
^(xor) Exponent(power) x ^ y [shift+6]
%% Modulus (Remainder from division) x %% y
%/% Integer Division x%/%y
miscllaneous operators:
=======================
: Creates a series of numbers in a sequence x <- 1:10
if statement
============
used to execute a block of code if condition is true.
syntax:
if(condition)
{
//code if condition is true
}
example:
ae<-6
if(ae%%2==0)
{
print("even")
}
if else statement
====================
operators to check conditions:
>
<
>=
<=
==
!=
example:
--------
n<-9
if(n%%2==0)
{
print("number is even")
} else {
print("number is odd")
}
if else if
==========
syntax:
if(condition)
{
//code if condition is true
}
else if(condition2)
{
//code if condition2 is true
}
else
{
//if all conditions are false
}
example:
---------
a<-23
b<-4
task<-"*"
if(task=="+")
{
print(a+b)
} else if(task=="-"){
print(a-b)
} else {
print("invalid")
}
function
===========
1.function is a block of code that executes when it is called.
2.function keyword is used to create a function.
syntax:
func_name <- function(arg_if_any)
{
//block of code
return()//if any
}
example:
tryfunction<-function(n=0){
if(n<0)
{
print("negative")
} else {
"positive"
}
}
tryfunction(-7)
recursion: function calling itself with a condition.
==========
example:
tryfunction<-function(n=0){
if(n>0)
{
print(n)
n<-n-1
tryfunction(n)
}
}
tryfunction(6)
data structures
==================
vector:
R Vectors are the same as the arrays in R language which are used to hold multiple data values of the same type.
One major key point is that in R Programming Language the indexing of the vector will start from '1' and not from '0'.
We can create numeric vectors and character vectors as well.
syntax:
var_name<-c()
example:
[-------]
data<-c(5,8,0,3,4)
print(data)
for(rtg in data)
{
print(rtg)
}
matrix
========
matrix(g<-matrix(c(45,3,345,5.6),nrow=2,ncol=2))
list(c(4,56,64))
In R programming, several built-in data structures are used to organize and store data efficiently. Here are some commonly used data structures in R:
Vectors:
A vector is a basic data structure in R and is a one-dimensional array that can hold elements of the same data type.
You can create a vector using the c() function.
# Example of creating a vector
my_vector <- c(1, 2, 3, 4, 5)
SORT THE VECTOR
=================
sort(vector_var)
vector
-------
data<-c(4,6,33,5.4)
sort(data)
length(data)
data[c(1)]
data[c(-2)]
Matrices:
=========
A matrix is a two-dimensional array that contains elements of the same data type.
You can create a matrix using the matrix() function.
# Example of creating a matrix
my_matrix <- matrix(1:6, nrow = 2, ncol = 3)
Arrays:
=======
An array is a multi-dimensional extension of a matrix. It can have more than two dimensions.
You can create an array using the array() function.
# Example of creating a 3D array
my_array <- array(1:24, dim = c(r,c,t))
my_array <- array(1:24, dim = c(2, 3, 4))
Lists:
======
A list is a versatile data structure that can contain elements of different data types.
It is often used to store heterogeneous data.
You can create a list using the list() function.
# Example of creating a list
my_list <- list(name = "John", age = 30, grades = c(90, 85, 92))
check existence of a data in list
------------------------------------
thislist <- list("apple", "banana", "cherry")
"apple" %in% thislist
add a element
-------------
append(thislist, "orange")
itr<-function(l)
{
for(a in l)
{
print(a)
}
}
t<-list(5,33,2,2,1)
itr(t)
t<-append(t,5.6)
itr(t)
Data Frames:
============
A data frame is a two-dimensional table similar to a matrix, but it can store columns of different data types.
You can create a data frame using the data.frame() function.
# Example of creating a data frame
my_dataframe <- data.frame(name = c("John", "Jane", "Bob"), age = c(25, 30, 22))
names<-c("lakshmi","rahul gandhi","satish tiwari","modi")
ages<-c(4,5,3,4)
d<-data.frame(name=names,age=ages)
print(d)
Factors:
========
A factor is used to represent categorical data in R. It is a vector that can take on a limited set of distinct values.
You can create a factor using the factor() function.
# Example of creating a factor
my_factor <- factor(c("Male", "Female", "Male", "Female"))
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