# R - Variables

Variable Name | Validity | Reason |
---|---|---|

var_name2. | valid | Has letters, numbers, dot and underscore |

var_name% | Invalid | Has the character '%'. Only dot(.) and underscore allowed. |

2var_name | invalid | Starts with a number |

.var_name , var.name | valid | Can start with a dot(.) but the dot(.)should not be followed by a number. |

.2var_name | invalid | The starting dot is followed by a number making it invalid. |

_var_name | invalid | Starts with _ which is not valid |

## 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()**or

**cat()**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 foundAll 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)

*Table of contents:*1. R - Overview

2. R - Environment Setup

3. R - Basic Syntax

4. R - Data Types

5. R - Variables

6. R - Operators

7. R - Decision Making

8. R - Loops

9. R - Functions

10. R - Strings

11. R - Vectors

12. R - Matrices

13. R - Arrays

14. R - Factors

15. R - Data Frames

16. R - Packages

17. R - Data Reshaping

18. R - CSV Files

19. R - Excel Files

20. R - Binary Files

21. R - XML Files

22. R - JSON Files

23. R - Web Data

24. R - Database

25. R - Pie Charts

26. R - Bar Charts

27. R - Boxplots

28. R - Histograms

29. R - Line Graphs

30. R - Scatterplots

31. R - Mean, Median and Mode

32. R - Linear Regression

33. R - Multiple Regression

34. R - Logistic Regression

35. R - Normal Distribution

36. R - Binomial Distribution

37. R - Poisson Regression

38. R - Analysis of Covariance

39. R - Time Series Analysis

40. R - Nonlinear Least Square

41. R - Decision Tree

42. R - Random Forest

43. R - Survival Analysis

44. R - Chi Square Tests

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