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- R Statistics Examples
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- R - Linear Regression
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- R - Analysis of Covariance
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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 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 |

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.

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

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"

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)

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