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How to add a column based on other columns in R DataFrame ?

  • Last Updated : 30 Apr, 2021

A data frame can be accessed and modified to store new insertions and deletions. The data frame can undergo mutations to increase its dimensions and store more data, as well as the rows and columns values, which can be modified based on other cell values. 

In this article, we will see how to add columns based on other columns in DataFrame in R Programming Language. There can be various methods to do the same. Let’s discuss them in detail.

Method 1 : Using transform() function

The transform() method in R is used to modify the data and perform mutations. It transforms the first argument that is supplied to the function. New columns can also be added as a second argument of the function, where it may be either a list declared at the beginning or initialized during run times using the desired regular expression evaluation. We can specify the newly added column name on the left side of the second argument, and declare the if-else expression on the right. The if-else expression consists of three parts,

  • The condition to test the data upon
  • The second part is evaluated when the condition is not satisfied
  • Third when it isn’t.

The result has to be explicitly into the original data frame, in order to pertain to the results. 

Syntax:



transform(dataframe,x=c(..))

where x is the newly added column. 

Example:

R




# creating a data frame
data_frame = data.frame(col1=c(1,2,3,-4),
                        col2=c(8,9,5,10),
                        col3=c(0,2,3,5))
  
# printing original data frame
print("Original Data Frame")
print (data_frame)
  
# transforming data frame
# declare col4 where if col1 is equal 
# to col3, replace by col1+col3 value, 
# otherwise by col1+col2 value
data_frame <- transform(
  data_frame, col4= ifelse(col1==col3, col1+col2, col1+col3))
  
print("Modified Data Frame")
print(data_frame)

Output

[1] "Original Data Frame"
 col1 col2 col3
1    1    8    0
2    2    9    2
3    3    5    3
4   -4   10    5
[1] "Modified Data Frame"
 col1 col2 col3 col4
1    1    8    0    1
2    2    9    2   11
3    3    5    3    8
4   -4   10    5    1

Method 2 : Using with() method

The with() method in R can be used to evaluate expressions and then transform the data contained in a data frame. With is a generic function that evaluates expression specified as the second argument of the function in a local environment constructed from data, which is defined in the first argument of the function. Any logical expression can be provided as the first argument of the method and the value in the new column is replaced depending on the truth value of the expression after evaluating the condition in the argument parts of the with method. 

Syntax:

with(data, expr, …)



Example:

R




# creating a data frame
data_frame = data.frame(col1=c(1,2,3,-4),
                        col2=c(8,9,5,10),
                        col3=c(0,2,3,5))
  
# printing original data frame
print("Original Data Frame")
print (data_frame)
  
# transforming data frame
# declare col4 where if col1 is equal
# to col3, replace by col1+col3 value,
# otherwise by col1+col2 value
data_frame$col4 <- with(
  data_frame, ifelse(col1+col3>5, col1+col3, col1+col2))
  
print("Modified Data Frame")
print(data_frame)

Output

[1] "Original Data Frame"
 col1 col2 col3
1    1    8    0
2    2    9    2
3    3    5    3
4   -4   10    5
[1] "Modified Data Frame"
 col1 col2 col3 col4
1    1    8    0    9
2    2    9    2   11
3    3    5    3    6
4   -4   10    5    6

Method 3 : Using apply() method 

apply() method in R takes a well-organized data frame or matrix as an input and gives as output a vector, list, or an array. apply() method is primarily used to avoid explicit uses of loop constructs. Any function can be specified into the apply() method. The result has to be explicitly into the original data frame, in order to pertain the results.  

Syntax: apply(X, margin, FUN)

Parameter : 

  • x: a data frame or a matrix
  • margin:  take a value or range between 1 and 2 to define where to apply the function:
    • margin=1 : the manipulation is performed on rows
    • margin=2 : the manipulation is performed on columns
    • margin=c(1,2) : the manipulation is performed on both rows and columns
  • FUN: the function to apply where in built functions like mean, median, sum, min, max and even user-defined functions can be applied

Example:

R




# creating a data frame
data_frame = data.frame(col1=c(1,2,3,-4),
                        col2=c(8,9,5,10),
                        col3=c(0,2,3,5))
  
# printing original data frame
print("Original Data Frame")
print (data_frame)
  
# transforming data frame
# declare col4 where if col1 is
# equal to col3, replace by col1+col2
# value, otherwise by col3-col2 value
data_frame$col4 <- apply(
  data_frame, 1, FUN = function(x) if(mean(x[1])>1) x[2]+x[1] else x[3]-x[2])
  
print("Modified Data Frame")
print(data_frame)

Output

[1] "Original Data Frame"
  col1 col2 col3
1    1    8    0
2    2    9    2
3    3    5    3
4   -4   10    5
[1] "Modified Data Frame"
  col1 col2 col3 col4
1    1    8    0   -8
2    2    9    2   11
3    3    5    3    8
4   -4   10    5   -5



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