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Mutating column in dplyr using rowSums

  • Last Updated : 24 Oct, 2021

In this article, we are going to discuss how to mutate columns in dataframes using the dplyr package in R Programming Language. 

Installation

The package can be downloaded and installed in the R working space using the following command : 

Install Command – install.packages(“dplyr”)

Load Command – library(“dplyr”)

Functions Used

  • mutate(): The mutate() method in this package adds new variables and preserves existing ones. The mutate method doesn’t affect the rows of the dataframe. However, re-grouping may happen based on the groups created in the dataframe. dataframe attributes are also preserved.

Syntax:



mutate (new-col-name = rowSums())

  • rowSums(): The rowSums() method calculates the sum of each row of a numeric array, matrix, or dataframe. We can select specific rows to compute the sum in this method. Since, the matrix created by default row and column names are labeled using the X1, X2.., etc. labels, we can specify them using these names. The rows can be selected using the select_ method. 

Syntax:

select_(. , col-names.. )

Parameters:

  • col-names : column names in the dataframe

Example 1:

In this example we are going to create a dataframe with a matrix with 3 columns – X1, X2, X3, and all columns are selected and their row sums are computed. An additional column “row_sum” is appended to the end of the dataframe.

R




# load the dplyr library
library("dplyr")
  
# creating the dataframe
# from the matrix
data_frame < - data.frame(matrix(rnorm(30), 10, 3),
                          stringsAsFactors=FALSE)
  
print("Original DataFrame")
print(data_frame)
  
# computing row sums
data_mod < - data_frame % > % mutate(row_sum=rowSums(
  select_(., "X1", "X2", "X3")))
  
# printing modified dataframe
print("Modified DataFrame")
print(data_mod)

Output

[1] "Original DataFrame" 
X1         X2         X3 
1  -2.1548694 -1.1243811 -1.3944730 
2   1.1023396 -2.0153914 -1.6321950 
3  -0.2959568 -0.6511423 -0.2601204 
4  -0.1503434 -0.3802135  0.5651982 
5   0.7330868  1.8792182  0.1205579 
6   0.5351399 -0.1250861 -0.4986981 
7  -0.4058386 -0.0359763 -0.8261032 
8  -1.3560053 -0.2901260 -1.1033241 
9  -0.6176755 -0.8223494  0.8507067 
10  0.7307755 -1.2664778  1.2097483 
[1] "Modified DataFrame" 
X1         X2         X3     row_sum 
1  -2.1548694 -1.1243811 -1.3944730 -4.67372344 
2   1.1023396 -2.0153914 -1.6321950 -2.54524683 
3  -0.2959568 -0.6511423 -0.2601204 -1.20721946 
4  -0.1503434 -0.3802135  0.5651982  0.03464132 
5   0.7330868  1.8792182  0.1205579  2.73286285 
6   0.5351399 -0.1250861 -0.4986981 -0.08864431 
7  -0.4058386 -0.0359763 -0.8261032 -1.26791811 
8  -1.3560053 -0.2901260 -1.1033241 -2.74945549 
9  -0.6176755 -0.8223494  0.8507067 -0.58931825 
10  0.7307755 -1.2664778  1.2097483  0.67404601

Example 2:

In this example,  rowSums of X1 and X3 are computed. Only these columns are returned in the final output.

R




# load the package
library("dplyr")
  
# creating the dataframe
data_frame < - data.frame(matrix(rnorm(30), 10, 3),
                          stringsAsFactors=FALSE)
  
print("Original DataFrame")
print(data_frame)
  
# computing row sums
data_mod < -
data_frame % >%
mutate(row_sum=rowSums(select(., .dots=all_of(c("X1", "X2")))))
  
# printing modified dataframe
print("Modified DataFrame")
print(data_mod)

Output

[1] "Original DataFrame" 
 X1         X2         X3 
1  -0.01475802 -2.0928792  0.6990158 
2   0.09758214  0.9327706 -0.7551849 
3   1.73099513 -2.0445329  0.7353809 
4  -0.98991323 -0.8638640  0.7545635 
5  -0.10079777 -1.0169922 -2.2176920 
6  -0.32026943 -0.2890030  1.0493662 
7   0.13442533 -2.3674214  0.4975756 
8  -1.47351401 -1.1391841 -1.0987409 
9   1.05674759 -0.7550495  1.0312730 
10 -0.14471879  0.7089866  0.1736686 
[1] "Modified DataFrame"            
X1         X2         X3    row_sum 
1  -0.01475802 -2.0928792  0.6990158 -2.1076372 
2   0.09758214  0.9327706 -0.7551849  1.0303527 
3   1.73099513 -2.0445329  0.7353809 -0.3135378 
4  -0.98991323 -0.8638640  0.7545635 -1.8537772 
5  -0.10079777 -1.0169922 -2.2176920 -1.1177900 
6  -0.32026943 -0.2890030  1.0493662 -0.6092725 
7   0.13442533 -2.3674214  0.4975756 -2.2329960 
8  -1.47351401 -1.1391841 -1.0987409 -2.6126981 
9   1.05674759 -0.7550495  1.0312730  0.3016981 
10 -0.14471879  0.7089866  0.1736686  0.5642678 



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