Sum of rows based on column value in R dataframe

In this article, we will be discussing how we can sum up row values based on column value in a data frame in R Programming Language. Suppose you have a data frame like this:

	fruits	shop_1	shop_2
1.	Apple	1	13
2.	Mango	9	5
3.	Strawberry	2	14
4.	Apple	10	6
5.	Apple	3	15
6.	Strawberry	11	7
7.	Mango	4	16
8.	Strawberry	12	8

This dataset consists of fruits name and shop_1, shop_2 as a column name. Here shop_1 and shop_2 show the number of fruits available in shops. Now you want to find the aggregate sum of all the rows in shope_1 that have the same fruit value. So our dataset looks like this :

	fruits	shop_1	shop_2
1.	Apple	14	34
2.	Mango	13	21
3.	Strawberry	25	29

Method 1: Using aggregate function

The aggregate function creates a subset of the original data and computes the statistical function for each subset and returns the result.

Syntax:

aggregate(.~fruit,data=df,FUN=sum)

Example:

R

# Sum of rows based on column values

# Creating dataset
# creating fuits column
x <- c("Apple","Mango","Strawberry",
       "Apple","Apple","Strawberry",
       "Mango","Strawberry")

# creating shop_1 column
y <- c(1,9,2,10,3,11,4,12)

# creating shop_2 column
z <- c(13,5,14,6,15,7,16,8)

# creating dataframe
df <- data.frame(fruits=x,shop_1=y,shop_2=z)

# applying aggregate function
aggregate(.~fruits,data=df,FUN=sum)

Output:

Using aggregate function

Method 2: Using ddply and  numcolwise function

ddply  simply split the given data frame and perform any  operation on it (probably apply a  function) and return the data frame.

colwise is a function from the famous plyr package. colwise function is used to compute a function on each column in data frame, it computes column wise.

Example:

R

# Sum of rows based on column values

# loading library
library(plyr)

# Creating dataset
# creating fuits column
x <- c("toy1","toy2","toy3",
       "toy1","toy1","toy3",
       "toy2","toy3")

# creating stock_1 column
y <- c(1,2,3,4,5,6,4,8)

# creating stock_2 column
z <- c(9,1,10,5,2,6,4,8)

# creating dataframe
df <- data.frame(toys=x,stock_1=y,stock_2=z)

# using sum function colwise
ddply(df,"toys",numcolwise(sum))

Output: 

Using ddply