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The dplyr package in R provides a convenient way to calculate a cumulative sum using the cumsum() function. This function takes a vector of values as its argument and returns the cumulative sum of the values as a vector. For example, if you have a vector of numbers, you can use cumsum() to obtain the cumulative sum of the numbers in the vector. This can be useful when you want to obtain the total of a sequence of values across multiple rows.
You can use the following methods to calculate the cumulative sum of a column in R using the package:
Method 1: Calculate Cumulative Sum of One Column
df %>% mutate(cum_sum = cumsum(var1))
Method 2: Calculate Cumulative Sum by Group
df %>% group_by(var1) %>% mutate(cum_sum = cumsum(var2))
The following examples show how to use each method in practice.
Example 1: Calculate Cumulative Sum Using dplyr
Suppose we have the following data frame in R:
#create dataset df <- data.frame(day=c(1, 2, 3, 4, 5, 6, 7, 8), sales=c(7, 12, 10, 9, 9, 11, 18, 23)) #view dataset df day sales 1 1 7 2 2 12 3 3 10 4 4 9 5 5 9 6 6 11 7 7 18 8 8 23
We can use the following code to create a new column that contains the cumulative sum of the values in the ‘sales’ column:
library(dplyr)
#calculate cumulative sum of sales
df %>% mutate(cum_sales = cumsum(sales))
day sales cum_sales
1 1 7 7
2 2 12 19
3 3 10 29
4 4 9 38
5 5 9 47
6 6 11 58
7 7 18 76
8 8 23 99
Example 2: Calculate Cumulative Sum by Group Using dplyr
Suppose we have the following data frame in R:
#create dataset
df <- data.frame(store=c('A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'),
day=c(1, 2, 3, 4, 1, 2, 3, 4),
sales=c(7, 12, 10, 9, 9, 11, 18, 23))
#view dataset
df
store day sales
1 A 1 7
2 A 2 12
3 A 3 10
4 A 4 9
5 B 1 9
6 B 2 11
7 B 3 18
8 B 4 23
We can use the following code to create a new column that contains the cumulative sum of the values in the ‘sales’ column, grouped by the ‘store’ column:
library(dplyr)
#calculate cumulative sum of sales by store
df %>% group_by(store) %>% mutate(cum_sales = cumsum(sales))
# A tibble: 8 x 4
# Groups: store [2]
store day sales cum_sales
1 A 1 7 7
2 A 2 12 19
3 A 3 10 29
4 A 4 9 38
5 B 1 9 9
6 B 2 11 20
7 B 3 18 38
8 B 4 23 61