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You can use the following methods to calculate the cumulative sum of a column in R using the dplyr 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 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 â€˜salesâ€™ 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 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 â€˜salesâ€™ column, grouped by the â€˜storeâ€™ 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

**Additional Resources**

The following tutorials explain how to perform other common calculations in R:

How to Calculate the Sum by Group in R

How to Calculate the Mean by Group in R