Cumulative Sum calculation in R, using the dplyr package in R, you can calculate the cumulative sum of a column using the following methods.
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Approach 1: Calculate Cumulative Sum of One Column
df %>% mutate(cum_sum = cumsum(var1))
Approach 2: Calculate Cumulative Sum by Group
df %>% group_by(var1) %>% mutate(cum_sum = cumsum(var2))
The examples below demonstrate how to apply each strategy in practice.
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Example 1: Using dplyr, calculate the cumulative sum.
Let’s say we have the following R data frame:
Let’s make a dataset
df <- data.frame(day=c(1, 2, 3, 4, 5, 6, 7, 8), sales=c(57, 42, 50, 99, 59, 51, 58, 45))
Now we can view the dataset
df
day sales 1 1 57 2 2 42 3 3 50 4 4 99 5 5 59 6 6 51 7 7 58 8 8 45
To create a new column that holds the cumulative sum of the values in the ‘sales’ column, use the following code.
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library(dplyr)
Let’s calculate the cumulative sum of sales
df %>% mutate(cum_sales = cumsum(sales))
day sales cum_sales 1 1 57 57 2 2 42 99 3 3 50 149 4 4 99 248 5 5 59 307 6 6 51 358 7 7 58 416 8 8 45 461
Example 2: Using dplyr, calculate the Cumulative Sum by Group.
Let’s say we have the following R data frame.
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Make a dataset
df <- data.frame(store=c('X', 'X', 'X', 'X', 'Y', 'Y', 'Y', 'Y'),
day=c(1, 2, 3, 4, 1, 2, 3, 4),
sales=c(87, 82, 80, 98, 98, 81, 88, 83))
View the dataset now
df
X 1 87 2 X 2 82 3 X 3 80 4 X 4 98 5 Y 1 98 6 Y 2 81 7 Y 3 88 8 Y 4 83
To construct a new column that holds the cumulative sum of the values in the ‘sales’ column, grouped by the ‘store’ column, we can use the following code:
library(dplyr)
Now we can calculate the cumulative sum of sales by store.
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df %>% group_by(store) %>% mutate(cum_sales = cumsum(sales))
store day sales cum_sales <chr> <dbl> <dbl> <dbl> 1 X 1 87 87 2 X 2 82 169 3 X 3 80 249 4 X 4 98 347 5 Y 1 98 98 6 Y 2 81 179 7 Y 3 88 267 8 Y 4 83 350
