We will first see two examples of using cumsum() on a vector to compute cumulative sum and then we will compute cumulative sum of a column in a dataframe.
library(tidyverse)
Let us create a simple vector with sequence of numbers from 1 to 10.
1:10 ## [1] 1 2 3 4 5 6 7 8 9 10
Applying cumulative sum on the vector 1:10 using cumsum() function in R, we get
cumsum(1:10) ## [1] 1 3 6 10 15 21 28 36 45 55
See that cumulative sum at any index is sum of all the elements including the value at the index. For example, cumulative sum at index 3 of the vector is 6, the sum of three elements 1,2, and 3.
Let us consider another example computing cumsum on a vector. And this time we use 10 random numbers generated from uniform distribution.
set.seed(1234) runif(10) ## [1] 0.113703411 0.622299405 0.609274733 0.623379442 0.860915384 0.640310605 ## [7] 0.009495756 0.232550506 0.666083758 0.514251141
By applying cumsum() on this random vector we get the following.
cumsum(runif(10)) ## [1] 0.6935913 1.2385661 1.5212997 2.4447332 2.7370490 3.5743447 3.8605679 ## [8] 4.1273887 4.3141115 4.5463374
We can see that when the input vector has posotive numbers cumulative sum keeps increasing.
cumulative sum of a column in a dataframe
Here we consider the example of computing cumulative sum of a column in a dataframe. For this example, we first create a dataframe with numbers from 1 to 10.
df <- tibble(x =1:10) df ## # A tibble: 10 × 1 ## x ## <int> ## 1 1 ## 2 2 ## 3 3 ## 4 4 ## 5 5 ## 6 6 ## 7 7 ## 8 8 ## 9 9 ## 10 10
We compute cumulative sum using cumsum() function as before and use mutate() function in dplyr to add the cumulative sum as a new column to the dataframe.
df %>% mutate(x_cumsum = cumsum(x)) ## # A tibble: 10 × 2 ## x x_cumsum ## <int> <int> ## 1 1 1 ## 2 2 3 ## 3 3 6 ## 4 4 10 ## 5 5 15 ## 6 6 21 ## 7 7 28 ## 8 8 36 ## 9 9 45 ## 10 10 55
We can visualise the growth cumulative sum when we sequence of numbers as in our example. Here the blue line the cumulative sum, while the dots are the sequence of numbers from 1 to 10.
