Weird : cumsum not working on dplyr

Context: I want to add cumulative sum column to my tibble named words_uni. I used library(dplyr), function mutate. I work with R version 3.4.1 64 bit - Windows 10 and RStudio Version 1.0.143

> head(words_uni) # A tibble: 6 x 3 # Groups: Type [6] Type Freq per <chr> <int> <dbl> 1 the 937839 0.010725848 2 i 918552 0.010505267 3 to 788892 0.009022376 4 a 615082 0.007034551 

Then I did the following:

> words_uni1 = words_uni %>% mutate( acum= cumsum(per)) > head(words_uni1) # A tibble: 6 x 4 # Groups: Type [6] Type Freq per acum <chr> <int> <dbl> <dbl> 1 the 937839 0.010725848 0.010725848 2 i 918552 0.010505267 0.010505267 3 to 788892 0.009022376 0.009022376 4 a 615082 0.007034551 0.007034551 

Problem: It is not doing what I was expecting, and I cannot see why.

I would appreciate your comments. Thanks in advance.

1 Answer

You must have previously grouped the tibble by type. This causes your mutate call to calculate it by type.

Here is some reproducible code:

require(readr) require(dplyr) x <- read_csv("type, freq, per the, 937839, 0.010725848 i, 918552, 0.010505267 to, 788892, 0.009022376 a, 615082, 0.007034551") ### ungrouped tibble, desired results x %>% mutate(acum = cumsum(per)) # A tibble: 4 x 4 type freq per acum <chr> <int> <dbl> <dbl> 1 the 937839 0.010725848 0.01072585 2 i 918552 0.010505267 0.02123112 3 to 788892 0.009022376 0.03025349 4 a 615082 0.007034551 0.03728804 ### grouped tibble x %>% group_by(type) %>% mutate(acum = cumsum(per)) # A tibble: 4 x 4 # Groups: type [4] type freq per acum <chr> <int> <dbl> <dbl> 1 the 937839 0.010725848 0.010725848 2 i 918552 0.010505267 0.010505267 3 to 788892 0.009022376 0.009022376 4 a 615082 0.007034551 0.007034551 

You need to simply ungroup your data.

word_uni %>% ungroup() %>% mutate(acum = cumsum(per)) 

Should do the trick.