I'd like to generate cumulative sums with a reset if the "current" sum exceeds some threshold, using dplyr. In the below, I want to cumsum over 'a'.
library(dplyr)
library(tibble)
tib <- tibble(
t = c(1,2,3,4,5,6),
a = c(2,3,1,2,2,3)
)
# what I want
## thresh = 5
# A tibble: 6 x 4
# t a g c
# <dbl> <dbl> <int> <dbl>
# 1 1.00 2.00 0 2.00
# 2 2.00 3.00 0 5.00
# 3 3.00 1.00 1 1.00
# 4 4.00 2.00 1 3.00
# 5 5.00 2.00 1 5.00
# 6 6.00 3.00 2 3.00
# what I want
## thresh = 4
# A tibble: 6 x 4
# t a g c
# <dbl> <dbl> <int> <dbl>
# 1 1.00 2.00 0 2.00
# 2 2.00 3.00 0 5.00
# 3 3.00 1.00 1 1.00
# 4 4.00 2.00 1 3.00
# 5 5.00 2.00 1 5.00
# 6 6.00 3.00 2 3.00
# what I want
## thresh = 6
# A tibble: 6 x 4
# t a g c
# <dbl> <dbl> <int> <dbl>
# 1 1.00 2.00 0 2.00
# 2 2.00 3.00 0 5.00
# 3 3.00 1.00 0 6.00
# 4 4.00 2.00 1 2.00
# 5 5.00 2.00 1 4.00
# 6 6.00 3.00 1 7.00
I've examined many of the similar questions here (such as resetting cumsum if value goes to negative in r ) and have gotten what I hoped was close, but no.
I've tried variants of
thresh <-5
tib %>%
group_by(g = cumsum(lag(cumsum(a) >= thresh, default = FALSE))) %>%
mutate(c = cumsum(a)) %>%
ungroup()
which returns
# A tibble: 6 x 4
t a g c
<dbl> <dbl> <int> <dbl>
1 1.00 2.00 0 2.00
2 2.00 3.00 0 5.00
3 3.00 1.00 1 1.00
4 4.00 2.00 2 2.00
5 5.00 2.00 3 2.00
6 6.00 3.00 4 3.00
You can see that the "group" is not getting reset after the first time.
I think you can use accumulate()
here to help. And i've also made a wrapper function to use for different thresholds
sum_reset_at <- function(thresh) {
function(x) {
accumulate(x, ~if_else(.x>=thresh, .y, .x+.y))
}
}
tib %>% mutate(c = sum_reset_at(5)(a))
# t a c
# <dbl> <dbl> <dbl>
# 1 1 2 2
# 2 2 3 5
# 3 3 1 1
# 4 4 2 3
# 5 5 2 5
# 6 6 3 3
tib %>% mutate(c = sum_reset_at(4)(a))
# t a c
# <dbl> <dbl> <dbl>
# 1 1 2 2
# 2 2 3 5
# 3 3 1 1
# 4 4 2 3
# 5 5 2 5
# 6 6 3 3
tib %>% mutate(c = sum_reset_at(6)(a))
# t a c
# <dbl> <dbl> <dbl>
# 1 1 2 2
# 2 2 3 5
# 3 3 1 6
# 4 4 2 2
# 5 5 2 4
# 6 6 3 7
if you're interested in the group building based on cumsum < threshold
You can use the following base::
function:
cumSumReset <- function(x, thresh = 4) {
ans <- numeric()
i <- 0
while(length(x) > 0) {
cs_over <- cumsum(x)
ntimes <- sum( cs_over <= thresh )
x <- x[-(1:ntimes)]
ans <- c(ans, rep(i, ntimes))
i <- i + 1
}
return(ans)
}
call:
tib %>% mutate(g = cumSumReset(a, 5))
result:
# A tibble: 6 x 3
# t a g
# <dbl> <dbl> <dbl>
#1 1 2 0
#2 2 3 0
#3 3 1 1
#4 4 2 1
#5 5 2 1
#6 6 3 2
g
you can now do whatever you like. I know it is a bit old question, but I came across this while searching for a similar question and thus thought to include this alternate approach here too.
library MESS
has a inbuilt function cumsumbinning()
for these kind of requirements. Since here you need to cross that threshold
before stopping, you can use it like this (using threshold - 1
and setting cutwhenpassed = TRUE
in the third argument.
library(tidyverse)
library(MESS)
tib <- tibble(
t = c(1,2,3,4,5,6),
a = c(2,3,1,2,2,3)
)
n <- 5 # threshold
tib %>%
group_by(g = cumsumbinning(a, n-1, TRUE) -1) %>%
mutate(c = cumsum(a))
#> # A tibble: 6 x 4
#> # Groups: g [3]
#> t a g c
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 2 0 2
#> 2 2 3 0 5
#> 3 3 1 1 1
#> 4 4 2 1 3
#> 5 5 2 1 5
#> 6 6 3 2 3
n <- 4 # threshold
tib %>%
group_by(g = cumsumbinning(a, n-1, TRUE) -1) %>%
mutate(c = cumsum(a))
#> # A tibble: 6 x 4
#> # Groups: g [3]
#> t a g c
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 2 0 2
#> 2 2 3 0 5
#> 3 3 1 1 1
#> 4 4 2 1 3
#> 5 5 2 1 5
#> 6 6 3 2 3
n <- 6 # threshold
tib %>%
group_by(g = cumsumbinning(a, n-1, TRUE) -1) %>%
mutate(c = cumsum(a))
#> # A tibble: 6 x 4
#> # Groups: g [2]
#> t a g c
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 2 0 2
#> 2 2 3 0 5
#> 3 3 1 0 6
#> 4 4 2 1 2
#> 5 5 2 1 4
#> 6 6 3 1 7
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