How to Get the Cumulative Sum by Group in R

Calculate cumulative sum (cumsum) by group

df$csum <- ave(df$value, df$id, FUN=cumsum)

ave is the "go-to" function if you want a by-group vector of equal length to an existing vector and it can be computed from those sub vectors alone. If you need by-group processing based on multiple "parallel" values, the base strategy is do.call(rbind, by(dfrm, grp, FUN)).

Cumulative sum with `all` or `any` by group

You're not actually doing a cumsum--nothing needs to be summed. You are looking for the row number within the group.

Here are a couple ways with dplyr:

df %>%
  group_by(group) %>%
  mutate(
    result1 = row_number() * any(y %% 3 == 0),
    result2 = case_when(
      any(y %% 3 == 0) ~ row_number(),
      TRUE ~ 0L
    )
  )
# # A tibble: 12 × 4
# # Groups:   group [6]
#    group     y result1 result2
#    <int> <int>   <int>   <int>
#  1     1     1       0       0
#  2     1     2       0       0
#  3     2     3       1       1
#  4     2     4       2       2
#  5     3     5       1       1
#  6     3     6       2       2
#  7     4     7       0       0
#  8     4     8       0       0
#  9     5     9       1       1
# 10     5    10       2       2
# 11     6    11       1       1
# 12     6    12       2       2

How to get the cumulative sum by group in R?

library(data.table)

# convert to data.table in place
setDT(df)

# dcast and do individual sums
dt.cast = dcast.data.table(df, group ~ rep, value.var = 'value',
                           fun.aggregate = sum)
dt.cast
#   group d1 d2
#1:     0  0  1
#2:     1  1  2

# cumsum
dt.cast[, as.list(cumsum(unlist(.SD))), by = group]
#   group d1 d2
#1:     0  0  1
#2:     1  1  3

Conditional cumulative sum and grouping in R

I am able to find an answer to it, credit to the link .

myDat %>% mutate(cumsum_15 = accumulate(Freq, ~ifelse(.x + .y <= 15000000, .x + .y, .y)),
                 group_15 = cumsum(Freq == cumsum_10))

Group according to cumulative sums

Two possible one-liners, with purrr::accumulate and with MESS::cumsumbinning:

purrr::accumulate

library(tidyverse)
group_by(input, grp = LETTERS[cumsum(value == accumulate(value, ~ ifelse(.x + .y <= 100, .x + .y, .y)))])

MESS::cumsumbinning

library(dplyr)
group_by(input, grp = LETTERS[MESS::cumsumbinning(value, 100)])

output

# A tibble: 6 x 3
# Groups:   grp [3]
     id value grp  
  <int> <dbl> <chr>
1     1    99 A    
2     2     1 A    
3     3    33 B    
4     4    33 B    
5     5    33 B    
6     6   150 C    

Group by cumulative sums with conditions

Use na.locf0 from zoo to fill in the NAs and then apply rleid from data.table:

library(data.table)
library(zoo)

rleid(na.locf0(df$ID))
##  [1] 1 2 2 2 2 3 4 4 5 5 5

Cumulative total by group

If you just want cumulative sums per group, then you can do

transform(d, new=ave(value,group,FUN=cumsum))

with base R.

Cumulative sum in R by group and start over when sum of values in group larger than maximum value

One purrr approach could be:

cumsum(c(FALSE, diff(accumulate(test, ~ ifelse(.x >= 10, .y, .x + .y))) <= 0))

[1] 0 0 1 1 1 2 2 2 3

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