Cumulative count of each value
The dplyr
way:
library(dplyr)
foo <- data.frame(id=c(1, 2, 3, 2, 2, 1, 2, 3))
foo <- foo %>% group_by(id) %>% mutate(count=row_number())
foo
# A tibble: 8 x 2
# Groups: id [3]
id count
<dbl> <int>
1 1 1
2 2 1
3 3 1
4 2 2
5 2 3
6 1 2
7 2 4
8 3 2
That ends up grouped by id
. If you want it not grouped, add %>% ungroup()
.
The ave
function computes a function by group.
> id <- c(1,2,3,2,2,1,2,3)
> data.frame(id,count=ave(id==id, id, FUN=cumsum))
id count
1 1 1
2 2 1
3 3 1
4 2 2
5 2 3
6 1 2
7 2 4
8 3 2
I use id==id
to create a vector of all TRUE
values, which get converted to numeric when passed to cumsum
. You could replace id==id
with rep(1,length(id))
.
Here is a way to get the counts:
id <- c(1,2,3,2,2,1,2,3)
sapply(1:length(id),function(i)sum(id[i]==id[1:i]))
Which gives you:
[1] 1 1 1 2 3 2 4 2
For completeness, adding a data.table way:
library(data.table)
DT <- data.table(id = c(1, 2, 3, 2, 2, 1, 2, 3))
DT[, count := seq(.N), by = id][]
Output:
id count
1: 1 1
2: 2 1
3: 3 1
4: 2 2
5: 2 3
6: 1 2
7: 2 4
8: 3 2