New variable for cumulative count of one variable in another variable

I have previously asked a question about how to create a variable which counts instances of one ID number appearing in another column (see here). However, I have now realised that I need the new columns to hold a cumulative sum of the number of times the victim (in a particular crime incident) has been recorded as a suspect (in incidents occurring previous to that incident), and one which counts the number of times the suspect (in a particular crime incident) has been recorded as a victim (in incidents occurring previous to that incident). My data is already ordered by date so all I need is a way of cumulatively counting.

Here's a simplified version of my data:

And here is what I want to create:

Note that, where there is an NA, I would like the NA to be preserved. I'm currently trying to work in tidyverse and piping where possible, so I would prefer answers in that kind of format, but I'm open to any solution!

I tried adapting user438383's answer to my previous question but it threw an error (I'm quite new to R so I wasn't sure what this meant!):

# DUMMY DATA TEST
s.uid <- c(1:5, 9, NA, 5, 9)
v.uid <- c(3, 9, 8, 5, 2, 2, 7, 9, 5)

dat <- tibble(s.uid, v.uid)

dat %>% 
  group_by(s.uid) %>% 
  mutate(s.in.v = cumsum(dat$v.uid %in% s.uid)) %>% 
  group_by(v.uid) %>% 
  mutate(v.in.s = cumsum(dat$s.uid %in% v.uid))

Error: Problem with `mutate()` input `s.in.v`.
x Input `s.in.v` can't be recycled to size 1.
ℹ Input `s.in.v` is `cumsum(dat$v.uid %in% s.uid)`.
ℹ Input `s.in.v` must be size 1, not 9.
ℹ The error occurred in group 1: s.uid = 1.

One approach is to use the magrittr pipe placeholder dot, and a rowwise approach, summing the number of TRUE values of a subset of the column.

dat %>%
  mutate(n = row_number()) %>%
  rowwise() %>%
  mutate(s.in.v = ifelse(is.na(s.uid), NA, sum(s.uid == .$v.uid[1:n], na.rm = T)),
         v.in.s = ifelse(is.na(v.uid), NA, sum(v.uid == .$s.uid[1:n], na.rm = T))) %>%
  ungroup() %>%
  select(-n)


# A tibble: 9 x 5
      n s.uid v.uid s.in.v v.in.s
  <int> <dbl> <dbl>  <int>  <int>
1     1     1     3      0      0
2     2     2     9      0      0
3     3     3     8      1      0
4     4     4     5      0      0
5     5     5     2      1      1
6     6     9     2      1      1
7     7    NA     7     NA      0
8     8     5     9      1      1
9     9     9     5      2      2

Note that this is likely not computationally efficient.