resetting cumsum if value goes to negative in r

Question

ve <- c(17, -9, 9, -17, 17, -17, 11, -9, 16, -18, 17, 0, 0, -18, 17, 0, 0, -17, 14, -14, 17, -2, 0, -15, 9, -9, 17, -16, 16, -17, 17, -17, 17, -17, 17, -17, 17, -8, 7, -16, 17, -14, 14, -10, 10, -16, 16, -10, 10, -12, 12, -11, 11, -17, 17, -17, 17, -9, 8, -17, 17, -17, 17, -16, 16, -17, 17, -8, 8, -9, 9, -17, 17, -17, 17, -13, 13, -10, 7, -10, 13, -16, 17, -13, 13, -13, 13, -9, 8, -17, 17, -10, 9, -17, 17, -17, 17, -16, 16, -10, 10, -15, 15, -14, 14, -14, 15, -13, 13, -9, 9, -13, 13, -12, 12, -10, 9, -11, 12, -8, 7, -10, 10, -9, 9, -11, 11, -9, 9, -7, 7, -12, 11, -11, 12, -11, 11, -14, 14, -13, 13, -10, 10, -13, 13, -17, 17, -7, 7, -17, 17, -17, 17, -14, 14, NA)

df <- data.frame(ve = ve, calc = 0)

I need to calculate cumsum in column calc, but it needs to reset to zero and start again whenever its value goes negative.. I've tried several conditions but it's not really working...

Also, is it possible to achieve this in dplyr? I'm new to dplyr and find it somewhat difficult whenever I need to use dependent value..

Thank you for your help!

it should go as..

     ve calc
1    17    17
2    -9    8
3     9    17
4   -17    0
5    17    17
6   -17    0
7    11    11
8    -9    2
9    16    18
10  -18    0
11   17    17
12    0    17
13    0    17
14  -18    0
15   17    17

If you see row 14 and 15, with the normal cumsum it would be -1 and 16 but I want it to reset to 0 instead of -1 and continue cumsum, hence the next would be 17

Answer 1

We can replace the NA values with 0 and use cumsum

library(dplyr)
df1 <- df %>%
      group_by(grp = cumsum(lag(cumsum(replace(ve, is.na(ve), 0)) < 0, default = TRUE))) %>%
     mutate(calc = cumsum(replace(ve, is.na(ve), 0)), calc = replace(calc, calc < 0, 0)) %>%
      ungroup() %>%
      select(-grp)
head(df1, 15)
# A tibble: 15 x 2
#      ve  calc
#   <dbl> <dbl>
# 1    17    17
# 2    -9     8
# 3     9    17
# 4   -17     0
# 5    17    17
# 6   -17     0
# 7    11    11
# 8    -9     2
# 9    16    18
#10   -18     0
#11    17    17
#12     0    17
#13     0    17
#14   -18     0
#15    17    17

Answer 2

Here is an iterative solution. I can't think of how to do this vectorized/using dplyr without multiple passes over the data, but I'm sure someone else will:

ve_csum = numeric(length(ve))

current_total = 0
for (i in 1:length(ve)) {
    if (is.na(ve[i])) {
        ve_csum[i] = current_total
        next
    }
    current_total = current_total + ve[i]
    if (current_total < 0) {
        current_total = 0
    }
    ve_csum[i] = current_total
}

result = data.frame(ve, ve_csum)

Answer 3

Not using dplyr, but this should work:

ve = as.data.frame(ve)
ve = na.omit(ve)
ve$cumS = 0
ve$cumS[1] = ve$ve[1]

for (i in 2 : length(ve$ve)) {

ve$cumS[i] = ifelse((ve$cumS[i - 1] + ve$ve[i]) < 0,
                     0, (ve$cumS[i - 1] + ve$ve[i]))
}

Answer 4

> df$calc=ifelse(cumsum(df$ve)<0,0,cumsum(df$ve))