Replace column value in a data frame based on other columns - r

Question

I have the following data frame ordered by name and time.
set.seed(100)
df <- data.frame('name' = c(rep('x', 6), rep('y', 4)),
'time' = c(rep(1, 2), rep(2, 3), 3, 1, 2, 3, 4),
'score' = c(0, sample(1:10, 3), 0, sample(1:10, 2), 0, sample(1:10, 2))
)
> df
name time score
1 x 1 0
2 x 1 4
3 x 2 3
4 x 2 5
5 x 2 0
6 x 3 1
7 y 1 5
8 y 2 0
9 y 3 5
10 y 4 8
In df$score there are zeros followed by an unknown number of actual values, i.e. df[1:4,], and sometimes there are overlapping df$name between two df$score == 0, i.e. df[6:7,].
I want to change df$time where df$score != 0. Specifically, I want to assign the time value of the closest upper row with df$score == 0 if df$name is matching.
The following code gives the good output but my data have millions of rows so this solution is very inefficient.
score_0 <- append(which(df$score == 0), dim(df)[1] + 1)
for(i in 1:(length(score_0) - 1)) {
df$time[score_0[i]:(score_0[i + 1] - 1)] <-
ifelse(df$name[score_0[i]:(score_0[i + 1] - 1)] == df$name[score_0[i]],
df$time[score_0[i]],
df$time[score_0[i]:(score_0[i + 1] - 1)])
}
> df
name time score
1 x 1 0
2 x 1 4
3 x 1 3
4 x 1 5
5 x 2 0
6 x 2 1
7 y 1 5
8 y 2 0
9 y 2 5
10 y 2 8
Where score_0 gives the index where df$score == 0. We see that df$time[2:4] are now all equal to 1, that in df$time[6:7] only the first one changed because the second have df$name == 'y' and the closest upper row with df$score == 0 has df$name == 'x'. The last two rows also have changed correctly.

Answer 1

You can do it like this:
library(dplyr)
df %>% group_by(name) %>% mutate(ID=cumsum(score==0)) %>%
group_by(name,ID) %>% mutate(time = head(time,1)) %>%
ungroup() %>% select(name,time,score) %>% as.data.frame()
# name time score
# 1 x 1 0
# 2 x 1 8
# 3 x 1 10
# 4 x 1 6
# 5 x 2 0
# 6 x 2 5
# 7 y 1 4
# 8 y 2 0
# 9 y 2 5
# 10 y 2 9

Answer 2

Solution using dplyr and data.table:
library(data.table)
library(dplyr)
df %>%
mutate(
chck = score == 0,
chck_rl = ifelse(score == 0, lead(rleid(chck)), rleid(chck))) %>%
group_by(name, chck_rl) %>% mutate(time = first(time)) %>%
ungroup() %>%
select(-chck_rl, -chck)
Output:
# A tibble: 10 x 3
name time score
<chr> <dbl> <int>
1 x 1 0
2 x 1 2
3 x 1 9
4 x 1 7
5 x 2 0
6 x 2 1
7 y 1 8
8 y 2 0
9 y 2 2
10 y 2 3
Solution only using data.table:
library(data.table)
setDT(df)[, chck_rl := ifelse(score == 0, shift(rleid(score == 0), type = "lead"),
rleid(score == 0))][, time := first(time), by = .(name, chck_rl)][, chck_rl := NULL]
Output:
name time score
1: x 1 0
2: x 1 2
3: x 1 9
4: x 1 7
5: x 2 0
6: x 2 1
7: y 1 8
8: y 2 0
9: y 2 2
10: y 2 3