Copy value from row, based on match of another row - r

Question

Having a rough time approaching this problem with a large dataset. Essentially there are multiple rows for the same item. However, only one of the items contains the required value. I need to copy that value to all matching items.
Eg. below, I need item 100 to have a cost of 1203 for every row.
df = data.frame("item" = c(100, 100, 100, 105, 105, 102, 102, 102),
"cost" = c(1203, 0, 0, 66, 0, 1200, 0, 0))
> df
item cost
1 100 1203
2 100 0
3 100 0
4 105 66
5 105 0
6 102 1200
7 102 0
8 102 0
Like so:
df_wanted = data.frame("item" = c(100, 100, 100, 105, 105, 102, 102, 102),
"cost" = c(1203, 1203, 1203, 66, 66, 1200, 1200, 1200))
> df_wanted
item cost
1 100 1203
2 100 1203
3 100 1203
4 105 66
5 105 66
6 102 1200
7 102 1200
8 102 1200
Below is my attempt at I think an inefficient method:
for (row in 1:length(df$cost)){
if (df$cost[row] == 0){
df$cost[row] = df$cost[row-1]
}
}

Answer 1

here is one option. After grouping by 'item', subset the 'cost' where the 'cost' is not 0 and select the first element
library(dplyr)
df %>%
group_by(item) %>%
mutate(cost = first(cost[cost!=0))
# A tibble: 8 x 2
# Groups: item [3]
# item cost
# <dbl> <dbl>
#1 100 1203
#2 100 1203
#3 100 1203
#4 105 66
#5 105 66
#6 102 1200
#7 102 1200
#8 102 1200

Answer 2

Looks like you want to group by item and then replace 0 in cost with the last non-zero value. In each group, cummax(which(cost != 0)) will give the index of the last non-zero value.
library(dplyr)
df %>%
group_by(item) %>%
mutate(cost = cost[cummax(which(cost != 0))]) %>%
ungroup()
## A tibble: 8 x 2
# item cost
# <dbl> <dbl>
#1 100 1203
#2 100 1203
#3 100 1203
#4 105 66
#5 105 66
#6 102 1200
#7 102 1200
#8 102 1200
Base R equivalent is
transform(df, cost = ave(cost, item, FUN = function(x) x[cummax(which(x != 0))]))

Answer 3

What I ended up going with after revisiting this problem as a left_join(). Which makes more sense to me intuitively though it may not be the best solution.
The original DF below.
df = tibble("item" = as.factor(c(100, 100, 100, 105, 105, 102, 102, 102)),
"cost" = c(1203, 0, 0, 66, 0, 0, 1200, 0))
> df
# A tibble: 8 x 2
item cost
<fct> <dbl>
1 100 1203
2 100 0
3 100 0
4 105 66
5 105 0
6 102 0
7 102 1200
8 102 0
Create an 'index' of item-value pairs
df_index <- df %>%
group_by(item) %>%
arrange(-cost) %>%
slice(1)
> df_index
# A tibble: 3 x 2
# Groups: item [3]
item cost
<fct> <dbl>
1 100 1203
2 102 1200
3 105 66
Finally, join the dataframes by item to fill in the empty row values.
df_joined <- df %>%
left_join(df_index, by="item")
> df_joined
# A tibble: 8 x 3
item cost.x cost.y
<fct> <dbl> <dbl>
1 100 1203 1203
2 100 0 1203
3 100 0 1203
4 105 66 66
5 105 0 66
6 102 0 1200
7 102 1200 1200
8 102 0 1200