How can I split rows up by the number of times located in a column in R?

Question

For example, suppose you have the following dataframe:

ID<-c("11", "12", "13", "14", "14")
Date<-c("2020-01-01", "2020-02-01", "2020-03-15", "2020-04-10", "2020-06-01")
Item<-c("Item1", "Item1", "Item2", "Item2", "Item2")
ItemPrice<-c(5, 5, 7, 7, 7)
Quantity<-c(1, 2, -2, 2, 3)
Cost<-c(5, 10, -14, 14, 21)
df<-data.frame(ID, Date, Item, ItemPrice, Quantity, Cost)
df

  ID       Date  Item ItemPrice Quantity Cost
1 11 2020-01-01 Item1         5        1    5
2 12 2020-02-01 Item1         5        2   10
3 13 2020-03-15 Item2         7       -2  -14
4 14 2020-04-10 Item2         7        2   14
5 14 2020-06-01 Item2         7        3   21

However, you wanted to separate the rows by Quantity so each one represents an individual sale like the following:

   ID       Date  Item ItemPrice Quantity Cost
1  11 2020-01-01 Item1         5        1    5
2  12 2020-02-01 Item1         5        1    5
3  12 2020-02-01 Item1         5        1    5
4  13 2020-03-15 Item2         7       -1   -7
5  13 2020-03-15 Item2         7       -1   -7
6  14 2020-04-10 Item2         7        1    7
7  14 2020-04-10 Item2         7        1    7
8  14 2020-06-01 Item2         7        1    7
9  14 2020-06-01 Item2         7        1    7
10 14 2020-06-01 Item2         7        1    7

How could this be achieved?

Answer 1

Create a count column with the absolute value of 'Quantity', change the 'Quantity' to sign of it, replace the 'Cost' by dividing it with 'cnt' column, and then replicate the rows with 'cnt' column

library(dplyr)
library(tidyr)
df %>% 
   mutate(cnt = abs(Quantity), Quantity = sign(Quantity), 
         Cost = Cost/cnt) %>%
   uncount(cnt) %>%
   as_tibble

-output

# A tibble: 10 x 6
#   ID    Date       Item  ItemPrice Quantity  Cost
#   <chr> <chr>      <chr>     <dbl>    <dbl> <dbl>
# 1 11    2020-01-01 Item1         5        1     5
# 2 12    2020-02-01 Item1         5        1     5
# 3 12    2020-02-01 Item1         5        1     5
# 4 13    2020-03-15 Item2         7       -1    -7
# 5 13    2020-03-15 Item2         7       -1    -7
# 6 14    2020-04-10 Item2         7        1     7
# 7 14    2020-04-10 Item2         7        1     7
# 8 14    2020-06-01 Item2         7        1     7
# 9 14    2020-06-01 Item2         7        1     7
#10 14    2020-06-01 Item2         7        1     7