Rolling calculations with lag and ifelse

Question

I'm trying to calculate the cumulative sum of a column in my data frame, but I only want it to be done based on a condition.

I am very new to R and trying to convert this spss code below in R to achieve the dataframe below with a new column called cumulative based on whether the lca is the same as the row above and if it is then add health net cost to the cumulative cost above.

spss code:
IF LAG(LCA) NE LCA cumulative=health_net_cost.
IF LAG(LCA)=LCA cumulative=LAG(cumulative)+health_net_cost.
EXECUTE.

Dataframe in R

Lca health_net_cost   cummulative
10  100         100     
10  200         300
10  300         600
28  400         1000
28  100         1100
8   100         1200
8   200         1400
8   300         1700

Answer 1

This may the solution you looking for your problem. Using cumsum function.

df <- data.frame("LCA" = c(10,10,10,28,28,8,8,8),"Health_Net_Cost" = c(100,200,300,400,100,100,200,300))
df

Output:

LCA Health_Net_Cost
10            100
10            200
10            300
28            400
28            100
 8            100
 8            200
 8            300

Run:

cum_df <- df%>%group_by(LCA)%>%mutate(Cumulative=cumsum(Health_Net_Cost))
cum_df

Yours expected output:

LCA Health_Net_Cost Cumulative
10             100        100
10             200        300
10             300        600
28             400        400
28             100        500
 8             100        100
 8             200        300
 8             300        600

Answer 2

EDIT :

If you want the cumulative sums grouped by LCA , this may help:

install.packages("dplyr")
library(dplyr)
df %>%
  group_by(LCA) %>%
  mutate("cumulative" = cumsum(Health_Net_Cost))
# A tibble: 8 x 3
# Groups:   LCA [3]
    LCA Health_Net_Cost cumulative
  <dbl>           <dbl>      <dbl>
1    10             100        100
2    10             200        300
3    10             300        600
4    28             400        400
5    28             100        500
6     8             100        100
7     8             200        300
8     8             300        600