I have a longhitudinal dataframe with a lot of missing values that looks like this.
ID = c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3)
date = c(1,2,3,4,5,1,2,3,4,5,1,2,3,4,5)
cond = c(0,0,0,1,0,0,0,0,1,0,0,0,0,0,0)
var = c(1, NA , 2, 0,NA, NA, 3, NA,0, NA, 2, NA, 1,NA,NA)
df = data.frame(ID, date, cond,var)
I would like to carry forward the last observation based on two conditions:
1) when cond=0 it should carry on the observation the higher value of the variable of interest.
2) when cond=1 it should carry forward the lower value of the variable of interest.
Does anyone have an idea on how I could do this in an elegant way?
The final dataset should look like this
ID = c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3)
date = c(1,2,3,4,5,1,2,3,4,5,1,2,3,4,5)
cond = c(0,0,0,1,0,0,0,0,1,0,0,0,0,0,0)
var = c(1, 1 , 2, 0, 0, NA, 3, 3, 0, 0,2,2,2,2,2)
final = data.frame(ID, date, cond,var)
So far I was able to carry forward the last observation, but I was unable to impose the conditions
library(zoo)
df <- df %>%
group_by(ID) %>%
mutate(var =
na.locf(var, na.rm = F))
any suggestion is welcomed
This is the use of accumulate2 ie
df%>%
group_by(ID)%>%
mutate(d = unlist(accumulate2(var,cond[-1],function(z,x,y) if(y) min(z,x,na.rm=TRUE) else max(z,x,na.rm=TRUE))))
# A tibble: 15 x 5
# Groups: ID [3]
ID date cond var d
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 0 1 1
2 1 2 0 NA 1
3 1 3 0 2 2
4 1 4 1 0 0
5 1 5 0 NA 0
6 2 1 0 NA NA
7 2 2 0 3 3
8 2 3 0 NA 3
9 2 4 1 0 0
10 2 5 0 NA 0
11 3 1 0 2 2
12 3 2 0 NA 2
13 3 3 0 1 2
14 3 4 0 NA 2
15 3 5 0 NA 2