r - Rolling mean (moving average) by group/id with dplyr

Question

I have a longitudinal follow-up of blood pressure recordings.

The value at a certain point is less predictive than is the moving average (rolling mean), which is why I'd like to calculate it. The data looks like

test <- read.table(header=TRUE, text = "
  ID  AGE   YEAR_VISIT  BLOOD_PRESSURE  TREATMENT
  1 20  2000    NA 3
  1 21  2001    129 2
  1 22  2002    145 3
  1 22  2002    130 2
  2 23  2003    NA  NA
  2 30  2010    150 2
  2 31  2011    110 3
  4 50  2005    140 3
  4 50  2005    130 3
  4 50  2005    NA  3
  4 51  2006    312 2
  5 27  2010    140 4
  5 28  2011    170 4
  5 29  2012    160 NA
  7 40  2007    120 NA
                   ")

I'd like to calculate a new variable, called BLOOD_PRESSURE_UPDATED. This variable should be the moving average for BLOOD_PRESSURE and have the following characteristics:

• A moving average is the current value plus the previous value divided by two.
• For the first observation, the BLOOD_PRESSURE_UPDATED is just the current BLOOD_PRESSURE. If that is missing, BLOOD_PRESSURE_UPDATED should be the overall mean.
• Missing values should be filled in with nearest previous value.

I've tried the following:

test2 <- test %>%
  group_by(ID) %>%
  arrange(ID, YEAR_VISIT) %>%
  mutate(BLOOD_PRESSURE_UPDATED = rollmean(x=BLOOD_PRESSURE, 2)) %>%
ungroup()

I have also tried rollaply and rollmeanr without succeeding.

Answer 1

How about this?

    library(dplyr)   
    test2<-arrange(test,ID,YEAR_VISIT) %>% 
           mutate(lag1=lag(BLOOD_PRESSURE),
                  lag2=lag(BLOOD_PRESSURE,2),
                  movave=(lag1+lag2)/2)

Another solution using 'rollapply' function in zoo package (I like more)

library(dplyr)
library(zoo)
test2<-arrange(test,ID,YEAR_VISIT) %>%
       mutate(ma2=rollapply(BLOOD_PRESSURE,2,mean,align='right',fill=NA))