Calculating rolling average over time by two conditions [duplicate] - r

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))

Answer 2

slider is a 'new-er' alternative that plays nicely with the tidyverse.
Something like this would do the trick
test2 <- test %>%
group_by(ID) %>%
arrange(ID, YEAR_VISIT) %>%
mutate(BLOOD_PRESSURE_UPDATED = slider::slide_dbl(BLOOD_PRESSURE, mean, .before = 1, .after = 0)) %>%
ungroup()

Answer 3

If you are not committed to to dplyr this should work:
get.mav <- function(bp,n=2){
require(zoo)
if(is.na(bp[1])) bp[1] <- mean(bp,na.rm=TRUE)
bp <- na.locf(bp,na.rm=FALSE)
if(length(bp)<n) return(bp)
c(bp[1:(n-1)],rollapply(bp,width=n,mean,align="right"))
}
test <- with(test,test[order(ID,YEAR_VISIT),])
test$BLOOD_PRESSURE_UPDATED <-
unlist(aggregate(BLOOD_PRESSURE~ID,test,get.mav,na.action=NULL,n=2)$BLOOD_PRESSURE)
test
# ID AGE YEAR_VISIT BLOOD_PRESSURE TREATMENT BLOOD_PRESSURE_UPDATED
# 1 1 20 2000 NA 3 134.6667
# 2 1 21 2001 129 2 131.8333
# 3 1 22 2002 145 3 137.0000
# 4 1 22 2002 130 2 137.5000
# 5 2 23 2003 NA NA 130.0000
# 6 2 30 2010 150 2 140.0000
# 7 2 31 2011 110 3 130.0000
# ...
This works for moving averages > 2 as well.
And here's a data.table solution, which is likely to be much faster if your dataset is large.
library(data.table)
setDT(test) # converts test to a data.table in place
setkey(test,ID,YEAR_VISIT)
test[,BLOOD_PRESSURE_UPDATED:=as.numeric(get.mav(BLOOD_PRESSURE,2)),by=ID]
test
# ID AGE YEAR_VISIT BLOOD_PRESSURE TREATMENT BLOOD_PRESSURE_UPDATED
# 1: 1 20 2000 NA 3 134.6667
# 2: 1 21 2001 129 2 131.8333
# 3: 1 22 2002 145 3 137.0000
# 4: 1 22 2002 130 2 137.5000
# 5: 2 23 2003 NA NA 130.0000
# 6: 2 30 2010 150 2 140.0000
# 7: 2 31 2011 110 3 130.0000
# ...

Answer 4

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