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Simple moving average on an unbalanced panel in R

I work with an unbalanced, irregularly spaced section of the time series. My goal is to get a moving average lagging vector for the Quantity vector, segmented by Subject.

In other words, let's say that for Subject_1 the following Quanatities are observed: [1,2,3,4,5]. First I need to be late by 1, giving [NA, 1,2,3,4].

Then I need to take a moving average of order 3, getting [NA, NA, NA, (3 + 2 + 1) / 3, (4 + 3 + 2) / 3]

The above should be done for all entities.

# Construct example balanced panel DF
panel <- data.frame(
  as.factor(sort(rep(1:6,5))),
  rep(1:5,6),
  rnorm(30)                
)
colnames(panel)<- c("Subject","Day","Quantity")

#Make panel DF unbalanced
panelUNB <- subset(panel,as.numeric(Subject)!= Day)
panelUNB <- panelUNB[-c(15,16),]

, "", plm lag. "Quanatity" , rollmean zoo:

panel$QuantityMA <- ave(panel$Quantity, panel$Subject, FUN = function(x) rollmean(
                     x,3,align="right",fill=NA,na.rm=TRUE))

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, , split-lapply (rollaverage) -unlist:

Temp <-with(panelUNB, split(Quantity, Subject))
Temp <- lapply(Temp, FUN=function (x) rollapplyr(
   x,2,align="right",fill=NA,na.rm=TRUE, FUN=mean))
QuantityMA <-unlist(Temp)

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library(reshape2)
library(zoo)

# create time series where each subject have an observation at each time step
d1 <- data.frame(subject = rep(letters[1:4], each = 5),
                 day = rep(1:5, 4),
                 quantity = sample(x = 1:4, size = 20, replace = TRUE))
d1

# select some random observations
d2 <- d1[sample(x = seq_len(nrow(d1)), size = 15), ]
d2

# reshape to wide format with dcast
# -> 'automatic' extension from irregular to regular series for each subject,
# _given_ that all time steps are represented.
# Alternative method below more explicit

# fill for structural missings defaults to NA
d3 <- dcast(d2, day ~ subject, value.var = "quantity")
d3

# convert to zoo time series 
z1 <- zoo(x = d3[ , -1], order.by = d3$day)

################################
# alternative method to extend time series
# time steps to include are given explicitly

# create a zero-dimensional zoo series
z0 <- zoo(, min(d1$day):max(d1$day))

# extend z1 to contain the same time indices as z0 
z1 <- merge(z1, z0) 
################################

# lag, defaults to one unit 
z2 <- lag(x = z1)
z2

# calculate rolling mean with window width 3
rollmeanr(x = z2, k = 3)

# Handling of NAs:
# from ?rollmean:
# "The default method of rollmean does not handle inputs that contain NAs.
# In such cases, use rollapply instead.": 
rollapplyr(data = z2, width = 3, FUN = mean, na.rm = TRUE)
+2

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