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rplyrmoving-averagepanel-data

R - Calculating 12 month moving average on panel data

First, full disclosure. I attempted to do this strictly in MS Access with correlated subqueries, and had some help on this post 12 month moving average by person, date. I originally thought my data would be small enough to chug through, but it is awful. As an alternative, I'm going to try running this in R and then writing results to a new table in MS Access. I have data such that I have the following fields:

rep, cyc_date, amt

Following the linked example by Andrie for a rolling 5-year period (as opposed to the 5-year average) R: Calculating 5 year averages in panel data, I am trying to get rolling 12 month average for amt field by rep. Here is my code:

library(zoo)
library(plyr)
library(RODBC)

# Pull data from local MS Access database.  The referenced sqlFetch is a query
# that pulls the data, ordered by `rep`, then `cyc_date`

channel <- odbcConnectAccess2007("C://MyDB.accdb")
data <- data.frame(sqlFetch(channel, "MyView"))

# Ensure coercion of `cyc_date` to date type
data$cyc_date <- as.Date(data$cyc_date)

# Function (take from post above)
rollmean12 <- function(x) {
                 rollmean(x, 12)
              }
# Calculate rolling average by person
rollvec <- ddply(data, .(data$rep), rollmean12(data$amt))

Unfortunately, this doesn't work. I'm getting the following error:

Error in llply(.data = .data, .fun = .fun, ..., .progress = .progress,  : 
.fun is not a function.

I'm not sure why this is happening. Do I need to explicitly convert data to a zoo object? If so, not sure how to handle the extra dimensionality resulting from the person_id field. Any help would be very much appreciated.

Solution

  • I found this code on the following post: applying rolling mean by group in R

    data$movavg <- ave(data$amt, data$rep, FUN = function(x) rollmean(x, k=12, align="right", na.pad=T)).

    ave saves the day!