How to calculate three year rolling return using R

I need to get a 3-year rolling return working (3-year return for each id, for each year).

I have tried to use the PerformanceAnalytics package but I keep getting an error that my data is not a time series.

When I use the function it says TRUE so I am completely stuck as to how to get the 3-year rolling return to work. So I just need someone to provide me with the R code that will produce the 3-year returns.

Here's a sample dataset

ppd_id   FY   TF_1YR
1      2001  -0.0636
1      2002  -0.0929
1      2003  0.1648
1      2004  0.1006
1      2005  0.1098
1      2006  0.0837
1      2007  0.1792
1      2008  -0.1521
1      2009  -0.1003
1      2010  0.0847
1      2011  0.0221
1      2012  0.1801
1      2013  0.146
1      2014  0.1202
1      2015  0.0105
1      2016  0.1022
1      2017  0.1286
1      2018  0.0929

Here's link to dataset

Here's my code

library(smooth)
library(readr)
pensionreturns <- read_csv("pensionreturns.csv")
sma(pensionreturns, h=

Solution

Assuming that:

we are starting out with the data frame DF2 in the Note at the end which is the data in question duplicated so that there are 2 id's
the third column represents returns so the 3 year returns are the product of one plus each of the last 3 values (current value and prior 2) all minus 1, i.e. (1 + r0) * (1 + r1) * (1 + r2) - 1 where r0, is the current year's return, r1 is the prior year's return and r2 is the return in the year prior to that.

convert the data to the wide form zoo series z and then use rollapplyr. Omit the fill= argument if the NA's at the beginning are not needed. The result will be a zoo series of returns. (We could use fortify.zoo, see ?fortify.zoo, to convert it to a data frame although it will be easier to perform further time series manipulations if you leave it as a time series.)

library(zoo)

z <- read.zoo(DF2, index = 2, split = 1, FUN = c)
rollapplyr(z + 1, 3, prod, fill = NA) - 1

giving this zoo series:

                1            2
2001           NA           NA
2002           NA           NA
2003 -0.010609049 -0.010609049
2004  0.162883042  0.162883042
2005  0.422740161  0.422740161
2006  0.323680900  0.323680900
2007  0.418212355  0.418212355
2008  0.083530596  0.083530596
2009 -0.100440641 -0.100440641
2010 -0.172530498 -0.172530498
2011 -0.002527919 -0.002527919
2012  0.308343674  0.308343674
2013  0.382282521  0.382282521
2014  0.514952431  0.514952431
2015  0.297228567  0.297228567
2016  0.247648627  0.247648627
2017  0.257004321  0.257004321
2018  0.359505217  0.359505217

Note

DF <- structure(list(ppd_id = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), FY = 2001:2018, TF_1YR = c(-0.0636, 
-0.0929, 0.1648, 0.1006, 0.1098, 0.0837, 0.1792, -0.1521, -0.1003, 
0.0847, 0.0221, 0.1801, 0.146, 0.1202, 0.0105, 0.1022, 0.1286, 
0.0929)), class = "data.frame", row.names = c(NA, -18L))

DF2 <- rbind(DF, transform(DF, ppd_id = 2))