I have daily prices series over a wide range of products; I want to convert to a new dataframe with weekly or monthly data.
I first used xts in order to apply the to.weekly function...which works only for OHLC format. I am sure there may exist a function similar to to.weekly but for dataframe where the format is not OHLC.
There a different posts already related to this as the following: Does rollapply() allow an array of results from call to function? or Averaging daily data into weekly data
I am eventually using:
length(bra)
1 2416
test
Would there be a more efficient approach? Thanks.
Let's try with this data:
library(zoo) tt
Now I define a function which extracts the year and the number of the week (drop %Y if you don't need to distinguish between years):
week
You can use this function to aggregate the zoo object with mean (for example):
aggregate(z, by=week, FUN=mean)
which produces this result:
A B C 2013.18 0.3455357 0.34129269 3 2013.19 0.4506297 0.57665133 9 2013.20 0.3950585 0.46197173 16 2013.21 0.5990886 -0.02689994 23 2013.22 0.5115043 0.18726564 30 2013.23 0.5327597 0.16250339 37
I'm fairly new to R but stumbled on this when I had a similar problem. I needed to convert xts data that isn't OHLC. to.monthly states that it can handle univariate series but it also says in the details that it only supports returning OHLC. I think it might work by just setting OHLC=FALSE. Alternatively, the source of to.period uses the following function which worked for me even to convert several series (all with same time index)
data.monthly
Short and clean and even copies the column names.
