问题
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<-bra[seq(1,2416,7),]
Would there be a more efficient approach? Thanks.
回答1:
Let's try with this data:
library(zoo)
tt <- seq(Sys.Date(), by='day', length=365)
vals <- data.frame(A=runif(365), B=rnorm(365), C=1:365)
z <- zoo(vals, 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 <- function(x)format(x, '%Y.%W')
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
回答2:
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 <- data[ endpoints(data, on="months", k=1), ]
Short and clean and even copies the column names.
来源：https://stackoverflow.com/questions/16442396/convert-daily-to-weekly-monthly-data-with-r