ulima2_ - 1 year ago 50

R Question

I have a time series object with daily values that starts in the 19th century and reaches into the 20th century. There are a lot of missing values in there.

I'm trying to calculate weekly means and here's a minimal example:

`library(zoo)`

library(xts)

# Create time series that starts in 19th century

T <- 100 # number of days

myTS <- xts(rnorm(T), as.Date(1:T, origin="1899-11-05"))

# Insert some missing values

myTS[4:7] <- NA

myTS[33:34] <- NA

myTS[67:87] <- NA

# Try calculating weekly means

weekData <- apply.weekly(myTS, colMeans, na.rm = TRUE)

which only returns the weekly mean for the last week:

1900-02-13 [some value]

I use

`colMeans`

`mean`

I would like the mean for all the weeks. Does somebody have an idea what I'm doing wrong?

Answer Source

Updated based on your comment to use week-year combination:

```
library(zoo)
library(xts)
# Create time series that starts in 19th century
T <- 100 # number of days
myTS <- xts(rnorm(T), as.Date(1:T, origin="1899-11-05"))
# Insert some missing values
myTS[4:7] <- NA
myTS[33:34] <- NA
myTS[67:87] <- NA
# Let's use a flexible class
myTS <- data.frame(dates=index(myTS),v1=myTS[,1])
# Here's an easy way to transform dates to weeks
require(lubridate)
week_num <- week(myTS[,1])
year_num <- year(myTS[,1])
week_yr <- paste(week_num, year_num)
# Weekly means
aggregate(myTS$v1,by=list(week_yr),mean,na.rm=T)
```

`Group.1 x 1 1 1900 0.05405322 2 2 1900 0.31981319 3 3 1900 NaN 4 4 1900 NaN 5 45 1899 0.85081053 6 46 1899 0.34064255 7 47 1899 0.02880424 8 48 1899 -0.34408119 9 49 1899 -0.38089026 10 5 1900 0.62292188 11 50 1899 -0.59666955 12 51 1899 0.57756987 13 52 1899 -0.41325485 14 53 1899 0.88013634 15 6 1900 0.01514668 16 7 1900 -0.50863942`