Analyzing Daily/Weekly Data Using Ts in R

Converting to time series data (using daily/weekly data)

As you want daily time series, you need to specify your frequency as 365:

weekly_ts <- ts(dat, 
                freq=365, 
                start= c(2020,245))

Daily Time Series Analysis

ts expects you to have values for each element of the time-series, i.e., it would expect you to have the seventh day values in the data.

One option is to expand the date index to include your missing observations. You could fill those missing observations with na.approx or na, but you can't give ts a six day week and expect it to comprehend it as a seven day cycle.

A good way to do this is to look at zoo, which has specific functions for dealing with these sorts of situations.

Analyzing weekly observations/data

Ok. So, I figured out the answer to my question:

I shouldn't be converting an object of class Date to TS because it isn't periodic. I should rather use zoo packages' classes to be consistent.

library("lubridate")
#take care of weeks:
testdata$Fiscal.Year <- paste("1/1/",testdata$Fiscal.Year,sep = "")
testdata$Fiscal.Date <- dmy(testdata$Fiscal.Year) + lubridate::weeks(testdata$Fiscal.Week)

#testdata$date_ts <- as.ts(testdata$Fiscal.Date)
ggplot(testdata,aes(x=year(testdata$Fiscal.Date),y=testdata$Product.Bookings.Net))+geom_bar(stat = "identity")

This works well.

The same holds good for the other two issues that were posted by me. I found this pdf extremely useful in my analysis: https://faculty.washington.edu/ezivot/econ424/Working%20with%20Time%20Series%20Data%20in%20R.pdf

This resolved my query. I am still open to any thoughts for reading "weekly" data and then efficiently converting it to of type "Date"

R : Converting Daily time series to monthly

The possible solution for OP issue can be based on:

#Code
y.mon<-aggregate(property_damages_in_dollars~format(as.Date(date_decision_made),
format="%Y/%m"),data=final_data, FUN=sum)

More variants can be explored around the format() options.

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