Hey I have some data aggregated at quarter level and there is a column contains data like this:
> unique(data$fiscalyearquarter)
[1] "2012Q3" "2010Q3" "2012Q1" "2011Q4" "2012Q4" "2008Q1" "2008Q2" "2010Q4" "2010Q1"
[10] "2009Q2" "2012Q2" "2011Q3" "2013Q2" "2013Q1" "2011Q2" "2013Q4" "2009Q4" "2009Q3"
[19] "2011Q1" "2010Q2" "2013Q3" "2008Q4" "2009Q1" "2014Q1" "2008Q3" "2014Q2"
I am thinking about writing a function that turn a string into a timestamp.
Something like this, split the the string to be year and quarter and then force the quarter to be converted to be month(the middle of the quarter).
convert <- function(myinput = "2008Q2"){
year <- substr(myinput, 1, 4)
quarter <- substr(myinput, 6, 6)
month <- 3 * as.numeric(quarter) - 1
date <- as.Date(paste0(year, sprintf("%02d", month), '01'), '%Y%m%d')
return(date)
}
I have to convert those strings to date format and then analyze it from there.
> convert("2010Q3")
[1] "2010-08-01"
Is there any way beyond my hard coding solution to analyze time series problem at quarterly level?