Weekly Data into Time Series for Forecasting in R

Question

I have data that looks like the following:

df <- structure(list(Variable =c("A", "B", "A", "B", "A", "B", "A", "B", "A"), 
 Quantity=c("1", "100", "2", "5", "6", "30", "8", "15", "133"),
 YearQuarter=c("2017Q2", "2017Q2", "2017Q3", "2017Q3", "2017Q4", "2017Q4", "2018Q1", "2018Q1", "2018Q2"),
 Week=c("1", "10", "1", "2", "1", "6", "2", "9", "13")),
 class= "data.frame", row.names=c(NA, -9L))

There are no actual dates but I would like to turn it into a time series data set so I can forecast. The format of the time series is week 1-13 for each year/qtr. Ideally, I can get this to a 52-week frequency so I can forecast the following 52 weeks for each forecast.

Answer 1

It's unclear what you want as a result but if we assume that df represents two series A and B and you want a regularly spaced ts series of frequency 52 as a result then use the following.

library(zoo)

df2 <- transform(df, Quantity = as.numeric(Quantity))
toTime <- function(yq, wk) as.numeric(as.yearqtr(yq)) + (as.numeric(wk) - 1)/13/4
z <- read.zoo(df2, index = c("YearQuarter", "Week"), split = "Variable", FUN = toTime)
tt <- as.ts(z)
frequency(tt)
## [1] 52

If you want to treat Variable as a series of 1's and 2's (for A and B respectively):

df3 <- transform(df, Quantity = as.numeric(Quantity), 
                     Variable = match(Variable, c("A", "B")))
z3 <- read.zoo(df3, index = c("YearQuarter", "Week"), FUN = toTime)
tt3 <- as.ts(z3)

Note that most time series forecasting routines require regularly spaced non-missing data and the data shown in the question will generate many NAs when regularly spaced.