How to insert "event dummy" (?) based on time interval in different dataframe

Question

I have a dataframe which provides the start and end date of an event for different countries. Events can occur for several times for each country (e.g. country A).

Start.Year <- c("1990","1992","1997","1995")
End.Year <- c("1995","1993","2000","1996")
Country <- c("A","B","A","C")
a <- data.frame(Start.Year,End.Year,Country)
a$Start.Year <- as.numeric(as.character(a$Start.Year))
a$End.Year <- as.numeric(as.character(a$End.Year))

      Start.Year End.Year Country
       1990     1995       A
       1992     1993       B
       1997     2000       A
       1995     1996       C 

I have a second data frame which is in a time-series cross section format (Year/Country/Event(Yes/No).

b1 <-as.data.frame(expand.grid(year=(1990:2000), Country=unique(a$Country)))
b1$Event <-0   
b1$year <- as.numeric(as.character(b1$year))

How can I obtain the result below (apologies for the clumsy presentation). Event should be "1" when the year is between the start and end year of the first dataframe; for each country; the second dataframe exists already, meaning that I don't want to convert the first dataframe, but rather match (?) the information from the first dataframe to the second one.

I tried

b1$Event[a$Start.Year<=b1$year & a$End.Year>=b1$year] <- 1 

but get "longer object length is not a multiple of shorter object length" as error message. Grateful for any hint/advice!

Result aimed at:

  Year Country Event
  1990       A     1
  1991       A     1
  1992       A     1
  1993       A     1
  1994       A     1
  1995       A     1
  1996       A     0
  1997       A     1
  1998       A     1
  1999       A     1
  2000       A     1
  1990       B     0
  1991       B     0
  1992       B     1
  1993       B     1
  1994       B     0
  1995       B     0
  1996       B     0
  1997       B     0
  1998       B     0
  1999       B     0
  2000       B     0
  1990       C     0
  1991       C     0
  1992       C     0
  1993       C     0
  1994       C     0
  1995       C     1
  1996       C     1
  1997       C     0
  1998       C     0
  1999       C     0
  2000       C     0

Answer 1

Here is a solution using the rolling join feature in data.table. I have slightly changed (fixed?) your definition of a and removed the Event column in b1.

require(data.table)

Start.Year <- c(1990, 1992, 1997, 1995)
End.Year <- c(1995, 1993, 2000, 1996)
Country <- c("A", "B", "A", "C")
a <- data.frame(Start.Year, End.Year, Country)
a <- data.table(a)   ## convert to use feature

b1 <-as.data.frame(expand.grid(year=(1990:2000), Country=unique(a$Country)))
b1 <- data.table(b1)  ## convert 

##  join by Start.Year, setting matching keys for each dataset
setkey(a, Country, Start.Year)
setkey(b1, Country, year)

# the tricky part
# roll=TRUE means all years will match to 
# next smallest event Start.Year
ab <- a[b1, roll=TRUE] 

setnames(ab, c('Country', 'Year', 'Event'))  ## fix names
ab[Year > Event, Event:=NA]  ## stop index at end year
ab[!is.na(Event), Event:=1]  ## transform year markers to 1
ab[is.na(Event), Event:=0]   ## transform missing matches to 0

ab is the data in the format you want. You can use it just like a data.frame or convert it back if you don't want to keep it in that class. The join should be very fast.

Answer 2

You can generate a list of all the years by

yrs <- mapply(seq, a[[1]], a[[2]])

and a list of corresponding countries by

ctr <- rep(a[[3]], times=sapply(yrs, length) )

and then set the values within b1 by

b1$Event[match(sprintf("%s-%s", unlist(yrs), ctr), sprintf("%s-%s", b1$year, b1$Country))] <- 1

It's creating a composite year:country index, and for every b1 that matches an a event, setting it to 1. If there are multiple events per country in a given year, they will get set to 1. (Also you've got different capitalisations of 'year' so watch out for that).