Create a binary variable based on the first appearance of another (date) variable

Question

Is it possible to create a binary variable based on the first appearance of another (date) variable?

For my thesis I am trying to create a variable that captures the number of first-time forecasts issued and revised during the month divided by the number of forecasts at the month-end for a firm in a given year. For convenience I would like to separate the first-time forecasts issued and revised in different columns.

Example data

dt <- data.table(
  analyst = rep((1:2),10),
  id = rep((1:5),4),
  year = rep(as.Date(c('2009-12-31','2009-12-31','2010-12-31','2010-12-31'),format='%Y-%m-%d'),5),
  fdate = rep(as.Date(c('2009-07-31','2009-02-26','2010-01-31','2010-05-15','2009-06-30','2009-10-08','2010-07-31','2010-11-30','2009-01-31','2009-06-26','2010-05-03','2010-04-13','2009-10-30','2009-11-02','2010-03-28','2010-10-14','2009-02-17','2009-09-14','2010-08-02','2010-10-03'),format='%Y-%m-%d')))

To create the variable, I used the following steps: First, identifying the issuance of the first-time forecasts for a given year (for firms by analysts) with the following code:

dt2 <- setkey(setDT(dt), id, year, analyst)[order(fdate),.SD[1L] ,by=list(id,year)]

However, this generates a table with only the first-time forecast by id, year and analyst. Secondly, I give the first-time forecasts the value 1 with:

dt3 <- print(dt2[, first:=1L])

Third, combine the two data.tables:

dt4 <- dt3[dt, on = c('id', 'year', 'analyst', 'fdate')]

Fourth, I replace the na for 0

dt4[is.na(dt4)] <- 0

Fifth, creating the revised binary variable:

dt4$rev <- ifelse(dt4$first == 0,"1", "0")

Last, I sum the number of first-time and revised forecasts for every month for a firm.

Is there a more elegant way of creating this variable so I can learn more of R/data.table? I have tried to incorporate the dcast function, based on the answers from:

• R data.table - categorical values in one column to binary values in multiple columns

• How to programmatically create binary columns based on a categorical variable in data.table?

• Data table dcast column headings

However, it doesn't work out for me.

Current result, based on the previous mentioned steps:

id        year  analyst        fdate first  rev
1   2009-12-31        1   2009-07-31    1     0
1   2009-12-31        2   2009-10-08    0     1
1   2010-12-31        1   2010-05-03    1     0
1   2010-12-31        2   2010-10-14    0     1
2   2009-12-31        1   2009-02-17    1     0
2   2009-12-31        2   2009-02-26    0     1
2   2010-12-31        1   2010-07-31    0     1
2   2010-12-31        2   2010-04-13    1     0
3   2009-12-31        1   2009-10-30    0     1
3   2009-12-31        2   2009-09-14    1     0
3   2010-12-31        1   2010-01-31    1     0
3   2010-12-31        2   2010-11-30    0     1
4   2009-12-31        1   2009-01-31    1     0
4   2009-12-31        2   2009-11-02    0     1
4   2010-12-31        1   2010-08-02    0     1
4   2010-12-31        2   2010-05-15    1     0
5   2009-12-31        1   2009-06-30    0     1
5   2009-12-31        2   2009-06-26    1     0
5   2010-12-31        1   2010-03-28    1     0
5   2010-12-31        2   2010-10-03    0     1

Answer 1

We can replace the ifelse and also the base R methods. Create the 'first' as 0, then do a join with 'dt2' based on the columns in the post, then assign those matching rows to 1 for 'first', negate ( ! ) the first and convert to integer with ( + ) or as.integer and assign it to rev

dt[, first := 0][dt2, first := 1, on = .(id, year, analyst, fdate)]
dt[, rev := +(!first)][]
#     analyst id       year      fdate first rev
# 1:       1  1 2009-12-31 2009-07-31     1   0
# 2:       2  1 2009-12-31 2009-10-08     0   1
# 3:       1  1 2010-12-31 2010-05-03     1   0
# 4:       2  1 2010-12-31 2010-10-14     0   1
# 5:       1  2 2009-12-31 2009-02-17     1   0
# 6:       2  2 2009-12-31 2009-02-26     0   1
# 7:       1  2 2010-12-31 2010-07-31     0   1
# 8:       2  2 2010-12-31 2010-04-13     1   0
# 9:       1  3 2009-12-31 2009-10-30     0   1
#10:       2  3 2009-12-31 2009-09-14     1   0
#11:       1  3 2010-12-31 2010-01-31     1   0
#12:       2  3 2010-12-31 2010-11-30     0   1
#13:       1  4 2009-12-31 2009-01-31     1   0
#14:       2  4 2009-12-31 2009-11-02     0   1
#15:       1  4 2010-12-31 2010-08-02     0   1
#16:       2  4 2010-12-31 2010-05-15     1   0
#17:       1  5 2009-12-31 2009-06-30     0   1
#18:       2  5 2009-12-31 2009-06-26     1   0
#19:       1  5 2010-12-31 2010-03-28     1   0
#20:       2  5 2010-12-31 2010-10-03     0   1