Match and replace columns of dataframe by multiple conditions

Question

Cheers, I have two data frames with the following structure.

DF1:
Airlines           HeadQ      Date           Cost_Index
American           PHX        07-31-2016     220
American           ATL        08-31-2016     150
American           ATL        10-31-2016     150
Delta              ATL        10-31-2016     180
American           ATL        08-31-2017     200

Second data frame DF2 has the following structure:

DF2:
Airlines           HeadQ      Date          
American           ATL        09-30-2016
Delta              ATL        03-31-2017

Now looking up with data frames DF1 and DF2, I would like to alter DF1 to the following data frame.

DF1:
Airlines           HeadQ      Date           Cost_Index
American           PHX        07-31-2016     220
American           ATL        08-31-2016     0
American           ATL        10-31-2016     150
Delta              ATL        10-31-2016     180
American           ATL        08-31-2017     200

The condition is, lookup for Airlines and HeadQ of DF1 from DF2 and if DF1$Date < DF2$Date then make Cost_Index as 0 or else continue with Cost_Index.

I tried, unsuccessfully, with:

DF1$Cost_Index <- ifelse(DF1$Airlines == DF2$Airlines & DF1$HeadQ == DF2$HeadQ 
        & DF1$Date < DF2$Date, 0, DF1$Cost_Index)


Warning:
1: In DF1$Airlines == DF2$Airlines : longer object
length is not a multiple of shorter object length". 
2: In<=.default(DF1$Date, DF2$Date) : longer object length is not a
multiple of shorter object length

DF1:
Airlines           HeadQ      Date           Cost_Index
American           PHX        07-31-2016     220
American           ATL        08-31-2016     0
American           ATL        10-31-2016     0
Delta              ATL        10-31-2016     0
American           ATL        08-31-2017     200

Can anyone point me to right direction?

Note:

str(DF1$Date): Date, format: "2016-10-31"
str(DF2$Date): Date, format: "2016-08-31"

Answer 1

Using the conditional joins feature (since 1.9.8), I'd do this as follows:

require(data.table) # v1.9.8+
# convert to data.tables, and Date column to Date class.
setDT(df1)[, Date := as.Date(Date, format = "%m-%d-%Y")]
setDT(df2)[, Date := as.Date(Date, format = "%m-%d-%Y")]

df1[df2, on = .(Airlines, HeadQ, Date < Date), # find matching rows based on condition
      Cost_Index := 0L]                        # update column with 0 for those rows

df1
#    Airlines HeadQ       Date Cost_Index
# 1: American   PHX 2016-07-31        220
# 2: American   ATL 2016-08-31          0
# 3: American   ATL 2016-10-31        150
# 4:    Delta   ATL 2016-10-31        180