Count based on multiple conditions using sqldf

Hi all I’m writing a sql query on R using sqldf and seem to hit a roadblock. I have a table with an Id column, two dates columns and a grouping by column.

AlertDate  AppointmentDate  ID  Branch
01/01/20   04/01/20         1   W1
01/01/20   09/01/20         1   W1
08/01/20   09/01/20         1   W2
01/01/20   23/01/20         1   W1

The query I’m writing is

sqldf('select Branch,count(ID) from df where AlertDate <= AppointmentDate 
and AppointmentDate <AlertDate+7 group by Branch')

From this query the result I’m getting is

Branch Count
W1      1
W2      1

Whichis correct based on the query. What I want to achieve is if my 2nd condition is false ie AppointmentDate is less than AlertDate+7. Instead of dropping the count it should be counted in the next group depending on date. Example if the alertdate is 01/01/20 and appointment date is 23/01/20 then it should be counted in W4. ceil((Appointmentdate-alertdate)/7) SO in the end I want the result as

Branch  Count
W1      1
W2      2
W4      1

The second row should be counted in W2 and 4th should be in W4 rather than being discarded. I was trying to achieve this in sql using sqldf in R. Any possible solution using R or Sql would work for me.

Output of dput(test)

structure(list(AlertDate = structure(c(18262, 18262, 18269, 18262), class = "Date"), AppointmentDate = structure(c(18265, 18270,18270, 18284), class = 
"Date"), ID = c(1, 1, 1, 1), Branch = c("W1","W1", "W2", "W1")), class = c("spec_tbl_df", "tbl_df", "tbl","data.frame"), row.names = c(NA, -4L), problems = 
structure(list( row = 4L, col = "Branch", expected = "", actual = "embedded null", 
file = "'C:/Users/FRssarin/Desktop/test.txt'"), row.names = c(NA,-1L), class = c("tbl_df", "tbl", "data.frame")), spec = structure(list(  cols = list(AlertDate = 
structure(list(format = "%d/%m/%y"), class = c("collector_date", 
"collector")), AppointmentDate = structure(list(format = "%d/%m/%y"), class = c("collector_date",  "collector")), ID = structure(list(), class = c("collector_double", "collector")), Branch = structure(list(), class = 
c("collector_character",  "collector"))), default = structure(list(), class = c("collector_guess",  "collector")), skip = 1), class = "col_spec"))

Answer

Here is one way to do it using data.table

df <- structure(list(AlertDate = structure(c(18262, 18262, 18269, 18262), class = "Date"), AppointmentDate = structure(c(18265, 18270,18270, 18284), class = 
                                                                                                                     "Date"), ID = c(1, 1, 1, 1), Branch = c("W1","W1", "W2", "W1")), class = c("spec_tbl_df", "tbl_df", "tbl","data.frame"), row.names = c(NA, -4L), problems = 
              structure(list( row = 4L, col = "Branch", expected = "", actual = "embedded null", 
                              file = "'C:/Users/FRssarin/Desktop/test.txt'"), row.names = c(NA,-1L), class = c("tbl_df", "tbl", "data.frame")), spec = structure(list(  cols = list(AlertDate = 
                                                                                                                                                                                      structure(list(format = "%d/%m/%y"), class = c("collector_date", 

I am converting this to data.table and creating a new column for your logic.

library(data.table)
df <- data.table(df)
df <- df[AlertDate <= AppointmentDate] 
df[, new_branch:= ifelse(as.numeric(AppointmentDate-AlertDate)>=7
            ,paste0("W", as.character(ceiling(as.numeric(AppointmentDate-AlertDate)/7))),Branch)]

Here is the resulting table

    AlertDate AppointmentDate ID Branch new_branch
1: 2020-01-01      2020-01-04  1     W1         W1
2: 2020-01-01      2020-01-09  1     W1         W2
3: 2020-01-08      2020-01-09  1     W2         W2
4: 2020-01-01      2020-01-23  1     W1         W4

This is the goupby result..

df[, .(.N, alert=head(AlertDate,1),  appoint=head(AppointmentDate,1)), by = list(new_branch)]
   new_branch N      alert    appoint
1:         W1 1 2020-01-01 2020-01-04
2:         W2 2 2020-01-01 2020-01-09
3:         W4 1 2020-01-01 2020-01-23