R data.table: (dynamic) forward looking Cross-Joins -


i wondering if there option cj() method in data.table take vectors formed evaluated condition instead of running full cross join.

data

library(data.table) df<-data.table(   id=c(18l,  18l,  18l,  46l,  74l,  74l, 165l, 165l),    cat=c(1300l, 1320l, 1325l, 1300l, 1300l, 1325l, 1300l, 1325l),   low=c(24.625, 16.250, 14.500, 43.625, 58.250, 45.375, 90.750, 77.875),   high=c(26.625, 17.500, 15.500, 45.625, 60.000, 47.375, 92.750, 79.875)   )  df     id  cat    low   high 1:  18 1300 24.625 26.625 2:  18 1320 16.250 17.500 3:  18 1325 14.500 15.500 4:  46 1300 43.625 45.625 5:  74 1300 58.250 60.000 6:  74 1325 45.375 47.375 7: 165 1300 90.750 92.750 8: 165 1325 77.875 79.875

here, have total of 8 observations of 4 different items (ids 18, 46, 74 , 165). each item recorded in several categories (cat 1300, 1320, 1325) , 2 measurements taken (low , high).

desired output

i want create table each item (id) joins low value of each category (cat) high values of categories that larger via cross join. desired output looks like

    id  cat  cat_large    low   high 1:  18 1300      1320  24.625 17.500 2:  18 1300      1325  24.625 15.500 3:  18 1320      1325  16.250 15.500 4:  74 1300      1325  58.250 47.375 5: 165 1300      1325  90.750 79.875

where added cat_high indicate 2 categories being joined in low/high.

unfortunately, cannot find correct way amend full cross join, df[,cj(low=low,high=high),by=.(id)], behave this. grateful help/hints.

one way:

df[, c(   cj(cat = cat, lcat = cat, sorted = false),   cj(low = low, high = high, sorted = false)   ), by=id][lcat > cat]      id  cat lcat    low   high 1:  18 1300 1320 24.625 17.500 2:  18 1300 1325 24.625 15.500 3:  18 1320 1325 16.250 15.500 4:  74 1300 1325 58.250 47.375 5: 165 1300 1325 90.750 79.875

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