Home Filling table column by matching several rows from different tables with data.table

Filling table column by matching several rows from different tables with data.table

H.Barras
1#
H.Barras Published in 2018-01-11 21:00:48Z
 I need help finding a solution to the following problem: Here is an example data set: library(data.table) x_coord <- rep(sort(rep(c(1:3),3)),2) y_coord <- rep(c(1:3),6) time_info <- c(rep(strptime("201701010000", tz = "UTC", format = "%Y%m%d%H%M"),9), rep(strptime("201701010005", tz = "UTC", format = "%Y%m%d%H%M"),9)) table1 <- data.table(x = x_coord, y = y_coord, time = time_info) table2 <- data.table(x = c(1,1,3,2), y = c(1,1,3,1),time = rep(time_info[1], 4), values = c(3,5,8,6)) # table2 has one unique time-value  The aim is to fill in table1 with the values from table2 so that it looks like this: table3 <- table1 for (i in c(1:nrow(table2))) { table3[x == table2$x[i] & y == table2$y[i] & time == table2$time[i],"values" := .(table2$values[i])] }  There are several constraints: table1 does not contain all coordinates from table2 and the reverse. Each coordinates/time combination has only one corresponding value so there is no issue with multiple values per coordinate/time. I can't do it manually, since "table1" has in reality 2*10^7 rows and I want to get the values from almost 100'000 "table2"s which each have the size of about 10000 rows and which come from a different file each. I've tried merging but then when it's Looping, it adds a new "values"-column at each Iteration (and on top of that it's taking really long). I've also tried matching with table1[time == table2$time[1] & paste0(x,y) %in% paste0(table2$x,table2$y), "values" := .(table2$values)]  but then I am not sure that the right value is assigned to the right coordinate. I hope my question is clear, sorry if not! Thank you very much for helping.
Jaap
2#
 A join with data.table: table1[table2, on = .(x, y, time), values := values][]  which gives:  x y time values 1: 1 1 2017-01-01 01:00:00 5 2: 1 2 2017-01-01 01:00:00 NA 3: 1 3 2017-01-01 01:00:00 NA 4: 2 1 2017-01-01 01:00:00 6 5: 2 2 2017-01-01 01:00:00 NA 6: 2 3 2017-01-01 01:00:00 NA 7: 3 1 2017-01-01 01:00:00 NA 8: 3 2 2017-01-01 01:00:00 NA 9: 3 3 2017-01-01 01:00:00 8 10: 1 1 2017-01-01 01:05:00 NA 11: 1 2 2017-01-01 01:05:00 NA 12: 1 3 2017-01-01 01:05:00 NA 13: 2 1 2017-01-01 01:05:00 NA 14: 2 2 2017-01-01 01:05:00 NA 15: 2 3 2017-01-01 01:05:00 NA 16: 3 1 2017-01-01 01:05:00 NA 17: 3 2 2017-01-01 01:05:00 NA 18: 3 3 2017-01-01 01:05:00 NA 
 If you're prepared to use the dplyr package you could do this. library(dplyr) table3 = table1 %>% left_join(table2)