Join two data frames in R based on closest timesta

问题:

Hi I have two tables (table1 and table2 below) and would like to join them based on the closest timestamp to form expected_output. Some kind of solution involving dplyr would be great if possible, but not if it further complicates things.

table1 = 
structure(list(date = structure(c(1437051300, 1434773700, 1431457200
), class = c("POSIXct", "POSIXt"), tzone = ""), val1 = c(94L, 
33L, 53L)), .Names = c("date", "val1"), row.names = c(NA, -3L
), class = "data.frame")

table2 = 
structure(list(date = structure(c(1430248288, 1435690482, 1434050843
), class = c("POSIXct", "POSIXt"), tzone = ""), val2 = c(67L, 
90L, 18L)), .Names = c("date", "val2"), row.names = c(NA, -3L
), class = "data.frame")

expected_output = 
structure(list(date = structure(c(1437051300, 1434773700, 1431457200
), class = c("POSIXct", "POSIXt"), tzone = ""), val1 = c(94L,
33L, 53L), val2 = c(90L, 18L, 67L)), .Names = c("date", "val1", 
"val2"), row.names = c(NA, -3L), class = "data.frame")

回答1:

Using rolling joins feature of data.table with roll = "nearest":

require(data.table) # v1.9.6+
setDT(table1)[, val2 := setDT(table2)[table1, val2, on = "date", roll = "nearest"]]

Here, val2 column is created by performing a join on the column date with roll = "nearest" option. For each row of table1$date, the closest matching row from table2$date is computed, and val2 for corresponding row is extracted.

回答2:

This is liable to be slow, but...

d   <- function(x,y) abs(x-y) # define the distance function
idx <- sapply( table1$date, function(x) which.min( d(x,table2$date) )) # find matches

cbind(table1,table2[idx,-1,drop=FALSE])
#                  date val1 val2
# 2 2015-07-16 08:55:00   94   90
# 3 2015-06-20 00:15:00   33   18
# 1 2015-05-12 15:00:00   53   67

Another way of constructing idx is max.col(-outer(table1$date, table2$date, d)).