join data based on a Date/Time range in R - r

Question

I have one file (location) that has an x,y coordinates and a date/time identification. I want to get information from a second table (weather) that has a "similar" date/time variable and the co-variables (temperature and wind speed). The trick is the date/time are not exactly the same numbers in both tables. I want to select the weather data that is closest from the location data. I know I need to do some loops and thats about it.
Example location example weather
x y date/time date/time temp wind
1 3 01/02/2003 18:00 01/01/2003 13:00 12 15
2 3 01/02/2003 19:00 01/02/2003 16:34 10 16
3 4 01/03/2003 23:00 01/02/2003 20:55 14 22
2 5 01/04/2003 02:00 01/02/2003 21:33 14 22
01/03/2003 00:22 13 19
01/03/2003 14:55 12 12
01/03/2003 18:00 10 12
01/03/2003 23:44 2 33
01/04/2003 01:55 6 22
So the final output would be a table with the correctly "best" matched weather data to the location data
x y datetime datetime temp wind
1 3 01/02/2003 18:00 ---- 01/02/2003 16:34 10 16
2 3 01/02/2003 19:00 ---- 01/02/2003 20:55 14 22
3 4 01/03/2003 23:00 ---- 01/03/2003 00:22 13 19
2 5 01/04/2003 02:00 ---- 01/04/2003 01:55 6 22
Any suggestions where to start? I am trying to do this in R

Answer 1

I needed to bring that data in as data and time separately and then paste and format
location$dt.time <- as.POSIXct(paste(location$date, location$time),
format="%m/%d/%Y %H:%M")
And the same for weather
Then for each value of date.time in location, find the entry in weather that has the lowest absolute values for the time differences:
sapply(location$dt.time, function(x) which.min(abs(difftime(x, weather$dt.time))))
# [1] 2 3 8 9
cbind(location, weather[ sapply(location$dt.time,
function(x) which.min(abs(difftime(x, weather$dt.time)))), ])
x y date time dt.time date time temp wind dt.time
2 1 3 01/02/2003 18:00 2003-01-02 18:00:00 01/02/2003 16:34 10 16 2003-01-02 16:34:00
3 2 3 01/02/2003 19:00 2003-01-02 19:00:00 01/02/2003 20:55 14 22 2003-01-02 20:55:00
8 3 4 01/03/2003 23:00 2003-01-03 23:00:00 01/03/2003 23:44 2 33 2003-01-03 23:44:00
9 2 5 01/04/2003 02:00 2003-01-04 02:00:00 01/04/2003 01:55 6 22 2003-01-04 01:55:00
cbind(location, weather[
sapply(location$dt.time,
function(x) which.min(abs(difftime(x, weather$dt.time)))), ])[ #pick columns
c(1,2,5,8,9,10)]
x y dt.time temp wind dt.time.1
2 1 3 2003-01-02 18:00:00 10 16 2003-01-02 16:34:00
3 2 3 2003-01-02 19:00:00 14 22 2003-01-02 20:55:00
8 3 4 2003-01-03 23:00:00 2 33 2003-01-03 23:44:00
9 2 5 2003-01-04 02:00:00 6 22 2003-01-04 01:55:00
My answers seem a bit different than yours but another reader has already questioned your abilities to do the matching properly by hand.

Answer 2

One fast and short way may be using data.table.
If you create two data.table's X and Y, both with keys, then the syntax is :
X[Y,roll=TRUE]
We call that a rolling join because we roll the prevailing observation in X forward to match the row in Y. See the examples in ?data.table and the introduction vignette.
Another way to do this is the zoo package which has locf (last observation carried forward), and possibly other packages too.
I'm not sure if you mean closest in terms of location, or time. If location, and that location is x,y coordinates then you will need some distance measure in 2D space I guess. data.table only does univariate 'closest' e.g. by time. Reading your question for a 2nd time it does seem you mean closest in the prevailing sense though.
EDIT: Seen the example data now. data.table won't do this in one step because although it can roll forwards or backwards, it won't roll to the nearest. You could do it with an extra step using which=TRUE and then test whether the one after the prevailing was actually closer.