It sounds like you might want a rolling join, e.g.
https://dplyr.tidyverse.org/reference/join_by.html#rolling-joins.
(And data.table has similar functionality which inspired dplyr)
Hadley
On Mon, Aug 7, 2023 at 9:32 PM Naresh Gurbuxani
<naresh_gurbux...@hotmail.com> wrote:
>
>
> I have two dataframes, each with a column for timestamp. I want to
> merge the two dataframes such that each row from first dataframe
> is matched with the row in the second dataframe with most recent but
> preceding timestamp. Here is an example.
>
> option.trades <- data.frame(timestamp = as.POSIXct(c("2023-08-07 10:23:22",
> "2023-08-07 10:25:33", "2023-08-07 10:28:41")), option.price = c(2.5, 2.7,
> 1.8))
>
> stock.trades <- data.frame(timestamp = as.POSIXct(c("2023-08-07 10:23:21",
> "2023-08-07 10:23:34", "2023-08-07 10:24:57", "2023-08-07 10:28:37",
> "2023-08-07 10:29:01")), stock.price = c(102.2, 102.9, 103.1, 101.8, 101.7))
>
> stock.trades <- stock.trades[order(stock.trades$timestamp),]
>
> library(plyr)
> mystock.prices <- ldply(option.trades$timestamp, function(tstamp)
> tail(subset(stock.trades, timestamp <= tstamp), 1))
> names(mystock.prices)[1] <- "stock.timestamp"
> myres <- cbind(option.trades, mystock.prices)
>
> This method works. But for large dataframes, it is very slow. Is there
> a way to speed up the merge?
>
> Thanks,
> Naresh
>
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--
http://hadley.nz
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