Thanks for your suggestion.
I have just returned from a vacation and started catching up on my emails.
Rolling join is an elegant and most suitable solution for my tasks. I invested
some time in learning data.table package. The vignette on secondary indices
and auto indexing refers to
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
wrote:
>
>
> I have two dataframes, each with a column for
On Mon, 07 Aug 2023, Naresh Gurbuxani writes:
> 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.
I was able to adapt your solution using packages with which I am more familiar.
myres2 <- merge(option.trades, stock.trades, by = "timestamp", all =
TRUE)
myres2[,"stock.timestamp"] <- ifelse(is.na(myres2$stock.price), NA,
myres2$timestamp)
myres2$stock.timestamp <-
Hi Naresh,
Perhaps the below is faster than your approach
library(dplyr)
library(tidyr)
merge(option.trades, stock.trades, by="timestamp", all=TRUE) |>
dplyr::arrange(timestamp) |>
dplyr::mutate(stock.timestamp =
as.POSIXct(ifelse(is.na(option.price), timestamp, NA))) |>
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