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 anoth
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$stock.timestamp <- as.POSIXct(myres2$stock.time
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))) |>
tidyr::fill(stock.pr
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(
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