Thank you so much for this elegant solution, Jeff.
Philip
On 2020-11-12 02:20, Jeff Newmiller wrote:
I am not a data.table afficiando, but here is how I would do it with
dplyr/tidyr:
library(dplyr)
library(tidyr)
do_per_REL <- function( DF ) {
rng <- range( DF$REF1 ) # watch out for missing
I am not a data.table afficiando, but here is how I would do it with
dplyr/tidyr:
library(dplyr)
library(tidyr)
do_per_REL <- function( DF ) {
rng <- range( DF$REF1 ) # watch out for missing months?
DF <- ( data.frame( REF1 = seq( rng[ 1 ], rng[ 2 ], by = "month" ) )
%>% left_join
I am stuck on a data transformation problem. I have a data frame, df1 in
my example, with some original "levels" data. The data pertain to some
variable, such as GDP, in various reference periods, REF, as estimated
and released in various release periods, REL. The release periods follow
after t
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