I would like to use another resolution approach that is faster to calculate the SPV. See that I use inner_join, which is a function that takes considerable computational time, so there is another way to calculate the SPV, other than inner_join and make it faster.?
library(dplyr)
library(tidyr)
library(lubridate)
library(data.table)
df1 <- structure(
list(date1= c("2021-06-28","2021-06-28","2021-06-28","2021-06-28","2021-06-28",
"2021-06-28","2021-06-28","2021-06-28"),
date2 = c("2021-06-25","2021-06-25","2021-06-27","2021-07-07","2021-07-07","2021-07-09","2021-07-09","2021-07-09"),
Code = c("FDE","ABC","ABC","ABC","CDE","FGE","ABC","CDE"),
Week= c("Wednesday","Wednesday","Friday","Wednesday","Wednesday","Friday","Friday","Friday"),
DR1 = c(4,1,4,3,3,4,3,5),
DR01 = c(4,1,4,3,3,4,3,6), DR02= c(4,2,6,7,3,2,7,4),DR03= c(9,5,4,3,3,2,1,5),
DR04 = c(5,4,3,3,6,2,1,9),DR05 = c(5,4,5,3,6,2,1,9),
DR06 = c(2,4,3,3,5,6,7,8),DR07 = c(2,5,4,4,9,4,7,8),
DR08 = c(0,0,0,1,2,0,0,0),DR09 = c(0,0,0,0,0,0,0,0),DR010 = c(0,0,0,0,0,0,0,0),DR011 = c(4,0,0,0,0,0,0,0),
DR012 = c(0,0,0,3,0,0,0,5),DR013 = c(0,0,1,0,0,0,2,0),DR014 = c(0,0,0,0,0,2,0,0)),
class = "data.frame", row.names = c(NA, -8L))
selection = startsWith(names(df1), "DRM")
df1[selection][is.na(df1[selection])] = 0
dt1 <- as.data.table(df1)
cols <- grep("^DR0", colnames(dt1), value = TRUE)
medi_ana <-
dt1[, (paste0(cols, "_PV")) := DR1 - .SD, .SDcols = cols
][, lapply(.SD, median), by = .(Code, Week), .SDcols = paste0(cols, "_PV") ]
SPV<-df1%>%
inner_join(medi_ana, by = c('Code', 'Week')) %>%
mutate(across(matches("^DR0\\d $"), ~.x
get(paste0(cur_column(), '_PV')),
.names = '{col}_{col}_PV')) %>%
select(date1:Week, DR01_DR01_PV:last_col())%>%
data.frame()
> SPV
date1 date2 Code Week DR01_DR01_PV DR02_DR02_PV DR03_DR03_PV DR04_DR04_PV DR05_DR05_PV DR06_DR06_PV DR07_DR07_PV
1 2021-06-28 2021-06-25 FDE Wednesday 4 4.0 4 4.0 4.0 4.0 4.0
2 2021-06-28 2021-06-25 ABC Wednesday 1 -0.5 3 2.5 2.5 2.5 2.5
3 2021-06-28 2021-06-27 ABC Friday 4 3.0 5 4.5 5.5 1.5 2.0
4 2021-06-28 2021-07-07 ABC Wednesday 3 4.5 1 1.5 1.5 1.5 1.5
5 2021-06-28 2021-07-07 CDE Wednesday 3 3.0 3 3.0 3.0 3.0 3.0
6 2021-06-28 2021-07-09 FGE Friday 4 4.0 4 4.0 4.0 4.0 4.0
7 2021-06-28 2021-07-09 ABC Friday 3 4.0 2 2.5 1.5 5.5 5.0
8 2021-06-28 2021-07-09 CDE Friday 5 5.0 5 5.0 5.0 5.0 5.0
DR08_DR08_PV DR09_DR09_PV DR010_DR010_PV DR011_DR011_PV DR012_DR012_PV DR013_DR013_PV DR014_DR014_PV
1 4.0 4.0 4.0 4.0 4.0 4 4.0
2 1.5 2.0 2.0 2.0 0.5 2 2.0
3 3.5 3.5 3.5 3.5 3.5 3 3.5
4 2.5 2.0 2.0 2.0 3.5 2 2.0
5 3.0 3.0 3.0 3.0 3.0 3 3.0
6 4.0 4.0 4.0 4.0 4.0 4 4.0
7 3.5 3.5 3.5 3.5 3.5 4 3.5
8 5.0 5.0 5.0 5.0 5.0 5 5.0
CodePudding user response：
As we are using data.table, data.table join could be faster
library(data.table)
f1 <- function(nm, pat) grep(pat, nm, value = TRUE)
nm1 <- f1(names(df1), "^DR0\\d $")
nm2 <- f1(names(medi_ana), "_PV")
nm3 <- paste0("i.", nm2)
setDT(df1)[medi_ana, (nm2) := Map(` `, mget(nm1), mget(nm3)), on = .(Code, Week)]
SPV2 <- df1[, c('date1', 'date2', 'Code', 'Week', nm2), with = FALSE]