Can you help me think of some way to reduce the computational time of a code that generates a certain value, which in this case I call coef, which will depend on id/date/category? Better explanations below.
I made two functions that generate the same result. As you can see in benchmark, the first function (return_values) takes twice as long as the second function (return_valuesX) to generate the same results. See that in the second function, I make some brief changes when calculating the coef variable. However, I strongly believe that there is a possibility of improving the code, as you can see in the second function, I managed to improve 50% of processing time compared to the first just with brief changes. But I'm out of ideas for new adjustments, so I would like your valuable opinion.
Code Explanations:
In general, the purpose of the code is to calculate a value, which I call a coef for each group of id, date and category. For this, the median of the values resulting from the subtraction between DR1 and the values of the DRM0 columns of the df1 database is first calculated. After obtaining the median (med variable), I add the values found with the values of the DRM0 columns of my df1 database. This calculation is my SPV variable. In both cases, I used the data.table function, which I believe is faster than using dplyr. After I get SPV, I need to calculate the coef variable for each id/date/category.
Below I will insert an example real easy to understand of the coef calculation. If for example I want to calculate coef of idd<-"3", dmda<-"2021-12-03", CategoryChosse<-"ABC", and I have the following:
> SPV %>% filter(Id==idd, date2 == ymd(dmda), Category == CategoryChosse)
Id date1 date2 Week Category DRM001_PV DRM002_PV DRM003_PV DRM004_PV DRM005_PV DRM006_PV DRM007_PV DRM008_PV DRM009_PV DRM010_PV DRM011_PV DRM012_PV
1: 3 2021-12-01 2021-12-03 Monday ABC -3 374 198 17 537 -54 330 -136 -116 534 18 -199
DRM013_PV DRM014_PV DRM015_PV DRM016_PV DRM017_PV DRM018_PV DRM019_PV DRM020_PV DRM021_PV DRM022_PV DRM023_PV DRM024_PV DRM025_PV DRM026_PV DRM027_PV DRM028_PV
1: 106 106 349 76 684 390 218 146 141 20 435 218 372 321 218 218
DRM029_PV DRM030_PV DRM031_PV DRM032_PV DRM033_PV DRM034_PV DRM035_PV DRM036_PV DRM037_PV DRM038_PV DRM039_PV DRM040_PV DRM041_PV DRM042_PV DRM043_PV DRM044_PV
1: 55 455 46 411 262 449 325 467 43 -114 191 167 63 -123 252 218
DRM045_PV DRM046_PV DRM047_PV DRM048_PV DRM049_PV DRM050_PV DRM051_PV DRM052_PV DRM053_PV DRM054_PV DRM055_PV DRM056_PV DRM057_PV DRM058_PV DRM059_PV DRM060_PV
1: 305 420 -296 596 200 218 190 203 607 218 442 -72 463 129 -39 333
DRM061_PV DRM062_PV DRM063_PV DRM064_PV DRM065_PV DRM066_PV DRM067_PV DRM068_PV DRM069_PV DRM070_PV DRM071_PV DRM072_PV DRM073_PV DRM074_PV DRM075_PV DRM076_PV
1: -26 160 -91 326 218 369 317 476 224 61 195 613 342 218 204 521
DRM077_PV DRM078_PV DRM079_PV DRM080_PV DRM081_PV DRM082_PV DRM083_PV DRM084_PV DRM085_PV DRM086_PV DRM087_PV DRM088_PV DRM089_PV DRM090_PV DRM091_PV DRM092_PV
1: 588 218 449 340 51 508 -72 42 492 510 328 818 -132 -105 210 -102
DRM093_PV DRM094_PV DRM095_PV DRM096_PV DRM097_PV DRM098_PV DRM099_PV DRM0100_PV DRM0101_PV DRM0102_PV DRM0103_PV DRM0104_PV DRM0105_PV DRM0106_PV DRM0107_PV
1: -137 94 639 265 -64 512 32 -53 414 340 -16 471 434 150 267
DRM0108_PV DRM0109_PV DRM0110_PV DRM0111_PV DRM0112_PV DRM0113_PV DRM0114_PV DRM0115_PV DRM0116_PV DRM0117_PV DRM0118_PV DRM0119_PV DRM0120_PV DRM0121_PV DRM0122_PV
1: 383 -162 434 -134 -39 450 212 146 -26 8 222 341 601 239 57
DRM0123_PV DRM0124_PV DRM0125_PV DRM0126_PV DRM0127_PV DRM0128_PV DRM0129_PV DRM0130_PV DRM0131_PV DRM0132_PV DRM0133_PV DRM0134_PV DRM0135_PV DRM0136_PV DRM0137_PV
1: 484 239 502 415 504 62 487 168 101 319 365 37 218 -50 230
DRM0138_PV DRM0139_PV DRM0140_PV DRM0141_PV DRM0142_PV DRM0143_PV DRM0144_PV DRM0145_PV DRM0146_PV DRM0147_PV DRM0148_PV DRM0149_PV DRM0150_PV DRM0151_PV DRM0152_PV
1: 493 159 150 132 58 21 468 -81 27 345 107 148 -66 -146 -185
DRM0153_PV DRM0154_PV DRM0155_PV DRM0156_PV DRM0157_PV DRM0158_PV DRM0159_PV DRM0160_PV DRM0161_PV DRM0162_PV DRM0163_PV DRM0164_PV DRM0165_PV DRM0166_PV DRM0167_PV
1: -14 562 68 140 353 120 130 301 76 441 218 370 218 378 -22
DRM0168_PV DRM0169_PV DRM0170_PV DRM0171_PV DRM0172_PV DRM0173_PV DRM0174_PV DRM0175_PV DRM0176_PV DRM0177_PV DRM0178_PV DRM0179_PV DRM0180_PV DRM0181_PV DRM0182_PV
1: -279 563 628 600 152 218 445 246 420 94 495 509 356 183 326
DRM0183_PV DRM0184_PV DRM0185_PV DRM0186_PV DRM0187_PV DRM0188_PV DRM0189_PV DRM0190_PV DRM0191_PV DRM0192_PV DRM0193_PV DRM0194_PV DRM0195_PV DRM0196_PV DRM0197_PV
1: 493 -190 -65 -123 376 357 473 112 -69 471 452 221 165 -44 87
DRM0198_PV DRM0199_PV DRM0200_PV DRM0201_PV DRM0202_PV DRM0203_PV DRM0204_PV DRM0205_PV DRM0206_PV DRM0207_PV DRM0208_PV DRM0209_PV DRM0210_PV DRM0211_PV DRM0212_PV
1: 239 285 521 -65 158 223 160 223 269 57 218 218 102 329 218
DRM0213_PV DRM0214_PV DRM0215_PV DRM0216_PV DRM0217_PV DRM0218_PV DRM0219_PV DRM0220_PV DRM0221_PV DRM0222_PV DRM0223_PV DRM0224_PV DRM0225_PV DRM0226_PV DRM0227_PV
1: 769 215 -68 218 347 18 218 547 759 278 -80 -37 629 -16 774
DRM0228_PV DRM0229_PV DRM0230_PV DRM0231_PV DRM0232_PV DRM0233_PV DRM0234_PV DRM0235_PV DRM0236_PV DRM0237_PV DRM0238_PV DRM0239_PV DRM0240_PV DRM0241_PV DRM0242_PV
1: 364 113 -132 31 536 118 248 385 218 202 218 41 23 218 379
DRM0243_PV DRM0244_PV DRM0245_PV DRM0246_PV DRM0247_PV DRM0248_PV DRM0249_PV DRM0250_PV DRM0251_PV DRM0252_PV DRM0253_PV DRM0254_PV DRM0255_PV DRM0256_PV DRM0257_PV
1: -158 462 600 221 218 221 442 218 53 218 176 504 -61 78 68
DRM0258_PV DRM0259_PV DRM0260_PV DRM0261_PV DRM0262_PV DRM0263_PV DRM0264_PV DRM0265_PV DRM0266_PV DRM0267_PV DRM0268_PV DRM0269_PV DRM0270_PV DRM0271_PV DRM0272_PV
1: 493 403 218 339 299 749 -18 465 686 -215 579 307 366 279 94
DRM0273_PV DRM0274_PV DRM0275_PV DRM0276_PV DRM0277_PV DRM0278_PV DRM0279_PV DRM0280_PV DRM0281_PV DRM0282_PV DRM0283_PV DRM0284_PV DRM0285_PV DRM0286_PV DRM0287_PV
1: 138 56 459 613 219 400 35 -74 516 218 -80 317 310 -231 229
DRM0288_PV DRM0289_PV DRM0290_PV DRM0291_PV DRM0292_PV DRM0293_PV DRM0294_PV DRM0295_PV DRM0296_PV DRM0297_PV DRM0298_PV DRM0299_PV DRM0300_PV DRM0301_PV DRM0302_PV
1: 345 -70 619 235 122 61 337 -163 210 586 127 -112 368 365 476
DRM0303_PV DRM0304_PV DRM0305_PV DRM0306_PV DRM0307_PV DRM0308_PV DRM0309_PV DRM0310_PV DRM0311_PV DRM0312_PV DRM0313_PV DRM0314_PV DRM0315_PV DRM0316_PV DRM0317_PV
1: 240 270 497 97 420 -184 212 -28 151 527 186 -32 60 96 -86
DRM0318_PV DRM0319_PV DRM0320_PV DRM0321_PV DRM0322_PV DRM0323_PV DRM0324_PV DRM0325_PV DRM0326_PV DRM0327_PV DRM0328_PV DRM0329_PV DRM0330_PV DRM0331_PV DRM0332_PV
1: 454 321 300 552 319 134 -63 622 441 297 507 578 198 360 542
DRM0333_PV DRM0334_PV DRM0335_PV DRM0336_PV DRM0337_PV DRM0338_PV DRM0339_PV DRM0340_PV DRM0341_PV DRM0342_PV DRM0343_PV DRM0344_PV DRM0345_PV DRM0346_PV DRM0347_PV
1: 153 318 68 763 370 337 633 469 453 146 428 418 169 468 526
DRM0348_PV DRM0349_PV DRM0350_PV DRM0351_PV DRM0352_PV DRM0353_PV DRM0354_PV DRM0355_PV DRM0356_PV DRM0357_PV DRM0358_PV DRM0359_PV DRM0360_PV DRM0361_PV DRM0362_PV
1: 441 674 21 -182 174 153 -158 268 191 460 10 82 543 -193 218
DRM0363_PV DRM0364_PV DRM0365_PV
1: -203 269 479
> SPV %>% filter(Id==idd, date2 == ymd(dmda), Category == CategoryChosse)
Id date1 date2 Week Category DRM001_PV DRM002_PV DRM003_PV DRM004_PV DRM005_PV DRM006_PV DRM007_PV DRM008_PV DRM009_PV DRM010_PV DRM011_PV DRM012_PV
1: 3 2021-12-01 2021-12-03 Monday ABC -3 374 198 17 537 -54 330 -136 -116 534 18 -199
DRM013_PV DRM014_PV DRM015_PV DRM016_PV DRM017_PV DRM018_PV DRM019_PV DRM020_PV DRM021_PV DRM022_PV DRM023_PV DRM024_PV DRM025_PV DRM026_PV DRM027_PV DRM028_PV
1: 106 106 349 76 684 390 218 146 141 20 435 218 372 321 218 218
DRM029_PV DRM030_PV DRM031_PV DRM032_PV DRM033_PV DRM034_PV DRM035_PV DRM036_PV DRM037_PV DRM038_PV DRM039_PV DRM040_PV DRM041_PV DRM042_PV DRM043_PV DRM044_PV
1: 55 455 46 411 262 449 325 467 43 -114 191 167 63 -123 252 218
DRM045_PV DRM046_PV DRM047_PV DRM048_PV DRM049_PV DRM050_PV DRM051_PV DRM052_PV DRM053_PV DRM054_PV DRM055_PV DRM056_PV DRM057_PV DRM058_PV DRM059_PV DRM060_PV
1: 305 420 -296 596 200 218 190 203 607 218 442 -72 463 129 -39 333
DRM061_PV DRM062_PV DRM063_PV DRM064_PV DRM065_PV DRM066_PV DRM067_PV DRM068_PV DRM069_PV DRM070_PV DRM071_PV DRM072_PV DRM073_PV DRM074_PV DRM075_PV DRM076_PV
1: -26 160 -91 326 218 369 317 476 224 61 195 613 342 218 204 521
DRM077_PV DRM078_PV DRM079_PV DRM080_PV DRM081_PV DRM082_PV DRM083_PV DRM084_PV DRM085_PV DRM086_PV DRM087_PV DRM088_PV DRM089_PV DRM090_PV DRM091_PV DRM092_PV
1: 588 218 449 340 51 508 -72 42 492 510 328 818 -132 -105 210 -102
DRM093_PV DRM094_PV DRM095_PV DRM096_PV DRM097_PV DRM098_PV DRM099_PV DRM0100_PV DRM0101_PV DRM0102_PV DRM0103_PV DRM0104_PV DRM0105_PV DRM0106_PV DRM0107_PV
1: -137 94 639 265 -64 512 32 -53 414 340 -16 471 434 150 267
DRM0108_PV DRM0109_PV DRM0110_PV DRM0111_PV DRM0112_PV DRM0113_PV DRM0114_PV DRM0115_PV DRM0116_PV DRM0117_PV DRM0118_PV DRM0119_PV DRM0120_PV DRM0121_PV DRM0122_PV
1: 383 -162 434 -134 -39 450 212 146 -26 8 222 341 601 239 57
DRM0123_PV DRM0124_PV DRM0125_PV DRM0126_PV DRM0127_PV DRM0128_PV DRM0129_PV DRM0130_PV DRM0131_PV DRM0132_PV DRM0133_PV DRM0134_PV DRM0135_PV DRM0136_PV DRM0137_PV
1: 484 239 502 415 504 62 487 168 101 319 365 37 218 -50 230
DRM0138_PV DRM0139_PV DRM0140_PV DRM0141_PV DRM0142_PV DRM0143_PV DRM0144_PV DRM0145_PV DRM0146_PV DRM0147_PV DRM0148_PV DRM0149_PV DRM0150_PV DRM0151_PV DRM0152_PV
1: 493 159 150 132 58 21 468 -81 27 345 107 148 -66 -146 -185
DRM0153_PV DRM0154_PV DRM0155_PV DRM0156_PV DRM0157_PV DRM0158_PV DRM0159_PV DRM0160_PV DRM0161_PV DRM0162_PV DRM0163_PV DRM0164_PV DRM0165_PV DRM0166_PV DRM0167_PV
1: -14 562 68 140 353 120 130 301 76 441 218 370 218 378 -22
DRM0168_PV DRM0169_PV DRM0170_PV DRM0171_PV DRM0172_PV DRM0173_PV DRM0174_PV DRM0175_PV DRM0176_PV DRM0177_PV DRM0178_PV DRM0179_PV DRM0180_PV DRM0181_PV DRM0182_PV
1: -279 563 628 600 152 218 445 246 420 94 495 509 356 183 326
DRM0183_PV DRM0184_PV DRM0185_PV DRM0186_PV DRM0187_PV DRM0188_PV DRM0189_PV DRM0190_PV DRM0191_PV DRM0192_PV DRM0193_PV DRM0194_PV DRM0195_PV DRM0196_PV DRM0197_PV
1: 493 -190 -65 -123 376 357 473 112 -69 471 452 221 165 -44 87
DRM0198_PV DRM0199_PV DRM0200_PV DRM0201_PV DRM0202_PV DRM0203_PV DRM0204_PV DRM0205_PV DRM0206_PV DRM0207_PV DRM0208_PV DRM0209_PV DRM0210_PV DRM0211_PV DRM0212_PV
1: 239 285 521 -65 158 223 160 223 269 57 218 218 102 329 218
DRM0213_PV DRM0214_PV DRM0215_PV DRM0216_PV DRM0217_PV DRM0218_PV DRM0219_PV DRM0220_PV DRM0221_PV DRM0222_PV DRM0223_PV DRM0224_PV DRM0225_PV DRM0226_PV DRM0227_PV
1: 769 215 -68 218 347 18 218 547 759 278 -80 -37 629 -16 774
DRM0228_PV DRM0229_PV DRM0230_PV DRM0231_PV DRM0232_PV DRM0233_PV DRM0234_PV DRM0235_PV DRM0236_PV DRM0237_PV DRM0238_PV DRM0239_PV DRM0240_PV DRM0241_PV DRM0242_PV
1: 364 113 -132 31 536 118 248 385 218 202 218 41 23 218 379
DRM0243_PV DRM0244_PV DRM0245_PV DRM0246_PV DRM0247_PV DRM0248_PV DRM0249_PV DRM0250_PV DRM0251_PV DRM0252_PV DRM0253_PV DRM0254_PV DRM0255_PV DRM0256_PV DRM0257_PV
1: -158 462 600 221 218 221 442 218 53 218 176 504 -61 78 68
DRM0258_PV DRM0259_PV DRM0260_PV DRM0261_PV DRM0262_PV DRM0263_PV DRM0264_PV DRM0265_PV DRM0266_PV DRM0267_PV DRM0268_PV DRM0269_PV DRM0270_PV DRM0271_PV DRM0272_PV
1: 493 403 218 339 299 749 -18 465 686 -215 579 307 366 279 94
DRM0273_PV DRM0274_PV DRM0275_PV DRM0276_PV DRM0277_PV DRM0278_PV DRM0279_PV DRM0280_PV DRM0281_PV DRM0282_PV DRM0283_PV DRM0284_PV DRM0285_PV DRM0286_PV DRM0287_PV
1: 138 56 459 613 219 400 35 -74 516 218 -80 317 310 -231 229
DRM0288_PV DRM0289_PV DRM0290_PV DRM0291_PV DRM0292_PV DRM0293_PV DRM0294_PV DRM0295_PV DRM0296_PV DRM0297_PV DRM0298_PV DRM0299_PV DRM0300_PV DRM0301_PV DRM0302_PV
1: 345 -70 619 235 122 61 337 -163 210 586 127 -112 368 365 476
DRM0303_PV DRM0304_PV DRM0305_PV DRM0306_PV DRM0307_PV DRM0308_PV DRM0309_PV DRM0310_PV DRM0311_PV DRM0312_PV DRM0313_PV DRM0314_PV DRM0315_PV DRM0316_PV DRM0317_PV
1: 240 270 497 97 420 -184 212 -28 151 527 186 -32 60 96 -86
DRM0318_PV DRM0319_PV DRM0320_PV DRM0321_PV DRM0322_PV DRM0323_PV DRM0324_PV DRM0325_PV DRM0326_PV DRM0327_PV DRM0328_PV DRM0329_PV DRM0330_PV DRM0331_PV DRM0332_PV
1: 454 321 300 552 319 134 -63 622 441 297 507 578 198 360 542
DRM0333_PV DRM0334_PV DRM0335_PV DRM0336_PV DRM0337_PV DRM0338_PV DRM0339_PV DRM0340_PV DRM0341_PV DRM0342_PV DRM0343_PV DRM0344_PV DRM0345_PV DRM0346_PV DRM0347_PV
1: 153 318 68 763 370 337 633 469 453 146 428 418 169 468 526
DRM0348_PV DRM0349_PV DRM0350_PV DRM0351_PV DRM0352_PV DRM0353_PV DRM0354_PV DRM0355_PV DRM0356_PV DRM0357_PV DRM0358_PV DRM0359_PV DRM0360_PV DRM0361_PV DRM0362_PV
1: 441 674 21 -182 174 153 -158 268 191 460 10 82 543 -193 218
DRM0363_PV DRM0364_PV DRM0365_PV
1: -203 269 479
So coef will be ymd(dmda) - ymd(min(df1$date1)). That is, if I do to this id/date/category that I mentioned I get a difference of 2 days, so the value I want is the DRM003_PV . So the value for this case is 198. Therefore, I made:
coef<-SPV %>%
filter(Id==idd, date2 == ymd(dmda), Category == CategoryChosse) %>%
pull(as.numeric(ymd(dmda)-ymd(min(df1$date1)))+6)
> coef
[1] 198
This issue has been resolved here: Adjust code to choose a specific column depending on the difference between dates
Libraries and database
library(tidyverse)
library(lubridate)
library(data.table)
library(bench)
set.seed(123)
df1 <- data.frame( Id = rep(1:5, length=800),
date1 = as.Date( "2021-12-01"),
date2= rep(seq( as.Date("2021-01-01"), length.out=400, by=1), each = 2),
Category = rep(c("ABC", "EFG"), length.out = 800),
Week = rep(c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday",
"Saturday", "Sunday"), length.out = 800),
DR1 = sample( 200:250, 800, repl=TRUE),
setNames( replicate(365, { sample(0:800, 800)}, simplify=FALSE),
paste0("DRM0", formatC(1:365, width = 2, format = "d", flag = "0"))))
First function
return_values <- function (df1,idd,dmda, CategoryChosse) {
# First idea: Calculate the median of the values resulting from the subtraction between DR1 and the values of the DRM0 columns
dt1 <- as.data.table(df1)
cols <- grep("^DRM0", colnames(dt1), value = TRUE)
med <-
dt1[, (paste0(cols, "_PV")) := DR1 - .SD, .SDcols = cols
][, lapply(.SD, median), by = .(Id, Category, Week), .SDcols = paste0(cols, "_PV") ]
# Second idea: After obtaining the median, I add the values found with the values of the DRM columns of my df1 database.
f2 <- function(nm, pat) grep(pat, nm, value = TRUE)
nm1 <- f2(names(df1), "^DRM0\\d+$")
nm2 <- f2(names(med), "_PV")
nm3 <- paste0("i.", nm2)
setDT(df1)[med,(nm2) := Map(`+`, mget(nm1), mget(nm3)), on = .(Id, Category, Week)]
SPV <- df1[, c('Id','date1', 'date2', 'Week','Category', nm2), with = FALSE]#%>%data.frame
# Third idea: Calculate the coef values
coef<-SPV %>%
filter(Id==idd, date2 == ymd(dmda), Category == CategoryChosse) %>%
pull(as.numeric(ymd(dmda)-ymd(min(df1$date1)))+6)
return(coef)
}
Results using first function
subset_df1 <- subset(df1, date2 > date1)
a<-subset_df1 %>%
rowwise %>%
select(-c(Week,starts_with('DR')))%>%
mutate(Result=return_values(df1,Id, date2, Category)) %>%
data.frame()
> a
Id date1 date2 Category Result
1 1 2021-12-01 2021-12-02 ABC 4.0
2 2 2021-12-01 2021-12-02 EFG 238.0
3 3 2021-12-01 2021-12-03 ABC 198.0
4 4 2021-12-01 2021-12-03 EFG 163.0
5 5 2021-12-01 2021-12-04 ABC 462.0
...........
Second function
return_valuesX <- function (df1,idd,dmda, CategoryChosse) {
# First idea: Calculate the median of the values resulting from the subtraction between DR1 and the values of the DRM columns
dt1 <- as.data.table(df1)
num_to_pull <- as.numeric(ymd(dmda)-ymd(min(df1$date1)))+6
cols <- grep("^DRM0", colnames(dt1), value = TRUE)[1:num_to_pull]
med <-
dt1[, (paste0(cols, "_PV")) := DR1 - .SD, .SDcols = cols
][, lapply(.SD, median), by = .(Id, Category, Week), .SDcols = paste0(cols, "_PV") ]
# Second idea: After obtaining the median, I add the values found with the values of the DRM columns of my df1 database.
f2 <- function(nm, pat) grep(pat, nm, value = TRUE)
nm1 <- f2(names(df1), "^DRM0\\d+$")[1:num_to_pull]
nm2 <- f2(names(med), "_PV")[1:num_to_pull]
nm3 <- paste0("i.", nm2)[1:num_to_pull]
setDT(df1)[med,(nm2) := Map(`+`, mget(nm1), mget(nm3)), on = .(Id, Category, Week)]
SPV <- df1[, c('Id','date1', 'date2', 'Week','Category', nm2), with = FALSE]#%>%data.frame
# Third idea: Calculate the coef values
coef<-SPV %>%
filter(Id==idd, date2 == ymd(dmda), Category == CategoryChosse) %>%
pull(num_to_pull)
return(coef)
}
Results using second function
b<-subset_df1 %>%
rowwise %>%
select(-c(Week,starts_with('DR')))%>%
mutate(Result = return_valuesX(df1,Id, date2, Category)) %>%
data.frame()
> b
Id date1 date2 Category Result
1 1 2021-12-01 2021-12-02 ABC 4.0
2 2 2021-12-01 2021-12-02 EFG 238.0
3 3 2021-12-01 2021-12-03 ABC 198.0
4 4 2021-12-01 2021-12-03 EFG 163.0
5 5 2021-12-01 2021-12-04 ABC 462.0
...............
Comparing the two results:
identical(a, b)
[1] TRUE
Calculate processing time using benchmark
subset_df1 <- subset(df1, date2 > date1)
bench::mark(a=subset_df1 %>%
rowwise %>%
select(-c(Week,starts_with('DR')))%>%
mutate(Result=return_values(df1,Id, date2, Category)),
b=subset_df1 %>%
rowwise %>%
select(-c(Week,starts_with('DR')))%>%
mutate(Result=return_valuesX(df1,Id, date2, Category)),iterations = 1)
# A tibble: 2 x 13
expression min median `itr/sec` mem_alloc `gc/sec` n_itr n_gc total_time result memory time gc
<bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl> <int> <dbl> <bch:tm> <list> <list> <list> <list>
1 a 53.7s 53.7s 0.0186 4.54GB 0.634 1 34 53.7s <rowwise_df [130 x 5]> <Rprofmem [981,580 x 3]> <bench_tm [1]> <tibble [1 x 3]>
2 b 21s 21s 0.0477 913.77MB 0.382 1 8 21s <rowwise_df [130 x 5]> <Rprofmem [278,340 x 3]> <bench_tm [1]> <tibble [1 x 3]>
To check df1 database 
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