How to reduce processing time of a code in R
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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