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Denormalize-A-DataFrame

Source: vignettes/Denormalize-A-DataFrame.Rmd
Denormalize-A-DataFrame.Rmd

Vignette Build Datetime 

message(paste0('Datetime: ',Sys.Date(),':',Sys.time()))
#> Datetime: 2026-01-21:2026-01-21 14:55:12.38782

Load Libraries

Set Up the Reporting Environment 

tmpdr <- tempdir()
datdir <- file.path(gsub("\\","/",tmpdr,fixed=TRUE),"datdir")
dir.create(datdir,showWarnings=FALSE)
repfun::copydata(datdir)
repfun::rs_setup(D_POP="SAFFL",D_POPLBL="Safety",
         D_POPDATA=repfun::adsl %>% dplyr::filter(SAFFL =='Y'), 
         D_SUBJID=c("STUDYID","USUBJID"), R_ADAMDATA=datdir)
repfun:::rfenv$G_POPDATA %>% dplyr::mutate(TRT01AN=ifelse(TRT01A=='Placebo',1,ifelse(TRT01A=='Xanomeline Low Dose',2,3))) %>% repfun::ru_labels(varlabels=list('TRT01AN'='Actual Treatment for Period 01 (n)')) -> G_POPDATA

Read in ADAE and Apply Population 

adae <- repfun:::rfenv$adamdata$adae.rda() %>% select(-SAFFL) %>% 
  repfun::ru_getdata(G_POPDATA, c("STUDYID", "USUBJID"), keeppopvars=c("TRT01AN", "TRT01A"))

Generate Counts and Percents for AE Body System and Preferred Term 

aesum <- repfun::ru_freq(adae,
                 dsetindenom=G_POPDATA,
                 countdistinctvars=c('STUDYID','USUBJID'),
                 groupbyvarsnumer=c('TRT01AN','TRT01A','AEBODSYS','AEDECOD'),
                 anyeventvars = c('AEBODSYS','AEDECOD'),
                 anyeventvalues = c('ANY EVENT','ANY EVENT'),
                 groupbyvarsdenom=c('TRT01AN'),
                 resultstyle="NUMERPCT",
                 totalforvar=c('TRT01AN'),
                 totalid=99,
                 totaldecode='Total',
                 codedecodevarpairs=c("TRT01AN", "TRT01A"),
                 varcodelistpairs=c(""),
                 codelistnames=list(),
                 resultpctdps=0)

Denormalize the AE Counts and Percents Data Set 

aesum_t <- repfun::ru_denorm(aesum,varstodenorm=c("tt_result", "PERCENT"), 
                     groupbyvars=c("tt_summarylevel", "AEBODSYS", "AEDECOD"), 
                     acrossvar="TRT01AN", acrossvarlabel="TRT01A", 
                     acrossvarprefix=c("tt_ac", "tt_p")) %>% 
  dplyr::arrange(tt_summarylevel, AEBODSYS, AEDECOD)

Display the Denormalized AE Counts and Percents Data Set 

lbls <- sapply(aesum_t,function(x){attr(x,"label")})
knitr::kable(head(aesum_t,10), col.names=paste(names(lbls),lbls,sep=": "), 
             caption = "Denormalized Data Set for Counts and Percents") %>%  
  kable_styling(full_width = T) %>% column_spec(c(2,3), width_min = c('2in','2in'))
Denormalized Data Set for Counts and Percents
tt_summarylevel: Summary Level AEBODSYS: Body System or Organ Class AEDECOD: Dictionary-Derived Term tt_ac01: Placebo tt_p01: Placebo tt_ac02: Xanomeline Low Dose tt_p02: Xanomeline Low Dose tt_ac03: Xanomeline High Dose tt_p03: Xanomeline High Dose tt_ac99: Total tt_p99: Total
0 ANY EVENT ANY EVENT 69 (80%) 80.232558 86 (90%) 89.583333 70 (97%) 97.222222 225 (89%) 88.5826772
1 CARDIAC DISORDERS ANY EVENT 13 (15%) 15.116279 16 (17%) 16.666667 15 (21%) 20.833333 44 (17%) 17.3228346
1 CONGENITAL, FAMILIAL AND GENETIC DISORDERS ANY EVENT 0 (0%) 0.000000 1 (1%) 1.041667 2 (3%) 2.777778 3 (1%) 1.1811024
1 EAR AND LABYRINTH DISORDERS ANY EVENT 1 (1%) 1.162791 2 (2%) 2.083333 1 (1%) 1.388889 4 (2%) 1.5748031
1 EYE DISORDERS ANY EVENT 4 (5%) 4.651163 2 (2%) 2.083333 1 (1%) 1.388889 7 (3%) 2.7559055
1 GASTROINTESTINAL DISORDERS ANY EVENT 17 (20%) 19.767442 16 (17%) 16.666667 20 (28%) 27.777778 53 (21%) 20.8661417
1 GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS ANY EVENT 21 (24%) 24.418605 51 (53%) 53.125000 36 (50%) 50.000000 108 (43%) 42.5196850
1 HEPATOBILIARY DISORDERS ANY EVENT 1 (1%) 1.162791 0 (0%) 0.000000 0 (0%) 0.000000 1 (0%) 0.3937008
1 IMMUNE SYSTEM DISORDERS ANY EVENT 0 (0%) 0.000000 1 (1%) 1.041667 1 (1%) 1.388889 2 (1%) 0.7874016
1 INFECTIONS AND INFESTATIONS ANY EVENT 16 (19%) 18.604651 10 (10%) 10.416667 13 (18%) 18.055556 39 (15%) 15.3543307

Derive Summary Statistics for Baseline Characteristics Data 

demstats <- repfun::ru_sumstats(G_POPDATA,
                        analysisvars=c("AGE","TRTDURD"),
                        groupbyvars=c("STUDYID","TRT01AN"),
                        codedecodevarpairs=c("TRT01AN", "TRT01A"),
                        totalforvar="TRT01AN", totalid=99,
                        totaldecode="Total",
                        statsinrowsyn = "Y",
                        analysisvardps=list("AGE"=1,"TRTDURD"=2),
                        statslist=c("n", "mean", "median", "sd", "min", "max"))

Denormalize the Baseline Characteristics Summary Statistics Data Set 

demprod_t <- repfun::ru_denorm(demstats, varstodenorm=c("tt_result"), 
                       groupbyvars=c("tt_avid", "tt_avnm", "tt_svid", "tt_svnm"),
                       acrossvar="TRT01AN", acrossvarlabel="TRT01A", 
                       acrossvarprefix=c("tt_ac"))

Display the Denormalized Baseline Characteristics Summary Statistics Data Set 

lbls <- sapply(demprod_t,function(x){attr(x,"label")})
knitr::kable(head(demprod_t,10), col.names=paste(names(lbls),lbls,sep=": "), 
             caption = "Denormalized Data Set for Baseline Characteristics Summary Statistics") %>%   
  kable_styling(full_width = T) %>% column_spec(c(2), width_min = c('3in'))
Denormalized Data Set for Baseline Characteristics Summary Statistics
tt_avid: Analysis Variable ID tt_avnm: Analysis Variable Name tt_svid: Statistical Parameter ID tt_svnm: Statistical Parameter Name tt_ac01: Placebo tt_ac02: Xanomeline Low Dose tt_ac03: Xanomeline High Dose tt_ac99: Total
1 AGE 1 n 86 96 72 254
1 AGE 2 Mean 75.21 75.96 73.78 75.09
1 AGE 3 Median 76.00 78.00 75.50 77.00
1 AGE 4 SD 8.590 8.114 7.944 8.246
1 AGE 5 Min 52.0 51.0 56.0 51.0
1 AGE 6 Max 89.0 88.0 88.0 89.0
2 TRTDURD 1 n 85 95 72 252
2 TRTDURD 2 Mean 149.541 86.811 112.222 115.230
2 TRTDURD 3 Median 182.000 63.000 96.500 132.000
2 TRTDURD 4 SD 60.3544 70.4737 65.5233 70.7137