Generate-Counts-and-Percents

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Vignette Build Datetime

message(paste0('Datetime: ',Sys.Date(),':',Sys.time()))
#> Datetime: 2026-01-21:2026-01-21 09:14:26

Load Libraries

library(repfun)
library(dplyr)
library(kableExtra)

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_POPDATA=repfun::adsl %>% dplyr::filter(SAFFL =='Y'), 
         D_SUBJID=c("STUDYID","USUBJID"),
         R_ADAMDATA=datdir)

Update ADSL and ADAE

repfun:::rfenv$G_POPDATA %>% dplyr::mutate(TRT01AN=ifelse(TRT01A=='Placebo',1,ifelse(TRT01A=='Xanomeline Low Dose',2,3)),
                     SEXN=ifelse(SEX=='F',1,ifelse(SEX=='M',2,NA)),
                     RACEN=ifelse(RACE=='AMERICAN INDIAN OR ALASKA NATIVE',1,
                                  ifelse(RACE=='BLACK OR AFRICAN AMERICAN',2,
                                         ifelse(RACE=='WHITE',3,NA))),
                     AGEGR1N=ifelse(AGEGR1=='18-64',1,ifelse(AGEGR1=='>64',2,NA))) %>% 
              repfun::ru_labels(varlabels=list('TRT01AN'='Actual Treatment for Period 01 (n)','SEXN'='Sex (n)',
                                       'RACEN'='Race (n)','AGEGR1N'='Pooled Age Group 1 (n)')) -> G_POPDATA

G_POPDATA %>% dplyr::select(STUDYID,USUBJID,SAFFL,TRT01AN,TRT01A) -> G_POPDATA1
adae <- repfun:::rfenv$adamdata$adae.rda() %>% dplyr::filter(TRTEMFL=='Y') %>% 
  dplyr::inner_join(G_POPDATA1, by=c('STUDYID','USUBJID','SAFFL','TRT01A'))

Generate Counts and Percents for AE Body System and Preferred Term

aeprod <- repfun::ru_freq(adae,
                  dsetindenom=G_POPDATA1,
                  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=2) %>% dplyr::select(TRT01AN,TRT01A,AEBODSYS,AEDECOD,NUMERCNT,
                                             DENOMCNT,PERCENT,tt_result) %>% 
                                      dplyr::arrange(TRT01AN,TRT01A,AEBODSYS,AEDECOD)

Display the Results for AE Body System and Preferred Term

lbls <- sapply(aeprod,function(x){attr(x,"label")})
knitr::kable(head(aeprod,10), col.names=paste(names(lbls),lbls,sep=": "), 
             caption = "Counts and Percents for AEs by Body System and Preferred Term") %>%
    kable_styling(full_width = T) %>% column_spec(c(3,4,8), width_min = c('2in','2in','2in'))

Counts and Percents for AEs by Body System and Preferred Term
TRT01AN: Actual Treatment for Period 01 (n)	TRT01A: Actual Treatment for Period 01	AEBODSYS: Body System or Organ Class	AEDECOD: Dictionary-Derived Term	NUMERCNT: Numerator Count	DENOMCNT: Denominator Count	PERCENT: Percent	tt_result: Result
1	Placebo	ANY EVENT	ANY EVENT	65	86	75.581395	65 (75.58%)
1	Placebo	CARDIAC DISORDERS	ANY EVENT	12	86	13.953488	12 (13.95%)
1	Placebo	CARDIAC DISORDERS	ATRIAL FIBRILLATION	1	86	1.162791	1 (1.16%)
1	Placebo	CARDIAC DISORDERS	ATRIAL FLUTTER	0	86	0.000000	0 (0.00%)
1	Placebo	CARDIAC DISORDERS	ATRIAL HYPERTROPHY	1	86	1.162791	1 (1.16%)
1	Placebo	CARDIAC DISORDERS	ATRIOVENTRICULAR BLOCK FIRST DEGREE	1	86	1.162791	1 (1.16%)
1	Placebo	CARDIAC DISORDERS	ATRIOVENTRICULAR BLOCK SECOND DEGREE	1	86	1.162791	1 (1.16%)
1	Placebo	CARDIAC DISORDERS	BRADYCARDIA	1	86	1.162791	1 (1.16%)
1	Placebo	CARDIAC DISORDERS	BUNDLE BRANCH BLOCK LEFT	1	86	1.162791	1 (1.16%)
1	Placebo	CARDIAC DISORDERS	BUNDLE BRANCH BLOCK RIGHT	1	86	1.162791	1 (1.16%)

Generate Counts and Percents for AE Preferred Term Only

aeprod2 <- repfun::ru_freq(adae,
                   dsetindenom=G_POPDATA1,
                   countdistinctvars=c('STUDYID','USUBJID'),
                   groupbyvarsnumer=c('TRT01AN','TRT01A','AEDECOD'),
                   anyeventvars = c('AEDECOD'),
                   anyeventvalues = c('ANY EVENT'),
                   groupbyvarsdenom=c('TRT01AN'),
                   resultstyle="NUMERPCT",
                   totalforvar=c('TRT01AN'),
                   totalid=99,
                   totaldecode='Total',
                   codedecodevarpairs=c("TRT01AN", "TRT01A"),
                   varcodelistpairs=c(""),
                   codelistnames=list(),
                   resultpctdps=2) %>% dplyr::select(TRT01AN,TRT01A,AEDECOD,NUMERCNT,
                                              DENOMCNT,PERCENT,tt_result) %>% 
                                       dplyr::arrange(TRT01AN,TRT01A,AEDECOD)

Display the Results for AE Preferred Term Counts and Percents

lbls <- sapply(aeprod2,function(x){attr(x,"label")})
knitr::kable(head(aeprod2,10), col.names=paste(names(lbls),lbls,sep=": "), 
             caption = "Counts and Percents for AEs by Preferred Term Only") %>%
  kable_styling(full_width = T) %>% column_spec(c(3,7), width_min = c('2in','2in'))

Counts and Percents for AEs by Preferred Term Only
TRT01AN: Actual Treatment for Period 01 (n)	TRT01A: Actual Treatment for Period 01	AEDECOD: Dictionary-Derived Term	NUMERCNT: Numerator Count	DENOMCNT: Denominator Count	PERCENT: Percent	tt_result: Result
1	Placebo	ABDOMINAL DISCOMFORT	0	86	0.000000	0 (0.00%)
1	Placebo	ABDOMINAL PAIN	1	86	1.162791	1 (1.16%)
1	Placebo	ACROCHORDON EXCISION	0	86	0.000000	0 (0.00%)
1	Placebo	ACTINIC KERATOSIS	0	86	0.000000	0 (0.00%)
1	Placebo	AGITATION	2	86	2.325581	2 (2.33%)
1	Placebo	ALCOHOL USE	0	86	0.000000	0 (0.00%)
1	Placebo	ALLERGIC GRANULOMATOUS ANGIITIS	0	86	0.000000	0 (0.00%)
1	Placebo	ALOPECIA	1	86	1.162791	1 (1.16%)
1	Placebo	AMNESIA	0	86	0.000000	0 (0.00%)
1	Placebo	ANXIETY	0	86	0.000000	0 (0.00%)

Generate Counts and Percents for Demographic Data

G_POPDATA %>% dplyr::select(STUDYID,USUBJID,SAFFL,TRT01AN,TRT01A,SEXN,SEX,
                     RACEN,RACE,AGEGR1N,AGEGR1) -> G_POPDATA2
dflst <- list()
basechar <- data.frame()
i <- 1
for(v in c('AGEGR1','SEX','RACE')){
  lbl <- attr(G_POPDATA[[v]],'label')
  dflst[[v]] <- repfun::ru_freq(G_POPDATA2, dsetindenom=G_POPDATA2, 
                        countdistinctvars=c("STUDYID", "USUBJID"),
                        groupbyvarsnumer=c("STUDYID", "TRT01AN", paste0(v,'N')),
                        anyeventvars = NULL, anyeventvalues = NULL, groupminmaxvar=NULL,
                        totalforvar=c("TRT01AN"), totalid = 99, totaldecode = 'Total',
                        groupbyvarsdenom=c("STUDYID", "TRT01AN"), resultstyle="NUMERPCT", 
                        codedecodevarpairs=c("TRT01AN", "TRT01A", paste0(v,'N'), v),
                        varcodelistpairs=c(""), codelistnames=list(), resultpctdps=0) %>% 
    {. ->> LBLS} %>%
    dplyr::mutate(tt_avid=i, tt_avnm=lbl) %>%
    rename(tt_svid=as.name(paste0(v,'N')), tt_svnm=as.name(v)) %>%
    dplyr::select(tt_avid,tt_avnm,TRT01AN,TRT01A,tt_svid,tt_svnm,NUMERCNT,
           DENOMCNT,PERCENT,tt_result) %>%
    dplyr::mutate(tt_avnm=paste0(tt_avnm,', n (%)'))
  row.names(dflst[[v]]) <- NULL
  basechar <- bind_rows(basechar,dflst[[v]])
  i <- i+1
  }
  basechar <- basechar %>% dplyr::arrange(TRT01AN,TRT01A,tt_avid,tt_avnm,tt_svid,tt_svnm)
  for (v in names(basechar)){
    if (v %in% names(LBLS)){
      attr(basechar[[v]],"label") <- attr(LBLS[[v]],"label")
    }
  }
  basechar <- repfun::ru_labels(basechar,varlabels=list('tt_svid'='Variable Order',
                                                'tt_svnm'='Variable Name',
                                                'tt_avid'='Value Order',
                                                'tt_avnm'='Value Name')) %>% 
              dplyr::arrange(tt_avid,tt_svid)

Display the Results for Counts and Percents of Demographic Data

lbls <- sapply(basechar,function(x){attr(x,"label")})
knitr::kable(head(basechar,10), col.names=paste(names(lbls),lbls,sep=": "), 
             caption = "Counts and Percents for Demographic Data") %>%
  kable_styling(full_width = T) %>% 
  column_spec(c(2,4,6,10), width_min = c('2in','2in','3in','2in'))

Counts and Percents for Demographic Data
tt_avid: Value Order	tt_avnm: Value Name	TRT01AN: Actual Treatment for Period 01 (n)	TRT01A: Actual Treatment for Period 01	tt_svid: Variable Order	tt_svnm: Variable Name	NUMERCNT: Numerator Count	DENOMCNT: Denominator Count	PERCENT: Percent	tt_result: Result
1	Pooled Age Group 1, n (%)	1	Placebo	1	18-64	14	86	16.279070	14 (16%)
1	Pooled Age Group 1, n (%)	2	Xanomeline Low Dose	1	18-64	8	96	8.333333	8 (8%)
1	Pooled Age Group 1, n (%)	3	Xanomeline High Dose	1	18-64	11	72	15.277778	11 (15%)
1	Pooled Age Group 1, n (%)	99	Total	1	18-64	33	254	12.992126	33 (13%)
1	Pooled Age Group 1, n (%)	1	Placebo	2	>64	72	86	83.720930	72 (84%)
1	Pooled Age Group 1, n (%)	2	Xanomeline Low Dose	2	>64	88	96	91.666667	88 (92%)
1	Pooled Age Group 1, n (%)	3	Xanomeline High Dose	2	>64	61	72	84.722222	61 (85%)
1	Pooled Age Group 1, n (%)	99	Total	2	>64	221	254	87.007874	221 (87%)
2	Sex, n (%)	1	Placebo	1	F	53	86	61.627907	53 (62%)
2	Sex, n (%)	2	Xanomeline Low Dose	1	F	55	96	57.291667	55 (57%)

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