# Example 10: Disposition Table

This example produces a subject disposition table. The report features extensive data preparation using the procs package. The example also shows how to create a stub column with summary rows.

## Program

    library(sassy)
    library(stringr)

    options("logr.autolog" = TRUE,
            "logr.notes" = FALSE,
            "logr.on" = TRUE,
            "procs.print" = FALSE)

    # Get temp directory
    tmp <- tempdir()

    # Open log
    lf <- log_open(file.path(tmp, "example10.log"))

    # Get data
    dir <- system.file("extdata", package = "sassy")

    # Load and Prepare Data ---------------------------------------------------

    sep("Prepare Data")

    put("Define data library")
    libname(sdtm, dir, "csv")

    put("Prepare DM data")
    datastep(sdtm$DM, keep = v(USUBJID, ARM),
             where = expression(ARM != "SCREEN FAILURE"), {}) -> dm_mod

    put("Prepare DS data")
    datastep(sdtm$DS, keep = v(USUBJID, DSTERM, DSDECOD, DSCAT),
             where = expression(DSCAT != "PROTOCOL MILESTONE"), {}) -> ds_mod

    put("Join DM with DS to get ARMs on DS")
    datastep(dm_mod, merge = ds_mod, merge_by = USUBJID, {}) -> dmds

    put("Change ARM to factor to assist with sparse data")
    dmds$ARM <- factor(dmds$ARM, levels = c("ARM A", "ARM B", "ARM C", "ARM D"))

    put("Get ARM population counts")
    proc_freq(dm_mod, tables = ARM, output = long,
              options = v(nonobs, nopercent)) -> arm_pop

    # Prepare formats ---------------------------------------------------------

    # Completed Study
    complete_fmt <- value(condition(x == "SUBJECT COMPLETED ALL VISITS AND PROTOCOL REQUIREMENTS",
                                    str_to_title("SUBJECT COMPLETED ALL VISITS AND PROTOCOL REQUIREMENTS")),
                          condition(x == "SUBJECT COMPLETED ALL VISITS BUT WITH MAJOR PROTOCOL VIOLATIONS",
                                    str_to_title("SUBJECT COMPLETED ALL VISITS BUT WITH MAJOR PROTOCOL VIOLATIONS")))

    # Subject Non-compliance
    noncomp_fmt <- value(condition(x == "NON-COMPLIANCE WITH STUDY DRUG",
                                   str_to_title("NON-COMPLIANCE WITH STUDY DRUG")))

    # Early Termination
    term_fmt <- value(condition(x == "LACK OF EFFICACY",
                                str_to_title("LACK OF EFFICACY")),
                      condition(str_detect(x, "LOST"),
                                str_to_title("LOST TO FOLLOW UP")),
                      condition(TRUE, str_to_title("LACK OF EFFICACY")))

    # Group labels
    group_fmt <- value(condition(x == "COMPLETED", "Subjects who Completed Study"),
                       condition(x == "NONCOMPLIANCE", "Subjects terminated due to non-compliance"),
                       condition(x == "OTHER", "Subjects who terminated early"))

    # Disposition Groups ------------------------------------------------------

    put("Create vector of final dataframe columns")
    cols <- v(group, cat, catseq, `ARM A`, `ARM B`, `ARM C`, `ARM D`) |> put()

    put("Get group counts")
    proc_freq(dmds, tables = DSDECOD, by = ARM) |>
      datastep(keep = v(BY, CAT, CNTPCT), {

        CNTPCT <- fmt_cnt_pct(CNT, arm_pop[[BY]])

      }) |>
      proc_transpose(var = CNTPCT, by = CAT, id = BY) |>
      datastep(keep = cols,
        {

        group <- ifelse(CAT == "NON-COMPLIANCE WITH STUDY DRUG", "NONCOMPLIANCE", CAT)
        cat = NA
        catseq = 1

      }) -> grps

    # Disposition Subgroups ----------------------------------------------------

    put("Pull out subjects who completed study.")
    datastep(dmds, where = expression(DSDECOD == "COMPLETED"),
             {
               TERMDECOD <- fapply(DSTERM, complete_fmt)

             }) |>
      proc_freq(tables = v(DSDECOD * TERMDECOD), by = ARM) |>
      datastep(keep = v(BY, CAT1, CAT2, CNTPCT),
        {

          CNTPCT <- fmt_cnt_pct(CNT, arm_pop[[BY]])

        }) |>
      proc_transpose(var = CNTPCT, by = v(CAT1, CAT2), id = BY) |>
      datastep(keep = cols,
        {
          group = CAT1
          cat = CAT2
          catseq = 2
        }) -> cmplt

    put("Pull out subjects who were non-compliant")
    datastep(dmds, where = expression(DSDECOD == "NON-COMPLIANCE WITH STUDY DRUG"),
             {
               TERMDECOD <- fapply(DSTERM, noncomp_fmt)

             }) |>
      proc_freq(tables = v(DSDECOD * TERMDECOD), by = ARM) |>
      datastep(keep = v(BY, CAT1, CAT2, CNTPCT),
               {

                 CNTPCT <- fmt_cnt_pct(CNT, arm_pop[[BY]])

               }) |>
      proc_transpose(var = CNTPCT, by = v(CAT1, CAT2), id = BY) |>
      datastep(keep = cols,
               {
                 group = "NONCOMPLIANCE"
                 cat = CAT2
                 catseq = 2
               }) -> noncompl

    put("Pull out subjects who terminated early")
    datastep(dmds, where = expression(DSDECOD == "OTHER"),
             {
               TERMDECOD <- fapply(DSTERM, term_fmt)

             }) |>
      proc_freq(tables = v(DSDECOD * TERMDECOD), by = ARM) |>
      datastep(keep = v(BY, CAT1, CAT2, CNTPCT),
               {

                 CNTPCT <- fmt_cnt_pct(CNT, arm_pop[[BY]])

               }) |>
      proc_transpose(var = CNTPCT, by = v(CAT1, CAT2), id = BY) |>
      datastep(keep = cols,
               {
                 group = "OTHER"
                 cat = CAT2
                 catseq = 2
               }) -> earlyterm

    put("Combine blocks into final data frame")
    datastep(grps, set = list(cmplt, noncompl, earlyterm),
        {
          lblind <- ifelse(is.na(cat), TRUE, FALSE)
        }) |>
      proc_sort(by = v(group, catseq, cat)) -> final

    # Report ------------------------------------------------------------------

    sep("Create and print report")

    # Create Table
    tbl <- create_table(final, first_row_blank = TRUE,
                        borders = "all", width = 8.5, header_bold = TRUE) |>
      column_defaults(from = `ARM A`, to = `ARM D`,
                      align = "center", width = 1) |>
      stub(vars = v(group, cat), "Completion Status",
           style = cell_style(bold = TRUE, indicator = "lblind")) |>
      define(group, blank_after = TRUE, dedupe = TRUE,
             format = group_fmt) |>
      define(cat, indent = .5) |>
      define(catseq, visible = FALSE) |>
      define(`ARM A`, label = "Placebo", n = arm_pop["ARM A"]) |>
      define(`ARM B`, label = "Drug 50mg", n = arm_pop["ARM B"]) |>
      define(`ARM C`, label = "Drug 100mg", n = arm_pop["ARM C"]) |>
      define(`ARM D`, label = "Competitor", n = arm_pop["ARM D"]) |>
      define(lblind, visible = FALSE) |>
      titles("Table 5.2.3", "Subject Disposition by Category and Treatment Group",
             "Safety Population", bold = TRUE, font_size = 11,
             borders = "outside", blank_row = "none") |>
      footnotes("Program: DS_Table.R",
                "NOTE: Denominator based on number of non-missing responses.",
                borders = "outside", blank_row = "none")

    pth <- file.path(tmp, "example10.pdf")

    rpt <- create_report(pth, output_type = "PDF", font = "Arial") |>
      set_margins(top = 1, bottom = 1) |>
      add_content(tbl)

    write_report(rpt)

    # Clean Up ----------------------------------------------------------------

    # Close log
    log_close()

    # Uncomment to view files
    # file.show(pth)
    # file.show(lf)

## Output

Here is the output report:

## Log

Here is the log:

    =========================================================================
    Log Path: C:/Users/dbosa/AppData/Local/Temp/RtmpEBPgPu/log/example10.log
    Program Path: C:/packages/Testing/procs/ProcsDisposition.R
    Working Directory: C:/packages/Testing/procs
    User Name: dbosa
    R Version: 4.3.1 (2023-06-16 ucrt)
    Machine: SOCRATES x86-64
    Operating System: Windows 10 x64 build 22621
    Base Packages: stats graphics grDevices utils datasets methods base Other
    Packages: tidylog_1.0.2 stringr_1.5.0 procs_1.0.3 reporter_1.4.1 libr_1.2.8
    fmtr_1.5.9 logr_1.3.4 common_1.0.8 sassy_1.1.0
    Log Start Time: 2023-09-05 22:06:05.090672
    =========================================================================

    =========================================================================
    Prepare Data
    =========================================================================

    Define data library

    # library 'sdtm': 7 items
    - attributes: csv not loaded
    - path: C:/Users/dbosa/AppData/Local/R/win-library/4.3/sassy/extdata
    - items:
      Name Extension Rows Cols     Size
    1   AE       csv  150   27  88.5 Kb
    2   DM       csv   87   24  45.5 Kb
    3   DS       csv  174    9  34.1 Kb
    4   EX       csv   84   11  26.4 Kb
    5   IE       csv    2   14  13.4 Kb
    6   SV       csv  685   10  70.3 Kb
    7   VS       csv 3358   17 467.4 Kb
             LastModified
    1 2023-08-07 17:51:40
    2 2023-08-07 17:51:40
    3 2023-08-07 17:51:40
    4 2023-08-07 17:51:40
    5 2023-08-07 17:51:40
    6 2023-08-07 17:51:40
    7 2023-08-07 17:51:40

    Prepare DM data

    datastep: columns decreased from 24 to 2

    # A tibble: 85 × 2
       USUBJID    ARM
       <chr>      <chr>
     1 ABC-01-049 ARM D
     2 ABC-01-050 ARM B
     3 ABC-01-051 ARM A
     4 ABC-01-052 ARM C
     5 ABC-01-053 ARM B
     6 ABC-01-054 ARM D
     7 ABC-01-055 ARM C
     8 ABC-01-056 ARM A
     9 ABC-01-113 ARM D
    10 ABC-01-114 ARM B
    # ℹ 75 more rows
    # ℹ Use `print(n = ...)` to see more rows

    Prepare DS data

    datastep: columns decreased from 9 to 4

    # A tibble: 87 × 4
       USUBJID    DSTERM                  DSDECOD DSCAT
       <chr>      <chr>                   <chr>   <chr>
     1 ABC-01-049 SUBJECT COMPLETED ALL … COMPLE… DISP…
     2 ABC-01-050 SUBJECT COMPLETED ALL … COMPLE… DISP…
     3 ABC-01-051 SUBJECT COMPLETED ALL … COMPLE… DISP…
     4 ABC-01-052 SUBJECT COMPLETED ALL … COMPLE… DISP…
     5 ABC-01-053 NON-COMPLIANCE WITH ST… NON-CO… DISP…
     6 ABC-01-054 SUBJECT COMPLETED ALL … COMPLE… DISP…
     7 ABC-01-055 SUBJECT COMPLETED ALL … COMPLE… DISP…
     8 ABC-01-056 SUBJECT COMPLETED ALL … COMPLE… DISP…
     9 ABC-01-113 SUBJECT COMPLETED ALL … COMPLE… DISP…
    10 ABC-01-114 SUBJECT COMPLETED ALL … COMPLE… DISP…
    # ℹ 77 more rows
    # ℹ Use `print(n = ...)` to see more rows

    Join DM with DS to get ARMs on DS

    datastep: columns increased from 2 to 5

    # A tibble: 87 × 5
       USUBJID    ARM   DSTERM            DSDECOD DSCAT
       <chr>      <chr> <chr>             <chr>   <chr>
     1 ABC-01-049 ARM D SUBJECT COMPLETE… COMPLE… DISP…
     2 ABC-01-050 ARM B SUBJECT COMPLETE… COMPLE… DISP…
     3 ABC-01-051 ARM A SUBJECT COMPLETE… COMPLE… DISP…
     4 ABC-01-052 ARM C SUBJECT COMPLETE… COMPLE… DISP…
     5 ABC-01-053 ARM B NON-COMPLIANCE W… NON-CO… DISP…
     6 ABC-01-054 ARM D SUBJECT COMPLETE… COMPLE… DISP…
     7 ABC-01-055 ARM C SUBJECT COMPLETE… COMPLE… DISP…
     8 ABC-01-056 ARM A SUBJECT COMPLETE… COMPLE… DISP…
     9 ABC-01-113 ARM D SUBJECT COMPLETE… COMPLE… DISP…
    10 ABC-01-114 ARM B SUBJECT COMPLETE… COMPLE… DISP…
    # ℹ 77 more rows
    # ℹ Use `print(n = ...)` to see more rows

    Change ARM to factor to assist with sparse data

    Get ARM population counts

    proc_freq: input data set 85 rows and 2 columns
               tables: ARM
               output: long
               view: TRUE
               output: 1 datasets

    # A tibble: 1 × 6
      VAR   STAT  `ARM A` `ARM B` `ARM C` `ARM D`
      <chr> <chr>   <dbl>   <dbl>   <dbl>   <dbl>
    1 ARM   CNT        20      21      21      23

    # A user-defined format: 2 conditions
      Name Type
    1  obj    U
    2  obj    U
                                                                  Expression
    1          x == "SUBJECT COMPLETED ALL VISITS AND PROTOCOL REQUIREMENTS"
    2 x == "SUBJECT COMPLETED ALL VISITS BUT WITH MAJOR PROTOCOL VIOLATIONS"
                                                                Label
    1          Subject Completed All Visits And Protocol Requirements
    2 Subject Completed All Visits But With Major Protocol Violations
      Order
    1    NA
    2    NA

    # A user-defined format: 1 conditions
      Name Type                            Expression
    1  obj    U x == "NON-COMPLIANCE WITH STUDY DRUG"
                               Label Order
    1 Non-Compliance With Study Drug    NA

    # A user-defined format: 3 conditions
      Name Type              Expression
    1  obj    U x == "LACK OF EFFICACY"
    2  obj    U   str_detect(x, "LOST")
    3  obj    U                    TRUE
                  Label Order
    1  Lack Of Efficacy    NA
    2 Lost To Follow Up    NA
    3  Lack Of Efficacy    NA

    # A user-defined format: 3 conditions
      Name Type           Expression
    1  obj    U     x == "COMPLETED"
    2  obj    U x == "NONCOMPLIANCE"
    3  obj    U         x == "OTHER"
                                          Label Order
    1              Subjects who Completed Study    NA
    2 Subjects terminated due to non-compliance    NA
    3             Subjects who terminated early    NA

    Create vector of final dataframe columns

    group
    cat
    catseq
    ARM A
    ARM B
    ARM C
    ARM D

    Get group counts

    proc_freq: input data set 87 rows and 5 columns
               tables: DSDECOD
               by: ARM
               view: TRUE
               output: 1 datasets

    # A tibble: 12 × 6
       BY    VAR     CAT                  N   CNT   PCT
       <chr> <chr>   <chr>            <dbl> <dbl> <dbl>
     1 ARM A DSDECOD COMPLETED           20    19 95
     2 ARM A DSDECOD NON-COMPLIANCE …    20     0  0
     3 ARM A DSDECOD OTHER               20     1  5
     4 ARM B DSDECOD COMPLETED           21    17 81.0
     5 ARM B DSDECOD NON-COMPLIANCE …    21     1  4.76
     6 ARM B DSDECOD OTHER               21     3 14.3
     7 ARM C DSDECOD COMPLETED           21    16 76.2
     8 ARM C DSDECOD NON-COMPLIANCE …    21     0  0
     9 ARM C DSDECOD OTHER               21     5 23.8
    10 ARM D DSDECOD COMPLETED           23    20 87.0
    11 ARM D DSDECOD NON-COMPLIANCE …    23     0  0
    12 ARM D DSDECOD OTHER               23     3 13.0

    datastep: columns decreased from 6 to 3

    # A tibble: 12 × 3
       BY    CAT                            CNTPCT
       <chr> <chr>                          <chr>
     1 ARM A COMPLETED                      19 ( 95.0%)
     2 ARM A NON-COMPLIANCE WITH STUDY DRUG 0 (  0.0%)
     3 ARM A OTHER                          1 (  5.0%)
     4 ARM B COMPLETED                      17 ( 81.0%)
     5 ARM B NON-COMPLIANCE WITH STUDY DRUG 1 (  4.8%)
     6 ARM B OTHER                          3 ( 14.3%)
     7 ARM C COMPLETED                      16 ( 76.2%)
     8 ARM C NON-COMPLIANCE WITH STUDY DRUG 0 (  0.0%)
     9 ARM C OTHER                          5 ( 23.8%)
    10 ARM D COMPLETED                      20 ( 87.0%)
    11 ARM D NON-COMPLIANCE WITH STUDY DRUG 0 (  0.0%)
    12 ARM D OTHER                          3 ( 13.0%)

    proc_transpose: input data set 12 rows and 3 columns
                    by: CAT
                    var: CNTPCT
                    id: BY
                    name: NAME
                    output dataset 3 rows and 6 columns

    # A tibble: 3 × 6
      CAT         NAME  `ARM A` `ARM B` `ARM C` `ARM D`
      <chr>       <chr> <chr>   <chr>   <chr>   <chr>
    1 COMPLETED   CNTP… 19 ( 9… 17 ( 8… 16 ( 7… 20 ( 8…
    2 NON-COMPLI… CNTP… 0 (  0… 1 (  4… 0 (  0… 0 (  0…
    3 OTHER       CNTP… 1 (  5… 3 ( 14… 5 ( 23… 3 ( 13…

    datastep: columns increased from 6 to 7

    # A tibble: 3 × 7
      group        cat   catseq `ARM A` `ARM B` `ARM C`
      <chr>        <lgl>  <dbl> <chr>   <chr>   <chr>
    1 COMPLETED    NA         1 19 ( 9… 17 ( 8… 16 ( 7…
    2 NONCOMPLIAN… NA         1 0 (  0… 1 (  4… 0 (  0…
    3 OTHER        NA         1 1 (  5… 3 ( 14… 5 ( 23…
    # ℹ 1 more variable: `ARM D` <chr>

    Pull out subjects who completed study.

    datastep: columns increased from 5 to 6

    # A tibble: 74 × 6
       USUBJID    ARM   DSTERM  DSDECOD DSCAT TERMDECOD
       <chr>      <fct> <chr>   <chr>   <chr> <chr>
     1 ABC-01-049 ARM D SUBJEC… COMPLE… DISP… Subject …
     2 ABC-01-050 ARM B SUBJEC… COMPLE… DISP… Subject …
     3 ABC-01-051 ARM A SUBJEC… COMPLE… DISP… Subject …
     4 ABC-01-052 ARM C SUBJEC… COMPLE… DISP… Subject …
     5 ABC-01-054 ARM D SUBJEC… COMPLE… DISP… Subject …
     6 ABC-01-055 ARM C SUBJEC… COMPLE… DISP… Subject …
     7 ABC-01-056 ARM A SUBJEC… COMPLE… DISP… Subject …
     8 ABC-01-113 ARM D SUBJEC… COMPLE… DISP… Subject …
     9 ABC-01-114 ARM B SUBJEC… COMPLE… DISP… Subject …
    10 ABC-02-033 ARM C SUBJEC… COMPLE… DISP… Subject …
    # ℹ 64 more rows
    # ℹ Use `print(n = ...)` to see more rows

    proc_freq: input data set 74 rows and 6 columns
               tables: DSDECOD * TERMDECOD
               by: ARM
               view: TRUE
               output: 1 datasets

    # A tibble: 8 × 8
      BY    VAR1   VAR2  CAT1  CAT2      N   CNT    PCT
      <chr> <chr>  <chr> <chr> <chr> <dbl> <dbl>  <dbl>
    1 ARM A DSDEC… TERM… COMP… Subj…    19    19 100
    2 ARM A DSDEC… TERM… COMP… Subj…    19     0   0
    3 ARM B DSDEC… TERM… COMP… Subj…    17    16  94.1
    4 ARM B DSDEC… TERM… COMP… Subj…    17     1   5.88
    5 ARM C DSDEC… TERM… COMP… Subj…    16    16 100
    6 ARM C DSDEC… TERM… COMP… Subj…    16     0   0
    7 ARM D DSDEC… TERM… COMP… Subj…    20    20 100
    8 ARM D DSDEC… TERM… COMP… Subj…    20     0   0

    datastep: columns decreased from 8 to 4

    # A tibble: 8 × 4
      BY    CAT1      CAT2                       CNTPCT
      <chr> <chr>     <chr>                      <chr>
    1 ARM A COMPLETED Subject Completed All Vis… 19 ( …
    2 ARM A COMPLETED Subject Completed All Vis… 0 (  …
    3 ARM B COMPLETED Subject Completed All Vis… 16 ( …
    4 ARM B COMPLETED Subject Completed All Vis… 1 (  …
    5 ARM C COMPLETED Subject Completed All Vis… 16 ( …
    6 ARM C COMPLETED Subject Completed All Vis… 0 (  …
    7 ARM D COMPLETED Subject Completed All Vis… 20 ( …
    8 ARM D COMPLETED Subject Completed All Vis… 0 (  …

    proc_transpose: input data set 8 rows and 4 columns
                    by: CAT1 CAT2
                    var: CNTPCT
                    id: BY
                    name: NAME
                    output dataset 2 rows and 7 columns

    # A tibble: 2 × 7
      CAT1  CAT2  NAME  `ARM A` `ARM B` `ARM C` `ARM D`
      <chr> <chr> <chr> <chr>   <chr>   <chr>   <chr>
    1 COMP… Subj… CNTP… 19 ( 9… 16 ( 7… 16 ( 7… 20 ( 8…
    2 COMP… Subj… CNTP… 0 (  0… 1 (  4… 0 (  0… 0 (  0…

    datastep: columns started with 7 and ended with 7

    # A tibble: 2 × 7
      group     cat      catseq `ARM A` `ARM B` `ARM C`
      <chr>     <chr>     <dbl> <chr>   <chr>   <chr>
    1 COMPLETED Subject…      2 19 ( 9… 16 ( 7… 16 ( 7…
    2 COMPLETED Subject…      2 0 (  0… 1 (  4… 0 (  0…
    # ℹ 1 more variable: `ARM D` <chr>

    Pull out subjects who were non-compliant

    datastep: columns increased from 5 to 6

    # A tibble: 1 × 6
      USUBJID    ARM   DSTERM   DSDECOD DSCAT TERMDECOD
      <chr>      <fct> <chr>    <chr>   <chr> <chr>
    1 ABC-01-053 ARM B NON-COM… NON-CO… DISP… Non-Comp…

    proc_freq: input data set 1 rows and 6 columns
               tables: DSDECOD * TERMDECOD
               by: ARM
               view: TRUE
               output: 1 datasets

    # A tibble: 4 × 8
      BY    VAR1    VAR2  CAT1  CAT2      N   CNT   PCT
      <chr> <chr>   <chr> <chr> <chr> <dbl> <dbl> <dbl>
    1 ARM A DSDECOD TERM… NON-… Non-…     0     0   NaN
    2 ARM B DSDECOD TERM… NON-… Non-…     1     1   100
    3 ARM C DSDECOD TERM… NON-… Non-…     0     0   NaN
    4 ARM D DSDECOD TERM… NON-… Non-…     0     0   NaN

    datastep: columns decreased from 8 to 4

    # A tibble: 4 × 4
      BY    CAT1                           CAT2  CNTPCT
      <chr> <chr>                          <chr> <chr>
    1 ARM A NON-COMPLIANCE WITH STUDY DRUG Non-… 0 (  …
    2 ARM B NON-COMPLIANCE WITH STUDY DRUG Non-… 1 (  …
    3 ARM C NON-COMPLIANCE WITH STUDY DRUG Non-… 0 (  …
    4 ARM D NON-COMPLIANCE WITH STUDY DRUG Non-… 0 (  …

    proc_transpose: input data set 4 rows and 4 columns
                    by: CAT1 CAT2
                    var: CNTPCT
                    id: BY
                    name: NAME
                    output dataset 1 rows and 7 columns

    # A tibble: 1 × 7
      CAT1  CAT2  NAME  `ARM A` `ARM B` `ARM C` `ARM D`
      <chr> <chr> <chr> <chr>   <chr>   <chr>   <chr>
    1 NON-… Non-… CNTP… 0 (  0… 1 (  4… 0 (  0… 0 (  0…

    datastep: columns started with 7 and ended with 7

    # A tibble: 1 × 7
      group        cat   catseq `ARM A` `ARM B` `ARM C`
      <chr>        <chr>  <dbl> <chr>   <chr>   <chr>
    1 NONCOMPLIAN… Non-…      2 0 (  0… 1 (  4… 0 (  0…
    # ℹ 1 more variable: `ARM D` <chr>

    Pull out subjects who terminated early

    datastep: columns increased from 5 to 6

    # A tibble: 12 × 6
       USUBJID    ARM   DSTERM  DSDECOD DSCAT TERMDECOD
       <chr>      <fct> <chr>   <chr>   <chr> <chr>
     1 ABC-03-005 ARM C LOST O… OTHER   DISP… Lost To …
     2 ABC-03-008 ARM D WORSEN… OTHER   DISP… Lack Of …
     3 ABC-04-074 ARM C LACK O… OTHER   DISP… Lack Of …
     4 ABC-04-128 ARM C LOST T… OTHER   DISP… Lost To …
     5 ABC-06-069 ARM A PSORIA… OTHER   DISP… Lack Of …
     6 ABC-06-161 ARM C NON  R… OTHER   DISP… Lack Of …
     7 ABC-08-103 ARM B LOST T… OTHER   DISP… Lost To …
     8 ABC-08-105 ARM C MOTHER… OTHER   DISP… Lack Of …
     9 ABC-08-107 ARM D PATIEN… OTHER   DISP… Lack Of …
    10 ABC-08-108 ARM B LOST T… OTHER   DISP… Lost To …
    11 ABC-09-021 ARM B LOST T… OTHER   DISP… Lost To …
    12 ABC-09-022 ARM D LOST T… OTHER   DISP… Lost To …

    proc_freq: input data set 12 rows and 6 columns
               tables: DSDECOD * TERMDECOD
               by: ARM
               view: TRUE
               output: 1 datasets

    # A tibble: 8 × 8
      BY    VAR1    VAR2  CAT1  CAT2      N   CNT   PCT
      <chr> <chr>   <chr> <chr> <chr> <dbl> <dbl> <dbl>
    1 ARM A DSDECOD TERM… OTHER Lack…     1     1 100
    2 ARM A DSDECOD TERM… OTHER Lost…     1     0   0
    3 ARM B DSDECOD TERM… OTHER Lack…     3     0   0
    4 ARM B DSDECOD TERM… OTHER Lost…     3     3 100
    5 ARM C DSDECOD TERM… OTHER Lack…     5     3  60
    6 ARM C DSDECOD TERM… OTHER Lost…     5     2  40
    7 ARM D DSDECOD TERM… OTHER Lack…     3     2  66.7
    8 ARM D DSDECOD TERM… OTHER Lost…     3     1  33.3

    datastep: columns decreased from 8 to 4

    # A tibble: 8 × 4
      BY    CAT1  CAT2              CNTPCT
      <chr> <chr> <chr>             <chr>
    1 ARM A OTHER Lack Of Efficacy  1 (  5.0%)
    2 ARM A OTHER Lost To Follow Up 0 (  0.0%)
    3 ARM B OTHER Lack Of Efficacy  0 (  0.0%)
    4 ARM B OTHER Lost To Follow Up 3 ( 14.3%)
    5 ARM C OTHER Lack Of Efficacy  3 ( 14.3%)
    6 ARM C OTHER Lost To Follow Up 2 (  9.5%)
    7 ARM D OTHER Lack Of Efficacy  2 (  8.7%)
    8 ARM D OTHER Lost To Follow Up 1 (  4.3%)

    proc_transpose: input data set 8 rows and 4 columns
                    by: CAT1 CAT2
                    var: CNTPCT
                    id: BY
                    name: NAME
                    output dataset 2 rows and 7 columns

    # A tibble: 2 × 7
      CAT1  CAT2  NAME  `ARM A` `ARM B` `ARM C` `ARM D`
      <chr> <chr> <chr> <chr>   <chr>   <chr>   <chr>
    1 OTHER Lack… CNTP… 1 (  5… 0 (  0… 3 ( 14… 2 (  8…
    2 OTHER Lost… CNTP… 0 (  0… 3 ( 14… 2 (  9… 1 (  4…

    datastep: columns started with 7 and ended with 7

    # A tibble: 2 × 7
      group cat          catseq `ARM A` `ARM B` `ARM C`
      <chr> <chr>         <dbl> <chr>   <chr>   <chr>
    1 OTHER Lack Of Eff…      2 1 (  5… 0 (  0… 3 ( 14…
    2 OTHER Lost To Fol…      2 0 (  0… 3 ( 14… 2 (  9…
    # ℹ 1 more variable: `ARM D` <chr>

    Combine blocks into final data frame

    datastep: columns increased from 7 to 8

    # A tibble: 8 × 8
      group        cat   catseq `ARM A` `ARM B` `ARM C`
      <chr>        <chr>  <dbl> <chr>   <chr>   <chr>
    1 COMPLETED    <NA>       1 19 ( 9… 17 ( 8… 16 ( 7…
    2 NONCOMPLIAN… <NA>       1 0 (  0… 1 (  4… 0 (  0…
    3 OTHER        <NA>       1 1 (  5… 3 ( 14… 5 ( 23…
    4 COMPLETED    Subj…      2 19 ( 9… 16 ( 7… 16 ( 7…
    5 COMPLETED    Subj…      2 0 (  0… 1 (  4… 0 (  0…
    6 NONCOMPLIAN… Non-…      2 0 (  0… 1 (  4… 0 (  0…
    7 OTHER        Lack…      2 1 (  5… 0 (  0… 3 ( 14…
    8 OTHER        Lost…      2 0 (  0… 3 ( 14… 2 (  9…
    # ℹ 2 more variables: `ARM D` <chr>, lblind <lgl>

    proc_sort: input data set 8 rows and 8 columns
               by: group catseq cat
               keep: group cat catseq ARM A ARM B ARM C ARM D lblind
               order: a a a
               output data set 8 rows and 8 columns

    # A tibble: 8 × 8
      group        cat   catseq `ARM A` `ARM B` `ARM C`
      <chr>        <chr>  <dbl> <chr>   <chr>   <chr>
    1 COMPLETED    <NA>       1 19 ( 9… 17 ( 8… 16 ( 7…
    2 COMPLETED    Subj…      2 19 ( 9… 16 ( 7… 16 ( 7…
    3 COMPLETED    Subj…      2 0 (  0… 1 (  4… 0 (  0…
    4 NONCOMPLIAN… <NA>       1 0 (  0… 1 (  4… 0 (  0…
    5 NONCOMPLIAN… Non-…      2 0 (  0… 1 (  4… 0 (  0…
    6 OTHER        <NA>       1 1 (  5… 3 ( 14… 5 ( 23…
    7 OTHER        Lack…      2 1 (  5… 0 (  0… 3 ( 14…
    8 OTHER        Lost…      2 0 (  0… 3 ( 14… 2 (  9…
    # ℹ 2 more variables: `ARM D` <chr>, lblind <lgl>

    =========================================================================
    Create and print report
    =========================================================================

    # A report specification: 1 pages
    - file_path: 'C:\Users\dbosa\AppData\Local\Temp\RtmpEBPgPu/example10.pdf'
    - output_type: PDF
    - units: inches
    - orientation: landscape
    - margins: top 1 bottom 1 left 1 right 1
    - line size/count: 9/41
    - content:
    # A table specification:
    - data: tibble 'final' 8 rows 8 cols
    - show_cols: all
    - use_attributes: all
    - width: 8.5
    - title 1: 'Table 5.2.3'
    - title 2: 'Subject Disposition by Category and Treatment Group'
    - title 3: 'Safety Population'
    - footnote 1: 'Program: DS_Table.R'
    - footnote 2: 'NOTE: Denominator based on number of non-missing responses.'
    - stub: group cat 'Completion Status' align='left'
    - define: group dedupe='TRUE'
    - define: cat
    - define: catseq visible='FALSE'
    - define: ARM A 'Placebo'
    - define: ARM B 'Drug 50mg'
    - define: ARM C 'Drug 100mg'
    - define: ARM D 'Competitor'
    - define: lblind visible='FALSE'

    lib_sync: synchronized data in library 'sdtm'

    =========================================================================
    Log End Time: 2023-09-05 22:06:09.329367
    Log Elapsed Time: 0 00:00:04
    =========================================================================

Next: Example 11: Subject Listing