# Example 2: Demographics Table

The second example produces a demographics summary table of selected variables. The report shows statistics for each of the four treatment groups.

## Program

Note the following about this example:

- The logr package provides automatic logging for many functions.
- The `datastep()` function from the libr package allows for easy data processing.
- The fmtr package provides several convenient functions for calculating and formatting summary statistics.
- Statistics from the procs package match SAS® out of the box.
- The reporter package supports ‘N=’ population counts in the header labels.
- The reporter package also allows you to define a stub column of hierarchical labels.

    library(sassy)

    # Prepare Log -------------------------------------------------------------

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

    # Get temp directory
    tmp <- tempdir()

    # Open log
    lf <- log_open(file.path(tmp, "example2.log"))

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

    sep("Prepare formats")

    put("Age categories")
    agecat <- value(condition(x >= 18 & x <= 29, "18 to 29"),
                    condition(x >=30 & x <= 39, "30 to 39"),
                    condition(x >=40 & x <=49, "40 to 49"),
                    condition(x >= 50, ">= 50"),
                    as.factor = TRUE)

    put("Sex decodes")
    fmt_sex <- value(condition(x == "M", "Male"),
                     condition(x == "F", "Female"),
                     condition(TRUE, "Other"),
                     as.factor = TRUE)

    put("Race decodes")
    fmt_race <- value(condition(x == "WHITE", "White"),
                      condition(x == "BLACK", "Black or African American"),
                      condition(TRUE, "Other"),
                      as.factor = TRUE)

    put("Compile format catalog")
    fc <- fcat(MEAN = "%.1f", STD = "(%.2f)",
               Q1 = "%.1f", Q3 = "%.1f",
               MIN = "%d", MAX = "%d",
               CNT = "%2d", PCT = "(%5.1f%%)",
               AGECAT = agecat,
               SEX = fmt_sex,
               RACE = fmt_race)

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

    sep("Prepare Data")

    put("Create sample ADSL data.")
    adsl <- read.table(header = TRUE, text = '
      SUBJID  ARM    SEX  RACE    AGE
      "001"   "ARM A" "F"  "WHITE" 19
      "002"   "ARM B" "F"  "WHITE" 21
      "003"   "ARM C" "F"  "WHITE" 23
      "004"   "ARM D" "F"  "BLACK" 28
      "005"   "ARM A" "M"  "WHITE" 37
      "006"   "ARM B" "M"  "WHITE" 34
      "007"   "ARM C" "M"  "WHITE" 36
      "008"   "ARM D" "M"  "WHITE" 30
      "009"   "ARM A" "F"  "WHITE" 39
      "010"   "ARM B" "F"  "WHITE" 31
      "011"   "ARM C" "F"  "BLACK" 33
      "012"   "ARM D" "F"  "WHITE" 38
      "013"   "ARM A" "M"  "BLACK" 37
      "014"   "ARM B" "M"  "WHITE" 34
      "015"   "ARM C" "M"  "WHITE" 36
      "016"   "ARM A" "M"  "WHITE" 40')

    put("Categorize AGE")
    adsl$AGECAT <- fapply(adsl$AGE, agecat)

    put("Log starting dataset")
    put(adsl)

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

    # Age Summary Block -------------------------------------------------------

    sep("Create summary statistics for age")

    put("Call means procedure to get summary statistics for age")
    proc_means(adsl, var = AGE,
               stats = v(n, mean, std, median, q1, q3, min, max),
               by = ARM,
               options = v(notype, nofreq)) -> age_stats

    put("Combine stats")
    datastep(age_stats,
             format = fc,
             drop = find.names(age_stats, start = 4),
             {
               `Mean (SD)` <- fapply2(MEAN, STD)
               Median <- MEDIAN
               `Q1 - Q3` <- fapply2(Q1, Q3, sep = " - ")
               `Min - Max` <- fapply2(MIN, MAX, sep = " - ")

             }) -> age_comb

    put("Transpose ARMs into columns")
    proc_transpose(age_comb,
                   var = names(age_comb),
                   copy = VAR, id = BY,
                   name = LABEL) -> age_block

    # Sex Block ---------------------------------------------------------------

    sep("Create frequency counts for SEX")

    put("Get sex frequency counts")
    proc_freq(adsl, tables = SEX,
              by = ARM,
              options = nonobs) -> sex_freq

    put("Combine counts and percents.")
    datastep(sex_freq,
             format = fc,
             rename = list(CAT = "LABEL"),
             drop = v(CNT, PCT),
             {

               CNTPCT <- fapply2(CNT, PCT)

             }) -> sex_comb

    put("Transpose ARMs into columns")
    proc_transpose(sex_comb, id = BY,
                   var = CNTPCT,
                   copy = VAR, by = LABEL,
                   options = noname) -> sex_trans

    put("Apply formats")
    datastep(sex_trans,
             {

               LABEL <- fapply(LABEL, fc$SEX)

             }) -> sex_cnts

    put("Sort by label")
    proc_sort(sex_cnts, by = LABEL) -> sex_block

    # Race block --------------------------------------------------------------

    sep("Create frequency counts for RACE")

    put("Get race frequency counts")
    proc_freq(adsl, tables = RACE,
              by = ARM,
              options = nonobs) -> race_freq

    put("Combine counts and percents.")
    datastep(race_freq,
             format = fc,
             rename = list(CAT = "LABEL"),
             drop = v(CNT, PCT),
             {

               CNTPCT <- fapply2(CNT, PCT)

             }) -> race_comb

    put("Transpose ARMs into columns")
    proc_transpose(race_comb, id = BY, var = CNTPCT,
                   copy = VAR, by = LABEL, options = noname) -> race_trans

    put("Clean up")
    datastep(race_trans,
             {
               LABEL <- fapply(LABEL, fc$RACE)

             }) -> race_cnts

    put("Sort by label")
    proc_sort(race_cnts, by = LABEL) -> race_block

    # Age Group Block ----------------------------------------------------------

    sep("Create frequency counts for Age Group")

    put("Get age group frequency counts")
    proc_freq(adsl,
              table = AGECAT,
              by = ARM,
              options = nonobs) -> ageg_freq

    put("Combine counts and percents and assign age group factor for sorting")
    datastep(ageg_freq,
             format = fc,
             keep = v(VAR, LABEL, BY, CNTPCT),
             {
               CNTPCT <- fapply2(CNT, PCT)
               LABEL <- CAT
             }) -> ageg_comb

    put("Sort by age group factor")
    proc_sort(ageg_comb, by = v(BY, LABEL)) -> ageg_sort

    put("Tranpose age group block")
    proc_transpose(ageg_sort,
                   var = CNTPCT,
                   copy = VAR,
                   id = BY,
                   by = LABEL,
                   options = noname) -> ageg_block

    put("Combine blocks into final data frame")
    datastep(age_block,
             set = list(ageg_block, sex_block, race_block),
             {}) -> final

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

    sep("Create and print report")

    var_fmt <- c("AGE" = "Age", "AGECAT" = "Age Group", "SEX" = "Sex", "RACE" = "Race")

    # Create Table
    tbl <- create_table(final, first_row_blank = TRUE) |>
      column_defaults(from = `ARM A`, to = `ARM D`, align = "center", width = 1.1) |>
      stub(vars = c("VAR", "LABEL"), "Variable", width = 2.5) |>
      define(VAR, blank_after = TRUE, dedupe = TRUE, label = "Variable",
             format = var_fmt,label_row = TRUE) |>
      define(LABEL, indent = .25, label = "Demographic Category") |>
      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"]) |>
      titles("Table 1.0", "Analysis of Demographic Characteristics",
             "Safety Population", bold = TRUE) |>
      footnotes("Program: DM_Table.R",
                "NOTE: Denominator based on number of non-missing responses.")

    rpt <- create_report(file.path(tmp, "example2.rtf"),
                         output_type = "RTF",
                         font = "Arial") |>
      page_header("Sponsor: Company", "Study: ABC") |>
      set_margins(top = 1, bottom = 1) |>
      add_content(tbl) |>
      page_footer("Date Produced: {Sys.Date()}", right = "Page [pg] of [tpg]")

    put("Write out the report")
    res <- write_report(rpt)

    # Clean Up ----------------------------------------------------------------
    sep("Clean Up")

    put("Close log")
    log_close()

    # Uncomment to view report
    # file.show(res$modified_path)

    # Uncomment to view log
    # file.show(lf)

## Output

Here is the output report:

## Log

And here is the log:

    =========================================================================
    Log Path: C:/Users/dbosa/AppData/Local/Temp/RtmpAXQUo8/log/example2.log
    Program Path: C:/Projects/Archytas/Westat/Tutorial2/Project/sassy-dm.R
    Working Directory: C:/Projects/Archytas/Westat/Tutorial2/Project
    User Name: dbosa
    R Version: 4.4.0 (2024-04-24 ucrt)
    Machine: SOCRATES x86-64
    Operating System: Windows 10 x64 build 22631
    Base Packages: stats graphics grDevices utils datasets methods base
    Other Packages: tidylog_1.0.2 ggplot2_3.5.1 procs_1.0.7 reporter_1.4.4
    libr_1.3.3 logr_1.3.7 fmtr_1.6.4 common_1.1.3 sassy_1.2.4
    Log Start Time: 2024-05-28 12:52:44.619869
    =========================================================================

    =========================================================================
    Prepare formats
    =========================================================================

    Age categories

    # A user-defined format: 4 conditions
    - as.factor: TRUE
      Name Type        Expression    Label Order
    1  obj    U x >= 18 & x <= 29 18 to 29    NA
    2  obj    U x >= 30 & x <= 39 30 to 39    NA
    3  obj    U x >= 40 & x <= 49 40 to 49    NA
    4  obj    U           x >= 50    >= 50    NA

    Sex decodes

    # A user-defined format: 3 conditions
    - as.factor: TRUE
      Name Type Expression  Label Order
    1  obj    U   x == "M"   Male    NA
    2  obj    U   x == "F" Female    NA
    3  obj    U       TRUE  Other    NA

    Race decodes

    # A user-defined format: 3 conditions
    - as.factor: TRUE
      Name Type   Expression                     Label Order
    1  obj    U x == "WHITE"                     White    NA
    2  obj    U x == "BLACK" Black or African American    NA
    3  obj    U         TRUE                     Other    NA

    Compile format catalog

    # A format catalog: 11 formats
    - $MEAN: type S, "%.1f"
    - $STD: type S, "(%.2f)"
    - $Q1: type S, "%.1f"
    - $Q3: type S, "%.1f"
    - $MIN: type S, "%d"
    - $MAX: type S, "%d"
    - $CNT: type S, "%2d"
    - $PCT: type S, "(%5.1f%%)"
    - $AGECAT: type U, 4 conditions
    - $SEX: type U, 3 conditions
    - $RACE: type U, 3 conditions

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

    Create sample ADSL data.

    Categorize AGE

    Log starting dataset

       SUBJID   ARM SEX  RACE AGE   AGECAT
    1       1 ARM A   F WHITE  19 18 to 29
    2       2 ARM B   F WHITE  21 18 to 29
    3       3 ARM C   F WHITE  23 18 to 29
    4       4 ARM D   F BLACK  28 18 to 29
    5       5 ARM A   M WHITE  37 30 to 39
    6       6 ARM B   M WHITE  34 30 to 39
    7       7 ARM C   M WHITE  36 30 to 39
    8       8 ARM D   M WHITE  30 30 to 39
    9       9 ARM A   F WHITE  39 30 to 39
    10     10 ARM B   F WHITE  31 30 to 39
    11     11 ARM C   F BLACK  33 30 to 39
    12     12 ARM D   F WHITE  38 30 to 39
    13     13 ARM A   M BLACK  37 30 to 39
    14     14 ARM B   M WHITE  34 30 to 39
    15     15 ARM C   M WHITE  36 30 to 39
    16     16 ARM A   M WHITE  40 40 to 49

    Get ARM population counts

    proc_freq: input data set 16 rows and 6 columns
               tables: ARM
               output: long
               view: TRUE
               output: 1 datasets

      VAR STAT ARM A ARM B ARM C ARM D
    1 ARM  CNT     5     4     4     3

    =========================================================================
    Create summary statistics for age
    =========================================================================

    Call means procedure to get summary statistics for age

    proc_means: input data set 16 rows and 6 columns
                by: ARM
                var: AGE
                stats: n mean std median q1 q3 min max
                view: TRUE
                output: 1 datasets

         BY VAR N MEAN      STD MEDIAN Q1 Q3 MIN MAX
    1 ARM A AGE 5 34.4 8.706320   37.0 37 39  19  40
    2 ARM B AGE 4 30.0 6.164414   32.5 26 34  21  34
    3 ARM C AGE 4 32.0 6.164414   34.5 28 36  23  36
    4 ARM D AGE 3 32.0 5.291503   30.0 28 38  28  38

    Combine stats

    datastep: columns decreased from 10 to 7

         BY VAR N   Mean (SD) Median     Q1 - Q3 Min - Max
    1 ARM A AGE 5 34.4 (8.71)   37.0 37.0 - 39.0   19 - 40
    2 ARM B AGE 4 30.0 (6.16)   32.5 26.0 - 34.0   21 - 34
    3 ARM C AGE 4 32.0 (6.16)   34.5 28.0 - 36.0   23 - 36
    4 ARM D AGE 3 32.0 (5.29)   30.0 28.0 - 38.0   28 - 38

    Transpose ARMs into columns

    proc_transpose: input data set 4 rows and 7 columns
                    var: BY VAR N Mean (SD) Median Q1 - Q3 Min - Max
                    id: BY
                    copy: VAR
                    name: LABEL
                    output dataset 5 rows and 6 columns

      VAR     LABEL       ARM A       ARM B       ARM C       ARM D
    1 AGE         N           5           4           4           3
    2 AGE Mean (SD) 34.4 (8.71) 30.0 (6.16) 32.0 (6.16) 32.0 (5.29)
    3 AGE    Median        37.0        32.5        34.5        30.0
    4 AGE   Q1 - Q3 37.0 - 39.0 26.0 - 34.0 28.0 - 36.0 28.0 - 38.0
    5 AGE Min - Max     19 - 40     21 - 34     23 - 36     28 - 38

    =========================================================================
    Create frequency counts for SEX
    =========================================================================

    Get sex frequency counts

    proc_freq: input data set 16 rows and 6 columns
               tables: SEX
               by: ARM
               view: TRUE
               output: 1 datasets

         BY VAR CAT CNT      PCT
    1 ARM A SEX   F   2 40.00000
    2 ARM A SEX   M   3 60.00000
    3 ARM B SEX   F   2 50.00000
    4 ARM B SEX   M   2 50.00000
    5 ARM C SEX   F   2 50.00000
    6 ARM C SEX   M   2 50.00000
    7 ARM D SEX   F   2 66.66667
    8 ARM D SEX   M   1 33.33333

    Combine counts and percents.

    datastep: columns decreased from 5 to 4

         BY VAR LABEL      CNTPCT
    1 ARM A SEX     F  2 ( 40.0%)
    2 ARM A SEX     M  3 ( 60.0%)
    3 ARM B SEX     F  2 ( 50.0%)
    4 ARM B SEX     M  2 ( 50.0%)
    5 ARM C SEX     F  2 ( 50.0%)
    6 ARM C SEX     M  2 ( 50.0%)
    7 ARM D SEX     F  2 ( 66.7%)
    8 ARM D SEX     M  1 ( 33.3%)

    Transpose ARMs into columns

    proc_transpose: input data set 8 rows and 4 columns
                    by: LABEL
                    var: CNTPCT
                    id: BY
                    copy: VAR
                    name: NAME
                    output dataset 2 rows and 6 columns

      VAR LABEL       ARM A       ARM B       ARM C       ARM D
    1 SEX     F  2 ( 40.0%)  2 ( 50.0%)  2 ( 50.0%)  2 ( 66.7%)
    2 SEX     M  3 ( 60.0%)  2 ( 50.0%)  2 ( 50.0%)  1 ( 33.3%)

    Apply formats

    datastep: columns started with 6 and ended with 6

      VAR  LABEL       ARM A       ARM B       ARM C       ARM D
    1 SEX Female  2 ( 40.0%)  2 ( 50.0%)  2 ( 50.0%)  2 ( 66.7%)
    2 SEX   Male  3 ( 60.0%)  2 ( 50.0%)  2 ( 50.0%)  1 ( 33.3%)

    Sort by label

    proc_sort: input data set 2 rows and 6 columns
               by: LABEL
               keep: VAR LABEL ARM A ARM B ARM C ARM D
               order: a
               output data set 2 rows and 6 columns

      VAR  LABEL       ARM A       ARM B       ARM C       ARM D
    2 SEX   Male  3 ( 60.0%)  2 ( 50.0%)  2 ( 50.0%)  1 ( 33.3%)
    1 SEX Female  2 ( 40.0%)  2 ( 50.0%)  2 ( 50.0%)  2 ( 66.7%)

    =========================================================================
    Create frequency counts for RACE
    =========================================================================

    Get race frequency counts

    proc_freq: input data set 16 rows and 6 columns
               tables: RACE
               by: ARM
               view: TRUE
               output: 1 datasets

         BY  VAR   CAT CNT       PCT
    1 ARM A RACE BLACK   1  20.00000
    2 ARM A RACE WHITE   4  80.00000
    3 ARM B RACE BLACK   0   0.00000
    4 ARM B RACE WHITE   4 100.00000
    5 ARM C RACE BLACK   1  25.00000
    6 ARM C RACE WHITE   3  75.00000
    7 ARM D RACE BLACK   1  33.33333
    8 ARM D RACE WHITE   2  66.66667

    Combine counts and percents.

    datastep: columns decreased from 5 to 4

         BY  VAR LABEL      CNTPCT
    1 ARM A RACE BLACK  1 ( 20.0%)
    2 ARM A RACE WHITE  4 ( 80.0%)
    3 ARM B RACE BLACK  0 (  0.0%)
    4 ARM B RACE WHITE  4 (100.0%)
    5 ARM C RACE BLACK  1 ( 25.0%)
    6 ARM C RACE WHITE  3 ( 75.0%)
    7 ARM D RACE BLACK  1 ( 33.3%)
    8 ARM D RACE WHITE  2 ( 66.7%)

    Transpose ARMs into columns

    proc_transpose: input data set 8 rows and 4 columns
                    by: LABEL
                    var: CNTPCT
                    id: BY
                    copy: VAR
                    name: NAME
                    output dataset 2 rows and 6 columns

       VAR LABEL       ARM A       ARM B       ARM C       ARM D
    1 RACE BLACK  1 ( 20.0%)  0 (  0.0%)  1 ( 25.0%)  1 ( 33.3%)
    2 RACE WHITE  4 ( 80.0%)  4 (100.0%)  3 ( 75.0%)  2 ( 66.7%)

    Clean up

    datastep: columns started with 6 and ended with 6

       VAR                     LABEL       ARM A       ARM B       ARM C       ARM D
    1 RACE Black or African American  1 ( 20.0%)  0 (  0.0%)  1 ( 25.0%)  1 ( 33.3%)
    2 RACE                     White  4 ( 80.0%)  4 (100.0%)  3 ( 75.0%)  2 ( 66.7%)

    Sort by label

    proc_sort: input data set 2 rows and 6 columns
               by: LABEL
               keep: VAR LABEL ARM A ARM B ARM C ARM D
               order: a
               output data set 2 rows and 6 columns

       VAR                     LABEL       ARM A       ARM B       ARM C       ARM D
    2 RACE                     White  4 ( 80.0%)  4 (100.0%)  3 ( 75.0%)  2 ( 66.7%)
    1 RACE Black or African American  1 ( 20.0%)  0 (  0.0%)  1 ( 25.0%)  1 ( 33.3%)

    =========================================================================
    Create frequency counts for Age Group
    =========================================================================

    Get age group frequency counts

    proc_freq: input data set 16 rows and 6 columns
               tables: AGECAT
               by: ARM
               view: TRUE
               output: 1 datasets

          BY    VAR      CAT CNT      PCT
    1  ARM A AGECAT 18 to 29   1 20.00000
    2  ARM A AGECAT 30 to 39   3 60.00000
    3  ARM A AGECAT 40 to 49   1 20.00000
    4  ARM A AGECAT    >= 50   0  0.00000
    5  ARM B AGECAT 18 to 29   1 25.00000
    6  ARM B AGECAT 30 to 39   3 75.00000
    7  ARM B AGECAT 40 to 49   0  0.00000
    8  ARM B AGECAT    >= 50   0  0.00000
    9  ARM C AGECAT 18 to 29   1 25.00000
    10 ARM C AGECAT 30 to 39   3 75.00000
    11 ARM C AGECAT 40 to 49   0  0.00000
    12 ARM C AGECAT    >= 50   0  0.00000
    13 ARM D AGECAT 18 to 29   1 33.33333
    14 ARM D AGECAT 30 to 39   2 66.66667
    15 ARM D AGECAT 40 to 49   0  0.00000
    16 ARM D AGECAT    >= 50   0  0.00000

    Combine counts and percents and assign age group factor for sorting

    datastep: columns decreased from 5 to 4

          VAR    LABEL    BY      CNTPCT
    1  AGECAT 18 to 29 ARM A  1 ( 20.0%)
    2  AGECAT 30 to 39 ARM A  3 ( 60.0%)
    3  AGECAT 40 to 49 ARM A  1 ( 20.0%)
    4  AGECAT    >= 50 ARM A  0 (  0.0%)
    5  AGECAT 18 to 29 ARM B  1 ( 25.0%)
    6  AGECAT 30 to 39 ARM B  3 ( 75.0%)
    7  AGECAT 40 to 49 ARM B  0 (  0.0%)
    8  AGECAT    >= 50 ARM B  0 (  0.0%)
    9  AGECAT 18 to 29 ARM C  1 ( 25.0%)
    10 AGECAT 30 to 39 ARM C  3 ( 75.0%)
    11 AGECAT 40 to 49 ARM C  0 (  0.0%)
    12 AGECAT    >= 50 ARM C  0 (  0.0%)
    13 AGECAT 18 to 29 ARM D  1 ( 33.3%)
    14 AGECAT 30 to 39 ARM D  2 ( 66.7%)
    15 AGECAT 40 to 49 ARM D  0 (  0.0%)
    16 AGECAT    >= 50 ARM D  0 (  0.0%)

    Sort by age group factor

    proc_sort: input data set 16 rows and 4 columns
               by: BY LABEL
               keep: VAR LABEL BY CNTPCT
               order: a a
               output data set 16 rows and 4 columns

          VAR    LABEL    BY      CNTPCT
    1  AGECAT 18 to 29 ARM A  1 ( 20.0%)
    2  AGECAT 30 to 39 ARM A  3 ( 60.0%)
    3  AGECAT 40 to 49 ARM A  1 ( 20.0%)
    4  AGECAT    >= 50 ARM A  0 (  0.0%)
    5  AGECAT 18 to 29 ARM B  1 ( 25.0%)
    6  AGECAT 30 to 39 ARM B  3 ( 75.0%)
    7  AGECAT 40 to 49 ARM B  0 (  0.0%)
    8  AGECAT    >= 50 ARM B  0 (  0.0%)
    9  AGECAT 18 to 29 ARM C  1 ( 25.0%)
    10 AGECAT 30 to 39 ARM C  3 ( 75.0%)
    11 AGECAT 40 to 49 ARM C  0 (  0.0%)
    12 AGECAT    >= 50 ARM C  0 (  0.0%)
    13 AGECAT 18 to 29 ARM D  1 ( 33.3%)
    14 AGECAT 30 to 39 ARM D  2 ( 66.7%)
    15 AGECAT 40 to 49 ARM D  0 (  0.0%)
    16 AGECAT    >= 50 ARM D  0 (  0.0%)

    Tranpose age group block

    proc_transpose: input data set 16 rows and 4 columns
                    by: LABEL
                    var: CNTPCT
                    id: BY
                    copy: VAR
                    name: NAME
                    output dataset 4 rows and 6 columns

         VAR    LABEL       ARM A       ARM B       ARM C       ARM D
    1 AGECAT 18 to 29  1 ( 20.0%)  1 ( 25.0%)  1 ( 25.0%)  1 ( 33.3%)
    2 AGECAT 30 to 39  3 ( 60.0%)  3 ( 75.0%)  3 ( 75.0%)  2 ( 66.7%)
    3 AGECAT 40 to 49  1 ( 20.0%)  0 (  0.0%)  0 (  0.0%)  0 (  0.0%)
    4 AGECAT    >= 50  0 (  0.0%)  0 (  0.0%)  0 (  0.0%)  0 (  0.0%)

    Combine blocks into final data frame

    datastep: columns started with 6 and ended with 6

          VAR                     LABEL       ARM A       ARM B       ARM C       ARM D
    1     AGE                         N           5           4           4           3
    2     AGE                 Mean (SD) 34.4 (8.71) 30.0 (6.16) 32.0 (6.16) 32.0 (5.29)
    3     AGE                    Median        37.0        32.5        34.5        30.0
    4     AGE                   Q1 - Q3 37.0 - 39.0 26.0 - 34.0 28.0 - 36.0 28.0 - 38.0
    5     AGE                 Min - Max     19 - 40     21 - 34     23 - 36     28 - 38
    6  AGECAT                  18 to 29  1 ( 20.0%)  1 ( 25.0%)  1 ( 25.0%)  1 ( 33.3%)
    7  AGECAT                  30 to 39  3 ( 60.0%)  3 ( 75.0%)  3 ( 75.0%)  2 ( 66.7%)
    8  AGECAT                  40 to 49  1 ( 20.0%)  0 (  0.0%)  0 (  0.0%)  0 (  0.0%)
    9     SEX                      Male  3 ( 60.0%)  2 ( 50.0%)  2 ( 50.0%)  1 ( 33.3%)
    10    SEX                    Female  2 ( 40.0%)  2 ( 50.0%)  2 ( 50.0%)  2 ( 66.7%)
    11   RACE                     White  4 ( 80.0%)  4 (100.0%)  3 ( 75.0%)  2 ( 66.7%)
    12   RACE Black or African American  1 ( 20.0%)  0 (  0.0%)  1 ( 25.0%)  1 ( 33.3%)

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

    Write out the report

    # A report specification: 1 pages
    - file_path: 'C:\Users\dbosa\AppData\Local\Temp\RtmpAXQUo8/example2.rtf'
    - output_type: RTF
    - units: inches
    - orientation: landscape
    - margins: top 1 bottom 1 left 1 right 1
    - line size/count: 9/36
    - page_header: left=Sponsor: Company right=Study: ABC
    - page_footer: left=Date Produced: 2024-05-28 center= right=Page [pg] of [tpg]
    - content:
    # A table specification:
    - data: data.frame 'final' 12 rows 6 cols
    - show_cols: all
    - use_attributes: all
    - title 1: 'Table 1.0'
    - title 2: 'Analysis of Demographic Characteristics'
    - title 3: 'Safety Population'
    - footnote 1: 'Program: DM_Table.R'
    - footnote 2: 'NOTE: Denominator based on number of non-missing responses.'
    - stub: VAR LABEL 'Variable' width=2.5 align='left'
    - define: VAR 'Variable' dedupe='TRUE'
    - define: LABEL 'Demographic Category'
    - define: ARM A 'Placebo'
    - define: ARM B 'Drug 50mg'
    - define: ARM C 'Drug 100mg'
    - define: ARM D 'Competitor'

    =========================================================================
    Clean Up
    =========================================================================

    Close log

    =========================================================================
    Log End Time: 2024-05-28 12:52:45.234875
    Log Elapsed Time: 0 00:00:00
    =========================================================================

Next: Example 3: Figures