# Example 1: Demographics

## Program

The following example shows a complete program. The example illustrates how procs functions work together, and interact with other sassy functions to create a demographics table with p-value statistics.

    library(sassy)
    library(procs)

    # 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, "example1.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"),
                    condition(TRUE, "Out of range"))

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

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

    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,
               AOV.F = "%5.3f",
               AOV.P = "(%5.3f)",
               CHISQ = "%5.3f",
               CHISQ.P = "(%5.3f)")

    # 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_trans

    put("Calculate aov")
    age_aov <- aov(AGE ~ ARM, data = adsl) |>
      summary()

    put("Get aov into proper data frame")

    age_aov <- age_aov[[1]][1, c("F value", "Pr(>F)")]
    names(age_aov) <- c("AOV.F", "AOV.P")
    age_aov <- as.data.frame(age_aov) |> put()

    put("Combine aov statistics")
    datastep(age_aov,
             keep = PVALUE,
             format = fc,
             {
               PVALUE <- fapply2(AOV.F, AOV.P)

             }) -> age_aov_comb

    put("Append aov")
    datastep(age_trans, merge = age_aov_comb, {}) -> 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) -> sex_trans

    put("Clean up")
    datastep(sex_trans, drop = NAME,
             {

               LABEL <- fapply(LABEL, fc$SEX)
               LABEL <- factor(LABEL, levels = levels(fc$SEX))

             }) -> sex_cnts

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

    put("Get sex chisq")
    proc_freq(adsl, tables = v(SEX * ARM),
              options = v(chisq, notable)) -> sex_chisq

    put("Combine chisq statistics")
    datastep(sex_chisq,
             format = fc,
             keep = PVALUE,
             {

               PVALUE = fapply2(CHISQ, CHISQ.P)
             }) -> sex_chisq_comb

    put("Append chisq")
    datastep(sex_cnts,
             merge = sex_chisq_comb,
             {}) -> 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) -> race_trans

    put("Clean up")

    datastep(race_trans, drop = NAME,
             where = expression(del == FALSE),
             {
               LABEL <- fapply(LABEL, fc$RACE)
               LABEL <- factor(LABEL, levels = levels(fc$RACE))

             }) -> race_cnts

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

    put("Get race chisq")
    proc_freq(adsl, tables = RACE * ARM,
              options = v(chisq, notable)) -> race_chisq

    put("Combine chisq statistics")
    datastep(race_chisq,
             format = fc,
             keep = c("PVALUE"),
             {

               PVALUE = fapply2(CHISQ, CHISQ.P)
             }) -> race_chisq_comb

    put("Append chisq")
    datastep(race_cnts, merge = race_chisq_comb, {}) -> 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 <- factor(CAT, levels = levels(fc$AGECAT))
             }) -> 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) -> ageg_trans

    put("Some clean up")
    datastep(ageg_trans,
             drop = NAME,
             {}) -> ageg_cnts

    put("Get ageg chisq")
    proc_freq(adsl, tables = AGECAT * ARM,
              options = v(chisq, notable)) -> ageg_chisq

    put("Combine chisq statistics")
    datastep(ageg_chisq,
             format = fc,
             keep = c("PVALUE"),
             {
               PVALUE = fapply2(CHISQ, CHISQ.P)
             }) -> ageg_chisq_comb

    put("Append chisq")
    datastep(ageg_cnts, merge = ageg_chisq_comb,
             {}) -> 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")

    plbl <- "Tests of Association{supsc('1')}\n Value (P-Value)"

    # 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"]) |>
      define(PVALUE, label = plbl, width = 2, dedupe = TRUE, align = "center") |>
      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.",
                "{supsc('1')}Pearson's Chi-Square tests will be used for "
                %p% "Categorical variables and ANOVA tests for continuous variables.")

    rpt <- create_report(file.path(tmp, "ProcsDemoDM.rtf"),
                         output_type = "RTF",
                         font = "Times") |>
      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:

## Log

And here is the log:

    =========================================================================
    Log Path: C:/Users/dbosa/AppData/Local/Temp/RtmpKQddL7/log/example1.log
    Program Path: C:/Projects/Archytas/Demo/example1.R
    Working Directory: C:/Projects/Archytas/Demo
    User Name: dbosa
    R Version: 4.2.1 (2022-06-23 ucrt)
    Machine: SOCRATES x86-64
    Operating System: Windows 10 x64 build 19044
    Base Packages: stats graphics grDevices utils datasets methods base Other
    Packages: tidylog_1.0.2 procs_0.0.9007 reporter_1.3.7 libr_1.2.6 fmtr_1.5.9
    logr_1.3.3 common_1.0.5 sassy_1.0.8
    Log Start Time: 2022-10-15 20:01:02
    =========================================================================

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

    Age categories

    # A user-defined format: 5 conditions
      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
    5  obj    U              TRUE Out of range    NA

    Sex decodes

    # A user-defined format: 4 conditions
      Name Type Expression   Label Order
    1  obj    U   is.na(x) Missing    NA
    2  obj    U   x == "M"    Male    NA
    3  obj    U   x == "F"  Female    NA
    4  obj    U       TRUE   Other    NA

    Race decodes

    # A user-defined format: 4 conditions
      Name Type   Expression                     Label Order
    1  obj    U     is.na(x)                   Missing    NA
    2  obj    U x == "WHITE"                     White    NA
    3  obj    U x == "BLACK" Black or African American    NA
    4  obj    U         TRUE                     Other    NA

    Compile format catalog

    # A format catalog: 15 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, 5 conditions
    - $SEX: type U, 4 conditions
    - $RACE: type U, 4 conditions
    - $AOV.F: type S, "%5.3f"
    - $AOV.P: type S, "(%5.3f)"
    - $CHISQ: type S, "%5.3f"
    - $CHISQ.P: type S, "(%5.3f)"

    =========================================================================
    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
               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

    Calculate aov

    Get aov into proper data frame

                    AOV.F     AOV.P
    ARM         0.2983651 0.8259486

    Combine aov statistics

    datastep: columns decreased from 2 to 1

             PVALUE
    1 0.298 (0.826)

    Append aov

    datastep: columns increased from 6 to 7

      VAR     LABEL       ARM A       ARM B       ARM C       ARM D        PVALUE
    1 AGE         N           5           4           4           3 0.298 (0.826)
    2 AGE Mean (SD) 34.4 (8.71) 30.0 (6.16) 32.0 (6.16) 32.0 (5.29)          <NA>
    3 AGE    Median        37.0        32.5        34.5        30.0          <NA>
    4 AGE   Q1 - Q3 37.0 - 39.0 26.0 - 34.0 28.0 - 36.0 28.0 - 38.0          <NA>
    5 AGE Min - Max     19 - 40     21 - 34     23 - 36     28 - 38          <NA>

    =========================================================================
    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 7 columns

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

    Clean up

    datastep: columns decreased from 7 to 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
               nodupkey: FALSE
               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%)

    Get sex chisq

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

          CHISQ CHISQ.DF   CHISQ.P
    1 0.5333333        3 0.9115095

    Combine chisq statistics

    datastep: columns decreased from 3 to 1

             PVALUE
    1 0.533 (0.912)

    Append chisq

    datastep: columns increased from 6 to 7

      VAR  LABEL       ARM A       ARM B       ARM C       ARM D        PVALUE
    1 SEX   Male  3 ( 60.0%)  2 ( 50.0%)  2 ( 50.0%)  1 ( 33.3%) 0.533 (0.912)
    2 SEX Female  2 ( 40.0%)  2 ( 50.0%)  2 ( 50.0%)  2 ( 66.7%)          <NA>

    =========================================================================
    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 7 columns

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

    Clean up

    datastep: columns decreased from 7 to 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
               nodupkey: FALSE
               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%)

    Get race chisq

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

         CHISQ CHISQ.DF   CHISQ.P
    1 1.449573        3 0.6939569

    Combine chisq statistics

    datastep: columns decreased from 3 to 1

             PVALUE
    1 1.450 (0.694)

    Append chisq

    datastep: columns increased from 6 to 7

       VAR                     LABEL       ARM A       ARM B       ARM C       ARM D
    1 RACE                     White  4 ( 80.0%)  4 (100.0%)  3 ( 75.0%)  2 ( 66.7%)
    2 RACE Black or African American  1 ( 20.0%)  0 (  0.0%)  1 ( 25.0%)  1 ( 33.3%)
             PVALUE
    1 1.450 (0.694)
    2          <NA>

    =========================================================================
    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 B AGECAT 18 to 29   1 25.00000
    5  ARM B AGECAT 30 to 39   3 75.00000
    6  ARM B AGECAT 40 to 49   0  0.00000
    7  ARM C AGECAT 18 to 29   1 25.00000
    8  ARM C AGECAT 30 to 39   3 75.00000
    9  ARM C AGECAT 40 to 49   0  0.00000
    10 ARM D AGECAT 18 to 29   1 33.33333
    11 ARM D AGECAT 30 to 39   2 66.66667
    12 ARM D AGECAT 40 to 49   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 18 to 29 ARM B  1 ( 25.0%)
    5  AGECAT 30 to 39 ARM B  3 ( 75.0%)
    6  AGECAT 40 to 49 ARM B  0 (  0.0%)
    7  AGECAT 18 to 29 ARM C  1 ( 25.0%)
    8  AGECAT 30 to 39 ARM C  3 ( 75.0%)
    9  AGECAT 40 to 49 ARM C  0 (  0.0%)
    10 AGECAT 18 to 29 ARM D  1 ( 33.3%)
    11 AGECAT 30 to 39 ARM D  2 ( 66.7%)
    12 AGECAT 40 to 49 ARM D  0 (  0.0%)

    Sort by age group factor

    proc_sort: input data set 12 rows and 4 columns
               by: BY LABEL
               keep: VAR LABEL BY CNTPCT
               order: a a
               nodupkey: FALSE
               output data set 12 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 18 to 29 ARM B  1 ( 25.0%)
    5  AGECAT 30 to 39 ARM B  3 ( 75.0%)
    6  AGECAT 40 to 49 ARM B  0 (  0.0%)
    7  AGECAT 18 to 29 ARM C  1 ( 25.0%)
    8  AGECAT 30 to 39 ARM C  3 ( 75.0%)
    9  AGECAT 40 to 49 ARM C  0 (  0.0%)
    10 AGECAT 18 to 29 ARM D  1 ( 33.3%)
    11 AGECAT 30 to 39 ARM D  2 ( 66.7%)
    12 AGECAT 40 to 49 ARM D  0 (  0.0%)

    Tranpose age group block

    proc_transpose: input data set 12 rows and 4 columns
                    by: LABEL
                    var: CNTPCT
                    id: BY
                    copy: VAR
                    name: NAME
                    output dataset 3 rows and 7 columns

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

    Some clean up

    datastep: columns decreased from 7 to 6

         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%)

    Get ageg chisq

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

         CHISQ CHISQ.DF   CHISQ.P
    1 2.436364        6 0.8755205

    Combine chisq statistics

    datastep: columns decreased from 3 to 1

             PVALUE
    1 2.436 (0.876)

    Append chisq

    datastep: columns increased from 6 to 7

         VAR    LABEL       ARM A       ARM B       ARM C       ARM D        PVALUE
    1 AGECAT 18 to 29  1 ( 20.0%)  1 ( 25.0%)  1 ( 25.0%)  1 ( 33.3%) 2.436 (0.876)
    2 AGECAT 30 to 39  3 ( 60.0%)  3 ( 75.0%)  3 ( 75.0%)  2 ( 66.7%)          <NA>
    3 AGECAT 40 to 49  1 ( 20.0%)  0 (  0.0%)  0 (  0.0%)  0 (  0.0%)          <NA>

    Combine blocks into final data frame

    datastep: columns started with 7 and ended with 7

          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%)
              PVALUE
    1  0.298 (0.826)
    2           <NA>
    3           <NA>
    4           <NA>
    5           <NA>
    6  2.436 (0.876)
    7           <NA>
    8           <NA>
    9  0.533 (0.912)
    10          <NA>
    11 1.450 (0.694)
    12          <NA>

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

    Write out the report

    # A report specification: 1 pages
    - file_path: 'C:\Users\dbosa\AppData\Local\Temp\RtmpKQddL7/ProcsDemoDM.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: 2022-10-15 center= right=Page [pg] of [tpg]
    - content:
    # A table specification:
    - data: data.frame 'final' 12 rows 7 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.'
    - footnote 3: '¹Pearson's Chi-Square tests will be used for Categorical variables and ANOVA tests for continuous variables.'
    - 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'
    - define: PVALUE 'Tests of Association¹
     Value (P-Value)' width=2 align='center' dedupe='TRUE'

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

    Unload libname

    lib_sync: synchronized data in library 'sdtm'

    lib_unload: library 'sdtm' unloaded

    Close log

    =========================================================================
    Log End Time: 2022-10-15 20:01:03
    Log Elapsed Time: 0 00:00:00
    =========================================================================