--- title: "Example 2: Demographics Table" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Example 2: Demographics Table} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ``` 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](https://logr.r-sassy.org)** package provides automatic logging for many functions. * The `datastep()` function from the **[libr](https://libr.r-sassy.org)** package allows for easy data processing. * The **[fmtr](https://fmtr.r-sassy.org)** package provides several convenient functions for calculating and formatting summary statistics. * Statistics from the **[procs](https://procs.r-sassy.org)** package match SAS® out of the box. * The **[reporter](https://reporter.r-sassy.org)** package supports 'N=' population counts in the header labels. * The **reporter** package also allows you to define a stub column of hierarchical labels. ```{r eval=FALSE, echo=TRUE} 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](sassy-figure.html)