In dbosak01/fmtr: Easily Apply Formats to Data
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
options(rmarkdown.html_vignette.check_title = FALSE)
Program
"Complete Example 1" showed how to create a simple demographics table using
the fmtr package and Tidyverse. Here is the same table created
with only Base R and the sassy system of packages.
The data for this example has been included in the fmtr package as an
external data file. It may be accessed using the system.file() function
as shown below, or downloaded directly from the fmtr GitHub site
here
library(sassy)
# Prepare Log -------------------------------------------------------------
options("logr.autolog" = TRUE,
"logr.notes" = FALSE)
# Get temp location for log and report output
tmp <- tempdir()
# Open log
lf <- log_open(file.path(tmp, "example2.log"))
# Load and Prepare Data ---------------------------------------------------
sep("Prepare Data")
# Get path to sample data
pkg <- system.file("extdata", package = "fmtr")
# Define data library
libname(sdtm, pkg, "csv")
# Prepare data
put("Subset DM dataset")
dm_mod <- subset(sdtm$DM, ARM != "SCREEN FAILURE",
v(USUBJID, SEX, AGE, ARM)) |> put()
put("Get ARM population counts")
arm_pop <- proc_freq(dm_mod, tables = ARM,
output = long,
options = v(nocum, nopercent, nonobs))
# Create Format Catalog --------------------------------------------------
sep("Create format catalog")
fmts <- fcat(AGECAT = value(condition(x >= 18 & x <= 24, "18 to 24"),
condition(x >= 25 & x <= 44, "25 to 44"),
condition(x >= 45 & x <= 64, "45 to 64"),
condition(x >= 65, ">= 65")),
SEX = value(condition(is.na(x), "Missing", order = 3),
condition(x == "M", "Male", order = 1),
condition(x == "F", "Female", order = 2)),
VAR = c("AGE" = "Age",
"AGECAT" = "Age Group",
"SEX" = "Sex"))
numfmts <- fcat(N = "%d", MEAN = "%.1f", STD = "(%04.2f)", MEDIAN = "%d", Q1 = "%.1f",
Q3 = "%.1f", MIN = "%d", MAX = "%d", CNT = "%d", PCT = "(%4.1f%%)")
numlbls <- c(N = "n", MEANSD = "Mean (SD)", MEDIAN = "Median", Q1Q3 = "Q1 - Q3",
MINMAX = "Min - Max")
# Age Summary Block -------------------------------------------------------
sep("Create summary statistics for age")
age_block <- proc_means(dm_mod, stats = v(n, mean, std, median, q1, q3, min, max),
class = ARM, options = v(nway, notype, nofreq)) |>
datastep(format = numfmts,
keep = v(CLASS, VAR, N, MEANSD, MEDIAN, Q1Q3, MINMAX),
{
MEANSD <- fapply2(MEAN, STD)
Q1Q3 <- fapply2(Q1, Q3, sep = " - ")
MINMAX <- fapply2(MIN, MAX, sep = " - ")
}) |>
proc_transpose(id = CLASS, copy = VAR,
var = v(N, MEANSD, MEDIAN, Q1Q3, MINMAX),
name = "LABEL") |>
datastep({LABEL <- fapply(LABEL, numlbls)})
# Age Group Block ----------------------------------------------------------
sep("Create frequency counts for Age Group")
put("Create age group frequency counts")
ageg_block <- dm_mod |>
datastep({AGECAT <- fapply(AGE, fmts$AGECAT)}) |>
proc_freq(tables = AGECAT, by = ARM,
options = nonobs) |>
datastep(format = numfmts,
keep = v(VAR, BY, LABEL, CNTPCT),
{
LABEL <- CAT
CNTPCT <- fapply2(CNT, PCT)
}) |>
proc_transpose(var = CNTPCT, by = LABEL, copy = VAR, id = BY, options = noname)
put("Sort age groups as desired")
ageg_block$LABEL <- factor(ageg_block$LABEL, levels = levels(fmts$AGECAT))
ageg_block <- proc_sort(ageg_block, by = LABEL, as.character = TRUE)
# Sex Block ---------------------------------------------------------------
sep("Create frequency counts for SEX")
put("Create sex frequency counts")
sex_block <- dm_mod |>
datastep({SEX <- fapply(SEX, fmts$SEX)}) |>
proc_freq(tables = SEX, by = ARM,
options = nonobs) |>
datastep(format = numfmts,
keep = v(VAR, BY, LABEL, CNTPCT),
{
LABEL <- CAT
CNTPCT <- fapply2(CNT, PCT)
}) |>
proc_transpose(var = CNTPCT, by = LABEL, copy = VAR, id = BY, options = noname)
put("Sort age groups as desired")
sex_block$LABEL <- factor(sex_block$LABEL, levels = levels(fmts$SEX))
sex_block <- proc_sort(sex_block, by = LABEL, as.character = TRUE)
put("Combine blocks into final data frame")
final <- bind_rows(age_block, ageg_block, sex_block) |> put()
# Report ------------------------------------------------------------------
sep("Create and print report")
# Create Table
tbl <- create_table(final, first_row_blank = TRUE, borders = c("top", "bottom")) |>
column_defaults(from = `ARM A`, to = `ARM D`, align = "center", width = 1.25) |>
stub(vars = v(VAR, LABEL), "Variable", width = 2.5) |>
define(VAR, blank_after = TRUE, dedupe = TRUE, label = "Variable",
format = fmts$VAR,label_row = TRUE) |>
define(LABEL, indent = .25, label = "Demographic Category") |>
define(`ARM A`, label = "Treatment Group 1", n = arm_pop["ARM A"]) |>
define(`ARM B`, label = "Treatment Group 2", n = arm_pop["ARM B"]) |>
define(`ARM C`, label = "Treatment Group 3", n = arm_pop["ARM C"]) |>
define(`ARM D`, label = "Treatment Group 4", n = arm_pop["ARM D"])
rpt <- create_report(file.path(tmp, "output/example2.rtf"),
output_type = "RTF", font = "Arial") |>
set_margins(top = 1, bottom = 1) |>
page_header("Sponsor: Company", "Study: ABC") |>
titles("Table 1.0", bold = TRUE, blank_row = "none") |>
titles("Analysis of Demographic Characteristics",
"Safety Population") |>
add_content(tbl) |>
footnotes("Program: DM_Table.R",
"NOTE: Denominator based on number of non-missing responses.") |>
page_footer(paste0("Date Produced: ", fapply(Sys.time(), "%d%b%y %H:%M")),
right = "Page [pg] of [tpg]")
res <- write_report(rpt)
# Clean Up ----------------------------------------------------------------
sep("Clean Up")
# Close log
log_close()
# View report
# file.show(res$modified_path)
# View log
# file.show(lf)
Output
Here is the report produced by the above sample program:
Log
Here is the log produced by the above sample program:
=========================================================================
Log Path: C:/Users/dbosa/AppData/Local/Temp/RtmpKc23Rz/log/example2.log
Program Path: C:/Users/dbosa/Documents/.active-rstudio-document
Working Directory: C:/Projects/Archytas/Sassy/Code
User Name: dbosa
R Version: 4.3.2 (2023-10-31 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 reporter_1.4.4 logr_1.3.5 sassy_1.2.1 lubridate_1.9.2
forcats_1.0.0 stringr_1.5.0 purrr_1.0.1 readr_2.1.4 tidyr_1.3.0 tibble_3.2.1
ggplot2_3.4.4 tidyverse_2.0.0 libr_1.2.8 dplyr_1.1.3 fmtr_1.6.2 procs_1.0.4
common_1.1.1
Log Start Time: 2024-01-07 18:26:25.636328
=========================================================================
=========================================================================
Prepare Data
=========================================================================
# library 'sdtm': 1 items
- attributes: csv not loaded
- path: C:/Users/dbosa/AppData/Local/R/win-library/4.3/fmtr/extdata
- items:
Name Extension Rows Cols Size LastModified
1 DM csv 87 24 45.5 Kb 2023-12-16 23:10:51
Subset DM dataset
# A tibble: 85 × 4
USUBJID SEX AGE ARM
<chr> <chr> <dbl> <chr>
1 ABC-01-049 M 39 ARM D
2 ABC-01-050 M 47 ARM B
3 ABC-01-051 M 34 ARM A
4 ABC-01-052 F 45 ARM C
5 ABC-01-053 F 26 ARM B
6 ABC-01-054 M 44 ARM D
7 ABC-01-055 F 47 ARM C
8 ABC-01-056 M 31 ARM A
9 ABC-01-113 M 74 ARM D
10 ABC-01-114 F 72 ARM B
# ℹ 75 more rows
# ℹ Use `print(n = ...)` to see more rows
Get ARM population counts
proc_freq: input data set 85 rows and 4 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
=========================================================================
Create format catalog
=========================================================================
# A user-defined format: 4 conditions
Name Type Expression Label Order
1 obj U x >= 18 & x <= 24 18 to 24 NA
2 obj U x >= 25 & x <= 44 25 to 44 NA
3 obj U x >= 45 & x <= 64 45 to 64 NA
4 obj U x >= 65 >= 65 NA
# A user-defined format: 3 conditions
Name Type Expression Label Order
1 obj U is.na(x) Missing 3
2 obj U x == "M" Male 1
3 obj U x == "F" Female 2
# A format catalog: 3 formats
- $AGECAT: type U, 4 conditions
- $SEX: type U, 3 conditions
- $VAR: type V, 3 elements
# A format catalog: 10 formats
- $N: type S, "%d"
- $MEAN: type S, "%.1f"
- $STD: type S, "(%04.2f)"
- $MEDIAN: type S, "%d"
- $Q1: type S, "%.1f"
- $Q3: type S, "%.1f"
- $MIN: type S, "%d"
- $MAX: type S, "%d"
- $CNT: type S, "%d"
- $PCT: type S, "(%4.1f%%)"
=========================================================================
Create summary statistics for age
=========================================================================
proc_means: input data set 85 rows and 4 columns
class: ARM
var: AGE
stats: n mean std median q1 q3 min max
view: TRUE
output: 1 datasets
CLASS VAR N MEAN STD MEDIAN Q1 Q3 MIN MAX
1 ARM A AGE 20 53.15000 11.89991 52.5 47.5 60 31 73
2 ARM B AGE 21 47.38095 16.25877 46.0 35.0 61 22 73
3 ARM C AGE 21 45.71429 14.41923 46.0 38.0 53 19 71
4 ARM D AGE 23 49.73913 14.32486 48.0 39.0 62 21 75
datastep: columns decreased from 10 to 7
CLASS VAR N MEANSD MEDIAN Q1Q3 MINMAX
1 ARM A AGE 20 53.1 (11.90) 52.5 47.5 - 60.0 31 - 73
2 ARM B AGE 21 47.4 (16.26) 46.0 35.0 - 61.0 22 - 73
3 ARM C AGE 21 45.7 (14.42) 46.0 38.0 - 53.0 19 - 71
4 ARM D AGE 23 49.7 (14.32) 48.0 39.0 - 62.0 21 - 75
proc_transpose: input data set 4 rows and 7 columns
var: N MEANSD MEDIAN Q1Q3 MINMAX
id: CLASS
copy: VAR
name: LABEL
output dataset 5 rows and 6 columns
VAR LABEL ARM A ARM B ARM C ARM D
1 AGE N 20 21 21 23
2 AGE MEANSD 53.1 (11.90) 47.4 (16.26) 45.7 (14.42) 49.7 (14.32)
3 AGE MEDIAN 52.5 46.0 46.0 48.0
4 AGE Q1Q3 47.5 - 60.0 35.0 - 61.0 38.0 - 53.0 39.0 - 62.0
5 AGE MINMAX 31 - 73 22 - 73 19 - 71 21 - 75
datastep: columns started with 6 and ended with 6
VAR LABEL ARM A ARM B ARM C ARM D
1 AGE n 20 21 21 23
2 AGE Mean (SD) 53.1 (11.90) 47.4 (16.26) 45.7 (14.42) 49.7 (14.32)
3 AGE Median 52.5 46.0 46.0 48.0
4 AGE Q1 - Q3 47.5 - 60.0 35.0 - 61.0 38.0 - 53.0 39.0 - 62.0
5 AGE Min - Max 31 - 73 22 - 73 19 - 71 21 - 75
=========================================================================
Create frequency counts for Age Group
=========================================================================
Create age group frequency counts
datastep: columns increased from 4 to 5
# A tibble: 85 × 5
USUBJID SEX AGE ARM AGECAT
<chr> <chr> <dbl> <chr> <chr>
1 ABC-01-049 M 39 ARM D 25 to 44
2 ABC-01-050 M 47 ARM B 45 to 64
3 ABC-01-051 M 34 ARM A 25 to 44
4 ABC-01-052 F 45 ARM C 45 to 64
5 ABC-01-053 F 26 ARM B 25 to 44
6 ABC-01-054 M 44 ARM D 25 to 44
7 ABC-01-055 F 47 ARM C 45 to 64
8 ABC-01-056 M 31 ARM A 25 to 44
9 ABC-01-113 M 74 ARM D >= 65
10 ABC-01-114 F 72 ARM B >= 65
# ℹ 75 more rows
# ℹ Use `print(n = ...)` to see more rows
proc_freq: input data set 85 rows and 5 columns
tables: AGECAT
by: ARM
view: TRUE
output: 1 datasets
# A tibble: 16 × 5
BY VAR CAT CNT PCT
<chr> <chr> <chr> <dbl> <dbl>
1 ARM A AGECAT >= 65 3 15
2 ARM A AGECAT 18 to 24 0 0
3 ARM A AGECAT 25 to 44 4 20
4 ARM A AGECAT 45 to 64 13 65
5 ARM B AGECAT >= 65 5 23.8
6 ARM B AGECAT 18 to 24 1 4.76
7 ARM B AGECAT 25 to 44 8 38.1
8 ARM B AGECAT 45 to 64 7 33.3
9 ARM C AGECAT >= 65 2 9.52
10 ARM C AGECAT 18 to 24 3 14.3
11 ARM C AGECAT 25 to 44 4 19.0
12 ARM C AGECAT 45 to 64 12 57.1
13 ARM D AGECAT >= 65 3 13.0
14 ARM D AGECAT 18 to 24 1 4.35
15 ARM D AGECAT 25 to 44 7 30.4
16 ARM D AGECAT 45 to 64 12 52.2
datastep: columns decreased from 5 to 4
# A tibble: 16 × 4
VAR BY LABEL CNTPCT
<chr> <chr> <chr> <chr>
1 AGECAT ARM A >= 65 3 (15.0%)
2 AGECAT ARM A 18 to 24 0 ( 0.0%)
3 AGECAT ARM A 25 to 44 4 (20.0%)
4 AGECAT ARM A 45 to 64 13 (65.0%)
5 AGECAT ARM B >= 65 5 (23.8%)
6 AGECAT ARM B 18 to 24 1 ( 4.8%)
7 AGECAT ARM B 25 to 44 8 (38.1%)
8 AGECAT ARM B 45 to 64 7 (33.3%)
9 AGECAT ARM C >= 65 2 ( 9.5%)
10 AGECAT ARM C 18 to 24 3 (14.3%)
11 AGECAT ARM C 25 to 44 4 (19.0%)
12 AGECAT ARM C 45 to 64 12 (57.1%)
13 AGECAT ARM D >= 65 3 (13.0%)
14 AGECAT ARM D 18 to 24 1 ( 4.3%)
15 AGECAT ARM D 25 to 44 7 (30.4%)
16 AGECAT ARM D 45 to 64 12 (52.2%)
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
# A tibble: 4 × 6
VAR LABEL `ARM A` `ARM B` `ARM C` `ARM D`
<chr> <chr> <chr> <chr> <chr> <chr>
1 AGECAT >= 65 3 (15.0%) 5 (23.8%) 2 ( 9.5%) 3 (13.0%)
2 AGECAT 18 to 24 0 ( 0.0%) 1 ( 4.8%) 3 (14.3%) 1 ( 4.3%)
3 AGECAT 25 to 44 4 (20.0%) 8 (38.1%) 4 (19.0%) 7 (30.4%)
4 AGECAT 45 to 64 13 (65.0%) 7 (33.3%) 12 (57.1%) 12 (52.2%)
Sort age groups as desired
proc_sort: input data set 4 rows and 6 columns
by: LABEL
keep: VAR LABEL ARM A ARM B ARM C ARM D
order: a
output data set 4 rows and 6 columns
# A tibble: 4 × 6
VAR LABEL `ARM A` `ARM B` `ARM C` `ARM D`
<chr> <chr> <chr> <chr> <chr> <chr>
1 AGECAT 18 to 24 0 ( 0.0%) 1 ( 4.8%) 3 (14.3%) 1 ( 4.3%)
2 AGECAT 25 to 44 4 (20.0%) 8 (38.1%) 4 (19.0%) 7 (30.4%)
3 AGECAT 45 to 64 13 (65.0%) 7 (33.3%) 12 (57.1%) 12 (52.2%)
4 AGECAT >= 65 3 (15.0%) 5 (23.8%) 2 ( 9.5%) 3 (13.0%)
=========================================================================
Create frequency counts for SEX
=========================================================================
Create sex frequency counts
datastep: columns started with 4 and ended with 4
# A tibble: 85 × 4
USUBJID SEX AGE ARM
<chr> <chr> <dbl> <chr>
1 ABC-01-049 Male 39 ARM D
2 ABC-01-050 Male 47 ARM B
3 ABC-01-051 Male 34 ARM A
4 ABC-01-052 Female 45 ARM C
5 ABC-01-053 Female 26 ARM B
6 ABC-01-054 Male 44 ARM D
7 ABC-01-055 Female 47 ARM C
8 ABC-01-056 Male 31 ARM A
9 ABC-01-113 Male 74 ARM D
10 ABC-01-114 Female 72 ARM B
# ℹ 75 more rows
# ℹ Use `print(n = ...)` to see more rows
proc_freq: input data set 85 rows and 4 columns
tables: SEX
by: ARM
view: TRUE
output: 1 datasets
# A tibble: 8 × 5
BY VAR CAT CNT PCT
<chr> <chr> <chr> <dbl> <dbl>
1 ARM A SEX Female 5 25
2 ARM A SEX Male 15 75
3 ARM B SEX Female 11 52.4
4 ARM B SEX Male 10 47.6
5 ARM C SEX Female 9 42.9
6 ARM C SEX Male 12 57.1
7 ARM D SEX Female 7 30.4
8 ARM D SEX Male 16 69.6
datastep: columns decreased from 5 to 4
# A tibble: 8 × 4
VAR BY LABEL CNTPCT
<chr> <chr> <chr> <chr>
1 SEX ARM A Female 5 (25.0%)
2 SEX ARM A Male 15 (75.0%)
3 SEX ARM B Female 11 (52.4%)
4 SEX ARM B Male 10 (47.6%)
5 SEX ARM C Female 9 (42.9%)
6 SEX ARM C Male 12 (57.1%)
7 SEX ARM D Female 7 (30.4%)
8 SEX ARM D Male 16 (69.6%)
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
# A tibble: 2 × 6
VAR LABEL `ARM A` `ARM B` `ARM C` `ARM D`
<chr> <chr> <chr> <chr> <chr> <chr>
1 SEX Female 5 (25.0%) 11 (52.4%) 9 (42.9%) 7 (30.4%)
2 SEX Male 15 (75.0%) 10 (47.6%) 12 (57.1%) 16 (69.6%)
Sort age groups as desired
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
# A tibble: 2 × 6
VAR LABEL `ARM A` `ARM B` `ARM C` `ARM D`
<chr> <chr> <chr> <chr> <chr> <chr>
1 SEX Male 15 (75.0%) 10 (47.6%) 12 (57.1%) 16 (69.6%)
2 SEX Female 5 (25.0%) 11 (52.4%) 9 (42.9%) 7 (30.4%)
Combine blocks into final data frame
VAR LABEL ARM A ARM B ARM C ARM D
1 AGE n 20 21 21 23
2 AGE Mean (SD) 53.1 (11.90) 47.4 (16.26) 45.7 (14.42) 49.7 (14.32)
3 AGE Median 52.5 46.0 46.0 48.0
4 AGE Q1 - Q3 47.5 - 60.0 35.0 - 61.0 38.0 - 53.0 39.0 - 62.0
5 AGE Min - Max 31 - 73 22 - 73 19 - 71 21 - 75
6 AGECAT 18 to 24 0 ( 0.0%) 1 ( 4.8%) 3 (14.3%) 1 ( 4.3%)
7 AGECAT 25 to 44 4 (20.0%) 8 (38.1%) 4 (19.0%) 7 (30.4%)
8 AGECAT 45 to 64 13 (65.0%) 7 (33.3%) 12 (57.1%) 12 (52.2%)
9 AGECAT >= 65 3 (15.0%) 5 (23.8%) 2 ( 9.5%) 3 (13.0%)
10 SEX Male 15 (75.0%) 10 (47.6%) 12 (57.1%) 16 (69.6%)
11 SEX Female 5 (25.0%) 11 (52.4%) 9 (42.9%) 7 (30.4%)
=========================================================================
Create and print report
=========================================================================
# A report specification: 1 pages
- file_path: 'C:\Users\dbosa\AppData\Local\Temp\RtmpKc23Rz/output/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
- 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.'
- page_footer: left=Date Produced: 07Jan24 18:26 center= right=Page [pg] of [tpg]
- content:
# A table specification:
- data: data.frame 'final' 11 rows 6 cols
- show_cols: all
- use_attributes: all
- stub: VAR LABEL 'Variable' width=2.5 align='left'
- define: VAR 'Variable' dedupe='TRUE'
- define: LABEL 'Demographic Category'
- define: ARM A 'Treatment Group 1'
- define: ARM B 'Treatment Group 2'
- define: ARM C 'Treatment Group 3'
- define: ARM D 'Treatment Group 4'
=========================================================================
Clean Up
=========================================================================
=========================================================================
Log End Time: 2024-01-07 18:26:27.426253
Log Elapsed Time: 0 00:00:01
=========================================================================
dbosak01/fmtr documentation built on June 15, 2024, 4:26 a.m.
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knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) options(rmarkdown.html_vignette.check_title = FALSE)
Program
"Complete Example 1" showed how to create a simple demographics table using the fmtr package and Tidyverse. Here is the same table created with only Base R and the sassy system of packages.
The data for this example has been included in the fmtr package as an
external data file. It may be accessed using the system.file() function
as shown below, or downloaded directly from the fmtr GitHub site
here
library(sassy) # Prepare Log ------------------------------------------------------------- options("logr.autolog" = TRUE, "logr.notes" = FALSE) # Get temp location for log and report output tmp <- tempdir() # Open log lf <- log_open(file.path(tmp, "example2.log")) # Load and Prepare Data --------------------------------------------------- sep("Prepare Data") # Get path to sample data pkg <- system.file("extdata", package = "fmtr") # Define data library libname(sdtm, pkg, "csv") # Prepare data put("Subset DM dataset") dm_mod <- subset(sdtm$DM, ARM != "SCREEN FAILURE", v(USUBJID, SEX, AGE, ARM)) |> put() put("Get ARM population counts") arm_pop <- proc_freq(dm_mod, tables = ARM, output = long, options = v(nocum, nopercent, nonobs)) # Create Format Catalog -------------------------------------------------- sep("Create format catalog") fmts <- fcat(AGECAT = value(condition(x >= 18 & x <= 24, "18 to 24"), condition(x >= 25 & x <= 44, "25 to 44"), condition(x >= 45 & x <= 64, "45 to 64"), condition(x >= 65, ">= 65")), SEX = value(condition(is.na(x), "Missing", order = 3), condition(x == "M", "Male", order = 1), condition(x == "F", "Female", order = 2)), VAR = c("AGE" = "Age", "AGECAT" = "Age Group", "SEX" = "Sex")) numfmts <- fcat(N = "%d", MEAN = "%.1f", STD = "(%04.2f)", MEDIAN = "%d", Q1 = "%.1f", Q3 = "%.1f", MIN = "%d", MAX = "%d", CNT = "%d", PCT = "(%4.1f%%)") numlbls <- c(N = "n", MEANSD = "Mean (SD)", MEDIAN = "Median", Q1Q3 = "Q1 - Q3", MINMAX = "Min - Max") # Age Summary Block ------------------------------------------------------- sep("Create summary statistics for age") age_block <- proc_means(dm_mod, stats = v(n, mean, std, median, q1, q3, min, max), class = ARM, options = v(nway, notype, nofreq)) |> datastep(format = numfmts, keep = v(CLASS, VAR, N, MEANSD, MEDIAN, Q1Q3, MINMAX), { MEANSD <- fapply2(MEAN, STD) Q1Q3 <- fapply2(Q1, Q3, sep = " - ") MINMAX <- fapply2(MIN, MAX, sep = " - ") }) |> proc_transpose(id = CLASS, copy = VAR, var = v(N, MEANSD, MEDIAN, Q1Q3, MINMAX), name = "LABEL") |> datastep({LABEL <- fapply(LABEL, numlbls)}) # Age Group Block ---------------------------------------------------------- sep("Create frequency counts for Age Group") put("Create age group frequency counts") ageg_block <- dm_mod |> datastep({AGECAT <- fapply(AGE, fmts$AGECAT)}) |> proc_freq(tables = AGECAT, by = ARM, options = nonobs) |> datastep(format = numfmts, keep = v(VAR, BY, LABEL, CNTPCT), { LABEL <- CAT CNTPCT <- fapply2(CNT, PCT) }) |> proc_transpose(var = CNTPCT, by = LABEL, copy = VAR, id = BY, options = noname) put("Sort age groups as desired") ageg_block$LABEL <- factor(ageg_block$LABEL, levels = levels(fmts$AGECAT)) ageg_block <- proc_sort(ageg_block, by = LABEL, as.character = TRUE) # Sex Block --------------------------------------------------------------- sep("Create frequency counts for SEX") put("Create sex frequency counts") sex_block <- dm_mod |> datastep({SEX <- fapply(SEX, fmts$SEX)}) |> proc_freq(tables = SEX, by = ARM, options = nonobs) |> datastep(format = numfmts, keep = v(VAR, BY, LABEL, CNTPCT), { LABEL <- CAT CNTPCT <- fapply2(CNT, PCT) }) |> proc_transpose(var = CNTPCT, by = LABEL, copy = VAR, id = BY, options = noname) put("Sort age groups as desired") sex_block$LABEL <- factor(sex_block$LABEL, levels = levels(fmts$SEX)) sex_block <- proc_sort(sex_block, by = LABEL, as.character = TRUE) put("Combine blocks into final data frame") final <- bind_rows(age_block, ageg_block, sex_block) |> put() # Report ------------------------------------------------------------------ sep("Create and print report") # Create Table tbl <- create_table(final, first_row_blank = TRUE, borders = c("top", "bottom")) |> column_defaults(from = `ARM A`, to = `ARM D`, align = "center", width = 1.25) |> stub(vars = v(VAR, LABEL), "Variable", width = 2.5) |> define(VAR, blank_after = TRUE, dedupe = TRUE, label = "Variable", format = fmts$VAR,label_row = TRUE) |> define(LABEL, indent = .25, label = "Demographic Category") |> define(`ARM A`, label = "Treatment Group 1", n = arm_pop["ARM A"]) |> define(`ARM B`, label = "Treatment Group 2", n = arm_pop["ARM B"]) |> define(`ARM C`, label = "Treatment Group 3", n = arm_pop["ARM C"]) |> define(`ARM D`, label = "Treatment Group 4", n = arm_pop["ARM D"]) rpt <- create_report(file.path(tmp, "output/example2.rtf"), output_type = "RTF", font = "Arial") |> set_margins(top = 1, bottom = 1) |> page_header("Sponsor: Company", "Study: ABC") |> titles("Table 1.0", bold = TRUE, blank_row = "none") |> titles("Analysis of Demographic Characteristics", "Safety Population") |> add_content(tbl) |> footnotes("Program: DM_Table.R", "NOTE: Denominator based on number of non-missing responses.") |> page_footer(paste0("Date Produced: ", fapply(Sys.time(), "%d%b%y %H:%M")), right = "Page [pg] of [tpg]") res <- write_report(rpt) # Clean Up ---------------------------------------------------------------- sep("Clean Up") # Close log log_close() # View report # file.show(res$modified_path) # View log # file.show(lf)
Output
Here is the report produced by the above sample program:
Log
Here is the log produced by the above sample program:
=========================================================================
Log Path: C:/Users/dbosa/AppData/Local/Temp/RtmpKc23Rz/log/example2.log
Program Path: C:/Users/dbosa/Documents/.active-rstudio-document
Working Directory: C:/Projects/Archytas/Sassy/Code
User Name: dbosa
R Version: 4.3.2 (2023-10-31 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 reporter_1.4.4 logr_1.3.5 sassy_1.2.1 lubridate_1.9.2
forcats_1.0.0 stringr_1.5.0 purrr_1.0.1 readr_2.1.4 tidyr_1.3.0 tibble_3.2.1
ggplot2_3.4.4 tidyverse_2.0.0 libr_1.2.8 dplyr_1.1.3 fmtr_1.6.2 procs_1.0.4
common_1.1.1
Log Start Time: 2024-01-07 18:26:25.636328
=========================================================================
=========================================================================
Prepare Data
=========================================================================
# library 'sdtm': 1 items
- attributes: csv not loaded
- path: C:/Users/dbosa/AppData/Local/R/win-library/4.3/fmtr/extdata
- items:
Name Extension Rows Cols Size LastModified
1 DM csv 87 24 45.5 Kb 2023-12-16 23:10:51
Subset DM dataset
# A tibble: 85 × 4
USUBJID SEX AGE ARM
<chr> <chr> <dbl> <chr>
1 ABC-01-049 M 39 ARM D
2 ABC-01-050 M 47 ARM B
3 ABC-01-051 M 34 ARM A
4 ABC-01-052 F 45 ARM C
5 ABC-01-053 F 26 ARM B
6 ABC-01-054 M 44 ARM D
7 ABC-01-055 F 47 ARM C
8 ABC-01-056 M 31 ARM A
9 ABC-01-113 M 74 ARM D
10 ABC-01-114 F 72 ARM B
# ℹ 75 more rows
# ℹ Use `print(n = ...)` to see more rows
Get ARM population counts
proc_freq: input data set 85 rows and 4 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
=========================================================================
Create format catalog
=========================================================================
# A user-defined format: 4 conditions
Name Type Expression Label Order
1 obj U x >= 18 & x <= 24 18 to 24 NA
2 obj U x >= 25 & x <= 44 25 to 44 NA
3 obj U x >= 45 & x <= 64 45 to 64 NA
4 obj U x >= 65 >= 65 NA
# A user-defined format: 3 conditions
Name Type Expression Label Order
1 obj U is.na(x) Missing 3
2 obj U x == "M" Male 1
3 obj U x == "F" Female 2
# A format catalog: 3 formats
- $AGECAT: type U, 4 conditions
- $SEX: type U, 3 conditions
- $VAR: type V, 3 elements
# A format catalog: 10 formats
- $N: type S, "%d"
- $MEAN: type S, "%.1f"
- $STD: type S, "(%04.2f)"
- $MEDIAN: type S, "%d"
- $Q1: type S, "%.1f"
- $Q3: type S, "%.1f"
- $MIN: type S, "%d"
- $MAX: type S, "%d"
- $CNT: type S, "%d"
- $PCT: type S, "(%4.1f%%)"
=========================================================================
Create summary statistics for age
=========================================================================
proc_means: input data set 85 rows and 4 columns
class: ARM
var: AGE
stats: n mean std median q1 q3 min max
view: TRUE
output: 1 datasets
CLASS VAR N MEAN STD MEDIAN Q1 Q3 MIN MAX
1 ARM A AGE 20 53.15000 11.89991 52.5 47.5 60 31 73
2 ARM B AGE 21 47.38095 16.25877 46.0 35.0 61 22 73
3 ARM C AGE 21 45.71429 14.41923 46.0 38.0 53 19 71
4 ARM D AGE 23 49.73913 14.32486 48.0 39.0 62 21 75
datastep: columns decreased from 10 to 7
CLASS VAR N MEANSD MEDIAN Q1Q3 MINMAX
1 ARM A AGE 20 53.1 (11.90) 52.5 47.5 - 60.0 31 - 73
2 ARM B AGE 21 47.4 (16.26) 46.0 35.0 - 61.0 22 - 73
3 ARM C AGE 21 45.7 (14.42) 46.0 38.0 - 53.0 19 - 71
4 ARM D AGE 23 49.7 (14.32) 48.0 39.0 - 62.0 21 - 75
proc_transpose: input data set 4 rows and 7 columns
var: N MEANSD MEDIAN Q1Q3 MINMAX
id: CLASS
copy: VAR
name: LABEL
output dataset 5 rows and 6 columns
VAR LABEL ARM A ARM B ARM C ARM D
1 AGE N 20 21 21 23
2 AGE MEANSD 53.1 (11.90) 47.4 (16.26) 45.7 (14.42) 49.7 (14.32)
3 AGE MEDIAN 52.5 46.0 46.0 48.0
4 AGE Q1Q3 47.5 - 60.0 35.0 - 61.0 38.0 - 53.0 39.0 - 62.0
5 AGE MINMAX 31 - 73 22 - 73 19 - 71 21 - 75
datastep: columns started with 6 and ended with 6
VAR LABEL ARM A ARM B ARM C ARM D
1 AGE n 20 21 21 23
2 AGE Mean (SD) 53.1 (11.90) 47.4 (16.26) 45.7 (14.42) 49.7 (14.32)
3 AGE Median 52.5 46.0 46.0 48.0
4 AGE Q1 - Q3 47.5 - 60.0 35.0 - 61.0 38.0 - 53.0 39.0 - 62.0
5 AGE Min - Max 31 - 73 22 - 73 19 - 71 21 - 75
=========================================================================
Create frequency counts for Age Group
=========================================================================
Create age group frequency counts
datastep: columns increased from 4 to 5
# A tibble: 85 × 5
USUBJID SEX AGE ARM AGECAT
<chr> <chr> <dbl> <chr> <chr>
1 ABC-01-049 M 39 ARM D 25 to 44
2 ABC-01-050 M 47 ARM B 45 to 64
3 ABC-01-051 M 34 ARM A 25 to 44
4 ABC-01-052 F 45 ARM C 45 to 64
5 ABC-01-053 F 26 ARM B 25 to 44
6 ABC-01-054 M 44 ARM D 25 to 44
7 ABC-01-055 F 47 ARM C 45 to 64
8 ABC-01-056 M 31 ARM A 25 to 44
9 ABC-01-113 M 74 ARM D >= 65
10 ABC-01-114 F 72 ARM B >= 65
# ℹ 75 more rows
# ℹ Use `print(n = ...)` to see more rows
proc_freq: input data set 85 rows and 5 columns
tables: AGECAT
by: ARM
view: TRUE
output: 1 datasets
# A tibble: 16 × 5
BY VAR CAT CNT PCT
<chr> <chr> <chr> <dbl> <dbl>
1 ARM A AGECAT >= 65 3 15
2 ARM A AGECAT 18 to 24 0 0
3 ARM A AGECAT 25 to 44 4 20
4 ARM A AGECAT 45 to 64 13 65
5 ARM B AGECAT >= 65 5 23.8
6 ARM B AGECAT 18 to 24 1 4.76
7 ARM B AGECAT 25 to 44 8 38.1
8 ARM B AGECAT 45 to 64 7 33.3
9 ARM C AGECAT >= 65 2 9.52
10 ARM C AGECAT 18 to 24 3 14.3
11 ARM C AGECAT 25 to 44 4 19.0
12 ARM C AGECAT 45 to 64 12 57.1
13 ARM D AGECAT >= 65 3 13.0
14 ARM D AGECAT 18 to 24 1 4.35
15 ARM D AGECAT 25 to 44 7 30.4
16 ARM D AGECAT 45 to 64 12 52.2
datastep: columns decreased from 5 to 4
# A tibble: 16 × 4
VAR BY LABEL CNTPCT
<chr> <chr> <chr> <chr>
1 AGECAT ARM A >= 65 3 (15.0%)
2 AGECAT ARM A 18 to 24 0 ( 0.0%)
3 AGECAT ARM A 25 to 44 4 (20.0%)
4 AGECAT ARM A 45 to 64 13 (65.0%)
5 AGECAT ARM B >= 65 5 (23.8%)
6 AGECAT ARM B 18 to 24 1 ( 4.8%)
7 AGECAT ARM B 25 to 44 8 (38.1%)
8 AGECAT ARM B 45 to 64 7 (33.3%)
9 AGECAT ARM C >= 65 2 ( 9.5%)
10 AGECAT ARM C 18 to 24 3 (14.3%)
11 AGECAT ARM C 25 to 44 4 (19.0%)
12 AGECAT ARM C 45 to 64 12 (57.1%)
13 AGECAT ARM D >= 65 3 (13.0%)
14 AGECAT ARM D 18 to 24 1 ( 4.3%)
15 AGECAT ARM D 25 to 44 7 (30.4%)
16 AGECAT ARM D 45 to 64 12 (52.2%)
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
# A tibble: 4 × 6
VAR LABEL `ARM A` `ARM B` `ARM C` `ARM D`
<chr> <chr> <chr> <chr> <chr> <chr>
1 AGECAT >= 65 3 (15.0%) 5 (23.8%) 2 ( 9.5%) 3 (13.0%)
2 AGECAT 18 to 24 0 ( 0.0%) 1 ( 4.8%) 3 (14.3%) 1 ( 4.3%)
3 AGECAT 25 to 44 4 (20.0%) 8 (38.1%) 4 (19.0%) 7 (30.4%)
4 AGECAT 45 to 64 13 (65.0%) 7 (33.3%) 12 (57.1%) 12 (52.2%)
Sort age groups as desired
proc_sort: input data set 4 rows and 6 columns
by: LABEL
keep: VAR LABEL ARM A ARM B ARM C ARM D
order: a
output data set 4 rows and 6 columns
# A tibble: 4 × 6
VAR LABEL `ARM A` `ARM B` `ARM C` `ARM D`
<chr> <chr> <chr> <chr> <chr> <chr>
1 AGECAT 18 to 24 0 ( 0.0%) 1 ( 4.8%) 3 (14.3%) 1 ( 4.3%)
2 AGECAT 25 to 44 4 (20.0%) 8 (38.1%) 4 (19.0%) 7 (30.4%)
3 AGECAT 45 to 64 13 (65.0%) 7 (33.3%) 12 (57.1%) 12 (52.2%)
4 AGECAT >= 65 3 (15.0%) 5 (23.8%) 2 ( 9.5%) 3 (13.0%)
=========================================================================
Create frequency counts for SEX
=========================================================================
Create sex frequency counts
datastep: columns started with 4 and ended with 4
# A tibble: 85 × 4
USUBJID SEX AGE ARM
<chr> <chr> <dbl> <chr>
1 ABC-01-049 Male 39 ARM D
2 ABC-01-050 Male 47 ARM B
3 ABC-01-051 Male 34 ARM A
4 ABC-01-052 Female 45 ARM C
5 ABC-01-053 Female 26 ARM B
6 ABC-01-054 Male 44 ARM D
7 ABC-01-055 Female 47 ARM C
8 ABC-01-056 Male 31 ARM A
9 ABC-01-113 Male 74 ARM D
10 ABC-01-114 Female 72 ARM B
# ℹ 75 more rows
# ℹ Use `print(n = ...)` to see more rows
proc_freq: input data set 85 rows and 4 columns
tables: SEX
by: ARM
view: TRUE
output: 1 datasets
# A tibble: 8 × 5
BY VAR CAT CNT PCT
<chr> <chr> <chr> <dbl> <dbl>
1 ARM A SEX Female 5 25
2 ARM A SEX Male 15 75
3 ARM B SEX Female 11 52.4
4 ARM B SEX Male 10 47.6
5 ARM C SEX Female 9 42.9
6 ARM C SEX Male 12 57.1
7 ARM D SEX Female 7 30.4
8 ARM D SEX Male 16 69.6
datastep: columns decreased from 5 to 4
# A tibble: 8 × 4
VAR BY LABEL CNTPCT
<chr> <chr> <chr> <chr>
1 SEX ARM A Female 5 (25.0%)
2 SEX ARM A Male 15 (75.0%)
3 SEX ARM B Female 11 (52.4%)
4 SEX ARM B Male 10 (47.6%)
5 SEX ARM C Female 9 (42.9%)
6 SEX ARM C Male 12 (57.1%)
7 SEX ARM D Female 7 (30.4%)
8 SEX ARM D Male 16 (69.6%)
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
# A tibble: 2 × 6
VAR LABEL `ARM A` `ARM B` `ARM C` `ARM D`
<chr> <chr> <chr> <chr> <chr> <chr>
1 SEX Female 5 (25.0%) 11 (52.4%) 9 (42.9%) 7 (30.4%)
2 SEX Male 15 (75.0%) 10 (47.6%) 12 (57.1%) 16 (69.6%)
Sort age groups as desired
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
# A tibble: 2 × 6
VAR LABEL `ARM A` `ARM B` `ARM C` `ARM D`
<chr> <chr> <chr> <chr> <chr> <chr>
1 SEX Male 15 (75.0%) 10 (47.6%) 12 (57.1%) 16 (69.6%)
2 SEX Female 5 (25.0%) 11 (52.4%) 9 (42.9%) 7 (30.4%)
Combine blocks into final data frame
VAR LABEL ARM A ARM B ARM C ARM D
1 AGE n 20 21 21 23
2 AGE Mean (SD) 53.1 (11.90) 47.4 (16.26) 45.7 (14.42) 49.7 (14.32)
3 AGE Median 52.5 46.0 46.0 48.0
4 AGE Q1 - Q3 47.5 - 60.0 35.0 - 61.0 38.0 - 53.0 39.0 - 62.0
5 AGE Min - Max 31 - 73 22 - 73 19 - 71 21 - 75
6 AGECAT 18 to 24 0 ( 0.0%) 1 ( 4.8%) 3 (14.3%) 1 ( 4.3%)
7 AGECAT 25 to 44 4 (20.0%) 8 (38.1%) 4 (19.0%) 7 (30.4%)
8 AGECAT 45 to 64 13 (65.0%) 7 (33.3%) 12 (57.1%) 12 (52.2%)
9 AGECAT >= 65 3 (15.0%) 5 (23.8%) 2 ( 9.5%) 3 (13.0%)
10 SEX Male 15 (75.0%) 10 (47.6%) 12 (57.1%) 16 (69.6%)
11 SEX Female 5 (25.0%) 11 (52.4%) 9 (42.9%) 7 (30.4%)
=========================================================================
Create and print report
=========================================================================
# A report specification: 1 pages
- file_path: 'C:\Users\dbosa\AppData\Local\Temp\RtmpKc23Rz/output/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
- 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.'
- page_footer: left=Date Produced: 07Jan24 18:26 center= right=Page [pg] of [tpg]
- content:
# A table specification:
- data: data.frame 'final' 11 rows 6 cols
- show_cols: all
- use_attributes: all
- stub: VAR LABEL 'Variable' width=2.5 align='left'
- define: VAR 'Variable' dedupe='TRUE'
- define: LABEL 'Demographic Category'
- define: ARM A 'Treatment Group 1'
- define: ARM B 'Treatment Group 2'
- define: ARM C 'Treatment Group 3'
- define: ARM D 'Treatment Group 4'
=========================================================================
Clean Up
=========================================================================
=========================================================================
Log End Time: 2024-01-07 18:26:27.426253
Log Elapsed Time: 0 00:00:01
=========================================================================
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