Output
Here is the report produced by the above sample program:
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The previous examples in the fmtr documentation were intentionally simplified to focus on the workings of a particular function. It is helpful to also view fmtr functions in the context of a complete program. The following example shows a complete program.
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(tidyverse)
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, "example1.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
dm_mod <- sdtm$DM %>%
select(USUBJID, SEX, AGE, ARM) %>%
filter(ARM != "SCREEN FAILURE") %>%
put()
put("Get ARM population counts")
arm_pop <- count(dm_mod, ARM) %>% deframe() %>% put()
# Create Format Catalog --------------------------------------------------
sep("Create format catalog")
fmts <- fcat(AGECAT = value(condition(x >= 18 & x <= 24, "18 to 24"),
condition(x >= 25 & x <= 44, "25 to 44"),
condition(x >= 45 & x <= 64, "45 to 64"),
condition(x >= 65, ">= 65"),
condition(TRUE, "Other")),
SEX = value(condition(is.na(x), "Missing"),
condition(x == "M", "Male"),
condition(x == "F", "Female"),
condition(TRUE, "Other")),
VAR = c("AGE" = "Age",
"AGECAT" = "Age Group",
"SEX" = "Sex"))
put(fmts)
# Age Summary Block -------------------------------------------------------
sep("Create summary statistics for age")
age_block <-
dm_mod %>%
group_by(ARM) %>%
summarise( N = fmt_n(AGE),
`Mean (SD)` = fmt_mean_sd(AGE),
Median = fmt_median(AGE),
`Q1 - Q3` = fmt_quantile_range(AGE),
Range = fmt_range(AGE)) %>%
pivot_longer(-ARM,
names_to = "label",
values_to = "value") %>%
pivot_wider(names_from = ARM,
values_from = "value") %>%
add_column(var = "AGE", .before = "label") %>%
put()
# Age Group Block ----------------------------------------------------------
sep("Create frequency counts for Age Group")
put("Create age group frequency counts")
ageg_block <-
dm_mod %>%
mutate(AGECAT = fapply(AGE, fmts$AGECAT)) %>%
select(ARM, AGECAT) %>%
group_by(ARM, AGECAT) %>%
summarize(n = n()) %>%
pivot_wider(names_from = ARM,
values_from = n,
values_fill = 0) %>%
transmute(var = "AGECAT",
label = factor(AGECAT, levels = c("18 to 24",
"25 to 44",
"45 to 64",
">= 65")),
`ARM A` = fmt_cnt_pct(`ARM A`, arm_pop["ARM A"]),
`ARM B` = fmt_cnt_pct(`ARM B`, arm_pop["ARM B"]),
`ARM C` = fmt_cnt_pct(`ARM C`, arm_pop["ARM C"]),
`ARM D` = fmt_cnt_pct(`ARM D`, arm_pop["ARM D"])) %>%
arrange(label) %>%
put()
# Sex Block ---------------------------------------------------------------
sep("Create frequency counts for SEX")
# Create sex frequency counts
sex_block <-
dm_mod %>%
select(ARM, SEX) %>%
group_by(ARM, SEX) %>%
summarize(n = n()) %>%
pivot_wider(names_from = ARM,
values_from = n,
values_fill = 0) %>%
transmute(var = "SEX",
label = fct_relevel(SEX, "M", "F"),
`ARM A` = fmt_cnt_pct(`ARM A`, arm_pop["ARM A"]),
`ARM B` = fmt_cnt_pct(`ARM B`, arm_pop["ARM B"]),
`ARM C` = fmt_cnt_pct(`ARM C`, arm_pop["ARM C"]),
`ARM D` = fmt_cnt_pct(`ARM D`, arm_pop["ARM D"])) %>%
arrange(label) %>%
mutate(label = fapply(label, fmts$SEX)) %>%
put()
put("Combine blocks into final data frame")
final <- bind_rows(age_block, ageg_block, sex_block) %>% put()
# Report ------------------------------------------------------------------
sep("Create and print report")
# Create Table
tbl <- create_table(final, first_row_blank = TRUE, borders = c("top", "bottom")) %>%
column_defaults(from = `ARM A`, to = `ARM D`, align = "center", width = 1.25) %>%
stub(vars = c("var", "label"), "Variable", width = 2.5) %>%
define(var, blank_after = TRUE, dedupe = TRUE, label = "Variable",
format = fmts$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/example1.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)Here is the report produced by the above sample program:
Here is the log produced by the above sample program:
=========================================================================
Log Path: C:/Users/dbosa/AppData/Local/Temp/RtmpcV9Bys/log/example1.log
Program Path: C:\packages\Testing\fmtr_example1.R
Working Directory: C:/packages/Testing
User Name: dbosa
R Version: 4.1.2 (2021-11-01)
Machine: SOCRATES x86-64
Operating System: Windows 10 x64 build 19041
Base Packages: stats graphics grDevices utils datasets methods base
Other Packages: tidylog_1.0.2 reporter_1.2.6 libr_1.2.1 fmtr_1.5.3 logr_1.2.7
sassy_1.0.5 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
readr_2.0.2 tidyr_1.1.4 tibble_3.1.5 ggplot2_3.3.5 tidyverse_1.3.1
Log Start Time: 2021-11-17 10:32:36
=========================================================================
=========================================================================
Prepare Data
=========================================================================
# library 'sdtm': 1 items
- attributes: csv not loaded
- path: C:/Users/dbosa/Documents/R/win-library/4.1/fmtr/extdata
- items:
Name Extension Rows Cols Size LastModified
1 DM csv 87 24 45.4 Kb 2021-11-16 10:34:25
lib_load: library 'sdtm' loaded
select: dropped 20 variables (STUDYID, DOMAIN, SUBJID, RFSTDTC, RFENDTC, <U+0085>)
filter: removed 2 rows (2%), 85 rows remaining
# A tibble: 85 x 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
# ... with 75 more rows
Get ARM population counts
count: now 4 rows and 2 columns, ungrouped
ARM A ARM B ARM C ARM D
20 21 21 23
=========================================================================
Create format catalog
=========================================================================
# A format catalog: 3 formats
- $AGECAT: type U, 5 conditions
- $SEX: type U, 4 conditions
- $VAR: type V, 3 elements
=========================================================================
Create summary statistics for age
=========================================================================
group_by: one grouping variable (ARM)
summarise: now 4 rows and 6 columns, ungrouped
pivot_longer: reorganized (N, Mean (SD), Median, Q1 - Q3, Range) into (label, value) [was 4x6, now 20x3]
pivot_wider: reorganized (ARM, value) into (ARM A, ARM B, ARM C, ARM D) [was 20x3, now 5x5]
# A tibble: 5 x 6
var label `ARM A` `ARM B` `ARM C` `ARM D`
<chr> <chr> <chr> <chr> <chr> <chr>
1 AGE N 20 21 21 23
2 AGE Mean (SD) 53.1 (11.9) 47.4 (16.3) 45.7 (14.4) 49.7 (14.3)
3 AGE Median 52.5 46.0 46.0 48.0
4 AGE Q1 - Q3 47.8 - 60.0 35.0 - 61.0 38.0 - 53.0 39.0 - 60.5
5 AGE Range 31 - 73 22 - 73 19 - 71 21 - 75
=========================================================================
Create frequency counts for Age Group
=========================================================================
Create age group frequency counts
mutate: new variable 'AGECAT' (character) with 4 unique values and 0% NA
select: dropped 3 variables (USUBJID, SEX, AGE)
group_by: 2 grouping variables (ARM, AGECAT)
summarize: now 15 rows and 3 columns, one group variable remaining (ARM)
pivot_wider: reorganized (ARM, n) into (ARM A, ARM B, ARM C, ARM D) [was 15x3, now 4x5]
transmute: dropped one variable (AGECAT)
new variable 'var' (character) with one unique value and 0% NA
new variable 'label' (factor) with 4 unique values and 0% NA
converted 'ARM A' from integer to character (0 new NA)
converted 'ARM B' from integer to character (0 new NA)
converted 'ARM C' from integer to character (0 new NA)
converted 'ARM D' from integer to character (0 new NA)
# A tibble: 4 x 6
var label `ARM A` `ARM B` `ARM C` `ARM D`
<chr> <fct> <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
=========================================================================
select: dropped 2 variables (USUBJID, AGE)
group_by: 2 grouping variables (ARM, SEX)
summarize: now 8 rows and 3 columns, one group variable remaining (ARM)
pivot_wider: reorganized (ARM, n) into (ARM A, ARM B, ARM C, ARM D) [was 8x3, now 2x5]
transmute: dropped one variable (SEX)
new variable 'var' (character) with one unique value and 0% NA
new variable 'label' (factor) with 2 unique values and 0% NA
converted 'ARM A' from integer to character (0 new NA)
converted 'ARM B' from integer to character (0 new NA)
converted 'ARM C' from integer to character (0 new NA)
converted 'ARM D' from integer to character (0 new NA)
mutate: converted 'label' from factor to character (0 new NA)
# A tibble: 2 x 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
# A tibble: 11 x 6
var label `ARM A` `ARM B` `ARM C` `ARM D`
<chr> <chr> <chr> <chr> <chr> <chr>
1 AGE N 20 21 21 23
2 AGE Mean (SD) 53.1 (11.9) 47.4 (16.3) 45.7 (14.4) 49.7 (14.3)
3 AGE Median 52.5 46.0 46.0 48.0
4 AGE Q1 - Q3 47.8 - 60.0 35.0 - 61.0 38.0 - 53.0 39.0 - 60.5
5 AGE Range 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: 'output/example1.rtf'
- output_type: RTF
- units: inches
- orientation: landscape
- margins: top 1 bottom 1 left 1 right 1
- line size/count: 9/40
- 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: 17Nov21 10:32 center= right=Page [pg] of [tpg]
- content:
# A table specification:
- data: tibble '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
=========================================================================
lib_sync: synchronized data in library 'sdtm'
lib_unload: library 'sdtm' unloaded
=========================================================================
Log End Time: 2021-11-17 10:32:36
Log Elapsed Time: 0 00:00:00
=========================================================================These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.