Program
Below is another complete example showing how the reporter package interacts with tidyverse and sassy functions to create an Adverse Events report.
library(tidyverse)
library(sassy)
options("logr.autolog" = TRUE,
"logr.notes" = FALSE)
# Get temp location for log and report output
tmp <- tempdir()
# Get Data ----------------------------------------------------------------
# Open log
lf <- log_open(file.path(tmp, "example3.log"))
sep("Prepare Data")
# Get path to sample data
pkg <- system.file("extdata", package = "reporter")
# Create libname for csv data
libname(sdtm, pkg, "csv", quiet = TRUE)
# Load data into workspace
lib_load(sdtm)
sep("Prepare table data")
dat <- sdtm.AE %>%
inner_join(select(sdtm.DM, USUBJID, ARM, ARMCD),
c("USUBJID" = "USUBJID"),
keep = FALSE) %>%
filter(ARM != "SCREEN FAILURE")
put("Get population counts")
arm_pop <- dat %>%
select(USUBJID, ARM) %>%
distinct() %>%
count(ARM) %>%
deframe() %>% put()
put ("Create lookup for AE severity")
sevn <- c(MILD = 1, MODERATE = 2, SEVERE = 3) %>% put()
put("Subset ADAE for needed rows and columns")
df_sub <- dat %>%
mutate(AESEVN = sevn[AESEV]) %>%
select(USUBJID, ARM, AESEV, AESEVN, AESOC, AEDECOD) %>%
distinct() %>%
put()
# Perform Calculations ----------------------------------------------------
sep("Perform Calculations")
put("Create template for needed ARM columns")
col_template <- paste0(c(rep("ARM A_", 3), rep("ARM B_", 3), rep("ARM C_", 3),
rep("ARM D_", 3)), rep(c(1, 2, 3), 3))
put("Identify and count highest severity grade for each subject")
df1 <- df_sub %>% arrange(USUBJID, AESOC, AEDECOD, AESEVN) %>%
group_by(USUBJID, ARM, AESOC, AEDECOD) %>%
mutate(max_sev = ifelse(row_number() == n(), TRUE, FALSE)) %>%
filter(max_sev == TRUE) %>%
group_by(ARM, AESOC, AEDECOD, AESEVN) %>%
summarize(cnt = n()) %>%
pivot_wider(names_from = c(ARM, AESEVN),
values_from = cnt,
values_fill = 0) %>%
put()
put("Fill in missing columns where there were no events.")
for (nm in col_template) {
if (!nm %in% names(df1))
df1[[nm]] <- 0
}
put(df1)
put("Format counts and percents for each column")
df_events <- df1 %>%
transmute(AESOC = AESOC,
AEDECOD = str_to_title(AEDECOD),
`ARM A_1` = fmt_cnt_pct(`ARM A_1`, arm_pop["ARM A"]),
`ARM A_2` = fmt_cnt_pct(`ARM A_2`, arm_pop["ARM A"]),
`ARM A_3` = fmt_cnt_pct(`ARM A_3`, arm_pop["ARM A"]),
`ARM B_1` = fmt_cnt_pct(`ARM B_1`, arm_pop["ARM B"]),
`ARM B_2` = fmt_cnt_pct(`ARM B_2`, arm_pop["ARM B"]),
`ARM B_3` = fmt_cnt_pct(`ARM B_3`, arm_pop["ARM B"]),
`ARM C_1` = fmt_cnt_pct(`ARM C_1`, arm_pop["ARM C"]),
`ARM C_2` = fmt_cnt_pct(`ARM C_2`, arm_pop["ARM C"]),
`ARM C_3` = fmt_cnt_pct(`ARM C_3`, arm_pop["ARM C"]),
`ARM D_1` = fmt_cnt_pct(`ARM D_1`, arm_pop["ARM D"]),
`ARM D_2` = fmt_cnt_pct(`ARM D_2`, arm_pop["ARM D"]),
`ARM D_3` = fmt_cnt_pct(`ARM D_3`, arm_pop["ARM D"])) %>%
arrange(AESOC, AEDECOD) %>%
ungroup() %>%
put()
put("Get counts for All Adverse Events")
df2 <- df_sub %>%
select(USUBJID, ARM, AESEVN) %>%
arrange(USUBJID, AESEVN) %>%
group_by(USUBJID) %>%
mutate(max_sev = ifelse(row_number() == n(), TRUE, FALSE)) %>%
filter(max_sev == TRUE) %>%
group_by(ARM, AESEVN) %>%
summarize(cnt = n()) %>%
pivot_wider(names_from = c(ARM, AESEVN),
values_from = cnt,
values_fill = 0) %>%
ungroup() %>%
put()
put("Fill in missing columns where there were no events.")
for (nm in col_template) {
if (!nm %in% names(df2))
df2[[nm]] <- 0
}
put(df2)
put("Format counts and percents for all adverse events.")
df_all <- df2 %>%
transmute(AESOC = "All System Organ Classes",
AEDECOD = "All Adverse Events",
`ARM A_1` = fmt_cnt_pct(`ARM A_1`, arm_pop["ARM A"]),
`ARM A_2` = fmt_cnt_pct(`ARM A_2`, arm_pop["ARM A"]),
`ARM A_3` = fmt_cnt_pct(`ARM A_3`, arm_pop["ARM A"]),
`ARM B_1` = fmt_cnt_pct(`ARM B_1`, arm_pop["ARM B"]),
`ARM B_2` = fmt_cnt_pct(`ARM B_2`, arm_pop["ARM B"]),
`ARM B_3` = fmt_cnt_pct(`ARM B_3`, arm_pop["ARM B"]),
`ARM C_1` = fmt_cnt_pct(`ARM C_1`, arm_pop["ARM C"]),
`ARM C_2` = fmt_cnt_pct(`ARM C_2`, arm_pop["ARM C"]),
`ARM C_3` = fmt_cnt_pct(`ARM C_3`, arm_pop["ARM C"]),
`ARM D_1` = fmt_cnt_pct(`ARM D_1`, arm_pop["ARM D"]),
`ARM D_2` = fmt_cnt_pct(`ARM D_2`, arm_pop["ARM D"]),
`ARM D_3` = fmt_cnt_pct(`ARM D_3`, arm_pop["ARM D"])) %>%
put()
# Final Data --------------------------------------------------------------
sep("Create final data frame")
final <- bind_rows(df_all, df_events) %>% put()
# Print Report ----------------------------------------------------------
sep("Create and print report")
put("Create table object")
tbl <- create_table(final, first_row_blank = TRUE, width = 9) %>%
column_defaults(from = `ARM A_1`, to = `ARM D_3`, width = 1) %>%
spanning_header("ARM A_1", "ARM A_3", label = "ARM A", n = arm_pop["ARM A"]) %>%
spanning_header("ARM B_1", "ARM B_3", label = "ARM B", n = arm_pop["ARM B"]) %>%
spanning_header("ARM C_1", "ARM C_3", label = "ARM C", n = arm_pop["ARM C"]) %>%
spanning_header("ARM D_1", "ARM D_3", label = "ARM D", n = arm_pop["ARM D"]) %>%
stub(vars = c("AESOC", "AEDECOD"), label = "System Organ Class\n Preferred Term", width = 5) %>%
define(AESOC, blank_after = TRUE, label_row = TRUE) %>%
define(AEDECOD, indent = .25) %>%
define(`ARM A_1`, align = "center", label = "Mild") %>%
define(`ARM A_2`, align = "center", label = "Moderate") %>%
define(`ARM A_3`, align = "center", label = "Severe") %>%
define(`ARM B_1`, align = "center", label = "Mild", page_wrap = TRUE) %>%
define(`ARM B_2`, align = "center", label = "Moderate") %>%
define(`ARM B_3`, align = "center", label = "Severe") %>%
define(`ARM C_1`, align = "center", label = "Mild", page_wrap = TRUE) %>%
define(`ARM C_2`, align = "center", label = "Moderate") %>%
define(`ARM C_3`, align = "center", label = "Severe") %>%
define(`ARM D_1`, align = "center", label = "Mild", page_wrap = TRUE) %>%
define(`ARM D_2`, align = "center", label = "Moderate") %>%
define(`ARM D_3`, align = "center", label = "Severe")
put("Create report object")
rpt <- create_report(file.path(tmp, "output/example3.rtf"), output_type = "RTF",
font = "Arial") %>%
options_fixed(font_size = 10) %>%
page_header("Sponsor: Company", "Study: ABC") %>%
titles("Table 5.0", "Adverse Events by Maximum Severity", bold = TRUE,
font_size = 12) %>%
add_content(tbl) %>%
footnotes("Program: AE_Table.R",
"Note: Adverse events were coded using MedDRA Version 9.1") %>%
page_footer(Sys.time(), "Confidential", "Page [pg] of [tpg]")
put("Print report")
res <- write_report(rpt) %>% put()
# Clean Up ----------------------------------------------------------------
sep("Clean Up")
# Remove library from workspace
lib_unload(sdtm)
# Close log
log_close()
# View log
writeLines(readLines(lf, encoding = "UTF-8"))
# View report
# file.show(res$file_path)Log
The above program produces the following log:
=========================================================================
Log Path: C:/Users/dbosa/AppData/Local/Temp/RtmpgrpGQ1/log/example3.log
Program Path: C:\packages\Testing\reporter_example3.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 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
magrittr_2.0.1 reporter_1.2.6 libr_1.2.1 fmtr_1.5.4 logr_1.2.7
sassy_1.0.5
Log Start Time: 2021-11-21 14:58:57
=========================================================================
=========================================================================
Prepare Data
=========================================================================
# library 'sdtm': 8 items
- attributes: csv not loaded
- path: C:/Users/dbosa/Documents/R/win-library/4.1/reporter/extdata
- items:
Name Extension Rows Cols Size LastModified
1 AE csv 150 27 88.3 Kb 2021-10-08 15:02:15
2 DA csv 3587 18 528.1 Kb 2021-10-08 15:02:15
3 DM csv 87 24 45.4 Kb 2021-10-08 15:02:15
4 DS csv 174 9 33.9 Kb 2021-10-08 15:02:15
5 EX csv 84 11 26.2 Kb 2021-10-08 15:02:15
6 IE csv 2 14 13.2 Kb 2021-10-08 15:02:15
7 SV csv 685 10 70.2 Kb 2021-10-08 15:02:15
8 VS csv 3358 17 467.3 Kb 2021-10-08 15:02:15
lib_load: library 'sdtm' loaded
=========================================================================
Prepare table data
=========================================================================
select: dropped 21 variables (STUDYID, DOMAIN, SUBJID, RFSTDTC, RFENDTC, <U+0085>)
inner_join: added 2 columns (ARM, ARMCD)
> rows only in x ( 0)
> rows only in y ( 30)
> matched rows 150
> =====
> rows total 150
filter: removed 5 rows (3%), 145 rows remaining
Get population counts
select: dropped 27 variables (STUDYID, DOMAIN, AESEQ, AETERM, AELLT, <U+0085>)
distinct: removed 90 rows (62%), 55 rows remaining
count: now 4 rows and 2 columns, ungrouped
ARM A ARM B ARM C ARM D
14 14 14 13
Create lookup for AE severity
MILD MODERATE SEVERE
1 2 3
Subset ADAE for needed rows and columns
mutate: new variable 'AESEVN' (double) with 3 unique values and 0% NA
select: dropped 24 variables (STUDYID, DOMAIN, AESEQ, AETERM, AELLT, <U+0085>)
distinct: removed 18 rows (12%), 127 rows remaining
# A tibble: 127 x 6
USUBJID ARM AESEV AESEVN AESOC AEDECOD
<chr> <chr> <chr> <dbl> <chr> <chr>
1 ABC-01-049 ARM D MODERATE 2 Investigations BLOOD GLUCOSE INCREA~
2 ABC-01-049 ARM D MODERATE 2 Investigations BLOOD TRIGLYCERIDES ~
3 ABC-01-049 ARM D MILD 1 Nervous system disorders HEADACHE
4 ABC-01-049 ARM D MODERATE 2 Investigations LABORATORY TEST ABNO~
5 ABC-01-049 ARM D MILD 1 Musculoskeletal and connectiv~ MUSCULOSKELETAL DISC~
6 ABC-01-050 ARM B MILD 1 Skin and subcutaneous tissue ~ RASH
7 ABC-01-050 ARM B MILD 1 Respiratory, thoracic and med~ UPPER RESPIRATORY TR~
8 ABC-01-051 ARM A MILD 1 Nervous system disorders HEADACHE
9 ABC-01-051 ARM A MILD 1 General disorders and adminis~ INFLUENZA LIKE ILLNE~
10 ABC-01-051 ARM A MILD 1 Respiratory, thoracic and med~ LOWER RESPIRATORY TR~
# ... with 117 more rows
=========================================================================
Perform Calculations
=========================================================================
Create template for needed ARM columns
Identify and count highest severity grade for each subject
group_by: 4 grouping variables (USUBJID, ARM, AESOC, AEDECOD)
mutate (grouped): new variable 'max_sev' (logical) with 2 unique values and 0% NA
filter (grouped): removed one row (1%), 126 rows remaining
group_by: 4 grouping variables (ARM, AESOC, AEDECOD, AESEVN)
summarize: now 101 rows and 5 columns, 3 group variables remaining (ARM, AESOC, AEDECOD)
pivot_wider: reorganized (ARM, AESEVN, cnt) into (ARM A_2, ARM A_1, ARM B_1, ARM B_2, ARM B_3, <U+0085>) [was 101x5, now 73x11]
# A tibble: 73 x 11
# Groups: AESOC, AEDECOD [73]
AESOC AEDECOD `ARM A_2` `ARM A_1` `ARM B_1` `ARM B_2` `ARM B_3` `ARM C_2` `ARM C_1`
<chr> <chr> <int> <int> <int> <int> <int> <int> <int>
1 Gastro~ TOOTHA~ 1 0 0 0 0 0 0
2 Genera~ INFLUE~ 0 1 0 0 0 0 0
3 Infect~ GASTRO~ 1 0 0 0 0 0 0
4 Infect~ INFLUE~ 1 0 0 0 0 0 0
5 Infect~ ORAL C~ 1 0 0 0 0 0 0
6 Infect~ PNEUMO~ 1 0 0 0 0 0 0
7 Infect~ RESPIR~ 0 1 0 0 0 1 2
8 Infect~ SINUSI~ 0 1 0 0 0 0 0
9 Infect~ TOOTH ~ 1 0 0 0 0 0 0
10 Infect~ UPPER ~ 0 4 5 1 0 0 3
# ... with 63 more rows, and 2 more variables: ARM D_1 <int>, ARM D_2 <int>
Fill in missing columns where there were no events.
# A tibble: 73 x 14
# Groups: AESOC, AEDECOD [73]
AESOC AEDECOD `ARM A_2` `ARM A_1` `ARM B_1` `ARM B_2` `ARM B_3` `ARM C_2` `ARM C_1`
<chr> <chr> <int> <int> <int> <int> <int> <int> <int>
1 Gastro~ TOOTHA~ 1 0 0 0 0 0 0
2 Genera~ INFLUE~ 0 1 0 0 0 0 0
3 Infect~ GASTRO~ 1 0 0 0 0 0 0
4 Infect~ INFLUE~ 1 0 0 0 0 0 0
5 Infect~ ORAL C~ 1 0 0 0 0 0 0
6 Infect~ PNEUMO~ 1 0 0 0 0 0 0
7 Infect~ RESPIR~ 0 1 0 0 0 1 2
8 Infect~ SINUSI~ 0 1 0 0 0 0 0
9 Infect~ TOOTH ~ 1 0 0 0 0 0 0
10 Infect~ UPPER ~ 0 4 5 1 0 0 3
# ... with 63 more rows, and 5 more variables: ARM D_1 <int>, ARM D_2 <int>,
# ARM A_3 <dbl>, ARM C_3 <dbl>, ARM D_3 <dbl>
Format counts and percents for each column
transmute (grouped): changed 73 values (100%) of 'AEDECOD' (0 new NA)
converted 'ARM A_1' from integer to character (0 new NA)
converted 'ARM A_2' from integer to character (0 new NA)
converted 'ARM A_3' from double to character (0 new NA)
converted 'ARM B_1' from integer to character (0 new NA)
converted 'ARM B_2' from integer to character (0 new NA)
converted 'ARM B_3' from integer to character (0 new NA)
converted 'ARM C_1' from integer to character (0 new NA)
converted 'ARM C_2' from integer to character (0 new NA)
converted 'ARM C_3' from double to character (0 new NA)
converted 'ARM D_1' from integer to character (0 new NA)
converted 'ARM D_2' from integer to character (0 new NA)
converted 'ARM D_3' from double to character (0 new NA)
ungroup: no grouping variables
# A tibble: 73 x 14
AESOC AEDECOD `ARM A_1` `ARM A_2` `ARM A_3` `ARM B_1` `ARM B_2` `ARM B_3` `ARM C_1`
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 Blood ~ Neutro~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~
2 Cardia~ Palpit~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~
3 Cardia~ Sinus ~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~
4 Congen~ Dermoi~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 1 ( 7.1~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~
5 Ear an~ Vertigo 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 1 ( 7.1~ 0 ( 0.0~ 0 ( 0.0~
6 Endocr~ Parath~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 1 ( 7.1~
7 Gastro~ Diarrh~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 1 ( 7.1~ 1 ( 7.1~ 0 ( 0.0~ 0 ( 0.0~
8 Gastro~ Food P~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 1 ( 7.1~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~
9 Gastro~ Tootha~ 0 ( 0.0~ 1 ( 7.1~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~
10 Gastro~ Vomiti~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 2 ( 14.3~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~
# ... with 63 more rows, and 5 more variables: ARM C_2 <chr>, ARM C_3 <chr>,
# ARM D_1 <chr>, ARM D_2 <chr>, ARM D_3 <chr>
Get counts for All Adverse Events
select: dropped 3 variables (AESEV, AESOC, AEDECOD)
group_by: one grouping variable (USUBJID)
mutate (grouped): new variable 'max_sev' (logical) with 2 unique values and 0% NA
filter (grouped): removed 72 rows (57%), 55 rows remaining
group_by: 2 grouping variables (ARM, AESEVN)
summarize: now 9 rows and 3 columns, one group variable remaining (ARM)
pivot_wider: reorganized (ARM, AESEVN, cnt) into (ARM A_1, ARM A_2, ARM B_1, ARM B_2, ARM B_3, <U+0085>) [was 9x3, now 1x9]
ungroup: no grouping variables
# A tibble: 1 x 9
`ARM A_1` `ARM A_2` `ARM B_1` `ARM B_2` `ARM B_3` `ARM C_1` `ARM C_2` `ARM D_1`
<int> <int> <int> <int> <int> <int> <int> <int>
1 6 8 8 4 2 8 6 7
# ... with 1 more variable: ARM D_2 <int>
Fill in missing columns where there were no events.
# A tibble: 1 x 12
`ARM A_1` `ARM A_2` `ARM B_1` `ARM B_2` `ARM B_3` `ARM C_1` `ARM C_2` `ARM D_1`
<int> <int> <int> <int> <int> <int> <int> <int>
1 6 8 8 4 2 8 6 7
# ... with 4 more variables: ARM D_2 <int>, ARM A_3 <dbl>, ARM C_3 <dbl>, ARM D_3 <dbl>
Format counts and percents for all adverse events.
transmute: new variable 'AESOC' (character) with one unique value and 0% NA
new variable 'AEDECOD' (character) with one unique value and 0% NA
converted 'ARM A_1' from integer to character (0 new NA)
converted 'ARM A_2' from integer to character (0 new NA)
converted 'ARM A_3' from double to character (0 new NA)
converted 'ARM B_1' from integer to character (0 new NA)
converted 'ARM B_2' from integer to character (0 new NA)
converted 'ARM B_3' from integer to character (0 new NA)
converted 'ARM C_1' from integer to character (0 new NA)
converted 'ARM C_2' from integer to character (0 new NA)
converted 'ARM C_3' from double to character (0 new NA)
converted 'ARM D_1' from integer to character (0 new NA)
converted 'ARM D_2' from integer to character (0 new NA)
converted 'ARM D_3' from double to character (0 new NA)
# A tibble: 1 x 14
AESOC AEDECOD `ARM A_1` `ARM A_2` `ARM A_3` `ARM B_1` `ARM B_2` `ARM B_3` `ARM C_1`
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 All Sy~ All Adv~ 6 ( 42.9~ 8 ( 57.1~ 0 ( 0.0~ 8 ( 57.1~ 4 ( 28.6~ 2 ( 14.3~ 8 ( 57.1~
# ... with 5 more variables: ARM C_2 <chr>, ARM C_3 <chr>, ARM D_1 <chr>,
# ARM D_2 <chr>, ARM D_3 <chr>
=========================================================================
Create final data frame
=========================================================================
# A tibble: 74 x 14
AESOC AEDECOD `ARM A_1` `ARM A_2` `ARM A_3` `ARM B_1` `ARM B_2` `ARM B_3` `ARM C_1`
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 All Sy~ All Ad~ 6 ( 42.9~ 8 ( 57.1~ 0 ( 0.0~ 8 ( 57.1~ 4 ( 28.6~ 2 ( 14.3~ 8 ( 57.1~
2 Blood ~ Neutro~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~
3 Cardia~ Palpit~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~
4 Cardia~ Sinus ~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~
5 Congen~ Dermoi~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 1 ( 7.1~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~
6 Ear an~ Vertigo 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 1 ( 7.1~ 0 ( 0.0~ 0 ( 0.0~
7 Endocr~ Parath~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 1 ( 7.1~
8 Gastro~ Diarrh~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 1 ( 7.1~ 1 ( 7.1~ 0 ( 0.0~ 0 ( 0.0~
9 Gastro~ Food P~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 1 ( 7.1~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~
10 Gastro~ Tootha~ 0 ( 0.0~ 1 ( 7.1~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~ 0 ( 0.0~
# ... with 64 more rows, and 5 more variables: ARM C_2 <chr>, ARM C_3 <chr>,
# ARM D_1 <chr>, ARM D_2 <chr>, ARM D_3 <chr>
=========================================================================
Create and print report
=========================================================================
Create table object
Create report object
Print report
# A report specification: 16 pages
- file_path: 'C:\Users\dbosa\AppData\Local\Temp\RtmpgrpGQ1/output/example3.rtf'
- output_type: RTF
- units: inches
- orientation: landscape
- margins: top 0.5 bottom 0.5 left 1 right 1
- line size/count: 9/46
- page_header: left=Sponsor: Company right=Study: ABC
- title 1: 'Table 5.0'
- title 2: 'Adverse Events by Maximum Severity'
- footnote 1: 'Program: AE_Table.R'
- footnote 2: 'Note: Adverse events were coded using MedDRA Version 9.1'
- page_footer: left=2021-11-21 14:58:58 center=Confidential right=Page [pg] of [tpg]
- content:
# A table specification:
- data: tibble 'final' 74 rows 14 cols
- show_cols: all
- use_attributes: all
- width: 9
- spanning_header: from='ARM A_1' to='ARM A_3' 'ARM A' level=1
- spanning_header: from='ARM B_1' to='ARM B_3' 'ARM B' level=1
- spanning_header: from='ARM C_1' to='ARM C_3' 'ARM C' level=1
- spanning_header: from='ARM D_1' to='ARM D_3' 'ARM D' level=1
- stub: AESOC AEDECOD 'System Organ Class
Preferred Term' width=5 align='left'
- define: AESOC dedupe='TRUE'
- define: AEDECOD
- define: ARM A_1 'Mild' align='center'
- define: ARM A_2 'Moderate' align='center'
- define: ARM A_3 'Severe' align='center'
- define: ARM B_1 'Mild' align='center' page_wrap='TRUE'
- define: ARM B_2 'Moderate' align='center'
- define: ARM B_3 'Severe' align='center'
- define: ARM C_1 'Mild' align='center' page_wrap='TRUE'
- define: ARM C_2 'Moderate' align='center'
- define: ARM C_3 'Severe' align='center'
- define: ARM D_1 'Mild' align='center' page_wrap='TRUE'
- define: ARM D_2 'Moderate' align='center'
- define: ARM D_3 'Severe' align='center'
# A report specification: 16 pages
- file_path: 'C:\Users\dbosa\AppData\Local\Temp\RtmpgrpGQ1/output/example3.rtf'
- output_type: RTF
- units: inches
- orientation: landscape
- margins: top 0.5 bottom 0.5 left 1 right 1
- line size/count: 9/46
- page_header: left=Sponsor: Company right=Study: ABC
- title 1: 'Table 5.0'
- title 2: 'Adverse Events by Maximum Severity'
- footnote 1: 'Program: AE_Table.R'
- footnote 2: 'Note: Adverse events were coded using MedDRA Version 9.1'
- page_footer: left=2021-11-21 14:58:58 center=Confidential right=Page [pg] of [tpg]
- content:
# A table specification:
- data: tibble 'final' 74 rows 14 cols
- show_cols: all
- use_attributes: all
- width: 9
- spanning_header: from='ARM A_1' to='ARM A_3' 'ARM A' level=1
- spanning_header: from='ARM B_1' to='ARM B_3' 'ARM B' level=1
- spanning_header: from='ARM C_1' to='ARM C_3' 'ARM C' level=1
- spanning_header: from='ARM D_1' to='ARM D_3' 'ARM D' level=1
- stub: AESOC AEDECOD 'System Organ Class
Preferred Term' width=5 align='left'
- define: AESOC dedupe='TRUE'
- define: AEDECOD
- define: ARM A_1 'Mild' align='center'
- define: ARM A_2 'Moderate' align='center'
- define: ARM A_3 'Severe' align='center'
- define: ARM B_1 'Mild' align='center' page_wrap='TRUE'
- define: ARM B_2 'Moderate' align='center'
- define: ARM B_3 'Severe' align='center'
- define: ARM C_1 'Mild' align='center' page_wrap='TRUE'
- define: ARM C_2 'Moderate' align='center'
- define: ARM C_3 'Severe' align='center'
- define: ARM D_1 'Mild' align='center' page_wrap='TRUE'
- define: ARM D_2 'Moderate' align='center'
- define: ARM D_3 'Severe' align='center'
=========================================================================
Clean Up
=========================================================================
lib_sync: synchronized data in library 'sdtm'
lib_unload: library 'sdtm' unloaded
=========================================================================
Log End Time: 2021-11-21 14:58:59
Log Elapsed Time: 0 00:00:01
=========================================================================
Output
Here are the first 4 pages of the report produced by the sample program above:
Next: Complete Example 4