Output
Here is the report produced by the sample program above:
Next: Complete Example 3
Below is another complete example showing how the reporter package interacts with tidyverse and sassy functions to create a statistical report.
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, "example2.log"))
# Load and Prepare Data ---------------------------------------------------
sep("Prepare Data")
# Get path to sample data
pkg <- system.file("extdata", package = "reporter")
# Define data library
libname(sdtm, pkg, "csv")
# Loads data into workspace
lib_load(sdtm)
# Prepare data
dm_mod <- sdtm.DM %>%
select(USUBJID, SEX, AGE, ARM) %>%
filter(ARM != "SCREEN FAILURE") %>%
datastep({
if (AGE >= 18 & AGE <= 24)
AGECAT = "18 to 24"
else if (AGE >= 25 & AGE <= 44)
AGECAT = "25 to 44"
else if (AGE >= 45 & AGE <= 64)
AGECAT <- "45 to 64"
else if (AGE >= 65)
AGECAT <- ">= 65"
}) %>% put()
put("Get ARM population counts")
arm_pop <- count(dm_mod, ARM) %>% deframe() %>% put()
# 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 %>%
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 user-defined format
fmt_sex <- value(condition(is.na(x), "Missing"),
condition(x == "M", "Male"),
condition(x == "F", "Female"),
condition(TRUE, "Other")) %>% put()
# 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, fmt_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")
var_fmt <- c("AGE" = "Age", "AGECAT" = "Age Group", "SEX" = "Sex")
# Create Table
tbl <- create_table(final, first_row_blank = TRUE, borders = "all") %>%
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 = var_fmt,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"]) %>%
titles("Table 1.0", "Analysis of Demographic Characteristics",
"Safety Population", borders = "outside", bold = TRUE) %>%
footnotes("Program: DM_Table.R",
"NOTE: Denominator based on number of non-missing responses.",
borders = "outside")
rpt <- create_report(file.path(tmp, "output/example2.rtf"), output_type = "RTF",
font = "Arial") %>%
set_margins(top = 1, bottom = 1) %>%
add_content(tbl)
res <- write_report(rpt)
# Clean Up ----------------------------------------------------------------
sep("Clean Up")
# Unload library from workspace
lib_unload(sdtm)
# Close log
log_close()
# View log
writeLines(readLines(lf, encoding = "UTF-8"))
# View report
# file.show(res$modified_path)The above program produces the following log:
=========================================================================
Log Path: C:/Users/dbosa/AppData/Local/Temp/RtmpgrpGQ1/log/example2.log
Program Path: C:\packages\Testing\reporter_example2.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 14:44:49
=========================================================================
=========================================================================
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
select: dropped 20 variables (STUDYID, DOMAIN, SUBJID, RFSTDTC, RFENDTC, <U+0085>)
filter: removed 2 rows (2%), 85 rows remaining
datastep: columns increased from 4 to 5
# A tibble: 85 x 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
# ... 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 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
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
=========================================================================
# 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
select: dropped 3 variables (USUBJID, AGE, AGECAT)
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: 'C:\Users\dbosa\AppData\Local\Temp\RtmpgrpGQ1/output/example2.rtf'
- output_type: RTF
- units: inches
- orientation: landscape
- margins: top 1 bottom 1 left 1 right 1
- line size/count: 9/40
- content:
# A table specification:
- data: tibble 'final' 11 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 '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: 2021-11-21 14:44:51
Log Elapsed Time: 0 00:00:01
=========================================================================```