Output
And here is the output:
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Here is another example of a complete program that demonstrates several of the key functions in the libr package. The example also shows how the package integrates with sassy.
library(sassy)
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"))
# Prepare Data ------------------------------------------------------------
sep("Prepare Data")
# Get path to sample data
pkg <- system.file("extdata", package = "libr")
# Create libname for csv data
libname(sdtm, pkg, "csv", quiet = TRUE)
put("Join and prepare data")
put("Join DM to VS and keep desired columns")
datastep(sdtm$DM, merge = sdtm$VS, merge_by = USUBJID,
keep = v(USUBJID, VSTESTCD, VISIT, VISITNUM, VSSTRESN, ARM, VSBLFL),
where = expression(VSTESTCD %in% c("PULSE", "RESP", "TEMP", "DIABP", "SYSBP") &
!(VISIT == "SCREENING" & VSBLFL != "Y")), {}) -> dm_joined
put("Sort by variables")
proc_sort(dm_joined, by = v(USUBJID, VSTESTCD, VISITNUM)) -> dm_sorted
put("Differentiate baseline from treated vital signs")
datastep(dm_sorted, by = v(USUBJID, VSTESTCD),
retain = list(BSTRESN = 0), {
# Combine treatment groups
# And distinguish baseline time points
if (ARM == "ARM A") {
if (VSBLFL %eq% "Y") {
GRP <- "A_BASE"
} else {
GRP <- "A_TRT"
}
} else {
if (VSBLFL %eq% "Y") {
GRP <- "O_BASE"
} else {
GRP <- "O_TRT"
}
}
# Populate baseline value
if (first.)
BSTRESN = VSSTRESN
}) -> prep
put("Get population counts")
pop_A <- subset(prep, GRP == "A_BASE", v(USUBJID, GRP)) |>
proc_sort(options = nodupkey) |>
proc_freq(tables = GRP,
options = v(nocum, nonobs, nopercent),
output = long) |>
subset(select = "A_BASE", drop = TRUE)
pop_O <- subset(prep, GRP == "O_BASE", v(USUBJID, GRP)) |>
proc_sort(options = nodupkey) |>
proc_freq(tables = GRP,
options = v(nocum, nonobs, nopercent),
output = long) |>
subset(select = "O_BASE", drop = TRUE)
# Prepare formats ---------------------------------------------------------
sep("Prepare formats")
put("Vital sign lookup format")
vs_fmt <- c(PULSE = "Pulse",
TEMP = "Temperature °C",
RESP = "Respirations/min",
SYSBP = "Systolic Blood Pressure",
DIABP = "Diastolic Blood Pressure") |> put()
put("Statistics lookup format")
stat_fmt <- c(MEANSTD = "Mean (SD)",
MEDIAN = "Median",
Q1Q3 = "Q1 - Q3",
MINMAX = "Min - Max") |> put()
put("Create format catalog")
fc <- fcat(MEAN = "%.1f",
STD = "(%.2f)",
MEDIAN = "%.1f",
Q1 = "%.1f",
Q3 = "%.1f",
MIN = "%.1f",
MAX = "%.1f")
# Prepare final data frame ------------------------------------------------
sep("Prepare final data")
put("Calculate statistics and prepare final data frame")
proc_means(prep, var = VSSTRESN, class = VSTESTCD, by = GRP,
stats = v(mean, std, median, q1, q3, min, max),
options = v(notype, nofreq, nway)) |>
datastep(format = fc,
drop = v(MEAN, STD, Q1, Q3, MIN, MAX, VAR),
rename = c("CLASS" = "VAR"),
{
MEANSTD <- fapply2(MEAN, STD)
Q1Q3 <- fapply2(Q1, Q3, sep = " - ")
MINMAX <- fapply2(MIN, MAX, sep = " - ")
}) |>
proc_transpose(id = BY, var = v(MEANSTD, MEDIAN, Q1Q3, MINMAX),
by = VAR, name = "LABEL") -> final
put("Prepare factor for sorting")
final$VAR <- factor(final$VAR, names(vs_fmt))
put("Final sort")
proc_sort(final, by = VAR) -> final
# Create Report -----------------------------------------------------------
sep("Create Report")
# Define table object
tbl <- create_table(final) |>
spanning_header(A_BASE, A_TRT, "Placebo", n = pop_A) |>
spanning_header(O_BASE, O_TRT, "Treated", n = pop_O) |>
column_defaults(width = 1.25, align = "center") |>
stub(c(VAR, LABEL), width = 2.5) |>
define(VAR, "Vital Sign", format = vs_fmt,
blank_after = TRUE, dedupe = TRUE, label_row = TRUE) |>
define(LABEL, indent = .25, format = stat_fmt) |>
define(A_BASE, "Baseline") |>
define(A_TRT, "After Treatment") |>
define(O_BASE, "Baseline") |>
define(O_TRT, "After Treatment")
# Define report object
rpt <- create_report(file.path(tmp, "./output/example2.rtf"), output_type = "RTF",
font = "Times", font_size = 12) |>
page_header("Sponsor: Company", "Study: ABC") |>
titles("Table 4.0", "Selected Vital Signs", bold = TRUE) |>
add_content(tbl, align = "center") |>
page_footer(Sys.time(), "CONFIDENTIAL", "Page [pg] of [tpg]")
# Write report to file system
res <- write_report(rpt)
# Clean Up ----------------------------------------------------------------
sep("Clean Up")
# Close log
log_close()
# View report
# file.show(res$file_path)
# View log
# file.show(lf)Here is the log from the above program:
=========================================================================
Log Path: C:/Users/dbosa/AppData/Local/Temp/RtmpiipZo8/log/example1.log
Program Path: C:/packages/Testing/Example2.R
Working Directory: C:/packages/Testing
User Name: dbosa
R Version: 4.3.1 (2023-06-16 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 procs_1.0.3 reporter_1.4.2 libr_1.2.8 logr_1.3.5
fmtr_1.6.1 common_1.1.0 sassy_1.2.1
Log Start Time: 2023-11-17 13:03:47.835721
=========================================================================
=========================================================================
Prepare Data
=========================================================================
# library 'sdtm': 8 items
- attributes: csv not loaded
- path: C:/Users/dbosa/AppData/Local/R/win-library/4.3/libr/extdata
- items:
Name Extension Rows Cols Size LastModified
1 AE csv 150 27 88.5 Kb 2023-09-09 22:45:51
2 DA csv 3587 18 528.2 Kb 2023-09-09 22:45:51
3 DM csv 87 24 45.5 Kb 2023-09-09 22:45:51
4 DS csv 174 9 34.1 Kb 2023-09-09 22:45:51
5 EX csv 84 11 26.4 Kb 2023-09-09 22:45:51
6 IE csv 2 14 13.4 Kb 2023-09-09 22:45:51
7 SV csv 685 10 70.3 Kb 2023-09-09 22:45:51
8 VS csv 3358 17 467.4 Kb 2023-09-09 22:45:51
Join and prepare data
Join DM to VS and keep desired columns
datastep: columns decreased from 24 to 7
# A tibble: 2,768 × 7
USUBJID VSTESTCD VISIT VISITNUM VSSTRESN ARM VSBLFL
<chr> <chr> <chr> <dbl> <dbl> <chr> <chr>
1 ABC-01-049 DIABP DAY 1 1 76 ARM D Y
2 ABC-01-049 DIABP WEEK 2 2 66 ARM D <NA>
3 ABC-01-049 DIABP WEEK 4 4 84 ARM D <NA>
4 ABC-01-049 DIABP WEEK 6 6 68 ARM D <NA>
5 ABC-01-049 DIABP WEEK 8 8 80 ARM D <NA>
6 ABC-01-049 DIABP WEEK 12 12 70 ARM D <NA>
7 ABC-01-049 DIABP WEEK 16 16 70 ARM D <NA>
8 ABC-01-049 PULSE DAY 1 1 84 ARM D Y
9 ABC-01-049 PULSE WEEK 2 2 84 ARM D <NA>
10 ABC-01-049 PULSE WEEK 4 4 76 ARM D <NA>
# ℹ 2,758 more rows
# ℹ Use `print(n = ...)` to see more rows
Sort by variables
proc_sort: input data set 2768 rows and 7 columns
by: USUBJID VSTESTCD VISITNUM
keep: USUBJID VSTESTCD VISIT VISITNUM VSSTRESN ARM VSBLFL
order: a a a
output data set 2768 rows and 7 columns
# A tibble: 2,768 × 7
USUBJID VSTESTCD VISIT VISITNUM VSSTRESN ARM VSBLFL
<chr> <chr> <chr> <dbl> <dbl> <chr> <chr>
1 ABC-01-049 DIABP DAY 1 1 76 ARM D Y
2 ABC-01-049 DIABP WEEK 2 2 66 ARM D <NA>
3 ABC-01-049 DIABP WEEK 4 4 84 ARM D <NA>
4 ABC-01-049 DIABP WEEK 6 6 68 ARM D <NA>
5 ABC-01-049 DIABP WEEK 8 8 80 ARM D <NA>
6 ABC-01-049 DIABP WEEK 12 12 70 ARM D <NA>
7 ABC-01-049 DIABP WEEK 16 16 70 ARM D <NA>
8 ABC-01-049 PULSE DAY 1 1 84 ARM D Y
9 ABC-01-049 PULSE WEEK 2 2 84 ARM D <NA>
10 ABC-01-049 PULSE WEEK 4 4 76 ARM D <NA>
# ℹ 2,758 more rows
# ℹ Use `print(n = ...)` to see more rows
Differentiate baseline from treated vital signs
datastep: columns increased from 7 to 9
# A tibble: 2,768 × 9
USUBJID VSTESTCD VISIT VISITNUM VSSTRESN ARM VSBLFL GRP BSTRESN
<chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <dbl>
1 ABC-01-049 DIABP DAY 1 1 76 ARM D Y O_BASE 76
2 ABC-01-049 DIABP WEEK 2 2 66 ARM D <NA> O_TRT 76
3 ABC-01-049 DIABP WEEK 4 4 84 ARM D <NA> O_TRT 76
4 ABC-01-049 DIABP WEEK 6 6 68 ARM D <NA> O_TRT 76
5 ABC-01-049 DIABP WEEK 8 8 80 ARM D <NA> O_TRT 76
6 ABC-01-049 DIABP WEEK 12 12 70 ARM D <NA> O_TRT 76
7 ABC-01-049 DIABP WEEK 16 16 70 ARM D <NA> O_TRT 76
8 ABC-01-049 PULSE DAY 1 1 84 ARM D Y O_BASE 84
9 ABC-01-049 PULSE WEEK 2 2 84 ARM D <NA> O_TRT 84
10 ABC-01-049 PULSE WEEK 4 4 76 ARM D <NA> O_TRT 84
# ℹ 2,758 more rows
# ℹ Use `print(n = ...)` to see more rows
Get population counts
proc_sort: input data set 20 rows and 2 columns
by: USUBJID GRP
keep: USUBJID GRP
order: a
options: nodupkey
output data set 20 rows and 2 columns
# A tibble: 20 × 2
USUBJID GRP
<chr> <chr>
1 ABC-01-051 A_BASE
2 ABC-01-056 A_BASE
3 ABC-02-034 A_BASE
4 ABC-02-038 A_BASE
5 ABC-02-109 A_BASE
6 ABC-03-002 A_BASE
7 ABC-03-006 A_BASE
8 ABC-03-091 A_BASE
9 ABC-04-075 A_BASE
10 ABC-04-080 A_BASE
11 ABC-04-126 A_BASE
12 ABC-06-068 A_BASE
13 ABC-06-069 A_BASE
14 ABC-07-012 A_BASE
15 ABC-07-016 A_BASE
16 ABC-08-104 A_BASE
17 ABC-08-106 A_BASE
18 ABC-09-020 A_BASE
19 ABC-09-023 A_BASE
20 ABC-09-137 A_BASE
proc_freq: input data set 20 rows and 2 columns
tables: GRP
output: long
view: TRUE
output: 1 datasets
# A tibble: 1 × 3
VAR STAT A_BASE
<chr> <chr> <dbl>
1 GRP CNT 20
proc_sort: input data set 67 rows and 2 columns
by: USUBJID GRP
keep: USUBJID GRP
order: a
options: nodupkey
output data set 67 rows and 2 columns
# A tibble: 67 × 2
USUBJID GRP
<chr> <chr>
1 ABC-01-049 O_BASE
2 ABC-01-050 O_BASE
3 ABC-01-052 O_BASE
4 ABC-01-053 O_BASE
5 ABC-01-054 O_BASE
6 ABC-01-055 O_BASE
7 ABC-01-113 O_BASE
8 ABC-01-114 O_BASE
9 ABC-02-033 O_BASE
10 ABC-02-035 O_BASE
# ℹ 57 more rows
# ℹ Use `print(n = ...)` to see more rows
proc_freq: input data set 67 rows and 2 columns
tables: GRP
output: long
view: TRUE
output: 1 datasets
# A tibble: 1 × 3
VAR STAT O_BASE
<chr> <chr> <dbl>
1 GRP CNT 67
=========================================================================
Prepare formats
=========================================================================
Vital sign lookup format
Pulse
Temperature °C
Respirations/min
Systolic Blood Pressure
Diastolic Blood Pressure
Statistics lookup format
Mean (SD)
Median
Q1 - Q3
Min - Max
Create format catalog
# A format catalog: 7 formats
- $MEAN: type S, "%.1f"
- $STD: type S, "(%.2f)"
- $MEDIAN: type S, "%.1f"
- $Q1: type S, "%.1f"
- $Q3: type S, "%.1f"
- $MIN: type S, "%.1f"
- $MAX: type S, "%.1f"
=========================================================================
Prepare final data
=========================================================================
Calculate statistics and prepare final data frame
proc_means: input data set 2768 rows and 9 columns
by: GRP
class: VSTESTCD
var: VSSTRESN
stats: mean std median q1 q3 min max
view: TRUE
output: 1 datasets
CLASS BY VAR MEAN STD MEDIAN Q1 Q3 MIN MAX
1 DIABP A_BASE VSSTRESN 77.15000 10.6537614 78.5 70.0 83.0 54.0 96.0
2 PULSE A_BASE VSSTRESN 72.75000 10.0518393 72.0 66.0 76.0 60.0 103.0
3 RESP A_BASE VSSTRESN 16.50000 2.5026302 16.0 16.0 18.0 12.0 20.0
4 SYSBP A_BASE VSSTRESN 128.15000 16.7120347 125.5 117.0 140.0 98.0 161.0
5 TEMP A_BASE VSSTRESN 36.52105 0.4171050 36.4 36.2 36.9 35.9 37.4
6 DIABP A_TRT VSSTRESN 77.08547 8.1182772 78.0 72.0 82.0 50.0 98.0
7 PULSE A_TRT VSSTRESN 74.58120 10.2201958 74.0 67.0 80.0 54.0 102.0
8 RESP A_TRT VSSTRESN 15.80342 3.4372185 16.0 16.0 18.0 8.0 24.0
9 SYSBP A_TRT VSSTRESN 130.43590 17.6304911 126.0 118.0 140.0 95.0 184.0
10 TEMP A_TRT VSSTRESN 36.45431 0.2857157 36.5 36.2 36.6 35.8 37.3
11 DIABP O_BASE VSSTRESN 77.48485 8.1415221 78.0 70.0 82.0 61.0 95.0
12 PULSE O_BASE VSSTRESN 73.55224 9.7051745 72.0 67.0 80.0 52.0 100.0
13 RESP O_BASE VSSTRESN 15.97015 3.1477272 16.0 15.0 18.0 8.0 22.0
14 SYSBP O_BASE VSSTRESN 126.68182 15.4672449 123.0 116.0 138.0 100.0 164.0
15 TEMP O_BASE VSSTRESN 36.42239 0.5376213 36.4 36.2 36.6 35.3 39.8
16 DIABP O_TRT VSSTRESN 76.94286 9.2058374 78.0 70.0 84.0 50.0 104.0
17 PULSE O_TRT VSSTRESN 74.02279 9.8592694 72.0 66.0 80.0 50.0 109.0
18 RESP O_TRT VSSTRESN 15.44444 3.4312291 16.0 12.0 18.0 8.0 24.0
19 SYSBP O_TRT VSSTRESN 125.90571 15.1715962 124.0 115.0 135.0 82.0 180.0
20 TEMP O_TRT VSSTRESN 36.31057 0.3903231 36.3 36.1 36.5 34.4 38.2
datastep: columns decreased from 10 to 6
VAR BY MEDIAN MEANSTD Q1Q3 MINMAX
1 DIABP A_BASE 78.5 77.2 (10.65) 70.0 - 83.0 54.0 - 96.0
2 PULSE A_BASE 72.0 72.8 (10.05) 66.0 - 76.0 60.0 - 103.0
3 RESP A_BASE 16.0 16.5 (2.50) 16.0 - 18.0 12.0 - 20.0
4 SYSBP A_BASE 125.5 128.2 (16.71) 117.0 - 140.0 98.0 - 161.0
5 TEMP A_BASE 36.4 36.5 (0.42) 36.2 - 36.9 35.9 - 37.4
6 DIABP A_TRT 78.0 77.1 (8.12) 72.0 - 82.0 50.0 - 98.0
7 PULSE A_TRT 74.0 74.6 (10.22) 67.0 - 80.0 54.0 - 102.0
8 RESP A_TRT 16.0 15.8 (3.44) 16.0 - 18.0 8.0 - 24.0
9 SYSBP A_TRT 126.0 130.4 (17.63) 118.0 - 140.0 95.0 - 184.0
10 TEMP A_TRT 36.5 36.5 (0.29) 36.2 - 36.6 35.8 - 37.3
11 DIABP O_BASE 78.0 77.5 (8.14) 70.0 - 82.0 61.0 - 95.0
12 PULSE O_BASE 72.0 73.6 (9.71) 67.0 - 80.0 52.0 - 100.0
13 RESP O_BASE 16.0 16.0 (3.15) 15.0 - 18.0 8.0 - 22.0
14 SYSBP O_BASE 123.0 126.7 (15.47) 116.0 - 138.0 100.0 - 164.0
15 TEMP O_BASE 36.4 36.4 (0.54) 36.2 - 36.6 35.3 - 39.8
16 DIABP O_TRT 78.0 76.9 (9.21) 70.0 - 84.0 50.0 - 104.0
17 PULSE O_TRT 72.0 74.0 (9.86) 66.0 - 80.0 50.0 - 109.0
18 RESP O_TRT 16.0 15.4 (3.43) 12.0 - 18.0 8.0 - 24.0
19 SYSBP O_TRT 124.0 125.9 (15.17) 115.0 - 135.0 82.0 - 180.0
20 TEMP O_TRT 36.3 36.3 (0.39) 36.1 - 36.5 34.4 - 38.2
proc_transpose: input data set 20 rows and 6 columns
by: VAR
var: MEANSTD MEDIAN Q1Q3 MINMAX
id: BY
name: LABEL
output dataset 20 rows and 6 columns
VAR LABEL A_BASE A_TRT O_BASE O_TRT
1 DIABP MEANSTD 77.2 (10.65) 77.1 (8.12) 77.5 (8.14) 76.9 (9.21)
2 DIABP MEDIAN 78.5 78.0 78.0 78.0
3 DIABP Q1Q3 70.0 - 83.0 72.0 - 82.0 70.0 - 82.0 70.0 - 84.0
4 DIABP MINMAX 54.0 - 96.0 50.0 - 98.0 61.0 - 95.0 50.0 - 104.0
5 PULSE MEANSTD 72.8 (10.05) 74.6 (10.22) 73.6 (9.71) 74.0 (9.86)
6 PULSE MEDIAN 72 74 72 72
7 PULSE Q1Q3 66.0 - 76.0 67.0 - 80.0 67.0 - 80.0 66.0 - 80.0
8 PULSE MINMAX 60.0 - 103.0 54.0 - 102.0 52.0 - 100.0 50.0 - 109.0
9 RESP MEANSTD 16.5 (2.50) 15.8 (3.44) 16.0 (3.15) 15.4 (3.43)
10 RESP MEDIAN 16 16 16 16
11 RESP Q1Q3 16.0 - 18.0 16.0 - 18.0 15.0 - 18.0 12.0 - 18.0
12 RESP MINMAX 12.0 - 20.0 8.0 - 24.0 8.0 - 22.0 8.0 - 24.0
13 SYSBP MEANSTD 128.2 (16.71) 130.4 (17.63) 126.7 (15.47) 125.9 (15.17)
14 SYSBP MEDIAN 125.5 126.0 123.0 124.0
15 SYSBP Q1Q3 117.0 - 140.0 118.0 - 140.0 116.0 - 138.0 115.0 - 135.0
16 SYSBP MINMAX 98.0 - 161.0 95.0 - 184.0 100.0 - 164.0 82.0 - 180.0
17 TEMP MEANSTD 36.5 (0.42) 36.5 (0.29) 36.4 (0.54) 36.3 (0.39)
18 TEMP MEDIAN 36.4 36.5 36.4 36.3
19 TEMP Q1Q3 36.2 - 36.9 36.2 - 36.6 36.2 - 36.6 36.1 - 36.5
20 TEMP MINMAX 35.9 - 37.4 35.8 - 37.3 35.3 - 39.8 34.4 - 38.2
Prepare factor for sorting
Final sort
proc_sort: input data set 20 rows and 6 columns
by: VAR
keep: VAR LABEL A_BASE A_TRT O_BASE O_TRT
order: a
output data set 20 rows and 6 columns
VAR LABEL A_BASE A_TRT O_BASE O_TRT
5 PULSE MEANSTD 72.8 (10.05) 74.6 (10.22) 73.6 (9.71) 74.0 (9.86)
6 PULSE MEDIAN 72 74 72 72
7 PULSE Q1Q3 66.0 - 76.0 67.0 - 80.0 67.0 - 80.0 66.0 - 80.0
8 PULSE MINMAX 60.0 - 103.0 54.0 - 102.0 52.0 - 100.0 50.0 - 109.0
17 TEMP MEANSTD 36.5 (0.42) 36.5 (0.29) 36.4 (0.54) 36.3 (0.39)
18 TEMP MEDIAN 36.4 36.5 36.4 36.3
19 TEMP Q1Q3 36.2 - 36.9 36.2 - 36.6 36.2 - 36.6 36.1 - 36.5
20 TEMP MINMAX 35.9 - 37.4 35.8 - 37.3 35.3 - 39.8 34.4 - 38.2
9 RESP MEANSTD 16.5 (2.50) 15.8 (3.44) 16.0 (3.15) 15.4 (3.43)
10 RESP MEDIAN 16 16 16 16
11 RESP Q1Q3 16.0 - 18.0 16.0 - 18.0 15.0 - 18.0 12.0 - 18.0
12 RESP MINMAX 12.0 - 20.0 8.0 - 24.0 8.0 - 22.0 8.0 - 24.0
13 SYSBP MEANSTD 128.2 (16.71) 130.4 (17.63) 126.7 (15.47) 125.9 (15.17)
14 SYSBP MEDIAN 125.5 126.0 123.0 124.0
15 SYSBP Q1Q3 117.0 - 140.0 118.0 - 140.0 116.0 - 138.0 115.0 - 135.0
16 SYSBP MINMAX 98.0 - 161.0 95.0 - 184.0 100.0 - 164.0 82.0 - 180.0
1 DIABP MEANSTD 77.2 (10.65) 77.1 (8.12) 77.5 (8.14) 76.9 (9.21)
2 DIABP MEDIAN 78.5 78.0 78.0 78.0
3 DIABP Q1Q3 70.0 - 83.0 72.0 - 82.0 70.0 - 82.0 70.0 - 84.0
4 DIABP MINMAX 54.0 - 96.0 50.0 - 98.0 61.0 - 95.0 50.0 - 104.0
=========================================================================
Create Report
=========================================================================
# A report specification: 1 pages
- file_path: 'C:\Users\dbosa\AppData\Local\Temp\RtmpiipZo8/./output/example2.rtf'
- output_type: RTF
- units: inches
- orientation: landscape
- margins: top 0.5 bottom 0.5 left 1 right 1
- line size/count: 9/38
- page_header: left=Sponsor: Company right=Study: ABC
- title 1: 'Table 4.0'
- title 2: 'Selected Vital Signs'
- page_footer: left=2023-11-17 13:03:53.809731 center=CONFIDENTIAL right=Page [pg] of [tpg]
- content:
# A table specification:
- data: data.frame 'final' 20 rows 6 cols
- show_cols: all
- use_attributes: all
- spanning_header: from='A_BASE' to='A_TRT' 'Placebo' level=1
- spanning_header: from='O_BASE' to='O_TRT' 'Treated' level=1
- stub: VAR LABEL width=2.5 align='left'
- define: VAR 'Vital Sign' dedupe='TRUE'
- define: LABEL
- define: A_BASE 'Baseline'
- define: A_TRT 'After Treatment'
- define: O_BASE 'Baseline'
- define: O_TRT 'After Treatment'
=========================================================================
Clean Up
=========================================================================
=========================================================================
Log End Time: 2023-11-17 13:03:54.012811
Log Elapsed Time: 0 00:00:06
=========================================================================
And here is the output:
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.
Health stats visible at Monitor.