-
-
Notifications
You must be signed in to change notification settings - Fork 4
/
sassy-dm.Rmd
778 lines (600 loc) · 23.5 KB
/
sassy-dm.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
---
title: "Example 2: Demographics Table"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Example 2: Demographics Table}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
The second example produces a demographics summary table of selected variables.
The report shows statistics for each of the four treatment groups.
## Program
Note the following about this example:
* The **[logr](https://logr.r-sassy.org)** package
provides automatic logging for many functions.
* The `datastep()` function from the **[libr](https://libr.r-sassy.org)**
package allows for easy data processing.
* The **[fmtr](https://fmtr.r-sassy.org)** package provides several
convenient functions for
calculating and formatting summary statistics.
* Statistics from the **[procs](https://procs.r-sassy.org)** package
match SAS® out of the box.
* The **[reporter](https://reporter.r-sassy.org)** package supports
'N=' population counts in the header labels.
* The **reporter** package also allows you to define a stub column of
hierarchical labels.
```{r eval=FALSE, echo=TRUE}
library(sassy)
# Prepare Log -------------------------------------------------------------
options("logr.autolog" = TRUE,
"logr.on" = TRUE,
"logr.notes" = FALSE,
"procs.print" = FALSE)
# Get temp directory
tmp <- tempdir()
# Open log
lf <- log_open(file.path(tmp, "example2.log"))
# Prepare formats ---------------------------------------------------------
sep("Prepare formats")
put("Age categories")
agecat <- value(condition(x >= 18 & x <= 29, "18 to 29"),
condition(x >=30 & x <= 39, "30 to 39"),
condition(x >=40 & x <=49, "40 to 49"),
condition(x >= 50, ">= 50"),
as.factor = TRUE)
put("Sex decodes")
fmt_sex <- value(condition(x == "M", "Male"),
condition(x == "F", "Female"),
condition(TRUE, "Other"),
as.factor = TRUE)
put("Race decodes")
fmt_race <- value(condition(x == "WHITE", "White"),
condition(x == "BLACK", "Black or African American"),
condition(TRUE, "Other"),
as.factor = TRUE)
put("Compile format catalog")
fc <- fcat(MEAN = "%.1f", STD = "(%.2f)",
Q1 = "%.1f", Q3 = "%.1f",
MIN = "%d", MAX = "%d",
CNT = "%2d", PCT = "(%5.1f%%)",
AGECAT = agecat,
SEX = fmt_sex,
RACE = fmt_race)
# Load and Prepare Data ---------------------------------------------------
sep("Prepare Data")
put("Create sample ADSL data.")
adsl <- read.table(header = TRUE, text = '
SUBJID ARM SEX RACE AGE
"001" "ARM A" "F" "WHITE" 19
"002" "ARM B" "F" "WHITE" 21
"003" "ARM C" "F" "WHITE" 23
"004" "ARM D" "F" "BLACK" 28
"005" "ARM A" "M" "WHITE" 37
"006" "ARM B" "M" "WHITE" 34
"007" "ARM C" "M" "WHITE" 36
"008" "ARM D" "M" "WHITE" 30
"009" "ARM A" "F" "WHITE" 39
"010" "ARM B" "F" "WHITE" 31
"011" "ARM C" "F" "BLACK" 33
"012" "ARM D" "F" "WHITE" 38
"013" "ARM A" "M" "BLACK" 37
"014" "ARM B" "M" "WHITE" 34
"015" "ARM C" "M" "WHITE" 36
"016" "ARM A" "M" "WHITE" 40')
put("Categorize AGE")
adsl$AGECAT <- fapply(adsl$AGE, agecat)
put("Log starting dataset")
put(adsl)
put("Get ARM population counts")
proc_freq(adsl, tables = ARM,
output = long,
options = v(nopercent, nonobs)) -> arm_pop
# Age Summary Block -------------------------------------------------------
sep("Create summary statistics for age")
put("Call means procedure to get summary statistics for age")
proc_means(adsl, var = AGE,
stats = v(n, mean, std, median, q1, q3, min, max),
by = ARM,
options = v(notype, nofreq)) -> age_stats
put("Combine stats")
datastep(age_stats,
format = fc,
drop = find.names(age_stats, start = 4),
{
`Mean (SD)` <- fapply2(MEAN, STD)
Median <- MEDIAN
`Q1 - Q3` <- fapply2(Q1, Q3, sep = " - ")
`Min - Max` <- fapply2(MIN, MAX, sep = " - ")
}) -> age_comb
put("Transpose ARMs into columns")
proc_transpose(age_comb,
var = names(age_comb),
copy = VAR, id = BY,
name = LABEL) -> age_block
# Sex Block ---------------------------------------------------------------
sep("Create frequency counts for SEX")
put("Get sex frequency counts")
proc_freq(adsl, tables = SEX,
by = ARM,
options = nonobs) -> sex_freq
put("Combine counts and percents.")
datastep(sex_freq,
format = fc,
rename = list(CAT = "LABEL"),
drop = v(CNT, PCT),
{
CNTPCT <- fapply2(CNT, PCT)
}) -> sex_comb
put("Transpose ARMs into columns")
proc_transpose(sex_comb, id = BY,
var = CNTPCT,
copy = VAR, by = LABEL,
options = noname) -> sex_trans
put("Apply formats")
datastep(sex_trans,
{
LABEL <- fapply(LABEL, fc$SEX)
}) -> sex_cnts
put("Sort by label")
proc_sort(sex_cnts, by = LABEL) -> sex_block
# Race block --------------------------------------------------------------
sep("Create frequency counts for RACE")
put("Get race frequency counts")
proc_freq(adsl, tables = RACE,
by = ARM,
options = nonobs) -> race_freq
put("Combine counts and percents.")
datastep(race_freq,
format = fc,
rename = list(CAT = "LABEL"),
drop = v(CNT, PCT),
{
CNTPCT <- fapply2(CNT, PCT)
}) -> race_comb
put("Transpose ARMs into columns")
proc_transpose(race_comb, id = BY, var = CNTPCT,
copy = VAR, by = LABEL, options = noname) -> race_trans
put("Clean up")
datastep(race_trans,
{
LABEL <- fapply(LABEL, fc$RACE)
}) -> race_cnts
put("Sort by label")
proc_sort(race_cnts, by = LABEL) -> race_block
# Age Group Block ----------------------------------------------------------
sep("Create frequency counts for Age Group")
put("Get age group frequency counts")
proc_freq(adsl,
table = AGECAT,
by = ARM,
options = nonobs) -> ageg_freq
put("Combine counts and percents and assign age group factor for sorting")
datastep(ageg_freq,
format = fc,
keep = v(VAR, LABEL, BY, CNTPCT),
{
CNTPCT <- fapply2(CNT, PCT)
LABEL <- CAT
}) -> ageg_comb
put("Sort by age group factor")
proc_sort(ageg_comb, by = v(BY, LABEL)) -> ageg_sort
put("Tranpose age group block")
proc_transpose(ageg_sort,
var = CNTPCT,
copy = VAR,
id = BY,
by = LABEL,
options = noname) -> ageg_trans
put("Combine blocks into final data frame")
datastep(age_block,
set = list(ageg_block, sex_block, race_block),
{}) -> final
# Report ------------------------------------------------------------------
sep("Create and print report")
var_fmt <- c("AGE" = "Age", "AGECAT" = "Age Group", "SEX" = "Sex", "RACE" = "Race")
# Create Table
tbl <- create_table(final, first_row_blank = TRUE) |>
column_defaults(from = `ARM A`, to = `ARM D`, align = "center", width = 1.1) |>
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 = "Placebo", n = arm_pop["ARM A"]) |>
define(`ARM B`, label = "Drug 50mg", n = arm_pop["ARM B"]) |>
define(`ARM C`, label = "Drug 100mg", n = arm_pop["ARM C"]) |>
define(`ARM D`, label = "Competitor", n = arm_pop["ARM D"]) |>
titles("Table 1.0", "Analysis of Demographic Characteristics",
"Safety Population", bold = TRUE) |>
footnotes("Program: DM_Table.R",
"NOTE: Denominator based on number of non-missing responses.")
rpt <- create_report(file.path(tmp, "example2.rtf"),
output_type = "RTF",
font = "Arial") |>
page_header("Sponsor: Company", "Study: ABC") |>
set_margins(top = 1, bottom = 1) |>
add_content(tbl) |>
page_footer("Date Produced: {Sys.Date()}", right = "Page [pg] of [tpg]")
put("Write out the report")
res <- write_report(rpt)
# Clean Up ----------------------------------------------------------------
sep("Clean Up")
put("Close log")
log_close()
# Uncomment to view report
# file.show(res$modified_path)
# Uncomment to view log
# file.show(lf)
```
## Output
Here is the output report:
<img src="../man/images/dm.png" align="center" />
## Log
And here is the log:
```
=========================================================================
Log Path: C:/Users/dbosa/AppData/Local/Temp/RtmpAXQUo8/log/example2.log
Program Path: C:/Projects/Archytas/Westat/Tutorial2/Project/sassy-dm.R
Working Directory: C:/Projects/Archytas/Westat/Tutorial2/Project
User Name: dbosa
R Version: 4.4.0 (2024-04-24 ucrt)
Machine: SOCRATES x86-64
Operating System: Windows 10 x64 build 22631
Base Packages: stats graphics grDevices utils datasets methods base
Other Packages: tidylog_1.0.2 ggplot2_3.5.1 procs_1.0.7 reporter_1.4.4
libr_1.3.3 logr_1.3.7 fmtr_1.6.4 common_1.1.3 sassy_1.2.4
Log Start Time: 2024-05-28 12:52:44.619869
=========================================================================
=========================================================================
Prepare formats
=========================================================================
Age categories
# A user-defined format: 4 conditions
- as.factor: TRUE
Name Type Expression Label Order
1 obj U x >= 18 & x <= 29 18 to 29 NA
2 obj U x >= 30 & x <= 39 30 to 39 NA
3 obj U x >= 40 & x <= 49 40 to 49 NA
4 obj U x >= 50 >= 50 NA
Sex decodes
# A user-defined format: 3 conditions
- as.factor: TRUE
Name Type Expression Label Order
1 obj U x == "M" Male NA
2 obj U x == "F" Female NA
3 obj U TRUE Other NA
Race decodes
# A user-defined format: 3 conditions
- as.factor: TRUE
Name Type Expression Label Order
1 obj U x == "WHITE" White NA
2 obj U x == "BLACK" Black or African American NA
3 obj U TRUE Other NA
Compile format catalog
# A format catalog: 11 formats
- $MEAN: type S, "%.1f"
- $STD: type S, "(%.2f)"
- $Q1: type S, "%.1f"
- $Q3: type S, "%.1f"
- $MIN: type S, "%d"
- $MAX: type S, "%d"
- $CNT: type S, "%2d"
- $PCT: type S, "(%5.1f%%)"
- $AGECAT: type U, 4 conditions
- $SEX: type U, 3 conditions
- $RACE: type U, 3 conditions
=========================================================================
Prepare Data
=========================================================================
Create sample ADSL data.
Categorize AGE
Log starting dataset
SUBJID ARM SEX RACE AGE AGECAT
1 1 ARM A F WHITE 19 18 to 29
2 2 ARM B F WHITE 21 18 to 29
3 3 ARM C F WHITE 23 18 to 29
4 4 ARM D F BLACK 28 18 to 29
5 5 ARM A M WHITE 37 30 to 39
6 6 ARM B M WHITE 34 30 to 39
7 7 ARM C M WHITE 36 30 to 39
8 8 ARM D M WHITE 30 30 to 39
9 9 ARM A F WHITE 39 30 to 39
10 10 ARM B F WHITE 31 30 to 39
11 11 ARM C F BLACK 33 30 to 39
12 12 ARM D F WHITE 38 30 to 39
13 13 ARM A M BLACK 37 30 to 39
14 14 ARM B M WHITE 34 30 to 39
15 15 ARM C M WHITE 36 30 to 39
16 16 ARM A M WHITE 40 40 to 49
Get ARM population counts
proc_freq: input data set 16 rows and 6 columns
tables: ARM
output: long
view: TRUE
output: 1 datasets
VAR STAT ARM A ARM B ARM C ARM D
1 ARM CNT 5 4 4 3
=========================================================================
Create summary statistics for age
=========================================================================
Call means procedure to get summary statistics for age
proc_means: input data set 16 rows and 6 columns
by: ARM
var: AGE
stats: n mean std median q1 q3 min max
view: TRUE
output: 1 datasets
BY VAR N MEAN STD MEDIAN Q1 Q3 MIN MAX
1 ARM A AGE 5 34.4 8.706320 37.0 37 39 19 40
2 ARM B AGE 4 30.0 6.164414 32.5 26 34 21 34
3 ARM C AGE 4 32.0 6.164414 34.5 28 36 23 36
4 ARM D AGE 3 32.0 5.291503 30.0 28 38 28 38
Combine stats
datastep: columns decreased from 10 to 7
BY VAR N Mean (SD) Median Q1 - Q3 Min - Max
1 ARM A AGE 5 34.4 (8.71) 37.0 37.0 - 39.0 19 - 40
2 ARM B AGE 4 30.0 (6.16) 32.5 26.0 - 34.0 21 - 34
3 ARM C AGE 4 32.0 (6.16) 34.5 28.0 - 36.0 23 - 36
4 ARM D AGE 3 32.0 (5.29) 30.0 28.0 - 38.0 28 - 38
Transpose ARMs into columns
proc_transpose: input data set 4 rows and 7 columns
var: BY VAR N Mean (SD) Median Q1 - Q3 Min - Max
id: BY
copy: VAR
name: LABEL
output dataset 5 rows and 6 columns
VAR LABEL ARM A ARM B ARM C ARM D
1 AGE N 5 4 4 3
2 AGE Mean (SD) 34.4 (8.71) 30.0 (6.16) 32.0 (6.16) 32.0 (5.29)
3 AGE Median 37.0 32.5 34.5 30.0
4 AGE Q1 - Q3 37.0 - 39.0 26.0 - 34.0 28.0 - 36.0 28.0 - 38.0
5 AGE Min - Max 19 - 40 21 - 34 23 - 36 28 - 38
=========================================================================
Create frequency counts for SEX
=========================================================================
Get sex frequency counts
proc_freq: input data set 16 rows and 6 columns
tables: SEX
by: ARM
view: TRUE
output: 1 datasets
BY VAR CAT CNT PCT
1 ARM A SEX F 2 40.00000
2 ARM A SEX M 3 60.00000
3 ARM B SEX F 2 50.00000
4 ARM B SEX M 2 50.00000
5 ARM C SEX F 2 50.00000
6 ARM C SEX M 2 50.00000
7 ARM D SEX F 2 66.66667
8 ARM D SEX M 1 33.33333
Combine counts and percents.
datastep: columns decreased from 5 to 4
BY VAR LABEL CNTPCT
1 ARM A SEX F 2 ( 40.0%)
2 ARM A SEX M 3 ( 60.0%)
3 ARM B SEX F 2 ( 50.0%)
4 ARM B SEX M 2 ( 50.0%)
5 ARM C SEX F 2 ( 50.0%)
6 ARM C SEX M 2 ( 50.0%)
7 ARM D SEX F 2 ( 66.7%)
8 ARM D SEX M 1 ( 33.3%)
Transpose ARMs into columns
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
VAR LABEL ARM A ARM B ARM C ARM D
1 SEX F 2 ( 40.0%) 2 ( 50.0%) 2 ( 50.0%) 2 ( 66.7%)
2 SEX M 3 ( 60.0%) 2 ( 50.0%) 2 ( 50.0%) 1 ( 33.3%)
Apply formats
datastep: columns started with 6 and ended with 6
VAR LABEL ARM A ARM B ARM C ARM D
1 SEX Female 2 ( 40.0%) 2 ( 50.0%) 2 ( 50.0%) 2 ( 66.7%)
2 SEX Male 3 ( 60.0%) 2 ( 50.0%) 2 ( 50.0%) 1 ( 33.3%)
Sort by label
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
VAR LABEL ARM A ARM B ARM C ARM D
2 SEX Male 3 ( 60.0%) 2 ( 50.0%) 2 ( 50.0%) 1 ( 33.3%)
1 SEX Female 2 ( 40.0%) 2 ( 50.0%) 2 ( 50.0%) 2 ( 66.7%)
=========================================================================
Create frequency counts for RACE
=========================================================================
Get race frequency counts
proc_freq: input data set 16 rows and 6 columns
tables: RACE
by: ARM
view: TRUE
output: 1 datasets
BY VAR CAT CNT PCT
1 ARM A RACE BLACK 1 20.00000
2 ARM A RACE WHITE 4 80.00000
3 ARM B RACE BLACK 0 0.00000
4 ARM B RACE WHITE 4 100.00000
5 ARM C RACE BLACK 1 25.00000
6 ARM C RACE WHITE 3 75.00000
7 ARM D RACE BLACK 1 33.33333
8 ARM D RACE WHITE 2 66.66667
Combine counts and percents.
datastep: columns decreased from 5 to 4
BY VAR LABEL CNTPCT
1 ARM A RACE BLACK 1 ( 20.0%)
2 ARM A RACE WHITE 4 ( 80.0%)
3 ARM B RACE BLACK 0 ( 0.0%)
4 ARM B RACE WHITE 4 (100.0%)
5 ARM C RACE BLACK 1 ( 25.0%)
6 ARM C RACE WHITE 3 ( 75.0%)
7 ARM D RACE BLACK 1 ( 33.3%)
8 ARM D RACE WHITE 2 ( 66.7%)
Transpose ARMs into columns
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
VAR LABEL ARM A ARM B ARM C ARM D
1 RACE BLACK 1 ( 20.0%) 0 ( 0.0%) 1 ( 25.0%) 1 ( 33.3%)
2 RACE WHITE 4 ( 80.0%) 4 (100.0%) 3 ( 75.0%) 2 ( 66.7%)
Clean up
datastep: columns started with 6 and ended with 6
VAR LABEL ARM A ARM B ARM C ARM D
1 RACE Black or African American 1 ( 20.0%) 0 ( 0.0%) 1 ( 25.0%) 1 ( 33.3%)
2 RACE White 4 ( 80.0%) 4 (100.0%) 3 ( 75.0%) 2 ( 66.7%)
Sort by label
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
VAR LABEL ARM A ARM B ARM C ARM D
2 RACE White 4 ( 80.0%) 4 (100.0%) 3 ( 75.0%) 2 ( 66.7%)
1 RACE Black or African American 1 ( 20.0%) 0 ( 0.0%) 1 ( 25.0%) 1 ( 33.3%)
=========================================================================
Create frequency counts for Age Group
=========================================================================
Get age group frequency counts
proc_freq: input data set 16 rows and 6 columns
tables: AGECAT
by: ARM
view: TRUE
output: 1 datasets
BY VAR CAT CNT PCT
1 ARM A AGECAT 18 to 29 1 20.00000
2 ARM A AGECAT 30 to 39 3 60.00000
3 ARM A AGECAT 40 to 49 1 20.00000
4 ARM A AGECAT >= 50 0 0.00000
5 ARM B AGECAT 18 to 29 1 25.00000
6 ARM B AGECAT 30 to 39 3 75.00000
7 ARM B AGECAT 40 to 49 0 0.00000
8 ARM B AGECAT >= 50 0 0.00000
9 ARM C AGECAT 18 to 29 1 25.00000
10 ARM C AGECAT 30 to 39 3 75.00000
11 ARM C AGECAT 40 to 49 0 0.00000
12 ARM C AGECAT >= 50 0 0.00000
13 ARM D AGECAT 18 to 29 1 33.33333
14 ARM D AGECAT 30 to 39 2 66.66667
15 ARM D AGECAT 40 to 49 0 0.00000
16 ARM D AGECAT >= 50 0 0.00000
Combine counts and percents and assign age group factor for sorting
datastep: columns decreased from 5 to 4
VAR LABEL BY CNTPCT
1 AGECAT 18 to 29 ARM A 1 ( 20.0%)
2 AGECAT 30 to 39 ARM A 3 ( 60.0%)
3 AGECAT 40 to 49 ARM A 1 ( 20.0%)
4 AGECAT >= 50 ARM A 0 ( 0.0%)
5 AGECAT 18 to 29 ARM B 1 ( 25.0%)
6 AGECAT 30 to 39 ARM B 3 ( 75.0%)
7 AGECAT 40 to 49 ARM B 0 ( 0.0%)
8 AGECAT >= 50 ARM B 0 ( 0.0%)
9 AGECAT 18 to 29 ARM C 1 ( 25.0%)
10 AGECAT 30 to 39 ARM C 3 ( 75.0%)
11 AGECAT 40 to 49 ARM C 0 ( 0.0%)
12 AGECAT >= 50 ARM C 0 ( 0.0%)
13 AGECAT 18 to 29 ARM D 1 ( 33.3%)
14 AGECAT 30 to 39 ARM D 2 ( 66.7%)
15 AGECAT 40 to 49 ARM D 0 ( 0.0%)
16 AGECAT >= 50 ARM D 0 ( 0.0%)
Sort by age group factor
proc_sort: input data set 16 rows and 4 columns
by: BY LABEL
keep: VAR LABEL BY CNTPCT
order: a a
output data set 16 rows and 4 columns
VAR LABEL BY CNTPCT
1 AGECAT 18 to 29 ARM A 1 ( 20.0%)
2 AGECAT 30 to 39 ARM A 3 ( 60.0%)
3 AGECAT 40 to 49 ARM A 1 ( 20.0%)
4 AGECAT >= 50 ARM A 0 ( 0.0%)
5 AGECAT 18 to 29 ARM B 1 ( 25.0%)
6 AGECAT 30 to 39 ARM B 3 ( 75.0%)
7 AGECAT 40 to 49 ARM B 0 ( 0.0%)
8 AGECAT >= 50 ARM B 0 ( 0.0%)
9 AGECAT 18 to 29 ARM C 1 ( 25.0%)
10 AGECAT 30 to 39 ARM C 3 ( 75.0%)
11 AGECAT 40 to 49 ARM C 0 ( 0.0%)
12 AGECAT >= 50 ARM C 0 ( 0.0%)
13 AGECAT 18 to 29 ARM D 1 ( 33.3%)
14 AGECAT 30 to 39 ARM D 2 ( 66.7%)
15 AGECAT 40 to 49 ARM D 0 ( 0.0%)
16 AGECAT >= 50 ARM D 0 ( 0.0%)
Tranpose age group block
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
VAR LABEL ARM A ARM B ARM C ARM D
1 AGECAT 18 to 29 1 ( 20.0%) 1 ( 25.0%) 1 ( 25.0%) 1 ( 33.3%)
2 AGECAT 30 to 39 3 ( 60.0%) 3 ( 75.0%) 3 ( 75.0%) 2 ( 66.7%)
3 AGECAT 40 to 49 1 ( 20.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%)
4 AGECAT >= 50 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%)
Combine blocks into final data frame
datastep: columns started with 6 and ended with 6
VAR LABEL ARM A ARM B ARM C ARM D
1 AGE N 5 4 4 3
2 AGE Mean (SD) 34.4 (8.71) 30.0 (6.16) 32.0 (6.16) 32.0 (5.29)
3 AGE Median 37.0 32.5 34.5 30.0
4 AGE Q1 - Q3 37.0 - 39.0 26.0 - 34.0 28.0 - 36.0 28.0 - 38.0
5 AGE Min - Max 19 - 40 21 - 34 23 - 36 28 - 38
6 AGECAT 18 to 29 1 ( 20.0%) 1 ( 25.0%) 1 ( 25.0%) 1 ( 33.3%)
7 AGECAT 30 to 39 3 ( 60.0%) 3 ( 75.0%) 3 ( 75.0%) 2 ( 66.7%)
8 AGECAT 40 to 49 1 ( 20.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%)
9 SEX Male 3 ( 60.0%) 2 ( 50.0%) 2 ( 50.0%) 1 ( 33.3%)
10 SEX Female 2 ( 40.0%) 2 ( 50.0%) 2 ( 50.0%) 2 ( 66.7%)
11 RACE White 4 ( 80.0%) 4 (100.0%) 3 ( 75.0%) 2 ( 66.7%)
12 RACE Black or African American 1 ( 20.0%) 0 ( 0.0%) 1 ( 25.0%) 1 ( 33.3%)
=========================================================================
Create and print report
=========================================================================
Write out the report
# A report specification: 1 pages
- file_path: 'C:\Users\dbosa\AppData\Local\Temp\RtmpAXQUo8/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
- page_footer: left=Date Produced: 2024-05-28 center= right=Page [pg] of [tpg]
- content:
# A table specification:
- data: data.frame 'final' 12 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 'Placebo'
- define: ARM B 'Drug 50mg'
- define: ARM C 'Drug 100mg'
- define: ARM D 'Competitor'
=========================================================================
Clean Up
=========================================================================
Close log
=========================================================================
Log End Time: 2024-05-28 12:52:45.234875
Log Elapsed Time: 0 00:00:00
=========================================================================
```
Next: [Example 3: Figures](sassy-figure.html)