dicho: Dichotomize variables
In sjmisc: Data and Variable Transformation Functions
Description
Usage
Arguments
Details
Value
Note
Examples
Description
Dichotomizes variables into dummy variables (0/1). Dichotomization is
either done by median, mean or a specific value (see dich.by).
dicho_if() is a scoped variant of dicho(), where recoding
will be applied only to those variables that match the logical condition
of predicate.
Usage
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Arguments
x
A vector or data frame.
...
Optional, unquoted names of variables that should be selected for
further processing. Required, if x is a data frame (and no
vector) and only selected variables from x should be processed.
You may also use functions like : or tidyselect's
select-helpers.
See 'Examples' or package-vignette.
dich.by
Indicates the split criterion where a variable is dichotomized.
Must be one of the following values (may be abbreviated):
"median" or "md"by default, x is split into two groups at the median.
"mean" or "m"splits x into two groups at the mean of x.
- numeric value
splits x into two groups at the specific value. Note that the value is inclusive, i.e. dich.by = 10 will split x into one group with values from lowest to 10 and another group with values greater than 10.
as.num
Logical, if TRUE, return value will be numeric, not a factor.
var.label
Optional string, to set variable label attribute for the
returned variable (see vignette Labelled Data and the sjlabelled-Package).
If NULL (default), variable label attribute of x will
be used (if present). If empty, variable label attributes will be removed.
val.labels
Optional character vector (of length two), to set value label
attributes of dichotomized variable (see set_labels).
If NULL (default), no value labels will be set.
append
Logical, if TRUE (the default) and x is a data frame,
x including the new variables as additional columns is returned;
if FALSE, only the new variables are returned.
suffix
Indicates which suffix will be added to each dummy variable.
Use "numeric" to number dummy variables, e.g. x_1,
x_2, x_3 etc. Use "label" to add value label,
e.g. x_low, x_mid, x_high. May be abbreviated.
predicate
A predicate function to be applied to the columns. The
variables for which predicate returns TRUE are selected.
Details
dicho() also works on grouped data frames (see group_by).
In this case, dichotomization is applied to the subsets of variables
in x. See 'Examples'.
Value
x, dichotomized. If x is a data frame,
for append = TRUE, x including the dichotomized. variables
as new columns is returned; if append = FALSE, only
the dichotomized variables will be returned. If append = TRUE and
suffix = "", recoded variables will replace (overwrite) existing
variables.
Note
Variable label attributes are preserved (unless changed via
var.label-argument).
Examples
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data(efc)
summary(efc$c12hour)
# split at median
table(dicho(efc$c12hour))
# split at mean
table(dicho(efc$c12hour, dich.by = "mean"))
# split between value lowest to 30, and above 30
table(dicho(efc$c12hour, dich.by = 30))
# sample data frame, values from 1-4
head(efc[, 6:10])
# dichtomized values (1 to 2 = 0, 3 to 4 = 1)
library(dplyr)
efc %>%
select(6:10) %>%
dicho(dich.by = 2) %>%
head()
# dichtomize several variables in a data frame
dicho(efc, c12hour, e17age, c160age, append = FALSE)
# dichotomize and set labels
frq(dicho(
efc, e42dep,
var.label = "Dependency (dichotomized)",
val.labels = c("lower", "higher"),
append = FALSE
))
# works also with gouped data frames
mtcars %>%
dicho(disp, append = FALSE) %>%
table()
mtcars %>%
group_by(cyl) %>%
dicho(disp, append = FALSE) %>%
table()
# dichotomizing grouped data frames leads to different
# results for a dichotomized variable, because the split
# value is different for each group.
# compare:
mtcars %>%
group_by(cyl) %>%
summarise(median = median(disp))
median(mtcars$disp)
# dichotomize only variables with more than 10 unique values
p <- function(x) dplyr::n_distinct(x) > 10
dicho_if(efc, predicate = p, append = FALSE)
Example output
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
4.00 10.00 20.00 42.40 42.75 168.00 6
0 1
464 438
0 1
676 226
0 1
606 296
c82cop1 c83cop2 c84cop3 c85cop4 c86cop5
1 3 2 2 2 1
2 3 3 3 3 4
3 2 2 1 4 1
4 4 1 3 1 1
5 3 2 1 2 2
6 2 2 3 3 3
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
c82cop1 c83cop2 c84cop3 c85cop4 c86cop5 c82cop1_d c83cop2_d c84cop3_d
1 3 2 2 2 1 1 0 0
2 3 3 3 3 4 1 1 1
3 2 2 1 4 1 0 0 0
4 4 1 3 1 1 1 0 1
5 3 2 1 2 2 1 0 0
6 2 2 3 3 3 0 0 1
c85cop4_d c86cop5_d
1 0 0
2 1 1
3 1 0
4 0 0
5 0 0
6 1 1
c12hour_d e17age_d c160age_d
1 0 1 1
2 1 1 0
3 1 1 1
4 1 0 1
5 1 1 0
6 0 1 1
7 1 0 1
8 1 1 1
9 1 0 1
10 1 1 0
11 1 0 1
12 1 1 0
13 1 1 1
14 1 0 1
15 1 1 0
16 0 1 1
17 1 1 1
18 1 0 1
19 1 1 0
20 1 0 0
21 1 1 1
22 1 0 1
23 0 1 0
24 1 1 1
25 0 1 1
26 1 1 1
27 0 1 1
28 0 0 1
29 1 1 1
30 1 1 1
31 1 1 1
32 1 1 1
33 1 1 1
34 1 1 1
35 1 0 1
36 1 1 1
37 1 0 1
38 1 1 1
39 1 1 1
40 1 1 0
41 0 1 0
42 1 0 1
43 1 0 1
44 1 0 1
45 1 0 1
46 1 0 1
47 1 0 1
48 1 0 1
49 1 0 1
50 1 1 1
51 1 0 1
52 1 0 1
53 1 0 1
54 1 0 1
55 1 1 1
56 1 1 1
57 1 <NA> 1
58 1 1 0
59 1 0 1
60 1 0 1
61 1 1 1
62 1 1 1
63 0 1 1
64 1 0 0
65 1 1 1
66 0 1 0
67 0 0 0
68 1 1 1
69 1 1 1
70 1 0 1
71 0 1 0
72 1 <NA> 1
73 1 1 1
74 1 0 1
75 1 1 1
76 1 0 1
77 1 0 0
78 1 1 1
79 0 0 0
80 1 0 1
81 1 0 1
82 1 <NA> 1
83 1 0 1
84 0 0 0
85 1 0 1
86 1 0 1
87 1 0 1
88 1 0 1
89 1 0 0
90 1 1 1
91 1 0 1
92 0 0 1
93 1 0 1
94 0 1 1
95 1 0 1
96 0 1 0
97 0 0 1
98 1 1 1
99 1 1 1
100 0 0 0
101 1 0 1
102 1 0 1
103 0 0 1
104 1 1 0
105 1 1 1
106 0 0 1
107 1 0 1
108 1 0 1
109 0 1 0
110 1 1 0
111 1 0 1
112 0 1 0
113 1 1 1
114 1 1 1
115 1 0 0
116 1 1 1
117 0 1 1
118 1 1 1
119 1 0 1
120 1 1 1
121 0 1 1
122 1 0 1
123 1 1 1
124 1 0 1
125 1 1 1
126 1 0 1
127 0 1 0
128 1 1 1
129 0 1 0
130 1 0 1
131 1 1 1
132 1 0 1
133 1 1 1
134 0 0 0
135 0 1 1
136 0 1 1
137 1 1 1
138 0 1 0
139 1 0 1
140 1 1 1
141 0 0 0
142 0 1 0
143 0 1 1
144 0 1 1
145 1 1 0
146 1 1 1
147 1 0 1
148 1 0 1
149 1 0 1
150 1 1 0
151 1 1 1
152 1 1 1
153 0 1 1
154 1 1 1
155 1 0 1
156 1 0 1
157 0 0 0
158 1 1 1
159 1 1 1
160 0 0 1
161 1 0 0
162 0 1 1
163 0 1 1
164 1 <NA> 1
165 1 1 0
166 1 0 1
167 0 1 0
168 1 1 1
169 0 0 0
170 0 0 0
171 0 0 1
172 1 1 1
173 0 0 1
174 0 0 1
175 1 1 1
176 0 0 0
177 1 1 1
178 1 1 1
179 0 1 1
180 1 0 1
181 1 1 1
182 0 1 0
183 1 0 1
184 0 1 0
185 1 0 1
186 1 0 0
187 1 0 0
188 1 1 0
189 0 0 1
190 1 0 1
191 0 0 1
192 1 <NA> 1
193 0 1 1
194 1 0 1
195 1 1 1
196 1 0 1
197 1 1 1
198 0 1 0
199 0 1 1
200 0 1 1
201 0 1 1
202 1 1 1
203 1 1 1
204 1 1 0
205 0 1 1
206 0 1 0
207 1 1 0
208 1 1 1
209 1 1 1
210 1 1 1
211 0 1 1
212 0 1 1
213 1 0 0
214 1 1 0
215 0 0 0
216 0 1 1
217 0 1 0
218 1 1 0
219 1 0 0
220 0 0 0
221 1 1 1
222 1 1 1
223 1 1 0
224 0 1 1
225 1 1 1
226 1 0 0
227 1 1 1
228 0 1 1
229 0 0 1
230 0 0 0
231 0 1 1
232 0 1 1
233 1 1 1
234 1 1 1
235 0 1 0
236 0 1 0
237 1 0 1
238 0 0 0
239 0 0 0
240 0 0 0
241 0 0 0
242 0 1 0
243 0 1 0
244 0 1 1
245 0 1 0
246 1 0 0
247 0 0 1
248 0 1 0
249 1 1 1
250 0 1 1
251 0 1 1
252 0 0 0
253 1 0 1
254 1 0 0
255 0 1 0
256 0 1 1
257 0 0 0
258 0 1 1
259 0 1 1
260 0 1 1
261 0 1 1
262 1 1 1
263 1 1 1
264 0 0 0
265 0 1 1
266 1 0 1
267 0 0 0
268 0 1 1
269 0 1 1
270 1 0 0
271 0 0 0
272 0 0 0
273 0 0 0
274 0 0 0
275 1 0 0
276 1 1 0
277 1 1 1
278 0 0 0
279 1 0 1
280 1 0 1
281 1 0 0
282 1 0 0
283 1 0 0
284 1 0 0
285 0 1 0
286 0 0 0
287 0 1 1
288 0 1 0
289 1 1 1
290 1 0 0
291 0 1 1
292 0 1 1
293 1 1 1
294 1 1 1
295 1 1 1
296 0 0 0
297 0 0 0
298 0 1 1
299 0 0 1
300 0 0 0
301 1 1 1
302 1 0 1
303 0 0 0
304 1 1 0
305 1 0 0
306 0 1 0
307 1 1 0
308 1 0 1
309 0 0 1
310 0 0 1
311 1 1 1
312 0 0 0
313 1 0 0
314 1 1 0
315 0 0 1
316 0 0 1
317 1 0 0
318 1 1 1
319 0 1 0
320 1 1 0
321 0 1 0
322 0 1 0
323 0 1 0
324 1 1 0
325 0 1 0
326 1 1 0
327 0 1 1
328 1 0 0
329 0 0 0
330 0 <NA> 0
331 0 1 0
332 0 0 0
333 0 1 0
334 1 1 1
335 0 1 0
336 1 0 0
337 1 1 0
338 0 1 1
339 1 1 1
340 0 0 0
341 1 0 0
342 0 0 0
343 0 0 0
344 1 <NA> 0
345 0 0 0
346 1 1 0
347 0 0 0
348 0 1 0
349 0 0 0
350 0 1 0
351 1 1 0
352 0 1 1
353 0 0 0
354 0 0 0
355 0 0 0
356 0 0 1
357 0 1 1
358 0 0 0
359 1 0 1
360 0 1 1
361 1 1 1
362 0 1 1
363 1 0 1
364 1 0 0
365 0 0 0
366 0 0 0
367 0 0 0
368 1 0 1
369 1 1 0
370 0 0 1
371 1 1 0
372 1 1 0
373 1 1 0
374 0 0 0
375 1 1 1
376 0 0 0
377 0 0 0
378 1 1 0
379 1 0 0
380 1 0 0
381 1 0 0
382 0 0 0
383 1 1 0
384 0 1 1
385 0 1 0
386 0 0 0
387 0 0 1
388 1 1 0
389 0 1 0
390 0 1 0
391 1 1 0
392 0 1 1
393 0 0 1
394 0 0 0
395 0 0 0
396 1 0 1
397 0 0 0
398 0 1 0
399 1 0 1
400 1 0 1
401 0 0 0
402 1 0 1
403 1 1 1
404 0 1 1
405 0 0 0
406 1 1 0
407 1 1 1
408 0 1 0
409 0 1 1
410 0 1 1
411 1 1 1
412 0 0 0
413 0 0 1
414 1 0 1
415 0 0 1
416 0 0 1
417 0 0 0
418 1 0 1
419 1 0 0
420 1 1 1
421 1 1 1
422 0 0 0
423 1 1 1
424 0 1 0
425 0 1 0
426 1 0 1
427 1 0 1
428 1 1 0
429 1 1 0
430 1 1 1
431 0 0 0
432 0 1 0
433 0 1 1
434 0 0 0
435 0 1 1
436 0 0 0
437 1 1 0
438 1 1 1
439 1 0 1
440 0 1 0
441 1 1 0
442 1 0 0
443 1 0 1
444 1 1 0
445 0 1 0
446 1 0 0
447 0 1 1
448 1 <NA> 1
449 0 1 1
450 1 0 1
451 1 0 0
452 0 0 0
453 0 1 <NA>
454 0 1 0
455 0 1 0
456 1 1 1
457 0 0 0
458 1 1 1
459 0 1 1
460 0 1 0
461 0 1 1
462 1 0 1
463 1 1 1
464 1 1 1
465 1 0 1
466 1 1 0
467 1 0 0
468 0 1 1
469 0 0 0
470 0 0 0
471 1 1 1
472 0 0 0
473 0 0 0
474 0 1 1
475 0 0 1
476 0 0 0
477 1 0 1
478 0 0 0
479 0 0 0
480 0 0 0
481 1 0 1
482 0 1 1
483 1 0 1
484 1 1 0
485 1 0 0
486 0 0 0
487 1 1 1
488 0 1 1
489 0 0 0
490 0 1 1
491 0 0 0
492 0 0 0
493 1 1 1
494 0 0 0
495 1 <NA> 0
496 0 1 0
497 0 1 1
498 0 0 0
499 1 0 0
500 0 0 0
501 0 0 0
502 1 1 0
503 0 0 0
504 0 1 0
505 1 1 1
506 0 1 1
507 0 0 0
508 0 0 1
509 0 0 0
510 0 1 1
511 0 0 1
512 0 1 0
513 0 0 0
514 0 1 1
515 1 1 1
516 0 0 1
517 1 1 0
518 1 0 1
519 1 0 1
520 1 1 1
521 0 0 1
522 1 1 0
523 0 0 0
524 0 0 0
525 0 0 1
526 0 0 0
527 0 1 0
528 0 0 0
529 0 1 0
530 0 1 1
531 1 1 0
532 0 0 0
533 0 0 0
534 0 1 0
535 0 0 0
536 0 0 0
537 0 1 0
538 1 1 0
539 0 1 0
540 1 1 0
541 0 1 1
542 0 1 0
543 1 0 1
544 0 0 0
545 1 0 0
546 1 0 0
547 1 0 0
548 1 1 1
549 0 0 0
550 0 1 0
551 1 0 0
552 0 1 0
553 0 1 0
554 1 1 0
555 0 0 0
556 0 0 0
557 1 0 1
558 1 1 0
559 1 1 0
560 1 0 0
561 1 0 1
562 1 1 0
563 1 0 1
564 0 1 1
565 1 0 0
566 0 1 1
567 0 1 0
568 1 0 1
569 1 <NA> 1
570 1 1 1
571 1 0 0
572 1 0 1
573 1 1 0
574 1 1 0
575 1 1 1
576 1 0 0
577 0 1 0
578 1 0 1
579 1 0 0
580 1 0 1
581 0 0 1
582 1 1 1
583 0 1 1
584 1 1 1
585 0 0 0
586 1 0 1
587 0 1 1
588 1 1 0
589 0 0 0
590 1 0 1
591 1 1 1
592 1 1 0
593 1 1 1
594 1 0 0
595 0 0 1
596 1 1 0
597 1 0 0
598 1 0 0
599 1 1 0
600 1 1 0
601 0 1 1
602 1 1 1
603 1 0 0
604 0 1 0
605 0 0 0
606 0 1 1
607 0 0 0
608 0 0 0
609 0 1 0
610 0 0 0
611 1 0 0
612 0 0 0
613 1 0 1
614 1 0 0
615 1 1 1
616 0 <NA> 0
617 0 1 0
618 0 0 0
619 0 1 0
620 0 1 0
621 0 1 0
622 0 1 0
623 0 1 1
624 0 0 0
625 0 1 0
626 0 0 0
627 0 0 0
628 0 1 0
629 0 1 1
630 1 1 1
631 0 0 0
632 0 0 1
633 1 0 0
634 0 1 1
635 1 1 1
636 0 0 0
637 0 0 0
638 0 0 1
639 0 1 0
640 0 0 1
641 1 1 0
642 1 0 1
643 1 0 0
644 1 0 0
645 1 0 0
646 1 0 0
647 0 1 1
648 1 0 0
649 0 0 1
650 0 0 0
651 0 0 0
652 1 1 1
653 1 0 0
654 1 1 1
655 0 0 0
656 0 0 0
657 1 0 0
658 1 1 1
659 1 1 1
660 1 1 1
661 1 1 1
662 0 0 0
663 0 0 1
664 1 0 0
665 0 1 0
666 0 0 0
667 0 0 1
668 0 1 1
669 0 0 0
670 0 0 0
671 1 0 1
672 0 0 0
673 1 0 0
674 1 0 1
675 1 0 0
676 1 0 1
677 1 1 1
678 1 1 1
679 0 0 0
680 1 1 1
681 0 0 0
682 0 0 0
683 0 0 0
684 0 1 0
685 0 1 0
686 0 0 1
687 0 0 0
688 0 0 1
689 0 0 1
690 1 1 1
691 1 1 0
692 0 0 0
693 1 1 1
694 0 0 0
695 1 1 0
696 1 1 1
697 1 0 1
698 0 1 0
699 0 1 1
700 1 1 0
701 0 0 0
702 0 0 0
703 0 0 1
704 0 1 0
705 0 0 0
706 0 1 1
707 0 0 0
708 0 0 1
709 1 0 1
710 0 0 1
711 0 1 0
712 1 0 1
713 0 0 0
714 1 0 1
715 1 0 0
716 1 0 0
717 1 0 0
718 0 0 1
719 0 0 0
720 0 1 1
721 0 0 0
722 0 0 0
723 0 1 0
724 1 0 0
725 1 0 0
726 1 1 1
727 1 0 1
728 1 0 0
729 1 1 1
730 0 1 0
731 0 0 0
732 1 1 1
733 0 1 1
734 1 1 0
735 0 0 1
736 1 1 1
737 0 0 1
738 1 1 1
739 1 1 1
740 0 1 1
741 1 1 0
742 0 0 0
743 1 1 0
744 0 1 0
745 1 1 1
746 0 0 0
747 0 0 0
748 1 0 0
749 1 1 0
750 1 1 1
751 1 1 1
752 0 1 1
753 1 1 0
754 1 1 1
755 1 1 1
756 0 0 1
757 0 0 0
758 0 1 1
759 0 1 0
760 0 0 1
761 0 0 0
762 0 1 1
763 0 0 1
764 0 1 0
765 0 1 1
766 0 1 0
767 0 0 1
768 0 0 0
769 1 0 1
770 0 0 0
771 0 1 1
772 0 0 0
773 0 1 1
774 0 1 0
775 0 1 0
776 0 0 0
777 0 1 1
778 0 0 0
779 0 0 0
780 1 0 1
781 0 0 1
782 0 0 0
783 1 0 0
784 1 0 0
785 1 1 0
786 0 1 0
787 1 0 0
788 1 0 0
789 0 1 0
790 0 0 0
791 0 0 0
792 0 0 0
793 0 0 1
794 1 1 1
795 0 1 0
796 0 0 0
797 0 1 0
798 0 0 0
799 0 0 0
800 1 1 0
801 1 1 1
802 1 0 0
803 1 1 0
804 1 0 1
805 1 1 1
806 1 0 1
807 1 0 1
808 1 1 1
809 1 1 1
810 1 1 1
811 0 1 0
812 0 1 0
813 0 0 0
814 1 0 0
815 0 0 0
816 0 0 0
817 1 1 1
818 0 1 0
819 0 0 0
820 0 0 0
821 0 0 0
822 0 0 0
823 0 0 0
824 0 0 0
825 0 0 0
826 0 0 0
827 1 0 0
828 0 0 0
829 0 0 0
830 0 0 0
831 0 1 0
832 1 0 1
833 0 0 1
834 1 1 0
835 0 0 0
836 0 0 0
837 0 0 1
838 0 1 1
839 1 0 0
840 1 0 0
841 1 1 0
842 1 1 1
843 0 1 1
844 0 0 0
845 0 0 0
846 0 0 0
847 0 1 1
848 0 0 0
849 0 0 0
850 0 0 0
851 0 0 0
852 0 0 0
853 0 1 0
854 0 0 0
855 1 1 0
856 0 1 0
857 0 1 0
858 0 0 0
859 1 1 1
860 0 0 0
861 0 1 0
862 0 0 0
863 0 1 0
864 0 1 1
865 0 1 1
866 0 1 0
867 0 0 1
868 0 0 1
869 0 0 0
870 0 0 0
871 0 0 0
872 0 0 0
873 0 0 0
874 0 1 0
875 0 0 1
876 0 0 0
877 0 1 1
878 1 0 0
879 1 0 1
880 0 0 0
881 1 1 0
882 0 0 0
883 0 1 0
884 0 1 1
885 1 0 0
886 1 0 1
887 1 1 1
888 0 0 0
889 1 0 1
890 1 1 1
891 0 0 0
892 1 0 0
893 1 0 0
894 1 1 1
895 0 1 0
896 0 1 1
897 1 0 0
898 1 1 1
899 1 1 0
900 1 0 1
901 0 0 0
902 0 0 0
903 <NA> <NA> <NA>
904 <NA> <NA> <NA>
905 <NA> <NA> <NA>
906 <NA> <NA> <NA>
907 <NA> <NA> <NA>
908 <NA> <NA> <NA>
# Dependency (dichotomized) (e42dep_d) <categorical>
# total N=908 valid N=901 mean=0.34 sd=0.47
val label frq raw.prc valid.prc cum.prc
0 lower 597 65.75 66.26 66.26
1 higher 304 33.48 33.74 100.00
NA NA 7 0.77 NA NA
.
0 1
16 16
.
1 2
18 14
# A tibble: 3 x 2
cyl median
<dbl> <dbl>
1 4 108
2 6 168.
3 8 350.
[1] 196.3
c12hour_d e17age_d c160age_d barthtot_d neg_c_7_d pos_v_4_d quol_5_d
1 0 1 1 1 1 0 0
2 1 1 0 1 1 0 0
3 1 1 1 0 0 0 0
4 1 0 1 0 0 1 0
5 1 1 0 0 1 1 1
6 0 1 1 0 1 0 0
7 1 0 1 0 1 0 1
8 1 1 1 0 0 1 1
9 1 0 1 0 1 0 0
10 1 1 0 0 0 0 0
11 1 0 1 0 1 0 0
12 1 1 0 1 1 0 1
13 1 1 1 0 1 1 0
14 1 0 1 0 1 1 0
15 1 1 0 <NA> 1 0 0
16 0 1 1 0 1 1 0
17 1 1 1 1 0 1 1
18 1 0 1 0 0 0 0
19 1 1 0 0 1 0 0
20 1 0 0 0 1 0 0
21 1 1 1 0 0 0 0
22 1 0 1 0 0 0 1
23 0 1 0 0 0 1 1
24 1 1 1 0 1 1 0
25 0 1 1 1 0 1 1
26 1 1 1 0 1 0 0
27 0 1 1 1 <NA> 0 0
28 0 0 1 1 0 0 0
29 1 1 1 0 1 0 0
30 1 1 1 0 0 <NA> 0
31 1 1 1 <NA> 1 0 0
32 1 1 1 0 0 0 1
33 1 1 1 1 1 0 0
34 1 1 1 0 1 0 0
35 1 0 1 0 0 1 0
36 1 1 1 0 0 1 0
37 1 0 1 0 1 0 0
38 1 1 1 1 0 1 1
39 1 1 1 0 1 1 0
40 1 1 0 0 1 0 0
41 0 1 0 1 1 0 0
42 1 0 1 <NA> 1 1 0
43 1 0 1 0 1 0 0
44 1 0 1 0 1 0 1
45 1 0 1 0 1 0 0
46 1 0 1 0 <NA> 0 0
47 1 0 1 <NA> 1 0 0
48 1 0 1 0 1 0 0
49 1 0 1 1 1 1 0
50 1 1 1 0 1 0 1
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851 0 0 0 1 0 0 1
852 0 0 0 0 0 1 0
853 0 1 0 1 0 0 1
854 0 0 0 1 0 1 1
855 1 1 0 0 0 1 1
856 0 1 0 0 0 1 1
857 0 1 0 1 0 1 1
858 0 0 0 1 0 0 1
859 1 1 1 1 1 0 0
860 0 0 0 1 0 0 1
861 0 1 0 1 0 1 1
862 0 0 0 1 0 1 1
863 0 1 0 1 0 0 0
864 0 1 1 1 0 0 1
865 0 1 1 1 0 0 0
866 0 1 0 1 0 1 1
867 0 0 1 1 0 0 1
868 0 0 1 1 0 1 1
869 0 0 0 1 0 0 0
870 0 0 0 1 0 1 1
871 0 0 0 1 0 1 1
872 0 0 0 1 0 0 1
873 0 0 0 1 0 0 1
874 0 1 0 1 0 0 0
875 0 0 1 1 0 1 1
876 0 0 0 1 1 0 1
877 0 1 1 0 1 0 0
878 1 0 0 0 0 0 0
879 1 0 1 0 1 0 0
880 0 0 0 1 0 0 1
881 1 1 0 1 0 0 0
882 0 0 0 1 0 0 0
883 0 1 0 1 0 0 0
884 0 1 1 0 1 0 0
885 1 0 0 0 1 0 0
886 1 0 1 0 1 0 0
887 1 1 1 0 1 0 0
888 0 0 0 0 0 1 1
889 1 0 1 1 0 0 0
890 1 1 1 1 1 0 0
891 0 0 0 1 0 1 0
892 1 0 0 0 0 0 0
893 1 0 0 0 1 1 0
894 1 1 1 0 1 0 0
895 0 1 0 0 1 0 0
896 0 1 1 1 1 0 0
897 1 0 0 0 1 0 0
898 1 1 1 1 0 1 1
899 1 1 0 0 0 0 0
900 1 0 1 0 1 1 0
901 0 0 0 1 0 1 1
902 0 0 0 1 0 0 1
903 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
904 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
905 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
906 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
907 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
908 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
sjmisc documentation built on Dec. 11, 2021, 9:34 a.m.
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Description Usage Arguments Details Value Note Examples
Description
Dichotomizes variables into dummy variables (0/1). Dichotomization is
either done by median, mean or a specific value (see dich.by).
dicho_if() is a scoped variant of dicho(), where recoding
will be applied only to those variables that match the logical condition
of predicate.
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 |
Arguments
x |
A vector or data frame. |
... |
Optional, unquoted names of variables that should be selected for
further processing. Required, if |
dich.by |
Indicates the split criterion where a variable is dichotomized. Must be one of the following values (may be abbreviated):
|
as.num |
Logical, if |
var.label |
Optional string, to set variable label attribute for the
returned variable (see vignette Labelled Data and the sjlabelled-Package).
If |
val.labels |
Optional character vector (of length two), to set value label
attributes of dichotomized variable (see |
append |
Logical, if |
suffix |
Indicates which suffix will be added to each dummy variable.
Use |
predicate |
A predicate function to be applied to the columns. The
variables for which |
Details
dicho() also works on grouped data frames (see group_by).
In this case, dichotomization is applied to the subsets of variables
in x. See 'Examples'.
Value
x, dichotomized. If x is a data frame,
for append = TRUE, x including the dichotomized. variables
as new columns is returned; if append = FALSE, only
the dichotomized variables will be returned. If append = TRUE and
suffix = "", recoded variables will replace (overwrite) existing
variables.
Note
Variable label attributes are preserved (unless changed via
var.label-argument).
Examples
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 | data(efc)
summary(efc$c12hour)
# split at median
table(dicho(efc$c12hour))
# split at mean
table(dicho(efc$c12hour, dich.by = "mean"))
# split between value lowest to 30, and above 30
table(dicho(efc$c12hour, dich.by = 30))
# sample data frame, values from 1-4
head(efc[, 6:10])
# dichtomized values (1 to 2 = 0, 3 to 4 = 1)
library(dplyr)
efc %>%
select(6:10) %>%
dicho(dich.by = 2) %>%
head()
# dichtomize several variables in a data frame
dicho(efc, c12hour, e17age, c160age, append = FALSE)
# dichotomize and set labels
frq(dicho(
efc, e42dep,
var.label = "Dependency (dichotomized)",
val.labels = c("lower", "higher"),
append = FALSE
))
# works also with gouped data frames
mtcars %>%
dicho(disp, append = FALSE) %>%
table()
mtcars %>%
group_by(cyl) %>%
dicho(disp, append = FALSE) %>%
table()
# dichotomizing grouped data frames leads to different
# results for a dichotomized variable, because the split
# value is different for each group.
# compare:
mtcars %>%
group_by(cyl) %>%
summarise(median = median(disp))
median(mtcars$disp)
# dichotomize only variables with more than 10 unique values
p <- function(x) dplyr::n_distinct(x) > 10
dicho_if(efc, predicate = p, append = FALSE)
|
Example output
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
4.00 10.00 20.00 42.40 42.75 168.00 6
0 1
464 438
0 1
676 226
0 1
606 296
c82cop1 c83cop2 c84cop3 c85cop4 c86cop5
1 3 2 2 2 1
2 3 3 3 3 4
3 2 2 1 4 1
4 4 1 3 1 1
5 3 2 1 2 2
6 2 2 3 3 3
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
c82cop1 c83cop2 c84cop3 c85cop4 c86cop5 c82cop1_d c83cop2_d c84cop3_d
1 3 2 2 2 1 1 0 0
2 3 3 3 3 4 1 1 1
3 2 2 1 4 1 0 0 0
4 4 1 3 1 1 1 0 1
5 3 2 1 2 2 1 0 0
6 2 2 3 3 3 0 0 1
c85cop4_d c86cop5_d
1 0 0
2 1 1
3 1 0
4 0 0
5 0 0
6 1 1
c12hour_d e17age_d c160age_d
1 0 1 1
2 1 1 0
3 1 1 1
4 1 0 1
5 1 1 0
6 0 1 1
7 1 0 1
8 1 1 1
9 1 0 1
10 1 1 0
11 1 0 1
12 1 1 0
13 1 1 1
14 1 0 1
15 1 1 0
16 0 1 1
17 1 1 1
18 1 0 1
19 1 1 0
20 1 0 0
21 1 1 1
22 1 0 1
23 0 1 0
24 1 1 1
25 0 1 1
26 1 1 1
27 0 1 1
28 0 0 1
29 1 1 1
30 1 1 1
31 1 1 1
32 1 1 1
33 1 1 1
34 1 1 1
35 1 0 1
36 1 1 1
37 1 0 1
38 1 1 1
39 1 1 1
40 1 1 0
41 0 1 0
42 1 0 1
43 1 0 1
44 1 0 1
45 1 0 1
46 1 0 1
47 1 0 1
48 1 0 1
49 1 0 1
50 1 1 1
51 1 0 1
52 1 0 1
53 1 0 1
54 1 0 1
55 1 1 1
56 1 1 1
57 1 <NA> 1
58 1 1 0
59 1 0 1
60 1 0 1
61 1 1 1
62 1 1 1
63 0 1 1
64 1 0 0
65 1 1 1
66 0 1 0
67 0 0 0
68 1 1 1
69 1 1 1
70 1 0 1
71 0 1 0
72 1 <NA> 1
73 1 1 1
74 1 0 1
75 1 1 1
76 1 0 1
77 1 0 0
78 1 1 1
79 0 0 0
80 1 0 1
81 1 0 1
82 1 <NA> 1
83 1 0 1
84 0 0 0
85 1 0 1
86 1 0 1
87 1 0 1
88 1 0 1
89 1 0 0
90 1 1 1
91 1 0 1
92 0 0 1
93 1 0 1
94 0 1 1
95 1 0 1
96 0 1 0
97 0 0 1
98 1 1 1
99 1 1 1
100 0 0 0
101 1 0 1
102 1 0 1
103 0 0 1
104 1 1 0
105 1 1 1
106 0 0 1
107 1 0 1
108 1 0 1
109 0 1 0
110 1 1 0
111 1 0 1
112 0 1 0
113 1 1 1
114 1 1 1
115 1 0 0
116 1 1 1
117 0 1 1
118 1 1 1
119 1 0 1
120 1 1 1
121 0 1 1
122 1 0 1
123 1 1 1
124 1 0 1
125 1 1 1
126 1 0 1
127 0 1 0
128 1 1 1
129 0 1 0
130 1 0 1
131 1 1 1
132 1 0 1
133 1 1 1
134 0 0 0
135 0 1 1
136 0 1 1
137 1 1 1
138 0 1 0
139 1 0 1
140 1 1 1
141 0 0 0
142 0 1 0
143 0 1 1
144 0 1 1
145 1 1 0
146 1 1 1
147 1 0 1
148 1 0 1
149 1 0 1
150 1 1 0
151 1 1 1
152 1 1 1
153 0 1 1
154 1 1 1
155 1 0 1
156 1 0 1
157 0 0 0
158 1 1 1
159 1 1 1
160 0 0 1
161 1 0 0
162 0 1 1
163 0 1 1
164 1 <NA> 1
165 1 1 0
166 1 0 1
167 0 1 0
168 1 1 1
169 0 0 0
170 0 0 0
171 0 0 1
172 1 1 1
173 0 0 1
174 0 0 1
175 1 1 1
176 0 0 0
177 1 1 1
178 1 1 1
179 0 1 1
180 1 0 1
181 1 1 1
182 0 1 0
183 1 0 1
184 0 1 0
185 1 0 1
186 1 0 0
187 1 0 0
188 1 1 0
189 0 0 1
190 1 0 1
191 0 0 1
192 1 <NA> 1
193 0 1 1
194 1 0 1
195 1 1 1
196 1 0 1
197 1 1 1
198 0 1 0
199 0 1 1
200 0 1 1
201 0 1 1
202 1 1 1
203 1 1 1
204 1 1 0
205 0 1 1
206 0 1 0
207 1 1 0
208 1 1 1
209 1 1 1
210 1 1 1
211 0 1 1
212 0 1 1
213 1 0 0
214 1 1 0
215 0 0 0
216 0 1 1
217 0 1 0
218 1 1 0
219 1 0 0
220 0 0 0
221 1 1 1
222 1 1 1
223 1 1 0
224 0 1 1
225 1 1 1
226 1 0 0
227 1 1 1
228 0 1 1
229 0 0 1
230 0 0 0
231 0 1 1
232 0 1 1
233 1 1 1
234 1 1 1
235 0 1 0
236 0 1 0
237 1 0 1
238 0 0 0
239 0 0 0
240 0 0 0
241 0 0 0
242 0 1 0
243 0 1 0
244 0 1 1
245 0 1 0
246 1 0 0
247 0 0 1
248 0 1 0
249 1 1 1
250 0 1 1
251 0 1 1
252 0 0 0
253 1 0 1
254 1 0 0
255 0 1 0
256 0 1 1
257 0 0 0
258 0 1 1
259 0 1 1
260 0 1 1
261 0 1 1
262 1 1 1
263 1 1 1
264 0 0 0
265 0 1 1
266 1 0 1
267 0 0 0
268 0 1 1
269 0 1 1
270 1 0 0
271 0 0 0
272 0 0 0
273 0 0 0
274 0 0 0
275 1 0 0
276 1 1 0
277 1 1 1
278 0 0 0
279 1 0 1
280 1 0 1
281 1 0 0
282 1 0 0
283 1 0 0
284 1 0 0
285 0 1 0
286 0 0 0
287 0 1 1
288 0 1 0
289 1 1 1
290 1 0 0
291 0 1 1
292 0 1 1
293 1 1 1
294 1 1 1
295 1 1 1
296 0 0 0
297 0 0 0
298 0 1 1
299 0 0 1
300 0 0 0
301 1 1 1
302 1 0 1
303 0 0 0
304 1 1 0
305 1 0 0
306 0 1 0
307 1 1 0
308 1 0 1
309 0 0 1
310 0 0 1
311 1 1 1
312 0 0 0
313 1 0 0
314 1 1 0
315 0 0 1
316 0 0 1
317 1 0 0
318 1 1 1
319 0 1 0
320 1 1 0
321 0 1 0
322 0 1 0
323 0 1 0
324 1 1 0
325 0 1 0
326 1 1 0
327 0 1 1
328 1 0 0
329 0 0 0
330 0 <NA> 0
331 0 1 0
332 0 0 0
333 0 1 0
334 1 1 1
335 0 1 0
336 1 0 0
337 1 1 0
338 0 1 1
339 1 1 1
340 0 0 0
341 1 0 0
342 0 0 0
343 0 0 0
344 1 <NA> 0
345 0 0 0
346 1 1 0
347 0 0 0
348 0 1 0
349 0 0 0
350 0 1 0
351 1 1 0
352 0 1 1
353 0 0 0
354 0 0 0
355 0 0 0
356 0 0 1
357 0 1 1
358 0 0 0
359 1 0 1
360 0 1 1
361 1 1 1
362 0 1 1
363 1 0 1
364 1 0 0
365 0 0 0
366 0 0 0
367 0 0 0
368 1 0 1
369 1 1 0
370 0 0 1
371 1 1 0
372 1 1 0
373 1 1 0
374 0 0 0
375 1 1 1
376 0 0 0
377 0 0 0
378 1 1 0
379 1 0 0
380 1 0 0
381 1 0 0
382 0 0 0
383 1 1 0
384 0 1 1
385 0 1 0
386 0 0 0
387 0 0 1
388 1 1 0
389 0 1 0
390 0 1 0
391 1 1 0
392 0 1 1
393 0 0 1
394 0 0 0
395 0 0 0
396 1 0 1
397 0 0 0
398 0 1 0
399 1 0 1
400 1 0 1
401 0 0 0
402 1 0 1
403 1 1 1
404 0 1 1
405 0 0 0
406 1 1 0
407 1 1 1
408 0 1 0
409 0 1 1
410 0 1 1
411 1 1 1
412 0 0 0
413 0 0 1
414 1 0 1
415 0 0 1
416 0 0 1
417 0 0 0
418 1 0 1
419 1 0 0
420 1 1 1
421 1 1 1
422 0 0 0
423 1 1 1
424 0 1 0
425 0 1 0
426 1 0 1
427 1 0 1
428 1 1 0
429 1 1 0
430 1 1 1
431 0 0 0
432 0 1 0
433 0 1 1
434 0 0 0
435 0 1 1
436 0 0 0
437 1 1 0
438 1 1 1
439 1 0 1
440 0 1 0
441 1 1 0
442 1 0 0
443 1 0 1
444 1 1 0
445 0 1 0
446 1 0 0
447 0 1 1
448 1 <NA> 1
449 0 1 1
450 1 0 1
451 1 0 0
452 0 0 0
453 0 1 <NA>
454 0 1 0
455 0 1 0
456 1 1 1
457 0 0 0
458 1 1 1
459 0 1 1
460 0 1 0
461 0 1 1
462 1 0 1
463 1 1 1
464 1 1 1
465 1 0 1
466 1 1 0
467 1 0 0
468 0 1 1
469 0 0 0
470 0 0 0
471 1 1 1
472 0 0 0
473 0 0 0
474 0 1 1
475 0 0 1
476 0 0 0
477 1 0 1
478 0 0 0
479 0 0 0
480 0 0 0
481 1 0 1
482 0 1 1
483 1 0 1
484 1 1 0
485 1 0 0
486 0 0 0
487 1 1 1
488 0 1 1
489 0 0 0
490 0 1 1
491 0 0 0
492 0 0 0
493 1 1 1
494 0 0 0
495 1 <NA> 0
496 0 1 0
497 0 1 1
498 0 0 0
499 1 0 0
500 0 0 0
501 0 0 0
502 1 1 0
503 0 0 0
504 0 1 0
505 1 1 1
506 0 1 1
507 0 0 0
508 0 0 1
509 0 0 0
510 0 1 1
511 0 0 1
512 0 1 0
513 0 0 0
514 0 1 1
515 1 1 1
516 0 0 1
517 1 1 0
518 1 0 1
519 1 0 1
520 1 1 1
521 0 0 1
522 1 1 0
523 0 0 0
524 0 0 0
525 0 0 1
526 0 0 0
527 0 1 0
528 0 0 0
529 0 1 0
530 0 1 1
531 1 1 0
532 0 0 0
533 0 0 0
534 0 1 0
535 0 0 0
536 0 0 0
537 0 1 0
538 1 1 0
539 0 1 0
540 1 1 0
541 0 1 1
542 0 1 0
543 1 0 1
544 0 0 0
545 1 0 0
546 1 0 0
547 1 0 0
548 1 1 1
549 0 0 0
550 0 1 0
551 1 0 0
552 0 1 0
553 0 1 0
554 1 1 0
555 0 0 0
556 0 0 0
557 1 0 1
558 1 1 0
559 1 1 0
560 1 0 0
561 1 0 1
562 1 1 0
563 1 0 1
564 0 1 1
565 1 0 0
566 0 1 1
567 0 1 0
568 1 0 1
569 1 <NA> 1
570 1 1 1
571 1 0 0
572 1 0 1
573 1 1 0
574 1 1 0
575 1 1 1
576 1 0 0
577 0 1 0
578 1 0 1
579 1 0 0
580 1 0 1
581 0 0 1
582 1 1 1
583 0 1 1
584 1 1 1
585 0 0 0
586 1 0 1
587 0 1 1
588 1 1 0
589 0 0 0
590 1 0 1
591 1 1 1
592 1 1 0
593 1 1 1
594 1 0 0
595 0 0 1
596 1 1 0
597 1 0 0
598 1 0 0
599 1 1 0
600 1 1 0
601 0 1 1
602 1 1 1
603 1 0 0
604 0 1 0
605 0 0 0
606 0 1 1
607 0 0 0
608 0 0 0
609 0 1 0
610 0 0 0
611 1 0 0
612 0 0 0
613 1 0 1
614 1 0 0
615 1 1 1
616 0 <NA> 0
617 0 1 0
618 0 0 0
619 0 1 0
620 0 1 0
621 0 1 0
622 0 1 0
623 0 1 1
624 0 0 0
625 0 1 0
626 0 0 0
627 0 0 0
628 0 1 0
629 0 1 1
630 1 1 1
631 0 0 0
632 0 0 1
633 1 0 0
634 0 1 1
635 1 1 1
636 0 0 0
637 0 0 0
638 0 0 1
639 0 1 0
640 0 0 1
641 1 1 0
642 1 0 1
643 1 0 0
644 1 0 0
645 1 0 0
646 1 0 0
647 0 1 1
648 1 0 0
649 0 0 1
650 0 0 0
651 0 0 0
652 1 1 1
653 1 0 0
654 1 1 1
655 0 0 0
656 0 0 0
657 1 0 0
658 1 1 1
659 1 1 1
660 1 1 1
661 1 1 1
662 0 0 0
663 0 0 1
664 1 0 0
665 0 1 0
666 0 0 0
667 0 0 1
668 0 1 1
669 0 0 0
670 0 0 0
671 1 0 1
672 0 0 0
673 1 0 0
674 1 0 1
675 1 0 0
676 1 0 1
677 1 1 1
678 1 1 1
679 0 0 0
680 1 1 1
681 0 0 0
682 0 0 0
683 0 0 0
684 0 1 0
685 0 1 0
686 0 0 1
687 0 0 0
688 0 0 1
689 0 0 1
690 1 1 1
691 1 1 0
692 0 0 0
693 1 1 1
694 0 0 0
695 1 1 0
696 1 1 1
697 1 0 1
698 0 1 0
699 0 1 1
700 1 1 0
701 0 0 0
702 0 0 0
703 0 0 1
704 0 1 0
705 0 0 0
706 0 1 1
707 0 0 0
708 0 0 1
709 1 0 1
710 0 0 1
711 0 1 0
712 1 0 1
713 0 0 0
714 1 0 1
715 1 0 0
716 1 0 0
717 1 0 0
718 0 0 1
719 0 0 0
720 0 1 1
721 0 0 0
722 0 0 0
723 0 1 0
724 1 0 0
725 1 0 0
726 1 1 1
727 1 0 1
728 1 0 0
729 1 1 1
730 0 1 0
731 0 0 0
732 1 1 1
733 0 1 1
734 1 1 0
735 0 0 1
736 1 1 1
737 0 0 1
738 1 1 1
739 1 1 1
740 0 1 1
741 1 1 0
742 0 0 0
743 1 1 0
744 0 1 0
745 1 1 1
746 0 0 0
747 0 0 0
748 1 0 0
749 1 1 0
750 1 1 1
751 1 1 1
752 0 1 1
753 1 1 0
754 1 1 1
755 1 1 1
756 0 0 1
757 0 0 0
758 0 1 1
759 0 1 0
760 0 0 1
761 0 0 0
762 0 1 1
763 0 0 1
764 0 1 0
765 0 1 1
766 0 1 0
767 0 0 1
768 0 0 0
769 1 0 1
770 0 0 0
771 0 1 1
772 0 0 0
773 0 1 1
774 0 1 0
775 0 1 0
776 0 0 0
777 0 1 1
778 0 0 0
779 0 0 0
780 1 0 1
781 0 0 1
782 0 0 0
783 1 0 0
784 1 0 0
785 1 1 0
786 0 1 0
787 1 0 0
788 1 0 0
789 0 1 0
790 0 0 0
791 0 0 0
792 0 0 0
793 0 0 1
794 1 1 1
795 0 1 0
796 0 0 0
797 0 1 0
798 0 0 0
799 0 0 0
800 1 1 0
801 1 1 1
802 1 0 0
803 1 1 0
804 1 0 1
805 1 1 1
806 1 0 1
807 1 0 1
808 1 1 1
809 1 1 1
810 1 1 1
811 0 1 0
812 0 1 0
813 0 0 0
814 1 0 0
815 0 0 0
816 0 0 0
817 1 1 1
818 0 1 0
819 0 0 0
820 0 0 0
821 0 0 0
822 0 0 0
823 0 0 0
824 0 0 0
825 0 0 0
826 0 0 0
827 1 0 0
828 0 0 0
829 0 0 0
830 0 0 0
831 0 1 0
832 1 0 1
833 0 0 1
834 1 1 0
835 0 0 0
836 0 0 0
837 0 0 1
838 0 1 1
839 1 0 0
840 1 0 0
841 1 1 0
842 1 1 1
843 0 1 1
844 0 0 0
845 0 0 0
846 0 0 0
847 0 1 1
848 0 0 0
849 0 0 0
850 0 0 0
851 0 0 0
852 0 0 0
853 0 1 0
854 0 0 0
855 1 1 0
856 0 1 0
857 0 1 0
858 0 0 0
859 1 1 1
860 0 0 0
861 0 1 0
862 0 0 0
863 0 1 0
864 0 1 1
865 0 1 1
866 0 1 0
867 0 0 1
868 0 0 1
869 0 0 0
870 0 0 0
871 0 0 0
872 0 0 0
873 0 0 0
874 0 1 0
875 0 0 1
876 0 0 0
877 0 1 1
878 1 0 0
879 1 0 1
880 0 0 0
881 1 1 0
882 0 0 0
883 0 1 0
884 0 1 1
885 1 0 0
886 1 0 1
887 1 1 1
888 0 0 0
889 1 0 1
890 1 1 1
891 0 0 0
892 1 0 0
893 1 0 0
894 1 1 1
895 0 1 0
896 0 1 1
897 1 0 0
898 1 1 1
899 1 1 0
900 1 0 1
901 0 0 0
902 0 0 0
903 <NA> <NA> <NA>
904 <NA> <NA> <NA>
905 <NA> <NA> <NA>
906 <NA> <NA> <NA>
907 <NA> <NA> <NA>
908 <NA> <NA> <NA>
# Dependency (dichotomized) (e42dep_d) <categorical>
# total N=908 valid N=901 mean=0.34 sd=0.47
val label frq raw.prc valid.prc cum.prc
0 lower 597 65.75 66.26 66.26
1 higher 304 33.48 33.74 100.00
NA NA 7 0.77 NA NA
.
0 1
16 16
.
1 2
18 14
# A tibble: 3 x 2
cyl median
<dbl> <dbl>
1 4 108
2 6 168.
3 8 350.
[1] 196.3
c12hour_d e17age_d c160age_d barthtot_d neg_c_7_d pos_v_4_d quol_5_d
1 0 1 1 1 1 0 0
2 1 1 0 1 1 0 0
3 1 1 1 0 0 0 0
4 1 0 1 0 0 1 0
5 1 1 0 0 1 1 1
6 0 1 1 0 1 0 0
7 1 0 1 0 1 0 1
8 1 1 1 0 0 1 1
9 1 0 1 0 1 0 0
10 1 1 0 0 0 0 0
11 1 0 1 0 1 0 0
12 1 1 0 1 1 0 1
13 1 1 1 0 1 1 0
14 1 0 1 0 1 1 0
15 1 1 0 <NA> 1 0 0
16 0 1 1 0 1 1 0
17 1 1 1 1 0 1 1
18 1 0 1 0 0 0 0
19 1 1 0 0 1 0 0
20 1 0 0 0 1 0 0
21 1 1 1 0 0 0 0
22 1 0 1 0 0 0 1
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824 0 0 0 1 1 0 1
825 0 0 0 1 1 1 1
826 0 0 0 1 1 1 1
827 1 0 0 1 1 1 1
828 0 0 0 0 0 0 0
829 0 0 0 1 1 0 0
830 0 0 0 0 1 0 1
831 0 1 0 1 0 1 1
832 1 0 1 0 0 1 0
833 0 0 1 1 0 1 1
834 1 1 0 1 0 0 1
835 0 0 0 1 0 0 0
836 0 0 0 1 1 0 1
837 0 0 1 1 0 0 1
838 0 1 1 1 0 0 1
839 1 0 0 1 0 1 1
840 1 0 0 0 1 1 1
841 1 1 0 0 1 1 1
842 1 1 1 0 1 1 0
843 0 1 1 1 0 1 1
844 0 0 0 1 0 1 1
845 0 0 0 1 0 0 1
846 0 0 0 1 0 0 1
847 0 1 1 1 0 1 1
848 0 0 0 1 0 0 1
849 0 0 0 1 0 0 1
850 0 0 0 1 0 1 1
851 0 0 0 1 0 0 1
852 0 0 0 0 0 1 0
853 0 1 0 1 0 0 1
854 0 0 0 1 0 1 1
855 1 1 0 0 0 1 1
856 0 1 0 0 0 1 1
857 0 1 0 1 0 1 1
858 0 0 0 1 0 0 1
859 1 1 1 1 1 0 0
860 0 0 0 1 0 0 1
861 0 1 0 1 0 1 1
862 0 0 0 1 0 1 1
863 0 1 0 1 0 0 0
864 0 1 1 1 0 0 1
865 0 1 1 1 0 0 0
866 0 1 0 1 0 1 1
867 0 0 1 1 0 0 1
868 0 0 1 1 0 1 1
869 0 0 0 1 0 0 0
870 0 0 0 1 0 1 1
871 0 0 0 1 0 1 1
872 0 0 0 1 0 0 1
873 0 0 0 1 0 0 1
874 0 1 0 1 0 0 0
875 0 0 1 1 0 1 1
876 0 0 0 1 1 0 1
877 0 1 1 0 1 0 0
878 1 0 0 0 0 0 0
879 1 0 1 0 1 0 0
880 0 0 0 1 0 0 1
881 1 1 0 1 0 0 0
882 0 0 0 1 0 0 0
883 0 1 0 1 0 0 0
884 0 1 1 0 1 0 0
885 1 0 0 0 1 0 0
886 1 0 1 0 1 0 0
887 1 1 1 0 1 0 0
888 0 0 0 0 0 1 1
889 1 0 1 1 0 0 0
890 1 1 1 1 1 0 0
891 0 0 0 1 0 1 0
892 1 0 0 0 0 0 0
893 1 0 0 0 1 1 0
894 1 1 1 0 1 0 0
895 0 1 0 0 1 0 0
896 0 1 1 1 1 0 0
897 1 0 0 0 1 0 0
898 1 1 1 1 0 1 1
899 1 1 0 0 0 0 0
900 1 0 1 0 1 1 0
901 0 0 0 1 0 1 1
902 0 0 0 1 0 0 1
903 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
904 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
905 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
906 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
907 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
908 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
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