Using the HELPmiss dataset, I am only interested in patients that were randomized to a HELP clinic (treat = yes). How do I create a new dataset of just those people?
CodePudding user response：
You can use the filter function to select only patients with treat = yes, using this code:
library(mosaicData)
library(tidyverse)
data(HELPmiss)
your_data <- HELPmiss %>%
filter(treat == "yes")
The data looks like this:
age anysub cesd d1 daysanysub dayslink drugrisk e2b female sex g1b homeless i1 i2 id indtot link mcs pcs
1 37 yes 49 3 177 225 0 NA 0 male yes housed 13 26 1 39 yes 25.11199 58.41369
2 37 yes 30 22 2 NA 0 NA 0 male yes homeless 56 62 2 43 <NA> 26.67031 36.03694
3 47 yes 6 1 31 365 0 NA 1 female no housed 4 4 6 29 no 55.50899 46.47521
4 28 yes 32 1 47 365 7 8 0 male yes homeless 12 24 8 44 no 9.16053 65.13801
5 39 yes 46 4 115 382 20 3 0 male no homeless 20 27 10 44 no 36.14376 22.61060
6 34 <NA> 46 0 NA 365 8 NA 1 female no housed 0 0 11 34 no 43.97468 60.07915
7 60 yes 36 10 6 22 0 1 0 male no homeless 13 20 15 41 yes 25.84616 31.82965
8 36 yes 43 2 0 443 0 NA 0 male no housed 51 51 16 38 no 23.60844 55.16998
9 28 yes 35 6 27 41 0 2 1 female yes homeless 0 0 17 26 yes 29.79983 44.77651
10 27 no 52 0 198 49 10 4 1 female yes housed 9 24 20 37 yes 15.45827 37.45214
11 41 <NA> 35 1 NA 391 12 1 0 male no housed 26 26 22 36 no 20.87145 36.58481
12 33 yes 18 1 129 272 0 NA 0 male no housed 0 0 23 27 yes 47.28674 61.64098
13 34 yes 30 1 154 56 0 NA 0 male no housed 3 3 28 34 yes 37.37156 63.06006
14 35 yes 27 0 34 361 1 NA 0 male no homeless 7 7 30 37 no 34.33567 61.82597
15 29 yes 47 1 142 79 0 3 0 male no homeless 0 0 32 37 yes 27.71771 42.22490
16 37 no 11 0 203 203 3 NA 0 male no homeless 6 6 35 35 yes 27.85261 63.52000
17 29 no 26 1 193 354 0 NA 0 male no housed 0 0 36 21 no 54.77435 53.35109
18 33 yes 29 1 10 29 0 NA 0 male no housed 0 0 37 30 yes 27.49548 56.73985
19 20 yes 34 1 177 365 0 NA 0 male no homeless 32 135 38 33 no 56.32433 53.23396
20 18 <NA> 38 1 NA 365 0 1 1 female no homeless 24 32 41 36 no 25.19557 34.28825
21 43 no 16 15 191 414 0 NA 0 male no homeless 24 36 44 41 no 15.86192 71.39259
22 28 yes 36 1 31 414 0 NA 0 male no homeless 6 12 45 39 no 24.14882 52.61977
23 42 yes 36 2 17 38 7 NA 0 male no housed 13 13 47 39 yes 29.41298 50.06427
24 34 yes 5 2 23 14 0 NA 1 female no housed 6 13 50 8 yes 59.45409 52.69898
25 44 <NA> 36 5 NA 321 19 1 0 male yes homeless 15 26 52 42 no 29.39028 40.38438
26 30 no 44 2 209 26 21 2 0 male yes homeless 9 15 54 44 yes 17.92525 45.48341
27 37 yes 29 2 111 18 0 NA 0 male no homeless 5 13 56 40 yes 34.43470 63.05807
28 35 yes 46 3 17 365 0 NA 1 female no housed 13 20 57 32 no 24.00031 46.75086
29 44 yes 44 1 4 27 0 NA 0 male yes housed 3 6 59 44 yes 26.65304 40.46056
30 38 yes 30 5 18 30 0 2 0 male no homeless 36 36 61 38 yes 26.06578 47.60514
31 41 no 29 3 181 19 0 2 1 female yes housed 3 6 65 20 yes 33.37417 55.23372
32 35 yes 28 1 36 400 0 1 0 male no housed 32 32 67 38 no 35.83964 52.68871
33 40 no 57 5 181 34 0 NA 1 female yes homeless 59 164 71 43 yes 17.70596 36.04016
34 38 <NA> 26 4 NA 133 1 NA 0 male no housed 0 0 72 38 yes 39.93416 53.15686
35 42 yes 31 2 103 48 8 3 0 male no homeless 26 51 73 44 yes 23.99673 45.18499
pss_fr racegrp satreat sexrisk substance treat avg_drinks max_drinks hospitalizations
1 0 black no 4 cocaine yes 13 26 3
2 1 white no 7 alcohol yes 56 62 22
3 5 black no 5 cocaine yes 4 4 1
4 4 white yes 6 alcohol yes 12 24 1
5 0 white yes 0 heroin yes 20 27 4
6 0 white no 2 heroin yes 0 0 0
7 1 black no 4 cocaine yes 13 20 10
8 1 white no 8 alcohol yes 51 51 2
9 7 hispanic yes 3 heroin yes 0 0 6
10 13 white no 3 heroin yes 9 24 0
11 8 black no 4 heroin yes 26 26 1
12 14 black no 4 cocaine yes 0 0 1
13 3 white no 5 cocaine yes 3 3 1
14 6 black no 4 heroin yes 7 7 0
15 5 black yes 2 cocaine yes 0 0 1
16 2 black yes 5 cocaine yes 6 6 0
17 10 black no 2 cocaine yes 0 0 1
18 10 black no 0 cocaine yes 0 0 1
19 8 black no 3 alcohol yes 32 135 1
20 8 other no 3 missing yes 24 32 1
21 3 white no 7 cocaine yes 24 36 15
22 4 black no 7 cocaine yes 6 12 1
23 14 white no 4 heroin yes 13 13 2
24 12 black no 4 cocaine yes 6 13 2
25 11 black no 10 heroin yes 15 26 5
26 6 other no 9 heroin yes 9 15 2
27 2 black no 7 alcohol yes 5 13 2
28 1 black no 7 cocaine yes 13 20 3
29 13 other no 4 cocaine yes 3 6 1
30 10 black no 4 alcohol yes 36 36 5
31 13 white yes 4 alcohol yes 3 6 3
32 12 black yes 6 cocaine yes 32 32 1
33 1 black no 4 alcohol yes 59 164 5
34 8 white yes 2 heroin yes 0 0 4
35 3 white yes 6 alcohol yes 26 51 2
When you check the treat column you will see only yes.