Praktikum
Membaca tabel dataset
#Load dan read tabel dataset
hmohiv<-read.table("https://stats.idre.ucla.edu/stat/r/examples/asa/hmohiv.csv", sep=",", header = TRUE)
#Cek isian dataset
hmohiv
## ID time age drug censor entdate enddate
## 1 1 5 46 0 1 5/15/1990 10/14/1990
## 2 2 6 35 1 0 9/19/1989 3/20/1990
## 3 3 8 30 1 1 4/21/1991 12/20/1991
## 4 4 3 30 1 1 1/3/1991 4/4/1991
## 5 5 22 36 0 1 9/18/1989 7/19/1991
## 6 6 1 32 1 0 3/18/1991 4/17/1991
## 7 7 7 36 1 1 11/11/1989 6/11/1990
## 8 8 9 31 1 1 11/25/1989 8/25/1990
## 9 9 3 48 0 1 2/11/1991 5/13/1991
## 10 10 12 47 0 1 8/11/1989 8/11/1990
## 11 11 2 28 1 0 4/11/1990 6/10/1990
## 12 12 12 34 0 1 5/11/1991 5/10/1992
## 13 13 1 44 1 1 1/17/1989 2/16/1989
## 14 14 15 32 1 1 2/16/1991 5/17/1992
## 15 15 34 36 0 1 4/9/1991 2/6/1994
## 16 16 1 36 0 1 3/9/1991 4/8/1991
## 17 17 4 54 0 1 8/3/1990 12/2/1990
## 18 18 19 35 0 0 6/10/1990 1/8/1992
## 19 19 3 44 1 0 6/12/1991 9/11/1991
## 20 20 2 38 0 1 1/7/1991 3/8/1991
## 21 21 2 40 0 0 8/29/1989 10/28/1989
## 22 22 6 34 1 1 5/29/1989 11/27/1989
## 23 23 60 25 0 0 11/16/1990 11/14/1995
## 24 24 11 32 0 1 5/9/1990 4/8/1991
## 25 25 2 42 1 0 9/10/1991 11/9/1991
## 26 26 5 47 0 1 12/26/1991 5/26/1992
## 27 27 4 30 0 0 5/29/1991 9/27/1991
## 28 28 1 47 1 1 5/1/1990 5/31/1990
## 29 29 13 41 0 1 3/24/1991 4/22/1992
## 30 30 3 40 1 1 7/18/1989 10/17/1989
## 31 31 2 43 0 1 9/16/1990 11/15/1990
## 32 32 1 41 0 1 6/22/1989 7/22/1989
## 33 33 30 30 0 1 4/27/1990 10/25/1992
## 34 34 7 37 0 1 5/16/1990 12/14/1990
## 35 35 4 42 1 1 2/19/1989 6/20/1989
## 36 36 8 31 1 1 2/17/1990 10/18/1990
## 37 37 5 39 1 1 8/6/1991 1/5/1992
## 38 38 10 32 0 1 8/10/1989 6/10/1990
## 39 39 2 51 0 1 12/27/1990 2/25/1991
## 40 40 9 36 0 1 4/26/1989 1/24/1990
## 41 41 36 43 0 1 12/4/1990 12/3/1993
## 42 42 3 39 0 1 4/28/1991 7/28/1991
## 43 43 9 33 0 1 7/9/1991 4/7/1992
## 44 44 3 45 1 1 12/31/1989 4/1/1990
## 45 45 35 33 0 1 12/20/1989 11/18/1992
## 46 46 8 28 0 1 6/22/1991 2/20/1992
## 47 47 11 31 0 1 4/11/1990 3/11/1991
## 48 48 56 20 1 0 5/22/1990 1/19/1995
## 49 49 2 44 0 0 11/11/1991 1/10/1992
## 50 50 3 39 1 1 1/18/1991 4/19/1991
## 51 51 15 33 0 1 11/11/1989 2/10/1991
## 52 52 1 31 0 1 10/1/1990 10/31/1990
## 53 53 10 33 0 1 3/20/1990 1/18/1991
## 54 54 1 50 1 1 7/30/1990 8/29/1990
## 55 55 7 36 1 1 7/17/1989 2/14/1990
## 56 56 3 30 1 1 11/10/1990 2/9/1991
## 57 57 3 42 1 1 3/5/1989 6/4/1989
## 58 58 2 32 1 1 3/2/1991 5/1/1991
## 59 59 32 34 0 1 9/11/1989 5/11/1992
## 60 60 3 38 1 1 9/12/1989 12/12/1989
## 61 61 10 33 0 0 4/8/1990 2/6/1991
## 62 62 11 39 1 1 4/20/1989 3/20/1990
## 63 63 3 39 1 1 1/31/1991 5/2/1991
## 64 64 7 33 1 1 9/15/1989 4/15/1990
## 65 65 5 34 1 1 12/7/1991 5/7/1992
## 66 66 31 34 0 1 3/4/1990 10/1/1992
## 67 67 5 46 1 1 4/20/1989 9/19/1989
## 68 68 58 22 0 1 6/16/1989 4/15/1994
## 69 69 1 44 1 1 10/1/1990 10/31/1990
## 70 70 3 37 0 0 2/1/1991 5/3/1991
## 71 71 43 25 0 1 5/13/1989 12/10/1992
## 72 72 1 38 0 1 8/9/1990 9/8/1990
## 73 73 6 32 0 1 12/18/1991 6/17/1992
## 74 74 53 34 0 1 8/23/1990 1/21/1995
## 75 75 14 29 0 1 1/19/1991 3/19/1992
## 76 76 4 36 1 1 8/26/1991 12/25/1991
## 77 77 54 21 0 1 5/16/1991 11/13/1995
## 78 78 1 26 1 1 3/20/1989 4/19/1989
## 79 79 1 32 1 1 10/5/1991 11/4/1991
## 80 80 8 42 0 1 5/21/1991 1/19/1992
## 81 81 5 40 1 1 6/10/1991 11/9/1991
## 82 82 1 37 1 1 8/31/1989 9/30/1989
## 83 83 1 47 0 1 12/28/1991 1/27/1992
## 84 84 2 32 1 1 9/29/1990 11/28/1990
## 85 85 7 41 1 0 11/20/1991 6/19/1992
## 86 86 1 46 1 0 7/2/1989 8/1/1989
## 87 87 10 26 1 1 10/11/1991 8/10/1992
## 88 88 24 30 0 0 10/11/1990 10/10/1992
## 89 89 7 32 1 1 12/5/1990 7/5/1991
## 90 90 12 31 1 0 9/8/1989 9/8/1990
## 91 91 4 35 0 1 4/10/1990 8/9/1990
## 92 92 57 36 0 1 12/11/1990 9/9/1995
## 93 93 1 41 1 1 12/15/1990 1/14/1991
## 94 94 12 36 1 0 1/13/1989 1/13/1990
## 95 95 7 35 1 1 8/22/1991 3/21/1992
## 96 96 1 34 1 1 8/2/1991 9/1/1991
## 97 97 5 28 0 1 5/22/1991 10/21/1991
## 98 98 60 29 0 0 4/2/1990 4/1/1995
## 99 99 2 35 1 0 5/1/1991 6/30/1991
## 100 100 1 34 1 1 5/11/1989 6/10/1989
hmohiv$time
## [1] 5 6 8 3 22 1 7 9 3 12 2 12 1 15 34 1 4 19 3 2 2 6 60 11 2
## [26] 5 4 1 13 3 2 1 30 7 4 8 5 10 2 9 36 3 9 3 35 8 11 56 2 3
## [51] 15 1 10 1 7 3 3 2 32 3 10 11 3 7 5 31 5 58 1 3 43 1 6 53 14
## [76] 4 54 1 1 8 5 1 1 2 7 1 10 24 7 12 4 57 1 12 7 1 5 60 2 1
hmohiv$censor
## [1] 1 0 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 0 0 1 0 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 0 1 1 1 0 1 1 1 0 0 1
Membuat Survival Object
#Membuat survival objek berdasarkan variable ‘time’ (survival time) dan ‘censor’
library(survival)
s_obj<-Surv(hmohiv$time,hmohiv$censor)
s_obj
## [1] 5 6+ 8 3 22 1+ 7 9 3 12 2+ 12 1 15 34 1 4 19+
## [19] 3+ 2 2+ 6 60+ 11 2+ 5 4+ 1 13 3 2 1 30 7 4 8
## [37] 5 10 2 9 36 3 9 3 35 8 11 56+ 2+ 3 15 1 10 1
## [55] 7 3 3 2 32 3 10+ 11 3 7 5 31 5 58 1 3+ 43 1
## [73] 6 53 14 4 54 1 1 8 5 1 1 2 7+ 1+ 10 24+ 7 12+
## [91] 4 57 1 12+ 7 1 5 60+ 2+ 1
#Menambahkan kolom survival object pada dataset
hmohiv["s_obj"]<-NA
hmohiv$s_obj<-Surv(hmohiv$time,hmohiv$censor)
hmohiv
## ID time age drug censor entdate enddate s_obj
## 1 1 5 46 0 1 5/15/1990 10/14/1990 5
## 2 2 6 35 1 0 9/19/1989 3/20/1990 6+
## 3 3 8 30 1 1 4/21/1991 12/20/1991 8
## 4 4 3 30 1 1 1/3/1991 4/4/1991 3
## 5 5 22 36 0 1 9/18/1989 7/19/1991 22
## 6 6 1 32 1 0 3/18/1991 4/17/1991 1+
## 7 7 7 36 1 1 11/11/1989 6/11/1990 7
## 8 8 9 31 1 1 11/25/1989 8/25/1990 9
## 9 9 3 48 0 1 2/11/1991 5/13/1991 3
## 10 10 12 47 0 1 8/11/1989 8/11/1990 12
## 11 11 2 28 1 0 4/11/1990 6/10/1990 2+
## 12 12 12 34 0 1 5/11/1991 5/10/1992 12
## 13 13 1 44 1 1 1/17/1989 2/16/1989 1
## 14 14 15 32 1 1 2/16/1991 5/17/1992 15
## 15 15 34 36 0 1 4/9/1991 2/6/1994 34
## 16 16 1 36 0 1 3/9/1991 4/8/1991 1
## 17 17 4 54 0 1 8/3/1990 12/2/1990 4
## 18 18 19 35 0 0 6/10/1990 1/8/1992 19+
## 19 19 3 44 1 0 6/12/1991 9/11/1991 3+
## 20 20 2 38 0 1 1/7/1991 3/8/1991 2
## 21 21 2 40 0 0 8/29/1989 10/28/1989 2+
## 22 22 6 34 1 1 5/29/1989 11/27/1989 6
## 23 23 60 25 0 0 11/16/1990 11/14/1995 60+
## 24 24 11 32 0 1 5/9/1990 4/8/1991 11
## 25 25 2 42 1 0 9/10/1991 11/9/1991 2+
## 26 26 5 47 0 1 12/26/1991 5/26/1992 5
## 27 27 4 30 0 0 5/29/1991 9/27/1991 4+
## 28 28 1 47 1 1 5/1/1990 5/31/1990 1
## 29 29 13 41 0 1 3/24/1991 4/22/1992 13
## 30 30 3 40 1 1 7/18/1989 10/17/1989 3
## 31 31 2 43 0 1 9/16/1990 11/15/1990 2
## 32 32 1 41 0 1 6/22/1989 7/22/1989 1
## 33 33 30 30 0 1 4/27/1990 10/25/1992 30
## 34 34 7 37 0 1 5/16/1990 12/14/1990 7
## 35 35 4 42 1 1 2/19/1989 6/20/1989 4
## 36 36 8 31 1 1 2/17/1990 10/18/1990 8
## 37 37 5 39 1 1 8/6/1991 1/5/1992 5
## 38 38 10 32 0 1 8/10/1989 6/10/1990 10
## 39 39 2 51 0 1 12/27/1990 2/25/1991 2
## 40 40 9 36 0 1 4/26/1989 1/24/1990 9
## 41 41 36 43 0 1 12/4/1990 12/3/1993 36
## 42 42 3 39 0 1 4/28/1991 7/28/1991 3
## 43 43 9 33 0 1 7/9/1991 4/7/1992 9
## 44 44 3 45 1 1 12/31/1989 4/1/1990 3
## 45 45 35 33 0 1 12/20/1989 11/18/1992 35
## 46 46 8 28 0 1 6/22/1991 2/20/1992 8
## 47 47 11 31 0 1 4/11/1990 3/11/1991 11
## 48 48 56 20 1 0 5/22/1990 1/19/1995 56+
## 49 49 2 44 0 0 11/11/1991 1/10/1992 2+
## 50 50 3 39 1 1 1/18/1991 4/19/1991 3
## 51 51 15 33 0 1 11/11/1989 2/10/1991 15
## 52 52 1 31 0 1 10/1/1990 10/31/1990 1
## 53 53 10 33 0 1 3/20/1990 1/18/1991 10
## 54 54 1 50 1 1 7/30/1990 8/29/1990 1
## 55 55 7 36 1 1 7/17/1989 2/14/1990 7
## 56 56 3 30 1 1 11/10/1990 2/9/1991 3
## 57 57 3 42 1 1 3/5/1989 6/4/1989 3
## 58 58 2 32 1 1 3/2/1991 5/1/1991 2
## 59 59 32 34 0 1 9/11/1989 5/11/1992 32
## 60 60 3 38 1 1 9/12/1989 12/12/1989 3
## 61 61 10 33 0 0 4/8/1990 2/6/1991 10+
## 62 62 11 39 1 1 4/20/1989 3/20/1990 11
## 63 63 3 39 1 1 1/31/1991 5/2/1991 3
## 64 64 7 33 1 1 9/15/1989 4/15/1990 7
## 65 65 5 34 1 1 12/7/1991 5/7/1992 5
## 66 66 31 34 0 1 3/4/1990 10/1/1992 31
## 67 67 5 46 1 1 4/20/1989 9/19/1989 5
## 68 68 58 22 0 1 6/16/1989 4/15/1994 58
## 69 69 1 44 1 1 10/1/1990 10/31/1990 1
## 70 70 3 37 0 0 2/1/1991 5/3/1991 3+
## 71 71 43 25 0 1 5/13/1989 12/10/1992 43
## 72 72 1 38 0 1 8/9/1990 9/8/1990 1
## 73 73 6 32 0 1 12/18/1991 6/17/1992 6
## 74 74 53 34 0 1 8/23/1990 1/21/1995 53
## 75 75 14 29 0 1 1/19/1991 3/19/1992 14
## 76 76 4 36 1 1 8/26/1991 12/25/1991 4
## 77 77 54 21 0 1 5/16/1991 11/13/1995 54
## 78 78 1 26 1 1 3/20/1989 4/19/1989 1
## 79 79 1 32 1 1 10/5/1991 11/4/1991 1
## 80 80 8 42 0 1 5/21/1991 1/19/1992 8
## 81 81 5 40 1 1 6/10/1991 11/9/1991 5
## 82 82 1 37 1 1 8/31/1989 9/30/1989 1
## 83 83 1 47 0 1 12/28/1991 1/27/1992 1
## 84 84 2 32 1 1 9/29/1990 11/28/1990 2
## 85 85 7 41 1 0 11/20/1991 6/19/1992 7+
## 86 86 1 46 1 0 7/2/1989 8/1/1989 1+
## 87 87 10 26 1 1 10/11/1991 8/10/1992 10
## 88 88 24 30 0 0 10/11/1990 10/10/1992 24+
## 89 89 7 32 1 1 12/5/1990 7/5/1991 7
## 90 90 12 31 1 0 9/8/1989 9/8/1990 12+
## 91 91 4 35 0 1 4/10/1990 8/9/1990 4
## 92 92 57 36 0 1 12/11/1990 9/9/1995 57
## 93 93 1 41 1 1 12/15/1990 1/14/1991 1
## 94 94 12 36 1 0 1/13/1989 1/13/1990 12+
## 95 95 7 35 1 1 8/22/1991 3/21/1992 7
## 96 96 1 34 1 1 8/2/1991 9/1/1991 1
## 97 97 5 28 0 1 5/22/1991 10/21/1991 5
## 98 98 60 29 0 0 4/2/1990 4/1/1995 60+
## 99 99 2 35 1 0 5/1/1991 6/30/1991 2+
## 100 100 1 34 1 1 5/11/1989 6/10/1989 1
Penugasan
Membaca tabel dataset
#Ambil ovarian dataset di R:
data(ovarian)
## Warning in data(ovarian): data set 'ovarian' not found
ovarian
## futime fustat age resid.ds rx ecog.ps
## 1 59 1 72.3315 2 1 1
## 2 115 1 74.4932 2 1 1
## 3 156 1 66.4658 2 1 2
## 4 421 0 53.3644 2 2 1
## 5 431 1 50.3397 2 1 1
## 6 448 0 56.4301 1 1 2
## 7 464 1 56.9370 2 2 2
## 8 475 1 59.8548 2 2 2
## 9 477 0 64.1753 2 1 1
## 10 563 1 55.1781 1 2 2
## 11 638 1 56.7562 1 1 2
## 12 744 0 50.1096 1 2 1
## 13 769 0 59.6301 2 2 2
## 14 770 0 57.0521 2 2 1
## 15 803 0 39.2712 1 1 1
## 16 855 0 43.1233 1 1 2
## 17 1040 0 38.8932 2 1 2
## 18 1106 0 44.6000 1 1 1
## 19 1129 0 53.9068 1 2 1
## 20 1206 0 44.2055 2 2 1
## 21 1227 0 59.5890 1 2 2
## 22 268 1 74.5041 2 1 2
## 23 329 1 43.1370 2 1 1
## 24 353 1 63.2192 1 2 2
## 25 365 1 64.4247 2 2 1
## 26 377 0 58.3096 1 2 1
Membuat Survival Object
#Membuat survival objek berdasarkan variable ‘futime’ (survival time) dan ‘fustat’
library(survival)
s_obj2<-Surv(ovarian$futime,ovarian$fustat)
s_obj2
## [1] 59 115 156 421+ 431 448+ 464 475 477+ 563 638 744+
## [13] 769+ 770+ 803+ 855+ 1040+ 1106+ 1129+ 1206+ 1227+ 268 329 353
## [25] 365 377+
#Menambahkan kolom survival object pada dataset
ovarian["s_obj2"]<-NA
ovarian$s_obj2<-Surv(ovarian$futime,ovarian$fustat)
ovarian
## futime fustat age resid.ds rx ecog.ps s_obj2
## 1 59 1 72.3315 2 1 1 59
## 2 115 1 74.4932 2 1 1 115
## 3 156 1 66.4658 2 1 2 156
## 4 421 0 53.3644 2 2 1 421+
## 5 431 1 50.3397 2 1 1 431
## 6 448 0 56.4301 1 1 2 448+
## 7 464 1 56.9370 2 2 2 464
## 8 475 1 59.8548 2 2 2 475
## 9 477 0 64.1753 2 1 1 477+
## 10 563 1 55.1781 1 2 2 563
## 11 638 1 56.7562 1 1 2 638
## 12 744 0 50.1096 1 2 1 744+
## 13 769 0 59.6301 2 2 2 769+
## 14 770 0 57.0521 2 2 1 770+
## 15 803 0 39.2712 1 1 1 803+
## 16 855 0 43.1233 1 1 2 855+
## 17 1040 0 38.8932 2 1 2 1040+
## 18 1106 0 44.6000 1 1 1 1106+
## 19 1129 0 53.9068 1 2 1 1129+
## 20 1206 0 44.2055 2 2 1 1206+
## 21 1227 0 59.5890 1 2 2 1227+
## 22 268 1 74.5041 2 1 2 268
## 23 329 1 43.1370 2 1 1 329
## 24 353 1 63.2192 1 2 2 353
## 25 365 1 64.4247 2 2 1 365
## 26 377 0 58.3096 1 2 1 377+