Create a condition for legend in ggsurvplot

Create a condition for legend in ggsurvplot - r

At the moment, the legend is Quartile 4. HR 0.62 (95%CI 0.10-3.72), P=0.60.
I would like to create a condition when as follow: if P-value is >=0.95, I would like to write Quartile 4. P=0.99. So without the HR and the 95% CI.
HR and Ci make nonsense writing like this for now.
With this code:
#libraries
library(readxl)
library(tidyverse)
library(tidytidbits)
library(survivalAnalysis)
library(dplyr)
library(survival)
library(survminer)
library(ggplot2)
library(ggthemes)
library(ggpubr)
df$quantile <- df$delta_mon1_baseline_to_d3
#define quartile
df$Quartile <- findInterval(df$quantile, quantile(df$quantile, na.rm = TRUE)[-5])
#factor Quartile
df$Quartile <- factor(df$Quartile)
#cox regression
cox <- coxph(Surv(mace_months_date_vs_date_sample, mace) ~ Quartile, data = df)
# create the tags
coxP <- data.frame(summary(cox)$coefficients)[,5]
coxConf <- data.frame(summary(cox)$conf.int) %>%
rownames_to_column() %>%
mutate(p = coxP,
p2 = case_when( # determine direction
round(p, 3) > p ~ '=',
round(p, 3) < p ~ '=',
round(p, 3) == p ~ '='
),
p3 = ifelse(round(p, 2) == 1, T, F), # id if p value is 1 (too high!)
# gsub adds space, round, keep trailing zeros
tag = paste0(rowname %>% gsub("(\\D)(\\d)", "\\1 \\2", .),
". HR ", exp.coef. %>% sprintf(fmt = "%.2f", .),
" (95% CI ",
lower..95 %>% sprintf(fmt = "%.2f", .),
"-", upper..95 %>% sprintf(fmt = "%.2f", .),
"), P", p2, "",
ifelse(p3,
yes = "0.99", # if p rounded = 1
no = sprintf(fmt = "%.2f", p)))) %>%
select(tag)
# validate as expected
coxConf
I have got this legend in the graph
Here are my data:
ID age sex mace mace_months_date_vs_date_sample trop egfr dm smoke delta_mon1_baseline_to_d3
1 44 52 1 1 30 2600 56 1 0 -822.
2 32 66 1 0 73 1710 90 1 0 -562.
3 20 56 1 1 5 NA 75 0 1 -502.
4 17 44 1 0 77 840 71 0 0 -389.
5 52 49 1 0 74 1740 71 0 1 -372.
6 57 58 1 0 74 5010 68 0 1 -308.
7 79 68 1 0 45 776 90 0 0 -284.
8 14 74 1 1 6 7120 78 0 0 -279.
9 223 63 1 0 46 4281 90 0 0 -218.
10 56 43 1 0 70 1360 90 1 0 -173.
11 50 54 1 0 70 15300 90 0 1 -163.
12 31 47 0 0 72 6490 77 1 1 -95.7
13 35 47 1 0 77 NA 83 0 0 -71.0
14 36 64 1 1 5 15940 52 0 1 -69.7
15 15 65 1 1 43 6300 49 1 0 -69.6
16 12 57 1 0 71 6020 88 0 1 -66.5
17 43 59 0 0 74 2100 84 0 1 -58.8
18 22 46 1 0 77 5330 88 0 1 -29.3
19 54 59 1 0 71 1500 81 1 1 -25.7
20 26 66 1 0 77 500 51 0 0 -12.5
21 29 73 0 0 77 NA 51 0 0 -2.99
22 25 54 1 0 73 1080 87 0 0 2.81
23 39 54 1 0 74 990 77 0 0 32.9
24 47 62 1 0 69 1420 85 0 0 33.0
25 49 54 1 1 28 NA 76 1 0 44.1
26 24 47 1 0 77 2390 90 0 1 47.7
27 45 51 0 0 73 3710 65 0 1 55.9
28 30 73 0 0 68 3340 48 1 1 117.
29 16 57 1 0 73 180 99 0 1 131.
30 55 47 1 0 70 NA 90 0 1 131.
31 37 81 1 0 74 NA 99 1 1 147.
32 21 46 1 0 75 3600 87 0 1 153.
33 60 72 1 0 76 470 62 0 0 160.
34 18 56 1 0 69 6390 90 0 1 165.
35 13 53 1 0 69 1970 87 1 1 180.
36 19 66 1 0 78 9320 59 0 0 180.
37 33 59 1 0 69 2260 79 0 1 193.
38 139 39 0 0 58 NA 90 0 1 209.
39 38 55 1 0 78 3930 90 1 0 244.
40 27 28 1 0 71 6440 90 0 1 248.
41 58 36 1 0 76 NA 78 1 1 327.
42 61 48 1 0 76 4470 90 0 1 336.
43 42 38 1 0 69 1800 69 0 1 375.
44 28 76 1 0 71 40 90 1 1 419.
Here is the console output:
structure(list(ID = c(44L, 32L, 20L, 17L, 52L, 57L, 79L, 14L,
223L, 56L, 50L, 31L, 35L, 36L, 15L, 12L, 43L, 22L, 54L, 26L,
29L, 25L, 39L, 47L, 49L, 24L, 45L, 30L, 16L, 55L, 37L, 21L, 60L,
18L, 13L, 19L, 33L, 139L, 38L, 27L, 58L, 61L, 42L, 28L, 121L,
192L, 120L, 68L, 41L, 23L, 216L, 136L, 88L, 87L, 182L, 93L, 154L,
94L, 116L, 145L, 228L, 76L, 63L, 59L, 219L, 175L, 164L, 181L,
234L, 146L, 242L, 71L, 67L, 187L, 128L, 151L, 215L, 132L, 173L,
124L, 119L, 224L, 140L, 221L, 172L, 115L, 103L, 73L, 194L, 106L,
193L, 148L, 156L, 203L, 100L, 81L, 190L, 206L, 233L, 189L, 105L,
220L, 85L, 11L, 205L, 131L, 1L, 225L, 183L, 213L, 7L, 147L, 134L,
86L, 69L, 212L, 199L, 75L, 137L, 191L, 245L, 111L, 153L, 112L,
89L, 243L, 109L, 165L, 95L, 231L, 5L, 168L, 159L, 6L, 179L, 77L,
155L, 171L, 174L, 84L, 102L, 207L, 230L, 138L, 188L, 241L, 72L,
235L, 211L, 127L, 237L, 70L, 210L, 110L, 133L, 2L, 218L, 180L,
229L, 65L, 130L, 96L, 226L, 152L, 197L, 178L, 141L, 195L, 92L,
162L, 201L, 217L, 222L, 208L, 104L, 160L, 66L, 74L, 185L, 177L,
123L, 184L, 204L, 227L, 125L, 83L, 8L, 143L, 9L, 3L, 117L, 10L,
198L, 244L, 108L, 34L, 214L, 4L, 97L, 200L, 113L, 80L, 166L,
98L, 238L, 239L, 114L, 167L, 64L, 157L, 90L, 149L, 129L, 170L,
91L, 135L, 122L, 240L, 99L, 236L, 144L, 53L, 176L, 107L, 232L,
163L, 142L, 118L, 126L, 158L, 186L, 82L, 78L, 48L, 62L, 209L,
196L, 46L, 150L, 161L, 169L, 101L, 202L, 51L, 40L), age = c(52L,
66L, 56L, 44L, 49L, 58L, 68L, 74L, 63L, 43L, 54L, 47L, 47L, 64L,
65L, 57L, 59L, 46L, 59L, 66L, 73L, 54L, 54L, 62L, 54L, 47L, 51L,
73L, 57L, 47L, 81L, 46L, 72L, 56L, 53L, 66L, 59L, 39L, 55L, 28L,
36L, 48L, 38L, 76L, NA, 59L, 71L, 58L, 57L, 54L, 69L, 49L, 65L,
48L, 35L, 44L, 65L, 56L, 66L, 41L, 55L, 52L, 67L, 61L, 65L, 61L,
75L, 56L, 37L, 75L, 68L, 59L, 52L, 59L, 59L, 63L, 62L, 57L, 48L,
65L, 41L, 60L, 77L, 66L, 50L, 51L, 81L, 61L, 64L, 48L, 63L, 78L,
79L, 51L, 74L, 52L, 73L, 82L, 58L, 72L, 63L, 67L, 72L, 51L, 68L,
41L, 66L, 69L, 60L, 66L, 71L, 45L, 81L, 52L, 67L, 58L, 63L, 47L,
63L, 67L, 62L, 72L, 75L, 46L, 73L, 57L, 75L, 68L, 68L, 73L, 51L,
59L, 59L, 60L, 54L, 62L, 64L, 48L, 73L, 79L, 58L, 46L, 75L, 63L,
68L, 60L, 54L, 78L, 54L, 46L, 49L, 67L, 79L, 54L, 47L, 51L, 66L,
61L, 64L, 79L, 73L, 51L, 52L, 52L, 76L, 75L, 56L, 76L, 54L, 82L,
62L, 57L, 53L, 42L, 63L, 37L, 66L, 46L, 76L, 39L, 51L, 80L, 69L,
76L, 48L, 65L, 59L, 76L, 57L, 66L, 69L, 68L, 72L, 62L, 56L, 51L,
60L, 71L, 68L, 77L, 28L, 62L, 51L, 61L, 56L, 72L, 79L, 62L, 68L,
68L, 49L, 75L, 64L, 48L, 51L, 68L, 68L, 70L, 73L, 54L, 47L, 79L,
40L, 52L, 58L, 69L, 61L, 44L, 57L, 55L, 43L, 61L, 44L, 77L, 35L,
74L, 72L, 60L, 44L, 53L, 61L, NA, 80L, 73L, 72L), sex = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L,
0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L,
1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L,
0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L,
1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L,
0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L,
0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 1L), mace = c(1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L,
0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L,
0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L,
0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L), mace_months_date_vs_date_sample = c(30L,
73L, 5L, 77L, 74L, 74L, 45L, 6L, 46L, 70L, 70L, 72L, 77L, 5L,
43L, 71L, 74L, 77L, 71L, 77L, 77L, 73L, 74L, 69L, 28L, 77L, 73L,
68L, 73L, 70L, 74L, 75L, 76L, 69L, 69L, 78L, 69L, 58L, 78L, 71L,
76L, 76L, 69L, 71L, 43L, 44L, 5L, 62L, 73L, 72L, 47L, 61L, 47L,
46L, 44L, 43L, 46L, 45L, 16L, 62L, 8L, 48L, 15L, 79L, 62L, 46L,
62L, 45L, 55L, 21L, 41L, 45L, 46L, 45L, 50L, 62L, 43L, 4L, 1L,
4L, 63L, 17L, 3L, 45L, 2L, 50L, 1L, 44L, 15L, 46L, 46L, 7L, 8L,
62L, 16L, 47L, 19L, 61L, 46L, 1L, 4L, 48L, 61L, 43L, 46L, 61L,
44L, 48L, 1L, 5L, 43L, 43L, 46L, 58L, 3L, 55L, 27L, 1L, 47L,
19L, 11L, 1L, 1L, 1L, 2L, 48L, 62L, 61L, 23L, 1L, 55L, 19L, 48L,
1L, 43L, 47L, 1L, 46L, 11L, 46L, 48L, 46L, 32L, 50L, 61L, 62L,
48L, 46L, 55L, 62L, 55L, 43L, 61L, 47L, 59L, 50L, 10L, 55L, 63L,
55L, 48L, 31L, 32L, 48L, 55L, 62L, 59L, 53L, 48L, 48L, 62L, 59L,
4L, 4L, 25L, 59L, 48L, 60L, 43L, 58L, 49L, 1L, 8L, 1L, 63L, 51L,
55L, 47L, 49L, 55L, 49L, 55L, 13L, 60L, 2L, 13L, 3L, 25L, 7L,
62L, 62L, 60L, 10L, 61L, 45L, 3L, 11L, 51L, 47L, 1L, 46L, 63L,
60L, 43L, 62L, 58L, 61L, 1L, 2L, 46L, 1L, 13L, 54L, 48L, 54L,
45L, 45L, 31L, 48L, 42L, 49L, 43L, 61L, 1L, 31L, 1L, 63L, 11L,
47L, 39L, 7L, 42L, 1L, 1L, 1L), trop = c(2600L, 1710L, NA, 840L,
1740L, 5010L, 776L, 7120L, 4281L, 1360L, 15300L, 6490L, NA, 15940L,
6300L, 6020L, 2100L, 5330L, 1500L, 500L, NA, 1080L, 990L, 1420L,
NA, 2390L, 3710L, 3340L, 180L, NA, NA, 3600L, 470L, 6390L, 1970L,
9320L, 2260L, NA, 3930L, 6440L, NA, 4470L, 1800L, 40L, 21876L,
871L, 7860L, NA, 320L, 8450L, 1730L, 262L, 16720L, 1247L, NA,
NA, 54592L, 1241L, 2413L, NA, 45649L, NA, NA, 160L, 843L, NA,
1470L, 372L, 844L, 1454L, 50000L, 11450L, 769L, 2234L, 349L,
250L, 3654L, 8421L, NA, 5204L, 440L, NA, 40273L, 90L, 9352L,
2177L, 10014L, 11L, 11135L, 5256L, 1753L, NA, NA, 50L, 8903L,
3598L, 2483L, NA, NA, NA, 1557L, 5247L, 24L, 2993L, 3624L, 751L,
NA, NA, 24160L, NA, NA, 5687L, 1911L, NA, NA, 1855L, 9951L, 13374L,
2107L, 4927L, 83L, 2380L, 663L, NA, NA, NA, NA, NA, NA, NA, 1627L,
NA, 1211L, 5654L, NA, NA, 10000L, NA, NA, NA, 2956L, 67927L,
NA, 63L, NA, 4790L, NA, NA, 3569L, 961L, 6581L, 253L, 2888L,
33017L, 1675L, 438L, 15543L, 6212L, 6694L, NA, 1945L, 3004L,
3789L, NA, 2844L, 950L, 123L, 6630L, 3220L, 2040L, NA, 6672L,
1480L, 6979L, NA, 1411L, 5711L, NA, 2340L, NA, 57L, NA, 33L,
5110L, NA, 2797L, 1035L, 2840L, 251L, 7671L, 6155L, 4299L, NA,
846L, 2339L, 400L, 86115L, 27L, 87355L, NA, 8669L, NA, NA, NA,
1258L, 3000L, NA, 137L, 3866L, NA, 1312L, NA, NA, NA, NA, NA,
2103L, 1586L, 601L, 1472L, 1692L, NA, 2102L, 6452L, NA, NA, 1244L,
2051L, 1007L, NA, NA, NA, 1726L, 3400L, 2143L, NA, 236L, 3930L,
31026L, NA, NA, NA, NA, 5280L, 1230L), egfr = c(56L, 90L, 75L,
71L, 71L, 68L, 90L, 78L, 90L, 90L, 90L, 77L, 83L, 52L, 49L, 88L,
84L, 88L, 81L, 51L, 51L, 87L, 77L, 85L, 76L, 90L, 65L, 48L, 99L,
90L, 99L, 87L, 62L, 90L, 87L, 59L, 79L, 90L, 90L, 90L, 78L, 90L,
69L, 90L, 87L, 90L, 58L, 90L, 79L, 55L, 51L, 90L, 58L, 90L, 56L,
62L, 86L, 61L, 84L, 63L, 63L, 90L, 90L, 85L, 64L, 67L, 45L, 90L,
78L, 65L, 69L, 90L, 90L, 59L, 54L, 60L, 68L, 86L, 42L, 73L, 90L,
85L, 63L, 90L, 86L, 68L, 71L, 90L, 68L, 63L, 81L, 76L, 61L, 75L,
84L, 90L, 90L, 48L, 90L, 77L, 68L, 90L, 64L, 90L, 90L, 61L, 84L,
80L, 69L, 58L, 65L, 86L, 86L, 46L, 56L, 90L, 46L, 87L, 84L, 68L,
67L, 72L, 35L, 86L, 74L, 78L, 67L, 90L, 90L, 90L, 76L, 90L, 86L,
63L, 63L, 53L, 90L, 90L, 75L, 64L, 69L, 68L, 52L, 49L, 90L, 65L,
86L, 42L, 63L, 90L, 90L, 73L, 75L, 66L, 64L, 90L, 35L, 90L, 82L,
90L, 84L, 61L, 90L, 86L, 51L, 52L, 69L, 54L, 90L, 75L, 85L, 72L,
90L, 80L, 54L, 58L, 90L, 89L, 72L, 90L, 84L, 33L, 74L, 36L, 56L,
61L, 63L, 77L, 84L, 85L, 90L, 90L, 51L, 90L, 90L, 68L, 90L, 52L,
90L, 48L, 90L, 82L, 86L, 67L, 90L, 76L, 14L, 63L, 59L, 82L, 90L,
39L, 77L, 90L, 78L, 74L, 54L, 36L, 58L, 69L, NA, 53L, 90L, 90L,
90L, 88L, 90L, 90L, 90L, 90L, 90L, 63L, 87L, 48L, 90L, 55L, 70L,
65L, 90L, NA, 90L, 90L, 72L, 66L, 55L), dm = c(1L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L,
0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L,
0L, 0L, 2L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, NA, 1L, 1L, 0L, 0L, 0L, 0L, 0L,
0L), smoke = c(0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L,
0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L,
0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L,
0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L,
0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L,
0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L,
0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L,
0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L,
1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L,
1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L,
1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
NA, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L), delta_mon1_baseline_to_d3 = c(-821.989379882808,
-562.289733886719, -502.216308593755, -388.549621582031, -372.002563476562,
-308.033813476562, -283.636077880859, -279.422790527344, -218.279922485352,
-173.209777832031, -162.939453124998, -95.700927734375, -70.961883544922,
-69.742797851558, -69.5900268554681, -66.49755859375, -58.77816772461,
-29.29504394531, -25.714111328125, -12.548919677734, -2.99462890625,
2.80639648437602, 32.883270263672, 33.036499023438, 44.05969238281,
47.6982421875, 55.87646484375, 116.716430664065, 130.585632324218,
131.392028808594, 146.8232421875, 153.190795898438, 159.863708496094,
164.985534667969, 179.8525390625, 180.041305541992, 192.978088378906,
208.791275024414, 243.90209960937, 248.09851074219, 327.035522460937,
336.011077880859, 375.086456298828, 419.108337402341, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA), stratum = structure(c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA), .Label = c("1", "2", "3", "4"), class = "factor"), surv = structure(c(30,
73, 5, 77, 74, 74, 45, 6, 46, 70, 70, 72, 77, 5, 43, 71, 74,
77, 71, 77, 77, 73, 74, 69, 28, 77, 73, 68, 73, 70, 74, 75, 76,
69, 69, 78, 69, 58, 78, 71, 76, 76, 69, 71, 43, 44, 5, 62, 73,
72, 47, 61, 47, 46, 44, 43, 46, 45, 16, 62, 8, 48, 15, 79, 62,
46, 62, 45, 55, 21, 41, 45, 46, 45, 50, 62, 43, 4, 1, 4, 63,
17, 3, 45, 2, 50, 1, 44, 15, 46, 46, 7, 8, 62, 16, 47, 19, 61,
46, 1, 4, 48, 61, 43, 46, 61, 44, 48, 1, 5, 43, 43, 46, 58, 3,
55, 27, 1, 47, 19, 11, 1, 1, 1, 2, 48, 62, 61, 23, 1, 55, 19,
48, 1, 43, 47, 1, 46, 11, 46, 48, 46, 32, 50, 61, 62, 48, 46,
55, 62, 55, 43, 61, 47, 59, 50, 10, 55, 63, 55, 48, 31, 32, 48,
55, 62, 59, 53, 48, 48, 62, 59, 4, 4, 25, 59, 48, 60, 43, 58,
49, 1, 8, 1, 63, 51, 55, 47, 49, 55, 49, 55, 13, 60, 2, 13, 3,
25, 7, 62, 62, 60, 10, 61, 45, 3, 11, 51, 47, 1, 46, 63, 60,
43, 62, 58, 61, 1, 2, 46, 1, 13, 54, 48, 54, 45, 45, 31, 48,
42, 49, 43, 61, 1, 31, 1, 63, 11, 47, 39, 7, 42, 1, 1, 1, 1,
0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1,
0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0,
0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0,
0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0,
0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1), .Dim = c(245L, 2L), .Dimnames = list(
NULL, c("time", "status")), type = "right", class = "Surv")), row.names = c(NA,
-245L), class = c("tbl_df", "tbl", "data.frame"))
Thank you very much for your help,

Related

How to display the intersection points of y axis?

library(data.table)
library(survival)
library(survminer)
library(dcurves)
I have df as follows
structure(list(PatientID = c(1001L, 1002L, 1003L, 1004L, 1006L,
1014L, 1015L, 1016L, 1018L, 1022L, 1024L, 1032L, 1040L, 1042L,
1049L, 1056L, 1059L, 1060L, 1066L, 1084L, 1087L, 1090L, 1093L,
1096L, 1097L, 1098L, 1099L, 1200L, 1205L, 1216L, 1221L, 1222L,
1225L, 1226L, 1233L, 1239L), TimeMonths = c(9L, 8L, 69L, 104L,
104L, 100L, 24L, 85L, 100L, 99L, 67L, 58L, 7L, 94L, 93L, 90L,
91L, 90L, 89L, 72L, 84L, 84L, 11L, 82L, 39L, 46L, 82L, 82L, 9L,
34L, 75L, 76L, 52L, 20L, 29L, 70L), Event = c(1L, 0L, 0L, 0L,
0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L
), P1 = c(0.1, 0.03, 0.02, 0.05, 0.01, 0.04, 0.03,
0.06, 0.02, 0.03, 0, 0, 0.11, 0.01, 0.03, 0, 0.01, 0.01, 0.01,
0, 0, 0, 0.05, 0.01, 0, 0, 0, 0, 0.04, 0, 0.07, 0.01, 0.01, 0,
0, 0), Age = c(88L, 49L, 60L, 46L, 50L, 60L, 38L, 74L, 39L, 65L,
80L, 35L, 54L, 40L, 54L, 55L, 60L, 38L, 64L, 74L, 71L, 57L, 55L,
49L, 42L, 30L, 63L, 46L, 47L, 58L, 34L, 72L, 50L, 60L, 73L, 51L
), Grade = c(2L, 2L, 2L, 3L, 3L, 1L, 2L, 2L, 2L, 1L, 2L, 1L,
3L, 1L, 3L, NA, 2L, 3L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L), Tsize = c(2.5, 6, 4, 4, 3.5,
1.8, 1.5, 1.5, 2, 3.5, 4, 2, 4.5, 3, 3, 2, 3, 1.5, 2.5, 5, 5,
3, 6, 3, 2.5, 5, 1.5, 2, 5.5, 5, 3, 6, 4, 3.5, 4, 5), LNstatus = c(0L,
1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L,
1L, 1L, 0L), Time = c(5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5), Model1 = c(0.0754111003682264, 0.789025824308725, 0.0888669573431788,
0.162944690463524, 0.148205788806964, 0.0232412222825853, 0.335639290548721,
0.427877640015971, 0.0437151408243315, 0.340118759660636, 0.104749557872707,
0.191041558905319, 0.901494025297558, 0.0271621117477482, 0.134690848022469,
NA, 0.0681964605449713, 0.0799110215826685, 0.0617151301818596,
0.129265190806531, 0.760358458474655, NA, 0.805815477115913,
0.0622010539620427, 0.4275233016386, 0.632818820863808, 0.39812097019303,
0.0217733118415042, 0.118190758626649, 0.721016961718498, 0.449778991144841,
0.164102951793386, 0.857780996059435, 0.576334735175668, 0.668186510212559,
0.107238793230798), Model2 = c(0.444752330666943, 0.930853771862407,
0.0980811416950194, 0.311714208416219, 0.108779198079316, 0.0297296718406789,
0.255165235219636, 0.652922467522166, 0.0301952043412281, 0.405213119172736,
0.0800242041001141, 0.0767147984837693, 0.999999999862285, 0.0181219543140756,
0.14865830843794, NA, 0.0496630840467432, 0.0382325860289233,
0.0421016493675688, 0.113455005788149, 0.665969059849803, NA,
0.991966233874329, 0.0424861062828985, 0.206267128670341, 0.453591375821231,
0.180419209497713, 0.00979786791522574, 0.243955485600603, 0.596589087934302,
0.778048018064795, 0.211053490646643, 0.747102269303274, 0.37666971629859,
0.511588083962541, 0.0826035699229486)), class = "data.frame", row.names = c(NA,
-36L))
I tried following code to generate the dca
dca(Surv(Time, Event) ~ Model1 + Model2, data = dfs,
label = list(Model1 = "Model1", Model2 = "Model2"), time = 5, thresholds = 1:35 / 100) %>% plot(smooth = TRUE)
Now I want to display the values of y axis at a specific x value for Model1 and Model2. It's approximately 0.255 and 0.300 respectively
Expected output

How to use Predict in rms package for a multiple values?

I used cox model for Predict function rms package for individual values they are returning the correct result where as when I give multiple values it gives me weired results?
library(data.table)
library(survival)
library(survminer)
library(rms)
dput(df)
structure(list(ID = c(1001L, 1002L, 1003L, 1004L, 1006L, 1014L,
1015L, 1016L, 1018L, 1022L, 1024L, 1032L, 1040L, 1042L, 1049L,
1056L, 1059L, 1060L, 1066L, 1084L, 1087L, 1090L, 1093L, 1096L,
1097L, 1098L, 1099L, 1200L, 1205L, 1216L, 1221L, 1222L, 1225L,
1226L, 1233L, 1239L), Time = c(9L, 8L, 69L, 104L, 104L, 100L,
24L, 85L, 100L, 99L, 67L, 58L, 7L, 94L, 93L, 90L, 91L, 90L, 89L,
72L, 84L, 84L, 11L, 82L, 39L, 46L, 82L, 82L, 9L, 34L, 75L, 76L,
52L, 20L, 29L, 70L), Event = c(1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L), Risk1 = c(0.1,
0.03, 0.02, 0.05, 0.01, 0.04, 0.03, 0.06, 0.02, 0.03, 0, 0, 0.11,
0.01, 0.03, 0, 0.01, 0.01, 0.01, 0, 0, 0, 0.05, 0.01, 0, 0, 0,
0, 0.04, 0, 0.07, 0.01, 0.01, 0, 0, 0), Risk2 = c(88L, 49L, 60L,
46L, 50L, 60L, 38L, 74L, 39L, 65L, 80L, 35L, 54L, 40L, 54L, 55L,
60L, 38L, 64L, 74L, 71L, 57L, 55L, 49L, 42L, 30L, 63L, 46L, 47L,
58L, 34L, 72L, 50L, 60L, 73L, 51L), Risk3 = c(2L, 2L, 2L, 3L,
3L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 3L, 1L, 3L, NA, 2L, 3L, 2L, 2L,
2L, NA, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L
)), class = "data.frame", row.names = c(NA, -36L)
followed by
ddist <- datadist(df)
options(datadist='ddist')
cox_model <-cph(Surv(Time,Event==1) ~ Risk1 + Risk2 + Risk3, x = T, y = T, data = df, surv = T)
Predict(cox_model, Risk1=3.2, Risk2=1, Risk3=0.5)
but when I give multiple values as follows:
Predict(cox_model,
Risk1=c(5,3,2,1.5,1.5,2,3,2.5,4,2,5.5,6,3,3.5,4,5,4.5,3,2,6,3,5,4,1.8,3,3.5,1.5,2.5,3.5,5,6,4,1.5,5,4,2.5),
Risk2=c(1,1,1,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,1,0,0,0,1,1,0,1,1,0,1,1,0,0,0),
Risk3=c(0,0.07,0,0.03,0.01,0.02,0.01,0,0.05,0,0.04,0.03,0.01,0.01,0.01,0,0.11,0.03,0,0.05,0,0,0.02,0.04,0.01,0,0,0.01,0.03,0,0.01,0,0.06,0,0,0.1))
It gives me a ouput with 46566 rows where as I have only 36 rows to predict

Cox regression stratified by another column than the predictor

I would like to perform a cox regression analysis with value_max_min as predictor, but according 2 groups peakand drop in the column peak_drop_status(to get 2 survival curves).
cox <- coxph(Surv(mace_months_date_vs_date_sample, mace) ~ value_max_min, data = df)
and to get the plot
fit <- survfit(Surv(mace_months_date_vs_date_sample, mace) ~ value_max_min,
data = df)
I thought about using the group_by or the slice functions?
Library used:
library(dplyr)
library(survival)
library(survminer)
Here is the console output:
structure(list(ID = c(136L, 136L, 200L, 200L, 146L, 146L, 153L,
153L, 137L, 137L, 214L, 214L, 85L, 85L, 96L, 96L, 172L, 172L,
182L, 182L, 87L, 87L, 93L, 93L, 210L, 210L, 69L, 69L, 132L, 132L,
68L, 68L, 74L, 74L, 9L, 9L, 159L, 159L, 7L, 7L, 154L, 154L, 94L,
94L, 113L, 113L, 124L, 124L, 131L, 131L, 143L, 143L, 151L, 151L,
213L, 213L, 225L, 225L, 160L, 160L, 226L, 226L, 133L, 133L, 105L,
105L, 119L, 119L, 156L, 156L, 208L, 208L, 117L, 117L, 227L, 227L,
97L, 97L, 221L, 221L, 187L, 187L, 155L, 155L, 189L, 189L, 219L,
219L, 178L, 178L, 181L, 181L, 184L, 184L, 165L, 165L, 233L, 233L,
180L, 180L, 192L, 192L, 79L, 79L, 183L, 183L, 139L, 139L, 199L,
199L, 81L, 81L, 162L, 162L, 104L, 104L, 237L, 237L, 128L, 128L,
73L, 73L, 229L, 229L, 138L, 138L, 218L, 218L, 95L, 95L, 110L,
110L, 190L, 190L, 72L, 72L, 127L, 127L, 164L, 164L, 111L, 111L,
194L, 194L, 216L, 216L, 188L, 188L, 71L, 71L, 67L, 67L, 88L,
88L, 123L, 123L, 173L, 173L, 223L, 223L, 152L, 152L, 238L, 238L,
63L, 63L, 10L, 10L, 75L, 75L, 109L, 109L, 197L, 197L, 193L, 193L,
89L, 89L, 106L, 106L, 205L, 205L, 125L, 125L, 121L, 121L, 100L,
100L, 234L, 234L, 6L, 6L, 211L, 211L, 228L, 228L, 175L, 175L,
84L, 84L, 191L, 191L, 243L, 243L, 115L, 115L, 220L, 220L, 242L,
242L, 2L, 2L, 222L, 222L, 203L, 203L, 201L, 201L, 224L, 224L,
4L, 4L, 102L, 102L, 76L, 76L, 239L, 239L, 231L, 231L, 195L, 195L,
134L, 134L, 171L, 171L, 83L, 83L, 217L, 217L, 5L, 5L, 141L, 141L,
3L, 3L, 112L, 112L, 235L, 235L, 185L, 185L, 103L, 103L, 120L,
120L, 207L, 207L, 166L, 166L, 174L, 174L, 116L, 116L, 8L, 8L,
140L, 140L, 14L, 14L, 27L, 27L, 30L, 30L, 23L, 23L, 54L, 54L,
13L, 13L, 16L, 16L, 39L, 39L, 44L, 44L, 42L, 42L, 51L, 51L, 245L,
245L, 59L, 59L, 28L, 28L, 45L, 45L, 34L, 34L, 49L, 49L, 43L,
43L, 24L, 24L, 19L, 19L, 12L, 12L, 41L, 41L, 47L, 47L, 32L, 32L,
50L, 50L, 31L, 31L, 18L, 18L, 15L, 15L, 60L, 60L, 52L, 52L, 21L,
21L, 29L, 29L, 38L, 38L, 55L, 55L, 33L, 33L, 56L, 56L, 244L,
244L, 36L, 36L, 20L, 20L, 17L, 17L, 57L, 57L, 35L, 35L, 22L,
22L, 26L, 26L, 25L, 25L, 37L, 37L, 58L, 58L, 61L, 61L), age = c(49L,
49L, 77L, 77L, 75L, 75L, 75L, 75L, 63L, 63L, 60L, 60L, 72L, 72L,
51L, 51L, 50L, 50L, 35L, 35L, 48L, 48L, 44L, 44L, 79L, 79L, 67L,
67L, 57L, 57L, 58L, 58L, 46L, 46L, 57L, 57L, 59L, 59L, 71L, 71L,
65L, 65L, 56L, 56L, 28L, 28L, 65L, 65L, 41L, 41L, 76L, 76L, 63L,
63L, 66L, 66L, 69L, 69L, 37L, 37L, 52L, 52L, 47L, 47L, 63L, 63L,
41L, 41L, 79L, 79L, 42L, 42L, 69L, 69L, 76L, 76L, 68L, 68L, 66L,
66L, 59L, 59L, 64L, 64L, 72L, 72L, 65L, 65L, 75L, 75L, 56L, 56L,
80L, 80L, 68L, 68L, 58L, 58L, 61L, 61L, 59L, 59L, 68L, 68L, 60L,
60L, 39L, 39L, 63L, 63L, 52L, 52L, 82L, 82L, 63L, 63L, 49L, 49L,
59L, 59L, 61L, 61L, 64L, 64L, 63L, 63L, 66L, 66L, 68L, 68L, 54L,
54L, 73L, 73L, 54L, 54L, 46L, 46L, 75L, 75L, 72L, 72L, 64L, 64L,
69L, 69L, 68L, 68L, 59L, 59L, 52L, 52L, 65L, 65L, 51L, 51L, 48L,
48L, 63L, 63L, 52L, 52L, 56L, 56L, 67L, 67L, 68L, 68L, 47L, 47L,
75L, 75L, 76L, 76L, 63L, 63L, 73L, 73L, 48L, 48L, 68L, 68L, 48L,
48L, NA, NA, 74L, 74L, 37L, 37L, 60L, 60L, 54L, 54L, 55L, 55L,
61L, 61L, 79L, 79L, 67L, 67L, 57L, 57L, 51L, 51L, 67L, 67L, 68L,
68L, 51L, 51L, 53L, 53L, 51L, 51L, 62L, 62L, 60L, 60L, 71L, 71L,
58L, 58L, 52L, 52L, 72L, 72L, 73L, 73L, 76L, 76L, 81L, 81L, 48L,
48L, 65L, 65L, 57L, 57L, 51L, 51L, 56L, 56L, 66L, 66L, 46L, 46L,
78L, 78L, 76L, 76L, 81L, 81L, 71L, 71L, 46L, 46L, 51L, 51L, 73L,
73L, 66L, 66L, 59L, 59L, 77L, 77L, 74L, 74L, 28L, 28L, 73L, 73L,
54L, 54L, 59L, 59L, 53L, 53L, 57L, 57L, 54L, 54L, 52L, 52L, 38L,
38L, 73L, 73L, 62L, 62L, 61L, 61L, 76L, 76L, 51L, 51L, 51L, 51L,
54L, 54L, 59L, 59L, 47L, 47L, 66L, 66L, 57L, 57L, 57L, 57L, 62L,
62L, 66L, 66L, 54L, 54L, 47L, 47L, 56L, 56L, 65L, 65L, 72L, 72L,
49L, 49L, 46L, 46L, 73L, 73L, 55L, 55L, 47L, 47L, 59L, 59L, 43L,
43L, 62L, 62L, 64L, 64L, 56L, 56L, 44L, 44L, 58L, 58L, 47L, 47L,
46L, 46L, 66L, 66L, 54L, 54L, 81L, 81L, 36L, 36L, 48L, 48L),
sex = c(1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L,
0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L,
0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L,
0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L,
0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L,
0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L,
1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L), mace = c(0L, 0L, 0L, 0L, 1L, 1L,
1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L,
0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L,
0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L,
0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L,
0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), mace_months_date_vs_date_sample = c(61L,
61L, 62L, 62L, 21L, 21L, 1L, 1L, 47L, 47L, 3L, 3L, 61L, 61L,
31L, 31L, 2L, 2L, 44L, 44L, 46L, 46L, 43L, 43L, 61L, 61L,
3L, 3L, 4L, 4L, 62L, 62L, 60L, 60L, 49L, 49L, 48L, 48L, 43L,
43L, 46L, 46L, 45L, 45L, 62L, 62L, 4L, 4L, 61L, 61L, 47L,
47L, 62L, 62L, 5L, 5L, 48L, 48L, 59L, 59L, 32L, 32L, 59L,
59L, 4L, 4L, 63L, 63L, 8L, 8L, 4L, 4L, 49L, 49L, 1L, 1L,
7L, 7L, 45L, 45L, 45L, 45L, 1L, 1L, 1L, 1L, 62L, 62L, 62L,
62L, 45L, 45L, 1L, 1L, 61L, 61L, 46L, 46L, 55L, 55L, 44L,
44L, 45L, 45L, 1L, 1L, 58L, 58L, 27L, 27L, 47L, 47L, 48L,
48L, 25L, 25L, 55L, 55L, 50L, 50L, 44L, 44L, 63L, 63L, 50L,
50L, 10L, 10L, 23L, 23L, 47L, 47L, 19L, 19L, 48L, 48L, 62L,
62L, 62L, 62L, 1L, 1L, 15L, 15L, 47L, 47L, 61L, 61L, 45L,
45L, 46L, 46L, 47L, 47L, 49L, 49L, 1L, 1L, 46L, 46L, 48L,
48L, 45L, 45L, 15L, 15L, 55L, 55L, 1L, 1L, 62L, 62L, 55L,
55L, 46L, 46L, 2L, 2L, 46L, 46L, 46L, 46L, 63L, 63L, 43L,
43L, 16L, 16L, 55L, 55L, 1L, 1L, 55L, 55L, 8L, 8L, 46L, 46L,
46L, 46L, 19L, 19L, 48L, 48L, 50L, 50L, 48L, 48L, 41L, 41L,
50L, 50L, 4L, 4L, 62L, 62L, 62L, 62L, 17L, 17L, 25L, 25L,
48L, 48L, 48L, 48L, 3L, 3L, 1L, 1L, 53L, 53L, 46L, 46L, 46L,
46L, 51L, 51L, 59L, 59L, 55L, 55L, 59L, 59L, 55L, 55L, 1L,
1L, 46L, 46L, 43L, 43L, 1L, 1L, 5L, 5L, 46L, 46L, 10L, 10L,
11L, 11L, 16L, 16L, 55L, 55L, 3L, 3L, 6L, 6L, 71L, 71L, 68L,
68L, 72L, 72L, 71L, 71L, 69L, 69L, 73L, 73L, 74L, 74L, 30L,
30L, 69L, 69L, 1L, 1L, 11L, 11L, 79L, 79L, 71L, 71L, 73L,
73L, 13L, 13L, 28L, 28L, 74L, 74L, 77L, 77L, 78L, 78L, 71L,
71L, 73L, 73L, 69L, 69L, 73L, 73L, 70L, 70L, 72L, 72L, 69L,
69L, 43L, 43L, 76L, 76L, 74L, 74L, 75L, 75L, 77L, 77L, 78L,
78L, 70L, 70L, 69L, 69L, 70L, 70L, 60L, 60L, 5L, 5L, 5L,
5L, 77L, 77L, 74L, 74L, 77L, 77L, 77L, 77L, 77L, 77L, 73L,
73L, 74L, 74L, 76L, 76L, 76L, 76L), trop = c(262L, 262L,
NA, NA, 1454L, 1454L, 663L, 663L, 2107L, 2107L, 86115L, 86115L,
24L, 24L, 3004L, 3004L, 9352L, 9352L, NA, NA, 1247L, 1247L,
NA, NA, 2888L, 2888L, NA, NA, 8421L, 8421L, NA, NA, NA, NA,
251L, 251L, 1211L, 1211L, NA, NA, 54592L, 54592L, 1241L,
1241L, 8669L, 8669L, 5204L, 5204L, 751L, 751L, 2840L, 2840L,
250L, 250L, NA, NA, NA, NA, 1411L, 1411L, 3789L, 3789L, 1675L,
1675L, 1557L, 1557L, 440L, 440L, NA, NA, 6979L, 6979L, 6155L,
6155L, 5110L, 5110L, 87355L, 87355L, 90L, 90L, 2234L, 2234L,
10000L, 10000L, NA, NA, 843L, 843L, 950L, 950L, 372L, 372L,
NA, NA, NA, NA, NA, NA, 6212L, 6212L, 871L, 871L, 776L, 776L,
24160L, 24160L, NA, NA, 9951L, 9951L, 3598L, 3598L, 2040L,
2040L, NA, NA, 6581L, 6581L, 349L, 349L, 11L, 11L, 6694L,
6694L, 63L, 63L, 15543L, 15543L, NA, NA, 33017L, 33017L,
2483L, 2483L, NA, NA, 961L, 961L, 1470L, 1470L, 2380L, 2380L,
11135L, 11135L, 1730L, 1730L, NA, NA, 11450L, 11450L, 769L,
769L, 16720L, 16720L, 57L, 57L, NA, NA, 4281L, 4281L, NA,
NA, 1258L, 1258L, NA, NA, 4299L, 4299L, 13374L, 13374L, NA,
NA, 2844L, 2844L, 1753L, 1753L, NA, NA, 5256L, 5256L, 3624L,
3624L, NA, NA, 21876L, 21876L, 8903L, 8903L, 844L, 844L,
5654L, 5654L, 3569L, 3569L, 45649L, 45649L, NA, NA, NA, NA,
4927L, 4927L, NA, NA, 2177L, 2177L, 5247L, 5247L, 50000L,
50000L, 438L, 438L, 1480L, 1480L, 50L, 50L, NA, NA, NA, NA,
27L, 27L, 2956L, 2956L, NA, NA, 3000L, 3000L, NA, NA, 6630L,
6630L, 1911L, 1911L, NA, NA, 2797L, 2797L, 6672L, 6672L,
1627L, 1627L, 123L, 123L, 7671L, 7671L, NA, NA, NA, NA, 2340L,
2340L, 10014L, 10014L, 7860L, 7860L, 67927L, 67927L, NA,
NA, NA, NA, 2413L, 2413L, 1035L, 1035L, 40273L, 40273L, 7120L,
7120L, 6440L, 6440L, 3340L, 3340L, 8450L, 8450L, 1500L, 1500L,
1970L, 1970L, 180L, 180L, 990L, 990L, 2600L, 2600L, 1800L,
1800L, 5280L, 5280L, 83L, 83L, 160L, 160L, 40L, 40L, 3710L,
3710L, 400L, 400L, NA, NA, 2100L, 2100L, 2390L, 2390L, 9320L,
9320L, 6020L, 6020L, 320L, 320L, 1420L, 1420L, 1710L, 1710L,
15300L, 15300L, 6490L, 6490L, 6390L, 6390L, 6300L, 6300L,
470L, 470L, 1740L, 1740L, 3600L, 3600L, NA, NA, 3930L, 3930L,
NA, NA, 2260L, 2260L, 1360L, 1360L, 846L, 846L, 15940L, 15940L,
NA, NA, 840L, 840L, 5010L, 5010L, NA, NA, 5330L, 5330L, 500L,
500L, 1080L, 1080L, NA, NA, NA, NA, 4470L, 4470L), egfr = c(90L,
90L, 48L, 48L, 65L, 65L, 35L, 35L, 84L, 84L, 90L, 90L, 64L,
64L, 61L, 61L, 86L, 86L, 56L, 56L, 90L, 90L, 62L, 62L, 75L,
75L, 56L, 56L, 86L, 86L, 90L, 90L, 89L, 89L, 84L, 84L, 86L,
86L, 65L, 65L, 86L, 86L, 61L, 61L, 90L, 90L, 73L, 73L, 61L,
61L, 77L, 77L, 60L, 60L, 58L, 58L, 80L, 80L, 58L, 58L, 90L,
90L, 64L, 64L, 68L, 68L, 90L, 90L, 61L, 61L, 80L, 80L, 90L,
90L, 36L, 36L, 90L, 90L, 90L, 90L, 59L, 59L, 90L, 90L, 77L,
77L, 64L, 64L, 52L, 52L, 90L, 90L, 33L, 33L, 90L, 90L, 90L,
90L, 90L, 90L, 90L, 90L, 90L, 90L, 69L, 69L, 90L, 90L, 46L,
46L, 90L, 90L, 75L, 75L, 54L, 54L, 90L, 90L, 54L, 54L, 90L,
90L, 82L, 82L, 49L, 49L, 35L, 35L, 90L, 90L, 66L, 66L, 90L,
90L, 86L, 86L, 90L, 90L, 45L, 45L, 72L, 72L, 68L, 68L, 51L,
51L, 90L, 90L, 90L, 90L, 90L, 90L, 58L, 58L, 84L, 84L, 42L,
42L, 90L, 90L, 86L, 86L, 90L, 90L, 90L, 90L, 90L, 90L, 87L,
87L, 67L, 67L, 51L, 51L, 81L, 81L, 74L, 74L, 63L, 63L, 90L,
90L, 56L, 56L, 87L, 87L, 84L, 84L, 78L, 78L, 63L, 63L, 63L,
63L, 63L, 63L, 67L, 67L, 64L, 64L, 68L, 68L, 78L, 78L, 68L,
68L, 90L, 90L, 69L, 69L, 90L, 90L, 90L, 90L, 75L, 75L, 85L,
85L, 85L, 85L, 52L, 52L, 69L, 69L, 90L, 90L, 76L, 76L, 90L,
90L, 54L, 54L, 86L, 86L, 90L, 90L, 61L, 61L, 72L, 72L, 76L,
76L, 69L, 69L, 85L, 85L, 86L, 86L, 42L, 42L, 72L, 72L, 71L,
71L, 58L, 58L, 68L, 68L, 86L, 86L, 75L, 75L, 84L, 84L, 63L,
63L, 63L, 63L, 78L, 78L, 90L, 90L, 48L, 48L, 55L, 55L, 81L,
81L, 87L, 87L, 99L, 99L, 77L, 77L, 56L, 56L, 69L, 69L, 66L,
66L, 67L, 67L, 85L, 85L, 90L, 90L, 65L, 65L, 68L, 68L, 76L,
76L, 84L, 84L, 90L, 90L, 59L, 59L, 88L, 88L, 79L, 79L, 85L,
85L, 90L, 90L, 90L, 90L, 77L, 77L, 90L, 90L, 49L, 49L, 62L,
62L, 71L, 71L, 87L, 87L, 51L, 51L, 90L, 90L, 90L, 90L, 79L,
79L, 90L, 90L, 90L, 90L, 52L, 52L, 75L, 75L, 71L, 71L, 68L,
68L, 83L, 83L, 88L, 88L, 51L, 51L, 87L, 87L, 99L, 99L, 78L,
78L, 90L, 90L), dm = c(0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L,
1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L,
0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L,
1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L), smoke = c(1L, 1L,
0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L,
0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L,
1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L,
0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L,
1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L,
1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L,
1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L,
1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L,
1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L,
0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L,
1L, 1L), peak_drop_status = c("max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val", "max_val", "min_val", "max_val", "min_val", "max_val",
"min_val"), value_max_min = c(NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 308.408676147461, -283.636077880859,
NA, NA, 208.791275024414, -5.3211898803711, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, -14.9628820419311, -218.279922485352, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -15.319938659668,
-279.422790527344, 248.09851074219, -30.822647094727, 116.716430664065,
-8.8325366973877, NA, NA, 10.0856704711914, -29.1057052612305,
179.8525390625, -10.1883692741394, 130.585632324218, -39.6044845581057,
32.883270263672, -5.3593330383301, -17.5934886932374, -821.989379882808,
375.086456298828, -5.7297992706299, NA, NA, NA, NA, NA, NA,
419.108337402341, -1.28273773193359, 55.87646484375, -17.770830154419,
NA, NA, 44.05969238281, -6.7603330612182, 36.9793767929077,
-58.77816772461, 47.6982421875, -48.2563076019287, 180.041305541992,
-3.5863590240479, 19.503479003907, -66.49755859375, NA, NA,
33.036499023438, -3.0688781738281, 83.0613746643061, -562.289733886719,
-5.5973930358887, -162.939453124998, 18.5003929138184, -95.700927734375,
164.985534667969, 9.7361946105957, 27.7907447814941, -69.5900268554681,
159.863708496094, -22.477741241455, -21.2021789550781, -372.002563476562,
153.190795898438, 5.9852733612061, 15.9482421875, -32.590072631836,
243.90209960937, -24.0595645904541, 131.392028808594, -5.5808029174805,
192.978088378906, -56.510217666626, 104.543823242188, -173.209777832031,
NA, NA, 174.83570098877, -69.742797851558, 12.743041992187,
-502.216308593755, 28.5669360160827, -388.549621582031, -15.8679084777832,
-308.033813476562, -12.657926082611, -133.534645795822, -0.800338745117202,
-170.645233154297, -8.9742355346679, -44.6486473083496, 3.6423645019532,
-0.407896041870121, 163.853607177735, -13.0253372192383,
327.035522460937, -3.5568542480469, 336.011077880859, -9.2046012878418
)), row.names = c(NA, -364L), class = c("tbl_df", "tbl",
"data.frame"))
Thank you,

To do a stratified Cox model, you would specify strata(var) in your model formula as follows:
library("survival")
library("survminer")
library("dplyr")
cox <- coxph(
Surv(mace_months_date_vs_date_sample, mace) ~ value_max_min + strata(peak_drop_status),
data = df
)
Your survfit isn't going to be very useful by the way, as you're supplying a numeric variable it's fitting a KM curve to every unique value of value_max_min. So you get a whole bunch of non-informative Kaplan-Meier curves:
fit <- survfit(
Surv(mace_months_date_vs_date_sample, mace) ~ value_max_min,
data = df
)
plot(fit)
It might be better for visualisation purposes to split your data into groups based on quantiles of the predictor:
med <- median(df$value_max_min, na.rm=TRUE)
fit <- survfit(
Surv(mace_months_date_vs_date_sample, mace) ~ (value_max_min > med),
data = df
)
plot(fit)

non-numeric argument to binary operator - log()

When I try to run log(x) on a variable in my dataset I get the error:
Error in oldat$gdp16 + 1 : non-numeric argument to binary operator
At first I thought that the reason is that these particular variables have NA's so I decided to deal with this problem like so:
oldat$gdp16[oldat$gdp16 == "#N/A"] <- "NA"
oldat$gdp16LOG <- log(oldat$gdp16 + 1, na.rm=TRUE)
This did not solve the problem.
Please find the excerpt of the data as an example below. The variable that fails the log transform is gdp16:
structure(list(gdp16 = c("19469", "159049", "554861", "10546",
"1208039", "390800", "37868", "11839", "32153", "47723", "467546",
"15649", "1793989", "53241", "32218", "1535768", "250036", "11190993",
"280091", "51339", "NA", "20154", "195305", "306900", "72343",
"98614", "332928", "5010", "23338", "73001", "4671", "238678",
"2465134", "14014", "14378", "3477796", "192691", "1056", "68664",
"320881", "125817", "20304", "2274230", "932256", "418977", "304819",
"317748", "1859384", "36375", "14057", "4949273", "137278", "70875",
"6715", "110912", "6813", "27572", "42773", "296536", "12232",
"1076912", "6796", "11183", "4374", "103606", "777228", "189286",
"404653", "7607", "1414804", "371075", "57821", "304889", "27424",
"471400", "205184", "105035", "152452", "187806", "1284728",
"644936", "38300", "309764", "89769", "44709", "295763", "1411042",
"1237255", "95584", "514460", "668745", "NA", "525608", "6952",
"411755", "4389", "22320", "42063", "863722", "24079", "93270",
"357045", "2650850", "18624475", "67068", "236", "205276", "16620"
), pop16 = c(34656L, 40606L, 43847L, 2925L, 24211L, 8737L, 9758L,
391L, 1425L, 9502L, 11331L, 2250L, 207653L, 7128L, 23439L, 36265L,
17910L, 1378665L, 48653L, 4174L, 11476L, 1170L, 10566L, 5728L,
10649L, 16385L, 95689L, 4955L, 1316L, 102403L, 899L, 5495L, 66860L,
1980L, 3719L, 82349L, 10776L, 107L, 16582L, 7337L, 9814L, 335L,
1324171L, 261115L, 80277L, 4755L, 8546L, 60627L, 23696L, 2881L,
126995L, 17794L, 48462L, 1816L, 4053L, 6080L, 1960L, 2868L, 31187L,
1263L, 127540L, 3552L, 3027L, 622L, 35277L, 17030L, 4693L, 185990L,
20673L, 51246L, 5235L, 4034L, 103320L, 6725L, 37970L, 10325L,
3407L, 2570L, 19702L, 144342L, 32276L, 7058L, 5607L, 5431L, 2065L,
56015L, 25369L, 46484L, 39579L, 9923L, 8373L, 18430L, 22465L,
8735L, 68864L, 7606L, 1365L, 11403L, 79512L, 41488L, 45005L,
9270L, 65596L, 323406L, 31848L, 31568L, 94569L, 16150L), gold16 = c(0L,
0L, 3L, 1L, 8L, 0L, 1L, 1L, 1L, 1L, 2L, 0L, 7L, 0L, 0L, 4L, 0L,
26L, 3L, 5L, 5L, 0L, 1L, 2L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L,
10L, 0L, 2L, 17L, 3L, 0L, 0L, 0L, 8L, 0L, 0L, 1L, 3L, 0L, 0L,
8L, 1L, 6L, 12L, 3L, 6L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 8L, 4L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 2L, 0L, 1L, 0L,
1L, 19L, 0L, 2L, 1L, 2L, 1L, 2L, 9L, 7L, 0L, 2L, 3L, 0L, 1L,
1L, 2L, 0L, 0L, 0L, 1L, 0L, 2L, 0L, 27L, 46L, 4L, 0L, 1L, 0L),
tot16 = c(0L, 2L, 4L, 4L, 29L, 1L, 18L, 2L, 2L, 9L, 6L, 0L,
19L, 3L, 0L, 22L, 0L, 70L, 8L, 10L, 11L, 0L, 10L, 15L, 1L,
0L, 3L, 0L, 1L, 8L, 1L, 1L, 42L, 0L, 7L, 42L, 6L, 1L, 0L,
0L, 15L, 0L, 2L, 3L, 8L, 2L, 2L, 28L, 2L, 11L, 41L, 18L,
13L, 1L, 0L, 0L, 0L, 4L, 5L, 0L, 5L, 0L, 2L, 0L, 1L, 19L,
18L, 1L, 1L, 7L, 4L, 0L, 1L, 0L, 11L, 1L, 1L, 1L, 4L, 55L,
0L, 8L, 1L, 4L, 4L, 10L, 21L, 17L, 0L, 11L, 7L, 0L, 3L, 1L,
6L, 0L, 1L, 3L, 8L, 0L, 11L, 1L, 67L, 121L, 13L, 3L, 2L,
0L), altitude = c(1790, 1, 10.5, 989, 605, 170, -28, 2, 6,
198, 76, 983, 1079, 580, 726, 74, 521, 44, 2625, 130, 4,
170, 244, 0, 0, 2850, 22, 2325, 37, 2355, 0, 25, 34, 0, 451,
34, 153, 25, 1529, 100, 102, 15, 210, 3, 1189, 8, 754, 14,
217, 53, 17, 338, 1795, 0, 5, 771, 8, 124, 60, 134, 2240,
80, 1350, 61, 53, -2, 10, 777, 207, 6, 12, 0, 7, 54, 93,
15, 3, 13, 70, 124, 624, 116, 0, 131, 281, 1271, 33, 667,
377, 15, 542, 691, 5, 789, 1, 63, 0, 0, 938, 1190, 168, 13,
14, 2, 459, 909, 25, 1483), athletes16 = c(3L, 64L, 215L,
31L, 420L, 72L, 56L, 29L, 33L, 120L, 104L, 12L, 462L, 50L,
24L, 310L, 42L, 392L, 143L, 85L, 117L, 15L, 104L, 119L, 26L,
37L, 121L, 12L, 46L, 37L, 53L, 54L, 393L, 6L, 40L, 418L,
92L, 7L, 21L, 37L, 154L, 8L, 112L, 28L, 63L, 76L, 47L, 309L,
12L, 56L, 336L, 101L, 79L, 8L, 0L, 19L, 32L, 67L, 32L, 2L,
123L, 23L, 43L, 35L, 48L, 237L, 195L, 71L, 6L, 31L, 62L,
10L, 13L, 11L, 234L, 90L, 40L, 37L, 95L, 285L, 10L, 103L,
25L, 52L, 63L, 135L, 135L, 307L, 6L, 151L, 103L, 7L, 57L,
7L, 54L, 5L, 28L, 61L, 100L, 21L, 204L, 12L, 360L, 555L,
70L, 86L, 22L, 30L)), class = "data.frame", row.names = c(NA,
-108L))

Your gdp16 variable is of class character. You cannot add a number to character. You need to convert your variable to the numeric type (and perhaps substitute NAs):
df$gdp16 <- as.numeric(df$gdp16)

You need to replace the "NA" with an actual NA, and do as.numeric:
oldat$gdp16LOG = log(as.numeric(replace(oldat$gdp16,oldat$gdp16=="NA",NA)))
You can just do as.numeric(oldat$gdp16) . It will return some error messages because any other string that is not numeric will be converted to NA..

How to force geom_density to start in (0,0)?

Question: how to force geom_density to start in (0,0)?
I have produced this plot:
Using this code (inputs on how to improve the script, are welcome):
ki60low <- subset(p, p$ki67in==0 & p$recurrence==1)
ki60in <- subset(p, p$ki67in==1 & p$recurrence==1)
ki60high <- subset(p, p$ki67in==2 & p$recurrence==1)
ki60low$time.recur.months1 <- ki60low$time.recur.months/12
ki60in$time.recur.months1 <- ki60in$time.recur.months/12
ki60high$time.recur.months1 <- ki60high$time.recur.months/12
theme <- theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_line(colour = "gray98"),
panel.grid.minor = element_line(colour = "gray98"),
panel.border = element_blank(),
panel.background = element_blank())
ggplot() + theme +
scale_x_continuous(name="Years to recurrence", breaks=c(0,1,2,3,4,5,6,7,8,9,10,11), labels=c("0","1","2","3","4","5","6","7","8","9","10","11"), limits=c(-1,11)) +
scale_y_continuous(name="Number of recurrences", limits=c(0, 6), seq(0,6,by=1)) +
geom_bar(aes(x=ki60low$time.recur.months1), colour="#1C73C2", fill="#1C73C2", alpha=0.2) +
geom_bar(aes(x=ki60in$time.recur.months1), colour="red", fill="red", alpha=0.2) +
geom_bar(aes(x=ki60high$time.recur.months1), colour="black", fill="black", alpha=0.7) +
geom_density(aes(x=ki60low$time.recur.months1, y=..count..), colour="#1C73C2", fill="#1C73C2", alpha=0.1) +
geom_density(aes(x=ki60in$time.recur.months1, y=..count..), colour="red", fill="red", alpha=0.1) +
geom_density(aes(x=ki60high$time.recur.months1, y=..count..), colour="black", fill="black", alpha=0.18) +
annotate("label", x = 8.28, y = 5.5, label = "Ki-67 proliferative index: 0 - 4%", label.size = 0.5, cex=8, colour="#1C73C2") +
annotate("label", x = 8.28, y = 4.5, label = "Ki-67 proliferative index: 5 - 9%", label.size = 0.5, cex=8, colour="red") +
annotate("label", x = 8.28, y = 3.5, label = "Ki-67 proliferative index: \u226510% ", label.size = 0.5, cex=8, colour="black") +
theme(axis.text.x = element_text(color = "grey20", size = 12), axis.title.x = element_text(color = "grey20", size = 14, face="bold", margin=margin(t=12))) +
theme(axis.text.y = element_text(color = "grey20", size = 11), axis.title.y = element_text(color = "grey20", size = 14, face="bold", margin=margin(r=12))) +
theme(legend.text=element_text(size=12)) + theme(legend.title=element_text(size=14))
I would like the geom_density to start in (0,0). How can this be done? I found this, which did not help.
My data p
p <- structure(list(ki67in = c(0L, 2L, 0L, 0L, 1L, 0L, 2L, 2L, 1L,
0L, 1L, 2L, 0L, 2L, 0L, 1L, 1L, 1L, 0L, 2L, 2L, 0L, 1L, 1L, 0L,
0L, 0L, 1L, 0L, 1L, 2L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 2L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 1L, 0L, 1L, 0L,
0L, 1L, 0L, 0L, 1L, 2L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 1L, 1L, 2L, 0L, 2L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 2L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 2L,
0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L), time.recur.months = c(0.75, 0.6, 4.665297741,
0.1, 0.75, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 6, 7, 8, 8,
8, 9, 11, 12, 13, 13, 15, 15, 15, 16, 17, 17, 18, 27, 28, 29,
30, 33, 34, 35, 37, 37, 38, 39, 40, 41, 45, 49, 49, 50, 52, 53,
54, 56, 56, 56, 56, 57, 58, 58, 60, 60, 60, 60, 61, 62, 63, 64,
66, 67, 67, 72, 72, 74, 78, 80, 80, 80, 81, 82, 83, 83, 84, 84,
85, 85, 86, 86, 88, 88, 88, 88, 89, 89, 89, 90, 90, 91, 91, 92,
92, 92, 92, 93, 93, 93, 93, 93, 93, 94, 97, 98, 98, 99, 99, 99,
100, 101, 101, 101, 103, 103, 103, 103, 104, 104, 106, 106, 109,
110, 111, 111, 112, 114, 114, 115, 116, 117, 118, 118, 118, 119,
120, 120, 120, 120, 120, 120, 121, 121, 123, 124, 124, 125, 125,
125, 125), recurrence = c(0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L,
0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 0L)), class = "data.frame", row.names = c(1L,
2L, 3L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 18L, 19L, 20L,
21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L,
34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 44L, 45L, 46L, 47L, 48L,
49L, 50L, 51L, 52L, 53L, 54L, 55L, 57L, 59L, 60L, 61L, 62L, 63L,
64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L,
77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 87L, 89L, 90L, 91L,
92L, 93L, 94L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L,
105L, 106L, 107L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 116L,
117L, 118L, 119L, 120L, 121L, 123L, 124L, 125L, 126L, 127L, 128L,
130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 139L, 140L,
141L, 142L, 143L, 144L, 145L, 146L, 147L, 148L, 149L, 150L, 151L,
152L, 153L, 154L, 155L, 156L, 157L, 158L, 159L, 160L, 161L, 162L,
163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L, 171L, 172L, 173L,
174L, 175L))

A few changes will allow you to generate your plot with much less code:
1) Use a single data frame with a category column that will be mapped to the colour and fill aesthetics, rather than separate data frames. Then you need to call each geom only once.
2) Generate the legend directly from the data, rather than with annotation.
3) Tweak an existing plot theme (theme_classic() in this case).
4) When setting breaks with scale_x/y_continuous(), you can often take advantage of various short cuts. For example: 0:11 instead of c(0,1,2,3,4,5,6,7,8,9,10,11). Also, if the labels are the same as the break values, then there's no need to add a labels argument.
In addition: I've switched from geom_bar to geom_rug, but you can of course go with a bar plot if you wish. For the rug plot, I've jittered the markers a bit so that points with the same x value will all be shown, rather than plotted on top of each other. Also, with ggplot, use bare column names rather than restating the data frame name. For example, in your original code, aes(time.recur.months1, ...) rather than aes(ki60low$time.recur.months1, ...).
For the axis limits, I'm not sure how you want them to look. You've set the x-axis limits at c(-1,11). To start at zero, there are two choices:
scale_x_continuous(limits=c(0,11)) will exclude data outside that range when calculating the density estimate.
coord_cartesian(xlim=c(0,11)) will include all data in the density estimate, even if it's outside the xlim range.
In either case, ggplot by default adds some padding before and after the axis limits. If you want less or no padding, use the expand argument in scale_x/y_continuous.
library(tidyverse)
library(ggstance)
p.for.plot = p %>%
filter(recurrence==1) %>%
arrange(ki67in) %>%
mutate(time.recur.years=time.recur.months/12,
ki67in=recode(ki67in,
"0"="Ki-67 proliferative index: 0 - 4%",
"1"="Ki-67 proliferative index: 5 - 9%",
"2"="Ki-67 proliferative index: \u226510%"),
ki67in=factor(ki67in, levels=unique(ki67in)))
cols = c("#1C73C2", "red", "black")
ggplot(p.for.plot, aes(time.recur.years, colour=ki67in, fill=ki67in)) +
geom_density(aes(y=..count..), alpha=0.2) +
#geom_bar(alpha=0.7) +
geom_rug(aes(y=0), position=position_jitter(width=0.05, height=0),
length=unit(0.05, "npc"), show.legend=FALSE) +
coord_cartesian(xlim=c(0,11)) +
scale_x_continuous(name="Years to recurrence", breaks=0:11, expand=c(0,0)) +
scale_y_continuous(name="Number of recurrences", limits=c(0, 6), breaks=0:6, expand=c(0,0)) +
scale_colour_manual(values=cols) +
scale_fill_manual(values=cols) +
labs(colour="", fill="") +
theme_classic() +
theme(panel.grid.major = element_line(colour = "gray98"),
panel.grid.minor = element_line(colour = "gray98"),
legend.position=c(0.7,0.8))

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