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

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))

Related

Create a condition for legend in ggsurvplot

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,
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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,

Kruskal.wallis gives out equal p-values

Friends,
I'm having an issue with the Kruskal wallis test in r, testing for stable seasonality with the Kruskal-wallis test. The p-values tested for each variable are coming out the same. Using Kruskal.test(formula, data = mydata) from the library(stats) package . I'm having a hard time believing that the pvalues would be the same.
My dataset is a monthly dataset with 163 obs, 3 macro economic variables in the model and two seasonal dummies.
I'm testing each independent macro economic variable with the dependent variable in the following way Kruskal.test(y~x, data = mydata). So for the data example below it would be Kruskal.test(pr~mev06_mp_lag2, data = mydata). And repeated for each mev in the dataset. All the pvalues for testing the 3 mev's (mev06_mp_lag2, mev29_lag2, mev108_lag1) comes out to be this output:
data: pr by mev29_lag2
Kruskal-Wallis chi-squared = 162, df = 162, p-value = 0.4852
Here is the data:
structure(list(date = structure(c(28L, 56L, 42L, 97L, 1L, 111L,
83L, 70L, 15L, 151L, 138L, 125L, 29L, 57L, 43L, 98L, 2L, 112L,
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85L, 72L, 17L, 153L, 140L, 127L, 31L, 59L, 45L, 100L, 4L, 114L,
86L, 73L, 18L, 154L, 141L, 128L, 32L, 60L, 46L, 101L, 5L, 115L,
87L, 74L, 19L, 155L, 142L, 129L, 33L, 61L, 47L, 102L, 6L, 116L,
88L, 75L, 20L, 156L, 143L, 130L, 34L, 62L, 48L, 103L, 7L, 117L,
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94L, 81L, 26L, 162L, 149L, 136L, 40L, 68L, 54L, 109L, 13L, 123L,
95L, 82L, 27L, 163L, 150L, 137L, 41L, 69L, 55L, 110L, 14L, 124L,
96L), .Label = c("01APR2006", "01APR2007", "01APR2008", "01APR2009",
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"01FEB2019", "01JAN2006", "01JAN2007", "01JAN2008", "01JAN2009",
"01JAN2010", "01JAN2011", "01JAN2012", "01JAN2013", "01JAN2014",
"01JAN2015", "01JAN2016", "01JAN2017", "01JAN2018", "01JAN2019",
"01JUL2006", "01JUL2007", "01JUL2008", "01JUL2009", "01JUL2010",
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"01JUL2016", "01JUL2017", "01JUL2018", "01JUN2006", "01JUN2007",
"01JUN2008", "01JUN2009", "01JUN2010", "01JUN2011", "01JUN2012",
"01JUN2013", "01JUN2014", "01JUN2015", "01JUN2016", "01JUN2017",
"01JUN2018", "01JUN2019", "01MAR2006", "01MAR2007", "01MAR2008",
"01MAR2009", "01MAR2010", "01MAR2011", "01MAR2012", "01MAR2013",
"01MAR2014", "01MAR2015", "01MAR2016", "01MAR2017", "01MAR2018",
"01MAR2019", "01MAY2006", "01MAY2007", "01MAY2008", "01MAY2009",
"01MAY2010", "01MAY2011", "01MAY2012", "01MAY2013", "01MAY2014",
"01MAY2015", "01MAY2016", "01MAY2017", "01MAY2018", "01MAY2019",
"01NOV2006", "01NOV2007", "01NOV2008", "01NOV2009", "01NOV2010",
"01NOV2011", "01NOV2012", "01NOV2013", "01NOV2014", "01NOV2015",
"01NOV2016", "01NOV2017", "01NOV2018", "01OCT2006", "01OCT2007",
"01OCT2008", "01OCT2009", "01OCT2010", "01OCT2011", "01OCT2012",
"01OCT2013", "01OCT2014", "01OCT2015", "01OCT2016", "01OCT2017",
"01OCT2018", "01SEP2006", "01SEP2007", "01SEP2008", "01SEP2009",
"01SEP2010", "01SEP2011", "01SEP2012", "01SEP2013", "01SEP2014",
"01SEP2015", "01SEP2016", "01SEP2017", "01SEP2018"), class = "factor"),
pr = c(0.1691759261, 0.1975689455, 0.1701795466, 0.1889038722,
0.1743304586, 0.1850822209, 0.1725476026, 0.1806130453, 0.1769864586,
0.1546961801, 0.18850436, 0.1695999754, 0.1660947088, 0.1929270116,
0.1629685381, 0.1716883769, 0.1782082767, 0.177316379, 0.1586548395,
0.1816295787, 0.1634939904, 0.1653658139, 0.1669465832, 0.1547769918,
0.17154596, 0.1824150313, 0.1600967574, 0.1819462462, 0.1625842114,
0.1605423212, 0.174298958, 0.16859091, 0.1567519737, 0.1549443922,
0.1528250707, 0.1563427163, 0.1562236709, 0.1544731644, 0.1595362963,
0.1749852828, 0.1536175907, 0.1668984941, 0.1532514745, 0.152745466,
0.1590015917, 0.1500819546, 0.1504755171, 0.1583227453, 0.1546476157,
0.1634331963, 0.1565167637, 0.1699421465, 0.1657200266, 0.1642684245,
0.1675084975, 0.1617848489, 0.1662501795, 0.1648139984, 0.1645302595,
0.169286769, 0.1707244798, 0.1845315559, 0.1752391568, 0.1899788506,
0.1784046029, 0.1842806875, 0.1836403012, 0.1753696341, 0.1738240496,
0.1747609205, 0.1724421753, 0.1803992831, 0.1763816185, 0.187630168,
0.1877238382, 0.1860668525, 0.1854666743, 0.1860146483, 0.1781037416,
0.185259322, 0.1879122146, 0.178520754, 0.1875367517, 0.18694397,
0.1860777227, 0.1979044449, 0.1833497201, 0.192027271, 0.1926325454,
0.1916103719, 0.1851319974, 0.1864458557, 0.1832327814, 0.1808570791,
0.1851145899, 0.1815387272, 0.1870942258, 0.1943564723, 0.1862582923,
0.1907279007, 0.1859213896, 0.1865372709, 0.1898453914, 0.1847275775,
0.1736567497, 0.1771092243, 0.1822902114, 0.1840752276, 0.1892670811,
0.1923250842, 0.1852956789, 0.1917880299, 0.18771724, 0.1857801687,
0.1868263217, 0.1867604143, 0.1824500898, 0.1758283625, 0.1829290332,
0.1808247326, 0.183507277, 0.1852845389, 0.1808714285, 0.1818222883,
0.1755951829, 0.1774808136, 0.1775837234, 0.1696830467, 0.172385402,
0.1694350722, 0.168336944, 0.1680335702, 0.1684147459, 0.1726731413,
0.1633235864, 0.1707780779, 0.1606329755, 0.1634684695, 0.1652849939,
0.15803428, 0.1616158193, 0.1527704105, 0.1584612931, 0.1550232032,
0.1534022945, 0.164970584, 0.1565023361, 0.1622506128, 0.1551517442,
0.1539405645, 0.152548495, 0.1516353176, 0.1523898229, 0.1477241538,
0.1502876518, 0.1515682192, 0.1540217905, 0.1589165786, 0.1531622236,
0.1583882529, 0.1532322761, 0.157552401, 0.1621688871), month = c(12L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L), mon1 = c(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, 1L, 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, 0L, 0L, 0L, 0L, 0L, 0L, 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, 1L, 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, 0L, 0L, 0L, 0L, 0L, 0L, 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, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L), mon3 = c(0L, 0L, 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, 1L, 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, 0L, 0L, 0L, 0L, 0L, 0L, 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, 1L, 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, 0L, 0L, 0L, 0L, 0L, 0L, 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, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L), mev06_mp_lag2 = c(0.2779810102,
0.1874272639, 0.1332826385, 0.1128640237, 0.1247535199, 0.1545791804,
0.2106891929, 0.2757365926, 0.329455103, 0.3808671396, 0.4450555294,
0.5340975751, 0.5971738413, 0.5881040948, 0.4793350636, 0.3124264887,
0.2197636246, 0.2206435437, 0.3113169675, 0.4196078671, 0.5003884945,
0.5494487995, 0.5369484545, 0.4606922562, 0.3338162715, 0.278520389,
0.3170366404, 0.4156696136, 0.4787532552, 0.4443344043, 0.3681819294,
0.2878537618, 0.2048228841, 0.1251537938, 0.0382989338, -0.058589422,
-0.142185008, -0.153725768, -0.074125689, 0.0484987522, 0.0608517463,
-0.079803144, -0.303655154, -0.429635585, -0.363580402, -0.1573843,
0.0420304555, 0.1835101363, 0.2542206609, 0.2533515836, 0.1774048348,
0.0536834552, -0.031620066, -0.048554527, -0.010029088, 0.0691957026,
0.1865379823, 0.314751579, 0.3867383564, 0.3849543674, 0.3270672177,
0.3352052154, 0.4333568873, 0.5807725419, 0.6594152281, 0.5820169704,
0.4614498827, 0.382189864, 0.3472850124, 0.3700953746, 0.4332794073,
0.5388940866, 0.6346031107, 0.6722549883, 0.6226019329, 0.5308626721,
0.5406836123, 0.652356085, 0.8470071782, 0.9341209812, 0.8264468016,
0.612419938, 0.5006911837, 0.5691599433, 0.7307708771, 0.8473791813,
0.8590757515, 0.7900410964, 0.7171039073, 0.6076028502, 0.5505395263,
0.5661995614, 0.631423817, 0.7324609809, 0.776800689, 0.7461146765,
0.6396693594, 0.5909067989, 0.6163303443, 0.6923212327, 0.7608602548,
0.7385415186, 0.7245230167, 0.735008075, 0.7303155287, 0.7306620594,
0.7216900251, 0.710357153, 0.668241137, 0.6465248078, 0.6386886106,
0.644503099, 0.6750915049, 0.6733980993, 0.707678618, 0.7411667711,
0.7159390625, 0.6659808449, 0.6197029436, 0.5965547889, 0.5673138317,
0.5608362128, 0.5669008884, 0.5795942214, 0.5905982279, 0.556992012,
0.5359266787, 0.5449271219, 0.5753646848, 0.6196930073, 0.6313425488,
0.6047324646, 0.5262327459, 0.4680502206, 0.4339327769, 0.422330442,
0.4388551617, 0.4449027001, 0.4724310877, 0.4603556503, 0.3559313099,
0.2192993453, 0.1752438701, 0.2708768468, 0.4398555582, 0.5419383533,
0.5258750189, 0.4264906744, 0.3512451556, 0.3047050285, 0.3177822041,
0.3703341357, 0.4374805453, 0.5119974656, 0.5479752418, 0.5383546522,
0.4763979544, 0.4418530239, 0.4423212346, 0.4638361889, 0.4725955269,
0.4199050848, 0.3677860365), mev29_lag2 = c(12052.672746,
12155.974991, 12259.977269, 12364.551523, 12471.923335, 12575.751994,
12681.578091, 12792.424151, 12903.799861, 13014.933326, 13125.644747,
13237.759633, 13347.540807, 13456.257594, 13563.261568, 13668.005405,
13772.061616, 13868.872889, 13963.208033, 14057.010446, 14145.406294,
14227.079383, 14301.142959, 14368.046479, 14424.924247, 14471.887375,
14508.019112, 14532.668323, 14547.065728, 14552.236417, 14550.020205,
14541.465439, 14527.537817, 14509.400483, 14488.246542, 14464.991414,
14441.692779, 14419.373969, 14399.416496, 14382.82297, 14369.044585,
14358.108259, 14348.715697, 14340.186543, 14332.550823, 14325.428273,
14318.322395, 14310.559769, 14301.864431, 14291.633935, 14279.435535,
14264.935547, 14247.97805, 14230.01465, 14210.49904, 14189.108376,
14166.881283, 14144.225632, 14121.472414, 14098.568702, 14076.59218,
14055.590158, 14035.983138, 14018.088095, 14001.533115, 13987.079436,
13973.759653, 13961.158726, 13949.839264, 13939.826368, 13931.070165,
13923.347123, 13916.816802, 13911.291278, 13906.706121, 13903.022798,
13900.161493, 13898.209865, 13897.051213, 13896.655547, 13897.047312,
13898.205564, 13900.125572, 13902.837452, 13906.230209, 13910.294112,
13914.960492, 13920.218961, 13926.287609, 13932.889015, 13940.451345,
13949.327157, 13959.352267, 13970.583834, 13983.14564, 13997.391872,
14012.965904, 14030.139859, 14048.917902, 14069.304752, 14091.541249,
14113.971365, 14137.471712, 14162.48361, 14187.783215, 14212.951734,
14237.687089, 14262.119284, 14285.160082, 14306.785799, 14326.567908,
14344.249129, 14360.498045, 14374.927988, 14388.841191, 14403.027623,
14417.285193, 14431.921345, 14447.347759, 14464.280067, 14482.60458,
14503.01009, 14525.873936, 14551.515778, 14580.356316, 14610.776601,
14643.555251, 14679.101052, 14716.763371, 14756.356798, 14797.710201,
14841.323243, 14885.552108, 14930.758122, 14976.563876, 15022.743933,
15070.254048, 15116.300407, 15163.332681, 15212.634721, 15262.129309,
15311.443993, 15360.633228, 15410.700926, 15460.012042, 15508.70943,
15555.948922, 15601.38129, 15647.017242, 15691.593748, 15737.814211,
15784.098257, 15824.336441, 15857.184087, 15890.739854, 15937.050823,
15997.292301, 16049.370568, 16063.033239, 16023.148233, 15962.775179,
15932.931115, 15961.380588), mev108_lag1 = c(3.4265582593,
3.8373450191, 4.1211669551, 4.2500265274, 4.2336477943, 4.1032530543,
3.9050112432, 3.691568661, 3.5215361911, 3.4547437295, 3.5245107487,
3.6740870118, 3.8205614376, 3.9060148228, 3.9500668579, 3.9928147249,
4.056423068, 4.097207087, 4.0423248638, 3.8590572205, 3.6249134397,
3.4534377102, 3.419037145, 3.448572797, 3.4287569276, 3.3235979183,
3.3376619007, 3.7361174237, 4.6156476062, 5.5516500424, 5.9018553329,
5.3364327802, 4.406525535, 3.9641497661, 4.5369688556, 5.6155652665,
6.3806850947, 6.3128039966, 5.8286655665, 5.6572058382, 6.1906323861,
7.0408483819, 7.4827400214, 7.0669869294, 6.1581569245, 5.3936717805,
5.2364436715, 5.4913612016, 5.777206406, 5.8339229216, 5.7719456704,
5.8170713396, 6.1029576358, 6.5263492298, 6.8736849118, 6.9975096947,
6.9363923153, 6.7924979551, 6.6668133872, 6.6299076039, 6.7439828613,
7.0243025303, 7.3370606372, 7.4869066644, 7.3844430207, 7.1374881632,
6.940002926, 6.9245088132, 7.0301738798, 7.1305865095, 7.1405475978,
7.1156467585, 7.1524809409, 7.3303394277, 7.6756343523, 8.1680801673,
8.7542261364, 9.1808145707, 9.1010680729, 8.4114150872, 7.6844861301,
7.7270955321, 8.9146989491, 10.361039125, 10.796323189, 9.4618739177,
7.2049954246, 5.5270537994, 5.2221817889, 5.905531143, 6.7592672119,
7.1298927381, 7.0304213613, 6.697874346, 6.3607611025, 6.1569021347,
6.2001333982, 6.5397429639, 7.0184856606, 7.3825719382, 7.5069332339,
7.4599546294, 7.377008726, 7.3638030204, 7.3988155209, 7.4176473452,
7.3829883718, 7.3415942425, 7.3652515353, 7.492033304, 7.6543284954,
7.7427624077, 7.7070473944, 7.6101649913, 7.5623895662, 7.6286991237,
7.7329248639, 7.7505651547, 7.6137269809, 7.4246691851, 7.337208565,
7.4360967197, 7.5892255476, 7.5910082105, 7.3256377393, 6.9067676469,
6.5375463809, 6.3577677595, 6.320229607, 6.3124546301, 6.2662262884,
6.2427837167, 6.3428922976, 6.6124818018, 6.9249171793, 7.0836464531,
6.9995311857, 6.784745399, 6.6375952256, 6.6797395345, 6.7927792813,
6.775540136, 6.5260699355, 6.2318486432, 6.1687507324, 6.4951667771,
7.0000862167, 7.3264282363, 7.2857205376, 6.9859881738, 6.6532338989,
6.4623367973, 6.4024537545, 6.3988018644, 6.3987025271, 6.4148188331,
6.4801548851, 6.6043861168, 6.7236064103, 6.7473536828, 6.6336225214,
6.4408520391, 6.2759289867), p_pr = c(0.1841979358, 0.1909299357,
0.1800235425, 0.1873193897, 0.1778321909, 0.1771717461, 0.1769871609,
0.1769369574, 0.1767002661, 0.1766514006, 0.1772474365, 0.1786372508,
0.1793958093, 0.1873407005, 0.1744738837, 0.1779058647, 0.1660300916,
0.165123522, 0.1662612377, 0.1675426585, 0.1680743656, 0.1680322376,
0.1668552618, 0.1643117778, 0.1604937471, 0.1674889291, 0.1589809185,
0.1707308583, 0.1656141418, 0.1669016231, 0.1658465865, 0.1626002246,
0.1584857239, 0.1556467109, 0.1550484409, 0.1554116407, 0.1553698903,
0.1642789961, 0.1562188049, 0.1676637554, 0.1607636607, 0.159365876,
0.154912779, 0.1508778098, 0.1504706517, 0.1538985266, 0.1585854408,
0.1628016268, 0.1653325485, 0.1746734474, 0.1636385773, 0.1694169075,
0.1595285254, 0.1602916429, 0.1622777106, 0.1647745096, 0.1677972871,
0.170901438, 0.1726448513, 0.1727558383, 0.1718106875, 0.182016627,
0.1762909312, 0.1891248658, 0.1824141631, 0.1800526397, 0.1767170916,
0.1748339829, 0.1743303929, 0.1752424115, 0.1769369171, 0.17959844,
0.182145123, 0.1926835257, 0.1831830764, 0.190698247, 0.1837433962,
0.1875573393, 0.1922445975, 0.1928025222, 0.1883983926, 0.1831397417,
0.1831222451, 0.1882066078, 0.1932319714, 0.2020834894, 0.1878958952,
0.1907776136, 0.179564677, 0.1783669915, 0.1788699402, 0.1800391448,
0.1813284168, 0.1829512395, 0.1831328753, 0.181735949, 0.1790137171,
0.1875337053, 0.1799754626, 0.191124027, 0.1842840392, 0.1833786054,
0.1825845794, 0.182550754, 0.1822481672, 0.1820347832, 0.1814673532,
0.18082831, 0.1795880318, 0.1882358605, 0.1790916575, 0.1878672726,
0.1797660056, 0.1793430747, 0.1799398102, 0.1807822543, 0.180246357,
0.1788849577, 0.1772437109, 0.1760414846, 0.1749113359, 0.1838871358,
0.1750360156, 0.1836953752, 0.1744313344, 0.1722844661, 0.170542729,
0.1699684655, 0.1702419601, 0.1709120463, 0.1706566897, 0.1694752567,
0.1672817086, 0.175105, 0.1653820849, 0.1735863964, 0.1646891174,
0.1638476083, 0.1636914003, 0.1629671545, 0.1601006771, 0.1561250286,
0.1539170317, 0.1550840353, 0.1586350423, 0.1705586865, 0.1617244458,
0.1681380973, 0.1570702457, 0.1547307475, 0.1537854739, 0.1541593825,
0.155270079, 0.1567753976, 0.1573188283, 0.1566263272, 0.154594785,
0.1625938782, 0.1536205501, 0.1632453909, 0.1552261163, 0.1537721633,
0.1517811103), r_pr = c(-0.01502201, 0.0066390098, -0.009843996,
0.0015844825, -0.003501732, 0.0079104748, -0.004439558, 0.003676088,
0.0002861925, -0.02195522, 0.0112569236, -0.009037275, -0.013301101,
0.0055863112, -0.011505346, -0.006217488, 0.0121781851, 0.0121928571,
-0.007606398, 0.0140869202, -0.004580375, -0.002666424, 9.13213e-05,
-0.009534786, 0.0110522129, 0.0149261022, 0.0011158389, 0.0112153879,
-0.00302993, -0.006359302, 0.0084523714, 0.0059906854, -0.00173375,
-0.000702319, -0.00222337, 0.0009310756, 0.0008537806, -0.009805832,
0.0033174915, 0.0073215274, -0.00714607, 0.0075326181, -0.001661304,
0.0018676562, 0.0085309399, -0.003816572, -0.008109924, -0.004478882,
-0.010684933, -0.011240251, -0.007121814, 0.000525239, 0.0061915012,
0.0039767816, 0.0052307869, -0.002989661, -0.001547108, -0.00608744,
-0.008114592, -0.003469069, -0.001086208, 0.0025149289, -0.001051774,
0.0008539848, -0.00400956, 0.0042280478, 0.0069232096, 0.0005356512,
-0.000506343, -0.000481491, -0.004494742, 0.0008008432, -0.005763504,
-0.005053358, 0.0045407618, -0.004631395, 0.0017232781, -0.001542691,
-0.014140856, -0.0075432, -0.000486178, -0.004618988, 0.0044145066,
-0.001262638, -0.007154249, -0.004179044, -0.004546175, 0.0012496574,
0.0130678684, 0.0132433805, 0.0062620573, 0.0064067109, 0.0019043646,
-0.00209416, 0.0019817146, -0.000197222, 0.0080805087, 0.0068227671,
0.0062828296, -0.000396126, 0.0016373504, 0.0031586655, 0.007260812,
0.0021768236, -0.008591417, -0.004925559, 0.0008228582, 0.0032469176,
0.0096790493, 0.0040892237, 0.0062040214, 0.0039207574, 0.0079512344,
0.006437094, 0.0068865115, 0.0059781601, 0.0022037328, -0.003056595,
0.0056853223, 0.004783248, 0.008595941, 0.0013974031, 0.0058354128,
-0.001873087, 0.0011638485, 0.0051963475, 0.0070409944, -0.000285419,
0.0021434419, -0.001476974, -0.002319746, -0.001441687, 0.0011330373,
-0.002431859, -0.002058499, -0.002808318, -0.004056142, -0.000379139,
0.0015935936, -0.004932874, 0.0015151421, -0.003354618, 0.0045442614,
-6.0832e-05, -0.005232748, -0.005588103, -0.00522211, -0.005887484,
-0.001918502, -0.000790183, -0.001236979, -0.002524065, -0.002880256,
-0.009051244, -0.007031176, -0.005058108, -0.000572995, -0.0036773,
-0.000458327, -0.004857138, -0.00199384, 0.0037802378, 0.0103877768
)), .Names = c("date", "pr", "month", "mon1", "mon3", "mev06_mp_lag2",
"mev29_lag2", "mev108_lag1", "p_pr", "r_pr"), class = "data.frame", row.names = c(NA,
-163L))
Am I missing something with the nuances of this test? Thoughts?
A Kruskal-Wallis test compares the dependent variable across groups defined by the unique values of the independent variable (analogous to one-way ANOVA). Your independent variables are continuous, so each splits your 163 observations into the same 163 different groups, each with one observation. This is why the tests come out the same.
A clue was in the output - the test had 162 degrees of freedom on 163 observations!
Kruskal-Wallis chi-squared = 162, df = 162, p-value = 0.4852
So the Kruskal-Wallis test isn't appropriate here, either you meant to bin your dependent variables first (although a K-W test still wouldn't be right as your groups would be ordered), or use a test for correlation.

Merge two legends (size and color) into one [duplicate]

This question already has answers here:
How to combine scales for colour and size into one legend?
(2 answers)
Closed 7 years ago.
What is the code to make the two legends into one: A circles legend with color?
I think, a single legend with circles colored according to "size" and "# total number of crimes" is the best way to show the legend.
Desired output:
1) There should be one legend: the circles, instead of black should be colored: 0 circle = "yellow" to 800 circle = "red".
My code:
library(maps)
library(ggmap)
Get map from Google Maps
lima <- get_map(location = "lima", zoom = 11, maptype = c("terrain"))
Plot
ggmap(lima) + geom_point(data = limanov2, aes(x = LONGITUD , y = LATITUD, color = TOTALES,
size = TOTALES)) +
scale_size_continuous(name = "Cantidad\ndelitos",range = c(2,12)) +
scale_color_gradient(name = "Cantidad\ndelitos", low = "yellow", high = "red") +
theme(legend.text= element_text(size=14)) +
ggtitle("TOTAL DELITOS - LIMA NOV 2012") +
theme(plot.title = element_text(size = 12, vjust=2, family="Verdana", face="italic"),
legend.position = 'left')
My data:
structure(list(DISTRITO = c("SAN JUAN DE LURIGANCHO", "CALLAO",
"LOS OLIVOS", "ATE", "LIMA", "SAN MARTIN DE PORRES", "SANTIAGO DE SURCO",
"CHORILLOS", "COMAS", "INDEPENDENCIA", "EL AGUSTINO", "LA VICTORIA",
"SAN JUAN DE MIRAFLORES", "VILLA EL SALVADOR", "SAN MIGUEL",
"CARABAYLLO", "MIRAFLORES", "SAN BORJA", "VENTANILLA", "SURQUILLO",
"BREÑA", "ANCON", "PTE. PIEDRA", "RIMAC", "BARRANCO", "LA MOLINA",
"SAN LUIS", "SANTA ANITA", "LURIGANCHO", "P. LIBRE", "MAGDALENA DEL MAR",
"LA PERLA", "CHACLACAYO", "PUENTE PIEDRA", "SAN ISIDRO", "JESUS MARIA",
"BELLAVISTA", "LINCE", "CARMEN DE LA LEGUA REYNOSO", "CIENEGUILLA",
"SANTA ROSA", "LURIN", "PUNTA NEGRA", "PUCUSANA", "LA PUNTA",
"PUNTA HERMOSA", "PACHACAMAC", "SAN BARTOLO", "SANTA MARIA"),
TOTALES = c(861L, 696L, 696L, 642L, 516L, 479L, 442L, 378L,
371L, 368L, 361L, 333L, 325L, 291L, 282L, 251L, 239L, 196L,
193L, 188L, 185L, 174L, 165L, 161L, 138L, 134L, 128L, 119L,
115L, 105L, 67L, 65L, 63L, 58L, 58L, 56L, 45L, 38L, 23L,
23L, 11L, 8L, 6L, 5L, 3L, 3L, 2L, 0L, 0L), HOMICIDIOS = c(1L,
7L, 0L, 1L, 2L, 0L, 0L, 1L, 7L, 4L, 4L, 4L, 0L, 0L, 0L, 2L,
0L, 0L, 7L, 0L, 0L, 0L, 0L, 4L, 0L, 0L, 2L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L), LESIONES = c(100L, 72L, 61L, 43L, 44L, 8L, 10L,
15L, 44L, 40L, 50L, 15L, 52L, 28L, 7L, 33L, 15L, 3L, 21L,
7L, 36L, 33L, 15L, 19L, 14L, 1L, 8L, 6L, 16L, 4L, 4L, 9L,
1L, 12L, 2L, 9L, 5L, 2L, 5L, 7L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L), VIO..DE.LA.LIBERTAD.PERSONAL = c(0L, 7L, 6L,
5L, 6L, 1L, 1L, 0L, 3L, 1L, 2L, 0L, 2L, 0L, 1L, 0L, 1L, 0L,
1L, 1L, 0L, 3L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L,
0L, 1L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L), VIO..DE.LA.LIBERTAD.SEXUAL = c(56L, 14L, 12L, 15L, 7L,
10L, 2L, 9L, 11L, 13L, 8L, 9L, 7L, 14L, 4L, 15L, 4L, 2L,
17L, 7L, 3L, 4L, 6L, 12L, 2L, 1L, 5L, 3L, 11L, 4L, 1L, 2L,
0L, 6L, 2L, 0L, 3L, 0L, 2L, 2L, 0L, 4L, 0L, 0L, 0L, 0L, 0L,
0L, 0L), HURTO.SIMPLE.Y.AGRAVADO = c(217L, 203L, 296L, 230L,
260L, 167L, 226L, 217L, 130L, 117L, 154L, 133L, 121L, 46L,
163L, 72L, 161L, 119L, 69L, 120L, 64L, 19L, 64L, 21L, 57L,
44L, 39L, 2L, 48L, 60L, 30L, 19L, 48L, 20L, 41L, 25L, 19L,
27L, 7L, 11L, 9L, 0L, 6L, 0L, 2L, 3L, 1L, 0L, 0L), ROBO.SIMPLE.Y.AGRAVADO = c(460L,
289L, 308L, 344L, 186L, 277L, 198L, 130L, 165L, 184L, 137L,
149L, 134L, 188L, 104L, 126L, 58L, 72L, 64L, 51L, 77L, 115L,
79L, 76L, 64L, 88L, 73L, 108L, 40L, 36L, 30L, 32L, 14L, 17L,
12L, 22L, 12L, 8L, 6L, 3L, 1L, 3L, 0L, 2L, 1L, 0L, 1L, 0L,
0L), MICRO.COM.DE.DROGAS = c(26L, 100L, 13L, 3L, 10L, 15L,
5L, 5L, 11L, 8L, 3L, 23L, 9L, 15L, 3L, 3L, 0L, 0L, 8L, 2L,
5L, 0L, 0L, 28L, 0L, 0L, 1L, 0L, 0L, 0L, 2L, 2L, 0L, 2L,
0L, 0L, 6L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 0L
), TENENCIA.ILEGAL.DE.ARMAS = c(1L, 4L, 0L, 1L, 1L, 1L, 0L,
1L, 0L, 1L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 6L, 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), LONGITUD = c(-77,
-77.12, -77.08, -76.89, -77.04, -77.09, -76.99, -77.01, -77.05,
-77.05, -77, -77.02, -76.97, -76.94, -77.09, -76.99, -77.03,
-77, -77.13, -77.01, -77.05, -77.11, -77.08, -76.7, -77.02,
-76.92, -77, -76.96, -76.86, -77.06, -77.07, -77.12, -76.76,
-77.08, -77.03, -77.05, -77.11, -77.04, -77.09, -76.78, -77.16,
-76.81, -76.73, -76.77, -77.16, -76.76, -76.83, -76.73, -76.77
), LATITUD = c(-11.99, -12.04, -11.95, -12.04, -12.06, -12,
-12.16, -12.2, -11.93, -11.99, -12.04, -12.08, -12.16, -12.23,
-12.08, -11.79, -12.12, -12.1, -11.89, -12.11, -12.06, -11.69,
-11.88, -11.94, -12.15, -12.09, -12.08, -12.04, -11.98, -12.08,
-12.09, -12.07, -11.99, -11.88, -12.1, -12.08, -12.06, -12.09,
-12.04, -12.07, -11.81, -12.24, -12.32, -12.47, -12.07, -12.28,
-12.18, -12.38, -12.42)), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -49L), .Names = c("DISTRITO", "TOTALES",
"HOMICIDIOS", "LESIONES", "VIO..DE.LA.LIBERTAD.PERSONAL", "VIO..DE.LA.LIBERTAD.SEXUAL",
"HURTO.SIMPLE.Y.AGRAVADO", "ROBO.SIMPLE.Y.AGRAVADO", "MICRO.COM.DE.DROGAS",
"TENENCIA.ILEGAL.DE.ARMAS", "LONGITUD", "LATITUD"))
I've found a solution. Reading the documention for GGPLOT2 V. 0.9
It is the new function: guide_legend() that should be used inside guides().
This is a function that lets you have more control over legend labels.
This is the end code with the resulting output (See the last line):
ggmap(lima) + geom_point(data = limanov2, aes(x = LONGITUD , y = LATITUD, color = TOTALES,
size = TOTALES)) +
scale_size_continuous(name = "Cantidad\ndelitos",range = c(2,12)) +
scale_color_gradient(name = "Cantidad\ndelitos", low = "yellow", high = "red") +
theme(legend.text= element_text(size=14)) +
ggtitle("TOTAL DELITOS - LIMA NOV 2012") +
theme(plot.title = element_text(size = 12, vjust=2, family="Verdana", face="italic"),
legend.position = 'left') +
guides(colour = guide_legend())

R: ggmap: containing missing values (geom_point) when plottinng but no NAs values found in data.frame

I'm plotting some points over a map with ggmap package.
The problem is that i get the message: "Removed 12 rows containing missing values (geom_point)".
But i don't have any NAs. I've looked the data, and used:
sum(is.na(limanov2)) #Gives 0
to prove it.
This is my code:
library(maps)
library(ggmap)
lima <- get_map(location = "lima", zoom = 11)
ggmap(lima) + geom_point(data = limanov2, aes(x = LONGITUD , y = LATITUD, color = TOTALES,
size = TOTALES)) +
scale_color_gradient(low = "yellow", high = "red")
My data:
structure(list(DISTRITO = c("SAN JUAN DE LURIGANCHO", "CALLAO",
"LOS OLIVOS", "ATE VITARTE", "LIMA CERCADO", "SAN MARTÍN", "SANTIAGO DE SURCO",
"CHORILLOS", "COMAS", "INDEPENDENCIA", "EL AGUSTINO", "LA VICTORIA",
"SAN JUAN DE MIRAFLORES", "VILLA EL SALVADOR", "S. MIGUEL", "CARABAYLLO",
"MIRAFLORES", "PTE. PIEDRA", "SAN BORJA", "VENTANILLA", "SURQUILLO",
"BREÑA", "ANCÓN", "EL RIMAC", "BARRANCO", "LA MOLINA", "SAN LUIS",
"STA. ANITA", "LURIGANCHO", "P. LIBRE", "MAGDALENA", "LA PERLA",
"CHACLACAYO", "SAN ISIDRO", "J. MARÍA", "BELLAVISTA", "LINCE",
"C. DE LA LEGUA", "CIENEGUILLA", "STA.ROSA", "LURÍN", "PTA.NEGRA",
"PUCUSANA", "LA PUNTA", "PTA. HERMOSA", "PACHACAMAC", "SAN BARTOLO",
"SANTA MARÍA"), TOTALES = c(861L, 696L, 696L, 642L, 516L, 479L,
442L, 378L, 371L, 368L, 361L, 333L, 325L, 291L, 282L, 251L, 239L,
223L, 196L, 193L, 188L, 185L, 174L, 161L, 138L, 134L, 128L, 119L,
115L, 105L, 67L, 65L, 63L, 58L, 56L, 45L, 38L, 23L, 23L, 11L,
8L, 6L, 5L, 3L, 3L, 2L, 0L, 0L), HOMICIDIOS = c(1L, 7L, 0L, 1L,
2L, 0L, 0L, 1L, 7L, 4L, 4L, 4L, 0L, 0L, 0L, 2L, 0L, 1L, 0L, 7L,
0L, 0L, 0L, 4L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), LESIONES = c(100L,
72L, 61L, 43L, 44L, 8L, 10L, 15L, 44L, 40L, 50L, 15L, 52L, 28L,
7L, 33L, 15L, 27L, 3L, 21L, 7L, 36L, 33L, 19L, 14L, 1L, 8L, 6L,
16L, 4L, 4L, 9L, 1L, 2L, 9L, 5L, 2L, 5L, 7L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L), VIO..DE.LA.LIBERTAD.PERSONAL = c(0L, 7L,
6L, 5L, 6L, 1L, 1L, 0L, 3L, 1L, 2L, 0L, 2L, 0L, 1L, 0L, 1L, 1L,
0L, 1L, 1L, 0L, 3L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L,
0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), VIO..DE.LA.LIBERTAD.SEXUAL = c(56L,
14L, 12L, 15L, 7L, 10L, 2L, 9L, 11L, 13L, 8L, 9L, 7L, 14L, 4L,
15L, 4L, 12L, 2L, 17L, 7L, 3L, 4L, 12L, 2L, 1L, 5L, 3L, 11L,
4L, 1L, 2L, 0L, 2L, 0L, 3L, 0L, 2L, 2L, 0L, 4L, 0L, 0L, 0L, 0L,
0L, 0L, 0L), HURTO.SIMPLE.Y.AGRAVADO = c(217L, 203L, 296L, 230L,
260L, 167L, 226L, 217L, 130L, 117L, 154L, 133L, 121L, 46L, 163L,
72L, 161L, 84L, 119L, 69L, 120L, 64L, 19L, 21L, 57L, 44L, 39L,
2L, 48L, 60L, 30L, 19L, 48L, 41L, 25L, 19L, 27L, 7L, 11L, 9L,
0L, 6L, 0L, 2L, 3L, 1L, 0L, 0L), ROBO.SIMPLE.Y.AGRAVADO = c(460L,
289L, 308L, 344L, 186L, 277L, 198L, 130L, 165L, 184L, 137L, 149L,
134L, 188L, 104L, 126L, 58L, 96L, 72L, 64L, 51L, 77L, 115L, 76L,
64L, 88L, 73L, 108L, 40L, 36L, 30L, 32L, 14L, 12L, 22L, 12L,
8L, 6L, 3L, 1L, 3L, 0L, 2L, 1L, 0L, 1L, 0L, 0L), MICRO.COM.DE.DROGAS = c(26L,
100L, 13L, 3L, 10L, 15L, 5L, 5L, 11L, 8L, 3L, 23L, 9L, 15L, 3L,
3L, 0L, 2L, 0L, 8L, 2L, 5L, 0L, 28L, 0L, 0L, 1L, 0L, 0L, 0L,
2L, 2L, 0L, 0L, 0L, 6L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L,
0L, 0L), TENENCIA.ILEGAL.DE.ARMAS = c(1L, 4L, 0L, 1L, 1L, 1L,
0L, 1L, 0L, 1L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 6L, 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), LONGITUD = c(-77, -77.12,
-77.08, -76.89, -77.04, -77.09, -76.99, -77.01, -77.05, -77.05,
-77, -77.02, -76.97, -76.94, -77.09, -76.99, -77.03, -77.08,
-77, -77.13, -77.01, -77.05, -77.11, -76.7, -77.02, -76.92, -77,
-76.96, -76.86, -77.06, -77.07, -77.12, -76.76, -77.03, -77.05,
-77.11, -77.04, -77.09, -76.78, -77.16, -76.81, -76.73, -76.77,
-77.16, -76.76, -76.83, -76.73, -76.77), LATITUD = c(-11.99,
-12.04, -11.97, -12.04, -12.06, -12, -12.16, -12.2, -11.93, -11.99,
-12.04, -12.08, -12.16, -12.23, -12.08, -11.79, -12.12, -11.88,
-12.1, -11.89, -12.11, -12.06, -11.69, -11.94, -12.15, -12.09,
-12.08, -12.04, -11.98, -12.08, -12.09, -12.07, -11.99, -12.1,
-12.08, -12.06, -12.09, -12.04, -12.07, -11.81, -12.24, -12.32,
-12.47, -12.07, -12.28, -12.18, -12.38, -12.42)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -48L), .Names = c("DISTRITO",
"TOTALES", "HOMICIDIOS", "LESIONES", "VIO..DE.LA.LIBERTAD.PERSONAL",
"VIO..DE.LA.LIBERTAD.SEXUAL", "HURTO.SIMPLE.Y.AGRAVADO", "ROBO.SIMPLE.Y.AGRAVADO",
"MICRO.COM.DE.DROGAS", "TENENCIA.ILEGAL.DE.ARMAS", "LONGITUD",
"LATITUD"))
You have values outside of the base map zoom range... try changing your zoom parameter.
library(maps)
library(ggmap)
lima <- get_map(location = "lima", zoom = 10)
ggmap(lima) +
geom_point(data = limanov2,
aes(x = LONGITUD , y = LATITUD,
color = TOTALES, size = TOTALES)) +
scale_color_gradient(low = "yellow", high = "red")

Panel Data Forecasting With R

I have data that is organized in panels like this (see below for output from the dput() function):
Country Year Month Var1 Var2
C1 2000 1 0 0
C1 2000 2 1 0
C1 2000 3 2 1
...
C2 2000 1 1 1
C2 2000 2 1 2
C2 2000 3 3 1
...
The data set has in total 27 countries for the years 1999 to 2008, but with unbalanced panels.
I want to be able to estimate a model for the full data set, and from this model do forecasting for each country in the data set. I have been looking into the YourCast package from King et al. but since I have all my data in a single file, I am at a loss as to how to create a data object that the yourcast() function will accept. Does anyone know how to do this without going through the tedious procedure of manually splitting the data file up into the different cross sections?
PS: 48 observations from the data set:
structure(list(Country = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Belgium",
"Denmark", "Czech.Republic", "Germany", "Estonia", "Greece",
"Spain", "France", "Ireland", "Italy", "Cyprus", "Latvia", "Lithuania",
"Luxembourg", "Hungary", "Malta", "Netherlands", "Austria", "Poland",
"Portugal", "Slovenia", "Slovakia", "Bulgaria", "Romania", "Finland",
"Sweden", "UK"), class = "factor"), Year = c(2003, 2003, 2003,
2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2004, 2004,
2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2003,
2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003,
2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004,
2004, 2005), Month = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8,
9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1), Yes = c(21L,
18L, 20L, 19L, 31L, 39L, 28L, 2L, 28L, 21L, 26L, 50L, 14L, 28L,
50L, 83L, 10L, 25L, 22L, 6L, 22L, 39L, 32L, 56L, 22L, 17L, 20L,
20L, 32L, 39L, 23L, 2L, 27L, 21L, 28L, 48L, 14L, 27L, 50L, 89L,
10L, 25L, 22L, 4L, 22L, 38L, 31L, 56L, 16L), No = c(0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 4L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 1L, 2L, 0L, 0L, 0L, 2L, 0L, 1L, 1L, 0L, 0L), Abstention = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 3L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 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
), No.Neg = c(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, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L), Abstention.Neg = c(0L, 0L, 0L, 1L, 1L, 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, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Yes.Neg = c(1L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 1L,
0L, 0L, 2L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L
), Yes.Pos = c(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, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L), Missing = c(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, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Enlargement = c(0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1)), .Names = c("Country", "Year", "Month", "Yes",
"No", "Abstention", "No.Neg", "Abstention.Neg", "Yes.Neg", "Yes.Pos",
"Missing", "Enlargement"), row.names = c(1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 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), class = "data.frame")
This is very simple
library(YourCast);
demo(chp.11.10)
You can prep your data to look like the data used in this demo with the yourprep command.
Type ?yourprep
If I understand your problem, splitting up the database could be quite easy. Supposing you named the dataset 'data':
results <- list()
for (i in 1:nlevels(data$Country)) {
results[[levels(data$Country)[i]]] <- yourcast(...)
}
In which simple loop you could do all forecasting to each country, and save the results to a list. Later you can read all results from the results list for all countries. E.g.: results[['Hungary']]
As I do not know anything about the package you use, here is a small example that could be fitted in the loop instead of the line containing yourcast() function:
results[[levels(data$Country)[i]]] <- c(levels(data$Country)[i], length(which(data$Country == levels(data$Country)[i])))
Which command will create a list containg all countries with two variables: name and sample size of given country.

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