dividing the values of data.table1 by data.table2 in R - r

I have created two fictional data.tables which summarise the cost and count of the items that contributed to the cost. I would like to calculate the average item cost = cost/count.
How can i divide the values of the two data.tables ?
combi_sum <- dcast(merge(mtcarsTOTAL[,.(cost, gear)], iris[, .N, .(carb, gear, gender, age)], by = "gear"),
gender + age ~ carb, value.var = "cost", fun.aggregate = sum, fill = 0)
structure(list(gender = c(1L, 1L, 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, 3L), age = c(1L, 2L,
3L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 11L, 12L, 13L, 14L), `1` = c(978, 978, 0, 0, 1074, 0, 0,
0, 2642, 2642, 0, 0, 0, 0, 3620, 0, 978, 2642, 0, 0, 978, 0,
0, 978, 2052, 0, 0, 0, 0, 1074, 978, 0, 0, 0, 3620, 0), `2` = c(0,
0, 0, 978, 0, 0, 0, 2052, 0, 2642, 978, 0, 0, 0, 0, 0, 0, 0,
1074, 2642, 0, 0, 0, 0, 0, 0, 0, 1074, 0, 978, 0, 2642, 0, 0,
978, 2642), `3` = c(0, 0, 0, 0, 0, 978, 2642, 0, 2642, 0, 0,
2642, 978, 0, 978, 2642, 0, 0, 0, 0, 1074, 3620, 2642, 0, 0,
0, 978, 0, 2642, 0, 0, 2642, 0, 2642, 0, 0), `4` = c(0, 0, 1074,
0, 0, 0, 978, 0, 0, 0, 1074, 1074, 0, 2052, 0, 0, 0, 0, 0, 1074,
0, 2642, 1074, 978, 978, 2642, 0, 0, 2642, 0, 2052, 0, 2642,
1074, 0, 0)), row.names = c(NA, -36L), class = c("data.table",
"data.frame"), .internal.selfref = <pointer: 0x7fb24d802ee0>, sorted = c("gender",
"age"))
combi_length <- dcast(merge(mtcarsTOTAL[,.(cost, gear)], iris[, .N, .(carb, gear, gender, age)], by = "gear"),
gender + age ~ carb, value.var = "cost", fun.aggregate = length, fill = 0)
structure(list(gender = c(1L, 1L, 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, 3L), age = c(1L, 2L,
3L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 11L, 12L, 13L, 14L), `1` = c(1L, 1L, 0L, 0L, 1L, 0L, 0L,
0L, 1L, 1L, 0L, 0L, 0L, 0L, 2L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L,
1L, 2L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 2L, 0L), `2` = c(0L,
0L, 0L, 1L, 0L, 0L, 0L, 2L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L,
0L, 1L, 1L), `3` = c(0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L,
0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 2L, 1L, 0L, 0L, 0L,
1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L), `4` = c(0L, 0L, 1L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 2L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 2L, 0L, 1L, 1L, 0L,
0L)), row.names = c(NA, -36L), class = c("data.table", "data.frame"
), .internal.selfref = <pointer: 0x7fb24d802ee0>, sorted = c("gender",
"age"))


Here is another option:
combi_sum[combi_length, as.character(1L:4L) := {
m <- unlist(mget(paste0("x.", 1L:4L))) / unlist(mget(paste0("i.", 1L:4L)))
as.data.table(matrix(replace(m, is.nan(m), 0), nrow=.N))
}]
data:
library(data.table)
combi_sum <-
structure(list(gender = c(1L, 1L, 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, 3L), age = c(1L, 2L,
3L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 11L, 12L, 13L, 14L), `1` = c(978, 978, 0, 0, 1074, 0, 0,
0, 2642, 2642, 0, 0, 0, 0, 3620, 0, 978, 2642, 0, 0, 978, 0,
0, 978, 2052, 0, 0, 0, 0, 1074, 978, 0, 0, 0, 3620, 0), `2` = c(0,
0, 0, 978, 0, 0, 0, 2052, 0, 2642, 978, 0, 0, 0, 0, 0, 0, 0,
1074, 2642, 0, 0, 0, 0, 0, 0, 0, 1074, 0, 978, 0, 2642, 0, 0,
978, 2642), `3` = c(0, 0, 0, 0, 0, 978, 2642, 0, 2642, 0, 0,
2642, 978, 0, 978, 2642, 0, 0, 0, 0, 1074, 3620, 2642, 0, 0,
0, 978, 0, 2642, 0, 0, 2642, 0, 2642, 0, 0), `4` = c(0, 0, 1074,
0, 0, 0, 978, 0, 0, 0, 1074, 1074, 0, 2052, 0, 0, 0, 0, 0, 1074,
0, 2642, 1074, 978, 978, 2642, 0, 0, 2642, 0, 2052, 0, 2642,
1074, 0, 0)), row.names = c(NA, -36L), class = c("data.table",
"data.frame"))
setDT(combi_sum, key=c("gender", "age"))
combi_length <- structure(list(gender = c(1L, 1L, 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, 3L), age = c(1L, 2L,
3L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 11L, 12L, 13L, 14L), `1` = c(1L, 1L, 0L, 0L, 1L, 0L, 0L,
0L, 1L, 1L, 0L, 0L, 0L, 0L, 2L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L,
1L, 2L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 2L, 0L), `2` = c(0L,
0L, 0L, 1L, 0L, 0L, 0L, 2L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L,
0L, 1L, 1L), `3` = c(0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L,
0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 2L, 1L, 0L, 0L, 0L,
1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L), `4` = c(0L, 0L, 1L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 2L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 2L, 0L, 1L, 1L, 0L,
0L)), row.names = c(NA, -36L), class = c("data.table", "data.frame"
))
setDT(combi_length, key=c("gender", "age"))


Maybe I am misunderstanding, but to divide values of one data.frame by another in R - you literally just divide them. I've created an example dataset to show you:
t1 <- data.frame(A1=c(10,2,4,1,4), B1=c(5,1,8,9,4), C1=c(12,10,10,5,1))
t2 <- data.frame(A2=c(8,2,5,10,1), B2=c(5,6,8,9,1), C2=c(6,5,15,10,12))
To divide t2/t1, you just do that:
t2/t1
Giving you:
A2 B2 C2
1 0.80 1.00 0.5
2 1.00 6.00 0.5
3 1.25 1.00 1.5
4 10.00 1.00 2.0
5 0.25 0.25 12.0
Basically, this can be understood as t1[x,y]/t2[x,y], giving you the answer dataset as t3[x,y]. Make sense? Is that what you were asking?

Related

DLNM: Error: coef/vcov not consistent with basis matrix. See help(crosspred)

I am using distributed lag non-linear models . I ran a glm model with a cross-basis matrix from the DLNM package. When I tried to get the predictions, I got this error:
Error in crosspred(cbpm1, Tp1, by = 1, bylag = 1, at = speimin:speimax) :
coef/vcov not consistent with basis matrix. See help(crosspred).
This happened when I tried lag 1,2, and 3, but there was no error when I tried lag 0, 4, and 5. I read about a similar question from this link. But still, I cannot figure it out with my own code. Your help is really meaningful for me. Thanks.
The code is:
Dis <- ss$dis1
vkt <- equalknots(ss$T,nk=2)
lkt = logknots(1,nk=2)
vkpm <- equalknots(ss$spei3,nk=2)
lkpm <- logknots(1,nk=2)
speimin <- min(ss$spei3, na.rm = TRUE)
speimax <- max(ss$spei3, na.rm = TRUE)
cbt1 = crossbasis(ss$T, lag=1, argvar=list(fun="bs",degree=2,knots=vkt), arglag=list(knots=lkt))
cbpm1 <- crossbasis(ss$spei3, lag=1, argvar=list(fun="bs",degree=2,knots=vkpm), arglag=list(knots=lkpm))
Tp1 <- glm(Dis ~ cbt1 + cbpm1 + ns(RH,3)+ns(timeseries,2*5),
family=poisson(link=log),ss)
at=speimin:speimax
predsltp1 <- crosspred(cbpm1,Tp1,by=1,bylag=1,at=speimin:speimax)
Here is the used library:
library(splines);library(class);library(stats);library(mda)
library(akima);library(gam);library(mgcv);library(foreign);library(som)
library(dlnm) #equalknots logknots crossbasis
library(splines) #ns
library(magrittr)
Here is the reproducible sample of my dataset:
a<-structure(list(job = 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, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "all", class = "factor"),
age3 = 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, 1L,
1L, 1L, 1L, 1L, 1L), .Label = "00_05", class = "factor"),
sexA = structure(c(1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L,
2L, 1L, 2L, 2L, 1L), .Label = c("F", "M"), class = "factor"),
All = c(65L, 53L, 92L, 68L, 81L, 103L, 144L, 92L, 44L, 40L,
54L, 19L, 55L, 61L, 72L, 89L, 77L, 68L, 71L, 27L, 15L, 18L,
39L, 52L, 52L, 58L, 27L, 44L, 32L, 37L), dis1 = c(6L, 0L,
9L, 0L, 0L, 0L, 9L, 0L, 3L, 6L, 3L, 0L, 0L, 3L, 6L, 0L, 9L,
3L, 0L, 3L, 6L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L), dis2 = c(3L,
6L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L, 3L, 0L, 0L, 6L, 6L,
0L, 0L, 0L, 3L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L),
T = c(20.39032258, 20.39032258, 19.78387097, 19.78387097,
19.64193548, 19.64193548, 18.78709677, 18.78709677, 19.17419355,
19.17419355, 20.46774194, 21.63870968, 21.85806452, 21.85806452,
19.73448276, 19.73448276, 20.55357143, 20.55357143, 19.925,
29.12580645, 29.12580645, 29.39354839, 29.39354839, 28.96129032,
28.96129032, 27.36666667, 27.40333333, 27.40333333, 27.82333333,
27.82333333), RH = c(70.09677419, 70.09677419, 70.03225806,
70.03225806, 70.35483871, 70.35483871, 72.32258065, 72.32258065,
69.80645161, 69.80645161, 74.58064516, 77.58064516, 71.32258065,
71.32258065, 75.82758621, 75.82758621, 62.28571429, 62.28571429,
72.60714286, 77.61290323, 77.61290323, 75.06451613, 75.06451613,
75.61290323, 75.61290323, 76.03333333, 76.23333333, 76.23333333,
75.03333333, 75.03333333), PP = c(11.5, 11.5, 44.5, 44.5,
25.9, 25.9, 14, 14, 5, 5, 35.7, 34.1, 30.8, 30.8, 44.4, 44.4,
15.6, 15.6, 40.7, 184, 184, 137.1, 137.1, 377, 377, 110.5,
129.8, 129.8, 292, 292), spei3 = c(0.447495072, 0.447495072,
1.537295165, 1.537295165, 1.285067571, 1.285067571, 0.441010834,
0.441010834, 1.505630159, 1.505630159, 1.725831329, 1.075029338,
-1.227673724, -1.227673724, 0.329690702, 0.329690702, 0.724314874,
0.724314874, 1.228544608, 0.60782059, 0.60782059, 0.191804009,
0.191804009, 1.752145476, 1.752145476, 1.94554333, 1.139058482,
1.139058482, -0.554472376, -0.554472376), timeseries = 1:30), class = "data.frame", row.names = c(NA,
-30L))

How can I apply restrictions and limited accessibility to inputs based on values from other inputs in Shiny?

I am completely new to shiny, and I am trying to create a quite simple app as part of learning it.
Questions:
(1) how can I apply a restriction on the sliderInput("n.sygdom" ...)? The intention is that the user receives an error if one chooses a value greater than the one set in sliderInput("n.fjernet" ...)?
(2) how can I make the radioButtons("ecs" ...) and radioButtons("contra.pos" ...) accessible only if sliderInput("n.sygdom" ...) ≥ 1? Preferable, if sliderInput("n.sygdom" ...)==0, then the radioButtons should be "removed" from the shinyapp
The shinyapp works by calculating a nomogram-score, which can be used to predict overall survival. It contains four components:
(1) n.fjernet is the number of lymph nodes removed on the cervical neck
(2) n.sygdom is the number of lymph nodes with evidence of cancer metastises. Therefore, n.sygdom cannot exceed the number of n.fjernet. If the user tries to choose n.sygdom greater than n.fjernet, the shinyapp should return: Error: Number of positive lymph nodes cannot exceed the lymph nodal yield (written in red)
(3) ecs is extracapsular spread (of metastises, i.e. the metastises penetrating one or more of the lymph nodes) in n.sygdom. Therefore, this option can only be available if n.sygdom ≥ 1
(4) contra.pos is whether the spread (of metastises) includes the uni- or contralateral side of the neck. Therefore, this option is only be available if n.sygdom ≥ 1.
The app function estimates the nomogram-score based on values stored in nom - which is attached below. If n.sygdom equals 0, and therefore unable access to ecs and contra.pos, then ecs and contra.pos should generate/add 0 to the calc_score printed in output$out.score <- renderText(calc_score()), i.e. exclusion from the nomogram.
My script
library(shiny)
library(survminer)
ui <- fluidPage(
titlePanel("Survival Curve"),
sidebarLayout(
sidebarPanel(
sliderInput("n.fjernet", "Lymph Nodal Yield", min = 2, max = 150, value = 30),
sliderInput("n.sygdom", "Number of positive lymph nodes", min = 0, max = 40, value = 0),
radioButtons("ecs", "Extracapsular extension", c("No","Yes")),
radioButtons("contra.pos", "Neck involvement", c("Contra.","Ipsi.")),
verbatimTextOutput("out.score"),
verbatimTextOutput("out.score.group")
),
mainPanel(
plotOutput("surv_plot")
)
)
)
server <- function(input, output, session) {
calc_score <- reactive({
round(nom$ecs$points[nom$ecs$ecs==input$ecs] +
nom$contra.pos$points[nom$contra.pos$contra.pos==input$contra.pos] +
nom$n.fjernet$points[nom$n.fjernet$n.fjernet==input$n.fjernet] +
nom$n.sygdom$points[nom$n.sygdom$n.sygdom==input$n.sygdom],digits=1)
})
calc_score_group <- function(score) {
cut(score, c(0,35.9,55.2,70.0,83.3,Inf), include.lowest = TRUE, labels = c("1","2","3","4","5"))
}
fit_data <- reactive({
p %>% filter(score.group == as.numeric(calc_score_group(calc_score())))
})
fit_model <- reactive({
survfit(Surv(os.neck, mors) ~ 1, data = fit_data())
})
output$out.score <- renderText(calc_score())
output$out.score.group <- renderText(calc_score_group(calc_score()))
output$surv_plot <- renderPlot({
ggsurvplot(
fit_model(),
data = fit_data(),
risk.table = TRUE,
pval = F,
pval.coord = c(0, 0.25),
conf.int = T,
size=1,
xlim = c(0,60),
conf.int.alpha=c(0.2),
break.x.by = 6,
xlab="Time in months",
ylab="Probability of overall survival",
ggtheme = theme,
surv.median.line = "v",
ylim=c(0,1),
palette="#2C77BF",
tables.theme=theme,
legend.title=paste("Score group", calc_score_group(calc_score())),
surv.scale="percent",
tables.col="strata",
risk.table.col = "strata",
risk.table.y.text = FALSE,
tables.y.text = FALSE)
})
}
shinyApp(ui, server)
My data (now with p$score.group)
p <- structure(list(contra.pos = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 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, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 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, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Ipsi.", "Contra."), class = "factor"),
ecs = structure(c(1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L,
2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L
), .Label = c("No", "Yes"), class = "factor"), n.fjernet = c(22L,
61L, 50L, 47L, 30L, 60L, 82L, 60L, 33L, 67L, 35L, 56L, 15L,
37L, 44L, 124L, 41L, 30L, 31L, 35L, 36L, 28L, 39L, 54L, 25L,
27L, 69L, 53L, 24L, 33L, 52L, 77L, 51L, 7L, 22L, 53L, 26L,
58L, 28L, 83L, 39L, 15L, 37L, 27L, 9L, 17L, 32L, 26L, 44L,
52L, 22L, 62L, 53L, 68L, 52L, 38L, 50L, 21L, 41L, 74L, 15L,
26L, 36L, 37L, 34L, 22L, 31L, 53L, 13L, 44L, 43L, 51L, 20L,
21L, 63L, 40L, 25L, 17L, 43L, 47L, 35L, 21L, 4L, 23L, 35L,
50L, 69L, 24L, 38L, 45L, 37L, 35L, 25L, 19L, 43L, 19L, 33L,
38L, 50L, 21L, 40L, 100L, 45L, 53L, 41L, 7L, 75L, 48L, 20L,
11L, 72L, 37L, 34L, 70L, 20L, 47L, 44L, 45L, 48L, 23L, 27L,
24L, 39L, 9L, 34L, 22L, 89L, 40L, 35L, 34L, 61L, 28L, 27L,
62L, 47L, 13L, 20L, 9L, 27L, 38L, 44L, 15L, 33L, 65L, 31L,
49L, 53L, 15L, 26L, 17L, 24L, 20L, 25L, 12L, 34L, 22L, 27L,
14L, 27L, 31L, 26L, 15L, 16L, 30L, 19L, 51L, 12L, 33L, 68L,
26L, 20L, 34L, 31L, 7L, 76L, 7L, 24L, 36L, 22L, 27L, 35L,
64L, 18L, 38L, 10L, 27L, 26L, 47L, 15L, 30L, 30L, 21L, 31L,
14L, 14L, 22L, 28L, 13L, 17L, 16L, 7L, 11L, 37L, 55L, 13L,
26L, 17L, 12L, 44L, 58L, 20L, 28L, 7L, 24L, 10L, 42L, 39L,
14L, 31L, 49L), n.sygdom = c(1L, 2L, 1L, 3L, 1L, 0L, 3L,
0L, 2L, 1L, 4L, 4L, 1L, 0L, 2L, 2L, 1L, 0L, 0L, 4L, 0L, 0L,
1L, 1L, 0L, 1L, 4L, 3L, 1L, 0L, 8L, 1L, 1L, 1L, 1L, 1L, 0L,
1L, 2L, 1L, 0L, 2L, 1L, 0L, 2L, 0L, 3L, 0L, 1L, 1L, 1L, 2L,
0L, 3L, 2L, 1L, 0L, 0L, 0L, 2L, 0L, 3L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 1L, 4L, 0L, 0L, 2L, 2L, 1L, 1L, 0L, 0L, 3L, 1L, 6L,
0L, 0L, 0L, 3L, 2L, 2L, 4L, 0L, 3L, 27L, 0L, 2L, 1L, 0L,
0L, 1L, 1L, 2L, 2L, 5L, 1L, 0L, 0L, 1L, 0L, 5L, 0L, 0L, 2L,
10L, 0L, 6L, 2L, 1L, 2L, 0L, 0L, 0L, 0L, 4L, 0L, 0L, 1L,
5L, 2L, 2L, 1L, 2L, 1L, 0L, 0L, 1L, 13L, 0L, 1L, 0L, 1L,
0L, 1L, 1L, 0L, 23L, 0L, 2L, 2L, 0L, 2L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 2L, 3L, 1L, 4L, 0L, 1L, 0L, 5L, 5L, 4L, 0L, 0L,
4L, 0L, 1L, 1L, 0L, 2L, 5L, 1L, 3L, 6L, 1L, 1L, 1L, 0L, 0L,
1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 2L, 0L, 1L, 1L,
0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 40L, 2L, 0L, 1L,
0L, 2L, 0L, 3L, 1L, 1L, 4L, 1L), mors = c(0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L,
0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L,
0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L,
0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L,
0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L,
1L, 0L, 0L, 1L, 0L, 1L), os.neck = c(9.63, 7.03, 9.17, 10.48,
7.69, 15.18, 13.5, 16.33, 15.31, 12.09, 12.35, 22.28, 15.77,
14.39, 10.02, 14.52, 8.44, 23.82, 5.95, 3.78, 19.32, 20.14,
15.51, 19.78, 12.98, 32.92, 9.76, 5.65, 30.75, 2.79, 33.58,
27.53, 27.63, 14.62, 29.17, 25.4, 18.43, 5.29, 30.75, 28.48,
14.69, 13.14, 6.6, 26.81, 40.74, 11.63, 13.31, 10.41, 9.56,
17.51, 35.78, 35.75, 37.62, 33.25, 36.96, 34.56, 40.05, 41.26,
24.34, 37.49, 40.94, 24.11, 39.33, 11.24, 39.1, 19.75, 38.93,
39.36, 36.34, 48, 29.17, 47.93, 3.68, 24.21, 46.36, 49.12,
50.96, 14.16, 54.01, 19.88, 50.86, 1.87, 54.24, 13.93, 11.6,
10.05, 23.1, 62.78, 12.58, 39, 59.83, 6.77, 60.39, 18.46,
61.77, 58.41, 49.45, 64.26, 2.4, 26.51, 58.94, 69.91, 64.66,
55.56, 46.55, 29.63, 55.66, 19.68, 7.62, 2.73, 17.77, 10.12,
9.95, 74.22, 57.3, 58.94, 27.01, 34.23, 78.82, 27.2, 83.02,
76.68, 58.15, 22.18, 14.49, 3.91, 25.92, 74.64, 66.83, 70.74,
38.08, 7.69, 74.55, 49.94, 11.1, 88.54, 6.44, 79.54, 80.82,
70.83, 12.91, 81.25, 17.38, 29.96, 94.72, 73.53, 72.54, 1.35,
89.69, 62.85, 7.62, 93.27, 5.09, 51.25, 62, 55.33, 44.62,
56.94, 94.55, 88.61, 32.46, 11.04, 16.53, 100.04, 24.74,
24.54, 5.75, 59.83, 59.83, 77.77, 92.78, 49.58, 91.2, 1.18,
18.92, 6.34, 32.46, 72.41, 105.82, 1.84, 12.78, 57.56, 59.14,
104.08, 15.54, 117.75, 4.27, 67.61, 19.78, 112.49, 53.59,
107.01, 47.57, 9.46, 53.59, 46.46, 57.33, 18.76, 82.04, 13.67,
67.45, 28.98, 21.19, 121.4, 91.07, 50.83, 121.72, 123.04,
6.31, 123.5, 58.68, 9.56, 34.1, 90.48, 71.1, 11.33, 65.35,
54.21, 34.99, 62.06), score = c(47.16, 47.55, 39.27, 72.23,
44.91, 25.74, 62.36, 25.74, 55.44, 34.48, 82.32, 76.4, 60.64,
32.22, 52.34, 36.13, 41.81, 34.2, 33.91, 65.66, 32.51, 34.76,
42.37, 38.14, 35.61, 45.75, 61.23, 59.04, 46.6, 33.35, 84.05,
26.5, 38.99, 62.89, 47.16, 38.42, 35.32, 37.01, 56.85, 29.96,
31.66, 60.52, 42.93, 35.04, 62.21, 37.86, 64.96, 35.32, 40.96,
38.71, 47.16, 58.77, 27.71, 54.81, 50.09, 42.65, 28.56, 36.73,
31.1, 43.88, 38.43, 78.15, 32.51, 32.22, 33.07, 47.16, 44.63,
27.71, 38.99, 40.96, 68.56, 28.28, 37.02, 58.83, 58.48, 42.09,
46.32, 37.86, 30.53, 60.73, 55, 77.47, 41.53, 36.17, 32.79,
71.38, 56.79, 57.98, 76.32, 29.97, 63.55, 123.86, 35.61,
59.39, 41.24, 37.3, 33.35, 37.5, 39.27, 58.83, 53.47, 52.02,
35.52, 27.71, 31.1, 51.39, 21.51, 83.34, 37.02, 39.55, 50.79,
91.42, 33.07, 63.65, 70.61, 51.62, 52.34, 29.97, 29.12, 36.17,
35.04, 85.42, 31.66, 40.12, 55.28, 90.67, 51.15, 53.47, 43.5,
55.16, 47.67, 34.76, 35.04, 35.89, 98.47, 38.99, 59.23, 40.12,
57.25, 31.94, 52.46, 49.14, 33.35, 92.46, 33.91, 50.93, 49.8,
38.43, 57.42, 37.86, 35.89, 47.73, 57.82, 39.27, 33.07, 36.45,
57.13, 70.03, 57.25, 71.95, 35.32, 60.64, 38.14, 71.76, 91.52,
61.15, 39.27, 33.35, 56.36, 35.32, 47.73, 43.78, 33.91, 62.77,
75.44, 51.39, 78.71, 78.39, 47.16, 45.75, 55, 24.61, 37.58,
42.65, 62.05, 35.04, 35.32, 51.62, 38.43, 34.2, 34.2, 42.29,
33.91, 49.42, 60.8, 36.45, 56.97, 61.2, 37.86, 38.14, 40.68,
61.77, 54.43, 27.15, 49.7, 46.04, 60.07, 39.27, 130.25, 59.89,
37.02, 40.32, 40.68, 69.48, 39.84, 73.64, 53.87, 60.92, 71.95,
51.05), score.group = structure(c(2L, 2L, 2L, 4L, 2L, 1L,
3L, 1L, 3L, 1L, 4L, 4L, 3L, 1L, 2L, 2L, 2L, 1L, 1L, 3L, 1L,
1L, 2L, 2L, 1L, 2L, 3L, 3L, 2L, 1L, 5L, 1L, 2L, 3L, 2L, 2L,
1L, 2L, 3L, 1L, 1L, 3L, 2L, 1L, 3L, 2L, 3L, 1L, 2L, 2L, 2L,
3L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 4L, 1L, 1L, 1L, 2L,
2L, 1L, 2L, 2L, 3L, 1L, 2L, 3L, 3L, 2L, 2L, 2L, 1L, 3L, 2L,
4L, 2L, 2L, 1L, 4L, 3L, 3L, 4L, 1L, 3L, 5L, 1L, 3L, 2L, 2L,
1L, 2L, 2L, 3L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 5L, 2L, 2L, 2L,
5L, 1L, 3L, 4L, 2L, 2L, 1L, 1L, 2L, 1L, 5L, 1L, 2L, 3L, 5L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 5L, 2L, 3L, 2L, 3L, 1L, 2L,
2L, 1L, 5L, 1L, 2L, 2L, 2L, 3L, 2L, 1L, 2L, 3L, 2L, 1L, 2L,
3L, 4L, 3L, 4L, 1L, 3L, 2L, 4L, 5L, 3L, 2L, 1L, 3L, 1L, 2L,
2L, 1L, 3L, 4L, 2L, 4L, 4L, 2L, 2L, 2L, 1L, 2L, 2L, 3L, 1L,
1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 2L,
3L, 2L, 1L, 2L, 2L, 3L, 2L, 5L, 3L, 2L, 2L, 2L, 3L, 2L, 4L,
2L, 3L, 4L, 2L), .Label = c("1", "2", "3", "4", "5"), class = "factor")), row.names = c(NA,
220L), class = "data.frame")
And
# plot(nom) for nomogram
nom <- structure(list(n.fjernet = structure(list(n.fjernet = c(2, 3,
4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,
53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,
69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,
85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100,
101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,
127, 128, 129, 130, 131, 132, 133), Xbeta = c(`1` = -0.0114560716414661,
`2` = -0.0171841074621991, `3` = -0.0229121432829322, `4` = -0.0286401791036652,
`5` = -0.0343682149243983, `6` = -0.0400962507451313, `7` = -0.0458242865658644,
`8` = -0.0515523223865974, `9` = -0.0572803582073305, `10` = -0.0630083940280635,
`11` = -0.0687364298487966, `12` = -0.0744644656695296, `13` = -0.0801925014902627,
`14` = -0.0859205373109957, `15` = -0.0916485731317288, `16` = -0.0973766089524618,
`17` = -0.103104644773195, `18` = -0.108832680593928, `19` = -0.114560716414661,
`20` = -0.120288752235394, `21` = -0.126016788056127, `22` = -0.13174482387686,
`23` = -0.137472859697593, `24` = -0.143200895518326, `25` = -0.148928931339059,
`26` = -0.154656967159792, `27` = -0.160385002980525, `28` = -0.166113038801258,
`29` = -0.171841074621991, `30` = -0.177569110442725, `31` = -0.183297146263458,
`32` = -0.189025182084191, `33` = -0.194753217904924, `34` = -0.200481253725657,
`35` = -0.20620928954639, `36` = -0.211937325367123, `37` = -0.217665361187856,
`38` = -0.223393397008589, `39` = -0.229121432829322, `40` = -0.234849468650055,
`41` = -0.240577504470788, `42` = -0.246305540291521, `43` = -0.252033576112254,
`44` = -0.257761611932987, `45` = -0.26348964775372, `46` = -0.269217683574453,
`47` = -0.274945719395186, `48` = -0.280673755215919, `49` = -0.286401791036652,
`50` = -0.292129826857386, `51` = -0.297857862678119, `52` = -0.303585898498852,
`53` = -0.309313934319585, `54` = -0.315041970140318, `55` = -0.320770005961051,
`56` = -0.326498041781784, `57` = -0.332226077602517, `58` = -0.33795411342325,
`59` = -0.343682149243983, `60` = -0.349410185064716, `61` = -0.355138220885449,
`62` = -0.360866256706182, `63` = -0.366594292526915, `64` = -0.372322328347648,
`65` = -0.378050364168381, `66` = -0.383778399989114, `67` = -0.389506435809847,
`68` = -0.39523447163058, `69` = -0.400962507451313, `70` = -0.406690543272047,
`71` = -0.41241857909278, `72` = -0.418146614913513, `73` = -0.423874650734246,
`74` = -0.429602686554979, `75` = -0.435330722375712, `76` = -0.441058758196445,
`77` = -0.446786794017178, `78` = -0.452514829837911, `79` = -0.458242865658644,
`80` = -0.463970901479377, `81` = -0.46969893730011, `82` = -0.475426973120843,
`83` = -0.481155008941576, `84` = -0.486883044762309, `85` = -0.492611080583042,
`86` = -0.498339116403775, `87` = -0.504067152224508, `88` = -0.509795188045241,
`89` = -0.515523223865974, `90` = -0.521251259686707, `91` = -0.526979295507441,
`92` = -0.532707331328174, `93` = -0.538435367148907, `94` = -0.54416340296964,
`95` = -0.549891438790373, `96` = -0.555619474611106, `97` = -0.561347510431839,
`98` = -0.567075546252572, `99` = -0.572803582073305, `100` = -0.578531617894038,
`101` = -0.584259653714771, `102` = -0.589987689535504, `103` = -0.595715725356237,
`104` = -0.60144376117697, `105` = -0.607171796997703, `106` = -0.612899832818436,
`107` = -0.618627868639169, `108` = -0.624355904459902, `109` = -0.630083940280635,
`110` = -0.635811976101369, `111` = -0.641540011922102, `112` = -0.647268047742835,
`113` = -0.652996083563568, `114` = -0.658724119384301, `115` = -0.664452155205034,
`116` = -0.670180191025767, `117` = -0.6759082268465, `118` = -0.681636262667233,
`119` = -0.687364298487966, `120` = -0.693092334308699, `121` = -0.698820370129432,
`122` = -0.704548405950165, `123` = -0.710276441770898, `124` = -0.716004477591631,
`125` = -0.721732513412364, `126` = -0.727460549233097, `127` = -0.73318858505383,
`128` = -0.738916620874563, `129` = -0.744644656695296, `130` = -0.750372692516029,
`131` = -0.756100728336763, `132` = -0.761828764157496), points = c(`1` = 27.84103949255,
`2` = 27.6285124735229, `3` = 27.4159854544958, `4` = 27.2034584354687,
`5` = 26.9909314164416, `6` = 26.7784043974145, `7` = 26.5658773783874,
`8` = 26.3533503593603, `9` = 26.1408233403332, `10` = 25.9282963213061,
`11` = 25.715769302279, `12` = 25.5032422832519, `13` = 25.2907152642248,
`14` = 25.0781882451977, `15` = 24.8656612261706, `16` = 24.6531342071435,
`17` = 24.4406071881164, `18` = 24.2280801690893, `19` = 24.0155531500622,
`20` = 23.8030261310351, `21` = 23.590499112008, `22` = 23.3779720929809,
`23` = 23.1654450739538, `24` = 22.9529180549267, `25` = 22.7403910358996,
`26` = 22.5278640168725, `27` = 22.3153369978454, `28` = 22.1028099788183,
`29` = 21.8902829597912, `30` = 21.6777559407641, `31` = 21.465228921737,
`32` = 21.2527019027099, `33` = 21.0401748836828, `34` = 20.8276478646557,
`35` = 20.6151208456286, `36` = 20.4025938266015, `37` = 20.1900668075744,
`38` = 19.9775397885473, `39` = 19.7650127695202, `40` = 19.5524857504931,
`41` = 19.339958731466, `42` = 19.1274317124389, `43` = 18.9149046934118,
`44` = 18.7023776743847, `45` = 18.4898506553576, `46` = 18.2773236363305,
`47` = 18.0647966173034, `48` = 17.8522695982763, `49` = 17.6397425792492,
`50` = 17.4272155602221, `51` = 17.214688541195, `52` = 17.0021615221679,
`53` = 16.7896345031408, `54` = 16.5771074841137, `55` = 16.3645804650866,
`56` = 16.1520534460595, `57` = 15.9395264270324, `58` = 15.7269994080053,
`59` = 15.5144723889782, `60` = 15.3019453699511, `61` = 15.089418350924,
`62` = 14.8768913318969, `63` = 14.6643643128698, `64` = 14.4518372938427,
`65` = 14.2393102748156, `66` = 14.0267832557885, `67` = 13.8142562367614,
`68` = 13.6017292177343, `69` = 13.3892021987072, `70` = 13.1766751796801,
`71` = 12.964148160653, `72` = 12.7516211416259, `73` = 12.5390941225988,
`74` = 12.3265671035717, `75` = 12.1140400845446, `76` = 11.9015130655175,
`77` = 11.6889860464904, `78` = 11.4764590274633, `79` = 11.2639320084362,
`80` = 11.0514049894091, `81` = 10.838877970382, `82` = 10.6263509513549,
`83` = 10.4138239323278, `84` = 10.2012969133007, `85` = 9.98876989427365,
`86` = 9.77624287524655, `87` = 9.56371585621945, `88` = 9.35118883719235,
`89` = 9.13866181816525, `90` = 8.92613479913816, `91` = 8.71360778011105,
`92` = 8.50108076108396, `93` = 8.28855374205686, `94` = 8.07602672302976,
`95` = 7.86349970400266, `96` = 7.65097268497556, `97` = 7.43844566594846,
`98` = 7.22591864692136, `99` = 7.01339162789426, `100` = 6.80086460886717,
`101` = 6.58833758984007, `102` = 6.37581057081297, `103` = 6.16328355178587,
`104` = 5.95075653275877, `105` = 5.73822951373167, `106` = 5.52570249470457,
`107` = 5.31317547567747, `108` = 5.10064845665037, `109` = 4.88812143762328,
`110` = 4.67559441859617, `111` = 4.46306739956908, `112` = 4.25054038054198,
`113` = 4.03801336151488, `114` = 3.82548634248778, `115` = 3.61295932346068,
`116` = 3.40043230443358, `117` = 3.18790528540648, `118` = 2.97537826637939,
`119` = 2.76285124735228, `120` = 2.55032422832519, `121` = 2.33779720929809,
`122` = 2.12527019027099, `123` = 1.91274317124389, `124` = 1.70021615221679,
`125` = 1.48768913318969, `126` = 1.27516211416259, `127` = 1.06263509513549,
`128` = 0.850108076108397, `129` = 0.637581057081296, `130` = 0.425054038054198,
`131` = 0.212527019027097, `132` = 0)), info = list(nfun = 3L,
predictor = "n.fjernet", effect.name = "n.fjernet", type = "main")),
n.sygdom = structure(list(n.sygdom = c(0, 1, 2, 3, 4, 5,
6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
37, 38, 39, 40), Xbeta = c(`133` = 0, `134` = 0.32236136668714,
`135` = 0.648650531337351, `136` = 0.909274081797897, `137` = 1.09719119915252,
`138` = 1.2269431700224, `139` = 1.31307128102872, `140` = 1.37011681879267,
`141` = 1.41262106993544, `142` = 1.452701773308, `143` = 1.49278247668057,
`144` = 1.53286318005315, `145` = 1.57294388342572, `146` = 1.61302458679829,
`147` = 1.65310529017085, `148` = 1.69318599354344, `149` = 1.733266696916,
`150` = 1.77334740028855, `151` = 1.81342810366113, `152` = 1.8535088070337,
`153` = 1.89358951040629, `154` = 1.93367021377884, `155` = 1.97375091715141,
`156` = 2.01383162052397, `157` = 2.05391232389658, `158` = 2.09399302726916,
`159` = 2.13407373064171, `160` = 2.17415443401433, `161` = 2.21423513738691,
`162` = 2.25431584075947, `163` = 2.29439654413205, `164` = 2.33447724750454,
`165` = 2.37455795087723, `166` = 2.41463865424957, `167` = 2.45471935762221,
`168` = 2.49480006099482, `169` = 2.53488076436739, `170` = 2.57496146774009,
`171` = 2.61504217111266, `172` = 2.65512287448523, `173` = 2.69520357785787
), points = c(`133` = 0, `134` = 11.9605572408505, `135` = 24.0668473679043,
`136` = 33.7367495824038, `137` = 40.7090287415156, `138` = 45.5232094563172,
`139` = 48.7188163378863, `140` = 50.8353739973004, `141` = 52.412407045637,
`142` = 53.8995193254604, `143` = 55.3866316052841, `144` = 56.8737438851078,
`145` = 58.3608561649314, `146` = 59.8479684447552, `147` = 61.3350807245785,
`148` = 62.8221930044026, `149` = 64.309305284226, `150` = 65.7964175640487,
`151` = 67.283529843873, `152` = 68.7706421236966, `153` = 70.257754403521,
`154` = 71.7448666833438, `155` = 73.2319789631674, `156` = 74.7190912429907,
`157` = 76.2062035228159, `158` = 77.6933158026398, `159` = 79.1804280824625,
`160` = 80.6675403622881, `161` = 82.1546526421122, `162` = 83.6417649219352,
`163` = 85.1288772017594, `164` = 86.6159894815798, `165` = 88.103101761408,
`166` = 89.5902140412232, `167` = 91.0773263210494, `168` = 92.5644386008743,
`169` = 94.0515508806979, `170` = 95.5386631605266, `171` = 97.0257754403502,
`172` = 98.5128877201739, `173` = 100)), info = list(nfun = 3L,
predictor = "n.sygdom", effect.name = "n.sygdom", type = "main")),
ecs = structure(list(ecs = c("No", "Yes"), Xbeta = c(`174` = 0,
`175` = 0.352802098746005), points = c(`174` = 0, `175` = 13.0899981598574
)), info = list(nfun = 3L, predictor = "ecs", effect.name = "ecs",
type = "main")), contra.pos = structure(list(contra.pos = c("Ipsi.",
"Contra."), Xbeta = c(`176` = 0, `177` = -0.149053853083395
), points = c(`176` = 5.53033745977221, `177` = 0)), info = list(
nfun = 3L, predictor = "contra.pos", effect.name = "contra.pos",
type = "main")), total.points = list(x = c(0, 10, 20,
30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140)), lp = list(
x = c(8.59901355289166, 27.1504878870641, 45.7019622212365,
64.253436555409, 82.8049108895814, 101.356385223754,
119.907859557926, 138.459333892099), x.real = c(-1, -0.5,
0, 0.5, 1, 1.5, 2, 2.5)), `Probability of 1 year survival` = list(
x = c(132.923978485611, 122.154519662532, 112.023605643525,
101.668496222993, 90.3441422980089, 77.0166169199793,
59.6149679036176, 31.771904926561), x.real = c(0.2, 0.3,
0.4, 0.5, 0.6, 0.7, 0.8, 0.9), fat = c("0.2", "0.3",
"0.4", "0.5", "0.6", "0.7", "0.8", "0.9"), which = c(FALSE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE
)), `Probability of 3 years survival` = list(x = c(111.455658509363,
98.1672795067317, 87.3978451509886, 77.2669361851509, 66.9118109221416,
55.5874579746061, 42.2599719337723, 24.8583283615229), x.real = c(0.1,
0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8), fat = c("0.1", "0.2",
"0.3", "0.4", "0.5", "0.6", "0.7", "0.8"), which = c(FALSE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE)),
`Probability of 5 years survival` = list(x = c(96.3366217164868,
83.0483098712202, 72.2788509036931, 62.1479468853665, 51.7928216700398,
40.468478298201, 27.1409533137375, 9.73931852863156), x.real = c(0.1,
0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8), fat = c("0.1", "0.2",
"0.3", "0.4", "0.5", "0.6", "0.7", "0.8"), which = c(FALSE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE))), info = list(
fun = list(function (x)
surv(12, x), function (x)
surv(36, x), function (x)
surv(60, x)), lp = TRUE, lp.at = c(-1, -0.5, 0, 0.5, 1, 1.5,
2, 2.5), discrete = c(n.fjernet = FALSE, n.sygdom = FALSE,
ecs = TRUE, contra.pos = TRUE, studie = TRUE), funlabel = c("Probability of 1 year survival",
"Probability of 3 years survival", "Probability of 5 years survival"
), fun.at = NULL, fun.lp.at = NULL, Abbrev = list(), minlength = 4,
conf.int = FALSE, R = structure(c(-0.761828764157496, -0.0114560716414661,
0, 2.69520357785787, 0, 0.352802098746005, -0.149053853083395,
0), .Dim = c(2L, 4L), .Dimnames = list(NULL, c("n.fjernet",
"n.sygdom", "ecs", "contra.pos"))), sc = 37.1029486683449,
maxscale = 100, Intercept = -1.23176092093802, nint = 10,
space.used = c(main = 4, ia = 0)), class = "nomogram")

your first problem can be solved with an updateSliderInput call where you update the max value to be equal the selected value from the slider above.
The second problem can besolved with an conditionalPanel here I needed to change the naming of the n.sygdom slider to n_sygdom because the java script couldn't handle to dot notation. I would recommend you to the same for all you variables whenever you are working with shiny.
Below is working example
ui <- fluidPage(
titlePanel("Survival Curve"),
sidebarLayout(
sidebarPanel(
sliderInput("n.fjernet", "Lymph Nodal Yield", min = 2, max = 150, value = 40),
sliderInput("n_sygdom", "Number of positive lymph nodes", min = 0, max = 40, value = 0),
conditionalPanel(
condition = "input.n_sygdom >= 1",
radioButtons("ecs", "Extracapsular extension", c("No","Yes")),
radioButtons("contra.pos", "Neck involvement", c("Contra.","Ipsi.")
)
),
verbatimTextOutput("out.score"),
verbatimTextOutput("out.score.group")
),
mainPanel(
plotOutput("surv_plot")
)
)
)
server <- function(input, output, session) {
calc_score <- reactive({
round(nom$ecs$points[nom$ecs$ecs==input$ecs] +
nom$contra.pos$points[nom$contra.pos$contra.pos==input$contra.pos] +
nom$n.fjernet$points[nom$n.fjernet$n.fjernet==input$n.fjernet] +
nom$n_sygdom$points[nom$n_sygdom$n_sygdom==input$n_sygdom],digits=1)
})
observe(
updateSliderInput(
session = session,
inputId = "n_sygdom",
max = min(40, input$n.fjernet),
value = min(input$n.fjernet, input$n_sygdom)
)
)
calc_score_group <- function(score) {
cut(score, c(0,35.9,55.2,70.0,83.3,Inf), include.lowest = TRUE, labels = c("1","2","3","4","5"))
}
fit_data <- reactive({
p %>% filter(score.group == as.numeric(calc_score_group(calc_score())))
})
fit_model <- reactive({
survfit(Surv(os.neck, mors) ~ 1, data = fit_data())
})
output$out.score <- renderText(calc_score())
output$out.score.group <- renderText(calc_score_group(calc_score()))
output$surv_plot <- renderPlot({
ggsurvplot(
fit_model(),
data = fit_data(),
risk.table = TRUE,
pval = F,
pval.coord = c(0, 0.25),
conf.int = T,
size=1,
xlim = c(0,60),
conf.int.alpha=c(0.2),
break.x.by = 6,
xlab="Time in months",
ylab="Probability of overall survival",
ggtheme = theme,
surv.median.line = "v",
ylim=c(0,1),
palette="#2C77BF",
tables.theme=theme,
legend.title=paste("Score group", calc_score_group(calc_score())),
surv.scale="percent",
tables.col="strata",
risk.table.col = "strata",
risk.table.y.text = FALSE,
tables.y.text = FALSE)
})
}
please not that I changed the naming also in the data set as well
Hope this helps!!

Analyzing covariation of variables over time in R

I am using Rstudio to analyze data. And - why else would I post this - I'm getting stuck!
The data is a time-series of the evolution of a community and their characteristics over time. For example, the n_members (the size) can increase over time.
Each combination of the variables 'randomseed' and 'lhsExperimentNumber' is 1 observation. In total, there are 80,000 observations. Each observation has a time-series going from 1 (month) to 204 (months). This time is characterized in the variable X.run.number
What I want:
See covariation of dependent and independent variables over time.
For example: I would like to see how the n_members (size) evolves over time, under different circumstances (exit_window, for example). I normally use dplyr and ggplot2, and I'd like to make some captivating images in ggplot.
My problem:
- I'm just a beginner in R so it's a little tricky for me to see the correct strategy here
- It would be sort of easier if I would just have 1 observation that evolves over time, but now I have 80,000 observations, which each independently evolve over time. So I don't really know how to work the data to make sensible graphs.
How would you guys tackle this? Maybe you can help me out with some tips (that would be so great!)
I used dput to reproduce the first 10 observations of my dataset:
library(ggplot2)
library(dplyr)
coop_dat <- structure(list(X.run.number. = c(2L, 3L, 4L, 1L, 2L, 3L, 4L,
1L, 3L, 3L), rewire_prop = c(0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1), prior_trust_std = c(0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1), max_trust = c(2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L), maxCR_trust = c(0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
0.2, 0.2, 0.2, 0.2), Ostrom. = structure(c(2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L), .Label = c("false", "true"), class = "factor"),
social_network = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), .Label = "\"small-world\"", class = "factor"),
external_ROI_influence = structure(c(2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L), .Label = c("false", "true"), class = "factor"),
randomseed = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), lhsExperimentNumber = c(1L,
2L, 3L, 0L, 1L, 2L, 3L, 0L, 2L, 2L), X.step. = c(0L, 0L,
0L, 0L, 1L, 1L, 1L, 1L, 2L, 3L), size_start_cooperative = c(12L,
4L, 20L, 17L, 12L, 4L, 20L, 17L, 4L, 4L), degree_prior = c(7L,
21L, 40L, 15L, 7L, 21L, 40L, 15L, 21L, 21L), degree_coop = c(17L,
47L, 20L, 31L, 17L, 47L, 20L, 31L, 47L, 47L), prior_trust_average = c(0.40574414878618,
0.631650829650462, 0.369752783104777, 0.585182260912843,
0.40574414878618, 0.631650829650462, 0.369752783104777, 0.585182260912843,
0.631650829650462, 0.631650829650462), number_of_meetings = c(10L,
5L, 2L, 2L, 10L, 5L, 2L, 2L, 5L, 5L), n_interactions = c(20L,
3L, 9L, 19L, 20L, 3L, 9L, 19L, 3L, 3L), exit_window = c(5L,
1L, 3L, 4L, 5L, 1L, 3L, 4L, 1L, 1L), info_peer_behavior = c(0.396114811487496,
0.627111892865505, 0.148686024846975, 0.281594149058219,
0.396114811487496, 0.627111892865505, 0.148686024846975,
0.281594149058219, 0.627111892865505, 0.627111892865505),
memory = c(8L, 11L, 20L, 1L, 8L, 11L, 20L, 1L, 11L, 11L),
n_shares_total_sum = c(0L, 0L, 0L, 0L, 21L, 13L, 32L, 31L,
13L, 13L), n_shares_total_mean = c(0, 0, 0, 0, 1.75, 3.25,
1.6, 1.82352941176471, 3.25, 3.25), members_at_meeting = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L), trust_coop_total = c(0,
0, 0, 0, 0.0457418505048186, 0.0563109517340759, 0.0349050771003757,
0.0587220121136852, 0.0563109517340759, 0.0563109517340759
), rep_total = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ROI = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), current_strategy = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), price = c(0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L), REfocus = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0), endpoint = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L), HG_membertypes = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
n_members = c(0L, 0L, 0L, 0L, 12L, 4L, 20L, 17L, 4L, 4L),
n_ids = c(0L, 0L, 0L, 0L, 12L, 4L, 20L, 17L, 4L, 4L), n_prods = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), n_cons = c(0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L), n_members_exit = c(0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L)), .Names = c("X.run.number.",
"rewire_prop", "prior_trust_std", "max_trust", "maxCR_trust",
"Ostrom.", "social_network", "external_ROI_influence", "randomseed",
"lhsExperimentNumber", "X.step.", "size_start_cooperative", "degree_prior",
"degree_coop", "prior_trust_average", "number_of_meetings", "n_interactions",
"exit_window", "info_peer_behavior", "memory", "n_shares_total_sum",
"n_shares_total_mean", "members_at_meeting", "trust_coop_total",
"rep_total", "ROI", "current_strategy", "price", "REfocus", "endpoint",
"HG_membertypes", "n_members", "n_ids", "n_prods", "n_cons",
"n_members_exit"), row.names = c(NA, 10L), class = "data.frame")
EDIT:
Right now I have this simple ggplot code:
ggplot(dat) +
geom_line(aes(x=month, y=trust_coop_total))
This produces this graph:
You can see that this is not really a line graph. It's more a graph with a bunch of vertical lines. What I would like is to decipher trends for each individual sample. Each sample is, as mentioned above, a combination of lhsExperimentnumber and X.run.number. There are so many samples that a line graph would be overwhelming, but it might be nice in combination with the alpha=1/10 command of geom_jitter. However, geom_jitter ignores the trend in the sample itself.
What should I do to get a good trend graph?

How to create score variable for predicted value from Logistic regression

I looked for previous posts and found this answer to a similar question from Aug '15. The code is:
logit <- glm(y~x1+x2+x3,family="binomial")
predict(logit)
pred <- predict(logit,newdata=data) #gives you b0 + b1x1 + b2x2 + b3x3
probs <- exp(pred)/(1+exp(pred)) #gives you probability that y=1 for each observation
What I don't understand is how I get the newdata=data part of the code.
How do I specify the data in the newdata part of the code?
How do I assign these predicted values to a variable?
Sample data
structure(list(CustomerID = 1:400, binary_depvar = c(1L, NA,
1L, NA, 1L, NA, NA, NA, 0L, NA, 0L, NA, 0L, NA, 1L, 1L, 1L, NA,
NA, NA, NA, 1L, NA, NA, 1L, NA, NA, NA, NA, 1L, 1L, NA, 0L, 1L,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0L, 1L, NA, 0L, NA,
NA, 1L, NA, NA, 1L, NA, 1L, 1L, 0L, 1L, 0L, 0L, 0L, NA, NA, NA,
1L, NA, 0L, NA, NA, NA, 0L, 1L, NA, 0L, 0L, NA, 1L, NA, 1L, NA,
NA, 1L, 1L, 1L, NA, NA, NA, 1L, 0L, NA, NA, 0L, NA, NA, NA, NA,
0L, 1L, NA, NA, NA, NA, 0L, 0L, NA, NA, NA, 0L, 1L, NA, 0L, NA,
NA, 1L, NA, 0L, NA, 1L, NA, NA, 1L, NA, NA, 1L, 1L, 0L, NA, NA,
NA, 1L, 1L, NA, NA, NA, 1L, NA, 1L, NA, NA, NA, NA, 1L, NA, NA,
NA, 1L, NA, NA, 0L, 1L, 1L, 1L, NA, 0L, NA, NA, NA, NA, 1L, NA,
0L, 0L, NA, 0L, 0L, NA, NA, 0L, 1L, 1L, 0L, 1L, 1L, NA, NA, NA,
NA, NA, NA, NA, NA, 1L, 1L, 0L, 1L, NA, NA, 0L, NA, NA, NA, 1L,
NA, NA, NA, NA, NA, NA, 0L, 1L, 0L, 0L, 0L, 1L, 1L, NA, 0L, NA,
NA, 1L, 1L, 0L, 0L, 0L, NA, 0L, 0L, 1L, NA, 0L, NA, 0L, 1L, NA,
0L, 1L, 1L, 1L, 1L, NA, 0L, NA, NA, NA, NA, NA, NA, NA, NA, NA,
0L, 1L, NA, 0L, 0L, NA, 1L, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 1L, 1L, 0L, NA, 1L, 0L, NA, 1L, NA, NA, 1L, 1L, 1L,
NA, 0L, 1L, 1L, 1L, 1L, NA, 0L, 0L, 1L, NA, NA, 1L, 1L, 0L, 1L,
NA, NA, NA, 0L, 1L, 1L, 1L, 0L, 0L, NA, 1L, 1L, NA, NA, NA, NA,
NA, NA, 0L, NA, 1L, 0L, 1L, NA, 1L, 0L, 0L, 1L, NA, NA, 1L, 1L,
1L, NA, 0L, 1L, 1L, NA, NA, 1L, NA, 0L, NA, NA, 1L, NA, NA, NA,
NA, 1L, NA, 0L, NA, 0L, NA, 0L, 1L, 1L, NA, 0L, 1L, NA, NA, 1L,
1L, 1L, NA, 1L, 0L, NA, NA, 0L, 0L, NA, NA, NA, 1L, 1L, NA, NA,
0L, 1L, NA, NA, 1L, 0L, 1L, NA, NA, NA, NA, NA, 1L, 1L, 0L, 0L,
NA, NA, NA, NA, 1L, NA, 1L, 0L, 1L, NA, NA, NA, 0L, 0L), binary_A = 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,
1L, 0L, 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, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 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, 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, 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, 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, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 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, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 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, 1L, 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, 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, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L),
binary_B = c(1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L,
1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L,
0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L,
0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L,
0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L,
1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L,
0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L,
1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L,
0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L,
1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L,
0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L,
1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L,
1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L,
0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L,
0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L,
1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L,
0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L),
binary_C = c(1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L,
0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L,
1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L,
1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L,
1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L,
1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L,
1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L,
1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L,
1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L,
1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L,
1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L,
0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L,
1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L,
0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L),
categ_A = c(4L, 4L, 1L, 1L, 37L, 4L, 17L, 55L, 7L, 4L, 62L,
11L, 56L, 38L, 39L, 13L, 62L, 10L, 13L, 6L, 4L, 7L, 57L,
1L, 9L, 69L, 22L, 17L, 13L, 6L, 7L, 7L, 13L, 7L, 27L, 12L,
4L, 7L, 13L, 62L, 25L, 17L, 17L, 19L, 27L, 7L, 7L, 13L, 17L,
7L, 27L, 4L, 38L, 37L, 13L, 1L, 37L, 33L, 13L, 44L, 22L,
53L, 17L, 17L, 38L, 2L, 1L, 19L, 19L, 11L, 31L, 4L, 57L,
37L, 13L, 30L, 17L, 13L, 17L, 27L, 11L, 53L, 7L, 25L, 20L,
6L, 6L, 7L, 7L, 41L, 7L, 7L, 62L, 12L, 4L, 53L, 13L, 53L,
37L, 5L, 4L, 1L, 57L, 1L, 2L, 37L, 17L, 39L, 53L, 17L, 38L,
22L, 62L, 12L, 5L, 1L, 6L, 1L, 1L, 4L, 1L, 53L, 37L, 5L,
4L, 4L, 4L, 27L, 17L, 22L, 4L, 7L, 6L, 52L, 2L, 46L, 20L,
11L, 48L, 53L, 19L, 13L, 19L, 57L, 27L, 1L, 33L, 17L, 7L,
53L, 37L, 37L, 36L, 1L, 37L, 17L, 47L, 55L, 33L, 11L, 34L,
13L, 1L, 57L, 17L, 53L, 27L, 48L, 41L, 7L, 11L, 7L, 62L,
17L, 4L, 1L, 19L, 27L, 27L, 37L, 13L, 5L, 41L, 62L, 27L,
38L, 48L, 11L, 27L, 46L, 13L, 37L, 17L, 3L, 7L, 4L, 1L, 10L,
1L, 2L, 5L, 37L, 34L, 6L, 2L, 4L, 33L, 2L, 47L, 7L, 3L, 4L,
1L, 6L, 13L, 13L, 31L, 13L, 24L, 1L, 7L, 7L, 4L, 55L, 11L,
4L, 19L, 4L, 1L, 37L, 27L, 17L, 13L, 4L, 13L, 19L, 26L, 62L,
5L, 24L, 38L, 27L, 2L, 8L, 19L, 4L, 38L, 1L, 13L, 4L, 4L,
17L, 54L, 4L, 17L, 17L, 2L, 11L, 13L, 17L, 4L, 6L, 8L, 9L,
38L, 40L, 17L, 70L, 11L, 50L, 14L, 7L, 8L, 7L, 17L, 17L,
62L, 1L, 4L, 17L, 4L, 4L, 6L, 38L, 17L, 4L, 53L, 59L, 13L,
7L, 17L, 4L, 7L, 13L, 7L, 38L, 24L, 20L, 17L, 4L, 4L, 13L,
7L, 7L, 4L, 19L, 7L, 7L, 38L, 62L, 4L, 17L, 17L, 19L, 36L,
17L, 47L, 13L, 13L, 2L, 36L, 26L, 25L, 1L, 2L, 4L, 4L, 27L,
27L, 19L, 41L, 53L, 11L, 62L, 37L, 47L, 37L, 13L, 1L, 27L,
17L, 24L, 11L, 17L, 17L, 27L, 62L, 38L, 38L, 7L, 17L, 53L,
37L, 7L, 17L, 7L, 7L, 4L, 11L, 26L, 13L, 4L, 7L, 38L, 24L,
37L, 12L, 1L, 17L, 25L, 26L, 19L, 25L, 33L, 27L, 53L, 5L,
27L, 7L, 62L, 4L, 1L, 1L, 25L, 5L, 62L, 47L, 4L, 7L, 48L,
12L, 17L, 18L, 7L, 9L, 37L, 63L, 37L, 46L, 1L), categ_B = c(0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 3L, 0L,
1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 3L, 1L,
0L, 0L, 2L, 2L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 0L,
1L, 3L, 0L, 2L, 1L, 0L, 1L, 0L, 2L, 1L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L,
1L, 0L, 2L, 0L, 0L, 0L, 2L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 2L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L,
1L, 0L, 3L, 0L, 3L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 2L, 0L,
0L, 0L, 3L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L,
0L, 3L, 0L, 0L, 2L, 0L, 1L, 1L, 1L, 2L, 3L, 1L, 0L, 3L, 0L,
0L, 0L, 1L, 3L, 0L, 3L, 0L, 0L, 0L, 1L, 3L, 0L, 0L, 0L, 0L,
0L, 0L, 3L, 0L, 2L, 0L, 0L, 1L, 0L, 0L, 2L, 3L, 1L, 3L, 0L,
0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L,
1L, 0L, 1L, 2L, 3L, 0L, 1L, 1L, 0L, 3L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 2L, 1L, 0L, 1L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L,
0L, 1L, 1L, 0L, 3L, 1L, 0L, 1L, 0L, 0L, 0L, 2L, 0L, 1L, 0L,
1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 1L,
3L, 0L, 3L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 2L, 1L, 0L, 1L,
0L, 3L, 0L, 0L, 3L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L,
1L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 0L,
2L, 0L, 0L, 0L, 1L, 0L, 3L, 0L, 3L, 0L, 1L, 0L, 3L, 0L, 3L,
0L, 1L, 1L, 0L, 0L, 0L, 0L, 3L, 1L, 1L, 1L, 1L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 3L, 0L, 2L, 0L, 3L, 0L, 3L, 0L, 1L, 0L,
1L, 0L, 0L, 0L, 1L, 3L, 3L, 0L, 0L), binary_D = c(1L, 0L,
0L, 1L, 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, 1L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 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,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L,
0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L,
0L, 1L, 0L, 1L, 0L, 1L, 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, 1L, 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, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L,
0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 1L, 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, 1L, 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, 1L, 0L, 0L,
1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 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, 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, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L), categ_C = c(2L, 1L, 1L,
1L, 6L, 3L, 9L, 8L, 3L, 2L, 0L, 2L, 0L, 3L, 0L, 0L, 10L,
3L, 5L, 8L, 4L, 6L, 0L, 2L, 2L, 0L, 6L, 6L, 0L, 2L, 1L, 8L,
7L, 3L, 3L, 5L, 2L, 2L, 9L, 0L, 6L, 4L, 6L, 7L, 3L, 0L, 0L,
4L, 0L, 4L, 0L, 1L, 7L, 8L, 6L, 1L, 6L, 6L, 7L, 10L, 6L,
7L, 4L, 3L, 3L, 2L, 2L, 3L, 5L, 1L, 5L, 5L, 8L, 7L, 2L, 6L,
5L, 8L, 6L, 4L, 5L, 5L, 7L, 8L, 3L, 4L, 6L, 8L, 2L, 0L, 3L,
4L, 0L, 4L, 3L, 7L, 4L, 8L, 10L, 1L, 5L, 1L, 0L, 1L, 1L,
3L, 0L, 7L, 6L, 2L, 4L, 7L, 0L, 4L, 5L, 3L, 5L, 1L, 2L, 1L,
1L, 6L, 6L, 2L, 1L, 3L, 3L, 1L, 4L, 4L, 2L, 3L, 2L, 0L, 2L,
7L, 5L, 4L, 10L, 9L, 5L, 5L, 9L, 0L, 0L, 1L, 0L, 7L, 10L,
7L, 3L, 0L, 1L, 1L, 9L, 0L, 2L, 5L, 4L, 3L, 2L, 0L, 1L, 0L,
4L, 7L, 6L, 0L, 3L, 0L, 0L, 0L, 0L, 2L, 1L, 2L, 0L, 9L, 3L,
0L, 6L, 9L, 0L, 8L, 0L, 6L, 8L, 9L, 0L, 8L, 1L, 3L, 4L, 1L,
0L, 4L, 2L, 2L, 1L, 0L, 0L, 3L, 5L, 3L, 2L, 2L, 0L, 3L, 8L,
4L, 2L, 2L, 2L, 9L, 0L, 0L, 3L, 0L, 0L, 1L, 6L, 7L, 1L, 7L,
2L, 3L, 0L, 1L, 2L, 3L, 0L, 0L, 7L, 5L, 0L, 1L, 0L, 0L, 5L,
4L, 5L, 6L, 1L, 2L, 1L, 5L, 2L, 2L, 5L, 4L, 2L, 4L, 4L, 2L,
5L, 6L, 3L, 0L, 8L, 8L, 3L, 1L, 7L, 2L, 6L, 10L, 2L, 0L,
5L, 5L, 9L, 5L, 7L, 5L, 2L, 0L, 0L, 1L, 2L, 6L, 4L, 4L, 2L,
1L, 3L, 3L, 0L, 0L, 4L, 4L, 9L, 1L, 0L, 4L, 6L, 8L, 1L, 3L,
1L, 1L, 1L, 8L, 5L, 0L, 2L, 0L, 8L, 5L, 9L, 4L, 1L, 2L, 0L,
3L, 0L, 0L, 0L, 0L, 0L, 2L, 5L, 0L, 4L, 2L, 1L, 4L, 3L, 6L,
1L, 0L, 0L, 4L, 0L, 7L, 9L, 0L, 9L, 8L, 1L, 5L, 0L, 3L, 3L,
9L, 0L, 10L, 0L, 0L, 2L, 0L, 7L, 8L, 7L, 2L, 0L, 6L, 7L,
4L, 4L, 0L, 6L, 2L, 4L, 5L, 0L, 7L, 3L, 1L, 10L, 6L, 5L,
2L, 10L, 0L, 2L, 0L, 1L, 5L, 5L, 4L, 3L, 3L, 3L, 8L, 1L,
0L, 3L, 3L, 5L, 6L, 1L, 6L, 6L, 0L, 1L, 0L, 0L, 6L, 10L,
2L), categ_D = c(1L, 2L, 4L, 8L, 5L, 2L, 4L, 5L, 4L, 3L,
4L, 3L, 6L, 2L, 3L, 3L, 7L, 2L, 4L, 7L, 8L, 3L, 8L, 4L, 10L,
2L, 5L, 2L, 1L, 8L, 3L, 2L, 3L, 2L, 2L, 4L, 2L, 6L, 3L, 1L,
9L, 5L, 4L, 3L, 5L, 8L, 2L, 4L, 5L, 2L, 5L, 2L, 4L, 6L, 7L,
1L, 6L, 3L, 3L, 9L, 5L, 2L, 8L, 3L, 6L, 3L, 8L, 3L, 5L, 3L,
4L, 4L, 5L, 2L, 1L, 7L, 5L, 5L, 6L, 5L, 1L, 1L, 1L, 7L, 4L,
5L, 7L, 9L, 3L, 3L, 2L, 2L, 1L, 4L, 1L, 9L, 2L, 8L, 3L, 4L,
1L, 6L, 2L, 2L, 2L, 2L, 1L, 8L, 5L, 1L, 3L, 3L, 1L, 3L, 4L,
3L, 2L, 2L, 2L, 2L, 2L, 7L, 5L, 2L, 2L, 3L, 4L, 5L, 7L, 4L,
1L, 3L, 5L, 3L, 5L, 4L, 3L, 1L, 7L, 9L, 6L, 6L, 6L, 3L, 3L,
3L, 2L, 5L, 3L, 3L, 2L, 4L, 9L, 5L, 4L, 3L, 6L, 5L, 6L, 3L,
8L, 6L, 2L, 2L, 9L, 1L, 2L, 9L, 3L, 2L, 1L, 1L, 3L, 4L, 8L,
3L, 4L, 6L, 2L, 3L, 3L, 10L, 6L, 2L, 3L, 3L, 7L, 2L, 6L,
9L, 5L, 3L, 2L, 2L, 2L, 3L, 4L, 4L, 3L, 3L, 1L, 4L, 5L, 1L,
4L, 3L, 1L, 1L, 5L, 3L, 5L, 3L, 1L, 7L, 1L, 6L, 2L, 2L, 1L,
4L, 4L, 2L, 2L, 7L, 3L, 7L, 4L, 2L, 2L, 3L, 2L, 7L, 5L, 2L,
5L, 6L, 3L, 1L, 9L, 7L, 4L, 3L, 1L, 5L, 1L, 1L, 3L, 1L, 3L,
10L, 3L, 3L, 8L, 3L, 5L, 3L, 5L, 3L, 4L, 5L, 3L, 9L, 4L,
2L, 3L, 8L, 5L, 8L, 2L, 9L, 5L, 4L, 4L, 6L, 6L, 2L, 1L, 6L,
7L, 6L, 7L, 2L, 8L, 7L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 5L,
2L, 2L, 6L, 4L, 3L, 3L, 4L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 2L,
5L, 1L, 1L, 4L, 1L, 6L, 9L, 4L, 2L, 1L, 2L, 2L, 9L, 1L, 9L,
2L, 2L, 1L, 5L, 4L, 4L, 4L, 9L, 8L, 5L, 1L, 5L, 10L, 6L,
8L, 2L, 3L, 5L, 7L, 1L, 4L, 3L, 4L, 3L, 3L, 7L, 1L, 6L, 5L,
3L, 3L, 1L, 7L, 2L, 2L, 5L, 1L, 1L, 5L, 3L, 6L, 6L, 4L, 3L,
2L, 4L, 7L, 4L, 3L, 8L, 2L, 1L, 4L, 2L, 5L, 3L, 1L, 2L, 2L,
4L, 9L, 1L, 4L, 4L, 6L, 5L, 5L, 5L, 4L, 8L, 3L, 6L, 1L, 6L,
6L, 7L, 3L), categ_E = c(4L, 4L, 1L, 1L, 9L, 3L, 1L, 10L,
1L, 8L, 8L, 1L, 1L, 1L, 9L, 1L, 10L, 1L, 5L, 2L, 5L, 6L,
2L, 1L, 7L, 9L, 6L, 5L, 1L, 3L, 1L, 4L, 6L, 6L, 7L, 7L, 1L,
6L, 8L, 1L, 9L, 6L, 9L, 1L, 4L, 5L, 4L, 6L, 8L, 4L, 5L, 5L,
8L, 8L, 1L, 3L, 9L, 7L, 7L, 7L, 6L, 6L, 7L, 5L, 8L, 1L, 1L,
7L, 1L, 4L, 9L, 6L, 9L, 1L, 6L, 2L, 6L, 6L, 8L, 1L, 1L, 7L,
5L, 8L, 5L, 4L, 3L, 8L, 5L, 8L, 3L, 7L, 9L, 4L, 5L, 3L, 7L,
7L, 8L, 1L, 1L, 1L, 2L, 1L, 4L, 6L, 6L, 3L, 1L, 5L, 6L, 7L,
8L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 10L, 9L, 1L, 5L, 5L,
7L, 5L, 1L, 6L, 7L, 1L, 6L, 2L, 1L, 7L, 7L, 1L, 8L, 10L,
1L, 6L, 6L, 4L, 7L, 5L, 2L, 7L, 1L, 9L, 8L, 7L, 9L, 6L, 7L,
7L, 9L, 8L, 2L, 7L, 9L, 7L, 1L, 4L, 7L, 5L, 8L, 8L, 2L, 7L,
1L, 4L, 9L, 1L, 7L, 5L, 5L, 8L, 5L, 9L, 8L, 5L, 2L, 1L, 6L,
4L, 1L, 1L, 8L, 10L, 1L, 6L, 5L, 5L, 5L, 6L, 5L, 7L, 1L,
1L, 3L, 6L, 9L, 1L, 5L, 3L, 9L, 4L, 9L, 6L, 1L, 1L, 1L, 3L,
8L, 8L, 8L, 3L, 1L, 3L, 7L, 2L, 1L, 9L, 5L, 5L, 7L, 5L, 1L,
8L, 6L, 6L, 6L, 1L, 7L, 6L, 3L, 5L, 2L, 3L, 9L, 1L, 1L, 7L,
7L, 7L, 7L, 6L, 4L, 5L, 4L, 1L, 4L, 4L, 7L, 6L, 1L, 5L, 9L,
7L, 3L, 3L, 6L, 4L, 10L, 5L, 7L, 4L, 5L, 8L, 6L, 6L, 6L,
1L, 6L, 7L, 5L, 1L, 2L, 4L, 8L, 4L, 3L, 8L, 1L, 5L, 10L,
2L, 3L, 1L, 9L, 1L, 5L, 6L, 4L, 9L, 3L, 6L, 4L, 2L, 4L, 4L,
5L, 5L, 3L, 8L, 7L, 7L, 8L, 8L, 4L, 7L, 7L, 7L, 6L, 5L, 8L,
6L, 7L, 3L, 9L, 5L, 1L, 3L, 1L, 7L, 1L, 1L, 7L, 8L, 8L, 9L,
2L, 6L, 10L, 5L, 9L, 6L, 5L, 7L, 7L, 2L, 4L, 6L, 4L, 5L,
8L, 1L, 10L, 3L, 7L, 8L, 9L, 3L, 6L, 2L, 7L, 3L, 5L, 1L,
5L, 1L, 7L, 8L, 2L, 1L, 5L, 2L, 4L, 8L, 5L, 8L, 2L, 7L, 6L,
6L, 4L, 1L, 6L, 6L, 1L, 2L, 1L, 6L, 1L, 3L, 4L, 7L, 8L, 10L,
7L, 3L, 7L, 6L, 1L, 7L, 2L, 1L, 10L, 5L), binary_E = c(1L,
1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 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, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L,
1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L,
1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L,
1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L,
0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 0L,
1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L,
1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L,
1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L,
0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L,
1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L,
0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L,
1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L,
1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L,
1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L), percentA = c(5.2, 5.28,
7.71, 3.78, 0, 4.16, 0, 6.42, 2.56, 2.69, 2.15, 3.95, 0,
2.06, 0, 5.45, 7.2, 1.51, 3.57, 12.74, 3.51, 5.06, 8.44,
8.43, 5.59, 6.85, 7.37, 7.37, 1.51, 5.23, 4.69, 1.91, 7.34,
5.32, 7.62, 5.06, 6.48, 7.38, 28.16, 3.66, 8.37, 4.01, 10.6,
6.46, 8.63, 3.76, 6.09, 8.03, 3.78, 7.44, 3.67, 10.24, 7.4,
3, 0, 8.88, 15.17, 6.92, 0, 4.24, 4.2, 10.17, 14.73, 9.22,
6.27, 4.11, 8.43, 8.56, 4.05, 0, 0, 3.5, 4.39, 13.19, 13.01,
6.9, 4.79, 7.23, 14.28, 15.67, 8.76, 3.86, 4.58, 6.51, 18.31,
3.92, 7.79, 6.94, 6.94, 8.76, 4.83, 10.54, 0, 0, 5.62, 0,
1.06, 4.13, 6.52, 4.17, 6.89, 4.25, 3.51, 6.4, 4.83, 5.01,
4.69, 3.54, 4.67, 9.5, 1.75, 0, 16.63, 2.6, 1.43, 3.59, 10.26,
6.15, 0, 5.81, 0, 7.28, 0, 8.91, 7.33, 4.89, 2.7, 13.14,
92.59, 11.03, 7.75, 5.49, 3.31, 7.82, 5.57, 0, 10.03, 7.35,
6.36, 8.94, 7.35, 0, 6.85, 3.85, 4.19, 4.48, 7.43, 10.47,
5.11, 9.42, 3.42, 4.74, 0.89, 6.12, 6.46, 11.31, 0, 4.19,
4.76, 5.86, 8.23, 17.76, 7.69, 18.39, 10.72, 12.73, 7.08,
9.44, 4.14, 26.5, 3.72, 9.47, 12.66, 29.22, 7.64, 3.9, 13.01,
3.53, 10.75, 8.26, 4.8, 14.54, 3.7, 14.15, 8.47, 7.33, 3.78,
5.78, 8.88, 0, 19.19, 16.91, 6.57, 6.36, 7.6, 6.52, 5.55,
9.05, 7.3, 2.81, 5.09, 5.25, 8.22, 7.31, 8.73, 5.27, 4.85,
2.31, 15.63, 0, 8.22, 9.19, 7.05, 6.47, 5.53, 0, 5.03, 0,
0, 6.77, 12.47, 5.71, 0, 6.55, 11.59, 25.13, 1.8, 7.35, 4.66,
7.56, 4.08, 2.98, 6.7, 8.91, 3.3, 5.85, 5.74, 8.73, 13.9,
9.97, 4.27, 10.56, 35.68, 6.56, 5.85, 35.95, 6.24, 1.95,
6.63, 4.23, 4.1, 4.03, 24.37, 4.68, 12.24, 2.41, 0, 3.47,
11.93, 4.15, 6.6, 8.17, 4.65, 4.09, 0, 1.44, 10.95, 0, 7.94,
4.38, 4.18, 1.93, 5.67, 9.42, 0, 13.1, 2.72, 7.25, 2, 5.27,
3.35, 2.12, 9.26, 7.11, 35.44, 2.06, 4.77, 4.24, 12.55, 8.71,
4.38, 5.42, 3.14, 2.7, 10.94, 0, 6.8, 2.47, 14.33, 4.6, 14.06,
5.82, 0, 14.25, 10.57, 9.29, 10.65, 5.58, 4.85, 2.33, 4.03,
6.41, 24.69, 7.61, 2.82, 6.78, 0, 0, 1.51, 0, 6.9, 3.97,
7.7, 0, 3.17, 8.43, 5.52, 5.68, 10.05, 15.86, 1.73, 4.1,
1.38, 5.35, 6.07, 5.68, 10.33, 10.42, 9.44, 0, 0, 7.21, 4.78,
0, 6.26, 7.39, 18.2, 3.44, 5.22, 3.41, 4.73, 10.21, 16.28,
8.51, 12.14, 4.94, 14.65, 3.23, 0, 4.2, 3.13, 4, 10.47, 2.81,
8.22, 0, 3.44, 0, 13.32, 6.93, 0, 3.3, 9.18, 6.71, 8.35,
0, 8.32, 5.43, 12.38, 5.15, 5.74, 0, 2.75, 10.21, 1.58, 8.86,
13.95, 7.1, 11.47, 11.72, 7.09, 0, 2.92, 7.55, 6.58, 14.32,
21.71, 4.01, 1.81, 2.39), percentB = c(43.97, 59.38, 43.37,
46.67, 13.04, 65.31, 43.82, 28.73, 32.44, 25.18, 34.82, 38.13,
19.15, 43.89, 100, 29.47, 43.16, 29.19, 37.42, 29.36, 47.94,
42.58, 18.11, 49.25, 40.12, 36.99, 39.65, 37.06, 48.05, 33.33,
43.79, 41.32, 41.1, 33.91, 25.45, 41.82, 45.68, 40.54, 37.92,
41.08, 34.3, 32.72, 46.55, 48.28, 39.66, 45.79, 42.67, 35.38,
48, 35.3, 45.96, 44.21, 36.27, 43.53, 45, 39.82, 22.19, 29.79,
70, 51.2, 53.52, 36.62, 22.75, 16.53, 46.78, 40.96, 53.79,
19.44, 28.05, 61.04, 42.86, 57.29, 32.62, 42.19, 34.15, 36.31,
41.66, 46.28, 33.92, 31.95, 40, 20.63, 45.59, 29.08, 24.84,
45.41, 39.32, 47.63, 33.85, 24.15, 43.13, 21.05, 12.5, 12.5,
42.67, 0, 63.06, 30.23, 46.15, 39.64, 41.34, 38.05, 38.82,
43.42, 52.62, 34.43, 54.7, 26.82, 36.86, 52.41, 43.85, 60.71,
33.33, 48.48, 47.56, 44.35, 39.47, 50, 57.32, 35.78, 41.67,
36.9, 5.26, 36.52, 44.6, 35.29, 47.31, 38.29, 0, 45.89, 45.83,
40.8, 47.42, 46.03, 31.22, 14.29, 36.08, 31.9, 23.04, 24.71,
42.52, 50, 29.71, 34.34, 43.46, 46.86, 51.31, 45.45, 42.34,
40, 27.54, 38.1, 42.5, 36.51, 35.26, 30.52, 54.05, 56, 28.3,
44.44, 33.03, 42.6, 36.01, 41.35, 34.07, 23.91, 41.32, 43.68,
44.44, 36.47, 46.33, 26.25, 23.33, 43.81, 52.12, 53.94, 8.45,
34.56, 43.2, 29.19, 38.1, 36.3, 53.57, 21.32, 17.11, 38.03,
42.95, 27.22, 41.67, 28.57, 47.37, 30.35, 42.65, 40, 45.25,
41.83, 38.55, 34.99, 30.34, 62.98, 32.14, 42.11, 45.65, 45.14,
40.41, 46.61, 15.47, 48.48, 37.01, 82.86, 42.96, 28.23, 34.36,
44.44, 33.22, 77.78, 41.79, 22.86, 42.59, 27.18, 46.81, 50.94,
38.75, 33.11, 25.73, 0, 52.11, 36.36, 34.89, 35.81, 37.84,
35.93, 43.75, 38.28, 20.68, 33.33, 34.38, 30.27, 8.33, 34.44,
15.16, 28.36, 25, 41.12, 39.76, 33.33, 31.65, 35.32, 39.81,
37.53, 52.73, 29.24, 26.12, 43.21, 29.29, 51.08, 32.14, 28.02,
29.41, 53.47, 27.27, 47.06, 20, 54.42, 12.5, 49.18, 36.85,
23.23, 38.55, 52.93, 39.24, 34.21, 26.73, 40.76, 26.09, 28.57,
50.67, 34.54, 39.07, 30.13, 39.77, 48.85, 46.15, 33.47, 56.52,
37.93, 32.35, 36.04, 45.29, 51.4, 40.51, 37.56, 42.61, 32.9,
41.07, 22.86, 37.86, 35.21, 37.35, 53.69, 31.5, 49.79, 29.55,
47.69, 29.09, 48.27, 41.15, 32.54, 31.95, 37.7, 53.6, 33.33,
20.41, 16.44, 45.65, 41.06, 33.33, 35.98, 36.77, 64.52, 39.26,
32.84, 40.96, 36.23, 44.71, 27.13, 39.65, 30.51, 43.96, 36.36,
35.71, 45.12, 40.27, 36.09, 40.41, 32.83, 28.19, 30.13, 21.51,
15, 28.57, 52.14, 22.66, 40.28, 35.24, 49.43, 25.54, 36.11,
41.62, 38.57, 32.06, 42.7, 29.61, 35.95, 32.78, 23.79, 31.71,
41.92, 2.94, 52.45, 47.82, 44.73, 45.32, 40.85, 51.83, 30.77,
31.24, 22.22, 11.74, 43.11, 42.86, 29.84, 47.89, 27.11, 34.91,
23.4, 38.36, 38.81, 36.31, 45.63, 41.51, 45.83, 36.5, 41.28,
37.28, 28.09, 51.72, 28.96, 34.5, 25.92, 41.56, 48.15, 50.1,
63.28, 49.39, 25, 55.1, 43.3, 8.08, 18.52), percentC = c(0.4,
4.69, 9.18, 0, 0, 6.12, 1.12, 0.67, 0.73, 3.08, 13.77, 0.43,
0, 2.26, 0, 0.97, 0.71, 0.62, 0.33, 0, 8.67, 3.57, 0, 3.14,
2.69, 0, 0.39, 0.94, 2.34, 10.42, 11.85, 3.4, 0, 2.3, 1.82,
2, 1.13, 0, 20.84, 1.29, 0.56, 0.89, 0.24, 3.45, 20.11, 3.18,
0.52, 35.35, 3.45, 1.53, 0.51, 9.5, 0.19, 2.52, 0, 0.3, 0.3,
0, 0, 10.47, 7.75, 0.33, 0, 0.45, 0.11, 1.2, 1.23, 8.33,
0, 23.38, 0, 0.75, 1.55, 1.56, 0.41, 0.83, 1.11, 2.66, 0,
0, 4.8, 0, 7.7, 0.49, 0.62, 4.03, 0.2, 0.38, 1.56, 0.52,
3.57, 0, 0, 0, 0, 0, 0.9, 0, 2.2, 2.16, 0.62, 1.33, 0.36,
1.84, 4.33, 0.55, 16.87, 0, 2.4, 9.65, 0, 0, 0.69, 6.06,
0.81, 1.66, 0, 2, 2.44, 0.83, 12.5, 1.47, 0, 0.34, 1.88,
0.59, 2.34, 1.46, 0, 1.43, 2.08, 1.35, 4.57, 3.17, 0.37,
0, 0.63, 0.31, 0.81, 0, 0.36, 0, 2.96, 0.54, 0.64, 4.02,
1.39, 2.02, 4.5, 0, 1.45, 1.07, 1.25, 0.74, 0.25, 2.28, 2.7,
4, 0.94, 0, 1.59, 0.59, 15.46, 6.02, 6.31, 0, 0, 0.99, 0.65,
2.35, 10.45, 1.67, 0, 0, 6.26, 1.52, 0.7, 0, 2.34, 1.16,
3.17, 2.42, 9.52, 0, 3.95, 1.43, 11.74, 0, 0, 0, 5.26, 13.33,
2.05, 2.29, 0.23, 0.58, 7.49, 5.64, 0.29, 0.88, 0.29, 0,
3.26, 25.4, 15.17, 1.73, 0.55, 0, 2.01, 0, 0.31, 1.88, 0.61,
2.96, 0.68, 0, 0.71, 0, 0, 4.01, 0, 4, 2.5, 0, 3.4, 12.5,
1.1, 4.24, 3.88, 2.43, 10.81, 0.85, 0, 4.31, 2.92, 0, 3.91,
0.43, 0, 0.75, 0.26, 0.84, 0, 0, 8.12, 0, 0.4, 1.37, 3.74,
0.87, 2.42, 0.26, 0, 1.54, 0.4, 4.98, 0, 0.6, 0, 1.81, 0,
0, 0, 1.13, 0, 0, 0.65, 2.02, 6.75, 9.14, 2.53, 25.54, 0.04,
7.82, 0, 7.14, 5.33, 3.65, 5.3, 1.95, 0.8, 0.71, 0, 20.15,
0, 0, 1.24, 3.9, 9.34, 4.2, 0, 9.58, 1.32, 0.92, 0.77, 0,
0.73, 1.41, 8.43, 2.29, 5.61, 6.83, 2.27, 5.09, 0, 5.31,
0.52, 1.83, 0.54, 2.19, 5.63, 0, 0, 35.62, 0, 4.35, 0, 0.53,
0.32, 0, 1.7, 1.49, 0.1, 0, 0.33, 0.33, 3.05, 5.08, 0, 0,
2.38, 1.83, 10.74, 1.38, 0.38, 0.33, 0.34, 3.33, 5.4, 11.43,
57.14, 3.45, 0, 16.67, 23.5, 0.76, 1.45, 0, 1.95, 0, 2.52,
4.55, 0, 2.87, 0.43, 1.17, 1.69, 0.8, 0, 1.4, 0.27, 0.27,
6.24, 6.81, 2.51, 7.69, 4.35, 0, 0, 0, 0, 0.14, 3.31, 0,
3.37, 0, 0.8, 0, 0.71, 4.51, 0.17, 0, 1.83, 4.18, 1.78, 1.87,
6.9, 0.26, 11.37, 2, 1.93, 0, 3.45, 3.39, 0.56, 0, 5.51,
2.3, 0, 0), percentD = c(0.93, 0, 0.86, 0, 0, 0.27, 0, 0,
0, 0, 0, 0, 0.33, 0, 0, 0, 0.75, 0, 1, 0, 0, 0, 0, 0.29,
1, 0, 0, 1, 0, 1, 0.75, 1, 0, 0, 0, 1, 1, 0.43, 0, 0.78,
0, 0.71, 0, 0, 0, 0, 0.29, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0.81, 0, 0.56, 0, 1, 0, 1, 0, 0, 0, 0.8, 0, 0.43, 0,
0, 0, 1, 1, 0, 0, 0.8, 0.67, 1, 0, 0, 0, 0.82, 0, 0, 0, 0.07,
1, 0, 0, 0, 1, 0, 0, 0.25, 0, 0, 0, 0.25, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0.75, 0, 0.98, 0, 0.9, 0, 0.67, 0,
0, 0.95, 0.67, 1, 0, 0, 0.33, 0, 0, 0.78, 0, 0.96, 0, 0,
0, 0, 0, 0, 0.78, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0.25,
0, 0, 0, 1, 1, 0, 0, 0.07, 0, 0.3, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0.08, 0, 0, 0, 0,
0, 0, 0, 0.88, 0, 0.58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0.99, 0, 0, 0.75, 0, 0, 0.22, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 0, 0, 0.86, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 0, 0.79, 1, 0, 1, 0, 0.95, 0, 0, 0, 1, 0, 1, 0,
0, 0, 0.13, 0, 0.93, 0, 0, 0, 0, 0.05, 1, 0, 0.93, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0.78,
0.25, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.67, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.99, 0, 0, 0, 0, 1, 0, 0, 0.8, 0,
0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0.24, 0, 0, 1, 0, 0, 0.45,
0, 0, 1, 1, 0, 0.44, 0, 0, 0, 0, 0, 0, 0.4, 0, 0, 0, 1, 0,
0.57, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0.49, 0, 0, 1, 0, 0, 0,
0.92, 0, 0.73, 0, 0, 0, 0, 0.9, 0, 0, 0, 1, 0, 1, 0.96, 0,
0, 0, 0, 0)), .Names = c("CustomerID", "binary_depvar", "binary_A",
"binary_B", "binary_C", "categ_A", "categ_B", "binary_D", "categ_C",
"categ_D", "categ_E", "binary_E", "percentA", "percentB", "percentC",
"percentD"), class = "data.frame", row.names = c(NA, -400L))

R: Recoding multiple dummy variables into a single variable and replacing the corresponding dummy value with the variable name

I have a dataset with 14 mutually exclusive categories of call type all coded as dummy variables. Here is a small sample:
dput(df)
structure(list(MON1_12 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), WEEK1_53 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), AGENT_ID = structure(c(3L,
4L, 7L, 8L, 1L, 6L, 5L, 9L, 2L, 10L), .Label = c("A129", "A360",
"A407", "B891", "D197", "L145", "L722", "O518", "T443", "W764"
), class = "factor"), CallsHandled = c(1L, 4L, 2L, 14L, 1L, 2L,
5L, 1L, 1L, 3L), CONTENT = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L), CLAIMS = c(1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L),
CREDIT_CARD = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L),
DEDUCT_BILL = c(0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L),
HCREFORM = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L)), .Names = c("MON1_12",
"WEEK1_53", "AGENT_ID", "CallsHandled", "CONTENT", "CLAIMS",
"CREDIT_CARD", "DEDUCT_BILL", "HCREFORM"), class = "data.frame", row.names = c(NA,
-10L))
I want to combine each of the dummy variables into a single new variable called "QUEUE" that replaces the value of "1" with the name of the dummy variable its corresponding dummy variable. Here is an example of what this would look like:
dput(df2)
structure(list(MON1_12 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), WEEK1_53 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), AGENT_ID = structure(c(3L,
4L, 7L, 8L, 1L, 6L, 5L, 9L, 2L, 10L), .Label = c("A129", "A360",
"A407", "B891", "D197", "L145", "L722", "O518", "T443", "W764"
), class = "factor"), CallsHandled = c(1L, 4L, 2L, 14L, 1L, 2L,
5L, 1L, 1L, 3L), QUEUE = structure(c(1L, 4L, 2L, 4L, 1L, 3L,
3L, 5L, 5L, 4L), .Label = c("CLAIMS", "CONTENT", "CREDIT_CARD",
"DEDUCT_BILL", "HCREFORM"), class = "factor")), .Names = c("MON1_12",
"WEEK1_53", "AGENT_ID", "CallsHandled", "QUEUE"), class = "data.frame", row.names = c(NA,
-10L))
Edit in response to having question marked down: This is what I had tried this afternoon on recommendation with a slightly different sample dataframe:
df$Queue <- as.factor(df$CONTENT + df$CLAIMS*2 + df$CREDIT_CARD*3 + df$DEDUCT_BILL*4 + df$HCREFORM*5)
levels(df$Queue) <- c("CONTENT", "CLAIMS", "CREDIT_CARD","DEDUCT_BILL","HCREFORM")
View(df)
But I received a column of NA's in the Queue column. So, I recreated another sample dataset here. This dataframe is adequately representative of what I'll receive in reality, except I'll have about 40 variables and 2 million rows. When I run what I tried above on "df" above I get the following incorrect result:
dput(df)
structure(list(MON1_12 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), WEEK1_53 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), AGENT_ID = structure(c(3L,
4L, 7L, 8L, 1L, 6L, 5L, 9L, 2L, 10L), .Label = c("A129", "A360",
"A407", "B891", "D197", "L145", "L722", "O518", "T443", "W764"
), class = "factor"), CallsHandled = c(1L, 4L, 2L, 14L, 1L, 2L,
5L, 1L, 1L, 3L), CONTENT = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L), CLAIMS = c(1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L),
CREDIT_CARD = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L),
DEDUCT_BILL = c(0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L),
HCREFORM = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Queue = structure(c(2L,
1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("CONTENT",
"CLAIMS", "CREDIT_CARD", "DEDUCT_BILL", "HCREFORM"), class = "factor")), .Names = c("MON1_12",
"WEEK1_53", "AGENT_ID", "CallsHandled", "CONTENT", "CLAIMS",
"CREDIT_CARD", "DEDUCT_BILL", "HCREFORM", "Queue"), row.names = c(NA,
-10L), class = "data.frame")
I also tried:
df3 <- cbind(df[1:4], QUEUE = apply(df[5:9], 1, function(N) names(N)[as.logical(N)]))
but received the following error: "Error in data.frame("CLAIMS", character(0), character(0), "DEDUCT_BILL", :
arguments imply differing number of rows: 1, 0:

You could use max.col to get the column index that have a value of '1' in each row for columns 5 to 9. (The 'df' example is not correct as most of the rows were all 0s. The corrected one is below).
df$QUEUE <- names(df)[-c(1:4)][max.col(df[-c(1:4)])]
Or you can do
df$QUEUE <- names(df)[-(1:4)][(as.matrix(df[-(1:4)]) %*%
seq_along(df[-(1:4)]))[,1]]
Update
Based on the edit dataset 'df', some rows are all '0's for the columns 5:9, and in the expected result, it is showed that 'QUEUE' as 'CONTENT'. In that case, we can first modify the 'CONTENT' column to change the values where rows are all 0's and then apply either of the code above
df$CONTENT[!rowSums(df[5:9])] <- 1
df$QUEUE1 <- names(df)[5:9][max.col(df[5:9])]
df$QUEUE1
#[1] "CLAIMS" "CONTENT" "CONTENT" "DEDUCT_BILL" "CONTENT"
#[6] "CONTENT" "CONTENT" "CONTENT" "CONTENT" "CONTENT"
data
df <- structure(list(MON1_12 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), WEEK1_53 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L),
AGENT_ID = structure(c(3L,
4L, 7L, 8L, 1L, 6L, 5L, 9L, 2L, 10L), .Label = c("A129", "A360",
"A407", "B891", "D197", "L145", "L722", "O518", "T443", "W764"
), class = "factor"), CallsHandled = c(1L, 4L, 2L, 14L, 1L, 2L,
5L, 1L, 1L, 3L), CONTENT = c(0, 0, 1, 0, 0, 0, 0, 0, 0, 0), CLAIMS = c(1,
0, 0, 0, 1, 0, 0, 0, 0, 0), CREDIT_CARD = c(0, 0, 0, 0, 0, 1,
1, 0, 0, 0), DEDUCT_BILL = c(0, 1, 0, 1, 0, 0, 0, 0, 0, 1),
HCREFORM = c(0,
0, 0, 0, 0, 0, 0, 1, 1, 0)), .Names = c("MON1_12", "WEEK1_53",
"AGENT_ID", "CallsHandled", "CONTENT", "CLAIMS", "CREDIT_CARD",
"DEDUCT_BILL", "HCREFORM"), row.names = c(NA, -10L), class = "data.frame")

This should produce the desired result:
df2 <- cbind(df[1:4], QUEUE = apply(df[5:9], 1, function(N) names(N)[as.logical(N)]))
provided that only one and exactly one of the dummy variables is 1 in any of the rows (which is not true in your original sample of df).
Explanation: df[1:4] selects the columns one through four to be preserved in the output. It is then column bound to QUEUE using cbind function. QUEUE is obtained by iterating through the dummy variables (columns five through nine), row-wise over the data set df and selecting the column-name that contains the value one.

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