Обычно мы хотим возвращать значения из функций, а не пытаться получить к ним доступ, например, [["plot_env"]][["plotGD"]]. В R, чтобы вернуть несколько элементов из функции, мы должны заключить их в list(). Для вашего приложения функция function.clustering() должна возвращать 3 элемента: данные покрытия, таблицу кластеризации и график рассеяния. Это обрабатывается:
return(list(
"Data" = data_table_1,
"Plot" = plotGD,
"Coverage" = coverage
))
Обратите внимание, что plotGD - это просто объект графика, а не печатный график. Последний печатает график в графическое окно / панель, поэтому вы должны выполнить двойную гимнастику [[]][[]].
Похоже на кабель. Верните data.frame (или data.table или matrix) и выполните стилизацию внутри функции сервера.
Наконец, чтобы использовать function.LetCoverage, мы просто передаем третий элемент, возвращаемый функцией кластеризации. Это создаст график и отобразит его.
HTH,
Рабочее приложение:
library(shiny)
library(ggplot2)
library(rdist)
library(geosphere)
library(kableExtra)
library(readxl)
library(tidyverse)
#database
df<-structure(list(Properties = c(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), Latitude = c(-23.8, -23.8, -23.9, -23.9, -23.9, -23.9, -23.9, -23.9, -23.9, -23.9, -23.9, -23.9, -23.9, -23.9,
+ -23.9, -23.9, -23.9, -23.9, -23.9,-23.9,-23.9,-23.9,-23.9,-23.9,-23.9,-23.9,-23.9,-23.9,-23.9,-23.9,-23.9,-23.9,-23.9,-23.9,-23.9), Longitude = c(-49.6, -49.6, -49.6, -49.6, -49.6, -49.6, -49.6, -49.6, -49.6, -49.6, -49.7,
+ -49.7, -49.7, -49.7, -49.7, -49.6, -49.6, -49.6, -49.6,-49.6,-49.6,-49.6,-49.6,-49.6,-49.6,-49.6,-49.6,-49.6,-49.6,-49.6,-49.6,-49.6,-49.6,-49.6,-49.6), Waste = c(526, 350, 526, 469, 285, 175, 175, 350, 350, 175, 350, 175, 175, 364,
+ 175, 175, 350, 45.5, 54.6,350,350,350,350,350,350,350,350,350,350,350,350,350,350,350,350)), class = "data.frame", row.names = c(NA, -35L))
function.clustering <- function(df, k, Filter1, Filter2) {
#df is database
#k is number of clusters
#Filter1 is equal to 1, if all properties are used
#Filter1 is equal to 2 is to limit the use of properties that have potential for waste production <L e >S
if (Filter1 == 2) {
Q1 <- matrix(quantile(df$Waste, probs = 0.25))
Q3 <- matrix(quantile(df$Waste, probs = 0.75))
L <- Q1 - 1.5 * (Q3 - Q1)
S <- Q3 + 1.5 * (Q3 - Q1)
df_1 <- subset(df, Waste > L[1])
df <- subset(df_1, Waste < S[1])
}
#cluster
coordinates <- df[c("Latitude", "Longitude")]
d <- as.dist(distm(coordinates[, 2:1]))
fit.average <- hclust(d, method = "average")
#Number of clusters
clusters <- cutree(fit.average, k)
nclusters <- matrix(table(clusters))
df$cluster <- clusters
#Localization
center_mass <- matrix(nrow = k, ncol = 2)
for (i in 1:k) {
center_mass[i, ] <-
c(
weighted.mean(
subset(df, cluster == i)$Latitude,
subset(df, cluster == i)$Waste
),
weighted.mean(
subset(df, cluster == i)$Longitude,
subset(df, cluster == i)$Waste
)
)
}
coordinates$cluster <- clusters
center_mass <- cbind(center_mass, matrix(c(1:k), ncol = 1))
#Coverage
coverage <- matrix(nrow = k, ncol = 1)
for (i in 1:k) {
aux_dist <-
distm(rbind(subset(coordinates, cluster == i), center_mass[i, ])[, 2:1])
coverage[i, ] <- max(aux_dist[nclusters[i, 1] + 1, ])
}
coverage <- cbind(coverage, matrix(c(1:k), ncol = 1))
colnames(coverage) <- c("Coverage_meters", "cluster")
#Sum of Waste from clusters
sum_waste <- matrix(nrow = k, ncol = 1)
for (i in 1:k) {
sum_waste[i, ] <- sum(subset(df, cluster == i)["Waste"])
}
sum_waste <- cbind(sum_waste, matrix(c(1:k), ncol = 1))
colnames(sum_waste) <- c("Potential_Waste_m3", "cluster")
#Output table
data_table <- Reduce(merge, list(df, coverage, sum_waste))
data_table <-
data_table[order(data_table$cluster, as.numeric(data_table$Properties)), ]
data_table_1 <-
aggregate(. ~ cluster + Coverage_meters + Potential_Waste_m3,
data_table[, c(1, 7, 6, 2)],
toString)
#Scatter Plot
suppressPackageStartupMessages(library(ggplot2))
df1 <- as.data.frame(center_mass)
colnames(df1) <- c("Latitude", "Longitude", "cluster")
g <-
ggplot(data = df, aes(
x = Longitude,
y = Latitude,
color = factor(clusters)
)) + geom_point(aes(x = Longitude, y = Latitude), size = 4)
Centro_View <-
g + geom_text(
data = df,
mapping = aes(
x = eval(Longitude),
y = eval(Latitude),
label = Waste
),
size = 3,
hjust = -0.1
) + geom_point(
data = df1,
mapping = aes(Longitude, Latitude),
color = "green",
size = 4
) + geom_text(
data = df1,
mapping = aes(x = Longitude, y = Latitude, label = 1:k),
color = "black",
size = 4
)
plotGD <-
Centro_View +
ggtitle("Scatter Plot") +
theme(plot.title = element_text(hjust = 0.5))
return(list(
"Data" = data_table_1,
"Plot" = plotGD,
"Coverage" = coverage
))
}
function.LetControl <- function(coverage) {
m <- mean(coverage[, 1])
MR <- mean(abs(diff(coverage[, 1])))
d2 <- 1.1284
LIC <- m - 3 * (MR / d2)
LSC <- m + 3 * (MR / d2)
plot(
coverage[, 1],
type = "b",
pch = 16,
ylim = c(LIC - 0.1 * LIC, LSC + 0.5 * LSC),
axes = FALSE
)
axis(1, at = 1:35)
axis(2)
box()
grid()
abline(h = MR,
lwd = 2)
abline(h = LSC, lwd = 2, col = "red")
abline(h = LIC, lwd = 2, col = "red")
}
ui <- fluidPage(
titlePanel("Clustering "),
sidebarLayout(
sidebarPanel(
helpText(h3("Generation of clustering")),
radioButtons("filter1", h3("Waste Potential"),
choices = list("Select all properties" = 1,
"Exclude properties that produce less than L and more than S" = 2),
selected = 1),
radioButtons("filter2", h3("Coverage do cluster"),
choices = list("Use default limitations" = 1,
"Do not limite coverage" = 2
),selected = 1),
tags$hr(),
helpText(h3("Are you satisfied with the solution?")),
helpText(h4("(1) Yes")),
helpText(h4("(2) No")),
helpText(h4("(a) Change the number of clusters")),
sliderInput("Slider", h3("Number of clusters"),
min = 2, max = 34, value = 8),
helpText(h4("(b) Change the filter options"))
),
mainPanel(
uiOutput("tabela"),
plotOutput("ScatterPlot"),
plotOutput("LetCoverage"),
)))
server <- function(input, output) {
f1<-renderText({input$filter1})
f2<-renderText({input$filter2})
Modelclustering<-reactive(function.clustering(df,input$Slider,1,1))
output$tabela <- renderUI({
data_table_1 <- Modelclustering()[[1]]
x <- kable(data_table_1[order(data_table_1$cluster), c(1, 4, 2, 3)], align = "c", row.names = FALSE)
x <- kable_styling(kable_input = x, full_width = FALSE)
HTML(x)
})
output$ScatterPlot <- renderPlot({
Modelclustering()[[2]]
})
output$LetCoverage <- renderPlot({
function.LetControl(Modelclustering()[[3]])
})
}
# Run the application
shinyApp(ui = ui, server = server)