r - 指定闪亮的表格行数
问题描述
当我执行闪亮时，我想显示表的前 5 行。可执行代码如下。您可以看到默认情况下它显示前 8 行。但是，如果我改变“集群数量”，它最多可以达到 34 个集群。因此，表会非常大。我想将其配置为最多显示前 5 行。如果您有更多，因此您有一个“按钮”可以转到接下来的五行。
有人可以帮我解决这个问题吗？？
非常感谢各位朋友。
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){
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<-print(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$tabela <- renderUI({
data_table_1 <- req(Modelclustering())[[1]]
x <- kable(data_table_1[order(data_table_1$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)
非常感谢各位朋友。
解决方案
您可以尝试使用 dataTable 而不是使用 kable 作为表输出吗？
如果这样做，您可以限制显示的行数，这意味着用户必须单击下一页才能查看接下来的 5 个集群。
具体来说， dataTable 中的 pageLength 选项就是您要查找的内容：
library(shiny)
library(ggplot2)
library(rdist)
library(geosphere)
library(kableExtra)
library(readxl)
library(tidyverse)
##Need to install this package if you don't have it
library(DT)
#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){
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<-print(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$hr(),
helpText(h3("Are you satisfied with the solution?")),
helpText(h4("(1) Yes")),
helpText(h4("(2) No")),
helpText(h4("(a) Change the number of clusters")),
sliderInput("Slider", h3("Number of clusters"),
min = 2, max = 34, value = 8),
helpText(h4("(b) Change the filter options"))
),
mainPanel(
##uiOutput("tabela"),
DTOutput("tabela"),
plotOutput("ScatterPlot"),
plotOutput("LetCoverage"),
)))
server <- function(input, output) {
f1<-renderText({input$filter1})
f2<-renderText({input$filter2})
Modelclustering<-reactive(function.clustering(df,input$Slider,1,1))
# output$tabela <- renderUI({
# data_table_1 <- req(Modelclustering())[[1]]
# x <- kable(data_table_1[order(data_table_1$cluster), c(1, 4, 2, 3)], align = "c", row.names = FALSE)
# x <- kable_styling(kable_input = x, full_width = FALSE)
# HTML(x)
output$tabela <- renderDataTable({
data_table_1 <- req(Modelclustering())[[1]]
x <- datatable(data_table_1[order(data_table_1$cluster), c(1, 4, 2, 3)],
options = list(
paging =TRUE,
pageLength = 5
)
)
return(x)
})
output$ScatterPlot <- renderPlot({
Modelclustering()[[2]]
})
output$LetCoverage <- renderPlot({
function.LetControl(Modelclustering()[[3]])
})
}
# Run the application
shinyApp(ui = ui, server = server)
我让你的应用程序现在可以工作了。这应该可以，无论您在输入中设置多少，它都只会显示 5 个集群。如果要更改此设置，只需添加一个新的输入滑块或控件小部件。
我注释掉了你的代码并添加了我的代码。我在 UI 和服务器端进行了更改。
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