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怎样用R-Shiny打造在线App

发表于：2026-11-02 作者：千家信息网编辑

千家信息网最后更新 2026年11月02日，怎样用R-Shiny打造在线App，相信很多没有经验的人对此束手无策，为此本文总结了问题出现的原因和解决方法，通过这篇文章希望你能解决这个问题。今天做一个小小的案例，算是shiny动态可视化的小开端…

千家信息网最后更新 2026年11月02日怎样用R-Shiny打造在线App

怎样用R-Shiny打造在线App，相信很多没有经验的人对此束手无策，为此本文总结了问题出现的原因和解决方法，通过这篇文章希望你能解决这个问题。

今天做一个小小的案例，算是shiny动态可视化的小开端……

这个案例是之前发过的中国人口结构动态金字塔图，这个图还是蛮不错，数据取自UN的官网，非常有现实意义的人口性别结构数据。

library(ggplot2)
library(animation)
library(dplyr)
library(tidyr)
library(xlsx)
library(ggthemes)
library(shiny)
library(shinythemes)

做简单的数据清洗工作，为shiny提供可用的数据源：

setwd("D:/R/File")
windowsFonts(myfont=windowsFont("微软雅黑"))
female<-read.xlsx("Population.xlsx",sheetName="Female",header=T,encoding='UTF-8',check.names = FALSE)
male<-read.xlsx("Population.xlsx",sheetName="Male",header=T,encoding='UTF-8',check.names = FALSE)
female<-female%>%gather(Year,Poputation,-1)
male<-male%>%gather(Year,Poputation,-1)
female$Poputation<-female$Poputation*-1
male$sex<-"male";female$sex<-"female"
China_Population<-rbind(male,female)%>%mutate(abs_pop=abs(Poputation))
China_Population$agegroup<-factor(China_Population$agegroup,
levels=c("0-4","5-9","10-14","15-19","20-24","25-29","30-34","35-39","40-44","45-49","50-54","55-59","60-64","65-69","70-74","75-79","80+") ,order=T)
China_Population_dd<-filter(China_Population,Year==1995)

定制shinyapp的ui：

ui <-shinyUI(fluidPage(
theme=shinytheme("cerulean"),
titlePanel("Population Structure Data"),
sidebarLayout(
sidebarPanel(
selectInput("var1", "x-axis",c("agegroup"="agegroup","Poputation"="Poputation","sex"="sex"),selected="agegroup"),
selectInput("var2", "y-axis",c("agegroup"="agegroup","Poputation"="Poputation","sex"="sex"),selected="Poputation"),
selectInput("var3", "Gender",c("agegroup"="agegroup","Poputation"="Poputation","sex"="sex"),selected="sex"),
selectInput("theme", "Choose a ShinyTheme:",choices ("cerulean","cosmo","cyborg","darkly","flatly","journal","lumen","paper",
"readable","sandstone","simplex","slate","spacelab","superhero","united","yeti")),
sliderInput("var4","Year",min=1950,max=2015,value=5,step=5)
),
mainPanel(h3('Dynamic pyramid of population structure in China'),plotOutput("distPlot"))
)
))

定制shiny的输出服务端：

server<-shinyServer(function(input,output){
output$distPlot <- renderPlot({
mydata=filter(China_Population,Year==input$var4)
argu1<-switch(input$var1,agegroup=mydata$agegroup,Poputation=mydata$Poputation,sex=mydata$sex)
argu2<-switch(input$var2,agegroup=mydata$agegroup,Poputation=mydata$Poputation,sex=mydata$sex)
argu3<-switch(input$var3,agegroup=mydata$agegroup,Poputation=mydata$Poputation,sex=mydata$sex)
ggplot(data=mydata,aes(x=argu1,y=argu2,fill=argu3))+
coord_fixed()+
coord_flip() +
geom_bar(stat="identity",width=1) +
scale_y_continuous(breaks = seq(-70000,70000,length=9),
labels = paste0(as.character(c(abs(seq(-70,70,length=9)))), "m"),
limits = c(-75000,75000)) +
theme_economist(base_size=14)+
scale_fill_manual(values=c('#D40225','#374F8F')) +
labs(title=paste0("Population structure of China:",input$var4),
caption="Data Source:United Nations Department of Economic and Docial Affairs\nPopulation Division\nWorld Population Prospects,the 2015 Revision"
,y="Population",x="Age") +
guides(fill=guide_legend(reverse=TRUE))+
theme(
text=element_text(family="myfont"),
legend.position =c(0.8,0.9),
legend.title = element_blank(),
plot.title = element_text(size=20),
plot.caption = element_text(size=12,hjust=0)
)
})
})