如果我的第一个闪亮的应用程序，我一直在尝试调试几天 - 我正在尝试创建一个闪亮的应用程序，使用折线图显示州的死亡率与全国平均值。每当我运行应用程序时，UI都会闪烁，页面会变为空白。我收到2条错误消息。任何建议将非常感谢
library(shiny)
url < "https://raw.githubusercontent.com/indianspice/IS608/master/Assign3/cleaned-cdc-mortality-1999-2010-2.csv"
mdata <- read.csv(url, stringsAsFactors = FALSE)
#Extract unique states and disease
states <- unique(mdata$State)
causes <- unique(mdata$ICD.Chapter)
#Define UI for application that draws a histogram
shinyUI(fluidPage(
#Application title
titlePanel("Mortality Improvement by State"),
#Dropdown state and cause
sidebarLayout(
sidebarPanel(selectInput("state_name", "State:", choices = states)
, radioButtons("cause_name", "Select disease - cause of Death:",
c("Infectious & parasitic" = "Certain infectious and parasitic diseases" ,
"Neoplasms" = "Neoplasms",
"Blood & blood-forming & immune mechanism disorders" = "Diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism",
"Endocrine, nutritional & metabolic" = "Endocrine, nutritional and metabolic diseases",
"Mental & behavioural disorders" = "Mental and behavioural disorders",
"Nervous system" = "Diseases of the nervous system",
"Ear & mastoid process" = "Diseases of the ear and mastoid process",
"Circulatory system" = "Diseases of the circulatory system",
"Respiratory system" = "Diseases of the respiratory system",
"Digestive system" = "Diseases of the digestive system",
"Skin & subcutaneous tissue" = "Diseases of the skin and subcutaneous tissue",
"Musculoskeletal system & connective tissue" = "Diseases of the musculoskeletal system and connective tissue",
"Genitourinary system" = "Diseases of the genitourinary system",
"Pregnancy, childbirth & the puerperium" = "Pregnancy, childbirth and the puerperium",
"Conditions originating in the perinatal period" = "Certain conditions originating in the perinatal period",
"Congenital malformations, deformations & chromosomal abnormalities" = "Congenital malformations, deformations and chromosomal abnormalities",
"Symptoms, signs & abnormal clinical & laboratory findings, not classified" = "Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified",
"External causes of morbidity & mortality" = "External causes of morbidity and mortality"))
),
# Show a plot of the generated distribution
mainPanel(
plotOutput("causev")
)
)
))
library(shiny)
library(ggplot2)
library(dplyr)
#Number per 50000
crude_rate <- 500000
url < "https://raw.githubusercontent.com/indianspice/IS608/master/Assign3/cleaned-cdc-mortality-1999-2010-2.csv"
mdata <- read.csv(url, stringsAsFactors = FALSE)
#Extract unique states and disease
#states <- unique(mdata$State) - remove
#causes <- unique(mdata$ICD.Chapter) remove
nat_cause <- aggregate(cbind(Deaths, Population) ~ ICD.Chapter + Year, mdata, FUN=sum)
#Add national average column
nat_cause$NatAvge <- round(nat_cause$Deaths / nat_cause$Population * crude_rate, 4)
#Define server logic required to visulaize the data
shinyServer(function(input, output, session) {
sub_mdata <- reactive({
paste("Cause of Death: ", input$causes)
output$caption <- renderText({
print(input$causes)
sub_mdata()
})
sub_mdata <- reactive({
subset(mdata, ICD.Chapter == input$causes & State == input$states,
select=c(Year, Crude.Rate, ICD.Chapter))
})
new_df <- reactive({
df <- sub_mdata()
colnames(df) <- c("Year", "Rate_State", "ICD.Chapter")
df <- merge(df, nat_cause, by=c("Year", "ICD.Chapter"))
df = dplyr::mutate(df, state_diff = lag(Rate_State) - Rate_State)
df = dplyr::mutate(df, nat_diff = lag(NatAvge) - NatAvge)
df = dplyr::mutate(df, st_nat = (state_diff - nat_diff))
return(df)
})
})
output$causev <- renderPlot({
p <- ggplot(data=new_df(), aes(x="Year", y="st_nat")) +
geom_line()
print(p)
})
})
Warning: Error in get: object 'FUN' of mode 'function' was not found
Stack trace (innermost first):
46: get
45: match.fun
44: aggregate.data.frame
43: aggregate.formula
42: aggregate
1: runApp
Error in get(as.character(FUN), mode = "function", envir = envir) :
object 'FUN' of mode 'function' was not found
ICD.Chapter State Year Deaths Population Crude.Rate
1 Certain infectious and parasitic diseases AL 1999 1092 4430141 24.6
2 Certain infectious and parasitic diseases AL 2000 1188 4447100 26.7
3 Certain infectious and parasitic diseases AL 2001 1211 4467634 27.1
4 Certain infectious and parasitic diseases AL 2002 1215 4480089 27.1
5 Certain infectious and parasitic diseases AL 2003 1350 4503491 30.0
6 Certain infectious and parasitic diseases AL 2004 1251 4530729 27.6
答案 0 :(得分：0)
以下代码是否适合您？我基本上只是拿了你给出的例子并删除了一些小错误。永远不会让Warning: Error in get: ...消息变得艰难。
注意：确保在干净的会话中运行此代码。您的错误原因可能是名称冲突。请参阅here。
library(shiny)
library(ggplot2)
library(dplyr)
url <- paste0("https://raw.githubusercontent.com/indianspice/IS608/",
"master/Assign3/cleaned-cdc-mortality-1999-2010-2.csv")
mdata <- read.csv(url, stringsAsFactors = FALSE)
crude_rate <- 500000
nat_cause <- aggregate(cbind(Deaths, Population) ~ ICD.Chapter + Year, mdata, FUN = sum)
nat_cause$NatAvge <- round(nat_cause$Deaths / nat_cause$Population * crude_rate, 4)
server <- function(input, output, session) {
sub_mdata <- reactive({subset(
mdata, ICD.Chapter == input$causes & State == input$state_name,
select = c(Year, Crude.Rate, ICD.Chapter)
)})
new_df <- reactive({
df <- sub_mdata()
colnames(df) <- c("Year", "Rate_State", "ICD.Chapter")
merge(df, nat_cause, by=c("Year", "ICD.Chapter")) %>%
dplyr::mutate(state_diff = lag(Rate_State) - Rate_State) %>%
dplyr::mutate(nat_diff = lag(NatAvge) - NatAvge) %>%
dplyr::mutate(st_nat = (state_diff - nat_diff))
})
output$causev <- renderPlot({
ggplot(data=new_df(), aes(x=Year, y=st_nat)) + geom_line()
})
}
ui <- fluidPage(
#Application title
titlePanel("Mortality Improvement by State"),
#Dropdown state and cause
sidebarLayout(
sidebarPanel(
selectInput("state_name", "State:", choices = unique(mdata$State)),
radioButtons("causes", "Select disease - cause of Death:",
unique(mdata$ICD.Chapter))
),
# Show a plot of the generated distribution
mainPanel(plotOutput("causev"))
)
)
shinyApp(ui, server)