Shinyapps.io error: Warning: Error in gsub: input string 31 is invalid in this locale
Shinyapps.io error: Warning: Error in gsub: input string 31 is invalid in this locale
美好的一天，过去几天我一直在使用我闪亮的应用程序，该应用程序在本地运行良好，但每当我在 shinyapps.io 上打开它时，它不会显示 plot_geo 地图。它只是说 Error: An error has occurred. Check your logs or contact app author for clarification .
这是我应用程序的 link。问题出在“世界二氧化碳排放量”地图上。
我也看到了类似的post here所以我尝试了这个解决方案，但问题仍然存在。
所以我检查了我的日志，这是我发现的：
2022-05-15T15:43:22.365650+00:00 shinyapps[6305171]: Warning: Error in gsub: input
string 31 is invalid in this locale
2022-05-15T15:43:22.365776+00:00 shinyapps[6305171]: 123: FUN
2022-05-15T15:43:22.365976+00:00 shinyapps[6305171]: 119: FUN
2022-05-15T15:43:22.365875+00:00 shinyapps[6305171]: 121: FUN
2022-05-15T15:43:22.365927+00:00 shinyapps[6305171]: 120: lapply
2022-05-15T15:43:22.366368+00:00 shinyapps[6305171]: 96: output$Map
2022-05-15T15:43:22.366025+00:00 shinyapps[6305171]: 118: lapply
2022-05-15T15:43:22.366573+00:00 shinyapps[6305171]: 7: connect$retryingStartServer
2022-05-15T15:43:22.366126+00:00 shinyapps[6305171]: 116: plotly_build.plotly
2022-05-15T15:43:22.366410+00:00 shinyapps[6305171]: 15: <Anonymous>
2022-05-15T15:43:22.366521+00:00 shinyapps[6305171]: 8: retry
2022-05-15T15:43:22.366280+00:00 shinyapps[6305171]: 110: func
2022-05-15T15:43:22.365718+00:00 shinyapps[6305171]: 124: gsub
2022-05-15T15:43:22.366327+00:00 shinyapps[6305171]: 97: renderFunc
2022-05-15T15:43:22.366177+00:00 shinyapps[6305171]: 112:
getFromNamespace("prepareWidget", "plotly")
2022-05-15T15:43:22.366470+00:00 shinyapps[6305171]: 13: fn
2022-05-15T15:43:22.366229+00:00 shinyapps[6305171]: 111: shinyRenderWidget
2022-05-15T15:43:22.366077+00:00 shinyapps[6305171]: 117: translate_linebreaks
2022-05-15T15:43:22.366622+00:00 shinyapps[6305171]: 6: eval
2022-05-15T15:43:22.365825+00:00 shinyapps[6305171]: 122: lapply
2022-05-15T15:43:22.366670+00:00 shinyapps[6305171]: 5: eval
这是我的代码：
app.R
library(tidyverse)
library(rnaturalearth)
library(shiny)
library(plyr)
library(dplyr)
library(plotly)
library(ggplot2)
library(gganimate)
library(sf)
library(uuid)
library(shinydashboard)
library(fontawesome)
source("curl.R")
world <- ne_countries(scale = "small", returnclass = "sf")
#####################
worldEmission <- world %>% select(geometry,name,iso_a3) %>%
right_join(data, by = c("iso_a3" = "ISO.3166.1.alpha.3" ))
worldEmission <- worldEmission %>% mutate(selected = TRUE)
maxperCapita <- max(worldEmission$Per.Capita)
worldEmission[is.na(worldEmission)] = 0
worldContinents <- data1 %>% select(Entity, Continent) %>%
right_join(worldEmission, by = c("Entity" = "Country"))
worldContinents$Year <- as.numeric(worldContinents$Year)
countryNames <-unique(worldEmission$Country)
#####################
ui <- dashboardPage(
dashboardHeader(),
dashboardSidebar(
sidebarMenu(
menuItem("World Co2 Emission", tabName = "Map", icon = icon('map')),
menuItem("Continent Co2 Emission", tabName = "Continent",icon = icon('chart-bar')),
menuItem("Emission Factors", tabName = "Emission", icon = icon('chart-line')) ,
menuItem("Linear Regression", tabName = "Regression", icon = icon('arrow-alt-circle-up'))
)
),
dashboardBody(
tabItems(
tabItem("Map",
h1("Map"),
box(plotlyOutput("Map"), wdith = 3)
),
tabItem("Continent",
box(plotOutput("Bargraph"), wdith = 12)
),
tabItem("Emission",
box(plotOutput("Linegraph"), wdith = 12)
),
tabItem("Regression",
box(plotOutput("Linearregression"), wdith = 12)
)
)#tabItems
)#dashboardBody
)#dashboardPage
server <- function(input, output) {
output$Map <- renderPlotly({
chorplethMap <- plot_geo(worldEmission,
locationmode = countryNames,
color = ("black"),
frame = ~Year) %>%
add_trace(worldEmission,
type = "choropleth",
locations = ~iso_a3,
z = ~Per.Capita,
zmin = 0,
color = ~Per.Capita,
colorscale = 'reds') %>%
layout(geo = list(scope = countryNames))
chorplethMap
})
output$Bargraph <- renderPlot({
worldContinents<-na.omit(worldContinents)
barchart <- ggplot(data = worldContinents,
aes(x = Continent, y = `Per.Capita`,fill = Continent)) +
stat_summary(geom = "col", fun = mean, width= 0.7, col = "gray50") +
geom_bar(stat = "identity") +
theme_light(base_size = 20)
barchart
})
output$Linegraph <- renderPlot({
data2 <- na.omit(data)
totalInYears <- aggregate(data2[c("Coal", "Oil", "Gas", "Cement")], by = data2["Year"], sum)
linechart <- ggplot(data = totalInYears, aes(x = Year)) +
geom_line(aes(y = Coal), color = "orange", lwd = 1) +
geom_line(aes(y = Oil), color = "pink",lwd = 1) +
geom_line(aes(y = Gas), color = "yellow",lwd = 1) +
geom_line(aes(y = Cement), color = "green",lwd = 1) + theme_dark()
linechart
})
output$Linearregression <- renderPlot({
linearRegression <- subset(data, select = c("Country", "Year", "Per.Capita"))
linearRegression1 <- linearRegression[which(linearRegression$Country == 'Global'),]
plot(linearRegression1$Year, linearRegression1$Per.Capita)
linearRegression2 <- lm(Per.Capita ~ Year, data = linearRegression1)
summary(linearRegression2)
abline(linearRegression2, col = "blue")
shapiro.test(linearRegression1$Per.Capita)
})
}
shinyApp(ui = ui, server = server)
curl.R
library(curl)
x <- tempfile()
y <- tempfile()
curl_download("https://raw.githubusercontent.com/FrancisDiesto/CarbonEmmissionPerCapita/main/GCB2021v34_MtCO2_flat.csv", x)
data <- read.csv(x)
curl_download("https://raw.githubusercontent.com/FrancisDiesto/WorldPopulation/main/continents-according-to-our-world-in-data.csv", y)
data1 <- read.csv(y)
这应该是结果：
谢谢
我的朋友找到了解决方案，@monte 也指出了这一点，我只是在之前的尝试中使用了不同的语法，但这个有效Sys.setlocale("LC_ALL", "C")谢谢。
美好的一天，过去几天我一直在使用我闪亮的应用程序，该应用程序在本地运行良好，但每当我在 shinyapps.io 上打开它时，它不会显示 plot_geo 地图。它只是说 Error: An error has occurred. Check your logs or contact app author for clarification .
这是我应用程序的 link。问题出在“世界二氧化碳排放量”地图上。
我也看到了类似的post here所以我尝试了这个解决方案，但问题仍然存在。
所以我检查了我的日志，这是我发现的：
2022-05-15T15:43:22.365650+00:00 shinyapps[6305171]: Warning: Error in gsub: input
string 31 is invalid in this locale
2022-05-15T15:43:22.365776+00:00 shinyapps[6305171]: 123: FUN
2022-05-15T15:43:22.365976+00:00 shinyapps[6305171]: 119: FUN
2022-05-15T15:43:22.365875+00:00 shinyapps[6305171]: 121: FUN
2022-05-15T15:43:22.365927+00:00 shinyapps[6305171]: 120: lapply
2022-05-15T15:43:22.366368+00:00 shinyapps[6305171]: 96: output$Map
2022-05-15T15:43:22.366025+00:00 shinyapps[6305171]: 118: lapply
2022-05-15T15:43:22.366573+00:00 shinyapps[6305171]: 7: connect$retryingStartServer
2022-05-15T15:43:22.366126+00:00 shinyapps[6305171]: 116: plotly_build.plotly
2022-05-15T15:43:22.366410+00:00 shinyapps[6305171]: 15: <Anonymous>
2022-05-15T15:43:22.366521+00:00 shinyapps[6305171]: 8: retry
2022-05-15T15:43:22.366280+00:00 shinyapps[6305171]: 110: func
2022-05-15T15:43:22.365718+00:00 shinyapps[6305171]: 124: gsub
2022-05-15T15:43:22.366327+00:00 shinyapps[6305171]: 97: renderFunc
2022-05-15T15:43:22.366177+00:00 shinyapps[6305171]: 112:
getFromNamespace("prepareWidget", "plotly")
2022-05-15T15:43:22.366470+00:00 shinyapps[6305171]: 13: fn
2022-05-15T15:43:22.366229+00:00 shinyapps[6305171]: 111: shinyRenderWidget
2022-05-15T15:43:22.366077+00:00 shinyapps[6305171]: 117: translate_linebreaks
2022-05-15T15:43:22.366622+00:00 shinyapps[6305171]: 6: eval
2022-05-15T15:43:22.365825+00:00 shinyapps[6305171]: 122: lapply
2022-05-15T15:43:22.366670+00:00 shinyapps[6305171]: 5: eval
这是我的代码：
app.R
library(tidyverse)
library(rnaturalearth)
library(shiny)
library(plyr)
library(dplyr)
library(plotly)
library(ggplot2)
library(gganimate)
library(sf)
library(uuid)
library(shinydashboard)
library(fontawesome)
source("curl.R")
world <- ne_countries(scale = "small", returnclass = "sf")
#####################
worldEmission <- world %>% select(geometry,name,iso_a3) %>%
right_join(data, by = c("iso_a3" = "ISO.3166.1.alpha.3" ))
worldEmission <- worldEmission %>% mutate(selected = TRUE)
maxperCapita <- max(worldEmission$Per.Capita)
worldEmission[is.na(worldEmission)] = 0
worldContinents <- data1 %>% select(Entity, Continent) %>%
right_join(worldEmission, by = c("Entity" = "Country"))
worldContinents$Year <- as.numeric(worldContinents$Year)
countryNames <-unique(worldEmission$Country)
#####################
ui <- dashboardPage(
dashboardHeader(),
dashboardSidebar(
sidebarMenu(
menuItem("World Co2 Emission", tabName = "Map", icon = icon('map')),
menuItem("Continent Co2 Emission", tabName = "Continent",icon = icon('chart-bar')),
menuItem("Emission Factors", tabName = "Emission", icon = icon('chart-line')) ,
menuItem("Linear Regression", tabName = "Regression", icon = icon('arrow-alt-circle-up'))
)
),
dashboardBody(
tabItems(
tabItem("Map",
h1("Map"),
box(plotlyOutput("Map"), wdith = 3)
),
tabItem("Continent",
box(plotOutput("Bargraph"), wdith = 12)
),
tabItem("Emission",
box(plotOutput("Linegraph"), wdith = 12)
),
tabItem("Regression",
box(plotOutput("Linearregression"), wdith = 12)
)
)#tabItems
)#dashboardBody
)#dashboardPage
server <- function(input, output) {
output$Map <- renderPlotly({
chorplethMap <- plot_geo(worldEmission,
locationmode = countryNames,
color = ("black"),
frame = ~Year) %>%
add_trace(worldEmission,
type = "choropleth",
locations = ~iso_a3,
z = ~Per.Capita,
zmin = 0,
color = ~Per.Capita,
colorscale = 'reds') %>%
layout(geo = list(scope = countryNames))
chorplethMap
})
output$Bargraph <- renderPlot({
worldContinents<-na.omit(worldContinents)
barchart <- ggplot(data = worldContinents,
aes(x = Continent, y = `Per.Capita`,fill = Continent)) +
stat_summary(geom = "col", fun = mean, width= 0.7, col = "gray50") +
geom_bar(stat = "identity") +
theme_light(base_size = 20)
barchart
})
output$Linegraph <- renderPlot({
data2 <- na.omit(data)
totalInYears <- aggregate(data2[c("Coal", "Oil", "Gas", "Cement")], by = data2["Year"], sum)
linechart <- ggplot(data = totalInYears, aes(x = Year)) +
geom_line(aes(y = Coal), color = "orange", lwd = 1) +
geom_line(aes(y = Oil), color = "pink",lwd = 1) +
geom_line(aes(y = Gas), color = "yellow",lwd = 1) +
geom_line(aes(y = Cement), color = "green",lwd = 1) + theme_dark()
linechart
})
output$Linearregression <- renderPlot({
linearRegression <- subset(data, select = c("Country", "Year", "Per.Capita"))
linearRegression1 <- linearRegression[which(linearRegression$Country == 'Global'),]
plot(linearRegression1$Year, linearRegression1$Per.Capita)
linearRegression2 <- lm(Per.Capita ~ Year, data = linearRegression1)
summary(linearRegression2)
abline(linearRegression2, col = "blue")
shapiro.test(linearRegression1$Per.Capita)
})
}
shinyApp(ui = ui, server = server)
curl.R
library(curl)
x <- tempfile()
y <- tempfile()
curl_download("https://raw.githubusercontent.com/FrancisDiesto/CarbonEmmissionPerCapita/main/GCB2021v34_MtCO2_flat.csv", x)
data <- read.csv(x)
curl_download("https://raw.githubusercontent.com/FrancisDiesto/WorldPopulation/main/continents-according-to-our-world-in-data.csv", y)
data1 <- read.csv(y)
这应该是结果：
谢谢
我的朋友找到了解决方案，@monte 也指出了这一点，我只是在之前的尝试中使用了不同的语法，但这个有效Sys.setlocale("LC_ALL", "C")谢谢。