大家下午好。
我正在创建一个应用程序分析工具，并在match.arg（position）中不断出现错误：'arg'必须为NULL或字符向量。我已经研究了几天，但似乎找不到问题。我感谢任何有第二眼的人，他们也许可以看看我可能会缺少什么。非常感谢！
library(shiny)
library(ggplot2)
library(stringr)
library(dplyr)
library(DT)
library(tools)
library(lubridate)
library(data.table)
library(plotly)
dtCLIPKey <- as.data.frame(updated_dtCLIPKey_11_16)
min_date <- min(dtCLIPKey$Date_1)
max_date <- max(dtCLIPKey$Date_1)
dtCLIPKey <- as.data.table(dtCLIPKey)
dtCLIPKey[,Short.NameN := .N,by="Short.Name"]
#NEED TO REMEMBER TO GET COUNT RIGHT TO INCLUDE HOURS AND DAY LIMITATION
dtCLIPKey$Short.NameRank <-frankv(dtCLIPKey$Short.NameN, ties.method = "dense")
# Define UI for application that plots features of movies
ui <- navbarPage("JRSS Application Analysis",
#First NavBar Page
tabPanel("Aggregate",
# Sidebar layout with a input and output definitions
sidebarLayout(
# Inputs
sidebarPanel(
wellPanel(
h3("Plotting"), # Third level header: Plotting
# Explanatory text
HTML(paste0("Trend will be based on traffic samples from the following date selection.
Pick dates between ", min_date, " and ", max_date, ".")),
# Break for visual separation
br(), br(),
# Date input
dateRangeInput(inputId = "date",
label = "Select dates:",
start = "2018-09-01", end = "2018-10-31",
min = min_date, max = max_date,
startview = "year")
),
# Select variable for x-axis
selectInput(inputId = "hours",
label = "Hour:",
choices = levels(factor(unique(dtCLIPKey$Hour_1))),
selected = "1"),
#Number of Applications
sliderInput("freq",
"Top N Applications:",
min = 1, max = 40, value = 10),
wellPanel(
# Show data table
checkboxInput(inputId = "show_data",
label = "Show data table",
value = TRUE)
),
# Built with Shiny by RStudio
br(), br(),
h5("Built with",
img(src = "https://www.rstudio.com/wp-content/uploads/2014/04/shiny.png", height = "30px"),
"by",
img(src = "https://www.rstudio.com/wp-content/uploads/2014/07/RStudio-Logo-Blue-Gray.png", height = "30px"),
".")
),
# Output:
mainPanel(
tabsetPanel(type = "tabs",
# Tab 1: Barchart
tabPanel(title = "Barchart",
plotOutput(outputId = "barchart"),
br(),
h5(textOutput("description")),
br(),
plotOutput(outputId = "boxplot")
),
# Tab 2: ScatterPlot
tabPanel(title = "ScatterPlot",
plotOutput(outputId = "scatterplot"),
plotOutput(outputId = "scatterplot2")
),
# Tab 3: Data
tabPanel(title = "Data",
br(),
DT::dataTableOutput(outputId = "traffictable"))
)#MainPanel
)#Sidebarpanel
)#sidebarpayout
),#tabPanel
tabPanel("App selection",
# Inputs
sidebarLayout(
sidebarPanel(
wellPanel(
selectInput(inputId = "App1",
label = "Application",
choices = unique(dtCLIPKey$Short.Name),
selected = "Application.XXX"),
# Break for visual separation
br(), br(),
# Break for visual separation
br(), br(),
# Date input
dateRangeInput(inputId = "dateapp",
label = "Select app dates:",
start = "2018-09-01", end = "2018-10-31",
min = min_date, max = max_date,
startview = "year")
),
# Select variable for x-axis
selectInput(inputId = "hoursapp",
label = "Hour:",
choices = levels(factor(unique(dtCLIPKey$Hour_1))),
selected = "1")),
# Select variables to download
wellPanel(
checkboxGroupInput(inputId = "selected_var",
label = "Select variables:",
choices = c("Date" = "Date_1",
"IP Pair" = "IPPair",
"Computer Name" = "comp_name",
"Computer Type" = "comp_type_string",
"Group" = "Group"),
selected = "Application")
)#wellpanel
)#sidebarpanel
)#sidebarlayout
)#Tabpanel
# Output:
mainPanel(
tabsetPanel(type = "tabs",
# Tab 1: Barchart
tabPanel(title = "Violin",
plotOutput(outputId = "histapp1")
),#TablPanel
tabPanel(title = "Trend",
plotOutput(outputId = "lineapp1")
),
tabPanel(title = "Plot by Variable",
plotOutput(outputId = "scattervar"),
plotOutput(outputId = "boxplotvar")
)
)#tabsetPanel
)#MainPanel
#sidebarPanel
# Define server function required to create the scatterplot
server <- function(input, output, session) {
# Create a subset of data filtering for selected time frame
traffic_selected <- reactive({
req(input$date) # ensure availablity of value before proceeding
filter(dtCLIPKey, Date_1 >= input$date[1] & Date_1 <= input$date[2] & Hour_1 == input$hours & Short.NameRank > (max(dtCLIPKey$Short.NameRank)-input$freq))
})
# Create a subset of data filtering for selected application
app_selected <- reactive({
req(input$date) # ensure availablity of value before proceeding
filter(dtCLIPKey, Date_1 >= input$dateapp[1] & Date_1 <= input$dateapp[2] & Hour_1 == input$hoursapp & Short.Name == input$App1)
})
app_selected_var <- reactive({
req(input$date) # ensure availablity of value before proceeding
filter(dtCLIPKey, Date_1 >= input$dateapp[1] & Date_1 <= input$dateapp[2] & Hour_1 == input$hoursapp & Short.Name == input$App1)
})
# Create the plot
output$barchart <- renderPlot({
req(input$date)
dtCLIPKey_selected_date <- dtCLIPKey %>%
filter(Date_1 >= input$date[1] & Date_1 <= input$date[2] & Hour_1 == input$hours & Short.NameRank > (max(dtCLIPKey$Short.NameRank)-input$freq))
ggplot(dtCLIPKey_selected_date, aes(x = Short.Name, y = Short.NameN, fill = Date_1)) +
geom_bar(stat = "identity", width = 0.5, position = "dodge") +
coord_flip()
})
output$histapp1 <- renderPlot({
req(input$dateapp)
dtCLIPKey_selected_date <- dtCLIPKey %>%
filter(Date_1 >= input$dateapp[1] & Date_1 <= input$dateapp[2] & Hour_1 == input$hoursapp & Short.Name == input$App1)
p <- ggplot(dtCLIPKey_selected_date, aes(x = Date_1, y = log(dt)))
p + geom_violin() + geom_boxplot(width = .1, fill = "blue", outlier.colour = NA) +
stat_summary(fun.y = median, geom = "point", fill = "white", shape = 21, size = 2.5)
})
output$lineapp1 <- renderPlot({
req(input$dateapp)
dtCLIPKey_selected_date <- dtCLIPKey %>%
filter(Date_1 >= input$dateapp[1] & Date_1 <= input$dateapp[2] & Hour_1 == input$hoursapp & Short.Name == input$App1)
ggplot(dtCLIPKey_selected_date, aes(x = Date_1, y = mean(dt))) +
geom_line() +
geom_point()
})
output$boxplot <- renderPlot({
req(input$date)
dtCLIPKey_selected_date <- dtCLIPKey %>%
mutate(Date_1 = as.Date(Date_1)) %>% # convert thtr_rel_date to Date format
filter(Date_1 >= input$date[1] & Date_1 <= input$date[2] & Hour_1 == input$hours & Short.NameRank > (max(dtCLIPKey$Short.NameRank)-input$freq))
ggplot(dtCLIPKey_selected_date, aes(x=Short.Name, y=log(dt), group = Short.Name)) +
geom_boxplot() + stat_summary(fun.y = "mean", geom = "point", shape=23, size=3, fill="blue") + ggtitle("Log of Latency by Date") + theme(plot.title = element_text(hjust = 0.5)) + labs(x= "Date", y= "Log of Latency (in microsecs)") +
coord_flip() +
facet_grid(~ Date_1)
})
output$scatterplot <- renderPlot({
req(input$date)
dtCLIPKey_selected_date <- dtCLIPKey %>%
mutate(Date_1 = as.Date(Date_1)) %>% # convert thtr_rel_date to Date format
filter(Date_1 >= input$date[1] & Date_1 <= input$date[2] & Hour_1 == input$hours & Short.NameRank > (max(dtCLIPKey$Short.NameRank)-input$freq))
ggplot(dtCLIPKey_selected_date, aes(x=log(Short.NameN), y=log(dt), color = Short.Name)) +
geom_point() +
facet_grid(~Date_1)
})
output$scatterplot2 <- renderPlot({
req(input$date)
dtCLIPKey_selected_date <- dtCLIPKey %>%
mutate(Date_1 = as.Date(Date_1)) %>% # convert thtr_rel_date to Date format
filter(Date_1 >= input$date[1] & Date_1 <= input$date[2] & Hour_1 == input$hours & Short.NameRank > (max(dtCLIPKey$Short.NameRank)-input$freq))
ggplot(dtCLIPKey_selected_date, aes(x=log(Short.NameN), y=log(dt), color = Short.Name)) +
geom_point()
})
output$scattervar <- renderPlot({
req(input$date)
req(input$selected_var)
dtCLIPKey_selected_date <- dtCLIPKey %>%
mutate(Date_1 = as.Date(Date_1)) %>% # convert thtr_rel_date to Date format
filter(Date_1 >= input$date[1] & Date_1 <= input$date[2] & Hour_1 == input$hours & Short.Name == input$app1)
ggplot(dtCLIPKey_selected_date, aes(x=log(Short.NameN), y=log(dt), color = Short.Name)) +
geom_point()
})
output$scattervar <- renderPlot({
req(input$date)
req(input$selected_var)
dtCLIPKey_selected_date <- dtCLIPKey %>%
mutate(Date_1 = as.Date(Date_1)) %>% # convert thtr_rel_date to Date format
filter(Date_1 >= input$date[1] & Date_1 <= input$date[2] & Hour_1 == input$hours & Short.Name == input$app1)
dtCLIPKey_selected_date <- subset(dtCLIPKey_selected_date, select = c(Short.Name, dt, input$selected_var))
ggplot(dtCLIPKey_selected_date, aes(x=input$selected_var, y=log(dt), color = Short.Name)) +
geom_boxplot()
})
# Print data table if checked
output$traffictable <- DT::renderDataTable(
if(input$show_data){
DT::datatable(data = traffic_selected()[, 1:6],
options = list(pageLength = 10),
rownames = FALSE)
}
)
}
# Create Shiny app object
shinyApp(ui = ui, server = server)