delete rsconnect

app.R

@@ -0,0 +1,62 @@
+library(shiny)
+library(ggplot2)
+library(dplyr)
+
+# Load dataset
+data <- read.csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-12-20/weather_forecasts.csv")
+
+# Define UI
+ui <- fluidPage(
+  # Add title
+  titlePanel("Temperature or Precipitation Trends"),
+
+  # Add sidebar layout
+  sidebarLayout(
+    # Add sidebar panel with inputs
+    sidebarPanel(
+
+      # Add slider input for selecting range of months
+      sliderInput("month_range", "Select Month Range:",
+                  min = 1, max = 12, value = c(1, 12)),
+
+
+      # Add dropdown menu input for selecting state
+      selectInput("state", "Select State:",
+                  choices = unique(data$state)),
+
+      # Add dropdown menu input for selecting city
+      selectInput("city", "Select City:",
+                  choices = unique(data$city)),
+
+      # Add radio button input for selecting temperature or precipitation
+      radioButtons("data_type", "Select Data Type:",
+                   choices = c("Temperature", "Precipitation"),
+                   selected = "Temperature")
+    ),
+
+    # Add main panel with plot output
+    mainPanel(
+      plotOutput("line_plot")
+    )
+  )
+)
+
+# Define server
+server <- function(input, output) {
+  # Filter data based on user inputs
+  filtered_data <- reactive({
+    data %>%
+      filter(month >= input$month_range[1], month <= input$month_range[2]) %>%
+      filter(state == input$state, city == input$city)
+  })
+
+  # Create line plot based on filtered data and user data type input
+  output$line_plot <- renderPlot({
+    ggplot(filtered_data(), aes(x = month, y = ifelse(input$data_type == "Temperature", observed_temp, observed_precip))) +
+      geom_line() +
+      labs(x = "Month", y = input$data_type)
+  })
+}
+
+# Run app
+shinyApp(ui, server)