Merge pull request #24 from UBC-MDS/data_pro

processed

.gitignore

@@ -0,0 +1,4 @@
+processed.R
+rsconnect/
+
+.Rhistory

app.R

@@ -1,9 +1,15 @@
 library(shiny)
 library(ggplot2)
 library(dplyr)
+library(plotly)
+library(leaflet)
+library(leaflet.extras)
+library(sf)
+library(countrycode)
+library(RColorBrewer)
 
 # Load dataset
-data <- read.csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-12-20/weather_forecasts.csv")
+weather <- read.csv("data/processed/weather_pro.csv")
 
 # Define UI
 ui <- fluidPage(
@@ -22,11 +28,11 @@ ui <- fluidPage(
 
       # Add dropdown menu input for selecting state
       selectInput("state", "Select State:",
-                  choices = unique(data$state)),
+                  choices = unique(weather$state)),
 
       # Add dropdown menu input for selecting city
       selectInput("city", "Select City:",
-                  choices = unique(data$city)),
+                  choices = NULL),
 
       # Add radio button input for selecting temperature or precipitation
       radioButtons("data_type", "Select Data Type:",
@@ -42,20 +48,41 @@ ui <- fluidPage(
 )
 
 # Define server
-server <- function(input, output) {
+server <- function(input, output, session) {
+
+  observe({
+    updateSelectInput(session, "city", 
+                      choices = unique(weather$city[weather$state == input$state]))
+  })
+
   # Filter data based on user inputs
   filtered_data <- reactive({
-    data %>%
-      filter(month >= input$month_range[1], month <= input$month_range[2]) %>%
-      filter(state == input$state, city == input$city)
+    weather %>%
+      filter(month >= input$month_range[1], month <= input$month_range[length(input$month_range)]) %>%
+      filter(state == input$state, city == input$city) %>%
+      group_by(month) %>%
+      summarise(observed_temp = mean(observed_temp, na.rm=TRUE),
+                observed_precip = mean(observed_precip, na.rm=TRUE))
   })
 
   # 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$data_type)
+    if (input$data_type == "Temperature"){
+      ggplot(filtered_data(), aes(x = month, y = observed_temp)) +
+        geom_point(color="violetred") +
+        geom_line(color="lightblue") +
+        scale_x_continuous(breaks = seq(1, 12, by = 1)) +
+        labs(x = "Month", y = "Temperature")
+    }
+    else{
+      ggplot(filtered_data(), aes(x = month, y = observed_precip)) +
+        geom_point(color="violetred") +
+        geom_line(color="lightblue") +
+        scale_x_continuous(breaks = seq(1, 12, by = 1)) +
+        labs(x = "Month", y = "Precipitation")
+    }
   })
+
 }
 
 # Run app