diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..cab71db
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,4 @@
+processed.R
+rsconnect/
+
+.Rhistory
diff --git a/app.R b/app.R
index 3900a01..d9a0b84 100644
--- a/app.R
+++ b/app.R
@@ -1,9 +1,16 @@
library(shiny)
library(ggplot2)
library(dplyr)
+library(plotly)
+library(leaflet)
+library(geosphere)
+library(leaflet.extras)
+library(sf)
+
# 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")
+cities <- read.csv("data/processed/cities.csv")
# Define UI
ui <- fluidPage(
@@ -19,44 +26,100 @@ ui <- fluidPage(
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$state)),
+ choices = unique(weather$state)),
# Add dropdown menu input for selecting city
selectInput("city", "Select City:",
- choices = unique(data$city)),
+ choices = NULL),
# Add radio button input for selecting temperature or precipitation
radioButtons("data_type", "Select Data Type:",
choices = c("Temperature", "Precipitation"),
- selected = "Temperature")
+ selected = "Temperature"),
),
# Add main panel with plot output
mainPanel(
- plotOutput("line_plot")
+ plotOutput("line_plot"),
+ leafletOutput("map")
)
)
)
# Define server
-server <- function(input, output) {
+server <- function(input, output, session) {
+
+ # Update city and state input based on map clicks
+ observeEvent(input$map_marker_click, {
+ updateSelectInput(session, "city", selected = input$map_marker_click$id)
+ updateSelectInput(session, "state", selected = cities$state[cities$city == input$map_marker_click$id])
+ })
+
+ # Update city input possible values based on selected state
+ observe({
+ updateSelectInput(session, "city",
+ choices = unique(weather$city[weather$state == input$state]))
+
+ })
+ # observe({
+ # updateSelectInput(session, "state",
+ # choices = unique(weather$state[weather$city == input$city]))
+ # })
+
+
+
# 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)
+ line_data <- reactive({
+ weather %>%
+ filter(month >= input$month_range[1], month <= input$month_range[length(input$month_range)]) %>%
+ filter(state == input$state, city == input$city) %>%
+ group_by(month, high_or_low) %>%
+ summarise(observed_temp = mean(observed_temp, na.rm=TRUE),
+ observed_precip = mean(observed_precip, na.rm=TRUE))
})
# Create line plot based on filtered data and user data type input
output$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$data_type == "Temperature"){
+ ggplot(line_data(), aes(x = month, y = observed_temp, col=high_or_low)) +
+ geom_point() +
+ geom_line() +
+ scale_x_continuous(breaks = seq(1, 12, by = 1)) +
+ labs(x = "Month", y = "Temperature (°F)", color="High/Low")
+ }
+ else{
+ ggplot(line_data(), aes(x = month, y = observed_precip)) +
+ geom_point(color="violetred") +
+ geom_line(color="lightblue") +
+ scale_x_continuous(breaks = seq(1, 12, by = 1)) +
+ labs(x = "Month", y = "Precipitation")
+ }
})
+
+ # Create leaflet map
+ output$map <- renderLeaflet({
+ leaflet(cities) |>
+ addTiles() |>
+ addCircleMarkers(~lon,
+ ~lat,
+ popup = paste0("City: ", cities$city, "
",
+ "State: ", cities$state, "
",
+ "Elevation: ", cities$elevation, " m
",
+ "Distance to Coast: ", cities$distance_to_coast, " mi