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add city ranking tab #39

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Mar 1, 2023
Merged

add city ranking tab #39

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253 changes: 186 additions & 67 deletions app.R
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Original file line number Diff line number Diff line change
Expand Up @@ -8,95 +8,143 @@ library(leaflet.extras)
library(sf)
library(shinydashboard)


# Load dataset
weather <- read.csv("data/processed/weather_pro.csv")
cities <- read.csv("data/processed/cities.csv")

# Change numeric month from number to name only for bar plots
weather_bar <- weather
weather_bar$month <- month.name[weather_bar$month]

# Define UI
ui <- dashboardPage(
# Add title
dashboardHeader(title = "City Weather"),

# Add sidebar layout
dashboardSidebar(# 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(weather$state)),

# Add dropdown menu input for selecting city
selectInput("city", "Select City:",
choices = NULL),

# Add radio button input for selecting temperature or precipitation
radioButtons("data_type", "Select Type:",
choices = c("Temperature", "Precipitation"),
selected = "Temperature")),
dashboardBody(
# Added a row for summary statistics
fluidRow(

valueBoxOutput("avgBox"),

valueBoxOutput("maxBox"),
dashboardHeader(title = "Citytemp Weather Dashboard"),

dashboardSidebar(sidebarMenu(
menuItem(
"Temperature or Precipitation Trends",
tabName = "temp_precip_trends"

valueBoxOutput("minBox")
),
box(plotOutput("line_plot")),
box(leafletOutput("map"))
menuItem(
"City Ranking by Temp/Rain",
tabName = "city_ranking"

)
)),
dashboardBody(tabItems(
# Tab 1: Temperature or Precipitation Trends
tabItem(tabName = "temp_precip_trends",
fluidRow(
column(
width = 4,
# 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(weather$state)),
# Add dropdown menu input for selecting city
selectInput("city",
"Select City:",
choices = NULL),
# Add radio button input for selecting temperature or precipitation
radioButtons(
"data_type",
"Select Data Type:",
choices = c("Temperature", "Precipitation"),
selected = "Temperature"
)
),
column(width = 8,

# Added a row for summary statistics
fluidRow(

valueBoxOutput("avgBox"),

valueBoxOutput("maxBox"),

valueBoxOutput("minBox")
),

# Add main panel with plot output
plotOutput("line_plot"),
leafletOutput("map"))
)),
# Tab 2: City Ranking by Temperature/Precipitation
tabItem(tabName = "city_ranking",
fluidRow(
column(
width = 4,
selectInput("statename",
"Select a state:",
choices = unique(weather_bar$state)),
selectInput(
"highlow",
"Select highest or lowest temperature:",
choices = c("Highest" = "high", "Lowest" = "low")
),
selectInput("month",
"Select a month:",
choices = unique(weather_bar$month))
),
column(
width = 8,
plotOutput("temp_barplot"),
plotOutput("rain_barplot")
)
))
))
)


# Define server
# Define server logic
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$id])
updateSelectInput(session,
"state",
selected = cities$state[cities$city == input$map_marker_click$id])
})

# Update city input possible values based on selected state
observe({
updateSelectInput(session, "city",
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
line_data <- reactive({
weather %>%
filter(month >= input$month_range[1], month <= input$month_range[length(input$month_range)]) %>%
weather %>% filter(month >= input$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))
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$line_plot <- renderPlot({
if (input$data_type == "Temperature"){
ggplot(line_data(), aes(x = month, y = observed_temp, col=high_or_low)) +
if (input$data_type == "Temperature") {
ggplot(line_data(),
aes(x = month, y = observed_temp, col = high_or_low)) +
geom_point() +
geom_line() +
scale_x_continuous(breaks = seq(1, 12, by = 1)) +
labs(x = "Month", y = "Temperature (°F)", color="High/Low")
labs(x = "Month", y = "Temperature (°F)", color = "High/Low")
}
else{
ggplot(line_data(), aes(x = month, y = observed_precip)) +
geom_point(color="violetred") +
geom_line(color="lightblue") +
geom_point(color = "violetred") +
geom_line(color = "lightblue") +
scale_x_continuous(breaks = seq(1, 12, by = 1)) +
labs(x = "Month", y = "Precipitation")
}
Expand All @@ -106,22 +154,91 @@ server <- function(input, output, session) {
output$map <- renderLeaflet({
leaflet(cities) |>
addTiles() |>
addCircleMarkers(~lon,
~lat,
popup = paste0("City: ", cities$city, "<br>",
"State: ", cities$state, "<br>",
"Elevation: ", cities$elevation, " m<br>",
"Distance to Coast: ", cities$distance_to_coast, " mi<br>",
"Average Annual Precipitation: ", cities$avg_annual_precip, " in"),
layerId = cities$city,
label = cities$city,
color = "navy",
radius = 5,
stroke = FALSE,
fillOpacity = 0.4) |>
addCircleMarkers(
~ lon,
~ lat,
popup = paste0(
"City: ",
cities$city,
"<br>",
"State: ",
cities$state,
"<br>",
"Elevation: ",
cities$elevation,
" m<br>",
"Distance to Coast: ",
cities$distance_to_coast,
" mi<br>",
"Average Annual Precipitation: ",
cities$avg_annual_precip,
" in"
),
layerId = cities$city,
label = cities$city,
color = "navy",
radius = 5,
stroke = FALSE,
fillOpacity = 0.4
) |>
setView(-100, 40, zoom = 3.3)
})



# --------------------------------------City Ranking Tab start here------------------------------------


# Filter data based on user input, and calculate avg temp and rain
bar_data <- reactive({
weather_bar %>% filter(state == input$statename &
high_or_low == input$highlow &
month == input$month) %>%
group_by(city) %>% summarize(
avg_temp = mean(observed_temp, na.rm = TRUE),
avg_rain = mean(observed_precip, na.rm = TRUE)
)
})



# Create bar chart of cities by temperature
output$temp_barplot <- renderPlot({
ggplot(bar_data(), aes(x = avg_temp, y = reorder(city, avg_temp),fill= avg_temp)) +
geom_bar(stat = "identity") + scale_fill_gradient(low="yellow", high="red") +
labs(
x = "Average Temperature",
y = "City",
title = paste0(
"Cities in ",
input$statename,
" by average ",
input$highlow,
"est temperature in ",
input$month
)
)
})


# Create bar chart of cities by precipitation
output$rain_barplot <- renderPlot({
ggplot(bar_data(), aes(x = avg_rain, y = reorder(city, avg_rain), fill = avg_rain)) +
geom_bar(stat = "identity") + scale_fill_gradient(low="lightblue", high="darkblue") +
labs(
x = "Average Precipitation",
y = "City",
title = paste0(
"Cities in ",
input$statename,
" by average precipitation in ",
input$month
)
)
})

# --------------------------------------summary statistics box start here------------------------------------

# data processing for summary statistics
stat_data <- reactive({
weather %>% filter(city == input$city ) %>%
Expand Down Expand Up @@ -182,7 +299,9 @@ server <- function(input, output, session) {
}
})


}

# Run app
shinyApp(ui, server)