Shiny Server

You can also use the data that comes with the package to create interactive webapps with shiny.

## Load required packages
library("dplyr")
library("mxmaps")
library("geojsonio")
library("jsonlite")
library("shiny")
library("leaflet")
library("RColorBrewer")
library("lubridate")
library("zoo")
library("stringi")

## Download archived crime data from elcri.men 1997-2017 (not updated anymore)
## All local crimes at the state level
tmpdir <- tempdir()
download.file("https://data.diegovalle.net/elcrimen/fuero-comun-estados.csv.gz",
              file.path(tmpdir, "fuero-comun-estados.csv.gz"))


## Load the crime data
crime <- read.csv(file.path(tmpdir, "fuero-comun-estados.csv.gz"))

## Only intentional homicides
crime <- subset(crime, modalidad == "HOMICIDIOS" & tipo == "DOLOSOS")

## subset the year from the date
crime$year <- as.integer(substr(crime$date, 1, 4))

## Yearly homicide rates
hom <- crime %>%
  mutate(year = year(as.yearmon(date))) %>%
  group_by(year, state_code, tipo, state) %>%
  summarise(total = sum(count, na.rm = TRUE),
            rate = total / mean(population) * 10^5) %>%
  mutate(region = str_mxstate(state_code),
         id = str_mxstate(state_code))

# Convert the topoJSON to spatial object
data(mxstate.topoJSON)
tmpdir <- tempdir()
# have to use RJSONIO or else the topojson isn't valid
write(RJSONIO::toJSON(mxstate.topoJSON), file.path(tmpdir, "state.topojson"))
# read the topojson file as an 'sf' object
states <- topojson_read(file.path(tmpdir, "state.topojson"))

# put state codes in a standard format
states$id <- str_mxstate(states$id)


ui <- bootstrapPage(
  tags$style(type = "text/css", "html, body {width:100%;height:100%}"),
  leafletOutput("map", width = "100%", height = "100%"),
  absolutePanel(top = 10, right = 10,
                sliderInput("range", "Year", 1997, 2017,
                            value = 2017, step = 1, sep="",
                            animate = animationOptions(interval = 3000, loop = FALSE, 
                                                       playButton = NULL,
                                                       pauseButton = NULL),
                )
  )
)

server <- function(input, output, session) {
  
  # Reactive expression for the data subsetted to what the user selected
  filteredData <- reactive({
    states <- left_join(states, subset(hom, year == input$range[1]), 
                             by = "id")
    states
    #hom[hom$year == input$range[1] ,]
  })
  
  # This reactive expression represents the palette function,
  # which changes as the user makes selections in UI.
  colorpal <- reactive({
    colorNumeric("Reds", hom$rate)
  })
  
  output$map <- renderLeaflet({
    # Use leaflet() here, and only include aspects of the map that
    # won't need to change dynamically (at least, not unless the
    # entire map is being torn down and recreated).
    states <- left_join(states, subset(hom, year == 2017), by = "id")
    pal <- colorpal()
    leaflet(states) %>% addTiles() %>%
      setView(-102, 23.8, 5)%>% 
      addLegend(position = "bottomright",
                pal = pal, values = ~hom$rate,
                title = "homicide<br>rate") %>% 
      addTiles()
  })
  
  # Incremental changes to the map (in this case, replacing the
  # circles when a new color is chosen) should be performed in
  # an observer. Each independent set of things that can change
  # should be managed in its own observer.
  observe({
    pal <- colorpal()
    leafletProxy("map", data = filteredData()) %>%
      clearShapes() %>%
      addPolygons(stroke = TRUE, weight = 1, color = "#000000",
                  fillOpacity = 0.8, smoothFactor = 0.5,
                  fillColor = ~pal(rate), 
                  popup = ~ sprintf("State: %s<br/>Rate: %s",
                                    stri_trans_totitle(state), 
                                    round(rate, 1)))
  })
  
  
}

shinyApp(ui, server)