In ryanwyg/Chicago-Data-Package: Contains some Chicago-relevant datasets, primarily sf objects.

library(leaflet)
library(spData)
library(dplyr)
library(ChicagoPackage)
library(tmap)
library(sf)
library(DT)

The Maps

Column {data-width=350}

Select Dataset and Attributes

span(tags$i(h6("Data current as of June 2020.")), style="color:#045a8d")
selectizeInput("dataset", "Choose a dataset:", choices = c("Chicago Public Shools 2018", "Divvy", "CTA Stations", "Social Vulnerability Index", selected = NULL, multiple = FALSE,
               options = NULL))
selectInput("variable", "Choose a variable:", choices = c("1", "2", "3"))
selectInput("by", "visualize by:", choices = c("Community Area", "2010 Cencus Tracts", "Police Beats", "Police Wards"))
selectInput("as", "show as:", choices = c("density dot", "dots", "Choropleth Map"))
sliderInput("year",
            "Year",
            min = 2001,
            max = 2018,
            step = 1,
            sep = "",
            value = 1)

Download the Plot

radioButtons("picformat", "Select the Image Format to Download", list("png", "jpeg"), "")

downloadButton(outputId = "down", label = "Download the Graph")
downloadHandler(filename = function() {
     paste('resultset-', Sys.Date(), '.csv', sep='')
   },
     content = function(file) {
     write.csv(subset_dataset(), file, row.names = FALSE)
   }
)

Output {data-width=650}

Output Map

# update region selections
  observeEvent(input$dataset, {
    if (input$dataset=="Divvy") {
      updateSelectizeInput(session = session, inputId = "variable", 
                        choices = c("Total Docks", "Docks in Service"))
      updateSelectInput(session = session, inputId = "by", 
                        choices = c("Community Area", "2010 Census Tracts", "Police Wards"))
      updateSelectInput(session = session, inputId = "as", 
                        choices = c("Choropleth Map", "Dots"))
    }
     if (input$dataset=="Chicago Public Shools 2018") {
      updateSelectizeInput(session = session, inputId = "variable", 
                        choices = c("School Type", "Primary Category", "Student Growth Rating", "Student Attainment Rating", "Culture Climate Rating", "Healthy School Certification", "School Survey Amibitious Instruction", "School Survey Safety", "Suspensions Per 100 Students Year 1 Percent"))
      updateSelectInput(session = session, inputId = "by", 
                        choices = c("NA"))
      updateSelectInput(session = session, inputId = "as", 
                        choices = c("Dots"))
    }
  }, ignoreInit = TRUE)

  # create dataframe with selected data
  reactive_db = reactive({
    if (input$dataset=="Divvy") { 
      db = divvy
    }
  })

renderLeaflet({
  leaflet(ca_boundaries) %>%
  addPolygons(color = "#444444", weight = 1, smoothFactor = 0.5,
    opacity = 1.0, fillOpacity = 0.5,
    fillColor = ~colorQuantile("YlOrRd", shape_area)(shape_area),
    highlightOptions = highlightOptions(color = "white", weight = 2,
      bringToFront = TRUE)) %>% 
    addProviderTiles(providers$CartoDB.Positron)
  })

renderLeaflet({
  leaflet(census_tracts_2010) %>%
  addPolygons(color = "#444444", weight = 1, smoothFactor = 0.5,
    opacity = 1.0, fillOpacity = 0.5,
    fillColor = ~colorQuantile("YlOrRd", commarea_n)(commarea_n),
    highlightOptions = highlightOptions(color = "white", weight = 2,
      bringToFront = TRUE)) %>% 
    addProviderTiles(providers$CartoDB.Positron)
  })
renderLeaflet({
      leaflet(divvy) %>% 
      addCircles(divvy$geometry) %>% 
      addTiles() %>%
      addCircleMarkers(data = divvy, divvy$geometry, 
                       radius = 3, popup = ~as.character(cntnt), 
                       color = ~pal(divvy$Status),
                       stroke = FALSE, fillOpacity = 0.8)%>%
      addLegend(pal=pal, values=bb_data$Category,opacity=1, na.label = "Not Available")%>%
      addEasyButton(easyButton(
        icon="fa-crosshairs", title="ME",
        onClick=JS("function(btn, map){ map.locate({setView: true}); }")))
        })
renderLeaflet({
      leaflet(cpsPR2018_sf) %>% 
      addCircles(cpsPR2018_sf$School_Type) %>% 
      addTiles() %>%
      addCircleMarkers(data = cpsPR2018_sf, cpsPR2018_sf$geometry, 
                       radius = 3, popup = ~as.character(cntnt), 
                       color = ~pal(cpsPR2018_sf$School_Type),
                       stroke = FALSE, fillOpacity = 0.8)%>%
      addLegend(pal=pal, values=bb_data$Category,opacity=1, na.label = "Not Available")%>%
      addEasyButton(easyButton(
        icon="fa-crosshairs", title="ME",
        onClick=JS("function(btn, map){ map.locate({setView: true}); }")))
        })

The Tables

Select {.sidebar}

#selectInput("data", "Choose a dataset:", choices = c("Divvy", "CTA Ridership", "CTA Stations", "Chicago SVI", "CPS Tiers", "Chicago Public Schoos", "Tax Increment Financing"))


conditionalPanel(
        'input.dataset === "Divvy Stations"',
        helpText("Click the column header to sort a column.")
      )
      conditionalPanel(
        'input.dataset === "CTA Ridership"',
        helpText("Click the column header to sort a column.")
      )
      conditionalPanel(
        'input.dataset === "CTA Stations"',
        helpText("Click the column header to sort a column.")
      )
      conditionalPanel(
        'input.dataset === "Chicago SVI"',
        helpText("Click the column header to sort a column.")
      )
      conditionalPanel(
        'input.dataset === "CPS Tiers"',
        helpText("Click the column header to sort a column.")
      )
      conditionalPanel(
        'input.dataset === "Chicago Public Schools"',
        checkboxGroupInput("show_vars", "Columns in dataset to show:",
                           names(cpsPR2018_sf), selected = names(cpsPR2018_sf))
      )
      conditionalPanel(
        'input.dataset === "Tax Increment Financing"',
        helpText("Click the column header to sort a column.")
      )
      conditionalPanel(
        'input.dataset === "Affordable Rental Housing"',
        helpText("Click the column header to sort a column.")
      )

downloadButton(outputId = "down", label = "Download the Data")

downloadHandler(filename = function() {
     paste('resultset-', Sys.Date(), '.csv', sep='')
   },
     content = function(file) {
     write.csv(subset_dataset(), file, row.names = FALSE)
   }
)

Output

Data

tabsetPanel(
        id = 'dataset',
        tabPanel("Divvy Stations", DT::dataTableOutput("mytable1")),
        tabPanel("CTA Ridership", DT::dataTableOutput("mytable2")),
        tabPanel("CTA Stations", DT::dataTableOutput("mytable3")),
        tabPanel("Chicago SVI", DT::dataTableOutput("mytable4")),
        tabPanel("CPS Tiers", DT::dataTableOutput("mytable5")),
        tabPanel("Chicago Public Schools", DT::dataTableOutput("mytable6")),
        tabPanel("Tax Increment Financing", DT::dataTableOutput("mytable7")),
        tabPanel("Affordable Rental Housing", DT::dataTableOutput("mytable8"))
      )

output$mytable1 <- DT::renderDataTable({
    DT::datatable(divvy, options(DT.options = list(scrollY="100vh")))
  })

output$mytable2 <- DT::renderDataTable({
    DT::datatable(cta_ridership, options = list(c(scrollY="200px", scrollX="300px", pageLength = 100)))
  })

output$mytable3 <- DT::renderDataTable({
    DT::datatable(cta_stations, options = list(c(scrollY="200px", scrollX="300px", pageLength = 100)))
  })

output$mytable4 <- DT::renderDataTable({
    DT::datatable(chicago_svi, options = list(c(scrollY="200px", scrollX="300px", pageLength = 100)))
  })

output$mytable5 <- DT::renderDataTable({
    DT::datatable(cps_tiers, options = list(c(scrollY="200px", scrollX="300px", pageLength = 100)))
  })

output$mytable6 <- DT::renderDataTable({
    DT::datatable(cpsPR2018_sf, options = list(c(scrollY="200px", scrollX="300px", pageLength = 100)))
  })

output$mytable7 <- DT::renderDataTable({
    DT::datatable(tif_sf, options = list(c(scrollY="200px", scrollX="300px", pageLength = 100)))
  })

output$mytable8 <- DT::renderDataTable({
    DT::datatable(tif_sf, options = list(c(scrollY="200px", scrollX="300px", pageLength = 100)))
  })

Codebook

Information Regarding the Data

Datasets to be included in the package include (All accessed through SODA API):

• Census Tracts 2010 (soon to be updated to 2020): Accessed here as a geojson file;
• Boundaries - Community Areas (current): Accessed here as a geojson file;
• Boundaries - Police Beats (current): Accessed here as a geojson file;
• Boundaries - Police Districts (current): Accessed here as a geojson file;
• Boundaries - Wards (2015-): Accessed here as a geojson file;
• Boundaries - Zoning Districts (current): Accessed here as a geojson file;
• Affordable Rental Housing Developments (points) (2019): Accessed here as a json file;
• Chicago Public Schools - School Progress Reports SY1617 (points, with information regarding community areas): Accessed here as a json file;
• CTA - Ridership - Monthly Day-Type Averages & Totals: Accessed here as a json file;
• CTA - 'L' (Rail) Stations: Accessed here as a zip file (points, with community area information) (will have to figure out a way to unzip and manage this dataset, possibly a new R script);
• Census Data - Selected socioeconomic indicators in Chicago, 2008 – 2012 (by community area): Accessed here as a json file. This dataset is outdated, but is the only one that shows community-area level data. It is possible to use CPS data directly from census bureau or IPUMS, but they are not as detailed to the community area level;
• Divvy Bicycle Stations (points, with community area info): Accessed here as a json file;
• Tax Increment Financing (TIF) Projects (points): Accessed here as a geojson file;
• 311 Service Requests - Vacant and Abandoned Buildings (points, with community area information): Accessed here as a geojson file;

The above datasets are all original sets included in Clyde’s Package, and I linked online API locations, and will update in the code. I also included an additional dataset below which I think is quite important:

• Crimes - 2001 to Present (points, with temporal information, can aggregate by community area, police wards, beats): Accessed here as a json file;
• Divvy Trips (points, with beginning and end point info): Accessed here as a json file;

More datasets will be added after the completion of the first stage of project.

 

About

My name is Ryan Wang, and I am a Master's student at the Univeristy of Chicago, concentrating on quantitative methods of social analyses. I created this flexdashboard app with RShiny code incorporated. This app uses the data and functionalities in the Chicago Package and it intends to be a tool for initial assessment of the Chicago data, aiming to quickly guide interests for further data discovery.

The "Chicago Package" is a continuation of Clyde Schwarb's Chicago Data Package (https://github.com/cschwab1/chicagodatapackage) project for GIS 3. Clyde is an undergraduate at the University of Chicago studying geography and environmental and urban studies. A year ago, Clyde initated this project to "provide a starting point for those interested in learning geocomputation with R using real Chicago data", and "building a larger collection of civic data for academic and amateur, journalist and researcher, non-profit and concerned citizens alike." Some most commonly used datasets were provided in the original package, along with initial processing code and vignettes from Clyde. I completed documentation of datasets and added two new datasets, Crime - from 2001 to present, and Divvy Trips.

I have further improved upon the package by providing complete data documentation and vignettes. I also added two new datasets and updated exisiting datasets to current June 2020 data.

The app is made into two sections, a spatial data explorer section ("The Maps") and a non-spatial data explorer section("The Tables"). Both uses a sidebar to select specific variables of interest, and has the functionality for downloading the data.

Copyright (c) 2019 Clyde Copyright (c) 2020 Ryan

ryanwyg/Chicago-Data-Package documentation built on June 22, 2020, 2:55 p.m.