hw2.R

library(plyr)
library(dplyr)
library(plotly)
library(ggplot2)
library(shiny)
library(reshape2)
library(data.table)
library(DT)
library(httr)
library(jsonlite)
library(plotly)
library(htmltools)

## Creation of
ckanSQL <- function(url) {
  # Make the Request
  r <- RETRY("GET", URLencode(url))
  # Extract Content
  c <- content(r, "text")
  # Basic gsub to make NA's consistent with R
  json <- gsub('NaN', 'NA', c, perl = TRUE)
  # Create Dataframe
  data.frame(jsonlite::fromJSON(json)$result$records)
}
# Unique values for Resource Field
ckanUnique <- function(id, field) {
  url <- paste0("https://data.wprdc.org/api/action/datastore_search_sql?sql=SELECT%20DISTINCT(%22", field, "%22)%20from%20%22", id, "%22")
  c(ckanSQL(URLencode(url)))
}
#Create choices for fields of Race and Gender
gender_choices <- sort(ckanUnique("7f5da957-01b5-4187-a842-3f215d30d7e8", "Gender")$Gender)
race_choices <- sort(ckanUnique("7f5da957-01b5-4187-a842-3f215d30d7e8", "Race")$Race)

#Relabel choices of gender and race (input in user interface)
# Moved gender choice refactoring to the reactive function
 race_choices <- recode(race_choices, A = "Asian", B = "Black", H = "Hispanic", I = "Indian", U = "Unknown", W = "White", x = "Unreported")

# Define UI for application using a fluid page layout
ui <- fluidPage(
# Layout of Basic Input          
  titlePanel("Allegheny County Jail Bookings for September "),
      tabsetPanel(
        #1st Panel for Gender and Race Information and Refresh Button
        tabPanel("Basic Information",
                  fluidRow(
                    column(4,

                           wellPanel(radioButtons("race", label = "Race", 
                                                 choiceNames = c('Asian' , 'Black','Hispanic','Indian','Unknown','White','Unreported'),
                                                 choiceValues = c("A", "B", "H", "I", "U", "W", "x")
                                                 )),
                           wellPanel(radioButtons("gender", label = "Options", choiceNames = c("Female", "Male"), choiceValues = c("F", "M"))),
                           wellPanel(actionButton("click", "Click to See What Happens"))),
                   column(8, wellPanel( plotlyOutput("race.info")), 
                             wellPanel(plotlyOutput("gender.info"))))),
         #2nd Panel for Download Button and Data Table
         tabPanel("Downloads and Table",
                   fluidRow(column(4, wellPanel(downloadButton("new.download", label = "Download New File"))),
                            column(8, wellPanel(dataTableOutput("table", width = "80%")))))
      ))

# Define server logic required to draw two bar graphs, a data table, and a download button
server <- function(input, output, session = session) {
# create reactive element  
df.filter <- reactive ({
  # FYI, you can use an IN statement instead of equals for any api calls where you would otherwise let folks select multiple things. https://www.w3schools.com/sql/sql_in.asp
   url <- paste0("https://data.wprdc.org/api/action/datastore_search_sql?sql=SELECT%20*%20FROM%20%227f5da957-01b5-4187-a842-3f215d30d7e8%22%20WHERE%20%22Gender%22%20=%20%27", 
                input$gender,"%27%20AND%20%22Race%22%20%3D%20%27", input$race, "%27%20")
     # use ckan on url and make clean data
  clean.data <- ckanSQL(url) %>% na.omit() %>%
   mutate(Gender = plyr::revalue(Gender,  c("F" = 'Female',  "M" = 'Male'))
          ) %>%
  mutate(Race = plyr::revalue(Race,  c("A" = 'Asian',  "B" = 'Black', "H" = 'Hispanic', "I" = 'Indian', "U" = 'Unknown', "W" = 'White', "x" = 'Unreported')))
  print(colnames(clean.data))
  return(clean.data)
   })
#Create Interactive Race and Gender Plot  
   output$race.info <- renderPlotly({
    df2 <- df.filter()
    # ggplots are easier to read when each plus starts a new line.
    ggplotly(ggplot(data = df2, aes(x = Race , fill = Race)) + 
               geom_bar() + 
               ggtitle("Arrests by Race and Gender for September") +
               scale_fill_manual(values = c("Asian" = "green", "White" = "orange", 
                                            "Black" = "purple", "Hispanic" = "yellow",
                                            "Indian" = "pink", "Unreported" = "red",
                                            "Unknown" = "grey")) +
               ylab("Total"))
    }) 
#Create Interactive Gender Plot
  output$gender.info <- renderPlotly({
    df1 <- df.filter()
    ggplotly(ggplot(data = df1, aes(x = Gender, fill = Gender)) + 
      geom_bar()+ ggtitle("Arrests by Sex for September") +  scale_fill_manual (values = c("Female" = "blue", "Male" = "brown")) + 
        ylab("Total"))})
#Create Download Button that allows people to download info
  output$new.download <- downloadHandler(
    filename = function(){ 
      paste("new.download", Sys.Date(), ".csv", sep = "" )},
    content = function(file) {
      write.csv(df.filter(), file, row.names = FALSE)
    })
#Create table  
  output$table <- renderDataTable({
    functioning.table <- df.filter()
    subset(functioning.table, select = c(Date, Gender, Race, Age.at.Booking, Current.Age))
  })
#Create Refresh Button   
  observeEvent(input$click, {
    updateRadioButtons(session, "race", selected = 'A')
    updateRadioButtons(session, "gender", selected = 'F')
    # This resets both filters, not just race. A few pts off in UI.
    showNotification("You have successfully reset the filters for race", type = "message")
      })
}
# Run the application 
shinyApp(ui = ui, server = server)