shinyapp.Rmd

---
title: "Analysis of NYC Sex-Related, Weapon-Related and Drug-Related Felonies"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    source_code: embed
runtime: shiny
---


```{r}
#### File for shiny app. It's not being run from this file. It's just showing the code. 
knitr::opts_chunk$set(eval = FALSE)
```


```{r global, include=FALSE}
# load data in 'global' chunk so it can be shared by all users of the dashboard
library(flexdashboard)
library(tidyverse)
library(viridis)
library(p8105.datasets)
library(plotly)
library(chron)
library(lubridate)
library(rvest)
library(tools)

```

```{r}
### Dataset for 4 years without addresses
crimes_14_17 = readRDS(file = "./sex_drug_weapons.rds")

season_df = crimes_14_17 %>% 
  mutate(quarter = as.numeric(quarter(cmplnt_fr_dt)),
         month = months(cmplnt_fr_dt),
         month = forcats::fct_relevel(month, c("January", "February", "March", "April", "May", "June", "July",
                                               "August", "September", "October", "November", "December")),
         day = weekdays(cmplnt_fr_dt),
         day = forcats::fct_relevel(day, c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday")),
         cmplnt_fr_tm = lubridate::hms(as.character(cmplnt_fr_tm)),
         hour = hour(cmplnt_fr_tm),
         prem_typ_desc = toTitleCase(prem_typ_desc)) %>% select(-cmplnt_fr_tm)

####Labels for axes
trendx <- list(
  title = "Month")

trendy <- list(
  title = "Number of Crimes")

heatx <- list(
  title = "Hour of the day")

heaty <- list(
  title = "Day of the week")

ratex <- list(
  title = "Year")

ratey <- list(
  title = "Crime rate")

barx <- list(
  title = "Premises of crime location")

bary <- list(
  title = "Number")
```


Inputs {.sidebar}
-----------------------------------------------------------------------

```{r}

crimegroups = season_df %>% distinct(crime_group) %>% pull()
# selectInput widget
selectInput("crime_types", label = h3("Select Crime Type"),
            choices = crimegroups, selected = "Sex-Related")

years = season_df %>% arrange(desc(year)) %>% distinct(year) %>% pull()

radioButtons("year_choice", label = h3("Choose Year"),
    choices = years, 
    selected = "2017")
```

NYPD Crime data for Sex-related, Weapon-Related and Drug-related felonies 
from 2014 to 2017. 


Row
-----------------------------------------------------------------------

### Heatmap
    
```{r}
renderPlotly({
season_df %>% mutate(hour = as.factor(hour)) %>% 
    filter(crime_group == input$crime_types,
                     year == input$year_choice) %>% 
  group_by(day, hour) %>% 
  summarize(number = n()) %>% 
  mutate(text_label = str_c('Hour: ', hour, '\nDay: ', day, '\nNumber: ', number)) %>% 
  plot_ly( x = ~ hour, y = ~ day, hoverinfo = "text",
         type = "heatmap", text = ~ text_label,
         colors = colorRamp(c("yellow", "red")),
         z = ~ number) %>% 
    layout(xaxis = heatx, yaxis = heaty)
})
```


Row {.tabset}
-----------------------------------------------------------------------

### Trend
    
```{r}
renderPlotly({
season_df %>% filter(!is.na(boro_nm)) %>% 
    filter(crime_group == input$crime_types,
                     year == input$year_choice) %>% 
  group_by(boro_nm, month) %>% 
  summarize(number = n()) %>% 
  mutate(text_label = str_c('Borough: ', boro_nm, '\nNumber: ', number)) %>% 
  plot_ly(x = ~ month, 
          y = ~ number, 
          color = ~ boro_nm, 
          type = 'scatter', 
          mode = 'lines', hoverinfo = "text",
          text = ~ text_label) %>% 
    layout(xaxis = trendx, yaxis = trendy)
})
```
   
### Crime rate

```{r}
url = "https://www.census.gov/quickfacts/fact/table/newyorkcitynewyork,bronxcountybronxboroughnewyork,kingscountybrooklynboroughnewyork,newyorkcountymanhattanboroughnewyork,queenscountyqueensboroughnewyork,richmondcountystatenislandboroughnewyork/PST045217"

nyc_population = read_html(url) %>%  html_nodes(css = "table") %>% .[[1]] %>% 
  html_table(header = TRUE) %>% as.tibble() %>% 
  janitor::clean_names()

names(nyc_population)[1:7] = c("estimate_date", "new_york_city", "bronx", "brooklyn", "manhattan", "queens", "staten_island")

nyc_population = nyc_population %>% 
  gather(key = boro_nm, value = population, estimate_date:staten_island) %>% 
  mutate(population = if_else(population == "Population estimates, July 1, 2017,  (V2017)", "2017", population),
         population = as.numeric(gsub("," , "", population)))

grouped_df = crimes_14_17 %>% group_by(boro_nm, crime_group, year) %>% 
  summarise(number = n())

population_df = left_join(grouped_df, nyc_population, by = "boro_nm") %>% 
  mutate(crime_rate = (number/population)*100,
         year = as.factor(year))

renderPlotly({
population_df %>% 
    filter(crime_group == input$crime_types) %>% 
  plot_ly(x = ~ year, 
          y = ~ crime_rate, 
          color = ~ boro_nm, 
          type = 'scatter', 
          mode = 'lines') %>% 
    layout(xaxis = ratex, yaxis = ratey)
})

```

### Location of crime

```{r}

renderPlotly({
season_df %>% 
    filter(crime_group == input$crime_types,
           year == input$year_choice) %>% 
    group_by(prem_typ_desc) %>%
    summarize(number = n()) %>% top_n(15) %>% 
  plot_ly(x = ~ reorder(prem_typ_desc, -number),
          y = ~ number,
          type = 'bar') %>% 
    layout(xaxis = barx, yaxis = bary)
})
```