flex_dashboard.Rmd

---
title: "Sex-Related, Weapon-Related & Drug-Related Crimes in NYC"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
---

```{r flex_libraries}
library(flexdashboard)
library(plotly)
library(tidyverse)
library(leaflet)
library(leaflet.extras)
library(sp)
library(maps)
library(chron)
library(lubridate)
library(rvest)
library(tools)
library(geojsonio)
```

```{r flex_data}
sex_drug_weapons = readRDS(file = "./datasets/sex_drug_weapons.rds")

###Dataset for 2017 with addresses
crimes_df = readRDS(file = "./datasets/all_sex_drug_weapon_zip_2017.rds")

```

Column {data-width=550}
-----------------------------------------------------------------------

### Chart A: Distribution for sex-related, drug-related and weapon-related felonies for 2017 by Zipcode

```{r flex_chatA}
map_json = geojson_read("./datasets/nyc_zip_map.geojson", what = "sp")

crimes_df = crimes_df %>% 
  mutate(zipcode = str_extract(zip, "[0-9][0-9][0-9][0-9][0-9]"),
         boro_nm = toTitleCase(boro_nm))

crime_group_sum = crimes_df %>% group_by(zipcode) %>% summarize(number = n())

map_json@data <-  
  map_json@data %>% left_join(crime_group_sum, by = c("postalCode" = "zipcode")) %>% 
  mutate(number = as.numeric(number)) %>% 
  mutate(number = if_else(is.na(number), 0, number))

# create color palette with colorNumeric()
nyc_pal <- colorNumeric("Blues", domain = map_json@data$number)

map_json %>% 
  leaflet() %>% 
  addProviderTiles("OpenStreetMap.BlackAndWhite", options = providerTileOptions(opacity = 0.4)) %>% 
  setView(lat = 40.7, lng = -73.9, zoom = 11) %>% 
  addPolygons(weight = 1, fillColor = ~ nyc_pal(number),
              color = "gray",
              fillOpacity = 3, opacity = 1, 
              smoothFactor = 0.2, 
  # add labels that display mumber
              label = ~ paste(postalCode, borough, ':', number, sep = '\n'),
              # highlight polygons on hover
              highlight = highlightOptions(weight = 2, color = "black",
              bringToFront = TRUE)) %>% 
   addLegend(pal = nyc_pal,
                   values = c(0:305),
                   opacity = 0.75,
                   title = "Number of incident crimes ",
                   position = "topleft")
```

Column {data-width=450}
-----------------------------------------------------------------------

### Chart B: Distribution of victims of sex and weapon-related felonies by age group 2014 - 2017

```{r flex_chartB}

sex_drug_weapons %>% filter(!is.na(vic_age_group), vic_age_group != "unknown") %>% 
  mutate(vic_age_group = str_c(vic_age_group, " years")) %>% 
  filter(crime_group == "Sex-Related" | crime_group == "Weapon-Related") %>% 
  group_by(crime_group, vic_age_group) %>% 
  summarize(number = n()) %>% 
  mutate(text_label = paste("Age group = ", vic_age_group, "\nCrime Group = ", crime_group, "\nCount = ", number)) %>% 
  plot_ly(x = ~ crime_group, y = ~ number, color = ~ vic_age_group, type = "bar", text = ~ text_label) %>% 
   layout(yaxis = list(title = 'Count'), xaxis = list(title = 'Crime Group'))

```

### Chart C: Distribution of victims of sex, drug and weapon-related felonies by race/ethnicity from 2014 - 2017

```{r flex_chartC}

season_df = sex_drug_weapons %>% 
  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)) %>% select(-cmplnt_fr_tm)

#### Filtering out unknown victim race

p = season_df %>% filter(vic_race != "unknown") %>% 
  group_by(vic_race, boro_nm) %>% summarize(number = n()) %>% 
  ggplot(aes(x = reorder(vic_race, number), y = number, fill = vic_race)) + geom_col()  + 
  theme(legend.position = "left", axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  labs(y = "Total Number") + viridis::scale_fill_viridis(discrete = TRUE,
                                                         name = "Race/Ethnicity")


ggplotly(p)
  
```