VellonINFO3500Project

---
title: "Denver Police Department Activity Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
  runtime: shiny
---

```{r setup, include=FALSE}

##Including all of the libraries necessary for this analysis. 
#Make sure the packages are installed before calling the library. 

library(flexdashboard)
library(knitr)
library(dplyr)
library(ggplot2)
library(shiny)
library(ggmap)
library(shiny)
library(shinydashboard)
library(lubridate)

```


DPD 911 Dispatches 
===================================== 

Column {data-width=350}
-----------------------------------------------------------------------

### Dispatch Time of Day by Day of Week

```{r}
#The text above the blue ====== line is the tab label. 
#The text to the right to ### is the title of the dashboard componenet. 

#There are 3 datasets used in this in this dashboard. 
#This is the first dataset stored in my setwd() location. 

df911<-readRDS("R 911 Dataset.rds")

#I used ggplot and dplyr to create the visuals on this dashboard. 
#The fill is Time.of.Day and the fill is what you are trying to group-by. 

df911 %>%
  ggplot(aes(x = DOW, fill = Time.of.Day)) +
    geom_bar()

```

### Top 5 "Problems" by Time of Day of Dispatches

```{r}

#There were too many "problems" to fit on one visual so I decided to use the top 5 by frequency. 

#This first chunck of code uses dyplr to determien what the top 5 problems are. 
d2 <- df911 %>%
  count(Problem) %>%
  top_n(5) %>%
  arrange(n, Problem) %>%
  mutate(Problem = factor(Problem, levels = unique(Problem)))

#This second chunk of code uses dplyr and ggplot to apply the top 5 problems to create the visual. 
df911 %>%
  filter(Problem %in% d2$Problem) %>%
  mutate(Problem = factor(Problem, levels = levels(d2$Problem))) %>%
  ggplot(aes(x = Problem, fill = Time.of.Day)) +
    geom_bar()

```

DPD Arrests 
===================================== 

Column {data-width=350}
-----------------------------------------------------------------------

### Arrest Reasons by Month

```{r}

#The second tab uses a different dataset, so we have to load in this other datset.
#This dataset is also located in my setwd() location. 

dfAR <-read.csv("ArrestData.csv", header=TRUE)

g<-ggplot(dfAR,aes(as.factor(MONTH)))

g+geom_bar(aes(fill=X.ARREST_REASON.))

```

### Top 5 Arrest Zones by Month 

```{r}

d3 <- dfAR %>%
  count(X.ZONE) %>%
  top_n(5) %>%
  arrange(n, X.ZONE) %>%
  mutate(X.ZONE = factor(X.ZONE, levels = unique(X.ZONE)))

dfAR %>%
  filter(X.ZONE %in% d3$X.ZONE) %>%
  mutate(X.ZONE = factor(X.ZONE, levels = levels(d3$X.ZONE))) %>%
  ggplot(aes(x = as.factor(MONTH), fill = X.ZONE)) +
    geom_bar()

```

DPD Offenses
===================================== 

Column {data-width=350}
-----------------------------------------------------------------------

### Top 5 Offenses by District

```{r}

#The third tab uses a different dataset, so we have to load in this other datset.
#This dataset is also located in my setwd() location. 

dfOD<-readRDS("OffensesData.rds")

d4 <- dfOD %>%
  count(Offense.Desc) %>%
  top_n(5) %>%
  arrange(n, Offense.Desc) %>%
  mutate(Offense.Desc = factor(Offense.Desc, levels = unique(Offense.Desc)))

dfOD %>%
  filter(Offense.Desc %in% d4$Offense.Desc) %>%
  mutate(Offense.Desc = factor(Offense.Desc, levels = levels(d4$Offense.Desc))) %>%
  ggplot(aes(x = as.factor(District), fill = Offense.Desc)) +
    geom_bar()

```

### Offenses in Districts by Day of Week

```{r}

dfOD %>%
  ggplot(aes(x = as.factor(District), fill = Occ.Dow)) +
    geom_bar()

```

Pot Crimes 
===================================== 

-----------------------------------------------------------------------
### Top 5 Offense Categories by District Involving MJ

```{r}

#The fourth tab uses a different dataset, so we have to load in this other datset.
#This dataset is also located in my setwd() location. 

dfPOT<-read.csv("crime_marijuana.csv")


d5<- dfPOT %>%
  count(OFFENSE_CATEGORY_ID) %>%
  top_n(5) %>%
  arrange(n, OFFENSE_CATEGORY_ID) %>%
  mutate(OFFENSE_CATEGORY_ID = factor(OFFENSE_CATEGORY_ID, levels = unique(OFFENSE_CATEGORY_ID)))

dfPOT %>%
  filter(OFFENSE_CATEGORY_ID %in% d5$OFFENSE_CATEGORY_ID) %>%
  mutate(OFFENSE_CATEGORY_ID = factor(OFFENSE_CATEGORY_ID, levels = levels(d5$OFFENSE_CATEGORY_ID))) %>%
  ggplot(aes(x = as.factor(DISTRICT_ID), fill = OFFENSE_CATEGORY_ID)) +
    geom_bar()