Dogs of New York.Rmd

---
title: "Dog Bites in New York in 2017"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    theme: united
---

```{r setup, include = FALSE}

# This is a setup environment for setting up libraries and creating necessary data transformations

library (flexdashboard)
library (tidyverse)
library (plotly)

#Importing the dataset
datadogs2017 <- read_csv ("datadogs2017.csv")


# This is data to be used for textual part related to boroughs

datadogsborougs <- datadogs2017 %>%
  
  group_by (borough) %>%
  
  tally() %>%
  
  arrange (desc (n)) %>%
  
  mutate (perc = round ((n/sum(n)) * 100, 0))


#Dataset for datatable

datadogstable <- datadogs2017 %>%
  select (-zip_code)

#Creating a subset for top three breeds for bar graph

datadogsbreed <- datadogs2017 %>%
  
  group_by (breed) %>%
  
  tally () %>%

  rename (Number_of_bites = n) %>%
  
  arrange (desc (Number_of_bites)) %>%
  
  top_n(3)

# Changing order of levels to be presented on the graph properly 

datadogsbreed$breed <- factor (datadogsbreed$breed,
                               levels = c ("Pit Bull", "Unknown", "Shih Tzu"))
 

# Creating subset for gender and spay/neutered for second stacked bar graph

datadogsgenderspay <- datadogs2017 %>%
  
  group_by (spay_neuter, gender) %>%
  
  tally () %>%
  
  #Creating another variable that will be called Info in order to be used as a label 
  
  mutate (Info = paste ('<br>', "Spay/Neuter:", 
                        
                        spay_neuter, '<br>',
                        
                        "Number of bites:", n, '<br>',
                        
                        "Gender:", gender, '<br>'))


```

Column {data-width=650} 
-----------------------------------------------------------------------
### **How much do dogs bite in New York City** {data-height=250, align=justify}

This dashboard shows statistics of dog bites in New York City in 2017. It shows that there were total of **`r nrow(datadogs2017)`** bites. The bar chart below shows which were the top three biters per breed. Although, there are certain patterns that might suggest that **Pit Bulls** are the most aggressive, it might just be the case that there are more of them in New York in comparison to other breeds. **`r datadogsborougs[1,1]`** and **`r datadogsborougs[2,1]`** are the two boroughs with the highest percentage of bites **`r datadogsborougs[1,3]`%** and **`r datadogsborougs[2,3]`%** respectively.

### **Table of Dog Bites in New York in 2017** {data-height=750}

```{r}

# Using DT package to create datatable

DT :: datatable (datadogstable, options = list(
  bPaginate = TRUE))

```




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

### **Three breeds with the highest number of bites in 2017**

```{r}
#Barchart for breed biters using ggplot and plotly package

p <- ggplot (data = datadogsbreed, aes (x = breed, y = Number_of_bites))+
  
     geom_col (fill = c("darkred", "darkgreen", "darkblue")) +
  
     ylab ("Number of Bites") +
  
     theme (legend.position = "none",
            
          panel.background = element_rect (fill = "lightcyan"))
  
# Turn it interactive with ggplotly

p <- ggplotly (p)

p

```

### **Bites based on dog's gender and whether they were spayed/neutered** {align=center}

```{r}

#Creating stacked bar using ggplot and plotly package
p1<- ggplot (data = datadogsgenderspay)+
  
  geom_col (aes (x = spay_neuter, y = n, fill = gender, label = Info)) +
  
  scale_fill_manual (values = c('cyan3', 'darkorange', "purple")) +
  
  ylab ("Number of bites") +
  
  theme (legend.position = "none",
    panel.background = element_blank())

p1<-ggplotly(p1,tooltip = "Info")

p1

```