---
title: "An Analysis on Airbnb Hosting in NYC 2019"
output: 
  flexdashboard::flex_dashboard:
    theme: journal
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(dplyr)
library(tidyverse)
library(glue)
library(plotly)
library(ggpubr)
```

```{r}
airbnb <- read_csv("AB_NYC_2019.csv")
```

```{r}
NAexpelled1 <- airbnb%>% 
  mutate(last_review = replace_na(last_review, replace = mean(last_review, na.rm = T))) %>% 
  mutate(reviews_per_month = replace_na(reviews_per_month, replace = mean(reviews_per_month, na.rm = T)))
```

```{r}
airbnbclean <- NAexpelled1%>% 
  select(-id, -host_name, -host_id, -reviews_per_month, -calculated_host_listings_count, -last_review)
```

```{r}
data_airbnb <- airbnbclean %>% 
  mutate(potential_earnings = price * minimum_nights * availability_365)%>%
  select(neighbourhood_group, potential_earnings) %>% 
  group_by(neighbourhood_group) %>% 
  summarise(max_potential_earnings = round(max(potential_earnings), 2))
```

```{r}
theme_algoritma <- theme(legend.key = element_rect(fill="black"),
           legend.background = element_rect(color="white", fill="#263238"),
           plot.subtitle = element_text(size=6, color="white"),
           panel.background = element_rect(fill="#dddddd"),
           panel.border = element_rect(fill=NA),
           panel.grid.minor.x = element_blank(),
           panel.grid.major.x = element_blank(),
           panel.grid.major.y = element_line(color="darkgrey", linetype=2),
           panel.grid.minor.y = element_blank(),
           plot.background = element_rect(fill="#263238"),
           text = element_text(color="white"),
           axis.text = element_text(color="white")
           
           )
```

```{r}
airbnbclean_vis <- airbnbclean %>% 
  group_by(neighbourhood_group, room_type) %>% 
  summarise(mean_price = round(mean(price),2)) %>% 
  ungroup() 
```

```{r}
airbnb3 <- airbnbclean[ airbnbclean$number_of_reviews >= 150, ]
```

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

### Total Maximum Potential Earning of Airbnb Hosts in NYC 
```{r}
Plot1_airbnb <- ggplot(data = data_airbnb, aes(x = reorder(neighbourhood_group, 
                                        max_potential_earnings), 
                                     y = max_potential_earnings,
                                      text = glue("neighbourhood_group:{neighbourhood_group} 
 max_potential_earnings : {max_potential_earnings}") 
                                     )) +
  geom_col(fill = "dodgerblue4") +
  geom_col(data = filter(data_airbnb, 
                         neighbourhood_group == "Manhattan"), 
           fill = "firebrick4") +
  labs(x = NULL,
       y = NULL,
       title = "Total Max Potential Earning of Airbnb Hosts in NYC in the Year 2019") +
  coord_flip() +
  theme_algoritma
```


```{r}
plot1_inter_airbnb <- ggplotly(Plot1_airbnb, tooltip = "text")
plot1_inter_airbnb
```

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

### Average Price of Airbnb Room in NYC Year 2019

```{r}
options(scipen = 999)
airbnb_avg_pricerange <- ggplot(airbnbclean_vis, aes(x = reorder(neighbourhood_group, 
                                             mean_price), 
                                 y = mean_price,
                                 text = glue("Type of Room: {room_type}
                                             Mean Price: {mean_price}"))) +
  geom_col(aes(fill = room_type), position = "dodge") +
  labs(x = "Neighbourhood Group of NYC",
       y = "Mean Price",
       title = "Average Price of Airbnb Room in NYC Year 2019") +
  theme(legend.position = "TRUE") +
  theme_algoritma
```

```{r}
airbnb_avg_pricerange1 <- ggplotly(airbnb_avg_pricerange, tooltip = "text")
airbnb_avg_pricerange1
```

### Distribution of Review Received by Airbnb Hosts > 150

```{r}
airbnb_review <- ggplot(data = airbnb3, mapping = aes(x = neighbourhood_group,
                                       y = number_of_reviews,
                                       text = glue("Airbnb Host Name: {name}"))) +
  geom_jitter(aes(color = neighbourhood_group)) +
  scale_color_brewer(palette = "Set2") +
  labs(x = "Neighbourhood Group",
       y = "Total Review",
       title = "Airbnb NYC Review Distribution Year 2019")+
  theme(legend.position = "none")
```

```{r}
airbnb_review1 <- ggplotly(airbnb_review)
airbnb_review1
```