AirbnbFinal
Amani Panduwawala
2023-03-14
Dataset - Airbnb Listings in New York City
This dataset contains information on Airbnb Listings in New York City in 2019. It includes various variables related to the listings such as the price, number of reviews, neighbourgood,minimum nights longitude and latitude, room type, availability and many other variables.
‘Price’ : The Price per night of the Airbnb listing USD
‘number_of_reviews’ : The total numver of reviews for the Airbnb listing
‘neighbourhood_group’ : The neighborhood in which the Airbnb listing is located
‘latitude’/‘longitude’: The cordinates of the airbnb listings
‘room_type’ : The type of room avaialbe for the Airbnb listing
‘minimum_nights’ : The minimum number of nights required to book the listing
‘host_name’ is the person who owns the property
I plan to explore the relationship between the price of airbnb listings and the number of reviwes they recieve, as well as how the price and avilability of Airbnb listings vary across different neighborhoods in New York City.
library(tidyverse)
library(ggfortify)
library(htmltools)
library(plotly)
library(ggplot2)
setwd("C:/Users/amani/OneDrive/Desktop/Data110")
airbnb_ny19 <- read_csv("airbnb_ny19.csv")head(airbnb_ny19)## # A tibble: 6 × 16
## id name host_id host_…¹ neigh…² neigh…³ latit…⁴ longi…⁵ room_…⁶ price
## <dbl> <chr> <dbl> <chr> <chr> <chr> <dbl> <dbl> <chr> <dbl>
## 1 2539 Clean & q… 2787 John Brookl… Kensin… 40.6 -74.0 Privat… 149
## 2 2595 Skylit Mi… 2845 Jennif… Manhat… Midtown 40.8 -74.0 Entire… 225
## 3 3647 THE VILLA… 4632 Elisab… Manhat… Harlem 40.8 -73.9 Privat… 150
## 4 3831 Cozy Enti… 4869 LisaRo… Brookl… Clinto… 40.7 -74.0 Entire… 89
## 5 5022 Entire Ap… 7192 Laura Manhat… East H… 40.8 -73.9 Entire… 80
## 6 5099 Large Coz… 7322 Chris Manhat… Murray… 40.7 -74.0 Entire… 200
## # … with 6 more variables: minimum_nights <dbl>, number_of_reviews <dbl>,
## # last_review <chr>, reviews_per_month <dbl>,
## # calculated_host_listings_count <dbl>, availability_365 <dbl>, and
## # abbreviated variable names ¹host_name, ²neighbourhood_group,
## # ³neighbourhood, ⁴latitude, ⁵longitude, ⁶room_typesummary(airbnb_ny19)## id name host_id host_name
## Min. : 2539 Length:48895 Min. : 2438 Length:48895
## 1st Qu.: 9471945 Class :character 1st Qu.: 7822033 Class :character
## Median :19677284 Mode :character Median : 30793816 Mode :character
## Mean :19017143 Mean : 67620011
## 3rd Qu.:29152178 3rd Qu.:107434423
## Max. :36487245 Max. :274321313
##
## neighbourhood_group neighbourhood latitude longitude
## Length:48895 Length:48895 Min. :40.50 Min. :-74.24
## Class :character Class :character 1st Qu.:40.69 1st Qu.:-73.98
## Mode :character Mode :character Median :40.72 Median :-73.96
## Mean :40.73 Mean :-73.95
## 3rd Qu.:40.76 3rd Qu.:-73.94
## Max. :40.91 Max. :-73.71
##
## room_type price minimum_nights number_of_reviews
## Length:48895 Min. : 0.0 Min. : 1.00 Min. : 0.00
## Class :character 1st Qu.: 69.0 1st Qu.: 1.00 1st Qu.: 1.00
## Mode :character Median : 106.0 Median : 3.00 Median : 5.00
## Mean : 152.7 Mean : 7.03 Mean : 23.27
## 3rd Qu.: 175.0 3rd Qu.: 5.00 3rd Qu.: 24.00
## Max. :10000.0 Max. :1250.00 Max. :629.00
##
## last_review reviews_per_month calculated_host_listings_count
## Length:48895 Min. : 0.010 Min. : 1.000
## Class :character 1st Qu.: 0.190 1st Qu.: 1.000
## Mode :character Median : 0.720 Median : 1.000
## Mean : 1.373 Mean : 7.144
## 3rd Qu.: 2.020 3rd Qu.: 2.000
## Max. :58.500 Max. :327.000
## NA's :10052
## availability_365
## Min. : 0.0
## 1st Qu.: 0.0
## Median : 45.0
## Mean :112.8
## 3rd Qu.:227.0
## Max. :365.0
## Average Prices for Different neighbourhood groups
avg_price <- airbnb_ny19 %>%
group_by(neighbourhood_group) %>%
summarize(avg_price = mean(price))library(dplyr)This plot shows the average price of listings in each neighborhood groups with the x-axis representing the different neighborhood groups and the y-axis representing the average price of listings in each group. Each bar colour represents neighborhood groups with a legend which shows the neighborhood group. It can be seen that Manhatten has the most expensive airbnbs and the Bronx has the lowest.
ggplot(avg_price, aes(x = neighbourhood_group, y = avg_price, fill = neighbourhood_group)) +
geom_bar(stat = "identity", width = 0.5) +
scale_fill_manual(values = c("#FF69F5", "#FF69B4", "#FF1493", "#C71585", "#FFB6C1")) +
labs(title = "Average Price by Neighborhood Group",
x = "Neighborhood Group",
y = "Average Price",
fill = "Neighborhood Group") +
theme_minimal() +
theme(legend.position = "bottom",
panel.background = element_rect(fill = "Grey"))
Using ggplot2 to create a more detailed graph
library(ggplot2)Filter for listings with at least one review I was just exploring to see what I can do
airbnb_ny19 <- airbnb_ny19 %>%
filter(number_of_reviews >0)Here I have used the ggplot2 to create a scatter plot of Airbnb listings in New York City. The x-axis represents the number pf reviews that each listing has recieved, and the y-axis represents the price per night in US dollars. The colour of each point represents the neighborhood group where the listing is located.
ggplot(data = airbnb_ny19, aes(x = number_of_reviews, y = price, color = neighbourhood_group)) +
geom_point(alpha = 0.7, size = 2) +
labs(title = "Airbnb Listings in New York City",
x = "Number of Reviews",
y = "Price (USD)")+
scale_y_continuous(limits = c(0, 2000)) +
theme_minimal() +
theme(plot.title = element_text(size = 20, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
legend.title = element_blank(),
legend.text = element_text(size = 12))## Warning: Removed 35 rows containing missing values (`geom_point()`).
Since I did not understand how to read that graph I tried to explore more and found out I can make graphs using the longitude and latitude
This scatterplot of Airbnb listings in New York City shows the longitude and latitude variables and are mapped on to the x-axis and y- acis.The color shows the neighborgood group of each listing and the size is used to show the price of each listings
ggplot(airbnb_ny19, aes(x = longitude, y = latitude, color = neighbourhood_group, size = price)) +
geom_point(alpha = 0.2) +
theme_dark(base_size = 10) +
scale_color_manual(values = c("darkgreen", "purple", "yellow", "white", "blue")) +
scale_size_continuous(name = "Price", range = c(1, 10)) +
labs(color = "Neighborhood Group") +
ggtitle("Airbnb Listings in New York City")