Airbnb - Bangkok
Aung Thura Htoo
2024-08-13
#https://insideairbnb.com/bangkok/Set the Directory, Loaded the data, and “tidyverse”
setwd("D:/me/R-Language/Practice/Dataset")
options(repos = c(CRAN = "https://cran.rstudio.com/"))
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors# Load the CSV file into a data frame
data <- read.csv("listings_airbnb_Aug2024.csv")Check the data and Columns
# View the first few rows of the data
head(data)## id name host_id host_name
## 1 27934 Nice room with superb city view 120437 Nuttee
## 2 27979 Easy going landlord,easy place 120541 Emy
## 3 28745 modern-style apartment in Bangkok 123784 Familyroom
## 4 35780 Spacious one bedroom at The Kris Condo Bldg. 3 153730 Sirilak
## 5 48736 Condo with Chaopraya River View 222005 Athitaya
## 6 55681 Sathorn Terrace Apartment(61) 263049 Tor
## neighbourhood_group neighbourhood latitude longitude room_type price
## 1 NA Ratchathewi 13.75983 100.5413 Entire home/apt 2020
## 2 NA Bang Na 13.66818 100.6167 Private room NA
## 3 NA Bang Kapi 13.75232 100.6240 Private room NA
## 4 NA Din Daeng 13.78823 100.5726 Private room 1286
## 5 NA Rat Burana 13.68556 100.4954 Private room 1653
## 6 NA Bang Rak 13.71934 100.5176 Private room 1150
## minimum_nights number_of_reviews last_review reviews_per_month
## 1 3 64 2020-01-06 0.43
## 2 1 0 NA
## 3 60 0 NA
## 4 14 6 2024-05-22 0.06
## 5 3 1 2014-02-03 0.01
## 6 2 34 2024-04-17 0.21
## calculated_host_listings_count availability_365 number_of_reviews_ltm license
## 1 2 362 0
## 2 2 0 0
## 3 1 0 0
## 4 1 309 2
## 5 1 365 0
## 6 7 356 5str(data)## 'data.frame': 23651 obs. of 18 variables:
## $ id : num 27934 27979 28745 35780 48736 ...
## $ name : chr "Nice room with superb city view" "Easy going landlord,easy place" "modern-style apartment in Bangkok" "Spacious one bedroom at The Kris Condo Bldg. 3" ...
## $ host_id : int 120437 120541 123784 153730 222005 263049 263049 294896 302658 272478 ...
## $ host_name : chr "Nuttee" "Emy" "Familyroom" "Sirilak" ...
## $ neighbourhood_group : logi NA NA NA NA NA NA ...
## $ neighbourhood : chr "Ratchathewi" "Bang Na" "Bang Kapi" "Din Daeng" ...
## $ latitude : num 13.8 13.7 13.8 13.8 13.7 ...
## $ longitude : num 101 101 101 101 100 ...
## $ room_type : chr "Entire home/apt" "Private room" "Private room" "Private room" ...
## $ price : int 2020 NA NA 1286 1653 1150 1384 1102 NA 1543 ...
## $ minimum_nights : int 3 1 60 14 3 2 2 30 1 90 ...
## $ number_of_reviews : int 64 0 0 6 1 34 210 2 0 18 ...
## $ last_review : chr "2020-01-06" "" "" "2024-05-22" ...
## $ reviews_per_month : num 0.43 NA NA 0.06 0.01 0.21 1.29 0.01 NA 0.11 ...
## $ calculated_host_listings_count: int 2 2 1 1 1 7 7 2 1 1 ...
## $ availability_365 : int 362 0 0 309 365 356 365 362 0 358 ...
## $ number_of_reviews_ltm : int 0 0 0 2 0 5 2 0 0 0 ...
## $ license : chr "" "" "" "" ...unique_room_type <- unique(data$room_type)
unique_room_type## [1] "Entire home/apt" "Private room" "Hotel room" "Shared room"Distribution of the Room Type
data %>%
count(room_type) %>%
ggplot(aes(x = reorder(room_type, -n), y = n)) +
geom_bar(stat = "identity", fill = "skyblue", color = "black", width = 0.7) +
geom_text(aes(label = n), vjust = -0.5, color = "black", size = 3.5) +
labs(title = "Distribution of Room Types", x = "Room Type", y = NULL) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
Hosts with multiple listings
top_hosts <- data %>%
group_by(host_name, room_type) %>%
summarize(listings_count = n(), .groups = 'drop') %>%
pivot_wider(
names_from = room_type,
values_from = listings_count,
values_fill = list(listings_count = 0) # Fill in 0 for missing values
) %>%
mutate(
`Listings` = `Entire home/apt` + `Private room` + `Shared room` + `Hotel room`
) %>% arrange(desc(Listings))
top_hosts## # A tibble: 6,263 × 6
## host_name `Entire home/apt` `Private room` `Hotel room` `Shared room`
## <chr> <int> <int> <int> <int>
## 1 Alice 80 173 0 1
## 2 Curry 225 0 0 1
## 3 Krittika 175 10 0 0
## 4 Elmer 0 153 0 8
## 5 Alex 151 6 1 0
## 6 Tony 145 0 0 0
## 7 Noons 123 0 0 2
## 8 Max 96 10 0 0
## 9 Yang 103 0 0 0
## 10 K 87 3 8 0
## # ℹ 6,253 more rows
## # ℹ 1 more variable: Listings <int>Availability Distribution for Short-Term vs Long-Term Rentals
short_term <- data %>% filter(minimum_nights <= 7)
long_term <- data %>% filter(minimum_nights > 7)
ggplot() +
geom_histogram(data = short_term, aes(x = availability_365, fill = 'Short-Term'), bins = 30, alpha = 0.5) +
geom_histogram(data = long_term, aes(x = availability_365, fill = 'Long-Term'), bins = 30, alpha = 0.5) +
labs(title = "Availability Distribution for Short-Term vs Long-Term Rentals",
x = "Availability (days per year)",
y = "Count") +
scale_fill_manual(values = c('Short-Term' = 'blue', 'Long-Term' = 'red')) +
theme_minimal()
Mean, Median, and Avg_availability of short-term vs long-term rentals
summary_short_term <- short_term %>%
summarise(average_price = mean(price, na.rm = TRUE),
median_price = median(price, na.rm = TRUE),
avg_availability = mean(availability_365, na.rm = TRUE))
summary_long_term <- long_term %>%
summarise(average_price = mean(price, na.rm = TRUE),
median_price = median(price, na.rm = TRUE),
avg_availability = mean(availability_365, na.rm = TRUE))
summary_short_term## average_price median_price avg_availability
## 1 2644.593 1472.5 221.0468summary_long_term## average_price median_price avg_availability
## 1 2074.552 1250 210.4231Clean NA and infinite numbers
sum(!is.finite(data$price))## [1] 4639sum(is.na(data$price))## [1] 4639data_clean <- data %>%
filter(is.finite(price) & !is.na(price))
str(data_clean)## 'data.frame': 19012 obs. of 18 variables:
## $ id : num 27934 35780 48736 55681 55686 ...
## $ name : chr "Nice room with superb city view" "Spacious one bedroom at The Kris Condo Bldg. 3" "Condo with Chaopraya River View" "Sathorn Terrace Apartment(61)" ...
## $ host_id : int 120437 153730 222005 263049 263049 294896 272478 545890 578110 610315 ...
## $ host_name : chr "Nuttee" "Sirilak" "Athitaya" "Tor" ...
## $ neighbourhood_group : logi NA NA NA NA NA NA ...
## $ neighbourhood : chr "Ratchathewi" "Din Daeng" "Rat Burana" "Bang Rak" ...
## $ latitude : num 13.8 13.8 13.7 13.7 13.7 ...
## $ longitude : num 101 101 100 101 101 ...
## $ room_type : chr "Entire home/apt" "Private room" "Private room" "Private room" ...
## $ price : int 2020 1286 1653 1150 1384 1102 1543 6024 1469 1190 ...
## $ minimum_nights : int 3 14 3 2 2 30 90 28 30 1 ...
## $ number_of_reviews : int 64 6 1 34 210 2 18 147 0 6 ...
## $ last_review : chr "2020-01-06" "2024-05-22" "2014-02-03" "2024-04-17" ...
## $ reviews_per_month : num 0.43 0.06 0.01 0.21 1.29 0.01 0.11 0.95 NA 0.35 ...
## $ calculated_host_listings_count: int 2 1 1 7 7 2 1 1 1 3 ...
## $ availability_365 : int 362 309 365 356 365 362 358 362 365 365 ...
## $ number_of_reviews_ltm : int 0 2 0 5 2 0 0 0 0 5 ...
## $ license : chr "" "" "" "" ...Boxplot of prices by room type
# Boxplot of prices by room type
data_clean %>% ggplot(aes(x = room_type, y = price)) +
geom_boxplot(fill = "lightblue", color = "blue") +
labs(title = "Price by Room Type", x = "Room Type", y = "Price")
Boxplot of prices by room type without outliers
# Boxplot of prices by room type with y-axis limits
data_clean %>% ggplot(aes(x = room_type, y = price)) +
geom_boxplot(fill = "lightblue", color = "blue") +
labs(title = "Price by Room Type", x = "Room Type", y = "Price") +
coord_cartesian(ylim = c(0, 6000))
Scatter plot of price vs. number of reviews
# Scatter plot of price vs. number of reviews
data_clean %>%
ggplot(aes(x = number_of_reviews, y = price)) +
geom_point() +
labs(title = "Price vs. Number of Reviews", x = "Number of Reviews", y = "Price")+
coord_cartesian(ylim = c(0, 10000))
# Compute correlation
cor(data_clean$price, data_clean$number_of_reviews, use = "complete.obs")## [1] -0.0050089215 highest Listings by Neighborhood
# Count of listings by neighborhood and select top 15
neighborhood_counts <- data_clean %>%
group_by(neighbourhood) %>%
summarise(count = n()) %>%
slice_max(order_by = count, n = 15)
neighborhood_counts## # A tibble: 15 × 2
## neighbourhood count
## <chr> <int>
## 1 Vadhana 3205
## 2 Khlong Toei 2870
## 3 Huai Khwang 1692
## 4 Ratchathewi 1250
## 5 Sathon 906
## 6 Phra Khanong 781
## 7 Phra Nakhon 735
## 8 Bang Rak 725
## 9 Chatu Chak 617
## 10 Parthum Wan 473
## 11 Din Daeng 470
## 12 Bang Na 454
## 13 Khlong San 452
## 14 Suanluang 444
## 15 Bang Phlat 363# Bar plot of listing counts by neighborhood, arranged in descending order
neighborhood_counts %>% ggplot(aes(x = reorder(neighbourhood, -count), y = count)) +
geom_bar(stat = "identity", fill = "lightcoral", color = "darkred") +
labs(title = "15 highest Listings by Neighborhood", x = "Neighborhood", y = "Number of Listings") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
15 Highest Average Price by Neighborhood
# Average price by neighborhood
neighborhood_avg_price <- data_clean %>%
group_by(neighbourhood) %>%
summarise(avg_price = mean(price, na.rm = TRUE))%>%
slice_max(order_by = avg_price, n = 15)
# Bar plot of average price by neighborhood
neighborhood_avg_price %>% ggplot(aes(x = reorder(neighbourhood, -avg_price), y = avg_price)) +
geom_bar(stat = "identity", fill = "lightblue", color = "blue") +
labs(title = "15 Highest Average Price by Neighborhood", x = "Neighborhood", y = "Average Price") +
coord_cartesian(ylim = c(0, 10000)) + # Adjust y-axis as needed
theme(axis.text.x = element_text(angle = 90, hjust = 1))
Cleaning Data with sf package to create a geometry column using
latitude and longitude
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## layoutGeographical Distribution of Airbnb - Bangkok Listings along with their Price
# Creating the ggplot object
p <- ggplot(data_clean_sf) +
geom_sf(aes(color = neighbourhood, text = paste("Neighbourhood:", neighbourhood, "<br>Price (THB):", price))) +
theme_minimal() +
labs(title = "Geographical Distribution of Airbnb - Bangkok Listings",
subtitle = "Colored by Neighborhood",
x = "Longitude",
y = "Latitude") +
theme(legend.position = "none") # Remove the legend## Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat,
## : Ignoring unknown aesthetics: text# Converting to an interactive plot
p_interactive <- ggplotly(p, tooltip = "text")
# Displaying the interactive plot
p_interactive