AirbnbFinal3

This dataset contains information on Airbnb Listings in New York City in 2019.Airbnb is one of the main ways of accomodation for travelers travelling to not only New York but many other parts of the world. This data set has 16 columns and 48895 observations.This dataset was originally compiled and made available by Inside Airbnb, an independent website that scrapes Airbnb’s website to provide public data on Airbnb listings in cities around the world. This dataset allows us to compare statistics for a wide range of airbnbs with different price points and locations. First I am going to see the average price of airbnb listing in each neighborhood groups. Then I will look at the relationship between the reviews and the prices

‘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

The data collection methodology for this dataset is collected through Airbnb’s internal platforms.I chose this dataset because I am interested in going to see some of these airbnb’s when I visit New York. This dataset also provides valuable insights into the dynamics of short-term rental markets in urban areas like New York City. According to the article in npr.org it discusses the potential impact of new short term regulations on the Airbnb market in New York City. There are new regulations which are set to go into effect in early 2023. this can have an impact on this dataset as it could result in a decrease in the number of Airbnb listings available in the city which could in turn affect the accuracy and completeness of the dataset. Reference- https://www.npr.org/2022/12/28/1145709106/nyc-could-lose-10-000-airbnb-listings-because-of-new-short-term-rental-regulatio

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
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library(tmap)

## Warning: package 'tmap' was built under R version 4.2.3

library(tmaptools)

## Warning: package 'tmaptools' was built under R version 4.2.3

library(leaflet)

## Warning: package 'leaflet' was built under R version 4.2.3

library(sf)

## Warning: package 'sf' was built under R version 4.2.3

## Linking to GEOS 3.9.3, GDAL 3.5.2, PROJ 8.2.1; sf_use_s2() is TRUE

library(leaflet.extras)

## Warning: package 'leaflet.extras' was built under R version 4.2.3

library(dplyr)
library(rio)

## Warning: package 'rio' was built under R version 4.2.3

library(sp)

## Warning: package 'sp' was built under R version 4.2.3

setwd("C:/Users/amani/OneDrive/Desktop/Data110")
airbnb_ny19 <- read_csv("airbnb_ny19.csv")

## Rows: 48895 Columns: 16
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (6): name, host_name, neighbourhood_group, neighbourhood, room_type, la...
## dbl (10): id, host_id, latitude, longitude, price, minimum_nights, number_of...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Loading the library to enter image

library(magick)

## Warning: package 'magick' was built under R version 4.2.3

## Linking to ImageMagick 6.9.12.3
## Enabled features: cairo, freetype, fftw, ghostscript, heic, lcms, pango, raw, rsvg, webp
## Disabled features: fontconfig, x11

library(jpeg)

airbnb_image <- readJPEG("airbnb.jpeg")
plot(0:1, 0:1, type = "n", xaxt = "n", yaxt = "n", bty = "n", ylab = "", xlab = "")
rasterImage(airbnb_image, 0.2, 0.2, 0.8, 0.8)

Removing all rows that contain missing values from the dataset. Any row that contains one missing value will be removed.

airbnb_ny19 <- airbnb_ny19 %>% drop_na()

Group by neighborhood and calculate mean price

price_by_neighborhood <- airbnb_ny19 %>%
  group_by(neighbourhood_group) %>%
  summarize(avg_price = mean(price))

Creating the bar plot. This graph shows the average price of Airbnb listings in each neighborhood group. It can be seen that the most expensive neighborhoods are in Mangatten. Brooklyn also has some expensive neighborhoods. The least expensive neighborhoods are in the Bronxs and Staten Island.

ggplot(data = airbnb_ny19, aes(x = neighbourhood_group, y = price)) +
  geom_bar(stat = "summary", fun = "mean", fill = "#FFC0CB") +
  labs(title = "Average Airbnb Listing Price by Neighborhood Group", 
       x = "Neighborhood Group", 
       y = "Average Price ($)") +
  theme_minimal()

Next I am going to see the correlation between the number of reviews and the price

airbnb_reviews_price <- airbnb_ny19 %>%
  select(price, number_of_reviews)

loading plotly to create an interactive plot.

library(plotly)

## 
## Attaching package: 'plotly'

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This plot shows a clear positive correlation between the number of reviews for an airbnb and its price. As the price of the listing increases there are generally more reviews. However there are also high priced listings with relatively few reviews which shows that they are less popular despite their high prices or not many people can afford to visit those airbnbs. There is a large spread of data points at lower prices showing that there are many listings with similar number of reviews at a range of prices. When you go over the points you can see the individual data points and neighborhoods making it easier to identify patterns and outliers.

p <- ggplot(airbnb_ny19, aes(x = number_of_reviews, y = price)) +
  geom_point(alpha = 0.6, color = "purple") +
  labs(x = "Number of Reviews", y = "Price") +
  theme_bw()

ggplotly(p)

It would be interesting to explore this relationship further by including additional variables, such as the type of listing (e.g. entire home vs private room), the location of the listing, and the time of year that the reviews were left.

library(dplyr)
library(ggplot2)

I am going to filter the dataset to only include listings with a price greater than 50 USD and more than 100 reviews.

airbnb_ny19_filtered <- airbnb_ny19 %>%
  filter(price > 50 & number_of_reviews > 100)

This is a scatter plot which shows the relationship between the price and number of reviews. The plot shows that there is a negative relationship between price and number of reviews with higher priced listings having fewer reviews. The plot shows that the majority of listings fall below 500 USD. This could be due to a number of factors such as high priced airbnbs being percieved as less afforadable or having more competition from other similar airbnbs. Lower priced airbnbs may more likely to attract budget conscious travelers who are more willing to leave a review whil higher -priced listings may attract travelers who are less price sensitive therefore less likely to leave reviews.

airbnb_ny19_filtered %>%
  ggplot(aes(x = price, y = number_of_reviews)) +
  geom_point(alpha = 0.2, size = 2, color = "#FF5733") + 
  geom_smooth(method = "lm", se = FALSE, color = "black") +
  scale_y_log10() +
  labs(title = "Price vs. Number of Reviews on Airbnb Listings in NYC",
       x = "Price (USD)",
       y = "Number of Reviews") 

## `geom_smooth()` using formula = 'y ~ x'

airbnb_ny19_filtered <- lm(number_of_reviews ~ price, data = airbnb_ny19_filtered)

summary(airbnb_ny19_filtered)

## 
## Call:
## lm(formula = number_of_reviews ~ price, data = airbnb_ny19_filtered)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -67.17 -42.97 -18.50  26.38 374.53 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 171.14752    2.14484  79.795  < 2e-16 ***
## price        -0.05732    0.01332  -4.302 1.75e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 60.56 on 2706 degrees of freedom
## Multiple R-squared:  0.006792,   Adjusted R-squared:  0.006425 
## F-statistic: 18.51 on 1 and 2706 DF,  p-value: 1.755e-05

The p-value associated with this coefficient estimate is very small (1.75e-05), which means that it is statistically significant at the 0.05 level, indicating that there is a significant linear relationship between price and the number of reviews. However, the R-squared value is quite low (0.006792), indicating that only a very small proportion of the variation in the number of reviews can be explained by the variation in price.

Creating a color palette

mypal <- colorNumeric(palette = "Purples", domain = airbnb_ny19$price)

#Defining the name and price of the airbnb listing to create interactive pop-up windows

mypopup <- paste("<strong>Name:</strong> ", airbnb_ny19$name,
                 "<br><strong>Price:</strong> $", airbnb_ny19$price)

This shows a map that shows the location of the Airbnb listings in New York City with the indicating price of the listings. The legend shows the price range and the corresponding color.

mymap <- leaflet() %>%
  addProviderTiles("CartoDB.Positron") %>%
  addCircleMarkers(data = airbnb_ny19, 
                   lat = ~latitude, 
                   lng = ~longitude,
                   fillColor = ~mypal(price),
                   color = "pink", 
                   fillOpacity = 0.9, 
                   weight = 1, 
                   radius = 3,
                   popup = mypopup) %>%
  addLegend(pal = mypal, 
            values = airbnb_ny19$price, 
            position = "bottomright", 
            title = "Price",
            labFormat = labelFormat(prefix = "$"))

mymap