Challenge 6 Air BnB Data by Last Review

challenge_6
Sue-Ellen Duffy
air_bnb
Visualizing Time and Relationships
Author

Sue-Ellen Duffy

Published

April 2, 2023

Code 
library(tidyverse)
library(ggplot2)
library(summarytools)
library(lubridate)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

AirBnB Listing Data in New York City 2019

This dataset shows AirBnB listings in NYC in 2019 with 48,895 rows (listings) and 17 columns (data for each listing). We see different types of observations including NYC neighborhood and neighborhood group, type of rental (entire home, private room, shared room), their prices, the minimum required number of nights, and number of guest reviews. Additionally we can see how many listing each host has on AirBnB, how many days a listing was available throughout 2019, and the date of the last guest review.

Read in the Data

I chose not to pivot this data because each listing was unique, even if a host had different listings, each had different price points, neighborhoods, room types, and names.

Code 
mydata <- read.csv("_data/AB_NYC_2019.csv", na.strings=c('','  ','   '))
tibble(mydata, 10)
# A tibble: 48,895 × 17
      id name      host_id host_…¹ neigh…² neigh…³ latit…⁴ longi…⁵ room_…⁶ price
   <int> <chr>       <int> <chr>   <chr>   <chr>     <dbl>   <dbl> <chr>   <int>
 1  2539 "Clean &…    2787 John    Brookl… Kensin…    40.6   -74.0 Privat…   149
 2  2595 "Skylit …    2845 Jennif… Manhat… Midtown    40.8   -74.0 Entire…   225
 3  3647 "THE VIL…    4632 Elisab… Manhat… Harlem     40.8   -73.9 Privat…   150
 4  3831 "Cozy En…    4869 LisaRo… Brookl… Clinto…    40.7   -74.0 Entire…    89
 5  5022 "Entire …    7192 Laura   Manhat… East H…    40.8   -73.9 Entire…    80
 6  5099 "Large C…    7322 Chris   Manhat… Murray…    40.7   -74.0 Entire…   200
 7  5121 "BlissAr…    7356 Garon   Brookl… Bedfor…    40.7   -74.0 Privat…    60
 8  5178 "Large F…    8967 Shunic… Manhat… Hell's…    40.8   -74.0 Privat…    79
 9  5203 "Cozy Cl…    7490 MaryEl… Manhat… Upper …    40.8   -74.0 Privat…    79
10  5238 "Cute & …    7549 Ben     Manhat… Chinat…    40.7   -74.0 Entire…   150
# … with 48,885 more rows, 7 more variables: minimum_nights <int>,
#   number_of_reviews <int>, last_review <chr>, reviews_per_month <dbl>,
#   calculated_host_listings_count <int>, availability_365 <int>, `10` <dbl>,
#   and abbreviated variable names ¹​host_name, ²​neighbourhood_group,
#   ³​neighbourhood, ⁴​latitude, ⁵​longitude, ⁶​room_type

The different data points for each listing:

Code 
colnames(mydata)
 [1] "id"                             "name"                          
 [3] "host_id"                        "host_name"                     
 [5] "neighbourhood_group"            "neighbourhood"                 
 [7] "latitude"                       "longitude"                     
 [9] "room_type"                      "price"                         
[11] "minimum_nights"                 "number_of_reviews"             
[13] "last_review"                    "reviews_per_month"             
[15] "calculated_host_listings_count" "availability_365"

Date Tidying

The date was originally characters, I used transform and as.date to mutate last_review into date format.

Code 
mydata <- transform(mydata, last_review=as.Date(last_review))
str(mydata)
'data.frame':   48895 obs. of  16 variables:
 $ id                            : int  2539 2595 3647 3831 5022 5099 5121 5178 5203 5238 ...
 $ name                          : chr  "Clean & quiet apt home by the park" "Skylit Midtown Castle" "THE VILLAGE OF HARLEM....NEW YORK !" "Cozy Entire Floor of Brownstone" ...
 $ host_id                       : int  2787 2845 4632 4869 7192 7322 7356 8967 7490 7549 ...
 $ host_name                     : chr  "John" "Jennifer" "Elisabeth" "LisaRoxanne" ...
 $ neighbourhood_group           : chr  "Brooklyn" "Manhattan" "Manhattan" "Brooklyn" ...
 $ neighbourhood                 : chr  "Kensington" "Midtown" "Harlem" "Clinton Hill" ...
 $ latitude                      : num  40.6 40.8 40.8 40.7 40.8 ...
 $ longitude                     : num  -74 -74 -73.9 -74 -73.9 ...
 $ room_type                     : chr  "Private room" "Entire home/apt" "Private room" "Entire home/apt" ...
 $ price                         : int  149 225 150 89 80 200 60 79 79 150 ...
 $ minimum_nights                : int  1 1 3 1 10 3 45 2 2 1 ...
 $ number_of_reviews             : int  9 45 0 270 9 74 49 430 118 160 ...
 $ last_review                   : Date, format: "2018-10-19" "2019-05-21" ...
 $ reviews_per_month             : num  0.21 0.38 NA 4.64 0.1 0.59 0.4 3.47 0.99 1.33 ...
 $ calculated_host_listings_count: int  6 2 1 1 1 1 1 1 1 4 ...
 $ availability_365              : int  365 355 365 194 0 129 0 220 0 188 ...

Time Dependent Visualization

This is the hard part. I’m not exactly sure what to show here, but I got this far so I won’t give up!

#1 There has gotta be something wrong with these outliers. Who is staying for more than 200 days?

Code 
ggplot(mydata, aes(x=`last_review`, y=`minimum_nights`)) + geom_point() +
  labs(title = "Date of Last Review by Minimum Nights Stay", x = "Date of Last Review", y = "Minimum Required Nights Stay")

#2 I am just going to making some assumptions here… Those folks who had their entire home/apt for rent and had last reviews in 2012-2014 probably sold their homes. These may have been some of those early starters who were buying up real estate and saw this as a great source of income before they were able to sell and move on to other ventures.

Code 
ggplot(mydata, aes(x=`last_review`, y=`room_type`)) + geom_point() +
    labs(title = "Date of Last Review by Room Type", x = "Date of Last Review", y = "Room Type")

#3 I filtered here for Staten Island, just for fun, and set the listing price to less than $500. Most of the last reviews here are in 2019.

Code 
mydata%>%
  filter(neighbourhood_group == "Staten Island", price < 500) %>%
    ggplot(aes(x=last_review, y=price)) +
  geom_point() + scale_x_date(date_labels = "%Y %m %d") +
  xlab("")  + scale_x_date(limits = as.Date(c("2012-01-01", "2020-01-01"))) +
    labs(title = "Staten Island - Date of Last Review by Price of Listing", x = "Date of Last Review", y = "Price of Listings")

#4 Here’s a comparasson - Manhattan has last reviews all over the place.

Code 
mydata%>%
  filter(neighbourhood_group == "Manhattan", price < 500) %>%
    ggplot(aes(x=last_review, y=price)) +
  geom_point() + scale_x_date(date_labels = "%Y %m %d") +
  xlab("")   + scale_x_date(limits = as.Date(c("2012-01-01", "2020-01-01"))) +
    labs(title = "Manhattan - Date of Last Review by Price of Listing", x = "Date of Last Review", y = "Price of Listings")

#5 I’m not exactly sure what to make of this graph. But I wanted to make it so here it is!

Code 
mydata%>%
  filter(last_review > "2019-01-01") %>%

ggplot(aes(x=last_review, y=price, fill=neighbourhood_group)) +
    geom_area() + ylim(0, 500) +
    labs(title = "2019 Neighborhood Group Data by date of last review and price", x = "Date of Last Review", y = "Price of Listings", fill = "Neighborhood Group")

Visualizing Part-Whole Relationships

#1 I chose this date to show the distribution of room type across Neighborhood Groups.

Code 
ggplot(mydata, aes(neighbourhood_group, fill = room_type)) + 
  geom_bar(position="fill", stat = "count") + 
  labs(title = "Percent Distribution of Room Type in each NYC Neighborhood Group", x = "Neighborhood Group", y = "Percentage of Room Type") +
  scale_fill_discrete(name = "Room Type") + 
  theme_bw()