2019 AirBnB Rentals in NYC Boroughs

challenge_5

Michele Carlin

air_bnb

Author

Michele Carlin

Published

March 28, 2023

Code

library(tidyverse)
library(summarytools)
library(dbplyr)
library(readr)
library(forcats)

knitr::opts_chunk$set(echo = TRUE)

Read in and view summary of ‘AB_NYC_2019’ dataset

Code

AB_NYC <- read_csv ("_data/AB_NYC_2019.csv")

Rows: 48895 Columns: 16
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr   (5): name, host_name, neighbourhood_group, neighbourhood, room_type
dbl  (10): id, host_id, latitude, longitude, price, minimum_nights, number_o...
date  (1): last_review

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.

Code

View(AB_NYC)
view(dfSummary(AB_NYC))

Switching method to 'browser'
Output file written: C:\Users\CarlinML\AppData\Local\Temp\RtmpsRk2DP\file54a44f12598d.html

This dataset contains 48895 rows and 16 columns. Each row/observation contains information on Airbnb rentals in NYC during 2019, including the type of rental (e.g., entire home, private room, shared room), the NYC borough, the price, minimum number of nights, and number of reviews.

Per the data frame summary, the price variable contains some $0’s; sort dataframe to review before recoding.

Code

AB_NYC <- AB_NYC[order(AB_NYC$price),]

Recode price=$0 to NA.

Code

AB_NYC <- AB_NYC %>% 
  mutate_at(c("price"), ~ na_if(., 0))

AB_NYC <- AB_NYC[order(AB_NYC$price, na.last=FALSE),]

Recode ‘price’ variable into new ‘price_range’ variable.

Code

AB_NYC <- AB_NYC %>%
  mutate(price_range = case_when(
         price < 100 ~ "less than $100",
         price >= 100 & price < 250 ~ "$100 - $249",
         price >= 250 & price < 500 ~ "$250 - $499",
         price >= 500 & price < 750 ~ "$500 - $749",
         price >= 750 & price < 1000 ~ "$750 - $999",
         price >= 1000 ~ "$1000 or more")
  )
AB_NYC %>% count(price_range)

# A tibble: 7 × 2
  price_range        n
  <chr>          <int>
1 $100 - $249    20792
2 $1000 or more    298
3 $250 - $499     4991
4 $500 - $749      682
5 $750 - $999      255
6 less than $100 21866
7 <NA>              11

Determine the values associated with each price range category.

Code

unique(AB_NYC$price_range)

[1] NA               "less than $100" "$100 - $249"    "$250 - $499"   
[5] "$500 - $749"    "$750 - $999"    "$1000 or more"

Specify the factor order. Can’t get factor levels to work…(why are they showing in correct order above all of a sudden, but weren’t before when I ran this over and over?).

Code

AB_NYC<-AB_NYC%>%
  mutate(price_range_ordered = factor(price, 
                       levels = price_range[c(2,3,4,5,6)]))

AB_NYC %>%
  filter (!is.na(price_range)) %>% 
 ggplot(aes(x=price_range)) + 
   geom_bar(position = "dodge",
           stat = "count", fill="purple", colour="black")+
   labs(title = "Frequency of Various Price Ranges", y = "Frequency", x = "Price Range")

Create ggplot of rentals by NYC Borough, order bars from highest to lowest value.

Code

AB_NYC %>%
 ggplot(aes(x=fct_infreq(neighbourhood_group))) + 
   geom_bar(stat = "count", fill="blue", colour="black")+
   labs(title = "AirBnB Rentals by NYC Borough", y = "Frequency", x = "NYC Borough")

Create crosstab of neighbourhood by price_range.

Code

xtabs(~ neighbourhood_group + price_range, AB_NYC)

                   price_range
neighbourhood_group $100 - $249 $1000 or more $250 - $499 $500 - $749
      Bronx                 229             2          30           6
      Brooklyn             7677            67        1244         158
      Manhattan           11234           213        3486         498
      Queens               1548            13         217          18
      Staten Island         104             3          14           2
                   price_range
neighbourhood_group $750 - $999 less than $100
      Bronx                   1            822
      Brooklyn               54          10895
      Manhattan             194           6035
      Queens                  5           3865
      Staten Island           1            249

Calculate min/max/mean price (price) and group data by neighbourhood_group and room_type.

Code

GrpByNeighborhoodRoom <- AB_NYC %>%
    group_by(neighbourhood_group) %>%
    summarise(minADR = min(price, na.rm = TRUE), maxADR = max(price, na.rm = TRUE), meanADR = mean(price, na.rm = TRUE)) 
head(GrpByNeighborhoodRoom)

# A tibble: 5 × 4
  neighbourhood_group minADR maxADR meanADR
  <chr>                <dbl>  <dbl>   <dbl>
1 Bronx                   10   2500    87.6
2 Brooklyn                10  10000   124. 
3 Manhattan               10  10000   197. 
4 Queens                  10  10000    99.5
5 Staten Island           13   5000   115.

Code

AB_NYC %>%
 ggplot(aes(x=neighbourhood_group, y=price)) + 
   geom_bar(position = "dodge",fill="pink", colour="black",
           stat = "summary",
           fun = "mean")+
   labs(title = "Average AirBnB Nightly Cost by NYC Borough", y = "Average Nighyly Cost", x = "NYC Borough")

Warning: Removed 11 rows containing non-finite values (`stat_summary()`).

Code

GrpByNeighborhoodRoom <- AB_NYC %>%
    group_by(neighbourhood_group, room_type) %>%
    summarise(minADR = min(price, na.rm = TRUE), maxADR = max(price, na.rm = TRUE), meanADR = mean(price, na.rm = TRUE))

`summarise()` has grouped output by 'neighbourhood_group'. You can override
using the `.groups` argument.

Code

head(GrpByNeighborhoodRoom)

# A tibble: 6 × 5
# Groups:   neighbourhood_group [2]
  neighbourhood_group room_type       minADR maxADR meanADR
  <chr>               <chr>            <dbl>  <dbl>   <dbl>
1 Bronx               Entire home/apt     28   1000   128. 
2 Bronx               Private room        10   2500    66.9
3 Bronx               Shared room         20    800    59.8
4 Brooklyn            Entire home/apt     10  10000   178. 
5 Brooklyn            Private room        10   7500    76.5
6 Brooklyn            Shared room         15    725    50.8

Code

tail(GrpByNeighborhoodRoom)

# A tibble: 6 × 5
# Groups:   neighbourhood_group [2]
  neighbourhood_group room_type       minADR maxADR meanADR
  <chr>               <chr>            <dbl>  <dbl>   <dbl>
1 Queens              Entire home/apt     10   2600   147. 
2 Queens              Private room        10  10000    71.8
3 Queens              Shared room         11   1800    69.0
4 Staten Island       Entire home/apt     48   5000   174. 
5 Staten Island       Private room        20    300    62.3
6 Staten Island       Shared room         13    150    57.4

Code

AB_NYC %>%
ggplot(aes(x=neighbourhood_group, y=price, fill=room_type)) + 
   geom_bar(position = "dodge",
           stat = "summary",
           fun = "mean")+
   facet_wrap(vars(room_type), scales = "free_x") +
  scale_fill_manual(values=c("green","orange","blue"))+
  theme(legend.position="none")+
   scale_x_discrete(guide = guide_axis(n.dodge=2))+
   labs(title = "Average AirBnB Nightly Cost by NYC Borough and Room Type", y = "Average Nightly Cost", x = "NYC Borough")

Warning: Removed 11 rows containing non-finite values (`stat_summary()`).

Code

AB_NYC %>%
  ggplot(aes(fill=price_range, x=room_type)) + 
    geom_bar(position="dodge", stat="count")