Challenge 5

challenge_5

air_bnb

Introduction to Visualization

Author

Siddharth Goel

Published

January 29, 2023

library(tidyverse)
library(ggplot2)
library(readr)

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

Challenge Overview

Today’s challenge is to:

read in a data set, and describe the data set using both words and any supporting information (e.g., tables, etc)
tidy data (as needed, including sanity checks)
mutate variables as needed (including sanity checks)
create at least two univariate visualizations

try to make them “publication” ready
Explain why you choose the specific graph type

Create at least one bivariate visualization

try to make them “publication” ready
Explain why you choose the specific graph type

Read in data

data <- read.csv("_data/AB_NYC_2019.csv")
head(data)

    id                                             name host_id   host_name
1 2539               Clean & quiet apt home by the park    2787        John
2 2595                            Skylit Midtown Castle    2845    Jennifer
3 3647              THE VILLAGE OF HARLEM....NEW YORK !    4632   Elisabeth
4 3831                  Cozy Entire Floor of Brownstone    4869 LisaRoxanne
5 5022 Entire Apt: Spacious Studio/Loft by central park    7192       Laura
6 5099        Large Cozy 1 BR Apartment In Midtown East    7322       Chris
  neighbourhood_group neighbourhood latitude longitude       room_type price
1            Brooklyn    Kensington 40.64749 -73.97237    Private room   149
2           Manhattan       Midtown 40.75362 -73.98377 Entire home/apt   225
3           Manhattan        Harlem 40.80902 -73.94190    Private room   150
4            Brooklyn  Clinton Hill 40.68514 -73.95976 Entire home/apt    89
5           Manhattan   East Harlem 40.79851 -73.94399 Entire home/apt    80
6           Manhattan   Murray Hill 40.74767 -73.97500 Entire home/apt   200
  minimum_nights number_of_reviews last_review reviews_per_month
1              1                 9  2018-10-19              0.21
2              1                45  2019-05-21              0.38
3              3                 0                            NA
4              1               270  2019-07-05              4.64
5             10                 9  2018-11-19              0.10
6              3                74  2019-06-22              0.59
  calculated_host_listings_count availability_365
1                              6              365
2                              2              355
3                              1              365
4                              1              194
5                              1                0
6                              1              129

Briefly describe the data

This dataset contains data for AirBNB hotels and rooms available for booking in New York City for the year 2019. It has 16 columns with details ranging from booking dates, locations to room type and reviews.

Tidy Data (as needed)

Is your data already tidy, or is there work to be done? Be sure to anticipate your end result to provide a sanity check, and document your work here.

I am tidying the data to remove the N/A values in reviews_per_month column with 0.

tidy_data <- data %>% replace_na(list(reviews_per_month = 0))

Are there any variables that require mutation to be usable in your analysis stream? For example, do you need to calculate new values in order to graph them? Can string values be represented numerically? Do you need to turn any variables into factors and reorder for ease of graphics and visualization?

head(tidy_data)

    id                                             name host_id   host_name
1 2539               Clean & quiet apt home by the park    2787        John
2 2595                            Skylit Midtown Castle    2845    Jennifer
3 3647              THE VILLAGE OF HARLEM....NEW YORK !    4632   Elisabeth
4 3831                  Cozy Entire Floor of Brownstone    4869 LisaRoxanne
5 5022 Entire Apt: Spacious Studio/Loft by central park    7192       Laura
6 5099        Large Cozy 1 BR Apartment In Midtown East    7322       Chris
  neighbourhood_group neighbourhood latitude longitude       room_type price
1            Brooklyn    Kensington 40.64749 -73.97237    Private room   149
2           Manhattan       Midtown 40.75362 -73.98377 Entire home/apt   225
3           Manhattan        Harlem 40.80902 -73.94190    Private room   150
4            Brooklyn  Clinton Hill 40.68514 -73.95976 Entire home/apt    89
5           Manhattan   East Harlem 40.79851 -73.94399 Entire home/apt    80
6           Manhattan   Murray Hill 40.74767 -73.97500 Entire home/apt   200
  minimum_nights number_of_reviews last_review reviews_per_month
1              1                 9  2018-10-19              0.21
2              1                45  2019-05-21              0.38
3              3                 0                          0.00
4              1               270  2019-07-05              4.64
5             10                 9  2018-11-19              0.10
6              3                74  2019-06-22              0.59
  calculated_host_listings_count availability_365
1                              6              365
2                              2              355
3                              1              365
4                              1              194
5                              1                0
6                              1              129

All the variables are in correct format and are usable in the analysis.

Univariate Visualizations

I will attempt to plot the reviews_per_month of listings in NYC as this is the closest metric we can get for occupancy in these listings considering occupancy to be directly proportional to reviews.

To achieve this plot, I first aggregated the data to get the total reviews in a month for each neighborhood group.

df_agg = tidy_data %>% group_by(neighbourhood_group)  %>%
                    summarise(total_reviews_per_month = sum(reviews_per_month),
                              .groups = 'drop')
head(df_agg)

# A tibble: 5 × 2
  neighbourhood_group total_reviews_per_month
  <chr>                                 <dbl>
1 Bronx                                 1610.
2 Brooklyn                             21105.
3 Manhattan                            21158.
4 Queens                                8879.
5 Staten Island                          588.

ggplot(data=df_agg, aes(x=neighbourhood_group, y=total_reviews_per_month)) +
  geom_bar(stat="identity") + labs(title = "Total Reviews Per Month")

Bivariate Visualization(s)

The pricing for each borough is also an important factor that is generally looked upon by customers before booking and is worth keeping track of.

tidy_data %>%
  ggplot(aes(neighbourhood_group, price), fill = neighbourhood) + 
  geom_boxplot() + 
  labs(title = "Borough-wise pricing") + 
  theme_classic()