HW4: Visualizations
This is the R Markdown file for the HW4 of DACS-601 Summer 2022. I’m using the New York City Airbnb csv file from the Sample Datasets.
loading data
# A tibble: 6 × 16
id name host_id host_name neighbourhood_g… neighbourhood
<dbl> <chr> <dbl> <chr> <chr> <chr>
1 2539 Clean & quie… 2787 John Brooklyn Kensington
2 2595 Skylit Midto… 2845 Jennifer Manhattan Midtown
3 3647 THE VILLAGE … 4632 Elisabeth Manhattan Harlem
4 3831 Cozy Entire … 4869 LisaRoxa… Brooklyn Clinton Hill
5 5022 Entire Apt: … 7192 Laura Manhattan East Harlem
6 5099 Large Cozy 1… 7322 Chris Manhattan Murray Hill
# … with 10 more variables: latitude <dbl>, longitude <dbl>,
# room_type <chr>, price <dbl>, minimum_nights <dbl>,
# number_of_reviews <dbl>, last_review <date>,
# reviews_per_month <dbl>, calculated_host_listings_count <dbl>,
# availability_365 <dbl>Data analysis
1. Number of airbnbs in each neighbourhood group
# A tibble: 5 × 2
neighbourhood_group n
<chr> <int>
1 Bronx 1091
2 Brooklyn 20104
3 Manhattan 21661
4 Queens 5666
5 Staten Island 3732. Average price of airbnb in each neighbourhood group
# A tibble: 5 × 2
neighbourhood_group avg_price
<chr> <dbl>
1 Manhattan 197.
2 Brooklyn 124.
3 Staten Island 115.
4 Queens 99.5
5 Bronx 87.53. Median price of airbnb in each neighbourhood group
# A tibble: 5 × 2
neighbourhood_group median_price
<chr> <dbl>
1 Manhattan 150
2 Brooklyn 90
3 Queens 75
4 Staten Island 75
5 Bronx 654. Standard deviation price of airbnb in each neighbourhood group
# A tibble: 5 × 2
neighbourhood_group sd_price
<chr> <dbl>
1 Manhattan 291.
2 Staten Island 278.
3 Brooklyn 187.
4 Queens 167.
5 Bronx 107.5. Host with the most stays
# A tibble: 6 × 2
host_id n
<dbl> <int>
1 219517861 327
2 107434423 232
3 30283594 121
4 137358866 103
5 12243051 96
6 16098958 966. Airbnb with most number of reviews
# A tibble: 6 × 3
# Groups: id [6]
id name reviews
<dbl> <chr> <dbl>
1 9145202 Room near JFK Queen Bed 629
2 903972 Great Bedroom in Manhattan 607
3 903947 Beautiful Bedroom in Manhattan 597
4 891117 Private Bedroom in Manhattan 594
5 10101135 Room Near JFK Twin Beds 576
6 8168619 Steps away from Laguardia airport 5437. Most available airbnb
# A tibble: 6 × 3
# Groups: id [6]
id name availability
<dbl> <chr> <dbl>
1 2539 Clean & quiet apt home by the park 365
2 3647 THE VILLAGE OF HARLEM....NEW YORK ! 365
3 11452 Clean and Quiet in Brooklyn 365
4 11943 Country space in the city 365
5 21644 Upper Manhattan, New York 365
6 32037 Huge Private Floor at The Waverly 3658. Least available airbnb
# A tibble: 6 × 3
# Groups: id [6]
id name availability
<dbl> <chr> <dbl>
1 5022 Entire Apt: Spacious Studio/Loft by central park 0
2 5121 BlissArtsSpace! 0
3 5203 Cozy Clean Guest Room - Family Apt 0
4 6090 West Village Nest - Superhost 0
5 7801 Sweet and Spacious Brooklyn Loft 0
6 8700 Magnifique Suite au N de Manhattan - vue Cloitres 0Plots
Univariate
Plot 1 -Price Distribution
Since this data is not that readable, we are zooming in on the values of x from 0 to 500 since the majority of the distribution is in this range
- What variables are you visualizing:
price - What questions are attempting to answer: What is the price distribution of all the airbnbs?
- What conclusions you can make from the visualization: Most of the airbnbs are priced in the range of 55-70
Limitations
What questions are left unanswered with your visualizations: Taking into account all the price values and their distribution
What about the visualizations may be unclear to a naive viewer: The price distribution looks skewed and difficult to interpret.
How could you improve the visualizations for the final project: I zoomed in on the visualisation over the majority distributions. There are other ways to deal with it like using logarithmic method to normalise the scale.
Bivariate
Plot 2 - Average price of airbnb for each neighbourhood group
- What variables are you visualizing:
price, neighbourhood_group - What questions are attempting to answer: What is the average price of airbnbs for each neighbourhood group?
- What conclusions you can make from the visualization: Airbnbs in the neighbourhood group Manhattan and Brooklyn have much higher average price than the other neighbourhood groups
Limitations
What questions are left unanswered with your visualizations: Actual trend of price, it could be the case that there are only a few values of ‘Staten Island’ neighborhood group and much more of the other groups. How do we know this is the real trend and not lack of data?
What about the visualizations may be unclear to a naive viewer: The y-axis might be unclear to a naive viewer
How could you improve the visualizations for the final project: I would make the graph more readable - scale y axis to human readable values
Plot 3 - Average price of airbnb for each room type
- What variables are you visualizing:
price, room_type - What questions are attempting to answer: What is the average price of airbnb for each room type?
- What conclusions you can make from the visualization: Airbnbs of room types ‘entire home/apt’ and ‘private room’ have much higher average price than ‘shared room’ room type
Limitations
What questions are left unanswered with your visualizations: Actual trend of price, it could be the case that there are only a few values of ‘shared room’ type and much more of the other room types. How do we know this is the real trend and not lack of data?
What about the visualizations may be unclear to a naive viewer: The y-axis might be unclear to a naive viewer
How could you improve the visualizations for the final project: I would make the graph more readable - scale y axis to human readable values