knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir = "C:/Users/apoor/OneDrive/Desktop/UMass/Summer 2022/DACS 601 - R Programming")
getwd()[1] "C:/Users/apoor/OneDrive/Desktop/UMass/Summer 2022/DACS 601 - R Programming/hw6"Introduction
In this analysis we analyse the New York Airbnb Dataset which includes information about various Airbnbs in the New York City, USA in various neighbourhoods. Airbnbs are houses/apartments that owners rent out to travellers looking for a play to stay. The airbnbs can be private or shared. We see the relation between the price, location, room type, reviews, etc of the airbnbs are try to see if there’s any trend being followed.
Data
The dataset includes 48895 rows and 16 columns which includes the information about the airbnbs in the New York City.
Loading data
[1] "Dimensions: "dim(ab_nyc_data)[1] 48895 16print("Column names: ")[1] "Column names: "colnames(ab_nyc_data) [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" print("Data types: ")[1] "Data types: "sapply(ab_nyc_data, class) id name
"numeric" "character"
host_id host_name
"numeric" "character"
neighbourhood_group neighbourhood
"character" "character"
latitude longitude
"numeric" "numeric"
room_type price
"character" "numeric"
minimum_nights number_of_reviews
"numeric" "numeric"
last_review reviews_per_month
"character" "numeric"
calculated_host_listings_count availability_365
"numeric" "numeric" print("Data head: ")[1] "Data head: "head(ab_nyc_data)# A tibble: 6 × 16
id name host_id host_…¹ neigh…² neigh…³ latit…⁴ longi…⁵ room_…⁶
<dbl> <chr> <dbl> <chr> <chr> <chr> <dbl> <dbl> <chr>
1 2539 Clean… 2787 John Brookl… Kensin… 40.6 -74.0 Privat…
2 2595 Skyli… 2845 Jennif… Manhat… Midtown 40.8 -74.0 Entire…
3 3647 THE V… 4632 Elisab… Manhat… Harlem 40.8 -73.9 Privat…
4 3831 Cozy … 4869 LisaRo… Brookl… Clinto… 40.7 -74.0 Entire…
5 5022 Entir… 7192 Laura Manhat… East H… 40.8 -73.9 Entire…
6 5099 Large… 7322 Chris Manhat… Murray… 40.7 -74.0 Entire…
# … with 7 more variables: price <dbl>, minimum_nights <dbl>,
# number_of_reviews <dbl>, last_review <chr>,
# reviews_per_month <dbl>, calculated_host_listings_count <dbl>,
# availability_365 <dbl>, and abbreviated variable names
# ¹host_name, ²neighbourhood_group, ³neighbourhood, ⁴latitude,
# ⁵longitude, ⁶room_type
# ℹ Use `colnames()` to see all variable namesExploring the dataset
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
head(ab_nyc_data %>% group_by(neighbourhood_group) %>% summarise(avg_price= mean(price)) %>% arrange(desc(avg_price)))# 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
head(ab_nyc_data %>% group_by(neighbourhood_group) %>% summarise(median_price= median(price)) %>% arrange(desc(median_price)))# 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
head(ab_nyc_data %>% group_by(neighbourhood_group) %>% summarise(sd_price= sd(price)/10) %>% arrange(desc(sd_price)))# A tibble: 5 × 2
neighbourhood_group sd_price
<chr> <dbl>
1 Manhattan 29.1
2 Staten Island 27.8
3 Brooklyn 18.7
4 Queens 16.7
5 Bronx 10.75. 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
head(ab_nyc_data %>% group_by(id, name) %>% summarise(reviews= sum(number_of_reviews)) %>% arrange(desc(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
head(ab_nyc_data %>% group_by(id, name) %>% summarise(availability= mean(availability_365)) %>% arrange(desc(availability)))# 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
head(ab_nyc_data %>% group_by(id, name) %>% summarise(availability= mean(availability_365)) %>% arrange(availability))# 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 0Visualisations
The following are the exploratory visualisations on the dataset where we try to figure out patterns in the data.
Univariate Analysis
Plot 1 - Price Distribution
ggplot(ab_nyc_data) + geom_histogram(aes(price), binwidth=15) + xlab("Price (USD)") + ylab("Frequency") + ggtitle("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
ggplot(ab_nyc_data) + geom_histogram(aes(price), binwidth=15) + xlab("Price (USD)") + ylab("Frequency") + ggtitle("Price Distribution") + xlim(0,500)
- 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 Analysis
Plot 2 - Median price of airbnb for each neighbourhood group
ab_nyc_data2 <- ab_nyc_data %>% group_by(neighbourhood_group) %>% summarise(sd=sd(price)/10, median=median(price))
ggplot(ab_nyc_data2, aes(x=neighbourhood_group, y=median, fill=neighbourhood_group)) + geom_bar(stat="identity") + xlab("Neighbourhood group") + ylab("Median Price (USD)") + ggtitle("Median Price by neighbourhood group") + geom_errorbar(aes(x=neighbourhood_group, ymin=median-sd, ymax=median+sd), width=0.6, colour="black")
- What variables are you visualizing:
price, neighbourhood_group - What questions are attempting to answer: What is the median 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 median 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
Is there anything you want to answer with your dataset, but can’t?: I would make the graph more readable - scale y axis to human readable values
Plot 3 - Median price of airbnb for each room type
ab_nyc_data3 <- ab_nyc_data %>% group_by(room_type) %>% summarise(sd=sd(price)/10, median=median(price))
ggplot(ab_nyc_data3, aes(x=room_type, y=median, fill=room_type)) + geom_bar(stat="identity") + xlab("Room type") + ylab("Median Price") + ggtitle("Median Price by room type") + geom_errorbar(aes(x=room_type, ymin=median-sd, ymax=median+sd), width=0.6, colour="black") 
- What variables are you visualizing:
price, room_type - What questions are attempting to answer: What is the median 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 median 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
Is there anything you want to answer with your dataset, but can’t?: I would make the graph more readable - scale y axis to human readable values
Facetwrap Analysis
Plot 4 - Median price of airbnb for each room type by neighbourhood group
ggplot(ab_nyc_data, aes(x=room_type, y=median(price)/10000, fill=room_type)) + geom_bar(stat="identity") + xlab("Room Type") + ylab("Median Price (USD)") + ggtitle("Median Price for each room type by neighbourhood group") + facet_wrap(vars(neighbourhood_group), scales="free_y") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
- What variables are you visualizing:
price, room_type, neighbourhood_group - What questions are attempting to answer: What is the median price of airbnb for each room type by neighbourhood group?
- What conclusions you can make from the visualization: Airbnbs of room types ‘entire home/apt’ and ‘private room’ have much higher average price for neighbourhood groups Brooklyn and Manhattan.
Limitations
What questions are left unanswered with your visualizations: In depth trend of the data, what more can we know about the price differences by neighbourhood groups?
What about the visualizations may be unclear to a naive viewer: The y-axis might be unclear to a naive viewer
Is there anything you want to answer with your dataset, but can’t?: I would make the graph more readable - scale y axis to human readable values
Multivariate Analysis
Plot 5 - Relationship between number of reviews and minimum nights
ggplot(ab_nyc_data, aes(x=number_of_reviews, y=price, col=price, size=minimum_nights)) +
theme(axis.title = element_text(), axis.title.x = element_text()) +
geom_point() +
xlab("Number of reviews") +
ylab("Price") +
ggtitle("Relationship between number of reviews and minimum nights",
subtitle = "The costlier airbnbs have lesser number of reviews")
Reflection
To be added
Describe your process/experience with the project, including the decisions you made, what was most challenging, and what you wish you would have known. You can also discuss what the next steps would be, were you to continue with the project.
Conclusion
The following are the conclusions made so far -
- Manhattan is the most expensive neighbourhood group compared to other neighborhood groups.
- Brooklyn can be the second best choice considering the price and number of reviews.
- Entire home/apts are much more expensive than private or shared rooms.
Final Questions
What is missing from your final project?
I think I’m missing the proper formatting of my final project. Adding the Reflection and Conclusion sections
What do you hope to accomplish between now and submission time?
I’m planning to add the remaining sections in my final report
Bibliography
- Source: https://www.kaggle.com/dgomonov/new-york-city-airbnb-open-data
- https://www.r-project.org/
- R Programming textbook - https://r4ds.had.co.nz/
- https://q4launch.com/blog/how-airbnb-works-for-hosts/
- https://rpubs.com/Dkanawat/652521
- https://web.stanford.edu/class/bios221/labs/multivariate/lab_5_multivariate.html
- https://medium.com/@svinetas/exploratory-analysis-of-nyc-airbnb-data-via-r-244215bc90bc
- https://yaolongchua.wordpress.com/2020/08/17/exploratory-data-analysis-of-new-york-city-airbnb-dataset-using-r/