DACS 601 - HW4
Apoorva Saraswat
2022-07-11
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 libraries
library(readr)
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✔ ggplot2 3.3.6 ✔ dplyr 1.0.9
## ✔ tibble 3.1.7 ✔ stringr 1.4.0
## ✔ tidyr 1.2.0 ✔ forcats 0.5.1
## ✔ purrr 0.3.4## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(dplyr)loading data
ab_nyc_data <- read_csv("C:/Users/apoor/Desktop/UMass/Summer 2022/DACS 601 - R Programming/datasets/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.head(ab_nyc_data)## # A tibble: 6 × 16
## id name host_id host_name neighbourhood_g… neighbourhood latitude
## <dbl> <chr> <dbl> <chr> <chr> <chr> <dbl>
## 1 2539 Clean & quiet… 2787 John Brooklyn Kensington 40.6
## 2 2595 Skylit Midtow… 2845 Jennifer Manhattan Midtown 40.8
## 3 3647 THE VILLAGE O… 4632 Elisabeth Manhattan Harlem 40.8
## 4 3831 Cozy Entire F… 4869 LisaRoxa… Brooklyn Clinton Hill 40.7
## 5 5022 Entire Apt: S… 7192 Laura Manhattan East Harlem 40.8
## 6 5099 Large Cozy 1 … 7322 Chris Manhattan Murray Hill 40.7
## # … with 9 more variables: 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
head(ab_nyc_data %>% count(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)) %>% arrange(desc(sd_price)))## # 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
head(ab_nyc_data %>% count(host_id) %>% arrange(desc(n)))## # 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)))## `summarise()` has grouped output by 'id'. You can override using the `.groups`
## argument.## # 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)))## `summarise()` has grouped output by 'id'. You can override using the `.groups`
## argument.## # 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))## `summarise()` has grouped output by 'id'. You can override using the `.groups`
## argument.## # 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
ggplot(ab_nyc_data) + geom_histogram(aes(price), binwidth=15) + xlab("Price") + 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") + ylab("Frequency") + ggtitle("Price Distribution") + xlim(0,500)## Warning: Removed 1044 rows containing non-finite values (stat_bin).## Warning: Removed 2 rows containing missing values (geom_bar).
- 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
ggplot(ab_nyc_data, aes(x=neighbourhood_group, y=mean(price), fill=neighbourhood_group)) + geom_bar(stat="identity") + xlab("Neighbourhood group") + ylab("Average Price") + ggtitle("Avg Price by 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
ggplot(ab_nyc_data, aes(x=room_type, y=mean(price), fill=room_type)) + geom_bar(stat="identity") + xlab("Room Type") + ylab("Average Price") + ggtitle("Avg Price by 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