Challenge 5

Code

library(tidyverse)
library(ggplot2)

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

Read in data

Read Autralian Marriage tidy data file.

Code

ab_nyc <- read_csv("_data/AB_NYC_2019.csv",
                   show_col_types = FALSE)
ab_nyc

# A tibble: 48,895 × 16
      id name      host_id host_…¹ neigh…² neigh…³ latit…⁴ longi…⁵ room_…⁶ price
   <dbl> <chr>       <dbl> <chr>   <chr>   <chr>     <dbl>   <dbl> <chr>   <dbl>
 1  2539 Clean & …    2787 John    Brookl… Kensin…    40.6   -74.0 Privat…   149
 2  2595 Skylit M…    2845 Jennif… Manhat… Midtown    40.8   -74.0 Entire…   225
 3  3647 THE VILL…    4632 Elisab… Manhat… Harlem     40.8   -73.9 Privat…   150
 4  3831 Cozy Ent…    4869 LisaRo… Brookl… Clinto…    40.7   -74.0 Entire…    89
 5  5022 Entire A…    7192 Laura   Manhat… East H…    40.8   -73.9 Entire…    80
 6  5099 Large Co…    7322 Chris   Manhat… Murray…    40.7   -74.0 Entire…   200
 7  5121 BlissArt…    7356 Garon   Brookl… Bedfor…    40.7   -74.0 Privat…    60
 8  5178 Large Fu…    8967 Shunic… Manhat… Hell's…    40.8   -74.0 Privat…    79
 9  5203 Cozy Cle…    7490 MaryEl… Manhat… Upper …    40.8   -74.0 Privat…    79
10  5238 Cute & C…    7549 Ben     Manhat… Chinat…    40.7   -74.0 Entire…   150
# … with 48,885 more rows, 6 more variables: minimum_nights <dbl>,
#   number_of_reviews <dbl>, last_review <date>, reviews_per_month <dbl>,
#   calculated_host_listings_count <dbl>, availability_365 <dbl>, and
#   abbreviated variable names ¹​host_name, ²​neighbourhood_group,
#   ³​neighbourhood, ⁴​latitude, ⁵​longitude, ⁶​room_type
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names

Briefly describe the data

This data set looks like a customer review collected by a company. It contains nearly 50,000 people’s review and each data distinguished by their id, neighborhood, and etc.

Univariate Visualizations

First of all, I took a look about their neighbourhood group. I choosed bar graph because it can be recognized easily.

Code

count(ab_nyc, 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         373

Code

ggplot(ab_nyc, aes(neighbourhood_group)) +
  geom_bar()

Most neighbourhood group are Manhattan(21661) and Brooklyn(20104).

Next, I took a closer look at the Manhattan.

Code

ab_nyc_man <- ab_nyc%>%
  filter(neighbourhood_group=="Manhattan")

count(ab_nyc_man, neighbourhood)

# A tibble: 32 × 2
   neighbourhood          n
   <chr>              <int>
 1 Battery Park City     70
 2 Chelsea             1113
 3 Chinatown            368
 4 Civic Center          52
 5 East Harlem         1117
 6 East Village        1853
 7 Financial District   744
 8 Flatiron District     80
 9 Gramercy             338
10 Greenwich Village    392
# … with 22 more rows
# ℹ Use `print(n = ...)` to see more rows

Code

ggplot(ab_nyc_man, aes(neighbourhood)) +
  geom_bar()

32 neighbourhood are in the Manhattan and Harlem’s proportion is the largest.

Bivariate Visualization

Now, I wondered whether availity and amount of review is related. So I made scatter plot regarding availity and reviews per month. I selected review per month to avoid distortion due to subscription period, etc.

Code

ggplot(ab_nyc, aes(x=availability_365, y=reviews_per_month)) +
  geom_point()

Anything is hard to recognized. It seems like there are no relations between availity and amount of reviews.