Challenge 7 Instructions

challenge_7
hotel_bookings
australian_marriage
air_bnb
eggs
abc_poll
faostat
usa_households
Visualizing Multiple Dimensions
Author

Kevin Martell Luya

Published

May 5, 2023

library(tidyverse)
library(ggplot2)
library(here)
library(ggthemes)

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

Challenge Overview

Today’s challenge is to:

  1. read in a data set, and describe the data set using both words and any supporting information (e.g., tables, etc)
  2. tidy data (as needed, including sanity checks)
  3. mutate variables as needed (including sanity checks)
  4. Recreate at least two graphs from previous exercises, but introduce at least one additional dimension that you omitted before using ggplot functionality (color, shape, line, facet, etc) The goal is not to create unneeded chart ink (Tufte), but to concisely capture variation in additional dimensions that were collapsed in your earlier 2 or 3 dimensional graphs.
  • Explain why you choose the specific graph type
  1. If you haven’t tried in previous weeks, work this week to make your graphs “publication” ready with titles, captions, and pretty axis labels and other viewer-friendly features

R Graph Gallery is a good starting point for thinking about what information is conveyed in standard graph types, and includes example R code. And anyone not familiar with Edward Tufte should check out his fantastic books and courses on data visualizaton.

(be sure to only include the category tags for the data you use!)

Read in data

Read in one (or more) of the following datasets, using the correct R package and command.

  • eggs ⭐
  • abc_poll ⭐⭐
  • australian_marriage ⭐⭐
  • hotel_bookings ⭐⭐⭐
  • air_bnb ⭐⭐⭐
  • us_hh ⭐⭐⭐⭐
  • faostat ⭐⭐⭐⭐⭐
airb<-here("posts","_data","AB_NYC_2019.csv") %>%
  read_csv()
airb
# 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

Briefly describe the data

AB_NYC_2019.csv contains listing data about rental places in New York. It would be interesting analyzing this data which comes from the concept of share economy.

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.

ggplot(airb,aes(minimum_nights))+
  geom_histogram()

For minimum_nights varible, the plotted histograms presents outliers, let’s remove them.

less_than_400 = airb%>%
  filter(minimum_nights < 400)
ggplot(less_than_400,aes(minimum_nights))+
  geom_histogram()

For minimum_nights varible, the plotted histograms presents outliers, let’s remove them.

less_than_200 = airb%>%
  filter(minimum_nights < 200)
ggplot(less_than_200,aes(minimum_nights))+
  geom_histogram()

Clearly we have more outliers, let’s filter data even more.

less_than_40 <- airb%>%
  filter(minimum_nights < 40)
ggplot(less_than_40,aes(minimum_nights))+
  geom_histogram()

The shape has two humps. It is bimobal.

Visualization with Multiple Dimensions

Now let’s consider rooms types and how they relate to the minimmum nights.

less_than_40 %>%
  ggplot(aes(room_type,minimum_nights))+
  geom_boxplot(alpha=.6)+
  scale_y_continuous(labels=scales::number_format())+
  labs(x="Type of room",
       y="number of nigths")+
  ggthemes::theme_few()

Now, lets check the minimmum nights per neighbourhood group and check if follow the same bimodal distribution.

less_than_40 %>%
  ggplot(aes(minimum_nights))+
  geom_histogram()+
  facet_wrap(vars(neighbourhood_group),scales="free")

We can observe that all the neighbourhoods follow the same distribution. This suggests they have the same behavior when in terms of minimum nights.

Now, let’s create a new column that shows 5-day intervals for minimum nights. Because there are outliers we take values below 200 minimum nights. We filtered this data in previous steps.

nights_ranges_by_5_days <- less_than_200 %>%
  mutate(nights_ranges = case_when(
    minimum_nights <= 5  ~ "1_5",
    minimum_nights > 5  & minimum_nights <= 10  ~ "5-10",
    minimum_nights > 10  & minimum_nights <= 15  ~ "10-15",
    minimum_nights > 15  & minimum_nights <= 20  ~ "15-20",
    minimum_nights > 20  & minimum_nights <= 25  ~ "20-25",
    minimum_nights > 25  & minimum_nights <= 30  ~ "25-30",
    minimum_nights > 30  & minimum_nights <= 35  ~ "30-35",
    minimum_nights > 35  & minimum_nights <= 40  ~ "35-40",
    minimum_nights > 40  & minimum_nights <= 45  ~ "40-45",
    minimum_nights > 45  & minimum_nights <= 50  ~ "45-50",
    minimum_nights > 50  & minimum_nights <= 55  ~ "50-55",
    minimum_nights > 55  & minimum_nights <= 100  ~ "55-100",
    minimum_nights > 100 ~ "+100" ))%>%
  fill(nights_ranges, .direction = "down")

nights_ranges_by_5_days_factor <- nights_ranges_by_5_days %>%
  mutate( nights_ranges = factor(nights_ranges))

Let’s check the intervals were created.

summary(nights_ranges_by_5_days_factor)
       id               name              host_id           host_name        
 Min.   :    2539   Length:48822       Min.   :     2438   Length:48822      
 1st Qu.: 9476788   Class :character   1st Qu.:  7831209   Class :character  
 Median :19692584   Mode  :character   Median : 30844240   Mode  :character  
 Mean   :19025154                      Mean   : 67666153                     
 3rd Qu.:29156076                      3rd Qu.:107434423                     
 Max.   :36487245                      Max.   :274321313                     
                                                                             
 neighbourhood_group neighbourhood         latitude       longitude     
 Length:48822        Length:48822       Min.   :40.50   Min.   :-74.24  
 Class :character    Class :character   1st Qu.:40.69   1st Qu.:-73.98  
 Mode  :character    Mode  :character   Median :40.72   Median :-73.96  
                                        Mean   :40.73   Mean   :-73.95  
                                        3rd Qu.:40.76   3rd Qu.:-73.94  
                                        Max.   :40.91   Max.   :-73.71  
                                                                        
  room_type             price         minimum_nights    number_of_reviews
 Length:48822       Min.   :    0.0   Min.   :  1.000   Min.   :  0.00   
 Class :character   1st Qu.:   69.0   1st Qu.:  1.000   1st Qu.:  1.00   
 Mode  :character   Median :  105.5   Median :  2.000   Median :  5.00   
                    Mean   :  152.6   Mean   :  6.461   Mean   : 23.29   
                    3rd Qu.:  175.0   3rd Qu.:  5.000   3rd Qu.: 24.00   
                    Max.   :10000.0   Max.   :198.000   Max.   :629.00   
                                                                         
  last_review         reviews_per_month calculated_host_listings_count
 Min.   :2011-03-28   Min.   : 0.010    Min.   :  1.000               
 1st Qu.:2018-07-10   1st Qu.: 0.190    1st Qu.:  1.000               
 Median :2019-05-19   Median : 0.720    Median :  1.000               
 Mean   :2018-10-04   Mean   : 1.374    Mean   :  7.152               
 3rd Qu.:2019-06-23   3rd Qu.: 2.020    3rd Qu.:  2.000               
 Max.   :2019-07-08   Max.   :58.500    Max.   :327.000               
 NA's   :10020        NA's   :10020                                   
 availability_365 nights_ranges  
 Min.   :  0.0    1_5    :38752  
 1st Qu.:  0.0    25-30  : 4336  
 Median : 45.0    5-10   : 3503  
 Mean   :112.6    10-15  : 1019  
 3rd Qu.:226.0    15-20  :  291  
 Max.   :365.0    55-100 :  273  
                  (Other):  648

Now, let see the 5-day intervals for minimum nights and how they relate to the average price for each interval.

nights_ranges_by_5_days_factor %>%

  group_by(nights_ranges) %>%
  summarise(mean_price=mean(price)) %>%
  ggplot(aes(reorder(nights_ranges,-mean_price), mean_price))+
  geom_col()+
  labs(x="minimum_nights ranges in days")

After tailoring the outliers, minimum_nights less than 200, data suggests that for minimum nights between 35 and 40, a host might charge around 365 dollars in average which is higer than minimum nights between 1 and 5, where the host might charge 150 dollars dollars in average.

However, if the 1-5-day hosts were able to rent the at least 4 consecutive times (4 _ 20 days), then the income would be higher in average compared to the 35-40-day hosts.

From the host perspective, data suggest that it is more profitable to rent a house for less number of days compared to other minimum nights 5-day interval.