1 Introduction

This is a data analysis project using R Software in which we perform data cleansing, present exploratory data analysis, visualizations and modelling of a large dataset of San Francisco Airbnb.

The focus of this project is also to first manipulate the data using the skills we have learnt during the course. So there will be a focus in both tools used and the analysis of the actual data. As this is a real world data set, it comes with challenges which we will most likely encounter and has thus given us an opportunity to experiment in all aspects.

The analysis focuses on exploring all possibilites and as such display tables, maps, graphs, boxplots, fitting regression lines using R to present the output on a well formatted RMarkdown document. Also all the data cleansing was performed within R even though it would be have been possible to do it with Excel or SQL.

This document also covers details such as the R packages used, data set information, HTML coding used to format the R Markdown HTML document, R Shiny Package that was published and all data manipulation tasks performed.

1.0.1 Why did we select the SF Airbnb Dataset?

The SF Airbnb Listing dataset was selected because San Francisco has always had a shortage of housing especially with the influx of wealthy Silicon Valley employees driving housing prices up and pushing San Francisco residents out of San Francisco.

Airbnb was definitely having an impact on the housing crises as people were looking for a short term gains and pulling long term housing out of the rental market and instead renting to Airbnb consumers.

In order to address this issue, in 2017, the City of San Francisco introduced some very strict rules regarding registration of Airbnb hosts which dropped the number of Airbnb rentals by 50%. A review of the rules for any Airbnb host is found in the following link https://www.airbnb.com/help/article/871/san-francisco-ca

1.0.2 What will we do next

We will explore the exist will give high level reviews on various variables in the dataset such as neighbourhoods, prices, superhosts vs regular hosts, review scores, minimum nights, number of rooms, accomodatability etc.

We performed the following tasks on this dataset:

  • Data Collection

  • Data Preparation

  • Data Cleansing

  • Data Exploratory Analysis

  • Modelling and Analysis

2 Data Collection

The SF Airbnb dataset was obtained from data.world. https://data.world/ajsanne/sf-airbnb-listings We have over 7500 records of the San Francisco Airbnb dataset that consists of numeric, date, characters, URLs, geocode, boolean data types. Initially the dataset was a comma separated values file with 107 columns .

Some data cleansing outside of R which was done using both Excel and Notepad++ to reduce the columns to 19. The dataset is rich with various variables which will require a lot of cleanup but also result in usage of lots of different R commands and packages.

2.1 Prerequisites

This section sets up the environment by setting the working directory where all files will be saved and loading required packages. Below are the list of packages that were used. It is essential to install and then load the library packages.

Name Usage
leaps leaps( performs an exhaustive search for the best subsets of the variables in x for predicting y in linear regression, using an efficient branch-and-bound algorithm.
mapproj Converts latitude/longitude into projected coordinates.
ggthemes Provides extra ‘ggplot2’ themes, theoms and scales.
tidyr For changing the layout of your data sets, to convert data into the tidy format
breakDown presents variable contributions in a concise graphical way.
ggmap Download street maps straight from Google maps and use them as a background in your ggplots.
DT For HTML display of data
ggplot2 For customizable graphical representation
leaflet Visualize maps
dplyr For data manipulation
tidyverse Collection of R packages designed for data science that works harmoniously with other packages
kableExtra To display table in a fancy way
readxl The readxl package makes it easy to get data out of Excel and into R
pixiedust pixiedust’ provides this functionality with a programming interface intended to be similar to ’ggplot2’s system of layers with fine tuned control over each cell of the table
vtable Automatically generates HTML variable documentation including variable names, labels, classes, value labels (if applicable), value ranges
scales Customised to control the appearance of axis and legend labels.
plotly Plotly’s R graphing library makes interactive, publication-quality graphs
shiny Shiny is an R package that makes it easy to build interactive web apps straight from R
zoo performing calculations containing irregular time series of numeric vectors, matrices & factors
summarytools  Provides tools to neatly and quickly summarize data
skimr Summarize data and extendes tidyverse packages
shiny to build interactive web apps straight from R.
ggfortify To perform multiple plots in a single instance
jtools Needed in order to for effect_tools to work

3 Data Preparation

In this section, we begin the process of cleaning and transforming raw data prior to processing and analysis. We will do the following

  • Load and View Dataset
  • Data Discovery
  • Cleanse Data
  • Data Transformation
  • Store Data

3.1 Load and View Dataset

Here we get a snapshot or full view of the entire dataset and we can query in all fields to return records. We can get a good idea of the type of data contained in the fields before cleansing and planning the visualization.

3.1.1 Interactive R Shiny Web App & Histogram Dataset

An R Shiny App was built to display an interactive table and histogram which allows searching and filtering of records by Neighbourhoods. Rshiny R package was used to enable this functionality.

A view of the interactive dataset can be visible in RShiny App here.

3.1.2 Interactive Table of Dataset

We load the data from a csv and immediately create a dataframe. We create a data table to visualize and browse through the dataset to ensure the import was successfully done. Datatable allows us to get the data loaded in scrollable views. We are also able to search for any values in any column and limit the result set.

We used this table familiarize ourselves with the data set in order to decide the type of visualizations we needed. Even though the data seems structured, a lot of numerical or date columns were imported into R as characters. Also there seemed to unicode related issues that needed to be addressed.

Table: SF Airbnb Dataset Table.

3.2 Data Discovery

Before cleansing the data, we need to understand the type of data we have and see if there are any missing data, wrongly assigned data types etc which will prevent us from creating interesting visualizations.

We have done the following:

  • We use glimpse in dplyr package to check column name, properties and subset of data

  • We use the knitr kable package to check statistics of the table to include means, medians, max, min for numeric columns

  • We use summary with -is.na option to check for columns and total count of nulls and/or NA

  • We also use dfSummary to display summary statistics and missing values

Because the results of this code chunk are not interesting and as well formatted, we have opted for the html to give the user to option to view the output. It also adds some interactivity to the document.

We find that review_scores_rating and reviews_per_month have about 180 records with null values. In order to aggregate the averages or perform any calculations, we need to updated these missing values with a value that makes sense.

## Data Frame Summary  
## df_sf  
## Dimensions: 999 x 18  
## Duplicates: 0  
## 
## --------------------------------------------------------------------------------------------------------------------------------
## No   Variable                 Stats / Values                    Freqs (% of Valid)    Graph                 Valid      Missing  
## ---- ------------------------ --------------------------------- --------------------- --------------------- ---------- ---------
## 1    Id                       Mean (sd) : 17536627 (11412873)   999 distinct values   :           .         999        0        
##      [integer]                min < med < max:                                        :       .   :         (100.0%)   (0.0%)   
##                               8339 < 18472576 < 35286441                              : :   . : : :                             
##                               IQR (CV) : 21283962 (0.7)                               : : : : : : :                             
##                                                                                       : : : : : : :                             
## 
## 2    host_name                1. David                           16 ( 1.6%)                                 999        0        
##      [character]              2. Michael                         14 ( 1.4%)                                 (100.0%)   (0.0%)   
##                               3. Sanford                         10 ( 1.0%)                                                     
##                               4. Angela                           9 ( 0.9%)                                                     
##                               5. John                             9 ( 0.9%)                                                     
##                               6. Laurence                         9 ( 0.9%)                                                     
##                               7. ResortShare1                     9 ( 0.9%)                                                     
##                               8. Jennifer                         8 ( 0.8%)                                                     
##                               9. Ross                             8 ( 0.8%)                                                     
##                               10. Joe                             7 ( 0.7%)                                                     
##                               [ 586 others ]                    900 (90.1%)           IIIIIIIIIIIIIIIIII                        
## 
## 3    host_id                  Mean (sd) : 44135416 (66500582)   859 distinct values   :                     999        0        
##      [integer]                min < med < max:                                        :                     (100.0%)   (0.0%)   
##                               6267 < 11527863 < 264634459                             :                                         
##                               IQR (CV) : 46717526 (1.5)                               :                                         
##                                                                                       : : . .           .                       
## 
## 4    host_since               1. 2/24/2010                       10 ( 1.0%)                                 999        0        
##      [character]              2. 1/21/2015                        9 ( 0.9%)                                 (100.0%)   (0.0%)   
##                               3. 4/7/2013                         9 ( 0.9%)                                                     
##                               4. 11/25/2014                       7 ( 0.7%)                                                     
##                               5. 3/25/2019                        6 ( 0.6%)                                                     
##                               6. 8/1/2015                         6 ( 0.6%)                                                     
##                               7. 9/20/2016                        6 ( 0.6%)                                                     
##                               8. 11/1/2012                        5 ( 0.5%)                                                     
##                               9. 4/8/2015                         5 ( 0.5%)                                                     
##                               10. 5/11/2014                       5 ( 0.5%)                                                     
##                               [ 724 others ]                    931 (93.2%)           IIIIIIIIIIIIIIIIII                        
## 
## 5    host_is_superhost        1. f                              599 (60.0%)           IIIIIIIIIII           999        0        
##      [character]              2. t                              400 (40.0%)           IIIIIIII              (100.0%)   (0.0%)   
## 
## 6    neighbourhood_cleansed   1. Mission                        105 (10.5%)           II                    999        0        
##      [character]              2. Western Addition                90 ( 9.0%)           I                     (100.0%)   (0.0%)   
##                               3. Castro/Upper Market             86 ( 8.6%)           I                                         
##                               4. Noe Valley                      60 ( 6.0%)           I                                         
##                               5. Haight Ashbury                  59 ( 5.9%)           I                                         
##                               6. SOMA                            56 ( 5.6%)           I                                         
##                               7. Marina                          46 ( 4.6%)                                                     
##                               8. Pacific Heights                 43 ( 4.3%)                                                     
##                               9. Russian Hill                    43 ( 4.3%)                                                     
##                               10. Bernal Heights                 42 ( 4.2%)                                                     
##                               [ 24 others ]                     369 (36.9%)           IIIIIII                                   
## 
## 7    zipcode                  1. 94110                          129 (12.9%)           II                    999        0        
##      [character]              2. 94114                          121 (12.1%)           II                    (100.0%)   (0.0%)   
##                               3. 94117                          105 (10.5%)           II                                        
##                               4. 94109                           90 ( 9.0%)           I                                         
##                               5. 94115                           59 ( 5.9%)           I                                         
##                               6. 94123                           59 ( 5.9%)           I                                         
##                               7. 94103                           53 ( 5.3%)           I                                         
##                               8. 94133                           48 ( 4.8%)                                                     
##                               9. 94131                           45 ( 4.5%)                                                     
##                               10. 94107                          42 ( 4.2%)                                                     
##                               [ 19 others ]                     248 (24.8%)           IIII                                      
## 
## 8    latitude                 Mean (sd) : 37.8 (0)              927 distinct values             : .         999        0        
##      [numeric]                min < med < max:                                                : : :   .     (100.0%)   (0.0%)   
##                               37.7 < 37.8 < 37.8                                              : : : : : :                       
##                               IQR (CV) : 0 (0)                                              : : : : : : :                       
##                                                                                       . . : : : : : : : :                       
## 
## 9    longitude                Mean (sd) : -122.4 (0)            913 distinct values             . :         999        0        
##      [numeric]                min < med < max:                                                  : : : :     (100.0%)   (0.0%)   
##                               -122.5 < -122.4 < -122.4                                          : : : :                         
##                               IQR (CV) : 0 (0)                                                . : : : :                         
##                                                                                       . . . : : : : : : :                       
## 
## 10   room_type                1. Entire home/apt                887 (88.8%)           IIIIIIIIIIIIIIIII     999        0        
##      [character]              2. Private room                   110 (11.0%)           II                    (100.0%)   (0.0%)   
##                               3. Shared room                      2 ( 0.2%)                                                     
## 
## 11   accommodates             Mean (sd) : 5.7 (2.4)             16 distinct values          :               999        0        
##      [integer]                min < med < max:                                          :   :               (100.0%)   (0.0%)   
##                               1 < 6 < 16                                                :   :                                   
##                               IQR (CV) : 3 (0.4)                                      . : . : :                                 
##                                                                                       : : : : : .   .                           
## 
## 12   beds                     Mean (sd) : 3.1 (1.6)             12 distinct values      : :                 999        0        
##      [integer]                min < med < max:                                          : : .               (100.0%)   (0.0%)   
##                               0 < 3 < 12                                                : : :                                   
##                               IQR (CV) : 2 (0.5)                                      : : : : :                                 
##                                                                                       : : : : : .                               
## 
## 13   price                    1. $350.00·                       108 (10.8%)           II                    999        0        
##      [character]              2. $400.00·                        72 ( 7.2%)           I                     (100.0%)   (0.0%)   
##                               3. $450.00·                        67 ( 6.7%)           I                                         
##                               4. $500.00·                        57 ( 5.7%)           I                                         
##                               5. $600.00·                        30 ( 3.0%)                                                     
##                               6. $550.00·                        29 ( 2.9%)                                                     
##                               7. $375.00·                        27 ( 2.7%)                                                     
##                               8. $700.00·                        22 ( 2.2%)                                                     
##                               9. $399.00·                        20 ( 2.0%)                                                     
##                               10. $425.00·                       20 ( 2.0%)                                                     
##                               [ 188 others ]                    547 (54.8%)           IIIIIIIIII                                
## 
## 14   minimum_nights           Mean (sd) : 9.9 (34)              20 distinct values    :                     999        0        
##      [integer]                min < med < max:                                        :                     (100.0%)   (0.0%)   
##                               1 < 3 < 999                                             :                                         
##                               IQR (CV) : 3 (3.4)                                      :                                         
##                                                                                       :                                         
## 
## 15   number_of_reviews        Mean (sd) : 21.1 (32.8)           122 distinct values   :                     999        0        
##      [integer]                min < med < max:                                        :                     (100.0%)   (0.0%)   
##                               0 < 9 < 281                                             :                                         
##                               IQR (CV) : 25 (1.6)                                     :                                         
##                                                                                       : : .                                     
## 
## 16   last_review              1. (Empty string)                 177 (17.7%)           III                   999        0        
##      [character]              2. 5/27/2019                       25 ( 2.5%)                                 (100.0%)   (0.0%)   
##                               3. 5/26/2019                       23 ( 2.3%)                                                     
##                               4. 9/28/2018                       18 ( 1.8%)                                                     
##                               5. 5/19/2019                       17 ( 1.7%)                                                     
##                               6. 5/20/2019                       16 ( 1.6%)                                                     
##                               7. 5/24/2019                       16 ( 1.6%)                                                     
##                               8. 5/23/2019                       15 ( 1.5%)                                                     
##                               9. 1/10/2019                       13 ( 1.3%)                                                     
##                               10. 4/12/2019                      13 ( 1.3%)                                                     
##                               [ 285 others ]                    666 (66.7%)           IIIIIIIIIIIII                             
## 
## 17   review_scores_rating     Mean (sd) : 97.5 (4.4)            21 distinct values                      :   817        182      
##      [integer]                min < med < max:                                                          :   (81.8%)    (18.2%)  
##                               40 < 99 < 100                                                             :                       
##                               IQR (CV) : 3 (0)                                                          :                       
##                                                                                                       . :                       
## 
## 18   reviews_per_month        Mean (sd) : 1 (1.2)               268 distinct values   :                     822        177      
##      [numeric]                min < med < max:                                        :                     (82.3%)    (17.7%)  
##                               0 < 0.6 < 9.2                                           :                                         
##                               IQR (CV) : 1 (1.2)                                      : :                                       
##                                                                                       : : .                                     
## --------------------------------------------------------------------------------------------------------------------------------
##      Id          host_name        host_id        host_since     
##  Mode :logical   Mode :logical   Mode :logical   Mode :logical  
##  FALSE:999       FALSE:999       FALSE:999       FALSE:999      
##                                                                 
##  host_is_superhost neighbourhood_cleansed  zipcode         latitude      
##  Mode :logical     Mode :logical          Mode :logical   Mode :logical  
##  FALSE:999         FALSE:999              FALSE:999       FALSE:999      
##                                                                          
##  longitude       room_type       accommodates       beds        
##  Mode :logical   Mode :logical   Mode :logical   Mode :logical  
##  FALSE:999       FALSE:999       FALSE:999       FALSE:999      
##                                                                 
##    price         minimum_nights  number_of_reviews last_review    
##  Mode :logical   Mode :logical   Mode :logical     Mode :logical  
##  FALSE:999       FALSE:999       FALSE:999         FALSE:999      
##                                                                   
##  review_scores_rating reviews_per_month
##  Mode :logical        Mode :logical    
##  FALSE:817            FALSE:822        
##  TRUE :182            TRUE :177

3.3 View Dataset

host_name host_id host_since host_is_superhost neighbourhood_cleansed zipcode latitude longitude room_type
Ding 15357373 5/11/2014 t Western Addition 94115 37.77832 -122.4456 Entire home/apt
Jonathan 16921283 6/17/2014 f Castro/Upper Market 94114 37.76022 -122.4441 Entire home/apt
Stephanie 13639065 3/28/2014 t Mission 94110 37.76291 -122.4216 Entire home/apt
Dagmar 35408771 6/9/2015 f Mission 94110 37.75426 -122.4238 Entire home/apt
Charlie 359544 1/23/2011 f Mission 94103 37.76775 -122.4080 Entire home/apt
Sonder (San Francisco) 219930816 10/10/2018 f Western Addition 94117 37.77652 -122.4344 Private room

4 Data Cleansing and Validation

In order to further manipulate the data, the data needs to be cleansed for further data analysis and visualization. The following tasks were necessary for various columns in the dataset:-

  • Convert column types i.e from character to numeric in this case price, last_review, host_since.
  • Delete characters from data i.e $ signs and commas from Price column and convert Price from character to numeric.
  • Convert character data to Date using as.Date method
  • Update null date columns with System date using dplyr replace_na fuction
  • Rename column names to be better descriptive using colnames
  • Drop unnecessary columns using
  • Replace all null values in the dataset with the aggregate using lapply and na.aggregate

4.1 Eliminate Special characters and update column type

The price column was imported into the data frame as a character column because it had both dollar signs and commas. We need to make calculations or aggregate the price and it is essential that it is a numerical field.

This section was to remove these special characters and change the price column to a numeric to allow for further statistic calculations.

# *********Remove the dollar sign and comma from Price column and change it Price from Character to Numeric ******# remove the dollar sign in price
df_sf$price = gsub("\\$", "", df_sf$price)
# remove the comma in price
df_sf$price = gsub(",", "", df_sf$price)
# change price from character to numeric
df_sf$price = as.numeric(df_sf$price)

tail(df_sf$price,20)
##  [1] 345 345 345 344 342 340 340 340 340 339 339 339 339 339 337 336 336 335 335
## [20] 335
# change reviews from character to numeric
df_sf$reviews_per_month = as.numeric(df_sf$reviews_per_month)

# summary
summary(df_sf$reviews_per_month)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.010   0.270   0.625   1.048   1.300   9.210     177

4.2 Transform and Enrich Data

We had a lot of NA columns which made it difficult to perform calculations, so a decision was made to replace the NA date values with the current system date and also for some null columns to update them with the averages.

4.2.1 Date and Null column modifications

# Replace NULLS with mean or today's date if using dates
library(zoo)
df_sf$last_review.new<-replace_na(df_sf$last_review,Sys.Date())

df_sf<- replace(df_sf, TRUE, lapply(df_sf, na.aggregate))

# Change last_review and host_since date columns from chr to Date columns
df_sf$last_review  <- as.Date(df_sf$last_review , format="%m/%d/%Y")
df_sf$host_since  <- as.Date(df_sf$host_since , format="%m/%d/%Y")

4.2.2 Drop and Rename Columns

Some columns were deemed uneccessary in the analysis and needed to be dropped. Also a better naming convention was required to allow ease in variable definition.

##  [1] "Id"                   "host_name"            "host_id"             
##  [4] "host_since"           "host_is_superhost"    "neighbourhood"       
##  [7] "zipcode"              "latitude"             "longitude"           
## [10] "room_type"            "capacity"             "beds"                
## [13] "price"                "minimum_nights"       "number_of_reviews"   
## [16] "last_review"          "review_scores_rating" "reviews_per_month"   
## [19] "last_review.new"

4.3 Store Transformed and Enriched Data

We make a backup of the enriched data by writing to csv file. From this point on, the cleaned dataset will be used in the next steps. As the dataset is large and some visualizations may not always be aesthetically pleasing, a reduced subset of the dataset is also generated.

##      Id          host_name        host_id        host_since     
##  Mode :logical   Mode :logical   Mode :logical   Mode :logical  
##  FALSE:999       FALSE:999       FALSE:999       FALSE:999      
##                                                                 
##  host_is_superhost neighbourhood    zipcode         latitude      
##  Mode :logical     Mode :logical   Mode :logical   Mode :logical  
##  FALSE:999         FALSE:999       FALSE:999       FALSE:999      
##                                                                   
##  longitude       room_type        capacity          beds        
##  Mode :logical   Mode :logical   Mode :logical   Mode :logical  
##  FALSE:999       FALSE:999       FALSE:999       FALSE:999      
##                                                                 
##    price         minimum_nights  number_of_reviews last_review    
##  Mode :logical   Mode :logical   Mode :logical     Mode :logical  
##  FALSE:999       FALSE:999       FALSE:999         FALSE:822      
##                                                    TRUE :177      
##  review_scores_rating reviews_per_month last_review.new
##  Mode :logical        Mode :logical     Mode :logical  
##  FALSE:999            FALSE:999         FALSE:999      
## 
Data summary
Name df_sf
Number of rows 999
Number of columns 19
_______________________
Column type frequency:
character 6
Date 2
numeric 11
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
host_name 0 1 1 30 0 596 0
host_is_superhost 0 1 1 1 0 2 0
neighbourhood 0 1 4 21 0 34 0
zipcode 0 1 0 5 1 29 0
room_type 0 1 11 15 0 3 0
last_review.new 0 1 0 10 177 295 0

Variable type: Date

skim_variable n_missing complete_rate min max median n_unique
host_since 0 1.00 2009-01-09 2019-05-28 2014-02-02 734
last_review 177 0.82 2012-09-14 2019-06-01 2019-03-22 294

Variable type: numeric

skim_variable n_missing complete_rate total
Id 0 1 1.751909e+10
host_id 0 1 4.409128e+10
latitude 0 1 3.773277e+04
longitude 0 1 -1.223090e+05
capacity 0 1 5.706000e+03
beds 0 1 3.131000e+03
price 0 1 5.915530e+05
minimum_nights 0 1 9.850000e+03
number_of_reviews 0 1 2.106800e+04
review_scores_rating 0 1 9.736521e+04
reviews_per_month 0 1 1.047180e+03

4.4 View Clean Subset Dataset

In this section, a subset of the dataset was created to speed up the creation of aesthetic plots. We also used the filter function of dplyr package to do the same in this and subsequent sections.

Airbnb Subset Dataset
neighbourhood room_type price capacity zipcode host_is_superhost review_scores_rating host_since number_of_reviews neighbourhood room_type price capacity zipcode host_is_superhost review_scores_rating host_since number_of_reviews
Western Addition Entire home/apt 3000 6 94115 t 97.00000 2014-05-11 13 Western Addition Entire home/apt 3000 6 94115 t 97.00000 2014-05-11 13
Castro/Upper Market Entire home/apt 3000 6 94114 f 97.46267 2014-06-17 0 Castro/Upper Market Entire home/apt 3000 6 94114 f 97.46267 2014-06-17 0
Mission Entire home/apt 2565 4 94110 t 99.00000 2014-03-28 30 Mission Entire home/apt 2565 4 94110 t 99.00000 2014-03-28 30
Mission Entire home/apt 2500 7 94110 f 100.00000 2015-06-09 2 Mission Entire home/apt 2500 7 94110 f 100.00000 2015-06-09 2
Mission Entire home/apt 2450 8 94103 f 100.00000 2011-01-23 5 Mission Entire home/apt 2450 8 94103 f 100.00000 2011-01-23 5
Western Addition Private room 2353 2 94117 f 97.46267 2018-10-10 0 Western Addition Private room 2353 2 94117 f 97.46267 2018-10-10 0
Western Addition Private room 2353 2 94117 f 97.46267 2018-10-10 0 Western Addition Private room 2353 2 94117 f 97.46267 2018-10-10 0
Marina Entire home/apt 2282 16 94123 t 100.00000 2018-07-28 17 Marina Entire home/apt 2282 16 94123 t 100.00000 2018-07-28 17
North Beach Private room 2200 6 94133 f 97.46267 2014-02-06 0 North Beach Private room 2200 6 94133 f 97.46267 2014-02-06 0
Financial District Entire home/apt 2010 2 94105 f 97.46267 2012-01-08 0 Financial District Entire home/apt 2010 2 94105 f 97.46267 2012-01-08 0
Western Addition Entire home/apt 2000 16 94115 t 100.00000 2012-09-01 18 Western Addition Entire home/apt 2000 16 94115 t 100.00000 2012-09-01 18
Western Addition Entire home/apt 2000 10 94115 f 97.46267 2016-12-25 0 Western Addition Entire home/apt 2000 10 94115 f 97.46267 2016-12-25 0
Marina Entire home/apt 1800 6 94123 t 100.00000 2012-07-28 14 Marina Entire home/apt 1800 6 94123 t 100.00000 2012-07-28 14
Marina Entire home/apt 1787 15 94123 t 100.00000 2018-07-28 11 Marina Entire home/apt 1787 15 94123 t 100.00000 2018-07-28 11
Outer Sunset Private room 1700 2 94122 f 97.46267 2016-09-28 0 Outer Sunset Private room 1700 2 94122 f 97.46267 2016-09-28 0
Outer Sunset Private room 1700 1 94122 f 97.46267 2016-09-28 0 Outer Sunset Private room 1700 1 94122 f 97.46267 2016-09-28 0
Western Addition Entire home/apt 1600 8 94102 f 100.00000 2014-07-19 6 Western Addition Entire home/apt 1600 8 94102 f 100.00000 2014-07-19 6
Marina Entire home/apt 1600 12 94123 t 100.00000 2016-01-06 5 Marina Entire home/apt 1600 12 94123 t 100.00000 2016-01-06 5
Russian Hill Private room 1600 6 94109 f 97.46267 2016-09-28 0 Russian Hill Private room 1600 6 94109 f 97.46267 2016-09-28 0
Presidio Heights Entire home/apt 1500 8 94118 t 96.00000 2013-02-17 11 Presidio Heights Entire home/apt 1500 8 94118 t 96.00000 2013-02-17 11
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Haight Ashbury Entire home/apt 350 4 94117 t 99.00000 2011-02-15 16 Haight Ashbury Entire home/apt 350 4 94117 t 99.00000 2011-02-15 16
Excelsior Entire home/apt 350 7 94112 t 97.00000 2016-12-11 18 Excelsior Entire home/apt 350 7 94112 t 97.00000 2016-12-11 18
Outer Sunset Entire home/apt 350 6 94122 f 100.00000 2017-01-19 6 Outer Sunset Entire home/apt 350 6 94122 f 100.00000 2017-01-19 6
Chinatown Private room 350 2 94108 f 100.00000 2010-02-24 5 Chinatown Private room 350 2 94108 f 100.00000 2010-02-24 5
Chinatown Private room 350 2 94108 f 93.00000 2010-02-24 3 Chinatown Private room 350 2 94108 f 93.00000 2010-02-24 3
Chinatown Private room 350 2 94108 f 100.00000 2010-02-24 1 Chinatown Private room 350 2 94108 f 100.00000 2010-02-24 1
Chinatown Private room 350 2 94108 f 97.46267 2010-02-24 0 Chinatown Private room 350 2 94108 f 97.46267 2010-02-24 0
Chinatown Private room 350 2 94108 f 80.00000 2010-02-24 2 Chinatown Private room 350 2 94108 f 80.00000 2010-02-24 2
Chinatown Private room 350 2 94108 f 100.00000 2010-02-24 1 Chinatown Private room 350 2 94108 f 100.00000 2010-02-24 1
Chinatown Private room 350 2 94108 f 97.46267 2010-02-24 0 Chinatown Private room 350 2 94108 f 97.46267 2010-02-24 0
Chinatown Private room 350 2 94108 f 100.00000 2010-02-24 4 Chinatown Private room 350 2 94108 f 100.00000 2010-02-24 4
Outer Richmond Entire home/apt 350 6 94121 f 100.00000 2012-03-06 4 Outer Richmond Entire home/apt 350 6 94121 f 100.00000 2012-03-06 4
North Beach Private room 350 1 94133 f 97.46267 2017-08-30 0 North Beach Private room 350 1 94133 f 97.46267 2017-08-30 0
Potrero Hill Private room 350 6 94107 f 100.00000 2015-05-03 21 Potrero Hill Private room 350 6 94107 f 100.00000 2015-05-03 21
Western Addition Entire home/apt 350 5 94117 f 100.00000 2010-09-26 1 Western Addition Entire home/apt 350 5 94117 f 100.00000 2010-09-26 1
Downtown/Civic Center Entire home/apt 350 4 94109 f 100.00000 2014-08-10 2 Downtown/Civic Center Entire home/apt 350 4 94109 f 100.00000 2014-08-10 2
Mission Entire home/apt 350 4 94110 t 99.00000 2013-09-01 51 Mission Entire home/apt 350 4 94110 t 99.00000 2013-09-01 51
Western Addition Entire home/apt 350 6 94117 f 100.00000 2017-10-20 3 Western Addition Entire home/apt 350 6 94117 f 100.00000 2017-10-20 3
Inner Sunset Entire home/apt 350 11 94122 t 99.00000 2012-04-15 27 Inner Sunset Entire home/apt 350 11 94122 t 99.00000 2012-04-15 27
Haight Ashbury Private room 350 6 94117 t 96.00000 2011-10-03 5 Haight Ashbury Private room 350 6 94117 t 96.00000 2011-10-03 5
Haight Ashbury Entire home/apt 350 7 94117 t 100.00000 2013-07-22 1 Haight Ashbury Entire home/apt 350 7 94117 t 100.00000 2013-07-22 1
Nob Hill Entire home/apt 350 6 94133 t 97.46267 2012-02-03 0 Nob Hill Entire home/apt 350 6 94133 t 97.46267 2012-02-03 0
Mission Entire home/apt 350 6 94110 f 80.00000 2010-07-05 4 Mission Entire home/apt 350 6 94110 f 80.00000 2010-07-05 4
Russian Hill Entire home/apt 350 4 94109 t 96.00000 2018-01-18 21 Russian Hill Entire home/apt 350 4 94109 t 96.00000 2018-01-18 21
Western Addition Entire home/apt 350 3 94117 f 100.00000 2011-06-27 5 Western Addition Entire home/apt 350 3 94117 f 100.00000 2011-06-27 5
Downtown/Civic Center Entire home/apt 350 2 94102 f 100.00000 2012-05-14 7 Downtown/Civic Center Entire home/apt 350 2 94102 f 100.00000 2012-05-14 7
Chinatown Private room 350 4 94108 f 85.00000 2012-07-02 4 Chinatown Private room 350 4 94108 f 85.00000 2012-07-02 4
Noe Valley Entire home/apt 350 6 94131 t 100.00000 2018-03-21 9 Noe Valley Entire home/apt 350 6 94131 t 100.00000 2018-03-21 9
Pacific Heights Private room 350 2 94123 t 100.00000 2011-06-23 16 Pacific Heights Private room 350 2 94123 t 100.00000 2011-06-23 16
Western Addition Entire home/apt 350 11 94117 t 99.00000 2011-02-23 43 Western Addition Entire home/apt 350 11 94117 t 99.00000 2011-02-23 43
Mission Entire home/apt 350 4 94110 t 98.00000 2012-11-15 11 Mission Entire home/apt 350 4 94110 t 98.00000 2012-11-15 11
Bernal Heights Entire home/apt 350 4 94110 f 100.00000 2014-08-26 6 Bernal Heights Entire home/apt 350 4 94110 f 100.00000 2014-08-26 6
SOMA Entire home/apt 350 4 94103 t 99.00000 2012-05-19 15 SOMA Entire home/apt 350 4 94103 t 99.00000 2012-05-19 15
Bayview Entire home/apt 350 5 94124 t 99.00000 2016-03-13 14 Bayview Entire home/apt 350 5 94124 t 99.00000 2016-03-13 14
Bernal Heights Entire home/apt 350 6 94110 f 96.00000 2016-09-20 5 Bernal Heights Entire home/apt 350 6 94110 f 96.00000 2016-09-20 5
Mission Entire home/apt 350 5 94110 t 98.00000 2012-02-22 22 Mission Entire home/apt 350 5 94110 t 98.00000 2012-02-22 22
Parkside Entire home/apt 350 4 94116 f 90.00000 2015-07-17 4 Parkside Entire home/apt 350 4 94116 f 90.00000 2015-07-17 4
Western Addition Entire home/apt 350 4 94115 f 100.00000 2013-07-02 2 Western Addition Entire home/apt 350 4 94115 f 100.00000 2013-07-02 2
Haight Ashbury Entire home/apt 350 2 94117 f 100.00000 2015-01-06 5 Haight Ashbury Entire home/apt 350 2 94117 f 100.00000 2015-01-06 5
Haight Ashbury Entire home/apt 350 6 94117 t 100.00000 2012-03-16 4 Haight Ashbury Entire home/apt 350 6 94117 t 100.00000 2012-03-16 4
Mission Private room 350 2 94110 f 100.00000 2014-08-08 1 Mission Private room 350 2 94110 f 100.00000 2014-08-08 1
Potrero Hill Entire home/apt 350 4 94107 t 100.00000 2015-10-05 7 Potrero Hill Entire home/apt 350 4 94107 t 100.00000 2015-10-05 7
Noe Valley Entire home/apt 350 4 94114 f 85.00000 2013-09-20 4 Noe Valley Entire home/apt 350 4 94114 f 85.00000 2013-09-20 4
Haight Ashbury Entire home/apt 350 4 94117 t 100.00000 2013-08-05 24 Haight Ashbury Entire home/apt 350 4 94117 t 100.00000 2013-08-05 24
Inner Richmond Entire home/apt 350 3 94118 t 97.46267 2012-10-03 0 Inner Richmond Entire home/apt 350 3 94118 t 97.46267 2012-10-03 0
Marina Private room 350 2 94123 f 97.46267 2018-12-07 0 Marina Private room 350 2 94123 f 97.46267 2018-12-07 0
Excelsior Private room 350 5 94112 t 97.46267 2017-10-18 0 Excelsior Private room 350 5 94112 t 97.46267 2017-10-18 0
Inner Richmond Entire home/apt 350 5 94118 t 97.46267 2012-10-03 0 Inner Richmond Entire home/apt 350 5 94118 t 97.46267 2012-10-03 0
Castro/Upper Market Entire home/apt 350 4 94114 f 97.46267 2013-12-18 0 Castro/Upper Market Entire home/apt 350 4 94114 f 97.46267 2013-12-18 0
Outer Sunset Entire home/apt 350 4 94122 f 97.46267 2017-01-06 0 Outer Sunset Entire home/apt 350 4 94122 f 97.46267 2017-01-06 0
Outer Richmond Entire home/apt 350 6 94121 t 100.00000 2014-05-05 3 Outer Richmond Entire home/apt 350 6 94121 t 100.00000 2014-05-05 3
SOMA Entire home/apt 350 4 94105 f 97.46267 2016-12-08 1 SOMA Entire home/apt 350 4 94105 f 97.46267 2016-12-08 1
Excelsior Entire home/apt 350 10 94112 f 96.00000 2019-03-20 15 Excelsior Entire home/apt 350 10 94112 f 96.00000 2019-03-20 15
Mission Entire home/apt 350 7 94110 f 100.00000 2012-12-22 1 Mission Entire home/apt 350 7 94110 f 100.00000 2012-12-22 1
Mission Entire home/apt 350 12 94110 f 100.00000 2014-03-04 1 Mission Entire home/apt 350 12 94110 f 100.00000 2014-03-04 1
Haight Ashbury Private room 350 4 94117 f 97.46267 2015-04-02 0 Haight Ashbury Private room 350 4 94117 f 97.46267 2015-04-02 0
Outer Mission Entire home/apt 350 6 94131 f 97.46267 2015-10-27 0 Outer Mission Entire home/apt 350 6 94131 f 97.46267 2015-10-27 0
Bernal Heights Entire home/apt 350 6 94110 f 100.00000 2012-12-07 1 Bernal Heights Entire home/apt 350 6 94110 f 100.00000 2012-12-07 1
Nob Hill Entire home/apt 350 5 94109 f 100.00000 2012-08-17 2 Nob Hill Entire home/apt 350 5 94109 f 100.00000 2012-08-17 2
Outer Richmond Entire home/apt 350 6 94121 f 97.46267 2012-07-24 0 Outer Richmond Entire home/apt 350 6 94121 f 97.46267 2012-07-24 0
Bernal Heights Entire home/apt 350 5 94110 f 97.46267 2018-03-14 0 Bernal Heights Entire home/apt 350 5 94110 f 97.46267 2018-03-14 0
Mission Entire home/apt 349 6 94110 f 90.00000 2014-10-28 7 Mission Entire home/apt 349 6 94110 f 90.00000 2014-10-28 7
Chinatown Entire home/apt 349 4 94108 t 94.00000 2013-08-12 126 Chinatown Entire home/apt 349 4 94108 t 94.00000 2013-08-12 126
Haight Ashbury Entire home/apt 349 4 94117 f 92.00000 2016-09-19 19 Haight Ashbury Entire home/apt 349 4 94117 f 92.00000 2016-09-19 19
Castro/Upper Market Entire home/apt 349 4 94114 t 95.00000 2013-04-10 25 Castro/Upper Market Entire home/apt 349 4 94114 t 95.00000 2013-04-10 25
Castro/Upper Market Entire home/apt 349 5 94114 t 93.00000 2011-07-04 8 Castro/Upper Market Entire home/apt 349 5 94114 t 93.00000 2011-07-04 8
SOMA Private room 349 1 94103 f 100.00000 2014-09-17 9 SOMA Private room 349 1 94103 f 100.00000 2014-09-17 9
Castro/Upper Market Entire home/apt 349 4 94114 t 100.00000 2013-12-08 15 Castro/Upper Market Entire home/apt 349 4 94114 t 100.00000 2013-12-08 15
Noe Valley Entire home/apt 349 4 94102 f 93.00000 2018-05-23 50 Noe Valley Entire home/apt 349 4 94102 f 93.00000 2018-05-23 50
SOMA Entire home/apt 349 2 94158 t 97.00000 2017-11-01 12 SOMA Entire home/apt 349 2 94158 t 97.00000 2017-11-01 12
Castro/Upper Market Entire home/apt 349 6 94114 f 97.46267 2011-02-18 0 Castro/Upper Market Entire home/apt 349 6 94114 f 97.46267 2011-02-18 0
Crocker Amazon Entire home/apt 349 7 94014 t 100.00000 2016-01-16 4 Crocker Amazon Entire home/apt 349 7 94014 t 100.00000 2016-01-16 4
Excelsior Entire home/apt 349 7 94112 t 97.46267 2013-09-01 0 Excelsior Entire home/apt 349 7 94112 t 97.46267 2013-09-01 0
West of Twin Peaks Entire home/apt 349 8 94116 f 96.00000 2019-01-26 5 West of Twin Peaks Entire home/apt 349 8 94116 f 96.00000 2019-01-26 5
Western Addition Private room 349 4 94102 f 80.00000 2018-10-10 1 Western Addition Private room 349 4 94102 f 80.00000 2018-10-10 1
Potrero Hill Entire home/apt 348 7 94107 t 97.00000 2010-10-13 20 Potrero Hill Entire home/apt 348 7 94107 t 97.00000 2010-10-13 20
Castro/Upper Market Entire home/apt 348 8 94114 f 98.00000 2014-11-12 10 Castro/Upper Market Entire home/apt 348 8 94114 f 98.00000 2014-11-12 10
Western Addition Entire home/apt 348 4 94102 t 100.00000 2017-07-06 76 Western Addition Entire home/apt 348 4 94102 t 100.00000 2017-07-06 76
Bernal Heights Entire home/apt 345 5 94110 f 99.00000 2013-05-28 44 Bernal Heights Entire home/apt 345 5 94110 f 99.00000 2013-05-28 44
Marina Entire home/apt 345 4 94123 f 98.00000 2013-08-04 49 Marina Entire home/apt 345 4 94123 f 98.00000 2013-08-04 49
Castro/Upper Market Entire home/apt 345 2 94114 f 96.00000 2015-04-07 5 Castro/Upper Market Entire home/apt 345 2 94114 f 96.00000 2015-04-07 5
Noe Valley Entire home/apt 345 4 94114 f 93.00000 2015-04-14 12 Noe Valley Entire home/apt 345 4 94114 f 93.00000 2015-04-14 12
Bernal Heights Entire home/apt 345 10 94112 t 99.00000 2017-12-11 32 Bernal Heights Entire home/apt 345 10 94112 t 99.00000 2017-12-11 32
Marina Entire home/apt 345 4 94123 f 97.46267 2019-05-15 0 Marina Entire home/apt 345 4 94123 f 97.46267 2019-05-15 0
Financial District Entire home/apt 344 2 94105 t 90.00000 2011-10-06 2 Financial District Entire home/apt 344 2 94105 t 90.00000 2011-10-06 2
Western Addition Private room 342 4 94117 f 89.00000 2018-10-10 9 Western Addition Private room 342 4 94117 f 89.00000 2018-10-10 9
Mission Entire home/apt 340 5 94103 t 99.00000 2012-05-09 37 Mission Entire home/apt 340 5 94103 t 99.00000 2012-05-09 37
Noe Valley Entire home/apt 340 6 94114 f 97.00000 2011-06-23 30 Noe Valley Entire home/apt 340 6 94114 f 97.00000 2011-06-23 30
Noe Valley Entire home/apt 340 4 94114 t 97.46267 2012-07-06 0 Noe Valley Entire home/apt 340 4 94114 t 97.46267 2012-07-06 0
Visitacion Valley Entire home/apt 340 8 94134 f 100.00000 2019-04-26 1 Visitacion Valley Entire home/apt 340 8 94134 f 100.00000 2019-04-26 1
Mission Entire home/apt 339 5 94110 t 99.00000 2013-07-22 39 Mission Entire home/apt 339 5 94110 t 99.00000 2013-07-22 39
SOMA Entire home/apt 339 5 94107 f 100.00000 2012-04-16 1 SOMA Entire home/apt 339 5 94107 f 100.00000 2012-04-16 1
Mission Entire home/apt 339 7 94110 f 100.00000 2013-11-21 5 Mission Entire home/apt 339 7 94110 f 100.00000 2013-11-21 5
Downtown/Civic Center Private room 339 4 94109 f 100.00000 2016-09-20 3 Downtown/Civic Center Private room 339 4 94109 f 100.00000 2016-09-20 3
Inner Richmond Entire home/apt 339 8 94118 t 97.00000 2016-09-07 63 Inner Richmond Entire home/apt 339 8 94118 t 97.00000 2016-09-07 63
Downtown/Civic Center Private room 337 4 94102 f 97.46267 2019-03-25 0 Downtown/Civic Center Private room 337 4 94102 f 97.46267 2019-03-25 0
Mission Entire home/apt 336 5 94110 t 100.00000 2014-06-26 1 Mission Entire home/apt 336 5 94110 t 100.00000 2014-06-26 1
North Beach Entire home/apt 336 4 94133 f 97.46267 2018-02-14 0 North Beach Entire home/apt 336 4 94133 f 97.46267 2018-02-14 0
Noe Valley Entire home/apt 335 4 94131 f 100.00000 2014-11-25 1 Noe Valley Entire home/apt 335 4 94131 f 100.00000 2014-11-25 1
Noe Valley Entire home/apt 335 6 94131 f 90.00000 2014-11-25 2 Noe Valley Entire home/apt 335 6 94131 f 90.00000 2014-11-25 2
Mission Entire home/apt 335 4 94110 t 100.00000 2011-10-14 5 Mission Entire home/apt 335 4 94110 t 100.00000 2011-10-14 5

5 Exploratory Data Analysis

In this step, we want to get maximum insights from the data set. While utilizing the various R packages offered, we have used tables, bar graphs, line graphs, boxplots and maps to extract information about the dataset.

This section contains groupings of various data combinations using dplyr. ggplot was used to visualize the data by mapping data variables. The first layer is the data itself, in this case, ggplot is used to map the data variables beds, accommodates, and scales maps the variables in the data space to values in the aesthetic space such as color and size. Here, we used variable room_type to map the variables above to the aesthetic space……

5.1 Total rentals by Room type Table

In this section, we have used both dplyr group by and summarize option to count the number of rentals for the different neighbourhoods. A well formatted table is generated using the kable R package.

Here we see the areas closest to the downtown area have a high number of rentals compared to the inner city areas. Also neighbourhoods deemed not so safe have less rentals available. These neighbourhoods also cost less when it comes to rentals and given the stringent rules implemented by the city, it is possible that they may have been more affected.

neighbourhood Count
Bayview 5
Bernal Heights 42
Castro/Upper Market 86
Chinatown 17
Crocker Amazon 3
Diamond Heights 2

5.2 Map showing Prices vs Neighbourhood

A static map of San Francisco neighbourhoods using the coordinate points provided in the dataset was used in conjunction with ggmap. The get_stamenmap function of the ggmap R package accesses a tile server for Stament maps. A visit to Openstreet was necessary to obtain the coordinates and zoom size.

It seems the average pricing for rentals is below 500 USD and we can see that most rentals are concentrated in the center of the city possibly for tourists and business travelers convienience.

5.3 Tables Statistics

5.3.1 Number of homes based on Room Type

A table using kable_styling option of the KabelExtra R package was utilized. There are 8 times more Entire homes or apartments for rent than Private rooms. It seems both hosts and possibly consumers prefer their privacy. Hosts make more money on renting out the entire apartment rather than a room.

room_type Count
1 Entire home/apt 887
2 Private room 110
3 Shared room 2

5.3.2 Ratings of Review Scores by Neighbourhood

The average reviews by neighbourhood also utilized both dplyr functionality for generating the data and kable_styling for visualizing the table.

The reviews per neighbourhood seem high even for the not so great neighbourhoods. It is important to note that San Francisco has seen a lot of growth and renovation into not so great neighbourhoods. This has also driven the price up as most original San Francisco resident (not working in the Tech industry) have left.

neighbourhood Mean_reviewscore
1 Bayview 96.00
2 Bernal Heights 97.64
3 Castro/Upper Market 97.90
4 Chinatown 95.70
5 Crocker Amazon 95.82
6 Diamond Heights 97.00

5.3.3 Average Price per Apt by Neighbourhood

This was similar to the above showing Average price per Rental per Neighbourhood. The average prices per apt per neighbourhood per Night is quite high but still cheaper than an average night in a San Francisco hotel, if one can find one available in their desired location.

neighbourhood MeanPrice
1 Bayview 418.20
2 Bernal Heights 476.55
3 Castro/Upper Market 559.90
4 Chinatown 479.35
5 Crocker Amazon 398.00
6 Diamond Heights 442.50

5.4 Interactive Map of Neighbourhoods and Price

This intereactive map built using leaflet, shows the San Francisco neighbourhoods and the popups contain information about the listing. The user has the ability to zoom in and out of the map. The popup displays Price, Neighbourood, Minimum Nights and Rating of the rental. This could be good information for renters to have an overview with maps.

It is interesting to see that the minimum number of nights is 2 or more at most properties. The prices are very high per neighbourhood and they are concentrated in the middle. San Francisco is only 7 miles long and 7 miles wide and the concentration of properties available for rent is alarming for a such a small city.

5.5 Boxplot of Room Type Prices

This Boxplot represents some select neighbourhoods in San Francisco and the Prices per night. This is a summary of different types of neighbourhoods. The idea was to test the boxplot functionality hence, we used ggplot with the filter functionality.

A filter of some select neighbourhoods, shows that even the median for the lowest priced neighbourhoods in this subset is about 350USD. Some outliers show some high prices from 1500USD to over 2500USD.

5.6 Bar graph

This bar graph shows super hosts and none super hosts based on sampled neighbourhoods in San Francisco. Boolean values in the dataset and the bar graph represent, super host as ‘t’ and ‘f’ for non-superhost. The ggplot2 R package was used to generate in conjunction with the filter to reduce the result set and produce an aesthetically pleasure bar graph.

The bar graph shows the number of super hosts. The Superhost badge is a symbol of the best in hospitality and is displayed on all Superhost listings and profiles. It is clear that neighbourhoods with high rentals and closer to the downtown area, also have more superhosts. Since the Superhost badge is a symbol of good hospitability, most likely many consumers are repeat customers or going by “word of mouth”. Its good business for the hosts. Fewer rentals in the outskirts of the downtown area equates to fewer Superhost badge.

5.7 Scatterplot distribution of Room types and Prices

This scatter plot shows the distribution of prices in all the neighborhoods of San Franscisco based on room types. Based on the analysis,it is obvious that the Shared rooms had the least observations and we can conclude that the price varies across different neighbourhoods for the Private rooms and or Entire home/apartments.

Since there are more Entire homes rented as previously seen, we can again see a concentration in the downtown areas and also a high concentration of pricess around 350-600USD.

5.8 Line Graph Prices and beds

The line graph shows the relationship between variable price and beds.Here we used a sample of the dataset and the filter function of the ggplot package.

It seems the most expensive are 3 bedrooms. Prices vary and that could be explained by neighbourhood or location.

5.9 Interactive Scatter Plot Analysis of prices with Neighbourhoods

This scatter plot has interactive functionality that allows a Play button that shows the room prices by neighbourhood changing by minimum nights.

Minimum nights of between 1-5 have the most activity and range below 1000USD. Shared rooms do not feature much.

6 Modeling and Analysis

This section will focus on the various tools that exist in plotting, analyzing, visualizing regression model data. The packages that transform these visualizations into less statistical will be used.

6.1 Fitting a Regression Model

This multi regression formula model is plotted to predict the outcome of prices on the basis of all the variables listed in the formula. As the R lm package does not provide easy to read and formatted tables, the pixiedust package was used with its various functions such as dust and sprinkle. It allowed for formatting of columns and provide column names.

Analyzing the multi regression results of the formula below:

Formula : - price ~ neighbourhood + room_type + minimum_nights + number_of_reviews +host_is_superhost + capacity

we can see that only the Predictor variables with smaller p-value are responsible for explanatory variable (Price). In this case, that is the capacity/number of people accomodated and Shared rooms. These two values have a p-value of less than 0.05 and they have statistically significant interaction. The other predictor variables in the formula are not statistically significant.

Parameters Estimate Std Error T-Statistic P-value
(Intercept) 97.401 164.437 0.592 0.55
neighbourhoodMarina 301.899 161.234 1.872 0.063
neighbourhoodMission 164.184 156.92 1.046 0.3
neighbourhoodOuter Sunset 232.755 178.964 1.301 0.19
neighbourhoodSOMA 181.259 160.946 1.126 0.26
room_typePrivate room 152.193 87.62 1.737 0.084
room_typeShared room 880.963 348.466 2.528 0.012
minimum_nights -0.408 0.343 -1.188 0.24
number_of_reviews -1.334 0.75 -1.78 0.076
host_is_superhostt 50.394 49.26 1.023 0.31
capacity 52.609 9.586 5.488 < 0.001

6.2 Plot of the Linear Regression Model

To perform multiple plots in a single instance, ggfortify package was used. Here we have bigger and clearer outputs that make it easier to analyze.

## [1] "ggmultiplot"
## attr(,"package")
## [1] "ggfortify"

6.2.1 Best Regression Model

Here we use regsubsets to view best fitting model. An option to view the data is given.

## Subset selection object
## Call: regsubsets.formula(price ~ neighbourhood + room_type + minimum_nights + 
##     number_of_reviews + host_is_superhost + capacity, data = df_sf3, 
##     nbest = 2, nvmax = 9)
## 10 Variables  (and intercept)
##                           Forced in Forced out
## neighbourhoodMarina           FALSE      FALSE
## neighbourhoodMission          FALSE      FALSE
## neighbourhoodOuter Sunset     FALSE      FALSE
## neighbourhoodSOMA             FALSE      FALSE
## room_typePrivate room         FALSE      FALSE
## room_typeShared room          FALSE      FALSE
## minimum_nights                FALSE      FALSE
## number_of_reviews             FALSE      FALSE
## host_is_superhostt            FALSE      FALSE
## capacity                      FALSE      FALSE
## 2 subsets of each size up to 9
## Selection Algorithm: exhaustive
##          neighbourhoodMarina neighbourhoodMission neighbourhoodOuter Sunset
## 1  ( 1 ) " "                 " "                  " "                      
## 1  ( 2 ) "*"                 " "                  " "                      
## 2  ( 1 ) " "                 " "                  " "                      
## 2  ( 2 ) "*"                 " "                  " "                      
## 3  ( 1 ) "*"                 " "                  " "                      
## 3  ( 2 ) " "                 " "                  " "                      
## 4  ( 1 ) "*"                 " "                  " "                      
## 4  ( 2 ) "*"                 " "                  " "                      
## 5  ( 1 ) "*"                 " "                  " "                      
## 5  ( 2 ) "*"                 " "                  " "                      
## 6  ( 1 ) "*"                 " "                  " "                      
## 6  ( 2 ) "*"                 " "                  " "                      
## 7  ( 1 ) "*"                 " "                  " "                      
## 7  ( 2 ) "*"                 " "                  "*"                      
## 8  ( 1 ) "*"                 " "                  "*"                      
## 8  ( 2 ) "*"                 " "                  " "                      
## 9  ( 1 ) "*"                 "*"                  "*"                      
## 9  ( 2 ) "*"                 " "                  "*"                      
##          neighbourhoodSOMA room_typePrivate room room_typeShared room
## 1  ( 1 ) " "               " "                   " "                 
## 1  ( 2 ) " "               " "                   " "                 
## 2  ( 1 ) " "               " "                   "*"                 
## 2  ( 2 ) " "               " "                   " "                 
## 3  ( 1 ) " "               " "                   "*"                 
## 3  ( 2 ) " "               "*"                   "*"                 
## 4  ( 1 ) " "               "*"                   "*"                 
## 4  ( 2 ) " "               " "                   "*"                 
## 5  ( 1 ) " "               "*"                   "*"                 
## 5  ( 2 ) " "               "*"                   "*"                 
## 6  ( 1 ) " "               "*"                   "*"                 
## 6  ( 2 ) " "               "*"                   "*"                 
## 7  ( 1 ) " "               "*"                   "*"                 
## 7  ( 2 ) " "               "*"                   "*"                 
## 8  ( 1 ) " "               "*"                   "*"                 
## 8  ( 2 ) "*"               "*"                   "*"                 
## 9  ( 1 ) "*"               "*"                   "*"                 
## 9  ( 2 ) "*"               "*"                   "*"                 
##          minimum_nights number_of_reviews host_is_superhostt capacity
## 1  ( 1 ) " "            " "               " "                "*"     
## 1  ( 2 ) " "            " "               " "                " "     
## 2  ( 1 ) " "            " "               " "                "*"     
## 2  ( 2 ) " "            " "               " "                "*"     
## 3  ( 1 ) " "            " "               " "                "*"     
## 3  ( 2 ) " "            " "               " "                "*"     
## 4  ( 1 ) " "            " "               " "                "*"     
## 4  ( 2 ) " "            "*"               " "                "*"     
## 5  ( 1 ) " "            "*"               " "                "*"     
## 5  ( 2 ) "*"            " "               " "                "*"     
## 6  ( 1 ) "*"            "*"               " "                "*"     
## 6  ( 2 ) " "            "*"               "*"                "*"     
## 7  ( 1 ) "*"            "*"               "*"                "*"     
## 7  ( 2 ) "*"            "*"               " "                "*"     
## 8  ( 1 ) "*"            "*"               "*"                "*"     
## 8  ( 2 ) "*"            "*"               "*"                "*"     
## 9  ( 1 ) "*"            "*"               " "                "*"     
## 9  ( 2 ) "*"            "*"               "*"                "*"

6.2.2 Plot of Model best

6.2.3 Plot Categorical Predictors

Here we plot for the categorical predictor Host_is_superhost and we have a number of options with the effect_plot option of the ggplot2 package.

The Super host has higher prices compared to non super hosts.

6.2.4 Plot for Room Type

Here the categorical room type is used and the display is also interesting in a different view. This shows the same data we have seen before that Entire homes which are the majority, are priced at around 300-400USD. It seems that Private rooms also hold their own in the price war.

6.2.5 Plot for Neighbourhood against Price

Here we predict another categorical variable, neighborhood but we want to use lines to convey the ordered nature of this predictor. The effect_plot function does this clearly.

7 Conclusion

We can conclude that they are distinct reasons contributing to the disparities displayed by the data exploration. First and foremost, prices varying across neighbourhoods could be for various reasons such as safety, proximity to downtown, host status, accomodatability and so on.

Majority of the rentals are located in the downtown area and its vicinity. There is some indication that the less expensive neighbourhoods are either based on neighbourhood safety reputation or distance from downtown area. San Francisco is a full of touristic attraction peppering the entire Bay Area so touristic locations should not be a driving factor for prices.

Additional scrutiny of the data unveils that rentals concentrated in the center of the city have an average price of below 500USD. Privacy factor could be an issue which accounts majority of rentals falling in the Entire Home or Apartment category.

Observations shows that there are none to few prolonged rentals but rather the minimum nights stayed is 2. This could be in line with the rules addressed in the article referenced above. Using Multiple linear regression, we attempted to model the relationship between price and the other variables and we came to the conclusion that some of the variables are statistically significant with a p-value of less than 0.05 while others are not.

8 Appendix

We created a few R Shiny Web Applications related to this project but they were not fully completed.

The Interactive Table and Histogram of the SF Airbnb Dataset R Shiny Web App is located in the following URL:

(https://cwnswiss2022.shinyapps.io/Wanyonyi_Vilakati_Rshiny/) – File Wanyonyi_Vilakati_Rshiny.Rmd has the code to execute it and it executes with the “Run Document” button.

The interactive map of the Neighbourhoods with zoom and popup details is not published but attached in the files as RShiny_InteractiveMap.R . It executes with Run App button.