Challenge 5 Instructions

challenge_5
railroads
cereal
air_bnb
pathogen_cost
australian_marriage
public_schools
usa_households
Introduction to Visualization
Author

Meredith Rolfe

Published

August 22, 2022

library(tidyverse)
library(ggplot2)

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. create at least two univariate visualizations
  • try to make them “publication” ready
  • Explain why you choose the specific graph type
  1. Create at least one bivariate visualization
  • try to make them “publication” ready
  • Explain why you choose the specific graph type

R Graph Gallery is a good starting point for thinking about what information is conveyed in standard graph types, and includes example R code.

(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.

  • cereal.csv ⭐
  • Total_cost_for_top_15_pathogens_2018.xlsx ⭐
  • Australian Marriage ⭐⭐
  • AB_NYC_2019.csv ⭐⭐⭐
  • StateCounty2012.xls ⭐⭐⭐
  • Public School Characteristics ⭐⭐⭐⭐
  • USA Households ⭐⭐⭐⭐⭐
# Read data into dataframe
data <- read_csv("_data/AB_NYC_2019.csv")

head(data, 5)
# A tibble: 5 × 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 & q…    2787 John    Brookl… Kensin…    40.6   -74.0 Privat…   149
2  2595 Skylit Mi…    2845 Jennif… Manhat… Midtown    40.8   -74.0 Entire…   225
3  3647 THE VILLA…    4632 Elisab… Manhat… Harlem     40.8   -73.9 Privat…   150
4  3831 Cozy Enti…    4869 LisaRo… Brookl… Clinto…    40.7   -74.0 Entire…    89
5  5022 Entire Ap…    7192 Laura   Manhat… East H…    40.8   -73.9 Entire…    80
# … with 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
# Get summary statistics of data
summary(data)
       id               name              host_id           host_name        
 Min.   :    2539   Length:48895       Min.   :     2438   Length:48895      
 1st Qu.: 9471945   Class :character   1st Qu.:  7822033   Class :character  
 Median :19677284   Mode  :character   Median : 30793816   Mode  :character  
 Mean   :19017143                      Mean   : 67620011                     
 3rd Qu.:29152178                      3rd Qu.:107434423                     
 Max.   :36487245                      Max.   :274321313                     
                                                                             
 neighbourhood_group neighbourhood         latitude       longitude     
 Length:48895        Length:48895       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:48895       Min.   :    0.0   Min.   :   1.00   Min.   :  0.00   
 Class :character   1st Qu.:   69.0   1st Qu.:   1.00   1st Qu.:  1.00   
 Mode  :character   Median :  106.0   Median :   3.00   Median :  5.00   
                    Mean   :  152.7   Mean   :   7.03   Mean   : 23.27   
                    3rd Qu.:  175.0   3rd Qu.:   5.00   3rd Qu.: 24.00   
                    Max.   :10000.0   Max.   :1250.00   Max.   :629.00   
                                                                         
  last_review         reviews_per_month calculated_host_listings_count
 Min.   :2011-03-28   Min.   : 0.010    Min.   :  1.000               
 1st Qu.:2018-07-08   1st Qu.: 0.190    1st Qu.:  1.000               
 Median :2019-05-19   Median : 0.720    Median :  1.000               
 Mean   :2018-10-04   Mean   : 1.373    Mean   :  7.144               
 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   :10052        NA's   :10052                                   
 availability_365
 Min.   :  0.0   
 1st Qu.:  0.0   
 Median : 45.0   
 Mean   :112.8   
 3rd Qu.:227.0   
 Max.   :365.0

Briefly describe the data

The data is composed of different AirBNB properties listed in New York City, in the year 2019. It consists of information such as the host name, the neighborhood and neighborhood group, the geographical locations, as well as the property type and price. It would be interesting to explore the data to identify differnt statistics by property type as well as neighborhood group.

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.

#Get NA columns count
get_na_col_count <-sapply(data, function(col_name) sum(length(which(is.na(col_name)))))
na_col_count <- data.frame(get_na_col_count)
na_col_count
                               get_na_col_count
id                                            0
name                                         16
host_id                                       0
host_name                                    21
neighbourhood_group                           0
neighbourhood                                 0
latitude                                      0
longitude                                     0
room_type                                     0
price                                         0
minimum_nights                                0
number_of_reviews                             0
last_review                               10052
reviews_per_month                         10052
calculated_host_listings_count                0
availability_365                              0

From the analysis, we can see that every row has a unique id associated with it, which means that there are no duplicate listings present in the dataset.

Are there any variables that require mutation to be usable in your analysis stream? For example, do you need to calculate new values in order to graph them? Can string values be represented numerically? Do you need to turn any variables into factors and reorder for ease of graphics and visualization?

Document your work here.

# Get number of unique room types
unique(data$room_type)
[1] "Private room"    "Entire home/apt" "Shared room"    
# Get number of unique neighborhood groups
unique(data$neighbourhood_group)
[1] "Brooklyn"      "Manhattan"     "Queens"        "Staten Island"
[5] "Bronx"

We can see that room_type and neighborhood_group columns are categorical, with limited unique values.

Univariate Visualizations

data %>%
  dplyr::count(room_type) %>%
  ggplot(aes(x = room_type, y = n, fill = room_type)) +
  geom_bar(stat = "identity") + 
  geom_text(aes(label = n), vjust = -0.5) +
  labs(title="Room Types vs Total Bookings", x="Room Types Available", y="Total Booking Count", fill="Room Type")

data %>%
  dplyr::count(neighbourhood_group) %>%
  ggplot(aes(x = neighbourhood_group, y = n, fill = neighbourhood_group)) +
  geom_bar(stat = "identity") + 
  geom_text(aes(label = n), vjust = -0.5) +
  labs(title="Neighborhood vs Total Bookings", x="Neighborhoods", y="Total Booking Count", fill="Neighborhoods")

# Get histogram plot to get price density of the properties listed
data %>%
  ggplot(aes(x=price)) + 
  geom_histogram() + 
  xlim(0, 2100) + 
  xlab("Price (in Dollars)") + 
  ylab("Number of properties") + 
  ggtitle("Histogram of property price distribution in dataset")

As the histogram is right skewed, we can conclude that most of the properties have price listings less than $500, with very few properties exceeding that value.

Bivariate Visualization(s)

ggplot(data) +
  geom_bar(aes(x = room_type, fill=neighbourhood_group)) + 
  labs(x = "Room Type", y = "Number of Airbnbs", title = "Scatterplot of Room Type vs # of Airbnbs",
       fill = "Neighbourhood Group")

ggplot(data) + 
  geom_point(mapping = aes(x = minimum_nights, y = price)) + 
  labs(x = "Minimum Nights",
       y = "Price",
       title = "Price vs Minimum Nights")

Any additional comments?

From the analysis, we get various visualizations which tell us that entire apartment bookings are most popular, with Manhattan and Brooklyn being the most sought after neighborhood groups. Within the bivariate visualizations we can also see a breakdown of the proportion of neighborhood group bookings for the property types available.