library(tidyverse)
library(ggplot2)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Challenge 5
challenge_5
Abhinav Reddy Yadatha
AB_NYC_2019.csv
Introduction to Visualization
Challenge Overview
Today’s challenge is to:
- read in a data set, and describe the data set using both words and any supporting information (e.g., tables, etc)
- tidy data (as needed, including sanity checks)
- mutate variables as needed (including sanity checks)
- create at least two univariate visualizations
- try to make them “publication” ready
- Explain why you choose the specific graph type
- 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 ⭐⭐⭐⭐⭐
ab_nyc_data <- read.csv("_data/AB_NYC_2019.csv")
head(ab_nyc_data) id name host_id host_name
1 2539 Clean & quiet apt home by the park 2787 John
2 2595 Skylit Midtown Castle 2845 Jennifer
3 3647 THE VILLAGE OF HARLEM....NEW YORK ! 4632 Elisabeth
4 3831 Cozy Entire Floor of Brownstone 4869 LisaRoxanne
5 5022 Entire Apt: Spacious Studio/Loft by central park 7192 Laura
6 5099 Large Cozy 1 BR Apartment In Midtown East 7322 Chris
neighbourhood_group neighbourhood latitude longitude room_type price
1 Brooklyn Kensington 40.64749 -73.97237 Private room 149
2 Manhattan Midtown 40.75362 -73.98377 Entire home/apt 225
3 Manhattan Harlem 40.80902 -73.94190 Private room 150
4 Brooklyn Clinton Hill 40.68514 -73.95976 Entire home/apt 89
5 Manhattan East Harlem 40.79851 -73.94399 Entire home/apt 80
6 Manhattan Murray Hill 40.74767 -73.97500 Entire home/apt 200
minimum_nights number_of_reviews last_review reviews_per_month
1 1 9 2018-10-19 0.21
2 1 45 2019-05-21 0.38
3 3 0 NA
4 1 270 2019-07-05 4.64
5 10 9 2018-11-19 0.10
6 3 74 2019-06-22 0.59
calculated_host_listings_count availability_365
1 6 365
2 2 355
3 1 365
4 1 194
5 1 0
6 1 129summary(ab_nyc_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
Length:48895 Min. : 0.010 Min. : 1.000
Class :character 1st Qu.: 0.190 1st Qu.: 1.000
Mode :character Median : 0.720 Median : 1.000
Mean : 1.373 Mean : 7.144
3rd Qu.: 2.020 3rd Qu.: 2.000
Max. :58.500 Max. :327.000
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
unique(ab_nyc_data$room_type)[1] "Private room" "Entire home/apt" "Shared room" Briefly describe the data
The dataset comprises 16 columns and primarily focuses on neighborhood groups and room types to determine housing prices. The price range in the dataset spans from 0 to 10000.
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.
The available room options are categorized into three types: Private room, Shared room, and Entire home/apartment.
sum(is.na(ab_nyc_data$reviews_per_month))[1] 10052ab_nyc_data <- ab_nyc_data %>% replace_na(list(reviews_per_month = 0))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?
Upon observation, I noticed that there were 10052 rows with missing values in the “reviews_per_month” column. In order to provide potential buyers with a more accurate understanding of the reviews for the property, I decided to replace these missing values with 0.
Document your work here.
Univariate Visualizations
Let us visualize on how the prices are scattered accross the dataset.
ggplot(ab_nyc_data,aes(price))+
geom_histogram()
airb_filt_data <- ab_nyc_data %>%
filter(price>0 & price<2500)
ggplot(airb_filt_data,aes(price))+
geom_histogram()
Bivariate Visualization(s)
Now, let us visualize two variables : price and room_type across the whole data.
ggplot(ab_nyc_data,aes(room_type,price))+geom_boxplot()+labs(title = "Distribution of Airbnb prices across various ranges of room types.")
Any additional comments?