2019-10-01

Pizza Party!

This week's data is from Jared Lander and Barstool Sports via Tyler Richards.

Credit for this week's concept goes to Ludmila who did a recent dataviz presentation and gave shoutouts to both #tidytuesday and a pizza dataset!

Check out her DataViz video and slides at her GitHub

Jared's data is from top NY pizza restaurants, with a 6 point likert scale survey on ratings. The Barstool sports dataset has critic, public, and the Barstool Staff's rating as well as pricing, location, and geo-location. There are 22 pizza places that overlap between the two datasets.

If you want to look more at geo-location of pizza places, checkout this one from DataFiniti. This includes 10000 pizza places, their price ranges and geo-locations.

Get the data!

pizza_jared <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-10-01/pizza_jared.csv")
pizza_barstool <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-10-01/pizza_barstool.csv")
pizza_datafiniti <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-10-01/pizza_datafiniti.csv")

Data Dictionary

pizza_jared.csv

variable	class	description
polla_qid	integer	Quiz ID
answer	character	Answer (likert scale)
votes	integer	Number of votes for that question/answer combo
pollq_id	integer	Poll Question ID
question	character	Question
place	character	Pizza Place
time	integer	Time of quiz
total_votes	integer	Total number of votes for that pizza place
percent	double	Vote percent of total for that pizza place

pizza_barstool.csv

variable	class	description
name	character	Pizza place name
address1	character	Pizza place address
city	character	City
zip	double	Zip
country	character	Country
latitude	double	Latitude
longitude	double	Longitude
price_level	double	Price rating (fewer $ = cheaper, more $$$ = expensive)
provider_rating	double	Provider review score
provider_review_count	double	Provider review count
review_stats_all_average_score	double	Average Score
review_stats_all_count	double	Count of all reviews
review_stats_all_total_score	double	Review total score
review_stats_community_average_score	double	Community average score
review_stats_community_count	double	community review count
review_stats_community_total_score	double	community review total score
review_stats_critic_average_score	double	Critic average score
review_stats_critic_count	double	Critic review count
review_stats_critic_total_score	double	Critic total score
review_stats_dave_average_score	double	Dave (Barstool) average score
review_stats_dave_count	double	Dave review count
review_stats_dave_total_score	double	Dave total score

pizza_datafiniti.csv

variable	class	description
name	character	Pizza place
address	character	Address
city	character	City
country	character	Country
province	character	State
latitude	double	Latitude
longitude	double	Longitude
categories	character	Restaurant category
price_range_min	double	Price range min
price_range_max	double	Price range max

Cleaning Script

library(tidyverse)
library(jsonlite)

# Get barstool data off github
pizza_raw <- read_csv("https://raw.githubusercontent.com/tylerjrichards/Barstool_Pizza/master/pizza_data.csv")

pizza_cooked <- pizza_raw %>% 
  select(name, address1, city, zip, country, latitude, longitude, priceLevel, 
         providerRating, providerReviewCount, 
         reviewStats.all.averageScore:reviewStats.dave.totalScore) %>% 
  janitor::clean_names()

# Get jared data off his website (json)

url <- "https://jaredlander.com/data/PizzaPollData.php"

jared_pizza <- fromJSON(readLines(url), flatten = TRUE) %>% 
  as_tibble() %>% 
  janitor::clean_names()

write_csv(jared_pizza, here::here("2019", "2019-10-01", "pizza_jared.csv"))

write_csv(pizza_cooked, here::here("2019", "2019-10-01", "pizza_barstool.csv"))