tidyTuesday does Pizza
Pizza Ratings
The #tidyTuesday for this week involves pizza shop ratings data. The data come from a variety of sources; it is price, ratings, and similar data for pizza restaurants. The actual contents vary depending on the data source. I will begin by loading the data and summarizing what data seem to be available so that we can figure out what we can do with it. Let’s see what we have.
pizza_jared <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-10-01/pizza_jared.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## polla_qid = col_double(),
## answer = col_character(),
## votes = col_double(),
## pollq_id = col_double(),
## question = col_character(),
## place = col_character(),
## time = col_double(),
## total_votes = col_double(),
## percent = col_double()
## )pizza_barstool <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-10-01/pizza_barstool.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_double(),
## name = col_character(),
## address1 = col_character(),
## city = col_character(),
## country = col_character()
## )
## ℹ Use `spec()` for the full column specifications.pizza_datafiniti <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-10-01/pizza_datafiniti.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## name = col_character(),
## address = col_character(),
## city = col_character(),
## country = col_character(),
## province = col_character(),
## latitude = col_double(),
## longitude = col_double(),
## categories = col_character(),
## price_range_min = col_double(),
## price_range_max = col_double()
## )summary(pizza_jared)## polla_qid answer votes pollq_id
## Min. : 2.00 Length:375 Min. : 0.000 Min. : 2.00
## 1st Qu.:21.00 Class :character 1st Qu.: 0.000 1st Qu.:21.00
## Median :40.00 Mode :character Median : 2.000 Median :40.00
## Mean :39.93 Mean : 2.832 Mean :39.93
## 3rd Qu.:59.00 3rd Qu.: 4.000 3rd Qu.:59.00
## Max. :77.00 Max. :26.000 Max. :77.00
##
## question place time total_votes
## Length:375 Length:375 Min. :1.344e+09 Min. : 0.00
## Class :character Class :character 1st Qu.:1.395e+09 1st Qu.: 7.00
## Mode :character Mode :character Median :1.467e+09 Median :12.00
## Mean :1.459e+09 Mean :14.16
## 3rd Qu.:1.519e+09 3rd Qu.:19.00
## Max. :1.569e+09 Max. :67.00
##
## percent
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.1667
## Mean :0.2000
## 3rd Qu.:0.3333
## Max. :1.0000
## NA's :5summary(pizza_datafiniti)## name address city country
## Length:10000 Length:10000 Length:10000 Length:10000
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## province latitude longitude categories
## Length:10000 Min. :21.42 Min. :-157.80 Length:10000
## Class :character 1st Qu.:34.42 1st Qu.:-104.80 Class :character
## Mode :character Median :40.12 Median : -82.91 Mode :character
## Mean :38.37 Mean : -90.06
## 3rd Qu.:40.91 3rd Qu.: -75.19
## Max. :64.85 Max. : -71.95
## price_range_min price_range_max
## Min. : 0.000 Min. : 7.00
## 1st Qu.: 0.000 1st Qu.:25.00
## Median : 0.000 Median :25.00
## Mean : 4.655 Mean :27.76
## 3rd Qu.: 0.000 3rd Qu.:25.00
## Max. :50.000 Max. :55.00summary(pizza_barstool)## name address1 city zip
## Length:463 Length:463 Length:463 Min. : 1748
## Class :character Class :character Class :character 1st Qu.:10009
## Mode :character Mode :character Mode :character Median :10019
## Mean :18531
## 3rd Qu.:11234
## Max. :94133
##
## country latitude longitude price_level
## Length:463 Min. :25.79 Min. :-122.41 Min. :0.00
## Class :character 1st Qu.:40.72 1st Qu.: -74.09 1st Qu.:1.00
## Mode :character Median :40.75 Median : -73.99 Median :1.00
## Mean :40.19 Mean : -77.44 Mean :1.46
## 3rd Qu.:40.78 3rd Qu.: -73.97 3rd Qu.:2.00
## Max. :45.00 Max. : -70.09 Max. :3.00
## NA's :2 NA's :2
## provider_rating provider_review_count review_stats_all_average_score
## Min. :2.000 Min. : 2.0 Min. :0.100
## 1st Qu.:3.500 1st Qu.: 74.0 1st Qu.:6.240
## Median :3.500 Median : 169.0 Median :7.162
## Mean :3.671 Mean : 386.1 Mean :6.876
## 3rd Qu.:4.000 3rd Qu.: 392.0 3rd Qu.:7.809
## Max. :5.000 Max. :5797.0 Max. :9.079
##
## review_stats_all_count review_stats_all_total_score
## Min. : 1.00 Min. : 0.10
## 1st Qu.: 4.00 1st Qu.: 23.65
## Median : 8.00 Median : 54.10
## Mean : 19.02 Mean : 149.93
## 3rd Qu.: 19.00 3rd Qu.: 140.20
## Max. :568.00 Max. :5045.60
##
## review_stats_community_average_score review_stats_community_count
## Min. : 0.000 Min. : 0.00
## 1st Qu.: 6.075 1st Qu.: 3.00
## Median : 7.225 Median : 7.00
## Mean : 6.457 Mean : 17.87
## 3rd Qu.: 7.873 3rd Qu.: 18.00
## Max. :10.000 Max. :567.00
##
## review_stats_community_total_score review_stats_critic_average_score
## Min. : 0.00 Min. : 0.0000
## 1st Qu.: 15.65 1st Qu.: 0.0000
## Median : 47.30 Median : 0.0000
## Mean : 142.28 Mean : 0.9717
## 3rd Qu.: 135.10 3rd Qu.: 0.0000
## Max. :5036.30 Max. :11.0000
##
## review_stats_critic_count review_stats_critic_total_score
## Min. :0.0000 Min. : 0.000
## 1st Qu.:0.0000 1st Qu.: 0.000
## Median :0.0000 Median : 0.000
## Mean :0.1425 Mean : 1.023
## 3rd Qu.:0.0000 3rd Qu.: 0.000
## Max. :5.0000 Max. :29.800
##
## review_stats_dave_average_score review_stats_dave_count
## Min. : 0.080 Min. :1
## 1st Qu.: 6.200 1st Qu.:1
## Median : 7.100 Median :1
## Mean : 6.623 Mean :1
## 3rd Qu.: 7.800 3rd Qu.:1
## Max. :10.000 Max. :1
##
## review_stats_dave_total_score
## Min. : 0.080
## 1st Qu.: 6.200
## Median : 7.100
## Mean : 6.623
## 3rd Qu.: 7.800
## Max. :10.000
## I will use this data; it contains some Oregon pizzarias.
pizza_datafiniti %>% filter(province=="OR")## # A tibble: 122 x 10
## name address city country province latitude longitude categories
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr>
## 1 Cobu… 1710 C… Spri… US OR 44.1 -123. Restauran…
## 2 Cobu… 1710 C… Spri… US OR 44.1 -123. Restauran…
## 3 Cobu… 1710 C… Spri… US OR 44.1 -123. Restauran…
## 4 Cobu… 1710 C… Spri… US OR 44.1 -123. Restauran…
## 5 Cobu… 1710 C… Spri… US OR 44.1 -123. Restauran…
## 6 Cobu… 1710 C… Spri… US OR 44.1 -123. Restauran…
## 7 Cobu… 1710 C… Spri… US OR 44.1 -123. Restauran…
## 8 Cobu… 1710 C… Spri… US OR 44.1 -123. Restauran…
## 9 Cobu… 1710 C… Spri… US OR 44.1 -123. Restauran…
## 10 Cobu… 1710 C… Spri… US OR 44.1 -123. Restauran…
## # … with 112 more rows, and 2 more variables: price_range_min <dbl>,
## # price_range_max <dbl>For the first plot, let me show what is going on in Oregon.
pizzaData <- pizza_datafiniti %>% filter(province=="OR") %>% group_by(name) %>% mutate(Tcount = n(), PriceAvg = mean(price_range_max - price_range_min)) %>% ungroup()
p <- pizzaData %>% ggplot(.) + aes(x=reorder(name,PriceAvg), fill=PriceAvg) + geom_bar() + coord_flip() + labs(x="Pizza Restaurant", y="Count", title="Oregon Pizzerias", caption = "data from #tidyTuesday; sorted by Average Price")
p
Now I want a map.
pizzaData$group <- 44
states <- map_data("state")
OR.df <- subset(states, region == "oregon")
OR_base <- ggplot(data = OR.df, mapping = aes(x = long, y = lat, group = group)) +
geom_polygon(color = "black", fill = "gray") + labs(title="Oregon Pizzerias")
OR_base
OR.Pizza <- OR_base + geom_point(data = pizzaData, aes(y=latitude, x=longitude, group=group), color = "red")
OR.Pizza
Combine them into one picture
grid.arrange(p,OR.Pizza, ncol=2)
Now to use a girafe to put this together interactively.
theme_set(theme_minimal())
pizzaData <- pizzaData %>% mutate(name = str_remove(name, "['``]"))
# Build the barplot
gg1 <- ggplot(pizzaData, aes(x=reorder(name,PriceAvg), fill=PriceAvg)) +
geom_bar_interactive(aes(x = name, tooltip = name, data_id = name)) +
coord_flip() + scale_fill_viridis_c() + theme(axis.text=element_text(size=8)) +
labs(x="Pizza Restaurant", y="Count", caption = "data from #tidyTuesday", sub="sorted by Average Price")
# Build the map
gg2 <- ggplot(data = OR.df, mapping = aes(x = long, y = lat, group = group)) +
geom_polygon(fill=gray(0.95)) +
geom_text_interactive(data = pizzaData, aes(y=latitude, x=longitude, label=emoji('pizza'), tooltip = name, data_id = name), family='EmojiOne', size=2, color="orange") +
scale_color_viridis_c(guide=FALSE) +
theme_nothing()
# Bind them together with girafe
a <- girafe( code = print(gg1 / gg2 + plot_annotation(title="Pizza in Oregon")))
library(widgetframe)## Loading required package: htmlwidgetsframeWidget(a, width = "100%", height = "100%")The formatting of this is quite off. For some reason, the frame doesn’t control scroll and overplots.
Rayshader
And a mini-rayshader. This is a complete lift and replace of the vignette after calculating the Price Midpoint.
states <- map_data("state")
PDF <- pizza_datafiniti %>% mutate(Price.Midpoint = (price_range_min + price_range_max / 2))
mtplot <- ggplot(data = states, mapping = aes(x = long, y = lat)) +
geom_polygon(color = "black", fill = "gray") +
geom_point(data=PDF, aes(x = longitude, y = latitude, color = Price.Midpoint)) +
scale_color_viridis_c() + theme_minimal()
plot_gg(mtplot, width = 3.5, multicore = TRUE, windowsize = c(1200, 1200),
zoom = 0.5, phi = 35, theta = 30, sunangle = 15, soliddepth = -20)## Warning in make_shadow(heightmap, shadowdepth, shadowwidth, background, :
## `magick` package required for smooth shadow--using basic shadow instead.render_snapshot(clear = TRUE)