Public
Edited
May 15
import {pizzaorders} from "@observablehq/data-vis-course-assignment"
pizzaorders
Type Table, then Shift-Enter. Ctrl-space for more options.
real0rders
X
shortname
Y
mean
orders
Color
#ff5375
Size
—
Facet X
season
Facet Y
—
Mark
rule
Type Chart, then Shift-Enter. Ctrl-space for more options.
pizzaorders
Type Table, then Shift-Enter. Ctrl-space for more options.
sh0rtorders = pizzaorders.map(d => ({
shortname: d.name.split(" ")[0] }));
pizzaorders.slice(0, 5).map(d => ({
orderDate: d.orderDate,
type: typeof d.orderDate,
dateValue: d.orderDate ? new Date(d.orderDate) : "Invalid Date"
}))
realorders = pizzaorders.map(d => ({
...d,
month: d.orderDate ? new Date(d.orderDate).getMonth() + 1 : "Unknown"
}));
real0rders = pizzaorders.map(d => ({
...d,
shortname: d.name.split(" ")[0],
month: d.orderDate ? new Date(d.orderDate).getMonth() + 1 : "Unknown",
season: d.orderDate ?
(new Date(d.orderDate).getMonth() + 1 <= 2 || new Date(d.orderDate).getMonth() + 1 === 12 ? "Winter" :
new Date(d.orderDate).getMonth() + 1 >= 3 && new Date(d.orderDate).getMonth() + 1 <= 5 ? "Spring" :
new Date(d.orderDate).getMonth() + 1 >= 6 && new Date(d.orderDate).getMonth() + 1 <= 8 ? "Summer" :
"Fall") : "Unknown"
}));
filteredOrders = real0rders.filter(d => d.season === ["Winter", "Spring", "Summer", "Fall"][selectedseason])
colorScale = d3.scaleOrdinal()
.domain(["Winter", "Spring", "Summer", "Fall"])
.range(["#1E90FF", "#32CD32", "#FFD700", "#8B0000"]);
Explore: I looked into the dataset and found ways to make it more legible and conveyable, such as extracting the first word out of the overly long pizza names so an actual visualization could be made. I attempted (somewhat successfully) to sort the dates into seasons by extracting the months and classifying them according to their rough season.
Explain: The people of California and Nevada consume more pizza in the summer. The patterns of meat consumption change as the seasons change, with buffalo and oxtail having significant increases as it gets colder. The largest single season change is sicilian consumption from around 600 in the spring down to around 375 in the summer.
Present: Overall, the data is remarkably consistent between the two states. Pizza consumption is roughly uniform among the heavy hitters of BBQ, Hawaiian, Margherita, Meat Lover, and Pepperoni. One interesting trend is that the standard deviation increases among the types of pizza ordered as the weather gets warmer, suggesting a possible link between temperature and disposable income or perhaps openness to trying different pizzas.
Purpose-built for displays of data
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