Public
Edited
Jul 10, 2023
Plot.plot({
y: { label: "proportion of elementary schools with security officers" },
marks: [Plot.barY(es_rollup, { x: "network", y: "proportion" })]
})
Plot.plot({
marginLeft: 100,
marginRight: 100,
y: {
grid: true
},
marks: [
Plot.boxX(hs_data, {
y: (d) => (d.security_guards > 0 ? "security" : "no security"),
x: "StudentCount",
fy: "network"
})
]
})
es_rollup = d3
.flatRollup(
raw_data.filter(
(d) => d.network.includes("Network") && d.primary_category === "ES"
),
(v) => d3.mean(v, (d) => d.security_guards > 0),
(d) => d.network
)
.map(([network, proportion]) => ({
network: Number(network.split(" ")[1]),
proportion
}))
.filter((d) => d.network < 14)
hs_data = raw_data.filter(
(d) => d.network.includes("Network") && d.primary_category === "HS"
)
Schools With Security Guards - School profiles and guard count.csv
Type Table, then Shift-Enter. Ctrl-space for more options.
data_for_analysis = raw_data
.map((d) => ({
...d,
has_security_guards: Number(d.security_guards > 0),
log_count: Math.log(d.StudentCount) - Math.log(1000)
}))
.map((r) => {
const factor = "primary_category";
// get uniqe factor levels
const factorLevels = [...new Set(raw_data.map((r) => r[factor]))];
const sortedLevels = factorLevels.sort();
// for each factor level, add a new column to our dataframe
// e.g. race.f1, race.f2 ...
sortedLevels.forEach(
(level) => (r[factor + ".f" + level] = r[factor] === level ? 1 : 0)
);
return r;
})
.map((r) => {
const factor = "network";
// get uniqe factor levels
const factorLevels = [...new Set(raw_data.map((r) => r[factor]))];
const sortedLevels = factorLevels.sort();
// for each factor level, add a new column to our dataframe
// e.g. race.f1, race.f2 ...
sortedLevels.forEach(
(level) => (r[factor + ".f" + level] = r[factor] === level ? 1 : 0)
);
return r;
})
d3.mean(raw_data.map((d) => d.StudentCount))
import { lm, summary } from "@chrispahm/linear-models-in-observable-notebooks"
model = lm(
"has_security_guards ~ primary_category.fHS + log_count*primary_category.fHS + network.fNetwork 1 + network.fNetwork 3 + network.fNetwork 4 + network.fNetwork 5 + network.fNetwork 6 + network.fNetwork 7 + network.fNetwork 8 + network.fNetwork 9 + network.fNetwork 10 + network.fNetwork 11 + network.fNetwork 12 + network.fNetwork 13 + network.fNetwork 14 + network.fNetwork 15 + network.fNetwork 16 + network.fNetwork 17ss",
data_for_analysis.filter(
(d) =>
d.governance === "District" &&
d.primary_category !== "MS" &&
d.network.includes("Network")
)
)
summary(model)
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