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Plot.plot({
color: country_colors[0],
marks: [
Plot.barY(kills_summary, {x: "President", y: deathSelect, fill: "Country"}),
Plot.ruleY([0])
]
})
kills_summary = strikes_df
.groupby("President", "Country")
.rollup({
min_civilian_deaths: d => op.sum(d["Minimum civilians reported killed"]),
max_civilian_deaths: d => op.sum(d["Maximum civilians reported killed"]),
min_deaths: d => op.sum(d["Minimum total people killed"]),
max_deaths: d => op.sum(d["Maximum total people killed"])
})
kills_summary_annual = strikes_df
.groupby("year")
.rollup({
min_civilian_deaths: d => op.sum(d["Minimum civilians reported killed"]),
max_civilian_deaths: d => op.sum(d["Maximum civilians reported killed"]),
min_deaths: d => op.sum(d["Minimum total people killed"]),
max_deaths: d => op.sum(d["Maximum total people killed"])
})
.ungroup()
.derive({
avg_civilian_deaths: d => (d.max_civilian_deaths + d.min_civilian_deaths) / 2,
avg_total_deaths: d => (d.max_deaths + d.min_deaths)/ 2
})
.derive({est_combatant_deaths: d => d.avg_total_deaths - d.avg_civilian_deaths})
.derive({combatant_kills_per_civilian: d => d.est_combatant_deaths / d.avg_civilian_deaths})
.orderby("year")
Plot.plot({
x:{
tickFormat: "d"
},
y: {
label: "Combatant to Civilian Kill Ratio"
},
marks:
[
Plot.line(kills_summary_annual, {x: "year", y: "combatant_kills_per_civilian"}),
Plot.ruleY([0]),
Plot.linearRegressionY(kills_summary_annual, {x: "year",
y: "combatant_kills_per_civilian",
stroke: "steelBlue",
ci:0.95})
]
})
Plot.plot({
x: {
tickFormat: "d"
},
y:{
tickFormat: "p"
},
marks:[
Plot.barY(strike_percentages_by_year, {x: "year", y: "percent_strikes_no_civilian_deaths"})
]
}
)
strikes_count_by_year = strikes_df
.groupby("year")
.rollup({
strike_count: d => op.count()
})
strikes_count_no_civilian_deaths = strikes_df
.filter(d => d["Maximum civilians reported killed"] == 0)
.groupby("year")
.rollup({
strike_count_no_civ: d => op.count()
})
strike_percentages_by_year = strikes_count_by_year.join_left(strikes_count_no_civilian_deaths)
.derive({
percent_strikes_no_civilian_deaths: d => d.strike_count_no_civ / d.strike_count
})
.orderby("year")
Plot.plot({
x:{
tickFormat: "d"
},
marks: [
Plot.line(strikes_df, Plot.groupX({y: "sum"},
{x: "year",
y: childDeathVariable // helper that reacts to radio buttons (see appendix)
})),
Plot.ruleY([0])
]
})
land = topojson.feature(land50m, land50m.objects.land)
land50m = FileAttachment("land-50m.json").json()
wb = await FileAttachment("DroneWarsData.xlsx").xlsx({typed: true})
all_strikes = wb.sheet(9, {headers: true})
all_strikes
Type Table, then Shift-Enter. Ctrl-space for more options.
strikes_df = aq.from(all_strikes)
.filter(d => d.President != undefined)
.derive({
date: aq.escape(d => stringToDate(d["Date (MM-DD-YYYY)"]))
})
.derive({
// break up
year: aq.escape(d => d.date.getFullYear()),
month: d => op.month(d.date)
})
function stringToDate(dateString) {
let dateParts = dateString.split("-");
// month is 0-based, so subtract 1
let month = parseInt(dateParts[0]) - 1;
let day = parseInt(dateParts[1]);
let year = parseInt(dateParts[2]);
return new Date(year, month, day);
}
childDeathVariable = { // this variable reacts to the radio buttons for the "Chidlen Killed plot"
if (reportingMethod === "Maximum"){
return("Maximum children reported killed")
} else {
return("Minimum children reported killed")
}
}
country_colors= [ {
scheme: "set1",
type: "categorical",
legend: true
} ,
{
scheme: "set1",
type: "categorical"
}]
Type JavaScript, then Shift-Enter. Ctrl-space for more options. Arrow ↑/↓ to switch modes.
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
