Data visualization software enginner: working to make visualization more accessible, equitable, and impactful.
Published
Edited
May 28, 2021
Importers
2 stars
// Remove special characters from columns and rename them
data = tidy(
voting,
mutate({
total_vote: (d) => clean_num(d, "Total vote"),
registration: (d) => clean_num(d, "Registration"),
voter_turnout: (d) => clean_percent(d, "Voter Turnout"),
vap_turnout: (d) => clean_percent(d, "VAP Turnout"),
invalid_votes: (d) => clean_percent(d, "Invalid votes"),
population: (d) => clean_num(d, "Population"),
voting_age_population: (d) => clean_num(d, "Voting age population"),
compulsory_voting: (d) => d["Compulsory voting"],
year: (d) => d["Year"],
election_type: (d) => d["Election type"],
country: (d) => d.Country
}),
select([
"country",
"population",
"election_type",
"total_vote",
"registration",
"voter_turnout",
"compulsory_voting",
"invalid_votes",
"voting_age_population",
"year"
])
)
// Sort the countries by the average voter turnout (highest to lowest)
sorted_countries = tidy(
data,
groupBy(
"country",
summarize({
mean: m("voter_turnout")
})
),
arrange(desc("mean"))
).map((d) => d.country)
// Function to remove commas and extra spaces from numeric columns
clean_num = (d, col) =>
d[col] ? +S(d[col]).replaceAll(",", "").trim().s : null
// Load the data
voting = FileAttachment("idea_export_40_60ae663e879af.csv").csv({ typed: true })
// Data wrangling functions
import {
tidy,
mutate,
select,
groupBy,
summarize,
mean,
arrange,
desc,
median as m
} from "@pbeshai/tidyjs"
// Function to percent signs and extra spaces from percentage columns
clean_percent = (d, col) =>
d[col] ? +S(d[col]).replaceAll("%", "").trim().s / 100 : null
// Load the `string` package into the variable `S`
S = require("string@3.3.3/dist/string.min.js")
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