👨💻 Modelling consultant
🧮 numbers 📊 actuarial
📈 viz, interactivity, transparency
domains = ({
age_0_in: _.range(20, 46),
//salary_age_0_in: _.range(20, 46),
age_in: _.range(20, 71),
contribution_charge_rate_in: _.range(-0.02, 0.1, 0.01),
management_charge_rate_in: _.range(-0.02, 0.03, 0.01),
empee_contribution_rate_in: _.range(0, 0.4, 0.01),
emper_matching_rate_in: _.range(0, 1.51, 0.05),
fund_value_0_in: _.range(0, 1e6 + 1, 1e6 / 25),
//missed_contribution_age_in: [0],
retirement_age_in: _.range(50, 78, 1),
salary_0_in: _.range(10e3, 200e3 + 1, 10e3),
salary_inflation_rate_in: _.range(-0.05, 0.1 + 0.01, 0.01),
unit_growth_rate_in: _.range(
-0.02,
0.08 + 0.001,
0.01 /* I get floating point matchup issues in interactions when I go real small here ! */
)
})
inputs
// do NOT include inputs that will change in form
mutable cursor = ({
age_0_in: 30,
//salary_age_0_in: 30,
unit_growth_rate_in: 0.06,
age_in: 50,
emper_matching_rate_in: 1,
contribution_charge_rate_in: 0.04,
management_charge_rate_in: 0.01,
empee_contribution_rate_in: 0.1,
fund_value_0_in: 0,
//missed_contribution_age_in: 15,
retirement_age_in: 65,
salary_0_in: 50000,
salary_inflation_rate_in: 0.02
})
// this drives data updates into the visual
viz.data("data", data).run()
data = data_inputs.map(d => ({...d, value: model[formula](d)}))
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.
