👨💻 Modelling consultant
🧮 numbers 📊 actuarial
📈 viz, interactivity, transparency
Formula Relationships as Treespension calculator browsersome SaaS cashflows 💵🌊loan validator 🕵️ (dev 📓)pension calculator cashflows 💱 interactionPension Calculator - Ireland 🚧 🧓👴 🇮🇪Monte Carlo Pension CalculatorPension Calculator 🧓👴 Rec. Concept 🚧 a0Coordinated Visuals: Pension Calculator Stories 📔🖼Function from São Paulo 🌇🎨Function from Copacabana 🎶🏖🌊🩴how the snowflakes fall 🌨️Function-Visual-InputsRetirement fund contour mapIt's pi day! 🥮Monte Carlo Pi 🥮calcudonuts 🍩💫You 👉 be the finance minister 🎩 (wip)Payroll Playground 🧸 (Ireland & 🚧)Tax calculator impacts interaction 📈🔨 (Ireland & 🚧)calculang and a fern 🌿 10 points at a time 🔎calculang and a fern 🌿Pension Calculator 🧓👴 🚧 easy versionZapis bracket counting problemSaaS business model, experience analysis 🕵️ (wip)Lorenz-Attractor ♾️calculang by coffee ☕covid19: cases-deaths link (wip)covid19: cases-deaths link (wip) w. lag inputcalculang bouncing ball model ⚽🏀🥎little calculang helpers (data/viz)example visuals via helpersfar smoother (and faster) snowflakespath of ☀️ and 🌙 todaycalculang bouncing ball, transitionscalculationships
Retirement fund heatmap
spec = calcuvizspec({
models: [pc],
input_cursors: [{
fund_value_0_in:0, unit_growth_rate_in, retirement_age_in, salary_0_in, salary_inflation_rate_in, emper_contribution_rate_in:0, contribution_charge_in
}],
mark: 'rect', // rect for heatmap
encodings: {
y: {name: 'empee_contribution_rate_in', domain: _.range(0.05,0.20,small ? 0.015 : 0.005), type:'ordinal'},
x: {name: 'age_0_in', type: 'ordinal', domain: _.range(20,50,small ? 3 : 1)},
color: {name: 'value', type: 'quantitative'},
row: {name: 'formula', domain: ['projected_fund_value']}
}
})
spec_custom = {
var o = {...spec};
o.width = 700 * (small ? 0.5 : 1)
o.height = 600 * (small ? 0.5 : 1)
o.encoding.y.axis = {format:'.1%'}
o.encoding.y.sort = 'descending'
o.encoding.color.scale = {/*range: 'diverging',*/ scheme:'turbo', reverse:true}
o.resolve = {"scale": {"color": "independent"}}
return o // other customisation like removing the redundant color legend, moving x axis to top, also possible; open below in Vega Editor & vega-lite has excellent documentation
}
spec_table = calcuvizspec({
models: [pc],
input_cursors: [{
fund_value_0_in:0, unit_growth_rate_in, retirement_age_in, salary_0_in, salary_inflation_rate_in, emper_contribution_rate_in:0, contribution_charge_in
}],
mark: 'text', // rect for heatmap
encodings: {
y: {name: 'empee_contribution_rate_in', domain: _.range(0.05,0.20,small ? 0.015 : 0.005), type:'ordinal'},
x: {name: 'age_0_in', type: 'ordinal', domain: _.range(20,50,small ? 3 : 1)},
color: {name: 'value', type: 'quantitative'},
text: {name: 'value', type: 'quantitative'},
row: {name: 'formula', domain: ['salaries_per_projected_fund']}
}
})
spec_table_custom = {
var o = {...spec_table};
o.width = 700 * (small ? 0.5 : 1)
o.height = 600 * (small ? 0.5 : 1)
o.encoding.y.axis = {format:'.1%'}
o.encoding.y.sort = 'descending'
o.encoding.text.format = '.1f'
if (o.encoding.color) { o.encoding.color.scale = {/*range: 'diverging',*/ scheme:'reds', reverse:true}; o.encoding.color.legend = null }
o.resolve = {"scale": {"color": "independent"}}
return o // other customisation like removing the redundant color legend, moving x axis to top, also possible; open below in Vega Editor & vega-lite has excellent documentation
}
One platform to build and deploy the best data apps
Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects are always up-to-date.