life_expectancy_income_population.json
county_presidential_election_income_unemployment.json
jupyter notebook for creating the life expectancy json
interactive widgets in jupyter notebook that create vega chart
{ | |
"$schema": "https://vega.github.io/schema/vega-lite/v2.json", | |
"description": "A scatterplot template with URL.", | |
"data": {"url": "https://gist.githubusercontent.com/ties/ddfedd35f9b34c23a8c2ffb9d7a83d4d/raw/7d5de711ff35cb1fdaefeba9055380b1209432c9/life_expectancy_income_population.json"}, | |
"mark": "point", | |
"encoding": { | |
"x": {"field": "corrected_income","type": "quantitative"}, | |
"y": {"field": "population","type": "quantitative"} | |
} | |
} |
{ | |
"$schema": "https://vega.github.io/schema/vega-lite/v2.1.json", | |
"vconcat": [ | |
{ | |
"width": 500, | |
"height": 300, | |
"layer": [ | |
{ | |
"data": { | |
"url": "https://raw.githubusercontent.com/ties/cbs_gebiedsindelingen_topojson/master/Buurt_2014_layer_q1e3.json", | |
"format": {"type": "topojson", "feature": "layer"} | |
}, | |
"transform": [{"filter": "substring(datum.id, 0, 6) == 'BU0153'"}], | |
"projection": {"type": "mercator", "center": [52.2215, 6.8937]}, | |
"mark": {"type": "geoshape", "fill": "lightgray", "stroke": "white"} | |
}, | |
{ | |
"data": { | |
"url": "https://gist.githubusercontent.com/ties/d1b86c2a17b7c9f7105212080011d611/raw/f7e3b5255df478f7eb7e53c2c86e253d44a7a64c/openbare_ruimte_enschede.json" | |
}, | |
"projection": {"type": "mercator", "center": [52.2215, 6.8937]}, | |
"transform": [ | |
{"filter": {"selection": "brush"}} | |
], | |
"mark": "circle", | |
"encoding": { | |
"longitude": {"field": "lon", "type": "quantitative"}, | |
"latitude": {"field": "lat", "type": "quantitative"}, | |
"color": {"field": "categorie", "type": "nominal"} | |
} | |
} | |
] | |
}, | |
{ | |
"width": 500, | |
"selection": { | |
"brush": {"type": "interval", "encodings": ["x"]} | |
}, | |
"data": { | |
"url": "https://gist.githubusercontent.com/ties/d1b86c2a17b7c9f7105212080011d611/raw/f7e3b5255df478f7eb7e53c2c86e253d44a7a64c/openbare_ruimte_enschede.json" | |
}, | |
"mark": "area", | |
"encoding": { | |
"x": {"field": "begindatum", "type": "temporal", "timeUnit": "yearmonthdate"}, | |
"y": {"field": "categorie", "aggregate": "count"} | |
} | |
} | |
] | |
} |
{ | |
"$schema": "https://vega.github.io/schema/vega-lite/v2.1.json", | |
"data": { | |
"url": "data/us-10m.json", | |
"format": {"type": "topojson", "feature": "counties"} | |
}, | |
"transform": [ | |
{ | |
"lookup": "id", | |
"from": { | |
"data": { | |
"url": "https://gist.githubusercontent.com/ties/fb0f28c3608ec6c1e02579de3132b26a/raw/b68ed5cd39bd9b1cd968422cd8ac733c3993acc5/pres_unemploy_income.json", | |
"format": {"type": "json"} | |
}, | |
"key": "id", | |
"fields": [ | |
"Unemployment_rate_2016", | |
"Median_Household_Income_2016", | |
"per_dem", | |
"per_gop", | |
"Civilian_labor_force_2016" | |
] | |
} | |
}, | |
{"calculate": "datum.per_dem - datum.per_gop", "as": "win_perc"} | |
], | |
"vconcat": [ | |
{ | |
"projection": {"type": "albersUsa"}, | |
"mark": "geoshape", | |
"encoding": { | |
"color": { | |
"condition": { | |
"selection": "brush", | |
"field": "win_perc", | |
"type": "quantitative", | |
"scale": { | |
"domain": [-1, 0, 1], | |
"range": ["#ff0000", "#ffffff", "#0000ff"] | |
} | |
}, | |
"value": "grey" | |
} | |
}, | |
"width": 500, | |
"height": 300 | |
}, | |
{ | |
"width": 500, | |
"height": 300, | |
"selection": {"brush": {"type": "interval"}}, | |
"mark": "point", | |
"encoding": { | |
"x": {"field": "Unemployment_rate_2016", "type": "quantitative"}, | |
"y": {"field": "Median_Household_Income_2016", "type": "quantitative"}, | |
"color": { | |
"condition": { | |
"selection": "brush", | |
"type": "quantitative" | |
}, | |
"value": "grey" | |
}, | |
"size": { | |
"field": "Civilian_labor_force_2016" | |
} | |
} | |
} | |
] | |
} |