Engineer on the Shiny team at RStudio. Biostats Ph.D. from Vanderbilt.
Global temperature trendsBeeswarm, Log ScaleContoursDensity contoursBubble chart componentCircle packing componentExploring Data with Vega-LiteStreaming data into VegaVega-Lite Line ChartBrushable scatterplotVega-Lite ScatterplotNight Skies — Lights and Light Pollution WebGL GlobeGraphvizRidgeline plotZoomable sunburstHierarchical edge bundlingMethods of Comparison, ComparedChord diagramA Guide to Guides: Axes & Legends in VegaPSR B1919+21HexbinStacked-to-grouped barsTree of LifeHeat indexZoomable circle packingMarimekkoVizsla and Vega-LiteDirectly labelling linesParallel coordinatesCollapsible treeTangled tree visualizationMarey’s TrainsWorld History TimelineSmall multiple chart cartogramThe Real MVP in the NBAAnimated treemapDensity Contour Matrix with BrushingStars and constellationsHertzsprung–Russell diagramThe Coronavirus landscapeGitHub BurndownCandlestick ChartConcentration values vs. TimeA few days of CO2 levels in my homeIrregular bins histogramELD ViewerDistributions and summary statistics - a collection of Plot examplesMermaidBivariate Bubble MapD3 galleryCalendarStacked bar chartDot plotConnected scatterplotCandlestick chartHistogramForce-directed graphDisjoint force-directed graphIndex chartSankey diagramLine chart, percent changeStacked area chartBubble chartArea chartHorizontal bar chartRadial tidy treeCircle packingIcicleStreamgraphTidy treeCluster treeSunburstHorizon chartBox plotScatterplotDifference chartBand chartBar chart transitionsTreemapLine chart, multiple seriesScatterplot matrixBrushable scatterplot matrixPlayfair's Wheat and Wages
Physics based t-SNE
make_centroid = (ndim) => {
const low = -spread/2;
const high = spread/2;
const point_gen = d3.randomUniform(low, high);
return () => [...(new Array(ndim))].map(point_gen);
}
gen_centroids = (n, ndim) => {
const center_maker = make_centroid(ndim);
return [...(new Array(n))].map(center_maker)
}
centroids = gen_centroids(number_of_centroids, number_of_dimensions)
sample_from_centroid = (nsamples, centroid, centroid_id) => {
const rnorm = d3.randomNormal(0, sample_std_dev);
const get_sample = () => ({group:centroid_id, location : centroid.map(d => d + rnorm())});
return [...(new Array(nsamples))].map(get_sample);
}
// Generate a bunch of data samples from our array of centroids keeping info on their centroid id with them.
function gen_data_from_centroids(centroids, nsamples){
return centroids.reduce(
(data, d, i) => [...data, ...sample_from_centroid(nsamples, d, i)],
[])
.map((d,i) => (Object.assign({id: i},d,{}))) // add in node id in addition to the centroid one.
}
// generate our data
point_data = gen_data_from_centroids(centroids, number_of_samples)
pairwise_distances = {
const distances = [];
for(let i = 0; i < point_data.length; i++){
for(let j = i; j < point_data.length; j++){
const p1_loc = point_data[i].location;
const p2_loc = point_data[j].location;
const sum_of_square_diffs = p1_loc
.map((d, ind) => Math.pow(d - p2_loc[ind], 2))
.reduce((summed, d) => summed + d, 0);
distances.push({source: i, target: j, value: Math.sqrt(sum_of_square_diffs)})
}
}
// filter out the self links as they are not needed.
return distances.filter(({value}) => value > 0)
}
number_of_dimensions = 10 // dimensionality of our generated data
spread = 25 // width of the distribution determining each centroids location on the dimensions
sample_std_dev = 1 // standard deviation of the normal samples around each centroid
number_of_samples = 25 // how many samples we have per centroid
number_of_centroids = 4 // how many centroids we have
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