This is my first visualization with D3, using data from a project I recently did with Metis. I took the yelp corpus from 1/2 star ratings and took the twitter vocabulary for tweets my model predicted were similar to the 1/2 star ratings and plotted the top 100 words for each (excluding stopwords).
Some things I think are interesting about this are how much the words overlap/how much they don't, and how accurate the words themselves are, despite possibly not occurring in the yelp reviews at all.
scroll bar side to side
yelp/twitter button to show only one or the other
hover over examples
animations just because animations
forked from mbostock's block: Stacked Bar Chart
forked from emilyinamillion's block: Stacked Bar Chart
xxxxxxxxxx<meta charset="utf-8"><style>body { font: 8px sans-serif;}.axis path,.axis line { fill: none; stroke: #000; shape-rendering: crispEdges;}.bar { fill: blue;}.x.axis path { display: none;}</style><body> <script src="//d3js.org/d3.v3.min.js"></script><script>var margin = {top: 20, right: -5, bottom: 50, left: 35}, width = 961 - margin.left - margin.right, height = 500 - margin.top - margin.bottom;var x = d3.scale.ordinal() .rangeRoundBands([0, width], .1);var y = d3.scale.linear() .rangeRound([height, 0]);var color = d3.scale.ordinal() .range(["#D98880", "#A9CCE3", ]);var xAxis = d3.svg.axis() .scale(x) .orient("bottom");var yAxis = d3.svg.axis() //.scale(y) .orient("left") .tickFormat(d3.format(".2s"));var svg = d3.select("body").append("svg") .attr("width", width + margin.left + margin.right) .attr("height", height + margin.top + margin.bottom) .append("g") .attr("transform", "translate(" + margin.left + "," + margin.top + ")");d3.csv("fullshebangdata4d3.csv", function(error, data) { if (error) throw error; color.domain(d3.keys(data[0]).filter(function(key) { return key !== "word"; })); data.forEach(function(d) { var y0 = 0; d.ages = color.domain().map(function(name) { return {name: name, y0: y0, y1: y0 += +d[name]}; }); d.total = d.ages[d.ages.length - 1].y1; }); data.sort(function(a, b) { return b.total - a.total; }); x.domain(data.map(function(d) { return d.word; })); y.domain([0, d3.max(data, function(d) { return d.total; })]); svg.append("g") .attr("class", "x axis") .attr("transform", "translate(0," + height + ")") .call(xAxis) .selectAll("text") .style("text-anchor", "end") .attr("dx", "-.8em") .attr("dy", "-.80em") .attr("transform", "rotate(-90)"); svg.append("g") .attr("class", "y axis") .call(yAxis) .append("text") .attr("transform", "rotate(-90)") .attr("y", 6) .attr("dy", "1.43704em") .attr("font-size", "20") .style("text-anchor", "end") .text("Word Frequency"); var state = svg.selectAll(".word") .data(data) .enter().append("g") .attr("class", "g") .attr("transform", function(d) { return "translate(" + x(d.word) + ",0)"; }); state.selectAll("rect") .data(function(d) { return d.ages; }) .enter().append("rect") .attr("width", x.rangeBand()) .attr("y", function(d) { return y(d.y1); }) .attr("height", function(d) { return y(d.y0) - y(d.y1); }) .style("fill", function(d) { return color(d.name); }); var legend = svg.selectAll(".legend") .data(color.domain().slice().reverse()) .enter().append("g") .attr("class", "legend") .attr("transform", function(d, i) { return "translate(0," + i * 20 + ")"; }); legend.append("rect") .attr("x", width - 59) .attr("y", 9) .attr("width", 27) .attr("height", 16) .style("fill", color); legend.append("text") .attr("x", width - 67) .attr("y", 18) .attr("font-size", 19) .attr("dy", ".35em") .style("text-anchor", "end") .text(function(d) { return d; }); });</script> https://d3js.org/d3.v3.min.js