31:T1324,congressionalCAGRs = { // Create the SVG container. const svg = d3 .create("svg") .attr("width", width) .attr("height", height) .attr("viewBox", `0 0 ${width} ${height}`) .attr("font-family", "'Helvetica Neue', sans-serif"); // Generate the compound annual growth rate (CAGR) for each Congress. const congressionalCAGRs = Object.entries(congressionalData).map( ([congress, data]) => ({ congress, party: congressesByParty[congress], cagr: cagr(data) }) ); // Add 1px of padding to account for the effect of the stroke on each rect. const padding = 1; // Add 5px of padding for labels attached to bars. const labelPadding = 5; // Define x and y scales, using .nice() on the x-axis to round to the nearest multiple of 2%. const x = d3 .scaleLinear() .domain([0, d3.max(congressionalCAGRs.map(({ cagr }) => cagr))]) .nice() .range([margin.left + padding, width - margin.right + padding]); const y = d3 .scaleBand() .domain(d3.range(congressionalCAGRs.length)) .range([margin.top, height - margin.bottom]) .padding(0.1); // Join the data to a element containing both the bar () and label (). const datum = svg .selectAll("g") .data(congressionalCAGRs) .join("g"); datum .append("rect") .attr("x", margin.left + padding) .attr("y", (_, i) => y(i)) .attr("width", d => x(d.cagr) - x(0)) .attr("height", y.bandwidth()) .attr("fill", d => colors[d.party]) .attr("fill-opacity", 0.25) .attr("stroke", d => colors[d.party]); datum .append("text") .attr("x", d => x(d.cagr) + labelPadding) .attr("y", (_, i) => y(i) + y.bandwidth() / 2) .attr("dominant-baseline", "middle") .attr("fill", colors.annotation) .attr("font-size", "0.7rem") .text(d => `${d.cagr.toFixed(2)}%`); // Define and draw the x and y axes, removing tick lines and adding labels. const xAxis = svg .append("g") .attr("transform", `translate(0, ${height - margin.bottom})`) .call( d3 .axisBottom(x) .tickSizeOuter(0) .tickFormat(d => `${d}%`) ); xAxis.selectAll(".tick line").remove(); xAxis .append("text") .attr("x", width / 2) .attr("y", 35) .attr("font-size", "0.7rem") .attr("fill", colors.label) .attr("text-anchor", "middle") .text("Compound Annual Growth Rate (CAGR)"); const yAxis = svg .append("g") .attr("transform", `translate(${margin.left}, 0)`) .call( d3 .axisLeft(y) .tickSizeOuter(0) .tickFormat(d => `${congressionalCAGRs[d].congress}th`) ); yAxis.selectAll(".tick line").remove(); yAxis .append("text") .attr("x", -height / 2) .attr("y", -45) .attr("fill", colors.label) .attr("text-anchor", "middle") .attr("transform", "rotate(-90)") .text("Congress"); // Derive the mean CAGR. const meanCAGR = d3.mean(congressionalCAGRs.map(({ cagr }) => cagr)); // Label the mean CAGR on the chart. const meanLabel = svg.append("g"); meanLabel .append("line") .attr("x1", x(meanCAGR)) .attr("y1", margin.top) .attr("x2", x(meanCAGR)) .attr("y2", height - margin.bottom) .attr("stroke", colors.annotation) .attr("stroke-dasharray", 2); meanLabel .append("text") .attr("font-size", "0.7rem") .attr("font-weight", "700") .attr("fill", colors.annotation) .attr("x", x(meanCAGR) + labelPadding) .attr("y", margin.top + 10) .text(`${meanCAGR.toFixed(2)}% Mean CAGR`); // Add the legend for bar colors. const legend = svg.append("g"); const legendData = ['rep', 'dem', 'split']; const legendRectDimensions = { width: 40, height: 20 }; const legendDatum = legend .selectAll("g") .data(legendData) .join("g"); legendDatum.attr( "transform", (_, i) => `translate(${width - margin.right - legendRectDimensions.width - padding}, ${i * legendRectDimensions.height + 40})` ); legendDatum .append("rect") .attr("x", 40) .attr("y", 0) .attr("width", 40) .attr("height", 20) .attr("fill", d => colors[d]) .attr("fill-opacity", "0.25") .attr("stroke", d => colors[d]); legendDatum .append("text") .attr("x", 0) .attr("y", legendRectDimensions.height / 2) .attr("font-size", "0.7rem") .attr("dominant-baseline", "middle") .text( d => `${d.charAt(0).toUpperCase()}${d.slice(1)}${d !== 'split' ? '.' : ''}` ); // Add the title. svg .append("text") .attr("x", width) .attr("y", 20) .attr("font-size", "1rem") .attr("font-weight", "700") .attr("fill", colors.label) .attr("text-anchor", "end") .text( "The Compound Annual Growth Rate of the National Debt under Each Congress from 1995 to 2021" ); return svg.node(); }32:T4ff,md`This view of the data yields a few key insights: 1. The **111th Congress** had the highest CAGR of any Congress since 1995. This makes some sense, given that it was in session during President Obama's first two years in office amidst recovery from [the Great Recession](https://en.wikipedia.org/wiki/Great_Recession). The **116th Congress**, which was split between a Democratic House and a Republican Senate, oversaw passage of [the CARES Act](https://en.wikipedia.org/wiki/CARES_Act) to address unemployment caused by the Covid-19 pandemic. At the time of its passage it was [the largest economic stimulus bill in U.S. history](https://www.latimes.com/politics/story/2020-03-25/vote-senate-on-2-trillion-economic-stimulus-package-coronavirus). 2. Over a very small set of samples, **Republican congresses** have the lowest mean CAGR at **4.97%**, **split congresses** are in the middle at **7.27%**, and **Democratic congresses** have the highest mean CAGR at **12.75%**. 3. Government spending began rising dramatically during the **108th Congress**, which coincides with the ramping up of the [Iraq War](https://en.wikipedia.org/wiki/Iraq_War). Spending increased throughout the years of the war into the Great Recession, but then decreased as the recovery took effect. `33:T1666,congressionalCAGRsWithPresidentialParty = { // Create the SVG container. const svg = d3 .create("svg") .attr("width", width) .attr("height", height) .attr("viewBox", `0 0 ${width} ${height}`) .attr("font-family", "'Helvetica Neue', sans-serif"); // Generate the compound annual growth rate (CAGR) for each Congress. const congressionalCAGRs = Object.entries(congressionalData).map( ([congress, data]) => ({ congress, cagr: cagr(data), party: `${congressesByParty[congress]}${congressesByPresidentialParty[congress]}` }) ); // Add 1px of padding to account for the effect of the stroke on each rect. const padding = 1; // Add 5px of padding for labels attached to bars. const labelPadding = 5; // Define x and y scales, using .nice() on the x-axis to round to the nearest multiple of 2%. const x = d3 .scaleLinear() .domain([0, d3.max(congressionalCAGRs.map(({ cagr }) => cagr))]) .nice() .range([margin.left + padding, width - margin.right]); const y = d3 .scaleBand() .domain(d3.range(congressionalCAGRs.length)) .range([margin.top, height - margin.bottom]) .padding(0.1); // Join the data to a element containing both the bar () and label (). const datum = svg .selectAll("g") .data(congressionalCAGRs) .join("g"); datum .append("rect") .attr("x", margin.left + padding) .attr("y", (_, i) => y(i)) .attr("width", d => x(d.cagr) - x(0)) .attr("height", y.bandwidth()) .attr("fill", d => colors[d.party]) .attr("fill-opacity", "0.25") .attr("stroke", d => colors[d.party]); datum .append("text") .attr("x", d => x(d.cagr) + labelPadding) .attr("y", (_, i) => y(i) + y.bandwidth() / 2) .attr("font-size", "0.7rem") .attr("dominant-baseline", "middle") .attr("fill", colors.annotation) .text(d => `${d.cagr.toFixed(2)}%`); // Define and draw the x and y axes, removing tick lines and adding labels. const xAxis = svg .append("g") .attr("transform", `translate(0, ${height - margin.bottom})`) .call( d3 .axisBottom(x) .tickSizeOuter(0) .tickFormat(d => `${d}%`) ); xAxis.selectAll(".tick line").remove(); xAxis .append("text") .attr("x", width / 2) .attr("y", 35) .attr("fill", colors.label) .attr("text-anchor", "middle") .text("Compound Annual Growth Rate (CAGR)"); const yAxis = svg .append("g") .attr("transform", `translate(${margin.left}, 0)`) .call( d3 .axisLeft(y) .tickSizeOuter(0) .tickFormat(d => `${congressionalCAGRs[d].congress}th`) ); yAxis.selectAll(".tick line").remove(); yAxis .append("text") .attr("x", -height / 2) .attr("y", -45) .attr("fill", colors.label) .attr("text-anchor", "middle") .attr("transform", "rotate(-90)") .text("Congress"); // Derive the mean CAGR. const meanCAGR = d3.mean(congressionalCAGRs.map(({ cagr }) => cagr)); // Label the mean CAGR on the chart. const meanLabel = svg.append("g"); meanLabel .append("line") .attr("x1", x(meanCAGR)) .attr("y1", margin.top) .attr("x2", x(meanCAGR)) .attr("y2", height - margin.bottom) .attr("stroke", colors.annotation) .attr("stroke-dasharray", 2); meanLabel .append("text") .attr("font-size", "0.7rem") .attr("font-weight", "700") .attr("fill", colors.annotation) .attr("x", x(meanCAGR) + labelPadding) .attr("y", margin.top + 10) .text(`${meanCAGR.toFixed(2)}% Mean CAGR`); // Generate the data for the legend. const legendData = Object.entries(colors) .slice(-6) .map(([key, color]) => { switch (key) { case 'reprep': return { color, label: 'Rep. Cong., Rep. Pres.' }; case 'splitrep': return { color, label: 'Split Cong., Rep. Pres.' }; case 'demrep': return { color, label: 'Dem. Cong., Rep. Pres.' }; case 'repdem': return { color, label: 'Rep. Cong., Dem. Pres.' }; case 'splitdem': return { color, label: 'Split Cong., Dem. Pres.' }; case 'demdem': return { color, label: 'Dem. Cong., Dem. Pres.' }; } }); const legendRectDimensions = { width: 40, height: 20 }; // Add the legend for bar colors. const legend = svg.append("g"); const legendDatum = legend .selectAll("g") .data(legendData) .join("g"); legendDatum.attr( "transform", (_, i) => `translate(${width - margin.right - legendRectDimensions.width - padding}, ${i * legendRectDimensions.height + 40})` ); legendDatum .append("rect") .attr("x", 40) .attr("y", 0) .attr("width", legendRectDimensions.width) .attr("height", legendRectDimensions.height) .attr("fill", d => d.color) .attr("fill-opacity", "0.25") .attr("stroke", d => d.color); legendDatum .append("text") .attr("x", 25) .attr("y", legendRectDimensions.height / 2) .attr("font-size", "0.7rem") .attr("dominant-baseline", "middle") .attr("text-anchor", "end") .text(d => d.label); svg .append("text") .attr("x", width) .attr("y", 20) .attr("font-size", "1rem") .attr("font-weight", "700") .attr("fill", colors.label) .attr("text-anchor", "end") .text( "The Compound Annual Growth Rate of the National Debt under Each Congress from 1995 to 2021" ); return svg.node(); }34:T7ec,congressionalEra = (era, values) => { const height = 200; // Create the SVG container. const svg = d3 .create("svg") .attr("width", width / 2) .attr("height", height) .attr("viewBox", `0 0 ${width / 2} ${height}`) .attr("font-family", "'Helvetica Neue', sans-serif"); // Define a temporal scale for the x-axis and a linear scale for the y-axis. const x = d3 .scaleUtc() .domain(d3.extent(values.map(d => d.date))) .range([margin.left, width / 2 - margin.right]); const y = d3 .scaleLinear() .domain(d3.extent(values.map(d => d.value))) .range([height - margin.bottom, margin.top]); // Draw x and y axes. const xAxis = d3 .axisBottom(x) .ticks(2) .tickSizeOuter(0); svg .append("g") .attr("transform", `translate(0, ${height - margin.bottom})`) .call(xAxis); const yAxis = d3 .axisLeft(y) .ticks(2) .tickFormat(d3.format("~s")) .tickSizeOuter(0); svg .append("g") .attr("transform", `translate(${margin.left}, 0)`) .call(yAxis); // Create a line generator. const line = d3 .line() .curve(d3.curveBasis) .x(d => x(d.date)) .y(d => y(d.value)); // Draw the actual path generated by the line generator. // Manually close the path with custom L and z instructions appended to the generated path. svg .append("path") .attr( "d", line(values) + `L${x(values[values.length - 1].date)},${height - margin.top} L${x( values[0].date )},${height - margin.top} z` ) .attr("stroke", colors[congressesByParty[era]]) .attr("fill", colors[congressesByParty[era]]) .attr("fill-opacity", 0.25); // Append a label for each Congress. svg .append("text") .attr("x", width / 2 - margin.right - 5) .attr("y", height - margin.bottom - 5) .attr("font-size", "0.7rem") .attr("font-weight", "500") .attr("fill", colors.label) .attr("text-anchor", "end") .text(`${era}th Congress`); return svg.node(); }35:T7c3,beeswarm = (era, values) => { const height = 100; // Create SVG container. const svg = d3 .create("svg") .attr("width", width / 2) .attr("height", height) .attr("viewBox", `0 0 ${width / 2} ${height}`); // Group debt values by month and year identifier, i.e. '04-1993'. const eraDataByMonth = _.groupBy(values, d => d.date.toISOString().slice(0, 7) ); // Get the debt at the start and end of the month and derive the percent change. const monthlyEraPercentChanges = Object.values(eraDataByMonth).map(d => { const [start, end] = [d[0], d[d.length - 1]]; return ((end.value - start.value) / start.value) * 100; }); const padding = 1; const radius = 3; // Derive a linear scale from the min and max of monthlyPercentChanges. const x = d3 .scaleLinear() .domain(d3.extent(monthlyPercentChanges)) .range([margin.left, width / 2 - margin.right]); // Draw a circle for each monthly percent change. svg .append("g") .selectAll("circle") .data(dodge(monthlyEraPercentChanges, { radius: radius * 2 + padding, x })) .join("circle") .attr("cx", d => d.x) .attr("cy", d => height - margin.bottom - radius - padding - d.y) .attr("r", radius) .attr("fill", colors[congressesByParty[era]]) .attr("fill-opacity", 0.25) .attr("stroke", colors[congressesByParty[era]]); // Append axes. const xAxis = d3 .axisBottom(x) .tickSizeOuter(0) .ticks(3) .tickFormat(d => `${d}%`); svg .append("g") .attr("transform", `translate(0, ${height - margin.bottom})`) .call(xAxis) .selectAll(".tick line") .remove(); // Append a title. svg .append("text") .attr("x", width / 2 - margin.right - 5) .attr("y", height - margin.bottom - 5) .attr("font-size", "0.7rem") .attr("font-weight", "500") .attr("font-family", "'HelveticaNeue', sans-serif") .attr("text-anchor", "end") .text(`${era}th Congress`); return svg.node(); }36:T538,dodge = (eraData, { x, radius }) => { const radius2 = radius ** 2; // Sort circles by x-coordinate along the horizontal axis. const circles = eraData.map(d => ({ x: x(d), d })).sort((a, b) => a.x - b.x); const epsilon = 1e-3; let head = null; let tail = null; // Returns true if circle ⟨x,y⟩ intersects with any circle in the queue. function intersects(x, y) { let a = head; while (a) { if (radius2 - epsilon > (a.x - x) ** 2 + (a.y - y) ** 2) { return true; } a = a.next; } return false; } // Place each circle sequentially. for (const circle of circles) { // Remove circles from the queue that can’t intersect the new circle. while (head && head.x < circle.x - radius2) head = head.next; // Choose the minimum non-intersecting tangent. if (intersects(circle.x, (circle.y = 0))) { let a = head; circle.y = Infinity; do { let y = a.y + Math.sqrt(radius2 - (a.x - circle.x) ** 2); if (y < circle.y && !intersects(circle.x, y)) { circle.y = y; } a = a.next; } while (a); } // Add the current circle to the queue. circle.next = null; if (head === null) { head = tail = circle; } else { tail = tail.next = circle; } } return circles; }37:T42c,congressionalData = { // Slice all data returned by the endpoint to just the periods representing complete Congresses. // For this analysis, Jan. 4, 1995 – Jan. 3, 2021 is the period of interest. const slice = data.slice( data.findIndex( d => d.date.getUTCMonth() === 0 && d.date.getUTCDate() >= 4 && d.date.getUTCFullYear() === 1995 ), _.findLastIndex( data, d => d.date.getUTCMonth() === 0 && d.date.getUTCDate() <= 4 && d.date.getUTCFullYear() === 2021 ) ); let congress = 104; let year = 1995; const congresses = {}; for (let i = 0; i < slice.length; i++) { const date = slice[i].date; // When we hit the 4th day of the year two years in the future, we've started the next Congress. if (date.getUTCFullYear() === year + 2 && date.getUTCDate() >= 4) { congress++; year += 2; } Array.isArray(congresses[congress]) ? congresses[congress].push(slice[i]) : (congresses[congress] = [slice[i]]); } return congresses; }38:[] 2f:{"title":"The National Debt Across Congressional Eras","cells":[{"id":0,"value":"md`# The National Debt Across Congressional Eras`","pinned":false,"hidden":false,"mode":"ojs"},{"id":31,"value":"md`When thinking about who's responsible for the national debt, it's important to look at who controls government spending in the first place. That power lies with the legislative branch, Congress.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":34,"value":"md`Since the 104th Congress began its legislative session in 1995, Republicans have fully controlled Congress **six times**; conversely, Democrats have only fully controlled it **twice**, with the remaining five sessions being split.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":37,"value":"md`To examine how each Congress has contributed to the national debt, we can start by visualizing the compound annual growth rate (CAGR) in the debt under each Congress.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":44,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":196,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":208,"value":"md`We can nuance the view of the data above by combining it with information on the party of the President in office. This can help us identify which party combinations across different branches of government affect spending most significantly.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":210,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":261,"value":"md`This view of the data complicates our story a bit. In particular, we can see that:\n\n1. The highest CAGR, under the 111th Congress, occurred when **Democrats fully controlled Congress and the presidency**. The second highest CAGR came under **a split Congress with a Republican president**, while the third occurred under **a Democratic Congress with a Republican president**.\n2. The two lowest CAGRs occurred under a **Republican Congress with a Democratic president**. The third lowest CAGR occurred under **full Republican control**.\n3. When the Congress is split and a Republican holds the presidency, the mean CAGR is **11.92%**. When the Congress is split and a Democrat holds the presidency, the mean CAGR drops to **6.06%**.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":311,"value":"md`## Congressional Spending Over Time`","pinned":false,"hidden":false,"mode":"ojs"},{"id":278,"value":"md`An important consideration in this analysis is that, in recent years, the debt has increasingly been used as a political weapon. Government shutdowns and threats concerning the debt ceiling are now common tactics to win legislative concessions, particularly among Republicans. We can see this in the data by visualizing the debt over time for each Congress in [small multiples](https://flowingdata.com/charttype/small-multiples/). Focus in particular on areas where the debt goes flat.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":26,"value":"html`\n ${Object.entries(congressionalData).map(([era, values]) => congressionalEra(era, values))}\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":18,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":318,"value":"md`These debt trendlines show an interesting temporal picture in how the national debt is accumulated. Starting with the **112th Congress**, we can see long periods of time where the national debt remains totally flat. These periods tend to coincide with government shutdowns. Prior to the 112th Congress, the only flat period occurred during the **104th Congress**, corresponding to [the 1995-1996 government shutdowns](https://en.wikipedia.org/wiki/1995%E2%80%931996_United_States_federal_government_shutdowns).\n\nDebt trendlines before the 112th Congress also tended to be more consistently linear in their growth. One significant outlier is the period of time around the **Great Recession** towards the end of the **110th Congress**. Another is the **106th Congress**, which saw a much more saw-toothed change in the debt involving multiple periods of debt reduction.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":324,"value":"md`Another useful way to look at this data is through a [Beeswarm chart](https://flowingdata.com/charttype/beeswarm/) looking at month-over-month percent change in the national debt. This can help us understand if the debt of a Congress was accumulated more gradually (indicated by a cluster of dots around 0 - +1%) or occurred in sharp spikes (indicated by dots in the >+2% range).`","pinned":false,"hidden":false,"mode":"ojs"},{"id":345,"value":"html`\n ${Object.entries(congressionalData).map(([era, values]) => beeswarm(era, values))}\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":367,"value":"monthlyPercentChanges = {\n // Group debt values by month and year identifier, i.e. '04-1993'.\n const dataByMonth = _.groupBy(data, d => d.date.toISOString().slice(0, 7));\n\n // Get the debt at the start and end of each month and derive the percent change.\n const monthlyPercentChanges = Object.values(dataByMonth).map(d => {\n const [start, end] = [d[0], d[d.length - 1]];\n\n return ((end.value - start.value) / start.value) * 100;\n });\n\n return monthlyPercentChanges;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":335,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":333,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":391,"value":"md`There are a few key insights we can derive from this view of the data:\n\n1. **From the 104th Congress to the 109th Congress, government spending tended to be more consistent month to month.** Moreover, debt increases and debt reduction were also more balanced, with the monthly percent change in the debt only cracking +2% twice.\n2. **The 110th and 116th Congress had the largest positive outliers, each with at least one monthly percent change of +4%.** This makes sense, given that both of these Congresses had urgent employment crises to deal with: recovery from the Great Recession for the 110th and Covid-19 for the 116th.\n3. **The 113th through 116th Congresses have high concentrations around 0% change.** These correspond to the months of major government shutdowns, which were less frequent in earlier Congresses.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":399,"value":"md`## Conclusion`","pinned":false,"hidden":false,"mode":"ojs"},{"id":535,"value":"md`As with all analysis, there's much more we could investigate here. This approach is relatively simplistic in that it's purely examining correlations between the debt and the political party of the President or the political makeup of Congress. However, we can still make some useful conclusions from this analysis:`","pinned":false,"hidden":false,"mode":"ojs"},{"id":537,"value":"md`\n1. **Republican narratives of fiscal conservatism around the debt aren't borne out in the data.** In large part, this is because Republican congresses and presidents still have priorities that involve government spending, particularly when it comes to national defense. The clearest example is the Iraq War, which represented a critical inflection point in changing the scale of government spending.\n2. **Economic crises influence government spending more than political party.** Government responses to the Great Recession and the Covid-19 pandemic were the two sharpest increases in spending, and both Democratic and Republican presidents and congresses oversaw this spending.\n3. **The debt can and is being used as a political weapon.** Periods of aggressive spending matched by periods of government shutdowns are becoming the norm in recent congresses. This largely mimics the political polarization we're seeing generally in the United States.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":547,"value":"md`Ultimately, disagreements about the role of government in citizens' lives go deeper than the national debt. However, diving into the debt can give us a picture into how these divisions do or do not manifest in the actual policy decisions our government makes.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":6,"value":"md`## Appendix`","pinned":false,"hidden":false,"mode":"ojs"},{"id":39,"value":"md`### Data Preparation`","pinned":false,"hidden":false,"mode":"ojs"},{"id":12,"value":"endpoint = 'https://api.fiscaldata.treasury.gov/services/api/fiscal_service/v2/accounting/od/debt_to_penny'","pinned":true,"hidden":false,"mode":"ojs"},{"id":14,"value":"data = {\n const resolve = (url = endpoint, acc = []) => {\n const data = d3.json(url).then((response) => {\n const next = acc.concat(\n response.data.map((d) => ({\n date: d3.utcParse(\"%Y-%m-%d\")(d.record_date),\n value: +d.tot_pub_debt_out_amt\n })\n ));\n\n if (response.links.next) {\n return resolve(`${endpoint}?${response.links.next}`, next);\n }\n\n return next;\n });\n \n return data;\n };\n \n return resolve();\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":42,"value":"cagr = slice => {\n const { date: startDate, value: startValue } = slice[0];\n const { date: endDate, value: endValue } = slice[slice.length - 1];\n const diffMs = endDate.getTime() - startDate.getTime();\n const totalYears = diffMs / (1000 * 60 * 60 * 24 * 365);\n\n return (Math.pow(endValue / startValue, 1 / totalYears) - 1) * 100;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":4,"value":"$37","pinned":true,"hidden":false,"mode":"ojs"},{"id":16,"value":"congressesByParty = ({\n 104: 'rep',\n 105: 'rep',\n 106: 'rep',\n 107: 'split',\n 108: 'rep',\n 109: 'rep',\n 110: 'dem',\n 111: 'dem',\n 112: 'split',\n 113: 'split',\n 114: 'split',\n 115: 'rep',\n 116: 'split'\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":213,"value":"congressesByPresidentialParty = ({\n 104: 'dem',\n 105: 'dem',\n 106: 'dem',\n 107: 'rep',\n 108: 'rep',\n 109: 'rep',\n 110: 'rep',\n 111: 'dem',\n 112: 'dem',\n 113: 'dem',\n 114: 'dem',\n 115: 'rep',\n 116: 'rep'\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":330,"value":"md`### Constants`","pinned":false,"hidden":false,"mode":"ojs"},{"id":20,"value":"colors = ({\n dem: '#2563EB',\n rep: '#DC2626',\n split: '#7C3AED',\n annotation: '#4B5563',\n label: '#000000',\n // Democratic Congress, Democratic President\n demdem: '#2563EB',\n // Democratic Congress, Republican President\n demrep: '#70a5ff',\n // Split Congress, Democratic President\n splitdem: '#96a2fb',\n // Split Congress, Republican President\n splitrep: '#d582f7',\n // Republican Congress, Democratic President\n repdem: '#f36785',\n // Republican Congress, Republican President\n reprep: '#DC2626'\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":45,"value":"height = 600","pinned":true,"hidden":false,"mode":"ojs"},{"id":24,"value":"margin = ({ top: 40, left: 60, right: 40, bottom: 40 });","pinned":true,"hidden":false,"mode":"ojs"},{"id":559,"value":"md`## Dependencies`","pinned":false,"hidden":false,"mode":"ojs"},{"id":10,"value":"d3 = require('d3@6');","pinned":true,"hidden":false,"mode":"ojs"},{"id":8,"value":"_ = require('lodash');","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W38","stdlib":"1","titling":"auto"}