2d:null 2e:["$","title",null,{"children":"Examples of bitemporal charts | Toph Tucker | Observable"}] 33:T557,md`# Examples of bitemporal charts Bitemporal data has two dimensions of time, like a table with two columns of dates. Often, one dimension is the date the data was made _on_, and one is the date the data is _about_. For example, consider data about some aspect of ${d3.timeFormat("%B %e, %Y")( d3.timeMonth.offset(d3.timeMonth(new Date()), 3) )} — what the world population will be, or what a store’s sales will be that day. Years before that date, we might have forecasts about it. We might have a forecast made _on_ April 11, 1970, and _on_ July 10, 1998, and _on_ February 3, 2016 — all _about_ the same date. Then the date arrives, and “on” and “about” coincide. In the case of store sales, that data may be the “actual” number. But for the world population, we can’t do an instantaneous count, so we continue to update our beliefs about that day even after it passes. And really, even for something like revenue, companies routinely restate numbers about the past — typically not out of any devious intent, but just because it is hard to count. I am a [longtime fan of the format](https://twitter.com/tophtucker/status/878942767317213185) and have been collecting examples for a few years. They can be separated into two kinds, which Tracy Alloway calls “Medusa” charts and “Fishhook” charts; the first is far more common. `34:T11e6,chart = { const svg = d3.create("svg").attr("viewBox", [0, 0, width, height]); svg.append("g").call(xAxis); svg.append("g").call(yAxis); const content = svg.append("g"); content .append("text") .attr("font-family", "sans-serif") .attr("font-size", 10) .attr("font-weight", "bold") .attr("x", margin.left) .attr("y", margin.top) .attr("dx", 3) .attr("dy", ".35em") .text(examples.find(d => d.value === example).label); const xLabels = content .append("g") .attr("font-family", "sans-serif") .attr("font-size", 10) .attr("font-weight", "bold") .attr("text-anchor", "end") .attr( "transform", `translate(${width - margin.right}, ${height - margin.bottom - 4})` ); const aboutLabel = xLabels.append("text").text("About"); const onLabel = xLabels.append("text").text("On"); const byOn = content .append("g") .attr("class", "by-on") .selectAll("path") .data(dataByOn) .join("path") .attr("fill", "none") .attr("stroke", "gray") .attr("stroke-width", .5) .attr("stroke-linejoin", "round") .attr("stroke-linecap", "round"); const byAbout = content .append("g") .attr("class", "by-about") .selectAll("path") .data(dataByAbout) .join("path") .attr("fill", "none") .attr("stroke", "gray") .attr("stroke-width", .5) .attr("stroke-linejoin", "round") .attr("stroke-linecap", "round"); const actual = content .append("path") .datum(dataActual) .attr("fill", "none") .attr("stroke", "steelblue") .attr("stroke-width", 1.5) .attr("stroke-linejoin", "round") .attr("stroke-linecap", "round"); return Object.assign(svg.node(), { async update(line) { const isOnOverAbout = line.name === "on-over-about"; svg.attr("data-type", line.name); if (!actual.attr("d")) { // init byOn.attr("d", line.line).attr("opacity", Number(isOnOverAbout)); byAbout.attr("d", line.line).attr("opacity", Number(!isOnOverAbout)); actual.attr("d", line.line); aboutLabel .attr("opacity", Number(isOnOverAbout)) .attr("x", isOnOverAbout ? 0 : -40); onLabel .attr("opacity", Number(!isOnOverAbout)) .attr("x", !isOnOverAbout ? 0 : -40); } else { // change const t0 = svg.transition().duration(500); byOn.transition(t0).attr("opacity", .5); byAbout.transition(t0).attr("opacity", .5); await t0.end(); const t1 = svg.transition().duration(5000); byOn.transition(t1).attr("d", line.line); byAbout.transition(t1).attr("d", line.line); actual.transition(t1).attr("d", line.line); aboutLabel .transition(t1) .attr("opacity", Number(isOnOverAbout)) .attr("x", isOnOverAbout ? 0 : -40); onLabel .transition(t1) .attr("opacity", Number(!isOnOverAbout)) .attr("x", !isOnOverAbout ? 0 : -40); await t1.end(); const t2 = svg.transition().duration(500); byOn.transition(t2).attr("opacity", Number(isOnOverAbout)); byAbout.transition(t2).attr("opacity", Number(!isOnOverAbout)); } }, async redraw(line) { actual.attr("d", null); const isOnOverAbout = svg.attr("data-type") === "on-over-about"; const prop = isOnOverAbout ? "on" : "about"; const whiskers = (isOnOverAbout ? byOn : byAbout) .each(function(d) { d.len = this.getTotalLength(); }) .attr("stroke-dasharray", d => `${d.len} ${d.len}`) .attr("stroke-dashoffset", d => d.len); const pts = actual.datum(); for (let i = 1; i <= pts.length; i++) { const latest = pts[i - 1] ? pts[i - 1][prop] : Infinity; const len0 = actual.node().getTotalLength(); actual.attr("d", line.line(pts.slice(0, i))); const len1 = actual.node().getTotalLength(); const t0 = actual .attr("stroke-dasharray", `${len0} ${len1}`) .transition(250) .attr("stroke-dasharray", `${len1} ${len1}`); await t0.end(); const t1 = whiskers .filter(d => d[0][prop] <= latest) .transition() .ease(d3.easeLinear) .duration(250) .attr("stroke-dashoffset", 0); await t1.end(); } actual.attr("stroke-dasharray", undefined); whiskers .attr("stroke-dasharray", undefined) .attr("stroke-dashoffset", undefined); } }); return svg.node(); }35:Ta6f,viewof table = { const svg = d3 .create("svg") .attr("viewBox", [ 0, 0, width, x(d3.max(data.map(d => d.on))) + margin.bottom ]) .style("overflow", "visible") .style("background", "white"); svg.on("click", () => svg.property("value", false).dispatch("input")); svg .append("path") .datum(dataActual) .attr("fill", "none") .attr("stroke", "steelblue") .attr("stroke-width", 1.5) .attr("stroke-linejoin", "round") .attr("stroke-linecap", "round") .attr("d", line2D); svg.append("g").call(xAxisTop); svg.append("g").call(xAxisLeft); const on = svg .append("g") .selectAll("path") .data(dataByOn) .join("path") .attr("fill", "none") .attr("stroke", "gray") .attr("stroke-width", .5) .attr("stroke-linejoin", "round") .attr("stroke-linecap", "round") .attr("d", line2D) .attr("opacity", Number(line.name === "on-over-about")); const about = svg .append("g") .selectAll("path") .data(dataByAbout) .join("path") .attr("fill", "none") .attr("stroke", "gray") .attr("stroke-width", .5) .attr("stroke-linejoin", "round") .attr("stroke-linecap", "round") .attr("d", line2D) .attr("opacity", Number(line.name === "about-over-on")); svg .append("g") .selectAll("text") .data( d3 .cross(x.ticks(width / 80), xOn.ticks(width / 80)) .map(([_about, _on]) => data.find(({ about, on }) => about >= _about && on >= _on) ) .filter(d => d) ) .join("text") .attr("x", ({ about }) => x(about)) .attr("y", ({ on }) => xOn(on)) .attr("text-anchor", "middle") .attr("dy", ".3em") .attr("font-family", "sans-serif") .attr("font-size", 10) .text(({ value }) => valueFormat(value)) .style("cursor", "pointer") .on("click", (event, d) => { svg.property("value", d).dispatch("input"); event.stopPropagation(); }) .call(halo); // svg // .append("g") // .selectAll("g") // .data( // resample(dataByOn, d => +d3.timeWeek.floor(d), d => d[0].on, d => d[0]) // ) // .join("g") // .selectAll("text") // .data(d => resample(d, d => +d3.timeWeek.floor(d), d => d.about, d => d[0])) // .join("text") // .attr("x", ({ about }) => x(about)) // .attr("y", ({ on }) => xOn(on)) // .attr("text-anchor", "middle") // .attr("dy", ".3em") // .attr("font-family", "sans-serif") // .attr("font-size", 10) // .text(({ value }) => valueFormat(value)) // .call(halo); svg.property("value", false).dispatch("input"); return svg.node(); }36:T7f2,md`I am grateful to Jonathan Garber at Business Insider for [posting](https://twitter.com/businessinsider/status/878939144457682944) the one that [got me started](https://www.businessinsider.com/wall-street-brutally-wrong-when-it-comes-to-predicting-the-10-year-2017-6) in June 2017. Joe Weisenthal [pointed](https://twitter.com/TheStalwart/status/885663793375784960) me toward Tracy Alloway’s typology of “[Medusas](https://twitter.com/tracyalloway/status/669602286587527168)” and “[fishhooks](https://twitter.com/tracyalloway/status/729591962920222720)”. Dominikus Baur [sent](https://twitter.com/dominikus/status/885642961194672128) an article about Amanda Cox’s “[porcupine](https://datajournalism.com/read/handbook/one/delivering-data/designing-with-data)”; Bryan Bischof calls them “[whiskers](https://twitter.com/BEBischof/status/885679548943171584)”; Ole Peters noted their “[strong southwest wind](https://twitter.com/ole_b_peters/status/981447771692650498)”. Robert Monfera [gave me](https://twitter.com/monfera/status/885653042409897984) the excellent term “bitemporal”, and the [Wikipedia entry](https://en.wikipedia.org/wiki/Bitemporal_Modeling). Adam Pearce and Evan Applegate sent lots. Claire got the LIBOR data for me. J.W. Mason helpfully [posted his R code](http://jwmason.org/slackwire/cbo-interest-rate-forecasts-2009-2019/) for getting the CBO data. I think the best way to understand these is to talk through specific examples with concrete language; this “on” and “about” thing I keep going on about just doesn’t resonate! It only really makes sense when it’s like, “On Saturday, I sent an email saying I thought we’d sell a hundred of these next month.” E.g. see also [my Coronavirus example](https://observablehq.com/@tophtucker/bitemporal-coronavirus-cases) with data from the [COVID Tracking Project](https://github.com/covid19Tracking/covid-tracking-data/), showing how estimates of the number of cases on a given day in the past have risen over time. --- `37:Tf63,md`Two and a half years after I started collecting, in December 2019, this chart of [Itanium sales forecasts](https://twitter.com/MooStation/status/1205388199293403138) was going around. As I went to add it to my thread, [I saw Chris Beiser doing the same](https://twitter.com/tophtucker/status/1205699961607933952). I scrolled back in awe as I realized he’d been doing the exact same thing for almost exactly as long, starting [his thread](https://twitter.com/ctbeiser/status/1205698719275864064) eleven days before I’d started mine. We had never met but in January I moved to San Francisco and by the end of the month I’d run into him on the street, thanks to Rob. Chris says: “I’ve always been a fan of the examples that show the persistence of belief in the face of reality. Sometimes (the IEA) that’s a malicious forecast; in other cases, (VMT) it’s the result of nonsensical predictions. But I also think they’re sobering, in the sense that we often see charts of the predictions alone, rather than the medusa-variants; in that guise (‘analysts say automation-driven unemployment will destroy over 100 million jobs by 2040’) they’re often treated far too credulously.” When you see a single-line forecast, try to imagine the bitemporal version. When I started my thread in 2017, [I wrote](https://twitter.com/tophtucker/status/878943479606501376): >[I especially love] the breed that shows some systematic updraft…feels almost like orbital vector field, angels beating wings centripetally (Feynman). They’re usually presented as showing forecasts to be “brutally wrong”, usually optimistic. >But it’s so systematic I think something’s up! The Earth constantly accelerates Sunward just to stay in elliptical orbit. Maybe forecasts gotta trend upward just to stay on “random walk”. I.e. I think it captures something of the essential orientation of life against disorder, and shouldn’t be dismissed/extinguished as “wrong”. >People forever have only stayed aloft through being “brutally wrong” about their own and collective prospects, for today and for posterity. I don’t mean “we should delude ourselves”; I mean that it might be a category error to just call it delusion. It might be cleverer than us. “Whether you think you can or think you can’t, you’re right!” is wrong, but nicely conveys that predictions are not independent of outcomes. >Anyway cool chart, our tools and data structures make it relatively hard to make, it could & should be easier. >Oh also it feels like the kind of thing that might disappear as soon as you look at it; balance masks evidence of the forces that caused it. The Feynman reference is to [his 1965 lecture on gravity](https://www.youtube.com/watch?v=j3mhkYbznBk&t=17m10s): >What makes planets go around the sun? At the time of Kepler some people answered this problem by saying that there were angels behind them beating their wings and pushing the planets around an orbit. As you will see, the answer is not very far from the truth. The only difference is that the angels sit in a different direction and their wings push inward. Feynman’s point conjures up an image of a vector field of just one of the forces acting on nature, with the “actual” line superimposed, flagrantly going against the current, to show there must be something else at work. --- Emanuel Derman flips the usual point people make with bitemporal line charts: instead of holding up the actual data to make past forecasts look silly (emphasizing the realness of what actually happened), he holds up past possibilities to make the actual reality look, perhaps, more contingent and less necessary (emphasizing the realness of what might have happened) — like [Kundera’s](https://www.goodreads.com/quotes/8198075-they-had-been-talking-about-his-friend-z-when-she) _es könnte auch anders sein_, “it could just as well be otherwise”:`38:T113a,normalized = { const svg = d3 .create("svg") .attr("viewBox", [0, 0, width, height]) .style("overflow", "visible") .style("font-family", "sans-serif") .style("font-size", "12px") .style("z-index", -1); const content = svg.append("g"); const yLabel = Math.abs(y.range()[1] - y.range()[0]) / 3; const labels = content .append("g") .attr("opacity", 0) .attr("transform", `translate(${margin.left}, ${y(0)})`); labels .append("text") .attr("y", -yLabel) .text("↑ Overestimate"); labels .append("text") .attr("y", yLabel) .text("↓ Underestimate"); const byOn = content .append("g") .attr("class", "by-on") .selectAll("path") // .data( // dataByOn.map(arr => // arr.filter(d => d.about < d3.max(dataActual, d => d.about)) // ) // ) .data(dataByOn) .join("path") .attr("fill", "none") .attr("stroke", "gray") .attr("stroke-width", .5) .attr("stroke-linejoin", "round") .attr("stroke-linecap", "round") .attr("d", lineAbout); const actual = content .append("path") .datum(dataActual) .attr("fill", "none") .attr("stroke", "steelblue") .attr("stroke-width", 1.5) .attr("stroke-linejoin", "round") .attr("stroke-linecap", "round") .attr("d", lineAbout); function original() { const t0 = svg.transition().duration(2000); content.transition(t0).attr("transform", `translate(0, 0)`); labels.transition(t0).attr("opacity", 0); actual.transition(t0).attr("d", lineAbout); byOn.transition(t0).attr("d", lineAbout); } function normalizeY() { const t0 = svg.transition().duration(2000); const line0 = d => lineAbout( d.map(({ about, value }) => ({ about, value: value - dataActualContinuous(+about) })) ); content .transition(t0) .attr("transform", `translate(0, ${-d3.mean(y.range())})`); labels.transition(t0).attr("opacity", 1); actual.transition(t0).attr("d", line0); byOn.transition(t0).attr("d", line0); } function normalizeX() { const t0 = svg.transition().duration(2000); const line0 = d => lineAbout( d.map(({ about, on, value }) => ({ about: new Date(+dataActual[0].on + +about - +on), value })) ); content.transition(t0).attr("transform", `translate(0, 0)`); labels.transition(t0).attr("opacity", 0); actual.transition(t0).attr("d", line0); byOn.transition(t0).attr("d", line0); } async function normalizeXY() { const t0 = svg.transition().duration(2000); const line0 = d => lineAbout( d.map(({ about, on, value }) => ({ about: new Date(+dataActual[0].on + +about - +on), value })) ); actual.transition(t0).attr("d", line0); byOn.transition(t0).attr("d", line0); await t0.end(); const t1 = svg.transition().duration(2000); const line1 = d => lineAbout( d.map(({ about, on, value }) => ({ about: new Date(+dataActual[0].on + +about - +on), value: value - dataActualContinuous(+on) })) ); content .transition(t1) .attr("transform", `translate(0, ${-d3.mean(y.range())})`); labels.transition(t1).attr("opacity", 1); actual.transition(t1).attr("d", line1); byOn.transition(t1).attr("d", line1); } async function normalizeYX() { const t0 = svg.transition().duration(2000); const line0 = d => lineAbout( d.map(({ about, value }) => ({ about, value: value - dataActualContinuous(+about) })) ); content .transition(t0) .attr("transform", `translate(0, ${-d3.mean(y.range())})`); labels.transition(t0).attr("opacity", 1); actual.transition(t0).attr("d", line0); byOn.transition(t0).attr("d", line0); await t0.end(); const t1 = svg.transition().duration(2000); const line1 = d => lineAbout( d.map(({ about, on, value }) => ({ about: new Date(+dataActual[0].on + +about - +on), value: value - dataActualContinuous(+about) })) ); actual.transition(t1).attr("d", line1); byOn.transition(t1).attr("d", line1); } return Object.assign(svg.node(), { original, normalizeX, normalizeY, normalizeXY, normalizeYX }); }39:T26ec,gallery = [ [ FileAttachment("594c2174a3630f88018b4bd6.jpeg"), `10yr rates from [Business Insider](https://www.businessinsider.com/wall-street-brutally-wrong-when-it-comes-to-predicting-the-10-year-2017-6)`, "medusa", "sw" ], [ FileAttachment("DZ6l-1yWsAAIyNa.jpeg"), `[Vehicle-miles forecasts](https://www.ssti.us/2013/12/new-travel-demand-projections-are-due-from-u-s-dot-will-they-be-accurate-this-time/)`, "medusa", "sw" ], [ FileAttachment("D8ZKPYfXoAAh0Ja.jpeg"), `Fed futures [via Joe Weisenthal](https://twitter.com/TheStalwart/status/1136677587328147457)`, "medusa", "sw" ], [ FileAttachment("D0zvgwVWwAE5OFU.jpeg"), `Euro inflation [via Joe Weisenthal](https://twitter.com/TheStalwart/status/1102519360529358851)`, "medusa", "sw" ], [ FileAttachment("ELpmLYHUUAAseVc.png"), `[Itanium sales forecasts](https://twitter.com/MooStation/status/1205388199293403138)`, "medusa", "sw" ], [ FileAttachment("EXC6tzVWsAIb1BP.png"), `[Rolling average death lag](https://twitter.com/DavidSteadson/status/1256709606950256640)`, "medusa", "nw" ], [ FileAttachment("CehLMs0WsAABgTa.jpeg"), `[Riksbank rates](https://twitter.com/vediense/status/713903232049745920)`, "medusa", "sw" ], [ FileAttachment("DVII8QbW4AAMhrq.jpeg"), `[Copper forecasts](https://www.princeton.edu/~deaton/downloads/Commodity_Prices_and_Growth_in_Africa.pdf) via [Morten Jerven](https://twitter.com/MJerven/status/959840601217945600)`, "medusa", "sw" ], [ FileAttachment("ELxsV8wXkAAievc.png"), `[Interest rates and unemployment](https://twitter.com/greg_ip/status/1205957928093671426)`, "medusa", "sw" ], [ FileAttachment("EJVF36gWkAA9t12.jpeg"), `[Solar capacity](https://twitter.com/Sustainable2050/status/1194938108665569281)`, "medusa", "w" ], [ FileAttachment("medusa.png"), `“Medusa” of Libor curve via [Tracy Alloway](https://twitter.com/tracyalloway/status/669602286587527168)`, "medusa", "sw" ], [ FileAttachment("Screen Shot 2020-06-22 at 11.13.09 AM.png"), `“[Porcupine](https://datajournalism.com/read/handbook/one/delivering-data/designing-with-data)” of [budget forecasts](https://archive.nytimes.com/www.nytimes.com/interactive/2010/02/02/us/politics/20100201-budget-porcupine-graphic.html) by [Amanda Cox](https://twitter.com/amandacox)`, "medusa", "sw" ], [ FileAttachment("Screen Shot 2020-06-22 at 11.12.36 AM.png"), `[Coronavirus models](https://www.nytimes.com/interactive/2020/04/22/upshot/coronavirus-models.html) via [Margot Sanger-Katz](https://twitter.com/sangerkatz/status/1253047421510340608)`, "medusa", "?" ], [ FileAttachment("Screen Shot 2020-06-22 at 11.15.37 AM.png"), `[Tesla production](https://www.bloomberg.com/graphics/tesla-model-3-vin-tracker/)`, "medusa", "sw" ], [ FileAttachment("DEpuzDyVYAAQhu8.jpeg"), `[S&P 500 earnings expectations](https://www.valuewalk.com/wp-content/uploads/2017/03/3-7-17-Total-Return-Webcast-FINAL-FINAL.pdf)`, "fishhook", "se" ], [ FileAttachment("CiAIBgBWUAApmRr.jpeg"), `“Fishhook” of EPS revisions via [Tracy Alloway](https://twitter.com/tracyalloway/status/729591962920222720)`, "fishhook", "se" ], [ FileAttachment("cbo-projected-rates-cropped.png"), `CBO projected rates via Chris Beiser and [Nathan Tankus](https://twitter.com/NathanTankus/status/1301227622656024577)`, "medusa", "sw" ], [ FileAttachment("productivity-growth.jpg"), `Productivity growth via Chris Beiser`, "medusa", "sw" ], [ FileAttachment("greenhouse-gas-emissions.jpg"), `Greenhouse gas emissions via Chris Beiser`, "medusa", "nw" ], [ FileAttachment("japan-birth-rate.jpg"), `Japan birth rate via Chris Beiser`, "medusa", "w" ], [ FileAttachment("coal-production.png"), `Coal production via Chris Beiser`, "medusa", "w" ], [ FileAttachment("Eg7ncadU0AAcQgk.png"), `IEA renewable energy forecasts via [@atmort](https://twitter.com/atmort/status/1301231933209194496/)`, "medusa", "w" ], [ FileAttachment("Eg70WCVWsAI73fU.png"), `IMF World GDP Growth via [@michaelsjoel](https://twitter.com/michaelsjoel/status/1301246147609145344/)`, "medusa", "w" ], [ FileAttachment("katie peek - energy_gap_o@1.jpg"), `Energy production vs consumption [by Katie Peek](https://projects.katiepeek.com/The-Energy-Gap)`, "medusa", "w" ], [ FileAttachment( "EnHW9ISVgAMixyA - share of balances that exceed original.jpeg" ), `[Share of loans exceeding original](https://twitter.com/Econ_Marshall/status/1329079855858630661) with rare relative x-axis`, "medusa", "sw" ], [ FileAttachment("boston globe - opioids - 2017-06-28.jpg"), `Boston Globe on opioid death projections ([based on](https://www.statnews.com/2017/06/27/opioid-deaths-forecast/))`, "medusa", "sw" ], [ FileAttachment("mattyglesias - cbo rates forecasts 1985-2020.jpeg"), `CBO projected rates via [Matt Yglesias](https://twitter.com/mattyglesias/status/1331199848465518592) → [Robert Lesser](https://twitter.com/RobertLesser_)`, "medusa", "w" ], [ FileAttachment("survey of professional forecasters - yields.jpeg"), `Survey of yield forecasts via [Kieran Healy](https://twitter.com/kjhealy/status/1336681782251573249) → [Robert Kosara](Robert Kosara) → [Alex Wein](https://twitter.com/alexwhines/status/1336746195473952768)`, "medusa", "sw" ], [ FileAttachment("euro zone inflation.png"), `Euro inflation forecasts via [Robin Brooks](https://twitter.com/RobinBrooksIIF/status/1337046997296148483/photo/1) → [Robin Sloan](https://twitter.com/robinsloan/status/1337049294684172288) → [Sha Hwang](https://twitter.com/shashashasha/status/1337097083090857985) → [Noah Veltman](https://twitter.com/veltman/status/1337101971463933952)`, "medusa", "sw" ], [ FileAttachment("us fed unemployment.png"), `Fed unemployment forecasts via [Robin Brooks](https://twitter.com/RobinBrooksIIF/status/1130414698061545477) → [Robin Sloan](https://twitter.com/robinsloan/status/1337053926793867265)`, "medusa", "w" ], [ FileAttachment("fed inflation.png"), `Fed inflation forecasts via [Robin Brooks](https://twitter.com/RobinBrooksIIF/status/1306320973168349185) → [Robin Sloan](https://twitter.com/robinsloan/status/1337054311067639815)`, "medusa", "sw" ], [ FileAttachment("fusion reactor funding.jpeg"), `Fusion reactor funding via [Ben Reinhardt](https://twitter.com/Ben_Reinhardt/status/1360443925400551424)`, "medusa", "sw" ], [ FileAttachment("fed rate thru 2021.png"), `Fed rates 2021 via [Tracy Alloway](https://twitter.com/tracyalloway/status/1368555570198839300)`, "medusa", "sw" ], [ FileAttachment("iea energy forecasts by aukehoekstra.jpeg"), `[IEA photovoltaic forecasts](https://maartensteinbuch.com/2017/06/12/photovoltaic-growth-reality-versus-projections-of-the-international-energy-agency/) by [Auke Hoekstra](https://twitter.com/AukeHoekstra/status/1355543250791239687/) via [Tom Critchlow](https://twitter.com/tomcritchlow/status/1384571283216637957/) → [Ryan Herr](https://twitter.com/rrherr/status/1384577439582597125) ([also](https://twitter.com/DrSimEvans/status/1392045749383352321))`, "medusa", "w" ], [ FileAttachment("apollo-inflation.png"), `[Inflation expectations](https://johnhcochrane.blogspot.com/2022/10/inflation-expectations.html) ([via](https://twitter.com/cauchyfriend/status/1588998613535195137/photo/1), via Ben, see [also](https://twitter.com/mattyglesias/status/1589235194120536065))`, "medusa", "nw" ], [ FileAttachment("roston-co2-cuts-needed.png"), `[CO2 cuts needed](https://www.bloomberg.com/news/articles/2022-11-08/is-the-1-5c-warming-goal-dead-cop27-and-climate-overshoot) to hit 1.5ºC of warming, by Eric Roston, data from OWID, [via](https://twitter.com/aaronrutkoff/status/1589896524569673728/photo/1), via Fil`, "medusa", "nw" ], [ FileAttachment("Screen Shot 2023-03-15 at 12.09.32 PM.png"), `[Government deficits](https://www.nytimes.com/interactive/2023/03/10/upshot/presidential-budgets-reality.html), by Josh Katz, Alicia Parlapiano and Margot Sanger-Katz, via Fil`, "medusa", "nw" ], [ FileAttachment("Screen Shot 2023-03-15 at 12.09.40 PM.png"), `[Government spending](https://www.nytimes.com/interactive/2023/03/10/upshot/presidential-budgets-reality.html), by Josh Katz, Alicia Parlapiano and Margot Sanger-Katz, via Fil`, "medusa", "w" ], [ FileAttachment("Screen Shot 2023-03-15 at 12.09.53 PM.png"), `[Government revenue](https://www.nytimes.com/interactive/2023/03/10/upshot/presidential-budgets-reality.html), by Josh Katz, Alicia Parlapiano and Margot Sanger-Katz, via Fil`, "medusa", "sw" ], [ FileAttachment("Screen Shot 2023-03-27 at 1.29.07 PM.png"), `[Fed projections](https://www.nytimes.com/2023/03/22/business/fed-powell-takeaways.html); bitemporal, but not a line chart`, "other", "?" ], [ FileAttachment("china gdp.jpeg"), `[China GDP projections](https://twitter.com/StefanFSchubert/status/1680116856470790144/photo/1), via [Matt Neary](https://twitter.com/_mattneary/status/1680391415988199424)`, "medusa", "sw" ], [ FileAttachment("IMG_8350.JPEG"), `[“Hairy” Fed projections](https://twitter.com/JamesEagle17/status/1752284677182988529), by James Eagle, via Ben Cohen`, "medusa", "sw" ], [ FileAttachment("image.png"), `[Divorce rate](https://livingpixel.io/blog/divorce-rates-stats-can), by Living Pixel, via Fil`, "other", "?" ] 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