31:T460,encoding_types = md`## Encoding Types In this section, you will visualize the same set of numbers from a dataset of your choice using four different encodings. For practice, **you must write the D3 code yourself** (though you can import from other notebooks _you_ have written). For each of the following encodings, you must do the following: > _Position, Length, Angle, Color_ - Create a visualization using the selected encoding. This includes appropriate axes, labels, and legends (if appropriate). - Describe **which encoding** the chart uses - Describe **how accurate** the encoding is (relative to other encodings), and note particular comparisons that are more or less visibly apparent in this encoding. If you want to get fancy, you can add your observations _about_ each to the chart itself through [annotations](https://d3-annotation.susielu.com/). Or, to practice your programming skills, you can make a single chart and **add a dropdown menu** to change the encoding of the data! Also feel free to organize your notebook as you see fit (e.g., putting the data loading section in the appendix, etc.)`32:T664,chart = { const svg = d3 .create('svg') .attr('viewBox', [0, 0, view_box.width, view_box.height]); const xAxis = d3.axisBottom().scale(scatter_xScale); svg .append('g') .attr( 'transform', 'translate(0,' + (view_box.height - view_box.marginBottom) + ')' ) .call(xAxis); const yAxis = d3.axisLeft().scale(scatter_yScale); svg .append('g') .attr('transform', 'translate(' + view_box.marginLeft + ',0)') .call(yAxis); // x axis label svg .append("text") .attr("class", "x label") .attr("x", view_box.width / 2) .attr("y", view_box.height - view_box.marginBottom + 35) .style('text-anchor', 'middle') .text("Week"); //y axis label svg .append("text") .attr("class", "y label") .attr("text-anchor", "end") .attr( "transform", `translate(${view_box.marginLeft - 30}, ${view_box.height / 2})rotate(-90)` ) .style('text-anchor', 'middle') .text("Weight (IB)"); //append title svg .append("text") .attr( 'transform', `translate(${view_box.width / 2}, ${view_box.marginTop / 2})` ) .style('text-anchor', 'middle') .style('font-size', '20px') .style('font-weight', 'bold') .text('Weight Over Time'); svg .selectAll('circle') .data(data) .join(enter => enter.append("circle")) .attr("cx", d => view_box.width / 2) .attr("cy", d => view_box.height / 2) .attr("r", 3) .attr('fill', 'blue'); svg .selectAll("circle") .attr("cx", d => scatter_xScale(d.x)) .attr("cy", d => scatter_yScale(d.y)); return svg.node(); }33:T41f,bars = { const container = html``; const svg = d3.select(container); svg .append('g') .attr('class', 'bars') .selectAll('rect') .data(data2) .join('rect') .attr('class', 'bar') .attr('x', d => x1(d.week)) .attr('y', d => y1(d.weight)) .attr('width', x1.bandwidth()) .attr('height', d => y1(0) - y1(d.weight)) .attr("fill", "yellow"); svg .append('g') .attr('class', 'x-axis') .attr('transform', `translate(0,${height - margin.bottom})`) .call(xAxis); svg .append('g') .attr('class', 'y-axis') .attr('transform', `translate(${margin.left},0)`) .call(yAxis); svg .append("text") .attr("dx", width / 2 + 20) .attr("dy", height - margin.top + 30) .attr("text-anchor", "middle") .text("Weeks"); svg .append("text") .attr("dx", margin.left - 440) .attr("dy", height - margin.top - 763) .style("text-anchor", "middle") .attr("transform", "rotate(-90)") .text("Weight"); return container; }34:T43b,chart3 = { const arcs = pie(data2); const svg = d3 .create("svg") .attr("viewBox", [-width / 2, -height / 2, width, height]); svg .append("g") .attr("stroke", "white") .selectAll("path") .data(arcs) .join("path") .attr("fill", d => color(d.data.week)) .attr("d", arc) .append("title") .text(d => `${d.data.week}: ${d.data.weight.toLocaleString()}`); svg .append("g") .attr("font-family", "sans-serif") .attr("font-size", 12) .attr("text-anchor", "middle") .selectAll("text") .data(arcs) .join("text") .attr("transform", d => `translate(${arcLabel.centroid(d)})`) .call(text => text .append("tspan") .attr("y", "-0.4em") .attr("font-weight", "bold") .text(d => d.data.week) ) .call(text => text .filter(d => d.endAngle - d.startAngle > 0.25) .append("tspan") .attr("x", 0) .attr("y", "0.7em") .attr("fill-opacity", 0.7) .text(d => d.data.weight.toLocaleString()) ); return svg.node(); }35:T432,color_bars = { const container = html``; const svg = d3.select(container); svg .append('g') .attr('class', 'bars') .selectAll('rect') .data(data2) .join('rect') .attr('class', 'bar') .attr('x', d => x1(d.week)) .attr('y', d => y1(d.weight)) .attr('width', x1.bandwidth()) .attr('height', d => y1(0) - y1(d.weight)) .attr("fill", d => color2(d.weight)); svg .append('g') .attr('class', 'x-axis') .attr('transform', `translate(0,${height - margin.bottom})`) .call(xAxis); svg .append('g') .attr('class', 'y-axis') .attr('transform', `translate(${margin.left},0)`) .call(yAxis); svg .append("text") .attr("dx", width / 2 + 20) .attr("dy", height - margin.top + 30) .attr("text-anchor", "middle") .text("Weeks"); svg .append("text") .attr("dx", margin.left - 440) .attr("dy", height - margin.top - 763) .style("text-anchor", "middle") .attr("transform", "rotate(-90)") .text("Weight"); return container; }36:[{"id":"a6740b420d340576224620265d240fb804c0b596f494036142ae1ae4e983616dd5be45c24b77858e5e0cd6e60dd74787546af5cedaba31e164d2d2a8b2f2330a","href":"https://static.observableusercontent.com/files/a6740b420d340576224620265d240fb804c0b596f494036142ae1ae4e983616dd5be45c24b77858e5e0cd6e60dd74787546af5cedaba31e164d2d2a8b2f2330a","name":"weightOverTime2.csv","type":"text/csv","lastModified":1589440830130.732,"size":165},{"id":"35faeb2fd2470cfee7e55711cd6cc5144e9b679482b5e6ce14465d1d82f26a493d74fa731e7ae481250efe48eb08a53592a012713585de412562d899d67a7f5a","href":"https://static.observableusercontent.com/files/35faeb2fd2470cfee7e55711cd6cc5144e9b679482b5e6ce14465d1d82f26a493d74fa731e7ae481250efe48eb08a53592a012713585de412562d899d67a7f5a","name":"weightOverTime.csv","type":"text/csv","lastModified":1588046935447.827,"size":165}] 2f:{"title":"Graphical Encodings Shah","cells":[{"id":0,"value":"md`# Graphical Encodings Shah`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4,"value":"summary = md`In this notebook, you will learn about best practices for **graphically encoding** your data through the following steps: \n1. Carefully reading the seminal Cleveland and McGill Paper _Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods_, which you need to access through [Canvas](https://canvas.uw.edu/courses/1370850/files/folder/papers)\n2. Demonstrating your understanding of the theories by creating graphics and explaining the perceptual accuracy of the selected encodings (using dataset(s) of your choice).`","pinned":false,"hidden":false,"mode":"ojs"},{"id":11,"value":"set_up = md`## Set up `","pinned":false,"hidden":false,"mode":"ojs"},{"id":67,"value":"displayCaution(\n \"Make sure you fork and rename this notebook before continuing!\"\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":74,"value":"md`To create a notebook for your assignment, please do the following (see [this notebook](https://observablehq.com/@info474/creating-assignments?collection=@info474/teaching-process) for more details on each step):\n

**Fork Assignment**: You should fork this assignment (make sure to fork it _within_ the Team). _Do not_ edit this notebook before forking it

**Rename Assignment**: Add your last name to the assignment name by changing the header cell value (e.g., to _Graphical Encodings Freeman_)

**Add Assignment to Collection**: add the assignment to the [appropriate collection](https://observablehq.com/collection/@info474/graphical-encoding-assignments)

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":84,"value":"reading_responses = md`## Reading Responses\nAfter **carefully** reading the academic paper linked above, answer the following questions (please use Markdown to provide a clear structure to your answers -- feel free to edit the question cells themselves, or create new cells as you see fit):\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":88,"value":"md`- In your own words, describe _what they did_ in the study to assess the perceptual accuracy of different encodings (e.g., what did they ask, and of whom?)?`","pinned":false,"hidden":false,"mode":"ojs"},{"id":134,"value":"md`The study concerned expirmentation to test theories on perceputal tasks when visually decoding information that are encoded in graphs. The study was broken down into two parts. The first part was defining the elementary perceptual tasks that would be used by the participants. The second part was the ordering of the tasks based on how accurately they were performed.`","pinned":true,"hidden":false,"mode":"ojs"},{"id":94,"value":"md`- Do you see any limitations to this study design (i.e., do you have any concerns about basing your visualizations on the results of the study)?`","pinned":false,"hidden":false,"mode":"ojs"},{"id":133,"value":"md` I dont see any limitations that would make me not want to base my visualizations on the results. However if I am nit picking I would say the participants arent neccessarily dvided by education level and technical profeciency isnt necessarily defined. `","pinned":true,"hidden":false,"mode":"ojs"},{"id":98,"value":"md`- The paper argues that cartesian graphs (scatter plots) are powerful for reasons beyond the fact that they allow us to encode two variables (e.g., \\`x\\` and \\`y\\`) using a position encoding. What additional information are we able to extract from a scatter plot, and which _elementary perceptual task_ enables us to do this?`","pinned":false,"hidden":false,"mode":"ojs"},{"id":132,"value":"md` Scatter plots allow us to deduce relationships/ differences between two or more points on the plot. The elementary perceptual task of direction allows us to do this. `","pinned":true,"hidden":false,"mode":"ojs"},{"id":33,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":131,"value":"data = Object.assign(\n d3.csvParse(\n await FileAttachment(\"weightOverTime.csv\").text(),\n ({ Weight: y, Week: x }) => ({\n x: +x,\n y: +y\n })\n )\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":130,"value":"view_box = ({\n width: 800, height: 500, marginLeft: 50, marginRight: 50, marginTop: 50, marginBottom: 50\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":129,"value":"weight_range = d3.extent(data, d => +d.y)","pinned":true,"hidden":false,"mode":"ojs"},{"id":128,"value":"week_range = d3.extent(data, d => +d.x)","pinned":true,"hidden":false,"mode":"ojs"},{"id":127,"value":"md`# Position: Scatter Plot`","pinned":true,"hidden":false,"mode":"ojs"},{"id":126,"value":"// define X scale\nscatter_xScale = d3\n .scaleLinear()\n .domain(week_range)\n .range([view_box.marginLeft, view_box.width - view_box.marginRight])","pinned":true,"hidden":false,"mode":"ojs"},{"id":125,"value":"scatter_yScale = d3\n .scaleLinear()\n .domain(weight_range)\n .range([\n view_box.height - view_box.marginBottom,\n view_box.marginTop\n ])","pinned":true,"hidden":false,"mode":"ojs"},{"id":179,"value":"md`Encoding Accuracy`","pinned":true,"hidden":false,"mode":"ojs"},{"id":124,"value":"$32","pinned":true,"hidden":false,"mode":"ojs"},{"id":123,"value":"md`# Length: Bar Chart`","pinned":true,"hidden":false,"mode":"ojs"},{"id":159,"value":"data2 = Object.assign(\n d3.csvParse(\n await FileAttachment(\"weightOverTime2.csv\").text(),\n ({ Week: x, Weight: y }) => ({\n week: +x,\n weight: +y\n })\n ),\n { format: \"Weeks\", y: \"↑ Weight (IBs)\" }\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":167,"value":"margin = ({ top: 25, right: 20, bottom: 35, left: 40 })","pinned":true,"hidden":false,"mode":"ojs"},{"id":172,"value":"height = 800","pinned":true,"hidden":false,"mode":"ojs"},{"id":175,"value":"width = 800","pinned":true,"hidden":false,"mode":"ojs"},{"id":122,"value":"x1 = d3\n .scaleBand()\n .domain(data2.map(d => d.week))\n .range([margin.left, width - margin.right])\n .padding(0.1)","pinned":true,"hidden":false,"mode":"ojs"},{"id":121,"value":"y1 = d3\n .scaleLinear()\n .domain([0, d3.max(data2, d => d.weight)])\n .nice()\n .range([height - margin.bottom, margin.top])","pinned":true,"hidden":false,"mode":"ojs"},{"id":139,"value":"xAxis = d3.axisBottom(x1).tickSizeOuter(0)","pinned":true,"hidden":false,"mode":"ojs"},{"id":140,"value":"yAxis = d3.axisLeft(y1).tickSizeOuter(0)","pinned":true,"hidden":false,"mode":"ojs"},{"id":181,"value":"md`Encoding Accuracy`","pinned":true,"hidden":false,"mode":"ojs"},{"id":120,"value":"$33","pinned":true,"hidden":false,"mode":"ojs"},{"id":119,"value":"md` # Angle: Pie Chart`","pinned":true,"hidden":false,"mode":"ojs"},{"id":183,"value":"color = d3\n .scaleOrdinal()\n .domain(data.map(d => d.week))\n .range(\n d3\n .quantize(t => d3.interpolateSpectral(t * 0.8 + 0.1), data.length)\n .reverse()\n )","pinned":true,"hidden":false,"mode":"ojs"},{"id":185,"value":"arc = d3.arc()\n .innerRadius(0)\n .outerRadius(Math.min(width, height) / 2 - 1)","pinned":true,"hidden":false,"mode":"ojs"},{"id":188,"value":"arcLabel = {\n const radius = (Math.min(width, height) / 2) * 0.8;\n return d3\n .arc()\n .innerRadius(radius)\n .outerRadius(radius);\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":190,"value":"pie = d3\n .pie()\n .sort(null)\n .value(d => d.weight)","pinned":true,"hidden":false,"mode":"ojs"},{"id":193,"value":"$34","pinned":true,"hidden":false,"mode":"ojs"},{"id":118,"value":"md` # Color: Bar Chart`","pinned":true,"hidden":false,"mode":"ojs"},{"id":199,"value":"color2 = d3.scaleSequential(\n d3.extent(data2, d => d.weight),\n d3.interpolateSpectral\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":203,"value":"$35","pinned":true,"hidden":false,"mode":"ojs"},{"id":54,"value":"curve_difference = md`## Curve Difference Charts\nThe paper introduces curve difference charts as a way of evaluating differences over time. However, there are many perceptual limitations to estimating the underlying values of the chart. In the section below, you should:\n- Describe limitations to the curve difference chart\n- Build a curve difference chart using D3 and a dataset of your choice (you may need to use different data than the example above, or a different subset of your data)\n- Build a D3 _alternative_ to your curve difference chart, and describe any differences you observe (_hint: the paper proposes some alternatives!_)\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":208,"value":"","pinned":true,"hidden":false,"mode":"ojs"},{"id":61,"value":"feedback = md`## Feedback\nIn the cell below, please let us know which pieces of the assignment you found particularly challenging, and if you had any issues specific to working with the Observable platform.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":7,"value":"md`## Appendix`","pinned":false,"hidden":false,"mode":"ojs"},{"id":117,"value":"d3 = require('d3@5')","pinned":true,"hidden":false,"mode":"ojs"},{"id":65,"value":"import { displayCaution } from \"@info474/utilities\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":49,"value":"html``","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W36","stdlib":"1","titling":"auto"}