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TO ADJUST DOMAIN BREAKS","create_time":"$D2023-02-19T21:25:23.649Z","update_time":null,"resolved":true,"user":{"type":"individual","id":"8b16ddb9dee32a3f","login":"mackcross","name":"mackcross","avatar_url":"https://avatars-next.observableusercontent.com/c748da9e5c023b7ec8437995e067e4e1268c70b0105b5e96414ce56afb2a3e50","tier":null}},{"id":"ffae1412de3a99f5","reply_to_id":null,"content":"Added this.","create_time":"$D2023-02-20T14:56:16.995Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"8b16ddb9dee32a3f","login":"mackcross","name":"mackcross","avatar_url":"https://avatars-next.observableusercontent.com/c748da9e5c023b7ec8437995e067e4e1268c70b0105b5e96414ce56afb2a3e50","tier":null}},{"id":"9b34ed429d3ebd57","reply_to_id":null,"content":"Added 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morning","create_time":"$D2023-02-20T14:58:02.361Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"8b16ddb9dee32a3f","login":"mackcross","name":"mackcross","avatar_url":"https://avatars-next.observableusercontent.com/c748da9e5c023b7ec8437995e067e4e1268c70b0105b5e96414ce56afb2a3e50","tier":null}}],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"ASSIGNMENT 2: ROCK COUNTY MAP","cells":[{"id":684,"value":"md`# ASSIGNMENT 2: ROCK COUNTY MAP`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":682,"value":"## PREPARATION FOR ASSIGNMENT 2\nThe data entered from assignment 1 contained null values. To prepare for the visualizations in subsequent assignments (shown below), I have copied code from https://observablehq.com/@geoviz/nullvalues AND https://observablehq.com/@geoviz/filtering_values_place_labels to modify my existing code--specifically in regards to its null/zero values. All modifications have been denoted in markdown above the line of code modified. These modifications are each labeled \"UPDATE_2/21:\". ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":52,"value":"d3 = require(\"d3@5\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":60,"value":"topojson = require(\"topojson-client@3\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":64,"value":"rockcounty = FileAttachment(\"rockcountymap.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":67,"value":"rockcounty_features = topojson.feature(rockcounty, rockcounty.objects.rockcountymap)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":70,"value":"csv_data = d3.csvParse(await FileAttachment(\"rockcountydata.csv\").text(),({CensusTract, Pop2010, lalowi1}) => [CensusTract, [+Pop2010, +lalowi1, +lalowi1/+Pop2010]])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":550,"value":"csv_data_objects = Object.assign((d3.csvParse(await FileAttachment(\"rockcountydata.csv\").text(), d3.autoType)).map(({CensusTract, Pop2010, lalowi1}) => ({CensusTract: +CensusTract, pctInterst: +lalowi1/+Pop2010})))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":374,"value":"CensusTract = Array.from(csv_data.values(), d => d[0])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":316,"value":"//this is how to take the values of a variable (column) from all variables you created from the csv file\nPop2010 = Array.from(csv_data.values(), d => d[1][0])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":698,"value":"**UPDATE_2/21:** I appended the line \".filter(d => d >0\" to the existing code below. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":343,"value":"lalowi1 = Array.from(csv_data.values(), d => d[1][1]).filter(d => d >0)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":700,"value":"**UPDATE_2/21:** I appended the line \".filter(d => d >0\" to the existing code below. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":361,"value":"Proportion_access = Array.from(csv_data.values(), d => d[1][2]).filter(d => d >0)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":702,"value":"**UPDATE_2/21:** I added this line of code to observe the minimum value of Proportion_access, as done in https://observablehq.com/@geoviz/nullvalues. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":646,"value":"d3.min(Proportion_access)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":73,"value":"//this is how you map the id column to the values. We will use this for joining with topojson later. \ndata = Object.assign(new Map(csv_data), {title: [\"Supermarket Accessibility\"]})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":389,"value":"# Assignment #2 ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":392,"value":"md`# Second Exercise on Observable: Classification and Colors`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":447,"value":"The following steps describe the process of producing the below visualizations. \n\n**STEP #1:** I forked my previous assignment (https://observablehq.com/d/9f7663c95783435f) then I added the code from \"Second Exercise on Observable: Classification and Colors\" (https://observablehq.com/@geoviz/second-exercise-on-observable) to the end of my previous assignment (staring below this line). As my data was already imported, I did not copy the beginning lines. Instead, I began my code with line 12 of \"Second Exercise on Observable\" which is: \n\nlinear = d3.scaleLinear()\n .domain(d3.extent(childpct))\n .range([\"white\", \"green\"])\n \nStarting from this line, I copied each line of code by clicking the \"+\" signage on the lefthand side of this notebook then selecting the Javascript option. Then I copied each line of code (the shaded lines) from the above notebook into this one. The subsequent steps of converting this code to fit my data are described throughout this notebook. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":404,"value":"# LINEAR SCALE (UNCLASSED)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":406,"value":"linear = d3.scaleLinear()\n .domain(d3.extent(Proportion_access))\n .range([\"white\", \"blue\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":453,"value":"**STEP 2a:** The scaleLinear() function enables the variable to be viewed in a linear way. The \".domain\" allows the user to identify which variable should be used. The above line originally identified \"childpct\" as the variable. This variable does not exist in my work. I replaced \"childpct\" with my variable \"proportion_access\" which I created from the previous assignment, as shown above. \n\n**STEP 2b:** the \".range\" function allows me to adjust the color. I changed the \"green\" value to \"blue\". ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":408,"value":"chart(numericSort(Proportion_access), linear)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":457,"value":"**STEP 3:** I adjusted the above line of code by replacing \"childpct\" with \"proportion_access\". This line allows visualization of this variable.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":410,"value":"# QUANTILE CLASSIFICATION","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":412,"value":"quantile = d3.scaleQuantile()\n .domain(Proportion_access)\n .range([\"#deebf7\", \"#9ecae1\", \"#3182bd\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":459,"value":"**STEP 4a:** scaleQuantile is a function which enables data to be organized divided into quantiles. I adjusted the above line of code by replacing \"childpct\" with \"proportion_access\" which indicated that this was the variable I wanted to be used by the function. \n\n**STEP 4b:** the \".range\" function allows me to adjust the color. I opened colorbrewer2.org. This website had an additional option box called \"Only show:\" and I selected the \"colorblind safe\" option. From there, I selected a blue color palette. This palette showed the colors and their respective hex values. I copied and pasted these three values (#deebf7, #9ecae1, #3182bd) into the code above--replacing the previous colors with these values. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":414,"value":"chart(numericSort(Proportion_access), quantile)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":463,"value":"**STEP 5:** I adjusted the above line of code by replacing \"childpct\" with \"proportion_access\"","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":416,"value":"# JENKS NATURAL BREAKS CLASSIFICATION","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":418,"value":"naturalbreaks = simple.ckmeans(Proportion_access, 3).map(v => v.pop())","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":469,"value":"**STEP 6:** I replaced the variable \"childpct\" with \"Proportion_access\". This line is creating a new variable \"naturalbreaks\" which breaks up the variable \"Proportion_access\" into its \"natural breaks\" (the dividing points within the variable that algorithmically make the most \"sense\") which can be seen in its array above. Additionally, the number 3 following Proportion_access in the above code indicates the number of classes. I selected 3 classes as this data set is rather small and 3 classes can capture a significant portion of the variation, while additional classes may make these visualizations unnecessarily overwrought. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":420,"value":"jenks = d3\n .scaleThreshold()\n .domain(naturalbreaks)\n .range([\"#f2f0f7\", \"#cbc9e2\", \"#9e9ac8\", \"#6a51a3\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":472,"value":"**STEP 7:** The function scaleThreshold() organizes the variable's values according to thresholds--either ones created with natural breaks, such as done above, or manually created (there's an example of this below). This example utilizes the natural breaks generated above, so the newly created variable naturalbreaks is used in .domain. \n\n**STEP 8:** I replaced the colors from the original code with my own color selections, using the process described above in STEP 4. This time I selected a purple palette: #f2f0f7, #cbc9e2, #9e9ac8, and #6a51a3. As there were only three colors listed in the previous example, I not only replaced the three with my own selections but also added another color. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":424,"value":"chart(numericSort(Proportion_access), jenks)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":475,"value":"**STEP 8:** I replaced the variable \"childpct\" with \"Proportion_access\".\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":422,"value":"# EQUAL INTERVAL CLASSIFICATION (QUANTIZE)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":426,"value":"quantize = d3.scaleQuantize()\n .domain([d3.min(Proportion_access),d3.max(Proportion_access)])\n .range([\"#fee5d9\", \"#fb6a4a\", \"#cb181d\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":478,"value":"**STEP 9a:** The function scaleQuantize() is used to organize the variable's values in an equal interval fashion. I replaced the variable \"childpct\" with \"Proportion_access\", so this variable is used by the function. \n\n**STEP 9b:** I replaced the colors from the original code with my own color selections, using the process described above in STEP 4. This time I selected a red palette: #fee5d9, #fb6a4a, and #cb181d.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":428,"value":"chart(numericSort(Proportion_access), quantize)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":481,"value":"**STEP 10:** I replaced the variable \"childpct\" with \"Proportion_access\".","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":430,"value":"# THRESHOLD","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":435,"value":"threshold = d3.scaleThreshold()\n .domain([0.09, 0.3, 0.4])\n .range([\"#ffffcc\", \"#78c679\", \"#238443\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":486,"value":"**STEP 11a:** This uses the same function, scaleThreshold(), as the natural breaks threshold example above, except this example contains manual breaks. As the breaks are manual, these values are input into the domain to be used by the function. I replaced the original values with .09, .3, and .4. I made the decision to raise the lowest value to .09 in order to capture the very low values together. I noticed that there were some extreme outliers, so I made the decision to move them out by making the upper range .4 to capture the upper outliers. \n**STEP 11B:** I replaced the colors from the original code with my own color selections, using the process described above in STEP 4. This time I selected a green-yellow palette: #ffffcc, #78c679, and #238443.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":437,"value":"chart(numericSort(Proportion_access), threshold)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":509,"value":"**STEP 12:** I replaced the original variable \"childpct\" with \"Proportion_access\"","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":439,"value":"showScaleGrouping(Proportion_access, {\n scaleQuantile: quantile,\n scaleThreshold: threshold,\n scaleJenks: jenks,\n scaleQuantize: quantize,\n scaleQuantizeNice: quantize.copy().nice()\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":512,"value":"**STEP 13:** I replaced the original variable \"childpct\" with \"Proportion_access\"","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":540,"value":"**STEP 14:** After completing the above work, I realized that I did not copy the histogram code. I returned to \"Second Exercise on Observable\" and copied lines 8 & 9, as shown below. I made additional modifications, which I describe below. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":536,"value":"viewof bins = Inputs.range([0, 20], {step: 1, label: \"Bins\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":538,"value":"Plot.plot({\n marks: [\n Plot.rectY(csv_data_objects, Plot.binX({y: \"count\"}, {x: \"pctInterst\", thresholds: bins})),\n Plot.ruleY([0])\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":543,"value":"**STEP 15:** I replaced \"pctAgeUnder5\" with \"pctInterst\" which was defined in the previous assignment (see above). \n\nThis graph is providing a visual of the number of individuals who w/out easy access to a grocery store compared to the number of individuals within the population. As demonstrated, this value is very, very small. Many of the values are clustering towards the low end (<.3) although there are a couple values which are higher. This is indicating that, in the area associated with these values, there is a proportionally higher number of individuals without easy access to grocery stories as opposed to the other areas. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":441,"value":"# ANNEX","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":502,"value":"function numericSort(x) {\n return (\n x\n // ensure the array is not changed in-place\n .slice()\n // comparator function that treats input as numeric\n .sort((a, b) => a - b)\n );\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":504,"value":"function chart(data, scale) {\n const w = 20,\n cols = Math.floor(Math.min(600, width) / w),\n lines = Math.ceil(200 / cols);\n const chart = d3\n .create(\"svg\")\n .attr(\"width\", cols * w)\n .attr(\"height\", lines * w);\n\n chart\n .append(\"g\")\n .attr(\"transform\", \"translate(2,2)\")\n .attr(\"style\", \"stroke:black; fill:white;\")\n .selectAll(\"rect\")\n .data(data)\n .join(\"rect\")\n .attr(\"width\", w - 3)\n .attr(\"height\", w - 3)\n .attr(\"x\", (_, i) => w * (i % cols))\n .attr(\"y\", (_, i) => w * ((i / cols) | 0))\n .style(\"fill\", d => (scale ? scale(d) : \"#ddd\"));\n return chart.node();\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":443,"value":"simple = require(\"simple-statistics@7.0.7/dist/simple-statistics.min.js\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":506,"value":"$49","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W4a","stdlib":"1"}} 24:null