2b:null 2c:["$","title",null,{"children":"Basic CSV Analyzer | Mark McClure | Observable"}] 31:T456,scatter_plot = { if (two_numeric_variables && two_numeric_variables != "no_numeric_data") { let regression = ss.linearRegression( two_numeric_variables.map((o) => [ o[two_numeric_variables_selector[0]], o[two_numeric_variables_selector[1]] ]) ); let m = regression.m; let b = regression.b; let xs = two_numeric_variables.map( (o) => o[two_numeric_variables_selector[0]] ); let xmin = d3.min(xs); let xmax = d3.max(xs); let ys = two_numeric_variables.map( (o) => o[two_numeric_variables_selector[1]] ); return Plot.plot({ height: 360, marks: [ Plot.dot(two_numeric_variables, { x: two_numeric_variables_selector[0], y: two_numeric_variables_selector[1], fill: "steelblue", opacity: 0.4, title: dot_title, tip: true }), Plot.line( [ [xmin, m * xmin + b], [xmax, m * xmax + b] ], { fill: "none", stroke: "black" } ) ] }); } else { return html``; } }32:T4b1,contingency_table = { if (two_categorical_variables_selector.length == 2) { let groups = d3.group( data.filter((o) => o[two_categorical_variables_selector[0]] !== false), (o) => o[two_categorical_variables_selector[1]] ); groups.forEach(function (v, k) { groups.set( k, d3.group(v, (o) => o[two_categorical_variables_selector[0]]) ); }); let div = d3.create("div"); div .append("div") .style("font-weight", "bold") .style("text-decoration", "underline") .text( `${two_categorical_variables_selector[0]} vs ${two_categorical_variables_selector[1]}` ); let table = div.append("table"); let top = table.append("tr"); top.append("td"); groups.forEach((v, k) => top.append("th").text(k)); let groups2 = d3.group( data, (o) => o[two_categorical_variables_selector[0]] ); Array.from(groups2.keys()).forEach(function (s) { let row = table.append("tr"); row.append("th").text(s); groups.forEach((v, k) => row.append("td").text(v.get(s) ? v.get(s).length : 0) ); }); return div.node(); } else { return html``; } }33:T4b8,samples = [ { name: "American Community Survey", attachment: FileAttachment("acs12.csv"), url: "https://www.openintro.org/data/index.php?data=acs12" }, { name: "Cherry Blossom Run", attachment: FileAttachment("run12.csv"), url: "https://www.openintro.org/data/index.php?data=run17" }, { name: "CDC", attachment: FileAttachment("cdc.csv"), url: "https://www.openintro.org/book/statdata/?data=cdc" }, { name: "70 Year Climate Change", attachment: FileAttachment("climate70.csv"), url: "https://www.openintro.org/data/index.php?data=climate70" }, { name: "GPA", attachment: FileAttachment("gpa.csv"), url: "https://www.openintro.org/data/index.php?data=gpa" }, { name: "NBA Players", attachment: FileAttachment("nba_players_19.csv"), url: "https://www.openintro.org/data/index.php?data=nba_players_19" }, { name: "Bkstr vs Amazon", attachment: FileAttachment("bkstr_amazon.csv"), url: "none" } // { // name: "Police use of force in MN", // attachment: FileAttachment("mn_police_use_of_force.csv"), // url: "https://www.openintro.org/data/index.php?data=mn_police_use_of_force" // } 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McClure","avatar_url":"https://avatars-next.observableusercontent.com/e615e819ce83d43faef607e48c53c5ff63943aa0ba9f5848499bd02fef7396f4","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":{"id":"6471c0828df362f2","slug":"teaching-statistics","title":"Teaching Statistics","owner":{"id":"d771c8ae812d5130","login":"mcmcclur","name":"Mark McClure"}},"type":"notebook","body":{"title":"Basic CSV Analyzer","cells":[{"id":0,"value":"top_matter = md`# Basic CSV Analyzer\n\nThis notebook allows you to explore some basic statistics associated with a CSV file. Simply upload a CSV file to get a quick look at the data as a table. You can then perform some basic analysis, assuming that the [data is tidy](https://en.wikipedia.org/wiki/Tidy_data) - i.e., the rows should correspond to sample observations and the columns to variables.\n\nThere are several sample files to explore, as well, which all come from [Open Intro Statistics](https://www.openintro.org/).`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":336,"value":"choose_header = md`## Choose the data`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":10,"value":"viewof selected_file = Inputs.select(samples, {\n label: html`Sample data file:`,\n format: (o) => o.name\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5,"value":"viewof input_file = {\n selected_file;\n let input_file = Inputs.file({\n accept: \".csv\",\n label: html`Upload your own:`\n });\n return input_file;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":340,"value":"view_header = md`## View the data`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":40,"value":"description = html`

This data has ${\n data.length\n} rows (or observations) and ${\n data.columns.length\n} columns (or variables), as shown in the table below. ${\n input_file\n ? \"\"\n : `You can get more information on this data file at this URL.`\n}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":19,"value":"viewof table = Inputs.table(data)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":146,"value":"analyze_one_numeric_header = md`## Analyze one numeric variable\n\nYou can now choose a numeric variable to analyze from the list below. Note that it is up to the user (you) to understand which variables are numerical and which are categorical.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":91,"value":"viewof numeric_variable = Inputs.radio(data.columns)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":200,"value":"non_numeric_warning = {\n if (numeric_variable && numeric_list.length == 0) {\n return html`Warning: Data does not appear to be numeric`;\n } else return html``;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":389,"value":"mean_std_header = md`### Mean and standard deviation`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":394,"value":"mean_std = {\n if (numeric_variable) {\n return md`\n * Mean: ${tex`\\mu=${d3.mean(numeric_list)}`} \n * Standard devation: ${tex`\\sigma=${d3.deviation(numeric_list)}`}`;\n } else {\n return html``;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":154,"value":"histogram_header = md`### A histogram`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":116,"value":"histogram = {\n if (numeric_variable) {\n return Plot.plot({\n y: {\n grid: true\n },\n marks: [\n Plot.rectY(\n data,\n Plot.binX(\n { y: \"count\", title: \"count\" },\n { x: numeric_variable, fill: \"steelblue\", tip: true }\n )\n ),\n Plot.ruleY([0])\n ]\n });\n } else {\n return html``;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":713,"value":"download_histogram = {\n if (numeric_variable) {\n return DOM.download(() => rasterize(histogram), \"histogram\", \"Save as PNG\");\n } else {\n return html``;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":406,"value":"five_point_summary_header = md`### Five point summary`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":401,"value":"five_point_summary = {\n if (numeric_variable) {\n return md`\n| min | 1st Q | median | 3rd Q | max |\n| --- | --- | --- | --- | --- |\n| ${jstat.min(numeric_list)} | ${jstat.percentile(\n numeric_list,\n 0.25\n )} | ${jstat.percentile(numeric_list, 0.5)} | ${jstat.percentile(\n numeric_list,\n 0.75\n )} | ${jstat.max(numeric_list)} |`;\n } else {\n return html``;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":221,"value":"box_plot_header = md`### Box-plot`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":244,"value":"box_plot = {\n if (numeric_variable) {\n return Plot.plot({\n x: {\n grid: true,\n inset: 6\n },\n marks: [boxX(data, { x: numeric_variable })]\n });\n } else {\n return html``;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":884,"value":"download_boxplot = {\n if (numeric_variable) {\n return DOM.download(() => rasterize(box_plot), \"boxplot\", \"Save as PNG\");\n } else {\n return html``;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":484,"value":"analyze_two_numeric_header = md`## Analyze two numeric variables\n\nChoose two numeric variables to analyze from the list below.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":488,"value":"viewof two_numeric_variables_selector = Inputs.checkbox(data.columns)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":822,"value":"linear_regression_header = md`### Linear regression`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":492,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":888,"value":"download_regression = {\n if (two_numeric_variables_selector.length == 2) {\n return DOM.download(\n () => rasterize(scatter_plot),\n \"scatter_plot\",\n \"Save as PNG\"\n );\n } else {\n return html``;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":538,"value":"correlation = {\n if (two_numeric_variables && two_numeric_variables != \"no_numeric_data\") {\n let c = ss.sampleCorrelation(\n two_numeric_variables.map((o) => o[two_numeric_variables_selector[0]]),\n two_numeric_variables.map((o) => o[two_numeric_variables_selector[1]])\n );\n return html`Correlation = ${d3.format(\"0.3f\")(c)}`;\n } else {\n return html``;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":761,"value":"regression_line = {\n if (two_numeric_variables && two_numeric_variables != \"no_numeric_data\") {\n let pts = two_numeric_variables.map((o) => [\n o[two_numeric_variables_selector[0]],\n o[two_numeric_variables_selector[1]]\n ]);\n let lr = ss.linearRegression(pts);\n return html`Regression line: ${tex`y=${d3.format(\"0.3f\")(lr.m)}x ${\n lr.b < 0 ? \"-\" : \"+\"\n } ${d3.format(\"0.3f\")(Math.abs(lr.b))}`}`;\n } else {\n return html``;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":974,"value":"regression_p_value = {\n if (two_numeric_variables && two_numeric_variables != \"no_numeric_data\") {\n let v1 = two_numeric_variables.map(\n (o) => [o[two_numeric_variables_selector[0]]][0]\n );\n let v2 = two_numeric_variables.map(\n (o) => [o[two_numeric_variables_selector[1]]][0]\n );\n let m = jstat.models.ols(\n v1,\n v2.map((x) => [1, x])\n );\n return html`${tex`p`} - value: ${d3.format(\"0.4f\")(m.f.pvalue)}`;\n } else {\n return html``;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":573,"value":"dot_title_label = md`You can display a variable on hover as well:`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":562,"value":"viewof dot_title = Inputs.radio(data.columns.concat(\"None\"), {\n label: md`**Title on hover:**`\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":249,"value":"analyze_categorical_header = md`## Analyze one categorical variable\n\nYou can also choose a categorical variables to analyze from the list below - though, it is again up to the you to understand which variables are categorical.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":265,"value":"viewof categoric_variable = Inputs.radio(data.columns)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":825,"value":"proportion_header = md`### Proportions`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":833,"value":"proportion_table = {\n if (categoric_variable) {\n let div = d3.create(\"div\");\n div\n .append(\"div\")\n .style(\"font-weight\", \"bold\")\n .style(\"text-decoration\", \"underline\")\n .text(categoric_variable);\n\n let groups = d3.group(data, (o) => o[categoric_variable]);\n let table = div.append(\"table\");\n let head = table.append(\"tr\");\n let row = table.append(\"tr\");\n groups.forEach(function (v, k) {\n head.append(\"th\").style(\"text-align\", \"center\").text(k);\n row.append(\"td\").style(\"text-align\", \"center\").text(v.length);\n });\n return div.node();\n } else {\n return html``;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":416,"value":"bar_chart_header = md`### Bar chart`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":251,"value":"bar_chart = {\n if (categoric_variable) {\n let plot = Plot.plot({\n y: {\n grid: true\n },\n width: 1000,\n height: 600,\n marks: [\n Plot.barY(\n data,\n Plot.groupX(\n {\n y: \"count\",\n title: (a, b) => {\n return `${a[0][categoric_variable]}: ${a.length}/${\n data.length\n } ≈ ${d3.format(\"0.3f\")(a.length / data.length)}`;\n }\n },\n { x: categoric_variable, fill: \"steelblue\", tip: true }\n )\n )\n ]\n });\n\n return plot;\n } else {\n return html``;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":727,"value":"download_barchart = {\n if (categoric_variable) {\n return DOM.download(() => rasterize(bar_chart), \"bar_chart\", \"Save as PNG\");\n } else {\n return html``;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":920,"value":"two_categorical_header = md`## Two categorical variables\n\nYou can compare two categorical variables with contingency tables and mosaic plots`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":933,"value":"viewof two_categorical_variables_selector = Inputs.checkbox(data.columns)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":628,"value":"contingency_table_header = md`### Contingency table`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":670,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":928,"value":"mosaic_header = md`### Mosaic plot`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":788,"value":"mosaic_plot = {\n if (two_categorical_variables_selector.length == 2) {\n let groups = d3.group(\n data.filter((o) => o[two_categorical_variables_selector[0]] !== false),\n (o) => o[two_categorical_variables_selector[1]]\n );\n let mosaic_data = [];\n groups.forEach(function (v, k) {\n let sub_groups = d3.group(\n v,\n (o) => o[two_categorical_variables_selector[0]]\n );\n sub_groups.forEach(function (vv, kk) {\n mosaic_data.push({ x: k, y: kk, value: vv.length });\n });\n });\n\n return make_mosaic_plot(mosaic_data);\n } else {\n return html``;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":902,"value":"download_mosaic_plot = {\n if (two_categorical_variables_selector.length == 2) {\n return DOM.download(() => rasterize(mosaic_plot), \"mosaic\", \"Save as PNG\");\n } else {\n return html``;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":586,"value":"side_by_side_box_header = md`### Side by side boxplots\n\nSide by side box plots can be used to explore the relationship between one numeric variable and one categorical variable. Choose one of each to see:\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":594,"value":"viewof side_by_side_categoric_variable = Inputs.radio(data.columns, {\n label: md`**Categorical variable:**`\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":604,"value":"viewof side_by_side_numeric_variable = Inputs.radio(data.columns, {\n label: md`**Numerical variable:**`\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":581,"value":"{\n if (side_by_side_numeric_variable && side_by_side_categoric_variable) {\n return Plot.plot({\n width: 1000,\n height: 600,\n y: {\n grid: true,\n inset: 6\n },\n marks: [\n boxY(data, {\n x: side_by_side_categoric_variable,\n y: side_by_side_numeric_variable\n })\n ]\n });\n } else {\n return html``;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":192,"value":"## Code","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":913,"value":"mosaic_data = {\n let groups = d3.group(data, (o) => o[\"What is your gender (optional):\"]);\n let mosaic_data = [];\n groups.forEach(function (v, k) {\n let sub_groups = d3.group(v, (o) => o[\"What is your eye color?\"]);\n sub_groups.forEach(function (vv, kk) {\n mosaic_data.push({ x: k, y: kk, value: vv.length });\n });\n });\n return mosaic_data;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":498,"value":"two_numeric_variables = {\n if (two_numeric_variables_selector.length == 2) {\n let c1 = data.filter(function (o) {\n let x = o[two_numeric_variables_selector[0]];\n if (parseFloat(x) && !isNaN(x)) {\n return true;\n }\n });\n let c2 = c1.filter(function (o) {\n let x = o[two_numeric_variables_selector[1]];\n if (parseFloat(x) && !isNaN(x)) {\n return true;\n }\n });\n if (c2.length > 0) {\n return c2;\n }\n } else {\n return \"no_numeric_data\";\n }\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":95,"value":"numeric_list = data\n .map((o) => parseFloat(o[numeric_variable]))\n .filter((x) => !isNaN(x))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":45,"value":"data = file.csv({ typed: true })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"file = {\n if (input_file) {\n return input_file;\n } else {\n return selected_file.attachment;\n }\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"$33","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":777,"value":"cdc_data = samples[2].attachment.csv({ typed: true })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":184,"value":"import { boxX, boxY } from \"@observablehq/plot-box\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":97,"value":"jstat = require(\"jstat\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":525,"value":"ss = require(\"simple-statistics\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":711,"value":"import { rasterize } from \"@mbostock/saving-svg\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":797,"value":"import { make_mosaic_plot } from \"8159a13658589863\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W34","stdlib":"1","titling":"auto"}} 30:"$2f:body"