2b:null 2c:["$","title",null,{"children":"Descriptive statistics: comparisons in JavaScript, Python, SQL, R, and Excel | Observable | Observable"}] 31:T30c3,compareFunctions = [ { method: "mean", language: "JavaScript", function: "d3.mean", link: "https://github.com/d3/d3-array/blob/v3.2.2/README.md#mean", example: '`d3.mean(waterUsage.map(d => d.waterGallons))`' }, { method: "mean", language: "Python", function: "pandas mean", link: "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.mean.html", example: "`waterUsage[\"waterGallons\"].mean()`" }, { method: "mean", language: "SQL", function: "AVG", link: "https://www.w3schools.com/sql/sql_where.asp", example: "`SELECT AVG(waterGallons) AS avgGallons FROM waterUsage`" }, {method: "mean", language: "R", function: "mean", link: "https://stat.ethz.ch/R-manual/R-devel/library/stats/html/median.html", example: "`mean(waterUsage$waterGallons)` " }, { method: "mean", language: 'Excel', function: 'AVERAGE', link: "https://support.microsoft.com/en-us/office/average-function-047bac88-d466-426c-a32b-8f33eb960cf6", example: '`=AVERAGE(E:E)` _(if waterGallons is in column E)_' }, { method: "standard_deviation", language: "JavaScript", function: "d3.deviation", link: "https://github.com/d3/d3-array/blob/v3.2.2/README.md#deviation", example: '`d3.deviation(waterUsage.map(d => d.waterGallons))`' }, { method: "standard_deviation", language: "Python", function: "pandas std", link: "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.std.html", example: "`waterUsage[\"waterGallons\"].std()`" }, { method: "standard_deviation", language: "SQL", function: "STDDEV_SAMP", link: "https://duckdb.org/docs/sql/aggregates.html", example: "`SELECT STDDEV_SAMP(waterGallons) AS sdGallons FROM waterUsage`" }, {method: "standard_deviation", language: "R", function: "sd", link: "https://stat.ethz.ch/R-manual/R-devel/library/stats/html/median.html", example: "`sd(waterUsage$waterGallons)` " }, { method: "standard_deviation", language: 'Excel', function: 'STDEV', link: "https://support.microsoft.com/en-us/office/stdev-function-51fecaaa-231e-4bbb-9230-33650a72c9b0", example: '`=STDEV(E:E)` _(if waterGallons is in column E)_' }, { method: "median", language: "JavaScript", function: "d3.median", link: "https://github.com/d3/d3-array/blob/v3.2.2/README.md#median", example: "`d3.median(waterUsage.map(d => d.waterGallons))`" }, { method: "median", language: "Python", function: "pandas median", link: "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.median.html", example: "`waterUsage[\"waterGallons\"].median()`" }, { method: "median", language: "SQL", function: "MEDIAN", link: "https://duckdb.org/docs/sql/aggregates.html#statistical-aggregates", example: "`SELECT MEDIAN(waterGallons) AS medWater FROM waterUsage`" }, { method: "median", language: "R", function: "median", link: "https://stat.ethz.ch/R-manual/R-devel/library/stats/html/median.html", example: "`median(waterUsage$waterGallons)`" }, { method: "median", language: 'Excel', function: 'MEDIAN', link: 'https://support.microsoft.com/en-us/office/median-function-d0916313-4753-414c-8537-ce85bdd967d2', example: '`=MEDIAN(E:E)` _(if waterGallons is in column E)_' }, { method: "range", language: "JavaScript", function: "d3.min, d3.max", link: "https://observablehq.com/@d3/d3-extent", example: "`d3.min(waterUsage.map(d => d.waterGallons))`" }, { method: "range", language: "Python", function: "pandas min, max", link: "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.median.html", example: "`waterUsage[\"waterGallons\"].min()`" }, { method: "range", language: "SQL", function: "MIN, MAX", link: "https://www.w3schools.com/sql/sql_min_max.asp", example: "`SELECT MIN(waterGallons) AS minWater FROM waterUsage`" }, { method: "range", language: "R", function: "min, max", link: "https://stat.ethz.ch/R-manual/R-devel/library/base/html/Extremes.html", example: "`min(waterUsage$waterGallons)`" }, { method: "range", language: 'Excel', function: 'MIN, MAX', link: 'https://support.microsoft.com/en-us/office/calculate-the-smallest-or-largest-number-in-a-range-45fe249f-96c3-443b-8e9f-87f16c48462c', example: '`=MIN(E:E)` _(if waterGallons is in column E)_' }, { method: "count", language: "JavaScript", function: "length property", link: "https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/length", example: "`waterUsage.length`" }, { method: "count", language: "Python", function: "pandas shape", link: "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.shape.html", example: '`waterUsage.shape[0]` _(use [1] to return number of columns)_' }, { method: "count", language: "SQL", function: "COUNT", link: "https://www.w3schools.com/sql/sql_count_avg_sum.asp", example: "`SELECT COUNT(*) AS countRows FROM waterUsage`" }, { method: "count", language: "R", function: "nrow", link: "https://stat.ethz.ch/R-manual/R-devel/library/base/html/nrow.html", example: "`nrow(waterUsage)`" }, { method: "count", language: 'Excel', function: 'ROWS', link: 'https://support.microsoft.com/en-us/office/rows-function-b592593e-3fc2-47f2-bec1-bda493811597', example: '`=ROWS(A2:E237)` _(if data are in columns A through E)_' }, { method: "grouped_mean", language: "JavaScript", function: "d3.rollup, d3.mean", link: "https://github.com/d3/d3-array/blob/v3.2.2/README.md#rollup", example: "`d3.rollup(waterUsage, v => d3.mean(v, d => d.waterGallons), d => d.propertyId)`" }, { method: "grouped_mean", language: "Python", function: "pandas groupby", link: "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html", example: "`waterUsage.groupby(\'propertyId\')[\'waterGallons\'].mean()`" }, { method: "grouped_mean", language: "SQL", function: 'GROUP BY', link: 'https://www.w3schools.com/sql/sql_groupby.asp', example: '`SELECT propertyId, AVG(waterGallons) AS mean FROM waterUsage GROUP BY propertyId`' }, { method: "grouped_mean", language: "R", function: "group_by, summarize", link: "https://dplyr.tidyverse.org/reference/summarise.html", example: "`waterUsage` |`>` `group_by(propertyId)` |`>` `summarize(mean = mean(waterGallons))`" }, { method: "grouped_mean", language: 'Excel', function: 'pivot table', link: 'https://support.microsoft.com/en-us/office/create-a-pivottable-to-analyze-worksheet-data-a9a84538-bfe9-40a9-a8e9-f99134456576', example: '_(Insert > PivotTable)_' }, { method: "grouped_count", language: "JavaScript", function: "d3.rollup, d3.count", link: "https://github.com/d3/d3-array/blob/v3.2.2/README.md#rollup", example: "`d3.rollup(waterUsage, v => d3.count(v, d => d.waterGallons), d => d.propertyId)`" }, { method: "grouped_count", language: "Python", function: "pandas groupby", link: "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html", example: "`waterUsage.groupby(\'propertyId\')[\'waterGallons\'].count()`" }, { method: "grouped_count", language: "SQL", function: 'GROUP BY, COUNT', link: 'https://www.w3schools.com/sql/sql_groupby.asp', example: '`SELECT propertyId, COUNT(waterGallons) AS counts FROM waterUsage GROUP BY propertyId`' }, { method: "grouped_count", language: "R", function: "dplyr count", link: "https://dplyr.tidyverse.org/reference/count.html", example: "`waterUsage` |`>` `count(propertyId)`" }, { method: "grouped_count", language: 'Excel', function: 'pivot table', link: 'https://support.microsoft.com/en-us/office/create-a-pivottable-to-analyze-worksheet-data-a9a84538-bfe9-40a9-a8e9-f99134456576', example: '_(Insert > PivotTable)_' }, { method: "conditional_count", language: "JavaScript", function: "Array filter, length", link: "https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/filter", example: "`waterUsage.filter(d => d.waterGallons > 90 && d.propertyId == \"B002\").length`" }, { method: "conditional_count", language: "Python", function: "pandas query, shape", link: "TODO", example: "`waterUsage.query('waterGallons > 90 & propertyId == \"B002\"').shape[0]`" }, { method: "conditional_count", language: "SQL", function: "GROUP BY, WHERE", link: "https://www.w3schools.com/sql/sql_groupby.asp", example: "`SELECT COUNT(*) AS exceedDays FROM waterUsage WHERE waterGallons > 90 AND propertyId = 'B002'`" }, { method: "conditional_count", language: "R", function: "dplyr::filter, dplyr::count", link: "TODO", example: "`waterUsageR` |`>` `filter(waterGallons > 90, propertyId == \"B002\")` |`>` `count()`" }, { method: "conditional_count", language: 'Excel', function: 'COUNTIFS', link: 'https://support.microsoft.com/en-us/office/countifs-function-dda3dc6e-f74e-4aee-88bc-aa8c2a866842', example: '`=COUNTIFS(B2:B237, "=B002", E2:E237,">90")` _(if propertyId is in column B, and waterGallons is in column E)_' }, { method: "percentile", language: "JavaScript", function: "d3.quantile", link: "https://github.com/d3/d3-array#quantile", example: "`d3.quantile(waterUsage.map(d => d.waterGallons), 0.9)`" }, { method: "percentile", language: "Python", function: "pandas quantile", link: "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.quantile.html", example: "`waterUsage['waterGallons'].quantile(0.9)`" }, { method: "percentile", language: "SQL", function: "quantile_cont", link: "https://duckdb.org/docs/sql/aggregates.html#statistical-aggregates", example: "`SELECT quantile_cont(waterGallons, 0.9) AS quant90 FROM waterUsage`" }, { method: "percentile", language: "R", function: "quantile", link: "https://stat.ethz.ch/R-manual/R-devel/library/stats/html/quantile.html", example: "`quantile(waterUsage$waterGallons, probs = 0.9)`" }, { method: "percentile", language: 'Excel', function: 'PERCENTILE', link: 'https://support.microsoft.com/en-us/office/percentile-function-91b43a53-543c-4708-93de-d626debdddca', example: '`=PERCENTILE(E2:E237, 0.9)`' }, { method: "ranks", language: "JavaScript", function: "d3.rank", link: "https://github.com/d3/d3-array/blob/main/README.md#rank", example: '`waterUsage.map((d, i) => ({...d, rank: d3.rank(waterUsage.map(d => d.waterGallons), d3.descending)[i] + 1}))` _(1 added so that ranks start at 1 instead of 0)_' }, { method: "ranks", language: "Python", function: "pandas rank", link: "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rank.html", example: "`waterUsage['rank'] = waterUsage['waterGallons'].rank(ascending=False, method = \"min\")`" }, { method: "ranks", language: "SQL", function: "RANK", link: "https://duckdb.org/docs/sql/window_functions.html#general-purpose-window-functions", example: "`SELECT *, RANK() OVER (ORDER BY waterGallons DESC) AS ranks FROM waterUsage`" }, { method: "ranks", language: "R", function: "rank", link: "https://stat.ethz.ch/R-manual/R-devel/library/base/html/rank.html", example: "`waterUsage` |`>` `mutate(rank = rank(-waterGallons, ties.method = \"min\"))`" }, { method: "ranks", language: 'Excel', function: 'RANK', link: 'https://support.microsoft.com/en-us/office/rank-function-6a2fc49d-1831-4a03-9d8c-c279cf99f723', example: '`=RANK(E2, $E$2:$E$237)`' } 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2f:{"id":"cffe12d0216ee668","slug":"descriptive-statistics-translations","title":"Descriptive statistics: comparisons in JavaScript, Python, SQL, R, and Excel","thumbnail":"16c5dea5b20e556b9b38b2e64fac40cdae1dd37e85ace14b8489fa338378d3bf","access_level":"public","create_time":"$D2023-03-03T04:45:05.932Z","update_time":"$D2023-03-30T15:09:03.372Z","view_time":null,"first_public_time":"$D2023-03-20T21:17:47.800Z","fork":false,"stars":51,"starred":false,"owner":{"type":"team","id":"f35c755083683fe5","login":"observablehq","name":"Observable","avatar_url":"https://avatars-next.observableusercontent.com/424c6c1f7794371c9915a160519bdfb34cf34e50069fcd6def38a1a04d4e9702","tier":"enterprise_2024"},"version":886,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"b71e24965feea6d8","login":"allisonhorst","name":"Allison Horst","avatar_url":"https://avatars-next.observableusercontent.com/323c217177f0f20e4e8ed3aa302fec1408cf22b57cbd726e37e76182d5f82a65","tier":null},{"type":"individual","id":"42520cea6c99d3c8","login":"pstuffa","name":"Paul Buffa","avatar_url":"https://avatars-next.observableusercontent.com/32c09bb97a2bb742225b949dcc4cf81a03dcd2b0caae97325970223a48a14b94","tier":null}],"members":[{"type":"individual","id":"4530d0072aa49b47","login":"erinn","name":"Erinn Wong","avatar_url":"https://avatars-next.observableusercontent.com/9119ac38e4f1c92d50b28822c47306212f06e085b7d1416e851a1c397194e2d4","tier":null,"role":"editor"},{"type":"individual","id":"074c414ad1d825f5","login":"mbostock","name":"Mike Bostock","avatar_url":"https://avatars-next.observableusercontent.com/7dbba99ced40bd5ab8013c99b1a11da61231bce321ba1148a9d16ad0f121550a","tier":null,"role":"editor"},{"type":"team","id":"f35c755083683fe5","login":"observablehq","name":"Observable","avatar_url":"https://avatars-next.observableusercontent.com/424c6c1f7794371c9915a160519bdfb34cf34e50069fcd6def38a1a04d4e9702","tier":"enterprise_2024","role":"viewer"}],"comments":[{"id":"2c7c8e6928c56537","reply_to_id":null,"content":"Yeah, lets not worry about seasonality index. That's going to be hard to show in a one liner for other langs","create_time":"$D2023-03-03T17:17:32.008Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"42520cea6c99d3c8","login":"pstuffa","name":"Paul Buffa","avatar_url":"https://avatars-next.observableusercontent.com/32c09bb97a2bb742225b949dcc4cf81a03dcd2b0caae97325970223a48a14b94","tier":null}},{"id":"bd6fb566e89a14a6","reply_to_id":null,"content":"Maybe add count if, sum if? Conditional rollups? Those are very popular in Excel","create_time":"$D2023-03-03T17:21:07.095Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"42520cea6c99d3c8","login":"pstuffa","name":"Paul Buffa","avatar_url":"https://avatars-next.observableusercontent.com/32c09bb97a2bb742225b949dcc4cf81a03dcd2b0caae97325970223a48a14b94","tier":null}},{"id":"6b405ad5a9544b9d","reply_to_id":"2c7c8e6928c56537","content":"👍 sounds good","create_time":"$D2023-03-03T17:29:30.405Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"b71e24965feea6d8","login":"allisonhorst","name":"Allison Horst","avatar_url":"https://avatars-next.observableusercontent.com/323c217177f0f20e4e8ed3aa302fec1408cf22b57cbd726e37e76182d5f82a65","tier":null}},{"id":"62ac1595499a4ee6","reply_to_id":"bd6fb566e89a14a6","content":"yeah good idea, added","create_time":"$D2023-03-03T17:29:58.320Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"b71e24965feea6d8","login":"allisonhorst","name":"Allison Horst","avatar_url":"https://avatars-next.observableusercontent.com/323c217177f0f20e4e8ed3aa302fec1408cf22b57cbd726e37e76182d5f82a65","tier":null}}],"custom_thumbnail":true,"trashed":false,"subscribed":null,"forks":20,"collection":{"id":"d7e0c450440adb47","slug":"workflow-resources","title":"Workflow Resources","owner":{"id":"f35c755083683fe5","login":"observablehq","name":"Observable"}},"type":"notebook","body":{"title":"Descriptive statistics: comparisons in JavaScript, Python, SQL, R, and Excel","cells":[{"id":0,"value":"# Descriptive statistics: comparisons in JavaScript, Python, SQL, R, and Excel","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":49,"value":"Here, we compare common descriptive statistics for data analysis (e.g. means, counts, quantiles) across languages. Examples shown use the mock data below (stored as _waterUsage_, which contains information on water usage (gallons), square footage, and number of residents for four different homes (A001, B002, C003 and D004).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":784,"value":"waterUsage = await FileAttachment(\"waterUsage.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":176,"value":"
\n## Comparison tables\nThe comparison tables below are not comprehensive; many good (in some cases, better) alternatives exist in each language, and we do not cover myriad caveats (e.g. handling missing values). In general, we use base and tidyverse functions for R examples, pandas functions for Python examples, and DuckDB’s SQL flavor.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":59,"value":"viewof langCheckbox = imageInput()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":554,"value":"

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":180,"value":"---\n${await FileAttachment(\"sample-mean.png\").image({width: 250, align: \"right\"})}\n### Find the sample mean value\nFind the mean of the entire _waterGallons_ column in _waterUsage_.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":77,"value":"$${comparisonTable(filteredFunctions.filter(d => d.method == \"mean\"))}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":703,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":705,"value":"---\n${await FileAttachment(\"standard-deviation.png\").image({width: 250, align: \"right\"})}\n### Find the sample standard deviation\nFind the standard deviation of the _waterGallons_ column in _waterUsage_.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":711,"value":"$${comparisonTable(filteredFunctions.filter(d => d.method == \"standard_deviation\"))}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":565,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":195,"value":"---\n${await FileAttachment(\"sample-median.png\").image({width: 250, align: \"right\"})}\n### Find the median value\nFind the median of _waterGallons_ column in _waterUsage_","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":198,"value":"$${comparisonTable(filteredFunctions.filter(d => d.method == \"median\"))}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":567,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":536,"value":"---\n${await FileAttachment(\"sample-min-max.png\").image({width: 250, align: \"right\"})}\n### Find minimum and maximum values\nReturn the minimum values of _waterGallons_ (syntax for maximum is the same for each).\n\n${comparisonTable(filteredFunctions.filter(d => d.method == \"range\"))}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":577,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":205,"value":"---\n${await FileAttachment(\"count-all@1.png\").image({width: 150, align: \"right\"})}\n### Total observations\nFind the total number of rows (total number of observations, if data are tidy) in _waterUsage_.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":214,"value":"$${comparisonTable(filteredFunctions.filter(d => d.method == \"count\"))}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":597,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":226,"value":"---\n${await FileAttachment(\"mean-by-group.png\").image({width: 250, align: \"right\"})}\n### Find mean values by group\nFind the mean daily water usage by _propertyId_.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":222,"value":"$${comparisonTable(filteredFunctions.filter(d => d.method == \"grouped_mean\"))}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":602,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":228,"value":"---\n${await FileAttachment(\"count-by-group@2.png\").image({width: 250, align: \"right\"})}\n### Count observations by group\nFind the total counts of observations by _propertyId_","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":224,"value":"$${comparisonTable(filteredFunctions.filter(d => d.method == \"grouped_count\"))}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":615,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":315,"value":"---\n${await FileAttachment(\"conditional-count.png\").image({width: 200, align: \"right\"})}\n### Find conditional counts\nFind the total number of days where house B002’s _waterGallons_ exceeds 90 gallons.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":330,"value":"$${comparisonTable(filteredFunctions.filter(d => d.method == \"conditional_count\"))}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":618,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":232,"value":"---\n${await FileAttachment(\"ranks.png\").image({width: 250, align: \"right\"})}\n### Rank values\nAdd a new column containing ranks based on the value in _waterGallons_, with the highest _waterGallons_ value ranked 1. In the case of ties, the minimum rank is used.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":230,"value":"$${comparisonTable(filteredFunctions.filter(d => d.method == \"ranks\"))}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":674,"value":"
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":237,"value":"---\n${await FileAttachment(\"percentile.png\").image({width: 250, align: \"right\"})}\n### Find percentiles\nFind the 90th percentile value for _waterGallons_.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":234,"value":"$${comparisonTable(filteredFunctions.filter(d => d.method == \"percentile\"))}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":726,"value":"

\n## Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":70,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":75,"value":"filteredFunctions = compareFunctions.filter(d => langCheckbox.includes(d.language))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":51,"value":"import {imageInput, comparisonTable} from \"@observablehq/data-wrangling-translations\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":55,"value":"import {set} from \"@tophtucker/custom-input-example\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":687,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined"}],"files":"$W32","stdlib":"1","titling":"auto"}} 30:"$2f:body"