From b5fc298efafbbf932ec7b396c6f031cc8dbd076f Mon Sep 17 00:00:00 2001 From: Andrew McNutt Date: Tue, 6 Aug 2019 15:29:40 -0700 Subject: [PATCH] updates --- paper-scripts/lint-rules.tsv | 143 ++++++++++++++------------ paper-scripts/prepare-mirage-table.js | 26 ++--- table.tex | 120 ++++++++++----------- vislint-ui-sketching.sketch | Bin 869716 -> 925719 bytes 4 files changed, 143 insertions(+), 146 deletions(-) diff --git a/paper-scripts/lint-rules.tsv b/paper-scripts/lint-rules.tsv index 4d5fade..34b0a2a 100644 --- a/paper-scripts/lint-rules.tsv +++ b/paper-scripts/lint-rules.tsv @@ -1,67 +1,76 @@ -Taxonomy (cause) Error Taxonomy (Mirage) Citations Notes Detection Technique Taxonomy Order Initial Taxonomy zone Group LOOKUP VALUE -Curation Missing/Repeated Records Aggregates might become inaccurate, impeding all comparisons Data, Spec 1 Totally Invisible Aggregation issues Curation 1 -Curation Outliers Aggregates might become inaccurate, impeding all comparisons Data, Spec 1 Totally Invisible Aggregation issues Preparation 2 -Curation Incorrectly assumed high Quality data Data 1 Visible Only With Effort Data quality errors Visualization 3 -Curation Multiple comparisons problem \cite{pu2018garden, zgraggen2018investigating} Garden of forking paths, Zerdraggen. Possibly not relevant as we've constrained ourselves to presentational VA ??? 1 Statistical Malpractice Comprehension 4 -Curation Dubious data Cairo's new book Data 1 Statistical Malpractice -Curation Insufficient data Cairo's new book Data 1 Statistical Malpractice -Curation * \cite{kim2003taxonomy} EVERYTHING ELSE IN THE Kim et al DIRTY DATA TAXONOMY 1 -Curation Numeric outliers \cite{wang2019uni} Data 1 -Curation Spelling mistakes Differently spelled groups might fall into different aggregates, causing inaccurate comparisons \cite{wang2019uni} Data 1 -Curation Uniqueness constraint failure Marks assumed to represent single values might represent several (which may then be further skewed by aggregates like SUM) \cite{wang2019uni} assumed singular values may be aggregated. Example: making a line chart of year has data from 1.5 years. Users assumes each mark indicates ONE measurement, when in fact their might be several Data 1 -Curation unidentified functional dependencies Visualization might show a strong relationship between variables, when in fact that relationship is part of the data composition (eg A+B = C, plot A vs C) Uni-Detect: A Unified Approach to Automated Error Detection in Tables Data 1 -Curation Drill-down bias Apparent trends might be attributed to the more specific/recently specified filters rather than relatively "simpler" explanations \cite{lee2019avoiding} Data, Spec 1 Statistical Malpractice -Preparation Differing Number of Records by group A mismatch in row cardinality (world indicators example) Data, Spec 2 Totally Invisible Aggregation issues -Preparation Simpson's Paradox Observed trend reverses \cite{guo2017you} Data, Spec 2 Totally Invisible Aggregation issues -Preparation Cherry Picking e.g. the chart filters out to only a tiny subset of data where something is true Data, Spec 2 Totally Invisible -Preparation Over dominating outlier wrecks scale Real trend is made invisible SLIGHTLY UNSURE ABOUT CLASSIFICATION Data, Spec 2 -Preparation Spurious correlation Gives the impression of an non-extant correlation Correlation is not robust to slight changes in the data Data 2 Statistical Malpractice -Preparation High variability or noise in contrast to the effect size Irrelevant conclusions could be made Data 2 Statistical Malpractice -Preparation P-hacking Gives the impression of an non-extant correlation Spurious difference (due to p-hacking, say. or just un-communicated sampling variability) Data 2 Statistical Malpractice -Preparation Outliers combined with the wrong aggregation type. Data, Spec, Render 2 -Preparation Aggregates mask second order statistics Two things may appear to be equal when in fact their distributions are fundamentally different \cite{matejka2017same, few2019loom} dinosaurus box plot, anscombe's quartet, variation blindness (the median isn't the message - gould) Data, Spec, Render 2 Totally Invisible Aggregation issues -Preparation Additional loom Statistical errors \cite{few2019loom} data loom: law of small numbers (cherry picking, bias sample fallacy), insensitivity to small sample size, base rate fallacy, hot hand fallacy, appeal to coincidence, regression fallacy, variation blindness (the median isn't the message - gould) 2 -Preparation Inappropriate use of mean Mean comparisons with normal distribution will be skewed \cite{few2019loom} Data loom (mean is design for normal distributions) Data, spec 2 -Preparation Confusing imputation Imputation that generates zeroes rather removing rows can radically alter aggregates \cite{song2018s} "Where's My Data?" 2 -Preparation Sampling at a rate less than the nyquist rate Apparent trend will be the aliases rather than the signal 2 -Visualization Banking to 45 failure Trend can appear from flat data or vice-versa HEER PAPER. Mirages can be perceptual in nature, but are driven by a decision somewhere in the analytics pipeline. The knob that controls this mirage is charts dimensions Data, spec, render 3 -Visualization area/length mismatches Correll YAxis truncation paper? e.g. “the incredible shrinking doctor” Spec 3 Visible Only With Effort Bad Quant Scales -Visualization Color too close Reader mistakes one group for another Spec 3 Visible Only With Effort Bad color scale -Visualization Time not aligned in direction of language Readers tend to think of time as flowing in the same direction as their language (CITATION), eg western language speakers tend to think of it as moving from left to right while REST OF EXAMPLE. Spec, Data, Render 3 -Visualization Overplotting Data, Spec, Render 3 Totally Invisible Rendering Issues -Visualization Singularities \cite{kindlmann2014algebraic} parallel coordinates AVD example Data, Render 3 Totally Invisible Rendering Issues -Visualization Improper Layering Overplotting can cause an non-existent trend to emerge from draw order \cite{kindlmann2014algebraic} taxi AVD example Data, Render 3 Totally Invisible Rendering Issues -Visualization Latent variables Missing Spec 3 Totally Invisible -Visualization Wrong/missing aggregation Aggregate marks with no opacity overlapping each other do an implicit max Spec, Render 3 Totally Invisible -Visualization Flipped Trend understood to be opposite \cite{pandey2015deceptive, correll2017black, cleveland1982variables} Black hat, panday, Variables on Scatter Plots Look More Highly Correlated When the Scales are Increased Spec 3 Visible Only With Effort Bad Quant Scales -Visualization Scale extents larger than the range of the data Differences compressed and visual variability is removed \cite{cleveland1982variables} Variables on Scatter Plots Look More Highly Correlated When the Scales are Increased Spec 3 Visible Only With Effort Bad Quant Scales -Visualization Distortions (log scales, say) and non-linearities Can cause readers to inaccurately correlate variables and cluster values Spec 3 Visible Only With Effort Bad Quant Scales -Visualization Truncated/expanded axes Can cause sorting errors and errors in characterizing distribution \cite{pandey2015deceptive, correll2017black, cleveland1982variables} Variables on Scatterplots Look More Highly Correlated When the Scales are Increased, Black hat, panday Spec, Data 3 -Visualization Colors binned unevenly Spec 3 Visible Only With Effort Bad color scale -Visualization Color just showing base rate Mistake base rate for data signal rate \cite{correll2016surprise} Surprise Maps Spec 3 Visible Only With Effort Bad color scale -Visualization Semantically color scale not relevant Reader may misinterpret color on a map as indicating the content of that region rather than a data variable Green or blue map regions can lead the reader to believe that those regions posses semantically meaningful trends (forest/ocean respectively). CITATION NEEDED, maybe Selecting Semantically‐Resonant Colors for Data Visualization Spec? 3 Bad color scale -Visualization Within-bar-bias \cite{newman2012bar} Bar graphs depicting averages are perceptually misinterpreted: The within-the-bar bias Spec 3 Visible Only With Effort Rhetorical Nudges -Visualization Highlight/downplaying outliers Spec 3 Visible Only With Effort Rhetorical Nudges -Visualization Clipped outliers Chosen domain hides outliers, impending extrema detection specified domain removes significant outliers, may be the same as cherry picking Data, Spec 3 -Visualization Continuous marks describe nominal quantities \cite{mcnuttlinting} Eg Line chart across nominal data. Citation VisLint Data, Spec 3 Charting malfeasance Expressiveness mismatches -Visualization Using ordinal measures as (ratio/interval) measures \cite{stevens1946theory, few2019loom} Stevens, also data loom as "conflating rankings with measure of worth" Data, spec 3 -Visualization V-hacking Purported insight not robust to minor changes CITATION? Spec, Render 3 Charting malfeasance -Visualization Charting parameter masking data error \cite{correll2018looks} Over/under smoothed KDE, Over/under binned histogram. Looks good to me Data, Spec, Render 3 Charting malfeasance -Visualization Poorly designed Charts Cairo's new book Spec 3 Charting malfeasance -Visualization Concealed uncertainty Cairo's new book, Slightly different than missing records, following "Where's My Data?" visualizations should represent their missing data in some way. Data, Spec, Render 3 Charting malfeasance -Visualization charts with misleading patterns Cairo's new book, black hat Spec, Render 3 Charting malfeasance -Visualization Sole reliance on measure of central tendency \cite{wall2017warning} Src: data loom. Could be tested by executing datasarus style data manipulation: keep an invariant the same and rotate the data via optimization Data, render 3 -Visualization Staircasing A trend on a noisy channel may appear to reverse because of the domain selection https://twitter.com/NASAClimate/status/1154503255486955520 setting domain to a small subrange, particularly on a line chart 3 -Comprehension Not accounting for bias \cite{wall2017warning} Ala "bias may occur" ?? 4 -Comprehension Causal Errors: Post hoc ergo propter hoc \cite{few2019loom} ?? 4 -Comprehension Causal Errors: unit bias \cite{few2019loom} ?? 4 -Comprehension Causal Errors: outcome bias \cite{few2019loom} ?? 4 -Comprehension Over-emphasizing data-ink minimalism Chart might not actually get read, minimalist charts are easier to ignore full message might get lost Data humanism/feminism: extremely minimalist data visualizations aren't memorable (MONSTER CITATION) or as emotionally impactful as illustrative ones (Data fem) . Visual Difficulties also might be of note here ?? 4 -Comprehension Familiarity Errors: confirmation bias Data loom ?? 4 -Comprehension Familiarity Errors: availability heuristic ?? 4 -Comprehension Familiarity Errors: status quo bias ?? 4 -Comprehension Familiarity Errors: default effect ?? 4 -Comprehension Familiarity Errors: anchoring effect ?? 4 -Comprehension Familiarity Errors: semmelweis effect ?? 4 -Comprehension Familiarity Errors: appeal to common belief ?? 4 -Comprehension Familiarity Errors: law of instrument ?? 4 +Taxonomy (cause) Error Taxonomy (Mirage) mirage-error Citations Hide Notes Detection Technique Taxonomy Order Initial Taxonomy zone Group LOOKUP VALUE +Curation Missing/Repeated Records Aggregates might become inaccurate, impeding all comparisons Faulty data can cause aggregages to be inaccurate, or at least different from the user intution. Data, Spec 1 Totally Invisible Aggregation issues Curation 1 +Curation Outliers Aggregates might become inaccurate, impeding all comparisons Outliers can cause upstream failures if not addressed properly by wrecking the visual scale and warping the perception of the real distribution. Can effect ability to read aggregate visualization accuracately Data, Spec 1 Totally Invisible Aggregation issues Wrangling 2 +Curation Dubious data TRUE Cairo's new book Data 1 Statistical Malpractice +Curation Insufficient data TRUE Cairo's new book Data 1 Statistical Malpractice +Curation Spelling mistakes Differently spelled groups might cause rows to fall into unitentionally aggregates, causing inaccurate comparisons. \cite{wang2019uni} Data 1 +Curation Uniqueness constraint failure Marks assumed to represent single values might represent several (which may then be further skewed by aggregates like SUM). \cite{wang2019uni} TRUE assumed singular values may be aggregated. Example: making a line chart of year has data from 1.5 years. Users assumes each mark indicates ONE measurement, when in fact their might be several Data 1 +Curation Unidentified functional dependencies Visualization might show a strong relationship between variables, when in fact that relationship is part of the data composition (eg A+B = C, plot A vs C). Uni-Detect: A Unified Approach to Automated Error Detection in Tables Data 1 +Curation Drill-down bias Apparent trends might be attributed to the more specific/recently specified filters rather than relatively "simpler" explanations. \cite{lee2019avoiding} Data, Spec 1 Statistical Malpractice +Curation For more see Kim etals Dirty Data Taxonomy * \cite{kim2003taxonomy} TRUE 1 +Wrangling Differing Number of Records by group Aggregate may be percieved as having a common cardinality Marks assumed to represent consistant number of values might have a variable number of entries, which can mask missing data or can lead to incorrect assumptions about aggregates. A mismatch in row cardinality (world indicators example) Data, Spec 2 Totally Invisible Aggregation issues +Wrangling Simpson's Paradox Observed trend reverses when aggregation changes An observed trends revereses when the aggregation level changes. \cite{guo2017you} Data, Spec 2 Totally Invisible Aggregation issues +Wrangling Cherry Picking Filtering down to an uncharacteristic offers an incorrect distribution e.g. the chart filters out to only a tiny subset of data where something is true. data loom: law of small numbers (cherry picking, bias sample fallacy) Data, Spec 2 Totally Invisible +Wrangling Over dominating outlier wrecks scale Real trend is made invisible TRUE SLIGHTLY UNSURE ABOUT CLASSIFICATION Data, Spec 2 +Wrangling Spurious correlation Gives the impression of an non-extant correlation Correlation is not robust to slight changes in the data Data 2 Statistical Malpractice +Wrangling High variability or noise in contrast to the effect size Irrelevant conclusions could be made Data 2 Statistical Malpractice +Wrangling P-hacking Gives the impression of an non-extant correlation QuickInsights, MORE Spurious difference (due to p-hacking, say. or just un-communicated sampling variability) Data 2 Statistical Malpractice +Wrangling Outliers combined with the wrong aggregation type. Data, Spec, Render 2 +Wrangling Aggregates mask second order statistics Two things may appear to be equal when in fact their distributions are fundamentally different \cite{matejka2017same, few2019loom} dinosaurus box plot, anscombe's quartet, variation blindness (the median isn't the message - gould) Data, Spec, Render 2 Totally Invisible Aggregation issues +Wrangling Additional loom Statistical errors \cite{few2019loom} TRUE data loom: law of small numbers (cherry picking, bias sample fallacy), insensitivity to small sample size, base rate fallacy, hot hand fallacy, appeal to coincidence, regression fallacy, variation blindness (the median isn't the message - gould) 2 +Wrangling insensitivity to small sample size data loom +Wrangling hot hand fallacy TRUE data loom +Wrangling appeal to coincidence TRUE data loom +Wrangling regression fallacy TRUE data loom +Wrangling Inappropriate use of mean Mean comparisons between non-normal and normal distribution will be skewed \cite{few2019loom} Data loom (mean is design for normal distributions) Data, spec 2 +Wrangling Confusing imputation Imputation that generates zeroes rather removing rows can radically alter aggregates. \cite{song2018s} "Where's My Data?" 2 +Wrangling Sampling rate errors Apparent trend will be the aliases rather than the signal An apparent trend may be an artifact of the sampling rate rather than the data. CITATION PLX 2 +Wrangling Distinct data classes treated as the same An apparent trend can be reversed Automatic Selection of Partitioning Variables for Small Multiple Displays,related to simpsons paradox Data, spec 2 +Visualization Banking to 45 failure Trend can appear from flat data or vice-versa HEER PAPER. Mirages can be perceptual in nature, but are driven by a decision somewhere in the analytics pipeline. The knob that controls this mirage is charts dimensions Data, spec, render 3 +Visualization area/length mismatches Misunderstand meaning of encoded variable Correll YAxis truncation paper? Pandey et al e.g. “the incredible shrinking doctor” Spec 3 Visible Only With Effort Bad Quant Scales +Visualization Color too close Reader mistakes one group for another Spec 3 Visible Only With Effort Bad color scale +Visualization Time not aligned in direction of language Reverseing convention can cause reveresed interpretation. \cite{correll2017black} Readers tend to think of time as flowing in the same direction as their language (CITATION), eg western language speakers tend to think of it as moving from left to right while REST OF EXAMPLE. Spec, Data, Render 3 +Visualization Overplotting TRUE Merged into impropert layering Data, Spec, Render 3 Totally Invisible Rendering Issues +Visualization Singularities Detail can become difficult to discern when collections of lines converge (such as in a parrallel coordinates chart). \cite{kindlmann2014algebraic} parallel coordinates AVD example Data, Render 3 Totally Invisible Rendering Issues +Visualization Improper Layering / Overplotting Overplotting can cause an non-existent trend to emerge due to the draw order. \cite{kindlmann2014algebraic} taxi AVD example Data, Render 3 Totally Invisible Rendering Issues +Visualization Latent variables Missing Spec 3 Totally Invisible +Visualization Wrong/missing aggregation Aggregate marks with no opacity overlapping each other do an implicit max Spec, Render 3 Totally Invisible +Visualization Flipped Trend understood to be opposite \cite{pandey2015deceptive, correll2017black, cleveland1982variables} Black hat, panday, Variables on Scatter Plots Look More Highly Correlated When the Scales are Increased Spec 3 Visible Only With Effort Bad Quant Scales +Visualization Scale extents larger than the range of the data Differences compressed and visual variability is removed \cite{cleveland1982variables} Variables on Scatter Plots Look More Highly Correlated When the Scales are Increased Spec 3 Visible Only With Effort Bad Quant Scales +Visualization Non-linear scales Can cause readers to inaccurately correlate variables and cluster values. Spec 3 Visible Only With Effort Bad Quant Scales +Visualization Truncated/expanded axes Can cause sorting errors and errors in characterizing distribution \cite{pandey2015deceptive, correll2017black, cleveland1982variables} Variables on Scatterplots Look More Highly Correlated When the Scales are Increased, Black hat, panday Spec, Data 3 +Visualization Colors binned unevenly May affect interpration of bin meaning Spec 3 Visible Only With Effort Bad color scale +Visualization Color just showing base rate Mistake base rate for data signal rate \cite{correll2016surprise} Surprise Maps Spec 3 Visible Only With Effort Bad color scale +Visualization Semantically color scale not relevant Reader may misinterpret color on a map as indicating the content of that region rather than a data variable. Green or blue map regions can lead the reader to believe that those regions posses semantically meaningful trends (forest/ocean respectively). CITATION NEEDED, maybe Selecting Semantically‐Resonant Colors for Data Visualization Spec? 3 Bad color scale +Visualization Within-bar-bias Readers misunderstand variablility \cite{newman2012bar} Bar graphs depicting averages are perceptually misinterpreted: The within-the-bar bias Spec 3 Visible Only With Effort Rhetorical Nudges +Visualization Highlight/downplaying outliers Spec 3 Visible Only With Effort Rhetorical Nudges +Visualization Clipped outliers Chosen domain hides outliers, impending the reader from accurate extrema detection. specified domain removes significant outliers, may be the same as cherry picking Data, Spec 3 +Visualization Continuous marks describe nominal quantities Reader may hallucinate a trend based on the rendered ordering. \cite{mcnuttlinting} Eg Line chart across nominal data. Citation VisLint Data, Spec 3 Charting malfeasance Expressiveness mismatches +Visualization Using ordinal measures as (ratio/interval) measures Related to "area/length mismatches" a mark might be encoded as big/medium/small which readers might then read as quantitative . \cite{stevens1946theory, few2019loom} Stevens, also data loom as "conflating rankings with measure of worth" Data, spec 3 +Visualization V-hacking Purported insight not robust to minor changes CITATION? TRUE Spec, Render 3 Charting malfeasance +Visualization Charting parameter masking data error Non-data parameter can mask a critical data error. Ex: a histogram binning hiding a missing value. \cite{correll2018looks} Over/under smoothed KDE, Over/under binned histogram. Looks good to me Data, Spec, Render 3 Charting malfeasance +Visualization Poorly designed Charts TRUE Cairo's new book Spec 3 Charting malfeasance +Visualization Concealed uncertainty Reader assume that task was completed with certainty Cairo's new book, Slightly different than missing records, following "Where's My Data?" visualizations should represent their missing data in some way. Data, Spec, Render 3 Charting malfeasance +Visualization charts with misleading patterns TRUE Cairo's new book, black hat Spec, Render 3 Charting malfeasance +Visualization Sole reliance on measure of central tendency Second order statistics carry more critical information or variance \cite{wall2017warning, few2019loom} Src: data loom. Could be tested by executing datasarus style data manipulation: keep an invariant the same and rotate the data via optimization Data, render 3 +Visualization Dual y-axes Relationship between lines is arbitrary (example: jason chaffetz chart) black hat, cairo? TRUE Cairo (poorly designed charts) +Visualization Uncorrelated data decorated with best fit line Uncareful reader might not investigate if the line of best fit actually matches with anything Uncorrelated data decorated with line of best fit. +Visualization Staircasing A trend on a noisy channel may appear to reverse because of the domain selection https://twitter.com/NASAClimate/status/1154503255486955520 setting domain to a small subrange, particularly on a line chart, this might also be a subcase of cherry picking? 3 +Visualization Nominal choropleth conflates color area with classed statistic Conflated color area with underlying value. Ex: Trump's election map is very red just because of the large and less populous parts of country CHOROPLETH CITATION? +Comprehension Incorrectly assumed high quality data Trusting untrust worthy data can lead to missing upstream errors (such as falsey data) Trusting untrust worthy data can lead to missing upstream errors (such as falsey data) Data 4 Visible Only With Effort Data quality errors Visualization 3 +Comprehension Multiple comparisons problem False inference from too many iterative comparisons Too many iterative comparisons has high probability of generating a configuration that offers a false comparison. \cite{pu2018garden, zgraggen2018investigating} Garden of forking paths, Zerdraggen. Possibly not relevant as we've constrained ourselves to presentational VA. ??? 4 Statistical Malpractice Comprehension 4 +Comprehension Not accounting for bias \cite{wall2017warning} Ala "bias may occur" ?? 4 +Comprehension Assuming view from no-where Falsely trust visualizations that have a bias The reader mighy falsey overly trust a visualization that doesn't present its origins. DATA FEM maybe same as not accounting for bias +Comprehension Causal Errors: Post hoc ergo propter hoc \cite{few2019loom} TRUE ?? 4 +Comprehension Causal Errors: unit bias \cite{few2019loom} TRUE ?? 4 +Comprehension Causal Errors: outcome bias \cite{few2019loom} TRUE ?? 4 +Comprehension Over-emphasizing data-ink minimalism Chart might not actually get read, minimalist charts are easier to ignore full message might get lost Data humanism/feminism: extremely minimalist data visualizations aren't memorable (MONSTER CITATION) or as emotionally impactful as illustrative ones (Data fem) . Visual Difficulties also might be of note here ?? 4 +Comprehension Confirmation bias Reader may see trend where one doesn't exisit \cite{valdez2017framework} Data loom ?? 4 +Comprehension Familiarity Errors: availability heuristic Falsey extrapolate meaning from data based on competionist set of data TRUE Data loom ?? 4 +Comprehension Familiarity Errors: status quo bias TRUE Data loom ?? 4 +Comprehension Default effect Visualization system defaults are most used, which can mask data and falsey increase trust. CITATION VERY MUCH NEEDED HERE Data loom ?? 4 +Comprehension Anchoring effect Reader may be blind to changes, framing whatever they see later in terms of what they've seen earlier. \cite{ritchie2019lie} Data loom ?? 4 +Comprehension Familiarity Errors: semmelweis effect \cite{valdez2017framework} TRUE MAybe should delete, covers same content as confirmation bias, Data loom ?? 4 +Comprehension Familiarity Errors: appeal to common belief TRUE Data loom ?? 4 +Comprehension Familiarity Errors: law of instrument TRUE Data loom, maybe same as default effect ?? 4 +Comprehension For more see Dimara etals taxonomy of biases in vis * \cite{dimara2018task} TRUE ?? 4 \ No newline at end of file diff --git a/paper-scripts/prepare-mirage-table.js b/paper-scripts/prepare-mirage-table.js index 4190740..1ffdc10 100644 --- a/paper-scripts/prepare-mirage-table.js +++ b/paper-scripts/prepare-mirage-table.js @@ -1,22 +1,24 @@ const tsv = require('tsv'); const {getFile, writeFile} = require('hoopoe'); -const toRow = isGray => row => - `& ${isGray ? '\\rowcolor{Gray}' : ''} ${row.Error} & ${row['Taxonomy (Mirage)']} ${row.Citations}`; -const toBlock = (rows, name, isGray) => { - return `\\multirow{${rows.length}}{1em}{\\rotatebox{90}{\\normalsize{${name}}}} ${rows.map(toRow(isGray)).join('\\\\\n')}\\\\`; +// const toRow = isGray => row => +// `& ${isGray ? '\\rowcolor{Gray}' : ''} ${row.Error} & ${row['Taxonomy (Mirage)']} ${row.Citations}`; +const toRow = colorSuffix => (row, idx) => + `& \\rowcolor{color${colorSuffix}${idx % 2 ? '-opaque' : ''}} ${row.Error} & ${row['mirage-error']} ${row.Citations}`; +const toBlock = (rows, name, colorSuffix) => { + return `\\multirow{${rows.length}}{1em}{\\rotatebox{90}{\\normalsize{${name}}}} ${rows.map(toRow(colorSuffix)).join('\\\\\n')}\\\\`; }; -const template = (curating, preparing, visualizing, comprehending) => ` +const template = (curating, wrangling, visualizing, comprehending) => ` % \\multirow{13}{1em}{\\rotatebox{90}{\\normalsize{Curation}}} \\begin{table*}[] \\centering -\\caption{Examples of errors arising at each of the stages in our taxonomy along with the ways that those errors can manifest themselves as mirages.} +\\caption{Examples of errors arising at each of the stages in our taxonomy along with the ways that those errors can manifest themselves as mirages. This list does not try to be comprehensive, only evocative.} \\small \\begin{tabular}{c|p{6cm}p{10cm}} & \\normalsize{Error} & \\normalsize{Mirage}\\\\ \\hline - ${toBlock(curating, 'Curating', false)} - ${toBlock(preparing, 'Preparing', true)} - ${toBlock(visualizing, 'Visualizing', false)} - ${toBlock(comprehending, 'Comprehending', true)} + ${toBlock(curating, 'Curating', 'a')} + ${toBlock(wrangling, 'Wrangling', 'b')} + ${toBlock(visualizing, 'Visualizing', 'c')} + ${toBlock(comprehending, 'Reading', 'd')} \\end{tabular} \\end{table*} `; @@ -31,9 +33,9 @@ function groupBy(data, key) { }, {}); } getFile('./paper-scripts/lint-rules.tsv') - .then(d => tsv.parse(d)) + .then(d => tsv.parse(d).filter(x => !x.Hide)) .then(d => { const groups = groupBy(d, 'Taxonomy (cause)'); writeFile('./table.tex', - template(groups.Curation, groups.Preparation, groups.Visualization, groups.Comprehension)); + template(groups.Curation, groups.Wrangling, groups.Visualization, groups.Comprehension)); }); diff --git a/table.tex b/table.tex index f488172..371687c 100644 --- a/table.tex +++ b/table.tex @@ -2,75 +2,61 @@ % \multirow{13}{1em}{\rotatebox{90}{\normalsize{Curation}}} \begin{table*}[] \centering -\caption{Examples of errors arising at each of the stages in our taxonomy along with the ways that those errors can manifest themselves as mirages.} +\caption{Examples of errors arising at each of the stages in our taxonomy along with the ways that those errors can manifest themselves as mirages. This list does not try to be comprehensive, only evocative.} \small \begin{tabular}{c|p{6cm}p{10cm}} & \normalsize{Error} & \normalsize{Mirage}\\ \hline - \multirow{12}{1em}{\rotatebox{90}{\normalsize{Curating}}} & Missing/Repeated Records & Aggregates might become inaccurate, impeding all comparisons \\ -& Outliers & Aggregates might become inaccurate, impeding all comparisons \\ -& Incorrectly assumed high Quality data & \\ -& Multiple comparisons problem & \cite{pu2018garden, zgraggen2018investigating}\\ -& Dubious data & \\ -& Insufficient data & \\ -& * & \cite{kim2003taxonomy}\\ -& Numeric outliers & \cite{wang2019uni}\\ -& Spelling mistakes & Differently spelled groups might fall into different aggregates, causing inaccurate comparisons \cite{wang2019uni}\\ -& Uniqueness constraint failure & Marks assumed to represent single values might represent several (which may then be further skewed by aggregates like SUM) \cite{wang2019uni}\\ -& unidentified functional dependencies & Visualization might show a strong relationship between variables, when in fact that relationship is part of the data composition (eg A+B = C, plot A vs C) \\ -& Drill-down bias & Apparent trends might be attributed to the more specific/recently specified filters rather than relatively "simpler" explanations \cite{lee2019avoiding}\\ - \multirow{13}{1em}{\rotatebox{90}{\normalsize{Preparing}}} & \rowcolor{Gray} Differing Number of Records by group & \\ -& \rowcolor{Gray} Simpson's Paradox & Observed trend reverses \cite{guo2017you}\\ -& \rowcolor{Gray} Cherry Picking & \\ -& \rowcolor{Gray} Over dominating outlier wrecks scale & Real trend is made invisible \\ -& \rowcolor{Gray} Spurious correlation & Gives the impression of an non-extant correlation \\ -& \rowcolor{Gray} High variability or noise in contrast to the effect size & Irrelevant conclusions could be made \\ -& \rowcolor{Gray} P-hacking & Gives the impression of an non-extant correlation \\ -& \rowcolor{Gray} Outliers combined with the wrong aggregation type. & \\ -& \rowcolor{Gray} Aggregates mask second order statistics & Two things may appear to be equal when in fact their distributions are fundamentally different \cite{matejka2017same, few2019loom}\\ -& \rowcolor{Gray} Additional loom Statistical errors & \cite{few2019loom}\\ -& \rowcolor{Gray} Inappropriate use of mean & Mean comparisons with normal distribution will be skewed \cite{few2019loom}\\ -& \rowcolor{Gray} Confusing imputation & Imputation that generates zeroes rather removing rows can radically alter aggregates \cite{song2018s}\\ -& \rowcolor{Gray} Sampling at a rate less than the nyquist rate & Apparent trend will be the aliases rather than the signal \\ - \multirow{28}{1em}{\rotatebox{90}{\normalsize{Visualizing}}} & Banking to 45 failure & Trend can appear from flat data or vice-versa \\ -& area/length mismatches & Correll YAxis truncation paper?\\ -& Color too close & Reader mistakes one group for another \\ -& Time not aligned in direction of language & \\ -& Overplotting & \\ -& Singularities & \cite{kindlmann2014algebraic}\\ -& Improper Layering & Overplotting can cause an non-existent trend to emerge from draw order \cite{kindlmann2014algebraic}\\ -& Latent variables Missing & \\ -& Wrong/missing aggregation & Aggregate marks with no opacity overlapping each other do an implicit max \\ -& Flipped & Trend understood to be opposite \cite{pandey2015deceptive, correll2017black, cleveland1982variables}\\ -& Scale extents larger than the range of the data & Differences compressed and visual variability is removed \cite{cleveland1982variables}\\ -& Distortions (log scales, say) and non-linearities & Can cause readers to inaccurately correlate variables and cluster values \\ -& Truncated/expanded axes & Can cause sorting errors and errors in characterizing distribution \cite{pandey2015deceptive, correll2017black, cleveland1982variables}\\ -& Colors binned unevenly & \\ -& Color just showing base rate & Mistake base rate for data signal rate \cite{correll2016surprise}\\ -& Semantically color scale not relevant & Reader may misinterpret color on a map as indicating the content of that region rather than a data variable \\ -& Within-bar-bias & \cite{newman2012bar}\\ -& Highlight/downplaying outliers & \\ -& Clipped outliers & Chosen domain hides outliers, impending extrema detection \\ -& Continuous marks describe nominal quantities & \cite{mcnuttlinting}\\ -& Using ordinal measures as (ratio/interval) measures & \cite{stevens1946theory, few2019loom}\\ -& V-hacking & Purported insight not robust to minor changes CITATION?\\ -& Charting parameter masking data error & \cite{correll2018looks}\\ -& Poorly designed Charts & \\ -& Concealed uncertainty & \\ -& charts with misleading patterns & \\ -& Sole reliance on measure of central tendency & \cite{wall2017warning}\\ -& Staircasing & A trend on a noisy channel may appear to reverse because of the domain selection \\ - \multirow{13}{1em}{\rotatebox{90}{\normalsize{Comprehending}}} & \rowcolor{Gray} Not accounting for bias & \cite{wall2017warning}\\ -& \rowcolor{Gray} Causal Errors: Post hoc ergo propter hoc & \cite{few2019loom}\\ -& \rowcolor{Gray} Causal Errors: unit bias & \cite{few2019loom}\\ -& \rowcolor{Gray} Causal Errors: outcome bias & \cite{few2019loom}\\ -& \rowcolor{Gray} Over-emphasizing data-ink minimalism & Chart might not actually get read, minimalist charts are easier to ignore full message might get lost \\ -& \rowcolor{Gray} Familiarity Errors: confirmation bias & \\ -& \rowcolor{Gray} Familiarity Errors: availability heuristic & \\ -& \rowcolor{Gray} Familiarity Errors: status quo bias & \\ -& \rowcolor{Gray} Familiarity Errors: default effect & \\ -& \rowcolor{Gray} Familiarity Errors: anchoring effect & \\ -& \rowcolor{Gray} Familiarity Errors: semmelweis effect & \\ -& \rowcolor{Gray} Familiarity Errors: appeal to common belief & \\ -& \rowcolor{Gray} Familiarity Errors: law of instrument & \\ + \multirow{5}{1em}{\rotatebox{90}{\normalsize{Curating}}} & \rowcolor{colora} Missing/Repeated Records & Faulty data can cause aggregages to be inaccurate, or at least different from the user intution. \\ +& \rowcolor{colora-opaque} Outliers & Outliers can cause upstream failures if not addressed properly by wrecking the visual scale and warping the perception of the real distribution. \\ +& \rowcolor{colora} Spelling mistakes & Differently spelled groups might cause rows to fall into unitentionally aggregates, causing inaccurate comparisons. \cite{wang2019uni}\\ +& \rowcolor{colora-opaque} Unidentified functional dependencies & Visualization might show a strong relationship between variables, when in fact that relationship is part of the data composition (eg A+B = C, plot A vs C). \\ +& \rowcolor{colora} Drill-down bias & Apparent trends might be attributed to the more specific/recently specified filters rather than relatively "simpler" explanations. \cite{lee2019avoiding}\\ + \multirow{13}{1em}{\rotatebox{90}{\normalsize{Wrangling}}} & \rowcolor{colorb} Differing Number of Records by group & Marks assumed to represent consistant number of values might have a variable number of entries, which can mask missing data or can lead to incorrect assumptions about aggregates. \\ +& \rowcolor{colorb-opaque} Simpson's Paradox & An observed trends revereses when the aggregation level changes. \cite{guo2017you}\\ +& \rowcolor{colorb} Cherry Picking & \\ +& \rowcolor{colorb-opaque} Spurious correlation & \\ +& \rowcolor{colorb} High variability or noise in contrast to the effect size & \\ +& \rowcolor{colorb-opaque} P-hacking & QuickInsights, MORE\\ +& \rowcolor{colorb} Outliers combined with the wrong aggregation type. & \\ +& \rowcolor{colorb-opaque} Aggregates mask second order statistics & \cite{matejka2017same, few2019loom}\\ +& \rowcolor{colorb} insensitivity to small sample size & \\ +& \rowcolor{colorb-opaque} Inappropriate use of mean & \cite{few2019loom}\\ +& \rowcolor{colorb} Confusing imputation & Imputation that generates zeroes rather removing rows can radically alter aggregates. \cite{song2018s}\\ +& \rowcolor{colorb-opaque} Sampling rate errors & An apparent trend may be an artifact of the sampling rate rather than the data. CITATION PLX\\ +& \rowcolor{colorb} Distinct data classes treated as the same & \\ + \multirow{26}{1em}{\rotatebox{90}{\normalsize{Visualizing}}} & \rowcolor{colorc} Banking to 45 failure & \\ +& \rowcolor{colorc-opaque} area/length mismatches & Correll YAxis truncation paper? Pandey et al\\ +& \rowcolor{colorc} Color too close & \\ +& \rowcolor{colorc-opaque} Time not aligned in direction of language & Reverseing convention can cause reveresed interpretation. \cite{correll2017black}\\ +& \rowcolor{colorc} Singularities & Detail can become difficult to discern when collections of lines converge (such as in a parrallel coordinates chart). \cite{kindlmann2014algebraic}\\ +& \rowcolor{colorc-opaque} Improper Layering / Overplotting & Overplotting can cause an non-existent trend to emerge due to the draw order. \cite{kindlmann2014algebraic}\\ +& \rowcolor{colorc} Latent variables Missing & \\ +& \rowcolor{colorc-opaque} Wrong/missing aggregation & \\ +& \rowcolor{colorc} Flipped & \cite{pandey2015deceptive, correll2017black, cleveland1982variables}\\ +& \rowcolor{colorc-opaque} Scale extents larger than the range of the data & \cite{cleveland1982variables}\\ +& \rowcolor{colorc} Non-linear scales & Can cause readers to inaccurately correlate variables and cluster values. \\ +& \rowcolor{colorc-opaque} Truncated/expanded axes & \cite{pandey2015deceptive, correll2017black, cleveland1982variables}\\ +& \rowcolor{colorc} Colors binned unevenly & \\ +& \rowcolor{colorc-opaque} Color just showing base rate & \cite{correll2016surprise}\\ +& \rowcolor{colorc} Semantically color scale not relevant & Reader may misinterpret color on a map as indicating the content of that region rather than a data variable. \\ +& \rowcolor{colorc-opaque} Within-bar-bias & \cite{newman2012bar}\\ +& \rowcolor{colorc} Highlight/downplaying outliers & \\ +& \rowcolor{colorc-opaque} Clipped outliers & Chosen domain hides outliers, impending the reader from accurate extrema detection. \\ +& \rowcolor{colorc} Continuous marks describe nominal quantities & Reader may hallucinate a trend based on the rendered ordering. \cite{mcnuttlinting}\\ +& \rowcolor{colorc-opaque} Using ordinal measures as (ratio/interval) measures & Related to "area/length mismatches" a mark might be encoded as big/medium/small which readers might then read as quantitative . \cite{stevens1946theory, few2019loom}\\ +& \rowcolor{colorc} Charting parameter masking data error & \cite{correll2018looks}\\ +& \rowcolor{colorc-opaque} Concealed uncertainty & \\ +& \rowcolor{colorc} Sole reliance on measure of central tendency & \cite{wall2017warning, few2019loom}\\ +& \rowcolor{colorc-opaque} Uncorrelated data decorated with best fit line & \\ +& \rowcolor{colorc} Staircasing & \\ +& \rowcolor{colorc-opaque} Nominal choropleth conflates color area with classed statistic & CHOROPLETH CITATION?\\ + \multirow{8}{1em}{\rotatebox{90}{\normalsize{Reading}}} & \rowcolor{colord} Incorrectly assumed high quality data & Trusting untrust worthy data can lead to missing upstream errors (such as falsey data) \\ +& \rowcolor{colord-opaque} Multiple comparisons problem & Too many iterative comparisons has high probability of generating a configuration that offers a false comparison. \cite{pu2018garden, zgraggen2018investigating}\\ +& \rowcolor{colord} Not accounting for bias & \cite{wall2017warning}\\ +& \rowcolor{colord-opaque} Assuming view from no-where & The reader mighy falsey overly trust a visualization that doesn't present its origins. DATA FEM\\ +& \rowcolor{colord} Over-emphasizing data-ink minimalism & \\ +& \rowcolor{colord-opaque} Confirmation bias & \cite{valdez2017framework}\\ +& \rowcolor{colord} Default effect & Visualization system defaults are most used, which can mask data and falsey increase trust. CITATION VERY MUCH NEEDED HERE\\ +& \rowcolor{colord-opaque} Anchoring effect & Reader may be blind to changes, framing whatever they see later in terms of what they've seen earlier. \cite{ritchie2019lie}\\ \end{tabular} \end{table*} diff --git a/vislint-ui-sketching.sketch b/vislint-ui-sketching.sketch index 36a4551d0a71fa3a019124dbcef8589a0085ba19..517473fb9d63ad7119196e2b772f862a97cee1a0 100644 GIT binary patch delta 214602 zcmZs?V{o9s)-^gwCUz#aor!JRwrx94GV#Q=olI;`Y}>Z&*=yf5NhMeMqdTRgs|i%9U+Nc*GYY&97n zcoIm~vWhIhfZB^sl4f4TKf};77CihAH^9l1*Y|^cN!0hHBCNeoTLW~0^H-JHo5|bTa-Vauj>wd+ z1A&6M;PwKR)uW-itkzk;Ira%UE%EjNCB|TlW&eg(%jCQ-hrbHHZVsIwY~{D88XLm| z1+8uxY(YDAQWD{dxb=z2qb~H213nZ$(${8gmBnZ-OqPT#>X|3%*#}ZnPdC|+Z19wB z>(8`C3eMjl4yBBMt_ClVX!TR@+nEI2+XE~xei;pdt&W!)fVcIW$wx=k*XM1)AZqF0etQ%qBl5BNl$GLjp z2~+Nm?TfEaZV~6-8vNdxBaR+{K|P+@93P|FzsYYT^qSRz^A;Q5%P$9Ft?xaXp)u#i z!Nl>3*7Yfth2U({bLo0eyFTEVWUfO89YX}midqamBZ6LXRgtZk%91af%~9z 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