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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">JDS</journal-id>
<journal-title-group><journal-title>Journal of Data Science</journal-title></journal-title-group>
<issn pub-type="epub">1683-8602</issn><issn pub-type="ppub">1680-743X</issn><issn-l>1680-743X</issn-l>
<publisher>
<publisher-name>School of Statistics, Renmin University of China</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">JDS1083</article-id>
<article-id pub-id-type="doi">10.6339/22-JDS1083</article-id>
<article-categories><subj-group subj-group-type="heading">
<subject>Computing in Data Science</subject></subj-group></article-categories>
<title-group>
<article-title>‘You Draw It’: Implementation of Visually Fitted Trends with <monospace>r2d3</monospace></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-9800-7304</contrib-id>
<name><surname>Robinson</surname><given-names>Emily A.</given-names></name><email xlink:href="mailto:erobin17@calpoly.edu">erobin17@calpoly.edu</email><xref ref-type="aff" rid="j_jds1083_aff_001">1</xref><xref ref-type="corresp" rid="cor1">∗</xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Howard</surname><given-names>Reka</given-names></name><xref ref-type="aff" rid="j_jds1083_aff_002">2</xref>
</contrib>
<contrib contrib-type="author">
<name><surname>VanderPlas</surname><given-names>Susan</given-names></name><xref ref-type="aff" rid="j_jds1083_aff_002">2</xref>
</contrib>
<aff id="j_jds1083_aff_001"><label>1</label>Department of Statistics, <institution>CalPoly-San Luis Obispo</institution>, <country>USA</country></aff>
<aff id="j_jds1083_aff_002"><label>2</label>Department of Statistics, <institution>University of Nebraska-Lincoln</institution>, <country>USA</country></aff>
</contrib-group>
<author-notes>
<corresp id="cor1"><label>∗</label>Corresponding author. Email: <ext-link ext-link-type="uri" xlink:href="mailto:erobin17@calpoly.edu">erobin17@calpoly.edu</ext-link>.</corresp>
</author-notes>
<pub-date pub-type="ppub"><year>2023</year></pub-date><pub-date pub-type="epub"><day>12</day><month>1</month><year>2023</year></pub-date><volume>21</volume><issue>2</issue><fpage>281</fpage><lpage>294</lpage><supplementary-material id="S1" content-type="archive" xlink:href="jds1083_s001.zip" mimetype="application" mime-subtype="x-zip-compressed">
<caption>
<title>Supplementary Material</title>
<p>
<list>
<list-item id="j_jds1083_li_001">
<label>•</label>
<p><bold>‘You Draw It’ Demonstration Applet:</bold> The shiny app used to demonstrate the ‘You Draw It’ method can be accessed at <uri>https://emily-robinson.shinyapps.io/can-you-draw-it/</uri>.</p>
</list-item>
<list-item id="j_jds1083_li_002">
<label>•</label>
<p><bold>Participant Data (Linear):</bold> De-identified participant data collected in the linear study and used for analyses are available to be downloaded from GitHub at <ext-link ext-link-type="uri" xlink:href="https://github.com/earobinson95/sdss-2022-you-draw-it-manuscript/raw/master/data/eyefitting-model-data.csv">https://github.com/earobinson95/sdss-2022-you-draw-it-manuscript/raw/master/data/eyefitting-model-data.csv</ext-link>.</p>
</list-item>
<list-item id="j_jds1083_li_003">
<label>•</label>
<p><bold>Participant Data (Non-linear):</bold> De-identified participant data collected in the non-linear study and used for analyses are available to be downloaded from GitHub at <ext-link ext-link-type="uri" xlink:href="https://github.com/earobinson95/sdss-2022-you-draw-it-manuscript/raw/master/data/youdrawit-model-data.csv">https://github.com/earobinson95/sdss-2022-you-draw-it-manuscript/raw/master/data/youdrawit-model-data.csv</ext-link>.</p>
</list-item>
<list-item id="j_jds1083_li_004">
<label>•</label>
<p><bold>Data Analysis Code:</bold> The code used to replicate the linear and non-linear study analysis in this paper can be found at <ext-link ext-link-type="uri" xlink:href="https://github.com/earobinson95/sdss-2022-you-draw-it-manuscript/blob/master/analysis/data-analysis.Rmd">https://github.com/earobinson95/sdss-2022-you-draw-it-manuscript/blob/master/analysis/data-analysis.Rmd</ext-link>.</p>
</list-item>
</list> 
</p>
</caption>
</supplementary-material><history><date date-type="received"><day>29</day><month>7</month><year>2022</year></date><date date-type="accepted"><day>23</day><month>12</month><year>2022</year></date></history>
<permissions><copyright-statement>2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.</copyright-statement><copyright-year>2023</copyright-year>
<license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/">
<license-p>Open access article under the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">CC BY</ext-link> license.</license-p></license></permissions>
<abstract>
<p>How do statistical regression results compare to intuitive, visually fitted results? Fitting lines by eye through a set of points has been explored since the 20th century. Common methods of fitting trends by eye involve maneuvering a string, black thread, or ruler until the fit is suitable, then drawing the line through the set of points. In 2015, the New York Times introduced an interactive feature, called ‘You Draw It,’ where readers are asked to input their own assumptions about various metrics and compare how these assumptions relate to reality. This research is intended to implement ‘You Draw It’, adapted from the New York Times, as a way to measure the patterns we see in data. In this paper, we describe the adaptation of an old tool for graphical testing and evaluation, eye-fitting, for use in modern web-applications suitable for testing statistical graphics. We present an empirical evaluation of this testing method for linear regression, and briefly discuss an extension of this method to non-linear applications.</p>
</abstract>
</article-meta>
</front>
<back>
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