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README.html
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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
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<h1 id="readme">README</h1>
<h1 id="dnddata">dnddata</h1>
<ul>
<li><a href="#dnddata">dnddata</a>
<ul>
<li><a href="#usageinstallation">Usage/installation</a></li>
<li><a href="#examples">Examples</a></li>
<li><a href="#about-the-data">About the data</a>
<ul>
<li><a href="#columnelement-description">Column/element description</a></li>
<li><a href="#caveats">Caveats</a>
<ul>
<li><a href="#possible-issues-with-data-fields">Possible Issues with data fields</a></li>
<li><a href="#possible-issues-with-detection-of-unique-characters">Possible issues with detection of unique characters</a></li>
<li><a href="#possible-issues-with-selection-bias">Possible issues with selection bias</a></li>
</ul></li>
</ul></li>
</ul></li>
</ul>
<p>This is a weekly updated dataset of character that are submitted to my web applications <a href="https://oganm.com/shiny/printSheetApp">printSheetApp</a> and <a href="https://oganm.com/shiny/interactiveSheet">interactiveSheet</a>. It is a superset of the dataset I previously released under <a href="https://oganm.github.io/dndstats">oganm/dndstats</a> with a much larger sample (3242 characters) size and more data fields. It was inspired by the <a href="https://fivethirtyeight.com/features/is-your-dd-character-rare/">FiveThirtyEight</a> article on race/class proportions and the data seems to correlate well with those results (see my <a href="https://oganm.github.io/dndstats">dndstats article</a>).</p>
<p>Along with a simple table (an R <code>data.frame</code> in package), the data is also present in json format (an R <code>list</code> in package). In the table version some data fields encode complex information that are represented in a more readable manner in the json format. The data included is otherwise identical.</p>
<h2 id="usageinstallation">Usage/installation</h2>
<p>If you are an R user, you can simply install this package and load it to access the dataset</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1">devtools<span class="op">::</span><span class="kw">install_github</span>(<span class="st">'oganm/dnddata'</span>)</a>
<a class="sourceLine" id="cb1-2" data-line-number="2"><span class="kw">library</span>(dnddata)</a></code></pre></div>
<p>Try <code>?tables</code>, <code>?lists</code> to see available objects and their descriptions</p>
<p>If you are not an R user, access the files within the <a href="data-raw">data-raw</a> directory. The files are available as JSON and TSV. You can find the field descriptions <a href="#columnelement-description">below</a>. <code>dnd_chars_all</code> files contain all characters that are submitted while <code>dnd_chars_unique</code> files are filtered to include unique characters.</p>
<h2 id="examples">Examples</h2>
<p>I will be using the list form of the dataset as a basis here.</p>
<p>Let’s replicate that plot from <a href="https://fivethirtyeight.com/features/is-your-dd-character-rare/">fivethirtyeight</a> as I did in my <a href="https://oganm.github.io/dndstats/">original article</a>.</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1"><span class="kw">library</span>(purrr)</a>
<a class="sourceLine" id="cb2-2" data-line-number="2"><span class="kw">library</span>(ggplot2)</a>
<a class="sourceLine" id="cb2-3" data-line-number="3"><span class="kw">library</span>(magrittr)</a>
<a class="sourceLine" id="cb2-4" data-line-number="4"><span class="kw">library</span>(dplyr)</a>
<a class="sourceLine" id="cb2-5" data-line-number="5"><span class="kw">library</span>(reshape2)</a>
<a class="sourceLine" id="cb2-6" data-line-number="6"></a>
<a class="sourceLine" id="cb2-7" data-line-number="7"><span class="co"># find all available races</span></a>
<a class="sourceLine" id="cb2-8" data-line-number="8">races =<span class="st"> </span>dnd_chars_unique_list <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-9" data-line-number="9"><span class="st"> </span>purrr<span class="op">::</span><span class="kw">map</span>(<span class="st">'race'</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-10" data-line-number="10"><span class="st"> </span>purrr<span class="op">::</span><span class="kw">map_chr</span>(<span class="st">'processedRace'</span>) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb2-11" data-line-number="11"><span class="st"> </span>unique <span class="op">%>%</span><span class="st"> </span>{.[.<span class="op">!=</span><span class="st">''</span>]}</a>
<a class="sourceLine" id="cb2-12" data-line-number="12"></a>
<a class="sourceLine" id="cb2-13" data-line-number="13"><span class="co"># find all available classes</span></a>
<a class="sourceLine" id="cb2-14" data-line-number="14">classes =<span class="st"> </span>dnd_chars_unique_list <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-15" data-line-number="15"><span class="st"> </span>purrr<span class="op">::</span><span class="kw">map</span>(<span class="st">'class'</span>) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb2-16" data-line-number="16"><span class="st"> </span><span class="kw">unlist</span>(<span class="dt">recursive =</span> <span class="ot">FALSE</span>) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb2-17" data-line-number="17"><span class="st"> </span>purrr<span class="op">::</span><span class="kw">map_chr</span>(<span class="st">'class'</span>) <span class="op">%>%</span><span class="st"> </span>unique</a>
<a class="sourceLine" id="cb2-18" data-line-number="18"></a>
<a class="sourceLine" id="cb2-19" data-line-number="19"><span class="co"># create an empty matrix</span></a>
<a class="sourceLine" id="cb2-20" data-line-number="20">coOccurenceMatrix =<span class="st"> </span><span class="kw">matrix</span>(<span class="dv">0</span> , <span class="dt">nrow=</span><span class="kw">length</span>(races),<span class="dt">ncol =</span> <span class="kw">length</span>(classes))</a>
<a class="sourceLine" id="cb2-21" data-line-number="21"><span class="kw">colnames</span>(coOccurenceMatrix) =<span class="st"> </span>classes</a>
<a class="sourceLine" id="cb2-22" data-line-number="22"><span class="kw">rownames</span>(coOccurenceMatrix) =<span class="st"> </span>races</a>
<a class="sourceLine" id="cb2-23" data-line-number="23"><span class="co"># fill the matrix with co-occurences of race and classes</span></a>
<a class="sourceLine" id="cb2-24" data-line-number="24"><span class="cf">for</span>(i <span class="cf">in</span> <span class="kw">seq_along</span>(races)){</a>
<a class="sourceLine" id="cb2-25" data-line-number="25"> <span class="cf">for</span>(j <span class="cf">in</span> <span class="kw">seq_along</span>(classes)){</a>
<a class="sourceLine" id="cb2-26" data-line-number="26"> <span class="co"># get characters with the right race</span></a>
<a class="sourceLine" id="cb2-27" data-line-number="27"> raceSubset =<span class="st"> </span>dnd_chars_unique_list[dnd_chars_unique_list <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-28" data-line-number="28"><span class="st"> </span>purrr<span class="op">::</span><span class="kw">map</span>(<span class="st">'race'</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-29" data-line-number="29"><span class="st"> </span>purrr<span class="op">::</span><span class="kw">map_chr</span>(<span class="st">'processedRace'</span>) <span class="op">%>%</span><span class="st"> </span>{.<span class="op">==</span>races[i]}]</a>
<a class="sourceLine" id="cb2-30" data-line-number="30"> </a>
<a class="sourceLine" id="cb2-31" data-line-number="31"> <span class="co"># get the characters with the right class. Weight multiclassed characters based on level</span></a>
<a class="sourceLine" id="cb2-32" data-line-number="32"> raceSubset <span class="op">%>%</span><span class="st"> </span>purrr<span class="op">::</span><span class="kw">map</span>(<span class="st">'class'</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-33" data-line-number="33"><span class="st"> </span>purrr<span class="op">::</span><span class="kw">map_dbl</span>(<span class="cf">function</span>(x){</a>
<a class="sourceLine" id="cb2-34" data-line-number="34"> x <span class="op">%>%</span><span class="st"> </span><span class="kw">sapply</span>(<span class="cf">function</span>(y){</a>
<a class="sourceLine" id="cb2-35" data-line-number="35"> (y<span class="op">$</span>class <span class="op">==</span><span class="st"> </span>classes[j])<span class="op">*</span>y<span class="op">$</span>level<span class="op">/</span>(<span class="kw">sum</span>(<span class="kw">map_int</span>(x,<span class="st">'level'</span>)))</a>
<a class="sourceLine" id="cb2-36" data-line-number="36"> }) <span class="op">%>%</span><span class="st"> </span>sum}) <span class="op">%>%</span><span class="st"> </span>sum -><span class="st"> </span>coOcc</a>
<a class="sourceLine" id="cb2-37" data-line-number="37"> </a>
<a class="sourceLine" id="cb2-38" data-line-number="38"> coOccurenceMatrix[i,j] =<span class="st"> </span>coOcc</a>
<a class="sourceLine" id="cb2-39" data-line-number="39"> }</a>
<a class="sourceLine" id="cb2-40" data-line-number="40">}</a>
<a class="sourceLine" id="cb2-41" data-line-number="41"></a>
<a class="sourceLine" id="cb2-42" data-line-number="42"><span class="co"># reorder the matrix a little bit</span></a>
<a class="sourceLine" id="cb2-43" data-line-number="43">coOccurenceMatrix =<span class="st"> </span></a>
<a class="sourceLine" id="cb2-44" data-line-number="44"><span class="st"> </span>coOccurenceMatrix[coOccurenceMatrix <span class="op">%>%</span><span class="st"> </span><span class="kw">apply</span>(<span class="dv">1</span>,sum) <span class="op">%>%</span><span class="st"> </span><span class="kw">order</span>(<span class="dt">decreasing =</span> <span class="ot">FALSE</span>),</a>
<a class="sourceLine" id="cb2-45" data-line-number="45"> coOccurenceMatrix <span class="op">%>%</span><span class="st"> </span><span class="kw">apply</span>(<span class="dv">2</span>,sum) <span class="op">%>%</span><span class="st"> </span><span class="kw">order</span>(<span class="dt">decreasing =</span> <span class="ot">TRUE</span>)]</a>
<a class="sourceLine" id="cb2-46" data-line-number="46"></a>
<a class="sourceLine" id="cb2-47" data-line-number="47"><span class="co"># calculate percentages</span></a>
<a class="sourceLine" id="cb2-48" data-line-number="48">coOccurenceMatrix =<span class="st"> </span>coOccurenceMatrix<span class="op">/</span>(<span class="kw">sum</span>(coOccurenceMatrix))<span class="op">*</span><span class="st"> </span><span class="dv">100</span></a>
<a class="sourceLine" id="cb2-49" data-line-number="49"></a>
<a class="sourceLine" id="cb2-50" data-line-number="50"><span class="co"># remove the rows and columns if they are less than 1%</span></a>
<a class="sourceLine" id="cb2-51" data-line-number="51">coOccurenceMatrixSubset =<span class="st"> </span>coOccurenceMatrix[,<span class="op">!</span>(coOccurenceMatrix <span class="op">%>%</span><span class="st"> </span><span class="kw">apply</span>(<span class="dv">2</span>,sum) <span class="op">%>%</span><span class="st"> </span>{.<span class="op"><</span><span class="dv">1</span>})]</a>
<a class="sourceLine" id="cb2-52" data-line-number="52">coOccurenceMatrixSubset =<span class="st"> </span>coOccurenceMatrixSubset[<span class="op">!</span>(coOccurenceMatrixSubset <span class="op">%>%</span><span class="st"> </span><span class="kw">apply</span>(<span class="dv">1</span>,sum) <span class="op">%>%</span><span class="st"> </span>{.<span class="op"><</span><span class="dv">1</span>}),]</a>
<a class="sourceLine" id="cb2-53" data-line-number="53"></a>
<a class="sourceLine" id="cb2-54" data-line-number="54"><span class="co"># add in class and race sums</span></a>
<a class="sourceLine" id="cb2-55" data-line-number="55">classSums =<span class="st"> </span>coOccurenceMatrix <span class="op">%>%</span><span class="st"> </span><span class="kw">apply</span>(<span class="dv">2</span>,sum) <span class="op">%>%</span><span class="st"> </span>{.[<span class="kw">colnames</span>(coOccurenceMatrixSubset)]}</a>
<a class="sourceLine" id="cb2-56" data-line-number="56">raceSums =<span class="st"> </span>coOccurenceMatrix <span class="op">%>%</span><span class="st"> </span><span class="kw">apply</span>(<span class="dv">1</span>,sum) <span class="op">%>%</span><span class="st"> </span>{.[<span class="kw">rownames</span>(coOccurenceMatrixSubset)]}</a>
<a class="sourceLine" id="cb2-57" data-line-number="57">coOccurenceMatrixSubset =<span class="st"> </span><span class="kw">cbind</span>(coOccurenceMatrixSubset,raceSums)</a>
<a class="sourceLine" id="cb2-58" data-line-number="58">coOccurenceMatrixSubset =<span class="st"> </span><span class="kw">rbind</span>(<span class="dt">Total =</span> <span class="kw">c</span>(classSums,<span class="ot">NA</span>), coOccurenceMatrixSubset)</a>
<a class="sourceLine" id="cb2-59" data-line-number="59"><span class="kw">colnames</span>(coOccurenceMatrixSubset)[<span class="kw">ncol</span>(coOccurenceMatrixSubset)] =<span class="st"> "Total"</span></a>
<a class="sourceLine" id="cb2-60" data-line-number="60"></a>
<a class="sourceLine" id="cb2-61" data-line-number="61"><span class="co"># ggplot</span></a>
<a class="sourceLine" id="cb2-62" data-line-number="62">coOccurenceFrame =<span class="st"> </span>coOccurenceMatrixSubset <span class="op">%>%</span><span class="st"> </span><span class="kw">melt</span>() </a>
<a class="sourceLine" id="cb2-63" data-line-number="63"><span class="kw">names</span>(coOccurenceFrame)[<span class="dv">1</span><span class="op">:</span><span class="dv">2</span>] =<span class="st"> </span><span class="kw">c</span>(<span class="st">'Race'</span>,<span class="st">'Class'</span>)</a>
<a class="sourceLine" id="cb2-64" data-line-number="64">coOccurenceFrame <span class="op">%<>%</span><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">fillCol =</span> value<span class="op">*</span>(Race<span class="op">!=</span><span class="st">'Total'</span> <span class="op">&</span><span class="st"> </span>Class<span class="op">!=</span><span class="st">'Total'</span>))</a>
<a class="sourceLine" id="cb2-65" data-line-number="65">coOccurenceFrame <span class="op">%>%</span><span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x =</span> Class,<span class="dt">y =</span> Race)) <span class="op">+</span></a>
<a class="sourceLine" id="cb2-66" data-line-number="66"><span class="st"> </span><span class="kw">geom_tile</span>(<span class="kw">aes</span>(<span class="dt">fill =</span> fillCol),<span class="dt">show.legend =</span> <span class="ot">FALSE</span>)<span class="op">+</span></a>
<a class="sourceLine" id="cb2-67" data-line-number="67"><span class="st"> </span><span class="kw">scale_fill_continuous</span>(<span class="dt">low =</span> <span class="st">'white'</span>,<span class="dt">high =</span> <span class="st">'#46A948'</span>,<span class="dt">na.value =</span> <span class="st">'white'</span>)<span class="op">+</span></a>
<a class="sourceLine" id="cb2-68" data-line-number="68"><span class="st"> </span>cowplot<span class="op">::</span><span class="kw">theme_cowplot</span>() <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-69" data-line-number="69"><span class="st"> </span><span class="kw">geom_text</span>(<span class="kw">aes</span>(<span class="dt">label =</span> value <span class="op">%>%</span><span class="st"> </span><span class="kw">round</span>(<span class="dv">2</span>) <span class="op">%>%</span><span class="st"> </span><span class="kw">format</span>(<span class="dt">nsmall=</span><span class="dv">2</span>))) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-70" data-line-number="70"><span class="st"> </span><span class="kw">scale_x_discrete</span>(<span class="dt">position=</span><span class="st">'top'</span>) <span class="op">+</span><span class="st"> </span><span class="kw">xlab</span>(<span class="st">''</span>) <span class="op">+</span><span class="st"> </span><span class="kw">ylab</span>(<span class="st">''</span>) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-71" data-line-number="71"><span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.text.x =</span> <span class="kw">element_text</span>(<span class="dt">angle =</span> <span class="dv">30</span>,<span class="dt">vjust =</span> <span class="fl">0.5</span>,<span class="dt">hjust =</span> <span class="dv">0</span>)) </a></code></pre></div>
<p><img 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" /><!-- --></p>
<p>Or try something new. Wonder which fighting style is more popular?</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" data-line-number="1">dnd_chars_unique_list <span class="op">%>%</span><span class="st"> </span>purrr<span class="op">::</span><span class="kw">map</span>(<span class="st">'choices'</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb3-2" data-line-number="2"><span class="st"> </span>purrr<span class="op">::</span><span class="kw">map</span>(<span class="st">'fighting style'</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb3-3" data-line-number="3"><span class="st"> </span>unlist <span class="op">%>%</span></a>
<a class="sourceLine" id="cb3-4" data-line-number="4"><span class="st"> </span>table <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb3-5" data-line-number="5"><span class="st"> </span><span class="kw">sort</span>(<span class="dt">decreasing =</span> <span class="ot">TRUE</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb3-6" data-line-number="6"><span class="st"> </span>as.data.frame <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb3-7" data-line-number="7"><span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x =</span> ., <span class="dt">y =</span> Freq)) <span class="op">+</span></a>
<a class="sourceLine" id="cb3-8" data-line-number="8"><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat=</span> <span class="st">'identity'</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb3-9" data-line-number="9"><span class="st"> </span>cowplot<span class="op">::</span><span class="kw">theme_cowplot</span>() <span class="op">+</span></a>
<a class="sourceLine" id="cb3-10" data-line-number="10"><span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.text.x=</span> <span class="kw">element_text</span>(<span class="dt">angle =</span> <span class="dv">45</span>,<span class="dt">hjust =</span> <span class="dv">1</span>))</a></code></pre></div>
<p><img 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" /><!-- --></p>
<h2 id="about-the-data">About the data</h2>
<h3 id="columnelement-description">Column/element description</h3>
<ul>
<li><p><strong>ip:</strong> A shortened hash of the IP address of the submitter</p></li>
<li><p><strong>finger:</strong> A shortened hash of the browser fingerprint of the submitter</p></li>
<li><p><strong>name:</strong> A shortened hash of character names</p></li>
<li><p><strong>race:</strong> Race of the character as coded by the app. May be unclear as the app inconsistently codes race/subrace information. See processedRace</p></li>
<li><p><strong>background:</strong> Background as it comes out of the application.</p></li>
<li><p><strong>date:</strong> Time & date of input. Dates before 2018-04-16 are unreliable as some has accidentally changed while moving files around.</p></li>
<li><p><strong>class:</strong> Class and level. Different classes are separated by | when needed.</p></li>
<li><p><strong>justClass:</strong> Class without level. Different classes are separated by | when needed.</p></li>
<li><p><strong>subclass:</strong> Subclass. Might be missing if the character is low level. Different classes are separated by | when needed.</p></li>
<li><p><strong>level:</strong> Total level</p></li>
<li><p><strong>feats:</strong> Feats chosen. Mutliple feats are separated by | when needed</p></li>
<li><p><strong>HP:</strong> Total HP</p></li>
<li><p><strong>AC:</strong> AC score</p></li>
<li><p><strong>Str, Dex, Con, Int, Wis, Cha:</strong> Ability score modifiers</p></li>
<li><p><strong>alignment:</strong> Alignment free text field. Since it’s a free text field, it includes alignments written in many forms. See processedAlignment, good and lawful to get the standardized alignment data.</p></li>
<li><p><strong>skills:</strong> List of proficient skills. Skills are separated by |.</p></li>
<li><p><strong>weapons:</strong> List of weapons, separated by |. This is a free text field. See processedWeapons for the standardized version</p></li>
<li><p><strong>spells:</strong> List of spells, separated by |. Each spell has its level next to it separated by *s. This is a free text field. See processedSpells for the standardized version</p></li>
<li><p><strong>castingStat:</strong> Casting stat as entered by the user. The format allows one casting stat so this is likely wrong if the character has different spellcasting classes. Also every character has a casting stat even if they are not casters due to the data format.</p></li>
<li><p><strong>choices:</strong> Character building choices. This field information about character properties such as fighting styles and skills chosen for expertise. Different choice types are separated by | when needed. The choice data is written as name of choice followed by a / followed by the choices that are separated by *s</p></li>
<li><p><strong>country:</strong> The origin of the submitter’s IP</p></li>
<li><p><strong>countryCode:</strong> 2 letter country code</p></li>
<li><p><strong>processedAlignment:</strong> Standardized version of the alignment column. I have manually matched each non standard spelling of alignment to its correct form. First character represents lawfulness (L, N, C), second one goodness (G,N,E). An empty string means alignment wasn’t written or unclear.</p></li>
<li><p><strong>good, lawful:</strong> Isolated columns for goodness and lawfulness</p></li>
<li><p><strong>processedRace:</strong> I have gone through the way race column is filled by the app and asigned them to correct races. Also includes some common races that are not natively supported such as warforged and changelings. If empty, indiciates a homebrew race not natively supported by the app.</p></li>
<li><p><strong>processedSpells:</strong> Formatting is same as spells. Standardized version of the spells column. Spells are matched to an official list using string similarity and some hardcoded rules.</p></li>
<li><p><strong>processedWeapons:</strong> Formatting is same as weapons. Standardized version of the weapons column. Created like the processedSpells column.</p></li>
<li><p><strong>levelGroup:</strong> Splits levels into groups. The groups represent the common ASI levels</p></li>
<li><p><strong>alias:</strong> A friendly alias that correspond to each uniqe name</p></li>
</ul>
<p>The list version of this dataset contains all of these fields but they are organised a little differently, keeping fields like <code>spells</code> and <code>processedSpells</code> together.</p>
<h3 id="caveats">Caveats</h3>
<h4 id="possible-issues-with-data-fields">Possible Issues with data fields</h4>
<p>Some data fields are more reliable than others. Below is a summary of all potential problems with the data fields</p>
<ul>
<li><p><strong>ip and browser fingerprints:</strong> Both IP and browser fingerprints are represented as hashes. I keep them to have an idea of individual users but did not make use of them so far. Note that same IPs can be shared by an entire region in some cases.</p></li>
<li><p><strong>processedAlignment:</strong> Alignment is a free text field in the app and optional. Many characters do not enter their alignments. To create the standardized alignment fields, I went through every entry and manually assigned every alternative spelling to the standardized version. These include mispelled entries, abreviations, entries in different languages etc. In cases where I wasn’t able to match (eg. what the hell is “lawful cute”), this field was left blank. Between automatic updates new and exciting ways to describe alignment can come into play. Unless I manually added these new entries, they will also appear blank.</p></li>
<li><p><strong>processedSpells:</strong> The mobile app allows entering free text into the spell fields. Which means I have to deal with people writing spells in a non-standard way with typos, abbreviations or additional information such as range, damage dice. I use some heuristics to match the entered text to a list of all published spells. Shortly, I look at the Levenshtein distance between the entry and the published spells and match the entry with the top result if</p>
<ul>
<li>the spell level is correct and,</li>
<li>there are not more than 10 substitutions/deletions/insertions or either entry or the potential match includes all words that the counterpart includes.</li>
<li>In addition, there are special cases for Bigby’s Hand, Tasha’s Hideous Laughter and Melf’s Acid Arrow as those spells are often written in their SRD form and match to wrong spells.</li>
</ul></li>
</ul>
<p>74% of all spells parsed did not require any modification. 21% of were only able to be matched through the heuristics. A manual examination of a random seleciton of these matches revealed 2/200 mistakes. 5% of the spell entries were not matched to an official spell. Manual observation of these entries revealed that the common reasons for a failure to match are users writing the spell under the wrong spell level, writing some class/race features such as blindsight as spells or adding/removing more than 10 charters when writing the spells either through abbreviation or adding additional information about the spell.</p>
<ul>
<li><strong>processedWeapons:</strong> Weapon names are also free text fields so a processing method similar to the one used for spells is used for weapon names. Instead of a threshold of 10 substititutions/deletions/insertions, 2 was used since weapon names typically did not include additional information like spell names did. Special cases were written for hand crossbow and heavy crossbow as they were typically mismatched to their official name (eg. “crossbow, hand”). Here the weapons that weren’t matched were spell names or homebrew weapons.</li>
</ul>
<p>81% of all weapons parsed did not require any modification. 14% of were only able to be matched through the heuristics. A manual examination of a random seleciton of these matches revealed 1/200 mistake. 5% of the weapon entries were not matched to an official weapon.</p>
<h4 id="possible-issues-with-detection-of-unique-characters">Possible issues with detection of unique characters</h4>
<p>Identification of unique characters rely on some heuristics. I assume any character with the same name and class is potentially the same character. In these cases I pick the highest level character. Race and other properties are not considered so some unique characters may be lost along the way. I have chosen to be less exact to reduce the nubmer of possible test characters since there were examples of people submitting essentially the same character with different races, presumably to test things out. For multiclassed characters, if a lower level character with the same name and a subset of classes exist, they are removed, again leaving the character with the highest level.</p>
<h4 id="possible-issues-with-selection-bias">Possible issues with selection bias</h4>
<p>This data comes from characters submitted to my web applications. The applications are written to support a popular third party character sheet app for mobile platforms. I have advertised my applications primarily on Reddit r/dndnext and r/dnd. I have seen them mentioned in a few other platforms by word of mouth. That means we are looking at subsamples of subsamples here, all of which can cause some amount of selection bias. Some characters could be thought experiments or for testing purposes and never see actual game play.</p>
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