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<a class='sr-only sr-only-focusable visually-hidden-focusable' href='#learnr-tutorial-content'>Skip to Tutorial Content</a>
<div class="pageContent band">
<main class="bandContent page">
<article class="topics" id="learnr-tutorial-content">
<div id="section-datenbasis" class="section level2">
<h2>Datenbasis</h2>
<div id="section-datenherkunft" class="section level3">
<h3>Datenherkunft</h3>
<p>In diesem Praktikum verwenden wir das Dataset “Board Games Analysis
🎲🎲🎯”. Die Daten wurden ursprünglich aus der BoardGameGeek-Datenbank
extrahiert und anschließend auf Kaggle hochgeladen, wo sie zu einem
Datensatz zusammengeführt wurden.</p>
<p>Link zum Notebook auf Kaggle: <a
href="https://www.kaggle.com/code/karnikakapoor/board-games-analysis"
class="uri">https://www.kaggle.com/code/karnikakapoor/board-games-analysis</a><br />
Link zum Datensatz auf Kaggle: <a
href="https://www.kaggle.com/code/karnikakapoor/board-games-analysis/input"
class="uri">https://www.kaggle.com/code/karnikakapoor/board-games-analysis/input</a></p>
</div>
<div id="section-inhalt" class="section level3">
<h3>Inhalt</h3>
<p>Der Datensatz umfasst 20.343 Einträge und enthält verschiedene
Informationen zu Brettspielen. Hier sind die Spalten und ihre jeweiligen
Inhalte:</p>
<p><strong>ID</strong>: Eine eindeutige Identifikationsnummer für jedes
Brettspiel im Datensatz.<em>(Integer)</em><br />
<strong>Name:</strong> Der Name des Brettspiels.<em>(String)</em><br />
<strong>Year:</strong> Das Jahr, in dem das Brettspiel veröffentlicht
wurde.<em>(Integer)</em><br />
<strong>Published:</strong> Informationen über die Veröffentlichung des
Spiels.<em>(Integer)</em><br />
<strong>Min Players:</strong> Die minimale Anzahl von Spielern, die das
Spiel spielen können.<em>(Integer)</em><br /><strong>Max Players:</strong> Die maximale Anzahl von Spielern, die das
Spiel unterstützt.<em>(Integer)</em><br />
<strong>Play Time:</strong> Die durchschnittliche Spielzeit des
Brettspiels.<em>(Integer)</em><br />
<strong>Min Age:</strong> Die empfohlene minimale Altersgruppe für
Spieler des Spiels.<em>(Integer)</em><br />
<strong>Users Rated:</strong> Die Anzahl der Benutzer, die das Spiel
bewertet haben.<em>(Double)</em><br />
<strong>Rating Average:</strong> Der durchschnittliche Bewertungswert
des Spiels durch Benutzer.<em>(Double)</em><br />
<strong>BGG Rank:</strong> Die Rangposition des Spiels auf
BoardGameGeek.<em>(Integer)</em><br />
<strong>Complexity Average:</strong> Der durchschnittliche
Schwierigkeitsgrad des Spiels.<em>(Double)</em><br />
<strong>Owned Users:</strong> Die Anzahl der Benutzer, die das Spiel
besitzen.<em>(Integer)</em><br />
<strong>Mechanics:</strong> Informationen über die Spielmechanismen, die
im Brettspiel verwendet werden.<em>(String)</em><br />
<strong>Domains:</strong> Die Domänen oder Kategorien, zu denen das
Brettspiel gehört.<em>(String)</em><br />
</p>
<p>Diese Spalten bieten eine breite Palette von Informationen über die
charakteristischen Merkmale, Bewertungen und Eigenschaften der
aufgeführten Brettspiele im Datensatz. Die Daten können für Analysen und
Erkenntnisse im Bereich der Brettspielindustrie sowie für die
Beurteilung von Präferenzen und Trends unter den Spielern verwendet
werden.</p>
</div>
<div id="section-hypothesen" class="section level3">
<h3>Hypothesen</h3>
<p>Wir haben zwei Hypothesen formuliert, die wir im
“Datenanalyse”-Abschnitt testen werden.</p>
<p><strong>Hypothese 1:</strong> Spiele mit höheren Complexity Score
haben niedriger anzahl von Owned Users.</p>
<p><strong>Hypothese 2:</strong> Spiele mit höheren Complexity Score
haben höhere Play Time.</p>
</div>
</div>
<div id="section-datenaufbereitung" class="section level2">
<h2>Datenaufbereitung</h2>
<div id="section-einlesen-von-daten" class="section level3">
<h3>Einlesen von Daten</h3>
<p>Um Datensätze mit R zu analysieren, mussen diese zuerst eingelesen
werden. Je nach Struktur des Datenfiles kommen verschiedene
Einlese-Befehle zur Anwendung. Es können u.a. Textfiles (.txt, .dat),
Datenfiles (.csv) und Excel-Files eingelesen werden.</p>
<div id="section-xlsx-dateien-in-r-importieren" class="section level4">
<h4>XLSX-Dateien in R importieren</h4>
<p>In diesem Tutorial benutzen wir eine .xlsx Datei, die wir einlesen
müssen. XLSX-Dateien lassen sich aus Excel heraus speichern und sind
heutzutage weit verbreitet.</p>
<p>Für diese Aufgabe bietet sich das readxl-Paket an, das am einfachsten
mit den folgenden Befehlen installiert und geladen wird:</p>
<div class="tutorial-exercise" data-label="install-readxl"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code>install.packages("readxl")
library (readxl)</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<p>Der Package ist schonmal installiert und wird verwendet, deswegen
lässt R nicht zu, dass es wieder installiert wird.</p>
<p>Wir jetzt können unser Datensatz einlesen:</p>
<div class="tutorial-exercise" data-label="read-data"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code>gameData <- read_excel("bgg_dataset.xlsx", sheet = "Sheet")</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<p>Aufgrund der Arbeitsverzeichnis funktioniert leider diese Funktion indiesem Dokument nicht, aber keine Sorge, wir haben sie im Hintergrund
genauso wie hier benutzt.</p>
<p>Wir können jetzt unsere Daten benutzen:</p>
<div class="tutorial-exercise" data-label="show-data"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code>gameData</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<p>Wir werden aber häufig nicht mit dem gesamten Datensatz arbeiten,
sondern nur mit einigen Spalten. Nur eine Spalte aus dem Datensatz lässt
sich wie folgt lesen:</p>
<div class="tutorial-exercise" data-label="read-column"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code>gameData$`Year Published`</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
</div>
<div id="section-zeit-zum-üben" class="section level4">
<h4>Zeit zum üben!</h4>
<p>Schreiben Sie den Code, um die Spalte <code>Mechanics</code> zu
lesen:</p>
<div class="tutorial-exercise" data-label="read-mechanics-column"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support"
data-label="read-mechanics-column-hint" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>gameData$`Mechanics`</code></pre>
</div>
<p><em>Wie Sie weitere Dateientypen einlesen und benutzen können, finden
Sie in diesem <a
href="https://bjoernwalther.com/daten-in-r-importieren/">Tutorial von
Björn Walther</a></em></p>
</div>
</div>
<div id="section-leerstellen-beheben" class="section level3">
<h3>Leerstellen beheben</h3>
<p>Das Beheben von Leerstellen in R Studio erfordert spezifische
Schritte, die je nach Art der Daten und der Art der Probleme variieren
können.</p>
<div id="section-in-einer-bestimmten-spalte" class="section level4">
<h4>In einer bestimmten Spalte:</h4>
<div class="tutorial-exercise" data-label="remove-empties-column"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code>gameData$`Year Published` <- trimws(gameData$`Year Published`)
gameData$`Year Published`</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
</div>
<div id="section-in-allen-spalten" class="section level4">
<h4>In allen Spalten:</h4>
<div class="tutorial-exercise" data-label="remove-empties"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code>gameData <- lapply(gameData, trimws)
gameData</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<p>In unserem Dataset scheint alles gut zu sein, deswegen haben wir
keine Änderungen nach Ausführung den Befehlen.</p>
</div>
</div>
<div id="section-inkonsistenzen-beheben" class="section level3">
<h3>Inkonsistenzen beheben</h3><p>In einem Datensatz beziehen sich Inkonsistenzen auf Unstimmigkeiten,
Widersprüche oder Fehler, die in den Daten vorhanden sind und
möglicherweise die Qualität der Daten beeinträchtigen. Die unterstehende
Befehle helfen uns, diese zu Inkonsistenzen zu beheben.</p>
<div id="section-standardisierung-der-schreibweise"
class="section level4">
<h4>Standardisierung der Schreibweise:</h4>
<div class="tutorial-exercise" data-label="to-lowercase-column"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code>gameData$`Mechanics` <- tolower(gameData$`Mechanics`)
gameData$`Mechanics`</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<p>Somit wurden alle Zeichenketten in der Spalte “Mechanics” in
Kleinbuchstaben umgewandelt.</p>
</div>
<div id="section-ersetzen-von-werten" class="section level4">
<h4>Ersetzen von Werten:</h4>
<div class="tutorial-exercise" data-label="replace-variable"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code>gameData$`Mechanics` <- gsub("Action", "!!!ACTION!!!", gameData$`Mechanics`)
gameData$`Mechanics`</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<p>Somit haben wir alle treffer für “Action” mit “!!!ACTION!!!”
ersetzt.</p>
</div>
<div id="section-behandlung-von-fehlenden-werten"
class="section level4">
<h4>Behandlung von fehlenden Werten:</h4>
<div class="tutorial-exercise" data-label="replace-missing-variable"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code>gameData$`Rating Average`[is.na(gameData$`Rating Average`)] <- mean(gameData$`Rating Average`, na.rm = TRUE)</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<p>Dieser Befehl ersetzt fehlende Werte durch der Median der vorhandenen
Werten in der Spalte. Was der Median ist, finden Sie in dem nächsten
Abschnitt über Datenanalyse.</p>
</div>
<div id="section-entfernen-von-duplikaten" class="section level4">
<h4>Entfernen von Duplikaten</h4>
<p>Dieser Befehl entfernt Duplikaten basierend auf allen Spalten. Wir
haben aber in unserem Datensatz keine Duplikaten.<br />
Zum Veranschaulichen, benutzen wir ein Vector.</p>
<div class="tutorial-exercise" data-label="remove-duplicates"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code>vector <- c(1, 2, 3, 3, 5, 2, 1, 7, 10, 5)
vector <- unique(vector)
vector</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
</div>
<div id="section-behandlung-von-sonderzeichen" class="section level4">
<h4>Behandlung von Sonderzeichen</h4>
<div class="tutorial-exercise" data-label="replace-special-chars"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code>gameData$`Mechanics` <- gsub("[^a-zA-Z0-9 ]", "", gameData$`Mechanics`)
gameData$`Mechanics`</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<p>Dieser Befehl entfernt Sonderzeichen in der Spalte “Mechanics”. In
diesem Fall sind Characters, die nicht eine Buchstabe oder Ziffer sind,
entfernt worden (auch die Kommas).</p>
</div><div id="section-notice" class="section level4">
<h4>Notice</h4>
<p>Diese Beispiele sind allgemeiner Natur, und Sie sollten sie an Ihre
spezifischen Daten und Probleme anpassen. Inkonsistenzen und Leerstellen
können in verschiedenen Formen auftreten, und es ist wichtig, die
geeigneten Methoden für Ihre speziellen Daten anzuwenden. Beachten Sie
auch, dass Datenbereinigung oft datenabhängig ist, und eine gründliche
Analyse der Daten ist vor der Bereinigung ratsam.</p>
</div>
</div>
<div
id="section-jetzt-sind-sie-dran-testen-sie-was-sie-in-diesem-abschnitt-über-daten-einlesen-gelernt-haben"
class="section level3">
<h3>Jetzt sind Sie dran! Testen Sie was Sie in diesem Abschnitt über
Daten einlesen gelernt haben:</h3>
<div class="panel-heading tutorial-quiz-title"><span data-i18n="text.quiz">Quiz</span></div>
<div class="panel panel-default tutorial-question-container">
<div data-label="second-quiz-1" class="tutorial-question panel-body">
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<div id="second-quiz-1-message_container" class="shiny-html-output"></div>
<div id="second-quiz-1-action_button_container" class="shiny-html-output"></div>
<script>if (Tutorial.triggerMathJax) Tutorial.triggerMathJax()</script>
</div>
</div>
<div class="panel panel-default tutorial-question-container">
<div data-label="second-quiz-2" class="tutorial-question panel-body">
<div id="second-quiz-2-answer_container" class="shiny-html-output"></div>
<div id="second-quiz-2-message_container" class="shiny-html-output"></div>
<div id="second-quiz-2-action_button_container" class="shiny-html-output"></div>
<script>if (Tutorial.triggerMathJax) Tutorial.triggerMathJax()</script>
</div>
</div>
<div class="panel panel-default tutorial-question-container">
<div data-label="second-quiz-3" class="tutorial-question panel-body">
<div id="second-quiz-3-answer_container" class="shiny-html-output"></div>
<div id="second-quiz-3-message_container" class="shiny-html-output"></div>
<div id="second-quiz-3-action_button_container" class="shiny-html-output"></div>
<script>if (Tutorial.triggerMathJax) Tutorial.triggerMathJax()</script>
</div>
</div>
<div class="panel panel-default tutorial-question-container">
<div data-label="second-quiz-4" class="tutorial-question panel-body">
<div id="second-quiz-4-answer_container" class="shiny-html-output"></div>
<div id="second-quiz-4-message_container" class="shiny-html-output"></div>
<div id="second-quiz-4-action_button_container" class="shiny-html-output"></div>
<script>if (Tutorial.triggerMathJax) Tutorial.triggerMathJax()</script>
</div>
</div>
</div>
</div>
<div id="section-datenanalyse" class="section level2">
<h2>Datenanalyse</h2>
In diesem Teil wird beschrieben, wie man verschiedene Daten mit
Funktionen ermitteln kann.<br />
Hierfuer sollte man sich die Daten erneut anschauen.<br />
Fuer diesen Teil ist es wichtig zu wissen, wie man auf eine Spalte
zugreifen kann. Dies macht man in dem man die Variable in dem der
Datensatz gespeichert ist (hier gameData) angibt und den Spaltennamen in
entweder zwei eckigen Klammern oder hinter einem $ Zeichen
definiert.<br />
Die Syntax waere dann beispielsweise:<br />
<code>data[["column"]])</code> oder <code>data$column</code>, bzw.
<code>data$"spaced column name"</code><br />
<br />
Die Tabelle unten zeigt alle Daten aus dem Datensatz. Mit den Pfeilen in
der oberen Reihe kann man die durch die Spalten schalten.
<div data-pagedtable="false">
<script data-pagedtable-source type="application/json">
{"columns":[{"label":["ID"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["Name"],"name":[2],"type":["chr"],"align":["left"]},{"label":["Year Published"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["Min Players"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["Max Players"],"name":[5],"type":["dbl"],"align":["right"]},{"label":["Play Time"],"name":[6],"type":["dbl"],"align":["right"]},{"label":["Min Age"],"name":[7],"type":["dbl"],"align":["right"]},{"label":["Users Rated"],"name":[8],"type":["dbl"],"align":["right"]},{"label":["Rating Average"],"name":[9],"type":["dbl"],"align":["right"]},{"label":["BGG Rank"],"name":[10],"type":["dbl"],"align":["right"]},{"label":["Complexity Average"],"name":[11],"type":["dbl"],"align":["right"]},{"label":["Owned Users"],"name":[12],"type":["dbl"],"align":["right"]},{"label":["Mechanics"],"name":[13],"type":["chr"],"align":["left"]},{"label":["Domains"],"name":[14],"type":["chr"],"align":["left"]}],"data":[{"1":"174430","2":"Gloomhaven","3":"2017","4":"1","5":"4","6":"120","7":"14","8":"42055","9":"8.79","10":"1","11":"3.86","12":"68323","13":"Action Queue, Action Retrieval, Campaign / Battle Card Driven, Card Play Conflict Resolution, Communication Limits, Cooperative Game, Deck Construction, Deck Bag and Pool Building, Grid Movement, Hand Management, Hexagon Grid, Legacy Game, Modular Board, Once-Per-Game Abilities, Scenario / Mission / Campaign Game, Simultaneous Action Selection, Solo / Solitaire Game, Storytelling, Variable Player Powers","14":"Strategy Games, Thematic Games"},{"1":"161936","2":"Pandemic Legacy: Season 1","3":"2015","4":"2","5":"4","6":"60","7":"13","8":"41643","9":"8.61","10":"2","11":"2.84","12":"65294","13":"Action Points, Cooperative Game, Hand Management, Legacy Game, Point to Point Movement, Set Collection, Trading, Variable Player Powers","14":"Strategy Games, Thematic Games"},{"1":"224517","2":"Brass: Birmingham","3":"2018","4":"2","5":"4","6":"120","7":"14","8":"19217","9":"8.66","10":"3","11":"3.91","12":"28785","13":"Hand Management, Income, Loans, Market, Network and Route Building, Score-and-Reset Game, Tech Trees / Tech Tracks, Turn Order: Stat-Based, Variable Set-up","14":"Strategy Games"},{"1":"167791","2":"Terraforming Mars","3":"2016","4":"1","5":"5","6":"120","7":"12","8":"64864","9":"8.43","10":"4","11":"3.24","12":"87099","13":"Card Drafting, Drafting, End Game Bonuses, Hand Management, Hexagon Grid, Income, Set Collection, Solo / Solitaire Game, Take That, Tile Placement, Turn Order: Progressive, Variable Player Powers","14":"Strategy Games"},{"1":"233078","2":"Twilight Imperium: Fourth Edition","3":"2017","4":"3","5":"6","6":"480","7":"14","8":"13468","9":"8.70","10":"5","11":"4.22","12":"16831","13":"Action Drafting, Area Majority / Influence, Area-Impulse, Dice Rolling, Follow, Grid Movement, Hexagon Grid, Modular Board, Trading, Variable Phase Order, Variable Player Powers, Voting","14":"Strategy Games, Thematic Games"},{"1":"291457","2":"Gloomhaven: Jaws of the Lion","3":"2020","4":"1","5":"4","6":"120","7":"14","8":"8392","9":"8.87","10":"6","11":"3.55","12":"21609","13":"Action Queue, Campaign / Battle Card Driven, Communication Limits, Cooperative Game, Critical Hits and Failures, Deck Construction, Grid Movement, Hand Management, Hexagon Grid, Legacy Game, Line of Sight, Once-Per-Game Abilities, Scenario / Mission / Campaign Game, Simultaneous Action Selection, Solo / Solitaire Game, Variable Player Powers","14":"Strategy Games, Thematic Games"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
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<p><br />
</p>
<div id="section-mittelwert" class="section level3">
<h3>Mittelwert</h3>
Der Mittelwert bestimmt den Durchschnitt aller gegebenen Werte. Die
Rechnung dafuer ist:<br />
<span class="math inline">\((x_{1} + x_{2} + \cdots + x_{n}) /
x\)</span><br />
<br />
Um den Mittelwert einer Spalte zu bestimmen, vewendet man die ‘mean()’
Funktion verwenden. In diese setzt man die Variable in der die Daten
gespeichert sind (gameData) und gibt die gewuenschte Spalte an.<br />
<br />
Bestimmen Sie den Mittelwert von “Play Time”.
<div class="tutorial-exercise" data-label="mean" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<p>Sie sollten hier den Wert 99.884 bekommen.<br />
</p>
</div>
<div id="section-median" class="section level3">
<h3>Median</h3>
Der Median ist der Mittelpunkt aller gegebenen Werte.<br />
<br />
Der Median wird durch die ‘median()’ Funktion bestimmt.<br />
Da das schreiben von <code>gameData[["Play Time"]]</code> etwas dauert,
kann man ab hier die Variable ‘ptime’ verwenden.<br />
Bestimmen Sie den Median von “Play Time”.
<div class="tutorial-exercise" data-label="median" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>ptime <- gameData[["Play Time"]]</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<p>Sie sollten hier den Wert 90 bekommen.<br />
</p>
</div>
<div id="section-varianz" class="section level3">
<h3>Varianz</h3>
Die Varianz bestimmt die Verteilung um den Mittelwert.<br />
<br />
Die Varianz wird durch die ‘var()’ Funktion bestimmt.<br />
Bestimmen sie die Varianz von “Play Time”.
<div class="tutorial-exercise" data-label="variance" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>ptime <- gameData$"Play Time"</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<p>Sie sollten hier den Wert 6070.055 bekommen.<br />
</p>
</div>
<div id="section-standardabweichung" class="section level3">
<h3>Standardabweichung</h3>
Die Standardabweichung bestimmt die Wurzel Verteilung um den
Mittelwert.<br />
<br />
Die Varianz wird durch die ‘sd()’ Funktion bestimmt.<br />
Bestimmen sie die Standardabweichung von “Play Time”.
<div class="tutorial-exercise" data-label="standard-deviation"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code>ptime <- gameData$"Play Time"</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<p>Sie sollten hier den Wert 77.91056 bekommen.<br />
</p></div>
<div id="section-lineare-regression" class="section level3">
<h3>Lineare Regression</h3>
Die Lineare Regression bestimmt das Verhaeltnis zwischen zwei
Werten(-sets).<br />
<br />
Die Varianz wird durch die ‘lm()’ Funktion bestimmt Hier uebergibt man
Daten welche mit einem Tilde-Symbol (~) verbunden sind, also
beispielsweise <code>lm(x~y)</code>.<br />
Bestimmen sie die Lineare Regression mit “Users Rated” im Verhaeltnis zu
“Owned Users”.
<div class="tutorial-exercise" data-label="linear-regression"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code>raters <- gameData$"Users Rated"
owned <- gameData$"Owned Users"</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<p>Falls man etwas mehr information will, kann man die Eingabe (oder die
Variale in welche man dies gespeichert hat) noch in die
<code>summary()</code> Funktion schreiben.<br />
Weiterhin kann man dies auf ein Plot abbilden. Dazu verwendet man die
<code>plot()</code> Funktion in welche man einen x und y Wert eingibt,
zusammen mit Optionen wie das Plot aussehen soll. Mehr information dazu
findet man <a
href="https://www.rdocumentation.org/packages/graphics/versions/3.6.2/topics/plot">in
der Dokumentation</a>.<br />
Mit <code>abline()</code> kann man die Regressionslinie der lm Funktion
abbilden. Dazu wird die lm Funktion, oder die Variable in der diese
gespeichert ist, an <code>abline()</code> uebergeben.
<img src="tutorial_page_files/figure-html/lm-plot-example-1.png" width="624" /></p>
Versuchen Sie nun den oben gezeigten Plot selbst zu erstellen. Die Farbe
und Parameter sind bereits vorgegeben.
<div class="tutorial-exercise" data-label="lm-plot" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="5"
data-pipe="|>">
<pre class="text"><code>raters <- gameData$"Users Rated"
owned <- gameData$"Owned Users"
cavg <- gameData$"Complexity Average"
x = lm()
plot(, , pch = 16, col = "blue")</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<p><br />
</p>
</div>
<div id="section-hypothesentest" class="section level3">
<h3>Hypothesentest</h3>
<p>R hat Funktionen zum Testen von Hypothesen. Bei den bereits gegebenen
Hypothesen eignet sich der <a
href="https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/chisq.test">Chi-squared
test</a>. Dieser wird mit <code>chisq.test()</code> aufgerufen und nimmt
zwei Wertesets an mit weiteren Parametern fuer genaueres einstellen. Der
ausgegebene p-Wert (p-value) kann dann mit dem Signifikanzwert
(typischerweise 5% / 0.05) verglichen werden. Ist der Wert groesser als
0.05 kann man annehmen, dass die Hypothese stimmt.</p>
<div class="tutorial-exercise" data-label="hypothesis-test"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="4" data-pipe="|>">
<pre class="text"><code>cavg <- gameData$"Complexity Average"
owned <- gameData$"Owned Users"
ptime <- gameData$"Play Time"</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="panel-heading tutorial-quiz-title"><span data-i18n="text.quiz">Quiz</span></div>
<div class="panel panel-default tutorial-question-container">
<div data-label="hypothesis-quiz-1" class="tutorial-question panel-body">
<div id="hypothesis-quiz-1-answer_container" class="shiny-html-output"></div>
<div id="hypothesis-quiz-1-message_container" class="shiny-html-output"></div>
<div id="hypothesis-quiz-1-action_button_container" class="shiny-html-output"></div><script>if (Tutorial.triggerMathJax) Tutorial.triggerMathJax()</script>
</div>
</div>
<div class="panel panel-default tutorial-question-container">
<div data-label="hypothesis-quiz-2" class="tutorial-question panel-body">
<div id="hypothesis-quiz-2-answer_container" class="shiny-html-output"></div>
<div id="hypothesis-quiz-2-message_container" class="shiny-html-output"></div>
<div id="hypothesis-quiz-2-action_button_container" class="shiny-html-output"></div>
<script>if (Tutorial.triggerMathJax) Tutorial.triggerMathJax()</script>
</div>
</div>
</div>
</div>
<div id="section-ergebnispräsentation" class="section level2">
<h2>Ergebnispräsentation</h2>
<p>In diesem Kapitel beschäftigen wir uns mit ausgewählten grafischen
Darstellungsmöglichkeiten von Daten und wie man diese in RStudio
erstellen kann.</p>
<div id="section-streudiagramm" class="section level3">
<h3>Streudiagramm</h3>
<p>Ein Steudiagramm wird häufig verwendet, um einen Zusammenhang
zwischen Variablen darzustellen. In RStudio kann man mit Hilfe des
plot()-Befehls ein solches Streudiagramm erzeugen. Hier ein
Beispiel:</p>
<img src="tutorial_page_files/figure-html/plot-1.png" width="624" /><br />
Der Code für dieses Streudiagramms ist grob zusammengefasst:<br />
<br />
<strong>plot(gameData[[“xxx”]],gameData[[“yyy”]],xlim=c(a,z))</strong><br />
<br />
Enthalten sind hier drei Parameter, die beiden Variablen und eine
Begrenzung. Wobei xlim eine Begrenzung der x-Achse, und damit die
Begrenzung der Werte von “xxx” darstellt, um ein genaueres Bild zu
erhalten.<br />
<br />
<br />
<br />
Erstellen Sie nun anhand des Beispiels ein einfaches Streudiagramm
welches den Zusammenhang zwischen “Min Age” und “Complexity Average”
darstellt:
<div class="tutorial-exercise" data-label="plotExercise"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support" data-label="plotExercise-hint"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code>plot(gameData[["Complexity Average"]],gameData[["Min Age"]])</code></pre>
</div>
<p><br />
<br />
</p>
</div>
<div id="section-boxplot-diagramm" class="section level3">
<h3>Boxplot-Diagramm</h3>
<p>Ein Boxplot ist ein Diagramm, dass verschiedene Lageparameter und
Streuparameter abbildet und damit einen ersten groben Überblick über
eine Verteilung gibt. Meistens verwendet man einen Boxplot um schnell
eine Übersicht über Median, Quartile, Minimal- und Maximalwerte sowie
Ausreißer zu erhalten.<br />
Folgend ein Beispiel:
<img src="tutorial_page_files/figure-html/ggplot-1.png" width="624" /><br />
<br />
Anhand des Beispiels nun ein kleines Quiz zur Verdeutlichung der
Begrifflichkeiten:<br />
</p>
<div class="panel-heading tutorial-quiz-title"><span data-i18n="text.quiz">Quiz</span></div>
<div class="panel panel-default tutorial-question-container">
<div data-label="quiz-ggplot-1" class="tutorial-question panel-body">
<div id="quiz-ggplot-1-answer_container" class="shiny-html-output"></div><div id="quiz-ggplot-1-message_container" class="shiny-html-output"></div>
<div id="quiz-ggplot-1-action_button_container" class="shiny-html-output"></div>
<script>if (Tutorial.triggerMathJax) Tutorial.triggerMathJax()</script>
</div>
</div>
<div class="panel panel-default tutorial-question-container">
<div data-label="quiz-ggplot-2" class="tutorial-question panel-body">
<div id="quiz-ggplot-2-answer_container" class="shiny-html-output"></div>
<div id="quiz-ggplot-2-message_container" class="shiny-html-output"></div>
<div id="quiz-ggplot-2-action_button_container" class="shiny-html-output"></div>
<script>if (Tutorial.triggerMathJax) Tutorial.triggerMathJax()</script>
</div>
</div>
<div class="panel panel-default tutorial-question-container">
<div data-label="quiz-ggplot-3" class="tutorial-question panel-body">
<div id="quiz-ggplot-3-answer_container" class="shiny-html-output"></div>
<div id="quiz-ggplot-3-message_container" class="shiny-html-output"></div>
<div id="quiz-ggplot-3-action_button_container" class="shiny-html-output"></div>
<script>if (Tutorial.triggerMathJax) Tutorial.triggerMathJax()</script>
</div>
</div>
<div class="panel panel-default tutorial-question-container">
<div data-label="quiz-ggplot-4" class="tutorial-question panel-body">
<div id="quiz-ggplot-4-answer_container" class="shiny-html-output"></div>
<div id="quiz-ggplot-4-message_container" class="shiny-html-output"></div>
<div id="quiz-ggplot-4-action_button_container" class="shiny-html-output"></div>
<script>if (Tutorial.triggerMathJax) Tutorial.triggerMathJax()</script>
</div>
</div>
<p><br />
<br />
Um ein Boxplot-Diagramm zu erstellen, muss man das ggplot2-package
installiert haben, der grobe Syntax fürs Diagramm selbst sieht wiefolgt
aus:<br />
<strong>ggplot(gameData, aes(y = .data[[“xxx”]], group = .data[[“yyy”]],
x = .data[[“yyy”]])) + geom_boxplot()</strong><br />
<br />
<br />
<br />
</p>
</div>
<div id="section-säulendiagramme" class="section level3">
<h3>Säulendiagramme</h3>
Das bekannte Säulendiagramm wird meistens verwendet um eine Veränderung
im Laufe der Zeit zu zeigen. Mit Säulendiagrammen kann man Steigerung,
Rückgang, Stagnation als auch eine Häufigkeitsverteilung deutlich
zeigen. Um ein Säulendiagramm in R mit Häufigkeitsverteilung zu
erhalten, gibt man den Befehl:<br />
<br />
<strong>barplot(table(gameData[[“xxx”]])</strong> ein und kann um die
Achsen näher zu beschreiben noch **xlab=““** und
<strong>ylab=””</strong> einfügen.<br />
<br />
Folgend ein Beispiel für ein Säulendiagramm mit
Häufigkeitsverteilung:<br />
<img src="tutorial_page_files/figure-html/barplot-1.png" width="624" /><br />
<br />
Geben Sie nun ein Säulendiagramm mit den Werten von”Complexity Average”
und Achsenbeschriftung aus:<br />
<div class="tutorial-exercise" data-label="barplotExercise"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support" data-label="barplotExercise-hint"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code>barplot(gameData[["Complexity Average"]], xlab = "Spiel" , ylab = "Score")</code></pre>
</div></div>
</div>
<div id="section-über-uns" class="section level2">
<h2>Über uns</h2>
<div id="section-lino-cortese" class="section level3">
<h3>Lino Cortese</h3>
<p>Lino ist verantwortlich für die Ausarbeitung des
Datenanalyse-Abschnitts.</p>
</div>
<div id="section-maximilian-fronmüller" class="section level3">
<h3>Maximilian Fronmüller</h3>
<p>Maxi hat sich auf die Erstellung des Ergebnispräsentation-Abschnitts
konzentriert.</p>
</div>
<div id="section-nikolay-nikolaev" class="section level3">
<h3>Nikolay Nikolaev</h3>
<p>Nikolay hat an die Abschnitte Datenbasis und Datenaufbereitung
gearbeitet.</p>
<p>
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engine = "r")), code_check = NULL, error_check = NULL, check = NULL,
solution = NULL, tests = NULL, options = list(eval = FALSE,
echo = TRUE, results = "markup", tidy = FALSE, tidy.opts = NULL,
collapse = FALSE, prompt = FALSE, comment = NA, highlight = FALSE,
size = "normalsize", background = "#F7F7F7", strip.white = TRUE,
cache = 0, cache.path = "tutorial_page_cache/html/",
cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,
autodep = FALSE, cache.rebuild = FALSE, fig.keep = "high",
fig.show = "asis", fig.align = "default", fig.path = "tutorial_page_files/figure-html/",
dev = "png", dev.args = NULL, dpi = 192, fig.ext = "png",
fig.width = 6.5, fig.height = 4, fig.env = "figure",
fig.cap = NULL, fig.scap = NULL, fig.lp = "fig:", fig.subcap = NULL,
fig.pos = "", out.width = 624, out.height = NULL, out.extra = NULL,
fig.retina = 2, external = TRUE, sanitize = FALSE, interval = 1,
aniopts = "controls,loop", warning = TRUE, error = FALSE,
message = TRUE, render = NULL, ref.label = NULL, child = NULL,
engine = "r", split = FALSE, include = TRUE, purl = TRUE,
max.print = 1000, label = "read-data", exercise = TRUE,
code = "gameData <- read_excel(\"bgg_dataset.xlsx\", sheet = \"Sheet\")",
out.width.px = 624, out.height.px = 384, params.src = "read-data, exercise=TRUE",
fig.num = 0, exercise.df_print = "paged", exercise.checker = "NULL"),
engine = "r", version = "4"), class = c("r", "tutorial_exercise"
)))
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-show-data-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-show-data-code-editor`)), session)
output$`tutorial-exercise-show-data-output` <- renderUI({
`tutorial-exercise-show-data-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
learnr:::store_exercise_cache(structure(list(label = "show-data", global_setup = structure(c("library(learnr)", "library(readxl)", "library(\"ggplot2\")", "options(scipen = 10)",
"gameData <- read_excel(\"bgg_dataset.xlsx\", sheet = \"Sheet\")",
"knitr::opts_chunk$set(echo = FALSE)"), chunk_opts = list(label = "setup",
include = FALSE)), setup = NULL, chunks = list(list(label = "show-data",
code = "gameData", opts = list(label = "\"show-data\"", exercise = "TRUE"),
engine = "r")), code_check = NULL, error_check = NULL, check = NULL,
solution = NULL, tests = NULL, options = list(eval = FALSE,
echo = TRUE, results = "markup", tidy = FALSE, tidy.opts = NULL,
collapse = FALSE, prompt = FALSE, comment = NA, highlight = FALSE,
size = "normalsize", background = "#F7F7F7", strip.white = TRUE,
cache = 0, cache.path = "tutorial_page_cache/html/",
cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,
autodep = FALSE, cache.rebuild = FALSE, fig.keep = "high",
fig.show = "asis", fig.align = "default", fig.path = "tutorial_page_files/figure-html/",
dev = "png", dev.args = NULL, dpi = 192, fig.ext = "png",
fig.width = 6.5, fig.height = 4, fig.env = "figure",
fig.cap = NULL, fig.scap = NULL, fig.lp = "fig:", fig.subcap = NULL,
fig.pos = "", out.width = 624, out.height = NULL, out.extra = NULL,
fig.retina = 2, external = TRUE, sanitize = FALSE, interval = 1,
aniopts = "controls,loop", warning = TRUE, error = FALSE,
message = TRUE, render = NULL, ref.label = NULL, child = NULL,
engine = "r", split = FALSE, include = TRUE, purl = TRUE,
max.print = 1000, label = "show-data", exercise = TRUE,
code = "gameData", out.width.px = 624, out.height.px = 384,
params.src = "show-data, exercise=TRUE", fig.num = 0,
exercise.df_print = "paged", exercise.checker = "NULL"),
engine = "r", version = "4"), class = c("r", "tutorial_exercise"
)))
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-read-column-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-read-column-code-editor`)), session)
output$`tutorial-exercise-read-column-output` <- renderUI({
`tutorial-exercise-read-column-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
learnr:::store_exercise_cache(structure(list(label = "read-column", global_setup = structure(c("library(learnr)",
"library(readxl)", "library(\"ggplot2\")", "options(scipen = 10)",
"gameData <- read_excel(\"bgg_dataset.xlsx\", sheet = \"Sheet\")",
"knitr::opts_chunk$set(echo = FALSE)"), chunk_opts = list(label = "setup",
include = FALSE)), setup = NULL, chunks = list(list(label = "read-column",
code = "gameData$`Year Published`", opts = list(label = "\"read-column\"",
exercise = "TRUE"), engine = "r")), code_check = NULL,
error_check = NULL, check = NULL, solution = NULL, tests = NULL,
options = list(eval = FALSE, echo = TRUE, results = "markup",
tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,
comment = NA, highlight = FALSE, size = "normalsize",
background = "#F7F7F7", strip.white = TRUE, cache = 0,
cache.path = "tutorial_page_cache/html/", cache.vars = NULL,
cache.lazy = TRUE, dependson = NULL, autodep = FALSE,
cache.rebuild = FALSE, fig.keep = "high", fig.show = "asis",
fig.align = "default", fig.path = "tutorial_page_files/figure-html/",
dev = "png", dev.args = NULL, dpi = 192, fig.ext = "png",
fig.width = 6.5, fig.height = 4, fig.env = "figure",
fig.cap = NULL, fig.scap = NULL, fig.lp = "fig:", fig.subcap = NULL,
fig.pos = "", out.width = 624, out.height = NULL, out.extra = NULL,
fig.retina = 2, external = TRUE, sanitize = FALSE, interval = 1,
aniopts = "controls,loop", warning = TRUE, error = FALSE,
message = TRUE, render = NULL, ref.label = NULL, child = NULL,
engine = "r", split = FALSE, include = TRUE, purl = TRUE,
max.print = 1000, label = "read-column", exercise = TRUE,
code = "gameData$`Year Published`", out.width.px = 624,
out.height.px = 384, params.src = "read-column, exercise=TRUE",
fig.num = 0, exercise.df_print = "paged", exercise.checker = "NULL"),
engine = "r", version = "4"), class = c("r", "tutorial_exercise"
)))
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-read-mechanics-column-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-read-mechanics-column-code-editor`)), session)
output$`tutorial-exercise-read-mechanics-column-output` <- renderUI({
`tutorial-exercise-read-mechanics-column-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
learnr:::store_exercise_cache(structure(list(label = "read-mechanics-column", global_setup = structure(c("library(learnr)",
"library(readxl)", "library(\"ggplot2\")", "options(scipen = 10)",
"gameData <- read_excel(\"bgg_dataset.xlsx\", sheet = \"Sheet\")",
"knitr::opts_chunk$set(echo = FALSE)"), chunk_opts = list(label = "setup",
include = FALSE)), setup = NULL, chunks = list(list(label = "read-mechanics-column",
code = "", opts = list(label = "\"read-mechanics-column\"",
exercise = "TRUE"), engine = "r")), code_check = NULL,
error_check = NULL, check = NULL, solution = NULL, tests = NULL,
options = list(eval = FALSE, echo = TRUE, results = "markup",
tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE,
comment = NA, highlight = FALSE, size = "normalsize",
background = "#F7F7F7", strip.white = TRUE, cache = 0,
cache.path = "tutorial_page_cache/html/", cache.vars = NULL,
cache.lazy = TRUE, dependson = NULL, autodep = FALSE,
cache.rebuild = FALSE, fig.keep = "high", fig.show = "asis",
fig.align = "default", fig.path = "tutorial_page_files/figure-html/",
dev = "png", dev.args = NULL, dpi = 192, fig.ext = "png",
fig.width = 6.5, fig.height = 4, fig.env = "figure",
fig.cap = NULL, fig.scap = NULL, fig.lp = "fig:", fig.subcap = NULL,
fig.pos = "", out.width = 624, out.height = NULL, out.extra = NULL,
fig.retina = 2, external = TRUE, sanitize = FALSE, interval = 1,
aniopts = "controls,loop", warning = TRUE, error = FALSE,
message = TRUE, render = NULL, ref.label = NULL, child = NULL,
engine = "r", split = FALSE, include = TRUE, purl = TRUE,
max.print = 1000, label = "read-mechanics-column", exercise = TRUE,
code = "", out.width.px = 624, out.height.px = 384, params.src = "read-mechanics-column, exercise=TRUE",
fig.num = 0L, exercise.df_print = "paged", exercise.checker = "NULL"),
engine = "r", version = "4"), class = c("r", "tutorial_exercise"
)))
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-remove-empties-column-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-remove-empties-column-code-editor`)), session)
output$`tutorial-exercise-remove-empties-column-output` <- renderUI({
`tutorial-exercise-remove-empties-column-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
learnr:::store_exercise_cache(structure(list(label = "remove-empties-column", global_setup = structure(c("library(learnr)",
"library(readxl)", "library(\"ggplot2\")", "options(scipen = 10)",
"gameData <- read_excel(\"bgg_dataset.xlsx\", sheet = \"Sheet\")",
"knitr::opts_chunk$set(echo = FALSE)"), chunk_opts = list(label = "setup",
include = FALSE)), setup = NULL, chunks = list(list(label = "remove-empties-column",
code = "gameData$`Year Published` <- trimws(gameData$`Year Published`)\ngameData$`Year Published`",
opts = list(label = "\"remove-empties-column\"", exercise = "TRUE"),
engine = "r")), code_check = NULL, error_check = NULL, check = NULL,
solution = NULL, tests = NULL, options = list(eval = FALSE,
echo = TRUE, results = "markup", tidy = FALSE, tidy.opts = NULL,
collapse = FALSE, prompt = FALSE, comment = NA, highlight = FALSE,
size = "normalsize", background = "#F7F7F7", strip.white = TRUE,
cache = 0, cache.path = "tutorial_page_cache/html/",
cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,
autodep = FALSE, cache.rebuild = FALSE, fig.keep = "high",
fig.show = "asis", fig.align = "default", fig.path = "tutorial_page_files/figure-html/",
dev = "png", dev.args = NULL, dpi = 192, fig.ext = "png",
fig.width = 6.5, fig.height = 4, fig.env = "figure",
fig.cap = NULL, fig.scap = NULL, fig.lp = "fig:", fig.subcap = NULL,
fig.pos = "", out.width = 624, out.height = NULL, out.extra = NULL, fig.retina = 2, external = TRUE, sanitize = FALSE, interval = 1,
aniopts = "controls,loop", warning = TRUE, error = FALSE,
message = TRUE, render = NULL, ref.label = NULL, child = NULL,
engine = "r", split = FALSE, include = TRUE, purl = TRUE,
max.print = 1000, label = "remove-empties-column", exercise = TRUE,
code = c("gameData$`Year Published` <- trimws(gameData$`Year Published`)",
"gameData$`Year Published`"), out.width.px = 624, out.height.px = 384,
params.src = "remove-empties-column, exercise=TRUE",
fig.num = 0, exercise.df_print = "paged", exercise.checker = "NULL"),
engine = "r", version = "4"), class = c("r", "tutorial_exercise"
)))
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-remove-empties-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-remove-empties-code-editor`)), session)
output$`tutorial-exercise-remove-empties-output` <- renderUI({
`tutorial-exercise-remove-empties-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
learnr:::store_exercise_cache(structure(list(label = "remove-empties", global_setup = structure(c("library(learnr)",
"library(readxl)", "library(\"ggplot2\")", "options(scipen = 10)",
"gameData <- read_excel(\"bgg_dataset.xlsx\", sheet = \"Sheet\")",
"knitr::opts_chunk$set(echo = FALSE)"), chunk_opts = list(label = "setup",
include = FALSE)), setup = NULL, chunks = list(list(label = "remove-empties",
code = "gameData <- lapply(gameData, trimws)\ngameData",
opts = list(label = "\"remove-empties\"", exercise = "TRUE"),
engine = "r")), code_check = NULL, error_check = NULL, check = NULL,
solution = NULL, tests = NULL, options = list(eval = FALSE,
echo = TRUE, results = "markup", tidy = FALSE, tidy.opts = NULL,
collapse = FALSE, prompt = FALSE, comment = NA, highlight = FALSE,
size = "normalsize", background = "#F7F7F7", strip.white = TRUE,
cache = 0, cache.path = "tutorial_page_cache/html/",
cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,
autodep = FALSE, cache.rebuild = FALSE, fig.keep = "high",
fig.show = "asis", fig.align = "default", fig.path = "tutorial_page_files/figure-html/",
dev = "png", dev.args = NULL, dpi = 192, fig.ext = "png",
fig.width = 6.5, fig.height = 4, fig.env = "figure",
fig.cap = NULL, fig.scap = NULL, fig.lp = "fig:", fig.subcap = NULL,
fig.pos = "", out.width = 624, out.height = NULL, out.extra = NULL,
fig.retina = 2, external = TRUE, sanitize = FALSE, interval = 1,
aniopts = "controls,loop", warning = TRUE, error = FALSE,
message = TRUE, render = NULL, ref.label = NULL, child = NULL,
engine = "r", split = FALSE, include = TRUE, purl = TRUE,
max.print = 1000, label = "remove-empties", exercise = TRUE,
code = c("gameData <- lapply(gameData, trimws)", "gameData"
), out.width.px = 624, out.height.px = 384, params.src = "remove-empties, exercise=TRUE",
fig.num = 0, exercise.df_print = "paged", exercise.checker = "NULL"),
engine = "r", version = "4"), class = c("r", "tutorial_exercise"
)))
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-to-lowercase-column-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-to-lowercase-column-code-editor`)), session)
output$`tutorial-exercise-to-lowercase-column-output` <- renderUI({
`tutorial-exercise-to-lowercase-column-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
learnr:::store_exercise_cache(structure(list(label = "to-lowercase-column", global_setup = structure(c("library(learnr)",
"library(readxl)", "library(\"ggplot2\")", "options(scipen = 10)",
"gameData <- read_excel(\"bgg_dataset.xlsx\", sheet = \"Sheet\")",
"knitr::opts_chunk$set(echo = FALSE)"), chunk_opts = list(label = "setup",
include = FALSE)), setup = NULL, chunks = list(list(label = "to-lowercase-column",
code = "gameData$`Mechanics` <- tolower(gameData$`Mechanics`)\ngameData$`Mechanics`",
opts = list(label = "\"to-lowercase-column\"", exercise = "TRUE"), engine = "r")), code_check = NULL, error_check = NULL, check = NULL,
solution = NULL, tests = NULL, options = list(eval = FALSE,
echo = TRUE, results = "markup", tidy = FALSE, tidy.opts = NULL,
collapse = FALSE, prompt = FALSE, comment = NA, highlight = FALSE,
size = "normalsize", background = "#F7F7F7", strip.white = TRUE,
cache = 0, cache.path = "tutorial_page_cache/html/",
cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,
autodep = FALSE, cache.rebuild = FALSE, fig.keep = "high",
fig.show = "asis", fig.align = "default", fig.path = "tutorial_page_files/figure-html/",
dev = "png", dev.args = NULL, dpi = 192, fig.ext = "png",
fig.width = 6.5, fig.height = 4, fig.env = "figure",
fig.cap = NULL, fig.scap = NULL, fig.lp = "fig:", fig.subcap = NULL,
fig.pos = "", out.width = 624, out.height = NULL, out.extra = NULL,
fig.retina = 2, external = TRUE, sanitize = FALSE, interval = 1,
aniopts = "controls,loop", warning = TRUE, error = FALSE,
message = TRUE, render = NULL, ref.label = NULL, child = NULL,
engine = "r", split = FALSE, include = TRUE, purl = TRUE,
max.print = 1000, label = "to-lowercase-column", exercise = TRUE,
code = c("gameData$`Mechanics` <- tolower(gameData$`Mechanics`)",
"gameData$`Mechanics`"), out.width.px = 624, out.height.px = 384,
params.src = "to-lowercase-column, exercise=TRUE", fig.num = 0,
exercise.df_print = "paged", exercise.checker = "NULL"),
engine = "r", version = "4"), class = c("r", "tutorial_exercise"
)))
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-replace-variable-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-replace-variable-code-editor`)), session)
output$`tutorial-exercise-replace-variable-output` <- renderUI({
`tutorial-exercise-replace-variable-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
learnr:::store_exercise_cache(structure(list(label = "replace-variable", global_setup = structure(c("library(learnr)",
"library(readxl)", "library(\"ggplot2\")", "options(scipen = 10)",
"gameData <- read_excel(\"bgg_dataset.xlsx\", sheet = \"Sheet\")",
"knitr::opts_chunk$set(echo = FALSE)"), chunk_opts = list(label = "setup",
include = FALSE)), setup = NULL, chunks = list(list(label = "replace-variable",
code = "gameData$`Mechanics` <- gsub(\"Action\", \"!!!ACTION!!!\", gameData$`Mechanics`)\ngameData$`Mechanics`",
opts = list(label = "\"replace-variable\"", exercise = "TRUE"),
engine = "r")), code_check = NULL, error_check = NULL, check = NULL,
solution = NULL, tests = NULL, options = list(eval = FALSE,
echo = TRUE, results = "markup", tidy = FALSE, tidy.opts = NULL,
collapse = FALSE, prompt = FALSE, comment = NA, highlight = FALSE,
size = "normalsize", background = "#F7F7F7", strip.white = TRUE,
cache = 0, cache.path = "tutorial_page_cache/html/",
cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,
autodep = FALSE, cache.rebuild = FALSE, fig.keep = "high",
fig.show = "asis", fig.align = "default", fig.path = "tutorial_page_files/figure-html/",
dev = "png", dev.args = NULL, dpi = 192, fig.ext = "png",
fig.width = 6.5, fig.height = 4, fig.env = "figure",
fig.cap = NULL, fig.scap = NULL, fig.lp = "fig:", fig.subcap = NULL,
fig.pos = "", out.width = 624, out.height = NULL, out.extra = NULL,
fig.retina = 2, external = TRUE, sanitize = FALSE, interval = 1,
aniopts = "controls,loop", warning = TRUE, error = FALSE,
message = TRUE, render = NULL, ref.label = NULL, child = NULL,
engine = "r", split = FALSE, include = TRUE, purl = TRUE,
max.print = 1000, label = "replace-variable", exercise = TRUE,
code = c("gameData$`Mechanics` <- gsub(\"Action\", \"!!!ACTION!!!\", gameData$`Mechanics`)",
"gameData$`Mechanics`"), out.width.px = 624, out.height.px = 384,
params.src = "replace-variable, exercise=TRUE", fig.num = 0,
exercise.df_print = "paged", exercise.checker = "NULL"),
engine = "r", version = "4"), class = c("r", "tutorial_exercise"
)))
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-replace-missing-variable-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-replace-missing-variable-code-editor`)), session)output$`tutorial-exercise-replace-missing-variable-output` <- renderUI({
`tutorial-exercise-replace-missing-variable-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
learnr:::store_exercise_cache(structure(list(label = "replace-missing-variable", global_setup = structure(c("library(learnr)",
"library(readxl)", "library(\"ggplot2\")", "options(scipen = 10)",
"gameData <- read_excel(\"bgg_dataset.xlsx\", sheet = \"Sheet\")",
"knitr::opts_chunk$set(echo = FALSE)"), chunk_opts = list(label = "setup",
include = FALSE)), setup = NULL, chunks = list(list(label = "replace-missing-variable",
code = "gameData$`Rating Average`[is.na(gameData$`Rating Average`)] <- mean(gameData$`Rating Average`, na.rm = TRUE)",
opts = list(label = "\"replace-missing-variable\"", exercise = "TRUE"),
engine = "r")), code_check = NULL, error_check = NULL, check = NULL,
solution = NULL, tests = NULL, options = list(eval = FALSE,
echo = TRUE, results = "markup", tidy = FALSE, tidy.opts = NULL,
collapse = FALSE, prompt = FALSE, comment = NA, highlight = FALSE,
size = "normalsize", background = "#F7F7F7", strip.white = TRUE,
cache = 0, cache.path = "tutorial_page_cache/html/",
cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,
autodep = FALSE, cache.rebuild = FALSE, fig.keep = "high",
fig.show = "asis", fig.align = "default", fig.path = "tutorial_page_files/figure-html/",
dev = "png", dev.args = NULL, dpi = 192, fig.ext = "png",
fig.width = 6.5, fig.height = 4, fig.env = "figure",
fig.cap = NULL, fig.scap = NULL, fig.lp = "fig:", fig.subcap = NULL,
fig.pos = "", out.width = 624, out.height = NULL, out.extra = NULL,
fig.retina = 2, external = TRUE, sanitize = FALSE, interval = 1,
aniopts = "controls,loop", warning = TRUE, error = FALSE,
message = TRUE, render = NULL, ref.label = NULL, child = NULL,
engine = "r", split = FALSE, include = TRUE, purl = TRUE,
max.print = 1000, label = "replace-missing-variable",
exercise = TRUE, code = "gameData$`Rating Average`[is.na(gameData$`Rating Average`)] <- mean(gameData$`Rating Average`, na.rm = TRUE)",
out.width.px = 624, out.height.px = 384, params.src = "replace-missing-variable, exercise=TRUE",
fig.num = 0, exercise.df_print = "paged", exercise.checker = "NULL"),
engine = "r", version = "4"), class = c("r", "tutorial_exercise"
)))
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-remove-duplicates-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-remove-duplicates-code-editor`)), session)
output$`tutorial-exercise-remove-duplicates-output` <- renderUI({
`tutorial-exercise-remove-duplicates-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
learnr:::store_exercise_cache(structure(list(label = "remove-duplicates", global_setup = structure(c("library(learnr)",
"library(readxl)", "library(\"ggplot2\")", "options(scipen = 10)",
"gameData <- read_excel(\"bgg_dataset.xlsx\", sheet = \"Sheet\")",
"knitr::opts_chunk$set(echo = FALSE)"), chunk_opts = list(label = "setup",
include = FALSE)), setup = NULL, chunks = list(list(label = "remove-duplicates",
code = "vector <- c(1, 2, 3, 3, 5, 2, 1, 7, 10, 5)\nvector <- unique(vector)\n\nvector",
opts = list(label = "\"remove-duplicates\"", exercise = "TRUE"),
engine = "r")), code_check = NULL, error_check = NULL, check = NULL,
solution = NULL, tests = NULL, options = list(eval = FALSE,
echo = TRUE, results = "markup", tidy = FALSE, tidy.opts = NULL,
collapse = FALSE, prompt = FALSE, comment = NA, highlight = FALSE,
size = "normalsize", background = "#F7F7F7", strip.white = TRUE,
cache = 0, cache.path = "tutorial_page_cache/html/",
cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,
autodep = FALSE, cache.rebuild = FALSE, fig.keep = "high",
fig.show = "asis", fig.align = "default", fig.path = "tutorial_page_files/figure-html/",
dev = "png", dev.args = NULL, dpi = 192, fig.ext = "png",
fig.width = 6.5, fig.height = 4, fig.env = "figure",
fig.cap = NULL, fig.scap = NULL, fig.lp = "fig:", fig.subcap = NULL,
fig.pos = "", out.width = 624, out.height = NULL, out.extra = NULL,
fig.retina = 2, external = TRUE, sanitize = FALSE, interval = 1,
aniopts = "controls,loop", warning = TRUE, error = FALSE, message = TRUE, render = NULL, ref.label = NULL, child = NULL,
engine = "r", split = FALSE, include = TRUE, purl = TRUE,
max.print = 1000, label = "remove-duplicates", exercise = TRUE,
code = c("vector <- c(1, 2, 3, 3, 5, 2, 1, 7, 10, 5)",
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</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-replace-special-chars-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-replace-special-chars-code-editor`)), session)
output$`tutorial-exercise-replace-special-chars-output` <- renderUI({
`tutorial-exercise-replace-special-chars-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
learnr:::store_exercise_cache(structure(list(label = "replace-special-chars", global_setup = structure(c("library(learnr)",
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params.src = "replace-special-chars, exercise=TRUE",
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learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "second-quiz-1",
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learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "second-quiz-2",
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learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "second-quiz-3",
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learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "second-quiz-4",
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<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-mean-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-mean-code-editor`)), session)
output$`tutorial-exercise-mean-output` <- renderUI({
`tutorial-exercise-mean-result`()
})
</script>
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learnr:::store_exercise_cache(structure(list(label = "mean", global_setup = structure(c("library(learnr)",
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include = FALSE)), setup = NULL, chunks = list(list(label = "mean",
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solution = NULL, tests = NULL, options = list(eval = FALSE,
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cache = 0, cache.path = "tutorial_page_cache/html/",
cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,
autodep = FALSE, cache.rebuild = FALSE, fig.keep = "high",
fig.show = "asis", fig.align = "default", fig.path = "tutorial_page_files/figure-html/",
dev = "png", dev.args = NULL, dpi = 192, fig.ext = "png",
fig.width = 6.5, fig.height = 4, fig.env = "figure",
fig.cap = NULL, fig.scap = NULL, fig.lp = "fig:", fig.subcap = NULL,
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)))
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-median-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-median-code-editor`)), session)
output$`tutorial-exercise-median-output` <- renderUI({
`tutorial-exercise-median-result`()
})
</script>
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learnr:::store_exercise_cache(structure(list(label = "median", global_setup = structure(c("library(learnr)",
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include = FALSE)), setup = NULL, chunks = list(list(label = "median",
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tests = NULL, options = list(eval = FALSE, echo = TRUE, results = "markup",
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engine = "r", split = FALSE, include = TRUE, purl = TRUE,
max.print = 1000, label = "median", exercise = TRUE,
code = "ptime <- gameData[[\"Play Time\"]]", out.width.px = 624,
out.height.px = 384, params.src = "median, exercise=TRUE",
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)))
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-variance-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-variance-code-editor`)), session)
output$`tutorial-exercise-variance-output` <- renderUI({
`tutorial-exercise-variance-result`()
})
</script>
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learnr:::store_exercise_cache(structure(list(label = "variance", global_setup = structure(c("library(learnr)",
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)))
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<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-standard-deviation-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-standard-deviation-code-editor`)), session)
output$`tutorial-exercise-standard-deviation-output` <- renderUI({
`tutorial-exercise-standard-deviation-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
learnr:::store_exercise_cache(structure(list(label = "standard-deviation", global_setup = structure(c("library(learnr)",
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)))
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<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-linear-regression-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-linear-regression-code-editor`)), session)
output$`tutorial-exercise-linear-regression-output` <- renderUI({
`tutorial-exercise-linear-regression-result`()
})
</script>
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learnr:::store_exercise_cache(structure(list(label = "linear-regression", global_setup = structure(c("library(learnr)",
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cache.vars = NULL, cache.lazy = TRUE, dependson = NULL,
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max.print = 1000, label = "linear-regression", exercise = TRUE,
code = c("raters <- gameData$\"Users Rated\"", "owned <- gameData$\"Owned Users\""
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)))
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<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-lm-plot-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-lm-plot-code-editor`)), session)
output$`tutorial-exercise-lm-plot-output` <- renderUI({
`tutorial-exercise-lm-plot-result`()
})
</script>
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learnr:::store_exercise_cache(structure(list(label = "lm-plot", global_setup = structure(c("library(learnr)",
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)))
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`tutorial-exercise-hypothesis-test-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-hypothesis-test-code-editor`)), session)
output$`tutorial-exercise-hypothesis-test-output` <- renderUI({ `tutorial-exercise-hypothesis-test-result`()
})
</script>
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learnr:::store_exercise_cache(structure(list(label = "hypothesis-test", global_setup = structure(c("library(learnr)",
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exercise.lines = 4, code = c("cavg <- gameData$\"Complexity Average\"",
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), out.width.px = 624, out.height.px = 384, params.src = "hypothesis-test, exercise=TRUE, exercise.lines=4",
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)))
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learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "hypothesis-quiz-1",
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learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "hypothesis-quiz-2",
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<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-plotExercise-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-plotExercise-code-editor`)), session)
output$`tutorial-exercise-plotExercise-output` <- renderUI({
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})
</script>
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learnr:::store_exercise_cache(structure(list(label = "plotExercise", global_setup = structure(c("library(learnr)",
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learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "quiz-ggplot-1",
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`tutorial-exercise-barplotExercise-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-barplotExercise-code-editor`)), session)
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