diff --git a/tutorial_page.Rmd b/tutorial_page.Rmd index be452298bb0875f43c49e5ad31d8679ebe5dc3b8..767f5e883c07ecf2b3d0357d7b0d1dfdb1a4a0e4 100644 --- a/tutorial_page.Rmd +++ b/tutorial_page.Rmd @@ -6,7 +6,10 @@ runtime: shiny_prerendered ```{r setup, include=FALSE} library(learnr) +library(readxl) +library("ggplot2") knitr::opts_chunk$set(echo = FALSE) +gameData <- read_excel("bgg_dataset.xlsx", sheet = "Sheet2", n_max = 500) ``` @@ -103,10 +106,115 @@ quiz( ### Hypothesentest ## Ergebnispräsentation +In diesem Kapitel beschäftigen wir uns mit ausgewählten grafischen Darstellungsmöglichkeiten von Daten und wie man diese in RStudio erstellen kann. ### Streudiagramm +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: + +```{r plot} +plot(gameData[["Year Published"]],gameData[["ID"]],ylab="ID des Spiels", xlab="Veröffentlichung des Spiels", main="Zusammenhang ID und Erstveröffentlichung eines Spiels",xlim=c(1960,2024)) + +``` +\ +Der Code für dieses Streudiagramms ist grob zusammengefasst: +\ +\ +**plot(gameData[["xxx"]],gameData[["yyy"]],xlim=c(a,z))** +\ +\ +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. +\ +\ +\ +\ +Erstellen Sie nun anhand des Beispiels ein einfaches Streudiagramm welches den Zusammenhang zwischen "Min Age" und "Complexity Average" darstellt: +```{r plotExercise, exercise=TRUE} + +``` +```{r plotExercise-hint} +plot(gameData[["Complexity Average"]],gameData[["Min Age"]]) + +``` +\ +\ + +### Boxplot-Diagramm +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. +\ +Folgend ein Beispiel: +```{r ggplot} +ggplot(gameData, aes(y = .data[["Play Time"]], group = .data[["Year Published"]], x = .data[["Year Published"]])) + geom_boxplot() + coord_cartesian(xlim = c(2000, 2020)) + +``` +\ +\ +Anhand des Beispiels nun ein kleines Quiz zur Verdeutlichung der Begrifflichkeiten: \ +```{r quiz-ggplot} +quiz( + question("Was ist der Median im Diagramm?", + answer("ein vertikaler Strich"), + answer("die Höhe der Box"), + answer("ein horizontaler Strich in der Mitter der Box", correct = TRUE), + answer("das untere Ende der Box")), + + question("Was sind die Quartile im Diagramm?(Es sind mehrere Antworten möglich)", + answer("das obere Ende der Box", correct = TRUE), + answer("Punkte oberhalb der Box"), + answer("ein Drittel des vertikalen Strichs"), + answer("das untere Ende der Box", correct = TRUE)), + + question("Was sind die Maximalwerte im Diagramm?", + answer("Punkte oberhalb der Box"), + answer("die Spitzen des vertikalen Strichs", correct = TRUE), + answer("die x-Achse und die y-Achse"), + answer("die Flaeche der Box") + ), + question("An was erkennt man im Diagramm die Ausreißer?", + answer("es sind Punkte außerhalb der Box", correct = TRUE), + answer("an der Größe der Box"), + answer("durch schlechtes Verhalten") + + ) +) +``` +\ +\ +Um ein Boxplot-Diagramm zu erstellen, muss man das ggplot2-package installiert haben, der grobe Syntax fürs Diagramm selbst sieht wiefolgt aus: +\ +**ggplot(gameData, aes(y = .data[["xxx"]], group = .data[["yyy"]], x = .data[["yyy"]])) + geom_boxplot()** +\ +\ +\ +\ + +### Säulendiagramme +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: +\ +\ +**barplot(table(gameData[["xxx"]])** ein und kann um die Achsen näher zu beschreiben noch **xlab=""** und **ylab=""** einfügen. +\ +\ +Folgend ein Beispiel für ein Säulendiagramm mit Häufigkeitsverteilung: +\ +```{r barplot} +barplot(table(gameData[["Min Age"]]), xlab = "Altersfreigabe" , ylab = "Häufigkeit", + main = "Häufigkeit der Altersfreigaben" ) +``` +\ +\ +Geben Sie nun ein Säulendiagramm mit den Werten von "Complexity Average" und Achsenbeschriftung aus: +\ +```{r barplotExercise, exercise=TRUE} + + +``` +```{r barplotExercise-hint} +barplot(gameData[["Complexity Average"]], xlab = "Spiel" , ylab = "Score") + +``` + -### Kein Plan ## Teaminfos diff --git a/tutorial_page.html b/tutorial_page.html index b9b19357ff29e883fed5d1d39f0aaffefc233b12..55e96d4f393a0e0db41c6e0dcb25b5f73e216596 100644 --- a/tutorial_page.html +++ b/tutorial_page.html @@ -14,7 +14,7 @@ <meta name="allow-skip" content="false" /> <meta name="learnr-version-prerender" content="0.11.5" /> -<title>Tutorial</title> +<title>Data Science Tutorial</title> <!-- header-includes START --> <!-- HEAD_CONTENT --> @@ -109,12 +109,11 @@ if (window.hljs) { <article class="topics" id="learnr-tutorial-content"> -<div id="section-topic-1" class="section level2"> -<h2>Topic 1</h2> -<div id="section-exercise" class="section level3"> -<h3>Exercise</h3> -<p><em>Here’s a simple exercise with an empty code chunk provided for -entering the answer.</em></p> +<div id="section-datenbasis" class="section level2"> +<h2>Datenbasis</h2> +<div id="section-datenherkunft" class="section level3"> +<h3>Datenherkunft</h3> +<p><em>Wo kommen die Daten her?</em></p> <p>Write the R code required to add two plus two:</p> <div class="tutorial-exercise" data-label="two-plus-two" data-completion="1" data-diagnostics="1" data-startover="1" @@ -122,11 +121,9 @@ 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> -<div id="section-exercise-with-code" class="section level3"> -<h3>Exercise with Code</h3> -<p><em>Here’s an exercise with some prepopulated code as well as -<code>exercise.lines = 5</code> to provide a bit more initial room to -work.</em></p> +<div id="section-inhalt" class="section level3"> +<h3>Inhalt</h3> +<p>Was ist der Inhalt der Datensatz.</p> <p>Now write a function that adds any two numbers and then call it:</p> <div class="tutorial-exercise" data-label="add-function" data-completion="1" data-diagnostics="1" data-startover="1" @@ -137,11 +134,16 @@ data-lines="5" 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> +<div id="section-hypothesen" class="section level3"> +<h3>Hypothesen</h3> +<p><em>Mindestens 2 Hypothesen</em></p> +<p>Mindestens 2 Hypothesen</p> </div> -<div id="section-topic-2" class="section level2"> -<h2>Topic 2</h2> -<div id="section-exercise-with-hint" class="section level3"> -<h3>Exercise with Hint</h3> +</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><em>Here’s an exercise where the chunk is pre-evaluated via the <code>exercise.eval</code> option (so the user can see the default output we’d like them to customize). We also add a “hint†to the correct @@ -166,6 +168,17 @@ data-lines="0" data-pipe="|>"> <pre class="text"><code>head(mtcars)</code></pre> </div> </div> +<div id="section-inkonsistenzen-und-leerstellen-beheben" +class="section level3"> +<h3>Inkonsistenzen und Leerstellen beheben</h3> +<p><em>Here’s an exercise where the chunk is pre-evaluated via the +<code>exercise.eval</code> option (so the user can see the default +output we’d like them to customize). We also add a “hint†to the correct +solution via the chunk immediate below labeled +<code>print-limit-hint</code>.</em></p> +<p>Modify the following code to limit the number of rows printed to +5:</p> +</div> <div id="section-quiz" class="section level3"> <h3>Quiz</h3> <p><em>You can include any number of single or multiple choice questions @@ -191,10 +204,175 @@ base and recommended R packages:</p> <script>if (Tutorial.triggerMathJax) Tutorial.triggerMathJax()</script> </div> </div> +</div> +</div> +<div id="section-datenanalyse" class="section level2"> +<h2>Datenanalyse</h2> +<div id="section-mittelwert" class="section level3"> +<h3>Mittelwert</h3> +</div> +<div id="section-median" class="section level3"> +<h3>Median</h3> +</div> +<div id="section-varianz" class="section level3"> +<h3>Varianz</h3> +</div> +<div id="section-standardabweichung" class="section level3"> +<h3>Standardabweichung</h3> +</div> +<div id="section-lineare-regression" class="section level3"> +<h3>Lineare Regression</h3> +</div> +<div id="section-hypothesentest" class="section level3"> +<h3>Hypothesentest</h3> +</div> +</div> +<div id="section-ergebnispräsentation" class="section level2"> +<h2>Ergebnispräsentation</h2> +<p>In diesem Kapitel beschäftigen wir uns mit einigen 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[[“xxxâ€]],gameData[[“yyyâ€]],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[[“xxxâ€]], group = .data[[“yyyâ€]], +x = .data[[“yyyâ€]])) + 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[[“xxxâ€]])</strong> ein und kann um die +Achsen näher zu beschreiben noch **xlab=““** und +<strong>ylab=â€â€</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 vonâ€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-teaminfos" class="section level2"> +<h2>Teaminfos</h2> +<div id="section-nikolay-nikolaev" class="section level3"> +<h3>Nikolay Nikolaev</h3> +</div> +<div id="section-maximilian-fronmüller" class="section level3"> +<h3>Maximilian Fronmüller</h3> +</div> +<div id="section-lino-cortese" class="section level3"> +<h3>Lino Cortese</h3> <p> <script type="application/shiny-prerendered" data-context="server-start"> library(learnr) +library(readxl) +library("ggplot2") knitr::opts_chunk$set(echo = FALSE) +gameData <- read_excel("bgg_dataset.xlsx", sheet = "Sheet2", n_max = 500) </script> <script type="application/shiny-prerendered" data-context="server"> @@ -227,18 +405,20 @@ output$`tutorial-exercise-two-plus-two-output` <- renderUI({ <script type="application/shiny-prerendered" data-context="server"> learnr:::store_exercise_cache(structure(list(label = "two-plus-two", global_setup = structure(c("library(learnr)", -"knitr::opts_chunk$set(echo = FALSE)"), chunk_opts = list(label = "setup", - include = FALSE)), setup = NULL, chunks = list(list(label = "two-plus-two", - code = "", opts = list(label = "\"two-plus-two\"", 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/", +"library(readxl)", "library(\"ggplot2\")", "knitr::opts_chunk$set(echo = FALSE)", +"gameData <- read_excel(\"bgg_dataset.xlsx\", sheet = \"Sheet2\", n_max = 500)" +), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL, + chunks = list(list(label = "two-plus-two", code = "", opts = list( + label = "\"two-plus-two\"", exercise = "TRUE"), engine = "r")), + code_check = NULL, error_check = NULL, check = NULL, solution = NULL, + tests = NULL, options = list(eval = FALSE, echo = TRUE, results = "markup", + tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE, + comment = NA, highlight = FALSE, size = "normalsize", + background = "#F7F7F7", strip.white = TRUE, cache = 0, + cache.path = "tutorial_page_cache/html/", cache.vars = NULL, + cache.lazy = TRUE, dependson = NULL, autodep = FALSE, + cache.rebuild = FALSE, fig.keep = "high", fig.show = "asis", + fig.align = "default", fig.path = "tutorial_page_files/figure-html/", dev = "png", dev.args = NULL, dpi = 192, fig.ext = "png", fig.width = 6.5, fig.height = 4, fig.env = "figure", fig.cap = NULL, fig.scap = NULL, fig.lp = "fig:", fig.subcap = NULL, @@ -264,12 +444,14 @@ output$`tutorial-exercise-add-function-output` <- renderUI({ <script type="application/shiny-prerendered" data-context="server"> learnr:::store_exercise_cache(structure(list(label = "add-function", global_setup = structure(c("library(learnr)", -"knitr::opts_chunk$set(echo = FALSE)"), chunk_opts = list(label = "setup", - include = FALSE)), setup = NULL, chunks = list(list(label = "add-function", - code = "add <- function() {\n \n}", opts = list(label = "\"add-function\"", - exercise = "TRUE", exercise.lines = "5"), engine = "r")), - code_check = NULL, error_check = NULL, check = NULL, solution = NULL, - tests = NULL, options = list(eval = FALSE, echo = TRUE, results = "markup", +"library(readxl)", "library(\"ggplot2\")", "knitr::opts_chunk$set(echo = FALSE)", +"gameData <- read_excel(\"bgg_dataset.xlsx\", sheet = \"Sheet2\", n_max = 500)" +), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL, + chunks = list(list(label = "add-function", code = "add <- function() {\n \n}", + opts = list(label = "\"add-function\"", exercise = "TRUE", + exercise.lines = "5"), 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, @@ -303,10 +485,12 @@ output$`tutorial-exercise-print-limit-output` <- renderUI({ <script type="application/shiny-prerendered" data-context="server"> learnr:::store_exercise_cache(structure(list(label = "print-limit", global_setup = structure(c("library(learnr)", -"knitr::opts_chunk$set(echo = FALSE)"), chunk_opts = list(label = "setup", - include = FALSE)), setup = NULL, chunks = list(list(label = "print-limit", - code = "mtcars", opts = list(label = "\"print-limit\"", exercise = "TRUE", - exercise.eval = "TRUE"), engine = "r")), code_check = NULL, +"library(readxl)", "library(\"ggplot2\")", "knitr::opts_chunk$set(echo = FALSE)", +"gameData <- read_excel(\"bgg_dataset.xlsx\", sheet = \"Sheet2\", n_max = 500)" +), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL, + chunks = list(list(label = "print-limit", code = "mtcars", + opts = list(label = "\"print-limit\"", exercise = "TRUE", + exercise.eval = "TRUE"), engine = "r")), code_check = NULL, error_check = NULL, check = NULL, solution = NULL, tests = NULL, options = list(eval = TRUE, echo = TRUE, results = "markup", tidy = FALSE, tidy.opts = NULL, collapse = FALSE, prompt = FALSE, @@ -334,16 +518,16 @@ learnr:::store_exercise_cache(structure(list(label = "print-limit", global_setup <script type="application/shiny-prerendered" data-context="server"> learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label = "quiz-1", question = structure("Which package contains functions for installing other R packages?", html = TRUE, class = c("html", -"character")), answers = list(structure(list(id = "lnr_ans_4fdf23c", +"character")), answers = list(structure(list(id = "lnr_ans_ea7089a", option = "base", value = "base", label = structure("base", html = TRUE, class = c("html", "character")), correct = FALSE, message = NULL, type = "literal"), class = c("tutorial_question_answer", -"tutorial_quiz_answer")), structure(list(id = "lnr_ans_2e0655d", +"tutorial_quiz_answer")), structure(list(id = "lnr_ans_86f42dc", option = "tools", value = "tools", label = structure("tools", html = TRUE, class = c("html", "character")), correct = FALSE, message = NULL, type = "literal"), class = c("tutorial_question_answer", -"tutorial_quiz_answer")), structure(list(id = "lnr_ans_488bd11", +"tutorial_quiz_answer")), structure(list(id = "lnr_ans_3b1f53c", option = "utils", value = "utils", label = structure("utils", html = TRUE, class = c("html", "character")), correct = TRUE, message = NULL, type = "literal"), class = c("tutorial_question_answer", -"tutorial_quiz_answer")), structure(list(id = "lnr_ans_66ce8da", +"tutorial_quiz_answer")), structure(list(id = "lnr_ans_cabaa4e", option = "codetools", value = "codetools", label = structure("codetools", html = TRUE, class = c("html", "character")), correct = FALSE, message = NULL, type = "literal"), class = c("tutorial_question_answer", "tutorial_quiz_answer"))), button_labels = list(submit = structure("<span data-i18n=\"button.questionsubmit\">Submit Answer<\u002fspan>", html = TRUE, class = c("html", @@ -353,7 +537,7 @@ learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label "character")), incorrect = structure("Incorrect", html = TRUE, class = c("html", "character")), message = NULL, post_message = NULL), ids = list( answer = "quiz-1-answer", question = "quiz-1"), loading = NULL, - random_answer_order = FALSE, allow_retry = FALSE, seed = 1925648562.1033, + random_answer_order = FALSE, allow_retry = FALSE, seed = 1452279270.82373, options = list()), class = c("learnr_radio", "tutorial_question" )), session = session) </script> @@ -361,16 +545,16 @@ learnr:::question_prerendered_chunk(structure(list(type = "learnr_radio", label <script type="application/shiny-prerendered" data-context="server"> learnr:::question_prerendered_chunk(structure(list(type = "learnr_checkbox", label = "quiz-2", question = structure("Which of the R packages listed below are used to create plots?", html = TRUE, class = c("html", -"character")), answers = list(structure(list(id = "lnr_ans_8af68bc", +"character")), answers = list(structure(list(id = "lnr_ans_4729d83", option = "lattice", value = "lattice", label = structure("lattice", html = TRUE, class = c("html", "character")), correct = TRUE, message = NULL, type = "literal"), class = 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