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<!DOCTYPE html>
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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">
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<div id="section-outline" class="section level2">
<h2>Outline</h2>
<p>In this tutorial, we learn about R and help you set up your first
Data Science Trip!!! Are you Ready? Let’s get started!</p>
<ul>
<li>Why R for Data Science?</li>
<li>What can R do?</li>
<li>Data Wrangling</li>
<li>Data Visualization</li>
<li>Be UT men basketball coach!!!</li>
</ul>
</div>
<div id="section-why-r-for-data-science" class="section level2">
<h2>Why R for Data Science?</h2>
<p>R is a <em>free</em> and open-source programming language for
statistical computing and graphics. The R Project for Statistical
Computing (<a href="https://www.r-project.org"
class="uri">https://www.r-project.org</a>) supports and develops R.</p>
<p><strong>Why learn R for Data Science?</strong></p>
<ul>
<li>R is free and open-source. Because it is free, everyone has access
to it! Because it is open-source, anyone can add functionality to R in
the form of packages. Also, statisticians and computer scientist can can
audit the code and edit any bugs (i.e., there is no black box).</li>
<li>R is the <em>lingua franca</em> of statistics. In other words, it is
the most common <em>data analysis tool</em>. Since R is the main
computing environment for statistical analysis, both established and new
methods in statistics, data mining, machine learning, and psychometrics
are often employed using R, so there is no need for specialized
software. Just learn R and you are ready to go.</li>
<li>R has beautiful <em>data visualization</em> capabilities. We will
learn the basics of making graphs in R in this microcredential. R has a
wide variety of packages for data visualization, including
<code>ggplot2</code>, <code>plotly</code>, and
<code>lattice</code>.</li>
<li>R has a great community! There are many talented R programmers that
have contributed helpful R packages. In addition, there are great R
mailing lists and Stack Overflow (<a href="https://stackoverflow.com"
class="uri">https://stackoverflow.com</a>) where you can find help using
R.</li>
</ul>
<p><strong>R you ready?</strong></p>
<p>The R community is made up of people passionate about the
intersection of numbers, data, analysis, and code. It was invented by
scientists for statistical computing and a community of specialized
packages has been built around the language. In this first section, you
will be introduced to some basic R syntax and discuss how R classifies
data types so that it can mathematically process them in analysis.</p>
<p><em>Background Information:</em> Unlike with other programming
languages, most beginners who want to learn R do so because they want to
analyze data. In this way, R is more of a tool to understand data than a
programming language used to build software applications. However, what
we are learning will build the foundation for you to start programming
in R.</p>
</div>
<div id="section-what-can-r-do" class="section level2">
<h2>What Can R do?</h2>
<p><strong>Mathematical calculations.</strong> Let’s start with the
basic syntax for mathematical calculations in R. R performs addition,
subtraction, multiplication, and division with +, -, *, and /
operators.</p>
<p><br> <strong>Comments.</strong> It’s often good practice to annotate
the code you write and not let the computer read it as code. We can
annotate or write text in our coding scripts using comments. Comments
are text written after a # in a program that is not run by the computer.
R interprets anything after a # as a comment. A brief example is
provided below demonstrating the use of comments. Run the following code
using the blue “Run Code” button on the right.</p>
<div class="tutorial-exercise" data-label="math_comment"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code>12 + 21 #this line of code sums 12 and 21</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>As you can see, the sum of 12 and 21 is shown when you run the code,
but the comment is not displayed in the output.</p>
<p><strong>Visualization.</strong> we can use R to make simple plot,
such as a bar plot, line plot, or scatter plot. We will use the
<code>ggplot2</code> package to create these plots. The
<code>ggplot2</code> package is a powerful and flexible package for
creating visualizations in R. We will learn more about data
visualization in the next section. Herw is an example of a simple bar
plot using the <code>ggplot2</code> package. <em>Run the code</em> below
to create a bar plot of the seed rating of the teams!!!</p>
<div class="tutorial-exercise" data-label="barplot" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code># Create a data frame
data <- data.frame(
team = c("Kansas", "Tennessee", "Duke", "Purdue"),
rating = c(98, 95, 92, 90))
# Create a bar plot using ggplot2
ggplot(data, aes(x = team, y = rating)) +
geom_bar(stat = "identity", fill = "orange") +
labs(title = "Seed Rating of Teams", x = "Team", y = "Seed Rating") +
theme_minimal()</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-before-analysisbe-famliar-with-the-dataset-codebook"
class="section level2">
<h2>Before Analysis:Be famliar with the <em>Dataset Codebook</em></h2>
<p>Before we start our analysis, Make sure you are famliar with your
dataset, the Codebook is a good start!!! The codebook is a document that
provides information about the variables in a dataset. It describes each
variable, the possible values it can take, and any other relevant
information. The data set we will be using is the March_Madness Data,
which contains information about the University NCAA Men’s Basketball
team March Madness performance from 2016 to 2023.</p>
<p><strong>March_Madness Codebook</strong></p>
<ul>
<li><strong>YEAR</strong>: sesson year（2016-2023）</li>
<li><strong>TEAM.NO</strong>: university team number</li>
<li><strong>TEAM</strong>: university team name</li>
<li><strong>SEED</strong>: university team seed</li>
<li><strong>ROUND</strong>: university team round</li>
<li><strong>POWER.RATING</strong>: university team power rating</li>
<li><strong>POWER.RATING.RANKING</strong>: university team power rating
ranking</li>
</ul>
</div>
<div id="section-data-wrangling" class="section level2">
<h2>Data Wrangling</h2>
<p>Data wrangling is the process of cleaning and transforming raw data
into a more usable format.In R, the <code>dplyr</code> package is a
powerful tool for data wrangling. It provides a set of functions that
can be used to manipulate data frames. The <code>dplyr</code> package is
part of the <code>tidyverse</code>, a collection of R packages designed
for data science.</p>
<p>Let’s take a look at an example of data wrangling using the
<code>dplyr</code> package.In this case,we will use NCAA men Baketball
data(March_Madness) to demonstrate how to filter and select data using
the <code>dplyr</code> package.</p>
<div id="section-viewing-data" class="section level3">
<h3>Viewing Data</h3>
<p>The first rule of data analysis is to view your data. This is
important to ensure the data were read in properly and nothing odd is
going on. We can accomplish this task with a few different functions in
and outside of the tidyverse. Our favorite functions to view our data
are the following:</p>
<ul>
<li><code>glimpse()</code> in the <code>tidyverse</code> package</li>
</ul>
<p>Lets go ahead and try <code>glimpse()</code>! <em>Run the code</em>
below to view the data.</p>
<div class="tutorial-exercise" data-label="glimpse" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>glimpse(March_Madness)</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>As you can see, <code>glimpse()</code> provides us with the some
information of data for each of variables. It even tells us the data
type for each of the variables. This is a great way to get a quick
overview of the data.</p>
</div>
<div id="section-basic-dplyr-verbs" class="section level3">
<h3>Basic <code>dplyr</code> verbs</h3>
<p>You can think of the functions within <code>dplyr</code> to
manipulate data as <em>verbs</em>. The function names tells you what
they do in terms of data manipulation. We’ve provided you with very
basic two functions below along with their actions.</p>
<ul>
<li><code>filter()</code> filters columns based on a given value</li>
<li><code>select()</code> selects columns to display in a data set</li>
</ul>
<p>Before we begin, we need to learn about the pipe.</p>
<p>Yes, that one! The pipe operator <code>%>%</code> is used to chain
together multiple functions in R. It takes the output of one function
and passes it as the first argument to the next function. This makes it
easier to read and write code.</p>
<div id="section-pipe" class="section level4">
<h4><code>pipe()</code></h4>
<p>More often than not, you will use multiple <code>dplyr</code>
functions to wrangle your data into the shape you need. Since the
<code>dplyr</code> functions take a data frame as the first argument, it
would be inefficient to save each step as a new object. Plus, you’d have
many different versions of a data frame in your global environment!
Instead, we can use the piper operator <code>%>%</code> or
<code>|></code> to perform multiple data manipulations in one chunk
of code. We like to think of the pipe operator as saying “and then” do
this. We will provide an example below using the <code>filter()</code>
function.</p>
</div>
<div id="section-filter" class="section level4">
<h4><code>filter()</code></h4>
<div class="tutorial-exercise" data-label="filter" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>filter(March_Madness, TEAM == 'Tennessee')
March_Madness |>
filter(TEAM == 'Tennessee')</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>Let’s talk about the first line of code. In this line, we don’t use
the pipe operator; instead we provide the name of the data set as the
first argument for the <code>filter()</code> function, and we use a
logical comparison for the second argument of the function by asking R
to filter and only display observations where the TEAM(team name) equals
“Tennessee.”</p>
<p>In the second line of code, we provide the name of the data set and
use the pipe operator to then filter the team “Tennessee”. For this code
structure, we tell R to take the <code>March_Madness</code> object AND
THEN (the pipe operator) filter the column <code>TEAM</code> which is
Tennessee.</p>
<p>We get the same result using both code structures above, but when we
start to combine <code>dplyr</code> functions in a chunk of code, it
will be more efficient to use the pipe operator.</p>
</div>
<div id="section-select" class="section level4">
<h4><code>select()</code></h4>
<div class="tutorial-exercise" data-label="select" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>March_Madness |>
select(YEAR, TEAM, POWER.RATING)</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 the code chunk above, we are asking R to select the columns YEAR,
TEAM, and POWER RATING. Notice that we use backticks around the column
name <code>POWER RATING</code> because it contains a space. This is a
common practice when selecting columns with spaces in the name.</p>
</div>
</div>
<div id="section-reflect" class="section level3">
<h3>Reflect</h3>
<p>Let’s take some time to reflect before moving on. What is one thing
that surprised you? What is one thing that confused you? Did you learn
anything that might be useful in the type of work you do?</p>
</div>
</div>
<div id="section-data-visualization---be-ut-men-basketball-coach"
class="section level2">
<h2>Data Visualization - BE UT Men Basketball coach</h2>
<p>Now, let’s apply your R skills to a real-world dataset: the NCAA
basketball March Madness. We’ll analyze Tennessee Vols team historical
data to understand past performance and perhaps predict future
outcomes.</p>
<p>Analysis Introduction:</p>
<p>In 2024, Tennessee achieved their best historical performance by
reaching the Elite Eight in March Madness. They are now set to compete
against Purdue University for a spot in the Final Four. As the coach,
you have received data from your team’s analysts, containing historical
March Madness data for both your team and the opponent. Your task is to
evaluate both teams’ performances to devise a winning strategy.</p>
<p>We’ll explore how the power rating for the Tennessee team has changed
over last three years in the March Madness tournaments. We’ll also
compare the performances between UT Vols and Purdue based on their power
ratings.</p>
<div id="section-task-1-tennessee-team-power-rating-analysis"
class="section level3">
<h3>Task 1: Tennessee Team Power Rating Analysis</h3>
<p>Explore how the power rating for the Tennessee team has changed over
the years in the March Madness tournaments. Create a line plot to
visualize the trend.</p>
<p><strong>Steps:</strong></p>
<ol style="list-style-type: decimal">
<li>Filter the dataset for team “Tennessee” and years 2021 to 2023.</li>
<li>select the columns “YEAR”,“TEAM” and “POWER RATING”.</li>
<li>Plot the POWER RATING over past 3 years.</li>
</ol>
<div class="tutorial-exercise" data-label="tennessee-PowerRating-trend"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code># Filter data for Tennessee and select relevant columns (YEAR, TEAM, POWER RATING) for years 2021 to 2023
tennessee_data <- March_Madness %>%
filter(TEAM == 'Tennessee', YEAR >= 2021, YEAR <= 2023) %>%
select(YEAR, TEAM, POWER.RATING)
# Plot Power Rating over the years using ggplot2 as a line plot
ggplot(tennessee_data, aes(x = YEAR, y = POWER.RATING)) +
geom_line(color = "orange") +
geom_point() +
labs(title = "Tennessee Vols Power Rating Trend in past 3 years' March Madness", x = "Year", y = "Power Rating") +
theme_minimal()</code></pre>
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</div>
</div>
<div
id="section-task-2-coach-its-your-turn.-purdue-team-power-rating-analysis"
class="section level3">
<h3>Task 2: Coach!! It’s your Turn. Purdue Team Power Rating
Analysis</h3>
<p><strong>object</strong>：Explore the Purde performances by analyzing
their Power Ratings.</p>
<p><strong>Steps:</strong></p>
<ol style="list-style-type: decimal">
<li>Filter the dataset for the Purdue university.</li>
<li>Select the columns “TEAM”,“YEAR” and POWER.RATING.</li>
<li>Create a bar plot to visualize the power ratings of Purdue . 4.Try
to think about the 2024 elite eight round result of Tennessee vs Purdue
based on the power rating.</li>
</ol>
<p>Let’s do! Step 1: Filter the dataset for Purdue University Filter the
dataset for team “Purdue” and years 2021 to 2023.</p>
<div class="tutorial-exercise" data-label="filter_data_for_Purdue"
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":true,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support"
data-label="filter_data_for_Purdue-hint" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code># Hint: Use the filter function from dplyr
Purdue_data <- March_Madness %>%
filter(TEAM == 'Purdue', YEAR >= 2021, YEAR <= 2023)</code></pre>
</div>
<div class="tutorial-exercise-support"
data-label="filter_data_for_Purdue-solution" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>Purdue_data <- March_Madness %>%
filter(TEAM == 'Purdue', YEAR >= 2021, YEAR <= 2023)</code></pre>
</div>
<p>Step 2: Select the columns “TEAM”,“YEAR” and POWER.RATING.</p>
<div class="tutorial-exercise" data-label="select_the_relevant_columns"
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":true,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support"
data-label="select_the_relevant_columns-hint" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code># Hint: Use the select function from dplyr
Purdue_data <- Purdue_data %>%
select(YEAR, TEAM, POWER.RATING)</code></pre>
</div>
<div class="tutorial-exercise-support"
data-label="select_the_relevant_columns-solution" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>Purdue_data <- Purdue_data %>%
select(YEAR, TEAM, POWER.RATING)</code></pre>
</div>
<p>Step 3: Create a line plot to visualize the power ratings of
Purdue.</p>
<div class="tutorial-exercise" data-label="create_lineplot_Purdue"
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":true,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support"
data-label="create_lineplot_Purdue-hint" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code># Hint: Use ggplot2 to create the line plot
ggplot(Purdue_data, aes(x = YEAR, y = POWER.RATING)) +
geom_line(color = "blue") +
geom_point(color = "blue")</code></pre>
</div>
<div class="tutorial-exercise-support"
data-label="create_lineplot_Purdue-solution" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>ggplot(Purdue_data, aes(x = YEAR, y = POWER.RATING)) +
geom_line(color = "blue") +
geom_point(color = "blue")</code></pre>
</div>
</div>
<div id="section-task-3-compare-tennessee-and-purdue-power-ratings"
class="section level3">
<h3>Task 3: Compare Tennessee and Purdue Power Ratings</h3>
<p>Now: hand over to the team’s analysts to compare the power ratings of
Tennessee and Purdue over the years.Run the code below to compare the
power ratings of Tennessee and Purdue over past 3 years.</p>
<div class="tutorial-exercise" data-label="power-rating-comparison"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code># draw UT vols Power Rating
p <- ggplot() +
geom_line(data = tennessee_data, aes(x = YEAR, y = POWER.RATING, color = "Tennessee"), size = 1) +
geom_point(data = tennessee_data, aes(x = YEAR, y = POWER.RATING, color = "Tennessee"))
# add Purdue Power Rating
p <- p +
geom_line(data = Purdue_data, aes(x = YEAR, y = POWER.RATING, color = "Purdue"), size = 1) +
geom_point(data = Purdue_data, aes(x = YEAR, y = POWER.RATING, color = "Purdue"))
# add labels and theme
p <- p + labs(title = "Tennessee vs Purdue Power Rating Comparison in past 3 years' March Madness",
x = "Year",
y = "Power Rating",
color = "Team") +
theme_minimal() +
scale_color_manual(values = c("Tennessee" = "orange", "Purdue" = "blue"))
# print the plot
print(p)</code></pre>
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"", "March_Madness |>", " filter(TEAM == 'Tennessee')"
), out.width.px = 624, out.height.px = 384, params.src = "filter, exercise=TRUE, exercise.eval = FALSE",
fig.num = 0, exercise.df_print = "paged"), engine = "r",
version = "4"), class = c("r", "tutorial_exercise")))
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-select-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-select-code-editor`)), session)
output$`tutorial-exercise-select-output` <- renderUI({
`tutorial-exercise-select-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
learnr:::store_exercise_cache(structure(list(label = "select", global_setup = structure(c("library(learnr)",
"library(readr)", "library(tidyverse)", "library(ggplot2)", "knitr::opts_chunk$set(echo = TRUE, eval = FALSE)",
"tutorial_options(exercise.timelimit = 60)", "gradethis::gradethis_setup()",
"March_Madness <- read.csv(\"March_Madness_Data.csv\")", "tennessee_data <- March_Madness %>%",
" filter(TEAM == 'Tennessee', YEAR >= 2021, YEAR <= 2023) %>%",
" select(YEAR, TEAM, POWER.RATING)", "Purdue_data <- March_Madness %>%",
" filter(TEAM == 'Purdue', YEAR >= 2021, YEAR <= 2023)"), chunk_opts = list(
label = "setup", include = FALSE)), setup = NULL, chunks = list(
list(label = "select", code = "March_Madness |>\n select(YEAR, TEAM, POWER.RATING)",
opts = list(label = "\"select\"", exercise = "TRUE",
exercise.eval = "FALSE"), 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 = "Intro-to-Data-Science_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 = "Intro-to-Data-Science_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, exercise.timelimit = 60, exercise.checker = "function (label = NULL, solution_code = NULL, user_code = NULL, \n check_code = NULL, envir_result = NULL, evaluate_result = NULL, \n envir_prep = NULL, last_value = NULL, stage = NULL, ..., \n solution_eval_fn = NULL) \n{\n (utils::getFromNamespace(\"check_exercise\", \"gradethis\"))(label = label, \n solution_code = solution_code, user_code = user_code, \n check_code = check_code, envir_result = envir_result, \n evaluate_result = evaluate_result, envir_prep = envir_prep, \n last_value = last_value, stage = stage, ...)\n}",
exercise.error.check.code = "gradethis_error_checker()",
label = "select", exercise = TRUE, exercise.eval = FALSE,
code = c("March_Madness |>", " select(YEAR, TEAM, POWER.RATING)"
), out.width.px = 624, out.height.px = 384, params.src = "select, exercise=TRUE, exercise.eval = FALSE",
fig.num = 0, exercise.df_print = "paged"), engine = "r",
version = "4"), class = c("r", "tutorial_exercise")))
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-tennessee-PowerRating-trend-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-tennessee-PowerRating-trend-code-editor`)), session)
output$`tutorial-exercise-tennessee-PowerRating-trend-output` <- renderUI({
`tutorial-exercise-tennessee-PowerRating-trend-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
learnr:::store_exercise_cache(structure(list(label = "tennessee-PowerRating-trend", global_setup = structure(c("library(learnr)",
"library(readr)", "library(tidyverse)", "library(ggplot2)", "knitr::opts_chunk$set(echo = TRUE, eval = FALSE)",
"tutorial_options(exercise.timelimit = 60)", "gradethis::gradethis_setup()",
"March_Madness <- read.csv(\"March_Madness_Data.csv\")", "tennessee_data <- March_Madness %>%",
" filter(TEAM == 'Tennessee', YEAR >= 2021, YEAR <= 2023) %>%",
" select(YEAR, TEAM, POWER.RATING)", "Purdue_data <- March_Madness %>%",
" filter(TEAM == 'Purdue', YEAR >= 2021, YEAR <= 2023)"), chunk_opts = list(
label = "setup", include = FALSE)), setup = NULL, chunks = list(
list(label = "tennessee-PowerRating-trend", code = "# Filter data for Tennessee and select relevant columns (YEAR, TEAM, POWER RATING) for years 2021 to 2023\ntennessee_data <- March_Madness %>%\n filter(TEAM == 'Tennessee', YEAR >= 2021, YEAR <= 2023) %>%\n select(YEAR, TEAM, POWER.RATING)\n\n# Plot Power Rating over the years using ggplot2 as a line plot\nggplot(tennessee_data, aes(x = YEAR, y = POWER.RATING)) +\n geom_line(color = \"orange\") + \n geom_point() + \n labs(title = \"Tennessee Vols Power Rating Trend in past 3 years' March Madness\", x = \"Year\", y = \"Power Rating\") +\n theme_minimal()",
opts = list(label = "\"tennessee-PowerRating-trend\"",
exercise = "TRUE", exercise.eval = "FALSE"), 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 = "Intro-to-Data-Science_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 = "Intro-to-Data-Science_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, exercise.timelimit = 60, exercise.checker = "function (label = NULL, solution_code = NULL, user_code = NULL, \n check_code = NULL, envir_result = NULL, evaluate_result = NULL, \n envir_prep = NULL, last_value = NULL, stage = NULL, ..., \n solution_eval_fn = NULL) \n{\n (utils::getFromNamespace(\"check_exercise\", \"gradethis\"))(label = label, \n solution_code = solution_code, user_code = user_code, \n check_code = check_code, envir_result = envir_result, \n evaluate_result = evaluate_result, envir_prep = envir_prep, \n last_value = last_value, stage = stage, ...)\n}",
exercise.error.check.code = "gradethis_error_checker()",
label = "tennessee-PowerRating-trend", exercise = TRUE,
exercise.eval = FALSE, code = c("# Filter data for Tennessee and select relevant columns (YEAR, TEAM, POWER RATING) for years 2021 to 2023",
"tennessee_data <- March_Madness %>%", " filter(TEAM == 'Tennessee', YEAR >= 2021, YEAR <= 2023) %>%",
" select(YEAR, TEAM, POWER.RATING)", "", "# Plot Power Rating over the years using ggplot2 as a line plot",
"ggplot(tennessee_data, aes(x = YEAR, y = POWER.RATING)) +",
" geom_line(color = \"orange\") + ", " geom_point() + ",
" labs(title = \"Tennessee Vols Power Rating Trend in past 3 years' March Madness\", x = \"Year\", y = \"Power Rating\") +",
" theme_minimal()"), out.width.px = 624, out.height.px = 384,
params.src = "tennessee-PowerRating-trend, exercise=TRUE,, exercise.eval = FALSE",
fig.num = 0, exercise.df_print = "paged"), engine = "r",
version = "4"), class = c("r", "tutorial_exercise")))
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-filter_data_for_Purdue-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-filter_data_for_Purdue-code-editor`)), session)
output$`tutorial-exercise-filter_data_for_Purdue-output` <- renderUI({
`tutorial-exercise-filter_data_for_Purdue-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
learnr:::store_exercise_cache(structure(list(label = "filter_data_for_Purdue", global_setup = structure(c("library(learnr)",
"library(readr)", "library(tidyverse)", "library(ggplot2)", "knitr::opts_chunk$set(echo = TRUE, eval = FALSE)",
"tutorial_options(exercise.timelimit = 60)", "gradethis::gradethis_setup()",
"March_Madness <- read.csv(\"March_Madness_Data.csv\")", "tennessee_data <- March_Madness %>%",
" filter(TEAM == 'Tennessee', YEAR >= 2021, YEAR <= 2023) %>%",
" select(YEAR, TEAM, POWER.RATING)", "Purdue_data <- March_Madness %>%",
" filter(TEAM == 'Purdue', YEAR >= 2021, YEAR <= 2023)"), chunk_opts = list(
label = "setup", include = FALSE)), setup = NULL, chunks = list(
list(label = "filter_data_for_Purdue", code = " \n", opts = list(
label = "\"filter_data_for_Purdue\"", exercise = "TRUE",
exercise.eval = "FALSE"), engine = "r")), code_check = NULL,
error_check = NULL, check = structure(c("gradethis::grade_result(gradethis::pass_if(~identical(.result, Purdue_data <- March_Madness %>%",
" filter(TEAM == 'Purdue', YEAR >= 2021, YEAR <= 2023))))"
), chunk_opts = list(label = "filter_data_for_Purdue-check")),
solution = structure(c("Purdue_data <- March_Madness %>%",
" filter(TEAM == 'Purdue', YEAR >= 2021, YEAR <= 2023)"), chunk_opts = list(
label = "filter_data_for_Purdue-solution")), 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 = "Intro-to-Data-Science_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 = "Intro-to-Data-Science_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, exercise.timelimit = 60, exercise.checker = "function (label = NULL, solution_code = NULL, user_code = NULL, \n check_code = NULL, envir_result = NULL, evaluate_result = NULL, \n envir_prep = NULL, last_value = NULL, stage = NULL, ..., \n solution_eval_fn = NULL) \n{\n (utils::getFromNamespace(\"check_exercise\", \"gradethis\"))(label = label, \n solution_code = solution_code, user_code = user_code, \n check_code = check_code, envir_result = envir_result, \n evaluate_result = evaluate_result, envir_prep = envir_prep, \n last_value = last_value, stage = stage, ...)\n}",
exercise.error.check.code = "gradethis_error_checker()",
label = "filter_data_for_Purdue", exercise = TRUE, exercise.eval = FALSE,
code = c(" ", ""), out.width.px = 624, out.height.px = 384,
params.src = "filter_data_for_Purdue, exercise = TRUE, exercise.eval = FALSE",
fig.num = 0L, exercise.df_print = "paged"), engine = "r",
version = "4"), class = c("r", "tutorial_exercise")))
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-select_the_relevant_columns-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-select_the_relevant_columns-code-editor`)), session)
output$`tutorial-exercise-select_the_relevant_columns-output` <- renderUI({
`tutorial-exercise-select_the_relevant_columns-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
learnr:::store_exercise_cache(structure(list(label = "select_the_relevant_columns", global_setup = structure(c("library(learnr)",
"library(readr)", "library(tidyverse)", "library(ggplot2)", "knitr::opts_chunk$set(echo = TRUE, eval = FALSE)",
"tutorial_options(exercise.timelimit = 60)", "gradethis::gradethis_setup()",
"March_Madness <- read.csv(\"March_Madness_Data.csv\")", "tennessee_data <- March_Madness %>%",
" filter(TEAM == 'Tennessee', YEAR >= 2021, YEAR <= 2023) %>%",
" select(YEAR, TEAM, POWER.RATING)", "Purdue_data <- March_Madness %>%",
" filter(TEAM == 'Purdue', YEAR >= 2021, YEAR <= 2023)"), chunk_opts = list(
label = "setup", include = FALSE)), setup = NULL, chunks = list(
list(label = "select_the_relevant_columns", code = " \n",
opts = list(label = "\"select_the_relevant_columns\"",
exercise = "TRUE", exercise.eval = "FALSE"), engine = "r")),
code_check = NULL, error_check = NULL, check = structure(c("gradethis::grade_result(gradethis::pass_if(~identical(.result, Purdue_data <- Purdue_data %>%",
" select(YEAR, TEAM, POWER.RATING))))"), chunk_opts = list(
label = "select_the_relevant_columns-check")), solution = structure(c("Purdue_data <- Purdue_data %>%",
" select(YEAR, TEAM, POWER.RATING)"), chunk_opts = list(
label = "select_the_relevant_columns-solution")), 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 = "Intro-to-Data-Science_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 = "Intro-to-Data-Science_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, exercise.timelimit = 60, exercise.checker = "function (label = NULL, solution_code = NULL, user_code = NULL, \n check_code = NULL, envir_result = NULL, evaluate_result = NULL, \n envir_prep = NULL, last_value = NULL, stage = NULL, ..., \n solution_eval_fn = NULL) \n{\n (utils::getFromNamespace(\"check_exercise\", \"gradethis\"))(label = label, \n solution_code = solution_code, user_code = user_code, \n check_code = check_code, envir_result = envir_result, \n evaluate_result = evaluate_result, envir_prep = envir_prep, \n last_value = last_value, stage = stage, ...)\n}",
exercise.error.check.code = "gradethis_error_checker()",
label = "select_the_relevant_columns", exercise = TRUE,
exercise.eval = FALSE, code = c(" ", ""), out.width.px = 624,
out.height.px = 384, params.src = "select_the_relevant_columns, exercise = TRUE, exercise.eval = FALSE",
fig.num = 0L, exercise.df_print = "paged"), engine = "r",
version = "4"), class = c("r", "tutorial_exercise")))
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-create_lineplot_Purdue-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-create_lineplot_Purdue-code-editor`)), session)
output$`tutorial-exercise-create_lineplot_Purdue-output` <- renderUI({
`tutorial-exercise-create_lineplot_Purdue-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
learnr:::store_exercise_cache(structure(list(label = "create_lineplot_Purdue", global_setup = structure(c("library(learnr)",
"library(readr)", "library(tidyverse)", "library(ggplot2)", "knitr::opts_chunk$set(echo = TRUE, eval = FALSE)",
"tutorial_options(exercise.timelimit = 60)", "gradethis::gradethis_setup()",
"March_Madness <- read.csv(\"March_Madness_Data.csv\")", "tennessee_data <- March_Madness %>%",
" filter(TEAM == 'Tennessee', YEAR >= 2021, YEAR <= 2023) %>%",
" select(YEAR, TEAM, POWER.RATING)", "Purdue_data <- March_Madness %>%",
" filter(TEAM == 'Purdue', YEAR >= 2021, YEAR <= 2023)"), chunk_opts = list(
label = "setup", include = FALSE)), setup = NULL, chunks = list(
list(label = "create_lineplot_Purdue", code = " \n", opts = list(
label = "\"create_lineplot_Purdue\"", exercise = "TRUE",
exercise.eval = "FALSE"), engine = "r")), code_check = NULL,
error_check = NULL, check = structure(c("gradethis::grade_result(gradethis::pass_if(~identical(.result, ggplot(Purdue_data, aes(x = YEAR, y = POWER.RATING)) +",
" geom_line(color = \"blue\") + ", " geom_point(color = \"blue\"))))"
), chunk_opts = list(label = "create_lineplot_Purdue-check")),
solution = structure(c("ggplot(Purdue_data, aes(x = YEAR, y = POWER.RATING)) +",
" geom_line(color = \"blue\") + ", " geom_point(color = \"blue\")"
), chunk_opts = list(label = "create_lineplot_Purdue-solution")),
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 = "Intro-to-Data-Science_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 = "Intro-to-Data-Science_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, exercise.timelimit = 60, exercise.checker = "function (label = NULL, solution_code = NULL, user_code = NULL, \n check_code = NULL, envir_result = NULL, evaluate_result = NULL, \n envir_prep = NULL, last_value = NULL, stage = NULL, ..., \n solution_eval_fn = NULL) \n{\n (utils::getFromNamespace(\"check_exercise\", \"gradethis\"))(label = label, \n solution_code = solution_code, user_code = user_code, \n check_code = check_code, envir_result = envir_result, \n evaluate_result = evaluate_result, envir_prep = envir_prep, \n last_value = last_value, stage = stage, ...)\n}",
exercise.error.check.code = "gradethis_error_checker()",
label = "create_lineplot_Purdue", exercise = TRUE, exercise.eval = FALSE,
code = c(" ", ""), out.width.px = 624, out.height.px = 384,
params.src = "create_lineplot_Purdue, exercise = TRUE, exercise.eval = FALSE",
fig.num = 0L, exercise.df_print = "paged"), engine = "r",
version = "4"), class = c("r", "tutorial_exercise")))
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-power-rating-comparison-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-power-rating-comparison-code-editor`)), session)
output$`tutorial-exercise-power-rating-comparison-output` <- renderUI({
`tutorial-exercise-power-rating-comparison-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
learnr:::store_exercise_cache(structure(list(label = "power-rating-comparison", global_setup = structure(c("library(learnr)",