knitr::include_graphics("img/laser-cycle.png")
A Coding Case Study with Quarto
Orientation Module: Case Study Key
0. INTRODUCTION
Welcome to your first LASER Case Study! The case study activities included in each module demonstrate how key Learning Analytics (LA) techniques featured in exemplary STEM education research studies can be implemented with R. Case studies also provide a holistic setting to explore important foundational topics integral to Learning Analytics such as reproducible research, use of APIs, and ethical use of educational data.
This orientation case study will also introduce you to Quarto, which is heavily integrated into each LASER Module. You may have used Qurto before - or you may not have! Either is fine as this task will be designed with the assumption that you have not used Quarto before.
In this interactive coding case study, we’ll focus on the following tasks:
- Reading data into R (in the Prepare section)
- Preparing and “wrangling” data in a tabular (think spreadsheet!) format (in the Wrangle section)
- Creating some basic plots (in the Explore section)
- Running a model - specifically, a simple regression model (in the Model section)
- Finally, creating a reproducible report of your work you can share with others (in the Communicate section)
How to use this Quarto document
What you are working in now is an Quarto markdown file as indicated by the .qmd file name extension. Following best practices for reproducible research (Gandrud 2013), Quarto files store information in plain text markdown syntax, which can be viewed as “Source” or by using “Visual” editor which converts markdown syntax to formatted text.
Quarto documents are fully reproducible and use a productive notebook interface to combine narrative text and “chunks” of code to produce a range of static and dynamic output formats including: HTML, PDF, Word, HTML5 slides, Tufte-style handouts, books, dashboards, shiny applications, scientific articles, websites, and more.
There are two keys to your use of Quarto for this activity:
- First, be sure that you are viewing the document in the “Visual Editor” mode. You can use this mode by clicking the word “Visual” on the left side of the toolbar above. The visual editor allows you to view formatted headers, text and code chunks as specified by the underlying markdown syntax, or “Source” text. Visual mode is a bit more “human readable” than markdown syntax but definitely take a look at the source text.
- Second, note the specially formatted text box below called a “code chunk.” These chunks allows you to run code from multiple languages including R, Python, and SQL. This specific code chunk contains a line of R code as specified by “r” inside the curly brackets
{}where you can also include other “chunk options.” You will also notice a set of buttons in the upper right corner for running the code.
Click the green arrow button on the right side of the code chunk to run the R code and view the image file name laser-cycle.png stored in the img folder in your files pane.
The Data-Intensive Research Workflow
You may have noticed that the words in this diagram correspond to the sections outlined at the beginning of this document. These terms, or processes, are part of a framework called the data-intensive research workflow and comes from the book Learning Analytics Goes to School (Krumm, Means, and Bienkowski 2018). You can check that out later, but don’t feel any need to dive deep into it for now - we’ll be spending more time on this throughout the week; just know that this document and all of our LASER Lab case studies are organized around these five components.
Now, let’s get started!
1. PREPARE
First and foremost, data-intensive research involves defining and refining a research question and developing an understanding of where your data comes from (Krumm, Means, and Bienkowski 2018). This part of the process also involves setting up a reproducible research environment (Gandrud 2013) so your work can be understood and replicated by other researchers. For now, we’ll focus on just a few parts of this process, diving in much more deeply into these components in later learning labs.
Research Question
In this case study, we’ll be working with data come from an unpublished research study, which utilized a number of different data sources to understand high school students’ motivation within the context of online courses. For the purpose of this case study, our analysis will be driven by the following research question:
Is there a relationship between the time students spend on a course (as measured through their learning management system) and their final course grade?
Packages 📦
As highlighted in Chapter 6 of Data Science in Education Using R (Estrellado et al. 2020), one of the first steps of every workflow should be to set up a “Project” within RStudio.
A Project is the home for all of the files, images, reports, and code that are used in any given project.
We are working in Posit Cloud with an R project cloned from GitHub, so a project has already been set up for you as indicated by the .Rproj file in the main directory. Locate the Files tab lower right hand window pane and see if you can find the file named laser-orientation.Rproj.
Since a project already set up for us, we will instead focus on loading the required packages we’ll need for analysis.
Packages, sometimes referred to as libraries, are shareable collections of R code that can contain functions, data, and/or documentation and extend the functionality of R.
You can always check to see which packages have already been installed and loaded into RStudio by looking at the Packages tab in the same pane as the Files tab. Click the packages tab to see which packages have already been installed for this project.
tidyverse 📦
One package that we’ll be using extensively in our learning labs is the {tidyverse} package. The {tidyverse} is actually a collection of R packages designed for wrangling and exploring data (sound familiar?) and which all share an underlying design philosophy, grammar, and data structures. These shared features are sometimes referred to as “tidy data principles” (Wickham and Grolemund 2016).
To load the tidyverse, we’ll use the library() function. Go ahead and run the code chunk below:
library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.0 ✔ tibble 3.2.1
✔ lubridate 1.9.2 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsPlease do not worry if you saw a number of messages: those probably mean that the tidyverse loaded just fine. If you see an error, though, try to interpret or search via your search engine the contents of the error, or reach out to us for assistance.
👉 Your Turn ⤵
As we noted in the beginning, these case studies are meant to be interactive. Throughout each case study, you’ll see Your Turn headings like the one above that will ask to you apply some of your R skills to help with the analysis. These Your Turns are intended to help you practice newly introduced functions or R code and reinforce R skills you have already learned.
Use the code chunk below to load the {skimr} package into our environment as well. Skimr is a handy package that provides summary statistics that you can skim quickly to understand your data and see what may be missing. We’ll be using this later in the Explore section of this case study.
library(skimr)Loading (or reading in) data
The data we’ll explore in this case study were originally collected for a research study, which utilized a number of different data sources to understand students’ course-related motivation. These courses were designed and taught by instructors through a state-wide online course provider designed to supplement—but not replace—students’ enrollment in their local school.
The data used in this case study has already been “wrangled” quite a bit, but the original datasets included:
A self-report survey assessing three aspects of students’ motivation
Log-trace data, such as data output from the learning management system (LMS)
Discussion board data
Academic achievement data
If you are interested in learning more about these datasets, you can visit Chapter 7 of the excellent book, Data Science in Education Using R(Estrellado et al. 2020).
Next, we’ll load our data - specifically, a CSV text file, the kind that you can export from Microsoft Excel or Google Sheets - into R, using the read_csv() function in the next chunk.
Clicking the green arrow runs the code; do that next to read the sci-online-classes.csv file stored in the data folder of your R project:
sci_data <- read_csv("data/sci-online-classes.csv")Rows: 603 Columns: 30
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (6): course_id, subject, semester, section, Gradebook_Item, Gender
dbl (23): student_id, total_points_possible, total_points_earned, percentage...
lgl (1): Grade_Category
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Nice work! You should now see a new data “object” named sci_data saved in your Environment pane. Try clicking on it and see what happens!
Viewing or inspecting data
Now let’s learn another way to inspect our data. Run the next chunk and look at the results, tabbing left or right with the arrows, or scanning through the rows by clicking the numbers at the bottom of the pane with the print-out of the data frame you “assigned” to the sci_data object in the previous code-chunk:
sci_data# A tibble: 603 × 30
student_id course_id total_points_possible total_points_earned
<dbl> <chr> <dbl> <dbl>
1 43146 FrScA-S216-02 3280 2220
2 44638 OcnA-S116-01 3531 2672
3 47448 FrScA-S216-01 2870 1897
4 47979 OcnA-S216-01 4562 3090
5 48797 PhysA-S116-01 2207 1910
6 51943 FrScA-S216-03 4208 3596
7 52326 AnPhA-S216-01 4325 2255
8 52446 PhysA-S116-01 2086 1719
9 53447 FrScA-S116-01 4655 3149
10 53475 FrScA-S116-02 1710 1402
# ℹ 593 more rows
# ℹ 26 more variables: percentage_earned <dbl>, subject <chr>, semester <chr>,
# section <chr>, Gradebook_Item <chr>, Grade_Category <lgl>,
# FinalGradeCEMS <dbl>, Points_Possible <dbl>, Points_Earned <dbl>,
# Gender <chr>, q1 <dbl>, q2 <dbl>, q3 <dbl>, q4 <dbl>, q5 <dbl>, q6 <dbl>,
# q7 <dbl>, q8 <dbl>, q9 <dbl>, q10 <dbl>, TimeSpent <dbl>,
# TimeSpent_hours <dbl>, TimeSpent_std <dbl>, int <dbl>, pc <dbl>, uv <dbl>Note: You can also enlarge this output by clicking the “Show in New Window” button located in the top right corner of the output.
👉 Your Turn ⤵
What do you notice about this data set? What do you wonder? Add one or two observations in the space below:
- YOUR RESPONSE HERE
There are many other ways to inspect your data; the glimpse() function provides one such way. Use the code chunk below to take a “glimpse” at your sci_data.
glimpse(sci_data)Rows: 603
Columns: 30
$ student_id <dbl> 43146, 44638, 47448, 47979, 48797, 51943, 52326,…
$ course_id <chr> "FrScA-S216-02", "OcnA-S116-01", "FrScA-S216-01"…
$ total_points_possible <dbl> 3280, 3531, 2870, 4562, 2207, 4208, 4325, 2086, …
$ total_points_earned <dbl> 2220, 2672, 1897, 3090, 1910, 3596, 2255, 1719, …
$ percentage_earned <dbl> 0.6768293, 0.7567261, 0.6609756, 0.6773345, 0.86…
$ subject <chr> "FrScA", "OcnA", "FrScA", "OcnA", "PhysA", "FrSc…
$ semester <chr> "S216", "S116", "S216", "S216", "S116", "S216", …
$ section <chr> "02", "01", "01", "01", "01", "03", "01", "01", …
$ Gradebook_Item <chr> "POINTS EARNED & TOTAL COURSE POINTS", "ATTEMPTE…
$ Grade_Category <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ FinalGradeCEMS <dbl> 93.45372, 81.70184, 88.48758, 81.85260, 84.00000…
$ Points_Possible <dbl> 5, 10, 10, 5, 438, 5, 10, 10, 443, 5, 12, 10, 5,…
$ Points_Earned <dbl> NA, 10.00, NA, 4.00, 399.00, NA, NA, 10.00, 425.…
$ Gender <chr> "M", "F", "M", "M", "F", "F", "M", "F", "F", "M"…
$ q1 <dbl> 5, 4, 5, 5, 4, NA, 5, 3, 4, NA, NA, 4, 3, 5, NA,…
$ q2 <dbl> 4, 4, 4, 5, 3, NA, 5, 3, 3, NA, NA, 5, 3, 3, NA,…
$ q3 <dbl> 4, 3, 4, 3, 3, NA, 3, 3, 3, NA, NA, 3, 3, 5, NA,…
$ q4 <dbl> 5, 4, 5, 5, 4, NA, 5, 3, 4, NA, NA, 5, 3, 5, NA,…
$ q5 <dbl> 5, 4, 5, 5, 4, NA, 5, 3, 4, NA, NA, 5, 4, 5, NA,…
$ q6 <dbl> 5, 4, 4, 5, 4, NA, 5, 4, 3, NA, NA, 5, 3, 5, NA,…
$ q7 <dbl> 5, 4, 4, 4, 4, NA, 4, 3, 3, NA, NA, 5, 3, 5, NA,…
$ q8 <dbl> 5, 5, 5, 5, 4, NA, 5, 3, 4, NA, NA, 4, 3, 5, NA,…
$ q9 <dbl> 4, 4, 3, 5, NA, NA, 5, 3, 2, NA, NA, 5, 2, 2, NA…
$ q10 <dbl> 5, 4, 5, 5, 3, NA, 5, 3, 5, NA, NA, 4, 4, 5, NA,…
$ TimeSpent <dbl> 1555.1667, 1382.7001, 860.4335, 1598.6166, 1481.…
$ TimeSpent_hours <dbl> 25.91944500, 23.04500167, 14.34055833, 26.643610…
$ TimeSpent_std <dbl> -0.18051496, -0.30780313, -0.69325954, -0.148446…
$ int <dbl> 5.0, 4.2, 5.0, 5.0, 3.8, 4.6, 5.0, 3.0, 4.2, NA,…
$ pc <dbl> 4.50, 3.50, 4.00, 3.50, 3.50, 4.00, 3.50, 3.00, …
$ uv <dbl> 4.333333, 4.000000, 3.666667, 5.000000, 3.500000…We have one more question to pose to you: What do rows and columns typically represent in your area of work and/or research?
Generally, rows typically represent “cases,” the units that we measure, or the units on which we collect data. This is not a trick question! What counts as a “case” (and therefore what is represented as a row) varies by (and within) fields. There may be multiple types or levels of units studied in your field; listing more than one is fine! Also, please consider what columns - which usually represent variables - represent in your area of work and/or research.
What do rows typically (or you think may) represent in your research:
- YOUR RESPONSE HERE
What do columns typically (or you think may) represent in your research:
- YOUR RESPONSE HERE
Next, we’ll use a few functions that are handy for preparing data in table form.
2. WRANGLE
By wrangle, we refer to the process of cleaning and processing data, and, in some cases, merging (or joining) data from multiple sources. Often, this part of the process is very (surprisingly) time-intensive! Wrangling your data into shape can itself be an important accomplishment! And documenting your code using R scripts oQuarto files will save yourself and others a great deal of time wrangling data in the future! There are great tools in R for data wrangling, especially through the use of the {dplyr} R package which is part of the {tidyverse} suite of packages.
Selecting variables
Recall from our Prepare section that we are interested the relationship between the time students spend on a course and their final course grade.
Let’s practice selecting a few variables by introducing a very powerful |> operator called a pipe. Pipes are a powerful tool for combining a sequence of functions or processes.
Run the following code chunk to “pipe” our sci_data to the select() function include the following two variables as arguments:
FinalGradeCEMS(i.e., students’ final grades on a 0-100 point scale)TimeSpent(i.e., the number of minutes they spent in the course’s learning management system)
sci_data |>
select(FinalGradeCEMS, TimeSpent)# A tibble: 603 × 2
FinalGradeCEMS TimeSpent
<dbl> <dbl>
1 93.5 1555.
2 81.7 1383.
3 88.5 860.
4 81.9 1599.
5 84 1482.
6 NA 3.45
7 83.6 1322.
8 97.8 1390.
9 96.1 1479.
10 NA NA
# ℹ 593 more rowsNotice how the number of columns (variables) is now different!
Let’s include one additional variable in the select function that you think might be a predictor of students’ final course grade or useful in addressing our research question.
First, we need to figure out what variables exist in our dataset (or be reminded of this - it’s very common in R to be continually checking and inspecting your data)!
Recall that you can use a function named glimpse() to do this.
glimpse(sci_data)Rows: 603
Columns: 30
$ student_id <dbl> 43146, 44638, 47448, 47979, 48797, 51943, 52326,…
$ course_id <chr> "FrScA-S216-02", "OcnA-S116-01", "FrScA-S216-01"…
$ total_points_possible <dbl> 3280, 3531, 2870, 4562, 2207, 4208, 4325, 2086, …
$ total_points_earned <dbl> 2220, 2672, 1897, 3090, 1910, 3596, 2255, 1719, …
$ percentage_earned <dbl> 0.6768293, 0.7567261, 0.6609756, 0.6773345, 0.86…
$ subject <chr> "FrScA", "OcnA", "FrScA", "OcnA", "PhysA", "FrSc…
$ semester <chr> "S216", "S116", "S216", "S216", "S116", "S216", …
$ section <chr> "02", "01", "01", "01", "01", "03", "01", "01", …
$ Gradebook_Item <chr> "POINTS EARNED & TOTAL COURSE POINTS", "ATTEMPTE…
$ Grade_Category <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ FinalGradeCEMS <dbl> 93.45372, 81.70184, 88.48758, 81.85260, 84.00000…
$ Points_Possible <dbl> 5, 10, 10, 5, 438, 5, 10, 10, 443, 5, 12, 10, 5,…
$ Points_Earned <dbl> NA, 10.00, NA, 4.00, 399.00, NA, NA, 10.00, 425.…
$ Gender <chr> "M", "F", "M", "M", "F", "F", "M", "F", "F", "M"…
$ q1 <dbl> 5, 4, 5, 5, 4, NA, 5, 3, 4, NA, NA, 4, 3, 5, NA,…
$ q2 <dbl> 4, 4, 4, 5, 3, NA, 5, 3, 3, NA, NA, 5, 3, 3, NA,…
$ q3 <dbl> 4, 3, 4, 3, 3, NA, 3, 3, 3, NA, NA, 3, 3, 5, NA,…
$ q4 <dbl> 5, 4, 5, 5, 4, NA, 5, 3, 4, NA, NA, 5, 3, 5, NA,…
$ q5 <dbl> 5, 4, 5, 5, 4, NA, 5, 3, 4, NA, NA, 5, 4, 5, NA,…
$ q6 <dbl> 5, 4, 4, 5, 4, NA, 5, 4, 3, NA, NA, 5, 3, 5, NA,…
$ q7 <dbl> 5, 4, 4, 4, 4, NA, 4, 3, 3, NA, NA, 5, 3, 5, NA,…
$ q8 <dbl> 5, 5, 5, 5, 4, NA, 5, 3, 4, NA, NA, 4, 3, 5, NA,…
$ q9 <dbl> 4, 4, 3, 5, NA, NA, 5, 3, 2, NA, NA, 5, 2, 2, NA…
$ q10 <dbl> 5, 4, 5, 5, 3, NA, 5, 3, 5, NA, NA, 4, 4, 5, NA,…
$ TimeSpent <dbl> 1555.1667, 1382.7001, 860.4335, 1598.6166, 1481.…
$ TimeSpent_hours <dbl> 25.91944500, 23.04500167, 14.34055833, 26.643610…
$ TimeSpent_std <dbl> -0.18051496, -0.30780313, -0.69325954, -0.148446…
$ int <dbl> 5.0, 4.2, 5.0, 5.0, 3.8, 4.6, 5.0, 3.0, 4.2, NA,…
$ pc <dbl> 4.50, 3.50, 4.00, 3.50, 3.50, 4.00, 3.50, 3.00, …
$ uv <dbl> 4.333333, 4.000000, 3.666667, 5.000000, 3.500000…👉 Your Turn ⤵
In the code chunk below, add a new variable, being careful to type the new variable name as it appears in the data. We’ve added some code to get you started. Consider how the names of the other variables are separated as you think about how to add an additional variable to this code.
sci_data |>
select(FinalGradeCEMS, TimeSpent)# A tibble: 603 × 2
FinalGradeCEMS TimeSpent
<dbl> <dbl>
1 93.5 1555.
2 81.7 1383.
3 88.5 860.
4 81.9 1599.
5 84 1482.
6 NA 3.45
7 83.6 1322.
8 97.8 1390.
9 96.1 1479.
10 NA NA
# ℹ 593 more rowsOnce added, the output should be different than in the code above - there should now be an additional variable included in the print-out.
A quick footnote about pipes: The original pipe operator, %>%, comes from the {magrittr} package but all packages in the tidyverse load %>% for you automatically, so you don’t usually load magrittr explicitly. The pipe has become such a useful and much used operator in R that it is now baked into R using the new and simpler native pipe |> operator. You can use both fairly interchangeably but there are a few differences between pipe operators.
Filtering variables
Next, let’s explore filtering variables. Check out and run the next chunk of code, imagining that we wish to filter our data to view only the rows associated with students who earned a final grade (as a percentage) of 70 - 70% - or higher.
sci_data |>
filter(FinalGradeCEMS > 70)# A tibble: 438 × 30
student_id course_id total_points_possible total_points_earned
<dbl> <chr> <dbl> <dbl>
1 43146 FrScA-S216-02 3280 2220
2 44638 OcnA-S116-01 3531 2672
3 47448 FrScA-S216-01 2870 1897
4 47979 OcnA-S216-01 4562 3090
5 48797 PhysA-S116-01 2207 1910
6 52326 AnPhA-S216-01 4325 2255
7 52446 PhysA-S116-01 2086 1719
8 53447 FrScA-S116-01 4655 3149
9 53475 FrScA-S216-01 1209 977
10 54066 OcnA-S116-01 4641 3429
# ℹ 428 more rows
# ℹ 26 more variables: percentage_earned <dbl>, subject <chr>, semester <chr>,
# section <chr>, Gradebook_Item <chr>, Grade_Category <lgl>,
# FinalGradeCEMS <dbl>, Points_Possible <dbl>, Points_Earned <dbl>,
# Gender <chr>, q1 <dbl>, q2 <dbl>, q3 <dbl>, q4 <dbl>, q5 <dbl>, q6 <dbl>,
# q7 <dbl>, q8 <dbl>, q9 <dbl>, q10 <dbl>, TimeSpent <dbl>,
# TimeSpent_hours <dbl>, TimeSpent_std <dbl>, int <dbl>, pc <dbl>, uv <dbl>👉 Your Turn ⤵
In the next code chunk, change the cut-off from 70% to some other value - larger or smaller (maybe much larger or smaller - feel free to play around with the code a bit!).
sci_data |>
filter(FinalGradeCEMS > 70)# A tibble: 438 × 30
student_id course_id total_points_possible total_points_earned
<dbl> <chr> <dbl> <dbl>
1 43146 FrScA-S216-02 3280 2220
2 44638 OcnA-S116-01 3531 2672
3 47448 FrScA-S216-01 2870 1897
4 47979 OcnA-S216-01 4562 3090
5 48797 PhysA-S116-01 2207 1910
6 52326 AnPhA-S216-01 4325 2255
7 52446 PhysA-S116-01 2086 1719
8 53447 FrScA-S116-01 4655 3149
9 53475 FrScA-S216-01 1209 977
10 54066 OcnA-S116-01 4641 3429
# ℹ 428 more rows
# ℹ 26 more variables: percentage_earned <dbl>, subject <chr>, semester <chr>,
# section <chr>, Gradebook_Item <chr>, Grade_Category <lgl>,
# FinalGradeCEMS <dbl>, Points_Possible <dbl>, Points_Earned <dbl>,
# Gender <chr>, q1 <dbl>, q2 <dbl>, q3 <dbl>, q4 <dbl>, q5 <dbl>, q6 <dbl>,
# q7 <dbl>, q8 <dbl>, q9 <dbl>, q10 <dbl>, TimeSpent <dbl>,
# TimeSpent_hours <dbl>, TimeSpent_std <dbl>, int <dbl>, pc <dbl>, uv <dbl>What happens when you change the cut-off from 70 to something else? Add a thought (or more) below:
- YOUR RESPONSE HERE
Arrange
The last function we’ll use for preparing tables is arrange. We’ll again use the |> to combine this arrange() function with a function we used already - select(). We do this so we can view only time spent and final grades.
sci_data |>
select(FinalGradeCEMS, TimeSpent) |>
arrange(FinalGradeCEMS)# A tibble: 603 × 2
FinalGradeCEMS TimeSpent
<dbl> <dbl>
1 0 13.9
2 0.535 306.
3 0.903 88.5
4 1.80 44.7
5 2.93 57.7
6 3.01 571.
7 3.06 0.7
8 3.43 245.
9 5.04 202.
10 5.2 11.0
# ℹ 593 more rowsNote that arrange works by sorting values in ascending order (from lowest to highest); you can change this by using the desc() function as an argument with arrange, like the following:
sci_data |>
select(FinalGradeCEMS, TimeSpent) |>
arrange(desc(FinalGradeCEMS))# A tibble: 603 × 2
FinalGradeCEMS TimeSpent
<dbl> <dbl>
1 100 2689.
2 99.8 2921.
3 99.3 965.
4 99.1 879.
5 99.0 1770.
6 98.6 1138.
7 98.6 1270.
8 98.6 1273.
9 98.2 1902.
10 98.2 5373.
# ℹ 593 more rowsJust at a quick cursory glance at our two variables, it does appear that students with higher grades also tend to have spent more time in the online course.
👉 Your Turn ⤵
In the code chunk below, replace FinalGradeCEMS that is used with both the select() and arrange() functions with a different variable in the data set. Consider returning to the code chunk above in which you glimpsed at the names of all of the variables.
sci_data |>
select(TimeSpent, FinalGradeCEMS) |>
arrange(desc(FinalGradeCEMS))# A tibble: 603 × 2
TimeSpent FinalGradeCEMS
<dbl> <dbl>
1 2689. 100
2 2921. 99.8
3 965. 99.3
4 879. 99.1
5 1770. 99.0
6 1138. 98.6
7 1270. 98.6
8 1273. 98.6
9 1902. 98.2
10 5373. 98.2
# ℹ 593 more rowsCan you compose a series of functions that include the select(), filter(), and arrange() functions? Recall that you can “pipe” the output from one function to the next as when we used select() and arrange() together in the code chunk above.
sci_data |>
select(TimeSpent, FinalGradeCEMS) |>
filter(FinalGradeCEMS > 70) |>
arrange(FinalGradeCEMS)# A tibble: 438 × 2
TimeSpent FinalGradeCEMS
<dbl> <dbl>
1 1480. 70.2
2 764. 70.4
3 608. 70.5
4 536. 70.6
5 2497. 70.6
6 232. 70.7
7 1665. 70.9
8 1075. 71.0
9 1978. 71.3
10 2774. 71.5
# ℹ 428 more rows3. EXPLORE
Exploratory data analysis, or exploring your data, involves processes of describing your data (such as by calculating the means and standard deviations of numeric variables, or counting the frequency of categorical variables) and, often, visualizing your data. As we’ll learn in later labs, the explore phase can also involve the process of “feature engineering,” or creating new variables within a dataset (Krumm, Means, and Bienkowski 2018).
In this section, we’ll quickly pull together some basic stats using a handy function from the {skimr} package, and introduce you to a basic data visualization “code template” for the {ggplot} package from the tidyverse.
Summary Statistics
Let’s repurpose what we learned from our wrangle section to select just a few variables and quickly gather some descriptive stats using the skim() function from the {skimr} package.
sci_data |>
select(TimeSpent, FinalGradeCEMS) |>
skim()| Name | select(sci_data, TimeSpen… |
| Number of rows | 603 |
| Number of columns | 2 |
| _______________________ | |
| Column type frequency: | |
| numeric | 2 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| TimeSpent | 5 | 0.99 | 1799.75 | 1354.93 | 0.45 | 851.90 | 1550.91 | 2426.09 | 8870.88 | ▇▅▁▁▁ |
| FinalGradeCEMS | 30 | 0.95 | 77.20 | 22.23 | 0.00 | 71.25 | 84.57 | 92.10 | 100.00 | ▁▁▁▃▇ |
👉 Your Turn ⤵
Use the code from the chunk from above to explore some other variables of interest from our sci_data.
sci_data |>
select(course_id, FinalGradeCEMS) |>
skim()| Name | select(sci_data, course_i… |
| Number of rows | 603 |
| Number of columns | 2 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| course_id | 0 | 1 | 12 | 13 | 0 | 26 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| FinalGradeCEMS | 30 | 0.95 | 77.2 | 22.23 | 0 | 71.25 | 84.57 | 92.1 | 100 | ▁▁▁▃▇ |
What happens if simply feed the skim function the entire sci_data object? Give it a try!
skim(sci_data)| Name | sci_data |
| Number of rows | 603 |
| Number of columns | 30 |
| _______________________ | |
| Column type frequency: | |
| character | 6 |
| logical | 1 |
| numeric | 23 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| course_id | 0 | 1 | 12 | 13 | 0 | 26 | 0 |
| subject | 0 | 1 | 4 | 5 | 0 | 5 | 0 |
| semester | 0 | 1 | 4 | 4 | 0 | 3 | 0 |
| section | 0 | 1 | 2 | 2 | 0 | 4 | 0 |
| Gradebook_Item | 0 | 1 | 9 | 35 | 0 | 3 | 0 |
| Gender | 0 | 1 | 1 | 1 | 0 | 2 | 0 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| Grade_Category | 603 | 0 | NaN | : |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| student_id | 0 | 1.00 | 86069.54 | 10548.60 | 43146.00 | 85612.50 | 88340.00 | 92730.50 | 97441.00 | ▁▁▁▃▇ |
| total_points_possible | 0 | 1.00 | 4274.41 | 2312.74 | 840.00 | 2809.50 | 3583.00 | 5069.00 | 15552.00 | ▇▅▂▁▁ |
| total_points_earned | 0 | 1.00 | 3244.69 | 1832.00 | 651.00 | 2050.50 | 2757.00 | 3875.00 | 12208.00 | ▇▅▁▁▁ |
| percentage_earned | 0 | 1.00 | 0.76 | 0.09 | 0.34 | 0.70 | 0.78 | 0.83 | 0.91 | ▁▁▃▇▇ |
| FinalGradeCEMS | 30 | 0.95 | 77.20 | 22.23 | 0.00 | 71.25 | 84.57 | 92.10 | 100.00 | ▁▁▁▃▇ |
| Points_Possible | 0 | 1.00 | 76.87 | 167.51 | 5.00 | 10.00 | 10.00 | 30.00 | 935.00 | ▇▁▁▁▁ |
| Points_Earned | 92 | 0.85 | 68.63 | 145.26 | 0.00 | 7.00 | 10.00 | 26.12 | 828.20 | ▇▁▁▁▁ |
| q1 | 123 | 0.80 | 4.30 | 0.68 | 1.00 | 4.00 | 4.00 | 5.00 | 5.00 | ▁▁▂▇▇ |
| q2 | 126 | 0.79 | 3.63 | 0.93 | 1.00 | 3.00 | 4.00 | 4.00 | 5.00 | ▁▂▆▇▃ |
| q3 | 123 | 0.80 | 3.33 | 0.91 | 1.00 | 3.00 | 3.00 | 4.00 | 5.00 | ▁▃▇▅▂ |
| q4 | 125 | 0.79 | 4.27 | 0.85 | 1.00 | 4.00 | 4.00 | 5.00 | 5.00 | ▁▁▂▇▇ |
| q5 | 127 | 0.79 | 4.19 | 0.68 | 2.00 | 4.00 | 4.00 | 5.00 | 5.00 | ▁▂▁▇▅ |
| q6 | 127 | 0.79 | 4.01 | 0.80 | 1.00 | 4.00 | 4.00 | 5.00 | 5.00 | ▁▁▃▇▅ |
| q7 | 129 | 0.79 | 3.91 | 0.82 | 1.00 | 3.00 | 4.00 | 4.75 | 5.00 | ▁▁▅▇▅ |
| q8 | 129 | 0.79 | 4.29 | 0.68 | 1.00 | 4.00 | 4.00 | 5.00 | 5.00 | ▁▁▂▇▆ |
| q9 | 129 | 0.79 | 3.49 | 0.98 | 1.00 | 3.00 | 4.00 | 4.00 | 5.00 | ▁▃▇▇▃ |
| q10 | 129 | 0.79 | 4.10 | 0.93 | 1.00 | 4.00 | 4.00 | 5.00 | 5.00 | ▁▂▃▇▇ |
| TimeSpent | 5 | 0.99 | 1799.75 | 1354.93 | 0.45 | 851.90 | 1550.91 | 2426.09 | 8870.88 | ▇▅▁▁▁ |
| TimeSpent_hours | 5 | 0.99 | 30.00 | 22.58 | 0.01 | 14.20 | 25.85 | 40.43 | 147.85 | ▇▅▁▁▁ |
| TimeSpent_std | 5 | 0.99 | 0.00 | 1.00 | -1.33 | -0.70 | -0.18 | 0.46 | 5.22 | ▇▅▁▁▁ |
| int | 76 | 0.87 | 4.22 | 0.59 | 2.00 | 3.90 | 4.20 | 4.70 | 5.00 | ▁▁▃▇▇ |
| pc | 75 | 0.88 | 3.61 | 0.64 | 1.50 | 3.00 | 3.50 | 4.00 | 5.00 | ▁▁▇▅▂ |
| uv | 75 | 0.88 | 3.72 | 0.70 | 1.00 | 3.33 | 3.67 | 4.17 | 5.00 | ▁▁▆▇▅ |
Data Visualization
Data visualization is an extremely common practice in Learning Analytics, especially in the use of data dashboards. Data visualization involves graphically representing one or more variables with the goal of discovering patterns in data. These patterns may help us to answer research questions or generate new questions about our data, to discover relationships between and among variables, and to create or select features for data modeling.
In this section we’ll focus on using a basic code template for the {ggplot2} package from the tidyverse. ggplot2 is a system for declaratively creating graphics, based on the grammar of graphics (Wickham 2016). You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical elements to use, and it takes care of the details.
The Graphing Workflow
At it’s core, you can create some very simple but attractive graphs with just a couple lines of code. {ggplot2} follows the common workflow for making graphs. To make a graph, you simply:
Start the graph with
ggplot()and include your data as an argument;“Add” elements to the graph using the
+operator ageom_()function;Select variables to graph on each axis with the
aes()argument.
Let’s give it a try by creating a simple histogram of our FinalGradeCEMS variable. The code below creates a histogram, or a distribution of the values, in this case for students’ final grades. Go ahead and run it:
ggplot(sci_data) +
geom_histogram(aes(x = FinalGradeCEMS))`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Warning: Removed 30 rows containing non-finite outside the scale range
(`stat_bin()`).
Note that the first function, ggplot(), creates a coordinate system that you can “add” layers to using additional functions and + operator. The first argument of ggplot() is the dataset, in our case sci_data, to use for the graph.
By itself, ggplot(data = mpg) just creates an empty graph. But when you add a required geom_() function like geom_histogram(), you tell it which type of graph you want to make, in our case a histogram. A geom is the geometrical object that a plot uses to represent observations. People often describe plots by the type of geom that the plot uses. For example, bar charts use bar geoms, line charts use line geoms, boxplots use boxplot geoms, and so on. Scatterplots, which we’ll see a in bit, break the trend; they use the point geom.
The final required element for any graph is a mapping = argument that defines which variables in your dataset are mapped to which axes in your graph. The mapping argument is always paired with the function aes(), which you use to gather together all of the mappings that you want to create. In our case, since we just created a simple histogram, we only had to specify what variable to place on the x axis, which in our case was FinalGradeCEMS.
We won’t spend a lot of time on it in this case study, but you can also add a wide range of aesthetic arguments to each geom, like changing the color of the histogram bars by adding an argument to specify color. Let’s give that a try using the fill = argument:
ggplot(sci_data) +
geom_histogram(aes(x = FinalGradeCEMS), fill = "blue")`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Warning: Removed 30 rows containing non-finite outside the scale range
(`stat_bin()`).
👉 Your Turn ⤵
Now use the code chunk below to visualize the distribution of another variable in the data, specifically TimeSpent. You can do so by swapping out the variable FinalGradeCEMS with our new variable. Also, change the color to one of your choosing; consider this list of valid color names here: http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf
ggplot(sci_data) +
geom_histogram(aes(x = TimeSpent), fill = "green")`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Warning: Removed 5 rows containing non-finite outside the scale range
(`stat_bin()`).
Tip: There is no shame in copying and pasting code from above. Remember, reproducible research is also intended to help you save time!
Scatterplots
Finally, let’s create a scatter plot for the relationship between these two variables. Scatterplots use the point geom, i.e., the geom_point() function, and are most useful for displaying the relationship between two continuous variables.
👉 Your Turn ⤵
Complete the code chunk below to create a simple scatterplot with TimeSpent on the x axis and FinalGradeCEMS on the y axis. Hint: something else important is also missing that you will need to “add” to your code.
ggplot(sci_data) +
geom_point(aes(x = TimeSpent,
y = FinalGradeCEMS))Warning: Removed 30 rows containing missing values or values outside the scale range
(`geom_point()`).
Well done! As you can see, there appears to be a positive relationship between the time students spend in the online course and their final grade!
To learn more about using {ggplot2} for data visualization, we highly recommend the Data visualization with ggplot2 :: Cheat Sheet.
4. MODEL
“Model” is one of those terms that has many different meanings. For our purpose, we refer to the process of simplifying and summarizing our data. Thus, models can take many forms; calculating means represents a legitimate form of modeling data, as does estimating more complex models, including linear regressions, and models and algorithms associated with machine learning tasks. For now, we’ll run a base linear regression model to further examine the relationship between TimeSpent and FinalGradeCEMS.
We’ll dive much deeper into modeling in subsequent learning labs, but for now let’s see if there is a statistically significant relationship between students’ final grades, FinaGradeCEMS, and the TimeSpent on the course:
m1 <- lm(FinalGradeCEMS ~ TimeSpent, data = sci_data)
summary(m1)
Call:
lm(formula = FinalGradeCEMS ~ TimeSpent, data = sci_data)
Residuals:
Min 1Q Median 3Q Max
-67.136 -7.805 4.723 14.471 30.317
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.581e+01 1.491e+00 44.13 <2e-16 ***
TimeSpent 6.081e-03 6.482e-04 9.38 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 20.71 on 571 degrees of freedom
(30 observations deleted due to missingness)
Multiple R-squared: 0.1335, Adjusted R-squared: 0.132
F-statistic: 87.99 on 1 and 571 DF, p-value: < 2.2e-16It looks like TimeSpent is associated with a higher final grade. That is, students who spent more time in the LMS also earned higher grades.
👉 Your Turn ⤵
Now let’s “add” another variable to the regression model. Specifically, use the + operator after TimeSpent to add the course subject variable as a predictor of students’ final grades.
m2 <- lm(FinalGradeCEMS ~ TimeSpent + subject, data = sci_data)
summary(m2)
Call:
lm(formula = FinalGradeCEMS ~ TimeSpent + subject, data = sci_data)
Residuals:
Min 1Q Median 3Q Max
-70.378 -8.836 4.816 12.855 36.047
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 57.3931739 2.3382193 24.546 < 2e-16 ***
TimeSpent 0.0071098 0.0006516 10.912 < 2e-16 ***
subjectBioA -1.5596482 3.6053075 -0.433 0.665
subjectFrScA 11.7306546 2.2143847 5.297 1.68e-07 ***
subjectOcnA 1.0974545 2.5771474 0.426 0.670
subjectPhysA 16.0357213 3.0712923 5.221 2.50e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 19.8 on 567 degrees of freedom
(30 observations deleted due to missingness)
Multiple R-squared: 0.213, Adjusted R-squared: 0.2061
F-statistic: 30.69 on 5 and 567 DF, p-value: < 2.2e-16What do you notice about the results? How would you interpret them? Add a comment or two below:
- In summary, the model suggests that there is a statistically significant, though relatively small, positive association between the time spent in the Learning Management System and a student’s final grade. However, the low R-squared value indicates that many other factors not included in the model may be influencing final grades.
5. COMMUNICATE
The final step in the workflow/process is sharing the results of your analysis with wider audience. Krumm et al. Krumm, Means, and Bienkowski (2018) have outlined the following 3-step process for communicating with education stakeholders findings from an analysis:
Select. Communicating what one has learned involves selecting among those analyses that are most important and most useful to an intended audience, as well as selecting a form for displaying that information, such as a graph or table in static or interactive form, i.e. a “data product.”
Polish. After creating initial versions of data products, research teams often spend time refining or polishing them, by adding or editing titles, labels, and notations and by working with colors and shapes to highlight key points.
Narrate. Writing a narrative to accompany the data products involves, at a minimum, pairing a data product with its related research question, describing how best to interpret the data product, and explaining the ways in which the data product helps answer the research question and might be used to inform new analyses or a “change idea” for improving student learning.
In later modules, you will have an opportunity to create a simple “data product” designed to illustrate some insights gained from your analysis and ideally highlight an action step or change idea that can be used to improve learning or the contexts in which learning occurs.
For now, we will wrap up this case study by converting your work to an HTML file that can be published and used to communicate your learning and demonstrate some of your new R skills. To do so, you will need to “render” your document by clicking the 
Render button in the menu bar at that the top of this file.
Rendering a document does two important things:
checks through all your code for any errors; and,
creates a file in your directory that you can use to share you work .
Now that you’ve finished your first case study, click the “Render” button in the toolbar at the top of your document to covert this Quarto document to a HTML web page, just one of the many publishing formats you can create with Quarto documents.
If the files rendered correctly, you should now see a new file named orientation-case-study-R.html in the Files tab located in the bottom right corner of R Studio. If so, congratulations, you just completed the getting started activity! You’re now ready for the unit Case Studies that we will complete during the third week of each unit.
Important
If you encounter errors when you try to render, first check the case study answer key located in the files pane and has the suggested code for the Your Turns. If you are still having difficulties, try copying and pasting the error into Google or ChatGPT to see if you can resolve the issue. Finally, contact your instructor to debug the code together if you’re still having issues.
Publish File
Rendered HTML files can be published online through a variety of ways including Posit Cloud, RPubs , GitHub Pages, Quarto Pub, or other methods. The easiest way to quickly publish your file online is to publish directly from RStudio. You can do so by clicking the “Publish” button located in the Viewer Pane after you render your document as illustrated in the screenshot below.
👉 Your Turn ⤵
Navigate the to the RPubs website and create a free account. Now, publish your rendered Quarto markdown file to the web using the RPubs option.
Your First LASER Badge
Congratulations, you’ve completed your first case study! Once you have shared a link to you published document with your instructor and they have reviewed your work, you will be provided a physical or digital version of the badge pictured at the top of this document!
References
Estrellado, Ryan A., Emily A. Freer, Jesse Mostipak, Joshua M. Rosenberg, and Isabella C. Velásquez. 2020. Data Science in Education Using r. Routledge. https://doi.org/10.4324/9780367822842.
Gandrud, Christopher. 2013. Reproducible Research with r and r Studio (3rd Edition). CRC Press. http://github.com/christophergandrud/Rep-Res-Book.
Krumm, Andrew, Barbara Means, and Marie Bienkowski. 2018. Learning Analytics Goes to School. Routledge. https://doi.org/10.4324/9781315650722.
Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.
Wickham, Hadley, and Garrett Grolemund. 2016. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. " O’Reilly Media, Inc.". https://r4ds.had.co.nz.