knitr::include_graphics("Img/research-workflow.png")
Rtutorial
Week3: Introduction to R Toolkit
This activity is prepared to warm-up your understanding of LA workflow. Through this exercise, we will practice basics of how RStudio works. In all of our practices, we will follow the LA workflow.
- Prepare: We will import our data file into R in the “Prepare” section.
- Wrangle: We will prepare and wrangle our data in the “Wrangle” section.
- Explore: We will check the patterns in the data through visualization.
- Model: We will run a regression model in the “Model” section.
- Communicate: Create a reproducible report of your work, you can share with others in the “Communicate” section.
How to follow this document?
In the R tutorial toolkit and case study week, we will be using a quarto markdown file, the extension of the file is “.qmd”. Unlike R markdown, Quarto documents are fully reproducible and support dozens of output formats, like PDFs, Word files, presentations, and more. In this document, you will see, we’re using visual mode to see the general parts.
There are two keys to your use of Quarto for this activity:
- First, you can see 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 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. If you wonder other chunk options, you can visit this link and learn more about it.
The Data-Intensive Research Workflow
Last week, in the class, we talked about the data-intensive research workflow.As we mentioned a couple of times, we will follow this workflow to present our research approach.
Let’s get started.
1. Prepare
As a first step in the data-intensive research workflow, we will need to define our research question(s) and need to understand where the data comes from (Krumm, 2018).
For this work, we will work with data come from an unpublished research study, which utilized a number of different data sources to understand high school students’ motivation within the context of online courses.
Our research question is:
“Is there a relationship between the time students spend on a course and their final course grade?”
For our analysis, we will need certain packages.One of the most common package is “Tidyverse” package. This package is actually a collection of R packages designed for wrangling and exploring data and which all share an underlying design philosophy, grammar, and data structure.
knitr::include_graphics("Img/tidyverse.png")
Through this class, we will have a chance to try different libraries in the tidyverse package. Let’s install our tidyverse package.
install.packages("tidyverse")We installed the package, what do we need to do now for using this package?
library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.6
✔ forcats 1.0.1 ✔ stringr 1.6.0
✔ ggplot2 4.0.1 ✔ tibble 3.3.1
✔ lubridate 1.9.4 ✔ tidyr 1.3.2
✔ purrr 1.2.1
── 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 errorsWe will also use another package called “skimr”. This is a handy package that provides summary statistics that you can skim quickly to understand your data. We’ll be using this later in the Explore section.
#install the {skimr} package below.
install.packages("skimr")#load the {skimr} package below.
library(skimr)Loading (or reading in) data
The data we’ll explore in this class were originally collected for a unpublished research study and 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 the 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 more interested in learning more about these datasets, you can visit Chapter 7 from your book, Data Science in Education Using R.
#load the sci-online-classes.cvs data
sci_online_classes <- 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.Your Turn:
Why do you think we included data/ before our sci-online-classes.csv file? Why quotation marks?
Add your responses after the dashes below:
- Your response here - This is to load the data based on the specific chosen file.
Hint: check the files panel.
Viewing or inspecting data
Let’s quickly check our data.
View(sci_online_classes)
sci_online_classes# 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>Your Turn:
What do you notice about this dataset? What do you wonder? Add one or two thoughts after the dash below:
- Your response here - There are some Null or N/A cells meaning either data was not collected or it was not answered.
There are other ways to inspect your data; the glimpse() function provides one such way. Let’s take a glimpse at our data.
glimpse(sci_online_classes)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…Generally, rows typically represent “cases”, the units that we measure, or the units on which we collect data. What counts as a “case” (and therefore what is represented as a row) varies by (and within) fields. There may be mutliple 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.
Your Turn:
How many “cases” or observations are in this dataset?
- Your response here - 603
Pick two columns (or more) and write what you think it represents:
- Your response here - 30
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 time-intensive! Wrangling your data into shape can itself be an important accomplishment! And documenting your code using R scripts or Markdown 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 {dplyr} R package which is part of the tidyverse.
Selecting Variables
Remember our research question, what we were interested in finding about this data?
Let’s practice selecting a few variables by introducing pipe operator, |>. 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_online_classes|>
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 check our data with View() function this time.
view(select(sci_online_classes,FinalGradeCEMS,TimeSpent))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
Let’s exploring filtering variables. We will filter our data to view only the rows associated with students who earned a final grade (as a percentage) of 70 or 70% or higher.
sci_online_classes|>
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_online_classes|>
filter(FinalGradeCEMS>90)# A tibble: 203 × 30
student_id course_id total_points_possible total_points_earned
<dbl> <chr> <dbl> <dbl>
1 43146 FrScA-S216-02 3280 2220
2 52446 PhysA-S116-01 2086 1719
3 53447 FrScA-S116-01 4655 3149
4 54066 OcnA-S116-01 4641 3429
5 54282 OcnA-S116-02 3581 2777
6 54434 PhysA-S116-01 3228 2506
7 55078 FrScA-S216-01 7000 4212
8 56152 AnPhA-S116-02 3323 2468
9 57489 PhysA-S116-01 2970 2050
10 58168 AnPhA-S216-01 4079 3327
# ℹ 193 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?
- Your response here - Only 33.7% of students received a final grade at or above 90.
Arrange
The last function we’ll use for preparing tables in arrange. We’ll again use the pipe operator to combine this with arrange() function we used already -select(). We do this so we can view only time spent and final grades.
sci_online_classes|>
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.
#let's change the order from asc to desc
sci_online_classes|>
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 rowsYour Turn:
In the next code chunk, replace FinalGradeCEMS that is used with both the select() and arrange() functions with a different variable in the dataset.
sci_online_classes|>
select(TimeSpent,Gender)|>
arrange(desc(TimeSpent))# A tibble: 603 × 2
TimeSpent Gender
<dbl> <chr>
1 8871. F
2 7085. M
3 7067. F
4 7003. M
5 6955. F
6 6772. F
7 6664. F
8 6348. F
9 6244. M
10 6162. M
# ℹ 593 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 in this class, the explore phase can also involve the process of “feature engineering,” or creating new variables within a dataset [@krumm2018]. 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_online_classes|>
select(FinalGradeCEMS,TimeSpent)|>
skim()| Name | select(sci_online_classes… |
| 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 |
|---|---|---|---|---|---|---|---|---|---|---|
| FinalGradeCEMS | 30 | 0.95 | 77.20 | 22.23 | 0.00 | 71.25 | 84.57 | 92.10 | 100.00 | ▁▁▁▃▇ |
| TimeSpent | 5 | 0.99 | 1799.75 | 1354.93 | 0.45 | 851.90 | 1550.91 | 2426.09 | 8870.88 | ▇▅▁▁▁ |
Your Turn:
Copy the code from the chunk from above and use it as a template to explore some other variables of interest from our sci_data.
#use skim() to summarize other variables of your choosing.
sci_online_classes|>
select(Gender,TimeSpent)|>
skim()| Name | select(sci_online_classes… |
| 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 |
|---|---|---|---|---|---|---|---|
| Gender | 0 | 1 | 1 | 1 | 0 | 2 | 0 |
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 | 851.9 | 1550.91 | 2426.09 | 8870.88 | ▇▅▁▁▁ |
What happens if simply feed the skim function the entire sci_data object? Give it a try!
#use skim() on the entire data frame
sci_online_classes|>
skim()| Name | sci_online_classes |
| 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]. 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 a geom_() 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.
ggplot(sci_online_classes)+
geom_histogram(aes(x=FinalGradeCEMS))`stat_bin()` using `bins = 30`. Pick better value `binwidth`.Warning: Removed 30 rows containing non-finite outside the scale range
(`stat_bin()`).
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_online_classes)+
geom_histogram(aes(x=FinalGradeCEMS), fill = "Blue")`stat_bin()` using `bins = 30`. Pick better value `binwidth`.Warning: Removed 30 rows containing non-finite outside the scale range
(`stat_bin()`).
Your Turn:
Now us the code chunk below to visualize the distribution of another variable in the data, specifically TimeSpent. 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
#create a histogram of TimeSpent using a different color
ggplot(sci_online_classes)+
geom_histogram(aes(x=FinalGradeCEMS), fill = "Orange")`stat_bin()` using `bins = 30`. Pick better value `binwidth`.Warning: Removed 30 rows containing non-finite outside the scale range
(`stat_bin()`).
Scatterplots
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 simplet scatterplot with TimeSpent on the x axis and FinalGradeCEMS on the y axis.
ggplot(sci_online_classes, aes(x=FinalGradeCEMS, y=TimeSpent))+
geom_point()Warning: Removed 30 rows containing missing values or values outside the scale range
(`geom_point()`).
What do you think about the relationship between TimeSpent and FinalGradeCEMS?
- Your response here - Data shows that it gets dense betrween 75-100%; showing a concentration. Students that study more (more time spent) show a correlation with receiving higher grades.
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:
model_1<- lm(FinalGradeCEMS~TimeSpent, data=sci_online_classes)
summary(model_1)
Call:
lm(formula = FinalGradeCEMS ~ TimeSpent, data = sci_online_classes)
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-16Your Turn:
Now let’s “add” another variable to the regression model. Specifically, use the + operator after TimeSpent to add the course subject variable, or another variable of your choosing, as a predictor of students’ final grades.
updated_model<- lm(FinalGradeCEMS~TimeSpent+Gender, data=sci_online_classes)
summary(updated_model)
Call:
lm(formula = FinalGradeCEMS ~ TimeSpent + Gender, data = sci_online_classes)
Residuals:
Min 1Q Median 3Q Max
-67.206 -7.886 4.643 14.528 30.503
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 65.8770987 1.5765890 41.785 <2e-16 ***
TimeSpent 0.0060849 0.0006496 9.368 <2e-16 ***
GenderM -0.2553153 1.8911994 -0.135 0.893
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 20.72 on 570 degrees of freedom
(30 observations deleted due to missingness)
Multiple R-squared: 0.1336, Adjusted R-squared: 0.1305
F-statistic: 43.93 on 2 and 570 DF, p-value: < 2.2e-16What do you notice about the results? Add a comment or two below:
- YOUR RESPONSE HERE - By adding another variable, the data changes but not too drastically; there is still a strong connection to more time spent to receiving a higher grade.
5. Communicate
The final step in the workflow/process is sharing the results of your analysis with wider audience. Krumm et al. @krumm2018 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.”
- The most important thing to share about this discovery was the connection between student time and their grades. This is how students spent their time in their learning management system (TimeSpent) and their final course grades (FinalGradeCEMS). This directly answers the research question: Does the time students spend on a course affect their final grade? One example was the scatter-plot, this was displaying TimeSpent on the x-axis and FinalGradeCEMS on y-axis is selected as the primary data product. This specific visualization allows stakeholders to see patterns, clustering, and variability in student performance.
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.
- After choosing the scatterplot option, the data products were clearer and easier to understand. An appropriate title was added (Relationship Between Time Spent in the Course and Final Grades) to the scatterplot, labeled the axes clearly, and used consistent colors to make it easier to read. There was unnecessary clutter removed automatically and handled missing values in a way that’s easy to follow. All these changes make sure that the data product clearly shows that the more time you spend in the course, the better your grades will be, without being too overwhelming visually.
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.
- The scatterplot results show a positive link between how much time students spend in the learning management system and their final grades. Those who spend more time on course materials tend to score higher, but it’s important to note that there’s still a lot of variation among learners. These findings can help us make better decisions about how we teach online. One idea is to put in place early warning systems or learning analytics dashboards that can spot students who aren’t really engaged. Then, we can step in earlier and give them extra support, reminders, or even change up the learning activities to get them more involved and improve their grades.
Render File
For your course project, 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 work by converting our work into a webpage that can be 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. This will do two things; it will:
check through all your code for any errors; and,
create a file in your directory that you can use to share you work through Posit Cloud, RPubs , GitHub Pages, Quarto Pub, or other methods.
Now that you’ve finished your first Rtutorial study, scroll back to the very top of this Quarto Document and change the author: “YOUR NAME HERE” to your actual name surrounded by quotation marks like so: author: “Dr. Cansu Tatar”.
Save as “RTutorial_Assignment1”
Acknowledgement:
Special thanks to Dr. Shaun Kellogg for his support and guidance to create these course materials.