LASER Badge
LASER Institute Orientation
Larisa Olesova
July 12, 2022
Introduction
Welcome to your first LASER badge! This LASER Orientation Badge is really a warm-up activitiy to introduce you to R Markdown and the coding case studies that we will be using in the machine learning, network analysis, and text mining labs. It is a chance to become familiar with how RStudio and R Markdown works.
You may have used R before-or you may not have! Either is fine as this task will be designed with the assumption that you have not used R before. It includes “reaches” for anyone who may want to do a bit more.
In the context of doing so, we’ll focus on the following tasks:
- Reading data into R (in the Prepare section)
- Preparing and “wrangling” data in table (think spreadsheet!) format (in the Wrangle section)
- Creating some plots (in the Explore section)
- Running a model - specifically, a regression model (in the Model section)
- Finally, creating a reproducible report of your work you can share with others (in the Communicate section)
The LASER Cycle
You may be wondering what these bolded terms above refer to; what’s so special about preparing, wrangling, exploring, and modeling data - and communicating results? We’re using these terms as a part of a framework, or model, for what we mean by doing learning in STEM education research.
The particular framework we are using comes from the work of Krumm et al.’s Learning Analytics Goes to School. You can check that out, but don’t feel any need to dive deep for now - we’ll be spending more time on this in first day of the summer institute. For now, know that this document is organized around three of the five components of what we’re referring to as the LASER cycle.
Click the green arrow to the right of the “code chunk” below to view the image (more on that process of clicking the green arrow and what it does, too, in a moment)!
knitr::include_graphics("img/laser-cycle.png")
How to use this R Markdown document
This is an R Markdown file as indicated by the .rmd extension at the end of the file name. R Markdown documents are fully reproducible and use a productive notebook interface to combine narrative text and “chunks” of code to produce a range of formatted outputs including: formats including HTML, PDF, MS Word, Beamer, HTML5 slides, Tufte-style handouts, books, dashboards, shiny applications, scientific articles, websites, and more.
There are two keys to your use of R Markdown 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.
- Second, click “Knit” next to the yarn ball at the top of this screen to preview the document as you work through it. This will allow you to see your code and the input in a rendered - easy-to-read - document, just as others will see this document when shared. Try knitting the document now and see what happens.
Let’s get started! We are glad you are here and to begin this exciting (and challenging) journey together.
1. PREPARE
By preparing, we refer to developing a question or purpose for the analysis, which you likely know from your research can be difficult! This part of the process also involves developing an understanding of the data and what you may need to analyze the data. This often involves looking at the data and its documentation. For now, we’ll focus on just a few parts of this process, diving in much more deeply over the coming weeks.
Packages 📦
R uses “packages,” add-ons that enhance its functionality. One package that we’ll be using is the tidyverse. To load the tidyverse, click the green arrow in the right corner of the block-or “chunk”-of code that follows.
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.2.1## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.7 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1## Warning: package 'ggplot2' was built under R version 4.2.1## Warning: package 'tibble' was built under R version 4.2.1## Warning: package 'tidyr' was built under R version 4.2.1## Warning: package 'readr' was built under R version 4.2.1## Warning: package 'purrr' was built under R version 4.2.1## Warning: package 'dplyr' was built under R version 4.2.1## Warning: package 'stringr' was built under R version 4.2.1## Warning: package 'forcats' was built under R version 4.2.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()Please 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.
Loading (or reading in) data
Next, we’ll load data-specifically, a CSV 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 your data folder into
your R environment:
d <- 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
d 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 you loaded:
d## # A tibble: 603 × 30
## student_id course_id total_points_poss… total_points_ea… percentage_earn…
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 43146 FrScA-S216-02 3280 2220 0.677
## 2 44638 OcnA-S116-01 3531 2672 0.757
## 3 47448 FrScA-S216-01 2870 1897 0.661
## 4 47979 OcnA-S216-01 4562 3090 0.677
## 5 48797 PhysA-S116-01 2207 1910 0.865
## 6 51943 FrScA-S216-03 4208 3596 0.855
## 7 52326 AnPhA-S216-01 4325 2255 0.521
## 8 52446 PhysA-S116-01 2086 1719 0.824
## 9 53447 FrScA-S116-01 4655 3149 0.676
## 10 53475 FrScA-S116-02 1710 1402 0.820
## # … with 593 more rows, and 25 more variables: 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 data set? What do you wonder? Add one-two thoughts following the dashes next (you can add additional dashes if you like!):
quiz data and how much time each student spent
there are the lastthree columns that are not clear at this point
There are other ways to inspect your data; the glimpse()
function provides one such way. Run the code below to take a glimpse at
your data.
glimpse(d)## 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.
Your Turn ⤵
What rows typically (or you think may) represent:
- cases or individual students in my research
What columns typically (or you think may) represent:
- variables, i.e., grades
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 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! There are great tools in R to do this, especially through the use of the {dplyr} R package.
Selecting variables
Let’s select only a few variables.
d |>
select(student_id, total_points_possible, total_points_earned)## # A tibble: 603 × 3
## student_id total_points_possible total_points_earned
## <dbl> <dbl> <dbl>
## 1 43146 3280 2220
## 2 44638 3531 2672
## 3 47448 2870 1897
## 4 47979 4562 3090
## 5 48797 2207 1910
## 6 51943 4208 3596
## 7 52326 4325 2255
## 8 52446 2086 1719
## 9 53447 4655 3149
## 10 53475 1710 1402
## # … with 593 more rowsNotice how the number of columns (variables) is now different.
Let’s include one additional variable in your select function.
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)!
You can use a function named glimpse() to do this.
glimpse(d)## 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 to the code below, 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.
d |>
select(student_id, total_points_possible, total_points_earned, TimeSpent)## # A tibble: 603 × 4
## student_id total_points_possible total_points_earned TimeSpent
## <dbl> <dbl> <dbl> <dbl>
## 1 43146 3280 2220 1555.
## 2 44638 3531 2672 1383.
## 3 47448 2870 1897 860.
## 4 47979 4562 3090 1599.
## 5 48797 2207 1910 1482.
## 6 51943 4208 3596 3.45
## 7 52326 4325 2255 1322.
## 8 52446 2086 1719 1390.
## 9 53447 4655 3149 1479.
## 10 53475 1710 1402 NA
## # … with 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.
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.
d |>
filter(FinalGradeCEMS > 70)## # A tibble: 438 × 30
## student_id course_id total_points_poss… total_points_ea… percentage_earn…
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 43146 FrScA-S216-02 3280 2220 0.677
## 2 44638 OcnA-S116-01 3531 2672 0.757
## 3 47448 FrScA-S216-01 2870 1897 0.661
## 4 47979 OcnA-S216-01 4562 3090 0.677
## 5 48797 PhysA-S116-01 2207 1910 0.865
## 6 52326 AnPhA-S216-01 4325 2255 0.521
## 7 52446 PhysA-S116-01 2086 1719 0.824
## 8 53447 FrScA-S116-01 4655 3149 0.676
## 9 53475 FrScA-S216-01 1209 977 0.808
## 10 54066 OcnA-S116-01 4641 3429 0.739
## # … with 428 more rows, and 25 more variables: 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!).
d |>
filter(FinalGradeCEMS > 50)## # A tibble: 510 × 30
## student_id course_id total_points_poss… total_points_ea… percentage_earn…
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 43146 FrScA-S216-02 3280 2220 0.677
## 2 44638 OcnA-S116-01 3531 2672 0.757
## 3 47448 FrScA-S216-01 2870 1897 0.661
## 4 47979 OcnA-S216-01 4562 3090 0.677
## 5 48797 PhysA-S116-01 2207 1910 0.865
## 6 52326 AnPhA-S216-01 4325 2255 0.521
## 7 52446 PhysA-S116-01 2086 1719 0.824
## 8 53447 FrScA-S116-01 4655 3149 0.676
## 9 53475 FrScA-S216-01 1209 977 0.808
## 10 54066 OcnA-S116-01 4641 3429 0.739
## # … with 500 more rows, and 25 more variables: 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):
- Cases changed from 438 rows to 510 rows meaning 510 students went cut-off from 50
Arrange
The last function we’ll use for preparing tables is arrange.
We’ll combine this arrange() function with a function we used already - select(). We do this so we can view only the student ID and their final grade.
d |>
select(student_id, FinalGradeCEMS) |>
arrange(FinalGradeCEMS)## # A tibble: 603 × 2
## student_id FinalGradeCEMS
## <dbl> <dbl>
## 1 90995 0
## 2 92606 0.535
## 3 95684 0.903
## 4 90996 1.80
## 5 94876 2.93
## 6 92633 3.01
## 7 85390 3.06
## 8 94630 3.43
## 9 90995 5.04
## 10 96677 5.2
## # … with 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 with arrange, like the following:
d |>
select(student_id, FinalGradeCEMS) |>
arrange(desc(FinalGradeCEMS))## # A tibble: 603 × 2
## student_id FinalGradeCEMS
## <dbl> <dbl>
## 1 85650 100
## 2 91067 99.8
## 3 66740 99.3
## 4 86792 99.1
## 5 78153 99.0
## 6 66689 98.6
## 7 88261 98.6
## 8 92740 98.6
## 9 92726 98.2
## 10 92741 98.2
## # … with 593 more rowsYour 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.
d |>
select(student_id, TimeSpent) |>
arrange(desc(TimeSpent))## # A tibble: 603 × 2
## student_id TimeSpent
## <dbl> <dbl>
## 1 94744 8871.
## 2 69743 7085.
## 3 86275 7067.
## 4 86429 7003.
## 5 88153 6955.
## 6 85410 6772.
## 7 70408 6664.
## 8 89435 6348.
## 9 91163 6244.
## 10 92185 6162.
## # … with 593 more rowsReach 1 🎉
Can 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.
This reach is not required/necessary to complete; it’s just for those who wish to do a bit more with these functions at this time (we’ll do more in class, too!)
3. 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 prior. In this section, we’ll create a few plots to explore our data.
Histogram
The code below creates a histogram, or a distribution of the values, in this case for students’ final grades.
ggplot(d, aes(x = FinalGradeCEMS)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 30 rows containing non-finite values (stat_bin).
You can change the color of the histogram bars by specifying a color as follows:
ggplot(d, aes(x = FinalGradeCEMS)) +
geom_histogram(fill = "blue")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 30 rows containing non-finite values (stat_bin).
Changing colors
Your Turn ⤵
In the code chunk below, 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(d, aes(x = FinalGradeCEMS)) +
geom_histogram(fill = "pink")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 30 rows containing non-finite values (stat_bin).
Finally, we’ll make one more change; visualize the distribution of another variable in the data - one other than FinalGradeCEMS. You can do so by swapping out the name for another variable with FinalGradeCEMS. Also, change the color to one other than blue.
ggplot(d, aes(x = TimeSpent)) +
geom_histogram(fill = "green")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 5 rows containing non-finite values (stat_bin).
Reach 2 🎉
Completed the above? Nice job! Try for a “reach” by creating a scatter plot for the relationship between two variables. You will need to pass the names of two variables to the code below for what is now simply XXX (a placeholder).
ggplot(d, aes(x = TimeSpent, y = FinalGradeCEMS)) +
geom_point()## Warning: Removed 30 rows containing missing values (geom_point).
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 linear regression to predict students’ final grades.
Below, we predict students’ final grades (FinaGradeCEMS,
which is on a 0-100 point scale) on the basis of the time they spent on
the course (measured through their learning management system in
minutes, TimeSpent, and the subject (one of five) of their
specific course.
m1 <- lm(FinalGradeCEMS ~ TimeSpent + subject, data = d)
summary(m1)##
## Call:
## lm(formula = FinalGradeCEMS ~ TimeSpent + subject, data = d)
##
## 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-16Your Turn ⤵
Notice how above the variables are separated by a + symbol. Below,
add another - a third - variable to the regression model.
Specifically, add a variable students’ initial, self-reported interest
in science, int - and any other variable(s) you like! What
do you notice about the results? We’re going to dive into this
much more: if you have many questions now, you’re in the right
spot!
m2 <- lm(FinalGradeCEMS ~ TimeSpent + semester, data = d)
summary(m2)##
## Call:
## lm(formula = FinalGradeCEMS ~ TimeSpent + semester, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -68.039 -7.442 4.962 14.592 30.359
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 67.6128776 1.7071997 39.605 <2e-16 ***
## TimeSpent 0.0060158 0.0006471 9.297 <2e-16 ***
## semesterS216 -4.0572474 1.7770965 -2.283 0.0228 *
## semesterT116 0.8319735 4.2202455 0.197 0.8438
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.64 on 569 degrees of freedom
## (30 observations deleted due to missingness)
## Multiple R-squared: 0.142, Adjusted R-squared: 0.1375
## F-statistic: 31.39 on 3 and 569 DF, p-value: < 2.2e-165. COMMUNICATE
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