R package {moodleQuiz} contains high-level functions for cleaning,
encoding, filtering, and combining student’s score and responses from
Moodle Quiz report.
You can install the development version from GitHub with:
# install.packages("remotes")
remotes::install_github("Lightbridge-KS/moodleQuiz")The 4 main functions of the {moodleQuiz} are as follows:
-
check_sub(): check student’s submission of the Moodle quiz report by looking at thestatecolumn. -
combine_resp(): combine student’s responses (Response x) from the Responses report. -
count_resp(): count how many responses each student answered from the Responses report. -
combine_grades(): filter, adjust, and combine student’s grade fromGrade/xxcolumn.
All of theses 4 main functions are generic function that operate on data frame and list of data frames of Moodle Quiz report. The latter will be particularly useful when performing a data aggregation across multiple Moodle quiz report of the same type.
A long time ago, I’ve build a Shiny app for my colleagues to manipulate Moodle Quiz report. As the app grows bigger, I realized that I have to separate the business logic from the application logic, so that the app can be maintainable. Instead, I’ve focus on writing functions, and shift my aim to build system of functions to manipulate Moodle Quiz report as easy as possible for R user.
Afterwards, My collection of functions become this {moodleQuiz}
package which drives the logic of this shiny
app to
facilitate the process of retrieve and combine student’s score from
SELECx (a Moodle platform from Faculty
of Medicine Siriraj Hospital, Mahidol University, Thailand).
library(moodleQuiz)
library(dplyr)Let’s say I have a Moodle Grades Report data frame of “Quiz_1”
glimpse(grades_ls$Quiz_1)
#> Rows: 27
#> Columns: 18
#> $ Surname <chr> "Roquemore", "Ali", "Hoffpauir", "Babbitt", "Huynh", "…
#> $ `First name` <chr> "Jada", "Ronin", "Jerry", "Nathan", "Rohith", "Joy", "…
#> $ Institution <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ Department <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ `Email address` <chr> "u017@example.com", "u001@example.com", "u002@example.…
#> $ State <chr> "Finished", "Finished", "Finished", "Finished", "Finis…
#> $ `Started on` <chr> "2 June 2021 8:28 AM", "2 June 2021 2:21 PM", "2 June …
#> $ `Time taken` <chr> "16 mins 49 secs", "17 mins 11 secs", "11 mins 30 secs…
#> $ Completed <chr> "2 June 2021 8:45 AM", "2 June 2021 2:38 PM", "2 June …
#> $ `Grade/10.00` <dbl> 9.47, 9.74, 9.47, 10.00, 10.00, 10.00, 9.21, 9.47, 10.…
#> $ `Q. 1 /0.79` <dbl> 0.26, 0.79, 0.26, 0.79, 0.79, 0.79, 0.79, 0.26, 0.79, …
#> $ `Q. 2 /0.26` <dbl> 0.26, 0.26, 0.26, 0.26, 0.26, 0.26, 0.26, 0.26, 0.26, …
#> $ `Q. 3 /2.37` <dbl> 2.37, 2.37, 2.37, 2.37, 2.37, 2.37, 2.11, 2.37, 2.37, …
#> $ `Q. 4 /2.11` <dbl> 2.11, 2.11, 2.11, 2.11, 2.11, 2.11, 1.58, 2.11, 2.11, …
#> $ `Q. 5 /2.11` <dbl> 2.11, 2.11, 2.11, 2.11, 2.11, 2.11, 2.11, 2.11, 2.11, …
#> $ `Q. 6 /0.26` <dbl> 0.26, 0.26, 0.26, 0.26, 0.26, 0.26, 0.26, 0.26, 0.26, …
#> $ `Q. 7 /1.05` <chr> "1.05", "0.79", "1.05", "1.05", "1.05", "1.05", "1.05"…
#> $ `Q. 8 /1.05` <dbl> 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, …Combine & Adjust Grades of Quiz 1
How to choose the maximum score of each student and readjust maximum grades to 100?
Calling combine_grades() will do that in one step, plus it also cleans
column names and extracts numeric student ID from Email address.
grades_ls$Quiz_1 %>%
combine_grades(
extract_id_from = "Email address", # Extract Student ID from Email
id_regex = "[:digit:]+", # Regular expression to extract student ID
choose_grade = "max", # Choose only maximum grade of each student
new_max_grade = 100 # Adjust maximum grade to 100
) %>%
select(Name, ID, starts_with("G"), starts_with("Q"))
#> # A tibble: 26 × 11
#> Name ID Grade_100 Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Jada Roquemo… 017 94.7 2.6 2.6 23.7 21.1 21.1 2.6 10.5 10.5
#> 2 Ronin Ali 001 97.4 7.9 2.6 23.7 21.1 21.1 2.6 7.9 10.5
#> 3 Jerry Hoffpa… 002 94.7 2.6 2.6 23.7 21.1 21.1 2.6 10.5 10.5
#> 4 Nathan Babbi… 026 100 7.9 2.6 23.7 21.1 21.1 2.6 10.5 10.5
#> 5 Rohith Huynh 024 100 7.9 2.6 23.7 21.1 21.1 2.6 10.5 10.5
#> 6 Joy Ellis 013 100 7.9 2.6 23.7 21.1 21.1 2.6 10.5 10.5
#> 7 Jeremy Nelson 023 92.1 7.9 2.6 21.1 15.8 21.1 2.6 10.5 10.5
#> 8 Christopher … 003 94.7 2.6 2.6 23.7 21.1 21.1 2.6 10.5 10.5
#> 9 Israel Munoz 018 100 7.9 2.6 23.7 21.1 21.1 2.6 10.5 10.5
#> 10 Zubaida al-A… 022 100 7.9 2.6 23.7 21.1 21.1 2.6 10.5 10.5
#> # … with 16 more rowsCombine Grades for All Quizzes
Supposed in a semester student have to do a graded assignment in a multiple quizzes, you can put each individual Grades Report in a list.
Now it’s a list of data frames (here as grades_ls).
grades_ls %>%
purrr::map_lgl(is.data.frame)
#> Quiz_1 Quiz_2 Quiz_3
#> TRUE TRUE TRUESupply a list of data frames into combine_grades(), you can combine
and weight grades from multiple quizzes into one data frame with a
sum of student’s score in the Total_x column.
grades_ls %>%
combine_grades(
extract_id_from = "Email address", # Extract Student ID from Email
id_regex = "[:digit:]+", # Regular expression to extract student ID
choose_grade = "max", # Choose only maximum grade of each student
new_max_grade = c(25, 25, 50) # Adjust maximum grade to 100
) %>%
select(Name, ID, contains("Grade"), starts_with("Total"))
#> # A tibble: 26 × 6
#> Name ID Quiz_1_Grade_25 Quiz_2_Grade_25 Quiz_3_Grade_50 Total_100
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Jada Roquemo… 017 23.7 19.4 50.0 93.1
#> 2 Ronin Ali 001 24.4 20.4 46.4 91.1
#> 3 Jerry Hoffpa… 002 23.7 16.6 50.0 90.3
#> 4 Nathan Babbi… 026 25 18.5 50.0 93.4
#> 5 Rohith Huynh 024 25 25 50.0 100.
#> 6 Joy Ellis 013 25 13.9 50.0 88.8
#> 7 Jeremy Nelson 023 23.0 25 46.4 94.4
#> 8 Christopher … 003 23.7 22.2 50.0 95.8
#> 9 Israel Munoz 018 25 21.3 50.0 96.2
#> 10 Zubaida al-A… 022 25 22.2 50.0 97.2
#> # … with 16 more rowsLast updated: 2022-06-02