Code to load dependencies
library(tidyverse)
library(data.table)
library(kableExtra)Usage report Privacy Basics for Researchers
1 About
In this report, you’ll find some data on the usage of the online training “Privacy Basics for Researchers”. This online module was created by Research Data Management Support at Utrecht University (NL) to provide a researcher-friendly introduction into the General Data Protection Regulation (GDPR), with a focus on how it applies to scientific research performed at Utrecht University (UU).
A description of and a registration link to the online module can be found on the RDM Support website. The module is embedded within the Utrecht University Moodle platform, “ULearning”, but the raw module materials are also available online via Zenodo.
Note
This report is primarily meant for internal monitoring purposes at the moment. We may adjust this report in a later stage, or move it to another web address!
2 Getting, reading and cleaning the data
2.1 Downloading the data
To obtain the data for this report from the ULearning platform, the following steps should be followed by a teacher/administrator in the ULearning platform:
2.1.0.1 1. List of enrolled participants
- From course main page, go to Participants.
- Set the following selection criteria: Match All of the following
- Match ANY Roles: Student, Guest, Authenticated user, Authenticated user on site home AND
- Match None Groups: Red
- Click “Apply filters”
- Select all users
- Under “With selected users…”, select “Comma-separated values (.csv)”
- Save the file in the
rawfolder. Add the date of downloading in the downloaded csv file “YYYYMMDD_courseid_838_participants.csv”
2.1.0.2 2. Progress report
- From course main page, go to Reports > Activity completion
- Download the file to the spreadsheet format (UTF-8 .csv)
- Save the file in the
rawfolder. Add the date of downloading in the downloaded csv file “YYYYMMDD_progress.pbfr.csv”
2.1.0.3 3. Quiz results
- From course main page, go to “Chapter 6 | Closing” > “Final Quiz”
- Click “Attempts: [##]” (The ## indicating the number of attempts)
- Under What to include in the report, select:
- Attempts from enrolled users who have attempted the quiz
- Attempts that are In progress, Overdue, Finished, and Never submitted
- Check Show at most one finished attempt per user (Highest grade)
- Under Display options:
- Make sure Page size is larger than the amount of attempts.
- Marks for each question: Yes
- Click Show report.
- Select all participants using the checkbox above the first name in the list.
- Download the data as Comma separated values (.csv)
- Save the file in the
rawfolder as “YYYYMMDD_PBfR-Quiz.csv”.
2.1.0.4 4. Privacy Basics for Researchers - Course participants report
- In ULearning, go to your profile picture and select Reports.
- Select the Custom report “Privacy Basics for Researchers e-learning”
- Scroll down and download the report as Comma separated values (.csv)
- Save the file in the
rawfolder as “YYYYMMDD_Privacy Basics for Researchers e-learning.csv”
The raw data are not shared because they contain personal data (e.g., names, email addresses and information about participants’ progress in the module).
2.2 Reading and cleaning the data
We first have to read and clean the data to get usable data frames. We don’t want to include people who were involved in the creation of the course or who provided feedback on it; we only need the actual users; people who enrolled after the launch of the course with the intention to actually learn something new!
Code to style graphs
# UU colors: https://www.uu.nl/en/organisation/corporate-identity/brand-policy/colour
UU_pallette <- c(
"#FFE6AB", # Lighter yellow
"#FFCD00", # UU yellow
"#F3965E", # Orange
"#C00A35", # Red
"#AA1555", # Bordeaux-red
"#6E3B23", # Brown
"#24A793", # Green
"#5287C6", # Blue
"#001240", # Dark blue
"#5B2182", # Purple
"#000000" # Black
)
uucol <- "#FFCD00"
styling <- list(
theme_classic(),
theme(legend.text = element_text(size = 10),
legend.position = "bottom",
legend.title = element_blank(),
axis.title.x = element_text(size = 11),
axis.text.y = element_text(size = 11),
axis.ticks = element_blank(),
panel.background = element_blank(),
panel.border=element_blank(),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
plot.background=element_blank())
)Code to read data
# 1. Read existing processed files
avg_progress_cats <- read.csv("data/processed/avg_progress_cats.csv")
nr_participants <- read.csv("data/processed/nr_participants.csv")
quizscores <- read.csv("data/processed/quizscores.csv")
total_quiz_scores <- read.csv("data/processed/total_quiz_scores.csv")
# 2. Read latest raw files
# See which files are in the raw folder
data_files <- data.frame(filename = list.files(path = "data/raw", pattern = ".csv"))
# Get the dates from the file names
data_files$filenamedates <- as.Date(str_extract(pattern = "[0-9]+[0-9]+[0-9]+",
string = data_files$filename),
format = "%Y%m%d")
# Sort by date using data.table::setorder (descending = most recent files first)
setorder(data_files, filenamedates, na.last = FALSE)
# Read the 3 files from the most recent date
most_recent_indices <- length(data_files$filename):(length(data_files$filename)-3)
participants <- read_csv(paste0("data/raw/",
data_files$filename[most_recent_indices][
str_detect(data_files$filename[most_recent_indices],
"courseid_838_participants.csv")
]))
progress <- read_csv(paste0("data/raw/",
data_files$filename[most_recent_indices][
str_detect(data_files$filename[most_recent_indices],
"progress.pbfr.csv")
]))
quiz <- read_csv(paste0("data/raw/",
data_files$filename[most_recent_indices][
str_detect(data_files$filename[most_recent_indices],
"PBfR-Quiz.csv")
]))
facultyinfo <- read_csv(paste0("data/raw/",
data_files$filename[most_recent_indices][
str_detect(data_files$filename[most_recent_indices],
"Privacy Basics for Researchers e-learning.csv")
]))
# Save the date for the coming calculations
date <- as.Date(data_files$filenamedates[length(data_files$filenamedates)],
"%Y-%m-%d")Code to assign the correct faculty to participants
# Create a named vector to store the recoding key
dept_recode <- c(
"Faculteit Geesteswetenschappen" = "GW",
"Faculteit Recht Economie Bestuur en Organisatie" = "REBO",
"Faculteit Diergeneeskunde" = "DGK",
"Universiteitsbibliotheek Utrecht" = "UB",
"Faculteit Sociale Wetenschappen" = "FSW",
"Faculteit Betawetenschappen" = "BETA",
"Universitaire Bestuursdienst" = "UBD",
"Faculteit Geowetenschappen" = "GEO",
"UMCU" = "MED"
)
participants_comb <- participants |>
# If Email address ends with @students.uu.nl, assign Groups = "Student"
mutate(Groups = case_when(
(is.na(Groups) & str_ends(`Email address`,
"@students.uu.nl")) ~ "Student",
# If email address does not end with uu.nl or umcutrecht, assign Groups = External
(is.na(Groups) & !str_ends(`Email address`,
"uu.nl") & !str_ends(`Email address`, "@umcutrecht.nl")) ~ "External",
.default = Groups)) |>
# Merge participants with facultyinfo
# left join: keep only the people from participants
# not from facultyinfo, as that also contains teacher/privacy officers
left_join(facultyinfo,
by = "Email address") |>
# Recode Department based on partial match with the dept_recode key
mutate(Department = map_chr(Department, function(dept) {
# Find the first match based on partial matching
match <- names(dept_recode)[str_detect(dept,
names(dept_recode))]
if (length(match) > 0) {
# If a match exists, use the recoded value
return(dept_recode[match[1]])
} else {
# If no match, return External
return("External")
}
}
)) |>
# Merge Groups and Department when Groups is NA
mutate(Groups = ifelse(is.na(Groups), Department, Groups)) |>
# Delete unused columns
select(-Department, -Completed)Code to select only relevant participants
# Filter participants to only contain the correct participants
participants_2 <- participants_comb |> filter(!(Groups == "Red" & !is.na(Groups)))
# Filter progress and quiz dataframes based on participants
progress_2 <- inner_join(participants_2, progress)
quiz_2 <- inner_join(participants_2, quiz) |>
# Filter out people who did the quiz multiple times: only select Finished
group_by(`Email address`) |>
mutate(has_finished = any(Status == "Finished")) |>
filter(!(Status == "In progress" & has_finished)) |>
select(-has_finished)3 Number of participants
Code to calculate the number of participants
# Calculate nr of participants of most recent download
new_row <- data.frame(date = date,
total = dim(participants_2)[1],
uu = sum(grepl("@uu.nl$",
participants_2$`Email address`)),
uu_students = sum(grepl("@students.uu.nl$",
participants_2$`Email address`)),
# other = total - uu - students
other = dim(participants_2)[1] -
sum(grepl("@uu.nl$", participants_2$`Email address`)) -
sum(grepl("@students.uu.nl$", participants_2$`Email address`)),
# Faculties
DGK = sum(participants_2$Groups=="DGK"),
REBO = sum(participants_2$Groups=="REBO"),
FSW = sum(participants_2$Groups=="FSW"),
GEO = sum(participants_2$Groups=="GEO"),
GW = sum(participants_2$Groups=="GW"),
BETA = sum(participants_2$Groups=="BETA"),
MED = sum(participants_2$Groups=="MED"),
UB = sum(participants_2$Groups=="UB"),
UBD = sum(participants_2$Groups=="UBD"),
Student = sum(participants_2$Groups=="Student"),
External = sum(participants_2$Groups=="External")
)
# Convert date to date type
nr_participants$date <- as.Date(nr_participants$date, "%Y-%m-%d")
# Paste new row below the existing data
nr_participants_all <- rbindlist(list(nr_participants, new_row),
use.names = TRUE,
fill = TRUE)As of 2025-05-26, there are 277 participants enrolled in the course. 166 of them are enrolled with their “@uu.nl” email address, and 85 of them with the “@students.uu.nl” email address. 26 participants are either from an external institution or have used a personal email address to enroll in the course.
In the below bar chart, you can see the development of the number of participants in the course over time.
Code to plot the participants over time
# From wide to long
nr_participants_long <- pivot_longer(data = nr_participants_all,
cols = c(uu, uu_students, other)
)
# Set the order of the variable levels
nr_participants_long$name <- factor(nr_participants_long$name,
levels = c("uu", "uu_students", "other"))
# Create a stacked bar plot
# Calculate midpoints for label positioning
nr_participants_long <- nr_participants_long |>
group_by(date) |>
arrange(desc(name)) |>
mutate(midpoint = cumsum(value) - 0.5 * value,
prev_height = lag(cumsum(value), default = 0))
# Adjust y-axis limits
y_max <- max(nr_participants_long$midpoint) + max(nr_participants_long$value) / 2
# Adjust label positioning
ggplot(nr_participants_long, aes(x = date, y = value, fill = name)) +
geom_bar(stat = "identity") +
geom_text(aes(label = ifelse(value > 0, value, ""),
y = prev_height + value / 2, group = name),
vjust = 0.5, color = "black", size = 3.5) +
ylim(0, y_max) + # Set y-axis limits
labs(title = "Course participants over time",
x = "Date", y = "Number of participants",
fill = "Type of participant") +
scale_fill_manual(name = "Group",
labels = c("uu" = "UU staff",
"uu_students" = "UU students",
"other" = "Others"),
values = UU_pallette) +
styling
4 Participation per faculty
On 2025-05-26, this was the division of faculties in the module (total: 277):
Code to create a table of participation per faculty
# Select the faculties and total values per date
nrs_faculties <- nr_participants_all |>
select(date, DGK:External, total) |>
filter(!is.na(DGK))
# make a long dataframe
nrs_faculties_long <- nrs_faculties |>
pivot_longer(cols = -date,
names_to = "Faculty",
values_to = "Participants") |>
mutate(date = as.character(date))
# make it wide again so that the df is in the right format for the table
nrs_faculties_wide <- nrs_faculties_long |>
pivot_wider(names_from = date,
values_from = Participants)
# Create the table with kableExtra
kable(nrs_faculties_wide, format = "html", output = FALSE,
caption = "<b>Participants per faculty</b>",
table.attr='cellpadding="3", cellspacing="3"') |>
kable_styling(bootstrap_options = c("striped",
"hover",
"condensed",
"responsive"),
fixed_thead = T) |>
# Highligh the last row (total) in yellow and bold
# Source: https://haozhu233.github.io/kableExtra/awesome_table_in_html.html
row_spec(length(nrs_faculties_wide$Faculty),
bold = T, color = "black", background = uucol)| Faculty | 2024-01-08 | 2024-02-01 | 2024-03-01 | 2024-04-02 | 2024-05-07 | 2024-06-03 | 2024-07-02 | 2024-08-02 | 2024-09-02 | 2024-10-01 | 2024-11-01 | 2024-12-02 | 2025-01-06 | 2025-02-03 | 2025-03-03 | 2025-04-01 | 2025-05-01 | 2025-05-26 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| DGK | 15 | 16 | 16 | 16 | 16 | 17 | 17 | 17 | 17 | 17 | 18 | 33 | 34 | 34 | 35 | 35 | 35 | 36 |
| REBO | 6 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 8 | 18 | 20 | 21 | 21 | 22 | 23 | 23 | 23 | 24 |
| FSW | 18 | 21 | 21 | 21 | 21 | 22 | 22 | 24 | 26 | 29 | 31 | 31 | 32 | 33 | 33 | 33 | 34 | 34 |
| GEO | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 8 | 8 | 8 | 8 | 10 | 10 | 10 | 11 | 12 | 13 | 14 |
| GW | 9 | 9 | 9 | 11 | 13 | 13 | 13 | 15 | 16 | 16 | 16 | 20 | 22 | 22 | 23 | 24 | 24 | 26 |
| BETA | 13 | 16 | 16 | 16 | 17 | 18 | 18 | 20 | 20 | 20 | 21 | 22 | 22 | 22 | 22 | 22 | 22 | 23 |
| MED | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 5 | 5 | 5 | 5 |
| UB | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 5 | 6 | 6 | 6 | 6 |
| UBD | 1 | 2 | 2 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 4 | 5 | 5 | 5 | 5 | 5 | 5 |
| Student | 13 | 14 | 17 | 19 | 26 | 27 | 27 | 30 | 30 | 41 | 46 | 49 | 52 | 55 | 58 | 61 | 71 | 82 |
| External | 9 | 11 | 11 | 13 | 14 | 16 | 18 | 20 | 21 | 21 | 22 | 22 | 22 | 22 | 22 | 22 | 22 | 22 |
| total | 94 | 107 | 111 | 119 | 131 | 138 | 141 | 152 | 157 | 181 | 193 | 220 | 228 | 234 | 243 | 248 | 260 | 277 |
5 Participants’ progress
Below you can see the average progress per group of participants for each block in the course as of 2025-05-26.
Code to plot latest progress per chapter and group
progress_3 <- progress_2 |>
# Delete columns we won't use
select(-starts_with("..."), -`First name`, -`Last name`) |>
# Turn character completion into numeric 0 or 1
mutate_at(vars(-`Email address`, -Groups),
~ifelse(. == "Completed", 1, 0)) |>
# Turn Groups variable into a factor
mutate(Groups = factor(Groups,
levels = c("DGK", "REBO", "FSW", "GEO", "GW",
"BETA", "MED", "UB", "UBD",
"Student", "External")),
#Create a factor variable for alternative group membership (UU, student or other)
group = as.factor(ifelse(grepl("@uu.nl$",
`Email address`),
"uu",
ifelse(grepl("@students.uu.nl$",
`Email address`),
"uu_students",
"other"))))
# Set order of the factor levels
progress_3$group <- factor(progress_3$group,
levels = c("uu", "uu_students", "other"))
# Group blocks into sections for easier visualization
# Commented below is the old code which is not very efficient. Because I am not 100% certain that the new code does its job, I am keeping this here. If it turns out that the newer code does work correctly, I will remove this commented code.
# latest_progress_long <- progress_3 |>
# pivot_longer(cols = -c(`Email address`, Groups, group),
# names_to = "block",
# values_to = "completion") |>
# mutate(chapter = ifelse(startsWith(block, "Welcome") |
# startsWith(block, "Introduction to Personal Data under the GDPR") |
# startsWith(block, "GDPR") |
# startsWith(block, "What is Personal Data") |
# startsWith(block, "Special Categories of Personal Data") |
# startsWith(block, "Roles in the GDPR"),
# "Chapter 1", ifelse(
# startsWith(block, "Introduction to Lawfulness") |
# startsWith(block, "Legal Basis for Processing Data") |
# startsWith(block, "Public Interest") |
# startsWith(block, "Consent") |
# startsWith(block, "How to Inform Data Subjects") |
# startsWith(block, "Data Subject Rights"),
# "Chapter 2", ifelse(
# startsWith(block, "Introduction to Planning Your Project") |
# startsWith(block, "Privacy by Design and Privacy by Default") |
# startsWith(block, "Demonstrating Compliance") |
# startsWith(block, "Privacy Scan and DPIA") |
# startsWith(block, "Common Privacy Risks") |
# startsWith(block, "Reporting a Data Breach"),
# "Chapter 3", ifelse(
# startsWith(block, "Introduction to Practical Measures") |
# startsWith(block, "Levels of Data Security") |
# startsWith(block, "Access Control") |
# startsWith(block, "Encryption") |
# startsWith(block, "Pseudonymisation and Anonymisation") |
# startsWith(block, "De-identification in Practice") |
# startsWith(block, "Processing Tools"),
# "Chapter 4", ifelse(
# startsWith(block, "Introduction to Storing and Sharing Personal Data") |
# startsWith(block, "Storing Personal Data") |
# startsWith(block, "Agreements in Research") |
# startsWith(block, "Sharing Personal Data") |
# startsWith(block, "Making Personal Data FAIR"),
# "Chapter 5", ifelse(
# startsWith(block, "Final Quiz") |
# startsWith(block, "Evaluate this course") |
# startsWith(block, "Available Support at Utrecht University"),
# "Chapter 6", "Not assigned"
# )
# )
# )
# )
# )
# )
# )
# Create a named vector to store the recoding key
chapter_mapping <- c(
"Welcome" = "Chapter 1",
"Introduction to Personal Data under the GDPR" = "Chapter 1",
"GDPR" = "Chapter 1",
"What is Personal Data" = "Chapter 1",
"Special Categories of Personal Data" = "Chapter 1",
"Roles in the GDPR" = "Chapter 1",
"Introduction to Lawfulness" = "Chapter 2",
"Legal Basis for Processing Data"= "Chapter 2",
"Public Interest" = "Chapter 2",
"Consent" = "Chapter 2",
"How to Inform Data Subjects" = "Chapter 2",
"Data Subject Rights" = "Chapter 2",
"Introduction to Planning Your Project" = "Chapter 3",
"Privacy by Design and Privacy by Default" = "Chapter 3",
"Demonstrating Compliance" = "Chapter 3",
"Privacy Scan and DPIA" = "Chapter 3",
"Common Privacy Risks" = "Chapter 3",
"Reporting a Data Breach" = "Chapter 3",
"Introduction to Practical Measures" = "Chapter 4",
"Levels of Data Security" = "Chapter 4",
"Access Control" = "Chapter 4",
"Encryption" = "Chapter 4",
"Pseudonymisation and Anonymisation" = "Chapter 4",
"De-identification in Practice" = "Chapter 4",
"Processing Tools" = "Chapter 4",
"Introduction to Storing and Sharing Personal Data" = "Chapter 5",
"Storing Personal Data" = "Chapter 5",
"Agreements in Research" = "Chapter 5",
"Sharing Personal Data" = "Chapter 5",
"Making Personal Data FAIR" = "Chapter 5",
"Final Quiz" = "Chapter 6",
"Evaluate this course" = "Chapter 6",
"Available Support at Utrecht University" = "Chapter 6"
)
latest_progress_long <- progress_3 |>
pivot_longer(cols = -c(`Email address`, Groups, group),
names_to = "block",
values_to = "completion") |>
mutate(chapter = map_chr(block, function(blck){
match <- names(chapter_mapping)[str_detect(blck, names(chapter_mapping))]
if(length(match) > 0){
# If a match exists, use the recoded value
return(chapter_mapping[match[1]])
} else {
# If no match, return "Not assigned"
return("Not assigned")
}
})
)
# Count people per faculty who appear in the progress file
n <- progress_3 |> count(Groups)
# Save completion rate per faculty
progress_per_fac <- latest_progress_long |>
group_by(Groups) |>
summarise(avg_completion_rate = mean(completion) * 100) |>
left_join(n) |>
mutate(date = date)
# Plot progress per faculty
progress_per_fac |>
mutate(graph_label = paste0(Groups, "\n (n = ", n, ")")) |>
ggplot(aes(x = graph_label,
y = avg_completion_rate)) +
geom_bar(stat = "identity",
position = position_dodge(0.9),
fill = uucol) +
geom_text(aes(label = paste0(round(avg_completion_rate, 0), "%"),
y = avg_completion_rate + 2), # Adjust label position as needed
size = 3.5, color = "black", position = position_dodge(0.9)) +
labs(x = "Faculty", y = "Average progress (%)",
title = paste0("Average progress (%) per faculty on ", date)) +
styling
See R code
# Plot average progress score (per person) per chapter
latest_progress_long |>
group_by(chapter) |>
summarise(avg_completion_rate = mean(completion)) |>
ungroup() |>
ggplot(aes(x = chapter,
y = avg_completion_rate * 100)) +
geom_bar(stat = "identity",
position = position_dodge(0.9),
fill = uucol) +
geom_text(aes(label = paste0(round(avg_completion_rate * 100, 0), "%"),
y = avg_completion_rate * 100 + 2), # Adjust label position as needed
size = 3.5, color = "black", position = position_dodge(0.9)) +
labs(x = "Chapter", y = "Average progress (%)",
title = paste0("Average progress (%) on ", date)) +
styling
Below, you can see the average progress over time.
On July 11th 2023, the ULearning platform got an update. Therefore, from then onwards, the progress for every user was set to 0 again, hence the drop in progress in July 2023.
In May 2025, we re-did a number of blocks, also updating the Completion settings. This resulted in some of the progress of users to be undone.
Code to plot average progress over time
progress_4_fac <- progress_3 |>
# From wide to long format based on the Email address and group
pivot_longer(cols = -c(`Email address`, Groups, group),
names_to = "block",
values_to = "completion") |>
group_by(`Email address`, Groups) |>
# Calculate average completion rate per participant
summarise(progress = mean(completion)) |>
# Put date in a new date column for all rows in the dataframe
mutate(date = as.Date(rep(date, n())))
progress_4_group <- progress_3 |>
# From wide to long format based on the Email address and group
pivot_longer(cols = -c(`Email address`, Groups, group),
names_to = "block",
values_to = "completion") |>
group_by(`Email address`, group) |>
# Calculate average completion rate per participant
summarise(progress = mean(completion)) |>
# Put date in a new date column for all rows in the dataframe
mutate(date = as.Date(rep(date, n())))
# Calculate new progress per faculty dataframe
avg_progress_new_fac <- progress_4_fac |>
group_by(Groups, date) |>
summarise(n = n(), # nr of people underlying each average
avg_progress = mean(progress) * 100)
# Calculate new progress per group dataframe
avg_progress_new_group <- progress_4_group |>
group_by(group, date) |>
summarise(n = n(), # nr of people underlying each average
avg_progress = mean(progress) * 100)
# In the old dataframe, make date as actual date + make group a factor
avg_progress_cats$date <- as.Date(avg_progress_cats$date, "%Y-%m-%d")
avg_progress_cats$group <- factor(avg_progress_cats$group,
levels = c("uu",
"uu_students",
"other"))
# Combine old and new data in 1 dataframe
avg_progress_cats_new <- bind_rows(avg_progress_cats,
avg_progress_new_fac,
avg_progress_new_group)
# Plot average progress over time per group
avg_progress_cats_new[!is.na(avg_progress_cats_new$group),] |>
ggplot(aes(x = date,
y = avg_progress,
color = group)) +
geom_point() +
geom_line(linewidth = 1) +
labs(x = "Date",
y = "Average Progress (%)",
title = "Average Progress Over Time per Group") +
scale_color_manual(name = "Group",
labels = c("uu" = "UU staff",
"uu_students" = "UU students",
"other" = "Others"),
values = UU_pallette) +
styling
6 Quiz results
Code to clean the new quiz data
quiz_3 <- quiz_2
# Make character grades numeric, and "-" into NA
quiz_3[quiz_3 == "-"] <- NA
quiz_3 <- quiz_3 |>
mutate(Grade = as.numeric(`Grade/10.0`)) |>
mutate_at(vars(starts_with("Q.")),
as.numeric)
# Create a factor variable for group membership (UU, student or other)
quiz_3$group <- as.factor(ifelse(grepl("@uu.nl$",
quiz_3$`Email address`),
"uu",
ifelse(grepl("@students.uu.nl$",
quiz_3$`Email address`),
"uu_students",
"other")))
# Rename question columns into something human-readable
quiz_3 <- rename_with(quiz_3, ~ str_extract(.x, "Q\\.\\s*\\d+") |>
str_replace_all("\\.|\\s", ""),
starts_with("Q"))
# Summarize the new quiz data per group
quiz_4 <- quiz_3 |>
# Make sure group is a factor variable
mutate(group = factor(group, levels = c("uu",
"uu_students",
"other"))) |>
# Group by UU / Students / Other / All for summary calculations
group_by(group) |>
# For every group, save the sample size, total grade, and mean grade per question
summarise(
n = n(),
total_grade = mean(Grade, na.rm = TRUE),
across(starts_with("Q"),
~ mean(., na.rm = TRUE)/0.6*100)
) |>
# Also save the date in the dataframe
mutate(date = as.Date(date, "%Y-%m-%d"))#
# Summarize the new quiz data per faculty
quiz_4_fac <- quiz_3 |>
# Group by faculty for summary calculations
group_by(Groups) |>
# For every faculty, save the sample size, total grade, and mean grade per question
summarise(
n = n(),
total_grade = mean(Grade, na.rm = TRUE),
across(starts_with("Q"),
~ mean(., na.rm = TRUE)/0.6*100)
) |>
# Also save the date in the dataframe
mutate(date = as.Date(date, "%Y-%m-%d")) |>
# Rearrange the columns for easier readability
select(date, Groups, n, total_grade, starts_with("Q"))
# Summarize new quiz data total (not per group)
quiz_4_total <- quiz_3 |>
ungroup() |>
# Save the sample size, total grade, and mean grade per question
summarise(
n = sum(!is.na(Grade)),
total_grade = mean(Grade, na.rm = TRUE),
across(starts_with("Q"), ~mean(., na.rm = TRUE) / 0.6 * 100)
) |>
# Also save the date in the dataframe
mutate(date = as.Date(date, "%Y-%m-%d")) |>
# Rearrange the columns for easier readability
select(date, n, total_grade, starts_with("Q"))
# Make date variables in old dataframes date too in order to merge
quizscores$date <- as.Date(quizscores$date, "%Y-%m-%d")
total_quiz_scores$date <- as.Date(total_quiz_scores$date, "%Y-%m-%d")
# Append new quizscores to old quiz scores
quizscores_new <- bind_rows(quizscores, quiz_4, quiz_4_fac)
quizscores_new$group <- factor(quizscores_new$group,
levels = c("uu",
"uu_students",
"other"))
total_quiz_scores_new <- bind_rows(total_quiz_scores, quiz_4_total)Below you can see the average final score on the quiz for the latest quiz results.
Code to plot the latest final grade per faculty
quiz_4_fac |>
mutate(graph_label = paste0(Groups, "\n (n = ", n, ")")) |>
ggplot(aes(x = graph_label,
y = total_grade)) +
geom_bar(stat = "identity",
position = position_dodge(0.9),
fill = uucol) +
geom_text(aes(label = round(total_grade, 2),
y = total_grade + 0.5), # Adjust label position as needed
size = 3.5, color = "black", position = position_dodge(0.9)) +
labs(x = "Faculty", y = "Average grade",
title = paste0("Average grade per faculty on ", date)) +
styling
Below is a graph with the average scores (in %) per question in the most recent quiz data.
Code to create the plot per question
latestquiz_total_long <- quiz_4_total |>
select(starts_with("Q")) |>
gather(key = "Question", value = "Score")
# Convert "Question" to a factor with the correct order
latestquiz_total_long$Question <- factor(latestquiz_total_long$Question,
levels = paste0("Q", 1:16))
# Plot
ggplot(latestquiz_total_long, aes(x = Question,
y = Score)) +
geom_bar(stat = "identity", fill = uucol) +
labs(x = "Question",
y = "Average Score",
title = "Average Score per Quiz Question (%)") +
geom_text(aes(label = sprintf("%.0f", Score)), vjust = -0.5, size = 3.5) +
styling
Below you can find the number of attempts (either in Progress or Finished)
Code to plot the number of attempts over time
quizscores_attempts <- quizscores_new |>
select(date,
group,
n) |>
filter(!is.na(group))
# Line plot
quizscores_attempts |>
ggplot(aes(x = date, y = n, color = group)) +
geom_line(linewidth = 1) +
geom_point(alpha = 0.7) +
labs(x = "Date",
y = "Number of attempts",
title = "Number of quiz attempts made over time per group") +
scale_color_manual(values = UU_pallette,
name = "Group", # Set the legend title
labels = c("uu" = "UU staff",
"uu_students" = "UU students",
"other" = "Others")) + # Set the legend labels
styling
Code to write new objects to the processed folder
# number of participants
write.csv(nr_participants_all,
"data/processed/nr_participants.csv",
row.names = FALSE)
# progress
write.csv(avg_progress_cats_new,
"data/processed/avg_progress_cats.csv",
row.names = FALSE)
# quiz
write.csv(quizscores_new,
"data/processed/quizscores.csv",
row.names = FALSE)
write.csv(total_quiz_scores_new,
"data/processed/total_quiz_scores.csv",
row.names = FALSE)