student_segmentation_r.Rmd

---
title: "Student Segmentation"
author: "Eli Nimy"
date: "2022-07-30"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```



# OULAD DATASET: 
- https://analyse.kmi.open.ac.uk/open_dataset
```{r}
# Open University Learning Analytics Dataset
library(oulad)

# Data Analyis and Visualisation
library(tidyverse)

# Statistical Summary
library(tigerstats)

# Gaussian mixture model (GMM)
library(mclust)

# Interactive Data Visualization 
library(highcharter)

# Fast Data operations: merging and manipulation 
library(data.table)
```


## Student Virtual Learning Environment: Sum Clicks Analysis
- *code_module* – an identification code for a module.
- *code_presentation* - the identification code of the module presentation.
- *id_student* – a unique identification number for the student.
- *id_site* - an identification number for the VLE material.
- *date* – the date of student’s interaction with the material measured as the number of days since the start of the - module-presentation.
- *sum_click* – the number of times a student interacts with the material in that day.

# BOTTLENECK 1
- Where the data frame has the largest memory (10,6 million observations)
```{r}
# To get information the OULAD Data set
help.search("oulad")

# Load student VLE data from oulad library
set.seed(1234)
svle_df <- student_vle
# svle_df %>% head()
student_vle %>% head()
```



```{r}
# Percentage of missing values
sum(is.na(student_vle$sum_click))/length(student_vle$sum_click) * 100
```




```{r}
# Statistical Summary of Date indicator
favstats(student_vle$date)
```



```{r}
# Statistical Summary of Sum of Clicks
favstats(student_vle$sum_click)
```



#### Clickstream Data
- The number of times a unique student interacted with material(s) throughout the day / week / month / term / semester/ year or a specific period will be summed
- Since engagement behavior can change, the number of interaction will vary with different days / weeks or etc. As a student may have a 100 clicks in one week and 200 the next (showing higher/lower engagement) so clustering can be performed for different weeks
```{r}
clicks_df <- student_vle %>% 
  # filter(date >=-25 & date <= 269 & 
  # code_presentation %in% 2013J) %>%  Filter for the dashboard (days) and semester
  group_by(id_student) %>% 
  summarise(sum_click = sum(sum_click))
clicks_df %>% head()
```



```{r}
# Statistical Summary of Sum of Clicks for the filtered Period 
favstats(clicks_df$sum_click)
```


## GMM MODEL SECTION
```{r}
# Selecting clicks for modelling
gmm_df <- select(clicks_df, sum_click)
gmm_df %>% head()
```


- Optimal Number of Components: 7 and 25
```{r}
# Fit a GMM model
set.seed(1837)
start_time <- Sys.time()
gmm_model <- Mclust(gmm_df, G=6)
end_time <- Sys.time()
```



```{r}
# Execution Time
end_time - start_time
```



```{r}
# Model Objects
print(gmm_model)
```



```{r}
# Summary table 
summary(gmm_model)
```



```{r}
# Cluster Means/Engagement Levels
gmm_model$parameters$mean
```


- Uncertainty: A value (0-1 or 0% - 100%) expressing the degree of uncertainty in a student belonging to a group
```{r}
# Add clusters and uncertainty values to original dataset
clicks_df$engagement_level <- gmm_model$classification
clicks_df$uncertainty <- gmm_model$uncertainty
clicks_df %>% head()
```



```{r}
# Exclude scientific outputs
options(scipen=999)
# View data set
# View(clicks_df) 
```


# Probability of Student belonging to each engagement level
```{r}
# Probability of a student belonging to a engagement level
engagement_level_probabilities <- gmm_model$z
colnames(engagement_level_probabilities) <- paste0('engagement_level', 1:gmm_model$G)
options(scipen=999)
# Create probabilities data frame
engagement_level_probabilities <- engagement_level_probabilities %>%
  round(2) %>% 
  as.data.frame()
engagement_level_probabilities %>% head()
```



- A table with the probability of a student belonging to each cluster, the cluster a student belongs to and the uncertainty with a student belonging to the particular cluster
```{r}
# Add cluster belonging of student
engagement_level_probabilities$engagement_level <- clicks_df$engagement_level
engagement_level_probabilities$uncertainty <- round(gmm_model$uncertainty*100,2)
engagement_level_probabilities$sum_click <- clicks_df$sum_click
engagement_level_probabilities$id_student <- clicks_df$id_student
engagement_level_probabilities %>% head()
```


#### Cluster Mean Table
```{r}
engagement_level_avg_df <- as.data.frame(round(gmm_model$parameters$mean))
colnames(engagement_level_avg_df) <- "average_clicks"
engagement_level_avg_df
# Rename rownames to engagement_level 
engagement_level_avg_df <- cbind(engagement_level = rownames(engagement_level_avg_df), engagement_level_avg_df)
# Remove rownames 
rownames(engagement_level_avg_df) <- NULL
engagement_level_avg_df 
```


#### Engagement Level Count Table
```{r}
# Obtain number of observations in each cluster. 
engagement_level_count_df <- clicks_df %>%
  group_by(engagement_level) %>%
  summarise(Number_of_students = n()) #%>% 
  # arrange(Number_of_students)
engagement_level_count_df
```


#### Engagement Level Statistics Table
```{r}
engagement_level_stats <- merge(engagement_level_avg_df, engagement_level_count_df) %>% arrange(average_clicks)
engagement_level_stats 
```



#### Distribution of Probabilities in each Engagement Level
```{r}
# probabilities <- select(cluster_probabilitics, -cluster, -uncertainty, -id_student) %>%
#   as.data.frame() %>%
#   mutate(id = row_number()) %>%
#   tidyr::gather(cluster, probability, -id)
# 
# ggplot(probabilities, aes(probability)) +
#   geom_histogram() +
#   facet_wrap(~ cluster, nrow = 6) 
```


## Engagement Level Analysis Tab
```{r}
# Data Visualization
library(highcharter)
```


- Highcharter default colors: https://api.highcharts.com/highcharts/colors
```{r}
hchart(engagement_level_stats, "column", hcaes(x = engagement_level, y = average_clicks), 
       dataLabels = list(enabled = TRUE),
       name = "Average No. of Clicks") %>% 
  hc_title(
    text = paste0("Average Number of Clicks for Each Engagement Level")
  ) %>% 
  hc_xAxis(title = list(text = "Engagement Level")) %>% 
  hc_yAxis(title = list(text = "Average Number of Clicks"))
```


```{r}
hchart(engagement_level_stats, "column", hcaes(x = engagement_level, y = Number_of_students), 
       dataLabels = list(enabled = TRUE),
       name = "No of Students") %>% 
  hc_title(
    text = paste0("Number of Students in Each Engagement Level")
  ) %>% 
  hc_xAxis(title = list(text = "Engagement Level")) %>% 
  hc_yAxis(title = list(text = "Number of Students")) %>% 
  hc_exporting(
    enabled = TRUE)
```


- Add min, max, average statistical summary of uncertainty
- Add short caption explanation
```{r}
hchart(engagement_level_probabilities$uncertainty, color = "#B71C1C", name = "Uncertainty") %>% 
  hc_title(
    text = paste0("Distribution of Engagement Level Assignment Uncertainty")
  ) %>% 
  hc_xAxis(title = list(text = "Uncertainty"),
           labels = list(format = "{value}%")) %>% 
  hc_yAxis(title = list(text = "Number of Students"))
```



```{r}
# Boxplot of clicks 
bp_clicks <- data_to_boxplot(data = clicks_df, 
                             variable = sum_click,
                             add_outliers = FALSE,
                             fillColor = "#B71C1C",
                             color = "black")
highchart() %>%
  hc_xAxis(type = "category",
           title = list(text = "Clicks")) %>%
  hc_yAxis(title = list(text = "Number of Clicks")) %>% 
  hc_title(
    text = paste0("Statistical Summary of Clickstream Data without Outliers")
  ) %>% 
  hc_add_series_list(bp_clicks) %>% 
  hc_legend(FALSE)
```



```{r}
# Boxplot of clicks 
bp_clicks_outlier <- data_to_boxplot(data = clicks_df, 
                             variable = sum_click,
                             add_outliers = TRUE,
                             fillColor = "#B71C1C",
                             color = "black")
highchart() %>%
  hc_xAxis(type = "category",
           title = list(text = "Clicks")) %>%
  hc_yAxis(title = list(text = "Sum of Clicks")) %>% 
  hc_title(
    text = paste0("Statistical Summary of Clickstream Data with Outliers")
  ) %>% 
  hc_add_series_list(bp_clicks_outlier) %>% 
  hc_legend(FALSE)
```


```{r}
hchart(clicks_df$sum_click, color = "#B71C1C", name = "Clicks") %>% 
  hc_title(
    text = paste0("Distribution of Clickstream Data")
  ) %>% 
  hc_xAxis(title = list(text = "Number of Clicks")) %>% 
  hc_yAxis(title = list(text = "Number of Students"))
```



```{r}
# Minimum, Maximum, Average Sum of clicks values
print(min(engagement_level_probabilities$sum_click))
print(max(engagement_level_probabilities$sum_click))
print(round(mean(engagement_level_probabilities$sum_click),2))

# Average Uncertainty value
print(round(mean(engagement_level_probabilities$uncertainty),2))
```



```{r}
# Probability Table
DT::datatable(engagement_level_probabilities,
              rownames = T,
              filter = "top",
              options = list(pageLength = 2, scrollX = TRUE, info = FALSE))
```


## INSTRUCTIONAL METHODS TAB
### Virtual Learning Environment Data 
The csv file contains information about the available materials in the VLE. Typically these are html pages, pdf files, etc. Students have access to these materials online and their interactions with the materials are recorded. The vle.csv file contains the following columns:

_ **id_site** – an identification number of the material.
- **code_module** – an identification code for module.
- **code_presentation** - the identification code of presentation.
- **activity_type** – the role associated with the module material.
- **week_from** – the week from which the material is planned to be used.
- **week_to** – week until which the material is planned to be used.
```{r}
#vle_df <- vle
# vle_df
vle %>% head()
```



# BOTTLENECK 2
- Where the code takes on the longest to execute and has the data frame has the largest memory
```{r}
# Merging the dataframes to link students with activities
# Long execution code
# Replace svle_df with student_vle
svle_vle_df <- data.table::merge.data.table(svle_df, vle, by = "id_site")
svle_vle_df
```



```{r}
# Long execution code
# Number of Times student accessed an activity 
activity_df <- svle_vle_df %>% 
  group_by(id_student,activity_type) %>% 
  summarise(activity_access_count = n()) %>% 
  as.data.frame()
activity_df
```



```{r}
# Merging data frames
clicks_activity_df <- merge.data.table(activity_df, clicks_df, by = "id_student")
clicks_activity_df
```


Activity Categories 
- Resources - resource, oucontent, folder, ouwiki, glossary, url
- Assessment - quiz, questionnaire, external quiz
- VLE Site/App - dualpane, repeatactivity, htmlactivity, sharedsubpage, ouelluminate, dataplus, page, homepage, forumng, subpage, oucollaborate 
```{r}
# clicks_activity_df <- clicks_activity_df %>%
#   mutate(vle_activity = case_when(grepl("resource", activity_type) ~ "Resource",
#                        grepl("oucontent", activity_type) ~ "Resource",
#                        grepl("url", activity_type) ~ "Resource",
#                        grepl("ouwiki", activity_type) ~ "Resource",
#                        grepl("glossary", activity_type) ~ "Resource",
#                        grepl("folder", activity_type) ~ "Resource",
#                        grepl("quiz", activity_type) ~ "Assessment",
#                        grepl("questionnaire", activity_type) ~ "Assessment",
#                        grepl("externalquiz", activity_type) ~ "Assessment",
#                        grepl("subpage", activity_type) ~ "VLE Site/App",
#                        grepl("forumng", activity_type) ~ "VLE Site/App",
#                        grepl("page", activity_type) ~ "VLE Site/App",
#                        grepl("oucollaborate", activity_type) ~ "VLE Site/App",
#                        grepl("dataplus", activity_type) ~ "VLE Site/App",
#                        grepl("homepage", activity_type) ~ "VLE Site/App",
#                        grepl("ouelluminate", activity_type) ~ "VLE Site/App",
#                        grepl("dualpane", activity_type) ~ "VLE Site/App",
#                        grepl("repeatactivity", activity_type) ~ "VLE Site/App",
#                        grepl("htmlactivity", activity_type) ~ "VLE Site/App",
#                        grepl("sharedsubpage", activity_type) ~ "VLE Site/App")) 
# clicks_activity_df
```



```{r}
# Number of occurrences for each activity 
# table(clicks_activity_df$vle_activity)
```



```{r}
# Occurrences of activity types per cluster
# clicks_activity_df %>% 
#   group_by(cluster, vle_activity) %>%
#   summarise(activity_access_count = n())
```



```{r}
activity_stats <- clicks_activity_df %>% 
  filter(engagement_level %in% 1) %>% # dashboard filter: single number select
  group_by(engagement_level, activity_type) %>%
  summarise(activity_access_count = n()) %>% 
  mutate(activity_access_percent = round(activity_access_count / sum(activity_access_count) * 100, 2)) %>% 
  arrange(desc(activity_access_percent))
activity_stats
```



```{r}
hchart(activity_stats, "column", hcaes(x = activity_type, y = activity_access_percent), 
       dataLabels = list(enabled = TRUE, format = "{y}%"),
       name = "% of Students") %>% 
  hc_title(
    text = paste0("Percentage of Students that Access each VLE Activity in Engagement Level ", 1)
  ) %>% 
  hc_xAxis(title = list(text = "VLE Activity")) %>% 
  hc_yAxis(title = list(text = "Percentage of Students"),
           labels = list(format = "{value}%")) %>% 
  hc_exporting(
    enabled = TRUE)
```


- source: https://www.datanovia.com/en/lessons/highchart-interactive-bar-plot-in-r/
- Color options are included
- New Title: VLE Activities Accessed by Engagement Level(s)
- description: Percentage of Students that Access each VLE Activity in Engagement Level
```{r}
# Comparison between engagement levels 
# Maximum number of comparison should be limited to 3 in dashboard select
activity_stats <- clicks_activity_df %>% 
  filter(engagement_level %in% c(1,2, 3)) %>%
  group_by(engagement_level, activity_type) %>%
  summarise(activity_access_count = n()) %>% 
  mutate(activity_access_percent = round(activity_access_count / sum(activity_access_count) * 100, 1)) %>% 
  arrange(desc(activity_access_percent))

hchart(activity_stats, "column", hcaes(x = activity_type, y = activity_access_percent, group = engagement_level), 
       dataLabels = list(enabled = TRUE, format = "{y}"),
       stacking = "normal") %>% 
  hc_title(
    text = paste0("Percentage of Students that Access each VLE Activity in Engagement Level")
  ) %>% 
  hc_xAxis(title = list(text = "VLE Activity")) %>% 
  hc_yAxis(title = list(text = "Percentage of Students"),
           labels = list(format = "{value}%")) %>% 
  hc_legend(title = list(text = "Engagement Level")) %>% 
  hc_exporting(
    enabled = TRUE) 
```






```{r}
# Module Analysis
# Number of Times student accessed a module on the VLE 
# module_df <- svle_vle_df %>% 
#   group_by(id_student,code_module.x) %>% 
#   summarise(module_acess_count = n()) %>% 
#   as.data.frame()
# module_df
# clicks_module_df <- merge.data.table(module_df, clicks_df, by = "id_student")
# clicks_module_df
# module_stats <- clicks_module_df %>% 
#   filter(engagement_level %in% c(1,2,3,4,5,6)) %>% # default: 4,5,6
#   group_by(engagement_level, code_module.x) %>%
#   summarise(module_access_count = n()) %>% 
#   mutate(module_access_percent = round(module_access_count / sum(module_access_count) * 100, 1)) %>% 
#   arrange(desc(module_access_percent))
# module_stats
```


```{r}
# Merging with student info data frame
# si <- read_csv("oulad/studentInfo.csv", show_col_types = FALSE)
# si$num_of_prev_attempts <- as.integer(si$num_of_prev_attempts)
# si <- as.data.frame(si)
clicks_student_df <- merge.data.table(student, clicks_df, by = "id_student") # si was student
clicks_student_df
```

```{r}
module_stats <- clicks_student_df %>% 
  filter(engagement_level %in% c(1:6)) %>% # default: all
  group_by(engagement_level, code_module) %>%
  summarise(number_of_students = n()) %>% 
  mutate(percentage_of_students = round(number_of_students / sum(number_of_students) * 100, 1)) %>% 
  arrange(desc(percentage_of_students))
module_stats
```



```{r}
hchart(module_stats, "column", hcaes(x = code_module, y = percentage_of_students, group = engagement_level), 
       dataLabels = list(enabled = TRUE, format = "{y}%")) %>% 
  hc_title(
    text = paste0("Modules Accessed by Engagement Level(s):")
  ) %>% 
  hc_xAxis(title = list(text = "Module")) %>% 
  hc_yAxis(title = list(text = "Percentage of Students"), 
           labels = list(format = "{value}%")) %>% 
  hc_legend(title = list(text = "Engagement Level")) %>% 
  hc_exporting(
    enabled = TRUE)
```



## Student Characteristic Tab
```{r}
clicks_df
```



```{r}
gender_stats <- clicks_student_df %>% 
  filter(engagement_level %in% c(1:6)) %>% # default: all
  group_by(engagement_level, gender) %>%
  summarise(number_of_students = n()) %>% 
  mutate(percentage_of_students = round(number_of_students / sum(number_of_students) * 100, 1)) %>% 
  arrange(desc(percentage_of_students))
gender_stats
```



```{r}
hchart(gender_stats, "column", hcaes(x = gender, y = percentage_of_students, group = engagement_level), 
       dataLabels = list(enabled = TRUE, format = "{y}%")) %>% 
  hc_title(
    text = paste0("Gender Representation in Engagement Level(s):")
  ) %>% 
  hc_xAxis(title = list(text = "Gender")) %>% 
  hc_yAxis(title = list(text = "Percentage of Students"), 
           labels = list(format = "{value}%")) %>% 
  hc_legend(title = list(text = "Engagement Level")) %>% 
  hc_exporting(
    enabled = TRUE)
```



```{r}
age_stats <- clicks_student_df %>% 
  filter(engagement_level %in% c(1:6)) %>% # default: all
  group_by(engagement_level, age_band) %>%
  summarise(number_of_students = n()) %>% 
  mutate(percentage_of_students = round(number_of_students / sum(number_of_students) * 100, 1)) %>% 
  arrange(desc(percentage_of_students))
age_stats
```



```{r}
hchart(age_stats, "column", hcaes(x = age_band, y = percentage_of_students, group = engagement_level), 
       dataLabels = list(enabled = TRUE, format = "{y}%")) %>% 
  hc_title(
    text = paste0("Student Age Band Representation in Engagement Level(s):")
  ) %>% 
  hc_xAxis(title = list(text = "Age Band")) %>% 
  hc_yAxis(title = list(text = "Percentage of Students"), 
           labels = list(format = "{value}%")) %>% 
  hc_legend(title = list(text = "Engagement Level")) %>% 
  hc_exporting(
    enabled = TRUE)
```



```{r}
disability_stats <- clicks_student_df %>% 
  filter(engagement_level %in% c(1:6)) %>% # default: all
  group_by(engagement_level, disability) %>%
  summarise(number_of_students = n()) %>% 
  mutate(percentage_of_students = round(number_of_students / sum(number_of_students) * 100, 1)) %>% 
  arrange(desc(percentage_of_students))
disability_stats$disability <- as.character(disability_stats$disability)
disability_stats
```



```{r}
hchart(disability_stats, "column", hcaes(x = disability, y = percentage_of_students, group = engagement_level), 
       dataLabels = list(enabled = TRUE, format = "{y}%")) %>% 
  hc_title(
    text = paste0("Student with/without a Disability Representation in Engagement Level(s):")
  ) %>% 
  hc_xAxis(title = list(text = "Disability")) %>% 
  hc_yAxis(title = list(text = "Percentage of Students"), 
           labels = list(format = "{value}%")) %>% 
  hc_legend(title = list(text = "Engagement Level")) %>% 
  hc_exporting(
    enabled = TRUE)
```



```{r}
final_results_stats <- clicks_student_df %>% 
  filter(engagement_level %in% c(1:6)) %>% # default: all
  group_by(engagement_level, final_result) %>%
  summarise(number_of_students = n()) %>% 
  mutate(percentage_of_students = round(number_of_students / sum(number_of_students) * 100, 1)) %>% 
  arrange(desc(percentage_of_students))
final_results_stats
```



```{r}
hchart(final_results_stats, "column", hcaes(x = final_result, 
                                            y = percentage_of_students, group = engagement_level), 
       dataLabels = list(enabled = TRUE, format = "{y}%")) %>% 
  hc_title(
    text = paste0("Student Final Academic Outcome in each Engagement Level")
  ) %>% 
  hc_xAxis(title = list(text = "Final Result")) %>% 
  hc_yAxis(title = list(text = "Percentage of Students"), 
           labels = list(format = "{value}%")) %>% 
  hc_legend(title = list(text = "Engagement Level")) %>% 
  hc_exporting(
    enabled = TRUE)
```



```{r}
prev_attempts_stats <- clicks_student_df %>% 
  filter(engagement_level %in% c(1:6)) %>% # default: all
  group_by(engagement_level, num_of_prev_attempts) %>%
  summarise(number_of_students = n()) %>% 
  mutate(percentage_of_students = round(number_of_students / sum(number_of_students) * 100, 1)) %>% 
  arrange(desc(percentage_of_students))
prev_attempts_stats$num_of_prev_attempts <- as.character(prev_attempts_stats$num_of_prev_attempts)
prev_attempts_stats
```



```{r}
hchart(prev_attempts_stats, "column", hcaes(x = num_of_prev_attempts, 
                                            y = percentage_of_students, group = engagement_level), 
       dataLabels = list(enabled = TRUE, format = "{y}%")) %>% 
  hc_title(
    text = paste0("Number of Previous Attempts in Course according to each Engagement Level")
  ) %>% 
  hc_xAxis(title = list(text = "Number of Previous Attempts")) %>% 
  hc_yAxis(title = list(text = "Percentage of Students"), 
           labels = list(format = "{value}%")) %>% 
  hc_legend(title = list(text = "Engagement Level")) %>% 
  hc_exporting(
    enabled = TRUE)
```



```{r}
region_stats <- clicks_student_df %>% 
  filter(engagement_level %in% c(1:2)) %>% # default: all
  group_by(engagement_level, region) %>%
  summarise(number_of_students = n()) %>% 
  mutate(percentage_of_students = round(number_of_students / sum(number_of_students) * 100, 1)) %>% 
  arrange(desc(percentage_of_students))
region_stats
```



```{r}
hchart(region_stats, "column", hcaes(x = region, y = percentage_of_students, group = engagement_level), 
       dataLabels = list(enabled = TRUE, format = "{y}%")) %>% 
  hc_title(
    text = paste0("Engagement Level Representation based on Student's Region of Stay")
  ) %>% 
  hc_xAxis(title = list(text = "Regions")) %>% 
  hc_yAxis(title = list(text = "Percentage of Students"), 
           labels = list(format = "{value}%")) %>% 
  hc_legend(title = list(text = "Engagement Level")) %>% 
  hc_exporting(
    enabled = TRUE)
```




## LIBRARY & DOWNLOADED DATA READ


```{r}
student_vle
read_csv("oulad_original/studentVle.csv")
```


```{r}
vle
read_csv("oulad_original/vle.csv")
```




```{r}
si <- read_csv("oulad_original/studentInfo.csv", show_col_types = FALSE)
si$num_of_prev_attempts <- as.integer(si$num_of_prev_attempts)
student
```


# Data Reduction
- Creating a subset of the original data to use on the deployed dashboard
```{r}
# Reading data
library(tidyverse)
svle_df <- read_csv("oulad_original/studentVle.csv") %>% as.data.frame()
#set.seed(1234)
# Sampling 100 000
svle_df2 <- sample_n(svle_df, 100000)

# Data Sampling Per Year
library(tigerstats)

#set.seed(1537)
B_2013 = subset(svle_df,code_presentation == "2013B")
B_2013 = popsamp(25000,B_2013)

#set.seed(1637)
J_2013 = subset(svle_df,code_presentation == "2013J")
J_2013 = popsamp(25000,J_2013)

#set.seed(1737)
B_2014 = subset(svle_df,code_presentation == "2014B")
B_2014 = popsamp(25000,B_2014)

#set.seed(1837)
J_2014 = subset(svle_df,code_presentation == "2014J")
J_2014 = popsamp(25000,J_2014)

svle_df3 <- rbind(B_2013, J_2013, B_2014, J_2014)
svle_df3
```


## COMPARING SAMPLE RESULTS
```{r}
# Ogriginal Data Sum of Clicks statistical summary
favstats(svle_df$sum_click)
```


```{r}
favstats(svle_df2$sum_click)
```


```{r}
favstats(svle_df3$sum_click)
```


```{r}
# For observing memory size
vle_df <- read_csv("oulad_original/vle.csv") %>% as.data.frame()
svle_vle_df <- data.table::merge.data.table(svle_df3, vle_df, by = "id_site")
svle_vle_df
```



```{r}
# Saving the subsetted data
# write.csv(svle_df3,'oulad2/studentVle.csv', row.names = FALSE)
```



```{r}
# read_csv("oulad2/studentVle.csv", show_col_types = FALSE)
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