Getting Started with moodleR
In moodleR: Helper Functions to Work with 'Moodle' Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE,
  message = FALSE
)

This vignette assumes that you have already been able to set up the connection to a Moodle Database and created a local cache (if chosen).

library(moodleR)
library(ggplot2)

Data access functions

Learning analytics practitioners working with LMS data will often be interested in a sub-set of the available tables with relevant references added. These can be accessed through several mdl_* functions which will connect to the relevant table(s) and return a reference to the data. Currently the package supports curated access through the following functions:

mdl_courses()
mdl_forum_posts()
mdl_grades()
mdl_log()
mdl_users()

For example:

my_grades <- mdl_grades()

creates an R object named my_grades containing the dbplyr reference to the table in question.

Summary functions

The R generic summary function is implemented with sensible defaults so that:

summary(my_grades)

provides useful information for the learning analytics practitioner.

Plot functions

Generic plot functions are also provided for all data-access objects:

plot(my_grades)

The plot functions return a ggplot by default. If you prefer base graphics you can call the function thus:

plot(my_grades, use_base_graphics = TRUE)

Additional columns

For convenience some additional columns have been added to the tables. These are all snake_case and are thus easy to spot:

library(dplyr)
my_grades %>% 
  select(contains("_")) %>% 
  colnames()

Objects of class dbplyr

The return value from the data-access functions are also dbplyr tables so dbplyr verbs and operation work on them as well. For example:

my_grades %>% 
  filter(courseid == 4957) %>% 
  summary()

my_grades %>% 
  filter(courseid == 4957) %>% 
  plot()

Using ggplot objects

Since the return value of a plot function is a ggplot object it is of course possible to add and/or manipulate the plot using the syntax from the ggplot2 package. For example by annotating the plot:

my_plot <- mdl_grades() %>% 
  plot()
my_plot +
  annotate("text",.45,10^5, label="Passing Grade",cex=5)+
  annotate("segment",x=.6,y=10^5,xend=.6,yend=0, lty=2)