Full Guide

Installation

Download the package directly from this repository using devtools:

library(devtools)
install_github("UCLATALL/CourseKataData")

Usage

This section details the usage of the functions in the CourseKataData package. If you would like to read more about the actual structure of the data downloaded from CourseKata, scroll down to the Data Structure section.

One and Done

The easiest way to get started working with your data is to just download it and run process_data with the path to the zip file (no need to unzip the file first).

library(CourseKataData)

process_data("path/to/downloaded/data.zip")

If you would like to unzip the file first (which can sometimes be faster), you can also pass the name of the directory to process_data(). If your data was in path/to/downloaded/data.zip and you extracted it to path/to/downloaded/data, then the call should be to the latter path:

process_data("path/to/downloaded/data")

When you run this function it will load all of the data and create six data frames (actually tibbles, but they work pretty much the same). These are the names of the data frames that are created:

• classes
• responses
• items
• tags
• media_views
• page_views

If there is already an R object with one of these names, you will be given the opportunity to abort the processing or overwrite the existing object.

Automatically Merge Multiple Downloads

If you have downloaded multiple data download zip files from CourseKata, don’t worry, this package takes care of merging the files for you. To load multiple data downloads into R, specify a vector of zip files or directories to load:

zip_paths <- c("path/to/first/zip", "path/to/second/zip", "path/to/a/directory")
process_data(zip_paths)

Time Zones

By default the data is parsed using the “UTC” time zone. If you would like to convert the date time data in each table to a different time zone you can specify it. In this example, your computer system’s time zone will be used:

process_data("path/to/downloaded/data", time_zone = Sys.timezone())

Splitting Responses

By default, all of the responses in the course are included in the responses table. These responses can be semantically split into three parts: the surveys at the beginning and end of the course, the practice quizzes at the end of each chapter, and the rest of the items in the text. If you would like to split the responses like this, there is a handy split_responses() function that will return a list of three tibbles:

parts <- split_responses(responses)
parts$in_text
parts$quizzes
parts$surveys

Piece by Piece

If you would instead like to process only part of the data, you can use the helpful sub-process functions that exist for each of the file types in the data download:

• process_classes()
• process_responses()
• process_items()
• process_tags()
• process_media_views()
• process_page_views()

As with process_data(), each of these functions will accept the path to a CourseKata data download zip file or the directory of the extracted zip file, or you can simply supply the name of the file that you want to process with the function.

# zip file
process_classes("path/to/downloaded/data.zip")

# directory
process_classes("path/to/downloaded/data)

The classes data file contains information about each class in the data download (see more about the structure of the data download and what is in each file in the Data Structure section below). However, the other files are specific to each class included in the download. If you want to extract a specific class, you can specify the class_id, which should correspond to the name of the class’s folder in the classes folder of the download.

# a single class
process_page_views(
  "path/to/downloaded/data.zip", 
  class_id = "dad3c954-3cb5-11ea-b81d-1d385b778009"
)

# two classes
process_page_views(
  "path/to/downloaded/data.zip", 
  class_id = c(
    "dad3c954-3cb5-11ea-b81d-1d385b778009",
    "1a99bb15-4d19-4e3f-bb60-6332141573ed"
  )
)

Into the Deep with Responses

The responses take a significant amount of processing. If you would like to do this in parts (perhaps to inject your own processing at some point along the way), each of the finer processing functions are available. The following is equivalent to process_responses():

read.csv("path/to/downloaded/data/classes/[class_id].csv") %>% 
  convert_types_in_responses() %>%
  ensure_data_in_responses() %>%
  map_response_options()

The part functions are purely functional, which means that they can be run in any order. If you alter the response data and a function doesn’t know how to handle it anymore, it will tell you what is missing or needed.