Preprocessing and analyzing web tracking data with webtrackR
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Researchers have relied on free/easy access to APIs from social media platforms for a very long time. But in the recent past, many prominent platforms revoked the free access to their API and made accessing the data almost unaffordable for regular researchers. The need for alternative data sources to study the online behaviour of individuals is big. One such alternative are studies that use webtracking to obtain the web browsing history of participants. This type of data is far richer than social media data but can also be far more heterogeneous and complex. Enter the R package webtrackR, a package to preprocess and analyze webtracking data.
Installation
You can install the development version of webtrackR from GitHub with:
# install.packages("remotes")
remotes::install_github("schochastics/webtrackR")
The CRAN version can be installed with:
install.packages("webtrackR")
The package is still under heavy development and new features are being added on regular basis. If you are working with webtracking data, feel free to reach out with your feature requests.
An S3 class for webtracking data
The package defines an S3 class called wt_dt which inherits most of the functionality from data.table. Each row in a web tracking data set represents a visit. Raw data read with the package need to have at least the following variables:
- panelist_id: the individual from which the data was collected
- url: the URL of the visit
- timestamp: the time of the URL visit
The function as.wt_dt assigns the class wt_dt to a raw web tracking data set. It also allows you to specify the name of the raw variables corresponding to panelist_id, url and timestamp.
All preprocessing functions check if these three variables are present and an error is thrown if one is not found
Data Preprocessing
Currently, the main functionality of the package is to preprocess a raw webtracking dataset and add some more helpful variables for later analysis:
add_duration()adds a variable calleddurationbased on the sequence of timestamps. The basic logic is that the duration of a visit is set to the time difference to the subsequent visit, unless this difference exceeds a certain value (defined by argumentcutoff), in which case the duration will be replaced byNAor some user-defined value (defined byreplace_by).add_session()adds a variable calledsession, which groups subsequent visits into a session until the difference to the next visit exceeds a certain value (defined bycutoff).extract_host(),extract_domain(),extract_path()extracts the host, domain and path of the raw URL and adds variables named accordingly. See function descriptions for definitions of these terms.drop_query()lets you drop the query and fragment components of the raw URL.add_next_visit()andadd_previous_visit()adds the previous or the next URL, domain, or host (defined bylevel) as a new variable.add_referral()adds a new variable indicating whether a visit was referred by a social media platform. Follows the logic of Schmidt et al., (2023).add_title()downloads the title of a website (the text within the<title>tag of a web site’s<head>) and adds it as a new variable.add_panelist_data(). Joins a data set containing information about participants such as a survey.
Classification
So far, one function, classify_visits(), is implemented which is used to categorize website visits by either extracting the URL’s domain or host and matching them to a list of domains or hosts, or by matching a list of regular expressions against the visit URL. Currently, some precompiled lists are included in the package, but these will move to a dedicated package domainator at a later stage.
Summarizing and aggregating
deduplicate()flags or drops (as defined by argumentmethod) consecutive visits to the same URL within a user-defined time frame (as set by argumentwithin). Alternatively to dropping or flagging visits, the function aggregates the durations of such duplicate visits.sum_visits()andsum_durations()aggregate the number or the durations of visits, by participant and by a time period (as set by argumenttimeframe). Optionally, the function aggregates the number / duration of visits to a certain class of visits.sum_activity()counts the number of active time periods (defined bytimeframe) by participant.
Example code
A typical workflow including preprocessing, classifying and aggregating web tracking data looks like this (using the in-built example data):
library(webtrackR)
# load example data and turn it into wt_dt
data("testdt_tracking")
wt <- as.wt_dt(testdt_tracking)
# add duration
wt <- add_duration(wt)
# extract domains
wt <- extract_domain(wt)
# drop duplicates (consecutive visits to the same URL within one second)
wt <- deduplicate(wt, within = 1, method = "drop")
# load example domain classification and classify domains
data("domain_list")
wt <- classify_visits(wt, classes = domain_list, match_by = "domain")
# load example survey data and join with web tracking data
data("testdt_survey_w")
wt <- add_panelist_data(wt, testdt_survey_w)
# aggregate number of visits by day and panelist, and by domain class
wt_summ <- sum_visits(wt, timeframe = "date", visit_class = "type")
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Citation
@online{schoch2023,
author = {Schoch, David},
title = {Preprocessing and Analyzing Web Tracking Data with
{webtrackR}},
date = {2023-09-12},
url = {http://blog.schochastics.net/posts/2023-09-12_preprocessing-and-analyzing-web-tracking-data-with-webtrackr},
langid = {en}
}
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