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webtrackR is an R package to preprocess and analyze web tracking data,
i.e., web browsing histories of participants in an academic study. Web
tracking data is oftentimes collected and analyzed in conjunction with
survey data of the same participants.
webtrackR is part of a series of R packages to analyse webtracking
data:
The package defines an S3 class called wt_dt which inherits most of
the functionality from the data.frame class. A summary and print
method are included in the package.
Each row in a web tracking data set represents a visit. Raw data 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. Additionally, it
turns the timestamp variable into POSIXct format.
All preprocessing functions check if these three variables are present.
Otherwise an error is thrown.
Preprocessing
Several other variables can be derived from the raw data with the
following functions:
add_duration() adds a variable called duration based 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 argument
cutoff), in which case the duration will be replaced by NA or some
user-defined value (defined by replace_by).
add_session() adds a variable called session, which groups
subsequent visits into a session until the difference to the next
visit exceeds a certain value (defined by cutoff).
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() and add_previous_visit() adds the previous or the
next URL, domain, or host (defined by level) 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
classify_visits() categorizes 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.
Summarizing and aggregating
deduplicate() flags or drops (as defined by argument method)
consecutive visits to the same URL within a user-defined time frame
(as set by argument within). Alternatively to dropping or flagging
visits, the function aggregates the durations of such duplicate
visits.
sum_visits() and sum_durations() aggregate the number or the
durations of visits, by participant and by a time period (as set by
argument timeframe). 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 by
timeframe) 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 durationwt<- add_duration(wt)
# extract domainswt<- 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 classwt_summ<- sum_visits(wt, timeframe="date", visit_class="type")
About
🔭 R package for Preprocessing and Analyzing Web Tracking Data
