functions for session reconstruction and analysis

Description

sessionreconstruct provides functions to aid in reconstructing and analysing user sessions. Although primarily designed for web sessions (see the introductory vignette), its session approach is plausibly applicable to other domains.

Author(s)

Oliver Keyes <okeyes@wikimedia.org>

calculate the bounce rate within a session dataset

Description

Calculates the "bounce rate" within a set of sessions - the proportion of sessions consisting only of a single event.

Usage

bounce_rate(sessions, user_id = NULL, precision = 2)

Arguments

Value

either a single numeric value, representing the percentage of sessions overall that are bounces, or a data.frame of user IDs and bounce rates if user_id is set to a column rather than NULL.

See Also

sessionise for session reconstruction, and session_length, session_count and time_on_page for other session-related metrics.

Examples

#Load and sessionise the dataset
data("session_dataset")
sessions <- sessionise(session_dataset, timestamp, uuid)

# Calculate overall bounce rate
rate <- bounce_rate(sessions)

# Calculate bounce rate on a per-user basis
per_user <- bounce_rate(sessions, user_id = uuid)

Count the number of sessions in a sessionised dataset

Description

link{session_count} counts the number of sessions in a sessionised dataset, producing either a count for the overall dataset or on a per-user basis (see below).

Usage

session_count(sessions, user_id = NULL)

Arguments

Value

either a single integer value or a data.frame (see above).

Examples

#Load and sessionise the dataset
data("session_dataset")
sessions <- sessionise(session_dataset, timestamp, uuid)

# Calculate overall bounce rate
count <- session_count(sessions)

# Calculate session count on a per-user basis
per_user <- session_count(sessions, user_id = uuid)

Example event dataset

Description

an example dataset of events, for experimenting with session reconstruction and analysis

Usage

session_dataset

Format

a data.frame of 63,524 rows consisting of:

uuid

Hashed and salted unique identifiers representing 10,000 unique clients.

timestamp

timestamps, as POSIXct objects

url

URLs, to demonstrate the carrying-along of metadata through the sessionisation process

Source

The uuid and timestamp columns come from an anonymised dataset of Wikipedia readers; the URLs are from NASA's internal web server, because space is awesome.

Calculate session length

Description

Calculate the overall length of each session.

Usage

session_length(sessions)

Arguments

Value

a data.frame of two columns - session_id, containing unique session IDs, and session_length, containing the length (in seconds) of that particular session.

Please note that these lengths should be considered a minimum; because of how sessions behave, calculating the time-on-page of the last event in a session is impossible.

See Also

sessionise for session reconstruction, and time_on_page, session_count and bounce_rate for other session-related metrics.

Examples

#Load and sessionise the dataset
data("session_dataset")
sessions <- sessionise(session_dataset, timestamp, uuid)

# Calculate session length
len <- session_length(sessions)

Reconstruct sessions (experimental)

Description

sessionise takes a data.frame of events (including timestamps and user IDs) and sessionises them, returning the same data.frame but with two additional columns - one containing a unique session ID, and one the time difference between successive events in the same session.

Usage

sessionise(x, timestamp, user_id, threshold = 3600)

Arguments

Value

x, ordered by userID and timestamp, with two new columns - session_id (containing a unique ID for the session a row is in) and delta (containing the time elapsed between that row's event, and the previous event, if they were both in the same session).

See Also

bounce_rate, time_on_page, session_length and session_count - common metrics that can be calculated with a sessionised dataset.

Examples

# Take a dataset with URLs and similar metadata and sessionise it -
# retaining that metadata

data("session_dataset")
sessionised_data <- sessionise(x = session_dataset,
                              timestamp = timestamp,
                              user_id = uuid,
                              threshold = 1800)

Calculate time-on-page metrics

Description

time_on_page generates metrics around the mean (or median) time-on-page - on an overall, per-user, or per-session basis.

Usage

time_on_page(sessions, by_session = FALSE, median = FALSE, precision = 2)

Arguments

Value

either a single numeric value, representing the mean/median time on page for the overall dataset, or a data.frame of session IDs and numeric values if by_session is TRUE.

See Also

sessionise for session reconstruction, and session_length, session_count and bounce_rate for other session-related metrics.

Examples

#Load and sessionise the dataset
data("session_dataset")
sessions <- sessionise(session_dataset, timestamp, uuid)

# Calculate overall time on page
top <- time_on_page(sessions)

# Calculate time-on-page on a per_session basis
per_session <- time_on_page(sessions, by_session = TRUE)

# Use median instead of mean
top_med <- time_on_page(sessions, median = TRUE)