Retention is an R package that contains datasets related to user activity, user details, and build versions. These datasets can be used for analyses such as user retention, activity patterns, and the impact of different build versions on user activity.
It has functions for simulating builds, users, and activity data, each of which is customisable to control scale of data output.
The core of the data is simulating a decrease in activity according to the number of days a player has been active. So, on day 0, there is a 100% chance of activity, but on day 1, only a 30% chance of activity, after day 30, a very small chance of activity. This mimics how player retention data is usually shaped.
This is done by creating a probability function dependent on days from start of activity, and sampling from a binomial distribution. See get_activity_probabilityand get_activity for more details.
Inspiration
The inspiration for these data is Unity Analytics game events. In order to present retention analytics on game data open source, I need simulated data that imitates the data structure I worked with at a video game studio.
Installation
You can install the Retention package from GitHub using the devtools package. Run the following commands in your R console:
Data
The data in this package was generated using the simulate_retention_data.R script. The datasets are stored as RDS files, called by the retention package, or accessed as csv files in retention_data/ and are:
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user_activity: This dataset tracks the activity of users across different build versions and dates. It contains 146,463 rows and 3 variables:user,build, andactivity_date.
dim(retention::user_activity)
## [1] 146463 3
retention::user_activity %>% head()
## # A tibble: 6 × 3
## user build activity_date
## <chr> <chr> <date>
## 1 user_1 0.0.0 2023-01-21
## 2 user_10 0.0.0 2023-01-23
## 3 user_10 0.0.0 2023-01-26
## 4 user_10 0.0.2 2023-02-06
## 5 user_100 0.0.0 2023-01-12
## 6 user_100 0.0.0 2023-01-13-
users: This dataset tracks the activity of users from their first build version and date. It contains 47,031 rows and 4 variables:user,first_build,activity_start, andactivity_days.
dim(retention::users)
## [1] 47031 4
retention::users %>% head()
## # A tibble: 6 × 4
## user first_build activity_start activity_days
## <chr> <chr> <date> <int>
## 1 user_1 0.0.0 2023-01-21 300
## 2 user_2 0.0.0 2023-01-17 443
## 3 user_3 0.0.0 2023-01-14 381
## 4 user_4 0.0.0 2023-01-09 190
## 5 user_5 0.0.0 2023-01-24 505
## 6 user_6 0.0.0 2023-01-11 552-
builds: This dataset tracks the release information of different build versions. It contains 57 rows and 4 variables:build,release_length,release_start, andrelease_end.
For more detailed information about these datasets, please refer to the documentation in the pkg_data.R file.
dim(retention::builds)
## [1] 57 4
retention::builds %>% head()
## # A tibble: 6 × 4
## build release_length release_start release_end
## <chr> <int> <date> <date>
## 1 0.0.0 24 2023-01-07 2023-01-30
## 2 0.0.1 4 2023-01-31 2023-02-03
## 3 0.0.2 27 2023-02-04 2023-03-02
## 4 0.1.0 30 2023-03-03 2023-04-01
## 5 0.1.1 12 2023-04-02 2023-04-13
## 6 0.1.2 15 2023-04-14 2023-04-28Functions that simulate user activity for different versions of an app
Simulate builds.
versions <-
get_versions(
major_change_max = 2,
minor_change_max = 1,
hot_fix_max = 1)
versions
## # A tibble: 6 × 4
## major_change minor_change hot_fix build
## <int> <int> <int> <chr>
## 1 0 0 0 0.0.0
## 2 0 0 1 0.0.1
## 3 0 1 0 0.1.0
## 4 1 0 0 1.0.0
## 5 1 0 1 1.0.1
## 6 2 0 0 2.0.0Simulate release dates for builds.
builds <- builds %>% set_build_releases(release_length_max = 7)
builds
## # A tibble: 57 × 4
## build release_length release_start release_end
## <chr> <int> <date> <date>
## 1 0.0.0 5 2023-01-07 2023-01-11
## 2 0.0.1 6 2023-01-12 2023-01-17
## 3 0.0.2 7 2023-01-18 2023-01-24
## 4 0.1.0 5 2023-01-25 2023-01-29
## 5 0.1.1 7 2023-01-30 2023-02-05
## 6 0.1.2 4 2023-02-06 2023-02-09
## 7 0.1.3 7 2023-02-10 2023-02-16
## 8 0.2.0 6 2023-02-17 2023-02-22
## 9 0.3.0 2 2023-02-23 2023-02-24
## 10 0.3.1 7 2023-02-25 2023-03-03
## # ℹ 47 more rowsSimulate users for builds.
users <- get_users(
builds,
new_users_max = 3,
max_activity_days = 14)
users
## # A tibble: 97 × 4
## user first_build activity_start activity_days
## <chr> <chr> <date> <int>
## 1 user_1 0.1.1 2023-02-02 6
## 2 user_2 0.1.1 2023-02-04 1
## 3 user_3 0.1.2 2023-02-08 6
## 4 user_4 0.1.2 2023-02-08 4
## 5 user_5 0.1.3 2023-02-13 14
## 6 user_6 0.1.3 2023-02-10 14
## 7 user_7 0.1.3 2023-02-15 14
## 8 user_8 0.2.0 2023-02-20 3
## 9 user_9 0.2.0 2023-02-18 2
## 10 user_10 0.2.0 2023-02-20 11
## # ℹ 87 more rowsSimulate activity.
user_activity_data <- get_activity(builds, users) %>%
dplyr::filter(active_on_date == TRUE)
user_activity_data
## # A tibble: 194 × 8
## user first_build activity_start activity_days build activity_date
## <chr> <chr> <date> <int> <chr> <date>
## 1 user_1 0.1.1 2023-02-02 6 0.1.1 2023-02-02
## 2 user_10 0.2.0 2023-02-20 11 0.2.0 2023-02-20
## 3 user_10 0.2.0 2023-02-20 11 0.2.0 2023-02-21
## 4 user_10 0.2.0 2023-02-20 11 0.2.0 2023-02-22
## 5 user_11 0.3.0 2023-02-23 9 0.3.0 2023-02-23
## 6 user_12 0.3.1 2023-02-25 6 0.3.1 2023-02-25
## 7 user_13 0.3.1 2023-02-27 2 0.3.1 2023-02-27
## 8 user_14 1.0.0 2023-03-04 6 1.0.0 2023-03-04
## 9 user_15 1.1.1 2023-03-15 11 1.1.1 2023-03-15
## 10 user_15 1.1.1 2023-03-15 11 1.1.1 2023-03-16
## # ℹ 184 more rows
## # ℹ 2 more variables: days_from_start <drtn>, active_on_date <lgl>