Title: | A Group of Fun Datasets of Various Sizes and Differing Levels of Quality |
---|---|
Description: | Four datasets are provided here from the 'Intendo' game 'Super Jetroid'. It is data from the 2015 year of operation and it comprises a revenue table ('all_revenue'), a daily users table ('users_daily'), a user summary table ('user_summary'), and a table with data on all user sessions ('all_sessions'). These core datasets come in different sizes, and, each of them has a variant that was intentionally made faulty (totally riddled with errors and inconsistencies). This suite of tables is useful for testing with packages that focus on data validation and data documentation. |
Authors: | Richard Iannone [aut, cre] |
Maintainer: | Richard Iannone <[email protected]> |
License: | MIT + file LICENSE |
Version: | 0.1.1.9000 |
Built: | 2024-10-31 22:11:01 UTC |
Source: | https://github.com/rich-iannone/intendo |
This summary table provides revenue data for every in-app purchase and ad view for players of Super Jetroid in 2015.
all_revenue( size = c("small", "medium", "large", "xlarge", "preview"), quality = c("perfect", "faulty"), type = c("tibble", "data.frame", "duckdb", "csv"), keep = FALSE )
all_revenue( size = c("small", "medium", "large", "xlarge", "preview"), quality = c("perfect", "faulty"), type = c("tibble", "data.frame", "duckdb", "csv"), keep = FALSE )
size |
A keyword that allows getting different variants of the table
based on the size of player base. The default |
quality |
The data quality level of the returned dataset. There are two
options: (1) |
type |
The table return type. By default, this is a |
keep |
Should the downloaded data be stored on disk in the working
directory? By default, this is |
A data table object, which could be a tibble (tbl_df
) a data
frame, or an in-memory DuckDB table (tbl_dbi
). If a CSV is written then
TRUE
will be invisibly returned.
# Get a preview of the `all_revenue` dataset # with the 'preview' size option all_revenue(size = "preview")
# Get a preview of the `all_revenue` dataset # with the 'preview' size option all_revenue(size = "preview")
all_revenue
The all_revenue_dd()
function generates a data dictionary based on the
all_revenue
table.
all_revenue_dd( size = c("small", "medium", "large", "xlarge", "preview"), quality = c("perfect", "faulty"), type = c("tibble", "data.frame", "duckdb") )
all_revenue_dd( size = c("small", "medium", "large", "xlarge", "preview"), quality = c("perfect", "faulty"), type = c("tibble", "data.frame", "duckdb") )
size |
A keyword that allows getting different variants of the table
based on the size of player base. The default |
quality |
The data quality level of the returned dataset. There are two
options: (1) |
type |
The table return type. By default, this is a |
A ptblank_informant
object.
# Get a preview of the `all_revenue` dataset # with the 'preview' size option all_revenue_dd(size = "preview")
# Get a preview of the `all_revenue` dataset # with the 'preview' size option all_revenue_dd(size = "preview")
This table provides information on player sessions and summarizes the number of revenue events (ad views and IAP spends) and provides total revenue amounts (in USD) broken down by type for the session.
all_sessions( size = c("small", "medium", "large", "xlarge", "preview"), quality = c("perfect", "faulty"), type = c("tibble", "data.frame", "duckdb", "csv"), keep = FALSE )
all_sessions( size = c("small", "medium", "large", "xlarge", "preview"), quality = c("perfect", "faulty"), type = c("tibble", "data.frame", "duckdb", "csv"), keep = FALSE )
size |
A keyword that allows getting different variants of the table
based on the size of player base. The default |
quality |
The data quality level of the returned dataset. There are two
options: (1) |
type |
The table return type. By default, this is a |
keep |
Should the downloaded data be stored on disk in the working
directory? By default, this is |
A data table object, which could be a tibble (tbl_df
) a data
frame, or an in-memory DuckDB table (tbl_dbi
). If a CSV is written then
TRUE
will be invisibly returned.
# Get a preview of the `all_sessions` dataset # with the 'preview' size option all_sessions(size = "preview")
# Get a preview of the `all_sessions` dataset # with the 'preview' size option all_sessions(size = "preview")
all_sessions
The all_sessions_dd()
function generates a data dictionary based on the
all_sessions
table.
all_sessions_dd( size = c("small", "medium", "large", "xlarge", "preview"), quality = c("perfect", "faulty"), type = c("tibble", "data.frame", "duckdb") )
all_sessions_dd( size = c("small", "medium", "large", "xlarge", "preview"), quality = c("perfect", "faulty"), type = c("tibble", "data.frame", "duckdb") )
size |
A keyword that allows getting different variants of the table
based on the size of player base. The default |
quality |
The data quality level of the returned dataset. There are two
options: (1) |
type |
The table return type. By default, this is a |
A ptblank_informant
object.
# Get a preview of the `all_sessions` dataset # with the 'preview' size option all_sessions_dd(size = "preview")
# Get a preview of the `all_sessions` dataset # with the 'preview' size option all_sessions_dd(size = "preview")
The intendo_metadata()
function provides a data frame containing metadata
for datasets in the intendo package.
intendo_metadata(include = c("all", "perfect", "faulty"))
intendo_metadata(include = c("all", "perfect", "faulty"))
include |
Should both perfect and faulty datasets be included in the
metadata table? This is the default ( |
A data frame.
# Obtain metadata on all datasets in the package but # only those in their 'perfect' form intendo_metadata(include = "perfect")
# Obtain metadata on all datasets in the package but # only those in their 'perfect' form intendo_metadata(include = "perfect")
This summary table provides information on each user. We get information here such as the first login/session time and some information useful for segmentation.
user_summary( size = c("small", "medium", "large", "xlarge", "preview"), quality = c("perfect", "faulty"), type = c("tibble", "data.frame", "duckdb", "csv"), keep = FALSE )
user_summary( size = c("small", "medium", "large", "xlarge", "preview"), quality = c("perfect", "faulty"), type = c("tibble", "data.frame", "duckdb", "csv"), keep = FALSE )
size |
A keyword that allows getting different variants of the table
based on the size of player base. The default |
quality |
The data quality level of the returned dataset. There are two
options: (1) |
type |
The table return type. By default, this is a |
keep |
Should the downloaded data be stored on disk in the working
directory? By default, this is |
A data table object, which could be a tibble (tbl_df
) a data
frame, or an in-memory DuckDB table (tbl_dbi
). If a CSV is written then
TRUE
will be invisibly returned.
# Get a preview of the `user_summary` dataset # with the 'preview' size option user_summary(size = "preview")
# Get a preview of the `user_summary` dataset # with the 'preview' size option user_summary(size = "preview")
user_summary
The user_summary_dd()
function generates a data dictionary based on the
user_summary
table.
user_summary_dd( size = c("small", "medium", "large", "xlarge", "preview"), quality = c("perfect", "faulty"), type = c("tibble", "data.frame", "duckdb") )
user_summary_dd( size = c("small", "medium", "large", "xlarge", "preview"), quality = c("perfect", "faulty"), type = c("tibble", "data.frame", "duckdb") )
size |
A keyword that allows getting different variants of the table
based on the size of player base. The default |
quality |
The data quality level of the returned dataset. There are two
options: (1) |
type |
The table return type. By default, this is a |
A ptblank_informant
object.
# Get a preview of the `user_summary` dataset # with the 'preview' size option user_summary_dd(size = "preview")
# Get a preview of the `user_summary` dataset # with the 'preview' size option user_summary_dd(size = "preview")
This summary table provides daily totals for every player that had at least one login/session in a day. We get measures such as daily sessions, time played, number of IAPs bought and ads viewed, revenue gained, progression info, and some segmentation categories.
users_daily( size = c("small", "medium", "large", "xlarge", "preview"), quality = c("perfect", "faulty"), type = c("tibble", "data.frame", "duckdb", "csv"), keep = FALSE )
users_daily( size = c("small", "medium", "large", "xlarge", "preview"), quality = c("perfect", "faulty"), type = c("tibble", "data.frame", "duckdb", "csv"), keep = FALSE )
size |
A keyword that allows getting different variants of the table
based on the size of player base. The default |
quality |
The data quality level of the returned dataset. There are two
options: (1) |
type |
The table return type. By default, this is a |
keep |
Should the downloaded data be stored on disk in the working
directory? By default, this is |
A data table object, which could be a tibble (tbl_df
) a data
frame, or an in-memory DuckDB table (tbl_dbi
). If a CSV is written then
TRUE
will be invisibly returned.
# Get a preview of the `users_daily` dataset # with the 'preview' size option users_daily(size = "preview")
# Get a preview of the `users_daily` dataset # with the 'preview' size option users_daily(size = "preview")
users_daily
The users_daily_dd()
function generates a data dictionary based on the
users_daily
table.
users_daily_dd( size = c("small", "medium", "large", "xlarge", "preview"), quality = c("perfect", "faulty"), type = c("tibble", "data.frame", "duckdb") )
users_daily_dd( size = c("small", "medium", "large", "xlarge", "preview"), quality = c("perfect", "faulty"), type = c("tibble", "data.frame", "duckdb") )
size |
A keyword that allows getting different variants of the table
based on the size of player base. The default |
quality |
The data quality level of the returned dataset. There are two
options: (1) |
type |
The table return type. By default, this is a |
A ptblank_informant
object.
# Get a preview of the `users_daily` dataset # with the 'preview' size option users_daily_dd(size = "preview")
# Get a preview of the `users_daily` dataset # with the 'preview' size option users_daily_dd(size = "preview")