The users_daily_dd() function generates a data dictionary based on the
users_daily table.
A keyword that allows getting different variants of the table
based on the size of player base. The default "small" table has the
lowest number of players/records. Increasing in size, we can also opt for
the "medium", "large", or "xlarge" versions.
The data quality level of the returned dataset. There are two
options: (1) "perfect" provides a pristine table with no errors at all
and (2) "faulty" gives you a table with a multitude of errors.
The table return type. By default, this is a "tibble" but a
"data.frame" can instead be returned if using that keyword. If you have
the duckdb package installed, you can instead obtain the table as an
in-memory DuckDB database table.
A ptblank_informant object.
# Get a preview of the `users_daily` dataset
# with the 'preview' size option
users_daily_dd(size = "preview")
#> # A tibble: 200 × 23
#> player_id login_date sessions_day playtime_day n_iap_day n_ads_day
#> <chr> <date> <int> <dbl> <int> <int>
#> 1 SBPFOHCVMNQI568 2015-01-01 1 34.9 1 4
#> 2 YMKOHGVFZWLJ836 2015-01-01 2 44.3 2 5
#> 3 DLCKJEZRSIGW561 2015-01-02 1 5.4 1 1
#> 4 SBPFOHCVMNQI568 2015-01-02 2 56.2 2 6
#> 5 IGOFAEVXNTUW479 2015-01-03 1 27.8 0 5
#> 6 LKCUQHXDRWAZ134 2015-01-03 1 22.3 1 3
#> 7 RNWFDZOSQPMB164 2015-01-03 1 10.1 1 2
#> 8 SBPFOHCVMNQI568 2015-01-03 2 32 1 2
#> 9 SOBKUJPREVCZ874 2015-01-03 1 8.5 1 1
#> 10 TDRXCWIOZEYM241 2015-01-03 2 37.3 1 5
#> # ℹ 190 more rows
#> # ℹ 17 more variables: rev_iap_day <dbl>, rev_ads_day <dbl>, rev_all_day <dbl>,
#> # start_day <date>, sessions_total <int>, playtime_total <dbl>,
#> # level_reached <dbl>, in_ftue <lgl>, at_eoc <lgl>, is_customer <lgl>,
#> # n_iap_total <int>, n_ads_total <int>, rev_iap_total <dbl>,
#> # rev_ads_total <dbl>, rev_all_total <dbl>, country <chr>, acquisition <chr>