Registers a data frame as the session's default, so the analysis helpers can
be called without repeating data every time. Set it once at the top of a
script or Quarto document and then omit data (or pass it by pipe).
Details
With a default set you can drop data entirely and name the column
positionally, the way you would in the tidyverse:
calc_percentage(demo_gender)– data taken from the default;podracing_survey %>% calc_percentage(demo_gender)– explicit pipe;calc_percentage(other_survey, demo_gender)– an explicit data frame always wins over the default.
The helpers tell a column from a data frame by looking at the first argument:
a data frame is used as the data; anything else (a bare column name) is taken
as the first column and the data comes from the default. Helpers that select
several columns through ... (e.g. diagnose(), calc_percentage_batch())
work the same way – diagnose(starts_with("ratings_")) needs no data.
The default applies to the analysis helpers that read a raw survey
(calc_percentage(), calc_summary(), calc_nps(), diagnose(),
crosstab(), ipm_model(), sample_comments(), ...). It does not apply
to plot/report helpers that consume an already-summarised table. The setting
lives only in the current R session; clear it with clear_dataset().
See also
get_dataset(), clear_dataset(), has_dataset().
Other config:
apply_order(),
edit_ezrsurvey_profile(),
ezrsurvey_options(),
ezrsurvey_profile_paths(),
get_dataset(),
get_order(),
list_orders(),
load_ezrsurvey_profile(),
order_for(),
register_order(),
register_order_presets(),
remove_order(),
reset_ezrsurvey_options(),
save_ezrsurvey_profile(),
use_ezrsurvey_profile()
Examples
use_dataset(podracing_survey)
calc_percentage(demo_gender) # data taken from the default
#> # A tibble: 3 × 3
#> demo_gender n pct
#> <chr> <int> <dbl>
#> 1 Female 399 42
#> 2 Male 525 55
#> 3 Non-binary 27 3
calc_nps(nps_value)
#> # A tibble: 1 × 8
#> n nps pct_detractors pct_passives pct_promoters detractors passives
#> <int> <dbl> <dbl> <dbl> <dbl> <int> <int>
#> 1 1000 7 25 43 32 252 426
#> # ℹ 1 more variable: promoters <int>
clear_dataset()