The headline diagnostics wrapper: for each selected column it auto-detects whether it is a point estimate (numeric, e.g. a 1-5 rating) or a proportion (categorical), and returns a tidy table of sample size, estimate, standard error, relative standard error and margin of error – i.e. the whole appendix "Data info" block as one call.
Usage
diagnose(
data = NULL,
...,
type = c("auto", "mean", "prop"),
by = NULL,
z = 1.96,
digits = 2
)Arguments
- data
A data frame.
- ...
Columns to diagnose, using tidyselect (e.g.
starts_with("ratings_"),demo_gender).- type
Force the estimate type:
"auto"(default, detect per column),"mean"or"prop".- by
Optional grouping column(s); diagnostics are computed within group.
- z
Margin-of-error multiplier passed to
margin_of_error(). Defaults to1.96(95%).- digits
Decimal places for the numeric output columns. Defaults to
2.
Value
A tibble with one row per column (per group), with
columns variable, type, unit, n, estimate, se, rse, moe and
precision. For type == "mean", unit is "points"; for proportions it
is "ppt" (percentage points) and estimate is a percentage.
Details
For categorical columns the reported estimate is the largest answer
category's share, which gives a concrete +/- percentage-point margin to
quote in a report footnote.
Each column is classified (or forced via type) as a point estimate or a
proportion. For numeric columns the estimate is the mean and the error is in
rating points; for categorical columns the estimate is the largest answer
category's share, which gives a single concrete +/- percentage-point
margin to quote (the largest category is also the worst case among the
observed categories, so it is a sensible headline). The precision column is
the five-band rating from rse_rating(). This is the per-variable form of the
report-appendix "Data info" block; for the narrative bullet-point version see
precision_summary().
See also
precision_summary(), rse_rating(), se_mean(), se_prop().
Other diagnostics:
margin_of_error(),
precision_summary(),
rse(),
rse_rating(),
se_mean(),
se_prop()
Examples
diagnose(podracing_survey, demo_gender, nps_value)
#> # A tibble: 2 × 9
#> variable type unit n estimate se rse moe precision
#> <chr> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 demo_gender prop ppt 951 55.2 1.61 2.92 3.16 high precision
#> 2 nps_value mean points 1000 7.56 0.06 0.74 0.11 high precision
diagnose(podracing_survey, starts_with("ratings_"))
#> # A tibble: 6 × 9
#> variable type unit n estimate se rse moe precision
#> <chr> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 ratings_atmosphere prop ppt 1000 30.1 1.45 4.82 2.84 high precision
#> 2 ratings_commentary prop ppt 1000 33 1.49 4.51 2.91 high precision
#> 3 ratings_safety prop ppt 1000 34.9 1.51 4.32 2.95 high precision
#> 4 ratings_speed prop ppt 1000 29.6 1.44 4.88 2.83 high precision
#> 5 ratings_value prop ppt 1000 28 1.42 5.07 2.78 precise
#> 6 ratings_venue prop ppt 1000 29.6 1.44 4.88 2.83 high precision