Add variable labels

Will add variable labels in a var_label column, based on:

1. labels provided in labels argument if provided;

2. variable labels defined in the original data frame with the label attribute (cf. labelled::var_label());

3. variable name otherwise.

tidy_add_variable_labels(
  x,
  labels = NULL,
  interaction_sep = " * ",
  instrumental_suffix = " (instrumental)",
  model = tidy_get_model(x)
)

Arguments

x

(data.frame)
A tidy tibble as produced by tidy_*() functions.

labels

(formula-list-selector)
An optional named list or a named vector of custom variable labels.

interaction_sep

(string)
Separator for interaction terms.

instrumental_suffix

(string)
Suffix added to variable labels for instrumental variables (fixest models). NULL to add nothing.

model

(a model object, e.g. glm)
The corresponding model, if not attached to x.

Details

If the variable column is not yet available in x, tidy_identify_variables() will be automatically applied.

It is possible to pass a custom label for an interaction term in labels (see examples).

See also

Other tidy_helpers: tidy_add_coefficients_type(), tidy_add_contrasts(), tidy_add_estimate_to_reference_rows(), tidy_add_header_rows(), tidy_add_n(), tidy_add_pairwise_contrasts(), tidy_add_reference_rows(), tidy_add_term_labels(), tidy_attach_model(), tidy_disambiguate_terms(), tidy_group_by(), tidy_identify_variables(), tidy_plus_plus(), tidy_remove_intercept(), tidy_select_variables()

Examples

df <- Titanic |>
  dplyr::as_tibble() |>
  dplyr::mutate(Survived = factor(Survived, c("No", "Yes"))) |>
  labelled::set_variable_labels(
    Class = "Passenger's class",
    Sex = "Sex"
  )

glm(Survived ~ Class * Age * Sex, data = df, weights = df$n, family = binomial) |>
  tidy_and_attach() |>
  tidy_add_variable_labels(
    labels = list(
      "(Intercept)" ~ "Custom intercept",
      Sex ~ "Gender",
      "Class:Age" ~ "Custom label"
    )
  )
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> # A tibble: 16 × 12
#>    term     variable var_label var_class var_type var_nlevels estimate std.error
#>    <chr>    <chr>    <chr>     <chr>     <chr>          <int>    <dbl>     <dbl>
#>  1 (Interc… (Interc… Custom i… NA        interce…          NA   3.56       0.507
#>  2 Class2nd Class    Passenge… character categor…           4  -1.74       0.589
#>  3 Class3rd Class    Passenge… character categor…           4  -3.71       0.531
#>  4 ClassCr… Class    Passenge… character categor…           4  -1.66       0.800
#>  5 AgeChild Age      Age       character dichoto…           2  11.0      883.   
#>  6 SexMale  Sex      Gender    character dichoto…           2  -4.28       0.532
#>  7 Class2n… Class:A… Custom l… NA        interac…          NA   3.53    1066.   
#>  8 Class3r… Class:A… Custom l… NA        interac…          NA -11.0      883.   
#>  9 ClassCr… Class:A… Custom l… NA        interac…          NA  NA         NA    
#> 10 Class2n… Class:S… Passenge… NA        interac…          NA   0.0680     0.671
#> 11 Class3r… Class:S… Passenge… NA        interac…          NA   2.80       0.569
#> 12 ClassCr… Class:S… Passenge… NA        interac…          NA   1.14       0.820
#> 13 AgeChil… Age:Sex  Age * Ge… NA        interac…          NA   5.25    1091.   
#> 14 Class2n… Class:A… Passenge… NA        interac…          NA  -1.19    1383.   
#> 15 Class3r… Class:A… Passenge… NA        interac…          NA  -4.57    1091.   
#> 16 ClassCr… Class:A… Passenge… NA        interac…          NA  NA         NA    
#> # ℹ 4 more variables: statistic <dbl>, p.value <dbl>, conf.low <dbl>,
#> #   conf.high <dbl>