The broom.helpers package provides suite of functions to work with regression model broom::tidy() tibbles.
The suite includes functions to group regression model terms by variable, insert reference and header rows for categorical variables, add variable labels, and more.
broom.helpers is used, in particular, by gtsummary::tbl_regression() for producing nice formatted tables of model coefficients and by ggstats::ggcoef_model() for plotting model coefficients.
To install stable version:
Documentation of stable version: https://larmarange.github.io/broom.helpers/
To install development version:
Documentation of development version: https://larmarange.github.io/broom.helpers/dev/
mod1 <- lm(Sepal.Length ~ Sepal.Width + Species, data = iris)
library(broom.helpers)
ex1 <- mod1 %>% tidy_plus_plus()
ex1
#> # A tibble: 4 × 17
#> term variable var_label var_class var_type var_nlevels contrasts
#> <chr> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 Sepal.Width Sepal.Wi… Sepal.Wi… numeric continu… NA <NA>
#> 2 Speciessetosa Species Species factor categor… 3 contr.tr…
#> 3 Speciesversicolor Species Species factor categor… 3 contr.tr…
#> 4 Speciesvirginica Species Species factor categor… 3 contr.tr…
#> # ℹ 10 more variables: contrasts_type <chr>, reference_row <lgl>, label <chr>,
#> # n_obs <dbl>, estimate <dbl>, std.error <dbl>, statistic <dbl>,
#> # p.value <dbl>, conf.low <dbl>, conf.high <dbl>
dplyr::glimpse(ex1)
#> Rows: 4
#> Columns: 17
#> $ term <chr> "Sepal.Width", "Speciessetosa", "Speciesversicolor", "S…
#> $ variable <chr> "Sepal.Width", "Species", "Species", "Species"
#> $ var_label <chr> "Sepal.Width", "Species", "Species", "Species"
#> $ var_class <chr> "numeric", "factor", "factor", "factor"
#> $ var_type <chr> "continuous", "categorical", "categorical", "categorica…
#> $ var_nlevels <int> NA, 3, 3, 3
#> $ contrasts <chr> NA, "contr.treatment", "contr.treatment", "contr.treatm…
#> $ contrasts_type <chr> NA, "treatment", "treatment", "treatment"
#> $ reference_row <lgl> NA, TRUE, FALSE, FALSE
#> $ label <chr> "Sepal.Width", "setosa", "versicolor", "virginica"
#> $ n_obs <dbl> 150, 50, 50, 50
#> $ estimate <dbl> 0.8035609, 0.0000000, 1.4587431, 1.9468166
#> $ std.error <dbl> 0.1063390, NA, 0.1121079, 0.1000150
#> $ statistic <dbl> 7.556598, NA, 13.011954, 19.465255
#> $ p.value <dbl> 4.187340e-12, NA, 3.478232e-26, 2.094475e-42
#> $ conf.low <dbl> 0.5933983, NA, 1.2371791, 1.7491525
#> $ conf.high <dbl> 1.013723, NA, 1.680307, 2.144481
mod2 <- glm(
response ~ poly(age, 3) + stage + grade * trt,
na.omit(gtsummary::trial),
family = binomial,
contrasts = list(
stage = contr.treatment(4, base = 3),
grade = contr.sum
)
)
ex2 <- mod2 %>%
tidy_plus_plus(
exponentiate = TRUE,
variable_labels = c(age = "Age (in years)"),
add_header_rows = TRUE,
show_single_row = "trt"
)
ex2
#> # A tibble: 17 × 19
#> term variable var_label var_class var_type var_nlevels header_row contrasts
#> <chr> <chr> <chr> <chr> <chr> <int> <lgl> <chr>
#> 1 <NA> age Age (in … nmatrix.3 continu… NA TRUE <NA>
#> 2 poly(… age Age (in … nmatrix.3 continu… NA FALSE <NA>
#> 3 poly(… age Age (in … nmatrix.3 continu… NA FALSE <NA>
#> 4 poly(… age Age (in … nmatrix.3 continu… NA FALSE <NA>
#> 5 <NA> stage T Stage factor categor… 4 TRUE contr.tr…
#> 6 stage1 stage T Stage factor categor… 4 FALSE contr.tr…
#> 7 stage2 stage T Stage factor categor… 4 FALSE contr.tr…
#> 8 stage3 stage T Stage factor categor… 4 FALSE contr.tr…
#> 9 stage4 stage T Stage factor categor… 4 FALSE contr.tr…
#> 10 <NA> grade Grade factor categor… 3 TRUE contr.sum
#> 11 grade1 grade Grade factor categor… 3 FALSE contr.sum
#> 12 grade2 grade Grade factor categor… 3 FALSE contr.sum
#> 13 grade3 grade Grade factor categor… 3 FALSE contr.sum
#> 14 trtDr… trt Chemothe… character dichoto… 2 NA contr.tr…
#> 15 <NA> grade:t… Grade * … <NA> interac… NA TRUE <NA>
#> 16 grade… grade:t… Grade * … <NA> interac… NA FALSE <NA>
#> 17 grade… grade:t… Grade * … <NA> interac… NA FALSE <NA>
#> # ℹ 11 more variables: contrasts_type <chr>, reference_row <lgl>, label <chr>,
#> # n_obs <dbl>, n_event <dbl>, estimate <dbl>, std.error <dbl>,
#> # statistic <dbl>, p.value <dbl>, conf.low <dbl>, conf.high <dbl>
dplyr::glimpse(ex2)
#> Rows: 17
#> Columns: 19
#> $ term <chr> NA, "poly(age, 3)1", "poly(age, 3)2", "poly(age, 3)3", …
#> $ variable <chr> "age", "age", "age", "age", "stage", "stage", "stage", …
#> $ var_label <chr> "Age (in years)", "Age (in years)", "Age (in years)", "…
#> $ var_class <chr> "nmatrix.3", "nmatrix.3", "nmatrix.3", "nmatrix.3", "fa…
#> $ var_type <chr> "continuous", "continuous", "continuous", "continuous",…
#> $ var_nlevels <int> NA, NA, NA, NA, 4, 4, 4, 4, 4, 3, 3, 3, 3, 2, NA, NA, NA
#> $ header_row <lgl> TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, F…
#> $ contrasts <chr> NA, NA, NA, NA, "contr.treatment(base=3)", "contr.treat…
#> $ contrasts_type <chr> NA, NA, NA, NA, "treatment", "treatment", "treatment", …
#> $ reference_row <lgl> NA, NA, NA, NA, NA, FALSE, FALSE, TRUE, FALSE, NA, FALS…
#> $ label <chr> "Age (in years)", "Age (in years)", "Age (in years)²", …
#> $ n_obs <dbl> NA, 92, 56, 80, NA, 46, 50, 35, 42, NA, 63, 53, 57, 90,…
#> $ n_event <dbl> NA, 31, 17, 22, NA, 17, 12, 13, 12, NA, 20, 16, 18, 30,…
#> $ estimate <dbl> NA, 20.2416394, 1.2337899, 0.4931553, NA, 1.0047885, 0.…
#> $ std.error <dbl> NA, 2.3254455, 2.3512842, 2.3936657, NA, 0.4959893, 0.5…
#> $ statistic <dbl> NA, 1.29340459, 0.08935144, -0.29533409, NA, 0.00963137…
#> $ p.value <dbl> NA, 0.1958712, 0.9288026, 0.7677387, NA, 0.9923154, 0.1…
#> $ conf.low <dbl> NA, 0.225454425, 0.007493208, 0.004745694, NA, 0.379776…
#> $ conf.high <dbl> NA, 2315.587655, 100.318341, 74.226179, NA, 2.683385, 1…ex3 <- mod1 %>%
# perform initial tidying of model
tidy_and_attach() %>%
# add reference row
tidy_add_reference_rows() %>%
# add term labels
tidy_add_term_labels() %>%
# remove intercept
tidy_remove_intercept()
ex3
#> # A tibble: 4 × 16
#> term variable var_label var_class var_type var_nlevels contrasts
#> <chr> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 Sepal.Width Sepal.Wi… Sepal.Wi… numeric continu… NA <NA>
#> 2 Speciessetosa Species Species factor categor… 3 contr.tr…
#> 3 Speciesversicolor Species Species factor categor… 3 contr.tr…
#> 4 Speciesvirginica Species Species factor categor… 3 contr.tr…
#> # ℹ 9 more variables: contrasts_type <chr>, reference_row <lgl>, label <chr>,
#> # estimate <dbl>, std.error <dbl>, statistic <dbl>, p.value <dbl>,
#> # conf.low <dbl>, conf.high <dbl>
dplyr::glimpse(ex3)
#> Rows: 4
#> Columns: 16
#> $ term <chr> "Sepal.Width", "Speciessetosa", "Speciesversicolor", "S…
#> $ variable <chr> "Sepal.Width", "Species", "Species", "Species"
#> $ var_label <chr> "Sepal.Width", "Species", "Species", "Species"
#> $ var_class <chr> "numeric", "factor", "factor", "factor"
#> $ var_type <chr> "continuous", "categorical", "categorical", "categorica…
#> $ var_nlevels <int> NA, 3, 3, 3
#> $ contrasts <chr> NA, "contr.treatment", "contr.treatment", "contr.treatm…
#> $ contrasts_type <chr> NA, "treatment", "treatment", "treatment"
#> $ reference_row <lgl> NA, TRUE, FALSE, FALSE
#> $ label <chr> "Sepal.Width", "setosa", "versicolor", "virginica"
#> $ estimate <dbl> 0.8035609, NA, 1.4587431, 1.9468166
#> $ std.error <dbl> 0.1063390, NA, 0.1121079, 0.1000150
#> $ statistic <dbl> 7.556598, NA, 13.011954, 19.465255
#> $ p.value <dbl> 4.187340e-12, NA, 3.478232e-26, 2.094475e-42
#> $ conf.low <dbl> 0.5933983, NA, 1.2371791, 1.7491525
#> $ conf.high <dbl> 1.013723, NA, 1.680307, 2.144481
ex4 <- mod2 %>%
# perform initial tidying of model
tidy_and_attach(exponentiate = TRUE) %>%
# add variable labels, including a custom value for age
tidy_add_variable_labels(labels = c(age = "Age in years")) %>%
# add reference rows for categorical variables
tidy_add_reference_rows() %>%
# add a, estimate value of reference terms
tidy_add_estimate_to_reference_rows(exponentiate = TRUE) %>%
# add header rows for categorical variables
tidy_add_header_rows()
ex4
#> # A tibble: 20 × 17
#> term variable var_label var_class var_type var_nlevels header_row contrasts
#> <chr> <chr> <chr> <chr> <chr> <int> <lgl> <chr>
#> 1 (Inte… (Interc… (Interce… <NA> interce… NA NA <NA>
#> 2 <NA> age Age in y… nmatrix.3 continu… NA TRUE <NA>
#> 3 poly(… age Age in y… nmatrix.3 continu… NA FALSE <NA>
#> 4 poly(… age Age in y… nmatrix.3 continu… NA FALSE <NA>
#> 5 poly(… age Age in y… nmatrix.3 continu… NA FALSE <NA>
#> 6 <NA> stage T Stage factor categor… 4 TRUE contr.tr…
#> 7 stage1 stage T Stage factor categor… 4 FALSE contr.tr…
#> 8 stage2 stage T Stage factor categor… 4 FALSE contr.tr…
#> 9 stage3 stage T Stage factor categor… 4 FALSE contr.tr…
#> 10 stage4 stage T Stage factor categor… 4 FALSE contr.tr…
#> 11 <NA> grade Grade factor categor… 3 TRUE contr.sum
#> 12 grade1 grade Grade factor categor… 3 FALSE contr.sum
#> 13 grade2 grade Grade factor categor… 3 FALSE contr.sum
#> 14 grade3 grade Grade factor categor… 3 FALSE contr.sum
#> 15 <NA> trt Chemothe… character dichoto… 2 TRUE contr.tr…
#> 16 trtDr… trt Chemothe… character dichoto… 2 FALSE contr.tr…
#> 17 trtDr… trt Chemothe… character dichoto… 2 FALSE contr.tr…
#> 18 <NA> grade:t… Grade * … <NA> interac… NA TRUE <NA>
#> 19 grade… grade:t… Grade * … <NA> interac… NA FALSE <NA>
#> 20 grade… grade:t… Grade * … <NA> interac… NA FALSE <NA>
#> # ℹ 9 more variables: contrasts_type <chr>, reference_row <lgl>, label <chr>,
#> # estimate <dbl>, std.error <dbl>, statistic <dbl>, p.value <dbl>,
#> # conf.low <dbl>, conf.high <dbl>
dplyr::glimpse(ex4)
#> Rows: 20
#> Columns: 17
#> $ term <chr> "(Intercept)", NA, "poly(age, 3)1", "poly(age, 3)2", "p…
#> $ variable <chr> "(Intercept)", "age", "age", "age", "age", "stage", "st…
#> $ var_label <chr> "(Intercept)", "Age in years", "Age in years", "Age in …
#> $ var_class <chr> NA, "nmatrix.3", "nmatrix.3", "nmatrix.3", "nmatrix.3",…
#> $ var_type <chr> "intercept", "continuous", "continuous", "continuous", …
#> $ var_nlevels <int> NA, NA, NA, NA, NA, 4, 4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2,…
#> $ header_row <lgl> NA, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALS…
#> $ contrasts <chr> NA, NA, NA, NA, NA, "contr.treatment(base=3)", "contr.t…
#> $ contrasts_type <chr> NA, NA, NA, NA, NA, "treatment", "treatment", "treatmen…
#> $ reference_row <lgl> NA, NA, NA, NA, NA, NA, FALSE, FALSE, TRUE, FALSE, NA, …
#> $ label <chr> "(Intercept)", "Age in years", "Age in years", "Age in …
#> $ estimate <dbl> 0.5266376, NA, 20.2416394, 1.2337899, 0.4931553, NA, 1.…
#> $ std.error <dbl> 0.4130930, NA, 2.3254455, 2.3512842, 2.3936657, NA, 0.4…
#> $ statistic <dbl> -1.55229592, NA, 1.29340459, 0.08935144, -0.29533409, N…
#> $ p.value <dbl> 0.1205914, NA, 0.1958712, 0.9288026, 0.7677387, NA, 0.9…
#> $ conf.low <dbl> 0.227717775, NA, 0.225454425, 0.007493208, 0.004745694,…
#> $ conf.high <dbl> 1.164600, NA, 2315.587655, 100.318341, 74.226179, NA, 2…