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.
This package is still experimental and under development.
To install it for testing purpose, use:
mod1 <- lm(Sepal.Length ~ Sepal.Width + Species, data = iris)
library(broom.helpers)
mod1 %>% tidy_plus_plus()
#> # A tibble: 4 x 14
#> term variable var_label var_class var_type contrasts reference_row label
#> <chr> <chr> <chr> <chr> <chr> <chr> <lgl> <chr>
#> 1 Sepa~ Sepal.W~ Sepal.Wi~ numeric continu~ <NA> NA Sepa~
#> 2 Spec~ Species Species factor categor~ contr.tr~ TRUE seto~
#> 3 Spec~ Species Species factor categor~ contr.tr~ FALSE vers~
#> 4 Spec~ Species Species factor categor~ contr.tr~ FALSE virg~
#> # ... with 6 more variables: estimate <dbl>, std.error <dbl>, statistic <dbl>,
#> # p.value <dbl>, conf.low <dbl>, conf.high <dbl>
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
)
)
mod2 %>%
tidy_plus_plus(
exponentiate = TRUE,
variable_labels = c(age = "Age (in years)"),
add_header_rows = TRUE,
show_single_row = "trt"
)
#> # A tibble: 17 x 15
#> term variable var_label var_class var_type header_row contrasts
#> <chr> <chr> <chr> <chr> <chr> <lgl> <chr>
#> 1 <NA> age Age (in ~ nmatrix.3 continu~ TRUE <NA>
#> 2 poly~ age Age (in ~ nmatrix.3 continu~ FALSE <NA>
#> 3 poly~ age Age (in ~ nmatrix.3 continu~ FALSE <NA>
#> 4 poly~ age Age (in ~ nmatrix.3 continu~ FALSE <NA>
#> 5 <NA> stage T Stage factor categor~ TRUE contr.tr~
#> 6 stag~ stage T Stage factor categor~ FALSE contr.tr~
#> 7 stag~ stage T Stage factor categor~ FALSE contr.tr~
#> 8 stag~ stage T Stage factor categor~ FALSE contr.tr~
#> 9 stag~ stage T Stage factor categor~ FALSE contr.tr~
#> 10 <NA> grade Grade factor categor~ TRUE contr.sum
#> 11 grad~ grade Grade factor categor~ FALSE contr.sum
#> 12 grad~ grade Grade factor categor~ FALSE contr.sum
#> 13 grad~ grade Grade factor categor~ FALSE contr.sum
#> 14 trtD~ trt Chemothe~ character categor~ NA contr.tr~
#> 15 <NA> grade:t~ Grade * ~ <NA> interac~ TRUE <NA>
#> 16 grad~ grade:t~ Grade * ~ <NA> interac~ FALSE <NA>
#> 17 grad~ grade:t~ Grade * ~ <NA> interac~ FALSE <NA>
#> # ... with 8 more variables: reference_row <lgl>, label <chr>, estimate <dbl>,
#> # std.error <dbl>, statistic <dbl>, p.value <dbl>, conf.low <dbl>,
#> # conf.high <dbl>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()
#> # A tibble: 4 x 12
#> term variable var_label var_class var_type contrasts reference_row label
#> <chr> <chr> <chr> <chr> <chr> <chr> <lgl> <chr>
#> 1 Sepa~ Sepal.W~ Sepal.Wi~ numeric continu~ <NA> NA Sepa~
#> 2 Spec~ Species Species factor categor~ contr.tr~ TRUE seto~
#> 3 Spec~ Species Species factor categor~ contr.tr~ FALSE vers~
#> 4 Spec~ Species Species factor categor~ contr.tr~ FALSE virg~
#> # ... with 4 more variables: estimate <dbl>, std.error <dbl>, statistic <dbl>,
#> # p.value <dbl>
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()
#> # A tibble: 20 x 13
#> term variable var_label var_class var_type header_row contrasts
#> <chr> <chr> <chr> <chr> <chr> <lgl> <chr>
#> 1 (Int~ <NA> (Interce~ <NA> interce~ NA <NA>
#> 2 <NA> age Age in y~ nmatrix.3 continu~ TRUE <NA>
#> 3 poly~ age Age in y~ nmatrix.3 continu~ FALSE <NA>
#> 4 poly~ age Age in y~ nmatrix.3 continu~ FALSE <NA>
#> 5 poly~ age Age in y~ nmatrix.3 continu~ FALSE <NA>
#> 6 <NA> stage T Stage factor categor~ TRUE contr.tr~
#> 7 stag~ stage T Stage factor categor~ FALSE contr.tr~
#> 8 stag~ stage T Stage factor categor~ FALSE contr.tr~
#> 9 stag~ stage T Stage factor categor~ FALSE contr.tr~
#> 10 stag~ stage T Stage factor categor~ FALSE contr.tr~
#> 11 <NA> grade Grade factor categor~ TRUE contr.sum
#> 12 grad~ grade Grade factor categor~ FALSE contr.sum
#> 13 grad~ grade Grade factor categor~ FALSE contr.sum
#> 14 grad~ grade Grade factor categor~ FALSE contr.sum
#> 15 <NA> trt Chemothe~ character categor~ TRUE contr.tr~
#> 16 trtD~ trt Chemothe~ character categor~ FALSE contr.tr~
#> 17 trtD~ trt Chemothe~ character categor~ FALSE contr.tr~
#> 18 <NA> grade:t~ Grade * ~ <NA> interac~ TRUE <NA>
#> 19 grad~ grade:t~ Grade * ~ <NA> interac~ FALSE <NA>
#> 20 grad~ grade:t~ Grade * ~ <NA> interac~ FALSE <NA>
#> # ... with 6 more variables: reference_row <lgl>, label <chr>, estimate <dbl>,
#> # std.error <dbl>, statistic <dbl>, p.value <dbl>