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.
Installation
To install stable version:
install.packages("broom.helpers")
To install development version:
devtools::install_github("larmarange/broom.helpers")
Examples
all-in-one wrapper
mod1 <- lm(Sepal.Length ~ Sepal.Width + Species, data = iris) library(broom.helpers) ex1 <- mod1 %>% tidy_plus_plus() ex1 #> # A tibble: 4 x 15 #> term variable var_label var_class var_type var_nlevels contrasts #> <chr> <chr> <chr> <chr> <chr> <int> <chr> #> 1 Sepa~ Sepal.W~ Sepal.Wi~ numeric continu~ NA <NA> #> 2 Spec~ Species Species factor categor~ 3 contr.tr~ #> 3 Spec~ Species Species factor categor~ 3 contr.tr~ #> 4 Spec~ Species Species factor categor~ 3 contr.tr~ #> # ... 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> dplyr::glimpse(ex1) #> Rows: 4 #> Columns: 15 #> $ term <chr> "Sepal.Width", "Speciessetosa", "Speciesversicolor", ... #> $ 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", "categori... #> $ var_nlevels <int> NA, 3, 3, 3 #> $ contrasts <chr> NA, "contr.treatment", "contr.treatment", "contr.trea... #> $ reference_row <lgl> NA, TRUE, FALSE, FALSE #> $ label <chr> "Sepal.Width", "setosa", "versicolor", "virginica" #> $ 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 x 16 #> 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 stag~ stage T Stage factor categor~ 4 FALSE contr.tr~ #> 7 stag~ stage T Stage factor categor~ 4 FALSE contr.tr~ #> 8 stag~ stage T Stage factor categor~ 4 FALSE contr.tr~ #> 9 stag~ stage T Stage factor categor~ 4 FALSE contr.tr~ #> 10 <NA> grade Grade factor categor~ 3 TRUE contr.sum #> 11 grad~ grade Grade factor categor~ 3 FALSE contr.sum #> 12 grad~ grade Grade factor categor~ 3 FALSE contr.sum #> 13 grad~ grade Grade factor categor~ 3 FALSE contr.sum #> 14 trtD~ trt Chemothe~ character dichoto~ 2 NA contr.tr~ #> 15 <NA> grade:t~ Grade * ~ <NA> interac~ NA TRUE <NA> #> 16 grad~ grade:t~ Grade * ~ <NA> interac~ NA FALSE <NA> #> 17 grad~ grade:t~ Grade * ~ <NA> interac~ NA 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> dplyr::glimpse(ex2) #> Rows: 17 #> Columns: 16 #> $ 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", "... #> $ 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,... #> $ contrasts <chr> NA, NA, NA, NA, "contr.treatment(base=3)", "contr.tre... #> $ reference_row <lgl> NA, NA, NA, NA, NA, FALSE, FALSE, TRUE, FALSE, NA, FA... #> $ label <chr> "Age (in years)", "Age (in years)", "Age (in years)²"... #> $ estimate <dbl> NA, 20.2416394, 1.2337899, 0.4931553, NA, 1.0047885, ... #> $ std.error <dbl> NA, 2.3254455, 2.3512842, 2.3936657, NA, 0.4959893, 0... #> $ statistic <dbl> NA, 1.29340459, 0.08935144, -0.29533409, NA, 0.009631... #> $ p.value <dbl> NA, 0.1958712, 0.9288026, 0.7677387, NA, 0.9923154, 0... #> $ conf.low <dbl> NA, 0.225454425, 0.007493208, 0.004745694, NA, 0.3797... #> $ conf.high <dbl> NA, 2315.587655, 100.318341, 74.226179, NA, 2.683385,...
fine control
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 x 13 #> term variable var_label var_class var_type var_nlevels contrasts #> <chr> <chr> <chr> <chr> <chr> <int> <chr> #> 1 Sepa~ Sepal.W~ Sepal.Wi~ numeric continu~ NA <NA> #> 2 Spec~ Species Species factor categor~ 3 contr.tr~ #> 3 Spec~ Species Species factor categor~ 3 contr.tr~ #> 4 Spec~ Species Species factor categor~ 3 contr.tr~ #> # ... with 6 more variables: reference_row <lgl>, label <chr>, estimate <dbl>, #> # std.error <dbl>, statistic <dbl>, p.value <dbl> dplyr::glimpse(ex3) #> Rows: 4 #> Columns: 13 #> $ term <chr> "Sepal.Width", "Speciessetosa", "Speciesversicolor", ... #> $ 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", "categori... #> $ var_nlevels <int> NA, 3, 3, 3 #> $ contrasts <chr> NA, "contr.treatment", "contr.treatment", "contr.trea... #> $ 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 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 x 14 #> term variable var_label var_class var_type var_nlevels header_row contrasts #> <chr> <chr> <chr> <chr> <chr> <int> <lgl> <chr> #> 1 (Int~ <NA> (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 stag~ stage T Stage factor categor~ 4 FALSE contr.tr~ #> 8 stag~ stage T Stage factor categor~ 4 FALSE contr.tr~ #> 9 stag~ stage T Stage factor categor~ 4 FALSE contr.tr~ #> 10 stag~ stage T Stage factor categor~ 4 FALSE contr.tr~ #> 11 <NA> grade Grade factor categor~ 3 TRUE contr.sum #> 12 grad~ grade Grade factor categor~ 3 FALSE contr.sum #> 13 grad~ grade Grade factor categor~ 3 FALSE contr.sum #> 14 grad~ grade Grade factor categor~ 3 FALSE contr.sum #> 15 <NA> trt Chemothe~ character dichoto~ 2 TRUE contr.tr~ #> 16 trtD~ trt Chemothe~ character dichoto~ 2 FALSE contr.tr~ #> 17 trtD~ trt Chemothe~ character dichoto~ 2 FALSE contr.tr~ #> 18 <NA> grade:t~ Grade * ~ <NA> interac~ NA TRUE <NA> #> 19 grad~ grade:t~ Grade * ~ <NA> interac~ NA FALSE <NA> #> 20 grad~ grade:t~ Grade * ~ <NA> interac~ NA FALSE <NA> #> # ... with 6 more variables: reference_row <lgl>, label <chr>, estimate <dbl>, #> # std.error <dbl>, statistic <dbl>, p.value <dbl> dplyr::glimpse(ex4) #> Rows: 20 #> Columns: 14 #> $ term <chr> "(Intercept)", NA, "poly(age, 3)1", "poly(age, 3)2", ... #> $ variable <chr> NA, "age", "age", "age", "age", "stage", "stage", "st... #> $ var_label <chr> "(Intercept)", "Age in years", "Age in years", "Age i... #> $ 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, ... #> $ header_row <lgl> NA, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FA... #> $ contrasts <chr> NA, NA, NA, NA, NA, "contr.treatment(base=3)", "contr... #> $ reference_row <lgl> NA, NA, NA, NA, NA, NA, FALSE, FALSE, TRUE, FALSE, NA... #> $ label <chr> "(Intercept)", "Age in years", "Age in years", "Age i... #> $ estimate <dbl> 0.5266376, NA, 20.2416394, 1.2337899, 0.4931553, NA, ... #> $ std.error <dbl> 0.4130930, NA, 2.3254455, 2.3512842, 2.3936657, NA, 0... #> $ statistic <dbl> -1.55229592, NA, 1.29340459, 0.08935144, -0.29533409,... #> $ p.value <dbl> 0.1205914, NA, 0.1958712, 0.9288026, 0.7677387, NA, 0...
