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broom.helpers

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 GGally::ggcoef_model() for plotting model coefficients.

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 × 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…
#> # … with 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>     
#> # … with 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…

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 × 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…
#> # … with 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>     
#> # … with 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…

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Version

Install

install.packages('broom.helpers')

Monthly Downloads

20,803

Version

1.7.0

License

GPL-3

Maintainer

Joseph Larmarange

Last Published

April 22nd, 2022

Functions in broom.helpers (1.7.0)

model_get_model

Get the model from model objects
model_get_model_frame

Get the model frame of a model
model_get_model_matrix

Get the model matrix of a model
model_get_n

Get the number of observations
.select_to_varnames

Variable selector
model_get_xlevels

Get xlevels used in the model
tidy_add_coefficients_type

Add coefficients type and label as attributes
model_get_weights

Get sampling weights used by a model
supported_models

Listing of Supported Models
model_get_terms

Get the terms of a model
model_get_response

Get model response
model_list_terms_levels

List levels of categorical terms
select_helpers

Select helper functions
reexports

Objects exported from other packages
model_list_variables

List all the variables used in a model
model_get_offset

Get model offset
model_get_nlevels

Get the number of levels for each factor used in xlevels
tidy_add_contrasts

Add contrasts type for categorical variables
model_identify_variables

Identify for each coefficient of a model the corresponding variable
tidy_add_term_labels

Add term labels
tidy_add_reference_rows

Add references rows for categorical variables
tidy_add_estimate_to_reference_rows

Add an estimate value to references rows for categorical variables
tidy_add_header_rows

Add header rows variables with several terms
tidy_add_n

Add the (weighted) number of observations
model_list_contrasts

List contrasts used by a model
tidy_plus_plus

Tidy a model and compute additional informations
tidy_parameters

Tidy a model with parameters package
tidy_add_variable_labels

Add variable labels
tidy_select_variables

Select variables to keep/drop
tidy_identify_variables

Identify the variable corresponding to each model coefficient
tidy_remove_intercept

Remove intercept(s)
tidy_disambiguate_terms

Disambiguate terms
tidy_with_broom_or_parameters

Tidy a model with broom or parameters
tidy_attach_model

Attach a full model to the tibble of model terms
model_compute_terms_contributions

Compute a matrix of terms contributions
.generic_selector

Generate a custom selector function
model_get_coefficients_type

Get coefficient type
assert_package

Check a package installation status or minimum required version
model_get_contrasts

Get contrasts used in the model
.escape_regex

Escapes any characters that would have special meaning in a regular expression
.clean_backticks

Remove backticks around variable names
model_get_assign

Get the assign attribute of model matrix of a model
.formula_list_to_named_list

Convert formula selector to a named list