Eta categorical covariate plots (typical)

Usage

eta_vs_catcov(
  xpdb,
  mapping = NULL,
  etavar = NULL,
  covvar = NULL,
  drop_fixed = TRUE,
  orientation = "x",
  show_n = check_xpdb_x(xpdb, .warn = FALSE),
  type = "bol",
  list = TRUE,
  title = "Eta versus categorical covariates | @run",
  subtitle = "Based on @nind individuals, Eta shrink: @etashk",
  caption = "@dir",
  tag = NULL,
  facets,
  .problem,
  quiet,
  ...
)

Arguments

xpdb: <xp_xtras> or <xpose_data`> object
mapping: ggplot2 style mapping
etavar: tidyselect for eta variables
covvar: tidyselect for categorical covariate variables; NULL (default) selects every categorical covariate in the xpdb data index.
drop_fixed: As in xpose
orientation: Passed to xplot_boxplot
show_n: Add "N=" to plot
type: Passed to xplot_boxplot
list: <logical> Only relevant when etavar resolves to more than one eta. If TRUE (default, for backwards compatibility), returns a plain list of one plot per eta. If FALSE, all etas are instead combined onto one shared plot – faceted by eta, in addition to the existing per-covariate facet – automatically paginating (at most 9 panels per page, i.e. ncol/nrow of 3) via xpose's own facet_wrap_paginate mechanism. Printing the returned plot renders every page; pass page to print() to select a specific one.
title: Plot title
subtitle: Plot subtitle
caption: Plot caption
tag: Plot tag
facets: Additional facets
.problem: Problem number
quiet: Silence output
...: Any additional aesthetics.

Value

The desired plot, or (when etavar resolves to more than one eta and list = TRUE) a plain list of one plot per eta.

Details

The ability to show number per covariate level is inspired by the package pmplots, but is implements here within the xpose ecosystem for consistency.

Examples

# \donttest{

eta_vs_catcov(xpdb_x)
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, DOSE, AMT, SS, II ... and 23 more variables
#> Warning: attributes are not identical across measure variables; they will be dropped
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, DOSE, AMT, SS, II ... and 23 more variables
#> Warning: attributes are not identical across measure variables; they will be dropped
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, DOSE, AMT, SS, II ... and 23 more variables
#> Warning: attributes are not identical across measure variables; they will be dropped
#> [[1]]

#> 
#> [[2]]

#> 
#> [[3]]

#> 

# Labels and units are also supported
xpdb_x %>%
  xpose::set_var_labels(AGE="Age", MED1 = "Digoxin") %>%
  xpose::set_var_units(AGE="yrs") %>%
  set_var_levels(SEX=lvl_sex(), MED1 = lvl_bin()) %>%
  eta_vs_catcov()
#> Warning: There was 1 warning in `dplyr::mutate()`.
#> ℹ In argument: `out = purrr::map_if(...)`.
#> Caused by warning:
#> ! In $prob no.2 columns: MED1 not present in the data.
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, DOSE, AMT, SS, II ... and 23 more variables
#> Warning: attributes are not identical across measure variables; they will be dropped
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, DOSE, AMT, SS, II ... and 23 more variables
#> Warning: attributes are not identical across measure variables; they will be dropped
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, DOSE, AMT, SS, II ... and 23 more variables
#> Warning: attributes are not identical across measure variables; they will be dropped
#> [[1]]

#> 
#> [[2]]

#> 
#> [[3]]

#> 

# Combine all etas onto one shared, faceted plot instead of a list
eta_vs_catcov(xpdb_x, list = FALSE)
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, DOSE, AMT, SS, II ... and 23 more variables
#> Warning: attributes are not identical across measure variables; they will be dropped


# Restrict to specific covariates with covvar, just like etavar
eta_vs_catcov(xpdb_x, covvar = SEX)
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, MED1, MED2, DOSE, AMT ... and 25 more variables
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, MED1, MED2, DOSE, AMT ... and 25 more variables
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, MED1, MED2, DOSE, AMT ... and 25 more variables
#> [[1]]

#> 
#> [[2]]

#> 
#> [[3]]

#> 
# }