plot_proportions: Plot proportions by sub-groups

Description

Plot one or several proportions (defined by logical conditions) by sub-groups. See proportion() for more details on the way proportions and confidence intervals are computed. By default, return a bar plot, but other geometries could be used (see examples). stratified_by() is an helper function facilitating a stratified analyses (i.e. proportions by groups stratified according to a third variable, see examples). dummy_proportions() is an helper to easily convert a categorical variable into dummy variables and therefore showing the proportion of each level of the original variable (see examples).

Usage

plot_proportions(
  data,
  condition,
  by = NULL,
  drop_na_by = FALSE,
  convert_continuous = TRUE,
  geom = "bar",
  ...,
  show_overall = TRUE,
  overall_label = "Overall",
  show_ci = TRUE,
  conf_level = 0.95,
  ci_color = "black",
  show_pvalues = TRUE,
  pvalues_test = c("fisher", "chisq"),
  pvalues_labeller = scales::label_pvalue(add_p = TRUE),
  pvalues_size = 3.5,
  show_labels = TRUE,
  labels_labeller = scales::label_percent(1),
  labels_size = 3.5,
  labels_color = "black",
  show_overall_line = FALSE,
  overall_line_type = "dashed",
  overall_line_color = "black",
  overall_line_width = 0.5,
  facet_labeller = ggplot2::label_wrap_gen(width = 50, multi_line = TRUE),
  flip = FALSE,
  free_scale = FALSE,
  return_data = FALSE
)
stratified_by(condition, strata)
dummy_proportions(variable)

Arguments

data: A data frame, data frame extension (e.g. a tibble), or a survey design object.
condition: <data-masking> A condition defining a proportion, or a dplyr::tibble() defining several proportions (see examples).
by: <tidy-select> List of variables to group by (comparison is done separately for each variable).
drop_na_by: Remove NA values in by variables?
convert_continuous: Should continuous variables (with 5 unique values or more) be converted to quartiles (using cut_quartiles())?
geom: Geometry to use for plotting proportions ("bar" by default).
...: Additional arguments passed to the geom defined by geom.
show_overall: Display "Overall" column?
overall_label: Label for the overall column.
show_ci: Display confidence intervals?
conf_level: Confidence level for the confidence intervals.
ci_color: Color of the error bars representing confidence intervals.
show_pvalues: Display p-values in the top-left corner?
pvalues_test: Test to compute p-values for data frames: "fisher" for stats::fisher.test() (with simulate.p.value = TRUE) or "chisq" for stats::chisq.test(). Has no effect on survey objects for those survey::svychisq() is used.
pvalues_labeller: Labeller function for p-values.
pvalues_size: Text size for p-values.
show_labels: Display proportion labels?
labels_labeller: Labeller function for proportion labels.
labels_size: Size of proportion labels.
labels_color: Color of proportion labels.
show_overall_line: Add an overall line?
overall_line_type: Line type of the overall line.
overall_line_color: Color of the overall line.
overall_line_width: Line width of the overall line.
facet_labeller: Labeller function for strip labels.
flip: Flip x and y axis?
free_scale: Allow y axis to vary between conditions?
return_data: Return data used instead of the plot?
strata: Stratification variable
variable: Variable to be converted into dummy variables.

Examples

Run this code

titanic |>
  plot_proportions(
    Survived == "Yes",
    overall_label = "All",
    labels_color = "white"
  )

titanic |>
  plot_proportions(
    Survived == "Yes",
    by = c(Class, Sex),
    fill = "lightblue"
  )

# \donttest{

titanic |>
  plot_proportions(
    Survived == "Yes",
    by = c(Class, Sex),
    fill = "lightblue",
    flip = TRUE
  )

titanic |>
  plot_proportions(
    Survived == "Yes",
    by = c(Class, Sex),
    geom = "point",
    color = "red",
    size = 3,
    show_labels = FALSE
  )

titanic |>
  plot_proportions(
    Survived == "Yes",
    by = c(Class, Sex),
    geom = "area",
    fill = "lightgreen",
    show_overall = FALSE
  )

titanic |>
  plot_proportions(
    Survived == "Yes",
    by = c(Class, Sex),
    geom = "line",
    color = "purple",
    ci_color = "darkblue",
    show_overall = FALSE
  )

titanic |>
  plot_proportions(
    Survived == "Yes",
    by = -Survived,
    mapping = ggplot2::aes(fill = variable),
    color = "black",
    show.legend = FALSE,
    show_overall_line = TRUE,
    show_pvalues = FALSE
 )

# defining several proportions

titanic |>
  plot_proportions(
    dplyr::tibble(
      Survived = Survived == "Yes",
      Male = Sex == "Male"
    ),
    by = c(Class),
    mapping = ggplot2::aes(fill = condition)
  )

titanic |>
  plot_proportions(
    dplyr::tibble(
      Survived = Survived == "Yes",
      Male = Sex == "Male"
    ),
    by = c(Class),
    mapping = ggplot2::aes(fill = condition),
    free_scale = TRUE
  )

iris |>
  plot_proportions(
    dplyr::tibble(
      "Long sepal" = Sepal.Length > 6,
      "Short petal" = Petal.Width < 1
    ),
    by = Species,
    fill = "palegreen"
  )

iris |>
  plot_proportions(
    dplyr::tibble(
      "Long sepal" = Sepal.Length > 6,
      "Short petal" = Petal.Width < 1
    ),
    by = Species,
    fill = "palegreen",
    flip = TRUE
  )

# works with continuous by variables
iris |>
  labelled::set_variable_labels(
    Sepal.Length = "Length of the sepal"
  ) |>
  plot_proportions(
    Species == "versicolor",
    by = dplyr::contains("leng"),
    fill = "plum",
    colour = "plum4"
  )

# works with survey object
titanic |>
  srvyr::as_survey() |>
  plot_proportions(
    Survived == "Yes",
    by = c(Class, Sex),
    fill = "darksalmon",
    color = "black",
    show_overall_line = TRUE,
    labels_labeller = scales::label_percent(.1)
 )
# }

# stratified analysis
titanic |>
  plot_proportions(
    (Survived == "Yes") |>  stratified_by(Sex),
    by = Class,
    mapping = ggplot2::aes(fill = condition)
  ) +
  ggplot2::theme(legend.position = "bottom") +
  ggplot2::labs(fill = NULL)

# Convert Class into dummy variables
titanic |>
  plot_proportions(
    dummy_proportions(Class),
    by = Sex,
    mapping = ggplot2::aes(fill = level)
  )

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