Plot categorical samples

plot() aggregates and plots categorical samples as violin plot.

Usage

# S3 method for class 'cat_sample'
plot(
  x,
  type = "violin",
  ...,
  option = "all",
  title = NULL,
  ylab = "Probability",
  colours = NULL,
  family = "sans",
  theme = NULL,
  beeswarm_cex = 0.6,
  beeswarm_corral = "none"
)

Arguments

x

an object of class cat_sample created by the function cat_sample_data.

type

character string with the type of plot, either beeswarm or violin.

...

Unused arguments, included only for future extensions of the function.

option

character string with the name of the option or character vector with the options that you want to extract from the data. Use all for all options.

title

character string with the title of the plot. If NULL, the title will be the topic name.

ylab

character string with the label of the y-axis.

colours

vector of colours to use for the categories.

family

character string with the font family to use in the plot.

theme

a theme function to overwrite the default theme.

beeswarm_cex

numeric, the space between points in the beeswarm plot.

beeswarm_corral

character string, the wrapping corral for the beeswarm plot. Anything accepted by the geom_beeswarm function.

Value

Invisibly a ggplot object.

Details

If a theme is provided, the family argument is ignored.

See also

Other plot helpers: plot.cont_sample(), plot.elic_cont()

Author

Sergio Vignali and Maude Vernet

Examples

# Create the elic_cat object for an elicitation process with three topics,
# four options, five categories and a maximum of six experts per topic
my_categories <- c("category_1", "category_2", "category_3",
                   "category_4", "category_5")
my_options <- c("option_1", "option_2", "option_3", "option_4")
my_topics <- c("topic_1", "topic_2", "topic_3")
my_elicit <- cat_start(categories = my_categories,
                       options = my_options,
                       experts = 6,
                       topics = my_topics) |>
  cat_add_data(data_source = topic_1, topic = "topic_1") |>
  cat_add_data(data_source = topic_2, topic = "topic_2") |>
  cat_add_data(data_source = topic_3, topic = "topic_3")
#> ✔ <elic_cat> object for "Elicitation" correctly initialised
#> ℹ Estimates sum to 1. Rescaling to 100.
#> ✔ Data added to Topic "topic_1" from "data.frame"
#> ℹ Estimates sum to 1. Rescaling to 100.
#> ✔ Data added to Topic "topic_2" from "data.frame"
#> ℹ Estimates sum to 1. Rescaling to 100.
#> ✔ Data added to Topic "topic_3" from "data.frame"

# Sample data from Topic 1 for all options using the unweighted method
samp <- cat_sample_data(my_elicit,
                        method = "unweighted",
                        topic = "topic_1")
#> ✔ Data sampled successfully using "unweighted" method.

# Plot the sampled data for all options
plot(samp)


# Plot the sampled data as beeswarm plot

if (FALSE) { # \dontrun{
plot(samp, type = "beeswarm", beeswarm_corral = "wrap")
} # }

if (FALSE) { # \dontrun{
# Plot the sampled data for option 1
plot(samp, option = "option_1")
} # }
if (FALSE) { # \dontrun{
# Plot the sampled data for option 1 and 3
plot(samp, option = c("option_1", "option_3"))
} # }
if (FALSE) { # \dontrun{
# Provide custom colours
plot(samp, colours = c("steelblue4", "darkcyan", "chocolate1",
                       "chocolate3", "orangered4"))
} # }
if (FALSE) { # \dontrun{
# Overwrite the default theme
plot(samp, theme = ggplot2::theme_minimal())
} # }