## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(collapse = FALSE, comment = "",
                      fig.width = 7, fig.height = 4.5, dpi = 96,
                      dev.args = list(bg = "transparent"))
# Console colour carries no meaning on a rendered page. pkgdown turns it on for
# its own build, and the escape sequences then reach the reader as literal text,
# so colour is switched off here for a plain vignette render and a site build
# alike. The fixed width keeps printed output inside the documentation column.
options(cli.num_colors = 1, cli.hyperlink = FALSE, crayon.enabled = FALSE,
        width = 80)

# Figures on the package website sit on a warm off-white page in light mode and
# are inverted by pkgdown in dark mode, so an opaque background would read as a
# pale slab one way and a black plate the other. Two things paint one. The
# device canvas is made transparent by `dev.args` above, and theme_depictr()
# then inherits theme_minimal()'s white plot.background, which is drawn over
# that canvas, so it is cleared as each figure is printed. This is deliberately
# a vignette-level choice: theme_depictr() keeps its opaque background, which
# is what a figure saved for a paper wants.
transparent_bg <- ggplot2::theme(
  plot.background  = ggplot2::element_rect(fill = NA, colour = NA),
  panel.background = ggplot2::element_rect(fill = NA, colour = NA)
)
knit_print.ggplot <- function(x, ...) knitr::normal_print(x + transparent_bg)
knit_print.patchwork <- function(x, ...) knitr::normal_print(x & transparent_bg)

library(depictr)

## -----------------------------------------------------------------------------
explore_distribution(lexical_decision, RT, group = condition, type = "density",
                     legend_inside = TRUE)

## -----------------------------------------------------------------------------
explore_distribution(wellbeing_survey, life_satisfaction, group = region,
                     type = "both", facet = TRUE)

## -----------------------------------------------------------------------------
ecdf_plot(lexical_decision, RT, group = condition,
          reference_quantiles = c(0.25, 0.5, 0.75), legend_inside = TRUE)

## -----------------------------------------------------------------------------
explore_categorical(wellbeing_survey, education, group = region,
                    proportion = TRUE, position = "dodge")

## -----------------------------------------------------------------------------
explore_bivariate(lexical_decision, condition, RT)

## -----------------------------------------------------------------------------
scatter_trend(crop_yield, fertiliser, yield, group = treatment)

## ----fig.height = 6-----------------------------------------------------------
explore_pairs(crop_yield,
              cols = c("rainfall", "fertiliser", "soil_ph", "yield"))

## ----fig.height = 5-----------------------------------------------------------
correlation_heatmap(wellbeing_survey)

## ----fig.height = 5-----------------------------------------------------------
correlation_heatmap(wellbeing_survey, reorder = TRUE)

## -----------------------------------------------------------------------------
raincloud_plot(lexical_decision, RT, group = condition)

## -----------------------------------------------------------------------------
group_comparison_plot(lexical_decision, RT, condition)

## ----fig.height = 4-----------------------------------------------------------
ridgeline_plot(wellbeing_survey, life_satisfaction, region)

## ----fig.height = 5-----------------------------------------------------------
set.seed(1)
estimation_plot(lexical_decision, RT, condition,
                title = "RT difference: unrelated vs. related priming")

## ----fig.height = 5-----------------------------------------------------------
set.seed(1)
group_comparison_plot(crop_yield, yield, treatment, differences = TRUE,
                      title = "Yield difference: enhanced vs. standard")

## -----------------------------------------------------------------------------
wb <- wellbeing_survey
wb$age_group <- ifelse(wb$age < median(wb$age), "younger", "older")
dumbbell_plot(wb, region, life_satisfaction, age_group, legend_inside = TRUE)

## -----------------------------------------------------------------------------
outlier_plot(crop_yield, yield)

## ----fig.height = 5-----------------------------------------------------------
missingness_map(wellbeing_survey, legend_inside = TRUE)

## -----------------------------------------------------------------------------
tab <- summary_table(
  wellbeing_survey,
  vars = c("life_satisfaction", "income", "stress", "education"),
  group = "region"
)
knitr::kable(tab)

## -----------------------------------------------------------------------------
library(ggplot2)

scatter_trend(crop_yield, fertiliser, yield, group = treatment) +
  labs(title = "Yield rises with fertiliser",
       subtitle = "More steeply under the enhanced treatment") +
  theme(legend.position = "bottom")

## -----------------------------------------------------------------------------
ecdf_plot(lexical_decision, RT, group = condition) +
  labs(colour = NULL) +
  guides(colour = guide_legend(reverse = TRUE))

## -----------------------------------------------------------------------------
explore_categorical(wellbeing_survey, education, group = region,
                    proportion = TRUE, position = "dodge") +
  theme(legend.position = "inside",
        legend.position.inside = c(0.98, 0.98),
        legend.justification = c(1, 1),
        legend.title = element_blank())

