depictr (R)
depictr is a single, consistent toolkit of publication-ready plots that span
the whole analysis workflow, from a first look at the data, through model
estimates and predictions, to diagnostics, uncertainty and reporting. Most
packages address one part of this work, whereas depictr aims to cover it from end
to end with one theme, one palette and one set of label conventions. Every
plotting function returns a ggplot2 object (or a
patchwork for composite panels), so a
plot can be refined further with the usual + syntax.
This is the R half of a sibling pair. The Python
package of the same name covers
most of the same workflow on top of plotnine. Its README
lists the handful of functions that have yet to be ported and the differences
that change what a figure shows, many of them arguments the Python functions do
not yet take.
Gallery
A grouped density (the default palette is a colourblind-safe set based on
Okabe-Ito) and Kaplan-Meier survival curves with confidence bands and a
number-at-risk table, each from a single function call:
Installation
depictr is on CRAN:
install.packages("depictr")
The development version installs from GitHub:
# install.packages("remotes")
remotes::install_github("pablobernabeu/depictr")
What's in the box
The functions fall into seven families that follow the analysis workflow.
Exploratory plots and a descriptive summary table open it. The model-estimate
views come next (forest plots, model comparison, predictions, interactions and
random effects), then residual diagnostics with the standard classification
curves, and then posterior summaries and power curves. Further families cover
principal components, clustering and survival, time series, and the shared theme
with the accessibility checks, composition, saving and reporting helpers. The
reference lists
every function by family.
Heavier modelling back-ends (lme4, broom, simr) are optional (in
Suggests) and used only when present. The core functions, examples, tests and
vignettes run on base lm/glm and the bundled data alone.
Measured accessibility
The default palette is based on the Okabe-Ito set, and that choice is checked.
The package ships a simulator of colour-vision deficiency based on the model of
Machado, Oliveira and Fernandes (2009), and palette_safety() measures how far
apart the palette's colours stay under each deficiency.
A safe palette is not a safe figure, though, so check_figure() audits the plot
you are about to submit. It measures how separable the encoding colours are under
each dichromacy and in greyscale, how small the text becomes at a stated output
width, how well text and geometry contrast with their backgrounds under WCAG, and
whether any distinction rests on colour alone. The size check covers the text
drawn inside the panel as well as around it, and the contrast check covers the
text around it alone. Each row reports the value it measured next to the
threshold, so a verdict can be argued with. For a figure saved at its final
width, give that width as both width_cm and render_width_cm, or neither, or
let save_plot(check = TRUE) audit it at the width it saves. A journal that
caps text size, as Nature does at 7 pt, can be held to its cap with
max_text_pt.
check_figure(explore_distribution(lexical_decision, RT, group = condition),
width_cm = 8.9)
The audit leaves continuous colour scales alone, and the colour rows of a
heatmap say so. For those, prefer a perceptually ordered map such as viridis or
the package's sequential and diverging ramps.
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