depictr is a single, consistent toolkit of plots
that span the whole analysis workflow, from a first look at the data,
through model estimates and predictions, to diagnostics, uncertainty and
reporting. Every plotting function returns a ggplot2 object
(Wickham,
2016) (or a patchwork for composite panels), so
you can keep customising with the usual + syntax, and every
plot shares one theme, one palette and one set of label conventions.
Five datasets to explore
The package ships with five reproducibly simulated datasets, each
chosen to exercise a different family of plots. They are documented
under their names (e.g. ?lexical_decision) and load with
data():
-
lexical_decision: a counterbalanced, crossed reaction-time/accuracy experiment (participant, item, condition, modality, word frequency). For mixed models and the classification plots. -
wellbeing_survey: a cross-sectional survey (life satisfaction, stress, sleep, income, age, ordered education, region) with informative missingness. For descriptives, correlations, regression and missing data. -
crop_yield: a field trial with a genuine fertiliser-by-treatment interaction. For regression, scatter-trend and interaction plots. -
clinical_trial: a two-arm trial with separating survival curves and a rare adverse-event outcome. For survival and imbalanced classification. -
monthly_sales: two seasonal monthly series (indoor/outdoor). For the time-series plots.
A tour by task
Begin with the data. explore_bivariate() chooses a
suitable plot for any pair of variables, here a scatter with a trend
because both are numeric.
explore_bivariate(crop_yield, fertiliser, yield)
Turn next to the model. After fitting it,
coefficient_plot() draws a forest plot of the
estimates.
fit <- lm(yield ~ rainfall + fertiliser + soil_ph + treatment, data = crop_yield)
coefficient_plot(fit, order = "descending", title = "Drivers of crop yield")
To see what the model implies, effects_plot() traces the
predicted response as one predictor varies.
effects_plot(fit, "fertiliser")
residual_diagnostics_plot() gathers the usual checks of
the fit into one panel.
For uncertainty, posterior_plot() summarises posterior
or simulation draws as a distribution per parameter. These are the real
fixed-effect posterior draws from a Bayesian fit of the lexical-decision
model, shipped with the package.
draws <- readRDS(system.file("extdata", "lexdec_draws.rds", package = "depictr"))
posterior_plot(draws[c("conditionunrelated", "modalityauditory",
"word_frequency")],
labels = c(conditionunrelated = "condition",
modalityauditory = "modality",
word_frequency = "word frequency"),
title = "Lexical-decision fixed effects (ms)")
The shared spine: tidy_estimates()
Most of the model functions rest on tidy_estimates(),
which turns a model, or a data frame of pre-computed estimates, into one
standard table. Because the plotting functions also accept that table,
estimates from any source (Bayesian posteriors, bootstrap intervals, or
figures taken from a paper) can be supplied directly.
tidy_estimates(fit)
#> term estimate std.error conf.low conf.high
#> 1 (Intercept) -7.156372471 0.7307020339 -8.597465983 -5.715278960
#> 2 rainfall 0.003869765 0.0006025038 0.002681505 0.005058026
#> 3 fertiliser 0.011266582 0.0010906005 0.009115695 0.013417469
#> 4 soil_ph 1.030217728 0.1056219670 0.821909657 1.238525800
#> 5 treatmentenhanced 1.317044684 0.0978270830 1.124109715 1.509979654A consistent, accessible look
theme_depictr(), depictr_palette() and
scale_colour_depictr() style your own plots too:
library(ggplot2)
ggplot(crop_yield, aes(fertiliser, yield, colour = treatment)) +
geom_point(alpha = 0.7) +
scale_colour_depictr() +
theme_depictr()
depictr_palette() returns the underlying hex colours
directly, ready to feed scale_fill_manual() or a
base-graphics col = argument:
depictr_palette(4)
#> [1] "#005b96" "#e69f00" "#009e73" "#d55e00"The qualitative palette is based on the Okabe-Ito set (Okabe & Ito, 2008), which stays distinguishable under the common forms of colour-vision deficiency; sequential and diverging variants are available too. Preview them with:
palette_preview(type = "all")
palette_preview() can also simulate a
colour-vision deficiency, so you can check a palette as a deuteranope
(red-green) would see it:
palette_preview(cvd = "deutan")
Set the look once for a whole script with
depictr_options() (base size, base family, brand and accent
colours, or a custom palette), instead of passing the same arguments to
every call. Called with no arguments it reports the current
settings:
depictr_options()
#> $base_size
#> [1] 11
#>
#> $base_family
#> [1] ""
#>
#> $brand
#> [1] "#005b96"
#>
#> $accent
#> [1] "#d55e00"
#>
#> $reference
#> [1] "grey60"
#>
#> $palette
#> NULL
#>
#> $na_value
#> [1] "grey80"Supplying arguments sets them for every later plot and returns the previous values, so you can put the look back afterwards:
old <- depictr_options(base_size = 13, accent = "#b3589a")
coefficient_plot(fit, title = "Set once, applied everywhere")
do.call(depictr_options, old) # restore the previous settingsWhere to next
The remaining articles go into each area in turn.
vignette("exploring-data") covers distributions,
categories, bivariate plots, scatter-plot matrices, correlations,
missingness, outliers, summary tables and the estimation plots.
vignette("model-estimates") is the flagship: forest plots,
model comparison, predicted values, interactions, random effects,
optimiser checks and the frequentist-over-Bayesian-posterior overlay.
vignette("diagnostics-and-uncertainty") covers residuals,
GLM-appropriate binned residuals, the classification suite (ROC, PR,
gains, lift, calibration, thresholds) on an imbalanced outcome, and
power curves. Two further articles,
vignette("multivariate-and-survival") and
vignette("time-series"), cover the remaining methods.