prettyGraphics is an R package designed to make the production of plots and data exploration easier, more flexible and prettier. prettyGraphics has been particularly inspired by the requirements of continuous ecological datasets. The package includes multiple ‘building block’ functions which help to define the initial arguments of a plot and then add elements to a plot in sequence. Some integrative functions draw on the flexibility of building blocks to define prettier plots for a variety of equivalent plotting functions in base R (e.g. graphics::plot(), graphics::hist() and more) or more specialised routines. Key functionality includes:

• The definition of pretty axes for plots.
• Tools to aid data exploration, including colouring lines by covariates and adding shading to elucidate relationships between several variables.
• Tools to aid statistical inference, including adding statistical summaries to reveal patterns and adding model predictions to plots to compare observations with model predictions.
• Integrative functions which create prettier versions of common plots more easily and facilitate visualisation of spatiotemporal data.
• The definition of pretty plot layouts.

You can install the development version of prettyGraphics from GitHub with:

devtools::install_github("edwardlavender/prettyGraphics", dependencies = TRUE, build_vignette = TRUE)

If you build the vignette, you can view this with vignette("introducing_prettyGraphics", package = "prettyGraphics"). The package can then be loaded and attached with:

library(prettyGraphics)

• pretty_seq() defines defines pretty sequences, given data, limits and pretty parameters;
• pretty_axis() is a very flexible function which is used to define and add pretty axes to plots (i.e., axes with intelligible tick mark labels that are positioned in appropriate, adjoining positions, rather than as an approximate box around a plot);
• pi_notation() translates numeric vectors into $\pi$ notation;
• sci_notation() translates the ‘e’ notation used by base R into scientific notation;
• add_lagging_point_zero() brings all numbers up to the same number of decimal places;
• add_grid_xy_rect() adds a rectangular grid to a plot at user-defined positions;

• add_lines() adds a line to a plot illustrating a relationship between y and x that can be coloured by the values of a third variable;
• add_colour_bar() adds a customisable colour bar legend to a plot;
• add_shading_bar() adds blocks of shading to a plot to elucidate relationships between a response and explanatory variables, one of which is a factor;
• add_shading_quantiles() adds shading for the quantiles of observed variation to a plot;
• add_boundary_box() adds a boundary box around observations at specified coordinates;

• summarise_in_bins() computes statistical summaries of continuous data in bins, which can be added to plots using add_lines();
• list_CIs() lists model predictions/confidence intervals from fitted values and standard errors (or similar);
• add_error_bars() adds error bars to a plot;
• add_error_envelope() adds model predictions (e.g. fitted lines, confidence intervals) to plots;
• pretty_predictions_1d() plots pretty one-dimensional predictions;
• pretty_predictions_2d() plots pretty two-dimensional predictions;
• pretty_smooth_1d() plots pretty one-dimensional smooths estimated by mgcv::gam();
• pretty_smooth_2d() plots pretty two-dimensional smooths estimated by mgcv::gam();

• pretty_plot() creates prettier plots for a variety of functions;
• pretty_panel() creates prettier grouped, multi-panel, plots;
• pretty_hist() creates prettier histograms;
• pretty_boxplot() creates prettier boxplots;
• pretty_curve() evaluates and plots functions;
• pretty_mat() creates pretty matrices;
• pretty_residuals() creates prettier diagnostic residual plots (including standard diagnostic plots alongside residuals against covariates, time stamps and the autocorrelation function, if applicable);

Here are some simple examples in which the default graphics and prettyGraphics plots are compared:

# Simulate some data 
n <- 100
x <- factor(sample(1:3, n, replace = TRUE))
y <- stats::rnorm(n, 0, 1)
# Compare graphics and prettyGraphics defaults for some example plots 
pp <- par(mfrow = c(1, 6), mar = c(2, 2, 2, 2))
plot.default(x, y, main = "[default] plot(x, y)")
pretty_plot(x, y, main = "pretty_plot(x, y)")
hist(y, main = "[default] hist(x)")
pretty_hist(y, main = "pretty_hist(x)")
boxplot(y ~ x, main = "[default] boxplot(y ~ x)")
pretty_boxplot(x, y, main = "pretty_boxplot(x, y)")

par(pp)

• pretty_line() creates pretty number lines and timelines;
• define_time_blocks() defines time blocks (i.e., diel periods or seasons) for each day in a time window (designed to work with add_shading_bar());
• pretty_ts() creates pretty time series plots;
• pretty_ts_mat() creates 2-dimensional plots of the within and between day variation in a time series;
• pretty_pgram() creates processed periodogram plots (power spectra);
• vis_ts() is an R Shiny-Dashboard user interface for the interactive exploration of (ecological) time series and creation of publication quality plots;
• add_moons() adds moons to a plot of lunar phase;

• pretty_map() produces pretty maps and pretty_map_from_file_raster() is a wrapper that creates maps directly from a list of source files. These functions are supported by a series of helper functions for adding spatial layers to a plot:
  • add_sp_raster() adds rasters to a background map;
  • add_sp_poly() adds polygons to a background map;
  • add_sp_line() adds lines to a background map;
  • add_sp_path() adds paths to a background map;
  • add_sp_points() adds locations to a background map;
  • add_sp_grid_ll() adds a longitude–latitude grid over a background (projected) map;
  • add_north_arrow() adds a North arrow to a map;
  • summarise_by_lat() and add_profile_lat() calculate and add latitudinal profiles to a plot;
• pretty_scape_3d() and vis_scape_3d() produce interactive, 3-dimensional visualisations of landscapes/seascapes and/or environmental conditions; for large rasters, crop_aggr_utm() helps reduce raster dimensions for these functions;

• pretty_cols_brewer() facilitates the creation of pretty colour schemes;
• pretty_cols_split_heat() generates a ‘split-heat’ colour scheme in which values either side of a break-point are coloured differently;

• par_mf() defines a suitable plotting window for a given number of plots;
• par_tri() defines the indices of plots along the lower or upper triangle of a square multi-panel matrix;

• tidy_numbers() tidies the number columns in a dataframe;
• tidy_write() writes a ‘tidy’ table to file;

Possible future functionality includes:

• Additional standard routines, such as pretty_barplot() for prettier barplots;
• Additional routines for visualising spatial data;
• Introduction of ggplot2 support;
• Additional specialised functions, such as add_hydrodynamic_ts() to add hydrodynamic model predictions to (depth) time series;

prettyGraphics is a new package whose contents are an early stage of evolution and primarily driven by the author’s ecological research. Please report issues, comments and suggestions!