Release notes:

Full reference to all functions available at grafify GitHub pages. There are also updates within all plot_ functions, which are a facet argument, and log-transformations with axis tick marks.

grafify v3.0.0

Major updates

This is a major update for grafify, which now provides wrappers for basic generalised additive models (gam) through the mgcv package. There are a more plot_ functions, a grafify theme for ggplot objects, and simple data wrangling before plotting.

Major additions

  1. Fit generalised additive models (gam) and get ANOVA tables with two new functions: ga_model and ga_anova. These are mainly for time-series analyses or where an assumption of linear relationship between predictor and outcomes is absent straight lines are not appropriate. Factor-wise smooths are fit with the by argument in mgcv, without or with a random factor. Random factors are also allowed with smooth re smooth. See documentation for mgcv smooths. Model diagnostics can be done with plot_qq_gam and plot_qq_model. Example data included as data_zooplankton is from Lathro RC, 2000.

  2. All plot_ functions now have two major updates:

  1. Log-transformation of axes: Axes can be transformed with log10 or log2 with LogYTrans and LogXTrans arguments. X axis transformations are only available for plot_xy_CatGroup and plot_xy_NumGroup. With log10 transformation, log-ticks will also appear. Default axes limits and labels should work in most cases, but if needed, three additional arguments are available: LogYBreaks, LogYLimits and LogYLabels (and respective ones for the X axis).
  2. facet argument to add another variable to created faceted plots with the facet_wrap layer in ggplot2. A related argument facet_scales can be used to set Y or X axis scales on faceted panels.
  1. New plot functions:
  1. plot_befafter_box is a new before-after plot function that includes a box and whisker plot to show data distribution in addition to lines joining matched data. In addition, both plot_befafter_colour and plot_befafter_shapes offer a box and whiskers summary of data.

  2. plot_lm_predict and plot_gam_predict can be used to plot observed (raw) data and predicted data from fitted linear models.

  3. plot_logscales is a function to easily perform “log10” or “log2” transformation on X or Y axes of any ggplot2 object along with log-ticks.

  1. Table manipulations:
  1. table_x_reorder is a function to reorder levels within a categorical variable. This uses factor from base R stats package to convert a column into a factor and reorders it based on a user-provided vector of group names.

  2. table_summary is a wrapper around aggregate (base R) function, which gives mean, median, SD, and counts grouped by one or more variables.

  1. A grafify theme for ggplot2: theme_grafify is a modification of theme_classic for making publication-ready grafify-like graphs easily when using ggplot2.

Minor changes

Much of the code has been edited and cleaned up. Among the main change is dropping unnecessary double curly brackets {{ }} within plot_ wrappers.

grafify v2.3.0

The main motivation behind this update was to simplify the package by reducing the number of exported functions. So some features that were previously in separate functions have been made available more easily via an additional argument to existing functions (e.g. single colour function (plot_..._sc) now offered in respective plot_ function with a new argument (see below). This has uncluttered the namespace of grafify. Most of the other additions are related to colour schemes.

Major additions

  1. A new SingleColour argument has been added to two-variables plot_ functions to generate graphs with a single colour along the X-axis aesthetic. This means the 8 plot_..._sc functions introduced in v1.5.0 are deprecated, but this feature is still retained in existing plot_ functions. This option also added to plot_3d_ functions for plots of one-way ANOVA data.

  2. Four new colourblind-friendly categorical colour schemes (chosen from cols4all package):

  1. Four new quantitative schemes for continuous or divergent colours.

All schemes also available through scale_fill.. and scale_colour_... calls to be used on any ggplot2 object.

  1. scale_fill_grafify and scale_colour_grafify (or scale_color_grafify) have been rewritten. These have two new arguments that offer features previously in scale_fill_grafify2/scale_colour_grafify2/ scale_color_grafify_c and scale_fill_grafify_c/scale_colour_grafify_c/ scale_color_grafify_C scale functions. These 6 functions are now deprecated to reduce exported namespace.

The new arguments are discrete (logical T/F) to select discrete or continuous palettes, and ColSeq (logical T/F) to pick sequential or distant colours from a chosen palette.

Minor changes & bug fixes

  1. Fixed the error in legend title in one-way ANOVA plots with plot_3d_ that incorrectly referred to xcol and shapes arguments.
  2. Fixed the error that led to depiction of different shapes in plot_3d_scatterviolin as compared to the other two plot_3d_ functions.
  3. posthoc_Trends... functions rewritten with stats::model.frame() to get model data frame as this is a more flexible method.
  4. Order of colours in light, bright and muted schemes changed slightly for better separation of colours when next to each other.
  5. The jitter setting in plot_scaltter_ is set to 0.2 so the graph as plotted with jitter by default.
  6. The default colour scheme for all graphs is now okabe_ito (the all_grafify palette is was just a concatenation of all palettes without real basis in good visualisation). Use one of the other palettes if more than 8 colours are needed (e.g. kelly, which has 20 discreet colours).

grafify v2.2.0

New features

plot_3d_scatterviolin and plot_4d_scatterviolin for one-way or two-way ANOVA design data to plot scatter plots with violins with box and whiskers.

Minor fixes

plot_qqmodel no longer relies on broom.mixed; instead uses rstudent from the base stats package to generate studentized residuals from a model.

grafify v2.1.0

New features

New experimental functions to compare slopes of linear regression via posthoc_Trends_Pairwise, posthoc_Trends_Levelwise and posthoc_Trends_vsRef.

Minor fixes

Minor changes to plot_qqmodel and plot_qqline to fix some OS-specific errors. QQ plots by default will have ok_orange colour within symbols when only one level is present within group. Both functions now use geom_qq and geom_qq_line (instead of stat_qq and stat_qq_line) internally.

grafify v2.0.0

This is a major update with some new features, bugfixes, and further cleaning up of code with consistent names of arguments in preparation for CRAN submission. Some previous code may not work because of renaming of some arguments for grouping variables in plot_ functions. But older arguments are retained with deprecation warnings in most cases, so old code should largely work.

New features

  1. plot_ functions have a new argument ColSeq (logical TRUE/FALSE) that picks colours sequentially from palette chosen by ColPal when TRUE (default). If set to FALSE, the most distant colours are chosen, as already implemented in scale_..._grafify2 functions.
  2. Violin plots get a major face-lift with a box-whiskers plot on top of the violin. This gives a clearer picture of data and dispersion than the default quantile lines in geom_violin. They also get new arguments to set thickness of lines (bvthick) and transparency of boxplots (b_alpha).
  3. There are new functions for fitting linear models with varying slopes and intercepts. These are mixed_model_slopes and mixed_anova_slopes.
  4. A function for comparing slopes of linear fits posthoc_Trends implements the emmeans::emtrends call.
  5. Most plot_ functions now have the ... argument forwarding dots for advanced users to add arguments to ggplot geometries where necessary.
  6. New plot_grafify_palette function that helps quickly visualise colours in palettes along with their names and hexcodes.
  7. plot_bar_sd and plot_bar_sd_sc have a new argument bthick to adjust the thickness of lines of the bars.

Bug fixes

  1. Distribution plots: the The Group grouping argument in plot_density, plot_histogram and plot_qqline is now called group for consistency with other plot_ functions.
  2. The Factor argument in post-hoc comparisons functions (posthoc_Pairwise, posthoc_vsRes, and posthoc_Levelwise) renamed as Fixed_Factor to be consistent with mixed_model, simple_model, mixed_anova and simple_anova functions.
  3. The plot_3d_scatterbar and plot_3d_scatterbox now correctly plot one-way ANOVA designs with randomised blocks with shapes mapped to levels of the random factor, and xcol as the grouping factor as originally intended but incorrectly implemented. This complements plot_4d_scatterbar and plot_4d_scatterbox which take two grouping factors and a random factor.
  4. Examples in help files have arguments explicitly labelled to make them easier to follow.
  5. groups in before-after plots is now called match as it is a bit more informative when showing matched data.
  6. For consistency, the argument for controlling opacity in distribution plots is renamed c_alpha in plot_density and plot_histogram (for colour opacity of colours under the density curve or histogram); opacity of symbols in plot_qqline is still called s_alpha.

grafify v1.5.1

This update fixes and cleans up code to remove all errors, warnings and notes from devtools::check(). All previous code should still work.

  1. The main update is that broom.mixed::augment is used to get model residuals than the fortify method as this will be deprecated soon. The broom.mixed package therefore required.
  2. The way ANOVA table is generated no longer relies on an internal function from lmerTest, but instead forces a mixed model object as lmerModLmerTest object to get F and P values in ANOVA tables from the stats::anova call.
  3. The magrittr package is required for internal use of pipes (%>%).
  4. Much of the code for simple_model and mixed_model was cleaned up so that model outputs are as close to objects generated by native calls to lm or lmer.
  5. Several internal functions related to the colour palettes have now been exported as this was easier.
  6. The make_1w_rb_data and make_2w_rb_data functions have been updated to have consistent factor and level names.

grafify v1.5.0

Major fixes

  1. New graph types

This version has 8 new plot_ functions ending in _sc for plotting data with two variables wherein the X variable is plotted in a single colour. This contrasts existing versions that plot the X variable with multiple colours chosen from the all_grafify palette. This is convenient when there are too many groups on the X axis and multiple colours are not necessary.

  1. Plotting Q-Q plot of model residuals

plot_qqmodel will plot a diagnostic Q-Q plot of a simple linear model (generated with simple_model or lm) or mixed effects linear model (generated with mixed_model or lmer) in a single step.

Minor fixes

Fixed a typo in posthoc_Levelwise where the adjust argument was not being correctly passed on to emmeans.

grafify v1.4.1

This version “breaks” a few arguments from v0.3.1, therefore is v1.4.1. Specifically, opacity for both symbols and bars/boxes/violins can be set using s_alpha and b_alpha or v_alpha, respectively; previously, only bars/boxes/violin opacity could be set with a single alpha parameter. Old code with just alpha will no longer work, sorry! There are also new graph types and arguments for ANOVAs as below.

Major fixes

  1. New graph types

    1. plot_density and plot_histogram for smooth density or histogram plots through geom_density and geom_histogram respectively.
    2. two new plot types plot_scatterbox and plot_scatterviolin that complement the plot_dot... versions and instead use geom_point with position_jitter. These versions are useful when a large number of data points are needed to be plotted.
  2. Updates

    1. simple_anova where the table also has Mean SS.
    2. mixed_anova now has two new arguments, one to change method for Df calculation and second to get type I or III SS (default is type II).
    3. jitter argument added to plot_3d.. and plot_4d.. functions for consistency with other scatter plots.
    4. bwid argument (for adjusting width of bars) added to plot_scatterbar_sd for consistency.

grafify v0.3.1

Bug fixes in mixed_model and simple_model which now correctly lists the data used in the call field.

grafify v0.3.0

  1. A new plot_4d_scatterbar function which is like plot_4d_scatterbox but plots bar and SD. So there are now two plot_3d_ and plot_4d_ functions.
  2. Text on X-axis on all graphs can be rotated from 0-90 using TextXAngle argument to prevent overlap.
  3. plot_dot_ functions now have dotthick option to set stroke thickness. This is similar to symthick for scatter/jitter plots.
  4. Using facet_wrap or facet_grid will not draw a box around panel text (unlike the default in theme_classic()).
  5. plot_3d_ and plot_4d_ functions draw symbols in black colour.

grafify v0.2.1:

  1. Bug fixes in plot_3d_scatterbar and plot_3d_scatterbox, which now correctly use the “shapes” variable to fill colour of bars/boxes and shape of the symbols; symbols are depicted in black.
  2. simple_anova generates type II ANOVA table through car::Anova(), so the car package is now a dependency. v0.1.0 and v0.2.0 generated type I ANOVA table through stats::anova().

grafify v0.2.0:

  1. the main difference from v0.1.0 is that all plot_ functions apply the all_grafify colour scheme by default (see plot_ vignettes on how to change colours)
  2. two new types of graphs are possible with two quantitative X-Y plots with a third variable that is either numeric (plot_xy_NumGroup) or categorical (plot_xy_CatGroup).
  3. there are two new continuous colour schemes (scale_fill_grafify_c and scale_colour_grafify_c), based on Paul Tol’s variant of YlOrBl scheme.

Minor changes

  1. plot_befafter... functions have a new logical TRUE/FALSE argument called boxplot to additionally show a box and whisker plot to show data distribution.

grafify v0.1.0

First release.