stat_density2d_filter().position_dodgenudge(), behaviour like position_dodge2nudge().x or y nudge vectors longer than one but shorter than the number of rows in data by reordering them based on the ordering of the data values to which they are applied.angle aesthetic in geom_label_npc(), geom_label_s() and geom_label_pairwise() matching the upcoming update to ggplot2::geom_label() (with help from teunbrand).linetype aesthetic to geom_label_npc(), for consistency with geom_label_s() and geom_label_pairwise().position_stack_minmax() to correctly position error bars in stacked plots.birch.dfand birch_dw.df with data for 350 birch seedlings.weather_18_june_2019.df with data at 1 min intercal for 24 h.quadrant_example.df and volcano_example.df from ‘ggpmisc’ to ‘ggpp’.geom_text_pairwise() and geom_label_pairwise()suitable for annotating/highlighting pairwise comparisons between levels of a factor mapped to the x aesthetic. They can also be used to highlight ranges of values when the variable mapped to x is a continuous numerical vector. The user interfaces are consistent with those of geom_text_s() and geom_label_s().stat_quadrant_counts() and stat_group_counts().stat_group_counts(): mismatch between counts and groups.stat_functions(), a replacement for ggplot2::stat_function() that fully complies with the grammar of graphics, supporting multiple functions per layer, grouping and facets.stat_dens2d_labels(), stat_dens2d_filter(), stat_dens2d_filter_g(), stat_dens1d_labels(), stat_dens1d_filter() and stat_dens1d_filter_g(): handle elegantly data for which density cannot be estimated (Issue #32 raised by mshubert to report bug).stat_dens2d_labels(), stat_dens2d_filter(), stat_dens2d_filter_g(), stat_dens1d_labels(), stat_dens1d_filter() and stat_dens1d_filter_g() use type = 8 when computing quantiles, instead of the default type = 7. Type 8 is recommended as better with data that are not normally distributed.exclude.these to stat_dens2d_labels(), stat_dens2d_filter(), stat_dens2d_filter_g(), stat_dens1d_labels(), stat_dens1d_filter() and stat_dens1d_filter_g() allowing forcing the exclusion of specific observations irrespective of the local density (See issue #19, partly addressing a suggestion by mshubert).stat_dens2d_labels(), stat_dens2d_filter(), stat_dens2d_filter_g(), stat_dens1d_labels(), stat_dens1d_filter() and stat_dens1d_filter_g() add parameter these.target making it possible to apply a function or vector of values passed through exclude.these or keep.these to any variable in the layer data (See issue #19, partly addressing a suggestion by mshubert)."spread" action in position_nudge_to().colour.target to improve consistency; now segments are never the target of colour by default.color.target and default.colors as synonyms of colour.target and default.colours.aes_string() in ‘ggplot2’ (>= 3.0.0).keep.these to stat_dens2d_labels(), stat_dens2d_filter(), stat_dens2d_filter_g(), stat_dens1d_labels(), stat_dens1d_filter() and stat_dens1d_filter_g() allowing forcing the inclusion of specific observations irrespective of the local density (See issue #19, partly addressing a suggestion by mshubert).stat_dens2d_labels(), stat_dens2d_filter(), stat_dens2d_filter_g(), stat_dens1d_labels(), stat_dens1d_filter() and stat_dens1d_filter_g() to allow separate control of kept observations or labels in each tail (1D) or in each quadrant (2D) of the density distribution, still using an estimate of the local density computed from the whole data set.return.density to stat_dens2d_labels(), stat_dens2d_filter(), stat_dens2d_filter_g(), stat_dens1d_labels(), stat_dens1d_filter() and stat_dens1d_filter_g() to request the local density estimate at the position of each observation to be returned in numeric variable density and the outcome of the test in logical variable keep.stat_panel_counts() and stat_group_counts().default.colour and colour.target to geom_label_s(), geom_text_s(), geom_point_s(), geom_plot(), geom_table() and geom_grob().default.alpha and alpha.target to geom_label_s(), geom_text_s(), geom_point_s(), geom_plot(), geom_table() and geom_grob().segment.colour, segment.alpha, and segment.size in geom_label_s(), geom_text_s(), geom_point_s(), geom_plot(), geom_table() and geom_grob(). (Segments are links, and not meant to inform about data values on their own.)dark_or_light().linewidth in geom_quadrant_lines() and geom_vhlines() for compatibility with ‘ggplot2’ (>= 3.3.7).geom_text_s() and geom_label_s() to support justification computed from positions, with hjust = "position" and vjust = "position" as new defaults. (The current, very simple, approach will be further tested and if necessary improved for the next release.)geom_text_s() and geom_label_s() to enable segment.size aesthetic and increase its default value, and add formal parameters point.padding, box.padding and min.segment.length to allow adjusting the length of the segments.position_nudge_center() for compatibility with ‘ggplot2’ (>= 3.3.7) and to ensure that every label gets nudged even if located exactly at the center (focus point of nudging).Multiple grobs and grob trees sharing the same name result in only the first one being rendered. This caused problems when multiple layers created with the same geometry were added to a plot.
Based on issues raised in the GitHub repository of ‘ggrepel’ and the nudge functions added some time ago to package ‘ggpp’ it became obvious that nudging can help in achieving good repulsion outcomes without need of tailored repulsion algorithms for specific cases. Obviously developing new ggplot position functions is much easier than tweaking the repulsion algorithm. It is also clear that not being able to combine nudging with stack, jitter and dodge positions made difficult to produce some types of plots. One case is replacing a key or legend with direct labels to plot elements, which is important in plots aimed at audiences outside academia.
In one of the issues in the GitHub repository of ‘ggrepel’ an answer by M. Krassowski included code that provided an elegant and simple approach to implementing combined position functions without duplicating code already in ‘ggplot2’ by instead calling methods of the parent class. I edited this code and included it in the package.
Except for the position functions with names ending in _keep, for which normal counterparts exist, the keeping of the original position can be disabled by passing kept.origin = "none" when they are called.
The renaming of geom_text_linked() to geom_text_s() is code breaking but I am now fairly confident this shorter name is easy to remember with s for segment.
position_stack_keep(), position_fill_keep(), position_jitter_keep(), position_dodge_keep() and position_dodge2_keep().position_stacknudge(), position_fillnudge(), position_jitternudge(), position_dodgenudge() and position_dodge2nudge() based on code by M. Krassowski for position_stack_and_nudge().position_nudge_to(), position_nudge_center() and position_nudge_line() adding support for disabling keeping of the original positions.geom_point_s() and geom_label_s() and update geom_text_s() renamed from geom_text_linked(). This is a code breaking change with respect to the previous (unstable) version.geom_plot(), geom_table() and geom_grob() to support plotting of segments when positions change, e.g., with nudging.With 12 new and four partly rewritten functions there is quite a lot of new code in this update, so even if tested and checked, it is possible that bugs may have slipped through. Please, do report them if you encounter any.
The initial implementation and user interface of three apply statistics first introduced in ‘ggpmisc’ 0.3.6 has been revised to expand their usefulness and to make them less error-prone, while the fourth one is now defunct. Note: The default argument for geom instat_centroid() is likely to change in the near future. Otherwise, the three statistics can be considered now stable.
Update stat_apply_group() to support summary functions like quantile() that return vectors with more than one value but shorter than the original number of observations.
Update stat_summary_xy() and stat_apply_group() to return NA in x and/or y when .fun.x or .fun.y are not passed an argument. This is a code breaking change with respect to the previous (unstable) version.
Update stat_summary_xy() and stat_centroid() to support functions that return a one row data frame, like those defined in ‘ggplot2’ to be passed as argument to parameter fun.data of ggplot2::stat_summary(), such as mean_se, mean_cl_boot , etc.
Fix bug in stat_centroid(), stat_summary_xy() and stat_apply_group() resulting in the return of a long data frame with NA values instead of a data frame with fewer rows.
Remove stat_apply_panel() , as it was redundant. Grouping can be modified per layer when needed.
Update compute_just2D() and compute_just() to work with any value for the angle aesthetic, as in the accepted version of the pull request in ‘ggplot2’.
Fix bug in geom_table() that would cause text left or right justified to be clipped when the text in a cell was very long (reported by dryguy). (Cell padding still needs improvement.)
This new package is the result of splitting package ‘ggpmisc’ into two packages: ‘ggpp’ containing extensions to the grammar of graphics and ‘ggpmisc’ containing extensions to ‘ggplot2’ related to plot decorations based on model fits, statistical summaries and other descriptors of the data being plotted. Package ‘ggpmisc’ depends on ‘ggpp’ with no visible changes for users. Package ‘ggpp’ can be loaded instead of ‘ggpmisc’ when only the extensions it contains are needed. Package ‘gginnards’ containing tools for editing ggplot objects as well as tools for inspecting them is an earlier spin-off from ‘gpmisc’.
Compared to ‘ggpmisc’ 0.3.9, the following changes have been introduced. New justification styles have being implemented to complement position_nudge_center() . They are supported in geom_text_s(), geom_plot(), geom_table(), geom_grob() and geom_marging_grob(). In the current implementation all rows in data should contain the same hjust or vjust value when using the new types of justification described here, this seems reasonable as they compute the individual justification values from the data. All other justification values, either numeric or character do not have this restriction and can be used as in geoms from ‘ggplot2’. These new features may change in the near future.
geom_linked_text() into geom_text_linked()."outward_mean" , "inward_mean" , "outward_median" and "inward_median" so that outward and inward are with respect to the centroid of the data instead of to the middle of the \(x\) or \(y\) scales. This should be useful in combination with position_nudge_center()."outward_nnn" and "inward_nnn" so that outward and inward are with respect to the number resulting from applying as.numeric() to the characters that replace nnn. For example strings like "outward_0.5" , "inward_3e5" or "outward_-3e-2" are supported. This should be useful when manual tweaking is desired. As special cases "outward_0" and "inward_0" apply justification outward and inward with respect to the origin. This should be useful for biplots used for PCA and similar cases with arrows radiating out of the origin. (The "outward" and "inward" justification implemented in ‘ggplot2’ is relative to the middle of the \(x\) or \(y\) scales.)compute_npcx() and compute_npcy() to support multiple steps per group (needed in ‘ggpmisc’)."outward" and "inward" justification of text labels when angle aesthetic takes values < -45 or > 45 degrees. This code change alters how old plots are rendered if text labels have been rotated by more than 45 degrees.ggplot2::geom_text() has been submitted and merged. This is now in the ‘ggplot2’ 3.3.4 milestone retaining consistent behaviour between ‘ggplot2’, ‘ggrepel’, ‘ggpp’ and ‘ggpmisc’.geom_plot().stat_poly_eq().geom_linked_text(). Except for the drawing of segments or arrows this new geometry behaves as ggplot2::geom_text() . Note: Segments and arrows are drawn only if the position function used returns both the repositioned and original coordinates.position_nudge_centre() and position_nudge_line() compute the direction of nudging and return both the nudged and original positions.position_nudge_to() nudges to new user-supplied position(s); position_nudge_keep() nudges to position(s) based on user-supplied position shift. These functions return both nudged and original position(s), which makes possible to draw connecting segments from text labels to the original position.stat_fit_glance() , stat_fit_augment() , stat_fit_tidy() and stat_fit_tb() now import the tidiers from package ‘generics’ instead of from ‘broom’. As a result, users must now explicitly load the package where the methods to be used are defined, such as ‘broom’ or ‘broom.mixed’ or define them before calling these statistics.glance.args to stat_fit_glance() , parameter tidy.ars to stat_fit_tidy() and stat_fit_tb() and parameter augment.args to stat_fit_augment() as some specializations of broom::glance(), broom::tidy() and stat_fit_augment() accept arguments specific to a given fitting method.stat_fit_tidy() would fail with quantreg::rq() and any other fit methods that do not return by default standard error estimates for parameter estimates (Thanks to Mark Neal for reporting the problem).stat_fit_glance(), stat_fit_augment() and stat_fit_tidy() to ensure compatibility with cor.test() and other functions that require an object rather than a quoted expression as argument for data .p.digits to stat_fit_tb().try_tibble.ts() and try_data_frame() did not handle correctly the conversion of dates for some time series, which also could affect ggplot.ts().stat_peaks() and stat_valleys() generated wrong labels if a Date object was mapped to x (the bug did not affect POSIX or datetime, and was obvious as it resulted in a shift in dates by several decades).stat_fit_tb() to support renaming of terms/parameter names in the table (Suggested by Big Old Dave and Z. Lin). In addition implement selection, reordering and renaming of columns and terms/parameters using positional indexes and pattern matching of truncated names in addition to whole names. Improve formatting of small P-values.stat_fmt_tb() to support the same expanded syntax as stat_fit_tb().stat_dens1d_filter(), stat_dens1d_filter_g() and stat_dens1d_labels(), to complement existing stat_dens2d_filter(), stat_dens2d_filter_g() and stat_dens2d_labels().stat_dens2d_filter(), stat_dens2d_filter_g() and stat_dens2d_labels() adding formal parameters keep.sparse and invert.selection, as available in the new 1D versions.stat_dens2d_labels() to accept not only character strings but also functions as argument to label.fill as the new stat_dens1d_labels() does.ggplot2::annotate() adding support for aesthetics npcx and npcy.stat_summary_xy() and stat_centroid().stat_poly_eq() to support labelling of equations according to group.output.type "markdown" in stat_poly_eq() usable with geom_richtext() from package ‘ggtext’.geom_table() and geom_table_npc().p.value.label and f.value.label to stat_poly_eq().stat_poly_eq().This version implements some new features and fixes bugs in the features introduced in version 0.3.1, please do rise an issue if you notice any remaining bugs! Some reported weaknesses in the documentation have been addressed. This updated version depends on ‘ggplot2’ (>= 3.2.1).
Add support for volcano and quadrant plots of outcomes.
Add geometries geom_vhlines() and geom_quadrant_lines().
Add convenience scales scale_x_logFC() and scale_y_logFC() for data expressed as fold change.
Add convenience scales scale_x_Pvalue(), scale_y_Pvalue(), scale_x_FDR(), scale_y_FDR().
Add convenience scales scale_colour_outcome(), scale_fill_outcome() and scale_shape_outcome() for data expressed as ternary or binary outcomes.
Add conversion functions outcome2factor() and threshold2factor() to convert vectors of numeric outcomes into factors with 2 or 3 levels.
Add conversion function xy_outcomes2factor() and xy_thresholds2factor() to combine two vectors of numeric outcomes into a 4-level factor.
Improve support for model-fit annotations.
Update stat_poly_eq() so that optionally instead of text labels it can return numeric values extracted from the fit object.
Document with examples how to pass weights and covariates to statistics based on methods from package ‘broom’. Highlight the differences among stat_poly_eq() and the stat_fit_xxx() statistics implemented using package ‘broom’.
Revise stat_apply_fun() to allow simultaneous application of functions to x and y aesthetics, and handling of diff() and other functions returning slightly shorter vectors than their input.
Support in stat_fit_tb(), stat_fit_augment(), stat_fit_tidy() and stat_fit_glance() the use of character strings as position arguments for parameters label.x and label.y when using geoms based on x and y aesthetics in addition to when using those taking the npcx and npcy aesthetics.
This is a major update, with a few cases in which old code may need to be revised to work, and many cases in which there will be subtle differences in the positions of labels used as annotations. The many new features may still have some bugs, please do rise an issue if you notice one!
Version requiring ‘ggplot2’ (>= 3.1.0).
Add new geometries, several of them accepting x and y in npc units through the new aesthetics npcx and npcy, allowing positioning relative to plotting area irrespective of native data units and scale limits. These geometries are useful on their own for annotations in particular they allow consistent positioning of textual summaries. By default they do not inherit the plot’s aesthetic mappings making their behaviour remain by default in-between that of true geometries and that of annotate().
geom_text_npc() and geom_label_npc() using aesthetics npcx and npcy.geom_table_npc() using aesthetics npcx and npcy.geom_plot() and geom_plot_npc() which can be used to add inset plots to a ggplot.geom_grob() and geom_grob_npc() which can be used to add inset grobs to a ggplot.geom_x_margin_point(), geom_y_margin_point(), geom_x_margin_arrow() and geom_y_margin_arrow() which behave similarly to geom_hline() and geom_vline() but plot points or arrows instead of lines. Add geom_x_margin_grob() and geom_y_margin_grob() with similar behaviour but for adding grobs.geom_table() and depended on the old default of inherit.aes=TRUE.stat_apply_panel() and stat_apply_group().stat_fit_glance() and improve diagnosis of unsupported input. Replace bad example in the corresponding documentation (workaround for bug reported by Robert White).Version requiring ‘ggplot2’ (>= 3.0.0), now in CRAN. Low level manipulation and debug methods and functions moved to new package ‘gginnards’ available through CRAN.
stat_poly_eq() (fixing bug reported by S.Al-Khalidi).stat_fit_tb().stat_fmt_tb() for formatting of tibbles for addition to plots as tables.stat_quadrat_count() into stat_quadrant_count() (miss-spelling).Non-CRAN version with additional functionality, but requiring the development version of ‘ggplot2’.
Non-CRAN version with additional functionality, but requiring the development version of ‘ggplot2’ >= 2.2.1.9000 (>= commit of 2017-02-09) from Github. Visit
geom_table(), a geom for adding a layer containing one or more tables to a plot panel.stat_fit_tb() a stat that computes a tidy tabular version of the summary or ANOVA table from a model fit.CRAN version
Add stat_quadrat_count() a stat that computes the number of observations in each quadrant of a plot panel ignoring grouping.
Fix bugs, one of which is code breaking: the names of returned parameter estimates have changed in stat_fit_tidy() now pasting "_estimate" to avoid name clashes with mapped variables.
stat_fit_tidy() so that it returns p-values for parameters, in addition to estimates and their standard errors.geom_debug() adding missing default arguments.delete_layers(), append_layers(), move_layers(), shift_layers(), which_layers(), extract_layers(), num_layers(), top_layer() and bottom_layer().Add stat_fit_tidy() implemented using broom::tidy(). Makes it possible to add the fitted equation for any fitted model supported by package ‘broom’, as long as the user supplies within aes() the code to build a label string. Update user guide.
Fix bug in stat_poly_equation() eq.x.rhs argument ignored when using expressions.
try_tibble() and try_data_frame() which made them fail silently with some objects of class "ts" in the case of numeric (decimal date) index for time. In addition lack of special handling for classes "yearmon" and "yearqrt" from package ‘zoo’, lead to erroneous date shifts by a few days.ggplot.ts() and ggplot.xts().label.fill in stat_dens2d_labels() from NA to "".stat_dens2d_labels() useful.Add stat_dens2d_labels(), a statistic that resets label values to NA by default, or any character string supplied as argument, in regions of a panel with high density of observations.
Add stat_den2d_filter(), a statistic that filters-out/filters-in observations in regions of a panel with high density of observations. These two statistics are useful for labeling or highlighting observations in regions of a panel with low density. Both stats use a compute_panel function.
Add stat_den2d_filter_g(), a statistic that filters-out/filters-in observations in regions of a group with high density of observations. This statistics is useful for highlighting observations. It uses a compute_group function. They use internally MASS:kde2d to estimate densities and default values for parameters are adjusted dynamically based on the number of observations.
stat_poly_eq().try_data_frame() to return an object of class "tibble" and add try_tibble() as synonym.stat_poly_eq().stat_poly_eq().geom_debug().stat_fit_augment().Enhance stat_poly_eq() so that 1) position of labels according to npc (relative positions using normalized coordinates), as well as by named positions "top", "bottom", "right", "left" and "center" is now implemented; 2) when grouping is present, suitable vjust values are computed to automatically position the labels for the different groups without overlap. Default label positions are now relative to the range of each panel’s \(x\) and \(y\) scales, eliminating in most cases the need to manually tweak label positions.
Add stat_fit_glance() uses package ‘broom’ for maximum flexibility in model function choice when wanting to add labels based on information from a model fit, at the expense of very frequently having to explicitly set aesthetics, and always having to add code to do the formatting of the values to be used in labels. Label position is as described above for stat_poly_eq().
Add stat_fit_deviations() for highlighting residuals in plots of fitted models. This statistic currently supports only lm() fits. By default geom “segment” is used to highlight the deviations of the observations from a fitted model.
Add stat_fit_residuals() for plotting residuals from a fitted model on their own in plots matching plots of lm fits plotted with stat_smooth() even with grouping or facets. This statistic currently supports only lm() fits. By default geom “point” is used to plot the residual from a fitted model.
Add preliminary version of stat_fit_augment(), which uses package ‘broom’ for maximum flexibility in model function choice, to augment the data with additional columns of values derived from a model fit.
stat_poly_eq().stat_poly_eq().stat_poly_eq().stat_debug_panel() and stat_debug_group() so that they can optionally print to the console a summary of the data received as input.geom_debug(), a geom that summarizes its data input to the console, and produces no visible graphical output.stat_poly_eq().try_data_frame().stat_poly_eq() changed to include the lhs (left hand side) of the equation by default.Add function try_data_frame() to convert R objects including time series objects of all classes accepted by try.xts() into data frames suitable for plotting with ggplot().
Update stat_peaks() and stat_valleys() to work correctly when the x aesthetic uses a Date or Datetime continuous scale such as ggplot() sets automatically for POSIXct variables mapped to the x aesthetic.
stat_debug() as stat_debug_group() and add stat_debug_panel().stat_peaks() and stat_valleys() (these are simpler versions of ggspectra::stat_peaks() and ggspectra::stat_valleys() for use with any numerical data (rather than light spectra).First version.
stat_poly_eq()stat_debug()