Package index • ggpp

Package overview

ggpp ggpp-package: ggpp: Grammar Extensions to 'ggplot2'

Scales

Scales control de mapping of values in data to values of an aesthetic, or the propeerties of the plot’s graphic elements.

scale_npcx_continuous() scale_npcy_continuous(): Position scales for continuous data (npcx & npcy)

compute_npcx() compute_npcy() as_npcx() as_npcy() compute_npc() as_npc(): Compute NPC coordinates

Geoms

Geoms, short for geometric objects, describe the type of plot you will produce. They add a plot layer containing graphic elements representing values in the data,

annotate(): Annotations supporting NPC

geom_label_npc() geom_text_npc(): Text with Normalised Parent Coordinates

geom_label_s() geom_text_s(): Linked Text

geom_label_pairwise() geom_text_pairwise(): Label pairwise comparisons

geom_point_s(): Points linked by a segment

geom_x_margin_point() geom_y_margin_point(): Reference points on the margins

geom_x_margin_arrow() geom_y_margin_arrow(): Reference arrows on the margins

geom_x_margin_grob() geom_y_margin_grob(): Add Grobs on the margins

geom_quadrant_lines() geom_vhlines(): Reference lines: horizontal plus vertical, and quadrants

geom_plot() geom_plot_npc(): Inset plots

geom_grob() geom_grob_npc(): Inset graphical objects

geom_table() geom_table_npc(): Inset tables

ttheme_gtdefault() ttheme_gtminimal() ttheme_gtbw() ttheme_gtplain() ttheme_gtdark() ttheme_gtlight() ttheme_gtsimple() ttheme_gtstripes(): Table themes

ttheme_set() set_ttheme(): Set default table theme

Statistics

It is often useful to summarize data before plotting, e.g., fiting models or computing means, or modifying the data in some other way.

stat_dens1d_filter() stat_dens1d_filter_g(): Filter observations by local 1D density

stat_dens1d_labels(): Replace labels in data based on 1D density

stat_dens2d_filter() stat_dens2d_filter_g(): Filter observations by local 2D density

stat_dens2d_labels(): Replace labels in data based on 2D density

stat_quadrant_counts(): Number of observations in quadrants

stat_panel_counts() stat_group_counts(): Number of observations in a plot panel

stat_fmt_tb(): Select and slice a tibble nested in data

stat_apply_group() stat_summary_xy() stat_centroid(): Apply a function to x or y values

stat_functions(): Draw functions as curves

Positions

It is often useful to displace plot elements away from their original positions to obtain stacked, side-by-side or non-overlapping plot elements.

position_nudge_keep(): Nudge points a fixed distance

position_nudge_center() position_nudge_centre(): Nudge labels away from a central point

position_nudge_line(): Nudge labels away from a line

position_nudge_to(): Nudge labels to new positions

position_dodgenudge() position_dodge_keep() position_dodge2nudge() position_dodge2_keep(): Combined positions dodge and nudge

position_stacknudge() position_fillnudge() position_stack_keep() position_fill_keep() position_stack_minmax(): Combined positions stack and nudge

position_jitternudge() position_jitter_keep(): Combined positions jitter and nudge

position_dodgenudge_to() position_dodge2nudge_to(): Dodge plus nudge labels to new positions

position_stacknudge_to() position_fillnudge_to(): Stack plus nudge labels to new positions

Plot creation

Additional ggplot methods for specific classes of data objects.

ggplot(<ts>) ggplot(<xts>): Create a new ggplot plot from time series data

Helper functions

Additional functions that can be useful when creating plots.

dark_or_light(): Chose between dark and light color

try_data_frame() try_tibble(): Convert an R object into a tibble

wrap_labels(): Wrap character strings in a vector

Example data

Data sets as R objects. The data are used for examples in the documentation.

birch.df birch_dw.df: Birch seedlings' size

ivy.df: Ivy photosynthesis light response

quadrant_example.df: Gene expression data

volcano_example.df: Gene expression data

weather_18_june_2019.df: Weather data