Obviously, it is possible to remember all the functions and commands to do visualization. One efficient way to index all these commands is the R Cheatsheets The cheatsheet for ggplot is available here1, which provides us a quick overview of the structure of the package.
geom_abline: straight line, usually added to the visualization to show trend or fitted linear regression.geom_vline: vertical line.geom_hline: horizontal line.geom_line: line plot, usually for time series data.geom_area: area plot, usually for time series data.geom_histogram: histogramgeom_bin2d: 2 dimensional histogramgeom_contour: contour lines to show the density estimation.geom_dotplot,geom_jitter: add random noise to scatterplot so that the points are identifiable.geom_pointrange:geom_ribbon,
geom_spoke,
geom_count,
geom_errorbar,
geom_label,
geom_polygon,
geom_rug,
geom_step,
geom_bar,
geom_crossbar,
geom_errorbarh,
geom_qq,
geom_segment,
geom_text,
geom_curve,
geom_freqpoly,
geom_linerange,
geom_qq_line,
geom_sf,
geom_tile,
geom_blank,
geom_density,
geom_hex,
geom_map,
geom_quantile,
geom_sf_label,
geom_violin,
geom_boxplot,
geom_density2d,
geom_path,
geom_raster,
geom_sf_text,
geom_col,
geom_density_2d,
geom_point,
geom_rect,
geom_smooth.The complete list of the scale functions is here2. We organize them according to their purposes as follows.
Here are the main categories (aesthetic mappings) of scale functions.
scale_x_*(): a class of functions to set up x axis.scale_y_*(): a class of functions to set up y axis.scale_color_*(): a class of functions to set up the symbol color dimension (color and colour are equivalent).scale_fill_*(): a class of functions to set up the inside color of the symbol (color is the exterior color while fill is the filled color).scale_shape_*(): a class of functions to set up the symbol shape dimension.scale_size_*(): a class of functions to set up the symbol size dimension.scale_linetype_*(): a class of function to set up the linetype dimension.scale_alpha_*(): a class of function to transparency level dimension.Within each category, we further list its members.
scale_x_*(): a class of functions to set up x axis.
scale_x_continuous(): use this function to set up x-axis if a quantitative/continuous variable is mapped to x-axis.scale_x_log10(): log10 transformation of x-axis if it is a quantitative variable.scale_x_sqrt(): square root transformation of x-axis if it is a quantitative variable.scale_x_reverse(): flip the order of x-axis if it is a quantitative variable.scale_x_discrete(): qualitative/discrete variable on x-axis.scale_x_binned(): binned variable on x-axis.scale_x_date(): date variable on x-axis.scale_x_datetime(): data and time variable on x-axis.scale_x_time(): time variable on x-axis.scale_y_*(): for y-axis.
scale_y_continuous(): for quantitative variable.scale_y_log10(): for log10 transformation of quantitative variable.scale_y_sqrt(): for square root transformation of quantitative variable.scale_y_reverse(): to flip the order of x-axisscale_y_discrete(): for qualitative variable.scale_y_binned(): for binned variable.scale_y_date(): for date variable.scale_y_datetime(): for data and time variable.scale_y_time(): for time variable.scale_color_*(): for color dimension.
scale_color_brewer(): for qualitative variable mapped to color, use R package RColorBrewer’s palatte.scale_color_distiller(): for quantitative variable mapped to color, use R package RColorBrewer’s palatte.scale_color_fermenter(): for binned variable mapped to color, use R package RColorBrewer’s palatte.scale_color_continuous(): default to scale_color_gradient().scale_color_binned(): default to scale_color_steps().scale_color_discrete(): default to scale_fill_hue()/scale_fill_brewer().scale_color_gradient(): for quantitative variable mapped to color, interpolate to two colors to get a palette (low-high).scale_color_gradient2(): for quantitative variable mapped to color, interpolate to three colors to get a palette (low-mid-high).scale_color_gradientn(): for quantitative variable mapped to color, interpolate to n colors to get a palette.scale_color_grey(): for quantitative variable mapped to color, interpolate to black and white to get a palette.scale_color_hue(): for qualitative variable mapped to color, not colour-blind safe palette.scale_color_identity(): for qualitative variable mapped to color, this variable has to already contain color as values.scale_color_manual(): for qualitative variable mapped to color, manually specify the color for each level.scale_color_steps(): for binned variable mapped to color, interpolate to two colors to get a palette (low-high).scale_color_steps2(): for binned variable mapped to color, interpolate to three colors to get a palette (low-mid-high).scale_color_stepsn(): for binned variable mapped to color, interpolate to n colors to get a palette.scale_color_viridis_d(): for qualitative variable mapped to color, use viridis color palette.scale_color_viridis_c(): for quantitative variable mapped to color, use viridis color palette.scale_color_viridis_b(): for binned variable mapped to color, use viridis color palette.scale_fill_*():
scale_fill_brewer(): for qualitative variable mapped to fill, use R package RColorBrewer’s palatte.scale_fill_distiller(): for quantitative variable mapped to fill, use R package RColorBrewer’s palatte.scale_fill_fermenter(): for binned variable mapped to fill, use R package RColorBrewer’s palatte.scale_fill_continuous(): default to scale_fill_gradient().scale_fill_binned(): default to scale_fill_steps().scale_fill_discrete(): default to scale_fill_hue()/scale_fill_brewer().scale_fill_gradient(): for quantitative variable mapped to fill, interpolate to two colors to get a palette (low-high).scale_fill_gradient2(): for quantitative variable mapped to fill, interpolate to three colors to get a palette (low-mid-high).scale_fill_gradientn(): for quantitative variable mapped to fill, interpolate to n colors to get a palette.scale_fill_grey(): for quantitative variable mapped to fill, interpolate to black and white to get a palette.scale_fill_hue(): for qualitative variable mapped to fill, not colour-blind safe palette.scale_fill_identity(): for qualitative variable mapped to fill, this variable has to already contain color as values.scale_fill_manual(): for qualitative variable mapped to fill, manually specify the color for each level.scale_fill_steps(): for binned variable mapped to fill, interpolate to two colors to get a palette (low-high).scale_fill_steps2(): for binned variable mapped to fill, interpolate to three colors to get a palette (low-mid-high).scale_fill_stepsn(): for binned variable mapped to fill, interpolate to n colors to get a palette.scale_fill_viridis_d(): for qualitative variable mapped to fill, use viridis color palette.scale_fill_viridis_c(): for quantitative variable mapped to fill, use viridis color palette.scale_fill_viridis_b(): for binned variable mapped to fill, use viridis color palette.scale_shape_*(): a class of functions to set up the symbol shape dimension.
scale_shape_manual()scale_shape_identity()scale_shape_binned()scale_shape()scale_size_*(): a class of functions to set up the symbol size dimension.
scale_size_manual()scale_size_identity()scale_size()scale_size_binned()scale_size_area()scale_size_binned_area()scale_linetype_*(): a class of function to set up the linetype dimension.
scale_linetype_manual()scale_linetype_identity()scale_linetype()scale_linetype_binned()scale_linetype_continuous()scale_linetype_discrete()scale_alpha_*(): a class of function to transparency level dimension.
scale_alpha_manual()scale_alpha_identity()scale_alpha()scale_alpha_continuous()scale_alpha_binned()scale_alpha_discrete()scale_alpha_ordinal()| Element | Setter | Description |
|---|---|---|
plot.background |
element_rect() |
plot background |
plot.title |
element_text() |
plot title |
plot.margin |
margin() |
margins around plot |
axis.line |
element_line() |
line parallel to axis (hidden in default themes) |
axis.text |
element_text() |
tick labels |
axis.text.x |
element_text() |
x-axis tick labels |
axis.text.y |
element_text() |
y-axis tick labels |
axis.title |
element_text() |
axis titles |
axis.title.x |
element_text() |
x-axis title |
axis.title.y |
element_text() |
y-axis title |
axis.ticks |
element_line() |
axis tick marks |
axis.ticks.length |
unit() |
length of tick marks |
legend.background |
element_rect() |
legend background |
legend.key |
element_rect() |
background of legend keys |
legend.key.size |
unit() |
legend key size |
legend.key.height |
unit() |
legend key height |
legend.key.width |
unit() |
legend key width |
legend.margin |
unit() |
legend margin |
legend.text |
element_text() |
legend labels |
legend.text.align |
0–1 | legend label alignment (0 = right, 1 = left) |
legend.title |
element_text() |
legend name |
legend.title.align |
0–1 | legend name alignment (0 = right, 1 = left) |
panel.background |
element_rect() |
panel background (under data) |
panel.border |
element_rect() |
panel border (over data) |
panel.grid.major |
element_line() |
major grid lines |
panel.grid.major.x |
element_line() |
vertical major grid lines |
panel.grid.major.y |
element_line() |
horizontal major grid lines |
panel.grid.minor |
element_line() |
minor grid lines |
panel.grid.minor.x |
element_line() |
vertical minor grid lines |
panel.grid.minor.y |
element_line() |
horizontal minor grid lines |
aspect.ratio |
numeric | plot aspect ratio |
strip.background |
element_rect() |
background of panel strips |
strip.text |
element_text() |
strip text |
strip.text.x |
element_text() |
horizontal strip text |
strip.text.y |
element_text() |
vertical strip text |
panel.margin |
unit() |
margin between facets |
panel.margin.x |
unit() |
margin between facets (vertical) |
panel.margin.y |
unit() |
margin between facets (horizontal) |
scale_color_brewer(): for qualitative variable mapped to color, use R package RColorBrewer’s palatte.scale_color_distiller(): for quantitative variable mapped to color, use R package RColorBrewer’s palatte.scale_color_viridis_d(): for qualitative variable mapped to color, use viridis color palette.scale_color_viridis_c(): for quantitative variable mapped to color, use viridis color palette.scale_color_gradient(): for quantitative variable mapped to color, interpolate to two colors to get a palette (low-high).scale_color_manual(): for qualitative variable mapped to color, manually specify the color for each level.The first two are for RColorBrewer color palette.
The second two are for viridis color palette.
The last two are the most flexible functions: scale_colour_gradient() and scale_colour_manual() for continuous and discrete variables, respectively.
Note that we also have another six functions for fill such as scale_fill_brewer() and etc.
For the aesthetic dimension of color, a complete list of scale functions are below.
For the aesthetic dimension of fill, a similar set of functions can be obtained by replacing *_color_*() with *_fill_*().
Their functions are the same.
scale_color_brewer(): for qualitative variable mapped to color, use R package RColorBrewer’s palatte.scale_color_distiller(): for quantitative variable mapped to color, use R package RColorBrewer’s palatte.scale_color_fermenter(): for binned variable mapped to color, use R package RColorBrewer’s palatte.scale_color_continuous(): default to scale_color_gradient().scale_color_binned(): default to scale_color_steps().scale_color_discrete(): default to scale_color_hue()/scale_color_brewer().scale_color_gradient(): for quantitative variable mapped to color, interpolate to two colors to get a palette (low-high).scale_color_gradient2(): for quantitative variable mapped to color, interpolate to three colors to get a palette (low-mid-high).scale_color_gradientn(): for quantitative variable mapped to color, interpolate to n colors to get a palette.scale_color_grey(): for quantitative variable mapped to color, interpolate to black and white to get a palette.scale_color_hue(): for qualitative variable mapped to color, not colour-blind safe palette.scale_color_identity(): for qualitative variable mapped to color, this variable has to already contain color as values.scale_color_manual(): for qualitative variable mapped to color, manually specify the color for each level.scale_color_steps(): for binned variable mapped to color, interpolate to two colors to get a palette (low-high).scale_color_steps2(): for binned variable mapped to color, interpolate to three colors to get a palette (low-mid-high).scale_color_stepsn(): for binned variable mapped to color, interpolate to n colors to get a palette.scale_color_viridis_d(): for qualitative variable mapped to color, use viridis color palette.scale_color_viridis_c(): for quantitative variable mapped to color, use viridis color palette.scale_color_viridis_b(): for binned variable mapped to color, use viridis color palette.