Function reference • animint2

Basics

All the necessities you need to render animints or static data visualizations. You begin with ggplot() and your data. Add geoms, aesthetics, and other components with +. Then make your plot interactive with animint().

ggplot(): Create a new ggplot plot.

animint(): Create an animint

aes(): Define aesthetic mappings.

`+`(<gganimint>) `%+%` `%+replace%`: Add a new component to a ggplot or theme object.

ggsave(): Save a ggplot (or other grid object) with sensible defaults

qplot() quickplot(): Quick plot

Layers

Layers determine how a graph looks and consist of the data; either a geom, a statistical transformation, or both; aesthetics; and possible position adjustments. Graphs may have more than one layer. It’s possible to use layer() to generate layers, but many people find it to be prolix. It’s convention to instead use a geom or stat function.

layer(): Create a new layer

Geoms

Geoms are geometric objects used to determine the kind of data visualization you’ll be generating. For example, you’d select geom_point() for a scatterplot and geom_line() for a line plot.

geom_abline() geom_hline() geom_vline(): Lines: horizontal, vertical, and specified by slope and intercept.

geom_bar() stat_count(): Bars, rectangles with bases on x-axis

geom_bin2d() stat_bin_2d(): Add heatmap of 2d bin counts.

geom_blank(): Blank, draws nothing.

geom_contour() stat_contour(): Display contours of a 3d surface in 2d.

geom_count() stat_sum(): Count the number of observations at each location.

geom_density() stat_density(): Display a smooth density estimate.

geom_density_2d() stat_density_2d(): Contours from a 2d density estimate.

geom_dotplot(): Dot plot

geom_errorbarh(): Horizontal error bars

geom_hex() stat_bin_hex(): Hexagon binning.

geom_freqpoly() geom_histogram() stat_bin(): Histograms and frequency polygons.

geom_jitter(): Points, jittered to reduce overplotting.

geom_crossbar() geom_errorbar() geom_linerange() geom_pointrange(): Vertical intervals: lines, crossbars & errorbars.

geom_map(): Polygons from a reference map.

geom_path() geom_line() geom_step(): Connect observations.

geom_point(): Points, as for a scatterplot

geom_polygon(): Polygon, a filled path.

geom_ribbon() geom_area(): Ribbons and area plots.

geom_rug(): Marginal rug plots.

geom_segment() geom_curve(): Line segments and curves.

geom_smooth() stat_smooth(): Add a smoothed conditional mean.

geom_spoke(): A line segment parameterised by location, direction and distance.

geom_tallrect(): ggplot2 geom with xmin and xmax aesthetics that covers the entire y range, useful for clickSelects background elements.

geom_label() geom_text(): Textual annotations.

geom_raster() geom_rect() geom_tile(): Draw rectangles.

geom_violin() stat_ydensity(): Violin plot.

geom_widerect(): ggplot2 geom with ymin and ymax aesthetics that covers the entire x range, useful for clickSelects background elements.

stat_qq() geom_qq(): Calculation for quantile-quantile plot.

update_geom_defaults() update_stat_defaults(): Modify geom/stat aesthetic defaults for future plots

Stats

Stats are similar to geoms. They differ only in that they summarize or transform the data in some way.

geom_bar() stat_count(): Bars, rectangles with bases on x-axis

geom_bin2d() stat_bin_2d(): Add heatmap of 2d bin counts.

geom_contour() stat_contour(): Display contours of a 3d surface in 2d.

geom_count() stat_sum(): Count the number of observations at each location.

geom_density() stat_density(): Display a smooth density estimate.

geom_density_2d() stat_density_2d(): Contours from a 2d density estimate.

geom_hex() stat_bin_hex(): Hexagon binning.

geom_freqpoly() geom_histogram() stat_bin(): Histograms and frequency polygons.

geom_smooth() stat_smooth(): Add a smoothed conditional mean.

geom_spoke(): A line segment parameterised by location, direction and distance.

geom_violin() stat_ydensity(): Violin plot.

stat_ecdf(): Empirical Cumulative Density Function

stat_ellipse(): Plot data ellipses.

stat_function(): Superimpose a function.

stat_identity(): Identity statistic.

stat_qq() geom_qq(): Calculation for quantile-quantile plot.

stat_summary_bin() stat_summary(): Summarise y values at unique/binned x x.

stat_summary_2d() stat_summary_hex(): Bin and summarise in 2d (rectangle & hexagons)

stat_unique(): Remove duplicates.

update_geom_defaults() update_stat_defaults(): Modify geom/stat aesthetic defaults for future plots

Position Adjustments

Position adjustments are used to stop overlapping geoms from overlapping.

position_dodge(): Adjust position by dodging overlaps to the side.

position_identity(): Don't adjust position

position_jitter(): Jitter points to avoid overplotting.

position_jitterdodge(): Adjust position by simultaneously dodging and jittering

position_nudge(): Nudge points.

position_fill() position_stack(): Stack overlapping objects on top of one another.

Annotations

Annotations denote reference points or important features in a data visualization. They don’t inherit settings from the animint or static data visualization.

geom_abline() geom_hline() geom_vline(): Lines: horizontal, vertical, and specified by slope and intercept.

annotate(): Create an annotation layer.

annotation_custom(): Annotation: Custom grob.

annotation_logticks(): Annotation: log tick marks

annotation_map(): Annotation: maps.

annotation_raster(): Annotation: High-performance rectangular tiling.

borders(): Create a layer of map borders.

Interactivity

Generate animints by first using ggplot() and creating a layer. Then place the resulting ggplot into animint(), which will automatically render the plot interactive.

animint(): Create an animint

animint2dir(): Compile and render an animint in a local directory.

animint2gist(): Convert a list of ggplots to an interactive animation and post files as a gist

animintOutput(): Shiny ui output function

renderAnimint(): Create an animint output element

Animint Helpers

These functions give you more control over your animint or make doing things with animints a little more convenient.

addShowSelectedForLegend(): Add a showSelected aesthetic if legend is specified

addSSandCSasAesthetics(): Add the showSelected/clickSelects params to the aesthetics mapping

make_bar(): Convenience function for an interactive bar that might otherwise be created using stat_summary(geom="bar").

make_tallrect(): Make a clickSelects geom_tallrect that completely tiles the x range. This makes it easy to construct tallrects for the common case of selecting a particular x value.

make_tallrect_or_widerect(): Make a clickSelects geom_widerect or geom_tallrect that completely tiles the x or y range. This function is used internally by make_tallrect or make_widerect, which are more user-friendly.

make_text(): Convenvience function for a showSelected plot label.

make_widerect(): Make a clickSelects geom_widerect that completely tiles the y range. This makes it easy to construct widerects for the common case of selecting a particular y value.

setPlotSizes(): Set plot width and height for all plots

switch_axes(): Flip axes in case of coord_flip Switches column names. Eg. xmin to ymin, yntercept to xintercept etc.

Data Manipulation

Animints take data, but they also output it. These functions give you control over both the data inputted and outputted.

colsNotToCopy(): Filter out columns that do not need to be copied

getCommonChunk(): Save the common columns for each tsv to one chunk

getLayerName(): Gives a unique name to each layer in saveLayer

getLayerParams(): Get all parameters for a layer

getLegend(): Function to get legend information for each scale

getLegendList(): Function to get legend information from ggplot

getTextSize(): Function to process text size with different types of unit

getUniqueAxisLabels(): Get unique axis labels for the plot

issueSelectorWarnings(): Issue warnings for selectors

merge_recurse(): Merge a list of data frames.

newEnvironment(): Environment to store meta data

parsePlot(): Convert a ggplot to a list.

saveChunks(): Split data set into chunks and save them to separate files.

selectSSandCS(): Separate .variable/.value selectors

split_recursive(): Split data.frame into recursive list of data.frame.

varied.chunk(): Extract subset for each data.frame in a list of data.frame

Coordinates

By default, animint2 uses a Cartesian coordinate system. These functions allow you to tweak or change the coordinate system your data visualization uses.

coord_cartesian(): Cartesian coordinates.

coord_fixed(): Cartesian coordinates with fixed relationship between x and y scales.

coord_flip(): Flipped cartesian coordinates.

coord_map() coord_quickmap(): Map projections.

coord_polar(): Polar coordinates.

coord_trans(): Transformed cartesian coordinate system.

Themes

Themes give you broad control over the appearance of everything in the data visualization orthogonal to the data. theme() gives you control over a single data visualization, while theme_set() modifies all future visualizations.

theme_grey() theme_gray() theme_bw() theme_linedraw() theme_light() theme_minimal() theme_classic() theme_dark() theme_void(): ggplot2 themes

theme(): Set theme elements

theme_animint(): theme for passing animint specific params

theme_update() theme_replace() theme_get() theme_set(): Get, set and update themes.

element_blank(): Theme element: blank. This theme element draws nothing, and assigns no space

element_line(): Theme element: line.

element_rect(): Theme element: rectangle.

element_text(): Theme element: text.

margin(): Define margins.

add_theme(): Modify properties of an element in a theme object

calc_element(): Calculate the element properties, by inheriting properties from its parents

rel(): Relative sizing for theme elements

Aesthetics

Aesthetics control how the data are connected to visual aspects of a data visualizations. You can use aes() to control color, transparency, size, shape, and more.

aes_() aes_string() aes_q(): Define aesthetic mappings from strings, or quoted calls and formulas.

aes_colour_fill_alpha colour color fill: Colour related aesthetics: colour, fill and alpha

aes_linetype_size_shape linetype size shape: Differentiation related aesthetics: linetype, size, shape

aes_position x y xmin xmax ymin ymax xend yend: Position related aesthetics: x, y, xmin, xmax, ymin, ymax, xend, yend

update_labels(): Update axis/legend labels

Scales

Scales are a subset of aesthetics. These functions control axes, legends, and the like.

labs() xlab() ylab() ggtitle(): Change axis labels, legend titles, plot title/subtitle and below-plot caption.

lims() xlim() ylim(): Convenience functions to set the axis limits.

expand_limits(): Expand the plot limits with data.

scale_alpha() scale_alpha_continuous() scale_alpha_discrete(): Alpha scales.

scale_colour_brewer() scale_fill_brewer() scale_colour_distiller() scale_fill_distiller(): Sequential, diverging and qualitative colour scales from colorbrewer.org

scale_x_continuous() scale_y_continuous() scale_x_log10() scale_y_log10() scale_x_reverse() scale_y_reverse() scale_x_sqrt() scale_y_sqrt(): Continuous position scales (x & y).

scale_x_date() scale_y_date() scale_x_datetime() scale_y_datetime(): Position scale, date & date times

scale_x_discrete() scale_y_discrete(): Discrete position.

scale_colour_gradient() scale_fill_gradient() scale_colour_gradient2() scale_fill_gradient2() scale_colour_gradientn() scale_fill_gradientn(): Smooth gradient between two colours

scale_colour_grey() scale_fill_grey(): Sequential grey colour scale.

scale_colour_hue() scale_fill_hue(): Qualitative colour scale with evenly spaced hues.

scale_colour_identity() scale_fill_identity() scale_shape_identity() scale_linetype_identity() scale_alpha_identity() scale_size_identity(): Use values without scaling.

scale_linetype() scale_linetype_continuous() scale_linetype_discrete(): Scale for line patterns.

scale_colour_manual() scale_fill_manual() scale_size_manual() scale_shape_manual() scale_linetype_manual() scale_alpha_manual(): Create your own discrete scale.

scale_shape(): Scale for shapes, aka glyphs.

scale_radius() scale_size() scale_size_area(): Scale size (area or radius).

scale_size_animint(): Scale point sizes using circle area, but specifying the radius in pixels.

Guides

Guides are a subset of scales. These functions give you finer control over scales.

guides(): Set guides for each scale.

guide_colourbar() guide_colorbar(): Continuous colour bar guide.

guide_legend(): Legend guide.

Facetting

Generate many small graphs for static data visualizations. These don’t work for animints.

facet_grid(): Lay out panels in a grid.

facet_null(): Facet specification: a single panel.

facet_wrap(): Wrap a 1d ribbon of panels into 2d.

labeller(): Generic labeller function for facets

label_value() label_both() label_context() label_parsed() label_wrap_gen(): Labeller functions

label_bquote(): Backquoted labeller

as_labeller(): Coerce to labeller function

Helpers

Some additional functions that aren’t at the core of animint2. Most of them are used for convenience or automation.

Automation

autoplot() is used to automate plot production, while the fortify functions are used to automate tidying dataframes.

autoplot(): Create a complete ggplot appropriate to a particular data type

fortify(<lm>): Supplement the data fitted to a linear model with model fit statistics.

fortify(<map>): Fortify method for map objects.

fortify(): Fortify a model with data.

fortify(<SpatialPolygonsDataFrame>) fortify(<SpatialPolygons>) fortify(<Polygons>) fortify(<Polygon>) fortify(<SpatialLinesDataFrame>) fortify(<Lines>) fortify(<Line>): Fortify method for classes from the sp package.

map_data(): Create a data frame of map data.

Extending Animint2

You don’t need to stick with just the functions animint2 gives you. Use these functions to make your own geoms, stats, and scales.

knit_print(<animint>): Insert an interactive animation into an R markdown document using a customized print method.

print(<animint>): print animint

print(<gganimintplot>) plot(<gganimintplot>): Draw plot on current graphics device.

print(<gganimintproto>): Print a gganimintproto object

as.list(<gganimintproto>): Convert a gganimintproto object to a list

format(<gganimintproto>): Format a gganimintproto object

gganimintproto() gganimintproto_parent(): Create a new gganimintproto object

is.gganimintproto(): Is an object a gganimintproto object?

Programming with Animint2

These functions help you program with animint2.

aes_() aes_string() aes_q(): Define aesthetic mappings from strings, or quoted calls and formulas.

knit_print(<animint>): Insert an interactive animation into an R markdown document using a customized print method.

print(<animint>): print animint

print(<gganimintplot>) plot(<gganimintplot>): Draw plot on current graphics device.

print(<gganimintproto>): Print a gganimintproto object

Helping Functions

These are convenience functions that may be helpful when making data visualizations.

cut_interval() cut_number() cut_width(): Cut up numeric vector into useful groups.

mean_cl_boot() mean_cl_normal() mean_sdl() median_hilow(): Wrap up a selection of summary functions from Hmisc to make it easy to use with stat_summary.

mean_se(): Calculate mean and standard errors on either side.

pt.to.lines(): Convert pt value to lines

resolution(): Compute the "resolution" of a data vector.

toRGB(): Convert R colors to RGB hexadecimal color values

Datasets

animint2 comes with a wide range of datasets. Use them to experiment with plot-building and animints.

breakpoints: The breakpointError of simulated signals

change: Graphical model structure change

ChromHMMiterations: ChromHMM iterations

climate: Climate data in central America from 1995 to 2000

compare: Testing rank and compare SVM on simulated patterns

diamonds: Prices of 50,000 round cut diamonds

economics economics_long: US economic time series.

faithfuld: 2d density estimate of Old Faithful data

FluView: FluView

FunctionalPruning: Functional Pruning Algorithm

generation.loci: Evolution simulation

intreg: Interval regression

luv_colours: colors() in Luv space.

malaria: Malaria parasite genome variants

midwest: Midwest demographics.

mixtureKNN: K-Nearest-Neighbors model of the mixture example data

montreal.bikes: Montreal bikes

mpg: Fuel economy data from 1999 and 2008 for 38 popular models of car

msleep: An updated and expanded version of the mammals sleep dataset.

PeakConsistency: Consistency of segmentation models in simulated Poisson peaks

pirates: Worldwide pirate attacks from 1978 to 2013

presidential: Terms of 11 presidents from Eisenhower to Obama.

prior: Class prior change

prostateLasso: Lasso model of the prostate cancer data set

seals: Vector field of seal movements.

TestROC: Test ROC curves

txhousing: Housing sales in TX.

UStornadoes: Tornadoes in the United States from 1950 to 2012

VariantModels: Error rates of supervised learning methods for variant calling

vervet: Vervet monkey intestinal microbiome

WorldBank: Demographics by country from 1960 to 2012

worldPop: World population by subcontinent

Testing

Some functions that may be useful for debugging.

checkAnimationTimeVar(): Check animation variable for errors

checkExtraParams(): Check extra_params argument for duplicates, non-named list

checkForSSandCSasAesthetics(): Check if showSelected and clickSelects have been used as aesthetics as in old syntax. If yes, raise error

checkPlotForAnimintExtensions(): Performs error checking on the plot for animint extensions

checkPlotList(): Check plot.list for errors

checkSingleShowSelectedValue(): Issue warnings for non interactive plots where there is only one showSelected value