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<!-- README.md is generated from README.Rmd. Please edit that file -->
# tweenr <img src="man/figures/logo.png" align="right" />
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[](https://github.com/thomasp85/tweenr/actions/workflows/R-CMD-check.yaml)
[](https://CRAN.R-project.org/package=tweenr)
[](https://CRAN.R-project.org/package=tweenr)
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## What is this?
`tweenr` is a package for interpolating data, mainly for animations. It
provides a range of functions that take data of different forms and
calculate intermediary values. It supports all atomic vector types along
with `factor`, `Date`, `POSIXct`, characters representing colours, and
`list`. `tweenr` is used extensibly by
[`gganimate`](https://github.com/thomasp85/gganimate) to create smooth
animations, but can also be used by itself to prepare data for animation
in another framework.
## How do I get it?
`tweenr` is available on CRAN and can be installed with
`install.packages('tweenr')`. In order to get the development version
you can install it from github with `devtools`
``` r
#install.packages('devtools')
devtools::install_github('thomasp85/tweenr')
```
## An example
Following is an example of using the pipeable `tween_state()` function
with our belowed iris data:
``` r
library(tweenr)
library(ggplot2)
# Prepare the data with some extra columns
iris$col <- c('firebrick', 'forestgreen', 'steelblue')[as.integer(iris$Species)]
iris$size <- 4
iris$alpha <- 1
iris <- split(iris, iris$Species)
# Here comes tweenr
iris_tween <- iris$setosa %>%
tween_state(iris$versicolor, ease = 'cubic-in-out', nframes = 30) %>%
keep_state(10) %>%
tween_state(iris$virginica, ease = 'elastic-out', nframes = 30) %>%
keep_state(10) %>%
tween_state(iris$setosa, ease = 'quadratic-in', nframes = 30) %>%
keep_state(10)
# Animate it to show the effect
p_base <- ggplot() +
geom_point(aes(x = Petal.Length, y = Petal.Width, alpha = alpha, colour = col,
size = size)) +
scale_colour_identity() +
scale_alpha_identity() +
scale_size_identity() +
coord_cartesian(xlim = range(iris_tween$Petal.Length),
ylim = range(iris_tween$Petal.Width))
iris_tween <- split(iris_tween, iris_tween$.frame)
for (d in iris_tween) {
p <- p_base %+% d
plot(p)
}
```
## Other functions
Besides the `tween_state()`/`keep_state()` combo showcased above, there
are a slew of other functions meant for data in different formats
**`tween_components`** takes a single data.frame, a vector of ids
identifying recurrent elements, and a vector of timepoints for each row
and interpolate each element between its specified time points.
**`tween_events`** takes a single data.frame where each row encodes a
single unique event, along with a start, and end time and expands the
data across a given number of frames.
**`tween_along`** takes a single data.frame along with an id and
timepoint vector and calculate evenly spaced intermediary values with
the possibility of keeping old values at each frame.
**`tween_at`** takes two data.frames or vectors along with a numeric
vector giving the interpolation point between the two data.frames to
calculate.
**`tween_fill`** fills missing values in a vector or data.frame by
interpolating between previous and next non-missing elements
## Easing
In order to get smooth transitions you’d often want a non-linear
interpolation. This can be achieved by using an easing function to
translate the equidistant interpolation points into new ones. `tweenr`
has support for a wide range of different easing functions, all of which
can be previewed using `display_ease()` as here where the popular
*cubic-in-out* is shown:
``` r
tweenr::display_ease('cubic-in-out')
```
<!-- -->
## Spatial interpolations
The purpose of `tweenr` is to interpolate values independently. If paths
and polygons needs to be transitioned the
[`transformr`](https://github.com/thomasp85/transformr) package should
be used as it expands tweenr into the spatial realm
|
