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https://github.com/thomasp85/tweenr

Interpolate your data
https://github.com/thomasp85/tweenr

animation plotting rstats transition tweening

Last synced: 3 months ago
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Interpolate your data

Lists

README 

        
---

output: github_document

---



```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  dev = 'jpeg',

  ffmpeg.format='gif',

  interval = 1/15

)

library(magrittr)

```



# tweenr 



[![R-CMD-check](https://github.com/thomasp85/tweenr/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/thomasp85/tweenr/actions/workflows/R-CMD-check.yaml)

[![CRAN\_Release\_Badge](http://www.r-pkg.org/badges/version-ago/tweenr)](https://CRAN.R-project.org/package=tweenr) [![CRAN\_Download\_Badge](http://cranlogs.r-pkg.org/badges/tweenr)](https://CRAN.R-project.org/package=tweenr)



## 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, eval=FALSE}

#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, fig.show='animate', interval=1/10, message=FALSE, cache=TRUE}

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, dev='png'}

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



```{r, echo=FALSE}

path <- dirname(knitr::fig_path())

animations <- list.files(path, pattern = 'README-.*.gif')

for (file in animations) {

  files <- list.files(path, pattern = paste0(sub('.gif', '', file), '.*jpeg'), full.names = TRUE)

  unlink(files)

}

```