The tweenr is all grown up

October 21, 2018
By

(This article was first published on Data Imaginist, and kindly contributed to R-bloggers)

NOTE: tweenr was released some time ago but a theft of my computer while
writing the release post meant that I only just finished writing about it now.

I’m very happy to once again announce a package update, as a new major version
of tweenr is now on CRAN. This release, while significant in itself, is also
an important part of getting gganimate on CRAN, so if you care about
gganimate but have never heard of tweenr you should be happy nonetheless.

tweenr was my first sort-of popular package and filled a gap in the gganimate
version of yore, where smooth transitions were something you had to bring
yourself. It has lived almost unchanged since its initial release, but as I
began to develop the next iteration of gganimate it became clear that new
functionality was needed. Some of it ended up in the
transformr but a huge chunk has
been added to tweenr itself. A description of everything new follows below.

Something new, something old {new_old}

The main API of the previous version of tweenr comprised of the
tween_states(), tween_elements(), and tween_appear(). All of these needed
serious change in both capabilities and API to the extend that all prior code
would break, so instead I decided to keep the old functions unchanged and create
new ones for a brighter future.

tween_states ⇒ tween_state/keep_state

tween_states() was perhaps the most used of the old functions. It takes a list
of data.frames and a specification of how long transitions between them should
take and how long it should pause at each data.frame. This is perfect for
situations where you have discrete states and want to have a smooth transition
between them. Still it had certain shortcomings, such as requiring that each
data.frame included the same number of rows etc. (meaning that each “element”
should be present in each state). Further I have ended up finding it a bit
clumsy to use. The new function(s) that should serve the same needs as
tween_states() is tween_state() (and keep_state()) and they are much more
powerful. The biggest difference, perhaps, is that tween_state() only takes a
from and to state and not an arbitrarily long list of states. If transitions
are needed between more states you’ll need to chain calls together (potentially
with keep_state() if the state should pause between transitions). It will
something like this:

library(tweenr)
irises <- split(iris, iris$Species)
iris_tween <- irises$setosa %>% 
  tween_state(irises$versicolor, ease = 'cubic-in-out', nframes = 10) %>% 
  keep_state(5) %>% 
  tween_state(irises$virginica, ease = 'linear', nframes = 15) %>% 
  keep_state(5)

As can be seen, if you like piping you’ll feel right at home. Apart from the
seemingly superficial change in API, the tween_state() also packs some new
tricks. One of these is per-column easing, so you can specify different easing
functions for different variables. Another, more fundamental one, is the
possibility of specifying an id to match rows by. Ultimately this means that
rows no longer needs to be matched by position and that you can now tween
between states with different numbers of elements. All of this is so important
that it will get its own section later on in Enter and Exit.

tween_elements ⇒ tween_components

While tween_states() was probably the most used of the legacy functions it was
by no means the only one. tween_elements() was a very powerful function that
let you specify different individual states for each element in a single
data.frame and then expand this to an arbitrary number of frames. The changes
that its hier bring is less dramatic than what happened with tween_states(),
and simply adds the same features that tween_state() introduced. This means
per-column easing and the same features as described below in
Enter and Exit. Further it changes the semantics of a couple of
variables so they are now tidy evaluated. This means that state specifications
can be calculated on the fly, rather than having to exist inside the data.frame
to be tweened.

tween_appear ⇒ tween_events

tween_appear() never really felt right. The purpose was to let each row appear
in a specific frame while still allowing the user to define how it should
appear. To this end it expanded the data.frame by giving each row an age in
each frame (negative age meant that it had yet to appear) and then let the user
do with this as they pleased. What was missing was the whole idea of
Enter and Exit which I have already plugged multiple times.
tween_event() is a pretty radical change in order to solve the problem that
tween_appear() originally tried (but failed) to solve.

Enter and Exit

Before we go any further with other new tweening functions I think I owe it to
you to describe what all this entering and exiting I’ve been talking about
really is. If you have dabbled in D3.js the two words will be familiar but they
have slightly different meaning in tweenr. In D3 enter and exit describe a
selection of data that did not match in a data join between current and next
state, while in tweenr it is a function that modifies data that is going to
appear or disappear. If enter and/or exit is not given, the data will just pop
into existence in the first frame it relates to and disappear without a trace
after the last frame it relates to has ended. if you provide e.g. an enter
function this will be applied to all elements when they first appear. The result
of the function will then be inserted into the tweening prior to the original
data so that any changes the function does will gradually change to the original
data. This may sound quite confusing, but in essence it means that if you pass
in an enter function that sets the transparancy variable to zero you’ll get a
gradual fade-in effect. The exit function is just like it, but in reverse. But
let’s see it in effect instead:

df1 <- data.frame(x = 1:2, y = 2, alpha = 1) # 2 rows
df2 <- data.frame(x = 2:0, y = 1, alpha = 1) # 3 rows

fade <- function(data) {
  data$alpha <- 0
  data
}

tween <- tween_state(df1, df2, ease = 'linear', nframes = 5, enter = fade)

tween$alpha[tween$.id == 3]
## [1] 0.25 0.50 0.75 1.00

We can see that the alpha value of the third element (the one that doesn’t exist
in the first state) gradually increase from zero to one. The reason why 0 isn’t
included is that the enter and exit function return virtual states that doens’t
remain in the data – only the transition to and from them does.

New tweens on the block

Appart from the new versions of the old functionality discussed above
this version also includes some brand new tweens, mainly implemented to serve
needs in gganimate but of course also available for everyone else.

tween_along

This is the tweening function that powers transition_reveal() in gganimate
if you have played with that you are fairly well situated to understand what it
does. In essence it allows you to specify time points for the different rows in
your data.frame and then tween between these. You might think that this is
exactly what tween_components() does and you’d partly be right. The big
difference is that tween_along() ensures equidistant frames, whereas
tween_components() assigns all rows in the data to the nearest frame and then
use the frame as a time variable. The latter will always have the raw data
appearing in one frame or another while the former will not. Further,
tween_along() will optionally keep earlier rows in your data at each frame,
which is useful if you e.g. want a line to gradually appear along an axis.

tween_at

This is a pretty low level tweening function intened to get an exact state
between two data frames or vectors. It takes two states and a numeric vector
giving the tween position for each row and then calculates the intermediary
rows.

tween_at(mtcars[1:3, ], mtcars[4:6, ], runif(3), 'linear')
##        mpg      cyl     disp       hp     drat       wt     qsec        vs
## 1 21.27039 6.000000 226.2455 110.0000 3.345701 3.022205 18.47440 0.6759747
## 2 20.51284 6.423621 202.3621 123.7677 3.741142 2.994673 17.02000 0.0000000
## 3 19.77526 5.287121 183.2966 100.7227 3.148519 3.053659 19.64613 1.0000000
##          am     gear     carb
## 1 0.3240253 3.324025 1.972076
## 2 0.7881894 3.788189 3.576379
## 3 0.3564393 3.356439 1.000000

This is unlikely to be useful for directly creating animations (though I guess
it is low-level enough to be able to be shoehorned into anything). It is being
used in gganimate for calculating shadow falloff in shadow_wake().

tween_fill

This tween takes a page out of tidyr::fill and simply fill out missing
elements in a data frame or vector. Instead of being boring and repeating the
prior or following data it doesn the tweenr thing and tweens between them:

mtcars2 <- mtcars[1:7, ]
mtcars2[2:6, ] <- NA

tween_fill(mtcars2, 'cubic-in-out')
##        mpg      cyl     disp    hp     drat       wt     qsec vs
## 1 21.00000 6.000000 160.0000 110.0 3.900000 2.620000 16.46000  0
## 2 20.87593 6.037037 163.7037 112.5 3.887222 2.637593 16.44852  0
## 3 20.00741 6.296296 189.6296 130.0 3.797778 2.760741 16.36815  0
## 4 17.65000 7.000000 260.0000 177.5 3.555000 3.095000 16.15000  0
## 5 15.29259 7.703704 330.3704 225.0 3.312222 3.429259 15.93185  0
## 6 14.42407 7.962963 356.2963 242.5 3.222778 3.552407 15.85148  0
## 7 14.30000 8.000000 360.0000 245.0 3.210000 3.570000 15.84000  0
##           am     gear carb
## 1 1.00000000 4.000000    4
## 2 0.98148148 3.981481    4
## 3 0.85185185 3.851852    4
## 4 0.50000000 3.500000    4
## 5 0.14814815 3.148148    4
## 6 0.01851852 3.018519    4
## 7 0.00000000 3.000000    4

Neat-o…

Grab bag of niceties

There are more subtle additions to tweenr as well, most of which has also been
driven by gganimate needs. Here’s an unceremonious list:

  • More information: In olden days tweenr simply added a .frame column to
    the output to identify the frame the row belonged to. It still does, but that
    column is now accompagnied by a .phase column that tells if the data is raw,
    static, entering, exiting, or transitioning, and an .id column that identifies
    the same data across frames.
  • More support: The supported data types has been expanded considerably. Most
    notably list columns are now accepted. If the list only contains numeric vectors
    these vectors will get tweened accordingly, and if not the list will be treated
    as constant.
  • Better colour support: Colour has always been tweened in the LAB
    representation to get a more natural transition. In the old version the native
    convertColor() function was used, but this could lead to substantial slowdown
    when tweening lots of data. To address this farver was developed and released
    and this version of tweenr naturally uses this for colour space conversions
    now. In addition, tweenr now supports hex-colours with alpha.

I think that is it, but frankly there has been so many additions that I may have
missed a few… First to find something I missed gets a sticker!

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