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transition_filter.md

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transition_filter

Anna Quaglieri 22/11/2018

devtools::install_github("thomasp85/gganimate")
devtools::install_github("thomasp85/transformr")
library(tidyverse)
library(gganimate)
library(transformr)
library(emo)

This is what the help function tells us transition_filter does.

?transition_filter

Transition between different filters

This transition allows you to transition between a range of filtering conditions. The conditions are expressed as logical statements and rows in the data will be retained if the statement evaluates to TRUE. It is possible to keep filtered data on display by setting keep = TRUE which will let data be retained as the result of the exit function. Note that if data is kept the enter function will have no effect.

Usage

transition_filter(transition_length, filter_length, ..., wrap = TRUE, keep = FALSE)

Let's start simple!! I believe that's where all big things start...

  • This is how a simple xy plot showing hp vs mpg from the mtcars data frame looks like using ggplot() + geom_point()
mydf <- mtcars
p1=ggplot(mydf, aes(x = hp, y = mpg)) +
geom_point()
p1

  • Let's add transition_filter()
p1 + transition_filter(transition_length = 10, 
                       filter_length = 0.2, 
                       wrap = TRUE, 
                       keep = FALSE,
                       qsec < 15,  qsec > 16) +
  labs(title="{closest_expression}")

To figure out what's happening at different stages of the transition you can ask gganimate to output the filters that are applied at different stages of the animation. This was achieved in the code above by setting the title to {closest_expression}. Every gganimate function has its own options which you can find listed in the Label variables section of a function's help page. For example these are three out of the Label variables for gganimate::transition_filter.

Label variables

  • transition_filter makes the following variables available for string literal interpretation
  • transitioning is a boolean indicating whether the frame is part of the transitioning phase
  • previous_filter The name of the last filter the animation was at

How do the filtering functions actually work?

transition_filter() requires at least two logical conditions that it will use to produce the different instances of the animation. For example, in the example below transition_filter() will plot first hp vs mpg only for rows that satisfies qsec < 15 and then it will plot hp vs mpg only for rows that satisfies qsec > 16. You can add as many conditions as you like!

Below I ran transition_filter() with three filters and added several Label variables to the title to give an idea about which filters are used at each state.

p1 + transition_filter(transition_length = 10, 
                       filter_length = 0.2, 
                       wrap = TRUE, 
                       keep = FALSE,
                       qsec < 14,  qsec > 16, qsec > 20) + 
  labs(title = "closest expression : {closest_expression}, \n transitioning : {transitioning}, \n closest_filter : {closest_filter}")

You can also provide a TRUE\FALSE column from your data frame as the logical condition!!!

mydf <- mydf %>% mutate(cond1 = qsec < 15,
                        cond2 = qsec > 15 & qsec < 20,
                        cond3 = qsec > 20)

# update ggplot plot
p1=ggplot(mydf, aes(x = hp, y = mpg)) +
geom_point()

head(mydf[,c("cond1","cond2","cond3")])
##   cond1 cond2 cond3
## 1 FALSE  TRUE FALSE
## 2 FALSE  TRUE FALSE
## 3 FALSE  TRUE FALSE
## 4 FALSE  TRUE FALSE
## 5 FALSE  TRUE FALSE
## 6 FALSE FALSE  TRUE

p1 + transition_filter(transition_length = 10, 
                       filter_length = 0.2, 
                       wrap = TRUE, 
                       keep = FALSE,
                       cond1,cond2,cond3) + 
  labs(title = "closest expression : {closest_expression}")

Let's investigate the keep argument.

In the previous examples keep has always been FALSE. To make things a little bit exciting I will set it now to TRUE.

g=ggplot(mtcars, aes(x = hp, y = mpg, colour = factor(cyl))) +
geom_point() + transition_filter(transition_length = 10, 
                            filter_length = 0.2, 
                            wrap = TRUE, 
                            keep = TRUE,
                            qsec < 14,qsec > 16,qsec > 20) + 
  labs(title = "closest expression : {closest_expression}, \n transitioning : {transitioning}, \n closest_filter : {closest_filter}")

animate(g, nframes = 10, fps = 2)

Great! But... now I cannot really see what's being filtered at each stage.

Thanks to the gganimate 📦 author Thomas for helping with understanding keep in this issue.

Normally, you would set keep = TRUE if you are interested in seeing how the filters modify your selection with respect to the whole background of observations (which comprises observations that would normally be excluded by the logical condition). The argument keep becomes useful in combination with an exit function that does not make the observations disappear. If you are not familar with exit functions you can find some examples here. In general, the exit function will do something, that you choose, to the observations that should be exiting a transition. The example below is adapted from this issue.

g=ggplot(mtcars, aes(x = hp, y = mpg, colour = factor(cyl))) + 
  geom_point(size = 2) + 
  transition_filter(transition_length = 10, 
                            filter_length = 0.2, 
                            wrap = TRUE, 
                            keep = TRUE,
                            qsec < 14,qsec > 16,qsec > 20) + 
  labs(title = "closest expression : {closest_expression}") +
  exit_manual(function(x) dplyr::mutate(x, colour = "grey",size= 1))

animate(g, nframes = 30, fps = 2)

In the example above, all the observations are always plotted, and by using the exit_manual function we can change the appearance of the observations that should actually be filtered out and highlight the ones that stay in!

On a different note, it is really interesting what happens with the code: dplyr::mutate(x, colour = "grey",size= 1)! The exter/exit functions receive the data with enter/exit the animations and they are stored in the standard data frame object. This means that we could use the dplyr::mutate() function to specify how we want to modify the entering/exiting data!

How does the wrap argument work?

wrap is a common argument to different gganimate() functions and it is pretty self explanatory:

wrap Should the animation wrap-around? If TRUE the last filter will be transitioned into the first.

When setting below, you can see that the transition between qsec > 20 (last condition) and qsec < 14 (first condition) happens with a sort of "jump". Whereas when setting wrap = TRUE the transition between the last and first condition is wrapped and happens in the same way as between the other ones, like in a cycle.

g=ggplot(mtcars, aes(x = hp, y = mpg, colour = factor(cyl))) + 
  geom_point(size = 2) + 
  transition_filter(transition_length = 10, 
                            filter_length = 0.2, 
                            wrap = FALSE, 
                            keep = TRUE,
                            qsec < 14,qsec > 16,qsec > 20) + 
  labs(title = "closest expression : {closest_expression}") +
  exit_manual(function(x) dplyr::mutate(x, colour = "grey",size= 1))

animate(g, nframes = 30, fps = 2)

g=ggplot(mtcars, aes(x = hp, y = mpg, colour = factor(cyl))) + 
  geom_point(size = 2) + 
  transition_filter(transition_length = 10, 
                            filter_length = 0.2, 
                            wrap = TRUE, 
                            keep = TRUE,
                            qsec < 14,qsec > 16,qsec > 20) + 
  labs(title = "closest expression : {closest_expression}") +
  exit_manual(function(x) dplyr::mutate(x, colour = "grey",size= 1))

animate(g, nframes = 30, fps = 2)

Errors encountered along the way

If you ever get: Error in transform_path(all_frames, next_filter, ease, params$transition_length[i],transformr is required to tween paths and lines install the package transformr.