The transitionPlot function aims at illustrating the transition between classes. The original intent was to show how self-administered the Charnley classification behaves before and after surgery. The plot is a fancier version than what can be achieved using packages such as diagram but at the cost of flexibility. The current function only allows to show a transition from one state to the next.
Note: The new Transition class will superseed the transitionPlot. See vignette("Introduction to the Transition-class")
for more info.
We will start by simulating some data similar to my article. Each observation has a sex and a Charnley class (A, B, or C). The transition is then dependent on both the sex and the Charnley class.
set.seed(1) library(magrittr) n <- 100 data <- data.frame( Sex = sample(c("Male", "Female"), size = n, replace = TRUE), Charnley_class = sample(c("A", "B", "C"), size = n, replace = TRUE)) prob <- data.frame( A = 1/3 + (data$Sex == "Male") * .25 + (data$Sex != "Male") * -.25 + (data$Charnley_class %in% "B") * -.5 + (data$Charnley_class %in% "C") * -2 , B = 1/3 + (data$Sex == "Male") * .1 + (data$Sex != "Male") * -.05 + (data$Charnley_class == "C") * -.5, C = 1/3 + (data$Sex == "Male") * -.25 + (data$Sex != "Male") * .25) # Remove negative probabilities prob <- t(apply(prob, 1, function(x) { if (any(x < 0)){ x <- x - min(x) + .05 } x })) data$Charnley_class_after <- apply(prob, 1, function(p) sample(c("A", "B", "C"), size = 1, prob = p)) %>% factor(levels = c("A", "B", "C")) # Create the transition matrix that # is the basis for the transition plot trn_mtrx <- with(data, table(Charnley_class, Charnley_class_after)) rm(prob)
knitr::opts_chunk$set(message=FALSE, warnings=FALSE)
The most simple use is to just supply the output from the table()
call:
knitr::opts_chunk$set(fig.height = 5, fig.width=5)
library(Gmisc) transitionPlot(trn_mtrx)
The out of the box arrows are rather in my opinion not well suited and I have therefore developed a few alternatives:
transitionPlot(trn_mtrx, type_of_arrow = "simple")
And a fancy variant that I call gradient arrows as they have a colored gradient inside that fades into the arrow color:
transitionPlot(trn_mtrx, type_of_arrow = "gradient")
knitr::opts_chunk$set(fig.height = 6)
Adding title, box labels, and customizing box text can easily be done using the main
, box_label
, and box_txt
. If you provide the box_txt
a vector it assumes the same text for both left and right boxes but you can also have separate texts as in the example below:
output_perc <- function(txt, n) sprintf("%s\n[%.0f%%]", txt, n) box_txt <- cbind(mapply(output_perc, txt = c("A", "B", "C"), n = prop.table(rowSums(trn_mtrx))*100), mapply(output_perc, txt = c("A", "B", "C"), n = prop.table(colSums(trn_mtrx))*100)) transitionPlot(trn_mtrx, main = "Charnley class changess", box_label = c("Before", "After"), box_txt = box_txt, cex = 1.2, type_of_arrow = "simple")
knitr::opts_chunk$set(fig.height = 5.5)
We can enhance the data-ink ratio by adding a color split for the boxes. This works only for factors with 2 levels such as sex in our example.
trn_mtrx_3D <- with(data, table(Charnley_class, Charnley_class_after, Sex)) transitionPlot(trn_mtrx_3D, fill_start_box = c("#5C246E", "#00688B"), type_of_arrow = "simple")
The gradient arrow is most useful in this setting. It mixes the two colors according to the particular transition composition and adds an explaining gradient bar:
transitionPlot(trn_mtrx_3D, fill_start_box = c("#5C246E", "#00688B"), type_of_arrow = "gradient")
Mixed text colors can be useful when one background color is brighter and white text becomes unreadable:
transitionPlot(trn_mtrx_3D, txt_start_clr = c("white", "black"), fill_start_box = c("#5C246E", "#BFEFFF"), type_of_arrow = "gradient")
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