ROC curve in ggplot calculation [r] - r

Question

I am trying to create a ROC curve in ggplot
I wrote function myself, however when I compare my results to results from roc_curve function from community (that I believe more) I get different results.
I would like to ask where is mistake in the function below?
library(ggplot2)
library(dplyr)
library(yardstick)
n <- 300 # sample size
data <-
data.frame(
real = sample(c(0,1), replace=TRUE, size=n),
pred = sample(runif(n), replace=TRUE, size=n)
)
simple_roc <- function(labels, scores){
labels <- labels[order(scores, decreasing=TRUE)]
data.frame(TPR=cumsum(labels)/sum(labels), FPR=cumsum(!labels)/sum(!labels), labels)
}
simple_roc(data$real, data$pred) %>%
ggplot(aes(TPR, FPR)) +
geom_line()
yardstick::roc_curve(data, factor(real), pred) %>%
ggplot(aes(1 - specificity, sensitivity)) +
geom_line()

Answer 1

First you need to anchor your ROC curve in the points (0, 0) and (1, 1).
simple_roc <- function(labels, scores){
labels <- labels[order(scores, decreasing=TRUE)]
data.frame(
TPR = c(0, cumsum(labels)/sum(labels), 1),
FPR = c(0, cumsum(!labels)/sum(!labels), 1)
)
}
Then the order in which your data is presented matters in ggplot2. Reversing the line direction should get you a bit closer:
yardstick::roc_curve(data, factor(real), pred) %>%
ggplot(aes(rev(1 - specificity), rev(sensitivity))) +
geom_line()
I would recommend against using your own function for any serious work. There are many other things that can go wrong and that well-maintained packages will handle properly such as missing values, infinite values, absence of some labels, and others that I can't even think about right now.