f_plot.R

library(data.table)
library(ROCR)
library(ggplot2)

f_plot  <- function(predictions, labels) {
    if (is.list(predictions) && is.atomic(labels))
        labels <- rep.int(list(labels), length(predictions))
    p <- prediction(predictions, labels)
    
    # Labels
    nm <- names(predictions)
    if (is.null(nm)) 
        nm <- make.names(seq_along(predictions))
    
    vals <- performance(p, "f")
    data <- data.table(
        name = rep.int(nm, lengths(vals@y.values)),
        y = unlist(vals@y.values),
        x = unlist(vals@x.values)
    )
    ggplot(data, aes(x = x, y = y, color = name)) + 
        geom_line() +
        labs(y = vals@y.name,
             x = vals@x.name,
             color = "") +
        facet_wrap(~ name, ncol = 1L, scales = "free") +
        theme(legend.position = "bottom")
}

pr_plot.R

library(data.table)
library(ROCR)
library(ggplot2)

pr_plot <- function(predictions, labels) {
    if (is.list(predictions) && is.atomic(labels))
        labels <- rep.int(list(labels), length(predictions))
    p <- prediction(predictions, labels)
    
    # Labels
    nm <- names(predictions)
    if (is.null(nm)) 
        nm <- make.names(seq_along(predictions))
    
    vals <- performance(p, "prec", "rec")
    data <- data.table(
        name = rep.int(nm, lengths(vals@y.values)),
        y1 = unlist(vals@x.values),
        y2 = unlist(vals@y.values),
        x = unlist(vals@alpha.values)
    )
    setnames(data, c("y1", "y2"), c(vals@x.name, vals@y.name))
    data <- melt(data, id.vars = c("x", "name"))
    ggplot(data, aes(x = x, y = value, color = variable)) + 
        geom_line() +
        labs(y = "Value",
             x = vals@alpha.name,
             color = "") +
        facet_wrap(~ name, ncol = 1L, scales = "free") +
        theme(legend.position = "bottom")
}

roc_plot.R

library(data.table)
library(ROCR)
library(ggplot2)

roc_plot <- function(predictions, labels) {
    if (is.list(predictions) && is.atomic(labels))
        labels <- rep.int(list(labels), length(predictions))
    
    p <- prediction(predictions, labels)
    
    # AUC
    auc <- performance(p, "auc")
    auc <- unlist(auc@y.values)
    
    # Labels
    nm <- names(predictions)
    if (is.null(nm)) 
        nm <- make.names(seq_along(predictions))
    nm <- sprintf("%s: AUC = %.3f", nm, auc)
    
    # ROC
    vals <- performance(p, "tpr", "fpr")
    data <- data.table(
        name = rep.int(nm, lengths(vals@y.values)),
        x = unlist(vals@x.values),
        y = unlist(vals@y.values)
    )
    
    # Plot
    ggplot(data, aes(x = x, y = y, color = name)) + 
        geom_path() + 
        labs(x = vals@x.name,
             y = vals@y.name,
             color = "") +
        theme(legend.position = "bottom",
              legend.direction = "vertical")
}