Plot confusion matrix in R using ggplot

This could be a good start

library(ggplot2)ggplot(data =  dframe, mapping = aes(x = label, y = method)) +  geom_tile(aes(fill = value), colour = "white") +  geom_text(aes(label = sprintf("%1.0f",value)), vjust = 1) +  scale_fill_gradient(low = "white", high = "steelblue")

Edited

TClass <- factor(c(0, 0, 1, 1))PClass <- factor(c(0, 1, 0, 1))Y      <- c(2816, 248, 34, 235)df <- data.frame(TClass, PClass, Y)library(ggplot2)ggplot(data =  df, mapping = aes(x = TClass, y = PClass)) +  geom_tile(aes(fill = Y), colour = "white") +  geom_text(aes(label = sprintf("%1.0f", Y)), vjust = 1) +  scale_fill_gradient(low = "blue", high = "red") +  theme_bw() + theme(legend.position = "none")

A slightly more modular solution based on MYaseen208's answer. Might be more effective for large datasets / multinomial classification:

confusion_matrix <- as.data.frame(table(predicted_class, actual_class))ggplot(data = confusion_matrix       mapping = aes(x = Var1,                     y = Var2)) +  geom_tile(aes(fill = Freq)) +  geom_text(aes(label = sprintf("%1.0f", Freq)), vjust = 1) +  scale_fill_gradient(low = "blue",                      high = "red",                      trans = "log") # if your results aren't quite as clear as the above example

Here's another ggplot2 based option; first the data (from caret):

library(caret)# data/code from "2 class example" example courtesy of ?caret::confusionMatrixlvs <- c("normal", "abnormal")truth <- factor(rep(lvs, times = c(86, 258)),                levels = rev(lvs))pred <- factor(  c(    rep(lvs, times = c(54, 32)),    rep(lvs, times = c(27, 231))),  levels = rev(lvs))confusionMatrix(pred, truth)

And to construct the plots (substitute your own matrix below as needed when setting up "table"):

library(ggplot2)library(dplyr)table <- data.frame(confusionMatrix(pred, truth)$table)plotTable <- table %>%  mutate(goodbad = ifelse(table$Prediction == table$Reference, "good", "bad")) %>%  group_by(Reference) %>%  mutate(prop = Freq/sum(Freq))# fill alpha relative to sensitivity/specificity by proportional outcomes within reference groups (see dplyr code above as well as original confusion matrix for comparison)ggplot(data = plotTable, mapping = aes(x = Reference, y = Prediction, fill = goodbad, alpha = prop)) +  geom_tile() +  geom_text(aes(label = Freq), vjust = .5, fontface  = "bold", alpha = 1) +  scale_fill_manual(values = c(good = "green", bad = "red")) +  theme_bw() +  xlim(rev(levels(table$Reference)))

# note: for simple alpha shading by frequency across the table at large, simply use "alpha = Freq" in place of "alpha = prop" when setting up the ggplot call above, e.g.,ggplot(data = plotTable, mapping = aes(x = Reference, y = Prediction, fill = goodbad, alpha = Freq)) +  geom_tile() +  geom_text(aes(label = Freq), vjust = .5, fontface  = "bold", alpha = 1) +  scale_fill_manual(values = c(good = "green", bad = "red")) +  theme_bw() +  xlim(rev(levels(table$Reference)))