Calculate accuracy by groups

Question

I have a data frame which looks like this:

df<- data.frame("iteration" = c(1,1,1,1,1,1), 
    "model" = c("RF","RF","RF","SVM", "SVM","SVM"),
    "label" = c(0,0,1,0,0,1), "prediction" = c(0,1,1,0,1,1))

  iteration model label prediction
1         1    RF     0          0
2         1    RF     0          1
3         1    RF     1          1
4         1   SVM     0          0
5         1   SVM     0          1
6         1   SVM     1          1

Actually, it has 10 iterations, more models and more data for each model.

What I am trying to do is basically to get the accuracy for each model.

So basically I want to apply this to each model group (RF,SVM):

table(df$label,df$prediction)

    0 1
  0 2 2
  1 0 2

Them sum the diagonal and divided by the total:

sum(diag(table(df$label,df$prediction)))/sum(table(df$label,df$prediction))
[1] 0.6666667

Is this a case where I can use tapply or is dplyrcomes in handy?

I am quite lost here.

Answer 1

Try:

library(dplyr)

df %>% 
  group_by(iteration, model) %>% 
  summarise(accuracy = sum(label == prediction) / n())

Which gives:

#Source: local data frame [2 x 3]
#Groups: iteration [?]
#
#  iteration  model  accuracy
#      (dbl) (fctr)     (dbl)
#1         1     RF 0.6666667
#2         1    SVM 0.6666667

The idea is to sum the number of times label == prediction returns TRUE and divide it by the size of the partition n()