R: Simplifying code to convert letter grades to numeric grades

Question

I have written a function in R that will convert a data frame containing letter grades into numeric grades. I then use sapply() on each column of the data frame. Is there a

Answer 1

First vectorize your function: you could do this with ifelse, or:

grvec <- c("A+"=4.3,"A"=4,"A-"=3.7,"B+"=3.3,"B"=3,"B-"=2.7,
           "C+"=2.3,"C"=2,"C-"=1.7,"D+"=1.3,"D"=1,"D-"= 0.7,
       "F"=0)

grades <- data.frame(final_exam=c("C","C-","D+"),
                     quiz_avg=c("A","B-","Q"))

##   final_exam quiz_avg
## 1          C        A
## 2         C-       B-
## 3         D+        Q

num_grades <- apply(grades,2,function(x) grvec[as.character(x)])
##      final_exam quiz_avg
## [1,]        2.0      4.0
## [2,]        1.7      2.7
## [3,]        1.3       NA

Answer 2

Here is a pretty fast hash approach that shines the more grades you have:

library(qdap)

grvec <- list("A+"=4.3,"A"=4,"A-"=3.7,"B+"=3.3,"B"=3,"B-"=2.7,
    "C+"=2.3,"C"=2,"C-"=1.7,"D+"=1.3,"D"=1,"D-"= 0.7, "F"=0)

dat[] <- lapply(dat, lookup, list2df(grvec)[, c(2:1)])

##   final_exam quiz_avg homework_avg
## 1        2.0      4.0            4
## 2        1.7      2.7            4
## 3        1.3      3.3            4
## 4        3.3      3.3            4
## 5        0.0      3.3            4
## 6        3.0      3.7            4

Answer 3

I would rewrite your convert_grades function as follows:

convert_grades <- function(x) {
  A <- factor(x, levels=c("A+", "A", "A-",
                     "B+", "B", "B-",
                     "C+", "C", "C-",
                     "D+", "D", "D-", "F"))
  values <- c(4.3, 4, 3.7, 
              3.3, 3, 2.7,
              2.3, 2, 1.7,
              1.3, 1, 0.7, 0)
  values[A]
}

Then, I would do the conversion like this:

num_grades <- grades
num_grades[] <- lapply(num_grades, convert_grades)
num_grades
  final_exam quiz_avg homework_avg
1        2.0      4.0            4
2        1.7      2.7            4
3        1.3      3.3            4
4        3.3      3.3            4
5        0.0      3.3            4
6        3.0      3.7            4