This gist provides a convenience function for a typical teacher duty: grade exams.

grade_exam.R

# Exam grading, as a convenience function for teachers

# Sebastian Sauer
# Stand: 2016-05-20


# need to be installed upfront with "install.packages()"
library(ggplot2)
library(car)
library(tidyr)
library(stringr)
library(magrittr)
library(gridExtra)
library(dplyr)
library(DT)



# solution vector(s)------------------------------------------------------------

# solution of my exam 01
solution_1 <-
  c(F, F, F, F, F, # 1 - 5
    T, F, T, F, F, # 6 - 10
    T, T, F, T, T, # 11 - 15
    T, F, F, T, F, # 16 - 20
    F, T, F, T, F, # 21 - 25
    F, F, F, F, T, # 26 - 30
    F, F, T, F, T, # 31- 35
    F, F, F, T, F) # 36 - 40


# solution of my exam 02
solution_2 <- c(T, T, T, T, F, # 1 - 5
             F, F, F, F, T, # 6 - 10
             T, T, F, T, T, # 11 - 15
             T, T, T, T, F, # 16 - 20
             F, T, T, F, T, # 21 - 25
             F, F, F, T, F, # 26 - 30
             T, T, F, T, T, # 31 - 35
             F, T, T, T, F # 36 - 40
)


# grading scheme ---------------------------------------------------------------
# please adjust to your scheme
# note that length of grading+1 must equal the length of the solution vector
grading <- c(5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,  #10 (starting from zero)
             5, 5, 5, 5, 5, 5, 5, 5, 5, 5,  #20
             5, 5, 5, 5, 4, 4, 4, 4, 4, 3,  #30
             3, 3, 3, 2, 2, 2, 2, 1, 1, 1) #40


# load data --------------------------------------------------------------------
# I used a google form for the exam. Works great.
# Export your data ax a CSV spreadsheet from google docs.

# either like this:
#pk <- read.csv(file.choose())

# or define path and file name:
path_data <- "~/Downloads"
data_file <- "pk_inf.csv"
setwd(path_data)
pk <- read.csv(file = data_file, header = TRUE)


# grading function -------------------------------------------------------------
# function for scoring, and matching scores to grades
grade_exam <- function(exam_data, solution_vector, grading_scheme, ID){
  # arguments:
    # exam_data: dataframe n*i with responses; n rows, i items
         # it is assumed that all questions are binary; Either correct or false.
    # solution_vector: Vector with correct solutions (logical vector)
    # grading_scheme: matching scheme for points --> grade (numeric vector)
    # ID: IDs of students/test takers, so to know who has which grade
  # returns:
    # score: vector
    # grade: integer



  # rename (shorten) variables, as google docs variable names are verbose
  items <- colnames(exam_data)
  colnames(exam_data) <- paste("i_", 1:ncol(exam_data), sep = "")


  # Recode responses to TRUE/FALSE: "Richtig" --> TRUE, "Falsch" --> FALSE
  # please adjust the responses as spit out by your form here!
  # I used German responses, you might use another language
  exam_data <- exam_data %>%
    mutate_each(funs(recode(.,
                            "'Richtig' = TRUE;
                          'Falsch' = FALSE;
                          else = NA")), 1:ncol(exam_data)) %>%
    mutate_each(funs(as.logical(.)), 1:ncol(exam_data))


  # Match responses to solution and tally up the score
  exam_data$score <- apply(exam_data, 1,
                    function(x) sum(x == solution_vector, na.rm = T))


  # add ID variable to dataframe
  exam_data$ID <- ID


  # Delete rows with score == 0, as these are mainly dummy data
  exam_data <- exam_data %>%
    filter(!(score == 0))


  # match scores to grade (assign grades)
  exam_data$grade <- grading_scheme[exam_data$score]


  # return scores and grade only
  exam_data <- exam_data %>%
    select(ID, score, grade)


  return(exam_data)
}



# now grade the test responses -------------------------------------------------

exam1 <- grade_exam(exam_data = select(pk_inf, 2:41),
                    solution_vector = solution_2,
                    grading_scheme = grading,
                    ID = pk_inf[, 42])


# plot grades
exam1 %>%
  ggplot(aes(x = grade)) %>%
  add(geom_histogram())



# plot ID alongside with score and grade
dev.off()
exam1 %>%
  arrange(ID) %>%
  grid.table()


# plot interactive table
exam1 %>%
  dplyr::filter(!is.na(ID), !(ID == "")) %>%
  arrange(ID) %>% datatable(., options = list(pagelength = 20))