In NicolasJBM/genexam: Creation and Generation of Exams

library(dplyr)
library(xtable)
options(xtable.comment = FALSE, xtable.floating = FALSE, xtable.timestamp = "")
library(ggplot2)

load("exam_data.RData")
load("student_info.RData")

base <- results %>%
  dplyr::select(student_id, score) %>%
  dplyr::group_by(student_id) %>%
  dplyr::summarize(score = sum(score, na.rm = TRUE)) %>%
  dplyr::ungroup()

student_score <- as.numeric(base[base$student_id == student_id,"score"])

group_mean <- base %>%
  dplyr::select(-student_id) %>%
  dplyr::summarize(score = mean(score, na.rm = TRUE)) %>%
  as.numeric()

if (student_score >= group_mean) col <- "darkgreen" else col <- "red"

Dear r first_name,

This is the individual report associated to the student number r as.character(student_id). Table 1 displays your answers as recorded by the scanner, and the corresponding score. Please make sure that the scanner did not make a mistake when reading your answser sheet (cf. figure 1). Check also whether points were correclty computed. Figure 2 gives you an indication about your position among all the participants to the exam. Finally, figures 3 to 5 give a detail of the percentages of points you earned in each chapter, section, or subsection. We hope this information will help you identify and address your main weaknesses.

quest_nbr <- length(unique(answers$question))
tbreaks <- unique(c(seq(from = 5, to = 45, by = 5), quest_nbr))
tbreaks <- tbreaks[tbreaks <= quest_nbr]

answers[answers$student_id == student_id,] %>%
  mutate(exam_id = as.character(exam_id)) %>%
  dplyr::select(-student_id) %>%
  xtable(
  digits = 2,
  caption = paste0("Answers recorded for the student id ", student_id)) %>%
  print(
    type = "latex",
    include.rownames = FALSE,
    format.args = list(big.mark = ",", decimal.mark = "."),
    size = "small",
    hline.after = c(-1, -1, 0, tbreaks),
    align = "ll|rrrrr",
    latex.environments = "center",
    caption.placement = "top",
    tabular.environment = "longtable",
    floating = FALSE
  )

base %>%
  ggplot(aes(x = score)) +
  geom_density() +
  geom_vline(xintercept = student_score, lty = 2, color = col) +
  annotate("text", x = student_score, y = 0.05, label = "Your\ngrade", color = col) +
  geom_vline(xintercept = group_mean, lty = 2, color = "blue") +
  annotate("text", x = group_mean, y = 0.025, label = "Group\nmean", color = "lightblue") +
  theme_bw()

reports[reports$student_id == student_id,] %>%
  dplyr::filter(level == "chapter") %>%
  dplyr::mutate(topic = reorder(topic, desc(topic))) %>%
  ggplot(aes(x = topic, y = percentage, fill = scope)) +
  geom_bar(stat = "identity", width = 0.5, position = position_dodge(width = 0.5)) +
  scale_fill_manual(values = c("lightblue", col)) +
  coord_flip() +
  ylim(0, 100) +
  theme_bw() +
  theme(legend.position = "bottom", legend.direction = "horizontal")

reports[reports$student_id == student_id,] %>%
  dplyr::filter(level == "section") %>%
  dplyr::mutate(topic = reorder(topic, desc(topic))) %>%
  ggplot(aes(x = topic, y = percentage, fill = scope)) +
  geom_bar(stat = "identity", width = 0.5, position = position_dodge(width = 0.5)) +
  scale_fill_manual(values = c("lightblue", col)) +
  coord_flip() +
  ylim(0, 100) +
  theme_bw() +
  theme(legend.position = "bottom", legend.direction = "horizontal")

reports[reports$student_id == student_id,] %>%
  dplyr::filter(level == "subsection") %>%
  dplyr::mutate(topic = reorder(topic, desc(topic))) %>%
  ggplot(aes(x = topic, y = percentage, fill = scope)) +
  geom_bar(stat = "identity", width = 0.5, position = position_dodge(width = 0.5)) +
  scale_fill_manual(values = c("lightblue", col)) +
  coord_flip() +
  ylim(0, 100) +
  theme_bw() +
  theme(legend.position = "bottom", legend.direction = "horizontal")

NicolasJBM/genexam documentation built on June 28, 2019, 2:42 p.m.