Grade analysis

rm(list = ls())
library(here)

## here() starts at /Users/ashish/files/research/projects/psych80_survey

library(tidyverse)

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library(janitor)

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library(tidylog)

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library(kableExtra)

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theme_set(theme_bw())

1 Read data

gradebook_filepath <- "data/2024-03-24T1119_Grades-W24-PSYCH-80-01.csv"

df_gradebook_raw <- read_csv(here(gradebook_filepath)) %>% 
  clean_names 

## Rows: 100 Columns: 35
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (34): Student, SIS User ID, SIS Login ID, Section, SU ID, Assignment #2 ...
## dbl  (1): ID
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

df_gradebook <- df_gradebook_raw %>% 
  drop_na(id) %>% 
  rename(suid = su_id) %>% 
  drop_na(suid) %>% 
  filter(suid != "06778030") %>% # jahfari
  mutate(suid = as.numeric(suid)) %>%
  rename_all(~str_replace(., "_[0-9]{6}$", "")) %>% 
  rename(exam_2 = exam_number_2) %>% 
  mutate_at(vars(
    exam_1, 
    exam_1_for_traveling_athletes,
    exam_number_2_traveling_athletes,
    exam_2,
    final_exam,
    assignment_number_1,
    assignment_number_2,
    assignment_number_3,
    assignment_number_4,
    extra_credit,
    participation), 
    as.numeric) %>%
  mutate(exam_2 = ifelse(!is.na(exam_number_2_traveling_athletes),
                         exam_number_2_traveling_athletes,
                          exam_2)) %>% 
  select(-matches("traveling_athletes")) %>%
  rename_at(vars(matches("assignment_number_[1-4]")), 
            ~str_replace(., "number_", "")) %>% 
  rename(sunet = sis_login_id) %>% 
  # -- dropped class -- #
  filter(!str_detect(student, "Dietrich"))

## drop_na: removed 2 rows (2%), 98 rows remaining
## rename: renamed one variable (suid)
## drop_na: removed 3 rows (3%), 95 rows remaining
## filter: removed one row (1%), 94 rows remaining
## mutate: converted 'suid' from character to double (0 new NA)
## rename_all: renamed 11 variables (assignment_number_2, assignment_number_3, exam_1, exam_number_2_traveling_athletes, assignment_number_1, …)
## rename: renamed one variable (exam_2)

## Warning: There were 3 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `exam_1_for_traveling_athletes =
##   .Primitive("as.double")(exam_1_for_traveling_athletes)`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.

## mutate_at: converted 'assignment_number_2' from character to double (0 new NA)
##            converted 'assignment_number_3' from character to double (0 new NA)
##            converted 'exam_1' from character to double (0 new NA)
##            converted 'exam_number_2_traveling_athletes' from character to double (93 new NA)
##            converted 'assignment_number_1' from character to double (0 new NA)
##            converted 'extra_credit' from character to double (0 new NA)
##            converted 'assignment_number_4' from character to double (0 new NA)
##            converted 'exam_2' from character to double (0 new NA)
##            converted 'final_exam' from character to double (92 new NA)
##            converted 'participation' from character to double (0 new NA)
##            converted 'exam_1_for_traveling_athletes' from character to double (92 new NA)
## mutate: no changes
## select: dropped 2 variables (exam_number_2_traveling_athletes, exam_1_for_traveling_athletes)
## rename_at: renamed 4 variables (assignment_2, assignment_3, assignment_1, assignment_4)
## rename: renamed one variable (sunet)
## filter: removed one row (1%), 93 rows remaining

2 Process data

exam_filepath <- "data/Final exam grading.xlsx"

df_exam_raw <- readxl::read_excel(here(exam_filepath), 
                                  sheet = "Update") %>% 
  clean_names


df_mc_key <- df_exam_raw %>% 
  slice(1) %>% 
  select(matches("mc[0-9]{,2}$")) %>% 
  pivot_longer(cols = everything(), 
               names_to = "mc",
               values_to = "key")

## slice: removed 96 rows (99%), one row remaining

## select: dropped 21 variables (exam_number, name, suid, sunet, mc_total, …)

## pivot_longer: reorganized (mc1, mc2, mc3, mc4, mc5, …) into (mc, key) [was 1x25, now 25x2]

df_mc <- df_exam_raw %>% 
  drop_na(suid) %>% 
  drop_na(total_score) %>% 
  select(name, suid, matches("mc[0-9]{,2}$")) %>% 
  pivot_longer(cols = -c(name, suid), 
               names_to = "mc",
               values_to = "response") %>%
  full_join(df_mc_key) %>% 
  mutate(score = if_else(response == key, 1, 0)) %>% 
  select(-key, -response) %>%
  mutate(mc = paste0(mc, "_score")) %>% 
  pivot_wider(id_cols = c(name, suid), 
              names_from = mc, 
              values_from = score) 

## drop_na: removed 3 rows (3%), 94 rows remaining

## drop_na: removed 3 rows (3%), 91 rows remaining

## select: dropped 19 variables (exam_number, sunet, mc_total, sa1, sa2, …)

## pivot_longer: reorganized (mc1, mc2, mc3, mc4, mc5, …) into (mc, response) [was 91x27, now 2275x4]

## Joining with `by = join_by(mc)`
## full_join: added one column (key)
## > rows only in x 0
## > rows only in y 0
## > matched rows 2,275
## > =======
## > rows total 2,275
## mutate: new variable 'score' (double) with 3 unique values and 1% NA
## select: dropped 2 variables (response, key)
## mutate: changed 2,275 values (100%) of 'mc' (0 new NA)
## pivot_wider: reorganized (mc, score) into (mc1_score, mc2_score, mc3_score,
## mc4_score, mc5_score, …) [was 2275x4, now 91x27]

df_exam <- df_exam_raw %>% 
  drop_na(suid) %>% 
  drop_na(total_score) %>% 
  left_join(df_mc) %>% 
  mutate(total_score = total_score + 3) %>%  # adding 3 points because of MC questions that most got wrong
  # left pad the numbers after mc in the column names to make them all the same length
  
  rename_at(vars(matches("mc[0-9]($|_)")), 
            function(x){
              x <- str_replace_all(x, "mc", "")
              x <- str_pad(x, width = 2, side = "left", pad = "0")
              paste0("mc", x)
            }) 

## drop_na: removed 3 rows (3%), 94 rows remaining
## drop_na: removed 3 rows (3%), 91 rows remaining
## Joining with `by = join_by(name, suid)`left_join: added 25 columns (mc1_score, mc2_score, mc3_score, mc4_score, mc5_score, …)
##            > rows only in x    0
##            > rows only in y  ( 0)
##            > matched rows     91
##            >                 ====
##            > rows total       91
## mutate: changed 91 values (100%) of 'total_score' (0 new NA)
## rename_at: renamed 9 variables (mc01, mc02, mc03, mc04, mc05, …)

3 Final exam

3.1 MC

3.1.1 Total MC Score

df_exam %>% 
  select(name, suid, matches("mc[0-9]{,2}_score$")) %>% 
  pivot_longer(cols = -c(name, suid), 
               names_to = "mc",
               values_to = "score") %>%
  drop_na(mc) %>% 
  group_by(name, suid) %>% 
  summarize(score = sum(score)) %>%
  ggplot(aes(x = score)) +
  geom_histogram(bins = 20) +
  geom_vline(aes(xintercept = mean(score, na.rm=T)), color = "red") +
  geom_label(
    aes(label = paste0("M = ", round(mean(score, na.rm=T), 2),
                       "\nSD = ", round(sd(score, na.rm=T), 2)
    )),
    x=15, y = 10,
    vjust = -1) +
  labs(title = "Multiple Choice Scores", x = "Score", y = "Count")

## select: dropped 44 variables (exam_number, sunet, mc01, mc02, mc03, …)

## pivot_longer: reorganized (mc1_score, mc2_score, mc3_score, mc4_score, mc5_score, …) into (mc, score) [was 91x27, now 2275x4]

## drop_na: no rows removed

## group_by: 2 grouping variables (name, suid)

## summarize: now 91 rows and 3 columns, one group variable remaining (name)

## Warning: Removed 4 rows containing non-finite values (`stat_bin()`).

3.1.2 By question

d <- df_exam %>% 
  select(name, suid, matches("mc[0-9]{,2}_score$")) %>% 
  pivot_longer(cols = -c(name, suid), 
               names_to = "mc",
               values_to = "score") %>%
  drop_na(mc) %>% 
  mutate(mc = str_replace_all(mc, "[a-z_]", "")) %>%
  mutate(mc = str_pad(mc, width = 2, side = "left", pad = "0")) 

## select: dropped 44 variables (exam_number, sunet, mc01, mc02, mc03, …)

## pivot_longer: reorganized (mc1_score, mc2_score, mc3_score, mc4_score, mc5_score, …) into (mc, score) [was 91x27, now 2275x4]

## drop_na: no rows removed

## mutate: changed 2,275 values (100%) of 'mc' (0 new NA)

## mutate: changed 819 values (36%) of 'mc' (0 new NA)

d %>%
  group_by(mc) %>%
  summarize(score = mean(score, na.rm=T)) %>%
  ggplot(aes(x = mc, y = score)) +
  geom_col() +
  geom_text(aes(label = round(score, 2)), vjust = -0.5, size = 3) +
  labs(title = "Multiple Choice by Question", y = "Proportion correct")

## group_by: one grouping variable (mc)

## summarize: now 25 rows and 2 columns, ungrouped

df_exam %>% 
  select(name, suid, matches("mc[0-9]{,2}$")) %>% 
  pivot_longer(cols = -c(name, suid), 
               names_to = "mc",
               values_to = "response") %>% 
  ggplot(aes(x = response)) + 
  geom_bar(stat = "count") + 
  facet_wrap(~mc, scales = "free") 

## select: dropped 44 variables (exam_number, sunet, mc_total, sa1, sa2, …)

## pivot_longer: reorganized (mc01, mc02, mc03, mc04, mc05, …) into (mc, response) [was 91x27, now 2275x4]

3.1.3 By grader

df_exam %>%
  ggplot(aes(x = mc_sa_grader, y = mc_total)) +
  geom_bar(stat = "summary", fun = "mean") +
  geom_errorbar(stat = "summary", 
                fun.data = mean_sdl,
                fun.args = list(mult = 1),
                width = 0.2) +
  geom_point( alpha = .2, 
              position = position_jitter(width = .2,
                                         height = 0))

3.2 SA

3.2.1 Total

df_exam %>% 
  ggplot(aes(x = sa_total)) + 
  geom_histogram(bins = 25) +
  labs(title = "Short Answer Scores", x = "Score", y = "Count") +
  geom_vline(aes(xintercept = mean(sa_total, na.rm=T)), color = "red") +
  geom_label(
    aes(label = paste0("M = ", round(mean(sa_total, na.rm=T), 2),
                       "\nSD = ", round(sd(sa_total, na.rm=T), 2)
    )),
    x=15, y = 10,
    vjust = -1)  +
  scale_x_continuous(breaks = seq(0, 25, 1))

3.2.2 By grader

df_exam %>%
  ggplot(aes(x = mc_sa_grader, y = sa_total)) + 
  geom_bar(stat = "summary", fun = "mean") +
  geom_point(alpha = .4, position=position_jitter(.2)) +
  geom_errorbar(stat = "summary", fun.data=mean_sdl, 
                fun.args = list(mult=1),
                width = 0.5) 

df_exam %>% 
  group_by(mc_sa_grader) %>% 
  summarize(mean_score = mean(sa_total),
            sd_score = sd(sa_total))

## group_by: one grouping variable (mc_sa_grader)

## summarize: now 4 rows and 3 columns, ungrouped

## # A tibble: 4 × 3
##   mc_sa_grader mean_score sd_score
##   <chr>             <dbl>    <dbl>
## 1 Allen              20.6     3.96
## 2 Arethea            19.6     3.90
## 3 Ashish             21.9     3.82
## 4 Kate               20.0     6.02

3.2.3 By question

d <- df_exam %>%
  select(name, suid, matches("sa[0-9]$"), mc_sa_grader) %>%
  pivot_longer(-c(name, suid, mc_sa_grader), 
               names_to = "sa",
               values_to = "score") %>%
  drop_na(score) 

## select: dropped 60 variables (exam_number, sunet, mc01, mc02, mc03, …)

## pivot_longer: reorganized (sa1, sa2, sa3, sa4, sa5, …) into (sa, score) [was 91x11, now 728x5]

## drop_na: removed 273 rows (38%), 455 rows remaining

d %>% 
  ggplot(aes(x = sa, y = score)) + 
  geom_bar(stat = "summary", fun = "mean") +
  geom_point(alpha = .4, position=position_jitter(.2)) +
  geom_errorbar(stat = "summary", fun.data=mean_sdl, 
                fun.args = list(mult=1),
                width = 0.5) 

d %>% 
  group_by(sa) %>% 
  summarize(mean_score = mean(score),
            sd_score = sd(score)) 

## group_by: one grouping variable (sa)

## summarize: now 8 rows and 3 columns, ungrouped

## # A tibble: 8 × 3
##   sa    mean_score sd_score
##   <chr>      <dbl>    <dbl>
## 1 sa1         4.14    1.30 
## 2 sa2         4.12    1.01 
## 3 sa3         4.35    0.724
## 4 sa4         4.15    1.25 
## 5 sa5         3.38    1.38 
## 6 sa6         4.02    1.23 
## 7 sa7         3.93    1.39 
## 8 sa8         4.52    0.903

3.2.4 By grader by question - 5

d <- df_exam %>%
  select(name, suid, matches("sa[0-9]$"), mc_sa_grader) %>%
  pivot_longer(-c(name, suid, mc_sa_grader), 
               names_to = "sa",
               values_to = "score") %>%
  filter(sa=="sa5") %>% 
  drop_na(score)

## select: dropped 60 variables (exam_number, sunet, mc01, mc02, mc03, …)

## pivot_longer: reorganized (sa1, sa2, sa3, sa4, sa5, …) into (sa, score) [was 91x11, now 728x5]

## filter: removed 637 rows (88%), 91 rows remaining

## drop_na: removed 40 rows (44%), 51 rows remaining

d %>% 
  ggplot(aes(x = mc_sa_grader, y = score)) + 
  geom_bar(stat = "summary", fun = "mean") +
  geom_point(alpha = .4, position=position_jitter(.2)) +
  geom_errorbar(stat = "summary", fun.data=mean_sdl, 
                fun.args = list(mult=1),
                width = 0.5) 

d %>% 
  group_by(mc_sa_grader) %>% 
  summarize(n = n())

## group_by: one grouping variable (mc_sa_grader)

## summarize: now 4 rows and 2 columns, ungrouped

## # A tibble: 4 × 2
##   mc_sa_grader     n
##   <chr>        <int>
## 1 Allen           13
## 2 Arethea         12
## 3 Ashish          16
## 4 Kate            10

3.2.5 By grader by question - all qs

d <- df_exam %>%
  select(name, suid, matches("sa[0-9]$"), mc_sa_grader) %>%
  pivot_longer(-c(name, suid, mc_sa_grader), 
               names_to = "sa",
               values_to = "score") %>%
  drop_na(score)

## select: dropped 60 variables (exam_number, sunet, mc01, mc02, mc03, …)

## pivot_longer: reorganized (sa1, sa2, sa3, sa4, sa5, …) into (sa, score) [was 91x11, now 728x5]

## drop_na: removed 273 rows (38%), 455 rows remaining

d %>% 
  ggplot(aes(x = mc_sa_grader, y = score)) + 
  geom_bar(stat = "summary", fun = "mean") +
  geom_point(alpha = .4, position=position_jitter(.2)) +
  geom_errorbar(stat = "summary", fun.data=mean_sdl, 
                fun.args = list(mult=1),
                width = 0.5) + 
  facet_wrap(~sa)

d %>% 
  group_by(mc_sa_grader) %>% 
  summarize(n = n())

## group_by: one grouping variable (mc_sa_grader)

## summarize: now 4 rows and 2 columns, ungrouped

## # A tibble: 4 × 2
##   mc_sa_grader     n
##   <chr>        <int>
## 1 Allen          120
## 2 Arethea        100
## 3 Ashish         115
## 4 Kate           120

3.2.6 By grader by question - 6

d <- df_exam %>%
  select(name, suid, matches("sa[0-9]$"), mc_sa_grader) %>%
  pivot_longer(-c(name, suid, mc_sa_grader), 
               names_to = "sa",
               values_to = "score") %>%
  filter(sa=="sa6") %>% 
  drop_na(score)

## select: dropped 60 variables (exam_number, sunet, mc01, mc02, mc03, …)

## pivot_longer: reorganized (sa1, sa2, sa3, sa4, sa5, …) into (sa, score) [was 91x11, now 728x5]

## filter: removed 637 rows (88%), 91 rows remaining

## drop_na: removed 20 rows (22%), 71 rows remaining

d %>% 
  ggplot(aes(x = mc_sa_grader, y = score)) + 
  geom_bar(stat = "summary", fun = "mean") +
  geom_point(alpha = .4, position=position_jitter(.2)) +
  geom_errorbar(stat = "summary", fun.data=mean_sdl, 
                fun.args = list(mult=1),
                width = 0.5) 

3.3 Essay

df_exam %>% 
  ggplot(aes(x = essay_total)) + 
  geom_histogram(bins = 25) +
  geom_label(
    aes(label = paste0("M = ", round(mean(essay_total, na.rm=T), 2),
                       "\nSD = ", round(sd(essay_total, na.rm=T), 2)
    )),
    x=15, y = 10,
    vjust = -1)  +
  labs(title = "Essay Scores", x = "Score", y = "Count") +
  scale_x_continuous(breaks = seq(0, 20, 1))

3.3.1 By grader

df_exam %>%
  ggplot(aes(x = essay_grader, y = essay_total)) + 
  geom_bar(stat = "summary", fun = "mean") + 
  geom_point(alpha = .4, position=position_jitter(.2)) +
  geom_errorbar(stat = "summary", fun.data=mean_sdl, 
                fun.args = list(mult=1),
                width = 0.5)

3.4 Total

df_exam %>% 
  ggplot(aes(x = total_score)) + 
  geom_histogram(bins = 25) +
  labs(title = "Total Scores", x = "Score", y = "Count") +
  scale_x_continuous(breaks = seq(0, 72, 5)) +
  geom_vline(aes(xintercept = mean(total_score, na.rm=T)), color = "red") +
  geom_label(
    aes(label = paste0("M = ", round(mean(total_score, na.rm=T), 2),
                       "\nSD = ", round(sd(total_score, na.rm=T), 2)
    )),
    x=40, y = 10,
    vjust = -1)

df_exam %>% 
  select(name, mc_total, sa_total, essay_total, total_score) %>% 
  view

## select: dropped 66 variables (exam_number, suid, sunet, mc01, mc02, …)

4 Class grades

# 5 + 5 + 5 + 5 + 40 + 40 + 70 + 5
df_gradebook_final <- df_gradebook %>% 
  left_join(df_exam %>% 
              select(suid, total_score) %>% 
              rename(final_exam_score = total_score)) %>% 
  mutate_at(vars(assignment_1,
                 assignment_2,
                 assignment_3,
                 assignment_4,
                 exam_1,
                 exam_2,
                 participation,
                 extra_credit,
                 final_exam_score), ~replace_na(as.numeric(.), 0)) %>% 
  mutate(total_class_score = 
           (assignment_1/5) * 5 + 
           (assignment_2/5) * 5  + 
           (assignment_3/5) * 5  + 
           (assignment_4/5) * 5  + 
           (exam_1 / 40) * 20 + 
           (exam_2 / 40) * 20 + 
           (participation / 5) * 5 + 
           extra_credit / 2 * 2 + 
           (final_exam_score / 70) * 35) %>% 
  select(student, sunet, suid, 
         assignment_1,
         assignment_2,
         assignment_3,
         assignment_4,
         exam_1,
         exam_2,
         participation,
         extra_credit,
         final_exam_score,
         total_class_score) 

## select: dropped 69 variables (exam_number, name, sunet, mc01, mc02, …)

## rename: renamed one variable (final_exam_score)

## Joining with `by = join_by(suid)`
## left_join: added one column (final_exam_score)
## > rows only in x 2
## > rows only in y ( 0)
## > matched rows 91
## > ====
## > rows total 93
## mutate_at: changed 2 values (2%) of 'assignment_2' (2 fewer NA)
## changed 2 values (2%) of 'assignment_3' (2 fewer NA)
## changed one value (1%) of 'exam_1' (1 fewer NA)
## changed 5 values (5%) of 'assignment_1' (5 fewer NA)
## changed 35 values (38%) of 'extra_credit' (35 fewer NA)
## changed 3 values (3%) of 'assignment_4' (3 fewer NA)
## changed 5 values (5%) of 'participation' (5 fewer NA)
## changed 2 values (2%) of 'final_exam_score' (2 fewer NA)
## mutate: new variable 'total_class_score' (double) with 90 unique values and 0%
## NA
## select: dropped 22 variables (id, sis_user_id, section, final_exam,
## assignments_current_points, …)

4.1 Total class scores

99+ = A+ 93-99 = A 90-92 = A- 87-89 = B+ 83-86 = B 80-82 = B- 77-79 = C+ 73-76 = C 70-72 = C- 67-69 = D+ 63-66 = D 60-62 = D- <60 = F

df_gradebook_final %>% 
  summarize(
    mean = mean(total_class_score),
    sd = sd(total_class_score)
  )

## summarize: now one row and 2 columns, ungrouped

## # A tibble: 1 × 2
##    mean    sd
##   <dbl> <dbl>
## 1  89.6  13.2

df_gradebook_final %>% 
  ggplot(aes(x = total_class_score)) +
  geom_histogram() + 
  geom_vline(aes(xintercept = mean(total_class_score)), color = "red") +
  geom_label(
    aes(label = paste0("M = ", round(mean(total_class_score), 2),
                       "\nSD = ", round(sd(total_class_score), 2)
    )),
    x=60, y = 10,
    vjust = -1) 

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

df_letter_grades <- df_gradebook_final %>% 
  mutate(grade = case_when(
    total_class_score >= 99 ~ "A+",
    total_class_score >= 93 & total_class_score < 99 ~ "A",
    total_class_score >= 90 & total_class_score < 93 ~ "A-",
    total_class_score >= 87 & total_class_score < 90 ~ "B+",
    total_class_score >= 83 & total_class_score < 87 ~ "B",
    total_class_score >= 80 & total_class_score < 83 ~ "B-",
    total_class_score >= 77 & total_class_score < 80 ~ "C+",
    total_class_score >= 73 & total_class_score < 77 ~ "C",
    total_class_score >= 70 & total_class_score < 73 ~ "C-",
    total_class_score >= 67 & total_class_score < 70 ~ "D+",
    total_class_score >= 63 & total_class_score < 67 ~ "D",
    total_class_score >= 60 & total_class_score < 63 ~ "D-",
    total_class_score < 60 ~ "F"
  )) %>% 
  arrange(desc(total_class_score)) %>% 
  mutate(rank = row_number()) %>% 
  group_by(grade) %>% 
  mutate(cumul_n = max(rank)) %>% 
  ungroup

## mutate: new variable 'grade' (character) with 12 unique values and 0% NA

## mutate: new variable 'rank' (integer) with 93 unique values and 0% NA

## group_by: one grouping variable (grade)

## mutate (grouped): new variable 'cumul_n' (integer) with 12 unique values and 0% NA

## ungroup: no grouping variables

df_letter_grades %>%
  # filter(final_exam_score > 0) %>% 
  mutate(grade = fct_reorder(grade, total_class_score, .desc = T)) %>%
  group_by(grade, cumul_n) %>% 
  summarize(
    n = n(),
    cumul_n = max(cumul_n),
    .groups = "drop"
  ) %>%
  ggplot(aes(x = grade, y = n)) +
  geom_col() + 
  geom_text(aes(label = n), vjust=-.5) +
  # geom_text(aes(label = cumul_n), y = 12) +
  labs(title = "Class Grades", x = "Grade", y = "Count")

## mutate: converted 'grade' from character to factor (0 new NA)

## group_by: 2 grouping variables (grade, cumul_n)

## summarize: now 12 rows and 3 columns, ungrouped

5 Each student’s final class grade

df_letter_grades %>% 
  select(-sunet, -suid, student, grade, total_class_score, everything()) %>% 
  kable()

## select: columns reordered (student, assignment_1, assignment_2, assignment_3, assignment_4, …)

student	assignment_1	assignment_2	assignment_3	assignment_4	exam_1	exam_2	participation	extra_credit	final_exam_score	total_class_score	grade	rank	cumul_n	sunet	suid
Roghair, Aurora Jade	5	4.90	4.40	5.0	40.25	42.00	5	2	73.00	103.925	A+	1	11	aroghair	6495846
Wang, Annabelle Anjie	5	5.00	5.00	5.0	41.25	41.50	5	2	69.00	102.875	A+	2	11	aawang0	6778775
Salahifar, Aylin G	5	5.00	4.66	5.0	40.50	41.00	5	2	70.50	102.660	A+	3	11	aysalah	6650094
Nguyen, Michelle	5	5.00	4.74	4.5	42.00	40.50	5	2	70.00	102.490	A+	4	11	micheiie	6682990
Park, Emma	5	4.70	4.53	5.0	41.00	41.00	5	0	73.00	101.730	A+	5	11	emmapark	6421329
Trockel, Rebecca Sabrina	5	4.50	5.00	5.0	40.25	39.50	5	2	70.25	101.500	A+	6	11	rtrockel	6376569
Mallen, Susie Lynn	5	4.50	4.48	5.0	41.75	37.50	5	2	71.00	101.105	A+	7	11	smallen	6640480
Barrientos, Isabella	5	5.00	4.83	5.0	40.00	41.00	5	2	67.50	101.080	A+	8	11	isabp03	6504013
Huang, Emily	5	5.00	4.13	5.0	40.50	41.00	5	0	71.50	100.630	A+	9	11	ehuang25	6603608
Seyahi, Lara Selin	5	5.00	4.40	5.0	38.50	40.00	5	2	68.50	99.900	A+	10	11	seyahi	6545140
Tiu, Ekin Yokhan	5	4.50	4.48	5.0	40.00	40.00	5	0	70.50	99.230	A+	11	11	ekintiu	6423010
Srinivasan, Maya Wells	5	5.00	4.66	5.0	41.25	36.00	5	2	67.00	98.785	A	12	49	mayasrin	6422613
Breschini, Alex Jean-Buenviaje	5	4.75	4.61	4.5	39.00	41.00	5	2	65.50	98.610	A	13	49	ajb2004	6683400
Citron, Emily	5	5.00	4.65	5.0	38.50	36.50	5	2	68.75	98.525	A	14	49	ecitron	6438897
Banks, Everett Samuel	5	2.45	4.22	5.0	39.88	39.50	5	2	70.00	98.360	A	15	49	evbanks	6731886
He, Shizhe	5	4.75	4.05	5.0	40.75	38.75	5	2	65.50	98.300	A	16	49	shizhehe	6661097
McNitt, Kaylee Sarah	5	4.25	4.66	5.0	37.00	38.50	5	2	69.00	98.160	A	17	49	ksmcnitt	6369630
Siggelkow, Katarina	5	5.00	4.66	5.0	41.00	39.50	5	2	62.50	98.160	A	18	49	ksig	6682708
Smith, Jerred Denali Villegas	5	5.00	4.74	5.0	39.50	41.00	5	0	66.00	97.990	A	19	49	jerred89	6512315
Movich-Fields, Tishrei Lee	5	5.00	4.66	5.0	39.25	38.50	5	0	68.75	97.910	A	20	49	tishrei	6420491
Sundar, Lavanya Marakatavalli	5	4.75	4.66	4.5	41.00	37.50	5	0	69.50	97.910	A	21	49	lsundar	6659969
Chang, Yujin	5	4.25	4.70	5.0	38.00	37.00	5	2	67.50	97.200	A	22	49	yujin9	6644961
Cortes, Nicole	5	4.75	4.74	5.0	37.25	38.00	5	2	66.00	97.115	A	23	49	nicortes	6504077
Mengheang, Marissa	5	5.00	4.27	5.0	41.25	34.50	5	2	65.50	96.895	A	24	49	mars7418	6683966
Torres Arsuaga, Alexandra Cecilia	5	4.75	4.58	5.0	35.50	41.25	5	2	64.00	96.705	A	25	49	torresar	6512328
Huang, Robin	5	3.50	4.70	5.0	40.00	33.50	5	2	69.50	96.700	A	26	49	rhuang42	6512167
Alvarado, Rodrigo	5	5.00	4.74	5.0	38.50	36.00	5	2	64.50	96.240	A	27	49	rodrigo0	6493795
Varlack, Vivianna Tiffany	5	5.00	3.97	5.0	40.50	37.75	5	0	66.00	96.095	A	28	49	vvarlack	6496691
Costa, Ana Ferreira da Motta	5	4.75	4.53	5.0	40.50	38.00	5	2	61.00	96.030	A	29	49	anafmc	6683414
Fung, Michael Benjamin	5	3.75	4.87	5.0	36.75	38.75	5	2	65.25	95.995	A	30	49	mikefung	6650042
Vachovska, Lora	5	5.00	4.54	5.0	36.25	39.00	5	0	67.50	95.915	A	31	49	lorapv	6544168
Dakad, Nazli Hilal	5	5.00	4.48	5.0	39.25	37.00	5	2	62.00	95.605	A	32	49	nhdakad	6455175
Alluri, Rishi	5	5.00	4.22	4.0	38.25	42.00	5	0	64.00	95.345	A	33	49	allurir	6543538
Jiang, Yuzhi	5	4.25	4.57	5.0	38.25	38.00	5	0	66.50	95.195	A	34	49	baggioj	6456134
Lu, Yuwen	5	5.00	4.57	5.0	38.75	37.00	5	0	65.50	95.195	A	35	49	tinaluyw	6486310
Marton, Selina Claire	5	4.75	3.53	5.0	39.25	35.50	5	2	65.00	95.155	A	36	49	selinacm	6743583
Anderson, Elijah Francisco	5	4.00	4.61	5.0	36.38	37.00	5	2	65.50	95.050	A	37	49	efa2003	6683927
Brindisi, Sophia Rose	5	5.00	4.83	5.0	40.38	36.00	5	2	60.00	95.020	A	38	49	sbrindis	6495775
Kisembo, Benita Kemigisa	5	5.00	3.92	5.0	39.88	37.50	5	0	64.50	94.860	A	39	49	kisemben	6504254
Martz, Eric	5	5.00	4.57	5.0	38.50	38.00	5	0	64.00	94.820	A	40	49	emartz	6448695
Garcia-Escobar, Eduardo	5	4.25	4.48	5.0	34.25	35.00	5	2	68.50	94.605	A	41	49	egarcia6	6504136
Lawson, Benjamin Richard	5	3.15	4.87	4.5	38.50	37.25	5	2	64.00	94.395	A	42	49	blawson3	6512205
Cushing, Stella Marie Reese	5	4.50	4.48	5.0	37.00	34.75	5	2	65.00	94.355	A	43	49	scushing	6745843
Sossa, Candice Rachel	5	5.00	3.41	5.0	39.25	38.75	5	0	63.00	93.910	A	44	49	csossa	6401980
Narayanan, Tejas	5	5.00	4.87	5.0	40.00	36.00	5	0	62.00	93.870	A	45	49	tejasn9	6458279
Temal, Anjali	5	4.50	4.46	5.0	37.25	38.75	5	0	63.50	93.710	A	46	49	anjali82	6456179
Ozoemelam, Liane	5	4.00	4.74	5.0	37.00	36.50	5	0	65.50	93.240	A	47	49	lianeozo	6544973
Tchakmakjian, Emmanuella Levon	5	5.00	4.27	4.5	38.25	38.00	5	2	58.50	93.145	A	48	49	etchak	6409933
Ballard, Tensaye Libra	5	4.50	4.57	5.0	38.75	30.50	5	2	64.75	93.070	A	49	49	tensayeb	6749550
Hernandez Leon, Brianna	5	3.75	4.31	5.0	34.88	38.00	5	2	62.00	92.500	A-	50	60	janay17	6647227
Ghimire, Samikshya	5	3.40	4.61	4.5	37.38	34.50	5	2	64.00	92.450	A-	51	60	samighim	6532372
Barone, Kamryn Regina	5	4.65	4.66	5.0	38.75	34.00	5	2	59.50	92.435	A-	52	60	kbarone	6739395
Morales-Madrid, Samuel	5	4.50	4.66	5.0	38.25	34.50	5	2	57.50	91.285	A-	53	60	samuelmm	6603236
Odell, Cherrial Ann	5	3.90	5.00	4.5	36.88	36.75	5	2	58.00	91.215	A-	54	60	cherrial	6360769
Tamor, Sydney Elizabeth	5	3.75	3.58	4.0	37.00	41.25	5	0	61.50	91.205	A-	55	60	sydtamor	6779178
Strutz, Patricia Marie	5	5.00	3.53	5.0	38.00	34.25	5	0	62.75	91.030	A-	56	60	pstrutz	6503711
MacCoun, Audrey	5	5.00	4.61	5.0	32.50	33.25	5	2	62.25	90.610	A-	57	60	amaccoun	6605504
Murphy, Cheyenne N	5	4.50	4.53	4.5	32.50	33.00	5	2	64.50	90.530	A-	58	60	cmurphy6	6493861
Rodriguez, Keila Samantha	5	4.50	4.00	5.0	35.38	36.00	5	2	58.50	90.440	A-	59	60	keila	6647252
Nordhagen, Andrew	5	4.75	4.48	5.0	33.00	35.50	5	0	63.50	90.230	A-	60	60	aenord	6504358
Pinciroli Pascual, Alissa	5	4.15	4.66	4.0	34.75	34.75	5	2	60.25	89.685	B+	61	73	pasc061	6495841
Vincent, Dylan Michael	0	5.00	3.97	5.0	37.75	33.25	5	2	66.00	89.470	B+	62	73	dylanv	6423286
Sturdza, Caroline Marie	5	4.25	4.40	5.0	33.75	29.00	5	2	64.50	89.275	B+	63	73	carostu	6486322
Aguilar, Karla Patricia	5	5.00	4.24	5.0	37.00	33.00	5	2	55.50	88.990	B+	64	73	kaguila5	6682225
Kehm, Andrew Thomas	5	5.00	4.18	5.0	33.75	33.25	5	0	62.00	88.680	B+	65	73	atkehm	6683181
Stewart, Riley Grace	5	4.75	4.22	4.5	36.75	28.50	5	2	61.00	88.595	B+	66	73	riley3	6644984
De Loera, Alan	5	4.00	4.74	5.0	40.50	39.00	5	0	50.00	88.490	B+	67	73	adeloera	6496650
Ramirez Gonzalez, Mirna Guadalupe	5	4.25	4.66	4.5	37.00	26.50	5	2	62.50	88.410	B+	68	73	mirna22	6649756
Shenoy, Thanh-Nga Catherine	5	3.75	5.00	5.0	36.00	37.50	5	2	51.50	88.250	B+	69	73	tshenoy	6544608
Hansen, Luke	0	4.25	4.74	5.0	37.00	36.00	5	0	65.00	87.990	B+	70	73	lrhansen	6252378
Garewal, Samuel Oliver	5	3.50	4.64	5.0	33.75	29.00	5	0	66.33	87.680	B+	71	73	sgarewal	6827043
Heinemann, Abigail Grace	5	3.75	5.00	5.0	33.25	36.50	0	0	67.50	87.375	B+	72	73	aheinem	6641596
Cortes, Maria	5	3.50	4.57	4.0	36.25	29.00	5	2	61.00	87.195	B+	73	73	mcortesc	6657464
Powell, Taylor Janai	5	5.00	4.27	5.0	36.00	33.00	5	2	51.25	86.395	B	74	77	tjp2608	6682994
Rojas, Tania Lisseth	5	3.75	4.59	4.0	34.00	36.50	5	0	55.75	85.465	B	75	77	trojas18	6421966
Cantin, Lauren Elizabeth	5	3.00	4.14	5.0	34.50	32.00	5	2	56.00	85.390	B	76	77	lecantin	6509449
Mijat, Sophia Grace	5	5.00	4.31	4.5	31.88	28.25	5	2	59.00	85.375	B	77	77	smijat	6650818
Campbell, Maxwell James	5	3.50	4.83	5.0	28.25	34.00	5	0	56.75	82.830	B-	78	80	maxjcamp	6659964
Nzelu, Kene Simone	5	4.15	3.23	5.0	33.75	30.50	5	2	49.50	81.255	B-	79	80	kenen	6421098
Bailey, Quincy	5	4.75	3.84	4.0	36.25	23.75	5	2	51.25	80.215	B-	80	80	qbailey	6778990
Thompson, Joshua Issa	5	4.40	3.85	4.5	34.75	32.00	5	0	46.75	79.500	C+	81	84	josht26	6640261
Taylor, Mikey Stephen	5	4.00	4.74	0.0	36.00	34.50	0	0	60.00	78.990	C+	82	84	mikey686	6778059
Johnson, Leah Yolanda	5	3.25	4.60	5.0	35.75	29.25	5	0	46.00	78.350	C+	83	84	bluerun2	6385807
Baker, Tahayla Brianna	5	3.00	4.01	5.0	27.88	28.75	5	2	52.00	78.325	C+	84	84	tahayla	6416268
Pacheco Ramirez, Jessica	5	4.25	3.92	5.0	31.38	25.00	5	2	40.50	73.610	C	85	85	jessipr	6657509
Leiva, Carlos D	5	3.75	4.18	4.5	37.00	24.50	5	0	35.50	70.930	C-	86	86	cdleiva	6749505
Kinney, Jasmine Waukela	5	3.30	4.44	5.0	29.38	27.25	5	2	32.50	69.305	D+	87	88	jwkinney	6455928
Ramos, Valeria	5	4.80	3.90	4.5	29.50	29.75	5	2	25.00	67.325	D+	88	88	ramos03	6683972
Cloud, Anne Elizabeth	0	4.75	2.54	5.0	24.10	26.00	5	2	41.50	65.090	D	89	89	acloud	6643281
Reliford, Dominique	0	0.00	0.00	0.0	23.00	32.00	0	0	39.83	47.415	F	90	93	reliford	9656716
Sun, Lilly	5	1.20	1.15	3.5	13.00	27.75	0	0	28.00	45.225	F	91	93	lillysun	6250630
Hicks, Christina Helena Pau	5	4.00	3.79	5.0	0.00	33.50	5	0	0.00	39.540	F	92	93	chickshp	6407150
Briones, Julia	0	0.00	0.00	0.0	29.25	28.75	0	0	0.00	29.000	F	93	93	jmba812	6261749

5.1 All assignment scores

Note that the mean for the final exam doesn’t match the earlier statistics, because this includes people who got a 0.

d <- df_gradebook_final %>% 
  select(student, 
         assignment_1,
         assignment_2,
         assignment_3,
         assignment_4,
         exam_1,
         exam_2,
         final_exam_score
  ) %>% 
  pivot_longer(cols = -student, 
               names_to = "assignment",
               values_to = "score") 

## select: dropped 5 variables (sunet, suid, participation, extra_credit, total_class_score)

## pivot_longer: reorganized (assignment_1, assignment_2, assignment_3, assignment_4, exam_1, …) into (assignment, score) [was 93x8, now 651x3]

d %>% 
  ggplot(aes(x = score)) +
  geom_histogram(bins = 20) +
  facet_wrap(~assignment, scales = "free") +
  labs(title = "Assignment Scores", x = "Score", y = "Count")

d %>% 
  group_by(assignment) %>%
  summarize(mean_score = mean(score),
            sd_score = sd(score)) %>% 
  mutate_at(vars(mean_score, sd_score), ~round(., 2)) %>%
  kbl() %>%
  kable_styling()

## group_by: one grouping variable (assignment)

## summarize: now 7 rows and 3 columns, ungrouped

## mutate_at: changed 7 values (100%) of 'mean_score' (0 new NA)

##            changed 7 values (100%) of 'sd_score' (0 new NA)

assignment	mean_score	sd_score
assignment_1	4.73	1.13
assignment_2	4.31	0.94
assignment_3	4.29	0.84
assignment_4	4.68	0.92
exam_1	36.18	5.87
exam_2	35.29	4.47
final_exam_score	59.68	13.06

5.2 Missing grades

df_gradebook_final %>% 
  pivot_longer(cols = -c(student, sunet, suid), 
               names_to = "assignment",
               values_to = "score") %>% 
  filter(is.na(score) | score == 0) %>% 
  arrange(assignment) %>% 
  kbl() %>% 
  kable_styling()

## pivot_longer: reorganized (assignment_1, assignment_2, assignment_3, assignment_4, exam_1, …) into (assignment, score) [was 93x13, now 930x5]

## filter: removed 875 rows (94%), 55 rows remaining

student	sunet	suid	assignment	score
Briones, Julia	jmba812	6261749	assignment_1	0
Cloud, Anne Elizabeth	acloud	6643281	assignment_1	0
Hansen, Luke	lrhansen	6252378	assignment_1	0
Reliford, Dominique	reliford	9656716	assignment_1	0
Vincent, Dylan Michael	dylanv	6423286	assignment_1	0
Briones, Julia	jmba812	6261749	assignment_2	0
Reliford, Dominique	reliford	9656716	assignment_2	0
Briones, Julia	jmba812	6261749	assignment_3	0
Reliford, Dominique	reliford	9656716	assignment_3	0
Briones, Julia	jmba812	6261749	assignment_4	0
Reliford, Dominique	reliford	9656716	assignment_4	0
Taylor, Mikey Stephen	mikey686	6778059	assignment_4	0
Hicks, Christina Helena Pau	chickshp	6407150	exam_1	0
Alluri, Rishi	allurir	6543538	extra_credit	0
Briones, Julia	jmba812	6261749	extra_credit	0
Campbell, Maxwell James	maxjcamp	6659964	extra_credit	0
De Loera, Alan	adeloera	6496650	extra_credit	0
Garewal, Samuel Oliver	sgarewal	6827043	extra_credit	0
Hansen, Luke	lrhansen	6252378	extra_credit	0
Heinemann, Abigail Grace	aheinem	6641596	extra_credit	0
Hicks, Christina Helena Pau	chickshp	6407150	extra_credit	0
Huang, Emily	ehuang25	6603608	extra_credit	0
Jiang, Yuzhi	baggioj	6456134	extra_credit	0
Johnson, Leah Yolanda	bluerun2	6385807	extra_credit	0
Kehm, Andrew Thomas	atkehm	6683181	extra_credit	0
Kisembo, Benita Kemigisa	kisemben	6504254	extra_credit	0
Leiva, Carlos D	cdleiva	6749505	extra_credit	0
Lu, Yuwen	tinaluyw	6486310	extra_credit	0
Martz, Eric	emartz	6448695	extra_credit	0
Movich-Fields, Tishrei Lee	tishrei	6420491	extra_credit	0
Narayanan, Tejas	tejasn9	6458279	extra_credit	0
Nordhagen, Andrew	aenord	6504358	extra_credit	0
Ozoemelam, Liane	lianeozo	6544973	extra_credit	0
Park, Emma	emmapark	6421329	extra_credit	0
Reliford, Dominique	reliford	9656716	extra_credit	0
Rojas, Tania Lisseth	trojas18	6421966	extra_credit	0
Smith, Jerred Denali Villegas	jerred89	6512315	extra_credit	0
Sossa, Candice Rachel	csossa	6401980	extra_credit	0
Strutz, Patricia Marie	pstrutz	6503711	extra_credit	0
Sun, Lilly	lillysun	6250630	extra_credit	0
Sundar, Lavanya Marakatavalli	lsundar	6659969	extra_credit	0
Tamor, Sydney Elizabeth	sydtamor	6779178	extra_credit	0
Taylor, Mikey Stephen	mikey686	6778059	extra_credit	0
Temal, Anjali	anjali82	6456179	extra_credit	0
Thompson, Joshua Issa	josht26	6640261	extra_credit	0
Tiu, Ekin Yokhan	ekintiu	6423010	extra_credit	0
Vachovska, Lora	lorapv	6544168	extra_credit	0
Varlack, Vivianna Tiffany	vvarlack	6496691	extra_credit	0
Briones, Julia	jmba812	6261749	final_exam_score	0
Hicks, Christina Helena Pau	chickshp	6407150	final_exam_score	0
Briones, Julia	jmba812	6261749	participation	0
Heinemann, Abigail Grace	aheinem	6641596	participation	0
Reliford, Dominique	reliford	9656716	participation	0
Sun, Lilly	lillysun	6250630	participation	0
Taylor, Mikey Stephen	mikey686	6778059	participation	0