save

scripts/summary.R

@@ -3,53 +3,78 @@ library(dplyr)
 # import dataset
 df <- read.csv(file = "data/uw_courses.csv", stringsAsFactors = F)
 
+
 # For this report, we are only concerned with INFO department
 df <- df %>% 
   filter(dept_abbrev == 'INFO')
 
-# Courses with lowest average class gpa within info department (@Nicholas)
-lowest_gpa <- df %>% 
-  filter(avg_gpa == min(df$avg_gpa))
+get_summary <- function(dataframe) {
+  # Courses with lowest average class gpa within info department (@Nicholas)
+  lowest_gpa <- df %>% 
+    filter(avg_gpa == min(df$avg_gpa)) %>% 
+    mutate(text=paste0(lastname, "'s ", course_code," ", course_title, " has an average GPA of ", avg_gpa)) %>% 
+    head(1) %>% 
+    pull()
+
+  # Courses with greatest number of Drop/Fail within info department (@Nicholas)
+  most_wf <- df %>% 
+    mutate(withdraw_fail = as.numeric(Fail) + as.numeric(W)) %>% 
+    filter(withdraw_fail == max(withdraw_fail, na.rm = T)) %>% 
+    mutate(text=paste0(lastname, "'s ", course_code," ", course_title, " has a total of ", withdraw_fail, " students withdrawed or failed")) %>% 
+    head(1) %>% 
+    pull()
+
+  # top 1 professor names with highest rating (@Lance)
+  highest_rated_prof <- df %>%
+    mutate(pro_name = paste(lastname, firstname)) %>%
+    select(professor_rating, pro_name) %>%
+    filter(is.na(professor_rating) == FALSE) %>%
+    group_by(pro_name) %>%
+    summarise(
+      ave_rate= sum(professor_rating) / n()
+    ) %>%
+    arrange(-ave_rate) %>% 
+    head(1) %>% 
+    select(pro_name) %>% 
+    pull()
 
-# Courses with greatest number of Drop/Fail within info department (@Nicholas)
-most_wf <- df %>% 
-  mutate(withdraw_fail = as.numeric(Fail) + as.numeric(W)) %>% 
-  filter(withdraw_fail == max(withdraw_fail, na.rm = T))
-
-# top 10 professor names with highest rating (@Lance)
-data_with_pros <- df %>%
-  mutate(pro_name = paste(lastname, firstname)) %>%
-  select(professor_rating, pro_name) %>%
-  filter(is.na(professor_rating) == FALSE) %>%
-  group_by(pro_name) %>%
-  summarise(
-    ave_rate= sum(professor_rating) / n()
-  ) %>%
-  arrange(-ave_rate) %>%
-  head(10)
-
 # courses with the biggest enrollment (@Chloe)
-biggest_class <- df %>%
-  mutate(course_tag = paste(dept_abbrev, course_no)) %>%
-  group_by(course_tag, course_title) %>%
-  summarize(class_size = sum(as.double(student_count))) %>%
-  arrange(-class_size) %>%
-  head(1)
-
-# The course with most 4.0 in percentage (@Keyan)
-most_four_course <- pull(df %>%
-  filter(dept_abbrev == "INFO") %>%
-  arrange(course_no) %>%
-  mutate(course_id = paste0(dept_abbrev, " ", course_no),
-  term = factor(term, levels = c("Autumn", "Winter", "Spring", "Summer")),
-                term = as.character(term)) %>%
-  select(course_id, course_title, section_id, term, year, A, student_count) %>%
-  group_by(course_id, course_title) %>%
-  summarise(A_percentage =
-    (sum(suppressWarnings(as.numeric(A)), na.rm = T) /
-     sum(suppressWarnings(as.numeric(student_count)), na.rm = T) * 100)) %>%
-  ungroup() %>%
-  ungroup() %>%
-  filter(A_percentage == max(A_percentage)) %>%
-  select(course_id))
+  biggest_class <- df %>%
+    mutate(course_tag = paste(dept_abbrev, course_no)) %>%
+    group_by(course_tag, course_title) %>%
+    summarize(class_size = sum(as.double(student_count))) %>%
+    arrange(-class_size) %>% 
+    mutate(course = paste(course_tag,course_title)) %>%
+    ungroup() %>% 
+    select(course) %>% 
+    head(1) %>% 
+    pull()
+
+  # The course with most 4.0 in percentage (@Keyan)
+  most_four_course <- df %>%
+                             filter(dept_abbrev == "INFO") %>%
+                             arrange(course_no) %>%
+                             mutate(course_id = paste0(dept_abbrev, " ", course_no),
+                                    term = factor(term, levels = c("Autumn", "Winter", "Spring", "Summer")),
+                                    term = as.character(term)) %>%
+                             select(course_id, course_title, section_id, term, year, A, student_count) %>%
+                             group_by(course_id, course_title) %>%
+                             summarise(A_percentage =
+                                         (sum(suppressWarnings(as.numeric(A)), na.rm = T) /
+                                            sum(suppressWarnings(as.numeric(student_count)), na.rm = T) * 100)) %>%
+                             ungroup() %>%
+                             ungroup() %>%
+                             filter(A_percentage == max(A_percentage)) %>%
+                             select(course_id) %>% 
+    pull()
+
+    summary <-list()
+    summary(min_gpa = lowest_gpa,
+            max_withdrawl_fail=most_wf,
+            largest_class=biggest_class,
+            best_performing_class=most_four_course,
+            best_prof=highest_rated_prof)
+  return(summary)
+}
+sth<-get_summary(df)