wcgrads: Williams College Graduating Seniors

pbk <- function(){

#' @title Phi Beta Kappa
#' @description pbk shows the percentage of each graduating class that is awarded Phi Beta Kappa.
#' @return A bar graph showing the percentage of each graduating class awarded Phi Beta Kappa
#' @usage pbk()
#' @import dplyr ggplot2
#' @export

a <- allyrs %>%
  select(grad.year, pbk) %>%
  group_by(grad.year, pbk) %>%
  summarize(count = n()) %>%
  ungroup() %>%
  group_by(grad.year) %>%
  mutate(yrly.count = sum(count)) %>%
  filter(pbk == TRUE) %>%
  mutate(yrly.pct = 100*count/yrly.count)

b <- a %>%
  ggplot(aes(grad.year, yrly.pct)) +
  geom_bar(stat = "identity") +
  ggtitle("Phi Beta Kappa") +
  xlab("Graduation Year") +
  ylab("Percent Phi Beta Kappa") +
  labs(caption = "Proportion of Phi Beta Kappa students each year.
       2003 has the highest percentage and 2006 has the lowest.")

return(b)

}

dept_honors<- function(){

  #' @title Departmental Honors
  #' @description dept_honors shows the total number of graduates that earned some level of departmental honors, by major and gender.
  #' @return A bar graph showing the total number of graduates that earned some level of departmental honors, by major and gender.
  #' @usage dept_honors()
  #' @import dplyr ggplot2 stringr
  #' @export

a <- allyrs %>%
  select(first.honors.dept, gender) %>%
  rename(honors.dept = first.honors.dept) %>%
  filter(is.na(honors.dept) == FALSE) %>%
  mutate(honors.dept = ifelse(str_detect(honors.dept, "Contract") == TRUE,
                              "Contract Major", honors.dept)) %>%
  group_by(honors.dept, gender) %>%
  summarize(count = n()) %>%
  arrange(desc(honors.dept))

b <- allyrs %>%
  select(second.honors.dept, gender) %>%
  rename(honors.dept = second.honors.dept) %>%
  filter(is.na(honors.dept) == FALSE) %>%
  mutate(honors.dept = ifelse(str_detect(honors.dept, "Contract") == TRUE,
                              "Contract Major", honors.dept)) %>%
  group_by(honors.dept, gender) %>%
  summarize(count = n()) %>%
  arrange(desc(honors.dept))

c <- left_join(a, b, by = c("honors.dept", "gender")) %>%
  group_by(honors.dept, gender) %>%
  mutate(count = sum(count.x ,count.y, na.rm = TRUE)) %>%
  select(-count.x, -count.y)

d <- c %>%
    ggplot(aes(honors.dept, count, fill = gender)) +
    geom_bar(stat = "identity", position = "dodge") +
    ggtitle("Departmental Honors") +
    xlab("Major") +
    ylab("Number of Graduates") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
    labs(caption = "Total number of graduates earning some level of departmental honors, by major.
         Gender is predicted from the R gender package.") +
    scale_fill_manual(values = c("#512698", "#fdcc09"), na.value="dimgray")

return(d)

}

latin_honors_gender <- function(){

  #' @title Latin Honors and Gender
  #' @description latin_honors_gender shows the total number of students of each predicted gender
  #'  who received each level of latin honors.
  #' @return A bar graph showing the total number of students of each predicted gender
  #'  who received each level of latin honors.
  #' @usage latin_honors_gender()
  #' @import dplyr ggplot2
  #' @export

  a <- allyrs %>%
    select(latin.honors, gender) %>%
    mutate(latin.honors = ifelse(is.na(latin.honors) == TRUE, "None", latin.honors)) %>%
    group_by(latin.honors, gender) %>%
    summarize(count = n())

  z <- a %>%
    ggplot(aes(latin.honors, count, fill = gender)) +
    geom_bar(stat = "identity", position = "dodge") +
    ggtitle("Latin Honors and Gender 2000-2016") +
    xlab("Latin Honors") +
    ylab("Number of Graduates") +
    guides(fill = guide_legend(title = "Gender")) +
    labs(caption = "The number of students of each gender acheiving each level of latin honors.") +
    ylim(0, 3000) +
    scale_fill_manual(values = c("#512698", "#fdcc09"), na.value="dimgray")

  return(z)
}