Assignment 4

Introduction

This Quarto I will be creating is using the Xavier Univeristy Peer Institution Data. It shows how firms identified numerous peer and competitor universities to which Xavier was compared.

Loading Data

I am loading in different libraries like Tidyverse to have all things apart of tidy, then lubridate in case I need to transform date values, and knitr for helping render documents. Then I will load in the data under the name xu.

Data Wrangling

formatting dates to correct US standards. Making NULL in total_exec and total_other into NA. Also making both of those numeric.

OER

Visualizations

#3.1
xu %>% 
  ggplot(aes(x = total_exec_comp,
             y = total_other_comp))+
  geom_point()+
  facet_wrap(~ ajcu, nrow = 2)

Warning: Removed 6 rows containing missing values or values outside the scale range
(`geom_point()`).

#3.2
xu_2 <- xu %>% 
  filter(ajcu == TRUE,
         big_east == TRUE) %>% 
  group_by(name) %>% 
  summarise(total_exec_comp = sum(total_exec_comp, na.rm = TRUE),
            total_other_comp = sum(total_other_comp, na.rm = TRUE)) %>% 
  mutate(ratio = total_exec_comp/total_other_comp)

xu_2 %>% 
  ggplot(aes(x = name, y = ratio)) +
  geom_col() +
  labs(title = "Ratio for executive comp to Other comp")

#3.3
xu %>% 
  ggplot(aes(x = as.factor(tax_file_yr), y = total_revenue))+
  geom_boxplot()+
  labs(title = "variance of total revenue by year",
       x = "Year"
       )

Analysis

rev_trends <- xu %>%
  group_by(tax_file_yr) %>%
  summarise(
    avg_tuition = mean(total_tuition_revenue, na.rm = TRUE),
    avg_gifts = mean(total_gifts, na.rm = TRUE),
    avg_expenses = mean(total_fun_expenses, na.rm = TRUE)
  )

rev_trends %>%
  ggplot(aes(x = as.factor(tax_file_yr))) +
  geom_point(aes(y = avg_tuition), color = "blue") +
  geom_point(aes(y = avg_gifts), color = "red") +
  geom_point(aes(y = avg_expenses), color = "green") +
  labs(
    title = "Average Tuition, Gifts, and Expenses by Year",
    x = "Year",
    y = "Average Amount"
  )

functional_expense <- xu %>% 
  group_by(tax_file_yr) %>% 
  summarise(total_fun_expenses = sum(total_fun_expenses))

functional_expense %>% 
  ggplot(aes(x = tax_file_yr, y = total_fun_expenses))+
  geom_point()+
  labs(
    title = "Total Functional Expenses Per Year",
    x = "Year",
    y = "Total Function Expenses"
  ) 

Responses to the Analysis

In the first graph I made it so the average tuition is blue, average gifts is red, average expenses are green. There is a normal increase with all three of the variables with tuition and expenses being much higher than gifts. However there is a steep decline in 2020 which makes sense because of COVID-19