Ranking of Master's in Finance - Financial Times

library(readxl)
library(tidyverse)
library(wbstats)
library(tictoc)
library(skimr)
library(countrycode)
library(here)
library(DBI)
library(dbplyr)
library(arrow)
library(rvest)
library(robotstxt) # check if we're allowed to scrape the data
library(scales)
library(sf)
library(readxl)
library(shiny)
library(gapminder)
library(GGally)
library(janitor)

ranking <- read_excel("C:/Users/gonza/Desktop/Personal Projects/Master Rankings/ranking-data-1685405201165.xlsx") %>% 
  clean_names()

#glimpse at variables
glimpse(ranking)

head(ranking)

reduced_names <- c("HEC", "ESCP", "Skema", "Essec", "Edhec", "Oxford", "Tsinghua", "IE", "LBS", "MIT", "Nova",
                   "Warwick", "Bocconi", "St Gallen", "WHU", "Imperial", "Católica", "Esade", "Peking", "Stockholm",
                   "Shanghai", "Bayes", "Rennes", "Grenoble", "McGill", "Trinity", "Frankfurt", "HEC Lausanne",
                   "Carlos III", "Kozminski", "Rotterdam", "Eada", "CUHK", "ISEG", "Vlerick", "UCD", "USI", "Henley",
                   "Lund", "SMU", "Iscte", "Edinburgh", "Rochester", "Cranfield", "Texas", "Lancaster", "Tilburg",
                   "BI Norwegian", "Porto", "Exeter", "Lucerne", "Amsterdam", "Durham", "Maryland", "Liverpool")

ranking <- ranking %>% mutate(short_name = reduced_names)

#Plotting the Salary today in USD against Unis

p1 <-ranking %>% 
  mutate(number=as.integer(number),
         salary_today_us = as.integer(str_replace(salary_today_us, ",", "")),
         international_students_percent=as.integer(international_students_percent),
         internships_percent = as.integer(internships_percent)) %>% 
  filter(number<=20) %>% 
  ggplot(aes(y=fct_reorder(short_name, -number), x=salary_today_us)) +
  geom_col() +
  geom_text(aes(label = salary_today_us),
            hjust = 1.5,
             vjust = 0.5,
             colour = "white", 
             size = 3) +
  theme(axis.text.x = element_text(angle = 90)) +
  theme_bw() +
  labs(y="", x="Salary Today in USD")


p1

ranking %>% 
  mutate(number=as.integer(number),
         salary_today_us = as.integer(str_replace(salary_today_us, ",", "")),
         international_students_percent=as.integer(international_students_percent),
         internships_percent = as.integer(internships_percent),
         location_main_campus = as.factor(location_main_campus),
         salary_percentage_increase=as.integer(salary_percentage_increase)) %>% 
  
  filter(number<=15) %>% 
  ggplot(aes(x=international_students_percent, 
             y=salary_today_us, 
             size=internships_percent, 
             color=location_main_campus)) +
  geom_point() +
  geom_text(aes(label = short_name),
            hjust = 0.5,
             vjust = 1.5,
             colour = "black",
             size = 3.5) +
  #scale_x_continuous(limits = c(0, 100)) +
  #scale_y_continuous(limits = c(50000, 200000)) +
  theme_bw() +
  labs(x="International Students (%)", y="Salary Today in USD") +
  ggtitle("Top Universities: Salary and international students") + 
  guides(size=guide_legend(title="Internships Achieved (%)"),
         color=guide_legend(title="Main Campus Location"))

ranking %>% 
  mutate(number=as.integer(number),
         salary_today_us = as.integer(str_replace(salary_today_us, ",", "")),
         international_students_percent=as.integer(international_students_percent),
         internships_percent = as.integer(internships_percent)) %>% 
  select(number, short_name, internships_percent)

ranking %>% 
  mutate(number=as.integer(number),
         salary_today_us = as.integer(str_replace(salary_today_us, ",", "")),
         international_students_percent=as.integer(international_students_percent),
         internships_percent = as.integer(internships_percent),
         location_main_campus = as.factor(location_main_campus),
         salary_percentage_increase=as.integer(salary_percentage_increase)) %>% 
  group_by(location_main_campus) %>% 
  summarise(mean_salary=mean(salary_today_us), count=n()) %>% 
  #slice_max(order_by = mean_salary, n=10) %>% 
  
  ggplot(aes(y=fct_reorder(location_main_campus, mean_salary), x=mean_salary)) +
  geom_col() +
  geom_text(aes(label = round(mean_salary,0)),
            hjust = 1.5,
             vjust = 0.5,
             colour = "white", 
             size = 3) +
  theme(axis.text.x = element_text(angle = 90)) +
  theme_bw() +
  labs(y="Main Campus Location", x="Mean Salary in USD")