Executive Summary

The Society of Jesus began building schools shortly after it was founded by Ignatius Loyola more than 400 years ago. There are more than 200 Jesuit educational institutions around the world. Jesuit education might have taken on various forms around the world, however, the main pillar of Jesuit education - educating individuals to be people for others- is present in every institution around the world.

For the purposes of this study, the data has been collected from Jesuit universities’ websites and compiled. R studio has been utilized to analyze the data and create the graphs. By conducting this study, we intend to identify how Jesuit institutions are spread geographically throughout the United States and the world. In addition, we are exploring how undergraduate enrollment differs based in geography.

Questions Posed

1. How are Jesuit institutions geographically spread throughout the world?

2. How are Jesuit institutions geographically spread throughout the United States?

3. How does undergraduate enrollment differ based in geography?

4. What is the ratio of staff to undergrad enrollment?

Data Analysis

Table of Jesuit Schools

datatable(JCDT, options=list(pageLength = 10))

To begin, here is a simple Graph of the U.S showing how many schools there are in each state. From here we see a large amount of states that have schools near the eastern side of the country. This makes senses seeing as this was the area where immigrants first came to the new world from England, France, Spain, Irland, Germany and Demark.

Jesuit Schools in the U.S

GVIS = read.csv("jcgvis.csv")

JCChart=GVIS%>%group_by(State)%>%dplyr::summarize(Count=length(State))

GeoStates <- gvisGeoChart(JCChart, "State", "Count",
                          options=list(region="US", 
                                       displayMode="regions", 
                                       resolution="provinces",
                                       width=600, height=400,colorAxis="{colors:['gold','lightblue','blue']}"))

plot(GeoStates)

Mean (Student vs Staff) Ratio By Country

geo3=JC%>%group_by(Country)%>%summarize(Mean=mean(Student.Staff.Ratio))


dimple(
  x="Country", y= "Mean", data = geo3,
  type = "bar") %>%
  add_title( "Student vs. Staff Ratio by Country") %>%
  set_bounds( x="10%", y="1%", width="90%",height="70%")

The map below shows the number of Jesuit institutions by each country. As indicated in the map, India has the highest number of Jesuit schools (52 schools), followed by the United States (28 schools), and Spain (10 schools). Spain has the highest number of schools in Europe and this can be explained by the fact that Spain is the home country of Ignatius of Loyola, the founder of the Jesuit society.

Number of Schools by Country

geo1=JC%>%group_by(Country)%>%summarize(Count=length(City))

#merg to geo-table
locs1=geo1

#add country code for tiles maps
locs1$iso_a3=as.factor(countrycode(geo1$Country,"country.name","iso3c"))

#Map Data   
url <- "http://www.naturalearthdata.com/http//www.naturalearthdata.com/download/50m/cultural/ne_50m_admin_0_countries.zip"

folder <- getwd() #set a folder where to download and extract the data
file <- basename(url) 
 download.file(url, file)
 unzip(file, exdir = folder)
    
#And read it with rgdal library
world <- readOGR(dsn = folder, 
                 layer = "ne_50m_admin_0_countries",
                 encoding = "UTF-8",
                 verbose = FALSE)
#setup to merge with "World geomap"
locs1=data.frame(locs1)

#world <- sp::merge(world,locs2, by="iso_a3")
world <- merge(world, locs1,
               by.x = "iso_a3",
               by.y = "iso_a3",
               sort = FALSE)

#world@data$iso_a3
#Colour palette. Check ?colorQuantile and ?RColorBrewer for more options
pal <- colorNumeric(palette = "Blues",domain = locs1$Count)

#Pop up: The info displayed when click on a country
world_popup <- paste(world$admin, 

                      ", Number of Schools:", 

                      world$Count)
#Tiles coming from stamen.com
tiles <- "http://{s}.tile.stamen.com/toner-lite/{z}/{x}/{y}.png"
attribution <- 'Map tiles by <a href="http://stamen.com">Stamen Design</a>, under <a href="http://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>. Map data by <a href="http://www.naturalearthdata.com/">Natural Earth</a>.'
leaflet(data = world) %>% 
  addTiles(urlTemplate = tiles,  
         attribution = attribution) %>%
  setView(0, 0, zoom = 2) %>% 
  addPolygons(fillColor = ~pal(world$Count), 
              fillOpacity = 1, 
              color = "#000000", 
              weight = 1, popup = world_popup) %>%
    addLegend("bottomright", pal = pal, values =~Count, 
              title ="Number of Schools", opacity = 1)

Schools by Region

TL=JC%>%group_by(Region)%>%summarize(Count=length(City))


dimple(
  x="Region", y= "Count", data = TL,
  type = "bar") %>%
  add_title( "Number of Schools vs Region") %>%
  set_bounds( x="5%", y="1%", width="90%",height="70%")

Additionally, the map below delves further into the geographic expansion of Jesuit institutions and their locations by city. THe reach of Jesuit Schools is far and has multiple sectors of their campuses that are not listed in the dataset, meaning there are even more institutions where Jesuit Education is a prevalent means of schooling.

Locations of Schools in the World

###NEW Table for ggmap function
#geo2=JC
#geo2=geo2[-c(1:15),c(8,10)]%>%group_by(City, Country)%>%summarize(Count=length(City))

### combining the city and country can help with the geocoding of the city, which we do next.
#geo2$CityCountry=paste(geo2$City,geo2$Country,sep=", ")  

#locs2=geocode(geo2$CityCountry, output = c("latlon"),
#source = c("google"), messaging = FALSE, sensor = FALSE,
#override_limit = FALSE)

#geo2$lat=locs2$lat
#geo2$lon=locs2$lon

#save(geo2,file="geo2.csv")

geo2 = read_excel("geo2.xlsx")

geo2$popup=paste("<table><tr><td>City:", geo2$City,"<br>Country:",geo2$Country, "<br>Number of Schools:", geo2$Count,"</td></tr></table>")

leaflet(geo2)%>%
  addProviderTiles("CartoDB.Positron") %>%
     setView(0, 0, zoom = 2) %>%
  addCircles(stroke=FALSE, fillOpacity = .5, color="blue", radius=~Count*50000,popup=~popup)

#saveWidget(JCSchools,"JCSchools.html", selfcontained = TRUE)

We also decided the explore the mean student-staff ratio per country. As the graph indicates, while the majority of the countries have a low student-staff ratio, Lebanon proved to be an outlier (student-staff ratio of 615-1). The majority of countries try to keep the student to staff ratio as low as possible. This is one of the more prominant reasons that many students and parents are attracted to these insitutions. Low ratio means smaller class sizes and more engaguement between student and professor.

Limitations

One of the limitations of the study was the quality of the data used for this project. Since institutions voluntarily post information on their websites, certain universities chose to not make their demographics and enrollment information Because of this, some of the data that would have otherwise been used in the study was missing.

Conclusion

In todays modern world where online schools and ‘For-profit’ colleges are seeming to pop up everywhere, one of the oldest institutions of professional education still remains. Not only have they expanded to different regions around the world but they have done so without the constant push to ‘Grow campus by size of undergards.’ Most of these Jesuit schools have remained small in size and have only promoted themselves through reputation, alumni excellence and positive word-of-mouth experience about being educated at a Jesuit School.