What can we do with this data?
We can use this package to address many questions such as:
- Which institutions have the qualities I’d like in an institution?
- What are the admission requirements for my preferred institution?
- What’s the relationship between the diversity of students and the diversity of staff?
To answer our questions we can make use of the existing functionality the package provides, as well as data wrangling and data visualization techniques. Some examples that address the question are below:
Example 1:
Which institutions have the qualities I’d like in an institution?
Let’s say Sophia, a senior at high school, is interested in going to a private college of relatively small size in the New England area that will accept the AP credits she’s earned, but is also slightly diverse and helps it’s students afford college.
Using the school_preferences function, Sophia can find a
school that perfectly fits her preferences.
- size: 2, selects a school size of 1,000 - 4,999 students
- region: “New England”, selects schools from a particular region
- alt_credits: “Yes”, selects schools that take either AP, Dual or Life Experience credits
- diversity_students: 36, selects schools where 30% of the students are non-white, or higher
- financial_aid: 70, selects school where 70% of the students receive financial aid
- affiliation: 3, selects private (not for profit) institutions
school_preferences(size = 2, region = "New England", alt_credits = "Yes", diversity_students = 36, financial_aid = 70, affiliation = 3)
#> Institution Institution ID % of Students Recieved Aid Institution Size Student Diversity Staff Diversity % of Students Disabled Region Type of Institution Religious Affiliation Calendar System Open Admissions Policy Years Required For Entering Vet Programs Alternative Credit Alternative Tuition Payment Distance Education Counseling Services Employment Services Daycare Services Live On-Campus Room Price Board Price Undergraduate Application Fee Graduate Application Fee
#> 1 University of Bridgeport 128744 78 2 67 20 1 New England 3 -2 1 2 -2 Programs Available Takes alternate credit Takes alternate tuition plans Offers distance education opportunities Offers counseling services Offers employment services Offers no daycare services 2 . . 0 0
#> 2 Goodwin University 129154 88 2 50 24 1 New England 3 -2 1 1 -2 Programs Available Takes alternate credit Takes alternate tuition plans Offers distance education opportunities Offers counseling services Offers employment services Offers no daycare services 2 4500 1700 50 50
#> 3 American International College 164447 97 2 53 14 1 New England 3 -2 1 2 -2 Programs Available Takes alternate credit Takes alternate tuition plans Offers distance education opportunities Offers counseling services Offers employment services Offers no daycare services 2 7420 7240 0 50
#> 4 Bay Path University 164632 81 2 41 9 1 New England 3 -2 1 2 -2 Programs Available Takes alternate credit Takes alternate tuition plans Offers distance education opportunities Offers counseling services Offers employment services Offers no daycare services 2 . . 25 0
#> 5 Clark University 165334 91 2 42 31 2 New England 3 -2 1 2 -2 Programs Available Takes alternate credit Takes alternate tuition plans Offers distance education opportunities Offers counseling services Offers employment services Offers no daycare services 2 5800 4000 0 75
#> 6 Lesley University 166452 85 2 37 19 2 New England 3 -2 1 2 -2 Programs Available Takes alternate credit Takes alternate tuition plans Offers distance education opportunities Offers counseling services Offers employment services Offers no daycare services 2 10060 6570 0 50
#> 7 Mount Holyoke College 166939 76 2 55 37 2 New England 3 -2 1 2 -2 Programs Available Takes alternate credit Takes alternate tuition plans Offers distance education opportunities Offers counseling services Offers employment services Offers no daycare services 2 7860 8160 60 50
#> 8 Smith College 167835 71 2 51 27 1 New England 3 -2 1 2 -2 Programs Available Takes alternate credit Takes alternate tuition plans Offers no distance education opportunities Offers counseling services Offers employment services Offers no daycare services 2 9400 9360 0 60
#> 9 Wentworth Institute of Technology 168227 84 2 36 26 2 New England 3 -2 1 2 -2 Programs Available Takes alternate credit Takes alternate tuition plans Offers distance education opportunities Offers counseling services Offers employment services Offers no daycare services 2 11766 3200 50 50
#> 10 New England Institute of Technology 217305 79 2 63 2 1 New England 3 -2 2 2 2 Programs Available Takes alternate credit Takes alternate tuition plans Offers distance education opportunities Offers counseling services Offers employment services Offers no daycare services 2 8730 5610 25 25The output is a data frame that includes The Institution name, ID, the % of students that receive aid, the size of the institution, the percent of non-white students and staff, the % of disabled students, the region of the institution, type, and other relevant information about the institution.
We can select the columns Sophia is most interested in:
school_preferences(size = 2, region = "New England", alt_credits = "Yes", diversity_students = 36, financial_aid = 70, affiliation = 3) %>%
select(`Institution`, `Institution Size`, `Region`, `Alternative Credit`, `Student Diversity`, `% of Students Recieved Aid`, `Type of Institution`)
#> Institution Institution Size Region Alternative Credit Student Diversity % of Students Recieved Aid Type of Institution
#> 1 University of Bridgeport 2 New England Takes alternate credit 67 78 3
#> 2 Goodwin University 2 New England Takes alternate credit 50 88 3
#> 3 American International College 2 New England Takes alternate credit 53 97 3
#> 4 Bay Path University 2 New England Takes alternate credit 41 81 3
#> 5 Clark University 2 New England Takes alternate credit 42 91 3
#> 6 Lesley University 2 New England Takes alternate credit 37 85 3
#> 7 Mount Holyoke College 2 New England Takes alternate credit 55 76 3
#> 8 Smith College 2 New England Takes alternate credit 51 71 3
#> 9 Wentworth Institute of Technology 2 New England Takes alternate credit 36 84 3
#> 10 New England Institute of Technology 2 New England Takes alternate credit 63 79 3Example 2
What are the admission requirements for my preferred institution?
If Sophia is interested in what it takes to apply to one of her
preferred schools, Sophia can use the admission_reqs
function that provides her with a list of the application
requirements.
admission_reqs(167835)
#> # A tibble: 9 × 2
#> Requirements Priority
#> <chr> <chr>
#> 1 High School Record Required
#> 2 Completion of College-Prepatory Program Required
#> 3 Recommendations Required
#> 4 High School GPA Recommended
#> 5 High School Rank Recommended
#> 6 Test of English as a Foreign Language Recommended
#> 7 Formal Demonstration of Competencies Neither_required_nor_recommended
#> 8 Admission Test Scores Neither_required_nor_recommended
#> 9 Other Tests Neither_required_nor_recommendedNow Sophia knows which application materials are required and recommended, and which ones are not necessary at all.
Example 3
What’s the relationship between the diversity of students and the diversity of staff?
In another scenario, a educational statistician is interested in the
potential relationship between how diverse a student body is and the
diversity of their staff. We’ll data visualize the % of diversity from
the resulting dataframe output by the school_preferences
function.
data <- school_preferences()
ggplot(data, aes(x = `Staff Diversity`, y = `Student Diversity`)) +
geom_point() +
geom_smooth(method = "lm") +
labs(title = "Student Diversity vs. Staff Diversity",
y = "Student Diversity (%)",
x = "Staff Diversity (%)")
#> `geom_smooth()` using formula 'y ~ x'
Due to it’s functionality, the statistician could also limit their research to explore this relationship to schools only located in the New England area:
data <- school_preferences(region = "New England")
ggplot(data, aes(x = `Staff Diversity`, y = `Student Diversity`)) +
geom_point() +
geom_smooth(method = "lm") +
labs(title = "Student Diversity vs. Staff Diversity in New England Institutions",
y = "Student Diversity (%)",
x = "Staff Diversity (%)")
#> `geom_smooth()` using formula 'y ~ x'
In both cases, we can see a moderate to strong positive relationship between student and staff diversity; after noting this relationship the statistician could go further by observing the how the size of an institution, can possibly influence this relationship.
data <- school_preferences(region = "New England") %>%
filter(`Institution Size` != -1 &`Institution Size` != -2 )
data$`Institution Size` <- as.factor(data$`Institution Size`)
ggplot(data, aes(x = `Staff Diversity`, y = `Student Diversity`, color = `Institution Size`)) +
geom_point() +
scale_fill_viridis_c(option = "magma") +
geom_smooth(method = "lm", aes(color=`Institution Size`), se = FALSE) +
labs(title = "Student Diversity vs. Staff Diversity in New England Institutions by Size",
y = "Student Diversity (%)",
x = "Staff Diversity (%)")
#> `geom_smooth()` using formula 'y ~ x'
And they can conclude here doesn’t seem to be much of a difference depending on Institution Size in New England Institutions.
Example 4:
What are the main similarities and differences between my two top college choices?
Amanda, a high school senior, has to decide where she will attend college soon, but is still debating between her top two choices.
Using the compare_int function, Amanda can take the two
schools she is interested in and compare them side by side in a table
that lists some of the major qualities of each institution.
compare_int(100654, 100663)
#> Alabama A & M University University of Alabama at Birmingham
#> Size 3 5
#> Full Time Students 1622 2102
#> Part Time Students 42 52
#> Average Aid Awarded 9872 9344
#> Average Award Size 9679 10435
#> City Normal Birmingham
#> State AL AL
#> Region Southeast Southeast
#> Urbanization 12 12
#> Calendar System 1 1
#> Admission Test Scores Required Required
#> Room & Board Cost . .
#> Degrees Offered Yes Yes
#> AP Credit Accepted Yes Yes
#> Dual Enrollment Credit Accepted Yes Yes
#> Study Abroad Programs Yes Yes
#> Freshman Required to Live on Campus No No
#> Meals per Week 19 .