Classifications and rankings of U.S. universities for the years 2017–2020.
Usage
data(qswur_usa)
Format
A tibble of 13 variables on 612 cases:
year
year of rankings
institution
institution of higher learning
size
size category of institution
focus
subject range of institution
res
research intensity of institution
age
age classification of institution
status
status of institution
rk_academic
rank by academic reputation
rk_employer
rank by employer reputation
rk_ratio
rank by faculty–student ratio
rk_citations
rank by citations per faculty
rk_intl_faculty
rank by international faculty ratio
rk_intl_students
rank by international student ratio
References
Quacquarelli Symonds (2021) "University Rankings". TopUniversities.com https://www.topuniversities.com/university-rankings.
Examples
# subset QS data to rank variables
head(qswur_usa)
#> # A tibble: 6 × 13
#> year institution size focus res age status rk_academic rk_employer
#> <int> <chr> <fct> <fct> <fct> <int> <chr> <int> <int>
#> 1 2017 MASSACHUSETTS IN… M CO VH 5 B 6 4
#> 2 2017 STANFORD UNIVERS… L FC VH 5 A 5 5
#> 3 2017 HARVARD UNIVERSI… L FC VH 5 B 1 1
#> 4 2017 CALIFORNIA INSTI… S CO VH 5 B 23 90
#> 5 2017 UNIVERSITY OF CH… L FC VH 5 B 13 47
#> 6 2017 PRINCETON UNIVER… M CO VH 5 B 10 32
#> # ℹ 4 more variables: rk_ratio <int>, rk_citations <int>,
#> # rk_intl_faculty <int>, rk_intl_students <int>
qs_ranks <- subset(
qswur_usa,
complete.cases(qswur_usa),
select = 8:13
)
# calculate Kendall correlation matrix
qs_cor <- cor(qs_ranks, method = "kendall")
# calculate eigendecomposition
qs_eigen <- eigen_ord(qs_cor)
# view correlations as cosines of biplot vectors
biplot(x = qs_eigen$vectors, y = qs_eigen$vectors, col = c(NA, "black"))