Chilean Universities Ranking September 2020

Precoding

## Packages
library(kableExtra)
library(tidyverse)

## Data
data <- read.csv("https://osf.io/mv9z8/download", fileEncoding = "UTF-8")

## Institutions Codification
usach <- subset(data, Affiliation == "USACH" | Affiliation == "OXF-USACH") 
puc <- subset(data, Affiliation == "PUC" | Affiliation == "PUC-VDEM") 
ua <- subset(data, Affiliation == "UA") 
uach <-subset(data, Affiliation == "UACH")
uah <- subset(data, Affiliation == "UAH" | Affiliation == "UCHILE-UAH") 
uai <- subset(data, Affiliation == "UAI") 
uchile <- subset(data, Affiliation == "UCHILE" | Affiliation == "UCHILE-UAH") 
uct <- subset(data, Affiliation == "UCT")
udd <- subset(data, Affiliation == "UDD")
udec <- subset(data, Affiliation == "UDEC")
udp <- subset(data, Affiliation == "UDP" | Affiliation == "UDP-COES" |
                Affiliation == "UDP-NYU" | Affiliation == "UDP-Leiden") 
ulagos <- subset(data, Affiliation == "ULAGOS") 
umayor <- subset(data, Affiliation == "UMAYOR") 
unab <- subset(data, Affiliation == "UNAB") 
utalca <- subset(data, Affiliation == "UTALCA") 
utem <- subset(data, Affiliation == "UTEM") 
uvalpo <- subset(data, Affiliation == "UVALPO") 

## Dataframe Construction
Name <- c("USACH", "PUC", "UA", "UACH", "UAI", "UCHILE", "UCT", "UDD", 
          "UDEC", "UDP", "ULAGOS", "UMAYOR", "UNAB", "UTALCA", "UTEM", 
          "UVALPO")
Cases <- c(nrow(usach), nrow(puc), nrow(ua), nrow(uach), nrow(uai), 
           nrow(uchile), nrow(uct), nrow(udd), nrow(udec), nrow(udp), 
           nrow(ulagos), nrow(umayor), nrow(unab), nrow(utalca), nrow(utem), 
           nrow(uvalpo))
Avg_Cites <- c(mean(usach$Cites), mean(puc$Cites), mean(ua$Cites), mean(uach$Cites),
               mean(uai$Cites), mean(uchile$Cites), mean(uct$Cites), mean(udd$Cites),
               mean(udec$Cites), mean(udp$Cites), mean(ulagos$Cites), mean(umayor$Cites),
               mean(unab$Cites), mean(utalca$Cites), mean(utem$Cites),mean(uvalpo$Cites))
Cum_Cites <- c(sum(usach$Cites), sum(puc$Cites), sum(ua$Cites), sum(uach$Cites),
               sum(uai$Cites), sum(uchile$Cites), sum(uct$Cites), sum(udd$Cites),
               sum(udec$Cites), sum(udp$Cites), sum(ulagos$Cites), sum(umayor$Cites),
               sum(unab$Cites), sum(utalca$Cites), sum(utem$Cites), sum(uvalpo$Cites))
Avg_H_Index <- c(mean(usach$H_Index), mean(puc$H_Index), mean(ua$H_Index), 
                 mean(uach$H_Index), mean(uai$H_Index), mean(uchile$H_Index), 
                 mean(uct$H_Index), mean(udd$H_Index), mean(udec$H_Index), 
                 mean(udp$H_Index), mean(ulagos$H_Index), mean(umayor$H_Index), 
                 mean(unab$H_Index), mean(utalca$H_Index), mean(utem$H_Index), 
                 mean(uvalpo$H_Index))
Cum_H_Index <- c(sum(usach$H_Index), sum(puc$H_Index), sum(ua$H_Index), 
                 sum(uach$H_Index), sum(uai$H_Index), sum(uchile$H_Index), sum(uct$H_Index), 
                 sum(udd$H_Index), sum(udec$H_Index), sum(udp$H_Index), sum(ulagos$H_Index), 
                 sum(umayor$H_Index), sum(unab$H_Index), sum(utalca$H_Index), 
                 sum(utem$H_Index), sum(uvalpo$H_Index))
Inv_Avg_Index <- Avg_H_Index*-1
Inv_Cum_Index <- Cum_H_Index*-1

Cumulative Ranking

Inst_Cum <- data.frame(Name, Cases, Cum_Cites, Cum_H_Index, Inv_Cum_Index)
Inst_Cum[is.na(Inst_Cum)] <- 0
Inst_Cum <- within(Inst_Cum, Quartile <- as.integer(cut(Inv_Cum_Index, 
                                                        quantile(Inv_Cum_Index, 
                                                                 probs = 0:4/4), 
                                                        include.lowest = TRUE)))
Inst_Cum$Inv_Cum_Index <- NULL
Inst_Cum <- Inst_Cum[order(-Inst_Cum$Cum_H_Index, -Inst_Cum$Cum_Cites), ]
Inst_Cum$Cum_Cites <- format(Inst_Cum$Cum_Cites, big.mark = ",")
rownames(Inst_Cum) <- NULL
NameCasesCum_CitesCum_H_IndexQuartile
PUC2616,5052351
UCHILE2111,0982081
UDP1715,9961841
USACH137,7711121
UDD71,389432
UDEC91,441412
UAI81,579392
UACH21,601322
UCT6419253
UTALCA11,083183
UVALPO11,016143
UA313183
UNAB128674
ULAGOS218274
UMAYOR27354
UTEM17844
Note:
Compiled using data from the CPS-Ranking. Data collected on September 5, 2020.

Average Ranking

Inst_Avg <- data.frame(Name, Cases, Avg_Cites, Avg_H_Index, Inv_Avg_Index)
Inst_Avg[is.na(Inst_Avg)] <- 0
Inst_Avg <- within(Inst_Avg, Quartile <- as.integer(cut(Inv_Avg_Index, 
                                                        quantile(Inv_Avg_Index, 
                                                                 probs = 0:4/4), 
                                                        include.lowest = TRUE)))
Inst_Avg$Inv_Avg_Index <- NULL
Inst_Avg <- Inst_Avg[order(-Inst_Avg$Avg_H_Index, -Inst_Avg$Avg_Cites), ]
rownames(Inst_Avg) <- NULL
Avg_Cites <- format(round(Inst_Avg$Avg_Cites, 2), nsmall = 2, big.mark = ",")
Avg_H_Index <- format(round(Inst_Avg$Avg_H_Index, 2), nsmall = 2, big.mark = ",")
Quartile <- Inst_Avg$Quartile
Inst_Avg <- select(Inst_Avg, Name, Cases)
Inst_Avg <- data.frame(Inst_Avg, Avg_Cites, Avg_H_Index, Quartile)
NameCasesAvg_CitesAvg_H_IndexQuartile
UTALCA11,083.0018.001
UACH2800.5016.001
UVALPO11,016.0014.001
UDP17940.9410.821
UCHILE21528.489.902
PUC26634.819.042
USACH13597.778.622
UNAB1286.007.002
UDD7198.436.143
UAI8197.384.883
UDEC9160.114.563
UCT669.834.173
UTEM178.004.004
ULAGOS291.003.504
UA343.672.674
UMAYOR236.502.504
Note:
Compiled using data from the CPS-Ranking. Data collected on September 5, 2020.


How to download and cite this dataset?

González-Bustamante, B. (2020). Chilean Political Science Impact Ranking Dataset [Data collected on September 5, 2020]. DOI: 10.17605/OSF.IO/M3NZD

Bastián González-Bustamante
Bastián González-Bustamante
DPhil (PhD) Researcher

DPhil (PhD) Researcher in the Department of Politics and International Relations and St Hilda’s College at the University of Oxford, United Kingdom.