Chilean Institutions Ranking March 2020

Precoding

## Packages
library(kableExtra)
library(tidyverse)

## Data
## data <- read.csv("20200305_ranking.csv")
data <- read.csv("https://osf.io/8hnx3/download", fileEncoding = "UTF-8")

## Institutions Codification
anepe <- subset(data, Affiliation == "ANEPE") 
coes <- subset(data, Affiliation == "COES" | Affiliation == "UDP-COES" | 
                 Affiliation == "LSE-COES") 
iipss <- subset(data, Affiliation == "IIPSS") 
il <- subset(data, Affiliation == "IL") 
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("ANEPE", "COES", "IIPSS", "IL", "USACH", "PUC", "UA", "UACH", "UAI",
          "UCHILE", "UCT", "UDD", "UDEC", "UDP", "ULAGOS", "UMAYOR", "UNAB",
          "UTALCA", "UTEM", "UVALPO")
Cases <- c(nrow(anepe), nrow(coes), nrow(iipss), nrow(il), 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(anepe$Cites), mean(coes$Cites), mean(iipss$Cites), mean(il$Cites),
               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(anepe$Cites), sum(coes$Cites), sum(iipss$Cites), sum(il$Cites),
               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(anepe$H_Index), mean(coes$H_Index), mean(iipss$H_Index), 
                 mean(il$H_Index), 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(anepe$H_Index), sum(coes$H_Index), sum(iipss$H_Index), 
                 sum(il$H_Index),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
PUC2615,7522341
UCHILE199,9721891
UDP1713,3241701
USACH136,8721011
COES46,452741
UDD71,225422
UAI81,396372
UDEC91,216372
UCT5356222
ULAGOS3742192
UTALCA1977153
UACH1727153
UNAB126383
UA39873
ANEPE14853
UMAYOR26534
UTEM15534
IIPSS14334
UVALPO13134
IL1524
Note:
Compiled using data from the CPS-Ranking. Data collected on March 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
COES41,613.0018.501
UTALCA1977.0015.001
UACH1727.0015.001
UDP17783.7610.001
UCHILE19524.849.951
PUC26605.859.002
UNAB1263.008.002
USACH13528.627.772
ULAGOS3247.336.332
UDD7175.006.002
ANEPE148.005.003
UAI8174.504.623
UCT571.204.403
UDEC9135.114.113
UTEM155.003.003
IIPSS143.003.003
UVALPO131.003.003
UA332.672.334
IL15.002.004
UMAYOR232.501.504
Note:
Compiled using data from the CPS-Ranking. Data collected on March 5, 2020.


How to download and cite this dataset?

González-Bustamante, B. (2020). Chilean Political Science Impact Ranking Dataset [Data collected on March 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.