Chilean Institutions Ranking March 2020
This project remains active, however, it was transferred to the Training Data Lab site in June 2023.
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*-1Cumulative 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$Cum_Cites, big.mark = ",")
rownames(Inst_Cum) <- NULL| Name | Cases | Cum_Cites | Cum_H_Index | Quartile |
|---|---|---|---|---|
| PUC | 26 | 15,752 | 234 | 1 |
| UCHILE | 19 | 9,972 | 189 | 1 |
| UDP | 17 | 13,324 | 170 | 1 |
| USACH | 13 | 6,872 | 101 | 1 |
| COES | 4 | 6,452 | 74 | 1 |
| UDD | 7 | 1,225 | 42 | 2 |
| UAI | 8 | 1,396 | 37 | 2 |
| UDEC | 9 | 1,216 | 37 | 2 |
| UCT | 5 | 356 | 22 | 2 |
| ULAGOS | 3 | 742 | 19 | 2 |
| UTALCA | 1 | 977 | 15 | 3 |
| UACH | 1 | 727 | 15 | 3 |
| UNAB | 1 | 263 | 8 | 3 |
| UA | 3 | 98 | 7 | 3 |
| ANEPE | 1 | 48 | 5 | 3 |
| UMAYOR | 2 | 65 | 3 | 4 |
| UTEM | 1 | 55 | 3 | 4 |
| IIPSS | 1 | 43 | 3 | 4 |
| UVALPO | 1 | 31 | 3 | 4 |
| IL | 1 | 5 | 2 | 4 |
| Note: | ||||
| Compiled using data from the CPS-Ranking. Data collected on March 5, 2020. |
Average Ranking
Inst_Avg <- data.frame(Name, Cases, Avg_Cites, Avg_H_Index, Inv_Avg_Index)
Inst_Avg[is.na(Inst_Avg)] <- 0
Inst_Avg <- within(Inst_Avg, Quartile <- as.integer(cut(Inv_Avg_Index,
quantile(Inv_Avg_Index,
probs = 0:4/4),
include.lowest = TRUE)))
Inst_Avg$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)| Name | Cases | Avg_Cites | Avg_H_Index | Quartile |
|---|---|---|---|---|
| COES | 4 | 1,613.00 | 18.50 | 1 |
| UTALCA | 1 | 977.00 | 15.00 | 1 |
| UACH | 1 | 727.00 | 15.00 | 1 |
| UDP | 17 | 783.76 | 10.00 | 1 |
| UCHILE | 19 | 524.84 | 9.95 | 1 |
| PUC | 26 | 605.85 | 9.00 | 2 |
| UNAB | 1 | 263.00 | 8.00 | 2 |
| USACH | 13 | 528.62 | 7.77 | 2 |
| ULAGOS | 3 | 247.33 | 6.33 | 2 |
| UDD | 7 | 175.00 | 6.00 | 2 |
| ANEPE | 1 | 48.00 | 5.00 | 3 |
| UAI | 8 | 174.50 | 4.62 | 3 |
| UCT | 5 | 71.20 | 4.40 | 3 |
| UDEC | 9 | 135.11 | 4.11 | 3 |
| UTEM | 1 | 55.00 | 3.00 | 3 |
| IIPSS | 1 | 43.00 | 3.00 | 3 |
| UVALPO | 1 | 31.00 | 3.00 | 3 |
| UA | 3 | 32.67 | 2.33 | 4 |
| IL | 1 | 5.00 | 2.00 | 4 |
| UMAYOR | 2 | 32.50 | 1.50 | 4 |
| Note: | ||||
| Compiled using data from the CPS-Ranking. Data collected on March 5, 2020. |
Bastián González-Bustamante
Post-doctoral Researcher
Post-doctoral Researcher in Computational Social Science and a lecturer in Governance and Development at the Institute of Public Administration at the Faculty of Governance and Global Affairs at Leiden University, Netherlands. Lecturer at the School of Public Administration at Universidad Diego Portales and Research Associate in Training Data Lab, Chile.