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 at the Institute of Security and Global Affairs (ISGA) at the Faculty of Governance and Global Affairs at Leiden University, Netherlands.