Lectura de datos
load("Icfes162.RData")
Cargando bibliotecas
library(dplyr)
library(ggplot2)
library(corrplot)
50. Partiendo del análisis global de estos resultados, ¿Es verdad que. . . “Antioquia la más educada”?
Matriz de correlaciones
datos <- icfes %>%
select(c(3, 4, 7, 16, 18, 20, 26, 27, 28, 29, 65:78, 80:81)) %>%
filter(ESTU_ESTUDIANTE == "ESTUDIANTE")
datoscor <- datos %>%
select(c(11:26))
color <- rainbow(n = 16, v = 0.6, start = 0.2, end = 1)
dfcor <- cor(datoscor, use = "na.or.complete")
corrplot(dfcor, method = 'circle', tl.col="black",
col = color, tl.cex = 2)
Resumen numérico
summary(datoscor)
## PUNT_LECTURA_CRITICA PERCENTIL_LECTURA_CRITICA DESEMP_LECTURA_CRITICA PUNT_MATEMATICAS
## Min. : 0.00 Min. : 1.00 Min. :1.000 Min. : 0.00
## 1st Qu.: 45.00 1st Qu.: 23.00 1st Qu.:2.000 1st Qu.: 41.00
## Median : 52.00 Median : 49.00 Median :3.000 Median : 51.00
## Mean : 51.39 Mean : 48.99 Mean :2.575 Mean : 49.64
## 3rd Qu.: 59.00 3rd Qu.: 75.00 3rd Qu.:3.000 3rd Qu.: 59.00
## Max. :100.00 Max. :100.00 Max. :4.000 Max. :100.00
## PERCENTIL_MATEMATICAS DESEMP_MATEMATICAS PUNT_C_NATURALES PERCENTIL_C_NATURALES DESEMP_C_NATURALES
## Min. : 1.00 Min. :1.000 Min. : 0.00 Min. : 1.00 Min. :1.000
## 1st Qu.: 23.00 1st Qu.:2.000 1st Qu.: 45.00 1st Qu.: 23.00 1st Qu.:2.000
## Median : 49.00 Median :2.000 Median : 52.00 Median : 49.00 Median :2.000
## Mean : 48.97 Mean :2.392 Mean : 51.41 Mean : 49.02 Mean :2.237
## 3rd Qu.: 75.00 3rd Qu.:3.000 3rd Qu.: 59.00 3rd Qu.: 75.00 3rd Qu.:3.000
## Max. :100.00 Max. :4.000 Max. :100.00 Max. :100.00 Max. :4.000
## PUNT_SOCIALES_CIUDADANAS PERCENTIL_SOCIALES_CIUDADANAS DESEMP_SOCIALES_CIUDADANAS PUNT_INGLES
## Min. : 0.0 Min. : 1.00 Min. :1.000 Min. : 0.00
## 1st Qu.: 42.0 1st Qu.: 23.00 1st Qu.:2.000 1st Qu.: 43.00
## Median : 50.0 Median : 49.00 Median :2.000 Median : 50.00
## Mean : 49.4 Mean : 49.01 Mean :2.108 Mean : 50.78
## 3rd Qu.: 58.0 3rd Qu.: 75.00 3rd Qu.:3.000 3rd Qu.: 59.00
## Max. :100.0 Max. :100.00 Max. :4.000 Max. :100.00
## PERCENTIL_INGLES PUNT_GLOBAL PERCENTIL_GLOBAL
## Min. : 1.00 Min. : 0.0 Min. : 1.00
## 1st Qu.: 23.00 1st Qu.:220.0 1st Qu.: 24.00
## Median : 49.00 Median :255.0 Median : 49.00
## Mean : 49.05 Mean :252.4 Mean : 49.35
## 3rd Qu.: 75.00 3rd Qu.:290.0 3rd Qu.: 75.00
## Max. :100.00 Max. :468.0 Max. :100.00
Estandarización de variables
datosstd <- data.frame(scale(datos[, 11:26]))
head(datosstd[1:5, 1:5])
## PUNT_LECTURA_CRITICA PERCENTIL_LECTURA_CRITICA DESEMP_LECTURA_CRITICA PUNT_MATEMATICAS
## 1 1.1871477 1.4570253 1.9546047 1.0475057
## 2 0.6996931 0.9149413 0.5826721 0.6099089
## 3 1.1871477 1.4570253 1.9546047 0.5369761
## 4 1.4308750 1.5925463 1.9546047 0.4640434
## 5 1.8370871 1.6941870 1.9546047 1.6309680
## PERCENTIL_MATEMATICAS
## 1 1.2542382
## 2 0.8139531
## 3 0.6784807
## 4 0.6107445
## 5 1.6267872
summary(datosstd)
## PUNT_LECTURA_CRITICA PERCENTIL_LECTURA_CRITICA DESEMP_LECTURA_CRITICA PUNT_MATEMATICAS
## Min. :-4.17485 Min. :-1.6260775 Min. :-2.1612 Min. :-3.62019
## 1st Qu.:-0.51894 1st Qu.:-0.8807120 1st Qu.:-0.7893 1st Qu.:-0.62995
## Median : 0.04975 Median : 0.0001745 Median : 0.5827 Median : 0.09938
## Mean : 0.00000 Mean : 0.0000000 Mean : 0.0000 Mean : 0.00000
## 3rd Qu.: 0.61845 3rd Qu.: 0.8810610 3rd Qu.: 0.5827 3rd Qu.: 0.68284
## Max. : 3.94939 Max. : 1.7280673 Max. : 1.9546 Max. : 3.67309
## PERCENTIL_MATEMATICAS DESEMP_MATEMATICAS PUNT_C_NATURALES PERCENTIL_C_NATURALES DESEMP_C_NATURALES
## Min. :-1.624549 Min. :-1.8123 Min. :-4.1922 Min. :-1.6273999 Min. :-1.7171
## 1st Qu.:-0.879451 1st Qu.:-0.5106 1st Qu.:-0.5227 1st Qu.:-0.8818757 1st Qu.:-0.3285
## Median : 0.001119 Median :-0.5106 Median : 0.0481 Median :-0.0008016 Median :-0.3285
## Mean : 0.000000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000000 Mean : 0.0000
## 3rd Qu.: 0.881689 3rd Qu.: 0.7911 3rd Qu.: 0.6189 3rd Qu.: 0.8802725 3rd Qu.: 1.0602
## Max. : 1.728392 Max. : 2.0929 Max. : 3.9622 Max. : 1.7274592 Max. : 2.4488
## PUNT_SOCIALES_CIUDADANAS PERCENTIL_SOCIALES_CIUDADANAS DESEMP_SOCIALES_CIUDADANAS PUNT_INGLES
## Min. :-3.81514 Min. :-1.6270687 Min. :-1.4196 Min. :-3.66815
## 1st Qu.:-0.57133 1st Qu.:-0.8814277 1st Qu.:-0.1379 1st Qu.:-0.56180
## Median : 0.04654 Median :-0.0002155 Median :-0.1379 Median :-0.05611
## Mean : 0.00000 Mean : 0.0000000 Mean : 0.0000 Mean : 0.00000
## 3rd Qu.: 0.66440 3rd Qu.: 0.8809967 3rd Qu.: 1.1437 3rd Qu.: 0.59406
## Max. : 3.90821 Max. : 1.7283160 Max. : 2.4254 Max. : 3.55593
## PERCENTIL_INGLES PUNT_GLOBAL PERCENTIL_GLOBAL
## Min. :-1.630374 Min. :-4.19401 Min. :-1.63968
## 1st Qu.:-0.883959 1st Qu.:-0.53858 1st Qu.:-0.85968
## Median :-0.001832 Median : 0.04297 Median :-0.01186
## Mean : 0.000000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.: 0.880294 3rd Qu.: 0.62451 3rd Qu.: 0.86988
## Max. : 1.728493 Max. : 3.58209 Max. : 1.71770
Puntaje lectura crítica vs Puntaje en sociales-ciudadanas (sin estandarizar)
ggplot(data = datos, aes(x = PUNT_LECTURA_CRITICA, y = PUNT_SOCIALES_CIUDADANAS)) +
geom_point(col = "gray10", fill = "magenta4")
Puntaje lectura crítica vs Puntaje en sociales-ciudadanas por género (estandarizadas)
names(datosstd) <- paste0(names(datosstd), "_E")
datosstd2 <- cbind(datos, datosstd) %>%
filter(ESTU_GENERO != "")
ggplot(data = datosstd2, aes(x = PUNT_LECTURA_CRITICA_E, y = PUNT_SOCIALES_CIUDADANAS_E,
color = ESTU_GENERO)) +
scale_color_manual(values = c("orange4", "navy")) +
geom_point(size = 4) +
geom_vline(xintercept = 0, col = "red", lty = 2, lwd = 0.9) +
geom_hline(yintercept = 0, col = "red", lty = 2, lwd = 0.9) +
theme_bw()
Puntaje lectura crítica vs Puntaje en sociales-ciudadanas por departamento (estandarizadas)
datosstd2_dpto <- datosstd2 %>%
filter(ESTU_RESIDE_DEPTO != "")
colores <- rainbow(n = 34, v = 0.6, start = 0, end = 1)
ggplot(data = datosstd2_dpto, aes(x = PUNT_LECTURA_CRITICA_E, y = PUNT_SOCIALES_CIUDADANAS_E,
color = ESTU_RESIDE_DEPTO)) +
scale_color_manual(values = colores) +
geom_jitter(size = 4) +
geom_vline(xintercept = 0, col = "red", lty = 2, lwd = 0.9) +
geom_hline(yintercept = 0, col = "red", lty = 2, lwd = 0.9) +
theme_bw()
Puntaje lectura crítica vs Puntaje en sociales-ciudadanas para 5 departamentos (estandarizadas)
datosstd2_dpto2 <- datosstd2_dpto %>%
filter(ESTU_RESIDE_DEPTO == "VALLE"
| ESTU_RESIDE_DEPTO == "SANTANDER" | ESTU_RESIDE_DEPTO == "ATLANTICO"
| ESTU_RESIDE_DEPTO == "ANTIOQUIA" | ESTU_RESIDE_DEPTO == "BOGOTA")
colores <- c("orangered1", "dodgerblue2", "magenta4", "gray10", "yellow2")
ggplot(data = datosstd2_dpto2, aes(x = PUNT_LECTURA_CRITICA_E, y = PUNT_SOCIALES_CIUDADANAS_E,
color = ESTU_RESIDE_DEPTO)) +
scale_color_manual(values = colores) +
geom_jitter(size = 4) +
geom_vline(xintercept = 0, col = "darkred", lty = 2, lwd = 0.9) +
geom_hline(yintercept = 0, col = "darkred", lty = 2, lwd = 0.9) +
theme_bw()
Puntaje lectura crítica vs Puntaje en sociales-ciudadanas para 5 departamentos (Antioquia)
colores <- c("orangered1", "gray20", "gray32", "gray44", "gray56")
ggplot(data = datosstd2_dpto2, aes(x = PUNT_LECTURA_CRITICA_E, y = PUNT_SOCIALES_CIUDADANAS_E,
color = ESTU_RESIDE_DEPTO)) +
scale_color_manual(values = colores) +
geom_jitter(size = 4) +
geom_vline(xintercept = 0, col = "darkred", lty = 2, lwd = 0.9) +
geom_hline(yintercept = 0, col = "darkred", lty = 2, lwd = 0.9) +
theme_bw()
Puntaje lectura crítica vs Puntaje en sociales-ciudadanas para 5 departamentos (Atláltico)
colores <- c("gray8", "dodgerblue2", "gray32", "gray44", "gray56")
ggplot(data = datosstd2_dpto2, aes(x = PUNT_LECTURA_CRITICA_E, y = PUNT_SOCIALES_CIUDADANAS_E,
color = ESTU_RESIDE_DEPTO)) +
scale_color_manual(values = colores) +
geom_jitter(size = 4) +
geom_vline(xintercept = 0, col = "darkred", lty = 2, lwd = 0.9) +
geom_hline(yintercept = 0, col = "darkred", lty = 2, lwd = 0.9) +
theme_bw()
Puntaje lectura crítica vs Puntaje en sociales-ciudadanas para 5 departamentos (Bogotá)
colores <- c("gray8", "gray20", "magenta4", "gray44", "gray56")
ggplot(data = datosstd2_dpto2, aes(x = PUNT_LECTURA_CRITICA_E, y = PUNT_SOCIALES_CIUDADANAS_E,
color = ESTU_RESIDE_DEPTO)) +
scale_color_manual(values = colores) +
geom_jitter(size = 4) +
geom_vline(xintercept = 0, col = "darkred", lty = 2, lwd = 0.9) +
geom_hline(yintercept = 0, col = "darkred", lty = 2, lwd = 0.9) +
theme_bw()
Puntaje lectura crítica vs Puntaje en sociales-ciudadanas para 5 departamentos (Santander)
colores <- c("gray8", "gray20", "gray32", "forestgreen", "gray56")
ggplot(data = datosstd2_dpto2, aes(x = PUNT_LECTURA_CRITICA_E, y = PUNT_SOCIALES_CIUDADANAS_E,
color = ESTU_RESIDE_DEPTO)) +
scale_color_manual(values = colores) +
geom_jitter(size = 4) +
geom_vline(xintercept = 0, col = "darkred", lty = 2, lwd = 0.9) +
geom_hline(yintercept = 0, col = "darkred", lty = 2, lwd = 0.9) +
theme_bw()
Puntaje lectura crítica vs Puntaje en sociales-ciudadanas para 5 departamentos (Valle del cauca)
colores <- c("gray8", "gray20", "gray32", "gray44", "yellow2")
ggplot(data = datosstd2_dpto2, aes(x = PUNT_LECTURA_CRITICA_E, y = PUNT_SOCIALES_CIUDADANAS_E,
color = ESTU_RESIDE_DEPTO)) +
scale_color_manual(values = colores) +
geom_jitter(size = 4) +
geom_vline(xintercept = 0, col = "darkred", lty = 2, lwd = 0.9) +
geom_hline(yintercept = 0, col = "darkred", lty = 2, lwd = 0.9) +
theme_bw()
Cálculo de las componentes principales
acp <- princomp(datosstd2[, 27:42], cor = TRUE)
Valores propios
summary(acp)
## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7
## Standard deviation 3.5562231 0.8480679 0.83377966 0.75286569 0.70770468 0.66236404 0.34009717
## Proportion of Variance 0.7904202 0.0449512 0.04344928 0.03542542 0.03130287 0.02742038 0.00722913
## Cumulative Proportion 0.7904202 0.8353714 0.87882065 0.91424607 0.94554894 0.97296933 0.98019846
## Comp.8 Comp.9 Comp.10 Comp.11 Comp.12 Comp.13
## Standard deviation 0.313671637 0.298547813 0.286949222 0.142014720 0.1034646038 0.0861031334
## Proportion of Variance 0.006149368 0.005570675 0.005146241 0.001260511 0.0006690578 0.0004633593
## Cumulative Proportion 0.986347826 0.991918500 0.997064741 0.998325253 0.9989943104 0.9994576697
## Comp.14 Comp.15 Comp.16
## Standard deviation 0.0809687169 0.044641138 1.133668e-02
## Proportion of Variance 0.0004097458 0.000124552 8.032518e-06
## Cumulative Proportion 0.9998674155 0.999991967 1.000000e+00
plot(acp$sdev, type="b",
xlab = "Componente principal",
ylab = "Valor propio",
main = "Gráfico de sedimentación (pareto)")
abline(v = 3, col = "red", lty = 2, lwd = 0.6)
abline(v = 7, col = "blue", lty = 2, lwd = 0.4)
Vectores propios
loadings(acp)
##
## Loadings:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9 Comp.10
## PUNT_LECTURA_CRITICA_E -0.252 -0.286 -0.213 -0.363 0.159 0.337 -0.136
## PERCENTIL_LECTURA_CRITICA_E -0.249 -0.132 -0.381 -0.124 -0.147 0.341 0.284 0.437 -0.109
## DESEMP_LECTURA_CRITICA_E -0.238 -0.215 -0.394 -0.258 0.324 -0.422 -0.609 0.124
## PUNT_MATEMATICAS_E -0.259 0.170 0.172 -0.322 -0.272 -0.108 -0.353
## PERCENTIL_MATEMATICAS_E -0.252 0.357 0.110 -0.142 -0.335 0.244 -0.171 -0.430
## DESEMP_MATEMATICAS_E -0.238 0.387 0.142 -0.237 -0.371 -0.411 0.238 0.587
## PUNT_C_NATURALES_E -0.256 0.187 -0.120 0.495 -0.166 0.233 -0.178
## PERCENTIL_C_NATURALES_E -0.254 0.246 0.108 0.173 0.262 0.321 0.335 -0.281 0.338 -0.181
## DESEMP_C_NATURALES_E -0.239 0.264 0.147 0.174 0.406 0.383 -0.446 0.345 -0.379 0.218
## PUNT_SOCIALES_CIUDADANAS_E -0.260 -0.119 -0.126 0.124 0.166 -0.447 0.361
## PERCENTIL_SOCIALES_CIUDADANAS_E -0.255 -0.249 0.400 -0.188 0.131 0.498
## DESEMP_SOCIALES_CIUDADANAS_E -0.240 -0.278 0.492 -0.255 -0.280 -0.131 -0.672
## PUNT_INGLES_E -0.234 -0.476 0.430 -0.116
## PERCENTIL_INGLES_E -0.223 -0.390 0.422 0.269 -0.345 0.253
## PUNT_GLOBAL_E -0.273 -0.166 0.169 -0.220
## PERCENTIL_GLOBAL_E -0.274 0.119 0.116 -0.137 0.146 0.269
## Comp.11 Comp.12 Comp.13 Comp.14 Comp.15 Comp.16
## PUNT_LECTURA_CRITICA_E 0.207 0.624 0.171 0.211
## PERCENTIL_LECTURA_CRITICA_E -0.178 -0.486 -0.159 -0.200
## DESEMP_LECTURA_CRITICA_E
## PUNT_MATEMATICAS_E -0.230 0.618 0.214 0.234
## PERCENTIL_MATEMATICAS_E 0.179 -0.480 -0.196 -0.292
## DESEMP_MATEMATICAS_E
## PUNT_C_NATURALES_E 0.169 -0.425 -0.454 0.220 0.210
## PERCENTIL_C_NATURALES_E -0.147 0.316 0.377 -0.184 -0.160
## DESEMP_C_NATURALES_E
## PUNT_SOCIALES_CIUDADANAS_E -0.682 0.221
## PERCENTIL_SOCIALES_CIUDADANAS_E 0.547 -0.298
## DESEMP_SOCIALES_CIUDADANAS_E
## PUNT_INGLES_E -0.704
## PERCENTIL_INGLES_E 0.590
## PUNT_GLOBAL_E -0.895
## PERCENTIL_GLOBAL_E -0.147 0.100 0.860
##
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9 Comp.10 Comp.11 Comp.12
## SS loadings 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
## Proportion Var 0.063 0.063 0.063 0.062 0.063 0.063 0.062 0.062 0.062 0.062 0.063 0.062
## Cumulative Var 0.063 0.125 0.188 0.250 0.313 0.375 0.438 0.500 0.563 0.625 0.688 0.750
## Comp.13 Comp.14 Comp.15 Comp.16
## SS loadings 1.000 1.000 1.000 1.000
## Proportion Var 0.063 0.063 0.062 0.062
## Cumulative Var 0.813 0.875 0.938 1.000
Puntajes
head(acp$scores, n = 10)
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8
## [1,] -4.243715 0.5980125 -1.4838740 -0.32704885 0.1967541 0.3730123 -0.01406390 -0.45692672
## [2,] -2.622393 -0.3490588 0.9294425 -0.37766459 -0.3758278 1.0066672 -0.02271030 0.37749253
## [3,] -4.695078 -0.4886441 -0.7279185 0.31860583 0.2413117 0.7250235 -0.08508084 -0.43465941
## [4,] -4.495903 -1.0444912 -0.5470604 0.18076910 -0.2198498 0.9206237 -0.34175678 -0.07138675
## [5,] -6.101369 0.2658524 -0.4284546 -0.74392393 -0.4260703 0.2041977 -0.07141037 -0.12418287
## [6,] 4.139839 -1.2424016 -0.6315911 -0.59232592 0.4286089 -0.2040696 0.75553464 0.10735257
## [7,] -5.691716 0.1889588 0.7952053 0.81511364 0.9077928 0.4083028 0.17732057 0.40025441
## [8,] 1.358756 -0.3393591 0.4159439 0.03932809 0.2062659 -0.1536299 0.12527600 0.31853438
## [9,] 3.923731 -0.9495590 0.4275942 -1.27494409 -1.0585215 0.1308668 0.09702469 0.40041390
## [10,] -2.467578 -1.6568852 -0.6921145 -1.04061533 -1.2502580 0.7905972 -0.17367955 -0.01080916
## Comp.9 Comp.10 Comp.11 Comp.12 Comp.13 Comp.14 Comp.15
## [1,] -0.1873425660 0.11042152 0.01435226 0.006056893 0.0008335378 0.028973617 -0.02988437
## [2,] -0.1260180858 0.15147094 -0.06396832 0.052852416 -0.0221689820 -0.051262056 0.03674905
## [3,] 0.4464597233 0.12824323 0.09321098 -0.025415955 -0.0600224070 -0.006773541 -0.01100345
## [4,] 0.2000970395 -0.02367566 -0.10796978 0.163064551 -0.0233291544 0.046391526 0.02801777
## [5,] 0.4809530315 0.05297200 0.22017428 0.153847633 0.0487153566 0.073398009 -0.07958630
## [6,] 0.0441735236 0.06904131 -0.08650860 -0.004921357 -0.0543198043 -0.036419436 -0.02288259
## [7,] -0.3643742958 0.31467075 -0.04645757 -0.208514576 -0.2134230794 0.001431311 -0.08603064
## [8,] -0.0962049248 -0.45231070 0.12466570 -0.027177695 0.0019482653 0.011305126 0.05579465
## [9,] -0.0004434339 -0.41585312 -0.05575884 0.177100560 0.1851495775 0.202856553 -0.13562300
## [10,] 0.3888494028 0.37771831 0.05811808 0.029272356 -0.0995082678 0.068591742 0.11848288
## Comp.16
## [1,] 2.911726e-04
## [2,] -6.764910e-03
## [3,] -2.271688e-02
## [4,] -1.537054e-02
## [5,] -5.396840e-06
## [6,] -3.151189e-03
## [7,] 2.744229e-02
## [8,] 2.128395e-02
## [9,] 1.504658e-02
## [10,] -1.831830e-02
Concatenando resultados
datosACP <- data.frame(datosstd2, acp$scores[, c(1, 2, 3)])
head(datosACP, n = 10)
## ESTU_ESTUDIANTE ESTU_EDAD ESTU_GENERO ESTU_AREA_RESIDE ESTU_RESIDE_MCPIO ESTU_RESIDE_DEPTO
## 1 ESTUDIANTE 18 M R LA ESTRELLA ANTIOQUIA
## 2 ESTUDIANTE 16 M R LA ESTRELLA ANTIOQUIA
## 3 ESTUDIANTE 17 M R ENVIGADO ANTIOQUIA
## 4 ESTUDIANTE 17 M R LA ESTRELLA ANTIOQUIA
## 5 ESTUDIANTE 16 M R PAIPA BOYACA
## 6 ESTUDIANTE 19 M R LA ESTRELLA ANTIOQUIA
## 7 ESTUDIANTE 16 F U CUCUTA NORTE SANTANDER
## 8 ESTUDIANTE 18 F U BARRANQUILLA ATLANTICO
## 9 ESTUDIANTE 17 M R ENVIGADO ANTIOQUIA
## 10 ESTUDIANTE 16 F U CUCUTA NORTE SANTANDER
## COLE_JORNADA COLE_GENERO COLE_CARACTER COLE_NATURALEZA PUNT_LECTURA_CRITICA
## 1 C MIXTO ACADÉMICO NO OFICIAL 66
## 2 C MIXTO ACADÉMICO NO OFICIAL 60
## 3 C MIXTO ACADÉMICO NO OFICIAL 66
## 4 C MIXTO ACADÉMICO NO OFICIAL 69
## 5 M MIXTO TÉCNICO/ACADÉMICO OFICIAL 74
## 6 C MIXTO ACADÉMICO NO OFICIAL 49
## 7 M MIXTO ACADÉMICO NO OFICIAL 64
## 8 M FEMENINO TÉCNICO/ACADÉMICO OFICIAL 50
## 9 C MIXTO ACADÉMICO NO OFICIAL 49
## 10 M MIXTO ACADÉMICO NO OFICIAL 68
## PERCENTIL_LECTURA_CRITICA DESEMP_LECTURA_CRITICA PUNT_MATEMATICAS PERCENTIL_MATEMATICAS
## 1 92 4 64 86
## 2 76 3 58 73
## 3 92 4 57 69
## 4 96 4 56 67
## 5 99 4 72 97
## 6 37 2 33 7
## 7 87 3 66 90
## 8 39 2 46 34
## 9 35 2 42 24
## 10 94 4 53 58
## DESEMP_MATEMATICAS PUNT_C_NATURALES PERCENTIL_C_NATURALES DESEMP_C_NATURALES PUNT_SOCIALES_CIUDADANAS
## 1 3 63 85 3 63
## 2 3 57 69 3 51
## 3 3 67 94 3 65
## 4 3 61 82 3 61
## 5 4 69 96 3 67
## 6 1 41 12 1 40
## 7 3 75 100 4 69
## 8 2 50 37 2 44
## 9 2 34 4 1 28
## 10 3 51 44 2 54
## PERCENTIL_SOCIALES_CIUDADANAS DESEMP_SOCIALES_CIUDADANAS PUNT_INGLES PERCENTIL_INGLES PUNT_GLOBAL
## 1 88 3 51 50 315
## 2 53 2 67 89 287
## 3 91 3 63 84 320
## 4 83 3 70 92 313
## 5 93 3 64 85 350
## 6 18 1 41 17 204
## 7 96 3 72 94 342
## 8 29 2 50 48 237
## 9 1 1 48 40 194
## 10 63 2 65 87 287
## PERCENTIL_GLOBAL PUNT_LECTURA_CRITICA_E PERCENTIL_LECTURA_CRITICA_E DESEMP_LECTURA_CRITICA_E
## 1 88 1.1871477 1.4570253 1.9546047
## 2 73 0.6996931 0.9149413 0.5826721
## 3 90 1.1871477 1.4570253 1.9546047
## 4 87 1.4308750 1.5925463 1.9546047
## 5 98 1.8370871 1.6941870 1.9546047
## 6 14 -0.1939737 -0.4063885 -0.7892605
## 7 96 1.0246628 1.2876240 0.5826721
## 8 36 -0.1127312 -0.3386280 -0.7892605
## 9 10 -0.1939737 -0.4741490 -0.7892605
## 10 73 1.3496325 1.5247858 1.9546047
## PUNT_MATEMATICAS_E PERCENTIL_MATEMATICAS_E DESEMP_MATEMATICAS_E PUNT_C_NATURALES_E
## 1 1.0475057 1.2542382 0.7911394 0.94508344
## 2 0.6099089 0.8139531 0.7911394 0.45582009
## 3 0.5369761 0.6784807 0.7911394 1.27125901
## 4 0.4640434 0.6107445 0.7911394 0.78199566
## 5 1.6309680 1.6267872 2.0928775 1.43434679
## 6 -1.2134109 -1.4213408 -1.8123368 -0.84888217
## 7 1.1933713 1.3897106 0.7911394 1.92361014
## 8 -0.2652846 -0.5069024 -0.5105987 -0.11498715
## 9 -0.5570157 -0.8455833 -0.5105987 -1.41968941
## 10 0.2452450 0.3059317 0.7911394 -0.03344325
## PERCENTIL_C_NATURALES_E DESEMP_C_NATURALES_E PUNT_SOCIALES_CIUDADANAS_E
## 1 1.2191472 1.0601857 1.0505711
## 2 0.6769477 1.0601857 0.1237694
## 3 1.5241344 1.0601857 1.2050380
## 4 1.1174848 1.0601857 0.8961042
## 5 1.5919093 1.0601857 1.3595050
## 6 -1.2546378 -1.7171200 -0.7257988
## 7 1.7274592 2.4488386 1.5139719
## 8 -0.4074511 -0.3284671 -0.4168649
## 9 -1.5257375 -1.7171200 -1.6526005
## 10 -0.1702389 -0.3284671 0.3554698
## PERCENTIL_SOCIALES_CIUDADANAS_E DESEMP_SOCIALES_CIUDADANAS_E PUNT_INGLES_E PERCENTIL_INGLES_E
## 1 1.3216027 1.1437322 0.01613195 0.03209566
## 2 0.1353556 -0.1379223 1.17198610 1.35528568
## 3 1.4232811 1.1437322 0.88302256 1.18564594
## 4 1.1521389 1.1437322 1.38870875 1.45706953
## 5 1.4910666 1.1437322 0.95526344 1.21957389
## 6 -1.0508915 -1.4195767 -0.70627689 -1.08752667
## 7 1.5927449 1.1437322 1.53319052 1.52492543
## 8 -0.6780710 -0.1379223 -0.05610894 -0.03576024
## 9 -1.6270687 -1.4195767 -0.20059070 -0.30718384
## 10 0.4742834 -0.1379223 1.02750433 1.28742978
## PUNT_GLOBAL_E PERCENTIL_GLOBAL_E Comp.1 Comp.2 Comp.3
## 1 1.0399020 1.3107442 -4.243715 0.5980125 -1.4838740
## 2 0.5746654 0.8020507 -2.622393 -0.3490588 0.9294425
## 3 1.1229800 1.3785701 -4.695078 -0.4886441 -0.7279185
## 4 1.0066708 1.2768313 -4.495903 -1.0444912 -0.5470604
## 5 1.6214478 1.6498733 -6.101369 0.2658524 -0.4284546
## 6 -0.8044287 -1.1988104 4.139839 -1.2424016 -0.6315911
## 7 1.4885230 1.5820475 -5.691716 0.1889588 0.7952053
## 8 -0.2561142 -0.4527266 1.358756 -0.3393591 0.4159439
## 9 -0.9705847 -1.3344620 3.923731 -0.9495590 0.4275942
## 10 0.5746654 0.8020507 -2.467578 -1.6568852 -0.6921145
Estudiantes proyectados sobre las componentes principales 1 y 2 por género
library(ggfortify)
autoplot(acp, data = datosstd2, col = 'ESTU_GENERO', loadings = TRUE,
loadings.colour = 'black', loadings.label = TRUE,
loadings.label.size = 4) +
scale_color_manual(values = c("green3", "midnightblue")) +
geom_vline(xintercept = 0, lty = 2, lwd = 1.2, col = "orangered2") +
geom_hline(yintercept = 0, lty = 2, lwd = 1.2, col = "orangered2") +
labs(x = "Componente principal 1",
y = "Componente principal 2",
color = "Género") +
theme_bw()
Estudiantes proyectados sobre las componentes principales 1 y 2 por departamento
colores <- rainbow(n = 34, v = 0.6, start = 0, end = 1)
autoplot(acp, data = datosstd2, col = 'ESTU_RESIDE_DEPTO', loadings = TRUE,
loadings.colour = 'black', loadings.label = TRUE,
loadings.label.size = 4) +
scale_color_manual(values = colores) +
geom_vline(xintercept = 0, lty = 2, lwd = 1.2, col = "blue") +
geom_hline(yintercept = 0, lty = 2, lwd = 1.2, col = "blue") +
labs(x = "Componente principal 1",
y = "Componente principal 2",
color = "Departamento") +
theme_bw()
Estudiantes proyectados sobre las componentes principales 1 y 2 por departamento (Antioquia)
coloresp <- paste0("gray", seq(5, 68, 2))
autoplot(acp, data = datosstd2, col = 'ESTU_RESIDE_DEPTO') +
scale_color_manual(values = c("gray1", "gray3", "green2", coloresp)) +
geom_vline(xintercept = 0, lty = 2, lwd = 1.2, col = "red") +
geom_hline(yintercept = 0, lty = 2, lwd = 1.2, col = "red") +
labs(x = "Componente principal 1",
y = "Componente principal 2",
color = "Departamento") +
theme_bw()
Estudiantes proyectados sobre las componentes principales 1 y 2 por jornada
colores <- rainbow(n = 7, v = 0.6, start = 0, end = 1)
autoplot(acp, data = datosstd2, col = 'COLE_JORNADA') +
scale_color_manual(values = colores) +
geom_vline(xintercept = 0, lty = 2, lwd = 1.2, col = "blue") +
geom_hline(yintercept = 0, lty = 2, lwd = 1.2, col = "blue") +
labs(x = "Componente principal 1",
y = "Componente principal 2",
color = "Jornada del colegio") +
theme_bw()
Estudiantes proyectados sobre las componentes principales 1 y 2 por naturaleza del colegio
autoplot(acp, data = datosstd2, col = 'COLE_NATURALEZA') +
scale_color_manual(values = c("gray10", "orangered2")) +
geom_vline(xintercept = 0, lty = 2, lwd = 1.2, col = "blue") +
geom_hline(yintercept = 0, lty = 2, lwd = 1.2, col = "blue") +
labs(x = "Componente principal 1",
y = "Componente principal 2",
color = "Naturaleza del coleio") +
theme_bw()
