library(rio)
Migrar=import("Base tarea 5 (3).xlsx")
library(readxl)
idioma <- read_excel("Base tarea 5 (3).xlsx", sheet = "idioma")
costo_de_vida <- read_excel("Base tarea 5 (3).xlsx", sheet = "costo de vida")
encontrar_empleo <- read_excel("Base tarea 5 (3).xlsx", sheet = "encontrar empleo")
criterios <- as.matrix(idioma[, -1])
criterios <- criterios / rowSums(criterios)
pesos_criterios <- colMeans(criterios)
print("Pesos de criterios:")
[1] "Pesos de criterios:"
print(pesos_criterios)
Brasil España E.E.U.U Inglaterra Francia
0.48025687 0.02996625 0.09787060 0.09787060 0.29403568
# Definir las matrices de comparación de alternativas
alternativas <- list(
"costo_de_vida" = as.matrix(costo_de_vida[, -1]),
"encontrar_empleo" = as.matrix(encontrar_empleo[, -1])
)
pesos_alternativas <- sapply(alternativas, function(m) {
m / rowSums(m)
})
pesos_global_alternativas <- apply(pesos_alternativas, 1, mean)
puntajes_globales <- pesos_global_alternativas * pesos_criterios
print("Pesos de criterios:")
[1] "Pesos de criterios:"
print(pesos_criterios)
Brasil España E.E.U.U Inglaterra Francia
0.48025687 0.02996625 0.09787060 0.09787060 0.29403568
print("Pesos de alternativas para cada criterio:")
[1] "Pesos de alternativas para cada criterio:"
print(pesos_alternativas)
costo_de_vida encontrar_empleo
[1,] 0.429184549 0.18083183
[2,] 0.399840064 0.18083183
[3,] 0.355019082 0.11956522
[4,] 0.120000000 0.16129032
[5,] 0.209150327 0.11956522
[6,] 0.214592275 0.18083183
[7,] 0.199920032 0.18083183
[8,] 0.532528623 0.11956522
[9,] 0.200000000 0.16129032
[10,] 0.366013072 0.11956522
[11,] 0.107296137 0.05967450
[12,] 0.331867253 0.05967450
[13,] 0.088754771 0.36231884
[14,] 0.320000000 0.32258065
[15,] 0.366013072 0.36231884
[16,] 0.141630901 0.03616637
[17,] 0.039984006 0.03616637
[18,] 0.011094346 0.03623188
[19,] 0.040000000 0.03225806
[20,] 0.006535948 0.03623188
[21,] 0.107296137 0.54249548
[22,] 0.028388645 0.54249548
[23,] 0.012603177 0.36231884
[24,] 0.320000000 0.32258065
[25,] 0.052287582 0.36231884
print("Pesos globales de alternativas:")
[1] "Pesos globales de alternativas:"
print(pesos_global_alternativas)
[1] 0.30500819 0.29033595 0.23729215 0.14064516 0.16435777 0.19771205 0.19037593 0.32604692 0.18064516
[10] 0.24278914 0.08348532 0.19577088 0.22553681 0.32129032 0.36416596 0.08889863 0.03807519 0.02366312
[19] 0.03612903 0.02138392 0.32489581 0.28544206 0.18746101 0.32129032 0.20730321
print("Puntajes globales de alternativas:")
[1] "Puntajes globales de alternativas:"
print(puntajes_globales)
[1] 0.146482276 0.008700280 0.023223926 0.013765027 0.048327049 0.094952570 0.005704853 0.031910408
[9] 0.017679851 0.071388671 0.040094398 0.005866519 0.022073423 0.031444877 0.107077784 0.042694179
[17] 0.001140971 0.002315923 0.003535970 0.006287634 0.156033442 0.008553629 0.018346922 0.031444877
[25] 0.060954540
resultados <- data.frame(
Criterios = names(pesos_criterios),
Pesos_Criterios = pesos_criterios,
Pesos_Globales_Alternativas = pesos_global_alternativas,
Puntajes_Globales_Alternativas = puntajes_globales
)
Warning in data.frame(Criterios = names(pesos_criterios), Pesos_Criterios = pesos_criterios, :
row names were found from a short variable and have been discarded
print("Resultados finales:")
[1] "Resultados finales:"
print(resultados)
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