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https://github.com/theoleuthardt/homelab-docker-compose.git
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68 lines
1.9 KiB
R
68 lines
1.9 KiB
R
library(readxl)
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library(ggplot2)
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library(GGally)
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library(corrplot)
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library(caret)
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library(rpart)
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library(rpart.plot)
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library(cluster)
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library(factoextra)
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data <- read_excel("EU2024.xlsx")
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colnames(data)
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# Korrelogramm
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cor_matrix <- cor(data[, sapply(data, is.numeric)], use = "pairwise.complete.obs")
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corrplot(cor_matrix, method = "color", addCoef.col = "black", tl.cex = 0.7)
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# Korrelierte Variablen
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#high_cor_vars <- which(abs(cor_matrix) > 0.8 & abs(cor_matrix) < 1, arr.ind = TRUE)
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#for (i in 1:nrow(high_cor_vars)) {
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#pair <- rownames(cor_matrix)[high_cor_vars[i, ]]
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#print(pair)
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#scatter_plot <- ggplot(data, aes_string(x = pair[1], y = pair[2], label = "Land")) +
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#geom_point() +
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#geom_text(vjust = -1) +
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#theme_minimal()
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#print(scatter_plot)
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#}
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# Lineare Regression
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#pair <- rownames(cor_matrix)[high_cor_vars[1, ]]
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#scatter_plot <- ggplot(data, aes_string(x = pair[1], y = pair[2], label = "Land")) +
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#geom_point() +
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#geom_text(vjust = -1) +
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#theme_minimal()
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#print(scatter_plot)
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# Entscheidungsbaum
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set.seed(123)
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decision_tree <- rpart(`BIP` ~ ., data = data, method = "anova")
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rpart.plot(decision_tree)
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# Hierarchisches Clustering
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dist_matrix <- dist(scale(data[, sapply(data, is.numeric)]))
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hclust_model <- hclust(dist_matrix)
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plot(hclust_model, labels = data$Land, main = "Dendrogramm")
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# K-Means Clustering
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set.seed(123)
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kmeans_model <- kmeans(scale(data[, sapply(data, is.numeric)]), centers = 3)
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fviz_cluster(kmeans_model, data = scale(data[, sapply(data, is.numeric)]), labelsize = 8)
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# PCA
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pca_model <- prcomp(data[, sapply(data, is.numeric)], scale = TRUE)
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fviz_pca_biplot(pca_model, label = "var", habillage = as.factor(data$Land))
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# PCA-Koordinatensystem
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fviz_pca_ind(pca_model, label = "none", habillage = as.factor(data$Land))
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# Chernoff-Faces
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#data_matrix <- as.data.frame(data[, sapply(data, is.numeric)])
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#fviz_pca_ind(scale(data_matrix))
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