feat: add rstudio deployment

rstudio/Klausur/EU2024.R

@@ -0,0 +1,68 @@
+library(readxl)
+library(ggplot2)
+library(GGally)
+library(corrplot)
+library(caret)
+library(rpart)
+library(rpart.plot)
+library(cluster)
+library(factoextra)
+
+data <- read_excel("EU2024.xlsx")
+colnames(data)
+
+
+# Korrelogramm
+cor_matrix <- cor(data[, sapply(data, is.numeric)], use = "pairwise.complete.obs")
+corrplot(cor_matrix, method = "color", addCoef.col = "black", tl.cex = 0.7)
+
+# Korrelierte Variablen
+#high_cor_vars <- which(abs(cor_matrix) > 0.8 & abs(cor_matrix) < 1, arr.ind = TRUE)
+#for (i in 1:nrow(high_cor_vars)) {
+  #pair <- rownames(cor_matrix)[high_cor_vars[i, ]]
+  
+  #print(pair)
+  
+  #scatter_plot <- ggplot(data, aes_string(x = pair[1], y = pair[2], label = "Land")) +
+    #geom_point() + 
+    #geom_text(vjust = -1) + 
+    #theme_minimal()
+  
+ #print(scatter_plot)
+#}
+
+# Lineare Regression
+#pair <- rownames(cor_matrix)[high_cor_vars[1, ]]
+#scatter_plot <- ggplot(data, aes_string(x = pair[1], y = pair[2], label = "Land")) +
+  #geom_point() + 
+  #geom_text(vjust = -1) + 
+  #theme_minimal()
+
+#print(scatter_plot)
+
+
+# Entscheidungsbaum
+set.seed(123)
+decision_tree <- rpart(`BIP` ~ ., data = data, method = "anova")
+rpart.plot(decision_tree)
+
+# Hierarchisches Clustering
+dist_matrix <- dist(scale(data[, sapply(data, is.numeric)]))
+hclust_model <- hclust(dist_matrix)
+plot(hclust_model, labels = data$Land, main = "Dendrogramm")
+
+# K-Means Clustering
+set.seed(123)
+kmeans_model <- kmeans(scale(data[, sapply(data, is.numeric)]), centers = 3)
+fviz_cluster(kmeans_model, data = scale(data[, sapply(data, is.numeric)]), labelsize = 8)
+
+# PCA
+pca_model <- prcomp(data[, sapply(data, is.numeric)], scale = TRUE)
+fviz_pca_biplot(pca_model, label = "var", habillage = as.factor(data$Land))
+
+# PCA-Koordinatensystem
+fviz_pca_ind(pca_model, label = "none", habillage = as.factor(data$Land))
+
+# Chernoff-Faces
+#data_matrix <- as.data.frame(data[, sapply(data, is.numeric)])
+#fviz_pca_ind(scale(data_matrix))

rstudio/Klausur/Probeklausur.R

@@ -0,0 +1,13 @@
+library(corrgram)
+
+xx <- data.frame(a=c(1,2,3,4,5), b=c(2,1,4,3,5))
+mean(xx$b)
+corelation <- cor(xx, use = "complete.obs")
+print(corelation)
+
+plot(xx)
+li <- lm(b~a, data=xx)
+abline(li)
+
+corrgram(xx, main="Korrelogramm", lower.panel=panel.pts,
+         upper.panel=panel.cor)

rstudio/Klausur/Quallen.R

@@ -0,0 +1,12 @@
+library(OneR)
+library(readxl)
+setwd("~/Klausur")
+qq <- read_excel("quallen.XLS")
+qq$giftig <- as.factor(qq$giftig)
+model <- OneR(giftig ~ ., data = qq)
+summary(model)
+
+tstdat <- data.frame(Farbe = "gelb", Größe = "mittel", transparent = "nein", 
+                     Geschmack = "süß")
+colnames(tstdat) <- c("Farbe","Größe","transparent","Geschmack")
+predict(model, tstdat)