Project 2
Kayla Williams
2025-03-19
library(dplyr)
df_original <- read.csv("C:/Users/kayla/OneDrive/Documents/TSU/Adv_Topics_CS497/Projects/HeartFailure_excerpt.csv", header=TRUE)
if ("Label" %in% colnames(df_original)) {
colnames(df_original)[colnames(df_original) == "Label"] <- "DEATH_EVENT"
}
df <- df_original %>% select(-Cluster_Hierarchical, -Cluster_KMeans)
expected_columns <- c("Age", "CPK_Levels", "Ejection_Fraction", "Platelets",
"Serum_Creatinine", "Serum_Sodium", "Time", "DEATH_EVENT")
if (length(colnames(df)) == length(expected_columns)) {
colnames(df) <- expected_columns
} else {
print("Warning: Column count mismatch. Here are the current column names:")
print(colnames(df))
}
print("Final Column Names in df (after removing clustering labels):")## [1] "Final Column Names in df (after removing clustering labels):"print(colnames(df))## [1] "Age" "CPK_Levels" "Ejection_Fraction"
## [4] "Platelets" "Serum_Creatinine" "Serum_Sodium"
## [7] "Time" "DEATH_EVENT"if ("DEATH_EVENT" %in% colnames(df)) {
X <- df %>% select(-DEATH_EVENT)
X_scaled <- scale(X)
} else {
stop("Error: Column 'DEATH_EVENT' not found in the dataset!")
}R Markdown
This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.
When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
library(cluster)
library(dplyr)
library(ggplot2)
chooseCRANmirror(graphics=FALSE, ind=66)
# print("Checking if X exists before scaling:")
# print("Checking if X is selected correctly:")
hc <- hclust(dist(X_scaled), method="ward.D2")
print(hc)##
## Call:
## hclust(d = dist(X_scaled), method = "ward.D2")
##
## Cluster method : ward.D2
## Distance : euclidean
## Number of objects: 299plot(hc, main="Hierarchical Clustering Dendrogram")
df$Cluster_Hierarchical <- cutree(hc, k=3)
print(dim(X)) ## [1] 299 7X_scaled <- scale(X)
silhouette_scores <- c()
for (k in 2:10) {
km <- kmeans(X_scaled, centers=k, nstart=25)
sil <- silhouette(km$cluster, dist(X_scaled))
silhouette_scores <- c(silhouette_scores, mean(sil[, 3]))
}
best_k <- which.max(silhouette_scores) + 1
kmeans_result <- kmeans(X_scaled, centers=best_k, nstart=25)
df$Cluster_KMeans <- kmeans_result$cluster
if ("Cluster_Hierarchical" %in% colnames(df) & "Cluster_KMeans" %in% colnames(df)) {
X <- df %>% select(-DEATH_EVENT, -Cluster_Hierarchical, -Cluster_KMeans)
} else {
stop("Error: Clustering columns are missing from df!")
}
install.packages("cluster", dependencies=TRUE)
set.seed(123)
print("Checking if X_scaled exists:")## [1] "Checking if X_scaled exists:"print(dim(X_scaled)) ## [1] 299 7print("Checking if K-Means clustering worked:")## [1] "Checking if K-Means clustering worked:"print(table(kmeans_result$cluster)) ##
## 1 2 3 4 5 6 7 8
## 6 41 80 38 5 28 24 77Including Plots
You can also embed plots, for example:
library(cluster)
hc <- hclust(dist(X_scaled), method="ward.D2")
plot(hc, main="Hierarchical Clustering Dendrogram", xlab="", sub="", cex=0.6) 
best_hierarchical_clusters <- 3
df$Cluster_Hierarchical <- cutree(hc, k=best_hierarchical_clusters)
df_original <- read.csv("C:/Users/kayla/OneDrive/Documents/TSU/Adv_Topics_CS497/Projects/HeartFailure_excerpt.csv", header=TRUE)
print("Checking column names before confusion matrix:")## [1] "Checking column names before confusion matrix:"print(colnames(df_original))## [1] "Age" "CPK_Levels" "Ejection_Fraction"
## [4] "Platelets" "Serum_Creatinine" "Serum_Sodium"
## [7] "Time" "DEATH_EVENT" "Cluster_Hierarchical"
## [10] "Cluster_KMeans"if ("Label" %in% colnames(df_original)) {
colnames(df_original)[colnames(df_original) == "Label"] <- "DEATH_EVENT"
}
print("First few rows of df:")## [1] "First few rows of df:"print(head(df)) ## Age CPK_Levels Ejection_Fraction Platelets Serum_Creatinine Serum_Sodium Time
## 1 75 582 20 265000 1.9 130 4
## 2 55 7861 38 263358 1.1 136 6
## 3 65 146 20 162000 1.3 129 7
## 4 50 111 20 210000 1.9 137 7
## 5 65 160 20 327000 2.7 116 8
## 6 90 47 40 204000 2.1 132 8
## DEATH_EVENT Cluster_Hierarchical Cluster_KMeans
## 1 1 1 7
## 2 1 2 1
## 3 1 1 7
## 4 1 3 3
## 5 1 1 7
## 6 1 1 4if ("DEATH_EVENT" %in% colnames(df_original)) {
print(paste("Length of df$Cluster_KMeans:", length(df$Cluster_KMeans)))
print(paste("Length of df_original$DEATH_EVENT:", length(df_original$DEATH_EVENT)))
if (length(df$Cluster_KMeans) == length(df_original$DEATH_EVENT)) {
print("Confusion Matrix for K-Means Clustering:")
print(table(df$Cluster_KMeans, df_original$DEATH_EVENT))
} else {
stop("Error: Cluster assignments and DEATH_EVENT do not have the same length!")
}
print("Confusion Matrix for Hierarchical Clustering:")
if (length(df$Cluster_Hierarchical) == length(df_original$DEATH_EVENT)) {
print(table(df$Cluster_Hierarchical, df_original$DEATH_EVENT))
} else {
stop("Error: Cluster assignments and DEATH_EVENT do not have the same length!")
}
}## [1] "Length of df$Cluster_KMeans: 299"
## [1] "Length of df_original$DEATH_EVENT: 299"
## [1] "Confusion Matrix for K-Means Clustering:"
##
## 0 1
## 1 3 3
## 2 35 6
## 3 50 30
## 4 16 22
## 5 1 4
## 6 23 5
## 7 6 18
## 8 69 8
## [1] "Confusion Matrix for Hierarchical Clustering:"
##
## 0 1
## 1 20 44
## 2 3 3
## 3 180 49sil_hc <- silhouette(df$Cluster_Hierarchical, dist(X_scaled))
plot(sil_hc[, 3], main="Silhouette Plot for Hierarchical Clustering",
xlab="Cluster Index", ylab="Silhouette Width", col="palevioletred1", pch=19)
sil_kmeans <- silhouette(df$Cluster_KMeans, dist(X_scaled))
plot(sil_kmeans[, 3], main="Silhouette Plot for K-Means Clustering",
xlab="Cluster Index", ylab="Silhouette Width", col="#40E0D0", pch=19)
library(ggplot2)
install.packages("ggplot2", dependencies=TRUE)
file_path <- "C:/Users/kayla/OneDrive/Documents/TSU/Adv_Topics_CS497/Projects/HeartFailure_excerpt.csv"
if (file.exists(file_path)) {
HeartFailure_excerpt <- read.csv(file_path, header=TRUE)
print("Dataset loaded successfully")
} else {
stop("Error: File not found! Check the path.")
}## [1] "Dataset loaded successfully"print(colnames(HeartFailure_excerpt))## [1] "Age" "CPK_Levels" "Ejection_Fraction"
## [4] "Platelets" "Serum_Creatinine" "Serum_Sodium"
## [7] "Time" "DEATH_EVENT" "Cluster_Hierarchical"
## [10] "Cluster_KMeans"silhouette_df <- data.frame(k=2:10, Silhouette=silhouette_scores)
ggplot(silhouette_df, aes(x=k, y=Silhouette)) +
geom_point(color="#FF34B3", size=3) +
geom_line(color="#00E5EE") +
labs(title="Silhouette Score for K-Means Clustering",
x="Number of Clusters (k)",
y="Silhouette Score") +
theme_minimal()
df$Cluster_KMeans <- as.factor(kmeans_result$cluster)
ggplot(df, aes(x=Age, y=CPK_Levels, color=Cluster_KMeans)) +
geom_point(size=3) +
labs(title="K-Means Clustering Visualization", x="Age", y="CPK_Levels") +
theme_minimal()
Note that the echo = FALSE parameter was added to the
code chunk to prevent printing of the R code that generated the
plot.
write.csv(df, "C:/Users/kayla/OneDrive/Documents/TSU/Adv_Topics_CS497/Projects/HeartFailure_excerpt.csv", row.names=FALSE)