Prerequisites:

1. R Installation:
If you don't have R installed, follow these steps:

o Download R from the official site: https://cran.r-project.org/.

o Install RStudio (an IDE for R): https://www.rstudio.com/.

2. Necessary Packages:
For the tasks, you will need the following R packages:

o ggplot2

o stats

o caret

o arules

Install them using: R

install.packages(c("ggplot2", "stats", "caret", "arules"))

Task 1: Numerical Operations in R

To get input from the user and perform numerical operations like MAX, MIN, AVG, SUM, SQRT, and
ROUND.

Code: R

# Input numbers from user

nums <- as.numeric(unlist(strsplit(readline(prompt="Enter numbers separated by commas: "), ",")))

# Perform operations

max_val <- max(nums)

min_val <- min(nums)

avg_val <- mean(nums)

sum_val <- sum(nums)

sqrt_vals <- sqrt(nums)

rounded_vals <- round(nums)

# Output results

cat("Maximum:", max_val, "\n")

cat("Minimum:", min_val, "\n")

cat("Average:", avg_val, "\n")

cat("Sum:", sum_val, "\n")

cat("Square roots:", sqrt_vals, "\n")

cat("Rounded values:", rounded_vals, "\n")

Task 2: Import/Export Data in R

To perform data import/export operations for .CSV, .XLS, and .TXT formats using data frames in R.

Code:R

# CSV import and export

data_csv <- read.csv("input.csv")

write.csv(data_csv, "output.csv")

# TXT import and export

data_txt <- read.table("input.txt", header=TRUE)

write.table(data_txt, "output.txt")

# XLS import and export requires the "readxl" package

install.packages("readxl")

library(readxl)

data_xls <- read_excel("input.xls")

write.csv(data_xls, "output_xls.csv") # R does not directly export to xls

Task 3: Matrix Operations in R

To perform matrix operations like addition, subtraction, multiplication, and division.

Code:R

# Input matrices

matrix1 <- matrix(as.numeric(unlist(strsplit(readline(prompt="Enter matrix 1 elements: "), ","))),

nrow=3, ncol=3)

matrix2 <- matrix(as.numeric(unlist(strsplit(readline(prompt="Enter matrix 2 elements: "), ","))),

nrow=3, ncol=3)

# Perform operations

addition <- matrix1 + matrix2

subtraction <- matrix1 - matrix2

multiplication <- matrix1 %*% matrix2

division <- matrix1 / matrix2 # Element-wise division

transpose1 <- t(matrix1)

# Output results

cat("Addition:\n", addition, "\n")

cat("Subtraction:\n", subtraction, "\n")

cat("Multiplication:\n", multiplication, "\n")

cat("Division:\n", division, "\n")

cat("Transpose of matrix1:\n", transpose1, "\n")

Task 4: Statistical Operations in R

To calculate Mean, Median, Mode, and Standard Deviation.

Code:R

# Input numbers

nums <- as.numeric(unlist(strsplit(readline(prompt="Enter numbers separated by commas: "), ",")))

# Calculate statistics

mean_val <- mean(nums)

median_val <- median(nums)

mode_val <- as.numeric(names(sort(table(nums), decreasing=TRUE)[1])) # Mode using table

sd_val <- sd(nums)

# Output results

cat("Mean:", mean_val, "\n")

cat("Median:", median_val, "\n")

cat("Mode:", mode_val, "\n")

cat("Standard Deviation:", sd_val, "\n")

Task 5: Data Preprocessing in R (Missing Values and Normalization)

To handle missing data and perform Min-Max normalization.

Code: R

# Example dataset with missing values

data <- data.frame(x=c(1, 2, NA, 4, 5), y=c(NA, 3, 6, 7, 8))

# Handling missing data by replacing with mean

data[is.na(data)] <- mean(data, na.rm=TRUE)

# Min-Max Normalization

normalized_data <- as.data.frame(lapply(data, function(x) (x - min(x)) / (max(x) - min(x))))

# Output results

cat("Data after handling missing values:\n", data, "\n")

cat("Normalized data:\n", normalized_data, "\n")

Task 6: PCA for Dimensionality Reduction

Perform PCA on the Houses dataset.

Code: R

# Using the iris dataset for example

data(iris)

iris_pca <- prcomp(iris[, 1:4], center=TRUE, scale.=TRUE)

# Output PCA result

summary(iris_pca)

plot(iris_pca)
Task 7: Simple Linear Regression in R

To perform simple linear regression.

Code: R

# Using built-in mtcars dataset

model <- lm(mpg ~ wt, data=mtcars)

summary(model)

plot(mtcars$wt, mtcars$mpg)

abline(model)
Task 8: K-Means Clustering

K-means clustering and visualization on the Iris dataset.

Code: R

data(iris)

kmeans_result <- kmeans(iris[, 1:4], centers=3)

# Plotting the clusters

plot(iris$Sepal.Length, iris$Sepal.Width, col=kmeans_result$cluster)

Task 9: Disease Diagnosis using KNN

KNN classification to diagnose disease and plot results.

Code: R

# Using the iris dataset as an example

library(caret)

set.seed(123)

trainIndex <- createDataPartition(iris$Species, p=0.7, list=FALSE)

trainData <- iris[trainIndex, ]

testData <- iris[-trainIndex, ]

model_knn <- train(Species ~ ., data=trainData, method="knn",

trControl=trainControl(method="cv"))

pred_knn <- predict(model_knn, newdata=testData)

confusionMatrix(pred_knn, testData$Species)
Task 10: Market Basket Analysis using Apriori Algorithm

To perform market basket analysis using association rules.

Code: R

library(arules)

data("Groceries")

rules <- apriori(Groceries, parameter=list(supp=0.01, conf=0.5))

inspect(head(rules))