R Programming Journal – Premitha Pagadala

  Assignment 9: Visualization in R – Base Graphics, Lattice, and ggplot2 Objectives Compare three major visualization systems in R: Base graphics , Lattice , and ggplot2 . Apply all three to the same dataset to observe syntactic , conceptual , and visual differences . Produce clear, reproducible code and interpret key findings. Dataset Overview For this assignment, I used the Breast Cancer Wisconsin Diagnostic dataset ( brca ) from the dslabs package — a common bioinformatics dataset used to classify tumor samples as Benign (B) or Malignant (M) based on cell nucleus measurements. This dataset contains features computed from breast mass cell nuclei. brca$y = Diagnosis (Benign or Malignant) brca$x = 30 numeric features describing cell characteristics Load and explore the data # Load libraries and dataset install.packages("dslabs") library(dslabs) data("brca") # 'brca$y' contains the diagnosis (Benign/ Malignant) # 'brca$x' is a matrix of 30...

  Assignment 8 — Input/Output, String Manipulation, and the plyr Package Objectives This assignment focused on using R to handle input/output operations , perform data summarization using the plyr package, and apply string manipulation techniques. By completing this exercise, I learned how to import data files, compute grouped statistics, filter data based on character patterns, and export processed results in multiple formats. R Code # Step 1: Import dataset into R # Choose file interactively or specify directly student6 <- read.table(   file.choose(),       header = TRUE,   sep = ",",   stringsAsFactors = FALSE ) # Step 2: Install and load plyr package install.packages("plyr") library(plyr) # Compute mean Grade by Sex gender_mean <- ddply(   student6,   "Sex",   summarise,   GradeAverage = mean(Grade, na.rm = TRUE) ) # Step 3: Write the grouped means to a text file write.table(   gender_mean,   file = "gend...

  Assignment 7: Exploring R’s Object-Oriented Systems (S3 & S4) In this assignment, I explored R's object-oriented systems, focusing on S3 and S4 classes. The objective was to understand how R dispatches generic functions based on object class and to practice creating and manipulating S3 and S4 objects. Objectives The goal of this assignment was to explore R’s two primary object-oriented systems— S3 and S4 —and understand how generic functions work with different object types. Specifically, we: Selected and inspected a dataset. Tested base R generic functions. Created custom S3 and S4 objects. Compared the behavior and structure of both systems. R Code Step 1: Loading the Dataset We begin by using one of R’s built-in datasets, PlantGrowth , which measures the effect of different treatments on plant growth. # Load dataset data("PlantGrowth") head(PlantGrowth) # Describe structure str(PlantGrowth) Output:  Step 2: Test Generic Functions # Summarize datas...

  Assignment 6: Matrix Operations and Construction In this assignment,  I practiced creating and manipulating matrices in R. The goal was to strengthen my understanding of how R handles matrix operations such as addition, subtraction, diagonal construction, and combining blocks of matrices to form structured arrays. R Code 🔹 Task 1: Matrix Addition & Subtraction # Task 1: define A and B A <- matrix(c(2, 0, 1, 3), ncol = 2) B <- matrix(c(5, 2, 4, -1), ncol = 2) # Addition A_plus_B <- A + B print("A + B:") print(A_plus_B) # Subtraction A_minus_B <- A - B print("A - B:") print(A_minus_B) 🔹 Task 2: Create a Diagonal Matrix # Task 2: Create a Diagonal Matrix D <- diag(c(4, 1, 2, 3)) print("Diagonal Matrix D:") print(D) 🔹 Task 3: Construct a Custom 5×5 Matrix # Task 3: Construct a Custom 5x5 Matrix # Step 1: create the first row row1 <- c(3, 1, 1, 1, 1) # Step 2: create the remaining rows: # - first column all 2’s (rows 2–5) # - diagonal...

Assignment 5: Matrix Algebra in R For this assignment, I explored how to create and inspect matrices in R, how to compute matrix inverses and determinants, and how to handle errors when working with non-square matrices. I also reflected on issues of numeric stability and performance that arise in matrix algebra computations. R Code # 1. Create the matrices A <- matrix(1:100, nrow = 10) B <- matrix(1:1000, nrow = 10) # 2. Inspect dimensions dimA <- dim(A) # should be 10 × 10 dimB <- dim(B) # should be 10 × 100 print("Dimensions of A:") print(dimA) print("Dimensions of B:") print(dimB) # 3. Compute inverse and determinant for A invA <- solve(A) detA <- det(A) print("Inverse of A:") print(invA) print("Determinant of A:") print(detA) # 4. Handle non-square matrix B with tryCatch invB <- tryCatch(solve(B), error = function(e) e) detB <- tryCatch(det(B), error = function(e) e) print("Inverse of B attempt:") print...