Exploring R

  Debugging is a crucial skill for programmers as it assists them in detecting and fixing errors or bugs in their code. In this detailed guide, we will take you through an example of debugging a code snippet and emphasize the essential tactics and tools needed to master the art of debugging. Understanding the Deliberate Bug Let's begin with the code snippet that contains a deliberate bug, which results in an error message during execution. Here's the faulty code, When this code is executed, it triggers an error message due to the unexpected placement of return(outlier.vec) within the for loop. Step 1: Identifying the Error To identify the error, we can use the traceback function, which prints the list of functions called before the error occurred. Running traceback("tukey_multiple") reveals the location of the error just before return(outlier.vec) . Step 2: Using Debugging Tools After locating the error, we implement the debug function to step through the code an...

A package in R is a standardized and structured unit that comprises various components, including  1. R code 2. Documentation 3. Data 4. External code Creating a package is initiated by generating a new blank project in R. Installing   ' devtools '   and   ' roxygen '   which help in creating packages and can be done by the following code: install.packages(“devtools”) install.packages(“roxygen2”) library(devtools) library(roxygen2) package.skeleton() It can also be installed directly by implementing code   devtools::install_github("hadley/devtools") It creates new files: Data, folder  DESCRIPTION, information file  Man, help folder R folder contains the . R files for functions  And “read-and-delete-me” file The  Description  consists of  fundamental information regarding the package in the following sequence:  The important and required fields are ‘Package’, ‘Version’, ‘License’, ‘Description’, ‘Title’, ‘Author’, and ...

Melanoma is a serious form of skin cancer that arises from the pigment-containing cells known as melanocytes. Understanding factors that contribute to melanoma development and progression is crucial for diagnosis and treatment. In this blog post, we'll explore a dataset containing information about patients diagnosed with melanoma and visualize the relationship between age, sex, thickness, and ulcer presence using R programming. To produce graphics in R, there are three types: 1. Base graphics 2. Lattice 3. ggplot2 1. Base graphics: Base graphics already exist in the R installation. It allows to create a wide range of two-dimensional plots, including scatterplots, line graphs, bar graphs, and more. 'plot' function is implemented which creates a scatterplot of a dataset. 'pch=21' is used to set the type of plotting to a filled circle with a solid fill. Different color palletes can be used, but for this code 'rainbow()' and 'heat.colors()' is utilized....