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Claude Code for Beginners: Step-by-Step AI Coding Tutorial

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 Artificial Intelligence is changing how developers write software. From generating code to fixing bugs and explaining complex logic, AI tools are becoming everyday companions for programmers. One such powerful tool is Claude Code , powered by Anthropic’s Claude AI model. If you’re a beginner or  an experienced developer looking to improve productivity, this guide will help you understand  what Claude Code is, how it works, and how to use it step-by-step . Let’s get started. What is Claude Code? Claude Code is an AI-powered coding assistant built on top of Anthropic’s Claude models. It helps developers by: Writing code from natural language prompts Explaining existing code Debugging errors Refactoring code for better readability Generating tests and documentation In simple words, you describe what you want in plain English, and Claude Code helps turn that into working code. It supports multiple programming languages, such as: Python JavaScri...
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R objects useful command to delete them

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R-Commands
R-Commands
The entities that R creates and manipulates are known as objects. These may be variables, arrays of numbers, character strings, functions, or more general structures built from such components. During an R session, objects are created and stored by name. This post tells you how to delete them.

 The R command
> objects()

 (alternatively, ls()) can be used to display the names of (most of) the objects which are currently stored within R. The collection of objects currently stored is called the workspace. The data visualization in R Language with GGplot a good idea to start.

 To remove objects the function rm is available:
> rm(x, y, z, ink, junk, temp, foo, bar)

 All objects created during an R session can be stored permanently in a file for use in future R sessions.

 At the end of each R session you are given the opportunity to save all the currently available objects. If you indicate that you want to do this, the objects are written to a file called .RData5 in the current directory, and the command lines used in the session are saved to a file called .Rhistory.

 When R is started at later time from the same directory it reloads the workspace from this file. At the same time the associated commands history is reloaded.
  • It is recommended that you should use separate working directories for analyses conducted  ith R. 
  • It is quite common for objects with names x and y to be created during an analysis. Names like this are often meaningful in the context of a single analysis, but it can be quite hard to decide what they might be when the several analyses have been conducted in the same directory.

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