R Programming: Conditions, Loops, Functions & Graphics

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Related lectures (32)

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Introduction to R Programming for Genetics & Genomics

Introduces a course on Genetics & Genomics, focusing on R programming with interactive exercises.

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Matlab 2: Vectorisation and Functions

Covers vectorisation, functions, and flow control in Matlab, emphasizing the importance of avoiding global variables and providing examples of simple plots and debugging techniques.

Gitlab Agent for Kubernetes (`agentk`)

Covers the setup of a Gitlab agent for Kubernetes, focusing on installation, version control, and troubleshooting.

Introduction to Data Science

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Excel Upgrade: Advanced Functions and Data Analysis

Covers advanced Excel functions and data analysis techniques, including automatic recording and using Solver.

Matlab: Interactive Mode and Project Steps

Introduces Matlab basics, error handling, and billiards project concepts.

File Management and Exception Handling in Python

Focuses on file management and exception handling in Python programming.

Total scattering and PDF analysis

Explores total scattering and PDF analysis in materials science, covering in-situ synthesis, data analysis techniques, and applications in host-guest systems.

Functions in Python

Introduces functions in Python, covering predefined and user-defined functions, formal and effective parameters, and the importance of docstrings.