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IT Management
R Programming
Overview:
Introduction:
This training program provides participants with essential knowledge and skills in R programming. It empowers them to use R for data analysis, statistical computing, and data visualization effectively.
Program Objectives:
At the end of this program, participants will be able to:
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Understand the basics of R programming language.
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Perform data manipulation and analysis using R.
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Create visualizations to represent data insights.
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Apply statistical methods and models using R.
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Utilize R for reproducible research and reporting.
Targeted Audience:
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Data Analysts.
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Statisticians.
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Data Scientists.
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Researchers.
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Professionals interested in data analysis.
Program Outline:
Unit 1:
Introduction to R Programming:
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Overview of R and its applications.
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Setting up the R environment.
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Basic syntax and data types in R.
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Working with vectors, matrices, and lists.
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Introduction to R packages and libraries.
Unit 2:
Data Manipulation in R:
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Importing and exporting data.
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Data cleaning and preprocessing.
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Using dplyr for data manipulation.
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Handling missing values and outliers.
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Summarizing and aggregating data.
Unit 3:
Data Visualization with R:
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Introduction to data visualization principles.
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Creating basic plots using base R.
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Advanced visualizations with ggplot2.
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Customizing and saving plots.
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Interactive visualizations with plotly.
Unit 4:
Statistical Analysis with R:
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Descriptive statistics and exploratory data analysis.
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Inferential statistics: hypothesis testing, t-tests, chi-square tests.
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Correlation and regression analysis.
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Analysis of variance (ANOVA).
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Time series analysis.
Unit 5:
Advanced Topics and Best Practices:
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Writing and debugging R scripts.
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Functional programming in R.
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Reproducible research with RMarkdown.
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Introduction to Shiny for building web applications.
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Best practices for efficient R programming.