R Programming for Data Analytics in Bioinformatics

One of the main attractions of using the R (http://cran.at.r-project.org) environment is the ease with which users can write their own programs and custom functions. The R programming syntax is extremely easy to learn, even for users with no previous programming experience. Once the basic R programming control structures are understood, users can use the R language as a powerful environment to perform complex custom analyses of almost any type of data.

The "R Programming for Data Analytics in Bioinformatics" workshop aims to equip participants with the essential skills to harness the power of R for data analysis, visualization, and statistical modeling. This 3-day workshop is designed to provide hands-on experience with R, enabling participants to transform raw data into actionable insights.

Day 1: Introduction to R and R Studio, Vector and Matrices

• Vector and matrices, importing data into R, Sub setting data, and logistics statement
• Setting up the working directory, Producing numeric summaries for categorical and numeric variables

Day 2: R packages and Statistical Analysis

• Working on dplyr, Amelia, gmodels R packages.
• Basics Statistical Analysis with R

Day 3: Data Visualization and prediction model

• Data Visualization with ggplot2
• Insulin prediction model