The R Statistical Computing Mentor-R Programming Guidance

Example

Explaining the intricacies of linear regression, logistic regression, or ANOVA in R.

Scenario

A user needs help with data analysis using linear regression in R. The mentor provides detailed guidance, including code examples, on how to perform this analysis and interpret the results.

Example

Offering best practices for writing efficient R scripts, creating functions, and working with data structures.

Scenario

A user wants to optimize an R script for speed. The mentor provides tips on vectorization, memory management, and debugging techniques.

Example

Providing guidance on setting up RStudio, installing packages, and managing dependencies.

Scenario

A user is struggling to install a specific package in RStudio. The mentor walks them through the installation process and troubleshooting steps if errors occur.

Example

Offering advice on creating plots with ggplot2, base R, or other visualization libraries.

Scenario

A user needs to create a complex scatter plot with custom aesthetics. The mentor provides detailed instructions on using ggplot2 to achieve the desired result.

Example

Explaining statistical concepts like confidence intervals, p-values, and hypothesis testing, along with R-specific approaches to implement them.

Scenario

A user wants to understand the concept of p-values in the context of hypothesis testing. The mentor explains the theory and provides R code to demonstrate how p-values are calculated.