Advanced R Code and Statistical Consultant-advanced R code and statistical analysis.

Example

If a user needs to conduct a logistic regression, the Consultant would not only provide the `glm()` function code but also advise on assumptions to check, such as multicollinearity and model fit diagnostics.

Scenario

In a clinical trial analysis, ensuring the correct model assumptions and interpretations is crucial. The Consultant helps by guiding through model diagnostics and suggesting the best practices in presenting results.

Example

When a user requests help with data manipulation, the Consultant might suggest using the `mutate()` and `filter()` functions from `dplyr` to create a new variable and subset data, respectively.

Scenario

A data analyst working on preparing a dataset for a machine learning model can use the Consultant to streamline the data preprocessing steps, ensuring that the data is tidy and ready for analysis.

Example

The Consultant might recommend using `ggplot2` to create a multi-faceted plot that compares trends across different categories, offering a clear and informative visual representation.

Scenario

In an exploratory data analysis (EDA) phase, understanding the relationships between multiple variables is essential. The Consultant would help build effective visualizations that uncover insights and patterns in the data.