Tidy Wizard-R programming guidance, tidyverse expertise

Example

Using functions like `dplyr::filter()` to select specific rows of data or `tidyr::pivot_longer()` to reshape data from wide to long format.

Scenario

A researcher has a dataset of daily weather observations for different cities in wide format (columns for each city). They need to convert this into long format for easy plotting and analysis. Tidy Wizard guides the user to use `pivot_longer()`, explaining the necessary steps in detail and demonstrating how to manage column names and values.

Example

Creating a scatter plot using `ggplot2` by specifying the aesthetics (`aes`) and adding geometries such as `geom_point()` and `geom_smooth()`.

Scenario

An analyst wishes to visualize the relationship between house prices and the size of houses from a real estate dataset. Tidy Wizard helps the user create a scatter plot with `ggplot2`, ensuring proper labeling, scaling, and the addition of a trend line to show the underlying relationship.

Example

Building a machine learning pipeline using tidymodels by first splitting the data (`initial_split()`), creating resamples (`vfold_cv()`), and fitting a model (e.g., `linear_reg()` or `random_forest()`).

Scenario

A data scientist needs to predict customer churn using historical customer data. Tidy Wizard walks them through setting up a data split for training and testing, using cross-validation, and building a logistic regression model with tidymodels. The wizard further explains how to evaluate the model using ROC curves and accuracy metrics.