Schedule of Topics

Identify methods for improving accessibility and interpretability of data visualizations.

Explain reactivity and construct the server function for a Shiny application.

No class.

Implement supervised and unsupervised learning methods for text data.

Introduce methods for text data, structuring text data in R, and conducting exploratory analysis.

Review methods for storing spatial data, define simple features data frames, and construct vector maps in ggplot2.

Identify components of geospatial visualizations and implement raster maps using ggplot2.

Practice scraping content from web pages using rvest.

Define an application program interface, write functions to query APIs, and practice tidying JSON objects.

Introduce tree-based predictive models, define hyperparameters, and implement tuning to optimize model performance.

Introduce and practice techniques for preprocessing data sets and developing model workflows using the tidymodels framework.

Review the goals of machine learning, introduce methods for estimating models in R using the tidymodels framework, and define a resampling framework for model validation.

Overview Before class Class materials Additional resources What you need to do after class Complete the homework assignment

Review of the importance of version control and practice implementing Git workflows.

Methods for implementing a tidy, reproducible workflow.

No class.

Extending Quarto to slide decks, websites, and deploying online.

All things related to Quarto and the knitr package.

Methods for identifying problems and generating reproducible examples.

Define computer bugs, discuss defensive programming tactics, and practice troubleshooting scripts.

Define and implement tidy evaluation.

Define a vector, review iterative operations, and practice using loops and map functions.

Review the pipe, define a function, and practice writing and debugging functions.

Introduce relational data structures, and practice working with factor columns.

Data frames, importing data files, and tidying data.

No class.

Define exploratory data analysis and practice exploring data with visualization methods.

Computational problem-solving, verbs for data manipulation, and practice transforming data frames using dplyr.

Introduction to data visualizations, the grammar of graphics, and ggplot2.

Overview of programming, applications to information science, reproducible research, and course logistics.