Schedule of Topics

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

Introduce Shiny applications and practice constructing a user interface.

No class.

Identify methods for improving accessibility and interpretability of data visualizations.

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, implement raster maps using ggplot2, and introduce geofaceting.

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.

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

Extended office hours to get support on homework 06.

No class.

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

All things related to Quarto and the knitr package.

Methods for implementing a tidy, reproducible workflow.

Methods for identifying problems and generating reproducible examples.

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

Define a vector, review iterative operations, and implement iteration using a range of techniques.

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.