Computing for Information Science
Computing for Information Science
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Overview
Introduction to the course
Data visualization
Why visualize data?
The grammar of graphics
How to build a complicated, layered graphic
Practice generating layered graphics using ggplot2
Data wrangling
Computer programming as a form of problem solving
dplyr in brief
Practice transforming college education (data)
Relational data: a quick review
Practice using relational data
Importing data into R
Practice transforming and visualizing factors
Tidy data
Practice tidying data
Exploratory data analysis
What is exploratory data analysis?
Practice exploring college education (data)
Geospatial visualization
Introduction to geospatial visualization
Drawing raster maps with ggmap
Practice drawing raster maps
Importing spatial data files using sf
Drawing vector maps with simple features and ggplot2
Practice drawing vector maps
Selecting optimal color palettes
Getting data from the web
Using APIs to get data
Practice getting data from the Twitter API
Writing API queries
Simplifying lists
Scraping web pages
Machine learning
The basics of statistical learning
Build a linear model
Logistic regression
Working with statistical models
Preprocess your data
Evaluate your model with resampling
Tune model parameters
Programming elements
Pipes in R
Practice the pipe
Functions in R
Vectors
Iteration
Bugs and styling code
Debugging and condition handling
Generating reproducible examples
Project management
Saving the source and blank slates
Project-oriented workflow
Use safe filepaths
A dive into R Markdown
R startup procedures
Recovering from common Git predicaments
Building Shiny applications
Text analysis
Basic workflow for text analysis
Practicing tidytext with song titles
Practicing sentiment analysis with Harry Potter
Practicing tidytext with Hamilton
Supervised classification with text data
Predicting song artist from lyrics
Topic modeling
Contents
Overview
This section contains lecture notes and exercises for the course.
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