Machine Learning with R

EDA for Text you own this product

This free project is part of the liveProject series Machine Learning with R for Text Data
intermediate R • data wrangling • data visualization • exploratory data analysis (EDA)
skills learned
import and clean data • generate basic data visualizations for time series datasets • tokenize and clean text data • calculate summary statistics for text data
Benjamin Soltoff
1 week · 4-6 hours per week · INTERMEDIATE
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Step into the shoes of an academic researcher tasked with predicting which areas will be the focus of the U.S. government’s policy-related efforts. In this liveProject, you’ll prepare for predictive modeling by exploring the policy areas and text descriptions in legislation data, using statistical visualizations and ggplot2, and identifying notable trends and outliers.

project author

Benjamin Soltoff
Benjamin Soltoff is an assistant senior instructional professor in computational social science at the University of Chicago. He’s the associate director of the Masters in Computational Social Science program and teaches courses in research design, programming in R, data visualization, and machine learning. He holds a PhD in political science from Pennsylvania State University. He develops training workshops for learners in academia and industry on data science techniques using R with an emphasis on reproducible workflows, and he’s an RStudio-certified trainer. For more information, you can view his personal site.


This liveProject is for intermediate R programmers who know the basics of data science. To begin these liveProjects you will need to be familiar with the following:

  • Intermediate R
  • Data wrangling
  • Data visualization
  • Exploratory data analysis

you will learn

In this liveProject, you’ll learn to develop a range of supervised machine learning classification models, widely used by data scientists and academic researchers, that will determine the model-fitting workflow.

  • Import and clean data
  • Generate basic data visualizations for time series datasets
  • Tokenize and clean text data
  • Calculate summary statistics for text data


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