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Packages in the
tidyverse suite, including
dplyr, represent amazing contributions data science. One thing that constantly vexed me, though, was the inability of
dplyr’s merge functions–
full_join, etc.– to actually let you know if the merges went through without having to do extra work. It just seemed so cumbersome to have to individually inspect each data frame after each merge. Well, thankfully, that is no longer necessary due to
R is a fantastic open-source program that allows users to do just about anything, but sometimes the program requires some tricks to accomplish seemingly simple tasks. Removing accents is a case in point, so I’d like to provide everyone with some guidance to overcome some of the thornier accent removal issues.
Properly balancing panel data and removing duplicate unique identifiers is a recurring challenge for students in my Research Practicum course. Accordingly, I thought it would be helpful to provide an example for everyone.
Hello, world! I have finally figured out GitHub and how to make a free website with Jekyll. It’s a great day!
Have you ever wondered how to make a map in ArcGIS? In this tutorial, we cover all of the basics: coordinate systems, how to work with shapefiles, spatial joins, and much more. By the end of the workshop, you will be able to plot point data and color in your map based on spatial polygons values using ArcGIS’ symbology function. [Slides Here]
Instructor (with Mike Findley), University of Texas at Austin, Department of Government (GOV 355M), 2021
This course provides students with a comprehensive introduction to data science for the political and economic world. By focusing on practical data skills coupled with strong social science reasoning, the course will enable students to acquire skills that will help them prepare for jobs in data science, industry, and academia. Organized around a set of substantive themes and practical tasks, each class topic is motivated by real-world problems and then backed with data science skills to solve those problems. Emphasis is placed on developing proficiency in cleaning, manipulating, wrangling, scraping, visualizing, and mapping data. Most work is conducted in the software programs R and Excel, and to a lesser extent through introductory exercises in other programs. In the process, students learn about good principles of working with data, including through version control with Github. The class takes place through asynchronous instruction, online coding practice problems, exams, and online instructor consultations. [Syllabus] [Summer 2021 Evaluation]
Instructor, University of Texas at Austin, Department of Government (GOV 355C), 2021
This course is the first semester of a Research Practicum program that attempts to provide undergraduates with a fairly comprehensive introduction to the research process in the social sciences. Classroom instruction covers arguments, concepts, measures, causality, and basic statistics. Given that knowledge of statistical software, text editors, reference management software, and mapping software is increasingly helpful for success in the social sciences, the course will also provide training in Stata, R, LaTeX, Mendeley, Excel, and ArcGIS. At the end of the first semester, students will hand-in their own well-developed Research Proposals in lieu of a final exam. [Syllabus] [Fall 2021 Evaluation] [Fall 2019 Evaluation]
Instructor, University of Texas at Austin, Department of Government (GOV 355D), 2022
This course is the second semester of a Research Practicum program that attempts to provide undergraduates with a fairly comprehensive introduction to the research process in the social sciences. Classroom instruction covers experiments, data structures, data cleaning, hypothesis testing, measurement challenges, linear regression, as well as the basics of panel data, regression discontinuity designs, difference-in-differences, synthetic controls, logistic regression, and network analysis. Training in Stata, R, LaTeX, Mendeley, and ArcGIS continues during the second semester of the course as well. At the end of the second semester, students complete their own research projects, write-up their results in a formal paper, and present their findings to the class. [Syllabus] [Spring 2022 Evaluation] [Spring 2021 Evaluation] [Spring 2020 Evaluation] [Spring 2019 Evaluation]