StatLab Articles

NOTE: As of March 2023, the free version of the Twitter API no longer allows read requests. This means the instructions below to create a developer account, access Twitter, and download tweets no longer works as written. If you have a paid "Basic" tier or higher then these instructions may work for you, but we have not verified this.

Whenever we are dealing with a dataset, we almost always run into a problem that may decrease our confidence in the results that we are getting - missing data! Examples of missing data can be found in surveys - where respondents intentionally refrained from answering a question, didn’t answer a question because it is not applicable to them, or simply forgot to give an answer. Or our dataset on trade in agricultural products for country-pairs over years could suffer from missing data as some countries fail to report their accounts for certain years.

Related Scholarship

• Claibourn, Michele, McClintock, Charlotte, and Public Interest Data Lab. 2018. Charlottesville Child Welfare Study 2018. Prepared by the Public Interest Data Lab for the Charlottesville Department of Social Services, Charlottesville, VA.
• Hatcher, Holly, McClintock, Charlotte, and Community Impact Lab. 2018. Early Childhood Mapping Project.

This post is something I’ve been thinking about writing for a while. I was inspired to write it by my own trials and tribulations, which are still ongoing, while working with the QGIS API, trying to programmatically do stuff in QGIS instead of relying on available widgets and plugins. I have spent, and will probably continue to spend, many hours scouring the internet and especially Stack Overflow looking for answers of how to use various classes, methods, attributes, etc.

I've been struggling off and on for literally months trying to create and export a print layout using Python for QGIS 3. Or PyQGIS 3 for short. I have finally figured out may of the ins and outs of the process and hopefully this will serve as a guide to save someone else a lot of effort and time.

Introduction

In the penultimate post of this series, we’ll use some unsupervised learning approaches to uncover comment clusters and latent themes among the comments to President Ryan’s Ours to Shape website.

The full code to recreate the analysis in the blog posts is available on GitHub.

Introduction

We're still analyzing the comments submitted to President Ryan’s Ours to Shape website.

In the fourth installment of this series (we’re almost done, I promise), we’ll look at the sentiment – aka positive-negative tone, polarity, affect – of the comments to President Ryan’s Ours to Shape website.

Introduction

To recap, we’re exploring the comments submitted to President Ryan’s Ours to Shape website (as of December 7, 2018).

Introduction

In the last post, we began exploring the comments submitted to the Ours to Shape website. We looked at the distribution across categories and contributors, the length and readability of the comments, and a few key words in context. While I did more exploration of the data than reported, the first post gives a taste of the kind of dive into the data that usefully proceeds analysis.

Introduction

As part of a series of workshops on quantitative analysis of text this fall, I started examining the comments submitted to President Ryan’s Ours to Shape website. The site invites people to share their ideas and insights for UVA going forward, particularly in the domains of service, discovery, and community.