Shiny is an R-based programming solution that allows building interactive web apps and combines the computational power of R with the interactivity of modern web. It allows to host standalone apps on a webpage, embed them in R Markdown documents, or build dashboards.
This section acts as a showroom of apps I’ve put together for conferences or simply as a presentation of my work.
Although there are several alternatives for doing systematic literature reviews for academics, none of them are really focused on field specific applications. Moreover, existing databases usually do not cover all relevant journals or their most recent publications including early view versions of accepted papers. The app is inspired by a routine I applied while working on literature reviews when I systematically checked my field specific journals using complex Boolean expressions.
The engine uses RSelenium web driver for navigating rvest web scraper to papers returned by a search query executed on a journal’s native homepage. A typical result is a database containing meta information on scraped papers which can be easily checked and further processed (qualitatively or quantitatively). Due to copyright reasons and restrictions concerning web scraping, the app is not freely available and can be ran only locally. Below is a video showing how the app works in practice. Currently, the database contains over 50 field-relevant journals published by various publishing houses.
CroPolarizeR is an online analytical tool for analyzing political debates in Croatian Sabor. It uses Wordfish algorithm for extracting political positions of speakers and projecting them onto a single scale defined by a topic under discussion. The algorithm relies on a statistical model of word counts and Poisson distribution of word frequencies. Positions of speakers are estimated using an expectation-maximization algorithm. Confidence intervals for estimated positions are generated from a parametric bootstrap. If you want to learn more about Wordfish, see Slapin, Jonathan and Sven-Oliver Proksch. 2008. “A Scaling Model for Estimating Time-Series Party Positions from Texts.” American Journal of Political Science 52(3): 705-772.
The app uses shiny servers for deploying a real time analysis of MPs’ positions in more than 4.500 debates collected from December 2003 to June 2019. Positions of MPs are summarized on a level of agenda capturing overall positions of speakers‘ contributions in a debate.
Although the app is still under development and might be buggy, its beta version can be tested and explored here. In the app, please select term you are interested in, year under that term, and agenda you would like to analyze. For the agenda field, either you can search in the list of available items or you can just start typing and use a full-text search query instead. The last step is to run the analysis with the button “Analyze”. The result will appear in the central part of the app; there are two graphs, the first visualizing overall positions of all MPs on a specific agenda, the second introducing groupings on a party level. Download button in the top-right corner allows users to download outcome of the analysis used for visualization. Although the data can be further analysed and processed, the visualization itself already provides useful empirical information – it can highlight overall positions of individual MPs on an agenda, identify existing cleavages among political parties, and recognize within-party divisions with dissent positions.
Mythologizing War: Legacies of Conflict in Croatian Parliament Debates
An interactive app presented as a part of Workshop on Authoritarian Backsliding, Electoral Misconduct and Violence organized at King’s College London (May 2019). It visualizes vector similarities of war-related concepts against a general concept of authoritarianism. The app is part of a paper Mythologizing War: Legacies of Conflict in Croatian Parliament Debates that is currently part of a proposal for special issue in International Political Science Review. Click here or the image below.