Chapter 1 Software Overview
The following software and programming languages are commonly used for conducting economic analyses:
R: R (R Core Team 2023) is a programming language designed for statistical computing and graphics. This language is widely used by data scientists and researchers for a range of tasks such as data processing, visualization, model estimation, and performing predictive or causal inference. For instance, one can use R to import GDP data, plot the data, compute the GDP growth rate from this data, and finally, apply time-series modeling techniques to predict future GDP growth.
LaTeX: LaTeX (Lamport 1986) is a powerful document preparation system widely used for typesetting scientific and technical documents. Similar to Microsoft Word, LaTeX is a text formatting software, but it offers advanced support for mathematical equations, cross-references, bibliographies, and more. LaTeX is particularly useful for creating professional-looking PDF documents with complex mathematical notation.
Markdown: Markdown (Gruber 2004) is designed for simple and easy formatting of plain text documents. It uses plain text characters and a simple syntax to add formatting elements such as headings, lists, emphasis, links, images, and code blocks. Markdown allows for quick and readable content creation without the need for complex formatting options. It is often used for creating documentation, writing blog posts, and formatting text in online forums. Markdown documents can be easily converted to other formats, making it highly portable.
R Markdown: R Markdown (Allaire et al. 2024; Xie 2023) combines R with Markdown, LaTeX, and Microsoft Word. This fusion creates an environment where data scientists and researchers can combine text and R code within the same document, eliminating the process of creating graphs in R and then transferring them to a Word or LaTeX document. An R Markdown document can be converted into several formats, including HTML, PDF, or Word. To generate a PDF, R Markdown initially crafts a LaTeX file which it then executes in the background. Thanks to the embedded R code in the R Markdown document, it’s possible to automate data downloading and updating to ensure a financial report remains up-to-date. In fact, the text you’re reading now was crafted with R Markdown.
RStudio: RStudio (Posit Team 2023) is an Integrated Development Environment (IDE) for R. An IDE is a software application that combines multiple programs into a single, user-friendly platform. Think of RStudio as the all-in-one tool you’ll use for conducting economic research - it will handle all tasks, running R, Markdown, and LaTeX in the background for you. However, for RStudio to work, R, R Markdown, and a LaTeX processor must be installed on your computer, so that RStudio can use these programs in the background.
Pandoc: Pandoc (MacFarlane 2023) is a document converter. Originating outside the R ecosystem, it has been adopted by RStudio and is essential to R Markdown’s flexibility. When you run an R Markdown document, under the hood, it’s Pandoc that transforms the Markdown file into a variety of outputs, whether that’s a Word document, an HTML web page, or a slide show. It comes automatically bundled with RStudio, ensuring that R Markdown users don’t have to go through the manual installation process.
TinyTeX: LaTeX is a markup language designed for document preparation and typesetting. By itself, it isn’t an executable software but relies on a TeX distribution for processing. While traditional TeX distributions like TeX Live or MikTeX might come with extensive components not commonly used by everyday R users, TinyTeX (Xie 2024b) offers a minimalistic, lightweight LaTeX distribution. It’s optimized for R Markdown users and is built upon the TeX Live system (TeX Users Group 1996).
R packages: R provides a rich set of basic functions that can be extended with R packages. These packages are a collection of functions written by contributors for specific tasks. For example, the
quantmod(Ryan and Ulrich 2024a) package provides functions for financial quantitative modeling.
All the software and programming languages mentioned above are open-source, meaning they are freely available and actively developed by a community of contributors. By mastering these tools, you will have the necessary skills to perform data analysis, create reproducible reports, and effectively communicate your findings in the field of economics.