Introduction to R for Economic Research

The first module provides an overview of freely accessible software widely used for data analysis in economic research. The central focus is R, a programming language specifically developed for statistical computing and graphics. Complementary software - RStudio, R Markdown, and LaTeX - are introduced as supportive tools for dynamic document creation based on R. This module will guide you through the installation process and will familiarize you with the utilization of these tools by exploring key syntax and its application to financial and economic data. Furthermore, the module discusses valuable resources such as DataCamp, an online learning platform, to enhance your expertise.

Module Overview

This module consists of nine chapters:

  • Chapter 1: “Software Overview” provides an overview of software commonly used for data analysis in economic research.
  • Chapter 2: “Software Installation” gives instructions for the installation of these software tools.
  • Chapter 3: “RStudio Interface” guides you through the interface of RStudio, a widely used IDE for R.
  • Chapter 4: “R Data Types and Structures” discusses the fundamental data types and structures in R.
  • Chapter 5: “Importing Data in R” explains how to import datasets into R for analysis.
  • Chapter 6: “Downloading Data in R” instructs on how to download data directly within the R environment.
  • Chapter 7: “Writing Reports with R Markdown” covers the use of R Markdown for creating dynamic, reproducible data reports.
  • Chapter 8: “Learning R with DataCamp” introduces DataCamp as an essential online resource for learning R.
  • Chapter 9: “Data Report on Yield Curve” provides an assessment to test the skills and knowledge gained throughout the module, centered around a data report on the yield curve.

In addition, the following DataCamp courses are integral to supplementing the content in Module I:

Refer to Chapter 8 for guidelines on how to optimize your learning experience from these DataCamp courses.

Learning Objectives

By the end of this module, you should be able to:

  1. Identify the role and importance of software tools such as R, RStudio, R Markdown, and LaTeX in conducting economic research.
  2. Install and set up necessary software including R, RStudio, R Markdown, and LaTeX for economic data analysis.
  3. Navigate the RStudio interface, understanding its various panels and functionalities.
  4. Understand and apply basic R data types and structures for data storage and manipulation.
  5. Import external datasets into R for analysis, and download data directly within the R environment.
  6. Create professional data reports using R Markdown, incorporating R code, text, plots, and tables seamlessly.
  7. Utilize online learning resources like DataCamp to enhance R skills and knowledge further.
  8. Apply the knowledge and skills acquired to write a detailed data report on yield curves, demonstrating proficiency in data import, manipulation, and presentation, as well as interpretation of economic data.

Learning Activities & Assessments for Module I

Throughout this module, you’ll participate in the following activities:

  1. Reading Material: Read the nine chapters in Module I. Each chapter covers different topics, including the importance of specific software tools, the installation process for these tools, using RStudio, understanding R’s data types and structures, importing and downloading data in R, creating reports with R Markdown, and learning R via online resources like DataCamp.

  2. Applying Reading Material: Implement the knowledge you’ve gained from the chapters by replicating the steps outlined. This includes software installation, working with different data types, importing and downloading data, and visualizing results. To ensure understanding, I recommend copying any provided code into your RStudio environment and attempting to reproduce the same output.

  3. Online Learning with DataCamp: Complete the DataCamp courses Introduction to R for Finance and Intermediate R for Finance. These courses will bolster the concepts discussed in this module and introduce you to new data analysis techniques.

Your learning will be assessed based on the following:

  1. DataCamp Course Completion: Completion of the designated DataCamp courses: Introduction to R for Finance and Intermediate R for Finance is a necessity and part of your overall assessment.

  2. Data Report on Yield Curve: Create a comprehensive data report on yield curves utilizing R Markdown. This report will act as a practical demonstration of your understanding of R, RStudio, and R Markdown. Your data report should showcase your proficiency in importing and manipulating data, visualizing the results, and delivering a sensible interpretation of yield curves. The quality of your report will form a significant part of your module assessment. For more details about this assignment, refer to Chapter 9.

Remember, mastery of R programming and data analysis is a step-by-step process. Make sure to thoroughly understand each data handling and manipulation technique before moving on to the next. If any challenges arise during your learning journey, do not hesitate to seek help. Good luck with your exploration of R!