Quantitative Economics with R

  • Vikram Dayal

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The objective of this video is to provide a user-friendly contemporary treatment of quantitative economics with a focus on data science. The subject and the audience are the same as the book –Quantitative Economics with R: a Data Science Approach – by the same author. The treatment here will be audio-visual, and highlight selections from the book, with clear demonstrations using the software. Some of the audience might prefer the video; others may see it as a complement to the book. Understanding that many in economics would like to learn R, the video project recognizes that people learning R with a typical economics background may find it frustrating trying to learn the software or not know how to go about learning it. The book is already being used for this purpose and the video will build on it. The video augments the book and is easier to follow.


This video provides a user-friendly contemporary treatment of quantitative economics with a focus on data science, and clear demonstrations using R.

About The Author

Vikram Dayal

Vikram Dayal is a Professor at the Institute of Economic Growth, Delhi. He has been using the R software in teaching quantitative economics to diverse audiences, and is the author of the Springer Brief titled An Introduction to R for Quantitative Economics: Graphing, Simulating and Computing. He has published research on a range of environmental and developmental issues, from outdoor and indoor air pollution in Goa, India, to tigers and Prosopis juliflora in Ranthambore National Park. He studied economics in India and the USA, and received his doctoral degree from the Delhi School of Economics, University of Delhi.


Supporting material

View source code at GitHub.

About this video

Vikram Dayal
Online ISBN
Total duration
1 hr 10 min
Springer, Singapore
Copyright information
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

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Video Transcript


Welcome to quantitative economics with a data science approach. I am the author of a book published by Springer, quantitative economics with R, a data science approach. The book and the video are complimentary, but they can also be used by themselves.

The book is far more detailed and comprehensive, but the video is intuitive and also would take far less time. Also you will see screenshots of the RStudio software, so you might find that helpful. The segments of the video are as follows. We will begin with R and RStudio. And we will be using R through RStudio, so initially we will see what different windows in RStudio are and how you can find your way around this software. This should be extremely helpful if you are new to R.

We then move to two key kinds of objects which are there in R, vectors and dataframes. Once we are familiar with that we are ready to wrangle data, which is that data don’t always come in the way, or are not arranged in the way that we require them to be arranged. So broadly this is a way of rearranging data.

Once we wrangle the data, we can graph data. And this has always been one of our strengths, so we will be looking at this in some detail. And this will also allow you to see what the data saying. We then move to causal inference, which is a question of what is the effect of dash on dash? What is the effect of a treatment on an outcome, of a policy on something that we want to affect in a positive way?

We’ll have a look at simulation, which is something that helps us understand data signs and also will give you tools for more advanced work in R. Finally we will look at prediction very briefly using a much used and a very versatile machine learning algorithm, random forests. But in order to get to random forests we will first see what trees are. So this is how we are going to proceed in this video on quantitative economics with R, a data science approach. I hope you will enjoy this as much as I did.