Course
Importing and Managing Financial Data in R
IntermediateSkill Level
Updated 04/2026RApplied Finance5 hr14 videos51 Exercises4,300 XP20,794Statement of Accomplishment
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Prerequisites
Manipulating Time Series Data in R1
Introduction and Downloading Data
A wealth of financial and economic data are available online. Learn how getSymbols() and Quandl() make it easy to access data from a variety of sources.
2
Extracting and Transforming Data
You've learned how to import data from online sources, now it's time to see how to extract columns from the imported data. After you've learned how to extract columns from a single object, you will explore how to import, transform, and extract data from multiple instruments.
3
Managing Data from Multiple Sources
Learn how to simplify and streamline your workflow by taking advantage of the ability to customize default arguments to getSymbols(). You will see how to customize defaults by data source, and then how to customize defaults by symbol. You will also learn how to handle problematic instrument symbols.
4
Aligning Data with Different Periodicities
You've learned how to import, extract, and transform data from multiple data sources. You often have to manipulate data from different sources in order to combine them into a single data set. First, you will learn how to convert sparse, irregular data into a regular series. Then you will review how to aggregate dense data to a lower frequency. Finally, you will learn how to handle issues with intra-day data.
5
Importing Text Data, and Adjusting for Corporate Actions
You've learned the core workflow of importing and manipulating financial data. Now you will see how to import data from text files of various formats. Then you will learn how to check data for weirdness and handle missing values. Finally, you will learn how to adjust stock prices for splits and dividends.
Importing and Managing Financial Data in R
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FAQs
What R functions are used to import financial data in this course?
You learn to use getSymbols and Quandl functions to access financial and economic data from multiple online sources and local files directly into R.
Does this course cover working with data from multiple sources?
Yes. A major focus is importing, transforming, and merging data from different sources with varying periodicities into a single unified dataset for analysis.
How does the course handle different data frequencies?
Chapter 4 teaches you to convert sparse irregular data to regular series, aggregate dense data to lower frequencies, and handle issues specific to intra-day financial data.
Does the course cover adjusting stock data for corporate actions?
Yes. The final chapter teaches you to adjust stock prices for splits and dividends, check data for anomalies, and handle missing values in financial time series.
How many chapters and exercises does this course contain?
It has 5 chapters with 53 exercises, covering data downloading, extraction, multi-source management, periodicity alignment, and text import with corporate action adjustments.
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