Interactive Course

Importing and Managing Financial Data in R

Learn how to access financial data from local files as well as from internet sources.

  • 5 hours
  • 15 Videos
  • 57 Exercises
  • 10,062 Participants
  • 4,850 XP

Loved by learners at thousands of top companies:

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Course Description

If you've ever done anything with financial or economic time series, you know the data come in various shapes, sizes, and periodicities. Getting the data into R can be stressful and time-consuming, especially when you need to merge data from several different sources into one data set. This course will cover importing data from local files as well as from internet sources.

  1. 1

    Introduction and downloading data

    Free

    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. 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.

  3. 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.

  4. 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.

  5. 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.

  1. 1

    Introduction and downloading data

    Free

    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.

What do other learners have to say?

Devon

“I've used other sites, but DataCamp's been the one that I've stuck with.”

Devon Edwards Joseph

Lloyd's Banking Group

Louis

“DataCamp is the top resource I recommend for learning data science.”

Louis Maiden

Harvard Business School

Ronbowers

“DataCamp is by far my favorite website to learn from.”

Ronald Bowers

Decision Science Analytics @ USAA

Joshua Ulrich
Joshua Ulrich

Quantitative Analyst & member of R/Finance Conference committee

Joshua Ulrich is a Quantitative Analyst & Programmer for an algorithmic market maker, a member of the R/Finance Conference organizing committee, and founder of the Saint Louis R User Group. He is the creator of TTR, and co-author of several other packages for R and finance. He is passionate about computational finance, algorithmic trading, risk management, and helping people solve problems.

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