1. Learn
    2. /
  2. Courses
    3. /
  3. Importing and Managing Financial Data in R
  • 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.

Initializing

Exercise

Create valid name for one instrument

If you are only downloading data for a single symbol and that symbol is not a syntactically valid name, you can set auto.assign = FALSE in your call to getSymbols(). That will allow you to directly assign the output to a syntactically valid name.

You may also want to convert the column names to syntactically valid names. That is a good idea if you plan to use the data in functions that expect column names to be syntactically valid names (e.g. lm()).

Instructions

100 XP
  • Use getSymbols() and its auto.assign argument to create an object named BRK.A containing data for "BRK-A".
  • Use colnames() to create an object named col_names that contains the column names for the BRK.A object.
  • Use make.names() to convert col_names to syntactically valid names.