Introduction to R for Finance
Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.
- 4 hours
- 14 Videos
- 62 Exercises
- 39,535 Participants
- 5,300 XP
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Course Description
In this finance-oriented introduction to R, you will learn essential data structures such as lists and data frames and have the chance to apply that knowledge to real-world financial examples. By the end of the course, you will be comfortable with the basics of manipulating your data to perform financial analysis in R.
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1
The Basics
FreeGet comfortable with the very basics of R and learn how to use it as a calculator. Also, create your first variables in R and explore some of the base data types such as numerics and characters.
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Data Frames
Arguably the most important data structure in R, the data frame is what most of your data will take the form of. It combines the structure of a matrix with the flexibility of having different types of data in each column.
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Lists
Wouldn't it be nice if there was a way to hold related vectors, matrices, or data frames together in R? In this final chapter, you will explore lists and many of their interesting features by building a small portfolio of stocks.
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Vectors and Matrices
In this chapter, you will learn all about vectors and matrices using historical stock prices for companies like Apple and IBM. You will then be able to feel confident creating, naming, manipulating, and selecting from vectors and matrices.
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Factors
Questions with answers that fall into a limited number of categories can be classified as factors. In this chapter, you will use bond credit ratings to learn all about creating, ordering, and subsetting factors.
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1
The Basics
FreeGet comfortable with the very basics of R and learn how to use it as a calculator. Also, create your first variables in R and explore some of the base data types such as numerics and characters.
-
Vectors and Matrices
In this chapter, you will learn all about vectors and matrices using historical stock prices for companies like Apple and IBM. You will then be able to feel confident creating, naming, manipulating, and selecting from vectors and matrices.
-
Data Frames
Arguably the most important data structure in R, the data frame is what most of your data will take the form of. It combines the structure of a matrix with the flexibility of having different types of data in each column.
-
Factors
Questions with answers that fall into a limited number of categories can be classified as factors. In this chapter, you will use bond credit ratings to learn all about creating, ordering, and subsetting factors.
-
Lists
Wouldn't it be nice if there was a way to hold related vectors, matrices, or data frames together in R? In this final chapter, you will explore lists and many of their interesting features by building a small portfolio of stocks.
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Lore Dirick
Manager of Data Science Curriculum at Flatiron School
Lore is a data scientist with expertise in applied finance. She obtained her PhD in Business Economics and Statistics at KU Leuven, Belgium. During her PhD, she collaborated with several banks working on advanced methods for the analysis of credit risk data. Lore formerly worked as a Data Science Curriculum Lead at DataCamp, and is now the Manager of Data Science Curriculum at Flatiron School, a coding bootcamp in NYC.
See MoreCollaborators
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Davis Vaughan