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Time Series with data.table in R

Master time series data using data.table in R.

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4 Hours14 Videos52 Exercises2,863 Learners4200 XP

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Course Description

Time series data is fun, but challenging. When ordering matters, your datasets get large, and timestamp precision differences can foil your merges, building reliable data processing pipelines requires a principled approach with the right tools. Enter data.table! Its expressive syntax will make your code powerful without sacrificing readability and its support for in-place operations will make your code super fast. Learn how to master time series data in data.table with this course!

  1. 1

    Review of data.table


    This chapter provides an overview of all the cool things that make data.table perfect for working with time series data, including its multiple column-selection options, how to modify data.tables by reference, and calling functions by taking advantage of non-standard evaluation.

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    Getting started
    50 xp
    Subsetting with .SD
    100 xp
    Subsetting with grep()
    100 xp
    Flexible data selection
    50 xp
    Individual column names
    100 xp
    Functions that modify the data.table
    100 xp
    Adapting to different input columns
    100 xp
    In-place name changes
    100 xp
    Executing functions inside data.tables
    50 xp
    Executing functions inside []
    100 xp
    Applying a function over every column
    100 xp
    Generating correlation matrices
    100 xp
  2. 3

    Generating lags, differences, and windowed aggregations

    Like most other data, time series data you find in the wild are rarely suitable to directly start using in model training. In this chapter, you'll learn how to write powerful, expressive data.table code to implement a few common forms of time series feature engineering.

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  3. 4

    Case study: financial data

    It's time to put it all together! In this chapter you'll consider a real-world dataset of spot metal prices from the London Metal Exchange (LME). By the end, you'll know how to write reusable functions to perform common time series feature engineering tasks and you'll have experience using those functions to build a statistical model.

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chesterChester Ismayamy-4121b590-cc52-442a-9779-03eb58089e08Amy Peterson
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James Lamb

Staff Data Scientist, Uptake

James is a data scientist / engineer based in Chicago, IL. He wrote "Time Series with data.table in R " on DataCamp but isn't actively maintaining it.
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