Time Series with data.table in R

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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!

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.

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.

Ok, so you have some time series data and you believe me that data.table is great for it. Before you can test that, you need to convert your data! In this chapter, you'll learn how to convert from popular time series data formats into data.table.

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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Review of data.table
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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.
View Chapter Details Play Chapter Now
Getting time series data into data.table

Ok, so you have some time series data and you believe me that data.table is great for it. Before you can test that, you need to convert your data! In this chapter, you'll learn how to convert from popular time series data formats into data.table.
View Chapter Details Play Chapter Now
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.
View Chapter Details Play Chapter Now
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.
View Chapter Details Play Chapter Now

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

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Time Series with data.table in R

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Loved by learners at thousands of top companies:

Course Description

Review of data.table

Getting started

Subsetting with .SD

Subsetting with grep()

Flexible data selection

Individual column names

Functions that modify the data.table

Adapting to different input columns

In-place name changes

Executing functions inside data.tables

Executing functions inside []

Applying a function over every column

Generating correlation matrices

Generating lags, differences, and windowed aggregations

Generating lags

Lags with setorder and shift

Bucketed lags with by

Fitting linear models with lags

Knowledge check: lags

Generating growth rates and differences

First differences

Growth rates

Knowledge check: differences

Windowing with j and by

Grouping data into windows with j and by

Windowed aggregations

Knowledge check: windowed aggregations

Getting time series data into data.table

Overview of the POSIXct type

Understanding POSIXct

Creating POSIXct objects

Creating dates with lubridate

Creating data.tables from vectors

Using seq.POSIXt()

Creating sample data with .N

Coercing from xts

Brief overview of xts

Why does anyone use xts for time series data?

Converting xts objects to data.tables

Combining datasets with merge() and rbindlist()

Merging on timestamp

Merging across different frequencies

Using rbindlist()

Case study: financial data

Case study: modeling metals prices

Cleaning up column names

Pipeline function to merge datasets

Timeseries feature engineering

Generating and tagging differences

Generating and tagging growth rates

Knowledge check: growth rates

EDA and model building

A safer function to generate correlation matrices

Adding features

Fitting a linear model

Congratulations