Course
Manipulating Time Series Data in R
IntermediateSkill Level
Updated 12/2024RData Manipulation4 hr15 videos49 Exercises4,100 XP7,095Statement of Accomplishment
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Learn All About Time Series Data
Working with data that changes over time is an essential skill in data science. This kind of data is known as a time series. You'll learn the foundations of what a time series represents, how to retrieve summary statistics about the data in a time series, and how to interpret a time series visually.Master Manipulation of Time Series with zoo, lubridate and xts
You’ll master using the zoo and lubridate packages to import, explore, and visualize time series data in R. You’ll learn to retrieve key attributes of time series information, such as the period of that data and how often the data was sampled, gaining fluency in converting between data frames and time series along the way. Further, by aggregating your data, you’ll learn to see the overall trends in the data using the xts package.Perfect Your Subsetting Skills
You’ll cover how to subset a window from a time series to focus on a particular period of interest. You’ll sample time series data at various rates, such as every minute, hour, month, or year. You'll also learn methods of 'imputing' your data – filling in missing values with constant fill, LOCF, or linear interpolation methods. You’ll also learn to create “rolling” windows of a time series that move, or "roll" along with data, making it possible to summarize trends in the data across time. You will also learn how to create expanding windows, which show how these summary statistics approach their final value.Prerequisites
Working with Dates and Times in R1
What Is Time Series Data?
You'll learn the foundations of what a time series represents, how to retrieve summary statistics about the data in a time series, and how to visually interpret a time series plot as part of the exploration step of your analysis. You’ll also cover how to manage date and time information within R objects and ways of incorporating consistent formatting for dates.
2
Manipulating Time Series with zoo
Here, you’ll learn to retrieve key attributes of time series information, such as the range in time of the data and how often the data were sampled, to understand your data better. You'll also be introduced to the zoo package, which contains tools and functions for creating and manipulating time series objects. Many data science applications in R use the data frame paradigm; you'll learn how to convert between a data frame and a time series.
3
Indexing Time Series Objects
You’ll cover how to subset a window from a time series to focus on a particular period of interest. You’ll see that when working with real-world time series data, the timespan of your dataset may cover more information than you need, which can clutter your visualizations. You’ll sample time series data at various rates, such as every minute, hour, month, or year. Further, by aggregating your data, you’ll learn to see the overall trends in the data using the xts package. You'll also learn methods of 'imputing' your data – filling in missing values with constant fill, LOCF, or linear interpolation methods.
4
Rolling and Expanding Windows
You’ll learn to create “rolling” windows of a time series that move, or "roll" along with data, making it possible to summarize trends in the data across time, such as the average over success months of observations or the sum over several weeks of sales. Overall summary statistics, like mean, median, sum, maximum, and so on, do not always provide insight into how data changes over time, and rolling windows will allow you to compute statistics dynamically. In addition to rolling windows, you will also learn how to create expanding windows, which show how these summary statistics approach their final value.
Manipulating Time Series Data in R
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FAQs
Which R packages are used for time series manipulation in this course?
You will work primarily with the zoo and xts packages for creating, manipulating, subsetting, and aggregating time series objects in R.
What methods for handling missing values in time series are taught?
You will learn three imputation methods: constant fill, last observation carried forward (LOCF), and linear interpolation for filling gaps in time series data.
What are rolling windows and why are they useful?
Rolling windows move across your data to compute summary statistics dynamically over time, such as a rolling average over successive months, revealing trends that overall statistics miss.
Do I need prior experience with time series data in R?
You need Intermediate R, Introduction to R, and Working with Dates and Times in R. No prior time series experience is required, as this is a beginner-level course.
Will I learn to convert between data frames and time series objects?
Yes. Chapter 2 covers converting between data frame and time series formats, which is essential since many R data science applications use the data frame paradigm.
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