9.1 Time series data manipulation (xts, zoo)

Time series data manipulation in R is all about working with data that changes over time. The xts and zoo packages are your go-to tools for handling this kind of data, offering powerful ways to create, modify, and analyze time-based information.

These packages let you easily subset, merge, and aggregate time series data. You can also visualize your data with specialized plots, making it simple to spot trends and patterns. It's like having a time machine for your data!

Time series data in R

Structure and characteristics

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• Time series data in R consists of a sequence of observations recorded at equally spaced time intervals, where each observation is associated with a specific timestamp or date
• The fundamental components of time series data include:
  • Timestamp (or date)
  • Corresponding value at each timestamp
  • Optional additional variables or attributes
• Time series data is typically stored in specialized data structures, such as:
  • xts (eXtensible Time Series)
  • zoo (Z's Ordered Observations)
  • These structures preserve the temporal ordering and provide efficient methods for manipulating and analyzing the data
• The index of a time series object represents the timestamps or dates associated with each observation, while the data itself can be:
  • Univariate (single variable)
  • Multivariate (multiple variables)

Types of time series

• Regular time series have observations recorded at fixed time intervals
  • Examples: hourly, daily, monthly
• Irregular time series may have varying time intervals between observations

Manipulating time series data

Creating and converting time series objects

• The xts package provides an extensible framework for working with time series data in R, building upon the functionality of the zoo package
• To create an xts object, use the

  xts()

  function, specifying the data and the corresponding time index
  • The time index can be a vector of dates, timestamps, or any ordered values
• The zoo package offers a more general framework for ordered observations, allowing for irregular time series and supporting various index types beyond dates and times
• Convert between different time series classes using functions like

  [as.xts()](https://www.fiveableKeyTerm:as.xts())

  and

  as.zoo()

  to take advantage of the specific features and methods provided by each package

Applying functions and modifying attributes

• Perform mathematical operations on time series objects, such as addition, subtraction, multiplication, and division, which are applied element-wise based on the aligned timestamps
• Apply functions to time series data using the

  [apply.daily()](https://www.fiveableKeyTerm:apply.daily())

  ,

  [apply.weekly()](https://www.fiveableKeyTerm:apply.weekly())

  ,

  [apply.monthly()](https://www.fiveableKeyTerm:apply.monthly())

  , and

  [apply.quarterly()](https://www.fiveableKeyTerm:apply.quarterly())

  functions to compute summary statistics or perform calculations at different time scales
• Modify the time zone of a time series object using the

  [tzone()](https://www.fiveableKeyTerm:tzone())

  function to set or retrieve the time zone attribute, ensuring consistent handling of timestamps across different regions

Operations on time series objects

Subsetting and merging

• Subset time series objects based on date ranges or conditional criteria using:
  • Square bracket notation (e.g.,

    ts_object["YYYY-MM-DD"]

    )

  • subset()

    function
  • Allows for flexible data extraction
• Use the

  [window()](https://www.fiveableKeyTerm:window())

  function to extract a specific time window from a time series object, specifying the start and end dates or timestamps
• Merge multiple time series objects based on their time indexes using the

  [merge()](https://www.fiveableKeyTerm:merge())

  function, which aligns the observations from different series based on their corresponding timestamps

Aggregation and resampling

• Perform aggregation operations on time series data using the

  apply.daily()

  ,

  apply.weekly()

  ,

  apply.monthly()

  , and

  apply.quarterly()

  functions, along with an aggregation function (e.g., mean, sum, max) to summarize data at different time scales
• Resample time series data to change the frequency of observations using the

  [to.period()](https://www.fiveableKeyTerm:to.period())

  function
  • Specify the desired time scale (e.g., daily to monthly) and the aggregation function to be applied
• Handle missing values in time series data using functions like:

  • [na.omit()](https://www.fiveableKeyTerm:na.omit())

    : Remove missing observations

  • [na.locf()](https://www.fiveableKeyTerm:na.locf())

    : Last observation carried forward imputation

  • [na.approx()](https://www.fiveableKeyTerm:na.approx())

    : Interpolation-based imputation

Visualizing time series data

Basic plotting techniques

• Create basic time series plots using the

  plot()

  function, which automatically handles the time index on the x-axis and the corresponding values on the y-axis
• Customize time series plots by specifying additional arguments, such as:

  • main

    : Title

  • xlab

    : X-axis label

  • ylab

    : Y-axis label

  • type

    : Line type

  • col

    : Color

  • lwd

    : Line width
• Use the

  lines()

  or

  points()

  functions to overlay multiple time series on the same plot, allowing for comparative analysis and visualization of relationships between different series

Advanced visualization techniques

• Apply techniques like smoothing or filtering to highlight trends or patterns in the time series data
  • Use functions such as

    [filter()](https://www.fiveableKeyTerm:filter())

    ,

    [loess()](https://www.fiveableKeyTerm:loess())

    , or

    [rollapply()](https://www.fiveableKeyTerm:rollapply())

    to compute moving averages or apply window-based transformations
• Create specialized plots for time series data, such as:
  • Seasonal plots (

    [seasonplot()](https://www.fiveableKeyTerm:seasonplot())

    ): Visualize seasonal patterns
  • Subseries plots (

    [monthplot()](https://www.fiveableKeyTerm:monthplot())

    ,

    [quarterplot()](https://www.fiveableKeyTerm:quarterplot())

    ): Examine the behavior of the series at different time scales
• Utilize interactive plotting libraries, such as dygraphs or plotly, to create dynamic and interactive time series visualizations
  • Allow for zooming, panning, and hovering over data points to retrieve additional information