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  3. Manipulating Time Series Data in R
  • 1

    What Is Time Series Data?

    Gratuito

    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.

Initializing

Ejercicio

Aggregating data

Aggregation is the process of reducing the sampling frequency of a time series and is important for understanding patterns within the data, allowing for cleaner visualization and interpretation. Especially if the temporal resolution is high – data sampled every hour or minute – it can be difficult to interpret the data or spot patterns, leading to a need for aggregation.

Here, you'll take a time series, hourly_temperature, containing hourly temperature readings from a town in the United States, and aggregate it to different levels.

hourly_temperature, xts, and ggplot2 are loaded for you.

Instrucciones 1/4

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  • 1
    • Autoplot the hourly_temperature time series with the y-axis label "Degrees Celsius".
  • 2
    • Using the apply-dot functions, aggregate hourly_temperature to the daily average.
  • 3
    • Aggregate hourly_temperature to find the weekly maximum, and autoplot with the 'light' theme and the same y-axis label.
  • 4
    • Aggregate hourly_temperature to find the monthly median and plot with the theme and y-axis label from Step 1.