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Manipulating Time Series Data in R

Master time series data manipulation in R, including importing, summarizing and subsetting, with zoo, lubridate and xts.

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4 Hours15 Videos49 Exercises4100 XP

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Course Description

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.
  1. 1

    What Is Time Series Data?

    Free

    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.

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    What is time series data?
    50 xp
    Plotting time series with autoplot
    100 xp
    Time series summary
    100 xp
    Interpreting time series plots
    100 xp
    Temporal data classes in R
    50 xp
    Finding the class
    50 xp
    is-dot functions
    100 xp
    lubridate's as_date
    100 xp
    Formatting dates in R
    50 xp
    Conversion specifications
    100 xp
    Parsing and formatting dates
    100 xp
  2. 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.

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  3. 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.

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  4. 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.

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Datasets

NOAA temperatures datasetMaunaloa datasetFTSE dataset

Collaborators

james-datacamp
James Chapman
izzyweber-9bc35945-95bd-423b-833e-40780c76586f
Izzy Weber
mahamfaisalkhan
Maham Khan
Harrison Brown Headshot

Harrison Brown

Graduate Researcher

Harrison Brown is a Graduate Researcher and Course Instructor at DataCamp! He has a background in Geography, Cartography, and GIS, and loves making maps and visualizations in R, Python, and ArcGIS.
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