R Tutorial: Manipulating and visualizing your data

Want to learn more? Take the full course at https://learn.datacamp.com/courses/case-studies-manipulating-time-series-data-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Now that you've practiced converting your data into an xts object, it's time to begin manipulating and exploring your data. First, you'll want to identify the qualities of your data. One important quality of any time series data is **periodicity**, or the units of time used to distinguish each observation. Time series data can be measured on any time scale, from observations every century to observations every second. The `periodicity()` command in xts will identify the time scale and scope of your data. Once you understand the periodicity of your data, another useful step is to plot your data over time. The most common methods for plotting time series data are the `plot.xts()` command and the `plot.zoo()` command. Both commands use the time index of your xts object as the x-axis by default. This allows you to easily visualize trends over time. Returning to our example from the previous video, we can see that the population in our city appears to be growing over time. But what if you'd like to add a more complex trend to this plot? Not only is population growth positive, it appears to be *accelerating*. Remember that xts objects have many of the qualities of matrix objects, including the ability to handle math operations. To derive more complex values from your time series data, you can simply calculate an additional column. In our city population data, we can calculate annual growth by taking the difference from year to year as a percentage. Plotting this new column of data, we can confirm that population growth is accelerating over time. Lets practice on our Boston flight data! #R #RTutorial #DataCamp #Manipulating #TimeSeries #Data