1. Time series
Moving on! In this lesson, we will review time series!
2. Time series
Time-dependent data may pop up during an interview for a position in which you analyze how variables change in time. Companies that collect time-dependent data operate in sectors such as finance, agriculture, and energy.
3. Time series
Wrangling time-dependent data using base R classes, like vectors, is hard, since time is irregular.
4. Time series
It's good to know at least one package for handling time series that will do the hard work for you.
In this lesson, we'll use the xts package because of its general application.
5. Time series
To check your skills, the interviewer might ask you to analyze time series in search of trends, seasonal variation, or serial correlation.
Another common task is to develop a time series prediction model, such as ARIMA.
6. Time series - object
A time-series object
7. Time series - object
consists of a set of dates or date times
8. Time series - object
and a set of corresponding values.
9. Time series - object
To initiate an xts object in R, you need to pass values and corresponding dates as arguments to the xts function.
10. Analysis - plot
The first step of a time series analysis is usually the visualization.
The plot function applied on an xts object outputs a nicely formatted linear plot.
11. Analysis - subsetting
Often, we are interested in a specific period rather than the whole history.
You can create a period as a set of dates using the sequence function.
12. Analysis - subsetting
The cool thing is that you can set the "by" argument using standard words, like "3 weeks" or "1 month".
13. Analysis - subsetting
Subsetting an xts object is analogous to subsetting a data frame.
14. Analysis - merging
During an interview, you may be asked to compare two time-series.
The merge function makes it easy to join time-dependent datasets.
15. Analysis - merging
By default, the merge function joins all data.
16. Analysis - merging
If you prefer to join only common dates, set the all parameter to FALSE.
17. Analysis - merging
You can also carry the last observation forward. Filling the gaps with the last observation can be achieved with the na.locf function applied upon the merge function.
18. Analysis - applying a function by calendar period
Another common task associated with a time series is performing calculations on a specific period.
19. Analysis - applying a function by calendar period
The apply.monthly function, as the name suggests, applies a given function on all data within one month.
20. Analysis - applying a function by calendar period
The apply.yearly function performs calculations on data within one year.
21. Summary
To summarize, we covered what a time series is, how to create a time series object in R, and went over a few ways to work with time-dependent data, namely: subsetting, merging, and applying a function over calendar periods.
22. Let's practice!
Let's wrangle some time-dependent data!