This function analyzes trends in an input table of GCAM data values over time. The time series are categorized into a user-defined number of trend types, and these are returned, along with a table identifying each time series with its trend type.
trend_analysis(d, n = 4, valuecol = "value", yearcol = "year")
The data to analyze. See details for a description of the input data format.
Number of categories to produce. For 32-region data, setting n > 8 is not recommended.
Name of the column with the data values in it. The output trends will prepend 'normalized.' to this name for the name of their value column.
Name of the column with the year values in it. The output trends will always call their year column "year".
Input data should be a data frame in long format, with year and value columns and at least one ID column. For example, you might have a table of region, year, and population. The output would then categorize regional population trends. It is also permissible to have more than one ID column; for example, region, sector, year, and output. In this case, each combination of region and sector would be a separate data point for categorization purposes.
The return value is a list containing two elements. The
element contains a table with columns
value. It represents a prototype time series for each trend category.
The prototype time series is in normalized units, with the largest value in
the time series defined to be 1.
categories element contains all of the columns from the input data
set, along with a
trend.category column. The original value column
will be normalized to the maximum value in each time series, just as the
prototype trend time series are. The trend category column indicates which
category that time series belonged to.
For example, if the input were a table of region, year, and population, then
trends output will be a table of trend category, year, and
population. If four categories were requested, then the prototype time
series might correspond to population time series that are (for example)
stable, rising then stable, rising continuously, and rising then declining.
The categories aren't labled in any way; an analyst would arrive at these
labels by looking at the prototype time series returned.
categories element would contain a table of region and trend
category, with each category being a number from 1-4, corresponding to one of
the trend categories returned. This table tells you which regions have
population trends like those in each of the trend categories.
The structure returned by the trend analysis has a summary method that prints a table of which data ponts were assigned to which categories, along with a count of how many data points were assigned to each category. There is also a plot method that will plot a visualization of the trend categories and their members.
1 2 3 4 5 6 7
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.