6 Relative Changes

This chapter shows how to chart relative changes in emissions and/or throughputs. By “relative change” we simply mean the percent change relative to a given year (i.e., the “base year” or “reference year”).

We don’t actually need to obtain any growth profile data yet; the function chart_annual_growth_by() will simply normalize whatever we supply it with. In the next chapter, we’ll look at growth profiles more specifically.

6.1 Relative changes in emissions

We can easily examine relative growth using chart_annual_growth_by(). It expects the same input as chart_annual_emissions(); the only additional requirement is that you supply a base_year.

Here is an example. It’s exactly the same as we saw in the Emissions chapter, except that we’re swapping chart_annual_growth_by() in for chart_annual_emissions_by().

#
# The `base_year` can be prefixed with `CY`, `BY`, `RY` or `PY`;
# `chart_annual_growth_by()` will ignore the differences.
# 
BY2011_annual_emission_data %>%
  filter_categories(
    "#283 Space Heating" = 283, 
    "#284 Water Heating" = 284) %>%
  filter_pollutants(
    "NOx") %>%
  chart_annual_growth_by(
    color = category,
    base_year = CY(2011)) # required; can be BY or CY

We can enhance it in exactly the same ways that we learned in the Emissions chapter, too. Using flag_years can be especially helpful, because then we can read off the exact growth for a given future (or historical) year.

BY2011_annual_emission_data %>%
  filter_categories(
    "#283 Space Heating" = 283, 
    "#284 Water Heating" = 284) %>%
  filter_pollutants(
    "NOx") %>%
  chart_annual_growth_by(
    color = category,
    base_year = BY(2011),
    flag_years = CY(1993, 2002, 2030),
    title = "Residential NG Combustion: Space and Water Heating",
    subtitle = str_c(
      "NOx emissions from water and space heating are projected to grow by +24% from CY2011 to CY2030.",
      "Labeled values are percent changes relative to the base year (CY2011).",
      sep = "\n")) # combine lines with a newline

6.2 Relative changes in throughputs

You can reuse the chunk above to work with throughput data, provided you drop the filter_pollutants(...) clause (two lines).

BY2011_area_source_throughput_data %>%
  filter_categories(
    "#283 Space Heating" = 283, 
    "#284 Water Heating" = 284) %>%
  chart_annual_growth_by(
    color = category,
    base_year = BY(2011),
    flag_years = CY(1993, 2002, 2030),
    title = "Residential NG Combustion: Space and Water Heating",
    subtitle = str_c(
      "Natural gas consumed by water and space heating is projected to grow by +24% from CY2011 to CY2030.",
      "Labeled values are percent changes relative to the base year (CY2011).",
      sep = "\n")) # combine lines with a newline

We can also look at the trend in total throughput for these two caegories, instead of splitting them apart. Just omit the color = cat_id, and swap in chart_annual_growth() (without the suffix _by()).

BY2011_area_source_throughput_data %>%
  filter_categories(
    "#283 Space Heating" = 283, 
    "#284 Water Heating" = 284) %>%
  chart_annual_growth(
    base_year = BY(2011),
    flag_years = CY(1993, 2002, 2030),
    title = "Residential NG Combustion: Space and Water Heating",
    subtitle = str_c(
      "Natural gas consumed by water and space heating is projected to grow by +24% from CY2011 to CY2030.",
      "Labeled values are percent changes relative to the base year (CY2011).",
      sep = "\n")) # combine lines with a newline

If you are trying this, you might notice that the throughput forecast doesn’t follow the same trajectory as the NO_x forecast. See the Appendix for an explanation and an advanced exercise.