3 Emissions

This chapter will show you how to create charts showing annual emissions for one or more categories and one or more pollutants.

It will also show you how to customize those charts with:

  • a unique color for each category;
  • directly labeled values at specific years; and
  • reader-friendly names for categories.

Finally, it will show you how to create area charts, consistent with all of the above.

All of what you learn will be extensible to the chapters on Throughputs, Growth Profiles, Emission Factors, and Control Factors.

3.1 Key functions

This section relies on three key functions:

  • filter_categories()
  • filter_pollutants()
  • chart_annual_emissions_by()

If you have read the vignette Introduction to BY Data1 then you are already familiar with filter_categories() and filter_pollutants().

The chart_annual_emissions_by() function — and its shorter form, chart_annual_emissions() — will be the foundation of this section. To learn more about chart_annual_emissions_by(), you can always type help("chart_annual_emissions_by").

3.2 Charting annual emissions

Let’s try it out on a relatively simple case: charting NOx emissions from BY2011 category #283 Water Heating.

#
# This is an absolutely minimal example for `chart_annual_emissions()`.
# We're going to build on it, adding improvements as we go.
#
BY2011_annual_emission_data %>%
  filter_categories(
    283) %>%
  filter_pollutants(
    "NOx") %>%
  chart_annual_emissions()

That’s a great first step!

3.3 Coloring by category

If you want to chart more than one category, you’ll probably want a way to distinguish them visually. Let’s use color to distinguish (and compare) #283 Water Heating and #284 Space Heating.

#
# Here we'll color by `category`. You could also color by `cnty_abbr`. (Try it!)
#
# By default, `filter_categories()` adds a new column `category`. If you don't
# name the categories --- which we will do, further along --- then they are 
# simply prefixed with "#". So, for example, when `cat_id` is 283, the default 
# for `category` will be "#283".
#
# The suffix `_by()` --- on `chart_annual_emissions_by()` --- is mostly just
# there for readability. Things should still work if you forget and use
# `chart_annual_emissions()` instead.
# 
BY2011_annual_emission_data %>%
  filter_categories(
    283, 
    284) %>%
  filter_pollutants(
    "NOx") %>%
  chart_annual_emissions_by(
    color = category)

The default colors are provided by scale_color_tableau(). You can try using a different color scheme if you like. Go to the “Help” pane in RStudio, and type “scale_color_”, then wait. You should see a list of choices pop up.

If you wind up with more than ten colors, rethink the chart. Maybe grouping categories would be helpful? In later sections, we will learn how.

3.4 Flagging specific values

Displaying exact values on a chart can be helpful to the reader. Then, they don’t have to consult a separate table.

To do this, simply supply flag_years. If you want to change the template for what exactly is displayed — digits, text, etc. — you can supply flag_labels too. See the Appendix.

#
# Use `flag_years` to display values directly in the chart.
#
BY2011_annual_emission_data %>%
  filter_categories(
    283, 
    284) %>%
  filter_pollutants(
    "NOx") %>%
  chart_annual_emissions_by(
    color = category,
    flag_years = CY(1993, 2011, 2030))

3.5 Humanizing category names

You can also, in the process of filtering, supply more human-readable names for the categories.

#
# Names supplied to `filter_categories()` appear in the data as a new column,
# `category`. Following that, we can ask `chart_annual_emissions_by()` to use
# `category` as the basis for coloring.
# 
BY2011_annual_emission_data %>%
  filter_categories(
    "#283 Space Heating" = 283, 
    "#284 Water Heating" = 284) %>%
  filter_pollutants(
    "NOx") %>%
  chart_annual_emissions_by(
    color = category,
    flag_years = CY(1993, 2011, 2030))

3.6 Titling and captioning

Last but not least, you can provide a title, subtitle, and/or caption.

#
# An explicit `title`, `subtitle`, and/or `caption` can be supplied to
# `chart_annual_emissions_by()`. This is strongly encouraged!!
#
# The default caption is just "DRAFT YYYY-mm-dd."
# 
BY2011_annual_emission_data %>%
  filter_categories(
    "#283 Space Heating" = 283, 
    "#284 Water Heating" = 284) %>%
  filter_pollutants(
    "NOx") %>%
  chart_annual_emissions_by(
    color = category,
    flag_years = CY(1993, 2011, 2030),
    title = "Residential NG Combustion: Space and Water Heating",
    subtitle = "Between CY1993-2002, controls on NOx from water heating were strengthened.")

3.7 Making an area chart

Stacking can be a good way to show how the total and the proportions vary over time. (Compare with the line-charts above, which are not stacked.)

To create a stacked (“area”) chart, simply pass fill = ... instead of color = ....

#
# `fill` can be supplied in place of `color`.
# 
# `subtitle = str_c(..., sep = "\n")` means "combine multiple lines of text".
# We can use this to avoid subtitles running off the chart.
#
BY2011_annual_emission_data %>%
  filter_categories(
    "#283 Space Heating" = 283, 
    "#284 Water Heating" = 284) %>%
  filter_pollutants(
    "NOx") %>%
  chart_annual_emissions_by(
    fill = category,
    title = "Residential NG Combustion: Space and Water Heating",
    subtitle = str_c(
      "Combined emissions from water and space heating have declined.",
      "But, they are projected to return to 1990 levels by 2030.",
      sep = "\n"))

3.8 More than one pollutant

Above, we’ve kept things simple by just focusing on NOx. But you’ll often want to show more than one pollutant at the same time. You can definitely do that!

#
# If multiple pollutants are represented in the data that is passed to
# `chart_annual_emissions_by()`, it will try to be helpful by creating a
# sub-plot for each pollutant. This ought to be a safe approach.
# 
BY2011_annual_emission_data %>%
  filter_categories(
    "#283 Space Heating" = 283, 
    "#284 Water Heating" = 284) %>%
  filter_pollutants(
    "NOx",
    "PM10") %>%
  chart_annual_emissions_by(
    color = category,
    title = "Residential NG Combustion: Space and Water Heating",
    subtitle = "Between CY1993-2002, controls on NOx from water heating were strengthened.")

  1. This is being migrated from an R vignette to a GitBook like this one. Stay tuned!↩︎