Some New Tools for Working with Data

There are two new tools we will use in working with data for this lab:

data(storms)
katrina = filter(storms, name == "Katrina", year == 2005)

storms %>% filter(name == "Katrina", year == 2005)

# First way
df1 = arrange(filter(mutate(storms, ts_area = pi * (ts_diameter / 2.0)^2), 
     ts_area > 10000), desc(ts_area))

# Second way, using a temporary variable
df2 = mutate(storms, ts_area = pi * (ts_diameter / 2.0)^2)
df2 = filter(df2, ts_area > 10000)
df2 = arrange(df2, desc(ts_area))

# Third way, using the pipe operator
df3 = storms %>% mutate(storms, ts_area = pi * (ts_diameter / 2.0)^2) %>%
     filter(ts_area > 10000) %>% arrange(desc(ts_area))

hurricanes_2005 = storms %>% filter(status == "hurricane", year == 2005)
lin_fit = lm(wind ~ pressure, data = hurricanes_2005)

lin_fit2 = storms %>% filter(status == "hurricane", year == 2005) %>%
           lm(wind ~ pressure, data = .)

. = foo(x)
bar(., y, z)

names = storms$name

cat_5 = storms %>% filter(category == 5)
names = cat_5$name

# This will give an error
names = storms %>% filter(category == 5)$name

# This will work
names = storms %>% filter(category == 5) %>% pull(name)

storms %>% filter(status == "hurricane", year == 2005) %>%
  ggplot(aes(x = pressure, y = wind)) + geom_point(na.rm = T)

lin_fit = storms %>% filter(status == "hurricane", year == 2005) %>%
           lm(wind ~ pressure, data = .)

lin_fit = storms %>% filter(status == "hurricane", year == 2005) %>%
           lm(wind ~ pressure, data = .)
tidy(lin_fit)

## # A tibble: 2 x 5
##   term        estimate std.error statistic  p.value
##   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
## 1 (Intercept)  947.      25.3         37.4 5.07e-86
## 2 pressure      -0.890    0.0263     -33.8 3.43e-79

lin_fit = storms %>% filter(status == "hurricane", year == 2005) %>%
           lm(wind ~ pressure, data = .)
slope = tidy(lin_fit) %>% filter(term == "pressure") %>% pull(estimate)
slope

## [1] -0.8896546

slope = storms %>% 
           filter(status == "hurricane", year == 2005) %>%
           lm(wind ~ pressure, data = .) %>% 
           tidy() %>% 
           filter(term == "pressure") %>% pull(estimate)
slope

## [1] -0.8896546

Writing functions

If you are going to do the same calculation many times in R it is often useful to define a function that performs that calculation and then you can simply call the function whenever you want to do the calculation with new values.

For instance, in the “Growth Rates and Trends” section of the assignment for the bottom-up decarbonization lab, we see that if we have a quantity q that is growing at a steady percentage rate \(r_q\) each year and we know the value in one year \(y_0\) and want to predict what the value will be at a future year \(y\), we can calculate this as follows: \[ q(y) = q(y_0) \times \exp(r_q \times (y - y_0)). \] We can write this as an R function that we can call for different quantities and years:

growth = function(q_0, rate, start_year, end_year) {
  q_0 * exp(rate * (end_year - start_year))
}

A function definition has three parts: First we have the name of the function (growth), then we have the word function, followed by the arguments that the user must supply to the function (q_0, rate, start_year, end_year), and then the body of the function, between curly braces { ... }. When you call the function, it executes all of the code between the curly braces and returns the last value calculated before the closing brace. Thus, we can call the function like this to estimate the world population in 2050

P_2017 = 7.53 # world population was 7.53 billion
r_P = 0.0141  # growth rate is 1.41% per yer
P_2050 = growth(q_0 = P_2017, rate = r_P, start_year = 2017, end_year = 2050)
P_2050

## [1] 11.99146

If you don’t specify the name of the parameter, the function fills them out in order, so we could also write

P_2017 = 7.53 # world population was 7.53 billion
r_P = 0.0141  # growth rate is 1.41% per yer
P_2050 = growth(P_2017, r_P, 2017, 2050)
P_2050

## [1] 11.99146

kayadata: an R package for analyzing Kaya-identity data

You can install the kayadata package as follows:

library(devtools)
install_github("jonathan-g/kayadata")

or

library(pacman)
p_load_current_gh("jonathan-g/kayadata")

Once you have installed the kayadata package, you load it as follows:

library(kayadata)

This package has several useful functions:

• get_kaya_data(region_name): Get Kaya-identity data for a country or region.

  • Argument: region_name is the name of the country or region you want data for (e.g., “United States”, “World”, “China”, etc.).

    You can inspect a list of countries and regions by running kaya_region_list().

    • Results: The function returns a data frame with the following columns:
      • region: The country or region.
      • year: The year.
      • P: The population (in billions).
      • G: The gross domestic product (in trillions).
      • E: The primary energy consumption (in quads).
      • F: The energy-related CO₂ emissions (in millions of metric tons of CO₂).
      • g: Per-capita GDP (in thousands of dollars per person).
      • e: The energy intensity of the economy (in quads per trillion dollars of GDP).
      • f: The emissions intensity of the energy supply (in millions of metric tons of CO₂ per quad).
      • ef: The emissions intensity of the economy (in millions of metric tons of CO₂ per trillion dollars of GDP).
• get_top_down_values(region_name): Get projections of the future values of Kaya-identity variables for a country, from the top-down International Energy Outlook analysis by the U.S. Energy Information Administration.
  • Argument: region_name is the name of the country or region you want data for (e.g., “United States”, “World”, “China”, etc.).
  • Results: The function returns a data frame with the following columns:
    • region: The country or region.
    • year: The year (five-year intervals from 2015–2050).
    • P: The population (in billions).
    • G: The gross domestic product (in trillions).
    • E: The primary energy consumption (in quads).
    • F: The energy-related CO₂ emissions (in millions of metric tons of CO₂).
    • g: Per-capita GDP (in thousands of dollars per person).
    • e: The energy intensity of the economy (in quads per trillion dollars of GDP).
    • f: The emissions intensity of the energy supply (in millions of metric tons of CO₂ per quad).
    • ef: The emissions intensity of the economy (in millions of metric tons of CO₂ per trillion dollars of GDP).
• get_top_down_trends(region_name): Get the trends for Kaya-identity variables for a country, from the top-down International Energy OUtlook analysis by the U.S. Energy Information Administration.
  • Argument: region_name is the name of the country or region you want data for (e.g., “United States”, “World”, “China”, etc.).
  • Results: The function returns a data frame with the following columns:
    • region: The country or region
    • P: The rate of change of the population (multiply by 100 to get the rate in percent per year).
    • G: The rate of change of GDP.
    • E: The rate of change of primary energy consumption.
    • F: The rate of change of CO₂ emissions.
    • g: The rate of change of per-capita GDP.
    • e: The rate of change of the energy intensity of the economy.
    • f: The rate of change of the emissions intensity of the energy supply.
    • ef: The rate of change of the emissions intensity of the economy.
• project_top_down(region_name, year): Calculate projected values for Kaya identity variables at a future year from the present through 2050.
  • Arguments:
    • region_name is the name of the country or region you want data for.
    • year: The year you want projected data for (through 2050).
    • Results: A data frame with one row and columns for region, year, and the Kaya variables: P, G, E, F, g, e, f, and ef.
• get_fuel_mix(region_name): Get the mixture of primary energy sources used by a country or region.
  • Argument: region_name is the name of the country or region you want data for (e.g., “United States”, “World”, “China”, etc.).
  • Results: The function returns a data frame with the following columns:
    • region: The country or region
    • region_code: A three-letter code for the country or region.
    • geography: The kind of region (“nation”, “region”, or “world”).
    • year: The year the data represents.
    • fuel: The kind of energy source (“Oil”, “Natural Gas”, “Coal”, “Nuclear”, “Hydro”, or “Renewables”)
    • quads: The number of quads of that kind of energy consumed in that year.
    • pct: The percent of total primary energy consumption that came from that kind of energy source.
• plot_kaya(kaya_data, variable, start_year, stop_year, y_lab, log_scale, trend_line, points, font_size): Plot a time series showing trends in a Kaya variable.

  • Arguments:

    • kaya_data: A data frame, like those returned by get_kaya_data.

    • variable: A quoted Kaya variable name (“P”, “G”, “E”, “F”, “g”, “e”, “f”, or “ef”).

    • start_year (optional): If you want to do trend analysis, you can specify the year to start calculating the trend. The graph will color the years from start_year to stop_year in a darker color.

      If you don’t specify start_year, the default value is 1980. You can eliminate the special coloring and trend analysis by setting start_year = NULL.

    • stop_year (optional): The year to stop the trend analysis. If you don’t specify stop_year, it defaults to the last year in the data frame.

    • y_lab (optional): The label for the y-axis. If you don’t specify this, R will pick an appropriate label based on which variable you choose.

    • log_scale (optional): Set this to TRUE or FALSE to specify whether to plot the y-axis on a logarithmic scale. The default is to set log_scale = FALSE if you don’t specify a value.

    • trend_line (optional): Set this to TRUE or FALSE to specify whether to plot a trend line. The default is to set trend_line = FALSE if you don’t specify a value.

    • points (optional): Set this to TRUE or FALSE to specify whether to plot points on the line. If you are making a small graph, the points may make the graph too busy. The default is to set points = TRUE if you don’t specify a value.

    • font_size (optional): This lets you adjust the size of the fonts used in labeling the axes on the graph. If you are making small plots and the y-axis label won’t fit on the side of the graph, you might want to use a smaller font size. The default is to set font_size = 20 if you don’t specify a value.

  • Result: This function returns a plot object.

  • Example:

    kd = get_kaya_data("World")
    plot_kaya(kd, "F")

    

    plot_kaya(kd, "F", log_scale = TRUE, font_size = 12, trend_line = TRUE)

    

    plot_kaya(kd, "F", points = FALSE, start_year = NULL, font_size = 12)

    

• plot_fuel_mix(fuel_mix, collapse_renewables): Make a “donut” chart showing the amount of energy the country got from different energy sources.
  • Arguments:

    • fuel_mix: A data frame with the fuel mix for the country, like the ones returned by get_fuel_mix()

    • collapse_renewables (optional): Set this to TRUE or FALSE to specify whether to combine “Hydro” with other renewables to make one “Renewables” category (TRUE), or plot “Hydro” separately from the other renewables (FALSE).

      If you don’t specify collapse_renewables, the default is to set collapse_renewables = TRUE. Setting collapse_renewables = FALSE will only work if you also specified collapse_renewables = FALSE when you retrieved the fuel mix using get_fuel_mix().

    • Results: A plot object with a donut chart.

    • Example:

      fm = get_fuel_mix("World")
      plot_fuel_mix(fm)

      

      fm = get_fuel_mix("World", collapse_renewables = FALSE)
      plot_fuel_mix(fm, collapse_renewables = FALSE)

      

• kaya_region_list(): Get a list of regions and countries for which you can get Kaya data.
• emissions_factors(): Get a data frame with columns for
  • fuel: The energy source
  • emission_factor: The emissions from that energy source, in million metric tons of CO₂ per quad of energy.
• generation_capacity(): Get a data frame with generation capacity of a typical power plant for different kinds of energy sources. This data frame has columns:

  • fuel: The energy source.

  • description: A description of the kind of power plant.

  • nameplate_capacity: The “nameplate capacity” of a typical power plant of that type. The nameplate capacity is the power output when the plant is running at its maximum capacity.

  • capacity_factor: The capacity factor. This is the fraction of the nameplate capacity that the power plant can produce over an average year.

    The average power output from a power plant is the nameplate capacity times the capacity factor: \[ \text{average power} = \text{nameplate capacity} \times \text{capacity factor} \]