dholstius

#
# If I wrap a single RasterLayer in a list, I can extract it
# with .[[1]] but not with unlist(). 
#
# This example just uses a single `RasterLayer` to make the point.
# The broader problem (for me) is that it seems to be precluding the
# possibility of using `purrr::map2()` to combine lists of RasterLayers
# in various ways.
#

dholstius

library(units)
install_symbolic_unit("person")
install_conversion_constant("person", "death", const = -1)
options(digits = 8)

# For convenience, let's assume a population of 1 million people.
pop <- as_units(1e6, "person")

# Let's assume that the baseline annual all-cause mortality rate is 
# about 1%, i.e., about 10,000 per million (per year).

dholstius

#
# BY2011 -> BY2015: emission factors for "animal waste" categories.
#
# Created by dholstius on 2019-11-21 for aguha.
#
# For the link between the two sets of categories, see (in Dropbox):
#
# - BY2015/Work/Crosswalks/Categories/BY2015_categories_geneaology.xlsx
#
# For more about the operations taking place in the code below, see (in R):

dholstius

#
# BY2015-area_source-forecast-chart_annual_growth.R
#
# Created 2019-11-18 by dholstius
#

library(inventory)
library(ggtools)

#

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#'
#' Summary Tree Tool
#' 
#' Discover which categories --- or groups of categories --- emit the largest
#' share(s) of a particular pollutant.
#' 
#' Version history:
#' 
#' - Created 2015-12-01 by dholstius
#' - Last updated 2019-10-23 by dholstius

dholstius

library(inventory)

show138 <- function (
  DB_year,
  category_id,
  verbose = TRUE
) {

  #' Define the preferred sorting order.
  PONSCO_vars <-

dholstius

# 
# Turns out that #1350 is the DataBank ID for "Diesel PM".
#
# Here's an "old-school" way to figure that out.
# t0064 contains pollutant metadata.
#
library(Ingres)

help(t0064)  # here's some Orange Binder material
view(t0064)  # type "Diesel" in the search box (upper right) 

dholstius

PY_CCl4_emission_data <-
  PY(1997:2016) %>%
  point_source_abated_emissions() %>%
  filter_pollutants(list(CCl4 = 60))

chart_data <-
  PY_CCl4_emission_data %>%
  mutate(fac_h1 = if_else(fac_id == 31, "Dow Chemical", "All Others")) %>%
  annual_emissions_by(fac_h1) %>%
  group_by(fac_h1)

dholstius

library(UScensus2010)
library(UScensus2010tract)
library(UScensus2010blkgrp)
library(UScensus2010blk)

Cook_tracts <- UScensus2010::county(state = "Illinois", name = "Cook", level = "tract")
Cook_tracts@data[1:8, 1:8]
plot(Cook_tracts)

Cook_blkgrps <- UScensus2010::county(state = "Illinois", name = "Cook", level = "blkgrp")

dholstius

library(dplyr)
library(htmlwidgets)
library(rpivotTable)

mtcars %>%
  rpivotTable(rows = "gear",
              cols = c("cyl", "carb"),
              vals = "hp",
              
              # Step 1. Make this line accomplish the same thing as the default