dholstius

old_pkgs <- dir("C:/Users/yfang/Documents/R/win-library/4.1")

for (pkg_name in old_pkgs) {

  # If we already installed it, don't do anything
  installed_pkgs <- row.names(installed.packages())
  
  if (pkg_name %in% installed_pkgs) {
    # pass
  } else {

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)

#

dholstius

#'
#' 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

#' Patch data on the fly.
#'
#' @param object  to be patched
#' @param cond    logical condition(s) to be evaluated within scope of object
#' @param \dots   name-value pairs
#' @param quiet   suppress messages
#'
#' @examples
#'   patch(mtcars, where(vs == 0, am == 1), gear = Inf, carb = carb + 10)
#'

dholstius

read.kml <- function(file, layers) {
    require(sp)
    require(rgdal)
    read.layer <- function (layer_name) {
        spobj <- rgdal::readOGR(dsn=file, layer=layer_name)
        coords <- coordinates(spobj)
        colnames(coords) <- c('x', 'y', 'z')[1:ncol(coords)]
        df <- data.frame(coords, spobj@data)
        transform(df, layer=layer_name)
    }