dholstius

eval({
  ensure_nonnull <- ensures_that(!is.null(.) ~ "Must not be NULL" )
  ensure_nonempty <- ensures_that(length(.) > 0 ~ "Must not be empty", +ensure_nonnull )
  ensure_finite <- ensures_that(all(is.finite(.)) ~ "Must be finite", +ensure_nonempty )
  ensure_positive <- ensures_that(all(. > 0) ~ "Must be positive", +ensure_finite )
  ensure_nonnegative <- ensures_that(all(. >= 0) ~ "Must be positive or zero", +ensure_finite )
  ensure_unique <- ensures_that(length(unique(.)) == 1)
})

# Wrapped in `eval({...})` b/c otherwise RStudio will complain about syntax

dholstius

#
# Context:
#
#  https://cran.r-project.org/web/packages/ensurer/vignettes/ensurer.html says:
#  "It is also possible to use the dot, ., directly in anonymous error handlers defined directly in the call to e.g. ensure_that."
#
# I must be misunderstanding the statement above!
#

library(ensurer)

dholstius

# $ brew install libssh2
# $ brew install libgit2
## $ export CMAKE_INCLUDE_PATH="/usr/local/Cellar/libssh2/1.7.0/include"
## $ export CMAKE_LIBRARY_PATH="/usr/local/Cellar/libssh2/1.7.0/lib"

# NOTE: re: git2r: https://github.com/ropensci/git2r/issues/236
install_CRAN <- function (pkg, repos = "http://cran.rstudio.com", verbose = TRUE, ...) {
  if (pkg %in% installed.packages()) message(pkg, " is already installed")
  else install.packages(pkg, repos = repos, verbose = verbose, ...)
}

dholstius

grouply <- function(f, ...) {
  groups <- lazyeval::lazy_dots(...)
  
  function(tbl, ...) {
    dplyr::group_by_(tbl, .dots = groups) %>%
      f(...) %>%
      dplyr::ungroup()
  }
}

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

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

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

# 
# 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

library(inventory)

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

  #' Define the preferred sorting order.
  PONSCO_vars <-

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