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#! /bin/sh

# purge_rstudio_configs
#
# Purpose: 
# Refresh RStudio settings by moving per-user RStudio configurations into dated copies. 
#
# Usage:
# $ purge_rstudio_configs
#

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#' Count intersections of y within geometries in x
#' 
#' @param x sf object
#' @param y sf object
#' @param varname the name of the new count column
#' @return x with an added 'count' column
st_count <- function(x, 
                     y,
                     varname = "count"){
  

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# Getting and saving a token for accessing documents in Google Drive

# this takes you through an interactive session tp exchange passcodes
token <-gargle::token_fetch(scopes = c("email", "https://www.googleapis.com/auth/drive"))

# then save it for use in another session
saveRDS(x, "~/.httr-token.rds")

token <- googlesheets::gs_auth(token = token)
# or for later session use

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#' Retrieve a stars layer of Auckland’s Maunga Whau volcano topography.  Adapted from
#' USGS-R inlmisc package.
#'
#' @seealso \url{https://waterdata.usgs.gov/blog/inlmiscmaps/}
#' @seealso \url{https://CRAN.R-project.org/package=inlmisc}
#' @export
#' @param indexed logical, if TRUE then assign 1,2,3,... as cell values
#' @return Stars
volcano_stars <- function(indexed = FALSE){
  require(magrittr)

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# Read a NanoSIMS file (possibly corrupted header line)
# into a data frame (tibble).  Possibly overwrite the orginal with the 
# correct header line.
#
# Requires [readr](https://CRAN.R-project.org/package=readr) package
# 
# Usage within R session:
# > source("/path/to/nanosims.R")
# # make resave FALSE to preserve the orginal file
# > x <- read_nanosims("/path/to/nanosims/file.csv", resave = TRUE)

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#' Build out an empty itemization table for a data frame
#'
#' @param x data.frame or tibble
#' @return character string, but it is printed, too
itemize_table <- function(x){
  klass <- sapply(x, function(x) class(x)[[1]])
  varnames <- names(klass)
  s <- c("\\itemize{",
         sprintf("  \\item{%s %s }", varnames, klass),
         "}")

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R packages

All components of R come in packages (compressed files with code and data inside) that are stored in one of two places on a computer: (1) a user's personal library OR (2) in a system-wide library available to all users. You'll be placing packages in your personal library as you don't have administrative credentials on the Mac Mini.

When R is installed it comes with 'base' packages for working with files, simple graphing, statistics, and working with the operating system (likely macOS in your case). When you start a new session (aka "instance") these 'base' packages are loaded

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#' Split a matrix by row much as is done for \code{data.frame}
#'
#' @param x the matrix
#' @param f a ‘factor’ see \code{split}
#' @param ... other arguments passed to \code{split}
#' @return a list of matrices
split_matrix <- function(x, f, ...){

    rnm <- rownames(x)
    if (is.null(rnm)){

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#' Compute the union of a list of vectors
#' 
#' @param x a list of zero or more vectors
#' @return a vector of the union or NULL is the input is empty
munion <- function(x){
    if (!is.list(x)) stop("input must be a list")
    
    n <- length(x)
    if (n == 0) return(NULL)
    if (n == 1) return(x[[1]])

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#' A function to audit disk usage by subdirectory
#'
#' @param path the path to audit 
#' @param extra character extra arguments for linux `du` command
#' @param app character the `du` executable path
#' @return data frame or NULL
#'  \itemize{
#'      \item{size numeric in megabytes}
#'      \item{name character}
#'  }