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library(packagemetrics) # devtools::install_github("ropenscilabs/packagemetrics")
library(ctv)
library(dplyr)

task_view   <- "Optimization"
out_path    <- paste0(task_view, ".rds")
pkgs        <- ctv:::.get_pkgs_from_ctv_or_repos(task_view)
pkgs        <- as.character(as.data.frame(pkgs[1])[,1])
if(!file.exists(out_path)){
  pkg_metrics <- packagemetrics::package_list_metrics(pkgs)

jsta

## Rsync Filter for $HOME

# Universal excludes -- These apply to all subdirs
- [Cc]ache
- temp
- tmp
- octave-core

# Emacs temp files
- .#*

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test.png: test.pdf
	convert -density 300 $<[0] -crop 100x20% -background white -alpha remove -strip -quality 70 test.png 
	mv test-0.png test.png
	rm test-*.png
	convert test.png -alpha set -background black -fill white \
   \( +clone -colorize 100 -gravity south -chop 0x6 -splice 0x6 \
   -spread 20 -paint 8 +transparent white -blur 0x0.8 \) \
   -background none -compose dstin -composite torn.png

jsta

library(rloadest)

my_model_no <- 1
loadreg_original <- loadReg(Phosphorus ~ model(1),
          data = app1.calib, dates = "DATES", flow = "FLOW")
loadreg_mod <- loadReg(as.formula(paste0("Phosphorus ~ model(", my_model_no, ")")),
          data = app1.calib, dates = "DATES", flow = "FLOW")

identical(
  loadreg_original,

jsta

1. Create an .envrc file:

export PATH=/home/<user>/anaconda3/bin:$PATH
# conda env create -n <env_name> -f environment.yml
source activate <env_name>

2. Create an environment.yml file:

jsta

library(rdrop2) # devtools::install_github("karthik/rdrop2")
library(dplyr)

top_level_path <- function(x){
  stringr::str_extract(x, "^(.+?)(\\/)")
}

rdrop2::drop_auth()

file_list_raw <- drop_dir(recursive = TRUE)

jsta

mediagoblin setup

Installation

See https://wiki.mediagoblin.org/HackingHowto#How_to_set_up_and_maintain_an_environment_for_hacking_with_virtualenv

Enable search plugin

Add [[indexedsearch]] to mediagoblin.ini

jsta

library(dams)
library(dplyr)
library(sf)
library(mapview)

dt <- nid_subset
dt <- dt[c(
  grep("edenville", tolower(dt$dam_name)), 
  grep("sanford", tolower(dt$dam_name))),] %>%
  dplyr::filter(state == "MI") %>%

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get_hypso <- function(rsub){  
  maxdepth <-  abs(cellStats(rsub, "min")) # set to "max" if depths are positive

  # define depth intervals by raster resolution  
  min_res   <- 0.5
  depth_int <- -1 * seq(0, round(maxdepth/min_res) * min_res, by = min_res)

  # calculate area of raster between depth intervals
  # reclassify raster based on depth intervals
  # calculate area of each class

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library(mboxr)
library(tm.plugin.webmining)
library(tm)
library(wordcloud)

fpath   <- path.expand("~/Downloads/jobs.mbox")
data    <- read_mbox(fpath)
content <- paste(unlist(data$content), collapse = " ")
test    <- extractHTMLStrip(content)
write.csv(test, "~/Downloads/jobs.csv")