jsta

library(sp)
library(rgdal)

exts <- c(".shp", ".dbf", ".sbn", ".sbx", ".shx")
dt_path <- paste0(tempfile(), exts)

dt <- download.file(paste0("http://geog.uoregon.edu/GeogR/data/shp/orstations", 
                      exts), dt_path)

dt <- readOGR(dt_path[1])

jsta

# See http://www.johnmyleswhite.com/notebook/2010/08/20/using-jags-in-r-with-the-rjags-package/

N <- 1000
x <- rnorm(N, 0, 5)

modelstring <- "
model {
  for(i in 1:N) {
    x[i] ~ dnorm(mu, tau)
  }

jsta

# National Landcover Database 2006 (NLCD)

nlcd_palette <- data.frame(
           class = c("water", "water", 
                     "developed", "developed", "developed", "developed", 
                     "barren", 
                     "forest", "forest", "forest", 
                     "shrubland", "shrubland", 
                     "herbaceous", "herbaceous", "herbaceous", "herbaceous", 
                     "planted", "planted", 

jsta

x <- seq(0, 10, length.out = 80)
y <- x

#' @param slope numeric slope of the original line
#' @param frac_along numeric fraction along the original line to draw control point array
#' @param seq_len numeric fraction of the line to include as a control point
#' 
generate_perp <- function(x, y, slope = 1, frac_along = 0.5, seq_len = c(0.25)){
  lapply(frac_along, function(i) {
    x_offset <- i * range(x)[2] - 5

jsta

library(sf)
library(ggplot2)

# crs       <- st_crs(orig_sf)
# test 	  <- st_geometry(orig_sf)
# test_text <- st_as_text(test)

crs       <- st_crs(32610)
test_text <- "LINESTRING(4683684.86627714 6127410.96399602, 4683791.86302937 6127277.7501861, 4683823.72428078 6127263.81933025, 4683937.27439384 6127249.11683816, 4683969.89585432 6127219.69092887, 4683951.89224796 6127168.37796377, 4683866.89142587 6127085.63923137, 4683850.50012205 6127051.83321428, 4683847.90414026 6126904.28325476, 4683913.46734349 6126776.99477916, 4683939.65765144 6126701.82749159, 4683999.27975858 6126605.97125794, 4684069.7019509 6126531.19991619, 4684137.39774915 6126498.57646262, 4684185.17287752 6126465.56639239)"

jsta

dt <- data.frame(a = seq(1, 10) + rnorm(1))

# option 1 ####
key_function <- function(x) {
  if(x > 7){
    "x"
  }else{
    if(x > 4 & x < 7){
      "y"
    }else{

jsta

library(glmtools)

estimate_linear <- function(nlayers, surface_area, max_depth){
  layer_thickness <- max_depth / nlayers
  depth_vec <- seq(0, max_depth, by = layer_thickness)
  
  df_pred <- data.frame(
    areas = c(surface_area, 0),
    layer_ind = c(1, nlayers + 1))
  

jsta

x <- 1:10
y <- 1 - (1 / (1 + 2*x))
yeps <- y + rnorm(length(y), sd = 0.01) # added noise
fit <- nls(yeps ~ 1 - (1 / (1 + b*x)), start = list(b = 2))

coef(fit)

plot(x, y)
lines(x, predict(fit))

jsta

ogr2ogr -f GPKG foo.gpkg foo.gdb

jsta

library(sbtools)
library(readxl)
library(dams)
library(dplyr)
library(sf)
library(maps)
library(ggplot2)

# Get Reservoir Morphology Database ####
temp_dir <- tempdir()