dill

# use MVB's foodweb to build a graphviz-compatible "dot" file
# this can then be used to generate a svg of the graph using:
# $ dot -Tsvg file.dot -o file.svg

# example usage:
# fw2dot("mrds","mrds.dot")

library(mvbutils)

fw2dot <- function(package.name,filename){

dill

# is Columbus, OH really the only place in the US where you're within
# 500 miles of half of the US population?

# claim: https://448f59f74df57015bbb8-a9447b7dfa4ae38e337b359963d557c4.ssl.cf3.rackcdn.com/9987%20Brewdog%20EFP%20Prospectus%20USA%20A4%20v9.pdf
# "Columbus is within 500 miles of over half of the US population"

# get the US 2010 census data
# http://www2.census.gov/geo/docs/reference/cenpop2010/county/CenPop2010_Mean_CO.txt
cent <- read.csv("CenPop2010_Mean_CO.txt")

dill

# try to understand measurements from the past...

# possible input types:
# - 5
# - 5'
# - 5'5
# - 5'7 1/2
# - 5'7\"
# - 5'8.5
# - 5'61/2\"

dill

# example from ?mgcv::concurvity

library(mgcv)
## simulate data with concurvity...
set.seed(8);n<- 200
f2 <- function(x) 0.2 * x^11 * (10 * (1 - x))^6 + 10 *
            (10 * x)^3 * (1 - x)^10
t <- sort(runif(n)) ## first covariate
## make covariate x a smooth function of t + noise...
x <- f2(t) + rnorm(n)*3

dill

library(emoGG)
library(ggplot2)

# set the am variable to be different emoji
mtcars$am[mtcars$am==1] <- "1f697"
mtcars$am[mtcars$am==0] <- "1f68c"

# use am as the emoji aesthetic
ggplot(mtcars, aes(wt, mpg, emoji=am))+ geom_emoji()

dill

# little animation to illustrate uncertainty in a density map

library(dsm)
library(mvtnorm)
library(ggplot2)
library(animation)
library(viridis)

# load the models and prediction data
# using data from the Duke spatial distance sampling course

dill

# make a boundary

library(mapdata)
library(maptools)
library(rgeos)
library(ggplot2)
library(sp)

# build the coastline
coastline <- map("world", c("USA", "Canada"), plot=FALSE)

dill

library(ggplot2)
library(emoGG)

source("imgcat.R")

p <- ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) + geom_emoji(emoji="1f337")
imgcat(print(p))

dill

# for LJT

# this wasn't as simple as I thought, it was the summary() method that does the smart thing
# so there is a bit of split() nonsense to deal with...

library(Distance)
library(dsm)
library(ggplot2)

dill

# watch all .Rmd files in a directory, if any change, knit them

library(knitr)

# use watch from Hadley's testthat package
library(testthat)


watchr<-function(path){
  # the path supplied here can just be something like "./"