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#Make sure wkhtmltopdf is installed to the path...

#Set the working directory
mainDir <- getwd()
targetFiles <- list.files(pattern = "html", recursive = T, full.names = T)
dir.create("0_HTML")
targetFilesUpdated <- sub('/0_', "_", targetFiles, fixed = TRUE)
targetFilesUpdated <- sub('./', '0_HTML/', targetFilesUpdated)
file.rename(targetFiles, targetFilesUpdated)

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library(taxize)
library(ape)

gen<-unique(unlist(lapply(strsplit(GenBank_Accession_Table$Taxon,' '), function(x)x[[1]][1])))


taxonomy<-do.call(rbind,pblapply(gen, function(x){
  tryCatch({
    tax_name(query = x, get = c('family',"superfamily"), db = "ncbi")
  }, error=function(e){})

cromanpa94

Instructions from Brian (https://github.com/bomeara/phydocker/blob/master/Dockerfile)

cd /usr/local/
sudo mkdir pathd8download
cd pathd8download
sudo wget http://www2.math.su.se/PATHd8/PATHd8.zip
sudo unzip PATHd8.zip
sudo cc PATHd8.c -O3 -lm -o PATHd8
sudo cp PATHd8 /usr/local/bin/PATHd8

cromanpa94

#Fasttree tree constraint in R
#http://www.microbesonline.org/fasttree/constrained.html

#Packages
library(ape)
library(phylobase)
library(adephylo)
library(bio3d)
library(pbapply)

cromanpa94

library(phytools)


# fullNew = path to tree files

trees<-lapply(seq_along(fullNew), function(x){
  cat(x,'_',fullNew[x], '\n')
  
  k<-grep('#NEXUS', readLines(fullNew[x]))
  

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#initialize
library(shiny)
library(ggplot2)
library(purrr)
library(dplyr)


#example data
data(iris)

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set.seed(123) # make reproducible
M = 10000 # Number of posterior samples

N = 20
# sample the IV
x = rnorm(N, 10, 5)
# regression model for DVs
y = 100 + 3*x + rnorm(N,0,10)

## Delete missing data (3 in each)

cromanpa94

annotate_rainbow_letters<-function(ggbasePlot, string, initialx, y, letterDistance,
                                   wordDistance,uppcaseadditionalDistance){

spup<-strsplit(string, split = "")[[1]]
uppLett<-grepl("^[[:upper:]]+$", spup)
dostanceVector<-ifelse(spup == " ", wordDistance, letterDistance)

if(any(uppLett)){
  for(i in 1:length(which(uppLett))){

cromanpa94

##Not my functions but slighly mod for my use

patchify2<-function(x, distance, p4s, givedist=TRUE) {
  # x: a binary Raster layer (0 or NA for background, and 1 for areas to be clumped)
  # distance: the neighbourhood distance. Patches that occur within this distance of 
  #           one another will be clumped. This should be in the units of the CRS given
  #           in p4s. If this is zero, the function will identify patches defined by 
  #           8-connectivity (i.e. queen's case).
  # p4s: an equal-area projection appropriate for the region. This will be used when 

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https://github.com/brentthorne/posterdown

https://github.com/rstudio/pagedown

https://github.com/GerkeLab/betterposter

https://github.com/pzhaonet/postr

https://github.com/odeleongt/postr