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# Welcome to the COPSAC ggplot Kaplan-Meier template. It builds nice and very customizable KM curves as shown below. Please run the two functions to load into memory, it works like a SAS macro.
# Load dependencies
library(survival)
library(ggplot2)
library(cowplot)
library(reshape2)

# Run these functions to load into memory
# Define custom function to create a survival data.frame. Credit http://www.ceb-institute.org/bbs/wp-content/uploads/2011/09/handout_ggplot2.pdf
createSurvivalFrame <- function(f.survfit){

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getSNPImputed <- function(SNPs)
{
	#Counting allele 1

	if (!require("data.table")) stop("Install packages data.table")
	if (!require("RMySQL")) stop("Install packages RMySQL")
	if (!require("rio")) stop("Install packages rio")
	if (!require("gtools")) stop("Install packages gtools")

	convertProbsToDoses <- function(props, alleleToReturnAorB="A")

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source("lib/check_packages.R")
check_packages()
load("data/ubp_phyloseq_metagenomeseq.RData")
library(vegan)
library(ggplot2)
library(cluster)
library(phyloseq)
library(cowplot)
library(reshape2)
library(scales)

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library("googlesheets")

# Forbind til google. Efter denne kommando åbner browseren, og du skal logge ind på google.
gs_ls()

# Herefter registerer du det pågældende ark med enten ark-navnet, eller som her, ark-nøglen (står i URLen)
sheet_key          <- "15WDkfscZ-3xuSE33WRmj5PIpC-q5Eto8wUlWqlv-x2w"

sheet_object       <- gs_key(sheet_key)
sheet_csv          <-  gs_read_csv(sheet_object, "Fanenavn")

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get_loadings <- function(physeq, ord, method = c("pearson", "spearman"), scaled=TRUE, cor = FALSE, axes=1:2, taxranks=6:7, top = NULL){
	if(!all(rownames(ord$vectors) == sample_names(physeq)))
		stop("Taxa names do not match")
	counts <- t(as(otu_table(physeq),"matrix"))
	scores <- ord$vectors
	cov <- cov(counts, scores, method=method[1])
	if(cor){
		cov <- cor(counts, scores, method=method[1])
	}
	if(scaled){

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ggroc <- function(roc, showAUC = TRUE, interval = 0.2, breaks = seq(0, 1, interval)){
  require(pROC)
  if(class(roc) != "roc")
    simpleError("Please provide roc object from pROC package")
  plotx <- rev(roc$specificities)
  ploty <- rev(roc$sensitivities)
  
  ggplot(NULL, aes(x = plotx, y = ploty)) +
    geom_segment(aes(x = 0, y = 1, xend = 1,yend = 0), alpha = 0.5) + 
    geom_step() +

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lowest <- function(tax){
  returnPhyseq <- FALSE
  if(class(tax) == "phyloseq"){
    physeq <- tax
    tax <- tax_table(physeq)
    returnPhyseq <- TRUE
  }
  tax <- tax[, colnames(tax) != "lowest"]
  tax <- tax %>% as("matrix")
  low <- tax[,ncol(tax)] %>% as.vector

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getBugsCV <- function(x, y, ncomp = 5, nfolds = 5, nvar = 5, direction = ">", seed = 42, nreps = 1){
  set.seed(seed)
  sigbugs <- list(nfolds*nreps)
  for(rep in 1:nreps){
    folds <- sample.int(nfolds, size = nrow(x), replace = TRUE)
    for(fold in 1:nfolds){
      train <- folds != fold
      test <- folds == fold
      fit <- splsda(x[train,], y[train], keepX = rep(nvar,ncomp), ncomp = ncomp)
      sigbugs[[(rep-1)*nfolds + fold]] <- names(fit$loadings$X[,1])[fit$loadings$X[,1] != 0]

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getBestFit <- function(x, y, ..., ncomp = 5, nfolds = 5, minvar = 1, maxvar = ncol(x), direction = ">", seed = 42, run = NULL){
  set.seed(seed)
  auc <- expand.grid(AUC = 0, ncomp = 1:ncomp, nvar = minvar:maxvar, stringsAsFactors = FALSE)
  folds <- sample.int(nfolds, size = nrow(x), replace = TRUE)
  for(i in minvar:maxvar){
    pred <- matrix(rep(NA,nrow(x)*ncomp), ncol = ncomp)
    for(fold in 1:nfolds){
      train <- folds != fold
      test <- folds == fold
      fit <- splsda(x[train,], y[train], keepX = rep(i,ncomp), ncomp = ncomp)

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ggphylobarplot <- function(physobj, 
      barlevel = "lowest", 
      colorlevel = "phylum", 
      splitvar = "Time", 
      splitlevelorder = c("1w", "1m", "3m"), 
      splitlevellabels = c("1 week", "1 month", "3 months"), 
      sortlevel = "1w", 
      mracut = .009, 
      agglomerate = FALSE, 
      agglomeratelevel = "genus",