muschellij2

library(oro.dicom)
library(oro.nifti)
library(shiny)
library(shinyIncubator)
options(shiny.maxRequestSize = -1)
shinyServer(function(input, output, session) {
  # output$filetable <- renderTable({
  makenii = reactive({
    rimg = c(0, 100)    
    nii = NULL

muschellij2

gistup

muschellij2

#' @title <brief desc>
#'
#' @description <full description>
#' @param  <what param does>
#' @param  <what param does>
#' @param  <what param does>
#' @export
#' @keywords
#' @seealso
#' @return

muschellij2

#' @name fslmask
#' @title Mask image using FSL
#' @param file (character) image to be masked
#' @param mask (character) mask given for image
#' @param outfile (character) resultant masked image name
#' @param retimg (logical) return image of class nifti
#' @param reorient (logical) If retimg, should file be reoriented when read in?
#' Passed to \code{\link{readNIfTI}}.
#' @param intern (logical) to be passed to \code{\link{system}}
#' @param opts (character) additional options to be passed to fslmask

muschellij2

> t(t(coef(SuBLIME_model)))
                              [,1]
(Intercept)          -9.828049e+00
FLAIR                 6.859367e-01
PD                    7.665177e-01
T2                   -3.484273e-01
T1                    5.850592e-01
FLAIR_diff            1.699155e-01
time_diff             2.533482e-03
PD_diff               3.295134e-01

muschellij2

So day 2 of the #JHUSMARTHack was last week, but I figured this would be a good time to discuss what was accomplished, but really discuss why I like using [RStudio](http://www.rstudio.com/) for making packages. 

# What was accomplished?
I had spoken [before](http://hopstat.wordpress.com/2014/05/01/smart-hackathon-day-1/) about the [repositories](http://github.com/muschellij2/) I had worked on an also [Developing Packages in RStudio](http://www.rstudio.com/ide/docs/packages/overview).  I'll discuss the workflow I settled into for for making a package.

## Workflow for an `R` package
I'm assuming `R` code is already available, presumably functions you had created during a project or analysis.  If code is not available, GREAT!  You can start your workflow for your new package or product all the same.  I'll try to put command-line equivalents in double parentheses.

Workflow:

muschellij2

library(ggplot2)
df = data.frame(x = rnorm(100), x2 = rnorm(100, mean=2))

g = ggplot(df, aes(x)) + geom_histogram( aes(x = x, y = ..density..), fill="blue") + 
  geom_histogram( aes(x = x2, y = -..density..), fill= "green")
print(g)

## using base
h1 = hist(df$x)
h2 = hist(df$x2)

muschellij2

#' @title Find matlab path
#'
#' @description This tries to find matlab's path using a system which
#' command, and then, if not found, looks at \code{getOption("matlab.path")}.  If not path is found, it fails.
#' @export
#' @return Character of command for matlab
get_matlab = function(){
  find.matlab <- system("which matlab", ignore.stdout=TRUE)
  matcmd <- 'matlab -nodesktop -nosplash -nodisplay -r '  
  if (find.matlab != 0){

muschellij2

<style type="text/css">
body {
    background-image: url('figure/rplot_loess.png');
    background-color: #cccccc;
}
</style>

muschellij2

title_desc = function(file) {
  require(Hmisc)
  desc = read.dcf(file)
  title = desc[, "Title"]
  ss = strsplit(title, " ")[[1]]
  ss = capitalize(ss)
  ss = paste(ss, collapse = " ")
  desc[, "Title"] = ss
  write.dcf(desc, file=file)
}