Setup_and_munge.R

##################
# Knitr settings #
##################

knitr::opts_chunk$set(warning=FALSE,
                      message=FALSE,
                      echo=FALSE,
                      dev="png", dev.args=list(type="cairo"), # The png device
                      # Change to dev="postscript" if you want the EPS-files
                      # for submitting. Also remove the dev.args() as the postscript
                      # doesn't accept the type="cairo" argument.
                      error=FALSE)

# Evaluate the figure caption after the plot
knitr::opts_knit$set(eval.after='fig.cap')

# Use the table counter that the htmlTable() provides
options(table_counter = TRUE)

# Use the figCapNo() with roman letters
options(fig_caption_no_roman = TRUE)

#################
# Load_packages #
#################
library(rms) # I use the cox regression from this package
library(boot) # The melanoma data set is used in this exampe
library(Gmisc) # Stuff I find convenient
library(Greg) # You need to get this from my GitHub see http://gforge.se/packages
library(magrittr) # The excellent piping package

##################
# Munge the data #
##################

# Here we go through and setup the variables so that
# they are in the proper format for the actual output

# Load the dataset - usually you would use read.csv
# or something similar
data("melanoma")

# Set time to years instead of days
melanoma$time_years <-
  melanoma$time / 365.25

# Factor the basic variables that
# we're interested in
melanoma$status <-
  factor(melanoma$status,
         levels=c(2, 1, 3),
         labels=c("Alive", # Reference
                  "Melanoma death",
                  "Non-melanoma death"))
melanoma$sex <-
  factor(melanoma$sex,
         labels=c("Male", # Reference
                  "Female"))

melanoma$ulcer <-
  factor(melanoma$ulcer,
         levels=0:1,
         labels=c("Absent", # Reference
                  "Present"))