hadley

How to write a reproducible example.

You are most likely to get good help with your R problem if you provide a reproducible example. A reproducible example allows someone else to recreate your problem by just copying and pasting R code.

There are four things you need to include to make your example reproducible: required packages, data, code, and a description of your R environment.

• Packages should be loaded at the top of the script, so it's easy to see which ones the example needs.

• The easiest way to include data in an email is to use dput() to generate

Sharpie

# Load Data
grainData <- read.csv('grainSize.csv', check.names=F, na.strings='--' )

# Calculate Derived Sample Values
grainData[['Phi Diameter']] <- -log2( grainData[['Grain Diameter']] )
totalWeight <- sum( grainData[['Sample Weight']] )
grainData[["Percent Retained"]] <- grainData[['Sample Weight']] / totalWeight
grainData[["Cumulative Percent"]] <- cumsum( grainData[["Percent Retained"]] )
grainData[['Percent Finer']] <- 1 - grainData[['Cumulative Percent']]

stephenturner

# To source this file into an environment to avoid cluttering the global workspace, put this in Rprofile:
# my.env <- new.env(); sys.source("C:/PathTo/THIS_FILE.r", my.env); attach(my.env)

#-----------------------------------------------------------------------
#     Load packages, set options and cwd, set up database connection
#-----------------------------------------------------------------------

## Load packages
# require(ggplot2)  #plotting
# require(plyr)		#data manipulation

benmarwick

# Load Data
grainData <- read.csv('grainSize.csv', check.names=F, na.strings='--' )

# Calculate Derived Sample Values
grainData[['Phi Diameter']] <- -log2( grainData[['Grain Diameter']] )
totalWeight <- sum( grainData[['Sample Weight']] )
grainData[["Percent Retained"]] <- grainData[['Sample Weight']] / totalWeight
grainData[["Cumulative Percent"]] <- cumsum( grainData[["Percent Retained"]] )
grainData[['Percent Finer']] <- 1 - grainData[['Cumulative Percent']]

mrdwab

read.tps = function(data) {
  # Reads the .tps file format produced by TPSDIG 
  # (http://life.bio.sunysb.edu/morph/ into a single data frame
  # USAGE: R> read.tps("filename.tps")
  a = readLines(data) # so we can do some searching and indexing
  LM = grep("LM", a) # find the line numbers for LM
  ID.ind = grep("ID", a) # find the line numbers for ID
  # and the ID values, SCALE values, and image names
  ID = gsub("(ID=)(.*)", "\\2", grep("ID", a, value=T)) 
  SCALE = gsub("(SCALE=)(.*)", "\\2", grep("SCALE", a, value=T)) 

GerHobbelt

# Editor backup files
*.bak
*~

stevenworthington

# ipak function: install and load multiple R packages.
# check to see if packages are installed. Install them if they are not, then load them into the R session.

ipak <- function(pkg){
    new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
    if (length(new.pkg)) 
        install.packages(new.pkg, dependencies = TRUE)
    sapply(pkg, require, character.only = TRUE)
}

stephenturner

## Correlation matrix with p-values. See http://goo.gl/nahmV for documentation of this function
cor.prob <- function (X, dfr = nrow(X) - 2) {
  R <- cor(X, use="pairwise.complete.obs")
  above <- row(R) < col(R)
  r2 <- R[above]^2
  Fstat <- r2 * dfr/(1 - r2)
  R[above] <- 1 - pf(Fstat, 1, dfr)
  R[row(R) == col(R)] <- NA
  R
}

rmflight

pasteLabel <- function(preText, inObj, objName, insLink=TRUE){
	objNum <- inObj[objName]
	
	useText <- paste(preText, objNum, sep=" ")
	if (insLink){
		useText <- paste("[", useText, "](#", objName, ")", sep="")
	}
	useText
}

rjw57

A Bayesian approach to seriation problems in archaeology
http://www.sciencedirect.com/science/article/pii/S0167947303000343

A Bayesian Approach to Ageing Perinatal Skeletal Material from Archaeological Sites: Implications for the Evidence for Infanticide in Roman-Britain
http://www.sciencedirect.com/science/article/pii/S0305440301907769#

Bayesian interpretation of radiocarbon results (thesis)
http://etheses.nottingham.ac.uk/1035/1/359686.pdf

Spatial and Multivariate Analysis, Random Sampling Error, and Analytical Noise: Empirical Bayesian Methods at Teotihuacan, Mexico