reading external files to R

file.exists("X")	does file/directory X exist?
dir.create("X")	create directory named X
download.file(X, destfile = Y)	download file at URI X to local file named Y
if(!file.exists("X)) {dir.create("X")}	create directory named X if directory X does not exist
	
read.table(X)	read tab-delimited data from file X
read.csv(X)	read comma-delimited data from file X
quote = " "	often fixes quote issues in tab or comma delimited files (optional argument)
na.strings	specifies NA character (eg 999) (optional argument)
skip = X	skip X number of lines at top of file to be read (optional argument)
	
library(xlsx)	library for reading Excel files
read.xlsx("X", sheetIndex = Y, header = T, colIndex = c(a:k), rowIndex = c(l:n))	read in Sheet Y from Excel file X, columns a through k, rows l through n
XLConnect	more powerful alternative to xlsx package for reading in Excel files
	
library(XML)	package for reading XML files
xmlTreeParse(URI, useInternal = T)	parse XML at address URI, using internal structure
xmlRoot(X)	extracts document wrapper from XML file
xmlObject[[1]][[2]]	access  node from xmlObject, second node of the first node
xmlSApply(object, xmlValue)	returns value from between the tags of xml object
htmlTreeParse(URI, useInternal = T)	parse HTML at addres URI, using internal structure
	
/node	top level node (Xpath)
//node	node at any level (Xpath)
node[@attr-name]	node with attribute name (Xpath)
node[@attr-name = 'bob']	node with attributed name equal to bob (Xpath)
xpathSApply(object, "//name", xmlValue)	returns values of element "name" from object 
	
library(jsonlite)	packing for reading json
fromJSON("URI")	reads data in neatly (can have dateframes nested within dataframes)
	
library(data.table)	package that uses C++ for speedy handling of big files
DT <- fread("filename")	fast-reads  filename into data.table format 
DT[,w:=z^2]	adds new variable w, which is the square of variable z, to DT
setkey(DT, x)	sets x as key variable in DT