1. Basics of R, Ch 2.6-6 Functions Help Managing your Objects
Getting Data into R
Getting Results out of R
© Fall 2004 Don Edwards and the University of South Carolina

2. 2.6 Functions Input arguments; output a new object (often a list)
Required and optional arguments
Built-in R functions
Output can depend on the object type
User-designed functions
Often developed iteratively
Functions are intimidating to write, but you can monitor progress as you build them sequentially. Optional arguments can be skipped—order matters! The output can be a list, though it is often just a single numerical vector.

3. 3 Help help, help.search, ?, ??,apropos
Generates extensive help file with several standard headings
Some of most relevant info is hard to find (e.g., plot())
Examples section is often disappointing
You have to “guess” at the right name. Help file includes Description, Value, Details, Examples, References. Relevant info for plot() is overwhelming. Examples are often arcane.

4. 4 Managing Your Objects search() ls()
attach() and detach() alternately place and remove objects from search path
attach() and source() are useful for reading files and inputting functions
Search() shows directories and order in which R searches them. Attach() puts an object in its own directory. Though attach and source are useful for inputting functions too, though I often copy and paste.

5. 4 Managing Your Objects (cont)
rm() to manage objects
Saving a workspace (.Rdata file)
Work with R in a specified (non-default) workspace by attaching the workspace
Use pos=2 or greater to attach a read-only workspace
Managing objects gets complex
You can attach a workspace to (or just double-click) to add it to the current workspace. It’s easy (and dangerous) to have objects in multiple directories.

6. 5 Getting Data into R 5.1 Creating Data
c(), seq(), rep()
rnorm(), etc
Creating matrices with matrix()
Excel files are much easier to import and export than before (see read.xlsx and write.xlsx in xlsx package)
The first bullet creates objects “by hand”. rnorm() creates data by simulation.

7. 5.2 The read.table() Function
Standardized text data form
brainbod example
as.is=T option
Missing values: NA
Note first row with “missing” column name. as.is can be used to ensure character data is not converted to factor data. Can use na.strings=c() option.

8. 5.2 The read.table() Function (cont)
No row names--header=T generates default integer row names
No (column) names--a couple different options
read.delim() and other variations appear to be more robust
First bullet--First row will have correct number of names now too. Default column names are V1, V2,… Use names()= in R itself or col.names=c() in read.table.

9. 5.3 The scan() Function A typical big-data-set setting
Thousands of records
Multiple lines per record
Variable-width fields
Advanced feature
scan() can also be used for the simplest types of data entry
Y=scan() needs two carriage returns to terminate.

10. 6. Getting Results Out of R
Not a strong point of R
Outputting matrices, data frames
t(), ncol
Recent improvements (write.table, write.xlsx)
round(), signif()
Functions: fix()
postscript and pdf commands save graphs and pictures
Other simple methods involve menu selection and right-clicking
Matrices and data frames are transposed and the number of columns will not be correct. write.table works correctly. For fixing functions, use Notepad window. Functions can be saved as a text file, but we usually work in the opposite direction (text file to function). Other methods for saving objects include right-clicking or menu-driven options.