(

post under construction)
This post is very generic cruft on basic data analysis designed around combining data with :

- cbind()
- rbind()
- matrix()
- as.matrix()
- data.frame()
- as.data.frame()

and visualizing data with the

* lattice *graphics package.

**Some quick and dirty notes on learning R**. I have found virtual libraries exist both on and offline for learning R. However, I have also found that R is a peculiar and specific language. I would compare the semantics most to SQL, but somehow that comparison stops being useful quickly. Ironically, given the power of R language quantitative analysis, I have found the user really wants to get the "feel of R" inside his forearms to become useful and self-confident.

**Spending time manipulating and re-organizing data is essential at each step of your curriculum in learning R. **Functionally, R is a mathematical platform and benefits from domain specific packages and knowledge. But the R language is also a unique engine type with programmable limits. R does certain functionality very well. Other functionality perhaps more typical of many programming languages is simply outside the subset of R. There is art to successful use of R. There is an important 'R' mentality that only serious practice will enjoin.

This post follows from my last post on combining data for analysis. I am using BEA, BLS, and Census data to understand 20 year macro-economic flows. Some examples on how to use cbind, rbind, matrix, as.data.frame commands to re-organize this data are

here. Below are some functions I have created to help explicate the data set ('dd'). They are slightly more concise/useful than the function 'str(dd)'. The user will recall that I have

concatenated data from mulitple sets into one

* dataframe*. I have used prefixes (BLS,PI,NS) designate "

Bureau of Labor Statistics", (

BEA) Personal Income, (

BEA) Net (residential) Stock.