Statistics 200: Lab 2 (Friday 12 September)

Getting Census data (using Netscape or other internet browser)

1. select 1990 Census Lookup;
2. select STF3A;
3. select Connecticut, and check the State-County level;
4. select New Haven County, and check State-County-County Subdivision;
5. check 'select/retrieve all ...';
6. (don't stare at the page for too long);
7. check table P13;
8. look at it in HTML;
9. back; then check 'tab-delimited format' ...
10. You should now be looking at a mess. Choose 'save-as' from the file menu, then save the mess as a text file called c:\user\NHage.txt

Getting data into Splus

> age <- read.table("NHage.txt")   # shouldn't work
> count.fields("NHage.txt")     # here's why
> age_read.table("NHage.txt",sep="\t")  #almost, but header messed up
> age_read.table("NHage.txt",sep="\t",header=T)

You now have a data frame. It acts like both a matrix and a list.

Save the first five rows of the data frame as age5. Use age5 as the test case for the first problem. It is a good idea to experiment with small data sets whenever you are trying to get a function to work.

Try dim(age), dimnames(age). Try running attributes() on each column of the data frame. (You can select columns by name or number, as for matrices, or by using the $ notation for lists.) Try

> lapply(age,attributes)       
 # applies the attributes() function
 # to each component of the data frame

Problem 1

Build yourself a function that takes a data frame as argument, and carries out the following operations. Assume that you will be feeding in a data frame whose rows have labels like "Ansonia town", ...

• Kill the first three columns.
• Create vectors under18 (sum the first twelve columns) and over18 (the remaining columns), giving the populations for each town under and over the age of 18 years. (Hint: ?apply or HELP)
• Strip the characters " town" from the end of each row name. (Hint: ?nchar, ?substring)
• Return a new data frame, with the stripped town names as row labels, and columns called under18, over18.

Run your function, saving the output as age.split.

Problem 2

Problem 3

Important HELP concerning saving your work.

Where Splus finds things

 > x <- 3 
> foo_function(y){  x<- 15;  y + x  } 
> foo(4) 
[1] 19 
> foo(x) 
[1] 18
> x 
[1] 3 

I assume you still have the data frame age.split lying around. Try

> search()
# This function returns the list of directories, 
# in order, where Splus looks for objects.  
# It is called the "search path".
> objects()
> attach(age.split,1)
# puts the data frame age in the first position
# on the search list
> total = under18 + over18
> search()
> objects()
> detach(1,save="junk")
> objects()
> junk

Note: If you create an object called foo in your working directory, and if there is another object called foo further down in the search list, Splus will find your foo first. Your foo object masks the other foo object. Be careful that you don't accidently mask a system object, such as the t() function or the c() function. You should take heed of any warnings about masking. See ?masked.

Evaluation frames

Libraries

We have created a special library for Statistic 200 at

H:\\courses\\stat200

The S-plus command

library(lib.loc="h:\\classes\\stat200") 

libary(help=nci,lib.loc="h:\\classes\\stat200") 

library(nci,lib.loc="h:\\classes\\stat200")