##Sat Oct  3 10:33:05 PDT 2009
## A more relaxed week :  review of R  for those who would rather
## nail down the basics and postpone the fancy bits about proportional
## hazard for another time.
##
##  PLEASE TALK TO ME before you decide to do this exercise instead of the
##                    proportional hazard exercise.
##
##  This file has a bunch of R exercises with the answers. This is purely
## a review of material that we have coverd earlier. The intention is
## to provide some drills in the hopes of developing some intuition
## and thereby making R code less mysterious
##
##  Read this file into your ~/213/Week7/exercise7.r buffer by telling emacs:
##  C-x i  to "include a file"  and then editting the suggested path to:
##  ~carlm/213/PropHazII/reviewR.r
##
##  The answers are in :
## http://www.demog.berkeley.edu/213/PropHazII/reviewRanswers.r
##
########################################################################

########################################################################
## Question
########################################################################

## 1)  create a vector called "vector1" of integers between 7and32
## can you think of two ways of doing this?



## 2)  create a vector called "vector2" consisting of all of the
## elements of vector1 divided by 3


## 3) what will this expression produce and why?
vector1 * c(1,0)


## 4) crate a vector cs.vector1 whose elements are the cumulative sum
## of the elements of vector1. In other words the ith element of
## cs.vector1 contains the sum the 1..i elements of vector1.




## 5) print to the screen, vector 1 in reverse order:


########################################################################
## 6) "modes" of vectors
########################################################################
## Vectors in R all have a "mode" the possible modes are "numeric",
## "character" and "logical".  Identify the mode of each of the
## following expressions:

1:7
(1:7)*T
(1:7) > T
(1:7)
(-7:T)
T:F
"1":"7"
(1:7) > "T"


########################################################################
##7) Using square brackets [ ] to select elements from objects:
########################################################################
## First create rv2 as below then write the expressions asked for below

rv2<- rnorm(100)  ## creates a vector rv2 which has 100 random normals

##a)  the last 5 elements of rv2


##b)  the largest 5 elements of rv2

##c)  the elements of rv2 which are greater than the 2

##d)  the index (that is the position in the vector) of the 5 largest
##  elements  of rv2


########################################################################
##8)  Arrays/Matricies:
########################################################################
## A vector with a dimension "attribute" is an
##array. If the dimension attribute has length 2, it is also a
##"matrix".

## wirte three different expressions that will create a 20X5 matrix from a
## vector such as rv2 above
##(1) using the matrix() function:

##(2) using the array() function

##(3) by setting the dimension attribute


########################################################################
## 9) Using square bracket selection with matrices:
########################################################################

## use mat1 to do what is asked for below:
mat1<- array(NA,c(10,8))

## change the 8th column of mat1 to be all 8s

## change the 2nd row of mat1 to be 2s BUT do not change the element
## in the 8th column

##  change the 10th row of mat1 to 2,1,2,1 ... including the 8th
##  column

## change every element whose row index is greater than it's column
## index to 5



########################################################################
## 10)Loops
########################################################################
## write a for loop that creates a vector containing the numbers
## between 1 and 1000 which are divisible by 27 (HINT: remember the
## modula operator "%%"


## write a while loop that does the same thing

## create a 10X9 matrix of NA and then use two for loops to populate
## each element of the matrix with product of the row and the column
## number



########################################################################
## 11) Using apply() to operate on rows and columns
########################################################################
## Do the following pointless tasks using the matrix that you produced
## in the previous exercise (a 10X9 matrix where element i,j = i*j

## use apply to find the column sums


## use apply (and selection with [ ] to find the colum sums of rows
## 5-9


## Divide each element of mat by it's collumn mean. In other words,
## write an expression that evaluates to a matrix with the same
## dimensions as mat and with each element being mat[i,j]/mean(mat[,j])



## write an expression that evaluates to a matrix with each element
## divided by the mean of the elements in its COLUMN (Why is this so
## much trickier than it looks)



########################################################################
## 12) factors and logical expressions, the %in% operator and tapply()
########################################################################
## read in the ACS05 data from a few weeks ago 

library(foreign)
acs<-read.dta(file='/data/commons/carlm/ACS05/ipumsACS05.dta')
acs.small<-acs[acs$serial %in% sort(unique(acs$serial))[1:1000],]

## What does this mean?
is.numeric(acs$age)
## and this:
levels(acs$age)
## Would this work to create a numeric age variable?
acs.small$Age<-as.numeric((acs.small$age))
## Hint: nope  what's wrong with it?

acs.small$Age<-as.numeric((acs.small$age)) -1


## write a logical expression that evaluates to true if the
## observation (in acs.small) is under 18 and not a Child of the Head.



## use the expression you just wrote to find the number of people in
## the small sample who are under 18 and NOT listed as Child of Head



## the %in% operator to find the intersection of two vectors
## EXAMPLE: How many households include both a parent and a child of the Head

sum(unique(acs$serial[acs$relate == "Child"]) %in%
 unique(acs$serial[acs$relate == "Parent"]))

## How many Children (of head) live in households that also contain a
## parent of the head?


## How many households contain at least 2 children of the Head


##How many households include at least 2 children
## and a parent of the


## use logical expressions and clever selection with [ ] to find the
## important demographic information asked for below

## Example -- find the mean age difference between Head and Spouse in
##each household that has both

sp.serial<-unique(acs.small$serial[acs.small$relate == "Spouse"])
sel<- acs.small$serial %in% sp.serial 
ageOfHead<-acs.small$Age[sel & acs.small$relate == "Head/Householder"]
ageOfSpouse<-acs.small$Age[sel & acs.small$relate == "Spouse"]
mean(ageOfHead - ageOfSpouse) 

## How many same sex couples are there in the full acs sample

## What is the average number of children in a female headed household


## Assuming that we have a numeric variable called Age
## use tapply() to find the mean age of males and females in acs.small


## use tapply() to find the mean age of spouses


## Use tapply() in addition to clever selection tricks and logical
## expressions to find these interesting demographic fun facts:

## What is the average size of a household that includes both Head and
## Spouse

## Find the median age of the oldest children (of the head) in each hh in the
## acs.small sample