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output: pdf_document
---
## Manipulating Vectors

Modify the following character vector to keep only street names, then sort and remove duplicates.

```{r}
x <- c("120 Main St", "231 Walnut Grove", "374 Central Pk", "402 Providence Ln", "555 Central Pk")
unique(sort(sub(" ", "", gsub("[0-9]", "", x))))
```

How could you sum all of the numbers between 1 and 1,000 that are evenly divisible by 3 or 5?  What about numbers between 1 and 100,000 divisible by 4, 7, or 13?

```{r}
# sum [1,10] divisible by 3 or 5
3 + 5 + 6 + 9 + 10
sum(unique(c(seq(3,1000,by=3), seq(5,1000,by=5))))
sum(unique(c(seq(4,100000,by=4), seq(7,100000,by=7), seq(13,100000,by=13))))
```

## Manipulating Data Frames

```{r}
size <- 1000
set.seed(475)
x <- data.frame(id=sample(100, size, replace=TRUE),
                visitdate=sample(365*2, size, replace=TRUE)
)
male <- sample(c(0,1,NA), 100, replace=TRUE, prob=c(45, 45, 10))
age <- round(runif(100, 40, 80))

# create male and age columns inside data.frame
# for example - use male[1] where id==1
# convert visitdate to Date field - assume day 0 is 2010-01-01
# order data.frame by id and visitdate
# show females age 75 or older
# display mean age by gender
x <- merge(x, cbind(male, id=seq(100)))
x <- merge(x, cbind(age, id=seq(100)))
x[,'visitdate'] <- as.Date(x[,'visitdate'], origin='2010-01-01')
x <- x[with(x, order(id, visitdate)),]
row.names(x) <- NULL

head(x)
```

## Writing Functions

Celsius to Fahrenheit: $f(x) = (x*9/5) + 32$

Celsius to Kelvin: $f(x) = x + 273.15$

Write a temperature conversion function.  It should take a vector of temperatures, the `from` type, and the `to` type.

```{r}
# test temp function with this data
set.seed(20)
x <- round(rnorm(30, 10, 10))

temp <- function(x, from='C', to='F') {
  if(from == 'F') {
    x <- (x - 32)*5/9
  } else if(from == 'K') {
    x <- x - 273.15
  }
  if(to == 'F') {
    x <- x*9/5 + 32
  } else if(to == 'K') {
    x <- x + 273.15
  }
  x
}

xf <- temp(x, from='C', to='F')
xk <- temp(x, from='C', to='K')
all.equal(temp(xf, from='F', 'K'), xk)
```

## Generating Plots

Given the following data set, use `ggplot2` and `qplot` and create several plots.

```{r}
library(ggplot2)
library(Hmisc)
getHdata(vlbw)

# scatterplot gest VS bwt
qplot(gest, bwt, data=vlbw)

# scatterplot gest VS bwt, add color and shape using variable sex
qplot(gest, bwt, data=vlbw, shape=sex, col=sex)

# boxplot of btw by sex
qplot(sex, bwt, data=vlbw, geom='boxplot')

# scatterplot of gest VS bwt, facet by race
qplot(gest, bwt, data=vlbw, facets=race ~ .)

# scatterplot of gest VS bwt, add regression line
qplot(gest, bwt, data=vlbw, geom=c('point','smooth'), method='lm', formula=y~x)
```