Basic graphics

References

• An R Companion to Applied Regression John Fox http://socserv.mcmaster.ca/jfox/Books/Companion/index.html
• Low birthweight dataset explanation: http://www.umass.edu/statdata/statdata/data/lowbwt.txt
• R Graphics: http://www.stat.auckland.ac.nz/~paul/RG2e/
• Lattice: http://lmdvr.r-forge.r-project.org/figures/figures.html
• ggplot2 (unofficial): http://www.cookbook-r.com/Graphs/

Prepare data

## Load dataset directly from the internet
lbw <- read.table("http://www.umass.edu/statdata/statdata/data/lowbwt.dat", head = T, skip = 4)

## "Fix" cases 10 and 39 to make them identical to BIO 213 dataset
lbw[c(10,39),"BWT"] <- c(2655,3035)

## Change variable names to lower cases
names(lbw) <- tolower(names(lbw))

## Recoding using within function
lbw <- within(lbw, {

    ## Relabel race: 1, 2, 3 -> White, Black, Other
    race.cat <- factor(race, levels = 1:3, labels = c("White","Black","Other"))

    ## Dichotomize ptl
    preterm  <- factor(ptl >= 1, levels = c(FALSE,TRUE), labels = c("0","1+"))
    ## You can alse use cut
    ## preterm  <- cut(ptl, breaks = c(-Inf, 0, Inf), labels = c("0","1+"))

    ## Change 0,1 binary to No,Yes binary
    smoke    <- factor(smoke, levels  = c(0,1), labels = c("No","Yes"))
    ht       <- factor(ht   , levels  = c(0,1), labels = c("No","Yes"))
    ui       <- factor(ui   , levels  = c(0,1), labels = c("No","Yes"))
    low      <- factor(low  , levels  = c(0,1), labels = c("No","Yes"))

    ## Categorize ftv (frequency of visit): 0   1   2   3   4   6 -> None(0), Normal(1-2), Many (>= 3)
    ftv.cat  <- cut(ftv, breaks = c(-Inf, 0, 2, Inf), labels = c("None","Normal","Many"))

    ## Make Normal the reference level
    ftv.cat  <- relevel(ftv.cat, ref = "Normal")
})