###### Chick weights data set
#1. look at the chickwts data multiple ways including plots
#2. fit a model that explains weight by feed (ANOVA) and keep summary
sumfit <- summary(aov (weight ~ feed, data=chickwts))
# Explore the object. Find the p-values and the group difference estimates.
##### DNase
# data on an elisa assay for DNAse
# 1. look at the DNase data. What is there?
# 2. plot the DNase standard curve with a best-fit line
# 3. make a new data frame with only run 1 data
# 4. make a new data frame with only duplicate 1 data
# 5. depict the runs with different colors, plotting characters, or lines
# 6. make a dataframe and plot of only the OD readings < 0.8 (linear region)
# 7. the sqrt() of conc
# 8. or the density ** 2
# 9. try log transformations
##### Especial para S van Liempd
# imagine the USJudgeRatings data is obtained with mass spec
#1 cluster judges
clusDred <- hclust(dist(USJudgeRatings))
# look at the hclust object and plot it
#2 cluster ratings (hint: you can transpose the data with t())
#3 heatmap(USJudgeRatings)