Microbial Informatics

• When you upload your assignments, upload the README.Rmd and README.md files generated by RStudio/knitr
• Will use first hour tomorrow to cover subject material and second hour to help people with assignment
• Start thinking about your project:
  • Emphasis on data analysis
  • Due 10/24/2104 (friday)
  • Feel free to come to office hours to discuss project ideas
  • I have some ideas for microbial ecology analysis projects

• Everything in R is some form of a vector - even output
• R has a rich set of descriptive statistics that can be used to simplify datasets

• Histograms
• Box plots
• Bar plots
• Strip charts

metadata <- read.table(file = "wild.metadata.txt", header = T)
rownames(metadata) <- metadata$Group
metadata <- metadata[, -1]

• This is a huge area of explorataion
• R is tremendously powerful for generating plots and data visualizaiton tools
• Can generally tell someone used MS Excel by how bad the plots look
• Can certainly generate crap in R, but upside is greater
• Numerous packages available, but we will focus on base package until the end of the semester:
  • Lattice
  • ggplot2
  • rgl

• Good for summarizing continuous data where you want to break it into discrete classes
• What do these two sets of commands do?

par(mfrow = c(2, 1))  # make two plotting windows
hist(metadata$Weight[metadata$Sex == "F"])
hist(metadata$Weight[metadata$Sex == "M"])
par(mfrow = c(1, 1))  # return to one plotting window

par(mfrow = c(2, 1))
hist(metadata$Weight[metadata$Sex == "F"], breaks = 10, ylim = c(0, 20), xlim = c(0, 
    30))
hist(metadata$Weight[metadata$Sex == "M"], breaks = 10, ylim = c(0, 20), xlim = c(0, 
    30), add = T)
par(mfrow = c(1, 1))

hist(metadata$Weight[metadata$Sex == "F"], breaks = 10, ylim = c(0, 20), xlim = c(0, 
    30), col = "pink")
hist(metadata$Weight[metadata$Sex == "M"], breaks = 10, col = "blue", add = T)

• Axis labels

hist(metadata$Weight, xlab = "Weights of Peromyscus spp.")

• Plot title

hist(metadata$Weight, main = "Distribution of Peromyscus spp. weights")

• Putting it together

hist(metadata$Weight, main = "Distribution of Peromyscus spp. weights", xlab = "Weights of Peromyscus spp.")
box()

m.hist <- hist(metadata$Weight[metadata$Sex == "F"], breaks = 10, ylim = c(0, 
    20), xlim = c(0, 30), col = "pink")
f.hist <- hist(metadata$Weight[metadata$Sex == "M"], breaks = 10, col = "blue", 
    add = T)

• Multiple histograms gets pretty cludgey
• Box plots allows you to plot out the results of the summary command

boxplot(metadata$Weight)  #\tmin, 25%tile, Median, 75%tile, max, outliers
boxplot(metadata$Weight[metadata$Sex == "F"], metadata$Weight[metadata$Sex == 
    "M"])
boxplot(metadata$Weight ~ metadata$Sex)

avg.weights <- aggregate(Weight~Sex, data=metadata, mean)
barplot(height=avg.weights$Weight, names.arg=avg.weights$Sex)

counts <- table(metadata$Sex,metadata$SP)
counts
barplot(counts)
barplot(counts, beside=T)

• Perhaps we don't have a ton of points and we want to see all of the data (think of animal experiments)

stripchart(metadata$Weight)
stripchart(metadata$Weight, method = "jitter")
stripchart(metadata$Weight ~ metadata$Sex, method = "jitter")
stripchart(metadata$Weight ~ metadata$Sex, method = "jitter", vertical = T)
stripchart(metadata$Weight ~ metadata$Sex, method = "jitter", jitter = 0.02, 
    vertical = T)
stripchart(metadata$Weight ~ metadata$Sex, method = "jitter", jitter = 0.02, 
    vertical = T, pch = 19)

stripchart(metadata$Weight ~ metadata$Sex, method = "jitter", jitter = 0.02, 
    vertical = T, pch = c(18, 19), col = c("red", "blue"))

• Assignment due Friday
• Read Introduction to Statistics with R (Chapter 8)