Microbial Informatics

• No class on Thursday. We will use first hour of Friday's lab to cover Thursday's material and then I will turn you loose to work on the lab material.
• Some of you have gotten comments back from me already about your "Pull Requests". Responding to these will enable you to get a better grade if you can satisfy what I comment on.
• Note that problems 2 and 3 need to be submitted as pull requests to different repositories as indicated in the assignment materials
• My office is 1520A, not 1150, MSRB I [sorry!]
  

• Learn some best practices in using git
• Learn how to integrate R code into R markdown documents using knitr
• Learn to navigate your way around R
• Learn the different types of variables within R

• When: Each commit should represent a single functionality or error fix.
• How ("The seven rules of a great git commit message"):
  1. Separate subject from body with a blank line
  2. Limit the subject line to 50 characters
  3. Capitalize the subject line
  4. Do not end the subject line with a period
  5. Use the imperative mood in the subject line
  6. Wrap the body at 72 characters
  7. Use the body to explain what and why vs. how
• git cheat sheet

• Think of this as a foreign language class.
• There's a lot of vocabulary to learn in R, but a lot of it you can create on your own. E.g. sum command
• Today we will be learning the basic parts of speech
• Later we will learn about sentence structure
• Programming languages have similar parts of speech and sentence structure, it's just a matter of getting the syntax right
• The hard part is getting the thinking right!

• Many ways to customize your version of R
• Get working directory: getwd()
• Set working directory: Files -> More -> Change working directory
• Quit R: q()
• If there is a + instead of > as the prompt: Hit Control+C or esc
• up arrow allows you to cycle through your history (it's also in the top right corner)
• hitting tab from the command line auto-completes command and file names

• google
• ?heatmap
• example(heatmap)
• apropos("heat")

• Embed R code into reports, documents, and slide shows
• Cheat sheet

• , cache=TRUE
• Evaluate it once so that you don't have to waste time redoing the analysis over and over
• Can cause problems as documents get bigger and bigger; may need to compile in command line R

2+2
exp(10)
sin(2*pi)
log(pi)
log2(pi)
log10(pi)
sqrt(pi)
abs(pi)
floor(pi)
ceiling(pi)
5 %% 2

plot(x=1:10,y=(1:10)^2)

    t.test(x=c(1:10),y=c(7:20))

## 
##  Welch Two Sample t-test
## 
## data:  c(1:10) and c(7:20)
## t = -5.435, df = 21.98, p-value = 1.855e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -11.053  -4.947
## sample estimates:
## mean of x mean of y 
##       5.5      13.5

• assignment: <- or =

    x <- 3
    x
    x * 3

• List current variables: ls()
• Remove a varaible: rm(x)
• ProTip: use <- for assignment and = for parameter option values

• Make variable names meaningful don't start with numbers can include periods / underscores try to avoid "reserved" names
• Good variable names are a type of commenting

 officeNumber <- "1520A"
 officeBuilding <- "MSRB I"

• Comments tell you what you meant so that when you come back to it in a year you know what you were doing.

    #this is a comment

• Comments are a required part of your assignments

    x <- 3
    typeof(x)

## [1] "double"

• Numeric/double/integer: counts of things, measurements
• Characters/strings: DNA sequence, amino acids, names
• Logical: is something true or not
• Functions: more complex...

x <- pi
y <- 2
z <- -3

office <- "1520A MSRB I"
grade <- "A"
genome <- "ATGCATCGTCCCGT"

• Note: the the grade value is in quotes. What happens if it is not in quotes?

    x <- TRUE
    y <- FALSE

    !x              # NOT operator
    x && y          # AND operator
    x & y           # bitwise AND operator (mainly for vectors)
    x || y          # OR operator
    x | y           # bitwise OR operator (mainly for vectors)
    x == y          # is equal operator
    x != y          # is not equal operator

    x <- 5
    y <- 3

    x > y          # greater than operator
    x >= y         # greater than or equal to operator
    x < y          # less than operator
    x <= y         # less than  or equal tooperator
    x == y         # is equal to operator
    x != y         # is not equal to operator

    x <- "ATG"
    y <- "CCC"

    x > y          # greater than operator
    x >= y         # greater than or equal to operator
    x < y          # less than operator
    x <= y         # less than  or equal tooperator
    x == y         # is equal to operator
    x != y         # is not equal to operator

    as.numeric(x)
    as.logical(x)
    as.character(x)

• There are other conversions that can be done. How would you figure out which converters are out there?
• Be sure to understand the "side effects" of the conversions

• Continue to work your way through the assignment that is due on Friday
• Read Introduction to Statistics with R (Chapters 1 and 2)
• Will post new homework will come out by the weekend that will be due in two weeks