Getting to Know R

Getting ready for the course

1. Download Intro_to_R.tar from https://ki-data.mit.edu/bcc/teaching/Intro_to_R/ onto Desktop.

2. Un-compress Intro_to_R.tar to a folder "Intro_to_R"

3. Open RStudio

4. Under the File menu, click on New project, choose Existing Directory

5. Use Browse to locate to the "Intro_to_R" folder and then Create Project This will be your working directory for the rest of the day (e.g. ~/Desktop/Intro_to_R)

6. Create a new R script (File > New File > R script) and save it in your working directory (e.g. intro_to_R.R). Here, you can type all the commands we run during the course, and save it for later reference.

Using R as a calculator

> 1+2
[1] 3
> 1+2*3+4*5
[1] 27

R is good at statistics

> group1<-c(4.5,4.7,4.2,4.8,3.9)
> group2<-c(6.0,5.9,5.8,5.5,6.2)
> t.test(group1,group2)

        Welch Two Sample t-test

data:  group1 and group2
t = -7.2281, df = 7.1573, p-value = 0.0001556
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -1.9355112 -0.9844888
sample estimates:
mean of x mean of y 
     4.42      5.88 

The R syntax from an example script

# Load libraries
library(Biobase)
library(limma)
library(ggplot2)

# Setup directory variables
baseDir <- getwd()
dataDir <- file.path(baseDir, "data")

# Load data
design_dat <- read.table(file.path(dataDir, 'mouse_exp_design.csv'), header=T, sep=",", row.names=1)

*The above snippet of R code has many different “parts of speech” for R (syntax):
*the comments # and how they are used to document function and its content
*the assignment operator <-
*variables and functions
*the = for arguments for functions