---
title: "1. Introduction to R "
author: "Sonali Arora"
output:
  BiocStyle::html_document:
    toc: true
    toc_depth: 2
vignette: >
  % \VignetteIndexEntry{1. Introduction to Bioconductor}
  % \VignetteEngine{knitr::rmarkdown}
---
```{r style, echo = FALSE, results = 'asis'}
BiocStyle::markdown()
options(width=100, max.print=1000)
options(useFancyQuotes=FALSE)
knitr::opts_chunk$set(
    eval=as.logical(Sys.getenv("KNITR_EVAL", "TRUE")),
    cache=as.logical(Sys.getenv("KNITR_CACHE", "TRUE")), 
    error=FALSE)
```

Author: Sonali Arora (<a
  href="mailto:sarora@fredhutch.org">sarora@fredhutch.org</a>)<br/ >
Date: 20-22 July, 2015<br />

The material in this course requires R version 3.2.1 and Bioconductor
version 3.2

## R 

- http://r-project.org
- Open-source, statistical programming language; widely used in academia, finance, pharma, . . .
- Core language, 'base' and > 4000 contributed packages
- Interactive sessions, scripts, packages

## Useful Functions in  base R 

- `dir`, `list.files`   
   - List files  
-  `read.table`, `scan`
   - Read Data into R 
- `c`, `factor`, `data.frame`, `matrix`
   - Create vectors , data.frame and matrices to store data
- `summary`, `table`, `xtabs`
   - Summarize or cross-tabulate data.
- `plot`
   -  Plot data to visualize it 
- `match`, `%in%`, `which`
   - find elements of one vector in another.
- `split`, `cut`
   - Split or cut vectors.
- `strsplit`, `grep`, `sub`
   -  Operate on character vectors. 
- `lapply`, `sapply`, `mapply`  
   -  Apply function to elements of lists.
- `t.test`, `lm`, `anova`
   - Compare two or several groups.
- `dist` , `hclust`
   - Cluster Data
- `biocLite`, `install.packages`
   - Install packages in R from online repository
- `traceback`, `debug`, `browser`
   - debug errors 


## Getting help in R 

- ?data.frame  
- methods(lm), methods(class=class(fit))  
- ?"plot<tab>"  
- help(package="Biostrings")  
- vignette(package="GenomicRanges")  
- StackOverflow; R-help mailing list

## Data types in R 

- Vectors 
  - logical, integer, numeric, character, . . .  
  - list() - contains other vectors (recursive)
  - factor(), NA - statistical concepts  
  - Can be named - c(Seattle=1, Portland=2)  
- matrix(), array() - a vector with a 'dim' attribute.  
- data.frame()  
   - like spreadsheets; list of equal length vectors.
   - Homogenous types within a column, heterogenous types across columns.  
- Other classes    
   - more complicated arrangement of vectors.
  - Examples  
     - the value returned by lm(); 
     - the DNAStringSet class used to hold DNA sequences.
  - plain, 'accessor', 'generic', and 'method' functions
- Packages  
  - base, recommended, contributed.  

## R programming concepts 

 - Functions - built-in (e.g., rnorm()); user-defined

```{r}
mean(1:10)
rnorm(1:10)
summary(rnorm(1:10))
```
- Subsetting - logical, numeric, character; 

```{r}
data(iris) 

# find those rows where petal.width is exactly 0.2
iris[iris$Petal.Width==0.2,]

# find those rows where sepal.length is less than 4.5
iris[iris$Sepal.Length < 4.5,]

# find all rows belonging to setosa
setosa_iris = iris[iris$Species=="setosa",]
dim(setosa_iris)
head(setosa_iris)
```

 - Iteration - over vector elements, lapply(), mapply(), apply()

```{r}
# drop the column containing characters i.e., Species 
iris <- iris[,!( names(iris) %in% "Species")]
dim(iris)

# find the mean of the first 4 numerical columns 
lapply(iris, mean) # simpler: colMeans(iris)

# simplify the result 
sapply(iris, mean)

# find the mean for each row. 
apply(iris, 1 , mean) #simpler : rowMeans(iris)
```

## R as a Statistical Computing Environment 

```{r}
# define a vector
x <- rnorm(1000) 

# vectorized calculation
y <- x + rnorm(1000, sd=.8) 

# object construction
df <- data.frame(x=x, y=y)
 
# linear model
fit <- lm(y ~ x, df)
```

## Visualizing Data in R 

```{r}
par(mfrow=c(1,2))
plot(y ~ x, df, cex.lab=2)
abline(fit, col="red", lwd=2)

library(ggplot2)
ggplot(df, aes(x, y)) + 
   geom_point() +
   stat_smooth(method="lm")
```

## `sessionInfo()`

```{r sessionInfo}
sessionInfo()
```