1. Data Exploration
2. ggplot2

If you have to repeat the same few lines of code more than once, then you really need to write a function. Functions are a fundamental building block of R. You use them all the time in R and it's not that much harder to string functions together (or write entirely new ones from scratch) to do more.

• R functions are objects just like anything else.
• By default, R function arguments are lazy - they're only evaluated if they're actually used:
• Every call on a R object is almost always a function call.

• The body(), the code inside the function.
• The formals(), the "formal" argument list, which controls how you can call the function.
• The environment()` which determines how variables referred to inside the function are found.
• args() to list arguments.

f <- function(x) x
f

## function(x) x

formals(f)

## $x

environment(f)

## <environment: R_GlobalEnv>

Question: How do we delete this function from our environment?

Variables defined inside functions exist in a different environment than the global environment. However, if a function is not defined inside one, it will look one level above.

x <- 2
g <- function() {
    y <- 1
    c(x, y)
}
g()

## [1] 2 1

first <- function(x, y) {
    z <- x + y
    return(z)
}

first(5, 7)

## [1] 12

• 1. Create a function that takes in two arguments, x and y, and computes the x² * y
• 2 Create a function that takes in three arguments, and makes a vector from the result
• 3. Create a function that counts the number of matching items. Hint: use %in% to create a logical statement

add <- function(a, b) {
    return(a + b)
}
vector <- c(3, 4, 5, 6)

add(vector[[1]], vector[[2]])

## [1] 7

x <- 5
f <- function() {
    y <- 10
    c(x = x, y = y)
}
f()

##  x  y 
##  5 10

temp <- function(a = 1, b = 2) {
    return(a + b)
}

temp()

## [1] 3

temp(5, 6)

## [1] 11

• Write a function that takes in a vector and multiplies the sum of the vector by 10. Return a logical statement based on whether the sum is under 1000.
• Write a function that calculates the mean of every column in a dataframe. Have it break gracefully if the column are not numbers, using class(x) == "numeric". Try your function on the iris dataset

f <- function(x) {
    if (x < 10) {
        0
    } else {
        10
    }
}
f(5)

## [1] 0

f(15)

## [1] 10

It's easy to generate reports dynamically in R.

Basic idea: Write data + software + documentation (or in this case manuscripts, reports) together.

Literate programming involves with three main steps:

1. Parse the source document and separate code from narratives.
2. Execute source code and return results.
3. Mix results from the source code with the original narratives.

Results from scientific research have to be reproducible to be trustworthy. We do not want a finding to be merely due to an isolated occurrence. Instill confidence, share data, results and scripts using github.

An incredibly simple semantic file format.Markdown makes it easy for even those without a web-publishing background to write any sort of text (including with links, lists, bullets, etc.) and have it displayed in a variety of formats.

• Markdown cheatsheet
• Original markdown reference

In RStudio, choose new R Markdown file (easiest way) or create a new file and save it with extension .Rmd.

```r
# some R code
```

Hit the Knit HTML button

What just happened?

knitr reads the Rmd file, finds and runs the code chunks identified by the backticks, and replaces it with the output of the functions.

Knitr will auto make tables, graphs, and even slides.

Everything in this course was made by typing into Rstudio and using knitr. Period. It is a powerful tool.

Make your life easier, safer, and more fun.

• Setup a GitHub account here.
• Download and install Rstudio.
• Download and install the platform-specific version of Git (not GitHub), default options work well.
• Configure Git with global commands. I have found this step necessary both times I ran through this process. Open up the bash version of Git and type the following:
  

git config –global user.name “your GitHub account name” git config –global user.email “your email”

• Open Rstudio and set the path to Git executable. Go to Tools > Options > Git/SVN
  

A repository is the location and name for all the files associated with a particular project. The first step is to log into your GitHub account and create a new repository. Make sure you check the box ‘Initialize this repository with a README.’

• Open Rstudio and go to Project > Create Project > Version Control > Git and paste the url in the github repo

• Now do some work in your new R project and create and save some files. The next step is to ‘commit’ your work – essentially making a copy of all of your script files (i.e., .R files) associated with the R project.

*Commit your work, give it some description

*push to the web.

sleep easy.