01-intro-to-R.Rmd

---
layout: topic
title: Introduction to R
author: Data Carpentry contributors
minutes: 45
---

```{r, echo=FALSE, purl=FALSE}
knitr::opts_chunk$set(results='hide', fig.path='img/r-lesson-')
```

------------

> ## Learning Objectives
>
> * Familiarize yourself with R syntax
> * Understand the concepts of objects and assignment
> * Understand the concepts of vector and data types
> * Get exposed to a few functions

------------

## The R syntax

<!--
_Start by showing an example of a script_

* Point to the different parts:
  - a function
  - the assignment operator `<-`
  - the `=` for arguments
  - the comments `#` and how they are used to document function and its content
  - the `$` operator
* Point to indentation and consistency in spacing to improve clarity


## Creating objects

```{r, echo=FALSE, purl=TRUE}
### Creating objects (assignments)
```
-->

You can get output from R simply by typing in math in the console

```{r, purl=FALSE}
3 + 5
12/7
```

We can also comment what it is we're doing

```{r, purl=FALSE}
# I am adding 3 and 5. R is fun!
3+5
```

What happens if we do that same command without the # sign in the front?

```{r, purl=FALSE, eval=FALSE}
I am adding 3 and 5. R is fun!
3+5
```

Now R is trying to run that sentence as a command, and it 
doesn't work. Now we're stuck over in the console. The 
`+` sign means that it's still waiting for input, so we 
can't type in a new command. To get out of this type `Esc`. This will work whenever you're stuck with that `+` sign. 

It's great that R is a glorified caluculator, but obviously
we want to do more interesting things. 

To do useful and interesting things, we need to assign _values_ to
_objects_. To create objects, we need to give it a name followed by the
assignment operator `<-` and the value we want to give it.

## Assignment operator


For instance, instead of adding 3 + 5, we can assign those
values to objects and then add them.

```{r, purl=FALSE}
# assign 3 to a
a <- 3
# assign 5 to b
b <- 5

# what now is a
a
# what now is b
b

#Add a and b
a + b
```

`<-` is the assignment operator. It assigns values on the right to objects on
the left. So, after executing `x <- 3`, the value of `x` is `3`. The arrow can
be read as 3 **goes into** `x`.  You can also use `=` for assignments but not in
all contexts so it is good practice to use `<-` for assignments. `=` should only
be used to specify the values of arguments in functions, see below.

In RStudio, typing `Alt + -` (push `Alt`, the key next to your space bar at the
same time as the `-` key) will write ` <- ` in a single keystroke.


### Exercise 

- What happens if we change `a` and then re-add `a` and `b`?
- Does it work if you just change `a` in the script and then add `a` and `b`? Did you still get the same answer after they changed `a`? If so, why do you think that might be?
- We can also assign a + b to a new variable, `c`. How would you do this?

## Notes on objects

Objects can be given any name such as `x`, `current_temperature`, or
`subject_id`. You want your object names to be explicit and not too long. They
cannot start with a number (`2x` is not valid but `x2` is). R is case sensitive
(e.g., `genome_length_mb` is different from `Genome_Length_MB`). There are some names that
cannot be used because they represent the names of fundamental functions in R
(e.g., `if`, `else`, `for`, see
[here](https://stat.ethz.ch/R-manual/R-devel/library/base/html/Reserved.html)
for a complete list). In general, even if it's allowed, it's best to not use
other function names (e.g., `c`, `T`, `mean`, `data`, `df`, `weights`). In doubt
check the help to see if the name is already in use. It's also best to avoid
dots (`.`) within a variable name as in `my.dataset`. There are many functions
in R with dots in their names for historical reasons, but because dots have a
special meaning in R (for methods) and other programming languages, it's best to
avoid them. It is also recommended to use nouns for variable names, and verbs
for function names. It's important to be consistent in the styling of your code
(where you put spaces, how you name variable, etc.). In R, two popular style
guides are [Hadley Wickham's](http://adv-r.had.co.nz/Style.html) and
[Google's](https://google-styleguide.googlecode.com/svn/trunk/Rguide.xml).

When assigning a value to an object, R does not print anything. You can force it to
print the value by using parentheses or by typing the name:


## Functions

The other key feature of R are functions. These are R's built in capabilities. Some examples of these are mathematical functions, like
`sqrt` and `round`. You can also get functions from libraries (which we'll talk about in a bit), or even write your own. 

## Functions and their arguments

Functions are "canned scripts" that automate something complicated or convenient
or both.  Many functions are predefined, or become available when using the
function `library()` (more on that later). A function usually gets one or more
inputs called *arguments*. Functions often (but not always) return a *value*. A
typical example would be the function `sqrt()`. The input (the argument) must be
a number, and the return value (in fact, the output) is the square root of that
number. Executing a function ('running it') is called *calling* the function. An
example of a function call is:

`sqrt(a)`

Here, the value of `a` is given to the `sqrt()` function, the `sqrt()` function
calculates the square root. This function is very simple, because it takes just one
argument. 

The return 'value' of a function need not be numerical (like that of `sqrt()`),
and it also does not need to be a single item: it can be a set of things, or
even a data set. We'll see that when we read data files in to R. 

Arguments can be anything, not only numbers or filenames, but also other
objects. Exactly what each argument means differs per function, and must be
looked up in the documentation (see below). If an argument alters the way the
function operates, such as whether to ignore 'bad values', such an argument is
sometimes called an *option*.

Most functions can take several arguments, but many have so-called *defaults*.
If you don't specify such an argument when calling the function, the function
itself will fall back on using the *default*. This is a standard value that the
author of the function specified as being "good enough in standard cases". An
example would be what symbol to use in a plot. However, if you want something
specific, simply change the argument yourself with a value of your choice.

Let's try a function that can take multiple arguments `round`.

```{r, results='show'}
round(3.14159)
```

We can see that we get `3`. That's because the default is to round
to the nearest whole number. If we want more digits we can see 
how to do that by getting information about the `round` function.
We can use `args(round)` or look at the
help for this function using `?round`.

```{r, results='show'}
args(round)
```

```{r, eval=FALSE}
?round
```


We see that if we want a different number of digits, we can 
type `digits=2` or however many we want.

```{r, results='show'}
round(3.14159, digits=2)
```

If you provide the arguments in the exact same order as they are defined you don't have to name them:

```{r, results='show'}
round(3.14159, 2)
```

However, it's usually not recommended practice because it's a lot of remembering
to do, and if you share your code with others that includes less known functions
it makes your code difficult to read. (It's however OK to not include the names
of the arguments for basic functions like `mean`, `min`, etc...)

Another advantage of naming arguments, is that the order doesn't matter.
This is useful when there start to be more arguments. 


### Exercise

We're going to work with genome lengths.
- Create a variable `genome_length_mb` and assign it the value `4.6`


```{r, purl=FALSE}
(genome_length_mb <- 4.6)
genome_length_mb
```

### Exercise

Now that R has `genome_length_mb` in memory, we can do arithmetic with it. For
instance, we may want to convert this to the weight of the genome in 
picograms (for some reason). 978Mb = 1picogram. Divide the 
genome length in Mb by 978. 


```{r, purl=FALSE}
genome_length_mb / 978.0
```

It turns out an E. coli genome doesn't weigh very much.

### Exercise

We can also change the variable's value by assigning it a new one. Say
we want to think about a human genome rather than E. coli. Change `genome_length_mb` to 3000 and figure out the weight of the human genome. 


```{r, purl=FALSE}
genome_length_mb <- 3000.0
genome_length_mb / 978.0
```


This means that assigning a value to one variable does not change the values of
other variables.  For example, let's store the genome's weight in a variable.

```{r, purl=FALSE}
genome_weight_pg <- genome_length_mb / 978.0
```

and then change `genome_length_mb` to 100.

```{r, purl=FALSE}
genome_length_mb <- 100
```

### Exercise

What do you think is the current content of the object `genome_weight_pg`? 3.06 or 0.102?


## Creating your own functions

Instead of having to remember that 978Mb = 1 picogram we could create a function to convert genome lengths to weights. The R code below defines a function to do this job.  

```{r, purl=FALSE}
genome_weight_from_length <- function(genome_length_mb){
	genome_length_mb / 978
}
```

Once we create this function we can get a genome's weight if we have its length by typing the name of our function and then the length in brackets.

```{r,purl=FALSE}
	# For the human genome
genome_weight_from_length(3000)
```


### Challenge

Create a function to calculate genome lengths in Mb from genome weights in picograms.  Test that your function works by using it to calculate the length of the human genome given its weight `3.07` picograms.


## Vectors and data types

```{r, echo=FALSE, purl=TRUE}
### Vectors and data types
```

A vector is the most common and basic data structure in R, and is pretty much
the workhorse of R. It's basically just a list of values, mainly either numbers or
characters. They're special lists that you can do math with. You can assign this list of values to a variable, just like you
would for one item. For example we can create a vector of genome lengths:

```{r, purl=FALSE}
glengths <- c(4.6, 3000, 50000)
glengths
```

A vector can also contain characters:

```{r, purl=FALSE}
species <- c("ecoli", "human", "corn")
species
```

There are many functions that allow you to inspect the content of a
vector. `length()` tells you how many elements are in a particular vector:

```{r, purl=FALSE}
length(glengths)
length(species)
```

These two vectors naturally fit together. We can formally associate the species names with their genome lengths using a named vector.

```{r, purl=FALSE}
names(glengths) <- species
```

You can also do math with whole vectors. For instance if we wanted to multiply the genome lengths of all the genomes in the list, we can do

```{r, purl=FALSE}
5 * glengths
```

or we can add the data in the two vectors together

```{r, purl=FALSE}
new_lengths <- glengths + glengths
new_lengths
```

This is very useful if we have data in different vectors that we 
want to combine or work with. 

Many functions in R accept vectors as arguments and operate accordingly.  For example we could use the function we created earlier to calculate genome weights to calculate a vector of genome weights given a vector of genome lengths.

```{r, purl=FALSE}
genome_weight_from_length(glengths)
```

There are few ways to figure out what's going on in a vector. 

`class()` indicates the class (the type of element) of an object:

```{r, purl=FALSE}
class(glengths)
class(species)
```

The function `str()` provides an overview of the object and the elements it
contains. It is a really useful function when working with large and complex
objects:

```{r, purl=FALSE}
str(glengths)
str(species)
```

You can add elements to your vector simply by using the `c()` function:

```{r, purl=FALSE}
lengths <- c(glengths, 90) # adding at the end
lengths <- c(30, glengths) # adding at the beginning
lengths
```

What happens here is that we take the original vector `glengths`, and we are
adding another item first to the end of the other ones, and then another item at
the beginning. We can do this over and over again to build a vector or a
dataset. As we program, this may be useful to autoupdate results that we are
collecting or calculating.

We just saw 2 of the 6 **data types** that R uses: `"character"` and
`"numeric"`. The other 4 are:       

* `"logical"` for `TRUE` and `FALSE` (the boolean data type)
* `"integer"` for integer numbers (e.g., `2L`, the `L` indicates to R that it's an integer)
* `"complex"` to represent complex numbers with real and imaginary parts (e.g.,
  `1+4i`) and that's all we're going to say about them
* `"raw"` that we won't discuss further

Vectors are one of the many **data structures** that R uses. Other important
ones are lists (`list`), matrices (`matrix`), data frames (`data.frame`) and
factors (`factor`).


## Solutions

```{r,purl=FALSE}
genome_length_from_weight <- function(genome_weight_picograms){
	genome_weight_picograms * 978
}

```