01-intro-to-R.Rmd

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


```{r, echo=FALSE, purl=FALSE, message = FALSE}
source("setup.R")
```

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

> ### Learning Objectives
>
> * Define the following terms as they relate to R: script, function, working directory, assign, object, variable.
> * Call functions with zero or more named or unnamed arguments.
> * Solve mathematical operations in R.
> * Assign values to objects and variables in R.
> * Describe what vectors are and how they are manipulated in R.
> * Inspect the content of vectors in R and manipulate their content.
> * Extract values from vectors in R.
> * Employ logic (i.e. TRUE, FALSE) to subset data in a vector.
> * Analyze vectors with missing data.

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


## Creating objects

```{r, echo=FALSE, purl=TRUE}
### Creating objects in R

```

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

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

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

```{r, purl=FALSE}
weight_kg <- 55
```

`<-` 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`.  For historical reasons, you can also use `=`
for assignments, but not in every context. Because of
the
[slight](http://blog.revolutionanalytics.com/2008/12/use-equals-or-arrow-for-assignment.html) [differences](https://web.archive.org/web/20130610005305/https://stat.ethz.ch/pipermail/r-help/2009-March/191462.html) in
syntax, it is good practice to use always `<-` for assignments.

In RStudio, typing <kbd>Alt</kbd> + <kbd>-</kbd> (push <kbd>Alt</kbd> at the
same time as the <kbd>-</kbd> key) will write ` <- ` in a single keystroke.


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., `weight_kg` is different from `Weight_kg`). There are some names that
cannot be used because they are 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`). If 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 variables, etc.). Using a consistent coding
style makes your code clearer to read for your future self and your
collaborators. In R, two popular style guides
are [Hadley Wickham's](http://adv-r.had.co.nz/Style.html)
and [Google's](https://google.github.io/styleguide/Rguide.xml)
(this [one](http://jef.works/R-style-guide/) is also comprehensive).

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

```{r, purl=FALSE}
weight_kg <- 55    # doesn't print anything
(weight_kg <- 55)  # but putting parenthesis around the call prints the value of `weight_kg`
weight_kg          # and so does typing the name of the object
```

Now that R has `weight_kg` in memory, we can do arithmetic with it. For
instance, we may want to convert this weight in pounds (weight in pounds is 2.2
times the weight in kg):

```{r, purl=FALSE}
2.2 * weight_kg
```

We can also change a variable's value by assigning it a new one:

```{r, purl=FALSE}
weight_kg <- 57.5
2.2 * weight_kg
```

This means that assigning a value to one variable does not change the values of
other variables.  For example, let's store the animal's weight in pounds in a new
variable, `weight_lb`:

```{r, purl=FALSE}
weight_lb <- 2.2 * weight_kg
```

and then change `weight_kg` to 100.

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

What do you think is the current content of the object `weight_lb`? 126.5 or 200?

> ### Comments
>
> The comment character in R is `#`, anything to the right of a `#` in a script
> will be ignored by R. It is useful to leave notes, and explanations in your
> scripts.
> RStudio makes it easy to comment or uncomment a paragraph: after selecting the
> lines you  want to comment, press at the same time on your keyboard
> <kbd>Crtl</kbd> + <kbd>Shift</kbd> + <kbd>C</kbd>.

> ### Challenge
>
> What are the values after each statement in the following?
>
> ```{r, purl=FALSE}
> mass <- 47.5            # mass?
> age  <- 122             # age?
> mass <- mass * 2.0      # mass?
> age  <- age - 20        # age?
> mass_index <- mass/age  # mass_index?
> ```

```{r, echo=FALSE, purl=TRUE}
### Challenge
##
## What are the values after each statement in the following?
##
## mass <- 47.5            # mass?
## age  <- 122             # age?
## mass <- mass * 2.0      # mass?
## age  <- age - 20        # age?
## mass_index <- mass/age  # mass_index?
```

### Functions and their arguments

Functions are "canned scripts" that automate something complicated or convenient
or both.  Many functions are predefined, or can be made available by importing R
*packages* (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:

```{r, eval=FALSE, purl=FALSE}
b <- sqrt(a)
```

Here, the value of `a` is given to the `sqrt()` function, the `sqrt()` function
calculates the square root, and returns the value which is then assigned to
variable `b`. 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 into 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). Some functions take arguments which
may either be specified by the user, or, if left out, take on a *default* value:
these are called *options*. Options are typically used to alter the way the
function operates, such as whether it ignores 'bad values', or what symbol to
use in a plot.  However, if you want something specific, you can specify a value
of your choice which will be used instead of the default.

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

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

Here, we've called `round()` with just one argument, `3.14159`, and it has
returned the value `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', purl=FALSE}
args(round)
```

```{r, eval=FALSE, purl=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', purl=FALSE}
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', purl=FALSE}
round(3.14159, 2)
```

And if you do name the arguments, you can switch their order:

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

It's good practice to put the non-optional arguments (like the number you're
rounding) first in your function call, and to specify the names of all optional
arguments.  If you don't, someone reading your code might have to look up the
definition of a function with unfamiliar arguments to understand what you're
doing.

> ### Call out
>
> What is called `objects` in `R` is known as `variables` in many other programming languages.
> Depending on the context `object` and `variable` can have drastically different meanings.
> However, in this lesson the two words are used synonymously.
> For more information see: https://cran.r-project.org/doc/manuals/r-release/R-lang.html#Objects

## 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 a group of values, mainly either numbers or
characters. 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 animal weights:

```{r, purl=FALSE}
weight_g <- c(50, 60, 65, 82)
weight_g
```

A vector can also contain characters:

```{r, purl=FALSE}
animals <- c("mouse", "rat", "dog")
animals
```

The quotes around "mouse", "rat", etc. are essential here. Without the quotes R
will assume there is a variable called mouse, rat, etc.

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(weight_g)
length(animals)
```

An important feature of a vector, is that all of the elements are the same type of data.
The function `class()` indicates the class (the type of element) of an object:

```{r, purl=FALSE}
class(weight_g)
class(animals)
```

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(weight_g)
str(animals)
```

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

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

What happens here is that we take the original vector `weight_g`, 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 grow a vector, or assemble
a dataset. As we program, this may be useful to add results that we are
collecting or calculating.

We just saw 2 of the 6 **atomic vector** types that R uses: `"character"` and
`"numeric"`. These are the basic building blocks that all R objects are built
from. 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`).


> ### Challenge
>
>
> * We’ve seen that atomic vectors can be of type character,
>   numeric, integer, and logical. But what happens if we try to mix these types in
>   a single vector?
> <!-- * _Answer_: R implicitly converts them to all be the same type -->
>
> * What will happen in each of these examples? (hint: use `class()`
>   to check the data type of your objects):
>
>     ```r
>     num_char <- c(1, 2, 3, 'a')
>     num_logical <- c(1, 2, 3, TRUE)
>     char_logical <- c('a', 'b', 'c', TRUE)
>     tricky <- c(1, 2, 3, '4')
>     ```
>
> * Why do you think it happens?
> <!-- * _Answer_: Vectors can be of only one data type. R tries to convert (coerce)
>   the content of this vector to find a "common denominator". -->
>

> * You've probably noticed that objects of different types get
>   converted into a single, shared type within a vector. In R, we
>   call converting objects from one class into another class
>   _coercion_. These conversions happen according to a hierarchy,
>   whereby some types get preferentially coerced into other
>   types. Can you draw a diagram that represents the hierarchy of how
>   these data types are coerced?
> <!-- * _Answer_: `logical -> numeric -> character <-- logical` -->

```{r, echo=FALSE, eval=FALSE, purl=TRUE}
## We’ve seen that atomic vectors can be of type character, numeric, integer, and
## logical. But what happens if we try to mix these types in a single
## vector?

## What will happen in each of these examples? (hint: use `class()` to
## check the data type of your object)
num_char <- c(1, 2, 3, "a")

num_logical <- c(1, 2, 3, TRUE)

char_logical <- c("a", "b", "c", TRUE)

tricky <- c(1, 2, 3, "4")

## Why do you think it happens?

## You've probably noticed that objects of different types get
## converted into a single, shared type within a vector. In R, we call
## converting objects from one class into another class
## _coercion_. These conversions happen according to a hierarchy,
## whereby some types get preferentially coerced into other types. Can
## you draw a diagram that represents the hierarchy of how these data
## types are coerced?

```



## Subsetting vectors

If we want to extract one or several values from a vector, we must provide one
or several indices in square brackets. For instance:

```{r, results='show', purl=FALSE}
animals <- c("mouse", "rat", "dog", "cat")
animals[2]
animals[c(3, 2)]
```

We can also repeat the indices to create an object with more elements than the
original one:

```{r, results='show', purl=FALSE}
more_animals <- animals[c(1, 2, 3, 2, 1, 4)]
more_animals
```

R indexes start at 1. Programming languages like Fortran, MATLAB, and R start
counting at 1, because that's what human beings typically do. Languages in the C
family (including C++, Java, Perl, and Python) count from 0 because that's
simpler for computers to do.

### Conditional subsetting

Another common way of subsetting is by using a logical vector: `TRUE` will
select the element with the same index, while `FALSE` will not:

```{r, results='show', purl=FALSE}
weight_g <- c(21, 34, 39, 54, 55)
weight_g[c(TRUE, FALSE, TRUE, TRUE, FALSE)]
```

Typically, these logical vectors are not typed by hand, but are the output of
other functions or logical tests. For instance, if you wanted to select only the
values above 50:

```{r, results='show', purl=FALSE}
weight_g > 50    # will return logicals with TRUE for the indices that meet the condition
## so we can use this to select only the values above 50
weight_g[weight_g > 50]
```

You can combine multiple tests using `&` (both conditions are true, AND) or `|`
(at least one of the conditions is true, OR):

```{r, results='show', purl=FALSE}
weight_g[weight_g < 30 | weight_g > 50]
weight_g[weight_g >= 30 & weight_g == 21]
```

Here, `<` stands for "smaller than", `>` or "bigger than", `>=` for "bigger or
equal to", and `==` for "equal to". The double equal sign `==` should not be
confused with the single `=` sign, which assigns to a variable (roughly speaking
the same as "`<-`").

A common task is to search for certain strings in a vector. One could use the "or"
operator `|` but this can become quickly tedious. The function `%in%` allows you to test
if a value is found in a vector:

```{r, results='show', purl=FALSE}
animals <- c("mouse", "rat", "dog", "cat")
animals[animals == "cat" | animals == "rat"] # returns both rat and cat
animals %in% c("rat", "cat", "dog", "duck")
animals[animals %in% c("rat", "cat", "dog", "duck")]
```

> ### Challenge (optional){.challenge}
>
> * Can you figure out why `"four" > "five"` returns `TRUE`?

```{r, echo=FALSE, purl=TRUE}
### Challenge (optional)
##
## * Can you figure out why `"four" > "five"` returns `TRUE`?
```

<!--
```{r, purl=FALSE}
## Answers
## * When using ">" or "<" on strings, R compares their alphabetical order. Here
##   "four" comes after "five", and therefore is "greater than" it.
```
-->


## Missing data

As R was designed to analyze datasets, it includes the concept of missing data
(which is uncommon in other programming languages). Missing data are represented
in vectors as `NA`.

When doing operations on numbers, most functions will return `NA` if the data
you are working with include missing values. This feature
makes it harder to overlook the cases where you are dealing with missing data.
You can add the argument `na.rm=TRUE` to calculate the result while ignoring
the missing values.

```{r, purl=FALSE}
heights <- c(2, 4, 4, NA, 6)
mean(heights)
max(heights)
mean(heights, na.rm = TRUE)
max(heights, na.rm = TRUE)
```

If your data include missing values, you may want to become familiar with the
functions `is.na()`, `na.omit()`, and `complete.cases()`. See below for
examples.


```{r, purl=FALSE}
## Extract those elements which are not missing values.
heights[!is.na(heights)]

## Returns the object with incomplete cases removed. The returned object is atomic.
na.omit(heights)

## Extract those elements which are complete cases.
heights[complete.cases(heights)]
```

> ### Challenge
>
> 1. Using this vector of length measurements, create a new vector with the NAs
> removed.
>
>     ```r
>     lengths <- c(10,24,NA,18,NA,20)
>     ```
>
> 2. Use the function `median()` to calculate the median of the `lengths` vector.


```{r, purl=TRUE, echo=FALSE}
## ### Challenge
## 1. Using this vector of length measurements, create a new vector with the NAs
## removed.
##
##    lengths <- c(10,24,NA,18,NA,20)
##
## 2. Use the function `median()` to calculate the median of the `lengths` vector.
```

Now that we have learned how to write scripts, and the basics of R's data
structures, we are ready to start working with the Portal dataset we have been
using in the other lessons, and learn about data frames.