01-intro-to-R.Rmd

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


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

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

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

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


## 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
```

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`). 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 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?

### 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?
```

## 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
```

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


* **Question**: 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 -->

* **Question**: 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')
```

* **Question**: 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". -->

* **Question**: Can you draw a diagram that represents the hierarchy of the data
  types?
<!-- * _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?

## Can you draw a diagram that represents the hierarchy of the data
## types?
```


## 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]
```

When working with vectors of characters, if you are trying to combine many
conditions it can become tedious to type. 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 {.challenge}
>
> * Can you figure out why `"four" > "five"` returns `TRUE`?

```{r, echo=FALSE, purl=TRUE}
# * 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`.

```{r, purl=FALSE}
planets <- c("Mercury", "Venus", "Earth", "Mars", "Jupiter", "Saturn", "Uranus",
             "Neptune", NA)
```

When doing operations on numbers, most functions will return `NA` if the data
you are working with include missing values. It is a safer behavior as otherwise
you may overlook that 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

* **Question**: Why does the following piece of code give an error message?
```{r, purl=FALSE}
sample <- c(2, 4, 4, "NA", 6)
mean(sample, na.rm = TRUE)
```
<!-- * _Answer_: Because R recognizes the NA in quotes as a character. -->

* **Question**: Why does the error message say the argument is not numeric?
<!-- * _Answer_: R converts the entire vector to character because of the "NA", and doesn't recognize it as numeric. -->

Next, we will use the "surveys" dataset to explore the `data.frame` data
structure, which is one of the most common types of R objects.