03-data-structures-part1.Rmd

---
layout: page
title: R for RNAseq analysis
subtitle: Data structures
minutes: 90
---

```{r, include=FALSE}
source("tools/chunk-options.R")
```

> ## Learning Objectives {.objectives}
>
> - To be aware of the different types of data
> - To begin exploring the data.frame, and understand how it's related to vectors, factors and lists
> - To be able to ask questions from R about the type, class, and structure of an object.
>

One of R's most powerful features is its ability to deal with tabular data - like what you might already have in a spreadsheet or a CSV. Let's start by making a toy dataset in your `data/` directory, called `feline-data.csv`:

Use your text editor to create this file:

```{r, eval=FALSE}
coat,weight,likes_string
calico,2.1,TRUE
black,5.0,FALSE
tabby,3.2,TRUE
```

```{r, echo = FALSE}
cats.df <- data.frame(coat = c("calico", "black", "tabby"),
                      weight = c(2.1, 5.0, 3.2),
                      likes_string = c(TRUE, FALSE, TRUE))
write.csv(cats.df, "data/feline-data.csv", row.names = FALSE)
```

We can load this into R via the following:

```{r}
cats <- read.csv(file = "data/feline-data.csv")
cats
```

We can begin exploring our dataset right away, pulling out columns and rows via the following:

```{r}
cats$weight
cats$weight[1]
cats$weight[2]
cats$weight[1] + cats$weight[2]
```

But what about

```{r}
cats$weight[1] + cats$coat[2]
```

Understanding what happened here is key to successfully analyzing data in R.

## Data Types

If you guessed that the last command will return an error because 2.1 plus black is nonsense, you're right - and you already have some intuition for an important concept in programming called *data types*. We can ask what type of data something is:

```{r}
typeof(cats$weight[1])
```

There are 5 main types: doubles, integers, complex, logical and character.

```
typeof(3.14)
typeof(1L)
typeof(1+1i)
typeof(TRUE)
typeof('banana')
```

Note the `L` suffix to insist that a number is an integer. No matter how complicated our analyses become, all data in R is interpreted as one of these basic data types. This strictness has some really important consequences. Go back to your text editor and add add this line to feline-data.csv:

```{r, eval=FALSE}
tabby,2.3 or 2.4,TRUE
```

Reload your cats data like before, and check what type of data we find in the `weight` column:

```{r}
cats <- read.csv(file="data/feline-data.csv")
typeof(cats$weight[1])
```

Oh no, our weights aren't the double type any more! If we try to do the same maths we did on them before, we run into trouble:

```{r}
cats$weight[1] + cats$weight[2]
```

What happened? When R reads a csv into one of these tables, it insists that everything in a column be the same basic type; if it can't understand *everything* in the column as a double, then *nobody* in the column gets to be a double. The table that R loaded our cats data into is something called a *data.frame*, and it is our first example of something called a *data structure* - things that R knows how to build out of the basic data types. In order to successfully use our data in R, we need to understand what these basic data structures are, and how they behave. For now, let's remove that extra line from our cats data and reload it, while we investigate this behaviour further:

feline-data.csv:

```
coat,weight,likes_string
calico,2.1,TRUE
black,5.0,FALSE
tabby,3.2,TRUE
```

And back in RStudio:

```
cats <- read.csv(file="data/feline-data.csv")
```

## Vectors & Type Coercion

To better understand the behaviour we just saw, let's meet another of the data structures: the *vector*.

```{r}
my_vector <- vector(length=3)
my_vector
```

Just like you might be familiar with from vectors elsewhere, a vector in R is essentially an ordered list of things, with the special condition that *everything in the vector must be the same basic data type*. If you don't choose the datatype, it'll default to `logical`; or, you can declare an empty vector of whatever type you like.

```{r}
another_vector <- vector(mode='character', length=3)
another_vector
```

You can check if something is a vector:

```{r}
str(another_vector)
```

The somewhat cryptic output from this command indicates the basic data type found in this vector; the number of things in the vector; and a few examples of what's actually in the vector. If we similarly do

```{r}
str(cats$weight)
```

we see that that's a vector, too - *the columns of data we load into R data.frames are all vectors*, and that's the root of why R forces everything in a column to be the same basic data type.

> ## Discussion 1 {.challenge}
>
> Why is R so opinionated about what we put in our columns of data?
> How does this help us?
>

You can also make vectors with explicit contents with the concatenate function:

```{r}
concat_vector <- c(2,6,3)
concat_vector
```

Given what we've learned so far, what do you think the following will produce?

```{r}
quiz_vector <- c(2,6,'3')
```

This is something called *type coercion*, and it is the source of many surprises and the reason why we need to be aware of the basic data types and how R will interpret them. Consider:

```{r}
coercion_vector <- c('a', TRUE)
coercion_vector
another_coercion_vector <- c(0, TRUE)
another_coercion_vector
```

The coercion rules go: `logical` -> `integer` -> `numeric` -> `complex` -> `character`. You can try to force coercion against this flow using the `as.` functions:

```{r}
character_vector_example <- c('0','2','4')
character_vector_example
character_coerced_to_numeric <- as.numeric(character_vector_example)
character_coerced_to_numeric
numeric_coerced_to_logical <- as.logical(character_coerced_to_numeric)
numeric_coerced_to_logical
```

As you can see, some surprising things can happen when R forces one basic data type into another! Nitty-gritty of type coercion aside, the point is: if your data doesn't look like what you thought it was going to look like, type coercion may well be to blame; make sure everything is the same type in your vectors and your columns of data.frames, or you will get nasty surprises!

Concatenate will also append things to an existing vector:

```{r}
ab_vector <- c('a', 'b')
ab_vector
concat_example <- c(ab_vector, 'SWC')
concat_example
```

You can also make series of numbers:

```{r}
mySeries <- 1:10
mySeries
seq(10)
seq(1,10, by=0.1)
```

In addition to asking for elements of a vector with the square bracket notation, we can ask a few other questions about vectors:

```{r}
sequence_example <- seq(10)
head(sequence_example, n=2)
tail(sequence_example, n=4)
length(sequence_example)
```

Finally, you can give names to elements in your vector, and ask for them that way:

```{r}
names_example <- 5:8
names(names_example) <- c("a", "b", "c", "d")
names_example
names_example['b']
```

> ## Challenge 1 {.challenge}
> Start by making a vector with the numbers 11 to 20.
> Then use the functions we just learned to extract the 3rd through 5th element in that vector into a new vector;
> name the elements in that new vector 'S', 'W', 'C'.
>

## Factors

We said that columns in data.frames were vectors:

```{r}
str(cats$weight)
str(cats$likes_string)
```

But what about

```{r}
str(cats$coat)
```

Another important data structure is called a *factor*. Factors usually look like character data, but are typically used to represent categorical information. For example, let's make a vector of strings labeling cat colorations for all the cats in our study:

```{r}
coats <- c('tabby', 'tortoiseshell', 'tortoiseshell', 'black', 'tabby')
coats
str(coats)
```

We can turn a vector into a factor like so:

```{r}
CATegories <- factor(coats)
str(CATegories)
```

Now R has noticed that there are three possible categories in our data - but it also did something surprising; instead of printing out the strings we gave it, we got a bunch of numbers instead. R has replaced our human-readable categories with numbered indices under the hood:

```{r}
typeof(coats[1])
typeof(CATegories[1])
```

> ## Challenge 2 {.challenge}
> Is there a factor in our `cats` data.frame? what is its name?
> Try using `?read.csv` to figure out how to keep text columns as character vectors instead of factors;
> then write a command or two to show that the factor in `cats` is actually is a character vector when loaded in this way.
>

In modelling functions, it's important to know what the baseline levels are. This is assumed to be the
first factor, but by default factors are labelled in alphabetical order. You can change this by specifying the levels:

```{r}
mydata <- c("case", "control", "control", "case")
factor_ordering_example <- factor(mydata, levels = c("control", "case"))
str(factor_ordering_example)
```

In this case, we've explicitly told R that "control" should represented by 1, and
"case" by 2. This designation can be very important for interpreting the
results of statistical models!

## Lists

Another data structure you'll want in your bag of tricks is the `list`. A list is simpler in some ways than the other types, because you can put anything you want in it:

```{r}
list_example <- list(1, "a", TRUE, 1+4i)
list_example
list_example[2]
another_list <- list(title = "Research Bazaar", numbers = 1:10, data = TRUE )
another_list
```

We can now understand something a bit surprising in our data.frame; what happens if we run:

```{r}
typeof(cats)
```

We see that data.frames look like lists 'under the hood' - this is because a data.frame is really a list of vectors and factors, as they have to be - in order to hold those columns that are a mix of vectors and factors, the data.frame needs something a bit more flexible than a vector to put all the columns together into a familiar table.

## Matrices

Last but not least is the matrix. We can declare a matrix full of zeros:

```{r}
matrix_example <- matrix(0, ncol=6, nrow=3)
matrix_example
```

and we can ask for and put values in the elements of our matrix with a couple of different notations:

```{r}
matrix_example[1,1] <- 1
matrix_example
matrix_example[1][1]
matrix_example[1][1] <- 2
matrix_example[1,1]
matrix_example
```

>
> ## Challenge 3 {.challenge}
>
> What do you think will be the result of
> `length(matrix_example)`?
> Try it.
> Were you right? Why / why not?
>

>
> ## Challenge 4 {.challenge}
>
> Make another matrix, this time containing the numbers 1:50,
> with 5 columns and 10 rows.
> Did the `matrix` function fill your matrix by column, or by
> row, as its default behaviour?
> See if you can figure out how to change this.
> (hint: read the documentation for `matrix`!)
>

> ## Challenge 5 {.challenge}
> Create a list of length two containing a character vector for each of the sections in this part of the workshop:
>
> - Data types
> - Data structures
>
> Populate each character vector with the names of the data types and data structures we've seen so far.
>

>
> ## Challenge 6 {.challenge}
>
> Consider the R output of the matrix below:
> ```{r, echo=FALSE}
> matrix(c(4, 1, 9, 5, 10, 7), ncol = 2, byrow = TRUE)
> ```
> What was the correct command used to write this matrix? Examine
> each command and try to figure out the correct one before typing them.
> Think about what matrices the other commands will produce.
>
> 1. `matrix(c(4, 1, 9, 5, 10, 7), nrow = 3)`
> 2. `matrix(c(4, 9, 10, 1, 5, 7), ncol = 2, byrow = TRUE)`
> 3. `matrix(c(4, 9, 10, 1, 5, 7), nrow = 2)`
> 4. `matrix(c(4, 1, 9, 5, 10, 7), ncol = 2, byrow = TRUE)`
>








## Challenge solutions

> ## Discussion 1 {.challenge}
> By keeping everything in a column the same, we allow ourselves to make simple assumptions about our data;
> if you can interpret one entry in the column as a number, then you can interpret *all* of them as numbers,
> so we don't have to check every time. This consistency, like consistently using the same separator in our
> data files, is what people mean when they talk about *clean data*; in the long run, strict consistency goes
> a long way to making our lives easier in R.
>

> ## Solution to Challenge 1 {.challenge}
> ```{r}
> x <- c(11:20)
> subset <- x[3:5]
> names(subset) <- c('S', 'W', 'C')
> ```
>

> ## Solution to Challenge 2 {.challenge}
> ```{r}
> cats <- read.csv(file="data/feline-data.csv", stringsAsFactors=FALSE)
> str(cats$coat)
> ```
> Note: new students find the help files difficult to understand; make sure to let them know
> that this is typical, and encourage them to take their best guess based on semantic meaning,
> even if they aren't sure.

>
> ## Solution to challenge 3 {.challenge}
>
> What do you think will be the result of
> `length(x)`?
>
> ```{r}
> length(matrix_example)
> ```
>
> Because a matrix is really just a vector with added dimension attributes, `length`
> gives you the total number of elements in the matrix.
>

>
> ## Solution to challenge 4 {.challenge}
>
> Make another matrix, this time containing the numbers 1:50,
> with 5 columns and 10 rows.
> Did the `matrix` function fill your matrix by column, or by
> row, as its default behaviour?
> See if you can figure out how to change this.
> (hint: read the documentation for `matrix`!)
>
> ```{r, eval=FALSE}
> x <- matrix(1:50, ncol=5, nrow=10)
> x <- matrix(1:50, ncol=5, nrow=10, byrow = TRUE) # to fill by row
> ```
>

> ## Solution to Challenge 5 {.challenge}
> ```{r}
> dataTypes <- c('double', 'complex', 'integer', 'character', 'logical')
> dataStructures <- c('data.frame', 'vector', 'factor', 'list', 'matrix')
> answer <- list(dataTypes, dataStructures)
> ```
> Note: it's nice to make a list in big writing on the board or taped to the wall
> listing all of these types and structures - leave it up for the rest of the workshop
> to remind people of the importance of these basics.
>

>
> ## Solution to challenge 6 {.challenge}
>
> Consider the R output of the matrix below:
> ```{r, echo=FALSE}
> matrix(c(4, 1, 9, 5, 10, 7), ncol = 2, byrow = TRUE)
> ```
> What was the correct command used to write this matrix? Examine
> each command and try to figure out the correct one before typing them.
> Think about what matrices the other commands will produce.
> ```{r, eval=FALSE}
> matrix(c(4, 1, 9, 5, 10, 7), ncol = 2, byrow = TRUE)
> ```
>