main.Rmd

---
title: "Introduction to R"
author: "FirstName1 LastName1, FirstName2 LastName2, ... " # REPLACE WITH YOUR OWN NAME(S) 
date: "`r format(Sys.time(), '%Y-%m-%d')`"
output:
  html_document:
    toc: true
    toc_depth: 4
    toc_float:
      collapse: false
    theme: "readable"
---
<!-- Lab creator: Alma Andersson -->
```{r setup, include=FALSE}
# Document configurations, DO NOT EDIT
knitr::opts_chunk$set(echo = TRUE, eval=FALSE)
options(max.print=200)
```

```{r,echo = FALSE, eval =TRUE}
## Change FALSE to TRUE
## as the last thing you do before
## knitting and hand in in the report

GRADE_MODE <- FALSE
```

Welcome to the first lab! The objective here is to get you _familiar_ with the
programming language `R` and thus prepare you for the more advanced labs. If you
work through these exercises, things will be much easier later on.

With that being said, let's get started!

_NOTE_: Instructions regarding how to hand in this report are found at the very
end of this document.

# Basic R operations

We will first work through some  - but far from all - of the most basic elements
of `R`, including:

* Help commands
* Arithmetics
* Logical operators
* Objects
* Data Types
* Arrays (Lists and vectors)
* Array operators
* DataFrames
* DataFrame operators
* Functions (Defining them)
* For loops
* Conditionals
* Plotting
* SummarizedExperiment

## Looking for help

Stack Overflow and Google are your friends, and you should use them vigorously.

![google-so](imgs/google-stack-overflow.png)

However, sometimes you don't have to leave your R session to get the answers you
are looking for. You can get tons of information and examples of usage for most
functions in `R` by typing: `?function_name` or `help(function_name)`, e.g.
`?sum` in your R console.

## Objects and variables

Variables is one of the most basic features of any programming language; in
short you can think of them as placeholders. $pi$ is a great example of a
real-life variable, we use it whenever we want to indicate the value
$3.14159265...$. Similarly, we can assign values or objects a specific name
(e.g, `fancy_variable`), one huge benefit with this is that if we decide to
change the value of `fancy_variable` from `3` to `4`, only the instance where we
define the value of `fancy_variable` will have to be changed rather than all places
where it is used.

In R, you have 2 options when you want to bind an object or value to a name
`var_name <- value` and `var_name = value`. For assignment, the best practice is
to always use `<-`, this is in line with Google's R Style Guide (widely used).

```{r,eval = TRUE, echo=TRUE}
# bind the values 1337 to the variable fancy_name_1
fancy_name_1 <- 1337
# print the variable fancy_name_1
print(sprintf("fancy_name_1 is : %d",fancy_name_1))
```

One variable can be used in the assignment of a value to another variable, as
you may see below:

```{r,echo=TRUE, eval=TRUE}
# bind the sum of fancy_name_1 and 6671 to the variable fancy_name_2
fancy_name_2 <- fancy_name_1 + 6671
# print fancy_name_2
print(sprintf("fancy_name_2 is : %d",fancy_name_2))
```
## Arithmetics

### Arithmetic operations

Computation with and manipulations of numbers are integral parts of any
programming language, hence it's essential to know how to call for these kind of
actions. Below you will see some examples of the more common mathemathical
operations that you will use.

```{r, eval=FALSE}
7 + 4  # => 11 (addition)
7 - 4  # => 3 (subtraction)
7 / 2  # => 3.5 (division)
7 * 2  # => 14 (multiplication)
2 ^ 3  # => 8 (exponents)
7 %% 3 # => 1 (modulo)
```

As in standard mathematics, parentheses are used to indicate the order by which
these operations should be executed. For example:

```{r,eval=TRUE,echo=TRUE}
ex_1 = 7 * 4 / 3 ^ 2
ex_2 = 7 * ( 4 / 3 ) ^ 2
ex_3 = ((7 * 4) / 3) ^ 2 

print(sprintf("ex_1 : %f",ex_1))
print(sprintf("ex_2 : %f",ex_2))
print(sprintf("ex_3 : %f",ex_3))

```

### Mathematical operators

There's a plethora of different mathematical functions in 'R', usually their
names resemble those of the mathematical operations they execute, for
example:


```{r, eval=FALSE}
log2(8) # => 3
abs(-9.12) # => 9.12
sqrt(4) # => 2
```

## Logical operators

Once calculations have been performed using different arithmetic operations,
we usually want to assess and _compare_ our results; that's when logical
operators come in handy; when used, they return a value that says either `TRUE` or
`FALSE` depending on the expression you wrote down. Some of the most useful
logical operators are:

* `==` : equal to - `TRUE` if LHS is equal to RHS
* `>` : greater than - `TRUE` if LHS is greater than RHS
* `>=` : greater than or equal to - `TRUE` if LHS is greater than or equal to RHS
* `<` : less than - `TRUE` if LHS is less than RHS
* `<=` : less than or equal to - `TRUE` if LHS is less than or equal to RHS
* `!=` : not equal to - `TRUE` if LHS is not equal to RHS

_COMMENT_: LHS = Left hand side, RHS = Right hand side

For some examples:

```{r,echo = TRUE, eval = FALSE}
3 == 3 # TRUE
2 > 1 # TRUE
9 < 7 # FALSE
4 != 5 #TRUE
5 < 5 #FALSE
5 <= 5 #TRUE
```

Of course we can also compare variables that we have assigned values to:

```{r, eval = TRUE, echo = TRUE}

# Assign values to two variables
foo <- 3.141
bar <- (foo / 2)^3 - 2
# compare their values
foo_greater_than_bar <- foo > bar
# print the value of foo_greater_than_bar
print(foo_greater_than_bar)
# fancy print
print(sprintf("Is foo greater than bar? Answer: %s!",
              ifelse(foo_greater_than_bar,"Yes","No")))
```

There are also ways of linking logical expressions, and thus creating a compound
expression. For example, you could check whether two expressions are true at the
_same_ time. The two variants that you mainly will encounter in these labs are:

* `&` - AND : both expressions must be `TRUE`
* `|` - OR : one of the two expressions must be `TRUE`

What do we mean by expressions then? This is best illustrated by some examples:

```{r, echo = TRUE, eval = TRUE}
# set variable values
expr_1 <- 4 < 3
expr_2 <- 9 < 13
# print values of expression 1 and 2
print(paste("The value of expr_1 is :",expr_1))
print(paste("The value of expr_2 is :",expr_2))

# make compound expressions
expr_3 <- expr_1 & expr_2 # is both expr_1 and expr_2 true? (AND operator)
expr_4 <- expr_1 | expr_2 # is either of expr_1 and expr_2 true? (OR operator)

# print values of expression 3 and 4
print(paste("The value of expr_3 is :",expr_3))
print(paste("The value of expr_4 is :",expr_4))

```

**NOTE**: if you want to link expressions rather than variables, it's good practice to use parentheses
```{r, echo = TRUE, eval = TRUE}
expr_1 <- 4 < 2 | 2 < 1  # bad practice
expr_1 <- (4 < 2) | (2 < 1 ) # good practice
```


### Data Classes

Every (programming) language has it's own classes of objects, and you have
already encountered some of them in this lab! Just to get you familiar with the
terminology, some of the _atomic classes_ (that build up every other class in R)
are:

* numerics : real numbers - e.g, `3.0`, `3.14` and `-1000`. More precisely $x \in \mathbb{R}$
* complex : complex numbers - e.g, `1 + 0i`, `-4 + 3i` and `0 + -1i` 
* character : characters and strings - e.g., `"a"`, `"hello"` and `"catfish"`
* logicals : boolean values - either `TRUE` or `FALSE`

A lot of errors that arise when coding can be traced back to combining the wrong
classes. For example, if we were to try and add a character to a numeric:

```{r, echo =TRUE, eval = FALSE}
test_1 <- 3 + "a"
```
Would give you the error message `Error in 3 + "a" : non-numeric argument to binary operator`

R, being a _high level_ programming language, implements automatic type coercion
- meaning you don't really have to pay too much attention to making sure you are
using the correct types all the time. For example, if you add a numeric and
complex value, the numeric is converted to a complex type and all is good in the hood:

```{r, echo = TRUE, eval = TRUE}
a_numeric <- 3
a_complex <- 4 - 2i

print(paste("a_numeric + a_complex = ",a_numeric + a_complex))
```
*WARNING* : while the programming becomes easier (e.g., compared to C/C++ or
Java) with this automatic coercion, you should also be cautious - sometimes unexpected
results and errors may be introduced. Take a look at what happens when
you compare a character with a numeric:

```{r, eval = TRUE, echo = FALSE}
result_1 <- "a" > 3
print(paste("a > 3 is : ",result_1))
```

Eh what...? This makes no sense at all, how can we even compare a character with
a number? Shouldn't this, if something, raise an error? It should indeed, but
this is an example of `R` trying to help us a little too much.

What's happening here is that the numeric `3` gets _coerced_ into the character
`"3"` which _can_ be compared to another character (if you, like in R, have
assigned an order to the characters).

## Arrays

The next element to introduce is the _array_, this is another fundamental structures
that you will use *a lot*. In a nutshell, arrays let us associate multiple
values to a single object. We'll be focusing on 1 and 2 dimensional arrays, let
us start with 1d arrays first.

### 1d arrays - lists and vectors

1d arrays come in two flavors, either as _lists_ or as _vectors_. Which you
create accordingly:

```{r,eval=TRUE, echo=TRUE}
a_list <- list(19660206,
               "Rick Astley",
               "never",
               0,
               "no",
               "you")

a_vector <- c("wind",
              "1A",
              "earth",
              "3B",
              "flat",
              "water",
              "5G",
              "round")
```

To access the i:th element of a:

* list -  do : `a_list[[i]]` (note the double parenthesis), 
* vector - do : `a_vector[i]` 

To exemplify, let us access the 4th element from our vector and list respectively:

```{r, echo = TRUE, eval =TRUE}

a_list_4th_element <- a_list[[4]]
a_vector_4th_element <- a_vector[4]

print(paste("The fourth element of our list is : ",a_list_4th_element))
print(paste("The fourth element of our vector is : ",a_vector_4th_element))
 
```

We could also assign "names" to our array elements (note how we are using `=` and not `<-`):

```{r,eval=TRUE, echo=TRUE}
a_list <- list(birth_date = 19660206,
               name = "Rick Astley",
               occurance = "never",
               problems = 0,
               beef = "no",
               whom  = "you")

a_vector <- c(element_1 = "wind",
              slot_1 = "A1",
              element_2 = "earth",
              slot_2 = "B2",
              shape_1 = "flat",
              element_3 = "water",
              slot_3 = "5G",
              shape_2 ="round")
```

We can still access our elements by specifying their position, but also by their
names; sometimes, this allows you to write more **readable code** and makes it
easier to access elements of your arrays.

To access an element by name, for _lists_ use the `$` symbol or `[['name']]`;
for vectors only the second option (`[[name]]`) is valid. To illustrate:

```{r, echo = TRUE, eval = TRUE}

# accessing the elements from our list
print(paste("His name is",
            a_list$name,
            "and he will",
            a_list[["occurance"]],
            "give",
            a_list[["whom"]],
            "up"
            )
      )
# accessing the elements from our vector
print(paste("We all know that the",
            a_vector[["element_2"]],
            "is",
            a_vector[["shape_1"]],
            "and that",
            a_vector[["slot_3"]],
            "caused COVID"
            )
      )
```

There's a nice command called `names` that we can use to check the names of our
list and vector elements respectively:

```{r, echo = TRUE, eval = TRUE}

print("The names of our list elements are:")
print(names(a_list)) # here we use the names function to access the names of a_list
print("The names of our vector elements are:")
print(names(a_vector)) # here we use the names function to access the names of a_vector
```

If we want to subset our lists and vectors we could either do:

* `a_list[1:3]` - creates a new list including the first to third element
* `a_list[c(name_1,name_2)]` - creates a new list with elements "name_1" and "name_2"
* `a_vector[1:3]` - creates a new vector including the first to third element
* `a_vector[c(name_1,name_3)]` - creates a new vector with elements "name_1" and "name_3"

As an example:

```{r, echo =TRUE, eval=TRUE}
a_list_subset <- a_list[1:3] #subset a_list
a_vector_subset <- a_vector[c("slot_1","slot_2","slot_3")] #subset a_vector

print('The subsetted list "a_list_subset" is:')
print(a_list_subset)
print('The subsetted vector "a_vector_subset" is:')
print(a_vector_subset)
```

Lists and vectors seem pretty similar, but there is one - very important -
feature that differs between them:

Vectors are *homogeneous* while lists can be *heterogeneous*.

This means that **all elements in a vector must be of the same type**, and if
they aren't they will be coerced to it, i.e., you can not have a vector of both
numerics and characters. See what happens when we try to declare such a vector:

```{r, echo = TRUE, eval = TRUE}

mixed_vector <- c(1, #numeric
                  "foo", #character
                  -2, #numeric
                  "bar" #character
                  )

# print the type of the first element in "mixed_vector"
print(paste("The type of the first element of mixed_vector is :",
            class(mixed_vector[1])))

```

If we use a list instead, the output will be different:

```{r, echo = TRUE, eval = TRUE}

mixed_list <- list(1, #numeric
                  "foo", #character
                  -2, #numeric
                  "bar" #character
                  )

# print the type of the first element in "mixed_list"
print(paste("The type of the first element of mixed_list is :",
            class(mixed_list[[1]])))

```

### Lists of Lists and so forth

The elements of our lists are not restricted to singular values, but could also
be another list or vector. For example:

```{r, echo = TRUE, eval = TRUE}

a_list_of_mixed <- list(list_1 = list("crystals","heals",FALSE),
                        list_2 = list(6,"days",7,"hours","stuck",TRUE,"Ever Given"),
                        vector_1 = c("Person", "Woman", "Man", "Camera", "TV")
                        )
```

*NOTE* : This functionality **does not** exist for vectors. If you try to put
vectors into a vector, they will be "flattened" and just appended to the vector.

If we want to access the second element in the vector that makes up the third
element of `a_list_of_mixed` we would do:

```{r, echo = TRUE, eval = TRUE}

# alternative 1
second_of_third_1 <- a_list_of_mixed[[3]][2]
# alternative 2
second_of_third_2 <- a_list_of_mixed$vector_1[2]

print(paste("(Alt 1) The second element of the third element is :",second_of_third_1))
print(paste("(Alt 2) The second element of the third element is :",second_of_third_2))

```

The object `a_list_of_mixed` to some extent represent a 2d array, we can
traverse both `a_list_of_mixed` itself (first dimension) and then the objects
that constitute its elements (second dimension).

When the lists or vectors that we want to store are of different length, using a
list is the best option, *BUT* if they are of equal length (structured arrays)
there are better options to choose from, being the `matrix` and `dataframe`
types - more on these soon.

### Vector functions

There are many functions that can help us extract useful information from our
vectors, just to expose you to a few:

```{r,echo = TRUE, eval = FALSE}

# define a new vector
a_new_vector <- c(4.1,-2,9.19,18.19,5.21)

#some functions and vector operations

max(a_new_vector) # => max value : 18.19
min(a_new_vector) # => min value : -2
range(a_new_vector) # => min and max value :c(-2, 18.19)


a_new_vector + a_new_vector # => elementwise addition : c(8.20,-4.00,18.38,36.38,10.42)
a_new_vector * a_new_vector # => elementwise multiplication :  c(16.8100,4.0000,84.4561,330.8761,27.1441)
a_new_vector %*% a_new_vector # => dot product : 463.2863

4.1 %in% a_new_vector # => check if 4.1 is in vector : TRUE
111 %in% a_new_vector # => check if 111 is in vector : FALSE
```
## Structured 2d Arrays

As eluded to above, when we have structured or _tabular_ data there are two data
structures that are particularly useful:

* Matrices - for homogeneous data
* Data frames - for heterogeneous data

As a reference:


```{r, echo = TRUE, eval = TRUE}

a_matrix <- matrix(data = c(4,1,2,9,-11,-8,1,3,4,21,0,1),
                   ncol = 4,
                   nrow = 3
)

a_df <- data.frame(birth_date = c(a_list$birth_date,19691294),
                   first_name = c(a_list$name,"Jay"),
                   problems = c(a_list$problems,99),
                   beef = c(a_list$beef,"yes"),
                   row.names = c("ind1","ind2")
)

print("this is a matrix")
print(a_matrix)
print("this is a data frame")
print(a_df)
```

Analogously to lists and vectors, we can also extract the names of the the
columns and rows in our 2D arrays, but now we use the functions `colnames` and
`rownames` instead.

```{r, echo = TRUE, eval = TRUE}

# print rownames and colnames of a_matrix
print("The row/colnames of a_matrix are:")
print(rownames(a_matrix))
print(colnames(a_matrix))
# print rownames and colnames of a_df
print("The row/colnames of a_df are:")
print(rownames(a_df))
print(colnames(a_df))
```

As you can see, our matrix does not yet have any row/colnames, to change this we can do:

```{r, echo = TRUE, eval = TRUE}

rownames(a_matrix) <- c("A","B","C")
colnames(a_matrix) <- c("a","b","c","d")

# print rownames and colnames of a_matrix
print("The row/colnames of a_matrix are:")
print(rownames(a_matrix))
print(colnames(a_matrix))
```

To access elements in a matrix we do `a_matrix[row_id,col_id]`, if we want to
access all elements in a specific column then we do `a_matrix[,col_id]` and
similarly for a row `a_matrix[row_id,]`. As an example:

```{r, echo = TRUE, eval = TRUE}
# access the element in the second row and third column in a_matrix
a_matrix[2,3]
# access all elements in the first column
a_matrix[,1]
# access the first and fourth in the second row
a_matrix[2,c(1,4)]
```

The same conventions can be used for data frames to access elements, but you can
also use the `$` operator to access columns in data frames. However, if you do
`a_df[,"col_name"]` or `a_df$col_name` this will return a vector and not a data
frame. To extract a column and preserve its row/colnames do `a_df["col_name"]`.

To exemplify:

```{r, echo = TRUE, eval = TRUE}

# Alt1. extract first columns by [,col_name] 
first_col_1 <- a_df[,"birth_date"]
# Alt2. extract first column by $col_name
first_col_2 <- a_df$birth_date
# Alt3. extract first column by [col_name]
first_col_3 <- a_df["birth_date"]
print("(Alt1) : By [,col_name]")
print(first_col_1)
print("(Alt2) By $col_name :")
print(first_col_2)
print("(Alt3) By [col_name] :")
print(first_col_3)

```

### Functions for 2D arrays

We saw how we could apply certain functions to our vectors in order to obtain
for example min and max values, we could do the same for our data frames and matrices.

```{r,echo = TRUE, eval = TRUE}

a_matrix_max <- max(a_matrix)
a_matrix_min <- min(a_matrix) 

print(paste("the max value of a_matrix is :", a_matrix_max))
print(paste("the min value of a_matrix is :", a_matrix_min))

```

that is convenient, but what if we wanted the max value in each column and
minimum value in respective row, how do we do this?

One possible solution would be:

```{r,echo=TRUE,eval=TRUE}
a_matrix_col_max <- c(max(a_matrix[,1]),max(a_matrix[,2]),max(a_matrix[,3]),max(a_matrix[,4]))
a_matrix_row_min <- c(min(a_matrix[1,]),min(a_matrix[2,]),min(a_matrix[3,]))
print("max in each column")
print(a_matrix_col_max)
print("min in each row")
print(a_matrix_row_min)
```
Whatever you do, please do not do this. In programming this is what we call a
"shitty ass solution". The main issue here is scaleability, this is not a
feasible approach if you had a 1000x1000 matrix for example. Instead of this
disaster of a solution, we will use a handy function called `apply`.

Let's see how this `apply`function works:

```{r,echo = TRUE, eval = TRUE}
?apply
```

The help section may a bit "icky" to interpret, but in short it says that if we
have an array `X` and a function `FUN` we can apply this function along any
dimension (`MARGIN`) by using apply, it will then treat the rows/columns as
individual vectors to which `FUN` is applied.

**NOTE** : rows are considered the first dimension, columns the second 

Let's say that we want to compute the max value of each column (dimension 2) in `a_matrix`, then we have:

* `X` : `a_matrix`
* `MARGIN` : 2
* `FUN` : `max` (note we do not _call_ `max` here, only provide the name)

in action this becomes:

```{r, echo = TRUE, eval = TRUE}
a_matrix_col_max <- apply(a_matrix,2,max) 
print(a_matrix_col_max)
```

*COMMENT*: For lists and vectors, similar functions to `apply` exist (check out `lapply` and `sapply`).

Since programmers are inherently lazy, some operations - like getting the column
sums - have been included as standard functions in R, meaning you don't have to
use `apply` but can call them directly. Some examples are:

* `{row,col}Sums`: compute and return the total sum for each row or column
* `{row,col}Means`: compute and return the mean for each row or column

As you can see the results are the same as if we had used `apply`:

```{r, echo = TRUE, eval = TRUE}
a_matrix_row_mean_1 <- apply(a_matrix,1,mean) 
a_matrix_row_mean_2 <- rowMeans(a_matrix)

print(paste("Alt1. rowmeans with apply"),a_matrix_row_mean_1)
print(a_matrix_row_mean_1)
print(paste("Alt2. rowmeans with rowMeans"),a_matrix_row_mean_2)
print(a_matrix_row_mean_2)
```

## Functions

Variables allow you to specify a certain value _once_ instead of every time
you use it, functions does the same but for _actions_. In short, you
associate a certain action to a function name, and whenever you want to execute that
action, you simply call the function.

We call the input to our functions "parameters" or "arguments".

You have already used several "pre-defined" functions, like `apply` and `max` -
but sometimes (quite often) we want to expand the repertoire of actions that we
can call for, as well as customizing them for our own needs, that's when we define our
own functions.

The basic structure for a function is as follows:

```{r, eval = TRUE, echo = FALSE}
# DO NOT RUN CHUNK

function_name <- function(parameter_1,parameter_2,...) {
  # function body
  # this is where the action happens
  
  return(result)
  
}
```

Say for example that we want a function called `sphere_volume` that computes the
volume of a sphere given its radius, the mathematical formula for this is:

\begin{equation}
V(r) = \frac{4\pi r^3}{3}
\end{equation}

Let us use $3.14$ as an approximation of $\pi$, then our function would look like:

```{r, echo = TRUE, eval = TRUE}

sphere_volume <- function(r) {
  pi_approx <- 3.14
  volume <- 4.0 / 3.0 * r ^3 * pi_approx
  return(volume)
}

```

Let us now test our function 
```{r, echo = TRUE, eval = TRUE}

# set radii values
radius_1 <- 9
radius_2 <- 2

# compute volumes
volume_1 <- sphere_volume(radius_1)
volume_2 <- sphere_volume(radius_2)

# print results
msg <- "The volume of a sphere with radius %0.f is %f"
print(sprintf(msg,radius_1,volume_1))
print(sprintf(msg,radius_2,volume_2))

```

## For loops

Just as variables and functions, loops are key features of almost any
programming language. In R we have `while`, `repeat` and `for` loops, but here
we'll focus to the `for` loops.

Briefly summarized, a for loop iterates over some values in a 1d array and, at
each iteration, executes a certain code block. The "skeleton" of a for loop is
as follows:

```{r, echo = TRUE, eval = FALSE}
# DO NOT RUN CHUNK

for (i in vec) {
  # loop body
}
```

here `vec` would be the array we iterate over. To give a less abstract example,
we can define a vector that contains all numbers from 1 to 5, done by `vec <-
c(1:5)` and then in each iteration print the value we are at:

```{r, echo = TRUE, eval = TRUE}

vec <- c(1:5)

for (i in vec) {
  print(i)
}

```

the elements in our arrays do not have to be numbers or given in a sequential order:

```{r, echo = TRUE, eval = TRUE}

vec <- c("gato","cat","katt","paka")
for (x in vec) {
  print(x)
}

```

Of course, for loops are not only used to print stuff, we could for example use
them to generate a Fibonacci series, defined as:

\begin{equation}\
F_n =\Bigg{\{}
\begin{array}{cl}
0 & \textrm{if }  n = 1 \\
1 & \textrm{if }  n = 2 \\
F_{n-1} + F_{n-2} & \textrm{else} & \\
\end{array}
\end{equation}

_NOTE_: the above equation looks better in the knitted document

Implementing this in could for example look like:

```{r, echo = TRUE, eval = TRUE}

# set desired number of elements in the Fibonacci series
n_elements <- 10

# create a vector F_ to store our elements
# we let F_[1] = 0 since F_1 = 0
# and
# F_[2] = 1 since F_2 = 1
# in the series

F_ <- c(0,1)

# iterate over n=3 to n=10
for (n in c(3:n_elements)) {
  
 # compute the next element in the sequence
 x <- F_[n-1] + F_[n-2]
 # add element to the F_ vector
 F_ <- c(F_,x)
}
# print result
print(sprintf("The first %d numbers of the Fibonacci series are: [%s]",
              n_elements,
              paste(F_,collapse = ", ")
              )
      )

```

## Conditionals 

Sometimes we want our actions to depend on the output of a function or the value
of a certain variable; as an example from real life, the clothes you put on is
(hopefully) fairly dependent on the temperature outside.

To implement these dependencies we use conditionals, which are : `if`, `else if`
and `else`. The way we structure our conditional statements is as follows:

```{r, echo = TRUE, eval = FALSE}
# DO NOT RUN CHUNK

if (condition_1) {
  # code to execute if condition_1 is true
} else if (condition_2) {
  # code to execute if condition_1 is FALSE
  # and condition_2 is TRUE
} else {
  # code to exectue if none of the
  # conditions above are TRUE
}
  
```

**NOTE**: You do not have to include an `else if` or `else` statement, an `if` statement can be used standalone, the reverse is not true. 
**NOTE**: You can add as many `else if` statements as you want, but only one `if` and `else`
**NOTE**: You should always follow the order of the statements above: first `if`, then `else if` (if you have any) and last `else`

To give you an example, run the chunk below _a couple of
times_ to see how the conditionals work:

```{r, echo = TRUE, eval = TRUE}

# get a random number between -10 to 25
temperature <- runif(1,-10,25)
# standard text to print
txt <- sprintf("The temperature is %0.f, so",temperature)

# if temperature is below 5 degrees
if (temperature < 5) {
  print(paste(txt,"put on a jacket!"))
# if temperature less than 12 degreees (but more than 5)
} else if ( temperature < 12) {
  print(paste(txt,"bring a shirt!"))
# if temperature is more than 12 degrees
} else {
  print(paste(txt,"it's summertime bby!"))
}
```

## Exercises

Now when we've walked through some of the basics of R, you are more than ready
for some exercises. To pass the lab, all exercises must be correctly completed.

### Exercise 1

Create a data frame (named `count.data`) with 3 samples and 5 genes. Rows are samples, genes are columns. Make all count values = 1
_Hint_: One way is to generate a `matrix` with the value one for all positions and then convert it to a `dataframe`.

```{r,echo = TRUE,eval = TRUE}
#INSERT CODE HERE
```

Make sample 2 have double the amount of counts for _each_ gene. Note that you 
should update the existing `count.data` object here not generate a new dataframe.

```{r,echo = TRUE,eval = TRUE}
#INSERT CODE HERE
```

Make sample 3 have the the following counts for each gene (32,16,8,4,2) 

```{r,echo = TRUE,eval = TRUE}
#INSERT CODE HERE
```

Cut the count values for gene 2 in half within sample 2 and 3, **and** add 3 counts to gene 4 for sample 1

```{r,echo = TRUE,eval = TRUE}
#INSERT CODE HERE
```

Calculate the mean value of each gene and store this as a vector named `vec`

```{r,echo = TRUE,eval = TRUE}
#INSERT CODE HERE
```

Test run the code below (if no error/warning -> continue with lab)

```{r,echo = TRUE,eval = GRADE_MODE}
source("checks.R")
check_exercise_1(vec)
```

### Exercise 2

In the code chunk below, a vector representing a genomic sequence is
generated, each element in the vector is one base in the sequence. You do
not have to pay attention to this particular code but should **run it**.

```{r, echo = TRUE, eval =TRUE}
sequence <- sample(c("A","C","T","G"),
                          size = 200,
                          replace = TRUE
                   )
sequence <- as.vector(sequence)

print(sprintf("the sequence is : %s",paste0(sequence,collapse = "")))
```

your task is to **write a function** called `base_count` that counts the number of occurrences of each base, i.e., how many A,C,T and G's does `sequence` contain, to be more specific:

`base_count(sequence)` should return a vector where the elements are named
"A,C,T,G" and their associated value represents the number of each base in
`sequence`. This vector could, for example, look like `c(A = 11, C = 132, T =
761, G = 41)`.


```{r, echo = TRUE, eval = GRADE_MODE}

# Possible long solution
base_count <- function(...){

      }

```

To test your solution, run the following code chunk:

```{r, echo = TRUE, eval = GRADE_MODE}
source("checks.R")
answer <- base_count(sequence)
check_exercise_2(sequence,answer)
```

### Exercise 3

We usually store data in external files, meaning we usually have to _read_ it
from these files. If we have a comma or tab separated file (extensions `.csv`
and `.tsv`) one way of doing this is by using the `read.table`
function.

If your fields are separated by commas (as in a `.csv`file ) use the argument
`sep=','`, if they are tab separated use the argument `sep = '\t'`.

We will read our data from the file "reads.tsv", it is a **count matrix** with
genes as rows and samples as columns. To read this file the command looks as
follows:

```{r,echo = TRUE, eval = TRUE}
counts <- read.table("reads.tsv", sep = "\t")
```

You could now answer the following questions:

#### Q1
> How many different genes have data been collected from? (help : use `nrow`)

#### Q2
> How many patients have data been collected from? (help : use `dim`)

#### Q3
>Which **5 genes** are most highly expressed across all samples? (help : use `sort` or `order`)

Put your answers into a list `ans_ex3` with the following structure:

```{r, echo = TRUE, eval = FALSE}
# THIS IS AN EXAMPLE, DO NOT EDIT
ans_ex3 <- list(a1 = NA, #answer to question 1 - numeric
                a2 = NA, #answer to question 2 - numeric
                a3 = NA, #answer to question 3 - character vector
                )
```

Answer with the specified format (e.g, a numeric), **not** a text or written answer.

```{r, echo = TRUE, eval = GRADE_MODE}

# WRITE CODE TO GENERATE ans_ex3 HERE


# modify this vector
ans_ex3 <- list(a1 = NA,
                a2 = NA,
                a3 = NA)

```

To check that your answers are correct, run the following code chunk:

```{r, echo = TRUE, eval = GRADE_MODE}
source("checks.R")
check_exercise_3(ans_ex3)

```

## Plotting

Sooner or later you will come to a point in your work where you want to
visualize your results. Visualization is usually helpful for you and others to
interpret the results. We will therefore explore how we may plot data in R.

There are several different built in functions that allow you to plot your data,
you find some examples of these below:

```{r,echo = TRUE, eval = TRUE}
# Define a vector with 5 values
c0 <- c(1, 3, 6, 4, 9)
# Graph the vector c0 
plot(c0)
# Set title of the current plot
title(main="Plot of single vector c0")

# Define 2 new vectors
c1 <- c(1, 3, 6, 4, 9)
c2 <- c(2, 5, 4, 5, 12)

# Plot c1 using a y axis that ranges from -1 to 13

plot(c1, # data to plot
     col="blue", # color
     ylim=c(-1,13) # y-axis range
     )

# Graph c2 with red dashed line and square points
lines(c2, # data to plot
      type = "b", #type of plot
      pch=22, # marker type
      lty=2, # line type
      col="red" # color
      )

# Create a title with a red, bold/italic font
title(main="Two Lines", col.main="black")

# We can also make a histogram of our data
hist(c1)

```

We can use a histogram to assess our count data; by plotting the rowsums
(total observed gene expression across all samples) in a histogram  we are able
to see how these (the sums) are distributed. We will `log` the sums in order to
reduce the variance and the dynamic range of our data.

```{r,eval=TRUE,echo=TRUE}

#Distribution of gene counts
hist(log(rowSums(counts)),
     main = "Logged Total Gene Counts"
     )

```

## R/Bioconductor

Bioconductor is a repository of R-packages specifically for biological analyses.
It has relatively strict requirements for submission, including installation on
every platform and proper documentation with a required tutorial (called a
vignette) explaining how the package should be used. **You will need this in the
upcoming labs**.

TLDR: "Bioconductor is a collection of bioinformatic tools".

Installs from Bioconductor is handled by the BiocManager library. BiocManager
should already be installed in your environment, but run the chunk below to 
make sure.

```{r, eval=FALSE, echo = TRUE}
if (!require("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
```

There are **a lot** of "packages" within bioconductor that you can install and
use. To install a package from bioconductor use
`BiocManager::install(PACKAGE_NAME)` where you replace `PACKAGE_NAME` with the
package of interest, e.g. `BiocManager::install("SummarizedExperiment")`. 

> NB! Notation like this `BiocManager::install(PACKAGE_NAME)` allow us to use a 
certain function (`install`) from a specific library (`BiocManager`) without 
importing said library into the namespace (calling `library(BiocManager)`). 
As some libraries will share functions, function could become shadowed, 
therefore this notation is useful to make sure that you are requesting a 
function from a particular library.

Again, "SummarizedExperiment" should allready be installed but you are welcome 
to try the command above yourself in the console.

## Working with SummarizedExperiment

The SummarizedExperiment class is used to store matrices containing experimental
results, which are commonly produced by sequencing experiments. Each object
stores observations of one or more samples, along with additional meta-data
describing both the observations (features) and samples (phenotypes).

A key aspect of the `SummarizedExperiment` class is its coordination of the
meta-data and assay when sub-setting. For example, if you want to exclude a
given sample you should do this both in the count and meta-data in one
operation, which ensures the meta-data and observed data will remain
synchronized.

Bad coordination of meta-and count data has resulted in a number of
incorrect results and paper retractions, hence why this is a very desirable
property.

Lets try it out!

We begin by loading the required package, the `SummarizedExperiment` class is
contained in - unsurprisingly - the package with the name
`SummarizedExperiment`. Go ahead and load it:

```{r, echo =TRUE, eval = TRUE, message=FALSE, warning=FALSE}
library(SummarizedExperiment)
```

Next we load a second package that contains same data, this package is called `airway`:

```{r, echo = TRUE, eval = TRUE, message=FALSE}
library(airway)
# Load data
data(airway)
# Bind object to new identifier 
se <- airway
se
```

Within the summarizedExperiment, you can store all kinds of data. Type
`colData(se)` to view all sample data included. Type `rowData(se)` to view all
gene data.

To access a particular sample's data, just type  `se$sample`. 

You can also store meta data for the entire data set, type `metadata(se)` to view it. 

## Hand In Guidelines

**How**:  First, go to the top of this document, on line 23 set
`GRADE_MODE = TRUE`. Next, knit this document, and hand in the resulting html-file
on Canvas. Make sure you have solved all the questions. If the document doesn't
compile, there is something wrong with the code.

**Deadline**: Your report is due 23:59 one week after the scheduled lab session;
if working in pairs - each of you should hand in two (identical) reports where the
names of both authors are clearly stated. For further information see the
guidelines on the course web-page.

```{r session_info}
sessionInfo()
```