Have you ever had only 5 minutes to hack into a high-security intranet to stop nuclear missiles from hitting the ground due to some cyber-terrorist activity?

Well, me neither…

• Speed is achieved by autocompleting with TAB and using only the keyboard when shortcuts are known.
• After using the same code over and over you end up memorising it (hopefully).
• Fail fast and often for cheap tasks. Set cheap tests for expensive tasks.

• Rstudio as a GUI is also called Integrated Development Environment (IDE). It has a CLI integrated under the Terminal tab.
• R is running on the Console tab in Rstudio. Which is a CLI or terminal where R as a program has been executed.

See the Getting Started for more information

Other mathematical functions are available such as exp(x), log(x), sqrt(x), abs(x), round(x, n), sum(x), max(x), etc.

In R spaces or identation do not matter like in Python

Factors are a special class of vector that can only contain predefined values. They represent categorical data.

Imagine that:

• x is a data structure with multiple elements
• i is an index or position in one dimension. It can be a number e.g. 1 (for the first position) or character if positions are named
• j is another index for another dimension
• a is an element name such a column name in a dataframe

NOTE: in R vectors start on 1 but in other languages such as Python it can be 0

# For vectors
x[i]
# Matrix or Data frame
x[i, j]
x[, j]
x[i, ]

# For lists and data frames
x[[i]]
x[[i, j]]
x$a

An object x type…

• can be tested with is._(x) where _ can be any of the available types. It returns a logical.
• can be coerced to another type if possible using as._(x)

• Missing or unknown values in R are indicated by NA (Not Applicable).
• Unless removed, operating with "NAs" will yield an NA.
• Be careful to not mistake missing values with NULL since the last one is a different type with length 0 and without attributes.

if ( statement1 ) {
    Do something
} else if ( statement2 ) {
    Do something else
} else if ( statement3 ) {
    Do something else
} else {
    Do something else
}

• The statement used must return a logical (TRUE or FALSE). eg x > 5 or patient == "sick"
• When there is a explicit condition defined one must use if or else if (for more than logical condition).
• else conditional will execute when the defined condition is not met.
• If we have a list of options then the function switch can be used.

# Risky due to unknown number of iterations
while ( statement1 ) {
    DO stuff until statement1 is FALSE
}

# Better use a for loop
for ( element in sequence ) {
    Do stuff based on element
}

There are extra controls for the loops:

• break : exit the current loop that is being executed
• next : starts the next iteration immediately from the beginning of the current loop.

These are functions that apply a function (FUN) to each element of a different data structure (x):

• apply(X, MARGIN, FUN, ...) where x is a matrix or dataframe, margin can be 1 to process rows and 2 for columns.
• lapply(X, FUN, ...) where x can also be list and it returns a list.
• sapply(X, FUN, ...) where x can also be a list but it returns a vector.

These are more efficient and fast ways to iterate in R. Always use if possible.

myfunction <- function ( argument1 = value1, argument2 = value2, ... ) {

    result = execute based on argument1 or argument2...
  
    return( result )
}

• Arguments are passed as a list
• return() is not necessary if the function just executes operations that are shown or elements stored outside the function

Different styles of building the structure and elements of computer programs. Inspired from Wikipedia:

• Object-oriented programming (OOP) is based on the concept of "objects" (often known as classes), which can contain data, in the form of fields (often known as attributes or properties), and code, in the form of procedures (often known as methods).
• Functional programming (FP) treats computation as the evaluation of mathematical functions and avoids changing-state and mutable data. The output value of a function depends only on its arguments, so calling a function with the same value for an argument always produces the same result.

In R:

• Everything that exists is an object.
• Everything that happens is a function call.

• Due to the open-source nature of R, members of the community create collections of code, data, objects and/or functions available for everybody else.
• These are are provided as packages and stores in repositories (being CRAN the main one).
• Packages can be installed by using install.packages("package_of_interest") and loaded in the beginning of a session by executing library("package_of_interest")

• Basic R cheatsheet
• Advanced R
• R language manual
• Awesome R- Curated list of R packages
• OOB, FP and R

Using all the elements described, let's make a function that estimates the ten-year risk of fatal cardiovascular disease in Europe based on the SCORE project. Click here for the instructions in the paper, Appendix A

Conroy, R. M. et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J 24, 987–1003 (2003).

To simplify, just focus on one type of patients such as Non-CHD CVD and low risk.

You can compare your results based on the provided charts by the European Society of Cardiology

• Copy and paste the syntax
• Use the cheat sheet as a guide
• Use if/else statements for the different cases
• Create an example of a patient as a vector or a list
• If you feel adventurous create multiple examples and run them by using a loop or even better with the apply() family