Template [R]

1. Data types, data structures and indexing

1.1 Basics

Object, assignment, functions, how to comment and get help

# we can use R as a calculator!

# Functions help us execute things; we usually have to provide arguments

# Don't know how a function works? Ask for help!

1.2 Data types

Numeric (integers or doubles)

# Naming objects tips

Character

Boolean

1.3 Data classes

Vectors

Ordered collection of elements.

What happens when we add 1 to a logical vector?

Matrix

Lists

Data frames

data.frame(vector1, vector2)	# bind vectors with the same length

1.4 Dimensions

dim(matrix)

length(object)

Can you guess the value of the length of my_df?

1.5 Indexing

vector[index]

matrix[row, column]

list[[element]]

1.6 Slicing

my_matrix <- matrix(c(34, 9, 6, 5, 3, 50, 43, 27, 98, 100), nrow=5)

2. Files

Absolute paths

C:/Users/RonBumblefootThal/Documents/RFolder/MyFirstProject/Draft/IDon'tKnowWhatI'mDoing/etc.R

Relative Paths

~/I_love_my_project/CoolCode.R

2.1 Working directories

2.2 Save/write files

Data frame example

soa_tour <- data.frame(country = c("USA", "UK", "FRA", "GER", "BRA"), 

                       frequency = c(34, 9, 6, 5, 3), 

                       continents = c("north_america", "europe", "europe", 

                                      "europe", "south_america"))

2.3 Load/read files

From your PC

From url

object <- read.csv(url("http://remote.repo/data/file.csv"))

Metabolic rates data: http://sciencecomputing.io/data/metabolicrates.csv

From url to your PC, then read

download.file(url = "http://sciencecomputing.io/data/metabolicrates.csv",

              destfile = "data/raw/metabolicrates.csv")

metabolic_rates <- read.csv("data/raw/metabolicrates.csv")

3. Control Flow

You already apply control flow when you decide how to go to work during winter.

For example:

• You take the metro if it's snowy

• You take the metro if it's cold

• You walk every other time

Now, let's put that into code!

3.1 Conditional evaluation

Simple if statement

Structure:

if (condition is true) {

	do expression

}

Example:

If/else statement

Structure:

if (condition) {

	expression 1

 } else { 

 	expression 2

}

Example:

Nested if /else statement

if (condition 1) {

	expression 1

  if (condition 2) {

    expression 2

  }

}

Example:

Best practice:

Adding a condition:

3.2 For loops

Simple for loops

Using for loops, you can then plan your schedule for a few days.

What we had:

weather <- "snowy"

temperature <- -15

But what about this?

weather_vec <- c("snowy", "cloudy", "snowy", "clear", "rainy")

temperature_vec <- c(-15, -23, -2, -40, 5)

Does the same code work?

if (weather_vec == "snowy" | temperature_vec < -20) {

  print("Take the metro!")

} else {

  print("Let's walk")

}

Iterations

for (i in iterations) {

	content of the for loop

}

More generally:

If statement inside for loops

Structure:

for (i in iterations) {

	if (condition) {

 	 	expression1

  } else {

  	expression2

  }

}

Example:

# Previous statement

if(weather_vec == "snowy" | temperature_vec < -20){

  	print("Take the metro!")

} else {

 	 	print("Just walk")

}

# Will this work?

3.3 Extras

Some logical operators

Comparisons:

• less than (<)

• more than (>)

• less than or equal to (<=)

• more than or equal to (>=)

• equal to (==)

• not equal to (!=)

Logic:

• not x (! x)

• x or y (x | y)

• x and y (x & y)

4. Functions

4.1 Syntax and arguments

Basic syntax

functionname <- function(argument1, argument2) { # Name and arguments 

	result = expression		# What the function does

  return(result) # What the function returns

}	

# Apply on values

# Apply on variables

Calling (personal) functions within functions

4.2 Scope

Variables can exist either in global or local scope.

Remember, the element to return in our function was called `abs_result`.

# What will this return, outside the function?

Here is a second example for ecologists who like to count living things:

# global variables

4.3 Integration

Combining functions and control flow

Let's come back to our previous example about transportation according to the weather.

Here is the forecast for the week and the weekend:

# Week forecast

weather_week <- c("snowy", "cloudy", "snowy", "clear", "rainy")

temperature_week <- c(-15.0, -23.0, -2.0, -40.0, 5.0)

# Weekend forecast

weather_weekend <- c("snowy", "rainy")

temperature_weekend <- c(-3.0, 2.0)

Now, let's build a function that will work with either the week or weekend forecasts.

It will look like:

transportation <- function {

	for (all days of the week/weekend) {

		if (snowy or cold) {

 				 take metro

    } else {

    		 walk

    }

 }

# Plan for the week

# Plan for the weekend

4.4 Exercise - Planning the week

Exercise to integrate the following:

• Functions

• Control flow

• Files

1. Write a function to read a file if it exists, downloading it first if it does not exist.

2. Apply the choose_transportation function to the data in the file

# Pseudocode

function (file, url)

	if (file exists)

  	read file

  else

  	download file

    read file

library("R.utils", quietly = TRUE)

# Useful functions

?file.exists     # library "R.Utils"

?read.csv

?download.file

Forecast data url: http://bit.ly/dt-forecast

Extras

Default values

# Define function