# Presentations day 1

### Introduction and basic function

Welcome to doctoral course 2657, "Introduction to R - data management, analysis and graphical presentation". Below are video recordings covering the lectures for day 1 of the course (except for the introductory lecture about the history of R). The video clips are in the same order as the topics in your handouts, although some of the slides may not be presented in the films. The recorded material can be used to prepare yourself for the live lectures, for students who are unable to attend, or for repetition of difficult topics.

If you think we speak too slowly, the video settings lets you increase presentation speed by 25, 50 or even 100%. If you prefer to watch all clips as a continuous stream, they are available as a playlist **here **(1h 10 min).

### RGui - R Graphical User Interface (consol and editor)

A brief introduction to the native R user interface.

### RStudio

Although we use RGui in this course (for its simplicity and compatibility with small screens), many R users prefer RStudio or other user interfaces with additional features. Feel free to use them during the course, if you like.

### R as a calculator

To get a lot of practice, make it a habit to use R for all calculations - even the basic ones.

### Variables

How to assign values to variables and use them in your calculations.

### Integers, decimals, logarithms, NaN and Inf

### Combine and vector calculus

Vectors are an integral part of R and getting used to them is really a necessity.

### Sequence and replicate

### Random samples

Draw random samples from a user-defined set of values.

### Random samples from different distributons

Draw random samples from specific distributions such as the normal or binomial distribution. Great tool for simulating studies at the design stage.

### Workspace

The workspace is a place in the computer's working memory where all your objects (data structures, functions etc.) are kept.

### Help

Very few (if any) know R by heart, and knowing where to find help will get you a long way.

### Data structures - introduction

R is a lot about nomenclature, and telling the various data structures (vectors, factors, arrays etc.) apart is necessary, although it may take some effort.

### Data structures - vectors

### Data structures - matrices

### Data structures - arrays

### Data structures - factors

### Data structures - data frames

### Data structures - lists

### Comments

To make the R script understandable to others and your future self, put a lot of comments into it. Comments are ignored by R, but they are invaluable to human beings.

That's all for day 1, now it's time for the assignments. We need your solutions no later than 9 AM November 17.