Inhalt

Abstract:

R is a programming language with statistical computing and data visualization functions. It has established itself as a workhorse in various branches of science due to its high modularity and package system, and the high-quality graphics it can produce with relative ease. While extremely powerful, R in itself is hard to master because its graphical interface is rudimentary. RStudio thrives to solve this problem by offering a development environment for R, with a console, syntax-highlighting editor that supports direct code execution, and tools for plotting, history, debugging and workspace management. This course offers beginners an easy, step-by-step introduction to the R and RStudio Environment with a gentle learning curve. It covers topics such as data import, basics of data handling as well as an introduction to data visualization and communication.

Learning Objectives:

Foundations: The students will learn about the basic software-side requirements, familiarize themselves with R and RStudio, and with the R syntax grammar. They will learn about packages, functions and objects. What is more important, the course will help increase the student’s self-belief needed for learning a new programming environment.
Capabilities and Skills: After completing the course, students will be able to complete their own R based projects. From data import to manipulating R content, the course will also give access to the rules and foundations of creating high-quality graphics with ggplot2. In addition, they will understand how basic modeling is done in R. Finally, they will be able to produce scientific-type documentation using RMarkdown.

Summary of Learning Objectives

After having completed the course, the students ...

1. Know how to install and how to get around RStudio.
2. Know and understand R and RStudio grammar.
3. Know and understand R objects and functions.
4. Know and understand what the Tidyverse is.
5. Know data import techniques and basic data manipulation steps.
6. Know how to create basic and more advanced graphs in R.
7. Will understand the functions needed to run a simple linear regression.
8. Know how to extract summary objects and create tables from them.
9. Know how create scientific documentation using RMarkdown.

Gliederung:

1. EXPLORATION OF THE R ECOSYSTEM

1. Course Introduction
2. Introducing the RStudio Environment
3. The Basic Elements of the R Language

2. DATA HANDLING

1. Importing Data Into R
2. The Basics of Data Handling
3. Advanced Data Handling
4. Data Cleaning Elements

3. VISUALIZATION

1. The Basics of Graphing Data
2. Advanced Graphing

4. MODELING

1. Basic Examples of Modeling

5. COMMUNICATING THE RESULTS

1. RMarkdown
2. RMarkdown Formatting
3. Code Chunks in RMarkdown
4. Tables
5. Citing Information in Rmarkdown
6. The papaja Package

Detaillierter Inhalt:

The course is structured around four blocks of general content. The first one, involves the presentation of the general software and of the programming environment. It follows a gentle learning curve, while giving detailed information where is needed.

In the second chapter, the basic elements of data import and handling are presented with examples, while the latter lessons in the chapter allow a more detailed look on data cleaning processes.

The third chapter introduces elements of plotting in R. The general grammar and syntax of a plot are introduced with the second lesson presenting plot customization options or labeling.

The fourth chapter deals with generating a simple linear regression model in R and presents an overview of data types that are available, such as vectors, data frames or lists.

The last chapter presents RMarkdown, a simple yet powerful scientific authoring system. It takes the student from the very basics such as simple Markdown syntax to methods of citation or linking code with scientific prose. In the very last lesson, the package papaja is presented, which is a general APA style reporting template.

Schwierigkeitsgrad:

Einsteiger

Lehr-/Lernform:

Virtuelle Vorlesung

Interaktionsformen mit dem System/Betreuer:

E-Mail

Interaktionsformen mit Mitlernenden:

Forum

Kursdemo:

zur Kursdemo

Schlagworte:

Programmieren

Nutzung

Zielgruppe:

Schlüsselqualifikationen für Studierende

Nutzbar im Studiengang:

Friedrich-Alexander-Universität

Bachelor

Sozialökonomik
Wirtschaftswissenschaften
Business and Economics
International Business Studies

Otto-Friedrich-Universität Bamberg

Bachelor

Psychologie

Geeignet für Berufsfeld:

Diverse Tätigkeiten in Wirtschaft, Verwaltung und Forschung

Formale Zugangsvoraussetzungen:

-

Erforderliche Vorkenntnisse:

Keine

Erforderliche Vorkenntnisse bzgl. Handhabung der Lernplattform:

-

Verantwortlich

Trägerhochschule:

Uni Erlangen-Nürnberg (FAU)

Anbieter:

Prof. Dr. Klaus Moser Prof. Dr. Judith Volmer

Autoren:

George Gunnesch-Luca, Klaus Moser

Betreuer:

Prof. Dr. Klaus Moser Dr. George Gunnesch-Luca Linnea Buschbeck

Prüfung

Written examination

Art der Prüfung:

schriftliche Prüfung

Prüfer:

Prof. Dr. Klaus Moser

Anmeldeverfahren:

–

Prüfungsanmeldefrist:

–

Prüfungsabmeldefrist:

–

Kapazität:

–

Prüfungsdatum:

–

Prüfungszeitraum:

–

Prüfungsdauer:

60 Minuten

Prüfungsort:

Nürnberg

Zustündiges Prüfungsamt:

Examination office of the students' home university

Zugelassene Hilfsmittel:

Open-books

Formale Voraussetzungen für die Prüfungsteilnahme:

–

Inhaltliche Voraussetzungen für die Prüfungsteilnahme:

–

Zertifikat:

Ja (Certificate (graded))

Anerkennung an folgenden Hochschulen:

Uni Erlangen-Nürnberg (FAU), Uni Bamberg

Sonstige Anerkennung:

The exam location will be communicated in the forum of the course.

Online-Prüfungsan-/-abmeldung:

Nein

Bemerkung:

The location of the exam will be communicated in the forum of the course.

Art der Prüfung:

Online-Testat

Prüfer:

Prof. Dr. Klaus Moser

Anmeldeverfahren:

keine Anmeldung erforderlich

Prüfungsanmeldefrist:

–

Prüfungsabmeldefrist:

–

Kapazität:

–

Prüfungsdatum:

Nach Absprache mit dem Prüfer

Prüfungsdauer:

–

Prüfungsort:

Online

Zustündiges Prüfungsamt:

Examination office of the students' home university

Zugelassene Hilfsmittel:

keine Beschränkung

Formale Voraussetzungen für die Prüfungsteilnahme:

–

Inhaltliche Voraussetzungen für die Prüfungsteilnahme:

–

Zertifikat:

Ja (Certificate of participation (not graded))

Anerkennung an folgenden Hochschulen:

Uni Erlangen-Nürnberg (FAU), Uni Bamberg

Sonstige Anerkennung:

noch nicht bekannt

Online-Prüfungsan-/-abmeldung:

Nein

Bemerkung:

In-course testing (i.e., testing during watching the course)

Erforderliche Technik

Spezielle Software:

RStudio

Spezielle Software:

R

Nutzungsbedingungen

Gebühren:

Nein

Nutzungsentgelte:

für andere Personen als (reguläre) Studenten der vhb Trägerhochschulen nach Maßgabe der Benutzungs- und Entgeltordnung der vhb

Copyright:

-

Hinweise zur Nutzung:

The whole course is in the English language.