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(in Polish) Introduction to Statistics with R

General data

Course ID:	2500-PL-PS-SP15-02
Erasmus code / ISCED:	14.4 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0313) Psychology The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	(unknown)
Name in Polish:	Introduction to Statistics with R
Organizational unit:	Faculty of Psychology
Course groups:
ECTS credit allocation (and other scores):	4.00 Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load.
Language:	English
Prerequisites (description):	spec 315
Short description:	This course is an introduction to the R programming language. The aim of the course is to give students knowledge and skills necessary to further work in R. During the course the students will learn the basics of working in the R environment, how to perform essential operations on data and basic statistical analyses in R. The classes will take a form of workshops during which students will first learn about different concepts and operations in R and next write their own code or work on the code provided by the instructor. Occasionally students will also have assigned readings.
Learning outcomes:	The students who complete this course will gain competence in the R programming language and will be able to perform basic operations and analyses on their own. They will be able to apply the knowledge and skills gained in the course to analyze datasets from their own research. Knowledge The student: - Knows basic interface of RStudio - Understands the specificity of R programming language - Knows the types of objects present in R - Knows the types of data structures in R - Knows what loops and functions are in R Skills The student: - Can create their own loops and functions in R - Can load different data formats into R - Can manipulate and join datasets in R - Can make simple visualizations using ggplot2 package - Can compute basic summary statistics - Can perform basic statistical analyses in R Attitudes The student: - Can independently look for solutions to basic problems encountered in R - Understands the importance of building readable and reproducible code for research

Classes in period "Winter semester 2023/24" (past)

Time span:	2023-10-01 - 2024-01-28	Selected timetable range: weekly course term Navigate to timetable MO TU W TH CW FR CW
Type of class:	Classes, 30 hours more information
Coordinators:	(unknown)
Group instructors:	Michał Wypych
Students list:	(inaccessible to you)
Examination:	Course - Grading Classes - Grading
Full description:	This course is an introduction to the R programming language. The aim of the course is to give students knowledge and skills necessary to further work in R. During the course the students will learn the basics of working in the R environment, how to perform essential operations on data and basic statistical analyses in R. The classes will take a form of workshops during which students will first learn about different concepts and operations in R and next write their own code or work on the code provided by the instructor. Occasionally students will also have assigned readings. The course requires students to bring their own computers to the class.
Bibliography:	1. Course website: https://mic-wypych.github.io/psychuwr/ 2. Working with covariates: https://arelbundock.com/posts/acmq_en_06_dag/ 3. Working with model results: https://www.andrewheiss.com/blog/2022/05/20/marginalia/

Course descriptions are protected by copyright.
Copyright by University of Warsaw.