Statistical Programming and Data Management

Study Course Description

Course Description Statuss:Approved

Course Description Version:5.00

Study Course Accepted:04.09.2023 14:59:47

Study Course Information
Course Code:		SL_108		LQF level:		Level 7
Credit Points:		4.00		ECTS:		6.00
Branch of Science:		Mathematics; Theory of Probability and Mathematical Statistics		Target Audience:		Life Science
Study Course Supervisor
Course Supervisor:		Andrejs Ivanovs
Study Course Implementer
Structural Unit:		Statistics Unit
The Head of Structural Unit:
Contacts:		23 Kapselu street, 2nd floor, Riga, statistikarsu[pnkts]lv, +371 67060897
Study Course Planning
Full-Time - Semester No.1
Lectures (count)		12	Lecture Length (academic hours)		2	Total Contact Hours of Lectures		24
Classes (count)		12	Class Length (academic hours)		2	Total Contact Hours of Classes		24
Total Contact Hours								48
Part-Time - Semester No.1
Lectures (count)		12	Lecture Length (academic hours)		1	Total Contact Hours of Lectures		12
Classes (count)		12	Class Length (academic hours)		2	Total Contact Hours of Classes		24
Total Contact Hours								36
Study course description
Preliminary Knowledge:
No specific prerequisites are demanded, however computer skills, high-school level algebra and statistics concepts will be used in the course.
Objective:
Skills of the data analysis and management are critical to modern applied statistical research. The aim of the course is to introduce students to two statistical software tools used in biostatistics research: R and Jamovi. Thus, the objectives of the course are: • Introduce the students to the statistical programming and data management using R and Jamovi statistical software; • Prepare students for computer work in other courses of the Biostatistics study programme.
Topic Layout (Full-Time)
No.	Topic			Type of Implementation		Number	Venue
1	Introduction to R language and R Studio. Interface, workflow, scripts and coding basics. Introduction to data visualization with ggplot – aesthetic mappings, geometric objects and statistical transformations.			Lectures		1.00	computer room
2	Practice using R interface. Practice answering simple questions about data through creating ggplot2 visualizations of R built-in datasets.			Classes		1.00	computer room
3	Introduction to data wrangling (transformation) with dplyr package: filter, arrange, select, create new variables and summarize. Introducing R projects workflow.			Lectures		1.00	computer room
4	Practice various data transformations with R dplyr package on R built-in datasets.			Classes		1.00	computer room
5	Exploratory data analysis: assessing variation and covariation. Statistical summaries with boxplots. Reading various types of data into R.			Lectures		1.00	computer room
6	Practice exploratory data analysis in R: data variation (barplots, histograms, boxplots) and covariation (visualizing two variable relation). Practicing readr package for reading data.			Classes		1.00	computer room
7	Principles of consistently organizing data in R with tidyr package: gather and spread datasets, deal with NA values. Introducing R Markdown reports system, various Markdown formats and principles of writing mathematical symbols.			Lectures		1.00	computer room
8	Importing and tidying a real-life dataset. Creating an R Markdown report.			Classes		1.00	computer room
9	Introduction to data management in R. Relational data principles: relations, keys, joins and set operations. Some common non-numeric variable types in R. Organizing multiple operations with pipes.			Lectures		1.00	computer room
10	Managing relational data (i.e., multiple tables) with dplyr. Practice dealing with non-numeric variable types in R: factors, strings, dates.			Classes		1.00	computer room
11	Advanced R programming: writing your own R functions, function vectorization, conditional execution (if, else statements), for loops and map functions.			Lectures		1.00	computer room
12	Exercises on function writing and programming elements.			Classes		1.00	computer room
13	The first stage of data preparation. Data structuring and preparation for the Jamovi software.			Lectures		1.00	computer room
14	Practical class in data structuring.			Classes		1.00	computer room
15	Second stage of data preparation. Data cleaning, creation and transformation of new variables.			Lectures		1.00	computer room
16	Practical class in data cleaning and creation of new variables.			Classes		1.00	computer room
17	Introduction to Power Query.			Lectures		1.00	computer room
18	Use of artificial intelligence in data analysis.			Classes		1.00	computer room
19	Introduction to Jamovi software. Data input, export. Teaching R using Jamovi.			Lectures		1.00	computer room
20	Functions: Filter, Compute, Transform.			Classes		1.00	computer room
21	Practical lesson with functions: Filter, Compute, Transform.			Lectures		1.00	computer room
22	Fast data exploration. Data visualization with Jamovi.			Classes		1.00	computer room
23	Work in groups. From paper to prepared data.			Lectures		1.00	computer room
24	Work in groups. From paper to prepared data.			Classes		1.00	computer room
Topic Layout (Part-Time)
No.	Topic			Type of Implementation		Number	Venue
1	Introduction to R language and R Studio. Interface, workflow, scripts and coding basics. Introduction to data visualization with ggplot – aesthetic mappings, geometric objects and statistical transformations.			Lectures		1.00	computer room
2	Practice using R interface. Practice answering simple questions about data through creating ggplot2 visualizations of R built-in datasets.			Classes		1.00	computer room
3	Introduction to data wrangling (transformation) with dplyr package: filter, arrange, select, create new variables and summarize. Introducing R projects workflow.			Lectures		1.00	computer room
4	Practice various data transformations with R dplyr package on R built-in datasets.			Classes		1.00	computer room
5	Exploratory data analysis: assessing variation and covariation. Statistical summaries with boxplots. Reading various types of data into R.			Lectures		1.00	computer room
6	Practice exploratory data analysis in R: data variation (barplots, histograms, boxplots) and covariation (visualizing two variable relation). Practicing readr package for reading data.			Classes		1.00	computer room
7	Principles of consistently organizing data in R with tidyr package: gather and spread datasets, deal with NA values. Introducing R Markdown reports system, various Markdown formats and principles of writing mathematical symbols.			Lectures		1.00	computer room
8	Importing and tidying a real-life dataset. Creating an R Markdown report.			Classes		1.00	computer room
9	Introduction to data management in R. Relational data principles: relations, keys, joins and set operations. Some common non-numeric variable types in R. Organizing multiple operations with pipes.			Lectures		1.00	computer room
10	Managing relational data (i.e., multiple tables) with dplyr. Practice dealing with non-numeric variable types in R: factors, strings, dates.			Classes		1.00	computer room
11	Advanced R programming: writing your own R functions, function vectorization, conditional execution (if, else statements), for loops and map functions.			Lectures		1.00	computer room
12	Exercises on function writing and programming elements.			Classes		1.00	computer room
13	The first stage of data preparation. Data structuring and preparation for the Jamovi software.			Lectures		1.00	computer room
14	Practical class in data structuring.			Classes		1.00	computer room
15	Second stage of data preparation. Data cleaning, creation and transformation of new variables.			Lectures		1.00	computer room
16	Practical class in data cleaning and creation of new variables.			Classes		1.00	computer room
17	Introduction to Power Query.			Lectures		1.00	computer room
18	Use of artificial intelligence in data analysis.			Classes		1.00	computer room
19	Introduction to Jamovi software. Data input, export. Teaching R using Jamovi.			Lectures		1.00	computer room
20	Functions: Filter, Compute, Transform.			Classes		1.00	computer room
21	Practical lesson with functions: Filter, Compute, Transform.			Lectures		1.00	computer room
22	Fast data exploration. Data visualization with Jamovi.			Classes		1.00	computer room
23	Work in groups. From paper to prepared data.			Lectures		1.00	computer room
24	Work in groups. From paper to prepared data.			Classes		1.00	computer room
Assessment
Unaided Work:
• Individual work with lecture material preparing to all lectures of study course. • Independently prepare assigned 2 computer projects practicing the concepts studied in the course. In order to evaluate the quality of the study course as a whole, the student must fill out the study course evaluation questionnaire on the Student Portal.
Assessment Criteria:
Assessment on the 10-point scale according to the RSU Educational Order: • Two computer projects to be signed in (one related to R and one related to Jamovi): 25% × 2 = 50% • Final written exam – 50%.
Final Examination (Full-Time):		Exam (Written)
Final Examination (Part-Time):		Exam (Written)
Learning Outcomes
Knowledge:		• Know, select and use independently main programming principles in R and Jamovi. • Use the database management principles in R and Jamovi. • Learn and operate advanced programming elements such as conditional execution, cycles and customized functions.
Skills:		After this course, students will be able to: • Examine various types of data into the software and organize it for analysis. • Complete data transformation and visualization with R and Jamovi. • Write and use their own R functions to automate common tasks.
Competencies:		Students will be able to: • Use qualitatively R and Jamovi software for statistical analysis in other biostatistics courses. • Recognize the differences between R and Jamovi programs and choose the most suitable for their analysis. • Profound their statistical programming skills independently, to perform research or analyse health-related data.
Bibliography
No.	Reference
Required Reading
1	Wickham, H. and Grolemund, G. 2016. R for Data Science. Import, Tidy, Transform, Visualize, and Model Data. O'Reilly.
2	Navarro, D. J. and Foxcroft, D. R. 2022. learning statistics with jamovi: a tutorial for psychology students and other beginners. (Version 0.75). DOI: 10.24384/hgc3-7p15.
Other Information Sources
1	https://www.statmethods.net/
2	https://rstudio.com/resources/cheatsheets/
3	https://ggplot2.tidyverse.org/