Advanced Visualisation in R 2400-DS2AV
R-CRAN is currently one of the most prevalent language for quantitative data analysis. One of the strongest aspect of R are data visualization packages with ggplot2 at the head. The course is designed for people who are familiar with the R program, want to specialize in it and want to master visualisation methods in this environment and then use it in quantitative analysis.
1. Introduction to ggplot2 (functions: ggplot(), aes(), geom_point(), ggsave())
2. Theme editing (functions: themes(), guide_legend(), guides(), labs(), packages: extrafont, ggthemes)
3. Labels on the graph (functions: geom_text(), geom_label(), geom_text_repel(), geom_label_repel())
4. Scale editing: (family of scale_* functions)
5. Barplot and pie plot (functions: geom_bar() and geom_arcpie())
6. Linear plot (functions: geom_line(), geom_vline(), geom_hline(), geom_rect())
7. Multiple graphs in ggplot2 (multiplot(), grid.arrange(), viewport())
8. Estimating trend on graph (geom_smooth())
9. Visualising 1d distributions: (functions: geom_histogram(), geom_density(), geom_boxplot(), geom_violin())
10. Visualising 2d dimentional distribution: (functions: geom_bin2d(), geom_hexbin(), geom_tile())
11. Maps in ggplot2 (functions: geom_polygon, ggmap)
12. Interactive visualisation in ggplot2(packages: ggiraph
13. Stat functions (stat_ family function)
14. htmlwidgets in R
15. Project presentations
Type of course
Course coordinators
Learning outcomes
KNOWLEDGE
1) Student at the end of the course knows how to use the R visualization packages to create advanced and publication ready graphs in R
2) Will have an in-depth knowledge of visualization techniques in R
3) Participant knows the application possibilities of visualization techniques in quantitative data analysis
SKILLS
1) Participant is skilled at working with statistical data using the R package, can automate and optimize data visualization
2) Student can design and write advanced procedures in the R program
SOCIAL SKILLS
1) The participant understands that the expert user of the R program is constantly learning about this environment and improving the workshop.
2) The student is aware that the R program with additional packages is constantly being developed and offers new opportunities over time.
3) The participant is aware that the R program is a universal tool and can be used in various fields of knowledge and that the course provides the basis for self-seeking such adaptations.
Students who complete the least-proficient course will know the program at the proficiency level, which will be a valuable position in the CV and a clear signal for employers with high analytical skills.
K_U02, K_U05
Assessment criteria
The final grade includes:
• credits for solving tasks performed in the course of self-study in class and homework (30 credits),
• points for preparing the semester project (70 points),
• extra points for activity.
Marks:
Point Mark
[0-60] ndst
(60-70] Dst
(70-80] dst +
(80-90] Db
(90-100] db +
(100-110] Bdb
>110 bdb !
Bibliography
- własne materiały
Literatura obowiązkowa:
-Biecek P., 2017, Przewodnik po pakiecie R, wydanie 4, Oficyna Wydawnicza GIS, Wrocław
- Kopczewska K., Kopczewski T., Wójcik P., (red), 2016, Metody ilościowe w R. Aplikacje ekonomiczne i finansowe, CeDeWu, wydanie 2,Warszawa
- Wickham, Hadley. Advanced R. CRC Press, 2014.
- Wickham, Hadley. ggplot2: elegant graphics for data analysis. Springer, 2016.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: