Advanced Programming in R

obowiązkowe

The aim of the course is to teach advanced programming methods in R, create complex scripts / programs, and evaluate their time complexity, as well as create own functions and packages. Firstly, the functions of the dplyr package will be presented to effectively aggregate and analyze data in subgroups, and use pipe operator %>% for a more readable code representation of several nested commands. Then the main focus will be on the automation of repetitive activities. In this context, while and for loops will be discussed, as well as the alternative R functions of the apply family. Also, elements will be introduced to conditionally execute program fragments and run code in batch mode. We will also discuss creating own functions and packages. An important part of the course will be showing tools for analyzing the code, evaluating its time effectiveness, and identifying and handling errors. The use of C ++ basics in R (Rcpp package) will also be discussed for example to replace slow R loops

R-CRAN is currently one of the most popular programs for statistical and econometric data analysis. Its advantages include: free/open-source license (also for commercial usage), versatility (new packages containing statistical procedures used for example in econometric, psychometric, sociological, geological, weather and biomedical analyzes are constantly being created) and a huge community of users contributing to it. new packages and supporting each other through online forums. R is also the best current data visualization program.

The course is designed for people who are familiar with the R program, want to specialize in it and want to master advanced programming methods in this environment and then use it in quantitative analysis. The use of this program requires expert knowledge of the programming language, which is a programming language.

Detailed course content:

• R as an object language - overview of objects, their methods and properties, creation of own objects, object programming in R

• R as a functional language - writing own functions, using loops and conditional processing, creating new methods for existing functions

• Analysis of code time complexity, effective loop alternatives (including the family of the apply () function)

• Code debugging tools (including features), defensive programming, algorithm optimization in R - benchmarking, profiling, memory management.

• Parallel processing

• Metaprogramming in R (non-standard code evaluation, R-macros, R expressions, domain languages in R)

• Using C ++ elements in R (Rcpp and others)

• Creating own packages in R and testing them, creating package documentation

- own materials

Compulsory literature:

- Wickham, Hadley. Advanced R. CRC Press, 2014.

- Gillespie, Colin i Lovelace, Robin (2016), Efficient R programming, O’Reilly Media, Inc.

- 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

KNOWLEDGE

1) Student at the end of the course knows how to use the R programming language to optimize quantitative data analysis procedures

2) Will have an in-depth knowledge of programming techniques in R

3) Participant knows the application possibilities of R programming in quantitative data analysis

1) Student can choose the optimal solution

2) Participant is skilled at working with statistical data using the R package, can automate and optimize data processing

3) Student can design and write advanced procedures and functions in the R program

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_W01, K_U01, K_U02, K_U03, K_U04, K_U05, KS_01, K_U06

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.

Oceny:

Punkty Ocena

[0-60] ndst

(60-70] Dst

(70-80] dst +

(80-90] Db

(90-100] db +

(100-110] Bdb

>110 bdb !