Data Science in R

Indicative content

The aim of this course is to provide knowledge in the field of data analysis in R and tools for their application in solving specific empirical problems. Emphasis is placed on the issues of data processing, selection, modeling and visualization. This course also contains basic information about the possibilities of working with large databases using the R program.

1. Mathematical operations in R, logical operators and comparison operators, data types in R, definition of variables and vectors, indexing of vectors and operations with vectors, lists.
2. Creation of matrices, operations with matrices, indexing of matrices, creation of table structures using data frames, selection and indexing of data frames and operations with data frames, import and export of data.
3. Basics of programming in R, condition if else, function ifelse, use of loops - for, while, creation of own functions.
4. Basic information about the group of packages tidyverse serving for data import, manipulation, modeling and visualization (packages such as readr, tibble, tidyr, dplyr, ggplot2, forcat, modelr…).
5. Data manipulation, use of dplyr package, selection of variables, filtering of variables, calculation of summary statistics, pipe operator (%>%).
6. Preparation and cleaning of data for data analysis (tidyr), grouping of data according to specific variables, work with categorical data, work with time formats.
7. Working with table structures (tibble), working with relational data, joining data from multiple tables based on keys, filtering using multiple tables.
8. Using the ggplot2 package to create various types of graphs (bar graph, pie graph, line graph, histogram, scatter graph, Boxplot…) and setting selected parameters of individual graphs.
9. Working with the Markdown R tool used to combine text, code and results.
10. Connecting and working with SQL database using dbplyr package. Working with large databases and connecting to other types of databases (dtplyr library, data.table).
11. Formulation and answering of a research question using the construction of a regression model and its testing (tidymodels, modelr).
12. Introduction to machine learning, overview of possibilities of using machine learning in R, application of machine learning using regression.
13. Basic information about the possibilities of data extraction from the web (import.io, rvest…).