R language for data analysis - free course from Skillbox, training, Date: November 29, 2023.

R programming language

Introduction to the R language and basic operations
Install R and RStudio, an R development environment, and get familiar with its interface. You'll learn how to create R and Rmarkdown files, begin to learn the syntax of the language, and become familiar with the concept of a vector in R.

Types and Data Structures
Explore data types in R and learn how to convert data from one type to another. You will understand data structures in R: vectors, matrices, dataframes and lists. Find out how to work with them.

Control structures
Learn to use the if-else conditional construct, test conditions, work with loops and functions.

Data processing. tidyverse library

Reading and writing files in R
You will learn how to work with files in the working folder, read and write files in csv, txt and excel formats.

Data processing using basic R tools
Learn to use dataframes and work with data using basic R tools. You will learn how to display a description of a dataframe and work with rows and columns.

Data processing with the tidyverse library: part 1
Get acquainted with the tidyverse library and its capabilities. You will understand the features of the tidyverse syntax and learn how to work with different functions. You will learn how to group and aggregate data and upload summary information using the stargazer library.

Data processing with the tidyverse library: part 2
Learn to transform data structures and join tables.

Working with missing values ​​in R
Learn to search for and count missing values ​​and look for patterns in them. You will understand how to visualize missing values ​​using the mice and VIM libraries and fill in the gaps using tidyverse.

Working with ordinal and categorical data in R
Learn data scales: numeric, ordinal, and categorical. You will understand the features of factor data in R and operations with them. Learn to work with categorical data with forcats.

Data visualization

Data visualization in R
Learn to build simple graphs using basic R tools - histogram, scatterplot and line graph. You will learn how to configure them and upload them to a file.

Data visualization with the ggplot2 library
Learn how to build plots with the ggplot2 library. Learn to work with one-dimensional, two-dimensional, and non-numeric data and group data in graphs.

Statistical data analysis in R

Intelligence Data Analysis in R
Learn about descriptive statistics in R. Learn to use the psych library and look for atypical values. Learn the Pearson and Spearman correlation coefficients and understand how to use them. You will learn about the concept of correlation matrices, you will be able to visualize them and upload them to a report.

A/B tests: selective evaluation
Learn how to set tasks and choose a design for A/B testing. Learn to conduct sampling, identify problems in the sample and calculate its size, taking into account the error and level of confidence in the data. You will be able to calculate and analyze confidence intervals in A/B testing.

A/B tests: testing statistical hypotheses
Learn to test statistical hypotheses using tests and understand possible errors during testing. Learn how to compare shares and averages in A/B testing and learn the algorithm for running an A/B test.

Finding relationships in data in R
Learn to identify relationships in quantitative and categorical data. Learn simple linear regression. You will learn how to work with a regression model, check its quality, upload the results and include them in an Rmarkdown report.

Advanced visualization and presentation of analysis results

Interactive graphs with the Plotly library
Get acquainted with the Plotly project, understand its capabilities, syntax features and functions. Learn to build interactive Plotly graphs in 2D and 3D and publish the results on RPubs.

Analytical dashboards in R: Shiny framework
Explore the Shiny project, its capabilities and code structure. Install the Shiny library, learn how to edit a template application, add menus, dataframe lines and interface elements to your dashboard.

Final project

Processing and analysis of socio-economic data
You will download data from different files, collect them into a single dataframe and process it. Conduct exploratory analysis, build regression models and graphs, and then present the results and interpretation in a report.