Visualization methods in data analysis by R: учебная программа учреждения высшего образования по учебной дисциплине для специальности: 7-06-0533-05 Applied Mathematics and Computer Science Profiling: Computer Data Analysis. № УД-1378/м.

Abstract

TEACHING MATERIAL CONTENTS Section 1. Introduction Topic 1.1. The tidyverse collection of R packages for data science The R language for statistical computing and graphics. The RStudio integrated development environment. The tidyverse collection of R packages for data science. Topic 1.2. The tidyverse style guide The tidyverse style guide. Automatic code formatting using the styler package. Automatic code checking for style guide compliance using the lintr package. Section 2. The ggplot2 package. Plot fundamentals in ggplot2 Topic 2.1. The ggplot2 package The ggplot2 package. Overview. Installation. Lifecycle. Ecosystem of extensions. Learning ggplot2. The plotly package for creating interactive graphics. Topic 2.2. Plot fundamentals in ggplot2 The ggplot(), aes(), `+`(<gg>), `%+%`, ggsave() functions and their arguments. Layers. Geometric objects. Statistical transformations. Position adjustments. Annotations. Aesthetics. Scales. Axes and legends. Facets. Coordinate systems. Themes. Topic 2.3. Visualization and recovery (imputation) of missing values The naniar, VIM packages for visualization and recovery (imputation) of missing values. Topic 2.4. Color palettes. Color blindness simulators Collection of color palettes of paletteer package. Color blindness simulators of colorBlindness, colorblindr packages. Section 3. Automation of reporting Topic 3.1. Automated graphical exploratory data analysis The dlookr, brinton packages for automated graphical exploratory data analysis. Topic 3.2. Quarto system for scientific and technical publishing An open-source Quarto system for scientific and technical publishing. Section 4. Graphing of a variable (probability) distribution Topic 4.1. Graphing of a continuous variable distribution

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Continuous variable. Features. Basic methods for graphing of a continuous variable (probability) distribution. Alternative methods for graphing of a continuous variable distribution. Plot options. Topic 4.2. Graphing of a categorical variable distribution Categorical variable. Nominal variable, ordinal variable, discrete variable. Features. Basic methods for graphing of a categorical variable (probability) distribution. Alternative methods for graphing of a categorical variable distribution. Plot options. Section 5. Graphing of multivariate data Topic 5.1. Graphing of multivariate continuous data Multivariate continuous data. Features. Basic methods for graphing of multivariate continuous data. Alternative methods for graphing of multivariate continuous data. Plot options. Topic 5.2. Graphing multivariate categorical data Multivariate categorical data. Features. Basic methods for graphing multivariate categorical data. Alternative methods for graphing multivariate categorical data. Plot options. Section 6. Time series graphing Topic 6.1. Time series graphing Time series. Features. Basic methods for time series graphing. Alternative methods for time series graphing. Plot options.