R programming fundamentals : deal with data using various modeling techniques / Kaelen Medeiros.

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Intro; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Introduction to R; Using R and RStudio, and Installing Useful Packages; Using R and RStudio; Executing Basic Functions in the R Console; Setting up a New Project ; Installing Packages; Activity: Installing the Tidyverse Packages; Variable Types and Data Structures; Variable Types; Numeric and Integers; Character; Dates; Activity: Identifying Variable Classes and Types; Data Structures; Vectors; Lists; Matrices; Dataframes; Activity: Creating Vectors, Lists, Matrices, and Dataframes.

Basic Flow ControlIf/else; For loop; While loop; Activity: Building Basic Loops; Data Import and Export; Excel Spreadsheets; Activity: Exporting and Importing the mtcars Dataset; Getting Help with R; Package Documentation and Vignettes; Activity: Exploring the Introduction to dplyr Vignette; RStudio Community, Stack Overflow, and the Rest of the Web; Summary; Data Visualization and Graphics; Creating Base Plots; The plot() Function; Factor Variables; Model Objects; Plotting More Than One Plot at a Time; Creating and Plotting a Linear Model Object; Titles and Axis Labels.

Changing the Color of Base PlotsActivity: Recreating Plots with Base Plot Methods; ggplot2; ggplot2 Basics; Histogram; Creating Histograms using ggplot2; Bar Chart; Creating a Bar Chart with ggplot2 using Two Different Methods; Scatterplot; Creating a Scatterplot of Two Continuous Variables; Boxplot; Creating Boxplots using ggplot2; Activity: Recreating Plots Using ggplot2; Digging into aes(); Bar Chart; Using Different Bar Chart Aesthetic Options; Facet Wrapping and Gridding; Utilizing Facet Wrapping and Gridding to Visualize Data Effectively; Boxplot + coord_flip(); Scatterplot.

Utilizing Different Aesthetics for Scatterplots, Including Shapes, Colors, and TransparenciesActivity: Utilizing ggplot2 Aesthetics; Extending the Plots with Titles, Axis Labels, and Themes; Interactive Plots; Plotly; Shiny; Exploring Shiny and Plotly; Summary; Data Management; Factor Variables; Creating Factor Variables in a Dataset; Using ifelse() Statements; Using the recode() Function; Examining and Changing the Levels of Pre-existing Factor Variables; Creating an Ordered Factor Variable; Activity: Creating and Manipulating Factor Variables; Summarizing Data; Data Summarization Tables.

Tables in RCreating Different Tables Using the table() Function; Using dplyr Methods to Create Data Summary Tables; Activity: Creating Data Summarization Tables; Summarizing Data with the Apply Family; Using the apply() Function to Create Numeric Data Summaries; Activity: Implementing Data Summary; Splitting, Combining, Merging, and Joining Datasets; Splitting and Combining Data and Datasets; Splitting and Unsplitting Data with Base R and the dplyr Methods; Splitting Datasets into Lists and Then Back Again; Combining Data; Combining Data with rbind().

Combining Matrices of Objects into Dataframes.

Data analysis is crucial to accurately predict the performance of an application. The book begins by getting you started with R, including basic programming and data import, data visualization, pivoting, merging, aggregating, and joins. Once you are comfortable with the basics, you can read ahead and learn all about data visualization and ...

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