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D. & Joe Wu Ph. D. [BTEP/GAU/CCR/NCI/NIH-email ncibtep@mail.nih.gov](BTEP/GAU/CCR/NCI/NIH-email ncibtep@mail.nih.gov)[ ](BTEP/GAU/CCR/NCI/NIH-email ncibtep@mail.nih.gov)Bioinformatics Training and Education Program  \nTable of Contents  \n\n| Course Overview |  |\n| --- | --- |\n| • Course Overview\u003Cbr>• Welcome to the Data Visualization with R Series\u003Cbr>• A series of lessons designed to introduce learners to the R package ggplot2\u003Cbr>• Course objectives\u003Cbr>• What this course is not!\u003Cbr>• Course Expectations\u003Cbr>• Lesson 1: Introduction to plot types\u003Cbr>• Lesson 2: Getting Started with ggplot2\u003Cbr>• Lesson 3: Scatter plots and Non-data elements of ggplot2 customization\u003Cbr>• Lesson 4: Visualizing summary statistics with ggplot2\u003Cbr>• Lesson 5: Visualizing clusters with heatmaps\u003Cbr>• Lesson 6: Combining multiple plots to create a figure panel\u003Cbr>• Required Course Materials\u003Cbr>Lesson 1: Course Introduction | 10 |\n|  | 10 |\n|  | 10 |\n|  | 10 |\n|  | 10 |\n|  | 10 |\n|  | 10 |\n|  | 11 |\n|  | 11 |\n|  | 11 |\n|  | 11 |\n|  | 11 |\n|  | 11 |\n| • Lesson 1: Course Introduction\u003Cbr>Lesson 2: The Basics of GGplot2 | 12 |\n| • Introduction to ggplot2 | 13 |\n\n• Objectives 13  \n\n| • Going beyond Excel | 13 |\n| --- | --- |\n| • Why ggplot2? | 16 |\n| • Getting started with ggplot2\u003Cbr>• Getting help\u003Cbr>• The ggplot2 template\u003Cbr>• Geom functions\u003Cbr>• Mapping and aesthetics (aes())\u003Cbr>• R objects can also store figures\u003Cbr>• Colors\u003Cbr>• Facets\u003Cbr>• A better example of facet_grid() using data(\"Titanic\") .\u003Cbr>• Building upon our template\u003Cbr>• Using multiple geoms per plot | 16 |\n|  | 17 |\n|  | 17 |\n|  | 20 |\n|  | 21 |\n|  | 24 |\n|  | 24 |\n|  | 30 |\n|  | 33 |\n|  | 34 |\n|  | 35 |\n| • Labels, legends, scales, and themes | 36 |\n| • Saving plots (ggsave()) | 37 |\n| • Resource list | 38 |\n| • Acknowledgements\u003Cbr>Lesson 3: Scatter plots and ggplot2 customization | 38 |\n\n•  Scatter plots and plot customization 39  \n• Objectives 39  \n• The Data 39  \n• The grammar of graphics 40  \n•  Scatter plots 41  \n• Basic Scatter 41  \n• Coordinate Systems 42  \n\n| •  Perform and plot PCA data using iris. 43\u003Cbr>• What is PCA? 43\u003Cbr>\u003Cbr>• Perform PCA 44\u003Cbr>\u003Cbr>• Plot PCA 45\u003Cbr>\u003Cbr>• Add custom axes labels 47\u003Cbr>\u003Cbr>• Add a stat to our plot with stat_ellipse() . 48\u003Cbr>• Plot customization 49 • Themes 49 • A helpful color trick 51 • Putting it all together 53 • Creating a publication ready volcano plot 53 • Changing axes scales 56 • Modifying legends 57 • Save to an . rds file 60 • References 61\u003Cbr>Lesson 4: Stat Transformations: Bar plots, box plots, and histograms |  |\n| --- | --- |\n| • Stat Transformations: Bar plots, box plots, and histograms\u003Cbr>• Our grammar of graphics template\u003Cbr>• Our grammar of graphics template\u003Cbr>• Libraries\u003Cbr>• The Data\u003Cbr>• Variable Types\u003Cbr>• The stat argument\u003Cbr>• Bar Plot\u003Cbr>• stat = count | 62 |\n|  | 62 |\n|  | 62 |\n|  | 63 |\n|  | 63 |\n|  | 64 |\n|  | 65 |\n|  | 66 |\n|  | 67 |\n\n• stat = identity 69  \n\n| •\u003Cbr>• | • Using factors | 71 |\n| --- | --- | --- |\n|  | • Adding error bars | 73 |\n|  | Histogram | 75 |\n|  | Box plot\u003Cbr>son5: Visualizing clusters with heatmap and dendrogram | 78 |\n| Les |  |  |\n| • Visualizing clusters with heatmaps 81\u003Cbr>• What is a heatmap? 81\u003Cbr>• Heatmap can be used to visualize the following 81 • What is a dendrogram? 81 • Applications of dendrograms 81 • Methods available to produce a heatmap in R 84 • Load the libraries 85 • Import data 85 • Heatmap of the top 20 genes from differential expression analysis 87 • Distance, clustering, and scaling 88 • Distance calculation 88\u003Cbr>• Cluster generation 88\u003Cbr>• Scaling 88\u003Cbr>• Working with color customization 92\u003Cbr>• Adding treatment group information to samples (annotation_col, annotation_colors) 93\u003Cbr>• Add the annotations column 94 |  |  |\n\n• Modify the annotations colors 95  \n• Splitting heatmap into multiple columns and rows 96  \n• Add a title to heatmap 98  \n• Saving a non-ggplot 100  \n\n| • Make this plot compatible with ggplot2 using the p","cbCaiebQXJTIUJ4G","https://ap.wps.com/l/cbCaiebQXJTIUJ4G","pdf",13112527,180,"English","# Course Overview\n## Welcome to the Data Visualization with R Series\n## Course objectives\n## What this course is not!\n## Course Expectations\n## Required Course Materials\n# Lesson 1: Course Introduction\n## Objectives\n## Going beyond Excel\n## Why ggplot2?\n## Getting started with ggplot2\n## Introduction to ggplot2\n# Lesson 2: The Basics of GGplot2\n## Going beyond Excel\n## Getting help\n## Geom functions\n## Mapping and aesthetics (aes())\n## Colors and facets\n## Saving plots (ggsave())\n# Lesson 3: Scatter plots and ggplot2 customization\n## The grammar of graphics\n## Coordinate Systems\n## PCA with iris (perform, plot, customize)\n# Lesson 4: Stat Transformations\n## Bar plots, box plots, and histograms\n## Stat arguments (count, identity)\n# Lesson 5: Visualizing clusters with heatmaps and dendrogram\n## Heatmaps and dendrograms\n## Distance, clustering, and scaling\n## Color customization and annotations\n# Lesson 6: Multi-figure panel\n## cowplot and ggdraw\n## patchwork and plot_layout()\n## Taking advantage of ggdraw\n# Practice Questions\n## Practice plotting using ggplot2: Lesson 2\n## Exercise Questions (Questions 1-6, challenges)","[{\"question\":\"What skills does this course aim to build for data visualization in R?\",\"answer\":\"The course builds proficiency in ggplot2 by teaching plot construction, customization with geoms and aesthetics, facets, legends, themes, and proper saving workflows. It also extends into stat-based plots, PCA visualization, and cluster heatmaps with dendrogram concepts.\"},{\"question\":\"Which ggplot2 topics are covered in the early lessons?\",\"answer\":\"Early lessons cover getting started with ggplot2, the ggplot2 template, geom functions, mapping aesthetics with aes(), working with colors, facets (including facet_grid ideas), and creating multiple geoms per plot. The materials also include labeled plots, legends, scales, and themes.\"},{\"question\":\"How does the course help learners create multi-figure panels?\",\"answer\":\"Lesson 6 explains combining plots using cowplot and patchwork, including arranging figures, plotting labels, using ggdraw, adding titles/spacers, and saving the final composed output.\"}]","Data Visualization with R - Course Overview - ggplot2 Lessons | PDF",454]