Search Results for: figures

As we have discussed, R objects are used to store things created in R to memory. This includes plots. scatter_plot

As we have discussed, R objects are used to store things created in R to memory. This includes plots. scatter_plot

As we have discussed, R objects are used to store things created in R to memory. This includes plots created with ggplot2 . dot_plot

(Inspired by Visualizing Data in the Tidyverse, a Coursera lesson) Consider whether the plot type you have chosen is the best way to convey your message Make your plot visually appealing Careful color selection - Read More...

(Inspired by Visualizing Data in the Tidyverse, a Coursera lesson) Consider whether the plot type you have chosen is the best way to convey your message Make your plot visually appealing Careful color selection - Read More...

#Setting a theme my_theme <- theme_bw() + theme( #Remove the border around the plot panel.border = element_blank(), # Add the axis lines back in axis.line = element_line(), #resize the major and Read More...

::: {.cell} #Setting a theme my_theme <- theme_bw() + theme( panel.border = element_blank(), axis.line = element_line(), panel.grid.major = element_line(size = 0.2), panel.grid.minor = element_line(size = 0.1), text = element_text( Read More...

Learning R and associated plotting packages is a great way to generate publishable figures in a reproducible fashion. With R you can: 1. Create simple or complex figures. 2. Create high resolution figures. 3. Generate scripts that can Read More...

There are many complementary R packages related to creating publishable figures using ggplot2. Check out the packages cowplot and ggpubr . Cowplot is particularly great for providing functions that facilitate arranging multiple plots in a grid Read More...

To learn how to create publishable figures using the ggplot2 package in R. By the end of this lesson, learners should be able to create simple, pretty, and effective figures.

This is ultimately up to you. In general, patchwork is easier to combine nicely aligned grid style plots. cowplot seems to include greater opportunities for customization, but you could easily customize things like titles using Read More...

You can nest figures by combining figures using plot_grid() , saving that to an object, and then plotting those pre-combined figures with another figure using plot_grid() again. Shared legends can be obtained using cowplot' Read More...

This is ultimately up to you. In general, patchwork is easier to combine nicely aligned grid style plots. cowplot seems to include greater opportunities for customization, but you could easily customize things like titles using Read More...

Learning R and associated plotting packages is a great way to generate publishable figures in a reproducible fashion. With R you can: 1. Create simple or complex figures. 2. Create high resolution figures. 3. Generate scripts that can Read More...

There are many complementary R packages related to creating publishable figures using ggplot2. Check out the packages cowplot and ggpubr . Cowplot is particularly great for providing functions that facilitate arranging multiple plots in a grid Read More...

Objectives To learn how to create publishable figures using the ggplot2 package in R. By the end of this lesson, learners should be able to create simple, pretty, and effective figures.

Review the grammar of graphics template. Learn about the statistical transformations inherent to geoms. Learn more about fine tuning figures with labels, legends, scales, and themes. Learn how to save plots with ggsave() . Review general Read More...

Review the grammar of graphics template. Learn about the statistical transformations inherent to geoms. Learn more about fine tuning figures with labels, legends, scales, and themes. Learn how to save plots with ggsave() . Review general Read More...

01/08/2026 - In this lesson, attendees will continue learning how to create publishable figures with ggplot2. Topics will include statistical transformations, coordinate systems, and themes.

01/06/2026 - In this lesson, attendees will learn the basics of ggplot2 to create simple, pretty, and effective figures with R.

In this lesson, attendees will continue learning how to plot publishable figures with ggplot2.

This lesson will introduce prominent ways to visualize data with R. The majority of the lesson will be devoted to learning how to create publishable figures using the ggplot2 package.

02/13/2024 - In this lesson, attendees will continue learning how to plot publishable figures with ggplot2. 

02/08/2024 - This lesson will introduce prominent ways to visualize data with R. The majority of the lesson will be devoted to learning how to create publishable figures using the ggplot2 package. 

Learn the ggplot2 syntax. Build a ggplot2 general template. By the end of the course, students should be able to create simple, pretty, and effective figures.

There are multiple ways to combine figures using R. In this lesson, we will learn how to combine figures primarily with patchwork . Though, we will also learn some components of cowplot . To get started, load Read More...

There are multiple ways to combine figures using R. In this lesson, we will learn how to combine figures using two different R packages cowplot and patchwork , beginning with cowplot . To get started, load the Read More...

The cowplot package provides various features that help with creating publication-quality figures, such as a set of themes, functions to align plots and arrange them into complex compound figures, and functions that make it easy Read More...

Scientific journals almost always have limits on the number of figures that can be included in a publication. Don't fret, in lesson 6, we will focus on generating sub plots and multi plot figure panels Read More...

The cowplot package provides various features that help with creating publication-quality figures, such as a set of themes, functions to align plots and arrange them into complex compound figures, and functions that make it easy Read More...

Data organization is extremely important to reproducible science. Consider organizing your project directory in a way that facilitates reproducibility. All inputs and outputs (where possible) should be contained within the project directory, and a consistent Read More...

A powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical Read More...

Outside of base R plotting, one of the most popular packages used to generate graphics in R is ggplot2 , which is associated with a family of packages collectively known as the tidyverse. GGplot2 allows the Read More...

Assuming you are still connected to a computational node on VSCode, open a new .R file in VSCode. At the bottom of the page, you will see R: (not attached) (See below). To attach the Read More...

Now that we have renv set up with our project, let's also establish a project structure. Let's exit R and edit our .Rprofile . Note When we ran renv::init() a local .Rprofile file Read More...

04/27/2023 - Scientific journals almost always have limits on the number of figures that can be included in a publication. Don't fret, in the 6th and final lesson of the Data Visualization with R course Read More...

Learn how to import spreadsheet data. Learn the ggplot2 syntax. Build a ggplot2 general template. By the end of the course, students should be able to create simple, pretty, and effective figures.

Journal Impact Factor Number of Figures Nature Cancer 60.72 5-8 Science 47.73 6 Cancer Cell 31.74 8 Journal of Clinical Oncology 44.54 6 JAMA Oncology 31.78 5 Cell Host and Microbe 21.02 7

This is the grammar of graphics. Adding layers to create unique figures. ggplot(data = ) + ( mapping = aes(), ) + Note that there are a lot of invisible (default) layers that often go into each ggplot2, and there are Read More...

Journal Impact Factor Number of Figures Nature Cancer 60.72 5-8 Science 47.73 6 Cancer Cell 31.74 8 Journal of Clinical Oncology 44.54 6 JAMA Oncology 31.78 5 Cell Host and Microbe 21.02 7 Example Multi-figure panel from Zhang et al.(2022). Longitudinal single-cell RNA-seq analysis reveals stress-promoted Read More...

Objectives Combine multiple plots into a single figure Learn how to use patchwork and cowplot The primary purpose of this lesson is to learn how to combine multiple figures into a single multi-panel figure using Read More...

Combine multiple plots into a single figure Learn how to use aspects of cowplot and patchwork The primary purpose of this lesson is to learn how to combine multiple figures into a single multi-panel figure Read More...

Outside of base R plotting, one of the most popular packages used to generate graphics in R is ggplot2 , which is associated with a family of packages collectively known as the tidyverse. GGplot2 allows the Read More...

The function plot_layout() can be used to control the layout, combine legends, and overwrite plot titles. pca1

The function plot_layout() can be used to control the layout, combine legends, and overwrite plot titles. pca1

In this section, we will learn how to create publishable figures using the R (ggplot2) package. This includes an introduction to mapping and aesthetics, building plots iteratively, and improving plot readability. Additional packages for colors, Read More...

Software Image Acquisition Commercial imaging systems of LRBGE Optical Microscopy Core are controlled by acquisition software specifically designed for the appropriate microscope, such as ZEN (Zeiss confocal microscopes), Nikon Elements (Nikon), Imspector (Abberior). Custom-built HILO Read More...

How do we ultimately get our figures to a publishable state? The bread and butter of pretty plots really falls to the additional non-data layers of our ggplot2 code. These layers will include code to Read More...

Last lesson we discussed the three basic components of creating a ggplot2 plot: the data , one or more geoms , and aesthetic mappings . ggplot(data = ) + (mapping = aes()) But, we also learned of other features that greatly Read More...

Course Overview Welcome to the R Introductory Series! A series of introductory lessons in R for scientists. This course will include a series of lessons for individuals new to R or with limited R experience . Read More...

You do not need to load a package to visually explore data. Rather, you can use base R graphics for plotting (from the graphics package). This plotting is fairly different from ggplot2 , which is based Read More...

Objectives Review the grammar of graphics template. Learn about the statistical transformations inherent to geoms. Learn more about fine tuning figures with labels, legends, scales, and themes. Learn how to save plots with ggsave() . Review Read More...

ggplot2 is an R graphics package from the tidyverse collection. It allows the user to create informative plots quickly by using a 'grammar of graphics' implementation, which is described as "a coherent system for Read More...

The following represents the basic ggplot2 template: ggplot(data = ) + (mapping = aes()) We need three basic components to create a plot: the data we want to plot , geom function(s) , and mapping aesthetics . Notice the + symbol Read More...

R is both a computational language and open-source environment for statistical computing and graphics. While R programming was ranked 20 th in popularity when compared with other programming languages in December 2023, it remains a favorite among Read More...

How do we ultimately get our figures to a publishable state? The bread and butter of pretty plots really falls to the additional non-data layers of our ggplot2 code. These layers will include code to Read More...

The following represents the basic ggplot2 template. ggplot(data = ) + (mapping = aes()) The only required components to begin plotting are the data we want to plot, geom function(s), and mapping aesthetics. Notice the + symbol following Read More...

We will be downloading a FASTQ file from the Sequence Read Archive to learn about trimming. But first, go back to the ~/biostar_class folder and then create a new directory named trimming. cd ~/biostar_ Read More...

Getting VsCode to work interactively with R Step 1: Creating an ssh key to use VSCode Setup VSCode to run on a compute node using instructions outlined here . Step 2: ssh to Biowulf and start an interactive Read More...

R scripts can be run from the command line with command line arguments. Here is a great resource from software carpentry explaining command line arguments. To use command line arguments with an R script, we Read More...

How do we ultimately get our figures to a publishable state? The bread and butter of pretty plots really falls to the additional non-data layers of our ggplot2 code. These layers will include code to Read More...

We can change the alignment of the plots by using the align argument. bc

Multi-figure panel Objectives Combine multiple plots into a single figure Learn how to use patchwork and cowplot The primary purpose of this lesson is to learn how to combine multiple figures into a single multi-panel Read More...

Objectives Combine multiple plots into a single figure Learn how to use aspects of cowplot and patchwork The primary purpose of this lesson is to learn how to combine multiple figures into a single multi-panel Read More...

The following represents the basic ggplot2 template. ggplot(data = ) + (mapping = aes()) The main components include the data we want to plot, geom function(s), and mapping aesthetics. Notice the + symbol following the ggplot() function. This Read More...

When you have a lot of colors and you want to keep these colors consistent, you can use the following convenient functions to set a name attribute for a vector of colors. Let's do Read More...

Course Overview Welcome to the Data Visualization with R Series A series of lessons designed to introduce learners to the R package ggplot2 This course will include a series of lessons for scientists with beginner Read More...

How do we ultimately get our figures to a publishable state? The bread and butter of pretty plots really falls to the additional non-data layers of our ggplot2 code. These layers will include code to Read More...

Last lesson we discussed the three basic components of creating a ggplot2 plot: the data, one or more geoms, and aesthetic mappings. ggplot(data = ) + (mapping = aes()) But, we also learned of other features that greatly Read More...

ggplot2 is a R graphics package from the tidyverse collection. It allows the user to create informative plots quickly by using a 'grammar of graphics' implementation, which is described as "a coherent system for Read More...

02/21/2024 - Dear colleagues, The Bioinformatics and Computational Biosciences Branch (BCBB) at NIAID invites you to join us for in-person hands-on workshops that will explore biovisualization techniques. Developers of UCSF ChimeraX and Cytoscape will be on Read More...

Data visualization with ggplot2 Objectives To learn how to create publishable figures using the ggplot2 package in R. By the end of this lesson, learners should be able to create simple, pretty, and effective figures. Read More...

Learning Objectives To understand: 1. the difference between R and RStudioIDE. 2. how to work within the RStudio environment including: creating an Rproject and Rscript navigating between directories using functions obtaining help how R can enhance data Read More...

Introduction to ggplot2 Objectives Learn the ggplot2 syntax. Build a ggplot2 general template. By the end of the course, students should be able to create simple, pretty, and effective figures. Data Visualization in the tidyverse Read More...

Lesson 2: Getting Started with QIIME2 Lesson Objectives Obtain sequence data and sample metadata Import data and metadata Discuss other useful QIIME2 features including view QIIME2, provenance tracking, and the QIIME2 forum. DNAnexus DNAnexus provides a Read More...

Step 1 for generating bigWig files is to convert the BAM alignment results to a bedGraph (with extension bg) file that contains coverage along genomic regions. Enhancing your vocabulary: BED file - this is also known Read More...

We previously stored FASTQC results for the HBR and UHR raw sequencing data in the ~/biostar_class/hbr_uhr/QC directory (recall that ~ denotes home directory). So before getting started, change into this folder. cd ~/ Read More...

Here, let's change back in the ~/biostar_class/hbr_uhr/hbr_uhr_hisat2 folder. cd $hbr_uhr_hisat2 To align FASTQ files for one sample, we construct the HISAT2 command with the following options Read More...

In lesson 9, we learned that reference genomes came in the form of FASTA files, which essentially store nucleotide sequences. In this lesson, we will learn about the FASTQ file, which is the file format that Read More...

Lesson 10: Introducing the FASTQ file and assessing sequencing data quality Before getting started, remember to be signed on to the DNAnexus GOLD environment. Lesson 9 Review In the previous lesson, we explored the reference genomes and Read More...

Lesson 11: Merging FASTQ quality reports and data cleanup Before getting started, remember to be signed on to the DNAnexus GOLD environment. Lesson 10 Review In the previous lesson, we learned about the structure of the FASTQ Read More...

To align FASTQ files for one sample, we construct the HISAT2 command with the following options. The "-x" flag prompts us to enter the base name (ie. without extension) of genome index. The Read More...

Lesson 14: Visualizing alignment results Before getting started, remember to be signed on to the DNAnexus GOLD environment. Lesson 13 Review Previously, we used the application HISAT2 to align the raw sequencing data from the Human Brain Read More...

Lesson 13: Aligning raw sequences to reference genome Before getting started, remember to be signed on to the DNAnexus GOLD environment. Lesson 11 Review In Lesson 11 we learned to aggregate multiple FASTQC reports into one using MultiQC, Read More...

Lesson 13: Aligning raw sequences to reference genome Before getting started, remember to be signed on to the DNAnexus GOLD environment. Lesson 11 Review In Lesson 11 we learned to aggregate multiple FASTQC reports into one using MultiQC, Read More...

Lesson 3: R Project Management and renv Learning objectives Discuss the importance of reproducibility Learn ways to make R analyses more reproducible Learn how to set up and organize an R project Learn how to use Read More...

Lesson 4: Submitting R Scripts via command line Learning Objectives Learn how to use R with less interaction Learn how to deploy sbatch R jobs, and learn about alternatives such as swarm . Learn about R job Read More...

Unix, what is it, and why should biologists take the time to learn it? The Unix operating system forms the basis of many bioinformatics analyses resources, such as the NIH High Performance Cluster (HPC) Biowulf/ Read More...

Excel is a great program for visualizing and manipulating small data sets. However, it isn't great for working with "big data", and resulting plots are generally not publishable. Learning R and associated Read More...

Introduction to ggplot2 Objectives Learn how to import spreadsheet data. Learn the ggplot2 syntax. Build a ggplot2 general template. By the end of the course, students should be able to create simple, pretty, and effective Read More...

Scatter plots and plot customization Objectives Learn to customize your ggplot with labels, axes, text annotations, and themes. Learn how to make and modify scatter plots to make fairly different overall plot representations. Load a Read More...