Quickly get started making nice plots in R to show off your data.
This course is a 3-hour quick start guide for making plots in R using ggplot and ComplexHeatmaps. This package includes the R code and example data I used in the videos, so you can follow along and play with the data yourself.
R is a great language for science, including statistics and making plots, and this course is designed to be accessible to people with no programming experience.
Here is the outline:
Lesson 1: A quick start guide — From data to plot with a few magic words
Lesson 2: Importing and downloading data — From Excel, text files, or publicly available data, this lesson covers how to get all of it into R and addresses a number of common problems with data formatting issues.
Lesson 3: Interrogating your data — Getting quick summary statistics and navigating data frames.
Lesson 4: Filtering and cleaning up data — Kicking out the data that annoys you and polishing up the rest
Lesson 5: Tweaking everything in your plots — Everything from color schemes to fonts to grid lines and tick marks, this lesson will show you how to change just about anything in a plot. Especially useful for creating plots for publication.
Lesson 6: Plot anything! — Quick guide to each plot type including which types of data fit into each one.
- Bar plots
- Scatter plots
- Box plots
- Violin plots
- Density plots
- Line-plots for time-course data
- Venn diagrams
Lesson 7: Multifaceted figures — Splitting up your data by some column into multiple plots arranged in rows, columns, or even tables.
Lesson 8: Heatmaps -- How to create everything from simple heatmaps to adding different clustering and trees, partitions, and labels on the sides.
Lesson 8: You may need to replace `source("https://bioconductor.org/biocLite.R")` with `biocLite("ComplexHeatmap")` if you are on R version 3.5+.
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