README.md
In ZachHunter/NicePlots.R: Nice Plots for Data Exploration
NicePlots.R
A series of S3 generic plotting functions to aid in data exploration and visualization for those just getting started in R
One of the fundamental problems in biomedical research is how interdependent bioinformatics specialists and wet lab biologist are and yet they have very few ways of effectively collaborating or sharing insights with each other. For a rigorous analysis or custom visualization, a lot of background knowledge is necessary. Not only does one need to learn R but the relevant statistics, specialized tools, then ggplot’s grammar of graphics and Bioconductor’s datatypes as well. With this project I hope to encourage biologists, physicians and other non-computational researchers to learn enough R to be able to interact with and visualize large datasets for the purposes of hypothesis generation. These plotting functions are S3 generics so that they can be easily adapted for use with complex next generation sequencing datasets from Bioconductor or any large dataset with a complex structure and make it invisible to the end user. The functions work like standard R functions allowing users to practice their basic R skills while being able to perform some basic exploratory analysis of their data.
A couple of details:
While not true for some of the early functions, the package makes heavy use of the tidyverse, but not ggplot2 at this time. Why not ggplot? Nothing against it, I just wanted to practice base R plotting when I wrote this. Anyone wanting to contribute ggplot based additions should feel free to do so. Kernsmooth is used for kernel density estimation and I plan to add some optional interactive shiny support in the future.
The code is still very much in flux but the aim is to get it submitted to CRAN sometime in 2019. Contributions are always welcome. A companion package integrating NicePlots and RNASeq data to serve as an example of how the package can be used is in the works as well and I will post more information on that here when it is a bit further along.
ZachHunter/NicePlots.R documentation built on Sept. 23, 2023, 4:04 a.m.
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NicePlots.R
A series of S3 generic plotting functions to aid in data exploration and visualization for those just getting started in R
One of the fundamental problems in biomedical research is how interdependent bioinformatics specialists and wet lab biologist are and yet they have very few ways of effectively collaborating or sharing insights with each other. For a rigorous analysis or custom visualization, a lot of background knowledge is necessary. Not only does one need to learn R but the relevant statistics, specialized tools, then ggplot’s grammar of graphics and Bioconductor’s datatypes as well. With this project I hope to encourage biologists, physicians and other non-computational researchers to learn enough R to be able to interact with and visualize large datasets for the purposes of hypothesis generation. These plotting functions are S3 generics so that they can be easily adapted for use with complex next generation sequencing datasets from Bioconductor or any large dataset with a complex structure and make it invisible to the end user. The functions work like standard R functions allowing users to practice their basic R skills while being able to perform some basic exploratory analysis of their data.
A couple of details: While not true for some of the early functions, the package makes heavy use of the tidyverse, but not ggplot2 at this time. Why not ggplot? Nothing against it, I just wanted to practice base R plotting when I wrote this. Anyone wanting to contribute ggplot based additions should feel free to do so. Kernsmooth is used for kernel density estimation and I plan to add some optional interactive shiny support in the future.
The code is still very much in flux but the aim is to get it submitted to CRAN sometime in 2019. Contributions are always welcome. A companion package integrating NicePlots and RNASeq data to serve as an example of how the package can be used is in the works as well and I will post more information on that here when it is a bit further along.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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