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@ -9,7 +9,7 @@
[![The MIT License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE.txt) [![The MIT License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE.txt)
[![DOI](https://zenodo.org/badge/283230219.svg)](https://zenodo.org/badge/latestdoi/283230219) [![DOI](https://zenodo.org/badge/283230219.svg)](https://zenodo.org/badge/latestdoi/283230219)
YouPlot is a command line tool that draws plots in a terminal. YouPlot is a command line tool that draws plots in the terminal.
:bar_chart: Powered by [UnicodePlot](https://github.com/red-data-tools/unicode_plot.rb) :bar_chart: Powered by [UnicodePlot](https://github.com/red-data-tools/unicode_plot.rb)
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## Quick Start ## Quick Start
* `cat data.tsv | uplot <command> [options]` or `uplot <command> [options] <data.tsv>`
* `uplot <command> [options] <data.tsv>`
### barplot ### barplot
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### count ### count
In this example, YouPlot counts the number of chromosomes where the gene is located from the human gene annotation file and it creates a bar chart. The human gene annotation file can be downloaded from the following website.
* https://www.gencodegenes.org/human/
```sh ```sh
cat gencode.v35.annotation.gff3 \ cat gencode.v35.annotation.gff3 \
| grep -v '#' | grep 'gene' | cut -f1 \ | grep -v '#' | grep 'gene' | cut -f1 \
@ -117,6 +112,9 @@ cat gencode.v35.annotation.gff3 \
<img alt="count" src="https://user-images.githubusercontent.com/5798442/101999832-30b1ae80-3d24-11eb-96fe-e5000bed1f5c.png"> <img alt="count" src="https://user-images.githubusercontent.com/5798442/101999832-30b1ae80-3d24-11eb-96fe-e5000bed1f5c.png">
</p> </p>
In this example, YouPlot counts the number of chromosomes where genes are located.
* [GENCODE - Human Release 38](https://www.gencodegenes.org/human/)
Note: `count` is not very fast because it runs in a Ruby script. Note: `count` is not very fast because it runs in a Ruby script.
This is fine in most cases, as long as the data size is small. If you want to visualize huge data, it is faster to use a combination of common Unix commands as shown below. This is fine in most cases, as long as the data size is small. If you want to visualize huge data, it is faster to use a combination of common Unix commands as shown below.