How to use Charty¶

Preparation¶

Load libraries¶

At first, we need to load libraries to be used here. We use the following libraries.

charty for data visualization
datasets-pandas for loading open datasets provided by red-datasets and using it with Pandas's data frame
numo/narray for some numerical array operations

You can execute the following code cell by selecting the cell and then hit Shift+Enter.

require "charty"
require "datasets-pandas"  # This loads "datasets" and "pandas"
require "numo/narray"

false

{
  charty: Charty::VERSION,
  datasets_pandas: DatasetsPandas::VERSION,
  numo_narray: Numo::NArray::VERSION
}

{:charty=>"0.2.10", :datasets_pandas=>"0.0.1", :numo_narray=>"0.9.2.0"}

Select visualization backend¶

In this notebook, we use plotly backend to create plots.

Charty::Backends.use(:plotly)

:plotly

Load dataset¶

Datasets::Penguins is a Ruby port of palmerpenguins dataset. This dataset includes measurements for penguin species, island in Palmer Archipelago, size (flipper length, body mass, bill dimensions), sex, and year.

We will use this dataset in this notebook.

penguins = Datasets::Penguins.new.to_pandas

And, we will use the fmri dataset provided in seaborn for the line plot examples. red-datasets also provides this dataset.

fmri = Datasets::SeabornData.new("fmri").to_pandas

Scatter plot¶

Simple scatter plot to show the relationship between bill_length_mm and bill_depth_mm.

Charty.scatter_plot(
  data: penguins,      # input table data
  x: :bill_length_mm,  # the column name for x-axis
  y: :bill_depth_mm    # the column name for y-axis
)

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	male	2007
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	female	2007
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	female	2007
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	None	2007
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	female	2007
...	...	...	...	...	...	...	...	...
339	Gentoo	Biscoe	NaN	NaN	NaN	NaN	None	2009
340	Gentoo	Biscoe	46.8	14.3	215.0	4850.0	female	2009
341	Gentoo	Biscoe	50.4	15.7	222.0	5750.0	male	2009
342	Gentoo	Biscoe	45.2	14.8	212.0	5200.0	female	2009
343	Gentoo	Biscoe	49.9	16.1	213.0	5400.0	male	2009

	subject	timepoint	event	region	signal
0	s13	18	stim	parietal	-0.017552
1	s5	14	stim	parietal	-0.080883
2	s12	18	stim	parietal	-0.081033
3	s11	18	stim	parietal	-0.046134
4	s10	18	stim	parietal	-0.037970
...	...	...	...	...	...
1059	s0	8	cue	frontal	0.018165
1060	s13	7	cue	frontal	-0.029130
1061	s12	7	cue	frontal	-0.004939
1062	s11	7	cue	frontal	-0.025367
1063	s0	0	cue	parietal	-0.006899