quarterly_sunspots

Quarterly mean relative sunspots number from 1749-1983

datasets

Alluvial plots are similar to sankey diagrams and visualise categorical data
over multiple dimensions as flows. (Rosvall M, Bergstrom CT (2010) Mapping Change in
Large Networks. PLoS ONE 5(1): e8694. <doi:10.1371/journal.pone.0008694>
Their graphical grammar however is a bit more complex then that of a regular x/y
plots. The 'ggalluvial' package made a great job of translating that grammar into
'ggplot2' syntax and gives you many options to tweak the appearance of an alluvial
plot, however there still remains a multi-layered complexity that makes it difficult
to use 'ggalluvial' for explorative data analysis. 'easyalluvial' provides a simple
interface to this package that allows you to produce a decent alluvial plot from any
dataframe in either long or wide format from a single line of code while also handling
continuous data. It is meant to allow a quick visualisation of entire dataframes
with a focus on different colouring options that can make alluvial plots a great
tool for data exploration.

Bjoern Koneswarakantha

easyalluvial

Generate Alluvial Plots with a Single Line of Code

quarterly_sunspots function

A data frame with 940 rows and 4 variables<dl>
 <dt>year</dt>
<dd></dd> <dt>qu</dt>
<dd>quarter</dd>
 <dt>spots</dt>
<dd>total number of sunspots</dd> <dt>mean_spots_per_year</dt>
<dd></dd>
 
</dl>

Format

Quarterly mean relative sunspots number from 1749-1983 — quarterly_sunspots

A data frame with 940 rows and 4 variables<dl>
 <dt>year</dt>
<dd></dd>

 <dt>qu</dt>
<dd>quarter</dd>
 <dt>spots</dt>
<dd>total number of sunspots</dd>

 <dt>mean_spots_per_year</dt>
<dd></dd>


 
</dl>

quarterly_sunspots: Quarterly mean relative sunspots number from 1749-1983

Description

Usage

Arguments

Format