heatmap.io: Heatmap Visualization

In ioanalysis: Input Output Analysis

Description

A visualization tool for matrices belonging to an input-output system.

Usage

heatmap.io(obj, RS_label = NULL, regions_x = 'all', sectors_x = 'all', 
           regions_y = 'all', sectors_y = 'all',
           ES_x = NULL, ES_y = NULL, FUN = NULL, low = NULL, high = NULL,
           min = NA, max = NA)

Arguments

obj	The object you wish to create a heat map for that corresponds to the RS_label
RS_label	The RS_label located in an InputOutput object. See as.inputoutput
regions_x	The regions you wish to plot on the x-axis. This can either be the numerical order the regions occur or the name of the regions. The default is 'all'.
sectors_x	The sectors you wish to plot on the x-axis. This can either be the numerical order the sectors occur or the name of the sectors. The default is 'all'.
regions_y	The regions you wish to plot on the y-axis. This can either be the numerical order the regions occur or the name of the regions. The default is 'all'.
sectors_y	The sectors you wish to plot on the y-axis. This can either be the numerical order the sectors occur or the name of the sectors. The default is 'all'.
ES_x	Instead of specifying regions and sectors individually, you can use an EasySelect object (see easy.select). If supplied, the regions and sectors are overridden.
ES_y	See ES_x
FUN	The transformation of the elements in obj such as log()
low	The color of the low values. Default is "yellow".
high	The color of the high values. Default is "blue".
min	The minimum value for the color legend. Default of NA == min(obj). Both min and max must be provided to change default.
max	The maximum value for the color legend. Default of NA == max(obj). Both min and max must be provided to change default.

Details

heatmap.io uses ggplot2::geom_tiles() to create the visualization of the object.

Note

The coloring follows the temperatures of stars!

Author(s)

John J. P. Wade

Examples

data(toy.IO)
class(toy.IO)

RS_label = toy.IO$RS_label
obj = toy.IO$L
heatmap.io(obj, RS_label, FUN = log, max = 3)

cuberoot = function(x){x^(1/3)}
heatmap.io(obj, RS_label, FUN = cuberoot)

# Total field of influence
fit = f.influence.total(toy.IO)
heatmap.io(fit, RS_label, sectors_x = c(1,3,4,5), regions_y = c(2), sectors = 1:3)

data(toy.ES)
ES2 = matrix(c(1,5,6,8,9))
class(ES2) = 'EasySelect'
heatmap.io(fit, RS_label, ES_x = toy.ES, ES_y = ES2, 
           low = '#00fcef', high = 'blueviolet')

ioanalysis documentation built on Jan. 13, 2021, 5:16 p.m.