Package 'ggDoubleHeat' reference manual

Title:	A Heatmap-Like Visualization Tool
Description:	A data visualization design that provides comparison between two (Double) data sources (usually on a par with each other) on one reformed heatmap, while inheriting 'ggplot2' features.
Authors:	Youzhi Yu [aut, cre], Trent Buskirk [aut, ths]
Maintainer:	Youzhi Yu <[email protected]>
License:	GPL (>= 3)
Version:	0.1.2
Built:	2025-02-14 04:41:26 UTC
Source:	https://github.com/pursuitofdatascience/ggdoubleheat

Heatcircle

Description

The heatcircle geom is used to create the two concentric circles that use luminance to show the values from two sources on the same plot.

Usage

geom_heat_circle(
  outside,
  outside_name = NULL,
  outside_colors = c("#FED7D8", "#FE8C91", "#F5636B", "#E72D3F", "#C20824"),
  inside,
  inside_name = NULL,
  inside_colors = c("gray100", "gray85", "gray50", "gray35", "gray0"),
  r = 2 * sqrt(2),
  ...
)
geom_heat_circle(
  outside,
  outside_name = NULL,
  outside_colors = c("#FED7D8", "#FE8C91", "#F5636B", "#E72D3F", "#C20824"),
  inside,
  inside_name = NULL,
  inside_colors = c("gray100", "gray85", "gray50", "gray35", "gray0"),
  r = 2 * sqrt(2),
  ...
)

Arguments

`outside`	The column name for the outside portion of heatcircle.
`outside_name`	The label name (in quotes) for the legend of the outside rendering. Default is `NULL`.
`outside_colors`	A color vector, usually as hex codes.
`inside`	The column name for the inside portion of heatcircle.
`inside_name`	The label name (in quotes) for the legend of the inside rendering. Default is `NULL`.
`inside_colors`	A color vector, usually as hex codes.
`r`	The value that controls how large of the inside portion with respect to the outside one. When `r` is larger, the inside get smaller. Default value is 2*sqrt(2) for achieving the equal inner and outer areas.
`...`	`...` accepts any arguments `scale_fill_gradientn()` has .

Value

A heatcircle comparing two data sources.

Examples


# heatcircle with categorical variables only

library(ggplot2)

data <- data.frame(x = rep(c("a", "b", "c"), 3),
                   y = rep(c("d", "e", "f"), 3),
                   outside_values = rep(c(1,5,7),3),
                   inside_values = rep(c(2,3,4),3))

ggplot(data, aes(x,y)) +
geom_heat_circle(outside = outside_values,
                 inside = inside_values)


# Making the inside smaller by setting r to be larger.

ggplot(data, aes(x,y)) +
geom_heat_circle(outside = outside_values,
                 inside = inside_values,
                 r = 5)


# heatcircle with numeric variables only

data <- data.frame(x = rep(c(1, 2, 3), 3),
                   y = rep(c(1, 2, 3), 3),
                   outside_values = rep(c(1,5,7),3),
                   inside_values = rep(c(2,3,4),3))

ggplot(data, aes(x,y)) +
geom_heat_circle(outside = outside_values,
                 inside = inside_values)


# heatcircle with a mixture of numeric and categorical variables

data <- data.frame(x = rep(c("a", "b", "c"), 3),
                   y = rep(c(1, 2, 3), 3),
                   outside_values = rep(c(1,5,7),3),
                   inside_values = rep(c(2,3,4),3))

ggplot(data, aes(x,y)) +
geom_heat_circle(outside = outside_values,
                 inside = inside_values)

# heatcircle with categorical variables only

library(ggplot2)

data <- data.frame(x = rep(c("a", "b", "c"), 3),
                   y = rep(c("d", "e", "f"), 3),
                   outside_values = rep(c(1,5,7),3),
                   inside_values = rep(c(2,3,4),3))

ggplot(data, aes(x,y)) +
geom_heat_circle(outside = outside_values,
                 inside = inside_values)


# Making the inside smaller by setting r to be larger.

ggplot(data, aes(x,y)) +
geom_heat_circle(outside = outside_values,
                 inside = inside_values,
                 r = 5)


# heatcircle with numeric variables only

data <- data.frame(x = rep(c(1, 2, 3), 3),
                   y = rep(c(1, 2, 3), 3),
                   outside_values = rep(c(1,5,7),3),
                   inside_values = rep(c(2,3,4),3))

ggplot(data, aes(x,y)) +
geom_heat_circle(outside = outside_values,
                 inside = inside_values)


# heatcircle with a mixture of numeric and categorical variables

data <- data.frame(x = rep(c("a", "b", "c"), 3),
                   y = rep(c(1, 2, 3), 3),
                   outside_values = rep(c(1,5,7),3),
                   inside_values = rep(c(2,3,4),3))

ggplot(data, aes(x,y)) +
geom_heat_circle(outside = outside_values,
                 inside = inside_values)

Heatgrid

Description

The heatgrid geom is used to create a modified heat map that uses luminance to show the values from two sources on the same plot.

Usage

geom_heat_grid(
  outside,
  outside_name = NULL,
  outside_colors = c("#FED7D8", "#FE8C91", "#F5636B", "#E72D3F", "#C20824"),
  inside,
  inside_name = NULL,
  inside_colors = c("gray100", "gray85", "gray50", "gray35", "gray0"),
  r = 2 * sqrt(2),
  ...
)
geom_heat_grid(
  outside,
  outside_name = NULL,
  outside_colors = c("#FED7D8", "#FE8C91", "#F5636B", "#E72D3F", "#C20824"),
  inside,
  inside_name = NULL,
  inside_colors = c("gray100", "gray85", "gray50", "gray35", "gray0"),
  r = 2 * sqrt(2),
  ...
)

Arguments

`outside`	The column name for the outside portion of heatgrid.
`outside_name`	The label name (in quotes) for the legend of the outside rendering. Default is `NULL`.
`outside_colors`	A color vector, usually as hex codes.
`inside`	The column name for the inside portion of heatgrid.
`inside_name`	The label name (in quotes) for the legend of the inside rendering. Default is `NULL`.
`inside_colors`	A color vector, usually as hex codes.
`r`	The value that controls how large of the inside portion with respect to the outside one. When `r` is larger, the inside get smaller. Default value is 2*sqrt(2) for achieving the equal inner and outer areas.
`...`	`...` accepts any arguments `scale_fill_gradientn()` has .

Value

A heatgrid comparing two data sources.

Examples


# heatgrid with categorical variables only

library(ggplot2)

data <- data.frame(x = rep(c("a", "b", "c"), 3),
                   y = rep(c("d", "e", "f"), 3),
                   outside_values = rep(c(1,5,7),3),
                   inside_values = rep(c(2,3,4),3))

ggplot(data, aes(x,y)) +
geom_heat_grid(outside = outside_values,
               inside = inside_values)

# Making the inside smaller by setting r to be larger.

ggplot(data, aes(x,y)) +
geom_heat_grid(outside = outside_values,
               inside = inside_values,
               r = 5)

# heatgrid with numeric variables only

data <- data.frame(x = rep(c(1, 2, 3), 3),
                   y = rep(c(1, 2, 3), 3),
                   outside_values = rep(c(1,5,7),3),
                   inside_values = rep(c(2,3,4),3))

ggplot(data, aes(x,y)) +
geom_heat_grid(outside = outside_values,
               inside = inside_values)


# heatgrid with a mixture of numeric and categorical variables

data <- data.frame(x = rep(c("a", "b", "c"), 3),
                   y = rep(c(1, 2, 3), 3),
                   outside_values = rep(c(1,5,7),3),
                   inside_values = rep(c(2,3,4),3))

ggplot(data, aes(x,y)) +
geom_heat_grid(outside = outside_values,
               inside = inside_values)




# heatgrid with categorical variables only

library(ggplot2)

data <- data.frame(x = rep(c("a", "b", "c"), 3),
                   y = rep(c("d", "e", "f"), 3),
                   outside_values = rep(c(1,5,7),3),
                   inside_values = rep(c(2,3,4),3))

ggplot(data, aes(x,y)) +
geom_heat_grid(outside = outside_values,
               inside = inside_values)

# Making the inside smaller by setting r to be larger.

ggplot(data, aes(x,y)) +
geom_heat_grid(outside = outside_values,
               inside = inside_values,
               r = 5)

# heatgrid with numeric variables only

data <- data.frame(x = rep(c(1, 2, 3), 3),
                   y = rep(c(1, 2, 3), 3),
                   outside_values = rep(c(1,5,7),3),
                   inside_values = rep(c(2,3,4),3))

ggplot(data, aes(x,y)) +
geom_heat_grid(outside = outside_values,
               inside = inside_values)


# heatgrid with a mixture of numeric and categorical variables

data <- data.frame(x = rep(c("a", "b", "c"), 3),
                   y = rep(c(1, 2, 3), 3),
                   outside_values = rep(c(1,5,7),3),
                   inside_values = rep(c(2,3,4),3))

ggplot(data, aes(x,y)) +
geom_heat_grid(outside = outside_values,
               inside = inside_values)

Heattriangle

Description

The heattriangle geom is used to create the two triangles split by a diagonal line of a rectangle that use luminance to show the values from two sources on the same plot.

Usage

geom_heat_tri(
  lower,
  lower_name = NULL,
  lower_colors = c("#FED7D8", "#FE8C91", "#F5636B", "#E72D3F", "#C20824"),
  upper,
  upper_name = NULL,
  upper_colors = c("gray100", "gray85", "gray50", "gray35", "gray0"),
  ...
)
geom_heat_tri(
  lower,
  lower_name = NULL,
  lower_colors = c("#FED7D8", "#FE8C91", "#F5636B", "#E72D3F", "#C20824"),
  upper,
  upper_name = NULL,
  upper_colors = c("gray100", "gray85", "gray50", "gray35", "gray0"),
  ...
)

Arguments

`lower`	The column name for the lower portion of heattriangle.
`lower_name`	The label name (in quotes) for the legend of the lower rendering. Default is `NULL`.
`lower_colors`	A color vector, usually as hex codes.
`upper`	The column name for the upper portion of heattriangle.
`upper_name`	The label name (in quotes) for the legend of the upper rendering. Default is `NULL`.
`upper_colors`	A color vector, usually as hex codes.
`...`	`...` accepts any arguments `scale_fill_gradientn()` has .

Value

A heattriangle with the main diagonal split by a line within each unit.

Examples


# heattriangle with categorical variables only

library(ggplot2)

data <- data.frame(x = rep(c("a", "b", "c"), 3),
                   y = rep(c("d", "e", "f"), 3),
                   lower_values = rep(c(1,5,7),3),
                   upper_values = rep(c(2,3,4),3))

ggplot(data, aes(x,y)) +
geom_heat_tri(lower = lower_values, upper = upper_values)


# heatcircle with numeric variables only

data <- data.frame(x = rep(c(1, 2, 3), 3),
                   y = rep(c(1, 2, 3), 3),
                   lower_values = rep(c(1,5,7),3),
                   upper_values = rep(c(2,3,4),3))

ggplot(data, aes(x,y)) +
geom_heat_tri(lower = lower_values, upper = upper_values)


# heatcircle with a mixture of numeric and categorical variables

data <- data.frame(x = rep(c("a", "b", "c"), 3),
                   y = rep(c(1, 2, 3), 3),
                   lower_values = rep(c(1,5,7),3),
                   upper_values = rep(c(2,3,4),3))

ggplot(data, aes(x,y)) +
geom_heat_tri(lower = lower_values, upper = upper_values)


# heattriangle with categorical variables only

library(ggplot2)

data <- data.frame(x = rep(c("a", "b", "c"), 3),
                   y = rep(c("d", "e", "f"), 3),
                   lower_values = rep(c(1,5,7),3),
                   upper_values = rep(c(2,3,4),3))

ggplot(data, aes(x,y)) +
geom_heat_tri(lower = lower_values, upper = upper_values)


# heatcircle with numeric variables only

data <- data.frame(x = rep(c(1, 2, 3), 3),
                   y = rep(c(1, 2, 3), 3),
                   lower_values = rep(c(1,5,7),3),
                   upper_values = rep(c(2,3,4),3))

ggplot(data, aes(x,y)) +
geom_heat_tri(lower = lower_values, upper = upper_values)


# heatcircle with a mixture of numeric and categorical variables

data <- data.frame(x = rep(c("a", "b", "c"), 3),
                   y = rep(c(1, 2, 3), 3),
                   lower_values = rep(c(1,5,7),3),
                   upper_values = rep(c(2,3,4),3))

ggplot(data, aes(x,y)) +
geom_heat_tri(lower = lower_values, upper = upper_values)

ggDoubleHeat: Data visualization for two sources

Description

ggDoubleHeat, which is a ggplot2 extension, provides visualization for a reformed heatmap. Instead of facetting heatmaps by data sources, they can be combined together for making one single heatmap, generated by geom_heat_*() functions built in the package. Prior to using the package, users should load ggplot2.

ggDoubleHeat functions

All functions in the package are named as geom_heat_*(), making the naming convention consistent. Also, the arguments for functions are relatively similar, although with slight variations due to where a specific argument will connect to the position of the rendering plot. Users should reference the documentation and possibly run examples presented in the help file when trying to understand what each argument means visually.

Popular Emojis

Description

The most popular Emoji of a given week in a given category from the Meltwater Tweet sample. They can be rendered by using "richtext" with annotate().

Usage

pitts_emojis
pitts_emojis

Format

An object of class character of length 270.

Pittsburgh COVID-related Google & Twitter incidence rates

Description

A data set containing the 30-week incidence rates of COVID related categories from week 1 starting from June 1, 2020 to week 30 that ended in the last Sunday of the year in Pittsburgh Metropolitan Statistical Area (MSA). The data columns are introduced below. One quick note about the columns of the data set: week_start as a column is present in the data set for illustration purposes, reminding users what week column is. In other words, it does not participate any visualization.

Usage

pitts_tg
pitts_tg

Format

A data frame with 270 rows and 6 columns:

msa: Metropolitan statistical area (Pittsburgh only).
week: week 1 to week 30.
week_start: The Monday date of the week started.
category: 9 Covid-related categories in total.
Twitter: weekly tweets percentage (%) in the MSA falling into each category.
Google: weekly Google search percentage (%) in the MSA falling into each category.

Source

Just like states_tg, Google is processed from Google Health API, and Twitter from Meltwater, a Twitter vendor. Both data sources are processed by the authors of the package.

Remove ggplot2 default padding

Description

The default ggplot2 plots give certain amount of padding for both continuous and discrete variables. Due to this padding, it makes the plots generated from ‘geom_heat_*()' look like there is something missing. Depends on users’ preference, they can remove the "empty space" by using this function. The only thing users need to figure out is whether the 'x' and 'y' scales are continuous or discrete.

Usage

remove_padding(x = "c", y = "d", ...)
remove_padding(x = "c", y = "d", ...)

Arguments

`x`	x-axis scale, if it is continuous scale, input "c"; discrete, "d".
`y`	y-axis scale, if it is continuous scale, input "c"; discrete, "d".
`...`	...

Value

remove_padding

States' COVID-related Google & Twitter incidence rates

Description

A data set containing the 30-week incidence rates of COVID related categories from week 1 starting from June 1, 2020 to week 30 that ended in the last Sunday of the year in 4 states (Florida, Missouri, New York, and Texas). The data columns are introduced below. One quick note about the columns of the data set: week_start as a column is present in the data set for illustration purposes, reminding users what week column is. In other words, it does not participate any visualization.

Usage

states_tg
states_tg

Format

A data frame with 1116 rows and 6 columns:

state: state
week: week 1 to week 30.
week_start: The Monday date of the week started.
category: 9 Covid-related categories in total.
Twitter: weekly tweets percentage (%) in state falling into each category.
Google: weekly Google search percentage (%) in state falling into each category.

Source

Just like pitts_tg, Google is processed from Google Health API, and Twitter from Meltwater, a Twitter vendor. Both data sources are processed by the authors of the package.

Plot Themes

Description

Plot Themes

Usage

theme_heat(
  base_size = 11,
  base_family = "",
  base_line_size = base_size/22,
  base_rect_size = base_size/22
)
theme_heat(
  base_size = 11,
  base_family = "",
  base_line_size = base_size/22,
  base_rect_size = base_size/22
)

Arguments

`base_size`	base font size
`base_family`	base font family
`base_line_size`	base size for line elements
`base_rect_size`	base size for rect elements

Value

Adding a heat theme to all plots generated by using the ggDoubleHeat package.

Package 'ggDoubleHeat'

Help Index

Heatcircle

Description

Usage

Arguments

Value

Examples

Heatgrid

Description

Usage

Arguments

Value

Examples

Heattriangle

Description

Usage

Arguments

Value

Examples

ggDoubleHeat: Data visualization for two sources

Description

ggDoubleHeat functions

Popular Emojis

Description

Usage

Format

Pittsburgh COVID-related Google & Twitter incidence rates

Description

Usage

Format

Source

Remove ggplot2 default padding

Description

Usage

Arguments

Value

States' COVID-related Google & Twitter incidence rates

Description

Usage

Format

Source

Plot Themes

Description

Usage

Arguments

Value