The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
The ggDoubleHeat package is a ggplot2
extension that provides visualization for data from two different
sources on a modified heat map. All functions from the package are named
as geom_heat_*(). A regular heat map, which can be made by
using geom_tile() from ggplot2, contains three
dimensions (variables). geom_heat_*(), however, can express
four dimensions of data on a single plot.
Please install the released version of ggDoubleHeat from
CRAN with:
install.packages("ggDoubleHeat")Alternatively, you can install the latest development version from Github with:
# install.packages("devtools")
devtools::install_github("PursuitOfDataScience/ggDoubleHeat")For demonstration purposes, the built-in dataset
pitts_tg is used to illustrate the basic usage of the
package.
library(ggDoubleHeat)
library(ggplot2)
pitts_tg## # A tibble: 270 x 6
## msa week week_start category Twitter Google
## <chr> <int> <date> <chr> <dbl> <dbl>
## 1 Pittsburgh 1 2020-06-01 Covid 0.965 0.681
## 2 Pittsburgh 1 2020-06-01 General Virus 0.538 0.0982
## 3 Pittsburgh 1 2020-06-01 Masks 0.466 0.117
## 4 Pittsburgh 1 2020-06-01 Sanitizing 0.0561 0.127
## 5 Pittsburgh 1 2020-06-01 Social Distancing 0.294 0.0386
## 6 Pittsburgh 1 2020-06-01 Symptoms 0.0457 0.0770
## 7 Pittsburgh 1 2020-06-01 Tests 0.0130 0.00415
## 8 Pittsburgh 1 2020-06-01 Treatment 0.0459 0.0376
## 9 Pittsburgh 1 2020-06-01 Working 0.295 0.160
## 10 Pittsburgh 2 2020-06-08 Covid 1.01 0.707
## # ... with 260 more rowspitts_tg is a dataset that collects the 30-week period
of COVID-related Google & Twitter incidence rate for 9 different
categories from the Pittsburgh Metropolitan Statistical Area (MSA). For
the complete information of the dataset, please type
?pitts_tg on the console.
Let’s start with geom_heat_grid():
ggplot(data = pitts_tg, aes(x = week, y = category)) +
geom_heat_grid(outside = Google, inside = Twitter) +
ggtitle("Pittsburgh Google & Twitter Incidence Rate (%) Comparison")
Now changing geom_heat_grid() to
geom_heat_circle():
ggplot(data = pitts_tg, aes(x = week, y = category)) +
geom_heat_circle(outside = Google, inside = Twitter) +
ggtitle("Pittsburgh Google & Twitter Incidence Rate (%) Comparison")
Let’s use geom_heat_tri():
ggplot(data = pitts_tg, aes(x = week, y = category)) +
geom_heat_tri(lower = Google, upper = Twitter) +
ggtitle("Pittsburgh Google & Twitter Incidence Rate (%) Comparison")
To make things a bit more colorful, the most popular emoji for a
given week in a given category from the respective Pittsburgh Twitter
daily sample files is rendered on each component of the heatgrid by
using ggtext. The following code is commented, as it takes
few minutes to generate. If you would like to run it, just simply
uncomment the code. But the generated heatgrid with emojis is
attached below as an image.
# install.packages("ggtext")
# library(ggtext)
#
# ggplot(data = pitts_tg, aes(x = week, y = category)) +
# geom_heat_grid(outside = Google, inside = Twitter) +
# # rendering emojis using "richtext"
# annotate("richtext", x = rep(c(1:30), 9), y = rep(1:9, each = 30),
# label = pitts_emojis, label.color = NA, fill = NA, size = 0.3) +
# ggtitle("Pittsburgh Google & Twitter Incidence Rate (%) Comparison")
Note: pitts_emojis is the Emoji metadata built in
ggDoubleHeat. Another thing worth noting is that there are
some grids not having Emoji, and the reason is that there is no Emoji
Unicode in the Twitter sample file.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.