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 rows
pitts_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.
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