This package allows users to create high-quality heatmaps from
labelled hierarchical data. Specifically, it requires input data in the
form of a two-level hierarchical structure. It will produce a heatmap
where each row and column represent a category at the lower
level. Rows and columns are then grouped into a higher-level
groupings. Names for each higher-level category and
groupings are shown in the margins. While other packages
(e.g. dendextend) allow heatmap rows and columns to be
arranged by groups only, hhmR also allows the labelling of
the data at both the category and group level.
The package has two main functions: hhm and
tshhm. These are explained in more detail below.
hhm (Hierarchical
Heatmap)hhm creates a labelled heatmap from hierarchical data.
This function is useful to create a heatmap where the categories shown
on both the x and y axis are grouped in some way. This heatmap will
order the categories by their assigned groupings, and present both the
categories and grouping labels along the axes. An example using
geographic data might be a series of smaller geographies (lower
categories) which aggregate into larger geographical regions (upper
groups).
This function requires a data.frame containing columns which specify
the lower categories (ylower, xlower) and
upper groupings (yupper, xupper). These
categories and groupings are used to arrange and label rows and columns
on a heatmap. The data.frame must contain a values variable
containing values to populate the heatmap. Note that the groupings will,
by default, be arranged alphabetically (top to bottom / left to right).
The ordering of the groups can be manually specified by converting
yupper and/or xupper to factors. In this case,
the groupings will be ordered based on the ordering of the factor levels
provided.
Below is an example of the hhm function’s application.
For a more in-depth description of it’s usage, see the package
vignette.
# Import package
library(hhmR)
# Import toy demonstration dataset (see `?example_migration` for details)
data(example_migration)
# Intial heatmap
hierarchical_heatmap = hhm(df = example_migration,
ylower = "Origin County",
xlower = "Destination County",
yupper = "Origin Region",
xupper = "Destination Region",
values = "Migration",
yttl_width = 0.22,
xttl_height = 0.4)
# View result
hierarchical_heatmap
tshhm
(Time-Series Hierarchical Heatmap)tshhm creates a labelled time-series heatmap from
heirarchical data. This function is useful if you wish to create a
time-series heatmap where the categories shown on the y-axis can be
grouped in some way. This heatmap will order the categories by their
assigned group and present both the categories and group labels along
the y-axis. An example might be a series of smaller geographies (lower
categories) which aggregate into larger geographical regions (upper
groups).
This function requires a data.frame containing columns that specify
the lower categories (lower) and upper groups
(upper) that each value corresponds to. These categories
and groups will be used to arrange and label the rows of the heatmap.
The data.frame must also contain a values variable,
containing the values used to populate the heatmap, and a
times variable, containing the time period during which
each value was observed. Note that the groups in upper will
by default be arranged alphabetically (top to bottom). The ordering of
the groups can be manually specified by converting upper to
a factor. In this case, the groups will be ordered based on the ordering
of the factor levels. The ordering of rows within each group can also be
specified using the sort_lower variable.
Below is an of how the tshhm function can be used. For
more details, see the package
vignette.
# Import packages
library(dplyr)
# Import toy demonstration dataset (see `?example_time_series` for details)
data(example_time_series)
# Intial heatmap
time_series_heatmap = tshhm(df = example_time_series,
lower = "County",
upper = "Region",
times = "Year",
values = "Immigration",
yttl_width = 0.25)
# View result
time_series_heatmap