Description: Corset plots are a new visualization technique, which are used strictly to visualize
repeat measures at 2 time points (such as pre- and post- data). The distribution of measurements
at each time point are visualized using half violins, whilst the trajectories of individual change
are visualized via spaghetti plots by connecting these two points linearly. These lines can be
coloured to represent percent change or other user-defined value. This method of visualization is
ideal for showing the heterogeneity of data, including differences by sub-groups.
The package relies on 'ggplot2' allowing for easy integration so that users can customize
their visualizations as required. Users can create corset plots using data in either wide or
long format using the functions gg_corset() or gg_corset_elongated(), respectively.
Imports: ggplot2, dplyr, gghalves, ggstance

Description: Corset plots are a visualization technique used strictly to visualize repeat measures
at 2 time points (such as pre- and post- data). The distribution of measurements are visualized at
each time point, whilst the trajectories of individual change are visualized by connecting the pre-
and post- values linearly. These lines can be coloured to represent the magnitude of change, or
other user-defined value. This method of visualization is ideal for showing the heterogeneity of
data, including differences by sub-groups. The package relies on 'ggplot2' allowing for easy
integration so that users can customize their visualizations as required. Users can create corset
plots using data in either wide or long format using the functions gg_corset() or gg_corset_elongated(), respectively.
Suggests: rmarkdown, knitr, viridis, MetBrewer

Title: Visualize Genome Coverage with Various Annotations
Description: The goal of 'ggcoverage' is to simplify the process of visualizing genome coverage. It contains functions to
load data from BAM, BigWig or BedGraph files, create genome coverage plot, add various annotations to
the coverage plot, including base and amino acid annotation, GC annotation, gene annotation, transcript annotation, ideogram annotation and peak annotation.
Imports: dplyr, GenomicRanges, ggbio, ggh4x, ggplot2, ggrepel,
grDevices, IRanges, magrittr, patchwork, RColorBrewer, rlang,
Rsamtools, rtracklayer, scales, stats, utils, methods,
Suggests: rmarkdown, knitr, BiocStyle, htmltools,
Packaged: 2022-08-16 13:24:48 UTC; songyabing