v5.0.0

This is the fifth major release of the R package 'ggcorset' to CRAN (version 0.5.0), which moves away from being dependent on the ggstance and dplyr packages. The package is used to create corset plots, which are a unique way to visualize individual and summary statistics of repeat measures data at 2 time points (such as pre- and post- data). The distribution of measurements at each time point is visualized using a half violin. The trajectory of individual change is visualized linearly between these distributions, which are coloured to visualize the magnitude of change or other user-defined value such as sub-groups. This method of visualization is ideal for showing the true heterogeneity of data, as well as the extent to which sub-groups capture this heterogeneity. Optional 'eyelets' provide either standard error means or the mean +/- 1 standard deviation of user-defined groups help to summarize change by sub-group, and 'facet_design' options aide in highlighting different sub-group trajectories or distributions.

The package relies on 'ggplot2' to produce the visualizations. As such, the corset plot allows for easy integration with 'ggplot2', so that users can customize their visualizations as required. This package is geared towards users with limited experience in R, creating corset plots using data in either wide or long format using the functions. Various supportive documents including a vignette is available to help users with limited 'ggplot2' experience.

v4.5.0

This is the fourth major release of the R package 'ggcorset' to CRAN (version 0.4.5), which offers multiple facet design options to display differences of sub-groups. The package is used to create corset plots, which are a unique way to visualize individual and summary statistics of repeat measures data at 2 time points (such as pre- and post- data). The distribution of measurements at each time point is visualized using a half violin. The trajectory of individual change is visualized linearly between these distributions, which are coloured to visualize the magnitude of change or other user-defined value such as sub-groups. This method of visualization is ideal for showing the true heterogeneity of data, as well as the extent to which sub-groups capture this heterogeneity. Optional 'eyelets' provide either standard error means or the mean +/- 1 standard deviation of user-defined groups help to summarize change by sub-group, and 'facet_design' options aide in highlighting different sub-group trajectories or distributions.

The package relies on 'ggplot2' to produce the visualizations. As such, the corset plot allows for easy integration with 'ggplot2', so that users can customize their visualizations as required. This package is geared towards users with limited experience in R, creating corset plots using data in either wide or long format using the functions. Various supportive documents including a vignette is available to help users with limited 'ggplot2' experience.

v4.0.0

This is the fourth release of the R package 'ggcorset' to CRAN (version 0.4.0). It is used to visualize repeat measures data at 2 time points (such as pre- and post- data). The distribution of measurements at each time point is visualized using a half violin. The trajectory of individual change is visualized linearly between these distributions, which are coloured to visualize the magnitude of change or other user-defined value such as sub-groups. This method of visualization is ideal for showing the true heterogeneity of data, as well as the extent to which sub-groups capture this heterogeneity. Optional 'eyelets' provide either standard error means or the mean +/- 1 standard deviation of user-defined groups help to summarize change by sub-group, and 'facet_design' options aide in highlighting different sub-group trajectories or distributions.

The package relies on 'ggplot2' to produce the visualizations. As such, the corset plot allows for easy integration with 'ggplot2', so that users can customize their visualizations as required. This package is geared towards users with limited experience in R, creating corset plots using data in either wide or long format using the functions. Various supportive documents including a vignette is available to help users with limited 'ggplot2' experience.

Corset Plots - Visualizing Heterogeneity in Change Outcomes

This is an exclusive GH version release which includes a new facet feature via the argument facet_design. All other features have been a part of the release to CRAN (Version 0.3.0).

The 'ggcorset' package is used to visualize repeat measures data at 2 time points (such as pre- and post- data). The distribution of measurements at each time point is visualized using a half violin. The trajectory of individual change is visualized linearly between these distributions, which are coloured to visualize the magnitude of change or other user-defined value such as sub-groups. This method of visualization is ideal for showing the true heterogeneity of data, as well as the extent to which sub-groups capture this heterogeneity. Optional 'eyelets' provide either standard error means or the mean +/- 1 standard deviation of user-defined groups help to summarize change by sub-group, and an optional 'faceted' option aides in highlighting different sub-group trajectories.

The package relies on 'ggplot2' to produce the visualizations. As such, the corset plot allows for easy integration with 'ggplot2', so that users can customize their visualizations as required. This package is geared towards users with limited experience in R, creating corset plots using data in either wide or long format using the functions. Various supportive documents including a vignette is available to help users with limited 'ggplot2' experience.

Corset Plots - Visualizing Heterogeneity in Change Outcomes

This is the third release of the modest R package 'ggcorset', which has been submitted and accepted by CRAN (version 0.3.0). It is used to visualize repeat measures data at 2 time points (such as pre- and post- data). The distribution of measurements at each time point is visualized using a half violin. The trajectory of individual change is visualized linearly between these distributions, which are coloured to visualize the magnitude of change or other user-defined value such as sub-groups. This method of visualization is ideal for showing the true heterogeneity of data, as well as the extent to which sub-groups capture this heterogeneity. Optional 'eyelets' provide either standard error means or the mean +/- 1 standard deviation of user-defined groups help to summarize change by sub-group, and an optional 'faceted' option aides in highlighting different sub-group trajectories.

The package relies on 'ggplot2' to produce the visualizations. As such, the corset plot allows for easy integration with 'ggplot2', so that users can customize their visualizations as required. This package is geared towards users with limited experience in R, creating corset plots using data in either wide or long format using the functions. Various supportive documents including a vignette is available to help users with limited 'ggplot2' experience.

Corset Plots - Visualizing Heterogeneity in Change Outcomes

This is the second release of the modest R package 'ggcorset', which has been submitted and accepted by CRAN (version 0.2.0). It is used to visualize repeat measures data at 2 time points (such as pre- and post- data). The distribution of measurements at each time point is visualized using a half violin. The trajectory of individual change is visualized linearly between these distributions, which are coloured to visualize the magnitude of change or other user-defined value such as sub-groups. This method of visualization is ideal for showing the true heterogeneity of data, as well as the extent to which sub-groups capture this heterogeneity. Optional 'eyelets' which provide standard error means of user-defined groups help to summarize change by sub-group, and an optional 'faceted' option aides in highlighting different sub-group trajectories.

The package relies on 'ggplot2' to produce the visualizations. As such, the corset plot allows for easy integration with 'ggplot2', so that users can customize their visualizations as required. This package is geared towards users with limited experience in R, creating corset plots using data in either wide or long format using the functions. Various supportive document including a vignette is available to help users with limited 'ggplot2' experience.

Corset Plots - Visualizing Heterogeneity in Discrete Change Outcomes

This is the first release of the modest R package {ggcorset}. It is used to visualize discrete repeat measures data at 2 time points (such as pre- and post- data). The distribution of measurements at each time point is visualized using a half violin. Additionally, the trajectory of individual change is visualized by connecting these two points linearly, which can be filled to visualize the magnitude of change or other user-defined observed value. This method of visualization is ideal for showing the true heterogeneity of data.

The package relies on {ggplot2} to produce the visualizations. As such, the corset plot allows for easy integration with {ggplot2}, so that users can customize their visualizations as required. This package is geared towards users with limited experience in R, creating corset plots using data in either wide or long format using the functions.