μPlot is a lightweight, interactive, scalable, high-performance chart library to visualize any time series data (a series of data points indexed in time order) using Canvas API. More features: Crosshair cursor.
This analysis was performed using R (ver. 3.1.0). Introduction to Gviz package The Gviz package aims to provide a structured visualization framework to plot any type of data along genomic coordinates. It also allows to integrate publicly available genomic annotation data from sources like UCSC or...
If you’re simply looking to compare trends and patterns across measures in your data, that might have separate scales or even different units, Mode’s dual axis feature may be a better fit for your charting needs. To learn about plotting data along more than one axis, seedual axes. Creat...
To use ggplot, we manipulate the data into “long format” using themeltfunction from the reshape2 package. We add names for all of the resulting columns for clarity. An unfortunate side effect of thepredictfunction used to populate the initial 3d dataset is that all of the row...
GeospaceLAB provides a framework of data access, analysis, and visualization for the researchers in space physics and space weather. The documentation can be found onreadthedocs.io. Features Class-based data manager, including DataHub: the core module (top-level class) to manage data from multipl...
In this project, I aimed to create a comprehensive data analysis portfolio piece using synthetic IoT (Internet of Things) sensor data. The goal was to showcase my skills in data manipulation, visualization, and analysis. To do this, I transitioned from using the wxPython framework in a previou...
plotting the H/C and O/C ranges for the collected data cannot accurately represent the true density of the dataset. To address this, we apply the kernel density of the training data to determine the appropriate ranges for the van Krevelen diagrams. The kernel density plots created for all da...
Shiny app generate publication-quality figures to visualize heterogeneity in the cell-cell interactions across patient-level covariates. Our framework is computationally efficient and requires no permutations to interpret measures of cellular interaction. The image-level and optional patient-level data along...
integration module monitors the event queue and dispatches appropriate events to either the HOOPS/MVO objects or directly to HOOPS/3dGS. The HOOPS/MVO application objects in turn engage the API of either the geometric modeler or HOOPS Visualize itself to interact with the data stored in each ...
The aim of this paper is to introduce a non-parametric framework to evaluate the calibration of multiclass risk models irrespective of the modeling technique used. Based on this framework we also derive a calibration measure to quantify and compare calibration performance between models. We illustrate...