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...
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.
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...
Matplotlib is a Python plotting library. It lets you create static, animated, and interactive visualizations from data. It’s perfect for what we’re doing today. Matplotlib is one of Python’s most popular data visualization libraries, and for good reason. Matplotlib is super useful. Some of ...
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...
We will be making the requests using Kaiko’s package made for this purpose. Afterwards, we will visualize the results. This tutorial is based on a python wrapper to use Kaiko’s data API. Please note that this repository is not officially maintained by Kaiko. Contributions are welcom...
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...
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...
Visual Explorer: what features of your data do you want to examine? The iteration process: Quick Charts: arrive with a starting graphic type in mind, then rebuild through until you arrive at something you like Visual Explorer: iterate toward your end visualization by using modular, independent ...