Use the Chart Viewer template to display bar charts, line charts, pie charts, histograms, and scatterplots to complement a map. Include multiple charts to view with a map or side by side with other charts for comparison. Up to three charts can be viewed side by side or stacked, but you can access and view all the charts that are authored in the map. Examples: Present a bar chart representing average property value by county for a given area. Compare charts based on multiple population statistics in your dataset. Display an interactive scatterplot based on two values in your dataset along with an essential set of map exploration tools. Data requirements The Chart Viewer template requires a map with at least one chart configured. Key app capabilities Multiple layout options - Choose Stack to display charts stacked with the map, or choose Side by side to display charts side by side with the map. Manage chart - Reorder, rename, or turn charts on and off in the app. Multiselect chart - Compare two charts in the panel at the same time. Bookmarks - Allow users to zoom and pan to a collection of preset extents that are saved in the map. Home, Zoom controls, Legend, Layer List, Search Supportability This web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

This visualization is a dashboard (2 pages), but the focus is on page 1, where there are 3 visuals, a word cloud, bubble chart and a bar chart, that are completely interactive (like every single visual can be interacted with to change the entire dashboard), along with selectable filters, you can use these to see real time, correlations between ingredients and cuisines, and visualize what cuisine leans towards what kind of ingredients, and even variants of specific ingredients. The second page contains, filters that show you more numerical data, where you can see side by side comparisons, of ingredients within two separate cuisines, or even the extent to which, two cuisines can use the same ingredient.This viz was submitted as part of the Data Bloom 2024 Viz competition.This viz was created using PowerBI and is based on the following data source: Kaggle - https://www.kaggle.com/datasets/kaggle/recipe-ingredients-dataset/dataPowerBI or a free viewer is required to render and view the full dynamic visualization within the PBIX file.

About Datasets:

Domain : Finance Project: Bank loan of customers Datasets: Finance_1.xlsx & Finance_2.xlsx Dataset Type: Excel Data Dataset Size: Each Excel file has 39k+ records

KPI's: 1. Year wise loan amount Stats 2. Grade and sub grade wise revolving balance 3. Total Payment for Verified Status Vs Total Payment for Non Verified Status 4. State wise loan status 5. Month wise loan status 6. Get more insights based on your understanding of the data

Process: 1. Understanding the problem 2. Data Collection 3. Data Cleaning 4. Exploring and analyzing the data 5. Interpreting the results

This data contains Power Query, Power Pivot, Merge data, Clustered Bar Chart, Clustered Column Chart, Line Chart, 3D Pie chart, Dashboard, slicers, timeline, formatting techniques.

Welcome to the Sales Insights Dashboard, a comprehensive analysis of sales data with interactive visualizations and key insights. Follow the steps below to explore the dashboard:

Overview:

The dashboard consist of charts representing Monthly Sales Trends, Regional Profitability, Top 5 Products, Sales by Category and Product Wise Sales and Quantity. Line chart, Area chart, Clustered Bar chart and Doughnut chart can be found in the dashboard.

Monthly Sales Trend

Explore the "Monthly Sales Trend" to understand how sales have evolved over time. Dynamic line charts showcase monthly trends, helping you spot patterns and seasonality.

Product-wise Sales and Quantity:

Delve into the "Product-wise Sales and Quantity" section for a granular view. Clustered bar charts display sales and quantity metrics for each product.

Top 5 Products by Sales:

Identify the "Top 5 Products by Sales" to focus on high-performing items. Doughnut chart offer insights into the top-selling products.

Regional Profitability:

Evaluate "Regional Profitability" to understand which regions contribute the most to profits. Area charts visually represent regional performance.

Sales by Category:

Dive into the "Sales by Category" to identify the most lucrative product categories. Interactive Doughnut charts reveal sales performance, aiding in strategic decision-making.

How to Use:

Interact with dropdowns, sliders, and buttons to customize your view. Hover over charts for detailed tooltips and information. Click on specific elements to filter data and uncover specific insights.

Feedback:

We welcome your feedback to enhance the dashboard further. Share your thoughts in the comments section. Explore the Sales Insights Dashboard now and transform your sales data into actionable insights!

The original imaging data and bar graph data for PCR in the study.

The Spokane Bridge Bicycle Counter records the number of bikes that cross the bridge using the pedestrian/bicycle pathway on the south side. Inductive loops on the pathway count the passing of bicycles with travel direction. The data consists of a date/time field, east pathway count field and west pathway count field. The count fields represent the total bicycles detected during the specified one hour period.

Drug discovery is accelerated with computational methods such as alchemical simulations to estimate ligand affinities. In particular, relative binding free energy (RBFE) simulations are beneficial for lead optimization. To use RBFE simulations to compare prospective ligands in silico, researchers first plan the simulation experiment, using graphs where nodes represent ligands and graph edges represent alchemical transformations between ligands. Recent work demonstrated that optimizing the statistical architecture of these perturbation graphs improves the accuracy of the predicted changes in the free energy of ligand binding. Therefore, to improve the success rate of computational drug discovery, we present the open-source software package High Information Mapper (HiMap)a new take on its predecessor, Lead Optimization Mapper (LOMAP). HiMap removes heuristics decisions from design selection and instead finds statistically optimal graphs over ligands clustered with machine learning. Beyond optimal design generation, we present theoretical insights for designing alchemical perturbation maps. Some of these results include that for n number of nodes, the precision of perturbation maps is stable at n·ln(n) edges. This result indicates that even an “optimal” graph can result in unexpectedly high errors if a plan includes too few alchemical transformations for the given number of ligands and edges. And, as a study compares more ligands, the performance of even optimal graphs will deteriorate with linear scaling of the edge count. In this sense, ensuring an A- or D-optimal topology is not enough to produce robust errors. We additionally find that optimal designs will converge more rapidly than radial and LOMAP designs. Moreover, we derive bounds for how clustering reduces cost for designs with a constant expected relative error per cluster, invariant of the size of the design. These results inform how to best design perturbation maps for computational drug discovery and have broader implications for experimental design.

Dataset

This dataset was created by Sanjana Murthy

Released under CC BY-NC-SA 4.0

Contents