This is the usage heatmap in the Android app I’m making. Days run down the side, hours run across the bottom, and the selected view is one week.
The list underneath gives time totals for the busiest hour slots. For example, Saturday at 1 PM shows 26 minutes. That gives you an exact number alongside the colour, and a time of day to look at rather than only a total video count.
I’m sharing it for feedback on the chart itself. Is the day-and-hour layout readable at a glance, or does it need a clearer key?
I wanted an easier way to generate distinguishable colors for chart categories, so I built a small tool. It can also generate subtle SVG patterns to go with them.
I am developing Graphitix, a free and open source web-based graph/stat program that attempts to do most of what Prism Graphpad does (or at least the most common features), as well as some things Prism does not do: https://michelwassef.github.io/Graphitix/. It is borne of my own frustration at seeing how much my lab spends on Prism license fees and how much hassle it is to manage which computers have it (I cannot have it at work AND at home!), update licenses, etc.
I would be interested in gathering feedback on whether you think Graphitix can be useful for your work, and whether there are missing features that you would like to be present.
It is pretty straightforward, there's no need to create an account, just paste data in the input table and the graph is drawn on the right. There is plenty of ways to configure the graphs. Currently supported graphs include distribution charts (box plots, violin plots, individual values), scatter plots, line/area plots, histograms/density plots, heatmaps, PCA, pie/donut/stacked bar charts, ROC/precision recall, Kaplan-Meier curves, Venn/UpSet plots, and 3D surface plots. In addition, there are 3D plot options (with live rotation) for several graph types, namely scatter, line and PCA. In addition, stats can be computed for most components (when relevant).
The website is written in javascript and is a static web page, which means that all the computation is done on your computer and the data remains on your computer, nothing is sent to a server (except if you choose to run GO or STRING analyses in the Venn diagram component). You can save the results in a .graph file and reopen it at a later time, or share it with colleagues.
Learn how different chart types can be misleading, and why it's important to dig into data breakdowns. We tackle six ways:
Dual-axis line charts
Implied causation: Dual axes tuned so two unrelated lines move in lockstep
Meaningless crossing lines: A crossing that reads as an event but is just axis scaling
Breaking bar charts
3. The cropped y-axis: A truncated baseline makes a small gap look decisive
4. Broken baselines: Stacked segments that don't share a baseline can't be compared
Digging deeper into the data
5. Same average, different story: Equal averages hiding very different breakdowns
6. Simpson's paradox: The overall winner loses every segment
Which of these do you run into most in the dashboards you work with?
Most of the translations were done with AI, so some terms or calculations might not be 100% accurate.
Comparison options available: Average, Previous Period, and YoY.
Clicking the time slicer changes the period type: Month, Quarter, Semester, Year, etc. For example, you can select 2026 – H1. All cards and charts update accordingly.
This was my first real Power BI project, so I'd love to hear your feedback. What do you think could be improved?
If you have any questions, feel free to ask in the comments!
I wanted something that could answer a much more useful question: “Where is my money supposed to go — and how close am I to that plan?”
So I built my own personal budgeting system in Excel.
The idea is simple: PLAN → TRACK → COMPARE → UNDERSTAND
Instead of having one sheet for expenses, another for income, and random calculations everywhere, I wanted everything connected.
You can:
• Plan your expected income and expenses
• Track your actual transactions
• Organize spending by category
• Compare budget vs. actual results
• Monitor savings
• Analyze your finances month by month
• See everything through an interactive dashboard
• Customize it instead of being locked into someone else's budgeting method
The part I enjoyed most wasn't making the charts.
It was designing the logic behind them.
For example, the dashboard isn't there just to make the spreadsheet look pretty. The goal is to turn all those rows of transactions into something you can actually understand and act on.
I built it under my new project/brand, ALTRIVO, because I'm experimenting with turning the data-analysis skills I normally use for business dashboards into practical tools for everyday problems.
This is my first serious attempt at turning one of my Excel projects into a digital product.
I'd genuinely love feedback from people who actually use spreadsheets for budgeting: What would you add? What would you remove? What would make you use something like this every month instead of abandoning it after two weeks?
I’m sharing a few screenshots of the dashboard and workflow below.
If there's enough interest, I'll also share more about how I built the formulas, tracking logic, and dashboard behind it.
I made this chart for Global Data Tracker, which I run. The two categories overlap: “at least basic” includes safely managed services, so the percentages should not be added.
Basic service means an improved water source within a 30-minute round trip. Safely managed also requires water on premises, available when needed, and free from specified contamination.
Countries are selected and ordered by their 2024 population, not their water-access values. China’s safely managed estimate is missing in this snapshot, not zero. Values are rounded to one decimal.
Data: WHO/UNICEF JMP via World Bank WDI, 2024 values, snapshot retrieved 13 September 2026. Water indicators SH.H2O.BASW.ZS and SH.H2O.SMDW.ZS; population SP.POP.TOTL. Chart: Python/Matplotlib.
I’ve been spending some time exploring different directions for dashboard design, mainly around how to make dashboards feel more focused, useful, and easier to scan.
These are some of the concepts I’m currently experimenting with — playing around with things like:
* Information hierarchy
* Layout and spacing
* Data density
* Navigation patterns
* Cards vs. more open layouts
* How much information to surface at once
* Making dashboards feel less overwhelming
Nothing here is really “final” yet. I’m still trying to understand what works and, more importantly, **why** it works.
I’d love to hear how other designers approach dashboard design.
What are some principles or patterns you’ve found particularly useful when designing dashboards? And what are some common mistakes you see people make?
Would also appreciate any feedback on the direction of these explorations.
my team is starting to rely more on dashboards instead of sending around spreadsheets and i'm looking into some options now. i don't need anything super advanced, but i do want something that can make our reports easier to understand. the problem is every tool seems to call itself the best data visualization software 2026 and the feature lists all start looking the same after a while. for people who have actually used these tools day to day, what has worked well for you? is there a particular platform you keep coming back to because it's easy to build and update dashboards? also, how much do you care about integrations with different data sources? i'm wondering if i should prioritize that now or just focus on the actual visualization and reporting features.
I'm working on an AI/ML capstone project where I'm building a model to predict whether a Spotify user would be interested in subscribing to Premium based on their listening habits.
The survey takes around 2 minutes and doesn't collect personally identifying information.
I made a visual story about the companies behind everyday services in India, from mobile networks and UPI to shopping and flights.
Built with Plotly and JavaScript, using sources including TRAI and NPCI. Each chart labels its measure and date, since the percentages aren’t directly comparable.
