Hi there,

I would like to visualize actual business process of large blue chips and build a software that allow everyone to become an insider of on-going routines of a big corporation.

I would like to squizze the syrop from the oranges (daily meetings,  chats across team members, on-going activities, emails correspondence, documentation, , blue print, decision making in reference to architecture , software , strategy, team , clients, etc ) , remove any identification, add extra visualization and offer this interactive software for educational institutions like colleges and universities. Students can benefit from a real time environment that allows them to get a grasp of what happens within a blue chip, why this and that decision is accepted or rejected, how the tools , people and processes are interrelated and integrated, what kind of issues pop up and how they are resolved , etc. This provides practical knowledge that can be useful for faster employment .

Example :

01.   daily activities of FICO reporting and consolidation department of a big pharma (the issues experienced, how they are resolved , teams dynamics over years, what kind of proposals they raise up and acceptance/rejection rate )

02.   daily activities of supply chain architect of a big pharma (the issues experienced, how they are resolved , teams dynamics over years, what kind of proposals they raise up and acceptance/rejection rate )

The second step is to make a software that visualize on-going activities across different departments in blue chips. This product can be offered to big corporations upon import of their source file of different origin.

I appreciate your critics and any other feedback.    

Thanks

Hi, I'm 26 year old and have 2 year gap as I quit my job in December 2022- BPO job started as customer service representative and got promoted to senior quality analyst by the time I quit with 3.6 years of experience. Now I've wasted 2 years of my life with nothing to show for the gap except 1 or 2 certificate courses. I'm learning data analytics to become a data analyst. How should I justify the gap and what should I do to get the job asap?

0 Abstract

In the age of digital transformation, voice assistants have become ubiquitous in daily life. However, the way we visualize their usage and interaction patterns can significantly influence the interpretation and decision-making process. This paper aims to explore the challenges in visualizing voice assistant usage data effectively. Using an example from UNESCO’s report, we critically analyze a flawed visualization that misrepresents the frequency of voice assistant use. By identifying these flaws, we propose better approaches for displaying such data to ensure clarity and accuracy. Ultimately, the goal is to enhance the understanding of user engagement with voice assistants, providing actionable insights for both developers and policymakers.

1 A Flawed Beginning: Representation of Voice Assistant Usage

Every story begins with a problem, and this one is no different.The visualization we selected comes from UNESCO’s report titled "I'd blush if I could: closing gender divides in digital skills through education". It displays the frequency of use for voice assistant features based on data from Voicebot.ai’s 2018 Smart Speaker Use Case Survey. At first glance, the chart seems informative, but upon closer inspection, it reveals critical flaws that undermine its credibility.

Figure 1.1 - Frequency of Use for Voice Assistant from UNESCO

The most obvious flaw lies in the stacked bar chart design, where categories like Use daily, Use monthly, and Tried at least once overlap, rather than being mutually exclusive. This results in some rows exceeding 100% when summed up—completely violating the basic principle of stacked bar charts. This leaves room for misinterpretation, leading readers to false conclusions. To make matters worse, the legend labels for “Use daily” and “Tried at least once” are swapped, a blunder that’s hard to overlook, especially in such a high-profile report. When such errors appear in an authoritative document, it shatters trust and highlights the need for rigorous quality control.

Figure 1.2 - 2018 Smart Speaker Use Case Frequency from Voicebot.ai

This visualization is important because it addresses the growing role of voice assistants in everyday life, shaping public perceptions and policy decisions. Flawed design and inaccurate labeling not only mislead readers but also undermine the very message the report seeks to convey. By fixing these issues, we can restore the chart’s integrity and ensure it delivers its critical insights effectively.

2 Unearthing Insights: Analyzing the Original Visualization

This chart was intended for a UNESCO report, a context where clarity and accessibility are paramount. The audience likely includes educators, policymakers, and researchers with varying levels of expertise in data interpretation. A visualization with design flaws not only risks misinforming readers but also diminishes the credibility of the report's conclusions.

2.1 What Story Does the Visualization Tell?

This chart provides a detailed display of the frequency distribution of smart speakers in different usage scenarios as of January 2018. These usage scenarios cover a wide range from basic functions such as "ask a question" to advanced functions such as "control smart home devices". The data is presented in three levels of classification, namely "Use daily" (dark purple), "Use monthly" (orange), and "Tried at least once" (light purple), clearly showing the proportion of each usage scenario in different frequency categories. Through this classification method, the chart not only reveals users' overall usage preferences for smart speaker functions, but also enables in-depth analysis of the proportion of active users and potential usage trends for different functions. However, the story is obscured by several design flaws that detract from the data's clarity and accessibility.

2.2 How to Read the Visualization?

To understand the chart, the reader must:

1. Refer to the legend to decode the colors, which represent the three categories.
2. Compare the bars across various activities to infer which tasks are most commonly performed daily, monthly, or at least once.
3. Navigate through the dense data to identify patterns or trends, such as the high daily usage of voice assistants for questions and music streaming.

However, the lack of labeling for the x-axis (percentage) leaves the reader guessing whether the values represent frequency rates or proportions of users. Moreover, the reliance on a legend forces the audience to repeatedly cross-reference colors with categories, further complicating the reading process.

2.3 Visual Variables and Their Flaws

1. Color: The chart uses three distinct colors to differentiate usage categories. While color is an effective visual variable, its application here is problematic:

• The legend requires constant reference, increasing the extraneous cognitive load.
• The colors for "Use daily" and "Tried at least once" are inadvertently swapped, introducing confusion.

1. Position and Length: The stacked bars are intended to communicate cumulative usage proportions. However:

• The bars do not sum to 100%, violating the procedural knowledge of how stacked bar charts typically function.
• The inconsistent starting points for "Use monthly" and "Tried at least once" hinder cross-category comparisons, encouraging cognitive tunneling on "Use daily" alone.

Procedural Knowledge: Procedural knowledge refers to the "how-to" aspect of cognition, where tasks are performed based on learned rules or processes. In the context of visualizations, readers rely on procedural knowledge to interpret common chart types correctly. For example, stacked bar charts are expected to display mutually exclusive categories that add up to 100%.

3. Data-Ink Ratio: A substantial amount of non-data ink is used for the legend and the large horizontal bars. This distracts from the actual data and reduces the chart's overall data-ink ratio, a principle advocated by Edward Tufte.

Data-Ink Ratio (Edward Tufte, 1984)

Tufte emphasizes minimizing non-essential visual elements (non-data ink) in charts. A high data-ink ratio ensures that visualizations focus the audience’s attention on the data itself rather than decorative or redundant elements.

2.4 Cognitive Theory and Visualization Effectiveness

This visualization violates several cognitive principles, which undermines its usability and interpretability:

1. Germane Cognitive Load (Audience):

Germane cognitive load refers to the mental effort required to integrate new information into existing knowledge structures.

• This visualization overwhelms viewers by cramming all tasks into a single chart without clear thematic grouping, making it difficult to discern key insights.
• For non-expert audiences, such as policymakers or educators unfamiliar with voice assistant usage, the chart's complexity is likely to feel intimidating or inaccessible.

1. Extraneous Cognitive Load (Visualization):

Extraneous cognitive load arises from poorly designed visualizations that demand unnecessary mental effort to process irrelevant details.

• The reliance on a legend increases cognitive effort, as readers must recall color-category associations while interpreting the chart.
• Because 'Tried at least once' includes' Use monthly 'and' Use daily ',the erroneous use of a stacked bar chart format for non-exclusive categories adds to the confusion, making the data harder to interpret accurately.

1. Cognitive Tunneling:

Cognitive tunneling occurs when a user focuses narrowly on one aspect of a visualization, neglecting other relevant information.

• The audience is likely to focus on "Use daily" since its starting point is consistent, while the other two categories (with floating baselines) are harder to compare.
• This design flaw may cause readers to overlook important data points, such as the relatively low "Tried at least once" rates for complex tasks like "Made a purchase."

3 The Process: Replicating the Graph

Replication was the first step in our journey. Using matplotlib, we recreated the original graph as faithfully as possible.

Figure 3.1 - Replicate the graph using matplotlib

4 A New Chapter: The Redesigned Visualization

The original graph we selected for improvement was marred by complexity and ambiguity, impeding its ability to convey the intended message. Our primary objective was to transform this visualization into a clear, insightful narrative that adheres to the principles of effective data representation.

The first step in this transformation involved selecting the most appropriate chart type. We then tackled the issue of inadequate labeling and annotations to ensure clarity. Increasing the data-ink ratio was another crucial change, enhancing focus on the core data. To address cognitive tunneling, we implemented an interactive chart using the Pyecharts library. Finally, the iterative process of revision and feedback played a vital role in refining our visualization.

4.1 Change 1: Select the Most Appropriate Chart Type

In our journey to transform a complex visualization into a clear and insightful narrative, the first pivotal step was selecting the most suitable chart type. The original visualization, a non-standard stacked bar chart, obscured the data's true story by merging disparate elements into a single, confusing image. This not only hindered comprehension but also risked misinterpretation by the audience.

Breaking Down the Complexity:

• We began by deconstructing the stacked bar chart. The original stacked bar chart attempted to convey multiple data points simultaneously, but its complexity obscured the underlying message. By splitting this into component bar charts, we can illuminate each data category individually. This approach not only simplifies the visualization but also allows viewers to focus on specific data segments without being overwhelmed by the entire dataset at once.

Choosing Grouped Bar Charts:

• The decision to use grouped bar charts was strategic. We consciously avoided the use of pie charts, which are often criticized for their inability to accurately convey differences in data magnitude.
• Unlike pie charts, which can mislead through visual distortion of proportions, grouped bar charts offer a straightforward comparison of categories across different groups. This format excels in displaying variations in data size and distribution, making it ideal for our dataset.

4.2 Change 2: Improve Labeling and Annotations

Clarity is paramount. A graph must serve as a transparent window into the data it represents, allowing viewers to grasp insights effortlessly. However, the original visualization we encountered was shrouded in ambiguity, primarily due to inadequate labeling and annotations.

"Frequency" or "Proportion"?

• The original graph failed to provide any annotations or explanations for the x-axis, leaving viewers puzzled about its meaning. Was it representing the "frequency of use" or the "proportion of a certain frequency of use population"? This lack of clarity not only hindered comprehension but also risked misinterpretation, which could lead to misguided conclusions.

Our Approach:

• To address this, we meticulously labeled each component of the graph. We began by clearly defining the y-axis, ensuring that its meaning was explicit. By specifying that it denotes the "Proportion among the surveyed population," we eliminated any potential confusion. This change transforms the graph from a cryptic image into a clear narrative, allowing viewers to grasp the data's implications swiftly.
• Furthermore, we enhanced the graph by labeling each group's task name and specific values directly on the graph. This direct approach provides an intuitive understanding of the data, enabling readers to engage with the information without the need for constant cross-referencing. The y-axis was also clearly marked, reinforcing the graph's message and ensuring that all visual elements worked cohesively to support the narrative.
• These approach aligns with Edward Tufte's principles for graphical integrity, particularly his assertion that "graphics must not quote data out of context." This contextual information acts as a narrative, guiding the reader through the data's intricacies and ensuring that no detail is overlooked.

4.3 Change 3: Increase Data-Ink Ratio

Edward Tufte's concept of the data-ink ratio (as we just introduced in Section 2.3) serves as a cornerstone for creating clear and impactful graphics.

Maximize the data-ink ratio

• The original visualization employed excessive ink on variable names and long horizontal bars, which did not directly enhance the viewer's understanding of the data. Additionally, the reliance on a separate legend consumed space and diverted attention from the core data. This design choice resulted in an inflated presence of non-data ink, thereby diminishing the clarity and effectiveness of the visualization.
• To address these challenges, we focused on increasing the data-ink ratio by streamlining the visualization. Our approach involved consolidating the legends of different subgraphs into a single, cohesive legend. This not only reduced clutter but also minimized the need for viewers to constantly shift their gaze away from the data to interpret the legend. By employing smaller subgraphs to categorize charts, we ensured that the focus remained on the data itself.
• Additionally, we wrapped long variable names, avoiding the cognitive strain associated with rotated labels. This subtle yet effective change enhanced readability without overwhelming the viewer. By breaking down the original stacked bar chart into grouped bar charts, we separated mixed colors, introducing appropriate white space between different groups. This separation not only clarified the data but also emphasized individual data points, allowing for a more nuanced interpretation.

4.4 Change 4: Avoid Cognitive Tunneling

In our original visualization, Figure 1, the reliance on legends to distinguish categories such as "daily use," "monthly use," and "at least once tried" presented a significant challenge. This setup forced readers into a repetitive back-and-forth between the bar chart and the legend, disrupting their cognitive process and inadvertently increasing cognitive load. Such interruptions can lead to cognitive tunneling, where the audience becomes overly focused on deciphering colors rather than grasping the overall data narrative.

Interactive chart implementation:

• To address this, we implemented an interactive chart using the Pyecharts library. This innovation allows users to effortlessly display the values of each feature by simply hovering their mouse over the chart. By eliminating the need to constantly reference the legend, readers can maintain their focus on the data itself, facilitating immediate comprehension and minimizing cognitive distractions.
• Furthermore, we organized the data into distinct categories such as "communication tasks" and "smart home management." This categorization enables readers to quickly understand the context and concentrate on areas of specific interest. By breaking down the data into smaller, thematic charts, we reduced confusion and enhanced the ease of comparison both within and across categories.

Figure 4.1 - Implementing dynamic interactive charts using pyechart

(This image is our demonstration GIF. To preview it, please check in the markdown report)

*Note: For those using Jupyter Notebook, please refer to the link provided in the appendix to experience this interactive visualization chart, as image previews may not be available.*</br> Preview interactive charts online: https://www.tarikvon.cn/files/pyechart.html

4.5 Change 5: Reduce Germane and Extraneous Cognitive Load

In the realm of information visualization, reducing cognitive load (as we just introduced in Section 2.4) is paramount to ensuring that audiences, irrespective of their expertise, can effectively interpret and engage with the data presented. In this section, we propose changes aimed at minimizing both germane and extraneous cognitive load in the original visualization, thereby enhancing its clarity and accessibility.

Germane Cognitive Load (Audience):

• For audiences with different professional backgrounds, the original stacked bar charts were not only daunting but also misleading. The misuse of these charts—where the sum does not equal 100%—creates confusion, particularly for those unfamiliar with such visualizations. This can lead to frustration and a loss of interest, especially among non-professional viewers.
• To address this, we restructured the data into clear, thematic categories that align with the audience's daily experiences. This approach not only reduces cognitive load but also enhances the audience's ability to consciously and confidently grasp the information. By categorizing topics, we introduce insights that were not immediately apparent in the original visualization. Viewers can now easily compare data features within and across different themes, uncovering new trends and insights. This method promotes faster information digestion, making it accessible to both experts and novices alike.

Extraneous Cognitive Load (Visualization):

• The original visualization also suffered from an overload of information presented within a single chart. This excessive detail imposed an additional cognitive burden, leaving audiences overwhelmed and impeding their ability to swiftly extract meaningful insights.
• The use of stacked bar charts, in this context, was uncommon and inappropriate. Such charts can obscure individual category values and make comparisons difficult, especially when the total does not equal 100%. This choice led to misinterpretations and hindered the audience's ability to draw accurate conclusions.
• To rectify these issues, we propose the use of simple and common bar charts, which are universally easier to understand. This approach, coupled with clear categorization, aids in accurate and rapid interpretation, allowing both experts and novices to digest the information with ease. By simplifying the visualization and aligning it with standard practices, we significantly enhance its effectiveness and accessibility.

4.6 Change 6: Revise and Get Feedback

In the journey of transforming our visualization, the significance of revision and soliciting feedback became our guiding compass.

Attempts:

• Our initial efforts involved adjusting the data scale to ensure that the sum of each stacked bar equaled 100%.
• We experimented with a unified vertical grouping bar chart, sorting the data by the frequency of function usage.

Feedback and Iterative Refinement:

• However, it was through the iterative process of revision and feedback that our visualization truly evolved. We engaged with classmates and professors, presenting each iteration and meticulously recording their insights. This collaborative effort provided a diverse range of perspectives, highlighting aspects we had not initially considered.
• The feedback illuminated the potential for a more thematic presentation, leading us to our current improved design. By categorizing functions into segments such as communication, entertainment, and information, and displaying them through a segmented bar chart, we achieved a more intuitive and engaging visualization.

4.7 Redesigned Graph

After implementing the proposed changes, we arrived at a redesigned graph that tells a clearer and more engaging story.

Figure 4.2 - Improvements to the Graph

5. Sharing with the Community

We have shared our project's work and records on the Reddit forum's visualization section, a vibrant community dedicated to the discussion and enhancement of information visualization. This platform is frequented by both enthusiasts and professionals who are passionate about the art and science of visualizing data. By posting our project here, we aim to contribute to the collective knowledge and encourage discourse on effective visualization practices.

Our decision to publish the project on this forum underscores the importance of community engagement in advancing information literacy. We hope to inspire others to prioritize quality in their visualizations and to recognize the transformative power of well-crafted data representations.

6. Conclusion: From Data to Storytelling

This project underscored the importance of storytelling in data visualization. A good graph is not just about presenting data; it’s about crafting a narrative that connects with the audience. Through this process, we learned:

1. The Power of Iteration: Revising and receiving feedback played a crucial role in improving the graph.
2. The Role of Narrative: Framing data within a storytelling structure (e.g., OCAR) significantly enhances its impact.
3. The Need for Accessibility: Inclusive design ensures that visualizations are effective for all audiences.

7. Appendix

1. Original Figure: https://www.tarikvon.cn/files/Original.png
2. Replication Figure: https://www.tarikvon.cn/files/Replication.svg
3. Improved Version: https://www.tarikvon.cn/files/Improved%20Version.svg
4. Improved Version (interactive): https://www.tarikvon.cn/files/pyechart.html

Poster I created during the pandemic while staying at home. The genes are color-coded and placed in a simple array with no specific scientific ordering. Designed more as a conversation starter.

Based on the genome information from GenBank: MN908947.3 - www.ncbi.nlm.nih.gov/nuccore/MN908947

https://preview.redd.it/ar628cqq996e1.jpg?width=1087&format=pjpg&auto=webp&s=30534104d0650865140c1ecbff9a299081fe227a

https://preview.redd.it/2x74laqq996e1.jpg?width=1175&format=pjpg&auto=webp&s=4eec111ba62cdb952d627205f5f4e57930cefe41

Using R and R Shiny for effective data visualization and risk assessment - Real world demo and presentation showing Raiffeisenlandesbank Oberösterreich’s (Austria) advanced risk management practices

Free R in Finance webinar - This week, Thurs, Dec 12, 2024 - Full recording provided to all registrants after webinar is completed

https://r-consortium.org/webinars/quantification-of-participation-risk-using-r-and-rshiny.html

Hi everyone,

I’m conducting a quick survey to gather feedback on graph visualization libraries and the features that matter most to users. Whether you’re a student, developer, data scientist, product manager etc. your insights would be incredibly valuable in helping improve tools for exploring and analyzing complex datasets.

The survey is short (just 3-5 minutes) and focuses on understanding what you look for in a graph visualization library.

Here’s the link to the survey: [Link]

Thank you so much!

A chart with the usual palette (left) and something more natural (right).

Lately, I've been wondering about why data visualizations customarily use highly-saturated color palettes. I understand the conventional wisdom is that vivid colors are supposedly more legible, distinct, and easier to visually map from chart to legend, but are these assumptions necessarily correct? Are there studies?

The human eye is incredibly sophisticated, and has evolved being able to discern a camouflaged predator from the grass it is hiding in. We can distinguish colors in all qualities of light, even across shadows. So, why not make visualizations that better respect what we can see?

My thinking is that not only are posterized palettes sometimes annoying to look at, but they could be more off-putting, too. Are more natural colors easier to look at? And would this tend to make more people look at things if they were easier on the eyes?

I recently did a chart (attached here) where I tried to see if I could add more visual nuance with colors and shading. I basically overlayed some texture (in Photoshop) and tinkered with some filters (the ribbing is a pattern I made from a photo of venetian blinds). I may have gone over the top (yes, probably so with the tungsten yellow), but I'm genuinely curious if anyone sees any benefit in exploring such things.

People who do data visualizations wrestle with legibility and color palettes all. the. time. Such a crowd must have an opinion.

I’ve tried a bunch of tools for work and wanted to do a review of why I think Visme stands out and if you find it worth considering for your infographics, charts, data analytics etc. ← Not paid or sponsored lol.

1. User-Friendly Interface

Visme uses a drag-and-drop editor that makes it easy to start without a steep learning curve. AKA you don’t have to be a designer or data specialist to make something look really good and really fast.

2. Editable Infographic Templates

Their templates are a lifesaver. You can customize every element to match your company colors and look.

3. Charts and Graphs

Visme allows you to upload your data spreadsheets, then generates totally customizable charts, maps, or graphs. Bar/Axis/Radial graphs, Pie or donut charts, scatter plots, histograms, pictograms.

You name it, they got it.

4. Live Editing with Coworkers

Working on a team or remotely? Me and my coworkers can edit together from our own PC’s and make comments in real time - sort of like a google doc. It helps us be aligned if we’re presenting on a last minute deadline.

5. Integration-Heavy

Almost every single platform you already use can be integrated. Think: Hubspot, Salesforce, Monday.com, Dropbox, the whole 9 yards. Makes it super easy to move across platforms in a project.

 Why You Should Try Visme

• Saves time with professional templates.
• Makes data visualization easier for teams.
• Ensures you won’t embarrass yourself trying to DIY from scratch lol.

Basically give it a try. I think it’s def worth it. 

I'm building this AI infographics generator product.

But I'm a developer not a designer. Want to understand more deeply from a designer's point of view about infographics design:

1, would you use an AI infographics product?
2, what's the biggest pain points?
3, what do you think is the best trade off between control and flexibility? what details do you want to control, what do you want to leave all to AI?

Thank you so much!

Hi, I’m on a elf growth journey and really would like to have a good visualisation meditation I can follow when I wake up in the morning. There are sooo many out there I’m unsure which to choose. Does anyone have any suggestions of what I can try?

Thanks :-)

hi r/visualization!

i'm comparing two groups to see if the treatments are significantly different, and originally, i had plotted bar charts with error bars (ggplot2 geom_bar and geom_errorbar), but when eyeballing my data, i noticed that the variance in the data is huge, regardless of treatment (means were not significantly different between treatments anyway).

i have four main metrics that i tested, so i had made four bar charts, but when i noticed the variability, i wondered if there's a better way to plot this. i calculated coefficients of variance both for metrics overall, and per treatment. certain metrics have higher CVs than others, and i want to figure out how to communicate this, while still displaying that no metrics had significant differences between treatments.

my thought process is, i change my four bar charts to be box plots and just put the p-value above (to indicate non-significance), then i create a grouped bar chart of the CVs (four groups of 3: treatment 1, treatment 2, overall- then times four).

is there a better way to do this? i don't want to have five bar charts on my research poster but i'm not sure what else to do. thanks!

🚀 I’m thrilled to present the Rose Pine Theme for R, a custom aesthetic for ggplot2 that blends modern minimalism with the serene elegance of the Rose Pine design philosophy. Drawing inspiration from its Python counterpart, this R-specific implementation has been refined to align with ggplot2’s unique functionality.

🎨 Key Features

Sophisticated Aesthetic: Offers a calm, polished appearance, perfect for professional and academic visualizations.

Rich Color Palette: Integrates the signature Rose Pine color scheme, complemented by customizable accent tones to suit diverse datasets.Seamless Integration: Designed to effortlessly fit into standard ggplot2 workflows, saving you time while enhancing your plots.

📚 How to Use

Installing and applying the Rose Pine Theme is straightforward, and the result is a visually striking output tailored for high-impact presentations or publications. Here’s a glimpse of the theme in action, showcasing its versatility across different types of data.

🔗 Documentation and Examples

Comprehensive documentation, including implementation guidelines, is available in the GitHub Repository. Check it out to explore usage tips and visual examples.

💡 Collaborate with Me

Feedback and contributions are highly encouraged! Whether you have suggestions for enhancements or want to contribute directly to the project, feel free to submit a pull request or open an issue on GitHub. Collaboration is the key to growth!

📩 Get in TouchFor any questions, feedback, or collaborative ideas, reach out via my portfolio website or connect with me on GitHub.

🔗 GitHub Repository: Explore the Rose Pine Theme for R

https://preview.redd.it/r5dr3m9iyk3e1.png?width=1700&format=png&auto=webp&s=9a9d5e8ed3d4a57385f1c38235e23a4a6734d3fd

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📌 ᴀʀᴇ ʏᴏᴜ ɪɴᴛᴏ ᴅᴀᴛᴀ ꜱᴄɪᴇɴᴄᴇ, ᴀɴᴀʟʏᴛɪᴄꜱ, ᴏʀ ᴠɪꜱᴜᴀʟɪᴢᴀᴛɪᴏɴ? ʟᴇᴛ’ꜱ ᴄᴏɴɴᴇᴄᴛ—ɪ’ᴅ ʟᴏᴠᴇ ᴛᴏ ᴄʜᴀᴛ ᴍᴏʀᴇ ᴀʙᴏᴜᴛ ɪᴛ!

Thank you

I have a large number of words, and I want to visualize their frequency of use in some data. This is exactly what a word cloud does. But i just don't like how.... floofy? they seem. Like something I'd see on etsy.

Beyond a bar plot with every word, is there another good way to visualize this data? Or ways to make the word cloud seem more scientific? I appreciate any advice

I’m playing WWE 2k24’s Universe Mode and keeping track of certain stats (who won what championships for how long, who’s friends/enemies with who, and how many matches a specific wrestler has won and lost). Initially, I used a coordinate graph on Desmos, but I wasn’t able to save it since it exceeded the file size limit. Other iOS apps wouldn’t let me add images instead of points (labelling each point would be messy since there are 96 wrestlers and there’s a lot of overlap), so while I’m looking for another app, I’m wondering if there’s a better way to accomplish my goal.

Also, if I’m able to store data for each wrestler (click on a name/point on the graph and get information about their championship stats, for example), I’d love it.

I never thought I’d be someone who could stick with a habit for this long, but here I am—116 days of meditation in a row. It started small, just 2 minutes a day, but tracking it in Mainspring habit tracker app kept me motivated to keep going.

At first, it felt like a chore, but now it’s something I actually look forward to. It’s helped me feel calmer, more focused, and way less stressed. Honestly, I’m just proud of myself for showing up every day.

Anyone else crushing their habit goals? Let’s celebrate some wins!

Hello guys, I am a masters student in data analytics and I have a data visualization team project.

I am a chess enthusiast who used to play a lot, I have a rating of around 2100-2200 on chess.com.

My team and I were really interested to find how psychological factors plays a role in chess. 

We conducted our entire project based on a lichess data set of 200k games. 

We studied how chess can teach us about psychology. We are presenting in front of a non chess audience with the hope to attract new players and show the beauty of the game. Here are the visuals as well as a blog post for some further explanation.

This is the link for the visuals:

https://public.tableau.com/app/profile/lynn.hajj/viz/Project_Chess41/Dashboard11?publish=yes 

This is the link for the blog:

https://sites.aub.edu.lb/datavisualization/2024/11/25/the-psychology-behind-chess/

We would be grateful for any feedback! If that project had any impact on you please let us know!!