13,000+ beans were classified according to 16 different attributes – we got our community to visualize the results.
How did Brexit change exports from the UK? Look at this week’s favorites to find out…
In week 12, #MakeoverMonday participants tackled data about consumption expenditure over time, including the impact of the pandemic on purchasing behaviors.
With the world’s population growing rapidly and agriculture intensifying in every region, we used week 11 to look at cash crops in different parts of the world.
For week 10 and in celebration of International Women’s Day 2021, we look at the progress that has been made to reach parity between genders in participation at the Olympic Games.
Week 9’s topic was the representation of women in national parliaments across Europe as seen by the number of seats they hold compared to men.
In week 8 the community tackled data about protests against the changes in Poland’s abortion policy
Valentine’s Day brings out a lot of creativity in our community each week, so this week’s favorites feature some rare data art examples that we’d like to showcase.
For the 12th Viz5 challenge, the community tackled perceptions of gender equality as reported from different countries.
Week 5 challenges the #MakeoverMonday community to create visualizations about renewable energy production in the EU. Check out the favorites in this video.
Indian coal mine production was the topic of week 4. Check out this week’s lessons learned and favorites.
What is warming the earth? That was the question we explored during week 3 of #MakeoverMonday.
Check out this week’s favorites as the community visualized the severe gender inequality in HIV infections in adolescents.
Welcome to #MakeoverMonday in 2021 – here are your favorites for week 1 plus a recap with three lessons learned.
The last week of 2020 had us visualize data about cocoa bean imports by different regions around the world. Check out these inspiring favorites that will leave you wanting chocolate…
For Charlie’s last #MakeoverMonday dataset he provided some data related to Arsenal football club which showed how much they’re struggling under Mikel Arteta as we approach Christmas 2020. How would the community choose to present this data?
In week 50 the community visualized data about Bob Ross’ paintings throughout his career as painter and artist who inspired millions with his artwork.
Looks can be deceptive! A simple dataset proved to be pretty versatile this week and the #MakeoverMonday community found a number of solutions when presenting this small dataset
This week saw our community tackle a #Viz5 dataset to visualize the success of Operation Fistula’s pilot program. Check out our favorite visualizations in this post.
For Andy Kriebel’s plaque-earning 1,000th Tableau Public viz, we looked at the development of GDP and Public Debt trends in the USA
In this week’s #MakeoverMonday challenge we tackled data on ad spending in the US and the community produced a lot of great visualizations. Check out the video here for this week’s favorites.
The week 45 data saw Nintendo Switch hardware and software sales charted by the #MakeoverMonday community. As ever a relatively small dataset was creatively approached and a wide variety of submissions were received
Check out this week’s video and links to the favorite visualizations for the #Viz5 challenge to visualize the digital gender gap, indicating how much access women have to the internet and mobile phones in different countries.
Some fairly depressing data this week (sorry!) looking at the way large corporate organisations in the US have exploited apparel suppliers worldwide as demand for products dipped in Q2 and Q3 2020
Check out the lessons learned for week 42 and see Eva’s favorite visualizations from this week’s data challenge. The community worked with data about healthcare spending.
To give frazzled minds a rest after the frenetic few days of Tableau Conferenceish, a simple dataset and a chart of dubious qualities was shared with the #MakeoverMonday community for some TLC
This week the #MakeoverMonday challenge consisted of visualizing a dataset about US GDP by counties. Check out the maps, bar charts, line charts and dashboards in this week’s favorites.
For week 39 we tackled another #Viz5 dataset, this time about child marriage. Our #MakeoverMonday community did a great job working with data about such a difficult topic and handled it with care and consideration. Check out this week’s favorites here!
Week 38 came with lots of submissions from the community, visualizing the price index of books and all other consumer goods for various countries. Check out this blog post for a video containing the lessons learned and this week’s favorites.
The data this week reflected a decade of aggregated teacher salary data from England. The #MakeoverMonday community did a great job of clearly and concisely representing this in an effective way.
A great variety of vizzes was created to visualize the nutritional value of different breakfast cereals. Check out the video to hear this week’s lessons and see Eva’s favorites.
Join us for MakeoverMonday Live(ish) on October 5th to analyze and visualize together with thousands of people around the globe. We’ll bring the live event right into your home office and we’d love to have you join us
Who would have thought that such a small dataset could incite such debate during Viz Review?! Lots of people were taken out of their comfort zone this week, and that’s a good thing. Lots of submissions were peppered with distracting icons and images; chart junk. In most cases, simplicity is best so the main lesson from this week for me is: if it doesn’t add to the understanding of the data, don’t add it to the visualisation!
For week 34 the community tackled another #Viz5 challenge to analyze and visualize data about women’s access to contraception in low and low-middle income countries. Check out the favorites for this week.
To tie in with his trip North of the border, Charlie shared some data related to tech companies based in Scotland. Do you always need to use maps when geographic data is provided? Maybe, maybe not. When executed well it certainly adds some flourish to a data viz.
During week 32 the community visualised the benefits of working from home. Check out the great charts and dashboards they created.
The #MakeoverMonday community tackled a fairly bland dataset with customary gusto this week. Here is a selection of the favourites this week.
For this #MakeoverMonday and #Viz5 challenge the community visualized the proportion of women in parliaments around the world. Check out this week’s favorites here.
Simple survey data this week and the challenge was as much about tone of voice and objectiveness as it was about data visualisation. It’s one thing to be able to present data in a captivating way. but understanding how to describe and position your wording is as important.
During week 28, the #MakeoverMonday community worked with data collected by scientists at Palmer Station, Antarctica. The data covers three different penguin species and some of their physical attributes.
This week we looked at data from 2014 via NHS Digital looking at self-reported symptoms of common mental health disorder. The challenge was as much the use of language and understanding of the limitations of the data, as it was to present it in a clear and effective way.
For week 26, the #MakeoverMonday community worked on another #Viz5 challenge, visualizing economic empowerment data. Check out this week’s favorites here.
Amazon post modest profit margins each quarter. Take a look to see the ways in which members of the #MakeoverMonday community presented that data in week 25 of the 2020 datasets
The #MakeoverMonday worked on high school sports participation rates for week 24, showing the increasing popularity of soccer among girls.
Attitudes to meat-free and animal-free foods in Great Britain came under the microscope in week 23. How many ways can survey results be visualised? Well, at least six!
During week 22, the MakeoverMonday community tackled the challenging topic of FGM, female genital mutilation, to raise awareness for charities striving to put an end to the practice in Tanzania.
An exceptional week of submissions this week, where the majority were subtle variations on a solid original visualisation. A reminder to all #MakeoverMonday participants: favourites can only be selected from Twitter submissions including a static screenshot of the final visualisation; we can’t incorporate .gifs in the Favourites post so they won’t be included.
In week 20 the #MakeoverMonday community tackled a dataset about car insurance rates in the US in 2020, highlighting rates for minimum and full cover.
The task this week was to try to improve upon a stacked bar chart representing metrics related to the 2020 World Happiness Report. There was a lot of variety in the submissions this week, which is reflected in the favourites. There were many, many more which came close!
During week 18 we tackled another #Viz5 dataset. This month the topic was obstetric fistula in Madagascar, with a detailed dataset provided by Operation Fistula. Check out this week’s favorites here.