Neither the forest nor the trees

On the NYT's twitter feed, they featured an article titled "These Seven Tech Stocks are Driving the Market". The first sentence of the article reads: "The S&P 500 is at an all-time high, and investors have just a handful of stocks to thank for it."

Without having seen any data, I'd surmise from that line that (a) the S&P 500 index has gone up recently, and (b) most if not all of the gain in the index can be attributed to gains in the tech stocks mentioned in the headline. (For purists, a handful is five, not seven.)

The chart accompanying the tweet is a treemap:

Nyt_magnificentseven

The treemap is possibly the most overhyped chart type of the modern era. Its use here is tangential to the story of surging market value. That's because the treemap presents a snapshot of the composition of the index, but contains nothing about the trend (change over time) of the average index value or of its components.

***

Even in representing composition, the treemap is inferior to, gasp, a pie chart. Of course, we can only use a pie chart for small numbers of components. The following illustration takes the data from the NYT chart on the Magnificent Seven tech stocks, and compares a treemap versus a pie chart side by side:

Junkcharts_redo_nyt_magnificent7

The reason why the treemap is worse is that both the width and the height of the boxes are changing while only the radius (or angle) of the pie slices is varying. (Not saying use a pie chart, just saying the treemap is worse.)

There is a reason why the designer appended data labels to each of the seven boxes. The effect of not having those labels is readily felt when our eyes reach the next set of stocks – which carry company names but not their market values. What is the market value of Berkshire Hathaway?

Even more so, what proportion of the total is the market value of Berkshire Hathaway? Indeed, if the designer did not write down 29%, it would take a bit of work to figure out the aggregate value of yellow boxes relative to the entire box!

This design sucessfully draws our attention to the structural importance of various components of the whole. There are three layers - the yellow boxes (Magnificent Seven), the gray boxes with company names, and the other gray boxes. I also like how they positioned the text on the right column.

***

Going inside the NYT article itself, we find two line charts that convey the story as told.

Here's the first one:

Nyt_magnificent7_linechart1

They are comparing the most recent stock prices with those from October 12 2022, which is identified as the previous "low". (I'm actually confused by how the most recent "low" is defined, but that's a different subject.)

This chart carries a lot of good information, even though it does not plot "all the data", as in each of the 500 S&P components individually. Over the period under analysis, the average index value has gone up about 35% while the Magnificent Seven's value have skyrocketed by 65% in aggregate. The latter accounted for 30% of the total value at the most recent time point.

If we set the S&P 500 index value in 2024 as 100, then the M7 value in 2024 is 30. After unwinding the 65% growth, the M7 value in October 2022 was 18; the S&P 500 in October 2022 was 74. Thus, the weight of M7 was 24% (18/74) in October 2022, compared to 30% now. Consequently, the weight of the other 473 stocks declined from 76% to 70%.

This isn't even the full story because most of the action within the M7 is in Nvidia, the stock most tightly associated with the current AI hype, as shown in the other line chart.

Nyt_magnificent7_linechart2

Nvidia's value jumped by 430% in that time window. From the treemap, the total current value of M7 is $12.3 b while Nvidia's value is $1.4 b, thus Nvidia is 11.4% of M7 currently. Since M7 is 29% of the total S&P 500, Nvidia is 11.4%*29% = 3% of the S&P. Thus, in 2024, against 100 for the S&P, Nvidia's share is 3. After unwinding the 430% growth, Nvidia's share in October 2022 was 0.6, about 0.8% of 74. Its weight tripled during this period of time.


To a new year of pleasant surprises

Happy new year!

This year promises to be the year of AI. Already last year, we pretty much couldn't lift an eyebrow without someone making an AI claim. This year will be even noisier. Visual Capitalist acknowledged this by making the noisiest map of 2023:

Visualcapitalist_01_Generative_AI_World_map sm

I kept thinking they have a geography teacher on the team, who really, really wants to give us a lesson of where each country is on the world map.

All our attention is drawn to the guiding lines and the random scatter of numbers. We have to squint to find the country names. All this noise drowns out the attempt to make sense of the data, namely, the inset of the top 10 countries in the lower left corner, and the classification of countries into five colored groups.

A small dose of editing helps. Remove most data labels except for the countries for which they have a story. Provide a data table below for those who want details.

***

In the Methodology section, the data analysts (possibly from a third party called ElectronicsHub) indicated that they used Google search volume of "over 90 of the most popular generative AI tools", calculating the "overall volume across all tools per 100k population". Then came a baffling line: "all search volumes were scaled up according to the search engine market share in each country, using figures from statscounter.com." (Note: in the following, I'm calling the data "AI-related search" for simplicity even though their measurement is restricted to the terms described above.)

It took me a while to comprehend what they could have meant by that line. I believe this is what that sentence means: Google is not the only search engine out there so by only researching Google search volume, they undercount the true search volume. How did they deal with the missing data problem? They "scaled up" so if Google is 80% of the search volume in a country, then they divide the Google volume by 80% to "scale up" to 100%.

Whenever we use heuristics like this, we should investigate its foundations. What is the implicit assumption behind this scaling-up procedure? It is that all search engines are effectively the same. The users of non-Google search engines behave exactly as the Google search engine users. If the analysts somehow could get their hands on the data of other search engines, they would discover that the proportion of search volume that is AI-related is effectively the same as seen on Google.

This is one of those convenient, and obviously wrong assumptions – if true, the market would have no need for more than one search engine. Each search engine's audience is just a random sample from the population of all users.

Let's make up some numbers. Let's say Google has 80% share of search volume in Country A, and AI-related search 10% of the overall Google search volume. The remaining search engines have 20% share. Scaling up here means taking the 8% of Google AI-related search volume, divide by 80%, which yields 10%. Since Google owns 8% of the 10%, the other search engines see 2% of overall search volume attributed to AI searches in Country A. Thus, the proportion of AI-related searches on those other search engines is 2%/20% = 10%.

Now, in certain countries, Google is not quite as dominant. Let's say Google only has 20% share of Country B's search volume. AI-related search on Google is 2%, which is 10% of its total. Using the same scaling-up procedure, the analysts have effectively assumed that the proportion of AI-related search volume in the dominant search engines in Country B to be also 10%.

I'm using the above calculations to illustrate a shortcoming of this heuristic. Using this procedure inflates the search volume in countries in which Google is less dominant because the inflation factor is the reciprocal of Google's market share. The less dominant Google is, the larger the inflation factor.

What's also true? The less dominant Google is, the smaller proportion of the total data the analysts are able to see, the lower the quality of the available information. So the heuristic is the most influential where it has the greatest uncertainty.

***

Hope your new year is full of uncertainty, and your heuristics shall lead you to pleasant surprises.

If you like the blog's content, please spread the word. I'm looking forward to sharing more content as the world of data continues to evolve at an amazing pace.

Disclosure: This blog post is not written by AI.


An elaborate data vessel

Visualcapitalist_globaloilproductionI recently came across the following dataviz showing global oil production (link).

This is an ambitious graphic that addresses several questions of composition.

The raw data show the amount of production by country adding up to the global total. The countries are then grouped by region. Further, the graph presents an oil-and-gas specific grouping, as indicated by the legend shown just below the chart title. This grouping is indicated by the color of the circumference of the circle containing the flag of the country.

This chart form is popular in modern online graphics programs. It is like an elaborate data vessel. Because the countries are lined up around the barrel, a space has been created on three sides to admit labels and text annotations. This is a strength of this chart form.

***

The chart conveys little information about the underlying data. Each country is given a unique odd shaped polygon, making it impossible to compare sizes. It’s definitely possible to pick out U.S., Russia, Saudi Arabia as the top producers. But in presenting the ranks of the data, this chart form pales in comparison to a straightforward data table, or a bar chart. The less said about presenting values, the better.

Indeed, our self-sufficiency test exposes the inability of these polygons to convey the data. This is precisely why almost all values of the dataset are present on the chart.

***

The dataviz subtly presumes some knowledge on the part of the readers.

The regions are not directly labeled. The readers must know that Saudi Arabia is in the Middle East, U.S. is part of North America, etc. Admittedly this is not a big ask, but it is an ask.

It is also assumed that readers know their flags, especially those of smaller countries. Some of the small polygons have no space left for country names and they are labeled with just flags.

Visualcapitalist_globaloilproduction_nocountrylabels

In addition, knowing country acronyms is required for smaller countries as well. For example, in Africa, we find AGO, COG and GAB.

Visualcapitalist_globaloilproduction_countryacronyms

For this chart form the designer treats each country according to the space it has on the chart (except those countries that found themselves on the edges of the barrel). Font sizes, icons, labels, acronyms, data labels, etc. vary.

The readers are assumed to know the significance of OPEC and OPEC+. This grouping is given second fiddle, and can be found via the color of the circumference of the flag icons.

Visualcapitalist_globaloilproduction_opeclegend

I’d have not assigned a color to the non-OPEC countries, and just use the yellow and blue for OPEC and OPEC+. This is a little edit but makes the search for the edges more efficient.

Visualcapitalist_globaloilproduction_twoopeclabels

***

Let’s now return to the perception of composition.

In exactly the same manner as individual countries, the larger regions are represented by polygons that have arbitrary shapes. One can strain to compile the rank order of regions but it’s impossible to compare the relative values of production across regions. Perhaps this explains the presence of another chart at the bottom that addresses this regional comparison.

The situation is worse for the OPEC/OPEC+ grouping. Now, the readers must find all flag icons with edges of a specific color, then mentally piece together these arbitrarily shaped polygons, then realizing that they won’t fit together nicely, and so must now mentally morph the shapes in an area-preserving manner, in order to complete this puzzle.

This is why I said earlier this is an elaborate data vessel. It’s nice to look at but it doesn’t convey information about composition as readers might expect it to.

Visualcapitalist_globaloilproduction_excerpt


What is the question is the question

I picked up a Fortune magazine while traveling, and saw this bag of bubbles chart.

Fortune_global500 copy

This chart is visually appealing, that must be said. Each circle represents the reported revenues of a corporation that belongs to the “Global 500 Companies” list. It is labeled by the location of the company’s headquarters. The largest bubble shows Beijing, the capital of China, indicating that companies based in Beijing count $6 trillion dollars of revenues amongst them. The color of the bubbles show large geographical units; the red bubbles are cities in Greater China.

I appreciate a couple of the design decisions. The chart title and legend are placed on the top, making it easy to find one’s bearing – effective while non-intrusive. The labeling signals a layering: the first and biggest group have icons; the second biggest group has both name and value inside the bubbles; the third group has values inside the bubbles but names outside; the smallest group contains no labels.

Note the judgement call the designer made. For cities that readers might not be familiar with, a country name (typically abbreviated) is added. This is a tough call since mileage varies.

***

As I discussed before (link), the bag of bubbles does not elevate comprehension. Just try answering any of the following questions, which any of us may have, using just the bag of bubbles:

  • What proportion of the total revenues are found in Beijing?
  • What proportion of the total revenues are found in Greater China?
  • What are the top 5 cities in Greater China?
  • What are the ranks of the six regions?

If we apply the self-sufficiency test and remove all the value labels, it’s even harder to figure out what’s what.

***

_trifectacheckup_image

Moving to the D corner of the Trifecta Checkup, we aren’t sure how to interpret this dataset. It’s unclear if these companies derive most of their revenues locally, or internationally. A company headquartered in Washington D.C. may earn most of its revenues in other places. Even if Beijing-based companies serve mostly Chinese customers, only a minority of revenues would be directly drawn from Beijing. Some U.S. corporations may choose its headquarters based on tax considerations. It’s a bit misleading to assign all revenues to one city.

As we explore this further, it becomes clear that the designer must establish a target – a strong idea of what question s/he wants to address. The Fortune piece comes with a paragraph. It appears that an important story is the spatial dispersion of corporate revenues in different countries. They point out that U.S. corporate HQs are more distributed geographically than Chinese corporate HQs, which tend to be found in the key cities.

There is a disconnect between the Question and the Data used to create the visualization. There is also a disconnect between the Question and the Visual display.


Graph workflow and defaults wreak havoc

For the past week or 10 days, every time I visited one news site, it insisted on showing me an article about precipitation in North Platte. It's baiting me to write a post about this lamentable bar chart (link):

Northplatte_rainfall

***

This chart got problems, and the problems start with the tooling, which dictates a workflow.

I imagine what the chart designer had to deal with.

For a bar chart, the tool requires one data series to be numeric, and the other to be categorical. A four-digit year is a number, which can be treated either as numeric or categorical. In most cases, and by default, numbers are considered numeric. To make this chart, the user asked the tool to treat years as categorical.

Junkcharts_northplattedry_datatypes

Many tools treat categories as distinct entities ("nominal"), mapping each category to a distinct color. So they have 11 colors for 11 years, which is surely excessive.

This happens because the year data is not truly categorical. These eleven years were picked based on the amount of rainfall. There isn't a single year with two values, it's not even possible. The years are just irregularly spaced indices. Nevertheless, the tool misbehaves if the year data are regarded as numeric. (It automatically selects a time-series line chart, because someone's data visualization flowchart says so.) Mis-specification in order to trick the tool has consequences.

The designer's intention is to compare the current year 2023 to the driest years in history. This is obvious from the subtitle in which 2023 is isolated and its purple color is foregrounded.

Junkcharts_northplattedry_titles

How unfortunate then that among the 11 colors, this tool grabbed 4 variations of purple! I like to think that the designer wanted to keep 2023 purple, and turn the other bars gray -- but the tool thwarted this effort.

Junkcharts_northplattedry_purples

The tool does other offensive things. By default, it makes a legend for categorical data. I like the placement of the legend right beneath the title, a recognition that on most charts, the reader must look at the legend first to comprehend what's on the chart.

Not so in this case. The legend is entirely redundant. Removing the legend does not affect our cognition one bit. That's because the colors encode nothing.

Worse, the legend sows confusion because it presents the same set of years in chronological order while the bars below are sorted by amount of precipitation: thus, the order of colors in the legend differs from that in the bar chart.

Junkcharts_northplattedry_legend

I can imagine the frustration of the designer who finds out that the tool offers no option to delete the legend. (I don't know this particular tool but I have encountered tools that are rigid in this manner.)

***

Something else went wrong. What's the variable being plotted on the numeric (horizontal) axis?

The answer is inches of rainfall but the answer is actually not found anywhere on the chart. How is it possible that a graphing tool does not indicate the variables being plotted?

I imagine the workflow like this: the tool by default puts an axis label which uses the name of the column that holds the data. That column may have a name that is not reader-friendly, e.g. PRECIP. The designer edits the name to "Rainfall in inches". Being a fan of the Economist graphics style, they move the axis label to the chart title area.

The designer now works the chart title. The title is made to spell out the story, which is that North Platte is experiencing a historically dry year. Instead of mentioning rainfall, the new title emphasizes the lack thereof.

The individual steps of this workflow make a lot of sense. It's great that the title is informative, and tells the story. It's great that the axis label was fixed to describe rainfall in words not database-speak. But the end result is a confusing mess.

The reader must now infer that the values being plotted are inches of rainfall.

Further, the tool also imposes a default sorting of the bars. The bars run from longest to shortest, in this case, the longest bar has the most rainfall. After reading the title, our expectation is to find data on the Top 11 driest years, from the driest of the driest to the least dry of the driest. But what we encounter is the opposite order.

Junkcharts_northplattedry_sorting

Most graphics software behaves like this as they are plotting the ranks of the categories with the driest being rank 1, counting up. Because the vertical axis moves upwards from zero, the top-ranked item ends up at the bottom of the chart.

***

_trifectacheckup_imageMoving now from the V corner to the D corner of the Trifecta checkup (link), I can't end this post without pointing out that the comparisons shown on the chart don't work. It's the first few months of 2023 versus the full years of the others.

The fix is to plot the same number of months for all years. This can be done in two ways: find the partial year data for the historical years, or project the 2023 data for the full year.

(If the rainy season is already over, then the chart will look exactly the same at the end of 2023 as it is now. Then, I'd just add a note to explain this.)

***

Here is a version of the chart after doing away with unhelpful default settings:


Redo_junkcharts_northplattedry


Showing both absolute and relative values on the same chart 1

Visual Capitalist has a helpful overview on the "uninsured" deposits problem that has become the talking point of the recent banking crisis. Here is a snippet of the chart that you can see in full at this link:

Visualcapitalist_uninsureddeposits_top

This is in infographics style. It's a bar chart that shows the top X banks. Even though the headline says "by uninsured deposits", the sort order is really based on the proportion of deposits that are uninsured, i.e. residing in accounts that exceed $250K.  They used a red color to highlight the two failed banks, both of which have at least 90% of deposits uninsured.

The right column provides further context: the total amounts of deposits, presented both as a list of numbers as well as a column of bubbles. As readers know, bubbles are not self-sufficient, and if the list of numbers were removed, the bubbles lost most of their power of communication. Big, small, but how much smaller?

There are little nuggets of text in various corners that provide other information.

Overall, this is a pretty good one as far as infographics go.

***

I'd prefer to elevate information about the Too Big to Fail banks (which are hiding in plain sight). Addressing this surfaces the usual battle between relative and absolute values. While the smaller banks have some of the highest concentrations of uninsured deposits, each TBTF bank has multiples of the absolute dollars of uninsured deposits as the smaller banks.

Here is a revised version:

Redo_visualcapitalist_uninsuredassets_1

The banks are still ordered in the same way by the proportions of uninsured value. The data being plotted are not the proportions but the actual deposit amounts. Thus, the three TBTF banks (Citibank, Chase and Bank of America) stand out of the crowd. Aside from Citibank, the other two have relatively moderate proportions of uninsured assets but the sizes of the red bars for any of these three dominate those of the smaller banks.

Notice that I added the gray segments, which portray the amount of deposits that are FDIC protected. I did this not just to show the relative sizes of the banks. Having the other part of the deposits allow readers to answer additional questions, such as which banks have the most insured deposits? They also visually present the relative proportions.

***

The most amazing part of this dataset is the amount of uninsured money. I'm trying to think who these account holders are. It would seem like a very small collection of people and/or businesses would be holding these accounts. If they are mostly businesses, is FDIC insurance designed to protect business deposits? If they are mostly personal accounts, then surely only very wealthy individuals hold most of these accounts.

In the above chart, I'm assuming that deposits and assets are referring to the same thing. This may not be the correct interpretation. Deposits may be only a portion of the assets. It would be strange though that the analysts only have the proportions but not the actual deposit amounts at these banks. Nevertheless, until proven otherwise, you should see my revision as a sketch - what you can do if you have both the total deposits and the proportions uninsured.


Finding the story in complex datasets

In CT Mirror's feature about Connecticut, which I wrote about in the previous post, there is one graphic that did not rise to the same level as the others.

Ctmirror_highschools

This section deals with graduation rates of the state's high school districts. The above chart focuses on exactly five districts. The line charts are organized in a stack. No year labels are provided. The time window is 11 years from 2010 to 2021. The column of numbers show the difference in graduation rates over the entire time window.

The five lines look basically the same, if we ignore what looks to be noisy year-to-year fluctuations. This is due to the weird aspect ratio imposed by stacking.

Why are those five districts chosen? Upon investigation, we learn that these are the five districts with the biggest improvement in graduation rates during the 11-year time window.

The same five schools also had some of the lowest graduation rates at the start of the analysis window (2010). This must be so because if a school graduated 90% of its class in 2010, it would be mathematically impossible for it to attain a 35% percent point improvement! This is a dissatisfactory feature of the dataviz.

***

In preparing an alternative version, I start by imagining how readers might want to utilize a visualization of this dataset. I assume that the readers may have certain school(s) they are particularly invested in, and want to see its/their graduation performance over these 11 years.

How does having the entire dataset help? For one thing, it provides context. What kind of context is relevant? As discussed above, it's futile to compare a school at the top of the ranking to one that is near the bottom. So I created groups of schools. Each school is compared to other schools that had comparable graduation rates at the start of the analysis period.

Amistad School District, which takes pole position in the original dataviz, graduated only 58% of its pupils in 2010 but vastly improved its graduation rate by 35% over the decade. In the chart below (left panel), I plotted all of the schools that had graduation rates between 50 and 74% in 2010. The chart shows that while Amistad is a standout, almost all schools in this group experienced steady improvements. (Whether this phenomenon represents true improvement, or just grade inflation, we can't tell from this dataset alone.)

Redo_junkcharts_ctmirrorhighschoolsgraduation_1

The right panel shows the group of schools with the next higher level of graduation rates in 2010. This group of schools too increased their graduation rates almost always. The rate of improvement in this group is lower than in the previous group of schools.

The next set of charts show school districts that already achieved excellent graduation rates (over 85%) by 2010. The most interesting group of schools consists of those with 85-89% rates in 2010. Their performance in 2021 is the most unpredictable of all the school groups. The majority of districts did even better while others regressed.

Redo_junkcharts_ctmirrorhighschoolsgraduation_2

Overall, there is less variability than I'd expect in the top two school groups. They generally appeared to have been able to raise or maintain their already-high graduation rates. (Note that the scale of each chart is different, and many of the lines in the second set of charts are moving within a few percentages.)

One more note about the charts: The trend lines are "smoothed" to focus on the trends rather than the year to year variability. Because of smoothing, there is some awkward-looking imprecision e.g. the end-to-end differences read from the curves versus the observed differences in the data. These discrepancies can easily be fixed if these charts were to be published.


Funnels and scatters

I took a peek at some of the work submitted by Ray Vella's students in his NYU dataviz class recently.

The following chart by Hosanah Bryan caught my eye:

Rich Get Richer_Hosanah Bryan (v2)

The data concern the GDP gap between rich and poor regions in various countries. In some countries, especially in the U.K., the gap is gigantic. In other countries, like Spain and Sweden, the gap is much smaller.

The above chart uses a funnel metaphor to organize the data, although the funnel does not add more meaning (not that it has to). Between that, the color scheme and the placement of text, it's visually clean and pleasant to look at.

The data being plotted are messy. They are not actual currency values of GDP. Each number is an index, and represents the relative level of the GDP gap in a given year and country. The gap being shown by the colored bars are differences in these indices 15 years apart. (The students were given this dataset to work with.)

So the chart is very hard to understand if one focuses on the underlying data. Nevertheless, the same visual form can hold other datasets which are less complicated.

One can nitpick about the slight misrepresentation of the values due to the slanted edges on both sides of the bars. This is yet another instance of the tradeoff between beauty and precision.

***

The next chart by Liz Delessert engages my mind for a different reason.

The Rich Get Richerv2

The scatter plot sets up four quadrants. The top right is "everyone gets richer". The top left, where most of the dots lie, is where "the rich get richer, the poor get poorer".  This chart shows a thoughtfulness about organizing the data, and the story-telling.

The grid setup cues readers toward a particular way of looking at the data.

But power comes with responsibility. Such scatter plots are particularly susceptible to the choice of data, in this case, countries. It is tempting to conclude that there are no countries in which everyone gets poorer. But that statement more likely tells us more about which countries were chosen than the real story.

I like to see the chart applied to other data transformations that are easier. For example, we can start with the % change in GDP computed separately for rich and for poor. Then we can form a ratio of these two percent changes.

 

 


A German obstacle course

Tagesschau_originalA twitter user sent me this chart from Germany.

It came with a translation:

"Explanation: The chart says how many car drivers plan to purchase a new state-sponsored ticket for public transport. And of those who do, how many plan to use their car less often."

Because visual language should be universal, we shouldn't be deterred by not knowing German.

The structure of the data can be readily understood: we expect three values that add up to 100% from the pie chart. The largest category accounts for 58% of the data, followed by the blue category (40%). The last and smallest category therefore has 2% of the data.

The blue category is of the most interest, and the designer breaks that up into four sub-groups, three of which are roughly similarly popular.

The puzzle is the identities of these categories.

The sub-categories are directly labeled so these are easy for German speakers. From a handy online translator, these labels mean "definitely", "probably", "rather not", "definitely not". Well, that's not too helpful when we don't know what the survey question is.

According to our correspondent, the question should be "of those who plan to buy the new ticket, how many plan to use their car less often?"

I suppose the question is found above the column chart under the car icon. The translator dutifully outputs "Thus rarer (i.e. less) car use". There is no visual cue to let readers know we are supposed to read the right hand side as a single column. In fact, for this reader, I was reading horizontally from top to bottom.

Now, the two icons on the left and the middle of the top row should map to not buying and buying the ticket. The check mark and cross convey that message. But... what do these icons map to on the chart below? We get no clue.

In fact, the will-buy ticket group is the 40% blue category while the will-not group is the 58% light gray category.

What about the dark gray thin sector? Well, one needs to read the fine print. The footnote says "I don't know/ no response".

Since this group is small and uninformative, it's fine to push it into the footnote. However, the choice of a dark color, and placing it at the 12-o'clock angle of the pie chart run counter to de-emphasizing this category!

Another twitter user visually depicts the journey we take to understand this chart:

Tagesschau_reply

The structure of the data is revealed better with something like this:

Redo_tagesschau_newticket

The chart doesn't need this many colors but why not? It's summer.

 

 

 

 


Improving simple bar charts

Here's another bar chart I came across recently. The chart - apparently published by Kaggle - appeared to present challenges data scientists face in industry:

Kaggle

This chart is pretty standard, and inoffensive. But we can still make it better.

Version 1

Redo_kaggle_nodecimals

I removed the decimals from the data labels.

Version 2

Redo_kaggle_noaxislabels

Since every bar is labelled, is anyone looking at the axis labels?

Version 3

Redo_kaggle_nodatalabels

You love axis labels. Then, let's drop the data labels.

Version 4

Redo_kaggle_categories

Ahh, so data scientists struggle with data problems, and people issues. They don't need better tools.