Suppose the question is to find the average of SALES PER VISIT (sales measured across the preceding 6 months) among the males and females in a sample of 25 shoppers.  The data look like this in Excel:

TIP:  We can easily input these data to Tableau by cutting and pasting the selection into the Tableau canvas:

Now to answer the question.

First time users of Tableau may correctly put GENDER on the row shelf and SALES PER VISIT on the column shelf.  Tableau defaults to a bar chart yielding:

First time users may also put SALES PER VISIT on the Label Marks card, which displays the value next to each bar in the chart.  They may even put GENDER on the Color Marks card to give the viz some pop.

And then they call it done.  Males spend more on average than females.

But do they?

We note that the green pills show SUM(Sales per Visit).  Tableau’s default “aggregation” is to sum the values across the rows in the data set.

Going back to Excel, if we sum SALES PER VISIT by GENDER across the 25 rows, we get, using the SUMIF function:

This is exactly what Tableau shows.

But we want to find the average.  In Excel, using the AVERAGEIF function, we see that females spend on average $11.62 while males spend $9.13 per visit.

To get Tableau to match, we simply change the aggregation by right-clicking on each of the green pills and select Measure (Average) from the drop-down menu.

Now we get the correct answer to our question.

If Tableau is not yielding the correct answer, try thinking about how you would do it in Excel.  Sometimes, but not always, this will provide the proper guidance.

The post Tableau Basics: SUM vs AVG appeared first on KDD Analytics.

This dashboard tracks the San Diego COVID experience: new cases, tests, and positivity rates at the county-level as well as new cases for each of the county’s ZIP Codes.

On this first-year anniversary of these daily postings, we thought we would look back at this roller coaster year.

And, although our dashboard does not include US data, we thought that comparing the San Diego COVID experience with the national average would be insightful.

San Diego COVID experience vs the nation

The following figure shows the 7-day moving average of daily new cases per 100,000, for both San Diego County and the entire US.[1]

As shown in the above figure, as a nation we have been through 3 waves with it being too soon to tell if the 4^th wave has crested. San Diego County’s experience was generally similar except for the 4^th wave.

Wave #1

The initial rise in daily new cases crested at a 7-day average of 10 per 100,000 for the US on April 12, 2020. San Diego’s first wave crested about a week earlier on April 4^th at about 4 per 100,000.

The US new case 7-day average fell to 6 per 100,000 by mid-June. San Diego’s briefly fell a bit but then rose back up to a daily rate of 3 to 4 per 100,000 till mid-June.

So, San Diego did not really experience the same recovery from the first wave as the US.

Wave #2

For both the US and San Diego, the second, much larger wave began in mid-June 2020. The US new case rate increased from a 7-day average of about 6 per 100,000 to a peak of 21 per 100,000 on July 23^rd. San Diego’s rate increased from about 4 per 100,000 to a peak of 16 per 100,000 on July 2^nd.

Both the US and San Diego new case rates declined through the end of the summer. The US new case rate bottomed at a 7-day average of 13 per 100,000 on September 13^th. San Diego’s bottomed at the end of August at about 8 per 100,000 and stayed essentially flat till mid-October.

Wave #3

The US third wave began in mid-September – a full month before San Diego was hit. Rising from a 7-day average low of about 11 per 100,000, the US new case rate increased throughout the fall and early winter, peaking at 76 cases per 100,000 on January 11, 2021.

San Diego’s third wave began in mid-October. From a 7-day average low of about 8 new cases per 100,000 on October 20, the new case rate peaked at a high of 109 per 100,000 on the same day that this third wave peaked for the country.

As the figure shows, San Diego (as well as Los Angeles) suffered much higher new case rates than the national average.

But daily new cases started to decline just as steeply as they increased. San Diego’s 7-day average new case rate fell from this high of 109 to around 7 per 100,000 by April 20, 2021.

The US new case rate fell as well, from a 7-day average of 76 to about 16 by March 19, 2021.

Wave #4

Until this point, San Diego’s experience, though different in severity, matched the general pattern of the country. However, a 4^th US wave began in mid-March 2021, driven by new outbreaks in Michigan and New Jersey. It is too soon to tell if this 4^th wave has crested but the most recent peak is a 7-day average of 21 new cases per 100,000 on April 13^th.

San Diego has been fortunate to escape this 4^th wave (so far).

Fingers crossed…

[1] San Diego new case data are from the San Diego County Health Department. US new case data are from the CDC.  The 7-day moving average is the average of the current and preceding 6 days. 2019 population is used to normalize case counts so we can compare San Diego with the nation.

The post San Diego and COVID-19 … A Very Challenging Year appeared first on KDD Analytics.

According to Intensity’s latest US recession start date forecast, there is a 50% probability of a recession starting sometime in the January to February 2019 period.  And a 97% probability of it starting sometime within the next 6 months.

Their “point estimate” of a recession start is January 2019.

Like, as in, right now!

If true, it will take awhile for the impacts to start showing up in the official government statistics.  But the stock market sell-off last quarter may be a harbinger of things to come.

Intensity, an economics and data science firm based in San Diego, CA, developed and back-tested a machine learning prediction algorithm for its clients.  The firm started releasing a monthly forecast of the next US recession start date to the public starting in March 2018.

Over the course of the last 11 months, it has been interesting following the updates to their forecast as economic conditions changed.

Intuitively, one would expect that the forecast would “settle down”, the closer the expected start date became.

And it got me thinking about what the best way is to visualize these changing forecasts.

Visualizing Forecast Updates Over Time

The forecasted recession start date is not linear with time.  For example, in March 2018, the next recession was forecasted by Intensity to start in April 2019.  But in April 2018, the forecast was revised, and the recession was to start 6 months earlier in October 2018.

Plotting the month of the forecast on the x-axis and the forecasted month of the recession start on the y-axis yields a “traditional time series” view as shown below.

As time progresses from left to right, we can see the forecasted recession start date fluctuating up and down, settling on January 2019, the most recent forecasted start date.

However, another way to visualize this is to show the progression of time vertically, from bottom to top.  In this case the forecasted recession start date would fluctuate horizontally, left and right, as shown below.

I don’t know about you, but I find this second view more appealing.  Maybe it is the old economist in me, trained on the Phillips Curve in graduate school.  But for me, the vertical, “up-down” orientation makes the variation in the forecasted recession start date “pop” more than in the horizontal, “left-to-right” view.

So, Recession in 2019?

It will be very interesting to see if Intensity sticks to its January 2019 point estimate.  Prior to the unexpectedly positive December 2018 jobs report, the consensus seemed to be a recession starting some time in 2019 or 2020.  For example, Gary Shilling recently tossed his hat into the recession ring with a predicted 66% chance of a recession in 2019.

However, the positive jobs report apparently has many economists now softening their stance on a recession this year.  And there is talk of policy makers being able to sidestep a recession.

Only time will tell…so stay tuned!

Plotting Ordered Times Series in Tableau

By the way, these charts were made in Tableau.  And it was not as straight forward as flipping the axes to get the vertical view.  Tableau’s default inclination is to “connect the dots” from left to right when time is involved.

Fortunately, there is an easy way to get Tableau to connect the dots vertically.  This makes use of the Path property in the Marks card.  I simply added a field to my raw data that indicated the order of my data, which, of course was calendar order.

Then dropping this field on the Path property in the Marks card tells Tableau to connect the dots (or “Marks” in Tableau-speak) in this order.  With the date of the forecast on the vertical, y-axis, Tableau connects the dots from bottom to top.

Very slick!

The post How to Visualize Changing Recession Start Date Forecasts appeared first on KDD Analytics.