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The Grey Lady Learns Pivot Tables: NY Times J-Course Data, Part 2

15 Jul

The Intermediate tine of the three-pronged New York Times data journalistic syllabus casts its analytic lot with pivot tables, and the kinds of questions properly placed before that mighty aggregating tool. Among its several data offerings awaits a 2900-record gathering of New York (i.e. Manhattan) real estate data, naming property owners, their holdings and their assessed value, and a trove of additional metrics. Per the course’s pedagogical remit, the learners are prompted to pose a few questions of their own of the data – surely a useful heuristic – before they turn to the assignments at hand, which among other things asks “What are the things you notice about this dataset that shows it isn’t perfect?”

A good, important, and generic question, one that nevertheless isn’t advanced by the Census data sheet we reviewed in the previous post. In fact, worksheet data imperfections can assume at least two forms: a discernible scruffiness among the records, and/or design impediments that could constrain analysis, and I’m conjecturing the Times wants it staffers to concern themselves with flaws of the former stripe.

For example, if this qualifies as a blemish: once downloaded to Excel, both the start and end_date year entries present themselves in text form, thus obstructing any unmediated attempt to group those data. Yet the year_built data remain indubitably numeric, and I can’t account for the discrepancy. But at the same time, however, these data in their native Google sheet mode appear appropriately numeric, and when I copied and pasted some of the start dates to a Google sheet of my own they behaved like good little values; and moreover, the left orientation imparted to the numbers in the end_date field suddenly righted themselves (that was a pun intended) via my paste. Another Google-Microsoft lost-in-translation flashpoint, perhaps, and not a species of data imperfection, if one remains in Sheets. (Another note: I’ve been heretofore unable to actually work with the Times sheets in the their Google trappings, as access to them defaults to View Only, and seems to require permission from the sheet owners in order to actually work with them. My requests for permission have gone unrequited to date, but in fact you can copy and paste the data to a blank Google sheet and go ahead. The data are open-sourced, aren’t they?)

Far more problematic however, and presumably one of the data failings over which the Times hoped its learners would puzzle, are the disparate spellings in the owner_name field of what appears to be the one and the same New York City Department of Housing:

nyt1

(Note the last entry above is simply misspelled. The data were drawn from the coredata site, by the way, a project of New York University’s Furman Center.) And, while we’re at it:

nyt2

But the Times’ marching orders don’t oblige its learners to proceed and do something about the inconsistencies. Absent that determination, no accurate answer to the Times’ question (number 6) – “Which owner has the most buildings?” – can be enabled. Remember that the Intermediate unit is pivot-table-driven, and any table invoking the owner_name field is going to loose the untidy spate of spellings reported above.

Yet one more imperfection besetting the selfsame owner_name field is the formidable complement of cells – 381 of them, to be exact, or about 13% of all the records – that contain no owner name at all, a lacuna that likewise comprises the analysis. The Times asks its learners “Who are the biggest owners in each neighborhood based on the number of units? Limit your table to owners who have more than 1,000 units”, an exercise which would appear to call for a pivot table that looks something like this:

Rows:  Neighborhood

owner_name

Values: res_units (filtered in the Rows area for sum of res_units equal to or greater than 1000)

And that alignment of parts kicks out a set of results that, in excerpt, embody the problem:

nyt3

Indeed, both data shortcomings – the blanks and the variant spellings – degrade the findings prohibitively.

The Times also wants its learners to “Compare the average value per unit for different neighborhoods. Which is the most expensive and which is the cheapest?” That chore seems to call for a calculated field, e.g. in Excel:

nyt4

I’m just wondering if the Times cohort learned the equivalent feature for Google Sheets; perhaps it did, after all.  Its Data Training Skills List merely records the Pivot Tables rubric without elaboration. (Note in addition that the housing data sheet hoards an Income sheet from which the Neighborhood population, income, and diversity fields on the Housing sheet have been presumably drawn, probably through a series of VLOOKUPS whose yields have been subject to a mass Copy > Paste Special routine directed to the Housing sheet.)

Of course, that surmise points to a larger question: the breadth of spreadsheet capabilities spanned by the Times training. How, for example, were learners expected to apply themselves to this assignment: “Which neighborhoods will be most affected (in terms of number of units) in each of the next 10 years by expiring subsidies and which one is the most secure?” I’d try this:

Rows: Neighborhood

Columns: end_date (filtered for years 2019-2028)

Values: program_name (Count, necessarily; the data are textual)

And my table looks like this:

nyt5

Thus Central Harlem is the neighborhood most vulnerable to short-term expirations of program subsidies – by far – with the Stuyvesant Town/Turtle Bay district, really a mélange of sections on Manhattan’s East Side, the least exposed. But does my pivot table approximate toward the strategy the Times was seeking?  Again I don’t know, but a conversation with the paper’s syllabus architects about their intentions for the exercises would prove instructive – at least for me.

And that conduces toward the inexorable follow-on, then: I’m happy to offer my services to the Times, in the edifying form of a weekly column on spreadsheets, and for a magnanimously modest emolument; and I’d make myself available to help with the in-house training, too.

Just one question: will my press pass get me into all the Yankees games for free?


Addendum to the above: My previous post recounted my inability to access and edit the Times’ files in native Google Sheet mode. The paper’s Elaine Chen did get back to me yesterday (July 16), pointing to the File > Download alternative. One assumes, after all, that Times doesn’t want to approve shared file access for the multitudes, and probably for good reason. I should add that if one downloads the data in CSV instead of Excel mode, the formatting discrepancies I described in Part 1 seem to disappear.

 

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The Grey Lady Learns Pivot Tables: NY Times J-Course Data, Part 1

28 Jun

This just in: The newspaper of record is rebranding itself into the newspaper of records. The Times – the one from New York, that is – has moved to evangelize the data-journalistic thing among its staff, and towards that admirable end has crafted an extended in-house workshop, syllabus and practice files/exercises made available to all the rest of us in Google Sheets here and here, respectively (ok, ok, call me the Luddite; I’m downloading the files into Excel).

The latter link above points to a sheaf of workbooks classed Advanced, Intermediate and Beginner (these rubrics sorted alphabetically, thus interpolating Beginner between the two ostensibly more challenging data collections. And note the Times cautions that even as the data sets have been mined from real-world repositories they’ve been alloyed, the better to serve their instructional purposes), and it occurred to me that a look some of the course contents might prove instructive in its own right.

We can begin with the Beginner Census_Characteristics of Older Americans (2016, 2019) workbook, whose associated Census: Worksheet exercise document asks us to unhide all its sequestered columns (about 65 of them in fact, most of which are massed at the far end of the data, something I missed repeatedly). Remember I’m downloading the data to Excel, an improvised recourse that bunches the field headers into ill-fitting Wrap Text mode. But by directing the download in Open Document mode instead the book nevertheless returns to Excel, but with the headers properly, visibly wrapped, though the columns could do with a bit of resizing (I don’t know if these little disjunctions bear traces of Google-Microsoft woofing).

The exercise text proceeds to let us know “We roughly color the group of categories. For example, the race and Hispanic stats are in light orange, and green columns are about marital status”. But no; these tinted ranges aren’t conditionally formatted, and to be fair can’t really lend themselves to those cellular  ornamentations. What shared textual/numeric datum, for example, could encourage all the ethnic data cells in columns K through V to turn orange? On the other hand, the columns brandish their colors all the way down to row 999, Google Sheet’s default row allotment maximum, though the data come to a halt at row 52.

Next, among other questions the exercise puts to us, we’re asked to “Take the average of the state mean earnings [presumably for Americans age 60 and over] and then look up the mean average for the US. Why do these numbers differ? “ Again, devoting ourselves to the 60-and-older data in the “states, 2016” sheet, and more particularly the 60+Pop; mean earnings field in column BB, that average is realized easily enough. But what mean average for the US does the Times want us to look up, and how? Of course, that very requisition may contribute to the exercise; and so after a bracing scroll-through across the 419 fields bulking up the “2016, US” sheet I stepped atop its cell K2, the one reporting mean household earnings for the 60+ plus demographic of $65,289 (sans curency format). But my lookup was eyeball-driven, and  certainly not under the steam of any maneuver typically entrusted to the redoutable V, or HLOOKUP function. Those instruments, after all, assume we know the identity of the pertinent lookup value – and we can’t know that the value reads “60 years and over; Estimate; INCOME IN THE PAST 12 MONTHS (IN 2016 INFLATION-ADJUSTED DOLLARS) – Households – With earnings – Mean earnings (dollars)”, the header entry in cell KL1:

Times1

And so by “look up” I’m constrained to assume that the Times is asking of us a simple, unmediated, visual hunt for that information. In other words: look up, not LOOKUP.

And with the respective means – the national average recorded unitarily in the “2016, US” sheet and the state-by-state average figured in “states, 2016” – in hand, we can propose an answer to the question the exercise puts to us: namely why the two averages differ. The answer, I hope: that the state average accords equivalent weight to each of the 51 (Washington DC appears in the list) income figures irrespective of population size, while the single national figure in effect tabulates the earnings of every American, thus flattening out any skew.

And speaking as a mere auditor of the Times workshop, I’d pose the same strategic conjecture about the exercise question “Which 3 states have had the largest percentage increase in their residents who are above 60 over that time?” That is, I’d wonder if the Times expects its tutees to simply do the basic math and literally look for the three most prominent state increases – or rather, filter out a top three, a la Excel’s filter option.

But the filtering alternatives in Google Sheets can pull users in two very directions. One pathway transports them to a filter view resembling the standard Excel dropdown-menu mechanism – but I can’t find a Top 10 (or 3) possibility registered among its capabilities here. The other byway to a top 3 drops us off at the most cool FILTER function, a de facto progenitor of the yet-to-be-released Excel dynamic array function of the same name; but its workings demand an intricacy not likely to be broached in a beginner class. Thus, I suspect that the Times again wants its learners to resort to simple visual inspection in order for them to glean that top 3.

As for the actual line-item math here, should you or a Times staffer choose to proceed with the exercise, I’d hammer in a new column somewhere in the “states, 2016” sheet and slot this formula in for the District of Columbia, the first “state” in row 2:

=D2/C2-J2/I2

The column references – D,C, J, and I – offer up the base and 60+ population data for 2016 and 2009. (And yes, the formula can make do without parentheses: The order of operations take care of themselves.)

Copy down the ad hoc column, and the trio of states divulging the largest increments in the 60+ cohort will be in there, somewhere.

And if the filter isn’t working for you, why not sort the numbers largest to smallest, and check out rows 2 through 4?

 

Philadelphia Police Complaints, Part 2: One Civilian’s Review

20 May

Now that I’ve spoken my piece about the putative redundancy of the two Philadelphia civilian police complaint workbooks, fairness insists that I actually take a look into the data that’s roosted inside worksheet number two – because there are findings to be found in there, in spite of all.

That book, ppd_complaints_disciplines, concentrates its gaze on the officers against whom civilians preferred a grievance. The complaints are additionally parsed into the districts in which the alleged offenses were perpetrated, and class the complaints along with their dispositions.

Once we’ve widened the fields via the usual auto-fit we see that a good many of the complaint incidents identify multiple officers, as well as different allegations. We could then move to determine, for starters, the average number of allegations lodged per complaint. But that simple-sounding intention isn’t realized quite so straightforwardly, because we need to isolate the number of unique complaint ids in column A before we divide them into all the complaint particulars; and the elegant way out would have us travel here, to this array formula:

=COUNTA(A2:A6313)/SUM(1/COUNTIF(A2:6313,A2:6313))

The denominator – or rather the pair of denominators commencing with the SUM function – exemplifies a well-known array formula for calculating unique values in a range. The COUNTIF element subjects the A2:A6313 range of complaint ids to what are in effect criteria furnished by each and every record. Thus each of the four instances of id 15-0001 are assessed against every id entry, four of which of course happen to present the selfsame 15-0001. Thus each instance here evaluates to a count of 4, and the formula’s “1/ “ numerator reduces each to ¼ – and by adding 4 1/4s a 1 is returned – tantamount to treating 15-0001 as a single instance. That reciprocal routine is applied to each and every value in column A and then summed – yielding in our case 2779. Divide that outcome into the field’s 6312 records and we wind up with average of 2.24 allegations per complaint. (It should be added that Excel’s dynamic-array UNIQUE function would streamline the approach on which I’ve embarked here, but the dynamic arrays remain behind a locked door somewhere in Redmond, and I have no idea when the key will be found. Note as well that the dynamic arrays will only download to the Office 365 iteration of Excel.)

But that average, however informative, doesn’t apprise us of the number of actual, discrete officers implicated by each complaint, because the same officer is often cited for multiple allegations laid to the same complaint. Again, for example, complaint 15-0001 and its four allegations actually identify but two different officers – and that is the number we’re seeking here, as it would go on to contribute to a real officer-per-case average.

One way – an inelegant one – for getting there would be to pour the data through the Remove Duplicates sieve, selecting the complaint_id and officer_id fields for the duplicate search. Click through and you’ll wind up with exactly 4700 unique, remaindered records, of which 313 are blank, however; and we can’t know how many of those ciphers do, and do not, point to a given officer but once per complaint. On the other hand, because most officers are in fact identified we can acceptably assume that for those complaints directed at multiple officers the unknown party is likely not the one(s) who is named.  That supposition can’t dispel all our questions, of course, but divide 4700 by the 2779 unique complaints we derived above, and we learn that 1.69 distinct officers fell under investigative scrutiny per case – although the real quotient is probably a bit smaller.

In any event, that figure emerges at the cost of dismissing 1600 records from the data set, after which can we subject the stragglers to a formula, e.g.

{=COUNTA(B2:B4701)/SUM(1/(COUNTIF(A2:A4701,A2:A4701)))}

Inelegant indeed. For a sleeker alternative, we could first concatenate the complaint and officer ids in a new field in column I that I’m calling complaintofficer, e.g. in I2:

=A2&B2

That step positions us to cull unique officer ids by case; by running the unique-record array formula at these data we should be able to emulate the 4700 total and divide it by the other unique-finding expression aimed at the complaint ids:

=SUM(1/COUNTIF(I2:I6313,I2:I6313))}/{=SUM(1/COUNTIF(A2:I6313,A2:I6313))}

Elegance is a relative term, mind you, but it works.

On the other hand, if you wanted to develop a racial breakout of the number of individual officers charged with at least one complaint, you may here want to mobilize a Remove Duplicates by the po_race variable, because the breakout comprises multiple items (i.e. “races”; and if you adopt this tack you could save the results under a different file name, thus conserving the original, complete data set). That sequence yields 2549 separate officers, and conduces toward this pivot table:

Rows: officer_id

Value: officer_id

officer_id (again, by % of Column Total)

I get:

blogphil1

Now of course the proportions tell us little, because we need to spread them atop the racial makeup of the Philadelphia police force before any exposition can rightly commence. Note by the way that only one UNKNOWN officer informs the count here, even as we earlier turned up 313 such blank records; that’s because, of course, all the unknowns have the same blank, “duplicate” id.

Returning to the data set in toto, we can distribute allegations by their varieties. Remember of course that the 2779 complaints have accumulated an average of 2.24 charges, but each charge is exactly that – a complaint in its own right. Thus this conventional pivot table:

Rows: allegations_investigated

Values: allegations_investigated

Allegations_investigated (again, % of Column Total)

reports:

blogphil2

Apart from the indefiniteness of the modal Departmental Violation type, we need to recall that our previous post numbered 2782 such allegations populating the ppd_complaints workbook we reviewed then. It’s seems clear that the count enumerated there imputed but one allegation per complaint, a coarser take on the data than the more detailed table we’ve just minted above. In the earlier sheet, for example, Departmental Violations contribute 24.05% of all complaints; here they amount to 31.07%.

We also need to explain why our array formula here totaled 2779 unique complaint ids, when the count in ppd_complaints came to 2782.  In that connection I simply copied the already-unique ids in the ppd_complaints to a new sheet, and directed a Remove Duplicates to ppd_complaint_disciplines keyed to the same field there, and copied these as well to the new sheet. Scrolling about and doing some due diligence, I did find a few discrepancies, e.g. an absent 15-0176 among the ppd_complaint_disciplines ids.

But what’s a mere three records between spreadsheets?

 

Philadelphia Police Complaints, Part 1: One Civilian’s Review

2 May

We’ve looked at civilian complaints about police conduct before – about three years ago, in fact, when I reviewed complaint data collected for the city of Indianapolis; and I’ve had to refresh my memory about that analytical encounter because a visit to Philadelphia’s open data site brought me to a similar, but not identical, record of allegations against the local constabulary. Indeed – a wider, cross-city study of how civilian complaints are conceived and organized might make for a most instructive, if subsidiary, comparative survey of spreadsheet construction.

But what of the Philadelphia complaints? In fact, two spreadsheets detail the histories here:

ppd_complaint_disciplines

ppd_complaints

The first, ppd_complaints, straightforwardly gathers the incidents into five fields via a neatly-sequenced id scheme, and Its dates received in column B are genuinely quantified beaides. You’ll want to auto-fit columns B and D, but probably not E, bearing the text-expressed summaries of complaints; because no field adjoins its right edge an auto-fit won’t realize any gain in visibility there. The data appear to span the complaints for 2015-18 in their entirety, and tack on complaints for the first month of this year as well (at least through January 30). Thus an obvious first reconnoiter would step us through complaint totals by year:

Row: date_received (Year)

Values: date_received (Count)

I get:   phil1

We see that civilian complaints have slackened steadily across the yearly bins, down 21% from their 2015 peak. Still, the January 2019 total of 31 seems low, projecting linearly for the year to about 360. But could it be, rather, that Januarys experience fewer complaints?

Of course we can answer the question by regrouping the complaint numbers both by year and month and shifting the month parameter (curiously and misleadingly holding fast to the date_received field name) into Columns:

phil2

We see then that January is something of a slow month for complaints, although 2019’s lowest reading suggests (but only suggests) that this year may drive the downward curve still further down its axis. Yet the figures for the contiguous December trend far higher – though a highly disproportionate accumulation of complaints dated the 31st of that month seem to prevail. Of the 267 December entries, 63 are time-stamped the 31st, even as chance would have projected a complaint total of around 9.

I arrived at the 63 by installing a set of temporary set of formulas in the next-available F column (don’t be fooled by the encroaching text in E – F is in fact free), starting with this one in F2:

=IF(AND(MONTH(B2)=12,DAY(B2)=31),1,0)

The formula asks if a given date evaluates both to the 12th month (December) and the month’s 31st day. Copy down, add the 1’s, and you get 63.

Is the skew a precipitate of some bookkeeping maneuver designed to hem complaints into a year about to elapse? I don’t know, but someone’s story-seeking antenna should be wagging madly. Indeed – 31 of the 87 December complaints lodged in 2015 fell on the 31st, a clustering that should have you reaching for your probability tables, and 17 of the 88 2016 petitions were similarly dated. That the December 31st numbers slinked back to 8 and 7 in 2017 and 2018 suggest in turn that some sort of correction was introduced to the archiving routine, but that speculation is exactly that.

We could continue and cross-tab complaint type of incident – what the worksheet calls general_cap_classification – by year, and because the classes outnumber the years I’d slide the latter into Columns for presentational fitness, plant general_cap_classification into Rows, and reprise the latter into Values. I get:

phil3

The categories beat out a relatively constant distribution, by and large, though Civil Rights Complaints – their small numbers duly noted – seem to have spiked in 2018. It should be added that the free-text precis of complaints in the summary field incline toward the vague, e.g., a civil-rights submission in which “…they were treated unprofessionally by an officer assigned to the 18th District,” a manner of understatement that could do with some expository padding (note too that the district references for the filings in the district_occurrence field report multiples of the actual district number, e.g. 1800 for 18).

But remember there is a second complaint worksheet among the Philadelphia holdings, ppd_complaint_disciplines, one that historicizes the same complaints and bears identical ids, but per a different complement of defining parameters. Here the complaints identify the race and gender of the officers charged, along with the disposition of the allegation brought against them (investigate_findings). Thus two sheets instigate a next question: since the sheets recall the same incidents, could they not have been consolidated into a single workbook, with each record roping in the fields from each sheet?

That question is a fair one indeed, but I think the Philadelphia data compilers opted for two sheets over a single, unifying rendition is because ppd_complaint_disciplines comprises multiple references of the same complaint id. Because a given complaint may have been levelled at several officers the data for each officer are set down distinctly, aggregating to 6312 records, about 3500 more than the ppd_complaints sheet in which the complaints are recorded uniquely. If each of these were to be matched with the complaints cited in the latter, those complaints would in many cases appear several times – once each for every officer charged in the incident, and those redundant citations might read awkwardly. But those reiterations aren’t necessarily superfluous, because some complaints triggered different charges.

The very first complaint entry, for example, 15-0001, is enumerated four times in the ppd_complaint_disciplines sheet, corresponding to the four actual complaint entries registered for the incident. But it gets a bit messier than that, because the four complaints in fact reference only two discrete officers, who are “named” in the officer_id field. It’s clear in this case that two charges were preferred against each, an recognition that uncovers another complication in turn: the allegations_investigated field discloses three Departmental Violation charges, and another brought for Verbal Abuse.

Yet 15-0001 is described in the ppd_complaints sheet as an instance of Departmental Violations only. For another example 15-0005 is named in that sheet as a case of Physical Abuse, even as the one officer charged in ppd_complaint_disciplines incurred two complaints, one for Criminal Allegation, the other for Harassment.

It’s possible, then, that because each of a set of multiple charges for the same complaint could be regarded independently, one workbook might suffice, with perhaps the date_received and summary fields appended to the others in ppd_complaint_disciplines.

In that light it should also be noted that the Indianapolis data at which I looked earlier works with but one workbook, featuring multiple complaint records as well as date information. Perhaps Philadelphia could have done the same.

But I’m not complaining.

The Hockey Stick Effect: Wayne Gretzky’s Goals, Part 2

12 Apr

There’s another parameter-in-waiting pacing behind the Wayne Gretzky goal data, one that might be worth dragging in front of the footlights and placed into dialogue with the Date field in column B. National Hockey League seasons bridge two calendar years, generally strapping on their blades in October and unlacing them in April. For example, Gretzky’s last goal – time-stamped March 29, 1999 – belongs to the 1998-1999 season, encouraging us to ask how those yearly parings might be sprung from the data, because they’re not there yet.

Of course, a catalogue of Gretzky’s season-by-season scoring accumulations is no gnostic secret; that bundle of information been in orbit in cyberspace for some time (here, for example), and so developing those data won’t presume to teach us something we don’t already know. But the seasonal goal breakdowns could be joined to other, more putatively novel findings awaiting discovery among the data, and so the exercise could be justified.

So here’s my season-welding formula. Pull into next-available-column R, head it Season, and enter in R2:

=IF(MONTH(B2)>=5,YEAR(B2)&”-“&YEAR(B2)+1,YEAR(B2)-1&”-“&YEAR(B2))

We’re looking to concatenate two consecutive years, and so the formula asks if the month of any given entry in B equals or exceeds 5, or May, or falls beneath that value. If the former, the year in B is joined to the following year, via the +1 addendum. If the month equals or postdates May, then the preceding years, operationalized by the -1, is concatenated with the year returned in the B column.

The formulas seemed to work, but as a precision check I rolled out this simple pivot table:

Row: Season

Values: Season (count, of necessity; the data are textual. The values should denote goal total by respective year).

I wound up with this, in excerpt:

Gretz1

Cross-referencing the results with the Gretzky goal data in the above hockey-reference.com link yielded a pleasing equivalence across the years.

Now for some looks in earnest at the data. Starting simply, we can juxtapose Gretzky’s goals scored at home to the ones he netted in away games:

Row: Home/Away

Values: Home/Away (count)

Home/Away (again, % of Column Total)

I get:

Gretz2

We learn that Gretzky scored a palpable majority of his goals at home, but we’d expect as much. As in nearly all team sports, NHL teams enjoy the proverbial home advantage, winning about 55% of the time – a near-equivalence to Gretzky’s ratio. That is, if home teams prevail disproportionately then their goal totals should exhibit a kindred disproportion, kind of. One difference with Gretzky, of course, is that he simply scored more of them.

And does the distribution of his goals by period pattern randomly? Let’s see:

Rows: Per (for Period)

Values: Per (Count)

Per (% of Column Totals)

I get:

Gretz3

Gretzky’s production appears to mount in games’ later stages (OT stands for the overtime period), but that finding needs to be qualified on a number of counts. We’d need first of all to track Gretzky’s average presence times on the ice; that is, was he deployed more often as games advanced toward their denouements and his valuable self was sent ice-bound at clutch time? And we’d also need to plot Gretzky’s goal timings against the league averages for such things; and while I haven’t seen those data, we can assume they’re out there somewhere.

Next, it occurred to me that a look at the winning percentages of games in which Gretzky scored might prove enlightening, once the task was properly conceived. Remember that, as a consequence of his numerous multi-goal flourishes, Gretzky’s goals scatter across far fewer than 894 games. The idea, then, is to fashion a discrete game count across which the goals were distributed; and that sounds like a call for the Discrete Count operation we’ve encountered elsewhere (here, for example). Once we isolate the actual-game totals – which should be associated uniquely with game dates – our answer should follow.

And this pivot table seems to do the job, enabled again by a tick of the Add this data to the Data Model box:

gretz4

Rows: Result

Values: Date (Distinct Count, % of Column Total)

I get:

gretz5

What have we learned? Apart from the up-front factoid that Gretzky scored in 638 of the 1487 games he played across his NHL career (638 is the numeric Grand Total above, before it was supplanted by the 100% figure in the pivot table; note Gretzky also appeared in 160 games in the World Hockey Association), we don’t know how his when-scoring 64.89% win percentage compares with his teams’ success rate when he didn’t score. I don’t have that information, and don’t know where to track it down. But it too is doubtless available.

For another analytical look-see, we can ask if Gretzky’s goals experienced some differential in the number of contributory assists that prefaced them. That is, players (up to two of them) whose passes to a teammate conduce toward the latter’s goal are rewarded with an assist; and the question could be asked, and answered here, if Gretzky’s assist-per-goal average fluctuated meaningfully.  We might to seek to know, for example, if during Gretzky’s heyday his improvisatory acumen freed him to score more unaided goals than in his career dotage, when he may have been bidden to rely more concertedly on his mates.

Since two Assist fields, one for each of the two potential per-goal assists, accompany each goal, the simplest way perhaps to initiate our query would be to enter column S, title it something like AssistCount, and enter in S2:

=COUNTA(J2:K2)

And copy down. That insurgent field readies this straightforward pivot table:

Rows: Season

Values: AssistCount (average, formatted to two decimals)

I get:

gretz6

Not much pattern guiding the data, but if you want to group the seasons in say, five-year bins, remember that because the season entries are purely textual you’ll have to mouse-select five seasons at a time and only then successively click the standard Group Selection command, ticking the collapse button as well if you wish:

gretz7

Even here, then, the variation, is minute – strikingly so.

Now for a last question we could ask about those teammates who were literally Gretzky’s most reliable assistants – that is, the players whose assist counts top their collaborative pairings with the Great One. The problem here is the two-columned distribution of the assist names, one for the first assist on a goal, the other for the (possible) second. I don’t know how a pivot table can return a unique complement of names across two fields simultaneously, preparatory to a count. If you do, get back to me; but in the meantime I turned again to the Get & Transform Data button group in the Data ribbon and moved to unpivot the data set via Power Query, by merging only the assist fields, e.g.:

gretz8

By selecting Assist1 and Assist2 and advancing to Transform > Unpivot Columns and Home > Close and Load the result looked like this, in excerpt:

gretz9

And of course you can rename Attribute and Value, say to Assist and Player.

Once there, this pivot table beckons – after you click TableTools > Tools > Summarize with Pivot Table:

Rows: Player

Values: Player (Count, sort Highest to Lowest)

I got, in excerpt:

gretz10

Nearly 22% of Gretzky’s goals received a helping hand – at least one wrapped around a stick – from his erstwhile Edmonton Oiler and Los Angeles King colleague Jari Kurri, no scoring slouch either with 601 goals of his own – a great many doubtless the beneficiary of a Gretzky assist. Then slip the Assist field beneath Player in Rows and:

gretz11

Now we learn that more than 60% of Kurri’s assists were of the proximate kind; that is, he was the penultimate custodian of the puck, before he shipped it to Gretzky for delivery into the net.

Now that’s how you Kurri favor with the Great One.

 

 

 

The Hockey Stick Effect: Wayne Gretzky’s Goals, Part 1

1 Apr

What is the measure of greatness? How about 894 records, one for each of the goals driven home by the National Hockey League’s Wayne Gretzky, aka the Great one?

That spreadsheet is as large as it gets for NHL scorers, and Tableau ace Ben Jones has infused the goal count with lots of supplementary background about each and every one of the 894, archiving the data for download on the data.world site here.

In fact the workbook makes itself available in both Excel and CSV mode, the latter requiring a text-to-columns parsing that likens it to the former. Either way, a few organizational points need to be entered.

For one thing, you’ll note that what’s called the Rank field in column A numerically ids Gretzky’s goals, in effect sorting them by newest to oldest. That is, Gretzky’s first goal – scored on October 14, 1979 – has received id 894, with the numbers decrementing ahead in time until his final score – tallied almost exactly 20 years ago on March 29, 1999 – has bottomed out with the number 1. It seems to me – and I suspect you’ll share the opinion – that the enumeration should have pulled in the opposite direction, with Gretzky’s last goal more properly checking in at 894. With that determination in mind I reversed the sequence via a standard autofill, entering 894 in cell A2, 893 in A3, and copying down.

You’ll also be struck by the unremittingly monotonic entries in the Scorer field, comprising 894 iterations of the name Wayne Gretzky. We’ve seen this before in other data sets, of course, being dragged into the data set as a likely accessory to some generic download protocol. Again, you can either ignore the field or delete it. Either way, you’re not going to use it.

And your curiosity will be stirred anew by the blank column-heading cells idling atop columns D, F, and G. It’s difficult to believe that Ben Jones, who doubtless knows whereof he speaks, would allow these most rudimentary oversights to escape his notice, but alternative explanations notwithstanding, the headings aren’t there and must be supplied.

Column D reports a binary datum – whether a Gretzky goal was scored at his team’s arena or at the rink to which his team traveled for an away game. I’ll thus entitle the field Home/Away and proceed to do something about the data themselves, whose cells remain empty when signifying a home goal and register an @ for “at”, that is, a goal netted at someone else’s arena. A pair of finds and replaces – the first, substituting an H for the blank cells, with the second supplanting the @ signs with a companion, alphabetized A – should sharpen the field’s intelligibility.

The headless column F archives game outcomes, i.e. wins, losses, or ties, and so I’ll call the field Result, or something like it. Column G denotes the phase of a game when the goal was scored, either during regulation time or overtime – or so I assumed. But a second thought soon followed on the heels of that hunch, if I may mangle the metaphor: it occurred to me that the Regulation/Overtime opposition simply recalls whether or not the game itself swung into an overtime period, irrespective of the actual times at which Gretzky scored. Could that uncertainty be relieved?

I think so, and I played it this way: first, I named the doubtful field Reg/OT, and ran a find and replace at the F column, substituting Reg for any empty cell therein. I then moved toward a pivot table:

Row Labels: Date (ungrouped, in order to exhibit each date)

Columns: Reg/OT

Values: Date (Count)

What I found is that no game date featured a value for both a regulation and overtime goal, a discovery that goes quite some way toward clinching the second speculation – namely, that the Reg/OT field entries do no more than inform us if the games necessitated an overtime period.

After all, if we confine the analysis momentarily to the games that spilled into overtime, one could most reasonably imagine that a scorer with Gretzky’s gifts would have occasionally lodged a goal in both the regulation and overtime phases of the same game; but the pivot table uncovers no such evidence. For any given date, Gretzky’s score(s) appear in either the OT or the Reg column. Moreover, some of the games – for example, November 27, 1985 – record two overtime goals, a unicorn-like impossibility in a sport in which overtime ends when the first goal is scored. (You’ll note by the way that the overtime-column goals only begin to appear in 1983, when a five-minute overtime period was instituted.)

Thus I’d aver that the Reg/OT field conveys little understanding of Gretzky’s scoring proclivities; all it does is identify games that happened to have extended themselves into overtime, and in which he scored – some time.

The Strength field cites the demographic possibilities under which Gretzky accrued his goals: EV refers to even strength, when both teams’ numeric complements on ice were equal, PP, or power play, during which the scoring team team temporarily outnumbered the other after a player was remanded to the penalty box, and SH or shorthanded, the rarest eventuality – when Gretzky scored while his team was outnumbered.

I do not, however, know with certainty what the EN entry in the Other field represents even though I probably should, and I see nothing in Data World’s data dictionary that moves to define it. It may very well stand for end, as in end of game, however; each of its 56 instances are joined to goals there were scored with fewer than two minutes left in their respective games. EN may then stand for scores achieved after the opposing goalie skated off in a losing cause and was replaced by offensive player, in order to buttress a desperate try at equalizing the game. Indeed – all 56 of the EN goals were scored in wins by Gretzky’s team.

As a matter of fact, I think I’m right. Filter the Other field for its ENs and look leftward at the Goalie field in L. There’s nothing there.

U.S. State Dept. Travel Advisories: Getting There

17 Mar

First principles: before you subject a dataset to your imposing, if caffeinated, spreadsheet acumen you have to actually get the data. But that blitheringly obvious stipulation is, nevertheless, sometimes easier stated than achieved.

It’s true that most open data sites aim to please and affably release their holdings, via a tick of a well-placed and intelligible Download or Export button, or something like it. But there is a particular brand of spreadsheet manqué that issues its data in stages – that is, it mothballs them across several web pages, and not then in a unitary place.

That segmented storage strategy might enhance the data’s readability, or might not, though if nothing else the multipage design spares viewers from scrolling relentlessly down the page. My question about these kinds of datasets is simple: can they be downloaded directly into a single spreadsheet without contortion?

And that question placed itself before me anew a short while ago when I met up with the US Department of State’s travel advisory dataset, brought to my attention by the Far and Wide site. That dataset looks like this, necessarily in part, given its multi-page distribution:

travel 1a

The set’s three fields seem perfectly limpid (save perhaps the Worldwide Caution entry, which appears to commend a global, and not a country-specific advisory); but their data are spread across five pages, each of which must be clicked separately:

travel5

Yes; as earlier indicated, It’s one of those kinds of datasets. And again – can I get it all into my spreadsheet without that most unbecoming of resorts: five copy-and-pastes, rife with pinched column widths and text that needs to be unwrapped? (Note by the way that as of this writing New Zealand’s Level 1 assessment I the data set hadn’t changed, having been last updated on November 15 2018. However, an alert for the country has been entered here.)

When assailed by such questions, a hopeful right-click upon the data gives us nothing to lose and something, perhaps, to gain e.g.

travel6

The third option from the bottom looks promising. Click there and we’re told that the data before us will find their way into my waiting spreadsheet. But I seem to recall having viewed that Export instruction elsewhere on other sites, with decidedly mixed results.

But why not. I gave it a click and to my surprise observed something actually happening on my blank worksheet, an ellipsis-freighted “External Data_1: Getting Data…” message that deliberated on screen for several minutes before finally giving way to an actual, unitary spreadsheet, e.g.

travel2

In other words, the export actually exported. Note that the advisory data in column A appears on site in a stream of hyperlinks that when clicked directs the viewer to a deeper background on the country in question; presumably the export routine thus executes a Paste Values protocol that strips the records of their more exotic contents. On the other hand, the download did introduce the Date Updated field to the spreadsheet in actual, numerically viable date mode.

So it worked, to my pleasant surprise, though a few significant qualifications of the process need be entered. First, a right-click on the first, Advisory field did not summon the Export to Microsoft Excel option; it was only when I attempted a click over the second or third field that the context menu disclosed the command. Second, and perhaps most importantly, the Export possibility only seems to make itself available when the sites are broached with Internet Explorer. Attempts to coax Export from the menu in the course of perusals conducted in Chrome or Firebox failed, and I am presently unaware of an enabling workaround for either browser, though I am happy to be reeducated on this count.

Now of course Excel and Internet Explorer spring from the same shop in Redmond, and so one could be left to draw one’s own conclusions on the matter. Is this what they mean by “seamless integration”? Seamless, but unseemly? I don’t know.

In any case, my success with the export fired up the obvious follow-on question: could the deed be replicated with similar datasets thronging the internet?

In search of answer, I stopped off at the US News and World Report rankings of law schools and clicked its Table View button, assuming that something like a spreadsheet would eventuate as a consequence. Once in view I again right-clicked the data’s second, Tuition field, revisited Export to Microsoft Excel (remember, I’m back in Internet Explorer), and was delivered this compendium (in excerpt):

travel3

While the above tableau and its quiver of alternating blank rows won’t pass Spreadsheet Design 101 (and I’m an easy grader), the “fault”, if I may be so judgmental, lies with the site and not the export, which seems to have captured the data as they appear on site. In short, the export seems to have worked again.

In the interests of building up a scientifically workable sample I turned next (again in Explorer) to the Times Higher Education world university rankings, which look something like this it situ:

travel4

Again, a right click upon the data ushered Export to Microsoft Excel to view (though the command appeared after clicking either the data’s first and third fields, but not the second), but this time the spreadsheet registered nothing but a companionless header row for the rankings. I retried the export numerous times, but met with the identical result on each attempt.

I can’t explain the discrepancy, i.e. why some data sets comply with the export request and others resist. That’s not to say an explanation can’t be adduced, of course, but I’ll have to assign that accounting to a web programmer. Clearly, the kinds of data tropes we’ve reviewed here embody a different genotype from that inherited by the standard open-data-site collections we usually confront here, those designed in large measure to download immediately into spreadsheets; and while it’s true that these web-emplaced data are probably meant to facilitate searches for a particular item and little more, it might be a good idea for their designers to anticipate the prospect that someone out there might wish to analyze the whole lot, and by treating the data as a whole, and seeing to it that they find their target in a spreadsheet all the time.