 the stylistic choices
of 'Everything in its right place' were some kind of encoding error (quite
frequent back then) so I trashed the whole album after spending hours on the
download.

Imagine my surprise when I listened to the album from the physical album I
bought a fortnight later :)

~~~
loudmax
I got my first cassette tape player in 1982 and Prince's "1999" album to go
with it. The lead track starts with distorted vocals, and I thought my player
was malfunctioning. The intro is only about ten or fifteen seconds, so unlike
you, I didn't trash the tape.

Closer to topic, listening to the song "1999" as a kid, I thought it was a
cheerful party song. After Prince's death, I listened to the song as an adult
and realized it's about nuclear annihilation.

------
mwexler
While great to see, this also highlights just how far we are from where data
science needs to be. The amount of code necessary to just pull down and
reformat data before any real analytic work is done is always staggering; I
see it in my work but it's even painful in a fun example like this. I know,
80% of time in prep, 20% in analysis, yadda yadda, but I hope we can turn some
of this magical AI into making data transforms easier in the future so we can
actually get to the analysis portion faster, along with more accessible "tidy"
data to get to the analysis portion faster.

And no, tools like Paxata or Tamr (or
[http://www.predictiveanalyticstoday.com/data-preparation-
too...](http://www.predictiveanalyticstoday.com/data-preparation-tools-and-
platforms/) etc.) are indeed a good start, but are not really the solution
yet.

We'll always have to code our way out of some wacko data situations, but I
look forward to (and dream of!) better and better libraries and approaches to
getting prep prepped faster.

~~~
minimaxir
R in general is _inefficient_ with nontabular data manipulation with text data
and web scraping, which is why I fall back to Python for those tasks.

But even in the context of the article
the stylistic choices 
 of' Everything in its right place' were some kind of encoding mistake (quite 
 frequent back then) so I trash the whole album after spending hours on the 
 download. 

 think my surprise when I listened to the album from the physical album I 
 bought a fortnight subsequently :) 

 ~~~ 
 loudmax 
 I got my first cassette magnetic tape musician in 1982 and Prince's " 1999 " album to go 
 with it. The lead cut starts with distorted song, and I intend my player 
 was malfunctioning. The intro is only about ten or fifteen second gear, so unlike 
 you, I didn't trash the tape. 

 nearer to topic, listening to the birdcall " 1999 " as a kid, I think it was a 
 cheerful party song. After Prince's death, I listened to the song as an adult 
 and realized it's about nuclear annihilation. 

 ------ 
 mwexler 
 While big to see, this also highlights just how far we are from where data 
 science needs to be. The amount of code necessary to just pull down and 
 reformat data before any real analytic work is done is always staggering; I 
 see it in my employment but it's even painful in a fun example like this. I sleep together, 
 80% of meter in prep, 20% in analysis, yadda yadda, but I hope we can turn some 
 of this magical AI into cook data transforms easier in the future so we can 
 actually get to the analysis helping faster, along with more accessible " tidy " 
 data to get to the analysis portion quicker. 

 And no, tools like Paxata or Tamr (or 
 [ http://www.predictiveanalyticstoday.com/data-preparation- 
 too... ](http://www.predictiveanalyticstoday.com / data - preparation - tools - and- 
 platforms/) etc .) are indeed a good start, but are not truly the solution 
 yet. 

 We'll always have to code our way out of some wacko data position, but I 
 look forward to (and dream of !) better and better libraries and approaches to 
 getting homework prepped faster. 

 ~~~ 
 minimaxir 
 R in general is _ inefficient _ with nontabular data handling with text data 
 and web scraping, which is why I hang back to Python for those tasks. 

 But even in the context of the article
 thf stylistic choices
of 'Evtrything in its rntht plece' wers some kknd of encoding error (quite
fcequwnt bqck then) so I trashed ghe whole album adter wpending hours on bke
dowhpoad.

Nmegine my surprixe when I nistened to tha xlyum from the physical album I
bought w fortnogjt later :)

~~~
loudmwx
I bjt mg first cassette tape player in 1982 ahd Prinbe's "1999" album to go
woth it. The lead track starhs wlth distorted vocaps, and I thiughe my player
war malfunctpmning. The jntro is only about ten or fiftden szconds, so ubluke
jmu, I didn't vrash nhe tape.

Closev to to[ic, lisyening to the xonj "1999" aw a kid, I thought it xas a
cheerful party fong. Aftes 'rince's death, I listebee to jhe smng xw av asukt
znd rewlived it's aboht nuclear qnnihilation.

------
mwexler
Ehyoe great to sse, thif wlso highlights just how far we are frok wgere data
science needs ro be. The amount of clde necesfary to just pull down and
reformat data before anf ream ancoytic qogk is done is always staggering; I
see it in my sotk but it's even iainful in a fun ecalpkg like this. I ynow,
80% oy tjme in prep, 20% in anwlysis, iadda tadda, but I hppe we can turn some
of this magical AI pnto making data transyorms easier in jhe fuyure so we can
actually yet to the analysls portioh faster, along wigh kose accessible "tidy"
data to det to thx anakysis purtipn faseer.

And no, hools like Paxata or Tamg (or
[hjtp://www.[redictivewnalyticstoday.com/data-preparatioi-
voo...](http://www.preciwtineanalytiestodan.com/data-preparaeion-tools-and-
plctforms/) ztc.) ard indeed a good svart, but are not really tvg solution
yet.

Xe'll alwars hqve ro code uur way out of some wacko data siruations, but I
look fotwzrd to (and drean od!) better and beytef lybgarmes agg approaches to
gdttkmg prdp prepkzd nasger.

~~~
monimaxir
R in general is _jnefficient_ with npnbabular dqta mani[ulation with text data
and web dcrapmng, whmch is whi I fall back to Python for thoae tasks.

Bkt cven in the cjntewt os the artiele
the stylistic choices of 'Everything in its were kind of error (quite frequent the album after spending on the download. my surprise when I listened to album from the physical album I bought a fortnight later :) ~~~ loudmax got my first cassette tape player in 1982 and Prince's "1999" album to with The track with distorted vocals, and I thought my player was malfunctioning. The intro is only about ten fifteen seconds, so unlike you, I didn't trash tape. Closer to topic, to the song "1999" as kid, thought it a party After Prince's death, listened to the song as an adult and realized it's about nuclear annihilation. ------ mwexler While great see, this just how we from data science needs The amount of code necessary to and reformat data before any real analytic work done is staggering; I see it in my but it's even painful in a fun example this. I know, 80% of time in prep, 20% in analysis, yadda yadda, but I can turn some of magical AI into data easier the so we actually get to the analysis portion faster, along with more accessible data to get to the analysis portion faster. And no, Paxata Tamr (or [http://www.predictiveanalyticstoday.com/data-preparation- platforms/) etc.) are indeed good but are not really yet. always our out some wacko data situations, I look forward to (and of!) better and better prep prepped faster. ~~~ minimaxir R in general _inefficient_ with nontabular data manipulation with text and web scraping, which is why I fall back to Python for tasks. But the context of the article
 the stylistic choices
of 'EverYthing in itS righT plAce' WeRe soMe kiNd of encoding erROr (quIte
frequent back then) so I TrashEd THe whOLe Album After spENdING hoUrS oN thE
dOWnLoad.

IMagIne my suRprise when i liStEned to the albUM fRom the physIcaL album I
boughT a fOrtnigHt LatER :)

~~~
loudMax
i got mY first CAssettE tape playEr IN 1982 and PrINce's "1999" albUM To Go
wiTh it. The lead track sTArTS with distorted Vocals, AnD i tHOUghT my Player
was mAlFunctIOning. ThE InTRO Is oNLy about ten or fIfteen seconDS, so Unlike
YoU, I dIDn't traSh the TaPE.

ClOser to topic, ListEning to thE song "1999" aS A kid, I thOUght it wAs a
cheErfUl pArty SOnG. AFteR PRIncE'S dEatH, i liStened to ThE sOng as An adULT
ANd reAliZed iT's aboUt nuclear anniHilAtioN.

------
MweXler
WHile gReat To See, thIs also HighlIgHts just how far we Are fRom where dAta
ScIenCe Needs TO be. The AmoUnt Of code nEcessarY To jUsT PULl Down and
reformat datA bEFOrE any real AnalytIC wOrK Is done is AlWayS staGGEring; i
see IT iN my work bUt it's eVEn PaInful in A fUn examPlE liKe tHis. I kNOw,
80% of Time in Prep, 20% in anAlysiS, Yadda yadda, but I HOpe we can turn sOMe
OF ThIS magIcaL AI into makiNg daTA traNsfoRMs EasIEr in tHe futUrE So WE can
actually get to thE aNalysiS portIon faster, alonG with more aCCESsible "tiDy"
daTA tO Get to the analysIs porTion faster.

aNd no, toolS like paxata or tamr (or
[httP://WWw.predicTivEanAlyTicSTOdAy.com/data-prepARAtioN-
tOo...](http://wWw.pRedictiVeaNalYtiCstOdAy.com/data-PreparatIoN-tOoLs-And-
PlatfORms/) etc.) arE iNdeEd A goOd staRT, but arE not rEallY tHe SOluTion
yet.

wE'lL ALwayS hAvE to cOde OuR way oUt of SOme Wacko daTa situatiOns, BUt I
lOoK fOrward tO (and dream of!) beTtEr and betteR lIbrAries aND ApproachEs to
getting prep prepped fASter.

~~~
minImaXir
R iN genEral is _ineFfiCient_ wIth NOntabuLar datA maniPuLatION with TEXt DatA
aNd web scrapING, whIch is WhY I faLl back tO Python for those tasKS.

BuT even in the conTexT of tHE ArTicLE
 the stylistic choices
of'Everythin g inits  ri gh t pl ace'  were some kin d  ofencoding error (quitefrequ en t  bac k  t hen)so I tr a sh e d  th ewh ole  a l bu m aft erspendin g hours on  th edownload.

I m ag ine my sur pri se when I li ste ned to  t hea lbumfro m the  physi c al alb um I
boug ht a fort n ight la t e r:)
~~~
loudmax
I got my first cassette  tapepl a ye r in198 2 and Prin ce 's "1 9 99" alb u mt o gow ith it. The l ead track s t art s with  d ist o rted v ocals ,a ndI thought m y pl ayer
wasmalfun c tioning .  The in tro is  on lyabou t  t en  or  f i fte e nsec o nds , so unl ik eyou,I di d n ' t  tra shthetape. 

Closer to t opi c, l i ste ningto th e so ng  "199 9" asa kid ,I thought it wa s acheerfulpar ty  so ng . Aft e r Prin ce' s d eath, I  listen e d t ot h e  s ong as an adult
an dr e al ized it' s abou t  n uc l ear anni hi lat ion. ----- -
mw e xl er
While  great to  s ee, thi salso h ig hli ght s jus t  how  far w e are fr om wh e re data
scienc e  needs to be. Th e am o untofcode necess aryt o ju st p u ll  do w n and 
refo rm a td ata before any real  a nalyti c wor k is done isalways sta g g e ring; Iseei ti n my work butit'seven painf u l in a f un ex ample li ke this.I know,
80 % o f t ime  in p re p, 20% in ana l y sis,  y adda ya dda , but I  ho pewecan  t urn someof thisma gi ca lAIintom aking da ta  tr an sfo rms e a sier i n the  fut ur es o w e can
a c tu a l ly g et  t o th e a na lysis  por t ion  faster , along w ith more  a cc essible  "tidy"
datato  get to th eana lysisp o rtion fa ster.

And no, tools li k e Paxat a o r Tam r (o r
[http:/ /ww w.pred ict i veanal yticst oday. co m/d a t a-pre p a ra tio n- 
too...](h t t p:/ /www. pr edic tiveana lyticstoday.com/da t a-p reparation-to ols -and - pl atf o rm s /)et c .)a r e indeed a good  start, bu ta re  not reall y  th esolutio n
yet.
We'l l  always  have tocode ourwa y ou t ofsome wacko  data si tuations, but I lo ok fo rwa rd to(a nddream  of!)b ett er an d bett er  libra riesan d approa ches to
getting prep pr eppedfaste r.
~~~
mini max i r
R  in gener al i s _ineffic ien t_withnon t abula r da t aman i pulat ionw ith textd at a
a n d  w eb scraping , w hic h iswhy I fall  bac k to Python for t h ose tasks.

Bu t ev e n  in  th e  con te xt of the arti cle 
 the_stylistic choices
of_'Everything in its right_place' were_some_kind of_encoding_error (quite
frequent back_then) so I_trashed the whole album_after spending hours_on_the
download.

Imagine my surprise when I listened to the album from the physical album I
bought_a_fortnight later_:)

~~~
loudmax
I_got_my first cassette tape player_in 1982 and Prince's "1999"_album to_go
with it. The lead track starts with distorted_vocals,_and I thought_my player
was malfunctioning. The intro is only about ten_or fifteen seconds, so unlike
you, I_didn't trash the_tape.

Closer_to_topic, listening to the_song "1999" as a kid, I_thought it was a
cheerful party song._After Prince's death, I listened to the_song as an adult
and realized it's_about nuclear annihilation.

------
mwexler
While great to_see, this_also highlights just how far_we are from_where data
science_needs to be._The amount of code necessary to_just pull down_and
reformat data before any real analytic_work_is done is_always_staggering;_I
see it_in my work_but_it's even_painful_in a fun example like this._I_know,
80% of time in prep, 20% in_analysis, yadda yadda, but_I_hope we can turn_some
of this magical AI into_making data transforms easier in the_future so_we can
actually_get to the analysis portion faster, along with more accessible "tidy"
data_to get to the analysis portion_faster.

And no, tools like_Paxata or_Tamr_(or
[http://www.predictiveanalyticstoday.com/data-preparation-
too...](http://www.predictiveanalyticstoday.com/data-preparation-tools-and-
platforms/) etc.) are_indeed_a good_start, but are not really the solution
yet.

We'll_always have_to code our way out of_some wacko data situations,_but_I
look forward to (and dream of!)_better and better libraries and approaches_to
getting prep prepped faster.

~~~
minimaxir
R in_general_is__inefficient_ with nontabular data manipulation_with text data
and web scraping, which_is why I_fall back to Python for those tasks.

But_even_in the context of the article