From 3 to 15: Milestones, dead ends, prospects. A subjective review - - PowerPoint PPT Presentation

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From 3 to 15: Milestones, dead ends, prospects. A subjective review - - PowerPoint PPT Presentation

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From 3 to 15: Milestones, dead ends, prospects. A subjective review of Statas history Ulrich Kohler ukohler@uni-potsdam.de University of Potsdam Faculty for Economics and Social Sciences German Stata Users Group Meeting June 22 nd 2018

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From 3 to 15: Milestones, dead ends, prospects. A subjective review of Stata’s history

Ulrich Kohler

ukohler@uni-potsdam.de

University of Potsdam Faculty for Economics and Social Sciences

German Stata Users Group Meeting June 22nd 2018 University of Konstanz, Germany

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Inhalt

Setting the Scene Milestones (and Dead Ends) Prospects (Why not Stata!?)

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Versions

v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v13 v14 v15

1.0 1.1 1.2 1.31.41.5 BiTurbo Sun/Unix 2.0 386/ix 2.05 IC HP/9000 2.1 DEC RISC 3.0 IBM RS/6000 MAC3.1 DEC Alpha Convex 4.0 Linux Win 95 Mac 5.0 6.0 7.0 Stata/SE OS X GLLAMM speedup 64−bit Solaris SGI Irix 8.0 8.1 8.2 64−bit Linux 9.0 9.1 9.2 10.010.1 11.0 11.1 11.2 12.0 12.1 13.0 13.1 14.0 14.1 14.2 15.0

1 9 8 5 1 9 8 6 1 9 8 8 1 9 9 1 9 9 2 1 9 9 4 1 9 9 6 1 9 9 8 2 2 2 2 4 2 6 2 8 2 1 2 1 2 2 1 4 2 1 6 2 1 8

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Education

v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v13 v14 v15

1.0 1.1 1.2 1.31.41.5 BiTurbo Sun/Unix 2.0 386/ix 2.05 IC HP/9000 2.1 DEC RISC 3.0 IBM RS/6000 MAC3.1 DEC Alpha Convex 4.0 Linux Win 95 Mac 5.0 6.0 7.0 Stata/SE OS X GLLAMM speedup 64−bit Solaris SGI Irix 8.0 8.1 8.2 64−bit Linux 9.0 9.1 9.2 10.010.1 11.0 11.1 11.2 12.0 12.1 13.0 13.1 14.0 14.1 14.2 15.0 NC 151 NC 101 NC 152 NC 200 MLEwS NC 631 GLM RMCDV ISA VGSG MlLMUS DAUS IMEUS MEUS PDF−doc WDAUS ISP DMUS Blog FPSAUS V−Tutorial TSUS SSG BAWS Webinar SBS MAUS FEUS IVRMUS HEUS SWUS Mata IRTUS

1 9 8 5 1 9 8 6 1 9 8 8 1 9 9 1 9 9 2 1 9 9 4 1 9 9 6 1 9 9 8 2 2 2 2 4 2 6 2 8 2 1 2 1 2 2 1 4 2 1 6 2 1 8

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Me

v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v13 v14 v15

1.0 1.1 1.2 1.31.41.5 BiTurbo Sun/Unix 2.0 386/ix 2.05 IC HP/9000 2.1 DEC RISC 3.0 IBM RS/6000 MAC3.1 DEC Alpha Convex 4.0 Linux Win 95 Mac 5.0 6.0 7.0 Stata/SE OS X GLLAMM speedup 64−bit Solaris SGI Irix 8.0 8.1 8.2 64−bit Linux 9.0 9.1 9.2 10.010.1 11.0 11.1 11.2 12.0 12.1 13.0 13.1 14.0 14.1 14.2 15.0 NC 151 NC 101 NC 152 NC 200 MLEwS NC 631 GLM RMCDV ISA VGSG MlLMUS DAUS IMEUS MEUS PDF−doc WDAUS ISP DMUS Blog FPSAUS V−Tutorial TSUS SSG BAWS Webinar SBS MAUS FEUS IVRMUS HEUS SWUS Mata IRTUS Me reading SwS me taking NC151 cdlplot biplot himatrix hist3 rgroup DAMS dsearch

  • utdat

NLSUG2 DESUG2 soepren mkdat biplot V2 DESUG3 DAUS DESUG4 sq DESUG5 DESUG6 DESUG7 sdlim DESUG8 khb cm2in _gapport DESUG9 nlcorr soepuse meresc DESUG10 DESUG11 dirtools DESUG12 psiduse DESUG14 sq V2 DESUG15 DESUG16 psidtools

1 9 8 5 1 9 8 6 1 9 8 8 1 9 9 1 9 9 2 1 9 9 4 1 9 9 6 1 9 9 8 2 2 2 2 4 2 6 2 8 2 1 2 1 2 2 1 4 2 1 6 2 1 8

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Plan of attack

Subjectively picking out

◮ milestones of development ◮ dead ends

to learn something on prospects.

Of course . . .

. . . any statements made here are just personal views. Others have different views. At best, my views are an inspiration for the wishes and grumbles session at the end of the meeting.

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Related

◮ Cox (2005) ◮ .

help whatsnew#to#

◮ .

ssc hot, author(name) n(#)

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Inhalt

Setting the Scene Milestones (and Dead Ends) Prospects (Why not Stata!?)

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Inhalt

Milestones (and Dead Ends) My Milestone Command milestones Other Milestones Dead ends

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Me reading “Statistics with Stata”

v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v13 v14 v15

1.0 1.1 1.2 1.31.41.5 BiTurbo Sun/Unix 2.0 386/ix 2.05 IC HP/9000 2.1 DEC RISC 3.0 IBM RS/6000 MAC3.1 DEC Alpha Convex 4.0 Linux Win 95 Mac 5.0 6.0 7.0 Stata/SE OS X GLLAMM speedup 64−bit Solaris SGI Irix 8.0 8.1 8.2 64−bit Linux 9.0 9.1 9.2 10.010.1 11.0 11.1 11.2 12.0 12.1 13.0 13.1 14.0 14.1 14.2 15.0 NC 151 NC 101 NC 152 NC 200 MLEwS NC 631 GLM RMCDV ISA VGSG MlLMUS DAUS IMEUS MEUS PDF−doc WDAUS ISP DMUS Blog FPSAUS V−Tutorial TSUS SSG BAWS Webinar SBS MAUS FEUS IVRMUS HEUS SWUS Mata IRTUS Me reading SwS me taking NC151 cdlplot biplot himatrix hist3 rgroup DAMS dsearch

  • utdat

NLSUG2 DESUG2 soepren mkdat biplot V2 DESUG3 DAUS DESUG4 sq DESUG5 DESUG6 DESUG7 sdlim DESUG8 khb cm2in _gapport DESUG9 nlcorr soepuse meresc DESUG10 DESUG11 dirtools DESUG12 psiduse DESUG14 sq V2 DESUG15 DESUG16 psidtools Me reading SwS

1985 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018

Why did I became a Stata user after reading SwS?

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Why Stata?

Command line interface

. use ../downloaded/data1, clear . reg incomeR age yedu income

Models

. mlogit lsat age yedu income

Speed

. set rmsg on . mlogit rep78 foreign r; t=0.04 14:54:11

Humor

endless loop → see “loop, endless” . . . loop, endless → see “endless loop”

Support

From: "William Gould" <wgould@stata.com> To: statalist@hsphsun2.harvard.edu Subject:Re: statalist: iweights and regress Date: Fri, 30 Jan 1998 10:11:57 -0600 xyz <xyz@abc.de> asked for a clarification on iweights. Stay away from them, I say, because they will invariably surprise you. Let me explain:...

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Inhalt

Milestones (and Dead Ends) My Milestone Command milestones Other Milestones Dead ends

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My 14 favorites

v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v13 v14 v15

1.0 1.1 1.2 1.31.41.5 BiTurbo Sun/Unix 2.0 386/ix 2.05 IC HP/9000 2.1 DEC RISC 3.0 IBM RS/6000 MAC3.1 DEC Alpha Convex 4.0 Linux Win 95 Mac 5.0 6.0 7.0 Stata/SE OS X GLLAMM speedup 64−bit Solaris SGI Irix 8.0 8.1 8.2 64−bit Linux 9.0 9.1 9.2 10.010.1 11.0 11.1 11.2 12.0 12.1 13.0 13.1 14.0 14.1 14.2 15.0 NC 151 NC 101 NC 152 NC 200 MLEwS NC 631 GLM RMCDV ISA VGSG MlLMUS DAUS IMEUS MEUS PDF−doc WDAUS ISP DMUS Blog FPSAUS V−Tutorial TSUS SSG BAWS Webinar SBS MAUS FEUS IVRMUS HEUS SWUS Mata IRTUS Me reading SwS me taking NC151 cdlplot biplot himatrix hist3 rgroup DAMS dsearch

  • utdat

NLSUG2 DESUG2 soepren mkdat biplot V2 DESUG3 DAUS DESUG4 sq DESUG5 DESUG6 DESUG7 sdlim DESUG8 khb cm2in _gapport DESUG9 nlcorr soepuse meresc DESUG10 DESUG11 dirtools DESUG12 psiduse DESUG14 sq V2 DESUG15 DESUG16 psidtools program svy net syntax archutil foreach file graph tw mata i.group marginsplot unicode npregress: bayes:

1985 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018

I’ll give some justifications for these choices.

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Statistical commands

svy Describers (like me) need to respect the compexity of samples – especially weights. marginsplot Makes understanding complicated models easy

. regress income i.sex##i.emp##c.age##c.age . margins, at(age=(20(5)80) emp=(1,2,3) sex=(1,2)) . marginsplot, by(emp)

npregress If you do not believe in homogenous treatment effects, this is for you . . . bayes: In my heart, I am Bayesian. bayesmh were introduced in Stata 14, but Stata 15’s bayes-prefix makes Bayesian analysis (syntactically) easy

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General usability

foreach/forvalues By-by endless loops, and by-by clumpsy for. graph twoway A command and a graphics programming language at the same time. Powerful and simple (but sometimes we want it even more powerful and much simpler at the same time.) fvvarlist Factor-variable notation lets you specify complicated models. Use marginsplot to interpret them. unicode America first? Perhaps, but American alone?

. display "✟❡t" ✟❡t

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Programmer commands

program Stata wouldn’t be Stata without program

. program hello . display "hello, world" . end

syntax Parsing made easy

. syntax [varlist] [if] [in]

file , the core of

. esttab . psiduse

mata Matrix calculations made easy

: b = invsym(X’X)*X’y

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Other

net Stata became Web-aware in 1998. It turned out to be a game changer. ssc the command formerly known as archutil made usage of user-written programs easy:

. ssc hot . ssc install estout

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Inhalt

Milestones (and Dead Ends) My Milestone Command milestones Other Milestones Dead ends

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Educational milestones

v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v13 v14 v15

1.0 1.1 1.2 1.31.41.5 BiTurbo Sun/Unix 2.0 386/ix 2.05 IC HP/9000 2.1 DEC RISC 3.0 IBM RS/6000 MAC3.1 DEC Alpha Convex 4.0 Linux Win 95 Mac 5.0 6.0 7.0 Stata/SE OS X GLLAMM speedup 64−bit Solaris SGI Irix 8.0 8.1 8.2 64−bit Linux 9.0 9.1 9.2 10.010.1 11.0 11.1 11.2 12.0 12.1 13.0 13.1 14.0 14.1 14.2 15.0 NC 151 NC 101 NC 152 NC 200 MLEwS NC 631 GLM RMCDV ISA VGSG MlLMUS DAUS IMEUS MEUS PDF−doc WDAUS ISP DMUS Blog FPSAUS V−Tutorial TSUS SSG BAWS Webinar SBS MAUS FEUS IVRMUS HEUS SWUS Mata IRTUS Me reading SwS me taking NC151 cdlplot biplot himatrix hist3 rgroup DAMS dsearch

  • utdat

NLSUG2 DESUG2 soepren mkdat biplot V2 DESUG3 DAUS DESUG4 sq DESUG5 DESUG6 DESUG7 sdlim DESUG8 khb cm2in _gapport DESUG9 nlcorr soepuse meresc DESUG10 DESUG11 dirtools DESUG12 psiduse DESUG14 sq V2 DESUG15 DESUG16 psidtools NC 151 MLEwS Blog V−Tutorial Webinar

1 9 8 5 1 9 8 6 1 9 8 8 1 9 9 1 9 9 2 1 9 9 4 1 9 9 6 1 9 9 8 2 2 2 2 4 2 6 2 8 2 1 2 1 2 2 1 4 2 1 6 2 1 8

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Users

v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v13 v14 v15

1.0 1.1 1.2 1.31.41.5 BiTurbo Sun/Unix 2.0 386/ix 2.05 IC HP/9000 2.1 DEC RISC 3.0 IBM RS/6000 MAC3.1 DEC Alpha Convex 4.0 Linux Win 95 Mac 5.0 6.0 7.0 Stata/SE OS X GLLAMM speedup 64−bit Solaris SGI Irix 8.0 8.1 8.2 64−bit Linux 9.0 9.1 9.2 10.010.1 11.0 11.1 11.2 12.0 12.1 13.0 13.1 14.0 14.1 14.2 15.0 NC 151 NC 101 NC 152 NC 200 MLEwS NC 631 GLM RMCDV ISA VGSG MlLMUS DAUS IMEUS MEUS PDF−doc WDAUS ISP DMUS Blog FPSAUS V−Tutorial TSUS SSG BAWS Webinar SBS MAUS FEUS IVRMUS HEUS SWUS Mata IRTUS Me reading SwS me taking NC151 cdlplot biplot himatrix hist3 rgroup DAMS dsearch

  • utdat

NLSUG2 DESUG2 soepren mkdat biplot V2 DESUG3 DAUS DESUG4 sq DESUG5 DESUG6 DESUG7 sdlim DESUG8 khb cm2in _gapport DESUG9 nlcorr soepuse meresc DESUG10 DESUG11 dirtools DESUG12 psiduse DESUG14 sq V2 DESUG15 DESUG16 psidtools Ado−disk STB UKSUG1 Statalist www.stata.com SSC SJ DESUG1 Forum

1 9 8 5 1 9 8 6 1 9 8 8 1 9 9 1 9 9 2 1 9 9 4 1 9 9 6 1 9 9 8 2 2 2 2 4 2 6 2 8 2 1 2 1 2 2 1 4 2 1 6 2 1 8

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Inhalt

Milestones (and Dead Ends) My Milestone Command milestones Other Milestones Dead ends

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Dead ends

◮ Stage. External command line editor for .gph-files.

Published 1989, never updated. Deprecated since Stata 5.

◮ gph commands (Stata 5). Low level graphics language

placed between . gph open ... . gph close gph continues to work under version 7; see help gph.

◮ Stata 7 had a “programmable bottom-layer graphics engine

You may wish to code your graphics programs using this new feature and, if so, point your browser at http://developer.stata.com/graphics Documentation for the new developmental system resides there.”

◮ for Loops as one-liner; Deprecated since Stata 8.

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Inhalt

Setting the Scene Milestones (and Dead Ends) Prospects (Why not Stata!?)

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Real Programmers

Source: https://xkcd.com/378/

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Speed

Speed was one (the) reason for me to start Stata. It is now a (the) reason for some to convert to R. I do not know, but speed has many dimensions:

◮ Speed of writing code ◮ Speed of writing correct code ◮ Speed of understanding written code ◮ Speed of the code written ◮ Speed of making written code running on different OS

In any case, C is faster than Mata, Mata is faster than Ado, but a well written Ado-file might still be faster than a badly written Mata program. Of course users can add their own C-code to Stata (Plugins); see http://www.stata.com/plugins.

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Commands

◮ The number of available techniques was one (the) reason

for me to start Stata. It is now a (the) reason for some to convert to R.

◮ I believe that R has more routines than Stata. ◮ As of today, I, personally don’t care. So far, I can do all I

want to do with Stata.

◮ Quality of routines? ◮ I am aware of colleagues saying that Stata cannot do

something, which in fact it can.

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Animated Graphs

◮ Animated graphs never been a top target of Stata’s

development

◮ Gould: animated graphs are for teaching not for

  • publication. Since many journals are now online, this is no

longer true.

◮ ’course, you can do animated graphs with gr7 from within

Stata: . do animated1

◮ ’course, you can build animated graphs by calling third

party software from within Stata (ffmpeg, convert, i.e. ImageMagic) . do animated2 Alos see https://blog.stata.com/2014/03/24/ how-to-create-animated-graphics-using-stata/

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Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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SLIDE 34

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Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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SLIDE 35

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Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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SLIDE 36

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Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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SLIDE 37

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-38
SLIDE 38

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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SLIDE 39

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-40
SLIDE 40

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-41
SLIDE 41

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Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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SLIDE 42

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Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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SLIDE 45

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Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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SLIDE 46

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-47
SLIDE 47

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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SLIDE 48

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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SLIDE 49

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-50
SLIDE 50

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-51
SLIDE 51

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-52
SLIDE 52

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-53
SLIDE 53

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-54
SLIDE 54

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-55
SLIDE 55

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-56
SLIDE 56

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-57
SLIDE 57

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-58
SLIDE 58

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-59
SLIDE 59

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-60
SLIDE 60

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-61
SLIDE 61

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-62
SLIDE 62

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Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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SLIDE 63

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Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-64
SLIDE 64

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Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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SLIDE 65

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Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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SLIDE 66

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Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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SLIDE 67

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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SLIDE 68

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Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

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Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-70
SLIDE 70

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-71
SLIDE 71

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-72
SLIDE 72

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-73
SLIDE 73

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-74
SLIDE 74

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-75
SLIDE 75

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-76
SLIDE 76

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-77
SLIDE 77

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-78
SLIDE 78

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-79
SLIDE 79

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-80
SLIDE 80

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-81
SLIDE 81

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-82
SLIDE 82

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-83
SLIDE 83

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-84
SLIDE 84

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-85
SLIDE 85

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-86
SLIDE 86

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-87
SLIDE 87

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-88
SLIDE 88

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-89
SLIDE 89

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-90
SLIDE 90

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-91
SLIDE 91

28/31

Animated Graph in L

A

T EX-Beamer Example

10 20 30 40 2,000 3,000 4,000 5,000 Weight (lbs.) mpg linear lowess

Of course we would like to see this realy interactive.

slide-92
SLIDE 92

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Web scraping

◮ Web scraping is yet another reason for some to convert to

R (and Python, of course)

◮ I realized that Python is much more powerful in processing

text data. Regular Expressions, in particular, are easier to use there.

◮ However still:

◮ copy lets you copy a file from the Internet to your hard disk,

which can then be processed with file.

◮ You can add Java plugins to Stata. ◮ Java plugins have been used to programm ◮ twitter2stata;

see https://blog.stata.com/2017/07/25/ importing-twitter-data-into-stata/

◮ facebook2stata;

see https://blog.stata.com/2018/01/16/ importing-facebook-data-into-stata/

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SLIDE 93

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’course there is an R-package . . .

◮ Need to rename this file using number found in the upper

left corner.

◮ An R-package finds that number ◮ ’course it is easy to the same with Stata

. do jpgrename

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SLIDE 94

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Literature

Cox, N. J. 2005. A brief history of Stata on its 20th anniversary. Stata Journal 5(1): 2005. Henkel, J. and E. von Hippel. 2005. Welfare Implications of User Innovation. In Essays in Honor of Edwin Mansfield. The Economics of R&D, Innovation and Technological Change, eds. A. N. Link and F. M. Scherer, 45–59. Springer. Jokisch, M. 2001. Open-Source Software-Entwicklung. Eine Analyse des Geschäftsmodells der StataCorp. Unpublished Master Thesis University of Munich.