데이터 사이언스와 플랫폼 정부

class: title-slide, left, bottom

# 데이터 사이언스와 플랫폼 정부
----
## **자동화와 추상화**
### 이광춘 / 한국 알(R) 사용자회
### 2022-04-25

---
class: inverse, middle
name: data-science-case

# 발표 개요

----

.pull-left[

**.warmyellow[데이터가 기하급수적으로 증가]**하고 있으며, 
지속적인 공공데이터 확대, 
데이터3법 및 데이터 기본법 통과에 따른 선진적인 데이터 법제도 기틀 마련되면서
이를 (데이터) 자원으로 가치를 창출하는 **디지털 전환**이 급속도로 진행되고 있으나,
디지털 전환에 따른 **.red[행정, 인력, 기술기반(도구와 언어)]**등 제반 여건은 성숙되지 못한 상황.

**.red[플랫폼 (지방)정부]**를 위해서는 선진 디지털 기업을 벤치마킹하고
디지털 기업을 지탱하는 **.green[자동화(Automation)와 추상화(Abstraction)]** 
내재화가 반드시 필요함.

]
 
.pull-right[   
.left[

1\. **[.warmyellow[데이터 과학]](#krila-01-data-science)**

2\. [디지털 기업](#krila-02-digital-company)

3\. [자동화](#krila-03-automation)

4\. [플랫폼 (정부)](#krila-04-platform)

5\. [문서 공장](#krila-05-factory)

6\. [마무리](#krila-goodbye)

]
]

---
name: data-science
# 데이터 과학

데이터 사이언스(Data Science)는 데이터를 다루는 과학이다. 따라서 자연법칙을 다루는 자연과학과 맥이 닿아있다.
자연법칙을 측정하여 데이터프레임(DataFrame)으로 나타내면 **변수(Variable)**, **관측점(Observation)**, **값(Value)**으로 표현된다.

.footnote[
[Google Search - "데이터 과학"](https://www.google.com/search?q=%EB%8D%B0%EC%9D%B4%ED%84%B0+%EA%B3%BC%ED%95%99)
]

---
name: tidyverse
# AI/ML/DL vs 데이터 과학

.center[
<img src="fig/tidyvese_os.png" width="77%" />
]

.footnote[
[한국통계학회 소식지 2019년 10월호 ](https://statkclee.github.io/ds-authoring/ds-stat-tidyverse.html)
]

---
name: tidyverse-definition
# tidyverse

.center[
<img src="fig/maso_tidyverse.png" width="77%" />
]

.pull-left[

- 기존 자료구조를 재사용
- 파이프 연산자로 간단한 함수를 조합
- 함수형 프로그래밍을 적극 사용
- 기계가 아닌 인간을 위한 설계

]

.pull-right[
 
- Reuse existing data structures.
- Compose simple functions with the pipe.
- Embrace functional programming.
- Design for humans.

]

---
name: y-fx
# 30년의 변화를 그림으로 표현

.center[
<img src="fig/intree-xy-f.png" alt="XY" width="77%" />
]

.footnote[
[이광춘(2019-01-26), "Data Scientist 가 바라본 제조업의 발전 전략 및 방안", Intree 4.0 World Forum](https://statkclee.github.io/ds-authoring/intree-seminar-2019-01.html)
]

---
name: data-science-graph
## Big Data/AI/ML/Data Science

.pull-left[
### Google Trends: 미국

![](fig/google-trends-comparison-us-1.png)
]

.pull-right[
### Google Trends: 대한민국

![](fig/google-trends-comparison-kr-1.png)
]

.footnote[
[이광춘 (2020-07-21), "기업운영혁신을 위한 데이터 과학: 기업의 활용방안", 포항산업과학연구원(RIST)](https://statkclee.github.io/ds-authoring/ds-rist.html)
]

---
name: data-science-resource
# 데이터 경제와 산업

.pull-left[
### 데이터 자원

![](fig/economist-bigdata-oil.png)
]

.pull-right[
### 파이프라인

![](fig/data-economy-industry.png)
]

---
name: data-sciece-value
## 가치

<ul>
 <li>기술 분석(Descriptive Analytics): 무슨 일이 있었나? (What happened?)</li>
 <li>진단 분석(Diagnostic Analytics): 왜 일어났나? (Why did it happen?)</li>
 <li>예측 분석(Predictive Analytics): 무슨 일이 일어날까? (What will happen?)</li>
 <li>처방 분석(Prescriptive Analytics): 어떻게 그런 일을 일으킬까? (How can we make it happen?)</li>
</ul>

.center[
<img src="fig/ds-gartner.png" width = "80%" />
]

---
name: data-sciece-cost
# 패러다임의 변화

.pull-left[
### 비용 (인건비 + 자본비용)

![](fig/data-science-cost.png)
]

.pull-right[
### 자동화 (소프트웨어 1.0 + 2.0)

![](fig/automated-machine-learning.png)
]

---
name: case-study-worldcup
## [실제 사례] 러시아 월드컵 (독일전)

.center[
<img src="fig/worldcup.gif" alt="월드컵" width="80%" />
]

.footnote[
[러시아 월드컵(2018) - 한국 vs 독일](https://statkclee.github.io/viz/viz-worldcup-germany.html)
]

---
name: case-study-earthquake
## [실제 사례] 지진해일 대피소

.center[
<img src="fig/earthquake.png" alt="월드컵" width="80%" />
]

.footnote[
[xwMOOC (2018-01-17), "지진해일 대피소 - crosstalk"](https://statkclee.github.io/viz/viz-earthquake-shelter.html)
]

---
name: case-study-congress
## [실제 사례] 국회의원 프로필

.center[
<img src="fig/trelliscopejs.png" alt="국회의원 사진" width="80%" />
]

.footnote[
[국회의원 사진 - trelliscope](https://statkclee.github.io/viz/viz-congressman.html)
]

---
name: case-study-avi
## [실제 사례] 동영상 감성 분석

.center[
<img src="fig/kbs-video.jpg" alt="슬픔은 어디에" width="80%" />
]

.footnote[
- [xwMOOC 딥러닝 - 동영상 감정 분석](https://statkclee.github.io/deep-learning/ms-oxford-video.html)
- [KBS NEWS, 인공지능으로 분석한 대통령의 마음…슬픔은 어디에?](http://news.kbs.co.kr/news/view.do?ncd=3390429&ref=D)
]

---
name: case-study-regression
## [실제 사례] 회귀분석 - MLB 투수 유전자는 유전이 될까?

.center[
<img src="fig/major-league.png" alt="MLB 투수 유전자" width="77%" />
]

.footnote[
[회귀분석 - MLB 투수 유전자는 유전이 될까?](https://statkclee.github.io/politics/pe-baseball-era.html)
]

---
name: case-study-birthrate
## [실제 사례] 출산장려 예산은 출산율을 높일까?

.center[
<img src="fig/budget-birthrate.png" width="57%" />
]

.footnote[
[저출산과 소요예산 - 상관관계](https://statkclee.github.io/viz/viz-low-birth-rate.html)
]

---
name: case-study-tax
## [실제 사례] 증가된 정부예산: 누가 세금을 많이 냈나?

.center[
<img src="fig/tax-burden.png"width="77%" />
]

.footnote[
[국세 세금 데이터 분석](https://statkclee.github.io/election/election_tax_EDA.html)
]

---
name: case-study-dashboard
## [실제 사례] 대쉬보드

.center[
<img src="fig/krila_dashboard.png" width="100%" />
]

.footnote[
[대통령선거 - 대쉬보드](https://aispiration.com/president/social-dashboard.html)
]

---
class: inverse, middle
name: krila-02-digital-company

# 발표 개요

----

.pull-left[

]
 
.pull-right[   
.left[

1\. [데이터 과학](#krila-01-data-science)

2\. **[.warmyellow[디지털 기업]](#krila-02-digital-company)**

3\. [자동화](#krila-03-automation)

4\. [플랫폼 (정부)](#krila-04-platform)

5\. [문서 공장](#krila-05-factory)

6\. [마무리](#krila-goodbye)

]
]

---
name: digital-traditional-company
# 전통기업: 사업 모형

.center[
<img src="fig/digital-business-model.png", width="67%" />
]

.footnote[

Klein, J. (2021). Competing in the Age of AI. By MarcoIansiti and KarimLakhani. Harvard Business Review Press: Boston, MA, 2020, ISBN<U+2010>13 978<U+2010>1633697621, hardback, ￡18, pp. 288. R&amp;D Management, 52(1), 153<U+2013>154. Portico. https://doi.org/10.1111/radm.12489

]

---
name: digital-traditional-company-om
# 전통기업: 운영 모형

.center[
<img src="fig/digital-operating-model.png", width="87%" />
]

---
name: digital-traditional-company-bm-om
# 전통기업: BM &rarr; OM

.center[
<img src="fig/digital-bm-om.png", width="100%" />
]

---
name: digital-traditional-company-problem
# 전통기업: 문제점

.center[
<img src="fig/traditional-value-of-firm.png", width="100%" />
]

---
name: digital-traditional-company-problem
# 전통기업: 근본 원인

.center[
<img src="fig/traditional-om.png", width="100%" />
]

---
name: digital-network-om
# 디지털 AI 기업: 운영모형 + 네트워크 효과

.center[
<img src="fig/network-effect-digital.png", width="100%" />
]

---
name: digital-network-ai-company
# 디지털 AI 기업: AI 공장과 운영모형

.center[
<img src="fig/ai-factory-om.png", width="100%" />
]

---
name: digital-value
# 디지털 AI 기업: 가치(Value), 효용(Utility)

.center[
<img src="fig/network-ai-value.png", width="87%" />
]
 
---
name: digital-value
# 디지털 AI 기업: 운영모형

.center[
<img src="fig/ai-network-om.png", width="100%" />
]
 
 
---
name: digital-value
# 디지털 AI 기업: 운영모형 비교

.center[
<img src="fig/digital-ai-om.png", width="100%" />
]
 
---
name: digital-collision
# 전통기업: 디지털 운영 모형 전환

.center[
<img src="fig/digital-transformation-om.png", width="100%" />
]

---
class: inverse, middle
name: krila-03-automation

# 발표 개요

----

.pull-left[

]
 
.pull-right[   
.left[

1\. [데이터 과학](#krila-01-data-science)

2\. [디지털 기업](#krila-02-digital-company)

3\. **[.warmyellow[자동화]](#krila-03-automation)**

4\. [플랫폼 (정부)](#krila-04-platform)

5\. [문서 공장](#krila-05-factory)

6\. [마무리](#krila-goodbye)

]
]

---
name: automation-tax
# 세무사 업무 변천과정

.center[

![세무 업무 변천사](fig/tax-preparation.png)

]

.footnote[
[이광춘 (2019-12-04), "자동화(Automation) - ~~로봇~~ 자동화가 빼앗는 일자리 그리고 나", 분당 서현 청소년 수련관 ](https://statkclee.github.io/ds-authoring/ds-bundang-2019.html)
]

---
name: automation-productivity-gap
# 생산성과 임금 격차

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1948 value: 0.0000 metric: Hourly compensation","Year: 1949 value: 0.0624 metric: Hourly compensation","Year: 1950 value: 0.1046 metric: Hourly compensation","Year: 1951 value: 0.1174 metric: Hourly compensation","Year: 1952 value: 0.1502 metric: Hourly compensation","Year: 1953 value: 0.2082 metric: Hourly compensation","Year: 1954 value: 0.2348 metric: Hourly compensation","Year: 1955 value: 0.2870 metric: Hourly compensation","Year: 1956 value: 0.3389 metric: Hourly compensation","Year: 1957 value: 0.3708 metric: Hourly compensation","Year: 1958 value: 0.3808 metric: Hourly compensation","Year: 1959 value: 0.4246 metric: Hourly compensation","Year: 1960 value: 0.4538 metric: Hourly compensation","Year: 1961 value: 0.4784 metric: Hourly compensation","Year: 1962 value: 0.5232 metric: Hourly compensation","Year: 1963 value: 0.5486 metric: Hourly compensation","Year: 1964 value: 0.5832 metric: Hourly compensation","Year: 1965 value: 0.6227 metric: Hourly compensation","Year: 1966 value: 0.6470 metric: Hourly compensation","Year: 1967 value: 0.6668 metric: Hourly compensation","Year: 1968 value: 0.7105 metric: Hourly compensation","Year: 1969 value: 0.7439 metric: Hourly compensation","Year: 1970 value: 0.7681 metric: Hourly compensation","Year: 1971 value: 0.8166 metric: Hourly compensation","Year: 1972 value: 0.9134 metric: Hourly compensation","Year: 1973 value: 0.9096 metric: Hourly compensation","Year: 1974 value: 0.8705 metric: Hourly compensation","Year: 1975 value: 0.8686 metric: Hourly compensation","Year: 1976 value: 0.8935 metric: Hourly compensation","Year: 1977 value: 0.9282 metric: Hourly compensation","Year: 1978 value: 0.9566 metric: Hourly compensation","Year: 1979 value: 0.9325 metric: Hourly compensation","Year: 1980 value: 0.8805 metric: Hourly compensation","Year: 1981 value: 0.8736 metric: Hourly compensation","Year: 1982 value: 0.8770 metric: Hourly compensation","Year: 1983 value: 0.8849 metric: Hourly compensation","Year: 1984 value: 0.8703 metric: Hourly compensation","Year: 1985 value: 0.8618 metric: Hourly compensation","Year: 1986 value: 0.8725 metric: Hourly compensation","Year: 1987 value: 0.8467 metric: Hourly compensation","Year: 1988 value: 0.8402 metric: Hourly compensation","Year: 1989 value: 0.8393 metric: Hourly compensation","Year: 1990 value: 0.8237 metric: Hourly compensation","Year: 1991 value: 0.8202 metric: Hourly compensation","Year: 1992 value: 0.8320 metric: Hourly compensation","Year: 1993 value: 0.8346 metric: Hourly compensation","Year: 1994 value: 0.8389 metric: Hourly compensation","Year: 1995 value: 0.8276 metric: Hourly compensation","Year: 1996 value: 0.8287 metric: Hourly compensation","Year: 1997 value: 0.8487 metric: Hourly compensation","Year: 1998 value: 0.8927 metric: Hourly compensation","Year: 1999 value: 0.9198 metric: Hourly compensation","Year: 2000 value: 0.9296 metric: Hourly compensation","Year: 2001 value: 0.9560 metric: Hourly compensation","Year: 2002 value: 0.9949 metric: Hourly compensation","Year: 2003 value: 1.0158 metric: Hourly compensation","Year: 2004 value: 1.0056 metric: Hourly compensation","Year: 2005 value: 0.9973 metric: Hourly compensation","Year: 2006 value: 0.9988 metric: Hourly compensation","Year: 2007 value: 1.0145 metric: Hourly compensation","Year: 2008 value: 1.0139 metric: Hourly compensation","Year: 2009 value: 1.0930 metric: Hourly compensation","Year: 2010 value: 1.1100 metric: Hourly compensation","Year: 2011 value: 1.0847 metric: Hourly compensation","Year: 2012 value: 1.0650 metric: Hourly compensation","Year: 2013 value: 1.0840 metric: Hourly compensation","Year: 2014 value: 1.0908 metric: Hourly compensation","Year: 2015 value: 1.1241 metric: Hourly compensation","Year: 2016 value: 1.1439 metric: Hourly compensation","Year: 2017 value: 1.1467 metric: Hourly compensation","Year: 2018 value: 1.1562 metric: Hourly compensation"],"type":"scatter","mode":"lines+markers","line":{"width":1.88976377952756,"color":"rgba(0,186,56,1)","dash":"solid"},"hoveron":"points","name":"Hourly compensation","legendgroup":"Hourly compensation","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","marker":{"autocolorscale":false,"color":"rgba(0,186,56,1)","opacity":1,"size":1.13385826771654,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(0,186,56,1)"}},"frame":null},{"x":[1948,1949,1950,1951,1952,1953,1954,1955,1956,1957,1958,1959,1960,1961,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018],"y":[0,0.0155,0.0934,0.1224,0.1549,0.1941,0.2144,0.2638,0.2659,0.3004,0.3272,0.3763,0.4006,0.4437,0.498,0.5503,0.5994,0.6492,0.6995,0.7198,0.7713,0.7785,0.8035,0.871,0.922,0.9696,0.9383,0.9811,1.0359,1.0605,1.0827,1.0811,1.0677,1.105,1.0837,1.1451,1.2021,1.2365,1.2828,1.288,1.3201,1.3412,1.3695,1.385,1.4748,1.4851,1.5054,1.5159,1.5624,1.6072,1.6621,1.7346,1.7947,1.8371,1.915,2.0122,2.0929,2.1529,2.1761,2.1978,2.2139,2.2875,2.3823,2.3821,2.3957,2.4096,2.4291,2.4575,2.4634,2.4978,2.529],"text":["Year: 1948 value: 0.0000 metric: Net productivity","Year: 1949 value: 0.0155 metric: Net productivity","Year: 1950 value: 0.0934 metric: Net productivity","Year: 1951 value: 0.1224 metric: Net productivity","Year: 1952 value: 0.1549 metric: Net productivity","Year: 1953 value: 0.1941 metric: Net productivity","Year: 1954 value: 0.2144 metric: Net productivity","Year: 1955 value: 0.2638 metric: Net productivity","Year: 1956 value: 0.2659 metric: Net productivity","Year: 1957 value: 0.3004 metric: Net productivity","Year: 1958 value: 0.3272 metric: Net productivity","Year: 1959 value: 0.3763 metric: Net productivity","Year: 1960 value: 0.4006 metric: Net productivity","Year: 1961 value: 0.4437 metric: Net productivity","Year: 1962 value: 0.4980 metric: Net productivity","Year: 1963 value: 0.5503 metric: Net productivity","Year: 1964 value: 0.5994 metric: Net productivity","Year: 1965 value: 0.6492 metric: Net productivity","Year: 1966 value: 0.6995 metric: Net productivity","Year: 1967 value: 0.7198 metric: Net productivity","Year: 1968 value: 0.7713 metric: Net productivity","Year: 1969 value: 0.7785 metric: Net productivity","Year: 1970 value: 0.8035 metric: Net productivity","Year: 1971 value: 0.8710 metric: Net productivity","Year: 1972 value: 0.9220 metric: Net productivity","Year: 1973 value: 0.9696 metric: Net productivity","Year: 1974 value: 0.9383 metric: Net productivity","Year: 1975 value: 0.9811 metric: Net productivity","Year: 1976 value: 1.0359 metric: Net productivity","Year: 1977 value: 1.0605 metric: Net productivity","Year: 1978 value: 1.0827 metric: Net productivity","Year: 1979 value: 1.0811 metric: Net productivity","Year: 1980 value: 1.0677 metric: Net productivity","Year: 1981 value: 1.1050 metric: Net productivity","Year: 1982 value: 1.0837 metric: Net productivity","Year: 1983 value: 1.1451 metric: Net productivity","Year: 1984 value: 1.2021 metric: Net productivity","Year: 1985 value: 1.2365 metric: Net productivity","Year: 1986 value: 1.2828 metric: Net productivity","Year: 1987 value: 1.2880 metric: Net productivity","Year: 1988 value: 1.3201 metric: Net productivity","Year: 1989 value: 1.3412 metric: Net productivity","Year: 1990 value: 1.3695 metric: Net productivity","Year: 1991 value: 1.3850 metric: Net productivity","Year: 1992 value: 1.4748 metric: Net productivity","Year: 1993 value: 1.4851 metric: Net productivity","Year: 1994 value: 1.5054 metric: Net productivity","Year: 1995 value: 1.5159 metric: Net productivity","Year: 1996 value: 1.5624 metric: Net productivity","Year: 1997 value: 1.6072 metric: Net productivity","Year: 1998 value: 1.6621 metric: Net productivity","Year: 1999 value: 1.7346 metric: Net productivity","Year: 2000 value: 1.7947 metric: Net productivity","Year: 2001 value: 1.8371 metric: Net productivity","Year: 2002 value: 1.9150 metric: Net productivity","Year: 2003 value: 2.0122 metric: Net productivity","Year: 2004 value: 2.0929 metric: Net productivity","Year: 2005 value: 2.1529 metric: Net productivity","Year: 2006 value: 2.1761 metric: Net productivity","Year: 2007 value: 2.1978 metric: Net productivity","Year: 2008 value: 2.2139 metric: Net productivity","Year: 2009 value: 2.2875 metric: Net productivity","Year: 2010 value: 2.3823 metric: Net productivity","Year: 2011 value: 2.3821 metric: Net productivity","Year: 2012 value: 2.3957 metric: Net productivity","Year: 2013 value: 2.4096 metric: Net productivity","Year: 2014 value: 2.4291 metric: Net productivity","Year: 2015 value: 2.4575 metric: Net productivity","Year: 2016 value: 2.4634 metric: Net productivity","Year: 2017 value: 2.4978 metric: Net productivity","Year: 2018 value: 2.5290 metric: Net productivity"],"type":"scatter","mode":"lines+markers","line":{"width":1.88976377952756,"color":"rgba(97,156,255,1)","dash":"solid"},"hoveron":"points","name":"Net productivity","legendgroup":"Net productivity","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","marker":{"autocolorscale":false,"color":"rgba(97,156,255,1)","opacity":1,"size":1.13385826771654,"symbol":"circle","line":{"width":1.88976377952756,"color":"rgba(97,156,255,1)"}},"frame":null}],"layout":{"margin":{"t":46.7762557077626,"r":7.30593607305936,"b":28.5844748858448,"l":34.337899543379},"plot_bgcolor":"rgba(255,255,255,1)","paper_bgcolor":"rgba(255,255,255,1)","font":{"color":"rgba(0,0,0,1)","family":"NanumGothic","size":14.6118721461187},"title":{"text":"Productivity growth and hourly compensation growth, 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시간당 급여","font":{"color":"rgba(0,0,0,1)","family":"NanumGothic","size":14.6118721461187}}},"hovermode":"closest","barmode":"relative"},"config":{"doubleClick":"reset","modeBarButtonsToAdd":["hoverclosest","hovercompare"],"showSendToCloud":false},"source":"A","attrs":{"5188e703273":{"x":{},"y":{},"colour":{},"type":"scatter"},"51884a466b21":{"x":{},"y":{},"colour":{}}},"cur_data":"5188e703273","visdat":{"5188e703273":["function (y) ","x"],"51884a466b21":["function (y) ","x"]},"highlight":{"on":"plotly_click","persistent":false,"dynamic":false,"selectize":false,"opacityDim":0.2,"selected":{"opacity":1},"debounce":0},"shinyEvents":["plotly_hover","plotly_click","plotly_selected","plotly_relayout","plotly_brushed","plotly_brushing","plotly_clickannotation","plotly_doubleclick","plotly_deselect","plotly_afterplot","plotly_sunburstclick"],"base_url":"https://plot.ly"},"evals":[],"jsHooks":[]}</script>

.footnote[
[Economic Policy Institute, "The Productivity - Pay Gap", July 2019](https://www.epi.org/productivity-pay-gap/)
]

---
name: automation-sourcing-strategy
# 소싱전략의 변화

.center[

![소싱 전략의 변화](fig/the-end-of-outsourcing.png)

]

.footnote[
자료출처: Kinetics consulting services / Automation Anywhere 
]

---
name: automation-paradox
# 모라벡의 역설(Moravec’s paradox)

.center[

]

.footnote[
미국 카네기 멜론 대학 (CMU) 로봇 공학자 한스 모라벡(Hans Moravec)이 1970년대에 ‘it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility’라는 표현으로 컴퓨터와 인간의 능력 차이를 역설적으로 표현하였다.
]

---
name: automation-comparison
# 중국어 방 (Chinese room)

.pull-left[
![중국어 방 주장을 모사한 도해](fig/chinese-room-argument.jpg)
]

.pull-right[

]

.footnote[
[중국어 방 역설 (Chinese room argument) - 대체 누가 중국어를 이해하고 있는가?](http://ko.experiments.wikidok.net/wp-d/592f718da44f1a4153e80611/View)
]

---
name: automation-apollo
# 아폴로 우주선

.center[

![아폴로 계획에 대한 역할 구분](fig/man-machine-role-allocation.png)

]

.footnote[

[Mary (Missy) Cummings (2014), "Man versus Machine or Man + Machine?", Duke University and MIT](https://hal.pratt.duke.edu/sites/hal.pratt.duke.edu/files/u10/IS-29-05-Expert%20Opinion[1]_0.pdf)

]

---
name: automation-role-allocation
# 인간과 기계 업무분장

.center[

]

.footnote[

[Mary (Missy) Cummings (2017), "Informing Autonomous System Design Through the Lens of Skill-, Rule-, and Knowledge-Based Behaviors", Duke University](https://hal.pratt.duke.edu/sites/hal.pratt.duke.edu/files/u10/IS-29-05-Expert%20Opinion[1]_0.pdf)

]

---
name: automation-jobs
# Human Supervisory Control

.center[

![human-supervisory-control](fig/human-supervisory-control.png)

]

.footnote[

]

---
class: inverse, middle
name: krila-04-platform

# 발표 개요

----

.pull-left[

]
 
.pull-right[   
.left[

1\. [데이터 과학](#krila-01-data-science)

2\. [디지털 기업](#krila-02-digital-company)

3\. [자동화](#krila-03-automation)

4\. **[.warmyellow[플랫폼 (정부)]](#krila-04-platform)**

5\. [문서 공장](#krila-05-factory)

6\. [마무리](#krila-goodbye)

]
]

---
name: platform-railroad
# 플랫폼

.center[
 <img src="fig/krila_railroad.png" alt="플랫폼" width="100%" />
]

---
name: platform-aa
# 자동화와 추상화

.center[
 <img src="fig/krila_aa.png" alt="자동화와 추상화" width="100%" />
]

---
name: platform-operating-system
# 플랫폼 - 운영체제(리눅스)

.center[
 <img src="fig/krila_os.png" alt="운영체제" width="100%" />
]

---
name: platform-government
# (지방) 정부 (X) 플랫폼

.center[
 <img src="fig/krila_open_gov_platform.png" alt="열린 정부 데이터 플랫폼" width="77%" />
]

.footnote[

Bonina, C., & Eaton, B. (2020). Cultivating open government data platform ecosystems through governance: Lessons from Buenos Aires, Mexico City and Montevideo. Government Information Quarterly, 37(3), 101479. https://doi.org/10.1016/j.giq.2020.101479

]

---
class: inverse, middle
name: krila-05-factory

# 발표 개요

----

.pull-left[

]
 
.pull-right[   
.left[

1\. [데이터 과학](#krila-01-data-science)

2\. [디지털 기업](#krila-02-digital-company)

3\. [자동화](#krila-03-automation)

4\. [플랫폼 (정부)](#krila-04-platform)

5\. **[.warmyellow[문서 공장]](#krila-05-factory)**

6\. [마무리](#krila-goodbye)

]
]

---
name: es-document-factory
# 문서 공장 (Document Factory)

.center[
<img src="fig/document-factory.png" width = "100%" />
]

.footnote[
- [데이터 과학 문서](https://statkclee.github.io/comp_document/)
- [PDF를 데이터로 보는 올바른 자세](https://statkclee.github.io/pdf/)
]

---
name: es-why
# [문서 공장] 문서 제작 문제

.center[
<img src="fig/current-document-problem.png" width = "100%" />
]

.footnote[
- [R마크다운 콘텐츠: 웹사이트, 블로그, 포트폴리오 등](https://statkclee.github.io/comp_document/cd-netlify.html)
]

---
name: es-communication
# [문서 공장] 데이터 과학 문서 커뮤니케이션 대상

.center[
<img src="fig/document-communication.png" width = "100%" />
]

.footnote[
- [R마크다운 콘텐츠: 웹사이트, 블로그, 포트폴리오 등](https://statkclee.github.io/comp_document/cd-netlify.html)
]

---
name: es-code-first
# [문서 공장] Code First 문서 제작

.center[
<img src="fig/document-engineering.png" width = "100%" />
]

.footnote[
- [code first 한글](https://github.com/statkclee/codefirst)
- [code first 영문](https://github.com/nrohr/codefirst)
]

---
name: es-html-word-pdf
# R Markdown &rarr; PDF, HTML, Word

.panelset[
.panel[.panel-name[R Code]

<pre>
library(knitr)
fs::dir_create("01_rmarkdown")

# 1. 보고서 만들기 ---------------------------------------
## 1.1. HTML 보고서 ---------------------------------------
render("01_bmi_rmarkdown_html.Rmd", 
    output_format="html_document",
    output_file = glue::glue("01_bmi_rmarkdown.html"),
    encoding = 'UTF-8', 
    output_dir = "01_rmarkdown")

## 1.2. PDF 보고서 ---------------------------------------
render("01_bmi_rmarkdown_pdf.Rmd", 
 output_format="pdf_document",
 output_file = glue::glue("01_bmi_rmarkdown.pdf"),
 encoding = 'UTF-8', 
 output_dir = "01_rmarkdown")
</pre>

]

.panel[.panel-name[문서 생성 결과]
.center[
<img src="fig/document-word-html-pdf.png" width = "57%" />
]
]
]

.footnote[
- [code first 한글](https://github.com/statkclee/codefirst)
]

---
name: es-basic-parameterized-report
# [문서 공장] R Markdown &rarr; Parameterized Report

.panelset[
.panel[.panel-name[R Code]

<pre>
fs::dir_create("02_parameterized")

# 1. 성별 ---------------------------------------
genders <- c("Male", "Female")

# 2. 남녀별 보고서 만들기 ---------------------------------------
for(gender in genders) {
 knitr::render("02_bmi_parameterized.Rmd", 
 output_format="html_document",
 params = list(gender = gender),
 output_file = glue::glue("02_bmi_parameterized_{gender}.html"),
 encoding = 'UTF-8', 
 output_dir = "02_parameterized")
}
</pre>

]

.panel[.panel-name[문서 생성 결과]
.center[
<img src="fig/parameterized-gender-document.png" width = "57%" />
]
]
]

.footnote[
- [code first 한글](https://github.com/statkclee/codefirst)
]

---
name: es-basic-parameterized-report
# [문서 공장] R Markdown &rarr; 전자우편 발송

.panelset[
.panel[.panel-name[R Code]

<pre>
fs::dir_create("03_email")

# 1. 전자우편 만들기 ---------------------------------------

bmi_email <- render_email('03_bmi_email.Rmd')

bmi_email

bmi_email %>%
  smtp_send(
    to = "steve.jobs@gmail.com",
    from = "victor@r2bit.com",
    subject = "한국 R 컨퍼런스 후원 테스트",
    credentials = creds_key("rconf")
  )

</pre>

]

.panel[.panel-name[문서 생성 결과]
.center[
<img src="fig/codefirst-email.png" width = "100%" />
]
]
]

.footnote[
- [code first 한글](https://github.com/statkclee/codefirst)
]

---
name: es-pro-report
# [문서 공장] 전문 보고서

.panelset[
.panel[.panel-name[작업 방식]
.center[
<img src="fig/front_rmarkdown.png" width = "77%" />
]
]

.panel[.panel-name[문서 생성 결과]
.center[
<img src="fig/pro-report-basic.png" width = "57%" />
]
]
]

.footnote[
- [Professional Report](https://github.com/statkclee/pro_report)
]

---
name: es-pro-report-backend
# [문서 공장] 전문 보고서 - 데이터베이스

.panelset[
.panel[.panel-name[작업 방식]
.center[
<img src="fig/pro_report_backend.png" width = "100%" />
]
]

.panel[.panel-name[문서 생성 결과]
.center[
<img src="fig/pro-report-basic.png" width = "57%" />
]
]
]

.footnote[
- [Professional Report](https://github.com/statkclee/pro_report/tree/backend)
]

---
name: es-pro-report-backend-parameter
# [문서 공장] 전문 보고서 - DB + Parameterized Report

.panelset[
.panel[.panel-name[작업 방식]
.center[
<img src="fig/pro_report_parameterized.png" width = "100%" />
]
]

.panel[.panel-name[문서 생성 결과]
.center[
<img src="fig/pro-report-parameter.png" width = "80%" />
]
]
]

.footnote[
- [Professional Report](https://github.com/statkclee/pro_report/tree/parameterized_document)
]

---
name: es-pro-report-dashboard
# [문서 공장] shiny 대쉬보드

.center[
<img src="fig/bmi_prediction.gif" width = "90%" />
]

.footnote[
- [Professional Report](https://github.com/statkclee/pro_report/tree/dashboard)
]

---
name: es-rconf-website
# 사례: [한국 R 컨퍼런스](https://use-r.kr/) - 웹사이트

.center[
<img src="fig/rconf-website.png" width = "80%" />
]

.footnote[
- [제작방법](https://github.com/tidyverse-korea/rconf)
]

---
name: es-rconf-template
# 사례: [한국 R 컨퍼런스](https://use-r.kr/) - [💻 발표자 템플릿](https://tidyverse-korea.github.io/templates/#1)

.center[
<img src="fig/rconf-template.png" width = "80%" />
]

.footnote[
- [제작방법](https://github.com/tidyverse-korea/templates) 
]

---
name: es-rconf-sponsor
# 사례: [한국 R 컨퍼런스](https://use-r.kr/) - [💁 후원](https://tidyverse-korea.github.io/sponsor/#1)

.center[
<img src="fig/rconf-sponsor.png" width = "100%" />
]

.footnote[
- [제작방법](https://github.com/tidyverse-korea/sponsor) 
]

---
name: es-rconf-poster
# 사례: [한국 R 컨퍼런스](https://use-r.kr/) - [🆘 포스터](https://tidyverse-korea.github.io/poster/)

.center[
<img src="fig/rconf-poster.png" width = "80%" />
]

.footnote[
- [제작방법](https://github.com/tidyverse-korea/poster) 
]

---
name: es-rconf-survey
# 사례: [한국 R 컨퍼런스](https://use-r.kr/) - [👀 여론조사](https://tidyverse-korea.github.io/rconf-2021-survey/)

.center[
<img src="fig/krila_rconf_survey.png" width = "80%" />
]

.footnote[
- [제작방법](https://github.com/tidyverse-korea/rconf-2021-survey) 
]

---
name: krila-goodbye
class: middle, inverse

.pull-left[
# **경청해 주셔서 감사합니다.**
 
## 한국 R 사용자회 / 오픈 통계 패키지

]

.pull-right[
.right[

]
]