R 이용 공공빅데이터 분석 경험

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# R 이용 공공빅데이터 분석 경험
## 을지의대 세미나
### 김진섭; <a href="https://github.com/jinseob2kim"><span><i class="fab  fa-github faa-float animated "></i>jinseob2kim</span></a>
### 차라투
### 2019-11-21

---

<div class="my-footer"><span><a href="https://www.zarathu.com">Zarathu Co.,Ltd</a>   
&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;
&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;
<a href="https://github.com/jinseob2kim">김진섭</a></span></div>

---

# News: 보건의료 빅데이터 플랫폼

[9월 17일 뉴스](http://www.medicaltimes.com/Users/News/NewsView.html?mode=view&ID=1128937&REFERER=NP). 공단/심평원/국건영/암센터 데이터 연계 플랫폼 오픈

연말까지 시범사업: 데이터비용 무료

---

# Trend: [공통데이터모델(CDM)](http://www.feedernet.org/html/)

전세계 병원/공공빅데이터 표준화 사업. 데이터정리부터 분석결과까지 공장화. 
- 최근 미국/아주대 공동 고혈압 1차치료 연구가 Lancet accept.

https://feedernet.co.kr/ 에서 심평원 샘플코호트 분석 가능.

---

# Executive Summary

* [dplyr](https://dplyr.tidyverse.org), [data.table](https://github.com/Rdatatable/data.table) 을 활용, 기존 [R](https://www.r-project.org/) 문법보다 빠르게 데이터 를 정리할 수 있다.

* [dbplyr](https://dplyr.tidyverse.org) 를 활용, [R](https://www.r-project.org/) 코드를 **PROC SQL** 문으로 바꿔 복잡한 SAS 작업을 수행할 수 있다. 
]

--
.large[
* 자체 개발한 [jsmodule](https://github.com/jinseob2kim/jsmodule) 패키지를 이용, GUI 환경에서 기술통계와 회귀/생존분석을 수행하고 테이블과 그림을 바로 다운받는다.
]

--
.large[ 
[jsmodule](https://github.com/jinseob2kim/jsmodule) 의 [표본조사 데이터 분석](http://app.zarathu.com/survey/) 기능을 활용, GUI 환경에서 **국건영** 데이터 분석을 수행하였다.
]

---
# Why [R](https://www.r-project.org/)?

- 더 간단한 코드: 특히 **Group by** 작업

- 테이블, 그림, 반출용 리포트를 바로 만들 수 있다.

**단점**

- Memory 문제: 용량 큰 데이터는 SAS 전처리 필요.

- 공단 명동분석실 환경악화: 램 8gb, 하드 100gb
]

---

# 데이터 매니지먼트

---
# Read SAS file

[haven](https://haven.tidyverse.org/) 패키지의 `read_sas` 이용

```r
library(haven)
*t20 <- read_sas("t20_example.sas7bdat")  
t20[1:100, ]                            ## Show first 100 rows
```

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  </script>
</div>

---
# Read multiple SAS file

공단데이터는 **월별**로 쪼개서 줌. `lapply` 와 `dplyr::bind_row` 로 합친 데이터 만듦

```r
library(dplyr)
## 월별 명세서 데이터
data.t20 <- list.files(pattern = "t20") %>%  ## filename contains "t20"
*  lapply(read_sas) %>% 
*  bind_rows

## 월별 x-ray 데이터
data.xray <- list.files(pattern = "t30") %>% 
  lapply(function(x){
    read_sas(x) %>% 
    filter(substr(MCARE_DIV_CD_ADJ, 1, 3) == "G21") %>% 
    select(CMN_KEY, MCARE_DIV_CD_ADJ)
    }) %>% 
  bind_rows 
```

---
# Grammar choice

.pull-left[
<center>
<a href="https://dplyr.tidyverse.org/"><img src="https://github.com/tidyverse/dplyr/raw/master/man/figures/logo.png" width=45%></a>
</center>

- 직관성: `%>%` 연산자, `group_by` 함수

- `read_sas` 후 바로 적용 가능.

https://blog.zarathu.com/posts/2019-01-03-rdatamanagement/

]

.pull-right[
<center>
<a href="https://rdatatable.gitlab.io/data.table/"><img src="https://raw.githubusercontent.com/Rdatatable/data.table/master/.graphics/logo.png" width=45%></a>
</center>

- 빠르고 간결한 코드

- Group 변수를 `setkey` 로 미리 설정하면 더 빠름.

- `read_sas` 후 `data.table` 클래스로 바꿔야 함.

https://jinseob2kim.github.io/radv1.html
]

---

# Comparison: 최초 자살시도

[dplyr](https://dplyr.tidyverse.org/)

```r
out <- t20 %>% 
  filter(substr(SICK_SYM1, 1, 3) %in% paste0("X", 60:84)) %>%  ## X60-84: suicide
  arrange(MDCARE_STRT_DT) %>%
  mutate(YEAR = substr(MDCARE_STRT_DT, 1, 4)) %>%              ## add year variable
  group_by(INDI_DSCM_NO) %>%                                   ## group by individual
  filter(row_number() == 1)                                    ## 1st row
```

[data.table](https://rdatatable.gitlab.io/data.table/)

```r
library(data.table)
t20 <- data.table(t20)
setkey(t20, INDI_DSCM_NO)  ## Fast groupby operation

out <- t20[substr(SICK_SYM1, 1, 3) %in% paste0("X", 60:84)][order(MDCARE_STRT_DT)][, YEAR := substr(MDCARE_STRT_DT, 1, 4)][, .SD[1], by = INDI_DSCM_NO] 
```

---
# [dplyr](https://dplyr.tidyverse.org/) to SQL

[dbplyr](https://dbplyr.tidyverse.org) 패키지로 [dplyr](https://dplyr.tidyverse.org/) 문법을 **SQL** 문으로 바꿀 수 있다.

```r
library(dbplyr)
*t20.db <- tbl_memdb(t20)                               ## DB type data
t20.db %>% 
  filter(substr(SICK_SYM1, 1, 3) %in% paste0("X", 60:84)) %>% 
  arrange(MDCARE_STRT_DT) %>%
  mutate(YEAR = substr(MDCARE_STRT_DT, 1, 4)) %>%       ## Add year variable
  group_by(INDI_DSCM_NO) %>%                            ## group by individual
  filter(row_number() == 1) %>%                         ## 1st row
* show_query                                            ## Show SQL query
```

```sql
SELECT `INDI_DSCM_NO`, `RN_KEY`, `RN_INST`, `MDCARE_STRT_DT`, `FORM_CD`, `MCARE_SUBJ_CD`, `SICK_SYM1`, `SICK_SYM2`, `HSPTZ_PATH_TYPE`, `OFIJ_TYPE`, `OPRTN_YN`, `MDCARE_DD_CNT`, `VSHSP_DD_CNT`, `TOT_PRSC_DD_CNT`, `MCARE_RSLT_TYPE`, `FST_HSPTZ_DT`, `EDC_ADD_RT`, `SPCF_SYM_TYPE`, `ED_RC_TOT_AMT`, `EDC_SBA`, `EDC_INSUR_BRDN_AMT`, `STD_YYYY`, `YEAR`
FROM (SELECT `INDI_DSCM_NO`, `RN_KEY`, `RN_INST`, `MDCARE_STRT_DT`, `FORM_CD`, `MCARE_SUBJ_CD`, `SICK_SYM1`, `SICK_SYM2`, `HSPTZ_PATH_TYPE`, `OFIJ_TYPE`, `OPRTN_YN`, `MDCARE_DD_CNT`, `VSHSP_DD_CNT`, `TOT_PRSC_DD_CNT`, `MCARE_RSLT_TYPE`, `FST_HSPTZ_DT`, `EDC_ADD_RT`, `SPCF_SYM_TYPE`, `ED_RC_TOT_AMT`, `EDC_SBA`, `EDC_INSUR_BRDN_AMT`, `STD_YYYY`, `YEAR`, ROW_NUMBER() OVER (PARTITION BY `INDI_DSCM_NO` ORDER BY `MDCARE_STRT_DT`) AS `zzz3`
      FROM (SELECT `INDI_DSCM_NO`, `RN_KEY`, `RN_INST`, `MDCARE_STRT_DT`, `FORM_CD`, `MCARE_SUBJ_CD`, `SICK_SYM1`, `SICK_SYM2`, `HSPTZ_PATH_TYPE`, `OFIJ_TYPE`, `OPRTN_YN`, `MDCARE_DD_CNT`, `VSHSP_DD_CNT`, `TOT_PRSC_DD_CNT`, `MCARE_RSLT_TYPE`, `FST_HSPTZ_DT`, `EDC_ADD_RT`, `SPCF_SYM_TYPE`, `ED_RC_TOT_AMT`, `EDC_SBA`, `EDC_INSUR_BRDN_AMT`, `STD_YYYY`, SUBSTR(`MDCARE_STRT_DT`, 1, 4) AS `YEAR`
            FROM (SELECT *
                    FROM (SELECT *
                            FROM `t20`
                          WHERE (SUBSTR(`SICK_SYM1`, 1, 3) IN 'X' || (60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84)))
                  ORDER BY `MDCARE_STRT_DT`)))
WHERE (`zzz3` = 1.0)
```

---
# SQL to SAS

생성된 **SQL** 은 바로 SAS **PROC SQL** 문으로 이용 가능.(따옴표는 삭제)

```sql
PROC SQL;
CREATE TABLE out AS
*SELECT INDI_DSCM_NO, RN_KEY, RN_INST, MDCARE_STRT_DT, FORM_CD, MCARE_SUBJ_CD, SICK_SYM1, SICK_SYM2, HSPTZ_PATH_TYPE, OFIJ_TYPE, OPRTN_YN, MDCARE_DD_CNT, VSHSP_DD_CNT, TOT_PRSC_DD_CNT, MCARE_RSLT_TYPE, FST_HSPTZ_DT, EDC_ADD_RT, SPCF_SYM_TYPE, ED_RC_TOT_AMT, EDC_SBA, EDC_INSUR_BRDN_AMT, STD_YYYY, YEAR
*FROM (SELECT INDI_DSCM_NO, RN_KEY, RN_INST, MDCARE_STRT_DT, FORM_CD, MCARE_SUBJ_CD, SICK_SYM1, SICK_SYM2, HSPTZ_PATH_TYPE, OFIJ_TYPE, OPRTN_YN, MDCARE_DD_CNT, VSHSP_DD_CNT, TOT_PRSC_DD_CNT, MCARE_RSLT_TYPE, FST_HSPTZ_DT, EDC_ADD_RT, SPCF_SYM_TYPE, ED_RC_TOT_AMT, EDC_SBA, EDC_INSUR_BRDN_AMT, STD_YYYY, YEAR, ROW_NUMBER() OVER (PARTITION BY INDI_DSCM_NO ORDER BY MDCARE_STRT_DT) AS zzz3
*       FROM (SELECT INDI_DSCM_NO, RN_KEY, RN_INST, MDCARE_STRT_DT, FORM_CD, MCARE_SUBJ_CD, SICK_SYM1, SICK_SYM2, HSPTZ_PATH_TYPE, OFIJ_TYPE, OPRTN_YN, MDCARE_DD_CNT, VSHSP_DD_CNT, TOT_PRSC_DD_CNT, MCARE_RSLT_TYPE, FST_HSPTZ_DT, EDC_ADD_RT, SPCF_SYM_TYPE, ED_RC_TOT_AMT, EDC_SBA, EDC_INSUR_BRDN_AMT, STD_YYYY, SUBSTR(MDCARE_STRT_DT, 1, 4) AS YEAR
*              FROM (SELECT *
*                       FROM (SELECT *
*                                FROM t20
*                              WHERE (SUBSTR(SICK_SYM1, 1, 3) IN 'X' || (60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84)))
*                     ORDER BY MDCARE_STRT_DT)))
*WHERE (zzz3 = 1.0)
QUIT;
```

--
[R](https://www.r-project.org/) 로 직관적인 코드를 만든 후, **PROC SQL** 문으로 바꿔 복잡한 SAS 작업을 수행할 수 있다. 
- 번역 불가능한 [R](https://www.r-project.org/) 함수도 있으므로 주의. 
- 도움말 https://dbplyr.tidyverse.org/articles/sql.html

---
# Tip: Date 다루기

[lubridate](https://lubridate.tidyverse.org/) 패키지의 `ymd` 함수 추천.

```r
library(lubridate)
t20 %>% 
*  mutate(MDCARE_STRT_DT = ymd(MDCARE_STRT_DT)) %>%  
*  mutate(YEAR = year(MDCARE_STRT_DT)) %>%            ## year: extract year
   select(MDCARE_STRT_DT, YEAR)
```

<div data-pagedtable="false">
  <script data-pagedtable-source type="application/json">
{"columns":[{"label":["MDCARE_STRT_DT"],"name":[1],"type":["date"],"align":["right"]},{"label":["YEAR"],"name":[2],"type":["dbl"],"align":["right"]}],"data":[{"1":"2002-10-16","2":"2002"},{"1":"2002-07-12","2":"2002"},{"1":"2002-09-23","2":"2002"},{"1":"2002-09-30","2":"2002"},{"1":"2002-08-26","2":"2002"},{"1":"2002-05-28","2":"2002"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
  </script>
</div>

설명서 - https://cran.r-project.org/web/packages/lubridate/vignettes/lubridate.html

---

# 통계 분석

---
# R 패키지 개발

3개의 패키지를 [CRAN](https://cran.r-project.org/)에 배포, 공단/심평원 분석실에서 다운 가능.

- [jstable](https://github.com/jinseob2kim/jstable): 논문용 테이블 만들기

- [jskm](https://github.com/jinseob2kim/jskm): kaplan-meier 생존곡선 그리기

- [jsmodule](https://github.com/jinseob2kim/jsmodule): 위 2 패키지를 적용한 통계분석 웹

---
# [jstable](https://github.com/jinseob2kim/jstable): table 1

```r
library(jstable)
*tb1 <- CreateTableOneJS(names(mtcars), strata = "vs", data = mtcars, factorVars = c("vs", "am", "gear", "carb"))
datatable(tb1$table[, -6], caption = tb1$caption)
```

<div id="htmlwidget-9dbce5895c976012c610" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-9dbce5895c976012c610">{"x":{"filter":"none","caption":"<caption>Stratified by vs<\/caption>","data":[["n","mpg ","cyl ","disp ","hp ","drat ","wt ","qsec ","vs (%)","","am (%)","","gear (%)","","","carb (%)","","","","",""],["","","","","","","","","0","1","0","1","3","4","5","1","2","3","4","6","8"],["    18"," 16.62 (3.86)","  7.44 (1.15)","307.15 (106.77)","189.72 (60.28)","  3.39 (0.47)","  3.69 (0.90)"," 16.69 (1.09)","    18 (100.0) ","     0 (  0.0) ","    12 ( 66.7) ","     6 ( 33.3) ","    12 ( 66.7) ","     2 ( 11.1) ","     4 ( 22.2) ","     0 (  0.0) ","     5 ( 27.8) ","     3 ( 16.7) ","     8 ( 44.4) ","     1 (  5.6) ","     1 (  5.6) "],["    14"," 24.56 (5.38)","  4.57 (0.94)","132.46 (56.89)"," 91.36 (24.42)","  3.86 (0.51)","  2.61 (0.72)"," 19.33 (1.35)","     0 (  0.0) ","    14 (100.0) ","     7 ( 50.0) ","     7 ( 50.0) ","     3 ( 21.4) ","    10 ( 71.4) ","     1 (  7.1) ","     7 ( 50.0) ","     5 ( 35.7) ","     0 (  0.0) ","     2 ( 14.3) ","     0 (  0.0) ","     0 (  0.0) "],["","<0.001","<0.001","<0.001","<0.001"," 0.013"," 0.001","<0.001","<0.001",""," 0.556",""," 0.001","",""," 0.002","","","","",""],["","","","","","","","","","","","","exact","","","exact","","","","",""]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>level<\/th>\n      <th>0<\/th>\n      <th>1<\/th>\n      <th>p<\/th>\n      <th>test<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"pageLength":5,"order":[],"autoWidth":false,"orderClasses":false,"columnDefs":[{"orderable":false,"targets":0}],"lengthMenu":[5,10,25,50,100]}},"evals":[],"jsHooks":[]}</script>

---
# [jstable](https://github.com/jinseob2kim/jstable): regression

```r
model.logisic <- glm(vs ~ wt + qsec, data = mtcars)
*res.logistic <- glmshow.display(model.logisic)
*out.logistic <- LabelepiDisplay(res.logistic)
datatable(out.logistic, caption = res.logistic$first.line)
```

<div id="htmlwidget-30d49496c11e853a5b47" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-30d49496c11e853a5b47">{"x":{"filter":"none","caption":"<caption>Linear regression predicting vs\n<\/caption>","data":[["wt","qsec","No. of observations","R-squared","AIC value"],["-0.29 (-0.44,-0.13)","0.21 (0.14,0.28)","32","0.7405","10.7788"],["< 0.001","< 0.001","","",""],["-0.23 (-0.32,-0.13)","0.19 (0.14,0.24)","","",""],["< 0.001","< 0.001","","",""],["**","**",null,null,null]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>crude coeff.(95%CI)<\/th>\n      <th>crude P value<\/th>\n      <th>adj. coeff.(95%CI)<\/th>\n      <th>adj. P value<\/th>\n      <th>sig<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"dom":"rt","order":[],"autoWidth":false,"orderClasses":false,"columnDefs":[{"orderable":false,"targets":0}]}},"evals":[],"jsHooks":[]}</script>

---
# [jskm](https://github.com/jinseob2kim/jskm)

```r
library(jskm);library(survival)
fit <- survfit(Surv(time, status) ~ rx, data = colon)
jskm(fit, timeby=365, marks = F, pval = T, table = T)
```

---
# [jsmodule](https://github.com/jinseob2kim/jsmodule)

<center>
<figure>
<a href="https://github.com/jinseob2kim/jsmodule"><img src="https://github.com/jinseob2kim/jsmodule/raw/master/vignettes/figures/addin.gif" width=100%></a>
<figurecaption>Rstudio Addins: jsmodule packages</figurecaption>
</figure>
</center>

통계분석 웹 : http://app.zarathu.com/

---
# [jsmodule](https://github.com/jinseob2kim/jsmodule) 특징

**Data type** 따라 분류

- [Basic statistics](http://app.zarathu.com/basic): 기본

- [Repeated measure](http://app.zarathu.com/repeated): 반복측정자료, **GEE**

- [Survey data](http://app.zarathu.com/survey): 표본조사(**국건영**), **survey regression**

**[Propensity score analysis](http://app.zarathu.com/ps)**

- PS 계산 후, **Original/Matching/IPTW** 분석 동시 수행.

주요 기능

- 범주형 변수 만들기, reference 바꾸기, Subgroup

- Table 1, 회귀분석, 생존분석, ROC 분석

---

<center>
<figure>
<img src="feature_jsmodule.jpg" width=100%>
<figurecaption>범주형 변수 만들기, reference 바꾸기, Subgroup</figurecaption>
</figure>
</center>

---
<center>
<figure>
<img src="tb1_jsmodule.jpg" width=100%>
<figurecaption>Table 1</figurecaption>
</figure>
</center>

---
<center>
<figure>
<img src="cox_jsmodule.jpg" width=100%>
<figurecaption>Cox model</figurecaption>
</figure>
</center>

---
<center>
<figure>
<img src="km_jsmodule.jpg" width=100%>
<figurecaption>Kaplan-meier plot</figurecaption>
</figure>
</center>

---
<center>
<figure>
<img src="timeroc_jsmodule.jpg" width=100%>
<figurecaption>Time dependent ROC</figurecaption>
</figure>
</center>

---
# Table download

<div id="htmlwidget-51b3c5e1a9ad58abe08a" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-51b3c5e1a9ad58abe08a">{"x":{"filter":"none","extensions":["Buttons"],"caption":"<caption>Linear regression predicting vs\n<\/caption>","data":[["wt","qsec","No. of observations","R-squared","AIC value"],["-0.29 (-0.44,-0.13)","0.21 (0.14,0.28)","32","0.7405","10.7788"],["< 0.001","< 0.001","","",""],["-0.23 (-0.32,-0.13)","0.19 (0.14,0.24)","","",""],["< 0.001","< 0.001","","",""],["**","**",null,null,null]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>crude coeff.(95%CI)<\/th>\n      <th>crude P value<\/th>\n      <th>adj. coeff.(95%CI)<\/th>\n      <th>adj. P value<\/th>\n      <th>sig<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"dom":"rtB","buttons":["copy","print",{"extend":"collection","buttons":[{"extend":"csv","filename":"resLogistic"},{"extend":"excel","filename":"resLogistic"},{"extend":"pdf","filename":"resLogistic"}],"text":"Download"}],"scrollX":true,"order":[],"autoWidth":false,"orderClasses":false,"columnDefs":[{"orderable":false,"targets":0}]}},"evals":[],"jsHooks":[]}</script>

---
# Figure download

`emf` 확장자 지원, **PPT**(2013 이상) 편집 가능.

---

# 국건영 데이터 분석

---
# 데이터 다운로드

https://knhanes.cdc.go.kr/knhanes/sub03/sub03_02_02.do - **sas, spss** 지원

---
# 표본조사 가중치

일반 통계 분석 안됨!! 표본구조 고려한 `svyglm`, `svycox`, `svykm` 필요.

- 가중치 : **wt_ex, wt_ex1, wt_itvex**

- 층화변수: **kstrata, kstrata1, kstratra2**

- 집락변수(1차 표본추출단위): **psu** 
]

.pull-right[
<center>
<figure>
<img src="weight_survey.jpg" width=100%>
<figurecaption> 통합 가중치</figurecaption>
</figure>
</center>
]

---
# [Survey data](http://app.zarathu.com/survey) in [jsmodule](https://github.com/jinseob2kim/jsmodule)

---
<center>
<figure>
<img src="untb1_jsmodule.jpg" width=100%>
<figurecaption>Table1: 가중치 미적용</figurecaption>
</figure>
</center>

---
<center>
<figure>
<img src="wttb1_jsmodule.jpg" width=100%>
<figurecaption>Table1: 가중치 적용</figurecaption>
</figure>
</center>

---
<center>
<figure>
<img src="svyglm_jsmodule.jpg" width=100%>
<figurecaption>svyglm</figurecaption>
</figure>
</center>

---

# Executive Summary

* [haven](https://github.com/tidyverse/haven) 패키지로 SAS 파일을 직접 읽을 수 있다.

* [dbplyr](https://dplyr.tidyverse.org) 를 활용, [R](https://www.r-project.org/) 코드를 **PROC SQL** 문으로 바꿔 복잡한 SAS 작업을 수행할 수 있다.

* 자체 개발한 [jsmodule](https://github.com/jinseob2kim/jsmodule) 패키지를 이용, GUI 환경에서 기술통계와 회귀/생존분석을 수행하고 테이블과 그림을 바로 다운받는다.

[jsmodule](https://github.com/jinseob2kim/jsmodule) 의 [표본조사 데이터 분석](http://app.zarathu.com/survey/) 기능을 활용, GUI 환경에서 **국건영** 데이터 분석을 수행하였다.
]

---

# END