R과 Rstudio를 이용한 재현가능한 연구

Why R ?

• (왜 R을 써야 하지?)

RStudio ?

• (이건 또 뭐지? 듣보잡)

Reproducible research using R and RStudio

• (이건 또 뭥미?)

왜 R을 써야 하지 ?

• 난 SPSS는 쓸줄 안다.
• SAS는 넘사벽…. R도 어렵다.
• 새로운 걸 배우기 싫다. 이 나이에…
• 그냥 하던대로 할랜다. 그게 속 편하다. 내 맘이다.

compareGroup 패키지

library(compareGroups)
data(predimed)

predimed 데이타의 모든 변수를 그룹별로 비교할거다

res=compareGroups(group ~ . , data=predimed)

표를 만들어라

createTable(res)

그룹별로 나눠서 비교할거다

res = compareGroups(group ~ .-sex-hormo, data=predimed)

모든 환자, 남자환자만, 여자환자만 따로 표를 세개 만든다

alltab=createTable(res)
femaletab=createTable(update(res,subset=sex=="Female"))
maletab=createTable(update(res,subset=sex=="Male"))

표 세개를 합친다.

cbind("ALL"=alltab,"FEMALE"=femaletab,"MALE"=maletab)                

Literate Programing

• (문학적 프로그래밍)

Reproducible Research

• (재현가능한 연구)

• “Remember that microarray analysis you did six months ago?
  We ran a few more arrays.
  Can you add them to the project and repeat the same analysis?”

• “The statistical analyst who looked at the data I generated previously is no longer available.
  Can you get someone else to analyze my new data set using the same methods (and thus producing a report I can expect to understand)”

• “Please write/edit the methods sections for the abstract/paper/grant proposal I am submitting based on the analysis you did several months ago.”

• Selected articles published in Nature Genetics between January 2005 and December 2006 that had used profiling with microarrays

• Of the 56 items retrieved electronically, 20 articles were considered potentially eligible for the project

• The four teams were from

  • University of Alabama at Birmingham(UAB)
  • Stanford/Dana-Farber(SD)
  • London(L)
  • Ioannina/Trento(IT)

• Each team was comprised of 3-6 scientists who worked together to evaluate each article.

• Result could be reproduced : n=2

• Reproduced with discrepancy : n=6

• Could not be reproduced : n=10

  • No data n=4 (no data n=2, subset n=1, no reporter data n=1)
  • Confusion over matching of data to analysis (n=2)
  • Specialized software required and not available (n=1)
  • Raw data available but could not be processed n=2

1. Collect and manage data(EXCEL)

2. Analysis (R/SPSS)

3. Writeup(WORD)

Integrating

• 1. Data management
• 2. Data analysis
• 3. Writing up results

in a single dynamic document

Reproducible research !!

- If spotting eror in data, or using different dataset…

- make changes in Rmarkdown and report will update automatically