Reproducibility
- Controversial software is proving surprisingly accurate at spotting errors in psychology papers
- We need a similar program for #pathology articles. Though most pathology articles do not report #statistics in APA style. #statcheck
- Stat-checking software stirs up psychology
- “Public Availability of Published Research Data in High-Impact Journals” by Alawi A. Alsheikh-Ali , Waqas Qureshi, Mouaz H. Al-Mallah, John P. A. Ioannidis.
- “Gene name errors are widespread in the scientific literature” by Mark Ziemann, Yotam Eren, and Assam El-Osta.
- “Deriving chemosensitivity from cell lines: Forensic bioinformatics and reproducible research in high-throughput biology” by Keith A. Baggerly and Kevin R. Coombes.
- by Thomas Herndon, Michael Ash, Robert Pollin.
- An example of an article given the highest designation for a fully reproducible article:published in 2009 by Duncan Lee, Claire Ferguson, and Richard Mitchell. To see the article's marking, download the PDF and look for the marking letter in a bold box at the top right.
- by Geir Kjetil Sandve, Anton Nekrutenko, James Taylor, Eivind Hovig.
- “Open-Source Genomic Analysis of Shiga-Toxin–Producing E. coli O104:H4” (multiple authors). The associated Dataset GigaScience and GitHub Wiki .
- A universal document converter
- There is an R package called “rrrpkg,” which was created to “facilitate reproducible research.” Their focus is on creating a Research Compendium. Here are some illustrations ofthey use that may be helpful to you.
- A push for reproducibility in biomedical research
- Controversial software is proving surprisingly accurate at spotting errors in psychology papers
- We need a similar program for #pathology articles. Though most pathology articles do not report #statistics in APA style. #statcheck
- Stat-checking software stirs up psychology
- “Public Availability of Published Research Data in High-Impact Journals” by Alawi A. Alsheikh-Ali , Waqas Qureshi, Mouaz H. Al-Mallah, John P. A. Ioannidis.
- “Gene name errors are widespread in the scientific literature” by Mark Ziemann, Yotam Eren, and Assam El-Osta.
- “Deriving chemosensitivity from cell lines: Forensic bioinformatics and reproducible research in high-throughput biology” by Keith A. Baggerly and Kevin R. Coombes.
- by Thomas Herndon, Michael Ash, Robert Pollin.
- An example of an article given the highest designation for a fully reproducible article:published in 2009 by Duncan Lee, Claire Ferguson, and Richard Mitchell. To see the article's marking, download the PDF and look for the marking letter in a bold box at the top right.
- by Geir Kjetil Sandve, Anton Nekrutenko, James Taylor, Eivind Hovig.
- “Open-Source Genomic Analysis of Shiga-Toxin–Producing E. coli O104:H4” (multiple authors). The associated Dataset GigaScience and GitHub Wiki .
- A universal document converter
- There is an R package called “rrrpkg,” which was created to “facilitate reproducible research.” Their focus is on creating a Research Compendium. Here are some illustrations ofthey use that may be helpful to you.
- A push for reproducibility in biomedical research
- Controversial software is proving surprisingly accurate at spotting errors in psychology papers
- We need a similar program for #pathology articles. Though most pathology articles do not report #statistics in APA style. #statcheck
- Stat-checking software stirs up psychology
- “Public Availability of Published Research Data in High-Impact Journals” by Alawi A. Alsheikh-Ali , Waqas Qureshi, Mouaz H. Al-Mallah, John P. A. Ioannidis.
- “Gene name errors are widespread in the scientific literature” by Mark Ziemann, Yotam Eren, and Assam El-Osta.
- “Deriving chemosensitivity from cell lines: Forensic bioinformatics and reproducible research in high-throughput biology” by Keith A. Baggerly and Kevin R. Coombes.
- by Thomas Herndon, Michael Ash, Robert Pollin.
- An example of an article given the highest designation for a fully reproducible article:published in 2009 by Duncan Lee, Claire Ferguson, and Richard Mitchell. To see the article's marking, download the PDF and look for the marking letter in a bold box at the top right.
- by Geir Kjetil Sandve, Anton Nekrutenko, James Taylor, Eivind Hovig.
- “Open-Source Genomic Analysis of Shiga-Toxin–Producing E. coli O104:H4” (multiple authors). The associated Dataset GigaScience and GitHub Wiki .
- A universal document converter
- There is an R package called “rrrpkg,” which was created to “facilitate reproducible research.” Their focus is on creating a Research Compendium. Here are some illustrations ofthey use that may be helpful to you.
Şöyle birşey düşünün, Pankreas patolojisi ile ilgileniyorsunuz. "Bizim pankreas serisi ne durumda" diye merak ettiniz. Yaptığınız şey birkaç düğmeye basmak, ve o zamana kadar bölümünüzde rapor edilen pankreas vakalarının yaş, cinsiyet, tümör çapı, tümör tipi, evre, derece, lenf nodu durumu vesair bilgileri sağ kalım grafikleri ile word dökümanı olarak oluşturuluveriyor. Bu hayal değil. Yapılabilir. Makul bir bilgi işlem çalışanı, CAP ve AJCC'ye uygun doldurulması zorunlu yapılandırılmış patoloji raporları, ana veri tablosuna erişim, biraz SQL, biraz R, biraz da R Markdown kullanarak bunu yapmak işten bile değil.
# https://github.com/spgarbet/tangram
# http://htmlpreview.github.io/?https://github.com/spgarbet/tg/blob/master/vignettes/example.html
library(tangram)
library(Hmisc)
getHdata(pbc)
# View(pbc)
table <- tangram(drug ~ bili + albumin + stage + protime + sex + age + spiders, data = pbc)
table
html5(table)
latex(table)
index(table)
write(
html5(tangram("drug ~ bili[2] + albumin + stage::Categorical + protime + sex + age + spiders", pbc, msd=TRUE, quant=seq(0, 1, 0.25)),
fragment=TRUE, inline="hmisc.css", caption = "HTML5 Table Hmisc Style", id="tbl2"),
"tangram1.html")
write(
html5(tangram("drug ~ bili[2] + albumin + stage::Categorical + protime + sex + age + spiders", pbc),
fragment=TRUE, inline="nejm.css", caption = "HTML5 Table NEJM Style", id="tbl3"),
"tangram_nejm.html")
tbl <- tangram("drug ~ bili[2] + albumin + stage::Categorical[1] + protime + sex[1] + age + spiders[1]",
data=pbc,
pformat = 5)
write(html5(tbl,
fragment=TRUE,
inline="lancet.css",
caption = "HTML5 Table Lancet Style", id="tbl4"
),
"tangram_lancet.html")
index(tangram("drug ~ bili + albumin + stage::Categorical + protime + sex + age + spiders", pbc))[1:20,]
library(readxl)
MDL307_Data <- read_excel("MDL307 - Data.xlsx")
MDL307_Data <- as.data.frame(MDL307_Data)
names(MDL307_Data)
View(MDL307_Data)
MDL307_Data$biyokimyasalrekurrens <- as.factor(MDL307_Data$biyokimyasalrekurrens)
levels(MDL307_Data$biyokimyasalrekurrens)[1] <- "yok"
levels(MDL307_Data$biyokimyasalrekurrens)[2] <- "var"
collist <- c("gleasonskor",
"tersiyer",
"kribriform",
"cerrahisinir",
"ekstaprostatik",
"lenfnodu",
"seminalvezikul"
)
MDL307_Data[collist] <- lapply(MDL307_Data[collist], as.factor)
table <- tangram(biyokimyasalrekurrens ~ yas +
gleasonskor +
tersiyer +
kribriform +
kribriformyuzde +
cerrahisinir +
ekstaprostatik +
lenfnodu +
seminalvezikul +
biyokimyasalrekurrens,
data = MDL307_Data)
table
- Export R output to a file
out <- capture.output(summary(my_very_time_consuming_regression))
cat("My title", out, file="summary_of_my_very_time_consuming_regression.txt", sep="n", append=TRUE)
Şöyle birşey düşünün, Pankreas patolojisi ile ilgileniyorsunuz. "Bizim pankreas serisi ne durumda" diye merak ettiniz. Yaptığınız şey birkaç düğmeye basmak, ve o zamana kadar bölümünüzde rapor edilen pankreas vakalarının yaş, cinsiyet, tümör çapı, tümör tipi, evre, derece, lenf nodu durumu vesair bilgileri sağ kalım grafikleri ile word dökümanı olarak oluşturuluveriyor. Bu hayal değil. Yapılabilir. Makul bir bilgi işlem çalışanı, CAP ve AJCC'ye uygun doldurulması zorunlu yapılandırılmış patoloji raporları, ana veri tablosuna erişim, biraz SQL, biraz R, biraz da R Markdown kullanarak bunu yapmak işten bile değil.
# https://github.com/spgarbet/tangram
# http://htmlpreview.github.io/?https://github.com/spgarbet/tg/blob/master/vignettes/example.html
library(tangram)
library(Hmisc)
getHdata(pbc)
# View(pbc)
table <- tangram(drug ~ bili + albumin + stage + protime + sex + age + spiders, data = pbc)
table
html5(table)
latex(table)
index(table)
write(
html5(tangram("drug ~ bili[2] + albumin + stage::Categorical + protime + sex + age + spiders", pbc, msd=TRUE, quant=seq(0, 1, 0.25)),
fragment=TRUE, inline="hmisc.css", caption = "HTML5 Table Hmisc Style", id="tbl2"),
"tangram1.html")
write(
html5(tangram("drug ~ bili[2] + albumin + stage::Categorical + protime + sex + age + spiders", pbc),
fragment=TRUE, inline="nejm.css", caption = "HTML5 Table NEJM Style", id="tbl3"),
"tangram_nejm.html")
tbl <- tangram("drug ~ bili[2] + albumin + stage::Categorical[1] + protime + sex[1] + age + spiders[1]",
data=pbc,
pformat = 5)
write(html5(tbl,
fragment=TRUE,
inline="lancet.css",
caption = "HTML5 Table Lancet Style", id="tbl4"
),
"tangram_lancet.html")
index(tangram("drug ~ bili + albumin + stage::Categorical + protime + sex + age + spiders", pbc))[1:20,]
library(readxl)
MDL307_Data <- read_excel("MDL307 - Data.xlsx")
MDL307_Data <- as.data.frame(MDL307_Data)
names(MDL307_Data)
View(MDL307_Data)
MDL307_Data$biyokimyasalrekurrens <- as.factor(MDL307_Data$biyokimyasalrekurrens)
levels(MDL307_Data$biyokimyasalrekurrens)[1] <- "yok"
levels(MDL307_Data$biyokimyasalrekurrens)[2] <- "var"
collist <- c("gleasonskor",
"tersiyer",
"kribriform",
"cerrahisinir",
"ekstaprostatik",
"lenfnodu",
"seminalvezikul"
)
MDL307_Data[collist] <- lapply(MDL307_Data[collist], as.factor)
table <- tangram(biyokimyasalrekurrens ~ yas +
gleasonskor +
tersiyer +
kribriform +
kribriformyuzde +
cerrahisinir +
ekstaprostatik +
lenfnodu +
seminalvezikul +
biyokimyasalrekurrens,
data = MDL307_Data)
table
- Export R output to a file
out <- capture.output(summary(my_very_time_consuming_regression))
cat("My title", out, file="summary_of_my_very_time_consuming_regression.txt", sep="n", append=TRUE)
Şöyle birşey düşünün, Pankreas patolojisi ile ilgileniyorsunuz. "Bizim pankreas serisi ne durumda" diye merak ettiniz. Yaptığınız şey birkaç düğmeye basmak, ve o zamana kadar bölümünüzde rapor edilen pankreas vakalarının yaş, cinsiyet, tümör çapı, tümör tipi, evre, derece, lenf nodu durumu vesair bilgileri sağ kalım grafikleri ile word dökümanı olarak oluşturuluveriyor. Bu hayal değil. Yapılabilir. Makul bir bilgi işlem çalışanı, CAP ve AJCC'ye uygun doldurulması zorunlu yapılandırılmış patoloji raporları, ana veri tablosuna erişim, biraz SQL, biraz R, biraz da R Markdown kullanarak bunu yapmak işten bile değil.
# https://github.com/spgarbet/tangram
# http://htmlpreview.github.io/?https://github.com/spgarbet/tg/blob/master/vignettes/example.html
library(tangram)
library(Hmisc)
getHdata(pbc)
# View(pbc)
table <- tangram(drug ~ bili + albumin + stage + protime + sex + age + spiders, data = pbc)
table
html5(table)
latex(table)
index(table)
write(
html5(tangram("drug ~ bili[2] + albumin + stage::Categorical + protime + sex + age + spiders", pbc, msd=TRUE, quant=seq(0, 1, 0.25)),
fragment=TRUE, inline="hmisc.css", caption = "HTML5 Table Hmisc Style", id="tbl2"),
"tangram1.html")
write(
html5(tangram("drug ~ bili[2] + albumin + stage::Categorical + protime + sex + age + spiders", pbc),
fragment=TRUE, inline="nejm.css", caption = "HTML5 Table NEJM Style", id="tbl3"),
"tangram_nejm.html")
tbl <- tangram("drug ~ bili[2] + albumin + stage::Categorical[1] + protime + sex[1] + age + spiders[1]",
data=pbc,
pformat = 5)
write(html5(tbl,
fragment=TRUE,
inline="lancet.css",
caption = "HTML5 Table Lancet Style", id="tbl4"
),
"tangram_lancet.html")
index(tangram("drug ~ bili + albumin + stage::Categorical + protime + sex + age + spiders", pbc))[1:20,]
library(readxl)
MDL307_Data <- read_excel("MDL307 - Data.xlsx")
MDL307_Data <- as.data.frame(MDL307_Data)
names(MDL307_Data)
View(MDL307_Data)
MDL307_Data$biyokimyasalrekurrens <- as.factor(MDL307_Data$biyokimyasalrekurrens)
levels(MDL307_Data$biyokimyasalrekurrens)[1] <- "yok"
levels(MDL307_Data$biyokimyasalrekurrens)[2] <- "var"
collist <- c("gleasonskor",
"tersiyer",
"kribriform",
"cerrahisinir",
"ekstaprostatik",
"lenfnodu",
"seminalvezikul"
)
MDL307_Data[collist] <- lapply(MDL307_Data[collist], as.factor)
table <- tangram(biyokimyasalrekurrens ~ yas +
gleasonskor +
tersiyer +
kribriform +
kribriformyuzde +
cerrahisinir +
ekstaprostatik +
lenfnodu +
seminalvezikul +
biyokimyasalrekurrens,
data = MDL307_Data)
table
- Export R output to a file
out <- capture.output(summary(my_very_time_consuming_regression))
cat("My title", out, file="summary_of_my_very_time_consuming_regression.txt", sep="n", append=TRUE)
Last modified 1mo ago