The document describes various pipe beveling machines and accessories for welding pipe. It provides specifications for 14 different pipe beveling machine models ranging from small portable models like the SM8 and S18 to larger stationary machines. It also lists accessories like mandrels, copying carriages, hydraulic power packs, tube cutting machines, and flange facing attachments that can be added to the pipe beveling machines.
This document outlines a final year project on a water cooling system for a ship's engine. The project aims to show how important water cooling is for maintaining safe engine temperatures. It involves constructing a basic cooling system using inexpensive and accessible materials like aquarium pumps, bottles, and tubing within a 3 month timeframe with a budget of under $200. The system works by filtering, heating, and circulating fresh water to cool the engine via a heat exchanger before returning it to the water tank.
Parallel R in snow (english after 2nd slide)Cdiscount
This presentation discusses parallelizing computations in R using the snow package. It demonstrates how to:
1. Create a cluster with multiple R sessions using makeCluster()
2. Split data across the sessions using clusterSplit() and export data to each node
3. Write functions to execute in parallel on each node using clusterEvalQ()
4. Collect the results, such as by summing outputs, to obtain the final parallelized computation. As an example, it shows how to parallelize the likelihood calculation for a probit regression model, reducing the computation time.
This document discusses mixing R source code and documentation in LaTeX documents using knitr. It recommends using knitr in RStudio to embed R code chunks and output (like graphs and tables) in LaTeX documents. Code chunks can include any R code to evaluate, show, or hide. Graphs and tables from R code chunks will be included in the LaTeX output.
The document describes various pipe beveling machines and accessories for welding pipe. It provides specifications for 14 different pipe beveling machine models ranging from small portable models like the SM8 and S18 to larger stationary machines. It also lists accessories like mandrels, copying carriages, hydraulic power packs, tube cutting machines, and flange facing attachments that can be added to the pipe beveling machines.
This document outlines a final year project on a water cooling system for a ship's engine. The project aims to show how important water cooling is for maintaining safe engine temperatures. It involves constructing a basic cooling system using inexpensive and accessible materials like aquarium pumps, bottles, and tubing within a 3 month timeframe with a budget of under $200. The system works by filtering, heating, and circulating fresh water to cool the engine via a heat exchanger before returning it to the water tank.
Parallel R in snow (english after 2nd slide)Cdiscount
This presentation discusses parallelizing computations in R using the snow package. It demonstrates how to:
1. Create a cluster with multiple R sessions using makeCluster()
2. Split data across the sessions using clusterSplit() and export data to each node
3. Write functions to execute in parallel on each node using clusterEvalQ()
4. Collect the results, such as by summing outputs, to obtain the final parallelized computation. As an example, it shows how to parallelize the likelihood calculation for a probit regression model, reducing the computation time.
This document discusses mixing R source code and documentation in LaTeX documents using knitr. It recommends using knitr in RStudio to embed R code chunks and output (like graphs and tables) in LaTeX documents. Code chunks can include any R code to evaluate, show, or hide. Graphs and tables from R code chunks will be included in the LaTeX output.
The document introduces building a data science platform in the cloud using Amazon Web Services and open source technologies. It discusses motivations for using a cloud-based approach for flexibility and cost effectiveness. The key building blocks are described as Amazon EC2 for infrastructure, Vertica for fast data storage and querying, and RStudio Server for analytical capabilities. Step-by-step instructions are provided to set up these components, including launching an EC2 instance, attaching an EBS volume for storage, installing Vertica and RStudio Server, and configuring connectivity between components. The platform allows for experimenting and iterating quickly on data analysis projects in the cloud.
- The document discusses strategies for analyzing large datasets that are too big to fit into memory, including using cloud computing, the ff and rsqlite packages in R, and sampling with the data.sample package.
- The ff and rsqlite packages allow working with data beyond RAM limits but require rewriting code, while data.sample provides sampling without rewriting code but introduces sampling error.
- Cloud computing avoids rewriting code and has no memory limits but requires setup, and sampling is good for analysis but not reporting exact values.
RStudio is a multi-platform integrated development environment (IDE) for R that allows users to develop R code on desktop or mobile devices. It provides features like code completion, executing code directly from source files, navigating to files and functions, version control, and interactive graphics. RStudio can be run locally or accessed via the web, making it a useful tool for developing R code from any device.
Add plots and images into a PowerPoint document from R softwarekassambara
This document contains a histogram plot of iris sepal width data generated using the R and ReporteRs packages, along with an image downloaded from the R website. The histogram shows the frequency of iris sepal widths ranging from 2.0 to 4.0.
Create a Powerpoint using R software and ReporteRs packagekassambara
This PowerPoint document created from R software contains 4 slides:
1) A bar plot of death rates by gender and location in Virginia.
2) A table showing measurements of iris flower species, including sepal length/width and petal length/width for 50 flowers from each of 3 iris species.
3) Information about the iris data set measurements.
4) A histogram plot of sepal width from the iris data set created by an R script.
Create a PowerPoint document from template using R software and ReporteRs pac...kassambara
This PowerPoint presentation created with R software contains 4 slides:
1) A bar plot showing death rates by age group and gender in rural and urban areas in Virginia.
2) A table showing measurements of sepal length, sepal width, petal length, and petal width for 50 flowers from each of 3 iris species.
3) An R script that generates a histogram of sepal width measurements from the iris data set.
4) No content is shown for the 4th slide.
The document introduces building a data science platform in the cloud using Amazon Web Services and open source technologies. It discusses motivations for using a cloud-based approach for flexibility and cost effectiveness. The key building blocks are described as Amazon EC2 for infrastructure, Vertica for fast data storage and querying, and RStudio Server for analytical capabilities. Step-by-step instructions are provided to set up these components, including launching an EC2 instance, attaching an EBS volume for storage, installing Vertica and RStudio Server, and configuring connectivity between components. The platform allows for experimenting and iterating quickly on data analysis projects in the cloud.
- The document discusses strategies for analyzing large datasets that are too big to fit into memory, including using cloud computing, the ff and rsqlite packages in R, and sampling with the data.sample package.
- The ff and rsqlite packages allow working with data beyond RAM limits but require rewriting code, while data.sample provides sampling without rewriting code but introduces sampling error.
- Cloud computing avoids rewriting code and has no memory limits but requires setup, and sampling is good for analysis but not reporting exact values.
RStudio is a multi-platform integrated development environment (IDE) for R that allows users to develop R code on desktop or mobile devices. It provides features like code completion, executing code directly from source files, navigating to files and functions, version control, and interactive graphics. RStudio can be run locally or accessed via the web, making it a useful tool for developing R code from any device.
Add plots and images into a PowerPoint document from R softwarekassambara
This document contains a histogram plot of iris sepal width data generated using the R and ReporteRs packages, along with an image downloaded from the R website. The histogram shows the frequency of iris sepal widths ranging from 2.0 to 4.0.
Create a Powerpoint using R software and ReporteRs packagekassambara
This PowerPoint document created from R software contains 4 slides:
1) A bar plot of death rates by gender and location in Virginia.
2) A table showing measurements of iris flower species, including sepal length/width and petal length/width for 50 flowers from each of 3 iris species.
3) Information about the iris data set measurements.
4) A histogram plot of sepal width from the iris data set created by an R script.
Create a PowerPoint document from template using R software and ReporteRs pac...kassambara
This PowerPoint presentation created with R software contains 4 slides:
1) A bar plot showing death rates by age group and gender in rural and urban areas in Virginia.
2) A table showing measurements of sepal length, sepal width, petal length, and petal width for 50 flowers from each of 3 iris species.
3) An R script that generates a histogram of sepal width measurements from the iris data set.
4) No content is shown for the 4th slide.
La prochaine version du framework .NET (.NET 4.5) apporte plusieurs innovations. Les performances ont été améliorées, les appels asynchrones ont été introduits dans de nombreuses API et le support au niveau de C# 5 des instructions async/await facilite leur usage. Le traitement parallèle a aussi été étendu avec le support des cœurs multiples et l'introduction des dataflow. Parmi les nouveautés on notera également le support étendu de MEF, des améliorations au niveau de WCF, de Workflow Foundation et de ASP.NET pour ne citer que ceux-ci.
Panorama des tendances, nouvelles normes, conseils précieux aux développeurs… Entre front, back et design, le Blend Web Mix offre chaque année un cocktail très prisé de technologies et de savoir-faire.
Slides présentés à l'occasion du premier meetup Paris R Addicts.
La présentation est destiné à ceux qui ne connaissent pas ou très peu. Elle montre les intérêts et les inconvénients du logiciel ainsi que des éléments de syntaxe et des liens qui aideront l'apprentissage.
3. Plusieurs packages coexistent :
dataframe2xls
xlsx
XLConnect
…
XLConnect présente quelques avantages :
Traite à la fois les fichiers xls et les fichiers xlsx
Utilise des scripts en java, ce qui ne nécessite pas en général d’installation supplémentaire
Le package continue d’être développé et il y a de plus en plus d’options
EXPORTS DE R VERS OFFICE 05/04/2013 3
4. Version longue
wb <- loadWorkbook(chemin, create = Crée un lien avec un fichier excel et éventuellement crée le fichier
TRUE)
createSheet(wb, name = "Data") Crée un onglet Data
writeWorksheet(wb, a, sheet = "Data") Sauve les données de a dans l’onglet Data créé
saveWorkbook(wb) Sauve le fichier excel
Version courte
writeWorksheetToFile(chemin, data a, Remarques :
sheet = "Data") Ne permet pas de créer le fichier ni l’onglet
Pas recommandé dans le cas d’exportations multiples dans un même
fichier, car R lance plusieurs scripts en Java destinés au même fichier excel
et ça finit par bloquer, peut-être pour des raisons de synchronisation
Les fonctions « symétriques » sont … readWorksheet / readWorksheetFromFile
EXPORTS DE R VERS OFFICE 05/04/2013 4
5. Exportations de plusieurs tables simultanément
writeWorksheet(wb, list(a,b), sheet = Colle les tables a et b dans les onglets Data1 et Data2
c("Data1","Data2"))
Choix des cellules cibles
createSheet(wb, name = "Data2") Colle la table a dans l’onglet Data2
writeWorksheet(wb, a, sheet =
"Data2",header=F,startCol=2,startRow=2
Colle les donnée à partir de la deuxième ligne et de la deuxième
) colonne
N’écrit pas les noms de colonnes
Dupplication d’onglets / Suppression du contenu
cloneSheet(wb, sheet = "Data2", name = Duplique l’onglet Data2
"Data3")
clearSheet(wb, sheet = "Data3") Supprime le contenu de Data3
EXPORTS DE R VERS OFFICE 05/04/2013 5
6. Possibilité de modifier des formats directement sous R
wb <- Exemple 1 : on change la taille des cellules dans l’onglet Data2
loadWorkbook(paste(nomDossier,"/Result
ats.xlsx",sep=""), create = FALSE)
setRowHeight(wb, sheet = "Data2", row
= 2:11, height = 30)
setColumnWidth(wb, sheet = "Data2",
column = 2:11, width = 4000)
saveWorkbook(wb)
wb <-
Exemple 2 : modification de la couleur de fond dans l’onglet Data2
loadWorkbook(paste(nomDossier,"/Result
ats.xlsx",sep=""), create = FALSE)
format1 <- createCellStyle(wb)
# On veut changer la couleur de fond
...
setFillPattern(format1, fill =
XLC$"FILL.SOLID_FOREGROUND")
# ... en rouge foncé
setFillForegroundColor(format1, color
= XLC$"COLOR.DARK_RED")
# on applique ici à la première ligne
setCellStyle(wb, sheet = "Data2", row
= 2, col = 2:11, C’est (excessivement) pénible
cellstyle = format1)
saveWorkbook(wb)
EXPORTS DE R VERS OFFICE 05/04/2013 6
7. Possibilité de ne pas écraser le format de la cellule d’arrivée
wb <- loadWorkbook(chemin, create = Garde le format de la cellule d’arrivée… pas très chic mais ça
FALSE) marche
setStyleAction(wb,XLC$"STYLE_ACTION.NO
NE")
writeWorksheet(wb, b, sheet =
"Data2",header=F,startCol=2,startRow=2
)
saveWorkbook(wb)
Possibilité d’appliquer des formats enregistrés dans le fichier excel
Il faut d’abord créer les formats que l’on souhaite dans un fichier excel
Ensuite, on peut partir de ce fichier excel en le dupliquant avant d’écrire dedans afin de disposer des « bons » formats
wb <- loadWorkbook(chemin, create = Récupère le format Format1 et l’applique à la cellule choisie
FALSE)
Format1 <- getCellStyle(wd, "Format
1")
setCellStyle(w,
sheet="Data2",row=1,col=1,cellstyle=Fo
rmat1)
saveWorkbook(wb)
EXPORTS DE R VERS OFFICE 05/04/2013 7
9. Ce package est en basé sur le package RCOM, qui lui-même utilise
l’application statconnDCOM
Pour installer statconnDCOM :
1. Utiliser l’instruction du package RCOM : installstatconnDCOM(id="rcom")
2. Télécharger depuis le site : http://rcom.univie.ac.at/
L’intérêt de R2wd est que ce package rend l’utilisation de RCOM facile
EXPORTS DE R VERS OFFICE 05/04/2013 9
10. Ouverture de word et création d’un document
wdGet() Ouverture de Word
wdNewDoc("chemin/Nouveau.doc") Création du document Nouveau.doc
wdSave() Enregistrement
wdQuit() Fermeture de Word
Ecriture de texte
wdTitle("Titre") Le format Titre du document word est utilisé
wdSection("Titre 1",newpage=TRUE) Le format Titre1 du document word est utilisé
wdSubsection("Titre 2") Le format Titre2 du document word est utilisé
wdSubsubsection("Titre 3") Le format Titre3 du document word est utilisé
wdBody("Normal") Le format Normal du document word est utilisé
EXPORTS DE R VERS OFFICE 05/04/2013 10
11. Mélanger des valeur et du texte
wdTitle(paste("Résultats du",date)
Coller des bouts de code R ou des sorties de console
Verbatim("ceci est mon code")
Faire des conversion d’unités (qu’est ce que ça fait dans ce package)
wdConvert(1,"in","pt")
wdConvert(1,"pt","in") Peut toujours servir…
wdConvert(1,"cm","pt")
EXPORTS DE R VERS OFFICE 05/04/2013 11
12. Export d’un graphique simple
wdPlot(1:100,1:100 )
Possibilité de choisir la fonction graphique
wdPlot(1:100,1:100,fun=boxplot )
EXPORTS DE R VERS OFFICE 05/04/2013 12
13. Export de base
wdTable(a)
On peut utiliser la fonction format() pour un formatage rapide
wdTable(format(a))
EXPORTS DE R VERS OFFICE 05/04/2013 13
15. Taille des lignes et des polices
wdTable(format(a),padding=10,pointsize
=5)
Formats « automatiques » autoformat = 1/2/3
EXPORTS DE R VERS OFFICE 05/04/2013 15
17. Ouverture de Word et création d’un document
comApp<-comCreateObject("Word.Application")
Instancie une nouvelle application Word
comSetProperty(comApp,"Visible",TRUE)
Rend Word visible
comDoc<-
comInvoke(comGetProperty(comApp,"Documents"),"Add") Instancie un nouveau document dans Word
Ecriture de texte
comApp[["Selection"]][["Text"]]<- "Ecrire du Pas très ergonomique
texte…"
Créer un graphique
win.metafile()
plot(density(rnorm(10000,0,1)))
dev.off()
# Etape 4: insertion graphique dans le workbook
comSelection<-comApp[["Selection"]]
docImage<-comInvoke(comSelection,"Paste")
EXPORTS DE R VERS OFFICE 05/04/2013 17
18. Ouverture de Excel et création d’un document
comApp<-comCreateObject("Excel.Application") Instancie une nouvelle application Excel
comSetProperty(pptApp,"Visible",TRUE) Rend Excel visible
comXls<- Instancie un nouveau document dans Excel
comInvoke(comGetProperty(comApp,"Workbooks"),"Add")
Ecriture
comSheet<-comGetProperty(comXls,"Worksheets",1)
comRange<-
comGetProperty(comSheet,"Range","A1","B4")
comSetProperty(comRange,"Value",cbind(c(11:14),c(15
:18)))
EXPORTS DE R VERS OFFICE 05/04/2013 18
19. Ouverture de PowerPoint et création d’un document
comApp<-comCreateObject("PowerPoint.Application") Instancie une nouvelle application PowerPoint
comSetProperty(comApp,"Visible",TRUE) Rend PowerPoint visible
pptPresentation<- Instancie un nouveau document dans
comInvoke(pptApp[["Presentations"]],"Add",-1)
PowerPoint
pptSlides<-comGetProperty(pptPresentation,"Slides")
Ecriture
pptCurrSlide<-comInvoke(pptSlides,"Add",1,11)
pptShapes<-pptCurrSlide[["Shapes"]]
pptCurrShape[["TextFrame"]][["TextRange"]][["Text"]
]<- "Slide créée depuis R"
EXPORTS DE R VERS OFFICE 05/04/2013 19