The slide shows a full gist of reading different types of data in R thanks to coursera it was much comprehensive and i made some additional changes too.
Overview of a few ways to group and summarize data in R using sample airfare data from DOT/BTS's O&D Survey.
Starts with naive approach with subset() & loops, shows base R's tapply() & aggregate(), highlights doBy and plyr packages.
Presented at the March 2011 meeting of the Greater Boston useR Group.
This set of slides is based on the presentation I gave at ACM DataScience camp 2014. This is suitable for those who are still new to R. It has a few basic data manipulation techniques, and then goes into the basics of using of the dplyr package (Hadley Wickham) #rstats #dplyr
Overview of a few ways to group and summarize data in R using sample airfare data from DOT/BTS's O&D Survey.
Starts with naive approach with subset() & loops, shows base R's tapply() & aggregate(), highlights doBy and plyr packages.
Presented at the March 2011 meeting of the Greater Boston useR Group.
This set of slides is based on the presentation I gave at ACM DataScience camp 2014. This is suitable for those who are still new to R. It has a few basic data manipulation techniques, and then goes into the basics of using of the dplyr package (Hadley Wickham) #rstats #dplyr
Learn to manipulate strings in R using the built in R functions. This tutorial is part of the Working With Data module of the R Programming Course offered by r-squared.
Desk reference for data transformation in Stata. Co-authored with Tim Essam (@StataRGIS, linkedin.com/in/timessam). See all cheat sheets at http://bit.ly/statacheatsheets. Updated 2016/06/03.
January 2016 Meetup: Speeding up (big) data manipulation with data.table packageZurich_R_User_Group
Abstract: Both practitioners and researchers spend significant amount of their time on data preparation, cleaning and exploration. It gets more complicated and interesting if a dataset is big, or if it has a lot of groups in it which require per-group analysis. In this talk I will introduce an innovative data.table package as an alternative to the standard data.frame which significantly cuts your programming and execution time with easier code. It is also the first step to working with big data in R. The talk will be beneficial for R users from all disciplines, as well as for big data professionals looking for more explicit data exploration tools.
Desk reference for data wrangling, analysis, visualization, and programming in Stata. Co-authored with Tim Essam(@StataRGIS, linkedin.com/in/timessam). See all cheat sheets at http://bit.ly/statacheatsheets. Updated 2016/06/03
Move your data (Hans Rosling style) with googleVis + 1 line of R codeJeffrey Breen
R's googleVis package makes it easy to use the Google Visualization API with your data. Here we demonstrate how to create a Hans Rosling-style motion chart with some sample data. Just one line of R code automatically generates 165 lines of HTML and JavaScript for us. This "lightning talk" was presented at the July 2011 meeting of the Greater Boston useR meeting.
Stata cheat sheet: programming. Co-authored with Tim Essam (linkedin.com/in/timessam). See all cheat sheets at http://bit.ly/statacheatsheets. Updated 2016/06/04
Learn to manipulate strings in R using the built in R functions. This tutorial is part of the Working With Data module of the R Programming Course offered by r-squared.
Desk reference for data transformation in Stata. Co-authored with Tim Essam (@StataRGIS, linkedin.com/in/timessam). See all cheat sheets at http://bit.ly/statacheatsheets. Updated 2016/06/03.
January 2016 Meetup: Speeding up (big) data manipulation with data.table packageZurich_R_User_Group
Abstract: Both practitioners and researchers spend significant amount of their time on data preparation, cleaning and exploration. It gets more complicated and interesting if a dataset is big, or if it has a lot of groups in it which require per-group analysis. In this talk I will introduce an innovative data.table package as an alternative to the standard data.frame which significantly cuts your programming and execution time with easier code. It is also the first step to working with big data in R. The talk will be beneficial for R users from all disciplines, as well as for big data professionals looking for more explicit data exploration tools.
Desk reference for data wrangling, analysis, visualization, and programming in Stata. Co-authored with Tim Essam(@StataRGIS, linkedin.com/in/timessam). See all cheat sheets at http://bit.ly/statacheatsheets. Updated 2016/06/03
Move your data (Hans Rosling style) with googleVis + 1 line of R codeJeffrey Breen
R's googleVis package makes it easy to use the Google Visualization API with your data. Here we demonstrate how to create a Hans Rosling-style motion chart with some sample data. Just one line of R code automatically generates 165 lines of HTML and JavaScript for us. This "lightning talk" was presented at the July 2011 meeting of the Greater Boston useR meeting.
Stata cheat sheet: programming. Co-authored with Tim Essam (linkedin.com/in/timessam). See all cheat sheets at http://bit.ly/statacheatsheets. Updated 2016/06/04
A QSAR is a mathematical relationship between a biological activity of a molecular system and its geometric and chemical characteristics.
QSAR attempts to find consistent relationship between biological activity and molecular properties, so that these “rules” can be used to evaluate the activity of new compounds.
Slick: Bringing Scala’s Powerful Features to Your Database Access Rebecca Grenier
This talk will teach you how to use Slick in practice, based on our experience at EatingWell Media Group. Slick is a totally different (and better!) relational database mapping tool that brings Scala’s powerful features to your database interactions, namely: static-checking, compile-time safety, and compositionality.
Here at EatingWell, we have learned quite a bit about Slick over the past two years as we transitioned from a PHP website to Scala. I will share with you tips and tricks we have learned, as well as everything you need to get started using Slick in your Scala application.
I will begin with Slick fundamentals: how to get started making your connection, the types of databases it can access, how to actually create table objects and make queries to and from them. We will using these fundamentals to demonstrate the powerful features inherited from the Scala language itself: static-checking, compile-time safety, and compositionality. And throughout I will share plenty of tips that will help you in everything from getting started to connection pooling options and configuration for use at scale.
Sequelize is a promise-based Node.js ORM for Postgres, MySQL, MariaDB, SQLite and Microsoft SQL Server. It features solid transaction support, relations, eager and lazy loading, read replication and more.
Introduction
Web Storage
WebSQL
IndexedDB
File System Access
Final Considerations
This presentation has been developed in the context of the Mobile Applications Development course, DISIM, University of L'Aquila (Italy), Spring 2015.
http://www.ivanomalavolta.com
ADO.NET Architecture
Data processing has traditionally relied primarily on a connection-based, two-tier model. As data
processing increasingly uses multi-tier architectures, programmers are switching to a
disconnected approach to provide better scalability for their applications.
Distributed, Incremental Dataflow Processing on AWS with GRAIL's Reflow (CMP3...Amazon Web Services
GRAIL is a life sciences company that analyzes large data sets from high-throughput DNA sequencers to develop methods for early cancer detection. In this session, hear how GRAIL's open-source, cloud-based batch processing system, Reflow, leverages Amazon EC2, Amazon S3, and Amazon DynamoDB to support the large-scale, high-throughput, and cost-efficient data analysis that enables GRAIL's research and development efforts. Reflow takes a modern, “cloud-native” approach to batch data processing, and is architected to run directly on the facilities offered by cloud providers like AWS. This approach allows Reflow to maintain a simple design and implementation while maximally utilizing the underlying AWS services and minimizing operational overhead and computing costs.
The workshop will present how to combine tools to quickly query, transform and model data using command line tools.
The goal is to show that command line tools are efficient at handling reasonable sizes of data and can accelerate the data science
process. We will show that in many instances, command line processing ends up being much faster than ‘big-data’ solutions. The content
of the workshop is derived from the book of the same name (http://datascienceatthecommandline.com/). In addition, we will cover
vowpal-wabbit (https://github.com/JohnLangford/vowpal_wabbit) as a versatile command line tool for modeling large datasets.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
2. Get/set your working directoryGet/set your working directory
A basic component of working with data is knowing your working directory
The two main commands are getwd() and setwd().
Be aware of relative versus absolute paths
Important difference in Windows setwd("C:UsersdatascDownloads")
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Relative - setwd("./data"), setwd("../")
Absolute - setwd("/Users/datasc/data/")
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3. Checking for and creating directoriesChecking for and creating directories
file.exists("directoryName") will check to see if the directory exists
dir.create("directoryName") will create a directory if it doesn't exist
Here is an example checking for a "data" directory and creating it if it doesn't exist
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if(!file.exists("data")){
dir.create("data")
}
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4. Reading data filesReading data files
We wil look at each of the methods
From Internet
Reading local files
Reading Excel Files
Reading XML
Reading JSON
Reading MySQL
Reading HDF5
Reading from other resources
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5. Getting data from InternetGetting data from Internet
Data from Healthit.gov
Use of download.file()
Useful for downloading tab-delimited, csv, and other files
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fileUrl <- "http://dashboard.healthit.gov/data/data/NAMCS_2008-2013.csv"
download.file(fileUrl,destfile="./data/NAMCS.csv",method="curl")
list.files("./data")
## [1] "NAMCS.csv"
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6. Getting data from InternetGetting data from Internet
Reading the data using read.csv()
data<-read.csv("http://dashboard.healthit.gov/data/data/NAMCS_2008-2013.csv")
head(data,2)
## Region Period Adoption.of.Basic.EHRs..Overall.Physician.Practices
## 1 Alabama 2013 0.48
## 2 Alaska 2013 0.50
## Adoption.of.Basic.EHRs..Primary.Care.Providers
## 1 0.50
## 2 0.52
## Adoption.of.Basic.EHRs..Rural.Providers
## 1 0.54
## 2 0.37
## Adoption.of.Basic.EHRs..Small.Practices
## 1 0.40
## 2 0.39
## Percent.of.office.based.physicians.with.computerized.capability.to.view.lab.results
## 1 0.74
## 2 0.75 6/40
7. Some notes about download.file()Some notes about download.file()
If the url starts with http you can use download.file()
If the url starts with https on Windows you may be ok
If the url starts with https on Mac you may need to set method="curl"
If the file is big, this might take a while
Be sure to record when you downloaded.
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8. Loading flat files - read.table()Loading flat files - read.table()
This is the main function for reading data into R
Flexible and robust but requires more parameters
Reads the data into RAM - big data can cause problems
Important parameters file, header, sep, row.names, nrows
Related: read.csv(), read.csv2()
Both read.table() and read.fwf() use scan to read the file, and then process the results of scan.
They are very convenient, but sometimes it is better to use scan directly
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9. Example dataExample data
fileUrl <- "http://dashboard.healthit.gov/data/data/NAMCS_2008-2013.csv"
download.file(fileUrl,destfile="./data/NAMCS.csv",method="curl")
list.files("./data")
## [1] "NAMCS.csv"
Data <- read.table("./data/NAMCS.csv")
## Error: line 2 did not have 87 elements
head(Data,2)
## Error: object 'Data' not found
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10. Example parametersExample parameters
read.csv sets sep="," and header=TRUE
same as
cameraData <- read.table("./data/NAMCS.csv",sep=",",header=TRUE)
cameraData <- read.csv("./data/NAMCS.csv")
head(cameraData)
## Region Period Adoption.of.Basic.EHRs..Overall.Physician.Practices
## 1 Alabama 2013 0.48
## 2 Alaska 2013 0.50
## 3 Arizona 2013 0.51
## 4 Arkansas 2013 0.46
## 5 California 2013 0.54
## 6 Colorado 2013 0.39
## Adoption.of.Basic.EHRs..Primary.Care.Providers
## 1 0.50
## 2 0.52
## 3 0.63
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11. Some more important parametersSome more important parameters
People face trouble with reading flat files those have quotation marks ` or " placed in data values,
setting quote="" often resolves these.
quote - you can tell R whether there are any quoted values quote="" means no quotes.
na.strings - set the character that represents a missing value.
nrows - how many rows to read of the file (e.g. nrows=10 reads 10 lines).
skip - number of lines to skip before starting to read
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12. read.xlsx(), read.xlsx2() {xlsx package}read.xlsx(), read.xlsx2() {xlsx package}
Reading specific rows and columnsReading specific rows and columns
library(xlsx)
Data <- read.xlsx("./data/ADME_genes.xlsx",sheetIndex=1,header=TRUE)
colIndex <- 2:3
rowIndex <- 1:4
dataSub <- read.xlsx("./data/ADME_genes.xlsx",sheetIndex=1,
colIndex=colIndex,rowIndex=rowIndex)
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13. Further notesFurther notes
The write.xlsx function will write out an Excel file with similar arguments.
read.xlsx2 is much faster than read.xlsx but for reading subsets of rows may be slightly unstable.
The XLConnect is a Java-based solution, so it is cross platform and returns satisfactory results.
For large data sets it may be very slow.
xlsReadWrite is very fast: it doesn't support .xlsx files
gdata package provides a good cross platform solutions. It is available for Windows, Mac or
Linux. gdata requires you to install additional Perl libraries. Perl is usually already installed in
Linux and Mac, but sometimes require more effort in Windows platforms.
In general it is advised to store your data in either a database or in comma separated files (.csv)
or tab separated files (.tab/.txt) as they are easier to distribute.
I found on the web a self made function to easily import xlsx files. It should work in all platforms
and use XML
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source("https://gist.github.com/schaunwheeler/5825002/raw/3526a15b032c06392740e20b6c9a179add2cee49/
xlsxToR = function("myfile.xlsx", header = TRUE)
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14. Working with XMLWorking with XML
http://en.wikipedia.org/wiki/XML
Extensible markup language
Frequently used to store structured data
Particularly widely used in internet applications
Extracting XML is the basis for most web scraping
Components
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Markup - labels that give the text structure
Content - the actual text of the document
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15. Read the file into RRead the file into R
library(XML)
fileUrl <- "http://www.w3schools.com/xml/simple.xml"
doc <- xmlTreeParse(fileUrl,useInternal=TRUE)
rootNode <- xmlRoot(doc)
xmlName(rootNode)
## [1] "breakfast_menu"
names(rootNode)
## food food food food food
## "food" "food" "food" "food" "food"
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16. Directly access parts of the XML documentDirectly access parts of the XML document
rootNode[[1]]
## <food>
## <name>Belgian Waffles</name>
## <price>$5.95</price>
## <description>Two of our famous Belgian Waffles with plenty of real maple syrup</description>
## <calories>650</calories>
## </food>
rootNode[[1]][[1]]
## <name>Belgian Waffles</name>
Go for a tour of XML package
Official XML tutorials short, long
An outstanding guide to the XML package
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17. JSONJSON
http://en.wikipedia.org/wiki/JSON
Javascript Object Notation
Lightweight data storage
Common format for data from application programming interfaces (APIs)
Similar structure to XML but different syntax/format
Data stored as
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Numbers (double)
Strings (double quoted)
Boolean (true or false)
Array (ordered, comma separated enclosed in square brackets [])
Object (unorderd, comma separated collection of key:value pairs in curley brackets {})
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29. Select a specific subsetSelect a specific subset
query <- dbSendQuery(hg19, "select * from affyU133Plus2 where misMatches between 1 and 3")
affyMis <- fetch(query); quantile(affyMis$misMatches)
## 0% 25% 50% 75% 100%
## 1 1 2 2 3
affyMisSmall <- fetch(query,n=10); dbClearResult(query);
## [1] TRUE
dim(affyMisSmall)
## [1] 10 22
# close connection
dbDisconnect(hg19)
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30. Further resourcesFurther resources
RMySQL vignette http://cran.r-project.org/web/packages/RMySQL/RMySQL.pdf
R data import and export
Set up R odbc with postgres
A nice blog post summarizing some other commands http://www.r-bloggers.com/mysql-and-r/
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31. HDF5HDF5
http://www.hdfgroup.org/
Used for storing large data sets
Supports storing a range of data types
Heirarchical data format
groups containing zero or more data sets and metadata
datasets multidimensional array of data elements with metadata
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Have a group header with group name and list of attributes
Have a group symbol table with a list of objects in group
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Have a header with name, datatype, dataspace, and storage layout
Have a data array with the data
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32. R HDF5 packageR HDF5 package
The rhdf5 package works really well, although it is not in CRAN. To install it:
source("http://bioconductor.org/biocLite.R")
## Bioconductor version 2.13 (BiocInstaller 1.12), ?biocLite for help
## A newer version of Bioconductor is available after installing a new
## version of R, ?BiocUpgrade for help
biocLite("rhdf5")
## BioC_mirror: http://bioconductor.org
## Using Bioconductor version 2.13 (BiocInstaller 1.12.1), R version 3.0.3.
## Installing package(s) 'rhdf5'
##
## The downloaded binary packages are in
## /var/folders/pm/jg6blwt55b71g8jl64wfw8ch0000gn/T//RtmpuYnNzs/downloaded_packages
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33. Creating an HDF5 file and group hierarchyCreating an HDF5 file and group hierarchy
library(rhdf5)
h5createFile("myhdf5.h5")
## [1] TRUE
h5createGroup("myhdf5.h5","foo")
## [1] TRUE
h5createGroup("myhdf5.h5","baa")
## [1] TRUE
h5createGroup("myhdf5.h5","foo/foobaa")
## [1] TRUE
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34. hdf5 continuedhdf5 continued
Saving multiple objects to an HDF5 file
h5ls("myhdf5.h5")
## group name otype dclass dim
## 0 / baa H5I_GROUP
## 1 / foo H5I_GROUP
## 2 /foo foobaa H5I_GROUP
A = 1:7; B = 1:18; D = seq(0,1,by=0.1)
h5save(A, B, D, file="newfile2.h5")
h5dump("newfile2.h5")
## $A
## [1] 1 2 3 4 5 6 7
##
## $B
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## 34/40
35. Reading from other resourcesReading from other resources
foreign package
Loads data from Minitab, S, SAS, SPSS, Stata,Systat
Basic functions read.foo
See the help page for more details http://cran.r-project.org/web/packages/foreign/foreign.pdf
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read.arff (Weka)
readline() read from console
read.dta (Stata)
read.clipboard()
read.mtp (Minitab)
read.octave (Octave)
read.spss (SPSS)
read.xport (SAS)
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