SlideShare ist ein Scribd-Unternehmen logo
1 von 50
Downloaden Sie, um offline zu lesen
Reading data
    into
  2012-09-28 @HSPH
 Kazuki Yoshida, M.D.
   MPH-CLE student

                        FREEDOM
                        TO(KNOW
Previously in this group


!   Group Website: http://rpubs.com/kaz_yos/useR_at_HSPH
!   Introduction to R
Menu
!   What statistics is all about.
!   Data-reading functions in R
!   Installing packages
!   Reading excel files
!   Reading other files
http://mediacrushllc.com/2012/internet-statistics-2012/




                            is the study of the
collection, organization, analysis, interpretation,
               and presentation of



               data
                 http://en.wikipedia.org/wiki/Statistics
No data,
  No life
No statistics
Loading data is
 the first step
  http://echrblog.blogspot.com/2011/04/statistics-on-states-with-systemic-or.html
Supported
!   .RData (native): load()
!   .csv: read.csv()
!   .xls/.xlsx: library(gdata) or library(XLConnect)
!   .sas7bdat: read.sas7bdat() via library(sas7bdat)
!   .dta: read.dta via library(foreign)
!   and more...
      http://cran.r-project.org/doc/manuals/R-data.html
library()
packages
4000+
   user-
contributed
 packages
           Fast
       development
         http://r4stats.com/articles/popularity/
Downside:
not much can be
 done without
    packages
CRAN
Comprehensive
      R
      Archive
      Network
http://cran.r-project.org/web/packages/
   available_packages_by_date.html
Let’s try
Open
R Studio
http://rstudio.org


Watch the screencast
Plot              Workspace
        switched


Console              Source
Menu: RStudio - Preferences     My configuration




                     Plot     Workspace




                 Console      Source
Menu: RStudio - Preferences     My configuration




                       Configure CRAN mirror
Comma Separated Values




            Use .CSV
            if possible
http://www.edrugsearch.com/edsblog/cvs-takes-on-wal-marts-generic-drug-prices-with-a-gimmicky-twist/#.UEfft0J8z0d
.csv
http://www.wondergraphs.com/img/SFO_Landings.csv
read.csv(“file.csv”)
http://www.wondergraphs.com/img/SFO_Landings.csv

                 Careful big file!
name of a dataset here




    new.dat <-
read.csv(“file.csv”)
                                file name here
function to read .csv files
alternatively
                 name of a dataset here




        new.dat <-
  read.csv(file.choose())
                                function to open a
  function to read .csv files   file-choose dialogue
Space separated

http://www.biostat.harvard.edu/~fitzmaur/ala2e/tlc.dat
read.table(“file.dat”)
                  or
  read.table(“file.dat”, header = T)

http://www.biostat.harvard.edu/~fitzmaur/ala2e/tlc.dat
tab-separated
read.delim(“file.tsv”)
     http://www.brookscole.com/cgi-wadsworth/
              course_products_wp.pl?
fid=M20b&flag=student&product_isbn_issn=9780495384
    960&disciplinenumber=1038&template=AUS
For comma-, tab-, or
space-separated text
       Let’s try!
Excel files
     prevalent
http://www.biography.com/people/bill-gates-9307520
     http://www.last.fm/music/Excel/+images/285200
http://www.philipcoppens.com/matrixconstructs.html




    We will use
  publicly available
         data
http://www.hsph.harvard.edu/faculty/miguel-
         hernan/files/nhefs_book.xls
        http://www.hsph.harvard.edu/faculty/miguel-hernan/causal-inference-book/
install.packages(“gdata”, dep = T)

  library(gdata)
read.xls(“file.xls”)

           Perl configuration necessary on Win
               http://cran.r-project.org/web/packages/gdata/INSTALL
install.packages(“XLConnect”, dep = T)

library(XLConnect)
readWorksheet(loadWorkbook(“file.xls”),
               sheet=1)

                Define a function for simplicity
  my.read.xls <- function(file) readWorksheet(loadWorkbook(file), sheet = 1)
  my.read.xls(“file.xls”)

        install.packages("XLConnect", type = "source") on Mac
To install a package
                            package name here


install.packages(“package”,
          dep = T)
                             short for TRUE
   short for dependencies
To load a package
                        package name here



library(package)
   double quote “” can be omitted
Just click
  box
Install package
       Load package
Read xls file chosen to nhefs
install.packages(“sas7bdat”, dep = T)

      library(sas7bdat)
read.sas7bdat(“file.sas7bdat”)
 http://www.biostat.harvard.edu/~fitzmaur/ala2e/
               smoking.sas7bdat
library(foreign)
    read.xport(“file.xpt”)
ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/nhanes/
                2009-2010/DEMO_F.xpt
library(foreign)
read.dta(“file.dta”)
http://www.biostat.harvard.edu/~fitzmaur/ala2e/
                 headache.dta
HTML table

            http://
        www.drugs.com/
       top200_2003.html
install.packages(“XML”, dep = T)
               library(XML)
readHTMLTable("http://www.drugs.com/top200_2003.html",
              which = 2, skip.rows = 1)




                                http://www.drugs.com/top200_2003.html
Fixed width
read.fwf(“file.txt”,
width = c(3, 5, ...))

       Use width = list(c(3,5,..), c(5,7,..))
         for multiple rows per subject
Important functions
!   install.packages(“PackageName”, dep = T)
!   library(PackageName)
!   str(dataset)
!   summary(dataset)
!   head(dataset)
Appendix:
Probability
Functions
what it
        -norm          -t      -binom -pois               does

                                                        density

 d-        dnorm        dt     dbinom       dpois
                                                         (mass)
                                                        given x-
                                                          axis
                                                         return

 p-        pnorm        pt     pbinom       ppois
                                                      probability,
                                                        given x-
                                                      axis(quan.)
                                                         return

 q-        qnorm        qt     qbinom       qpois
                                                        quantile
                                                       (x-axis),
                                                      given prob.
        library(BS    t.test,                          return p-

-test       DA):
           z.test,
                   library(BS
                      DA):
                              binom.test poisson.test
                                                       value and
                                                      confidence
         zsum.test tsum.test                            interval

Weitere ähnliche Inhalte

Was ist angesagt?

5.2 mining time series data
5.2 mining time series data5.2 mining time series data
5.2 mining time series dataKrish_ver2
 
05 Clustering in Data Mining
05 Clustering in Data Mining05 Clustering in Data Mining
05 Clustering in Data MiningValerii Klymchuk
 
DBMS 3 | ER Diagram to Relational Schema
DBMS 3 | ER Diagram to Relational SchemaDBMS 3 | ER Diagram to Relational Schema
DBMS 3 | ER Diagram to Relational SchemaMohammad Imam Hossain
 
Asymptotic notations
Asymptotic notationsAsymptotic notations
Asymptotic notationsNikhil Sharma
 
Presentation on Elementary data structures
Presentation on Elementary data structuresPresentation on Elementary data structures
Presentation on Elementary data structuresKuber Chandra
 
Introduction to pandas
Introduction to pandasIntroduction to pandas
Introduction to pandasPiyush rai
 
2. R-basics, Vectors, Arrays, Matrices, Factors
2. R-basics, Vectors, Arrays, Matrices, Factors2. R-basics, Vectors, Arrays, Matrices, Factors
2. R-basics, Vectors, Arrays, Matrices, Factorskrishna singh
 
2.3 bayesian classification
2.3 bayesian classification2.3 bayesian classification
2.3 bayesian classificationKrish_ver2
 
List,tuple,dictionary
List,tuple,dictionaryList,tuple,dictionary
List,tuple,dictionarynitamhaske
 
7. Relational Database Design in DBMS
7. Relational Database Design in DBMS7. Relational Database Design in DBMS
7. Relational Database Design in DBMSkoolkampus
 
2.2 decision tree
2.2 decision tree2.2 decision tree
2.2 decision treeKrish_ver2
 
Dbms 14: Relational Calculus
Dbms 14: Relational CalculusDbms 14: Relational Calculus
Dbms 14: Relational CalculusAmiya9439793168
 
Sparse matrix and its representation data structure
Sparse matrix and its representation data structureSparse matrix and its representation data structure
Sparse matrix and its representation data structureVardhil Patel
 
data generalization and summarization
data generalization and summarization data generalization and summarization
data generalization and summarization janani thirupathi
 

Was ist angesagt? (20)

5.2 mining time series data
5.2 mining time series data5.2 mining time series data
5.2 mining time series data
 
05 Clustering in Data Mining
05 Clustering in Data Mining05 Clustering in Data Mining
05 Clustering in Data Mining
 
DBMS 3 | ER Diagram to Relational Schema
DBMS 3 | ER Diagram to Relational SchemaDBMS 3 | ER Diagram to Relational Schema
DBMS 3 | ER Diagram to Relational Schema
 
Asymptotic notations
Asymptotic notationsAsymptotic notations
Asymptotic notations
 
Presentation on Elementary data structures
Presentation on Elementary data structuresPresentation on Elementary data structures
Presentation on Elementary data structures
 
Introduction to pandas
Introduction to pandasIntroduction to pandas
Introduction to pandas
 
01 Data Mining: Concepts and Techniques, 2nd ed.
01 Data Mining: Concepts and Techniques, 2nd ed.01 Data Mining: Concepts and Techniques, 2nd ed.
01 Data Mining: Concepts and Techniques, 2nd ed.
 
2. R-basics, Vectors, Arrays, Matrices, Factors
2. R-basics, Vectors, Arrays, Matrices, Factors2. R-basics, Vectors, Arrays, Matrices, Factors
2. R-basics, Vectors, Arrays, Matrices, Factors
 
R decision tree
R   decision treeR   decision tree
R decision tree
 
Branch and bound
Branch and boundBranch and bound
Branch and bound
 
2.3 bayesian classification
2.3 bayesian classification2.3 bayesian classification
2.3 bayesian classification
 
Random forest
Random forestRandom forest
Random forest
 
List,tuple,dictionary
List,tuple,dictionaryList,tuple,dictionary
List,tuple,dictionary
 
7. Relational Database Design in DBMS
7. Relational Database Design in DBMS7. Relational Database Design in DBMS
7. Relational Database Design in DBMS
 
2.2 decision tree
2.2 decision tree2.2 decision tree
2.2 decision tree
 
Introduction to R
Introduction to RIntroduction to R
Introduction to R
 
Dbms 14: Relational Calculus
Dbms 14: Relational CalculusDbms 14: Relational Calculus
Dbms 14: Relational Calculus
 
Sparse matrix and its representation data structure
Sparse matrix and its representation data structureSparse matrix and its representation data structure
Sparse matrix and its representation data structure
 
Dijkstra
DijkstraDijkstra
Dijkstra
 
data generalization and summarization
data generalization and summarization data generalization and summarization
data generalization and summarization
 

Ähnlich wie Reading Data into R

Reading Data into R REVISED
Reading Data into R REVISEDReading Data into R REVISED
Reading Data into R REVISEDKazuki Yoshida
 
Genomic Analysis in Scala
Genomic Analysis in ScalaGenomic Analysis in Scala
Genomic Analysis in ScalaRyan Williams
 
20130215 Reading data into R
20130215 Reading data into R20130215 Reading data into R
20130215 Reading data into RKazuki Yoshida
 
Introduction to source{d} Engine and source{d} Lookout
Introduction to source{d} Engine and source{d} Lookout Introduction to source{d} Engine and source{d} Lookout
Introduction to source{d} Engine and source{d} Lookout source{d}
 
Extending lifespan with Hadoop and R
Extending lifespan with Hadoop and RExtending lifespan with Hadoop and R
Extending lifespan with Hadoop and RRadek Maciaszek
 
Diagnosing Open-Source Community Health with Spark-(William Benton, Red Hat)
Diagnosing Open-Source Community Health with Spark-(William Benton, Red Hat)Diagnosing Open-Source Community Health with Spark-(William Benton, Red Hat)
Diagnosing Open-Source Community Health with Spark-(William Benton, Red Hat)Spark Summit
 
Scalable and Flexible Machine Learning With Scala @ LinkedIn
Scalable and Flexible Machine Learning With Scala @ LinkedInScalable and Flexible Machine Learning With Scala @ LinkedIn
Scalable and Flexible Machine Learning With Scala @ LinkedInVitaly Gordon
 
IntroductionSTATA.ppt
IntroductionSTATA.pptIntroductionSTATA.ppt
IntroductionSTATA.pptssuser3840bc
 
200612_BioPackathon_ss
200612_BioPackathon_ss200612_BioPackathon_ss
200612_BioPackathon_ssSatoshi Kume
 
Hadoop Integration in Cassandra
Hadoop Integration in CassandraHadoop Integration in Cassandra
Hadoop Integration in CassandraJairam Chandar
 
Stream or not to Stream?

Stream or not to Stream?
Stream or not to Stream?

Stream or not to Stream?
Lukasz Byczynski
 
R Analytics in the Cloud
R Analytics in the CloudR Analytics in the Cloud
R Analytics in the CloudDataMine Lab
 
Processing OWL2 Ontologies using Thea: An application of Logic Programming
Processing OWL2 Ontologies using Thea: An application of Logic ProgrammingProcessing OWL2 Ontologies using Thea: An application of Logic Programming
Processing OWL2 Ontologies using Thea: An application of Logic ProgrammingVangelis Vassiliadis
 
Thea: Processing OWL Ontologies - An application of logic programming
Thea: Processing OWL Ontologies - An application of logic programmingThea: Processing OWL Ontologies - An application of logic programming
Thea: Processing OWL Ontologies - An application of logic programmingguest57f623bf
 
Elasticsearch And Apache Lucene For Apache Spark And MLlib
Elasticsearch And Apache Lucene For Apache Spark And MLlibElasticsearch And Apache Lucene For Apache Spark And MLlib
Elasticsearch And Apache Lucene For Apache Spark And MLlibJen Aman
 
Psycopg2 - Connect to PostgreSQL using Python Script
Psycopg2 - Connect to PostgreSQL using Python ScriptPsycopg2 - Connect to PostgreSQL using Python Script
Psycopg2 - Connect to PostgreSQL using Python ScriptSurvey Department
 
So various polymorphism in Scala
So various polymorphism in ScalaSo various polymorphism in Scala
So various polymorphism in Scalab0ris_1
 
Introduction To Scala
Introduction To ScalaIntroduction To Scala
Introduction To ScalaInnar Made
 
Spark 4th Meetup Londond - Building a Product with Spark
Spark 4th Meetup Londond - Building a Product with SparkSpark 4th Meetup Londond - Building a Product with Spark
Spark 4th Meetup Londond - Building a Product with Sparksamthemonad
 

Ähnlich wie Reading Data into R (20)

Reading Data into R REVISED
Reading Data into R REVISEDReading Data into R REVISED
Reading Data into R REVISED
 
Genomic Analysis in Scala
Genomic Analysis in ScalaGenomic Analysis in Scala
Genomic Analysis in Scala
 
20130215 Reading data into R
20130215 Reading data into R20130215 Reading data into R
20130215 Reading data into R
 
Introduction to source{d} Engine and source{d} Lookout
Introduction to source{d} Engine and source{d} Lookout Introduction to source{d} Engine and source{d} Lookout
Introduction to source{d} Engine and source{d} Lookout
 
Extending lifespan with Hadoop and R
Extending lifespan with Hadoop and RExtending lifespan with Hadoop and R
Extending lifespan with Hadoop and R
 
Diagnosing Open-Source Community Health with Spark-(William Benton, Red Hat)
Diagnosing Open-Source Community Health with Spark-(William Benton, Red Hat)Diagnosing Open-Source Community Health with Spark-(William Benton, Red Hat)
Diagnosing Open-Source Community Health with Spark-(William Benton, Red Hat)
 
Scalable and Flexible Machine Learning With Scala @ LinkedIn
Scalable and Flexible Machine Learning With Scala @ LinkedInScalable and Flexible Machine Learning With Scala @ LinkedIn
Scalable and Flexible Machine Learning With Scala @ LinkedIn
 
IntroductionSTATA.ppt
IntroductionSTATA.pptIntroductionSTATA.ppt
IntroductionSTATA.ppt
 
200612_BioPackathon_ss
200612_BioPackathon_ss200612_BioPackathon_ss
200612_BioPackathon_ss
 
Hadoop Integration in Cassandra
Hadoop Integration in CassandraHadoop Integration in Cassandra
Hadoop Integration in Cassandra
 
Stream or not to Stream?

Stream or not to Stream?
Stream or not to Stream?

Stream or not to Stream?

 
R Analytics in the Cloud
R Analytics in the CloudR Analytics in the Cloud
R Analytics in the Cloud
 
Processing OWL2 Ontologies using Thea: An application of Logic Programming
Processing OWL2 Ontologies using Thea: An application of Logic ProgrammingProcessing OWL2 Ontologies using Thea: An application of Logic Programming
Processing OWL2 Ontologies using Thea: An application of Logic Programming
 
Thea: Processing OWL Ontologies - An application of logic programming
Thea: Processing OWL Ontologies - An application of logic programmingThea: Processing OWL Ontologies - An application of logic programming
Thea: Processing OWL Ontologies - An application of logic programming
 
Elasticsearch And Apache Lucene For Apache Spark And MLlib
Elasticsearch And Apache Lucene For Apache Spark And MLlibElasticsearch And Apache Lucene For Apache Spark And MLlib
Elasticsearch And Apache Lucene For Apache Spark And MLlib
 
Psycopg2 - Connect to PostgreSQL using Python Script
Psycopg2 - Connect to PostgreSQL using Python ScriptPsycopg2 - Connect to PostgreSQL using Python Script
Psycopg2 - Connect to PostgreSQL using Python Script
 
So various polymorphism in Scala
So various polymorphism in ScalaSo various polymorphism in Scala
So various polymorphism in Scala
 
User biglm
User biglmUser biglm
User biglm
 
Introduction To Scala
Introduction To ScalaIntroduction To Scala
Introduction To Scala
 
Spark 4th Meetup Londond - Building a Product with Spark
Spark 4th Meetup Londond - Building a Product with SparkSpark 4th Meetup Londond - Building a Product with Spark
Spark 4th Meetup Londond - Building a Product with Spark
 

Mehr von Kazuki Yoshida

Graphical explanation of causal mediation analysis
Graphical explanation of causal mediation analysisGraphical explanation of causal mediation analysis
Graphical explanation of causal mediation analysisKazuki Yoshida
 
Pharmacoepidemiology Lecture: Designing Observational CER to Emulate an RCT
Pharmacoepidemiology Lecture: Designing Observational CER to Emulate an RCTPharmacoepidemiology Lecture: Designing Observational CER to Emulate an RCT
Pharmacoepidemiology Lecture: Designing Observational CER to Emulate an RCTKazuki Yoshida
 
What is the Expectation Maximization (EM) Algorithm?
What is the Expectation Maximization (EM) Algorithm?What is the Expectation Maximization (EM) Algorithm?
What is the Expectation Maximization (EM) Algorithm?Kazuki Yoshida
 
Propensity Score Methods for Comparative Effectiveness Research with Multiple...
Propensity Score Methods for Comparative Effectiveness Research with Multiple...Propensity Score Methods for Comparative Effectiveness Research with Multiple...
Propensity Score Methods for Comparative Effectiveness Research with Multiple...Kazuki Yoshida
 
Visual Explanation of Ridge Regression and LASSO
Visual Explanation of Ridge Regression and LASSOVisual Explanation of Ridge Regression and LASSO
Visual Explanation of Ridge Regression and LASSOKazuki Yoshida
 
ENAR 2018 Matching Weights to Simultaneously Compare Three Treatment Groups: ...
ENAR 2018 Matching Weights to Simultaneously Compare Three Treatment Groups: ...ENAR 2018 Matching Weights to Simultaneously Compare Three Treatment Groups: ...
ENAR 2018 Matching Weights to Simultaneously Compare Three Treatment Groups: ...Kazuki Yoshida
 
Search and Replacement Techniques in Emacs: avy, swiper, multiple-cursor, ag,...
Search and Replacement Techniques in Emacs: avy, swiper, multiple-cursor, ag,...Search and Replacement Techniques in Emacs: avy, swiper, multiple-cursor, ag,...
Search and Replacement Techniques in Emacs: avy, swiper, multiple-cursor, ag,...Kazuki Yoshida
 
Comparison of Privacy-Protecting Analytic and Data-sharing Methods: a Simulat...
Comparison of Privacy-Protecting Analytic and Data-sharing Methods: a Simulat...Comparison of Privacy-Protecting Analytic and Data-sharing Methods: a Simulat...
Comparison of Privacy-Protecting Analytic and Data-sharing Methods: a Simulat...Kazuki Yoshida
 
Spacemacs: emacs user's first impression
Spacemacs: emacs user's first impressionSpacemacs: emacs user's first impression
Spacemacs: emacs user's first impressionKazuki Yoshida
 
Matching Weights to Simultaneously Compare Three Treatment Groups: a Simulati...
Matching Weights to Simultaneously Compare Three Treatment Groups: a Simulati...Matching Weights to Simultaneously Compare Three Treatment Groups: a Simulati...
Matching Weights to Simultaneously Compare Three Treatment Groups: a Simulati...Kazuki Yoshida
 
Multiple Imputation: Joint and Conditional Modeling of Missing Data
Multiple Imputation: Joint and Conditional Modeling of Missing DataMultiple Imputation: Joint and Conditional Modeling of Missing Data
Multiple Imputation: Joint and Conditional Modeling of Missing DataKazuki Yoshida
 
20130222 Data structures and manipulation in R
20130222 Data structures and manipulation in R20130222 Data structures and manipulation in R
20130222 Data structures and manipulation in RKazuki Yoshida
 
Linear regression with R 2
Linear regression with R 2Linear regression with R 2
Linear regression with R 2Kazuki Yoshida
 
Linear regression with R 1
Linear regression with R 1Linear regression with R 1
Linear regression with R 1Kazuki Yoshida
 
(Very) Basic graphing with R
(Very) Basic graphing with R(Very) Basic graphing with R
(Very) Basic graphing with RKazuki Yoshida
 
Introduction to Deducer
Introduction to DeducerIntroduction to Deducer
Introduction to DeducerKazuki Yoshida
 
Groupwise comparison of continuous data
Groupwise comparison of continuous dataGroupwise comparison of continuous data
Groupwise comparison of continuous dataKazuki Yoshida
 
Categorical data with R
Categorical data with RCategorical data with R
Categorical data with RKazuki Yoshida
 
Install and Configure R and RStudio
Install and Configure R and RStudioInstall and Configure R and RStudio
Install and Configure R and RStudioKazuki Yoshida
 

Mehr von Kazuki Yoshida (20)

Graphical explanation of causal mediation analysis
Graphical explanation of causal mediation analysisGraphical explanation of causal mediation analysis
Graphical explanation of causal mediation analysis
 
Pharmacoepidemiology Lecture: Designing Observational CER to Emulate an RCT
Pharmacoepidemiology Lecture: Designing Observational CER to Emulate an RCTPharmacoepidemiology Lecture: Designing Observational CER to Emulate an RCT
Pharmacoepidemiology Lecture: Designing Observational CER to Emulate an RCT
 
What is the Expectation Maximization (EM) Algorithm?
What is the Expectation Maximization (EM) Algorithm?What is the Expectation Maximization (EM) Algorithm?
What is the Expectation Maximization (EM) Algorithm?
 
Propensity Score Methods for Comparative Effectiveness Research with Multiple...
Propensity Score Methods for Comparative Effectiveness Research with Multiple...Propensity Score Methods for Comparative Effectiveness Research with Multiple...
Propensity Score Methods for Comparative Effectiveness Research with Multiple...
 
Emacs Key Bindings
Emacs Key BindingsEmacs Key Bindings
Emacs Key Bindings
 
Visual Explanation of Ridge Regression and LASSO
Visual Explanation of Ridge Regression and LASSOVisual Explanation of Ridge Regression and LASSO
Visual Explanation of Ridge Regression and LASSO
 
ENAR 2018 Matching Weights to Simultaneously Compare Three Treatment Groups: ...
ENAR 2018 Matching Weights to Simultaneously Compare Three Treatment Groups: ...ENAR 2018 Matching Weights to Simultaneously Compare Three Treatment Groups: ...
ENAR 2018 Matching Weights to Simultaneously Compare Three Treatment Groups: ...
 
Search and Replacement Techniques in Emacs: avy, swiper, multiple-cursor, ag,...
Search and Replacement Techniques in Emacs: avy, swiper, multiple-cursor, ag,...Search and Replacement Techniques in Emacs: avy, swiper, multiple-cursor, ag,...
Search and Replacement Techniques in Emacs: avy, swiper, multiple-cursor, ag,...
 
Comparison of Privacy-Protecting Analytic and Data-sharing Methods: a Simulat...
Comparison of Privacy-Protecting Analytic and Data-sharing Methods: a Simulat...Comparison of Privacy-Protecting Analytic and Data-sharing Methods: a Simulat...
Comparison of Privacy-Protecting Analytic and Data-sharing Methods: a Simulat...
 
Spacemacs: emacs user's first impression
Spacemacs: emacs user's first impressionSpacemacs: emacs user's first impression
Spacemacs: emacs user's first impression
 
Matching Weights to Simultaneously Compare Three Treatment Groups: a Simulati...
Matching Weights to Simultaneously Compare Three Treatment Groups: a Simulati...Matching Weights to Simultaneously Compare Three Treatment Groups: a Simulati...
Matching Weights to Simultaneously Compare Three Treatment Groups: a Simulati...
 
Multiple Imputation: Joint and Conditional Modeling of Missing Data
Multiple Imputation: Joint and Conditional Modeling of Missing DataMultiple Imputation: Joint and Conditional Modeling of Missing Data
Multiple Imputation: Joint and Conditional Modeling of Missing Data
 
20130222 Data structures and manipulation in R
20130222 Data structures and manipulation in R20130222 Data structures and manipulation in R
20130222 Data structures and manipulation in R
 
Linear regression with R 2
Linear regression with R 2Linear regression with R 2
Linear regression with R 2
 
Linear regression with R 1
Linear regression with R 1Linear regression with R 1
Linear regression with R 1
 
(Very) Basic graphing with R
(Very) Basic graphing with R(Very) Basic graphing with R
(Very) Basic graphing with R
 
Introduction to Deducer
Introduction to DeducerIntroduction to Deducer
Introduction to Deducer
 
Groupwise comparison of continuous data
Groupwise comparison of continuous dataGroupwise comparison of continuous data
Groupwise comparison of continuous data
 
Categorical data with R
Categorical data with RCategorical data with R
Categorical data with R
 
Install and Configure R and RStudio
Install and Configure R and RStudioInstall and Configure R and RStudio
Install and Configure R and RStudio
 

Kürzlich hochgeladen

The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxheathfieldcps1
 
Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfciinovamais
 
Introduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptxIntroduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptxpboyjonauth
 
Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3JemimahLaneBuaron
 
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Sapana Sha
 
Contemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptx
Contemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptxContemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptx
Contemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptxRoyAbrique
 
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdfBASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdfSoniaTolstoy
 
Measures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeMeasures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeThiyagu K
 
Interactive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communicationInteractive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communicationnomboosow
 
mini mental status format.docx
mini    mental       status     format.docxmini    mental       status     format.docx
mini mental status format.docxPoojaSen20
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104misteraugie
 
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 -  Danh muc Sach Giao Khoa 10 . pdf1029 -  Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdfQucHHunhnh
 
Z Score,T Score, Percential Rank and Box Plot Graph
Z Score,T Score, Percential Rank and Box Plot GraphZ Score,T Score, Percential Rank and Box Plot Graph
Z Score,T Score, Percential Rank and Box Plot GraphThiyagu K
 
Employee wellbeing at the workplace.pptx
Employee wellbeing at the workplace.pptxEmployee wellbeing at the workplace.pptx
Employee wellbeing at the workplace.pptxNirmalaLoungPoorunde1
 
Mastering the Unannounced Regulatory Inspection
Mastering the Unannounced Regulatory InspectionMastering the Unannounced Regulatory Inspection
Mastering the Unannounced Regulatory InspectionSafetyChain Software
 
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdfssuser54595a
 

Kürzlich hochgeladen (20)

The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptx
 
Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdf
 
Introduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptxIntroduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptx
 
Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3
 
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
 
Staff of Color (SOC) Retention Efforts DDSD
Staff of Color (SOC) Retention Efforts DDSDStaff of Color (SOC) Retention Efforts DDSD
Staff of Color (SOC) Retention Efforts DDSD
 
Contemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptx
Contemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptxContemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptx
Contemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptx
 
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdfBASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
 
Measures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeMeasures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and Mode
 
Interactive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communicationInteractive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communication
 
mini mental status format.docx
mini    mental       status     format.docxmini    mental       status     format.docx
mini mental status format.docx
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104
 
TataKelola dan KamSiber Kecerdasan Buatan v022.pdf
TataKelola dan KamSiber Kecerdasan Buatan v022.pdfTataKelola dan KamSiber Kecerdasan Buatan v022.pdf
TataKelola dan KamSiber Kecerdasan Buatan v022.pdf
 
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 -  Danh muc Sach Giao Khoa 10 . pdf1029 -  Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf
 
Z Score,T Score, Percential Rank and Box Plot Graph
Z Score,T Score, Percential Rank and Box Plot GraphZ Score,T Score, Percential Rank and Box Plot Graph
Z Score,T Score, Percential Rank and Box Plot Graph
 
Employee wellbeing at the workplace.pptx
Employee wellbeing at the workplace.pptxEmployee wellbeing at the workplace.pptx
Employee wellbeing at the workplace.pptx
 
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptxINDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
 
Mattingly "AI & Prompt Design: The Basics of Prompt Design"
Mattingly "AI & Prompt Design: The Basics of Prompt Design"Mattingly "AI & Prompt Design: The Basics of Prompt Design"
Mattingly "AI & Prompt Design: The Basics of Prompt Design"
 
Mastering the Unannounced Regulatory Inspection
Mastering the Unannounced Regulatory InspectionMastering the Unannounced Regulatory Inspection
Mastering the Unannounced Regulatory Inspection
 
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf
 

Reading Data into R