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Text
presentations.
CHECK FOR UPDATE
Running this function will perform the following steps: Check what is the latest R version. If the current installed R version is up-to-date, the function ends (and returns FALSE) If a newer version of R is available, you will be asked if to review the NEWS of the latest R version – in EZANOVA | R-STATISTICS BLOG ezANOVA { ez } – This function provides easy analysis of data from factorial experiments, including purely within-Ss designs (a.k.a. “repeated measures”), purely between-Ss designs, and mixed within-and-between-Ss designs, yielding ANOVA results and assumption checks. It is a wrapper of the Anova {car} function, and is easier touse.
A STEP BY STEP (SCREENSHOTS) TUTORIAL FOR UPGRADING R ONSEE MORE ONR-STATISTICS.COM
RUNNING MEDIAN
We elaborate on the sources of noise, and propose a mix of LOWESS (Cleveland, 1977) and the repeated running median (RRM; Tukey, 1977) to cope with these challenges. If all we wanted to do was to perform moving average (running average) on the data, using R, we could simply use the rollmean function from the zoo package.IRIS DATA SET
FRIEDMAN TEST
The test statistic for the Friedman’s test is a Chi-square with degrees of freedom. A detailed explanation of the method for computing the Friedman test is available on Wikipedia. Performing Friedman’s Test in R is very simple, and is by using the “friedman.test” command. INSTALLING PANDOC FROM R (ON WINDOWS) The R blogger Rolf Fredheim has recently wrote a great piece called “Reproducible research with R, Knitr, Pandoc and Word“, where he advocates for Pandoc as an essential part of reproducible research workflow in R, in helping to turn documents which are knitted in R into high quality Word for exchanging with our colleagues. It Continue reading "Installing Pandoc from R (on Windows R 3.5.0 IS RELEASED! (MAJOR RELEASE WITH MANY NEW FEATURESSEE MORE ONR-STATISTICS.COM
ABOUT | R-STATISTICS BLOG About R If this is your first time encountering "R", The R language (and open-source software) has become a de facto standard among statisticians for the development of statistical software, and is widely used for statistical software development and data analysis (for more details about R you can read the post "What is R?") About Continue reading "About" RSTUDIO | R-STATISTICS BLOG R 3.0.2 and RStudio 0.9.8 are released! R 3.0.2 (codename “Frisbee Sailing”) was released yesterday. The full list of new features and bug fixes is provided below. Also, RStudio v0.98 (in a “secret” preview) was announced two days ago with MANY new features, including: Amazing new debugging tools (!) An engine for creating Rpresentations.
CHECK FOR UPDATE
Running this function will perform the following steps: Check what is the latest R version. If the current installed R version is up-to-date, the function ends (and returns FALSE) If a newer version of R is available, you will be asked if to review the NEWS of the latest R version – in EZANOVA | R-STATISTICS BLOG ezANOVA { ez } – This function provides easy analysis of data from factorial experiments, including purely within-Ss designs (a.k.a. “repeated measures”), purely between-Ss designs, and mixed within-and-between-Ss designs, yielding ANOVA results and assumption checks. It is a wrapper of the Anova {car} function, and is easier touse.
A STEP BY STEP (SCREENSHOTS) TUTORIAL FOR UPGRADING R ONSEE MORE ONR-STATISTICS.COM
RUNNING MEDIAN
We elaborate on the sources of noise, and propose a mix of LOWESS (Cleveland, 1977) and the repeated running median (RRM; Tukey, 1977) to cope with these challenges. If all we wanted to do was to perform moving average (running average) on the data, using R, we could simply use the rollmean function from the zoo package.IRIS DATA SET
FRIEDMAN TEST
The test statistic for the Friedman’s test is a Chi-square with degrees of freedom. A detailed explanation of the method for computing the Friedman test is available on Wikipedia. Performing Friedman’s Test in R is very simple, and is by using the “friedman.test” command. INSTALLING PANDOC FROM R (ON WINDOWS) The R blogger Rolf Fredheim has recently wrote a great piece called “Reproducible research with R, Knitr, Pandoc and Word“, where he advocates for Pandoc as an essential part of reproducible research workflow in R, in helping to turn documents which are knitted in R into high quality Word for exchanging with our colleagues. It Continue reading "Installing Pandoc from R (on Windows R 3.5.0 IS RELEASED! (MAJOR RELEASE WITH MANY NEW FEATURESSEE MORE ONR-STATISTICS.COM
RSTUDIO | R-STATISTICS BLOG R 3.0.2 and RStudio 0.9.8 are released! R 3.0.2 (codename “Frisbee Sailing”) was released yesterday. The full list of new features and bug fixes is provided below. Also, RStudio v0.98 (in a “secret” preview) was announced two days ago with MANY new features, including: Amazing new debugging tools (!) An engine for creating Rpresentations.
CHECK FOR UPDATE
Running this function will perform the following steps: Check what is the latest R version. If the current installed R version is up-to-date, the function ends (and returns FALSE) If a newer version of R is available, you will be asked if to review the NEWS of the latest R version – in MULTIDIMENSIONAL SCALING WITH R (FROM “MASTERING DATA Multidimensional Scaling (MDS) is a multivariate statistical technique first used in geography. The main goal of MDS it is to plot multivariate data points in two dimensions, thus revealing the structure of the dataset by visualizing the relative distance of theobservations.
RJAVA | R-STATISTICS BLOG Deducer 0.4-2 contains a few bug fixes, and an interface to the iplots package.With the new iplots interface it is now possible to do interactive plots with Deducer. An introductory example screen cast (by Ian) is available on the tube:FRIEDMAN TEST
The test statistic for the Friedman’s test is a Chi-square with degrees of freedom. A detailed explanation of the method for computing the Friedman test is available on Wikipedia. Performing Friedman’s Test in R is very simple, and is by using the “friedman.test” command. DATA.FRAME OBJECTS IN R (VIA “R IN ACTION”) For readers of this blog, there is a 38% discount off the “R in Action” book (as well as all other eBooks, pBooks and MEAPs at Manning publishing house), simply by using the code rblogg38 when reaching checkout.. Let us now talk about data frames: Data Frames. A data frame is more general than a matrix in that different columns can contain different modes of data (numeric, character, and TRANSPOSE | R-STATISTICS BLOG Transpose. The transpose (reversing rows and columns) is perhaps the simplest method of reshaping a dataset. Use the t () function to transpose a matrix or a data frame. In the latter case, row names become variable (column) names. An example is presented in the nextlisting.
UPDATING R FROM R (ON WINDOWS) Upgrading R on Windows is not easy. While the R FAQ offer guidelines, some users may prefer to simply run a command in order to upgrade their R to the latest version. That is what the new {installr} package is all about. The {installr} package offers a set of R functions for the installation and Continue reading "Updating R from R (on Windows) – using the {installr} package" {STARGAZER} PACKAGE FOR BEAUTIFUL LATEX TABLES FROM R stargazer is a new R package that creates LaTeX code for well-formatted regression tables, with multiple models side-by-side, as well as for summary statistics tables. It can also output the content of data frames directly into LaTeX. Compared to available alternatives, stargazer excels in three regards: its ease of use, the large number of models K-MEANS CLUSTERING (FROM "R IN ACTION") In R’s partitioning approach, observations are divided into K groups and reshuffled to form the most cohesive clusters possible according to a given criterion. There are two methods—K-means and partitioning around mediods (PAM). In this article, based on chapter 16 of R in Action, Second Edition, author Rob Kabacoff discusses K-meansclustering.
ABOUT | R-STATISTICS BLOG About R If this is your first time encountering "R", The R language (and open-source software) has become a de facto standard among statisticians for the development of statistical software, and is widely used for statistical software development and data analysis (for more details about R you can read the post "What is R?") About Continue reading "About" RSTUDIO | R-STATISTICS BLOG R 3.0.2 and RStudio 0.9.8 are released! R 3.0.2 (codename “Frisbee Sailing”) was released yesterday. The full list of new features and bug fixes is provided below. Also, RStudio v0.98 (in a “secret” preview) was announced two days ago with MANY new features, including: Amazing new debugging tools (!) An engine for creating Rpresentations.
CHECK FOR UPDATE
Running this function will perform the following steps: Check what is the latest R version. If the current installed R version is up-to-date, the function ends (and returns FALSE) If a newer version of R is available, you will be asked if to review the NEWS of the latest R version – in EZANOVA | R-STATISTICS BLOG ezANOVA { ez } – This function provides easy analysis of data from factorial experiments, including purely within-Ss designs (a.k.a. “repeated measures”), purely between-Ss designs, and mixed within-and-between-Ss designs, yielding ANOVA results and assumption checks. It is a wrapper of the Anova {car} function, and is easier touse.
A STEP BY STEP (SCREENSHOTS) TUTORIAL FOR UPGRADING R ONSEE MORE ONR-STATISTICS.COM
RUNNING MEDIAN
We elaborate on the sources of noise, and propose a mix of LOWESS (Cleveland, 1977) and the repeated running median (RRM; Tukey, 1977) to cope with these challenges. If all we wanted to do was to perform moving average (running average) on the data, using R, we could simply use the rollmean function from the zoo package.IRIS DATA SET
FRIEDMAN TEST
The test statistic for the Friedman’s test is a Chi-square with degrees of freedom. A detailed explanation of the method for computing the Friedman test is available on Wikipedia. Performing Friedman’s Test in R is very simple, and is by using the “friedman.test” command. INSTALLING PANDOC FROM R (ON WINDOWS) The R blogger Rolf Fredheim has recently wrote a great piece called “Reproducible research with R, Knitr, Pandoc and Word“, where he advocates for Pandoc as an essential part of reproducible research workflow in R, in helping to turn documents which are knitted in R into high quality Word for exchanging with our colleagues. It Continue reading "Installing Pandoc from R (on Windows R 3.5.0 IS RELEASED! (MAJOR RELEASE WITH MANY NEW FEATURESSEE MORE ON R-STATISTICS.COMDOWNLOAD R 3 5 0 FOR WINDOWSR 3 5 3 MAC ABOUT | R-STATISTICS BLOG About R If this is your first time encountering "R", The R language (and open-source software) has become a de facto standard among statisticians for the development of statistical software, and is widely used for statistical software development and data analysis (for more details about R you can read the post "What is R?") About Continue reading "About" RSTUDIO | R-STATISTICS BLOG R 3.0.2 and RStudio 0.9.8 are released! R 3.0.2 (codename “Frisbee Sailing”) was released yesterday. The full list of new features and bug fixes is provided below. Also, RStudio v0.98 (in a “secret” preview) was announced two days ago with MANY new features, including: Amazing new debugging tools (!) An engine for creating Rpresentations.
CHECK FOR UPDATE
Running this function will perform the following steps: Check what is the latest R version. If the current installed R version is up-to-date, the function ends (and returns FALSE) If a newer version of R is available, you will be asked if to review the NEWS of the latest R version – in EZANOVA | R-STATISTICS BLOG ezANOVA { ez } – This function provides easy analysis of data from factorial experiments, including purely within-Ss designs (a.k.a. “repeated measures”), purely between-Ss designs, and mixed within-and-between-Ss designs, yielding ANOVA results and assumption checks. It is a wrapper of the Anova {car} function, and is easier touse.
A STEP BY STEP (SCREENSHOTS) TUTORIAL FOR UPGRADING R ONSEE MORE ONR-STATISTICS.COM
RUNNING MEDIAN
We elaborate on the sources of noise, and propose a mix of LOWESS (Cleveland, 1977) and the repeated running median (RRM; Tukey, 1977) to cope with these challenges. If all we wanted to do was to perform moving average (running average) on the data, using R, we could simply use the rollmean function from the zoo package.IRIS DATA SET
FRIEDMAN TEST
The test statistic for the Friedman’s test is a Chi-square with degrees of freedom. A detailed explanation of the method for computing the Friedman test is available on Wikipedia. Performing Friedman’s Test in R is very simple, and is by using the “friedman.test” command. INSTALLING PANDOC FROM R (ON WINDOWS) The R blogger Rolf Fredheim has recently wrote a great piece called “Reproducible research with R, Knitr, Pandoc and Word“, where he advocates for Pandoc as an essential part of reproducible research workflow in R, in helping to turn documents which are knitted in R into high quality Word for exchanging with our colleagues. It Continue reading "Installing Pandoc from R (on Windows R 3.5.0 IS RELEASED! (MAJOR RELEASE WITH MANY NEW FEATURESSEE MORE ON R-STATISTICS.COMDOWNLOAD R 3 5 0 FOR WINDOWSR 3 5 3 MAC RSTUDIO | R-STATISTICS BLOG R 3.0.2 and RStudio 0.9.8 are released! R 3.0.2 (codename “Frisbee Sailing”) was released yesterday. The full list of new features and bug fixes is provided below. Also, RStudio v0.98 (in a “secret” preview) was announced two days ago with MANY new features, including: Amazing new debugging tools (!) An engine for creating Rpresentations.
CHECK FOR UPDATE
Running this function will perform the following steps: Check what is the latest R version. If the current installed R version is up-to-date, the function ends (and returns FALSE) If a newer version of R is available, you will be asked if to review the NEWS of the latest R version – in MULTIDIMENSIONAL SCALING WITH R (FROM “MASTERING DATA Multidimensional Scaling (MDS) is a multivariate statistical technique first used in geography. The main goal of MDS it is to plot multivariate data points in two dimensions, thus revealing the structure of the dataset by visualizing the relative distance of theobservations.
RJAVA | R-STATISTICS BLOG Deducer 0.4-2 contains a few bug fixes, and an interface to the iplots package.With the new iplots interface it is now possible to do interactive plots with Deducer. An introductory example screen cast (by Ian) is available on the tube:FRIEDMAN TEST
The test statistic for the Friedman’s test is a Chi-square with degrees of freedom. A detailed explanation of the method for computing the Friedman test is available on Wikipedia. Performing Friedman’s Test in R is very simple, and is by using the “friedman.test” command. DATA.FRAME OBJECTS IN R (VIA “R IN ACTION”) For readers of this blog, there is a 38% discount off the “R in Action” book (as well as all other eBooks, pBooks and MEAPs at Manning publishing house), simply by using the code rblogg38 when reaching checkout.. Let us now talk about data frames: Data Frames. A data frame is more general than a matrix in that different columns can contain different modes of data (numeric, character, and TRANSPOSE | R-STATISTICS BLOG Transpose. The transpose (reversing rows and columns) is perhaps the simplest method of reshaping a dataset. Use the t () function to transpose a matrix or a data frame. In the latter case, row names become variable (column) names. An example is presented in the nextlisting.
UPDATING R FROM R (ON WINDOWS) Upgrading R on Windows is not easy. While the R FAQ offer guidelines, some users may prefer to simply run a command in order to upgrade their R to the latest version. That is what the new {installr} package is all about. The {installr} package offers a set of R functions for the installation and Continue reading "Updating R from R (on Windows) – using the {installr} package" {STARGAZER} PACKAGE FOR BEAUTIFUL LATEX TABLES FROM R stargazer is a new R package that creates LaTeX code for well-formatted regression tables, with multiple models side-by-side, as well as for summary statistics tables. It can also output the content of data frames directly into LaTeX. Compared to available alternatives, stargazer excels in three regards: its ease of use, the large number of models K-MEANS CLUSTERING (FROM "R IN ACTION") In R’s partitioning approach, observations are divided into K groups and reshuffled to form the most cohesive clusters possible according to a given criterion. There are two methods—K-means and partitioning around mediods (PAM). In this article, based on chapter 16 of R in Action, Second Edition, author Rob Kabacoff discusses K-meansclustering.
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HEATMAPLY 1.0.0 – BEAUTIFUL INTERACTIVE CLUSTER HEATMAPS IN R I’M EXCITED TO ANNOUNCE THAT HEATMAPLY VERSION 1.0.0 HAS BEENPUBLISHED TO CRAN!
(getting started
vignette is available here)
WHAT IS HEATMAPLY?
_HEATMAPLY _ is an R package for easily creating interactive cluster heatmaps that can be shared online as a stand-alone HTML file. INTERACTIVITY includes a tooltip display of values when hovering over cells, as well as the ability to zoom in to specific sections of the figure from the data matrix, the side dendrograms, or annotated labels. The package aims to be compatible with gplots::heatmap.2 so you could take code written for it and just change the heatmap.2 command to be heatmaply, and get the interactive version of the plot (although with slightly different, improved, defaults for colors and dendrogram ordering). Thanks to the synergistic relationship between _heatmaply_ and other R packages, the user is empowered by a refined control over the statistical and visual aspects of the heatmap layout. WHAT MAKES HEATMAPLY GREAT? The change from version 0.16.0 to version 1.0.0 is to indicate the maturity of the package. It is to reflect the following facts: Continue reading “heatmaply 1.0.0 – beautiful interactive clusterheatmaps in R”
Author Tal Galili
Posted on January 8,2020January 8, 2020
Categories
R , visualization
Tags cluster
heatmaps , heatmap
, heatmaply
, R
, visualization
Leave a comment on
heatmaply 1.0.0 – beautiful interactive cluster heatmaps in R REGISTRATION FOR ERUM 2018 CLOSES IN TWO DAYS! WHY I’M GOING TO ERUM THIS YEAR INSTEAD OF USER! I have attended the useR! conferences every year now for the past 9 years, and loved it! However, this year I’m saddened that I won’t be able to go. This is because this year the conference will be held in Australia , and going there would require me to be away from home for at least 8 days (my heart goes to the people of Australia who had a hard time coming to useR all these years). Ordinarily I would do it, but given that my wife and I have a sweet 8 months year old baby (called Maya), I’m very reluctant to be away from home for that long. THE ERUM 2018 CONFERENCE Fortunately for me, and for many other R users out there, we have a backup plan called eRum (a.k.a: The European R Users Meeting). It is an international conference, similar to useR! , that occurs every two years (specifically, in the years in which useR is taking place outside of Europe), and organized by Gergely Darocziand others.
About the plan for this year: * TIME AND LOCATION: the conference will take place on May 14-16, 2018 @ Budapest, Hungary * CROWD SIZE: The expectation is for ~500 R users from mostly Europe (you can see a visual breakdown of people’s country of origin here)
* CONTENT: The program has 5 keynote speakers , 12 invited speakers , 7 tracks of workshops and 2 tracks for contributed talks (picked after sifting over 150 abstracts). Knowing some of the people in the program, I can vouch for the high quality of the program. * THE REGISTRATION CLOSES THIS SUNDAY, SO HURRY UP AND REGISTER ! (the price is relatively cheap, starting from 80 Euro for students, and up to 275 Euro for industry). If you get to see me around, feel free to come and say HiAuthor Tal Galili
Posted on April 27,
2018April 27, 2018
Categories
R 1 Comment on Registration for eRum 2018 closes in two days! R 3.5.0 IS RELEASED! (MAJOR RELEASE WITH MANY NEW FEATURES) R 3.5.0 (codename “Joy in Playing”) was released yesterday. You can
get the latest binaries version FROM HERE . (or the .tar.gz SOURCE code from here).
This is a major release with many new features and bug fixes, the full list is provided below. UPGRADING R ON WINDOWS AND MAC If you are using WINDOWS you can easily upgrade to the latest version of R using the installr package. Simply run the
following code in Rgui: install.packages("installr") # install setInternet2(TRUE) # only for R versions older than 3.3.0 installr::updateR() # updating R. # If you wish it to go faster, run: installr::updateR(T) install.packages("installr") # install setInternet2(TRUE) # only for R versions older than 3.3.0 installr::updateR() # updating R. # If you wish it to go faster, run: installr::updateR(T) install.packages("installr") # install setInternet2(TRUE) # only for R versions older than 3.3.0 installr::updateR() # updating R. # If you wish it to go faster, run: installr::updateR(T) Running “updateR()” will detect if there is a new R version available, and if so it will download+install it (etc.). There is also a step by step tutorial (with screenshots) on how to upgrade R on Windows, using the _installr_package.
If you only see the option to upgrade to an older version of R, then change your mirror or try again in a few hours (it usually take around 24 hours for all CRAN mirrors to get the latest version of R). If you are using MAC you can easily upgrade to the latest version of R using Andrea Cirillo’s updateR package . The package is not on CRAN, so you’ll need to run the following code in Rgui: install.packages("devtools") devtools::install_github("AndreaCirilloAC/updateR") updateR(admin_password = "PASSWORD") # Where "PASSWORD" stands for your system password install.packages("devtools") devtools::install_github("AndreaCirilloAC/updateR") updateR(admin_password = "PASSWORD") # Where "PASSWORD" stands for your system password install.packages("devtools") devtools::install_github("AndreaCirilloAC/updateR") updateR(admin_password = "PASSWORD") # Where "PASSWORD" stands for your system password Later this year Andrea and I intend to merge the updateR package into installr so that the updateR function will work seamlessly in both Windows and Mac. Stay tuned Continue reading “R 3.5.0 is released! (major release with many newfeatures)”
Author Tal Galili
Posted on April 24,
2018
Categories
R Tags installr
12 Comments on R 3.5.0 is released! (major release with many new features) R 3.4.3 IS RELEASED (A BUG-FIX RELEASE) R 3.4.3 (codename “Kite-Eating Tree”) was released last week. You can
get the latest binaries version FROM HERE . (or the .tar.gz SOURCE code from here).
As mentioned by David Smith, R
3.4.3 is primarily a bug-fix release: > It fixes an issue with incorrect time zones on MacOS High Sierra, > and some issues with handling Unicode characters. (Incidentally, > representing international and special characters is something that > R takes great care in handling properly. It’s not an easy task: a > 2003 essay by Joel Spolsky describes the minefield that is > character representation> ,
> and not much has changed since then.) The full list of bug fixes and new features is provided below. UPGRADING TO R 3.4.3 ON WINDOWS If you are using WINDOWS you can easily upgrade to the latest version of R using the installr package. Simply run the
following code in Rgui: install.packages("installr") # install setInternet2(TRUE) # only for R versions older than 3.3.0 installr::updateR() # updating R. # If you wish it to go faster, run: installr::updateR(T) install.packages("installr") # install setInternet2(TRUE) # only for R versions older than 3.3.0 installr::updateR() # updating R. # If you wish it to go faster, run: installr::updateR(T) install.packages("installr") # install setInternet2(TRUE) # only for R versions older than 3.3.0 installr::updateR() # updating R. # If you wish it to go faster, run: installr::updateR(T) Running “updateR()” will detect if there is a new R version available, and if so it will download+install it (etc.). There is also a step by step tutorial (with screenshots) on how to upgrade R on Windows, using the _installr_package.
If you only see the option to upgrade to an older version of R, then change your mirror or try again in a few hours (it usually take around 24 hours for all CRAN mirrors to get the latest version of R). _I try to keep the installr package updated and useful, so if you have any suggestions or remarks on the package – you are invited to open an issue in the github page._
Continue reading “R 3.4.3 is released (a bug-fix release)”Author Tal Galili
Posted on December 8,2017
Categories
R Leave a comment on R 3.4.3 is released (a bug-fix release) HEATMAPLY: AN R PACKAGE FOR CREATING INTERACTIVE CLUSTER HEATMAPS FORONLINE PUBLISHING
_This post on the heatmaply packageis based on
my recent paper fromthe journal
bioinformatics (a link to a stable DOI ). The paper was published just last week, and since it is releasedas CC-BY , I am
permitted (and delighted) to republish it here in full. My co-authors for this paper are Jonathan Sidi , Alan O’Callaghan , and Carson Sievert._
SUMMARY: _heatmaply_is an R package
for easily creating interactive cluster heatmaps that can be shared online as a stand-alone HTML file. Interactivity includes a tooltip display of values when hovering over cells, as well as the ability to zoom in to specific sections of the figure from the data matrix, the side dendrograms, or annotated labels. Thanks to the synergistic relationship between _heatmaply_ and other R packages, the user is empowered by a refined control over the statistical and visual aspects of the heatmap layout. AVAILABILITY: The _heatmaply_ package is available under the GPL-2 Open Source license. It comes with a detailed vignette, and is freely available from: http://cran.r-project.org/package=_heatmaply_ Continue reading “heatmaply: an R package for creating interactive cluster heatmaps for online publishing”Author Tal Galili
Posted on October 30, 2017October 30, 2017Categories
R , R programming
, visualization
Tags Alan
O'Callaghan ,
Carson Sievert ,
dendextend , ggplot2, heatmaply
, Jonathan Sidi
, R
, R package
, visualization
, yoni sidi
3 Comments on heatmaply: an R package for creating interactive cluster heatmaps for onlinepublishing
R 3.4.2 IS RELEASED (WITH SEVERAL BUG FIXES AND A FEW PERFORMANCEIMPROVEMENTS)
R 3.4.2 (codename “Short Summer”) was released yesterday. You can
get the latest binaries version FROM HERE . (or the .tar.gz SOURCE code from here).
As mentioned by David Smith, R
3.4.2 includes a performance improvement for names: > c()c() and unlist()unlist() are now more efficient in > constructing the names(.)names(.) of their return value, thanks > to a proposal by Suharto Anggono. (PR#17284> )
The full list of bug fixes and new features is provided below. THANK YOU DUNCAN MURDOCH ! On a related note, following the announcement on R 3.4.2, Duncan Murdoch wrote yesterday:
> I’ve just finished the Windows build of R 3.4.2. It will make it > to CRAN and its mirrors over the next few hours.>
> This is the last binary release that I will be producing. I’ve > been building them for about 15 years, and it’s time to retire. > Builds using different tools and scripts are available > from https://mran.microsoft.com/download/. I’ll be putting my > own scripts on CRAN soon in case anyone wants to duplicate them.>
> Nightly builds of R-patched and R-devel will continue to run on > autopilot for the time being, without maintenance.>
> I will also be retiring from maintenance of the Rtools collection. I am grateful to Duncan for contributing so much of his time and expertise throughout the years. And I am confident that other R users, using the binaries for the Windows OS, share this sentiment. UPGRADING TO R 3.4.2 ON WINDOWS If you are using WINDOWS you can easily upgrade to the latest version of R using the installr package. Simply run the
following code in Rgui: install.packages("installr") # install setInternet2(TRUE) # only for R versions older than 3.3.0 installr::updateR() # updating R. # If you wish it to go faster, run: installr::updateR(T) install.packages("installr") # install setInternet2(TRUE) # only for R versions older than 3.3.0 installr::updateR() # updating R. # If you wish it to go faster, run: installr::updateR(T) install.packages("installr") # install setInternet2(TRUE) # only for R versions older than 3.3.0 installr::updateR() # updating R. # If you wish it to go faster, run: installr::updateR(T) Running “updateR()” will detect if there is a new R version available, and if so it will download+install it (etc.). There is also a step by step tutorial (with screenshots) on how to upgrade R on Windows, using the _installr_package.
If you only see the option to upgrade to an older version of R, then change your mirror or try again in a few hours (it usually take around 24 hours for all CRAN mirrors to get the latest version of R). _I try to keep the installr package updated and useful, so if you have any suggestions or remarks on the package – you are invited to open an issue in the github page._
Continue reading “R 3.4.2 is released (with several bug fixes and a few performance improvements)”Author Tal Galili
Posted on September
29, 2017September 29, 2017Categories
R Leave a comment on R 3.4.2 is released (with several bug fixes and a few performanceimprovements)
R 3.4.1 IS RELEASED – WITH SOME WINDOWS RELATED BUG-FIXES R 3.4.1 (codename “Single Candle”) was released several days ago. You can
get the latest binaries version FROM HERE . (or the .tar.gz SOURCE code from here).
As mentioned last week by David Smith, R 3.4.1 includes several Windows related bug fixed: > including an issue sometimes encountered when attempting to install > packages on Windows, and problems displaying functions including > Unicode characters (like “日本語”) in the Windows GUI. The full list of bug fixes and new features is provided below. UPGRADING TO R 3.4.1 ON WINDOWS If you are using WINDOWS you can easily upgrade to the latest version of R using the installr package. Simply run the
following code in Rgui: install.packages("installr") # install setInternet2(TRUE) # only for R versions older than 3.3.0 installr::updateR() # updating R. # If you wish it to go faster, run: installr::updateR(T) install.packages("installr") # install setInternet2(TRUE) # only for R versions older than 3.3.0 installr::updateR() # updating R. # If you wish it to go faster, run: installr::updateR(T) install.packages("installr") # install setInternet2(TRUE) # only for R versions older than 3.3.0 installr::updateR() # updating R. # If you wish it to go faster, run: installr::updateR(T) Running “updateR()” will detect if there is a new R version available, and if so it will download+install it (etc.). There is also a step by step tutorial (with screenshots) on how to upgrade R on Windows, using the _installr_package.
If you only see the option to upgrade to an older version of R, then change your mirror or try again in a few hours (it usually take around 24 hours for all CRAN mirrors to get the latest version of R). _I try to keep the installr package updated and useful, so if you have any suggestions or remarks on the package – you are invited to open an issue in the github page._
Continue reading “R 3.4.1 is released – with some Windows relatedbug-fixes”
Author Tal Galili
Posted on July 11,
2017July 11, 2017
Categories
R 2 Comments on R 3.4.1 is released – with some Windows related bug-fixes R 3.4.0 IS RELEASED – WITH NEW SPEED UPGRADES AND BUG-FIXES R 3.4.0 (codename “You Stupid Darkness”) was released 3 days ago. You can
get the latest binaries version FROM HERE . (or the .tar.gz SOURCE code from here). The full
list of bug fixes and new features is provided below. As mentioned two months agoby
David Smith, R 3.4.0 indicates several major changes aimed at IMPROVING THE PERFORMANCE OF R in various ways. These includes: * The JIT (‘Just In Time’) byte-code compiler is now enabled by default at its level 3. This means functions will be compiled on first or second use and top-level loops will be compiled and then run. (Thanks to Tomas Kalibera for extensive work to make this possible.) For now, the compiler will not compile code containing explicit calls to browser(): this is to support single stepping from the browser() call. JIT compilation can be disabled for the rest of the session using compiler::enableJIT(0) or by setting environment variable R_ENABLE_JIT to 0. * Matrix products now consistently bypass BLAS when the inputs have NaN/Inf values. Performance of the check of inputs has been improved. Performance when BLAS is used is improved for matrix/vector and vector/matrix multiplication (DGEMV is now used instead of DGEMM). One can now choose from alternative matrix product implementations via options(matprod = ). The “internal” implementation is not optimized for speed but consistent in precision with other summations in R (using long double accumulators where available). “blas” calls BLAS directly for best speed, but usually with undefined behavior for inputs with NaN/Inf. * Speedup in simplify2array() and hence sapply() and mapply() (for the case of names and common length #> 1), thanks to Suharto Anggono’s PR#17118. * Accumulating vectors in a loop is faster – Assigning to an element of a vector beyond the current length now over-allocates by a small fraction. The new vector is marked internally as growable, and the true length of the new vector is stored in the truelength field. This makes building up a vector result by assigning to the next element beyond the current length more efficient, though pre-allocating is still preferred. The implementation is subject to change and not intended to be used in packages at this time. * C-LEVEL FACILITIES have been extended. * Radix sort (which can be considered more efficient for some cases) is now chosen
by method = “auto” for sort.int() for double vectors (and hence used for sort() for unclassed double vectors), excluding ‘long’ vectors. sort.int(method = “radix”) no longer rounds double vectors. The default method until R 3.2.0 was “shell”. A minimal comparison between the two shows that for very short vectors (100 values), “shell” would perform better. From a 1000 values, they are comparable, and for larger vectors – “radix” is doing 2-3 times faster (which is probably the use case for which we would care about more). More about this can be read in ?sort.int#>
#> set.seed(2017-04-24) #> x microbenchmark(shell = sort.int(x, method = "shell"), radix = sort.int(x, method = "radix"))Unit: microseconds
expr min lq mean median uq max neval cld shell 15.775 16.606 17.80971 17.989 18.543 33.211 100 a radix 32.657 34.595 35.67700 35.148 35.702 88.561 100 b#>
#> set.seed(2017-04-24) #> x microbenchmark(shell = sort.int(x, method = "shell"), radix = sort.int(x, method = "radix"))Unit: microseconds
expr min lq mean median uq max neval cld shell 53.414 55.074 56.54395 56.182 57.0120 96.034 100 b radix 45.665 46.772 48.04222 47.325 48.1555 78.598 100 a#>
#> set.seed(2017-04-24) #> x microbenchmark(shell = sort.int(x, method = "shell"), radix = sort.int(x, method = "radix"))Unit: milliseconds
expr min lq mean median uq max neval cld shell 93.33140 95.94478 107.75347 103.02756 115.33709 221.0800 100 b radix 38.18241 39.01516 46.47038 41.45722 47.49596 159.3518 100 a#>
#>
#> #> set.seed(2017-04-24) #> x microbenchmark(shell = sort.int(x, method = "shell"), radix = sort.int(x, method = "radix")) Unit: microseconds expr min lq mean median uq max neval cld shell 15.775 16.606 17.80971 17.989 18.543 33.211 100 a radix 32.657 34.595 35.67700 35.148 35.702 88.561 100 b #> #> set.seed(2017-04-24) #> x microbenchmark(shell = sort.int(x, method = "shell"), radix = sort.int(x, method = "radix")) Unit: microseconds expr min lq mean median uq max neval cld shell 53.414 55.074 56.54395 56.182 57.0120 96.034 100 b radix 45.665 46.772 48.04222 47.325 48.1555 78.598 100 a #> #> set.seed(2017-04-24) #> x microbenchmark(shell = sort.int(x, method = "shell"), radix = sort.int(x, method = "radix")) Unit: milliseconds expr min lq mean median uq max neval cld shell 93.33140 95.94478 107.75347 103.02756 115.33709 221.0800 100 b radix 38.18241 39.01516 46.47038 41.45722 47.49596 159.3518 100 a #> #>#>
#> set.seed(2017-04-24) #> x microbenchmark(shell = sort.int(x, method = "shell"), radix = sort.int(x, method = "radix"))Unit: microseconds
expr min lq mean median uq max neval cld shell 15.775 16.606 17.80971 17.989 18.543 33.211 100 a radix 32.657 34.595 35.67700 35.148 35.702 88.561 100 b#>
#> set.seed(2017-04-24) #> x microbenchmark(shell = sort.int(x, method = "shell"), radix = sort.int(x, method = "radix"))Unit: microseconds
expr min lq mean median uq max neval cld shell 53.414 55.074 56.54395 56.182 57.0120 96.034 100 b radix 45.665 46.772 48.04222 47.325 48.1555 78.598 100 a#>
#> set.seed(2017-04-24) #> x microbenchmark(shell = sort.int(x, method = "shell"), radix = sort.int(x, method = "radix"))Unit: milliseconds
expr min lq mean median uq max neval cld shell 93.33140 95.94478 107.75347 103.02756 115.33709 221.0800 100 b radix 38.18241 39.01516 46.47038 41.45722 47.49596 159.3518 100 a#>
#>
More about the changes in R case be read at the nice post by DavidSmith
, or
in the list of changes given below. UPGRADING TO R 3.4.0 ON WINDOWS If you are using WINDOWS you can easily upgrade to the latest version of R using the installr package. Simply run the
following code in Rgui: install.packages("installr") # install setInternet2(TRUE) # only for R versions older than 3.3.0 installr::updateR() # updating R. # If you wish it to go faster, run: installr::updateR(T) install.packages("installr") # install setInternet2(TRUE) # only for R versions older than 3.3.0 installr::updateR() # updating R. # If you wish it to go faster, run: installr::updateR(T) install.packages("installr") # install setInternet2(TRUE) # only for R versions older than 3.3.0 installr::updateR() # updating R. # If you wish it to go faster, run: installr::updateR(T) Running “updateR()” will detect if there is a new R version available, and if so it will download+install it (etc.). There is also a step by step tutorial (with screenshots) on how to upgrade R on Windows, using the _installr_package.
If you only see the option to upgrade to an older version of R, then change your mirror or try again in a few hours (it usually take around 24 hours for all CRAN mirrors to get the latest version of R). _I try to keep the installr package updated and useful, so if you have any suggestions or remarks on the package – you are invited to open an issue in the github page._
Continue reading “R 3.4.0 is released – with new speed upgradesand bug-fixes”
Author Tal Galili
Posted on April 24,
2017April 24, 2017
Categories
R 4 Comments on R 3.4.0 is released – with new speed upgrades and bug-fixes SHINYHEATMAPLY – A SHINY APP FOR CREATING INTERACTIVE CLUSTERHEATMAPS
My friend Jonathan Sidi and I (Tal Galili ) are pleased to announce the releaseof shinyHeatmaply
(0.1.0): a new
Shiny application (and Shiny gadget) for creating interactive cluster heatmaps. shinyHeatmaply is based on the heatmaplyR
package which strives to make it easy as possible to create interactive cluster heatmaps . The app introduces a functionality that saves to disk a self contained copy of the htmlwidget as an html file with your data and specifications you set from the UI, so it can be embedded in webpages, blogposts and online web appendices for academic publications. You can see some of shinyHeatmaply‘s capabilities
in the following 40 seconds video: INSTALLING SHINYHEATMAPLYFrom CRAN :
install.packages('shinyHeatmaply') install.packages('shinyHeatmaply') install.packages('shinyHeatmaply')From github :
devtools::install_github('yonicd/shinyHeatmaply') devtools::install_github('yonicd/shinyHeatmaply') devtools::install_github('yonicd/shinyHeatmaply') RUNNING THE APP/GADGET The application has an import interface as part of the application which currently supports csv, txt, tab, xls, xlsx, rd, rda. You can start the app using:library(shiny)
library(heatmaply)
# If you didn't get shinyHeatmaply yet, you can run it through github: # runGitHub("yonicd/shinyHeatmaply",subdir = 'inst/shinyapp') # or just use your locally installed package: library(shinyHeatmaply) runApp(system.file("shinyapp", package = "shinyHeatmaply")) library(shiny) library(heatmaply) # If you didn't get shinyHeatmaply yet, you can run it through github: # runGitHub("yonicd/shinyHeatmaply",subdir = 'inst/shinyapp') # or just use your locally installed package: library(shinyHeatmaply) runApp(system.file("shinyapp", package = "shinyHeatmaply"))library(shiny)
library(heatmaply)
# If you didn't get shinyHeatmaply yet, you can run it through github: # runGitHub("yonicd/shinyHeatmaply",subdir = 'inst/shinyapp') # or just use your locally installed package: library(shinyHeatmaply) runApp(system.file("shinyapp", package = "shinyHeatmaply")) The gadget is called from the R console and accepts input arguments. The object defined as the input to the shinyHeatmaply gadget is a data.frame or a list of data.frames. You can start it using thefollowing code:
library(shinyHeatmaply)#single data.frame
data(mtcars)
launch_heatmaply(mtcars)#list
data(iris)
launch_heatmaply(list('Example1'=mtcars,'Example2'=iris)) library(shinyHeatmaply) #single data.frame data(mtcars) launch_heatmaply(mtcars) #list data(iris) launch_heatmaply(list('Example1'=mtcars,'Example2'=iris)) library(shinyHeatmaply)#single data.frame
data(mtcars)
launch_heatmaply(mtcars)#list
data(iris)
launch_heatmaply(list('Example1'=mtcars,'Example2'=iris)) You can see an example of a saved shinyHeatmaply output HERE . Or view the followingiframe:
Continue reading “shinyHeatmaply – a shiny app for creating interactive cluster heatmaps”Author Tal Galili
Posted on March 28,
2017April 24, 2017
Categories
R Tags app
, gadget
, heatmaply
, R
, Shiny
, shiny app
, shiny gadget
, shinyHeatmaply
8 Comments on
shinyHeatmaply – a shiny app for creating interactive clusterheatmaps
R 3.3.3 IS RELEASED! R 3.3.3 (codename “Another Canoe”) was released yesterdayYou
can get the latest binaries version FROM HERE . (or the .tar.gz SOURCE code from here). The full
list of bug fixes and new features is provided below. A quick summary by David Smith:
> R 3.3.3 fixes an issue related to attempting to use download.file>
> on sites that automatically redirect from http to https: now, > R will re-attempt to download the secure link rather than failing. > Other fixes include support for long vectors in the vapply>
> function, the ability to use pmax>
> (and pmin) on ordered factors, improved accuracy for qbeta for some > extreme cases, corrected spelling for “Cincinnati” in the precip>
> data set, and a few other minor issues. UPGRADING TO R 3.3.3 ON WINDOWS If you are using WINDOWS you can easily upgrade to the latest version of R using the installr package. Simply run the
following code in Rgui: install.packages("installr") # install setInternet2(TRUE) # only for R versions older than 3.3.0 installr::updateR() # updating R. install.packages("installr") # install setInternet2(TRUE) # only for R versions older than 3.3.0 installr::updateR() # updating R. install.packages("installr") # install setInternet2(TRUE) # only for R versions older than 3.3.0 installr::updateR() # updating R. Running “updateR()” will detect if there is a new R version available, and if so it will download+install it (etc.). There is also a step by step tutorial (with screenshots) on how to upgrade R on Windows, using the _installr_package.
If you only see the option to upgrade to an older version of R, then change your mirror or try again in a few hours (it usually take around 24 hours for all CRAN mirrors to get the latest version of R). _I try to keep the installr package updated and useful, so if you have any suggestions or remarks on the package – you are invited to open an issue in the github page._
Continue reading “R 3.3.3 is released!”Author Tal Galili
Posted on March 7,
2017
Categories
R Leave a comment on R 3.3.3is released!
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