heatmaply 1.0.0 – beautiful interactive cluster heatmaps in R

I’m excited to announce that version 1.0.0 has been published to 牵手常德棋牌官方下载 (getting started vignette is )

What is 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 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 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 , 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

牵手常德棋牌官方下载Fortunately for me, and for many other R users out there, we have a backup plan called  (a.k.a: The European R Users Meeting). It is an international conference, similar to , that occurs every two years (specifically, in the years in which useR is taking place outside of Europe), and organized by and others.

牵手常德棋牌官方下载About the plan for this year:

  • Time and location: the conference will take place on May 14-16, 2018 @
  • Crowd size: The expectation is for ~500 R users from mostly Europe (you can see a visual breakdown of people’s )
  • Content: The program has , , and (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 ! (the price is relatively cheap, starting from 80 Euro for students, and up to 275 Euro for industry).

牵手常德棋牌官方下载If you get to see me around, feel free to come and say Hi 🙂

 

 

R 3.5.0 is released! (major release with many new features)

R 3.5.0 (codename “Joy in Playing”) was . You can get the latest binaries version . (or the .tar.gz source code from ).

牵手常德棋牌官方下载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 牵手常德棋牌官方下载you can easily upgrade to the latest version of R using . 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)

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 牵手常德棋牌官方下载you can easily upgrade to the latest version of R using Andrea Cirillo’s . 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

牵手常德棋牌官方下载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 new features)”

R 3.4.3 is released (a bug-fix release)

R 3.4.3 (codename “Kite-Eating Tree”) was . You can get the latest binaries version . (or the .tar.gz source code from ).

牵手常德棋牌官方下载As mentioned by , R 3.4.3 is primarily a bug-fix release:

牵手常德棋牌官方下载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 , and not much has changed since then.)

牵手常德棋牌官方下载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 . 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)

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牵手常德棋牌官方下载 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 package updated and useful, so if you have any suggestions or remarks on the package – you are invited to .

Continue reading “R 3.4.3 is released (a bug-fix release)”

heatmaply: an R package for creating interactive cluster heatmaps for online publishing

This post on the  is based on my  the journal  (a link to ). The paper was published just last week, and since it is released as , I am permitted (and delighted) to republish it here in full. My co-authors for this paper are , , and .

Summary: 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牵手常德棋牌官方下载 package is available under the GPL-2 Open Source license. It comes with a detailed vignette, and is freely available from:

Continue reading “heatmaply: an R package for creating interactive cluster heatmaps for online publishing”

R 3.4.2 is released (with several bug fixes and a few performance improvements)

R 3.4.2 (codename “Short Summer”) was . You can get the latest binaries version . (or the .tar.gz source code from ).

牵手常德棋牌官方下载As mentioned by , R 3.4.2 includes a performance improvement for names:

c() and unlist() are now more efficient in constructing the names(.) of their return value, thanks to a proposal by Suharto Anggono. ()

The full list of bug fixes and new features is provided below.

Thank you Duncan Murdoch !

牵手常德棋牌官方下载On a related note, following the announcement on R 3.4.2, Duncan Murdoch :

牵手常德棋牌官方下载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.

牵手常德棋牌官方下载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 .  I’ll be putting my own scripts on CRAN soon in case anyone wants to duplicate them.

牵手常德棋牌官方下载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 . 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)

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牵手常德棋牌官方下载 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 package updated and useful, so if you have any suggestions or remarks on the package – you are invited to .

Continue reading “R 3.4.2 is released (with several bug fixes and a few performance improvements)”

R 3.4.1 is released – with some Windows related bug-fixes

R 3.4.1 (codename “Single Candle”) was . You can get the latest binaries version . (or the .tar.gz source牵手常德棋牌官方下载 code from ).

As mentioned by David Smith, R 3.4.1 includes several Windows related bug fixed:

牵手常德棋牌官方下载including an issue sometimes encountered when attempting to install packages on Windows, and problems displaying functions including Unicode characters (like “RIBENYU”) 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 . 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)

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 package updated and useful, so if you have any suggestions or remarks on the package – you are invited to .

Continue reading “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 . You can get the latest binaries version . (or the .tar.gz source code from ). The full list of bug fixes and new features is provided below.

As mentioned by 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 ) 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(2016-04-24)
#> 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(2016-04-24)
#> 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(2016-04-24)
#> 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 
#> 
#>

牵手常德棋牌官方下载More about the changes in R case be read at , or in the list of changes given below.

 

Upgrading to R 3.4.0 on Windows

If you are using Windows 牵手常德棋牌官方下载you can easily upgrade to the latest version of R using . 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)

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 package updated and useful, so if you have any suggestions or remarks on the package – you are invited to .

Continue reading “R 3.4.0 is released – with new speed upgrades and bug-fixes”

shinyHeatmaply – a shiny app for creating interactive cluster heatmaps

My friend and I () are pleased to announce the release of  (0.1.0): a new Shiny application (and Shiny gadget) for creating interactive cluster heatmaps. shinyHeatmaply is based on the  R package which strives to make it easy as possible to create interactive cluster .

The app introduces a functionality that 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 ‘s capabilities in the following 40 seconds video:

 

Installing shinyHeatmaply

From :

install.packages('shinyHeatmaply')

From :

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"))

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 the following code:

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 . Or view the following iframe:

Continue reading “shinyHeatmaply – a shiny app for creating interactive cluster heatmaps”

R 3.3.3 is released!

R 3.3.3 (codename “Another Canoe”) was  You can get the latest binaries version . (or the .tar.gz source code from ). The full list of bug fixes and new features is provided below.

A quick :

R 3.3.3 fixes an issue related to attempting to use 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 function, the ability to use (and pmin) on ordered factors, improved accuracy for qbeta for some extreme cases, corrected spelling for “Cincinnati” in the 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 . 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.

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 package updated and useful, so if you have any suggestions or remarks on the package – you are invited to .

Continue reading “R 3.3.3 is released!”