shinyHeatmaply – a shiny app for creating interactive cluster heatmaps

My friend Jonathan Sidi and I (Tal Galili) are pleased to announce the release of shinyHeatmaply (0.1.0): a new Shiny application (and Shiny gadget) for creating interactive cluster heatmaps. shinyHeatmaply is based on the heatmaply R 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 shinyHeatmaply

From CRAN:

install.packages('shinyHeatmaply')

From github:

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 a saved shinyHeatmaply output here. Or view the following iframe:

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ggedit 0.0.2: a GUI for advanced editing of ggplot2 objects

Guest post by Jonathan Sidi, Metrum Research Group

Last week the updated version of ggedit was presented in RStudio::conf2017. First, a BIG thank you to the whole RStudio team for a great conference and being so awesome to answer the insane amount of questions I had (sorry!). For a quick intro to the package see the previous post.

To install the package:

devtools::install_github("metrumresearchgroup/ggedit",subdir="ggedit")

Highlights of the updated version.

  • verbose script handling during updating in the gagdet (see video below)
  • verbose script output for updated layers and theme to parse and evaluate in console or editor
  • colourpicker control for both single colours/fills and and palletes
  • output for scale objects eg scale*grandient,scale*grandientn and scale*manual
  • verbose script output for scales eg scale*grandient,scale*grandientn and scale*manual to parse and evaluate in console or editor
  • input plot objects can have the data in the layer object and in the base object.
    • ggplot(data=iris,aes(x=Sepal.Width,y=Sepal.Length,colour=Species))+geom_point()
    • ggplot(data=iris,aes(x=Sepal.Width,y=Sepal.Length))+geom_point(aes(colour=Species))
    • ggplot()+geom_point(data=iris,aes(x=Sepal.Width,y=Sepal.Length,colour=Species))
  • plot.theme(): S3 method for class ‘theme’
    • visualizing theme objects in single output
    • visual comparison of two themes objects in single output
    • will be expanded upon in upcoming post

Continue reading “ggedit 0.0.2: a GUI for advanced editing of ggplot2 objects”

ggedit – interactive ggplot aesthetic and theme editor

Guest post by Jonathan Sidi, Metrum Research Group

ggplot2 has become the standard of plotting in R for many users. New users, however, may find the learning curve steep at first, and more experienced users may find it challenging to keep track of all the options (especially in the theme!).

ggedit is a package that helps users bridge the gap between making a plot and getting all of those pesky plot aesthetics just right, all while keeping everything portable for further research and collaboration.

ggedit is powered by a Shiny gadget where the user inputs a ggplot plot object or a list of ggplot objects. You can run ggedit directly from the console from the Addin menu within RStudio.

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R 3.3.2 is released!

R 3.3.2 (codename “Sincere Pumpkin Patch”) was released yesterday 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.

Upgrading to R 3.3.2 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.

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 3.3.2 is released!”

Set Application Domain Name with Shiny Server

Guest post by AVNER KANTOR

I used the wonderful tutorial of Dean Attall to set my machine in Google cloud. After I finished to configure it successfully I wanted to redirect my domain to the Shiny application URL. This is a short description how you can do it.

Continue reading “Set Application Domain Name with Shiny Server”

Presidential Election Predictions 2016 (an ASA competition)

Guest post by Jo Hardinprofessor of mathematics, Pomona College.

ASA’s Prediction Competition

In this election year, the American Statistical Association (ASA) has put together a competition for students to predict the exact percentages for the winner of the 2016 presidential election. They are offering cash prizes for the entry that gets closest to the national vote percentage and that best predicts the winners for each state and the District of Columbia. For more details see:

http://thisisstatistics.org/electionprediction2016/

To get you started, I’ve written an analysis of data scraped from fivethirtyeight.com. The analysis uses weighted means and a formula for the standard error (SE) of a weighted mean. For your analysis, you might consider a similar analysis on the state data (what assumptions would you make for a new weight function?). Or you might try some kind of model – either a generalized linear model or a Bayesian analysis with an informed prior. The world is your oyster!

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Using 2D Contour Plots within {ggplot2} to Visualize Relationships between Three Variables

Guest post by John Bellettiere, Vincent Berardi, Santiago Estrada

The Goal

To visually explore relations between two related variables and an outcome using contour plots. We use the contour function in Base R to produce contour plots that are well-suited for initial investigations into three dimensional data. We then develop visualizations using ggplot2 to gain more control over the graphical output. We also describe several data transformations needed to accomplish this visual exploration.

Continue reading “Using 2D Contour Plots within {ggplot2} to Visualize Relationships between Three Variables”

heatmaply: interactive heat maps (with R)

I am pleased to announce heatmaply, my new R package for generating interactive heat maps, based on the plotly R package.

tl;dr

By running the following 3 lines of code:

install.packages("heatmaply")
library(heatmaply)
heatmaply(mtcars, k_col = 2, k_row = 3) %>% layout(margin = list(l = 130, b = 40))

You will get this output in your browser (or RStudio console):

Continue reading “heatmaply: interactive heat maps (with R)”

R 3.3.0 is released!

R 3.3.0 (codename “Supposedly Educational”) was released today. You can get the latest binaries version from here. (or the .tar.gz source code from here). The full list of new features and bug fixes is provided below.

Upgrading to R 3.3.0 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)
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.

CHANGES IN R 3.3.0

SIGNIFICANT USER-VISIBLE CHANGES

  • nchar(x, *)‘s argument keepNA governing how the result for NAs in x is determined, gets a new default keepNA = NA which returns NA where x is NA, except for type = "width" which still returns 2, the formatting / printing width of NA.
  • All builds have support for https: URLs in the default methods for download.file(), url() and code making use of them.Unfortunately that cannot guarantee that any particular https: URL can be accessed. For example, server and client have to successfully negotiate a cryptographic protocol (TLS/SSL, …) and the server’s identity has to be verifiable via the available certificates. Different access methods may allow different protocols or use private certificate bundles: we encountered a https: CRAN mirror which could be accessed by one browser but not by another nor by download.file() on the same Linux machine.

NEW FEATURES

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Election tRends: An interactive US election tracker (using Shiny and Plotly)

Guest post by Jonathan Sidi

Introduction

The US primaries are coming on fast with almost 120 days left until the conventions. After building a shinyapp for the Israeli Elections I decided to update features in the app and tried out plotly in the shiny framework.

As a casual voter, trying to gauge the true temperature of the political landscape from the overwhelming abundance of polling is a heavy task. Polling data is continuously published during the state primaries and the variety of pollsters makes it hard to keep track what is going on. The app self updates using data published publicly by realclearpolitics.com.

The app keeps track of polling trends and delegate count daily for you. You create a personal analysis from the granular level data all the way to distributions using interactive ggplot2 and plotly graphs and check out the general elections polling to peak into the near future.

The app can be accessed through a couple of places. I set up an AWS instance to host the app for realtime use and there is the Github repository that is the maintained home of the app that is meant for the R community that can host shiny locally.

Running the App through Github

(github repo: yonicd/Elections)

#changing locale to run on Windows
if (Sys.info()[1] == "Windows") Sys.setlocale("LC_TIME","C") 
 
#check to see if libraries need to be installed
libs=c("shiny","shinyAce","plotly","ggplot2","rvest","reshape2","zoo","stringr","scales","plyr","dplyr")
x=sapply(libs,function(x)if(!require(x,character.only = T)) install.packages(x));rm(x,libs)
 
#run App
shiny::runGitHub("yonicd/Elections",subdir="USA2016/shiny")
 
#reset to original locale on Windows
if (Sys.info()[1] == "Windows") Sys.setlocale("LC_ALL")

Application Layout:

(see next section for details)

  1. Current Polling
  2. Election Analyis
  3. General Elections
  4. Polling Database

Usage Instructions:

Current Polling

  • The top row depicts the current accumulation of delegates by party and candidate is shown in a step plot, with a horizontal reference line for the threshold needed per party to recieve the nomination. Ther accumulation does not include super delegates since it is uncertain which way they will vote. Currently this dataset is updated offline due to its somewhat static nature and the way the data is posted online forces the use of Selenium drivers. An action button will be added to invoke refreshing of the data by users as needed.
  • The bottom row is a 7 day moving average of all polling results published on the state and national level. The ribbon around the moving average is the moving standard deviation on the same window. This is helpful to pick up any changes in uncertainty regarding how the voting public is percieving the candidates. It can be seen that candidates with lower polling averages and increased variance trend up while the opposite is true with the leading candidates, where voter uncertainty is a bad thing for them.

Snapshot of Overview Plot

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