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:
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:
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.
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.
#changing locale to run on Windowsif(Sys.info()=="Windows")Sys.setlocale("LC_TIME","C")#check to see if libraries need to be installed
shiny::runGitHub("yonicd/Elections",subdir="USA2016/shiny")#reset to original locale on Windowsif(Sys.info()=="Windows")Sys.setlocale("LC_ALL")
(see next section for details)
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.
(This is a guest post by my friend Yoni Sidi, a PhD candidate in statistics at the Hebrew University)
The Israeli elections are coming up this Tuesday, 17/3/2015 (i.e.: tomorrow!). They are a bit more complicated than your average US presidential race. The elections in Israel are based on nationwide proportional representation. The electoral threshold is 3.25% and the number of seats (or mandates) out of a total of 120 is proportional to the number of votes it recieves, so the threshold roughly translates to at least four mandates. The Israeli system is a multi-party system and is based on coalition governments. Multi-party is putting it mildly, there are 11 that have a chance (and are expected) to pass the mandate threshold.
There are two major parties, Hamachane Hazioni (Left Wing) and the Likud (Right Wing), that are hoping to garner between 16%-25% of the votes, 20-30 mandates. The main winners though are the medium size parties that recomend to the President who they think has the best chance to construct the next government, so yes there is a good possibility that the general elections winner will not be one constructing the coalition. Making the actual winners the parties that create the biggest coalition which exceeds 60 mandates.
An abundance of polling has been continually published during the run up and the variaety of pollsters and publishers is hard to keep track of as a casual voter trying to gauge the temperature of the political landscape. I came across a great realtime database by Project 61 on google docs of past and present polling result information and decided that it was a great opportunity to learn the Shiny library of RStudio and create an app that I can check current and past results. So after I figured out how to connect google docs to R, I created a self updating app that became a nice place to keep track of polling every day, check trends and distributions using interactive ggplot2 graphs and simulate coalition outcomes.
Please note that as of Friday (March 13th), until election day (March 17th), it is forbidden to perform new polls in Israel, hence the data presented here cannot allow for an up-to-date inference about the expected results of the election. This post is for educational purposes.
Running the election polls Shiny app on your computer
#changing locale to run on Windowsif(Sys.info()=="Windows")Sys.setlocale("LC_ALL","Hebrew_Israel.1255")#check to see if libraries need to be installed
x <-sapply(libs,function(x)if(!require(x,character.only=T))install.packages(x))rm(x,libs)#run App
shiny::runGitHub("Elections","yonicd",subdir="shiny")#reset to original locale on Windowsif(Sys.info()=="Windows")Sys.setlocale("LC_ALL")
The latest polling day results published in the media and the prediction made using the Project 61 weighting schemes. The parties are stacked into blocks to see which block has best chance to create a coalition.
The Project 61 prediction is based past pollster error deriving weights from the 2003,2006,2009 and 2013 elections, dependant on days to elections and parties. In their site there is an extensive analysis on pollster bias towards certain parties and party blocks.
An interactive polling analysis layout where the user can filter elections, parties, publishers and pollster, dates and create different types of plots using any variable as the x and y axis.
The default layer is the 60 day trend (estimated with loess smoother) of mandates published by each pollster by party
The user can choose to include in the plots Elections (2003,2006,2009,2013,2015) and the subsequent filters are populated with the relevant parties, pollsters and publishers relevant to the chosen elections. Next there is a slider to choose the days before the election you want to view in the plot. This was used instead of a calendar to make a uniform timeline when comparing across elections.
In addition the plot itself is a ggplot thus the options above the graph give the user control on nearly all the options to build a plot. The user can choose from the following variables:
Ideology (5 Party Blocks)
Ideology.Group (2 Party Blocks)
Attribute (Party History)
To define the following plot attributes:
X axis variable
Split Y by colors using a different variable
Rotation of X tick labels
Y axis variable
Create Facets to display subsets of the data in different panels (two more variables to cut data) there are two type of facets to choose from
Wrap: Wrap 1d ribbon of panels into 2d
Grid: Layout panels in a grid (matrix)
An example of filtering pollsters to compare different tendencies for each party in the 2015 elections:
An example of comparing distribution mandates per party in the last two months of polling
An example of comparing distribution of pollster errors across elections (up to 10 days prior end of polling), by splitting the parties into five groups compared to previous election: old party,new party, combined (combination of two or more old parties), new.split (new party created from a split of a party from last election), old.split (old party that was a left from the split).
As we can see the pollster do not get a good indication of new,new.split or combined parties, which could be a problem this election since there are: 3 combined, 2 new splits.
If you are an R user and know ggplot there is an additional editor console,below the plot, where you can create advanced plots freehand, just add to the final object from the GUI called p and the data.frame is x, eg p+geom_point(). Just notice that all aesthetics must be given they are not defined in the original ggplot() definition. It is also possible to use any library you want just add it to the top of the code, the end object must be a ggplot.
You can also remove the original layer if you want using the function remove_geom(ggplot_object,geom_layer), eg p=p+remove_geom(p,“point”) will remove the geom_point layer in the original graph
p=remove_geom(p,"point")#blank ggplot with facets in place#new layerp+geom_smooth(aes(x=DaysLeft,y=Mandates,fill=Party.En))+scale_x_reverse()+scale_fill_discrete(name="Party")
Finally the plots can be viewed in English or Hebrew, and can be downloaded to you local computer using the download button.
Mandate Simulator and Coalition Whiteboard
A bootstrap simulation is run on Polling results from up to 10 of the latest polls using the sampling error as the uncertainty of each mandate published. Taking into account mandate surplus agreements using the Hagenbach-Bischoff quota method and the mandate threshold limit (in this election it is 4 mandates), calculating the simulated final tally of mandates. The distributions are plotted per party and the location of the median published results in the media.
The user can choose how many polls to take into account, up to last 10 polls, and how big a simulation they want to run: 50,100,500,1000 random polling results per each party and poll.
Once the simulator is complete you can create coalitions based on either the simulated distribution or actual published polls and see who can pass 60 mandates. Choose the coalition parties and the opposition parties from dropdown lists. (Yes the ones chosen are nonsensical on purpose…)
All raw data used in the application can be viewed and filtered in a datatable.