Presidential Election Predictions 2016 (an ASA competition)

Guest post by Jo professor 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!

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.

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):

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 `NA`s 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.

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.

50 years of Data Science – by David Donoho

David Donoho published a fascinating paper based on a presentation at the Tukey Centennial workshop, Princeton NJ Sept 18 2015. You can download the full paper from here.

The paper got quite the attention on Hacker News, Data Science Central, Simply Stats, Xi’an’s blog, srown ion medium, and probably others. Share your thoughts in the comments.

Here is the abstract and table of content.

Abstract

More than 50 years ago, John Tukey called for a reformation of academic statistics. In ‘The Future of Data Analysis’, he pointed to the existence of an as-yet unrecognized science, whose subject of interest was learning from data, or ‘data analysis’. Ten to twenty years ago, John Chambers, Bill Cleveland and Leo Breiman independently once again urged academic statistics to expand its boundaries beyond the classical domain of theoretical statistics; Chambers called for more emphasis on data preparation and presentation rather than statistical modeling; and Breiman called for emphasis on prediction rather than inference. Cleveland even suggested the catchy name “Data Science” for his envisioned field.

A recent and growing phenomenon is the emergence of “Data Science” programs at major universities, including UC Berkeley, NYU, MIT, and most recently the Univ. of Michigan, which on September 8, 2015 announced a \$100M “Data Science Initiative” that will hire 35 new faculty. Teaching in these new programs has significant overlap in curricular subject matter with traditional statistics courses; in general, though, the new initiatives steer away from close involvement with academic statistics departments.

This paper reviews some ingredients of the current “Data Science moment”, including recent commentary about data science in the popular media, and about how/whether Data Science is really different from Statistics.

The now-contemplated field of Data Science amounts to a superset of the fields of statistics and machine learning which adds some technology for ‘scaling up’ to ‘big data’. This chosen superset is motivated by commercial rather than intellectual developments. Choosing in this way is likely to miss out on the really important intellectual event of the next fifty years.

Because all of science itself will soon become data that can be mined, the imminent revolution in Data Science is not about mere ‘scaling up’, but instead the emergence of scientific studies of data analysis science-wide. In the future, we will be able to predict how a proposal to change data analysis workflows would impact the validity of data analysis across all of science, even predicting the impacts field-by-field. Drawing on work by Tukey, Cleveland, Chambers and Breiman, I present a vision of data science based on the activities of people who are ‘learning from data’, and I describe an academic field dedicated to improving that activity in an evidence-based manner. This new field is a better academic enlargement of statistics and machine learning than today’s Data Science Initiatives, while being able to accommodate the same short-term goals.

Contents

1 Today’s Data Science Moment

2 Data Science ‘versus’ Statistics

2.1 The ‘Big Data’ Meme

2.2 The ‘Skills’ Meme

2.3 The ‘Jobs’ Meme

2.4 What here is real?

2.5 A Better Framework

3 The Future of Data Analysis, 1962

4 The 50 years since FoDA

4.1 Exhortations

4.2 Reification

5 Breiman’s ‘Two Cultures’, 2001

6 The Predictive Culture’s Secret Sauce

6.2 Experience with CTF

6.3 The Secret Sauce

6.4 Required Skills

7 Teaching of today’s consensus Data Science

8 The Full Scope of Data Science

8.1 The Six Divisions

8.2 Discussion

8.3 Teaching of GDS

8.4 Research in GDS

8.4.1 Quantitative Programming Environments: R

8.4.2 Data Wrangling: Tidy Data

8.4.3 Research Presentation: Knitr

8.5 Discussion

9.1 Science-Wide Meta Analysis

9.2 Cross-Study Analysis

9.3 Cross-Workflow Analysis

9.4 Summary

10 The Next 50 Years of Data Science

10.1 Open Science takes over

10.2 Science as data

10.3 Scientific Data Analysis, tested Empirically

10.3.1 DJ Hand (2006)

10.3.2 Donoho and Jin (2008)

10.3.3 Zhao, Parmigiani, Huttenhower and Waldron (2014)

10.4 Data Science in 2065

11 Conclusion

R 3.2.3 is released (with improvements for Windows users, and general bug fixes)

R 3.2.3 (codename “Wooden Christmas Tree”) was released several days ago. 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.

Major changes in R 3.2.3

As highlighted by David Smith, this release makes a few small improvements and bug fixes to R, including:

• Improved support for users of the Windows OS in time zones, OS version identification, FTP connections, and printing (in the GUI).
• Performance improvements and more support for long vectors in some functions including which.max
• Improved accuracy for the Chi-Square distribution functions in some extreme cases

Upgrading to R 3.2.3 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) 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.

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.

NEW FEATURES

• Some recently-added Windows time zone names have been added to the conversion table used to convert these to Olson names. (Including those relating to changes for Russia in Oct 2014, as in PR#16503.)
• (Windows) Compatibility information has been added to the manifests for ‘Rgui.exe’, ‘Rterm.exe’ and ‘Rscript.exe’. This should allow `win.version()` and`Sys.info()` to report the actual Windows version up to Windows 10.
• Windows `"wininet"` FTP first tries EPSV / PASV mode rather than only using active mode (reported by Dan Tenenbaum).
• `which.min(x)` and `which.max(x)` may be much faster for logical and integer `x` and now also work for long vectors.
• The ‘emulation’ part of `tools::texi2dvi()` has been somewhat enhanced, including supporting `quiet = TRUE`. It can be selected by `texi2dvi = "emulation"`.(Windows) MiKTeX removed its `texi2dvi.exe` command in Sept 2015: `tools::texi2dvi()` tries `texify.exe` if it is not found.
• (Windows only) Shortcuts for printing and saving have been added to menus in `Rgui.exe`. (Request of PR#16572.)
• `loess(..., iterTrace=TRUE)` now provides diagnostics for robustness iterations, and the `print()` method for `summary(<loess>)` shows slightly more.
• The included version of PCRE has been updated to 8.38, a bug-fix release.
• `View()` now displays nested data frames in a more friendly way. (Request with patch in PR#15915.)

BUG FIXES

• `regexpr(pat, x, perl = TRUE)` with Python-style named capture did not work correctly when `x` contained `NA` strings. (PR#16484)
• The description of dataset `ToothGrowth` has been improved/corrected. (PR#15953)
• `model.tables(type = "means")` and hence `TukeyHSD()` now support `"aov"` fits without an intercept term. (PR#16437)
• `close()` now reports the status of a `pipe()` connection opened with an explicit `open` argument. (PR#16481)
• Coercing a list without names to a data frame is faster if the elements are very long. (PR#16467)
• (Unix-only) Under some rare circumstances piping the output from `Rscript` or `R -f` could result in attempting to close the input file twice, possibly crashing the process. (PR#16500)
• (Windows) `Sys.info()` was out of step with `win.version()` and did not report Windows 8.
• `topenv(baseenv())` returns `baseenv()` again as in R 3.1.0 and earlier. This also fixes `compilerJIT(3)` when used in ‘.Rprofile’.
• `detach()`ing the methods package keeps `.isMethodsDispatchOn()` true, as long as the methods namespace is not unloaded.
• Removed some spurious warnings from `configure` about the preprocessor not finding header files. (PR#15989)
• `rchisq(*, df=0, ncp=0)` now returns `0` instead of `NaN`, and `dchisq(*, df=0, ncp=*)` also no longer returns `NaN` in limit cases (where the limit is unique). (PR#16521)
• `pchisq(*, df=0, ncp > 0, log.p=TRUE)` no longer underflows (for ncp > ~60).
• `nchar(x, "w")` returned -1 for characters it did not know about (e.g. zero-width spaces): it now assumes 1. It now knows about most zero-width characters and a few more double-width characters.
• Help for `which.min()` is now more precise about behavior with logical arguments. (PR#16532)
• The print width of character strings marked as `"latin1"` or `"bytes"` was in some cases computed incorrectly.
• `abbreviate()` did not give names to the return value if `minlength` was zero, unlike when it was positive.
• (Windows only) `dir.create()` did not always warn when it failed to create a directory. (PR#16537)
• When operating in a non-UTF-8 multibyte locale (e.g. an East Asian locale on Windows), `grep()` and related functions did not handle UTF-8 strings properly. (PR#16264)
• `read.dcf()` sometimes misread lines longer than 8191 characters. (Reported by Hervé Pagès with a patch.)
• `within(df, ..)` no longer drops columns whose name start with a `"."`.
• The built-in `HTTP` server converted entire `Content-Type` to lowercase including parameters which can cause issues for multi-part form boundaries (PR#16541).
• Modifying slots of S4 objects could fail when the methods package was not attached. (PR#16545)
• `splineDesign(*, outer.ok=TRUE)` (splines) is better now (PR#16549), and `interpSpline()` now allows `sparse=TRUE` for speedup with non-small sizes.
• If the expression in the traceback was too long, `traceback()` did not report the source line number. (Patch by Kirill Müller.)
• The browser did not truncate the display of the function when exiting with `options("deparse.max.lines")` set. (PR#16581)
• When `bs(*, Boundary.knots=)` had boundary knots inside the data range, extrapolation was somewhat off. (Patch by Trevor Hastie.)
• `var()` and hence `sd()` warn about `factor` arguments which are deprecated now. (PR#16564)
• `loess(*, weights = *)` stored wrong weights and hence gave slightly wrong predictions for `newdata`. (PR#16587)
• `aperm(a, *)` now preserves `names(dim(a))`.
• `poly(x, ..)` now works when either `raw=TRUE` or `coef` is specified. (PR#16597)
• `data(package=*)` is more careful in determining the path.
• `prettyNum(*, decimal.mark, big.mark)`: fixed bug introduced when fixing PR#16411.

INSTALLATION and INCLUDED SOFTWARE

• The included configuration code for `libintl` has been updated to that from `gettext` version 0.19.5.1 — this should only affect how an external library is detected (and the only known instance is under OpenBSD). (Wish of PR#16464.)
• `configure` has a new argument –disable-java to disable the checks for Java.
• The `configure` default for `MAIN_LDFLAGS` has been changed for the FreeBSD, NetBSD and Hurd OSes to one more likely to work with compilers other than `gcc`(FreeBSD 10 defaults to `clang`).
• `configure` now supports the OpenMP flags -fopenmp=libomp (clang) and -qopenmp (Intel C).
• Various macros can be set to override the default behaviour of `configure` when detecting OpenMP: see file ‘config.site’.
• Source installation on Windows has been modified to allow for MiKTeX installations without `texi2dvi.exe`. See file ‘MkRules.dist’.

“Why do people contribute to the R?” – concolusions from a new PNAS article

tl;dr: People contribute to R for various reasons, which evolves with time. The main reasons appear to be: “fun coding”, personal commitment to the community, interaction with like-minded and/or important people  – leading to higher self-esteem, future job opportunities, a chance to express oneself and enjoyable social inclusion.

From the abstract

One of the cornerstones of the R system for statistical computing is the multitude of packages contributed by numerous package authors. This amount of packages makes an extremely broad range of statistical techniques and other quantitative methods freely available. Thus far, no empirical study has investigated psychological factors that drive authors to participate in the R project. This article presents a study of R package authors, collecting data on different types of participation (number of packages, participation in mailing lists, participation in conferences), three psychological scales (types of motivation, psychological values, and work design characteristics), and various socio-demographic factors. The data are analyzed using item response models and subsequent generalized linear models, showing that the most important determinants for participation are a hybrid form of motivation and the social characteristics of the work design. Other factors are found to have less impact or influence only specific aspects of participation.

Summary of results

R developers, statisticians, and psychologists from Harvard University, University of Vienna, WU Vienna University of Economics, and University of Innsbruck empirically studied psychosocial drivers of participation of R package authors. Through an online survey they collected data from 1,448 package authors. The questionnaire included psychometric scales (types of motivation, psychological values, work design), sociodemografic variables related to the work on R, and three participation measures (number of packages, participation in mailing lists, participation in conferences).

The data were analyzed using item response models and subsequently generalized linear models (logistic regressions, negative-binomial regression) with SIMEX corrected parameters.

The analysis reveals that the most important determinants for participation are a hybrid form of motivation and the social characteristics of the work design. Hybrid motivation acknowledges that motivation is a complex continuum of intrinsic, extrinsic, and internalized extrinsic motives.
Motives evolve over time, as task characteristics shift from need-driven problem solving to mundane maintenance tasks within the R community.
For instance, motivation can evolve from pure “fun coding” towards a personal commitment with associated higher responsibilities within the community. The community itself provides a social work environment with high degrees of interaction, two facets of which are strong motivators. First, interaction with persons perceived as important increases one’s own reputation (self-esteem, future job opportunities, etc.) Second, interaction with alike minded persons (i.e., interested in solving statistical problems) creates opportunities to express oneself and enjoy social inclusion.

The findings do not substantiate the commonly held perception that people develop packages out of purely altruistic motives. It is also notable that in most cases package development is undertaken as part of an individual’s research, which is paid by an (academic) institution, rather than uncompensated developments that cut into leisure time.

Full paper (behind PNAS’s paywall for now) is available here:

R 3.2.2 is released

R 3.2.2 (codename “Fire Safety”) was released last weekend. 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.

SOME OF THE CHANGES

I personally found two things particularly interesting in this release:

1. setInternet2(TRUE) is now the default for windows (which will save people from getting “Error in file(con, “r”)” when using the installr package)
2. The dendrogram method of labels() is much more efficient for large dendrograms since it now uses rapply(). This is expected to speedup various functions in the dendextend R package (a package for visualizing, adjusting, and comparing dendrograms, which heavily relies on labels.dendrogram).

Also, David Smith (from Revolution/Microsoft) highlighted in his post several of the updates in R 3.2.2 he found interesting – mentioning how the new default for accessing the web with R will rely on the HTTPS protocol, and of improving the accuracy in the extreme tails of the t and hypergeometric distributions.

Upgrading to R 3.2.2 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) 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.

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 3.2.2:

SIGNIFICANT USER-VISIBLE CHANGES

• It is now easier to use secure downloads from https:// URLs on builds which support them: no longer do non-default options need to be selected to do so. In particular, packages can be installed from repositories which offer https:// URLs, and those listed by `setRepositories()`now do so (for some of their mirrors).Support for https:// URLs is available on Windows, and on other platforms if support for`libcurl` was compiled in and if that supports the `https` protocol (system installations can be expected to do). So https:// support can be expected except on rather old OSes (an example being OS X ‘Snow Leopard’, where a non-system version of `libcurl` can be used).(Windows only) The default method for accessing URLs via `download.file()` and `url()` has been changed to be `"wininet"` using Windows API calls. This changes the way proxies need to be set and security settings made: there have been some reports of sites being inaccessible under the new default method (but the previous methods remain available).

Slides from my JSM 2015 talk on dendextend

If you happen to be at the JSM 2015 conference this week, then this Monday, at 2pm, I will give a talk on the dendextend R package  (in the session “Advances in Graphical Frameworks and Methods Part 1“) – feel free to drop by and say hi.

Here are my slides for the intended talk:

p.s.: Yes – this presentation is very similar, although not identical, to the one I gave at useR2015. For example, I mention the new bioinformatics paper on dendextend.