14 search results for "solomon messing"

Collapsing a bipartite co-occurrence network

Collapsing a bipartite co-occurrence network

This note is a follow-up to the previous one. It shows how to use student-submitted keywords to find clusters of shared interests between the students. Dear students If you enjoyed my previous note, this one might also entertain you. And since your real first names are used in the data, you should be able to tell me later if...

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Facebook teaches you exploratory data analysis with R

May 12, 2014
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Facebook is a company that deals with a lot of data — more than 500 terabytes a day — and R is widely used at Facebook to visualize and analyze that data. Applications of R at Facebook include user behaviour, content trends, human resources and even graphics for the IPO prospectus. Now, four R users at Facebook (Moira Burke,...

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Data visualization

March 4, 2012
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For those who have not read the seminal works of Tufte and Cleveland, please hang your heads in shame. To salvage some sense of self-worth, you can then head over to Solomon Messing’s blog where he is starting a series on data visualization based on ...

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R’s role in science breakthrough: reproducibility of psychology studies

January 8, 2016
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The scientific process has been going through a welcome period of introspection recently, with a focus on understanding just how reliable the results of scientific studies are. We're not talking here about scientific fraud, but how the scientific process itself and the focus on p-values (which not even statisticians can easily explain) as the criterion for a positive result...

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Why you should learn R first for data science

January 26, 2015
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Why you should learn R first for data science

Over and over, when talking with people who are starting to learn data science, there’s a frustration that comes up: I don’t know which programming language to start with.” And it’s not just programming languages, it’s also software systems like Tableau, SPSS, etc. There is an ever widening range of tools and programming languages and The post

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When to Use Stacked Barcharts?

October 11, 2014
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When to Use Stacked Barcharts?

Yesterday a few of us on Facebook’s Data Science Team released a blogpost showing how candidates are campaigning on Facebook in the 2014 U.S. midterm elections. It was picked up in the Washington Post, in which Reid Wilson calls us “data … Continue reading →

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Visualization Series: Using Scatterplots and Models to Understand the Diamond Market (so You Don’t Get Ripped Off)

January 19, 2014
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Visualization Series: Using Scatterplots and Models to Understand the Diamond Market (so You Don’t Get Ripped Off)

My last post railed against the bad visualizations that people often use to plot quantitive data by groups, and pitted pie charts, bar charts and dot plots against each other for two visualization tasks.  Dot plots came out on top. … Continue reading →

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Streamline Your Mechanical Turk Workflow with MTurkR

June 24, 2013
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Streamline Your Mechanical Turk Workflow with MTurkR

I’ve been using Thomas Leeper‘s MTurkR package to administer my most recent Mechanical Turk study—an extension of work on representative-constituent communication claiming credit for pork benefits, with Justin Grimmer and Sean Westwood.  MTurkR is excellent, making it quick and easy to: test … Continue reading →

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Generating Labels for Supervised Text Classification using CAT and R

February 4, 2013
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Generating Labels for Supervised Text Classification using CAT and R

The explosion in the availability of text has opened new opportunities to exploit text as data for research. As Justin Grimmer and Brandon Stewart discuss in the above paper, there are a number of approaches to reducing human text to … Continue reading →

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Working with Bipartite/Affiliation Network Data in R

September 30, 2012
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Working with Bipartite/Affiliation Network Data in R

Data can often be usefully conceptualized in terms affiliations between people (or other key data entities). It might be useful analyze common group membership, common purchasing decisions, or common patterns of behavior. This post introduces bipartite/affiliation network data and provides … Continue reading →

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