Although it’s been in development for 10 years it seems that Tableau is suddenly making a splash in the education community.  It’s a great product — makes it easy to create pretty complex analyses using point and click.

The software seems to be super expensive — hundreds of thousands of dollars for an enterprise license.

I didn’t find it all that intuitive at first, and I got tired of so much pointing and clicking.  But I can see it’s going to revolutionize how people interact with data.

I found that it was missing some obvious stuff (you can’t compute a standard error directly) and in the end I may prefer to just continue working with R and ggplot.

I began by looking at this article in the New York Times on racial gaps in higher education:

http://www.nytimes.com/interactive/2013/05/07/education/college-admissions-gap.html?_r=1&

It turns out that the website contains data on 999 universities in the US.

I used the R package XML to read the table into JSON format and then the R package jsonlite to turn the table into a .csv file. Then I opened the file in Tableau.

It was easy to see the proportion of each university that was African American

    Drag a discrete variable “school name” to columns

    Drag a continuous variable “percent black” to rows

    Sort descending on “percent black.”

NB (from http://www.theinformationlab.co.uk/2011/09/23/blue-things-and-green-things/)

 

 

Let’s put them on a tree map and add a filter that allows you to choose the size of school. This allows you to do something pretty important – namely to see schools that meet criteria.

 

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So, if you’re an African American student looking for a smaller college (less than about 10,00 students) and you want (1) a high graduation rate and (2) a high proportion of black students, you’d look for schools to the left side of the map but which are also in a dark color. Howard University pops out (you have to mouse over the smaller squares to see the school name).

Tableau