Influence Explorer has a nice interface that lets you explore the political inclination of companies. The above graphic compares four tech companies. It is not surprising that Microsoft is more Republican than the other tech companies, and that Facebook’s younger and smaller employee base means more Democrat supporters and less total political contributions. It did surprise me though that Apple employees supports the Democrats more than Google employees.
The package “maps” contains geographical information very useful for producing maps, and it’s fairly easy to use this to make plots in ggplot2. This is a short tutorial showing how to create a map with shaded areas, like the one below.
DataSF hosts government data released by the city and county of San Francisco. Among all the data sets in its collection, it has a data sets showing the name, description, tags, etc of all the data sets in DataSF. “DataSF Data Set Tags” is a quick visualization of the tags in DataSF.
I wanted to play with Protovis for a while, and The Bay Citizen Code-a-Thon was the perfect opportunity to do so. So far I found Protovis to be pretty easy to use, with an ample of examples to
copy study from. The graphics it creates are quite elegant as well.
This is part 3 of the “Hacking Education” series exploring the DonorsChoose dataset. If you missed parts 1 and 2, check it out here and here.
In the second half of part 1 of this series, we looked at the type of projects that donors prefer by studying the projects that are more likely to become fully funded. An equally important factor to consider is return donorship. About one in three donors make subsequent donations within one year of their first. Having returning donors mean that donors were happy about the impacts they made, and that future projects are more likely to be funded. In part 3, we look at factors affecting whether a first-time donor would return and continue to contribute to DonorsChoose.
For the purpose of this analysis, new donors are considered to have “returned” if they made new contributions on DonorsChoose within the next 365 days.
Percentage return donorship have declined a little in the past few years, dipping down from 35% in 2008 to a little less than 30% in 2010. (There is some abnormality in the year 2006; it is unclear why this is the case.)
This is part 2 of the “Hacking Education” series exploring the DonorsChoose dataset. If you missed part 1, check it out here.
Differences between men and women are plentiful, so it’s not surprising for there to be differences between male and female teachers. But are there differences between married and unmarried teachers? More specifically, are there differences between teachers with prefix “Ms” and teachers with prefix “Mrs”?
In the DonorsChoose data set there certainly are. For one, a project posted by a “Mrs” has about 7% fewer chances of being fully funded than those posted by her male or unmarried colleagues. Even after accounting for other factors described in part 1, having a teacher prefix of “Mrs” lowers the odds of project completion by 10% as compared to “Ms”, and 15% as compared to “Mr”. In this post, we illustrate other differences between “Ms”, “Mrs”, and “Mr”.