A bit more on ffplot

This week I spent a few evenings and train journeys developing ffplot. The ffplot README gives technical details, here I'll talk about the motivation.

I spend a lot of time plotting my data, either to explore it or to create output for papers and presentations. I'm pretty experienced with basic R plotting, and I also use the excellent ggplot2. But I kept finding myself looking up solutions on Stackoverflow and mailing lists, and thinking "but what I want is so simple! Why can't I understand how to do it?"

ffplot is a simple frontend to ggplot - basically, a hack, born out of my own stupidity. It doesn't do anything new, it just makes it easier for the user to think about.

For example, for my honesty paper, I had people from 15 countries who reported either heads or tails in a coin flip experiment. So, I wanted a bar chart of the proportions of people reporting heads in each country, with some confidence intervals.

In ffplot, this looks like:

ffplot(prop(heads) + ci(heads) ~ country, data)

This gets you:


I like this way of thinking, because what you say is what you get.

Another example: my data includes a test of "citizen integrity", as well as a quiz which respondents could cheat on. I'd like to know if there's a relationship between answers to the test, and scores on the quiz. I'd also like to spot patterns in the data.

So, let's plot integrity score and quiz score, and add a smooth mean. Again, what you say to ffplot corresponds to what you want:

ffplot(quizperf + smooth(mean(quizperf)) ~ integ, data)

Hmm, no obvious linear relationship, but people with very low scores either got 0 or full marks in the quiz - probably because they either cheated, or didn't bother with it at all.

Lastly, let's look at the relationship between reporting heads on the coin flip – which gave respondents a cash reward – and scoring high on hard quiz questions. I hope this is positive, as that will support the idea that both the quiz and the coin flip are tapping the same underlying dimension of willingness to lie. In fact, I want to make sure it's positive in every age group.

So, I'll plot a bar chart of proportions reporting heads, with confidence intervals, split up by age.  Again, the command corresponds to what you want:

ffplot(bar(prop(heads), fill = "red") + ci(heads) ~ quizp.hard | age, data)

This looks pretty reasonable - top scores on the hard questions are generally associated with more reporting of heads.

There's lots still to do, and this is definitely a hack, based on my own ignorance of ggplot2 – if I was smarter I could get it to work more easily. But I like it when things are simple.

Many more examples, and installation instructions, can be found at the github page for ffplot.

Dani Rodrik on economists vs economics

Dani Rodrik critiques the critique of economics.

He argues for a "many models" approach. This certainly happens in the experimental economics world where there are several models of behaviour, each of which fits one or some experimental situation. The downside of this approach is that you lose the point that these models contradict each other. (Either politicians are making campaign commitments in one dimension, and you get the median voter theorem, or in many dimensions and you don't.) If many models are useful, and choosing the right one is a "craft", then do we end up vulnerable to the 1-800 critique?

Linkage

Lord Green on universities and migration.

Worries about executing Syrians by drone.

ffplot: a fast and friendly plotting function for R

I threw this together over the weekend.

I love ggplot2 but I always find myself looking up documentation for how to do things. I just want to say "plot the mean of y against x" and for that to work. Or "show me the distribution of gender by age group". ffplot tries to do that.

Please try it out and feed back!

Honesty paper

Here is the paper for the cross-cultural experiment on honesty I blogged about previously.

A small nudge backlash in Heidelberg

At ESA 2015. Alexia Gaudeul:

... the nudge did work in eliciting more pledges (promises of contributions)... but did not generate more actual contributions to charities...

Anett John:

Commitment problems are widely regarded as a remedy for self-control problems.... Individuals chose the stakes of the contract (a default penalty) themselves. A majority appears to choose a harmful contract... 55 per cent of clients default on their savings contract.

Mette Damgaard:

we present a ... model which contrasts the often documented benefit of reminders with a previously neglected cost of annoyance that a large number of messages can cause.... a reminder leads to more donations but we also document that the reminder leads to more unsubscriptions.