Come and see

Yesterday, as a pre-amble to an ordinary newsletter sent out via listserv to most PhD students at UPenn, we were offered this piece of advice:

Tip of the day: You should all know this by now: It is incorrect to say “come and see” or “come out and help”, or any other “come…and…” phrase. It is an infinitive phrase: “Come to see”, “Come out to help”, “Come to have fun”. Don’t aggravate anyone’s pet peeves; just write and say it correctly. You’re welcome.

Well, many of us linguistics graduate students felt this merited some kind of response. I don't know about other linguists out there, but if someone said this to me in a personal e-mail, or in conversation, I couldn't not respond.

And then, an amazing thing happened. We started drafting a letter in a Google document with 16 contributors. It was a litte chaotic, but we marshaled together intuitions, data, and argumentation, and had drafted this message in about an hour's time.

To whom it may concern:

We were recently sent a grammar “tip” via the [redacted] listserv which read:

Tip of the day: You should all know this by now: It is incorrect to say “come and see” or “come out and help”, or any other “come…and…” phrase. It is an infinitive phrase: “Come to see”, “Come out to help”, “Come to have fun”. Don’t aggravate anyone’s pet peeves; just write and say it correctly. You’re welcome.

The linguistics graduate students felt that this required a response, as in fact, the cited examples “come and see” and “come out and help” are both grammatical and widely used constructions in American English.

The two constructions differ slightly in meaning. If one says,

• Mary came and saw Tupac’s hologram perform.

it must be the case that the performance actually occurred; it cannot be the case that there were technical difficulties and the performance was cancelled. However,

• Mary came to see Tupac’s hologram perform.

admits the possibility that the performance was cancelled due to technical difficulties. Therefore, asserting that the infinitive phrase is a uniformly appropriate replacement for the conjoined phrase is not an appropriate representation of the linguistic facts.

Phrases like “come and see” are not restricted to the spoken idiom, but are also used in the written language. They even occur in texts considered by some to be canonical, as the following examples show:

He saith unto them, “Come and see”. (John 1:39, King James Bible) 

“Then you may come and see the picture”. (Merry Wives of Windsor II:II, William Shakespeare) 

“Will you come and see me?” (Pride & Prejudice, chap. 26, Jane Austen)

Generally, grammatical prescriptivism contributes little to useful discourse, and may even cause intelligent language users to be unfairly stigmatized. Thus, while we appreciate [redacted]'s light-hearted "tips-of-the-day," we would encourage authors to keep an open mind about the breadth of possible language use, especially in public forums.

Sincerely,

Jana Beck*
Claire Crawford*
[redacted]*
Sabriya Fisher*
Aaron Freeman*
Lauren Friedman*
Josef Fruehwald*
Kyle Gorman*
Marielle Lerner*
Caitlin Light*
Laurel MacKenzie*
Brittany McLaughlin*
Hilary Prichard*
Kobey Shwayder*
Jon Stevens*
[redacted]*

*Department of Linguistics

Thinking about it some more, I think at least the past tense "came to see" even has the implicature that either the seeing was unsuccessful, or there is some other more relevant event than the seeing which the speaker is about to tell us about.

Anyway, I think we did a bang up job, and produced a really excellent message, especially considering there were 16 authors!

Linguistic Notation Inside of R Plots!

So, I've been playing around with learning knitr, which is a Sweave-like R package for combining LaTeX and R code into one document. There's almost no learning curve if you already use Sweave, and I find a lot of knitr's design and usage to be a lot nicer.

I wasn't going to make a blog post or tutorial about knitr, because the documentation is already pretty good, and contains a lot of tutorials.  However, I've just had a major victory in incorporating linguistic notations into plots using knitr, and I just had to share. I'll show you the payoff first, and then include the details.

First, I managed to successfully use IPA characters as plot symbols and legend keys.

The actual data in the plot is on car fuel economy, but that's not the point. Look at that IPA!

Then, I tried to expand on the principles that got me the IPA, and look what I produced.

Yes, that is a syntax tree overlaid on top of the plot. But why stop there when you could go completely crazy?

How to do it.

The important thing about making these plots is that they were easy given my pre-existing knowledge of R, LaTeX and what I've learned about knitr.  The crucial element here is that knitr supports tikz graphics. I don't know anything about tikz graphics, and I still don't, which means that if you don't know anything about tikz graphics, you can still make plots like these.

Like most linguists who use LaTeX, I already know how to include IPA characters and draw syntactic trees in a LaTeX document. It's simple as

...
\usepackage{tipa}
\usepackage{qtree}
...
\textipa{D C P}
\Tree [.S NP VP ]
...

What is so cool about the tikz device is that it lets you define these notations in LaTeX syntax, and then incorporates them into R graphs. Here are the important code chunks to include in your knitr document to make it all work.

1 — Load the right R packages

Early on, load the ggplot2 and tikzDevice R packages.

<<>>=
    library(ggplot2)
    library(tikzDevice)
@

2 — Define your LaTeX libraries

Then, you need to tell the tikz device which LaTeX packages you want to use.

<<>>=
    options(tikzLatexPackages = c(getOption("tikzLatexPackages"),
                                  "\\usepackage{tipa}",
                                  "\\usepackage{qtree}"))
@

3 — Define the plotting elements in LaTeX

We're done with the hard part. Now, it's as simple as faking up some data...

<<>>=
    levels(mpg$drv) <- c("\\textipa{D}",
                         "\\textipa{C}",
                         "\\textipa{P}")
 
    mpg$tree <- "{\\footnotesize \\Tree [.S NP VP ]}"
@

4 — Plot the data using the tikz device

...and plotting it, using the tikz device.

<<dev="tikz", fig.width=8, fig.height=5, out.width="0.9\\textwidth", fig.align="center">>=
    ggplot(mpg, aes(displ, hwy, label = drv, color = drv)) + 
            geom_text() + 
            stat_smooth()+
            xlab("\\textipa{IPA!}")    
@

Or, in the case of the syntactic trees,

<<dev="tikz", fig.width=8, fig.height=5, out.width="0.7\\textwidth", fig.align="center">>=
    ggplot(mpg, aes(displ, hwy, label = tree))+
            geom_text() + 
            stat_smooth()+
            xlab("TREES")
@

5 — Compile the .Rnw to a .tex document

Here's some source code to embed these plots in a beamer presentation. To compile a .tex document from the .Rnw source, you can run

library(knitr)
knit("./ling-plot.Rnw")

Then, just compile the .tex document however your little heart desires.

How to do it with one click

As if this weren't awesome and easy enough yet, it's possible to compile the whole document in one click using RStudio, as outlined on this knitr page. You'll need to download the development (i.e. not guaranteed to be stable) RStudio release, then set the compilation option to use knitr, and you're done!

I have to say that from  a practical standpoint, I've found writing Sweave documents in RStudio to be a much better experience than what I was doing before, because I can run and debug the R code from within the .Rnw source document. No need to go flipping back and forth between a Tex editor and R.

P.S. I highlighted the code above at http://www.inside-r.org/pretty-r

More on Philadelphia Homicide

I've been doing more analysis of the Philadelphia Homicide data that the Philadelphia Inquirer has published, and presented some of it at the Philadelphia UseR group yesterday. My slides [pdf] and source [knitr .Rnw] are on github.

I should be clear that I am not an expert on crime and murder. In fact, I'm not even fairly knowledgeable. If anyone out there with more expertise has strong criticism of my "analysis" (really, it's just a rough exploration of the data), I'll eat it, and I'll look forward to your own analysis of the data (again, it's right here). Here are some of the most striking patterns that I found.

Results

First, here is the total number of murders that occurred over the past 23 years, broken down by the day of the week. The weekends are worse than the weekdays.

Next, here are the total number of murders by hour of the day. The hour of the day was not included in the data until 2006, so this only represents murders between 2006 and 2011. The plot is centered around midnight, so the afternoon of Day 1 is on the left, and the morning of Day 2 is on the right.

It looks like there's something weird going on around 11pm and midnight, which I have to chalk up to the reporting patterns of the PPD. For some reason, it seems like murders which occurred in the midnight hour are more likely to be logged as occurring at 11PM.

Here is the most striking plot that I produced this time around. It plots, by month, the average frequency of murders. The y-axis represents 1 murder every X days.

Since 1988, the African American community has been living in a Philadelphia with approximately a murder every day, or every other day. The White community, on the other hand, has been living in a Philadelphia with a murder once a week.

I also did some meager statistical analysis, specifically poisson regression with terms for the month (that is, January, February, etc, to look for a seasonal pattern), race of the victim, and weapon used. There was a significant month effect, but the coefficients didn't have much of a pattern to them. I did use number of days in the month as an offset in the regression, so it's not that. More importantly, there was an unsurprising main effect of race, but also a big interaction between race and weapon. Specifically, African American victims were way more likely to be killed by a gun.

Guns and knives are the two most common weapons used in murders in the data. White murder victims are 2.54x more likely to have been shot than stabbed, while an African American murder victim is 7.19x more likely to have been shot than stabbed, meaning that African American murder victims are 2.83x more likely to have been shot than a White murder victim was.
Update: There was a pretty serious flaw in my regression, in that if there was a Month where, say, no African Americans were murdered with a knife (and there were plenty), that month's data was missing, rather than 0. Filling in the data appropriately to reflect months with 0 murders for a particular race x weapon combination, the estimates are pretty different. White murder victims are 5.71x times more likely to be murdered with a gun than a knife, while African American murder victims were 8.62x times more likely to be murdered with a gun than a knife, meaning African Americans are 1.51x times more likely to be shot than stabbed. So, that's a pretty serious revision approximately halving the multiplier. I've already updated the linked code and slides.
So, gun deaths are an especially acute problem in the African American community. In fact, if you exclude gun deaths from the data, it actually looks like the racial disparity in murder rates has been narrowing.

It is purely coincidental that I'm posting this on the same day that the Philadelphia Police Department are doing a gun buyback. You can bring in a gun and receive a $100 Shoprite voucher, no questions asked. Seems like a good initiative.

Analysis Discussion

I spent a bit of time trying to figure out what I thought the most meaningful way to represent the murder rate was. First, I calculated the murder frequency by counting how many n murders there were a month, then divided that by the number of days in the month for (n murders/n days)=murders per day. But the resulting measure had values like 0.14 murders per day, which isn't too informative. What people want to know about murders, or at least what I want to know, is how often murders happen, not how many happened in a given time window. So, instead, I calculated (n days/n murders)=days per murder.

The y-axis for the murder rate figures is also a logarithmic scale, which is both reasonable given the distribution of the data, and the impression of the timescale. From a human perspective, the difference between 1 day and 2 days feels larger than the difference between 3 weeks and 4 weeks. The y axis is also flipped, to indicate that smaller numbers mean "more often".  I managed the reversed log transformation by writing my own coordinate transformation using the new scales package. Here's the R code.