Miskatonic University Press

If you feel like augmenting your reality by making a virtual copy of my two-volume boxed set of The Compact Edition of the Oxford English Dictionary appear on your table, here's how.

Step 1. Install Augment on your smartphone or tablet. Augment is an augmented reality application that runs on iOS and Android. It's easy to download and install: if you've got an iPhone you can find it by searching in the App Store, and if you have and Android phone you can find it in Google Play. It's free, and no sign-up is required.

Step 2. Download a PDF of the Augment marker and print it out. Put it on the table.

You need this becaus Augment is marker-based augmened reality: it looks at what the camera is seeing and if it detects a particular pre-defined pattern (the marker) it will overlay it with something else. Some marker-based AR programs have different markers for different augments, but in Augment there's just one marker and you choose what object to show on it.

Step 3. Run Augment. Go to the "Cubes Gallery" and search for "compact oed". Look for the "Compact OED" object (ignore the "Compact Oxford English Dictionary" if you see it---that was a first attempt with the dimensions wrong, which I need to get removed). Click on it to show the full entry, then click on the eye or the description to load up the object.

Step 4. Point the camera on your phone or tablet at the marker on the table. Marvel! And walk around it.

Since it's my dictionary, I can compare the real one to the virtual. First, here it is beside the marker:

Viewed through Augment, it looks like this from the front:

It looks like I mismeasured the height a bit, but so what. From the right:

From the top (notice the matching dust):

It goes a bit wonky when something is in front of the marker:

From the back:

I made the virtual OED inside Augment, and it was very easy. I took photos of all of the sides of the object, such as this of the front:

(I didn't pay much attention to lighting, but comparing the real to the virtual it's easy to see how better photography would help. Still, I think it's incredible anyway.)

Then I used "Custom Cubes" in Augment to add the pictures to each side of the object and to assign dimensions. Very simple (except that they have height and depth switched, as I consider them, so mind that). I cropped the pictures in the GIMP but you can do it in Augment too.

Augment is very nice. Try making other objects appear! Follow the company at @augmentedev on Twitter if you're interested.

I saw the Robert Wilson/Philip Glass opera Einstein on the Beach about a month ago and wrote about it here. It was astounding. I've kept on listening to it and done some reading about it, and here's something I found about a very small part of the music, about thirty seconds, that intrigued me.

Open up this audio clip in another window and play it a couple of times. It's the first fragment of "Knee Play 3." The knee plays are short pieces that comes between the acts.

Audio clip of the first fragment of "Knee Play 3" from Einstein on the Beach

It's a lot of numbers, eh? Over and over, changing slightly. Just hearing it on its own I found it hard to understand, and I got curious about how Glass had scored it. What did the singers have in front of them?

Well, I still don't know what the full score looks like, but here's what it originally looked like, written in Philip Glass's own hand, taken from Einstein on the Beach, a limited edition book from 1976 edited by Vicky Alliata:

It's an algorithm. It's code.

Here's what's going on, as I understand it. The score written down sets out six bars of music, and the whole notes used there indicate the pitches used to sing the music but without getting into finicky details. The double line at the end means the whole thing is repeated.

"8 (4 + 4) x 2" means count eight, four plus four, one-two-three-four one-two-three-our, for each bar in the score (that is, six times), and repeat it (x 2).

Fully expanded, Glass's formula becomes:

(1) 8 (4 + 4) x 2

    12341234 12341234 12341234 12341234 12341234 12341234
    12341234 12341234 12341234 12341234 12341234 12341234

(2) 7 (4 + 3) x 2

    1234123 1234123 1234123 1234123 1234123 1234123    
    1234123 1234123 1234123 1234123 1234123 1234123

(3) 6 (3 + 3) x 2

    123123 123123 123123 123123 123123 123123
    123123 123123 123123 123123 123123 123123

(4) 5 (3 + 2) x 2

    12312 12312 12312 12312 12312 12312
    12312 12312 12312 12312 12312 12312

I don't know what to make of "count (3 + 2 x 2)," though. If you know what that means I'd like to hear.

See also: Music by Philip Glass, by Philip Glass with Robert T. Jones, which covers Einstein on the Beach, Satyagraha and Akhnaten.

Thanks to Rob van der Bliek and Tim Knight for helping me understand this. Glass uses solfège---where the singer sings the name of a note when singing that note---elsewhere in the opera, and Tim said the counting here reminded him, on similar lines, of the Indian method of solkattu, for speaking rhythms (Glass studied Indian classical music in the 1960s).

There's something oddly beautiful about how Thunderbird shows part of an email message in a thread that's been going on so long that it's deep into quoting and requoting:

Last night I saw the new production of the Philip Glass/Robert Wilson (with Lucinda Childs) opera Einstein on the Beach. It was an astounding performance of an incredible work, and one of the best shows I've ever seen. It's four hours and twenty minutes long---that's 260 minutes---without any intermission, and though the audience can come and go as they please, most people stayed in their seats the entire time. I did. I didn't want to miss any of it. It was enthralling and exciting and hypnotic and marvellous.

• Einstein on the Beach (Pomegranate Arts tour official page)
• Einstein on the Beach (Philip Glass official page)
• Einstein on the Beach (unofficial page with many notes)

When I came into the hall, about fifteen minutes early, it had already started.

That's Kate Moran on the left and Helga Davis on the right. They sat there for a while, just like that, and then the chorus slowly began to walk into the orchestra pit, and then Moran and Davis began to talk. "Knee Play 1" had begun and the opera was underway. The two of them were in all of the "Knee Plays" (which serve as the introduction, intervals between acts, and conclusion), as well as in some of the acts. Both of them were excellent but Kate Moran stood out specially for her work in "Trial/Prison."

The program showed the original storyboard done by Robert Wilson, the director:

K1 there is "Knee Play 1." The square in the bottom right is the white square behind the two in the picture I took.

There's no story to the opera. Or perhaps there is. I don't know. But what you see in the storyboard is, for some of the acts, all there is. In act two, "Train" (top of the middle column) we see two people at the back of a train. They slowly come out onto the back deck, then they go back in. The Philip Glass Ensemble and the choir are playing. It takes about twenty minutes. It's repetitive. It's wonderful.

There are two dances, choreographed by Lucinda Childs, that are glorious and energetic, with the eight dancers (four men and four women) coming together in various combinations, then breaking up and reforming new groups, running and twirling and leaping beautifully.

The final scene, "Spaceship," I can hardly begin to describe. It follows "Bed," which (in this production at least---I suspect it was different earlier) is ten minutes of a long bright white rectangle moving from horizontal to vertical and then ascending up to the top of the stage. The soprano sings a beautiful solo. I admit there were a few minute here where my interest flagged.

But then "Spaceship" began. The curtains pull back to show actors and the Ensemble on a three-storey structure at the back of the stage, the musicians playing and the actors doing something with the light display on the back wall that gets brighter and busier and more complex as the act goes on. A boy in a glass box comes up through a trap door and ascends to the top of the stage, then moves back down and up. A man in a glass box moves horizontally across the stage about twenty feet up. Two dancers wave lights around as if directing a ship in for a landing. A man flies across the stage a few times. The choir is singing. The band is playing. Then the curtain comes down, the music keeps playing, and a little toy rocket in a spotlight moves up from the floor, stage right, to the top, stage left. Then the curtains open again and we see it all again, with the lights and actors and musicians and flying people.

And then in the last "Knee Play" it's back to Kate Doran and Helga Davis, plus a bus driver (whose bus is in the same spot as the white square at the start) who tells a touching story about two lovers in a park.

I'll never forget it. If you have a chance to see it, go.

Further reading: Three relevant articles from The Economist:

• Breaking the waves: An avant-garde opera on a rare world tour
• The music of Philip Glass: Still crazy after all these years
• Lucinda Childs's choreography: Everything old becomes new again

I was chuffed to see Kevin Ford report that the Unix utility file now recognizes MARC records:

$ file 101015_001.mp3 
101015_001.mp3: Audio file with ID3 version 2.3.0, contains: MPEG ADTS, layer III, v1, 192 kbps, 44.1 kHz, Stereo
$ file my-cats.jpg
my-cats.jpg: JPEG image data, JFIF standard 1.02
$ file OL.20100104.01.mrc
OL.20100104.01: MARC21 Bibliographic

If you download the source and look at the magic/Magdir/marc21 file you'll see what makes it work. Every file type has some "magic" that lets you identify it:

#--------------------------------------------
# marc21: file(1) magic for MARC 21 Format
#
# Kevin Ford (kefo@loc.gov)
# 
# MARC21 formats are for the representation and communication
# of bibliographic and related information in machine-readable
# form.  For more info, see http://www.loc.gov/marc/


# leader position 20-21 must be 45
20      string  45      

# leader starts with 5 digits, followed by codes specific to MARC format
>0      regex/1 (^[0-9]{5})[acdnp][^bhlnqsu-z]  MARC21 Bibliographic
!:mime  application/marc
>0      regex/1 (^[0-9]{5})[acdnosx][z] MARC21 Authority
!:mime  application/marc
>0      regex/1 (^[0-9]{5})[cdn][uvxy]  MARC21 Holdings
!:mime  application/marc
0       regex/1 (^[0-9]{5})[acdn][w]    MARC21 Classification
!:mime  application/marc
>0      regex/1 (^[0-9]{5})[cdn][q]     MARC21 Community
!:mime  application/marc

# leader position 22-23, should be "00" but is it?
>0      regex/1 (^.{21})([^0]{2})       (non-conforming)
!:mime  application/marc

A small victory now that a basic Unix/Linux utility can recognize a key library file format, but as Kyle Bannerjee put it on the Code4Lib mailing list, "I'm not sure whether to laugh or cry that it's a sign of progress that a 40 year old utility designed to identify file types is now just beginning to be able to recognize a format that's been around for almost 50 years."

About a month ago I was at Great Lakes THAT Camp 2012 (staying in a former convent) and I made a graph of tweeting action and posted about it at #thatcamp hashtags the Tony Hirst way.

The last couple of days I was at Code4Lib North 2012 (staying in a former convent) and on the train back home I made a graph of tweeting action, this time using ggplot2 in R the way Tony Hirst had. This should let you recreate it:

> require(ggplot2)
> require(twitteR)
> require(plyr)
> tweets <- searchTwitter("#c4ln", n=500)
> tw <- twListToDF(tweets) # turn results into a data frame
> tw1 <- ddply(tw, .var = "screenName", .fun = function(x) {return(subset(x, created %in% min(created),select=c(screenName,created)))})
> tw2 <- arrange(tw1, -desc(created))
> tw$screenName <- factor(tw$screenName, levels = tw2$screenName)
> library(stringr)
> trim <- function (x) sub('@','',x)
> tw$rt=sapply(tw$text,function(tweet) trim(str_match(tweet,"^RT (@[[:alnum:]_]*)")[2]))
> tw$rtt=sapply(tw$rt,function(rt) if (is.na(rt)) 'T' else 'RT')
> png(filename = "20120525-c4ln-tweets.png", height=800, width=600)
> ggplot(tw)+geom_point(aes(x=created,y=screenName,col=rtt))
> dev.off()

(If you don't have the twitteR (or ggplot2 or plyr) package installed then run install.packages("twitteR") (or the other package name) to get it.)

What does this show us? Original tweets and old-style RT retweets, first off. That a few people started using the hash tag early(@ajmcalorum was first off the bat). That a few people used it a lot (the usernames, lower down, with a lot of dots along their horizontal line) and more people used it only once (usually retweets). That there were a number of tweets on 23 May (the day before, when people were travelling to Windsor to get there), but things started in earnest on the first day, 24 May.

I have an Acer Liquid E phone running Android. All of the photographs it takes are timestamped in the internal EXIF metadata to 8 December 2002. Turns out there's a bug in the camera app: it seems that instead of using "insert correct date here" in libcamera.so somehow the December 2002 date got hardcoded in.

The timestamp on the file itself is correct, however, so I wrote this script to use that to edit the EXIF times. It uses ExifTool, which is probably in your package manager:

#!/bin/bash

for I in 2002-12-08*jpg
do
  TIMESTAMP=`stat --printf "%y" "$I"`
  NAME=`echo "$I" | sed 's/2002-12-08 12.00.00-//'`
  NAME="acer-$NAME"
  echo "$I --> $NAME ($TIMESTAMP)"
  cp -p "$I" $NAME
  exiftool -P -DateTimeOriginal="$TIMESTAMP" -CreateDate="$TIMESTAMP" $NAME
done

For example, a file named 2002-12-08 12.00.00-460.jpg timestamped 2012-04-10 19:30 would have the DateTimeOriginal and CreateDate EXIF fields corrected to to 2012-04-10 19:30 and the file would be renamed to acer-460.jpg. The original file is left untouched.

It worked for me, and it won't delete your files, so use it if it helps. Make sure that whenever you copy files off your Acer phone you use cp -p to preserve the original timestamp. Otherwise your photos will have their internal dates set to today!

This is the fifth and last in a series about using R to look at reference desk statistics recorded in LibStats. Previously:

• Ref desk 1: LibStats
• Ref desk 2: Questions asked per week at a branch
• Ref desk 3: Comparing question.type across branches
• Ref desk 4: Calculating hours of interactions

I've been making some other charts showing other kinds of ratios and calculations but I'm going to skip to one last pair of charts where I bring in the number of our students to figure out how many students we help with research help each week and for how long.

First, a brief review of the four branches of the York University Libraries system we're looking at:

• Scott is arts, humanities and social sciences, and the building includes the map library, the archives, and music/film library
• Bronfman is business
• Frost is on the Glendon campus in another part of the city and handles all of the students there
• Steacie is science, engineering and health

(Osgoode is law but they don't use LibStats so we'll forget about them for now.)

I calculated how many "home students" each library has. Bronfman handles everyone in the business school and in the administrative studies program in another faculty. Steacie handles everyone in the science and health faculties (except psychology, which is handled at Scott). Frost handles everyone at Glendon. Scott handles everyone else. The York University Factbook let me look up how many students were in each faculty, and I did a bit of adding and subtracting and figured out:

• Scott has 34,388 "home students"
• Bronfman has 6,050
• Frost has 2,677
• Steacie has 10,018

That's 53,133 students total, as of last fall. (We have about 43 librarians and archivists, for a ratio of 1235 students to each librarian, which is one of the worst in Canada.)

You can figure out something very similar for your library, probably.

With those numbers, we're all set for some more work in R.

First, I make a libstats.bigscott data frame, which gloms together all of the reference desk activities that happen in the Scott Library building (which as I said contains three smaller libraries) into one. This is necessary to group together all possible arts/humanities/social sciences questions. These lines below rename certain library.name fields by saying, for example for SMIL, for every entry in this data frame where library.name equals "SMIL", make library.name equal "Scott." Nice example of vector thinking in R.

> libstats.bigscott <- libstats
> libstats.bigscott$library.name[libstats.bigscott$library.name == "SMIL"] <- "Scott"
> libstats.bigscott$library.name[libstats.bigscott$library.name == "ASC"] <- "Scott"
> libstats.bigscott$library.name[libstats.bigscott$library.name == "Maps"] <- "Scott"
> libstats.bigscott$week <- as.Date(cut(as.Date(libstats.bigscott$timestamp, format="%m/%d/%Y %r"), "week", start.on.monday=TRUE))

Next, use our old friend ddply to count how many research questions are asked each week.

> research.users <- ddply(subset(libstats.bigscott,
                                 question.type %in% c("4. Strategy-Based", "5. Specialized")),
                          .(library.name, week), nrow)
> names(research.users)[3] <- "users"
> research.users$user.ratio <- NA
> head(research.users)
> library.name       week users user.ratio
1     Bronfman 2011-01-31    48         NA
2     Bronfman 2011-02-07    80         NA
3     Bronfman 2011-02-14    42         NA
4     Bronfman 2011-02-21    61         NA
5     Bronfman 2011-02-28    53         NA
6     Bronfman 2011-03-07    59         NA

Now, another probably heinous non-R way of dividing the number of users (or, actually, questions) each week by the number of "home students":

> for (i in 1:nrow(research.users)) { 
    if (research.users[i,1] == "Bronfman"          ) { research.users[i,4] = research.users[i,3] / 6050  }
    if (research.users[i,1] == "Frost"             ) { research.users[i,4] = research.users[i,3] / 2677  }
    if (research.users[i,1] == "Scott"             ) { research.users[i,4] = research.users[i,3] / 34388 }
    if (research.users[i,1] == "Steacie"           ) { research.users[i,4] = research.users[i,3] / 10018 }
  }
> library.name       week users  user.ratio
1     Bronfman 2011-01-31    48 0.007933884
2     Bronfman 2011-02-07    80 0.013223140
3     Bronfman 2011-02-14    42 0.006942149
4     Bronfman 2011-02-21    61 0.010082645
5     Bronfman 2011-02-28    53 0.008760331
6     Bronfman 2011-03-07    59 0.009752066

user.ratio there is what we're after. It looks low, doesn't it? Multiply it by 100 to get a percentage. It's still low.

The y-axis is per cent, so this shows that usually through term time we see give research help to under 1% of our students. There are a few weeks in some branches where it gets above that, but it's never above 1.5%.

That really surprised me. I have no idea what the numbers are like at other universities. If you figure it out for where you work, let me know. Perhaps one per cent is a common figure? Could it be five per cent at some universities? It would have to be a small university, I think, or have a lot of librarians.

Know that we know how many students we help with research, I wondered how long we spend helping them. More calculations in R, using ref.desk.spent, the function I defined in the last post to add up an estimate of how much time is spent at the desk. Here we break it down by branch by week, create a research.time.bigscott data frame, which I then merge with research.users so I can divide to create the research.mins.ratio which is what I'm after:

> research.time.bigscott <- data.frame(library.name = factor(), week = factor(), research.mins = numeric())
> branches <- c("Scott", "Frost", "Bronfman", "Steacie")
> for (i in 1:length(branches)) {
    branchname <- branches[i]
    for (j in 1:length(weeks)) {
      spent <- desk.time.spent(ddply(subset(libstats.bigscott,
                                            library.name == branchname & week==weeks[j] &
                                            question.type %in% c("4. Strategy-Based", "5. Specialized")),
                                     .(time.spent), nrow))
      rbind(research.time.bigscott,
            data.frame(library.name = branchname, week = weeks[j], research.mins = spent)) -> research.time.bigscott
    }
  }
> research.users$week <- as.factor(research.users$week) # Necessary for merge to work cleanly
> research.time.bigscott <- merge(research.time.bigscott, research.users, by=c("library.name", "week"))
> research.time.bigscott$research.mins.ratio <- research.time.bigscott$research.mins / research.time.bigscott$users
> head(research.time.bigscott)
  library.name       week research.mins users  user.ratio research.mins.ratio
1     Bronfman 2011-01-31           758    48 0.007933884            15.79167
2     Bronfman 2011-02-07          1340    80 0.013223140            16.75000
3     Bronfman 2011-02-14           595    42 0.006942149            14.16667
4     Bronfman 2011-02-21           997    61 0.010082645            16.34426
5     Bronfman 2011-02-28           775    53 0.008760331            14.62264
6     Bronfman 2011-03-07           901    59 0.009752066            15.27119
> xyplot(research.mins.ratio ~ as.Date(week) | library.name, data = research.time.bigscott,
         type = "h",
         ylab = "Length of average research interaction (minutes)",
         xlab = "Week",
         main = "Average length of research interactions (Scott includes ASC/Maps/SMIL)",
         sub = paste("From Feb 2011 to", up.to.week),
         abline=list(h=15, lty=3, col="lightgrey"),
        )

In this xyplot command I throw in an extra abline to draw a dashed light grey line along y=15 to help point out that generally we spend about fifteen minutes on each research interaction.

The Steacie library stands out from the others, and there are some peaks here and there, but overall we spend on average about fifteen minutes on each research interaction with students.

Put those two charts together and it shows that during term time we spend on average about fifteen minutes a week giving research help to each of under one per cent of our students.

This is Hat Rack, by Marcel Duchamp, at the Art Institute of Chicago. The label dates it at 1964 and adds "(1916 original now lost)."

Here is the signature on the bottom:

Seeing Duchamp's work in any gallery is a joy.

(Fourth in a series about using R to look at reference desk statistics recorded in LibStats. Third was Ref desk 3: Comparing question.type across branches.)

The last two posts looked at two straightforward breakdowns of reference desk activity at a library with several branches: questions by branch (to look at all activity at a single branch) and branches by question (to compare how many questions of the same type are asked at all branches). Both are very useful and lead to interesting questions and discussions. I'm sticking to numbers and charts here, but they are just the beginning of a larger discussion about the purpose of the reference desk is and how it works at a library---and how reference desk statistics are recorded, and what the act of recording means.

It's important to talk about all of that, and I do enjoy those discussions. But right now I'm going to make another chart.

Another fact we record about each reference desk interaction is its duration, which in our libstats data frame is in the time.spent column. As I explained in Ref Desk 1: LibStats, these are the options:

• NA ("not applicable," which I've used, though I can't remember why)
• 0-1 minute
• 1-5 minutes
• 5-10 minutes
• 10-20 minutes
• 20-30 minutes
• 30-60 minutes
• 60+ minutes

We can use this information to estimate the total amount of time we spend working with people at the desk: it's just a matter of multiplying the number of interactions by their duration.

Except we don't know the exact length of each duration, we only know it with some error bars: if we say an interaction took 5-10 minutes then it could have taken 5, 6, 7, 8, 9, or 10 minutes. 10 is 100% more than 5: relatively that's a pretty big range. (Of course, mathematically it makes no sense to have a 5-10 minute range and a 10-20 minute range, because if something took exactly 10 minutes it could go in either category.)

Let's make some generous estimates about a single number we can assign to the duration of reference desk interactions.

This means that if we have 10 transactions of duration 1-5 minutes we'll call it 10 * 5 = 50 minutes total. If we have 10 transactions of duration 20-30 minutes we'll call it a 10 * 25 = 250 minutes total. These estimates are arguable but I think they're good enough. They're on the generous side for the shorter durations, which make up most of the interactions.

Next, we need to figure out how many interactions happened of each possible duration. This is easily done using ddply as we did before, but this time to count by time.spent:

> tmp <- ddply(libstats, .(time.spent, week), nrow)
> head(tmp)
  time.spent       week   V1
1 0-1 minute 2011-01-31  811
2 0-1 minute 2011-02-07 1220
3 0-1 minute 2011-02-14 1177
4 0-1 minute 2011-02-21  592
5 0-1 minute 2011-02-28  949
6 0-1 minute 2011-03-07 1037

This tells us that across our library system in the week starting 2011-01-31 there were 811 interactions that took 0-1 minutes. What else happened that week? How many interactions were there of all other durations?

> subset(tmp, week == " 2011-01-31")
       time.spent       week  V1
1      0-1 minute 2011-01-31 811
65    1-5 minutes 2011-01-31 299
129 10-20 minutes 2011-01-31  72
212 20-30 minutes 2011-01-31  28
274 30-60 minutes 2011-01-31   6
332  5-10 minutes 2011-01-31 153
447          <NA> 2011-01-31  11

What's the total amount of time spent dealing with people at the desk? We'll use our rule defined above, and just ignore the NAs:

> 811*1 + 299*5 + 72*15 + 28*25 + 6*40 + 153*10
[1] 5856
> round(5856/60)
[1] 98

Answer: about 98 hours.

That's just one week, though. How can we figure it out for each week, and then chart that? I'd hoped to be able to do it in a nice R way with one of the apply functions, because I was in exactly the situation that Neil Saunders describes in A brief introduction to "apply" in R:

At any R Q&A site, you'll frequently see an exchange like this one:

Q: How can I use a loop to [...insert task here...] ?

A: Don't. Use one of the apply functions.

I'd hoped to go at it with some kind of arrangement with a data frame of duration factors and estimated times:

     duration       estimate
   0-1 minute              1
  1-5 minutes              5
 5-10 minutes             10
10-20 minutes             15
20-30 minutes             25
30-60 minutes             40
  60+ minutes             60

Then I'd apply a function to each row of tmp that would check the value of time.spent, look up the equivalent duration in this data frame to find the estimate that goes with it, multiply V1 * estimate and then put the result in an interaction.time column. I took a stab at but couldn't get it working, so I did it with a loop. It's not the R way, but it worked, and I'll take another crack at it another day.

So to do it the ugly way, first I define a function:

> desk.time.spent <- function(x) {
    if (nrow(x) == 0) { return(0) }
    sum = 0
    for (i in 1:nrow(na.omit(x))) { 
      if      (x[i,1] == "0-1 minute"   ) { sum = sum + 1 * x[i,2]  }
      else if (x[i,1] == "1-5 minutes"  ) { sum = sum + 5 * x[i,2]  }
      else if (x[i,1] == "5-10 minutes" ) { sum = sum + 10 * x[i,2]  }
      else if (x[i,1] == "10-20 minutes") { sum = sum + 15 * x[i,2] }
      else if (x[i,1] == "20-30 minutes") { sum = sum + 25 * x[i,2] }
      else if (x[i,1] == "30-60 minutes") { sum = sum + 40 * x[i,2] }
      else if (x[i,1] == "60+ minutes"  ) { sum = sum + 65 * x[i,2] }
    }
    return(sum)
  }

Then I create an empty data frame and reset which branches to look at, just in case I fiddled that somewhere before:

> interaction.time <- data.frame(library.name = factor(), week = factor(), desk.mins = numeric())
> interaction.time$week = as.Date(interaction.time$week) # Can't set this in the line above
> branches <- levels(libstats$library.name)

Then I loop through the branches, using ddply to calculate how many of each question was asked each week, and passing that data frame to the desk.time.spent() function for multiplication. It returns the number of minutes spent that week helping people, and then rbind matches things up to add a new row to the interaction.time data frame.

> for (i in 1:length(branches)) {
    branchname <- branches[i]
    write (branchname, stderr())
    for (j in 1:length(weeks)) {
      spent <- desk.time.spent(ddply(subset(libstats, 
    library.name == branchname & week==weeks[j]), .(time.spent), nrow))
      rbind(interaction.time, 
        data.frame(library.name = branchname, week = weeks[j], desk.mins = spent)) -> interaction.time
    }
  }
> tail(interaction.time)
    library.name       week desk.mins
429      Steacie 2012-02-27      1460
430      Steacie 2012-03-05      1180
431      Steacie 2012-03-12      1110
432      Steacie 2012-03-19      1286
433      Steacie 2012-03-26      1042
434      Steacie 2012-04-02      1523

(desk.mins is actually time spent not only at the desk but in offices and anywhere else. Just ignore the name, or change it---I'm copying this from a script that does something slightly different.)

> xyplot(desk.mins/60 ~ as.Date(week)|library.name, data = interaction.time,
         type = "h",
         ylab = "Hours",
         xlab = "Week",
         main = "Hours of interactions",
         sub = paste("From Feb 2011 to", up.to.week),
        )

Let's narrow things down and look at only research questions (4s and 5s) at the reference desk. Librarians and archivists often help people with research questions in an office, not at the desk---such practices vary from library to library and discipline to discipline, and of course not all branches have reference/research desks---and those aren't counted here. Nevertheless, it's an interesting view on what happens at the desk, and it's an instructive example about how to slice the data more finely, but as always we have to remember what data is being shown.

> branches <- c("Bronfman", "Frost", "Scott", "Steacie")
> research.interaction.time <- data.frame(library.name = factor(), week = factor(), research.mins = numeric())
> for (i in 1:length(branches)) {
    branchname <- branches[i]
    write (branchname, stderr())
    for (j in 1:length(weeks)) {
      spent <- desk.time.spent(ddply(subset(libstats,
                                          library.name == branchname & week==weeks[j] &
                                          location.name %in% c("Consultation Desk", "Drop-in Desk", "Reference Desk") &
                                          question.type %in% c("4. Strategy-Based", "5. Specialized")
                                          ),
                                   .(time.spent), nrow))
      rbind(research.interaction.time, data.frame(library.name = branchname, week = weeks[j], 
      research.mins = spent)) -> research.interaction.time
    }
  }
> interaction.time <- merge(interaction.time, research.interaction.time, by=c("week", "library.name"))
> tail(interaction.time)
          week library.name desk.mins research.mins
243 2012-03-26        Scott      2856          1946
244 2012-03-26      Steacie      1042           110
245 2012-04-02     Bronfman       781           270
246 2012-04-02        Frost       351            55
247 2012-04-02        Scott      1467           960
248 2012-04-02      Steacie      1523            20
> xyplot(research.mins/60 ~ as.Date(week) | library.name, data = interaction.time,
         type = "h",
         ylab = "Hours",
         xlab = "Week",
         main = "Research desk research interactions (hours)",
         sub = paste("From Feb 2011 to", up.to.week),
        )

In the next post, the last of this short series, I'll bring in the number of students and do a couple of calculations based on that.