I love using array (vector) languages. Amongst them J (http://www.jsoftware.com) and R (http://www.r-project.org/) are my two most preferred languages. Why? Because, they enable exploratory, interactive programming. This is such an under appreciated feature that I also became aware of it, only, very recently.
As a part of overseeing graduate students’ work (as a part of my job) I get asked questions about student’s theses research. In our discussion it will, more often than not, happen that the student will say “Let me show you how I did it” and will want to show me how they went about doing whatever it is that they tried to do. I observe what they are doing and I start getting questions in my head. If the student has thought about my questions they’ll be able to answer them promptly, but if they haven’t thought of it we’re stuck! I ask my questions. Either I let the student try to figure it out and provide a reasonable answer or get ready to do some very rough/quick exploratory analysis. Whenever this situation occurs I always remember my graduate school years. When I was a graduate student I didn’t know R (only a little Python…most definitely not J) or any other exploratory/interactive language and I would spend hours to find basic information which now takes me a couple of minutes. Let me try to explain with an example.
Let’s say a student is very annoyed with all the stuff that [s]he’s hearing about racism/militarization of police (Michael Brown Case Wikipedia article and Death of Eric Garner Wikipedia article) and has read/heard about Radley Balko’s Rise of the Warrior Cop: The Militarization of America’s Police Forces and has definitely read the NY Times article: In Wake of Clashes, Calls to Demilitarize Police and wants to recreate the map found on Mapping the Spread of the Military’s Surplus Gear. [S]He has found out that the data can be downloaded from https://github.com/TheUpshot/Military-Surplus-Gear. The student comes to me and asks, “Vijay, I’m having a difficult time generating these numbers!” What they mean is: “I’m having a difficult time generating count of items grouped by counties.” I so wish some student had come to me with this problem. But, then I remember the graduate school Vijay (who didn’t care about anything in the world but himself) and everything’s fine! So, how do I help this hypothetical student? Well, I use what I know to explore this data and also show him/her how they can do this all by themselves.
I will assume that you have access to a computer (sorry a tablet/iphone/android just won’t do for now) on which you have installed R and the package data.table (http://cran.r-project.org/web/packages/data.table/index.html) installed. Also, you have 1033-program-foia-may-2014.csv stored in a directory somewhere and your R session’s current working directory (inquired by
getwd() in an R session) workspace is currently that directory. Below is the session of my usage of R to get this information from the csv file.
$ represents shell prompt and
> represents R prompt.
$ R --no-init-file > getwd()  "/v/tmp" > list.files()  "1033-program-foia-may-2014.csv" > library(data.table) data.table 1.9.4 For help type: ?data.table *** NB: by=.EACHI is now explicit. See README to restore previous behaviour. > d <- fread('1033-program-foia-may-2014.csv') > > d State County NSN Item Name Quantity UI 1: AK ANCHORAGE 1005-00-073-9421 RIFLE,5.56 MILLIMETER 1 Each 2: AK ANCHORAGE 1005-00-073-9421 RIFLE,5.56 MILLIMETER 1 Each 3: AK ANCHORAGE 1005-00-073-9421 RIFLE,5.56 MILLIMETER 1 Each 4: AK ANCHORAGE 1005-00-073-9421 RIFLE,5.56 MILLIMETER 1 Each 5: AK ANCHORAGE 1005-00-073-9421 RIFLE,5.56 MILLIMETER 1 Each --- 243488: WY WESTON 1005-00-589-1271 RIFLE,7.62 MILLIMETER 1 Each 243489: WY WESTON 1005-00-589-1271 RIFLE,7.62 MILLIMETER 1 Each 243490: WY WESTON 1005-00-589-1271 RIFLE,7.62 MILLIMETER 1 Each 243491: WY WESTON 1005-00-589-1271 RIFLE,7.62 MILLIMETER 1 Each 243492: WY WESTON 1005-00-589-1271 RIFLE,7.62 MILLIMETER 1 Each Acquisition Cost Ship Date 1: 499 2012-08-30 2: 499 2012-08-30 3: 499 2012-08-30 4: 499 2012-08-30 5: 499 2012-08-30 --- 243488: 138 2008-10-20 243489: 138 2008-10-20 243490: 138 2008-10-20 243491: 138 2008-10-20 243492: 138 2008-10-20 > setnames(d, gsub(" ", "_",colnames(d))) # ?setnames > d[,list(State,County,.N),by=list(State,County,Item_Name)] State County 1: AK ANCHORAGE 2: AK ANCHORAGE 3: AK ANCHORAGE 4: AK ANCHORAGE 5: AK ANCHORAGE --- 84404: WY WASHAKIE 84405: WY WASHAKIE 84406: WY WASHAKIE 84407: WY WASHAKIE 84408: WY WESTON Item_Name State 1: RIFLE,5.56 MILLIMETER AK 2: HOLDER,MULTIPLE MAGAZINE AK 3: CAMOUFLAGE SCREENING SYSTEM,SNOW LIGHT WEIGHT RADAR TRANSPARENT AK 4: CAMOUFLAGE NET SYSTEM,RADAR SCATTERING AK 5: BINOCULAR AK --- 84404: PISTOL,CALIBER .45,AUTOMATIC WY 84405: TRUCK,UTILITY WY 84406: CARRIER,CARGO WY 84407: MODULAR SLEEP SYSTE WY 84408: RIFLE,7.62 MILLIMETER WY County N 1: ANCHORAGE 123 2: ANCHORAGE 1 3: ANCHORAGE 1 4: ANCHORAGE 2 5: ANCHORAGE 2 --- 84404: WASHAKIE 10 84405: WASHAKIE 5 84406: WASHAKIE 1 84407: WASHAKIE 1 84408: WESTON 7 > write.csv(d[,list(State,County,.N),by=list(State,County,Item_Name)],'aggregation.csv') > list.files()  "1033-program-foia-may-2014.csv" "aggregation.csv" >
Now you can generate all the pretty maps by joining
aggregation.csv with your counties shapefile. I’m not quite sure how the items were aggregated to generate the map found on Mapping the Spread of the Military’s Surplus Gear.