for(p in cran.packages) {
    if(!suppressWarnings(require(p, character.only = TRUE, quietly = TRUE))) {
        cat(paste(p, "missing, will attempt to install\n"))
        install.packages(p)
    }
    else {
        cat(paste(p, "installed OK\n"))
    }
}

Rainy City Music / Dub Phizix
Waveform / Phace & Misanthrop
Thorn / BTK
Kingpin (Calyx & Teebee Remix) / Friction & Skream feat Scrufizzer P Money & Riko Dan
Ignition (TR Tactics Remix) / Optiv & BTK feat Ryme Tyme
Beyond Our Dreams / J Majik & Wickaman
Hey Brother / Nu Elementz & Ruffstuff
Quartz Controlled / June Miller & Mindmapper
Roxy / Nymfo & State Of Mind
Run / Spinline
Pacman (Ram Trilogy Remix) / Ed Rush & Optical
The Unspoken / Technimatic
Dribble VIP / Muffler
This Is My World (Icicle Remix) / Jade
Midnight Nation / Optiv
Riptide / Optiv & BTK
Right Here / Ulterior Motive
Everything Is Everything / Utah Jazz & Alex Reece
Let It Be (DJ Marky & SPY Remix) / Artificial Intelligence feat Steo
Stand Alone / Artificial Intelligence
Guardian / SpectraSoul
Epic / Mikal
From Autumn To Ashes / June Miller
Blackout / Hive
Drop It (Optiv Remix) / BTK
The Kindred / Ed Rush & Optical
Arcadia / Ed Rush
Epic / Mikal
ni ten ichi ryu / teebee
Run It Like The President (feat. T Man) / Dub Phizix & Skeptical
Rainy City Music / Dub Phizix

_.map = _.collect = function(obj, iterator, context) {
var results = [];
if (obj == null) return results;
if (nativeMap && obj.map === nativeMap) return obj.map(iterator, context);
each(obj, function(value, index, list) {
  results.push(iterator.call(context, value, index, list));
});
return results;
};

map :: (a -> b) -> [a] -> [b]
map _ []     = []
map f (x:xs) = f x : map f xs

get.location <- function(l) {
  split.location <- tryCatch(strsplit(l,",")[[1]], error= function(e) return(c(NA, NA)))
  clean.location <- gsub("^ ", "", split.location)
  if(length(clean.location) > 2) {
    return(c(NA, NA))
  } else {
    return(clean.location)
  }
}
city.state <- lapply(ufo$Location, get.location)

library(ggplot2)
g <- ggplot(data=iris, aes_string(x='Sepal.Length', y='Sepal.Width', color='Petal.Length'))
g + geom_point() + facet_wrap(~Species) + geom_smooth(method='lm')

In [2]: execfile("ml1.py")

In [3]: d.head()
Out[3]: 
                     DateOccurred  DateReported  Location  YEAR  MONTH
Location YEAR MONTH                                                   
AB       2004 3                 1             1         1     1      1
         2005 4                 1             1         1     1      1
         2010 7                 1             1         1     1      1
AK       1990 1                 1             1         1     1      1
              3                 1             1         1     1      1

import pandas as pd
from datetime import datetime
from numpy import nan
import re

re_date = re.compile("\d{4}[01]\d[0123]\d")

with open("ufo.tsv", "w") as w:
    with open("ufo_awesome.tsv") as f:
        for l in f:
            s = l.split("\t")
            if len(s) == 6:
                if re_date.match(s[0]) and re_date.match(s[1]):
                    if int(s[0][4:6]) > 0 and int(s[0][6:8]) > 0:
                        w.write(l)

def get_loc_code(loc):
    l = loc.split()
    if len(l) == 2 and len(l[1]) == 2:
        return l[1].upper()
    else:
        return nan

d = pd.read_table("ufo.tsv",
                  header=None,
                  names=["DateOccurred", "DateReported",
                           "Location", "ShortDescription",
                           "Duration", "LongDescription"
                         ],
                  parse_dates=["DateOccurred", "DateReported"],
                  date_parser=lambda x: datetime.strptime(x, '%Y%m%d')
                  )
d = d.drop(["LongDescription", "ShortDescription", "Duration"], axis=1)
d.Location = d.Location.apply(get_loc_code)
d = d.dropna()
d = d[d.DateOccurred > datetime(1990, 01, 01)]
d["YEAR"] = d.DateOccurred.apply(lambda x: x.year)
d["MONTH"] = d.DateOccurred.apply(lambda x: x.month)
d = d.groupby(["Location", "YEAR", "MONTH"]).count()