robbrit

Keybase proof

I hereby claim:

• I am robbrit on github.
• I am robbrit (https://keybase.io/robbrit) on keybase.
• I have a public key ASCUVTrohgtc4kUEs-wThDE7PkxLcrnt1qnm7CoNCoNj5wo

To claim this, I am signing this object:

robbrit

<canvas id = "cvs"></canvas>

<script>
/**
 * HSV to RGB color conversion
 *
 * H runs from 0 to 360 degrees
 * S and V run from 0 to 100
 * 
 * Ported from the excellent java algorithm by Eugene Vishnevsky at:

robbrit

$ ->
  c = $("canvas").get(0)
  ctx = c.getContext "2d"
  #ctx.font="20px sans-serif"
  #mainLoop
  #running
  initial_sleep_length=250
  sleep_length=initial_sleep_length
  img_data_for_player=ctx.createImageData c.width,c.height
  tile_size=25

robbrit

function rnorm(mean, sd){
  if (mean === undefined){
    mean = 0;
  }
  if (sd === undefined){
    sd = 1;
  }

  do {
    var x1 = 2.0 * Math.random() - 1.0;

robbrit

# calculates the best vector of cointegration for a group of series.
#
# @param series A matrix with the series in columns
#
# @return The best vector of cointegration.
#
get_coint = function(series){
  # does this by performing a regression of time on
  # a linear combination of the series, but only take
  # the slope parameter

robbrit

# oil production per day by country
production = read.csv("production.csv", header = T)
# proven oil reserves by country
reserves = read.csv("reserves.csv", header = T)
# oil consumption by country
consumption = read.csv("consumption.csv", header = T)

# the total amount of oil production per year
total_production = sum(production$Production) * 365

robbrit

oil = read.csv("oil.csv", header = T)

# convert cubic metres to barrels
total = ts(oil$Total * 6.28981077 / 1000, start = 1971, end = 2010)
t = 1:length(total)

linear = lm(total ~ t)
expn   = lm(log(total) ~ t)

# do a linear model

robbrit

[1] "Average wages for degree in education:"
[1] "Confidence for 1986: 45951.512708 to 47462.916536"
[1] "Confidence for 2006: 48453.487226 to 49640.726011"
[1] "Standard Deviation for 1986: 28759.942381"
[1] "Standard Deviation for 2006: 37696.910023"
[1] "Median Confidence for 1986: 49299.946203 to 51002.542615"
[1] "Median Confidence for 2006: 47308.440547 to 48645.865039"
[1] ""
[1] "Average wages for degree in fine arts:"
[1] "Confidence for 1986: 30804.704353 to 34896.729876"

robbrit

data2006 <- read.csv("census2006.csv", header = TRUE)
data1986 <- read.csv("census1986.csv", header = TRUE)

# Get the CPI for 1986, 2006, and 2010
cpi2006 <- 1.091
cpi1986 <- 0.656
cpi2010 <- 1.165

calc_avg <- function(w1986, w2006, message, png_file){
  # scale by CPIs to give 2010 dollars

robbrit

data2006 <- read.csv("census2006.csv", header = TRUE)
data1986 <- read.csv("census1986.csv", header = TRUE)

# Get the CPI for 1986, 2006, and 2010
cpi2006 <- 1.091
cpi1986 <- 0.656
cpi2010 <- 1.165

calc_avg <- function(w1986, w2006, message, png_file){
  # scale by CPIs to give 2010 dollars