actuaryactually

#===============================================================================================
#Monty Hall Problem - Game Theory around Choice

#Three options available.  Two options have no prize, third option delivers a prize.  You select one, and 
#then the host of the game removes one of the losing options.  You are then offered the chance to reselect
#For this code, I assume that you always switch from your first choice.  It then examines the number of times
#you win based on this "always-switching" strategy

#===============================================================================================

actuaryactually

#--------------------------------------------------------------------------------------#
# 
# FANPLOT EXAMPLE
# 
# Made famous by the BoE inflation report, fanplots are good for showing distributions where 
# there is a time series. In this script I have created a toy dataset that looks like a dispersing time series
# to illustrate the fan syntax and .
# In future posts I'll show how to build a time-series using arbitrary data for an equity share 
#
# D Menezes 20161111

actuaryactually

setwd("C:/OldWorld/Blog/DataWrangling")

#Output console results to file:

# anova test
anova_test = aov(yield ~ block + N * P + K, npk)
anova_test

# t test
t_test = t.test(1:10, y = c(7:20))

actuaryactually

#preliminaries:
  wkg.dir = "C:/OldWorld/Blog/DataWrangling" # define where we'll work, save files, etc. for this post
  setwd(wkg.dir)
  
#Install a helper piece of code (pacman) and then load required code libraries in one-go:  
  install.packages("pacman")
  pacman::p_load(ChainLadder,ggplot2,data.table,tidyr)
  #Step 1: load some data and view
  RAA
  plot(RAA/1e3,lattice=TRUE, main="RAA Data by Accident Year (USD000s)",xlab="Dev Yrs")

actuaryactually

#PRELIMINARIES:
  install.packages("pacman")
  pacman::p_load(XML,rvest,magrittr,RCurl,stringi)

#Step 1: Establish location names and addresses for Harvey Norman Electrical stores

#draw in address locations from website:
  url<-"http://www.harveynorman.co.nz/store-finder.html"

actuaryactually

#PRELIMINARIES:
pacman::p_load(XML)

#recode as data frame :
output.addresses.df<-data.frame(c(output.addresses))
output.addresses.df<-cbind(output.addresses.df,long=1,lat=2)


#assign long/lat
for (i in 1:length(output.addresses)){

actuaryactually

#PRELIMINARIES:
  install.packages("pacman")
  pacman::p_load(XML,rvest,magrittr,RCurl,stringi)

#Step 1: Establish location names and addresses for Harvey Norman Electrical stores

#draw in address locations from website:
  url<-"http://www.harveynorman.co.nz/store-finder.html"
  rvest_target<-read_html(url)
  rvest_table_nodes <- html_nodes(rvest_target,"p.address ")

actuaryactually

#PRELIMINARIES:
pacman::p_load(data.table)

mydat1 <- fread('http://quakesearch.geonet.org.nz/csv?region=wellington&minmag=1&startdate=2014-05-01&enddate=2017-1-30T17:00:00')
head(mydat)

#the following site contains historic earthquake data for New Zealand: http://quakesearch.geonet.org.nz/
#Using the build query option on this page, we can look-up quakes in specific regions and time frames.  
#Let's look up Wellington's seismic profile since 1 Jan 2011

actuaryactually

pacman::p_load(ggmap,fitdistrplus,maps,mapproj,ggrepel)

wellington = c(lon = 174.7762, lat =  -41.28646)
wellington.map=get_map(location=wellington,zoom=8,color="bw")
    
r <- ggmap(wellington.map,extent="panel",maprange=FALSE)%+% mydat + aes(x = long, y = lat)
r <- r + geom_density2d()
r <- r + stat_density2d(aes(fill = ..level.., alpha = ..level..), size = 0.1, bins = 100, geom = 'polygon') 
r <- r + scale_fill_gradient(low = "green", high = "red") + scale_alpha(range = c(0.00, 0.25), guide = FALSE) 

actuaryactually

#FITTING SPLINES IN R
# D Menezes 20170531


#0. PRELIMINARIES
#install.packages("pacman") #only needed if you don't have it installed
pacman::p_load("splines")

#1. INPUT VALUES
input.x<-c(seq(from=0,to=120,by=12)) #assuming we have annual spline points