MonkmanMH

# PRINT THE INTEGERS 1 THROUGH 10
#
# VERSION 1 -- using while()
# make the initial assignment of variable count_1 to 0 (not necessary)
count_1 <- 0
# the while loop - conditional statement in the first parenthesis, 
# then the repeated steps within the {}
while (count_1 < 10) 
{  count_1 <- count_1 + 1
   print(count_1)

MonkmanMH

# BINOMIAL CONFIDENCE INTERVAL CALCULATOR
#
# the binomial distribuion approximates the Normal distribution
# http://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval
#
# read the data file
bin_data <- read.csv("bin_data.csv")
#
#
# the binomial confidence calculator function

MonkmanMH

Random numbers in R

The creation of random numbers, or the random selection of elements in a set (or population), is an important part of statistics and data science. From simulating coin tosses to selecting potential respondents for a survey, we have a heavy reliance on random number generation.

R offers us a variety of solutions for random number generation; here's a quick overview of some of the options.

runif, rbinom, rnorm

One simple solution is to use the runif function, which generates a stated number of values between two end points (but not the end points themselves!) The function uses the continuous uniform distribution, meaning that every value between the two end points has an equal probability of being sampled.

MonkmanMH

# CALCULATING PERCENTILES IN R 
#
# a basic percentile function using "ecdf" [Empirical Cumulative Distribution Function]
# using a data file "percentiledata" with variable VALUE
percentileFUN <- ecdf(percentiledata$VALUE)
percentileFUN
percentileFUN(percentiledata$VALUE)
# write the percentile values to the source file
percentiledata$pctl <- percentilefunction(percentiledata$VALUE)
#

MonkmanMH

Categorical data analysis in R - a resource list

R packages

vcd & vcdExtra http://cran.r-project.org/web/packages/vcd/ http://cran.r-project.org/web/packages/vcdExtra/

MonkmanMH

#
# setwd("D:/R_the software/datatrials/Lahman")
#
require(Lahman)
require(dplyr)
#
# throwing by position
# version 1 - "merge"
MasterFielding <- data.frame(merge(Master, Fielding, by="playerID"))
MasterFielding <- merge(Master, Fielding, by="playerID")

MonkmanMH

if (!require(wesanderson)) install.packages("wesanderson")
library(wesanderson)
# for more on the Wes Anderson colour palette:
# https://github.com/karthik/wesanderson#wes-anderson-palettes
# http://blog.revolutionanalytics.com/2014/03/give-your-r-charts-that-wes-anderson-style.html
#
#
#
# add some Wes Anderson "Grand Budapest Hotel" colour to print object "p2"
p2 + scale_fill_manual(values = wes.palette(4, "GrandBudapest")) 

MonkmanMH

Input Output models

##BC Stats BC Stats Input-Output model

KITSAULT MINE PROJECT, ENVIRONMENTAL ASSESSMENT: APPENDIX 7.0-B BC Stats BC Input - Output Model Report

Mountain Biking in Rossland and Golden

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---
title: "Testing Lahman 3.0"
author: "Martin Monkman"
date: "Sunday, August 31, 2014"
output: html_document
---

This markdown document incorporates a variety of short scripts that draw on the various tables in the `Lahman` package. (See the Lahman project page on RForge for more details <http://lahman.r-forge.r-project.org/>.)

Note that some of scripts appear in the documentation of other R packages; in those cases, the original source is noted prior to the script.

MonkmanMH

#
library(Lahman)
data(Master)
#
# `debut` variable; create new version `debutDate`
Master$debutDate <- (as.Date(Master$debut, "%m/%d/%Y"))
Master$debutDate[is.na(Master$debutDate)] <- 
  as.Date(Master$debut[is.na(Master$debutDate)])
#
# `finalGame` variable; create new version `finalGameDate`