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@jeromyanglim
jeromyanglim / charnum2num.r
Last active Aug 29, 2015
Take a data.frame x and convert all columns of class character that appear to be numeric into numeric
View charnum2num.r
charnum2num <- function(x) {
# Purpose
# Take a data.frame x and convert all columns of class character that appear to be numeric into numeric
# x is a data.frame
# code taken from library(Hmisc) and included here to avoid the dependency
all.is.numeric <- function (x, what = c("test", "vector"), extras = c(".", "NA"))
{
what <- match.arg(what)
old <- options(warn = -1)
on.exit(options(old))
@jeromyanglim
jeromyanglim / winterOlympicsMedals.r
Created Mar 21, 2010
Analysis of Winter Olympic Medals
View winterOlympicsMedals.r
# tips on reading a Google Spreadsheet:
# http://blog.revolution-computing.com/2009/09/how-to-use-a-google-spreadsheet-as-data-in-r.html
# Data taken from:"https://spreadsheets.google.com/ccc?key=0AgdO92JOXxAOdDVlaUpkNlB2WERtV3l1ZVFYbzllQWc"
# http://www.guardian.co.uk/news/datablog/2010/feb/11/winter-olympics-medals-by-country
googleLink <- "http://spreadsheets.google.com/pub?key=tsddww6vOYePkhPSxRpDeYw&single=true&gid=1&output=csv"
medals <- read.csv(googleLink, stringsAsFactors = FALSE)
savePlot <- TRUE # optional variable used to save or not save plots in code
# remove rows that do not contain data
@jeromyanglim
jeromyanglim / factorAnalysisExampleFromJeromyAnglimBlog.r
Created Mar 26, 2010
Factor Analysis of 50 item personality test
View factorAnalysisExampleFromJeromyAnglimBlog.r
# Example from: http://jeromyanglim.blogspot.com/2009/10/factor-analysis-in-r.html
# Jeromy Anglim
# Required packages.
require(psych);
require(foreign);
# Import data from SPSS data file.
personality <- foreign::read.spss("spss\\personality.sav",
to.data.frame = TRUE)
@jeromyanglim
jeromyanglim / hello_world.r
Created Nov 19, 2010
simple example of a gist
View hello_world.r
hello_world <- function() {
print("Hello World")
}
hello_world()
@jeromyanglim
jeromyanglim / reaction.exp
Created Apr 23, 2011
Inquisit script showing an example simple RT task, four choice RT task, and typing test
View reaction.exp
# Experiment
<expt ability>
/preinstructions = (initial.instructions)
/blocks=[
1=pm.simple;
2=pm.4choice;
3=ac.test.type]
</expt>
<data>
@jeromyanglim
jeromyanglim / percentile.r
Created Jul 24, 2011
Percentile example for Stats.SE
View percentile.r
# Get data
x <- c(93150, 93116, 93096, 93085, 92923, 92823, 92745, 92150, 91785, 91775,
91775, 91735, 91727, 91633, 91616, 91604, 91587, 91579, 91488, 91427, 91398,
91339, 91338, 91290, 91268, 91084, 91072, 90909, 86164, 85372, 83835, 83428,
81372, 81281, 81238, 81195, 81131, 81030, 81011, 80730, 80721, 80682, 80666,
80585, 80565, 80534, 80497, 80464, 80374, 80226, 80223, 80178, 80178, 80147,
80137, 80111, 80048, 80027, 79948, 79902, 79818, 79785, 79752, 79675, 79651,
79620, 79586, 79535, 79491, 79388, 79277, 79269, 79254, 79194, 79191, 79180,
79170, 79162, 79154, 79142, 79129, 79090, 79062, 79039, 79011, 78981, 78979,
78936, 78923, 78913, 78829, 78809, 78742, 78735, 78725, 78618, 78606, 78577,
View data.txt
6162.178176 0.049820046
4675.14432 0.145022261
5969.056896 0.47210138
5357.506176 0.052263122
33.796224 16.45154204
6162.178176 0.064262991
6725.448576 0.419005508
3247.656192 0.867394771
5357.506176 0.052263122
3612.97728 0.091337414
@jeromyanglim
jeromyanglim / abilitytests.exp
Created May 11, 2012
Simple, 2-choice, and 4-choice reaction time experiment using Inquisit
View abilitytests.exp
<expt ability>
/preinstructions = (initial.instructions)
/blocks=[1=pm.simple; 2=pm.4choice; 3=ac.test.type]
</expt>
<data>
/columns=[date, time, build, subject, trialcode, blockcode, blocknum, trialnum, latency, response, pretrialpause,
posttrialpause, trialtimeout, blocktimeout, correct, stimulusitem, stimulusnumber,
display.height, display.width,
computer.cpuspeed, computer.os,
View caschools-analysis.rmd
`r opts_chunk$set(cache=TRUE)`
This is a quick set of analyses of the California Test Score dataset. The post was produced using R Markdown in RStudio 0.96. The main purpose of this post is to provide a case study of using R Markdown to prepare a quick reproducible report. It provides examples of using plots, output, in-line R code, and markdown. The post is designed to be read along side the R Markdown source code, which is available as a gist on github.
<!-- more -->
### Preliminaries
* This post builds on my earlier post which provided a guide for [Getting Started with R Markdown, knitr, and RStudio 0.96](jeromyanglim.blogspot.com/2012/05/getting-started-with-r-markdown-knitr.html)
* The dataset analysed comes from the `AER` package which is an accompaniment to the book [Applied Econometrics with R](http://www.amazon.com/Applied-Econometrics-R-Use/dp/0387773169) written by [Christian Kleiber](http://wwz.unibas.ch/personen/profil/person/kleiber/) and [Achim Zeileis](http://eeecon.uibk.ac.at/~zeileis/
@jeromyanglim
jeromyanglim / r-markdown-example.html
Created May 21, 2012
file produced by R Markdown example
View r-markdown-example.html
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<!-- saved from url=(0014)about:internet -->
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
<title></title>
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