jtleek

col3 col2 col4 col1
h    a    t    t
i    v    i    g
k    s    g    n
n    g    n    i

jtleek

library(lubridate)
dt = "2015-11-18"
predict_function = function(dt){
dt_ymd = ymd(dt)

## Get some info
mday_dt = mday(dt_ymd)
wday_dt = wday(d_ymd)

## Download data

jtleek

Blah blah blah 2

jtleek

## Load library/set seed
set.seed(1325)
library(qvalue)
library(RSkittleBrewer)
trop = RSkittleBrewer("tropical")

## Set a target p-value
target_pval = 0.8

## Not significant if we 

jtleek

## Load libraries
library(XML)
library(dplyr)
library(RCurl)

## Get the results for a specific term
scrape_term = function(search_term,npages){
  base_url = "http://scholar.google.com/scholar?"
  search_string = paste0("q=",paste0(strsplit(search_term," ")[[1]],collapse="+"))
  dat = data.frame(NA,nrow=10*npages,ncol=3)

jtleek

## See the paper by Jager and Leek and associated discussion
## for more information: http://biostatistics.oxfordjournals.org/content/15/1/1
## For the code to perform the analysis in that paper or to see how the p-values
## were collected see the repo: https://github.com/jtleek/swfdr

## The data for this gist are available here:
## https://github.com/jtleek/swfdr/blob/master/pvalueData.rda

## Load packages
library(ggplot2)

jtleek

# Load library
library(RSkittleBrewer)

# Differences in mean between 0 and 5
sig = seq(0,5,length=100)

# This is the sample size
ss = 20

# Calculate power from 200 replicated tests

jtleek

#########################################################################################
#   Some functions to quantify your Google Scholar citations page. 
#   R functions Copyright (C) 2011 John Muschelli (jmuschel@jhsph.edu), Andrew Jaffe (ajaffe@jhsph.edu),
#   Jeffrey Leek (jtleek@gmail.com), and the Simply Statistics Blog
#   (http://simplystatistics.tumblr.com, http://twitter.com/simplystats)
#
#   This program is free software: you can redistribute it and/or modify
#   it under the terms of the GNU General Public License as published by
#   the Free Software Foundation, either version 3 of the License, or
#   (at your option) any later version.

jtleek

# Make the canvas
plot(1:10,1:10,xlim=c(-5,5),ylim=c(0,10),type="n",xlab="",ylab="",xaxt="n",yaxt="n")
# Make the branches
rect(-1,0,1,2,col="tan3",border="tan4",lwd=3)
polygon(c(-5,0,5),c(2,4,2),col="palegreen3",border="palegreen4",lwd=3)
polygon(c(-4,0,4),c(3.5,5.5,3.5),col="palegreen4",border="palegreen3",lwd=3)
polygon(c(-3,0,3),c(5,6.5,5),col="palegreen3",border="palegreen4",lwd=3)
polygon(c(-2,0,2),c(6.25,7.5,6.25),col="palegreen4",border="palegreen3",lwd=3)

jtleek

pvaluesPain = c(0.02, 0.06, 0.75, 0.38, 0.3, 0.07, 0.22, 0.27)
adjustedPvaluesPain = p.adjust(pvaluesPain,method="bonferroni")
adjustedPvaluesPain
sum(adjustedPvaluesPain < 0.05)

pvaluesNoPain = c(0.82, 0.98, 0.65, 0.39, 0.88, 0.95, 0.29, 0.64, 0.81)
adjustedPvaluesNoPain = p.adjust(pvaluesNoPain,method="bonferroni")
adjustedPvaluesNoPain
sum(adjustedPvaluesNoPain < 0.05)