Chrissy h Roberts (He/Him) chrissyhroberts

## Media_File_Consent_LSHTM.xml
<?xml version="1.0"?><h:html xmlns="http://www.w3.org/2002/xforms" xmlns:ev="http://www.w3.org/2001/xml-events" xmlns:h="http://www.w3.org/1999/xhtml" xmlns:jr="http://openrosa.org/javarosa" xmlns:odk="http://www.opendatakit.org/xforms" xmlns:orx="http://openrosa.org/xforms" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><h:head><h:title>media_file_consent</h:title><model><submission base64RsaPublicKey="MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEA1DXk7dbAI89DscB5M+aBlbvtIUcelkwdYX+CBV9uHdvkCm6g0CJM0nrzjRrhKyty0hSgIRfhIBozIfEJYSwRusz/ClGeNiL8Fz3JGYfnFWLw4ZmNKQwAz2CS/zoI4Mu7QRjmeWPIBohdjHo1hJNIjogme0Iip4GDn+3DgsuvFYXxjkWlXN7opEkxAeBQukQzAxCiWbwdhKWKQzwgzmSu5HqCldkkQQ1Q5Zd/KsdmejWQa/5xDd/gOJ0ql+AVzZC1Z9fE0+2HLEEQca8pgWUPXAnPEK2BdNI/ltfPhgKOCE1inXAZxIrDSybePUIyYbIj14aQ30osMpp4EMFFU1rYbQIDAQAB" method="form-data-post"/><instance><Media_File_Consent_LSHTM id="media_file_consent"><add_file/><add_link/><resource_and_attribution><subject/><attribution_name/><attribution_entity/><attribution_date>today()</attribution_dat

## MICE_simple


data(cars)
#make some missing values
cars[sample(1:50,size = 10),1]<-NA
cars[sample(1:50,size = 10),2]<-NA

require(mice)
#impute a bunch of data sets
cars.mice<-mice(cars,m=5,maxit=50,meth='pmm',seed=500)

## OR_Wald.R
oddsratioWald.proc <- function(n00, n01, n10, n11, alpha = 0.05)
  {


  #  Compute the odds ratio between two binary variables, x and y,
  #  as defined by the four numbers nij:
  #
  alpha<-0.05
  #    n00 = number of cases where x = 0 and y = 0
  n00<-14

## ODKGPS.to.KML
#R script to make a KML file of GPS data points collected in ODK
library(sp)
library(rgdal)

#make some dummy data
lon<-c(-123.736597,6.263428,147.263399,83.263430,169.263481,7.736540,0.0)
lat<-c(21.04847,-66.04859,42.04850,66.04854,44.04862,50.04865,52.00000)
status<-c(1,1,1,1,2,2,2)
names<-c("Mid_Pacific_Off_Mexico","North_Of_Antarctica","East_Of_Hokkaido","North_Central_Russia","Suiko_Seamount","Bacharach_Germany","Barkaway_UK")
df<-data.frame(lon,lat,status,names)

## ODK_Download_Decrypt with cleartext password, autoupdate on ODK Briefcase, simple report and options to purge all data before and after running
######################################################
# SETTINGS
#
# You need to install a version of Java that has the Java Cryptography Extensions - see instructions here https://github.com/chrissyhroberts/ODK_TRAINING/blob/master/User_Guide.md
# URL of aggregate server [must include top level directory]
# This script requires one package dependency from package "getPass". This will be installed automatically if you don't have it.
#                 i.e. "https://test.odk.lshtm.ac.uk/test"
# USER NAME
#                 i.e. "chrissy"
# DECRYPTION PEM LOCATION : Please enter the full path to the decryption key

## HOTExport
Setting up ODK collect for use with offline maps

https://export.hotosm.org/

Sign up for an account

Describe the data set in tab 1.
In tab 2 select only “MBTiles”
In tab 3 choose the source “OpenStreetMap” and set zoom range to 1-14
In the map viewer, navigate to your region of interest.

## R_worldmap_political
library(rworldmap)

theCountries<-read.table("Countries.txt",header=T,sep="\t")

# These are the ISO3 names of the countries you'd like to plot in red


malMap <- joinCountryData2Map(theCountries, joinCode = "ISO3",
                              nameJoinColumn = "ISO.ALPHA.3.Code")
# This will join your malDF data.frame to the country map data

## Gannt.chart.R
library(reshape2)
library(ggplot2)

#make csv file as below
#name,start.date,end.date,WP
#Task 1,1,10,1
#Task 2,2,8,1
#Task 3,12,16,2
#Task 4,22,32,2
#Task 5,18,22,3

## snp_gene_overlaps
#find overlap between some SNP locations and a list of gene locations

#test SNPs

#df = snps
#chr  start stop
#1    1232  1232
#2    12444 12444

#gene positions

## genetic_power_calc_case_control.R
#power calculations for genetic case control study
#using gap package and pbsize2 command
#citation      Jing Hua Zhao (2013). gap: Genetic Analysis Package. R package version 1.1-10.


#define study characteristics
require(gap)
prevalence=0.1
alpha=0.05


	data(cars)
	#make some missing values
	cars[sample(1:50,size = 10),1]<-NA
	cars[sample(1:50,size = 10),2]<-NA

	require(mice)
	#impute a bunch of data sets
	cars.mice<-mice(cars,m=5,maxit=50,meth='pmm',seed=500)
	oddsratioWald.proc <- function(n00, n01, n10, n11, alpha = 0.05)
	{


	# Compute the odds ratio between two binary variables, x and y,
	# as defined by the four numbers nij:
	#
	alpha<-0.05
	# n00 = number of cases where x = 0 and y = 0
	n00<-14
	#R script to make a KML file of GPS data points collected in ODK
	library(sp)
	library(rgdal)

	#make some dummy data
	lon<-c(-123.736597,6.263428,147.263399,83.263430,169.263481,7.736540,0.0)
	lat<-c(21.04847,-66.04859,42.04850,66.04854,44.04862,50.04865,52.00000)
	status<-c(1,1,1,1,2,2,2)
	names<-c("Mid_Pacific_Off_Mexico","North_Of_Antarctica","East_Of_Hokkaido","North_Central_Russia","Suiko_Seamount","Bacharach_Germany","Barkaway_UK")
	df<-data.frame(lon,lat,status,names)
	######################################################
	# SETTINGS
	#
	# You need to install a version of Java that has the Java Cryptography Extensions - see instructions here https://github.com/chrissyhroberts/ODK_TRAINING/blob/master/User_Guide.md
	# URL of aggregate server [must include top level directory]
	# This script requires one package dependency from package "getPass". This will be installed automatically if you don't have it.
	# i.e. "https://test.odk.lshtm.ac.uk/test"
	# USER NAME
	# i.e. "chrissy"
	# DECRYPTION PEM LOCATION : Please enter the full path to the decryption key
	Setting up ODK collect for use with offline maps

	https://export.hotosm.org/

	Sign up for an account

	Describe the data set in tab 1.
	In tab 2 select only “MBTiles”
	In tab 3 choose the source “OpenStreetMap” and set zoom range to 1-14
	In the map viewer, navigate to your region of interest.
	library(rworldmap)

	theCountries<-read.table("Countries.txt",header=T,sep="\t")

	# These are the ISO3 names of the countries you'd like to plot in red


	malMap <- joinCountryData2Map(theCountries, joinCode = "ISO3",
	nameJoinColumn = "ISO.ALPHA.3.Code")
	# This will join your malDF data.frame to the country map data
	library(reshape2)
	library(ggplot2)

	#make csv file as below
	#name,start.date,end.date,WP
	#Task 1,1,10,1
	#Task 2,2,8,1
	#Task 3,12,16,2
	#Task 4,22,32,2
	#Task 5,18,22,3
	#find overlap between some SNP locations and a list of gene locations

	#test SNPs

	#df = snps
	#chr start stop
	#1 1232 1232
	#2 12444 12444

	#gene positions
	#power calculations for genetic case control study
	#using gap package and pbsize2 command
	#citation Jing Hua Zhao (2013). gap: Genetic Analysis Package. R package version 1.1-10.



	#define study characteristics
	require(gap)
	prevalence=0.1
	alpha=0.05