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191c2c300beab54091f522b1777e28e9ed6dabd4
|
0d54162fb6c64534561f1793b34d3446eb569723
|
/Step 1 - reads BBS data and runs detectability model.R
|
68156515055800e28cd80f22d11dfecb7b6e20ec
|
[] |
no_license
|
samfranks/bbs
|
536817f3c14ba60350202e56f79bafb6b0400d60
|
0015dc2242a41b7daf9a9b74a94729d85c209df7
|
refs/heads/master
| 2021-01-24T06:40:32.947971
| 2014-11-04T10:55:18
| 2014-11-04T10:55:18
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 14,828
|
r
|
Step 1 - reads BBS data and runs detectability model.R
|
########################################################################
# DISTANCE SAMPLING OF BBS LINE TRANSECT DATA TO #
# OBTAIN DENSITY AND ABUNDANCE ESTIMATES #
########################################################################
# The core program below was written by Ali Johnston 2009 with some original
# code from Eric Rexstad and modified by Dario Massimino 2011
# The programs make use of the R library mrds
# The data formatting code was written by Ali Johnston 2009
# IMPORTANT! Please type the name of the species and the visit here:
species <- "SC"
Visit <- "M" # It can be E (early), L (late), or M (maximum)
min.year <- 1994
max.year <- 2011 # Final year (2 digits)
setwd("D:/Whinchat")
# These source functions to read and manipulate the bbs data:
source("D:/R functions/get.data.per.yr.per.square function.txt")
source("D:/R functions/format.data.all.yr.square function.txt")
source("D:/R functions/get.data.per.yr function.txt")
source("D:/R functions/format.data.all.yr function.txt")
source("D:/R functions/get.zero.obs.weather function.txt")
library(mrds)
#######################
# MODEL DETECTABILITY
# READING IN BIRD DATA
# Need transect observations of all transects in which there WERE observations.
miny <- substr(min.year,3,4)
maxy <- substr(max.year,3,4)
filename <- ifelse(substr(species,2,2)==".",
paste(substr(species,1,1),"_ ",miny,maxy," tran zF.txt", sep=""),
paste(species," ",miny,maxy," tran zF.txt", sep=""))
if (length(list.files("Data",pattern=filename))==1) {
bird.dat <- read.table(paste("Data/",filename,sep=""),sep=",")
} else {
print(Sys.time())
print(paste(filename,"has not been found"))
print("Data are now being read from the BBS folder")
print("This may take very long (several hours for the most abundant species!)")
wd <- getwd()
setwd("Z:/bbs")
bird.dat.xx <- format.data.all.yr(spec.code=species, min.year=min.year,
max.year=max.year, zero.obs.squares=F)
# It often gives me this warnings, but the data frame seems ok:
# In readLines(file.loc) : incomplete final line found on 'data05/ebird05'
setwd(wd)
write.table(bird.dat.xx,paste("Data/",filename,sep=""),sep=",")
bird.dat <- bird.dat.xx
print(Sys.time())
}
# Use the top one for transects and the bottom one for squares
bird.dat2 <- subset(bird.dat,select=c("site","observer","detected","year","distance","distbegin","distend","effort","eorl","habs1","tran.sec"))
visit <- ifelse(bird.dat2$eorl=="E",1,0) # 1=Early; 0=Late
bird.dat2 <- cbind(bird.dat2,visit)
# Check for wrong effort. Anything over 0.4 is wrong. This is 2km*100m*2 = 2*0.1*2 km2
# Often the effort is wrong because the square is entered twice in the habitat file, so the real
# effort is half of the recorded effort. Should check these, but for now is fudged:
sub1 <- subset(bird.dat2,effort>0.4)
sub2 <- subset(bird.dat2,effort<=0.4)
sub1$effort <- sub1$effort/2
bird.dat2 <- rbind(sub1,sub2)
# Values of the year as actual numbers (e.g. 1996) make convergence hard as they are
# such big numbers. So scale to be 1=min.year:
bird.dat2$year <- as.numeric(as.character(bird.dat2$year))
bird.dat2$year <- bird.dat2$year-min.year+1
# Create an 'object' column, with a unique index for each row.
bird.dat2$object <- seq(1,nrow(bird.dat2))
# The procedure in mrds assumes that the 'observer' field is used for double observer
# transects, so change the observer field to a vector of 1s, and put the observer code in
# a different field 'observer.code'.
bird.dat2$observer.code <- bird.dat2$observer
bird.dat2$observer <- rep(1,length(bird.dat2$observer))
# Remove any habitat types which don't have a big enough sample size of observations.
# Also remove any observations which don't have a habitat type recorded.
# Use table(bird.dat2$habs1) to check the sample sizes.
bird.dat2 <- bird.dat2[bird.dat2$habs1%in% c("A","B","C","D","E","F","G","H","I"),]
bird.dat2$habs1 <- bird.dat2$habs1[drop=T]
# Removing any with less than 20:
table.hab <- table(bird.dat2$habs1)
bird.dat2$habs1 <- as.character(bird.dat2$habs1)
bird.dat2$habs2 <- bird.dat2$habs1
if ((table.hab["H"]<=20|is.na(table.hab["H"])) | (table.hab["I"]<=20|is.na(table.hab["I"])) | (table.hab["G"]<=20|is.na(table.hab["G"]))) {
for(i in 1:nrow(bird.dat2)){
bird.dat2$habs2[i] <- ifelse(bird.dat2$habs1[i] %in% c("G","H","I"),"GHI",bird.dat2$habs1[i])
}
}
if ((table.hab["C"]<=20|is.na(table.hab["C"]<=20)) | (table.hab["D"]<=20|is.na(table.hab["D"]))) {
for(i in 1:nrow(bird.dat2)){
bird.dat2$habs2[i] <- ifelse(bird.dat2$habs1[i] %in% c("C","D"),"CD",bird.dat2$habs2[i])
}
}
if ((table.hab["A"]<=20|is.na(table.hab["A"]<=20)) | (table.hab["B"]<=20|is.na(table.hab["B"]<=20))) {
for(i in 1:nrow(bird.dat2)){
bird.dat2$habs2[i] <- ifelse(bird.dat2$habs1[i] %in% c("A","B"),"AB",bird.dat2$habs2[i])
}
}
if (any(table(bird.dat2$habs2)<=20)) print ("There are still habitats <=20")
bird.dat2$habs1.old <- bird.dat2$habs1
bird.dat2$habs1 <- bird.dat2$habs2
bird.dat2$habs1 <- as.factor(bird.dat2$habs1)
bird.dat3 <- bird.dat2
bird.dat3$habs1 <- bird.dat3$habs1[drop=T]
bird.dat8 <- bird.dat3 # Jumped to bird.dat8 as we don't consider speed, etc.
#n<-function(x){ as.numeric(as.character(x))}
#bird.dat8$vis<-n(bird.dat8$vis)
bird.dat8 <- subset(bird.dat8, !is.na(habs1))
# Makes one line for each individual observation:
bird.dat9 <- bird.dat8
bird.dat8 <- bird.dat8[-c(1:nrow(bird.dat8)),]
bird.dat.null <- bird.dat8
print(Sys.time())
batch <- 0
if (nrow(bird.dat9)>10000) {
for (batch in 1:(floor(nrow(bird.dat9)/10000))){
print (paste("Processing batch number",batch,"of",floor(nrow(bird.dat9)/10000)+1))
linecounter <- 1
temp <- bird.dat.null
for(i in 1:10000){
n <- bird.dat9$detected[(batch-1)*10000+i]
temp[linecounter:(linecounter+n-1),] <- bird.dat9[(batch-1)*10000+i,]
linecounter <- linecounter+n
} # closes i
bird.dat8 <- rbind(bird.dat8,temp)
} # closes batch
} # closes if
batch <- batch+1
print (paste("Processing batch number",batch,"of",floor(nrow(bird.dat9)/10000)+1))
print (Sys.time())
linecounter <- 1
temp <- bird.dat.null
for(i in 1:(ifelse(nrow(bird.dat9)>10000, nrow(bird.dat9)%%((batch-1)*10000), nrow(bird.dat9)))){
n <- bird.dat9$detected[(batch-1)*10000+i]
temp[linecounter:(linecounter+n-1),] <- bird.dat9[(batch-1)*10000+i,]
linecounter <- linecounter+n
}
bird.dat8 <- rbind(bird.dat8,temp)
bird.dat8$detected <- rep(1,nrow(bird.dat8))
bird.dat8$size <- rep(1,nrow(bird.dat8))
bird.dat8$object <- seq(1,nrow(bird.dat8),by=1)
bird.dat8$habs1 <- as.character(bird.dat8$habs1)
# gets rid of 9s (code for missing values)
bird.dat8 <- subset(bird.dat8, visit!=9)
########################
# RUNNING THE MODELS
#
# As the purpose is running these models for a wide range of species, we need to keep
# computing time within reasonable limits.
# For this reason we decided to fit only one models with visit and habitat
# as predictors.
Sys.time()
ddf.visit.habs1 <- ddf(~mcds(key="hn",formula=~as.factor(habs1)+visit),
data=bird.dat8, method="ds")
Sys.time()
final <- ddf.visit.habs1
save.image("Data/temporary rescue point.Rdata")
load("Data/temporary rescue point.Rdata")
###############################################################
# ESTIMATE DETECTABILITY AT THE SQUARE LEVEL ON ALL SQUARES
# Need the program "final" (a distance sampling model output) and the counts from
# each transect section of each square with the habitat for each transect section.
# This is used to sum the counts from each habitat, on each square.
# For the prediction step if the bird is a resident, only the first visit is used.
# And if it is a migrant, only the second visit is used.
# Therefore, if "visit" is a variable in the model "final", be careful to predict
# detecability in squares based only on the visit type which is going to be used
# for the abundance estimation. e.g for residents predict detectability in the
# first visit of the season.
filename <- ifelse(substr(species,2,2)==".",
paste(substr(species,1,1),"_ ",miny,maxy," squr zT.txt", sep=""),
paste(species," ",miny,maxy," squr zT.txt", sep=""))
if (length(list.files("Data",pattern=filename))==1) {
bird.dat.xx.sz<-read.table(paste("Data/",filename,sep=""), sep=",", header=T)
} else {
print(Sys.time())
print(paste(filename,"has not been found"))
print("Data are now being read from the BBS folder")
print("This may take very long(several hours for the most abundant species!)")
wd <- getwd()
setwd("Z:/bbs")
bird.dat.xx.sz <- format.data.all.yr.square(spec.code=species,
min.year=min.year, max.year=max.year, zero.obs.squares=T)
# It often gives me this warnings, but the data frame seems ok:
# In readLines(file.loc) : incomplete final line found on 'data05/ebird05'
setwd(wd)
write.table(bird.dat.xx.sz,paste("Data/",filename,sep=""),sep=",")
print(Sys.time())
}
# Run the following to get the square-visit-level information:
# Include zero squares for the prediction step
# Need to work out how many observation there were in each habitat per square:
ag.t <- aggregate(bird.dat$detect, by=list(bird.dat$site, bird.dat$year, bird.dat$eorl), mean)
sites <- as.character(ag.t$Group.1)
years <- ag.t$Group.2
eorls <- as.character(ag.t$Group.3)
habAdt<-habBdt<-habCdt<-habDdt<-habEdt<-habFdt<-habGdt<-habHdt<-habIdt<-vector(length=length(sites))
hab.det <- as.data.frame(cbind(habAdt,habBdt,habCdt,habDdt,habEdt,habFdt,habGdt,habHdt,habIdt,sites,eorls,years))
hab.det[,c(1:9)] <- matrix(nrow=length(sites),ncol=9,rep(0,9*length(sites)))
habs <- c("A","B","C","D","E","F","G","H","I")
for(i in 1:nrow(hab.det)){
sub<-subset(bird.dat,site==sites[i] & eorl==eorls[i] & year==years[i])
if(nrow(sub)>0){
for(j in 1:length(habs)){
g<-grep(habs[j],sub$habs1)
if(length(g)>0){
hab.det[i,j]<-sum(sub$detected[g])
} # close if
} # close j
} # close if
} # close i
hab.det$siteeorl <- paste(hab.det$sites,hab.det$eorl,hab.det$year,sep="")
# Match up the habitat counts with the square level information:
# Don't have the no of transects info for some squares:
bird.s2<-subset(bird.dat.xx.sz,!is.na(habA))
## Chose whether "E"arly or "L"ate visits:
## "E" if resident; "L" if migratory.
#if (Visit=="E"|Visit=="L") bird.s3 <- subset(bird.s2,eorl==Visit)
#if (Visit=="M") {
# bird.s2.1 <- aggregate(bird.s2$detected, by=list(bird.s2$site, bird.s2$year, bird.s2$dist.band), FUN=max)
# names(bird.s2.1)<-c("site","year","dist.band","detected")
# bird.s3<-bird.s2.1
#}
#bird.s3<-subset(bird.s3,dist.band==1)
bird.s3<-subset(bird.s2,dist.band==1)
# Merge the two datasets and delete the variable "detected" to avoid confusion
bird.s3$siteeorl <- paste(bird.s3$site, bird.s3$eorl, bird.s3$year, sep="")
bird.s4 <- merge(bird.s3,hab.det,by="siteeorl",all.x=T)
bird.s4 <- subset(bird.s4, select=-detected)
# Fill in zeros where no observations were made:
null.sites<-grep(TRUE,is.na(bird.s4$sites))
bird.s4[null.sites,c("habAdt","habBdt","habCdt","habDdt","habEdt","habFdt","habGdt","habHdt","habIdt")]<-0
# Amalgamate habitats which are from now on "the same":
# This will be different for each species!
# Also, be careful not to run more than once, otherwise the estimates are out.
#bird.s4$habH<-bird.s4$habH+bird.s4$habI
#bird.s4$habI <- 0
#bird.s4$habHdt<-bird.s4$habHdt+bird.s4$habIdt
#bird.s4$habIdt <- 0
bird.s5<-bird.s4
# Specify the pooled habitats:
# A B C D E F G H I
habs<-c("A","B","C","D","E","F","G","H","I")
if (any(names(table(bird.dat8$habs1))=="GHI")) {
habs[c(7,8,9)]<-c("GHI","GHI","GHI")
}
if (any(names(table(bird.dat8$habs1))=="CD")) {
habs[c(3,4)]<-c("CD","CD")
}
if (any(names(table(bird.dat8$habs1))=="AB")) {
habs[c(1,2)]<-c("AB","AB")
}
# Work out the total detections and total transects for each square:
st<-grep("habA",colnames(bird.s4))
st1<-st[1]-1
st2<-st[2]-1
tot.tran<-tot.det<-vector(length=nrow(bird.s5))
for(i in 1:nrow(bird.s5)){
tot.tran[i]<-sum(bird.s5[i,c(st1+1:9)])
tot.det[i]<-sum(bird.s5[i,c(st2+1:9)])
}
bird.s5<-cbind(bird.s5,tot.tran,tot.det)
bird.s5<-subset(bird.s5,tot.tran<11)
bird.s5<-subset(bird.s5,tot.tran>0)
# create visit and speed in the same way when we ran the model
bird.s5$visit<-bird.s5$speed<-NA
bird.s5$visit<-ifelse(bird.s5$eorl=="E", 1,0)
# Temporary rescue point 2
save.image("Data/temporary rescue point 2.Rdata")
#load("Data/temporary rescue point 2.Rdata")
# The following loop takes about 20 mins
habs1 <- as.vector(names(table(bird.dat8$habs1)))
fit.vec<-vector()
Sys.time()
for(i in 1:nrow(bird.s5)){
sub <- bird.s5[i,]
visit <- rep(sub$visit,length(habs1))
nd <- as.data.frame(cbind(habs1,visit))
nd$visit <- as.numeric(as.character(nd$visit))
fitted <- predict.ds(final, newdata=nd, compute=T)$fitted[,1]
fit <- as.data.frame(cbind(habs1,fitted))
colnames(fit)[1]<-"habs"
# Match up pooled habitats:
ha <- as.data.frame(cbind(1:9,habs))
ha2 <- merge(ha,fit,by="habs",all.x=T)
fit.hab <- as.numeric(as.character(ha2$fitted))
# Find which habitats are in the square and in what nos:
g1 <- grep(TRUE,bird.s5[i,st1+1:9]>0)
# Calculate the counts in each habitat and the weights:
ct <- bird.s5[i,st2+g1]
wt <- ct+bird.s5[i,st1+g1]
# Calculate the square detectability:
fit.vec[i] <- sum(fit.hab[g1]*wt)/sum(wt)
if (i%%10000==0) print(paste(i,"of",nrow(bird.s5)))
}
bird.s6 <-cbind(bird.s5,fit.vec)
save.image("temporary rescue point whinchat.Rdata")
Sys.time()
# THIS MUST BE CHECKED
#if (Visit=="E"|Visit=="L") {
# birds<-subset(bird.s6,eorl==Visit, select=c("site","year","eorl","tot.det","fit.vec","birds"))
#}
if (Visit=="M") {
#bird.s6$est.birds <- bird.s6$tot.det/bird.s6$fit.vec
bird.s6$eorl <- as.character(bird.s6$eorl)
bird.s6.E <- subset(bird.s6, eorl=="E")
bird.s6.L <- subset(bird.s6, eorl=="L")
bird.s7 <- merge(bird.s6.E,bird.s6.L,by=c("site","year"))
bird.s7 <- subset(bird.s7, select=c("site","year","eorl.x","tot.det.x","fit.vec.x","eorl.y","tot.det.y","fit.vec.y"))
# compare birds detected during early and late visit and set best to:
# 0 if same number; 1 if more birds for early visit; 2 for late visit
best <- ifelse(bird.s7$tot.det.x==bird.s7$tot.det.y, 0, 2)
best <- ifelse(bird.s7$tot.det.x>bird.s7$tot.det.y, 1, best)
birds <- bird.s7[,1:2]
birds$eorl <- ifelse(best==0, "B", "L")
birds$eorl <- ifelse(best==1, "E", birds$eorl)
birds$tot.det <- ifelse(best==1, bird.s7$tot.det.x, bird.s7$tot.det.y)
birds$fit.vec <- ifelse(best==0, (bird.s7$fit.vec.x+bird.s7$fit.vec.y)/2, bird.s7$fit.vec.y)
birds$fit.vec <- ifelse(best==1, bird.s7$fit.vec.x, birds$fit.vec)
}
imagename <- paste("Data/",species," Step 1.Rdata",sep="")
save.image(imagename)
#load(imagename)
filename <- paste("Data/",species,"detect.csv",sep="")
write.csv(birds, filename, row.names=F)
print(paste("End of Step 1 for ",species,". Visit: ",Visit,sep=""))
|
6a1f581174bd779fc89210f6d9c7e4ea560a61d7
|
5eb745b3373899c8531e180b1404e545cf3ba150
|
/man/calc.ctmax.Rd
|
b665da89f9cd6c0be2d56ecee1eeec86d3e602d6
|
[] |
no_license
|
qPharmetra/qpNCA
|
8a8e5a4bebf22ef9e3b005f7a3809102aacdb250
|
bde363e64dba0ce5ddbc01fc4211dab471a1f3ee
|
refs/heads/master
| 2022-01-16T18:05:24.976496
| 2022-01-11T16:38:36
| 2022-01-11T16:38:36
| 171,545,252
| 4
| 2
| null | 2021-10-17T18:16:50
| 2019-02-19T20:33:30
|
R
|
UTF-8
|
R
| false
| true
| 1,159
|
rd
|
calc.ctmax.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/calc.ctmax.r
\name{calc.ctmax}
\alias{calc.ctmax}
\title{Calculate Cmax and Tmax}
\usage{
calc.ctmax(x, by = NULL, timevar = "time", depvar = "dv")
}
\arguments{
\item{x}{data.frame}
\item{by}{character: column names in x indicating grouping variables; default is as.character(dplyr::groups(x))}
\item{timevar}{variable name containing the actual sampling time after dose}
\item{depvar}{variable name containing the dependent variable (e.g., concentration)}
}
\value{
A dataset with estimates for the Cmax (maximum concentration)
and Tmax (time of first occurence of cmax) parameters: one observation per subject
}
\description{
Calculates Cmax and Tmax from raw data for each PK curve defined using \code{by}. \cr
}
\details{
Input dataset can contain all uncorrected data, including LOQ;
estimate first occurence of maximum concentration for each PK curve;
if all concentrations are NA, sets Cmax and Tmax also to NA.
}
\examples{
\donttest{
library(magrittr)
library(dplyr)
data(ncx)
x <- ncx
x \%<>\% group_by(subject)
x \%<>\% correct.loq
x \%>\% calc.ctmax \%>\% head
}
}
|
28d65786e8f814049c011dce4c044c6790703646
|
a0aacfded5e9cc6b90ee9e4a5dcd41d80f6015e9
|
/tests/testthat/test_loading.R
|
9baeec71b0a5f7aec3b76fb0d4039add13cf9d4a
|
[
"MIT",
"CC-BY-4.0"
] |
permissive
|
lbusett/antanym
|
f86746bcb777e699c75cd6354188540d8b9a337d
|
2a6fee4e09dc039922832a04b6ec14ba3032e659
|
refs/heads/master
| 2020-03-11T05:15:28.618319
| 2018-04-22T21:13:56
| 2018-04-22T21:13:56
| 129,797,394
| 0
| 0
|
MIT
| 2018-04-16T19:50:51
| 2018-04-16T19:50:51
| null |
UTF-8
|
R
| false
| false
| 600
|
r
|
test_loading.R
|
context("fetching and caching data")
test_that("caching works", {
skip_on_cran()
cdir <- tempdir()
cfile <- file.path(cdir,"gaz_data.csv")
if (file.exists(cfile)) file.remove(cfile)
g <- an_read(cache_dir=cdir)
expect_true(file.exists(cfile))
finfo <- file.info(cfile)
## re-read using cache
g <- an_read(cache_dir=cdir)
expect_identical(finfo$mtime,file.info(cfile)$mtime)
## refresh cache
g <- an_read(cache_dir=cdir,refresh_cache=TRUE)
## mtime should have changed
expect_gt(as.numeric(file.info(cfile)$mtime),as.numeric(finfo$mtime))
})
|
c81281f0483975d4a07edf712d68227e0f0f7ad1
|
f3ed3d52b590f89643c66eb95c649c9e69d54387
|
/MSDS6306_DoingDS/class_work/Unit10/livesession_code_EDA.r
|
97885d577a11b6e818c9ede5fef032a0733978ba
|
[] |
no_license
|
khthomas/SMU_MSDS
|
39803cbac55d04bdae2fdc2c520fde2ac8b403cb
|
25b76caa80ff7e1c5a88951b993bf464247142cc
|
refs/heads/master
| 2021-09-06T15:27:09.069180
| 2018-02-08T02:23:12
| 2018-02-08T02:23:12
| 104,568,801
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,794
|
r
|
livesession_code_EDA.r
|
library(ggplot2)
library(reshape2)
files = "http://stat.columbia.edu/~rachel/datasets/nyt"
listofsets = data.frame(col1="test")
#This will grab all of the data and put it into a list of dataframes... it is really slow.
for (x in 1:31) {
listDF = list()
link = paste(files, x, ".csv", sep="")
holding = read.csv(url(link))
listDF[[x]] = holding
}
### This will get you as far as needed for the live session. You may need to change which dataset is needed
fileLocation <- "http://stat.columbia.edu/~rachel/datasets/nyt2.csv"
data1 <- read.csv(url(fileLocation))
#Cut takes a variable and binds it. Above the first group is -Inf to 18, then 18 to 24 etc.
data1$Age_Group <- cut(data1$Age, c(-Inf, 18, 24, 34, 44, 54, 64, Inf))
#change the levels to be more meaningful when read (changed the factors)
levels(data1$Age_Group) <- c("<18", "18-24", "25-34", "35-44", "45-54", "55-64", "65+")
#Problem: Click Through Rate (CTR) is notoriously small -- very few people click at all
# Can I get click through rate for our website? That could be interesting. Where can you get that data?
d1 = subset(data1, Impressions > 0)
d1$CTR = d1$Clicks/d1$Impressions
#change gender variable to a factor for GGplot
d1$Gender = as.factor(d1$Gender)
#Impressions by Gender
ggplot(d1, aes(x=Impressions, fill=Age_Group)) + theme(plot.title =element_text(hjust = 0.5)) +
geom_histogram(binwidth = 1) + ggtitle("Impressions by Age Group")
#CTR by Gender
ggplot(subset(d1, CTR>0), aes(x=CTR, fill=Gender)) +
geom_histogram(binwidth = 0.05) +
ggtitle("CTR by Gender") +
theme(plot.title = element_text(hjust = 0.5)) +
xlab("Probability of Click Through Rate: Clicks per Impression") +
ylab("Count") +
scale_fill_brewer(palette = "Dark2", labels=c("Female", "Male"))
|
ebef5c886a7f082ae4a1b921930847b1e278b3bd
|
0a0a04adad2a286a74017b572e51dc82dc3ef786
|
/inst/examplepkg/R/hypotenuse.R
|
44cfd202889469dd16e99252a0d6216e1b099362
|
[
"MIT"
] |
permissive
|
armcn/covtracer
|
7e408e67a8ac1376e397a19cff7dc9dfa23f1ac2
|
9ccfacd171a2698bca7935f6bc88241d04b78d11
|
refs/heads/main
| 2023-08-18T20:40:23.765988
| 2021-09-30T15:56:29
| 2021-09-30T15:56:29
| 411,432,330
| 0
| 0
|
NOASSERTION
| 2021-09-28T20:40:59
| 2021-09-28T20:40:58
| null |
UTF-8
|
R
| false
| false
| 160
|
r
|
hypotenuse.R
|
#' Calculate the hypotenuse provided two edge lengths
#'
#' @param a,b edge lengths
#'
#' @export
#'
hypotenuse <- function(a, b) {
return(sqrt(a^2 + b^2))
}
|
68634ca1d8319c0c39b4458b224f96b723399780
|
9fde3b252f2064d92ed109656802ff87b32d3084
|
/enhancer.R
|
d255e5d52ab4af485499516ac2ab0b8de1797828
|
[] |
no_license
|
rachelGoldfeder/cfDNA
|
e87b6b8698e1b6b6925d566bbd1a3d220b4eef9c
|
a686bdcf8d27c3df9cabbd2818991918649e7d61
|
refs/heads/master
| 2021-07-24T18:37:05.908782
| 2019-01-08T21:04:34
| 2019-01-08T21:04:34
| 104,079,609
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,974
|
r
|
enhancer.R
|
#! /usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
samphmc=args[1]
sampmc=args[2]
sampname=args[3]
library(data.table)
methcalls=getwd()
setwd(paste0(methcalls))
a=as.data.frame(fread(paste0(samphmc)))
b=as.data.frame(fread(paste0(sampmc)))
feat.mat=matrix(ncol=3,nrow=7)
ct=1
sum=0
for(i in c("enhancer","super_enhancer")){
feat=nrow(unique((subset(a,V15==i))[c(1:3)]))
feat.mat[ct,1] = paste0("hmc_",sampname)
feat.mat[ct,2] = i
feat.mat[ct,3] = feat
ct=ct+1
sum=sum+feat
}
not=nrow(unique((subset(a,V15!="enhancer"&V15!="super_enhancer"))[c(1:3)]))
feat.mat[3,]=c(paste0("hmc_",sampname),".",not)
sum=sum+not
feat.mat[4,]= c(paste0("hmc_",sampname),"sum",sum)
ct=5
sum=0
for(i in c("enhancer",".")){
feat=nrow(unique((subset(a,V14==i))[c(1:3)]))
feat.mat[ct,1] = paste0("hmc_",sampname)
feat.mat[ct,2] = i
feat.mat[ct,3] = feat
ct=ct+1
sum=sum+feat
}
feat.mat[7,]= c(paste0("hmc_",sampname),"sum",sum)
setwd(paste0(methcalls,"/plots"))
write.table(feat.mat,paste0(sampname,"_feat_gene.txt"),col.names = F,row.names=FALSE, quote=FALSE, sep = "\t", append=T)
feat.mat=matrix(ncol=3,nrow=7)
ct=1
sum=0
for(i in c("enhancer","super_enhancer")){
feat=nrow(unique((subset(b,V12==i))[c(1:3)]))
feat.mat[ct,1] = paste0("mc_",sampname)
feat.mat[ct,2] = i
feat.mat[ct,3] = feat
ct=ct+1
sum=sum+feat
}
not=nrow(unique((subset(b,V12!="enhancer"&V12!="super_enhancer"))[c(1:3)]))
feat.mat[3,]=c(paste0("mc_",sampname),".",not)
sum=sum+not
feat.mat[4,]= c(paste0("mc_",sampname),"sum",sum)
ct=5
sum=0
for(i in c("enhancer",".")){
feat=nrow(unique((subset(b,V11==i))[c(1:3)]))
feat.mat[ct,1] = paste0("mc_",sampname)
feat.mat[ct,2] = i
feat.mat[ct,3] = feat
ct=ct+1
sum=sum+feat
}
feat.mat[7,]= c(paste0("mc_",sampname),"sum",sum)
write.table(feat.mat,paste0(sampname,"_feat_gene.txt"),col.names = F,row.names=FALSE, quote=FALSE, sep = "\t", append=T)
|
e076561ef694415949afe4bcd79772aff55662bf
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/TreeBUGS/examples/getParam.Rd.R
|
6cd16a4eabad0ce6edfc1d7e943ec734ea258d85
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 387
|
r
|
getParam.Rd.R
|
library(TreeBUGS)
### Name: getParam
### Title: Get Parameter Posterior Statistics
### Aliases: getParam
### ** Examples
## Not run:
##D # mean estimates per person:
##D getParam(fittedModel, parameter = "theta")
##D
##D # save summary of individual estimates:
##D getParam(fittedModel, parameter = "theta",
##D stat = "summary", file= "ind_summ.csv")
## End(Not run)
|
c2c483c710e28b67e5c0daf206f7c2d09fba76dc
|
9c781801315fb834cf4a39db4fb12db7a585002e
|
/Genetics/finalSubmission/SupportingInformation/plotLikelihoodComparisons.R
|
3d785d578d47ea1396f6bdf81718194bd57f11d3
|
[] |
no_license
|
alexpopinga/CoalSIR
|
64a57ef6a79a73d61aafb3c548de522015e205ef
|
1f32e1ead268468c269f2f7ab730dbead6b04977
|
refs/heads/master
| 2021-04-26T23:27:15.868645
| 2018-03-06T00:43:56
| 2018-03-06T00:43:56
| 123,997,532
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,807
|
r
|
plotLikelihoodComparisons.R
|
source('likelihoodEstimator.R')
regenerate <- FALSE
###### MAIN ######
if (regenerate) {
gamma <- 0.3
beta <- 0.00075
S0 <- 999
origin <- 12.7808530307
tree <- read.tree('VolzSIRgamma_truth.tree')
gammaVec <- seq(.1,.7,by=.05)
# Estimate STOCHASTIC coalescent likelihoods for different gammas
Ntraj <- 10000
Nensemb <- 10
llensemb <- list()
for (e in 1:Nensemb)
llensemb[[e]] <- rep(0,length(gammaVec))
Nensemb <- 10
llensemb <- list()
for (e in 1:Nensemb)
llensemb[[e]] <- rep(0,length(gammaVec))
for (i in 1:length(gammaVec)) {
for (e in 1:Nensemb) {
llensemb[[e]][i] <- getCoalescentTreeDensity(tree, beta, gammaVec[i], S0, origin, Ntraj)
}
}
llmean <- rep(0,length(gammaVec))
llsd <- rep(0, length(gammaVec))
for (i in 1:length(gammaVec)) {
thisEnsemble <- rep(0, Nensemb)
for (e in 1:Nensemb) {
thisEnsemble[e] <- llensemb[[e]][i]
}
llmean[i] <- mean(thisEnsemble)
llsd[i] <- sd(thisEnsemble)
}
# Estimate DETERMINISTIC coalescent likelihoods for different gammas
gammaVecDet <- seq(0.1,0.35,by=0.01)
lldet <- rep(0, length(gammaVecDet))
for (i in 1:length(gammaVecDet)) {
lldet[i] <- getDeterministicCoalescentTreeDensity(tree, beta, gammaVecDet[i], S0, origin)
}
} else {
load(file='likelihoodResultsFromR10000_noCorrection.RData')
}
# Load in Java code results for same tree:
df <- read.table('likelihoodResultsFromJava_noCorrection.txt', header=T)
javaGamma <- df$gamma
javaLogP <- df$logP
javaSD <- apply(df[,3:12], 1, sd)
# Create figure
pdf('gammaLikelihoodComparison_noCorrection.pdf', width=7, height=5)
plot(gammaVec, llmean, 'o', #ylim=c(-440,-400),
xlab=expression(gamma),
ylab='Log likelihood',
main='Log likelihoods from simulated tree',
col='blue')
lines(gammaVec, llmean+2*llsd, lty=2, col='blue')
lines(gammaVec, llmean-2*llsd, lty=2, col='blue')
lines(javaGamma, javaLogP, 'o', col='red')
lines(javaGamma, javaLogP+2*javaSD, lty=2, col='red')
lines(javaGamma, javaLogP-2*javaSD, lty=2, col='red')
#lines(gammaVecDet, lldet, 'o', col='purple')
lines(c(0.3,0.3), c(-1e10,1e10), lty=2, col='grey', lwd=2)
#legend('bottomright', inset=.05, c('R','Java (10000)','R (det.)', 'Truth'), lty=c(1,1,1,2), pch=c(1,1,1,NA), lwd=c(1,1,1,2), col=c('blue','red','purple','grey'))
legend('bottomright', inset=.05, c('R 10*(10^4+)','Java 10*(10^4+)', '+/- 2*SD', 'Truth'), lty=c(1,1,2,2), pch=c(1,1,NA,NA), lwd=c(1,1,1,2), col=c('blue','red','black','grey'))
#legend('bottomright', inset=.05, c('R','Java (10000)', '+/- 2*SD', 'Truth'), lty=c(1,1,2,2), pch=c(1,1,NA,NA), lwd=c(1,1,1,2), col=c('blue','red','black','grey'))
dev.off()
|
51f590c56530d7a77a9bbec8b502626cdddf6a35
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/gtx/examples/make.moments2.Rd.R
|
2a91c952123841c6836998a7859f07e445bba9da
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 247
|
r
|
make.moments2.Rd.R
|
library(gtx)
### Name: make.moments2
### Title: Build matrix of second moments from subject-specific data.
### Aliases: make.moments2
### ** Examples
data(mthfrex)
xtx <- make.moments2(mthfr.params, c("SBP", "DBP", "SexC", "Age"), mthfrex)
|
4e1fe3de4bfa4475a3ae9706a3b3d8e0e227daff
|
e79b09555dfc727b7e88a5942a155e1847630732
|
/plot_strat_ox_budget_eval.R
|
9120b1acb1b4dd0da62e8713924bf3b02e4b9397
|
[] |
no_license
|
paultgriffiths/R_pyle
|
70b15e96c5dd7b2eef04aca69e5d11a8673c229c
|
0bc83cb1f3c2fa3c04c4cca36861c936163a9eb4
|
refs/heads/master
| 2021-09-10T21:22:11.776270
| 2018-04-02T12:09:10
| 2018-04-02T12:12:37
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,950
|
r
|
plot_strat_ox_budget_eval.R
|
# R script to plot and calculate the contribution of
# different ozone loss reactions to the total loss
# Alex Archibald, July 2012
# loop vars
i <- NULL; j <- NULL
nav <- 6.02214179E23
conv.factor <- 60*60*24*30*48*(1e-12)
# extract/define variables
lon <- get.var.ncdf(nc1, "longitude")
lat <- get.var.ncdf(nc1, "latitude")
hgt <- get.var.ncdf(nc1, "hybrid_ht")*1E-3
time <- get.var.ncdf(nc1, "t")
# define empty arrays to hold data
for (i in 1:12) {
assign(paste("l",i,".std", sep=""), array(NA, dim=c(length(lon), length(lat), length(hgt), length(time)) ) ) }
# Check to see if a trop. mask and mass exist?
if ( exists("mask") == TRUE) print("Tropospheric Mask exists, carrying on") else (source(paste(script.dir, "calc_trop_mask.R", sep="")))
# copy the mask that is for the troposphere
strat.mask <- 1-(mask)
# extract the fluxes. These are based on the 12 Ox cycles
# from Lee et al., JGR 2001
p1 <- get.var.ncdf(nc1, st.prod.code)
l1 <- get.var.ncdf(nc1, st.loss1.code)
l2 <- get.var.ncdf(nc1, st.loss2.code)
l3 <- get.var.ncdf(nc1, st.loss3.code)
l4 <- get.var.ncdf(nc1, st.loss4.code)
l5 <- get.var.ncdf(nc1, st.loss5.code)
l6 <- get.var.ncdf(nc1, st.loss6.code)
l7 <- get.var.ncdf(nc1, st.loss7.code)
l8 <- get.var.ncdf(nc1, st.loss8.code)
l9 <- get.var.ncdf(nc1, st.loss9.code)
l10 <- get.var.ncdf(nc1, st.loss10.code)
l11 <- get.var.ncdf(nc1, st.loss11.code)
l12 <- get.var.ncdf(nc1, st.loss12.code)
# calculate the total fluxes in Tg/y
# in the stratosphere
production <- sum( (p1*strat.mask) )*conv.factor
cyc1 <- sum( (l1*strat.mask) )*conv.factor
cyc1 <- sum( (l1*strat.mask) )*conv.factor
cyc2 <- sum( (l2*strat.mask) )*conv.factor
cyc3 <- sum( (l3*strat.mask) )*conv.factor
cyc4 <- sum( (l4*strat.mask) )*conv.factor
cyc5 <- sum( (l5*strat.mask) )*conv.factor
cyc6 <- sum( (l6*strat.mask) )*conv.factor
cyc7 <- sum( (l7*strat.mask) )*conv.factor
cyc8 <- sum( (l8*strat.mask) )*conv.factor
cyc9 <- sum( (l9*strat.mask) )*conv.factor
cyc10 <- sum( (l10*strat.mask) )*conv.factor
cyc11 <- sum( (l11*strat.mask) )*conv.factor
cyc12 <- sum( (l12*strat.mask) )*conv.factor
# convert from moles/gridbox/s -> molecules/cm3/s
for (i in 1:length(time) ) {
l1.std[,,,i] <- ( l1[,,,i]/(vol*1E6))*nav
l2.std[,,,i] <- ( l2[,,,i]/(vol*1E6))*nav
l3.std[,,,i] <- ( l3[,,,i]/(vol*1E6))*nav
l4.std[,,,i] <- ( l4[,,,i]/(vol*1E6))*nav
l5.std[,,,i] <- ( l5[,,,i]/(vol*1E6))*nav
l6.std[,,,i] <- ( l6[,,,i]/(vol*1E6))*nav
l7.std[,,,i] <- ( l7[,,,i]/(vol*1E6))*nav
l8.std[,,,i] <- ( l8[,,,i]/(vol*1E6))*nav
l9.std[,,,i] <- ( l9[,,,i]/(vol*1E6))*nav
l10.std[,,,i] <- ( l10[,,,i]/(vol*1E6))*nav
l11.std[,,,i] <- ( l11[,,,i]/(vol*1E6))*nav
l12.std[,,,i] <- ( l12[,,,i]/(vol*1E6))*nav }
# find mid latitude grid box and do a zonal mean
mid.lat <- which(lat >=37.50)[1]
cyc.1 <- apply(l1.std[,mid.lat,,4], c(2), mean)
cyc.2 <- apply(l2.std[,mid.lat,,4], c(2), mean)
cyc.3 <- apply(l3.std[,mid.lat,,4], c(2), mean)
cyc.4 <- apply(l4.std[,mid.lat,,4], c(2), mean)
cyc.5 <- apply(l5.std[,mid.lat,,4], c(2), mean)
cyc.6 <- apply(l6.std[,mid.lat,,4], c(2), mean)
cyc.7 <- apply(l7.std[,mid.lat,,4], c(2), mean)
cyc.8 <- apply(l8.std[,mid.lat,,4], c(2), mean)
cyc.9 <- apply(l9.std[,mid.lat,,4], c(2), mean)
cyc.10 <- apply(l10.std[,mid.lat,,4], c(2), mean)
cyc.11 <- apply(l11.std[,mid.lat,,4], c(2), mean)
cyc.12 <- apply(l12.std[,mid.lat,,4], c(2), mean)
# ###################################################################################################################################
# plot a mid latitude profile of the different cycles
pdf(file=paste(out.dir,mod1.name,"_strat_o3_loss.pdf", sep=""),width=8,height=6,paper="special",onefile=TRUE,pointsize=12)
par(mfrow=c(1,2))
## Is this just repetition?? ##
cyc.1 <- apply(l1.std[,mid.lat,,4], c(2), mean)
cyc.2 <- apply(l2.std[,mid.lat,,4], c(2), mean)
cyc.3 <- apply(l3.std[,mid.lat,,4], c(2), mean)
cyc.4 <- apply(l4.std[,mid.lat,,4], c(2), mean)
cyc.5 <- apply(l5.std[,mid.lat,,4], c(2), mean)
cyc.6 <- apply(l6.std[,mid.lat,,4], c(2), mean)
cyc.7 <- apply(l7.std[,mid.lat,,4], c(2), mean)
cyc.8 <- apply(l8.std[,mid.lat,,4], c(2), mean)
cyc.9 <- apply(l9.std[,mid.lat,,4], c(2), mean)
cyc.10 <- apply(l10.std[,mid.lat,,4], c(2), mean)
cyc.11 <- apply(l11.std[,mid.lat,,4], c(2), mean)
cyc.12 <- apply(l12.std[,mid.lat,,4], c(2), mean)
# plot the data
plot(log10(cyc.1), hgt, type="l", col="red", ylim=c(10,52), xlim=c(0,8), xaxt="n", ylab="Altitude (km)", xlab="Rate of Ox loss", lwd=1.5)
lines(log10(cyc.2), hgt, col="red", lty=2, lwd=1.5)
lines(log10(cyc.3), hgt, col="blue", lty=1, lwd=1.5)
lines(log10(cyc.4), hgt, col="red", lty=3, lwd=1.5)
lines(log10(cyc.5), hgt, col="purple", lty=1, lwd=1.5)
lines(log10(cyc.6), hgt, col="red", lty=4, lwd=1.5)
lines(log10(cyc.7), hgt, col="green", lty=1, lwd=1.5)
lines(log10(cyc.8), hgt, col="purple", lty=2, lwd=1.5)
lines(log10(cyc.9), hgt, col="blue", lty=2, lwd=1.5)
lines(log10(cyc.10), hgt, col="blue", lty=3, lwd=1.5)
lines(log10(cyc.11), hgt, col="green", lty=3, lwd=1.5)
lines(log10(cyc.12), hgt, col="black", lty=1, lwd=1.5)
minor.ticks.axis(1,9,mn=0,mx=8)
grid()
legend("bottomright",
c( expression(paste(h,nu,+Cl[2],O[2], sep="")), "ClO+BrO", expression(paste(HO[2],+O[3], sep="")), expression(paste(ClO+HO[2], sep="")),
expression(paste(BrO+HO[2], sep="")), "ClO+O", expression(paste(NO[2],"+O", sep="")), "BrO+O", expression(paste(HO[2],"+O", sep="")),
expression(paste(H+O[3], sep="")), expression(paste(h,nu,+NO[3], sep="")), expression(paste(O[3],"+O", sep="")) ),
col=c("red","red","blue","red","purple","red","green","purple","blue","blue","green","black"), lty=c(1,2,1,3,1,4,1,2,2,3,3,1), bty="n", cex=0.8 )
par(xpd=NA)
text(4,3, expression(paste("(molecules cm"^"-3"," s"^"-1",")", sep="") ))
text(12,60, paste("37.5N, March, stratospheric Ox loss:",mod1.name, sep=" "))
par(xpd=FALSE)
total <- rowSums(cbind(cyc.1, cyc.2, cyc.3, cyc.4, cyc.5, cyc.6, cyc.7, cyc.8, cyc.8, cyc.9, cyc.10, cyc.11, cyc.12) )
clox <- rowSums(cbind(cyc.1, cyc.2, cyc.4, cyc.6) ) / total
hox <- rowSums(cbind(cyc.3, cyc.9, cyc.10) ) / total
brox <- rowSums(cbind(cyc.5, cyc.8) ) / total
nox <- rowSums(cbind(cyc.7, cyc.11) ) / total
ox <- cyc.12 / total
ox.loss <- data.frame(clox, hox, brox, nox, ox)
plot(ox.loss$clox*100, hgt, ylim=c(10,52), xlim=c(0,100), lwd=2, col="red", type="l", yaxt="n", xlab="Percentage", ylab="")
lines(ox.loss$hox*100, hgt, lwd=2, col="blue")
lines(ox.loss$brox*100, hgt, lwd=2, col="purple")
lines(ox.loss$nox*100, hgt, lwd=2, col="green")
lines(ox.loss$ox*100, hgt, lwd=2, col="black")
grid()
legend(70,30, c( expression(paste(ClO[x], sep="")), expression(paste(HO[x], sep="")), expression(paste(BrO[x], sep="")), expression(paste(NO[x], sep="")), expression(paste(O[x], sep="")) ),
col=c("red","blue","purple","green","black"), lty=c(1,1,1,1,1), bty="n", cex=0.8)
dev.off()
|
bf3270b27582914aea9ce0f04c8bb992c1fe4b30
|
e039685fc9bdac3a7ffbeedb5aa22e4275f5c6a0
|
/classification/Naive Bayes Classifier.R
|
48e620e24da2d36010ef49ad069cd8e1fc70268f
|
[] |
no_license
|
cajogos/r-machine-learning
|
fb227124d2a393a612b22c065421a96b16c0cbe8
|
261ebe2c5def39a6db4f31395a9d92fe26a81eda
|
refs/heads/master
| 2020-08-21T05:27:38.660252
| 2019-12-25T19:02:35
| 2019-12-25T19:02:35
| 216,102,102
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 898
|
r
|
Naive Bayes Classifier.R
|
# Classifying data with the Naïve Bayes classifier
rm(list = ls(all = TRUE)) # Clean-up environment
dev.off() # Clean-up any plots
# --- The prepared churn dataset --- #
library(C50)
data(churn)
churnTrain <- churnTrain[, ! names(churnTrain) %in% c("state", "area_code", "account_length")]
set.seed(2)
ind <- sample(2, nrow(churnTrain), replace = TRUE, prob = c(0.7, 0.3))
trainset <- churnTrain[ind == 1,]
testset <- churnTrain[ind == 2, ]
# ------ #
library(e1071) # install.packages("e1071")
classifier <- naiveBayes(trainset[, !names(trainset) %in% c("churn")],
trainset$churn)
classifier
# Generate the classification table
bayes.table <- table(predict(classifier, testset[, !names(testset) %in% c("churn")]),
testset$churn)
bayes.table
# Generate a confusion matrix
library(caret)
confusionMatrix(bayes.table)
|
a00921785b8b863533ff68d7d62e0f4d63058579
|
118bc327b85a3ac1b40649dd4559d5f75b913a43
|
/man/removeCols.Rd
|
71625c667951126a0eb17bf7ccf3c2963c968135
|
[
"LicenseRef-scancode-warranty-disclaimer",
"MIT"
] |
permissive
|
bobverity/bobFunctions
|
2c613da2db8a3c2eaaffab0a8a5df170240891d4
|
3bce3fd92e4c30710413b02ea338ad1d987e5782
|
refs/heads/master
| 2021-01-17T12:36:57.566104
| 2018-10-05T10:57:10
| 2018-10-05T10:57:10
| 59,672,304
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 229
|
rd
|
removeCols.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/main.R
\name{removeCols}
\alias{removeCols}
\title{removeCols}
\usage{
removeCols(df, nameVec)
}
\description{
Remove named columns from data frame.
}
|
9a5b0ba82171140ac0c39ff6f1cf2fa2d155c3bc
|
0dd3258d8c04a59d937529b49c8c64f054a23a60
|
/ruvseq/src/volplot.R
|
f43b4039471488158089763692c943687ab49de7
|
[] |
no_license
|
knguyen4221/ruvseq-ag
|
5f63b1c18073884c8d3770434377eada3857d5ec
|
4c1b0e9fee27db6b357507169f89997fe7a2ae05
|
refs/heads/master
| 2021-09-04T06:53:01.085101
| 2018-01-11T20:36:17
| 2018-01-11T20:36:17
| 114,688,331
| 0
| 0
| null | 2018-01-11T20:36:18
| 2017-12-18T21:14:19
|
R
|
UTF-8
|
R
| false
| false
| 294
|
r
|
volplot.R
|
library(manhattanly)
d <- read.table("S:\\Development\\fastq\\analyses\\graphs\\AST\\counts_differentialExpressionAnalysisWithDESeq2_spikeInNorm.csv",
header=TRUE, sep = ",", na.rm)
colnames(d) <- c( "X", "baseMean", "EFFECTSIZE", "lfcSE", "stat", "pvalue", "P")
volcanoly(d)
|
0a002daa5738039ed36ec3887909e968a30bc90d
|
9c077831aaa80a56cff9e78303e3b923ff9c66d3
|
/R/fct_helpers.R
|
9e8f45c2c7ca666b80870d0b71d5d968715742fe
|
[
"MIT"
] |
permissive
|
MaryleneH/Exo_activity
|
ae5df7c3ef80d4a211a8491c4cad3169d8e1707b
|
18a132862e3ed8fd57c9f3e3eb8154042eee8bb2
|
refs/heads/main
| 2023-03-08T10:53:56.720355
| 2021-02-24T14:36:15
| 2021-02-24T14:36:15
| 340,514,066
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,396
|
r
|
fct_helpers.R
|
# Fonction pour échantillonner une table
my_sample <- function(dataset,number){
dataset %>%
sample_n(number)
}
# Fonction pour filtrer selon le type d'activité
filtrer_activites <- function(dataset,type_activity){
dataset %>%
filter(`Activity Type` == type_activity)
}
# Fonction pour créer un nuage de points selon Distance / Time et Activity Type
utils::globalVariables(c("Distance", "Time", "Activity Type","Elev Gain","Date"))
graph_dist_time <- function(dataset){
dataset %>%
ggplot() +
geom_point(aes(x = Distance , y =Time, color = `Activity Type`))+
theme_minimal()
}
# Fonction pour créer un box_plot selon Time /Distance / Activity type
graph_act_time <- function(dataset){
dataset %>%
ggplot(aes(x = `Activity Type` , y =Time, color = `Activity Type`)) +
geom_boxplot()+
theme_minimal()
}
# Fonction pour créer un box-plot selon Activity type / Elev Gain
graph_act_elev <- function(dataset){
dataset %>%
ggplot(aes(x = `Activity Type`, y =`Elev Gain`, color = `Activity Type`) ) +
geom_boxplot()+
theme_minimal()
}
# Fonction pour créer un diagramme en barres selon Date / Distance et Activity Type
graph_dist_month <- function(dataset){
dataset %>%
ggplot() +
aes(x = lubridate::month(Date), y = Distance, fill = `Activity Type`) +
geom_col()+
theme_minimal()+
labs(x ="Month")
}
|
6ca6add3ec3d06291c8c7a4c09b8f9df2ef0a416
|
a4f6b121c2f5a5fa124b464530b783852154ab61
|
/12. 회귀식(R).R
|
cb4af3874ec311a1c99c05ccbbfff06a1af0a2ae
|
[] |
no_license
|
mjkim9001/R_basic
|
cc1b00a52175643e25be5163b187e33d6c254963
|
82e3d141de678cc72e72718d14ad5dca7f8d1e58
|
refs/heads/master
| 2022-11-20T19:52:52.206895
| 2020-07-15T07:02:01
| 2020-07-15T07:02:01
| 276,788,126
| 0
| 0
| null | null | null | null |
UHC
|
R
| false
| false
| 1,714
|
r
|
12. 회귀식(R).R
|
setwd('c:/Rdata')
library(rvest)
library(stringr)
library(dplyr)
View(attitude)
cov(attitude)
cor(attitude)
with(attitude, cor.test(rating, complaints))
cor.test(attitude)
plot(attitude)
fasu = data.frame(fa, su)
fasu
lm(su~fa, data = fasu)
data = read.csv("cars.csv")
data
out = lm(dist~speed, data = data) #설명변수를 종속변수에 회귀분석
summary(out)
plot(dist~speed, data = data, col="blue")
abline(out, col="red")
lm(dist~speed+0, data = data)
out1 = lm(dist~speed+0, data = data)
plot(out1)
par(mfrow = c(2, 2))
shapiro.test(data$dist)
shapiro.test(log(data$dist))
shapiro.test(sqrt(data$dist))
out3 = lm(sqrt(dist)~speed+0, data = data)
summary(out3)
plot(out3)
out3$fitted.values
cbind(data$speed, out3$fitted.values)
out2 = lm(sqrt(dist)~speed+0, data = cars)
plot(out2)
shapiro.test(resid(out2))
data_new = data.frame(speed = data$speed)
predict(out2, data_new)
predict(out2,data_new,interval="confidence")
cbind(data_new$speed, fitted(out2))
# 다중회귀분석
data = read.csv("salary_data.csv")
data
out = lm(Salary~Experience+score, data=data)
out = lm(rating~.-critical, data = attitude)
summary(out)
backward = step(out, direction = "backward", trace=FALSE)
summary(backward)
both = step(out, direction = "both", trace = FALSE)
summary(both)
install.packages("leaps")
library(leaps)
leaps = regsubsets(rating~., data = attitude, nbest=5)
summary(leaps)
plot(leaps)
plot(leaps, scale= "bic")
plot(leaps, scale="adjr2")
plot(leaps, scale = "Cp")
out_bic=glm(rating~complaints, data = attitude)
summary(out_bic)
summary.out = summary(leaps)
which.max(summary.out$adjr2)
summary.out$which[11,]
out3 = lm(rating~complaints+learning+advance, data = attitude)
summary(out3)
|
16a090fa493819e389065d8ebc0b144789d4633d
|
2f6860bf6c18c42b67b563e22a46ea3f6af8207f
|
/cachematrix.R
|
5a5d2b24c83a7e38b640bb99c5c0c2e61fabded3
|
[] |
no_license
|
PooriaM/ProgrammingAssignment2
|
70aa8bc80f4d1008e4781991bd42f7805f62e1a1
|
1f20efd140c7641518b5f790ace9d464dd74b9fe
|
refs/heads/master
| 2020-07-14T15:47:21.679249
| 2016-08-03T23:01:35
| 2016-08-03T23:01:35
| 64,812,896
| 0
| 0
| null | 2016-08-03T03:43:33
| 2016-08-03T03:43:31
| null |
UTF-8
|
R
| false
| false
| 2,124
|
r
|
cachematrix.R
|
## Maxtrix inversion is usually costly, especially when running inside a loop.
## The following functions can compute and cache the inverse of a matrix so
## that they can be looked up later instead of recomputing the inverse.
## Written by:PooriaM
## "makeCacheMatrix"function creates a special "matrix" object that can cache
## its inverse.
makeCacheMatrix <- function(x = matrix()) {
## @x: a square invertible matrix
## return: a list containing functions to
## 1. set the matrix
## 2. get the matrix
## 3. set the inverse
## 4. get the inverse
## this list is used as the input to cacheSolve()
inv <- NULL
set <- function(y) {
# use `<<-` to assign a value to an object in an environment
# different from the current environment.
x <<- y
inv <<- NULL
}
get <- function() x
setInverse <- function(inverse) inv <<- inverse
getInverse <- function() inv
list(set = set, get = get,
setInverse = setInverse,
getInverse = getInverse)
}
## "cacheSolve" function computes the inverse of the "matrix" returned by
## makeCacheMatrix(). If the inverse has already been calculated and the matrix
## has not changed, it'll retrieves the inverse from the cache directly.
cacheSolve <- function(x, ...) {
## @x: output of makeCacheMatrix()
## return: inverse of the original matrix input to makeCacheMatrix()
inv <- x$getInverse()
# if the inverse has already been calculated
if(!is.null(inv)) {
# get it from the cache and skips the computation.
message("getting cached data")
return(inv)
}
# otherwise, calculates the inverse
mat <- x$get()
inv <- solve(mat, ...)
# sets the value of the inverse in the cache via the setInverse function.
x$setInverse(inv)
return(inv)
}
|
d4b5984bffbdf3fcfd5c19a93bfab9682e3700e2
|
95cfd39bbc815ddaf2b790dade4c933a2f5cdc7a
|
/man/fars_read.Rd
|
85bb311cc70d9732fe626a318ee04345a331650d
|
[] |
no_license
|
B0Ib0ivrb63B/farsRPackageProj-
|
81888d906384624d5d451869f12feffff71a1116
|
6019ab34bae149f00e619f8e2585c8933ccf13d4
|
refs/heads/master
| 2020-03-14T08:19:58.317395
| 2018-04-29T22:47:13
| 2018-04-29T22:47:13
| 131,522,740
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 713
|
rd
|
fars_read.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fars_functions.R
\name{fars_read}
\alias{fars_read}
\title{fars_read}
\usage{
fars_read(filename)
}
\arguments{
\item{filename}{filename of data file}
}
\value{
A tibble: <number of rows> x 50 cols
}
\description{
Function to read a semicolon (;) delimited file (including csv, tsv, .gz, .bz2, or .zip)
into a tibble from data from the US National Highway Traffic Safety Administration's Fatality Analysis
Reporting System. SOURCE: http://www.nhtsa.gov/Data/Fatality-Analysis-Reporting-System-(FARS)
}
\note{
Using a non-existant filename will result in a trapped error.
}
\examples{
\dontrun{
fars_read("C://User//data.zip")
}
}
|
2a920cbefc0dc9d982fe05fb9a30f5b43b823e61
|
b7c166227a8ca6773e600bee3f82ed6e4ca61143
|
/man/BootPI.Rd
|
4deb183a5d50883d1fbf8af2e53e34c55d261dc5
|
[] |
no_license
|
cran/BootPR
|
972b9db21662ec2f1555f8179dd1181d348e1d03
|
1c4d85c84317defd33826c4a4fe1d1e49e736cb5
|
refs/heads/master
| 2022-07-16T01:28:29.178653
| 2022-06-29T15:10:16
| 2022-06-29T15:10:16
| 17,717,260
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,174
|
rd
|
BootPI.Rd
|
\name{BootPI}
\alias{BootPI}
\title{ Bootstrap prediction intevals and point forecasts with no bias-correction}
\description{This function returns bootstrap forecasts and prediction intervals with no bias-correction
}
\usage{
BootPI(x, p, h, nboot, prob, type)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{x}{ a time series data set}
\item{p}{ AR order }
\item{h}{ the number of forecast periods }
\item{nboot}{number of bootstrap iterations }
\item{prob}{a vector of probabilities }
\item{type}{ "const" for the AR model with intercept only, "const+trend" for the AR model with intercept and trend }
}
\value{
\item{PI }{ prediction intervals}
\item{forecast }{bias-corrected point forecasts}
}
\references{
Thombs, L. A., & Schucany, W. R. (1990). Bootstrap prediction intervals for autoregression. Journal of the American Statistical Association, 85, 486-492.
}
\author{ Jae H. Kim }
\examples{
data(IPdata)
BootPI(IPdata,p=1,h=10,nboot=100,prob=c(0.05,0.95),type="const+trend")
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{ ts}
|
e9a7482904cc5c2bca991d6faf5a9e12451e8ae6
|
a026f85dbdd045ea2dc5b74df474afd02c3eb9af
|
/man/last_n_years.Rd
|
41c1b5c2c20108445a83e3f842843314376a61aa
|
[] |
no_license
|
selesnow/timeperiodsR
|
93df215538e9091fd9a9f0f0cb8e95db7735dc9d
|
3612001767f0dce942cea54f17de22b1d97863af
|
refs/heads/master
| 2023-04-27T15:52:19.511667
| 2023-04-20T10:15:49
| 2023-04-20T10:15:49
| 208,013,525
| 7
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,769
|
rd
|
last_n_years.Rd
|
\name{last_n_years}
\alias{last_n_years}
\title{
Start and end of last n years
}
\description{
Defines first and last date in previous period
}
\usage{
last_n_years(x = Sys.Date(),
n = 1,
part = getOption("timeperiodsR.parts"),
include_current = F)
}
\arguments{
\item{x}{Date object}
\item{n}{Number of periods for offset}
\item{part}{Part of period you need to receive, one of "all", "start", "end","sequence", "length". See details.}
\item{include_current}{If TRUE incliding current period in sequence}
}
\details{
You can get object of tpr class with all components or specify which component you need, use \code{part} for manage this option:
\itemize{
\item all - get all components
\item start - get only first date of period
\item end - get only last date of period
\item start - get vector of all dates in period
\item length - get number of dates in period
}
}
\value{Object of tpr class}
\author{
Alexey Seleznev
}
\seealso{
For get next other periods see \code{\link[timeperiodsR:last_n_months]{last_n_months()}}, \code{\link[timeperiodsR:last_n_days]{last_n_days()}}, \code{\link[timeperiodsR:last_n_weeks]{last_n_weeks()}}, \code{\link[timeperiodsR:last_n_quarters]{last_n_quarters()}}
}
\examples{
## To get start, end and sequence of last 2 years,
## exclude current year
last2years <- last_n_years(n = 2)
## include current year
last2years_2 <- last_n_years(n = 2, include_current = TRUE)
## To get vector of date sequences
last_n_years(n = 2, part = "sequence")
last_n_years(n = 2)$sequence
seq(last2years)
## Get number of days of last 2 years
day_nums <- last_n_years(n = 2, part = "length")
last_n_years()$length
length(last2years)
}
|
67380e1228dfe3286fb77ed8341e6d8191e5d166
|
ab76845b0cd8b17b74525042b0c83fbb93cc49d1
|
/R/redshift_add_column.R
|
c49a831efebdc8518f9f7879d5a18c1e3dbd5394
|
[
"MIT"
] |
permissive
|
zapier/redshiftTools
|
88a9ca49a55b6665b76ae3facc5d01b1dbbcd74d
|
a2258ab6aa0b3f3250190da436ac020da3d972bb
|
refs/heads/develop
| 2021-07-24T19:38:02.674339
| 2021-04-21T17:03:46
| 2021-04-21T17:03:46
| 59,214,680
| 2
| 5
|
MIT
| 2021-07-14T10:18:40
| 2016-05-19T14:35:05
|
R
|
UTF-8
|
R
| false
| false
| 857
|
r
|
redshift_add_column.R
|
#' Add a column to a redshift table
#'
#' Add a typed column to a redshift database
#'
#' @param dbcon Database connection object of type RPostgreSQL
#' @param table_name the name of the target table (character)
#' @param column_name (character)
#' @param redshift_type (character) for the redshift type to assign for this column
#'
#' @return NULL
#' @export
rs_add_column <- function(dbcon, table_name, column_name, redshift_type) {
if (is_schema(table_name)) {
table_name <- schema_to_character(table_name)
}
if (!column_name %in% DBI::dbListFields(dbcon, table_name)) {
DBI::dbGetQuery(
dbcon,
whisker.render("alter table {{table_name}}
add column {{column_name}} {{redshift_type}}
default NULL", list(table_name = table_name, column_name = column_name, redshift_type = redshift_type))
)
}
return(NULL)
}
|
6aa3a1be75b6c329cff4190302dd7d3e0526bd41
|
4b23658a53d0f7502e2c9a6d67eff57a9f3791dd
|
/cachematrix.R
|
3c021c5e2ebaf4423512e99f18aa9842e7cac5c0
|
[] |
no_license
|
LonePine/ProgrammingAssignment2
|
8d6ad0ee650aa3c22ee6bb7e12d849c8ab18fe24
|
f2a371b11ab946e4a070f66d0dd856272359080a
|
refs/heads/master
| 2020-05-02T09:52:56.772191
| 2015-07-22T23:47:37
| 2015-07-22T23:47:37
| 39,369,287
| 0
| 0
| null | 2015-07-20T07:23:14
| 2015-07-20T07:23:14
| null |
UTF-8
|
R
| false
| false
| 1,651
|
r
|
cachematrix.R
|
## Since calculating inverse of a matrix is time consuming in a repeated operation the two
## functions below help with that. makeCacheMatrix creates a special "matrix" that can cache its inverse.
## function cacheSolve computes the inverse of the matrix returned by makeCacheMatrix.
## function makeCacheMatrix caches the inverse of a matrix and returns a list of four functions:
## getmatrix, setmatrix, getinverse, setinverse.
makeCacheMatrix <- function(x = matrix()) {
IM <- NULL
setmatrix <- function(y){
x <<- y
IM <<- NULL
}
getmatrix <- function()x
setinverse <- function(solve) IM <<- solve
getinverse <- function()IM
list(setmatrix = setmatrix,getmatrix = getmatrix,setinverse = setinverse,getinverse = getinverse)
}
## function cacheSolve calculates the inverse of a matrix returned by the above function and checks
## for condition to see if inverse already exists. If not, then it calculates inverse and stores in IM.
cacheSolve <- function(x, ...) {
IM <- x$getinverse()
if(!is.null(IM)){
message(" getting cached matrix")
return(IM)
}
matrix <- x$getmatrix()
IM <- solve(matrix,...)
x$setinverse(IM)
IM
}
## created the matrix below and tested the functons makeCacheMatrix and cacheSolve above with
## print(mat), matrixx <-makeCacheMatrix(mat), matrixx$getmatrix(), matrixx$getinverse(),
## cacheSolve(matrixx), matrixx$getinverse(),class(matrixx$getmatrix()),
## class(matrixx$getinverse()) and results were found to be correct and valid.
mat <- matrix(c(1,0,5,2,1,6,3,4,0), nrow = 3,ncol = 3)
|
4da3a053c29e295cfaaf4fb32d4314d2154aeaf2
|
cc11ab7cf4531b687e1ec0b0cdaef97183fd949d
|
/tests/testthat/test_minimal.R
|
54ed9d1806a116b107e57bbd922ed9a790f3e365
|
[] |
no_license
|
mablab/rpostgisLT
|
7234b0d4f15bf10adea6a6fdb085055dc39e6cdb
|
af566be34182c8bd8bdac0a30b6ff39e5c4f9a04
|
refs/heads/master
| 2020-05-21T19:18:31.490234
| 2018-03-13T17:28:54
| 2018-03-13T17:28:54
| 61,544,009
| 9
| 3
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,451
|
r
|
test_minimal.R
|
context("rpostgisLT: minimal")
# source_test_helpers(path = "tests/testthat", env = test_env())
test_that("minimal ltraj transfer is equal and identical", {
testthat::skip_on_cran()
ib_min <- dl(ld(ibexraw[1])[1:10,]) # note that step parameters are recomputed on purpose
expect_true(ltraj2pgtraj(conn_empty,
ltraj = ib_min,
schema = "traj_min",
pgtraj = "ib_min"))
expect_message(ib_min_re <- pgtraj2ltraj(conn_empty, schema = "traj_min", pgtraj = "ib_min"),
"Ltraj successfully created from ib_min.")
expect_equal(ib_min, ib_min_re)
expect_false(identical(ib_min, ib_min_re))
expect_true(pgtrajDrop(conn_empty, "ib_min", "traj_min"))
})
test_that("overwrite with null proj4string", {
testthat::skip_on_cran()
ib_min <- dl(ld(ibexraw[1])[1:10, ])
expect_true(ltraj2pgtraj(
conn_empty,
ltraj = ib_min,
schema = "traj_min",
pgtraj = "ib_min"
))
attr(ib_min, "proj4string") <- NULL
expect_true(
ltraj2pgtraj(
conn_empty,
ltraj = ib_min,
schema = "traj_min",
pgtraj = "ib_min",
overwrite = TRUE
)
)
expect_true(pgtrajDrop(conn_empty, "ib_min", "traj_min"))
})
test_that("transfer with projection", {
testthat::skip_on_cran()
ib_min_srs <- dl(ld(ibexraw[2])[1:10, ], proj4string = srs)
# note that step parameters are recomputed on purpose
expect_true(ltraj2pgtraj(
conn_empty,
ltraj = ib_min_srs,
schema = "traj_min",
pgtraj = "ib_min_3395"
))
expect_message(
ib_min_srs_re <-
pgtraj2ltraj(conn_empty, schema = "traj_min", pgtraj = "ib_min_3395"),
"Ltraj successfully created from ib_min_3395."
)
expect_equal(ib_min_srs, ib_min_srs_re)
expect_true(pgtrajDrop(conn_empty, "ib_min_3395", "traj_min"))
})
test_that("ibexraw is not regular", {
testthat::skip_on_cran()
expect_false(is.regular(ibexraw))
})
test_that("pgtraj and schema defaults", {
testthat::skip_on_cran()
expect_message(ltraj2pgtraj(conn_empty, ibex, overwrite = TRUE),
"('ibex')|('traj')", fixed = FALSE)
expect_message(ibexTest <- pgtraj2ltraj(conn_empty, pgtraj = "ibex"),
"ibex")
expect_equal(ibex, ibexTest)
expect_true(pgtrajDrop(conn_empty, "ibex", "traj"))
})
|
75717bc62faa1efc54277eddaf9973cfa5b4fa71
|
d859174ad3cb31ab87088437cd1f0411a9d7449b
|
/autonomics.import/man/replace_nas_with_zeros.Rd
|
00ceca7cdc6125f9729b5525c3af7a4bf599fa48
|
[] |
no_license
|
bhagwataditya/autonomics0
|
97c73d0a809aea5b4c9ef2bf3f886614eceb7a3c
|
c7ca7b69161e5181409c6b1ebcbeede4afde9974
|
refs/heads/master
| 2023-02-24T21:33:02.717621
| 2021-01-29T16:30:54
| 2021-01-29T16:30:54
| 133,491,102
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 527
|
rd
|
replace_nas_with_zeros.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mutators.R
\name{replace_nas_with_zeros}
\alias{replace_nas_with_zeros}
\title{Replace NAs with zeros}
\usage{
replace_nas_with_zeros(object, verbose = TRUE)
}
\arguments{
\item{object}{SummarizedExperiment}
\item{verbose}{TRUE or FALSE}
}
\value{
eset
}
\description{
Replace NAs with zeros
}
\examples{
if (require(autonomics.data)){
require(magrittr)
object <- autonomics.data::stemcomp.proteinratios
replace_nas_with_zeros(object)
}
}
|
6e27716861977e8337bed86b9fd15bc13eea37ab
|
df0579b5738f674c9673780a4bd50cab37e94615
|
/ui.R
|
320552216a3b9976e14c24ee06d9515b1fc683f6
|
[] |
no_license
|
poolupsoon/DataScienceCapstone
|
0968c7a7256b4faee54dcc14f52cdb59d5b374e9
|
c10f8e159312fa5972798d667702e7edee50f614
|
refs/heads/master
| 2021-01-25T01:21:17.360270
| 2017-06-19T07:28:52
| 2017-06-19T07:28:52
| 94,747,111
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,373
|
r
|
ui.R
|
library(shiny)
library(tm)
library(wordnet)
set.seed(12345)
shinyUI(fluidPage(
splitLayout(cellWidths = c("20%", "60%", "20%"),
div(),
div(titlePanel("Data Science Capstone: Predictive Text Model")),
div()
),
splitLayout(cellWidths = c("20%", "60%", "20%"),
div(),
div(a(em("Click Here to View Documentation"), href = "documentation.html", target = "_blank")),
div()
),
hr(style = "border-color: black"),
splitLayout(cellWidths = c("20%", "10%", "50%", "20%"),
div(),
div(strong("Phrase:")),
div(textInput("textIn", label = NULL, width = "100%", placeholder = "Enter text in English")),
div()
),
splitLayout(cellWidths = c("20%", "10%", "50%", "20%"),
div(),
div(),
div(submitButton("Predict")),
div()
),
br(), br(),
splitLayout(cellWidths = c("20%", "10%", "50%", "20%"),
div(),
div(strong("Results:")),
div(textOutput("textOut1")),
div()
),
splitLayout(cellWidths = c("20%", "10%", "50%", "20%"),
div(),
div(),
div(textOutput("textOut2")),
div()
),
splitLayout(cellWidths = c("20%", "10%", "50%", "20%"),
div(),
div(),
div(textOutput("textOut3")),
div()
),
splitLayout(cellWidths = c("20%", "10%", "50%", "20%"),
div(),
div(),
div(textOutput("textOut4")),
div()
),
splitLayout(cellWidths = c("20%", "10%", "50%", "20%"),
div(),
div(),
div(textOutput("textOut5")),
div()
),
splitLayout(cellWidths = c("20%", "10%", "50%", "20%"),
div(),
div(),
div(textOutput("textOut6")),
div()
),
br(), br(),
hr(style = "border-color: black"),
div(em("DISCLAIMER: This Shiny Web App is for educational purposes only. The data and prediction accuracies are not guaranteed."), style = "text-align: center")
))
|
c177b4327e515dddf6e3d814669359b1520995b0
|
65cf48ec1a1c6cebdc2c9a1fedd0f40f0caf73a2
|
/R/ppsale.R
|
d34ee1406ccb6cecacf6165a5499c4598dc41f8f
|
[] |
no_license
|
Menglinucas/extd.comavm
|
e3c59f06202acbe87f5ea394af22deefa15d2431
|
409d6394e68c9d3232d36f1a8cd7a5d1a2e9e036
|
refs/heads/master
| 2021-09-08T09:06:30.390886
| 2018-03-09T01:19:47
| 2018-03-09T01:19:47
| 114,219,130
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 12,105
|
r
|
ppsale.R
|
#' joining ha and price data
#'
#' This function can join price and ha info
#' @param tabln.vec basic data provided by \code{\link{loadData}}
#' @param proptype type code of properties.The defalut is 11 indiciting housing
#' @return data including ha info and ha price
ppsale<-function(tabln.vec=NULL,proptype='11',modelpath1=NULL,city_code=NULL)
{
if(is.null(tabln.vec)){
stop('must provide a tabln.vec!!!')
}
library(dplyr)
# saleprice of property "11" (residence area)
price_sale<-tabln.vec$ha_price%>%filter(proptype==proptype)
# saleprice = 0 and Inf, not meaningful
price_sale$saleprice[which(price_sale$saleprice<=0 | price_sale$saleprice>1000000)] <- NA
# # mean saleprice of ha
# price_sale <- price_sale[!duplicated(price_sale),] %>% group_by(ha_code) %>% summarise(saleprice=mean(saleprice,na.rm=T))
# add pricesale to ha_info, note: mybe exist duplicated ha_code, that's not a wrong
ppi_sale<-merge(price_sale,tabln.vec$ha_info.sp,by="ha_code",all.y=T)
# ppi_sale<-ppi_sale%>%
# # mutate(year=floor(ymid/12),
# # month=ifelse(!ymid%%12,12,ymid%%12))%>%
# group_by(ha_code,x,y,name,ha_cl_code,ha_cl_name,dist_code,
# dist_name,buildyear,bldg_code) %>%
# summarise(saleprice=mean(saleprice,na.rm=T),
# volume_rate=mean(volume_rate,na.rm=T),greening_rate=mean(greening_rate,na.rm=T),
# edu.poi.sp_dens=mean(edu.poi.sp_dens,na.rm=T),hosi.poi.sp_dens=mean(hosi.poi.sp_dens,na.rm=T),
# trans.poi.sp_dens=mean(trans.poi.sp_dens,na.rm=T),busi.poi.sp_dens=mean(busi.poi.sp_dens,na.rm=T),
# poi.diversity.pts=mean(poi.diversity.pts,na.rm=T),
# edu.poi.sp_mindist=mean(edu.poi.sp_mindist,na.rm=T),hosi.poi.sp_mindist=mean(hosi.poi.sp_mindist,na.rm=T),
# trans.poi.sp_mindist=mean(trans.poi.sp_mindist,na.rm=T),busi.poi.sp_mindist=mean(busi.poi.sp_mindist,na.rm=T))%>%
# as.data.frame()
ppi_sale[ppi_sale=='NaN'] <- NA
# substitute the price by Community avm values, should ensure that comavm model contains all bldg_code of a community
ppi_sale <- subsp(ppi_sale,modelpath1,city_code)
return(ppi_sale)
}
# substitue saleprice of tabln$vec$ha_price by community avm
subsp <- function(sp,modelpath1,city_code){
# library(rjson)
filenames <- list.files(paste0(modelpath1,"/",city_code))
filepos <- paste0(modelpath1,"/",city_code,"/",filenames)
# number of files
nha <- length(filenames)
# create a dataframe to store all ha_code+saleprice of a city
comsp <- data.frame("ha_code"=rep(NA,nha*4),"bldg_code"=rep(NA,nha*4),
"K0"=rep(0,nha*4),"Kbyear"=rep(0,nha*4),"Kheight"=rep(0,nha*4),"Ktime"=rep(0,nha*4),
"Kfloor"=rep(0,nha*4),"KbrArea"=rep(0,nha*4),"Kstru"=rep(0,nha*4),"Kface_through"=rep(0,nha*4),
"Kface_sun"=rep(0,nha*4),"Kdeco_if"=rep(0,nha*4),"Kdeco_good"=rep(0,nha*4),"Kprop_rt"=rep(0,nha*4))
# extract the ha_code from filesnames, put in comsp@ha_code
comsp$ha_code <- sapply(sapply(filenames,unlist(strsplit),split="[_]"),function(x) x[1])
# extract the bldg_code from json, put in comsp@bldg_code
comsp$bldg_code[1:nha] <- rep("11",nha)
comsp$bldg_code[(nha+1):(2*nha)] <- rep("12",nha)
comsp$bldg_code[(2*nha+1):(3*nha)] <- rep("13",nha)
comsp$bldg_code[(3*nha+1):(4*nha)] <- rep("21",nha)
# extract the coefficients from json
features <- sapply(filepos,jsp,bldg_code='11')
comsp[1:nha,"K0"] <- features[1,1:nha]
comsp[1:nha,"Kbyear"] <- features[2,1:nha]
comsp[1:nha,"Kheight"] <- features[3,1:nha]
comsp[1:nha,"Ktime"] <- features[4,1:nha]
comsp[1:nha,"Kfloor"] <- features[5,1:nha]
comsp[1:nha,"KbrArea"] <- features[6,1:nha]
comsp[1:nha,"Kstru"] <- features[7,1:nha]
comsp[1:nha,"Kface_through"] <- features[8,1:nha]
comsp[1:nha,"Kface_sun"] <- features[9,1:nha]
comsp[1:nha,"Kdeco_if"] <- features[10,1:nha]
comsp[1:nha,"Kdeco_good"] <- features[11,1:nha]
comsp[1:nha,"Kprop_rt"] <- features[12,1:nha]
features <- sapply(filepos,jsp,bldg_code='12')
comsp[(nha+1):(2*nha),"K0"] <- features[1,1:nha]
comsp[(nha+1):(2*nha),"Kbyear"] <- features[2,1:nha]
comsp[(nha+1):(2*nha),"Kheight"] <- features[3,1:nha]
comsp[(nha+1):(2*nha),"Ktime"] <- features[4,1:nha]
comsp[(nha+1):(2*nha),"Kfloor"] <- features[5,1:nha]
comsp[(nha+1):(2*nha),"KbrArea"] <- features[6,1:nha]
comsp[(nha+1):(2*nha),"Kstru"] <- features[7,1:nha]
comsp[(nha+1):(2*nha),"Kface_through"] <- features[8,1:nha]
comsp[(nha+1):(2*nha),"Kface_sun"] <- features[9,1:nha]
comsp[(nha+1):(2*nha),"Kdeco_if"] <- features[10,1:nha]
comsp[(nha+1):(2*nha),"Kdeco_good"] <- features[11,1:nha]
comsp[(nha+1):(2*nha),"Kprop_rt"] <- features[12,1:nha]
features <- sapply(filepos,jsp,bldg_code='13')
comsp[(2*nha+1):(3*nha),"K0"] <- features[1,1:nha]
comsp[(2*nha+1):(3*nha),"Kbyear"] <- features[2,1:nha]
comsp[(2*nha+1):(3*nha),"Kheight"] <- features[3,1:nha]
comsp[(2*nha+1):(3*nha),"Ktime"] <- features[4,1:nha]
comsp[(2*nha+1):(3*nha),"Kfloor"] <- features[5,1:nha]
comsp[(2*nha+1):(3*nha),"KbrArea"] <- features[6,1:nha]
comsp[(2*nha+1):(3*nha),"Kstru"] <- features[7,1:nha]
comsp[(2*nha+1):(3*nha),"Kface_through"] <- features[8,1:nha]
comsp[(2*nha+1):(3*nha),"Kface_sun"] <- features[9,1:nha]
comsp[(2*nha+1):(3*nha),"Kdeco_if"] <- features[10,1:nha]
comsp[(2*nha+1):(3*nha),"Kdeco_good"] <- features[11,1:nha]
comsp[(2*nha+1):(3*nha),"Kprop_rt"] <- features[12,1:nha]
features <- sapply(filepos,jsp,bldg_code='21')
comsp[(3*nha+1):(4*nha),"K0"] <- features[1,1:nha]
comsp[(3*nha+1):(4*nha),"Kbyear"] <- features[2,1:nha]
comsp[(3*nha+1):(4*nha),"Kheight"] <- features[3,1:nha]
comsp[(3*nha+1):(4*nha),"Ktime"] <- features[4,1:nha]
comsp[(3*nha+1):(4*nha),"Kfloor"] <- features[5,1:nha]
comsp[(3*nha+1):(4*nha),"KbrArea"] <- features[6,1:nha]
comsp[(3*nha+1):(4*nha),"Kstru"] <- features[7,1:nha]
comsp[(3*nha+1):(4*nha),"Kface_through"] <- features[8,1:nha]
comsp[(3*nha+1):(4*nha),"Kface_sun"] <- features[9,1:nha]
comsp[(3*nha+1):(4*nha),"Kdeco_if"] <- features[10,1:nha]
comsp[(3*nha+1):(4*nha),"Kdeco_good"] <- features[11,1:nha]
comsp[(3*nha+1):(4*nha),"Kprop_rt"] <- features[12,1:nha]
# saleprice = 0 and Inf, not meaningful
comsp$K0[which(comsp$K0<=6 | comsp$K0>14)] <- NA
# delete no price data
comsp <- na.omit(comsp)
# delete the duplicated row
comsp <- comsp[!duplicated(subset(comsp,select=c(ha_code,bldg_code))),]
# dataframe: ha_code, bldg_code, ..., bc11, bc12, bc13, bc21
comsp$bc11 <- 0
comsp$bc12 <- 0
comsp$bc13 <- 0
comsp$bc21 <- 0
idy <- sapply(1:nrow(comsp),
function(i,df=comsp){
id <- which(substr(names(df),3,4)==df[i,'bldg_code'])}
)
for (i in 1:nrow(comsp)){
comsp[i,idy[i]] <- 1
}
# the coefficients sould not be too large
comsp[(which(abs(comsp[4])>1)),4] <- sign(comsp[(which(abs(comsp[4])>1)),4])
comsp[(which(abs(comsp[5])>1)),5] <- sign(comsp[(which(abs(comsp[5])>1)),5])
comsp[(which(abs(comsp[6])>1)),6] <- sign(comsp[(which(abs(comsp[6])>1)),6])
comsp[(which(abs(comsp[7])>1)),7] <- sign(comsp[(which(abs(comsp[7])>1)),7])
comsp[(which(abs(comsp[8])>1)),8] <- sign(comsp[(which(abs(comsp[8])>1)),8])
comsp[(which(abs(comsp[9])>1)),9] <- sign(comsp[(which(abs(comsp[9])>1)),9])
comsp[(which(abs(comsp[10])>1)),10] <- sign(comsp[(which(abs(comsp[10])>1)),10])
comsp[(which(abs(comsp[11])>1)),11] <- sign(comsp[(which(abs(comsp[11])>1)),11])
comsp[(which(abs(comsp[12])>1)),12] <- sign(comsp[(which(abs(comsp[12])>1)),12])
comsp[(which(abs(comsp[13])>1)),13] <- sign(comsp[(which(abs(comsp[13])>1)),13])
comsp[(which(abs(comsp[14])>1)),14] <- sign(comsp[(which(abs(comsp[14])>1)),14])
# price_sale <- group_by(comsp,ha_code) %>% summarise(saleprice=mean(saleprice,na.rm=T)) %>% as.data.frame()
# bldg <- table(comsp[1:2]) %>% unclass() %>% as.data.frame()
# names(bldg) <- paste0("bc",names(bldg))
# bldg$ha_code <- row.names(bldg)
# row.names(bldg) <- c(1:nrow(bldg))
# price_sale <- merge(bldg,price_sale,by="ha_code")
# replace saleprice
sp <- subset(sp,select=-c(saleprice,bldg_code))
result <- merge(comsp,sp,by='ha_code')
# the last checking
result <- result[!duplicated(result) & !is.na(result$K0),]
return(result)
}
# read (building year, height) and all coefficients from json file based on bldg_code
jsp <- function(filepos,bldg_code){
# all the variables
# standard variables including time(1:5), floor, brArea, stru, face_through, face_sun, deco_if, deco_good and prop_rt
coeffs <- c("price","buildyear","height",
"time","floor","brArea","stru","face_through","face_sun","deco_if","deco_good","prop_rt")
# the values of coefficients
coeffsValue <- c(rep(0,length(coeffs)))
# read Json
temp <- fromJSON(file = filepos)
# if the json file is null
if (length(temp)!=0){
# if existing the bldg_code in the json file
if (bldg_code %in% names(temp[[1]])){
bldgfeatures <- temp[[1]][[which(names(temp[[1]])==bldg_code)]]
features <- bldgfeatures$listFeature
# update time of the model
t1 <- as.Date(bldgfeatures$modelUpdateDate)
# the first day of this month
t2 <- lubridate::floor_date(Sys.Date(),unit='month')
# extract the coefficients of standard variables
featurenames <- c()
for (i in 1:length(features))
{
featurenames[i] <- features[[i]]$featureName
}
# select the time segment (0-?)
idt <- which(featurenames=='time')
if (length(idt) > 0){
for (i in 1:length(idt))
{
if (features[[idt[i]]]$min<2) { #the minimum time value is less than 2, maybe 0 or 1 in json file
idt_select <- idt[i]
coeffsValue[4] <- features[[idt_select]]$beta
}
}
}
if (coeffsValue[4]!=0){
stdtime <- as.numeric(t1-t2)/features[[idt_select]]$max
coeffsValue[4] <- coeffsValue[4] * stdtime
}
# select the floor segment (1-?)
idflr <- which(featurenames=='floor')
if (length(idflr) > 0){
for (i in 1:length(idflr))
{
if (features[[idflr[i]]]$min==1) { #the minimum floor value is 1
idflr_select <- idflr[i]
coeffsValue[5] <- features[[idflr_select]]$beta
}
}
}
# stdflr == 0
# select the area segment (?<90<?)
idarea <- which(featurenames=='brArea')
if (length(idarea) > 0){
for (i in 1:length(idarea))
{
if (features[[idarea[i]]]$min<90 & features[[idarea[i]]]$max>90) { # contain area = 90 m^2
idarea_select <- idarea[i]
coeffsValue[6] <- features[[idarea_select]]$beta
}
}
}
# stdarea == log(90)/log(500)
# # the maximum and minimum value of building year
# idbuildyear <- which(featurenames=='buildyear') # must be a 1 dim
# if (length(idbuildyear)==1){
# buildyear_max <- features[[idbuildyear]]$max
# buildyear_min <- features[[idbuildyear]]$min
# stdbuildyear <- (buildyear-buildyear_min)/(buildyear_max-buildyear_min)
# }else{stdbuildyear <- 0}
# # the maximum and minimum value of height
# idheight <- which(featurenames=='height') # must be a 1 dim
# if (length(idheight)==1){
# height_max <- features[[idheight]]$max
# height_min <- features[[idheight]]$min
# stdheight <- (height-height_min)/(height_max-height_min)
# }else{stdheight <- 0}
# else coefficients extraction
idf <- which(featurenames!='time' & featurenames!='floor' & featurenames!='brArea')
featurenames <- featurenames[idf]
if (length(featurenames)>0){
id <- sapply(1:length(featurenames),function(i) which(coeffs==featurenames[i]))
coeffsValue[id] <- sapply(1:length(featurenames),function(i) features[[idf[i]]]$beta)
}
return(coeffsValue)
}else{return(coeffsValue)}
}else{return(coeffsValue)}
}
|
9e3e296439bab4d6c9983878e1b06de1bfaf81ce
|
64ecf2a801522d5c105d921ffeb413af201a78f4
|
/R/NorgastAnastomoselekkasje.R
|
f67226d49e6a9adc6ffb428235e455d3aa2d1a6d
|
[] |
no_license
|
Rapporteket/norgast
|
c3a979915d4f6baca2d34130baf1684074037d89
|
a30ea9b2fa32ff9f224d7771f5f08a8802ecb721
|
refs/heads/rel
| 2023-06-10T11:47:55.564563
| 2023-06-09T14:05:19
| 2023-06-09T14:05:19
| 49,719,854
| 0
| 0
| null | 2023-02-22T09:55:01
| 2016-01-15T13:18:24
|
R
|
UTF-8
|
R
| false
| false
| 7,508
|
r
|
NorgastAnastomoselekkasje.R
|
#' Lag tabell over anastomoselekkasjerate
#'
#' Denne funksjonen lager tre tabeller for bruk i samlerapport
#'
#' @inheritParams FigAndeler
#'
#' @return Tabell En list med tre tabeller over anastomoselekkasjerater
#'
#' @export
NorgastAnastomoselekkasje <- function(RegData=RegData, datoFra='2014-01-01', datoTil='2050-12-31',
minald=0, maxald=130, erMann=99, reshID=601225, outfile='', elektiv=99,
BMI='', valgtShus='')
{
RegData <- RegData[which(RegData$Op_gr2 != 9), ]
RegData$Op_gr2 <- factor(RegData$Op_gr2)
grtxt <- c('Kolonreseksjoner, ny anastomose', 'Kolonreseksjoner, øvrige', "Rektumreseksjoner, ny anastomose",
"Rektumreseksjoner, øvrige", 'Øsofagusreseksjoner', 'Ventrikkelreseksjoner, ny anastomose',
'Ventrikkelreseksjoner, øvrige','Whipples operasjon')
RegData$variabel <- 0
RegData$variabel[RegData$ViktigsteFunn==1] <- 1
NorgastUtvalg <- NorgastLibUtvalg(RegData=RegData, datoFra=datoFra, datoTil=datoTil, minald=minald, maxald=maxald,
erMann=erMann, elektiv=elektiv, BMI=BMI, valgtShus=valgtShus)
RegData <- NorgastUtvalg$RegData
utvalgTxt <- NorgastUtvalg$utvalgTxt
indSh <-which(RegData$AvdRESH == reshID)
indRest <- which(RegData$AvdRESH != reshID)
RegDataSh <- RegData[indSh,]
RegDataRest <- RegData[indRest,]
### Lage tabeller #####################################################################
Tabell1 <- data.frame(Operasjonsgruppe=grtxt, N_lokal=numeric(8), RateAnastomoselekkasje_lokal=numeric(8),
N_ovrig=numeric(8), RateAnastomoselekkasje_ovrig=numeric(8))
Tabell1$N_lokal <- tapply(RegDataSh$variabel, RegDataSh$Op_gr2, length)[1:8]
Tabell1$RateAnastomoselekkasje_lokal <- round(tapply(RegDataSh$variabel, RegDataSh$Op_gr2, sum)/
tapply(RegDataSh$variabel, RegDataSh$Op_gr2, length)*100, 2)[1:8]
Tabell1$N_ovrig <- tapply(RegDataRest$variabel, RegDataRest$Op_gr2, length)[1:8]
Tabell1$RateAnastomoselekkasje_ovrig <- round(tapply(RegDataRest$variabel, RegDataRest$Op_gr2, sum)/
tapply(RegDataRest$variabel, RegDataRest$Op_gr2, length)*100, 2)[1:8]
Tabell1$RateAnastomoselekkasje_lokal[2]<-NA
Tabell1$RateAnastomoselekkasje_ovrig[2]<-NA
Tabell1$RateAnastomoselekkasje_lokal[4]<-NA
Tabell1$RateAnastomoselekkasje_ovrig[4]<-NA
Tabell1$RateAnastomoselekkasje_lokal[7]<-NA
Tabell1$RateAnastomoselekkasje_ovrig[7]<-NA
### Begrenset til rektum ##################################
regdata <- RegData # Beholde det fulle datasettet
RegData <- RegData[RegData$Op_gr2==3,] # Velg bare rektum
indSh <-which(RegData$AvdRESH == reshID)
indRest <- which(RegData$AvdRESH != reshID)
RegDataSh <- RegData[indSh,]
RegDataRest <- RegData[indRest,]
Tabell2 <- data.frame(Operasjonsgruppe=c('Rektumreseksjoner, ny anastomose', '\\quad Uten avlastende stomi', '\\quad Med avlastende stomi'),
N_lokal=numeric(3), RateAnastomoselekkasje_lokal=numeric(3),
N_ovrig=numeric(3), RateAnastomoselekkasje_ovrig=numeric(3))
Tabell2$N_lokal[2:3] <- tapply(RegDataSh$variabel, RegDataSh$AvlastendeStomiRektum, length)[1:2]
Tabell2$RateAnastomoselekkasje_lokal[2:3] <- round(tapply(RegDataSh$variabel, RegDataSh$AvlastendeStomiRektum, sum)/
tapply(RegDataSh$variabel, RegDataSh$AvlastendeStomiRektum, length)*100, 2)[1:2]
Tabell2$N_ovrig[2:3] <- tapply(RegDataRest$variabel, RegDataRest$AvlastendeStomiRektum, length)[1:2]
Tabell2$RateAnastomoselekkasje_ovrig[2:3] <- round(tapply(RegDataRest$variabel, RegDataRest$AvlastendeStomiRektum, sum)/
tapply(RegDataRest$variabel, RegDataRest$AvlastendeStomiRektum, length)*100, 2)[1:2]
Tabell2[1,2:5] <- NA
### Onkologisk forbehandling ##################################
regdata$ForbehandlingBinaer <- NA
regdata$ForbehandlingBinaer[regdata$Forbehandling %in% c(1,2,3)] <- 1
regdata$ForbehandlingBinaer[regdata$Forbehandling == 4] <- 0
RegData <- regdata
Tabell3 <- data.frame(Operasjonsgruppe=c('Rektumreseksjon, ny anastomose', '\\quad Ingen forbehandling', '\\quad Enhver forbehandling',
'Ventrikkelreseksjon, ny anastomose', '\\quad Ingen forbehandling', '\\quad Enhver forbehandling',
'Øsofagusreseksjon', '\\quad Ingen forbehandling', '\\quad Enhver forbehandling'),
N_lokal=numeric(9), RateAnastomoselekkasje_lokal=numeric(9),
N_ovrig=numeric(9), RateAnastomoselekkasje_ovrig=numeric(9))
RegData <- RegData[RegData$Op_gr2==3,]
indSh <-which(RegData$AvdRESH == reshID)
indRest <- which(RegData$AvdRESH != reshID)
RegDataSh <- RegData[indSh,]
RegDataRest <- RegData[indRest,]
Tabell3$N_lokal[2:3] <- tapply(RegDataSh$variabel, RegDataSh$ForbehandlingBinaer, length)[1:2]
Tabell3$RateAnastomoselekkasje_lokal[2:3] <- round(tapply(RegDataSh$variabel, RegDataSh$ForbehandlingBinaer, sum)/
tapply(RegDataSh$variabel, RegDataSh$ForbehandlingBinaer, length)*100, 2)[1:2]
Tabell3$N_ovrig[2:3] <- tapply(RegDataRest$variabel, RegDataRest$ForbehandlingBinaer, length)[1:2]
Tabell3$RateAnastomoselekkasje_ovrig[2:3] <- round(tapply(RegDataRest$variabel, RegDataRest$ForbehandlingBinaer, sum)/
tapply(RegDataRest$variabel, RegDataRest$ForbehandlingBinaer, length)*100, 2)[1:2]
RegData <- regdata
RegData <- RegData[RegData$Op_gr2==6,]
indSh <-which(RegData$AvdRESH == reshID)
indRest <- which(RegData$AvdRESH != reshID)
RegDataSh <- RegData[indSh,]
RegDataRest <- RegData[indRest,]
Tabell3$N_lokal[5:6] <- tapply(RegDataSh$variabel, RegDataSh$ForbehandlingBinaer, length)[1:2]
Tabell3$RateAnastomoselekkasje_lokal[5:6] <- round(tapply(RegDataSh$variabel, RegDataSh$ForbehandlingBinaer, sum)/
tapply(RegDataSh$variabel, RegDataSh$ForbehandlingBinaer, length)*100, 2)[1:2]
Tabell3$N_ovrig[5:6] <- tapply(RegDataRest$variabel, RegDataRest$ForbehandlingBinaer, length)[1:2]
Tabell3$RateAnastomoselekkasje_ovrig[5:6] <- round(tapply(RegDataRest$variabel, RegDataRest$ForbehandlingBinaer, sum)/
tapply(RegDataRest$variabel, RegDataRest$ForbehandlingBinaer, length)*100, 2)[1:2]
RegData <- regdata
RegData <- RegData[RegData$Op_gr2==5,]
indSh <-which(RegData$AvdRESH == reshID)
indRest <- which(RegData$AvdRESH != reshID)
RegDataSh <- RegData[indSh,]
RegDataRest <- RegData[indRest,]
Tabell3$N_lokal[8:9] <- tapply(RegDataSh$variabel, RegDataSh$ForbehandlingBinaer, length)[1:2]
Tabell3$RateAnastomoselekkasje_lokal[8:9] <- round(tapply(RegDataSh$variabel, RegDataSh$ForbehandlingBinaer, sum)/
tapply(RegDataSh$variabel, RegDataSh$ForbehandlingBinaer, length)*100, 2)[1:2]
Tabell3$N_ovrig[8:9] <- tapply(RegDataRest$variabel, RegDataRest$ForbehandlingBinaer, length)[1:2]
Tabell3$RateAnastomoselekkasje_ovrig[8:9] <- round(tapply(RegDataRest$variabel, RegDataRest$ForbehandlingBinaer, sum)/
tapply(RegDataRest$variabel, RegDataRest$ForbehandlingBinaer, length)*100, 2)[1:2]
Tabell3[c(1,4,7),2:5] <- NA
Tabell <- list(Tabell1=Tabell1, Tabell2=Tabell2, Tabell3=Tabell3)
return(invisible(Tabell))
}
|
1dc7edfdc605756506068f153ff8281033aaf683
|
e869d56b65fdbaf8b2b5d273c8ca67dd4b3c25d2
|
/capstone.R
|
8a8128fb301a788f5c5aece3d849e7cdbae96796
|
[] |
no_license
|
jodeck80/591-Capstone
|
ff35354b0eec4582c769d7835316e1b026058f7e
|
4c49627716f06ef0e7e8f5a6b0cfee8e6c05604e
|
refs/heads/master
| 2021-01-01T19:30:23.125263
| 2015-05-10T13:14:22
| 2015-05-10T13:14:22
| 34,755,991
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 18,227
|
r
|
capstone.R
|
library(streamR)
library(plyr)
library(ggplot2)
library(grid)
library(maps)
library(RStorm)
library(tm)
library(stringr)
library(ROAuth)
load("my_oauth.Rdata")
# Color blind friendly colors:
cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
#1 minute tweet duration
TWEET_DURATION <- 60
#number of times to iterate process
ITERATIONS <- 3
MLBTerms <- c("MLB","yankees", "red sox", "orioles", "rays", "blue jays",
"mets", "braves", "marlins", "phillies", "nationals",
"tigers", "twins", "indians", "royals", "white sox",
"cubs", "cardinals", "reds", "pirates", "brewers",
"mariners", "a's", "astros", "angels", "rangers",
"dodgers", "giants", "diamondbacks", "padres", "rockies")
NBATerms <- c("NBA","hawks", "cavaliers", "bulls", "raptors", "wizards",
"bucks", "celtics", "nets", "pacers", "heat",
"hornets", "pistons", "magic", "76ers", "knicks",
"warriors", "rockets", "clippers", "spurs", "trail blazers",
"grizzlies", "mavericks", "pelicans", "thunder", "suns",
"jazz", "nuggets", "kings", "lakers", "timberwolves")
NFLTerms <- c("NFL","eagles", "giants", "cowboys", "redskins", "bills",
"patriots", "dolphins", "jets", "bears", "lions",
"packers", "vikings", "browns", "bengals", "steelers",
"ravens", "panthers", "saints", "buccaneers", "falcons",
"titans", "colts", "jaguars", "texans", "49ers",
"cardinals", "seahawks", "rams", "broncos", "chargers", "raiders", "chiefs")
NHLTerms <- c("NHL","rangers", "canadiens", "lightning", "capitals", "islanders",
"red wings", "senators", "penguins", "bruins", "panthers",
"blue jackets", "flyers", "devils", "hurricanes", "maple leafs",
"sabres", "ducks", "blues", "predators", "blackhawks",
"canucks", "wild", "jets", "flames", "kings",
"stars", "avalanche", "sharks", "oilers", "coyotes")
maxMLB<-function(a,b)
{
max1 = 0
max2 = 0
MLB=NULL
for(i in 2:length(b))
{
max1 = 0
for(j in 1:dim(a)[1])
{
if(length(grep(b[i],a[j,]$word,ignore.case=TRUE))>0)
{
max1 = max1 + a[j,]$count
}
}
if(max2<max1)
{
max2 = max1
MLB = b[i]
}
}
return(MLB)
}
maxMLBcount<-function(a,b)
{
max1 = 0
for(i in 1:length(b))
{
for(j in 1:dim(a)[1])
{
if(length(grep(b[i],a[j,]$word,ignore.case=TRUE))>0)
{
max1 = max1 + a[j,]$count
}
}
}
return(max1)
}
# R word counting function:
CountWord <- function(tuple,...){
# Get the hashmap "word count"
counts <- GetHash("wordcount")
if (tuple$word %in% counts$word) {
# Increment the word count:
counts[counts$word == tuple$word,]$count <-counts[counts$word == tuple$word,]$count + 1
} else {
# If the word does not exist
# Add the word with count 1
counts <- rbind(counts, data.frame(word = tuple$word, count = 1,stringsAsFactors=F))
}
# Store the hashmap
SetHash("wordcount", counts)
}
# and splits it into words:
SplitSentence <- function(tuple,...)
{
if((tuple$text!="")||(tuple$text!=" "))
{
# Split the sentence into words
words <- unlist(strsplit(as.character(tuple$text), " "))
# For each word emit a tuple
for (word in words)
{
if (word!="")
{
Emit(Tuple(data.frame(word = word,stringsAsFactors=F)),...)
}
}
}
}
countMin<-function(tweets.running){
oneData<-as.data.frame(tweets.running$text)
oneData[,1]<-as.data.frame(str_replace_all(oneData[,1],"[^[:graph:]]", " "))
oneData[,1] <- sapply(oneData[,1] ,function(row){
iconv(row, "ISO_8859-2", "ASCII", sub="")
iconv(row, "latin1", "ASCII", sub="")
iconv(row, "LATIN2", "ASCII", sub="")
})
s <- Corpus(VectorSource(oneData[,1]),readerControl=list(language="en"))
s <- tm_map(s, tolower)
s <- tm_map(s, removeWords, c(stopwords("english"),"rt","http","retweet"))
s <- tm_map(s, removePunctuation)
s <- tm_map(s, PlainTextDocument)
s <- tm_map(s, stripWhitespace)
tweets<-data.frame(text=sapply(s, '[[', "content"), stringsAsFactors=FALSE)
#function to pre-process tweet text
topology = Topology(tweets)
# Add the bolts:
topology <- AddBolt(
topology, Bolt(SplitSentence, listen = 0)
)
topology <- AddBolt(
topology, Bolt(CountWord, listen = 1)
)
# R function that receives a tuple
# (a sentence in this case)
# Run the stream:
resultLoc <- RStorm(topology)
# Obtain results stored in "wordcount"
return (resultLoc)
}
#function to remove tweets having both the words love and hate
filterTweetsMLB<-function(a)
{
# check if text doesn't have both love and hate in it
if((length(grep(MLBTerms[1],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[2],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[3],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[4],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[5],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[6],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[7],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[8],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[9],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[10],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[11],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[12],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[13],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[14],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[15],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[16],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[17],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[18],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[19],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[20],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[21],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[22],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[23],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[24],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[25],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[26],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[27],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[28],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[29],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[30],a,ignore.case=TRUE))>0) |
(length(grep(MLBTerms[31],a,ignore.case=TRUE))>0))
{
if(a$league != 0)
{
#another league was also mentioned, will remove tweet
a$league = 5
}
else
{
#set league to 1 if league wasnt previously set
a$league = "MLB"
}
}
data.frame(a)
}
#function to remove tweets having both the words love and hate
filterTweetsNBA<-function(a)
{
# check if text doesn't have both love and hate in it
if((length(grep(NBATerms[1],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[2],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[3],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[4],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[5],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[6],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[7],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[8],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[9],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[10],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[11],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[12],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[13],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[14],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[15],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[16],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[17],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[18],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[19],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[20],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[21],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[22],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[23],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[24],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[25],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[26],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[27],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[28],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[29],a,ignore.case=TRUE))>0) |
(length(grep(NBATerms[30],a,ignore.case=TRUE))>0) )
{
if(a$league != 0)
{
#another league was also mentioned, will remove tweet
a$league = 5
}
else
{
#set league to 2 if league wasnt previously set
a$league = "NBA"
}
}
data.frame(a)
}
#function to remove tweets having both the words love and hate
filterTweetsNFL<-function(a)
{
# check if text doesn't have both love and hate in it
if((length(grep(NFLTerms[1],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[2],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[3],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[4],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[5],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[6],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[7],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[8],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[9],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[10],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[11],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[12],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[13],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[14],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[15],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[16],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[17],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[18],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[19],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[20],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[21],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[22],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[23],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[24],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[25],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[26],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[27],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[28],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[29],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[30],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[31],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[32],a,ignore.case=TRUE))>0) |
(length(grep(NFLTerms[33],a,ignore.case=TRUE))>0) )
{
if(a$league != 0)
{
#another league was also mentioned, will remove tweet
a$league = 5
}
else
{
#set league to 3 if league wasnt previously set
a$league = "NFL"
}
}
data.frame(a)
}
#function to remove tweets having both the words love and hate
filterTweetsNHL<-function(a)
{
# check if text doesn't have both love and hate in it
if((length(grep(NHLTerms[1],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[2],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[3],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[4],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[5],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[6],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[7],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[8],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[9],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[10],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[11],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[12],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[13],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[14],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[15],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[16],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[17],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[18],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[19],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[20],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[21],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[22],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[23],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[24],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[25],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[26],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[27],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[28],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[29],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[30],a,ignore.case=TRUE))>0) |
(length(grep(NHLTerms[31],a,ignore.case=TRUE))>0))
{
if(a$league != 0)
{
#another league was also mentioned, will remove tweet
a$league = 5
}
else
{
#set league to 4 if league wasnt previously set
a$league = "NHL"
}
}
data.frame(a)
}
#Capture new tweets and
updateTweets<-function(a, firstTime){
#stream tweets only in US locations
filterStream("tweetsUS.json", locations = c(-125, 25, -66, 50), timeout = TWEET_DURATION, oauth = my_oauth)
#parse tweets from .json file
tweets.df <- parseTweets("tweetsUS.json", verbose = FALSE)
#delete file once it is stored, to be written to again
file.remove("tweetsUS.json")
#copy parsed tweets, init league to 0
tweets.filter <- tweets.df
tweets.filter$league = 0
# push tweets through all league filters
tweets.filter<-ddply(tweets.filter,.(text),filterTweetsMLB)
tweets.filter<-ddply(tweets.filter,.(text),filterTweetsNBA)
tweets.filter<-ddply(tweets.filter,.(text),filterTweetsNFL)
tweets.filter<-ddply(tweets.filter,.(text),filterTweetsNHL)
#Only look at rows where one league was selected
tweets.filter <- subset(tweets.filter, tweets.filter$league == "MLB" |
tweets.filter$league == "NBA" |
tweets.filter$league == "NFL" |
tweets.filter$league == "NHL")
#if first time, cant bind yet
if(firstTime)
{
tweets.running <- tweets.filter
}
else
{
#append new filtered tweets to old
tweets.running <- rbind(a, tweets.filter)
}
return (tweets.running)
}
mapData <- function(a){
#US map
map.data <- map_data("state")
p <- ggplot(map.data) +
geom_map(aes(map_id = region), map = map.data, fill = "white", color = "grey20", size = 0.25) +
expand_limits(x = map.data$long, y = map.data$lat) +
theme(axis.line = element_blank(), axis.text = element_blank(), axis.ticks = element_blank(),
axis.title = element_blank(), panel.background = element_blank(), panel.border = element_blank(),
panel.grid.major = element_blank(), plot.background = element_blank(), legend.title=element_blank(),
plot.margin = unit(0 * c(-1.5, -1.5, -1.5, -1.5), "lines")) +
geom_point(data = points, aes(x = x, y = y, color = factor(points$league)), size = 2 , alpha = 1/2) +
scale_colour_manual(values=cbbPalette) +
coord_cartesian(xlim = c(-60, -130)) #trim off unused edges on x axis, not lways needed
return (p)
}
#print total counts of each league
printTotals <- function()
{
#print totals
print(paste0("Total MLB Tweets: ", sum(tweets.running$league=="MLB", na.rm=TRUE)))
print(paste0("Total NBA Tweets: ", sum(tweets.running$league=="NBA", na.rm=TRUE)))
print(paste0("Total NFL Tweets: ", sum(tweets.running$league=="NFL", na.rm=TRUE)))
print(paste0("Total NHL Tweets: ", sum(tweets.running$league=="NHL", na.rm=TRUE)))
}
#run these prior to starting loop
#handshake for twitter credentials
my_oauth$handshake(cainfo = system.file("CurlSSL", "cacert.pem", package = "RCurl"))
#define globally, used in many functions
tweets.running <- data.frame(0)
points <- data.frame(0)
result <- data.frame(0)
#Set hash count
counts <- GetHash("wordcount")
#Gather new tweets and update filter for first time
tweets.running <- updateTweets(tweets.running, TRUE)
result <- countMin(tweets.running)
#determine good length
for (i in 1:ITERATIONS)
{
#Gather new tweets and update filter
tweets.running <- updateTweets(tweets.running, FALSE)
result <- countMin(tweets.running)
#define points with only latitude, longitude, and league class
#update global so it can be used in ggplot
points <- data.frame(x = as.numeric(tweets.running$lon), y = as.numeric(tweets.running$lat),
league = tweets.running$league)
#Define map and print it
map <- mapData(points)
print(map)
#Print total counts
printTotals()
}
#Prints the most successful franchises in one league
MLB=NULL
pop = NULL
MLBcount = 0
counts <- GetHash("wordcount", result)
MLB = maxMLB(counts,MLBTerms)
if(MLBcount<maxMLBcount(counts,MLBTerms))
{
MLBcount = maxMLBcount(counts,MLBTerms)
pop = "MLB"
}
print("Most Tweeted Baseball Team:")
print(MLB)
MLB = maxMLB(counts,NBATerms)
if(MLBcount<maxMLBcount(counts,NBATerms))
{
MLBcount = maxMLBcount(counts,NBATerms)
pop = "NBA"
}
print("Most Tweeted Basketball Team:")
print(MLB)
MLB = maxMLB(counts,NHLTerms)
if(MLBcount<maxMLBcount(counts,NHLTerms))
{
MLBcount = maxMLBcount(counts,NHLTerms)
pop = "NHL"
}
print("Most Tweeted Hockey Team:")
print(MLB)
MLB = maxMLB(counts,NFLTerms)
if(MLBcount<maxMLBcount(counts,NFLTerms))
{
MLBcount = maxMLBcount(counts,NFLTerms)
pop = "NFL"
}
print("Most Tweeted Football Team:")
print(MLB)
print("Most Tweeted League:")
print(pop)
|
ba14df1dcd84053ea2d7873f3a50ba03cfa4c27e
|
2f5db3fb3d5841cca614df7d5c881dc7004b0054
|
/tests/testthat/test-tree2mat.R
|
182fd99a15eacdce0d0eb04fca2808a89db6a2d8
|
[] |
no_license
|
darcyj/specificity
|
d7e62e3cbe84ca20d3703aaf981ef2dc31f61db8
|
f826719f153656ddce6c7347ac6bbcd08be12e3d
|
refs/heads/master
| 2023-08-08T12:02:59.686467
| 2023-07-28T02:47:55
| 2023-07-28T02:47:55
| 205,035,039
| 7
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 934
|
r
|
test-tree2mat.R
|
library(specificity)
library(testthat)
test_that("tree2mat matches ape::cophenetic.phylo", {
# use plant genera tree from specificity::endophyte
set.seed(12345)
a <- endophyte$supertree
atips <- sample(a$tip.label, 20)
cph_mat <- ape::cophenetic.phylo(ape::keep.tip(a, atips))
# re-order cph_mat to match atips
cph_mat <- cph_mat[order(match(rownames(cph_mat),atips)), order(match(colnames(cph_mat),atips))]
cph_dis <- as.dist(cph_mat)
t2m_dis <- tree2mat(a, atips)
# round to 4 decimal places gets around C vs R precision balogna
expect_true(all(round(cph_dis, 4) == round(t2m_dis, 4)))
})
test_that("tree2mat error if tip not in tree", {
expect_error(tree2mat(endophyte$supertree, c("bob", "Cyanea", "Euphorbia")))
})
test_that("tree2mat error if tip in tree multiple times", {
a <- endophyte$supertree
a$tip.label[1:4] <- "bad_label"
expect_error(tree2mat(a, c("Lycium", "Cyanea", "Euphorbia", "bad_label")))
})
|
b2689b7db0ee5accd63bea50db6745c28c4a9225
|
6e17a70abf17794d29f482336a805ae468096fb0
|
/man/scale_color_dubois1.Rd
|
ac32cd1b04fd91c3fc0d91851f614f93bafef83a
|
[
"MIT"
] |
permissive
|
brevinf1/themedubois
|
544cd4e7a67c51d8a494304eb453fe89a76b5733
|
a80e44571aed55d3d06ed53706e31e8742296be4
|
refs/heads/master
| 2023-03-09T20:22:15.501152
| 2021-02-22T18:06:34
| 2021-02-22T18:06:34
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 305
|
rd
|
scale_color_dubois1.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/scale_color_dubois1.R
\name{scale_color_dubois1}
\alias{scale_color_dubois1}
\title{Long color palette (color)}
\usage{
scale_color_dubois1()
}
\value{
Color with long color palette
}
\description{
Long color palette (color)
}
|
04479578c0c1cae83ddede789cba7ba976f475bf
|
fb66254c9f6579e7b5a3bac623c16e63c9c3f987
|
/updateWeight.ConvLayer.R
|
5db628943f0d54e922436e6a185d0ca3e6deff29
|
[] |
no_license
|
LinHungShi/ConvolutionalNeuralNetwork
|
049573d1f9b400d84da55d47c1d0053c0f109da4
|
02d4e6b2e5c480951c0474cff5c732d1a1cc4a39
|
refs/heads/master
| 2020-04-14T13:16:53.313601
| 2015-09-07T15:59:23
| 2015-09-07T15:59:23
| 40,487,913
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 324
|
r
|
updateWeight.ConvLayer.R
|
updateWeight.ConvLayer <- function(layer, n_layer, alpha){
input <- layer$input
output <- layer$output
n_grad <- n_layer$grad
layer$grad <- updateGradient(layer, n_layer)
layer$w_grad <- updateWgradient(layer)
layer$weight <- updateWeight_(layer$weight, layer$w_grad, alpha)
return (layer)
}
|
6a21051aa0f52b3cf0153fab0ed63c18b647bba8
|
e61f27d49a8a975b37b7b93ea79d605e430c4c57
|
/Module-2-Data-Warehousing/workspace/case 4/etl.r
|
a87e5fdba5cbea42fb81ddaa862f0cd966110db3
|
[] |
no_license
|
Cbanzaime23/up-nec-business-intelligence
|
1c58fec21b5fbdf79f72c8fd9f7c0148aa7458b1
|
24c1d1d97b461e0e26a79cf6129f51b3cc545d2c
|
refs/heads/master
| 2020-08-28T15:24:33.874816
| 2017-09-03T09:07:04
| 2017-09-03T09:07:04
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 9,173
|
r
|
etl.r
|
# 3.1 Extraction of Data from Source and Insert to S
library(RSQLite)
#Connect to the Source System
db <- dbConnect(SQLite(), dbname="source.db")
#Extract Today's Shippers Data
shippers_extract = dbGetQuery( db,'SELECT * FROM shippers' )
#Close Source Connection
dbDisconnect(db)
#Connect to the DW System
dwcon <- dbConnect(SQLite(), dbname="datawarehousenew.db")
#Insert into S
deletesshippers = dbGetQuery(dwcon,"DELETE FROM s_shippers" )
dbWriteTable(conn = dwcon, name = "s_shippers", value =shippers_extract, row.names = FALSE, append = TRUE)
#Validate
dbGetQuery(dwcon, "SELECT * FROM s_shippers")
# 3.2 Get New and Changed Data and Insert into X
#Get New and Changed Data
s_table_new_data <- dbGetQuery(dwcon, "SELECT * FROM s_shippers WHERE shipperid NOT IN (SELECT shipperid FROM m_shippers)")
s_table_changed_company_name <-dbGetQuery(dwcon, "SELECT s.ShipperID, s.CompanyName,s.Phone FROM S_Shippers s INNER JOIN M_Shippers m ON s.ShipperID = m.ShipperID WHERE NOT s.CompanyName = m.CompanyName")
s_table_changed_phone_number <-dbGetQuery(dwcon, "SELECT s.ShipperID, s.CompanyName,s.Phone FROM S_Shippers s INNER JOIN M_Shippers m ON s.ShipperID = m.ShipperID WHERE NOT s.Phone = m.Phone")
s_table_changed_data <- rbind(s_table_changed_company_name,s_table_changed_phone_number)
s_table_extract <- rbind(s_table_new_data,s_table_changed_data)
#Insert into X
deletequery=dbGetQuery(dwcon, "DELETE FROM X_Shippers")
dbWriteTable(conn = dwcon, name = "X_Shippers", value =s_table_extract, row.names = FALSE, append = TRUE)
#Validate
dbGetQuery(dwcon, "SELECT * FROM X_shippers")
# 3.3 Clean X and Insert Errors into E
#Clean X
#Select Companies with Null Names
x_table_no_companyname = dbGetQuery(dwcon, "SELECT * FROM x_shippers WHERE companyname = ''")
x_table_no_companyname$ErrorType = "No Company Name"
#Select Duplicate Companies
x_table_duplicate_companies= dbGetQuery(dwcon, "SELECT * FROM x_shippers WHERE companyname IN (SELECT companyname FROM s_shippers GROUP BY companyname HAVING COUNT(companyname) > 1)")
x_table_duplicate_companies$ErrorType = "Duplicate Company Name"
x_table_errors = rbind(x_table_duplicate_companies,x_table_no_companyname)
#Set Unknown to Missing Phone Number
updatequery =dbGetQuery(dwcon, "UPDATE X_shippers SET Phone='Unknown Phone Number' WHERE Phone = ''")
#Insert into E
deletequery=dbGetQuery(dwcon, "DELETE FROM e_shippers")
dbWriteTable(conn = dwcon, name = "e_shippers", value =x_table_errors, row.names = FALSE, append = TRUE)
#Validate
dbGetQuery(dwcon, "SELECT * FROM e_shippers")
# 3.4. Select Clean Data and Insert into C
#Select Clean Data
x_table_clean_data = dbGetQuery(dwcon, "SELECT * FROM X_Shippers WHERE ShipperID NOT IN (SELECT ShipperID FROM E_Shippers)")
#Insert into C
query=dbGetQuery(dwcon, "DELETE FROM C_Shippers")
dbWriteTable(conn = dwcon, name = "C_Shippers", value =x_table_clean_data, row.names = FALSE, append = TRUE)
#Validate
dbGetQuery(dwcon, "SELECT * FROM C_Shippers")
# 3.5. Update M Table
#Update M Table
#Select All New From C
c_table_new_data = dbGetQuery(dwcon, "SELECT * from C_Shippers c WHERE c.ShipperID NOT IN (SELECT m.ShipperID FROM M_shippers m )")
dbWriteTable(conn = dwcon, name = "M_Shippers_Test", value =c_table_new_data, row.names = FALSE, append = TRUE)
#Select All Changed From C
c_table_changed_data = dbGetQuery(dwcon, "SELECT c.* FROM C_Shippers c, M_Shippers m WHERE c.ShipperID = m.ShipperID and (c.CompanyName <> m.CompanyName or c.Phone <> m.Phone)")
deletequery = dbGetQuery(dwcon, "DELETE FROM M_Shippers_Test WHERE ShipperID IN (SELECT m.ShipperID FROM C_Shippers c, M_Shippers m WHERE c.ShipperID = m.ShipperID and (c.CompanyName <> m.CompanyName or c.Phone <> m.Phone))")
dbWriteTable(conn = dwcon, name = "M_Shippers_Test", value=c_table_changed_data, row.names = FALSE, append = TRUE)
#Validate
dbGetQuery(dwcon, "SELECT * FROM M_Shippers_Test")
# 3.6. Select From C, Transform and Insert into T
#Select From C and Transform to DW Format
c_table_data = dbGetQuery(dwcon, "SELECT ShipperID as [Shipper_ID], CompanyName as [Shipper_Name], Phone as [Current_Shipper_Phone], DATE() as [Effective_Date] FROM C_Shippers" )
c_table_data$Previous_Shipper_Phone = "Previous_Shipper_Phone"
c_table_data = c_table_data[,c("Shipper_ID", "Shipper_Name", "Current_Shipper_Phone","Previous_Shipper_Phone", "Effective_Date" ) ]
#Insert into T
query=dbGetQuery(dwcon, "DELETE FROM T_Shipper")
dbWriteTable(conn = dwcon, name = "T_Shipper", value =c_table_data, row.names = FALSE, append = TRUE)
#Validate
dbGetQuery(dwcon, "SELECT * FROM T_Shipper")
# 3.7. Select New From T Insert Into I
#Select New from T
t_table_new_data = dbGetQuery(dwcon, "SELECT t.* FROM t_shipper t LEFT JOIN d_shipper d ON t.Shipper_ID = d.Shipper_ID WHERE d.Shipper_ID IS NULL")
t_table_new_data$Current_Row_Ind = 'Y'
#Insert New into I
query=dbGetQuery(dwcon, "DELETE FROM I_Shipper")
dbWriteTable(conn = dwcon, name = "I_Shipper", value =t_table_new_data, row.names = FALSE, append = TRUE)
#Validate
dbGetQuery(dwcon, "SELECT * FROM I_Shipper")
# 3.8. Select Updated From T Insert Into U
#Select Changed from T
t_table_changed_data=dbGetQuery(dwcon, "SELECT t.* FROM t_shipper t inner join d_shipper d ON t.Shipper_ID = d.Shipper_ID WHERE (NOT t.Shipper_Name = d.Shipper_Name or NOT t.Current_Shipper_Phone = d.Current_Shipper_Phone) AND d.Current_Row_Ind = 'Y'")
t_table_changed_data$Current_Row_Ind = 'Y'
#Insert into U
deletequery=dbGetQuery(dwcon, "DELETE FROM U_Shipper")
dbWriteTable(conn = dwcon, name = "U_Shipper", value =t_table_changed_data, row.names = FALSE, append = TRUE)
#Validate
dbGetQuery(dwcon, "SELECT * FROM U_Shipper")
# 3.9. Select from I Insert into D
maxkey <- dbGetQuery(dwcon, "SELECT MAX(Shipper_Key) as MAX FROM D_Shipper")
i_table_data <- dbGetQuery(dwcon, "SELECT * FROM I_Shipper")
i_table_data$Shipper_Key = (maxkey[1,1]+1):(maxkey[1,1]+nrow(i_table_data))
#Insert New into D
i_table_data = i_table_data[,c("Shipper_Key","Shipper_ID","Shipper_Name", "Current_Shipper_Phone", "Previous_Shipper_Phone", "Effective_Date", "Current_Row_Ind" )]
dbWriteTable(conn = dwcon, name = "D_Shipper", value =i_table_data, row.names = FALSE, append = TRUE)
#Validate
dbGetQuery(dwcon, "SELECT * FROM D_Shipper")
# 3.10. Select Type 3 Updates from U, Update D
#Select Type 3 from U and D
u_table_type3_data <- dbGetQuery(dwcon, "SELECT u.* from u_shipper u inner join d_shipper d on u.Shipper_ID = d.Shipper_ID where NOT (u.Current_Shipper_Phone = d.Current_Shipper_Phone) and d.Current_Row_Ind = 'Y'")
d_table_type3_data <- dbGetQuery(dwcon, "SELECT d.* from u_shipper u inner join d_shipper d on u.Shipper_ID = d.Shipper_ID where NOT (u.Current_Shipper_Phone = d.Current_Shipper_Phone) and d.Current_Row_Ind = 'Y'")
u_table_type3_data$Previous_Shipper_Phone = d_table_type3_data$Current_Shipper_Phone
u_table_type3_data$Shipper_Key = d_table_type3_data$Shipper_Key
u_table_type3_data = u_table_type3_data[,c("Shipper_Key","Shipper_ID","Shipper_Name", "Current_Shipper_Phone", "Previous_Shipper_Phone", "Effective_Date", "Current_Row_Ind" )]
deletequery = dbGetQuery(dwcon, "DELETE FROM d_shipper WHERE Shipper_ID IN (SELECT d.Shipper_ID from u_shipper u inner join d_shipper d on u.Shipper_ID = d.Shipper_ID where NOT (u.Current_Shipper_Phone = d.Current_Shipper_Phone) and d.Current_Row_Ind = 'Y')")
dbWriteTable(conn = dwcon, name = "d_shipper", value =u_table_type3_data, row.names = FALSE, append = TRUE)
#Validate
dbGetQuery(dwcon, "SELECT * FROM D_Shipper")
# 3.11. Select Type 2 Updates from U, Insert into D
#Select Type 2
maxkey <- dbGetQuery(dwcon, "SELECT MAX(Shipper_Key) as MAX FROM D_Shipper")
u_table_type2_data <- dbGetQuery(dwcon, "SELECT u.* FROM u_shipper u INNER JOIN d_shipper d ON u.Shipper_ID = d.Shipper_ID WHERE NOT (u.Shipper_Name = d.Shipper_Name) AND d.Current_Row_Ind = 'Y'")
u_table_type2_data$Shipper_Key = (maxkey[1,1]+1):(maxkey[1,1]+nrow(u_table_type2_data))
#Insert New into D
u_table_type2_data=u_table_type2_data[,c("Shipper_Key","Shipper_ID","Shipper_Name", "Current_Shipper_Phone", "Previous_Shipper_Phone", "Effective_Date", "Current_Row_Ind" )]
dbWriteTable(conn = dwcon, name = "d_shipper", value =u_table_type2_data, row.names = FALSE, append = TRUE)
#Validate
dbGetQuery(dwcon, "SELECT * FROM D_Shipper")
# 3.12. Update Current Indicators in D
#Update Shipper Current Value
forupdatedata <- dbGetQuery(dwcon, "SELECT d.* FROM u_shipper u INNER JOIN d_shipper d ON u.Shipper_Id = d.Shipper_Id WHERE d.Current_Row_Ind = 'Y' AND Not u.Shipper_Name = d.Shipper_Name ")
forupdatedata$Current_Row_Ind = 'N'
deletequery = dbGetQuery(dwcon, "DELETE FROM d_shipper WHERE Shipper_Key IN (SELECT d.Shipper_Key FROM u_shipper u INNER JOIN d_shipper d ON u.Shipper_Id = d.Shipper_Id WHERE d.Current_Row_Ind = 'Y' AND Not u.Shipper_Name = d.Shipper_Name)")
dbWriteTable(conn = dwcon, name = "d_shipper", value =forupdatedata, row.names = FALSE, append = TRUE)
dbGetQuery(dwcon, "SELECT * FROM D_Shipper")
dbDisconnect(dwcon)
|
1b0ee8b57ee0954be2c858bb770fb821e46596f0
|
faec5b544d361a40e1955c776fd5483547d299b8
|
/MovieLens-project.R
|
3f0834fcd320fbd8daee6ecef716cf6e53b6e4ad
|
[] |
no_license
|
kinube/edX-DataScience
|
62ce88b8cbf510025a32d72fa433b85512b357fe
|
24447dfc802269501cf7308dfd00d6bf24f65513
|
refs/heads/master
| 2020-04-05T16:45:50.507630
| 2020-01-06T01:50:06
| 2020-01-06T01:50:06
| 157,027,485
| 0
| 1
| null | null | null | null |
WINDOWS-1252
|
R
| false
| false
| 13,180
|
r
|
MovieLens-project.R
|
#### MovieLens project ####
# Author: Kiko Núñez
# Start Date: 09/08/2019
# End Date: 13/10/2019
## Load libraries needed:
library(stringr)
library(dplyr)
library(caret)
library(ggplot2)
library(grid)
library(scales)
################################
# Create edx set, validation set
################################
# Note: this process could take a couple of minutes
if(!require(tidyverse)) install.packages("tidyverse", repos = "http://cran.us.r-project.org")
if(!require(caret)) install.packages("caret", repos = "http://cran.us.r-project.org")
if(!require(data.table)) install.packages("data.table", repos = "http://cran.us.r-project.org")
# MovieLens 10M dataset:
# https://grouplens.org/datasets/movielens/10m/
# http://files.grouplens.org/datasets/movielens/ml-10m.zip
dl <- tempfile()
download.file("http://files.grouplens.org/datasets/movielens/ml-10m.zip", dl)
ratings <- fread(text = gsub("::", "\t", readLines(unzip(dl, "ml-10M100K/ratings.dat"))),
col.names = c("userId", "movieId", "rating", "timestamp"))
movies <- str_split_fixed(readLines(unzip(dl, "ml-10M100K/movies.dat")), "\\::", 3)
colnames(movies) <- c("movieId", "title", "genres")
movies <- as.data.frame(movies) %>% mutate(movieId = as.numeric(levels(movieId))[movieId],
title = as.character(title),
genres = as.character(genres))
movielens <- left_join(ratings, movies, by = "movieId")
# Validation set will be 10% of MovieLens data
set.seed(1, sample.kind="Rounding")
# if using R 3.5 or earlier, use `set.seed(1)` instead
test_index <- createDataPartition(y = movielens$rating, times = 1, p = 0.1, list = FALSE)
edx <- movielens[-test_index,]
temp <- movielens[test_index,]
# Make sure userId and movieId in validation set are also in edx set
validation <- temp %>%
semi_join(edx, by = "movieId") %>%
semi_join(edx, by = "userId")
# Add rows removed from validation set back into edx set
removed <- anti_join(temp, validation)
edx <- rbind(edx, removed)
rm(dl, ratings, movies, test_index, temp, movielens, removed)
#### 1. Initial analysis ####
# Visual inspection:
str(edx)
#### 2. Preprocessing ####
## Extract year of rating from timestamp:
edx <- transform(edx, timestamp = as.POSIXct(timestamp, origin = "1970-01-01"))
edx$yearRating <- as.integer(format(edx$timestamp, '%Y')) # Add yearRating column to edx dataset
## Add year of movie column:
edx$yearMovie <- as.integer(sub("\\).*", "", sub(".*\\(", "", edx$title)))
## Genre bagging:
GenresBags <- unique(unlist(str_split(edx$genres, "\\|"))) # Split genre value by '|'
print(GenresBags)
print(paste("Movies in the dataset have",
length(GenresBags), "different types of genres."))
GenresEdx <- matrix(, nrow(edx), length(GenresBags)) # set-up a matrix for bagging genres
colnames(GenresEdx) <- GenresBags
## Populate the matrix of genres (this may take a while):
pb <- txtProgressBar(min = 0, max = length(GenresBags), style = 3)
for (i in 1:length(GenresBags)) {
GenresEdx[grep(GenresBags[i],edx$genres), i] <- 1
setTxtProgressBar(pb, i)
}
GenresEdx[is.na(GenresEdx)] <- 0
edx <- cbind(edx, GenresEdx)
## Drop useless columns: timestamp and title:
edx_clean <- edx %>% select(-c(timestamp, title))
#### 3. Understanding the dataset ####
## Overall ratings distribution:
edx_clean %>% ggplot(aes(x=factor(rating))) +
geom_bar(color="steelblue", fill="steelblue") +
labs(x = "Ratings", y = "Counts") +
scale_y_continuous(labels = comma)
summary(edx_clean$rating)
## Ratings (counts and mean value) by users:
edx_clean %>% group_by(userId) %>% summarise(Users = n()) %>%
ggplot(aes(Users)) + geom_histogram(bins = 50, fill = "steelblue", color = "white") +
scale_x_log10() + labs(title = "Number of ratings per user") +
xlab("Number of ratings") + ylab("Number of users")
edx_clean %>% group_by(userId) %>% summarise(meanRating = mean(rating)) %>%
ggplot(aes(meanRating)) + geom_histogram(bins = 50, fill = "salmon4", color = "white") +
labs(title = "Mean ratings per users") +
xlab("Mean Rating") + ylab("Number of users")
## Ratings (counts and mean value) by movies:
edx_clean %>% group_by(movieId) %>% summarise(Movies = n()) %>%
ggplot(aes(Movies)) + geom_histogram(bins = 50, fill = "steelblue", color = "white") +
scale_x_log10() + labs(title = "Number of ratings per movie") +
xlab("Number of ratings") + ylab("Number of movies")
edx_clean %>% group_by(movieId) %>% summarise(meanRatingMovie = mean(rating)) %>%
ggplot(aes(meanRatingMovie)) + geom_histogram(bins = 50, fill = "salmon4", color = "white") +
labs(title = "Mean ratings per movie") +
xlab("Mean rating") + ylab("Number of movies")
## Ratings (counts and mean value) by movie antiquity:
edx_clean %>% mutate(yearDiff = yearRating - yearMovie) %>%
group_by(yearDiff) %>% summarise(Difference = n()) %>%
ggplot(aes(yearDiff, Difference)) + geom_bar(stat = "identity", fill = "steelblue", color = "white") +
scale_y_continuous(labels = comma) + labs(title = "Number of ratings per movie antiquity") +
xlab("Movie antiquity") + ylab("Number of ratings")
edx_clean %>% mutate(yearDiff = yearRating - yearMovie) %>%
group_by(yearDiff) %>% summarise(meanRatingYearDiff = mean(rating)) %>%
ggplot(aes(yearDiff, meanRatingYearDiff)) + geom_point(color = "salmon4") +
geom_smooth(method = "loess", formula = y ~ x) +
labs(title = "Mean rating per movie antiquity",
x = "Movie antiquity (in years)", y = "Mean rating")
## Ratings (counts and mean values) by genres (aggregated):
edx_clean %>% group_by(genres) %>% summarise(Genres = n()) %>%
ggplot(aes(Genres)) + geom_histogram(bins = 50, fill = "steelblue", color = "white") +
scale_x_log10(labels = comma) + labs(title = "Number of ratings per genre group") +
xlab("Number of ratings") + ylab("Number of genre groups")
edx_clean %>% group_by(genres) %>% summarise(meanRatingGenres = mean(rating)) %>%
ggplot(aes(meanRatingGenres)) + geom_histogram(bins = 50, fill = "salmon4", color = "white") +
labs(title = "Mean ratings per genres (aggregated)") +
xlab("Mean rating") + ylab("Number of genres")
## Ratings (counts and mean values) by genres (atomic):
p_countGenres <- NULL
p_ratingGenres <- NULL
for (i in 1:length(GenresBags)) { # Calculate counts and mean ratings per genre
index <- which(edx_clean[GenresBags[i]] == 1)
p_ratingGenres <- append(p_ratingGenres, mean(edx_clean[index, "rating"]))
p_countGenres <- append(p_countGenres, length(index))
}
names(p_ratingGenres) <- GenresBags
names(p_countGenres) <- GenresBags
# Plot the results
par(mai = c(1.8, 1, 1, 1))
barplot(p_countGenres/1000000, ylim = c(-5, 5), axes = FALSE, border = NA,
col = "steelblue", las = 2, main = "Number of ratings and mean ratings by genre")
barplot(-p_ratingGenres, add = TRUE, axes = FALSE, col = "salmon4", border = NA, names.arg = NA)
axis(2, at = seq(-5, 5, 1),
labels = c(rev(seq(0, 5, 1)), seq(1, 5, 1)), las = 2)
mtext("Mean", 2, line = 3, at = -2.5, col = "salmon4")
mtext("Number (Mill.)", 2, line = 3, at = 2.5, col = "steelblue")
## Ratings (count and mean values) by year of movie:
edx_clean %>% group_by(yearMovie) %>% summarise(Years = n()) %>%
ggplot(aes(yearMovie, Years)) + geom_bar(stat = "identity", fill = "steelblue", color = "white") +
labs(title = "Number of ratings per year of movie") +
xlab("Year") + ylab("Number of ratings") + scale_y_continuous(labels = comma)
edx_clean %>% group_by(yearMovie) %>% summarise(meanRatingYear = mean(rating)) %>%
ggplot(aes(yearMovie, meanRatingYear)) + geom_point(color = "salmon4") +
geom_smooth(method = "loess", formula = y ~ x) + labs(title = "Mean ratings per year of movie") +
xlab("Year") + ylab("Mean rating")
#### 4. Building the model to predict ratings ####
## Baseline (mean) model:
# Baseline calculation:
Baseline <- mean(edx_clean$rating)
print(paste("Baseline model (average): ", Baseline))
## Model includeing user effect:
# Penalty term due to user effect (p_user):
meanUsers <- edx_clean %>% group_by(userId) %>%
summarise(p_user = mean(rating - Baseline))
meanUsers %>% ggplot(aes(p_user)) +
geom_histogram(bins = 50, fill = "darkgreen", color = "white") +
labs(title = "Effect of users") +
xlab("Penalty due to user effect") + ylab("Frequency")
## Model including user and movie effect:
# Penalty term due to movie effect:
meanMovies <- edx_clean %>% left_join(meanUsers, by = "userId") %>%
group_by(movieId) %>% summarise(p_movie = mean(rating - Baseline - p_user))
meanMovies %>% ggplot(aes(p_movie)) +
geom_histogram(bins = 50, fill = "darkgreen", color = "white") +
labs(title = "Effect of movies") +
xlab("Penalty due to movie effect") + ylab("Frequency")
## Model including user, movie and antiquity:
# Penalty term due to antiquity effect:
meanDiffYear <- edx_clean %>% mutate(diffYear = yearRating - yearMovie) %>%
left_join(meanUsers, by = "userId") %>% left_join(meanMovies, by = "movieId") %>%
group_by(diffYear) %>% summarise(p_diffYear = mean(rating - Baseline - p_user - p_movie))
meanDiffYear %>% ggplot(aes(p_diffYear)) +
geom_histogram(bins = 50, fill = "darkgreen", color = "white") +
labs(title = "Effect of movie antiquity") +
xlab("Penalty due to year of movie antiquity effect") + ylab("Frequency")
## Model including user, movie, antiquity and genre:
# Penalty term due to genre effect:
meanGenre <- edx_clean %>% mutate(diffYear = yearRating - yearMovie) %>%
left_join(meanUsers, by = "userId") %>% left_join(meanMovies, by = "movieId") %>%
left_join(meanDiffYear, by = "diffYear") %>% group_by(genres) %>%
summarise(p_genres = mean(rating - Baseline - p_user - p_movie - p_diffYear))
meanGenre %>% ggplot(aes(p_genres)) +
geom_histogram(bins = 50, fill = "darkgreen", color = "white") +
labs(title = "Effect of movie genre") +
xlab("Penalty due to movie genre") + ylab("Frequency")
#### 5. Apply trained model to validation dataset ####
### First perform the same preprocessing:
## Extract year of rating from timestamp:
validation <- transform(validation, timestamp = as.POSIXct(timestamp, origin = "1970-01-01"))
validation$yearRating <- as.integer(format(validation$timestamp, '%Y')) # Add yearRating column to validation dataset
## Add year of movie column:
validation$yearMovie <- as.integer(sub("\\).*", "", sub(".*\\(", "", validation$title)))
## Genre bagging:
GenresVal <- matrix(, nrow(validation), length(GenresBags)) # set-up a matrix for bagging genres
colnames(GenresVal) <- GenresBags
pb <- txtProgressBar(min = 0, max = length(GenresBags), style = 3)
for (i in 1:length(GenresBags)) {
GenresVal[grep(GenresBags[i],validation$genres), i] <- 1
setTxtProgressBar(pb, i, title = "Populating genres")
}
GenresVal[is.na(GenresVal)] <- 0
validation <- cbind(validation, GenresVal)
## Drop useless columns (timestamp and title):
validation_clean <- validation %>% select(-c(timestamp, title))
## Now calculate RMSE of the model:
# RMSE of baseline model:
RMSE_Baseline <- RMSE(validation_clean$rating, Baseline)
print(paste("RMSE in baseline model: ", RMSE_Baseline))
# RMSE including user effect:
y_hat_users <- validation_clean %>% left_join(meanUsers, by = "userId") %>%
mutate(pred_rating = Baseline + p_user)
RMSE_Users <- RMSE(validation_clean$rating, y_hat_users$pred_rating)
print(paste("RMSE adding user effect: ", RMSE_Users))
# RMSE including user and movie effect:
y_hat_movies <- validation_clean %>% left_join(meanUsers, by = "userId") %>%
left_join(meanMovies, by = "movieId") %>%
mutate(pred_rating = Baseline + p_user + p_movie)
RMSE_UsersMovies <- RMSE(validation_clean$rating, y_hat_movies$pred_rating)
print(paste("RMSE adding movie and user effects: ", RMSE_UsersMovies))
# RMSE including user, movie and antiquity effects:
y_hat_diffYear <- validation_clean %>% mutate(diffYear = yearRating - yearMovie) %>%
left_join(meanUsers, by = "userId") %>% left_join(meanMovies, by = "movieId") %>%
left_join(meanDiffYear, by = "diffYear") %>%
mutate(pred_rating = Baseline + p_user + p_movie + p_diffYear)
RMSE_UsersMoviesDiffYear <- RMSE(validation_clean$rating, y_hat_diffYear$pred_rating)
print(paste("RMSE adding user, movie and antiquity effects: ", RMSE_UsersMoviesDiffYear))
# RMSE including user, movie, antiquity and genre effects:
y_hat_genre <- validation_clean %>% mutate(diffYear = yearRating - yearMovie) %>%
left_join(meanUsers, by = "userId") %>% left_join(meanMovies, by = "movieId") %>%
left_join(meanDiffYear, by = "diffYear") %>% left_join(meanGenre, by = "genres") %>%
mutate(pred_rating = Baseline + p_user + p_movie + p_diffYear + p_genres)
RMSE_UsersMoviesDiffYearGenre <- RMSE(validation_clean$rating, y_hat_genre$pred_rating)
print(paste("RMSE adding user, movie, antiquity and genre effects: ", RMSE_UsersMoviesDiffYearGenre))
|
c8e4a7c03ac2284f3f865fb3075b94896dc87a6d
|
31103bc586e6ea4959749543101d0d07679d53ec
|
/simulatePower.R
|
72c75875c882d1119f3cd5637c8aa5b99294cb4a
|
[] |
no_license
|
luboRprojects/TDTmethodology
|
43e5b6d480ff78e1640f81475b7d92d86c60a30b
|
ad43770f1b064365b982935e7e8f872b1836ce21
|
refs/heads/master
| 2020-07-15T19:56:25.889499
| 2019-09-01T06:48:41
| 2019-09-01T06:48:41
| 205,638,413
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 760
|
r
|
simulatePower.R
|
# This file allows simulation of large number of
# theoretical results if the DGP (data-generating process) is
# know th the researcher. DGP is in the expressed in the:
# - type of data distribution: rnorm
# - parameters of such a distributions: mean, sd
# Create a variable to which we store results
pval <- c()
# Run the simulation of n studies
# Note that the research design is about number
# of respondents: n
nStudies <- 10000
for(i in 1:nstudies) {
data1 <- rnorm(n=50, mean=15, sd=10)
data2 <- rnorm(n=50, mean=11, sd=10)
pval[i] <- t.test(data1, data2, alternative="greater")$p.val
}
# Table of p-values
table(pval<0.05) # TRUE/nStudies is statistical power
# What is the number of respondents (n) to reach power 0.8?
# Check the G*power!
|
ff4a2262206e7e40ba882311a255717b75633f9a
|
121f6db541bfdaba545cd70742ef951b75c90e73
|
/R/analysis/statistics.R
|
93df4afcfc6f6c498b97cebcb16cb4e2089c1021
|
[
"MIT"
] |
permissive
|
bwhsleepamu/sleep.tools
|
22444ae127f695b3ba4d5e35b67e07212e8c563d
|
b0d3e3801f60fd9c0daffe9d5c63e7eccfb3ef38
|
refs/heads/master
| 2021-09-25T02:04:53.439361
| 2018-10-16T18:52:45
| 2018-10-16T18:52:45
| 19,426,999
| 0
| 0
|
MIT
| 2018-10-16T18:52:46
| 2014-05-04T13:25:52
|
HTML
|
UTF-8
|
R
| false
| false
| 10,601
|
r
|
statistics.R
|
## For periods:
### NEED:
#DONE #### Agreement stats by subject and or subject group FOR EACH METHOD
#### Number of periods per sleep episode for each period type by each method
#### Distribution of period lengths for each type by subject and or subject group for each method
#### OTHER COMPARISONS??
## For cycles:
### NEED: distribution of lengths and counts for 1st, 2nd...cycles by subject and/or subject group FOR EACH METHOD
### Possible others: start times by cycle,
# young_s <- subjects[study=="NIAPPG" & age_group=='Y']
# old_s <- subjects[study=="NIAPPG" & age_group=='O']
# csr_s <- subjects[study=="T20CSR-CSR"]
# control_s <- subjects[study=="T20CSR-Control"]
## Agreement
function() {
# Cleaning:
## No sleep periods
clean.periods <- copy(periods)
clean.periods <- clean.periods[sleep_wake_period>0]
## Strip wake at beginning and end of sleep periods
clean.periods <- clean.periods[,strip_wake(.SD),by='subject_code,sleep_wake_period']
clean.periods[,pik:=.I]
clean.periods[,agreement:=sum(sleep_data[start_position:end_position]$epoch_type == label)/length,by='pik']
## Agreement graph
p <- ggplot(clean.periods[label %in% c('NREM', 'REM')], aes(x=agreement, color=method))
p <- p + scale_fill_manual(values=cbbPalette) + scale_colour_manual(values=cbbPalette)
p <- p + ggtitle("Agreement within Periods by Method")
p <- p + xlab("Agreement")
p <- p + facet_grid(label ~ ., scales='free_y')
p <- p + geom_density(alpha=.3)
p
ggsave("/home/pwm4/Desktop/Visualizations/agreement_full.svg", scale=2, width=9, height=4)
# Young
p <- ggplot(clean.periods[label %in% c('NREM', 'REM') & subject_code %in% young_s$subject_code], aes(x=agreement, color=method))
p <- p + scale_fill_manual(values=cbbPalette) + scale_colour_manual(values=cbbPalette)
p <- p + ggtitle("Agreement within Periods by Method\nYounger Subjects")
p <- p + xlab("Agreement")
p <- p + facet_grid(label ~ ., scales='free_y')
p <- p + geom_density(alpha=.3)
p
ggsave("/home/pwm4/Desktop/Visualizations/agreement_young.svg", scale=2, width=9, height=4)
# Old
p <- ggplot(clean.periods[label %in% c('NREM', 'REM') & subject_code %in% old_s$subject_code], aes(x=agreement, color=method))
p <- p + scale_fill_manual(values=cbbPalette) + scale_colour_manual(values=cbbPalette)
p <- p + ggtitle("Agreement within Periods by Method\nOlder Subjects")
p <- p + xlab("Agreement")
p <- p + facet_grid(label ~ ., scales='free_y')
p <- p + geom_density(alpha=.3)
p
ggsave("/home/pwm4/Desktop/Visualizations/agreement_old.svg", scale=2, width=9, height=4)
# CSR
p <- ggplot(clean.periods[label %in% c('NREM', 'REM') & subject_code %in% csr_s$subject_code], aes(x=agreement, color=method))
p <- p + scale_fill_manual(values=cbbPalette) + scale_colour_manual(values=cbbPalette)
p <- p + ggtitle("Agreement within Periods by Method\nChronic Sleep Restriction")
p <- p + xlab("Agreement")
p <- p + facet_grid(label ~ ., scales='free_y')
p <- p + geom_density(alpha=.3)
p
ggsave("/home/pwm4/Desktop/Visualizations/agreement_csr.svg", scale=2, width=9, height=4)
# NON-CSR
p <- ggplot(clean.periods[label %in% c('NREM', 'REM') & subject_code %in% control_s$subject_code], aes(x=agreement, color=method))
p <- p + scale_fill_manual(values=cbbPalette) + scale_colour_manual(values=cbbPalette)
p <- p + ggtitle("Agreement within Periods by Method\nControl")
p <- p + xlab("Agreement")
p <- p + facet_grid(label ~ ., scales='free_y')
p <- p + geom_density(alpha=.3)
p
ggsave("/home/pwm4/Desktop/Visualizations/agreement_control.svg", scale=2, width=9, height=4)
}
function() {
epoch_length <- EPOCH_LENGTH
# period stats
#period_counts <- clean.periods[,list(nrem_count=sum(label=='NREM'),rem_count=sum(label=='REM'),wake_count=sum(label=='WAKE')),by='subject_code,sleep_wake_period,method']
period_counts <- copy(clean.periods)
period_counts <- period_counts[,list(count=.N),by='subject_code,sleep_wake_period,method,label']
plot.period_counts(period_counts, subjects, "period_counts_all", "All Subjects")
plot.period_counts(period_counts, young_s, "period_counts_young", "Younger Subjects")
plot.period_counts(period_counts, old_s, "period_counts_old", "Older Subjects")
plot.period_counts(period_counts, control_s, "period_counts_control", "Control Subjects")
plot.period_counts(period_counts, csr_s, "period_counts_csr", "Chronic Sleep Restriction Subjects")
## Length distribution of periods
period_lengths <- copy(clean.periods)
period_lengths[,length:=length*epoch_length*60]
period_lengths[,period_number:=seq(.N),by='subject_code,sleep_wake_period,method,label']
plot.period_lengths(period_lengths, subjects, "period_lengths_all", "All Subjects")
plot.period_lengths(period_lengths, young_s, "period_lengths_young", "Younger Subjects")
plot.period_lengths(period_lengths, old_s, "period_lengths_old", "Older Subjects")
plot.period_lengths(period_lengths, control_s, "period_lengths_control", "Control Subjects")
plot.period_lengths(period_lengths, csr_s, "period_lengths_csr", "Chronic Sleep Restriction Subjects")
}
plot.period_counts <- function(period_counts, subject_group, file_name, label) {
## Number of periods of each type per sleep episode
p <- ggplot(period_counts[label %in% c('NREM', 'REM') & subject_code %in% subject_group$subject_code], aes(x=count, fill=method, color=method))
p <- p + ggtitle(paste("Periods per Sleep Episode", label, sep="\n"))
p <- p + scale_fill_manual(values=cbbPalette) + scale_colour_manual(values=cbbPalette)
p <- p + facet_grid(label ~ ., scales='free')
p <- p + geom_freqpoly(binwidth=1, origin=-0.5)
#p + geom_histogram(binwidth=1)
ggsave(paste("/home/pwm4/Desktop/Visualizations/", file_name, '.svg', sep=''), scale=2, width=9, height=4)
}
plot.period_lengths <- function(period_lengths, subject_group, file_name, label, by_period_count=FALSE, show_outliers=FALSE) {
pl <- period_lengths[label %in% c('NREM', 'REM') & subject_code %in% subject_group$subject_code]
ul <- unique(pl[method=='classic']$period_number)
p <- ggplot(pl[period_number %in% ul], aes(factor(period_number), length, color=method))
#p <- ggplot(period_lengths[label %in% c('NREM', 'REM') & subject_code %in% subject_group$subject_code], aes(method, length, color=method))
p <- p + ggtitle(paste("Distribution of Period Lengths", label, sep="\n"))
p <- p + scale_fill_manual(values=cbbPalette) + scale_colour_manual(values=cbbPalette)
p <- p + facet_grid(label ~ .)
p <- p + geom_boxplot(outlier.shape = NA) + scale_y_continuous(limits = quantile(period_lengths$length, c(0.1, 0.9)))
ggsave(paste("/home/pwm4/Desktop/Visualizations/", file_name, '.svg', sep=''), scale=2, width=9, height=4)
}
## Cycles
function() {
nrem_cycles.s <- copy(nrem_cycles)
nrem_cycles.s[,length:=length*epoch_length*60]
# p <- ggplot(nrem_cycles.s, aes(factor(cycle_number), length, color=method))
# p + geom_boxplot()
cycle_counts <- nrem_cycles[,list(count=.N),by='subject_code,sleep_wake_period,method']
# p <- ggplot(cycle_counts, aes(x=count, fill=method, color=method))
#p <- p + facet_grid(label ~ ., scales='free')
#p <- p + scale_x_discrete()
#p + geom_freqpoly(binwidth=1)
plot.cycles(nrem_cycles.s, cycle_counts, subjects, 'all')
plot.cycles(nrem_cycles.s, cycle_counts, old_s, 'older')
plot.cycles(nrem_cycles.s, cycle_counts, young_s, 'younger')
plot.cycles(nrem_cycles.s, cycle_counts, csr_s, 'csr')
plot.cycles(nrem_cycles.s, cycle_counts, control_s, 'control')
}
plot.cycles <- function(nrem_cycles, cycle_counts, subject_group, label, by_cycle_count = FALSE) {
p <- ggplot(nrem_cycles[subject_code %in% subject_group$subject_code], aes(factor(cycle_number), length, color=method))
p <- p + scale_fill_manual(values=cbbPalette) + scale_colour_manual(values=cbbPalette)
p <- p + ggtitle(paste("Distribution of Lengths of NREM Cycles by Method", label, sep="\n")) + ylab("Length (minutes)") + xlab("Cycle Number")
p <- p + geom_boxplot(outlier.shape = NA) + scale_y_continuous(limits = quantile(nrem_cycles$length, c(0.1, 0.9)))
ggsave(plot = p, filename=paste("/home/pwm4/Desktop/Visualizations/cycle_length_", label, '.svg', sep=''), scale=2, width=9, height=4)
cycle_counts <- nrem_cycles[,list(count=.N),by='subject_code,sleep_wake_period,method']
p <- ggplot(cycle_counts[subject_code %in% subject_group$subject_code], aes(x=count, fill=method, color=method))
p <- p + scale_fill_manual(values=cbbPalette) + scale_colour_manual(values=cbbPalette)
p <- p + geom_freqpoly(binwidth=1, origin=-.5)
p <- p + ggtitle(paste("Number of NREM cycles per Sleep Episode", label, sep="\n")) + ylab("Number of Sleep Episodes") + xlab("Number of NREM Cycles")
p <- p + scale_x_continuous(breaks=seq(from=0, to=14), limits=c(0,14))
ggsave(plot = p, filename=paste("/home/pwm4/Desktop/Visualizations/cycle_counts_", label, '.svg', sep=''), scale=2, width=9, height=4)
}
tmp <- function(lim) {
seq(from=lim[1], to=lim[2], by=1)
}
#periods
function(){
wd <- periods[subject_code=="3335GX" & sleep_wake_period==2]
tst <- wd[method=='changepoint']
# Cleaning:
## No sleep periods
clean.periods <- periods[sleep_wake_period>0]
## Strip wake at beginning and end of sleep periods
clean.periods <- clean.periods[,strip_wake(.SD),by='subject_code,sleep_wake_period']
psts <- clean.periods[,list(count=length(length)),by='subject_code,sleep_wake_period,method,label']
psts[, mean(NREM_count), by='method']
plot <- ggplot(psts, aes(method, count))
plot <- plot + facet_wrap(~ label)
plot + geom_boxplot()
plot <- ggplot(psts, aes(x=count))
plot <- plot + facet_grid(method ~ label)
plot <- plot + geom_histogram()
plot
#clean.periods[,table(label,method), by='subject_code,sleep_wake_period']
#summary(table(clean.periods$method,clean.periods$label,clean.periods$subject_code,clean.periods$sleep_wake_period))
}
## Stats for number of periods
period_stats <- function(labels) {
t <- table(labels)
list(NREM_count=t[names(t)=='NREM'], REM_count=t[names(t)=='REM'],WAKE_count=t[names(t)=='WAKE'], total=length(labels))
}
## Strips start and end wake off
strip_wake <- function(dt) {
sleep_onset <- min(match(c("NREM", "REM"), dt$label, nomatch=1L))
sleep_end <- (length(dt$label) - min(match(c("NREM", "REM"), rev(dt$label), nomatch=1L))) + 1L
dt[sleep_onset:sleep_end]
}
|
f135f5c8274f155d46ea09996b5156dde1a529ea
|
5dcb6a2aa0b0ded3f265140c7c213c369289aa7f
|
/R/gradient_factory.R
|
42381502fe2e6696cc52eaf1dc89cefadaba669b
|
[
"MIT"
] |
permissive
|
leeper/margins
|
f8086ca2abaf8e78d3d79244c6e7777ac1d1a2a1
|
bd374239f5da0a92e8e27d093785850933fdfa9e
|
refs/heads/main
| 2021-07-14T12:00:50.926131
| 2021-01-21T22:54:28
| 2021-01-21T22:54:28
| 25,176,098
| 269
| 56
|
NOASSERTION
| 2021-04-08T00:49:03
| 2014-10-13T20:21:42
|
R
|
UTF-8
|
R
| false
| false
| 2,100
|
r
|
gradient_factory.R
|
# a factory function that returns a function to give the gradient (as a vector)
## used as first argument to jacobian()
gradient_factory <- function(data, model, variables = NULL, type = "response", weights = NULL, eps = 1e-7, varslist = NULL, ...) {
UseMethod("gradient_factory", model)
}
gradient_factory.default <- function(data, model, variables = NULL, type = "response", weights = NULL, eps = 1e-7, varslist = NULL, ...) {
# identify classes of terms in `model`
if (is.null(varslist)) {
varslist <- find_terms_in_model(model, variables = variables)
}
# factory function to return marginal effects holding data constant but varying coefficients
FUN <- function(coefs, weights = NULL) {
model <- reset_coefs(model, coefs)
if (is.null(weights)) {
# build matrix of unit-specific marginal effects
if (is.null(type)) {
me_tmp <- marginal_effects(model = model, data = data, variables = variables, eps = eps, as.data.frame = FALSE, varslist = varslist, ...)
} else {
me_tmp <- marginal_effects(model = model, data = data, variables = variables, type = type, eps = eps, as.data.frame = FALSE, varslist = varslist, ...)
}
# apply colMeans to get average marginal effects
means <- stats::setNames(.colMeans(me_tmp, nrow(me_tmp), ncol(me_tmp), na.rm = TRUE), colnames(me_tmp))
} else {
# build matrix of unit-specific marginal effects
if (is.null(type)) {
me_tmp <- marginal_effects(model = model, data = data, variables = variables, eps = eps, as.data.frame = FALSE, varslist = varslist, ...)
} else {
me_tmp <- marginal_effects(model = model, data = data, variables = variables, type = type, eps = eps, as.data.frame = FALSE, varslist = varslist, ...)
}
# apply colMeans to get average marginal effects
means <- apply(me_tmp, 2L, stats::weighted.mean, w = weights, na.rm = TRUE)
}
means
}
return(FUN)
}
|
9f661e226748424bc0cba22bf188af82d93ae12e
|
33e51cf37c476e94808b9a96dbbeb72f76a208a7
|
/downloadData.R
|
2d8b8722258f166e0382622d0e3e51afb8d6f5a3
|
[] |
no_license
|
humberto-ortiz/PR2017replicaton
|
e81b9e3fabdb18d6cf10542e535680d694b6e099
|
ab0e35e2fc91c7b375a5c15151281006db3e69b8
|
refs/heads/master
| 2021-01-23T02:41:10.050982
| 2017-03-24T02:17:37
| 2017-03-24T02:17:37
| 86,019,834
| 1
| 0
| null | 2017-03-24T02:43:11
| 2017-03-24T02:43:11
| null |
UTF-8
|
R
| false
| false
| 3,026
|
r
|
downloadData.R
|
library(Biobase)
library(PharmacoGx)
GDSC <- downloadPSet("GDSC")
CCLE <- downloadPSet("CCLE")
common <- intersectPSet(
list( 'CCLE'=CCLE,
'GDSC'=GDSC ),
intersectOn=c("cell.lines", "drugs"),
strictIntersect=TRUE)
rawToDataFrame <- function( dataset ){
rawSensData <- common[[dataset]]@sensitivity$raw
names( dimnames(rawSensData) ) <- c("drugCell", "doseID", "doseData")
spNames <- strsplit( dimnames( rawSensData )[[1]], "_" )
allCells <- unique( sapply( spNames, "[[", 1 ) )
allDrugs <- unique( sapply( spNames, "[[", 2 ) )
allData <- expand.grid(
dimnames(rawSensData)[["doseID"]],
allCells,
allDrugs, stringsAsFactors=FALSE )
colnames(allData) <- c("doseID", "cellLine", "drug")
concatName <- with( allData,
paste( cellLine, drug, sep="_" ) )
allData$concentration <- NA
allData$viability <- NA
for( i in seq_len( nrow( allData ) ) ){
x <- dimnames(rawSensData)[[1]] %in% concatName[i]
y <- dimnames(rawSensData)[[2]] %in% allData$doseID[i]
if( any( x ) & any(y) ){
allData$viability[i] <- rawSensData[x,y,"Viability"]
allData$concentration[i] <- rawSensData[x,y,"Dose"]
}
}
allData <- na.omit(allData)
allData$doseID <- as.factor( allData$doseID )
allData$cellLine <- as.factor( allData$cellLine )
allData$drug <- as.factor( allData$drug )
allData$concentration <- as.numeric( allData$concentration )
allData$viability <- as.numeric( allData$viability )
allData$study <- dataset
allData
}
rawSensitivityDf<- rbind( rawToDataFrame("CCLE"), rawToDataFrame("GDSC") )
rawSensitivityDf <- rawSensitivityDf[,c("cellLine", "drug", "doseID", "concentration", "viability", "study")]
sdList <- lapply( c("CCLE", "GDSC"), function(dataset){
summarizedData <- common[[dataset]]@sensitivity$profiles
keepCols <- c("ic50_published", "auc_published")
summarizedData <- summarizedData[,keepCols]
spNames <- as.data.frame( do.call(rbind, strsplit( rownames( summarizedData ), "_" )) )
colnames( spNames ) <- c("cellLine", "drug")
summarizedData <- cbind( spNames, summarizedData )
colnames(summarizedData) <- gsub("_published", "", colnames( summarizedData ))
rownames( summarizedData ) <- NULL
summarizedData
} )
names(sdList) <- c("CCLE", "GDSC")
library(plyr)
joinedSumData <- join( sdList[[1]], sdList[[2]], by=c("cellLine", "drug") )
colnames(joinedSumData) <- c("cellLine", "drug", "ic50_CCLE", "auc_CCLE", "ic50_GDSC", "auc_GDSC")
write.table( joinedSumData, sep=",",
quote=FALSE, col.names=TRUE,
row.names=FALSE, file="summarizedPharmacoData.csv" )
write.table( rawSensitivityDf, sep=",",
quote=FALSE, col.names=TRUE,
row.names=FALSE, file="rawPharmacoData.csv" )
#library(dplyr)
#library(ggplot2)
#ggplot( dplyr:::filter( joinedSumData, drug == "Sorafenib" ), aes(-log10(ic50_GDSC), -log10(ic50_CCLE)) ) +
# geom_point()
#dev.off()
|
58a69558f014573b6e00df4a2c89e7f0b25b80b6
|
3542aa74a2aa1b8bb1f501f53dc05cf1ea7323ca
|
/plot3.R
|
522e77945e829f47fed1b5f0be5d2e38ed546a17
|
[] |
no_license
|
rrnisha/ExData_Plotting1
|
723895e682c5739683565213fd3bd20d63cecf3c
|
df5b5f2f048db9e9ce820e11588069b505f1fcd3
|
refs/heads/master
| 2020-12-25T07:06:25.509570
| 2014-07-13T13:49:33
| 2014-07-13T13:49:33
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 527
|
r
|
plot3.R
|
# Plot Sub_metering_1,2,3 for Feb 01, 02
source("prepData.R")
doPlot3 <- function() {
data <- prepData()
png(filename="plot3.png", width=480, height=480)
plot(data$DateTime, data$Sub_metering_1, type="l", xlab="", ylab="Energy sub metering")
lines(data$DateTime, data$Sub_metering_2, col="red")
lines(data$DateTime, data$Sub_metering_3, col="blue")
cols = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3")
legend("topright", lty=1, lwd=1, col=c("black","blue","red"), legend=cols)
dev.off()
}
doPlot3()
|
2a23d9089de76fb259174355810fff52c76445ed
|
c459dd32d88158cb064c3af2bc2ea8c7ab77c667
|
/clonality/calculate_ccf.20191031.v1.R
|
ef3e8e21e0d95338a20d82fce9ecf59fba213a50
|
[] |
no_license
|
ding-lab/ccRCC_snRNA_analysis
|
d06b8af60717779671debe3632cad744467a9668
|
ac852b3209d2479a199aa96eed3096db0b5c66f4
|
refs/heads/master
| 2023-06-21T15:57:54.088257
| 2023-06-09T20:41:56
| 2023-06-09T20:41:56
| 203,657,413
| 6
| 3
| null | null | null | null |
UTF-8
|
R
| false
| false
| 7,126
|
r
|
calculate_ccf.20191031.v1.R
|
# Yige Wu @WashU Oct 2019
## for calculating cancer cell fraction of CNAs and Mutations
# source ------------------------------------------------------------------
setwd(dir = "~/Box/")
source("./Ding_Lab/Projects_Current/RCC/ccRCC_snRNA/ccRCC_snRNA_analysis/ccRCC_snRNA_shared.R")
# set run id ----------------------------------------------------------
version_tmp <- 1
run_id <- paste0(format(Sys.Date(), "%Y%m%d") , ".v", version_tmp)
# set output directory ----------------------------------------------------
dir_out <- paste0(makeOutDir(), run_id, "/")
dir.create(dir_out)
# input VAF table ---------------------------------------------------------
vaf_tab <- fread("./Ding_Lab/Projects_Current/RCC/ccRCC_snRNA/Resources/Analysis_Results/mutation/generate_bulk_mutation_table/20191024.v1/snRNA_ccRCC_Mutation_VAF_Table.20191024.v1.csv", data.table = F)
# load meta data ----------------------------------------------------------
meta_tab <- fread(input = "./Ding_Lab/Projects_Current/RCC/ccRCC_snRNA/Resources/Analysis_Results/sample_info/make_meta_data/meta_data.20190924.v1.tsv", data.table = F)
# add snRNA_aliquot_id to vaf table ---------------------------------------
vaf_tab <- merge(vaf_tab, meta_tab, by.x = c("Aliquot"), by.y = c("Specimen.ID.bulk"), all.x = T)
# input CNA frequency by gene ---------------------------------------------
# gene_cna_state_tab <-
# set samples to process ----------------------------------------------------------
snRNA_aliquot_ids <- c("CPT0019130004", "CPT0001260013", "CPT0086350004", "CPT0010110013", "CPT0001180011", "CPT0020120013", "CPT0001220012", "CPT0014450005")
# using VHL mutation or BAP1 or SETD2 or PBRM1 mutation to estimate tumor purity by WES sample -------------------------------------
ccf_tab <- NULL
for (snRNA_aliquot_id_tmp in snRNA_aliquot_ids) {
# snRNA_aliquot_id_tmp <- "CPT0001260013"
## choose which mutated gene will be used for tumpor purity estiamte
## choose the highest VAF among VHL, PBRM1, SETD2 and BAP1
vaf_tmp <- vaf_tab %>%
filter(Specimen.ID.snRNA == snRNA_aliquot_id_tmp) %>%
select(VHL, PBRM1, BAP1, SETD2)
gene0 <- colnames(vaf_tmp)[which.max(vaf_tmp)]
gene0
vaf0 <- max(vaf_tmp, na.rm = T)
vaf0
# get the cancer cell fraction of different copy number for the gene (for example VHL) --------
tumor_perc_0x <- as.numeric(gene_cna_state_tab$perc_cna_in_tumor_cell[gene_cna_state_tab$gene_symbol == gene0 & gene_cna_state_tab$cna_state == 0 & gene_cna_state_tab$snRNA_aliquot_id == snRNA_aliquot_id_tmp])
tumor_perc_1x <- as.numeric(gene_cna_state_tab$perc_cna_in_tumor_cell[gene_cna_state_tab$gene_symbol == gene0 & gene_cna_state_tab$cna_state == 0.5 & gene_cna_state_tab$snRNA_aliquot_id == snRNA_aliquot_id_tmp])
tumor_perc_3x <- as.numeric(gene_cna_state_tab$perc_cna_in_tumor_cell[gene_cna_state_tab$gene_symbol == gene0 & gene_cna_state_tab$cna_state == 1.5 & gene_cna_state_tab$snRNA_aliquot_id == snRNA_aliquot_id_tmp])
tumor_perc_4x <- as.numeric(gene_cna_state_tab$perc_cna_in_tumor_cell[gene_cna_state_tab$gene_symbol == gene0 & gene_cna_state_tab$cna_state == 2 & gene_cna_state_tab$snRNA_aliquot_id == snRNA_aliquot_id_tmp])
tumor_perc_over_4x <- as.numeric(gene_cna_state_tab$perc_cna_in_tumor_cell[gene_cna_state_tab$gene_symbol == gene0 & gene_cna_state_tab$cna_state == 3 & gene_cna_state_tab$snRNA_aliquot_id == snRNA_aliquot_id_tmp])
if (length(tumor_perc_over_4x) != 0) {
tumor_perc_2x <- (1-tumor_perc_0x-tumor_perc_1x-tumor_perc_3x-tumor_perc_4x-tumor_perc_over_4x)
} else {
tumor_perc_2x <- (1-tumor_perc_0x-tumor_perc_1x-tumor_perc_3x-tumor_perc_4x)
}
a0 <- (1-tumor_perc_0x)
b0 <- (1*tumor_perc_1x + 2*tumor_perc_2x + 3*tumor_perc_3x + 4*tumor_perc_4x)
ccf0_to_test <- seq(from = 1, to = 0, by = -0.05)
## create a vector to hold the estimated tumor purity
tumor_puritys <- NULL
## create a matrix to hold the CCF for the rest of the mutated gene
ccf_mat <- matrix(data = 0, nrow = length(ccf0_to_test), ncol = length(SMGs[["CCRCC"]]))
colnames(ccf_mat) <- SMGs[["CCRCC"]]
for (i in 1:length(ccf0_to_test)) {
ccf0 <- ccf0_to_test[i]
# calculate purity according to the assumed ccf for the gene (for exapmle VHL--------------------------------------------------------
p <- 2/((a0*ccf0)/vaf0 + 2 - b0)
tumor_puritys <- c(tumor_puritys, p)
genes2test <- vaf_tab %>%
filter(Specimen.ID.snRNA == snRNA_aliquot_id_tmp)
genes2test <- colnames(genes2test[1, !is.na(genes2test)])
genes2test <- intersect(genes2test, SMGs[["CCRCC"]])
for (gene_tmp in genes2test) {
tumor_perc_0x <- as.numeric(gene_cna_state_tab$perc_cna_in_tumor_cell[gene_cna_state_tab$gene_symbol == gene_tmp & gene_cna_state_tab$cna_state == 0 & gene_cna_state_tab$snRNA_aliquot_id == snRNA_aliquot_id_tmp])
tumor_perc_1x <- as.numeric(gene_cna_state_tab$perc_cna_in_tumor_cell[gene_cna_state_tab$gene_symbol == gene_tmp & gene_cna_state_tab$cna_state == 0.5 & gene_cna_state_tab$snRNA_aliquot_id == snRNA_aliquot_id_tmp])
tumor_perc_3x <- as.numeric(gene_cna_state_tab$perc_cna_in_tumor_cell[gene_cna_state_tab$gene_symbol == gene_tmp & gene_cna_state_tab$cna_state == 1.5 & gene_cna_state_tab$snRNA_aliquot_id == snRNA_aliquot_id_tmp])
tumor_perc_4x <- as.numeric(gene_cna_state_tab$perc_cna_in_tumor_cell[gene_cna_state_tab$gene_symbol == gene_tmp & gene_cna_state_tab$cna_state == 2 & gene_cna_state_tab$snRNA_aliquot_id == snRNA_aliquot_id_tmp])
tumor_perc_over_4x <- as.numeric(gene_cna_state_tab$perc_cna_in_tumor_cell[gene_cna_state_tab$gene_symbol == gene_tmp & gene_cna_state_tab$cna_state == 3 & gene_cna_state_tab$snRNA_aliquot_id == snRNA_aliquot_id_tmp])
if (length(tumor_perc_over_4x) != 0) {
tumor_perc_2x <- (1-tumor_perc_0x-tumor_perc_1x-tumor_perc_3x-tumor_perc_4x-tumor_perc_over_4x)
} else {
tumor_perc_2x <- (1-tumor_perc_0x-tumor_perc_1x-tumor_perc_3x-tumor_perc_4x)
}
a <- (1-tumor_perc_0x)
if (gene_tmp != "KDM5C") {
b <- (1*tumor_perc_1x + 2*tumor_perc_2x + 3*tumor_perc_3x + 4*tumor_perc_4x)
} else {
b <- (1*tumor_perc_2x + 1.5*tumor_perc_3x + 2*tumor_perc_4x)
}
v <- as.numeric(vaf_tab %>%
filter(Specimen.ID.snRNA == snRNA_aliquot_id_tmp) %>%
select(gene_tmp))
ccf <- (v*(b*p+2-2*p))/(p*a)
ccf_mat[i,gene_tmp] <- ccf
}
}
ccf_tab_tmp <- data.frame(snRNA_aliquot_id = snRNA_aliquot_id_tmp, gene0 = gene2use, ccf0 = ccf0_to_test, tumor_purity = tumor_puritys, ccf_mat)
ccf_tab <- rbind(ccf_tab_tmp, ccf_tab)
}
write.table(x = ccf_tab, file = paste0(dir_out, "CCF_CNmut1.", run_id, ".tsv"), quote = F, row.names = F, col.names = T, sep = "\t")
# estimate CCF for somatic mutations using estimated tumor purity --------------------------------------
# input CNA CCF by chromosome arm results ---------------------------
# merge Mutation CCF with CNA CCF -----------------------------------------
# write out results -------------------------------------------------------
|
a9cc8fbe50784db132049b88c711f84de7f75fd2
|
f53d33f6a26d33d6f15632841bf805e5074cef99
|
/R/check_empty_elements.R
|
362d7c120f0635e957108028c5cd5213b97b4b4b
|
[] |
no_license
|
levisc8/Fun_Phylo_Package
|
6add7e6cd4e3cc0f1dfd21e669f0a55c143ee02a
|
224521f794d35297c1f3a0f835a6cb20f5e9c6fb
|
refs/heads/master
| 2021-06-10T04:56:43.768628
| 2020-06-01T12:07:58
| 2020-06-01T12:07:58
| 99,753,085
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 301
|
r
|
check_empty_elements.R
|
check_empty_elements <- function(...){
rmlist <- NULL
iniList <- list(...)
for(i in 1:length(iniList)){
if(is.null(iniList[[i]])){
rmlist <- c(rmlist, i)
}
}
if(!is.null(rmlist)){
outList <- iniList[-c(rmlist)]
} else {
outList <- iniList
}
return(outList)
}
|
ff64c2961cf7818d1aefbbd8c0ba1ea39879077d
|
fddcbf7ff2dad9d8d781da1dd9205a3dc22bf772
|
/server.R
|
1cb256bd3e621df760961264644b3bd425636a71
|
[] |
no_license
|
Khominets/lab_6
|
1733530972dcb4faf066ca1bf8e32e536de59ae0
|
dc5129be28d4e668a66c9b273ef8d7f71b794aec
|
refs/heads/master
| 2021-01-11T10:47:23.895505
| 2016-12-11T20:04:24
| 2016-12-11T20:04:24
| 76,195,721
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 549
|
r
|
server.R
|
library(shiny)
shinyServer(
function(input, output) {
output$func<-renderText({
input$start
if (input$num1 >= 3) {
paste0('Функція: ', '(' ,input$num1, '/' , '4' , ')' , '*', input$num2, '^', '2','*','tan','(' ,'pi', '/', input$num1, ')' )
}
else if (input$num1 < 3 ) {
paste0('Помилка')
}
})
output$dy<- renderText({
paste0('Площа правильного ',input$num1,'-кутника = ',
((input$num1/4)*input$num2^2*tan(pi/input$num1)))
})
})
|
3ab1a59c25bbd903b59fa065bdfd3d59d973fa84
|
cea51b60f43efac0959bb6e52749f608e1eddd13
|
/nlsralt/R/nlxbs.R
|
c59edc8fa2417647c1bd33df20b58b1c0e4c8cb1
|
[] |
no_license
|
ArkaB-DS/GSOC21-improveNLS
|
6bd711bca3ad1deac5aff3128cfc02e0237915a9
|
f33839f8ceef78591ee296c6e515cd52339bb2b0
|
refs/heads/master
| 2023-07-16T21:07:33.900548
| 2021-08-22T05:41:26
| 2021-08-22T05:41:26
| 398,542,725
| 3
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,498
|
r
|
nlxbs.R
|
nlxbs <- function(formula, start, trace = FALSE, data=NULL, subset=NULL, lower = -Inf,
upper = Inf, masked = NULL, weights=NULL, control=list()) {
# A simplified and hopefully robust alternative to finding
# the nonlinear least squares minimizer that causes
# 'formula' to give a minimal residual sum of squares.
#
# Modified 2021-6-26 to only used Identity matrix Marquardt stabilization
#
# nlxb is particularly intended to allow for the
# resolution of very ill-conditioned or else near
# zero-residual problems for which the regular nls()
# function is ill-suited.
#
# J C Nash 2014-7-16 nashjc _at_ uottawa.ca
#
# formula looks like 'y~b1/(1+b2*exp(-b3*T))' start MUST be
# a vector where all the elements are named: e.g.,
# start=c(b1=200, b2=50, b3=0.3) trace -- TRUE for console
# output data is a data frame containing data for variables
# used in the formula that are NOT the parameters. This
# data may also be defined in the parent frame i.e.,
# 'global' to this function lower is a vector of lower
# bounds upper is a vector of upper bounds masked is a
# character vector of names of parameters that are fixed.
# control is a list of control parameters. These are: ...
#
# This variant uses a qr solution without forming the sum
# of squares and cross products t(J)%*%J
#
# ?? and put in the weights
# ######### get data from data frame if exists
# ######### print(str(data))
# if (!is.null(data)) {
# for (dfn in names(data)) {
# cmd <- paste(dfn, "<-data$", dfn, "")
# eval(parse(text = cmd))
# }
# } else stop("'data' must be a list or an environment")
# ensure params in vector
pnames <- names(start)
start <- as.numeric(start) # ensure we convert (e.g., if matrix)
names(start) <- pnames ## as.numeric strips names, so this is needed
# bounds
npar <- length(start) # number of parameters
if (length(lower) == 1)
lower <- rep(lower, npar) # expand to full dimension
if (length(upper) == 1)
upper <- rep(upper, npar)
# more tests on bounds
if (length(lower) != npar)
stop("Wrong length: lower")
if (length(upper) != npar)
stop("Wrong length: upper")
if (any(start < lower) || any(start > upper))
stop("Infeasible start")
if (trace) {
cat("formula: ")
print(formula)
cat("lower:")
print(lower)
cat("upper:")
print(upper)
}
# controls
ctrl <- list(watch = FALSE, phi = 1, lamda = 1e-04, offset = 100,
laminc = 10, lamdec = 4, femax = 10000, jemax = 5000, rofftest = TRUE,
smallsstest = TRUE)
## maxlamda <- 1e+60) ## dropped 130709 ??why?
## epstol <- (.Machine$double.eps) * ctrl$offset # ??161018 - not used elsewhere
ncontrol <- names(control)
nctrl <- names(ctrl)
for (onename in ncontrol) {
if (!(onename %in% nctrl)) {
if (trace) cat("control ", onename, " is not in default set\n")
stop(onename," is not a control for nlxb")
}
ctrl[onename] <- control[onename]
}
if (trace) print(ctrl)
phiroot <- sqrt(ctrl$phi)
# Note spelling of lamda -- a throwback to Ag Can 1974 and way to see if folk are copying code.
# First get all the variable names:
# vn <- all.vars(parse(text = formula))
# ??? need to fix -- why?, what is wrong
vn <- all.vars(formula)
# Then see which ones are parameters (get their positions
# in the set xx
pnum <- start # may simplify later??
pnames <- names(pnum)
bdmsk <- rep(1, npar) # set all params free for now
# ?? put in lower==upper mask defn
maskidx <- union(which(lower==upper), which(pnames %in% masked)) # use both methods for masks
# NOTE: %in% not == or order gives trouble
if (length(maskidx) > 0 && trace) {
cat("The following parameters are masked:")
print(pnames[maskidx])
}
bdmsk[maskidx] <- 0 # fixed parameters
if (trace) { # diagnostic printout
cat("Finished masks check\n")
parpos <- match(pnames, vn) # ?? check this is right??
datvar <- vn[-parpos] # NOT the parameters
cat("datvar:")
print(datvar)
for (i in 1:length(datvar)) {
dvn <- datvar[[i]]
cat("Data variable ", dvn, ":")
if (is.null(data)) {
print(eval(parse(text = dvn)))
} else {
print(with(data, eval(parse(text = dvn))))
}
}
}
trjfn<-model2rjfun(formula, pnum, data=data)
if (trace) {
cat("trjfn:\n")
print(trjfn)
}
## Call the nlfb function here
## ?? problem is getting the data into the tresfn and tjacfn?? How?
## which gets data into the functions
resfb <- nlfbs(start=pnum, resfn=trjfn, jacfn=trjfn, trace=trace,
data=data, subset, lower=lower, upper=upper, maskidx=maskidx,
weights=weights, control=ctrl)
## control=ctrl, ...)
resfb$formula <- formula # 190805 to add formula
# ?? should there be any ... arguments
pnum <- as.vector(resfb$coefficients)
names(pnum) <- pnames # Make sure names re-attached. ??Is this needed??
## resfb$coefficients <- pnum ## commented 190821
result <- resfb
## attr(result, "pkgname") <- "nlsr"
class(result) <- "nlsr" ## CAUSES ERRORS ?? Does it?? 190821
result
}
|
47512cf818fc46625ec542d6e9d4ad4c5ffa0a1b
|
cbc4f1708ef51093fcf0bebe5c2a3ee8403d0f9a
|
/human_motif_analysis/parsing_GOrilla.R
|
488f2d65e5db56e6d9980249a7d0fa12e190725a
|
[] |
no_license
|
AndyFeinberg/methyl_entropy
|
540cf6d97d0dcaf3f3cc3799e7bacac5f27ef1b3
|
0da2e12cf2226cd51a09365981352805b49fdee6
|
refs/heads/main
| 2023-04-18T23:45:18.571277
| 2023-01-05T04:59:10
| 2023-01-05T04:59:10
| 584,472,340
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 941
|
r
|
parsing_GOrilla.R
|
rm(list=ls())
library(xlsx)
GO_in=as.data.table(read.xlsx('../downstream/output/graphs_tables/regulatory_non_regulatory_GOrilla.xlsx',1))
for(i in 1:nrow(GO_in)){
if(!is.na(GO_in$Description[i])){GO_term=GO_in$Description[i]}
else{GO_in$Description[i]=GO_term}
}
GO_in=GO_in[,list(P.value=max(P.value,na.rm=T),
FDR.q.value=max(FDR.q.value,na.rm=T),
Enrichment..N..B..n..b.=max(as.numeric(gsub('\\(.*','',Enrichment..N..B..n..b.)),na.rm=T),
Genes=gsub('\\[-] Hide genes,','',paste(unique(gsub(' - .*','',Genes)),collapse =','))),by=Description]
top50_gene=read.xlsx2('../downstream/output/human_analysis/NME_motif/NME_regulaotry_Ken.xlsx',1,startRow = 2)
GO_in$Genes=unlist(lapply(strsplit(GO_in$Genes,','),function(x) paste(x[x%in%top50_gene[1:50,]$Transcription.facotrs],collapse = ',')))
write.csv(GO_in,'../downstream/output/graphs_tables/regulatory_non_regulatory_GOrilla_processed.csv')
|
415f6d537d82bde63ba2ab2d874b43eff3ec828b
|
0b5910a5e63a5d6e5fb49ea610014ef8688a0179
|
/man/dGAselID.Rd
|
960dfcf0c3ab6135b293b26a29daf7bca4ff201e
|
[] |
no_license
|
cran/dGAselID
|
2b8d7c5cda432dc5e24f08998035cc979ae5e03f
|
3e7338b2c19a8f0a3af749a20716cd6bdc395d8b
|
refs/heads/master
| 2020-07-02T18:59:35.171136
| 2017-07-10T04:02:55
| 2017-07-10T04:02:55
| 74,287,862
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 3,827
|
rd
|
dGAselID.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dGAselID.R
\name{dGAselID}
\alias{dGAselID}
\title{dGAselID}
\usage{
dGAselID(x, response, method = knn.cvI(k = 3, l = 2), trainTest = "LOG",
startGenes, populationSize, iterations, noChr = 22, elitism = NA,
ID = "ID1", pMutationChance = 0, nSMutationChance = 0,
fSMutationChance = 0, lSDeletionChance = 0, wChrDeletionChance = 0,
transposonChance = 0, randomAssortment = TRUE, embryonicSelection = NA,
EveryGeneInInitialPopulation = TRUE, nnetSize = NA, nnetDecay = NA,
rdaAlpha = NA, rdaDelta = NA, ...)
}
\arguments{
\item{x}{Dataset in ExpressionSet format.}
\item{response}{Response variable}
\item{method}{Supervised classifier for fitness evaluation. Most of the supervised classifiers in MLInterfaces are acceptable. The default is knn.cvI(k=3, l=2).}
\item{trainTest}{Cross-validation method. The default is "LOG".}
\item{startGenes}{Genes in the genotypes at initialization.}
\item{populationSize}{Number of genotypes in initial population.}
\item{iterations}{Number of iterations.}
\item{noChr}{Number of chromosomes. The default value is 22.}
\item{elitism}{Elite population in percentages.}
\item{ID}{Dominance. The default value is "ID1". Use "ID2" for Incomplete Dominance.}
\item{pMutationChance}{Chance for a Point Mutation to occur. The default value is 0.}
\item{nSMutationChance}{Chance for a Non-sense Mutation to occur. The default value is 0.}
\item{fSMutationChance}{Chance for a Frameshift Mutation to occur. The default value is 0.}
\item{lSDeletionChance}{Chance for a Large Segment Deletion to occur. The default value is 0.}
\item{wChrDeletionChance}{Chance for a Whole Chromosome Deletion to occur. The default value is 0.}
\item{transposonChance}{Chance for a Transposon Mutation to occur. The default value is 0.}
\item{randomAssortment}{Random Assortment of Chromosomes for recombinations. The default value is TRUE.}
\item{embryonicSelection}{Remove chromosomes with fitness < specified value. The default value is NA.}
\item{EveryGeneInInitialPopulation}{Request for every gene to be present in the initial population. The default value is TRUE.}
\item{nnetSize}{for nnetI. The default value is NA.}
\item{nnetDecay}{for nnetI. The default value is NA.}
\item{rdaAlpha}{for rdaI. The default value is NA.}
\item{rdaDelta}{for rdaI. The default value is NA.}
\item{...}{Additional arguments.}
}
\value{
The output is a list containing 5 named vectors, records of the evolution:
\item{DGenes}{The occurrences in selected genotypes for every gene,}
\item{dGenes}{The occurrences in discarded genotypes for every gene,}
\item{MaximumAccuracy}{Maximum accuracy in every generation,}
\item{MeanAccuracy}{Average accuracy in every generation,}
\item{MinAccuracy}{Minimum accuracy in every generation,}
\item{BestIndividuals}{Best individual in every generation.}
}
\description{
Initializes and starts the search with the genetic algorithm.
}
\examples{
\dontrun{
library(genefilter)
library(ALL)
data(ALL)
bALL = ALL[, substr(ALL$BT,1,1) == "B"]
smallALL = bALL[, bALL$mol.biol \%in\% c("BCR/ABL", "NEG")]
smallALL$mol.biol = factor(smallALL$mol.biol)
smallALL$BT = factor(smallALL$BT)
f1 <- pOverA(0.25, log2(100))
f2 <- function(x) (IQR(x) > 0.5)
f3 <- ttest(smallALL$mol.biol, p=0.1)
ff <- filterfun(f1, f2, f3)
selectedsmallALL <- genefilter(exprs(smallALL), ff)
smallALL = smallALL[selectedsmallALL, ]
rm(f1)
rm(f2)
rm(f3)
rm(ff)
rm(bALL)
sum(selectedsmallALL)
set.seed(149)
res<-dGAselID(smallALL, "mol.biol", trainTest=1:79, startGenes=12, populationSize=200,
iterations=150, noChr=5, pMutationChance=0.0075, elitism=4)
}
}
|
09f932618afac04328758c9dd235e3ab570ae1d6
|
695b88a36f548e410d8a4181ed7c6f433c7515a1
|
/R/lstrends.R
|
b6f19dea19cca06928ec6c0401b7c358d8c51946
|
[] |
no_license
|
jonathon-love/lsmeans
|
16054e0a830df482fd6aa41b5a461535afb8d4bb
|
c6e91712705647bbd9aa2fa37e65929907fecca9
|
refs/heads/master
| 2021-01-12T00:31:38.801483
| 2017-08-02T11:31:32
| 2017-08-02T11:31:32
| 78,736,500
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,556
|
r
|
lstrends.R
|
##############################################################################
# Copyright (c) 2012-2016 Russell V. Lenth #
# #
# This file is part of the lsmeans package for R (*lsmeans*) #
# #
# *lsmeans* 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 2 of the License, or #
# (at your option) any later version. #
# #
# *lsmeans* is distributed in the hope that it will be useful, #
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
# GNU General Public License for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with R and *lsmeans*. If not, see #
# <https://www.r-project.org/Licenses/> and/or #
# <http://www.gnu.org/licenses/>. #
##############################################################################
### Code for lstrends
### lstrends function
lstrends = function(model, specs, var, delta.var=.01*rng, data,
transform = c("none", "response"), ...) {
estName = paste(var, "trend", sep=".") # Do now as I may replace var later
if (missing(data)) {
data = try(recover.data (model, data = NULL))
if (inherits(data, "try-error"))
stop("Possible remedy: Supply the data used in the 'data' argument")
}
else # attach needed attributes to given data
data = recover.data(model, data = data)
x = data[[var]]
fcn = NULL # differential
if (is.null(x)) {
fcn = var
var = .all.vars(as.formula(paste("~",var)))
if (length(var) > 1)
stop("Can only support a function of one variable")
else {
x = data[[var]]
if (is.null(x)) stop("Variable '", var, "' is not in the dataset")
}
}
rng = diff(range(x))
if (delta.var == 0) stop("Provide a nonzero value of 'delta.var'")
RG = orig.rg = ref.grid(model, data = data, ...)
grid = RG@grid
if (!is.null(mr <- RG@roles$multresp)) {
# use the grid value only for the 1st mult resp (no dupes)
if (length(mr) > 0)
grid = grid[grid[[mr]] == RG@levels[[mr]][1], ]
}
grid[[var]] = grid[[var]] + delta.var
basis = lsm.basis(model, attr(data, "terms"), RG@roles$xlev, grid, ...)
if (is.null(fcn))
newlf = (basis$X - RG@linfct) / delta.var
else {
y0 = with(RG@grid, eval(parse(text = fcn)))
yh = with(grid, eval(parse(text = fcn)))
diffl = (yh - y0)
if (any(diffl == 0)) warning("Some differentials are zero")
newlf = (basis$X - RG@linfct) / diffl
}
transform = match.arg(transform)
# Now replace linfct w/ difference quotient
RG@linfct = newlf
RG@roles$trend = var
if(hasName(RG@misc, "tran")) {
tran = RG@misc$tran
if (is.list(tran)) tran = tran$name
if (transform == "response") {
prd = .est.se.df(orig.rg, do.se = FALSE)
lnk = attr(prd, "link")
deriv = lnk$mu.eta(prd[[1]])
RG@linfct = diag(deriv) %*% RG@linfct
RG@misc$initMesg = paste("Trends are obtained after back-transforming from the", tran, "scale")
}
else
RG@misc$initMesg = paste("Trends are based on the", tran, "(transformed) scale")
}
RG@misc$tran = RG@misc$tran.mult = NULL
RG@misc$estName = estName
RG@misc$methDesc = "lstrends"
.save.ref.grid(RG) # save in .Last.ref.grid, if enabled
# args for lsmeans calls
args = list(object=RG, specs=specs, ...)
args$at = args$cov.reduce = args$mult.levs = args$vcov. = args$data = args$trend = NULL
do.call("lsmeans", args)
}
|
881c0876f0635baadc452e8ddc6ab3d6721135ae
|
4344aa4529953e5261e834af33fdf17d229cc844
|
/input/gcamdata/man/module_energy_elec_bio_low_xml.Rd
|
dbc11e5364215292ade54df501d76114f5a015cd
|
[
"ECL-2.0",
"LicenseRef-scancode-unknown-license-reference"
] |
permissive
|
JGCRI/gcam-core
|
a20c01106fd40847ed0a803969633861795c00b7
|
912f1b00086be6c18224e2777f1b4bf1c8a1dc5d
|
refs/heads/master
| 2023-08-07T18:28:19.251044
| 2023-06-05T20:22:04
| 2023-06-05T20:22:04
| 50,672,978
| 238
| 145
|
NOASSERTION
| 2023-07-31T16:39:21
| 2016-01-29T15:57:28
|
R
|
UTF-8
|
R
| false
| true
| 749
|
rd
|
module_energy_elec_bio_low_xml.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/zenergy_xml_elec_bio_low.R
\name{module_energy_elec_bio_low_xml}
\alias{module_energy_elec_bio_low_xml}
\title{module_energy_elec_bio_low_xml}
\usage{
module_energy_elec_bio_low_xml(command, ...)
}
\arguments{
\item{command}{API command to execute}
\item{...}{other optional parameters, depending on command}
}
\value{
Depends on \code{command}: either a vector of required inputs,
a vector of output names, or (if \code{command} is "MAKE") all
the generated outputs: \code{elec_bio_low.xml}. The corresponding file in the
original data system was \code{batch_elec_bio_low_xml.R} (energy XML).
}
\description{
Construct XML data structure for \code{elec_bio_low.xml}.
}
|
d90fc1db16cc666eb7abd019469c6a0abb3d6ef7
|
f99fa98d3f573726bcc18c9d87e257f79814e697
|
/basic_mapping_demo.R
|
9d9c5de354595336387919a90f7aff5a7a6b779a
|
[] |
no_license
|
cjbattey/Rseminar_GIS
|
c938d21bd643c4b1672330abd73a3b5ce6da568e
|
f919feff759a255f31becf225df3de1c539b1b1e
|
refs/heads/master
| 2021-01-10T13:08:05.874891
| 2015-11-03T03:45:17
| 2015-11-03T03:45:17
| 45,404,659
| 0
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,863
|
r
|
basic_mapping_demo.R
|
###########################
#### FUN WITH MAPS!!!! ####
###########################
### what you'll need
getwd()
install.packages('ggplot2')
install.packages('ggmap')
install.packages('mapdata')
install.packages('maps')
library(ggplot2)
library(ggmap)
library(mapdata)
library(maps)
################################
#### making basic maps in R ####
################################
map("worldHires", "Mexico") #pick your basemap, define your country
map("worldHires", "Mexico", col="grey90", fill=TRUE) #example visual tweak
map("worldHires", xlim=c(-130, -53), ylim=c(15,35)) #define by lat / long instead
localities <- read.csv("PABU.csv") #format is a column "lat" and a column "long" with data in decimal degrees
colnames(localities) <-c("num","lat","long","species")
map("worldHires", "Mexico", col="grey90", fill=TRUE)
points(localities$long, localities$lat, pch=19, col="red", cex=0.5) #plot localities data, choose aesthetic parameters
##################################################
#### more of the same but better with ggplot2 ####
##################################################
map <- map_data("world", "Mexico") #pick basemap -- higher res options available
ggplot() + theme_bw() + geom_path(data=map, aes(x=long, y=lat, group=group)) + coord_map() #look at the basemap
ggplot() + geom_point(data=localities, aes(x=long, y=lat)) #look at the points in space
#### and together now!
ggplot() +
geom_path(data=map, aes(x=long, y=lat, group=group)) +
geom_point(data=localities, aes(x=long, y=lat))
ggplot() + coord_map()+ #hold ratios / project constant
geom_path(data=map, aes(x=long, y=lat, group=group)) +
geom_point(data=localities, aes(x=long, y=lat))
#### add more graphical parameters
ggplot() + coord_map()+
geom_path(data=map, aes(x=long, y=lat, group=group)) +
geom_point(data=localities, aes(x=long, y=lat, col=species, size=num)) #color code points by species, scale by size
ggplot() + coord_map()+
geom_path(data=map, aes(x=long, y=lat, group=group)) +
geom_point(data=localities, aes(x=long, y=lat, col=species, size=num)) +
scale_size_continuous(range = c(3,13)) #tweak acceptable range of point sizes
ggplot() + coord_map()+
geom_path(data=map, aes(x=long, y=lat, group=group)) +
geom_point(data=localities, aes(x=long, y=lat, col=species, size=num)) +
scale_size_continuous(range = c(3,13)) +
theme_bw() # remove greyscale background
ggplot() + coord_map()+
geom_path(data=map, aes(x=long, y=lat, group=group)) +
geom_point(data=localities, aes(x=long, y=lat, col=species, size=num)) +
scale_size_continuous(range = c(3,13)) +
theme_bw() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) #remove background grid
ggplot() + coord_map()+
geom_path(data=map, aes(x=long, y=lat, group=group)) +
geom_point(data=localities, aes(x=long, y=lat, col=species, size=num)) +
scale_size_continuous(range = c(3,13)) +
theme_bw() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme(legend.title = element_blank(),legend.text = element_text(face = "italic"), axis.title.x = element_blank(), axis.title.y = element_blank()) #remove labels
#### a few other tricks
ggplot() + coord_map()+
geom_path(data=map, aes(x=long, y=lat, group=group)) +
#geom_point(data=localities, aes(x=long, y=lat, col=species, size=num)) +
#scale_size_continuous(range = c(3,13)) +
theme_bw() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme(legend.title = element_blank(),legend.text = element_text(face = "italic"), axis.title.x = element_blank(), axis.title.y = element_blank()) +
geom_bin2d(data=localities,aes(x=long,y=lat)) #"rasterize" your data (record density)
ggplot() + coord_map()+
geom_path(data=map, aes(x=long, y=lat, group=group)) +
#geom_point(data=localities, aes(x=long, y=lat, col=species, size=num)) +
#scale_size_continuous(range = c(3,13)) +
theme_bw() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme(legend.title = element_blank(),legend.text = element_text(face = "italic"), axis.title.x = element_blank(), axis.title.y = element_blank()) +
stat_summary2d(data=localities,aes(x=long,y=lat,z=num,fun="mean")) # visualize a summary statistic of it
ggplot() + coord_map()+
geom_path(data=map, aes(x=long, y=lat, group=group)) +
#geom_point(data=localities, aes(x=long, y=lat, col=species, size=num)) +
#scale_size_continuous(range = c(3,13)) +
theme_bw() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme(legend.title = element_blank(),legend.text = element_text(face = "italic"), axis.title.x = element_blank(), axis.title.y = element_blank()) +
stat_density2d(data=localities,aes(x=long,y=lat)) ### cool topographic overlay of density
|
1ee371af06457ee6ec3b2c79d8ee9351c9ea95a8
|
2da2406aff1f6318cba7453db555c7ed4d2ea0d3
|
/inst/snippet/balldrop-nls07-fig.R
|
2942ed5774ce72fd91830a3bc03ae63bce2a1069
|
[] |
no_license
|
rpruim/fastR2
|
4efe9742f56fe7fcee0ede1c1ec1203abb312f34
|
d0fe0464ea6a6258b2414e4fcd59166eaf3103f8
|
refs/heads/main
| 2022-05-05T23:24:55.024994
| 2022-03-15T23:06:08
| 2022-03-15T23:06:08
| 3,821,177
| 11
| 8
| null | null | null | null |
UTF-8
|
R
| false
| false
| 206
|
r
|
balldrop-nls07-fig.R
|
plot(balldrop.nls)
plot(balldrop.lm, w = 1)
gf_qq( ~ resid(balldrop.nls))
gf_qq( ~ resid(balldrop.lm))
gf_point(resid(balldrop.nls) ~ f(BallDrop$height))
gf_point(resid(balldrop.lm) ~ g(BallDrop$height))
|
6a90027b2e6bbe46f28044a424cab0b5e3003c6e
|
da725622bc962b639e1eb6df535b433e4366bcc5
|
/shinyCredentialsAndEducation/ui.r
|
2d0484e38312cfb48b03e8520cfe13c199a48423
|
[] |
no_license
|
bekahdevore/rKW
|
5649a24e803b88aa51a3e64020b232a23bd459fa
|
970dcf8dc93d4ec0e5e6a79552e27ddc0f850b91
|
refs/heads/master
| 2020-04-15T12:41:49.567456
| 2017-07-25T16:29:31
| 2017-07-25T16:29:31
| 63,880,311
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,122
|
r
|
ui.r
|
# This is the user-interface definition of a Shiny web application.
# You can find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com
#
library(shiny)
library(dplyr)
library(googleVis)
credentialsData <- as.data.frame(read.csv("credentialsAndEducation.csv"))
occupationsList <- as.character(unique(credentialsData$source))
shinyUI(fluidPage(
# Application title
titlePanel("Louisville MSA Credentials"),
# Sidebar with a slider input for number of bins
fluidRow(
column(12,
selectInput("credential",
"Select a credential",
choices = occupationsList)),
fluidRow(
column(4,
(htmlOutput("credentials"))),
column(4, offset = 4,
h4("Occupations"),
DT::dataTableOutput("credentialTable"))
)
)))
|
5f2a1875c84ef11de8d9d20c6a614cfbf329c586
|
c2b9eb709f1e5bf19b83d0977b5f3ff2c89d255c
|
/R/utils-httr.R
|
d6a6fd95b0175dcd41071b950fbed8a5dd293c7f
|
[] |
no_license
|
StevenMMortimer/rdynamicscrm
|
0a2d0b60ebb26bea741d94351f844d6b7b4babef
|
0077326f8f770eef582adaf12a4becb590ab426d
|
refs/heads/main
| 2021-06-02T10:16:23.849693
| 2019-07-08T15:09:08
| 2019-07-08T15:09:08
| 144,392,345
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,656
|
r
|
utils-httr.R
|
#' Function to catch and print HTTP errors
#'
#' @importFrom httr content http_error status_code POST add_headers
#' @importFrom xml2 xml_ns_strip xml_find_all xml_text
#' @note This function is meant to be used internally. Only use when debugging.
#' @keywords internal
#' @export
catch_errors <- function(x, retry=TRUE, verbose=FALSE){
if(http_error(x)){
response_parsed <- content(x, as="parsed", type="text/xml", encoding="UTF-8")
if(status_code(x) == 500){
error_code <- response_parsed %>%
xml_ns_strip() %>%
xml_find_all("s:Body//s:Fault//s:Code//s:Value") %>%
xml_text()
error_text <- response_parsed %>%
xml_ns_strip() %>%
xml_find_all("s:Body//s:Fault//s:Reason//s:Text") %>%
xml_text()
if(retry & error_text == "An error occurred when verifying security for the message."){
dyn_auth_refresh(verbose = verbose)
if(x$request$options$post){
this_body <- update_header(rawToChar(x$request$options$postfields))
x <- POST(x$request$url,
add_headers(x$request$headers),
body = this_body)
} else {
message(sprintf("%s: %s", error_code, error_text))
stop()
}
catch_errors(x, retry=FALSE, verbose = verbose) # retry=FALSE prevents infinite looping if we can't re-authenticate
} else {
message(sprintf("%s: %s", error_code, error_text))
stop()
}
} else {
message(response_parsed)
stop()
}
}
invisible(x)
}
#' Another function to catch and print HTTP errors
#'
#' @importFrom httr content http_error status_code
#' @importFrom xml2 xml_ns_strip xml_find_all xml_text
#' @note This function is meant to be used internally. Only use when debugging.
#' @keywords internal
#' @export
catch_errors2 <- function(x, verbose=FALSE){
retry <- FALSE
if(http_error(x)){
response_parsed <- content(x, as="parsed", type="text/xml", encoding="UTF-8")
if(status_code(x) == 500){
error_code <- response_parsed %>%
xml_ns_strip() %>%
xml_find_all("s:Body//s:Fault//s:Code//s:Value") %>%
xml_text()
error_text <- response_parsed %>%
xml_ns_strip() %>%
xml_find_all("s:Body//s:Fault//s:Reason//s:Text") %>%
xml_text()
if(error_text == "An error occurred when verifying security for the message."){
if(verbose) message('Refreshing Authentication')
dyn_auth_refresh(verbose = verbose)
retry <- TRUE
} else {
message(sprintf("%s: %s", error_code, error_text))
stop()
}
} else {
message(response_parsed)
stop()
}
}
return(retry)
}
#' Another function to catch and print HTTP errors
#'
#' @importFrom httr content http_error status_code
#' @importFrom xml2 xml_ns_strip xml_find_all xml_text
#' @note This function is meant to be used internally. Only use when debugging.
#' @keywords internal
#' @export
catch_errors_wo_retry <- function(x, verbose=FALSE){
retry <- FALSE
if(http_error(x)){
response_parsed <- content(x, as="parsed", type="text/xml", encoding="UTF-8")
if(status_code(x) == 500){
error_code <- response_parsed %>%
xml_ns_strip() %>%
xml_find_all("s:Body//s:Fault//s:Code//s:Value") %>%
xml_text()
error_text <- response_parsed %>%
xml_ns_strip() %>%
xml_find_all("s:Body//s:Fault//s:Reason//s:Text") %>%
xml_text()
message(sprintf("%s: %s", error_code, error_text))
stop()
} else {
message(response_parsed)
stop()
}
}
return(invisible(retry))
}
|
72dd6caef8397e6c4e636e2b38561066a5e3294c
|
b287e9f0550018796dccf868de06aef5fadf0558
|
/R/events_guildrolecreate.r
|
f7d0345826e1a118d3f9262540573e9df1401375
|
[] |
no_license
|
TheOnlyArtz/Pirate
|
ffedc098db2f847c2b1fb3329fd94b6393d5af99
|
f68e1a0f16f872387b6a1d46d7a62ae6eb0c3ed1
|
refs/heads/master
| 2020-05-17T15:40:19.106630
| 2019-08-13T11:58:46
| 2019-08-13T11:58:46
| 183,796,627
| 1
| 0
| null | 2019-08-13T11:58:47
| 2019-04-27T16:29:31
|
R
|
UTF-8
|
R
| false
| false
| 714
|
r
|
events_guildrolecreate.r
|
#' Event, emitted whenever a role is being created
#' @param data The event fields
#' @param client The client object
#'\dontrun{
#' client$emitter$on("GUILD_ROLE_CREATE", function(guild, role) {
#' cat("A new role:", role$name, "has been created in:", guild$name, "!")
#' })
#'}
#' @section Disclaimer:
#' This event will return guild id instead of guild object if not cached.
#' this can be used in order to fetch the guild from the API (DISABLED FOR NOW)
events.guild_role_create <- function(data, client) {
guild <- client$guilds$get(data$guild_id)
if (is.null(guild)) return()
role <- Role(data$role, guild)
guild$roles$set(role$id, role)
client$emitter$emit("GUILD_ROLE_CREATE", guild, role)
}
|
8ca99b520c5d0f98e57a60b14964f2cf3d5e51a4
|
777ac67d5ce4447560305313871ab8c4b0fc9c8d
|
/module01_data_and_files/materials/process_course_data.R
|
ffb2278313367989cba589518bef054e1ec8685f
|
[] |
no_license
|
tavareshugo/slcu_r_course
|
c31ac0b4350e2597e72ad174f672ea91232364d4
|
4bb4b37e794d706d6bd69a6ede8eef2faa18608e
|
refs/heads/master
| 2021-09-11T08:22:42.931505
| 2018-04-06T08:25:52
| 2018-04-06T08:25:52
| 103,411,299
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,080
|
r
|
process_course_data.R
|
# Script can be run from the command line as
# Rscript process_course_data.R
library(tidyverse)
# Read data directly from repository
x <- read.table("https://datadryad.org/bitstream/handle/10255/dryad.104508/Experiment1.txt?sequence=2",
header = TRUE, stringsAsFactors = FALSE)
# Rename all variables to be lowercase
x <- x %>%
rename_all(funs(tolower(.)))
# Retain only the genotypes shown in Fig. 1b-c
x <- x %>%
filter(genotype %in% c("Ler-1", "Col Ama", "fca-6", "flc-3 FRI", "FRI", "Col FRI", "ld-1", "flk-1", "fve-3", "prmt5 FRI", "vin3-4 FRI"))
# Keep only a few of the variables (remove some redundant ones)
x <- x %>%
select(genotype, background, temperature, fluctuation, daylength,
vernalization, survival.bolt, bolt, days.to.bolt, days.to.flower,
rosette.leaf.num, cauline.leaf.num, blade.length.mm, total.leaf.length.mm,
blade.ratio)
# Rename "daylength" to "day.length" for good example of consistency
x <- rename(x, day.length = daylength)
# Save as CSV
write_csv(x, "burghardt_et_al_2015_expt1.csv")
|
9ef3296689b1638bc09b7a0f70b401e8d9be3652
|
ba7e5cd3bd2d27da96a784954cb4478676decbce
|
/r/mfa.R
|
a8cce7be245fe6177341dec62f1dc066757d79b0
|
[] |
no_license
|
nfagan/hwwa
|
2179b7817b478208f150d6fab90a6ad85e3cc2fc
|
be173e4c0947589e380436a8f5bae5a3ebc339ae
|
refs/heads/master
| 2021-06-12T07:32:28.064086
| 2021-02-22T20:45:54
| 2021-02-22T20:45:54
| 128,787,231
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,423
|
r
|
mfa.R
|
library(R.matlab)
library(FactoMineR)
combine_data_labels <- function(data, labels) {
all_data <- data
for (name in names(labels)) {
all_data$name = labels$name
}
return(all_data)
}
raw_data_to_frame <- function(raw_data) {
mat_data <- data.frame(raw_data$data)
names(mat_data) <- unlist(raw_data$data.header)
return(mat_data)
}
extract_labels <- function(raw_data) {
categories <- unlist(raw_data$label.categories)
entries <- unlist(raw_data$label.entries)
indices <- raw_data$label.indices
label_entries <- matrix(0, nrow(indices), ncol(indices))
for (i in 1:ncol(label_entries)) {
label_entries[, i] = entries[indices[, i]]
}
labels <- data.frame(label_entries)
names(labels) <- categories
return(labels)
}
raw_data <- readMat("/Users/Nick/Desktop/hwwa/pca_data.mat")
labels <- extract_labels(raw_data)
data <- raw_data_to_frame(raw_data)
all_data <- cbind(data, labels)
keep_categories <- c("drug", "trial_type")
keep_columns <- c(names(data), keep_categories)
mfa_data <- all_data[keep_columns]
any_nan = apply(apply(mfa_data[names(data)], 2, is.nan), 1, any)
res.mfa <- MFA(mfa_data[!any_nan,],
group = c(ncol(data), length(keep_categories)),
type = c("s", "n"),
name.group = c("saccade_info", "behavior"),
num.group.sup = 1,
graph = FALSE)
quanti_var <- get_mfa_var(res.mfa)
|
403c34948e19042ccb11a7d3327df8a30019a844
|
b1c6daedae2a5cc1692a813249d91945765c1de5
|
/server.R
|
b0ee9690239f64ba55d26f4d9172f9ca44b43e76
|
[] |
no_license
|
jyhsieh/boardgame-match
|
23fe0b9894f47dfd3d8a3c7442cd4ab7704fc558
|
06443aa3bc4613c7e33a6457230df8effd42d94b
|
refs/heads/master
| 2020-03-20T08:03:03.050398
| 2018-09-18T14:26:05
| 2018-09-18T14:26:05
| 137,297,549
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,628
|
r
|
server.R
|
library(shiny)
library(tidyverse)
library(wordcloud2)
library(ggplot2)
#=========================================================================
# Expand Mechanic and Category Variable
#=========================================================================
All_mechanic = unlist(strsplit(boardgame$mechanic, ", "))
Uniq_mechanic = unique(All_mechanic)
All_cat = unlist(strsplit(boardgame$category, ", "))
Uniq_cat = unique(All_cat)
boardgame_cluster <- matrix(0,nrow = dim(boardgame)[1],ncol = length(Uniq_mechanic)+length(Uniq_cat))
for(i in 1:length(Uniq_mechanic)){
boardgame_cluster[,i] <- grepl(Uniq_mechanic[i],boardgame$mechanic)*1
}
for(i in 1:length(Uniq_cat)){
boardgame_cluster[,length(Uniq_mechanic)+i] <- grepl(Uniq_cat[i],boardgame$category)*1
}
dim(boardgame_cluster) # 4999 136
boardgame_cluster <- as.data.frame(boardgame_cluster)
colnames(boardgame_cluster)[1:length(Uniq_mechanic)] <- Uniq_mechanic
colnames(boardgame_cluster)[(1+length(Uniq_mechanic)):136] <- Uniq_cat
colnames(boardgame_cluster)[which(duplicated(colnames(boardgame_cluster)))] #"none" "Memory"
which(colnames(boardgame_cluster)=="none") #50 114
colnames(boardgame_cluster)[50] <- "none_mechanic"
colnames(boardgame_cluster)[114] <- "none_cat"
which(colnames(boardgame_cluster)=="Memory") # 26 117
colnames(boardgame_cluster)[26] <- "Memory_mechanic"
colnames(boardgame_cluster)[117] <- "Memory_cat"
#========================================================
# cluster determined by max mode
#========================================================
getmode <- function(v) {
uniqv <- unique(v)
uniqv[which.max(tabulate(match(v, uniqv)))]
}
cluster_max <- function(dat,n_cluster,num_iter){
result <- matrix(0,nrow = dim(dat)[1],ncol = num_iter)
for(i in 1:num_iter){
result[,i] <- kmeans(dat,centers = n_cluster)$cluster
}
return(apply(result,MARGIN = 1,getmode))
}
#========================================================
server <- function(input,output){
n_cluster <- reactive({input$num_cluster})
i <- reactive({input$which_cluster})
cluster_result <- reactive({
cluster_max(boardgame_cluster,n_cluster = n_cluster(),num_iter = 20)
})
boardgame_cluster_all <- reactive({
cbind(names = boardgame$names,boardgame_cluster,cluster = cluster_result())
})
d <- reactive({
word_freq <- c()
for(i in 1:n_cluster()){
word_freq <- cbind(word_freq,apply(boardgame_cluster_all()[boardgame_cluster_all()$cluster==i,-c(1,138)],2,sum))
}
#=========================================================================
# Word Cloud and Frequency Plot
#=========================================================================
data.frame(word=names(sort(word_freq[,i()],decreasing = T)),freq = sort(word_freq[,i()],decreasing = T))
})
output$freqplot <- renderPlot({
ggplot(data = d()[1:10,],aes(x = reorder(word,-freq),y = freq)) +
geom_bar(stat = "identity", fill = "steelblue") +
xlab("Game Type")+ ylab("Frequency") +
ggtitle(paste("Top 10 Game Types of Cluster ",i()))+
theme_classic() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(size=24, face="bold.italic",hjust = 0.5),
axis.title.x = element_text(size=14, face="bold"),
axis.title.y = element_text(size=14, face="bold")
)
})
output$wordcloud <- renderWordcloud2({
set.seed(1234)
if(d()$freq[1]>3*d()$freq[2]){
size <- 0.1
} else{size <- 0.3}
wordcloud2(d(), size = size, fontWeight = "bold", color = "random-light",backgroundColor = "grey")
})
}
|
fc61ffa5b5c459273880ecfdf8436008e2b69cba
|
dbf4d16af283045abb830df0ec5445b0dbd4af6c
|
/covid/acces OurWorldinData cases/read_in_our_worldindata.R
|
bb1930a8145440ad6beb6e7289613ad9eaafb197
|
[] |
no_license
|
ammapanin/striking-statistics-tap
|
1d7845e4b78fde053332141a64c8a5a82a5abb02
|
135ed9c902271ccfbf4803b466a3181995735817
|
refs/heads/master
| 2020-08-01T20:07:59.450793
| 2020-04-10T13:22:55
| 2020-04-10T13:22:55
| 211,101,091
| 1
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 8,507
|
r
|
read_in_our_worldindata.R
|
library(tidyverse)
library(countrycode)
library(readxl)
library(magick)
### Setup paths
covid.path <- file.path(normalizePath("~"),
"Dropbox",
"Work Documents",
"World Bank",
"amma_striking_stats_local",
"covid")
setwd(covid.path)
final.figures.path <- "final figures"
#trend.plot.path <- file.path(final.figures.path)
full.data.link <- "https://covid.ourworldindata.org/data/ecdc/full_data.csv"
full.dt.in <- read.csv(full.data.link)
### Get a list of SSA countries
wb.path <- file.path(shared.data.path, "wb_country_codes.csv")
wb.in <- read.csv(wb.path)
ssa.codes <- wb.in %>%
filter(region == "Sub-Saharan Africa") %>%
pull(code) %>%
as.character()
### Prepare dataset for plotting
comparison.countries <- c("United States", "China")
dt <- full.dt.in %>%
mutate(ccode = countrycode(
sourcevar = .$location,
origin = "country.name",
destination ="iso3c"),
is.ssa = ifelse(ccode %in% ssa.codes,
TRUE, FALSE),
date = as.Date(date))
dt.ssa <- dt %>%
filter((is.ssa == TRUE |
location %in% comparison.countries))
latest.date <- max(dt$date)
latest.date.text <- format(latest.date, "%d %B %Y")
dt.today <- dt %>%
filter(date== latest.date)
### Define some general parameters
general.theme <- theme_minimal() +
theme(axis.text.x = element_text(size = 6))
### Plot cases
latest.cases <- dt.ssa %>%
filter(date == latest.date) %>%
ggplot(aes(x = ccode, y = total_cases)) +
geom_point() +
scale_y_continuous(trans = "log10") +
general.theme
### Make the trend plots
translate.date <- function(date.col){
seq_along(date.col) - 1
}
get.earliest.date <- function(country.dt, n.cases = Ncases){
earliest.date <- country.dt %>%
mutate(more.than.n = total_cases >= n.cases) %>%
filter(more.than.n == TRUE) %>%
pull(date) %>%
min()
names(earliest.date) <- country.dt$location %>%
unique() %>%
as.character()
return(earliest.date)
}
get.days.since.n <- function(country.dt, n.cases = Ncases){
earliest.date <- get.earliest.date(country.dt, n.cases)
more.cases.df <- country.dt %>%
filter(date >= earliest.date) %>%
mutate(date.zeroed = translate.date(date))
return(more.cases.df)
}
double.day.function <- function(doubling.days,
time.vec,
n.cases = Ncases){
n.cases * (2 ^ (time.vec/doubling.days))
}
get.doubling.plot.coords <- function(ddf,
max.cases.in = max.cases.plot,
max.days.in = max.days.plot){
coords.df.out <- ddf %>%
filter(number_cases < max.cases.in) %>%
filter(date.zeroed == max.days.in)
return(coords.df.out)
}
pretty.doubling.names <- function(xstr){
paste(gsub("days",
"doubling every ",
xstr),
"days")
}
make.doubling.df <- function(doubling.days.list,
time.vec.in,
ncases.in = Ncases){
doubling.times.list <- lapply(
doubling.days.list,
double.day.function,
time.vec = time.vec.in,
n.cases = ncases.in)
names(doubling.times.list) <- paste0("days",
doubling.days.list)
doubling.df <- data.frame(doubling.times.list) %>%
mutate(date.zeroed = time.vec.in) %>%
pivot_longer(cols = starts_with("days"),
names_to = "doubling_time",
values_to = "number_cases")
plot.names.df <- doubling.df %>%
group_by(doubling_time) %>%
group_modify(~get.doubling.plot.coords(.x)) %>%
ungroup() %>%
mutate(line_name = pretty.doubling.names(.$doubling_time))
return(list(doubling.df, plot.names.df))
}
get.levels.of.factor <- function(df.in, factor.name, value.name){
ordered.out <- df.in %>%
arrange_(value.name) %>%
select_(factor.name) %>%
pluck(1) %>%
unique() %>%
as.character()
return(rev(ordered.out))
}
add.e4t.branding <- function(plot, plot.name,
width.in, height.in){
plot.png <- paste0(plot.name, ".png")
ggsave(plot.png,
width = width.in,
height = height.in,
units = "cm",
plot = plot)
stats.png <- image_read(plot.png)
logo <- image_read("e4t_logo.png")%>%
image_resize("x150")
twitter <- image_read("e4t_twitter.png") %>%
image_resize("x90")
logo.offset <- paste0("+",
round((width.in - 1)*100, 0),
"+",
"0")
print(logo.offset)
plot.img <- image_composite(stats.png, logo,
offset = logo.offset) #%>%
# image_composite(twitter, offset = "+1250+1950")
image_write(plot.img,
path = file.path(final.figures.path, plot.png))
return(plot.img)
}
### Check when different countries crossed the N threshold
Ncases <- 150
country.cross.n.list <- dt %>%
group_by(location) %>%
group_map(~get.earliest.date(.x, n.cases = Ncases),
keep = TRUE)
country.cross.n <- do.call("c", country.cross.n.list)
N.more.than.n <- dt.today %>%
filter(total_cases > Ncases) %>%
pull(location) %>%
as.character() %>%
length() %>%
`-`(1)
ssa.N <- dt.ssa %>%
group_by(location) %>%
group_modify(~get.days.since.n(.x, n.cases = Ncases)) %>%
ungroup() %>%
mutate(location = as.character(location),
location = factor(
location,
levels = get.levels.of.factor(
df.in = filter(., date == latest.date),
"location", "total_cases")),
ordered = TRUE)
ssa.only.df <- ssa.N %>%
filter(is.ssa)
max.cases.plot <- ssa.only.df %>%
pull(total_cases) %>%
max() %>%
`+`(20)
min.cases.plot <- ssa.only.df %>%
pull(total_cases) %>%
min() %>%
`-`(1)
max.days.plot <- ssa.only.df %>%
pull(date.zeroed) %>%
max() %>%
`+`(1)
N.countries <- length(ssa.N$location %>% unique())
days.since.n <- ssa.N$date.zeroed %>% unique()
doubling.days.plot <- c(1, 5, 10)
doubling.df.list <- make.doubling.df(doubling.days.plot,
days.since.n)
doubling.df <- doubling.df.list[[1]]
doubling.df.names <- doubling.df.list[[2]]
plot.title.base <- paste("%s African countries have more",
"than %s confirmed COVID-19 cases")
caption.text <- paste(
"Source: Our World in Data. Plot inspired by FT.",
sprintf("Last accessed on %s", latest.date.text))
xlab.text <- paste(
sprintf("Days since case %s was reported", Ncases))
plot.title <- sprintf(plot.title.base, N.countries, Ncases)
trend.plot <- ssa.N %>%
ggplot(aes(x = date.zeroed,
y = total_cases, colour = location)) +
geom_point() +
geom_line() +
geom_line(data = doubling.df,
inherit.aes = FALSE,
aes(x = date.zeroed,
y = number_cases,
linetype = doubling_time),
colour = "gray") +
geom_text(data = doubling.df.names,
inherit.aes = FALSE,
aes(x = date.zeroed,
y = number_cases,
label = line_name),
colour = "grey67",
hjust = "right",
nudge_x = -0.5) +
scale_y_continuous(trans = "log10",
limits = c(min.cases.plot, max.cases.plot)) +
scale_x_continuous(limits = c(0, max.days.plot)) +
labs(caption = caption.text) +
xlab(xlab.text) +
ylab("Total number of cases") +
guides(linetype = "none") +
general.theme +
theme(legend.title = element_blank(),
plot.title.position = "plot",
plot.caption.position = "plot",
plot.caption = element_text(
hjust = 0,
size = 7,
margin = margin(t = 0.5, unit = "cm")))
plot.final <- add.e4t.branding(trend.plot,
"SSA_more_than_100a",
width.in = 26.9,
height.in = 15.5)
|
d9aca7633db92bb9df6c0d0fd6e3d5f743685ca0
|
87f85a565768dbf5418516b1c742f45f2342f524
|
/scripts/misc/RMSDcov.R
|
a9aebba18cef3bbaf2e714251151bb63750986d1
|
[
"Apache-2.0"
] |
permissive
|
genomicsengland/gel-coverage
|
1ad6716efaa3059146c30812f1f7e21a0f4a9236
|
61a671a53ac52a0b62c8aea983ced65fd0bed6cc
|
refs/heads/master
| 2022-07-07T00:04:27.718426
| 2021-05-06T16:55:58
| 2021-05-06T16:55:58
| 70,790,696
| 2
| 0
|
Apache-2.0
| 2022-07-05T21:29:25
| 2016-10-13T09:27:20
|
Python
|
UTF-8
|
R
| false
| false
| 215
|
r
|
RMSDcov.R
|
args = commandArgs(trailingOnly = TRUE)
bwtool.file = args[1]
data <- read.delim(bwtool.file, as.is=T, sep = "\t", header = TRUE)
median <- median(sqrt(data$sum_of_squares/100000))
writeLines(paste(median,sep=" "))
|
09d718b4a74c51a5732be610e5f46ce64e55ede7
|
223756424b32600a66b505e886fbad9c9fe47e7b
|
/Plot1.R
|
8c5190d683b7b628d25d77a58fa25784a78d370e
|
[] |
no_license
|
ghostdatalearner/ExData_project2
|
12070e2091b54d0a6a1da7012e1fa849b4ab010b
|
867d83df40d8fabe459c0a41838086520ccf04c0
|
refs/heads/master
| 2021-01-22T23:11:14.325383
| 2014-06-21T16:32:27
| 2014-06-21T16:32:27
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 849
|
r
|
Plot1.R
|
# Coursera JHU Spec Data Science
#
# Exploratory Data Analysis
#
# Project 2
#
# Plot1.R
#
NEI <- readRDS("summarySCC_PM25.rds")
# Total emissions by year.
acc_emiss_year <- tapply(NEI$Emissions,NEI$year,sum)
# We open the graphic
png(file = "Plot1.png", width = 480, height = 480, bg = "transparent")
# As the accumulated emissions are in the order of millions of tons, we divide by 1000.000
# and add the proper indication in the y label
df_scaled <- acc_emiss_year/1000000
b<-barplot(df_scaled,ylab="PM_2.5 (Million tons)",main="Total PM_2.5 yearly emissions in USA",xlab="",axis.lty=1,ylim=c(0, 1.3*round(df_scaled[1],2)))
percent_labels <- paste0(100*(round((df_scaled-df_scaled[1])/df_scaled[1],3)),"%")
percent_labels[1] <- "1999 Reference: 100%"
text(x=b,y=as.vector(df_scaled),labels=percent_labels, pos=3,col="black",cex=0.75)
dev.off()
|
0b7bed3adeedcd7ae881fcb17e285d75ee191f37
|
22dc322d68a8bfaecf3c57be5ec99a433f0a95a8
|
/man/data_heckman.Rd
|
98353ea9902ab2b578c673b7672ca40bdef01bff
|
[] |
no_license
|
cran/micemd
|
19a1acfeb69da9e62d1639265a518ecebeb1f3a5
|
e5adbe076babd9f6c9aa3926eaabdd73d76dd69f
|
refs/heads/master
| 2023-06-09T21:04:43.211056
| 2023-06-01T11:00:04
| 2023-06-01T11:00:04
| 91,136,501
| 0
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,325
|
rd
|
data_heckman.Rd
|
\name{data_heckman}
\docType{data}
\alias{data_heckman}
\title{ A two-level incomplete dataset based on an online obesity survey}
\description{
The dataset used here was based on data collected from 2111 individuals in an online obesity survey in different locations. The data were simplified and grouped into five clusters.
The values and observability of the weight variable were defined according to Heckman's model in a herarchical model, and a systematic loss of this variable was assumed in one cluster.
Additionally, the predictor variables Age, Height and FAVC follow a MAR missing mechanism. Response time (Time) was used as an exclusion restriction variable.
}
\format{
A dataframe with 2111 observations with the following variables:
\tabular{rll}{
\tab Gender \tab a factor value with two levels: 1 ("Female"), 0 ("Male").\cr
\tab Age \tab a numeric value indicating age of subject in years.\cr
\tab Height\tab a numeric value with Height in meters.\cr
\tab FAVC\tab a factor value describing the frequent consumption of high caloric food (FAVC) with two levels:1("Yes"), 0("Male").\cr
\tab Weight\tab a numeric value with Weight in Kilograms.\cr
\tab Time\tab a numeric value indicating time in responding the questions in minutes.\cr
\tab Cluster\tab a numeric indexing the cluster.\cr
}
}
\source{Dataset obtained from "https://www.kaggle.com/datasets/fabinmndez/obesitydata?select=ObesityDataSet_raw_and_data_sinthetic.csv"}
\references{
Palechor, F. M., & de la Hoz Manotas, A. (2019). Dataset for estimation of obesity levels based on eating habits and physical condition in individuals from Colombia, Peru and Mexico. Data in brief, 25, 104344.
}
\details{
Simulation data code on gen_dataObs.R github repository
}
\examples{
require(mice)
require(ggplot2)
data(data_heckman)
summary(data_heckman)
# missing data pattern
md.pattern(data_heckman)
# Count missingness per group
by(data_heckman,
INDICES = data_heckman$Cluster,
FUN=md.pattern)
# Plot weight
ggplot(data_heckman, aes(x = Weight, group=as.factor(Cluster))) +
geom_histogram(aes(color = as.factor(Cluster),fill= as.factor(Cluster)),
position = "identity", bins = 30)+facet_grid(Cluster~.)
}
\keyword{datasets}
|
b7bbd4945c8b51d422702c63e28d63d286f23104
|
aedc3ee164734a8c42d5c535a02ee1acdf3443fb
|
/R/scRNA-seq/1.3_integrate_SCT-RPCA_fromMatrix.R
|
d50dea83cff643421f1a1b357a3b998cf13ecb63
|
[] |
no_license
|
zglu/Scripts_Bioinfo
|
60fdd5fe70b4900d981cc06560595ecb4fb9690e
|
8589ed613868f88ae12b8a629156c0a12b420bbc
|
refs/heads/master
| 2023-01-27T11:13:18.140272
| 2023-01-23T13:19:09
| 2023-01-23T13:19:09
| 96,612,675
| 3
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,118
|
r
|
1.3_integrate_SCT-RPCA_fromMatrix.R
|
# Rscript 1.3_integrate_SCT-RPCA_fromMatrix.R
library(Seurat)
library(dplyr)
# each data
Data1<-Read10X("~/zl3/Inqueries/Cheng_Sj_scRNA/_newCellRangerMapping2021-12/CellRanger_mito-4genes/F16/filtered_feature_bc_matrix/", gene.column=1) # default is col2: gene names. col1 is gene ids
Data2<-Read10X("~/zl3/Inqueries/Cheng_Sj_scRNA/_newCellRangerMapping2021-12/CellRanger_mito-4genes/F26/filtered_feature_bc_matrix/", gene.column=1) # default is col2: gene names. col1 is gene ids
Data1<-CreateSeuratObject(Data1, project = "F16", min.cells = 3, min.features = 200)
Data2<-CreateSeuratObject(Data2, project = "F26", min.cells = 3, min.features = 200)
Data1[["percent.mt"]] <- PercentageFeatureSet(object = Data1, pattern = "^Sj-")
Data2[["percent.mt"]] <- PercentageFeatureSet(object = Data2, pattern = "^Sj-")
Fil1 <- subset(x = Data1, subset = nFeature_RNA > 500 & nFeature_RNA < 4000 & nCount_RNA > 2000 & nCount_RNA < 30000 & percent.mt < 2.5)
Fil2 <- subset(x = Data2, subset = nFeature_RNA > 500 & nFeature_RNA < 4000 & nCount_RNA > 2000 & nCount_RNA < 30000 & percent.mt < 2.5)
int_list<-list(Fil1, Fil2)
int_list <- lapply(X = int_list, FUN = SCTransform, method = "glmGamPoi", vars.to.regress = "percent.mt")
features <- SelectIntegrationFeatures(object.list = int_list, nfeatures = 3000)
int_list <- PrepSCTIntegration(object.list = int_list, anchor.features = features)
## integration using RPCA
int_list <- lapply(X = int_list, FUN = RunPCA, features = features)
int.anchors <- FindIntegrationAnchors(object.list = int_list, normalization.method = "SCT",
anchor.features = features, reduction = "rpca")# , dims = 1:30, k.anchor = 20)
int.combined.sct <- IntegrateData(anchorset = int.anchors, normalization.method = "SCT")#, dims = 1:30)
int.combined.sct <- RunPCA(int.combined.sct, verbose = FALSE)
int.combined.sct <- RunUMAP(int.combined.sct, reduction = "pca", dims = 1:30)
int.combined.sct <- FindNeighbors(int.combined.sct, reduction = "pca", dims = 1:30)
int.combined.sct <- FindClusters(int.combined.sct, resolution = 0.5)
saveRDS(int.combined.sct, file="integrated_SCT-RPCA.rds")
|
16eec0354085a15393087ebc20bb338c4803d488
|
72d9009d19e92b721d5cc0e8f8045e1145921130
|
/SpaDES.tools/man/heading.Rd
|
04bcfa72fdfe5cc90494b061fcb6005335d501f2
|
[] |
no_license
|
akhikolla/TestedPackages-NoIssues
|
be46c49c0836b3f0cf60e247087089868adf7a62
|
eb8d498cc132def615c090941bc172e17fdce267
|
refs/heads/master
| 2023-03-01T09:10:17.227119
| 2021-01-25T19:44:44
| 2021-01-25T19:44:44
| 332,027,727
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,742
|
rd
|
heading.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/heading.R
\name{heading}
\alias{heading}
\alias{heading,SpatialPoints,SpatialPoints-method}
\alias{heading,matrix,matrix-method}
\alias{heading,matrix,SpatialPoints-method}
\alias{heading,SpatialPoints,matrix-method}
\title{Heading between spatial points.}
\usage{
heading(from, to)
\S4method{heading}{SpatialPoints,SpatialPoints}(from, to)
\S4method{heading}{matrix,matrix}(from, to)
\S4method{heading}{matrix,SpatialPoints}(from, to)
\S4method{heading}{SpatialPoints,matrix}(from, to)
}
\arguments{
\item{from}{The starting position; an object of class SpatialPoints.}
\item{to}{The ending position; an object of class SpatialPoints.}
}
\value{
The heading between the points, in degrees.
}
\description{
Determines the heading between spatial points.
}
\examples{
library(sp)
N <- 10L # number of agents
x1 <- stats::runif(N, -50, 50) # previous X location
y1 <- stats::runif(N, -50, 50) # previous Y location
x0 <- stats::rnorm(N, x1, 5) # current X location
y0 <- stats::rnorm(N, y1, 5) # current Y location
# using SpatialPoints
prev <- SpatialPoints(cbind(x = x1, y = y1))
curr <- SpatialPoints(cbind(x = x0, y = y0))
heading(prev, curr)
# using matrix
prev <- matrix(c(x1, y1), ncol = 2, dimnames = list(NULL, c("x","y")))
curr <- matrix(c(x0, y0), ncol = 2, dimnames = list(NULL, c("x","y")))
heading(prev, curr)
#using both
prev <- SpatialPoints(cbind(x = x1, y = y1))
curr <- matrix(c(x0, y0), ncol = 2, dimnames = list(NULL, c("x","y")))
heading(prev, curr)
prev <- matrix(c(x1, y1), ncol = 2, dimnames = list(NULL, c("x","y")))
curr <- SpatialPoints(cbind(x = x0, y = y0))
heading(prev, curr)
}
\author{
Eliot McIntire
}
|
6214b3b0a411afb2e64cdce7714de86546cad967
|
d7d2382f23fe3296d1528d17220ba2d29dc915b0
|
/SimpleInterpolationApp/TokenizeData_tm.R
|
6cb49174430c234614ee3e66e579ef584e80eafc
|
[] |
no_license
|
fredcaram/JohnHopkinsDataScienceCapstone
|
7f86988ec567d83d075408115ff795d8e4a0bbea
|
f53c23e7d884e9a9e445ccf3696486a90a4bbb35
|
refs/heads/master
| 2021-01-15T16:53:06.677732
| 2017-08-11T12:57:55
| 2017-08-11T12:57:55
| 99,732,754
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,837
|
r
|
TokenizeData_tm.R
|
library("RColorBrewer")
library("tm")
#library("ngramrr")
library("SnowballC")
library("wordcloud")
library(rJava)
.jinit(parameters="-Xmx128g")
library("RWeka")
#badWords <- VectorSource(readLines("./dirty/english.txt"))
tm.GetCorpus <- function(path){
corp <- VCorpus(DirSource(path))
corp <- tm.CleanCorpus(corp)
corp
}
tm.GetTextCorpus <- function(text){
corp <- VCorpus(VectorSource(text))
corp
}
tm.RemoveNaFilter <- function(x) any(grep("^na$", x, ignore.case = TRUE, invert = TRUE))
tm.CleanCorpus <- function(corp, removeStopWords = FALSE){
corp <- tm_map(corp, removePunctuation)
corp <- tm_map(corp, removeNumbers)
corp <- tm_map(corp, content_transformer(tolower))
if(removeStopWords){
corp <- tm_map(corp, removeWords, stopwords("english"))
}
corp <- tm_map(corp, removeWords, badWords$content)
corp <- tm_map(corp, stripWhitespace)
corp <- tm_map(corp, PlainTextDocument)
corp <- tm_filter(corp, tm.RemoveNaFilter)
corp
}
tm.GetDTM <- function(corp, ng, sparsity, removeStopWords){
options(mc.cores=1)
ptm <- proc.time()
ngramTokenizer <- function(x) RWeka::NGramTokenizer(x, RWeka::Weka_control(min = ng, max = ng))
if(removeStopWords){
corp <- tm_map(corp, removeWords, stopwords("english"))
}
my_dtm <- DocumentTermMatrix(corp, control = list(tokenize = ngramTokenizer))
if(!is.null(sparsity))
{
my_dtm <- removeSparseTerms(my_dtm, sparse= sparsity)
}
# Stop the clock
print(proc.time() - ptm)
my_dtm
}
tm.GetTermsFrequency <- function(mydtm){
freq <- colSums(as.matrix(mydtm))
freq <- freq[order(freq * -1)]
freq
}
tm.PlotFrequency <- function(freq){
qplot(freq, xlim=c(0, 50), bins=100)
}
tm.PlotWordCloud <- function(freq, n){
wordcloud(names(head(freq, n=n)), head(freq, n=n), c(4,.01), colors = brewer.pal(6, "Dark2"))
}
|
eea5d85c4c6393fab6070eb393d4a08e88f4a537
|
547660ed83f72f861078c2e9ab32255e112b8ac8
|
/inst/ignore/phylomedb.R
|
2f9019217c7a07baccd26be0b1caacde9fb01c8c
|
[
"MIT"
] |
permissive
|
ropensci/brranching
|
6d84f64ae7b97d873191be03ed2c7580efa07a32
|
efd089fd8218de75b1148d14db3e9926552fa705
|
refs/heads/master
| 2023-05-23T05:26:09.112878
| 2022-12-05T08:37:57
| 2022-12-05T08:37:57
| 30,196,325
| 18
| 10
|
NOASSERTION
| 2022-11-17T21:54:34
| 2015-02-02T16:31:55
|
R
|
UTF-8
|
R
| false
| false
| 817
|
r
|
phylomedb.R
|
#' @title PhylomeDB
#'
#' @description Fetch phylome trees from PhylomeDB
#'
#' @export
#' @param seqid An id
#' @param ... Curl options passed on to \code{\link[httr]{GET}}
#' @return Newick formatted tree or nexml text.
#' @examples \dontrun{
#' # Fetch by seqid
#' id <- "Phy004LGJW_CROPO"
#' tree <- phylomedb(seqid = id)
#' plot(tree, no.margin=TRUE)
#' }
phylomedb <- function(seqid, ...) {
args <- list(q = "search_tree", seqid = seqid)
gzpath <- tempfile(fileext = ".tar.gz")
tt <- GET(phydb_base, query = args, config(followlocation=1))
stop_for_status(tt)
out <- content(tt, as = "text")
}
phydb_base <- "http://phylomedb.org"
tar_url <- function(x) {
txt <- content(x, 'text')
grep("download data\\.tar\\.gz", txt)
}
do_tar <- function(x) {
tt <- GET(url, query = args, write_disk())
}
|
4abe7a5b8b60c0bfe4ea6ff9fa0a4aa9521e539b
|
16e3ea1b885ea80b6b6e1c8a76d085c0bed97462
|
/maineQTL.R
|
b1dad8914bc1ede819bd5a1b403bbb663e567b9f
|
[] |
no_license
|
CreRecombinase/MatrixeQTLGLM
|
5ba48a6877c38b57585ac56ce5627a424376acd1
|
c78e7d848f9b668b5ed840d6d5ac7880ccf1b62f
|
refs/heads/master
| 2020-12-24T14:53:48.802827
| 2013-04-23T18:29:11
| 2013-04-23T18:29:11
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,823
|
r
|
maineQTL.R
|
#First revision of Andy's pseudocode for unimputed BRCA SNP and Expression Data
#2/6/13
#NWK
library(sqldf)
library(plyr)
library(BatchExperiments)
library(MatrixEQTL)
###USAGE maineQTL.R <out.files> <root.dir> <out-dir> <annofile> <snp.expfile> <samples> <fold-validation> <time> <queue> <memory> <CISTRA|CIS>
makeClusterFunctionsLSF("~/lsf-standard.tmpl")
oargs <- commandArgs(trailingOnly=TRUE)
args <- list()
args$OUT.FILES <- oargs[1]
args$ROOT.DIR <- oargs[2]
args$OUT.DIR <- oargs[3]
args$ANNOFILE <- oargs[4]
args$SNP.EXPFILE <- oargs[5]
args$SAMPLES <- as.integer(oargs[6])
args$FOLD.VALIDATION <- as.integer(oargs[7])
args$TIME <- oargs[8]
args$QUEUE <- oargs[9]
args$MEMORY <- oargs[10]
args$CISTRA <- oargs[11]
root.dir <- args$ROOT.DIR
out.dir <- args$OUT.DIR
setwd(root.dir)
annofile <- args$ANNOFILE
snp.expdata <- args$SNP.EXPFILE
###Function for cross validation
mat.train <- function(i,snp.exploc,anno.loc,train.indices,MEQTL.params){
load(snp.exploc)
load(anno.loc)
total.ids <- snp.exp$snps$nCols()
snp.exp$snps$ColumnSubsample(train.indices)
snp.exp$gene$ColumnSubsample(match(colnames(snp.exp$snps),colnames(snp.exp$gene)))
with(MEQTL.params,
Matrix_eQTL_main(
snps=snp.exp$snps,
gene=snp.exp$gene,
output_file_name=paste(output.file.name.tra,i,".txt",sep=""),
output_file_name.cis=paste(output.file.name.cis,i,".txt",sep=""),
useModel=useModel,
verbose=verbose,
pvOutputThreshold=pvOutputThreshold.tra,
pvOutputThreshold.cis=pvOutputThreshold.cis,
snpspos=annolist$snp.anno,
genepos=annolist$exp.anno,
cisDist=cisDist,
pvalue.hist=pvalue.hist
)
)
}
samples <- args$SAMPLES
train.indices <- chunk(rep(1:samples,args$FOLD.VALIDATION),n.chunks=args$FOLD.VALIDATION)
test.indices <- chunk(1:samples,chunk.size=ceiling(samples/args$FOLD.VALIDATION))
train.indices <- mapply(FUN=function(x,y)x[-y],train.indices,test.indices,SIMPLIFY=F)
MEQTL.params <- list(
output.file.name.tra=paste(out.dir,args$OUT.FILES,"_trans",sep=""),
output.file.name.cis=paste(out.dir,args$OUT.FILES,"_cis",sep=""),
useModel=modelANOVA,
verbose=T,
pvOutputThreshold.tra=ifelse(args$CISTRA=="CISTRA",1e-8,0),
pvOutputThreshold.cis=1e-8,
cisDist=1e6,
pvalue.hist=F
)
m.dir <- tempfile(paste("meqtl.res",args$OUT.FILES,sep=""),tmpdir=out.dir)
registry.name <- paste("meqtl_reg_",args$OUT.FILES,sep="")
MEQTL.reg <- makeRegistry(registry.name,file.dir=m.dir,packages="MatrixEQTL")
batchMap(MEQTL.reg,mat.train,train.indices=train.indices,i=1:length(train.indices),more.args=list(
MEQTL.params=MEQTL.params,
snp.exploc=snp.expdata,
anno.loc=annofile))
submitJobs(MEQTL.reg,resources=list(queue=args$QUEUE,memory=args$MEMORY,time=args$TIME,threads=1))
Sys.sleep(35)
|
213f47b39253b8a1df2885d2ad303b38d2c39cca
|
39940b7cc4ce9470f5e8201e1f82b02b98984601
|
/utils/Nonpareil/man/summary.Nonpareil.Curve.Rd
|
8c4a4edefa927c09d59106b8c8f1fbf03d8ea876
|
[
"Artistic-2.0"
] |
permissive
|
lmrodriguezr/nonpareil
|
dcd7ac99330b9dc1737f0a46f0985095e58d6e65
|
162f1697ab1a21128e1857dd87fa93011e30c1ba
|
refs/heads/master
| 2022-02-23T11:52:11.279418
| 2022-02-22T19:51:05
| 2022-02-22T19:51:05
| 5,099,209
| 36
| 15
| null | 2017-11-14T15:13:17
| 2012-07-18T17:02:52
|
C++
|
UTF-8
|
R
| false
| true
| 1,240
|
rd
|
summary.Nonpareil.Curve.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Nonpareil.R
\name{summary.Nonpareil.Curve}
\alias{summary.Nonpareil.Curve}
\title{Returns a summary of the \code{Nonpareil.Curve} results.}
\usage{
\method{summary}{Nonpareil.Curve}(object, ...)
}
\arguments{
\item{object}{\code{Nonpareil.Curve} object.}
\item{...}{Additional parameters ignored.}
}
\value{
Returns a matrix with the following values for the dataset:
\itemize{
\item kappa: "Redundancy" value of the entire dataset.
\item C: Average coverage of the entire dataset.
\item LRstar: Estimated sequencing effort required to reach the objective
average coverage (star, 95% by default).
\item LR: Actual sequencing effort of the dataset.
\item modelR: Pearson's R coefficient betweeen the rarefied data and the
projected model.
\item diversity: Nonpareil sequence-diversity index (Nd). This value's
units are the natural logarithm of the units of sequencing effort
(log-bp), and indicates the inflection point of the fitted model for
the Nonpareil curve. If the fit doesn't converge, or the model is not
estimated, the value is zero (0).
}
}
\description{
Returns a summary of the \code{Nonpareil.Curve} results.
}
|
40fc0d38d99ac760d92574bf365a85c1fd44fe7c
|
bc2a485ffe10d8e42b0a5e100fbaeed83a6d57d4
|
/3.r
|
391e0957bbbb4bcb812a2bbb3a30d547bed2f462
|
[] |
no_license
|
Phanideep007/datascience
|
4989a11fc11aa8eb3a32ffd0644a087ffba0c837
|
eb1dfd08b57b397f97eb69605f156a531829c377
|
refs/heads/master
| 2020-03-16T03:28:10.537415
| 2018-05-18T16:11:28
| 2018-05-18T16:11:28
| 132,488,245
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 365
|
r
|
3.r
|
usedcar=read.csv(file.choose())
scale(usedcar$Price)
usedcar=usedcar[-1]
head(usedcar)
boxprice=BoxCoxTrans(usedcar$Price)
priceboxcox=predict(boxprice,usedcar$Price)
head(priceboxcox)
hist(priceboxcox)
plot(density(priceboxcox))
plot(density(usedcar$Price))
skewness(priceboxcox)
skewness(usedcar$Price)
par(mfrow=c(1,2))
dev.off()
princomp()
|
b36f893db7a6c1e1a570a48579e0fdaf0fd22bdb
|
9d0c396fc511e651a1a58d6f6c62b3667695d60f
|
/R/KMCox_RacoonOlyLarvaeSurvival.R
|
9fb298adca52a203752eb28782bc5631bf4a7224
|
[] |
no_license
|
lalma/RacoonOlyLarvalSurvival
|
fc902e502ca77c33317d057d1875ce4c9294943f
|
521f8626d79476e9f891fca4a0397588ba0fd5cc
|
refs/heads/main
| 2023-08-16T06:05:24.001055
| 2021-10-22T02:48:42
| 2021-10-22T02:48:42
| 393,133,714
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 7,719
|
r
|
KMCox_RacoonOlyLarvaeSurvival.R
|
#kaplan meier survival
#load libraries
library(survival)
library(survminer)
library(ggplot2)
library(rms)
library(readxl)
library(survELtest)
#upload datasheet 38,400 lines. once for each larvae
OlyLarvaeKMforR <- read_excel("C:/Users/Lindsay/Dropbox/Raccoon/larvae/oyster/larvae survival stats/GitHub/RacoonOlyLarvalSurvival/OlyLarvaeKMforR.xlsx")
View(OlyLarvaeKMforR)
#make surv object
KMsurv = Surv(time = OlyLarvaeKMforR$day, OlyLarvaeKMforR$dead, type = "right")
# fit KM model and plot without random effect of tank
sf <- survfit(KMsurv ~ Treatment+Location, data = OlyLarvaeKMforR)
summary(coxph(KMsurv ~ Treatment*Location, data = OlyLarvaeKMforR))
# Graph the KMsurvival distribution
#you can add pval = TRUE, but we know our p>0.0001
ggsurvplot(sf, conf.int = 0.05)
# another type of graph
plot(sf, xlab="Larval Age",
ylab="% Surviving", yscale=100, col=c("springgreen4", "royalblue3", "red","orange2","springgreen2","dodgerblue","indianred","gold1"),
main="% Larval Survival")
#Plot with breaktime by = # of days, palette=number of treatments listed colors
fontsize<-20
pCox <- ggsurvplot(sf, data=OlyLarvaeKMforR, risk.table = FALSE, pval = FALSE, conf.int = TRUE,
font.main = fontSize, font.x = fontSize, font.y = fontSize,
font.tickslab = fontSize, font.legend = fontSize,
palette = c("springgreen4", "royalblue3", "red","orange2","springgreen2","dodgerblue","indianred","gold1"), legend = "none"
) + xlab("Time (d)")
pCox$plot
#add a legend, dont need. probably make one not on R
legend("topright" , c("CI20-14C","CI5-14C","DB-14C","PW-14C", "CI20-20C", "CI5-20C", "DB-20C", "PW-20C"),
fill=c("springgreen4", "royalblue3", "red","orange2","springgreen2","dodgerblue","indianred","gold1"))
#another type of graph- will prob use this one
ggsurvplot(sf, data=OlyLarvaeKMforR, conf.int=T, risk.table=F, pval=F,legend=c("right"),
legend.labs=c("CI20-14C","CI5-14C","DB-14C","PW-14C", "CI20-20C", "CI5-20C", "DB-20C", "PW-20C"),legend.title="Treatment",
palette = c('darkgreen', 'blue4', 'darkred', 'darkgoldenrod', '#33CC66','steelblue','red','darkgoldenrod1'),
risk.table.height=.25,xlab="Time (days)", size=0.7, break.time.by = 3, break.y.by=.2, ggtheme = theme_bw() + theme(
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank()
))
#Test non parametric (logrank test) p<0.0001 chisq=4053
#report this value in paper to compare data from multuple pairwise groups
#log-rank test does not make assumptions about survival distribution. Analyzes each cohort separetely.
" When
survival curves cross, the log-rank test should not be
used because the probability of a type II error will be
increased, and the test has low power."
survdiff(formula=Surv(day,dead)~Treatment +Location, data=OlyLarvaeKMforR)
#emperica likelyhood-optimize power
#p-value <0.0001
surv_pvalue(sf)
#cox model sample code without interaction
cox1<-coxph(KMsurv~Treatment+Location, data=OlyLarvaeKMforR)
summary(cox1)
#cox model sample code with interaction, this is the model for the main analysis
cox<-coxph(KMsurv~Treatment*Location, data=OlyLarvaeKMforR)
cox##<here results!!!
summary(cox)
#38400 larvae, 25506 death, 12894 censored
#Looking at exp(coef)-- for example the Hazard Ratio of 20C is 0.586, this means that each additional
#day is associated with a 0.586 fold increse in the hazard death when compared to 14C, and its 95% confidence
#interval is (0.56,0.61). I think..Since this confidence interval is <1
#it indicates a decrease in the hazard survival compared to 14C, and there is a significant association
#between days and temperature (p<0.0001)
anova(cox)
cox.zph(cox)
plot(cox.zph(cox))
#A hazard ratio of 1 indicates no effect
#A hazard ratio > 1 indicates an increase in the hazard as the covariate rises- It gets to death faster than the control
#A hazard ratio < 1 indicates a decrease in the hazard as the covariate rises- it is slower go get to the event (death) than the cotnrol
ggforest(cox, data=OlyLarvaeKMforR)
#time-dependent covariate to fix proportional hazard assumption
#A Cox model with time-dependent covariate would compare the risk of an event between transplant and nontransplant at each event time, but would re-evaluate
#which risk group each person belonged in based on whether
#they'd had a transplant by that time
#model with temp only
coxTemp<-coxph(KMsurv~Treatment, data=OlyLarvaeKMforR)
ggforest(coxTemp, data=OlyLarvaeKMforR)
#model with locaiton only
coxLocation<-coxph(KMsurv~Location, data=OlyLarvaeKMforR)
ggforest(coxLocation, data=OlyLarvaeKMforR)
#model with non categorial veraibles
cox.number<-coxph(KMsurv~Temp+LocationNumber+Temp*LocationNumber, data=OlyLarvaeKMforR)
cox.number
summary(cox.number)
ggforest(cox.number)
############time varying coefficients
#show time points in time at which an individual died
cut.points <- unique(OlyLarvaeKMforR$day[OlyLarvaeKMforR$dead == 1])
#duplicate 4 line per individual, times 0-1, 1-7, 7-10, 10-13 =time0 and time
SURV2 <- survSplit(data = OlyLarvaeKMforR, cut = cut.points, end = "day", start = "day0", event = "dead")
View(SURV2)
cut.points
#run the cox model on orginal data- estimates one treatment male-21yrs
model.1 <- coxph(Surv(day, dead) ~ Treatment+Location+Treatment:Location, data = OlyLarvaeKMforR)
model.1
summary(model.1)
#Schoenfeld's global test for the violation of proportional assumption-- Grambsch PM, Therneau TM. Proportional hazards tests
# diagnostics based on weighted residuals. Biometrika 1994;81:515-26
zph<-cox.zph(model.1)
zph
#results of cox.zph show there is significant deviation from the proportional hazards assumption for the variable
covs <- data.frame(Location = "CI20", Treatment = "14C")
covs
summary(survfit(model.1, newdata = covs, type = "aalen"))
#output gives us probability of survival for CI20 14C for each day, i.e. a larvae has a .708 chance of survivind to day 4, but 0.163 chance of surviving to day 15
cox.zph(model.1)
#The result of zph shows that there is significant deviation from the proportional hazards assumption for all veriables
ggforest(model.1, SURV2)
plot(cox.zph(model.1))
####Percent mortality
CI2020C<-subset(OlyLarvaeKMforR, TreatmentLocation=="20CCI20")
CI2020Cend<-sum(CI2020C$Status)
CI2020Cend
CI2020Ctot<-length(CI2020C$Status)
CI2020Ctot
CI2020Cend/CI2020Ctot
CI2014C<-subset(OlyLarvaeKMforR, TreatmentLocation=="14CCI20")
CI2014Cend<-sum(CI2014C$Status)
CI2014Cend
CI2014Ctot<-length(CI2014C$Status)
CI2014Ctot
CI2014Cend/CI2014Ctot
CI520C<-subset(OlyLarvaeKMforR, TreatmentLocation=="20CCI5")
CI520Cend<-sum(CI520C$Status)
CI520Cend
CI520Ctot<-length(CI520C$Status)
CI520Ctot
CI520Cend/CI520C
CI514C<-subset(OlyLarvaeKMforR, TreatmentLocation=="14CCI5")
CI514Cend<-sum(CI514C$Status)
CI514Cend
CI514Ctot<-length(CI514C$Status)
CI514Ctot
CI514Cend/CI514Ctot
DB520C<-subset(OlyLarvaeKMforR, TreatmentLocation=="20CDB")
DB520Cend<-sum(DB520C$Status)
DB520Cend
DB520Ctot<-length(DB520C$Status)
DB520Ctot
DB520Cend/DB520Ctot
DB514C<-subset(OlyLarvaeKMforR, TreatmentLocation=="14CDB")
DB514Cend<-sum(DB514C$Status)
DB514Cend
DB514Ctot<-length(DB514C$Status)
DB514Ctot
DB514Cend/DB514Ctot
PW520C<-subset(OlyLarvaeKMforR, TreatmentLocation=="20CPW")
PW520Cend<-sum(PW520C$Status)
PW520Cend
PW520Ctot<-length(PW520C$Status)
PW520Ctot
PW520Cend/PW520Ctot
PW514C<-subset(OlyLarvaeKMforR, TreatmentLocation=="14CPW")
PW514Cend<-sum(PW514C$Status)
PW514Cend
PW514Ctot<-length(PW514C$Status)
PW514Ctot
PW514Cend/PW514Ctot
|
7fb74ab947e7d703e607a228207fb4b3711a49fc
|
68654fffeaba279324df9493948c0b63c0b23cc6
|
/odds_and_ends/former/08_paleo_monthly_gen_percentile_pred.R
|
f8532ca3383c4781c42d5a6a1c7bc1c58d426abd
|
[
"MIT"
] |
permissive
|
jstagge/weber_paleo_clim
|
dcb0fee723c920f90fc3ca343a8386aba427de7a
|
45d3f824265f37d8f9b916102240d8d145dcc2c2
|
refs/heads/master
| 2022-02-26T09:48:24.994255
| 2019-08-22T20:05:01
| 2019-08-22T20:05:01
| 133,690,915
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 19,011
|
r
|
08_paleo_monthly_gen_percentile_pred.R
|
# *------------------------------------------------------------------
# | PROGRAM NAME: Data Music
# | FILE NAME: paleo_monthly_gen_null.R
# | DATE:
# | CREATED BY: Jim Stagge
# *----------------------------------------------------------------
# | PURPOSE: This is a code wrapper to generate a midicsv file from data.
# | The resulting file can be processed into a midi file using the program csvmidi.
# | This midi file can then be played using timidity.
# | Check the ToRun.txt file for detailed code
# |
# *------------------------------------------------------------------
# | COMMENTS:
# |
# | 1:
# | 2:
# | 3:
# |*------------------------------------------------------------------
# | DATA USED:
# | This is a test instance using reconstructed climate indices ENSO and PDO
# |
# |*------------------------------------------------------------------
# | CONTENTS:
# |
# | PART 1:
# | PART 2:
# | PART 3:
# *-----------------------------------------------------------------
# | UPDATES:
# |
# |
# *------------------------------------------------------------------
### Clear any existing data or functions.
rm(list=ls())
###########################################################################
## Set the Paths
###########################################################################
### Path for Data and Output
data_path <- "../../data"
output_path <- "../../output"
global_path <- "../global_func"
function_path <- "./functions"
### Set output location
output_name <- "apr_model"
weber_output_path <- file.path(output_path,"paleo_weber")
write_output_path <- file.path(weber_output_path,output_name)
write_figures_path <- file.path(weber_output_path, "figures")
write_figures_path <- file.path(write_figures_path, output_name)
dir.create(write_output_path)
dir.create(write_figures_path)
###########################################################################
### Load functions
###########################################################################
### Load these functions for all code
require(colorout)
require(assertthat)
### Load these functions for this unique project
require(ggplot2)
require(monthlypaleo)
require(staggefuncs)
### Load project specific functions
file.sources = list.files(function_path, pattern="*.R", recursive=TRUE)
sapply(file.path(function_path, file.sources),source)
### Load global functions
file.sources = list.files(global_path, pattern="*.R", recursive=TRUE)
sapply(file.path(global_path, file.sources),source)
###########################################################################
## Set Initial Values
###########################################################################
### Set site data
site_id_list <- c("10128500")
site_name_list <- c("Weber River")
recons_file_name_list <- c("weber2014flow.txt")
first_month_wy <- 10 ### Water Year starts on Oct 1
param_cd <- "00060"
monthly_distr <- "gamma"
annual_distr <- "logis"
ref_period <- c(1900,2005)
### Number of PCs to consider as predictors
pc_pred <- 8
### Lags to consider
lags_pred <- seq(-2,2)
###########################################################################
## Read in reconstructed flows, tree rings and climate indices
###########################################################################
### Read in PC scores
pc_score_impute_res <- read.csv(file.path(file.path(weber_output_path,"pca_chronol"), "PC_Score_impute_res.csv"))
pc_score_impute_std <- read.csv(file.path(file.path(weber_output_path,"pca_chronol"), "PC_Score_impute_std.csv"))
### Read in climate indices
clim_ind <- read.csv(file.path(file.path(data_path,"paleo_clim_ind"), "clim_ind.csv"))
### Remove ENSO_var, this is simply a running variance calculation - not useful as a predictor
clim_ind <- subset(clim_ind, select=-c(ENSO_var))
################################################
### Create matrix of potential predictor values
#################################################
## Cut PCs to only number in initial settings
pc_cut <- seq(1,pc_pred+1)
pc_score_impute_std <- pc_score_impute_std[,pc_cut]
pc_score_impute_res <- pc_score_impute_res[,pc_cut]
### Create only climate index predictors
pred_clim_only <- clim_ind
pred_clim_only_lag <- shift_indices_df(pred_clim_only, lags=lags_pred)
pred_enso_ind <- clim_ind[,seq(1,3)]
pred_enso_ind_lag <- shift_indices_df(enso_ind, lags=lags_pred)
### Create concurrent predictors (same calendar year), with climate indices and first 8 PCs
pred_clim_pca_impute_std_concur <- merge(clim_ind, pc_score_impute_std, by.x="Year", by.y="X", all=TRUE)
pred_clim_pca_impute_res_concur <- merge(clim_ind, pc_score_impute_res, by.x="Year", by.y="X", all=TRUE)
pred_enso_pca_impute_std_concur <- merge(enso_ind, pc_score_impute_std, by.x="Year", by.y="X", all=TRUE)
pred_enso_pca_impute_res_concur <- merge(enso_ind, pc_score_impute_res, by.x="Year", by.y="X", all=TRUE)
### Lag the predictors
pred_clim_pca_impute_std_lag <- shift_indices_df(pred_clim_pca_impute_std_concur, lags=lags_pred)
pred_clim_pca_impute_res_lag <- shift_indices_df(pred_clim_pca_impute_res_concur, lags=lags_pred)
pred_enso_pca_impute_std_lag <- shift_indices_df(pred_enso_pca_impute_std_concur, lags=lags_pred)
pred_enso_pca_impute_res_lag <- shift_indices_df(pred_enso_pca_impute_res_concur, lags=lags_pred)
###########################################################################
### Set up a loop to run through all site_ides and Transform based on the Percentile Model
###########################################################################
for (n in seq(1,length(site_id_list))) {
site_id <- site_id_list[n]
site_name <- site_name_list[n]
recons_file_name <- recons_file_name_list[n]
###########################################################################
### Read in Flow Data
###########################################################################
### Read in observed flow and fix data type
obs_file_name <- paste0(site_id,"_",param_cd,"_mon_wy.csv")
flow_obs <- read.csv(file.path(weber_output_path,paste0("observed_flow/",obs_file_name)))
flow_obs$date <- as.Date(flow_obs$date)
#head(flow_obs) # Review data frame
### Read in reconst flows (use fread because of large header)
flow_recon <- read_table_wheaders(file.path(data_path,paste0("paleo_flow_annual/",recons_file_name)), sep="\t", na.string="-9999")
flow_recon <- merge(flow_recon, data.frame(age_AD=flow_obs$water_year, flow.obs.m3s=flow_obs$annual_mean), by="age_AD", all.x=TRUE)
################################################
### Read in monthly and annual parameters to Transform Percentiles
#################################################
percentile_path <- file.path(file.path(weber_output_path, "paleo_reconst"), "ap_model")
monthly_param <- list(param=read.csv(file.path(percentile_path, paste0(site_id,"/",site_id,"_param_month_",monthly_distr,".csv"))), distr=monthly_distr)
monthly_param$param <- monthly_param$param[,c(2,3)]
annual_param <- list(param=read.csv(file.path(percentile_path, paste0(site_id,"/",site_id,"_param_annual_",annual_distr,".csv"))), distr=annual_distr)
annual_param$param <- annual_param$param[,seq(1,3)]
#################################################
### Apply the Percentile Model with Lagged years (1 year previous, 1 year following)
#################################################
### Set up list of data combinations to fit
### Only use the imputed values
#pred_list <- c("enso_ind", "clim_only", "enso_ind_lag", "clim_only_lag", "enso_pca_impute_std_concur", "enso_pca_impute_res_concur", "clim_pca_impute_std_concur", "clim_pca_impute_lag")
pred_list <- c("enso_ind", "clim_only", "enso_pca_impute_std_concur", "enso_pca_impute_res_concur", "clim_pca_impute_std_concur", "clim_pca_impute_res_concur")
data_list <- c("observ_annual", "rec_local_m3s")
run_combinations <- expand.grid(predictors=pred_list, data=data_list)
### Loop through all combations, fitting, and saving
for (c in seq(1,dim(run_combinations)[1])){
pred_c <- run_combinations$predictors[c]
data_c <- run_combinations$data[c]
if(data_c != "observ_annual"){
data_name <- as.character(paste0("annual_flow_",data_c))
} else {
data_name <- as.character(data_c)
}
### Fit model
lag_fit <- perc_fit_pred(data=flow_obs, predictors=get(paste0("pred_",pred_c
)), monthly_param, annual_param, data_name= data_name)
### Write to csv file
write_location <- file.path(write_output_path, paste0(site_id,"_",output_name, "_coef_",data_c,"_",pred_c,".csv"))
write.csv(lag_fit$coef, file = write_location,row.names=TRUE)
write_location <- file.path(write_output_path, paste0(site_id,"_",output_name, "_alpha_lambda_",data_c,"_",pred_c,".csv"))
write.csv(lag_fit$alpha_lambda, file = write_location,row.names=TRUE)
}
}
###########################################################################
### Set up a loop to run through all site_ides and Plot results
###########################################################################
dir.create(file.path(write_figures_path,"png/"), showWarnings=FALSE)
dir.create(file.path(write_figures_path,"svg/"), showWarnings=FALSE)
dir.create(file.path(write_figures_path,"pdf/"), showWarnings=FALSE)
for (n in seq(1,length(site_id_list))) {
site_id <- site_id_list[n]
site_name <- site_name_list[n]
recons_file_name <- recons_file_name_list[n]
### Create table of all combinations
### Chose not to plot all the lags (too much)
#pred_list <- c("clim_only","clim_only_lag","clim_pca_impute_concur", "clim_pca_impute_lag")
pred_list <- c("enso_ind", "clim_only", "enso_pca_impute_std_concur", "enso_pca_impute_res_concur", "clim_pca_impute_std_concur", "clim_pca_impute_res_concur")
data_list <- c("observ_annual", "rec_local_m3s")
run_combinations <- expand.grid(predictors=pred_list, data=data_list)
### Loop through all combations, reading in data and saving
for (c in seq(1,dim(run_combinations)[1])){
pred_c <- as.character(run_combinations$predictors[c])
data_c <- run_combinations$data[c]
if(data_c != "observ_annual"){
data_name <- as.character(paste0("annual_flow_",data_c))
} else {
data_name <- as.character(data_c)
}
### Read in CSV file
file_location <- file.path(write_output_path, paste0(site_id,"_",output_name, "_coef_",data_c,"_",pred_c,".csv"))
coef_df <- read.csv(file_location)
rownames(coef_df) <- as.character(coef_df$X)
### Reorganize dataframe, remove the first column, set class
coef_df <- subset(coef_df, select=-c(X))
colnames(coef_df) <- seq(1,12)
coef_df <- data.frame(coef_df)
### Create plot of annual drivers
predictor_levels <- c("annual_norm", "annual_norm_neg_1year", "annual_norm_pos_1year")
predictor_labels <- c("Concurrent Water Year\nStd Normal", "Previous (-1) Water Year\nStd Normal", "Next (+1) Water Year\nStd Normal")
p <- norm_coef_plot(coef_df, predictor_levels=predictor_levels, predictor_labels=predictor_labels)
### Save annual drivers
ggsave(paste0(file.path(write_figures_path,"png/"), site_id, "_percent_pred_coef_", data_name,"_",pred_c,"_annual.png"), p, width=6, height=4, dpi=600)
ggsave(paste0(file.path(write_figures_path,"svg/"), site_id, "_percent_pred_coef_", data_name,"_",pred_c,"_annual.svg"), p, width=6, height=4)
ggsave(paste0(file.path(write_figures_path,"pdf/"), site_id, "_percent_pred_coef_", data_name,"_",pred_c,"_annual.pdf"), p, width=6, height=4)
#### Add a combination of 2 ENSO methods, not needed
#coef_df_enso_comb <- coef_df[rownames(coef_df) == "ENSO",] + coef_df[rownames(coef_df) == "ENSO_short",]
#rownames(coef_df_enso_comb) <- "ENSO_comb"
#coef_df <- rbind(coef_df, coef_df_enso_comb)
### Create plot of climate drivers
predictor_levels <- c("ENSO", "ENSO_short", "PDO")
predictor_labels <- c("ENSO (NADA)", "ENSO (Pacific Proxy)", "PDO")
p <- norm_coef_plot(coef_df, predictor_levels=predictor_levels, predictor_labels=predictor_labels)
### Reset the color scheme
p <- p + scale_colour_brewer(name="Predictor", type="qual", palette = "Accent")
### Save annual drivers
ggsave(paste0(file.path(write_figures_path,"png/"), site_id, "_percent_pred_coef_", data_name,"_",pred_c,"_clim_ind.png"), p, width=6, height=4, dpi=600)
ggsave(paste0(file.path(write_figures_path,"svg/"), site_id, "_percent_pred_coef_", data_name,"_",pred_c,"_clim_ind.svg"), p, width=6, height=4)
ggsave(paste0(file.path(write_figures_path,"pdf/"), site_id, "_percent_pred_coef_", data_name,"_",pred_c,"_clim_ind.pdf"), p, width=6, height=4)
### If it includes pca, create plots
if (grepl("pca",pred_c)) {
### Create a plot of PC coefficients
predictor_levels <- paste0("PC",seq(1,8))
predictor_labels <- predictor_levels
p <- norm_coef_plot(coef_df, predictor_levels=predictor_levels, predictor_labels=predictor_labels)
### Reset color scale
p <- p + scale_colour_brewer(name="Predictor", type="qual", palette = "Set2")
### Save annual drivers
ggsave(paste0(file.path(write_figures_path,"png/"), site_id, "_percent_pred_coef_", data_name,"_",pred_c,"_pcs.png"), p, width=6, height=4, dpi=600)
ggsave(paste0(file.path(write_figures_path,"svg/"), site_id, "_percent_pred_coef_", data_name,"_",pred_c,"_pcs.svg"), p, width=6, height=4)
ggsave(paste0(file.path(write_figures_path,"pdf/"), site_id, "_percent_pred_coef_", data_name,"_",pred_c,"_pcs.pdf"), p, width=6, height=4)
}
}
}
###########################################################################
### Set up a loop to run through all site_ides and Plot results
###########################################################################
for (n in seq(1,length(site_id_list))) {
site_id <- site_id_list[n]
site_name <- site_name_list[n]
recons_file_name <- recons_file_name_list[n]
### Create table of all combinations
### Chose not to plot all the lags (too much)
pred_list <- c("clim_only_lag","clim_pca_impute_lag")
if (site_id == "10109001") {
data_list <- c("observ_annual", "rec_local_m3s", "rec_region_m3s")
} else {
data_list <- c("observ_annual", "rec_m3s")
}
run_combinations <- expand.grid(predictors=pred_list, data=data_list)
### Loop through all combations, reading in data and saving
for (c in seq(1,dim(run_combinations)[1])){
pred_c <- as.character(run_combinations$predictors[c])
data_c <- run_combinations$data[c]
if(data_c != "observ_annual"){
data_name <- as.character(paste0("annual_flow_",data_c))
} else {
data_name <- as.character(data_c)
}
### Read in CSV file
file_location <- file.path(write_output_path, paste0(site_id,"_",output_name, "_coef_",data_c,"_",pred_c,".csv"))
coef_df <- read.csv(file_location)
rownames(coef_df) <- as.character(coef_df$X)
### Reorganize dataframe, remove the first column, set class
coef_df <- subset(coef_df, select=-c(X))
colnames(coef_df) <- seq(1,12)
coef_df <- data.frame(coef_df)
### Create plot of annual drivers
predictor_levels <- c("annual_norm", "annual_norm_neg_1year", "annual_norm_pos_1year")
predictor_labels <- c("Concurrent Water Year\nStd Normal", "Previous (-1) Water Year\nStd Normal", "Next (+1) Water Year\nStd Normal")
p <- norm_coef_plot(coef_df, predictor_levels=predictor_levels, predictor_labels=predictor_labels)
### Save annual drivers
ggsave(paste0(file.path(write_figures_path,"png/"), site_id, "_percent_pred_coef_", data_name,"_",pred_c,"_annual.png"), p, width=6, height=4, dpi=600)
ggsave(paste0(file.path(write_figures_path,"svg/"), site_id, "_percent_pred_coef_", data_name,"_",pred_c,"_annual.svg"), p, width=6, height=4)
ggsave(paste0(file.path(write_figures_path,"pdf/"), site_id, "_percent_pred_coef_", data_name,"_",pred_c,"_annual.pdf"), p, width=6, height=4)
#### Add a combination of 2 ENSO methods, not needed
#coef_df_enso_comb <- coef_df[rownames(coef_df) == "ENSO",] + coef_df[rownames(coef_df) == "ENSO_short",]
#rownames(coef_df_enso_comb) <- "ENSO_comb"
#coef_df <- rbind(coef_df, coef_df_enso_comb)
### Create plot of climate drivers
predictor_levels <- c("ENSO_0", "ENSO_short_0", "PDO_0")
predictor_labels <- c("ENSO (NADA)", "ENSO (Pacific Proxy)", "PDO")
p <- norm_coef_plot(coef_df, predictor_levels=predictor_levels, predictor_labels=predictor_labels)
### Reset the color scheme
p <- p + scale_colour_brewer(name="Predictor", type="qual", palette = "Accent")
### Save annual drivers
ggsave(paste0(file.path(write_figures_path,"png/"), site_id, "_percent_pred_coef_", data_name,"_",pred_c,"_clim_ind.png"), p, width=6, height=4, dpi=600)
ggsave(paste0(file.path(write_figures_path,"svg/"), site_id, "_percent_pred_coef_", data_name,"_",pred_c,"_clim_ind.svg"), p, width=6, height=4)
ggsave(paste0(file.path(write_figures_path,"pdf/"), site_id, "_percent_pred_coef_", data_name,"_",pred_c,"_clim_ind.pdf"), p, width=6, height=4)
### Create plots of PCs at different lags
lag_names <- c("-2", "-1", "0", "1", "2")
lag_numbers <- c(".2", ".1", "0", "1", "2")
### If it includes pca, create plots
if (grepl("pca",pred_c)) {
for (k in seq(1,length(lag_numbers))) {
### Create a plot of PC coefficients
predictor_labels <- paste0("PC",seq(1,8))
predictor_levels <- paste0(predictor_labels, "_",lag_numbers[k])
predictor_labels <- paste0(predictor_labels, " Lag ", lag_names[k])
p <- norm_coef_plot(coef_df, predictor_levels=predictor_levels, predictor_labels=predictor_labels)
### Reset color scale
p <- p + scale_colour_brewer(name="Predictor", type="qual", palette = "Set2")
p <- p + coord_cartesian(ylim=c(-0.2,0.2))
#
### Save annual drivers
ggsave(paste0(file.path(write_figures_path,"png/"), site_id, "_percent_pred_coef_", data_name,"_",pred_c,"_pcs_lag_",lag_names[k],".png"), p, width=6, height=4, dpi=600)
ggsave(paste0(file.path(write_figures_path,"svg/"), site_id, "_percent_pred_coef_", data_name,"_",pred_c,"_pcs_lag_",lag_names[k],".svg"), p, width=6, height=4)
ggsave(paste0(file.path(write_figures_path,"pdf/"), site_id, "_percent_pred_coef_", data_name,"_",pred_c,"_pcs_lag_",lag_names[k],".pdf"), p, width=6, height=4)
}
### Extract PC names
coef_df_names <- rownames(coef_df)
coef_df_pc <- substr(coef_df_names,1,2) == "PC"
coef_df_pc[[1]] <- TRUE
coef_df_pc <- coef_df[coef_df_pc,]
coef_df_names <- rownames(coef_df_pc)
for (k in seq(1,8)) {
### Subset to each PC
pc_test <- substr(coef_df_names,1,3) == paste0("PC", k)
pc_test[[1]] <- TRUE
pc_subset <- coef_df_pc[pc_test,]
### Create plot of climate drivers
predictor_levels <- rownames(pc_subset)
predictor_levels <- predictor_levels[seq(2,length(predictor_levels))]
predictor_labels <- c("Lag -2", "Lag -1", "Concur", "Lag +1", "Lag +2")
p <- norm_coef_plot(pc_subset, predictor_levels=predictor_levels, predictor_labels=predictor_labels)
### Save plot
ggsave(paste0(file.path(write_figures_path,"png/"), site_id, "_percent_pred_coef_", data_name,"_",pred_c,"_pc",k,"_lag",".png"), p, width=6, height=4, dpi=600)
ggsave(paste0(file.path(write_figures_path,"svg/"), site_id, "_percent_pred_coef_", data_name,"_",pred_c,"_pc",k,"_lag",".svg"), p, width=6, height=4)
ggsave(paste0(file.path(write_figures_path,"pdf/"), site_id, "_percent_pred_coef_", data_name,"_",pred_c,"_pc",k,"_lag",".pdf"), p, width=6, height=4)
}
}
}
}
|
d8657ef4c06d2bb9f7a3cea86b2692a1dd8a4f55
|
bad164cec8333f690f187f53643aaa9e9e70061c
|
/counts_forloop.R
|
6d9b81fc55f28f3fd09329628ff388dd917f5b8c
|
[] |
no_license
|
cocomushroom/meta_commu
|
7151c5d48164d55832644a4323757f5e1cf6abe0
|
568c2edc5049fdc694fa77e0dc9137ca2fd00ac9
|
refs/heads/master
| 2021-01-10T13:05:06.661282
| 2017-03-02T19:42:48
| 2017-03-02T19:42:48
| 55,309,772
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,988
|
r
|
counts_forloop.R
|
setwd("/Users/kc178/Downloads/all_GB+customized_silva_wLRORLR3_1208/cutfirst")
list_tab <- dir(pattern = "*IdxStats.tabular")
#make sure the files are read in the correct order
list_tab <- list_tab[c(3,2,1,6,5,4,9,8,7)]
ldf <- list()
present <- list()
for (k in 1:length(list_tab)) {
ldf[[k]] <- read.delim(list_tab[k], header=FALSE)
names(ldf[[k]]) <- c("seqID", "length", "mapped", "unmapped")
}
for (a in 1:length(ldf)) {
data0 <- ldf[[a]]
present[[a]] <- data0[data0$mapped>0, c(1,3)]
present[[a]]$seqID <- as.character(present[[a]]$seqID)
}
#ll<-vector(length=9, mode="list")
#combine count from all condition/replicate
allcount <- present[[1]][,c(1,2)]
#allcount <- present1[,c(1,2)]
# this conmmand can make sure exactly how many "0" are assigned; otherwise in some case only 1 "0" is assigned
#note that "$new column name" is enough to add a new column
for (i in 3:10) {
allcount[,i] <- rep(0, NROW(allcount))
}
names(allcount) <- c("seqID", "t1", "m1", "b1", "t2", "m2", "b2", "t3", "m3", "b3")
numCol <- ncol(allcount)
numColZero <- rep(0, numCol)
for (q in 2:length(present)) {
for (i in 1:NROW(present[[q]])) {
index<- present[[q]]$seqID[i]==allcount$seqID
if( any(index)){
allcount[index, 1+q] <- present[[q]]$mapped[i]
}else {
allcount <- rbind(allcount, numColZero)
rownumber <- NROW(allcount)
allcount[rownumber, 1] <- present[[q]]$seqID[i]
allcount[rownumber, q+1] <- present[[q]]$mapped[i]
# temp <- c(present2$seqID[i], 0, present2$mapped[i])
} #this syntax will only return "TRUE"
}
}
write.csv(allcount, row.names = FALSE, "allcount.csv")
#remove contaminated sequences checked by BLAST & MEGAN
not_fungi <- read.delim("/Users/kc178/Documents/HiSeq1269/community_RDP_NC_J_CF/velvet91/vel91_c95_meganNonfungi", header=FALSE)
not_fungi <- as.character(not_fungi$V1)
remove <- allcount$seqID %in% not_fungi
write.csv(allcount[!remove,], row.names = FALSE, "allcount_removenonfungi.csv")
|
d037a95f33fc722ff803f420ab01e161ac2ac604
|
63d97198709f3368d1c6d36739442efa699fe61d
|
/advanced algorithm/round3/k-server-analysis-master/data/tests/case184.rd
|
2d7b27ce86c03d418cbf45a45be8f3b2de6a842e
|
[] |
no_license
|
tawlas/master_2_school_projects
|
f6138d5ade91e924454b93dd8f4902ca5db6fd3c
|
03ce4847155432053d7883f3b5c2debe9fbe1f5f
|
refs/heads/master
| 2023-04-16T15:25:09.640859
| 2021-04-21T03:11:04
| 2021-04-21T03:11:04
| 360,009,035
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,265
|
rd
|
case184.rd
|
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141 [22, 11, 10] 1 2 380 2 393
142 [23, 11, 10] 1 2 382 2 395
143 [22, 11, 10] 1 2 384 2 397
144 [23, 11, 10] 1 2 386 2 399
145 [22, 11, 10] 1 2 388 2 401
146 [23, 11, 10] 1 2 390 2 403
147 [22, 11, 10] 1 2 392 2 405
148 [23, 11, 10] 1 2 394 2 407
149 [22, 11, 10] 1 2 396 2 409
150 [23, 11, 10] 1 2 398 2 411
151 [22, 11, 10] 1 2 400 2 413
152 [23, 11, 10] 1 2 402 2 415
153 [22, 11, 10] 1 2 404 2 417
154 [23, 11, 10] 1 2 406 2 419
155 [22, 11, 10] 1 2 408 2 421
156 [23, 11, 10] 1 2 410 2 423
157 [22, 11, 10] 1 2 412 2 425
158 [23, 11, 10] 1 2 414 2 427
159 [23, 22, 10] 11 0 414 0 427
160 [23, 22, 34] 12 14 428 14 441
161 [23, 22, 35] 1 2 430 2 443
162 [23, 22, 34] 1 2 432 2 445
163 [23, 22, 35] 1 2 434 2 447
164 [23, 22, 34] 1 2 436 2 449
165 [23, 22, 35] 1 2 438 2 451
166 [23, 22, 34] 1 2 440 2 453
167 [23, 22, 35] 1 2 442 2 455
168 [23, 22, 34] 1 2 444 2 457
169 [23, 22, 35] 1 2 446 2 459
170 [23, 22, 34] 1 2 448 2 461
171 [23, 22, 35] 1 2 450 2 463
172 [23, 22, 34] 1 2 452 2 465
173 [23, 22, 35] 1 2 454 2 467
174 [23, 22, 34] 1 2 456 2 469
175 [23, 22, 35] 1 2 458 2 471
176 [23, 22, 34] 1 2 460 2 473
177 [23, 22, 35] 1 2 462 2 475
178 [23, 22, 34] 1 2 464 2 477
179 [23, 22, 35] 1 2 466 2 479
180 [23, 22, 34] 1 2 468 2 481
181 [23, 22, 35] 1 2 470 2 483
182 [34, 22, 35] 11 2 472 2 485
183 [34, 10, 35] 12 22 494 22 507
184 [34, 11, 35] 1 2 496 2 509
185 [34, 10, 35] 1 2 498 2 511
186 [34, 11, 35] 1 2 500 2 513
187 [34, 10, 35] 1 2 502 2 515
188 [34, 11, 35] 1 2 504 2 517
189 [34, 10, 35] 1 2 506 2 519
190 [34, 11, 35] 1 2 508 2 521
191 [34, 10, 35] 1 2 510 2 523
192 [34, 11, 35] 1 2 512 2 525
193 [34, 10, 35] 1 2 514 2 527
194 [34, 11, 35] 1 2 516 2 529
195 [34, 10, 35] 1 2 518 2 531
196 [34, 11, 35] 1 2 520 2 533
197 [34, 10, 35] 1 2 522 2 535
198 [34, 11, 35] 1 2 524 2 537
199 [34, 10, 35] 1 2 526 2 539
200 [34, 11, 35] 1 2 528 2 541
201 [34, 10, 35] 1 2 530 2 543
202 [34, 11, 35] 1 2 532 2 545
203 [34, 10, 35] 1 2 534 2 547
204 [34, 11, 35] 1 2 536 2 549
205 [34, 10, 35] 1 2 538 2 551
206 [34, 11, 35] 1 2 540 2 553
207 [34, 11, 10] 11 0 540 0 553
540 553 415
|
2f26db8310aa3ef7627ac37330322f5d02813db0
|
1e8dd86a73b6a8d678f94933cf1cfc9e453cb2ae
|
/SurveyQs/ui.R
|
d61d52e71954657e1224c9be999dc7612627cbec
|
[] |
no_license
|
aclheexn/myrepo
|
4b3870ebd22481b7f0450b4b9a1acdf0c2afea34
|
5193994a56d9cbdffb0009472b6fcf46171f1060
|
refs/heads/master
| 2021-01-22T08:48:51.442258
| 2017-06-05T21:34:59
| 2017-06-05T21:34:59
| 92,636,372
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,968
|
r
|
ui.R
|
#
# This is the user-interface definition of a Shiny web application. You can
# run the application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
# input a class code
# Get classes or everybody's data
# Collecting, saving, Download
# Number of responses per quesiton
# Question bank of 10 then giving options to add another question
# Age Gender Major <-- Important Qs(Need 20. Get info Qs from other groups)
library(shinydashboard)
library(shiny)
# Define UI for application that draws a histogram
shinyUI(fluidPage(
# Application title
titlePanel("Survey Data"),
numericInput("code",
"Class Code",
value = 000),
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
numericInput("score",
"Input your SAT Math Score(To the nearest 25)",
min = 0,
max = 800,
step = 25,
value = 0),
sliderInput("hours",
"Input how many hours you spend on the internet",
min = 0,
max = 12,
value = 0,
step = 0.25),
textInput(inputId = "text", label = "Name", value = "None"),
selectInput(inputId = "height", label = "Input Your Height(Inches)",
choices = seq(48, 78)),
sliderInput("age",
"What is your age",
min = 0,
max = 100,
value = 0),
selectInput("major",
"What college are you in",
choices = c("Engineering",
"Science",
"Agricultural Sciences",
"Arts & Architecture",
"Communications",
"Earth & Mineral Sciences",
"Education",
"Health & Human Development",
"IST",
"Liberal Arts",
"DUS",
"Business")),
radioButtons("gender",
"Gender",
choices = c("Female", "Male")),
# actionButton("folder", label = "Create Folder"),
actionButton("save", label = "Save"),
actionButton("update", label = "Update Table"),
actionButton("clear", label = "Clear Table"),
actionButton("show", label = "Show All Data"),
actionButton("show2", label = "Show Class Data"),
helpText(textOutput("Updated"), style = "color:red"),
br(),
downloadButton("downloadData",
"Download All Existing Data")
),
# Show a plot of the generated distribution
mainPanel(
verbatimTextOutput("table"),
tableOutput("tote")
)
)
))
|
d960ef09ffafd033eb404510a8fabde05afb6d7b
|
002929791137054e4f3557cd1411a65ef7cad74b
|
/tests/testthat/test_getProbandPedigree.R
|
9a273523d3e93d8fcf0f78091789c5daad94ae85
|
[
"MIT"
] |
permissive
|
jhagberg/nprcgenekeepr
|
42b453e3d7b25607b5f39fe70cd2f47bda1e4b82
|
41a57f65f7084eccd8f73be75da431f094688c7b
|
refs/heads/master
| 2023-03-04T07:57:40.896714
| 2023-02-27T09:43:07
| 2023-02-27T09:43:07
| 301,739,629
| 0
| 0
|
NOASSERTION
| 2023-02-27T09:43:08
| 2020-10-06T13:40:28
| null |
UTF-8
|
R
| false
| false
| 926
|
r
|
test_getProbandPedigree.R
|
#' Copyright(c) 2017-2020 R. Mark Sharp
#' This file is part of nprcgenekeepr
context("getProbandPedigree")
library(testthat)
library(stringi)
data("lacy1989Ped")
ped <- lacy1989Ped
test_that("getProbandPedigree returns the correct pedigree", {
expect_true(all(getProbandPedigree(probands = c("A", "B"), ped)$id %in%
c("A", "B")))
expect_true(all(getProbandPedigree(probands = c("A", "B", "E"), ped)$id %in%
c("A", "B", "E")))
expect_true(all(getProbandPedigree(probands = c("F"), ped)$id %in%
c("A", "B", "D", "E", "F")))
expect_true(all(c("A", "B", "D", "E", "F") %in%
getProbandPedigree(probands = c("F"), ped)$id))
expect_true(all(getProbandPedigree(probands = c("D"), ped)$id %in%
c("A", "B", "D")))
expect_true(all(c("A", "B", "D") %in%
getProbandPedigree(probands = c("D"), ped)$id))
})
|
9f677b520e6b99620ebf278a9ae3b657585f9919
|
6f52523561fee8f1244d1cc2be3abf4124352e8a
|
/plot2.R
|
ff4234de200d9d1fa941391855a2267424cc3a4b
|
[] |
no_license
|
ishaanagw/ExData_Plotting1
|
6584148e1e98088d14ef641c3ca216c1c3ef62af
|
5eeea11ff0bdaf626192b555a96883caebcd49c9
|
refs/heads/master
| 2022-09-01T22:24:02.404093
| 2020-05-24T08:30:29
| 2020-05-24T08:30:29
| 266,476,688
| 0
| 0
| null | 2020-05-24T05:33:01
| 2020-05-24T05:33:00
| null |
UTF-8
|
R
| false
| false
| 1,700
|
r
|
plot2.R
|
## This part of code is common to all the plots
#========================================================================
## Reading the data
## calculating memory required for the data
## memory for a data set = number of rows* number of columns* 8 bytes
memory_bytes <- 2075259*9*8
memory_mb <- memory_bytes/10^6
cat("The memory required to store the data set is: ",memory_mb,"mb")
##loading the full data
data <- read.table("household_power_consumption.txt",sep = ";",na.strings = "?",header = TRUE)
##Saving the Date in the data set as Date
data$Date <- as.Date(data$Date,format = "%d/%m/%Y")
## subsetting the data based on the dates mentioned in the project
data_req <- subset(data,Date == "2007-02-01"| Date == "2007-02-02")
##Merging the Date and time column of the new data set to a new column called date_time
data_req$date_time <- paste(data_req$Date,data_req$Time)
data_req$date_time <- as.character(data_req$date_time)
## Changing the Time in the data set as POSXlt format
data_req$date_time <- as.POSIXlt(data_req$date_time)
#=================================================================
##Plot 2
## The plot is a line plot with the x axis as the time of the day for each day
## and the y axis is the Global Active Power
par(mfrow = c(1,1))
with(data_req,plot(date_time,Global_active_power,type = "l",ylab = "Global Active Power (killowatts)",xlab = ""))
## copying the plot to a png file with height and width 480 using the dev.copy function
dev.copy(png,"plot2.png",width = 480,height = 480)
## switching off the currently active file device
dev.off()
|
9e8af75290ae33090656b62b448b9979ad1db5cb
|
523090931e3508b00eb5d7b32986ae99a219c4a3
|
/server.R
|
3c7610c544c259057ae3eff687d8a43651bafbc2
|
[] |
no_license
|
PeiWeiChi/Shiny-Application-and-Reproducible-Pitch
|
a5f69da61bde2e47a10fc319448f2805ecbb98f3
|
45045a1f3d217711c8e8992734c351a2f13be718
|
refs/heads/master
| 2021-05-08T14:44:00.428075
| 2018-02-04T03:25:29
| 2018-02-04T03:25:29
| 120,096,809
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 746
|
r
|
server.R
|
library(shiny)
# Define server logic required to draw a histogram
shinyServer(function(input, output) {
output$dtext <- renderText({
n <- input$myMonth
y<-as.vector(window(AirPassengers,n,end = c(n,12)))
word <- paste0("Yearly total passengers:", sum(y))
print( word)
})
output$distPlot <- renderPlot({
n <- input$myMonth
y<-as.vector(window(AirPassengers,n,end = c(n,12)))
x <- c(1:12)
mydata<- cbind(x,y)
# draw the histogram with the specified number of bins
plot(mydata,type="b",xlab="Month",ylab="number of passengers",col="blue")
points(mydata,pch=1,col="red")
})
})
|
89fa15b20686d76e3f7e87f23b220338fa0cf14f
|
0bb0975ad2c2fd6c3afa41908a59ac7615082aa8
|
/Rjunk.R
|
ec065663fa777f427a8ada714c2efc21b42cd669
|
[] |
no_license
|
CoppellMustang/6306_BLT9.5
|
c852aa9aedfdef178050a7ee98d1ec2f3455a229
|
d246c9e5da8db398c471fb72ca60667eb6ce70ab
|
refs/heads/master
| 2021-01-20T19:59:45.456670
| 2016-07-20T17:19:50
| 2016-07-20T17:19:50
| 63,285,082
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 718
|
r
|
Rjunk.R
|
install.packages("tseries")
library("tseries")
SNPdata <- get.hist.quote('^gspc',quote="Close")
length(SNPdata)
SNPret <- log(lag(SNPdata)) - log(SNPdata)
length(SNPret)
SNPvol <- sd(SNPret) * sqrt(250) *100
Vol <- function(d, logrets){
var = 0
lam = 0
varlist <- c()
for (r in logrets){
lam = lam * (1 - 1/d) +1
var = (1 - 1/lam)*var+ (1/lam)*r^2
varlist <- c(varlist, var)
}
sqrt(varlist)
}
volest <- Vol(10, SNPret)
volest2 <- Vol(30, SNPret)
volest3 <- Vol(100, SNPret)
plot(volest,type='l')
lines(volest2,type='l', col = 'red')
lines(volest3,type='l', col = 'blue') # smooth with higher weight
|
0001876efb1b9e9f9ca30971ad7938e138b9c125
|
ab8d2c2ecd193cfdf7e4bed46e7585d51e71ed6c
|
/man/trait.dictionary.Rd
|
9a16ec1c48b969e3f91d78dfdd8a6d8ebb2daf78
|
[
"NCSA",
"LicenseRef-scancode-unknown-license-reference"
] |
permissive
|
dlebauer/pecan-priors
|
17cc74617485ce6c59e4e8e48e52773baabaf42a
|
919eed91e77d7d445e052f3f50e235a23f16f966
|
refs/heads/master
| 2021-06-02T09:49:36.286969
| 2019-09-25T04:40:17
| 2019-09-25T04:40:17
| 7,034,392
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 598
|
rd
|
trait.dictionary.Rd
|
\name{trait.dictionary}
\alias{trait.dictionary}
\title{Dictionary of terms used to identify traits in ed, filenames, and figures}
\usage{
trait.dictionary(traits = NULL)
}
\arguments{
\item{traits}{a vector of trait names, if traits = NULL,
all of the traits will be returned.}
}
\value{
a dataframe with id, the name used by ED and BETY for a
parameter; fileid, an abbreviated name used for files;
figid, the parameter name written out as best known in
english for figures and tables.
}
\description{
Dictionary of terms used to identify traits in ed,
filenames, and figures
}
|
ae4271dcf6c35db203e93249b7e71d0c7f5c167a
|
ddcfacca3170c9de0185ae08bc8ad3df3a4213c3
|
/man/T2O.Rd
|
9ea70a22f574d4af217191a38e58002572c4a619
|
[] |
no_license
|
cran/TVMM
|
042db76fcbb90ee557149879b5ba9ae4d85aae4f
|
a1564c4589ae9500d679a1df0ffef5477561ae09
|
refs/heads/master
| 2021-04-05T11:33:27.126549
| 2020-12-14T18:50:02
| 2020-12-14T18:50:02
| 248,552,850
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 937
|
rd
|
T2O.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/T2O.R
\name{T2O}
\alias{T2O}
\title{The traditional T2 test (T2).}
\usage{
T2O(X, mu0)
}
\arguments{
\item{X}{a matrix n x p containing n observations and p variables. It should not contain missing values (NA).}
\item{mu0}{a vector containing the mean population to be tested.}
}
\value{
the numerical value and the p-value of the test statistic.
}
\description{
The traditional T2 test (T2).
}
\examples{
set.seed(0)
library(MASS)
n <- 30
p <- 2
rho <- 0.9
delta <- 0.9
mu <- rep(0, times = p)
Sigma <- (1 - rho) * diag(p) + rho * matrix(1, p, p)
mu0 <- rep(0.3271,times = p)
X <- mvrnorm(n, mu, Sigma)
T2O(X=X, mu0=mu0)
}
\references{
Henrique J. P. Alves & Daniel F. Ferreira (2019): Proposition of new alternative tests adapted to the traditional T2 test, Communications in Statistics - Simulation and Computation, DOI: 10.1080/03610918.2019.1693596
}
|
08e1e72539e8b8446ab424b44649a4008dade1a1
|
57744ab6fedc2d4b8719fc51dce84e10189a0a7f
|
/rrdfqbcrnd0/R/GetDescrStatProcedure.R
|
68cabd4f5e1abd820f6716a8347fd5c0b3c4ce23
|
[] |
no_license
|
rjsheperd/rrdfqbcrnd0
|
3e808ccd56ccf0b26c3c5f80bec9e4d1c83e4f84
|
f7131281d5e4a415451dbd08859fac50d9b8a46d
|
refs/heads/master
| 2023-04-03T01:00:46.279742
| 2020-05-04T19:10:43
| 2020-05-04T19:10:43
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,616
|
r
|
GetDescrStatProcedure.R
|
##' Get list of Descriptive statistics
##'
##' For q1 and q3 type=2 is used, as it gives same results as SAS. However,
##' the R help on quantile states type=3 gives same result as SAS.
##' TODO(MJA): investigate which definition is the preferable.
##' @return List providing function for each descriptive statistics
##' @export
GetDescrStatProcedure<- function( ) {
list(
"code:procedure-mean"=list(fun=function(x){mean(x, na.rm=TRUE)}, univfunc="univfunc1" ),
"code:procedure-stddev"=list(fun=function(x){sd(x, na.rm=TRUE)}, univfunc="univfunc1" ),
"code:procedure-stdev"=list(fun=function(x){sd(x, na.rm=TRUE)}, univfunc="univfunc1" ),
"code:procedure-std"=list(fun=function(x){sd(x, na.rm=TRUE)}, univfunc="univfunc1" ),
"code:procedure-median"=list(fun=function(x){median(x, na.rm=TRUE)}, univfunc="univfunc1" ),
"code:procedure-min"=list(fun=function(x){min(x, na.rm=TRUE)}, univfunc="univfunc1" ),
"code:procedure-max"=list(fun=function(x){max(x, na.rm=TRUE)}, univfunc="univfunc1" ),
"code:procedure-n"=list(fun=function(x){length(x[!is.na(x)])}, univfunc="univfunc1" ),
"code:procedure-q1"=list(fun=function(x){quantile(x, probs=c(0.25),type=2, na.rm=TRUE)}, univfunc="univfunc1" ),
"code:procedure-q3"=list(fun=function(x){quantile(x, probs=c(0.75),type=2, na.rm=TRUE)}, univfunc="univfunc1" ),
"code:procedure-count"=list(fun=function(x){length(x)}, univfunc="univfunc2" ),
"code:procedure-countdistinct"=list(fun=function(x){length(unique(x))}, univfunc="univfunc2" ),
"code:procedure-percent"=list(fun=function(x){-1}, univfunc="univfunc3" )
)
}
|
f1434fcf3815bc4e61861fc8b9d9d1d72492c50e
|
b110ef8ad122bfbdcf65eac900680b0cb62ac851
|
/R/classContainer.R
|
493824651b429787dc093c388ca694e7a703ae8a
|
[] |
no_license
|
adelabriere/proFIA
|
fcebc86845c908e0c5978b3d603e2f48af9e64e5
|
8eaf9aa3105ca7bd9b71bf3754007a867afa522f
|
refs/heads/master
| 2020-03-13T09:11:17.646649
| 2019-07-10T13:31:11
| 2019-07-10T13:31:11
| 131,059,383
| 0
| 1
| null | 2019-04-07T11:17:12
| 2018-04-25T20:23:38
|
R
|
UTF-8
|
R
| false
| false
| 3,925
|
r
|
classContainer.R
|
#####Annotate FIA
#An object to contain all the information from an FIA acquisition.
#' An S4 class to represent an FIA experiments.
#'
#' The S4 class also includes all the informations about processing, and the detected signals
#' are stored.
#'
#' @include noiseEstimator.R
#' @param object A proFIAset object.
#' @export
#' @slot peaks A matrix containg all the peaks which have been detected in each
#' individual files.
#' \itemize{
#' \item mzmin the minimum value of the mass traces in the m/z dimension.
#' \item mzmax the maximum value of the mass traces in the m/z dimension.
#' \item scanMin the first scan on which the signal is detected.
#' \item scanMax the last scan on which the signal is detected.
#' \item areaIntensity the integrated area of the signal.
#' \item maxIntensity the maximum intensity of the signal.
#' \item solventIntensity the intensity of the solvent, 0 means that no significant
#' solvent was detected.
#' \item corSampPeak An idicator of matrix effect, if it's close to 1, the compound
#' does not suffer from heavy matrix effect, if it is inferior to 0.5, the compound
#' suffer from heavy matrix effect.
#' \item signalOverSolventRatio The ratio of the signal max intensity on the oslvent max intensity.
#' }
#' @include noiseEstimator.R
#' @slot group A matrix containing the information on the groups done between all the
#' acquisitions.
#' \itemize{
#' \item mzMed the median value of group in the m/z dimension.
#' \item mzMin the minimum value of the group in the m/z dimension.
#' \item mzMax the maximum value of the group in the m/z dimension.
#' \item scanMin the first scan on which the signal is detected.
#' \item scanMax the last scan on which the signal is detected.
#' \item nPeaks The number of peaks grouped in a group.
#' \item meanSolvent The mean of solvent in the acquisition.
#' \item pvalueMedian The median p-value of the group.
#' \item corMean The mean of the matrix effect indicator.
#' \item signalOverSolventMean The mean of ratio of the signal max
#' intensity on the oslvent max intensity.
#' \item corSd The standard deviation of the matrix effect indicator.
#' \item timeShifted Is the peak shifted compared to the injection peak.
#' }
#' @slot groupidx The row of the peaks corresponding to each group
#' in \code{peaks}.
#' @slot step The step of processing of the experiment.
#' @slot path The path of the experiment.
#' @slot classes A table with two columns, "rname" the absolute path
#' of a file, and "group" the class to which the file belong.
#' @slot dataMatrix A matrix variables x experiments suitable for
#' statistical analysis.
#' @slot noiseEstimation A model of noise as estimated by
#' \link{estimateNoiseMS}
#' @slot injectionPeaks A list of the injection peak which have been
#' detected for each experiment.
#' @slot injectionScan A numeric vector giving the scan of injection of
#' sample.
#' @aliases proFIAset-class dataMatrix peaks groupMatrix phenoClasses injectionPeaks
setClass(
"proFIAset",
slot = list(
peaks = "matrix",
group = "matrix",
groupidx = "list",
step = "character",
path = "character",
classes = "data.frame",
dataMatrix = "matrix",
noiseEstimation = "noiseEstimation",
injectionPeaks = "list",
injectionScan = "numeric"
),
prototype = list(
peaks = matrix(nrow = 0, ncol = 0),
group = matrix(nrow = 0, ncol = 0),
groupidx = list(),
step = "empty",
path = character(),
classes = data.frame(),
dataMatrix = matrix(nrow = 0, ncol = 0),
noiseEstimation = new("noiseEstimation"),
injectionPeaks = list(),
injectionScan = numeric(0)
)
)
|
d398b576c9ae97b1322f64ec982a43ee939627cf
|
73b297f2e53e18fc7a3d52de4658fede00a0319c
|
/man/arrayToDf.Rd
|
bbb020e66db60eeae1d5643e0a4f29bb4deb3e7a
|
[
"MIT"
] |
permissive
|
bentyeh/r_bentyeh
|
4f5d0f403dceffb450bd77d8ff87adcaa6c222da
|
971c63f4ec1486366e0fe07fc705c0589ac7ea39
|
refs/heads/master
| 2023-02-06T19:10:18.055017
| 2020-07-21T18:18:48
| 2020-07-21T18:18:48
| 281,471,203
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,190
|
rd
|
arrayToDf.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{arrayToDf}
\alias{arrayToDf}
\title{Convert an array to a data frame.}
\usage{
arrayToDf(ar)
}
\arguments{
\item{ar}{An array.}
}
\value{
\code{data.frame. dim = c(prod(dim(ar)), length(dim(ar)) + 1)}.
\itemize{
\item Rows: One row per element of the array. Rows are "entered" into the data frame in
\href{https://en.wikipedia.org/wiki/Row-_and_column-major_order}{column major order}.
\item Columns: One column per dimension of the array, plus one column for the values in the
array. \itemize{
\item Column names are retained from names of array dimensions (\code{names(dimnames(ar)}).
Where non-existant, column names are given as "d#", where # is the index of the dimension.
\item For each column, if the corresponding array dimension was named, then the column is of
class \code{character}. Otherwise, the column is of class \code{numeric}.
\item The last column (the values from the array) is named "value".
}
}
}
\description{
Convert an array to a data frame.
}
\seealso{
\code{\link[tidyr]{pivot_longer}}, \url{https://stackoverflow.com/a/42810479}.
}
|
0da5041389b93b17b08f7180217a4794edcfc26c
|
9a79d2eb242a5aa1ea537b395726bbb1b615582c
|
/data/readdata_POS_CASH_balance.R
|
4e38ed3d63d30c9ef57321e129b5752d1459c7d1
|
[] |
no_license
|
HeHuangDortmund/HomeCredit
|
80d49e1d6c417e4434cab98649893bfee0d1f7cc
|
f7d809539d1e48bbf00db38489d5f7a695d1e86d
|
refs/heads/master
| 2020-03-20T01:28:41.060990
| 2018-08-31T22:41:06
| 2018-08-31T22:41:06
| 137,077,343
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,235
|
r
|
readdata_POS_CASH_balance.R
|
readData = function(version = 1){
library(rprojroot)
library(data.table)
root = find_root(is_git_root)
setwd(root)
POS_CASH_balance = fread("../data/POS_CASH_balance.csv", na.strings = "")
if (version == 2){ # 在汇总前先填一部分NA
POS_CASH_balance$CNT_INSTALMENT[is.na(POS_CASH_balance$CNT_INSTALMENT)] = mean(POS_CASH_balance$CNT_INSTALMENT, na.rm = TRUE)
POS_CASH_balance$CNT_INSTALMENT_FUTURE[is.na(POS_CASH_balance$CNT_INSTALMENT_FUTURE)] = mean(POS_CASH_balance$CNT_INSTALMENT_FUTURE, na.rm = TRUE)
}
POS_CASH_balance[, Add_RATIO_INSTALMENT_LEFT := CNT_INSTALMENT_FUTURE / CNT_INSTALMENT]
############################## calculate the trend and intercept variables ###################################################################
# POS_CASH_balance = POS_CASH_balance[order(SK_ID_CURR, SK_ID_PREV, MONTHS_BALANCE)]
# temp_data = POS_CASH_balance[!is.na(Add_RATIO_INSTALMENT_LEFT)]
# temp_trend1 = temp_data[, lapply(.SD, function(x) {lm(x~(seq(1,length(MONTHS_BALANCE),by=1)))$coefficients[1]}),
# .SDcols = c("Add_RATIO_INSTALMENT_LEFT"),
# by = SK_ID_PREV] # 14min
# temp_trend2 = temp_data[, lapply(.SD, function(x) {lm(x~(seq(1,length(MONTHS_BALANCE),by=1)))$coefficients[2]}),
# .SDcols = c("Add_RATIO_INSTALMENT_LEFT"),
# by = SK_ID_PREV]
# temp_trend = merge(temp_trend1, temp_trend2, all = TRUE, by = c("SK_ID_PREV"))
##############################################################################################################################################
temp_trend = fread("trend_pos_cash.txt", drop = "V1")
# 对每个变量通过求MEAN和MAX进行汇总
temp_name = setdiff(names(POS_CASH_balance),c("SK_ID_PREV","SK_ID_CURR","NAME_CONTRACT_STATUS"))
temp = POS_CASH_balance[, c(lapply(.SD, mean, na.rm = TRUE),
lapply(.SD, max, na.rm = TRUE)), .SDcols = temp_name, by = SK_ID_CURR]
setnames(temp, 2:ncol(temp),paste(temp_name, rep(c('MEAN','MAX'),each = length(temp_name)), sep = "_"))
temp = temp[,c("CNT_INSTALMENT_MAX","CNT_INSTALMENT_FUTURE_MAX") := NULL] # drop 2 variables
temp[temp == Inf | temp == -Inf] = NA # 对于Inf/-Inf用NA替换
# 对于每一个SK_ID_PREV求MONTH_BALANCE的数量, 然后求MEAN进行汇总
temp2 = POS_CASH_balance[,.(Nr_POSCASH_MONTH = .N),by = list(SK_ID_CURR,SK_ID_PREV)]
## merge temp_trend
temp2 = merge(temp2, temp_trend, all = TRUE, by = "SK_ID_PREV")
temp2 = temp2[, lapply(.SD, mean, na.rm = TRUE), .SDcols = names(temp2)[-c(1,2)],by = SK_ID_CURR]
names(temp2)[-1] = paste(names(temp2)[-1],"MEAN",sep = "_")
# 对于每一个SK_ID_CURR求相对应的SK_ID_PREV的数量
temp3 = POS_CASH_balance[,.(Nr_POS_CASH = length(unique(SK_ID_PREV))), by = "SK_ID_CURR"]
# merge
temp = merge(temp2, temp, all = TRUE, by = "SK_ID_CURR")
temp = merge(temp3, temp, all = TRUE, by = "SK_ID_CURR")
rm(temp2,temp3)
# 处理唯一的一个categorical变量
# XNA = NA
POS_CASH_balance$NAME_CONTRACT_STATUS[POS_CASH_balance$NAME_CONTRACT_STATUS == "XNA"] = NA
# 此处或先不把Canceled和NA两个类别合并, 之后尝试使用mergeSmallFactorLevels合并?
POS_CASH_balance$NAME_CONTRACT_STATUS[POS_CASH_balance$NAME_CONTRACT_STATUS == "Canceled" | is.na(POS_CASH_balance$NAME_CONTRACT_STATUS)] = "Other"
temp_cat = POS_CASH_balance[,.N,by = list(SK_ID_CURR, NAME_CONTRACT_STATUS)]
temp_cat_wide = dcast(temp_cat, SK_ID_CURR~NAME_CONTRACT_STATUS, fill = 0, value.var = "N")
names(temp_cat_wide)[-1] = paste("NAME_CONTRACT_STATUS_POSCASH", names(temp_cat_wide)[-1],sep = "_")
# merge
POS_CASH_balance = merge(temp,temp_cat_wide,all = TRUE, by = "SK_ID_CURR")
rm(temp,temp_cat,temp_cat_wide)
POS_CASH_balance[,c("Add_RATIO_INSTALMENT_LEFT_MAX")] = NULL
names(POS_CASH_balance)[grep("SK_DPD",names(POS_CASH_balance))] = paste(names(POS_CASH_balance)[grep("SK_DPD",names(POS_CASH_balance))],"POS",sep="_")
names(POS_CASH_balance)[grep("MONTHS_BALANCE",names(POS_CASH_balance))] = paste(names(POS_CASH_balance)[grep("MONTHS_BALANCE",names(POS_CASH_balance))],"POS",sep="_")
return(POS_CASH_balance)
}
|
8e1c1cbfb76a8f64c81ca99bdf004202fd9feaa3
|
3f85f047f5d22d44f50e420ea64b6592f5fa349d
|
/plot4.R
|
f404c052eaac3bb9dd4d514201abbebf1c74ed0b
|
[] |
no_license
|
byronrdz/ExData_Plotting1
|
fb657b39a00fa66979373d8b1d0c3f59b8e6180b
|
63d5d52fca24f61947a5100becf427cc4b6502ac
|
refs/heads/master
| 2020-12-01T03:08:22.325941
| 2015-03-08T23:29:47
| 2015-03-08T23:29:47
| 31,691,325
| 0
| 0
| null | 2015-03-05T02:23:01
| 2015-03-05T02:22:59
| null |
UTF-8
|
R
| false
| false
| 1,172
|
r
|
plot4.R
|
#PLOT4
#Settting working directory where the data file is located
setwd("C:/Users/Byron/Documents/Coursera Data Science Sp/Exploratory Data Analysis/ExData_Plotting1")
#Loading filtered data from the file. Just rows corresponding to 1/2/2007 and 2/2/2007
library(sqldf)
data <- read.csv.sql("household_power_consumption.txt",header=TRUE,sep=";",sql="select * from file where Date in('1/2/2007','2/2/2007')")
#Creating the time vector for the graphic.
days <- paste(as.Date(data$Date,"%d/%m/%Y"),data$Time)
days <- strptime(days,"%Y-%m-%d %H:%M:%S")
#Creating the graphic 4
png("plot4.png",width=480,height=480)
par(mfrow=c(2,2))
plot(days,data$Global_active_power,type="l",ylab="Global Active Power (kilowats)",xlab="")
plot(days,data$Voltage,type="l",ylab="Voltage",xlab="datetime")
plot(days,data$Sub_metering_1,type="l",ylab="Energy sub metering",xlab="")
lines(days,data$Sub_metering_2,col="red")
lines(days,data$Sub_metering_3,col="blue")
legend("topright",lty=c(1,1,1),col=c("black","red","blue"),legend=c("Sub_metering_1","Sub_metering_2","Sub_metering_3"))
plot(days,data$Global_reactive_power,type="l",ylab="Global_reactive_power",xlab="datetime")
dev.off()
|
14fb10fd8f8ba403a4e47c304870a1bf29d1297d
|
3396a0f6202149a80a6170ab088bf407f5e30169
|
/analysis/networks-statistics.R
|
84db725eb504a1dddf1527c12d8577aea52db172
|
[] |
no_license
|
akastrin/medline-evolution
|
c997e51f155b2ffea73374fa0b9a48edeb2436cc
|
294f7414d8d2d6b50e0ffa4806057519fc91a1b7
|
refs/heads/master
| 2021-08-24T06:50:04.338605
| 2017-12-08T13:42:56
| 2017-12-08T13:42:56
| 112,579,727
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,455
|
r
|
networks-statistics.R
|
library(igraph)
library(data.table)
library(Rcpp)
library(R.matlab)
library(tidyverse)
library(Matrix)
# source("clustercoef_kaiser.R")
sourceCpp("./scripts/kaiser.cpp")
# Create network-statistics.csv file
files <- paste0("../data/matlab/adj-mats/adj-mat-", 1:49, ".mm")
n_nodes <- vector(mode = "integer", length = length(files))
n_edges <- vector(mode = "integer", length = length(files))
ave_deg <- vector(mode = "double", length = length(files))
cen <- vector(mode = "integer", length = length(files))
apl <- vector(mode = "double", length = length(files))
cc <- vector(mode = "double", length = length(files))
for (i in 1:length(files)) {
file <- files[i]
data <- readMM(file)
g <- graph_from_adjacency_matrix(adjmatrix = data, mode = "undirected", weighted = TRUE)
adj <- get.adjacency(g, sparse = FALSE)
n_nodes[i] <- vcount(g)
n_edges[i] <- ecount(g)
ave_deg[i] <- mean(degree(g))
cen[i] <- mean(eigen_centrality(g, directed = FALSE)$vector)
apl[i] <- mean_distance(g = g, directed = FALSE)
cc[i] <- kaiser(adj)
cat(i, "\n")
}
modularity <- read_tsv("../data/matlab/other/modularity.txt")
comm_size <- readMat("../data/matlab/other/comm-sizes.mat")
comm_size <- apply(comm_size$commSizes != 0, 1, sum)
year <- 1966:2014
tab <- data.frame(year, n_nodes, n_edges, ave_deg, cen, apl, cc, modul, comm_size)
write_tsv(tab, path = "../data/networks_statistics.txt")
####################################
# Heatmap
comm_evol_size <- readMat("../data/matlab/other/comm-evol-size.mat")$commEvolSize
# library(heatmap3)
library(RColorBrewer)
data <- t(comm_evol_size)
data2 <- data
data2 <- log(data2 + 1)
library(reshape2)
data_melt <- melt(data)
data_melt2 <- melt(data2)
library(ggplot2)
library(scales)
plt <- ggplot(data_melt2, aes(Var2, Var1)) +
geom_tile(aes(fill = value)) +
scale_fill_gradient(low = "white", high = "black") +
scale_x_continuous(labels = seq(1965, 2015, 5), breaks = seq(1, 51, 5)) +
scale_y_reverse() +
labs(x = "Year", y = "Community") +
theme(panel.background = element_blank(),
panel.border=element_rect(fill=NA),
legend.position = "none")
plt
ggsave("../figures/community-heatmap.pdf", plt, height = 9, width = 5)
####################################
# How many ... each particular community active
life_span <- apply(X = comm_evol_size, MARGIN = 2, FUN = function(x) sum(x != 0))
med_comm_size <- apply(X = comm_evol_size, MARGIN = 2, FUN = function(x) median(x[x != 0]))
# Read community structure from MATLAB
comm1 <- readMat("/home/andrej/Documents/dev/community-evolution-analysis/data/mats/medline/strComms1.mat")$strComms
mesh2cluster <- function(str_comms) {
res <- list()
n <- length(str_comms)
for (i in 1:n) {
mesh_terms <- unlist(str_comms[[i]])
setNames(object = mesh_terms, nm = rep(n, length(mesh_terms)))
res[[i]] <- mesh_terms
}
return(res)
}
####################################
data <- readMM("../data/matlab/adj-mats/adj-mat-1.mm")
g <- graph_from_adjacency_matrix(adjmatrix = data, mode = "undirected", weighted = TRUE)
g <- simplify(as.undirected(g))
l <- layout.auto(g)
png("../figures/net.png")
plot(g, vertex.size = 0, vertex.shape = "none", vertex.label = NA,
edge.color = adjustcolor("black", alpha = .1), edge.curved = TRUE, edge.width=0.2, layout = l)
dev.off()
library(HiveR)
gAdj <- get.adjacency(g, type = "upper", edges = FALSE, names = TRUE, sparse = FALSE)
hive1 <- adj2HPD(gAdj, type = "2D")
|
d19a55a2377991e72edc0e8135637e57010cadd2
|
b82c3a70ecc18227493239f0a2037592a0e767d2
|
/collection/mysql_config.R
|
1f062927d9f7d9e0e81e3c415a0552718ff5637b
|
[] |
no_license
|
hzontine/polarbear
|
8fed9f3befe7c4e40547df5c0463bb14d15c454f
|
893a322a566d05f00b47fbbd70d8987ffd5a11ce
|
refs/heads/master
| 2020-05-21T15:04:26.039931
| 2017-09-30T21:43:25
| 2017-09-30T21:43:25
| 58,944,871
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 191
|
r
|
mysql_config.R
|
# MySQL configuration.
#mysql.db.name <- "polarbear"
mysql.db.name <- "polarbear"
mysql.user <- "stephen"
mysql.password <- "iloverae"
# git update-index --assume-unchanged mysql_config.R
|
4c41b1bc45f0ec7a40e460f7ddf820832f6132b1
|
a0a9934e32e7c68e57af0eddf83b00d3189cca1d
|
/Animal_Matching.R
|
0e3ed9f4da5b11a371029de4235489ff7f81a63c
|
[] |
no_license
|
mathemagicalgames/mathe-magical-games
|
708cc51c43a8cff94068e82b540508a88ad4ef9a
|
164b497910575382ccf9fc4c5cb31c57ea280eff
|
refs/heads/master
| 2020-08-01T08:09:51.128294
| 2019-09-25T19:54:45
| 2019-09-25T19:54:45
| 210,926,486
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,534
|
r
|
Animal_Matching.R
|
# Copyright (c) 2017 - 2018
# Nicolai Meinshausen [meinshausen@stat.math.ethz.ch]
# Jonas Peters [jonas.peters@math.ku.dk]
# All rights reserved. See the file COPYING for license terms.
## dimension (will result in p^2+p+1 players and unique animals)
p <- 3
## check whether p is a small prime number
if(! p %in% c(2,3,5,7,11, 13)) stop(" Dimension p has to be a small
prime: 2,3,5,7 or (if you are not impatient) also 11 or 13")
## names for animals -- is padded with characters in the end for a potentially larger number of players -- can be easily changed here
names <- c("Lion", "Giraffe", "Dog", "Cat", "Mouse", "Elephant", "Snake","Eagle","Frog","Spider","Dolphin","Jellyfish","Earthworm", as.character(1:150))
## start from {0,1,...,p-1}
set <- 0:(p-1)
## embed into three dimensions
three.dim <- expand.grid(set,set,set)
## select points that lead to unique lines through origin (this is not just one possible choice -- keeping those vectors whose first nonzero entry is a 1)
keep <- which( apply(three.dim,1, function(x) if(identical(as.numeric(x),c(0,0,0))) FALSE else x[which(x!=0)[1]]==1))
## for the p^2+p+1 unique lines through the origin, keep a representative point
lines <- as.matrix(three.dim[keep,])
## m is now the number of players and unique animals
m <- p^2+p+1
## q is the number of possible pairs between players (or animals)
q <- (m*(m-1))/2
## s is the number of animals each player will have (or players that will have a specific animal)
s <- p+1
## the matrix M will store the indices of s animals of player i in row i
M <- matrix(nrow=q,ncol=s)
cc <- 0
for (j1 in 1:(m-1)){
for (j2 in (j1+1):m){
o1 <- lines[j1,]
o2 <- lines[j2,]
tmp <- c(j1,j2)
for (c1 in 0:p){
for (c2 in 0:p){
vec <- as.numeric((c1*o1 + c2*o2) %% p)
new <- which( apply( sweep( lines,2,vec,FUN="-")==0, 1, all))
tmp <- sort(unique(c(tmp, new)))
}
}
cc <- cc+1
M[ cc,] <- tmp
}
}
M <- unique(M)
## A is the incidence matrix for the choice of animals in M
A <- matrix(0,nrow=m,ncol=m)
for (j in 1:m) A[j, M[j,]] <- 1
## can check that A satisfies A A^{\transp} =pI + J
print( A%*%t(A) )
## write out table of animals (latex version TRUE or FALSE )
latex <- FALSE
for (j in 1:m){
if (latex){
cat("\n Player ",j," & ", paste(names[M[j,]],collapse=" & " )," \\\\ ")
}else{
cat("\n Player ",j," takes ", paste(names[M[j,]],collapse=", " )," ")
}
}
|
55765445891239364a14c077a3b14fce07004e33
|
d34170b0244866131fd5d959688f908f02ea5404
|
/R/corrXY.r
|
8e96b9006a494b57b7ea51acb7d32bd173c3ffad
|
[] |
no_license
|
ClementCalenge/adehabitatLT
|
7ac4182ce35abc6b8d85f8929017102e5a430bf3
|
1da9d94626de2ed4c069ba4fedef16cf9ddf5c8f
|
refs/heads/master
| 2023-04-14T12:32:51.368684
| 2023-04-07T07:25:26
| 2023-04-07T07:25:26
| 81,651,833
| 5
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,783
|
r
|
corrXY.r
|
.convtime <- function(step, units)
{
if (units=="min")
step <- step*60
if (units=="hour")
step <- step*60*60
if (units=="day")
step <- step*60*60*24
return(step)
}
.corrXY <- function(x, y, dab, daa)
{
x1 <- x[1]
y1 <- y[1]
if (daa[1]!=dab[1]) {
dt <- daa[2]-daa[1]
dif <- abs(daa[1]-dab[1])
alpha <- atan2( (y[2]-y[1]), (x[2]-x[1]) )
r <- sqrt( (x[2]-x[1])^2 + (y[2]-y[1])^2 )*dif/dt
if (daa[1] > dab[1]) {
x1 <- x[1]+cos(alpha)*r
y1 <- y[1]+sin(alpha)*r
}
if (daa[1] < dab[1]) {
alpha <- pi + alpha
x1 <- x[1]+cos(alpha)*r
y1 <- y[1]+sin(alpha)*r
}
}
xn <- x[length(y)]
yn <- y[length(y)]
if (daa[length(daa)]!=dab[length(dab)]) {
dt <- daa[length(daa)]-daa[length(daa)-1]
dif <- abs(daa[length(daa)]-dab[length(daa)])
alpha <- atan2((y[length(daa)]-y[length(daa)-1]),
(x[length(daa)]-x[length(daa)-1]))
r <- sqrt((x[length(daa)]-x[length(daa)-1])^2 +
(y[length(daa)]-y[length(daa)-1])^2)*dif/dt
if (daa[length(dab)]<dab[length(dab)]) {
alpha <- pi + alpha
xn <- x[length(daa)]+cos(alpha)*r
yn <- y[length(daa)]+sin(alpha)*r
} else {
xn <- x[length(daa)]+cos(alpha)*r
yn <- y[length(daa)]+sin(alpha)*r
}
}
x[1] <- x1
y[1] <- y1
x[length(x)] <- xn
y[length(x)] <- yn
xb <- x[1:(length(x)-2)]
xc <- x[2:(length(x)-1)]
xa <- x[3:length(x)]
yb <- y[1:(length(x)-2)]
yc <- y[2:(length(x)-1)]
ya <- y[3:length(x)]
daat <- daa
dabt <- dab
if (any(daa[-c(1,length(daa))]<dab[-c(1,length(daa))])) {
dt <- diff(daa[1:(length(daa)-1)])
daa <- daa[2:(length(x)-1)]
dab <- dab[2:(length(x)-1)]
dif <- abs(daa-dab)
alpha <- atan2( (yc-yb),(xc-xb) )
r <- sqrt( (xc-xb)^2 + (yc-yb)^2 )*dif/dt
xc[daa<dab] <- xc[daa<dab] + cos(alpha[daa<dab] + pi)*r[daa<dab]
yc[daa<dab] <- yc[daa<dab] + sin(alpha[daa<dab] + pi)*r[daa<dab]
}
daa <- daat
dab <- dabt
if (any(daa[-c(1,length(daa))] > dab[-c(1,length(daa))])) {
dt <- diff(daat[2:(length(daat))])
daa <- daa[2:(length(x)-1)]
dab <- dab[2:(length(x)-1)]
dif <- abs(daa-dab)
alpha <- atan2( (ya-yc),(xa-xc) )
r <- sqrt( (xa-xc)^2 + (ya-yc)^2 )*dif/dt
xc[daa>dab] <- xc[daa>dab] + cos(alpha[daa>dab])*r[daa>dab]
yc[daa>dab] <- yc[daa>dab] + sin(alpha[daa>dab])*r[daa>dab]
}
xn <- c(x1, xc, xn)
yn <- c(y1, yc, yn)
return(list(x=xn,y=yn))
}
|
a751159ff1fad33e8cffa5a118274f07c4d19792
|
0fb33ca8eef07fcb5d3687f4cf2793ef187f79f4
|
/R/ca-scoreFACT_EN.R
|
b64db6bda8b8ac8bdb482b7107c789c31d990e69
|
[
"MIT"
] |
permissive
|
raybaser/FACTscorer
|
e3c10b9a065cb5b6290b211519b72ed9171a1fc2
|
070a1cf479ee8c1f19bf6a295c2ed0d544ff6406
|
refs/heads/master
| 2022-03-16T20:20:29.198088
| 2022-03-12T09:42:36
| 2022-03-12T09:42:36
| 61,918,573
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,334
|
r
|
ca-scoreFACT_EN.R
|
#' @title Score the FACT-En
#'
#' @description
#' Generates all of the scores of the Functional Assessment of Cancer Therapy -
#' Endometrial Cancer (FACT-En, v4) from item responses.
#'
#' @templateVar MEASURE FACT-En
#' @templateVar SCOREFUN scoreFACT_En
#' @template templateDetailsAC
#'
#'
#' @template templateParamsAC
#'
#'
#' @return A data frame with the following scale scores is returned:
#'
#' \itemize{
#' \item \strong{PWB} - Physical Well-Being subscale
#' \item \strong{SWB} - Social/Family Well-Being subscale
#' \item \strong{EWB} - Emotional Well-Being subscale
#' \item \strong{FWB} - Physical Well-Being subscale
#' \item \strong{FACTG} - FACT-G Total Score (PWB+SWB+EWB+FWB)
#' \item \strong{ENCS} - Endometrial Cancer subscale
#' \item \strong{FACT_En_TOTAL} - FACT-En Total Score (PWB+SWB+EWB+FWB+ENCS)
#' \item \strong{FACT_En_TOI} - FACT-En Trial Outcome Index (PWB+FWB+ENCS)
#' }
#'
#' If \code{AConly = TRUE}, the only scale score returned is \strong{ENCS}.
#'
#' If a variable was given to the \code{id} argument, then that variable will
#' also be in the returned data frame. Additional, relatively unimportant,
#' variables will be returned if \code{updateItems = TRUE} or \code{keepNvalid =
#' TRUE}.
#'
#' @references FACT-En Scoring Guidelines, available at
#' \url{http://www.facit.org}
#'
#'
#' @export
#'
#' @examples
#' \dontshow{
#' ## FIRST creating a df with fake item data to score
#' itemNames <- c('O1', 'O3', 'Hep8', 'ES6', 'ES4', 'Hep1', 'ES1',
#' 'ES2', 'ES3', 'HI7', 'ES8', 'En1', 'B1', 'Cx6', 'Bl2', 'En2')
#' exampleDat <- make_FACTdata(namesAC = itemNames)
#'
#' ## NOW scoring the items in exampleDat
#'
#' ## Returns data frame with ONLY scale scores
#' (scoredDat <- scoreFACT_En(exampleDat))
#'
#' ## Using the id argument (makes merging with original data more fool-proof):
#' (scoredDat <- scoreFACT_En(exampleDat, id = "ID"))
#'
#' ## Merge back with original data, exampleDat:
#' mergeDat <- merge(exampleDat, scoredDat, by = "ID")
#' names(mergeDat)
#'
#' ## If ONLY the "Additional Concerns" items are in df, use AConly = TRUE
#' (scoredDat <- scoreFACT_En(exampleDat, AConly = TRUE))
#'
#' ## AConly = TRUE with an id variable
#' (scoredDat <- scoreFACT_En(exampleDat, id = "ID", AConly = TRUE))
#'
#' ## Returns scale scores, plus recoded items (updateItems = TRUE)
#' ## Also illustrates effect of setting keepNvalid = TRUE.
#' scoredDat <- scoreFACT_En(exampleDat, updateItems = TRUE, keepNvalid = TRUE)
#' names(scoredDat)
#' ## Descriptives of scored scales
#' summary(scoredDat[, c('PWB', 'SWB', 'EWB', 'FWB', 'FACTG',
#' 'ENCS', 'FACT_En_TOTAL', 'FACT_En_TOI')])
#' }
scoreFACT_En <- function(df, id = NULL, AConly = FALSE, updateItems = FALSE,
keepNvalid = FALSE) {
df_scores <-
scoreFACT_any(
df = df,
id = id,
namesAC = c("O1", "O3", "Hep8", "ES6", "ES4", "Hep1", "ES1",
"ES2", "ES3", "HI7", "ES8", "En1", "B1", "Cx6", "Bl2", "En2"),
namesRev = c("O1", "O3", "Hep8", "ES6", "ES4", "Hep1", "ES1",
"ES2", "ES3", "HI7", "ES8", "En1", "B1", "Cx6", "Bl2", "En2"),
nameSub = "ENCS",
nameTot = "FACT_En",
AConly = AConly,
updateItems = updateItems,
keepNvalid = keepNvalid
)
return(df_scores)
}
|
f29143b43266c6f0b522c9f106c3dc96e452a151
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/TPD/examples/Rao.Rd.R
|
efd378be1148c0012196665be1e97c77ac8514f7
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,054
|
r
|
Rao.Rd.R
|
library(TPD)
### Name: Rao
### Title: Rao's Quadratic Entropy and its Partition
### Aliases: Rao
### ** Examples
# 1. Compute the TPDs of three different species.
traits_iris <- iris[, c("Sepal.Length", "Sepal.Width")]
sp_iris <- iris$Species
TPDs_iris <- TPDs(species = sp_iris, traits_iris)
#2. Compute the dissimilarity between the three species:
dissim_iris_sp <- dissim(TPDs_iris)
#3. Compute the TPDc of five different communities:
abundances_comm_iris <- matrix(c(c(0.9, 0.1, 0), # setosa dominates
c(0.4, 0.5, 0.1 ),
c(0.15, 0.7, 0.15), #versicolor dominates
c(0.1, 0.5, 0.4),
c(0, 0.1, 0.9)), #virginica dominates
ncol = 3, byrow = TRUE, dimnames = list(paste0("Comm.",1:5),
unique(iris$Species)))
TPDc_iris <- TPDc( TPDs = TPDs_iris, sampUnit = abundances_comm_iris)
#4. Compute Rao:
Rao_iris <- Rao(diss = dissim_iris_sp, TPDc = TPDc_iris)
|
527f30ebbdde326a045271de5e635fb20782cdca
|
089626fd5094bbf9478bd11c1b16331675082113
|
/week1/plot_trips.R
|
0b8cf4e57dab957faf50e6f6094733f3948a610e
|
[] |
no_license
|
BasiraS/coursework
|
6ed0aa3f35bcb1b0a73b4251bf3c13526911868e
|
6bd93c96374fb5ec442d0992069be3084c488536
|
refs/heads/master
| 2022-11-05T18:44:37.686794
| 2020-06-19T19:53:01
| 2020-06-19T19:53:01
| 268,619,251
| 0
| 0
| null | 2020-06-01T19:58:03
| 2020-06-01T19:58:02
| null |
UTF-8
|
R
| false
| false
| 12,211
|
r
|
plot_trips.R
|
########################################
# load libraries
########################################
# load some packages that we'll need
library(tidyverse)
library(scales)
# be picky about white backgrounds on our plots
theme_set(theme_bw())
# load RData file output by load_trips.R
load('trips.RData')
########################################
# plot trip data
########################################
# plot the distribution of trip times across all rides (compare a histogram vs. a density plot)
# Histogram
trips %>% filter(tripduration < 3600) %>% ggplot(aes(x = tripduration)) + geom_histogram()
# Density
trips %>% filter(tripduration < 3600) %>% ggplot(aes(x = tripduration)) + geom_density(fill = "grey")
# plot the distribution of trip times by rider type indicated using color and fill (compare a histogram vs. a density plot)
# Histogram
trips %>%
filter(tripduration < 3600) %>%
ggplot(aes(x = tripduration, colour = usertype)) +
geom_histogram(position = "identity", alpha = 0.25) +
facet_wrap(~ usertype, scales = "free")
# Density
trips %>%
filter(tripduration < 3600) %>%
ggplot(aes(x = tripduration, colour = usertype, )) +
geom_density(fill = "grey") +
facet_wrap(~ usertype, scales = "free")
# plot the total number of trips on each day in the dataset
trips %>%
mutate(day = as.Date(starttime)) %>%
group_by(day) %>%
summarize(num_trips = n()) %>%
ggplot(aes(x = day, y = num_trips)) +
geom_line() +
scale_y_continuous(label = comma) +
xlab('Day') + ylab('Number of Trips')
trips %>%
mutate(day = as.Date(starttime)) %>%
ggplot(aes(x = day)) +
geom_histogram()+
scale_y_continuous(label = comma) +
xlab('Day') + ylab('Number of Trips')
trips %>%
mutate(day = factor((weekdays(as.POSIXct(starttime), abbreviate = F)), levels=c("Monday","Tuesday","Wednesday","Thursday","Friday"))) %>%
ggplot(aes(x = day)) +
geom_histogram(stat = "count") +
scale_y_continuous(label = comma) +
xlab('Day') + ylab('Number of Trips')
# plot the total number of trips (on the y axis) by age (on the x axis) and gender (indicated with color)
trips %>%
mutate(age = 2014 - birth_year) %>%
group_by(age, gender) %>%
summarize(num_trips = n()) %>%
filter(num_trips <= 600000) %>%
ggplot(aes(x = age, y = num_trips, colour = gender)) +
geom_point() +
scale_y_continuous(label = comma)
trips %>%
mutate(age = 2014 - birth_year) %>%
group_by(age) %>%
ggplot(aes(x = age, colour = gender)) +
geom_histogram(position = "identity", alpha = 0.25) +
scale_y_continuous(label = comma) +
facet_wrap(~ gender, scales = "free")
# plot the ratio of male to female trips (on the y axis) by age (on the x axis)
# hint: use the spread() function to reshape things to make it easier to compute this ratio
# (you can skip this and come back to it tomorrow if we haven't covered spread() yet)
trips %>%
mutate(age = 2014 - birth_year) %>%
group_by(age, gender) %>%
summarize(count = n()) %>%
pivot_wider(names_from = gender, values_from = count) %>%
mutate(ratio = Male / Female) %>%
ggplot(aes(x = age, y = ratio)) +
geom_point()
########################################
# plot weather data
########################################
# plot the minimum temperature (on the y axis) over each day (on the x axis)
weather %>%
ggplot(aes(x = ymd, y = tmin)) +
geom_point() +
geom_smooth(se = FALSE)
# plot the minimum temperature and maximum temperature (on the y axis, with different colors) over each day (on the x axis)
# hint: try using the gather() function for this to reshape things before plotting
# (you can skip this and come back to it tomorrow if we haven't covered gather() yet)
weather %>%
select(tmin, tmax, ymd) %>%
pivot_longer(names_to = "temp_type", values_to = "temp", cols = c('tmin', 'tmax')) %>%
ggplot(aes(x = ymd, y = temp, group = temp_type)) +
geom_point(aes(colour = temp_type)) +
geom_smooth(se = FALSE)
########################################
# plot trip and weather data
########################################
# join trips and weather
trips_with_weather <- inner_join(trips, weather, by="ymd")
# plot the number of trips as a function of the minimum temperature, where each point represents a day
# you'll need to summarize the trips and join to the weather data to do this
trips_with_weather %>%
group_by(tmin, ymd) %>%
summarize(num_trips = n()) %>%
ggplot(aes(x = tmin, y = num_trips)) +
geom_point(aes(colour = ymd)) +
scale_y_continuous(label = comma)
# repeat this, splitting results by whether there was substantial precipitation or not
# you'll need to decide what constitutes "substantial precipitation" and create a new T/F column to indicate this
trips_with_weather %>%
mutate(prcp_chance = (prcp > 0)) %>%
group_by(tmin, ymd, prcp_chance) %>%
summarize(num_trips = n()) %>%
ggplot(aes(x = tmin, y = num_trips)) +
geom_point(aes(colour = prcp_chance)) +
scale_y_continuous(label = comma)
# add a smoothed fit on top of the previous plot, using geom_smooth
trips_with_weather %>%
mutate(prcp_chance = (prcp > 0.5)) %>%
group_by(tmin, ymd, prcp_chance) %>%
summarize(num_trips = n()) %>%
ggplot(aes(x = tmin, y = num_trips)) +
geom_point(aes(colour = prcp_chance)) +
geom_smooth(aes(colour = prcp_chance), se = FALSE) +
scale_y_continuous(label = comma)
# compute the average number of trips and standard deviation in number of trips by hour of the day
# hint: use the hour() function from the lubridate package
# plot the above
trips_with_weather %>%
mutate(hourTime = hour(starttime)) %>%
group_by(hourTime, ymd) %>%
summarize(count = n()) %>%
group_by(hourTime) %>%
summarize(average_trips = mean(count), sd_trips = sd(count)) %>%
pivot_longer(names_to = "computation", values_to = "result", cols = c('average_trips', 'sd_trips')) %>%
ggplot(aes(x = hourTime, y = result)) +
geom_point() +
scale_y_continuous(label = comma) +
facet_wrap(~ computation, scales = "free")
# repeat this, but now split the results by day of the week (Monday, Tuesday, ...) or weekday vs. weekend days
# hint: use the wday() function from the lubridate package
trips_with_weather %>%
mutate(hourTime = hour(starttime)) %>%
group_by(hourTime, wday(ymd)) %>%
summarize(count = n()) %>%
group_by(hourTime) %>%
summarize(average_trips = mean(count), sd_trips = sd(count)) %>%
pivot_longer(names_to = "computation", values_to = "result", cols = c('average_trips', 'sd_trips')) %>%
ggplot(aes(x = hourTime, y = result)) +
geom_point() +
scale_y_continuous(label = comma) +
facet_wrap(~ computation, scales = "free")
########################################
# Chapter 3
########################################
library(tidyverse)
ggplot2::mpg
########################################
# SOLUTIONS
########################################
# 3.3.1
# Exercise 1: What's gone wrong with this code? Why are the points not blue?
#Original Code:
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = "blue"))
#Modified Code:
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy), color = "blue")
# Exercise 2: Which variables in mpg are categorical? Which variables are continuous? (Hint: type ?mpg to read the documentation for the dataset). How can you see this information when you run mpg?
#Categorical variables: manufacturer, model, year, cyl, trans, drv, fl, class
#Continuous variables: displ, cty, hwy
# Exercise 3: Map a continuous variable to color, size, and shape. How do these aesthetics behave differently for categorical vs. continuous variables?
# It works well for categorical since there is a finite set, however, it does not work so well for
# continuous variables because of the various values types. Color and Size still can work, however, it cannot be mapped to shape
# 3.5.1
# Exercise 1: What happens if you facet on a continuous variable?
# The continuous variable is converted to a categorical variable, and the plot contains a facet for each distinct value.
# Exercise 4: What are the advantages to using faceting instead of the color aesthetic? What are the disadvantages? How might the balance change if you had a larger data set?
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_wrap(~ class, nrow = 2)
# The advantage is that you get to see a more detail view of the data, easier to distinguish
# The disadvantage is that it becomes difficult to compare the values of observations between categories since the observations for each category are on different plots.
# 3.6.1
# Exercise 5: Will these two graphs look different? Why/why not?
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_point() +
geom_smooth()
ggplot() +
geom_point(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_smooth(data = mpg, mapping = aes(x = displ, y = hwy))
# The two graphs will not be different because both geom_point() and geom_smooth() will use the same data and mappings.
# They will inherit those options from the ggplot() object, so the mappings don't need to specified again.
# Exercise 6: Recreate the R code necessary to generate the following graphs.
ggplot(mpg, aes(x = displ, y = hwy)) +
geom_point() +
geom_smooth(se = FALSE)
ggplot(mpg, aes(x = displ, y = hwy, group = drv)) +
geom_point() +
geom_smooth(se = FALSE)
ggplot(mpg, aes(x = displ, y = hwy, colour = drv)) +
geom_point() +
geom_smooth(se = FALSE)
ggplot(mpg, aes(x = displ, y = hwy)) +
geom_point(aes(colour = drv)) +
geom_smooth(se = FALSE)
ggplot(mpg, aes(x = displ, y = hwy)) +
geom_point(aes(colour = drv)) +
geom_smooth(aes(linetype = drv), se = FALSE)
ggplot(mpg, aes(x = displ, y = hwy)) +
geom_point(size = 4, color = "white") +
geom_point(aes(colour = drv))
# 3.8.1
# Question 1: What is the problem with this plot? How could you improve it?
# Original Code:
ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
geom_point()
# Modified Code:
ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
geom_point(position = "jitter")
# Question 2: What parameters to geom_jitter() control the amount of jittering?
# There are two parameters that control the amount of jittering: width and height
########################################
# Chapter 12
########################################
library(tidyverse)
ggplot2::mpg
########################################
# SOLUTIONS
########################################
# 12.2.1
# Exercise 2: Using prose, describe how the variables and observations are organized in each of the sample tables.
# Table 1: Observations: country and year; Variables: cases and population
# Table 2: Observations: country, year, and type of count; Variable: the count (value)
# Table 3: Observations: country and year; Variable: rate
# Table 4a: Observations: country; Variable: year 1999 and 2000 (value for cases)
# Table 4b: Observations: country; Variable: year 1999 and 2000 (value for population)
# 12.3.3
# Exercise 1: Why are pivot_longer() and pivot_wider() not perfectly symmetrical?
# The two are not symmetrical because of the variation on column.
# Exercise 3: What would happen if you widen this table? Why? How could you add a new column to uniquely identify each value?
people <- tribble(
~name, ~names, ~values,
#-----------------|--------|------
"Phillip Woods", "age", 45,
"Phillip Woods", "height", 186,
"Phillip Woods", "age", 50,
"Jessica Cordero", "age", 37,
"Jessica Cordero", "height", 156
)
pivot_wider(people, names_from = "names", values_from = "values")
# It will fail because columns do not uniquely identity rows
# Modification:
people %>%
group_by(name, names) %>%
mutate(identify = row_number()) %>%
pivot_wider(names_from = "names", values_from = "values")
|
35d9ae20034f29785a18298bf63af0b6d92d334a
|
6a5a13aa76f394b99c3ba24580c63e492c72314f
|
/exploratory_data_analysis/week1/week1_course.R
|
6c4e038704ea4d5d2c0b3fcd735c3da00c1ee899
|
[] |
no_license
|
sriharshams/datasciencecoursera
|
6de4616997c687e14c62d816d19859bffc1f7f98
|
e34263d3080e5b272d2d61fa323439c353effa61
|
refs/heads/master
| 2020-04-15T08:46:39.289593
| 2017-06-24T22:09:29
| 2017-06-24T22:09:29
| 52,250,701
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,349
|
r
|
week1_course.R
|
#Get data
getwd()
#Note: This directory needs to be changed based on the required directory for data location
setwd("C:/learning/coursera/coursera-data_science-data/exploratory_data_analysis/week1/data")
filename <- "avgpm25.csv"
## Download the dataset:
if (!file.exists(filename)){
fileURL <- "https://raw.githubusercontent.com/jtleek/modules/master/04_ExploratoryAnalysis/exploratoryGraphs/data/avgpm25.csv"
download.file(fileURL, filename, mode='wb', cacheOK=FALSE)
}
pollution <- read.csv(filename, colClasses = c("numeric", "character", "factor", "numeric", "numeric"))
head(pollution)
#Simple summaries of data, one dimension
#Five-number summary (6-number)
summary(pollution$pm25)
# Box plot
boxplot(pollution$pm25, col = "blue")
abline(h = 12)
# Histograms & density plot
hist(pollution$pm25, col = "green", breaks = 100)
abline(v = 12, lwd = 2)
abline(v = median(pollution$pm25), col = "magenta", lwd = 4)
rug(pollution$pm25)
#barplot
barplot(table(pollution$region), col = "wheat", main = "Number of Counties in Each Region")
# Simple smmaries of Data 2 dimensoins
# Multiple / overlayed 1-D plots(Lattice / ggplot2)
# Multople Boxplots
boxplot(pm25 ~ region, data = pollution, col = "red")
par(mfrow = c(2, 1), mar = c(4, 4, 2, 1))
hist(subset(pollution, region == "east")$pm25, col = "green")
hist(subset(pollution, region == "west")$pm25, col = "green")
# Scatterplots
with(pollution, plot(latitude, pm25, col = region))
abline(h = 12, lwd = 2, lty = 2)
par(mfrow = c(2, 1), mar = c(5, 4, 2, 1))
with(subset(pollution, region == "west"), plot(latitude, pm25, main = "West"))
with(subset(pollution, region == "east"), plot(latitude, pm25, main = "East"))
# Smooth scatterplots
# > 2 dimensions
# Overlayed/ multiple 2-D plots : coplots
# Use color, size, shape to add dimensions
# Spinning plots
# Actual 3-D plots (not that useful)
#baseplot
library(datasets)
data(cars)
with(cars, plot(speed, dist))
#latticeplot xyplot, bwplot
library(lattice)
state <- data.frame(state.x77, region = state.region)
xyplot(Life.Exp ~ Income | region, data = state, layout = c(4,1))
# ggplot2 mixes baseplot & lattice plot
library(ggplot2)
data(mpg)
qplot(displ, hwy, data = mpg)
#baseplot system (graphics : plot, hist, boxplot, grDevices : X11, PDF, PostScript, PNG etc)
#Latticeplot system (lattice : xyplot, bwplot, levelplot, grid : lattice uses grid)
hist(airquality$Ozone)
with(airquality, plot(Wind, Ozone))
boxplot(Ozone ~ Month, airquality, xlab = "Month", ylab = "Ozone (ppb)")
par("lty")
par("mar")
par("mfrow")
with(airquality, plot(Wind, Ozone))
title(main = "Ozone and Wind in New York City")
#baseplot with annotation
with(airquality, plot(Wind, Ozone, main = "Ozone and Wind in New York City"))
with(subset(airquality, Month == 5), points(Wind, Ozone, col = "blue"))
with(subset(airquality, Month != 5), points(Wind, Ozone, col = "red"))
legend("topright", pch = 1, col = c("blue", "red"), legend = c("May", "Other Months"))
#baseplot with regression line
with(airquality, plot(Wind, Ozone, main = "Ozone and Wind in New York City", pch = 20))
model <- lm(Ozone ~ Wind, airquality)
abline(model, lwd = 2)
#multiple baseplots
par(mfrow = c(1,2))
with(airquality, {
plot(Wind, Ozone, main = "Ozone and Wind")
plot(Solar.R, Ozone, main = "Ozone and Solr Radiation")
})
par(mfrow = c(1, 3), mar = c(4, 4, 2, 1), oma = c(0, 0, 2, 0))
with(airquality, {
plot(Wind, Ozone, main = "Ozone and Wind" )
plot(Solar.R, Ozone, main = "Ozone and Solar Radiation")
plot(Temp, Ozone, main = "Ozone and Temperature")
mtext("Ozone and Weather in New York City", outer = TRUE)
})
x <- rnorm(100)
hist(x)
y <- rnorm(100)
plot(x, y)
z <- rnorm(100)
plot(x, z)
par(mar = c(4, 4, 2, 2))
plot(x, y, pch = 20)
plot(x, y, pch = 19)
plot(x, y, pch = 2)
plot(x, y, pch = 3)
plot(x, y, pch = 4)
example(points)
plot(x, y, pch = 20)
title("Scatterplot")
text(-2, -2, "Label")
legend("topright", legend = "Data", pch = 20)
fit <- lm(y ~ x)
abline(fit)
abline(fit, lwd = 3, col = "red")
plot(x, y, xlab ="Weight", ylab = "height", main = "Scatterplot", pch = 20)
z <- rpois(100, 2)
par(mfrow = c(2,1))
plot(x, y, pch = 20 )
plot(x, z, pch = 20)
par("mar")
par(mar = c(2, 2, 1, 1))
par(mfrow = c(1,2))
plot(x, y, pch = 20 )
plot(x, z, pch = 20)
par(mar = c(4, 4, 2, 2))
par(mfrow = c(2, 2))
plot(x, y)
plot(x, z)
plot(z, x)
plot(y, x)
par(mfcol = c(2, 2))
plot(x, y)
plot(x, z)
plot(z, x)
plot(y, x)
par(mfrow = c(1, 1))
x <- rnorm(100)
y <- x + rnorm(100)
g <- gl(2, 50)
g <- gl(2, 50, labels = c("Male", "Female"))
str(g)
plot(x, y)
plot(x, y, type = "n")
points(x[g=="Male"], y[g=="Male"], col ="blue")
points(x[g=="Female"], y[g=="Female"], col ="red", pch = 19)
?Devices
with(faithful, plot(eruptions, waiting))
title(main = "Old Faithful gyser data")
getwd()
setwd("C:/learning/coursera/coursera-data_science-data/exploratory_data_analysis/week1")
pdf(file = "myplot.pdf")
with(faithful, plot(eruptions, waiting))
title(main = "Old Faithful gyser data")
dev.off()
with(faithful, plot(eruptions, waiting))
title(main = "Old Faithful gyser data")
dev.copy(png, file = "geyserplot.png")
dev.off()
|
c7e201eb87579bd3ffb8fbbb57d49853fc939285
|
33b7262af06cab5cd28c4821ead49b3a0c24bb9d
|
/pkg/caret/tests/testthat/test_sampling_options.R
|
b5ae0ef8f56243b3997b70272e0c1b30b10110f2
|
[] |
no_license
|
topepo/caret
|
d54ea1125ad41396fd86808c609aee58cbcf287d
|
5f4bd2069bf486ae92240979f9d65b5c138ca8d4
|
refs/heads/master
| 2023-06-01T09:12:56.022839
| 2023-03-21T18:00:51
| 2023-03-21T18:00:51
| 19,862,061
| 1,642
| 858
| null | 2023-03-30T20:55:19
| 2014-05-16T15:50:16
|
R
|
UTF-8
|
R
| false
| false
| 2,493
|
r
|
test_sampling_options.R
|
library(caret)
library(testthat)
context("sampling options")
load(system.file("models", "sampling.RData", package = "caret"))
test_that('check appropriate sampling calls by name', {
skip_on_cran()
arg_names <- c("up", "down", "rose", "smote")
arg_funcs <- sampling_methods
arg_first <- c(TRUE, FALSE)
## test that calling by string gives the right result
for(i in arg_names) {
out <- caret:::parse_sampling(i, check_install = FALSE)
expected <- list(name = i,
func = sampling_methods[[i]],
first = TRUE)
expect_equivalent(out, expected)
}
})
test_that('check appropriate sampling calls by function', {
skip_on_cran()
arg_names <- c("up", "down", "rose", "smote")
arg_funcs <- sampling_methods
arg_first <- c(TRUE, FALSE)
## test that calling by function gives the right result
for(i in arg_names) {
out <- caret:::parse_sampling(sampling_methods[[i]],
check_install = FALSE)
expected <- list(name = "custom",
func = sampling_methods[[i]],
first = TRUE)
expect_equivalent(out, expected)
}
})
test_that('check bad sampling name', {
skip_on_cran()
expect_error(caret:::parse_sampling("what?"))
})
test_that('check bad first arg', {
skip_on_cran()
expect_error(
caret:::parse_sampling(
list(name = "yep", func = sampling_methods[["up"]], first = 2),
check_install = FALSE)
)
})
test_that('check bad func arg', {
skip_on_cran()
expect_error(
caret:::parse_sampling(
list(name = "yep", func = I, first = 2),
check_install = FALSE)
)
})
test_that('check incomplete list', {
skip_on_cran()
expect_error(caret:::parse_sampling(list(name = "yep"), check_install = FALSE))
})
test_that('check call', {
skip_on_cran()
expect_error(caret:::parse_sampling(14, check_install = FALSE))
})
###################################################################
##
test_that('check getting all methods', {
skip_on_cran()
expect_equivalent(getSamplingInfo(), sampling_methods)
})
test_that('check getting one method', {
skip_on_cran()
arg_names <- c("up", "down", "rose", "smote")
for(i in arg_names) {
out <- getSamplingInfo(i, regex = FALSE)
expected <- list(sampling_methods[[i]])
names(expected) <- i
expect_equivalent(out, expected)
}
})
test_that('check missing method', {
skip_on_cran()
expect_error(getSamplingInfo("plum"))
})
|
b99835ac2fde0eae55f8b0d0cc3d6f20f57bedc1
|
accc9bcaf489cbd6d9394a6b7971cb0f80b1d350
|
/Lung_asthma/Celda_code.R
|
b2ffcbe66ae3636931f8cba283a41d4706b31731
|
[] |
no_license
|
zx1an95/JH
|
f7a6e1d6ca827e3ad81857c4b42d0970aacd731a
|
5c6de3003f95eacd7b1208164a7d24e2635fdf51
|
refs/heads/master
| 2020-12-29T08:40:03.438489
| 2020-05-05T18:54:28
| 2020-05-05T18:54:28
| 238,539,667
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 10,873
|
r
|
Celda_code.R
|
load("GSE130148_raw_counts.RData")
lung_resection <- CreateSeuratObject(counts = raw_counts, project = "lung_resection",
min.cells = 3, min.features = 250)
lung_resection[["percent.mt"]] <- PercentageFeatureSet(lung_resection, pattern = "^MT-")
lung_resection <- subset(lung_resection, subset = nFeature_RNA > 200 & nFeature_RNA < 4000 & percent.mt < 20)
lung_resection <- NormalizeData(lung_resection, normalization.method = "LogNormalize", scale.factor = 10000)
lung_resection <- FindVariableFeatures(lung_resection, selection.method = "vst", nfeatures = 4500)
topgenes <- lung_resection@assays$RNA@var.features
library(celda)
new_lung_resection <- as.matrix(lung_resection@assays$RNA@counts)
new_lung_resection <- new_lung_resection[topgenes,]
moduleSplit <- recursiveSplitModule(counts = new_lung_resection,
initialL = 3, maxL = 150) #L = 85
plotGridSearchPerplexity(celdaList = moduleSplit)
moduleSplitSelect <- subsetCeldaList(moduleSplit, params = list(L = 85))
cellSplit <- recursiveSplitCell(counts = new_lung_resection,
initialK = 3,
maxK = 45,
yInit = clusters(moduleSplitSelect)$y) #K = 30
plotGridSearchPerplexity(celdaList = cellSplit)
goodceldaModel <- subsetCeldaList(celdaList = cellSplit, params = list(K = 30, L = 85))
factorized <- factorizeMatrix(new_lung_resection, celdaMod = goodceldaModel)
names(factorized)
cellPop <- factorized$proportions$cellPopulation
topGenes <- topRank(matrix = factorized$proportions$module,
n = 5, threshold = NULL)
celdaHeatmap(counts = new_lung_resection, celdaMod = goodceldaModel, nfeatures = 5)
tsne <- celdaTsne(counts = new_lung_resection, celdaMod = goodceldaModel)
plotDimReduceCluster(dim1 = tsne[, 1],
dim2 = tsne[, 2],
cluster = clusters(goodceldaModel)$z)
plotDimReduceFeature(dim1 = tsne[, 1],
dim2 = tsne[, 2],
counts = new_lung_resection,
features = "DCN")
celdaProbabilityMap(counts = new_lung_resection, celdaMod = goodceldaModel)
moduleHeatmap(counts = new_lung_resection, celdaMod = goodceldaModel,
featureModule = 55, topCells = 100)
genes <- c(topGenes$names$L76, topGenes$names$L78)
geneIx <- which(rownames(new_lung_resection) %in% genes)
normCounts <- normalizeCounts(counts = new_lung_resection, scaleFun = scale)
plotHeatmap(counts = normCounts,
z = clusters(goodceldaModel)$z,
y = clusters(goodceldaModel)$y,
featureIx = geneIx,
showNamesFeature = TRUE)
plotDimReduceCluster(dim1 = tsne[, 1], dim2 = tsne[, 2], cluster = clusters(goodceldaModel)$z, specificClusters = c(2,3))
plotDimReduceCluster(dim1 = tsne[, 1], dim2 = tsne[, 2], cluster = clusters(goodceldaModel)$z)
plotDimReduceFeature(dim1 = tsne[, 1],dim2 = tsne[, 2],counts = new_lung_resection,features = c("APOC1", "MARCO", "C1QB", "C19orf59", "FABP4"))
plotDimReduceFeature(dim1 = tsne[, 1],dim2 = tsne[, 2],counts = new_lung_resection,features = c("SFTPB", "SFTPC", "SFTPA2", "SLPI", "NAPSA"))
plotDimReduceFeature(dim1 = tsne[, 1],dim2 = tsne[, 2],counts = new_lung_resection,features = c("IGHA1", "IGHM", "IGHG3", "IGHG1", "IGKC"))
plotDimReduceFeature(dim1 = tsne[, 1],dim2 = tsne[, 2],counts = new_lung_resection,features = c("CCL5", "TRBC2", "CD2", "IL32", "TRAC"))
plotDimReduceFeature(dim1 = tsne[, 1],dim2 = tsne[, 2],counts = new_lung_resection,features = c("S100A9", "VCAN", "APOBEC3A", "CD300E", "THBS1"))
plotDimReduceFeature(dim1 = tsne[, 1],dim2 = tsne[, 2],counts = new_lung_resection,features = c("SCGB1A1", "SCGB3A1", "SCGB3A2", "BPIFB1", "WFDC2"))
plotDimReduceFeature(dim1 = tsne[, 1],dim2 = tsne[, 2],counts = new_lung_resection,features = c("TPSAB1", "CPA3", "HPGDS", "KIT", "TPSD1"))
plotDimReduceFeature(dim1 = tsne[, 1],dim2 = tsne[, 2],counts = new_lung_resection,features = c("PRG4", "MT2A", "MT1E", "KRT19", "SLC6A14"))
plotDimReduceFeature(dim1 = tsne[, 1],dim2 = tsne[, 2],counts = new_lung_resection,features = c("C9orf24", "TPPP3", "C20orf85", "RSPH1", "C11orf88"))
plotDimReduceFeature(dim1 = tsne[, 1],dim2 = tsne[, 2],counts = new_lung_resection,features = c("EMP2", "AGER", "CAV1", "RTKN2", "CEACAM6"))
plotDimReduceFeature(dim1 = tsne[, 1],dim2 = tsne[, 2],counts = new_lung_resection,features = c("SPARCL1", "EPAS1", "EMP1", "VWF", "SERPINE1"))
plotDimReduceFeature(dim1 = tsne[, 1],dim2 = tsne[, 2],counts = new_lung_resection,features = c("GNLY", "NKG7", "GZMA", "FGFBP2"))
plotDimReduceFeature(dim1 = tsne[, 1],dim2 = tsne[, 2],counts = new_lung_resection,features = c("HLA-DPA1", "HLA-DQA1", "HLA-DQB1", "GPR183", "FCER1A"))
plotDimReduceFeature(dim1 = tsne[, 1],dim2 = tsne[, 2],counts = new_lung_resection,features = c("DCN", "COL1A2", "LUM", "COL3A1", "A2M"))
plotDimReduceFeature(dim1 = tsne[, 1],dim2 = tsne[, 2],counts = new_lung_resection,features = c("CCL21", "TFF3", "GNG11", "MMRN1", "LYVE1"))
plotDimReduceFeature(dim1 = tsne[, 1],dim2 = tsne[, 2],counts = new_lung_resection,features = c("FCGR3B","S100A9","CSF3R","S100A8","IFITM2"))
plotDimReduceFeature(dim1 = tsne[, 1],dim2 = tsne[, 2],counts = new_lung_resection,features = c("CMTM2","MNDA","VNN2","CXCR2","S100P"))
plotDimReduceFeature(dim1 = tsne[, 1],dim2 = tsne[, 2],counts = new_lung_resection,features = c("S100A12","RGS2","FPR1","NAMPT","G0S2","IFITM3"))
DE <- array()
cluster1 <- which(rownames(new_lung_resection) %in% head(diffexpClust1$Gene,20))
Indlist <- c()
for (ct in cell_type){
y <- differentialExpression(counts = new_lung_resection,
celdaMod = goodceldaModel,
c1 = ct,
c2 = NULL)
y <- y[y$FDR < 0.05 & abs(y$Log2_FC) > 2, ]
Indlist <- append(Indlist,which(rownames(new_lung_resection) %in% head(y$Gene,5)))
}
Indexlist <- which(rownames(new_lung_resection) %in% head(cluster1$Gene,5))
Indexlist <- append(Indexlist,which(rownames(new_lung_resection) %in% head(cluster35$Gene,5)))
Indexlist2 <- which((rownames(new_lung_resection) %in% c("APOC1", "MARCO", "C1QB", "C19orf59", "FABP4",
"SFTPB", "SFTPC", "SFTPA2", "SLPI", "NAPSA",
"IGHA1", "IGHM", "IGHG3", "IGHG1", "IGKC",
"CCL5", "TRBC2", "CD2", "IL32", "TRAC",
"S100A9", "VCAN", "APOBEC3A", "CD300E", "THBS1",
"SCGB1A1", "SCGB3A1", "SCGB3A2", "BPIFB1", "WFDC2",
"TPSAB1", "CPA3", "HPGDS", "KIT", "TPSD1",
"PRG4", "MT2A", "MT1E", "KRT19", "SLC6A14",
"C9orf24", "TPPP3", "C20orf85", "RSPH1", "C11orf88",
"EMP2", "AGER", "CAV1", "RTKN2", "CEACAM6",
"SPARCL1", "EPAS1", "EMP1", "VWF", "SERPINE1",
"GNLY", "NKG7", "GZMA", "FGFBP2",
"HLA-DPA1", "HLA-DQA1", "HLA-DQB1", "GPR183", "FCER1A",
"DCN", "COL1A2", "LUM", "COL3A1", "A2M",
"CCL21", "TFF3", "GNG11", "MMRN1", "LYVE1")))
normCounts <- normalizeCounts(counts = new_lung_resection, scaleFun = scale)
cell_type <- c("Macrophages", "Type 2", "Neutrophils", "B cell", "T cell", "Type 1",
"Ciliated", "Club", "Endothelium","NK cell", "Mast cell", "Transformed epithelium",
"Dendritic cell", "Fibroblast", "Lymphatic")
plotHeatmap(counts = normCounts[, clusters(goodceldaModel)$z %in% cell_type],
z = clusters(goodceldaModel)$z[clusters(goodceldaModel)$z %in% cell_type],
featureIx = Indlist,
clusterFeature = FALSE,
clusterCell = TRUE,
showNamesFeature = TRUE)
plotHeatmap(counts = normCounts[, clusters(goodceldaModel)$z %in% cell_type],
clusterCell = TRUE,
clusterFeature = FALSE,
z = clusters(goodceldaModel)$z[clusters(goodceldaModel)$z %in% cell_type],
y = clusters(goodceldaModel)$y,
featureIx = Indexlist2,
showNamesFeature = TRUE)
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='27'] <- "26"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='28'] <- "26"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='9'] <- "8"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='10'] <- "8"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='29'] <- "7"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='5'] <- "4"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='6'] <- "4"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='23'] <- "22"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='21'] <- "20"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='26'] <- "Macrophages"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='8'] <- "Type 2"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='7'] <- "B cell"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='18'] <- "T cell"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='24'] <- "Neutrophils"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='4'] <- "Club"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='22'] <- "Mast cell"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='11'] <- "Transformed epithelium"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='11'] <- "Ciliated"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='20'] <- "Type 1"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='15'] <- "Endothelium"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='19'] <- "NK cell"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='30'] <- "Dendritic cell"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='13'] <- "Lymphatic"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='1'] <- "B cell"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='12'] <- "Club"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='14'] <- "B cell"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='2'] <- "B cell"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='3'] <- "B cell"
goodceldaModel@clusters$z[goodceldaModel@clusters$z=='25'] <- "Neutrophils"
length(unique(goodceldaModel@clusters$z))
|
d08c3a08948203fc8d97b1a89cbdf6676248d67d
|
7090f45f05d288c426d91515ea0a80371c402fcc
|
/tests/testthat/test-search.R
|
bdc29b349b76c4f2f460ec9507731b57705c6c97
|
[
"MIT"
] |
permissive
|
KTH-Library/dblp
|
5969671b9d6a11c19646aac3a309ad6aaa1966ef
|
8a5488b2a9c13c9506c497fdfaa75b4ec7384e50
|
refs/heads/master
| 2021-08-26T01:01:14.531024
| 2021-08-18T15:15:44
| 2021-08-18T15:15:44
| 246,881,922
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 400
|
r
|
test-search.R
|
test_that("search works", {
t1 <- dblp_search("European", entity = "venues")$content
expect_gt(nrow(t1), 5)
})
test_that("search venues w zero hits works", {
t1 <- dblp_search("venues", entity = "venues")$content
expect_equal(nrow(t1), 0)
})
test_that("search authors w zero hits works", {
t1 <- dblp_search("nooneinparticular", entity = "authors")$content
expect_equal(nrow(t1), 0)
})
|
6307f3c80c9aea88ca07aba9504bbfb424262a6f
|
1277466828a8aa3bfd8e3d3280b9f21a8b575cf4
|
/app/global.R
|
159186d51bcccc901e321d9ed697839cad0deb8b
|
[] |
no_license
|
gabrielteotonio/pca-image-compression
|
c8e52acd855cc2aff842967c11b7aeeacf8e0f3d
|
734282c32ab76da9a55b6a912c1ababfade6cb23
|
refs/heads/master
| 2020-05-15T10:38:52.418552
| 2019-04-20T23:45:23
| 2019-04-20T23:45:23
| 182,195,726
| 4
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,124
|
r
|
global.R
|
# Packages ----
library(shiny)
library(EMD) # Lena's image
library(blockmatrix) # For creating block matrices
library(ggplot2) # Graphics
library(redR) # For MSE and PSNR measures
library(SPUTNIK) # For SSIM measure
# Loading data ----
data(lena) # Storage the image
# Functions ----
blockToObs <- function(block_matrix) {
obs_matrix <- c()
for (i in 1:64) {
for (j in 1:64) {
obs <- as.vector(t(block_matrix[i,j]))
obs_matrix <- rbind(obs_matrix, obs)
}
}
return(obs_matrix)
}
obsToBlock <- function(Obs_matrix) {
matrix <- matrix(0, nrow = 512, ncol = 512)
cont <- 1
for (i in seq(1,512,8)) {
for (j in seq(1,512,8)) {
block_matrix <- matrix(Obs_matrix[cont,], nrow = 8, ncol = 8, byrow = T)
matrix[i:(i+8-1), j:(j+8-1)] <- block_matrix
cont <- cont + 1
}
}
return(matrix)
}
Tmatrix <- function(m) {
t_m <- diag(0, ncol = 64, nrow = 64)
for (i in 1:m) {
t_m[i,i] <- 1
}
return(t_m)
}
pca <- function(data) {
lena_block <- as.blockmatrix(data, nrowe = 8, ncole = 8) # Create a block matrix for image data
x <- blockToObs(lena_block) # Transform a block matrix into a "dataframe" (blocks by row)
x_cent <- scale(x, center = T, scale = F) # Centering the matrix
cov_matrix <- t(x_cent) %*% x_cent # Calculating the Covariance matrix
eigen_pairs <- eigen(cov_matrix) # Calculating eigenvalues and eigenvectors
eigenvalues <- eigen_pairs$values # Accessing eigenvalues
eigenvectors <- eigen_pairs$vectors # Accessing eigenvectors
results <- list("x" = x, "eigenvalues" = eigenvalues, "eigenvectors" = eigenvectors)
return(results)
}
compression <- function(x, eigenvectors, m) {
y <- x %*% eigenvectors # Creating the new "dataframe" with new p principal components
t_m <- Tmatrix(m) # T matrix for define the number of p taking into account
y_m <- y %*% t_m # Creating the new "dataframe" with new p = dim(t_m) principal components
b <- y_m %*% t(eigenvectors) # Inverse of the transformation
B <- obsToBlock(b) # The image generating matrix
return(B) # Returning the image matrix
}
PCA <- pca(lena)
|
ad2106406837b11d40646fb906428e47a756a8f9
|
034af5f06904647579ab75acca51ca597611eca8
|
/func-room.R
|
eb1ba58e2e1af5c674741df519ac14a10cfe7556
|
[
"MIT"
] |
permissive
|
rintukutum/DREAM-PretermBirth-SC2
|
bc1218a0d70755655ce59ff8d2dfdcc6df803adf
|
f49a0ed96d36941a7ff14764594eb0fe73d4e024
|
refs/heads/master
| 2020-12-02T03:34:11.741355
| 2019-12-31T14:24:37
| 2019-12-31T14:24:37
| 230,874,237
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,804
|
r
|
func-room.R
|
ctrl.sptd <- function(x){
x.ctrl <- x[idx.ctrl]
x.sptd <- x[idx.sptd]
df_ <- data.frame(
class = rep(
c('ctrl','sptd'),
c(length(x.ctrl),
length(x.sptd))),
value = c(
x.ctrl,
x.sptd
)
)
normal <- shapiro.test(df_$value)$p.value > 0.05
if(normal){
pval <- t.test(value~class,data=df_)$p.value
}else{
pval <- wilcox.test(value~class,data=df_)$p.value
}
return(pval)
}
ctrl.pprom <- function(x){
x.ctrl <- x[idx.ctrl]
x.pprom <- x[idx.pprom]
df_ <- data.frame(
class = rep(
c('ctrl','pprom'),
c(length(x.ctrl),
length(x.pprom))),
value = c(
x.ctrl,
x.pprom
)
)
normal <- shapiro.test(df_$value)$p.value > 0.05
if(normal){
pval <- t.test(value~class,data=df_)$p.value
}else{
pval <- wilcox.test(value~class,data=df_)$p.value
}
return(pval)
}
performSVM <- function(tr.x,tr.y,tt.x,SEED=7860){
# tr.x <- ctrl.PPROM.sig.2H.probes.data$tr.x[,1:100]
# tr.y <- ctrl.PPROM.sig.2H.probes.data$tr.y
# tt.x <- ctrl.PPROM.sig.2H.probes.data$tt.x[,1:100]
library(e1071)
library(caret)
library(foreach)
set.seed(SEED)
cv.5 <- createFolds(tr.y,k = 5)
out <- list()
for(i in 1:length(cv.5)){
idx.tr <- unlist(cv.5[-i])
idx.tt <- unlist(cv.5[i])
mini.tr.x <- tr.x[idx.tr,]
mini.tr.y <- tr.y[idx.tr]
mini.tt.x <- tr.x[idx.tt,]
mini.tt.y <- tr.y[idx.tt]
#--------------------
# MODELS
mod.linear <- svm(
x = mini.tr.x,
y= mini.tr.y,
kernel = 'linear',
probability = TRUE
)
pred.linear <- predict(
mod.linear,
mini.tt.x
)
mod.radial <- svm(
x = mini.tr.x,
y= mini.tr.y,
kernel = 'radial',
probability = TRUE
)
pred.radial <- predict(
mod.radial,
mini.tt.x
)
mod.sigmoid <- svm(
x = mini.tr.x,
y= mini.tr.y,
kernel = 'sigmoid',
probability = TRUE
)
pred.sigmoid <- predict(
mod.sigmoid,
mini.tt.x
)
tmpOUT.r <- getCVperf(
pred = pred.radial,
orig = mini.tt.y,
mod.method = 'SVM.radial'
)
tmpOUT.s <- getCVperf(
pred = pred.sigmoid,
orig = mini.tt.y,
mod.method = 'SVM.sigmoid'
)
tmpOUT.l <- getCVperf(
pred = pred.linear,
orig = mini.tt.y,
mod.method = 'SVM.linear'
)
perf.svm <- list(
tmpOUT.r,
tmpOUT.s,
tmpOUT.l
)
perf.svm.df <- plyr::ldply(perf.svm)
#---------
# PREDICT TT
tt.linear <- predict(mod.linear,tt.x,probability = TRUE)
tt.linear <- data.frame(round(attr(tt.linear,'probabilities'),4))
tt.linear$SampleID <- rownames(tt.linear)
tt.sigmoid <- predict(mod.sigmoid,tt.x,probability = TRUE)
tt.sigmoid <- data.frame(round(attr(tt.sigmoid,'probabilities'),4))
tt.sigmoid$SampleID <- rownames(tt.sigmoid)
tt.radial <- predict(mod.radial,tt.x,probability = TRUE)
tt.radial <- data.frame(round(attr(tt.radial,'probabilities'),4))
tt.radial$SampleID <- rownames(tt.radial)
tt.out <- list(
'linear' = tt.linear,
'sigmoid' = tt.sigmoid,
'radial' = tt.radial
)
OUT <- list(
perf = perf.svm.df,
tt.prediction = plyr::ldply(tt.out)
)
out[[i]] <- OUT
}
names(out) <- paste0('CV',1:length(cv.5))
return(out)
}
getCVperf <- function(pred,
orig,
mod.method){
xout <- caret::confusionMatrix(
data = pred,
reference=orig
)
df_ <- data.frame(xout$byClass[c('Sensitivity','Specificity')])
colnames(df_)[1] <- 'val'
df_$property <- rownames(df_)
df_$method <- mod.method
return(df_)
}
extractMODpred <- function(x,method){
x <- x$tt.prediction
idx <- x$.id %in% method
return(x[idx,])
}
|
34eb6636e8a3f8a1d17c6f1f6e76b73a35377358
|
df5cfd2a2c753abae911f275b8842ae2905b0e98
|
/tests/testthat/test-correlation.R
|
13d8f0144c42cf0de5c91ecd6b11ad1ec9766887
|
[] |
no_license
|
cran/roahd
|
badc33125a145e40ad62ca4cb46dbb3686d1a004
|
678f7c77246bcbd290673ce405519eddb3a323f7
|
refs/heads/master
| 2021-11-23T08:04:45.984396
| 2021-11-03T23:10:02
| 2021-11-03T23:10:02
| 56,397,799
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 16,268
|
r
|
test-correlation.R
|
# maxima() & minima() -----------------------------------------------------
test_that("maxima() works for functional data when `which = TRUE`", {
# Arrange
P <- 1e4
time_grid <- seq(0, 1, length.out = P)
h <- time_grid[2] - time_grid[1]
Data <- matrix(c(1 * time_grid,
2 * time_grid,
3 * ( 0.5 - abs( time_grid - 0.5))),
nrow = 3, ncol = P, byrow = TRUE)
fD <- fData(time_grid, Data)
# Act
actual <- maxima(fD, which = TRUE)
# Assert
expected_grid <- c(1, 1, 0 + 4999 * h)
expected_value <- c(1, 2, 3 * ( 0.5 - abs( 0.5 - 4999 * h)))
expect_equal(actual$grid, expected_grid)
expect_equal(actual$value, expected_value)
})
test_that("minima() works for functional data when `which = TRUE`", {
# Arrange
P <- 1e4
time_grid <- seq(0, 1, length.out = P)
Data <- matrix(c(1 * time_grid,
2 * time_grid,
3 * ( 0.5 - abs( time_grid - 0.5))),
nrow = 3, ncol = P, byrow = TRUE)
fD <- fData(time_grid, Data)
# Act
actual <- minima(fD, which = TRUE)
# Assert
expected_grid <- rep(0, 3)
expected_value <- rep(0, 3)
expect_equal(actual$grid, expected_grid)
expect_equal(actual$value, expected_value)
})
test_that("maxima() works for functional data when `which = FALSE`", {
# Arrange
P <- 1e4
time_grid <- seq(0, 1, length.out = P)
h <- time_grid[2] - time_grid[1]
Data <- matrix(c(1 * time_grid,
2 * time_grid,
3 * ( 0.5 - abs( time_grid - 0.5))),
nrow = 3, ncol = P, byrow = TRUE)
fD <- fData(time_grid, Data)
# Act
actual <- maxima(fD, which = FALSE)
# Assert
expected <- c(1, 2, 3 * (0.5 - abs( 0.5 - 4999 * h)))
expect_equal(actual, expected)
})
test_that("minima() works for functional data when `which = FALSE`", {
# Arrange
P <- 1e4
time_grid <- seq(0, 1, length.out = P)
Data <- matrix(c(1 * time_grid,
2 * time_grid,
3 * ( 0.5 - abs( time_grid - 0.5))),
nrow = 3, ncol = P, byrow = TRUE)
fD <- fData(time_grid, Data)
# Act
actual <- minima(fD, which = FALSE)
# Assert
expected <- rep(0, 3)
expect_equal(actual, expected)
})
# area_under_curve() ------------------------------------------------------
test_that("area_under_curve() works for functional data", {
# Arrange
P <- 1e4
time_grid <- seq(0, 1, length.out = P)
fD_1 <- fData(time_grid,
matrix(c(1 * time_grid,
2 * time_grid,
3 * ( 0.5 - abs( time_grid - 0.5))),
nrow = 3, ncol = P, byrow = TRUE))
fD_2 <- fData(time_grid,
matrix(c(sin(2 * pi * time_grid),
cos(2 * pi * time_grid),
4 * time_grid * (1 - time_grid)),
nrow = 3, ncol = P, byrow = TRUE))
# Act
actual <- area_under_curve(fD_1)
# Assert
expected <- c(0.5, 1, 0.75)
expect_equal(actual, expected)
expect_true(all(c(
area_under_curve(fD_2)[1:2],
abs(area_under_curve(fD_2[3, ]) - 2/3)
) <= .Machine$double.eps^0.5))
})
# Ordering functions ------------------------------------------------------
test_that("max_ordered() works as expected", {
# Arrange
P <- 1e3
time_grid <- seq(0, 1, length.out = P)
h <- time_grid[2] - time_grid[1]
Data_1 <- matrix(
c(1 * time_grid, 2 * time_grid),
nrow = 2, ncol = P, byrow = TRUE
)
Data_2 <- matrix(
3 * (0.5 - abs(time_grid - 0.5)),
nrow = 1, byrow = TRUE
)
Data_3 <- rbind(Data_1, Data_1)
fD_1 <- fData(time_grid, Data_1)
fD_2 <- fData(time_grid, Data_2)
fD_3 <- fData(time_grid, Data_3)
# Act
actual_max_1 <- max_ordered(fD_1, fD_2)
actual_max_2 <- max_ordered(fD_2, fD_1)
actual_max_3 <- max_ordered(fD_2, fD_3)
actual_max_4 <- max_ordered(fD_3, fD_2)
# Assert
expected_max_1 <- c(TRUE, FALSE)
expect_equal(actual_max_1, expected_max_1)
expected_max_2 <- c(FALSE, TRUE)
expect_equal(actual_max_2, expected_max_2)
expect_error(max_ordered(fD_1, fD_3))
expect_error(max_ordered(fD_3, fD_1))
expected_max_3 <- c(FALSE, TRUE, FALSE, TRUE)
expect_equal(actual_max_3, expected_max_3)
expected_max_4 <- c(TRUE, FALSE, TRUE, FALSE)
expect_equal(actual_max_4, expected_max_4)
})
test_that("area_ordered() works as expected", {
# Arrange
P <- 1e3
time_grid <- seq(0, 1, length.out = P)
h <- time_grid[2] - time_grid[1]
Data_1 <- matrix(
c(1 * time_grid, 2 * time_grid),
nrow = 2, ncol = P, byrow = TRUE
)
Data_2 <- matrix(
3 * (0.5 - abs(time_grid - 0.5)),
nrow = 1, byrow = TRUE
)
Data_3 <- rbind(Data_1, Data_1)
fD_1 <- fData(time_grid, Data_1)
fD_2 <- fData(time_grid, Data_2)
fD_3 <- fData(time_grid, Data_3)
# Act
actual_area_1 <- area_ordered(fD_1, fD_2)
actual_area_2 <- area_ordered(fD_2, fD_1)
actual_area_3 <- area_ordered(fD_2, fD_3)
actual_area_4 <- area_ordered(fD_3, fD_2)
# Assert
expected_area_1 <- c(TRUE, FALSE)
expect_equal(actual_area_1, expected_area_1)
expected_area_2 <- c(FALSE, TRUE)
expect_equal(actual_area_2, expected_area_2)
expect_error(area_ordered(fD_1, fD_3))
expect_error(area_ordered(fD_3, fD_1))
expected_area_3 <- c(FALSE, TRUE, FALSE, TRUE)
expect_equal(actual_area_3, expected_area_3)
expected_area_4 <- c(TRUE, FALSE, TRUE, FALSE)
expect_equal(actual_area_4, expected_area_4)
})
# cor_kendall() & cor_spearman() ------------------------------------------
test_that("cor_kendall() and cor_spearman() work as expected", {
# Arrange
withr::local_seed(1234)
N <- 2e2
P <- 1e3
time_grid <- seq(0, 1, length.out = P)
Cov <- exp_cov_function(time_grid, alpha = 0.3, beta = 0.4)
Data_1 <- generate_gauss_fdata(
N,
centerline = sin(2 * pi * time_grid),
Cov = Cov
)
Data_2 <- generate_gauss_fdata(
N,
centerline = sin(2 * pi * time_grid),
Cov = Cov
)
mfD <- mfData(time_grid, list(Data_1, Data_2))
# Act
actual_kendall_max <- cor_kendall(mfD, ordering = 'max')
actual_kendall_area <- cor_kendall(mfD, ordering = 'area')
actual_spearman_mei <- cor_spearman(mfD, ordering = 'MEI')
actual_spearman_mhi <- cor_spearman(mfD, ordering = 'MHI')
# Assert
expect_snapshot_value(actual_kendall_max, style = "serialize")
expect_snapshot_value(actual_kendall_area, style = "serialize")
expect_snapshot_value(actual_spearman_mei, style = "serialize")
expect_snapshot_value(actual_spearman_mhi, style = "serialize")
})
# Case studies from Dalia Valencia, Rosa Lillo, Juan Romo -----------------
test_that("cor_kendall() & cor_spearman() work on Case Study 1 from Dalia Valencia, Rosa Lillo, Juan Romo.", {
withr::local_seed(1234)
N <- 50
P <- 50
time_grid<- seq(0, 1, length.out = P)
sigma_12 <- 0.8
R <- matrix(c(1, sigma_12, sigma_12, 1), ncol = 2, nrow = 2)
Z <- matrix(rnorm(N * 2, 0, 1), ncol = 2, nrow = N) %*% chol(R)
X <- (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 1])^3 +
(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 1])^2 +
(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 1]) * 3
Y <- (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 2])^2 +
(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 2]) * 7 / 8 +
- 10
mfD <- mfData(time_grid, list(X, Y))
expect_snapshot_value(cor_kendall(mfD, ordering = 'max'), style = "serialize")
expect_snapshot_value(cor_kendall(mfD, ordering = 'area'), style = "serialize")
expect_snapshot_value(cor_spearman(mfD, ordering = 'MEI'), style = "serialize")
expect_snapshot_value(cor_spearman(mfD, ordering = 'MHI'), style = "serialize")
})
test_that("cor_kendall() & cor_spearman() work on Case Study 2 from Dalia Valencia, Rosa Lillo, Juan Romo.", {
withr::local_seed(1234)
N <- 50
P <- 50
time_grid<- seq(0, 1, length.out = P)
sigma_12 <- - 0.7
R <- matrix(c(1, sigma_12, sigma_12, 1), ncol = 2, nrow = 2)
Z <- matrix(rnorm(N * 2, 0, 1), ncol = 2, nrow = N) %*% chol(R)
X <- sin(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 1])
Y <- cos(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 2])
mfD <- mfData(time_grid, list(X, Y))
expect_snapshot_value(cor_kendall(mfD, ordering = 'max'), style = "serialize")
expect_snapshot_value(cor_kendall(mfD, ordering = 'area'), style = "serialize")
expect_snapshot_value(cor_spearman(mfD, ordering = 'MEI'), style = "serialize")
expect_snapshot_value(cor_spearman(mfD, ordering = 'MHI'), style = "serialize")
})
test_that("cor_kendall() & cor_spearman() work on Case Study 3 from Dalia Valencia, Rosa Lillo, Juan Romo.", {
withr::local_seed(1234)
N <- 50
P <- 50
time_grid<- seq(0, 1, length.out = P)
sigma_12 <- 1
Z <- rnorm(N, 0, 1)
X <- (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z)^2
Y <- (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z)^4
mfD <- mfData(time_grid, list(X, Y))
expect_equal(cor_kendall(mfD, ordering = 'max' ), 1)
expect_equal(cor_kendall(mfD, ordering = 'area'), 1)
expect_equal(cor_spearman(mfD, ordering = 'MEI'), 1)
expect_equal(cor_spearman(mfD, ordering = 'MHI'), 1)
})
test_that("cor_kendall() & cor_spearman() work on Case Study 4 from Dalia Valencia, Rosa Lillo, Juan Romo.", {
withr::local_seed(1234)
N <- 50
P <- 50
time_grid<- seq(0, 1, length.out = P)
sigma_12 <- 1
Z <- rnorm(N, 0, 1)
X <- (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z)^2 +
(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z) * 7 +
2
Y <- ((matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z)^2 +
(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z) * 7 +
2)^3
mfD <- mfData(time_grid, list(X, Y))
expect_equal(cor_kendall(mfD, ordering = 'max' ), 1)
expect_equal(cor_kendall(mfD, ordering = 'area'), 1)
expect_equal(cor_spearman(mfD, ordering = 'MEI'), 1)
expect_equal(cor_spearman(mfD, ordering = 'MHI'), 1)
})
test_that("cor_kendall() & cor_spearman() work on Case Study 5 from Dalia Valencia, Rosa Lillo, Juan Romo.", {
withr::local_seed(1234)
N <- 50
P <- 50
time_grid<- seq(0, 1, length.out = P)
sigma_12 <- 1
Z <- rnorm(N, 0, 1)
X <- (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z)^2 +
(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z) * 7 +
2
Y <- 1 - ((matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z)^2 +
(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z) * 7 +
2)^3
mfD <- mfData(time_grid, list(X, Y))
expect_equal(cor_kendall(mfD, ordering = 'max' ), -1)
expect_equal(cor_kendall(mfD, ordering = 'area'), -1)
expect_equal(cor_spearman(mfD, ordering = 'MEI'), -1)
expect_equal(cor_spearman(mfD, ordering = 'MHI'), -1)
})
test_that("cor_kendall() & cor_spearman() work on Case Study 6 from Dalia Valencia, Rosa Lillo, Juan Romo.", {
withr::local_seed(1234)
N <- 50
P <- 50
time_grid<- seq(0, 1, length.out = P)
sigma_12 <- 0.6
R <- matrix(c(1, sigma_12, sigma_12, 1), ncol = 2, nrow = 2)
Z <- matrix(rnorm(N * 2, 0, 1), ncol = 2, nrow = N) %*% chol(R)
X <- exp(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 1])
Y <- (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 2])^3 +
(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 2])^2 +
(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 2]) * 3
mfD <- mfData(time_grid, list(X, Y))
expect_snapshot_value(cor_kendall(mfD, ordering = 'max'), style = "serialize")
expect_snapshot_value(cor_kendall(mfD, ordering = 'area'), style = "serialize")
expect_snapshot_value(cor_spearman(mfD, ordering = 'MEI'), style = "serialize")
expect_snapshot_value(cor_spearman(mfD, ordering = 'MHI'), style = "serialize")
})
test_that("cor_kendall() & cor_spearman() work on Case Study 7 from Dalia Valencia, Rosa Lillo, Juan Romo.", {
withr::local_seed(1234)
N <- 50
P <- 50
time_grid<- seq(0, 1, length.out = P)
sigma_12 <- -0.8
R <- matrix(c(1, sigma_12, sigma_12, 1), ncol = 2, nrow = 2)
Z <- matrix(rnorm(N * 2, 0, 1), ncol = 2, nrow = N) %*% chol(R)
X <- exp(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 1])^2
Y <- cos((matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 2]))
mfD <- mfData(time_grid, list(X, Y))
expect_snapshot_value(cor_kendall(mfD, ordering = 'max'), style = "serialize")
expect_snapshot_value(cor_kendall(mfD, ordering = 'area'), style = "serialize")
expect_snapshot_value(cor_spearman(mfD, ordering = 'MEI'), style = "serialize")
expect_snapshot_value(cor_spearman(mfD, ordering = 'MHI'), style = "serialize")
})
test_that("cor_kendall() & cor_spearman() work on Case Study 8 from Dalia Valencia, Rosa Lillo, Juan Romo.", {
withr::local_seed(1234)
N <- 50
P <- 50
time_grid<- seq(0, 1, length.out = P)
sigma_12 <- 0.4
R <- matrix(c(1, sigma_12, sigma_12, 1), ncol = 2, nrow = 2)
Z <- matrix(rnorm(N * 2, 0, 1), ncol = 2, nrow = N) %*% chol(R)
X <- sin(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 1])
Y <- (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 2])^2
mfD <- mfData(time_grid, list(X, Y))
expect_snapshot_value(cor_kendall(mfD, ordering = 'max'), style = "serialize")
expect_snapshot_value(cor_kendall(mfD, ordering = 'area'), style = "serialize")
expect_snapshot_value(cor_spearman(mfD, ordering = 'MEI'), style = "serialize")
expect_snapshot_value(cor_spearman(mfD, ordering = 'MHI'), style = "serialize")
})
test_that("cor_kendall() & cor_spearman() work on Case Study 9 from Dalia Valencia, Rosa Lillo, Juan Romo.", {
withr::local_seed(1234)
N <- 50
P <- 50
time_grid<- seq(0, 1, length.out = P)
sigma_12 <- 1
Z <- rnorm(N, 0, 1)
X <- (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z)^2 +
(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z) * 9 +
- 5
Y <- cos(matrix(3 * time_grid, nrow = N, ncol = P, byrow = TRUE) + Z)
mfD <- mfData(time_grid, list(X, Y))
expect_snapshot_value(cor_kendall(mfD, ordering = 'max'), style = "serialize")
expect_snapshot_value(cor_kendall(mfD, ordering = 'area'), style = "serialize")
expect_snapshot_value(cor_spearman(mfD, ordering = 'MEI'), style = "serialize")
expect_snapshot_value(cor_spearman(mfD, ordering = 'MHI'), style = "serialize")
})
test_that("cor_kendall() & cor_spearman() work on Case Study 10 from Dalia Valencia, Rosa Lillo, Juan Romo.", {
withr::local_seed(1234)
N <- 50
P <- 50
time_grid<- seq(0, 1, length.out = P)
sigma_12 <- 0.9
R <- matrix(c(1, sigma_12, sigma_12, 1), ncol = 2, nrow = 2)
Z <- matrix(rnorm(N * 2, 0, 1), ncol = 2, nrow = N) %*% chol(R)
X <- exp(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE)^2 + Z[, 1])
Y <- (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 2])^2 +
matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) * (-8) +
(matrix(0, nrow = N, ncol = P, byrow = TRUE) + Z[, 2])
mfD <- mfData(time_grid, list(X, Y))
expect_snapshot_value(cor_kendall(mfD, ordering = 'max'), style = "serialize")
expect_snapshot_value(cor_kendall(mfD, ordering = 'area'), style = "serialize")
expect_snapshot_value(cor_spearman(mfD, ordering = 'MEI'), style = "serialize")
expect_snapshot_value(cor_spearman(mfD, ordering = 'MHI'), style = "serialize")
})
test_that("cor_kendall() & cor_spearman() work on Case Study 11 from Dalia Valencia, Rosa Lillo, Juan Romo.", {
withr::local_seed(1234)
N <- 50
P <- 50
time_grid <- seq(0, 1, length.out = P)
sigma_12 <- 0.
R <- matrix(c(1, sigma_12, sigma_12, 1), ncol = 2, nrow = 2)
Z <- matrix(rnorm(N * 2, 0, 1), ncol = 2, nrow = N) %*% chol(R)
X <- exp(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 1])
Y <- sin(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 2])
mfD <- mfData(time_grid, list(X, Y))
expect_snapshot_value(cor_kendall(mfD, ordering = 'max'), style = "serialize")
expect_snapshot_value(cor_kendall(mfD, ordering = 'area'), style = "serialize")
expect_snapshot_value(cor_spearman(mfD, ordering = 'MEI'), style = "serialize")
expect_snapshot_value(cor_spearman(mfD, ordering = 'MHI'), style = "serialize")
})
|
c3c0c5281b141ecd384fe2a9fa73d679bdcdb6ab
|
7eb63399fa00e3c547e5933ffa4f47de515fe2c6
|
/man/serr.lgcpPredict.Rd
|
71f11a4aa35097a21ca750e1c25fbd13e4b8d2b6
|
[] |
no_license
|
bentaylor1/lgcp
|
a5cda731f413fb30e1c40de1b3360be3a6a53f19
|
2343d88e5d25ecacd6dbe5d6fcc8ace9cae7b136
|
refs/heads/master
| 2021-01-10T14:11:38.067639
| 2015-11-19T13:22:19
| 2015-11-19T13:22:19
| 45,768,716
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 514
|
rd
|
serr.lgcpPredict.Rd
|
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/lgcpMethods.R
\name{serr.lgcpPredict}
\alias{serr.lgcpPredict}
\title{serr.lgcpPredict function}
\usage{
\method{serr}{lgcpPredict}(obj, ...)
}
\arguments{
\item{obj}{an lgcpPredict object}
\item{...}{additional arguments}
}
\value{
Standard error of the relative risk as computed by MCMC.
}
\description{
Accessor function returning the standard error of relative risk as an lgcpgrid object.
}
\seealso{
\link{lgcpPredict}
}
|
501faccfb23e26f090d46c5a4b5bc247d20813ca
|
89f7311158c333e2e35f12fda6af01e00d9ca55d
|
/man/preprocess_experiment.Rd
|
b7cf0ff711c0b9e22a565280e120912ef1175def
|
[] |
no_license
|
BrainVR/brainvr-gonogo
|
282b3165f606203cc4c20c6baaeeea04b923996f
|
b7c133822d2c54311c229a89f106f9c2229e644e
|
refs/heads/main
| 2023-09-01T14:59:20.664380
| 2021-10-30T20:44:14
| 2021-10-30T20:44:14
| 384,252,148
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 385
|
rd
|
preprocess_experiment.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gonogo-preprocessing.R
\name{preprocess_experiment}
\alias{preprocess_experiment}
\title{Prepares experimental data to cleaner stats}
\usage{
preprocess_experiment(df_experiment)
}
\arguments{
\item{df_experiment}{}
}
\value{
}
\description{
Renames columns, recodes some values, adds new deduced values
}
|
89d2353b8781f73823b73d2141d4d1b3582c03d9
|
5bae3c3cd5e47b3088e0fb6478a60ae74e8f7767
|
/rawdata/rawcleandata.R
|
d2e0b157772f1cb3dabe98a640275598fa5d2eea
|
[] |
no_license
|
gravatarsmiley/capstoneR
|
04d4093e63815b3ec91102c1a0ec8f6b869e5838
|
28154a9e672b1a23d5a76233ef380eb193b08b85
|
refs/heads/master
| 2020-03-20T19:15:23.746641
| 2018-06-24T22:27:07
| 2018-06-24T22:27:07
| 137,628,993
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 365
|
r
|
rawcleandata.R
|
# Load and clean up the NOAA data, and put .rdata into the data directory
# need devtools, dplyr, readr
library(devtools)
library(dplyr)
library(lubridate)
library(readr)
library(stringr)
# read in the raw data
raw_NOAA <- readr::read_delim('./rawdata/signif.txt',delim = '\t')
# now use data
clean_NOAA <- eq_clean_data(raw_NOAA)
devtools::use_data(clean_NOAA)
|
693251f3d29d788d5547590e0ef8d8195e886491
|
7f1fbfc87e553e2304f80c16b523ffd3dd57dedf
|
/plot4.R
|
b457820a5312cf684fda17136786fedaf8f3352b
|
[] |
no_license
|
rupesh2017/ExData_Plotting1
|
b8cb1821b1b9397b0b93274a32daa0589c1b1f52
|
652bea9363743d7aa972a05859fd7f0e7ad1136d
|
refs/heads/master
| 2020-03-22T00:12:27.840224
| 2018-06-30T08:01:48
| 2018-06-30T08:01:48
| 138,697,007
| 0
| 0
| null | 2018-06-26T06:55:02
| 2018-06-26T06:55:00
| null |
UTF-8
|
R
| false
| false
| 1,661
|
r
|
plot4.R
|
#set working directory
#reading data
#find column class to read file faster
a <- read.table(file="household_power_consumption.txt",header=TRUE,sep=";",nrows = 5)
classes <- sapply(a,class)
#read the file
file <- read.table(file="household_power_consumption.txt",header=TRUE,sep=";",nrow=2075259,colClasses = classes,na.strings = "?")
#change the date which was factor to date class
file$Date <- as.Date(file$Date,"%d/%m/%Y")
#subset required date
data <- file[file$Date >="2007-02-01" & file$Date<="2007-02-02",]
#format both date and time in combination,also paste convert the time of factor class to
#character class and strptime change it to time class
x <- strptime(paste(data$Date,data$Time),"%Y-%m-%d %H:%M:%S")
#-----------------------------------------------------------------------------------------
#initialise png graphics
# no need for width and height argument by default it is 480p both
png("plot4.png")
#set 2 column and 2 row using mfrow for 4 graphs and choose margin using mar
par(mfrow=c(2,2),mar=c(4,4,4,4))
#1
plot(x,data$Global_active_power,type="l",xlab="",ylab="Global Active Power")
#2
plot(x,data$Voltage,type="l",xlab="datatime",ylab="Voltage")
#3
plot(x,data$Sub_metering_1,type="l",xlab="",ylab="Energy sub metering")
lines(x,data$Sub_metering_2,type="l",col="red")
lines(x,data$Sub_metering_3,type="l",col="blue")
legend("topright",legend=c("sub_metering_1","sub_metering_2","sub_metering_3"),col=c("black","red","blue"),lty=(rep(1,3)),bty="n",cex=0.70)
#4
plot(x,data$Global_reactive_power,type="l",xlab="datatime",ylab="Global_reactive_power")
#turn off graphics
dev.off()
|
782ef9e894eb5c17c5994ebc040cc187d8770ca2
|
cbb287e2060194451f8f4dc4b68dc9de5b20a8d9
|
/cachematrix.R
|
b682eb00d48ce24653af49c05ffdb5405cadb710
|
[] |
no_license
|
DonResnik/ProgrammingAssignment2
|
7410c028818a2d8f6483c392e0de20cfa370e57a
|
fdff4bdba81e3ba9f533194eee1221838b9898f8
|
refs/heads/master
| 2021-01-15T13:52:35.622413
| 2016-03-25T01:08:05
| 2016-03-25T01:08:05
| 54,630,293
| 0
| 0
| null | 2016-03-24T09:27:26
| 2016-03-24T09:27:25
| null |
UTF-8
|
R
| false
| false
| 1,200
|
r
|
cachematrix.R
|
## makeCacheMatrix - takes in a matrix, and sets up
## a cache to store a value related to the matrix
## that can be retrieved using internal getmatrix() method
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
set <- function(y) {
x <<- y
m <<- NULL
}
get <- function()
setmatrix <- function(solve) m <<- solve
getmatrix <- function() m
list(set = set, get = get,
setmatrix = setmatrix,
getmatrix = getmatrix)
}
## cacheSolve - checks the cacheMatrix to see if the
## matrix already has a stored result. If it
## does it returns it from the cache and notifies the
## user that the result came from cached data. If not, it runs
## solve() on the matrix and stores the result in the cache.
## sample code to test the methods:
## d <- matrix(c(10,0,9,6), nrow=2, ncol=2)
## myMatrix <- makeCacheMatrix(d)
## cacheSolve(myMatrix)
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
m <- x$getmatrix()
if(!is.null(m)) {
message("getting cached data")
return(m)
}
data <- x$get()
m <- solve(data, ...)
x$setmatrix(m)
m
}
|
928ca4f8d6b749e6604c4cfbcebfe9a3d3a43a52
|
77ef73c072c75fc92d313d404fa1b6df50a53e40
|
/man/cyto_details_save.Rd
|
c8824cc4e1ac40d215c710e6f12ed42eeb79d30d
|
[] |
no_license
|
DillonHammill/CytoExploreR
|
8eccabf1d761c29790c1d5c1921e1bd7089d9e09
|
0efb1cc19fc701ae03905cf1b8484c1dfeb387df
|
refs/heads/master
| 2023-08-17T06:31:48.958379
| 2023-02-28T09:31:08
| 2023-02-28T09:31:08
| 214,059,913
| 60
| 17
| null | 2020-08-12T11:41:37
| 2019-10-10T01:35:16
|
R
|
UTF-8
|
R
| false
| true
| 893
|
rd
|
cyto_details_save.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cyto-helpers.R
\name{cyto_details_save}
\alias{cyto_details_save}
\title{Save experiment details to csv file}
\usage{
cyto_details_save(x, save_as = NULL)
}
\arguments{
\item{x}{object of class \code{flowSet} or \code{GatingSet} annotated with
experiment details.}
\item{save_as}{name of csv file to which the experiment details shuld be
saved.}
}
\value{
write experiment details to named csv file.
}
\description{
Save experiment details to csv file
}
\examples{
\dontrun{
library(CytoExploreRData)
# Activation GatingSet
gs <- GatingSet(Activation)
# Modify experiment details manually
cyto_details(gs)$Treatment <- c(
rep("A", 8),
rep("B", 8),
rep("C", 8),
rep("D", 8),
NA
)
# Save experiment details to file
cyto_details_save(gs)
}
}
\author{
Dillon Hammill, \email{Dillon.Hammill@anu.edu.au}
}
|
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