content large_stringlengths 0 6.46M | path large_stringlengths 3 331 | license_type large_stringclasses 2 values | repo_name large_stringlengths 5 125 | language large_stringclasses 1 value | is_vendor bool 2 classes | is_generated bool 2 classes | length_bytes int64 4 6.46M | extension large_stringclasses 75 values | text stringlengths 0 6.46M |
|---|---|---|---|---|---|---|---|---|---|
#
library(yaml)
l <- yaml.load_file("tree1.yaml")
library(data.tree)
jl <- as.Node(l)
#Plotting
library(igraph)
library(ape)
jl$Revert()
jlp <- as.phylo(jl)
library(igraph)
G <- graph.tree(n=13,children=2)
# let's print it using a tree-specific layout
# (N.B. you must specify the root node)
co <- layout.reingold.tilford(G, params=list(root=1))
plot(G, layout=co)
acme <- Node$new("Acme Inc.")
accounting <- acme$AddChild("Accounting")
software <- accounting$AddChild("New Software")
standards <- accounting$AddChild("New Accounting Standards")
research <- acme$AddChild("Research")
newProductLine <- research$AddChild("New Product Line")
newLabs <- research$AddChild("New Labs")
it <- acme$AddChild("IT")
outsource <- it$AddChild("Outsource")
agile <- it$AddChild("Go agile")
goToR <- it$AddChild("Switch to R")
print(acme)
ti <- Node$new("Topic indicators")
do <- ti$AddChild("dobj")
va <- do$AddChild("Verb=hankkia/etsiä/etc")
fp <- va$AddChild("First person")
fp$AddChild("Asumisen järjestin itselleni jo Suomesta käsin. ")
fp$AddChild("Asunnon hankin yksityiseltä vuokranantajalta, ja se oli valmiina saapuessani. ")
va$AddChild("third person / passive")
do$AddChild("other")
do <- ti$AddChild("nmod")
fp <- do$AddChild("First person")
fp$AddChild("Kuljin rautatieasemalta metrolla yliopiston asuntolalle, missä... ")
ot <- do$AddChild("Other")
lc <- ot$AddChild("Locative cases")
ela <- lc$AddChild("Elative")
ela$AddChild("Asunnosta muodostui kriittisin osa koko vaihtoa. ")
ila <- lc$AddChild("Illative")
ila$AddChild("Suurin osa Pietarin vaihtareista majoittuu samaan asuntolaan, ")
ine <- lc$AddChild("Inessive")
ine$AddChild("Meidän lisäksi samassa asunnossa asui italialainen tyttö, jonka kanssa jaoimme vessan, suihkun ja jääkaapin. ")
ot <- do$AddChild("Asunnon suhteen / kanssa")
ot$AddChild("Minulla kävi tuuri asunnon kanssa, sillä eräs tuttuni omistaa asunnon Berliinissä. ")
do <- ti$AddChild("")
print(ti)
collapsibleTree(ti, fontSize=20, height=900, width=1200, linkLength=200)
collapsibleTreeNetwork(ti, fontSize=20, height=900, width=1200, linkLength=200)
simpleNetwork(tiNetwork[-3], fontSize = 15)
tiNetwork <- ToDataFrameNetwork(ti, "name")
acmeNetwork <- ToDataFrameNetwork(acme, "name")
other:
Rekistöröityminen paikalliseksi asukkaaksi sujui helposti,
accounting <- acme$AddChild("Accounting")
software <- accounting$AddChild("New Software")
standards <- accounting$AddChild("New Accounting Standards")
research <- acme$AddChild("Research")
newProductLine <- research$AddChild("New Product Line")
newLabs <- research$AddChild("New Labs")
it <- acme$AddChild("IT")
outsource <- it$AddChild("Outsource")
agile <- it$AddChild("Go agile")
goToR <- it$AddChild("Switch to R")
| /teksti/shiny_tree.R | no_license | hrmJ/matkakertomukset | R | false | false | 3,051 | r | #
library(yaml)
l <- yaml.load_file("tree1.yaml")
library(data.tree)
jl <- as.Node(l)
#Plotting
library(igraph)
library(ape)
jl$Revert()
jlp <- as.phylo(jl)
library(igraph)
G <- graph.tree(n=13,children=2)
# let's print it using a tree-specific layout
# (N.B. you must specify the root node)
co <- layout.reingold.tilford(G, params=list(root=1))
plot(G, layout=co)
acme <- Node$new("Acme Inc.")
accounting <- acme$AddChild("Accounting")
software <- accounting$AddChild("New Software")
standards <- accounting$AddChild("New Accounting Standards")
research <- acme$AddChild("Research")
newProductLine <- research$AddChild("New Product Line")
newLabs <- research$AddChild("New Labs")
it <- acme$AddChild("IT")
outsource <- it$AddChild("Outsource")
agile <- it$AddChild("Go agile")
goToR <- it$AddChild("Switch to R")
print(acme)
ti <- Node$new("Topic indicators")
do <- ti$AddChild("dobj")
va <- do$AddChild("Verb=hankkia/etsiä/etc")
fp <- va$AddChild("First person")
fp$AddChild("Asumisen järjestin itselleni jo Suomesta käsin. ")
fp$AddChild("Asunnon hankin yksityiseltä vuokranantajalta, ja se oli valmiina saapuessani. ")
va$AddChild("third person / passive")
do$AddChild("other")
do <- ti$AddChild("nmod")
fp <- do$AddChild("First person")
fp$AddChild("Kuljin rautatieasemalta metrolla yliopiston asuntolalle, missä... ")
ot <- do$AddChild("Other")
lc <- ot$AddChild("Locative cases")
ela <- lc$AddChild("Elative")
ela$AddChild("Asunnosta muodostui kriittisin osa koko vaihtoa. ")
ila <- lc$AddChild("Illative")
ila$AddChild("Suurin osa Pietarin vaihtareista majoittuu samaan asuntolaan, ")
ine <- lc$AddChild("Inessive")
ine$AddChild("Meidän lisäksi samassa asunnossa asui italialainen tyttö, jonka kanssa jaoimme vessan, suihkun ja jääkaapin. ")
ot <- do$AddChild("Asunnon suhteen / kanssa")
ot$AddChild("Minulla kävi tuuri asunnon kanssa, sillä eräs tuttuni omistaa asunnon Berliinissä. ")
do <- ti$AddChild("")
print(ti)
collapsibleTree(ti, fontSize=20, height=900, width=1200, linkLength=200)
collapsibleTreeNetwork(ti, fontSize=20, height=900, width=1200, linkLength=200)
simpleNetwork(tiNetwork[-3], fontSize = 15)
tiNetwork <- ToDataFrameNetwork(ti, "name")
acmeNetwork <- ToDataFrameNetwork(acme, "name")
other:
Rekistöröityminen paikalliseksi asukkaaksi sujui helposti,
accounting <- acme$AddChild("Accounting")
software <- accounting$AddChild("New Software")
standards <- accounting$AddChild("New Accounting Standards")
research <- acme$AddChild("Research")
newProductLine <- research$AddChild("New Product Line")
newLabs <- research$AddChild("New Labs")
it <- acme$AddChild("IT")
outsource <- it$AddChild("Outsource")
agile <- it$AddChild("Go agile")
goToR <- it$AddChild("Switch to R")
|
#' Retrieval functions for USGS data
#'
#' \tabular{ll}{
#' Package: \tab USGSwsDataRetrieval\cr
#' Type: \tab Package\cr
#' Version: \tab 0.0.0\cr
#' Date: \tab 2014-07-03\cr
#' License: \tab Unlimited for this package, dependencies have more restrictive licensing.\cr
#' Copyright: \tab This software is in the public domain because it contains materials
#' that originally came from the United States Geological Survey, an agency of
#' the United States Department of Interior. For more information, see the
#' official USGS copyright policy at
#' http://www.usgs.gov/visual-id/credit_usgs.html#copyright\cr
#' LazyLoad: \tab yes\cr
#' }
#'
#' Collection of functions to help retrieve USGS data from either web services or user provided data files.
#'
#' @name USGSwsDataRetrieval-package
#' @import RCurl XML reshape2
#' @docType package
#' @author David Lorenz \email{lorenz@@usgs.gov}, Laura De Cicco \email{ldecicco@@usgs.gov}
#' @keywords data, retrieval
NULL
#' List of USGS parameter codes
#'
#' Complete list of USGS parameter codes as of September 25, 2013.
#'
#' @name parameterCdFile
#' @docType data
#' @keywords USGS parameterCd
NULL | /R/USGSwsDataRetrieval.R | permissive | wcdamsch/USGSwsDataRetrieval | R | false | false | 1,150 | r | #' Retrieval functions for USGS data
#'
#' \tabular{ll}{
#' Package: \tab USGSwsDataRetrieval\cr
#' Type: \tab Package\cr
#' Version: \tab 0.0.0\cr
#' Date: \tab 2014-07-03\cr
#' License: \tab Unlimited for this package, dependencies have more restrictive licensing.\cr
#' Copyright: \tab This software is in the public domain because it contains materials
#' that originally came from the United States Geological Survey, an agency of
#' the United States Department of Interior. For more information, see the
#' official USGS copyright policy at
#' http://www.usgs.gov/visual-id/credit_usgs.html#copyright\cr
#' LazyLoad: \tab yes\cr
#' }
#'
#' Collection of functions to help retrieve USGS data from either web services or user provided data files.
#'
#' @name USGSwsDataRetrieval-package
#' @import RCurl XML reshape2
#' @docType package
#' @author David Lorenz \email{lorenz@@usgs.gov}, Laura De Cicco \email{ldecicco@@usgs.gov}
#' @keywords data, retrieval
NULL
#' List of USGS parameter codes
#'
#' Complete list of USGS parameter codes as of September 25, 2013.
#'
#' @name parameterCdFile
#' @docType data
#' @keywords USGS parameterCd
NULL |
rm(list=ls())
subtype <- "~/GDAN/fpkm-uq/TCGA-BRCA/BRCA_subtype.txt"
geneannot_file <- "~/GDAN/scripts/geneannot.rds"
outdir <- "~/GDAN//fpkm-uq/TCGA-BRCA/output/"
active_file_rds <- paste(outdir,"active.rds",sep="")
legacy_file_rds <- paste(outdir,"legacy.rds",sep="")
merged_file_rds <- paste(outdir,"merged_active_legacy.rds",sep="")
functions_file <- "~/GDAN/scripts/rsem_fpkm_functions.R"
cancer_var <- "BRCA"
subtype_var <- "Expression_subtype"
data.full <- readRDS(file=merged_file_rds)
data.ge <- subset(data.full,! row.names(data.full) %in% c("dataset",subtype_var,"entrezgene"))
dataset <- data.full["dataset",]
data_df1 <- rbind(data.ge,cl=dataset)
dim(data_df1)
source(functions_file)
analysis(data_df1,"BRCA\n",outdir)
#subtype analysis
pat <- "-\\d\\d\\w-\\d\\d\\w-\\w\\w\\w\\w-\\d\\d$"
actdf <- readRDS(file=active_file_rds)
actdf <- add_subtype_row(actdf,subtype,subtype_var,"Barcode",pat)
rownames(actdf) <- gsub("\\.\\d+","",rownames(actdf), perl=TRUE)
actdf <- rbind(actdf,dataset=rep(38,dim(actdf)[2]))
legacy <- readRDS(file=legacy_file_rds)
en2hg <- readRDS(file=geneannot_file)
legacy$hgnc <- gsub("\\|\\d+$","",rownames(legacy),perl=TRUE)
legacy$entrezgene <- gsub("^[\\w\\-]+\\|","",rownames(legacy),perl=TRUE)
legacy <- legacy[!duplicated(legacy$entrezgene),]
legacy <- merge(legacy,en2hg,by.x='entrezgene',by.y='entrezgene')
legacy <- legacy[!duplicated(legacy$ensembl_gene_id),]
rownames(legacy) <- legacy$ensembl_gene_id
legacy <- rbind(legacy,dataset=rep(19,dim(legacy)[2]))
legacy <- add_subtype_row(legacy,subtype,subtype_var,"Barcode",pat)
mergeddf <- merge(legacy,actdf,by="row.names")
rownames(mergeddf) <- mergeddf$Row.names
dim(mergeddf)
mergeddf$Row.names <- NULL
mergeddf$hgnc <- NULL
mergeddf$ensembl_gene_id <- NULL
mergeddf$entrezgene <- NULL
fileout=paste(outdir,"merged_with_subtype.rds",sep="")
saveRDS(mergeddf,file=fileout)
m <- readRDS(file=paste(outdir,"merged_with_subtype.rds",sep=""))
plotname <- paste(cancer_var,subtype_var)
class_analysis(m,"dataset",subtype_var,plotname,outdir,cancer_var)
| /tcga_brca_2.R | no_license | joelsparker1/gdan_rna_qc | R | false | false | 2,140 | r | rm(list=ls())
subtype <- "~/GDAN/fpkm-uq/TCGA-BRCA/BRCA_subtype.txt"
geneannot_file <- "~/GDAN/scripts/geneannot.rds"
outdir <- "~/GDAN//fpkm-uq/TCGA-BRCA/output/"
active_file_rds <- paste(outdir,"active.rds",sep="")
legacy_file_rds <- paste(outdir,"legacy.rds",sep="")
merged_file_rds <- paste(outdir,"merged_active_legacy.rds",sep="")
functions_file <- "~/GDAN/scripts/rsem_fpkm_functions.R"
cancer_var <- "BRCA"
subtype_var <- "Expression_subtype"
data.full <- readRDS(file=merged_file_rds)
data.ge <- subset(data.full,! row.names(data.full) %in% c("dataset",subtype_var,"entrezgene"))
dataset <- data.full["dataset",]
data_df1 <- rbind(data.ge,cl=dataset)
dim(data_df1)
source(functions_file)
analysis(data_df1,"BRCA\n",outdir)
#subtype analysis
pat <- "-\\d\\d\\w-\\d\\d\\w-\\w\\w\\w\\w-\\d\\d$"
actdf <- readRDS(file=active_file_rds)
actdf <- add_subtype_row(actdf,subtype,subtype_var,"Barcode",pat)
rownames(actdf) <- gsub("\\.\\d+","",rownames(actdf), perl=TRUE)
actdf <- rbind(actdf,dataset=rep(38,dim(actdf)[2]))
legacy <- readRDS(file=legacy_file_rds)
en2hg <- readRDS(file=geneannot_file)
legacy$hgnc <- gsub("\\|\\d+$","",rownames(legacy),perl=TRUE)
legacy$entrezgene <- gsub("^[\\w\\-]+\\|","",rownames(legacy),perl=TRUE)
legacy <- legacy[!duplicated(legacy$entrezgene),]
legacy <- merge(legacy,en2hg,by.x='entrezgene',by.y='entrezgene')
legacy <- legacy[!duplicated(legacy$ensembl_gene_id),]
rownames(legacy) <- legacy$ensembl_gene_id
legacy <- rbind(legacy,dataset=rep(19,dim(legacy)[2]))
legacy <- add_subtype_row(legacy,subtype,subtype_var,"Barcode",pat)
mergeddf <- merge(legacy,actdf,by="row.names")
rownames(mergeddf) <- mergeddf$Row.names
dim(mergeddf)
mergeddf$Row.names <- NULL
mergeddf$hgnc <- NULL
mergeddf$ensembl_gene_id <- NULL
mergeddf$entrezgene <- NULL
fileout=paste(outdir,"merged_with_subtype.rds",sep="")
saveRDS(mergeddf,file=fileout)
m <- readRDS(file=paste(outdir,"merged_with_subtype.rds",sep=""))
plotname <- paste(cancer_var,subtype_var)
class_analysis(m,"dataset",subtype_var,plotname,outdir,cancer_var)
|
\alias{gtkRecentInfoGetDescription}
\name{gtkRecentInfoGetDescription}
\title{gtkRecentInfoGetDescription}
\description{Gets the (short) description of the resource.}
\usage{gtkRecentInfoGetDescription(object)}
\arguments{\item{\code{object}}{[\code{\link{GtkRecentInfo}}] a \code{\link{GtkRecentInfo}}}}
\details{ Since 2.10}
\value{[character] the description of the resource.}
\author{Derived by RGtkGen from GTK+ documentation}
\keyword{internal}
| /man/gtkRecentInfoGetDescription.Rd | no_license | cran/RGtk2.10 | R | false | false | 455 | rd | \alias{gtkRecentInfoGetDescription}
\name{gtkRecentInfoGetDescription}
\title{gtkRecentInfoGetDescription}
\description{Gets the (short) description of the resource.}
\usage{gtkRecentInfoGetDescription(object)}
\arguments{\item{\code{object}}{[\code{\link{GtkRecentInfo}}] a \code{\link{GtkRecentInfo}}}}
\details{ Since 2.10}
\value{[character] the description of the resource.}
\author{Derived by RGtkGen from GTK+ documentation}
\keyword{internal}
|
#' Dupla Sena lottery game probability
#'
#' Does the ticket have 50 tens, in which the bettor can? dial from 6 to 15 numbers,
#' consists of extracting 6 different numbers, you can win by hitting 3, 4, 5 or
#' 6 numbers, with a differential, the player has two chances to win.
#'
#' To choose 6 numbers, the bet is simple, where it offers the lowest odds
#' win: combination of 50 numbers taken from 6 to 6 = 50!/6!44! = 15890700
#' However, as there are two draws, the player has two chances out of approximately 16 million.
#' 2/15890700 = 1/7945350, to win any of the SENAS, the player has approximately 1 chance in 8 million.
#'
#'x is the amount of numbers bet
#'
#'y is the amount number of hits
#'
#' @param x number
#' @param y number
#'
#' @return number
#' @export
#'
#' @examples ProbDSena(15,5)
ProbDSena = function(x,y){
prob = (choose(x,y)*choose(50-x,6-y)*2)/choose(50,6)
if(y>6){
prob = 0
message("The maximum number of correct answers is six")
}
else if(y<=2){
stop("There is no such probability of hits in Dupla Sena")
}
else
if(y==3)
message("Happy birthday. you hit the Terno!")
if(y==4)
message("Happy birthday. you hit the Quadra!")
if(y==5)
message("Happy birthday. you hit the Quina!")
if(y==6)
message("Happy birthday. You are champion of the Dupla Sena!")
return(prob)
}
| /R/ProbDSena.R | permissive | farias741/Lotto | R | false | false | 1,361 | r | #' Dupla Sena lottery game probability
#'
#' Does the ticket have 50 tens, in which the bettor can? dial from 6 to 15 numbers,
#' consists of extracting 6 different numbers, you can win by hitting 3, 4, 5 or
#' 6 numbers, with a differential, the player has two chances to win.
#'
#' To choose 6 numbers, the bet is simple, where it offers the lowest odds
#' win: combination of 50 numbers taken from 6 to 6 = 50!/6!44! = 15890700
#' However, as there are two draws, the player has two chances out of approximately 16 million.
#' 2/15890700 = 1/7945350, to win any of the SENAS, the player has approximately 1 chance in 8 million.
#'
#'x is the amount of numbers bet
#'
#'y is the amount number of hits
#'
#' @param x number
#' @param y number
#'
#' @return number
#' @export
#'
#' @examples ProbDSena(15,5)
ProbDSena = function(x,y){
prob = (choose(x,y)*choose(50-x,6-y)*2)/choose(50,6)
if(y>6){
prob = 0
message("The maximum number of correct answers is six")
}
else if(y<=2){
stop("There is no such probability of hits in Dupla Sena")
}
else
if(y==3)
message("Happy birthday. you hit the Terno!")
if(y==4)
message("Happy birthday. you hit the Quadra!")
if(y==5)
message("Happy birthday. you hit the Quina!")
if(y==6)
message("Happy birthday. You are champion of the Dupla Sena!")
return(prob)
}
|
# connectivity with macrobond, try around when you get a chance
# https://help.macrobond.com/add-ins/the-macrobond-api-for-r/
| /Macrobond.R | no_license | mustang115/Rsetup | R | false | false | 126 | r | # connectivity with macrobond, try around when you get a chance
# https://help.macrobond.com/add-ins/the-macrobond-api-for-r/
|
# setup temp folders ---------------------------
temp_folder <- tempdir()
dir2 <- file.path(temp_folder, "tmp2")
dir3 <- file.path(temp_folder, "tmp3")
setup({
dir.create(dir2)
dir.create(dir3)
})
teardown({
unlink(dir2, recursive = TRUE)
unlink(dir3, recursive = TRUE)
})
p1 <- ".."
rr <- sample(1:29, 4) # get 4 random nodes
message('\n4 random nodes are: ',paste(rr, collapse = " "))
r2u <- zoo::as.yearmon(c('1950-01','1954-12'))
# check errors -------------------
test_that("Upfront errors post correctly", {
expect_error(
crssi_create_dnf_files(
'CoRiverNF',
oFolder = 'doesNotExist',
startYear = 2017,
endYear = 2021,
recordToUse = r2u
),
paste0(file.path("doesNotExist"), " folder does not exist.", "\n",
"Create the directory before calling crssi_create_dnf_files()")
)
expect_error(
crssi_create_dnf_files(
"CoRiverNF.txt",
oFolder = 'doesNotExist',
startYear = 2017,
endYear = 2021,
recordToUse = r2u
),
paste0("CoRiverNF.txt does not appear to be valid.\n",
"It should be either an Excel (xlsx) file or 'CoRiverNF'"),
fixed = TRUE
)
expect_error(
crssi_create_dnf_files(
'CoRiverNF',
oFolder = dir2,
startYear = 2017,
endYear = 2021,
recordToUse = c("1906-01", "1997-12")
),
"recordToUse must be class 'yearmon'."
)
expect_error(
crssi_create_dnf_files(
'CoRiverNF',
oFolder = dir2,
startYear = 2017,
endYear = 2021,
recordToUse = zoo::as.yearmon(c("1906-01", "1997-12", "1999-12"))
),
"recordToUse should only contain two entries, or be 'NA'."
)
expect_error(
crssi_create_dnf_files(
'CoRiverNF',
oFolder = dir2,
startYear = 2017,
endYear = 2021,
recordToUse = zoo::as.yearmon(c("1906-2", "1997-12"))
),
"The first entry to recordToUse should be January of some year."
)
expect_error(
crssi_create_dnf_files(
'CoRiverNF',
oFolder = dir2,
startYear = 2017,
endYear = 2021,
recordToUse = zoo::as.yearmon(c("1906-1", "1997-11"))
),
"The second entry to recordToUse should be December of some year."
)
expect_error(
crssi_create_dnf_files(
'CoRiverNF',
oFolder = dir2,
startYear = 2017,
endYear = 2021,
recordToUse = zoo::as.yearmon(c("1905-1", "1997-12"))
),
"Years in recordToUse should not be before 1906."
)
expect_error(
crssi_create_dnf_files(
'CoRiverNF',
oFolder = dir2,
startYear = 2017,
endYear = 2021,
recordToUse = zoo::as.yearmon(c("1906-1", "1905-12"))
),
"Years in recordToUse should not be before 1906."
)
expect_error(
crssi_create_dnf_files(
'CoRiverNF',
oFolder = dir2,
startYear = 2017,
endYear = 2021,
recordToUse = zoo::as.yearmon(c("1988-1", "1980-12"))
),
"The second entry in recordToUse should be after the first entry."
)
expect_error(
crssi_create_dnf_files(
'CoRiverNF',
oFolder = dir2,
startYear = 2017,
endYear = 2021,
recordToUse = zoo::as.yearmon(c("1988-1", "2100-12"))
),
paste(
"The end year in `recordToUse` must be <=",
as.integer(format(tail(zoo::index(CoRiverNF::monthlyInt), 1), "%Y"))
)
)
})
# check the two different fucntions create data -------
# because we are using pre- 1971 data, we do not need to regenerate the data
# in the provided trace folders each time the natural flow are updated
test_that('can create files',{
expect_message(
crssi_create_dnf_files(
'CoRiverNF',
oFolder = dir2,
startYear = 2017,
endYear = 2021,
recordToUse = r2u
)
)
expect_warning(expect_message(
crssi_create_dnf_files(
"../NaturalFlows_Sample.xlsx",
oFolder = dir3,
startYear = 2017,
endYear = 2021,
recordToUse = r2u
)
))
})
# check that all files in the three directories are the same -------------
dirs <- list.dirs(dir2, recursive = FALSE, full.names = FALSE)
test_that("all files are the same", {
for(curDir in dirs){
allFiles <- list.files(file.path(dir2, curDir))
for(ff in allFiles){
#message(curDir, "/", ff)
expect_identical(
scan(file.path(dir2, curDir, ff), what = "character", quiet = TRUE),
scan(file.path(dir3, curDir, ff), what = "character", quiet = TRUE),
info = paste(curDir, ff)
)
}
}
})
allFiles <- c(nf_file_names(), "MWD_ICS.SacWYType",
"MeadFloodControlData.hydrologyIncrement",
"HydrologyParameters.TraceNumber",
"HydrologyParameters.SupplyScenario")
test_that("all files exist", {
expect_setequal(allFiles, list.files(file.path(dir2, "trace1")))
expect_setequal(allFiles, list.files(file.path(dir2, "trace3")))
})
test_that('files created from "CoRiverNF" are the same as from Excel', {
expect_equal(
as.matrix(read.csv(
file.path(dir2, 'trace1',nf_file_names()[rr[1]]),
skip = 1
)),
as.matrix(read.csv(
file.path(p1,'trace1', nf_file_names()[rr[1]]),
skip = 1
))
)
expect_equal(
as.matrix(read.csv(
file.path(dir2, 'trace5',nf_file_names()[rr[1]]),
skip = 1
)),
as.matrix(read.csv(
file.path(p1,'trace5', nf_file_names()[rr[1]]),
skip = 1
))
)
expect_equal(
as.matrix(read.csv(
file.path(dir2, 'trace2',nf_file_names()[rr[2]]),
skip = 1
)),
as.matrix(read.csv(
file.path(p1,'trace2', nf_file_names()[rr[2]]),
skip = 1
))
)
expect_equal(
as.matrix(read.csv(
file.path(dir2, 'trace3',nf_file_names()[rr[3]]),
skip = 1
)),
as.matrix(read.csv(
file.path(p1,'trace3', nf_file_names()[rr[3]]),
skip = 1
))
)
expect_equal(
as.matrix(read.csv(
file.path(dir2, 'trace4',nf_file_names()[rr[4]]),
skip = 1
)),
as.matrix(read.csv(
file.path(p1,'trace4', nf_file_names()[rr[4]]),
skip = 1
))
)
})
test_that('ism files match each other as expected', {
expect_equal(
as.matrix(read.csv(
file.path(dir2, 'trace1',nf_file_names()[rr[1]]),
skip = 1
))[13:24],
as.matrix(read.csv(
file.path(p1,'trace2', nf_file_names()[rr[1]]),
skip = 1
))[1:12]
)
expect_equal(
as.matrix(read.csv(
file.path(dir2, 'trace1',nf_file_names()[rr[2]]),
skip = 1
))[49:60],
as.matrix(read.csv(
file.path(p1,'trace5', nf_file_names()[rr[2]]),
skip = 1
))[1:12]
)
expect_equal(
as.matrix(read.csv(
file.path(dir2, 'trace1',nf_file_names()[rr[2]]),
skip = 1
))[1:12],
as.matrix(read.csv(
file.path(p1,'trace4', nf_file_names()[rr[2]]),
skip = 1
))[25:36]
)
})
# internal format excel function -------------------
test_that("Excel formatting works", {
expect_warning(expect_s3_class(
tmp <- CRSSIO:::read_and_format_nf_excel("../NaturalFlows_Sample.xlsx"),
c("xts", "zoo")
))
expect_equal(ncol(tmp), 29)
expect_equal(nrow(tmp) %% 12, 0)
expect_equal(zoo::index(tmp)[1], zoo::as.yearmon("Jan 1906"))
expect_equal(format(tail(zoo::index(tmp),1), "%b"), "Dec")
})
| /tests/testthat/test-nfFileCreation.R | no_license | rabutler-usbr/CRSSIO | R | false | false | 7,342 | r |
# setup temp folders ---------------------------
temp_folder <- tempdir()
dir2 <- file.path(temp_folder, "tmp2")
dir3 <- file.path(temp_folder, "tmp3")
setup({
dir.create(dir2)
dir.create(dir3)
})
teardown({
unlink(dir2, recursive = TRUE)
unlink(dir3, recursive = TRUE)
})
p1 <- ".."
rr <- sample(1:29, 4) # get 4 random nodes
message('\n4 random nodes are: ',paste(rr, collapse = " "))
r2u <- zoo::as.yearmon(c('1950-01','1954-12'))
# check errors -------------------
test_that("Upfront errors post correctly", {
expect_error(
crssi_create_dnf_files(
'CoRiverNF',
oFolder = 'doesNotExist',
startYear = 2017,
endYear = 2021,
recordToUse = r2u
),
paste0(file.path("doesNotExist"), " folder does not exist.", "\n",
"Create the directory before calling crssi_create_dnf_files()")
)
expect_error(
crssi_create_dnf_files(
"CoRiverNF.txt",
oFolder = 'doesNotExist',
startYear = 2017,
endYear = 2021,
recordToUse = r2u
),
paste0("CoRiverNF.txt does not appear to be valid.\n",
"It should be either an Excel (xlsx) file or 'CoRiverNF'"),
fixed = TRUE
)
expect_error(
crssi_create_dnf_files(
'CoRiverNF',
oFolder = dir2,
startYear = 2017,
endYear = 2021,
recordToUse = c("1906-01", "1997-12")
),
"recordToUse must be class 'yearmon'."
)
expect_error(
crssi_create_dnf_files(
'CoRiverNF',
oFolder = dir2,
startYear = 2017,
endYear = 2021,
recordToUse = zoo::as.yearmon(c("1906-01", "1997-12", "1999-12"))
),
"recordToUse should only contain two entries, or be 'NA'."
)
expect_error(
crssi_create_dnf_files(
'CoRiverNF',
oFolder = dir2,
startYear = 2017,
endYear = 2021,
recordToUse = zoo::as.yearmon(c("1906-2", "1997-12"))
),
"The first entry to recordToUse should be January of some year."
)
expect_error(
crssi_create_dnf_files(
'CoRiverNF',
oFolder = dir2,
startYear = 2017,
endYear = 2021,
recordToUse = zoo::as.yearmon(c("1906-1", "1997-11"))
),
"The second entry to recordToUse should be December of some year."
)
expect_error(
crssi_create_dnf_files(
'CoRiverNF',
oFolder = dir2,
startYear = 2017,
endYear = 2021,
recordToUse = zoo::as.yearmon(c("1905-1", "1997-12"))
),
"Years in recordToUse should not be before 1906."
)
expect_error(
crssi_create_dnf_files(
'CoRiverNF',
oFolder = dir2,
startYear = 2017,
endYear = 2021,
recordToUse = zoo::as.yearmon(c("1906-1", "1905-12"))
),
"Years in recordToUse should not be before 1906."
)
expect_error(
crssi_create_dnf_files(
'CoRiverNF',
oFolder = dir2,
startYear = 2017,
endYear = 2021,
recordToUse = zoo::as.yearmon(c("1988-1", "1980-12"))
),
"The second entry in recordToUse should be after the first entry."
)
expect_error(
crssi_create_dnf_files(
'CoRiverNF',
oFolder = dir2,
startYear = 2017,
endYear = 2021,
recordToUse = zoo::as.yearmon(c("1988-1", "2100-12"))
),
paste(
"The end year in `recordToUse` must be <=",
as.integer(format(tail(zoo::index(CoRiverNF::monthlyInt), 1), "%Y"))
)
)
})
# check the two different fucntions create data -------
# because we are using pre- 1971 data, we do not need to regenerate the data
# in the provided trace folders each time the natural flow are updated
test_that('can create files',{
expect_message(
crssi_create_dnf_files(
'CoRiverNF',
oFolder = dir2,
startYear = 2017,
endYear = 2021,
recordToUse = r2u
)
)
expect_warning(expect_message(
crssi_create_dnf_files(
"../NaturalFlows_Sample.xlsx",
oFolder = dir3,
startYear = 2017,
endYear = 2021,
recordToUse = r2u
)
))
})
# check that all files in the three directories are the same -------------
dirs <- list.dirs(dir2, recursive = FALSE, full.names = FALSE)
test_that("all files are the same", {
for(curDir in dirs){
allFiles <- list.files(file.path(dir2, curDir))
for(ff in allFiles){
#message(curDir, "/", ff)
expect_identical(
scan(file.path(dir2, curDir, ff), what = "character", quiet = TRUE),
scan(file.path(dir3, curDir, ff), what = "character", quiet = TRUE),
info = paste(curDir, ff)
)
}
}
})
allFiles <- c(nf_file_names(), "MWD_ICS.SacWYType",
"MeadFloodControlData.hydrologyIncrement",
"HydrologyParameters.TraceNumber",
"HydrologyParameters.SupplyScenario")
test_that("all files exist", {
expect_setequal(allFiles, list.files(file.path(dir2, "trace1")))
expect_setequal(allFiles, list.files(file.path(dir2, "trace3")))
})
test_that('files created from "CoRiverNF" are the same as from Excel', {
expect_equal(
as.matrix(read.csv(
file.path(dir2, 'trace1',nf_file_names()[rr[1]]),
skip = 1
)),
as.matrix(read.csv(
file.path(p1,'trace1', nf_file_names()[rr[1]]),
skip = 1
))
)
expect_equal(
as.matrix(read.csv(
file.path(dir2, 'trace5',nf_file_names()[rr[1]]),
skip = 1
)),
as.matrix(read.csv(
file.path(p1,'trace5', nf_file_names()[rr[1]]),
skip = 1
))
)
expect_equal(
as.matrix(read.csv(
file.path(dir2, 'trace2',nf_file_names()[rr[2]]),
skip = 1
)),
as.matrix(read.csv(
file.path(p1,'trace2', nf_file_names()[rr[2]]),
skip = 1
))
)
expect_equal(
as.matrix(read.csv(
file.path(dir2, 'trace3',nf_file_names()[rr[3]]),
skip = 1
)),
as.matrix(read.csv(
file.path(p1,'trace3', nf_file_names()[rr[3]]),
skip = 1
))
)
expect_equal(
as.matrix(read.csv(
file.path(dir2, 'trace4',nf_file_names()[rr[4]]),
skip = 1
)),
as.matrix(read.csv(
file.path(p1,'trace4', nf_file_names()[rr[4]]),
skip = 1
))
)
})
test_that('ism files match each other as expected', {
expect_equal(
as.matrix(read.csv(
file.path(dir2, 'trace1',nf_file_names()[rr[1]]),
skip = 1
))[13:24],
as.matrix(read.csv(
file.path(p1,'trace2', nf_file_names()[rr[1]]),
skip = 1
))[1:12]
)
expect_equal(
as.matrix(read.csv(
file.path(dir2, 'trace1',nf_file_names()[rr[2]]),
skip = 1
))[49:60],
as.matrix(read.csv(
file.path(p1,'trace5', nf_file_names()[rr[2]]),
skip = 1
))[1:12]
)
expect_equal(
as.matrix(read.csv(
file.path(dir2, 'trace1',nf_file_names()[rr[2]]),
skip = 1
))[1:12],
as.matrix(read.csv(
file.path(p1,'trace4', nf_file_names()[rr[2]]),
skip = 1
))[25:36]
)
})
# internal format excel function -------------------
test_that("Excel formatting works", {
expect_warning(expect_s3_class(
tmp <- CRSSIO:::read_and_format_nf_excel("../NaturalFlows_Sample.xlsx"),
c("xts", "zoo")
))
expect_equal(ncol(tmp), 29)
expect_equal(nrow(tmp) %% 12, 0)
expect_equal(zoo::index(tmp)[1], zoo::as.yearmon("Jan 1906"))
expect_equal(format(tail(zoo::index(tmp),1), "%b"), "Dec")
})
|
# Hello, world!
#
# This is an example function named 'hello'
# which prints 'Hello, world!'.
#
# You can learn more about package authoring with RStudio at:
#
# http://r-pkgs.had.co.nz/
#
# Some useful keyboard shortcuts for package authoring:
#
# Build and Reload Package: 'Ctrl + Shift + B'
# Check Package: 'Ctrl + Shift + E'
# Test Package: 'Ctrl + Shift + T'
#
| /R/hello.R | no_license | jimmywle/tsoutlier | R | false | false | 401 | r | # Hello, world!
#
# This is an example function named 'hello'
# which prints 'Hello, world!'.
#
# You can learn more about package authoring with RStudio at:
#
# http://r-pkgs.had.co.nz/
#
# Some useful keyboard shortcuts for package authoring:
#
# Build and Reload Package: 'Ctrl + Shift + B'
# Check Package: 'Ctrl + Shift + E'
# Test Package: 'Ctrl + Shift + T'
#
|
\name{midint}
\alias{midint}
\title{
Calculation of the middle of time interval
}
\description{
Calculates the middle of observing time interval for a given visual meteor dataset.
}
\usage{
midint(data)
}
\arguments{
\item{data}{
data frame consisting of visual meteor data (rate or magnitude data).
}
}
\value{
\code{midint} returns the middle of observing time interval, in \code{"\%Y-\%m-\%d \%H:\%M:\%S"} format, UTC timezone
(object of \code{POSIXct} class).
}
\author{
Kristina Veljkovic
}
\note{
Argument \code{data} has to consist of the columns named "Start.Date" and "End.Date". These dates should be given in
\code{"\%Y-\%m-\%d \%H:\%M:\%S"} format (UTC timezone).
}
\seealso{
\code{\link{solar.long}}
}
\examples{
## calculate middle of time interval for rate and magnitude data, year 2015
midint(rate2015)
midint(magn2015)
}
| /man/midint.Rd | no_license | cran/MetFns | R | false | false | 885 | rd | \name{midint}
\alias{midint}
\title{
Calculation of the middle of time interval
}
\description{
Calculates the middle of observing time interval for a given visual meteor dataset.
}
\usage{
midint(data)
}
\arguments{
\item{data}{
data frame consisting of visual meteor data (rate or magnitude data).
}
}
\value{
\code{midint} returns the middle of observing time interval, in \code{"\%Y-\%m-\%d \%H:\%M:\%S"} format, UTC timezone
(object of \code{POSIXct} class).
}
\author{
Kristina Veljkovic
}
\note{
Argument \code{data} has to consist of the columns named "Start.Date" and "End.Date". These dates should be given in
\code{"\%Y-\%m-\%d \%H:\%M:\%S"} format (UTC timezone).
}
\seealso{
\code{\link{solar.long}}
}
\examples{
## calculate middle of time interval for rate and magnitude data, year 2015
midint(rate2015)
midint(magn2015)
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Misc_Exported.R
\name{setup}
\alias{setup}
\title{Setup parallel processing}
\usage{
setup(cpus = parallel::detectCores() * 0.5, ...)
}
\arguments{
\item{cpus}{number of CPUs}
\item{...}{other arguments passed to 'snowfall::sfInit'}
}
\description{
Sets up parallel processing using the snowfall package
}
\examples{
\dontrun{
setup() # set-up half the available processors
setup(6) # set-up 6 processors
}
}
| /man/setup.Rd | no_license | DLMtool/DLMtool | R | false | true | 488 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Misc_Exported.R
\name{setup}
\alias{setup}
\title{Setup parallel processing}
\usage{
setup(cpus = parallel::detectCores() * 0.5, ...)
}
\arguments{
\item{cpus}{number of CPUs}
\item{...}{other arguments passed to 'snowfall::sfInit'}
}
\description{
Sets up parallel processing using the snowfall package
}
\examples{
\dontrun{
setup() # set-up half the available processors
setup(6) # set-up 6 processors
}
}
|
#' IOHexperimenter-based wrapper
#'
#' For easier use with the IOHexperimenter
#'
#' @rdname random_search
#' @param IOHproblem An IOHproblem object
#'
#' @export
#' @examples
#' \donttest{
#' benchmark_algorithm(IOH_random_search, data.dir = NULL)
#' }
IOH_random_search <- function(IOHproblem, budget = NULL) {
if (IOHproblem$suite == "PBO")
random_search_PB(IOHproblem$dimension, IOHproblem$obj_func, IOHproblem$target_hit, budget)
else
random_search(IOHproblem$dimension, IOHproblem$obj_func, IOHproblem$target_hit, budget,
IOHproblem$lbound, IOHproblem$ubound, IOHproblem$maximization)
}
#' IOHexperimenter-based wrapper
#'
#' For easier use with the IOHexperimenter
#'
#' @param IOHproblem An IOHproblem object
#'
#' @rdname random_local_search
#' @export
#' @examples
#' \donttest{
#' benchmark_algorithm(IOH_random_local_search, data.dir = NULL)
#' }
IOH_random_local_search <- function(IOHproblem, budget = NULL) {
random_local_search(IOHproblem$dimension, IOHproblem$obj_func, IOHproblem$target_hit, budget)
}
#' IOHexperimenter-based wrapper
#'
#' For easier use with the IOHexperimenter
#'
#' @param IOHproblem An IOHproblem object
#'
#' @rdname self_adaptive_GA
#' @export
#' @examples
#' \donttest{
#' one_comma_two_EA <- function(IOHproblem) { IOH_self_adaptive_GA(IOHproblem, lambda_=2) }
#'
#' benchmark_algorithm(one_comma_two_EA, params.track = "Mutation rate",
#' algorithm.name = "one_comma_two_EA", data.dir = NULL,
#' algorithm.info = "Using one_comma_two_EA with specific parameter" )
#' }
IOH_self_adaptive_GA <- function(IOHproblem, lambda_ = 1, budget = NULL) {
self_adaptive_GA(IOHproblem$dimension, IOHproblem$obj_func,
target_hit = IOHproblem$target_hit, budget = budget,
lambda_ = lambda_, set_parameters = IOHproblem$set_parameters)
}
#' IOHexperimenter-based wrapper
#'
#' For easier use with the IOHexperimenter
#'
#' @param IOHproblem An IOHproblem object
#'
#' @rdname two_rate_GA
#' @export
#' @examples
#' \donttest{
#' bechmark_algorithm(IOH_two_rate_GA)
#' }
IOH_two_rate_GA <- function(IOHproblem, lambda_ = 1, budget = NULL) {
two_rate_GA(IOHproblem$dimension, IOHproblem$obj_func, budget = budget,
lambda_ = lambda_, set_parameters = IOHproblem$set_parameters,
target_hit = IOHproblem$target_hit)
}
#' Random Search
#'
#' Random walk in \eqn{{0, 1}^d} space; Maximization
#'
#' @rdname random_search
#' @export
random_search_PB <- function(dim, obj_func, target_hit = function(){ FALSE }, budget = NULL) {
if (is.null(budget)) budget <- 10 * dim
fopt <- -Inf
xopt <- NULL
while (budget > 0 && !target_hit()) {
x <- sample(c(0, 1), dim, TRUE)
f <- obj_func(x)
budget <- budget - 1
if (f > fopt) {
xopt <- x
fopt <- f
}
}
list(xopt = xopt, fopt = fopt)
}
#' Random Search
#'
#' Random walk in continuous space;
#'
#' @param dim Dimension of search space
#' @param obj_func The evaluation function
#' @param target_hit Optional, function which enables early stopping if a target value is reached
#' @param budget Integer, maximal allowable number of function evaluations
#' @param lbound Lower bound of search space. Either single number or vector of size `dim`
#' @param ubound Upper bound of search space. Either single number or vector of size `dim`
#' @param maximize Whether to perform maximization or minimization.
#' The function assumes minimization, achieved by inverting the obj_func when `maximize` is FALSE
#' @export
random_search <- function(dim, obj_func, target_hit = function(){ FALSE }, budget = NULL,
lbound = -1, ubound = 1, maximize = T) {
if (is.null(budget)) budget <- 10 * dim
if (maximize) { #Assume mimimization in the remainder of this function
obj_func_transformed <- function(x) {return(-1*obj_func(x))}
}
else{
obj_func_transformed <- obj_func
}
fopt <- Inf
xopt <- NULL
while (budget > 0 && !target_hit()) {
x <- runif(dim, lbound, ubound)
f <- obj_func_transformed(x)
budget <- budget - 1
if (f < fopt) {
xopt <- x
fopt <- f
}
}
list(xopt = xopt, fopt = fopt)
}
#' Random Local Search (RLS) Algorithm
#'
#' The simplest stochastic optimization algorithm for discrete problems. A randomly
#' chosen position in the solution vector is perturbated in each iteration. Only
#' improvements are accepted after perturbation.
#'
#'
#' @param dimension Dimension of search space
#' @param obj_func The evaluation function
#' @param target_hit Optional, function which enables early stopping if a target value is reached
#' @param budget integer, maximal allowable number of function evaluations
#'
#' @export
random_local_search <- function(dimension, obj_func, target_hit = function(){ FALSE },
budget = NULL) {
if (is.null(budget)) budget <- 10*dimension
starting_point <- sample(c(0, 1), dimension, TRUE)
fopt <- obj_func(starting_point)
xopt <- starting_point
iter <- 1
while (iter < budget && !target_hit() ) {
candidate <- xopt
switch_idx <- sample(1:dimension, 1)
candidate[switch_idx] <- 1 - candidate[switch_idx]
fval <- obj_func(candidate)
if (fval >= fopt) {
fopt <- fval
xopt <- candidate
}
iter <- iter + 1
}
list(xopt = xopt, fopt = fopt)
}
#' Mutation operator for 1+lambda EA
#'
#'
#' @param ind The individual to mutate
#' @param mutation_rate The mutation rate
#' @noRd
mutate <- function(ind, mutation_rate){
dim <- length(ind)
mutations <- seq(0, 0, length.out = dim)
while (sum(mutations) == 0) {
mutations <- sample(c(0, 1), dim, prob = c(1 - mutation_rate, mutation_rate), replace = TRUE)
}
as.integer( xor(ind, mutations) )
}
#' One-Comma-Lambda Self-Adapative Genetic Algorithm
#'
#' A genetic algorithm that controls the mutation rate (strength) using the so-called
#' self-adaptation mechanism: the mutation rate is firstly perturbated and then the
#' resulting value is taken to mutate Lambda solution vector. The best solution is
#' selected along with its mutation rate.
#'
#' @param lambda_ The size of the offspring
#' @param budget How many times the objective function can be evaluated
#' @param dimension Dimension of search space
#' @param obj_func The evaluation function
#' @param target_hit Optional, function which enables early stopping if a target value is reached
#' @param set_parameters Function to call to store the value of the registered parameters
#'
#' @export
self_adaptive_GA <- function(dimension, obj_func, lambda_ = 10, budget = NULL,
set_parameters = NULL, target_hit = function(){ FALSE }) {
obj_func <- obj_func
if (is.null(budget)) budget <- 10 * dimension
r <- 1.0 / dimension
if (is.function(set_parameters)) set_parameters(r)
x <- sample(c(0, 1), dimension, TRUE)
xopt <- x
fopt <- fx <- obj_func(x)
budget <- budget - 1
tau <- 0.22
while (budget > 0 && !target_hit()) {
lambda_ <- min(lambda_, budget) #ensure budget is not exceeded
x_ <- tcrossprod(rep(1, lambda_), x)
r_ <- (1.0 / (1 + (1 - r) / r * exp(tau * rnorm(lambda_)))) %*% t(rep(1, dimension))
idx <- matrix(runif(lambda_ * dimension), lambda_, dimension) < r_
x_[idx] <- 1 - x_[idx]
if (is.function(set_parameters)) set_parameters(r)
f <- obj_func(x_)
budget <- budget - lambda_
selected <- which(min(f) == f)[[1]]
x <- x_[selected, ]
r <- r_[selected, 1]
if (f[selected] > fopt) {
fopt <- f[selected]
xopt <- x
}
}
list(xopt = xopt, fopt = fopt)
}
#' One-Comma-Lambda Genetic Algorithm with 2-rate self-adaptive mutation rate
#'
#' A genetic algorithm that controls the mutation rate (strength) using the so-called
#' 2-rate self-adaptation mechanism: the mutation rate is based on a parameter r. For
#' each generation, half offspring are generated by mutation rate 2r/dim, and half by
#' r/2dim. r that the best offspring has been created with will be inherited by
#' probability 3/4, the other by 1/4.
#'
#' @param lambda_ The size of the offspring
#' @param budget How many times the objective function can be evaluated
#' @param dimension Dimension of search space
#' @param obj_func The evaluation function
#' @param target_hit Optional, function which enables early stopping if a target value is reached
#' @param set_parameters Function to call to store the value of the registered parameters
#'
#' @export
two_rate_GA <- function(dimension, obj_func, target_hit = function() { FALSE }, lambda_ = 2,
budget = NULL, set_parameters = NULL){
if (is.null(budget)) budget <- 100*dimension
parent <- sample(c(0, 1), dimension, TRUE)
best <- parent
r <- 2.0
fopt <- obj_func(parent)
budget <- budget - 1
if (is.function(set_parameters)) set_parameters(r)
while (budget > 0 && !target_hit()) {
selected_r <- r;
selected_obj <- -Inf
for (i in 1:lambda_) {
offspring <- parent
if (i <= lambda_/2) {
mutation_rate = r / 2.0 / dimension;
} else{
mutation_rate = 2.0 * r / dimension;
}
offspring <- mutate(offspring, mutation_rate)
v <- obj_func(offspring)
if (v >= fopt) {
fopt <- v
best <- offspring
}
if (v >= selected_obj) {
selected_obj = v
selected_r = mutation_rate * dimension;
}
budget <- budget - 1
if (budget == 0 ) break
}
parent <- best
if (runif(1) > 0.5) {
r = selected_r
}
else{
if (runif(1) > 0.5) {
r = r / 2.0
} else{
r = 2.0 * r
}
}
if (r < 2.0) r = 2.0
if (r > dimension / 4.0) r = dimension / 4.0
if (is.function(set_parameters)) set_parameters(r)
}
list(xopt = best, fopt = fopt)
}
| /build/R/R/Algorithms.R | permissive | nojhan/IOHexperimenter | R | false | false | 9,877 | r | #' IOHexperimenter-based wrapper
#'
#' For easier use with the IOHexperimenter
#'
#' @rdname random_search
#' @param IOHproblem An IOHproblem object
#'
#' @export
#' @examples
#' \donttest{
#' benchmark_algorithm(IOH_random_search, data.dir = NULL)
#' }
IOH_random_search <- function(IOHproblem, budget = NULL) {
if (IOHproblem$suite == "PBO")
random_search_PB(IOHproblem$dimension, IOHproblem$obj_func, IOHproblem$target_hit, budget)
else
random_search(IOHproblem$dimension, IOHproblem$obj_func, IOHproblem$target_hit, budget,
IOHproblem$lbound, IOHproblem$ubound, IOHproblem$maximization)
}
#' IOHexperimenter-based wrapper
#'
#' For easier use with the IOHexperimenter
#'
#' @param IOHproblem An IOHproblem object
#'
#' @rdname random_local_search
#' @export
#' @examples
#' \donttest{
#' benchmark_algorithm(IOH_random_local_search, data.dir = NULL)
#' }
IOH_random_local_search <- function(IOHproblem, budget = NULL) {
random_local_search(IOHproblem$dimension, IOHproblem$obj_func, IOHproblem$target_hit, budget)
}
#' IOHexperimenter-based wrapper
#'
#' For easier use with the IOHexperimenter
#'
#' @param IOHproblem An IOHproblem object
#'
#' @rdname self_adaptive_GA
#' @export
#' @examples
#' \donttest{
#' one_comma_two_EA <- function(IOHproblem) { IOH_self_adaptive_GA(IOHproblem, lambda_=2) }
#'
#' benchmark_algorithm(one_comma_two_EA, params.track = "Mutation rate",
#' algorithm.name = "one_comma_two_EA", data.dir = NULL,
#' algorithm.info = "Using one_comma_two_EA with specific parameter" )
#' }
IOH_self_adaptive_GA <- function(IOHproblem, lambda_ = 1, budget = NULL) {
self_adaptive_GA(IOHproblem$dimension, IOHproblem$obj_func,
target_hit = IOHproblem$target_hit, budget = budget,
lambda_ = lambda_, set_parameters = IOHproblem$set_parameters)
}
#' IOHexperimenter-based wrapper
#'
#' For easier use with the IOHexperimenter
#'
#' @param IOHproblem An IOHproblem object
#'
#' @rdname two_rate_GA
#' @export
#' @examples
#' \donttest{
#' bechmark_algorithm(IOH_two_rate_GA)
#' }
IOH_two_rate_GA <- function(IOHproblem, lambda_ = 1, budget = NULL) {
two_rate_GA(IOHproblem$dimension, IOHproblem$obj_func, budget = budget,
lambda_ = lambda_, set_parameters = IOHproblem$set_parameters,
target_hit = IOHproblem$target_hit)
}
#' Random Search
#'
#' Random walk in \eqn{{0, 1}^d} space; Maximization
#'
#' @rdname random_search
#' @export
random_search_PB <- function(dim, obj_func, target_hit = function(){ FALSE }, budget = NULL) {
if (is.null(budget)) budget <- 10 * dim
fopt <- -Inf
xopt <- NULL
while (budget > 0 && !target_hit()) {
x <- sample(c(0, 1), dim, TRUE)
f <- obj_func(x)
budget <- budget - 1
if (f > fopt) {
xopt <- x
fopt <- f
}
}
list(xopt = xopt, fopt = fopt)
}
#' Random Search
#'
#' Random walk in continuous space;
#'
#' @param dim Dimension of search space
#' @param obj_func The evaluation function
#' @param target_hit Optional, function which enables early stopping if a target value is reached
#' @param budget Integer, maximal allowable number of function evaluations
#' @param lbound Lower bound of search space. Either single number or vector of size `dim`
#' @param ubound Upper bound of search space. Either single number or vector of size `dim`
#' @param maximize Whether to perform maximization or minimization.
#' The function assumes minimization, achieved by inverting the obj_func when `maximize` is FALSE
#' @export
random_search <- function(dim, obj_func, target_hit = function(){ FALSE }, budget = NULL,
lbound = -1, ubound = 1, maximize = T) {
if (is.null(budget)) budget <- 10 * dim
if (maximize) { #Assume mimimization in the remainder of this function
obj_func_transformed <- function(x) {return(-1*obj_func(x))}
}
else{
obj_func_transformed <- obj_func
}
fopt <- Inf
xopt <- NULL
while (budget > 0 && !target_hit()) {
x <- runif(dim, lbound, ubound)
f <- obj_func_transformed(x)
budget <- budget - 1
if (f < fopt) {
xopt <- x
fopt <- f
}
}
list(xopt = xopt, fopt = fopt)
}
#' Random Local Search (RLS) Algorithm
#'
#' The simplest stochastic optimization algorithm for discrete problems. A randomly
#' chosen position in the solution vector is perturbated in each iteration. Only
#' improvements are accepted after perturbation.
#'
#'
#' @param dimension Dimension of search space
#' @param obj_func The evaluation function
#' @param target_hit Optional, function which enables early stopping if a target value is reached
#' @param budget integer, maximal allowable number of function evaluations
#'
#' @export
random_local_search <- function(dimension, obj_func, target_hit = function(){ FALSE },
budget = NULL) {
if (is.null(budget)) budget <- 10*dimension
starting_point <- sample(c(0, 1), dimension, TRUE)
fopt <- obj_func(starting_point)
xopt <- starting_point
iter <- 1
while (iter < budget && !target_hit() ) {
candidate <- xopt
switch_idx <- sample(1:dimension, 1)
candidate[switch_idx] <- 1 - candidate[switch_idx]
fval <- obj_func(candidate)
if (fval >= fopt) {
fopt <- fval
xopt <- candidate
}
iter <- iter + 1
}
list(xopt = xopt, fopt = fopt)
}
#' Mutation operator for 1+lambda EA
#'
#'
#' @param ind The individual to mutate
#' @param mutation_rate The mutation rate
#' @noRd
mutate <- function(ind, mutation_rate){
dim <- length(ind)
mutations <- seq(0, 0, length.out = dim)
while (sum(mutations) == 0) {
mutations <- sample(c(0, 1), dim, prob = c(1 - mutation_rate, mutation_rate), replace = TRUE)
}
as.integer( xor(ind, mutations) )
}
#' One-Comma-Lambda Self-Adapative Genetic Algorithm
#'
#' A genetic algorithm that controls the mutation rate (strength) using the so-called
#' self-adaptation mechanism: the mutation rate is firstly perturbated and then the
#' resulting value is taken to mutate Lambda solution vector. The best solution is
#' selected along with its mutation rate.
#'
#' @param lambda_ The size of the offspring
#' @param budget How many times the objective function can be evaluated
#' @param dimension Dimension of search space
#' @param obj_func The evaluation function
#' @param target_hit Optional, function which enables early stopping if a target value is reached
#' @param set_parameters Function to call to store the value of the registered parameters
#'
#' @export
self_adaptive_GA <- function(dimension, obj_func, lambda_ = 10, budget = NULL,
set_parameters = NULL, target_hit = function(){ FALSE }) {
obj_func <- obj_func
if (is.null(budget)) budget <- 10 * dimension
r <- 1.0 / dimension
if (is.function(set_parameters)) set_parameters(r)
x <- sample(c(0, 1), dimension, TRUE)
xopt <- x
fopt <- fx <- obj_func(x)
budget <- budget - 1
tau <- 0.22
while (budget > 0 && !target_hit()) {
lambda_ <- min(lambda_, budget) #ensure budget is not exceeded
x_ <- tcrossprod(rep(1, lambda_), x)
r_ <- (1.0 / (1 + (1 - r) / r * exp(tau * rnorm(lambda_)))) %*% t(rep(1, dimension))
idx <- matrix(runif(lambda_ * dimension), lambda_, dimension) < r_
x_[idx] <- 1 - x_[idx]
if (is.function(set_parameters)) set_parameters(r)
f <- obj_func(x_)
budget <- budget - lambda_
selected <- which(min(f) == f)[[1]]
x <- x_[selected, ]
r <- r_[selected, 1]
if (f[selected] > fopt) {
fopt <- f[selected]
xopt <- x
}
}
list(xopt = xopt, fopt = fopt)
}
#' One-Comma-Lambda Genetic Algorithm with 2-rate self-adaptive mutation rate
#'
#' A genetic algorithm that controls the mutation rate (strength) using the so-called
#' 2-rate self-adaptation mechanism: the mutation rate is based on a parameter r. For
#' each generation, half offspring are generated by mutation rate 2r/dim, and half by
#' r/2dim. r that the best offspring has been created with will be inherited by
#' probability 3/4, the other by 1/4.
#'
#' @param lambda_ The size of the offspring
#' @param budget How many times the objective function can be evaluated
#' @param dimension Dimension of search space
#' @param obj_func The evaluation function
#' @param target_hit Optional, function which enables early stopping if a target value is reached
#' @param set_parameters Function to call to store the value of the registered parameters
#'
#' @export
two_rate_GA <- function(dimension, obj_func, target_hit = function() { FALSE }, lambda_ = 2,
budget = NULL, set_parameters = NULL){
if (is.null(budget)) budget <- 100*dimension
parent <- sample(c(0, 1), dimension, TRUE)
best <- parent
r <- 2.0
fopt <- obj_func(parent)
budget <- budget - 1
if (is.function(set_parameters)) set_parameters(r)
while (budget > 0 && !target_hit()) {
selected_r <- r;
selected_obj <- -Inf
for (i in 1:lambda_) {
offspring <- parent
if (i <= lambda_/2) {
mutation_rate = r / 2.0 / dimension;
} else{
mutation_rate = 2.0 * r / dimension;
}
offspring <- mutate(offspring, mutation_rate)
v <- obj_func(offspring)
if (v >= fopt) {
fopt <- v
best <- offspring
}
if (v >= selected_obj) {
selected_obj = v
selected_r = mutation_rate * dimension;
}
budget <- budget - 1
if (budget == 0 ) break
}
parent <- best
if (runif(1) > 0.5) {
r = selected_r
}
else{
if (runif(1) > 0.5) {
r = r / 2.0
} else{
r = 2.0 * r
}
}
if (r < 2.0) r = 2.0
if (r > dimension / 4.0) r = dimension / 4.0
if (is.function(set_parameters)) set_parameters(r)
}
list(xopt = best, fopt = fopt)
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gh3.R
\name{gh_gists_get}
\alias{gh_gists_get}
\title{List the authenticated user's gists or if called anonymously, this will}
\usage{
gh_gists_get(...)
}
\arguments{
\item{...}{Additional parameters to pass to \code{\link[gh]{gh}}. See details.}
}
\description{
return all public gists.
}
\details{
Additional parameters that can be passed:
\describe{
\item{type}{ It takes values in: all, public, private, forks, sources, member.
The default is: all}
\item{Accept}{Is used to set specified media type. }
}
}
| /man/gh_gists_get.Rd | permissive | ramnathv/gh3 | R | false | true | 589 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gh3.R
\name{gh_gists_get}
\alias{gh_gists_get}
\title{List the authenticated user's gists or if called anonymously, this will}
\usage{
gh_gists_get(...)
}
\arguments{
\item{...}{Additional parameters to pass to \code{\link[gh]{gh}}. See details.}
}
\description{
return all public gists.
}
\details{
Additional parameters that can be passed:
\describe{
\item{type}{ It takes values in: all, public, private, forks, sources, member.
The default is: all}
\item{Accept}{Is used to set specified media type. }
}
}
|
if(!exists("NEI")){
NEI <- readRDS("C:\\Users\\winchesters\\Desktop\\coursera\\exploratory\\summarySCC_PM25.rds")
}
if(!exists("SCC")){
SCC <- readRDS("C:\\Users\\winchesters\\Desktop\\coursera\\exploratory\\Source_Classification_Code.rds")
}
TotalEmissionPerYear <- aggregate(Emissions ~ year, NEI,sum)
png("Plot1.png")
barplot(height = TotalEmissionPerYear$Emissions,
names.arg = TotalEmissionPerYear$year,
xlab = "years",
ylab = expression("Total PM[2.5] multiply by emission"),
main = expression("PM[2.5] in different years"))
dev.off()
| /Plot1.R | no_license | sahil411/Exploratory_analysis2 | R | false | false | 599 | r | if(!exists("NEI")){
NEI <- readRDS("C:\\Users\\winchesters\\Desktop\\coursera\\exploratory\\summarySCC_PM25.rds")
}
if(!exists("SCC")){
SCC <- readRDS("C:\\Users\\winchesters\\Desktop\\coursera\\exploratory\\Source_Classification_Code.rds")
}
TotalEmissionPerYear <- aggregate(Emissions ~ year, NEI,sum)
png("Plot1.png")
barplot(height = TotalEmissionPerYear$Emissions,
names.arg = TotalEmissionPerYear$year,
xlab = "years",
ylab = expression("Total PM[2.5] multiply by emission"),
main = expression("PM[2.5] in different years"))
dev.off()
|
#' Simulation of Synthetic Populations for Survey Data
#' Considering Auxiliary Information
#'
#' The production of synthetic datasets has been proposed as a
#' statistical disclosure control solution to generate public use
#' files out of protected data, and as a tool to
#' create ``augmented datasets'' to serve as input for
#' micro-simulation models.
#' Synthetic data have become an important instrument
#' for \emph{ex-ante} assessments of policies' impact.
#' The performance and acceptability of such a tool relies
#' heavily on the quality of the synthetic populations,
#' i.e., on the statistical similarity between the synthetic
#' and the true population of interest.
#'
#' Multiple approaches and tools have been developed to
#' generate synthetic data. These approaches can be
#' categorized into three main groups:
#' synthetic reconstruction, combinatorial optimization,
#' and model-based generation.
#'
#' The package:
#' \pkg{simPop} is a user-friendly R-package based on a modular object-oriented concept.
#' It provides a highly optimized S4 class implementation
#' of various methods, including calibration by iterative
#' proportional fitting and simulated annealing, and
#' modeling or data fusion by logistic regression.
#'
#' The following applications further shows the methods and package:
#' We firstly demonstrated the use of \pkg{simPop} by creating
#' a synthetic population of Austria based on the
#' European Statistics of Income and Living Conditions (Alfons et al., 2011)
#' including the evaluation of the quality of the generated population.
#' In this contribution, the mathematical details of functions \code{simStructure}, \code{simCategorical},
#' \code{simContinuous} and \code{simComponents} are given in detail.
#' The disclosure risk of this synthetic population has been evaluated in (Templ and Alfons, 2012) using large-scale simulation studies.
#'
#' Employer-employee data were created in Templ and Filzmoser (2014) whereby
#' the structure of companies and employees are considered.
#'
#' Finally, the R package \pkg{simPop} is presented in full detail
#' in Templ et al. (2017). In this paper - the main reference to this work -
#' all functions and the S4 class
#' structure of the package are described in detail. For beginners, this paper might be
#' the starting point to learn about the methods and package.
#'
#'
#' \tabular{ll}{ Package: \tab simPop\cr Type: \tab Package\cr Version: \tab
#' 1.0.0\cr Date: \tab 20017-08-07\cr License: \tab GPL (>= 2) \cr }
#'
#' @name simPop-package
#' @aliases simPop-package simPop
#' @docType package
#' @author Bernhard Meindl, Matthias Templ, Andreas Alfons, Alexander Kowarik,
#'
#' Maintainer: Matthias Templ <matthias.templ@@gmail.com>
#' @references
#' M. Templ, B. Meindl, A. Kowarik, A. Alfons, O. Dupriez (2017) Simulation of Synthetic Populations for Survey Data Considering Auxiliary
#' Information. \emph{Journal of Statistical Survey}, \strong{79} (10), 1--38. \doi{10.18637/jss.v079.i10}
#'
#' A. Alfons, M. Templ (2011) Simulation of close-to-reality population data for household surveys with application to EU-SILC. \emph{Statistical Methods & Applications}, \strong{20} (3), 383--407. doi: 10.1007/s10260-011-0163-2
#'
#' M. Templ, P. Filzmoser (2014) Simulation and quality of a synthetic close-to-reality employer-employee population.
#' Journal of Applied Statistics, \strong{41} (5), 1053--1072. \doi{10.1080/02664763.2013.859237}
#'
#' M. Templ, A. Alfons (2012) Disclosure Risk of Synthetic Population Data
#' with Application in the Case of EU-SILC. In J Domingo-Ferrer, E Magkos (eds.),
#' \emph{Privacy in Statistical Databases}, \strong{6344} of Lecture Notes in Computer Science, 174--186. Springer Verlag, Heidelberg. \doi{10.1007/978-3-642-15838-4_16}
#'
#' @keywords package
#' @examples
#'
#' ## we use synthetic eusilcS survey sample data
#' ## included in the package to simulate a population
#'
#' ## create the structure
#' data(eusilcS)
#' \donttest{
#' ## approx. 20 seconds computation time
#' inp <- specifyInput(data=eusilcS, hhid="db030", hhsize="hsize", strata="db040", weight="db090")
#' ## in the following, nr_cpus are selected automatically
#' simPop <- simStructure(data=inp, method="direct", basicHHvars=c("age", "rb090"))
#' simPop <- simCategorical(simPop, additional=c("pl030", "pb220a"), method="multinom", nr_cpus=1)
#' simPop
#' class(simPop)
#' regModel = ~rb090+hsize+pl030+pb220a
#'
#' ## multinomial model with random draws
#' eusilcM <- simContinuous(simPop, additional="netIncome",
#' regModel = regModel,
#' upper=200000, equidist=FALSE, nr_cpus=1)
#' class(eusilcM)
#' }
#'
#' ## this is already a basic synthetic population, but
#' ## many other functions in the package might now
#' ## be used for fine-tuning, adding further variables,
#' ## evaluating the quality, adding finer geographical details,
#' ## using different methods, calibrating surveys or populations, etc.
#' ## -- see Templ et al. (2017) for more details.
#'
NULL
| /R/simPop-package.R | no_license | statistikat/simPop | R | false | false | 5,083 | r | #' Simulation of Synthetic Populations for Survey Data
#' Considering Auxiliary Information
#'
#' The production of synthetic datasets has been proposed as a
#' statistical disclosure control solution to generate public use
#' files out of protected data, and as a tool to
#' create ``augmented datasets'' to serve as input for
#' micro-simulation models.
#' Synthetic data have become an important instrument
#' for \emph{ex-ante} assessments of policies' impact.
#' The performance and acceptability of such a tool relies
#' heavily on the quality of the synthetic populations,
#' i.e., on the statistical similarity between the synthetic
#' and the true population of interest.
#'
#' Multiple approaches and tools have been developed to
#' generate synthetic data. These approaches can be
#' categorized into three main groups:
#' synthetic reconstruction, combinatorial optimization,
#' and model-based generation.
#'
#' The package:
#' \pkg{simPop} is a user-friendly R-package based on a modular object-oriented concept.
#' It provides a highly optimized S4 class implementation
#' of various methods, including calibration by iterative
#' proportional fitting and simulated annealing, and
#' modeling or data fusion by logistic regression.
#'
#' The following applications further shows the methods and package:
#' We firstly demonstrated the use of \pkg{simPop} by creating
#' a synthetic population of Austria based on the
#' European Statistics of Income and Living Conditions (Alfons et al., 2011)
#' including the evaluation of the quality of the generated population.
#' In this contribution, the mathematical details of functions \code{simStructure}, \code{simCategorical},
#' \code{simContinuous} and \code{simComponents} are given in detail.
#' The disclosure risk of this synthetic population has been evaluated in (Templ and Alfons, 2012) using large-scale simulation studies.
#'
#' Employer-employee data were created in Templ and Filzmoser (2014) whereby
#' the structure of companies and employees are considered.
#'
#' Finally, the R package \pkg{simPop} is presented in full detail
#' in Templ et al. (2017). In this paper - the main reference to this work -
#' all functions and the S4 class
#' structure of the package are described in detail. For beginners, this paper might be
#' the starting point to learn about the methods and package.
#'
#'
#' \tabular{ll}{ Package: \tab simPop\cr Type: \tab Package\cr Version: \tab
#' 1.0.0\cr Date: \tab 20017-08-07\cr License: \tab GPL (>= 2) \cr }
#'
#' @name simPop-package
#' @aliases simPop-package simPop
#' @docType package
#' @author Bernhard Meindl, Matthias Templ, Andreas Alfons, Alexander Kowarik,
#'
#' Maintainer: Matthias Templ <matthias.templ@@gmail.com>
#' @references
#' M. Templ, B. Meindl, A. Kowarik, A. Alfons, O. Dupriez (2017) Simulation of Synthetic Populations for Survey Data Considering Auxiliary
#' Information. \emph{Journal of Statistical Survey}, \strong{79} (10), 1--38. \doi{10.18637/jss.v079.i10}
#'
#' A. Alfons, M. Templ (2011) Simulation of close-to-reality population data for household surveys with application to EU-SILC. \emph{Statistical Methods & Applications}, \strong{20} (3), 383--407. doi: 10.1007/s10260-011-0163-2
#'
#' M. Templ, P. Filzmoser (2014) Simulation and quality of a synthetic close-to-reality employer-employee population.
#' Journal of Applied Statistics, \strong{41} (5), 1053--1072. \doi{10.1080/02664763.2013.859237}
#'
#' M. Templ, A. Alfons (2012) Disclosure Risk of Synthetic Population Data
#' with Application in the Case of EU-SILC. In J Domingo-Ferrer, E Magkos (eds.),
#' \emph{Privacy in Statistical Databases}, \strong{6344} of Lecture Notes in Computer Science, 174--186. Springer Verlag, Heidelberg. \doi{10.1007/978-3-642-15838-4_16}
#'
#' @keywords package
#' @examples
#'
#' ## we use synthetic eusilcS survey sample data
#' ## included in the package to simulate a population
#'
#' ## create the structure
#' data(eusilcS)
#' \donttest{
#' ## approx. 20 seconds computation time
#' inp <- specifyInput(data=eusilcS, hhid="db030", hhsize="hsize", strata="db040", weight="db090")
#' ## in the following, nr_cpus are selected automatically
#' simPop <- simStructure(data=inp, method="direct", basicHHvars=c("age", "rb090"))
#' simPop <- simCategorical(simPop, additional=c("pl030", "pb220a"), method="multinom", nr_cpus=1)
#' simPop
#' class(simPop)
#' regModel = ~rb090+hsize+pl030+pb220a
#'
#' ## multinomial model with random draws
#' eusilcM <- simContinuous(simPop, additional="netIncome",
#' regModel = regModel,
#' upper=200000, equidist=FALSE, nr_cpus=1)
#' class(eusilcM)
#' }
#'
#' ## this is already a basic synthetic population, but
#' ## many other functions in the package might now
#' ## be used for fine-tuning, adding further variables,
#' ## evaluating the quality, adding finer geographical details,
#' ## using different methods, calibrating surveys or populations, etc.
#' ## -- see Templ et al. (2017) for more details.
#'
NULL
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ggthemes_data.R
\docType{data}
\name{ggthemes_data}
\alias{ggthemes_data}
\title{Palette and theme data}
\format{A \code{list} object.}
\usage{
ggthemes_data
}
\description{
The \code{ggthemes} environment contains various values used in
themes and palettes. This is undocumented and subject to change.
}
\keyword{datasets}
| /man/ggthemes_data.Rd | no_license | d2squared/ggthemes | R | false | true | 402 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ggthemes_data.R
\docType{data}
\name{ggthemes_data}
\alias{ggthemes_data}
\title{Palette and theme data}
\format{A \code{list} object.}
\usage{
ggthemes_data
}
\description{
The \code{ggthemes} environment contains various values used in
themes and palettes. This is undocumented and subject to change.
}
\keyword{datasets}
|
file <- file('./household_power_consumption.txt')
data <- read.table(text = grep("^[1,2]/2/2007",readLines(file),value=TRUE), sep = ';', col.names = c("Date", "Time", "Global_active_power", "Global_reactive_power", "Voltage", "Global_intensity", "Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), na.strings = '?')
data$Date <- as.Date(data$Date, format = '%d/%m/%Y')
data$DateTime <- as.POSIXct(paste(data$Date, data$Time))
if(!file.exists('figures')) dir.create('figures')
png(filename = './figures/plot4.png', width = 480, height = 480, units='px')
par(mfrow = c(2, 2))
plot(data$DateTime, data$Global_active_power, xlab = '', ylab = 'Global Active Power (kilowatt)', type = 'l')
plot(data$DateTime, data$Voltage, xlab = 'datetime', ylab = 'Voltage', type = 'l')
plot(data$DateTime, data$Sub_metering_1, xlab = '', ylab = 'Energy sub metering', type = 'l')
lines(data$DateTime, data$Sub_metering_2, col = 'red')
lines(data$DateTime, data$Sub_metering_3, col = 'blue')
legend('topright', col = c('black', 'red', 'blue'), legend = c('Sub_metering_1', 'Sub_metering_2', 'Sub_metering_3'), lwd = 1)
plot(data$DateTime, data$Global_reactive_power, xlab = 'datetime', ylab = 'Global_reactive_power', type = 'l')
dev.off() | /plot4.R | no_license | pam-wichit/ExData_Plotting1 | R | false | false | 1,224 | r | file <- file('./household_power_consumption.txt')
data <- read.table(text = grep("^[1,2]/2/2007",readLines(file),value=TRUE), sep = ';', col.names = c("Date", "Time", "Global_active_power", "Global_reactive_power", "Voltage", "Global_intensity", "Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), na.strings = '?')
data$Date <- as.Date(data$Date, format = '%d/%m/%Y')
data$DateTime <- as.POSIXct(paste(data$Date, data$Time))
if(!file.exists('figures')) dir.create('figures')
png(filename = './figures/plot4.png', width = 480, height = 480, units='px')
par(mfrow = c(2, 2))
plot(data$DateTime, data$Global_active_power, xlab = '', ylab = 'Global Active Power (kilowatt)', type = 'l')
plot(data$DateTime, data$Voltage, xlab = 'datetime', ylab = 'Voltage', type = 'l')
plot(data$DateTime, data$Sub_metering_1, xlab = '', ylab = 'Energy sub metering', type = 'l')
lines(data$DateTime, data$Sub_metering_2, col = 'red')
lines(data$DateTime, data$Sub_metering_3, col = 'blue')
legend('topright', col = c('black', 'red', 'blue'), legend = c('Sub_metering_1', 'Sub_metering_2', 'Sub_metering_3'), lwd = 1)
plot(data$DateTime, data$Global_reactive_power, xlab = 'datetime', ylab = 'Global_reactive_power', type = 'l')
dev.off() |
#please change the address before excution! Thank You:)
data <- read.csv("G:/subjects/statistics and probabilities/HW4/movie_metadata.csv")
correctData <- function(data){
data <- na.omit(data)
color <- vector("numeric")
# assign 1 for colory and assign 0 for black and white to make the comparison possible!:)
for(i in 1 : nrow(data)){
if(data[i , "color"] == "Color"){
color <- c(color , 1)
}
else{
color <- c(color , 0)
}
}
data <- data.frame(color , data$budget, data$num_critic_for_reviews,data$duration,data$director_facebook_likes,
data$actor_1_facebook_likes, data$num_voted_users, data$cast_total_facebook_likes,
data$num_user_for_reviews,data$imdb_score,data$movie_facebook_likes)
return(data)
}
data <- correctData(data)
#part-1
covFinder <- function(data){
covMatrix <- matrix(c(0), nrow = 11, ncol = 11)
for(i in 1:11){
for(j in 1:11){
covMatrix[i,j] = cov(data[,i],data[,j])
}
}
colnames(covMatrix) <- c("color" , "budget", "num critic for reviews", "duration", "director facebook likes", "actor 1 facebook likes", "num voted users", "cast total facebook likes", "num user for reviews","imdb score", "movie facebook likes")
rownames(covMatrix) <- c("color" , "budget", "num critic for reviews", "duration", "director facebook likes", "actor 1 facebook likes", "num voted users", "cast total facebook likes", "num user for reviews","imdb score", "movie facebook likes")
write.csv(covMatrix, "G:/subjects/statistics and probabilities/HW4/cov_mat.csv")
return(covMatrix)
}
covMatrix <- covFinder(data)
View(covMatrix)
#part-2
#It can be seen that some features have a the same or opposite mean monotoy with film's success but in this way that if they have
# positive/negetive Cov then they have the same/oppsite monotony but because it involves the mean amount , makes it hard to compare
# effects of the fatures in success rate so to standardize this comparison (adjusting the scales) and easily comparing factors it's better to use correlations
# which is done below:
corFinder <- function(data){
corMatrix <- matrix(c(0), nrow = 11, ncol = 11)
for(i in 1:11){
for(j in 1:11){
corMatrix[i,j] = cor(data[,i],data[,j])
}
}
colnames(corMatrix) <- c("color" , "budget", "num critic for reviews", "duration", "director facebook likes", "actor 1 facebook likes", "num voted users", "cast total facebook likes", "num user for reviews","imdb score", "movie facebook likes")
rownames(corMatrix) <- c("color" , "budget", "num critic for reviews", "duration", "director facebook likes", "actor 1 facebook likes", "num voted users", "cast total facebook likes", "num user for reviews","imdb score", "movie facebook likes")
write.csv(corMatrix, "G:/subjects/statistics and probabilities/HW4/cor_mat.csv")
return(corMatrix)
}
corMatrix <- corFinder(data)
View(corMatrix)
#as it can be seen in the corMatrix : if we declare success with imdb score then these factors are the most improtant respectively as
# can be seen in the corMatrix:
#num voted users , duration , num critic for reviews , num user for reviews , movie facebook likes , director facebook likes
# cast total facebook likes , actor 1 facebook likes , budget , color
# it's clear that actor 1 & budget don;t have so effects!
#part-3
names <- c("color" , "budget", "num_critic_for_reviews", "duration", "director_facebook_likes", "actor_1_facebook_likes", "num _voted_users", "cast_total_facebook_likes", "num_user_for_reviews","imdb_score", "movie_facebook_likes")
for(i in 1:10){
for(j in (i+1):11){
plot(covMatrix[,i], covMatrix[,j],xlab = names[i], ylab = names[j])
}
}
names <- c("color" , "budget", "num_critic_for_reviews", "duration", "director_facebook_likes", "actor_1_facebook_likes", "num _voted_users", "cast_total_facebook_likes", "num_user_for_reviews","imdb_score", "movie_facebook_likes")
for(i in 1:10){
for(j in (i+1):11){
plot(corMatrix[,i], corMatrix[,j],xlab = names[i], ylab = names[j])
}
}
| /Assignments/Assignment4/Practical Solution/IMDB.R | no_license | Ashkan-Soleymani98/ProbabilityStatistics---Fall2017-2018 | R | false | false | 4,061 | r | #please change the address before excution! Thank You:)
data <- read.csv("G:/subjects/statistics and probabilities/HW4/movie_metadata.csv")
correctData <- function(data){
data <- na.omit(data)
color <- vector("numeric")
# assign 1 for colory and assign 0 for black and white to make the comparison possible!:)
for(i in 1 : nrow(data)){
if(data[i , "color"] == "Color"){
color <- c(color , 1)
}
else{
color <- c(color , 0)
}
}
data <- data.frame(color , data$budget, data$num_critic_for_reviews,data$duration,data$director_facebook_likes,
data$actor_1_facebook_likes, data$num_voted_users, data$cast_total_facebook_likes,
data$num_user_for_reviews,data$imdb_score,data$movie_facebook_likes)
return(data)
}
data <- correctData(data)
#part-1
covFinder <- function(data){
covMatrix <- matrix(c(0), nrow = 11, ncol = 11)
for(i in 1:11){
for(j in 1:11){
covMatrix[i,j] = cov(data[,i],data[,j])
}
}
colnames(covMatrix) <- c("color" , "budget", "num critic for reviews", "duration", "director facebook likes", "actor 1 facebook likes", "num voted users", "cast total facebook likes", "num user for reviews","imdb score", "movie facebook likes")
rownames(covMatrix) <- c("color" , "budget", "num critic for reviews", "duration", "director facebook likes", "actor 1 facebook likes", "num voted users", "cast total facebook likes", "num user for reviews","imdb score", "movie facebook likes")
write.csv(covMatrix, "G:/subjects/statistics and probabilities/HW4/cov_mat.csv")
return(covMatrix)
}
covMatrix <- covFinder(data)
View(covMatrix)
#part-2
#It can be seen that some features have a the same or opposite mean monotoy with film's success but in this way that if they have
# positive/negetive Cov then they have the same/oppsite monotony but because it involves the mean amount , makes it hard to compare
# effects of the fatures in success rate so to standardize this comparison (adjusting the scales) and easily comparing factors it's better to use correlations
# which is done below:
corFinder <- function(data){
corMatrix <- matrix(c(0), nrow = 11, ncol = 11)
for(i in 1:11){
for(j in 1:11){
corMatrix[i,j] = cor(data[,i],data[,j])
}
}
colnames(corMatrix) <- c("color" , "budget", "num critic for reviews", "duration", "director facebook likes", "actor 1 facebook likes", "num voted users", "cast total facebook likes", "num user for reviews","imdb score", "movie facebook likes")
rownames(corMatrix) <- c("color" , "budget", "num critic for reviews", "duration", "director facebook likes", "actor 1 facebook likes", "num voted users", "cast total facebook likes", "num user for reviews","imdb score", "movie facebook likes")
write.csv(corMatrix, "G:/subjects/statistics and probabilities/HW4/cor_mat.csv")
return(corMatrix)
}
corMatrix <- corFinder(data)
View(corMatrix)
#as it can be seen in the corMatrix : if we declare success with imdb score then these factors are the most improtant respectively as
# can be seen in the corMatrix:
#num voted users , duration , num critic for reviews , num user for reviews , movie facebook likes , director facebook likes
# cast total facebook likes , actor 1 facebook likes , budget , color
# it's clear that actor 1 & budget don;t have so effects!
#part-3
names <- c("color" , "budget", "num_critic_for_reviews", "duration", "director_facebook_likes", "actor_1_facebook_likes", "num _voted_users", "cast_total_facebook_likes", "num_user_for_reviews","imdb_score", "movie_facebook_likes")
for(i in 1:10){
for(j in (i+1):11){
plot(covMatrix[,i], covMatrix[,j],xlab = names[i], ylab = names[j])
}
}
names <- c("color" , "budget", "num_critic_for_reviews", "duration", "director_facebook_likes", "actor_1_facebook_likes", "num _voted_users", "cast_total_facebook_likes", "num_user_for_reviews","imdb_score", "movie_facebook_likes")
for(i in 1:10){
for(j in (i+1):11){
plot(corMatrix[,i], corMatrix[,j],xlab = names[i], ylab = names[j])
}
}
|
library(ape)
testtree <- read.tree("2999_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="2999_0_unrooted.txt") | /codeml_files/newick_trees_processed/2999_0/rinput.R | no_license | DaniBoo/cyanobacteria_project | R | false | false | 135 | r | library(ape)
testtree <- read.tree("2999_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="2999_0_unrooted.txt") |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fun.list.rw.R
\name{make.robust}
\alias{make.robust}
\title{Add a vague componenet to mixture model to make robust}
\usage{
make.robust(object, weight)
}
\arguments{
\item{object}{mixture.prior to update}
\item{weight}{weight of vague component in updated model}
}
\value{
A \code{mixture.prior} object with updated weights and parameters
}
\description{
Add a vague componenet to mixture model to make robust
}
| /StudyPrior/man/make.robust.Rd | no_license | igrave/StudyPrior | R | false | true | 491 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fun.list.rw.R
\name{make.robust}
\alias{make.robust}
\title{Add a vague componenet to mixture model to make robust}
\usage{
make.robust(object, weight)
}
\arguments{
\item{object}{mixture.prior to update}
\item{weight}{weight of vague component in updated model}
}
\value{
A \code{mixture.prior} object with updated weights and parameters
}
\description{
Add a vague componenet to mixture model to make robust
}
|
# Required data ----------------------------------------------------------------------------------------------------------------------
data("dataHigherMoments")
# Formula transformations ------------------------------------------------------------------------------------------------------------
context("Correctness - higherMomentsIV - Formula transformations")
# Data sorting -----------------------------------------------------------------------------------------------------------------------
context("Correctness - higherMomentsIV - Data sorting")
test_that("Differently sorted data produces same results", {
data.altered <- dataHigherMoments
data.altered[sample(nrow(data.altered), nrow(data.altered), replace = FALSE), ]
expect_silent(res.orig <- higherMomentsIV(y~X1+X2+P|P|IIV(g=x2,iiv=g, X1), data=dataHigherMoments, verbose=FALSE))
expect_silent(res.diff.sorted <- higherMomentsIV(y~X1+X2+P|P|IIV(g=x2,iiv=g, X1), data=data.altered, verbose=FALSE))
expect_equal(coef(res.orig), coef(res.diff.sorted))
expect_equal(coef(summary(res.orig)), coef(summary(res.diff.sorted)))
})
# Predict ----------------------------------------------------------------------------------------------------------------------------
context("Correctness - higherMomentsIV - Predict")
test_that("No newdata results in fitted values", {
expect_silent(higher.1 <- higherMomentsIV(y~X1+X2+P|P|IIV(iiv=gp, g=x2, X1), data=dataHigherMoments, verbose=FALSE))
expect_equal(predict(higher.1), fitted(higher.1))
expect_silent(higher.2 <- higherMomentsIV(y~X1+X2+P|P|IIV(iiv=y2) + IIV(iiv=yp) + IIV(iiv=g,g=x3,X1),
data=dataHigherMoments, verbose=FALSE))
expect_equal(predict(higher.2), fitted(higher.2))
})
test_that("Same prediction data as for fitting results in fitted values", {
expect_silent(higher.1 <- higherMomentsIV(y~X1+X2+P|P|IIV(iiv=gp, g=x2, X1), data=dataHigherMoments, verbose=FALSE))
expect_equal(predict(higher.1, newdata=dataHigherMoments), fitted(higher.1))
expect_silent(higher.2 <- higherMomentsIV(y~X1+X2+P|P|IIV(iiv=y2) + IIV(iiv=yp) + IIV(iiv=g,g=x3,X1),
data=dataHigherMoments, verbose=FALSE))
expect_equal(predict(higher.2, newdata=dataHigherMoments), fitted(higher.2))
})
test_that("Same results as ivreg with useless iiv dummies", {
expect_silent(higher.1 <- higherMomentsIV(y~X1+X2+P|P|IIV(iiv=gp, g=x2, X1), data=dataHigherMoments, verbose=FALSE))
expect_equal(predict(higher.1, newdata=dataHigherMoments),
AER:::predict.ivreg(higher.1, newdata = cbind(dataHigherMoments,IIV.isgp.gisx2.regisX1=-11)))
expect_silent(higher.2 <- higherMomentsIV(y~X1+X2+P|P|IIV(iiv=y2) + IIV(iiv=yp) + IIV(iiv=g,g=x3,X1),
data=dataHigherMoments, verbose=FALSE))
expect_equal(predict(higher.2, newdata=dataHigherMoments),
AER:::predict.ivreg(higher.2, newdata = cbind(dataHigherMoments,IIV.isy2=1.23, IIV.isyp=2.34, IIV.isg.gisx3.regisX1=3.45)))
})
test_that("Correct structure of predictions", {
expect_silent(higher.1 <- higherMomentsIV(y~X1+X2+P|P|IIV(iiv=gp, g=x2, X1), data=dataHigherMoments, verbose=FALSE))
expect_silent(pred.1 <- predict(higher.1, dataHigherMoments))
expect_true(is.numeric(pred.1))
expect_true(length(pred.1) == nrow(dataHigherMoments))
expect_true(all(names(pred.1) == names(fitted(higher.1))))
expect_true(all(names(pred.1) == rownames(dataHigherMoments)))
})
test_that("Correct when using transformations in the formula", {
# transformation in regressor
expect_silent(higher.1 <- higherMomentsIV(y~I((X1+1)/2)+X2+P|P|IIV(iiv=gp, g=x2, I((X1+1)/2)), data=dataHigherMoments, verbose=FALSE))
expect_equal(predict(higher.1, newdata=dataHigherMoments), fitted(higher.1))
# transformation in endogenous
expect_silent(higher.1 <- higherMomentsIV(y~X1+X2+I((P+14)/3)|I((P+14)/3)|IIV(iiv=gp, g=x2, X1), data=dataHigherMoments, verbose=FALSE))
expect_equal(predict(higher.1, newdata=dataHigherMoments), fitted(higher.1))
})
# ** iiv built correctly
| /tests/testthat/test-correctness_highermoments.R | no_license | mmeierer/REndo | R | false | false | 4,104 | r | # Required data ----------------------------------------------------------------------------------------------------------------------
data("dataHigherMoments")
# Formula transformations ------------------------------------------------------------------------------------------------------------
context("Correctness - higherMomentsIV - Formula transformations")
# Data sorting -----------------------------------------------------------------------------------------------------------------------
context("Correctness - higherMomentsIV - Data sorting")
test_that("Differently sorted data produces same results", {
data.altered <- dataHigherMoments
data.altered[sample(nrow(data.altered), nrow(data.altered), replace = FALSE), ]
expect_silent(res.orig <- higherMomentsIV(y~X1+X2+P|P|IIV(g=x2,iiv=g, X1), data=dataHigherMoments, verbose=FALSE))
expect_silent(res.diff.sorted <- higherMomentsIV(y~X1+X2+P|P|IIV(g=x2,iiv=g, X1), data=data.altered, verbose=FALSE))
expect_equal(coef(res.orig), coef(res.diff.sorted))
expect_equal(coef(summary(res.orig)), coef(summary(res.diff.sorted)))
})
# Predict ----------------------------------------------------------------------------------------------------------------------------
context("Correctness - higherMomentsIV - Predict")
test_that("No newdata results in fitted values", {
expect_silent(higher.1 <- higherMomentsIV(y~X1+X2+P|P|IIV(iiv=gp, g=x2, X1), data=dataHigherMoments, verbose=FALSE))
expect_equal(predict(higher.1), fitted(higher.1))
expect_silent(higher.2 <- higherMomentsIV(y~X1+X2+P|P|IIV(iiv=y2) + IIV(iiv=yp) + IIV(iiv=g,g=x3,X1),
data=dataHigherMoments, verbose=FALSE))
expect_equal(predict(higher.2), fitted(higher.2))
})
test_that("Same prediction data as for fitting results in fitted values", {
expect_silent(higher.1 <- higherMomentsIV(y~X1+X2+P|P|IIV(iiv=gp, g=x2, X1), data=dataHigherMoments, verbose=FALSE))
expect_equal(predict(higher.1, newdata=dataHigherMoments), fitted(higher.1))
expect_silent(higher.2 <- higherMomentsIV(y~X1+X2+P|P|IIV(iiv=y2) + IIV(iiv=yp) + IIV(iiv=g,g=x3,X1),
data=dataHigherMoments, verbose=FALSE))
expect_equal(predict(higher.2, newdata=dataHigherMoments), fitted(higher.2))
})
test_that("Same results as ivreg with useless iiv dummies", {
expect_silent(higher.1 <- higherMomentsIV(y~X1+X2+P|P|IIV(iiv=gp, g=x2, X1), data=dataHigherMoments, verbose=FALSE))
expect_equal(predict(higher.1, newdata=dataHigherMoments),
AER:::predict.ivreg(higher.1, newdata = cbind(dataHigherMoments,IIV.isgp.gisx2.regisX1=-11)))
expect_silent(higher.2 <- higherMomentsIV(y~X1+X2+P|P|IIV(iiv=y2) + IIV(iiv=yp) + IIV(iiv=g,g=x3,X1),
data=dataHigherMoments, verbose=FALSE))
expect_equal(predict(higher.2, newdata=dataHigherMoments),
AER:::predict.ivreg(higher.2, newdata = cbind(dataHigherMoments,IIV.isy2=1.23, IIV.isyp=2.34, IIV.isg.gisx3.regisX1=3.45)))
})
test_that("Correct structure of predictions", {
expect_silent(higher.1 <- higherMomentsIV(y~X1+X2+P|P|IIV(iiv=gp, g=x2, X1), data=dataHigherMoments, verbose=FALSE))
expect_silent(pred.1 <- predict(higher.1, dataHigherMoments))
expect_true(is.numeric(pred.1))
expect_true(length(pred.1) == nrow(dataHigherMoments))
expect_true(all(names(pred.1) == names(fitted(higher.1))))
expect_true(all(names(pred.1) == rownames(dataHigherMoments)))
})
test_that("Correct when using transformations in the formula", {
# transformation in regressor
expect_silent(higher.1 <- higherMomentsIV(y~I((X1+1)/2)+X2+P|P|IIV(iiv=gp, g=x2, I((X1+1)/2)), data=dataHigherMoments, verbose=FALSE))
expect_equal(predict(higher.1, newdata=dataHigherMoments), fitted(higher.1))
# transformation in endogenous
expect_silent(higher.1 <- higherMomentsIV(y~X1+X2+I((P+14)/3)|I((P+14)/3)|IIV(iiv=gp, g=x2, X1), data=dataHigherMoments, verbose=FALSE))
expect_equal(predict(higher.1, newdata=dataHigherMoments), fitted(higher.1))
})
# ** iiv built correctly
|
M = cor(mtcars)
(order.AOE = corrMatOrder(M, order = 'AOE'))
(order.FPC = corrMatOrder(M, order = 'FPC'))
(order.hc = corrMatOrder(M, order = 'hclust'))
(order.hc2 = corrMatOrder(M, order = 'hclust', hclust.method = 'ward.D'))
M.AOE = M[order.AOE,order.AOE]
M.FPC = M[order.FPC,order.FPC]
M.hc = M[order.hc, order.hc]
M.hc2 = M[order.hc2,order.hc2]
par(ask = TRUE)
corrplot(M)
corrplot(M.AOE)
corrplot(M.FPC)
corrplot(M.hc)
corrplot(M.hc)
corrRect.hclust(corr = M.hc, k = 2)
corrplot(M.hc)
corrRect.hclust(corr = M.hc, k = 3)
corrplot(M.hc2)
corrRect.hclust(M.hc2, k = 2, method = 'ward.D')
| /vignettes/example-corrMatOrder.R | permissive | ZhangYet/corrplot | R | false | false | 600 | r | M = cor(mtcars)
(order.AOE = corrMatOrder(M, order = 'AOE'))
(order.FPC = corrMatOrder(M, order = 'FPC'))
(order.hc = corrMatOrder(M, order = 'hclust'))
(order.hc2 = corrMatOrder(M, order = 'hclust', hclust.method = 'ward.D'))
M.AOE = M[order.AOE,order.AOE]
M.FPC = M[order.FPC,order.FPC]
M.hc = M[order.hc, order.hc]
M.hc2 = M[order.hc2,order.hc2]
par(ask = TRUE)
corrplot(M)
corrplot(M.AOE)
corrplot(M.FPC)
corrplot(M.hc)
corrplot(M.hc)
corrRect.hclust(corr = M.hc, k = 2)
corrplot(M.hc)
corrRect.hclust(corr = M.hc, k = 3)
corrplot(M.hc2)
corrRect.hclust(M.hc2, k = 2, method = 'ward.D')
|
#
# test filtering via factors
#
setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source('../h2o-runit.R')
factorfilter <- function(conn){
Log.info('uploading ddply testing dataset')
df.h <- h2o.importFile(conn, normalizePath(locate('smalldata/jira/pub-180.csv')))
Log.info('printing from h2o')
Log.info( head(df.h) )
Log.info('subsetting via factor')
df.h.1 <- df.h[ df.h$colgroup == 'a', ]
expect_that( dim(df.h.1), equals(c(3,4) ))
df.h.2 <- df.h[ df.h[,2] == "group2", ]
expect_that( dim(df.h.2), equals(c(2, 4) ))
df.h.3 <- df.h[ df.h[,2] == 'group1' & df.h$colgroup == 'c', ]
expect_that( dim(df.h.3), equals( c(1,4) ))
Log.info('localizing')
df.1 <- as.data.frame(df.h.1)
df.2 <- as.data.frame(df.h.2)
df.3 <- as.data.frame(df.h.3)
Log.info('testing')
expect_that( dim(df.1), equals(c(3, 4) ))
checkTrue( unique( df.1[,1] ) == 'a' && unique(df.1[,2]) == 'group1')
checkTrue(all( df.1[,3] == c(1,2,1) ))
checkTrue(all( df.1[,4] == c(2,3,2) ))
expect_that( dim(df.2), equals(c(2, 4) ))
expect_that( unique( df.2[,1] ), equals(factor('c')))
expect_that(unique(df.2[,2]), equals(factor('group2')))
checkTrue(all( df.2[,3] == c(5,5) ))
checkTrue(all( df.2[,4] == c(6,6) ))
expect_that( dim(df.3), equals( c(1, 4) ))
expect_that( df.3[1,1], equals( factor('c')))
expect_that(df.3[1,2], equals(factor('group1' )))
expect_that( df.3[1,3], equals(5 ))
expect_that( df.3[1,4], equals(6 ))
testEnd()
}
if(F){
# R code that does the same as above
data <- read.csv(locate('smalldata/jira/pub-180.csv'), header=T)
data[ data$colgroup == 'a', ]
data[ data[,2] == 'group2', ]
data[ data[,2] == 'group1' & data$colgroup == 'c', ]
}
doTest('factor filtering', factorfilter)
| /h2o-r/tests/testdir_jira/runit_pub_168_dfpredicates.R | permissive | mrgloom/h2o-3 | R | false | false | 1,780 | r | #
# test filtering via factors
#
setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source('../h2o-runit.R')
factorfilter <- function(conn){
Log.info('uploading ddply testing dataset')
df.h <- h2o.importFile(conn, normalizePath(locate('smalldata/jira/pub-180.csv')))
Log.info('printing from h2o')
Log.info( head(df.h) )
Log.info('subsetting via factor')
df.h.1 <- df.h[ df.h$colgroup == 'a', ]
expect_that( dim(df.h.1), equals(c(3,4) ))
df.h.2 <- df.h[ df.h[,2] == "group2", ]
expect_that( dim(df.h.2), equals(c(2, 4) ))
df.h.3 <- df.h[ df.h[,2] == 'group1' & df.h$colgroup == 'c', ]
expect_that( dim(df.h.3), equals( c(1,4) ))
Log.info('localizing')
df.1 <- as.data.frame(df.h.1)
df.2 <- as.data.frame(df.h.2)
df.3 <- as.data.frame(df.h.3)
Log.info('testing')
expect_that( dim(df.1), equals(c(3, 4) ))
checkTrue( unique( df.1[,1] ) == 'a' && unique(df.1[,2]) == 'group1')
checkTrue(all( df.1[,3] == c(1,2,1) ))
checkTrue(all( df.1[,4] == c(2,3,2) ))
expect_that( dim(df.2), equals(c(2, 4) ))
expect_that( unique( df.2[,1] ), equals(factor('c')))
expect_that(unique(df.2[,2]), equals(factor('group2')))
checkTrue(all( df.2[,3] == c(5,5) ))
checkTrue(all( df.2[,4] == c(6,6) ))
expect_that( dim(df.3), equals( c(1, 4) ))
expect_that( df.3[1,1], equals( factor('c')))
expect_that(df.3[1,2], equals(factor('group1' )))
expect_that( df.3[1,3], equals(5 ))
expect_that( df.3[1,4], equals(6 ))
testEnd()
}
if(F){
# R code that does the same as above
data <- read.csv(locate('smalldata/jira/pub-180.csv'), header=T)
data[ data$colgroup == 'a', ]
data[ data[,2] == 'group2', ]
data[ data[,2] == 'group1' & data$colgroup == 'c', ]
}
doTest('factor filtering', factorfilter)
|
library(ape)
testtree <- read.tree("3924_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="3924_0_unrooted.txt") | /codeml_files/newick_trees_processed/3924_0/rinput.R | no_license | DaniBoo/cyanobacteria_project | R | false | false | 135 | r | library(ape)
testtree <- read.tree("3924_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="3924_0_unrooted.txt") |
#A script to calculate similarity scores for gene expression
library(proxy)
setwd('Documents/public-datasets/TCGA/expressionData/Expression-Genes/UNC__AgilentG4502A_07_1/')
source('~/Documents/Rscripts/120518-initialize.R')
geneMatrix = read.delim('~/Documents/public-datasets/TCGA/expressionData/Expression-Genes/UNC__AgilentG4502A_07_1/120814-CREBexpressionMatrixSorted.txt')
row.names(geneMatrix) = geneMatrix$genes
geneMatrix = geneMatrix[,2:92]
#Get the summary stats for the matrix
stats = summary(geneMatrix)
#In excel get the upper and lower quartiles and put into a vector for the CREB signature. Order alphabetically
sig = read.delim('120813-CREBsignatureSorted.txt', header=FALSE)
signature = as.matrix(sig$V2)
row.names(signature) = sig$V1
#generate the distance matrix. Make sure that the order of genes in the expression matrix and signature are the same!
distanceMatrix = function(geneExpressionMatrix, GeneSignature) {
index = 1
distanceMatrix = geneExpressionMatrix[,,]
while (index <= length(geneExpressionMatrix)) {
distanceMatrix[,index] = dist(geneExpressionMatrix[,index], GeneSignature, by_rows=FALSE, pairwise=TRUE) #distance measure
index = index + 1
}
return (distanceMatrix)
}
similarityMatrix = function(geneExpressionMatrix, GeneSignature) {
index = 1
distanceMatrix = geneExpressionMatrix[,,]
while (index <= length(geneExpressionMatrix)) {
distanceMatrix[,index] = simil(geneExpressionMatrix[,index], GeneSignature, pairwise=TRUE, by_rows=FALSE) #similarity measure
index = index + 1
}
return (distanceMatrix)
}
#Obtain the column sum and mean for the distance matrix
meanDistance = colMeans(distanceMatrix)
sumDistance = colSums(distanceMatrix)
hist(sumDistance, xlim=c(10, 40), breaks='Scott', freq=T, xlab='Sum distance from CREB signature', ylab='Frequency', main='TCGA Agilent 1 sample similarity to 38 gene CREB signature', col='blue')
distanceSummary = summary(sumDistance)
#bind the survival data to the colSum similarity data
survival = read.delim('~/Documents/public-datasets/TCGA/clinicalData/120731-survivalDataStructered.txt')
sur = survival[,c(4,5)]
distPaitents = colnames(distanceMatrix)
sur1 = sur[distPaitents,] | /PhD/120813-GeneExpressionSignature.R | no_license | dvbrown/Rscripts | R | false | false | 2,203 | r | #A script to calculate similarity scores for gene expression
library(proxy)
setwd('Documents/public-datasets/TCGA/expressionData/Expression-Genes/UNC__AgilentG4502A_07_1/')
source('~/Documents/Rscripts/120518-initialize.R')
geneMatrix = read.delim('~/Documents/public-datasets/TCGA/expressionData/Expression-Genes/UNC__AgilentG4502A_07_1/120814-CREBexpressionMatrixSorted.txt')
row.names(geneMatrix) = geneMatrix$genes
geneMatrix = geneMatrix[,2:92]
#Get the summary stats for the matrix
stats = summary(geneMatrix)
#In excel get the upper and lower quartiles and put into a vector for the CREB signature. Order alphabetically
sig = read.delim('120813-CREBsignatureSorted.txt', header=FALSE)
signature = as.matrix(sig$V2)
row.names(signature) = sig$V1
#generate the distance matrix. Make sure that the order of genes in the expression matrix and signature are the same!
distanceMatrix = function(geneExpressionMatrix, GeneSignature) {
index = 1
distanceMatrix = geneExpressionMatrix[,,]
while (index <= length(geneExpressionMatrix)) {
distanceMatrix[,index] = dist(geneExpressionMatrix[,index], GeneSignature, by_rows=FALSE, pairwise=TRUE) #distance measure
index = index + 1
}
return (distanceMatrix)
}
similarityMatrix = function(geneExpressionMatrix, GeneSignature) {
index = 1
distanceMatrix = geneExpressionMatrix[,,]
while (index <= length(geneExpressionMatrix)) {
distanceMatrix[,index] = simil(geneExpressionMatrix[,index], GeneSignature, pairwise=TRUE, by_rows=FALSE) #similarity measure
index = index + 1
}
return (distanceMatrix)
}
#Obtain the column sum and mean for the distance matrix
meanDistance = colMeans(distanceMatrix)
sumDistance = colSums(distanceMatrix)
hist(sumDistance, xlim=c(10, 40), breaks='Scott', freq=T, xlab='Sum distance from CREB signature', ylab='Frequency', main='TCGA Agilent 1 sample similarity to 38 gene CREB signature', col='blue')
distanceSummary = summary(sumDistance)
#bind the survival data to the colSum similarity data
survival = read.delim('~/Documents/public-datasets/TCGA/clinicalData/120731-survivalDataStructered.txt')
sur = survival[,c(4,5)]
distPaitents = colnames(distanceMatrix)
sur1 = sur[distPaitents,] |
rm(list=ls())
library(MASS)
library(far)
# remotes::install_github("isglobal-brge/mgcca")
library(curatedTCGAData)
library(TCGAutils)
# Libraries needed by mgcca package
library(MultiAssayExperiment)
library(RSpectra)
library(impute)
# Benchmark
library(microbenchmark)
library(mgcca)
fsim <- function(n, p, q, prop.miss, ncomp, ncomp.true, sigma.noise, difer){
# n <- 100
# p <- 5
# q <- 5
# prop.miss <- 0.1
# ncomp <- 2
# sigma.noise <- 0.001
# ncomp.true <- 2
# difer <- 1
# n = number of individuals
# p = number of variables in X matrix
# q = number of variables in Y matrix
# prop.miss= proportion of missing values
# ncomp = number of correlation functions
# sigma.noise = sigma of noise added to Xi.
# ncomp.true = number of true canonical variables.
# ... = arguments passed to mgcc function (currently ignored)
if (p>n | q>n) stop("number of individuals must be larger than the number of variables")
if (n%%2!=0) stop("n must be pair")
# simulate data (as in "Generalized canonical correlation analysis of matrices with missing rows: A simulation study" paper)
# note: we add noise to X1 instead of Y, if noise is added to Ytrue as specified in paper var(Xi) is not singular!!!
Ysim <- matrix(rnorm(n*ncomp.true), n, ncomp.true) # simulate from a standard normal variables
group <- rep(1:2, each=n/2)
names(group) <- 1:n
Ysim[group==1,] <- Ysim[group==1,]+difer/2
Ysim[group==2,] <- Ysim[group==2,]-difer/2
# plot(Ysim, col=rep(1:2, each=n/2))
Ytrue <- Ysim
pval.true <- anova(manova(Ytrue ~ group))[2,6]
b1 <- matrix(runif(ncomp.true*p,-1,1), ncomp.true, p)
X1 <- Ytrue%*%b1 + matrix(rnorm(n*p, 0, sigma.noise), n, p) # add noise to X1 instead of Y!!!
b2 <- matrix(runif(ncomp.true*q,-1,1), ncomp.true, q)
X2 <- Ytrue%*%b2 + matrix(rnorm(n*q, 0, sigma.noise), n, q) # add noise to X2 instead of Y!!!
rownames(X1) <- rownames(X2) <- 1:n
X<-Xori <- list(X1,X2)
####### introduce individuals missings at random
n<- nrow(X[[1]])
Xmiss <- Xori
k <- length(Xmiss)
which.miss.prev <- integer()
for (i in 1:k){
Xi <- X[[i]]
n <- nrow(Xi)
p <- ncol(Xi)
which.miss.i <- sample(1:n,round(prop.miss*n))
which.miss.i <- which.miss.i[!which.miss.i%in%which.miss.prev]
if (length(which.miss.i)>0) Xi[which.miss.i,] <- NA
Xmiss[[i]] <- Xi
which.miss.prev <- which.miss.i
}
## average imputation
Ximp <- lapply(Xmiss, function(Xi){
apply(Xi,2, function(xx){
m <- mean(xx,na.rm=TRUE)
ifelse(is.na(xx), m , xx)
})
})
#### fit mgcc to different methods
## gold (all cases)
resultAll <- mgcca(Xori, nfac = ncomp, scores=TRUE)
#plot(resultAll$Y, col=rep(1:2, each=n/2))
pval.all <- anova(manova(resultAll$Y ~ group))[2,6]
## average imputation
resultImp <- mgcca(Ximp, nfac = ncomp, scores=TRUE)
pval.imp <- anova(manova(resultImp$Y ~ group))[2,6]
## complete cases
common <- Reduce(intersect,lapply(lapply(Xmiss, na.omit),rownames))
Xcommon <- lapply(Xmiss, function(xi) xi[common,,drop=FALSE])
resultCommon <- mgcca(Xcommon, nfac = ncomp, scores=TRUE)
#cat("n common=", nrow(Xcommon[[1]]),"\n") # number of individuals with complete data
pval.common <- anova(manova(resultCommon$Y ~ group[common]))[2,6]
## mcca
Xr <- lapply(Xmiss, na.omit)
resultMGCCA <- mgcca(Xr, nfac = ncomp, scores=TRUE)
pval.mgcca <- anova(manova(resultMGCCA$Y ~ group))[2,6]
c(pval.true, pval.all, pval.imp, pval.common, pval.mgcca)
}
#replicate(4,fsim(n=500, p=50, q=50, prop.miss=0.2, ncomp=2, ncomp.true=2, sigma.noise=0.2, difer=0.5))
## fixed parameters
n <- 500
ncomp <- 2
ncomp.true <- 2
nsim <- 100
vars <- 50
sigma.noise <- 0.2
nsim <- 1000
## variable parameters
esc <- expand.grid(difer=c(0, 0.25, 0.5), prop.miss=c(0.1,0.2,0.3))
res <- list()
set.seed(123456)
for (i in 1:nrow(esc)){
cat("----Escenari",i,"\n-difer=",esc$difer[i],"\n-prop.miss=",esc$prop.miss[i],"\n\n")
iter <- 0
res[[i]] <- replicate(nsim,{
iter <<- iter+1
if (iter%%10==0) cat("iteration",iter,"\n")
res <- try(fsim(n, p=vars, q=vars, prop.miss=esc$prop.miss[i], ncomp, ncomp.true, sigma.noise=sigma.noise, difer=esc$difer[i]), silent=TRUE)
if (inherits(res, "try-error"))
return(c(NA,NA,NA))
else
return(res)
})
cat("\n\n\n")
}
save(res, file="sim2res.rda")
| /simulations/case2/simulate2.R | permissive | isglobal-brge/paperGCCA | R | false | false | 4,361 | r | rm(list=ls())
library(MASS)
library(far)
# remotes::install_github("isglobal-brge/mgcca")
library(curatedTCGAData)
library(TCGAutils)
# Libraries needed by mgcca package
library(MultiAssayExperiment)
library(RSpectra)
library(impute)
# Benchmark
library(microbenchmark)
library(mgcca)
fsim <- function(n, p, q, prop.miss, ncomp, ncomp.true, sigma.noise, difer){
# n <- 100
# p <- 5
# q <- 5
# prop.miss <- 0.1
# ncomp <- 2
# sigma.noise <- 0.001
# ncomp.true <- 2
# difer <- 1
# n = number of individuals
# p = number of variables in X matrix
# q = number of variables in Y matrix
# prop.miss= proportion of missing values
# ncomp = number of correlation functions
# sigma.noise = sigma of noise added to Xi.
# ncomp.true = number of true canonical variables.
# ... = arguments passed to mgcc function (currently ignored)
if (p>n | q>n) stop("number of individuals must be larger than the number of variables")
if (n%%2!=0) stop("n must be pair")
# simulate data (as in "Generalized canonical correlation analysis of matrices with missing rows: A simulation study" paper)
# note: we add noise to X1 instead of Y, if noise is added to Ytrue as specified in paper var(Xi) is not singular!!!
Ysim <- matrix(rnorm(n*ncomp.true), n, ncomp.true) # simulate from a standard normal variables
group <- rep(1:2, each=n/2)
names(group) <- 1:n
Ysim[group==1,] <- Ysim[group==1,]+difer/2
Ysim[group==2,] <- Ysim[group==2,]-difer/2
# plot(Ysim, col=rep(1:2, each=n/2))
Ytrue <- Ysim
pval.true <- anova(manova(Ytrue ~ group))[2,6]
b1 <- matrix(runif(ncomp.true*p,-1,1), ncomp.true, p)
X1 <- Ytrue%*%b1 + matrix(rnorm(n*p, 0, sigma.noise), n, p) # add noise to X1 instead of Y!!!
b2 <- matrix(runif(ncomp.true*q,-1,1), ncomp.true, q)
X2 <- Ytrue%*%b2 + matrix(rnorm(n*q, 0, sigma.noise), n, q) # add noise to X2 instead of Y!!!
rownames(X1) <- rownames(X2) <- 1:n
X<-Xori <- list(X1,X2)
####### introduce individuals missings at random
n<- nrow(X[[1]])
Xmiss <- Xori
k <- length(Xmiss)
which.miss.prev <- integer()
for (i in 1:k){
Xi <- X[[i]]
n <- nrow(Xi)
p <- ncol(Xi)
which.miss.i <- sample(1:n,round(prop.miss*n))
which.miss.i <- which.miss.i[!which.miss.i%in%which.miss.prev]
if (length(which.miss.i)>0) Xi[which.miss.i,] <- NA
Xmiss[[i]] <- Xi
which.miss.prev <- which.miss.i
}
## average imputation
Ximp <- lapply(Xmiss, function(Xi){
apply(Xi,2, function(xx){
m <- mean(xx,na.rm=TRUE)
ifelse(is.na(xx), m , xx)
})
})
#### fit mgcc to different methods
## gold (all cases)
resultAll <- mgcca(Xori, nfac = ncomp, scores=TRUE)
#plot(resultAll$Y, col=rep(1:2, each=n/2))
pval.all <- anova(manova(resultAll$Y ~ group))[2,6]
## average imputation
resultImp <- mgcca(Ximp, nfac = ncomp, scores=TRUE)
pval.imp <- anova(manova(resultImp$Y ~ group))[2,6]
## complete cases
common <- Reduce(intersect,lapply(lapply(Xmiss, na.omit),rownames))
Xcommon <- lapply(Xmiss, function(xi) xi[common,,drop=FALSE])
resultCommon <- mgcca(Xcommon, nfac = ncomp, scores=TRUE)
#cat("n common=", nrow(Xcommon[[1]]),"\n") # number of individuals with complete data
pval.common <- anova(manova(resultCommon$Y ~ group[common]))[2,6]
## mcca
Xr <- lapply(Xmiss, na.omit)
resultMGCCA <- mgcca(Xr, nfac = ncomp, scores=TRUE)
pval.mgcca <- anova(manova(resultMGCCA$Y ~ group))[2,6]
c(pval.true, pval.all, pval.imp, pval.common, pval.mgcca)
}
#replicate(4,fsim(n=500, p=50, q=50, prop.miss=0.2, ncomp=2, ncomp.true=2, sigma.noise=0.2, difer=0.5))
## fixed parameters
n <- 500
ncomp <- 2
ncomp.true <- 2
nsim <- 100
vars <- 50
sigma.noise <- 0.2
nsim <- 1000
## variable parameters
esc <- expand.grid(difer=c(0, 0.25, 0.5), prop.miss=c(0.1,0.2,0.3))
res <- list()
set.seed(123456)
for (i in 1:nrow(esc)){
cat("----Escenari",i,"\n-difer=",esc$difer[i],"\n-prop.miss=",esc$prop.miss[i],"\n\n")
iter <- 0
res[[i]] <- replicate(nsim,{
iter <<- iter+1
if (iter%%10==0) cat("iteration",iter,"\n")
res <- try(fsim(n, p=vars, q=vars, prop.miss=esc$prop.miss[i], ncomp, ncomp.true, sigma.noise=sigma.noise, difer=esc$difer[i]), silent=TRUE)
if (inherits(res, "try-error"))
return(c(NA,NA,NA))
else
return(res)
})
cat("\n\n\n")
}
save(res, file="sim2res.rda")
|
if(getRversion() >= "3.1.0") {
utils::globalVariables(
c(
'.',
'loc_lat',
'loc_lng',
'stop_id',
'stop_lat',
'stop_lon',
'stop_name',
'trip_id'
)
)
} | /R/globals.R | no_license | r-transit/transitfeedr | R | false | false | 208 | r | if(getRversion() >= "3.1.0") {
utils::globalVariables(
c(
'.',
'loc_lat',
'loc_lng',
'stop_id',
'stop_lat',
'stop_lon',
'stop_name',
'trip_id'
)
)
} |
library(dplyr)
library(ggplot2)
#to set outputs in english
Sys.setlocale("LC_ALL", "en_US")
#reading in temp file
temp <- tempfile()
download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip", temp)
data <- read.csv(unz(temp, "activity.csv"), stringsAsFactors = F)
data$date <- as.Date(data$date)
daily_steps <- data %>% group_by(date) %>% summarise(total_daily_steps = sum(steps))
#What is mean total number of steps taken per day?
#histogram
ggplot(daily_steps, aes(x = total_daily_steps)) +
geom_histogram()
#mean and median of steps each day
mean(daily_steps$total_daily_steps, na.rm = T)
median(daily_steps$total_daily_steps, na.rm = T)
#What is the average daily activity pattern?
#mean steps by interval + time series
mean_per_interval <- data %>% group_by(interval) %>% summarise(mean = mean(steps, na.rm = T))
ggplot(mean_per_interval, aes(x = interval, y = mean)) +
geom_line()
#interval wth mean highest number of steps
mean_per_interval %>% filter(mean == max(mean))
###Imputing missing values
#total number os NAs
summary(data)
#filling missing values
no_missing <- transform(data, steps = ifelse(is.na(steps), ))
data2 <- data
x <- data.frame(0,0,0,0)
for(i in 1:nrow(data2)) {
if(is.na(data2[i,1] == T)) {
x <- left_join(data2[i,], mean_per_interval, by = 'interval')
data2[i,1] <- x[,4]
}
}
#histogram of data2 - with no missing
daily_steps2 <- data2 %>% group_by(date) %>% summarise(total_daily_steps = sum(steps))
#histogram
ggplot(daily_steps2, aes(x = total_daily_steps)) +
geom_histogram()
#mean and median of steps each day
mean(daily_steps2$total_daily_steps, na.rm = T)
median(daily_steps2$total_daily_steps, na.rm = T)
#we can see that the mean and median values have no changed for
#the overall data set, but now we have a more complete dataset
mean(data$steps, na.rm = T)
mean(data2$steps)
###Are there differences in activity patterns between weekdays and weekends?
data2 <- data2 %>% mutate(weekday = weekdays(date), day_of_week = ifelse(weekday == "Saturday" | weekday == "Sunday", "weekend", "weekday"))
data2$day_of_week <- as.factor(data2$day_of_week)
mean_per_interval2 <- data2 %>% group_by(interval, day_of_week) %>% summarise(mean = mean(steps, na.rm = T))
##panel plot
ggplot(mean_per_interval2, aes(x = interval, y = mean)) +
geom_line() +
facet_grid( day_of_week ~ .)
| /PA1_script.R | no_license | andreluna15/reproducible_research | R | false | false | 2,395 | r | library(dplyr)
library(ggplot2)
#to set outputs in english
Sys.setlocale("LC_ALL", "en_US")
#reading in temp file
temp <- tempfile()
download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip", temp)
data <- read.csv(unz(temp, "activity.csv"), stringsAsFactors = F)
data$date <- as.Date(data$date)
daily_steps <- data %>% group_by(date) %>% summarise(total_daily_steps = sum(steps))
#What is mean total number of steps taken per day?
#histogram
ggplot(daily_steps, aes(x = total_daily_steps)) +
geom_histogram()
#mean and median of steps each day
mean(daily_steps$total_daily_steps, na.rm = T)
median(daily_steps$total_daily_steps, na.rm = T)
#What is the average daily activity pattern?
#mean steps by interval + time series
mean_per_interval <- data %>% group_by(interval) %>% summarise(mean = mean(steps, na.rm = T))
ggplot(mean_per_interval, aes(x = interval, y = mean)) +
geom_line()
#interval wth mean highest number of steps
mean_per_interval %>% filter(mean == max(mean))
###Imputing missing values
#total number os NAs
summary(data)
#filling missing values
no_missing <- transform(data, steps = ifelse(is.na(steps), ))
data2 <- data
x <- data.frame(0,0,0,0)
for(i in 1:nrow(data2)) {
if(is.na(data2[i,1] == T)) {
x <- left_join(data2[i,], mean_per_interval, by = 'interval')
data2[i,1] <- x[,4]
}
}
#histogram of data2 - with no missing
daily_steps2 <- data2 %>% group_by(date) %>% summarise(total_daily_steps = sum(steps))
#histogram
ggplot(daily_steps2, aes(x = total_daily_steps)) +
geom_histogram()
#mean and median of steps each day
mean(daily_steps2$total_daily_steps, na.rm = T)
median(daily_steps2$total_daily_steps, na.rm = T)
#we can see that the mean and median values have no changed for
#the overall data set, but now we have a more complete dataset
mean(data$steps, na.rm = T)
mean(data2$steps)
###Are there differences in activity patterns between weekdays and weekends?
data2 <- data2 %>% mutate(weekday = weekdays(date), day_of_week = ifelse(weekday == "Saturday" | weekday == "Sunday", "weekend", "weekday"))
data2$day_of_week <- as.factor(data2$day_of_week)
mean_per_interval2 <- data2 %>% group_by(interval, day_of_week) %>% summarise(mean = mean(steps, na.rm = T))
##panel plot
ggplot(mean_per_interval2, aes(x = interval, y = mean)) +
geom_line() +
facet_grid( day_of_week ~ .)
|
#' Plot the biotracer data with one biplot for each combination of 2 biotracers
#'
#' @inheritParams plot_data
#'
#' @import ggplot2
#'
#' @keywords internal
#' @noRd
plot_biotracer_data <- function(biotracer_data, save=FALSE, save_path){
# If the biotracer data contains 3 elements called d13C, d15N and d125I, then we will plot 3 figures,
# because there are 3 ways to choose an unordered subset of 2 elements from a fixed set of 3 elements:
# d13C vs. d15N,
# d13C vs. d125I and
# d15N vs. d125I.
#
# With the following code, we select all the possible combinations without repetition:
nb_biotracers <- ncol(biotracer_data) - 1
if(nb_biotracers==1){
figure <- ggplot(biotracer_data,
aes(x = biotracer_data$group, y=get(names(biotracer_data)[2]),
colour = biotracer_data$group)) +
ggtitle("Isotopic measurements") +
geom_point(data=biotracer_data, aes(x = biotracer_data$group, y=get(names(biotracer_data)[2]), colour = biotracer_data$group), position=position_jitter(width=.2,height=.1), show.legend=T) +
guides(colour = guide_legend()) +
theme_bw() +
#xlab(names(biotracer_data)[element1 + 1]) +
ylab(names(biotracer_data)[element1 + 1]) +
theme(panel.grid.major = element_line(colour = "grey"),
panel.grid.minor = element_blank(),
axis.title = element_text(size = 15),
axis.text.y = element_text(size = 12),
axis.text.x = element_text(margin = margin(3, 0, 0, 0), size = 12, angle=45, hjust=1),
plot.title = element_text(hjust = 0.5),
legend.position="none")
print(figure)
if (save == TRUE){
save_path <- ifelse(!missing(save_path), save_path, getwd())
ggsave(paste0(save_path, "/figure_biotracer_", colnames(biotracer_data)[element1],
".png"),
height = 4.4, width = 8)
}
}else{
for (element1 in 1:nb_biotracers){
for (element2 in 1:nb_biotracers){
if (element2 > element1){
figure <- ggplot(data=biotracer_data,
aes(x = biotracer_data[, element1 + 1],
y = biotracer_data[, element2 + 1],
colour = biotracer_data$group)) +
ggtitle("Isotopic measurements") +
xlab(names(biotracer_data)[element1 + 1]) +
ylab(names(biotracer_data)[element2 + 1]) +
geom_point(size = 3, na.rm = TRUE) +
guides(colour = guide_legend()) +
theme_bw() +
theme(panel.grid.major = element_line(colour = "grey"),
panel.grid.minor = element_blank(),
axis.title = element_text(size = 15),
axis.text.y = element_text(size = 12),
axis.text.x = element_text(margin = margin(3, 0, 0, 0), size = 12),
plot.title = element_text(hjust = 0.5))
print(figure)
if (save == TRUE){
save_path <- ifelse(!missing(save_path), save_path, getwd())
ggsave(paste0(save_path, "/figure_biotracer_", colnames(biotracer_data)[element1+1], "_", colnames(biotracer_data)[element2+1],
".png"),
height = 4.4, width = 8)
}
}
}
}
}
}
#' Plot any matrix data with a raster plot
#'
#' @param matrix the matrix ready to be plotted
#' @param title the title to put (depends on the variable)
#' @inheritParams plot_data
#'
#' @import ggplot2
#'
#' @keywords internal
#' @noRd
plot_matrix <- function(matrix, title, save = FALSE, save_path){
matrix <- as.data.frame(matrix)
df <- data.frame(rep(colnames(matrix), each = nrow(matrix)),
rep(rownames(matrix), nrow(matrix)),
unlist(matrix))
colnames(df) <- c("pred", "prey", "value")
df$value <- round(df$value, 2)
df$pred <- as.factor(df$pred)
df$prey <- rev(as.factor(df$prey))
figure <- ggplot(df, aes_string(x = "pred", y = "prey", fill = "value")) + geom_raster() + theme_bw() +
#scale_x_continuous(labels = colnames(matrix)), breaks = seq(1, ncol(matrix))) +
scale_y_discrete(labels = rev(rownames(matrix)), limits=rev) +
scale_fill_gradient(low = "white", high = "blue3", limit = c(0, 1)) +
ggtitle(title) +
ylab("Preys") +
xlab("Predators") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title = element_text(size = 15),
axis.text.x = element_text(size = 12, angle = 45, vjust = 1, hjust = 1),
axis.text.y = element_text(size = 12, angle = 45, vjust = 1, hjust = 0),
legend.title = element_blank(),
plot.title = element_text(hjust = 0.5))
if (ncol(matrix) < 15){
figure <- figure + geom_text(data = df[!is.na(df$value), ], aes_string(label = "value"))
}
print(figure)
if (save == TRUE){
save_path <- ifelse(!missing(save_path), save_path, getwd())
ggsave(paste0(save_path, "figure_", gsub(" ", "_", title),
".png"),
height = 4.4, width = 8)
}
}
#' Plot the input data
#'
#' @description This function is used to plot the input biotracer and/or the stomach content data.
#' You can use the function with only one parameter to plot only one kind of data.
#'
#' The figure(s) can be saved as PNG using: \code{save = TRUE}, and the directory path to which
#' the figures are saved can be precised with: \code{save_path = "."}.
#'
#' If only the stomach content data is entered, there will be a single raster plot containing the proportions
#' of occurences in the stomachs.
#'
#' For the biotracer data, there will be as many plots as the number of
#' combinations of elements. For example if only two isotopes are entered, there will be a single biplot
#' plotted. If three elements are entered (element A, B and C), three biplots will be shown : A vs. B,
#' B vs. C and A vs. C.
#'
#' @inheritParams preprocess_data
#' @inheritParams plot_prior
#'
#' @examples
#'
#' example_biotracer_data <- read.csv(system.file("extdata", "example_biotracer_data.csv",
#' package = "EcoDiet"))
#' plot_data(biotracer_data = example_biotracer_data)
#'
#' example_stomach_data <- read.csv(system.file("extdata", "example_stomach_data.csv",
#' package = "EcoDiet"))
#'
#' plot_data(biotracer_data = example_biotracer_data,
#' stomach_data = example_stomach_data)
#'
#'
#' @seealso \code{\link{plot_prior}} to plot the prior means or probability distribution(s),
#' \code{\link{plot_results}} to plot the posterior means or probability distribution(s)
#'
#' @export
plot_data <- function(biotracer_data = NULL, stomach_data = NULL,
save = FALSE, save_path = "."){
if (!is.null(stomach_data)){
# Clean the stomach data similarly as in the preprocess_data function except for the commented parts
if (!colnames(stomach_data)[1] %in% rownames(stomach_data)){
row.names(stomach_data) <- stomach_data[, 1]
stomach_data[, 1] <- NULL
}
# Divide the number of stomachs by the total number of full stomachs to obtain proportions
stomach_data[] <- sapply(stomach_data, function(X) X/X[nrow(stomach_data)])
# Remove the NA caused by division by zero for the trophic groups at the base of the ecosystem
stomach_data[is.na(stomach_data)] <- 0
stomach_data <- stomach_data[-nrow(stomach_data), ]
stomach_data <- stomach_data[, order(colnames(stomach_data))]
stomach_data <- stomach_data[order(rownames(stomach_data)), ]
stomach_data[stomach_data == 0] <- NA
plot_matrix(stomach_data, title = "Proportion of occurences in stomachs",
save, save_path)
}
if (!is.null(biotracer_data)){
plot_biotracer_data(biotracer_data, save, save_path)
}
}
#' Plot the prior probability distribution(s) for a given variable, a given predator and its given preys
#'
#' @inheritParams plot_prior
#' @param title the title to put (depends on the variable and the predator to plot)
#'
#' @import ggplot2
#'
#' @keywords internal
#' @noRd
plot_prior_distribution <- function(data, literature_configuration, pred, prey,
variable, title, save, save_path){
# Check that the entered predator is correct
pred_index <- which(colnames(data$o) == pred)
if (length(pred_index) == 0){
stop("You entered a wrong predator name in the `pred` argument.\n",
"Please check and ensure to pick one of the following: \"",
paste(colnames(data$o), collapse = "\", \""), "\".\n")
}
if (data$nb_prey[pred_index] == 0){
stop("The predator you have chosen (\"", pred, "\") has no prey.")
}
# Check that the entered prey(s) is/are correct
if (is.null(prey)){
prey_index <- data$list_prey[pred_index, ]
prey_index <- prey_index[!is.na(prey_index)]
prey <- colnames(data$o)[prey_index]
} else {
prey_index <- which(colnames(data$o) %in% prey)
if (length(prey) != length(prey_index)){
stop("Please check the values entered in `prey` argument.\n",
"The following names don't correspond to any trophic groups: \n",
"\".\n But the prey names are actually: \"",
paste(prey[which(!prey%in%colnames(data$o))], collapse = "\", \""), "\".\n")
}
if (!all(prey_index %in% data$list_prey[pred_index, ])){
stop("You have entered at least one prey that is not eaten by the predator \"",
pred ,"\".\n", " Here are the preys you have entered: \"",
paste(prey, collapse = "\", \""),
"\".\n And here are the predator's preys: \"",
paste(colnames(data$o)[data$list_prey[pred_index, 1:data$nb_prey[pred_index]]],
collapse = "\", \""),
"\".\n Please rename your prey input to be consistent.")
}
}
# Construct the corresponding data frame
x <- seq(0, 1, length = 512)
df_to_plot <- data.frame(Prey = c(), x = c(), Density = c(), type=c())
df_to_plot_cond <- data.frame(Prey = c(), x = c(), Density = c(), type=c())
if (variable == "PI"){
if (literature_configuration) {
scalar <- ((length(which(!is.na(data$list_prey[pred_index, ]))) - 1) / (data$CV[pred_index]^2))-1
scalar <- ifelse(length(which(!is.na(data$list_prey[pred_index, ]))) == 1, 1, scalar)
df_cond_allprey <- sapply(data$alpha_lit[, pred_index], function(x){stats::rgamma(1000000,x*scalar)})
df_cond_allprey <- apply(df_cond_allprey, 2, function(X) X/rowSums(df_cond_allprey))
df_cond_allprey <- apply(df_cond_allprey, 2, function(X) stats::density(X, from=0, to=1)$y)
}else{
vecprey <- rep(0,ncol(data$list_prey))
vecprey[prey_index] <- 1
df_cond_allprey <- sapply(vecprey, function(x){stats::rgamma(1000000,x)})
df_cond_allprey <- apply(df_cond_allprey, 2, function(X) X/rowSums(df_cond_allprey))
df_cond_allprey <- apply(df_cond_allprey, 2, function(X) stats::density(X, from=0, to=1)$y)
}
}
for (each_prey in prey){
prey_idx <- which(rownames(data$o) == each_prey)
if (prey_idx %in% data$list_prey[pred_index, ]){
if (variable == "PI"){
if (literature_configuration) {
Density <- stats::dbeta(x, data$alpha_lit[prey_idx, pred_index] * scalar,
(colSums(data$alpha_lit)[pred_index] -
data$alpha_lit[prey_idx, pred_index]) * scalar)
df_to_plot_cond <- rbind(df_to_plot_cond, data.frame(Prey = rep(each_prey, 512), x = x, Density = df_cond_allprey[, prey_idx], type=" conditional"))
} else {
Density <- stats::dbeta(x, 1, data$nb_prey[pred_index] - 1)
}
df_to_plot <- rbind(df_to_plot, data.frame(Prey = rep(each_prey, 512), x = x, Density = Density, type="marginal"))
} else if (variable == "eta"){
if (literature_configuration) {
Density <- stats::dbeta(x, data$eta_hyperparam_1[prey_idx, pred_index],
data$eta_hyperparam_2[prey_idx, pred_index])
} else {
Density <- stats::dbeta(x, 1, 1)
}
df_to_plot <- rbind(df_to_plot, data.frame(Prey = rep(each_prey, 512), x = x, Density = Density, type="marginal"))
}
}
}
if (variable == "PI" & literature_configuration==T){
df_to_plot <- rbind(df_to_plot, df_to_plot_cond)
}
# Plot the figure
figure <- ggplot(df_to_plot, aes_string(x = "x", y = "Density", colour = "Prey", linetype = "Prey")) +
geom_line(size = 1.25) +
ggtitle(paste(title, "\nfor the", pred, "predator")) +
xlim(0, 1) +
theme_bw() +
theme(axis.title.x = element_blank(),
plot.title = element_text(hjust = 0.5)) +
facet_wrap(~type, scales="free_y")
print(figure)
if (save == TRUE){
ggsave(paste0("figure_", gsub(" ", "_", title), "_for_the_", pred, "_predator",
format(Sys.time(),'_%Y-%m-%d_%H-%M-%S'), ".png"),
height = 4.4, width = 6.2, path = save_path)
}
}
#' Plot the prior means or probability distribution(s)
#'
#' @description This function plots the prior means or probability distribution(s) for one or the two
#' variable(s) of interest : the trophic link probabilities ("eta") and/or the diet proportions ("PI").
#'
#' The figure(s) can be saved as PNG using: \code{save = TRUE}, and the directory path to which
#' the figures are saved can be precised with: \code{save_path = "."}.
#'
#' If no "pred" nor "prey" parameter is entered, the plot will be a raster plot with the mean priors for
#' all the trophic groups.
#'
#' If one predator name is entered as "pred", the probability distribution(s) will be plotted for all its
#' prey(s) by default. Some specific prey(s) name(s) can also be entered because if a predator has
#' 22 preys, plotting them all will make the plot hard to read. So you can specify the one or many prey(s)
#' of interest and only display their corresponding probability distribution(s).
#'
#' The "variable" parameter can be specified if one wants to plot the priors for only one variable
#' ("PI" or "eta").
#'
#' @param data the preprocessed data list output by the preprocess_data() function
#' @param pred the predator name for which we want to plot the probability densities
#' @param prey the prey(s) name(s) for which we want to plot the probability densities
#' @param variable the variable(s) for which we want to plot the probability densities. By default
#' we will plot the two variables of interest: eta and PI.
#' @param save A boolean describing whether the figure should be saved as PNG.
#' By default the figures are not saved.
#' @param save_path A string describing the path to which the figures should be saved.
#' By default the figures are saved in a temporary directory.
#' @inheritParams preprocess_data
#'
#' @examples
#'
#' realistic_biotracer_data <- read.csv(system.file("extdata", "realistic_biotracer_data.csv",
#' package = "EcoDiet"))
#' realistic_stomach_data <- read.csv(system.file("extdata", "realistic_stomach_data.csv",
#' package = "EcoDiet"))
#'
#' data <- preprocess_data(biotracer_data = realistic_biotracer_data,
#' trophic_discrimination_factor = c(0.8, 3.4),
#' literature_configuration = FALSE,
#' stomach_data = realistic_stomach_data)
#'
#' plot_prior(data, literature_configuration = FALSE)
#' plot_prior(data, literature_configuration = FALSE, pred = "Cod")
#' plot_prior(data, literature_configuration = FALSE, pred = "Cod",
#' prey = c("Crabs", "Shrimps"), variable = "eta")
#'
#' @seealso \code{\link{plot_results}} to plot the posterior means or probability distribution(s),
#' \code{\link{plot_data}} to plot the input data
#'
#' @export
plot_prior <- function(data, literature_configuration,
pred = NULL, prey = NULL,
variable = c("eta", "PI"),
save = FALSE,
save_path = "."){
if (!all(variable %in% c("eta", "PI"))){
stop("This function has only be designed to plot the priors of PI or eta.")
}
for (var in variable){
if (is.null(pred) & is.null(prey)){
title <- switch(var,
PI = "Mean of the prior diet proportions",
eta = "Mean of the prior trophic link probabilities")
mean_prior <- matrix(NA, ncol = data$nb_group, nrow = data$nb_group)
colnames(mean_prior) <- rownames(mean_prior) <- colnames(data$o)
for (i in data$list_pred){
for (k in data$list_prey[i, 1:data$nb_prey[i]]){
if (var == "eta"){
if (literature_configuration){
mean_prior[k, i] <- (data$eta_hyperparam_1[k, i]/
(data$eta_hyperparam_1[k, i] + data$eta_hyperparam_2[k, i]))
} else {
mean_prior[k, i] <- 1/2
}
} else if (var == "PI"){
if (literature_configuration){
mean_prior[k, i] <- data$alpha_lit[k, i]/colSums(data$alpha_lit)[i]
} else {
mean_prior[k, i] <- 1/data$nb_prey[i]
}
}
}
}
plot_matrix(mean_prior, title, save, save_path)
} else {
title <- switch(var,
PI = "Marginal and Conditional prior distribution of the diet proportions",
eta = "Marginal prior distribution of the trophic link probabilities")
plot_prior_distribution(data, literature_configuration, pred, prey,
variable = var, title, save, save_path)
}
}
}
#' Extract the means of the posterior distribution for a specific variable (PI or eta)
#' in a matrix format (with the predators in the columns, and the preys in the rows)
#'
#' @inheritParams plot_results
#' @param mcmc_output A matrix generated in plot_results containing the MCMC samples
#'
#' @keywords internal
#' @noRd
extract_mean <- function(mcmc_output, data, variable = "PI"){
# keep only the means for the relevant variable
raw_means <- colMeans(mcmc_output)
raw_means <- raw_means[startsWith(names(raw_means), variable)]
# prepare an empty matrix with the correct format
matrix_mean <- matrix(NA, data$nb_group, data$nb_group)
colnames(matrix_mean) <- rownames(matrix_mean) <- colnames(data$o)
for (i in seq_along(raw_means)){
# extract the indices that are between the brackets: "PI[2, 4]" -> "2,4"
correct_indices <- regmatches(names(raw_means)[i], regexec("\\[(.*?)\\]", names(raw_means)[i]))[[1]][2]
# re-format the indices: "2,4" -> c(2L, 4L)
correct_indices <- as.integer(strsplit(correct_indices, ",")[[1]])
# use the indices to fill the matrix with the correct format
matrix_mean[correct_indices[1], correct_indices[2]] <- raw_means[i]
}
return(matrix_mean)
}
#' Plot the posterior probability density(ies) for a given variable and predator
#'
#' @inheritParams plot_results
#' @param mcmc_output A matrix generated in plot_results containing the MCMC samples
#' @param title the title to put (depends on the variable and the predator to plot)
#'
#' @import ggplot2
#'
#' @keywords internal
#' @noRd
plot_posterior_distribution <- function(mcmc_output, data, pred, prey,
variable, title, save = FALSE, save_path){
# Check that the entered predator is correct
pred_index <- which(colnames(data$o) == pred)
if (length(pred_index) == 0){
stop("You did not put a correct predator name in the `pred` argument.\n",
" You entered the name \"", pred,"\", while the predator names are actually: \"",
paste(colnames(data$o), collapse = "\", \""), "\".\n",
" Please use one of the above names in the `pred` argument.")
}
if (data$nb_prey[pred_index] == 0){
stop("The predator you have chosen (\"", pred, "\") has no prey and thus cannot be plotted.")
}
# Check that the entered prey(s) is/are correct
if (!is.null(prey)){
prey_index <- which(colnames(data$o) %in% prey)
if (length(prey) != length(prey_index)){
stop("You used an incorrect prey name in the `prey` argument.\n",
" You have entered the names: \"", paste(prey, collapse = "\", \""),
"\".\n But the prey names are actually: \"",
paste(colnames(data$o), collapse = "\", \""), "\".\n",
" Please put correct names in the `prey` argument.")
}
if (!all(prey_index %in% data$list_prey[pred_index, ])){
stop("You have entered at least one prey that is not eaten by the predator \"",
pred ,"\".\n", " Here are the preys you have entered: \"",
paste(prey, collapse = "\", \""),
"\".\n And here are the predator's preys: \"",
paste(colnames(data$o)[data$list_prey[pred_index, 1:data$nb_prey[pred_index]]],
collapse = "\", \""),
"\".\n Please rename your prey input to be consistent.")
}
}
# Keep only the variable of interest (all the "PI" or all the "eta")
mcmc_output <- mcmc_output[, startsWith(colnames(mcmc_output), variable)]
# Create a lookup table between the model output's names and the prey's and predator's indices:
# names prey pred
# 1 PI[2,1] 2 1
# 2 PI[3,1] 3 1
# 3 PI[3,2] 3 2
lookup <- sapply(colnames(mcmc_output), function(X) regmatches(X, regexec("\\[(.*?)\\]", X))[[1]][2])
prey_idx <- sapply(lookup, function(X) strsplit(X, split=',')[[1]][[1]])
pred_idx <- sapply(lookup, function(X) strsplit(X, split=',')[[1]][[2]])
lookup_table <- data.frame(names = colnames(mcmc_output),
prey = as.integer(prey_idx),
pred = as.integer(pred_idx),
stringsAsFactors = FALSE)
# Prepare a data frame with the values for one predator's preys
if (is.null(prey)){
variables_to_extract <- lookup_table[lookup_table$pred == pred_index, ]$names
prey <- colnames(data$o)[lookup_table[lookup_table$pred == pred_index, ]$prey]
} else {
variables_to_extract <- lookup_table[(lookup_table$pred == pred_index &
lookup_table$prey %in% prey_index &
lookup_table$prey %in% data$list_prey[pred_index, ]), ]$names
}
values_to_extract <- mcmc_output[, variables_to_extract]
df_to_plot <- data.frame(Prey = rep(prey, each = dim(mcmc_output)[1]),
variable_to_plot = c(values_to_extract))
# Trick to scale the plot and not have a warning from the CRAN check:
variable_to_plot <- NULL
..scaled.. <- NULL
Prey <- NULL
# Plot these values to represent the approximated probability densities
figure <- ggplot(df_to_plot) +
geom_density(aes(x = variable_to_plot, y = ..scaled.., fill = Prey),
alpha = .3, adjust = 1/2, na.rm = TRUE) +
geom_density(aes(x = variable_to_plot, y = ..scaled.., color = Prey),
size = 1.25, adjust = 1/2, na.rm = TRUE) +
ggtitle(paste(title, "\nfor the", colnames(data$o)[pred_index], "predator")) +
ylab("Density") +
scale_shape_manual(values = c(seq(1:10))) +
guides(colour = guide_legend(byrow = 1, ncol = 1), shape = guide_legend(byrow = 1, ncol = 1)) +
xlim(0, 1) +
theme_bw() +
theme(axis.title.x = element_blank(),
plot.title = element_text(hjust = 0.5))
print(figure)
if (save == TRUE){
ggsave(paste0("figure_", gsub(" ", "_", title), "_for_the_", colnames(data$o)[pred_index], "_predator",
format(Sys.time(),'_%Y-%m-%d_%H-%M-%S'), ".png"),
height = 4.4, width = 6.2, path = save_path)
}
}
#' Plot the posterior means or probability distribution(s)
#'
#' @description This function plots the posterior means or probability distribution(s) for one
#' or the two variable(s) of interest : the trophic link probabilities ("eta") and/or
#' the diet proportions ("PI").
#'
#' The figure(s) can be saved as PNG using: \code{save = TRUE}, and the directory path to which
#' the figures are saved can be precised with: \code{save_path = "."}.
#'
#' If no "pred" nor "prey" parameter is entered, the plot will be a raster plot with the mean priors for
#' all the trophic groups.
#'
#' If one predator name is entered as "pred", the probability distribution(s) will be plotted for all its
#' prey(s) by default. Some specific prey(s) name(s) can also be entered because if a predator has
#' 22 preys, plotting them all will make the plot hard to read. So you can specify the one or many prey(s)
#' of interest and only display their corresponding probability distribution(s).
#'
#' The "variable" parameter can be specified if one wants to plot the priors for only one variable
#' ("PI" or "eta").
#'
#' @param jags_output the mcmc.list object output by the run_model() function
#' @inheritParams plot_prior
#'
#' @examples
#'
#' \donttest{
#' realistic_biotracer_data <- read.csv(system.file("extdata", "realistic_biotracer_data.csv",
#' package = "EcoDiet"))
#' realistic_stomach_data <- read.csv(system.file("extdata", "realistic_stomach_data.csv",
#' package = "EcoDiet"))
#'
#' data <- preprocess_data(biotracer_data = realistic_biotracer_data,
#' trophic_discrimination_factor = c(0.8, 3.4),
#' literature_configuration = FALSE,
#' stomach_data = realistic_stomach_data)
#'
#' write_model(literature_configuration = FALSE)
#'
#' mcmc_output <- run_model("EcoDiet_model.txt", data, run_param="test")
#'
#' plot_results(mcmc_output, data)
#' plot_results(mcmc_output, data, pred = "Crabs")
#' plot_results(mcmc_output, data, pred = "Crabs",
#' variable = "PI", prey = c("Bivalves", "Shrimps"))
#'
#' }
#'
#' @seealso \code{\link{plot_prior}} to plot the prior means or probability distribution(s),
#' \code{\link{plot_data}} to plot the input data
#'
#' @export
plot_results <- function(jags_output, data,
pred = NULL, prey = NULL,
variable = c("eta", "PI"),
save = FALSE,
save_path = "."){
if (!all(variable %in% c("eta", "PI"))){
stop("This function can only print a figure for the PI or eta variable.\n",
" But you have entered this variable name: \"", variable, "\".\n",
" Please use rather `variable = \"PI\"` or `variable = \"eta\"` for this function.")
}
mcmc_output <- as.matrix(jags_output$samples)
for (var in variable){
if (is.null(pred) & is.null(prey)){
title <- switch(var,
PI = "Mean of the posterior diet proportions",
eta = "Mean of the posterior trophic link probabilities")
mean <- extract_mean(mcmc_output, data, variable = var)
plot_matrix(mean, title, save, save_path)
} else {
title <- switch(var,
PI = "Marginal posterior distribution of the diet proportions",
eta = "Marginal posterior distribution of the trophic link probabilities")
plot_posterior_distribution(mcmc_output, data, pred, prey,
variable = var, title, save, save_path)
}
}
}
| /R/plot.R | no_license | cran/EcoDiet | R | false | false | 28,694 | r | #' Plot the biotracer data with one biplot for each combination of 2 biotracers
#'
#' @inheritParams plot_data
#'
#' @import ggplot2
#'
#' @keywords internal
#' @noRd
plot_biotracer_data <- function(biotracer_data, save=FALSE, save_path){
# If the biotracer data contains 3 elements called d13C, d15N and d125I, then we will plot 3 figures,
# because there are 3 ways to choose an unordered subset of 2 elements from a fixed set of 3 elements:
# d13C vs. d15N,
# d13C vs. d125I and
# d15N vs. d125I.
#
# With the following code, we select all the possible combinations without repetition:
nb_biotracers <- ncol(biotracer_data) - 1
if(nb_biotracers==1){
figure <- ggplot(biotracer_data,
aes(x = biotracer_data$group, y=get(names(biotracer_data)[2]),
colour = biotracer_data$group)) +
ggtitle("Isotopic measurements") +
geom_point(data=biotracer_data, aes(x = biotracer_data$group, y=get(names(biotracer_data)[2]), colour = biotracer_data$group), position=position_jitter(width=.2,height=.1), show.legend=T) +
guides(colour = guide_legend()) +
theme_bw() +
#xlab(names(biotracer_data)[element1 + 1]) +
ylab(names(biotracer_data)[element1 + 1]) +
theme(panel.grid.major = element_line(colour = "grey"),
panel.grid.minor = element_blank(),
axis.title = element_text(size = 15),
axis.text.y = element_text(size = 12),
axis.text.x = element_text(margin = margin(3, 0, 0, 0), size = 12, angle=45, hjust=1),
plot.title = element_text(hjust = 0.5),
legend.position="none")
print(figure)
if (save == TRUE){
save_path <- ifelse(!missing(save_path), save_path, getwd())
ggsave(paste0(save_path, "/figure_biotracer_", colnames(biotracer_data)[element1],
".png"),
height = 4.4, width = 8)
}
}else{
for (element1 in 1:nb_biotracers){
for (element2 in 1:nb_biotracers){
if (element2 > element1){
figure <- ggplot(data=biotracer_data,
aes(x = biotracer_data[, element1 + 1],
y = biotracer_data[, element2 + 1],
colour = biotracer_data$group)) +
ggtitle("Isotopic measurements") +
xlab(names(biotracer_data)[element1 + 1]) +
ylab(names(biotracer_data)[element2 + 1]) +
geom_point(size = 3, na.rm = TRUE) +
guides(colour = guide_legend()) +
theme_bw() +
theme(panel.grid.major = element_line(colour = "grey"),
panel.grid.minor = element_blank(),
axis.title = element_text(size = 15),
axis.text.y = element_text(size = 12),
axis.text.x = element_text(margin = margin(3, 0, 0, 0), size = 12),
plot.title = element_text(hjust = 0.5))
print(figure)
if (save == TRUE){
save_path <- ifelse(!missing(save_path), save_path, getwd())
ggsave(paste0(save_path, "/figure_biotracer_", colnames(biotracer_data)[element1+1], "_", colnames(biotracer_data)[element2+1],
".png"),
height = 4.4, width = 8)
}
}
}
}
}
}
#' Plot any matrix data with a raster plot
#'
#' @param matrix the matrix ready to be plotted
#' @param title the title to put (depends on the variable)
#' @inheritParams plot_data
#'
#' @import ggplot2
#'
#' @keywords internal
#' @noRd
plot_matrix <- function(matrix, title, save = FALSE, save_path){
matrix <- as.data.frame(matrix)
df <- data.frame(rep(colnames(matrix), each = nrow(matrix)),
rep(rownames(matrix), nrow(matrix)),
unlist(matrix))
colnames(df) <- c("pred", "prey", "value")
df$value <- round(df$value, 2)
df$pred <- as.factor(df$pred)
df$prey <- rev(as.factor(df$prey))
figure <- ggplot(df, aes_string(x = "pred", y = "prey", fill = "value")) + geom_raster() + theme_bw() +
#scale_x_continuous(labels = colnames(matrix)), breaks = seq(1, ncol(matrix))) +
scale_y_discrete(labels = rev(rownames(matrix)), limits=rev) +
scale_fill_gradient(low = "white", high = "blue3", limit = c(0, 1)) +
ggtitle(title) +
ylab("Preys") +
xlab("Predators") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title = element_text(size = 15),
axis.text.x = element_text(size = 12, angle = 45, vjust = 1, hjust = 1),
axis.text.y = element_text(size = 12, angle = 45, vjust = 1, hjust = 0),
legend.title = element_blank(),
plot.title = element_text(hjust = 0.5))
if (ncol(matrix) < 15){
figure <- figure + geom_text(data = df[!is.na(df$value), ], aes_string(label = "value"))
}
print(figure)
if (save == TRUE){
save_path <- ifelse(!missing(save_path), save_path, getwd())
ggsave(paste0(save_path, "figure_", gsub(" ", "_", title),
".png"),
height = 4.4, width = 8)
}
}
#' Plot the input data
#'
#' @description This function is used to plot the input biotracer and/or the stomach content data.
#' You can use the function with only one parameter to plot only one kind of data.
#'
#' The figure(s) can be saved as PNG using: \code{save = TRUE}, and the directory path to which
#' the figures are saved can be precised with: \code{save_path = "."}.
#'
#' If only the stomach content data is entered, there will be a single raster plot containing the proportions
#' of occurences in the stomachs.
#'
#' For the biotracer data, there will be as many plots as the number of
#' combinations of elements. For example if only two isotopes are entered, there will be a single biplot
#' plotted. If three elements are entered (element A, B and C), three biplots will be shown : A vs. B,
#' B vs. C and A vs. C.
#'
#' @inheritParams preprocess_data
#' @inheritParams plot_prior
#'
#' @examples
#'
#' example_biotracer_data <- read.csv(system.file("extdata", "example_biotracer_data.csv",
#' package = "EcoDiet"))
#' plot_data(biotracer_data = example_biotracer_data)
#'
#' example_stomach_data <- read.csv(system.file("extdata", "example_stomach_data.csv",
#' package = "EcoDiet"))
#'
#' plot_data(biotracer_data = example_biotracer_data,
#' stomach_data = example_stomach_data)
#'
#'
#' @seealso \code{\link{plot_prior}} to plot the prior means or probability distribution(s),
#' \code{\link{plot_results}} to plot the posterior means or probability distribution(s)
#'
#' @export
plot_data <- function(biotracer_data = NULL, stomach_data = NULL,
save = FALSE, save_path = "."){
if (!is.null(stomach_data)){
# Clean the stomach data similarly as in the preprocess_data function except for the commented parts
if (!colnames(stomach_data)[1] %in% rownames(stomach_data)){
row.names(stomach_data) <- stomach_data[, 1]
stomach_data[, 1] <- NULL
}
# Divide the number of stomachs by the total number of full stomachs to obtain proportions
stomach_data[] <- sapply(stomach_data, function(X) X/X[nrow(stomach_data)])
# Remove the NA caused by division by zero for the trophic groups at the base of the ecosystem
stomach_data[is.na(stomach_data)] <- 0
stomach_data <- stomach_data[-nrow(stomach_data), ]
stomach_data <- stomach_data[, order(colnames(stomach_data))]
stomach_data <- stomach_data[order(rownames(stomach_data)), ]
stomach_data[stomach_data == 0] <- NA
plot_matrix(stomach_data, title = "Proportion of occurences in stomachs",
save, save_path)
}
if (!is.null(biotracer_data)){
plot_biotracer_data(biotracer_data, save, save_path)
}
}
#' Plot the prior probability distribution(s) for a given variable, a given predator and its given preys
#'
#' @inheritParams plot_prior
#' @param title the title to put (depends on the variable and the predator to plot)
#'
#' @import ggplot2
#'
#' @keywords internal
#' @noRd
plot_prior_distribution <- function(data, literature_configuration, pred, prey,
variable, title, save, save_path){
# Check that the entered predator is correct
pred_index <- which(colnames(data$o) == pred)
if (length(pred_index) == 0){
stop("You entered a wrong predator name in the `pred` argument.\n",
"Please check and ensure to pick one of the following: \"",
paste(colnames(data$o), collapse = "\", \""), "\".\n")
}
if (data$nb_prey[pred_index] == 0){
stop("The predator you have chosen (\"", pred, "\") has no prey.")
}
# Check that the entered prey(s) is/are correct
if (is.null(prey)){
prey_index <- data$list_prey[pred_index, ]
prey_index <- prey_index[!is.na(prey_index)]
prey <- colnames(data$o)[prey_index]
} else {
prey_index <- which(colnames(data$o) %in% prey)
if (length(prey) != length(prey_index)){
stop("Please check the values entered in `prey` argument.\n",
"The following names don't correspond to any trophic groups: \n",
"\".\n But the prey names are actually: \"",
paste(prey[which(!prey%in%colnames(data$o))], collapse = "\", \""), "\".\n")
}
if (!all(prey_index %in% data$list_prey[pred_index, ])){
stop("You have entered at least one prey that is not eaten by the predator \"",
pred ,"\".\n", " Here are the preys you have entered: \"",
paste(prey, collapse = "\", \""),
"\".\n And here are the predator's preys: \"",
paste(colnames(data$o)[data$list_prey[pred_index, 1:data$nb_prey[pred_index]]],
collapse = "\", \""),
"\".\n Please rename your prey input to be consistent.")
}
}
# Construct the corresponding data frame
x <- seq(0, 1, length = 512)
df_to_plot <- data.frame(Prey = c(), x = c(), Density = c(), type=c())
df_to_plot_cond <- data.frame(Prey = c(), x = c(), Density = c(), type=c())
if (variable == "PI"){
if (literature_configuration) {
scalar <- ((length(which(!is.na(data$list_prey[pred_index, ]))) - 1) / (data$CV[pred_index]^2))-1
scalar <- ifelse(length(which(!is.na(data$list_prey[pred_index, ]))) == 1, 1, scalar)
df_cond_allprey <- sapply(data$alpha_lit[, pred_index], function(x){stats::rgamma(1000000,x*scalar)})
df_cond_allprey <- apply(df_cond_allprey, 2, function(X) X/rowSums(df_cond_allprey))
df_cond_allprey <- apply(df_cond_allprey, 2, function(X) stats::density(X, from=0, to=1)$y)
}else{
vecprey <- rep(0,ncol(data$list_prey))
vecprey[prey_index] <- 1
df_cond_allprey <- sapply(vecprey, function(x){stats::rgamma(1000000,x)})
df_cond_allprey <- apply(df_cond_allprey, 2, function(X) X/rowSums(df_cond_allprey))
df_cond_allprey <- apply(df_cond_allprey, 2, function(X) stats::density(X, from=0, to=1)$y)
}
}
for (each_prey in prey){
prey_idx <- which(rownames(data$o) == each_prey)
if (prey_idx %in% data$list_prey[pred_index, ]){
if (variable == "PI"){
if (literature_configuration) {
Density <- stats::dbeta(x, data$alpha_lit[prey_idx, pred_index] * scalar,
(colSums(data$alpha_lit)[pred_index] -
data$alpha_lit[prey_idx, pred_index]) * scalar)
df_to_plot_cond <- rbind(df_to_plot_cond, data.frame(Prey = rep(each_prey, 512), x = x, Density = df_cond_allprey[, prey_idx], type=" conditional"))
} else {
Density <- stats::dbeta(x, 1, data$nb_prey[pred_index] - 1)
}
df_to_plot <- rbind(df_to_plot, data.frame(Prey = rep(each_prey, 512), x = x, Density = Density, type="marginal"))
} else if (variable == "eta"){
if (literature_configuration) {
Density <- stats::dbeta(x, data$eta_hyperparam_1[prey_idx, pred_index],
data$eta_hyperparam_2[prey_idx, pred_index])
} else {
Density <- stats::dbeta(x, 1, 1)
}
df_to_plot <- rbind(df_to_plot, data.frame(Prey = rep(each_prey, 512), x = x, Density = Density, type="marginal"))
}
}
}
if (variable == "PI" & literature_configuration==T){
df_to_plot <- rbind(df_to_plot, df_to_plot_cond)
}
# Plot the figure
figure <- ggplot(df_to_plot, aes_string(x = "x", y = "Density", colour = "Prey", linetype = "Prey")) +
geom_line(size = 1.25) +
ggtitle(paste(title, "\nfor the", pred, "predator")) +
xlim(0, 1) +
theme_bw() +
theme(axis.title.x = element_blank(),
plot.title = element_text(hjust = 0.5)) +
facet_wrap(~type, scales="free_y")
print(figure)
if (save == TRUE){
ggsave(paste0("figure_", gsub(" ", "_", title), "_for_the_", pred, "_predator",
format(Sys.time(),'_%Y-%m-%d_%H-%M-%S'), ".png"),
height = 4.4, width = 6.2, path = save_path)
}
}
#' Plot the prior means or probability distribution(s)
#'
#' @description This function plots the prior means or probability distribution(s) for one or the two
#' variable(s) of interest : the trophic link probabilities ("eta") and/or the diet proportions ("PI").
#'
#' The figure(s) can be saved as PNG using: \code{save = TRUE}, and the directory path to which
#' the figures are saved can be precised with: \code{save_path = "."}.
#'
#' If no "pred" nor "prey" parameter is entered, the plot will be a raster plot with the mean priors for
#' all the trophic groups.
#'
#' If one predator name is entered as "pred", the probability distribution(s) will be plotted for all its
#' prey(s) by default. Some specific prey(s) name(s) can also be entered because if a predator has
#' 22 preys, plotting them all will make the plot hard to read. So you can specify the one or many prey(s)
#' of interest and only display their corresponding probability distribution(s).
#'
#' The "variable" parameter can be specified if one wants to plot the priors for only one variable
#' ("PI" or "eta").
#'
#' @param data the preprocessed data list output by the preprocess_data() function
#' @param pred the predator name for which we want to plot the probability densities
#' @param prey the prey(s) name(s) for which we want to plot the probability densities
#' @param variable the variable(s) for which we want to plot the probability densities. By default
#' we will plot the two variables of interest: eta and PI.
#' @param save A boolean describing whether the figure should be saved as PNG.
#' By default the figures are not saved.
#' @param save_path A string describing the path to which the figures should be saved.
#' By default the figures are saved in a temporary directory.
#' @inheritParams preprocess_data
#'
#' @examples
#'
#' realistic_biotracer_data <- read.csv(system.file("extdata", "realistic_biotracer_data.csv",
#' package = "EcoDiet"))
#' realistic_stomach_data <- read.csv(system.file("extdata", "realistic_stomach_data.csv",
#' package = "EcoDiet"))
#'
#' data <- preprocess_data(biotracer_data = realistic_biotracer_data,
#' trophic_discrimination_factor = c(0.8, 3.4),
#' literature_configuration = FALSE,
#' stomach_data = realistic_stomach_data)
#'
#' plot_prior(data, literature_configuration = FALSE)
#' plot_prior(data, literature_configuration = FALSE, pred = "Cod")
#' plot_prior(data, literature_configuration = FALSE, pred = "Cod",
#' prey = c("Crabs", "Shrimps"), variable = "eta")
#'
#' @seealso \code{\link{plot_results}} to plot the posterior means or probability distribution(s),
#' \code{\link{plot_data}} to plot the input data
#'
#' @export
plot_prior <- function(data, literature_configuration,
pred = NULL, prey = NULL,
variable = c("eta", "PI"),
save = FALSE,
save_path = "."){
if (!all(variable %in% c("eta", "PI"))){
stop("This function has only be designed to plot the priors of PI or eta.")
}
for (var in variable){
if (is.null(pred) & is.null(prey)){
title <- switch(var,
PI = "Mean of the prior diet proportions",
eta = "Mean of the prior trophic link probabilities")
mean_prior <- matrix(NA, ncol = data$nb_group, nrow = data$nb_group)
colnames(mean_prior) <- rownames(mean_prior) <- colnames(data$o)
for (i in data$list_pred){
for (k in data$list_prey[i, 1:data$nb_prey[i]]){
if (var == "eta"){
if (literature_configuration){
mean_prior[k, i] <- (data$eta_hyperparam_1[k, i]/
(data$eta_hyperparam_1[k, i] + data$eta_hyperparam_2[k, i]))
} else {
mean_prior[k, i] <- 1/2
}
} else if (var == "PI"){
if (literature_configuration){
mean_prior[k, i] <- data$alpha_lit[k, i]/colSums(data$alpha_lit)[i]
} else {
mean_prior[k, i] <- 1/data$nb_prey[i]
}
}
}
}
plot_matrix(mean_prior, title, save, save_path)
} else {
title <- switch(var,
PI = "Marginal and Conditional prior distribution of the diet proportions",
eta = "Marginal prior distribution of the trophic link probabilities")
plot_prior_distribution(data, literature_configuration, pred, prey,
variable = var, title, save, save_path)
}
}
}
#' Extract the means of the posterior distribution for a specific variable (PI or eta)
#' in a matrix format (with the predators in the columns, and the preys in the rows)
#'
#' @inheritParams plot_results
#' @param mcmc_output A matrix generated in plot_results containing the MCMC samples
#'
#' @keywords internal
#' @noRd
extract_mean <- function(mcmc_output, data, variable = "PI"){
# keep only the means for the relevant variable
raw_means <- colMeans(mcmc_output)
raw_means <- raw_means[startsWith(names(raw_means), variable)]
# prepare an empty matrix with the correct format
matrix_mean <- matrix(NA, data$nb_group, data$nb_group)
colnames(matrix_mean) <- rownames(matrix_mean) <- colnames(data$o)
for (i in seq_along(raw_means)){
# extract the indices that are between the brackets: "PI[2, 4]" -> "2,4"
correct_indices <- regmatches(names(raw_means)[i], regexec("\\[(.*?)\\]", names(raw_means)[i]))[[1]][2]
# re-format the indices: "2,4" -> c(2L, 4L)
correct_indices <- as.integer(strsplit(correct_indices, ",")[[1]])
# use the indices to fill the matrix with the correct format
matrix_mean[correct_indices[1], correct_indices[2]] <- raw_means[i]
}
return(matrix_mean)
}
#' Plot the posterior probability density(ies) for a given variable and predator
#'
#' @inheritParams plot_results
#' @param mcmc_output A matrix generated in plot_results containing the MCMC samples
#' @param title the title to put (depends on the variable and the predator to plot)
#'
#' @import ggplot2
#'
#' @keywords internal
#' @noRd
plot_posterior_distribution <- function(mcmc_output, data, pred, prey,
variable, title, save = FALSE, save_path){
# Check that the entered predator is correct
pred_index <- which(colnames(data$o) == pred)
if (length(pred_index) == 0){
stop("You did not put a correct predator name in the `pred` argument.\n",
" You entered the name \"", pred,"\", while the predator names are actually: \"",
paste(colnames(data$o), collapse = "\", \""), "\".\n",
" Please use one of the above names in the `pred` argument.")
}
if (data$nb_prey[pred_index] == 0){
stop("The predator you have chosen (\"", pred, "\") has no prey and thus cannot be plotted.")
}
# Check that the entered prey(s) is/are correct
if (!is.null(prey)){
prey_index <- which(colnames(data$o) %in% prey)
if (length(prey) != length(prey_index)){
stop("You used an incorrect prey name in the `prey` argument.\n",
" You have entered the names: \"", paste(prey, collapse = "\", \""),
"\".\n But the prey names are actually: \"",
paste(colnames(data$o), collapse = "\", \""), "\".\n",
" Please put correct names in the `prey` argument.")
}
if (!all(prey_index %in% data$list_prey[pred_index, ])){
stop("You have entered at least one prey that is not eaten by the predator \"",
pred ,"\".\n", " Here are the preys you have entered: \"",
paste(prey, collapse = "\", \""),
"\".\n And here are the predator's preys: \"",
paste(colnames(data$o)[data$list_prey[pred_index, 1:data$nb_prey[pred_index]]],
collapse = "\", \""),
"\".\n Please rename your prey input to be consistent.")
}
}
# Keep only the variable of interest (all the "PI" or all the "eta")
mcmc_output <- mcmc_output[, startsWith(colnames(mcmc_output), variable)]
# Create a lookup table between the model output's names and the prey's and predator's indices:
# names prey pred
# 1 PI[2,1] 2 1
# 2 PI[3,1] 3 1
# 3 PI[3,2] 3 2
lookup <- sapply(colnames(mcmc_output), function(X) regmatches(X, regexec("\\[(.*?)\\]", X))[[1]][2])
prey_idx <- sapply(lookup, function(X) strsplit(X, split=',')[[1]][[1]])
pred_idx <- sapply(lookup, function(X) strsplit(X, split=',')[[1]][[2]])
lookup_table <- data.frame(names = colnames(mcmc_output),
prey = as.integer(prey_idx),
pred = as.integer(pred_idx),
stringsAsFactors = FALSE)
# Prepare a data frame with the values for one predator's preys
if (is.null(prey)){
variables_to_extract <- lookup_table[lookup_table$pred == pred_index, ]$names
prey <- colnames(data$o)[lookup_table[lookup_table$pred == pred_index, ]$prey]
} else {
variables_to_extract <- lookup_table[(lookup_table$pred == pred_index &
lookup_table$prey %in% prey_index &
lookup_table$prey %in% data$list_prey[pred_index, ]), ]$names
}
values_to_extract <- mcmc_output[, variables_to_extract]
df_to_plot <- data.frame(Prey = rep(prey, each = dim(mcmc_output)[1]),
variable_to_plot = c(values_to_extract))
# Trick to scale the plot and not have a warning from the CRAN check:
variable_to_plot <- NULL
..scaled.. <- NULL
Prey <- NULL
# Plot these values to represent the approximated probability densities
figure <- ggplot(df_to_plot) +
geom_density(aes(x = variable_to_plot, y = ..scaled.., fill = Prey),
alpha = .3, adjust = 1/2, na.rm = TRUE) +
geom_density(aes(x = variable_to_plot, y = ..scaled.., color = Prey),
size = 1.25, adjust = 1/2, na.rm = TRUE) +
ggtitle(paste(title, "\nfor the", colnames(data$o)[pred_index], "predator")) +
ylab("Density") +
scale_shape_manual(values = c(seq(1:10))) +
guides(colour = guide_legend(byrow = 1, ncol = 1), shape = guide_legend(byrow = 1, ncol = 1)) +
xlim(0, 1) +
theme_bw() +
theme(axis.title.x = element_blank(),
plot.title = element_text(hjust = 0.5))
print(figure)
if (save == TRUE){
ggsave(paste0("figure_", gsub(" ", "_", title), "_for_the_", colnames(data$o)[pred_index], "_predator",
format(Sys.time(),'_%Y-%m-%d_%H-%M-%S'), ".png"),
height = 4.4, width = 6.2, path = save_path)
}
}
#' Plot the posterior means or probability distribution(s)
#'
#' @description This function plots the posterior means or probability distribution(s) for one
#' or the two variable(s) of interest : the trophic link probabilities ("eta") and/or
#' the diet proportions ("PI").
#'
#' The figure(s) can be saved as PNG using: \code{save = TRUE}, and the directory path to which
#' the figures are saved can be precised with: \code{save_path = "."}.
#'
#' If no "pred" nor "prey" parameter is entered, the plot will be a raster plot with the mean priors for
#' all the trophic groups.
#'
#' If one predator name is entered as "pred", the probability distribution(s) will be plotted for all its
#' prey(s) by default. Some specific prey(s) name(s) can also be entered because if a predator has
#' 22 preys, plotting them all will make the plot hard to read. So you can specify the one or many prey(s)
#' of interest and only display their corresponding probability distribution(s).
#'
#' The "variable" parameter can be specified if one wants to plot the priors for only one variable
#' ("PI" or "eta").
#'
#' @param jags_output the mcmc.list object output by the run_model() function
#' @inheritParams plot_prior
#'
#' @examples
#'
#' \donttest{
#' realistic_biotracer_data <- read.csv(system.file("extdata", "realistic_biotracer_data.csv",
#' package = "EcoDiet"))
#' realistic_stomach_data <- read.csv(system.file("extdata", "realistic_stomach_data.csv",
#' package = "EcoDiet"))
#'
#' data <- preprocess_data(biotracer_data = realistic_biotracer_data,
#' trophic_discrimination_factor = c(0.8, 3.4),
#' literature_configuration = FALSE,
#' stomach_data = realistic_stomach_data)
#'
#' write_model(literature_configuration = FALSE)
#'
#' mcmc_output <- run_model("EcoDiet_model.txt", data, run_param="test")
#'
#' plot_results(mcmc_output, data)
#' plot_results(mcmc_output, data, pred = "Crabs")
#' plot_results(mcmc_output, data, pred = "Crabs",
#' variable = "PI", prey = c("Bivalves", "Shrimps"))
#'
#' }
#'
#' @seealso \code{\link{plot_prior}} to plot the prior means or probability distribution(s),
#' \code{\link{plot_data}} to plot the input data
#'
#' @export
plot_results <- function(jags_output, data,
pred = NULL, prey = NULL,
variable = c("eta", "PI"),
save = FALSE,
save_path = "."){
if (!all(variable %in% c("eta", "PI"))){
stop("This function can only print a figure for the PI or eta variable.\n",
" But you have entered this variable name: \"", variable, "\".\n",
" Please use rather `variable = \"PI\"` or `variable = \"eta\"` for this function.")
}
mcmc_output <- as.matrix(jags_output$samples)
for (var in variable){
if (is.null(pred) & is.null(prey)){
title <- switch(var,
PI = "Mean of the posterior diet proportions",
eta = "Mean of the posterior trophic link probabilities")
mean <- extract_mean(mcmc_output, data, variable = var)
plot_matrix(mean, title, save, save_path)
} else {
title <- switch(var,
PI = "Marginal posterior distribution of the diet proportions",
eta = "Marginal posterior distribution of the trophic link probabilities")
plot_posterior_distribution(mcmc_output, data, pred, prey,
variable = var, title, save, save_path)
}
}
}
|
#week4 finalExcersie
#Jorge Balderas
#You should create one R script called run_analysis.R that does the following.
#Merges the training and the test sets to create one data set.
#Extracts only the measurements on the mean and standard deviation for each measurement.
#Uses descriptive activity names to name the activities in the data set
#Appropriately labels the data set with descriptive variable names.
#From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.
----
library(data.table)
library(dplyr)
--
setwd("C:/Users/jorge.balderas.ayala/Documents/R/data/Getting and Cleaning Data/UCI HAR Dataset/test")
setwd("C:/Users/jorge.balderas.ayala/Documents/R/data/Getting and Cleaning Data/UCI HAR Dataset/train")
#Cols&Names and Activity Labels
features <- read.table("C:/Users/jorge.balderas.ayala/Documents/R/data/Getting and Cleaning Data/UCI HAR Dataset/features.txt")[,2]
activity_labels <- read.table("C:/Users/jorge.balderas.ayala/Documents/R/data/Getting and Cleaning Data/UCI HAR Dataset/activity_labels.txt")[,2]
# TEST
x_test <- read.table("C:/Users/jorge.balderas.ayala/Documents/R/data/Getting and Cleaning Data/UCI HAR Dataset/test/X_test.txt")
y_test <- read.table("C:/Users/jorge.balderas.ayala/Documents/R/data/Getting and Cleaning Data/UCI HAR Dataset/test/y_test.txt")
subject_test <-read.table("C:/Users/jorge.balderas.ayala/Documents/R/data/Getting and Cleaning Data/UCI HAR Dataset/test/subject_test.txt")
# TRAIN
x_train <- read.table("C:/Users/jorge.balderas.ayala/Documents/R/data/Getting and Cleaning Data/UCI HAR Dataset/train/X_train.txt")
y_train <- read.table("C:/Users/jorge.balderas.ayala/Documents/R/data/Getting and Cleaning Data/UCI HAR Dataset/train/y_train.txt")
subject_train <-read.table("C:/Users/jorge.balderas.ayala/Documents/R/data/Getting and Cleaning Data/UCI HAR Dataset/train/subject_train.txt")
# mean & Std
names (x_test) = features
names (x_train) = features
extract_features <- grepl("mean|std", features)
x_test1= x_test[,extract_features]
x_train1 = x_train[,extract_features]
# Labels
y_test[,2] = activity_labels[y_test[,1]]
y_train[,2] = activity_labels[y_train[,1]]
names(y_test) = c("Activity_ID", "Activity_Label")
names(y_train) = c("Activity_ID", "Activity_Label")
names(subject_test) = "subject"
names(subject_train) = "subject"
### Data
test_data <- cbind(subject_test, y_test, x_test1)
train_data <- cbind(subject_train, y_train, x_train1)
finaldata = rbind(test_data, train_data)
dim(finaldata)
finaldataavg <- aggregate(.~finaldata$Activity_Label, finaldata, mean)
write.table(finaldataavg, "tidy.txt", row.name = FALSE)
dim(finaldataavg)
| /run_analysis.R | no_license | Jgbalderasa/Getting-and-Cleaning-Data-Course-Project | R | false | false | 2,784 | r | #week4 finalExcersie
#Jorge Balderas
#You should create one R script called run_analysis.R that does the following.
#Merges the training and the test sets to create one data set.
#Extracts only the measurements on the mean and standard deviation for each measurement.
#Uses descriptive activity names to name the activities in the data set
#Appropriately labels the data set with descriptive variable names.
#From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.
----
library(data.table)
library(dplyr)
--
setwd("C:/Users/jorge.balderas.ayala/Documents/R/data/Getting and Cleaning Data/UCI HAR Dataset/test")
setwd("C:/Users/jorge.balderas.ayala/Documents/R/data/Getting and Cleaning Data/UCI HAR Dataset/train")
#Cols&Names and Activity Labels
features <- read.table("C:/Users/jorge.balderas.ayala/Documents/R/data/Getting and Cleaning Data/UCI HAR Dataset/features.txt")[,2]
activity_labels <- read.table("C:/Users/jorge.balderas.ayala/Documents/R/data/Getting and Cleaning Data/UCI HAR Dataset/activity_labels.txt")[,2]
# TEST
x_test <- read.table("C:/Users/jorge.balderas.ayala/Documents/R/data/Getting and Cleaning Data/UCI HAR Dataset/test/X_test.txt")
y_test <- read.table("C:/Users/jorge.balderas.ayala/Documents/R/data/Getting and Cleaning Data/UCI HAR Dataset/test/y_test.txt")
subject_test <-read.table("C:/Users/jorge.balderas.ayala/Documents/R/data/Getting and Cleaning Data/UCI HAR Dataset/test/subject_test.txt")
# TRAIN
x_train <- read.table("C:/Users/jorge.balderas.ayala/Documents/R/data/Getting and Cleaning Data/UCI HAR Dataset/train/X_train.txt")
y_train <- read.table("C:/Users/jorge.balderas.ayala/Documents/R/data/Getting and Cleaning Data/UCI HAR Dataset/train/y_train.txt")
subject_train <-read.table("C:/Users/jorge.balderas.ayala/Documents/R/data/Getting and Cleaning Data/UCI HAR Dataset/train/subject_train.txt")
# mean & Std
names (x_test) = features
names (x_train) = features
extract_features <- grepl("mean|std", features)
x_test1= x_test[,extract_features]
x_train1 = x_train[,extract_features]
# Labels
y_test[,2] = activity_labels[y_test[,1]]
y_train[,2] = activity_labels[y_train[,1]]
names(y_test) = c("Activity_ID", "Activity_Label")
names(y_train) = c("Activity_ID", "Activity_Label")
names(subject_test) = "subject"
names(subject_train) = "subject"
### Data
test_data <- cbind(subject_test, y_test, x_test1)
train_data <- cbind(subject_train, y_train, x_train1)
finaldata = rbind(test_data, train_data)
dim(finaldata)
finaldataavg <- aggregate(.~finaldata$Activity_Label, finaldata, mean)
write.table(finaldataavg, "tidy.txt", row.name = FALSE)
dim(finaldataavg)
|
#' Dependency Summary (Console Output)
#'
#' This function gives a summary of the dependency structure of a given package
#' and points out opportunities to eliminate depdendencies completely. The default
#' assumption is that there is an R package in the current working
#' directory and that the dependencies to be analyzed are given in the DESCRIPTION
#' file. Use the parameters ‘githublink’ and/or 'pkg' to alter the package/s
#' to be analyzed.
#' @param githublink A link to a github repository of an R package.
#' @param pkg Character vector of CRAN package name/s you want to see the
#' dependencies of. In the case that githublink is also set, the github package
#' is considered as the root package and the packages provided
#' by the pkg parameter are considered to be first level packages, e.g. on
#' the same level as the packages in the DESCRIPTION file of the github package.
#' This is to help answer the question "How would the dependency structure change
#' if the package on github would also depend on a few more packages (provided by
#' the pkg parameter)?".
#' @param includebasepkgs Whether to include base packages in the analysis.
#' @export
#' @examples
#' dstr(githublink = "tidyverse/ggplot2",
#' pkg = c("astro", "celestial"), includebasepkgs = TRUE)
dstr <- function(githublink = NULL, pkg = NULL, includebasepkgs = F){
writeLines("Loading...\n")
data <- dstr_data(githublink, pkg, c("root", "unique2",
"all", "list"),
includebasepkgs = includebasepkgs)
uniquelist <- data[[2]]
allpkg <- data[[3]]
dlist <- data[[4]]
if(is.null(githublink) & !is.null(pkg)){
writeLines(paste0("--- [dstr] DEPENDENCY STRUCTURE ANALYSIS OF { ",
paste0(pkg, collapse = ", ")," } ---"))
} else if(is.null(pkg) & !is.null(githublink)){
writeLines(paste0("--- [dstr] DEPENDENCY STRUCTURE ANALYSIS OF '", data[[1]],"' ---"))
} else if(!is.null(pkg) & !is.null(githublink)){
writeLines(paste0("--- [dstr] DEPENDENCY STRUCTURE ANALYSIS OF '", data[[1]],
" + { ",paste0(pkg, collapse = ", ")," }' ---"))
} else {
writeLines(paste0("--- [dstr] DEPENDENCY STRUCTURE ANALYSIS OF '", data[[1]],"' ---"))
}
if(!includebasepkgs){
writeLines("Base packages are ignored in this analysis.")
}
writeLines("\n")
if(is.null(pkg)){
writeLines(paste0("First Level Dependencies (Packages Found In The DESCRIPTION File) (",
length(data[[2]]),")"))
} else if (!is.null(githublink)){
# both githublink and pkg are set
writeLines(paste0("First Level Dependencies (Packages Found In The DESCRIPTION File)",
"\n+ Input Packages From The 'pkg' Parameter (",
length(data[[2]])," In Total)"))
} else {
# only pkg is set
# writeLines(paste0("Input Packages From The 'pkg' Parameter (",
# length(data[[2]]),")"))
writeLines(paste0("First Level Dependencies (Packages Found In The DESCRIPTION File/s",
"\nOf The Specified Package/s In The 'pkg' Parameter) (",
length(data[[2]]),")"))
}
paragraphsep___ <- paste0(rep("-", 80), collapse = "")
writeLines(paragraphsep___)
#writeLines(paste(names(data[[2]]), collapse = ", "))
print(names(data[[2]]))
writeLines("\n")
writeLines(paste("All", length(data[[3]]), "Eventually Loaded Packages (Dependencies Of Dependencies Of...)"))
writeLines(paragraphsep___)
print(data[[3]])
writeLines("\n")
writeLines("Opportunities To Reduce Dependencies (Iterating Through All First Level Dependencies)")
writeLines(paragraphsep___)
# Sort the list so that packages with most dependencies are first in list
uniquelist <- uniquelist[names(sort(sapply(uniquelist, length),
decreasing = T))]
for (j in 1:length(uniquelist)){
if(length(uniquelist[[j]]) > 1){
writeLines(paste0("If you remove '", names(uniquelist)[j],
"' you will remove the following ", length(uniquelist[[j]]),
" packages completely:"))
print(uniquelist[[j]])
} else if (length(uniquelist[[j]]) == 1){
writeLines(paste0("If you remove '", names(uniquelist)[j],
"' you will remove the following ", length(uniquelist[[j]]),
" package completely:"))
print(uniquelist[[j]])
} else {
sought <- names(uniquelist)[j]
soughtinlist <- sapply(dlist, function(x) sought %in% x)
loaders <- names(soughtinlist)[soughtinlist]
writeLines(paste0("If you remove '", names(uniquelist)[j],"' you will remove 0 other packages and also not '", names(uniquelist)[j],
"' istelf because it is a deeper level dependency from the following first level dependencies:"))
print(loaders)
}
writeLines("\n")
}
writeLines("Shared Dependencies / Hard To Remove")
writeLines(paragraphsep___)
shareddeps <- list()
for(i in 1:length(allpkg)){
soughtinlist <- sapply(dlist, function(y) allpkg[i] %in% y)
loaders <- names(soughtinlist)[soughtinlist]
if(length(loaders) > 1){
shareddeps[[length(shareddeps)+1]] <- loaders
names(shareddeps)[length(shareddeps)] <- allpkg[i]
}
}
if(length(shareddeps) > 0){
# Sort the list so that packages with most dependencies are first in list
shareddeps <- shareddeps[names(sort(sapply(shareddeps, length),
decreasing = T))]
unique_loaders <- unique(shareddeps)
#sapply(shareddeps, function(x){all(x == y)})
collapsed_loaded <- lapply(unique_loaders,
function(y) names(shareddeps)[sapply(shareddeps,
function(x){identical(x, y)})])
for(i in 1:length(unique_loaders)){
#writeLines(paste0("The packages '", paste(collapsed_loaded[[i]], collapse = ", "),
# "' are loaded by your (",length(unique_loaders[[i]]) ,") first level packages '",
# paste(unique_loaders[[i]], collapse = ", ", "'")))
writeLines(paste0(length(unique_loaders[[i]]),
" first level packages ('",
paste0(unique_loaders[[i]], collapse = ", ", "'"),
") depend on the following packages:"))
print(collapsed_loaded[[i]])
writeLines("\n")
}
} else {
writeLines("You don't have shared dependencies, e.g. none of the ulimatively loaded packages is loaded because of two or more first level packages.")
}
}
| /R/dstr.R | no_license | iagomosqueira/dstr | R | false | false | 6,688 | r | #' Dependency Summary (Console Output)
#'
#' This function gives a summary of the dependency structure of a given package
#' and points out opportunities to eliminate depdendencies completely. The default
#' assumption is that there is an R package in the current working
#' directory and that the dependencies to be analyzed are given in the DESCRIPTION
#' file. Use the parameters ‘githublink’ and/or 'pkg' to alter the package/s
#' to be analyzed.
#' @param githublink A link to a github repository of an R package.
#' @param pkg Character vector of CRAN package name/s you want to see the
#' dependencies of. In the case that githublink is also set, the github package
#' is considered as the root package and the packages provided
#' by the pkg parameter are considered to be first level packages, e.g. on
#' the same level as the packages in the DESCRIPTION file of the github package.
#' This is to help answer the question "How would the dependency structure change
#' if the package on github would also depend on a few more packages (provided by
#' the pkg parameter)?".
#' @param includebasepkgs Whether to include base packages in the analysis.
#' @export
#' @examples
#' dstr(githublink = "tidyverse/ggplot2",
#' pkg = c("astro", "celestial"), includebasepkgs = TRUE)
dstr <- function(githublink = NULL, pkg = NULL, includebasepkgs = F){
writeLines("Loading...\n")
data <- dstr_data(githublink, pkg, c("root", "unique2",
"all", "list"),
includebasepkgs = includebasepkgs)
uniquelist <- data[[2]]
allpkg <- data[[3]]
dlist <- data[[4]]
if(is.null(githublink) & !is.null(pkg)){
writeLines(paste0("--- [dstr] DEPENDENCY STRUCTURE ANALYSIS OF { ",
paste0(pkg, collapse = ", ")," } ---"))
} else if(is.null(pkg) & !is.null(githublink)){
writeLines(paste0("--- [dstr] DEPENDENCY STRUCTURE ANALYSIS OF '", data[[1]],"' ---"))
} else if(!is.null(pkg) & !is.null(githublink)){
writeLines(paste0("--- [dstr] DEPENDENCY STRUCTURE ANALYSIS OF '", data[[1]],
" + { ",paste0(pkg, collapse = ", ")," }' ---"))
} else {
writeLines(paste0("--- [dstr] DEPENDENCY STRUCTURE ANALYSIS OF '", data[[1]],"' ---"))
}
if(!includebasepkgs){
writeLines("Base packages are ignored in this analysis.")
}
writeLines("\n")
if(is.null(pkg)){
writeLines(paste0("First Level Dependencies (Packages Found In The DESCRIPTION File) (",
length(data[[2]]),")"))
} else if (!is.null(githublink)){
# both githublink and pkg are set
writeLines(paste0("First Level Dependencies (Packages Found In The DESCRIPTION File)",
"\n+ Input Packages From The 'pkg' Parameter (",
length(data[[2]])," In Total)"))
} else {
# only pkg is set
# writeLines(paste0("Input Packages From The 'pkg' Parameter (",
# length(data[[2]]),")"))
writeLines(paste0("First Level Dependencies (Packages Found In The DESCRIPTION File/s",
"\nOf The Specified Package/s In The 'pkg' Parameter) (",
length(data[[2]]),")"))
}
paragraphsep___ <- paste0(rep("-", 80), collapse = "")
writeLines(paragraphsep___)
#writeLines(paste(names(data[[2]]), collapse = ", "))
print(names(data[[2]]))
writeLines("\n")
writeLines(paste("All", length(data[[3]]), "Eventually Loaded Packages (Dependencies Of Dependencies Of...)"))
writeLines(paragraphsep___)
print(data[[3]])
writeLines("\n")
writeLines("Opportunities To Reduce Dependencies (Iterating Through All First Level Dependencies)")
writeLines(paragraphsep___)
# Sort the list so that packages with most dependencies are first in list
uniquelist <- uniquelist[names(sort(sapply(uniquelist, length),
decreasing = T))]
for (j in 1:length(uniquelist)){
if(length(uniquelist[[j]]) > 1){
writeLines(paste0("If you remove '", names(uniquelist)[j],
"' you will remove the following ", length(uniquelist[[j]]),
" packages completely:"))
print(uniquelist[[j]])
} else if (length(uniquelist[[j]]) == 1){
writeLines(paste0("If you remove '", names(uniquelist)[j],
"' you will remove the following ", length(uniquelist[[j]]),
" package completely:"))
print(uniquelist[[j]])
} else {
sought <- names(uniquelist)[j]
soughtinlist <- sapply(dlist, function(x) sought %in% x)
loaders <- names(soughtinlist)[soughtinlist]
writeLines(paste0("If you remove '", names(uniquelist)[j],"' you will remove 0 other packages and also not '", names(uniquelist)[j],
"' istelf because it is a deeper level dependency from the following first level dependencies:"))
print(loaders)
}
writeLines("\n")
}
writeLines("Shared Dependencies / Hard To Remove")
writeLines(paragraphsep___)
shareddeps <- list()
for(i in 1:length(allpkg)){
soughtinlist <- sapply(dlist, function(y) allpkg[i] %in% y)
loaders <- names(soughtinlist)[soughtinlist]
if(length(loaders) > 1){
shareddeps[[length(shareddeps)+1]] <- loaders
names(shareddeps)[length(shareddeps)] <- allpkg[i]
}
}
if(length(shareddeps) > 0){
# Sort the list so that packages with most dependencies are first in list
shareddeps <- shareddeps[names(sort(sapply(shareddeps, length),
decreasing = T))]
unique_loaders <- unique(shareddeps)
#sapply(shareddeps, function(x){all(x == y)})
collapsed_loaded <- lapply(unique_loaders,
function(y) names(shareddeps)[sapply(shareddeps,
function(x){identical(x, y)})])
for(i in 1:length(unique_loaders)){
#writeLines(paste0("The packages '", paste(collapsed_loaded[[i]], collapse = ", "),
# "' are loaded by your (",length(unique_loaders[[i]]) ,") first level packages '",
# paste(unique_loaders[[i]], collapse = ", ", "'")))
writeLines(paste0(length(unique_loaders[[i]]),
" first level packages ('",
paste0(unique_loaders[[i]], collapse = ", ", "'"),
") depend on the following packages:"))
print(collapsed_loaded[[i]])
writeLines("\n")
}
} else {
writeLines("You don't have shared dependencies, e.g. none of the ulimatively loaded packages is loaded because of two or more first level packages.")
}
}
|
# This module create investment return series.
gen_returns <- function( sim_paramlist_ = sim_paramlist,
Global_paramlist_ = Global_paramlist,
returnScenarios_ = returnScenarios
){
## Unquote for development
# sim_paramlist_ = sim_paramlist
# Global_paramlist_ = Global_paramlist
# returnScenarios_ = returnScenarios
assign_parmsList(Global_paramlist_, envir = environment())
assign_parmsList(sim_paramlist_, envir = environment())
## Constant return distributions defined by mean and sd
if(return_type == "simple"){
set.seed(sim_paramlist$seed)
i.r <- matrix(rnorm(nyear*nsim, ir.mean, ir.sd), nyear, nsim)
i.r <- cbind(rep(ir.mean - ir.sd^2/2, nyear), i.r) # add deterministic returns
colnames(i.r) <- 0:nsim
}
## Time varying return distributions defined by scenarios
if (return_type == "internal"){
# return_scenario <- "RS1"
# nsim = 5
returnScenarios_local <- returnScenarios_ %>% filter(scenario == return_scenario)
set.seed(sim_paramlist_$seed)
i.r <- cbind(
with(returnScenarios_local, create_returns(return_det, 0, period)), # add deterministic returns
replicate(nsim, with(returnScenarios_local, create_returns(r.mean, r.sd, period)))
)
colnames(i.r) <- 0:nsim
}
## T
i.r <- cbind(rep(i, nyear), i.r) # add constant return that equals discount rate for checking model consistency
i.shock <- i.r[, 2]
i.shock[3:6] <- c(-0.24, 0.12, 0.12, 0.12)
i.r <- cbind(i.shock, i.r)
colnames(i.r) <- c(-2:nsim)
return(i.r)
}
#gen_returns()[,2]
| /model/simulation/model_sim_invReturns.R | no_license | yimengyin16/model_SJ | R | false | false | 1,634 | r |
# This module create investment return series.
gen_returns <- function( sim_paramlist_ = sim_paramlist,
Global_paramlist_ = Global_paramlist,
returnScenarios_ = returnScenarios
){
## Unquote for development
# sim_paramlist_ = sim_paramlist
# Global_paramlist_ = Global_paramlist
# returnScenarios_ = returnScenarios
assign_parmsList(Global_paramlist_, envir = environment())
assign_parmsList(sim_paramlist_, envir = environment())
## Constant return distributions defined by mean and sd
if(return_type == "simple"){
set.seed(sim_paramlist$seed)
i.r <- matrix(rnorm(nyear*nsim, ir.mean, ir.sd), nyear, nsim)
i.r <- cbind(rep(ir.mean - ir.sd^2/2, nyear), i.r) # add deterministic returns
colnames(i.r) <- 0:nsim
}
## Time varying return distributions defined by scenarios
if (return_type == "internal"){
# return_scenario <- "RS1"
# nsim = 5
returnScenarios_local <- returnScenarios_ %>% filter(scenario == return_scenario)
set.seed(sim_paramlist_$seed)
i.r <- cbind(
with(returnScenarios_local, create_returns(return_det, 0, period)), # add deterministic returns
replicate(nsim, with(returnScenarios_local, create_returns(r.mean, r.sd, period)))
)
colnames(i.r) <- 0:nsim
}
## T
i.r <- cbind(rep(i, nyear), i.r) # add constant return that equals discount rate for checking model consistency
i.shock <- i.r[, 2]
i.shock[3:6] <- c(-0.24, 0.12, 0.12, 0.12)
i.r <- cbind(i.shock, i.r)
colnames(i.r) <- c(-2:nsim)
return(i.r)
}
#gen_returns()[,2]
|
##############################################################
# seperate and total variance #
##############################################################
variance<-function(){
varmat<-matrix(0,10,6)
colnames(varmat)<-c("variance1-200","variance201-400",
"variance401-600","variance601-800",
"variance801-1000","intergratedVariance")
for (i in 1:10) {
#variance of sections
varmat[i,1]<-round(var(dataList[[i]][c(1,200),"Close"]),digits = 3)
varmat[i,2]<-round(var(dataList[[i]][c(201,400),"Close"]),digits = 3)
varmat[i,3]<-round(var(dataList[[i]][c(401,600),"Close"]),digits = 3)
varmat[i,4]<-round(var(dataList[[i]][c(601,800),"Close"]),digits = 3)
varmat[i,5]<-round(var(dataList[[i]][c(801,1000),"Close"]),digits = 3)
#integrated variance
varmat[i,6]<-round(var(dataList[[i]][,"Close"]),digits = 3)
}
plot.zoo(varmat,xlab = c(1,10),ylab = c("1-200","201-400","401-600",
"601-800","801-1000"))
return(varmat)
} | /variance.R | no_license | TANKERS888/COMP396 | R | false | false | 1,037 | r | ##############################################################
# seperate and total variance #
##############################################################
variance<-function(){
varmat<-matrix(0,10,6)
colnames(varmat)<-c("variance1-200","variance201-400",
"variance401-600","variance601-800",
"variance801-1000","intergratedVariance")
for (i in 1:10) {
#variance of sections
varmat[i,1]<-round(var(dataList[[i]][c(1,200),"Close"]),digits = 3)
varmat[i,2]<-round(var(dataList[[i]][c(201,400),"Close"]),digits = 3)
varmat[i,3]<-round(var(dataList[[i]][c(401,600),"Close"]),digits = 3)
varmat[i,4]<-round(var(dataList[[i]][c(601,800),"Close"]),digits = 3)
varmat[i,5]<-round(var(dataList[[i]][c(801,1000),"Close"]),digits = 3)
#integrated variance
varmat[i,6]<-round(var(dataList[[i]][,"Close"]),digits = 3)
}
plot.zoo(varmat,xlab = c(1,10),ylab = c("1-200","201-400","401-600",
"601-800","801-1000"))
return(varmat)
} |
unzipFile <- "household_power_consumption.txt"
downloadFile <- "household_power_consumption.zip"
downloadLink <- "https://d396qusza40orc.cloudfront.net/exdata/data/household_power_consumption.zip"
plot3 <- function(){
## Check if dataset file has already been unzipped
if(!file.exists(unzipFile)){
## Check if file has already been downloaded
if (!file.exists(downloadFile)){
## File not downloaded, hence download file
download.file(downloadLink, downloadFile)
} else {
## File already downloaded
}
## now Unzip file and extract dataset
unzip(downloadFile)
} else {
## Dataset file exist already
}
t <- read.table("household_power_consumption.txt", header=TRUE, sep=";", na.strings = "?", colClasses = c('character','character','numeric','numeric','numeric','numeric','numeric','numeric','numeric'))
## Format date to Type Date
t$Date <- as.Date(t$Date, "%d/%m/%Y")
## Filter data set from Feb. 1, 2007 to Feb. 2, 2007
t <- subset(t,Date >= as.Date("2007-2-1") & Date <= as.Date("2007-2-2"))
## Remove incomplete observation
t <- t[complete.cases(t),]
## Combine Date and Time column
dateTime <- paste(t$Date, t$Time)
## Name the vector
dateTime <- setNames(dateTime, "DateTime")
## Remove Date and Time column
t <- t[ ,!(names(t) %in% c("Date","Time"))]
## Add DateTime column
t <- cbind(dateTime, t)
## Format dateTime Column
t$dateTime <- as.POSIXct(dateTime)
with(t, {
plot(Sub_metering_1~dateTime, type="l",
ylab="Global Active Power (kilowatts)", xlab="")
lines(Sub_metering_2~dateTime,col='Red')
lines(Sub_metering_3~dateTime,col='Blue')
})
legend("topright", col=c("black", "red", "blue"), lwd=c(1,1,1),
c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"))
## Saving to file
dev.copy(png, file="plot3.png", height=480, width=480)
dev.off()
}
| /plot3.R | no_license | Vichar-Dev/ExData_Plotting1 | R | false | false | 1,951 | r | unzipFile <- "household_power_consumption.txt"
downloadFile <- "household_power_consumption.zip"
downloadLink <- "https://d396qusza40orc.cloudfront.net/exdata/data/household_power_consumption.zip"
plot3 <- function(){
## Check if dataset file has already been unzipped
if(!file.exists(unzipFile)){
## Check if file has already been downloaded
if (!file.exists(downloadFile)){
## File not downloaded, hence download file
download.file(downloadLink, downloadFile)
} else {
## File already downloaded
}
## now Unzip file and extract dataset
unzip(downloadFile)
} else {
## Dataset file exist already
}
t <- read.table("household_power_consumption.txt", header=TRUE, sep=";", na.strings = "?", colClasses = c('character','character','numeric','numeric','numeric','numeric','numeric','numeric','numeric'))
## Format date to Type Date
t$Date <- as.Date(t$Date, "%d/%m/%Y")
## Filter data set from Feb. 1, 2007 to Feb. 2, 2007
t <- subset(t,Date >= as.Date("2007-2-1") & Date <= as.Date("2007-2-2"))
## Remove incomplete observation
t <- t[complete.cases(t),]
## Combine Date and Time column
dateTime <- paste(t$Date, t$Time)
## Name the vector
dateTime <- setNames(dateTime, "DateTime")
## Remove Date and Time column
t <- t[ ,!(names(t) %in% c("Date","Time"))]
## Add DateTime column
t <- cbind(dateTime, t)
## Format dateTime Column
t$dateTime <- as.POSIXct(dateTime)
with(t, {
plot(Sub_metering_1~dateTime, type="l",
ylab="Global Active Power (kilowatts)", xlab="")
lines(Sub_metering_2~dateTime,col='Red')
lines(Sub_metering_3~dateTime,col='Blue')
})
legend("topright", col=c("black", "red", "blue"), lwd=c(1,1,1),
c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"))
## Saving to file
dev.copy(png, file="plot3.png", height=480, width=480)
dev.off()
}
|
\name{founder.event}
\alias{founder.event}
\title{Simulation of a founder event}
\usage{
founder.event(p0=0.5,Ne=1000,Nf=10,ttime=100,etime=50,show="p")
}
\arguments{
\item{p0}{Starting frequency for the A allele.}
\item{Ne}{Effective population size at the start of the simulation and after the founding event.}
\item{Nf}{Size of the founding population.}
\item{ttime}{Total time of the simulation.}
\item{etime}{Time for the founding event.}
\item{show}{Two different options for plotting. \code{"p"} shows the frequency of the A allele through time; \code{"var"} shows the genetic variation in the population, calculated as p*(1-p). The default is \code{show="p"}.}
}
\description{
This function simulates genetic drift with a founding event at time \code{etime}.
}
\value{
The function creates one of two different plots, depending on the value of \code{show}.
}
\author{Liam Revell \email{liam.revell@umb.edu}}
\seealso{
\code{\link{genetic.drift}}
}
\examples{
founder.event()
founder.event(show="variation")
}
\keyword{population genetics}
\keyword{drift}
| /man/founder.event.Rd | no_license | jmallon/PopGen | R | false | false | 1,076 | rd | \name{founder.event}
\alias{founder.event}
\title{Simulation of a founder event}
\usage{
founder.event(p0=0.5,Ne=1000,Nf=10,ttime=100,etime=50,show="p")
}
\arguments{
\item{p0}{Starting frequency for the A allele.}
\item{Ne}{Effective population size at the start of the simulation and after the founding event.}
\item{Nf}{Size of the founding population.}
\item{ttime}{Total time of the simulation.}
\item{etime}{Time for the founding event.}
\item{show}{Two different options for plotting. \code{"p"} shows the frequency of the A allele through time; \code{"var"} shows the genetic variation in the population, calculated as p*(1-p). The default is \code{show="p"}.}
}
\description{
This function simulates genetic drift with a founding event at time \code{etime}.
}
\value{
The function creates one of two different plots, depending on the value of \code{show}.
}
\author{Liam Revell \email{liam.revell@umb.edu}}
\seealso{
\code{\link{genetic.drift}}
}
\examples{
founder.event()
founder.event(show="variation")
}
\keyword{population genetics}
\keyword{drift}
|
Geocode_Combined<-function(tidy_tracks,stormvalue,cname){
tryCatch ( {
CountryMap = ggmap(get_googlemap(center=as.numeric(geocode(cname)), scale=2, zoom=4), extent="normal")
},
warning = function(w) {
message("Failure to fetch geo-location. Try again ")
message(w)
return(NULL)
},
error = function(e) {
message("error Failure to fetch geo-location. Try again ")
message(e)
return(NULL)
}
)
finalgeom = CountryMap
tidy_tracks1 <- subset(tidy_tracks,storm_id_unique == stormvalue & ne > 0 & nw > 0 & se > 0 & sw > 0)
KATRINA2005f = tidy_tracks1
filter1 = unique(KATRINA2005f$latitude)
track_list1<- vector("list", length(filter1))
code1<- vector("list", nrow(KATRINA2005f))
filter1 = unique(KATRINA2005f$latitude)
for (k in 1: length(filter1)) {
tidy_tracks2 <- subset(KATRINA2005f, latitude == filter1[k])
tidy_tracks2<- tidy_tracks2 %>% mutate(ne = ne*1852*1,se = se*1852*1,
sw = sw*1852*1,
nw = nw*1852*1 )
l_points_1 = setNames(data.frame(matrix(ncol = 9, nrow = 0)), c("longitude", "latitude","stormid","wind_speed","ne","se","sw","nw"))
x_tracks = tidy_tracks2
for (i in 1:nrow(x_tracks)) {
ne_tracks <- base::data.frame(geosphere::destPoint(p = c(x_tracks[i,]$longitude,x_tracks[i,]$latitude),b = 1:90,d = x_tracks[i,]$ne))
ne_tracks$latitude <- x_tracks[i,]$latitude
ne_tracks$longitude <- x_tracks[i,]$longitude
ne_tracks$stormid <- x_tracks[i,]$storm_id_unique
ne_tracks$wind_speed<- x_tracks[i,]$wind_speed
ne_tracks$ne = x_tracks[i,]$ne
ne_tracks$se = x_tracks[i,]$se
ne_tracks$sw = x_tracks[i,]$sw
ne_tracks$nw = x_tracks[i,]$nw
nw_tracks<- base::data.frame(geosphere::destPoint(p = c(x_tracks[i,]$longitude,x_tracks[i,]$latitude),b = 271:360,d = x_tracks[i,]$nw))
nw_tracks$latitude <- x_tracks[i,]$latitude
nw_tracks$longitude <- x_tracks[i,]$longitude
nw_tracks$stormid <- x_tracks[i,]$storm_id_unique
nw_tracks$wind_speed<-x_tracks[i,]$wind_speed
nw_tracks$ne = x_tracks[i,]$ne
nw_tracks$se = x_tracks[i,]$se
nw_tracks$sw = x_tracks[i,]$sw
nw_tracks$nw = x_tracks[i,]$nw
se_tracks <- base::data.frame(geosphere::destPoint(p = c(x_tracks[i,]$longitude,x_tracks[i,]$latitude),b = 91:180,d = x_tracks[i,]$se))
se_tracks$latitude <- x_tracks[i,]$latitude
se_tracks$longitude <- x_tracks[i,]$longitude
se_tracks$stormid <- x_tracks[i,]$storm_id_unique
se_tracks$wind_speed<-x_tracks[i,]$wind_speed
se_tracks$ne = x_tracks[i,]$ne
se_tracks$se = x_tracks[i,]$se
se_tracks$sw = x_tracks[i,]$sw
se_tracks$nw = x_tracks[i,]$nw
sw_tracks <- base::data.frame(geosphere::destPoint(p = c(x_tracks[i,]$longitude,x_tracks[i,]$latitude),b = 181:270,d = x_tracks[i,]$sw))
sw_tracks$latitude <- x_tracks[i,]$latitude
sw_tracks$longitude <- x_tracks[i,]$longitude
sw_tracks$stormid <- x_tracks[i,]$storm_id_unique
sw_tracks$wind_speed<-x_tracks[i,]$wind_speed
sw_tracks$ne = x_tracks[i,]$ne
sw_tracks$se = x_tracks[i,]$se
sw_tracks$sw = x_tracks[i,]$sw
sw_tracks$nw = x_tracks[i,]$nw
l_points <- dplyr::bind_rows(list(ne_tracks,se_tracks,sw_tracks,nw_tracks))
l_points_1 = dplyr::bind_rows(list(l_points,l_points_1))
}
track_list1[[k]] = l_points_1
}
for (k in 1:length(track_list1)){
track_list1[[k]]$wind_speed = as.factor(track_list1[[k]]$wind_speed)
}
for (k in 1:length(track_list1)){
finalgeom= finalgeom +
geom_hurricane(data = track_list1[[k]],
aes(x = lon, y = lat,
ne = ne, se = se, nw = nw,sw = sw,
fill = wind_speed, color = wind_speed)) +
scale_color_manual(name = "Wind speed (kts)",
values = c("red", "orange", "yellow")) +
scale_fill_manual(name = "Wind speed (kts)",
values = c("red", "orange", "yellow"))}
return(finalgeom)
}
| /R/geocode - clubbed.R | no_license | vanita1/Hurricane1 | R | false | false | 3,944 | r | Geocode_Combined<-function(tidy_tracks,stormvalue,cname){
tryCatch ( {
CountryMap = ggmap(get_googlemap(center=as.numeric(geocode(cname)), scale=2, zoom=4), extent="normal")
},
warning = function(w) {
message("Failure to fetch geo-location. Try again ")
message(w)
return(NULL)
},
error = function(e) {
message("error Failure to fetch geo-location. Try again ")
message(e)
return(NULL)
}
)
finalgeom = CountryMap
tidy_tracks1 <- subset(tidy_tracks,storm_id_unique == stormvalue & ne > 0 & nw > 0 & se > 0 & sw > 0)
KATRINA2005f = tidy_tracks1
filter1 = unique(KATRINA2005f$latitude)
track_list1<- vector("list", length(filter1))
code1<- vector("list", nrow(KATRINA2005f))
filter1 = unique(KATRINA2005f$latitude)
for (k in 1: length(filter1)) {
tidy_tracks2 <- subset(KATRINA2005f, latitude == filter1[k])
tidy_tracks2<- tidy_tracks2 %>% mutate(ne = ne*1852*1,se = se*1852*1,
sw = sw*1852*1,
nw = nw*1852*1 )
l_points_1 = setNames(data.frame(matrix(ncol = 9, nrow = 0)), c("longitude", "latitude","stormid","wind_speed","ne","se","sw","nw"))
x_tracks = tidy_tracks2
for (i in 1:nrow(x_tracks)) {
ne_tracks <- base::data.frame(geosphere::destPoint(p = c(x_tracks[i,]$longitude,x_tracks[i,]$latitude),b = 1:90,d = x_tracks[i,]$ne))
ne_tracks$latitude <- x_tracks[i,]$latitude
ne_tracks$longitude <- x_tracks[i,]$longitude
ne_tracks$stormid <- x_tracks[i,]$storm_id_unique
ne_tracks$wind_speed<- x_tracks[i,]$wind_speed
ne_tracks$ne = x_tracks[i,]$ne
ne_tracks$se = x_tracks[i,]$se
ne_tracks$sw = x_tracks[i,]$sw
ne_tracks$nw = x_tracks[i,]$nw
nw_tracks<- base::data.frame(geosphere::destPoint(p = c(x_tracks[i,]$longitude,x_tracks[i,]$latitude),b = 271:360,d = x_tracks[i,]$nw))
nw_tracks$latitude <- x_tracks[i,]$latitude
nw_tracks$longitude <- x_tracks[i,]$longitude
nw_tracks$stormid <- x_tracks[i,]$storm_id_unique
nw_tracks$wind_speed<-x_tracks[i,]$wind_speed
nw_tracks$ne = x_tracks[i,]$ne
nw_tracks$se = x_tracks[i,]$se
nw_tracks$sw = x_tracks[i,]$sw
nw_tracks$nw = x_tracks[i,]$nw
se_tracks <- base::data.frame(geosphere::destPoint(p = c(x_tracks[i,]$longitude,x_tracks[i,]$latitude),b = 91:180,d = x_tracks[i,]$se))
se_tracks$latitude <- x_tracks[i,]$latitude
se_tracks$longitude <- x_tracks[i,]$longitude
se_tracks$stormid <- x_tracks[i,]$storm_id_unique
se_tracks$wind_speed<-x_tracks[i,]$wind_speed
se_tracks$ne = x_tracks[i,]$ne
se_tracks$se = x_tracks[i,]$se
se_tracks$sw = x_tracks[i,]$sw
se_tracks$nw = x_tracks[i,]$nw
sw_tracks <- base::data.frame(geosphere::destPoint(p = c(x_tracks[i,]$longitude,x_tracks[i,]$latitude),b = 181:270,d = x_tracks[i,]$sw))
sw_tracks$latitude <- x_tracks[i,]$latitude
sw_tracks$longitude <- x_tracks[i,]$longitude
sw_tracks$stormid <- x_tracks[i,]$storm_id_unique
sw_tracks$wind_speed<-x_tracks[i,]$wind_speed
sw_tracks$ne = x_tracks[i,]$ne
sw_tracks$se = x_tracks[i,]$se
sw_tracks$sw = x_tracks[i,]$sw
sw_tracks$nw = x_tracks[i,]$nw
l_points <- dplyr::bind_rows(list(ne_tracks,se_tracks,sw_tracks,nw_tracks))
l_points_1 = dplyr::bind_rows(list(l_points,l_points_1))
}
track_list1[[k]] = l_points_1
}
for (k in 1:length(track_list1)){
track_list1[[k]]$wind_speed = as.factor(track_list1[[k]]$wind_speed)
}
for (k in 1:length(track_list1)){
finalgeom= finalgeom +
geom_hurricane(data = track_list1[[k]],
aes(x = lon, y = lat,
ne = ne, se = se, nw = nw,sw = sw,
fill = wind_speed, color = wind_speed)) +
scale_color_manual(name = "Wind speed (kts)",
values = c("red", "orange", "yellow")) +
scale_fill_manual(name = "Wind speed (kts)",
values = c("red", "orange", "yellow"))}
return(finalgeom)
}
|
weld_bmillion <- function(filename){
lines <- readLines(filename)
lines <- gsub("([0-9]) ([bm]illion)", "\\1~\\2", lines)
writeLines(lines, filename)
}
| /R/weld_bmillion.R | permissive | grattaninstitute/Cost-overruns-report | R | false | false | 159 | r |
weld_bmillion <- function(filename){
lines <- readLines(filename)
lines <- gsub("([0-9]) ([bm]illion)", "\\1~\\2", lines)
writeLines(lines, filename)
}
|
#install and load plotrix-package / neccessary to use pyramid.plot
install.packages("plotrix")
library("plotrix")
#function to calculate relative frequencies in % table for variable k with l different characteristics
frequency = function(k, l){
if (missing(k))
stop("No data passed to the function. Variable k has to be defined.")
if (missing(l))
stop("No data passed to the function. Variable l has to be defined.")
100*sweep(table(k,l), 2, colSums(table(k,l)), "/")
}
#function to build population pyramid and store it as pdf
buildpopulation = function(k, l, popname){
if (missing(popname))
stop('No data passed to the function. Variable popname has to be defined.
Please define a plot name such as "populationpyramid.pdf".
Use quotation marks, at the beginning and the end of the plot name.')
pop = frequency(k, l)
pdf(popname)
pyramid.plot(pop[,1], pop[,2], labels = rownames(pop),
gap = 2, lxcol = "blue", rxcol = "red")
dev.off()
}
#subsample with employees covered by the union contract (with FC or SC)
datFCSC = dat[ which(SCTariffDummy == 1 | FCTariffDummy == 1),]
#subsample with employees which ar not covered by the union contract (no FC & no SC)
datNoFCSC = dat[ which(SCTariffDummy == 0 & FCTariffDummy == 0),]
#population pyramid on the whole dataset
buildpopulation(dat$ef41, dat$ef10, "population_all.pdf")
#population pyramid of employees covered by the union contract
buildpopulation(datFCSC$ef41, datFCSC$ef10, "populationFCSC.pdf")
#population pyramid of employees not covered by the union contract
buildpopulation(datNoFCSC$ef41, datNoFCSC$ef10, "population-noFCSC.pdf")
#calculate arithmetic mean
mean(dat$ef41, na.rm = TRUE) #all population
mean(datFCSC$ef41, na.rm = TRUE) #subsample with union covered workers
mean(datNoFCSC$ef41, na.rm = TRUE) #subsample with workers without a union contract
#calculate median
median(dat$ef41, na.rm = TRUE) #all population
median(datFCSC$ef41, na.rm = TRUE) #subsample with union covered workers
median(datNoFCSC$ef41, na.rm = TRUE) #subsample with workers without a union contract
#function to simultaneously generate a boxplot and save it in the seperate pdf-file
buildboxplot = function (v, w , boxname, z){
if (missing(v))
stop("No data passed to the function. Variable v has to be defined.")
if (missing(w))
stop("No data passed to the function. Variable w has to be defined.")
if (missing(z))
stop("No data passed to the function. Variable z has to be defined.
z is a vector which should contain labels for the characteristics of the variable w.
The number of the characteristics of w must equal the number of elements in z.")
if (missing(boxname))
stop('No data passed to the function.boxname has to be defined such as "graph.pdf".')
if (is.numeric(v)!= TRUE)
stop("Numeric data needed. k has to be a numeric variable.")
pdf(boxname, width = 11, height = 7)
boxplot(v~w, range=2.5, width=NULL, notch=FALSE,varwidth=FALSE, names = z,
boxwex=0.8, outline=FALSE, staplewex=0.5, horizontal=FALSE, border="black",
col="#94d639", add=FALSE, at=NULL)
abline(h = median(v, na.rm = TRUE), col="red", lwd = 1.5)
dev.off()
}
#define a vector with label names for gender and education
genderLAB = c("male", "female")
educLAB = c("Educ A", "Educ B", "Educ C", "Educ D", "Educ E", "Educ F", "Educ G")
#boxplot ln(wage)~gender of all employees
buildboxplot(dat$lnWage, dat$ef10, "boxplot_lnwage_gen.pdf", genderLAB)
#boxplot ln(wage)~education of all employees
buildboxplot(dat$lnWage, dat$ef16u2, "boxplot_lnwage_educ.pdf", educLAB)
#boxplot ln(wage)~education of employees which are covered by an union contract
buildboxplot(datFCSC$lnWage, datFCSC$ef16u2, "boxploteducFCSC.pdf", educLAB)
#boxplot ln(wage)~education of employees which are not covered by an union contract
buildboxplot(datNoFCSC$lnWage, datNoFCSC$ef16u2, "boxploteduc-NoFCSC.pdf", educLAB)
#calculate median of the variable ln(wage)
median(dat$lnWage, na.rm = TRUE)
median(datFCSC$lnWage, na.rm = TRUE)
median(datNoFCSC$lnWage, na.rm = TRUE)
| /SPLFreqPlot/SPLFreqPlot.R | no_license | maxr91/SPLUnionWageEffects | R | false | false | 4,179 | r | #install and load plotrix-package / neccessary to use pyramid.plot
install.packages("plotrix")
library("plotrix")
#function to calculate relative frequencies in % table for variable k with l different characteristics
frequency = function(k, l){
if (missing(k))
stop("No data passed to the function. Variable k has to be defined.")
if (missing(l))
stop("No data passed to the function. Variable l has to be defined.")
100*sweep(table(k,l), 2, colSums(table(k,l)), "/")
}
#function to build population pyramid and store it as pdf
buildpopulation = function(k, l, popname){
if (missing(popname))
stop('No data passed to the function. Variable popname has to be defined.
Please define a plot name such as "populationpyramid.pdf".
Use quotation marks, at the beginning and the end of the plot name.')
pop = frequency(k, l)
pdf(popname)
pyramid.plot(pop[,1], pop[,2], labels = rownames(pop),
gap = 2, lxcol = "blue", rxcol = "red")
dev.off()
}
#subsample with employees covered by the union contract (with FC or SC)
datFCSC = dat[ which(SCTariffDummy == 1 | FCTariffDummy == 1),]
#subsample with employees which ar not covered by the union contract (no FC & no SC)
datNoFCSC = dat[ which(SCTariffDummy == 0 & FCTariffDummy == 0),]
#population pyramid on the whole dataset
buildpopulation(dat$ef41, dat$ef10, "population_all.pdf")
#population pyramid of employees covered by the union contract
buildpopulation(datFCSC$ef41, datFCSC$ef10, "populationFCSC.pdf")
#population pyramid of employees not covered by the union contract
buildpopulation(datNoFCSC$ef41, datNoFCSC$ef10, "population-noFCSC.pdf")
#calculate arithmetic mean
mean(dat$ef41, na.rm = TRUE) #all population
mean(datFCSC$ef41, na.rm = TRUE) #subsample with union covered workers
mean(datNoFCSC$ef41, na.rm = TRUE) #subsample with workers without a union contract
#calculate median
median(dat$ef41, na.rm = TRUE) #all population
median(datFCSC$ef41, na.rm = TRUE) #subsample with union covered workers
median(datNoFCSC$ef41, na.rm = TRUE) #subsample with workers without a union contract
#function to simultaneously generate a boxplot and save it in the seperate pdf-file
buildboxplot = function (v, w , boxname, z){
if (missing(v))
stop("No data passed to the function. Variable v has to be defined.")
if (missing(w))
stop("No data passed to the function. Variable w has to be defined.")
if (missing(z))
stop("No data passed to the function. Variable z has to be defined.
z is a vector which should contain labels for the characteristics of the variable w.
The number of the characteristics of w must equal the number of elements in z.")
if (missing(boxname))
stop('No data passed to the function.boxname has to be defined such as "graph.pdf".')
if (is.numeric(v)!= TRUE)
stop("Numeric data needed. k has to be a numeric variable.")
pdf(boxname, width = 11, height = 7)
boxplot(v~w, range=2.5, width=NULL, notch=FALSE,varwidth=FALSE, names = z,
boxwex=0.8, outline=FALSE, staplewex=0.5, horizontal=FALSE, border="black",
col="#94d639", add=FALSE, at=NULL)
abline(h = median(v, na.rm = TRUE), col="red", lwd = 1.5)
dev.off()
}
#define a vector with label names for gender and education
genderLAB = c("male", "female")
educLAB = c("Educ A", "Educ B", "Educ C", "Educ D", "Educ E", "Educ F", "Educ G")
#boxplot ln(wage)~gender of all employees
buildboxplot(dat$lnWage, dat$ef10, "boxplot_lnwage_gen.pdf", genderLAB)
#boxplot ln(wage)~education of all employees
buildboxplot(dat$lnWage, dat$ef16u2, "boxplot_lnwage_educ.pdf", educLAB)
#boxplot ln(wage)~education of employees which are covered by an union contract
buildboxplot(datFCSC$lnWage, datFCSC$ef16u2, "boxploteducFCSC.pdf", educLAB)
#boxplot ln(wage)~education of employees which are not covered by an union contract
buildboxplot(datNoFCSC$lnWage, datNoFCSC$ef16u2, "boxploteduc-NoFCSC.pdf", educLAB)
#calculate median of the variable ln(wage)
median(dat$lnWage, na.rm = TRUE)
median(datFCSC$lnWage, na.rm = TRUE)
median(datNoFCSC$lnWage, na.rm = TRUE)
|
## ----------------------------------------------------------------------
##
## IGraph R package
## Copyright (C) 2005-2014 Gabor Csardi <csardi.gabor@gmail.com>
## 334 Harvard street, Cambridge, MA 02139 USA
##
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 2 of the License, or
## (at your option) any later version.
##
## This program 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 this program; if not, write to the Free Software
## Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA
## 02110-1301 USA
##
## ----------------------------------------------------------------------
###################################################################
# Convert graphs to human readable forms
###################################################################
.get.attr.codes <- function(object) {
ga <- va <- ea <- ""
gal <- graph_attr_names(object)
if (length(gal) != 0) {
ga <- paste(sep="", gal, " (g/",
.Call("R_igraph_get_attr_mode", object, 2L, PACKAGE="igraph"),
")")
}
val <- vertex_attr_names(object)
if (length(val) != 0) {
va <- paste(sep="", val, " (v/",
.Call("R_igraph_get_attr_mode", object, 3L, PACKAGE="igraph"),
")")
}
eal <- edge_attr_names(object)
if (length(eal) != 0) {
ea <- paste(sep="", edge_attr_names(object), " (e/",
.Call("R_igraph_get_attr_mode", object, 4L, PACKAGE="igraph"),
")")
}
c(ga, va, ea)
}
.print.header <- function(object) {
if (!is_igraph(object)) {
stop("Not a graph object")
}
title <- paste(sep="", "IGRAPH ",
c("U","D")[is_directed(object)+1],
c("-","N")[is_named(object)+1],
c("-","W")[is_weighted(object)+1],
c("-","B")[is_bipartite(object)+1], " ",
vcount(object), " ", ecount(object), " -- ")
w <- getOption("width")
if (nchar(title) < w && "name" %in% graph_attr_names(object)) {
title <- substring(paste(sep="", title,
as.character(object$name)[1]), 1, w-1)
}
cat(title, "\n", sep="")
atxt <- .get.attr.codes(object)
atxt <- paste(atxt[atxt!=""], collapse=", ")
if (atxt != "") {
atxt <- strwrap(paste(sep="", "+ attr: ", atxt), prefix = "| ",
initial = "")
cat(atxt, sep="\n")
}
1 + if (length(atxt) == 1 && atxt == "") 0 else length(atxt)
}
indent_print <- printr$indent_print
.print.graph.attributes <- function(x, full, max.lines) {
list <- graph_attr_names(x)
if (length(list)!=0) {
cat("+ graph attributes:\n")
out <- capture.output({
lapply(list, function(n) {
cat(sep="", "+ ", n, ":\n")
indent_print(graph_attr(x, n), .indent = " ")
})
invisible(NULL)
})
indent_print(out, sep = "\n", .indent = "| ", .printer = cat)
length(out) + 1
} else {
0
}
}
## IGRAPH U--- 10 10 -- Ring graph
## + attr: name (g/c), mutual (g/l), circular (g/l)
## + graph attributes:
## | + name:
## | [1] "Ring graph"
## | + mutual:
## | [1] FALSE
## | + circular=
## | [1] TRUE
## | + layout =
## | [,1] [,2]
## | [1,] 0.000000 0.000000e+00
## | [2,] 1.000000 0.000000e+00
## | [3,] 0.809017 5.877853e-01
## | [4,] 0.309017 9.510565e-01
## | [5,] -0.309017 9.510565e-01
## | [6,] -0.809017 5.877853e-01
## | [7,] -1.000000 1.224647e-16
## | [8,] -0.809017 -5.877853e-01
## | [9,] -0.309017 -9.510565e-01
## | [10,] 0.309017 -9.510565e-01
## | [11,] 0.809017 -5.877853e-01
## + edges:
## [1] 1-- 2 2-- 3 3-- 4 4-- 5 5-- 6 6-- 7 7-- 8 8-- 9 9--10 1--10
.print.vertex.attributes <- function(x, full, max.lines) {
pf <- function(x) .print.vertex.attributes.old(x, full, max.lines)
if (length(vertex_attr_names(x))) cat("+ vertex attributes:\n")
indent_print(x, .indent = "| ", .printer = pf)
}
.print.vertex.attributes.old <- function(x, full, max.lines) {
vc <- vcount(x)
list <- vertex_attr_names(x)
if (length(list) != 0) {
mp <- getOption("max.print")
options(max.print=1000000000)
if (vc <= mp) {
omitted.vertices <- 0
ind <- as.numeric(V(x))
} else {
omitted.vertices <- vc-mp
ind <- seq(length=mp)
}
if (vc==0 ||
all(sapply(list, function(v)
is.numeric(vertex_attr(x, v)) ||
is.character(vertex_attr(x, v)) ||
is.logical(vertex_attr(x, v))))) {
## create a table
tab <- data.frame(v=paste(sep="", "[", ind, "]"), row.names="v")
for (i in list) {
tab[i] <- vertex_attr(x, i, ind)
}
print(tab)
} else {
for (i in ind) {
cat(sep="", "[[", i, "]]\n")
lapply(list, function(n) {
cat(sep="", "[[", i, "]][[", n, "]]\n")
print(vertex_attr(x, n, i))})
}
}
options(max.print=mp)
if (omitted.vertices != 0) {
cat(paste('[ reached getOption("max.print") -- omitted',
omitted.vertices, "vertices ]\n\n"))
}
}
}
.print.edges.edgelist <- function(x, edges = E(x), names) {
ec <- length(edges)
list <- edge_attr_names(x)
list <- list[list!="name"]
arrow <- ifelse(is_directed(x), "->", "--")
if (is_named(x)) {
cat("+ edges (vertex names) and their attributes:\n")
} else {
cat("+ edges and their attributes:\n")
}
if (names && ! "name" %in% vertex_attr_names(x)) {
names <- FALSE
}
if (names && "name" %in% vertex_attr_names(x) &&
!is.numeric(vertex_attr(x, "name")) &&
!is.character(vertex_attr(x, "name")) &&
!is.logical(vertex_attr(x, "name"))) {
warning("Can't print vertex names, complex `name' vertex attribute")
names <- FALSE
}
mp <- getOption("max.print")
if (mp >= ec) {
omitted.edges <- 0
el <- ends(x, edges, names=names)
} else {
omitted.edges <- ec-mp
el <- ends(x, ends[seq_len(mp)])
if (names) { el[] <- V(x)$name[el] }
}
ename <- if ("name" %in% edge_attr_names(x)) {
paste(sep="", "'", E(x)$name, "'")
} else {
seq(length=nrow(el))
}
if (ec==0 ||
all(sapply(list, function(v) is.numeric(edge_attr(x, v)) |
is.character(edge_attr(x,v)) |
is.logical(edge_attr(x, v))))) {
## create a table
tab <- data.frame(row.names=paste(sep="", "[", ename, "]"))
if (is.numeric(el)) { w <- nchar(max(el)) } else { w <- max(nchar(el)) }
tab["edge"] <- paste(sep="", format(el[,1], width=w),
arrow, format(el[,2], width=w))
for (i in list) {
tab[i] <- edge_attr(x, i)
}
print(tab)
} else {
i <- 1
apply(el, 1, function(v) {
cat(sep="", "[", ename[i], "] ", v[1], " ", arrow, " ", v[2]);
lapply(list, function(n) {
cat(sep="", "\n[[", i, "]][[", n, "]]\n")
print(edge_attr(x, n, i))})
cat("\n")
i <<- i+1
})
}
if (omitted.edges != 0) {
cat(paste('[ reached getOption("max.print") -- omitted', omitted.edges,
'edges ]\n\n'))
}
}
#' @include printr.R
head_print <- printr$head_print
printer_callback <- printr$printer_callback
.print.edges.compressed <- function(x, edges = E(x), names, num = FALSE,
max.lines = igraph_opt("auto.print.lines")) {
len <- length(edges)
title <- "+" %+%
(if (num) " " %+% chr(len) %+% "/" %+%
(if (is.null(x)) "?" else chr(gsize(x))) else "") %+%
(if (len == 1) " edge" else " edges") %+%
(if (is.null(x)) ", unknown graph" else "") %+%
(if (is.null(attr(edges, "vnames"))) "" else " (vertex names)") %+%
":\n"
cat(title)
if (!is.null(attr(edges, "single")) && attr(edges, "single") &&
!is.null(x)) {
## Double bracket
ea <- edge_attr(x)
if (all(sapply(ea, is.atomic))) {
etail <- tail_of(x, edges)
ehead <- head_of(x, edges)
df <- data.frame(
stringsAsFactors = FALSE,
tail = as_ids(etail),
head = as_ids(ehead),
tid = as.vector(etail),
hid = as.vector(ehead)
)
if (length(ea)) {
ea <- do_call(data.frame, .args = ea, stringsAsFactors = FALSE)
df <- cbind(df, ea[as.vector(edges), , drop = FALSE])
}
print(df)
} else {
print(lapply(ea, "[", as.vector(edges)))
}
} else if (is.null(max.lines)) {
.print.edges.compressed.all(x, edges, names)
} else {
.print.edges.compressed.limit(x, edges, names, max.lines)
}
}
.print.edges.compressed.all <- function(x, edges, names) {
arrow <- c("--", "->")[is_directed(x)+1]
if (!is.null(x)) {
el <- ends(x, edges, names=names)
pr <- paste(sep="", format(el[,1]), arrow, format(el[,2]))
print(pr, quote=FALSE)
} else {
if (!is.null(attr(edges, "vnames"))) {
print(as.vector(attr(edges, "vnames")), quote = FALSE)
} else if (!is.null(names(edges))) {
print(names(edges), quote = FALSE)
} else {
print(as.vector(edges))
}
}
}
.print.edges.compressed.limit <- function(x, edges, names, max.lines) {
if (!is.null(x)) {
arrow <- c("--", "->")[is_directed(x)+1]
can_max <- NA
el <- NA
fun <- function(q, no) {
if (q == "length") {
length(edges)
} else if (q == "min_width") {
5
} else if (q == "width") {
el <<- ends(x, edges[seq_len(no)], names = names)
cummax(nchar(el[,1])) + nchar(arrow) + cummax(nchar(el[,2])) + 1
} else if (q == "print") {
el <<- el[seq_len(no), , drop = FALSE]
out <- paste(sep="", format(el[,1]), arrow, format(el[,2]))
capture.output(print(out, quote = FALSE))
} else if (q == "max") {
can_max <<- no
} else if (q == "done") {
if (no["tried_items"] < length(edges) ||
no["printed_lines"] < no["tried_lines"]) {
cat("+ ... omitted several edges\n")
}
}
}
fun <- printer_callback(fun)
head_print(fun, max_lines = max.lines)
} else {
if (!is.null(attr(edges, "vnames"))) {
head_print(as.vector(attr(edges, "vnames")), quote = FALSE)
} else if (!is.null(names(edges))) {
head_print(names(edges), quote = FALSE)
} else {
head_print(as.vector(edges))
}
}
}
.print.edges.adjlist <- function(x) {
## TODO: getOption("max.print")
cat("+ edges:\n")
vc <- vcount(x)
arrow <- c(" -- ", " -> ")[is_directed(x)+1]
al <- as_adj_list(x, mode="out")
w <- nchar(max(which(degree(x, mode="in") != 0)))
mpl <- trunc((getOption("width")-nchar(arrow)-nchar(vc)) / (w+1))
if (any(sapply(al, length) > mpl)) {
## Wrapping needed
mw <- nchar(vcount(x))
sm <- paste(collapse="", rep(" ", mw+4))
alstr <- lapply(seq_along(al), function(x) {
len <- length(al[[x]])
fac <- rep(1:(len/mpl+1), each=mpl, length=len)
nei <- tapply(format(al[[x]], width=mw), fac, paste, collapse=" ")
mark <- paste(sep="", format(x, width=mw), arrow)
mark <- c(mark, rep(sm, max(0, length(nei)-1)))
paste(sep="", mark, nei)
})
cat(unlist(alstr), sep="\n")
} else {
alstr <- sapply(al, function(x) {
paste(format(x, width=w), collapse=" ")
})
mark <- paste(sep="", format(seq_len(vc)), arrow)
alstr <- paste(sep="", mark, alstr)
maxw <- max(nchar(alstr))
sep <- " "
ncol <- trunc((getOption("width")-1+nchar(sep)) / (maxw+nchar(sep)))
if (ncol > 1) {
alstr <- format(alstr, width=maxw, justify="left")
fac <- rep(1:(vc/ncol+1), each=ncol, length=vc)
alstr <- tapply(alstr, fac, paste, collapse=sep)
}
cat(alstr, sep="\n")
}
}
.print.edges.adjlist.named <- function(x, edges = E(x)) {
## TODO getOption("max.print")
cat("+ edges (vertex names):\n")
arrow <- c(" -- ", " -> ")[is_directed(x)+1]
vn <- V(x)$name
al <- as_adj_list(x, mode="out")
alstr <- sapply(al, function(x) { paste(collapse=", ", vn[x]) })
alstr <- paste(sep="", format(vn), arrow, alstr)
alstr <- strwrap(alstr, exdent=max(nchar(vn))+nchar(arrow))
cat(alstr, sep="\n")
}
#' @method str igraph
#' @export
str.igraph <- function(object, ...) {
print.igraph(object, full=TRUE, ...)
}
#' Print graphs to the terminal
#'
#' These functions attempt to print a graph to the terminal in a human readable
#' form.
#'
#' \code{summary.igraph} prints the number of vertices, edges and whether the
#' graph is directed.
#'
#' \code{str.igraph} prints the same information, and also lists the edges, and
#' optionally graph, vertex and/or edge attributes.
#'
#' \code{print.igraph} behaves either as \code{summary.igraph} or
#' \code{str.igraph} depending on the \code{full} argument. See also the
#' \sQuote{print.full} igraph option and \code{\link{igraph_opt}}.
#'
#' The graph summary printed by \code{summary.igraph} (and \code{print.igraph}
#' and \code{str.igraph}) consists one or more lines. The first line contains
#' the basic properties of the graph, and the rest contains its attributes.
#' Here is an example, a small star graph with weighed directed edges and named
#' vertices: \preformatted{ IGRAPH DNW- 10 9 -- In-star
#' + attr: name (g/c), mode (g/c), center (g/n), name (v/c),
#' weight (e/n) }
#' The first line always
#' starts with \code{IGRAPH}, showing you that the object is an igraph graph.
#' Then a four letter long code string is printed. The first letter
#' distinguishes between directed (\sQuote{\code{D}}) and undirected
#' (\sQuote{\code{U}}) graphs. The second letter is \sQuote{\code{N}} for named
#' graphs, i.e. graphs with the \code{name} vertex attribute set. The third
#' letter is \sQuote{\code{W}} for weighted graphs, i.e. graphs with the
#' \code{weight} edge attribute set. The fourth letter is \sQuote{\code{B}} for
#' bipartite graphs, i.e. for graphs with the \code{type} vertex attribute set.
#'
#' Then, after two dashes, the name of the graph is printed, if it has one,
#' i.e. if the \code{name} graph attribute is set.
#'
#' From the second line, the attributes of the graph are listed, separated by a
#' comma. After the attribute names, the kind of the attribute -- graph
#' (\sQuote{\code{g}}), vertex (\sQuote{\code{v}}) or edge (\sQuote{\code{e}})
#' -- is denoted, and the type of the attribute as well, character
#' (\sQuote{\code{c}}), numeric (\sQuote{\code{n}}), logical
#' (\sQuote{\code{l}}), or other (\sQuote{\code{x}}).
#'
#' As of igraph 0.4 \code{str.igraph} and \code{print.igraph} use the
#' \code{max.print} option, see \code{\link[base]{options}} for details.
#'
#' @aliases print.igraph str.igraph summary.igraph
#' @param x The graph to print.
#' @param full Logical scalar, whether to print the graph structure itself as
#' well.
#' @param graph.attributes Logical constant, whether to print graph attributes.
#' @param vertex.attributes Logical constant, whether to print vertex
#' attributes.
#' @param edge.attributes Logical constant, whether to print edge attributes.
#' @param names Logical constant, whether to print symbolic vertex names (ie.
#' the \code{name} vertex attribute) or vertex ids.
#' @param max.lines The maximum number of lines to use. The rest of the
#' output will be truncated.
#' @param object The graph of which the summary will be printed.
#' @param \dots Additional agruments.
#' @return All these functions return the graph invisibly.
#' @author Gabor Csardi \email{csardi.gabor@@gmail.com}
#' @method print igraph
#' @export
#' @export print.igraph
#' @keywords graphs
#' @examples
#'
#' g <- make_ring(10)
#' g
#' summary(g)
#'
print.igraph <- function(x, full=igraph_opt("print.full"),
graph.attributes=igraph_opt("print.graph.attributes"),
vertex.attributes=igraph_opt("print.vertex.attributes"),
edge.attributes=igraph_opt("print.edge.attributes"),
names=TRUE, max.lines = igraph_opt("auto.print.lines"), ...) {
if (!is_igraph(x)) {
stop("Not a graph object")
}
head_lines <- .print.header(x)
if (is.logical(full) && full) {
if (graph.attributes) {
head_lines <- head_lines + .print.graph.attributes(x, full, max.lines)
}
if (vertex.attributes) {
head_lines <- head_lines + .print.vertex.attributes(x, full, max.lines)
}
if (ecount(x)==0) {
## Do nothing
} else if (edge.attributes && length(edge_attr_names(x)) != 0 ) {
.print.edges.edgelist(x, names = names)
} else if (median(degree(x, mode="out")) < 3) {
.print.edges.compressed(x, names = names, max.lines = NULL)
} else if (is_named(x)) {
.print.edges.adjlist.named(x)
} else {
.print.edges.adjlist(x)
}
} else if (full == "auto") {
.print.edges.compressed(x, names = names, max.lines =
max.lines - head_lines)
}
invisible(x)
}
#' @rdname print.igraph
#' @method summary igraph
#' @export
summary.igraph <- function(object, ...) {
.print.header(object)
invisible(object)
}
"
####################################################################
## Various designs for printing graphs
## Summary
IGRAPH UNW- 5 5 -- A ring
Attr: name (g/c), name (v/c), weight (e/n)
IGRAPH D-W- 100 200 -- Gnm random graph
## Printing, edge list
IGRAPH-UNW--V5-E5----------------------------------------- A ring -
+ attributes: name (g), name (v), weight (e).
+ edges:
edge weight
[1]' a--b 1
[2]' b--c 2
[3]' c--d -1
[4]' d--e 0.5
[5]' a--e 1
## Compressed edge list
IGRAPH UNW- 5 10 -- A ring
+ attributes: name (g/c), name (v/n), weight (e/n)
+ edges:
[1]' 1--2 2--3 3--4 4--5 1--5 2--5 5--1
[8]' 1--4 4--2 1--3
## This is good if vertices are named
IGRAPH UNW- 10 18 -- Krackhardt kite
+ attributes: name (g/c), name (v/c), weight (e/n)
+ edges:
Andre -- [1] Beverly, Carol, Diane, Fernando
Beverly -- [1] Andre, Diane, Ed, Garth
Carol -- [1] Andre, Diane, Fernando
Diane -- [1] Andre, Beverly, Carol, Diane, Ed
-- [6] Garth
Ed -- [1] Beverly, Diane, Garth
Fernando -- [1] Andre, Carol, Diane, Garth
Garth -- [1] Beverly, Diane, Ed, Fernando
Heather -- [1] Fernando, Garth
Ike -- [1] Heather, Jane
Jane -- [1] Ike
IGRAPH UNW- 10 18 -- Krackhardt kite
+ attributes: name (g/c), name (v/c), weight (e/n)
+ edges:
Andre -- Beverly, Carol, Diane, Fernando
Beverly -- Andre, Diane, Ed, Garth
Carol -- Andre, Diane, Fernando
Diane -- Andre, Beverly, Carol, Diane, Ed, Garth
Ed -- Beverly, Diane, Garth
Fernando -- Andre, Carol, Diane, Garth
Garth -- Beverly, Diane, Ed, Fernando
Heather -- Fernando, Garth
Ike -- Heather, Jane
Jane -- Ike
## This is the good one if vertices are not named
IGRAPH U--- 100 200 -- Gnm random graph
+ edges:
[ 1] 28 46 89 90 [ 2] 47 69 72 89
[ 3] 29 [ 4] 17 20
[ 5] 11 40 42 51 78 89 [ 6] 27 32 70 87 93
[ 7] 18 27 87 [ 8] 18 24 82
[ 9] 18 20 85 94 [ 10] 24 70 77 91
[ 11] 5 12 34 61 62 [ 12] 11 41 44 61 65 80
...
## Alternative designs, summary
IGRAPH-UNW--V5-E5,---------------------------------------- A ring -
+ attributes: name (g/c), name (v/c), weight (e/n)
IGRAPH. |V|=5, |E|=5, undirected, named, weighted.
Attributes: name (g/c), name (v/c), weight (e/n)
IGRAPH: 'A ring'
Graph attributes: |V|=5, |E|=5, undirected, name.
Vertex attributes: name.
Edge attributes: weight.
## Alternative designs, printing
IGRAPH-UNW--V5-E5----------------------------------------- A ring -
'- attributes: name (g), name (v), weight (e).
' edge weight
[1] 'a' -- 'b' 1
[2] 'b' -- 'c' 2
[3] 'c' -- 'd' -1
[4] 'd' -- 'e' 0.5
[5] 'a' -- 'e' 1
IGRAPH-UNW--V-5-E-10-------------------------------------- A ring -
|- attributes: name (g), name (v), weight (e).
|- edges:
[1] 'a'--'b' 'b'--'c' 'c'--'d' 'd'--'e' 'a'--'e' 'b'-'e'
[7] 'e'--'a' 'a'--'d' 'd'--'b' 'a'--'c'
IGRAPH-UNW--V-5-E-10-------------------------------------- A ring -
+ attributes: name (g), name (v), weight (e).
+ vertices:
| name
| [1] a
| [2] b
| [3] c
| [4] d
| [5] e
+ edges:
[1] 'a'--'b' 'b'--'c' 'c'--'d' 'd'--'e' 'a'--'e' 'b'-'e'
[7] 'e'--'a' 'a'--'d' 'd'--'b' 'a'--'c'
IGRAPH-UNW--V-5-E-10-------------------------------------- A ring -
+ graph attributes: name
+ vertex attributes: name
+ edge attributes: weight
+ vertices:
| name
|1] a
|2] b
|3] c
|4] d
|5] e
+ edges:
|1] a--b b--c c--d d--e a--e b-e
|7] e--a a--d d--b a--c
IGRAPH-UNW--V-5-E-10-------------------------------------- A ring -
+ graph attributes: name (c)
+ vertex attributes: name (c)
+ edge attributes: weight (n)
+ edges:
[1] a--b b--c c--d d--e a--e b-e
[7] e--a a--d d--b a--c
IGRAPH-UNW--V-5-E-10-------------------------------------- A ring -
+ attributes: name (g/c), name (v/c), weight (e/n)
+ edges:
[ 1] a--b b--c c--d d--e a--e b--e e--a a--d d--b
[10] a--c
IGRAPH-DNW--V-5-E-10-------------------------------------- A ring -
+ attributes: name (g/c), name (v/n), weight (e/n)
+ edges:
[1]' 1->2 2->3 3->4 4->5 1->5 2->5 5->1
[8]' 1->4 4->2 1->3
IGRAPH-UNW--V-5-E-20-------------------------------------- A ring -
+ attributes: name (g/c), name (v/c), weight (e/n)
+ edges:
[ 1] a-b b-c c-d d-e a-e b-e e-a a-d d-b a-c
[11] a-b b-c c-d d-e a-e b-e e-a a-d d-b a-c
IGRAPH-UNW--V-8-E-10-------------------------------------- A ring -
+ attributes: name (g/c), name (v/c), weight (e/n)
+ edges:
[a] b c e f h
[b] a c e
[c] a b d
[d] a b c h
[e] a b d
[f] a
[g]
[h] a d
IGRAPH-UNW--V-10-E-18------------------------------------- A ring -
+ attributes: name (g/c), name (v/c), weight (e/n)
+ edges:
[a] a--{b,c,e,f,h} b--{a,c,e} c--{a,b,d} d--{a,b,c,h}
[e] e--{a,b,d} f--{a} g--{} h--{a,d}
IGRAPH-UNW--V10-E18------------------------------Krackhardt kite--
+ attributes: name (g/c), name (v/c), weight (e/n)
+ edges:
[ Andre][1] Beverly Carol Diane Fernando
[ Beverly][1] Andre Diane Ed Garth
[ Carol][1] Andre Diane Fernando
[ Diane][1] Andre Beverly Carol Diane Ed
[ Diane][6] Garth
[ Ed][1] Beverly Diane Garth
[Fernando][1] Andre Carol Diane Garth
[ Garth][1] Beverly Diane Ed Fernando
[ Heather][1] Fernando Garth
[ Ike][1] Heather Jane
[ Jane][1] Ike
IGRAPH-UNW--V10-E18-------------------------------Krackhardt kite--
+ attributes: name (g/c), name (v/c), weight (e/n)
+ edges:
[ Andre][1] Beverly/1 Carol/3 Diane/3 Fernando/1
[ Beverly][1] Andre/1 Diane/1 Ed/2 Garth/2
[ Carol][1] Andre/2 Diane/2 Fernando/1
[ Diane][1] Andre/5 Beverly/1 Carol/0.4 Diane/2
[ Diane][5] Ed/1.5 Garth/2.5
[ Ed][1] Beverly/-1 Diane/1.5 Garth/2
[Fernando][1] Andre/1 Carol/2 Diane/1 Garth/1
[ Garth][1] Beverly/2 Diane/3 Ed/1 Fernando/-1
[ Heather][1] Fernando/3 Garth/1
[ Ike][1] Heather/1 Jane/-1
[ Jane][1] Ike/-2
IGRAPH-UNW--V10-E18-------------------------------Krackhardt kite--
+ attributes: name (g/c), name (v/c), weight (e/n)
+ edges:
[ Andre][1] Beverly (1) Carol (3) Diane (3) Fernando (1)
[ Beverly][1] Andre (1) Diane (1) Ed (2) Garth (2)
[ Carol][1] Andre (2) Diane (2) Fernando (1)
[ Diane][1] Andre (5) Beverly (1) Carol (0.5) Diane (2)
[ Diane][5] Ed (1.5) Garth (2.5)
[ Ed][1] Beverly (-1) Diane (1.5) Garth (2)
[Fernando][1] Andre (1) Carol (2) Diane (1) Garth (1)
[ Garth][1] Beverly (2) Diane (3) Ed (1) Fernando (-1)
[ Heather][1] Fernando (3) Garth (1)
[ Ike][1] Heather (1) Jane (-1)
[ Jane][1] Ike (-2)
IGRAPH UNW- V10 E18 -- Krackhardt kite
+ attr: name (g/c), name (v/c), weight (e/n)
+ edges:
[ Andre][1] Beverly (1) Carol (3) Diane (3) Fernando (1)
[ Beverly][1] Andre (1) Diane (1) Ed (2) Garth (2)
[ Carol][1] Andre (2) Diane (2) Fernando (1)
[ Diane][1] Andre (5) Beverly (1) Carol (0.5) Diane (2)
[ Diane][5] Ed (1.5) Garth (2.5)
[ Ed][1] Beverly (-1) Diane (1.5) Garth (2)
[Fernando][1] Andre (1) Carol (2) Diane (1) Garth (1)
[ Garth][1] Beverly (2) Diane (3) Ed (1) Fernando (-1)
[ Heather][1] Fernando (3) Garth (1)
[ Ike][1] Heather (1) Jane (-1)
[ Jane][1] Ike (-2)
IGRAPH-U----V100-E200----------------------------Gnm random graph--
+ edges:
[ 1] 28 46 89 90
[ 2] 47 69 72 89
[ 3] 29
[ 4] 17 20
[ 5] 11 40 42 51 78 89
[ 6] 27 32 70 87 93
[ 7] 18 27 87
[ 8] 18 24 82
[ 9] 18 20 85 94
[ 10] 24 70 77 91
[ 11] 5 12 34 61 62
[ 12] 11 41 44 61 65 80
...
IGRAPH-U----100-200------------------------------Gnm random graph--
+ edges:
[ 1] 28 46 89 90 [ 2] 47 69 72 89
[ 3] 29 [ 4] 17 20
[ 5] 11 40 42 51 78 89 [ 6] 27 32 70 87 93
[ 7] 18 27 87 [ 8] 18 24 82
[ 9] 18 20 85 94 [ 10] 24 70 77 91
[ 11] 5 12 34 61 62 [ 12] 11 41 44 61 65 80
...
"
| /R/print.R | no_license | Ruchika8/Dgraph | R | false | false | 25,526 | r |
## ----------------------------------------------------------------------
##
## IGraph R package
## Copyright (C) 2005-2014 Gabor Csardi <csardi.gabor@gmail.com>
## 334 Harvard street, Cambridge, MA 02139 USA
##
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 2 of the License, or
## (at your option) any later version.
##
## This program 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 this program; if not, write to the Free Software
## Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA
## 02110-1301 USA
##
## ----------------------------------------------------------------------
###################################################################
# Convert graphs to human readable forms
###################################################################
.get.attr.codes <- function(object) {
ga <- va <- ea <- ""
gal <- graph_attr_names(object)
if (length(gal) != 0) {
ga <- paste(sep="", gal, " (g/",
.Call("R_igraph_get_attr_mode", object, 2L, PACKAGE="igraph"),
")")
}
val <- vertex_attr_names(object)
if (length(val) != 0) {
va <- paste(sep="", val, " (v/",
.Call("R_igraph_get_attr_mode", object, 3L, PACKAGE="igraph"),
")")
}
eal <- edge_attr_names(object)
if (length(eal) != 0) {
ea <- paste(sep="", edge_attr_names(object), " (e/",
.Call("R_igraph_get_attr_mode", object, 4L, PACKAGE="igraph"),
")")
}
c(ga, va, ea)
}
.print.header <- function(object) {
if (!is_igraph(object)) {
stop("Not a graph object")
}
title <- paste(sep="", "IGRAPH ",
c("U","D")[is_directed(object)+1],
c("-","N")[is_named(object)+1],
c("-","W")[is_weighted(object)+1],
c("-","B")[is_bipartite(object)+1], " ",
vcount(object), " ", ecount(object), " -- ")
w <- getOption("width")
if (nchar(title) < w && "name" %in% graph_attr_names(object)) {
title <- substring(paste(sep="", title,
as.character(object$name)[1]), 1, w-1)
}
cat(title, "\n", sep="")
atxt <- .get.attr.codes(object)
atxt <- paste(atxt[atxt!=""], collapse=", ")
if (atxt != "") {
atxt <- strwrap(paste(sep="", "+ attr: ", atxt), prefix = "| ",
initial = "")
cat(atxt, sep="\n")
}
1 + if (length(atxt) == 1 && atxt == "") 0 else length(atxt)
}
indent_print <- printr$indent_print
.print.graph.attributes <- function(x, full, max.lines) {
list <- graph_attr_names(x)
if (length(list)!=0) {
cat("+ graph attributes:\n")
out <- capture.output({
lapply(list, function(n) {
cat(sep="", "+ ", n, ":\n")
indent_print(graph_attr(x, n), .indent = " ")
})
invisible(NULL)
})
indent_print(out, sep = "\n", .indent = "| ", .printer = cat)
length(out) + 1
} else {
0
}
}
## IGRAPH U--- 10 10 -- Ring graph
## + attr: name (g/c), mutual (g/l), circular (g/l)
## + graph attributes:
## | + name:
## | [1] "Ring graph"
## | + mutual:
## | [1] FALSE
## | + circular=
## | [1] TRUE
## | + layout =
## | [,1] [,2]
## | [1,] 0.000000 0.000000e+00
## | [2,] 1.000000 0.000000e+00
## | [3,] 0.809017 5.877853e-01
## | [4,] 0.309017 9.510565e-01
## | [5,] -0.309017 9.510565e-01
## | [6,] -0.809017 5.877853e-01
## | [7,] -1.000000 1.224647e-16
## | [8,] -0.809017 -5.877853e-01
## | [9,] -0.309017 -9.510565e-01
## | [10,] 0.309017 -9.510565e-01
## | [11,] 0.809017 -5.877853e-01
## + edges:
## [1] 1-- 2 2-- 3 3-- 4 4-- 5 5-- 6 6-- 7 7-- 8 8-- 9 9--10 1--10
.print.vertex.attributes <- function(x, full, max.lines) {
pf <- function(x) .print.vertex.attributes.old(x, full, max.lines)
if (length(vertex_attr_names(x))) cat("+ vertex attributes:\n")
indent_print(x, .indent = "| ", .printer = pf)
}
.print.vertex.attributes.old <- function(x, full, max.lines) {
vc <- vcount(x)
list <- vertex_attr_names(x)
if (length(list) != 0) {
mp <- getOption("max.print")
options(max.print=1000000000)
if (vc <= mp) {
omitted.vertices <- 0
ind <- as.numeric(V(x))
} else {
omitted.vertices <- vc-mp
ind <- seq(length=mp)
}
if (vc==0 ||
all(sapply(list, function(v)
is.numeric(vertex_attr(x, v)) ||
is.character(vertex_attr(x, v)) ||
is.logical(vertex_attr(x, v))))) {
## create a table
tab <- data.frame(v=paste(sep="", "[", ind, "]"), row.names="v")
for (i in list) {
tab[i] <- vertex_attr(x, i, ind)
}
print(tab)
} else {
for (i in ind) {
cat(sep="", "[[", i, "]]\n")
lapply(list, function(n) {
cat(sep="", "[[", i, "]][[", n, "]]\n")
print(vertex_attr(x, n, i))})
}
}
options(max.print=mp)
if (omitted.vertices != 0) {
cat(paste('[ reached getOption("max.print") -- omitted',
omitted.vertices, "vertices ]\n\n"))
}
}
}
.print.edges.edgelist <- function(x, edges = E(x), names) {
ec <- length(edges)
list <- edge_attr_names(x)
list <- list[list!="name"]
arrow <- ifelse(is_directed(x), "->", "--")
if (is_named(x)) {
cat("+ edges (vertex names) and their attributes:\n")
} else {
cat("+ edges and their attributes:\n")
}
if (names && ! "name" %in% vertex_attr_names(x)) {
names <- FALSE
}
if (names && "name" %in% vertex_attr_names(x) &&
!is.numeric(vertex_attr(x, "name")) &&
!is.character(vertex_attr(x, "name")) &&
!is.logical(vertex_attr(x, "name"))) {
warning("Can't print vertex names, complex `name' vertex attribute")
names <- FALSE
}
mp <- getOption("max.print")
if (mp >= ec) {
omitted.edges <- 0
el <- ends(x, edges, names=names)
} else {
omitted.edges <- ec-mp
el <- ends(x, ends[seq_len(mp)])
if (names) { el[] <- V(x)$name[el] }
}
ename <- if ("name" %in% edge_attr_names(x)) {
paste(sep="", "'", E(x)$name, "'")
} else {
seq(length=nrow(el))
}
if (ec==0 ||
all(sapply(list, function(v) is.numeric(edge_attr(x, v)) |
is.character(edge_attr(x,v)) |
is.logical(edge_attr(x, v))))) {
## create a table
tab <- data.frame(row.names=paste(sep="", "[", ename, "]"))
if (is.numeric(el)) { w <- nchar(max(el)) } else { w <- max(nchar(el)) }
tab["edge"] <- paste(sep="", format(el[,1], width=w),
arrow, format(el[,2], width=w))
for (i in list) {
tab[i] <- edge_attr(x, i)
}
print(tab)
} else {
i <- 1
apply(el, 1, function(v) {
cat(sep="", "[", ename[i], "] ", v[1], " ", arrow, " ", v[2]);
lapply(list, function(n) {
cat(sep="", "\n[[", i, "]][[", n, "]]\n")
print(edge_attr(x, n, i))})
cat("\n")
i <<- i+1
})
}
if (omitted.edges != 0) {
cat(paste('[ reached getOption("max.print") -- omitted', omitted.edges,
'edges ]\n\n'))
}
}
#' @include printr.R
head_print <- printr$head_print
printer_callback <- printr$printer_callback
.print.edges.compressed <- function(x, edges = E(x), names, num = FALSE,
max.lines = igraph_opt("auto.print.lines")) {
len <- length(edges)
title <- "+" %+%
(if (num) " " %+% chr(len) %+% "/" %+%
(if (is.null(x)) "?" else chr(gsize(x))) else "") %+%
(if (len == 1) " edge" else " edges") %+%
(if (is.null(x)) ", unknown graph" else "") %+%
(if (is.null(attr(edges, "vnames"))) "" else " (vertex names)") %+%
":\n"
cat(title)
if (!is.null(attr(edges, "single")) && attr(edges, "single") &&
!is.null(x)) {
## Double bracket
ea <- edge_attr(x)
if (all(sapply(ea, is.atomic))) {
etail <- tail_of(x, edges)
ehead <- head_of(x, edges)
df <- data.frame(
stringsAsFactors = FALSE,
tail = as_ids(etail),
head = as_ids(ehead),
tid = as.vector(etail),
hid = as.vector(ehead)
)
if (length(ea)) {
ea <- do_call(data.frame, .args = ea, stringsAsFactors = FALSE)
df <- cbind(df, ea[as.vector(edges), , drop = FALSE])
}
print(df)
} else {
print(lapply(ea, "[", as.vector(edges)))
}
} else if (is.null(max.lines)) {
.print.edges.compressed.all(x, edges, names)
} else {
.print.edges.compressed.limit(x, edges, names, max.lines)
}
}
.print.edges.compressed.all <- function(x, edges, names) {
arrow <- c("--", "->")[is_directed(x)+1]
if (!is.null(x)) {
el <- ends(x, edges, names=names)
pr <- paste(sep="", format(el[,1]), arrow, format(el[,2]))
print(pr, quote=FALSE)
} else {
if (!is.null(attr(edges, "vnames"))) {
print(as.vector(attr(edges, "vnames")), quote = FALSE)
} else if (!is.null(names(edges))) {
print(names(edges), quote = FALSE)
} else {
print(as.vector(edges))
}
}
}
.print.edges.compressed.limit <- function(x, edges, names, max.lines) {
if (!is.null(x)) {
arrow <- c("--", "->")[is_directed(x)+1]
can_max <- NA
el <- NA
fun <- function(q, no) {
if (q == "length") {
length(edges)
} else if (q == "min_width") {
5
} else if (q == "width") {
el <<- ends(x, edges[seq_len(no)], names = names)
cummax(nchar(el[,1])) + nchar(arrow) + cummax(nchar(el[,2])) + 1
} else if (q == "print") {
el <<- el[seq_len(no), , drop = FALSE]
out <- paste(sep="", format(el[,1]), arrow, format(el[,2]))
capture.output(print(out, quote = FALSE))
} else if (q == "max") {
can_max <<- no
} else if (q == "done") {
if (no["tried_items"] < length(edges) ||
no["printed_lines"] < no["tried_lines"]) {
cat("+ ... omitted several edges\n")
}
}
}
fun <- printer_callback(fun)
head_print(fun, max_lines = max.lines)
} else {
if (!is.null(attr(edges, "vnames"))) {
head_print(as.vector(attr(edges, "vnames")), quote = FALSE)
} else if (!is.null(names(edges))) {
head_print(names(edges), quote = FALSE)
} else {
head_print(as.vector(edges))
}
}
}
.print.edges.adjlist <- function(x) {
## TODO: getOption("max.print")
cat("+ edges:\n")
vc <- vcount(x)
arrow <- c(" -- ", " -> ")[is_directed(x)+1]
al <- as_adj_list(x, mode="out")
w <- nchar(max(which(degree(x, mode="in") != 0)))
mpl <- trunc((getOption("width")-nchar(arrow)-nchar(vc)) / (w+1))
if (any(sapply(al, length) > mpl)) {
## Wrapping needed
mw <- nchar(vcount(x))
sm <- paste(collapse="", rep(" ", mw+4))
alstr <- lapply(seq_along(al), function(x) {
len <- length(al[[x]])
fac <- rep(1:(len/mpl+1), each=mpl, length=len)
nei <- tapply(format(al[[x]], width=mw), fac, paste, collapse=" ")
mark <- paste(sep="", format(x, width=mw), arrow)
mark <- c(mark, rep(sm, max(0, length(nei)-1)))
paste(sep="", mark, nei)
})
cat(unlist(alstr), sep="\n")
} else {
alstr <- sapply(al, function(x) {
paste(format(x, width=w), collapse=" ")
})
mark <- paste(sep="", format(seq_len(vc)), arrow)
alstr <- paste(sep="", mark, alstr)
maxw <- max(nchar(alstr))
sep <- " "
ncol <- trunc((getOption("width")-1+nchar(sep)) / (maxw+nchar(sep)))
if (ncol > 1) {
alstr <- format(alstr, width=maxw, justify="left")
fac <- rep(1:(vc/ncol+1), each=ncol, length=vc)
alstr <- tapply(alstr, fac, paste, collapse=sep)
}
cat(alstr, sep="\n")
}
}
.print.edges.adjlist.named <- function(x, edges = E(x)) {
## TODO getOption("max.print")
cat("+ edges (vertex names):\n")
arrow <- c(" -- ", " -> ")[is_directed(x)+1]
vn <- V(x)$name
al <- as_adj_list(x, mode="out")
alstr <- sapply(al, function(x) { paste(collapse=", ", vn[x]) })
alstr <- paste(sep="", format(vn), arrow, alstr)
alstr <- strwrap(alstr, exdent=max(nchar(vn))+nchar(arrow))
cat(alstr, sep="\n")
}
#' @method str igraph
#' @export
str.igraph <- function(object, ...) {
print.igraph(object, full=TRUE, ...)
}
#' Print graphs to the terminal
#'
#' These functions attempt to print a graph to the terminal in a human readable
#' form.
#'
#' \code{summary.igraph} prints the number of vertices, edges and whether the
#' graph is directed.
#'
#' \code{str.igraph} prints the same information, and also lists the edges, and
#' optionally graph, vertex and/or edge attributes.
#'
#' \code{print.igraph} behaves either as \code{summary.igraph} or
#' \code{str.igraph} depending on the \code{full} argument. See also the
#' \sQuote{print.full} igraph option and \code{\link{igraph_opt}}.
#'
#' The graph summary printed by \code{summary.igraph} (and \code{print.igraph}
#' and \code{str.igraph}) consists one or more lines. The first line contains
#' the basic properties of the graph, and the rest contains its attributes.
#' Here is an example, a small star graph with weighed directed edges and named
#' vertices: \preformatted{ IGRAPH DNW- 10 9 -- In-star
#' + attr: name (g/c), mode (g/c), center (g/n), name (v/c),
#' weight (e/n) }
#' The first line always
#' starts with \code{IGRAPH}, showing you that the object is an igraph graph.
#' Then a four letter long code string is printed. The first letter
#' distinguishes between directed (\sQuote{\code{D}}) and undirected
#' (\sQuote{\code{U}}) graphs. The second letter is \sQuote{\code{N}} for named
#' graphs, i.e. graphs with the \code{name} vertex attribute set. The third
#' letter is \sQuote{\code{W}} for weighted graphs, i.e. graphs with the
#' \code{weight} edge attribute set. The fourth letter is \sQuote{\code{B}} for
#' bipartite graphs, i.e. for graphs with the \code{type} vertex attribute set.
#'
#' Then, after two dashes, the name of the graph is printed, if it has one,
#' i.e. if the \code{name} graph attribute is set.
#'
#' From the second line, the attributes of the graph are listed, separated by a
#' comma. After the attribute names, the kind of the attribute -- graph
#' (\sQuote{\code{g}}), vertex (\sQuote{\code{v}}) or edge (\sQuote{\code{e}})
#' -- is denoted, and the type of the attribute as well, character
#' (\sQuote{\code{c}}), numeric (\sQuote{\code{n}}), logical
#' (\sQuote{\code{l}}), or other (\sQuote{\code{x}}).
#'
#' As of igraph 0.4 \code{str.igraph} and \code{print.igraph} use the
#' \code{max.print} option, see \code{\link[base]{options}} for details.
#'
#' @aliases print.igraph str.igraph summary.igraph
#' @param x The graph to print.
#' @param full Logical scalar, whether to print the graph structure itself as
#' well.
#' @param graph.attributes Logical constant, whether to print graph attributes.
#' @param vertex.attributes Logical constant, whether to print vertex
#' attributes.
#' @param edge.attributes Logical constant, whether to print edge attributes.
#' @param names Logical constant, whether to print symbolic vertex names (ie.
#' the \code{name} vertex attribute) or vertex ids.
#' @param max.lines The maximum number of lines to use. The rest of the
#' output will be truncated.
#' @param object The graph of which the summary will be printed.
#' @param \dots Additional agruments.
#' @return All these functions return the graph invisibly.
#' @author Gabor Csardi \email{csardi.gabor@@gmail.com}
#' @method print igraph
#' @export
#' @export print.igraph
#' @keywords graphs
#' @examples
#'
#' g <- make_ring(10)
#' g
#' summary(g)
#'
print.igraph <- function(x, full=igraph_opt("print.full"),
graph.attributes=igraph_opt("print.graph.attributes"),
vertex.attributes=igraph_opt("print.vertex.attributes"),
edge.attributes=igraph_opt("print.edge.attributes"),
names=TRUE, max.lines = igraph_opt("auto.print.lines"), ...) {
if (!is_igraph(x)) {
stop("Not a graph object")
}
head_lines <- .print.header(x)
if (is.logical(full) && full) {
if (graph.attributes) {
head_lines <- head_lines + .print.graph.attributes(x, full, max.lines)
}
if (vertex.attributes) {
head_lines <- head_lines + .print.vertex.attributes(x, full, max.lines)
}
if (ecount(x)==0) {
## Do nothing
} else if (edge.attributes && length(edge_attr_names(x)) != 0 ) {
.print.edges.edgelist(x, names = names)
} else if (median(degree(x, mode="out")) < 3) {
.print.edges.compressed(x, names = names, max.lines = NULL)
} else if (is_named(x)) {
.print.edges.adjlist.named(x)
} else {
.print.edges.adjlist(x)
}
} else if (full == "auto") {
.print.edges.compressed(x, names = names, max.lines =
max.lines - head_lines)
}
invisible(x)
}
#' @rdname print.igraph
#' @method summary igraph
#' @export
summary.igraph <- function(object, ...) {
.print.header(object)
invisible(object)
}
"
####################################################################
## Various designs for printing graphs
## Summary
IGRAPH UNW- 5 5 -- A ring
Attr: name (g/c), name (v/c), weight (e/n)
IGRAPH D-W- 100 200 -- Gnm random graph
## Printing, edge list
IGRAPH-UNW--V5-E5----------------------------------------- A ring -
+ attributes: name (g), name (v), weight (e).
+ edges:
edge weight
[1]' a--b 1
[2]' b--c 2
[3]' c--d -1
[4]' d--e 0.5
[5]' a--e 1
## Compressed edge list
IGRAPH UNW- 5 10 -- A ring
+ attributes: name (g/c), name (v/n), weight (e/n)
+ edges:
[1]' 1--2 2--3 3--4 4--5 1--5 2--5 5--1
[8]' 1--4 4--2 1--3
## This is good if vertices are named
IGRAPH UNW- 10 18 -- Krackhardt kite
+ attributes: name (g/c), name (v/c), weight (e/n)
+ edges:
Andre -- [1] Beverly, Carol, Diane, Fernando
Beverly -- [1] Andre, Diane, Ed, Garth
Carol -- [1] Andre, Diane, Fernando
Diane -- [1] Andre, Beverly, Carol, Diane, Ed
-- [6] Garth
Ed -- [1] Beverly, Diane, Garth
Fernando -- [1] Andre, Carol, Diane, Garth
Garth -- [1] Beverly, Diane, Ed, Fernando
Heather -- [1] Fernando, Garth
Ike -- [1] Heather, Jane
Jane -- [1] Ike
IGRAPH UNW- 10 18 -- Krackhardt kite
+ attributes: name (g/c), name (v/c), weight (e/n)
+ edges:
Andre -- Beverly, Carol, Diane, Fernando
Beverly -- Andre, Diane, Ed, Garth
Carol -- Andre, Diane, Fernando
Diane -- Andre, Beverly, Carol, Diane, Ed, Garth
Ed -- Beverly, Diane, Garth
Fernando -- Andre, Carol, Diane, Garth
Garth -- Beverly, Diane, Ed, Fernando
Heather -- Fernando, Garth
Ike -- Heather, Jane
Jane -- Ike
## This is the good one if vertices are not named
IGRAPH U--- 100 200 -- Gnm random graph
+ edges:
[ 1] 28 46 89 90 [ 2] 47 69 72 89
[ 3] 29 [ 4] 17 20
[ 5] 11 40 42 51 78 89 [ 6] 27 32 70 87 93
[ 7] 18 27 87 [ 8] 18 24 82
[ 9] 18 20 85 94 [ 10] 24 70 77 91
[ 11] 5 12 34 61 62 [ 12] 11 41 44 61 65 80
...
## Alternative designs, summary
IGRAPH-UNW--V5-E5,---------------------------------------- A ring -
+ attributes: name (g/c), name (v/c), weight (e/n)
IGRAPH. |V|=5, |E|=5, undirected, named, weighted.
Attributes: name (g/c), name (v/c), weight (e/n)
IGRAPH: 'A ring'
Graph attributes: |V|=5, |E|=5, undirected, name.
Vertex attributes: name.
Edge attributes: weight.
## Alternative designs, printing
IGRAPH-UNW--V5-E5----------------------------------------- A ring -
'- attributes: name (g), name (v), weight (e).
' edge weight
[1] 'a' -- 'b' 1
[2] 'b' -- 'c' 2
[3] 'c' -- 'd' -1
[4] 'd' -- 'e' 0.5
[5] 'a' -- 'e' 1
IGRAPH-UNW--V-5-E-10-------------------------------------- A ring -
|- attributes: name (g), name (v), weight (e).
|- edges:
[1] 'a'--'b' 'b'--'c' 'c'--'d' 'd'--'e' 'a'--'e' 'b'-'e'
[7] 'e'--'a' 'a'--'d' 'd'--'b' 'a'--'c'
IGRAPH-UNW--V-5-E-10-------------------------------------- A ring -
+ attributes: name (g), name (v), weight (e).
+ vertices:
| name
| [1] a
| [2] b
| [3] c
| [4] d
| [5] e
+ edges:
[1] 'a'--'b' 'b'--'c' 'c'--'d' 'd'--'e' 'a'--'e' 'b'-'e'
[7] 'e'--'a' 'a'--'d' 'd'--'b' 'a'--'c'
IGRAPH-UNW--V-5-E-10-------------------------------------- A ring -
+ graph attributes: name
+ vertex attributes: name
+ edge attributes: weight
+ vertices:
| name
|1] a
|2] b
|3] c
|4] d
|5] e
+ edges:
|1] a--b b--c c--d d--e a--e b-e
|7] e--a a--d d--b a--c
IGRAPH-UNW--V-5-E-10-------------------------------------- A ring -
+ graph attributes: name (c)
+ vertex attributes: name (c)
+ edge attributes: weight (n)
+ edges:
[1] a--b b--c c--d d--e a--e b-e
[7] e--a a--d d--b a--c
IGRAPH-UNW--V-5-E-10-------------------------------------- A ring -
+ attributes: name (g/c), name (v/c), weight (e/n)
+ edges:
[ 1] a--b b--c c--d d--e a--e b--e e--a a--d d--b
[10] a--c
IGRAPH-DNW--V-5-E-10-------------------------------------- A ring -
+ attributes: name (g/c), name (v/n), weight (e/n)
+ edges:
[1]' 1->2 2->3 3->4 4->5 1->5 2->5 5->1
[8]' 1->4 4->2 1->3
IGRAPH-UNW--V-5-E-20-------------------------------------- A ring -
+ attributes: name (g/c), name (v/c), weight (e/n)
+ edges:
[ 1] a-b b-c c-d d-e a-e b-e e-a a-d d-b a-c
[11] a-b b-c c-d d-e a-e b-e e-a a-d d-b a-c
IGRAPH-UNW--V-8-E-10-------------------------------------- A ring -
+ attributes: name (g/c), name (v/c), weight (e/n)
+ edges:
[a] b c e f h
[b] a c e
[c] a b d
[d] a b c h
[e] a b d
[f] a
[g]
[h] a d
IGRAPH-UNW--V-10-E-18------------------------------------- A ring -
+ attributes: name (g/c), name (v/c), weight (e/n)
+ edges:
[a] a--{b,c,e,f,h} b--{a,c,e} c--{a,b,d} d--{a,b,c,h}
[e] e--{a,b,d} f--{a} g--{} h--{a,d}
IGRAPH-UNW--V10-E18------------------------------Krackhardt kite--
+ attributes: name (g/c), name (v/c), weight (e/n)
+ edges:
[ Andre][1] Beverly Carol Diane Fernando
[ Beverly][1] Andre Diane Ed Garth
[ Carol][1] Andre Diane Fernando
[ Diane][1] Andre Beverly Carol Diane Ed
[ Diane][6] Garth
[ Ed][1] Beverly Diane Garth
[Fernando][1] Andre Carol Diane Garth
[ Garth][1] Beverly Diane Ed Fernando
[ Heather][1] Fernando Garth
[ Ike][1] Heather Jane
[ Jane][1] Ike
IGRAPH-UNW--V10-E18-------------------------------Krackhardt kite--
+ attributes: name (g/c), name (v/c), weight (e/n)
+ edges:
[ Andre][1] Beverly/1 Carol/3 Diane/3 Fernando/1
[ Beverly][1] Andre/1 Diane/1 Ed/2 Garth/2
[ Carol][1] Andre/2 Diane/2 Fernando/1
[ Diane][1] Andre/5 Beverly/1 Carol/0.4 Diane/2
[ Diane][5] Ed/1.5 Garth/2.5
[ Ed][1] Beverly/-1 Diane/1.5 Garth/2
[Fernando][1] Andre/1 Carol/2 Diane/1 Garth/1
[ Garth][1] Beverly/2 Diane/3 Ed/1 Fernando/-1
[ Heather][1] Fernando/3 Garth/1
[ Ike][1] Heather/1 Jane/-1
[ Jane][1] Ike/-2
IGRAPH-UNW--V10-E18-------------------------------Krackhardt kite--
+ attributes: name (g/c), name (v/c), weight (e/n)
+ edges:
[ Andre][1] Beverly (1) Carol (3) Diane (3) Fernando (1)
[ Beverly][1] Andre (1) Diane (1) Ed (2) Garth (2)
[ Carol][1] Andre (2) Diane (2) Fernando (1)
[ Diane][1] Andre (5) Beverly (1) Carol (0.5) Diane (2)
[ Diane][5] Ed (1.5) Garth (2.5)
[ Ed][1] Beverly (-1) Diane (1.5) Garth (2)
[Fernando][1] Andre (1) Carol (2) Diane (1) Garth (1)
[ Garth][1] Beverly (2) Diane (3) Ed (1) Fernando (-1)
[ Heather][1] Fernando (3) Garth (1)
[ Ike][1] Heather (1) Jane (-1)
[ Jane][1] Ike (-2)
IGRAPH UNW- V10 E18 -- Krackhardt kite
+ attr: name (g/c), name (v/c), weight (e/n)
+ edges:
[ Andre][1] Beverly (1) Carol (3) Diane (3) Fernando (1)
[ Beverly][1] Andre (1) Diane (1) Ed (2) Garth (2)
[ Carol][1] Andre (2) Diane (2) Fernando (1)
[ Diane][1] Andre (5) Beverly (1) Carol (0.5) Diane (2)
[ Diane][5] Ed (1.5) Garth (2.5)
[ Ed][1] Beverly (-1) Diane (1.5) Garth (2)
[Fernando][1] Andre (1) Carol (2) Diane (1) Garth (1)
[ Garth][1] Beverly (2) Diane (3) Ed (1) Fernando (-1)
[ Heather][1] Fernando (3) Garth (1)
[ Ike][1] Heather (1) Jane (-1)
[ Jane][1] Ike (-2)
IGRAPH-U----V100-E200----------------------------Gnm random graph--
+ edges:
[ 1] 28 46 89 90
[ 2] 47 69 72 89
[ 3] 29
[ 4] 17 20
[ 5] 11 40 42 51 78 89
[ 6] 27 32 70 87 93
[ 7] 18 27 87
[ 8] 18 24 82
[ 9] 18 20 85 94
[ 10] 24 70 77 91
[ 11] 5 12 34 61 62
[ 12] 11 41 44 61 65 80
...
IGRAPH-U----100-200------------------------------Gnm random graph--
+ edges:
[ 1] 28 46 89 90 [ 2] 47 69 72 89
[ 3] 29 [ 4] 17 20
[ 5] 11 40 42 51 78 89 [ 6] 27 32 70 87 93
[ 7] 18 27 87 [ 8] 18 24 82
[ 9] 18 20 85 94 [ 10] 24 70 77 91
[ 11] 5 12 34 61 62 [ 12] 11 41 44 61 65 80
...
"
|
rfiles <- list.files(path = "../data/classify/results", pattern = "HNG_res", full.names = T)
wfiles <- list.files(path = "../data/classify/welcalc", pattern = "subdat", full.names = T)
resdat <- list()
for (i in 1:length(rfiles)) {
print(rfiles[i])
print(wfiles[i])
load(rfiles[i])
load(wfiles[i])
resdat[[i]] <- cbind(HNG.res, HNG.wel)
}
HNG <- do.call(rbind, resdat)
save(HNG, file = "../data/classify/merged/HNG.Rda")
rm(HNG, resdat)
rfiles <- list.files(path = "../data/classify/results", pattern = "HO_res", full.names = T)
wfiles <- list.files(path = "../data/classify/welcalc", pattern = "subdat", full.names = T)
resdat <- list()
for (i in 1:length(rfiles)) {
print(rfiles[i])
print(wfiles[i])
load(rfiles[i])
load(wfiles[i])
resdat[[i]] <- cbind(HO.res, HO.wel)
}
HO <- do.call(rbind, resdat)
save(HO, file = "../data/classify/merged/HO.Rda")
rm(HO, resdat)
| /append/mergeres.R | no_license | bamonroe/code-ch4 | R | false | false | 882 | r | rfiles <- list.files(path = "../data/classify/results", pattern = "HNG_res", full.names = T)
wfiles <- list.files(path = "../data/classify/welcalc", pattern = "subdat", full.names = T)
resdat <- list()
for (i in 1:length(rfiles)) {
print(rfiles[i])
print(wfiles[i])
load(rfiles[i])
load(wfiles[i])
resdat[[i]] <- cbind(HNG.res, HNG.wel)
}
HNG <- do.call(rbind, resdat)
save(HNG, file = "../data/classify/merged/HNG.Rda")
rm(HNG, resdat)
rfiles <- list.files(path = "../data/classify/results", pattern = "HO_res", full.names = T)
wfiles <- list.files(path = "../data/classify/welcalc", pattern = "subdat", full.names = T)
resdat <- list()
for (i in 1:length(rfiles)) {
print(rfiles[i])
print(wfiles[i])
load(rfiles[i])
load(wfiles[i])
resdat[[i]] <- cbind(HO.res, HO.wel)
}
HO <- do.call(rbind, resdat)
save(HO, file = "../data/classify/merged/HO.Rda")
rm(HO, resdat)
|
# Name: Nithin S Nair
#Last updated: June 19, 2019
#--------------------------------------------------------------------------------------------------------------#
#RELATIVE WORKING DIRECTORY
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
## IMPORTING REQUIRED PACKAGES
#Packages will be installed if not available in the device
packages <- c("dplyr","tidyverse","tidyr","DT","shiny","ggplot2","ggmap")
packages_new <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(packages_new)) install.packages(packages_new)
library(dplyr)
library(tidyverse)
library(tidyr)
library(DT)
library(shiny)
library(ggplot2)
library(ggmap)
# Reading Data
collision.df <- read.csv("traffic-collision-data-from-2010-to-present.csv", stringsAsFactors = F)
## Extracting Latitudes and Longitudes from Location
collision.df <- tidyr::separate(data=collision.df,
col=Location,
into=c("Latitude", "Longitude"),
sep=",",
remove=FALSE)
collision.df$Latitude <- stringr::str_replace_all(collision.df$Latitude, "\\{'latitude': '", "")
collision.df$Latitude <- stringr::str_replace_all(collision.df$Latitude, "'", "")
collision.df$Longitude <- stringr::str_replace_all(collision.df$Longitude, " 'longitude': '", "")
collision.df$Longitude <- stringr::str_replace_all(collision.df$Longitude, "'", "")
collision.df$Latitude <- as.numeric(collision.df$Latitude)
collision.df$Longitude <- as.numeric(collision.df$Longitude)
## Extracting Month and Year from Date Occurred
collision.df <- tidyr::separate(data=collision.df,
col=Date.Occurred,
into=c("Year","Month"),
sep="-",
remove=FALSE)
collision.df$Year <- as.numeric(collision.df$Year)
collision.df$Month <- as.numeric(collision.df$Month)
## Filtering Values of Year 2018
collision.df <- collision.df %>% filter(Year == 2018)
# Extract Hour of the Accident
collision.df$Hour <- as.numeric(collision.df$Time.Occurred)%/%100
#--------------------------------------------------------------------------------------------------------------#
# Define UI for application
ui <- fluidPage(
# App title
titlePanel("Vehicle Collisions in Los Angeles"),
# Sidebar layout with input and output definitions
sidebarLayout(
# Sidebar panel for inputs
sidebarPanel(
sliderInput(inputId="Mon",label = "Month of the Year (Select 0 to see for the whole year)",value = 0,min = 0,max = 12)
),
# Outputs
mainPanel(
tabsetPanel(type = "tabs",id = "tabselected",
tabPanel("Bar Chart", value=1, plotOutput(outputId = "BarPlot", height=500),textOutput(outputId = "frequency")),
tabPanel("Location of Collisions in Map",value=2, plotOutput(outputId = "maplocation", height=500)),
tabPanel("Heat Map", value=3, plotOutput(outputId = "Heatmap", height=500))
)
)
)
)
# Define Server
server <- function(input, output) {
# Create Barplot
output$BarPlot <- renderPlot({
filtered.df <- collision.df
if (input$Mon >=1) {
filtered.df <- filtered.df %>%filter(filtered.df$Month == input$Mon)
}
ggplot(data=filtered.df, aes(x=filtered.df$Hour)) +
geom_bar(aes(y = (..count..))) + theme_minimal()+xlab("Hour of the Day")+ylab("Frequency")+
ggtitle("Frequency of Collisions by the Time of the Day")
})
# Create text output stating the top frequency
output$frequency <- renderText({
filtered.df <- collision.df
if (input$Mon >=1) {
filtered.df <- filtered.df %>%filter(filtered.df$Month == input$Mon)
}
histo <- hist(filtered.df$Hour, breaks=24, freq=TRUE)
Bin <- histo$breaks[which.max(histo$counts)]
#Top <- max(histo$counts)
Top <- round(max(histo$counts)*100/length(filtered.df$Hour),2)
paste0("The highest number of collisions occur in the hour ",Bin+1," and its Percentage Frequency is ", Top)
})
# Rendering Map
filtered2.df <- collision.df%>%filter(Latitude!=0, Longitude!=0)
output$maplocation <- renderPlot({
if (input$Mon >=1) {
filtered2.df <- filtered2.df %>%filter(filtered2.df$Month == input$Mon)
}
maploc<- qmplot(Longitude, Latitude, data=filtered2.df, geom = "blank",
zoom = 11, maptype = "terrain", darken = .5)+
ggtitle("Map Locations of Collisions")
maploc+geom_point(aes(Longitude,Latitude),data = filtered2.df, size =0.1, color ="red")
})
# Rendering Heatmap
output$Heatmap <- renderPlot({
filtered.df <- collision.df
if (input$Mon >=1) {
filtered.df <- filtered.df %>%filter(filtered.df$Month == input$Mon)
}
ggplot(filtered.df, aes(x = filtered.df$Area.Name, y = filtered.df$Hour))+
geom_bin2d()+
scale_fill_gradient(low="white", high="red")+
ggtitle("Heatmap of Locations of Collisions") +
labs(x="Area Name of Collision",y="Hour of the day")
})
}
# Creating a Shiny app object
shinyApp(ui = ui, server = server)
#--------------------------------------------------------------------------------------------------------------#
| /Traffic-Collision-Incidents-in-LA-R-Shiny-app.R | no_license | NithinNair/Traffic-Collision-Incidents-in-LA-R-Shiny-app- | R | false | false | 5,421 | r | # Name: Nithin S Nair
#Last updated: June 19, 2019
#--------------------------------------------------------------------------------------------------------------#
#RELATIVE WORKING DIRECTORY
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
## IMPORTING REQUIRED PACKAGES
#Packages will be installed if not available in the device
packages <- c("dplyr","tidyverse","tidyr","DT","shiny","ggplot2","ggmap")
packages_new <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(packages_new)) install.packages(packages_new)
library(dplyr)
library(tidyverse)
library(tidyr)
library(DT)
library(shiny)
library(ggplot2)
library(ggmap)
# Reading Data
collision.df <- read.csv("traffic-collision-data-from-2010-to-present.csv", stringsAsFactors = F)
## Extracting Latitudes and Longitudes from Location
collision.df <- tidyr::separate(data=collision.df,
col=Location,
into=c("Latitude", "Longitude"),
sep=",",
remove=FALSE)
collision.df$Latitude <- stringr::str_replace_all(collision.df$Latitude, "\\{'latitude': '", "")
collision.df$Latitude <- stringr::str_replace_all(collision.df$Latitude, "'", "")
collision.df$Longitude <- stringr::str_replace_all(collision.df$Longitude, " 'longitude': '", "")
collision.df$Longitude <- stringr::str_replace_all(collision.df$Longitude, "'", "")
collision.df$Latitude <- as.numeric(collision.df$Latitude)
collision.df$Longitude <- as.numeric(collision.df$Longitude)
## Extracting Month and Year from Date Occurred
collision.df <- tidyr::separate(data=collision.df,
col=Date.Occurred,
into=c("Year","Month"),
sep="-",
remove=FALSE)
collision.df$Year <- as.numeric(collision.df$Year)
collision.df$Month <- as.numeric(collision.df$Month)
## Filtering Values of Year 2018
collision.df <- collision.df %>% filter(Year == 2018)
# Extract Hour of the Accident
collision.df$Hour <- as.numeric(collision.df$Time.Occurred)%/%100
#--------------------------------------------------------------------------------------------------------------#
# Define UI for application
ui <- fluidPage(
# App title
titlePanel("Vehicle Collisions in Los Angeles"),
# Sidebar layout with input and output definitions
sidebarLayout(
# Sidebar panel for inputs
sidebarPanel(
sliderInput(inputId="Mon",label = "Month of the Year (Select 0 to see for the whole year)",value = 0,min = 0,max = 12)
),
# Outputs
mainPanel(
tabsetPanel(type = "tabs",id = "tabselected",
tabPanel("Bar Chart", value=1, plotOutput(outputId = "BarPlot", height=500),textOutput(outputId = "frequency")),
tabPanel("Location of Collisions in Map",value=2, plotOutput(outputId = "maplocation", height=500)),
tabPanel("Heat Map", value=3, plotOutput(outputId = "Heatmap", height=500))
)
)
)
)
# Define Server
server <- function(input, output) {
# Create Barplot
output$BarPlot <- renderPlot({
filtered.df <- collision.df
if (input$Mon >=1) {
filtered.df <- filtered.df %>%filter(filtered.df$Month == input$Mon)
}
ggplot(data=filtered.df, aes(x=filtered.df$Hour)) +
geom_bar(aes(y = (..count..))) + theme_minimal()+xlab("Hour of the Day")+ylab("Frequency")+
ggtitle("Frequency of Collisions by the Time of the Day")
})
# Create text output stating the top frequency
output$frequency <- renderText({
filtered.df <- collision.df
if (input$Mon >=1) {
filtered.df <- filtered.df %>%filter(filtered.df$Month == input$Mon)
}
histo <- hist(filtered.df$Hour, breaks=24, freq=TRUE)
Bin <- histo$breaks[which.max(histo$counts)]
#Top <- max(histo$counts)
Top <- round(max(histo$counts)*100/length(filtered.df$Hour),2)
paste0("The highest number of collisions occur in the hour ",Bin+1," and its Percentage Frequency is ", Top)
})
# Rendering Map
filtered2.df <- collision.df%>%filter(Latitude!=0, Longitude!=0)
output$maplocation <- renderPlot({
if (input$Mon >=1) {
filtered2.df <- filtered2.df %>%filter(filtered2.df$Month == input$Mon)
}
maploc<- qmplot(Longitude, Latitude, data=filtered2.df, geom = "blank",
zoom = 11, maptype = "terrain", darken = .5)+
ggtitle("Map Locations of Collisions")
maploc+geom_point(aes(Longitude,Latitude),data = filtered2.df, size =0.1, color ="red")
})
# Rendering Heatmap
output$Heatmap <- renderPlot({
filtered.df <- collision.df
if (input$Mon >=1) {
filtered.df <- filtered.df %>%filter(filtered.df$Month == input$Mon)
}
ggplot(filtered.df, aes(x = filtered.df$Area.Name, y = filtered.df$Hour))+
geom_bin2d()+
scale_fill_gradient(low="white", high="red")+
ggtitle("Heatmap of Locations of Collisions") +
labs(x="Area Name of Collision",y="Hour of the day")
})
}
# Creating a Shiny app object
shinyApp(ui = ui, server = server)
#--------------------------------------------------------------------------------------------------------------#
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/execute_order.R
\name{tn_place_order}
\alias{tn_place_order}
\title{Place an order (testnet)}
\usage{
tn_place_order(
symbol = NULL,
side = NULL,
orderQty = NULL,
price = NULL,
displayQty = NULL,
stopPx = NULL,
clOrdID = NULL,
pegOffsetValue = NULL,
pegPriceType = NULL,
ordType = NULL,
timeInForce = NULL,
execInst = NULL,
text = NULL
)
}
\arguments{
\item{symbol}{string. Instrument symbol. e.g. 'XBTUSD'.}
\item{side}{string. Order side. Valid options: Buy, Sell. Defaults to 'Buy' unless \code{orderQty}is negative.}
\item{orderQty}{double. Order quantity in units of the instrument (i.e. contracts).}
\item{price}{double. Optional limit price for 'Limit', 'StopLimit', and 'LimitIfTouched' orders.}
\item{displayQty}{double. Optional quantity to display in the book. Use 0 for a fully hidden order.}
\item{stopPx}{double. Optional trigger price for 'Stop', 'StopLimit', 'MarketIfTouched', and 'LimitIfTouched' orders.
Use a price below the current price for stop-sell orders and buy-if-touched orders.
Use \code{execInst} of 'MarkPrice' or 'LastPrice' to define the current price used for triggering.}
\item{clOrdID}{string. Optional Client Order ID. This clOrdID will come back on the order and any related executions.}
\item{pegOffsetValue}{string. Optional trailing offset from the current price for 'Stop', 'StopLimit', '
MarketIfTouched', and 'LimitIfTouched' orders; use a negative offset for stop-sell orders and buy-if-touched orders.
Optional offset from the peg price for 'Pegged' orders.}
\item{pegPriceType}{string. Optional peg price type.
Valid options: LastPeg, MidPricePeg, MarketPeg, PrimaryPeg, TrailingStopPeg.}
\item{ordType}{string. Order type. Valid options: Market, Limit, Stop, StopLimit, MarketIfTouched, LimitIfTouched, Pegged.
Defaults to 'Limit' when \code{price} is specified. Defaults to 'Stop' when \code{stopPx} is specified.
Defaults to 'StopLimit' when \code{price} and \code{stopPx} are specified.}
\item{timeInForce}{string. Time in force. Valid options: Day, GoodTillCancel, ImmediateOrCancel, FillOrKill.
Defaults to 'GoodTillCancel' for 'Limit', 'StopLimit', and 'LimitIfTouched' orders.}
\item{execInst}{string. Optional execution instructions. Valid options: ParticipateDoNotInitiate,
AllOrNone, MarkPrice, IndexPrice, LastPrice, Close, ReduceOnly, Fixed.
'AllOrNone' instruction requires \code{displayQty} to be 0. 'MarkPrice', 'IndexPrice' or 'LastPrice'
instruction valid for 'Stop', 'StopLimit', 'MarketIfTouched', and 'LimitIfTouched' orders.}
\item{text}{string. Optional order annotation. e.g. 'Take profit'.}
}
\value{
Returns a \code{tibble} containing information about the trade that has been placed.
See \url{https://testnet.bitmex.com/api/explorer/#!/Order/Order_new} for more details.
}
\description{
Place an order using the Bitmex testnet API. Requires testnet API key.
}
\examples{
\dontrun{
# place limit order to Buy at specific price
tn_place_order(symbol = "XBTUSD", price = 6000, orderQty = 10)
}
}
| /man/tn_place_order.Rd | permissive | hfshr/bitmexr | R | false | true | 3,084 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/execute_order.R
\name{tn_place_order}
\alias{tn_place_order}
\title{Place an order (testnet)}
\usage{
tn_place_order(
symbol = NULL,
side = NULL,
orderQty = NULL,
price = NULL,
displayQty = NULL,
stopPx = NULL,
clOrdID = NULL,
pegOffsetValue = NULL,
pegPriceType = NULL,
ordType = NULL,
timeInForce = NULL,
execInst = NULL,
text = NULL
)
}
\arguments{
\item{symbol}{string. Instrument symbol. e.g. 'XBTUSD'.}
\item{side}{string. Order side. Valid options: Buy, Sell. Defaults to 'Buy' unless \code{orderQty}is negative.}
\item{orderQty}{double. Order quantity in units of the instrument (i.e. contracts).}
\item{price}{double. Optional limit price for 'Limit', 'StopLimit', and 'LimitIfTouched' orders.}
\item{displayQty}{double. Optional quantity to display in the book. Use 0 for a fully hidden order.}
\item{stopPx}{double. Optional trigger price for 'Stop', 'StopLimit', 'MarketIfTouched', and 'LimitIfTouched' orders.
Use a price below the current price for stop-sell orders and buy-if-touched orders.
Use \code{execInst} of 'MarkPrice' or 'LastPrice' to define the current price used for triggering.}
\item{clOrdID}{string. Optional Client Order ID. This clOrdID will come back on the order and any related executions.}
\item{pegOffsetValue}{string. Optional trailing offset from the current price for 'Stop', 'StopLimit', '
MarketIfTouched', and 'LimitIfTouched' orders; use a negative offset for stop-sell orders and buy-if-touched orders.
Optional offset from the peg price for 'Pegged' orders.}
\item{pegPriceType}{string. Optional peg price type.
Valid options: LastPeg, MidPricePeg, MarketPeg, PrimaryPeg, TrailingStopPeg.}
\item{ordType}{string. Order type. Valid options: Market, Limit, Stop, StopLimit, MarketIfTouched, LimitIfTouched, Pegged.
Defaults to 'Limit' when \code{price} is specified. Defaults to 'Stop' when \code{stopPx} is specified.
Defaults to 'StopLimit' when \code{price} and \code{stopPx} are specified.}
\item{timeInForce}{string. Time in force. Valid options: Day, GoodTillCancel, ImmediateOrCancel, FillOrKill.
Defaults to 'GoodTillCancel' for 'Limit', 'StopLimit', and 'LimitIfTouched' orders.}
\item{execInst}{string. Optional execution instructions. Valid options: ParticipateDoNotInitiate,
AllOrNone, MarkPrice, IndexPrice, LastPrice, Close, ReduceOnly, Fixed.
'AllOrNone' instruction requires \code{displayQty} to be 0. 'MarkPrice', 'IndexPrice' or 'LastPrice'
instruction valid for 'Stop', 'StopLimit', 'MarketIfTouched', and 'LimitIfTouched' orders.}
\item{text}{string. Optional order annotation. e.g. 'Take profit'.}
}
\value{
Returns a \code{tibble} containing information about the trade that has been placed.
See \url{https://testnet.bitmex.com/api/explorer/#!/Order/Order_new} for more details.
}
\description{
Place an order using the Bitmex testnet API. Requires testnet API key.
}
\examples{
\dontrun{
# place limit order to Buy at specific price
tn_place_order(symbol = "XBTUSD", price = 6000, orderQty = 10)
}
}
|
rm()
#options(shiny.autoreload = TRUE)
#options(shiny.maxRequestSize=2000*1024^2)
library(shiny)
library(shinyBS)
library(shinyjs)
#library(STRINGdb)
library(ggplot2)
library(tidyverse)
library(data.table)
library(MSstatsTMT)
library(knitr)
library(readxl)
if (FALSE) require("V8")
#library(MSnbase)
#####################################
# Global functions #
radioTooltip <- function(id, choice, title, placement = "bottom", trigger = "hover", options = NULL){
options = shinyBS:::buildTooltipOrPopoverOptionsList(title, placement, trigger, options)
options = paste0("{'", paste(names(options), options, sep = "': '", collapse = "', '"), "'}")
bsTag <- shiny::tags$script(shiny::HTML(paste0("
$(document).ready(function() {
setTimeout(function() {
$('input', $('#", id, "')).each(function(){
if(this.getAttribute('value') == '", choice, "') {
opts = $.extend(", options, ", {html: true});
$(this.parentElement).tooltip('destroy');
$(this.parentElement).tooltip(opts);
}
})
}, 500)
});
")))
htmltools::attachDependencies(bsTag, shinyBS:::shinyBSDep)
}
# shinyjs.disableTab = function() {
# vartabs = $('tablist').find('li:not(.active) a');
# tabs.bind('click.tab', function(e) {
# e.preventDefault();
# return false;
# });
# tabs.addClass('disabled');
# }
#
# shinyjs.enableTab = function(param) {
# vartabs = $('tablist').find('li:not(.active):nth-child(' + param + ') a');
# tab.unbind('click.tab');
# tab.removeClass('disabled');
# }
####################################
source("panels/home-ui.R", local = T)
source("panels/loadpage-ui.R", local = T)
source("panels/qc-ui.R", local = T)
source("panels/pq-ui.R", local = T)
source("panels/statmodel-ui.R", local = T)
source("panels/expdes-ui.R", local = T)
#source("panels/analysis-ui.R", local = T)
#source("panels/clust-ui.R", local = T)
source("panels/report-ui.R", local = T)
source("panels/help-ui.R", local = T)
#########################################################################
jsCode = '
shinyjs.init = function() {
$(document).keypress(function(e) { alert("Key pressed: " + e.which); });
alert("fooo");
console.log("initttttt");
$("#tablist li a").addClass("disabled");
$(".nav").on("click", ".disabled", function (e) {
e.preventDefault();
return false;
});
}
shinyjs.enableTab = function(value) {
$("#tablist li a[data-value=" + value + "]").removeClass("disabled");
}
'
if(!exists('currentTab') || is.null(currentTab)){
currentTab <- "Homepage"
}
css <- "
.disabled {
background: #eee !important;
cursor: default !important;
color: black !important;
}
"
ui <- navbarPage(
title = "MSstats-Shiny",
id = "tablist",
selected = currentTab,
tags$head(
tags$style(HTML("
.shiny-output-error-validation {
color: red;
}
"))
),
useShinyjs(),
extendShinyjs(text = jsCode,functions = c("init","enableTab")),
tags$style(css),
tabPanel("Homepage", icon = icon("home"), home),
tabPanel("Upload data",value = "Uploaddata", icon = icon("send"), loadpage),
tabPanel("Data processing",value = "DataProcessing", icon = icon("gears"), qc),
tabPanel("Protein quantification", value = "PQ",icon = icon("calculator"), pq),
tabPanel("Statistical model", value = "StatsModel", icon = icon("magic"), statmodel),
tabPanel("Future experiments", value = "Future", icon = icon("flask"), expdes),
tabPanel("Download logfile", icon = icon("download"), report),
tabPanel("Help", icon = icon("ambulance"), help),
inverse = T,
collapsible = T,
windowTitle = "Shiny-MSstats"
)
shinyUI(ui) | /ui.R | no_license | dhavalmohandas/ShinyMSstats | R | false | false | 4,221 | r | rm()
#options(shiny.autoreload = TRUE)
#options(shiny.maxRequestSize=2000*1024^2)
library(shiny)
library(shinyBS)
library(shinyjs)
#library(STRINGdb)
library(ggplot2)
library(tidyverse)
library(data.table)
library(MSstatsTMT)
library(knitr)
library(readxl)
if (FALSE) require("V8")
#library(MSnbase)
#####################################
# Global functions #
radioTooltip <- function(id, choice, title, placement = "bottom", trigger = "hover", options = NULL){
options = shinyBS:::buildTooltipOrPopoverOptionsList(title, placement, trigger, options)
options = paste0("{'", paste(names(options), options, sep = "': '", collapse = "', '"), "'}")
bsTag <- shiny::tags$script(shiny::HTML(paste0("
$(document).ready(function() {
setTimeout(function() {
$('input', $('#", id, "')).each(function(){
if(this.getAttribute('value') == '", choice, "') {
opts = $.extend(", options, ", {html: true});
$(this.parentElement).tooltip('destroy');
$(this.parentElement).tooltip(opts);
}
})
}, 500)
});
")))
htmltools::attachDependencies(bsTag, shinyBS:::shinyBSDep)
}
# shinyjs.disableTab = function() {
# vartabs = $('tablist').find('li:not(.active) a');
# tabs.bind('click.tab', function(e) {
# e.preventDefault();
# return false;
# });
# tabs.addClass('disabled');
# }
#
# shinyjs.enableTab = function(param) {
# vartabs = $('tablist').find('li:not(.active):nth-child(' + param + ') a');
# tab.unbind('click.tab');
# tab.removeClass('disabled');
# }
####################################
source("panels/home-ui.R", local = T)
source("panels/loadpage-ui.R", local = T)
source("panels/qc-ui.R", local = T)
source("panels/pq-ui.R", local = T)
source("panels/statmodel-ui.R", local = T)
source("panels/expdes-ui.R", local = T)
#source("panels/analysis-ui.R", local = T)
#source("panels/clust-ui.R", local = T)
source("panels/report-ui.R", local = T)
source("panels/help-ui.R", local = T)
#########################################################################
jsCode = '
shinyjs.init = function() {
$(document).keypress(function(e) { alert("Key pressed: " + e.which); });
alert("fooo");
console.log("initttttt");
$("#tablist li a").addClass("disabled");
$(".nav").on("click", ".disabled", function (e) {
e.preventDefault();
return false;
});
}
shinyjs.enableTab = function(value) {
$("#tablist li a[data-value=" + value + "]").removeClass("disabled");
}
'
if(!exists('currentTab') || is.null(currentTab)){
currentTab <- "Homepage"
}
css <- "
.disabled {
background: #eee !important;
cursor: default !important;
color: black !important;
}
"
ui <- navbarPage(
title = "MSstats-Shiny",
id = "tablist",
selected = currentTab,
tags$head(
tags$style(HTML("
.shiny-output-error-validation {
color: red;
}
"))
),
useShinyjs(),
extendShinyjs(text = jsCode,functions = c("init","enableTab")),
tags$style(css),
tabPanel("Homepage", icon = icon("home"), home),
tabPanel("Upload data",value = "Uploaddata", icon = icon("send"), loadpage),
tabPanel("Data processing",value = "DataProcessing", icon = icon("gears"), qc),
tabPanel("Protein quantification", value = "PQ",icon = icon("calculator"), pq),
tabPanel("Statistical model", value = "StatsModel", icon = icon("magic"), statmodel),
tabPanel("Future experiments", value = "Future", icon = icon("flask"), expdes),
tabPanel("Download logfile", icon = icon("download"), report),
tabPanel("Help", icon = icon("ambulance"), help),
inverse = T,
collapsible = T,
windowTitle = "Shiny-MSstats"
)
shinyUI(ui) |
if (!exists("hpc"))
source("load-dataset.R")
png(filename = "figure/plot1.png", bg = "transparent")
hist(hpc$Global_active_power,
main = "Global Active Power",
xlab = "Global Active Power (kilowatts)",
col = "red")
dev.off()
| /plot1.R | no_license | sunsure/ExData_Plotting1 | R | false | false | 245 | r | if (!exists("hpc"))
source("load-dataset.R")
png(filename = "figure/plot1.png", bg = "transparent")
hist(hpc$Global_active_power,
main = "Global Active Power",
xlab = "Global Active Power (kilowatts)",
col = "red")
dev.off()
|
# ============================================================
# Similarity measures for sparse matrices.
# ============================================================
#' Calculates correlations between columns of two sparse matrices.
#'
#' @param X (dgCMatrix)
#' @param Y (dgCMatrix)
#' @returns Matrix of correlations.
cal_cor <- function(X, Y){
availX <- X!=0
availY <- Y!=0
# TODO: to optimize further
X<- t(as(t(X) - colMeans(X), "dgCMatrix"))
Y<- t(as(t(Y) - colMeans(Y), "dgCMatrix"))
R <- crossprod(X,Y)
N <- crossprod(X^2, availY)
M <- crossprod(availX, Y^2)
cor <- R
cor@x <- cor@x/((N@x^0.5) * (M@x^0.5))
cor
}
#' Calculates cosine between columns of two sparse matrices.
#'
#' @param X (dgCMatrix)
#' @param Y (dgCMatrix)
#' @returns Matrix of cosine measures.
cal_cos <- function(X, Y){
ones <- rep(1,nrow(X))
means <- drop(crossprod(X^2, ones)) ^ 0.5
diagonal <- Diagonal( x = means^-1 )
X <- X %*% diagonal
ones <- rep(1,nrow(Y))
means <- drop(crossprod(Y^2, ones)) ^ 0.5
diagonal <- Diagonal( x = means^-1 )
Y <- Y %*% diagonal
crossprod(X, Y)
}
| /R/similarity_measures.R | no_license | Sandy4321/collaboratory | R | false | false | 1,126 | r | # ============================================================
# Similarity measures for sparse matrices.
# ============================================================
#' Calculates correlations between columns of two sparse matrices.
#'
#' @param X (dgCMatrix)
#' @param Y (dgCMatrix)
#' @returns Matrix of correlations.
cal_cor <- function(X, Y){
availX <- X!=0
availY <- Y!=0
# TODO: to optimize further
X<- t(as(t(X) - colMeans(X), "dgCMatrix"))
Y<- t(as(t(Y) - colMeans(Y), "dgCMatrix"))
R <- crossprod(X,Y)
N <- crossprod(X^2, availY)
M <- crossprod(availX, Y^2)
cor <- R
cor@x <- cor@x/((N@x^0.5) * (M@x^0.5))
cor
}
#' Calculates cosine between columns of two sparse matrices.
#'
#' @param X (dgCMatrix)
#' @param Y (dgCMatrix)
#' @returns Matrix of cosine measures.
cal_cos <- function(X, Y){
ones <- rep(1,nrow(X))
means <- drop(crossprod(X^2, ones)) ^ 0.5
diagonal <- Diagonal( x = means^-1 )
X <- X %*% diagonal
ones <- rep(1,nrow(Y))
means <- drop(crossprod(Y^2, ones)) ^ 0.5
diagonal <- Diagonal( x = means^-1 )
Y <- Y %*% diagonal
crossprod(X, Y)
}
|
library(raster)
#library(dplyr)
#library(lubridate)
library(rwrfhydro)
library(rgdal)
library(tmap)
library(parallel)
###############################################################
################# Parametry do ustawienia #####################
###############################################################
args = commandArgs(trailingOnly=TRUE)
print(args[1])
print(args[2])
#args <- c(20190526,1)
start_wrf <- as.Date(paste0(args[1]), format="%Y%m%d")
dzien <- as.numeric(as.character(args[2])) # tutaj chodzi o dzien wyprzedzenia wzgledem startu prognozy (np. +1, +2)
patt <- as.character(start_wrf+dzien)
#print()
#patt<- "2019-05-20"
pathway <- paste0("/media/wh/dysk12/wrfout/", format(start_wrf, "%Y%m%d") ,"/wrfprd")
day <- dir(path=pathway , pattern = patt,full.names = T)
day <- day[grep(pattern = "00$", x = day)] # bez geotiffow
if(length(day[grep(pattern = "d03", x = day)])>0) day <- day[grep(pattern = "d03", x = day)] # i tylko domena 03
day <- as.list(day)
# warstwy GIS:
load(file = "data/gisy.Rdata")
# wojewodztwa <- readOGR("data/POL_adm1.shp")
# pol <- readOGR("data/POL_adm0.shp")
# rzeki <- readOGR("data/rzekiPL.shp")
# jeziora <- readOGR("data/jeziora.shp")
# proj4 <- "+proj=lcc +lat_1=49.826000213623 +lat_2=49.826000213623 +lat_0=51.8421516418457 +lon_0=16.2469997406006 +x_0=0 +y_0=0 +a=6370000 +b=6370000 +units=m +no_defs"
# wojewodztwa <- spTransform(wojewodztwa,proj4)
# pol <- spTransform(pol, proj4)
# jeziora <- spTransform(jeziora, proj4)
# rzeki <- spTransform(rzeki, proj4)
# centroidy <- gCentroid(wojewodztwa,byid=TRUE)
##########################################################################
# liczymy srednia z poszczegolnych warstw:
tempfil<-dir(path=pathway, pattern = "T2", full.names = T)
if(length(tempfil[grep(pattern = patt, x = tempfil)])>24) tempfil <- tempfil[grep(pattern = paste0("d03_",patt), x = tempfil)] # i tylko domena 03
tempfil<- stack(tempfil[grepl(pattern=patt, tempfil)])
meantemp<- calc(tempfil,min)-0.5
#beginCluster(4)
#meantemp <- clusterR(tempfil, calc, args=list(mean, na.rm=T))
#endCluster()
# Creating figure
#color scale
tempcolores<- c("#f6c39f","#e3ac89","#cb9881","#b58575","#9c716e","#865c62","#704754",
"#57344a","#3f1f3f","#240d2b","#260225","#2e0331","#370938","#420a40",
"#431243","#481046","#571658","#5e185e","#5f1b60","#671e67","#6d2069",
"#853a85","#964299","#9a559d","#a665a3","#ae74a9","#b485b3","#ba93b9",
"#c6a5c5","#cbb4cb","#d3c1d2","#c3cad5","#b6b7c6","#9ca4b9","#8992b0",
"#5e689b","#5e699d","#48528f","#374182","#1d2e77","#0b1761","#162e74",
"#234080","#37578c","#456f9a","#5a88ab","#78b2c4","#9fdbdc","#b1f0ee",
"#83c9a7","#72c29a","#67b78c","#69ba8f","#61b080","#56a573","#4c9d64",
"#3a9152","#368a45","#2a7f39","#2b7234","#1d681c","#29741a","#44851e",
"#578c25","#759c2b","#84a935","#afbf3c","#d8d952","#d4d755","#efe362",
"#e9d04f","#e1b845","#d9a53f","#c68f3d","#cc8c38","#c27b31","#ba6323",
"#b74d22","#ac4e28","#9f2715","#7b1b11","#80110c","#741105","#6f0d07",
"#630c06","#5a0c0c","#540904","#4b0504","#400401","#3f0101","#2d0708",
"#442321","#583e3a","#6f5652","#866e6a","#9c8982","#b2a59c","#c8bcb1",
"#c9bdb1","#ddd5c9","#f5efe3","#f4efe3")
temperatura_map<- function(input="inp", output="outp"){
obj1<- mask(input-273.15, pol)
centroidy$column <- sprintf(round(as.vector(raster::extract(obj1, centroidy)),1),fmt = '%#.1f')
breaks <-round(seq(-32, 31, length.out = length(tempcolores)),1)
range_min <- floor(quantile(obj1, p=0.01))
range_max <- ceiling(max(maxValue(obj1)))
ind <- which(breaks> range_min & breaks < range_max)
breaks2 <- round(breaks[ind],1)
tempcolores2 <- tempcolores[ind[-length(ind)]]
tm_shape(obj1) +
tm_raster(title= paste0("Minimalna dobowa temperatura powietrza [°C] \n", patt , " (00-23 UTC)"),
interval.closure = "left",legend.hist = T,
palette = tempcolores2, breaks=breaks2,
legend.is.portrait = FALSE,
interpolate = FALSE) +
#Border
tm_shape(pol) +
tm_polygons(alpha = 0.001, lwd=1.5) +
#Border of counties
tm_shape(wojewodztwa)+
tm_polygons(alpha = 0.01, lwd=0.7)+
#Rivers
tm_shape(rzeki)+
tm_lines(col="#2669d6", lwd=1.1) +
#Lakes
tm_shape(jeziora)+
tm_polygons(col="#2669d6") +
#Title of the figure
tm_layout(
aes.palette = "div",
sepia.intensity = 0.2,
legend.just = "right",
title.color = "blue",
compass.type = "arrow",
title.bg.color = "white",
title.bg.alpha = 0.5,
title.size = 45,
#title.position = c(0.02,0.06),
legend.outside = T,
legend.outside.position = "bottom",
legend.width = 5,
legend.hist.width = 0.9,
legend.hist.height = 0.6,
legend.title.size = 0.90,
legend.text.size = 0.5,
#legend.position = c("right","bottom"),
legend.bg.color = "#FFFFFF60",
legend.height = 0.9,
legend.frame.lwd = 0.2,
legend.frame = F,
legend.bg.alpha = 1,
space.color="grey90",
legend.format = list(text.separator = " ", format = formatC("f")))+
#Lon/Lat
tm_grid(projection = "longlat", x = 10:30, y=40:60, labels.col = "black", col = "gray",lwd = 0.5,
labels.size = 0.4, labels.inside.frame = T) +
#Mean values of counties
tm_shape(centroidy)+
tm_text("column", size = 0.6) +
#Compass
tm_compass(size = 1, fontsize = 0.7,
position = c(0.04,0.9), color.light = "grey90") +
# scale bar
tm_scale_bar(width = 0.12,size = 0.35,breaks = c(0,50,100,150), position = c("left","bottom")) +
# windhydro credits
tm_credits("(c) Wind-Hydro 2019", position = c("left", "bottom"),
size = 0.35, bg.color = "white")
}
www_path <- gsub(x = pathway, pattern = "wrfprd","www")
dir.create(www_path)
p <- temperatura_map(input=meantemp)
# ciecie w inner margins: dol, lewa, gora, prawa
tmap_save(p + tm_layout(inner.margins = c(-0.1, -0.06, -0.1, -0.1)),
filename = paste0(www_path, "/t2m_min_",patt,".png"), width=1000, height=1300)
writeRaster(meantemp-273.15, filename = paste0(www_path,"/t2m_min_",patt,".tif"),overwrite=TRUE) | /R/temperatura_min.R | no_license | bczernecki/drought | R | false | false | 6,455 | r | library(raster)
#library(dplyr)
#library(lubridate)
library(rwrfhydro)
library(rgdal)
library(tmap)
library(parallel)
###############################################################
################# Parametry do ustawienia #####################
###############################################################
args = commandArgs(trailingOnly=TRUE)
print(args[1])
print(args[2])
#args <- c(20190526,1)
start_wrf <- as.Date(paste0(args[1]), format="%Y%m%d")
dzien <- as.numeric(as.character(args[2])) # tutaj chodzi o dzien wyprzedzenia wzgledem startu prognozy (np. +1, +2)
patt <- as.character(start_wrf+dzien)
#print()
#patt<- "2019-05-20"
pathway <- paste0("/media/wh/dysk12/wrfout/", format(start_wrf, "%Y%m%d") ,"/wrfprd")
day <- dir(path=pathway , pattern = patt,full.names = T)
day <- day[grep(pattern = "00$", x = day)] # bez geotiffow
if(length(day[grep(pattern = "d03", x = day)])>0) day <- day[grep(pattern = "d03", x = day)] # i tylko domena 03
day <- as.list(day)
# warstwy GIS:
load(file = "data/gisy.Rdata")
# wojewodztwa <- readOGR("data/POL_adm1.shp")
# pol <- readOGR("data/POL_adm0.shp")
# rzeki <- readOGR("data/rzekiPL.shp")
# jeziora <- readOGR("data/jeziora.shp")
# proj4 <- "+proj=lcc +lat_1=49.826000213623 +lat_2=49.826000213623 +lat_0=51.8421516418457 +lon_0=16.2469997406006 +x_0=0 +y_0=0 +a=6370000 +b=6370000 +units=m +no_defs"
# wojewodztwa <- spTransform(wojewodztwa,proj4)
# pol <- spTransform(pol, proj4)
# jeziora <- spTransform(jeziora, proj4)
# rzeki <- spTransform(rzeki, proj4)
# centroidy <- gCentroid(wojewodztwa,byid=TRUE)
##########################################################################
# liczymy srednia z poszczegolnych warstw:
tempfil<-dir(path=pathway, pattern = "T2", full.names = T)
if(length(tempfil[grep(pattern = patt, x = tempfil)])>24) tempfil <- tempfil[grep(pattern = paste0("d03_",patt), x = tempfil)] # i tylko domena 03
tempfil<- stack(tempfil[grepl(pattern=patt, tempfil)])
meantemp<- calc(tempfil,min)-0.5
#beginCluster(4)
#meantemp <- clusterR(tempfil, calc, args=list(mean, na.rm=T))
#endCluster()
# Creating figure
#color scale
tempcolores<- c("#f6c39f","#e3ac89","#cb9881","#b58575","#9c716e","#865c62","#704754",
"#57344a","#3f1f3f","#240d2b","#260225","#2e0331","#370938","#420a40",
"#431243","#481046","#571658","#5e185e","#5f1b60","#671e67","#6d2069",
"#853a85","#964299","#9a559d","#a665a3","#ae74a9","#b485b3","#ba93b9",
"#c6a5c5","#cbb4cb","#d3c1d2","#c3cad5","#b6b7c6","#9ca4b9","#8992b0",
"#5e689b","#5e699d","#48528f","#374182","#1d2e77","#0b1761","#162e74",
"#234080","#37578c","#456f9a","#5a88ab","#78b2c4","#9fdbdc","#b1f0ee",
"#83c9a7","#72c29a","#67b78c","#69ba8f","#61b080","#56a573","#4c9d64",
"#3a9152","#368a45","#2a7f39","#2b7234","#1d681c","#29741a","#44851e",
"#578c25","#759c2b","#84a935","#afbf3c","#d8d952","#d4d755","#efe362",
"#e9d04f","#e1b845","#d9a53f","#c68f3d","#cc8c38","#c27b31","#ba6323",
"#b74d22","#ac4e28","#9f2715","#7b1b11","#80110c","#741105","#6f0d07",
"#630c06","#5a0c0c","#540904","#4b0504","#400401","#3f0101","#2d0708",
"#442321","#583e3a","#6f5652","#866e6a","#9c8982","#b2a59c","#c8bcb1",
"#c9bdb1","#ddd5c9","#f5efe3","#f4efe3")
temperatura_map<- function(input="inp", output="outp"){
obj1<- mask(input-273.15, pol)
centroidy$column <- sprintf(round(as.vector(raster::extract(obj1, centroidy)),1),fmt = '%#.1f')
breaks <-round(seq(-32, 31, length.out = length(tempcolores)),1)
range_min <- floor(quantile(obj1, p=0.01))
range_max <- ceiling(max(maxValue(obj1)))
ind <- which(breaks> range_min & breaks < range_max)
breaks2 <- round(breaks[ind],1)
tempcolores2 <- tempcolores[ind[-length(ind)]]
tm_shape(obj1) +
tm_raster(title= paste0("Minimalna dobowa temperatura powietrza [°C] \n", patt , " (00-23 UTC)"),
interval.closure = "left",legend.hist = T,
palette = tempcolores2, breaks=breaks2,
legend.is.portrait = FALSE,
interpolate = FALSE) +
#Border
tm_shape(pol) +
tm_polygons(alpha = 0.001, lwd=1.5) +
#Border of counties
tm_shape(wojewodztwa)+
tm_polygons(alpha = 0.01, lwd=0.7)+
#Rivers
tm_shape(rzeki)+
tm_lines(col="#2669d6", lwd=1.1) +
#Lakes
tm_shape(jeziora)+
tm_polygons(col="#2669d6") +
#Title of the figure
tm_layout(
aes.palette = "div",
sepia.intensity = 0.2,
legend.just = "right",
title.color = "blue",
compass.type = "arrow",
title.bg.color = "white",
title.bg.alpha = 0.5,
title.size = 45,
#title.position = c(0.02,0.06),
legend.outside = T,
legend.outside.position = "bottom",
legend.width = 5,
legend.hist.width = 0.9,
legend.hist.height = 0.6,
legend.title.size = 0.90,
legend.text.size = 0.5,
#legend.position = c("right","bottom"),
legend.bg.color = "#FFFFFF60",
legend.height = 0.9,
legend.frame.lwd = 0.2,
legend.frame = F,
legend.bg.alpha = 1,
space.color="grey90",
legend.format = list(text.separator = " ", format = formatC("f")))+
#Lon/Lat
tm_grid(projection = "longlat", x = 10:30, y=40:60, labels.col = "black", col = "gray",lwd = 0.5,
labels.size = 0.4, labels.inside.frame = T) +
#Mean values of counties
tm_shape(centroidy)+
tm_text("column", size = 0.6) +
#Compass
tm_compass(size = 1, fontsize = 0.7,
position = c(0.04,0.9), color.light = "grey90") +
# scale bar
tm_scale_bar(width = 0.12,size = 0.35,breaks = c(0,50,100,150), position = c("left","bottom")) +
# windhydro credits
tm_credits("(c) Wind-Hydro 2019", position = c("left", "bottom"),
size = 0.35, bg.color = "white")
}
www_path <- gsub(x = pathway, pattern = "wrfprd","www")
dir.create(www_path)
p <- temperatura_map(input=meantemp)
# ciecie w inner margins: dol, lewa, gora, prawa
tmap_save(p + tm_layout(inner.margins = c(-0.1, -0.06, -0.1, -0.1)),
filename = paste0(www_path, "/t2m_min_",patt,".png"), width=1000, height=1300)
writeRaster(meantemp-273.15, filename = paste0(www_path,"/t2m_min_",patt,".tif"),overwrite=TRUE) |
#You should create one R script called run_analysis.R that does the following.
#---Merges the training and the test sets to create one data set.
#---Extracts only the measurements on the mean and standard deviation for each measurement.
#---Uses descriptive activity names to name the activities in the data set
#---Appropriately labels the data set with descriptive variable names.
#---From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.
#the code runs assuming we are in the UCI HAR Dataset unzipped folder
require("data.table")
require("reshape2")
################ 1. Merges the training and the test sets to create one data set ##############
# Load: data column names
features <- read.table("features.txt",stringsAsFactors=F) #reading features
features<-features$V2 #working with the labels alone, excluding indexes from the table
x_test <- read.table("./test/X_test.txt",stringsAsFactors=F) #reading test set
colnames(x_test)<-features #labeling data with descriptive variable names
y_test <- read.table("./test/y_test.txt",stringsAsFactors=F) #reading test labels
subject_test <- read.table("./test/subject_test.txt",stringsAsFactors=F)
x_test$subject<-subject_test[,1] #getting the ids for the subjects in the test data set
x_test$activity = y_test[,1] #getting the ids for the activities in the training data set
x_train<-read.table("./train/X_train.txt",stringsAsFactors=F) #reading training set
colnames(x_train)<-features #labeling data with descriptive variable names
y_train<-read.table("./train/y_train.txt") #reading training labels
subject_train<-read.table("./train/subject_train.txt",stringsAsFactors=F)
x_train$subject<-subject_train[,1] #getting the ids for the subjects in the test data set
x_train$activity = y_train[,1] #getting the ids for the subjects in the test data set
merged<-rbind(x_train,x_test) #merginf training and test data, binding by row
################ 2. Get mean and standard deviation columns #########
sd.mean.features<-subset(features, (grepl("std|mean",features)==TRUE)) #extracting features containing only the mean and standard deviation
extracted<-merged[,sd.mean.features] #extracting from the main dataset the columns corresponding
#to mean and standard deviation only
extracted$subject<-merged$subject #add subject from main data frame, excluded above
extracted$activity<-merged$activity # add activity from main data frame excluded above
############# 3 Uses descriptive activity names to name the activities in the data set ######
activity_labels <- read.table("activity_labels.txt",stringsAsFactors=F) #reading activity labels
activity_labels<-activity_labels$V2 #working with the labels alone, excluding indexes from the table
activity_labels<-as.factor(activity_labels) # converting column to factor
extracted$activity<-as.factor(extracted$activity) #converting column to factor
extracted$activity_label = factor(extracted$activity, levels=c(1,2,3,4,5,6),
labels=activity_labels) # applying corresponding labels to the activity ids
#using factor(); each id c(1,2,3,4,5,6) corresponds to a single activity
############# 4 Appropriately labels the data set with descriptive variable names. ######
### achievied at step 1: by assigning the column names with the second column from the features table
######### 5 From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.
id_labels = c("subject", "activity", "activity_label") # creating a labels vector with the measures
labels = setdiff(colnames(extracted), id_labels) #extracting columns corresponding to variables, excluding the list above
melted = melt(extracted, id = id_labels, measure.vars = labels) #re-arranging dataframe - measures, variables and values
# Generating the tidy dataset with the average of each variable for each activity and each subject
# The dataset is aggregated by the casting formula and applying the mean function to the re-arranged dataset using melt()
tidy_data = dcast(melted, subject + activity_label ~ variable, mean)
write.table(tidy_data,"tidy_data.txt",sep="\t",row.names=F,col.names=T,quote=F)
| /run_analysis.R | no_license | andratolbus/CleaningData | R | false | false | 4,476 | r | #You should create one R script called run_analysis.R that does the following.
#---Merges the training and the test sets to create one data set.
#---Extracts only the measurements on the mean and standard deviation for each measurement.
#---Uses descriptive activity names to name the activities in the data set
#---Appropriately labels the data set with descriptive variable names.
#---From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.
#the code runs assuming we are in the UCI HAR Dataset unzipped folder
require("data.table")
require("reshape2")
################ 1. Merges the training and the test sets to create one data set ##############
# Load: data column names
features <- read.table("features.txt",stringsAsFactors=F) #reading features
features<-features$V2 #working with the labels alone, excluding indexes from the table
x_test <- read.table("./test/X_test.txt",stringsAsFactors=F) #reading test set
colnames(x_test)<-features #labeling data with descriptive variable names
y_test <- read.table("./test/y_test.txt",stringsAsFactors=F) #reading test labels
subject_test <- read.table("./test/subject_test.txt",stringsAsFactors=F)
x_test$subject<-subject_test[,1] #getting the ids for the subjects in the test data set
x_test$activity = y_test[,1] #getting the ids for the activities in the training data set
x_train<-read.table("./train/X_train.txt",stringsAsFactors=F) #reading training set
colnames(x_train)<-features #labeling data with descriptive variable names
y_train<-read.table("./train/y_train.txt") #reading training labels
subject_train<-read.table("./train/subject_train.txt",stringsAsFactors=F)
x_train$subject<-subject_train[,1] #getting the ids for the subjects in the test data set
x_train$activity = y_train[,1] #getting the ids for the subjects in the test data set
merged<-rbind(x_train,x_test) #merginf training and test data, binding by row
################ 2. Get mean and standard deviation columns #########
sd.mean.features<-subset(features, (grepl("std|mean",features)==TRUE)) #extracting features containing only the mean and standard deviation
extracted<-merged[,sd.mean.features] #extracting from the main dataset the columns corresponding
#to mean and standard deviation only
extracted$subject<-merged$subject #add subject from main data frame, excluded above
extracted$activity<-merged$activity # add activity from main data frame excluded above
############# 3 Uses descriptive activity names to name the activities in the data set ######
activity_labels <- read.table("activity_labels.txt",stringsAsFactors=F) #reading activity labels
activity_labels<-activity_labels$V2 #working with the labels alone, excluding indexes from the table
activity_labels<-as.factor(activity_labels) # converting column to factor
extracted$activity<-as.factor(extracted$activity) #converting column to factor
extracted$activity_label = factor(extracted$activity, levels=c(1,2,3,4,5,6),
labels=activity_labels) # applying corresponding labels to the activity ids
#using factor(); each id c(1,2,3,4,5,6) corresponds to a single activity
############# 4 Appropriately labels the data set with descriptive variable names. ######
### achievied at step 1: by assigning the column names with the second column from the features table
######### 5 From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.
id_labels = c("subject", "activity", "activity_label") # creating a labels vector with the measures
labels = setdiff(colnames(extracted), id_labels) #extracting columns corresponding to variables, excluding the list above
melted = melt(extracted, id = id_labels, measure.vars = labels) #re-arranging dataframe - measures, variables and values
# Generating the tidy dataset with the average of each variable for each activity and each subject
# The dataset is aggregated by the casting formula and applying the mean function to the re-arranged dataset using melt()
tidy_data = dcast(melted, subject + activity_label ~ variable, mean)
write.table(tidy_data,"tidy_data.txt",sep="\t",row.names=F,col.names=T,quote=F)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/counts.R
\name{sens}
\alias{sens}
\title{Sensitivity}
\usage{
sens(thres, y, y.hat)
}
\arguments{
\item{thres}{thershold where to split. Must be in range of \code{y.hat}}
\item{y}{status yes=1, no=0 or dead=1, alive=0}
\item{y.hat}{numeric. risk between 0 and 1}
}
\description{
Sensitivity
}
| /man/sens.Rd | no_license | guhjy/Atools | R | false | true | 391 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/counts.R
\name{sens}
\alias{sens}
\title{Sensitivity}
\usage{
sens(thres, y, y.hat)
}
\arguments{
\item{thres}{thershold where to split. Must be in range of \code{y.hat}}
\item{y}{status yes=1, no=0 or dead=1, alive=0}
\item{y.hat}{numeric. risk between 0 and 1}
}
\description{
Sensitivity
}
|
###########################################################################/**
# @RdocFunction withRepos
#
# @title "Evaluate an R expression with repositories set temporarily"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{expr}{The R expression to be evaluated.}
# \item{repos}{A @character @vector of repositories to use.}
# \item{...}{Additional arguments passed to @see "useRepos".}
# \item{substitute}{If @TRUE, argument \code{expr} is
# \code{\link[base]{substitute}()}:ed, otherwise not.}
# \item{envir}{The @environment in which the expression should be evaluated.}
# }
#
# \value{
# Returns the results of the expression evaluated.
# }
#
# @author
#
# \examples{\dontrun{
# # Install from BioC related repositories only
# withRepos(install.packages("edgeR"), repos="[[BioC]]")
#
# # Install from CRAN or BioC related repositories only
# withRepos(install.packages("edgeR"), repos=c("CRAN", "[[BioC]]"))
#
# # Install from mainstream repositories only (same as previous)
# withRepos(install.packages("edgeR"), repos="[[mainstream]]")
#
# # Install from R-Forge and mainstream repositories only
# withRepos(install.packages("R.utils"), repos="[[R-Forge]]")
#
# # Update only CRAN packages
# withRepos(update.packages(ask=FALSE), repos="[[CRAN]]")
#
# # Update only Bioconductor packages
# withRepos(update.packages(ask=FALSE), repos="[[BioC]]")
# }}
#
# \seealso{
# Internally, @see "base::eval" is used to evaluate the expression.
# See also @see "base::options" and @see "utils::install.packages".
# }
#
# @keyword IO
# @keyword programming
#*/###########################################################################
withRepos <- function(expr, repos="[[mainstream]]", ..., substitute=TRUE, envir=parent.frame()) {
# Argument 'expr':
if (substitute) expr <- substitute(expr)
# Argument 'envir':
if (!is.environment(envir))
throw("Argument 'envir' is not a list: ", class(envir)[1L])
# Parse and set repositories temporarily
prev <- useRepos(repos, ...)
on.exit(useRepos(prev))
# Evaluate expression
eval(expr, envir = envir, enclos = baseenv())
} # withOptions()
| /R/withRepos.R | no_license | HenrikBengtsson/R.utils | R | false | false | 2,173 | r | ###########################################################################/**
# @RdocFunction withRepos
#
# @title "Evaluate an R expression with repositories set temporarily"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{expr}{The R expression to be evaluated.}
# \item{repos}{A @character @vector of repositories to use.}
# \item{...}{Additional arguments passed to @see "useRepos".}
# \item{substitute}{If @TRUE, argument \code{expr} is
# \code{\link[base]{substitute}()}:ed, otherwise not.}
# \item{envir}{The @environment in which the expression should be evaluated.}
# }
#
# \value{
# Returns the results of the expression evaluated.
# }
#
# @author
#
# \examples{\dontrun{
# # Install from BioC related repositories only
# withRepos(install.packages("edgeR"), repos="[[BioC]]")
#
# # Install from CRAN or BioC related repositories only
# withRepos(install.packages("edgeR"), repos=c("CRAN", "[[BioC]]"))
#
# # Install from mainstream repositories only (same as previous)
# withRepos(install.packages("edgeR"), repos="[[mainstream]]")
#
# # Install from R-Forge and mainstream repositories only
# withRepos(install.packages("R.utils"), repos="[[R-Forge]]")
#
# # Update only CRAN packages
# withRepos(update.packages(ask=FALSE), repos="[[CRAN]]")
#
# # Update only Bioconductor packages
# withRepos(update.packages(ask=FALSE), repos="[[BioC]]")
# }}
#
# \seealso{
# Internally, @see "base::eval" is used to evaluate the expression.
# See also @see "base::options" and @see "utils::install.packages".
# }
#
# @keyword IO
# @keyword programming
#*/###########################################################################
withRepos <- function(expr, repos="[[mainstream]]", ..., substitute=TRUE, envir=parent.frame()) {
# Argument 'expr':
if (substitute) expr <- substitute(expr)
# Argument 'envir':
if (!is.environment(envir))
throw("Argument 'envir' is not a list: ", class(envir)[1L])
# Parse and set repositories temporarily
prev <- useRepos(repos, ...)
on.exit(useRepos(prev))
# Evaluate expression
eval(expr, envir = envir, enclos = baseenv())
} # withOptions()
|
dataFile <- "./data/household_power_consumption.txt"
data <- read.table(dataFile, header=TRUE, sep=";", stringsAsFactors=FALSE, dec=".")
subSetData <- data[data$Date %in% c("1/2/2007","2/2/2007") ,]
#str(subSetData)
datetime <- strptime(paste(subSetData$Date, subSetData$Time, sep=" "), "%d/%m/%Y %H:%M:%S")
globalActivePower <- as.numeric(subSetData$Global_active_power)
subMetering1 <- as.numeric(subSetData$Sub_metering_1)
subMetering2 <- as.numeric(subSetData$Sub_metering_2)
subMetering3 <- as.numeric(subSetData$Sub_metering_3)
png("plot3.png", width=480, height=480)
plot(datetime, subMetering1, type="l", ylab="Energy Submetering", xlab="")
lines(datetime, subMetering2, type="l", col="red")
lines(datetime, subMetering3, type="l", col="blue")
legend("topright", c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), lty=1, lwd=2.5, col=c("black", "red", "blue"))
dev.off()
| /Plot3.R | no_license | GWdata/Exploratory-Course-Project1 | R | false | false | 908 | r | dataFile <- "./data/household_power_consumption.txt"
data <- read.table(dataFile, header=TRUE, sep=";", stringsAsFactors=FALSE, dec=".")
subSetData <- data[data$Date %in% c("1/2/2007","2/2/2007") ,]
#str(subSetData)
datetime <- strptime(paste(subSetData$Date, subSetData$Time, sep=" "), "%d/%m/%Y %H:%M:%S")
globalActivePower <- as.numeric(subSetData$Global_active_power)
subMetering1 <- as.numeric(subSetData$Sub_metering_1)
subMetering2 <- as.numeric(subSetData$Sub_metering_2)
subMetering3 <- as.numeric(subSetData$Sub_metering_3)
png("plot3.png", width=480, height=480)
plot(datetime, subMetering1, type="l", ylab="Energy Submetering", xlab="")
lines(datetime, subMetering2, type="l", col="red")
lines(datetime, subMetering3, type="l", col="blue")
legend("topright", c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), lty=1, lwd=2.5, col=c("black", "red", "blue"))
dev.off()
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
context("dbCreateTableAs and sqlCreateTableAs")
source("utilities.R")
test_that("sqlCreateTableAs works", {
conn <- setup_live_connection()
test_table_name <- "test_sqlcreatetableas"
expect_equal(
sqlCreateTableAs(
conn, test_table_name,
sql = "SELECT * FROM iris"
),
DBI::SQL(
paste0(
"CREATE TABLE ", dbQuoteIdentifier(conn, test_table_name), "\n",
"AS\n",
"SELECT * FROM iris"
)
)
)
expect_equal(
sqlCreateTableAs(
conn, test_table_name,
sql = "SELECT * FROM iris",
with = "WITH (format = 'ORC')"
),
DBI::SQL(
paste0(
"CREATE TABLE ", dbQuoteIdentifier(conn, test_table_name), "\n",
"WITH (format = 'ORC')\n",
"AS\n",
"SELECT * FROM iris"
)
)
)
})
test_equal_tables <- function(conn, test_table_name, test_origin_table) {
expect_equal(
dbListFields(conn, test_table_name),
dbListFields(conn, test_origin_table)
)
expect_equal(
get_nrow(conn, test_table_name),
get_nrow(conn, test_origin_table)
)
}
test_that("dbCreateTableAS works with live database", {
conn <- setup_live_connection()
test_table_name <- "test_createtableas"
test_origin_table <- "iris"
test_statement <- paste0("SELECT * FROM ", test_origin_table)
if (dbExistsTable(conn, test_table_name)) {
dbRemoveTable(conn, test_table_name)
}
expect_false(dbExistsTable(conn, test_table_name))
expect_true(dbCreateTableAs(conn, test_table_name, test_statement))
expect_true(dbExistsTable(conn, test_table_name))
test_equal_tables(conn, test_table_name, test_origin_table)
expect_error(
dbCreateTableAs(conn, test_table_name, test_statement),
"The table .* exists but overwrite is set to FALSE"
)
expect_message(
res <- dbCreateTableAs(
conn, test_table_name, test_statement,
overwrite = TRUE
),
"is overwritten"
)
expect_true(res)
expect_true(dbExistsTable(conn, test_table_name))
test_equal_tables(conn, test_table_name, test_origin_table)
})
| /tests/testthat/test-dbCreateTableAs.R | permissive | prestodb/RPresto | R | false | false | 2,264 | r | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
context("dbCreateTableAs and sqlCreateTableAs")
source("utilities.R")
test_that("sqlCreateTableAs works", {
conn <- setup_live_connection()
test_table_name <- "test_sqlcreatetableas"
expect_equal(
sqlCreateTableAs(
conn, test_table_name,
sql = "SELECT * FROM iris"
),
DBI::SQL(
paste0(
"CREATE TABLE ", dbQuoteIdentifier(conn, test_table_name), "\n",
"AS\n",
"SELECT * FROM iris"
)
)
)
expect_equal(
sqlCreateTableAs(
conn, test_table_name,
sql = "SELECT * FROM iris",
with = "WITH (format = 'ORC')"
),
DBI::SQL(
paste0(
"CREATE TABLE ", dbQuoteIdentifier(conn, test_table_name), "\n",
"WITH (format = 'ORC')\n",
"AS\n",
"SELECT * FROM iris"
)
)
)
})
test_equal_tables <- function(conn, test_table_name, test_origin_table) {
expect_equal(
dbListFields(conn, test_table_name),
dbListFields(conn, test_origin_table)
)
expect_equal(
get_nrow(conn, test_table_name),
get_nrow(conn, test_origin_table)
)
}
test_that("dbCreateTableAS works with live database", {
conn <- setup_live_connection()
test_table_name <- "test_createtableas"
test_origin_table <- "iris"
test_statement <- paste0("SELECT * FROM ", test_origin_table)
if (dbExistsTable(conn, test_table_name)) {
dbRemoveTable(conn, test_table_name)
}
expect_false(dbExistsTable(conn, test_table_name))
expect_true(dbCreateTableAs(conn, test_table_name, test_statement))
expect_true(dbExistsTable(conn, test_table_name))
test_equal_tables(conn, test_table_name, test_origin_table)
expect_error(
dbCreateTableAs(conn, test_table_name, test_statement),
"The table .* exists but overwrite is set to FALSE"
)
expect_message(
res <- dbCreateTableAs(
conn, test_table_name, test_statement,
overwrite = TRUE
),
"is overwritten"
)
expect_true(res)
expect_true(dbExistsTable(conn, test_table_name))
test_equal_tables(conn, test_table_name, test_origin_table)
})
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/helpers.R
\name{round_up_nice}
\alias{round_up_nice}
\title{Round up to nearest 'nice' number
source: https://stackoverflow.com/a/6463946}
\usage{
round_up_nice(x, nice = c(1, 2, 4, 5, 6, 8, 10))
}
\arguments{
\item{x}{Number to round up}
\item{nice}{Vector of 'nice' numbers (no need to change this)}
}
\description{
Round up to nearest 'nice' number
source: https://stackoverflow.com/a/6463946
}
| /man/round_up_nice.Rd | permissive | matthewgthomas/brclib | R | false | true | 477 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/helpers.R
\name{round_up_nice}
\alias{round_up_nice}
\title{Round up to nearest 'nice' number
source: https://stackoverflow.com/a/6463946}
\usage{
round_up_nice(x, nice = c(1, 2, 4, 5, 6, 8, 10))
}
\arguments{
\item{x}{Number to round up}
\item{nice}{Vector of 'nice' numbers (no need to change this)}
}
\description{
Round up to nearest 'nice' number
source: https://stackoverflow.com/a/6463946
}
|
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/providers.R
\name{provider}
\alias{provider}
\title{Create a new provider.}
\usage{
provider(tile_f, attr_tile, attr_data = osm_license)
}
\arguments{
\item{tile_f}{A tile function has inputs x, y and z and returns
a url.}
\item{attr_title,attr_data}{Attribution information for rendered tiles
and underlying data.}
}
\description{
A provider wrappers a tile function with attribution details.
}
\keyword{internal}
| /man/provider.Rd | no_license | junior128/rastermap | R | false | false | 503 | rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/providers.R
\name{provider}
\alias{provider}
\title{Create a new provider.}
\usage{
provider(tile_f, attr_tile, attr_data = osm_license)
}
\arguments{
\item{tile_f}{A tile function has inputs x, y and z and returns
a url.}
\item{attr_title,attr_data}{Attribution information for rendered tiles
and underlying data.}
}
\description{
A provider wrappers a tile function with attribution details.
}
\keyword{internal}
|
#
# This is the server logic 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/
#
library(shiny)
options(shiny.maxRequestSize=100*1024^2)
# Define server logic required to draw a histogram
shinyServer(
function(input, output) {
data <- reactive({
file1 <- input$file
if(is.null(file1)) { return()}
#read.table(file=file1$datapath, sep=input$sep, header=input$header, stringAsFactors=input$stringAsFactors)
read.table(file=file1$datapath, sep=input$sep, header=input$header)
})
output$filedf <- renderTable ({
if(is.null(data())) { return () }
input$file
})
output$sum <- renderTable({
if(is.null(data())) {return ()}
summary(data())
})
output$table <- renderTable({
if(is.null(data())) { return ()}
head(data())
})
output$tb <- renderUI({
if(is.null(data())) { return () }
else
tabsetPanel(
tabPanel("About file", tableOutput("filedf")),
tabPanel("Data", tableOutput("table")),
tabPanel("Summary", tableOutput("sum"))
)
})
}
) | /server.R | no_license | gnetsanet/scViz | R | false | false | 1,452 | r | #
# This is the server logic of a Shiny web application. You can run the
# application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
options(shiny.maxRequestSize=100*1024^2)
# Define server logic required to draw a histogram
shinyServer(
function(input, output) {
data <- reactive({
file1 <- input$file
if(is.null(file1)) { return()}
#read.table(file=file1$datapath, sep=input$sep, header=input$header, stringAsFactors=input$stringAsFactors)
read.table(file=file1$datapath, sep=input$sep, header=input$header)
})
output$filedf <- renderTable ({
if(is.null(data())) { return () }
input$file
})
output$sum <- renderTable({
if(is.null(data())) {return ()}
summary(data())
})
output$table <- renderTable({
if(is.null(data())) { return ()}
head(data())
})
output$tb <- renderUI({
if(is.null(data())) { return () }
else
tabsetPanel(
tabPanel("About file", tableOutput("filedf")),
tabPanel("Data", tableOutput("table")),
tabPanel("Summary", tableOutput("sum"))
)
})
}
) |
#' @title Bayesian VAR
#' @author Nikolas Kuschnig, Lukas Vashold
#' @description Bayesian VAR with Hyperpriors
#'
#' @param data
#' @param lags Number of lags to include.
#' @param nsave Number of MCMC-draws to store.
#' @param nburn Number of MCMC-draws to discard
#' @param irf Boolean determining whether to compute impulse responses.
#' @param horizon Integer stating the horizon of the impulse responses.
#' @param sign_res Numeric matrix of sign restrictions.
#' @param mn_prior placeholder
#' @param soc_prior placeholder
#' @param sur_prior placeholder
#' @param scale_hess Number or numerical vector scaling the hessian matrix.
#'
#' @return Returns a list with stored draws of beta, sigma, the impulse responses,
#' as well as all draws of parameters and log-likelihood.
#' @export
#'
bvar <- function(data,
lags,
nsave = 5000, nburn = 1000,
irf = TRUE, horizon = 20, sign_res = NULL,
# pred_density = FALSE,
mn_prior = TRUE, soc_prior = TRUE, sur_prior = TRUE,
scale_hess = c(0.1, 0.03, 0.03)) {
# Robustness Checks -------------------------------------------------------
if(nsave < 0 || nburn < 0 || !is.numeric(nsave) || !is.numeric(nburn)) {
stop("Iterations were not specified properly.")
}
# Preparation -------------------------------------------------------------
Y <- as.matrix(data)
X <- lag_data(Y, lags)
Y <- Y[(lags + 1):nrow(Y), ]
X <- X[(lags + 1):nrow(X), ]
X <- cbind(1, X)
K <- ncol(X)
M <- ncol(Y)
N <- nrow(Y)
# OLS
beta_ols <- solve(crossprod(X)) %*% crossprod(X, Y)
sse_ols <- crossprod(Y - X %*% beta_ols)
sigma_ols <- sigma <- sse_ols / (N - K)
# Hyperpriors -------------------------------------------------------------
n_priors <- mn_prior + soc_prior + sur_prior
# Prior parameters
Y0 <- colMeans(Y[1:lags, ])
par_min <- list("lambda" = 0.0001, "miu" = 0.0001, "theta" = 0.0001)
par_max <- list("lambda" = 5, "miu" = 50, "theta" = 50)
par_modes <- list("lambda" = 0.2, "miu" = 1, "theta" = 1)
par_sd <- list("lambda" = 0.4, "miu" = 1, "theta" = 1)
mn_alpha <- 2
# Coefficients of the hyperpriors
prior_coef <- list("lambda" = gamma_coef(par_modes$lambda, par_sd$lambda),
"miu" = gamma_coef(par_modes$miu, par_sd$miu),
"theta" = gamma_coef(par_modes$theta, par_sd$theta))
# Inverse Hessian and Jacobian for drawing proposals
H <- diag(n_priors) * scale_hess
exp_modes <- exp(c(par_modes$lambda, par_modes$miu, par_modes$theta))
J <- exp_modes / (1 + exp_modes) ^ 2
J[1] <- J[1] * (par_max$lambda - par_min$lambda)
J[2] <- J[2] * (par_max$miu - par_min$miu)
J[3] <- J[3] * (par_max$theta - par_min$theta)
J <- diag(J)
HH <- J %*% H %*% t(J)
mn_mean <- matrix(0, K, M)
mn_mean[2:(M + 1), ] <- diag(M)
mn_sd <- apply(Y, 2, function(x) {
sqrt(arima(x, order = c(lags, 0, 0))$sigma2)
})
mn_var <- 1e06
post_mode <- c("lambda" = par_modes$lambda,
"miu" = par_modes$miu,
"theta" = par_modes$theta)
# Initial Values ----------------------------------------------------------
par_draw <- bounded_rnorm(post_mode, diag(HH),
bounds = list("min" = par_min, "max" = par_max))
logML_draw <- logML(Y, X, lags, Y_row = N, Y_col = M,
par = list("lambda" = par_draw[1],
"miu" = par_draw[2],
"theta" = par_draw[3],
"alpha" = mn_alpha, "psi" = mn_sd),
mn_mean, mn_sd, mn_var, Y0, prior_coef)
# Storage
par_store <- matrix(NA, nsave + nburn, length(post_mode))
ml_store <- vector("numeric", nsave + nburn)
beta_store <- array(NA, c(nsave, K, M))
sigma_store <- array(NA, c(nsave, M, M))
accepted <- 0
if(irf) {irf_store <- array(NA, c(nsave, M, M, horizon))}
if(!is.null(sign_res)) {sign_res <- as.vector(sign_res)}
# if(pred_density) {y_store <- matrix(NA, nrow = nsave, M)}
# Loop --------------------------------------------------------------------
for(i in (1 - nburn):nsave) {
# Metropolis-Hastings
par_temp <- bounded_rnorm(par_draw, diag(HH),
bounds = list("min" = par_min, "max" = par_max))
logML_temp <- logML(Y, X, lags, Y_row = N, Y_col = M,
par = list("lambda" = par_temp[1],
"miu" = par_temp[2],
"theta" = par_temp[3],
"alpha" = mn_alpha, "psi" = mn_sd),
mn_mean, mn_sd, mn_var, Y0, prior_coef)
if(runif(1) < exp(logML_temp$logML - logML_draw$logML)) {
logML_draw <- logML_temp
par_draw <- par_temp
accepted <- accepted + 1
} else {
logML_draw <- logML(Y, X, lags, Y_row = N, Y_col = M,
par = list("lambda" = par_draw[1],
"miu" = par_draw[2],
"theta" = par_draw[3],
"alpha" = mn_alpha, "psi" = mn_sd),
mn_mean, mn_sd, mn_var, Y0, prior_coef)
}
# Storage
par_store[i + nburn, ] <- par_draw
ml_store[i + nburn] <- logML_draw$logML
if(i > 0) {
beta_store[i, , ] <- logML_draw$beta_draw
sigma_store[i, , ] <- logML_draw$sigma_draw
# Impulse Responses
if(irf) {
# Companion matrix
beta_comp <- matrix(0, K - 1, K - 1)
beta_comp[1:M, ] <- t(logML_draw$beta_draw[2:K, ])
if(lags > 1) {
beta_comp[(M + 1):(K - 1), 1:(K - 1 - M)] <- diag(M * (lags - 1))
}
# Identification
if(!is.null(sign_res)) {
restrictions <- FALSE
counter <- 0
while(!restrictions) {
counter <- counter + 1
R_tilde <- matrix(rnorm(M^2, 0, 1), M, M)
qr_object <- qr(R_tilde)
R <- qr.Q(qr_object)
R <- R %*% diag((diag(R) > 0) - (diag(R) < 0))
shock <- t(chol(logML_draw$sigma_draw)) %*% R
shock_vec <- as.vector(shock)
shock_vec[which(shock_vec < 0)] <- -1
shock_vec[which(shock_vec > 0)] <- 1
if(all(shock_vec == sign_res)) {restrictions <- TRUE}
if(counter > 2500) {
stop("No matrix fitting the sign-restrictions found.")
}
}
} else {
shock <- t(chol(logML_draw$sigma_draw))
}
# Compute
irf_draw <- array(0, c(M * lags, M * lags, horizon))
irf_draw[1:M, 1:M, 1] <- shock
for(j in 2:horizon) {
irf_draw[, , j] <- irf_draw[, , j - 1] %*% t(beta_comp)
}
irf_store[i, , , ] <- irf_draw[1:M, 1:M, ]
}
# if(pred_density) {
# X_new <- c(1,Y[N,],X[N,2:(M*(lags-1)+1)])
# y_store[i,] <- X_new%*%logML_draw$beta_draw+t(t(chol(logML_draw$sigma_draw))%*%rnorm(M))
# }
}
}
# Outputs -----------------------------------------------------------------
acc_rate <- accepted / (nburn + nsave)
if(irf) {
out <- list("beta" = beta_store, "sigma" = sigma_store,
"parameters" = par_store, "log_ml" = ml_store,
"acceptance" = acc_rate, "irf" = irf_store)
} else {
out <- list("beta" = beta_store, "sigma" = sigma_store,
"parameters" = par_store, "log_ml" = ml_store,
"acceptance" = acc_rate)
}
return(out)
}
| /exercise_4/exercise_4_var.R | permissive | lnsongxf/econ_exercises | R | false | false | 7,717 | r | #' @title Bayesian VAR
#' @author Nikolas Kuschnig, Lukas Vashold
#' @description Bayesian VAR with Hyperpriors
#'
#' @param data
#' @param lags Number of lags to include.
#' @param nsave Number of MCMC-draws to store.
#' @param nburn Number of MCMC-draws to discard
#' @param irf Boolean determining whether to compute impulse responses.
#' @param horizon Integer stating the horizon of the impulse responses.
#' @param sign_res Numeric matrix of sign restrictions.
#' @param mn_prior placeholder
#' @param soc_prior placeholder
#' @param sur_prior placeholder
#' @param scale_hess Number or numerical vector scaling the hessian matrix.
#'
#' @return Returns a list with stored draws of beta, sigma, the impulse responses,
#' as well as all draws of parameters and log-likelihood.
#' @export
#'
bvar <- function(data,
lags,
nsave = 5000, nburn = 1000,
irf = TRUE, horizon = 20, sign_res = NULL,
# pred_density = FALSE,
mn_prior = TRUE, soc_prior = TRUE, sur_prior = TRUE,
scale_hess = c(0.1, 0.03, 0.03)) {
# Robustness Checks -------------------------------------------------------
if(nsave < 0 || nburn < 0 || !is.numeric(nsave) || !is.numeric(nburn)) {
stop("Iterations were not specified properly.")
}
# Preparation -------------------------------------------------------------
Y <- as.matrix(data)
X <- lag_data(Y, lags)
Y <- Y[(lags + 1):nrow(Y), ]
X <- X[(lags + 1):nrow(X), ]
X <- cbind(1, X)
K <- ncol(X)
M <- ncol(Y)
N <- nrow(Y)
# OLS
beta_ols <- solve(crossprod(X)) %*% crossprod(X, Y)
sse_ols <- crossprod(Y - X %*% beta_ols)
sigma_ols <- sigma <- sse_ols / (N - K)
# Hyperpriors -------------------------------------------------------------
n_priors <- mn_prior + soc_prior + sur_prior
# Prior parameters
Y0 <- colMeans(Y[1:lags, ])
par_min <- list("lambda" = 0.0001, "miu" = 0.0001, "theta" = 0.0001)
par_max <- list("lambda" = 5, "miu" = 50, "theta" = 50)
par_modes <- list("lambda" = 0.2, "miu" = 1, "theta" = 1)
par_sd <- list("lambda" = 0.4, "miu" = 1, "theta" = 1)
mn_alpha <- 2
# Coefficients of the hyperpriors
prior_coef <- list("lambda" = gamma_coef(par_modes$lambda, par_sd$lambda),
"miu" = gamma_coef(par_modes$miu, par_sd$miu),
"theta" = gamma_coef(par_modes$theta, par_sd$theta))
# Inverse Hessian and Jacobian for drawing proposals
H <- diag(n_priors) * scale_hess
exp_modes <- exp(c(par_modes$lambda, par_modes$miu, par_modes$theta))
J <- exp_modes / (1 + exp_modes) ^ 2
J[1] <- J[1] * (par_max$lambda - par_min$lambda)
J[2] <- J[2] * (par_max$miu - par_min$miu)
J[3] <- J[3] * (par_max$theta - par_min$theta)
J <- diag(J)
HH <- J %*% H %*% t(J)
mn_mean <- matrix(0, K, M)
mn_mean[2:(M + 1), ] <- diag(M)
mn_sd <- apply(Y, 2, function(x) {
sqrt(arima(x, order = c(lags, 0, 0))$sigma2)
})
mn_var <- 1e06
post_mode <- c("lambda" = par_modes$lambda,
"miu" = par_modes$miu,
"theta" = par_modes$theta)
# Initial Values ----------------------------------------------------------
par_draw <- bounded_rnorm(post_mode, diag(HH),
bounds = list("min" = par_min, "max" = par_max))
logML_draw <- logML(Y, X, lags, Y_row = N, Y_col = M,
par = list("lambda" = par_draw[1],
"miu" = par_draw[2],
"theta" = par_draw[3],
"alpha" = mn_alpha, "psi" = mn_sd),
mn_mean, mn_sd, mn_var, Y0, prior_coef)
# Storage
par_store <- matrix(NA, nsave + nburn, length(post_mode))
ml_store <- vector("numeric", nsave + nburn)
beta_store <- array(NA, c(nsave, K, M))
sigma_store <- array(NA, c(nsave, M, M))
accepted <- 0
if(irf) {irf_store <- array(NA, c(nsave, M, M, horizon))}
if(!is.null(sign_res)) {sign_res <- as.vector(sign_res)}
# if(pred_density) {y_store <- matrix(NA, nrow = nsave, M)}
# Loop --------------------------------------------------------------------
for(i in (1 - nburn):nsave) {
# Metropolis-Hastings
par_temp <- bounded_rnorm(par_draw, diag(HH),
bounds = list("min" = par_min, "max" = par_max))
logML_temp <- logML(Y, X, lags, Y_row = N, Y_col = M,
par = list("lambda" = par_temp[1],
"miu" = par_temp[2],
"theta" = par_temp[3],
"alpha" = mn_alpha, "psi" = mn_sd),
mn_mean, mn_sd, mn_var, Y0, prior_coef)
if(runif(1) < exp(logML_temp$logML - logML_draw$logML)) {
logML_draw <- logML_temp
par_draw <- par_temp
accepted <- accepted + 1
} else {
logML_draw <- logML(Y, X, lags, Y_row = N, Y_col = M,
par = list("lambda" = par_draw[1],
"miu" = par_draw[2],
"theta" = par_draw[3],
"alpha" = mn_alpha, "psi" = mn_sd),
mn_mean, mn_sd, mn_var, Y0, prior_coef)
}
# Storage
par_store[i + nburn, ] <- par_draw
ml_store[i + nburn] <- logML_draw$logML
if(i > 0) {
beta_store[i, , ] <- logML_draw$beta_draw
sigma_store[i, , ] <- logML_draw$sigma_draw
# Impulse Responses
if(irf) {
# Companion matrix
beta_comp <- matrix(0, K - 1, K - 1)
beta_comp[1:M, ] <- t(logML_draw$beta_draw[2:K, ])
if(lags > 1) {
beta_comp[(M + 1):(K - 1), 1:(K - 1 - M)] <- diag(M * (lags - 1))
}
# Identification
if(!is.null(sign_res)) {
restrictions <- FALSE
counter <- 0
while(!restrictions) {
counter <- counter + 1
R_tilde <- matrix(rnorm(M^2, 0, 1), M, M)
qr_object <- qr(R_tilde)
R <- qr.Q(qr_object)
R <- R %*% diag((diag(R) > 0) - (diag(R) < 0))
shock <- t(chol(logML_draw$sigma_draw)) %*% R
shock_vec <- as.vector(shock)
shock_vec[which(shock_vec < 0)] <- -1
shock_vec[which(shock_vec > 0)] <- 1
if(all(shock_vec == sign_res)) {restrictions <- TRUE}
if(counter > 2500) {
stop("No matrix fitting the sign-restrictions found.")
}
}
} else {
shock <- t(chol(logML_draw$sigma_draw))
}
# Compute
irf_draw <- array(0, c(M * lags, M * lags, horizon))
irf_draw[1:M, 1:M, 1] <- shock
for(j in 2:horizon) {
irf_draw[, , j] <- irf_draw[, , j - 1] %*% t(beta_comp)
}
irf_store[i, , , ] <- irf_draw[1:M, 1:M, ]
}
# if(pred_density) {
# X_new <- c(1,Y[N,],X[N,2:(M*(lags-1)+1)])
# y_store[i,] <- X_new%*%logML_draw$beta_draw+t(t(chol(logML_draw$sigma_draw))%*%rnorm(M))
# }
}
}
# Outputs -----------------------------------------------------------------
acc_rate <- accepted / (nburn + nsave)
if(irf) {
out <- list("beta" = beta_store, "sigma" = sigma_store,
"parameters" = par_store, "log_ml" = ml_store,
"acceptance" = acc_rate, "irf" = irf_store)
} else {
out <- list("beta" = beta_store, "sigma" = sigma_store,
"parameters" = par_store, "log_ml" = ml_store,
"acceptance" = acc_rate)
}
return(out)
}
|
#' Load in data from 10x experiment
#'
#' Creates a full or sparse matrix from a sparse data matrix provided by 10X
#' genomics.
#'
#' @param data_dir Directory containing the matrix.mtx, genes.tsv, and barcodes.tsv
#' files provided by 10x. A vector or named vector can be given in order to load
#' several data directories. If a named vector is given, the cell barcode names
#' will be prefixed with the name.
#' @param min_total_cell_counts integer(1) threshold such that cells (barcodes)
#' with total counts below the threshold are filtered out
#' @param min_mean_gene_counts numeric(1) threshold such that genes with mean
#' counts below the threshold are filtered out.
#' @param ... passed arguments
#'
#' @details This function was developed from the \code{Read10X} function from
#' the \code{Seurat} package.
#'
#' @return If \code{expand} is TRUE, returns an SCESet object with counts data
#' and log2(cpm + offset) as expression data; else returns a sparse matrix with
#' rows and columns labeled.
#'
#' @importFrom Matrix readMM
#' @rdname read10xResults
#' @aliases read10xResults read10XResults
#' @export
#' @examples
#' \dontrun{
#' sce10x <- read10Xxesults("path/to/data/directory")
#' count_matrix_10x <- read10xResults("path/to/data/directory", expand = FALSE)
#' }
read10xResults <- function(data_dir, min_total_cell_counts = NULL,
min_mean_gene_counts = NULL) {
nsets <- length(data_dir)
full_data <- vector("list", nsets)
gene_info_list <- vector("list", nsets)
cell_info_list <- vector("list", nsets)
for (i in seq_len(nsets)) {
run <- data_dir[i]
barcode.loc <- file.path(run, "barcodes.tsv")
gene.loc <- file.path(run, "genes.tsv")
matrix.loc <- file.path(run, "matrix.mtx")
## read sparse count matrix
data_mat <- Matrix::readMM(matrix.loc)
## define filters
if (!is.null(min_total_cell_counts)) {
keep_barcode <- (Matrix::colSums(data_mat) >= min_total_cell_counts)
data_mat <- data_mat[, keep_barcode]
}
cell.names <- utils::read.table(barcode.loc, header = FALSE,
colClasses = "character")[[1]]
dataset <- i
if (!is.null(names(data_dir))) {
dataset <- names(data_dir)[i]
}
full_data[[i]] <- data_mat
gene_info_list[[i]] <- utils::read.table(gene.loc, header = FALSE,
colClasses = "character")
cell_info_list[[i]] <- DataFrame(dataset = dataset, barcode = cell.names)
}
# Checking gene uniqueness.
if (nsets > 1 && length(unique(gene_info_list)) != 1L) {
stop("gene information differs between runs")
}
gene_info <- gene_info_list[[1]]
colnames(gene_info) <- c("id", "symbol")
rownames(gene_info) <- gene_info$id
# Forming the full data matrix.
full_data <- do.call(cbind, full_data)
rownames(full_data) <- gene_info$id
# Applying some filtering if requested.
if (!is.null(min_mean_gene_counts)) {
keep_gene <- (Matrix::rowSums(data_mat) >= min_mean_gene_counts)
full_data <- full_data[keep_gene,]
gene_info <- gene_info[keep_gene,]
}
# Adding the cell data.
cell_info <- do.call(rbind, cell_info_list)
SingleCellExperiment(list(counts = full_data), rowData = gene_info,
colData = cell_info)
}
#' @rdname read10xResults
#' @export
read10XResults <- function(...) {
read10xResults(...)
}
| /R/10ximport-wrapper.R | permissive | Jun-Lizst/archive-scater | R | false | false | 3,611 | r | #' Load in data from 10x experiment
#'
#' Creates a full or sparse matrix from a sparse data matrix provided by 10X
#' genomics.
#'
#' @param data_dir Directory containing the matrix.mtx, genes.tsv, and barcodes.tsv
#' files provided by 10x. A vector or named vector can be given in order to load
#' several data directories. If a named vector is given, the cell barcode names
#' will be prefixed with the name.
#' @param min_total_cell_counts integer(1) threshold such that cells (barcodes)
#' with total counts below the threshold are filtered out
#' @param min_mean_gene_counts numeric(1) threshold such that genes with mean
#' counts below the threshold are filtered out.
#' @param ... passed arguments
#'
#' @details This function was developed from the \code{Read10X} function from
#' the \code{Seurat} package.
#'
#' @return If \code{expand} is TRUE, returns an SCESet object with counts data
#' and log2(cpm + offset) as expression data; else returns a sparse matrix with
#' rows and columns labeled.
#'
#' @importFrom Matrix readMM
#' @rdname read10xResults
#' @aliases read10xResults read10XResults
#' @export
#' @examples
#' \dontrun{
#' sce10x <- read10Xxesults("path/to/data/directory")
#' count_matrix_10x <- read10xResults("path/to/data/directory", expand = FALSE)
#' }
read10xResults <- function(data_dir, min_total_cell_counts = NULL,
min_mean_gene_counts = NULL) {
nsets <- length(data_dir)
full_data <- vector("list", nsets)
gene_info_list <- vector("list", nsets)
cell_info_list <- vector("list", nsets)
for (i in seq_len(nsets)) {
run <- data_dir[i]
barcode.loc <- file.path(run, "barcodes.tsv")
gene.loc <- file.path(run, "genes.tsv")
matrix.loc <- file.path(run, "matrix.mtx")
## read sparse count matrix
data_mat <- Matrix::readMM(matrix.loc)
## define filters
if (!is.null(min_total_cell_counts)) {
keep_barcode <- (Matrix::colSums(data_mat) >= min_total_cell_counts)
data_mat <- data_mat[, keep_barcode]
}
cell.names <- utils::read.table(barcode.loc, header = FALSE,
colClasses = "character")[[1]]
dataset <- i
if (!is.null(names(data_dir))) {
dataset <- names(data_dir)[i]
}
full_data[[i]] <- data_mat
gene_info_list[[i]] <- utils::read.table(gene.loc, header = FALSE,
colClasses = "character")
cell_info_list[[i]] <- DataFrame(dataset = dataset, barcode = cell.names)
}
# Checking gene uniqueness.
if (nsets > 1 && length(unique(gene_info_list)) != 1L) {
stop("gene information differs between runs")
}
gene_info <- gene_info_list[[1]]
colnames(gene_info) <- c("id", "symbol")
rownames(gene_info) <- gene_info$id
# Forming the full data matrix.
full_data <- do.call(cbind, full_data)
rownames(full_data) <- gene_info$id
# Applying some filtering if requested.
if (!is.null(min_mean_gene_counts)) {
keep_gene <- (Matrix::rowSums(data_mat) >= min_mean_gene_counts)
full_data <- full_data[keep_gene,]
gene_info <- gene_info[keep_gene,]
}
# Adding the cell data.
cell_info <- do.call(rbind, cell_info_list)
SingleCellExperiment(list(counts = full_data), rowData = gene_info,
colData = cell_info)
}
#' @rdname read10xResults
#' @export
read10XResults <- function(...) {
read10xResults(...)
}
|
library(pirate)
### Name: gem_test
### Title: Implement Fitted GEM criterior on a Data Set
### Aliases: gem_test gem_test_sample gem_test_simsample
### ** Examples
#constructing the covariance matrix
co <- matrix(0.2, 10, 10)
diag(co) <- 1
dataEx <- data_generator1(d = 0.3, R2 = 0.5, v2 = 1, n = 300,
co = co, beta1 = rep(1,10),inter = c(0,0))
#fit the GEM
dat <- dataEx[[1]]
model_nu <- gem_fit(dat = dat, method = "nu")
#calculate the population average benefit in the data sample
gem_test_sample(dat,model_nu[[2]])
#calculate the population average benefit when outcome under both treatment conditions
#is known, usually in a simulated sample
bigData <- data_generator3(n = 1000,co = co,bet =dataEx[[2]], inter = c(0,0))
gem_test_simsample(bigData[[1]],bigData[[2]],bigData[[3]],model_nu[[2]])
| /data/genthat_extracted_code/pirate/examples/gem_test.Rd.R | no_license | surayaaramli/typeRrh | R | false | false | 835 | r | library(pirate)
### Name: gem_test
### Title: Implement Fitted GEM criterior on a Data Set
### Aliases: gem_test gem_test_sample gem_test_simsample
### ** Examples
#constructing the covariance matrix
co <- matrix(0.2, 10, 10)
diag(co) <- 1
dataEx <- data_generator1(d = 0.3, R2 = 0.5, v2 = 1, n = 300,
co = co, beta1 = rep(1,10),inter = c(0,0))
#fit the GEM
dat <- dataEx[[1]]
model_nu <- gem_fit(dat = dat, method = "nu")
#calculate the population average benefit in the data sample
gem_test_sample(dat,model_nu[[2]])
#calculate the population average benefit when outcome under both treatment conditions
#is known, usually in a simulated sample
bigData <- data_generator3(n = 1000,co = co,bet =dataEx[[2]], inter = c(0,0))
gem_test_simsample(bigData[[1]],bigData[[2]],bigData[[3]],model_nu[[2]])
|
atom_nom <- function(residuetype, atom, mode){
delta <- -1
natom <- ""
switch(mode,
full = switch(residuetype,
ALA = switch(atom,
HB1 = {natom = "QB"
delta=1},
HB2 = {natom = "QB"
delta=1},
HB3 = {natom = "QB"
delta=1},
QB = c()),
ARG = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HG2 = c(),
HG3 = c(),
QG = c(),
HD2 = c(),
HD3 = c(),
QD = c(),
HH11 = c(),
HH12 = c(),
QH1 = c(),
HH21 = c(),
HH22 = c(),
QH2 = c()),
ASN = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HD21 = c(),
HD22 = c(),
QD2 = c()),
ASP = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c()),
CYS = switch(atom,
HB2 = c(),
HB3 = c(),
HG = c(),
QB = c()),
CYSS = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c()),
GLN = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HG2 = c(),
HG3 = c(),
QG = c(),
HE21 = c(),
HE22 = c(),
QE2 = c()),
GLU = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HG2 = c(),
HG3 = c(),
QG = c()),
GLY = switch(atom,
HA2 = c(),
HA3 = c(),
QA = c()),
HIS = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HD1 = c(),
HD2 = c(),
HE1 = c()),
ILE = switch(atom,
HB = c(),
HG21 = {natom = "QG2"
delta=1},
HG22 = {natom = "QG2"
delta=1},
HG23 = {natom = "QG2"
delta=1},
QG2 = c(),
HG12 = c(),
HG13 = c(),
QG1 = c(),
HD11 = {natom = "QD1"
delta=1},
HD12 = {natom = "QD1"
delta=1},
HD13 = {natom = "QD1"
delta=1},
QD1 = c()),
LEU = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HG = c(),
HD11 = {natom = "QD1"
delta=1},
HD12 = {natom = "QD1"
delta=1},
HD13 = {natom = "QD1"
delta=1},
QD1 = c(),
HD21 = {natom = "QD2"
delta=1},
HD22 = {natom = "QD2"
delta=1},
HD23 = {natom = "QD2"
delta=1},
QD2 = c(),
QQD = c()),
LYS = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HG2 = c(),
HG3 = c(),
QG = c(),
HD2 = c(),
HD3 = c(),
QD = c(),
HE2 = c(),
HE3 = c(),
QE = c(),
HZ1 = {natom = "QZ"
delta=1},
HZ2 = {natom = "QZ"
delta=1},
HZ3 = {natom = "QZ"
delta=1},
QZ = c()),
MET = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HG2 = c(),
HG3 = c(),
QG = c(),
HE1 = {natom = "QE"
delta=1},
HE2 = {natom = "QE"
delta=1},
HE3 = {natom = "QE"
delta=1},
QE = c()),
PHE = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HD1 = c(),
HD2 = c(),
QD = c(),
HB2 = c(),
HB3 = c(),
QB = c(),
HE1 = c(),
HE2 = c(),
QE = c(),
QR = c(),
HZ = c()),
PRO = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HG2 = c(),
HG3 = c(),
QG = c(),
HD2 = c(),
HD3 = c(),
QD = c()),
SER = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HG = c()),
THR = switch(atom,
HB = c(),
HG21 = {natom = "QG2"
delta=1},
HG22 = {natom = "QG2"
delta=1},
HG23 = {natom = "QG2"
delta=1},
QG2 = c(),
HG1 = c()),
TRP = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HD1 = c(),
HE1 = c(),
HE3 = c(),
HZ2 = c(),
HZ3 = c(),
HH2 = c()),
TYR = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HD1 = c(),
HD2 = c(),
QD = c(),
HE1 = c(),
HE2 = c(),
QE = c(),
QR = c(),
HH = c()),
VAL = switch(atom,
HB = c(),
HG11 = {natom = "QG1"
delta=1},
HG12 = {natom = "QG1"
delta=1},
HG13 = {natom = "QG1"
delta=1},
QG1 = c(),
HG21 = {natom = "QG2"
delta=1},
HG22 = {natom = "QG2"
delta=1},
HG23 = {natom = "QG2"
delta=1},
QG2 = c(),
QQG = c())),
homitted = switch(residuetype,
ALA = switch(atom,
HB1 = {natom = "CB"
delta=1},
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = c()),
ARG = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HG2 = {natom = "CG"
delta=1},
HG3 = {natom = "CG"
delta=1},
QG = {natom = "CG"
delta=1},
HD2 = {natom = "CD"
delta=1},
HD3 = {natom = "CD"
delta=1},
QD = {natom = "CD"
delta=1},
HH11 = c(),
HH12 = c(),
QH1 = c(),
HH21 = c(),
HH22 = c(),
QH2 = c()),
ASN = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HD21 = c(),
HD22 = c(),
QD2 = c()),
ASP = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1}),
CYS = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
HG = {natom = "SG"
delta=1},
QB = {natom = "CB"
delta=1}),
CYSS = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1}),
GLN = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HG2 = {natom = "CG"
delta=1},
HG3 = {natom = "CG"
delta=1},
QG = {natom = "CG"
delta=1},
HE21 = c(),
HE22 = c(),
QE2 = c()),
GLU = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HG2 = {natom = "CG"
delta=1},
HG3 = {natom = "CG"
delta=1},
QG = {natom = "CG"
delta=1}),
GLY = switch(atom,
HA2 = c(),
HA3 = c(),
QA = c()),
HIS = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HD1 = c(),
HD2 = c(),
HE1 = c()),
ILE = switch(atom,
HB = c(),
HG21 = {natom = "QG2"
delta=1},
HG22 = {natom = "QG2"
delta=1},
HG23 = {natom = "QG2"
delta=1},
QG2 = c(),
HG12 = {natom = "CG1"
delta=1},
HG13 = {natom = "CG1"
delta=1},
QG1 = {natom = "CG1"
delta=1},
HD11 = {natom = "QD1"
delta=1},
HD12 = {natom = "QD1"
delta=1},
HD13 = {natom = "QD1"
delta=1},
QD1 = c()),
LEU = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HG = c(),
HD11 = {natom = "QD1"
delta=1},
HD12 = {natom = "QD1"
delta=1},
HD13 = {natom = "QD1"
delta=1},
QD1 = c(),
HD21 = {natom = "QD2"
delta=1},
HD22 = {natom = "QD2"
delta=1},
HD23 = {natom = "QD2"
delta=1},
QD2 = c(),
QQD = c()),
LYS = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HG2 = {natom = "CG"
delta=1},
HG3 = {natom = "CG"
delta=1},
QG = {natom = "CG"
delta=1},
HD2 = {natom = "CD"
delta=1},
HD3 = {natom = "CD"
delta=1},
QD = {natom = "CD"
delta=1},
HE2 = {natom = "CE"
delta=1},
HE3 = {natom = "CE"
delta=1},
QE = {natom = "CE"
delta=1},
HZ1 = {natom = "QZ"
delta=1},
HZ2 = {natom = "QZ"
delta=1},
HZ3 = {natom = "QZ"
delta=1},
QZ = c()),
MET = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HG2 = {natom = "CG"
delta=1},
HG3 = {natom = "CG"
delta=1},
QG = {natom = "CG"
delta=1},
HE1 = {natom = "QE"
delta=1},
HE2 = {natom = "QE"
delta=1},
HE3 = {natom = "QE"
delta=1},
QE = c()),
PHE = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HD1 = c(),
HD2 = c(),
QD = c(),
HB2 = c(),
HB3 = c(),
QB = c(),
HE1 = c(),
HE2 = c(),
QE = c(),
QR = c(),
HZ = c()),
PRO = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HG2 = c(),
HG3 = c(),
QG = c(),
HD2 = c(),
HD3 = c(),
QD = c()),
SER = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HG = {natom = "OG"
delta=1}),
THR = switch(atom,
HB = c(),
HG21 = {natom = "QG2"
delta=1},
HG22 = {natom = "QG2"
delta=1},
HG23 = {natom = "QG2"
delta=1},
QG2 = c(),
HG1 = {natom = "OG1"
delta=1}),
TRP = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HD1 = c(),
HE1 = c(),
HE3 = c(),
HZ2 = c(),
HZ3 = c(),
HH2 = c()),
TYR = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HD1 = c(),
HD2 = c(),
QD = c(),
HE1 = c(),
HE2 = c(),
QE = c(),
QR = c(),
HH = {natom = "OH"
delta=1}),
VAL = switch(atom,
HB = c(),
HG11 = {natom = "QG1"
delta=1},
HG12 = {natom = "QG1"
delta=1},
HG13 = {natom = "QG1"
delta=1},
QG1 = c(),
HG21 = {natom = "QG2"
delta=1},
HG22 = {natom = "QG2"
delta=1},
HG23 = {natom = "QG2"
delta=1},
QG2 = c(),
QQG = c())),
xplor = switch(residuetype,
ALA = switch(atom,
HB1 = c(),
HB2 = c(),
HB3 = c(),
QB = c()),
ARG = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HG2 = c(),
HG1 = {natom="HG3"
delta=-1},
QG = c(),
HD2 = c(),
HD1 = {natom="HD3"
delta=-1},
QD = c(),
HH11 = c(),
HH12 = c(),
QH1 = c(),
HH21 = c(),
HH22 = c(),
QH2 = c()),
ASN = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HD21 = c(),
HD22 = c(),
QD2 = c()),
ASP = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c()),
CYS = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
HG = c(),
QB = c()),
CYSS = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c()),
GLN = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HG2 = c(),
HG1 = {natom="HG3"
delta=-1},
QG = c(),
HE21 = c(),
HE22 = c(),
QE2 = c()),
GLU = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HG2 = c(),
HG1 = {natom="HG3"
delta=-1},
QG = c()),
GLY = switch(atom,
HA2 = c(),
HA1 = {natom="HA3"
delta=-1},
QA = c()),
HIS = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HD1 = c(),
HD2 = c(),
HE1 = c()),
ILE = switch(atom,
HB = c(),
HG21 = c(),
HG22 = c(),
HG23 = c(),
QG2 = c(),
HG12 = c(),
HG11 = {natom="HG13"
delta=-1},
QG1 = c(),
HD11 = c(),
HD12 = c(),
HD13 = c(),
QD1 = c()),
LEU = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HG = c(),
HD11 = c(),
HD12 = c(),
HD13 = c(),
QD1 = c(),
HD21 = c(),
HD22 = c(),
HD23 = c(),
QD2 = c(),
QQD = c()),
LYS = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HG2 = c(),
HG1 = {natom="HG3"
delta=-1},
QG = c(),
HD2 = c(),
HD1 = {natom="HD3"
delta=-1},
QD = c(),
HE2 = c(),
HE1 = {natom="HE3"
delta=-1},
QE = c(),
HZ1 = c(),
HZ2 = c(),
HZ3 = c(),
QZ = c()),
MET = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HG2 = c(),
HG1 = {natom="HG3"
delta=-1},
QG = c(),
HE1 = c(),
HE2 = c(),
HE3 = c(),
QE = c()),
PHE = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HD1 = c(),
HD2 = c(),
QD = c(),
HB2 = c(),
HB3 = c(),
QB = c(),
HE1 = c(),
HE2 = c(),
QE = c(),
QR = c(),
HZ = c()),
PRO = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HG2 = c(),
HG1 = {natom="HG3"
delta=-1},
QG = c(),
HD2 = c(),
HD1 = {natom="HD3"
delta=-1},
QD = c()),
SER = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HG = c()),
THR = switch(atom,
HB = c(),
HG21 = c(),
HG22 = c(),
HG23 = c(),
QG2 = c(),
HG1 = c()),
TRP = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HD1 = c(),
HE1 = c(),
HE3 = c(),
HZ2 = c(),
HZ3 = c(),
HH2 = c()),
TYR = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HD1 = c(),
HD2 = c(),
QD = c(),
HE1 = c(),
HE2 = c(),
QE = c(),
QR = c(),
HH = c()),
VAL = switch(atom,
HB = c(),
HG11 = c(),
HG12 = c(),
HG13 = c(),
QG1 = c(),
HG21 = c(),
HG22 = c(),
HG23 = c(),
QG2 = c(),
QQG = c())))
return(list(natom=natom, delta=delta))
}
| /R/atom_nom.R | no_license | AdamRahman/sprosr | R | false | false | 38,766 | r | atom_nom <- function(residuetype, atom, mode){
delta <- -1
natom <- ""
switch(mode,
full = switch(residuetype,
ALA = switch(atom,
HB1 = {natom = "QB"
delta=1},
HB2 = {natom = "QB"
delta=1},
HB3 = {natom = "QB"
delta=1},
QB = c()),
ARG = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HG2 = c(),
HG3 = c(),
QG = c(),
HD2 = c(),
HD3 = c(),
QD = c(),
HH11 = c(),
HH12 = c(),
QH1 = c(),
HH21 = c(),
HH22 = c(),
QH2 = c()),
ASN = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HD21 = c(),
HD22 = c(),
QD2 = c()),
ASP = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c()),
CYS = switch(atom,
HB2 = c(),
HB3 = c(),
HG = c(),
QB = c()),
CYSS = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c()),
GLN = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HG2 = c(),
HG3 = c(),
QG = c(),
HE21 = c(),
HE22 = c(),
QE2 = c()),
GLU = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HG2 = c(),
HG3 = c(),
QG = c()),
GLY = switch(atom,
HA2 = c(),
HA3 = c(),
QA = c()),
HIS = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HD1 = c(),
HD2 = c(),
HE1 = c()),
ILE = switch(atom,
HB = c(),
HG21 = {natom = "QG2"
delta=1},
HG22 = {natom = "QG2"
delta=1},
HG23 = {natom = "QG2"
delta=1},
QG2 = c(),
HG12 = c(),
HG13 = c(),
QG1 = c(),
HD11 = {natom = "QD1"
delta=1},
HD12 = {natom = "QD1"
delta=1},
HD13 = {natom = "QD1"
delta=1},
QD1 = c()),
LEU = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HG = c(),
HD11 = {natom = "QD1"
delta=1},
HD12 = {natom = "QD1"
delta=1},
HD13 = {natom = "QD1"
delta=1},
QD1 = c(),
HD21 = {natom = "QD2"
delta=1},
HD22 = {natom = "QD2"
delta=1},
HD23 = {natom = "QD2"
delta=1},
QD2 = c(),
QQD = c()),
LYS = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HG2 = c(),
HG3 = c(),
QG = c(),
HD2 = c(),
HD3 = c(),
QD = c(),
HE2 = c(),
HE3 = c(),
QE = c(),
HZ1 = {natom = "QZ"
delta=1},
HZ2 = {natom = "QZ"
delta=1},
HZ3 = {natom = "QZ"
delta=1},
QZ = c()),
MET = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HG2 = c(),
HG3 = c(),
QG = c(),
HE1 = {natom = "QE"
delta=1},
HE2 = {natom = "QE"
delta=1},
HE3 = {natom = "QE"
delta=1},
QE = c()),
PHE = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HD1 = c(),
HD2 = c(),
QD = c(),
HB2 = c(),
HB3 = c(),
QB = c(),
HE1 = c(),
HE2 = c(),
QE = c(),
QR = c(),
HZ = c()),
PRO = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HG2 = c(),
HG3 = c(),
QG = c(),
HD2 = c(),
HD3 = c(),
QD = c()),
SER = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HG = c()),
THR = switch(atom,
HB = c(),
HG21 = {natom = "QG2"
delta=1},
HG22 = {natom = "QG2"
delta=1},
HG23 = {natom = "QG2"
delta=1},
QG2 = c(),
HG1 = c()),
TRP = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HD1 = c(),
HE1 = c(),
HE3 = c(),
HZ2 = c(),
HZ3 = c(),
HH2 = c()),
TYR = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HD1 = c(),
HD2 = c(),
QD = c(),
HE1 = c(),
HE2 = c(),
QE = c(),
QR = c(),
HH = c()),
VAL = switch(atom,
HB = c(),
HG11 = {natom = "QG1"
delta=1},
HG12 = {natom = "QG1"
delta=1},
HG13 = {natom = "QG1"
delta=1},
QG1 = c(),
HG21 = {natom = "QG2"
delta=1},
HG22 = {natom = "QG2"
delta=1},
HG23 = {natom = "QG2"
delta=1},
QG2 = c(),
QQG = c())),
homitted = switch(residuetype,
ALA = switch(atom,
HB1 = {natom = "CB"
delta=1},
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = c()),
ARG = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HG2 = {natom = "CG"
delta=1},
HG3 = {natom = "CG"
delta=1},
QG = {natom = "CG"
delta=1},
HD2 = {natom = "CD"
delta=1},
HD3 = {natom = "CD"
delta=1},
QD = {natom = "CD"
delta=1},
HH11 = c(),
HH12 = c(),
QH1 = c(),
HH21 = c(),
HH22 = c(),
QH2 = c()),
ASN = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HD21 = c(),
HD22 = c(),
QD2 = c()),
ASP = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1}),
CYS = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
HG = {natom = "SG"
delta=1},
QB = {natom = "CB"
delta=1}),
CYSS = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1}),
GLN = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HG2 = {natom = "CG"
delta=1},
HG3 = {natom = "CG"
delta=1},
QG = {natom = "CG"
delta=1},
HE21 = c(),
HE22 = c(),
QE2 = c()),
GLU = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HG2 = {natom = "CG"
delta=1},
HG3 = {natom = "CG"
delta=1},
QG = {natom = "CG"
delta=1}),
GLY = switch(atom,
HA2 = c(),
HA3 = c(),
QA = c()),
HIS = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HD1 = c(),
HD2 = c(),
HE1 = c()),
ILE = switch(atom,
HB = c(),
HG21 = {natom = "QG2"
delta=1},
HG22 = {natom = "QG2"
delta=1},
HG23 = {natom = "QG2"
delta=1},
QG2 = c(),
HG12 = {natom = "CG1"
delta=1},
HG13 = {natom = "CG1"
delta=1},
QG1 = {natom = "CG1"
delta=1},
HD11 = {natom = "QD1"
delta=1},
HD12 = {natom = "QD1"
delta=1},
HD13 = {natom = "QD1"
delta=1},
QD1 = c()),
LEU = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HG = c(),
HD11 = {natom = "QD1"
delta=1},
HD12 = {natom = "QD1"
delta=1},
HD13 = {natom = "QD1"
delta=1},
QD1 = c(),
HD21 = {natom = "QD2"
delta=1},
HD22 = {natom = "QD2"
delta=1},
HD23 = {natom = "QD2"
delta=1},
QD2 = c(),
QQD = c()),
LYS = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HG2 = {natom = "CG"
delta=1},
HG3 = {natom = "CG"
delta=1},
QG = {natom = "CG"
delta=1},
HD2 = {natom = "CD"
delta=1},
HD3 = {natom = "CD"
delta=1},
QD = {natom = "CD"
delta=1},
HE2 = {natom = "CE"
delta=1},
HE3 = {natom = "CE"
delta=1},
QE = {natom = "CE"
delta=1},
HZ1 = {natom = "QZ"
delta=1},
HZ2 = {natom = "QZ"
delta=1},
HZ3 = {natom = "QZ"
delta=1},
QZ = c()),
MET = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HG2 = {natom = "CG"
delta=1},
HG3 = {natom = "CG"
delta=1},
QG = {natom = "CG"
delta=1},
HE1 = {natom = "QE"
delta=1},
HE2 = {natom = "QE"
delta=1},
HE3 = {natom = "QE"
delta=1},
QE = c()),
PHE = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HD1 = c(),
HD2 = c(),
QD = c(),
HB2 = c(),
HB3 = c(),
QB = c(),
HE1 = c(),
HE2 = c(),
QE = c(),
QR = c(),
HZ = c()),
PRO = switch(atom,
HB2 = c(),
HB3 = c(),
QB = c(),
HG2 = c(),
HG3 = c(),
QG = c(),
HD2 = c(),
HD3 = c(),
QD = c()),
SER = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HG = {natom = "OG"
delta=1}),
THR = switch(atom,
HB = c(),
HG21 = {natom = "QG2"
delta=1},
HG22 = {natom = "QG2"
delta=1},
HG23 = {natom = "QG2"
delta=1},
QG2 = c(),
HG1 = {natom = "OG1"
delta=1}),
TRP = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HD1 = c(),
HE1 = c(),
HE3 = c(),
HZ2 = c(),
HZ3 = c(),
HH2 = c()),
TYR = switch(atom,
HB2 = {natom = "CB"
delta=1},
HB3 = {natom = "CB"
delta=1},
QB = {natom = "CB"
delta=1},
HD1 = c(),
HD2 = c(),
QD = c(),
HE1 = c(),
HE2 = c(),
QE = c(),
QR = c(),
HH = {natom = "OH"
delta=1}),
VAL = switch(atom,
HB = c(),
HG11 = {natom = "QG1"
delta=1},
HG12 = {natom = "QG1"
delta=1},
HG13 = {natom = "QG1"
delta=1},
QG1 = c(),
HG21 = {natom = "QG2"
delta=1},
HG22 = {natom = "QG2"
delta=1},
HG23 = {natom = "QG2"
delta=1},
QG2 = c(),
QQG = c())),
xplor = switch(residuetype,
ALA = switch(atom,
HB1 = c(),
HB2 = c(),
HB3 = c(),
QB = c()),
ARG = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HG2 = c(),
HG1 = {natom="HG3"
delta=-1},
QG = c(),
HD2 = c(),
HD1 = {natom="HD3"
delta=-1},
QD = c(),
HH11 = c(),
HH12 = c(),
QH1 = c(),
HH21 = c(),
HH22 = c(),
QH2 = c()),
ASN = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HD21 = c(),
HD22 = c(),
QD2 = c()),
ASP = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c()),
CYS = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
HG = c(),
QB = c()),
CYSS = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c()),
GLN = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HG2 = c(),
HG1 = {natom="HG3"
delta=-1},
QG = c(),
HE21 = c(),
HE22 = c(),
QE2 = c()),
GLU = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HG2 = c(),
HG1 = {natom="HG3"
delta=-1},
QG = c()),
GLY = switch(atom,
HA2 = c(),
HA1 = {natom="HA3"
delta=-1},
QA = c()),
HIS = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HD1 = c(),
HD2 = c(),
HE1 = c()),
ILE = switch(atom,
HB = c(),
HG21 = c(),
HG22 = c(),
HG23 = c(),
QG2 = c(),
HG12 = c(),
HG11 = {natom="HG13"
delta=-1},
QG1 = c(),
HD11 = c(),
HD12 = c(),
HD13 = c(),
QD1 = c()),
LEU = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HG = c(),
HD11 = c(),
HD12 = c(),
HD13 = c(),
QD1 = c(),
HD21 = c(),
HD22 = c(),
HD23 = c(),
QD2 = c(),
QQD = c()),
LYS = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HG2 = c(),
HG1 = {natom="HG3"
delta=-1},
QG = c(),
HD2 = c(),
HD1 = {natom="HD3"
delta=-1},
QD = c(),
HE2 = c(),
HE1 = {natom="HE3"
delta=-1},
QE = c(),
HZ1 = c(),
HZ2 = c(),
HZ3 = c(),
QZ = c()),
MET = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HG2 = c(),
HG1 = {natom="HG3"
delta=-1},
QG = c(),
HE1 = c(),
HE2 = c(),
HE3 = c(),
QE = c()),
PHE = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HD1 = c(),
HD2 = c(),
QD = c(),
HB2 = c(),
HB3 = c(),
QB = c(),
HE1 = c(),
HE2 = c(),
QE = c(),
QR = c(),
HZ = c()),
PRO = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HG2 = c(),
HG1 = {natom="HG3"
delta=-1},
QG = c(),
HD2 = c(),
HD1 = {natom="HD3"
delta=-1},
QD = c()),
SER = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HG = c()),
THR = switch(atom,
HB = c(),
HG21 = c(),
HG22 = c(),
HG23 = c(),
QG2 = c(),
HG1 = c()),
TRP = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HD1 = c(),
HE1 = c(),
HE3 = c(),
HZ2 = c(),
HZ3 = c(),
HH2 = c()),
TYR = switch(atom,
HB2 = c(),
HB1 = {natom="HB3"
delta=-1},
QB = c(),
HD1 = c(),
HD2 = c(),
QD = c(),
HE1 = c(),
HE2 = c(),
QE = c(),
QR = c(),
HH = c()),
VAL = switch(atom,
HB = c(),
HG11 = c(),
HG12 = c(),
HG13 = c(),
QG1 = c(),
HG21 = c(),
HG22 = c(),
HG23 = c(),
QG2 = c(),
QQG = c())))
return(list(natom=natom, delta=delta))
}
|
# THIS FUNCTION CALCULATES CLIMATIC WATER DEFICIT USING EITHER MONTHLY ANNUAL DATA oR MEAN MONTHLY DATA
# DATA INPUT: site (i.e. a unique id for a given location), slope (degrees), latitude (decimal degrees), folded aspect (degrees), ppt, tmean, soilawc (in cm.; default is 200 cm), month, and year (if using annual data only; default is null))
####note: you input each variable as a vector, but they need to all line up correctly and be the same length, except for soil awc (that way if you don't have soil awc data for each site, you can specify a specific value, such as soilawc = 300)
# PACKAGES THAT MUST BE INSTALLED BEFORE RUNNING THE SCRIPT: data.table and geosphere
# EXAMPLE SCRIPTS:
###cwd_data<-cwd_function(site=data$site,slope=data$slope,latitude=data$latitude,foldedaspect=data$foldedaspect,ppt=data$ppt,tmean=data$tmean,month=data$month,year=data$year,type="annual")
###cwd_normal_data<-cwd_function(site=data$site,slope=data$slope,latitude=data$latitude,foldedaspect=data$foldedaspect,ppt=data$ppt,tmean=data$tmean,month=data$month,type="normal")
#example script with soil awc specified as one number across all sites:
### cwd_normal_data<-cwd_function(site=data$site,slope=data$slope,latitude=data$latitude,foldedaspect=data$foldedaspect,ppt=data$ppt,tmean=data$tmean,month=data$month,soilawc=300,type="normal")
#example script where I have unique soil awc data for each site:
### cwd_normal_data<-cwd_function(site=data$site,slope=data$slope,latitude=data$latitude,foldedaspect=data$foldedaspect,ppt=data$ppt,tmean=data$tmean,month=data$month,soilawc=data$soilawc,type="normal")
cwd_function <- function(site,slope,latitude,foldedaspect,ppt,tmean,month,soilawc=200,year=NULL,type=c('normal','annual')) {
# Packages
require(data.table)
require(geosphere)
#R SCRIPT
if (type =="annual"){
data<-as.data.table(cbind(as.character(site),slope,latitude,foldedaspect,ppt,tmean,month,year))[,soilawc:=soilawc]
colnames(data)<-c("site","slope","latitude","foldedaspect","ppt","tmean","month","year","soilawc")}
if (type =="normal"){
data<-as.data.table(cbind(site,slope,latitude,foldedaspect,ppt,tmean,month))[,soilawc:=soilawc]
colnames(data)<-c("site","slope","latitude","foldedaspect","ppt","tmean","month","soilawc")
}
data$site<-as.character(data$site)
data$slope<-as.numeric(as.character(data$slope))
data$latitude<-as.numeric(as.character(data$latitude))
data$foldedaspect<-as.numeric(as.character(data$foldedaspect))
data$ppt<-as.numeric(as.character(data$ppt))
data$tmean<-as.numeric(as.character(data$tmean))
data$month<-as.numeric(as.character(data$month))
### for 30 year normal data only, we'll create iterations so that way the water storage can carry over from one year to the next
if (type == "normal"){year<-c(rep(1,length(data$site)),rep(2,length(data$site)),rep(3,length(data$site)),rep(4,length(data$site)),rep(5,length(data$site)),rep(6,length(data$site)),rep(7,length(data$site)),rep(8,length(data$site)),rep(9,length(data$site)),rep(10,length(data$site)))
data<-rbind(data,data,data,data,data,data,data,data,data,data)}
data$year<-as.numeric(as.character(year))
# calculate daylength
daylength<-vector()
datasite<-data[,.(latitude=mean(latitude)),by=.(site)]
for (i in 1:length(datasite$latitude)){
dl<-daylength(datasite$latitude[i],1:365)
day<- tapply(dl, rep(1:12, c(31,28,31,30,31,30,31,31,30,31,30,31)), mean)
site <- as.vector(rep(datasite$site[i],length(day)))
month<-as.vector(c(1,2,3,4,5,6,7,8,9,10,11,12))
join<-cbind(site,month,day)
daylength<-rbind(daylength,join)
}
daylength<-as.data.frame(daylength)
daylength$site<-as.character(daylength$site)
daylength$month<-as.numeric(as.character(daylength$month))
daylength$day<-as.numeric(as.character(daylength$day))
data<-merge(data,daylength,by=c("site","month"))
##########data$year<-year
# calculate all other variables
data[,fm:=ifelse(tmean<0,0,ifelse(tmean>=6,1,tmean*0.166666666))]
data[,rainm:=fm*ppt]
data[,snowm:=(1-fm)*ppt]
data<-setorder(data,site,year,month)
sites<-unique(data$site)
mergedata<-vector()
for (i in 1:length(sites)){
packm<-vector()
sitedat<-setorder(data,site,year,month)[site==sites[i],]
for (j in 1:length(sitedat$site)){
packmsite<-ifelse(j>1,(((1-sitedat$fm[j])^2)*sitedat$ppt[j])+((1-sitedat$fm[j])*packm[j-1]),(((1-sitedat$fm[j])^2)*sitedat$ppt[j]))
packm<-c(packm,packmsite)
site<-as.character(sitedat$site)
year<-as.numeric(as.character(sitedat$year))
month<-as.numeric(as.character(sitedat$month))
}
mergedat<-cbind(site,year,month,packm)
mergedata<-rbind(mergedata,mergedat)
}
mergedt<-as.data.table(mergedata)
mergedt$year<-as.numeric(as.character(mergedt$year))
mergedt$month<-as.numeric(as.character(mergedt$month))
mergedt$site<-as.character(mergedt$site)
data<-merge(data,mergedt,by=c("site","year","month"))
data$packm<-as.numeric(as.character(data$packm))
data[,meltm:=fm*snowm*data.table::shift(packm,1L,type="lag",fill=0)]
data[,wm:=rainm+meltm]
data[month==1 | month==3 |month==5|month==7|month==8|month==10|month==12,days:=31]
data[month==4 | month==6 |month==9|month==11,days:=30]
data[month==2,days:=28]
data[,ea:=exp(((17.3*tmean)/(tmean+273.2)))*0.611]
# convert slope, folded aspect, and latitude to radians
data[,sloprad:=slope*0.0174532925]
data[,afrad:=foldedaspect*0.0174532925]
data[,latrad:=latitude*0.0174532925]
#calculate heat load
data[,heatload:=0.339+0.808*(cos(latrad)*cos(sloprad))-0.196*(sin(latrad)*sin(sloprad))-0.482*(cos(afrad)*sin(sloprad))]
data[,petm:=ifelse(tmean<0,0,((((ea*tmean)/(tmean+273.3))*day*days*29.8)*heatload/10))]
mergedata<-vector()
for (i in 1:length(sites)){
soilm<-vector()
sitedat<-setorder(data,site,year,month)[site==sites[i],]
for (j in 1:length(sitedat$site)){
soilmsite<-ifelse(j>1,ifelse((sitedat$wm[j]-sitedat$petm[j]+soilm[j-1])<=0,0,ifelse((sitedat$wm[j]-sitedat$petm[j]+soilm[j-1])<sitedat$soilawc[j],(sitedat$wm[j]-sitedat$petm[j]+soilm[j-1]),sitedat$soilawc[j])),ifelse((sitedat$wm[j]-sitedat$petm[j])<=0,0,ifelse((sitedat$wm[j]-sitedat$petm[j])<sitedat$soilawc[j],(sitedat$wm[j]-sitedat$petm[j]),sitedat$soilawc[j])))
soilm<-c(soilm,soilmsite)
site<-as.character(sitedat$site)
year<-as.numeric(as.character(sitedat$year))
month<-as.numeric(as.character(sitedat$month))
}
mergedat<-cbind(site,year,month,soilm)
mergedata<-rbind(mergedata,mergedat)
}
mergedt<-as.data.table(mergedata)
mergedt$year<-as.numeric(as.character(mergedt$year))
mergedt$month<-as.numeric(as.character(mergedt$month))
mergedt$site<-as.character(mergedt$site)
data<-merge(data,mergedt,by=c("site","year","month"))
data$soilm<-as.numeric(as.character(data$soilm))
data[,soilm1:=data.table::shift(soilm,1L,type="lag",fill=0)]
data[,deltsoil:=(soilm1*(1-(exp(-1*(petm-wm)/soilawc))))]
data[,deltsoilwm:=ifelse(deltsoil>0,wm+deltsoil,wm)]
data[,aet:=ifelse(deltsoilwm<petm,deltsoilwm,petm)]
data[,cwd:=petm-aet]
##### for 800 m normal data then we subset to get just the last simulation
if (type == "normal"){data<-data[year==10,]}
return(data)}
| /resources/calculations/cwd_function.R | no_license | rahuezo/gdp_temperature_project | R | false | false | 7,009 | r |
# THIS FUNCTION CALCULATES CLIMATIC WATER DEFICIT USING EITHER MONTHLY ANNUAL DATA oR MEAN MONTHLY DATA
# DATA INPUT: site (i.e. a unique id for a given location), slope (degrees), latitude (decimal degrees), folded aspect (degrees), ppt, tmean, soilawc (in cm.; default is 200 cm), month, and year (if using annual data only; default is null))
####note: you input each variable as a vector, but they need to all line up correctly and be the same length, except for soil awc (that way if you don't have soil awc data for each site, you can specify a specific value, such as soilawc = 300)
# PACKAGES THAT MUST BE INSTALLED BEFORE RUNNING THE SCRIPT: data.table and geosphere
# EXAMPLE SCRIPTS:
###cwd_data<-cwd_function(site=data$site,slope=data$slope,latitude=data$latitude,foldedaspect=data$foldedaspect,ppt=data$ppt,tmean=data$tmean,month=data$month,year=data$year,type="annual")
###cwd_normal_data<-cwd_function(site=data$site,slope=data$slope,latitude=data$latitude,foldedaspect=data$foldedaspect,ppt=data$ppt,tmean=data$tmean,month=data$month,type="normal")
#example script with soil awc specified as one number across all sites:
### cwd_normal_data<-cwd_function(site=data$site,slope=data$slope,latitude=data$latitude,foldedaspect=data$foldedaspect,ppt=data$ppt,tmean=data$tmean,month=data$month,soilawc=300,type="normal")
#example script where I have unique soil awc data for each site:
### cwd_normal_data<-cwd_function(site=data$site,slope=data$slope,latitude=data$latitude,foldedaspect=data$foldedaspect,ppt=data$ppt,tmean=data$tmean,month=data$month,soilawc=data$soilawc,type="normal")
cwd_function <- function(site,slope,latitude,foldedaspect,ppt,tmean,month,soilawc=200,year=NULL,type=c('normal','annual')) {
# Packages
require(data.table)
require(geosphere)
#R SCRIPT
if (type =="annual"){
data<-as.data.table(cbind(as.character(site),slope,latitude,foldedaspect,ppt,tmean,month,year))[,soilawc:=soilawc]
colnames(data)<-c("site","slope","latitude","foldedaspect","ppt","tmean","month","year","soilawc")}
if (type =="normal"){
data<-as.data.table(cbind(site,slope,latitude,foldedaspect,ppt,tmean,month))[,soilawc:=soilawc]
colnames(data)<-c("site","slope","latitude","foldedaspect","ppt","tmean","month","soilawc")
}
data$site<-as.character(data$site)
data$slope<-as.numeric(as.character(data$slope))
data$latitude<-as.numeric(as.character(data$latitude))
data$foldedaspect<-as.numeric(as.character(data$foldedaspect))
data$ppt<-as.numeric(as.character(data$ppt))
data$tmean<-as.numeric(as.character(data$tmean))
data$month<-as.numeric(as.character(data$month))
### for 30 year normal data only, we'll create iterations so that way the water storage can carry over from one year to the next
if (type == "normal"){year<-c(rep(1,length(data$site)),rep(2,length(data$site)),rep(3,length(data$site)),rep(4,length(data$site)),rep(5,length(data$site)),rep(6,length(data$site)),rep(7,length(data$site)),rep(8,length(data$site)),rep(9,length(data$site)),rep(10,length(data$site)))
data<-rbind(data,data,data,data,data,data,data,data,data,data)}
data$year<-as.numeric(as.character(year))
# calculate daylength
daylength<-vector()
datasite<-data[,.(latitude=mean(latitude)),by=.(site)]
for (i in 1:length(datasite$latitude)){
dl<-daylength(datasite$latitude[i],1:365)
day<- tapply(dl, rep(1:12, c(31,28,31,30,31,30,31,31,30,31,30,31)), mean)
site <- as.vector(rep(datasite$site[i],length(day)))
month<-as.vector(c(1,2,3,4,5,6,7,8,9,10,11,12))
join<-cbind(site,month,day)
daylength<-rbind(daylength,join)
}
daylength<-as.data.frame(daylength)
daylength$site<-as.character(daylength$site)
daylength$month<-as.numeric(as.character(daylength$month))
daylength$day<-as.numeric(as.character(daylength$day))
data<-merge(data,daylength,by=c("site","month"))
##########data$year<-year
# calculate all other variables
data[,fm:=ifelse(tmean<0,0,ifelse(tmean>=6,1,tmean*0.166666666))]
data[,rainm:=fm*ppt]
data[,snowm:=(1-fm)*ppt]
data<-setorder(data,site,year,month)
sites<-unique(data$site)
mergedata<-vector()
for (i in 1:length(sites)){
packm<-vector()
sitedat<-setorder(data,site,year,month)[site==sites[i],]
for (j in 1:length(sitedat$site)){
packmsite<-ifelse(j>1,(((1-sitedat$fm[j])^2)*sitedat$ppt[j])+((1-sitedat$fm[j])*packm[j-1]),(((1-sitedat$fm[j])^2)*sitedat$ppt[j]))
packm<-c(packm,packmsite)
site<-as.character(sitedat$site)
year<-as.numeric(as.character(sitedat$year))
month<-as.numeric(as.character(sitedat$month))
}
mergedat<-cbind(site,year,month,packm)
mergedata<-rbind(mergedata,mergedat)
}
mergedt<-as.data.table(mergedata)
mergedt$year<-as.numeric(as.character(mergedt$year))
mergedt$month<-as.numeric(as.character(mergedt$month))
mergedt$site<-as.character(mergedt$site)
data<-merge(data,mergedt,by=c("site","year","month"))
data$packm<-as.numeric(as.character(data$packm))
data[,meltm:=fm*snowm*data.table::shift(packm,1L,type="lag",fill=0)]
data[,wm:=rainm+meltm]
data[month==1 | month==3 |month==5|month==7|month==8|month==10|month==12,days:=31]
data[month==4 | month==6 |month==9|month==11,days:=30]
data[month==2,days:=28]
data[,ea:=exp(((17.3*tmean)/(tmean+273.2)))*0.611]
# convert slope, folded aspect, and latitude to radians
data[,sloprad:=slope*0.0174532925]
data[,afrad:=foldedaspect*0.0174532925]
data[,latrad:=latitude*0.0174532925]
#calculate heat load
data[,heatload:=0.339+0.808*(cos(latrad)*cos(sloprad))-0.196*(sin(latrad)*sin(sloprad))-0.482*(cos(afrad)*sin(sloprad))]
data[,petm:=ifelse(tmean<0,0,((((ea*tmean)/(tmean+273.3))*day*days*29.8)*heatload/10))]
mergedata<-vector()
for (i in 1:length(sites)){
soilm<-vector()
sitedat<-setorder(data,site,year,month)[site==sites[i],]
for (j in 1:length(sitedat$site)){
soilmsite<-ifelse(j>1,ifelse((sitedat$wm[j]-sitedat$petm[j]+soilm[j-1])<=0,0,ifelse((sitedat$wm[j]-sitedat$petm[j]+soilm[j-1])<sitedat$soilawc[j],(sitedat$wm[j]-sitedat$petm[j]+soilm[j-1]),sitedat$soilawc[j])),ifelse((sitedat$wm[j]-sitedat$petm[j])<=0,0,ifelse((sitedat$wm[j]-sitedat$petm[j])<sitedat$soilawc[j],(sitedat$wm[j]-sitedat$petm[j]),sitedat$soilawc[j])))
soilm<-c(soilm,soilmsite)
site<-as.character(sitedat$site)
year<-as.numeric(as.character(sitedat$year))
month<-as.numeric(as.character(sitedat$month))
}
mergedat<-cbind(site,year,month,soilm)
mergedata<-rbind(mergedata,mergedat)
}
mergedt<-as.data.table(mergedata)
mergedt$year<-as.numeric(as.character(mergedt$year))
mergedt$month<-as.numeric(as.character(mergedt$month))
mergedt$site<-as.character(mergedt$site)
data<-merge(data,mergedt,by=c("site","year","month"))
data$soilm<-as.numeric(as.character(data$soilm))
data[,soilm1:=data.table::shift(soilm,1L,type="lag",fill=0)]
data[,deltsoil:=(soilm1*(1-(exp(-1*(petm-wm)/soilawc))))]
data[,deltsoilwm:=ifelse(deltsoil>0,wm+deltsoil,wm)]
data[,aet:=ifelse(deltsoilwm<petm,deltsoilwm,petm)]
data[,cwd:=petm-aet]
##### for 800 m normal data then we subset to get just the last simulation
if (type == "normal"){data<-data[year==10,]}
return(data)}
|
testlist <- list(x = c(2.58981145684914e-307, 4.94065645841247e-324, 0, 5.92314661134617e-304, 0, 0, 3.21687814903302e+181, 1.81250437761959e-207, 7.48552777617268e+247, 5.77636796151408e-275, NaN, -9.40591696517169e+24, 1.00496080260073e+180, -7.99990688549151e-280, 1.53047510903779e-314, 0, 0, -5.15273907786594e-36, -6.95715257111252e+306, NaN, -1.65652687672654e-181, NaN, -1.06817373190923e+297, 3.53369545915248e+72, 7.21902962651992e-304, 5.50339608387342e-310, 0, 1.390671161567e-308, -4.73574659118105e+305, NaN, 5.0453059431966e+182, 3.91135427752315e-274, -1.34765550943377e+28, 9.11136727638145e-311, -6.67113314602392e+306, -5.82852024984172e+303, 8.85449539944218e-159, 2.14327978499502e-312, 128978.107570072, 1.59875023337884e-112, 8.98637298518713e-243, 1.6690200483343e-308, -7.98824203857711e-280, 9.11136590428902e-311, -6.67113314602392e+306, 4.8212389505418e+279, 1.62597454369523e-260, 2.13959396519322e+181, NaN, 5.2404436192131e+279, 9.94246486511075e-297, -1.34747548880033e+28, 2.33251002275375e-308, -4.19987381709656e-140, 7.20834935387592e-304, 8.85470246026103e-159, 5.48679624958878e-310, -1.27856197940926e+306, -5.15273908894671e-36, -7.72134029854232e-84, 7.15597569347109e-251, 2.42061551367857e+35, 2.41751947069162e+35, -4.19983489031012e-140), y = c(NaN, -8.15391703403251e-280, 1.00496080260072e+180, 8.02122686268216e-312, -4.16709697076072e+304, NaN, 3.53369545917587e+72, -6.671133146024e+306, 7.20834927233968e-304, -4.80964726313466e+306, -1.50540387407455e+85, -3.82914315864077e+305, -1.10534589198562e-176, NaN, -5.83693282055951e+294, 4.18006618602297e-297, 5.0758839169449e-116, 2.58981098488467e-307, 6.05156985652021e+34, 1.06096233928348e+37, NaN, 3.54164535367902e-317, 9.94246486511075e-297, 5.48671568880241e-310, -1.06812920585108e+297, -1.06812920585333e+297, -4.4610881607846e-140, 7.29112201950355e-304, -1.34763370135724e+28, -1.34765550943381e+28, -1.34765550943381e+28, -1.34765550943381e+28, -1.34765550943381e+28, -1.34765550943381e+28, -5.48684606877171e+303, 1.61363344975071e+184, -4.72620764279479e-85, -3.3131898344407e+304, 9.74576447753728e-113, 5.07588390157017e-116, 5.09911232106546e+182, 5.77636807742164e-275, NaN, 4.77830972673675e-299, 9.34609791357e-307, NaN, -3.07918242955014e+296, 4.18006618602297e-297, 1.26707338641151e-279, -2.94449594723056e+47, -2.94449594579902e+47, NaN, NaN, 3.53369910561397e+72, 3.13151305916514e-294, 1.52973827771342e-308, 1.3953860590809e-258, 5.48663231790394e-310, 2.31663158087643e+77, -1.49960546094526e+126, 0))
result <- do.call(blorr:::blr_pairs_cpp,testlist)
str(result) | /blorr/inst/testfiles/blr_pairs_cpp/libFuzzer_blr_pairs_cpp/blr_pairs_cpp_valgrind_files/1609956838-test.R | no_license | akhikolla/updated-only-Issues | R | false | false | 2,626 | r | testlist <- list(x = c(2.58981145684914e-307, 4.94065645841247e-324, 0, 5.92314661134617e-304, 0, 0, 3.21687814903302e+181, 1.81250437761959e-207, 7.48552777617268e+247, 5.77636796151408e-275, NaN, -9.40591696517169e+24, 1.00496080260073e+180, -7.99990688549151e-280, 1.53047510903779e-314, 0, 0, -5.15273907786594e-36, -6.95715257111252e+306, NaN, -1.65652687672654e-181, NaN, -1.06817373190923e+297, 3.53369545915248e+72, 7.21902962651992e-304, 5.50339608387342e-310, 0, 1.390671161567e-308, -4.73574659118105e+305, NaN, 5.0453059431966e+182, 3.91135427752315e-274, -1.34765550943377e+28, 9.11136727638145e-311, -6.67113314602392e+306, -5.82852024984172e+303, 8.85449539944218e-159, 2.14327978499502e-312, 128978.107570072, 1.59875023337884e-112, 8.98637298518713e-243, 1.6690200483343e-308, -7.98824203857711e-280, 9.11136590428902e-311, -6.67113314602392e+306, 4.8212389505418e+279, 1.62597454369523e-260, 2.13959396519322e+181, NaN, 5.2404436192131e+279, 9.94246486511075e-297, -1.34747548880033e+28, 2.33251002275375e-308, -4.19987381709656e-140, 7.20834935387592e-304, 8.85470246026103e-159, 5.48679624958878e-310, -1.27856197940926e+306, -5.15273908894671e-36, -7.72134029854232e-84, 7.15597569347109e-251, 2.42061551367857e+35, 2.41751947069162e+35, -4.19983489031012e-140), y = c(NaN, -8.15391703403251e-280, 1.00496080260072e+180, 8.02122686268216e-312, -4.16709697076072e+304, NaN, 3.53369545917587e+72, -6.671133146024e+306, 7.20834927233968e-304, -4.80964726313466e+306, -1.50540387407455e+85, -3.82914315864077e+305, -1.10534589198562e-176, NaN, -5.83693282055951e+294, 4.18006618602297e-297, 5.0758839169449e-116, 2.58981098488467e-307, 6.05156985652021e+34, 1.06096233928348e+37, NaN, 3.54164535367902e-317, 9.94246486511075e-297, 5.48671568880241e-310, -1.06812920585108e+297, -1.06812920585333e+297, -4.4610881607846e-140, 7.29112201950355e-304, -1.34763370135724e+28, -1.34765550943381e+28, -1.34765550943381e+28, -1.34765550943381e+28, -1.34765550943381e+28, -1.34765550943381e+28, -5.48684606877171e+303, 1.61363344975071e+184, -4.72620764279479e-85, -3.3131898344407e+304, 9.74576447753728e-113, 5.07588390157017e-116, 5.09911232106546e+182, 5.77636807742164e-275, NaN, 4.77830972673675e-299, 9.34609791357e-307, NaN, -3.07918242955014e+296, 4.18006618602297e-297, 1.26707338641151e-279, -2.94449594723056e+47, -2.94449594579902e+47, NaN, NaN, 3.53369910561397e+72, 3.13151305916514e-294, 1.52973827771342e-308, 1.3953860590809e-258, 5.48663231790394e-310, 2.31663158087643e+77, -1.49960546094526e+126, 0))
result <- do.call(blorr:::blr_pairs_cpp,testlist)
str(result) |
Nginx provisioning | /Deploy/roles/nginx/readme.rd | no_license | lion-u/Mentorship_program | R | false | false | 18 | rd | Nginx provisioning |
#Q1 Linear Regression Error
sigma=0.1
d=8
N=10
ExpectedEin = sigma^2 *(1-(d+1)/N)
ExpectedEin
#0.001
N=25
sigma^2 *(1-(d+1)/N)
#0.0064
N=100
sigma^2 *(1-(d+1)/N)
#0.0091
N=500
#0.00982
#Q2 NonLinear Transforms
#Q3
#number of parameters + 1
#Q4 Gradient Descent
#use chain rule
#https://www.khanacademy.org/math/multivariable-calculus/partial_derivatives_topic/partial_derivatives/v/partial-derivatives-2
#Q5
surfaceErrorFunction <- function(u,v) {
(u * exp(v) - 2 * v * exp(-u)) ^ 2
}
PDEu <- function(u,v) {
2*(exp(v) + 2 * v * exp(-u)) * (u * exp(v) - 2 * v * exp(-u))
}
PDEv <- function(u, v) {
2*(exp(v) * u - 2*v*exp(-u)) * (exp(v) * u - 2 * exp(-u))
}
learningRate <- 0.1
tolerance <- 10.0 ^ -14
u=1.0
v=1.0
step <- 0
surfaceError <- surfaceErrorFunction(u,v)
surfaceError
while(surfaceError > tolerance) {
uDescent <- PDEu(u,v)
vDescent <- PDEv(u,v)
u <- u - uDescent * learningRate
v <- v - vDescent * learningRate
surfaceError <- surfaceErrorFunction(u,v)
surfaceError
step <- step + 1
step
}
step
#10
#q6
# u
#[1] 0.04473629
#> v
#[1] 0.02395871
#Q7
u=1.0
v=1.0
step <- 0
maxSteps <- 15
while(step <= maxSteps) {
uDescent <- PDEu(u,v)
vDescent <- PDEv(u,v)
u <- u - uDescent * learningRate
uDescent <- PDEu(u,v)
vDescent <- PDEv(u,v)
v <- v - vDescent * learningRate
step <- step + 1
step
}
surfaceError <- surfaceErrorFunction(u,v)
surfaceError
# 0.1326554
#8. logistic regression
fTargetFunction <- function(trainingPoint, targetLineSlope,targetLineCoeff){
predictedYValue <- (targetLineSlope * trainingPoint$x) + targetLineCoeff
if(trainingPoint$y > predictedYValue ) {
trainingPointOrientation = 1 #above line
} else {
trainingPointOrientation = -1 #below line
}
trainingPointOrientation
}
crossEntropyFn <- function(trainingPoint, result, weights) {
error <- log(1 + exp(-result * tcrossprod(as.matrix(trainingPoint), as.matrix(weights))))
return(error)
}
gradient <- function( point, result, weights){
num = -(result * point)
den = 1 + (exp(result * tcrossprod(as.matrix(point), as.matrix(weights))))
return (num/den)
}
train <- function(trainingSet, weights, targetLineSlope, targetLineCoeff){
targetVector <- numeric()
for (i in 1:nrow(trainingSet)) {
trainingPoint <- trainingSet [i,]
targetValue <- fTargetFunction(trainingPoint,targetLineSlope, targetLineCoeff);
targetVector <- c(targetVector, targetValue);
}
learningRate = 0.01
previousWeights = data.frame(-1,-1,-1)
step = 0
displacement = 1
threshold = 0.01
while (displacement >= threshold) {
sampleSet = sample(1:nrow(trainingSet))
for (i in 1:length(sampleSet)) {
feature = trainingSet[i,]
result = targetVector[i]
descent = gradient(feature, result,weights)
weights = weights - learningRate * descent
}
displacement = norm(as.matrix(previousWeights) - as.matrix(weights))
previousWeights = weights
step = step + 1
#print( paste("Disp", displacement))
}
print(paste("Wts", weights, "step", step))
return(weights)
}
avgCrossEntropy <- 0.0
trainingPoints <- 100
testingPoints <- 1000
maxIteration=10
iterationCount = 1
while (iterationCount <= maxIteration) {
#Generate targetLine
targetLine <- data.frame(runif(2,-1,1),runif(2,-1,1))
names(targetLine ) <- c('x','y')
fit <- lm(targetLine$y~targetLine$x)
targetLineSlope <- fit$coefficients[2]
targetLineCoeff <- fit$coefficients[1]
#Initialize weights to 0
weights <- data.frame(0,0,0)
names(weights) <- c("w0","w1","w2")
trainingSet <- data.frame(1,runif(trainingPoints, -1, 1),runif(trainingPoints, -1, 1))
names(trainingSet) <- c('c','x','y')
weights <- train(trainingSet, weights, targetLineSlope, targetLineCoeff)
testingSet <- data.frame(1,runif(testingPoints, -1, 1),runif(testingPoints, -1, 1))
names(testingSet) <- c('c','x','y')
crossEntropy <- 0
for (i in 1:nrow(testingSet)) {
testingPoint <- testingSet [i,]
result <- fTargetFunction(testingPoint,targetLineSlope, targetLineCoeff);
crossEntropyVal <- crossEntropyFn(testingPoint,result, weights);
#print(crossEntropyVal);
crossEntropy = crossEntropy + crossEntropyVal
}
print(paste("CrossEntropy Error Avg",crossEntropy/nrow(testingSet)))
avgCrossEntropy <- avgCrossEntropy + (crossEntropy/nrow(testingSet))
iterationCount = iterationCount +1
}
print(paste("CrossEntropy Error Avg Mult Iterations",avgCrossEntropy/maxIteration))
#q8.
# 0.11
#q9
#353
#q1. c
#q2
#q3. c
#q4. e
#q5. d
#q6. e
#q7. a
#q8. d
#q9. b x a
#q10
| /Assignment5/AssignWk5_V1.R | no_license | dxrodri/Caltech_ML | R | false | false | 4,573 | r | #Q1 Linear Regression Error
sigma=0.1
d=8
N=10
ExpectedEin = sigma^2 *(1-(d+1)/N)
ExpectedEin
#0.001
N=25
sigma^2 *(1-(d+1)/N)
#0.0064
N=100
sigma^2 *(1-(d+1)/N)
#0.0091
N=500
#0.00982
#Q2 NonLinear Transforms
#Q3
#number of parameters + 1
#Q4 Gradient Descent
#use chain rule
#https://www.khanacademy.org/math/multivariable-calculus/partial_derivatives_topic/partial_derivatives/v/partial-derivatives-2
#Q5
surfaceErrorFunction <- function(u,v) {
(u * exp(v) - 2 * v * exp(-u)) ^ 2
}
PDEu <- function(u,v) {
2*(exp(v) + 2 * v * exp(-u)) * (u * exp(v) - 2 * v * exp(-u))
}
PDEv <- function(u, v) {
2*(exp(v) * u - 2*v*exp(-u)) * (exp(v) * u - 2 * exp(-u))
}
learningRate <- 0.1
tolerance <- 10.0 ^ -14
u=1.0
v=1.0
step <- 0
surfaceError <- surfaceErrorFunction(u,v)
surfaceError
while(surfaceError > tolerance) {
uDescent <- PDEu(u,v)
vDescent <- PDEv(u,v)
u <- u - uDescent * learningRate
v <- v - vDescent * learningRate
surfaceError <- surfaceErrorFunction(u,v)
surfaceError
step <- step + 1
step
}
step
#10
#q6
# u
#[1] 0.04473629
#> v
#[1] 0.02395871
#Q7
u=1.0
v=1.0
step <- 0
maxSteps <- 15
while(step <= maxSteps) {
uDescent <- PDEu(u,v)
vDescent <- PDEv(u,v)
u <- u - uDescent * learningRate
uDescent <- PDEu(u,v)
vDescent <- PDEv(u,v)
v <- v - vDescent * learningRate
step <- step + 1
step
}
surfaceError <- surfaceErrorFunction(u,v)
surfaceError
# 0.1326554
#8. logistic regression
fTargetFunction <- function(trainingPoint, targetLineSlope,targetLineCoeff){
predictedYValue <- (targetLineSlope * trainingPoint$x) + targetLineCoeff
if(trainingPoint$y > predictedYValue ) {
trainingPointOrientation = 1 #above line
} else {
trainingPointOrientation = -1 #below line
}
trainingPointOrientation
}
crossEntropyFn <- function(trainingPoint, result, weights) {
error <- log(1 + exp(-result * tcrossprod(as.matrix(trainingPoint), as.matrix(weights))))
return(error)
}
gradient <- function( point, result, weights){
num = -(result * point)
den = 1 + (exp(result * tcrossprod(as.matrix(point), as.matrix(weights))))
return (num/den)
}
train <- function(trainingSet, weights, targetLineSlope, targetLineCoeff){
targetVector <- numeric()
for (i in 1:nrow(trainingSet)) {
trainingPoint <- trainingSet [i,]
targetValue <- fTargetFunction(trainingPoint,targetLineSlope, targetLineCoeff);
targetVector <- c(targetVector, targetValue);
}
learningRate = 0.01
previousWeights = data.frame(-1,-1,-1)
step = 0
displacement = 1
threshold = 0.01
while (displacement >= threshold) {
sampleSet = sample(1:nrow(trainingSet))
for (i in 1:length(sampleSet)) {
feature = trainingSet[i,]
result = targetVector[i]
descent = gradient(feature, result,weights)
weights = weights - learningRate * descent
}
displacement = norm(as.matrix(previousWeights) - as.matrix(weights))
previousWeights = weights
step = step + 1
#print( paste("Disp", displacement))
}
print(paste("Wts", weights, "step", step))
return(weights)
}
avgCrossEntropy <- 0.0
trainingPoints <- 100
testingPoints <- 1000
maxIteration=10
iterationCount = 1
while (iterationCount <= maxIteration) {
#Generate targetLine
targetLine <- data.frame(runif(2,-1,1),runif(2,-1,1))
names(targetLine ) <- c('x','y')
fit <- lm(targetLine$y~targetLine$x)
targetLineSlope <- fit$coefficients[2]
targetLineCoeff <- fit$coefficients[1]
#Initialize weights to 0
weights <- data.frame(0,0,0)
names(weights) <- c("w0","w1","w2")
trainingSet <- data.frame(1,runif(trainingPoints, -1, 1),runif(trainingPoints, -1, 1))
names(trainingSet) <- c('c','x','y')
weights <- train(trainingSet, weights, targetLineSlope, targetLineCoeff)
testingSet <- data.frame(1,runif(testingPoints, -1, 1),runif(testingPoints, -1, 1))
names(testingSet) <- c('c','x','y')
crossEntropy <- 0
for (i in 1:nrow(testingSet)) {
testingPoint <- testingSet [i,]
result <- fTargetFunction(testingPoint,targetLineSlope, targetLineCoeff);
crossEntropyVal <- crossEntropyFn(testingPoint,result, weights);
#print(crossEntropyVal);
crossEntropy = crossEntropy + crossEntropyVal
}
print(paste("CrossEntropy Error Avg",crossEntropy/nrow(testingSet)))
avgCrossEntropy <- avgCrossEntropy + (crossEntropy/nrow(testingSet))
iterationCount = iterationCount +1
}
print(paste("CrossEntropy Error Avg Mult Iterations",avgCrossEntropy/maxIteration))
#q8.
# 0.11
#q9
#353
#q1. c
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#q3. c
#q4. e
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#q10
|
# Script to update model hyperparameters and check convergence
#source("check_conv.R")
# First need script to update sigma2 and nu
kappa.nu <- 5
lambda.nu <- 5/kappa.nu
kappa.sigma2 <- 5
lambda.sigma2 <- 0.05/kappa.sigma2
inv.chisq.like <- function(par,tau2.vec){
nu <- par[1]
sigma2 <- par[2]
N.tau2 <- length(tau2.vec)
no.data.term <- (nu/2)*log(nu/2) - lgamma(nu/2) + (nu/2)*log(sigma2)
log.like <- N.tau2*no.data.term - (nu/2 + 1)*sum(log(tau2.vec)) -
(nu*sigma2/2)*sum(1/tau2.vec)
return(log.like)
}
nu.log.prior <- function(nu,kappa,lambda){
log.prior <- (kappa-1)*log(nu) - (nu/lambda)
#log.prior <- -log(nu)
return(log.prior)
}
sigma2.log.prior <- function(sigma2,kappa,lambda){
log.prior <- (kappa-1)*log(sigma2) - (sigma2/lambda)
#log.prior <- -log(sigma2)
return(log.prior)
}
inv.chisq.post <- function(par,tau2.vec,kappa.nu,lambda.nu,
kappa.sigma2,lambda.sigma2){
# Optimize in log space
par <- exp(par)
log.post <- inv.chisq.like(par=par,tau2.vec=tau2.vec) +
nu.log.prior(nu=par[1],kappa=kappa.nu,lambda=lambda.nu) +
sigma2.log.prior(sigma2=par[2],kappa=kappa.sigma2,lambda=lambda.sigma2)
return(log.post)
}
joint.inv.chisq.optim <- function(tau2.vec,kappa.nu,lambda.nu,
kappa.sigma2,lambda.sigma2){
# Initialize parameters
sigma2 <- sigma2.update(tau2.vec=tau2.vec)
nu <- initialize.nu(tau2.vec=tau2.vec,sigma2=sigma2)
# Optimize in log space
par <- log(c(nu,sigma2))
# Get optimium of joint posterior
par.out <- optim(par=par,fn=inv.chisq.post,
tau2.vec=tau2.vec,
kappa.nu=kappa.nu,lambda.nu=lambda.nu,
kappa.sigma2=kappa.sigma2,
lambda.sigma2=lambda.sigma2,
control=list(fnscale=-1))$par
# Go back to constrained space
par.out <- exp(par.out)
names(par.out) <- c("nu","sigma2")
return(par.out)
}
###############################
nu.profile.log.post <- function(nu,sigma2,tau2.vec,kappa,lambda){
log.post <- inv.chisq.like(c(nu,sigma2),tau2.vec) +
nu.log.prior(nu,kappa,lambda)
return(log.post)
}
sigma2.profile.log.post <- function(nu,sigma2,tau2.vec,kappa,lambda){
log.post <- inv.chisq.like(c(nu,sigma2),tau2.vec) +
sigma2.log.prior(sigma2,kappa,lambda)
return(log.post)
}
nu.optimize <- function(tau2.vec,sigma2,kappa=NULL,lambda=NULL,prior=TRUE){
if(prior){
nu.new <- optimize(f=nu.profile.log.post,interval=c(0,1000),maximum=TRUE,
tau2.vec=tau2.vec,sigma2=sigma2,kappa=kappa,lambda=lambda)
} else {
nu.new <- optimize(f=inv.chisq.like,interval=c(0,1000),maximum=TRUE,
tau2.vec=tau2.vec,sigma2=sigma2)}
return(nu.new)
}
sigma2.optimize <- function(tau2.vec,nu,kappa,lambda){
# Initialize parameter
sigma2 <- sigma2.update(tau2.vec=tau2.vec)
sigma2.new <- optimize(f=sigma2.profile.log.post,interval=c(0,1000),maximum=TRUE,
tau2.vec=tau2.vec,nu=nu,kappa=kappa.sigma2,lambda=lambda.sigma2)
return(sigma2.new)
}
# sigma2 update is analytic
sigma2.update <- function(tau2.vec){
N.tau2 <- length(tau2.vec)
sigma2.new <- N.tau2/sum(1/tau2.vec)
return(sigma2.new)
}
# Get derivates of profile likelihood of nu
nu.fd <- function(nu,tau2.vec,sigma2,kappa,lambda){
N.tau2 <- length(tau2.vec)
fd <- N.tau2*log(nu/2) - N.tau2*digamma(nu/2) + N.tau2*log(sigma2) -
sum(log(tau2.vec)) + (kappa-1)/nu - 1/lambda
return(fd)
}
nu.sd <- function(nu,tau2.vec,sigma2,kappa,lambda){
N.tau2 <- length(tau2.vec)
sd <- N.tau2/nu - (N.tau2/2)*trigamma(nu/2) - (kappa-1)/nu^2
return(sd)
}
# One Newton-Raphson update for nu
nu.update <- function(nu,tau2.vec,sigma2,kappa,lambda,log.param=TRUE){
fd <- nu.fd(nu=nu,tau2.vec=tau2.vec,sigma2=sigma2,
kappa=kappa,lambda=lambda)
sd <- nu.sd(nu=nu,tau2.vec=tau2.vec,sigma2=sigma2,
kappa=kappa,lambda=lambda)
if(log.param){
# Transform nu to log-nu
log.nu <- log(nu)
# Need chain rule
fd.log <- fd*nu
sd.log <- nu*(nu*sd + fd)
nu.log.new <- log.nu - (fd.log/sd.log)
nu.new <- exp(nu.log.new)
} else {nu.new <- nu - (fd/sd)}
return(nu.new)
}
# Function to initialize nu with moment estimator
initialize.nu <- function(tau2.vec,sigma2){
mean.tau2 <- mean(tau2.vec)
nu.start <- 2*mean.tau2/(mean.tau2-sigma2)
# Make sure have reasonable first guess
#default.value <- sqrt(.Machine$double.eps)
default.value <- 1
nu.start <- max(nu.start,default.value)
return(nu.start)
}
# Newton-Raphson function for nu
nu.newton.raphson <- function(tau2.vec,sigma2,kappa,lambda,
reltol=1e-8,iter.max=100,
verbose=FALSE){
nu <- initialize.nu(tau2.vec,sigma2)
nu.vec <- nu
for(i in 1:iter.max){
print(nu)
nu.old <- nu
nu <- nu.update(nu=nu,tau2.vec=tau2.vec,sigma2=sigma2,
kappa=kappa,lambda=lambda)
nu.vec <- c(nu.vec,nu)
conv <- check.conv(old.param.vec=nu.old,new.param.vec=nu,
reltol=reltol)
if(conv){break}
}
if(verbose){
if(i < iter.max){print(paste("Newton-Raphson for nu converged in",i,"iterations"))
} else {print(paste("Newton-Raphson for nu failed to converge in",
iter.max,"iterations"))}
ts.plot(nu.vec)
}
#print(nu.vec)
return(nu)
}
# Overall function to update hyperparamters
update.hparam <- function(current.param.list,reltol=1e-6,
kappa.nu,lambda.nu,kappa.sigma2,
lambda.sigma2){
# Grab relevant parameters out of list
mu.corpus.vec <- current.param.list$mu.corpus.vec
tau2.vec <- as.vector(current.param.list$tau2.param.vecs)
print(summary(tau2.vec))
psi.old <- current.param.list$psi
gamma.old <- current.param.list$gamma
sigma2.old <- current.param.list$sigma2
nu.old <- current.param.list$nu
# Update hparams
#psi.new <- mean(mu.corpus.vec)
#gamma.new <- sd(mu.corpus.vec)
psi.new <- log(0.05)
gamma.new <- 0.1
n.tau2 <- length(tau2.vec)
nu.new <- mean(log(tau2.vec))
## nu.new <- -1
sigma2.new <- var(log(tau2.vec))*((n.tau2-1)/n.tau2)
## sigma2.new <- 2
## sigma2.new <- 1
## nu.new <- nu.old
## sigma2.new <- sigma2.optimize(tau2.vec=tau2.vec,nu=nu.new,kappa=kappa.sigma2,
## lambda=lambda.sigma2)$maximum
## par.inv.chisq.new <- joint.inv.chisq.optim(tau2.vec=tau2.vec,
## kappa.nu=kappa.nu,
## lambda.nu=lambda.nu,
## kappa.sigma2=kappa.sigma2,
## lambda.sigma2=lambda.sigma2)
## nu.new <- par.inv.chisq.new["nu"]
## sigma2.new <- par.inv.chisq.new["sigma2"]
## sigma2.new <- sigma2.update(tau2.vec=tau2.vec)
## print(sigma2.new)
## sigma2.new <- 0.05
## nu.new <- nu.optimize(tau2.vec=tau2.vec,sigma2=sigma2.new,
## kappa=kappa,lambda=lambda)$maximum
## nu.new <- 500
## print(nu.new)
## nu.new <- nu.newton.raphson(tau2.vec=tau2.vec,sigma2=sigma2.new,
## kappa=kappa,lambda=lambda)
# Put new hparams in current.param.list
current.param.list$psi <- psi.new
current.param.list$gamma <- gamma.new
current.param.list$sigma2 <- sigma2.new
current.param.list$nu <- nu.new
# Check global convergence of hyperparameters
global.conv <- check.conv(old.param.vec=c(psi.old,gamma.old,sigma2.old,nu.old),
new.param.vec=c(psi.new,gamma.new,sigma2.new,nu.new),
reltol=1e-6)
hparam.outlist <- list(current.param.list=current.param.list,
global.conv=global.conv)
return(hparam.outlist)
}
| /mmm_fit_code/mmm_fit_functions/hparam_update_OLD.R | no_license | jbischof/HPC_model | R | false | false | 7,857 | r | # Script to update model hyperparameters and check convergence
#source("check_conv.R")
# First need script to update sigma2 and nu
kappa.nu <- 5
lambda.nu <- 5/kappa.nu
kappa.sigma2 <- 5
lambda.sigma2 <- 0.05/kappa.sigma2
inv.chisq.like <- function(par,tau2.vec){
nu <- par[1]
sigma2 <- par[2]
N.tau2 <- length(tau2.vec)
no.data.term <- (nu/2)*log(nu/2) - lgamma(nu/2) + (nu/2)*log(sigma2)
log.like <- N.tau2*no.data.term - (nu/2 + 1)*sum(log(tau2.vec)) -
(nu*sigma2/2)*sum(1/tau2.vec)
return(log.like)
}
nu.log.prior <- function(nu,kappa,lambda){
log.prior <- (kappa-1)*log(nu) - (nu/lambda)
#log.prior <- -log(nu)
return(log.prior)
}
sigma2.log.prior <- function(sigma2,kappa,lambda){
log.prior <- (kappa-1)*log(sigma2) - (sigma2/lambda)
#log.prior <- -log(sigma2)
return(log.prior)
}
inv.chisq.post <- function(par,tau2.vec,kappa.nu,lambda.nu,
kappa.sigma2,lambda.sigma2){
# Optimize in log space
par <- exp(par)
log.post <- inv.chisq.like(par=par,tau2.vec=tau2.vec) +
nu.log.prior(nu=par[1],kappa=kappa.nu,lambda=lambda.nu) +
sigma2.log.prior(sigma2=par[2],kappa=kappa.sigma2,lambda=lambda.sigma2)
return(log.post)
}
joint.inv.chisq.optim <- function(tau2.vec,kappa.nu,lambda.nu,
kappa.sigma2,lambda.sigma2){
# Initialize parameters
sigma2 <- sigma2.update(tau2.vec=tau2.vec)
nu <- initialize.nu(tau2.vec=tau2.vec,sigma2=sigma2)
# Optimize in log space
par <- log(c(nu,sigma2))
# Get optimium of joint posterior
par.out <- optim(par=par,fn=inv.chisq.post,
tau2.vec=tau2.vec,
kappa.nu=kappa.nu,lambda.nu=lambda.nu,
kappa.sigma2=kappa.sigma2,
lambda.sigma2=lambda.sigma2,
control=list(fnscale=-1))$par
# Go back to constrained space
par.out <- exp(par.out)
names(par.out) <- c("nu","sigma2")
return(par.out)
}
###############################
nu.profile.log.post <- function(nu,sigma2,tau2.vec,kappa,lambda){
log.post <- inv.chisq.like(c(nu,sigma2),tau2.vec) +
nu.log.prior(nu,kappa,lambda)
return(log.post)
}
sigma2.profile.log.post <- function(nu,sigma2,tau2.vec,kappa,lambda){
log.post <- inv.chisq.like(c(nu,sigma2),tau2.vec) +
sigma2.log.prior(sigma2,kappa,lambda)
return(log.post)
}
nu.optimize <- function(tau2.vec,sigma2,kappa=NULL,lambda=NULL,prior=TRUE){
if(prior){
nu.new <- optimize(f=nu.profile.log.post,interval=c(0,1000),maximum=TRUE,
tau2.vec=tau2.vec,sigma2=sigma2,kappa=kappa,lambda=lambda)
} else {
nu.new <- optimize(f=inv.chisq.like,interval=c(0,1000),maximum=TRUE,
tau2.vec=tau2.vec,sigma2=sigma2)}
return(nu.new)
}
sigma2.optimize <- function(tau2.vec,nu,kappa,lambda){
# Initialize parameter
sigma2 <- sigma2.update(tau2.vec=tau2.vec)
sigma2.new <- optimize(f=sigma2.profile.log.post,interval=c(0,1000),maximum=TRUE,
tau2.vec=tau2.vec,nu=nu,kappa=kappa.sigma2,lambda=lambda.sigma2)
return(sigma2.new)
}
# sigma2 update is analytic
sigma2.update <- function(tau2.vec){
N.tau2 <- length(tau2.vec)
sigma2.new <- N.tau2/sum(1/tau2.vec)
return(sigma2.new)
}
# Get derivates of profile likelihood of nu
nu.fd <- function(nu,tau2.vec,sigma2,kappa,lambda){
N.tau2 <- length(tau2.vec)
fd <- N.tau2*log(nu/2) - N.tau2*digamma(nu/2) + N.tau2*log(sigma2) -
sum(log(tau2.vec)) + (kappa-1)/nu - 1/lambda
return(fd)
}
nu.sd <- function(nu,tau2.vec,sigma2,kappa,lambda){
N.tau2 <- length(tau2.vec)
sd <- N.tau2/nu - (N.tau2/2)*trigamma(nu/2) - (kappa-1)/nu^2
return(sd)
}
# One Newton-Raphson update for nu
nu.update <- function(nu,tau2.vec,sigma2,kappa,lambda,log.param=TRUE){
fd <- nu.fd(nu=nu,tau2.vec=tau2.vec,sigma2=sigma2,
kappa=kappa,lambda=lambda)
sd <- nu.sd(nu=nu,tau2.vec=tau2.vec,sigma2=sigma2,
kappa=kappa,lambda=lambda)
if(log.param){
# Transform nu to log-nu
log.nu <- log(nu)
# Need chain rule
fd.log <- fd*nu
sd.log <- nu*(nu*sd + fd)
nu.log.new <- log.nu - (fd.log/sd.log)
nu.new <- exp(nu.log.new)
} else {nu.new <- nu - (fd/sd)}
return(nu.new)
}
# Function to initialize nu with moment estimator
initialize.nu <- function(tau2.vec,sigma2){
mean.tau2 <- mean(tau2.vec)
nu.start <- 2*mean.tau2/(mean.tau2-sigma2)
# Make sure have reasonable first guess
#default.value <- sqrt(.Machine$double.eps)
default.value <- 1
nu.start <- max(nu.start,default.value)
return(nu.start)
}
# Newton-Raphson function for nu
nu.newton.raphson <- function(tau2.vec,sigma2,kappa,lambda,
reltol=1e-8,iter.max=100,
verbose=FALSE){
nu <- initialize.nu(tau2.vec,sigma2)
nu.vec <- nu
for(i in 1:iter.max){
print(nu)
nu.old <- nu
nu <- nu.update(nu=nu,tau2.vec=tau2.vec,sigma2=sigma2,
kappa=kappa,lambda=lambda)
nu.vec <- c(nu.vec,nu)
conv <- check.conv(old.param.vec=nu.old,new.param.vec=nu,
reltol=reltol)
if(conv){break}
}
if(verbose){
if(i < iter.max){print(paste("Newton-Raphson for nu converged in",i,"iterations"))
} else {print(paste("Newton-Raphson for nu failed to converge in",
iter.max,"iterations"))}
ts.plot(nu.vec)
}
#print(nu.vec)
return(nu)
}
# Overall function to update hyperparamters
update.hparam <- function(current.param.list,reltol=1e-6,
kappa.nu,lambda.nu,kappa.sigma2,
lambda.sigma2){
# Grab relevant parameters out of list
mu.corpus.vec <- current.param.list$mu.corpus.vec
tau2.vec <- as.vector(current.param.list$tau2.param.vecs)
print(summary(tau2.vec))
psi.old <- current.param.list$psi
gamma.old <- current.param.list$gamma
sigma2.old <- current.param.list$sigma2
nu.old <- current.param.list$nu
# Update hparams
#psi.new <- mean(mu.corpus.vec)
#gamma.new <- sd(mu.corpus.vec)
psi.new <- log(0.05)
gamma.new <- 0.1
n.tau2 <- length(tau2.vec)
nu.new <- mean(log(tau2.vec))
## nu.new <- -1
sigma2.new <- var(log(tau2.vec))*((n.tau2-1)/n.tau2)
## sigma2.new <- 2
## sigma2.new <- 1
## nu.new <- nu.old
## sigma2.new <- sigma2.optimize(tau2.vec=tau2.vec,nu=nu.new,kappa=kappa.sigma2,
## lambda=lambda.sigma2)$maximum
## par.inv.chisq.new <- joint.inv.chisq.optim(tau2.vec=tau2.vec,
## kappa.nu=kappa.nu,
## lambda.nu=lambda.nu,
## kappa.sigma2=kappa.sigma2,
## lambda.sigma2=lambda.sigma2)
## nu.new <- par.inv.chisq.new["nu"]
## sigma2.new <- par.inv.chisq.new["sigma2"]
## sigma2.new <- sigma2.update(tau2.vec=tau2.vec)
## print(sigma2.new)
## sigma2.new <- 0.05
## nu.new <- nu.optimize(tau2.vec=tau2.vec,sigma2=sigma2.new,
## kappa=kappa,lambda=lambda)$maximum
## nu.new <- 500
## print(nu.new)
## nu.new <- nu.newton.raphson(tau2.vec=tau2.vec,sigma2=sigma2.new,
## kappa=kappa,lambda=lambda)
# Put new hparams in current.param.list
current.param.list$psi <- psi.new
current.param.list$gamma <- gamma.new
current.param.list$sigma2 <- sigma2.new
current.param.list$nu <- nu.new
# Check global convergence of hyperparameters
global.conv <- check.conv(old.param.vec=c(psi.old,gamma.old,sigma2.old,nu.old),
new.param.vec=c(psi.new,gamma.new,sigma2.new,nu.new),
reltol=1e-6)
hparam.outlist <- list(current.param.list=current.param.list,
global.conv=global.conv)
return(hparam.outlist)
}
|
infoprobs <-
function (betas, z) {
cpr <- cprobs(betas, z)
ipr <- iprobs(betas, z)
sum.cprs <- lapply(cpr, function (x) {
nr <- nrow(x)
t((1 - rbind(x[1, ], x[-nr, ] + x[-1, ]))^2)
})
betas. <- sapply(betas, function (x) x[length(x)])
for (i in 1:length(betas))
sum.cprs[[i]] <- betas.[i]^2 * ipr[[i]] * sum.cprs[[i]]
do.call(cbind, lapply(sum.cprs, rowSums))
}
| /R/infoprobs.R | no_license | cran/ltm | R | false | false | 427 | r | infoprobs <-
function (betas, z) {
cpr <- cprobs(betas, z)
ipr <- iprobs(betas, z)
sum.cprs <- lapply(cpr, function (x) {
nr <- nrow(x)
t((1 - rbind(x[1, ], x[-nr, ] + x[-1, ]))^2)
})
betas. <- sapply(betas, function (x) x[length(x)])
for (i in 1:length(betas))
sum.cprs[[i]] <- betas.[i]^2 * ipr[[i]] * sum.cprs[[i]]
do.call(cbind, lapply(sum.cprs, rowSums))
}
|
library("deSolve")
growth = function(t, y, params) {
with(as.list(c(params,y)), {
## function values
zigma = P+FF+K+V
f = (zigma/80)*(637.27 - 0.305*density)/12/7 # [density] = 1/(dm^2), (zigma/80) er et gæt for hver meget mere større larver spiser
g = 0.125 * P # "gæt", sørger for maks 46% proteinindhold
kCombustC = 0.08473955701/0.5
h = kCombustC/2*K
#kCombust = -0.3485+0.033054*temp #For temp 10-30 grader. Ellers død?
kCombust = kCombustC/2*K
fCombust = 0.05298229217*FF
radius = (zigma/(10*pi))^(1/3) # approx radius (as if cylinder of water 5x as long as the diameter)
j = (0.22 * pi * radius^2) * # surface area
1440 * # minutes/day
0.89750342 *
(V/zigma - H) * # humidity difference, diffusion boundary assumed linear, replaces (1-H) in eq
(temp + 273.15)^(-1.4) * sqrt(vAir) * Pwater(temp)
lumenSize = 0.15*zigma
ppl = Pl/lumenSize
fpl = Fl/lumenSize
kpl = Kl/lumenSize
pa = Dp * Pl/lumenSize
fa = Df * Fl/lumenSize
ka = Dk * Kl/lumenSize # / lumenSize [mg]
## derivatives
dPx = ppl*f
dFx = fpl*f
dKx = kpl*f
dP = pa-g
dUx = 0.5*g
dF = fa + h - fCombust
dK = ka - h - kCombust
#dV = 0.5*g + kCombust + fCombust - j
dV = 0.5*g + (0.02108 * zigma) - j # (kCombust + fCombust) = 0.021808 uden skelnen, desværre
dVx = j
dPl = pp*f - ppl*f - pa
dFl = fp*f - fpl*f - fa
dKl = kp*f - kpl*f - ka
#print(j)
return(list(c(dPx, dFx, dKx, dUx, dVx,
dP, dF, dK, dV,
dPl, dFl, dKl, f, pa+fa+ka),
c(f = f, pa = pa, fa = fa, ka = ka, zigma = zigma, kCombust = kCombust, fCombust = fCombust, j = j, pp = 100*P/zigma, kp = 100*K/zigma, fp = 100*FF/zigma, vp = 100*V/zigma, ppt = 100*P/(P+K+FF), kpt = 100*K/(P+K+FF), fpt = 100*FF/(P+K+FF), urp = Ux/(Px+Fx+Kx+Ux), growthrate=dK+dF+dK)))
})
}
Pwater = function(temp) { # Antoine ligningen
10^(
2.124903 # for at omregne til Pascal
+ 8.07131 # A
- (
1730.63/ # B
(233.426 # C
+ temp) # [temp] = °C
)
)
}
params = c(
temp = 28,
density = 50,
H = 0.65,
pressure = 101325, # Pa
vAir = 0.15, # m/s
Dp = 100, # gæt
Df = 100, # gæt
Dk = 100, # (0.015umol/min)/(3 mol/L) * 1 kg/L omregnet til mg/d
pp = 0.33,
fp = 0.06,
kp = 0.61
)
initials = c(
Px = 0, Fx = 0, Kx = 0, Ux = 0, Vx = 0,
P = 2, FF = 3, K = 5, V = 5,
Pl = 0.2, Fl = 0.3, Kl = 0.5,
FoodConsumed = 0, FoodDigested = 0
)
sols = ode(initials,c(1:84),growth,params)
plot(sols)
print(sols[84,])
# Scatterplot
mi <- as.vector(c(0,10,0,101325/2,0.001,1,1,1,0,0,0),"numeric")
ma <- as.vector(c(100,100,1,101325*2,10,100,100,100,1,1,1),"numeric")
attribute.names = c('temp','density','H','pressure','vAir','Dp','Df','Dk','pp','fp','kp')
N.iter <- 500
P.end <- vector("numeric",length = N.iter)
FF.end <- vector("numeric",N.iter)
K.end <- vector("numeric",N.iter)
param.list = matrix(data = NA,nrow = length(params),ncol = N.iter)
#parms = matrix(data = NA,nrow = length(params),ncol = N.iter)
parms = vector("numeric", length = length(params))
GSA.res = matrix(data = NA,nrow = length(params),ncol = N.iter)
#output = matrix(data = NA,nrow = length(params),ncol = N.iter)
#j = 1
#loop param
set.seed(3)
for (j in 1:length(params)){
param.list[j,] = runif(N.iter,min=mi[j],max=ma[j])
}
for ( i in 1:N.iter){
# simulate the epidemic
parms = param.list[,i]
names(parms)<-attribute.names
output <- ode(initials,times = c(1:84), growth, parms)
# antal syge på samme tid
P.end[i] <- output[84,'ppt']
# t.max
FF.end[i] <- output[84,'fpt']
# totale antal syge
K.end[i] <- output[84,'kpt']
}
GSA.res <- data.frame(id=1:N.iter,P.end,FF.end,K.end)
names(GSA.res) <- c("id","P end","F end", "K end")
pairs(GSA.res)
# Andre metoder følger...
library(ggplot2)
library(reshape2)
#head(GSA.res)
#GSA.res[1] <- as.factor(P.max)
nyGSA = melt(GSA.res,id.vars=c("id"))
nyGSA$variable <- as.factor(nyGSA$variable)
ggplot(nyGSA, aes(x=variable,y=value)) +
geom_violin(trim=T, fill='pink', color="darkred")+
geom_boxplot(width=0.1) + theme_minimal() + ggtitle("Violinplot")+xlab("Slut næringsindhold")+ylab("Værdier")
| /globalsensitivitet.R | no_license | Dynamisk-Modellering-AEMMS/melorme-fagprojekt | R | false | false | 4,249 | r | library("deSolve")
growth = function(t, y, params) {
with(as.list(c(params,y)), {
## function values
zigma = P+FF+K+V
f = (zigma/80)*(637.27 - 0.305*density)/12/7 # [density] = 1/(dm^2), (zigma/80) er et gæt for hver meget mere større larver spiser
g = 0.125 * P # "gæt", sørger for maks 46% proteinindhold
kCombustC = 0.08473955701/0.5
h = kCombustC/2*K
#kCombust = -0.3485+0.033054*temp #For temp 10-30 grader. Ellers død?
kCombust = kCombustC/2*K
fCombust = 0.05298229217*FF
radius = (zigma/(10*pi))^(1/3) # approx radius (as if cylinder of water 5x as long as the diameter)
j = (0.22 * pi * radius^2) * # surface area
1440 * # minutes/day
0.89750342 *
(V/zigma - H) * # humidity difference, diffusion boundary assumed linear, replaces (1-H) in eq
(temp + 273.15)^(-1.4) * sqrt(vAir) * Pwater(temp)
lumenSize = 0.15*zigma
ppl = Pl/lumenSize
fpl = Fl/lumenSize
kpl = Kl/lumenSize
pa = Dp * Pl/lumenSize
fa = Df * Fl/lumenSize
ka = Dk * Kl/lumenSize # / lumenSize [mg]
## derivatives
dPx = ppl*f
dFx = fpl*f
dKx = kpl*f
dP = pa-g
dUx = 0.5*g
dF = fa + h - fCombust
dK = ka - h - kCombust
#dV = 0.5*g + kCombust + fCombust - j
dV = 0.5*g + (0.02108 * zigma) - j # (kCombust + fCombust) = 0.021808 uden skelnen, desværre
dVx = j
dPl = pp*f - ppl*f - pa
dFl = fp*f - fpl*f - fa
dKl = kp*f - kpl*f - ka
#print(j)
return(list(c(dPx, dFx, dKx, dUx, dVx,
dP, dF, dK, dV,
dPl, dFl, dKl, f, pa+fa+ka),
c(f = f, pa = pa, fa = fa, ka = ka, zigma = zigma, kCombust = kCombust, fCombust = fCombust, j = j, pp = 100*P/zigma, kp = 100*K/zigma, fp = 100*FF/zigma, vp = 100*V/zigma, ppt = 100*P/(P+K+FF), kpt = 100*K/(P+K+FF), fpt = 100*FF/(P+K+FF), urp = Ux/(Px+Fx+Kx+Ux), growthrate=dK+dF+dK)))
})
}
Pwater = function(temp) { # Antoine ligningen
10^(
2.124903 # for at omregne til Pascal
+ 8.07131 # A
- (
1730.63/ # B
(233.426 # C
+ temp) # [temp] = °C
)
)
}
params = c(
temp = 28,
density = 50,
H = 0.65,
pressure = 101325, # Pa
vAir = 0.15, # m/s
Dp = 100, # gæt
Df = 100, # gæt
Dk = 100, # (0.015umol/min)/(3 mol/L) * 1 kg/L omregnet til mg/d
pp = 0.33,
fp = 0.06,
kp = 0.61
)
initials = c(
Px = 0, Fx = 0, Kx = 0, Ux = 0, Vx = 0,
P = 2, FF = 3, K = 5, V = 5,
Pl = 0.2, Fl = 0.3, Kl = 0.5,
FoodConsumed = 0, FoodDigested = 0
)
sols = ode(initials,c(1:84),growth,params)
plot(sols)
print(sols[84,])
# Scatterplot
mi <- as.vector(c(0,10,0,101325/2,0.001,1,1,1,0,0,0),"numeric")
ma <- as.vector(c(100,100,1,101325*2,10,100,100,100,1,1,1),"numeric")
attribute.names = c('temp','density','H','pressure','vAir','Dp','Df','Dk','pp','fp','kp')
N.iter <- 500
P.end <- vector("numeric",length = N.iter)
FF.end <- vector("numeric",N.iter)
K.end <- vector("numeric",N.iter)
param.list = matrix(data = NA,nrow = length(params),ncol = N.iter)
#parms = matrix(data = NA,nrow = length(params),ncol = N.iter)
parms = vector("numeric", length = length(params))
GSA.res = matrix(data = NA,nrow = length(params),ncol = N.iter)
#output = matrix(data = NA,nrow = length(params),ncol = N.iter)
#j = 1
#loop param
set.seed(3)
for (j in 1:length(params)){
param.list[j,] = runif(N.iter,min=mi[j],max=ma[j])
}
for ( i in 1:N.iter){
# simulate the epidemic
parms = param.list[,i]
names(parms)<-attribute.names
output <- ode(initials,times = c(1:84), growth, parms)
# antal syge på samme tid
P.end[i] <- output[84,'ppt']
# t.max
FF.end[i] <- output[84,'fpt']
# totale antal syge
K.end[i] <- output[84,'kpt']
}
GSA.res <- data.frame(id=1:N.iter,P.end,FF.end,K.end)
names(GSA.res) <- c("id","P end","F end", "K end")
pairs(GSA.res)
# Andre metoder følger...
library(ggplot2)
library(reshape2)
#head(GSA.res)
#GSA.res[1] <- as.factor(P.max)
nyGSA = melt(GSA.res,id.vars=c("id"))
nyGSA$variable <- as.factor(nyGSA$variable)
ggplot(nyGSA, aes(x=variable,y=value)) +
geom_violin(trim=T, fill='pink', color="darkred")+
geom_boxplot(width=0.1) + theme_minimal() + ggtitle("Violinplot")+xlab("Slut næringsindhold")+ylab("Værdier")
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ani-compression-profile.R
\docType{class}
\name{anisotropyprofile_summary-class}
\alias{anisotropyprofile_summary-class}
\title{Summary of Anisotropy profile}
\description{
Information about the anisotropy-profile.
}
| /man/anisotropyprofile_summary-class.Rd | no_license | antiphon/Kdirectional | R | false | true | 295 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ani-compression-profile.R
\docType{class}
\name{anisotropyprofile_summary-class}
\alias{anisotropyprofile_summary-class}
\title{Summary of Anisotropy profile}
\description{
Information about the anisotropy-profile.
}
|
\name{see.citations}
\alias{see.citations}
\title{see.citations}
\description{
Extract author information from a saved search.
}
\usage{
see.citations(search = NULL, results = NULL, index = 'article.titles', ...)
}
\arguments{
\item{search}{a string or regular expression which identifies paper(s) to open in search results}
\item{results}{saved search results from a search.pubmed( ) call}
\item{index}{results column to search in saved search results}
\item{...}{optional arguments passed to rcrossref::cr_cn}
}
\value{
tibble
}
\examples{
## save search
#saved.search <- search.pubmed(term = 'baby')
##see citations from article with 'Postnatal' title
#see.citations('Postnatal', results = saved.search)
## see citations from article with "Children's" in abstract
#see.citations("Children's", results = saved.search, index = 'article.abstracts')
## see citations from article with "Children's" in abstract in text format
#see.citations("Children's", results = saved.search, index = 'article.abstracts', format = 'text')
}
| /man/see.citations.Rd | no_license | ShawnBrad/SearchPubmed | R | false | false | 1,029 | rd | \name{see.citations}
\alias{see.citations}
\title{see.citations}
\description{
Extract author information from a saved search.
}
\usage{
see.citations(search = NULL, results = NULL, index = 'article.titles', ...)
}
\arguments{
\item{search}{a string or regular expression which identifies paper(s) to open in search results}
\item{results}{saved search results from a search.pubmed( ) call}
\item{index}{results column to search in saved search results}
\item{...}{optional arguments passed to rcrossref::cr_cn}
}
\value{
tibble
}
\examples{
## save search
#saved.search <- search.pubmed(term = 'baby')
##see citations from article with 'Postnatal' title
#see.citations('Postnatal', results = saved.search)
## see citations from article with "Children's" in abstract
#see.citations("Children's", results = saved.search, index = 'article.abstracts')
## see citations from article with "Children's" in abstract in text format
#see.citations("Children's", results = saved.search, index = 'article.abstracts', format = 'text')
}
|
setwd("C:/Users/dusti/Box Sync/Personal Computer/Quantitative Biodiversity/Group Project/GroupProject")
rm(list=ls())
library(vegan)
library(psych)
library(ggplot2)
library(forcats)
library(dplyr)
library(corrplot)
# SPECIES RICHNESS CODE, WITHOUT FORAGING GUILDS
data.without.guilds <- read.csv("hf085-01-bird.csv", header = TRUE)
data.num <- data.without.guilds[ ,3:51]
S.obs <- function(x = ""){
rowSums(x > 0) * 1
}
obs.rich <- S.obs(data.num)
obs.rich
S.chao2 <- function(site = "", SbyS = ""){
SbyS = as.data.frame(SbyS)
x = SbyS[site, ]
SbyS.pa <- (SbyS > 0) * 1
Q1 = sum(colSums(SbyS.pa) == 1)
Q2 = sum(colSums(SbyS.pa) == 2)
S.chao2 = S.obs(x) + (Q1^2)/(2*Q2)
return(S.chao2)
}
S.chao2
est.rich <- S.chao2(1:40, data.num)
est.rich
# FORAGING GUILDS CODE, MEAN ABUNDANCE
Guilds <- read.csv("ForagingGuildsDataFile.csv")
str(Guilds)
summary(Guilds)
sem <- function(x){
sd(na.omit(x))/sqrt(length(na.omit(x)))
}
#Compute means for omnivores and insectivores in the different mortality classes
Omnivores.Mean <- tapply(Guilds$Omn_16, Guilds$mortality.class, mean)
Omnivores.Mean
Insectivores.Mean <- tapply(Guilds$Ins_29, Guilds$mortality.class, mean)
Insectivores.Mean
#Compute standard errors for those means
Omnivores.SE <- tapply(Guilds$Omn_16, Guilds$mortality.class, sem)
Omnivores.SE
Insectivores.SE <- tapply(Guilds$Ins_29, Guilds$mortality.class, sem)
Insectivores.SE
#Omnivore bar plot
OmnivorePlot <- barplot(Omnivores.Mean, ylim = c(0, round(1.5*max(Omnivores.Mean), digits = 0)),
pch = 15, cex = 1.25, las = 1, cex.lab = 1.4, cex.axis = 1.25,
xlab = "mortality class",
ylab = "mean site abundance",
names.arg = c("low", "medium", "high A", "high B"))
arrows(x0 = OmnivorePlot, y0 = Omnivores.Mean, y1 = Omnivores.Mean - Omnivores.SE, angle = 90,
length = 0.1, lwd = 1)
arrows(x0 = OmnivorePlot, y0 = Omnivores.Mean , y1 = Omnivores.Mean + Omnivores.SE, angle = 90,
length = 0.1, lwd = 1)
#Insectivore bar plot
InsectivorePlot <- barplot(Insectivores.Mean, ylim = c(0, round(1.5*max(Insectivores.Mean), digits = 0)),
pch = 15, cex = 1.25, las = 1, cex.lab = 1.4, cex.axis = 1.25,
xlab = "mortality class",
ylab = "mean site abundance",
names.arg = c("low", "medium", "high A", "high B"))
arrows(x0 = InsectivorePlot, y0 = Insectivores.Mean, y1 = Insectivores.Mean - Insectivores.SE, angle = 90,
length = 0.1, lwd = 1)
arrows(x0 = InsectivorePlot, y0 = Insectivores.Mean , y1 = Insectivores.Mean + Insectivores.SE, angle = 90,
length = 0.1, lwd = 1)
#Get omnivore and insectivore bars on same plot
#Load excel file with means and S.E.s for both guilds at each mortality class
ClusteredBar <- read.csv("GuildsClusteredBar.csv", header = TRUE)
ClusteredBar
str(ClusteredBar)
#Reorder x axis
ClusteredBar$Mortality <- fct_relevel(ClusteredBar$Mortality, "Low", "Medium", "High-B", "High-A")
ggplot(ClusteredBar, aes(x=Mortality, y = Mean, fill = Guild)) +
geom_bar(position=position_dodge(), stat ="identity", colour = 'black') + geom_errorbar(aes(ymin=Mean-S.E., ymax=Mean+S.E.), width = .2, position = position_dodge(.9)) + ggtitle("Bird Abundance")
#Run Omnivore Anova
OmnivoreAnova <- aov(Guilds$Omn_16~Guilds$mortality.class, data = Guilds)
OmnivoreAnova
summary(OmnivoreAnova)
TukeyHSD(OmnivoreAnova)
#Run Insectivore ANOVA
InsectivoreAnova <- aov(Guilds$Ins_29~Guilds$mortality.class, data = Guilds)
summary(InsectivoreAnova)
TukeyHSD(InsectivoreAnova)
| /GuildsData.R | no_license | dubrewer92/GroupProject | R | false | false | 3,634 | r | setwd("C:/Users/dusti/Box Sync/Personal Computer/Quantitative Biodiversity/Group Project/GroupProject")
rm(list=ls())
library(vegan)
library(psych)
library(ggplot2)
library(forcats)
library(dplyr)
library(corrplot)
# SPECIES RICHNESS CODE, WITHOUT FORAGING GUILDS
data.without.guilds <- read.csv("hf085-01-bird.csv", header = TRUE)
data.num <- data.without.guilds[ ,3:51]
S.obs <- function(x = ""){
rowSums(x > 0) * 1
}
obs.rich <- S.obs(data.num)
obs.rich
S.chao2 <- function(site = "", SbyS = ""){
SbyS = as.data.frame(SbyS)
x = SbyS[site, ]
SbyS.pa <- (SbyS > 0) * 1
Q1 = sum(colSums(SbyS.pa) == 1)
Q2 = sum(colSums(SbyS.pa) == 2)
S.chao2 = S.obs(x) + (Q1^2)/(2*Q2)
return(S.chao2)
}
S.chao2
est.rich <- S.chao2(1:40, data.num)
est.rich
# FORAGING GUILDS CODE, MEAN ABUNDANCE
Guilds <- read.csv("ForagingGuildsDataFile.csv")
str(Guilds)
summary(Guilds)
sem <- function(x){
sd(na.omit(x))/sqrt(length(na.omit(x)))
}
#Compute means for omnivores and insectivores in the different mortality classes
Omnivores.Mean <- tapply(Guilds$Omn_16, Guilds$mortality.class, mean)
Omnivores.Mean
Insectivores.Mean <- tapply(Guilds$Ins_29, Guilds$mortality.class, mean)
Insectivores.Mean
#Compute standard errors for those means
Omnivores.SE <- tapply(Guilds$Omn_16, Guilds$mortality.class, sem)
Omnivores.SE
Insectivores.SE <- tapply(Guilds$Ins_29, Guilds$mortality.class, sem)
Insectivores.SE
#Omnivore bar plot
OmnivorePlot <- barplot(Omnivores.Mean, ylim = c(0, round(1.5*max(Omnivores.Mean), digits = 0)),
pch = 15, cex = 1.25, las = 1, cex.lab = 1.4, cex.axis = 1.25,
xlab = "mortality class",
ylab = "mean site abundance",
names.arg = c("low", "medium", "high A", "high B"))
arrows(x0 = OmnivorePlot, y0 = Omnivores.Mean, y1 = Omnivores.Mean - Omnivores.SE, angle = 90,
length = 0.1, lwd = 1)
arrows(x0 = OmnivorePlot, y0 = Omnivores.Mean , y1 = Omnivores.Mean + Omnivores.SE, angle = 90,
length = 0.1, lwd = 1)
#Insectivore bar plot
InsectivorePlot <- barplot(Insectivores.Mean, ylim = c(0, round(1.5*max(Insectivores.Mean), digits = 0)),
pch = 15, cex = 1.25, las = 1, cex.lab = 1.4, cex.axis = 1.25,
xlab = "mortality class",
ylab = "mean site abundance",
names.arg = c("low", "medium", "high A", "high B"))
arrows(x0 = InsectivorePlot, y0 = Insectivores.Mean, y1 = Insectivores.Mean - Insectivores.SE, angle = 90,
length = 0.1, lwd = 1)
arrows(x0 = InsectivorePlot, y0 = Insectivores.Mean , y1 = Insectivores.Mean + Insectivores.SE, angle = 90,
length = 0.1, lwd = 1)
#Get omnivore and insectivore bars on same plot
#Load excel file with means and S.E.s for both guilds at each mortality class
ClusteredBar <- read.csv("GuildsClusteredBar.csv", header = TRUE)
ClusteredBar
str(ClusteredBar)
#Reorder x axis
ClusteredBar$Mortality <- fct_relevel(ClusteredBar$Mortality, "Low", "Medium", "High-B", "High-A")
ggplot(ClusteredBar, aes(x=Mortality, y = Mean, fill = Guild)) +
geom_bar(position=position_dodge(), stat ="identity", colour = 'black') + geom_errorbar(aes(ymin=Mean-S.E., ymax=Mean+S.E.), width = .2, position = position_dodge(.9)) + ggtitle("Bird Abundance")
#Run Omnivore Anova
OmnivoreAnova <- aov(Guilds$Omn_16~Guilds$mortality.class, data = Guilds)
OmnivoreAnova
summary(OmnivoreAnova)
TukeyHSD(OmnivoreAnova)
#Run Insectivore ANOVA
InsectivoreAnova <- aov(Guilds$Ins_29~Guilds$mortality.class, data = Guilds)
summary(InsectivoreAnova)
TukeyHSD(InsectivoreAnova)
|
#' Inverse logit
#'
#' Return inverse logit of values. `-Inf` or `Inf` return logits of 0 or 1, respectively.
#'
#' @md
#' @param x Values on logit scale
#'
#' @export
#'
inv_logit = function (x) {
p = 1/(1 + exp(-x))
p[is.infinite(p)] = 1
p
}
| /R/inv_logit.R | no_license | hinkelman/YoloBypassSBM | R | false | false | 264 | r | #' Inverse logit
#'
#' Return inverse logit of values. `-Inf` or `Inf` return logits of 0 or 1, respectively.
#'
#' @md
#' @param x Values on logit scale
#'
#' @export
#'
inv_logit = function (x) {
p = 1/(1 + exp(-x))
p[is.infinite(p)] = 1
p
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mwu.R
\name{mwu}
\alias{mwu}
\title{Mann-Whitney-U-Test}
\usage{
mwu(x, grp, distribution = "asymptotic", weight.by = NULL)
}
\arguments{
\item{x}{Numeric vector or variable.}
\item{grp}{Grouping variable indicating the groups that should be used for comparison.}
\item{distribution}{Indicates how the null distribution of the test statistic should be computed.
May be one of \code{"exact"}, \code{"approximate"} or \code{"asymptotic"}
(default). See \code{\link[coin]{wilcox_test}} for details.}
\item{weight.by}{Vector of weights that will be applied to weight all observations.
Must be a vector of same length as the input vector. Default is
\code{NULL}, so no weights are used.}
}
\value{
(Invisibly) returns a data frame with U, p and Z-values for each group-comparison
as well as effect-size r; additionally, group-labels and groups' n's are
also included.
}
\description{
This function performs a Mann-Whitney-U-Test (or Wilcoxon rank sum test,
see \code{\link{wilcox.test}} and \code{\link[coin]{wilcox_test}})
for \code{x}, for each group indicated by \code{grp}. If \code{grp}
has more than two categories, a comparison between each combination of
two groups is performed. \cr \cr
The function reports U, p and Z-values as well as effect size r
and group-rank-means.
}
\note{
This function calls the \code{\link[coin]{wilcox_test}} with formula. If \code{grp}
has more than two groups, additionally a Kruskal-Wallis-Test (see \code{\link{kruskal.test}})
is performed. \cr \cr
Interpretation of effect sizes, as a rule-of-thumb:
\itemize{
\item small effect >= 0.1
\item medium effect >= 0.3
\item large effect >= 0.5
}
}
\examples{
data(efc)
# Mann-Whitney-U-Tests for elder's age by elder's dependency.
mwu(efc$e17age, efc$e42dep)
}
| /man/mwu.Rd | no_license | stefanfritsch/sjstats | R | false | true | 1,998 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mwu.R
\name{mwu}
\alias{mwu}
\title{Mann-Whitney-U-Test}
\usage{
mwu(x, grp, distribution = "asymptotic", weight.by = NULL)
}
\arguments{
\item{x}{Numeric vector or variable.}
\item{grp}{Grouping variable indicating the groups that should be used for comparison.}
\item{distribution}{Indicates how the null distribution of the test statistic should be computed.
May be one of \code{"exact"}, \code{"approximate"} or \code{"asymptotic"}
(default). See \code{\link[coin]{wilcox_test}} for details.}
\item{weight.by}{Vector of weights that will be applied to weight all observations.
Must be a vector of same length as the input vector. Default is
\code{NULL}, so no weights are used.}
}
\value{
(Invisibly) returns a data frame with U, p and Z-values for each group-comparison
as well as effect-size r; additionally, group-labels and groups' n's are
also included.
}
\description{
This function performs a Mann-Whitney-U-Test (or Wilcoxon rank sum test,
see \code{\link{wilcox.test}} and \code{\link[coin]{wilcox_test}})
for \code{x}, for each group indicated by \code{grp}. If \code{grp}
has more than two categories, a comparison between each combination of
two groups is performed. \cr \cr
The function reports U, p and Z-values as well as effect size r
and group-rank-means.
}
\note{
This function calls the \code{\link[coin]{wilcox_test}} with formula. If \code{grp}
has more than two groups, additionally a Kruskal-Wallis-Test (see \code{\link{kruskal.test}})
is performed. \cr \cr
Interpretation of effect sizes, as a rule-of-thumb:
\itemize{
\item small effect >= 0.1
\item medium effect >= 0.3
\item large effect >= 0.5
}
}
\examples{
data(efc)
# Mann-Whitney-U-Tests for elder's age by elder's dependency.
mwu(efc$e17age, efc$e42dep)
}
|
print.dspat=function(x,...)
##################################################################################
# Prints relevant parts of a dspat object
#
# Jeff Laake
# 9 April 2008
##################################################################################
{
cat("\nStudy area: ")
print(x$study.area)
cat("\nLines: ")
print(x$lines.psp)
cat("\nObservations: ")
print(x$model$Q$data)
cat("\nFitted model: ")
print(x$model)
return(NULL)
}
summary.dspat=function(object,...)
##################################################################################
# Summarizes ppm model fit
#
# Jeff Laake
# 9 April 2008
##################################################################################
{
summary(object$model)
}
coef.dspat=function(object,...)
##################################################################################
# Extracts coefficients and separates into intensity and detection parameters
#
# Jeff Laake
# 9 April 2008
##################################################################################
{
coeff=coef(object$model)
detection=grep("distance",names(coeff))
if(length(detection)!=0)
return(list(intensity=coeff[-detection],detection=coeff[detection]))
else
return(list(intensity=coeff,detection=NULL))
}
vcov.dspat=function(object,...)
##################################################################################
# Extracts variance-covariance matrix for coefficients of fitted model
#
# Jeff Laake
# 9 April 2008
##################################################################################
{
return(vcov.ppm(object$model,gamaction="silent"))
}
AIC.dspat=function(object,...,k)
##################################################################################
# Extracts AIC for fitted model
#
# Jeff Laake
# 9 April 2008
##################################################################################
{
if(missing(k)) k=2
return(-2*object$model$maxlogpl+k*length(coef(object$model)))
}
im.clipped=function(x,window)
##################################################################################
# Fills an image with values x into a clipped non-rectangular window and
# returns an image (class im). An im must be rectagular, so this function
# creates a rectangular image using the bounding box for window with a 0 value
# and then fills in the values (x) that are in window.
#
# Arguments:
# x -a vector of image values in order defined by spatstat with
# y increasing fastest and then x increasing (see rev_val)
#
# window - a polygonal window of class owin
#
# Value: an image with NA outside window and image values in window of the
# image
#
#
#
# Jeff Laake
# 20 April 2008
##################################################################################
{
x.im=as.im(0,W=window)
x.im[window]=x
return(x.im)
}
rev_val=function(x,y,val)
##################################################################################
# Reverses order of vector val such that it matches order needed for
# an image with y increasing within x increasing. If val aleady in that
# order it will remain unchanged.
#
# Arguments:
# x - x coordinates
# y - y coordinates
# val - values at x,y
#
# Value: reordered vector of values
#
# Jeff Laake
# 20 April 2008
##################################################################################
{
X=cbind(x,y,val)
return(X[order(x,y),3])
}
Ops.psp=function(e1,e2)
##################################################################################
# Allows syntax of x==y or x!=y where x and y are 2 psp objects.
# Tests whether 2 line segment objects are = or !=
#
# Arguments: e1,e2 - psp objects
# Value: TRUE or FALSE
#
# Jeff Laake
# 14 April 2008
##################################################################################
{
ok <- switch(.Generic, "==" = , "!=" = TRUE, FALSE)
if (!ok) {
warning(.Generic, " not meaningful for psp")
return(rep.int(NA, max(length(e1), if (!missing(e2)) length(e2))))
}
if(!class(e1)[1]=="psp" | !class(e2)[1]=="psp") stop("\nOne or more arguments is not of class psp")
x.end=endpoints.psp(e1)
y.end=endpoints.psp(e2)
if(.Generic == "==")
return(x.end$n==y.end$n & all(x.end$x==y.end$x) & all(x.end$y==y.end$y) )
else
return(!(x.end$n==y.end$n & all(x.end$x==y.end$x) & all(x.end$y==y.end$y)) )
}
owin.gpc.poly=function(window)
##################################################################################
# Converts an owin class composed of a single polygon to a gpc.poly
#
# Arguments: window - an owin class
#
# Value : gpc.poly from first polygon in owin
#
# Jeff Laake
# 18 April 2008
##################################################################################
{
if(is.null(window$bdry))
return(as(cbind(c(window$xrange,rev(window$xrange)),
c(rep(window$yrange[1],2),rep(window$yrange[2],2))),
"gpc.poly"))
else
return(as(cbind(window$bdry[[1]]$x,window$bdry[[1]]$y),"gpc.poly"))
}
| /DSpat/R/internal.R | no_license | jlaake/DSpat | R | false | false | 5,207 | r | print.dspat=function(x,...)
##################################################################################
# Prints relevant parts of a dspat object
#
# Jeff Laake
# 9 April 2008
##################################################################################
{
cat("\nStudy area: ")
print(x$study.area)
cat("\nLines: ")
print(x$lines.psp)
cat("\nObservations: ")
print(x$model$Q$data)
cat("\nFitted model: ")
print(x$model)
return(NULL)
}
summary.dspat=function(object,...)
##################################################################################
# Summarizes ppm model fit
#
# Jeff Laake
# 9 April 2008
##################################################################################
{
summary(object$model)
}
coef.dspat=function(object,...)
##################################################################################
# Extracts coefficients and separates into intensity and detection parameters
#
# Jeff Laake
# 9 April 2008
##################################################################################
{
coeff=coef(object$model)
detection=grep("distance",names(coeff))
if(length(detection)!=0)
return(list(intensity=coeff[-detection],detection=coeff[detection]))
else
return(list(intensity=coeff,detection=NULL))
}
vcov.dspat=function(object,...)
##################################################################################
# Extracts variance-covariance matrix for coefficients of fitted model
#
# Jeff Laake
# 9 April 2008
##################################################################################
{
return(vcov.ppm(object$model,gamaction="silent"))
}
AIC.dspat=function(object,...,k)
##################################################################################
# Extracts AIC for fitted model
#
# Jeff Laake
# 9 April 2008
##################################################################################
{
if(missing(k)) k=2
return(-2*object$model$maxlogpl+k*length(coef(object$model)))
}
im.clipped=function(x,window)
##################################################################################
# Fills an image with values x into a clipped non-rectangular window and
# returns an image (class im). An im must be rectagular, so this function
# creates a rectangular image using the bounding box for window with a 0 value
# and then fills in the values (x) that are in window.
#
# Arguments:
# x -a vector of image values in order defined by spatstat with
# y increasing fastest and then x increasing (see rev_val)
#
# window - a polygonal window of class owin
#
# Value: an image with NA outside window and image values in window of the
# image
#
#
#
# Jeff Laake
# 20 April 2008
##################################################################################
{
x.im=as.im(0,W=window)
x.im[window]=x
return(x.im)
}
rev_val=function(x,y,val)
##################################################################################
# Reverses order of vector val such that it matches order needed for
# an image with y increasing within x increasing. If val aleady in that
# order it will remain unchanged.
#
# Arguments:
# x - x coordinates
# y - y coordinates
# val - values at x,y
#
# Value: reordered vector of values
#
# Jeff Laake
# 20 April 2008
##################################################################################
{
X=cbind(x,y,val)
return(X[order(x,y),3])
}
Ops.psp=function(e1,e2)
##################################################################################
# Allows syntax of x==y or x!=y where x and y are 2 psp objects.
# Tests whether 2 line segment objects are = or !=
#
# Arguments: e1,e2 - psp objects
# Value: TRUE or FALSE
#
# Jeff Laake
# 14 April 2008
##################################################################################
{
ok <- switch(.Generic, "==" = , "!=" = TRUE, FALSE)
if (!ok) {
warning(.Generic, " not meaningful for psp")
return(rep.int(NA, max(length(e1), if (!missing(e2)) length(e2))))
}
if(!class(e1)[1]=="psp" | !class(e2)[1]=="psp") stop("\nOne or more arguments is not of class psp")
x.end=endpoints.psp(e1)
y.end=endpoints.psp(e2)
if(.Generic == "==")
return(x.end$n==y.end$n & all(x.end$x==y.end$x) & all(x.end$y==y.end$y) )
else
return(!(x.end$n==y.end$n & all(x.end$x==y.end$x) & all(x.end$y==y.end$y)) )
}
owin.gpc.poly=function(window)
##################################################################################
# Converts an owin class composed of a single polygon to a gpc.poly
#
# Arguments: window - an owin class
#
# Value : gpc.poly from first polygon in owin
#
# Jeff Laake
# 18 April 2008
##################################################################################
{
if(is.null(window$bdry))
return(as(cbind(c(window$xrange,rev(window$xrange)),
c(rep(window$yrange[1],2),rep(window$yrange[2],2))),
"gpc.poly"))
else
return(as(cbind(window$bdry[[1]]$x,window$bdry[[1]]$y),"gpc.poly"))
}
|
testlist <- list(ExpressionSet = structure(c(3.10503529560174e+231, 1.23181983389617e+58, 1.52478221747831e+245, 6.36967296041789e+178, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(5L, 7L)), Phylostratum = numeric(0))
result <- do.call(myTAI:::cpp_TAI,testlist)
str(result) | /myTAI/inst/testfiles/cpp_TAI/AFL_cpp_TAI/cpp_TAI_valgrind_files/1615762279-test.R | no_license | akhikolla/updatedatatype-list3 | R | false | false | 334 | r | testlist <- list(ExpressionSet = structure(c(3.10503529560174e+231, 1.23181983389617e+58, 1.52478221747831e+245, 6.36967296041789e+178, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(5L, 7L)), Phylostratum = numeric(0))
result <- do.call(myTAI:::cpp_TAI,testlist)
str(result) |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/maps.R
\name{agg_station_map}
\alias{agg_station_map}
\title{Aggregate scores by station}
\usage{
agg_station_map(map.me, pct_sum, spatial, taxon.rank, todays.date)
}
\description{
Aggregate scores by station
}
| /man/agg_station_map.Rd | no_license | zsmith27/CHESSIE | R | false | true | 289 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/maps.R
\name{agg_station_map}
\alias{agg_station_map}
\title{Aggregate scores by station}
\usage{
agg_station_map(map.me, pct_sum, spatial, taxon.rank, todays.date)
}
\description{
Aggregate scores by station
}
|
## this script won't download the data file for you
## download and extract the zip file to ./data directory in you working directory
## data url: https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip
## this script assume you have household_power_consumption.txt in ./data directory
## tidy data
library(dplyr)
data <- read.csv("./data/household_power_consumption.txt", stringsAsFactors=F, sep = ";")
data <- filter(data, Date=="1/2/2007" | Date=="2/2/2007")
data[,3] <- as.numeric(data[,3])
data[,4] <- as.numeric(data[,4])
data[,5] <- as.numeric(data[,5])
data[,6] <- as.numeric(data[,6])
data[,7] <- as.numeric(data[,7])
data[,8] <- as.numeric(data[,8])
data$TD <- strptime(paste(data$Date, data$Time, sep=","),
format="%d/%m/%Y, %H:%M:%S")
## plot line
par(mfrow = c(1,1),oma=c(0,1,0,0))
plot(data$TD,data$Global_active_power,
type="l", ylab="Global Active Power (kilowatts)", xlab="",
cex.lab=0.8, cex.axis=0.8)
## output to png file
dev.copy(png, file="plot2.png")
dev.off() | /coursera-exploratory data analysis/project 1/plot2.R | no_license | adamantoi/datasciencecoursera | R | false | false | 1,049 | r | ## this script won't download the data file for you
## download and extract the zip file to ./data directory in you working directory
## data url: https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip
## this script assume you have household_power_consumption.txt in ./data directory
## tidy data
library(dplyr)
data <- read.csv("./data/household_power_consumption.txt", stringsAsFactors=F, sep = ";")
data <- filter(data, Date=="1/2/2007" | Date=="2/2/2007")
data[,3] <- as.numeric(data[,3])
data[,4] <- as.numeric(data[,4])
data[,5] <- as.numeric(data[,5])
data[,6] <- as.numeric(data[,6])
data[,7] <- as.numeric(data[,7])
data[,8] <- as.numeric(data[,8])
data$TD <- strptime(paste(data$Date, data$Time, sep=","),
format="%d/%m/%Y, %H:%M:%S")
## plot line
par(mfrow = c(1,1),oma=c(0,1,0,0))
plot(data$TD,data$Global_active_power,
type="l", ylab="Global Active Power (kilowatts)", xlab="",
cex.lab=0.8, cex.axis=0.8)
## output to png file
dev.copy(png, file="plot2.png")
dev.off() |
suppressMessages(library(cacheSweave))
infile <- system.file("example", "simpleRR.Rnw", package = "cacheSweave")
setCacheDir("cache")
Sweave(infile, driver = cacheSweaveDriver)
ls()
## Check to see that objects were properly cached
## Database should contain 'airquality' and 'fit'
dname <- dir("cache", full.names = TRUE)
suppressMessages(library(stashR))
db <- new("localDB", dir = dname[2], name = "cacheDB")
dbList(db)
| /tests/reg-tests.R | no_license | mclements/cachesweave | R | false | false | 430 | r | suppressMessages(library(cacheSweave))
infile <- system.file("example", "simpleRR.Rnw", package = "cacheSweave")
setCacheDir("cache")
Sweave(infile, driver = cacheSweaveDriver)
ls()
## Check to see that objects were properly cached
## Database should contain 'airquality' and 'fit'
dname <- dir("cache", full.names = TRUE)
suppressMessages(library(stashR))
db <- new("localDB", dir = dname[2], name = "cacheDB")
dbList(db)
|
CETTActionsParser <-
R6::R6Class(
"CETTActionsParser",
inherit = AbstractParser,
private = list(
parse_record = function() {
cett_type <- strsplit(private$tibble_name, "_")[[1]][1]
drugs <- xmlChildren(pkg_env$root)
pb <- progress_bar$new(total = xmlSize(drugs))
actions_tbl <-
map_df(drugs, ~ private$actions_rec(., cett_type, pb)) %>% unique()
if (nrow(actions_tbl) > 0) {
colnames(actions_tbl) <- c("action", "parent_id")
}
return(actions_tbl)
},
actions_rec = function(rec, cett_type, pb) {
pb$tick()
return(map_df(xmlChildren(rec[[cett_type]]),
~ drug_sub_df(., "actions", id = "id")))
}
)
)
#' Carriers/ Enzymes/ Targets/ Transporters Actions parsers
#'
#' Collection of related actions
#'
#' @return a tibble with 2 variables:
#' \describe{
#' \item{action}{describe related action}
#' \item{\emph{parent_id}}{carrier/ target/ enzyme/ transporter id}
#' }
#' @keywords internal
#' @name cett_actions_doc
NULL
#' @rdname cett_actions_doc
carriers_actions <- function() {
CETTActionsParser$new(
"carriers_actions"
)$parse()
}
#' @rdname cett_actions_doc
enzymes_actions <- function() {
CETTActionsParser$new(
"enzymes_actions"
)$parse()
}
#' @rdname cett_actions_doc
targets_actions <- function() {
CETTActionsParser$new(
"targets_actions"
)$parse()
}
#' @rdname cett_actions_doc
transporters_actions <- function() {
CETTActionsParser$new(
"transporters_actions"
)$parse()
}
| /R/cett_actions_parsers.R | no_license | cran/dbparser | R | false | false | 1,644 | r | CETTActionsParser <-
R6::R6Class(
"CETTActionsParser",
inherit = AbstractParser,
private = list(
parse_record = function() {
cett_type <- strsplit(private$tibble_name, "_")[[1]][1]
drugs <- xmlChildren(pkg_env$root)
pb <- progress_bar$new(total = xmlSize(drugs))
actions_tbl <-
map_df(drugs, ~ private$actions_rec(., cett_type, pb)) %>% unique()
if (nrow(actions_tbl) > 0) {
colnames(actions_tbl) <- c("action", "parent_id")
}
return(actions_tbl)
},
actions_rec = function(rec, cett_type, pb) {
pb$tick()
return(map_df(xmlChildren(rec[[cett_type]]),
~ drug_sub_df(., "actions", id = "id")))
}
)
)
#' Carriers/ Enzymes/ Targets/ Transporters Actions parsers
#'
#' Collection of related actions
#'
#' @return a tibble with 2 variables:
#' \describe{
#' \item{action}{describe related action}
#' \item{\emph{parent_id}}{carrier/ target/ enzyme/ transporter id}
#' }
#' @keywords internal
#' @name cett_actions_doc
NULL
#' @rdname cett_actions_doc
carriers_actions <- function() {
CETTActionsParser$new(
"carriers_actions"
)$parse()
}
#' @rdname cett_actions_doc
enzymes_actions <- function() {
CETTActionsParser$new(
"enzymes_actions"
)$parse()
}
#' @rdname cett_actions_doc
targets_actions <- function() {
CETTActionsParser$new(
"targets_actions"
)$parse()
}
#' @rdname cett_actions_doc
transporters_actions <- function() {
CETTActionsParser$new(
"transporters_actions"
)$parse()
}
|
TEAM_NAME="jakl"
VERSION="R version 3.6.0 (2019-04-26)"
load_data <- function(path){
# Loads the data using base R.
#
data <- read.csv(path, stringsAsFactors = T)
data
}
computer_use_001 <- function(data){
# Computer use is defined using 'comp_week' and 'comp_wend'
# IMPORTS
# DEFINE COMPUTER USE
# Assign average numeric values for each of the categories
# reorder the levels of comp_week and comp_wend
data$comp_week <- factor(data$comp_week,levels(data$comp_week)[c(4,3,1,2)])
data$comp_wend <- factor(data$comp_wend,levels(data$comp_wend)[c(4,3,1,2)])
# keep these names for plotting later
#iv.levels <- levels(data$comp_week)
data
}
depression_001 <- function(data){
# This function defines depression using 'prim_diag' and
# 'secd_diag', values 10-12 and also 4 'mixed anxiety and depression'
#IMPORTS:
#PARAMETERS:
#DEFINE DEPRESSION:
data$anydepdiag <- 0
data$anydepdiag[data$prim_diag==4 |
data$prim_diag==10 |
data$prim_diag==11 |
data$prim_diag==12 ] <- 1
data$anydepdiag[data$secd_diag==4 |
data$secd_diag==10 |
data$secd_diag==11 |
data$secd_diag==12 ] <- 1
data
}
transformation_001 <- function(data){
# This selects some variables to use in final model
# IMPORTS
# PARAMETERS
VARIABLES_TO_USE <- c('anydepdiag', 'comp_week', 'comp_wend')
# select data
data <- select(data, VARIABLES_TO_USE)
data
}
specify_model <- function(data){
# Logistic regression to predict anydepdiag from computer use on weekdays and at weekends
# follows examples at https://stats.idre.ucla.edu/r/dae/logit-regression/
# uses package aod
# IMPORTS
library(aod)
library(tidyverse)
# logistic regression model
model <- glm(data=data, anydepdiag ~ comp_week + comp_wend, family="binomial")
summary(model)
confint(model) # confidence intervals on co-efficients estimates based on the profiled log-likelihood function
# log odds ratio s.e. and lower/upper bounds for CI
lor <- model$coefficients[2:7]
lse <- summary(model)$coefficients[2:7,2]
llb <- lor - 2*lse
lub <- lor + 2*lse
# exponentiate
or <- exp(lor)
ci <- exp(c(llb, lub))
# the results
# Note: DIC not necessary (not Bayes/Hierarchical model)
# data must be returned as part of a list because R can't return multiple objects
results <- list('aic' = model$aic,
'or_1' = exp(model$coefficients[2:7]),
'p_1' = summary(model)$coefficients[2:7,4],
'ci_1' = ci,
'mod' = model,
'data' = data)
results
}
output <- function(results){
# make some visible output so we can see what the model has done
# IMPORTS
library(tidyverse)
library(pander)
# send reportables to console
message(paste("AIC=",results$aic))
message(results$or_1 %>% pandoc.table(caption="Odds Ratios"))
message(results$p_1 %>% pandoc.table(caption="Probabilities"))
message(results$ci_1 %>% pandoc.table(caption="Confidence Intervals"))
}
#### lines to run functions
data <- load_data(path = 'https://raw.githubusercontent.com/knedza/JAKL/master/maps-synthetic-data-v1.1.csv')
data <- computer_use_001(data)
data <- depression_001(data)
data <- transformation_001(data)
results <- specify_model(data)
output(results)
| /JAKLinFunctions.R | no_license | knedza/JAKL | R | false | false | 3,518 | r | TEAM_NAME="jakl"
VERSION="R version 3.6.0 (2019-04-26)"
load_data <- function(path){
# Loads the data using base R.
#
data <- read.csv(path, stringsAsFactors = T)
data
}
computer_use_001 <- function(data){
# Computer use is defined using 'comp_week' and 'comp_wend'
# IMPORTS
# DEFINE COMPUTER USE
# Assign average numeric values for each of the categories
# reorder the levels of comp_week and comp_wend
data$comp_week <- factor(data$comp_week,levels(data$comp_week)[c(4,3,1,2)])
data$comp_wend <- factor(data$comp_wend,levels(data$comp_wend)[c(4,3,1,2)])
# keep these names for plotting later
#iv.levels <- levels(data$comp_week)
data
}
depression_001 <- function(data){
# This function defines depression using 'prim_diag' and
# 'secd_diag', values 10-12 and also 4 'mixed anxiety and depression'
#IMPORTS:
#PARAMETERS:
#DEFINE DEPRESSION:
data$anydepdiag <- 0
data$anydepdiag[data$prim_diag==4 |
data$prim_diag==10 |
data$prim_diag==11 |
data$prim_diag==12 ] <- 1
data$anydepdiag[data$secd_diag==4 |
data$secd_diag==10 |
data$secd_diag==11 |
data$secd_diag==12 ] <- 1
data
}
transformation_001 <- function(data){
# This selects some variables to use in final model
# IMPORTS
# PARAMETERS
VARIABLES_TO_USE <- c('anydepdiag', 'comp_week', 'comp_wend')
# select data
data <- select(data, VARIABLES_TO_USE)
data
}
specify_model <- function(data){
# Logistic regression to predict anydepdiag from computer use on weekdays and at weekends
# follows examples at https://stats.idre.ucla.edu/r/dae/logit-regression/
# uses package aod
# IMPORTS
library(aod)
library(tidyverse)
# logistic regression model
model <- glm(data=data, anydepdiag ~ comp_week + comp_wend, family="binomial")
summary(model)
confint(model) # confidence intervals on co-efficients estimates based on the profiled log-likelihood function
# log odds ratio s.e. and lower/upper bounds for CI
lor <- model$coefficients[2:7]
lse <- summary(model)$coefficients[2:7,2]
llb <- lor - 2*lse
lub <- lor + 2*lse
# exponentiate
or <- exp(lor)
ci <- exp(c(llb, lub))
# the results
# Note: DIC not necessary (not Bayes/Hierarchical model)
# data must be returned as part of a list because R can't return multiple objects
results <- list('aic' = model$aic,
'or_1' = exp(model$coefficients[2:7]),
'p_1' = summary(model)$coefficients[2:7,4],
'ci_1' = ci,
'mod' = model,
'data' = data)
results
}
output <- function(results){
# make some visible output so we can see what the model has done
# IMPORTS
library(tidyverse)
library(pander)
# send reportables to console
message(paste("AIC=",results$aic))
message(results$or_1 %>% pandoc.table(caption="Odds Ratios"))
message(results$p_1 %>% pandoc.table(caption="Probabilities"))
message(results$ci_1 %>% pandoc.table(caption="Confidence Intervals"))
}
#### lines to run functions
data <- load_data(path = 'https://raw.githubusercontent.com/knedza/JAKL/master/maps-synthetic-data-v1.1.csv')
data <- computer_use_001(data)
data <- depression_001(data)
data <- transformation_001(data)
results <- specify_model(data)
output(results)
|
library(ggplot2)
library(ez)
library(Hmisc)
library(reshape2)
library(psychReport)
library(lsr)
library(bayestestR)
library(BayesFactor)
library(TOSTER)
#### clear environment
rm(list = ls())
#### load data
# on Inspiron 13
setwd("C:/Users/wuxiu/Documents/PhD@UBC/Lab/2ndYear/AnticipatoryPursuit/AnticipatoryPursuitMotionPerception/analysis/R")
source("pairwise.t.test.with.t.and.df.R")
plotFolder <- ("C:/Users/wuxiu/Documents/PhD@UBC/Lab/2ndYear/AnticipatoryPursuit/AnticipatoryPursuitMotionPerception/results/manuscript/figures/rawPlots/")
### modify these parameters to plot different conditions
# dataFileName <- "timeBinPSE_exp1.csv"
dataFileName <- "PSE_exp1vs3.csv"
# pdfFileName <- "timeBinPSE_exp1.pdf"
pdfInteractionFileName <- "PSE_exp1vs3_interaction.pdf"
# pdfFileNameD <- "slopeDiff_exp1vs3.pdf"
# for plotting
textSize <- 25
axisLineWidth <- 0.5
dotSize <- 3
# slope
ylimLow <- 10
ylimHigh <- 50
# PSE
ylimLow <- -0.15
ylimHigh <- 0.15
# # ASP
# ylimLow <- -1
# ylimHigh <- 5
# # # ASP gain
# # ylimLow <- -0.1
# # ylimHigh <- 0.4
# # clp gain in context trials
# ylimLow <- 0
# ylimHigh <- 1
data <- read.csv(dataFileName)
subAll <- unique(data["sub"])
subTotalN <- dim(subAll)[1]
# dataD <- read.csv(dataDFileName)
# data <- data[data.exp==3]
# # exclude bad fitting...
# data <- subset(data[which(data$sub!=8),])
## compare two experiments
# PSE anova
sub <- data["sub"]
exp <- data["exp"]
# timeBin <- data["timeBin"]
prob <- data["prob"]
measure <- data["PSE"]
dataAnova <- data.frame(sub, prob, exp, measure)
dataAnova$prob <- as.factor(dataAnova$prob)
dataAnova$sub <- as.factor(dataAnova$sub)
dataAnova$exp <- as.factor(dataAnova$exp)
# dataAnova$timeBin <- as.factor(dataAnova$timeBin)
colnames(dataAnova)[4] <- "measure"
# dataAnova <- aggregate(perceptualErrorMean ~ sub * rotationSpeed * exp,
# data = dataTemp, FUN = "mean")
anovaData <- ezANOVA(dataAnova, dv = .(measure), wid = .(sub),
within = .(prob, exp), type = 3, return_aov = TRUE, detailed = TRUE)
# print(anovaData)
aovEffectSize(anovaData, 'pes')
# Equivalence test for the differences of PSE between experiments
dataE <- data.frame(matrix(ncol=3,nrow=dim(subAll)[1], dimnames=list(NULL, c("sub", "exp1", "exp2"))))
for (subN in 1:subTotalN) {
data150 <- dataAnova[which(sub==subAll[subN, 1] & exp==1 & prob==50), ]$measure
data190 <- dataAnova[which(sub==subAll[subN, 1] & exp==1 & prob==90), ]$measure
data250 <- dataAnova[which(sub==subAll[subN, 1] & exp!=1 & prob==50), ]$measure
data290 <- dataAnova[which(sub==subAll[subN, 1] & exp!=1 & prob==90), ]$measure
dataE["sub"][subN, 1] <- subN
dataE["exp1"][subN, 1] <- data190-data150
dataE["exp2"][subN, 1] <- data290-data250
}
show(dataE)
dataTOSTpaired(data = dataE, pairs = list((c(i1="exp1",i2="exp2"))), low_eqbound = -0.36, high_eqbound = 0.36, eqbound_type = "d", alpha = 0.05, desc = TRUE, plots = TRUE)
# # compute Bayes Factor inclusion...
# bf <- anovaBF(measure ~ prob + timeBin + prob*timeBin + sub, data = dataAnova,
# whichRandom="sub")
# bayesfactor_inclusion(bf, match_models = TRUE)
# p <- ggplot(dataAnova, aes(x = prob, y = measure, color = exp)) +
# stat_summary(aes(y = measure), fun.y = mean, geom = "point", shape = 95, size = 15) +
# stat_summary(fun.data = 'mean_sdl',
# fun.args = list(mult = 1.96/sqrt(subTotalN)),
# geom = 'errorbar', width = .1) +
# # geom = 'smooth', se = 'TRUE') +
# # stat_summary(aes(y = measure), fun.data = mean_se, geom = "errorbar", width = 0.1) +
# geom_point(aes(x = prob, y = measure), size = dotSize, shape = 1) +
# # geom_segment(aes_all(c('x', 'y', 'xend', 'yend')), data = data.frame(x = c(50, 40), xend = c(90, 40), y = c(-0.1, -0.1), yend = c(-0.1, 0.15)), size = axisLineWidth) +
# scale_y_continuous(name = "Anticipatory pursuit velocity (°/s)") + #, limits = c(-0.1, 0.55), expand = c(0, 0)) +
# # scale_y_continuous(name = "PSE") +
# scale_x_discrete(name = "Probability of rightward motion", breaks=c("50", "90")) +
# # scale_x_discrete(name = "Probability of rightward motion", breaks=c(50, 70, 90)) +
# # scale_colour_discrete(name = "After reversal\ndirection", labels = c("CCW", "CW")) +
# theme(axis.text=element_text(colour="black"),
# axis.ticks=element_line(colour="black", size = axisLineWidth),
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank(),
# panel.border = element_blank(),
# panel.background = element_blank(),
# text = element_text(size = textSize, colour = "black"),
# legend.background = element_rect(fill="transparent"),
# legend.key = element_rect(colour = "transparent", fill = "white"))
# # facet_wrap(~exp)
# print(p)
# ggsave(paste(plotFolder, pdfFileName, sep = ""))
# ## t-test of the simple main effect of probability in the control experiment
# dataD <- dataAnova[dataAnova$exp==3,]
# # show(dataD)
# res <- pairwise.t.test.with.t.and.df(x = dataD$measure, g = dataD$prob, paired = TRUE, p.adj="none")
# show(res) # [[3]] = p value table, un adjusted
# res[[5]] # t-value
# res[[6]] # dfs
# res[[3]]
# p.adjust(res[[3]], method = "bonferroni", n = 4)
# cohensd <- cohensD(subset(dataD, prob==50)$measure, subset(dataD, prob==90)$measure, method = 'paired')
# show(cohensd)
## interaction plot
dataPlot <- data.frame(sub, prob, exp, measure)
colnames(dataPlot)[4] <- "measure"
dataPlot$sub <- as.factor(dataPlot$sub)
# dataPlot$prob <- as.factor(dataPlot$prob)
# is.numeric(dataPlot$timeBin)
dataPlot$exp <- as.factor(dataPlot$exp)
# dataPlot <- aggregate(measure ~ exp+prob, data = dataPlot, FUN = "mean")
# show(dataPlot)
# # for time bin plots
# p <- ggplot(dataPlot, aes(x = timeBin, y = measure, color = prob)) +
# stat_summary(fun.y = mean, geom = "point", shape = 95, size = 17.5) +
# stat_summary(fun.y = mean, geom = "line", width = 1) +
# stat_summary(fun.data = 'mean_sdl', fun.args = list(mult = 1.96/sqrt(subTotalN)), geom = 'errorbar', width = 1.5, size = 1) +
# scale_x_continuous(name = "time bin of trials", breaks=c(1, 2), limits = c(0.5, 2.5), expand = c(0, 0)) +
p <- ggplot(dataPlot, aes(x = prob, y = measure, color = exp)) +
stat_summary(fun.y = mean, geom = "point", shape = 95, size = 17.5) +
stat_summary(fun.y = mean, geom = "line", width = 1) +
stat_summary(fun.data = 'mean_sdl', fun.args = list(mult = 1.96/sqrt(subTotalN)), geom = 'errorbar', width = 1.5, size = 1) +
stat_summary(aes(y = measure), fun.data = mean_se, geom = "errorbar", width = 0.1) +
# geom_point(aes(x = prob, y = measure), size = dotSize, shape = 1) +
geom_segment(aes_all(c('x', 'y', 'xend', 'yend')), data = data.frame(x = c(50, 45), y = c(ylimLow, ylimLow), xend = c(90, 45), yend = c(ylimLow, ylimHigh)), size = axisLineWidth, inherit.aes = FALSE) +
# scale_y_continuous(name = "Anticipatory pursuit velocity (°/s)", breaks = seq(ylimLow, ylimHigh, 1), expand = c(0, 0)) +
# scale_y_continuous(name = "Anticipatory pursuit velocity gain", breaks = seq(ylimLow, ylimHigh, 0.1), expand = c(0, 0)) +
# coord_cartesian(ylim=c(ylimLow, ylimHigh)) +
scale_y_continuous(name = "PSE", limits = c(ylimLow, ylimHigh), breaks = c(ylimLow, 0, ylimHigh), expand = c(0, 0)) +
scale_x_continuous(name = "Probability of rightward motion", breaks=c(50, 90), limits = c(45, 95), expand = c(0, 0)) +
# scale_x_discrete(name = "Probability of rightward motion", breaks=c("50", "90")) +
# scale_colour_discrete(name = "After reversal\ndirection", labels = c("CCW", "CW")) +
theme(axis.text=element_text(colour="black"),
axis.ticks=element_line(colour="black", size = axisLineWidth),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
text = element_text(size = textSize, colour = "black"),
legend.background = element_rect(fill="transparent"),
legend.key = element_rect(colour = "transparent", fill = "white"))
# facet_wrap(~exp)
print(p)
ggsave(paste(plotFolder, pdfInteractionFileName, sep = ""))
# ## t-test of the difference
# sub <- dataD["sub"]
# exp <- dataD["exp"]
# measure <- dataD["slopeDiff"]
# dataDtemp <- data.frame(sub, exp, measure)
# dataDtemp$sub <- as.factor(dataDtemp$sub)
# dataDtemp$exp <- as.factor(dataDtemp$exp)
# colnames(dataDtemp)[3] <- "measure"
# # dataDttest <- aggregate(measure ~ exp, data = dataDtemp, FUN = "mean")
# # res <- pairwise.t.test.with.t.and.df(x = dataDtemp$measure, g = dataDtemp$exp, paired = TRUE, p.adj="none")
# # show(res) # [[3]] = p value table, un adjusted
# # res[[5]] # t-value
# # res[[6]] # dfs
# # res[[3]]
# # p.adjust(res[[3]], method = "bonferroni", n = 3)
# # # bias in PSE
# # ylimLow <- -0.05
# # ylimHigh <- 0.2
# # # bias in ASP
# # ylimLow <- 0
# # ylimHigh <- 3
# # bias in slope
# ylimLow <- -40
# ylimHigh <- 30
# p <- ggplot(dataDtemp, aes(x = exp, y = measure)) +
# stat_summary(aes(y = measure), fun.y = mean, geom = "point", shape = 95, size = 15) +
# stat_summary(fun.data = 'mean_sdl',
# fun.args = list(mult = 1.96/sqrt(subTotalN)),
# geom = 'linerange', size = 1) +
# geom_line(aes(x = exp, y = measure, group = sub), size = 0.5, linetype = "dashed") +
# geom_point(aes(x = exp, y = measure), size = dotSize, shape = 1) +
# geom_segment(aes_all(c('x', 'y', 'xend', 'yend')), data = data.frame(x = c(0), y = c(ylimLow), xend = c(0), yend = c(ylimHigh)), size = axisLineWidth) +
# # scale_y_continuous(name = "Bias of PSE") + #, limits = c(0, 0.15), expand = c(0, 0.01)) +
# scale_y_continuous(name = "Bias of slope") + #, limits = c(0, 0.15), expand = c(0, 0.01)) +
# # scale_y_continuous(name = "Bias of anticipatory pursuit velocity(deg/s)") + #, limits = c(0, 0.15), expand = c(0, 0.01)) +
# scale_x_discrete(name = "Experiment", limits = c("1", "3"), labels = c("1" = "Exp1", "3" = "Exp3")) +
# # scale_x_discrete(name = "Experiment", limits = c("1", "2"), labels = c("1" = "Exp1", "2" = "Exp2")) +
# theme(axis.text=element_text(colour="black"),
# axis.ticks=element_line(colour="black", size = axisLineWidth),
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank(),
# panel.border = element_blank(),
# panel.background = element_blank(),
# text = element_text(size = textSize, colour = "black"),
# legend.background = element_rect(fill="transparent"),
# legend.key = element_rect(colour = "transparent", fill = "white"))
# # facet_wrap(~prob)
# print(p)
# ggsave(paste(plotFolder, pdfFileNameD, sep = "")) | /analysis/R/twowayANOVAs&plots.R | no_license | CaptainS5/AnticipatoryPursuit-MotionPerception | R | false | false | 11,178 | r | library(ggplot2)
library(ez)
library(Hmisc)
library(reshape2)
library(psychReport)
library(lsr)
library(bayestestR)
library(BayesFactor)
library(TOSTER)
#### clear environment
rm(list = ls())
#### load data
# on Inspiron 13
setwd("C:/Users/wuxiu/Documents/PhD@UBC/Lab/2ndYear/AnticipatoryPursuit/AnticipatoryPursuitMotionPerception/analysis/R")
source("pairwise.t.test.with.t.and.df.R")
plotFolder <- ("C:/Users/wuxiu/Documents/PhD@UBC/Lab/2ndYear/AnticipatoryPursuit/AnticipatoryPursuitMotionPerception/results/manuscript/figures/rawPlots/")
### modify these parameters to plot different conditions
# dataFileName <- "timeBinPSE_exp1.csv"
dataFileName <- "PSE_exp1vs3.csv"
# pdfFileName <- "timeBinPSE_exp1.pdf"
pdfInteractionFileName <- "PSE_exp1vs3_interaction.pdf"
# pdfFileNameD <- "slopeDiff_exp1vs3.pdf"
# for plotting
textSize <- 25
axisLineWidth <- 0.5
dotSize <- 3
# slope
ylimLow <- 10
ylimHigh <- 50
# PSE
ylimLow <- -0.15
ylimHigh <- 0.15
# # ASP
# ylimLow <- -1
# ylimHigh <- 5
# # # ASP gain
# # ylimLow <- -0.1
# # ylimHigh <- 0.4
# # clp gain in context trials
# ylimLow <- 0
# ylimHigh <- 1
data <- read.csv(dataFileName)
subAll <- unique(data["sub"])
subTotalN <- dim(subAll)[1]
# dataD <- read.csv(dataDFileName)
# data <- data[data.exp==3]
# # exclude bad fitting...
# data <- subset(data[which(data$sub!=8),])
## compare two experiments
# PSE anova
sub <- data["sub"]
exp <- data["exp"]
# timeBin <- data["timeBin"]
prob <- data["prob"]
measure <- data["PSE"]
dataAnova <- data.frame(sub, prob, exp, measure)
dataAnova$prob <- as.factor(dataAnova$prob)
dataAnova$sub <- as.factor(dataAnova$sub)
dataAnova$exp <- as.factor(dataAnova$exp)
# dataAnova$timeBin <- as.factor(dataAnova$timeBin)
colnames(dataAnova)[4] <- "measure"
# dataAnova <- aggregate(perceptualErrorMean ~ sub * rotationSpeed * exp,
# data = dataTemp, FUN = "mean")
anovaData <- ezANOVA(dataAnova, dv = .(measure), wid = .(sub),
within = .(prob, exp), type = 3, return_aov = TRUE, detailed = TRUE)
# print(anovaData)
aovEffectSize(anovaData, 'pes')
# Equivalence test for the differences of PSE between experiments
dataE <- data.frame(matrix(ncol=3,nrow=dim(subAll)[1], dimnames=list(NULL, c("sub", "exp1", "exp2"))))
for (subN in 1:subTotalN) {
data150 <- dataAnova[which(sub==subAll[subN, 1] & exp==1 & prob==50), ]$measure
data190 <- dataAnova[which(sub==subAll[subN, 1] & exp==1 & prob==90), ]$measure
data250 <- dataAnova[which(sub==subAll[subN, 1] & exp!=1 & prob==50), ]$measure
data290 <- dataAnova[which(sub==subAll[subN, 1] & exp!=1 & prob==90), ]$measure
dataE["sub"][subN, 1] <- subN
dataE["exp1"][subN, 1] <- data190-data150
dataE["exp2"][subN, 1] <- data290-data250
}
show(dataE)
dataTOSTpaired(data = dataE, pairs = list((c(i1="exp1",i2="exp2"))), low_eqbound = -0.36, high_eqbound = 0.36, eqbound_type = "d", alpha = 0.05, desc = TRUE, plots = TRUE)
# # compute Bayes Factor inclusion...
# bf <- anovaBF(measure ~ prob + timeBin + prob*timeBin + sub, data = dataAnova,
# whichRandom="sub")
# bayesfactor_inclusion(bf, match_models = TRUE)
# p <- ggplot(dataAnova, aes(x = prob, y = measure, color = exp)) +
# stat_summary(aes(y = measure), fun.y = mean, geom = "point", shape = 95, size = 15) +
# stat_summary(fun.data = 'mean_sdl',
# fun.args = list(mult = 1.96/sqrt(subTotalN)),
# geom = 'errorbar', width = .1) +
# # geom = 'smooth', se = 'TRUE') +
# # stat_summary(aes(y = measure), fun.data = mean_se, geom = "errorbar", width = 0.1) +
# geom_point(aes(x = prob, y = measure), size = dotSize, shape = 1) +
# # geom_segment(aes_all(c('x', 'y', 'xend', 'yend')), data = data.frame(x = c(50, 40), xend = c(90, 40), y = c(-0.1, -0.1), yend = c(-0.1, 0.15)), size = axisLineWidth) +
# scale_y_continuous(name = "Anticipatory pursuit velocity (°/s)") + #, limits = c(-0.1, 0.55), expand = c(0, 0)) +
# # scale_y_continuous(name = "PSE") +
# scale_x_discrete(name = "Probability of rightward motion", breaks=c("50", "90")) +
# # scale_x_discrete(name = "Probability of rightward motion", breaks=c(50, 70, 90)) +
# # scale_colour_discrete(name = "After reversal\ndirection", labels = c("CCW", "CW")) +
# theme(axis.text=element_text(colour="black"),
# axis.ticks=element_line(colour="black", size = axisLineWidth),
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank(),
# panel.border = element_blank(),
# panel.background = element_blank(),
# text = element_text(size = textSize, colour = "black"),
# legend.background = element_rect(fill="transparent"),
# legend.key = element_rect(colour = "transparent", fill = "white"))
# # facet_wrap(~exp)
# print(p)
# ggsave(paste(plotFolder, pdfFileName, sep = ""))
# ## t-test of the simple main effect of probability in the control experiment
# dataD <- dataAnova[dataAnova$exp==3,]
# # show(dataD)
# res <- pairwise.t.test.with.t.and.df(x = dataD$measure, g = dataD$prob, paired = TRUE, p.adj="none")
# show(res) # [[3]] = p value table, un adjusted
# res[[5]] # t-value
# res[[6]] # dfs
# res[[3]]
# p.adjust(res[[3]], method = "bonferroni", n = 4)
# cohensd <- cohensD(subset(dataD, prob==50)$measure, subset(dataD, prob==90)$measure, method = 'paired')
# show(cohensd)
## interaction plot
dataPlot <- data.frame(sub, prob, exp, measure)
colnames(dataPlot)[4] <- "measure"
dataPlot$sub <- as.factor(dataPlot$sub)
# dataPlot$prob <- as.factor(dataPlot$prob)
# is.numeric(dataPlot$timeBin)
dataPlot$exp <- as.factor(dataPlot$exp)
# dataPlot <- aggregate(measure ~ exp+prob, data = dataPlot, FUN = "mean")
# show(dataPlot)
# # for time bin plots
# p <- ggplot(dataPlot, aes(x = timeBin, y = measure, color = prob)) +
# stat_summary(fun.y = mean, geom = "point", shape = 95, size = 17.5) +
# stat_summary(fun.y = mean, geom = "line", width = 1) +
# stat_summary(fun.data = 'mean_sdl', fun.args = list(mult = 1.96/sqrt(subTotalN)), geom = 'errorbar', width = 1.5, size = 1) +
# scale_x_continuous(name = "time bin of trials", breaks=c(1, 2), limits = c(0.5, 2.5), expand = c(0, 0)) +
p <- ggplot(dataPlot, aes(x = prob, y = measure, color = exp)) +
stat_summary(fun.y = mean, geom = "point", shape = 95, size = 17.5) +
stat_summary(fun.y = mean, geom = "line", width = 1) +
stat_summary(fun.data = 'mean_sdl', fun.args = list(mult = 1.96/sqrt(subTotalN)), geom = 'errorbar', width = 1.5, size = 1) +
stat_summary(aes(y = measure), fun.data = mean_se, geom = "errorbar", width = 0.1) +
# geom_point(aes(x = prob, y = measure), size = dotSize, shape = 1) +
geom_segment(aes_all(c('x', 'y', 'xend', 'yend')), data = data.frame(x = c(50, 45), y = c(ylimLow, ylimLow), xend = c(90, 45), yend = c(ylimLow, ylimHigh)), size = axisLineWidth, inherit.aes = FALSE) +
# scale_y_continuous(name = "Anticipatory pursuit velocity (°/s)", breaks = seq(ylimLow, ylimHigh, 1), expand = c(0, 0)) +
# scale_y_continuous(name = "Anticipatory pursuit velocity gain", breaks = seq(ylimLow, ylimHigh, 0.1), expand = c(0, 0)) +
# coord_cartesian(ylim=c(ylimLow, ylimHigh)) +
scale_y_continuous(name = "PSE", limits = c(ylimLow, ylimHigh), breaks = c(ylimLow, 0, ylimHigh), expand = c(0, 0)) +
scale_x_continuous(name = "Probability of rightward motion", breaks=c(50, 90), limits = c(45, 95), expand = c(0, 0)) +
# scale_x_discrete(name = "Probability of rightward motion", breaks=c("50", "90")) +
# scale_colour_discrete(name = "After reversal\ndirection", labels = c("CCW", "CW")) +
theme(axis.text=element_text(colour="black"),
axis.ticks=element_line(colour="black", size = axisLineWidth),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
text = element_text(size = textSize, colour = "black"),
legend.background = element_rect(fill="transparent"),
legend.key = element_rect(colour = "transparent", fill = "white"))
# facet_wrap(~exp)
print(p)
ggsave(paste(plotFolder, pdfInteractionFileName, sep = ""))
# ## t-test of the difference
# sub <- dataD["sub"]
# exp <- dataD["exp"]
# measure <- dataD["slopeDiff"]
# dataDtemp <- data.frame(sub, exp, measure)
# dataDtemp$sub <- as.factor(dataDtemp$sub)
# dataDtemp$exp <- as.factor(dataDtemp$exp)
# colnames(dataDtemp)[3] <- "measure"
# # dataDttest <- aggregate(measure ~ exp, data = dataDtemp, FUN = "mean")
# # res <- pairwise.t.test.with.t.and.df(x = dataDtemp$measure, g = dataDtemp$exp, paired = TRUE, p.adj="none")
# # show(res) # [[3]] = p value table, un adjusted
# # res[[5]] # t-value
# # res[[6]] # dfs
# # res[[3]]
# # p.adjust(res[[3]], method = "bonferroni", n = 3)
# # # bias in PSE
# # ylimLow <- -0.05
# # ylimHigh <- 0.2
# # # bias in ASP
# # ylimLow <- 0
# # ylimHigh <- 3
# # bias in slope
# ylimLow <- -40
# ylimHigh <- 30
# p <- ggplot(dataDtemp, aes(x = exp, y = measure)) +
# stat_summary(aes(y = measure), fun.y = mean, geom = "point", shape = 95, size = 15) +
# stat_summary(fun.data = 'mean_sdl',
# fun.args = list(mult = 1.96/sqrt(subTotalN)),
# geom = 'linerange', size = 1) +
# geom_line(aes(x = exp, y = measure, group = sub), size = 0.5, linetype = "dashed") +
# geom_point(aes(x = exp, y = measure), size = dotSize, shape = 1) +
# geom_segment(aes_all(c('x', 'y', 'xend', 'yend')), data = data.frame(x = c(0), y = c(ylimLow), xend = c(0), yend = c(ylimHigh)), size = axisLineWidth) +
# # scale_y_continuous(name = "Bias of PSE") + #, limits = c(0, 0.15), expand = c(0, 0.01)) +
# scale_y_continuous(name = "Bias of slope") + #, limits = c(0, 0.15), expand = c(0, 0.01)) +
# # scale_y_continuous(name = "Bias of anticipatory pursuit velocity(deg/s)") + #, limits = c(0, 0.15), expand = c(0, 0.01)) +
# scale_x_discrete(name = "Experiment", limits = c("1", "3"), labels = c("1" = "Exp1", "3" = "Exp3")) +
# # scale_x_discrete(name = "Experiment", limits = c("1", "2"), labels = c("1" = "Exp1", "2" = "Exp2")) +
# theme(axis.text=element_text(colour="black"),
# axis.ticks=element_line(colour="black", size = axisLineWidth),
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank(),
# panel.border = element_blank(),
# panel.background = element_blank(),
# text = element_text(size = textSize, colour = "black"),
# legend.background = element_rect(fill="transparent"),
# legend.key = element_rect(colour = "transparent", fill = "white"))
# # facet_wrap(~prob)
# print(p)
# ggsave(paste(plotFolder, pdfFileNameD, sep = "")) |
tabPanel(
title = tagList(
icon("globe-asia"),
i18n$t("感染状況マップ")
),
fluidRow(
column(
width = 5,
tags$div(
fluidRow(
column(
width = 6,
switchInput(
inputId = "switchMapVersion",
value = T,
onLabel = i18n$t("シンプル"),
onStatus = "danger",
offStatus = "danger",
offLabel = i18n$t("詳細"),
label = i18n$t("表示モード"),
inline = T,
size = "small",
width = "300px",
labelWidth = "200px",
handleWidth = "100px"
),
),
column(
width = 6,
tags$span(
dropdownButton(
tags$h4(i18n$t("表示設定")),
materialSwitch(
inputId = "showPopupOnMap",
label = i18n$t("日次増加数のポップアップ"),
status = "danger",
value = T
),
materialSwitch(
inputId = "replyMapLoop",
label = i18n$t("ループ再生"),
status = "danger",
value = T
),
dateRangeInput(
inputId = "mapDateRange",
label = i18n$t("表示日付"),
start = byDate$date[nrow(byDate) - 15],
end = byDate$date[nrow(byDate)],
min = byDate$date[1],
max = byDate$date[nrow(byDate)],
separator = " ~ ",
language = "ja"
),
sliderInput(
inputId = "mapFrameSpeed",
label = i18n$t("再生速度（秒/日）"),
min = 0.5,
max = 3,
step = 0.1,
value = 0.8
),
circle = F,
inline = T,
status = "danger",
icon = icon("gear"),
size = "sm",
width = "300px",
tooltip = tooltipOptions(title = i18n$t("表示設定"), placement = "top")
),
style = "float:right;"
)
),
),
style = "margin-top:10px;"
),
uiOutput("comfirmedMapWrapper") %>% withSpinner(proxy.height = "550px"),
# TODO もし全部の都道府県に感染者報告がある場合、こちらのバーを再検討する
progressBar(
id = "activePatients",
value = TOTAL_JAPAN - DEATH_JAPAN - 40 - sum(mhlwSummary[日付 == max(日付)]$退院者),
total = TOTAL_JAPAN - DEATH_JAPAN - 40,
title = tagList(
icon("procedures"),
i18n$t("現在患者数")
),
striped = T,
status = "danger",
display_pct = T
),
bsTooltip(
id = "activePatients",
placement = "top",
title = i18n$t("分母には死亡者、チャーター便で帰国したクルーズ船の乗客40名は含まれていません。")
),
tagList(icon("shield-alt"), tags$b(i18n$t("感染者報告なし"))),
uiOutput("saveArea"),
),
column(
width = 7,
boxPad(
fluidRow(
column(
width = 9,
radioGroupButtons(
inputId = "switchTableVersion",
label = NULL,
justified = T,
choiceNames = c(
paste(icon("procedures"), i18n$t("感染")),
paste(icon("vials"), i18n$t("検査")),
paste(icon("hospital"), i18n$t("回復・死亡"))
),
choiceValues = c("confirmed", "test", "discharged"),
status = "danger"
)
),
column(
width = 3,
tags$span(
awesomeCheckbox(
inputId = "tableShowSetting",
label = i18n$t("グルーピング表示"),
status = "danger",
value = T
),
style = "float:right;"
)
)
),
uiOutput("summaryTable") %>% withSpinner()
)
)
)
)
| /03_Components/Main/ConfirmedMap.ui.R | permissive | peppy/2019-ncov-japan | R | false | false | 4,329 | r | tabPanel(
title = tagList(
icon("globe-asia"),
i18n$t("感染状況マップ")
),
fluidRow(
column(
width = 5,
tags$div(
fluidRow(
column(
width = 6,
switchInput(
inputId = "switchMapVersion",
value = T,
onLabel = i18n$t("シンプル"),
onStatus = "danger",
offStatus = "danger",
offLabel = i18n$t("詳細"),
label = i18n$t("表示モード"),
inline = T,
size = "small",
width = "300px",
labelWidth = "200px",
handleWidth = "100px"
),
),
column(
width = 6,
tags$span(
dropdownButton(
tags$h4(i18n$t("表示設定")),
materialSwitch(
inputId = "showPopupOnMap",
label = i18n$t("日次増加数のポップアップ"),
status = "danger",
value = T
),
materialSwitch(
inputId = "replyMapLoop",
label = i18n$t("ループ再生"),
status = "danger",
value = T
),
dateRangeInput(
inputId = "mapDateRange",
label = i18n$t("表示日付"),
start = byDate$date[nrow(byDate) - 15],
end = byDate$date[nrow(byDate)],
min = byDate$date[1],
max = byDate$date[nrow(byDate)],
separator = " ~ ",
language = "ja"
),
sliderInput(
inputId = "mapFrameSpeed",
label = i18n$t("再生速度（秒/日）"),
min = 0.5,
max = 3,
step = 0.1,
value = 0.8
),
circle = F,
inline = T,
status = "danger",
icon = icon("gear"),
size = "sm",
width = "300px",
tooltip = tooltipOptions(title = i18n$t("表示設定"), placement = "top")
),
style = "float:right;"
)
),
),
style = "margin-top:10px;"
),
uiOutput("comfirmedMapWrapper") %>% withSpinner(proxy.height = "550px"),
# TODO もし全部の都道府県に感染者報告がある場合、こちらのバーを再検討する
progressBar(
id = "activePatients",
value = TOTAL_JAPAN - DEATH_JAPAN - 40 - sum(mhlwSummary[日付 == max(日付)]$退院者),
total = TOTAL_JAPAN - DEATH_JAPAN - 40,
title = tagList(
icon("procedures"),
i18n$t("現在患者数")
),
striped = T,
status = "danger",
display_pct = T
),
bsTooltip(
id = "activePatients",
placement = "top",
title = i18n$t("分母には死亡者、チャーター便で帰国したクルーズ船の乗客40名は含まれていません。")
),
tagList(icon("shield-alt"), tags$b(i18n$t("感染者報告なし"))),
uiOutput("saveArea"),
),
column(
width = 7,
boxPad(
fluidRow(
column(
width = 9,
radioGroupButtons(
inputId = "switchTableVersion",
label = NULL,
justified = T,
choiceNames = c(
paste(icon("procedures"), i18n$t("感染")),
paste(icon("vials"), i18n$t("検査")),
paste(icon("hospital"), i18n$t("回復・死亡"))
),
choiceValues = c("confirmed", "test", "discharged"),
status = "danger"
)
),
column(
width = 3,
tags$span(
awesomeCheckbox(
inputId = "tableShowSetting",
label = i18n$t("グルーピング表示"),
status = "danger",
value = T
),
style = "float:right;"
)
)
),
uiOutput("summaryTable") %>% withSpinner()
)
)
)
)
|
fileile <- "household_power_consumption.txt"
data <- read.table(file, header=TRUE, sep=";", stringsAsFactors=FALSE, dec=".")
subdata <- data[data$Date %in% c("1/2/2007","2/2/2007") ,]
plot1 <- as.numeric(subdata$Global_active_power)
png("plot1.png", width=480, height=480)
hist(plot1, col="red", main="Global Active Power", xlab="Global Active Power (kilowatts)")
| /plot1.r | no_license | mikeauld74/ExData_Plotting1 | R | false | false | 370 | r | fileile <- "household_power_consumption.txt"
data <- read.table(file, header=TRUE, sep=";", stringsAsFactors=FALSE, dec=".")
subdata <- data[data$Date %in% c("1/2/2007","2/2/2007") ,]
plot1 <- as.numeric(subdata$Global_active_power)
png("plot1.png", width=480, height=480)
hist(plot1, col="red", main="Global Active Power", xlab="Global Active Power (kilowatts)")
|
#Load Source File
#-the source file is a .RData file where all the functions and codes are preloaded
place<-getwd()
source("/home/tcrnerc/Scratch/models/incubator/SpatialModel.R") #load ABM
load("/home/tcrnerc/Scratch/models/incubator/sweep1.RData") #load sweep
setwd(place)#Load Source File
#This allows the definition of arguments (this allows the definition of the random seeds as an input argument)
Args<-as.numeric(commandArgs(TRUE))
NewArgs=c(45,7282,7941,7971,8681,8714,9435,9436);
Args=NewArgs[Args]
set.seed(Args)
number.sim.per.job = 3
#Subset sweep for current run
thissweep<-as.data.frame(paramsweep[((Args-1)*number.sim.per.job+1):(Args*number.sim.per.job),])
resultMean<-numeric() #place holder for containing results
resultMedian<-numeric() #place holder for containing results
#Simulation loop
for (x in 1:number.sim.per.job)
{
print(x)
result<-sim(mat=matrixGenerator(randomPoints(1000),k=thissweep$k[x]),
z=thissweep$z[x],mu=thissweep$mu[x],sigma=thissweep$sigma[x],
timeSteps=1000,verbose=TRUE,mode="model")
resultMean[x]=result[1]
resultMedian[x]=result[2]
}
#When the computation is done, create the name of the RData as the name of the calling code...
thissweep$Mean=resultMean
thissweep$Median=resultMedian
name<-paste("./res",Args,".csv",sep="")
write.csv(thissweep,file=name)
| /submitLegion/completeRuns/submit_model1ext.R | no_license | ercrema/culturalincubators | R | false | false | 1,340 | r | #Load Source File
#-the source file is a .RData file where all the functions and codes are preloaded
place<-getwd()
source("/home/tcrnerc/Scratch/models/incubator/SpatialModel.R") #load ABM
load("/home/tcrnerc/Scratch/models/incubator/sweep1.RData") #load sweep
setwd(place)#Load Source File
#This allows the definition of arguments (this allows the definition of the random seeds as an input argument)
Args<-as.numeric(commandArgs(TRUE))
NewArgs=c(45,7282,7941,7971,8681,8714,9435,9436);
Args=NewArgs[Args]
set.seed(Args)
number.sim.per.job = 3
#Subset sweep for current run
thissweep<-as.data.frame(paramsweep[((Args-1)*number.sim.per.job+1):(Args*number.sim.per.job),])
resultMean<-numeric() #place holder for containing results
resultMedian<-numeric() #place holder for containing results
#Simulation loop
for (x in 1:number.sim.per.job)
{
print(x)
result<-sim(mat=matrixGenerator(randomPoints(1000),k=thissweep$k[x]),
z=thissweep$z[x],mu=thissweep$mu[x],sigma=thissweep$sigma[x],
timeSteps=1000,verbose=TRUE,mode="model")
resultMean[x]=result[1]
resultMedian[x]=result[2]
}
#When the computation is done, create the name of the RData as the name of the calling code...
thissweep$Mean=resultMean
thissweep$Median=resultMedian
name<-paste("./res",Args,".csv",sep="")
write.csv(thissweep,file=name)
|
#' @param infile Path to the input file
#' @param header T (Default) or F
#' @return A matrix of the infile
#' @export
load_ped <- function(infile, header = TRUE){
## read file as a matrix ##
in.dt <- data.table::fread(infile, header = header)
## conduct checks ##
#- check column names
nameVec = c('IID', 'sample', 'FID', 'family', 'PID', 'father', 'DID',
'MID', 'mother', 'mom', 'sex', 'gender', 'phenotype',
'phen', 'pheno' ,'trait')
nameVec = toupper(nameVec)
colnames(in.dt) = toupper(colnames(in.dt))
if(!any(colnames(in.dt) %in% nameVec)) stop(name.rules())
colnames(in.dt)[which(colnames(in.dt) %in% c("IID", "SAMPLE"))] <- 'IID'
colnames(in.dt)[which(colnames(in.dt) %in% c("FID", 'FAMILY'))] <- 'FID'
colnames(in.dt)[which(colnames(in.dt) %in% c("PID", "FATHER", 'DID', 'PID'))] <- 'PID'
colnames(in.dt)[which(colnames(in.dt) %in% c("MID", "MOTHER", "MOM"))] <- 'MID'
colnames(in.dt)[which(colnames(in.dt) %in% c("SEX", "GENDER"))] <- 'SEX'
colnames(in.dt)[which(colnames(in.dt) %in% c("PHENOTYPE", "PHEN", "PHENO", "TRAIT"))] <- 'PHEN'
#- check for duplicated column names
if (length(unique(colnames(in.dt))) < length(colnames(in.dt))) stop(col.duplicated())
#- check if there's duplicated IDs
if (length(unique(paste0(in.dt$IID,'-', in.dt$FID))) < length(paste0(in.dt$IID, '-', in.dt$FID))) stop(id.duplicatd())
## functions ##
name.rules <- function(){
writeLines("Column names should be as listed (not case-sensitive):
Sample can be 'IID', 'sample'
Family can be 'FID', 'family'
Father can be 'PID', 'dad', 'DID', 'father'
Mother can be 'MID', 'mother', 'mom'
Sex can be 'sex', 'gender'
Phenotype can be 'phenotype', 'phen','pheno' , 'trait'" )
}
value.rules <- function(){
writeLines("Columns can contain the following values (not case-sensitive):
Males as 'M', 'male', 1
Females as 'F', 'female', 2
Unknown as NA, -9, 'unk'")
}
col.duplicated <- function(){
writeLines('Your table has duplicated column names')
}
id.duplicated <- function(){
writeLines('You have multiple samples within a family with the same ID')
}
in.dt
}
| /R/load_ped.R | no_license | kopalgarg/gwas-vis | R | false | false | 2,361 | r | #' @param infile Path to the input file
#' @param header T (Default) or F
#' @return A matrix of the infile
#' @export
load_ped <- function(infile, header = TRUE){
## read file as a matrix ##
in.dt <- data.table::fread(infile, header = header)
## conduct checks ##
#- check column names
nameVec = c('IID', 'sample', 'FID', 'family', 'PID', 'father', 'DID',
'MID', 'mother', 'mom', 'sex', 'gender', 'phenotype',
'phen', 'pheno' ,'trait')
nameVec = toupper(nameVec)
colnames(in.dt) = toupper(colnames(in.dt))
if(!any(colnames(in.dt) %in% nameVec)) stop(name.rules())
colnames(in.dt)[which(colnames(in.dt) %in% c("IID", "SAMPLE"))] <- 'IID'
colnames(in.dt)[which(colnames(in.dt) %in% c("FID", 'FAMILY'))] <- 'FID'
colnames(in.dt)[which(colnames(in.dt) %in% c("PID", "FATHER", 'DID', 'PID'))] <- 'PID'
colnames(in.dt)[which(colnames(in.dt) %in% c("MID", "MOTHER", "MOM"))] <- 'MID'
colnames(in.dt)[which(colnames(in.dt) %in% c("SEX", "GENDER"))] <- 'SEX'
colnames(in.dt)[which(colnames(in.dt) %in% c("PHENOTYPE", "PHEN", "PHENO", "TRAIT"))] <- 'PHEN'
#- check for duplicated column names
if (length(unique(colnames(in.dt))) < length(colnames(in.dt))) stop(col.duplicated())
#- check if there's duplicated IDs
if (length(unique(paste0(in.dt$IID,'-', in.dt$FID))) < length(paste0(in.dt$IID, '-', in.dt$FID))) stop(id.duplicatd())
## functions ##
name.rules <- function(){
writeLines("Column names should be as listed (not case-sensitive):
Sample can be 'IID', 'sample'
Family can be 'FID', 'family'
Father can be 'PID', 'dad', 'DID', 'father'
Mother can be 'MID', 'mother', 'mom'
Sex can be 'sex', 'gender'
Phenotype can be 'phenotype', 'phen','pheno' , 'trait'" )
}
value.rules <- function(){
writeLines("Columns can contain the following values (not case-sensitive):
Males as 'M', 'male', 1
Females as 'F', 'female', 2
Unknown as NA, -9, 'unk'")
}
col.duplicated <- function(){
writeLines('Your table has duplicated column names')
}
id.duplicated <- function(){
writeLines('You have multiple samples within a family with the same ID')
}
in.dt
}
|
\name{grasp.cormat}
\alias{grasp.cormat}
\title{GRASP-R correlations map and matrix}
\description{Calculates correlation between PVs and plots a matrix of correlation}
\usage{grasp.cormat(gr.Yi, cols, thin = 1)}
\arguments{
\item{gr.Yi}{Selected responses to be used in the form of a vector with column numbers}
\item{cols}{Selected predictors to be used in the form of a vector with column numbers}
\item{thin}{Leave 1}
}
\details{
This function calculates correlation between PVs and plots a matrix of correlation. It is a visual check.
Too highly correlated PVs have to be eliminated manually, after checking.
}
\author{
Fabien Fivaz \email{fabien.fivaz@bluewin.ch}. Ported to R from GRASP \url{http://www.cscf.ch/grasp/} for S-Plus written by A. Lehmann,
J.R. Leathwich and J. McC Overton. Look at \url{http://www.cscf.ch/grasp} for details and update.
}
\seealso{
\code{\link{grasp.histo}}, \code{\link{grasp.datamap}}, \code{\link{grasp.RvsP}} and \code{\link{grasp.corlim}}
}
\keyword{models} \keyword{smooth} \keyword{regression} \keyword{plot}
| /man/grasp.cormat.Rd | no_license | cran/grasper | R | false | false | 1,087 | rd | \name{grasp.cormat}
\alias{grasp.cormat}
\title{GRASP-R correlations map and matrix}
\description{Calculates correlation between PVs and plots a matrix of correlation}
\usage{grasp.cormat(gr.Yi, cols, thin = 1)}
\arguments{
\item{gr.Yi}{Selected responses to be used in the form of a vector with column numbers}
\item{cols}{Selected predictors to be used in the form of a vector with column numbers}
\item{thin}{Leave 1}
}
\details{
This function calculates correlation between PVs and plots a matrix of correlation. It is a visual check.
Too highly correlated PVs have to be eliminated manually, after checking.
}
\author{
Fabien Fivaz \email{fabien.fivaz@bluewin.ch}. Ported to R from GRASP \url{http://www.cscf.ch/grasp/} for S-Plus written by A. Lehmann,
J.R. Leathwich and J. McC Overton. Look at \url{http://www.cscf.ch/grasp} for details and update.
}
\seealso{
\code{\link{grasp.histo}}, \code{\link{grasp.datamap}}, \code{\link{grasp.RvsP}} and \code{\link{grasp.corlim}}
}
\keyword{models} \keyword{smooth} \keyword{regression} \keyword{plot}
|
source("shared.R")
rankall <- function(outcome, num = "best") {
ranked <- rankedByState(outcome = outcome)
## For each state, find the hospital of the given rank
## Return a data frame with the hospital names and the
## (abbreviated) state name
byState <- split(ranked, ranked$state)
if (num == "best") idx <- 1
else if (num == "worst") idx <- nrow(byState[[1]])
else idx <- num
result <- data.frame(byState[[1]][idx,])
for(i in 2:length(byState)) {
if (num == "worst") idx <- nrow(byState[[i]])
state <- byState[[i]][idx,]
if (!is.na(state$state)) {
result <- rbind(result, state)
}
}
allStates <- levels(byState[[1]]$state)
resultStates <- result$state
naStates <- setdiff(allStates, resultStates)
for(nas in naStates) {
r <- data.frame(name=NA, state=nas, score=NaN)
result <- rbind(result, r)
}
result[with(result, order(state, name, na.last = TRUE)),][,c(1,2)]
}
| /rankall.R | no_license | paulCrsr/RProgrammingAssignment3 | R | false | false | 1,192 | r | source("shared.R")
rankall <- function(outcome, num = "best") {
ranked <- rankedByState(outcome = outcome)
## For each state, find the hospital of the given rank
## Return a data frame with the hospital names and the
## (abbreviated) state name
byState <- split(ranked, ranked$state)
if (num == "best") idx <- 1
else if (num == "worst") idx <- nrow(byState[[1]])
else idx <- num
result <- data.frame(byState[[1]][idx,])
for(i in 2:length(byState)) {
if (num == "worst") idx <- nrow(byState[[i]])
state <- byState[[i]][idx,]
if (!is.na(state$state)) {
result <- rbind(result, state)
}
}
allStates <- levels(byState[[1]]$state)
resultStates <- result$state
naStates <- setdiff(allStates, resultStates)
for(nas in naStates) {
r <- data.frame(name=NA, state=nas, score=NaN)
result <- rbind(result, r)
}
result[with(result, order(state, name, na.last = TRUE)),][,c(1,2)]
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functions_other.R
\name{getRhat}
\alias{getRhat}
\title{Get rhat}
\usage{
getRhat(stan_fit, parameters)
}
\arguments{
\item{stan_fit}{Output of stan}
\item{parameters}{Parameter names to extract}
}
\description{
Get rhat
}
| /man/getRhat.Rd | no_license | wjoycezhao/hddmRstan | R | false | true | 302 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functions_other.R
\name{getRhat}
\alias{getRhat}
\title{Get rhat}
\usage{
getRhat(stan_fit, parameters)
}
\arguments{
\item{stan_fit}{Output of stan}
\item{parameters}{Parameter names to extract}
}
\description{
Get rhat
}
|
#
# vim:set ff=unix expandtab ts=2 sw=2:
#################################################################
writeGenericFunctionRdFiles <- function(path,docs,nsi){
gens<-nsi[["gens_defined_by_package"]]
funNamesToDocument <-names(gens)
#funNamesToDocument <-names(nsi[["documentableMeths"]])
# note that ls will not find S4 methods for generic functions
# these are treated elsewhere
list0 <- fixPackageFileNames(funNamesToDocument)
names(list0) <- funNamesToDocument
sapply(
funNamesToDocument,
function(item) {
docs_i<-docs[[item]]
if (!is.null(docs_i)){
fn <- file.path(path, paste(list0[[item]],".Rd",sep=""))
#fff<-getGeneric(item,where=e)
fff<-gens[[item]]
fdo=genericFunctionDocObject(name=item,l=docs_i,functionObject=fff)
write_Rd_file(fdo,fn)
}else{
# there are two possibilities
warning(sprintf("## mm ## No documentation found for item:%s.",item))
}
}
)
}
| /pkg/R/deprecated/writeGenericFunctionRdFiles.R | no_license | mamueller/linkeddocs | R | false | false | 995 | r | #
# vim:set ff=unix expandtab ts=2 sw=2:
#################################################################
writeGenericFunctionRdFiles <- function(path,docs,nsi){
gens<-nsi[["gens_defined_by_package"]]
funNamesToDocument <-names(gens)
#funNamesToDocument <-names(nsi[["documentableMeths"]])
# note that ls will not find S4 methods for generic functions
# these are treated elsewhere
list0 <- fixPackageFileNames(funNamesToDocument)
names(list0) <- funNamesToDocument
sapply(
funNamesToDocument,
function(item) {
docs_i<-docs[[item]]
if (!is.null(docs_i)){
fn <- file.path(path, paste(list0[[item]],".Rd",sep=""))
#fff<-getGeneric(item,where=e)
fff<-gens[[item]]
fdo=genericFunctionDocObject(name=item,l=docs_i,functionObject=fff)
write_Rd_file(fdo,fn)
}else{
# there are two possibilities
warning(sprintf("## mm ## No documentation found for item:%s.",item))
}
}
)
}
|
setwd("D:/FAIMS/20180822_DI2A/")
############### PEPTIDE LEVEL FDRs
## 3 dalton isolation, 60k OT resolution
mso60k3i_3p10f<-peplvlfdr(msplitresults="mso3p10f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_3p20f<-peplvlfdr(msplitresults="mso3p20f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_3p30f<-peplvlfdr(msplitresults="mso3p30f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_3p40f<-peplvlfdr(msplitresults="mso3p40f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_3p50f<-peplvlfdr(msplitresults="mso3p50f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_3p60f<-peplvlfdr(msplitresults="mso3p60f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
## 3 dalton isolation, 15k OT resolution
mso15k3i_3p10f<-peplvlfdr(msplitresults="mso3p10f_DIA2_OT_15k_3i1o_1.txt") ## < 1% FDR
mso15k3i_3p20f<-peplvlfdr(msplitresults="mso3p20f_DIA2_OT_15k_3i1o_1.txt") ## < 1% FDR
mso15k3i_3p30f<-peplvlfdr(msplitresults="mso3p30f_DIA2_OT_15k_3i1o_1.txt") ## < 1% FDR
mso15k3i_3p40f<-peplvlfdr(msplitresults="mso3p40f_DIA2_OT_15k_3i1o_1.txt") ## < 1% FDR
mso15k3i_3p50f<-peplvlfdr(msplitresults="mso3p50f_DIA2_OT_15k_3i1o_1.txt") ## < 1% FDR
mso15k3i_3p60f<-peplvlfdr(msplitresults="mso3p60f_DIA2_OT_15k_3i1o_1.txt") ## < 1% FDR
## p8 dalton isolation, 60k OT resolution
mso60kp8i_p8p10f<-peplvlfdr(msplitresults="msop8p10f_DIA2_OT_60k.txt") ## < 1% FDR
mso60kp8i_p8p20f<-peplvlfdr(msplitresults="msop8p20f_DIA2_OT_60k.txt") ## < 1% FDR
mso60kp8i_p8p30f<-peplvlfdr(msplitresults="msop8p30f_DIA2_OT_60k.txt") ## < 1% FDR
mso60kp8i_p8p40f<-peplvlfdr(msplitresults="msop8p40f_DIA2_OT_60k.txt") ## < 1% FDR
mso60kp8i_p8p50f<-peplvlfdr(msplitresults="msop8p50f_DIA2_OT_60k.txt") ## < 1% FDR
mso60kp8i_p8p60f<-peplvlfdr(msplitresults="msop8p60f_DIA2_OT_60k.txt") ## < 1% FDR
## p8 dalton isolation, 15k OT resolution
mso15kp8i_p8p10f<-peplvlfdr(msplitresults="msop8p10f_DIA2_OT_15k.txt") ## < 1% FDR
mso15kp8i_p8p20f<-peplvlfdr(msplitresults="msop8p20f_DIA2_OT_15k.txt") ## < 1% FDR
mso15kp8i_p8p30f<-peplvlfdr(msplitresults="msop8p30f_DIA2_OT_15k.txt") ## < 1% FDR
mso15kp8i_p8p40f<-peplvlfdr(msplitresults="msop8p40f_DIA2_OT_15k.txt") ## < 1% FDR
mso15kp8i_p8p50f<-peplvlfdr(msplitresults="msop8p50f_DIA2_OT_15k.txt") ## < 1% FDR
mso15kp8i_p8p60f<-peplvlfdr(msplitresults="msop8p60f_DIA2_OT_15k.txt") ## < 1% FDR
#### 30k resolution
## 3 mz isolation
mso30k3i_3p10f<-peplvlfdr(msplitresults="mso3p10f_DIA2_OT_30k_3i1o_1.txt") ## < 1% FDR
mso30k3i_3p20f<-peplvlfdr(msplitresults="mso3p20f_DIA2_OT_30k_3i1o_1.txt") ## < 1% FDR
mso30k3i_3p30f<-peplvlfdr(msplitresults="mso3p30f_DIA2_OT_30k_3i1o_1.txt") ## < 1% FDR
mso30k3i_3p40f<-peplvlfdr(msplitresults="mso3p40f_DIA2_OT_30k_3i1o_1.txt") ## < 1% FDR
mso30k3i_3p50f<-peplvlfdr(msplitresults="mso3p50f_DIA2_OT_30k_3i1o_1.txt") ## < 1% FDR
mso30k3i_3p60f<-peplvlfdr(msplitresults="mso3p60f_DIA2_OT_30k_3i1o_1.txt") ## < 1% FDR
## 0.8 mz isolation
mso30kp8i_p8p10f<-peplvlfdr(msplitresults="msop8p10f_DIA2_OT_30k.txt") ## < 1% FDR
mso30kp8i_p8p20f<-peplvlfdr(msplitresults="msop8p20f_DIA2_OT_30k.txt") ## < 1% FDR
mso30kp8i_p8p30f<-peplvlfdr(msplitresults="msop8p30f_DIA2_OT_30k.txt") ## < 1% FDR
mso30kp8i_p8p40f<-peplvlfdr(msplitresults="msop8p40f_DIA2_OT_30k.txt") ## < 1% FDR
mso30kp8i_p8p50f<-peplvlfdr(msplitresults="msop8p50f_DIA2_OT_30k.txt") ## < 1% FDR
mso30kp8i_p8p60f<-peplvlfdr(msplitresults="msop8p60f_DIA2_OT_30k.txt") ## < 1% FDR
#### 50k resolution
## 3 mz isolation
mso50k3i_3p10f<-peplvlfdr(msplitresults="mso3p10f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i_3p20f<-peplvlfdr(msplitresults="mso3p20f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i_3p30f<-peplvlfdr(msplitresults="mso3p30f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i_3p40f<-peplvlfdr(msplitresults="mso3p40f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i_3p50f<-peplvlfdr(msplitresults="mso3p50f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i_3p60f<-peplvlfdr(msplitresults="mso3p60f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
## 0.8 mz isolation
mso50kp8i_p8p10f<-peplvlfdr(msplitresults="msop8p10f_DIA2_OT_50k.txt") ## < 1% FDR
mso50kp8i_p8p20f<-peplvlfdr(msplitresults="msop8p20f_DIA2_OT_50k.txt") ## < 1% FDR
mso50kp8i_p8p30f<-peplvlfdr(msplitresults="msop8p30f_DIA2_OT_50k.txt") ## < 1% FDR
mso50kp8i_p8p40f<-peplvlfdr(msplitresults="msop8p40f_DIA2_OT_50k.txt") ## < 1% FDR
mso50kp8i_p8p50f<-peplvlfdr(msplitresults="msop8p50f_DIA2_OT_50k.txt") ## < 1% FDR
mso50kp8i_p8p60f<-peplvlfdr(msplitresults="msop8p60f_DIA2_OT_50k.txt") ## < 1% FDR
### PRECURSOR TOLERANCES
# 60k, 3mz iso
mso60k3i_1p20f<-peplvlfdr(msplitresults="mso1p20f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_1p5p20f<-peplvlfdr(msplitresults="mso1p5p20f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_2p20f<-peplvlfdr(msplitresults="mso2p20f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_2p5p20f<-peplvlfdr(msplitresults="mso2p5p20f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_3.50f<-peplvlfdr(msplitresults="mso3p5p20f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_420f<-peplvlfdr(msplitresults="mso4p20f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_4.5p20f<-peplvlfdr(msplitresults="mso4p5p20f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_5p20f<-peplvlfdr(msplitresults="mso5p20f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i<-peplvlfdr(msplitresults="mso7p20f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i<-peplvlfdr(msplitresults="mso5p5p20f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
# 60k, 0.8 mz iso
mso60k<-peplvlfdr(msplitresults="mso1p2p20f_DIA2_OT_60k.txt") ## < 1% FDR
mso60k<-peplvlfdr(msplitresults="mso1p6p20f_DIA2_OT_60k.txt") ## < 1% FDR
mso60k<-peplvlfdr(msplitresults="mso2p4p20f_DIA2_OT_60k.txt") ## < 1% FDR
# 50k, 3mz iso
mso50k3i<-peplvlfdr(msplitresults="mso3p5_20f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i<-peplvlfdr(msplitresults="mso0p0_20f_DIA2_OT_50k_3i1o_1.txt") ## 5.0 typo
mso50k3i<-peplvlfdr(msplitresults="mso6p5_20f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
# repeat 3 more
mso50k3i<-peplvlfdr(msplitresults="mso5p5p_20f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i<-peplvlfdr(msplitresults="mso6p0p_20f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i<-peplvlfdr(msplitresults="mso7p0p_20f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i<-peplvlfdr(msplitresults="mso4p20f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i<-peplvlfdr(msplitresults="mso4p5p20f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i<-peplvlfdr(msplitresults="mso5p20f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i<-peplvlfdr(msplitresults="mso5p5p20f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i<-peplvlfdr(msplitresults="mso6p20f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
######################################################33
#############3 NO FAIMS
#############################################################
mso15k3mz<-peplvlfdr(msplitresults="D:/FAIMS/20180823_noFAIMS/mso_3p_20f_noFAIMS_OT_15k_3i1o_1.txt") ## < 1% FDR
mso30k3mz<-peplvlfdr(msplitresults="D:/FAIMS/20180823_noFAIMS/mso_3p_20f_noFAIMS_OT_30k_3i1o_1.txt")
mso15kp8mz<-peplvlfdr(msplitresults="D:/FAIMS/20180823_noFAIMS/mso_0p8p_20f_noFAIMS_OT_15k_p8ip4o_1.txt") ## < 1% FDR
mso30kp8mz<-peplvlfdr(msplitresults="D:/FAIMS/20180823_noFAIMS/mso_0p8p_20f_noFAIMS_OT_30k_p8ip4o_1.txt") ## < 1% FDR
mso50kp8mz<-peplvlfdr(msplitresults="D:/FAIMS/20180823_noFAIMS/mso_0p8p_20f_noFAIMS_OT_50k_p8ip4o_1.txt") ## < 1% FDR
mso60kp8mz<-peplvlfdr(msplitresults="D:/FAIMS/20180823_noFAIMS/mso_0p8p_20f_noFAIMS_OT_60k_p8ip4o_1.txt") ## < 1% FDR
mso60kp8mz<-peplvlfdr(msplitresults="D:/FAIMS/20180822_DI2A/mso0p8p_500f_DIA2_ITp8_30m_p1f.txt")
msoitp8mz<-peplvlfdr(msplitresults="D:/FAIMS/20180822_DI2A/mso0p8p_500f_DIA2_ITp8.txt")
| /explore_searchparams_DI2A.R | no_license | jgmeyerucsd/pRoteomics | R | false | false | 7,554 | r |
setwd("D:/FAIMS/20180822_DI2A/")
############### PEPTIDE LEVEL FDRs
## 3 dalton isolation, 60k OT resolution
mso60k3i_3p10f<-peplvlfdr(msplitresults="mso3p10f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_3p20f<-peplvlfdr(msplitresults="mso3p20f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_3p30f<-peplvlfdr(msplitresults="mso3p30f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_3p40f<-peplvlfdr(msplitresults="mso3p40f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_3p50f<-peplvlfdr(msplitresults="mso3p50f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_3p60f<-peplvlfdr(msplitresults="mso3p60f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
## 3 dalton isolation, 15k OT resolution
mso15k3i_3p10f<-peplvlfdr(msplitresults="mso3p10f_DIA2_OT_15k_3i1o_1.txt") ## < 1% FDR
mso15k3i_3p20f<-peplvlfdr(msplitresults="mso3p20f_DIA2_OT_15k_3i1o_1.txt") ## < 1% FDR
mso15k3i_3p30f<-peplvlfdr(msplitresults="mso3p30f_DIA2_OT_15k_3i1o_1.txt") ## < 1% FDR
mso15k3i_3p40f<-peplvlfdr(msplitresults="mso3p40f_DIA2_OT_15k_3i1o_1.txt") ## < 1% FDR
mso15k3i_3p50f<-peplvlfdr(msplitresults="mso3p50f_DIA2_OT_15k_3i1o_1.txt") ## < 1% FDR
mso15k3i_3p60f<-peplvlfdr(msplitresults="mso3p60f_DIA2_OT_15k_3i1o_1.txt") ## < 1% FDR
## p8 dalton isolation, 60k OT resolution
mso60kp8i_p8p10f<-peplvlfdr(msplitresults="msop8p10f_DIA2_OT_60k.txt") ## < 1% FDR
mso60kp8i_p8p20f<-peplvlfdr(msplitresults="msop8p20f_DIA2_OT_60k.txt") ## < 1% FDR
mso60kp8i_p8p30f<-peplvlfdr(msplitresults="msop8p30f_DIA2_OT_60k.txt") ## < 1% FDR
mso60kp8i_p8p40f<-peplvlfdr(msplitresults="msop8p40f_DIA2_OT_60k.txt") ## < 1% FDR
mso60kp8i_p8p50f<-peplvlfdr(msplitresults="msop8p50f_DIA2_OT_60k.txt") ## < 1% FDR
mso60kp8i_p8p60f<-peplvlfdr(msplitresults="msop8p60f_DIA2_OT_60k.txt") ## < 1% FDR
## p8 dalton isolation, 15k OT resolution
mso15kp8i_p8p10f<-peplvlfdr(msplitresults="msop8p10f_DIA2_OT_15k.txt") ## < 1% FDR
mso15kp8i_p8p20f<-peplvlfdr(msplitresults="msop8p20f_DIA2_OT_15k.txt") ## < 1% FDR
mso15kp8i_p8p30f<-peplvlfdr(msplitresults="msop8p30f_DIA2_OT_15k.txt") ## < 1% FDR
mso15kp8i_p8p40f<-peplvlfdr(msplitresults="msop8p40f_DIA2_OT_15k.txt") ## < 1% FDR
mso15kp8i_p8p50f<-peplvlfdr(msplitresults="msop8p50f_DIA2_OT_15k.txt") ## < 1% FDR
mso15kp8i_p8p60f<-peplvlfdr(msplitresults="msop8p60f_DIA2_OT_15k.txt") ## < 1% FDR
#### 30k resolution
## 3 mz isolation
mso30k3i_3p10f<-peplvlfdr(msplitresults="mso3p10f_DIA2_OT_30k_3i1o_1.txt") ## < 1% FDR
mso30k3i_3p20f<-peplvlfdr(msplitresults="mso3p20f_DIA2_OT_30k_3i1o_1.txt") ## < 1% FDR
mso30k3i_3p30f<-peplvlfdr(msplitresults="mso3p30f_DIA2_OT_30k_3i1o_1.txt") ## < 1% FDR
mso30k3i_3p40f<-peplvlfdr(msplitresults="mso3p40f_DIA2_OT_30k_3i1o_1.txt") ## < 1% FDR
mso30k3i_3p50f<-peplvlfdr(msplitresults="mso3p50f_DIA2_OT_30k_3i1o_1.txt") ## < 1% FDR
mso30k3i_3p60f<-peplvlfdr(msplitresults="mso3p60f_DIA2_OT_30k_3i1o_1.txt") ## < 1% FDR
## 0.8 mz isolation
mso30kp8i_p8p10f<-peplvlfdr(msplitresults="msop8p10f_DIA2_OT_30k.txt") ## < 1% FDR
mso30kp8i_p8p20f<-peplvlfdr(msplitresults="msop8p20f_DIA2_OT_30k.txt") ## < 1% FDR
mso30kp8i_p8p30f<-peplvlfdr(msplitresults="msop8p30f_DIA2_OT_30k.txt") ## < 1% FDR
mso30kp8i_p8p40f<-peplvlfdr(msplitresults="msop8p40f_DIA2_OT_30k.txt") ## < 1% FDR
mso30kp8i_p8p50f<-peplvlfdr(msplitresults="msop8p50f_DIA2_OT_30k.txt") ## < 1% FDR
mso30kp8i_p8p60f<-peplvlfdr(msplitresults="msop8p60f_DIA2_OT_30k.txt") ## < 1% FDR
#### 50k resolution
## 3 mz isolation
mso50k3i_3p10f<-peplvlfdr(msplitresults="mso3p10f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i_3p20f<-peplvlfdr(msplitresults="mso3p20f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i_3p30f<-peplvlfdr(msplitresults="mso3p30f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i_3p40f<-peplvlfdr(msplitresults="mso3p40f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i_3p50f<-peplvlfdr(msplitresults="mso3p50f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i_3p60f<-peplvlfdr(msplitresults="mso3p60f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
## 0.8 mz isolation
mso50kp8i_p8p10f<-peplvlfdr(msplitresults="msop8p10f_DIA2_OT_50k.txt") ## < 1% FDR
mso50kp8i_p8p20f<-peplvlfdr(msplitresults="msop8p20f_DIA2_OT_50k.txt") ## < 1% FDR
mso50kp8i_p8p30f<-peplvlfdr(msplitresults="msop8p30f_DIA2_OT_50k.txt") ## < 1% FDR
mso50kp8i_p8p40f<-peplvlfdr(msplitresults="msop8p40f_DIA2_OT_50k.txt") ## < 1% FDR
mso50kp8i_p8p50f<-peplvlfdr(msplitresults="msop8p50f_DIA2_OT_50k.txt") ## < 1% FDR
mso50kp8i_p8p60f<-peplvlfdr(msplitresults="msop8p60f_DIA2_OT_50k.txt") ## < 1% FDR
### PRECURSOR TOLERANCES
# 60k, 3mz iso
mso60k3i_1p20f<-peplvlfdr(msplitresults="mso1p20f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_1p5p20f<-peplvlfdr(msplitresults="mso1p5p20f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_2p20f<-peplvlfdr(msplitresults="mso2p20f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_2p5p20f<-peplvlfdr(msplitresults="mso2p5p20f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_3.50f<-peplvlfdr(msplitresults="mso3p5p20f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_420f<-peplvlfdr(msplitresults="mso4p20f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_4.5p20f<-peplvlfdr(msplitresults="mso4p5p20f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i_5p20f<-peplvlfdr(msplitresults="mso5p20f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i<-peplvlfdr(msplitresults="mso7p20f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
mso60k3i<-peplvlfdr(msplitresults="mso5p5p20f_DIA2_OT_60k_3i1o_1.txt") ## < 1% FDR
# 60k, 0.8 mz iso
mso60k<-peplvlfdr(msplitresults="mso1p2p20f_DIA2_OT_60k.txt") ## < 1% FDR
mso60k<-peplvlfdr(msplitresults="mso1p6p20f_DIA2_OT_60k.txt") ## < 1% FDR
mso60k<-peplvlfdr(msplitresults="mso2p4p20f_DIA2_OT_60k.txt") ## < 1% FDR
# 50k, 3mz iso
mso50k3i<-peplvlfdr(msplitresults="mso3p5_20f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i<-peplvlfdr(msplitresults="mso0p0_20f_DIA2_OT_50k_3i1o_1.txt") ## 5.0 typo
mso50k3i<-peplvlfdr(msplitresults="mso6p5_20f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
# repeat 3 more
mso50k3i<-peplvlfdr(msplitresults="mso5p5p_20f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i<-peplvlfdr(msplitresults="mso6p0p_20f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i<-peplvlfdr(msplitresults="mso7p0p_20f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i<-peplvlfdr(msplitresults="mso4p20f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i<-peplvlfdr(msplitresults="mso4p5p20f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i<-peplvlfdr(msplitresults="mso5p20f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i<-peplvlfdr(msplitresults="mso5p5p20f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
mso50k3i<-peplvlfdr(msplitresults="mso6p20f_DIA2_OT_50k_3i1o_1.txt") ## < 1% FDR
######################################################33
#############3 NO FAIMS
#############################################################
mso15k3mz<-peplvlfdr(msplitresults="D:/FAIMS/20180823_noFAIMS/mso_3p_20f_noFAIMS_OT_15k_3i1o_1.txt") ## < 1% FDR
mso30k3mz<-peplvlfdr(msplitresults="D:/FAIMS/20180823_noFAIMS/mso_3p_20f_noFAIMS_OT_30k_3i1o_1.txt")
mso15kp8mz<-peplvlfdr(msplitresults="D:/FAIMS/20180823_noFAIMS/mso_0p8p_20f_noFAIMS_OT_15k_p8ip4o_1.txt") ## < 1% FDR
mso30kp8mz<-peplvlfdr(msplitresults="D:/FAIMS/20180823_noFAIMS/mso_0p8p_20f_noFAIMS_OT_30k_p8ip4o_1.txt") ## < 1% FDR
mso50kp8mz<-peplvlfdr(msplitresults="D:/FAIMS/20180823_noFAIMS/mso_0p8p_20f_noFAIMS_OT_50k_p8ip4o_1.txt") ## < 1% FDR
mso60kp8mz<-peplvlfdr(msplitresults="D:/FAIMS/20180823_noFAIMS/mso_0p8p_20f_noFAIMS_OT_60k_p8ip4o_1.txt") ## < 1% FDR
mso60kp8mz<-peplvlfdr(msplitresults="D:/FAIMS/20180822_DI2A/mso0p8p_500f_DIA2_ITp8_30m_p1f.txt")
msoitp8mz<-peplvlfdr(msplitresults="D:/FAIMS/20180822_DI2A/mso0p8p_500f_DIA2_ITp8.txt")
|
# Copyright 2014 Google Inc. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Script to auto-generate BigQuery schema and clean the data for BigQuery import.
library(reshape)
library(plyr)
library(dplyr)
library(testthat)
dataDir <- './'
#----------------------------------------------------------------------------
# Load Demographic Data
demo <- read.csv(file.path(dataDir, 'PGPParticipantSurvey-20140506220023.csv'),
stringsAsFactors=FALSE,
na.strings=c('NA', 'N/A', 'No response', 'null', ''))
expect_equal(nrow(demo), 2627)
expect_equal(ncol(demo), 52)
# Substitute whitespace and punctuation for underscores
colnames(demo) <- gsub('\\W+', '_', colnames(demo))
# Trim trailing underscores
colnames(demo) <- gsub('_+$', '', colnames(demo))
# PGP participants have filled out the surveys multiple times
expect_less_than(length(unique(demo$Participant)), nrow(demo))
# Drop a few columns
drops <- c('Do_not_touch',
'Do_you_have_a_severe_genetic_disease_or_rare_genetic_trait_If_so_you_can_add_a_description_for_your_public_profile',
'Disease_trait_Documentation_description')
demo <- demo[,!(names(demo) %in% drops)]
# Convert Timestamp column to dates
demo$Timestamp <- strptime(as.character(demo$Timestamp), '%m/%d/%Y %H:%M:%S')
demo$Timestamp <- as.POSIXct(demo$Timestamp)
# Filter, keeping only most recent survey per participant
recentDemo <- demo %.%
group_by(Participant) %.%
arrange(desc(Timestamp)) %.%
filter(row_number(Participant) == 1)
expect_equal(length(unique(demo$Participant)), nrow(recentDemo))
# Spot check the data
expect_equal(recentDemo[recentDemo$Participant == 'huD554DB',]$Timestamp,
as.POSIXct('2014-02-07 12:20:52 PST'))
#----------------------------------------------------------------------------
# Load Phenotypic Trait Data
files <- c(
'PGPTrait&DiseaseSurvey2012-Blood-20140506220045.csv',
'PGPTrait&DiseaseSurvey2012-Cancers-20140506220037.csv',
'PGPTrait&DiseaseSurvey2012-CirculatorySystem-20140506220056.csv',
'PGPTrait&DiseaseSurvey2012-CongenitalTraitsAndAnomalies-20140506220117.csv',
'PGPTrait&DiseaseSurvey2012-DigestiveSystem-20140506220103.csv',
'PGPTrait&DiseaseSurvey2012-Endocrine,Metabolic,Nutritional,AndImmunity-20140506220041.csv',
'PGPTrait&DiseaseSurvey2012-GenitourinarySystems-20140506220107.csv',
'PGPTrait&DiseaseSurvey2012-MusculoskeletalSystemAndConnectiveTissue-20140506220114.csv',
'PGPTrait&DiseaseSurvey2012-NervousSystem-20140506220048.csv',
'PGPTrait&DiseaseSurvey2012-RespiratorySystem-20140506220059.csv',
'PGPTrait&DiseaseSurvey2012-SkinAndSubcutaneousTissue-20140506220111.csv',
'PGPTrait&DiseaseSurvey2012-VisionAndHearing-20140506220052.csv'
)
traits <- lapply(files, function(file) {
data <- read.csv(file.path(dataDir, file),
stringsAsFactors=FALSE,
na.strings=c('NA', 'N/A', 'No response', 'null', ''))
print(paste('file:', file, 'nrow:', nrow(data), 'ncol:', ncol(data)))
expect_equal(ncol(data), 5)
# This column name differs between the surveys but its the same data. Update
# the column name so that we can join all this data together.
if('Have.you.ever.been.diagnosed.with.one.of.the.following.conditions.' == colnames(data)[4]) {
colnames(data)[4] <- 'Have.you.ever.been.diagnosed.with.any.of.the.following.conditions.'
}
expect_equal(colnames(data), c('Participant',
'Timestamp',
'Do.not.touch.',
'Have.you.ever.been.diagnosed.with.any.of.the.following.conditions.',
'Other.condition.not.listed.here.'))
# PGP participants have filled out the surveys multiple times
expect_less_than(length(unique(data$Participant)), nrow(data))
data
})
trait <- do.call(rbind, traits)
expect_equal(ncol(trait), 5)
expect_equal(nrow(trait), sum(unlist(lapply(traits, nrow))))
# Convert Timestamp column to dates
trait$Timestamp <- strptime(as.character(trait$Timestamp), '%m/%d/%Y %H:%M:%S')
trait$Timestamp <- as.POSIXct(trait$Timestamp)
trait <- arrange(trait, desc(Timestamp))
# Reshape the trait data such that conditions are individual columns.
longTrait <- ddply(trait, .(Participant), function(data) {
conditions <- unlist(strsplit(c(as.character(data$Have.you.ever.been.diagnosed.with.any.of.the.following.conditions.)), ','))
# Trim leading and trailing whitespace and punctuation
conditions <- gsub('^\\W+', '', conditions)
conditions <- gsub('\\W+$', '', conditions)
# Substitute whitespace and punctuation for underscores
conditions <- gsub('\\W+', '_', conditions)
data.frame(Participant = rep(unique(data$Participant), length(conditions)),
Timestamp = rep(unique(data$Timestamp), length(conditions)),
conditions = conditions)
})
longTrait <- cbind(longTrait, has=rep(TRUE, nrow(longTrait)))
wideTrait <- reshape(longTrait,
idvar = 'Participant',
timevar='conditions',
v.names='has',
direction = 'wide')
expect_equal(length(unique(wideTrait$Participant)), nrow(wideTrait))
# Substitute whitespace and punctuation for underscores
colnames(wideTrait) <- gsub('\\W$', '', colnames(wideTrait))
colnames(wideTrait) <- gsub('\\W+', '_', colnames(wideTrait))
# Spot check our data to verify that it was reshaped correctly.
expect_true(wideTrait[wideTrait$Participant=='hu005023', 'has_Dental_cavities'])
expect_true(is.na(wideTrait[wideTrait$Participant=='hu005023', 'has_Gastroesophageal_reflux_disease_GERD']))
expect_true(wideTrait[wideTrait$Participant=='hu005023', 'has_Impacted_tooth'])
expect_true(is.na(wideTrait[wideTrait$Participant=='hu005023', 'has_Skin_tags']))
expect_true(is.na(wideTrait[wideTrait$Participant=='hu005023', 'has_Hair_loss_includes_female_and_male_pattern_baldness']))
expect_true(is.na(wideTrait[wideTrait$Participant=='hu005023', 'has_Acne']))
expect_true(wideTrait[wideTrait$Participant=='hu005023', 'has_Allergic_contact_dermatitis'])
expect_true(wideTrait[wideTrait$Participant=='hu627574', 'has_Dental_cavities'])
expect_true(wideTrait[wideTrait$Participant=='hu627574', 'has_Gastroesophageal_reflux_disease_GERD'])
expect_true(wideTrait[wideTrait$Participant=='hu627574', 'has_Impacted_tooth'])
expect_true(is.na(wideTrait[wideTrait$Participant=='hu627574', 'has_Skin_tags']))
expect_true(wideTrait[wideTrait$Participant=='hu627574', 'has_Hair_loss_includes_female_and_male_pattern_baldness'])
expect_true(wideTrait[wideTrait$Participant=='hu627574', 'has_Acne'])
expect_true(is.na(wideTrait[wideTrait$Participant=='hu627574', 'has_Allergic_contact_dermatitis']))
expect_true(wideTrait[wideTrait$Participant=='hu8E2A35', 'has_Dental_cavities'])
expect_true(is.na(wideTrait[wideTrait$Participant=='hu8E2A35', 'has_Gastroesophageal_reflux_disease_GERD']))
expect_true(is.na(wideTrait[wideTrait$Participant=='hu8E2A35', 'has_Impacted_tooth']))
expect_true(wideTrait[wideTrait$Participant=='hu8E2A35', 'has_Skin_tags'])
expect_true(wideTrait[wideTrait$Participant=='hu8E2A35', 'has_Hair_loss_includes_female_and_male_pattern_baldness'])
expect_true(wideTrait[wideTrait$Participant=='hu8E2A35', 'has_Acne'])
expect_true(is.na(wideTrait[wideTrait$Participant=='hu8E2A35', 'has_Allergic_contact_dermatitis']))
#----------------------------------------------------------------------------
# Some participants only filled out trait surveys, others only filled out the
# participant survey
expect_false(setequal(recentDemo$Participant,
intersect(wideTrait$Participant, recentDemo$Participant)))
expect_false(setequal(wideTrait$Participant,
intersect(wideTrait$Participant, recentDemo$Participant)))
# JOIN it all together, dropping the Timestamp columns
pheno <- join(recentDemo[,-2], wideTrait[,-2], type='full')
expect_equal(nrow(pheno),
length(union(wideTrait$Participant, recentDemo$Participant)))
# Generate BQ Schema
cols <- colnames(pheno)
bool_ints <- c(0, 1, NA)
schema <- c()
for (i in 1:length(cols)) {
type <- 'STRING'
if ('logical' == class(pheno[, i])) {
type <- 'BOOLEAN'
} else if ('numeric' == class(pheno[, i])) {
type <- 'FLOAT'
} else if ('integer' == class(pheno[, i])) {
if (setequal(bool_ints, union(bool_ints, pheno[, i]))) {
type <- 'BOOLEAN'
} else {
type <- 'INTEGER'
}
}
schema <- append(schema, paste(cols[i], type,
sep=":", collapse=","))
}
print(paste(schema, collapse=','))
# Write out file to load into BigQuery
write.table(pheno, file.path(dataDir, 'pgp-phenotypes.tsv'),
row.names=FALSE, sep='\t', na='')
| /pgp/provenance/phenotype-prep.R | permissive | deflaux/bigquery-examples | R | false | false | 9,276 | r | # Copyright 2014 Google Inc. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Script to auto-generate BigQuery schema and clean the data for BigQuery import.
library(reshape)
library(plyr)
library(dplyr)
library(testthat)
dataDir <- './'
#----------------------------------------------------------------------------
# Load Demographic Data
demo <- read.csv(file.path(dataDir, 'PGPParticipantSurvey-20140506220023.csv'),
stringsAsFactors=FALSE,
na.strings=c('NA', 'N/A', 'No response', 'null', ''))
expect_equal(nrow(demo), 2627)
expect_equal(ncol(demo), 52)
# Substitute whitespace and punctuation for underscores
colnames(demo) <- gsub('\\W+', '_', colnames(demo))
# Trim trailing underscores
colnames(demo) <- gsub('_+$', '', colnames(demo))
# PGP participants have filled out the surveys multiple times
expect_less_than(length(unique(demo$Participant)), nrow(demo))
# Drop a few columns
drops <- c('Do_not_touch',
'Do_you_have_a_severe_genetic_disease_or_rare_genetic_trait_If_so_you_can_add_a_description_for_your_public_profile',
'Disease_trait_Documentation_description')
demo <- demo[,!(names(demo) %in% drops)]
# Convert Timestamp column to dates
demo$Timestamp <- strptime(as.character(demo$Timestamp), '%m/%d/%Y %H:%M:%S')
demo$Timestamp <- as.POSIXct(demo$Timestamp)
# Filter, keeping only most recent survey per participant
recentDemo <- demo %.%
group_by(Participant) %.%
arrange(desc(Timestamp)) %.%
filter(row_number(Participant) == 1)
expect_equal(length(unique(demo$Participant)), nrow(recentDemo))
# Spot check the data
expect_equal(recentDemo[recentDemo$Participant == 'huD554DB',]$Timestamp,
as.POSIXct('2014-02-07 12:20:52 PST'))
#----------------------------------------------------------------------------
# Load Phenotypic Trait Data
files <- c(
'PGPTrait&DiseaseSurvey2012-Blood-20140506220045.csv',
'PGPTrait&DiseaseSurvey2012-Cancers-20140506220037.csv',
'PGPTrait&DiseaseSurvey2012-CirculatorySystem-20140506220056.csv',
'PGPTrait&DiseaseSurvey2012-CongenitalTraitsAndAnomalies-20140506220117.csv',
'PGPTrait&DiseaseSurvey2012-DigestiveSystem-20140506220103.csv',
'PGPTrait&DiseaseSurvey2012-Endocrine,Metabolic,Nutritional,AndImmunity-20140506220041.csv',
'PGPTrait&DiseaseSurvey2012-GenitourinarySystems-20140506220107.csv',
'PGPTrait&DiseaseSurvey2012-MusculoskeletalSystemAndConnectiveTissue-20140506220114.csv',
'PGPTrait&DiseaseSurvey2012-NervousSystem-20140506220048.csv',
'PGPTrait&DiseaseSurvey2012-RespiratorySystem-20140506220059.csv',
'PGPTrait&DiseaseSurvey2012-SkinAndSubcutaneousTissue-20140506220111.csv',
'PGPTrait&DiseaseSurvey2012-VisionAndHearing-20140506220052.csv'
)
traits <- lapply(files, function(file) {
data <- read.csv(file.path(dataDir, file),
stringsAsFactors=FALSE,
na.strings=c('NA', 'N/A', 'No response', 'null', ''))
print(paste('file:', file, 'nrow:', nrow(data), 'ncol:', ncol(data)))
expect_equal(ncol(data), 5)
# This column name differs between the surveys but its the same data. Update
# the column name so that we can join all this data together.
if('Have.you.ever.been.diagnosed.with.one.of.the.following.conditions.' == colnames(data)[4]) {
colnames(data)[4] <- 'Have.you.ever.been.diagnosed.with.any.of.the.following.conditions.'
}
expect_equal(colnames(data), c('Participant',
'Timestamp',
'Do.not.touch.',
'Have.you.ever.been.diagnosed.with.any.of.the.following.conditions.',
'Other.condition.not.listed.here.'))
# PGP participants have filled out the surveys multiple times
expect_less_than(length(unique(data$Participant)), nrow(data))
data
})
trait <- do.call(rbind, traits)
expect_equal(ncol(trait), 5)
expect_equal(nrow(trait), sum(unlist(lapply(traits, nrow))))
# Convert Timestamp column to dates
trait$Timestamp <- strptime(as.character(trait$Timestamp), '%m/%d/%Y %H:%M:%S')
trait$Timestamp <- as.POSIXct(trait$Timestamp)
trait <- arrange(trait, desc(Timestamp))
# Reshape the trait data such that conditions are individual columns.
longTrait <- ddply(trait, .(Participant), function(data) {
conditions <- unlist(strsplit(c(as.character(data$Have.you.ever.been.diagnosed.with.any.of.the.following.conditions.)), ','))
# Trim leading and trailing whitespace and punctuation
conditions <- gsub('^\\W+', '', conditions)
conditions <- gsub('\\W+$', '', conditions)
# Substitute whitespace and punctuation for underscores
conditions <- gsub('\\W+', '_', conditions)
data.frame(Participant = rep(unique(data$Participant), length(conditions)),
Timestamp = rep(unique(data$Timestamp), length(conditions)),
conditions = conditions)
})
longTrait <- cbind(longTrait, has=rep(TRUE, nrow(longTrait)))
wideTrait <- reshape(longTrait,
idvar = 'Participant',
timevar='conditions',
v.names='has',
direction = 'wide')
expect_equal(length(unique(wideTrait$Participant)), nrow(wideTrait))
# Substitute whitespace and punctuation for underscores
colnames(wideTrait) <- gsub('\\W$', '', colnames(wideTrait))
colnames(wideTrait) <- gsub('\\W+', '_', colnames(wideTrait))
# Spot check our data to verify that it was reshaped correctly.
expect_true(wideTrait[wideTrait$Participant=='hu005023', 'has_Dental_cavities'])
expect_true(is.na(wideTrait[wideTrait$Participant=='hu005023', 'has_Gastroesophageal_reflux_disease_GERD']))
expect_true(wideTrait[wideTrait$Participant=='hu005023', 'has_Impacted_tooth'])
expect_true(is.na(wideTrait[wideTrait$Participant=='hu005023', 'has_Skin_tags']))
expect_true(is.na(wideTrait[wideTrait$Participant=='hu005023', 'has_Hair_loss_includes_female_and_male_pattern_baldness']))
expect_true(is.na(wideTrait[wideTrait$Participant=='hu005023', 'has_Acne']))
expect_true(wideTrait[wideTrait$Participant=='hu005023', 'has_Allergic_contact_dermatitis'])
expect_true(wideTrait[wideTrait$Participant=='hu627574', 'has_Dental_cavities'])
expect_true(wideTrait[wideTrait$Participant=='hu627574', 'has_Gastroesophageal_reflux_disease_GERD'])
expect_true(wideTrait[wideTrait$Participant=='hu627574', 'has_Impacted_tooth'])
expect_true(is.na(wideTrait[wideTrait$Participant=='hu627574', 'has_Skin_tags']))
expect_true(wideTrait[wideTrait$Participant=='hu627574', 'has_Hair_loss_includes_female_and_male_pattern_baldness'])
expect_true(wideTrait[wideTrait$Participant=='hu627574', 'has_Acne'])
expect_true(is.na(wideTrait[wideTrait$Participant=='hu627574', 'has_Allergic_contact_dermatitis']))
expect_true(wideTrait[wideTrait$Participant=='hu8E2A35', 'has_Dental_cavities'])
expect_true(is.na(wideTrait[wideTrait$Participant=='hu8E2A35', 'has_Gastroesophageal_reflux_disease_GERD']))
expect_true(is.na(wideTrait[wideTrait$Participant=='hu8E2A35', 'has_Impacted_tooth']))
expect_true(wideTrait[wideTrait$Participant=='hu8E2A35', 'has_Skin_tags'])
expect_true(wideTrait[wideTrait$Participant=='hu8E2A35', 'has_Hair_loss_includes_female_and_male_pattern_baldness'])
expect_true(wideTrait[wideTrait$Participant=='hu8E2A35', 'has_Acne'])
expect_true(is.na(wideTrait[wideTrait$Participant=='hu8E2A35', 'has_Allergic_contact_dermatitis']))
#----------------------------------------------------------------------------
# Some participants only filled out trait surveys, others only filled out the
# participant survey
expect_false(setequal(recentDemo$Participant,
intersect(wideTrait$Participant, recentDemo$Participant)))
expect_false(setequal(wideTrait$Participant,
intersect(wideTrait$Participant, recentDemo$Participant)))
# JOIN it all together, dropping the Timestamp columns
pheno <- join(recentDemo[,-2], wideTrait[,-2], type='full')
expect_equal(nrow(pheno),
length(union(wideTrait$Participant, recentDemo$Participant)))
# Generate BQ Schema
cols <- colnames(pheno)
bool_ints <- c(0, 1, NA)
schema <- c()
for (i in 1:length(cols)) {
type <- 'STRING'
if ('logical' == class(pheno[, i])) {
type <- 'BOOLEAN'
} else if ('numeric' == class(pheno[, i])) {
type <- 'FLOAT'
} else if ('integer' == class(pheno[, i])) {
if (setequal(bool_ints, union(bool_ints, pheno[, i]))) {
type <- 'BOOLEAN'
} else {
type <- 'INTEGER'
}
}
schema <- append(schema, paste(cols[i], type,
sep=":", collapse=","))
}
print(paste(schema, collapse=','))
# Write out file to load into BigQuery
write.table(pheno, file.path(dataDir, 'pgp-phenotypes.tsv'),
row.names=FALSE, sep='\t', na='')
|
#Answer 2
NEI2<-NEI[NEI$fips=="24510",]
ans2<-sapply(split(NEI2, NEI2$year),function(k) sum(k$Emissions))
png('plot2a.png', width=480, height=480)
barplot(ans2, xlab="years", ylab="PM2.5 Emission", main="Total PM2.5 Emission across years for Baltimore City ")
dev.off()
| /plot2.R | no_license | adityatare/Exploratory-Data-Analysis2 | R | false | false | 278 | r | #Answer 2
NEI2<-NEI[NEI$fips=="24510",]
ans2<-sapply(split(NEI2, NEI2$year),function(k) sum(k$Emissions))
png('plot2a.png', width=480, height=480)
barplot(ans2, xlab="years", ylab="PM2.5 Emission", main="Total PM2.5 Emission across years for Baltimore City ")
dev.off()
|
## these functions will cache the inverse of a matrix by 1) storing the
## value of the inverse and 2) calculates the inverse of the matrix using the first function
## this function creates a special matrix object and stores 1) the matrix ad 2) the inverse of the matrix
makeCacheMatrix <- function(x = matrix()) {
i <- NULL
set <- function(y) {
x <<- y
i <<- NULL
}
get <- function() x
setInverse <- function(inverse) i <<- inverse
getInverse <- function() i
list(set = set, get = get, setInverse = setInverse,
getInverse = getInverse)
}
## this function calculates the inverse using the above function - first it checks to see
## if the mean has already been calculated and if not completes the calculation
cacheSolve <- function(x, ...) {
i <- x$getInverse()
if(!is.null(i)) {
message("getting cached data")
return(i)
}
data <- x$get()
i <- solve(data, ...)
x$setInverse(i)
i ## return a matrix that is the inverse of x
}
| /cachematrix.R | no_license | jmoss13148/ProgrammingAssignment2 | R | false | false | 1,019 | r | ## these functions will cache the inverse of a matrix by 1) storing the
## value of the inverse and 2) calculates the inverse of the matrix using the first function
## this function creates a special matrix object and stores 1) the matrix ad 2) the inverse of the matrix
makeCacheMatrix <- function(x = matrix()) {
i <- NULL
set <- function(y) {
x <<- y
i <<- NULL
}
get <- function() x
setInverse <- function(inverse) i <<- inverse
getInverse <- function() i
list(set = set, get = get, setInverse = setInverse,
getInverse = getInverse)
}
## this function calculates the inverse using the above function - first it checks to see
## if the mean has already been calculated and if not completes the calculation
cacheSolve <- function(x, ...) {
i <- x$getInverse()
if(!is.null(i)) {
message("getting cached data")
return(i)
}
data <- x$get()
i <- solve(data, ...)
x$setInverse(i)
i ## return a matrix that is the inverse of x
}
|
\name{circos.genomicDensity}
\alias{circos.genomicDensity}
\title{
Calculate and add genomic density track
}
\description{
Calculate and add genomic density track
}
\usage{
circos.genomicDensity(
data,
ylim.force = FALSE,
window.size = NULL,
overlap = TRUE,
col = ifelse(area, "grey", "black"),
lwd = par("lwd"),
lty = par("lty"),
type = "l",
area = TRUE,
area.baseline = NULL,
baseline = 0,
border = NA,
...)
}
\arguments{
\item{data}{A bed-file-like data frame or a list of data frames
}
\item{ylim.force}{Whether to force upper bound of \code{ylim} to be 1.
}
\item{window.size}{Pass to \code{\link{genomicDensity}}
}
\item{overlap}{Pass to \code{\link{genomicDensity}}
}
\item{col}{Colors. It should be length of one. If \code{data} is a list of data frames, the length of \code{col}
can also be the length of the list.
}
\item{lwd}{Width of lines
}
\item{lty}{Style of lines
}
\item{type}{Type of lines, see \code{\link{circos.lines}}
}
\item{area}{See \code{\link{circos.lines}}
}
\item{area.baseline}{Deprecated, use \code{baseline} instead.
}
\item{baseline}{See \code{\link{circos.lines}}
}
\item{border}{See \code{\link{circos.lines}}
}
\item{...}{Pass to \code{\link{circos.trackPlotRegion}}
}
}
\details{
This function is a high-level graphical function, and it will create a new track.
}
\seealso{
\url{https://jokergoo.github.io/circlize_book/book/high-level-genomic-functions.html#genomic-density-and-rainfall-plot}
}
\examples{
load(system.file(package = "circlize", "extdata", "DMR.RData"))
# rainfall
circos.initializeWithIdeogram(plotType = c("axis", "labels"))
bed_list = list(DMR_hyper, DMR_hypo)
circos.genomicRainfall(bed_list, pch = 16, cex = 0.4, col = c("#FF000080", "#0000FF80"))
circos.genomicDensity(bed_list[[1]], col = c("#FF000080"), track.height = 0.1)
circos.genomicDensity(bed_list[[2]], col = c("#0000FF80"), track.height = 0.1)
circos.clear()
}
| /man/circos.genomicDensity.Rd | permissive | Amz965/circlize | R | false | false | 1,970 | rd | \name{circos.genomicDensity}
\alias{circos.genomicDensity}
\title{
Calculate and add genomic density track
}
\description{
Calculate and add genomic density track
}
\usage{
circos.genomicDensity(
data,
ylim.force = FALSE,
window.size = NULL,
overlap = TRUE,
col = ifelse(area, "grey", "black"),
lwd = par("lwd"),
lty = par("lty"),
type = "l",
area = TRUE,
area.baseline = NULL,
baseline = 0,
border = NA,
...)
}
\arguments{
\item{data}{A bed-file-like data frame or a list of data frames
}
\item{ylim.force}{Whether to force upper bound of \code{ylim} to be 1.
}
\item{window.size}{Pass to \code{\link{genomicDensity}}
}
\item{overlap}{Pass to \code{\link{genomicDensity}}
}
\item{col}{Colors. It should be length of one. If \code{data} is a list of data frames, the length of \code{col}
can also be the length of the list.
}
\item{lwd}{Width of lines
}
\item{lty}{Style of lines
}
\item{type}{Type of lines, see \code{\link{circos.lines}}
}
\item{area}{See \code{\link{circos.lines}}
}
\item{area.baseline}{Deprecated, use \code{baseline} instead.
}
\item{baseline}{See \code{\link{circos.lines}}
}
\item{border}{See \code{\link{circos.lines}}
}
\item{...}{Pass to \code{\link{circos.trackPlotRegion}}
}
}
\details{
This function is a high-level graphical function, and it will create a new track.
}
\seealso{
\url{https://jokergoo.github.io/circlize_book/book/high-level-genomic-functions.html#genomic-density-and-rainfall-plot}
}
\examples{
load(system.file(package = "circlize", "extdata", "DMR.RData"))
# rainfall
circos.initializeWithIdeogram(plotType = c("axis", "labels"))
bed_list = list(DMR_hyper, DMR_hypo)
circos.genomicRainfall(bed_list, pch = 16, cex = 0.4, col = c("#FF000080", "#0000FF80"))
circos.genomicDensity(bed_list[[1]], col = c("#FF000080"), track.height = 0.1)
circos.genomicDensity(bed_list[[2]], col = c("#0000FF80"), track.height = 0.1)
circos.clear()
}
|
`rclust` <-
function(dist, clusters = 2, runs = 1000, counter = FALSE) {
if (runs == 1) return(relational.clustering(dist, clusters))
else {
rc2return <- NULL
rc2 <- sum(dist)
for (i in 1:runs) {
npart <- relational.clustering(dist, clusters)
if (i%%10==0 && counter==TRUE) print(paste('Calculating run number ',i,sep=''))
if (length(npart) == 1) {
next(i)
}
nd <- as.matrix(dist)
n<-dim(nd)[1]
nd[upper.tri(nd, diag = TRUE)] <- 0
outer <- NULL
inner <- NULL
inout <- matrix(0,n,n)
for (i in 1:(n-1)) {
for (j in (i+1):n) {
if (npart[i] != npart[j]) inout[j,i] <- 1
}
}
sumin <- sum(nd[which(inout==0)])
sumn<-sum(1:(n-1))
np <- sumin/(sumn-sum(inout))
if (np < rc2) {
rc2return <- npart
rc2 <- np
}
}
}
return(rc2return)
}
| /fossil/R/rclust.R | no_license | ingted/R-Examples | R | false | false | 907 | r | `rclust` <-
function(dist, clusters = 2, runs = 1000, counter = FALSE) {
if (runs == 1) return(relational.clustering(dist, clusters))
else {
rc2return <- NULL
rc2 <- sum(dist)
for (i in 1:runs) {
npart <- relational.clustering(dist, clusters)
if (i%%10==0 && counter==TRUE) print(paste('Calculating run number ',i,sep=''))
if (length(npart) == 1) {
next(i)
}
nd <- as.matrix(dist)
n<-dim(nd)[1]
nd[upper.tri(nd, diag = TRUE)] <- 0
outer <- NULL
inner <- NULL
inout <- matrix(0,n,n)
for (i in 1:(n-1)) {
for (j in (i+1):n) {
if (npart[i] != npart[j]) inout[j,i] <- 1
}
}
sumin <- sum(nd[which(inout==0)])
sumn<-sum(1:(n-1))
np <- sumin/(sumn-sum(inout))
if (np < rc2) {
rc2return <- npart
rc2 <- np
}
}
}
return(rc2return)
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_theme.R
\name{plot_theme}
\alias{plot_theme}
\title{Plot Theme}
\usage{
plot_theme(fontFamily = "Purisa", titleFont = 26)
}
\arguments{
\item{fontFamily}{A character string that defines the font
family to be used for the axis / title / legend etc.}
}
\description{
Holds the theme for the plots to be used in the
README
}
| /man/plot_theme.Rd | no_license | O1sims/FootballStats | R | false | true | 406 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_theme.R
\name{plot_theme}
\alias{plot_theme}
\title{Plot Theme}
\usage{
plot_theme(fontFamily = "Purisa", titleFont = 26)
}
\arguments{
\item{fontFamily}{A character string that defines the font
family to be used for the axis / title / legend etc.}
}
\description{
Holds the theme for the plots to be used in the
README
}
|
options(repos = c(cran = 'https://cran.rstudio.com'))
## local library for project-specific packages
dir.create('.Rlib', showWarnings = FALSE, recursive = TRUE)
.libPaths(new = '.Rlib')
print("working in power-analyses repo")
| /.Rprofile | no_license | jburos/power-analyses | R | false | false | 226 | rprofile | options(repos = c(cran = 'https://cran.rstudio.com'))
## local library for project-specific packages
dir.create('.Rlib', showWarnings = FALSE, recursive = TRUE)
.libPaths(new = '.Rlib')
print("working in power-analyses repo")
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Rsamplers.R
\name{mvtnormSelectionProbs}
\alias{mvtnormSelectionProbs}
\title{A function for computing the probability of positive/negative selections}
\usage{
mvtnormSelectionProbs(y, mu, sigma, threshold, selected = NULL)
}
\arguments{
\item{y}{the observed normal vector, or an initial sample point that
satisfies the selection event}
\item{mu}{the mean vector of the (untruncated) normal vector}
\item{sigma}{the covariance of the (untruncated) normal vector}
\item{threshold}{a vector of size length(y), the selection threshold}
\item{selected}{which coordinates were selected? if NULL function
will attempt to figure it out on its own but my fail if there are
both observations that are larger than \code{threshold} and observations
that are smaller than \code{-threshold}.}
}
\description{
This function uses the mvtnorm package to compute the probability of
positive and negative selection events of selective ROI problems.
The selection event is assumed to be, {selected > threshold} or
{selected < -threshold} (with coordinates which were not selected not
satisfying the selection event)
}
| /man/mvtnormSelectionProbs.Rd | no_license | ammeir2/selectiveROI | R | false | true | 1,182 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Rsamplers.R
\name{mvtnormSelectionProbs}
\alias{mvtnormSelectionProbs}
\title{A function for computing the probability of positive/negative selections}
\usage{
mvtnormSelectionProbs(y, mu, sigma, threshold, selected = NULL)
}
\arguments{
\item{y}{the observed normal vector, or an initial sample point that
satisfies the selection event}
\item{mu}{the mean vector of the (untruncated) normal vector}
\item{sigma}{the covariance of the (untruncated) normal vector}
\item{threshold}{a vector of size length(y), the selection threshold}
\item{selected}{which coordinates were selected? if NULL function
will attempt to figure it out on its own but my fail if there are
both observations that are larger than \code{threshold} and observations
that are smaller than \code{-threshold}.}
}
\description{
This function uses the mvtnorm package to compute the probability of
positive and negative selection events of selective ROI problems.
The selection event is assumed to be, {selected > threshold} or
{selected < -threshold} (with coordinates which were not selected not
satisfying the selection event)
}
|
\name{metaSub.character}
\alias{as.filename}
\alias{as.filename.character}
\alias{metaSub.filename}
\alias{metaSub.character}
\alias{metaSub}
\title{Create Replicate Text files with Systematic Substitutions}
\description{
\code{metaSub} is generic. A method is defined for character;
a convenience wrapper is provided for passing names of text files to be
read and then resampled.
\code{metaSub} collapses a character vector to one line of text.
The vector is replicated as many times as there are elements in \code{names},
with flexible substitution of text fragments. If \code{out} is supplied, the
replicates are saved as text files identified with \code{names}
and a \code{suffix}.
\code{metaSub.filename} will process multiple filenames, if \code{x} is as long as \code{names}.
}
\usage{
\method{as.filename}{character}(x, ...)
\method{metaSub}{filename}(x, names, pattern=NULL, replacement=NULL, ...)
\method{metaSub}{character}(
x,
names,
pattern = NULL,
replacement = NULL,
out = NULL,
suffix = '.txt',
fixed = TRUE,
...
)
}
\arguments{
\item{x}{scalar character, or (second form) filename(s). Multi-element character
will be collapsed to one element, with newline as the separator.}
\item{names}{a list of (unique) names for resulting output elements.
A vector of names will be coerced to character and to list.}
\item{pattern}{a character vector of text fragments to replace, optionally
encoded as regular expressions (\code{fixed==FALSE}, See \code{?gsub}, \code{?regex}). Can also be a list. See details.}
\item{replacement}{A character vector of substitutions for patterns.
The wildcard \sQuote{*} is available to represent the corresponding value
of \code{names}. Can also be a list with as many elements as \code{pattern}
(see details). Expressions are supported (see details).}
\item{out}{(path and) directory in which to write the resulting
control streams}
\item{suffix}{file extension for filenames, if \code{out} is supplied}
\item{fixed}{passed to \code{gsub}: use \code{FALSE} if \code{pattern} contains
regular expressions. Scalar, or same length as \code{pattern}.}
\item{\dots}{extra arguments, available to expressions or passed to \code{gsub}}
}
\details{
Typical usages are
\preformatted{
metaSub(x, names, ...)
metaSub(as.filename(x), names, ...)
}
Replacement is performed by \code{gsub}, so an element of \code{pattern}
will be replaced everywhere it occurs in a line.
if \code{pattern} or \code{replacement} is a list, each element should
be of length one, or as long as \code{names}. In the latter case,
substitutions can be specific on a per-name basis. The wild card \sQuote{*} is
still available.
It is necessary that \code{pattern} and \code{replacement} be of the
same length, but it is not necessary that their corresponding elements
have equal lengths. Thus, one can specify name-specific replacements
for a single pattern, or a single replacement for name-specific patterns.
An expression can be specified for \code{replacement} itself, or one of
its pattern-wise elements, or one of the name-wise elements of a pattern-wise
element. Expressions are evaluated in an environment containing \dQuote{name}
(same meaning as \sQuote{*} above) and all other \dots arguments. This is useful
if extra arguments have a dimension indexed, at least loosely, by \code{names}.
The evaluated expression is treated as character, and wildcard substitution
is attempted. Use \code{\*} for a literal asterisk: in R: \code{\\\\*}.
NOTE: be very careful not to have trailing commas in your lists! An
error results that is very hard to track. e.g. \code{c(a,b,c,)}.
}
\value{
an invisible named character vector.
If \code{out} is supplied, elements are written as files with corresponding
names.
}
\references{\url{http://mifuns.googlecode.com}}
\author{Tim Bergsma}
\seealso{
\itemize{
\item \code{\link{gsub}}
\item \code{\link{regex}}
}
}
\examples{
data(ctl)
dir()
e <- metaSub(
ctl,
names=1:3,
pattern=c(
'PROBLEM 8',
'data8.csv',
'8.MSF'
),
replacement=c(
'PROBLEM *',
'*.csv',
'*.MSF'
),
out='.',
suffix='.ctl'
)
t <- metaSub(
ctl,
names=c('test1','test2'),
pattern=c('PROBLEM 8','data8.csv','METH=0'),
replacement=c('PROBLEM *','*.csv','METH=1'),
)
t <- metaSub(
ctl,
names=c('test1','test2'),
pattern=c(
'PROBLEM 8',
'data8.csv',
'METH=0'
),
replacement=list(
'PROBLEM *',
'*.csv',
c('METH=1','METH=2')
),
out='.',
suffix='.ctl'
)
#just a file copy...
metaSub(as.filename('1.ctl'),names='4',out='.',suffix='.ctl')
#using a (nonsense) replacement expression...
options <- data.frame(var=c(8,9),alt=c(10,11))
a <- metaSub(
ctl,
names=rownames(options),
pattern='9999',
replacement=expression(
options$var[rownames(options)==name]
),
options=options
)
cat(a[[2]])
#replacement expression in a 'mixed' list...
b <- metaSub(
ctl,
names=rownames(options),
pattern=list(
'PRINT=2',
'9999'
),
replacement=list(
'PRINT=3',
expression(options$var[rownames(options)==name])
),
options=options
)
cat(b[[2]])
#replacement expressions on a per-name basis
d <- metaSub(
ctl,
names=rownames(options),
pattern='9999',
replacement=list( #so that replacement is as long as pattern
list( #with different options for each 'name'
expression(options$var[rownames(options)==name]),
expression(options$alt[rownames(options)==name])
)
),
options=options
)
cat(d[[2]])
}
\keyword{manip}
| /man/metaSub.character.Rd | no_license | cran/MIfuns | R | false | false | 5,532 | rd | \name{metaSub.character}
\alias{as.filename}
\alias{as.filename.character}
\alias{metaSub.filename}
\alias{metaSub.character}
\alias{metaSub}
\title{Create Replicate Text files with Systematic Substitutions}
\description{
\code{metaSub} is generic. A method is defined for character;
a convenience wrapper is provided for passing names of text files to be
read and then resampled.
\code{metaSub} collapses a character vector to one line of text.
The vector is replicated as many times as there are elements in \code{names},
with flexible substitution of text fragments. If \code{out} is supplied, the
replicates are saved as text files identified with \code{names}
and a \code{suffix}.
\code{metaSub.filename} will process multiple filenames, if \code{x} is as long as \code{names}.
}
\usage{
\method{as.filename}{character}(x, ...)
\method{metaSub}{filename}(x, names, pattern=NULL, replacement=NULL, ...)
\method{metaSub}{character}(
x,
names,
pattern = NULL,
replacement = NULL,
out = NULL,
suffix = '.txt',
fixed = TRUE,
...
)
}
\arguments{
\item{x}{scalar character, or (second form) filename(s). Multi-element character
will be collapsed to one element, with newline as the separator.}
\item{names}{a list of (unique) names for resulting output elements.
A vector of names will be coerced to character and to list.}
\item{pattern}{a character vector of text fragments to replace, optionally
encoded as regular expressions (\code{fixed==FALSE}, See \code{?gsub}, \code{?regex}). Can also be a list. See details.}
\item{replacement}{A character vector of substitutions for patterns.
The wildcard \sQuote{*} is available to represent the corresponding value
of \code{names}. Can also be a list with as many elements as \code{pattern}
(see details). Expressions are supported (see details).}
\item{out}{(path and) directory in which to write the resulting
control streams}
\item{suffix}{file extension for filenames, if \code{out} is supplied}
\item{fixed}{passed to \code{gsub}: use \code{FALSE} if \code{pattern} contains
regular expressions. Scalar, or same length as \code{pattern}.}
\item{\dots}{extra arguments, available to expressions or passed to \code{gsub}}
}
\details{
Typical usages are
\preformatted{
metaSub(x, names, ...)
metaSub(as.filename(x), names, ...)
}
Replacement is performed by \code{gsub}, so an element of \code{pattern}
will be replaced everywhere it occurs in a line.
if \code{pattern} or \code{replacement} is a list, each element should
be of length one, or as long as \code{names}. In the latter case,
substitutions can be specific on a per-name basis. The wild card \sQuote{*} is
still available.
It is necessary that \code{pattern} and \code{replacement} be of the
same length, but it is not necessary that their corresponding elements
have equal lengths. Thus, one can specify name-specific replacements
for a single pattern, or a single replacement for name-specific patterns.
An expression can be specified for \code{replacement} itself, or one of
its pattern-wise elements, or one of the name-wise elements of a pattern-wise
element. Expressions are evaluated in an environment containing \dQuote{name}
(same meaning as \sQuote{*} above) and all other \dots arguments. This is useful
if extra arguments have a dimension indexed, at least loosely, by \code{names}.
The evaluated expression is treated as character, and wildcard substitution
is attempted. Use \code{\*} for a literal asterisk: in R: \code{\\\\*}.
NOTE: be very careful not to have trailing commas in your lists! An
error results that is very hard to track. e.g. \code{c(a,b,c,)}.
}
\value{
an invisible named character vector.
If \code{out} is supplied, elements are written as files with corresponding
names.
}
\references{\url{http://mifuns.googlecode.com}}
\author{Tim Bergsma}
\seealso{
\itemize{
\item \code{\link{gsub}}
\item \code{\link{regex}}
}
}
\examples{
data(ctl)
dir()
e <- metaSub(
ctl,
names=1:3,
pattern=c(
'PROBLEM 8',
'data8.csv',
'8.MSF'
),
replacement=c(
'PROBLEM *',
'*.csv',
'*.MSF'
),
out='.',
suffix='.ctl'
)
t <- metaSub(
ctl,
names=c('test1','test2'),
pattern=c('PROBLEM 8','data8.csv','METH=0'),
replacement=c('PROBLEM *','*.csv','METH=1'),
)
t <- metaSub(
ctl,
names=c('test1','test2'),
pattern=c(
'PROBLEM 8',
'data8.csv',
'METH=0'
),
replacement=list(
'PROBLEM *',
'*.csv',
c('METH=1','METH=2')
),
out='.',
suffix='.ctl'
)
#just a file copy...
metaSub(as.filename('1.ctl'),names='4',out='.',suffix='.ctl')
#using a (nonsense) replacement expression...
options <- data.frame(var=c(8,9),alt=c(10,11))
a <- metaSub(
ctl,
names=rownames(options),
pattern='9999',
replacement=expression(
options$var[rownames(options)==name]
),
options=options
)
cat(a[[2]])
#replacement expression in a 'mixed' list...
b <- metaSub(
ctl,
names=rownames(options),
pattern=list(
'PRINT=2',
'9999'
),
replacement=list(
'PRINT=3',
expression(options$var[rownames(options)==name])
),
options=options
)
cat(b[[2]])
#replacement expressions on a per-name basis
d <- metaSub(
ctl,
names=rownames(options),
pattern='9999',
replacement=list( #so that replacement is as long as pattern
list( #with different options for each 'name'
expression(options$var[rownames(options)==name]),
expression(options$alt[rownames(options)==name])
)
),
options=options
)
cat(d[[2]])
}
\keyword{manip}
|
dataSetsPath <- "C:\\Users\\samarths\\Desktop\\newdata\\"
require(e1071)
svm.models <- list()
parentTestSet <- read.csv(paste(dataSetsPath, "test_FS.csv", sep=''), header = TRUE)
parentTrainingSet <- read.csv(paste(dataSetsPath, "train_FS.csv", sep=''), header=TRUE)
parentTrainingSet <- parentTrainingSet[,-1]
mergedSet <- rbind(parentTrainingSet, parentTestSet)
# PCA on merged set.
pca <- prcomp(mergedSet, scale = TRUE)
# Find 95% threshold of cumulative variance proportion of the principal components.
thresh <- max(which(cumsum(pca$sdev^2 / sum(pca$sdev^2)) <= 0.95))
# for each digit
for (i in 0:9) {
print(i)
trainingSet <- read.csv(paste(dataSetsPath, "Train_FS_scaled\\", "trainFS-digit", i, ".csv", sep=''), header=TRUE)
# Apply PCA to training set.
train.pca <- predict(pca, trainingSet[,-ncol(trainingSet)])
# Train SVM model.
model <- svm(train.pca[,1:thresh], as.factor(trainingSet[,ncol(trainingSet)]), probability = TRUE)
# Add the SVM model to the list of models, one per digit.
svm.models[[length(svm.models)+1]] <- model
}
testSet <- read.csv(paste(dataSetsPath, "test_FS.csv", sep=''), header = TRUE)
# apply PCA to test set
test.pca <- predict(pca, testSet)
predictions <- matrix(nrow = nrow(testSet), ncol=0)
# for each digit
for (i in 0:9) {
print(i)
# Predictions from SVM for this digit.
prediction <- predict(svm.models[[i+1]], test.pca[,1:thresh], probability = TRUE)
# Store predicted probability for this digit.
predictions <- cbind(predictions, attr(prediction, "probabilities")[,"1"])
}
require(MASS)
write.matrix(predictions, paste(dataSetsPath, "probabilities.csv", sep=''), sep=',')
| /pca_merged_plus_svm/pcasvm.R | no_license | geramirez/digit-recognizer | R | false | false | 1,665 | r | dataSetsPath <- "C:\\Users\\samarths\\Desktop\\newdata\\"
require(e1071)
svm.models <- list()
parentTestSet <- read.csv(paste(dataSetsPath, "test_FS.csv", sep=''), header = TRUE)
parentTrainingSet <- read.csv(paste(dataSetsPath, "train_FS.csv", sep=''), header=TRUE)
parentTrainingSet <- parentTrainingSet[,-1]
mergedSet <- rbind(parentTrainingSet, parentTestSet)
# PCA on merged set.
pca <- prcomp(mergedSet, scale = TRUE)
# Find 95% threshold of cumulative variance proportion of the principal components.
thresh <- max(which(cumsum(pca$sdev^2 / sum(pca$sdev^2)) <= 0.95))
# for each digit
for (i in 0:9) {
print(i)
trainingSet <- read.csv(paste(dataSetsPath, "Train_FS_scaled\\", "trainFS-digit", i, ".csv", sep=''), header=TRUE)
# Apply PCA to training set.
train.pca <- predict(pca, trainingSet[,-ncol(trainingSet)])
# Train SVM model.
model <- svm(train.pca[,1:thresh], as.factor(trainingSet[,ncol(trainingSet)]), probability = TRUE)
# Add the SVM model to the list of models, one per digit.
svm.models[[length(svm.models)+1]] <- model
}
testSet <- read.csv(paste(dataSetsPath, "test_FS.csv", sep=''), header = TRUE)
# apply PCA to test set
test.pca <- predict(pca, testSet)
predictions <- matrix(nrow = nrow(testSet), ncol=0)
# for each digit
for (i in 0:9) {
print(i)
# Predictions from SVM for this digit.
prediction <- predict(svm.models[[i+1]], test.pca[,1:thresh], probability = TRUE)
# Store predicted probability for this digit.
predictions <- cbind(predictions, attr(prediction, "probabilities")[,"1"])
}
require(MASS)
write.matrix(predictions, paste(dataSetsPath, "probabilities.csv", sep=''), sep=',')
|
# TODO: Add comment
#
# Author: Ruth
###############################################################################
mytheme <-theme_bw(base_size=10) + theme(plot.title = element_text(vjust=2), plot.margin=unit(c(0.08, 0.08, 0.0, 0.08),"cm"),
axis.title.y = element_text(vjust=0.50),
axis.line = element_line(colour = "grey6", linetype=1, size = 0.3),
panel.border = element_blank(),
panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme(strip.background = element_rect(fill = 'white'))
colorList <- c("orangered2", "dodgerblue1")
png("RuthSync/Thesis/Presentation/1_FeedingVsCond.png", width = 1000, height = 800, units = "px", res = 200)
ggplot(subset(BoxComboMorn, IndFeed != "NA"), aes(x = residCond, y = IndFeedNum, color = Treatment, linecolor = Treatment)) +
ylab("Individual fed?") +
geom_point(aes(shape = Treatment), size = 1, position = position_jitter(width = 0.00, height = 0.03)) +
stat_smooth(aes(color = Treatment), method="glm", method.args = list(family = "binomial"), se=FALSE, size = 1) +
mytheme + xlab("Individual Condition") +
scale_shape_manual(values = c(16, 18), name = "Prey Size") +
scale_color_manual(values = colorList, name = "Prey Size")
dev.off() | /EcuRCode/BoxTrials/ThesisPresentation/BoxTrialsGraphs.R | no_license | ruthubc/ruthubc | R | false | false | 1,284 | r | # TODO: Add comment
#
# Author: Ruth
###############################################################################
mytheme <-theme_bw(base_size=10) + theme(plot.title = element_text(vjust=2), plot.margin=unit(c(0.08, 0.08, 0.0, 0.08),"cm"),
axis.title.y = element_text(vjust=0.50),
axis.line = element_line(colour = "grey6", linetype=1, size = 0.3),
panel.border = element_blank(),
panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme(strip.background = element_rect(fill = 'white'))
colorList <- c("orangered2", "dodgerblue1")
png("RuthSync/Thesis/Presentation/1_FeedingVsCond.png", width = 1000, height = 800, units = "px", res = 200)
ggplot(subset(BoxComboMorn, IndFeed != "NA"), aes(x = residCond, y = IndFeedNum, color = Treatment, linecolor = Treatment)) +
ylab("Individual fed?") +
geom_point(aes(shape = Treatment), size = 1, position = position_jitter(width = 0.00, height = 0.03)) +
stat_smooth(aes(color = Treatment), method="glm", method.args = list(family = "binomial"), se=FALSE, size = 1) +
mytheme + xlab("Individual Condition") +
scale_shape_manual(values = c(16, 18), name = "Prey Size") +
scale_color_manual(values = colorList, name = "Prey Size")
dev.off() |
#' Number of dimensions.
#' Number of dimensions of an array or vector
#'
#' @param x array
#' @keywords internal
dims <- function(x) length(amv_dim(x))
#' Dimensions.
#' Consistent dimensions for vectors, matrices and arrays.
#'
#' @param x array, matrix or vector
#' @keywords internal
amv_dim <- function(x) if (is.vector(x)) length(x) else dim(x)
#' Dimension names.
#' Consistent dimnames for vectors, matrices and arrays.
#'
#' Unlike \code{\link{dimnames}} no part of the output will ever be
#' null. If a component of dimnames is omitted, \code{amv_dimnames}
#' will return an integer sequence of the appropriate length.
#'
#' @param x array, matrix or vector
#' @keywords internal
#' @export
amv_dimnames <- function(x) {
d <- if (is.vector(x)) list(names(x)) else dimnames(x)
if (is.null(d)) d <- rep(list(NULL), dims(x))
null_names <- which(unlist(llply(d, is.null)))
d[null_names] <- llply(null_names, function(i) seq.int(amv_dim(x)[i]))
# if (is.null(names(d))) names(d) <- paste("X", 1:length(d), sep="")
d
}
#' Reduce dimensions.
#' Remove extraneous dimensions
#'
#' @param x array
#' @keywords internal
reduce_dim <- function(x) {
do.call("[", c(list(x), lapply(dim(x), function(x) if (x==1) 1 else TRUE), drop=TRUE))
}
| /R/dimensions.r | no_license | froh/plyr | R | false | false | 1,273 | r | #' Number of dimensions.
#' Number of dimensions of an array or vector
#'
#' @param x array
#' @keywords internal
dims <- function(x) length(amv_dim(x))
#' Dimensions.
#' Consistent dimensions for vectors, matrices and arrays.
#'
#' @param x array, matrix or vector
#' @keywords internal
amv_dim <- function(x) if (is.vector(x)) length(x) else dim(x)
#' Dimension names.
#' Consistent dimnames for vectors, matrices and arrays.
#'
#' Unlike \code{\link{dimnames}} no part of the output will ever be
#' null. If a component of dimnames is omitted, \code{amv_dimnames}
#' will return an integer sequence of the appropriate length.
#'
#' @param x array, matrix or vector
#' @keywords internal
#' @export
amv_dimnames <- function(x) {
d <- if (is.vector(x)) list(names(x)) else dimnames(x)
if (is.null(d)) d <- rep(list(NULL), dims(x))
null_names <- which(unlist(llply(d, is.null)))
d[null_names] <- llply(null_names, function(i) seq.int(amv_dim(x)[i]))
# if (is.null(names(d))) names(d) <- paste("X", 1:length(d), sep="")
d
}
#' Reduce dimensions.
#' Remove extraneous dimensions
#'
#' @param x array
#' @keywords internal
reduce_dim <- function(x) {
do.call("[", c(list(x), lapply(dim(x), function(x) if (x==1) 1 else TRUE), drop=TRUE))
}
|
library(lubridate)
library(plyr)
library(dplyr)
rawattendance<-read.csv("attendance2018.csv")
cleanattendance <- rawattendance %>%
filter(Attendance.Location.Name=="Seymour Dual Language Academy")%>%
mutate(Day=1)%>%
mutate(Pres=ifelse(Attendance.Desc=="Present",1,0))%>%
mutate(Abs=ifelse(Pres==0,1,0))%>%
mutate(StudentName=paste(Student.Last.Nm,Student.First.Nm,sep=", "))%>%
group_by(Student.Id,StudentName,Rptg.Race.Ethnicity.Desc,Has.Iep,Male) %>%
summarise(TotalEnrolled=sum(Day),DaysPresent=sum(Pres),DaysAbsent=sum(Abs))%>%
mutate(AttendancePercentage=DaysPresent/TotalEnrolled)%>%
mutate(AttendancePercentage=(paste(round((AttendancePercentage)*100,digits=1),"%",sep="")))%>%
rename(Race=Rptg.Race.Ethnicity.Desc,Gender=Male,IEP=Has.Iep)%>%
select(Student.Id,StudentName,Race,Gender,IEP,TotalEnrolled,DaysPresent,AttendancePercentage)
Demo<-rawattendance %>%
filter(Attendance.Location.Name=="Seymour Dual Language Academy")%>%
select(Student.Id,Student.Last.Nm,Student.First.Nm,Male,Rptg.Race.Ethnicity.Desc,Has.Iep)%>%
mutate(StudentName=paste(Student.Last.Nm,Student.First.Nm,sep=","))
rawattendance$Percent<-as.numeric(rawattendance$'Period.Absence..'>.5)
rawattendance$daycount = 1
rawattendance$Month<-month(rawattendance$cal_date)
rawattendance$Month<-month.abb[rawattendance$Month]
YTDdays<-as.data.frame(aggregate(daycount ~ STUDENT_ID, data=rawattendance, FUN=sum))
YTDabsent<-as.data.frame(aggregate(Absent ~ STUDENT_ID, data=rawattendance, FUN=sum))
YTD<-merge(YTDdays,YTDabsent,by="STUDENT_ID",all=TRUE)
YTD$YTDAtt<-((YTD$daycount-YTD$Absent)/(YTD$daycount))
YTD$YTDAtt <- paste(round((YTD$YTDAtt)*100,digits=1),"%",sep="")
YTD<-YTD[,c("STUDENT_ID","YTDAtt")]
totaldays<-as.data.frame(aggregate(daycount ~ STUDENT_ID + Month, data=rawattendance, FUN=sum))
totalabsent<-as.data.frame(aggregate(Absent ~ STUDENT_ID + Month, data=rawattendance, FUN=sum))
together<-merge(totaldays,totalabsent, by=c("STUDENT_ID","Month"),all=TRUE)
together$Percentage<-((together$daycount-together$Absent)/(together$daycount))
together$Percentage <- paste(round((together$Percentage)*100,digits=1),"%",sep="")
together<-together[,c("STUDENT_ID","Month","Percentage")]
togetherfinal<-reshape(together, idvar="STUDENT_ID", timevar="Month", direction="wide")
togetherfinal<-rename(togetherfinal,DecAtt=Percentage.Dec,
FebAtt=Percentage.Feb,
JanAtt=Percentage.Jan,
NovAtt=Percentage.Nov,
OctAtt=Percentage.Oct,
SepAtt=Percentage.Sep,
MarAtt=Percentage.Mar,
AprAtt=Percentage.Apr,
MayAtt=Percentage.May,
JuneAtt=Percentage.Jun)
togetherfinal<-merge(togetherfinal,YTD,by="STUDENT_ID",all=TRUE)
rawattendance<-togetherfinal[,c("STUDENT_ID","YTDAtt","SepAtt","OctAtt","NovAtt","DecAtt","JanAtt","FebAtt","MarAtt","AprAtt","MayAtt","JuneAtt")]
### STAR DATA
#STAR Data
#load all files
FSM<-read.csv("Fall16SM_cleaned.csv")
FSR<-read.csv("Fall16SR_cleaned.csv")
FSEL<-read.csv("Fall16SEL_cleaned.csv")
WSM<-read.csv("WinterSM_cleaned.csv")
WSR<-read.csv("WinterSR_cleaned.csv")
WSEL<-read.csv("WinterSEL_cleaned.csv")
#cleaning STAR information
FSEL<-FSEL[c("F16EL_StudentLocalID","F16EL_LiteracyClassification")]
FSM<-FSM[c("F16Math_StudentLocalID","F16Math_GradeEquivalent")]
FSR<-FSR[c("F16Read_StudentLocalID","F16Read_GradeEquivalent")]
WSM<-WSM[c("Win16Math_StudentLocalID","Win16Math_GradeEquivalent")]
WSR<-WSR[c("Win16Read_StudentLocalID","Win16Read_GradeEquivalent")]
WSEL<-WSEL[c("Win16EL_StudentLocalID","Win16EL_LiteracyClassification")]
#change column names
colnames(FSM)[which(names(FSM)=="F16Math_GradeEquivalent")]<-"Fall Math"
colnames(FSM)[which(names(FSM)=="F16Math_StudentLocalID")]<-"STUDENT_ID"
colnames(FSR)[which(names(FSR)=="F16Read_GradeEquivalent")]<-"Fall Reading"
colnames(FSR)[which(names(FSR)=="F16Read_StudentLocalID")]<-"STUDENT_ID"
colnames(FSEL)[which(names(FSEL)=="F16EL_StudentLocalID")]<-"STUDENT_ID"
colnames(FSEL)[which(names(FSEL)=="F16EL_LiteracyClassification")]<-"Fall EL"
colnames(WSM)[which(names(WSM)=="Win16Math_GradeEquivalent")]<-"Winter Math"
colnames(WSM)[which(names(WSM)=="Win16Math_StudentLocalID")]<-"STUDENT_ID"
colnames(WSR)[which(names(WSR)=="Win16Read_GradeEquivalent")]<-"Winter Reading"
colnames(WSR)[which(names(WSR)=="Win16Read_StudentLocalID")]<-"STUDENT_ID"
colnames(WSEL)[which(names(WSEL)=="Win16EL_LiteracyClassification")]<-"Winter EL"
colnames(WSEL)[which(names(WSEL)=="Win16EL_StudentLocalID")]<-"STUDENT_ID"
#merging
StarbyMP<-merge(FSM,FSR,by="STUDENT_ID", all=TRUE)
StarbyMP<-merge(StarbyMP,FSEL,by="STUDENT_ID",all=TRUE)
StarbyMP<-merge(StarbyMP,WSM,by="STUDENT_ID",all=TRUE)
StarbyMP<-merge(StarbyMP,WSR,by="STUDENT_ID",all=TRUE)
StarbyMP<-merge(StarbyMP,WSEL,by="STUDENT_ID",all=TRUE)
####
#getting columns from enrollment
enroll1<-read.csv("enrollmentallyears.csv",strip.white = TRUE)
enroll1<-rename(enroll1,SchoolCode=EnrollmentSchoolID)
enroll1<-filter(enroll1,SCHOOL_YEAR=="2017")
enroll1$Active<-ifelse(enroll1$WITHDRAWAL_CODE=="",1,0)
enroll<-filter(enroll1,Active=="1")
##student names
Demo<-read.csv("Demographics.csv")
Demo<-select(Demo,STUDENT_ID,LastName,FirstName)
#merging all four
final1<-merge(enroll,rawattendance,by="STUDENT_ID",all=TRUE)
final<-merge(final1,togetherfinal,by="STUDENT_ID",all=TRUE)
final<-merge(final,Demo,by="STUDENT_ID",all=TRUE)
#cleanup
Report<-subset(final1,EnrollmentSchoolName=="Seymour Dual Language Academy")
Report<-Report[,c("STUDENT_ID","LastName","FirstName","IEP","Gender","Grade","ELL","Ethnicity","YTDAtt","SepAtt","OctAtt","NovAtt","DecAtt","JanAtt","FebAtt","MarAtt","AprAtt","MayAtt","JuneAtt")]
Report<-sapply(Report, as.character)
Report[is.na(Report)]<-" "
write.csv(cleanattendance,"C:/Users/drobil66/Desktop/RFiles/R Reports/SeymourAttendance2018.csv")
| /Seymour Request.R | no_license | djrobillard/SCSD | R | false | false | 6,096 | r | library(lubridate)
library(plyr)
library(dplyr)
rawattendance<-read.csv("attendance2018.csv")
cleanattendance <- rawattendance %>%
filter(Attendance.Location.Name=="Seymour Dual Language Academy")%>%
mutate(Day=1)%>%
mutate(Pres=ifelse(Attendance.Desc=="Present",1,0))%>%
mutate(Abs=ifelse(Pres==0,1,0))%>%
mutate(StudentName=paste(Student.Last.Nm,Student.First.Nm,sep=", "))%>%
group_by(Student.Id,StudentName,Rptg.Race.Ethnicity.Desc,Has.Iep,Male) %>%
summarise(TotalEnrolled=sum(Day),DaysPresent=sum(Pres),DaysAbsent=sum(Abs))%>%
mutate(AttendancePercentage=DaysPresent/TotalEnrolled)%>%
mutate(AttendancePercentage=(paste(round((AttendancePercentage)*100,digits=1),"%",sep="")))%>%
rename(Race=Rptg.Race.Ethnicity.Desc,Gender=Male,IEP=Has.Iep)%>%
select(Student.Id,StudentName,Race,Gender,IEP,TotalEnrolled,DaysPresent,AttendancePercentage)
Demo<-rawattendance %>%
filter(Attendance.Location.Name=="Seymour Dual Language Academy")%>%
select(Student.Id,Student.Last.Nm,Student.First.Nm,Male,Rptg.Race.Ethnicity.Desc,Has.Iep)%>%
mutate(StudentName=paste(Student.Last.Nm,Student.First.Nm,sep=","))
rawattendance$Percent<-as.numeric(rawattendance$'Period.Absence..'>.5)
rawattendance$daycount = 1
rawattendance$Month<-month(rawattendance$cal_date)
rawattendance$Month<-month.abb[rawattendance$Month]
YTDdays<-as.data.frame(aggregate(daycount ~ STUDENT_ID, data=rawattendance, FUN=sum))
YTDabsent<-as.data.frame(aggregate(Absent ~ STUDENT_ID, data=rawattendance, FUN=sum))
YTD<-merge(YTDdays,YTDabsent,by="STUDENT_ID",all=TRUE)
YTD$YTDAtt<-((YTD$daycount-YTD$Absent)/(YTD$daycount))
YTD$YTDAtt <- paste(round((YTD$YTDAtt)*100,digits=1),"%",sep="")
YTD<-YTD[,c("STUDENT_ID","YTDAtt")]
totaldays<-as.data.frame(aggregate(daycount ~ STUDENT_ID + Month, data=rawattendance, FUN=sum))
totalabsent<-as.data.frame(aggregate(Absent ~ STUDENT_ID + Month, data=rawattendance, FUN=sum))
together<-merge(totaldays,totalabsent, by=c("STUDENT_ID","Month"),all=TRUE)
together$Percentage<-((together$daycount-together$Absent)/(together$daycount))
together$Percentage <- paste(round((together$Percentage)*100,digits=1),"%",sep="")
together<-together[,c("STUDENT_ID","Month","Percentage")]
togetherfinal<-reshape(together, idvar="STUDENT_ID", timevar="Month", direction="wide")
togetherfinal<-rename(togetherfinal,DecAtt=Percentage.Dec,
FebAtt=Percentage.Feb,
JanAtt=Percentage.Jan,
NovAtt=Percentage.Nov,
OctAtt=Percentage.Oct,
SepAtt=Percentage.Sep,
MarAtt=Percentage.Mar,
AprAtt=Percentage.Apr,
MayAtt=Percentage.May,
JuneAtt=Percentage.Jun)
togetherfinal<-merge(togetherfinal,YTD,by="STUDENT_ID",all=TRUE)
rawattendance<-togetherfinal[,c("STUDENT_ID","YTDAtt","SepAtt","OctAtt","NovAtt","DecAtt","JanAtt","FebAtt","MarAtt","AprAtt","MayAtt","JuneAtt")]
### STAR DATA
#STAR Data
#load all files
FSM<-read.csv("Fall16SM_cleaned.csv")
FSR<-read.csv("Fall16SR_cleaned.csv")
FSEL<-read.csv("Fall16SEL_cleaned.csv")
WSM<-read.csv("WinterSM_cleaned.csv")
WSR<-read.csv("WinterSR_cleaned.csv")
WSEL<-read.csv("WinterSEL_cleaned.csv")
#cleaning STAR information
FSEL<-FSEL[c("F16EL_StudentLocalID","F16EL_LiteracyClassification")]
FSM<-FSM[c("F16Math_StudentLocalID","F16Math_GradeEquivalent")]
FSR<-FSR[c("F16Read_StudentLocalID","F16Read_GradeEquivalent")]
WSM<-WSM[c("Win16Math_StudentLocalID","Win16Math_GradeEquivalent")]
WSR<-WSR[c("Win16Read_StudentLocalID","Win16Read_GradeEquivalent")]
WSEL<-WSEL[c("Win16EL_StudentLocalID","Win16EL_LiteracyClassification")]
#change column names
colnames(FSM)[which(names(FSM)=="F16Math_GradeEquivalent")]<-"Fall Math"
colnames(FSM)[which(names(FSM)=="F16Math_StudentLocalID")]<-"STUDENT_ID"
colnames(FSR)[which(names(FSR)=="F16Read_GradeEquivalent")]<-"Fall Reading"
colnames(FSR)[which(names(FSR)=="F16Read_StudentLocalID")]<-"STUDENT_ID"
colnames(FSEL)[which(names(FSEL)=="F16EL_StudentLocalID")]<-"STUDENT_ID"
colnames(FSEL)[which(names(FSEL)=="F16EL_LiteracyClassification")]<-"Fall EL"
colnames(WSM)[which(names(WSM)=="Win16Math_GradeEquivalent")]<-"Winter Math"
colnames(WSM)[which(names(WSM)=="Win16Math_StudentLocalID")]<-"STUDENT_ID"
colnames(WSR)[which(names(WSR)=="Win16Read_GradeEquivalent")]<-"Winter Reading"
colnames(WSR)[which(names(WSR)=="Win16Read_StudentLocalID")]<-"STUDENT_ID"
colnames(WSEL)[which(names(WSEL)=="Win16EL_LiteracyClassification")]<-"Winter EL"
colnames(WSEL)[which(names(WSEL)=="Win16EL_StudentLocalID")]<-"STUDENT_ID"
#merging
StarbyMP<-merge(FSM,FSR,by="STUDENT_ID", all=TRUE)
StarbyMP<-merge(StarbyMP,FSEL,by="STUDENT_ID",all=TRUE)
StarbyMP<-merge(StarbyMP,WSM,by="STUDENT_ID",all=TRUE)
StarbyMP<-merge(StarbyMP,WSR,by="STUDENT_ID",all=TRUE)
StarbyMP<-merge(StarbyMP,WSEL,by="STUDENT_ID",all=TRUE)
####
#getting columns from enrollment
enroll1<-read.csv("enrollmentallyears.csv",strip.white = TRUE)
enroll1<-rename(enroll1,SchoolCode=EnrollmentSchoolID)
enroll1<-filter(enroll1,SCHOOL_YEAR=="2017")
enroll1$Active<-ifelse(enroll1$WITHDRAWAL_CODE=="",1,0)
enroll<-filter(enroll1,Active=="1")
##student names
Demo<-read.csv("Demographics.csv")
Demo<-select(Demo,STUDENT_ID,LastName,FirstName)
#merging all four
final1<-merge(enroll,rawattendance,by="STUDENT_ID",all=TRUE)
final<-merge(final1,togetherfinal,by="STUDENT_ID",all=TRUE)
final<-merge(final,Demo,by="STUDENT_ID",all=TRUE)
#cleanup
Report<-subset(final1,EnrollmentSchoolName=="Seymour Dual Language Academy")
Report<-Report[,c("STUDENT_ID","LastName","FirstName","IEP","Gender","Grade","ELL","Ethnicity","YTDAtt","SepAtt","OctAtt","NovAtt","DecAtt","JanAtt","FebAtt","MarAtt","AprAtt","MayAtt","JuneAtt")]
Report<-sapply(Report, as.character)
Report[is.na(Report)]<-" "
write.csv(cleanattendance,"C:/Users/drobil66/Desktop/RFiles/R Reports/SeymourAttendance2018.csv")
|
library(shiny)
library(poppr)
library(phangorn)
library(ape)
library(igraph)
#####################################################
# IMPORTANT: ALl the functions written before line 68 refer to functions loaded by R
# before shiny is deployed and executes the custom functions. These functions are definitions of'
# global functions and variables. Make sure to add these kinds of functions here. For more information
# refer ro the shiny manual.
####################################################
########### MICROBE-ID customization ############
# Here's where you add your database file (Comma Separated Object). Make sure
# that the database is in the same folder than this file (server.R)
df <- read.table("Ramorum_ssr.csv", header = TRUE, sep = "\t")
##################################
df.m <- as.matrix(df)
########### MICROBE-ID customization ############
# Change these values to the repeat lenghts and names of your SSR markers.
ssr <- c(PrMS6 = 3,
PRMS9c3 = 2,
PrMS39a = 2,
PrMS45 = 4,
KI18 = 2,
KI64 = 2,
ILVOPrMS131 = 2
)
##################################
# Functions to create elements to plot
## 1. Distance Tree
plot.tree <- function (tree, type = input$tree, ...){
ARGS <- c("nj", "upgma")
type <- match.arg(type, ARGS)
barlen <- min(median(tree$edge.length), 0.1)
if (barlen < 0.1)
barlen <- 0.01
plot.phylo(tree, cex = 0.8, font = 2, adj = 0, xpd = TRUE,
label.offset = 0.0125, ...)
nodelabels(tree$node.label, adj = c(1.3, -0.5), frame = "n",
cex = 0.8, font = 3, xpd = TRUE)
if (type == "nj") {
add.scale.bar(lwd = 5, length = barlen)
tree <- ladderize(tree)
}
else {
axisPhylo(3)
}
}
## 2. Minimum spanning network
plot.minspan <- function(gen, mst, gadj=3, inds = "none", ...){
plot_poppr_msn(gen, mst, gadj=gadj, vertex.label.color = "firebrick", inds = inds,
vertex.label.font = 2, vertex.label.dist = 0.5, nodelab = 100,
quantiles = FALSE)
}
########### MICROBE-ID customization ############
# From this line on, every one of the functions is going to be used by shiny in a reactive way. All modifications
# of processes and outputs for the User interface file (in this case, the www/index.html) are found here.
#
# INPUT FROM index.html: All variables that start with index$...
# OUTPUT TO index.html: All variables that start with output$...
#
# To determine which variable communicates with whic <div> in the index.html file, search for the line with the
# class=shiny*.(e.g. The input$table variable is gonna be filled with info from the <div class="shiny-bound-input" id="table">.
#
# For more information refer to the shiny manual
shinyServer(function(input, output) {
#Data input and manipulation
data.genoid <- reactive({
if (gsub("\\s", "", input$table) == ""){
return(NULL)
} else {
input_table <- read.table(text = input$table, stringsAsFactors = FALSE)
colnames(input_table) <- colnames(df.m)
input_data.genoid <- input_table[[1]]
df.m <- rbind(df.m, input_table, deparse.level = 0)
df.m <- as.data.frame(df.m)
gen <- df2genind(df.m[, -c(1, 2)], ploid = 2, sep = "/", pop = df.m[, 2],
ind.names = df.m[, 1])
#Adding colors to the tip values according to the clonal lineage
gen$other$tipcolor <- pop(gen)
gen$other$input_data.genoid <- input_data.genoid
ngroups <- length(levels(gen$other$tipcolor))
########### MICROBE-ID customization ############
# Change these colors to represent the groups defined in your data.genoid set.
#
defined_groups <- c("blue", "darkcyan", "darkolivegreen", "darkgoldenrod","red")
#
# Change heat.colors to whatever color palette you want to represent
# submitted data.genoid.
#
input_colors <- heat.colors(ngroups - length(defined_groups))
#
##################################
levels(gen$other$tipcolor) <- c(defined_groups, input_colors)
gen$other$tipcolor <- as.character(gen$other$tipcolor)
return(gen)
}
})
# Setting a random seed for the current session from the user interface (<input type = "number" name = "seed" id = "seed" value = "9449" min = "0" />)
seed <- reactive({
return(input$seed)
})
# Greyscale slider settings from the user interface (<input id="integer" type="slider" name="integer" value="3" class="jslider" data-from="0" data-to="50" data-step="1" data-skin="plastic" data-round="FALSE" data-locale="us" data-format="#,##0.#####" data-smooth="FALSE"/>)
slider <- reactive({
slider.a <- (input$integer)
return(slider.a)
})
# Processing the results. The functions here create the figures to be displayed by the user interface.
# Bootstrap of a distance tree out of the data
boottree <- reactive({
# Running the tree, setting a cutoff of 50 and saving it into a variable to
# be plotted (tree)
if (input$boot > 1000){
return(1000L)
} else if (input$boot < 10){
return(10L)
}
set.seed(seed())
tree <- try(bruvo.boot(data.genoid(), replen = ssr, sample = input$boot,
tree = input$tree, cutoff = 50), silent = TRUE)
# This is a catch to avoid having missing data within the distance matrix.
if ("try-error" %in% class(tree)){
for (i in sample(100)){
tree <- try(bruvo.boot(data.genoid(), replen = ssr, sample = input$boot,
tree = input$tree, cutoff = 50), silent = TRUE)
if (!"try-error" %in% class(tree)){
print(paste0("Success: ", i))
break
}
print(paste0("Failed: ", i))
}
}
tree$tip.label <- paste(tree$tip.label, as.character(pop(data.genoid())))
if (input$tree=="nj"){
tree <- phangorn::midpoint(ladderize(tree))
}
return(tree)
})
#Minimum spanning network creation
msnet <- reactive ({
msn.plot <- bruvo.msn(data.genoid(), replen = ssr)
V(msn.plot$graph)$size <- 3
return(msn.plot)
})
############ MICROBE-ID customization ############
# The following lines of code communicate with the user interface to
# plot the outputs from the processes in the server script.
## Distance Tree (<div id="distPlotTree" class="span6 shiny-plot-output">)
output$distPlotTree <- renderPlot({
if (is.null(data.genoid())){
plot.new()
rect(0, 1, 1, 0.8, col = "indianred2", border = 'transparent' ) +
text(x = 0.5, y = 0.9, "No SSR data has been input.", cex = 1.6, col = "white")
} else if (is.integer(boottree())){
msg <- ifelse(boottree() > 10L, "\nless than or equal to 1000",
"greater than 10")
msg <- paste("The number of bootstrap replicates should be", msg)
plot.new()
rect(0, 1, 1, 0.8, col = "indianred2", border = 'transparent' ) +
text(x = 0.5, y = 0.9, msg, cex = 1.6, col = "white")
} else {
plot.tree(boottree(), type=input$tree, tip.col=as.character(unlist(data.genoid()$other$tipcolor)))
}
})
##Minimum Spanning Network (<div id="MinSpanTree" class="shiny-plot-output")
output$MinSpanTree <- renderPlot({
if (is.null(data.genoid())){
plot.new()
rect(0, 1, 1, 0.8, col = "indianred2", border = 'transparent' ) +
text(x = 0.5, y = 0.9, "No SSR data has been input.", cex = 1.6, col = "white")
} else {
set.seed(seed())
plot.minspan(data.genoid(), msnet(), gadj=slider(), inds = data.genoid()$other$input_data.genoid)
}
})
############ MICROBE-ID customization ############
# The following lines of code communicate with the user interface to
# download the outputs from the processes in the server script.
## Distance tree in .tre format (<a id="downloadData" class="btn btn-primary shiny-download-link">")
output$downloadData <- downloadHandler(
filename = function() { paste0(input$tree, '.tre') },
content = function(file) {
write.tree(boottree(), file)
})
## Distance tree in PDF format (<a id="downloadPdf" class="btn btn-info shiny-download-link">)
output$downloadPdf <- downloadHandler(
filename = function() { paste0(input$tree, '.pdf') },
content = function(file) {
pdf(file, width=11, height=8.5)
plot.tree(boottree(), type=input$tree, tip.col=as.character(unlist(data.genoid()$other$tipcolor)))
dev.off()
})
## Minimum spanning network in PDF format (<a id="downloadPdfMst" class="btn btn-info shiny-download-link">)
output$downloadPdfMst <- downloadHandler(
filename = function() { paste0("min_span_net", '.pdf')} ,
content = function(file) {
pdf(file, width=11, height=8.5)
set.seed(seed())
plot.minspan( data.genoid(), msnet(), gadj=slider(), inds = data.genoid()$other$input_data.genoid)
dev.off()
}
)
#EOF
})
| /shiny-server/www/genoid_ramorum/server.R | no_license | grunwaldlab/phytophthora_id | R | false | false | 8,942 | r | library(shiny)
library(poppr)
library(phangorn)
library(ape)
library(igraph)
#####################################################
# IMPORTANT: ALl the functions written before line 68 refer to functions loaded by R
# before shiny is deployed and executes the custom functions. These functions are definitions of'
# global functions and variables. Make sure to add these kinds of functions here. For more information
# refer ro the shiny manual.
####################################################
########### MICROBE-ID customization ############
# Here's where you add your database file (Comma Separated Object). Make sure
# that the database is in the same folder than this file (server.R)
df <- read.table("Ramorum_ssr.csv", header = TRUE, sep = "\t")
##################################
df.m <- as.matrix(df)
########### MICROBE-ID customization ############
# Change these values to the repeat lenghts and names of your SSR markers.
ssr <- c(PrMS6 = 3,
PRMS9c3 = 2,
PrMS39a = 2,
PrMS45 = 4,
KI18 = 2,
KI64 = 2,
ILVOPrMS131 = 2
)
##################################
# Functions to create elements to plot
## 1. Distance Tree
plot.tree <- function (tree, type = input$tree, ...){
ARGS <- c("nj", "upgma")
type <- match.arg(type, ARGS)
barlen <- min(median(tree$edge.length), 0.1)
if (barlen < 0.1)
barlen <- 0.01
plot.phylo(tree, cex = 0.8, font = 2, adj = 0, xpd = TRUE,
label.offset = 0.0125, ...)
nodelabels(tree$node.label, adj = c(1.3, -0.5), frame = "n",
cex = 0.8, font = 3, xpd = TRUE)
if (type == "nj") {
add.scale.bar(lwd = 5, length = barlen)
tree <- ladderize(tree)
}
else {
axisPhylo(3)
}
}
## 2. Minimum spanning network
plot.minspan <- function(gen, mst, gadj=3, inds = "none", ...){
plot_poppr_msn(gen, mst, gadj=gadj, vertex.label.color = "firebrick", inds = inds,
vertex.label.font = 2, vertex.label.dist = 0.5, nodelab = 100,
quantiles = FALSE)
}
########### MICROBE-ID customization ############
# From this line on, every one of the functions is going to be used by shiny in a reactive way. All modifications
# of processes and outputs for the User interface file (in this case, the www/index.html) are found here.
#
# INPUT FROM index.html: All variables that start with index$...
# OUTPUT TO index.html: All variables that start with output$...
#
# To determine which variable communicates with whic <div> in the index.html file, search for the line with the
# class=shiny*.(e.g. The input$table variable is gonna be filled with info from the <div class="shiny-bound-input" id="table">.
#
# For more information refer to the shiny manual
shinyServer(function(input, output) {
#Data input and manipulation
data.genoid <- reactive({
if (gsub("\\s", "", input$table) == ""){
return(NULL)
} else {
input_table <- read.table(text = input$table, stringsAsFactors = FALSE)
colnames(input_table) <- colnames(df.m)
input_data.genoid <- input_table[[1]]
df.m <- rbind(df.m, input_table, deparse.level = 0)
df.m <- as.data.frame(df.m)
gen <- df2genind(df.m[, -c(1, 2)], ploid = 2, sep = "/", pop = df.m[, 2],
ind.names = df.m[, 1])
#Adding colors to the tip values according to the clonal lineage
gen$other$tipcolor <- pop(gen)
gen$other$input_data.genoid <- input_data.genoid
ngroups <- length(levels(gen$other$tipcolor))
########### MICROBE-ID customization ############
# Change these colors to represent the groups defined in your data.genoid set.
#
defined_groups <- c("blue", "darkcyan", "darkolivegreen", "darkgoldenrod","red")
#
# Change heat.colors to whatever color palette you want to represent
# submitted data.genoid.
#
input_colors <- heat.colors(ngroups - length(defined_groups))
#
##################################
levels(gen$other$tipcolor) <- c(defined_groups, input_colors)
gen$other$tipcolor <- as.character(gen$other$tipcolor)
return(gen)
}
})
# Setting a random seed for the current session from the user interface (<input type = "number" name = "seed" id = "seed" value = "9449" min = "0" />)
seed <- reactive({
return(input$seed)
})
# Greyscale slider settings from the user interface (<input id="integer" type="slider" name="integer" value="3" class="jslider" data-from="0" data-to="50" data-step="1" data-skin="plastic" data-round="FALSE" data-locale="us" data-format="#,##0.#####" data-smooth="FALSE"/>)
slider <- reactive({
slider.a <- (input$integer)
return(slider.a)
})
# Processing the results. The functions here create the figures to be displayed by the user interface.
# Bootstrap of a distance tree out of the data
boottree <- reactive({
# Running the tree, setting a cutoff of 50 and saving it into a variable to
# be plotted (tree)
if (input$boot > 1000){
return(1000L)
} else if (input$boot < 10){
return(10L)
}
set.seed(seed())
tree <- try(bruvo.boot(data.genoid(), replen = ssr, sample = input$boot,
tree = input$tree, cutoff = 50), silent = TRUE)
# This is a catch to avoid having missing data within the distance matrix.
if ("try-error" %in% class(tree)){
for (i in sample(100)){
tree <- try(bruvo.boot(data.genoid(), replen = ssr, sample = input$boot,
tree = input$tree, cutoff = 50), silent = TRUE)
if (!"try-error" %in% class(tree)){
print(paste0("Success: ", i))
break
}
print(paste0("Failed: ", i))
}
}
tree$tip.label <- paste(tree$tip.label, as.character(pop(data.genoid())))
if (input$tree=="nj"){
tree <- phangorn::midpoint(ladderize(tree))
}
return(tree)
})
#Minimum spanning network creation
msnet <- reactive ({
msn.plot <- bruvo.msn(data.genoid(), replen = ssr)
V(msn.plot$graph)$size <- 3
return(msn.plot)
})
############ MICROBE-ID customization ############
# The following lines of code communicate with the user interface to
# plot the outputs from the processes in the server script.
## Distance Tree (<div id="distPlotTree" class="span6 shiny-plot-output">)
output$distPlotTree <- renderPlot({
if (is.null(data.genoid())){
plot.new()
rect(0, 1, 1, 0.8, col = "indianred2", border = 'transparent' ) +
text(x = 0.5, y = 0.9, "No SSR data has been input.", cex = 1.6, col = "white")
} else if (is.integer(boottree())){
msg <- ifelse(boottree() > 10L, "\nless than or equal to 1000",
"greater than 10")
msg <- paste("The number of bootstrap replicates should be", msg)
plot.new()
rect(0, 1, 1, 0.8, col = "indianred2", border = 'transparent' ) +
text(x = 0.5, y = 0.9, msg, cex = 1.6, col = "white")
} else {
plot.tree(boottree(), type=input$tree, tip.col=as.character(unlist(data.genoid()$other$tipcolor)))
}
})
##Minimum Spanning Network (<div id="MinSpanTree" class="shiny-plot-output")
output$MinSpanTree <- renderPlot({
if (is.null(data.genoid())){
plot.new()
rect(0, 1, 1, 0.8, col = "indianred2", border = 'transparent' ) +
text(x = 0.5, y = 0.9, "No SSR data has been input.", cex = 1.6, col = "white")
} else {
set.seed(seed())
plot.minspan(data.genoid(), msnet(), gadj=slider(), inds = data.genoid()$other$input_data.genoid)
}
})
############ MICROBE-ID customization ############
# The following lines of code communicate with the user interface to
# download the outputs from the processes in the server script.
## Distance tree in .tre format (<a id="downloadData" class="btn btn-primary shiny-download-link">")
output$downloadData <- downloadHandler(
filename = function() { paste0(input$tree, '.tre') },
content = function(file) {
write.tree(boottree(), file)
})
## Distance tree in PDF format (<a id="downloadPdf" class="btn btn-info shiny-download-link">)
output$downloadPdf <- downloadHandler(
filename = function() { paste0(input$tree, '.pdf') },
content = function(file) {
pdf(file, width=11, height=8.5)
plot.tree(boottree(), type=input$tree, tip.col=as.character(unlist(data.genoid()$other$tipcolor)))
dev.off()
})
## Minimum spanning network in PDF format (<a id="downloadPdfMst" class="btn btn-info shiny-download-link">)
output$downloadPdfMst <- downloadHandler(
filename = function() { paste0("min_span_net", '.pdf')} ,
content = function(file) {
pdf(file, width=11, height=8.5)
set.seed(seed())
plot.minspan( data.genoid(), msnet(), gadj=slider(), inds = data.genoid()$other$input_data.genoid)
dev.off()
}
)
#EOF
})
|
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