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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
905a787f5c16471279a934b262fced7b8f9cdfa0 | aaec73a1cc215e604b33dd2c57d8ea12ed07b64f | /MRSA_MLST_read.R | e599b20084ef87efac2e802eec3e44c1347e3f8d | [] | no_license | eugejoh/MRSA_MLST | 8fb2bbbd654eb6d62f4bb6eb9301ab2b069b16a4 | 9fcd1d5687b2e6b9d4c15be504ce57a784bbeca9 | refs/heads/master | 2021-01-11T15:53:31.348380 | 2017-01-24T20:09:02 | 2017-01-24T20:09:02 | 79,949,566 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,927 | r | MRSA_MLST_read.R | # MRSA MLST Database
rm(list=ls())
# Taken from http://saureus.beta.mlst.net/ Jan 8, 2017
setwd("/Users/eugenejoh/Documents/Post-Grad/ID Data/MRSA/MLST")
# portable_profile_edit #includes
# filenames follow format...
# portable_genename_edit
#
profile<-read.csv("portable_profile_edit.csv",header=T,na.strings=c("",NA))
dim(profile) #dimensions of the read-in dataset
names(profile) # names of the columns in dataset
str(profile) #structure of the data frame
#################
# DATA CLEANING #
#################
# Identify missing values
levels(profile$country) #names of countries included in the dataset
summary(profile$country) #summary of counts for each country
# Change country to character from factor type
profile$country <- as.character(profile$country)
# Standardize resistiance factors ($methicillin, $vancomycin)
summary(profile$methicillin)
str(profile$methicillin) # nine levels for definitions for resistance?
# we want to simplify this to binary output for resistant(R) and susceptible(S)
factor(profile$methicillin)
# factor(profile$methicillin) gives us I, MIC 4mgl/L, MIC 64mg/L, r, R, s, S, Unknown, Unspecified
# now we merge r with R and s with S, while renaming the MICs
levels(profile$methicillin) <- c("I","MIC4","MIC64","R","R","S","S","Unknown","Unspecified")
# we also recognize MIC's of 4mg/L and 64mg/L are considered resistant. Important to note that breakpoints for methicillin MIC tests are now limited - see CDC website for info - https://www.cdc.gov/mrsa/lab/
# we are also going to combine the I, MIC4, MIC64 into the resistant category (R)
levels(profile$methicillin) <- c("R","R","R","R","S","Unknown","Unspecified")
# now we only have 4 levels : R, S, Unknown, Unspecified
factor(profile$methicillin)
################
################
#mis_profile<-sapply(profile,function(x) sum(is.na(x)))
report.NA <- function(v){
nam <- deparse(substitute(v))
varNA <-paste0(nam,"NAs")
#assign(newvar,sapply(v,function(x) sum(is.na(x))),envir = parent.frame())
#assign(newvar,apply(is.na(v),2,sum))
assign(varNA,as.data.frame(colSums(is.na(v))),envir=parent.frame())
message(paste("Sum of NAs in",nam,"dataset:",varNA),appendLF=T)
} # Why you need to change the function environment, http://emilkirkegaard.dk/en/?p=5718
report.NA(profile);ls()
summary(profile$st) #2594 different types of STs, 4 missing values
summary(profile$methicillin) #review the MIC for each one (which ones are considered R or S)
profileNAs
# 20 strain names are missing
# 4 STs are missing
# 15 countries
################
################
no.st<-which(is.na(profile$st)) #row index of those with missing STs in dataset
profile[no.st,] # info on the missing values of STs
# England, Eire (Ireland), 2 USA
profile[which(is.na(profile$st)),]
####################
# QUESTIONS TO ASK #
####################
### Which countries have reported ST 239?
id.239 <- which(profile$st==239) #index of rows with ST-239
st.239 <-profile[id.239,] #new dataframe of only ST-239
sort(unique(st.239$country)) # alphabetical list of countries with ST-239
### Which countries have report ST8 spa type t008?
id.ST8.t008 <- which(profile$st==8 & profile$spa_type=="t008")
ST8.t008 <- profile[id.ST8.t008,]
dim(ST8.t008)
sort(unique(ST8.t008$country))
length(sort(unique(ST8.t008$country)))
# What is the proportion of S vs. R S. aureus reports are there in North America?
table(profile$methicillin,useNA="always") #number of counts for R, S, Unknown, Unspecified and NA values for methicillin resistance
sum(is.na(profile$methicillin)) #counts of NAs in dataset
NA.MRSA <- profile[profile$country=="USA" | profile$country=="Canada",] #selection of North American countries
table(NA.MRSA$methicillin,useNA="always")
NA.MRSA <- NA.MRSA[complete.cases(NA.MRSA$country),] #removal of NAs
# ggplot
# http://stackoverflow.com/questions/16184188/ggplot-facet-piechart-placing-text-in-the-middle-of-pie-chart-slices
library(ggplot2)
install.packages("viridis")
library(viridis)
NA.MRSA$st <- as.numeric(NA.MRSA$st) #convert the STs to numeric type
c.NorthA.MRSA <-as.data.frame(table(NA.MRSA$st));names(c.NorthA.MRSA) <- c("ST","count") #data frame containing count frequencies of the STs
c.NorthA.MRSA<-c.NorthA.MRSA[order(-c.NorthA.MRSA$count),] #order the STs by count frequencies
c.NorthA.MRSA[1:10,] # top ten
blank_t <- theme_minimal()+
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.border = element_blank(),
panel.grid=element_blank(),
axis.ticks = element_blank(),
plot.title=element_text(size=20, face="bold",hjust=0.6),
plot.subtitle=element_text(size=15,face=c("bold","italic"),hjust=0.6),
axis.text.x=element_blank(),
legend.title=element_text(size=14,face="bold",vjust=0.5),
panel.margin=unit(2,"cm")
)
ggplot(c.NorthA.MRSA[1:10,], aes(x="",y=count,fill=ST,order=ST)) +
geom_bar(width=1, stat="identity") +
ggtitle(element_text(size=50))+
labs(title = "STs in North America", subtitle = "Canada and United States",y=NULL) +
coord_polar(theta="y")+
geom_text(aes(label = scales::percent(count/sum(c.NorthA.MRSA[1:10,2]))),size=4, position = position_stack(vjust = 0.6)) +
blank_t +
scale_fill_manual(guide_legend(title="STs"),
values=c("#e6b4c9",
"#bce5c2", # colour palette resource for data scientists
"#c7b5de", # http://tools.medialab.sciences-po.fr/iwanthue/
"#e3e4c5",
"#a1bbdd",
"#e5b6a5",
"#99d5e5",
"#bdbda0",
"#dcd1e1",
"#99c6b8"))
# http://www.sthda.com/english/wiki/ggplot2-pie-chart-quick-start-guide-r-software-and-data-visualization
# http://graphicdesign.stackexchange.com/questions/3682/where-can-i-find-a-large-palette-set-of-contrasting-colors-for-coloring-many-d
# changing order of scale # https://learnr.wordpress.com/2010/03/23/ggplot2-changing-the-default-order-of-legend-labels-and-stacking-of-data/
install.packages("randomcoloR")
library(randomcoloR)
distinctColorPalette(k=15)
#+facet_grid(Channel ~ ., scales = "free")
bp <- ggplot(NA.MRSA,aes(x="",y=st,fill=country)) + geom_bar(width = 1,stat = "identity")
bp
pie <- bp + coord_polar("y",start=0)
pie + geom_text(aes(label = country), position = position_stack(vjust = 0.5))
bp1 <- ggplot(NA.MRSA,aes(x=factor(1),fill=country)) + geom_bar(width = 1)
bp1
pie1 <- bp1 + coord_polar("y",start=0)
pie1
pie + scale_fill_brewer(palette="Blues")
# How many STs are there in China, S Korea, Taiwan and Japan?
F.east <- profile[profile$country=="China" | profile$country=="South Korea" | profile$country=="Japan" | profile$country=="Taiwan",] #subset the data by Far East countries. | logic operator is OR, & would be AND
dim(F.east) #573 entries
factor(F.east$st) # 289 different STs
sum(is.na(F.east$st)) #15 missing values
table(F.east$st) #counts of STs
table(F.east$country) #counts of STs by country
FE.final<-F.east[complete.cases(F.east$country),] #removal of all rows that are missing an entry for country
dim(FE.final) #check the dimensions is 573-15 = ?
# Country Specific Details
# China
length(which(FE.final$country=="China")) #counts for China
FE.China <- FE.final[FE.final$country=="China",]
FE.China$country
dim(FE.China) # 229 entries
sort(table(FE.China$st),decreasing=T) #most common STs are 97, 239 and 398
sort(table(FE.China$st),decreasing=T)[1:5] #top 10 most frequent
factor(FE.China$st) #163 different type of STs
China.tab <- table(FE.China$st)
bp <- ggplot(FE.China,aes(x=st)) + geom_bar()
bp + coord_polar("y",start=0)
x <- c("Insurance", "Insurance", "Capital Goods", "Food markets", "Food markets")
tt <- table(x)
names(tt[tt==max(tt)])
# S Korea
# Taiwan
# Japan
# Mapping out Countries using ggmap() package
install.packages(c("ggmap","maptools","maps"))
library(ggplot2,ggmap,maptools)
library(maps)
# HOW TO PLOT POINTS ON MAPS IN R
# https://www.r-bloggers.com/r-beginners-plotting-locations-on-to-a-world-map/
# http://stackoverflow.com/questions/11201997/world-map-with-ggmap/13222504#13222504
# setup cities of interest
visited <- c("SFO", "Chennai", "London", "Melbourne", "Johannesbury, SA")
ll.visited <- geocode(visited) #get
visit.x <- ll.visited$lon
visit.y <- ll.visited$lat
mp <- NULL
mapWorld <- borders("world", colour="gray50", fill="gray50") # create a layer of borders
mp <- ggplot() + mapWorld
mp <- mp+ geom_point(aes(x=visit.x, y=visit.y) ,color="blue", size=3)
# http://stackoverflow.com/questions/41018634/continuous-colour-gradient-that-applies-to-a-single-geom-polygon-element-with-gg
library(ggplot2)
map <- map_data("world") #map of the world from
map$value <- setNames(sample(1:50, 252, T), unique(map$region))[map$region]
world.p <- ggplot(map, aes(long, lat, group=group, fill=value)) +
geom_polygon()+
coord_map(projection = "mercator",xlim=c(180,-180),ylim=c(75,-75))
p <- ggplot(map, aes(long, lat, group=group, fill=value)) +
geom_polygon() +
coord_quickmap(xlim = c(-50,50), ylim=c(25,75))
p + geom_polygon(data = subset(map, region=="Germany"), fill = "red")
|
f8e4fb8b9dc98f447ae79ada86b94e808958eb6c | 904022448a1599c2e6dcb2a438beb4b64efb65bc | /adr_no_cells.R | e9b626725e472d7ff226c756f5de3fde1ea7f6f4 | [] | no_license | GKild/neuroblastoma | 1bb9ec3df959482d6d7e9a718fc98bb813033a8f | a17d359b8ad915ce3eb831739254c951df1719e4 | refs/heads/master | 2023-03-04T04:24:57.783627 | 2021-02-16T11:12:00 | 2021-02-16T11:12:00 | 217,528,698 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,167 | r | adr_no_cells.R | #get all adrenal datasets
ba1=readRDS("/lustre/scratch117/casm/team274/my4/oldScratch/preProcessSCP/output/babyAdrenal1_Seurat.RDS")
ba2=readRDS("/lustre/scratch117/casm/team274/my4/oldScratch/preProcessSCP/output/babyAdrenal2_Seurat.RDS")
bilateral=readRDS("/lustre/scratch117/casm/team274/my4/oldScratch/preProcessSCP/output/bilateralAdrenals_Seurat.RDS")
bilat_tech=readRDS("/lustre/scratch117/casm/team274/my4/oldScratch/preProcessSCP/output/techComparison_Seurat.RDS")
adr3=readRDS("/lustre/scratch117/casm/team274/my4/oldScratch/ProjectsExtras/SCP/Data/fAdrenal19wk/seurat.RDS")
DimPlot(adr3, label=T)
dim(ba1)
dim(ba2)
dim(bilateral)
dim(bilat_tech)
DimPlot(ba1, label=T)
DimPlot(ba2, label=T)
DimPlot(bilateral, label=T)
DimPlot(bilat_tech,label=T)
FeaturePlot(ba1, features=c('HBB','HBA1','PECAM1','PTPRC','EPCAM','PDGFRB','MYCN','SOX10','SOX2','PHOX2A','CHGB','PHOX2B'))
FeaturePlot(ba2, features=c('HBB','HBA1','PECAM1','PTPRC','EPCAM','PDGFRB','MYCN','SOX10','SOX2','PHOX2A','CHGB','PHOX2B'))
FeaturePlot(bilat_tech, features=c('HBB','HBA1','PECAM1','PTPRC','EPCAM','PDGFRB','MYCN','SOX10','SOX2','PHOX2A','CHGB','PHOX2B'))
length(WhichCells(ba2, idents = c(19,7,11,17,10)))
length(WhichCells(ba2, idents = c(0,1,2,4,6,8,12,13)))
length(WhichCells(ba2, idents = c(18)))
length(WhichCells(ba2, idents = c(14)))
length(WhichCells(ba2, idents = c(3,9,15)))
length(WhichCells(ba2, idents = c(5,16)))
FeaturePlot(bilateral, features=c("EPCAM"))
length(WhichCells(ba1, idents = c(14,19,20)))
length(WhichCells(ba1, idents = c(0,1,2,4,6,7,8,10,11,13)))
length(WhichCells(ba1, idents = c(5,17)))
length(WhichCells(ba1, idents = c(16,18)))
length(WhichCells(ba1, idents = c(3,9,15,12)))
length(WhichCells(bilateral, idents = c(12,15,16,20,22)))
length(WhichCells(bilateral, idents = c(0,2,4,5,9,10,23,24)))
length(WhichCells(bilat_tech, idents = c(0,2,3,4,9,10,16,11)))
length(WhichCells(bilat_tech, idents = c(6,7,14)))
FeaturePlot(bilat_tech, c("SOX2"))
length(WhichCells(adr3,idents=c(0,1,2,4,6,9,13,14)))
length(WhichCells(adr3,idents=c(23)))
length(WhichCells(bilateral, idents = c(22)))
length(WhichCells(ba2, idents = c(17,10)))
length(WhichCells(bilat_tech, idents = c(11)))
WhichCells(bilateral, orig.ident="5388STDY7717452")
left=rownames(bilateral@meta.data)[which(bilateral@meta.data$orig.ident%in%c("5388STDY7717452", "5388STDY7717453", "5388STDY7717454", "5388STDY7717455"))]
right=rownames(bilateral@meta.data)[which(!bilateral@meta.data$orig.ident%in%c("5388STDY7717452", "5388STDY7717453", "5388STDY7717454", "5388STDY7717455"))]
left_srat=subset(bilateral, cells=left)
right_srat=subset(bilateral, cells=right)
length(WhichCells(left_srat, idents = c(12,15,16,20,22)))
length(WhichCells(left_srat, idents = c(0,2,4,5,9,10,23,24)))
length(WhichCells(left_srat, idents = c(18,19)))
length(WhichCells(left_srat, idents = c(1,21,13)))
length(WhichCells(left_srat, idents = c(8,11)))
length(WhichCells(left_srat, idents = c(3,6,7,14,17)))
length(WhichCells(right_srat, idents = c(12,15,16,20,22)))
length(WhichCells(right_srat, idents = c(0,2,4,5,9,10,23,24)))
length(WhichCells(right_srat, idents = c(18,19)))
length(WhichCells(right_srat, idents = c(1,21,13)))
length(WhichCells(right_srat, idents = c(8,11)))
length(WhichCells(right_srat, idents = c(3,6,7,14,17)))
length(WhichCells(right_srat, idents = c(22)))
sapply(strsplit(names(adr3@active.ident), "_"), "[", 6)
old_left=rownames(adr3@meta.data)[which(sapply(strsplit(names(adr3@active.ident), "_"), "[", 6)%in%c("Adr8710632", "Adr8710633"))]
old_right=rownames(adr3@meta.data)[which(!sapply(strsplit(names(adr3@active.ident), "_"), "[", 6)%in%c("Adr8710632", "Adr8710633"))]
old_left_srat=subset(adr3, cells=old_left)
old_right_srat=subset(adr3, cells=old_right)
length(WhichCells(old_left_srat,idents=c(0,1,2,4,6,9,13,14)))
length(WhichCells(old_left_srat,idents=c(23)))
length(WhichCells(old_left_srat,idents=c(12,17,27,19,20)))
length(WhichCells(old_left_srat,idents=c(3,5,7,24,21,25)))
length(WhichCells(old_left_srat,idents=c(18,15)))
length(WhichCells(old_left_srat,idents=c(22,8,11,10,16)))
length(WhichCells(old_right_srat,idents=c(0,1,2,4,6,9,13,14)))
length(WhichCells(old_right_srat,idents=c(23)))
length(WhichCells(old_right_srat,idents=c(12,17,27,19,20)))
length(WhichCells(old_right_srat,idents=c(3,5,7,24,21,25)))
length(WhichCells(old_right_srat,idents=c(18,15)))
length(WhichCells(old_right_srat,idents=c(22,8,11,10,16)))
length(WhichCells(bilat_tech,idents=c(1,5,15)))
length(WhichCells(bilat_tech,idents=c(17,13,18)))
length(WhichCells(bilat_tech,idents=c(19,8,12)))
length(WhichCells(bilat_tech,idents=c(20,21)))
DimPlot(ba1, label=T)
tab=read.csv("plts/Book3.csv", header = T)
tab_melt=melt(tab,id.vars = "Sample")
ggplot(tab_melt, aes(fill=variable, y=value, x=Sample)) +
geom_bar(position="fill", stat="identity") + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black")) +
scale_fill_manual(values=c("#332288", "#88CCEE", "#117733", "#DDCC77", "#CC6677","#AA4499"))
|
2304729c9de1e5dd83612f40613537ad1adbbb4e | 4b5c970ac1dc877453c7a13f921c807b4177ae6c | /plot5.R | b9234270b55bb0f78d5d542f8521591f82309914 | [] | no_license | ManuChalela/JHU_EDA_Project2 | dec3e8fa7d02b6c2f57036b706154eba591e0070 | 18751cf6f6dee335cb9cd5a70dcfeee307c20501 | refs/heads/main | 2023-01-18T16:05:18.348466 | 2020-12-12T04:19:31 | 2020-12-12T04:19:31 | 320,467,620 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,208 | r | plot5.R | library(data.table)
library(reshape2)
library(png)
library(dplyr)
library(fs)
library(lubridate)
library(ggplot2)
path <- file.path(getwd(), "data/")
fs::dir_create(path = path)
url <-
"https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
download.file(url, file.path(path, "dataFiles.zip"))
unzip(zipfile = file.path(path, "dataFiles.zip"), exdir = path)
SCC <- data.table::as.data.table(x = readRDS(file = file.path(path, "Source_Classification_Code.rds")))
NEI <- data.table::as.data.table(x = readRDS(file = file.path(path, "summarySCC_PM25.rds")))
# Gather the subset of the NEI data which corresponds to vehicles
vehiclesSCC <- SCC[grepl("vehicle", SCC.Level.Two, ignore.case=TRUE)
, SCC]
vehiclesNEI <- NEI[NEI[, SCC] %in% vehiclesSCC,]
# Subset the vehicles NEI data to Baltimore's fip
baltimoreVehiclesNEI <- vehiclesNEI[fips=="24510",]
png("plot5.png")
ggplot(baltimoreVehiclesNEI,aes(factor(year),Emissions)) +
geom_bar(stat="identity", fill ="#FF9999" ,width=0.75) +
labs(x="year", y=expression("Total PM"[2.5]*" Emission (10^5 Tons)")) +
labs(title=expression("PM"[2.5]*" Motor Vehicle Source Emissions in Baltimore from 1999-2008"))
dev.off()
|
5c2c21814c12d3e0b9cb528f024a53158d7c5a4a | 43ac1b022fb160479e9baf022d522857323878d2 | /analysis/dir-utility.r | 4cd3d97e41967605094589f658a7727c16cef257 | [] | no_license | franciscastro/research-plan-composition | 6b532f4ea4ff339ef36948d886505f8b86e719a1 | 503108ea8393a6b03faed347ab91e65fcadc8008 | refs/heads/master | 2021-01-10T11:46:13.741052 | 2016-04-22T20:17:45 | 2016-04-22T20:17:45 | 45,146,889 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,078 | r | dir-utility.r | #' ---
#' title: "Directory creation utility script"
#' author: "Francisco Castro (fgcastro@wpi.edu)"
#' date: "15 March 2016"
#' ---
# NOTES
#==================================================
#
# This script is used to create directories for each
# sub-sample generated in data-setup.r and data-sample.r
#
# Run data-setup.r prior to running this script.
#
#==================================================
# Set directory paths for each sample group
#==================================================
dir_1101_A <- "C:/Git Repositories/files/sampled/1101-a"
dir_1101_B <- "C:/Git Repositories/files/sampled/1101-b"
dir_1101_C <- "C:/Git Repositories/files/sampled/1101-c"
dir_1102_A <- "C:/Git Repositories/files/sampled/1102-a"
dir_1102_B <- "C:/Git Repositories/files/sampled/1102-b"
#==================================================
# Read files (csv files have been pre-generated)
#==================================================
data_1101a <- read.csv("sample-cs1101-A.csv")
data_1101b <- read.csv("sample-cs1101-B.csv")
data_1101c <- read.csv("sample-cs1101-C.csv")
data_1102a <- read.csv("sample-cs1102-A.csv")
data_1102b <- read.csv("sample-cs1102-B.csv")
#==================================================
# Fetch directory names (usernames) from data frames
#==================================================
data_1101a <- data_1101a$username
data_1101b <- data_1101b$username
data_1101c <- data_1101c$username
data_1102a <- data_1102a$username
data_1102b <- data_1102b$username
#==================================================
# Create directories for each sample group
#==================================================
create_dirs <- function(maindir, subdir) {
dir.create(file.path(maindir, subdir))
}
sapply(data_1101a, create_dirs, maindir = dir_1101_A)
sapply(data_1101b, create_dirs, maindir = dir_1101_B)
sapply(data_1101c, create_dirs, maindir = dir_1101_C)
sapply(data_1102a, create_dirs, maindir = dir_1102_A)
sapply(data_1102b, create_dirs, maindir = dir_1102_B)
#==================================================
|
3ed23d80bb0869d75cede7a6279326b3b8186fc8 | 560f1adefa19f293eafbe63fc6bd18b0769bc154 | /R programming/corr.R | c97c535675b1d7594333ee5646aab563792eeb0d | [] | no_license | kmj9000/datasciencecoursera | 10e78b1889887536b1e8ecdab96aacf9e597b658 | a1f7fd8515a8d13e68d1bee843627b035fbf895d | refs/heads/master | 2020-05-30T16:03:48.949857 | 2015-11-22T17:13:27 | 2015-11-22T17:13:27 | 29,430,090 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 754 | r | corr.R | corr <- function(directory, threshold = 0) {
## 'directory' is a character vector of length 1 indicating
## the location of the CSV files
## 'threshold' is a numeric vector of length 1 indicating the
## number of completely observed observations (on all
## variables) required to compute the correlation between
## nitrate and sulfate; the default is 0
## Return a numeric vector of correlations
files <- Sys.glob(paste0(directory, "/", "*.csv"))
correlns <- c()
for (i in files) {
eachFile <- read.csv(i);
completeCases <- eachFile[complete.cases(eachFile),]
if (nrow(completeCases) > threshold)
correlns <- c(correlns, cor(completeCases$sulfate, completeCases$nitrate))
}
correlns
} |
7ee30b714afe9cb13a25be1ba3d880ff19a6dc57 | 2447be673b4bceaf193a5ced248dd0f62912d994 | /man/extract-methods.Rd | 643226eb2e1f5f81be34b12f452ed47ae8075a85 | [] | no_license | benneic/gpuRcuda | 024b6f1af5a08257a9c3e51ef68dc5014712d74d | 23a4ad805c738da7227176f99ecdae1750fda00c | refs/heads/master | 2020-07-22T03:38:06.289601 | 2018-01-10T17:50:15 | 2018-01-10T17:50:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 844 | rd | extract-methods.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/methods-cudaVector.R, R/methods-nvVector.R,
% R/methods.R
\docType{methods}
\name{[,cudaVector,missing,missing,missing-method}
\alias{[,cudaVector,missing,missing,missing-method}
\alias{[,nvVector,missing,missing,missing-method}
\alias{[,cudaMatrix,missing,missing,missing-method}
\alias{[,nvMatrix,missing,missing,missing-method}
\title{Extract gpuRcuda elements}
\usage{
\S4method{[}{cudaVector,missing,missing,missing}(x, i, j, drop)
\S4method{[}{nvVector,missing,missing,missing}(x, i, j, drop)
\S4method{[}{cudaMatrix,missing,missing,missing}(x, i, j, drop)
\S4method{[}{nvMatrix,missing,missing,missing}(x, i, j, drop)
}
\arguments{
\item{x}{A gpuRcuda object}
\item{i}{missing}
\item{j}{missing}
\item{drop}{missing}
}
\author{
Charles Determan Jr.
}
|
592cf590c688fb9e504bdae57f5b5358f685c96a | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/1842_12/rinput.R | 63ad90d1e113c9f7dce696364438dbb816a130c9 | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 137 | r | rinput.R | library(ape)
testtree <- read.tree("1842_12.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="1842_12_unrooted.txt") |
fe11c8160f62d13202ba1153617c73d04289f170 | fdfe62a9b82d6537d633849a8f3ad5a24130bbeb | /irods-3.3.1-cyverse/iRODS/clients/icommands/test/rules3.0/rulemsiTarFileExtract.r | 4d8af5cfbb0fdd6f808f4d96fd831199068db188 | [] | no_license | bogaotory/irods-cyverse | 659dbef3652c1713a419d588adf0b866474dea9a | 2aceaf7c318c2fb581fcca2d62968f69460dcc9c | refs/heads/master | 2021-01-10T14:15:27.267337 | 2016-02-25T00:44:06 | 2016-02-25T00:44:06 | 52,032,218 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 690 | r | rulemsiTarFileExtract.r | myTestRule {
# Input parameters are:
# Tar file within iRODS that will have its files extracted
# Collection where the extracted files will be placed
# Resource where the extracted files will be written
# Output parameter:
# Status flag for the operation
# Output from running the example is:
# Extract files from a tar file into collection /tempZone/home/rods/ruletest/sub on resource demoResc
msiTarFileExtract(*File,*Coll,*Resc,*Status);
writeLine("stdout","Extract files from a tar file *File into collection *Coll on resource *Resc");
}
INPUT *File="/tempZone/home/rods/test/testcoll.tar", *Coll="/tempZone/home/rods/ruletest/sub", *Resc="demoResc"
OUTPUT ruleExecOut
|
700463f468d4da491b3ed47c51e528b0d2103ca6 | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /twosamples/man/ks_test.Rd | 6b361c542c7d35e43351928ea11eb35a948a451e | [] | no_license | akhikolla/TestedPackages-NoIssues | be46c49c0836b3f0cf60e247087089868adf7a62 | eb8d498cc132def615c090941bc172e17fdce267 | refs/heads/master | 2023-03-01T09:10:17.227119 | 2021-01-25T19:44:44 | 2021-01-25T19:44:44 | 332,027,727 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,719 | rd | ks_test.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R, R/documentation.R,
% R/two_samples.R
\name{ks_stat}
\alias{ks_stat}
\alias{ks_test}
\title{Kolmogorov-Smirnov Test}
\usage{
ks_stat(a, b, power = 1)
ks_test(a, b, nboots = 2000, p = default.p)
}
\arguments{
\item{a}{a vector of numbers}
\item{b}{a vector of numbers}
\item{power}{power to raise test stat to}
\item{nboots}{Number of bootstrap iterations}
\item{p}{power to raise test stat to}
}
\value{
Output is a length 2 Vector with test stat and p-value in that order. That vector has 3 attributes -- the sample sizes of each sample, and the number of bootstraps performed for the pvalue.
}
\description{
A two-sample test using the Kolmogorov-Smirnov test statistic (\code{ks_stat}).
}
\details{
The KS test compares two ECDFs by looking at the maximum difference between them. Formally -- if E is the ECDF of sample 1 and F is the ECDF of sample 2, then \deqn{KS = max |E(x)-F(x)|^p} for values of x in the joint sample. The test p-value is calculated by randomly resampling two samples of the same size using the combined sample.
In the example plot below, the KS statistic is the height of the vertical black line. \figure{ks.png}{Example KS stat plot}
}
\section{Functions}{
\itemize{
\item \code{ks_stat}: Kolmogorov-Smirnov test statistic
\item \code{ks_test}: Permutation based two sample Kolmogorov-Smirnov test
}}
\examples{
vec1 = rnorm(20)
vec2 = rnorm(20,4)
ks_test(vec1,vec2)
}
\seealso{
\code{\link[=dts_test]{dts_test()}} for a more powerful test statistic. See \code{\link[=kuiper_test]{kuiper_test()}} or \code{\link[=cvm_test]{cvm_test()}} for the natural successors to this test statistic.
}
|
dfa47530f78ff6a56276ad3ee7863bb633a40d03 | 669e0ccbaf738f5c0e0f6b8d790217c6ff5e15b4 | /import_and_sample.R | a9e3528a6045540e85b44f97f29fb694294978d5 | [] | no_license | hytsang/Capstone-Project | 9550d161e8563a80de68ddefdb66018ffbf11682 | dceb836ce63d46dc6d0be6015418fc4a1edb9a09 | refs/heads/master | 2021-01-11T14:00:43.052533 | 2015-04-19T11:35:07 | 2015-04-19T11:35:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 921 | r | import_and_sample.R | # importing and sampling the data
# load stringi library
library(stringi)
setwd("~/Capstone Project")
list.files("final")
list.files("final/en_US")
# import the blogs and twitter datasets in text mode
blogs <- readLines("final/en_US/en_US.blogs.txt", encoding="UTF-8")
twitter <- readLines("final/en_US/en_US.twitter.txt", encoding="UTF-8")
#importing news dataset
con <- file("final/en_US/en_US.news.txt", open="rb")
news <- readLines(con, encoding="UTF-8")
close(con)
rm(con)
# dropping non UTF-8 character
twitter <- iconv(twitter, from = "latin1", to = "UTF-8", sub="")
twitter <- stri_replace_all_regex(twitter, "\u2019|`","'")
twitter <- stri_replace_all_regex(twitter, "\u201c|\u201d|u201f|``",'"')
# Sampling the data
blogs <- sample(blogs,10000)
news <- sample(news,10000)
twitter <- sample(twitter,10000)
data <- c(twitter,blogs,news)
rm(blogs)
rm(news)
rm(twitter)
save(data,file="sample_data.Rdata") |
3f05634a5ead7cbf1bf90c9c161cb3dcff2e2454 | 72279a412260a94bdf5ae70db4cea2e153689331 | /functional_response&Fig2_AP.R | 704e5e10bca5680f52dc79e63434c7aa4997e6fc | [] | no_license | koehnl/Seabird_ForageFish_model | 21b599eef6e5c4d143c2e21f2bf41148434eb691 | 54a105bf03eda27e4b6214208e7a3bd733de5207 | refs/heads/main | 2023-03-21T18:01:37.217260 | 2021-03-14T23:07:08 | 2021-03-14T23:07:08 | 344,362,258 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,696 | r | functional_response&Fig2_AP.R | # CODE TO PLAY AROUND WITH FUNCTIONAL RESPONSES
# and make Figure 2 from Koehn et al. 2021
# acknowledgments to Andre Punt (University of Washington)
# for supplying lines of code and help
alpha <- 0 # lowest possible impact on survival
slope <-30 # how fast it drops from 1 to the lowest possible impact on suvival
beta <- 0.2 # ratio (P/P0) where predator would switch?
# P/P0
xx <- seq(from=0,to=1.5,by=0.01)
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
plot(xx,yy,type='l')
abline(v = 0.15, lty = 3)
######################################
alpha <- 0
slope <- 40
beta <- 0.15
# P/P0
xx <- seq(from=0,to=1.5,by=0.01)
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
plot(xx,yy,type='l')
alpha <- 0
slope <- 30
beta <- 0.2
# P/P0
yy2 <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx, yy2, col = "red")
functionalresponse <- function(alpha, slope, beta) {
xx <- seq(from=0,to=1.1,by=0.01)
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
plot(xx,yy,type='l', ylim = c(0,1), ylab = "", xlab = "", yaxt = "n", xaxt = "n")
#abline(v = 0.3, lty = 3)
axis(side = 1, at = c(0,0.1,0.2,0.3,0.4,0.5,1), las = 0)
axis(side = 2, at = c(0,0.2,0.4,0.6,0.8,1), las = 2)
return(cbind(yy,xx))
}
xx <- seq(from=0,to=1.1,by=0.01)
functionalresponse(0.1, 40, 0.3) # test
zz <- seq(from=0,to=1.1,by=0.01)
lines(zz,xx)
par(mfrow = c(3,2))
par(mar=c(3,4,3,2))
par(oma=c(3,3,2,2))
# BREEDER ATTENDANCE
temp =functionalresponse(0.1,20,0.3)
functionalresponse(0.1,30,0.3) #breeders specialist
#functionalresponse(0.6,20,0.3) #generalist
#functionalresponse(0.3,20,0.3) #middle
alpha = 0.6; slope = 20; beta = 0.3
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "red")
mtext("% Breeder Attendance", side=3, line = 1)
#mtext("Proportion Prey Available", side=1, line = 3)
alpha = 0.3; slope = 20; beta = 0.3
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "blue")
legend("bottomright", legend = c("Specialist", "Generalist"),
col = c("Black", "Red"), lty = c(1,1), y.intersp=1, bty = 'n')
# fledgling
functionalresponse(-0.3,30,0.2) # have a max(0, yy) so will give 0 if yy goes negative
alpha = 0.51; slope = 15; beta = 0.1
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "red")
mtext("% Fledge 1 chick - if 3 chicks", side=3, line = 1)
#mtext("Proportion Prey Available", side=1, line = 3)
alpha = 0.27; slope = 20; beta = 0.15
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "blue")
legend("bottomright", legend = c("Specialist", "Generalist", "Middle"),
col = c("Black", "Red", "Blue"), lty = c(1,1,1), y.intersp=1)
# Second chick
functionalresponse(-0.3,30,0.15)
# vs. firrst chick
alpha = -0.3; slope = 30; beta = 0.2
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "red")
functionalresponse(-0.3,30,0.15)
alpha = 0.41; slope = 15; beta = 0.05
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "red")
mtext("% Fledge 1 chick - if 2 chicks", side=3, line = 1)
#mtext("Proportion Prey Available", side=1, line = 3)
alpha = 0.21; slope = 20; beta = 0.1
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "blue")
legend("bottomright", legend = c("Specialist", "Generalist", "Middle"),
col = c("Black", "Red", "Blue"), lty = c(1,1,1), y.intersp=1)
# 3rd chick
functionalresponse(-0.3,30,0.1)
alpha = 0.2; slope = 15; beta = 0
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "red")
mtext("% Fledge 1 chick", side=3, line = 1)
#mtext("Proportion Prey Available", side=1, line = 3)
alpha = 0.05; slope = 20; beta = 0.05
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "blue")
legend("bottomright", legend = c("Specialist", "Generalist", "Middle"),
col = c("Black", "Red", "Blue"), lty = c(1,1,1), y.intersp=1)
param_breeders_special = c(0.2,30,0.3) # colony/breeder attendance (not survival)
param_breeders_general = c(0.6,30,0.3)
param_breeders_mid = c(0.3,20,0.3)
# OK? based on Piatt et al. 2007/Cairns should be shifted even further right.
# also right shape?
param_fledge_special = c(-0.3,30,0.2)
# enough like 1/3 for the birds - i think so, and looks like Andre's
param_fledge_general = c(0.3,15,0.1) # so at some lower survival, switch and survival goes back to 1?
param_fledge_mid = c(0.1,20,0.15)
param_fledge2_special = c(-0.3,30,0.10)
paramfledge2_general = c(0.2,20,0.05) # *will 2nd and 3rd chick still apply for generalist who just switched anyway?
paramfledge2_mid = c()
paramfledge3_special = c(-0.3,30,0.05)
# adult survival
functionalresponse(0.1,20,0.15)
alpha = 0.6; slope = 20; beta = 0.15
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "red")
mtext("% Adult Survival", side=3, line = 1)
#mtext("Proportion Prey Available", side=1, line = 3)
alpha = 0.3; slope = 20; beta = 0.15
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "blue")
legend("bottomright", legend = c("Specialist", "Generalist", "Middle"),
col = c("Black", "Red", "Blue"), lty = c(1,1,1), y.intersp=1)
param_adult_special = c(-0.1,20,0.15) # pretty good based on Piatt et al. 2007
param_adult_general = c(0.5,20,0.15)
param_adult_mid = c(0.25,20,0.15)
functionalresponse(0.1,10,0.3)
alpha = 0.6; slope = 10; beta = 0.3
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "red")
mtext("% Juvenile Survival", side=3, line = 1)
#mtext("Proportion Prey Available", side=1, line = 3)
alpha = 0.3; slope = 10; beta = 0.3
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "blue")
legend("bottomright", legend = c("Specialist", "Generalist", "Middle"),
col = c("Black", "Red", "Blue"), lty = c(1,1,1), y.intersp=1)
par(xpd = NA)
mtext(expression(paste('Relative Prey Availability (P'['y,s,l'],' / P'[0],')')),
side = 1, outer = TRUE, line = 1)
mtext(expression(paste('Demographic Rate Modifier (',delta['y,s,l'],')')), side = 2, outer = TRUE, line = 0, padj = 0)
param_juv_special = c(0,10,0.3) # need lots of prey or else only the strong survive
param_juv_general = c(0.5,10,0.3)
param_juv_mid = c(0.25,10,0.3)
# 1st vs. 2nd vs. 3rd chick
functionalresponse(-0.3,30,0.2) # have a max(0, yy) so will give 0 if yy goes negative
alpha = -0.3; slope = 30; beta = 0.15
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "red")
mtext("% Chick Fledge", side=2, line = 3)
mtext("Proportion Prey Available", side=1, line = 3)
alpha = -0.3; slope = 30; beta = 0.1
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "blue")
legend("bottomright", legend = c("1st chick", "2nd chick", "3rd chick"),
col = c("Black", "Red", "Blue"), lty = c(1,1,1), y.intersp=1)
functionalresponse(0.1,20,0.15)
alpha = 0.1; slope = 20; beta = 0.1
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "grey50")
alpha = 0.1; slope = 20; beta = 0.3
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "grey60")
alpha = 0.3; slope = 20; beta = 0.3
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "grey70")
alpha = 0.5; slope = 20; beta = 0.15
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "grey30")
######################### new plot 3/3/2020
functionalresponse <- function(alpha, slope, beta) {
xx <- seq(from=0,to=0.5,by=0.01)
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
plot(xx,yy,type='l', ylim = c(0,1), ylab = "", xlab = "", yaxt = "n", xaxt = "n")
#abline(v = 0.3, lty = 3)
axis(side = 1, at = c(0,0.1,0.2,0.3,0.4,0.5,1), las = 0)
axis(side = 2, at = c(0,0.2,0.4,0.6,0.8,1), las = 2)
return(cbind(yy,xx))
}
xx <- seq(from=0,to=0.5,by=0.01)
par(xpd = FALSE)
par(mfrow = c(2,1))
par(mar = c(2,2,1,1))
par(oma = c(3,4,1,1))
functionalresponse(0.2,30,0.3) #breeders specialist
alpha = 0.6; slope = 30; beta = 0.3
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "black", lty = 2)
#mtext(expression(paste("Rate Modifier (", delta, ")" )), side=2, line = 2.5)
#mtext("Proportion Prey Available", side=1, line = 3)
#breeder = expression(paste("Breeder attendance", gamma))
legend("bottomright",
legend = c("Specialist", "Generalist", expression(paste("Breeder attendance ",gamma)), "Adult survival", "Juvenile survival"),
col = c("grey", "grey","black", "red", "blue"), lty = c(1,2,1,1,1), y.intersp=1, bty = 'n')
alpha = 0.6; slope = 30; beta = 0.15
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "red", lty = 2)
alpha = 0.2; slope = 30; beta = 0.15
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "red", lty = 1)
alpha = 0.6; slope = 30; beta = 0.2
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "blue", lty = 2)
alpha = 0.2; slope = 30; beta = 0.2
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "blue", lty = 1)
text(0,0.95, label = "(A)", xpd = NA)
functionalresponse(-0.3,30,0.2) # have a max(0, yy) so will give 0 if yy goes negative
alpha = 0.51; slope = 15; beta = 0.1
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "black", lty = 2)
alpha = -0.3; slope = 30; beta = 0.15
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "red")
alpha = 0.41; slope = 15; beta = 0.05
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "red", lty = 2)
alpha = -0.3; slope = 30; beta = 0.1
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "blue")
alpha = 0.2; slope = 15; beta = 0
yy <- alpha + (1-alpha)/(1.0+exp(-slope*(xx-beta)))
lines(xx,yy, col = "blue", lty = 2)
legend("bottomright", legend = c("Fledge 1 of 3 chicks", "Fledge 1 of 2 chicks", "Fledge 1 of 1 chick"),
col = c("Black", "Red", "Blue"), lty = c(1,1,1), y.intersp=1, bty = 'n')
abline(v = 0.33, col = 'grey', lty = 4)
par(xpd = NA)
mtext(expression(paste('Relative Prey Availability (P'['y,s,l'],' / ',tilde(P)['l'],')')),
side = 1, outer = TRUE, line = 1)
mtext(expression(paste('Demographic Rate Modifier (',delta['y,s,l'],' or ',gamma['y,s,l'], ')')), side = 2, outer = TRUE, line = 1, padj = 0)
text(0,0.95, label = "(B)", xpd = NA)
# param_fledge_special = c(-0.3,30,0.2)
# # enough like 1/3 for the birds - i think so, and looks like Andre's
# param_fledge_general = c(0.51,15,0.1) # so at some lower survival, switch and survival goes back to 1?
#
# param_fledge2_special = c(-0.3,30,0.15)
# param_fledge2_general = c(0.41,15,0.05) # *will 2nd and 3rd chick still apply for generalist who just switched anyway?
#
# param_fledge3_special = c(-0.3,30,0.1)
# param_fledge3_general = c(0.2,15,0)
|
c56fe033019049ef5b80d3b8d65ceb552198014b | dde67e60fd78b0b30c68c3a877ee095aa931da55 | /man/scNGP.data.Rd | 09ed8c201948332f826898f32f2df60393633186 | [] | no_license | CenterForStatistics-UGent/PIMseq | 0ff61a203c806fa3ea65c15f097ef0500e8d800b | 31e0463a09b1eb56a3be018cb26fcfe072f180a7 | refs/heads/master | 2021-07-09T16:33:17.211694 | 2020-09-05T10:20:31 | 2020-09-05T10:20:31 | 191,369,264 | 2 | 1 | null | 2019-07-10T10:25:58 | 2019-06-11T12:41:45 | R | UTF-8 | R | false | true | 725 | rd | scNGP.data.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/NGPscData.R
\docType{data}
\name{scNGP.data}
\alias{scNGP.data}
\title{Neuroblastoma cell line single cell RNA-seq.}
\format{A SingleCellExperiment object}
\source{
\url{http://dx.doi.org/10.1101/430090}
}
\usage{
scNGP.data
}
\description{
Contains 83 cell line neuroblastoma cells (31 nutlin-3 treated and 52 controls). The data
is generated using SMARTer/C1 protocol.
}
\references{
Verboom, K., Everaert, C., Bolduc, N., Livak, K. J., Yigit, N., Rombaut, D., ... & Mestdagh, P. (2018). SMARTer single cell total RNA sequencing. BioRxiv, 430090.
\describe{
\item{SingleCellExperiment}{counts + gene info+cell infro}
}
}
\keyword{datasets}
|
13dfdf6070c787dc8ddeef5c77fe60876226f56a | d3f96c9ca845fc5b38ef9e7bd4c53b3f06beb49b | /man/system_check_requirements.Rd | 93c656b96b4da081160d5340395c3a5492d08ed5 | [
"BSD-3-Clause"
] | permissive | john-harrold/ubiquity | 92f08e4bebf0c4a17f61e2a96ae35d01ba47e5a2 | bb0532915b63f02f701148e5ae097222fef50a2d | refs/heads/master | 2023-08-16T17:43:52.698487 | 2023-08-16T01:24:48 | 2023-08-16T01:24:48 | 121,585,550 | 9 | 3 | null | null | null | null | UTF-8 | R | false | true | 684 | rd | system_check_requirements.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ubiquity.R
\name{system_check_requirements}
\alias{system_check_requirements}
\title{Check For Perl and C Tools}
\usage{
system_check_requirements(
checklist = list(perl = list(check = TRUE, perlcmd = "perl"), C = list(check = TRUE)),
verbose = TRUE
)
}
\arguments{
\item{checklist}{list with names corresponding to elements of the system to check.}
\item{verbose}{enable verbose messaging}
}
\value{
List fn result of all packages
}
\description{
Check the local installation for perl and verify C compiler is installed and working.
}
\examples{
\donttest{
invisible(system_check_requirements())
}
}
|
510fc5761191d4e059e6494071c4da75100d752a | 580484276bd35b209fb3bf1a8ad134226c9363da | /Code/Download_Files.R | ba6688f28a067514b98d67c763c6d11dcab7b98c | [] | no_license | jbrittain72/MSDS_6306_Case_Study_1 | 8d4ba5ceb1e0f711319d7c0712fb36e8fe8cd5fb | 240977dd29682b0af8c93f2c3e6cad7a484eeec4 | refs/heads/master | 2021-01-12T14:40:29.868900 | 2016-10-29T03:17:21 | 2016-10-29T03:17:21 | 72,046,599 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 846 | r | Download_Files.R | ############################################
## Download Case Study 1 Data Files
## Jim Brittain
## 2016-10-27
############################################
### This make file will creat a folder (if it doesn't already exist) and
### download the required files for the Case Study 1
# Create a subfolder to download data files to if not present
if (file.exists("Download_Files")){
setwd(file.path(MainDir, "Download_Files"))
} else {
dir.create(file.path(MainDir, "Download_Files"))
setwd(file.path(MainDir, "Download_Files"))
}
# Download files to Download folder
download.file(url="https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2FGDP.csv",
destfile="GDP.csv")
download.file(url="https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2FEDSTATS_Country.csv", destfile="EDSTATS_Country.csv")
|
8e0cef26e98b2eee337f7a55f1bc227b8d72a0bc | c2af459079b844373ffbb97c15afd93a785c107a | /textmining.R | a58c2b883b084d12133511736f4b24c44033753c | [] | no_license | TonyNdungu/Unsupervised_learning | 4af19516ef509a18a20c48f76b8df45d5bd33f29 | da030047114792860a6683df91c283cd7c9d2b67 | refs/heads/master | 2020-04-22T05:15:05.598461 | 2019-03-27T13:37:45 | 2019-03-27T13:37:45 | 170,153,302 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,516 | r | textmining.R | # Load libraries
library(twitteR)
# Twitter API Oauth process.
consumer_key <- 'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'
consumer_secret <- 'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'
access_token <- 'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'
access_secret <- 'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'
setup_twitter_oauth(consumer_key, consumer_secret, access_token, access_secret)
# retrieve the first 200 tweets from the timeline of @Rdatamining
rdmTweets <- userTimeline("rdatamining", n=200)
(nDocs <- length(rdmTweets))
# Have a look at the five tweets numbered 11 to 15
rdmTweets[11:15]
# Wrapping the tweets to fit the width of paper
for (i in 11:15) {
cat(paste("[[", i, "]] ", sep=""))
writeLines(strwrap(rdmTweets[[i]]$getText(), width=73))
}
#convert tweets to a data frame
df <- do.call("rbind", lapply(rdmTweets, as.data.frame))
dim(df)
# build a corpus, and specify the source to be character vectors
library(tm)
myCorpus <- Corpus(VectorSource(df$text))
# convert to lower case
myCorpus <- tm_map(myCorpus, tolower)
# remove punctuation
myCorpus <- tm_map(myCorpus, removePunctuation)
# remove numbers
myCorpus <- tm_map(myCorpus, removeNumbers)
# remove URLs
removeURL <- function(x) gsub("http[[:alnum:]]*", "", x)
myCorpus <- tm_map(myCorpus, removeURL)
# add two extra stop words: "available" and "via"
myStopwords <- c(stopwords('english'), "available", "via")
# remove "r" and "big" from stopwords
myStopwords <- setdiff(myStopwords, c("r", "big"))
# remove stopwords from corpus
myCorpus <- tm_map(myCorpus, removeWords, myStopwords)
library(SnowballC)
# keep a copy of corpus to use later as a dictionary for stem completion
myCorpusCopy <- myCorpus
# stem words
myCorpus <- tm_map(myCorpus, stemDocument)
# inspect documents (tweets) numbered 11 to 15
# inspect(myCorpus[11:15])
# The code below is used for to make text fit for paper width
for (i in 11:15) {
cat(paste("[[", i, "]] ", sep=""))
writeLines(strwrap(myCorpus[[i]], width=73))
}
# stem completion
myCorpus <- tm_map(myCorpus, stemCompletion, dictionary=myCorpusCopy)
#count frequency of "mining"
miningCases <- tm_map(myCorpusCopy, grep, pattern="\\<mining")
sum(unlist(miningCases))
# count frequency of "miners"
minerCases <- tm_map(myCorpusCopy, grep, pattern="\\<miners")
sum(unlist(minerCases))
# replace "miners" with "mining"
myCorpus <- tm_map(myCorpus, gsub, pattern="miners", replacement="mining")
# Build a term document matrix
myTdm <- TermDocumentMatrix(myCorpus, control=list(wordLengths=c(1,Inf)))
myTdm <- TermDocumentMatrix(myCorpus, control=list(minWordLength=1))
# inspect frequent words
findFreqTerms(myTdm, lowfreq=10)
termFrequency <- rowSums(as.matrix(myTdm))
termFrequency <- subset(termFrequency, termFrequency>=10)
library(ggplot2)
qplot(names(termFrequency), termFrequency, geom="bar", xlab="Terms") + coord_flip()
barplot(termFrequency, las=2)
# which words are associated with "r"?
findAssocs(myTdm, 'r', 0.25)
# which words are associated with "mining"?
findAssocs(myTdm, 'mining', 0.25)
library(wordcloud)
m <- as.matrix(myTdm)
# calculate the frequency of words and sort it descendingly by frequency
wordFreq <- sort(rowSums(m), decreasing=TRUE)
# word cloud
set.seed(375) # to make it reproducible
grayLevels <- gray( (wordFreq+10) / (max(wordFreq)+10) )
wordcloud(words=names(wordFreq), freq=wordFreq, min.freq=3, random.order=F,
colors=grayLevels)
# Clustering words
# remove sparse terms
myTdm2 <- removeSparseTerms(myTdm, sparse=0.95)
m2 <- as.matrix(myTdm2)
# cluster terms
distMatrix <- dist(scale(m2))
fit <- hclust(distMatrix, method="ward")
plot(fit)
# cut tree into 10 clusters
rect.hclust(fit, k=10)
(groups <- cutree(fit, k=10))
# change it to a Boolean matrix
termDocMatrix[termDocMatrix>=1] <- 1
# transform into a term-term adjacency matrix
termMatrix <- termDocMatrix %*% t(termDocMatrix)
# inspect terms numbered 5 to 10
termMatrix[5:10,5:10]
#============================================================
# transpose the matrix to cluster documents (tweets)
m3 <- t(m2)
# set a fixed random seed
set.seed(122)
# k-means clustering of tweets
k <- 8
kmeansResult <- kmeans(m3, k)
# cluster centers
round(kmeansResult$centers, digits=3)
#Check the top 3 words in every cluster
for (i in 1:k) {
cat(paste("cluster ", i, ": ", sep=""))
s <- sort(kmeansResult$centers[i,], decreasing=T)
cat(names(s)[1:3], "\n")
# print the tweets of every cluster
# print(rdmTweets[which(kmeansResult$cluster==i)])
}
# Clustering tweets with k-medoid algorithm
library(fpc)
# partitioning around medoids with estimation of number of clusters
pamResult <- pamk(m3, metric="manhattan")
# number of clusters identified
(k <- pamResult$nc)
pamResult <- pamResult$pamobject
# print cluster medoids
for (i in 1:k) {
cat(paste("cluster", i, ": "))
cat(colnames(pamResult$medoids)[which(pamResult$medoids[i,]==1)], "\n")
# print tweets in cluster i
# print(rdmTweets[pamResult$clustering==i])
}
# plot clustering result
layout(matrix(c(1,2),2,1)) # set to two graphs per page
plot(pamResult, color=F, labels=4, lines=0, cex=.8, col.clus=1,
col.p=pamResult$clustering)
layout(matrix(1)) # change back to one graph per page
pamResult2 <- pamk(m3, krange=2:8, metric="manhattan")
# LoadTerm document matrix
#
#Inspect part of the matrix
m2[5:10,1:20]
# change it to a Boolean matrix
m2[m2>=1] <- 1
# transform into a term-term adjacency matrix
m2 <- m2 %*% t(m2)
# inspect terms numbered 5 to 10
m2[5:10,5:10]
library(igraph)
# build a graph from the above matrix
g <- graph.adjacency(m2, weighted=T, mode="undirected")
# remove loops
g <- simplify(g)
# set labels and degrees of vertices
V(g)$label <- V(g)$name
V(g)$degree <- degree(g)
# Plot the network with layout
# set seed to make the layout reproducible
set.seed(3952)
layout1 <- layout.fruchterman.reingold(g)
plot(g, layout=layout1)
#Details about other layout options
plot(g, layout=layout.kamada.kawai)
tkplot(g, layout=layout.kamada.kawai)
V(g)$label.cex <- 2.2 * V(g)$degree / max(V(g)$degree)+ .2
V(g)$label.color <- rgb(0, 0, .2, .8)
V(g)$frame.color <- NA
egam <- (log(E(g)$weight)+.4) / max(log(E(g)$weight)+.4)
E(g)$color <- rgb(.5, .5, 0, egam)
E(g)$width <- egam
# plot the graph in layout1
plot(g, layout=layout1)
|
ea5847a3845b6cf23dafc7092bdf66c869b83b51 | 517f85614328e6facc3134dcbda0efffca9c4370 | /recode.R | 2527c1fbe4ae975002cf32afb30b2d56e206f153 | [] | no_license | fishforwish/fgc | 2dce9636aebaee4a73396de8a9dd52b7603b190d | 1cf825b1b35214fc7829816b64970d8c768738ac | refs/heads/master | 2023-03-17T19:14:45.927645 | 2023-03-09T10:27:26 | 2023-03-09T10:27:26 | 47,423,818 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,388 | r | recode.R | ## Recode all problematic DHS entries
print("####################### RTARGET ####################")
library(gdata)
library(dplyr)
## Reweight before subsetting.
Answers <- (Answers
## Reweight before subsetting
%>% mutate(sampleWeight = sampleWeight/sum(sampleWeight))
## subset: Don't want people who never heard of FGC (heardFGC and heardGC) and visitors
%>% filter((!is.na(heardFGC) & substr(heardFGC, 1, 3) == "Yes")
| (!is.na(heardGC) & substr(heardGC, 1, 3) == "Yes")
)
%>% filter(!is.na(visitorResident) & !grepl("Visitor", visitorResident))
)
# Ideally, I like to have a variable as daughterFGC (daughter's FGC status) which involves a few variables: numDaughterFgced (already cut; 95 and 0=none cut), and daughterToFgc. Those variables provides answers of yes (already cut), no (none cut), plan to cut, plan not to be cut, and don't know the plan yet. I like to make it a single outcome measurement with 6 levels: yes/to be cut, yes/not to be cut, no/to be cut, no/not to be cut, yes/don't know, no/don't know.
Answers <- (Answers
%>% mutate(daughterFgced = ifelse(numDaughterFgced == 95, "No", "Yes")
, daughterFgced = ifelse(numDaughterFgced == 0, "Yes", daughterFgced)
, daughterFgced = factor(daughterFgced)
, continueFgc = ifelse(continueFgc == "Don't know", "Depends", as.character(continueFgc))
, continueFgc = factor(continueFgc)
, daughterNotFgced = ifelse(daughterNotFgced == "Don't know", NA, as.character(daughterNotFgced))
, daughterNotFgced = factor(daughterNotFgced)
, CC = substring(survey, 1, 2)
, CC = as.factor(CC)
, recode = substring(survey, 3, 3)
, recode = as.numeric(recode)
, region = as.factor(paste(CC, region, sep="_"))
, clusterId = as.factor(paste(survey, clusterId, sep="_"))
, ethni = as.factor(paste(CC, ethni, sep="_"))
, religion = tableRecode(religion, "religion", maxCat=4)
, maritalStat = tableRecode(maritalStat, "partnership", maxCat=4)
, wealth = wealth/100000
, fgcstatus = fgc
, fgc = rightfactor(fgc)
, Education = edu
, edu = rightfactor(edu)
, edu = edu / mean(edu, na.rm = TRUE)
, Persist = contfgc(continueFgc)
, Persist01 = contfgc01(continueFgc)
, daughterPlan = yesnodkFactor(daughterToFgc)
, daughterPlan01 = yesnodk01(daughterToFgc)
, Religion = religion
, MaritalStatus = maritalStat
, Residence = urRural
, Job = job
)
)
# rdsave(Answers)
|
645ebb9516e6391075d90abc2b0a171a6e94d4a5 | 06772dd41870da689df082609992e032970bac12 | /R/plot.kora.samples.R | 785fe00116f079df144370dfc97c3f1abe272c5a | [] | no_license | TheSeoman/Scripts | 6c5ffa94d4c0e144a31f9f9e54ca324d90586ee0 | 3fb59b6ac7e24c6dba266d47ca7aeedbb2bb57c1 | refs/heads/master | 2021-05-15T15:00:56.679283 | 2018-04-11T18:03:39 | 2018-04-11T18:03:39 | 107,265,319 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,787 | r | plot.kora.samples.R | source('Scripts/R/paths.R')
require(gplots)
library(VennDiagram)
load(PATHS$METH.RESIDUALS.DATA)
load(PATHS$EXPR.RESIDUALS.DATA)
load(PATHS$SNP.SAMPLES.DATA)
covariates.all <- read.table(PATHS$F.COVARIATES, sep = ";", header = TRUE)
covariates.all$expr_s4f4ogtt <- as.character(covariates.all$expr_s4f4ogtt)
covariates.all$axio_s4f4 <- as.character(covariates.all$axio_s4f4)
covariates.all$meth_f4 <- as.character(covariates.all$meth_f4)
id.map <- covariates.all[as.character(covariates.all$expr_s4f4ogtt) %in% rownames(expr.residuals)
| as.character(covariates.all$axio_s4f4) %in% snp.samples
| covariates.all$meth_f4 %in% rownames(meth.residuals), ]
id.map <- id.map[order(id.map$expr_s4f4ogtt),]
get.covariate.overview <- function(covariates.map) {
overview <- data.frame(matrix(ncol = 4, nrow = 3))
rownames(overview) <- c('Age', 'BMI', 'WBC')
colnames(overview) <- c('Covariate', 'Minimum', 'Maximum', 'Mean')
overview[1, ] <- c('Age', min(covariates.map$utalteru), max(covariates.map$utalteru), format(mean(covariates.map$utalteru), digits = 4))
overview[2, ] <- c('BMI' ,min(na.omit(covariates.map$utbmi)), max(na.omit(covariates.map$utbmi)), format(mean(na.omit(covariates.map$utbmi)), digits = 4))
overview[3, ] <- c('WBC', min(na.omit(covariates.map$ul_wbc)), max(na.omit(covariates.map$ul_wbc)), format(mean(na.omit(covariates.map$ul_wbc)), digits = 3))
return(overview)
}
full.kora.overview <- get.covariate.overview(id.map)
select.id.map <- id.map[id.map$expr_s4f4ogtt %in% rownames(expr.residuals)
& id.map$axio_s4f4 %in% snp.samples
& id.map$meth_f4 %in% rownames(meth.residuals),]
select.kora.overview <- get.covariate.overview(select.id.map)
write.table(full.kora.overview, file = paste0(PATHS$TABLE.DIR, 'full.covariate.overview.tsv'),
quote = F, sep = '\t', row.names = F, col.names = T)
write.table(select.kora.overview, file = paste0(PATHS$TABLE.DIR, 'select.covariate.overview.tsv'),
quote = F, sep = '\t', row.names = F, col.names = T)
expr.count <- sum(id.map$expr_s4f4ogtt %in% rownames(expr.residuals))
snp.count <- sum(id.map$axio_s4f4 %in% snp.samples)
meth.count <- sum(id.map$meth_f4 %in% rownames(meth.residuals))
pdf(file = paste0(PATHS$PLOT.DIR, 'samples_venn.pdf'), width = 4.5, height = 2)
grid.newpage()
venn.plot <- draw.triple.venn(area1 = expr.count,
area2 = snp.count,
area3 = meth.count,
n12 = sum(id.map$expr_s4f4ogtt %in% rownames(expr.residuals) & id.map$axio_s4f4 %in% snp.samples),
n13 = sum(id.map$expr_s4f4ogtt %in% rownames(expr.residuals) & id.map$meth_f4 %in% rownames(meth.residuals)),
n23 = sum(id.map$axio_s4f4 %in% snp.samples & id.map$meth_f4 %in% rownames(meth.residuals)),
n123 = sum(id.map$expr_s4f4ogtt %in% rownames(expr.residuals) & id.map$axio_s4f4 %in% snp.samples & id.map$meth_f4 %in% rownames(meth.residuals)),
category = c(paste0('Expression (', expr.count, ')'),
paste0('Genotype (', snp.count, ')'),
paste0('Methylation (', meth.count, ')')),
fill = c('red', 'blue', 'yellow'),
cex = c(1),
cat.cex = c(1),
euler.d = T,
scaled = T,
cat.dist = c(0.07, 0.07, 0.04),
cat.pos = c(330, 30, 180))
dev.off()
grid.draw(venn.plot)
|
67660fe00164e929cde897920127c0de5e6f2bc2 | f89f43fe4f7fc6fec03716da4271679a605114a5 | /scripts/ggprob.R | d846f2446f337924547517c717f57ac9180b1170 | [] | no_license | DrSpecial/An-in-depth-analysis-about-how-the-NBA-has-changed-over-time | 4f941ffd482c13b0645b69fc1333859b3612cc72 | 0ee32e2248959569f5f4618b0858c8f0662c4adc | refs/heads/main | 2023-08-12T22:56:32.307323 | 2021-09-30T20:43:39 | 2021-09-30T20:43:39 | 412,182,960 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,396 | r | ggprob.R | require(tidyverse)
### binomial
gbinom_default_a = function(n, p, scale = FALSE)
{
a = ifelse(scale,
floor(n*p - 4*sqrt(n*p*(1-p))),0)
return (a)
}
gbinom_default_b = function(n, p, scale = FALSE)
{
b = ifelse(scale,
floor(n*p + 4*sqrt(n*p*(1-p))),n)
return (b)
}
geom_binom_density = function(n = 1, p = 0.5, scale = FALSE, a=NULL, b=NULL, color="blue", ...)
{
if ( is.null(a) )
{
a = gbinom_default_a(n, p, scale)
}
if ( is.null(b) )
{
b = gbinom_default_b(n, p, scale)
}
# make sure a and b are integers
a = round(a)
b = round(b)
# make sure a < b
if(a > b) {
temp = a
a = b
b = temp
}
# make sure a and b are in range
if(a < 0)
a = 0
if(b > n)
b = n
dat = tibble( x = seq(a,b,1),
xend = x,
y = dbinom(x,n,p),
yend = rep(0, length(y)))
geom_segment(aes(x = x, xend = xend, y = y, yend = yend), data = dat, color = color, ...)
}
gbinom = function(n,p,scale = FALSE,
a = gbinom_default_a(n,p,scale),
b = gbinom_default_b(n,p,scale),
color = "blue",
title = TRUE, ...) {
g = ggplot()
g = g +
geom_binom_density(n, p, scale, a, b, color, ...) +
xlab('x') +
ylab('Probability') +
geom_hline(yintercept=0)
if ( title )
{
g = g + ggtitle( paste("Binomial(",n,",",p,")") )
}
return( g )
}
### normal
geom_norm_density = function(mu=0,sigma=1,a=NULL,b=NULL,color="blue",...)
{
if ( is.null(a) )
{
a = qnorm(0.0001,mu,sigma)
}
if ( is.null(b) )
{
b = qnorm(0.9999,mu,sigma)
}
x = seq(a,b,length.out=1001)
df = data.frame(
x=x,
y=dnorm(x,mu,sigma)
)
geom_line(aes(x=x,y=y), data = df, color=color,...)
}
geom_norm_fill = function(mu=0,sigma=1,a=NULL,b=NULL,
fill="firebrick4",...)
{
if ( is.null(a) )
{
a = qnorm(0.0001,mu,sigma)
}
if ( is.null(b) )
{
b = qnorm(0.9999,mu,sigma)
}
x = seq(a,b,length.out=1001)
df = data.frame(
x=x,
ymin=rep(0,length(x)),
ymax = dnorm(x,mu,sigma)
)
geom_ribbon(aes(x=x,ymin=ymin,ymax=ymax,y=NULL), data = df, fill = fill, ...)
}
gnorm = function(mu=0,sigma=1,a=NULL,b=NULL,color="blue",
fill=NULL,title=TRUE,...)
{
g = ggplot()
if ( !is.null(fill) )
g = g + geom_norm_fill(mu,sigma,a,b,fill)
g = g +
geom_norm_density(mu,sigma,a,b,color,...) +
geom_hline(yintercept=0) +
ylab('density')
if ( title )
g = g +
ggtitle(paste("N(",mu,",",sigma,")"))
return ( g )
}
### chi-square
geom_chisq_null_a = function(df)
{
if ( df < 2 )
a = qchisq(0.05,df)
else
a = qchisq(0.0001,df)
return ( a )
}
geom_chisq_null_b = function(df)
{
if ( df < 2 )
b = qchisq(0.95,df)
else
b = qchisq(0.9999,df)
return ( b )
}
geom_chisq_density = function(df=1,a=NULL,b=NULL,color="blue",...)
{
if ( is.null(a) )
a = geom_chisq_null_a(df)
if ( is.null(b) )
b = geom_chisq_null_b(df)
x = seq(a,b,length.out=1001)
dat = data.frame(
x=x,
y=dchisq(x,df)
)
geom_line(data=dat,aes(x=x,y=y),color=color,...)
}
geom_chisq_fill = function(df=1,a=NULL,b=NULL,
fill="firebrick4",...)
{
if ( is.null(a) )
a = geom_chisq_null_a(df)
if ( is.null(b) )
b = geom_chisq_null_b(df)
x = seq(a,b,length.out=1001)
dat = data.frame(
x=x,
ymin=rep(0,length(x)),
ymax = dchisq(x,df)
)
geom_ribbon(data=dat,aes(x=x,ymin=ymin,ymax=ymax,y=NULL),fill=fill,...)
}
gchisq = function(df=1,a=NULL,b=NULL,color="blue",
fill=NULL,title=TRUE,...)
{
g = ggplot()
if ( !is.null(fill) )
g = g + geom_chisq_fill(df,a,b,fill)
g = g +
geom_chisq_density(df,a,b,color,...) +
geom_hline(yintercept=0) +
ylab('density')
if ( title )
g = g +
ggtitle(paste("Chi-square(",df,")"))
return ( g )
}
### t
geom_t_density = function(df=1,a=NULL,b=NULL,color="blue",...)
{
if ( is.null(a) )
a = qt(0.0001,df)
if ( is.null(b) )
b = qt(0.9999,df)
x = seq(a,b,length.out=1001)
dat = data.frame(
x=x,
y=dt(x,df)
)
geom_line(data=dat,aes(x=x,y=y),color=color,...)
}
geom_t_fill = function(df=1,a=NULL,b=NULL,
fill="firebrick4",...)
{
if ( is.null(a) )
a = qt(0.0001,df)
if ( is.null(b) )
b = qt(0.9999,df)
x = seq(a,b,length.out=1001)
dat = data.frame(
x=x,
ymin=rep(0,length(x)),
ymax = dt(x,df)
)
geom_ribbon(data=dat,aes(x=x,ymin=ymin,ymax=ymax,y=NULL),fill=fill,...)
}
gt = function(df=1,a=NULL,b=NULL,color="blue",
fill=NULL,title=TRUE,...)
{
g = ggplot()
if ( !is.null(fill) )
g = g + geom_t_fill(df,a,b,fill)
g = g +
geom_t_density(df,a,b,color,...) +
geom_hline(yintercept=0) +
ylab('density')
if ( title )
g = g +
ggtitle(paste("t(",round(df,3),")"))
return ( g )
}
### F
geom_f_density = function(df1=1,df2=1,a=NULL,b=NULL,color="blue",...)
{
if ( is.null(a) )
a = qf(0.0001,df1,df2)
if ( is.null(b) )
b = qf(0.9999,df1,df2)
x = seq(a,b,length.out=1001)
dat = data.frame(
x=x,
y=df(x,df1,df2)
)
geom_line(data=dat,aes(x=x,y=y),color=color,...)
}
geom_f_fill = function(d1f=1,df2=1,a=NULL,b=NULL,
fill="firebrick4",...)
{
if ( is.null(a) )
a = qf(0.0001,df1,df2)
if ( is.null(b) )
b = qf(0.9999,df1,df2)
x = seq(a,b,length.out=1001)
dat = data.frame(
x=x,
ymin=rep(0,length(x)),
ymax = df(x,df1,df2)
)
geom_ribbon(data=dat,aes(x=x,ymin=ymin,ymax=ymax,y=NULL),fill=fill,...)
}
gf = function(df1=1,df2=1,a=NULL,b=NULL,color="blue",
fill=NULL,title=TRUE,...)
{
g = ggplot()
if ( !is.null(fill) )
g = g + geom_f_fill(df1,df2,a,b,fill)
g = g +
geom_f_density(df1,df2,a,b,color,...) +
geom_hline(yintercept=0) +
ylab('density')
if ( title )
g = g +
ggtitle(paste("F(",df1,",",df2,")"))
return ( g )
}
### beta
geom_beta_null_a = function(alpha,beta)
{
if ( alpha >= 1)
a = 0
else
a = 0.01
return ( a )
}
geom_beta_null_b = function(alpha,beta)
{
if ( beta >= 1 )
b = 1
else
b = 0.99
return ( b )
}
geom_beta_density = function(alpha=1, beta=1, a=NULL, b=NULL, color="blue",...)
{
if ( is.null(a) )
a = geom_beta_null_a(alpha,beta)
if ( is.null(b) )
b = geom_beta_null_b(alpha,beta)
x = seq(a,b,length.out=1001)
dat = data.frame(
x=x,
y=dbeta(x,alpha,beta)
)
geom_line(data=dat,aes(x=x,y=y),color=color,...)
}
geom_beta_fill = function(alpha=1,beta=1,a=NULL,b=NULL,
fill="firebrick4",...)
{
if ( is.null(a) )
a = geom_beta_null_a(alpha,beta)
if ( is.null(b) )
b = geom_beta_null_b(alpha,beta)
x = seq(a,b,length.out=1001)
dat = data.frame(
x=x,
ymin=rep(0,length(x)),
ymax = dbeta(x,alpha,beta)
)
geom_ribbon(data=dat,aes(x=x,ymin=ymin,ymax=ymax,y=NULL),fill=fill,...)
}
gbeta = function(alpha=1,beta=1,a=NULL,b=NULL,color="blue",
fill=NULL,title=TRUE,...)
{
g = ggplot()
if ( !is.null(fill) )
g = g + geom_beta_fill(alpha,beta,a,b,fill)
g = g +
geom_beta_density(alpha,beta,a,b,color,...) +
geom_hline(yintercept=0) +
ylab('density')
if ( title )
g = g +
ggtitle(paste("Beta(",alpha,",",beta,")"))
return ( g )
}
|
da24ea459e701f25d004b3b41f605cb22bbdad2d | 499e407e5f3f16b7f5ed1905a9e150d41a8fe3ba | /workingfiles/ProjRShiny_my/global.R | a3e5af6ba4b5d85223d6b3cdbd152553789400ff | [] | no_license | cjy93/LTA_bus_analysis | 93b360052680551d101f2b64781fb11a0262e73b | 4293a2868500e7f566eef0b561aaf93075881f6b | refs/heads/master | 2022-05-27T18:21:05.073801 | 2020-05-02T12:30:17 | 2020-05-02T12:30:17 | 242,526,627 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,187 | r | global.R | ## Library packages
library(shiny)
library(dplyr)
library(tidyverse)
library(shinydashboard)
library(flows)
library(maptools)
library(st)
library(leaflet)
library(reshape2)
library(igraph)
library(ggraph)
library(tidygraph)
library(tmap)
library(flows)
library(sp)
library(RColorBrewer)
library(plotly)
library(ggthemes)
suppressWarnings(as.numeric(c("1", "2", "X")))
##################################################### Import data here #########################################################
# busstop volume
busstop_volume <- read.csv("data/passenger volume by busstop.csv")
colnames(busstop_volume)[5] = "BusStopCode"
busstop_volume$BusStopCode <- as.character(busstop_volume$BusStopCode)
# busstop information
busstops <- read.csv("data/busstop_lonlat_subzone_District.csv")%>%
dplyr::filter(planning_area != "Invalid")
busstops$subzone_name_my <- busstops$subzone_name
busstops$BusStopCode <- as.integer(busstops$BusStopCode)
busstops$BusStopCode <- as.character(busstops$BusStopCode)
busstops$planning_area <- as.character(busstops$planning_area)
busstops$planning_area[busstops$planning_area %in% c('Central Water Catchment', 'Mandai', 'Marina South', 'Museum', 'Newton', 'Orchard', 'Outram',
'Seletar', 'Rochor', 'Singapore River', 'Tanglin', 'Southern Islands', 'River Valley', 'Paya Lebar',
'Straits View', 'Tengah')] <- "Others"
#bus route
busroute <- read_csv('data/bus_route_overall.csv')
busroute$BusStopCode <- as.integer(busroute$BusStopCode)
busroute$BusStopCode <- as.character(busroute$BusStopCode)
busroute <- busroute[c('BusStopCode', 'Direction', 'Distance', 'ServiceNo', 'StopSequence')]
busroute <- busroute[busroute$BusStopCode %in% as.list(unique(busstops['BusStopCode']))[['BusStopCode']], ]
## Origin Destination data
##################################################### Mengyong Proportionate symbol map#########################################################
busstop_volume_lat_long_my <- dplyr::inner_join(busstop_volume, busstops, by ='BusStopCode')
location_my <- busstop_volume_lat_long_my %>%
dplyr::group_by(BusStopCode)%>%
dplyr::arrange(desc(BusStopCode))
location_my <- plyr::rename(location_my, c('Latitude'= 'lat' , 'Longitude'= 'lon' ))
#location_my$tap_in_out_radius <- (location_my$TOTAL_TAP_IN_VOLUME + location_my$TOTAL_TAP_OUT_VOLUME)**(1/2)/6
location_my <- location_my[c('planning_area', 'subzone_name_my', 'DAY_TYPE', 'TIME_PER_HOUR', 'BusStopCode', 'Description', 'RoadName', 'TOTAL_TAP_IN_VOLUME', 'TOTAL_TAP_OUT_VOLUME', 'lon', 'lat')]
location_my <- plyr::rename(location_my, c("DAY_TYPE" = "Day" , "TOTAL_TAP_IN_VOLUME"= "TapIns" , "TOTAL_TAP_OUT_VOLUME"= "TapOuts" ,"TIME_PER_HOUR"= "Time" , "planning_area"= "PlanningArea" ))
location_my <- location_my %>% dplyr::filter(Time >=6 & Time <= 23)
planning_area_list_my <-sort(unique(location_my$PlanningArea))
#pal <- colorNumeric(palette = "RdPu", domain = location_my$TapIns*2)
##################################################### Mengyong Centrality#########################################################
busroute_2 <- busroute
busroute_2['StopSequence'] = busroute_2['StopSequence']-1
busroute_2['BusStopCode_dest'] = busroute_2['BusStopCode']
busroute_2 <- busroute_2[c('BusStopCode_dest', 'Direction', 'ServiceNo', 'StopSequence')]
busstops_from_to <- dplyr::inner_join(busroute, busroute_2, by =c('StopSequence', 'ServiceNo', 'Direction'))
#join the two tables together
busroute_busstop <- dplyr::inner_join(busstops_from_to, busstops, by ='BusStopCode')
keeps <- c('BusStopCode', 'BusStopCode_dest')
busroute_busstop <- busroute_busstop[, keeps, drop = FALSE]
busroute_busstop <- plyr::rename(busroute_busstop, c("BusStopCode" = "from"))
busroute_busstop <- plyr::rename(busroute_busstop,c("BusStopCode_dest" = "to"))
#groupby
busroute_busstop_aggregated <- busroute_busstop %>%
#group_by(from, to, planning_area) %>%
dplyr::group_by(from, to) %>%
dplyr::summarise(Weight = n()) %>%
dplyr::filter(from!=to) %>%
dplyr::filter(Weight > 0) %>%
dplyr::ungroup()
busroute_busstop_aggregated$from <- as.character(busroute_busstop_aggregated$from)
busroute_busstop_aggregated$to <- as.character(busroute_busstop_aggregated$to)
#nodes
nodes_my <- busstops
nodes_my <- plyr::rename(nodes_my,c("BusStopCode" = "id" ))
nodes_my$id <- as.character(nodes_my$id)
#create graph structure
bus_graph <- tbl_graph(nodes = nodes_my, edges = busroute_busstop_aggregated, directed = TRUE)
#extract centrality
bus_graph=bus_graph%>%mutate(betweenness_centrality = centrality_betweenness(normalized = TRUE)) %>%mutate(closeness_centrality = centrality_closeness(normalized = TRUE)) %>%
dplyr::mutate(degree_centrality=centrality_degree(mode='out',normalized = TRUE))
bus_graph = bus_graph %>% dplyr::mutate(eigen_centrality=centrality_eigen(weight = bus_graph$betweenness_centrality, directed = TRUE, scale = FALSE))
#get edge table
plot_vector2<- as.data.frame(cbind(V(bus_graph)$Longitude,V(bus_graph)$Latitude,V(bus_graph)$betweenness_centrality,V(bus_graph)$closeness_centrality,
V(bus_graph)$eigen_centrality,V(bus_graph)$degree_centrality))
edgelist <- get.edgelist(bus_graph)
edgelist[,1]<-as.numeric(match(edgelist[,1],V(bus_graph)))
edgelist[,2]<-as.numeric(match(edgelist[,2],V(bus_graph)))
node1=data.frame(plot_vector2[edgelist[,1],])
node2=data.frame(plot_vector2[edgelist[,2],])
node3=data.frame(cbind(node1,node2))
edge_table <-
plyr::rename(node3,c("V1" = "long.f" , "V2" = "lat.f" , "V1.1" = "long.t" , "V2.1"= "lat.t" , "V3" = "between.f" , "V4"= "closeness.f","V5"= "eigen.f" , "V6" = "degree.f" ))
edge_table<- edge_table %>% dplyr::left_join(busstops, by =c("long.f"= "Longitude", "lat.f" = "Latitude"))
keeps <- c("long.f","lat.f","long.t","lat.t", "planning_area", 'subzone_name_my', "between.f", "closeness.f", "eigen.f","degree.f" )
edge_table <- edge_table[ , (names(edge_table) %in% keeps)]
#range01 <- function(x){(x-min(x))/(max(x)-min(x))}
range01 <- function(x) trunc(rank(x))/length(x)
edge_table$between.f <-range01(edge_table$between.f)
edge_table$closeness.f <-range01(edge_table$closeness.f)
edge_table$eigen.f <-range01(edge_table$eigen.f)
edge_table$degree.f <-range01(edge_table$degree.f)
# get node table
map_table <-
plyr::rename(plot_vector2,c("V1"="long.f" , "V2"="lat.f" , "V3"="between.f" , "V4"="closeness.f" , "V5"="eigen.f" ,"V6"= "degree.f" ))
map_table <- map_table %>% dplyr::left_join(busstops, by =c("long.f"= "Longitude", "lat.f" = "Latitude"))
map_table$between.f <-round(range01(map_table$between.f),3)
map_table$closeness.f <-round(range01(map_table$closeness.f),3)
map_table$eigen.f <-round(range01(map_table$eigen.f),3)
map_table$degree.f <-round(range01(map_table$degree.f),3)
#write.csv(map_table,"Path where you'd like to export the DataFrame\\File Name.csv", row.names = FALSE)
#get the radius of the bubbles
map_table$combined.f = (map_table$between.f*3+1)**(3/4) + (map_table$closeness.f*3+1)**(3/4) + (map_table$eigen.f*3+1)**(3/4) + (map_table$degree.f*3+1)**(3/4) |
7b3dd89a99c73e48fa30ca25d2a5d1764db51d2d | 0d0104d63657026b3c987e65ba07e0c87e8cb549 | /theApp/data_GSE148729_Calu3_totalRNA/deprecated_theApp.R | dd9b6d731f50f2eca63a5dc39dc8ec1107d554af | [] | no_license | EuancRNA/Pan-Coronavirus-Gene-Regulatory-Networks | 765b1fa09968622f7acdf0a4cec7707179279f62 | 99c956abaec9eb8432790f3c9365c69bbc0b1476 | refs/heads/master | 2022-07-20T05:35:36.165289 | 2020-05-23T23:31:42 | 2020-05-23T23:31:42 | 250,493,330 | 5 | 2 | null | 2020-05-21T04:18:18 | 2020-03-27T09:32:34 | R | UTF-8 | R | false | false | 3,362 | r | deprecated_theApp.R | library(ggplot2)
library(stringr)
library(ggplot2)
setwd("/home/zuhaib/Desktop/covid19Research/rnaSeqData/GSE148729/dataFiles/GSE148729_Calu3_totalRNA/dataLongFormat")
data <- read.table("../GSE148729_Calu3_totalRNA_readcounts.txt", header = T, sep = "\t")
fls <- list.files()[grep("GSE", list.files())]
datasets <- lapply(fls, function(x) {
read.table(x, header = T, sep = " ")
})
names(datasets) <- fls
names(datasets) <- str_replace_all(names(datasets), "\\.txt", "")
# Takes in the DE between time points of some gene, and returns x,y coordinates for the line
# as well as the color of the points based on whether it was significantly expressed.
# Note: Time points must be sorted
makeLine <- function(timePoints) {
minTimePoint <- timePoints[1,2]
retDF <- data.frame(x = c(minTimePoint, timePoints$T2),
y = c(0, cumsum(timePoints$log2FoldChange)),
sig = c("Significant", timePoints$Colour),
gene = timePoints[1,1])
return(retDF)
}
makePlot <- function(goi, doi) {
dataToPlot <- lapply(doi, function(y) {
ds <- datasets[[y]]
lst <- lapply(goi, function(x) {
return(ds[grep(x, ds[,1]),])
})
lns <- lapply(lst, function(x) {
return(makeLine(x))
})
xMax <- max(unlist(lapply(lns, function(x) return(x[,1])))) + 1
yMin <- min(unlist(lapply(lns, function(x) return(x[,2])))) - 1
yMax <- max(unlist(lapply(lns, function(x) return(x[,2])))) + length(lns)
return(list(Plot = lns, xMax = xMax, yMin = yMin, yMax = yMax, Name = y))
})
maxX <- max(unlist(lapply(dataToPlot, function(x) return(x$xMax))))
minY <- min(unlist(lapply(dataToPlot, function(x) return(x$yMin))))
maxY <- max(unlist(lapply(dataToPlot, function(x) return(x$yMax))))
# pointShift <- (maxY - minY) / length(goi)
# maxY <- maxY + 2
# print(maxY)
for (d in dataToPlot) {
vShift <- 0
plot(1, type="n", xlab="Time (h)", ylab="", xlim=c(-9, maxX), ylim=c(minY, maxY), main = d$Name)
for (i in d$Plot) {
lines(i$x,
i$y + vShift,
type = "b",
col = sapply(i$sig, function(x) if (x == "Significant") return("black") else return("yellow")),
cex = 2,
pch = 16)
text(-5, vShift, labels = i$gene)
vShift <- vShift + 1
}
}
}
# Old Code
# makePlot <- function(goi, doi) {
# lst <- lapply(goi, function(x) {
# return(datasets$GSE148729_Calu3_sarsCov2[grep(x, datasets$GSE148729_Calu3_sarsCov2[,1]),])
# })
# lns <- lapply(lst, function(x) {
# return(makeLine(x))
# })
# xMax <- max(unlist(lapply(lns, function(x) return(x[,1])))) + 1
# yMin <- min(unlist(lapply(lns, function(x) return(x[,2])))) - 1
# yMax <- max(unlist(lapply(lns, function(x) return(x[,2])))) + length(lns)
# vShift <- 0
# plot(1, type="n", xlab="", ylab="", xlim=c(-5, xMax), ylim=c(yMin, yMax))
# # rect(par("usr")[1], par("usr")[3], par("usr")[2], par("usr")[4], col =
# # "antiquewhite")
# for (i in lns) {
# lines(i$x,
# i$y + vShift,
# type = "b",
# col = sapply(i$sig, function(x) if (x == "Significant") return("black") else return("yellow")),
# cex = 2,
# pch = 16)
# text(-1, vShift, labels = i$gene)
# vShift <- vShift + 1
# }
# }
|
d617ed5e434791be3b97667d1a600cd94d5f83b1 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/diagram/examples/coordinates.Rd.R | 35dbc7a20f9e179ec0c19db2fa5c9b6c1d18faf6 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 557 | r | coordinates.Rd.R | library(diagram)
### Name: coordinates
### Title: coordinates of elements on a plot
### Aliases: coordinates
### Keywords: manip
### ** Examples
openplotmat(main = "coordinates")
text(coordinates(N = 6), lab = LETTERS[1:6], cex = 2)
text(coordinates(N = 8, relsize = 0.5), lab = letters[1:8], cex = 2)
openplotmat(main = "coordinates")
text(coordinates(pos = c(2, 4, 2)), lab = letters[1:8], cex = 2)
plot(0, type = "n", xlim = c(0, 5), ylim = c(2, 8), main = "coordinates")
text(coordinates(pos = c(2, 4, 3), hor = FALSE), lab = 1:9, cex = 2)
|
08463022f9668a4c5d416768f42a4931f48317e9 | e662def6f0876bdd2908d17601b0060e52d31577 | /R/RcppExports.R | 12816a383718ef71a10aea4e98a971fe0ebe094c | [] | no_license | XiangyuLuo/BLGGM | e2794e1c0016830b46e4d858780d74d8de3717d0 | 1b37a4e418f669207cd2e6a2171c243233c4c94d | refs/heads/main | 2023-07-05T18:02:59.844543 | 2021-08-09T08:35:16 | 2021-08-09T08:35:16 | 330,578,042 | 0 | 0 | null | 2021-01-18T06:43:10 | 2021-01-18T06:43:09 | null | UTF-8 | R | false | false | 1,000 | r | RcppExports.R | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
MCMC_full <- function(num_iter, num_save, theta_t, ind_zero, mu_t, invcov_t, cov_t, edge_t, group_t, lambda0_t, lambda1_t, pi_t, gam, G, N, K, ssp_v0, ssp_v1, ssp_l, ssp_pi, epsilon_theta = 0.2, num_step_theta = 20L, eta_mu = 0, tau_sq_mu = 1, lam0_0 = 2, lam1_0 = -2, sigma2_lam0 = 0.25, sigma2_lam1 = 0.25, epsilon_lam = 0.01, num_step_lam = 10L, iter_save = FALSE, n_threads = 1L, iter_print = 1000L, class_print = FALSE) {
.Call(`_BLGGM_MCMC_full`, num_iter, num_save, theta_t, ind_zero, mu_t, invcov_t, cov_t, edge_t, group_t, lambda0_t, lambda1_t, pi_t, gam, G, N, K, ssp_v0, ssp_v1, ssp_l, ssp_pi, epsilon_theta, num_step_theta, eta_mu, tau_sq_mu, lam0_0, lam1_0, sigma2_lam0, sigma2_lam1, epsilon_lam, num_step_lam, iter_save, n_threads, iter_print, class_print)
}
update_pi_R <- function(group_t, gam, K) {
.Call(`_BLGGM_update_pi_R`, group_t, gam, K)
}
|
a68bae85ce3cc017a47840d15f55880ea556e7b0 | c87f61d1f1e048d52ca528093dff47823d92cad6 | /R/wpmed-global.R | 39f37e0ac4c789907c27516a5ec1b3962994dc03 | [
"MIT"
] | permissive | nettrom/importance | 96629fd2ff67ad6100172aa5aaca63d4a386c9d7 | 6e74623f87862d29ce423769f723adb7ff3d0a20 | refs/heads/master | 2021-01-17T15:17:59.831166 | 2017-08-14T23:02:53 | 2017-08-14T23:02:53 | 84,104,642 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,265 | r | wpmed-global.R | ## Comparing the global classifier on the WPMED dataset, and vice versa.
## This involves a little bit of fiddling because our measure of proportions
## of inlinks from WPMED is not in the global dataset. But we'll get to that later…
## Global classifier on the WPMED dataset.
## This is the classifier we used on that dataset.
## Based on an earlier run, I use gamma=1.40, cost=16, which produced err=0.5065
## imp_svm = svm(rating ~ log_inlinks + log_views, data=training.set,
## cost=16, gamma=1.4, cross=10);
summary(imp_svm);
## Our current test set is the WPMED test set, but I decided to name the
## log_inlinks column differently, let's fix that
test.set[, log_inlinks := log_links];
wpmed[, log_inlinks := log_links];
svm_predictions = predict(object=imp_svm,
newdata=test.set);
test.set$global_pred = svm_predictions;
conf_svm = table(test.set$global_pred, test.set$rating);
confusionMatrix(conf_svm);
test.set[rating == "Top" & global_pred == 'High'];
test.set[rating == "High" & global_pred == 'Top'];
svm_predictions = predict(object=imp_svm,
newdata=wpmed);
wpmed$global_pred = svm_predictions;
conf_svm = table(wpmed$rating, wpmed$global_pred);
cf = confusionMatrix(conf_svm);
## Switch the test set to the unanimous one we previously used.
test.set = unanimous_dataset[is_training == 0];
## Add the n_local_dampened to the test.set with a default value of 1
## (which means no inlinks are from WPMED)
test.set[, n_local_dampened := 1.0];
## Let's see if any articles in this test set are in WPMED.
test.set[talk_page_id %in% wpmed$talk_page_id];
## We find 28 articles in WPMED, set their value to the one in the WPMED dataset.
for(page in test.set[talk_page_id %in% wpmed$talk_page_id]$talk_page_id) {
test.set[talk_page_id == page,
n_local_dampened := wpmed[talk_page_id == page]$n_local_dampened];
}
## Also need to rename log_inlinks to log_links
test.set[, log_links := log_inlinks];
## Looks like that worked, let's make some predictions!
svm_predictions = predict(object=imp_svm.kamps,
newdata=test.set);
test.set$kamps_pred = svm_predictions;
cf_global_kamps = confusionMatrix(table(test.set$rating, test.set$kamps_pred))
|
f69329b06d132356530f89820b0a8cf54ba1b1b9 | 42dedcc81d5dc9a61a79dbcea9bdd7363cad97be | /age+gender/analysis/01_resolution/C-ROIs_01-extract.R | b46c17a16735607bd9b82854af8dc9367a12df50 | [] | no_license | vishalmeeni/cwas-paper | 31f4bf36919bba6caf287eca2abd7b57f03d2c99 | 7d8fe59e68bc7c242f9b3cfcd1ebe6fe6918225c | refs/heads/master | 2020-04-05T18:32:46.641314 | 2015-09-02T18:45:10 | 2015-09-02T18:45:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,006 | r | C-ROIs_01-extract.R | # This script will extract the time-series from each of the ROI sets
###
# Setup
###
library(Rsge)
library(tools)
tmpdf <- read.csv("z_details.csv")
njobs <- nrow(tmpdf) # number of jobs = number of subjects
nthreads <- 1
nforks <- 100
rbase <- "/home2/data/Projects/CWAS/share/age+gender/analysis/01_resolution/rois"
mask_file <- file.path(rbase, "mask_4mm.nii.gz")
ks <- c(25,50,100,200,400,800,1600,3200,6400)
func_files <- as.character(read.table("z_funcpaths_4mm.txt")[,1])
####
## ROI Extraction (Derived)
####
#
#roi_files <- file.path(rbase, sprintf("rois_k%04i.nii.gz", ks))
#
#for (roi_file in roi_files) {
# cat(sprintf("ROI: %s\n", roi_file))
# roi_base <- file_path_sans_ext(file_path_sans_ext(basename(roi_file)))
# out_files <- sge.parLapply(func_files, function(func_file) {
# set_parallel_procs(1, 1)
# out_file <- file.path(dirname(func_file), paste(roi_base, ".nii.gz", sep=""))
# roi_mean_wrapper(func_file, roi_file, mask_file, out_file)
# return(out_file)
# }, packages=c("connectir"), function.savelist=ls(), njobs=njobs)
# out_files <- unlist(out_files)
# ofile <- paste("z_", roi_base, ".txt", sep="")
# write.table(out_files, file=ofile, row.names=F, col.names=F)
#}
###
# ROI Extraction (Random)
###
roi_files <- file.path(rbase, sprintf("rois_random_k%04i.nii.gz", ks))
for (roi_file in roi_files) {
cat(sprintf("ROI: %s\n", roi_file))
roi_base <- file_path_sans_ext(file_path_sans_ext(basename(roi_file)))
out_files <- sge.parLapply(func_files, function(func_file) {
set_parallel_procs(1, 1)
out_file <- file.path(dirname(func_file), paste(roi_base, ".nii.gz", sep=""))
roi_mean_wrapper(func_file, roi_file, mask_file, out_file)
return(out_file)
}, packages=c("connectir"), function.savelist=ls(), njobs=njobs)
out_files <- unlist(out_files)
ofile <- paste("z_", roi_base, ".txt", sep="")
write.table(out_files, file=ofile, row.names=F, col.names=F)
}
|
8ea68769e6b362a9c97804f253c1d914770a8112 | 11e7d531fcf7ea80b4c1a04fc6ab0e6efa8a7a7b | /man/abb2state.Rd | f0e5c1da8d2fdedd05eae9a194d9e5cee8187976 | [] | no_license | nbarsch/tfwsp | 81e5b82aa9b8f1ed0ed13b9e55457e4a565339f6 | 28e25f48c4d3180fa411f71dd7eef1382a33b153 | refs/heads/master | 2023-04-06T20:27:33.192381 | 2021-04-12T06:52:09 | 2021-04-12T06:52:09 | 287,382,128 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 312 | rd | abb2state.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/abb2state.R
\name{abb2state}
\alias{abb2state}
\title{abb2state()}
\usage{
abb2state(stateabb)
}
\arguments{
\item{stateabb}{state abb as two character abb i.e. "CA", "TX", or "NY"}
}
\description{
Get state from state abbreviation
}
|
43b571c21dd0bbb85e89cb550039ca967fd9c8be | ab26402079755c8eff8d4221267720dd44d89da0 | /R/S3Method.R | 0f3f5550b380148b0fa53ff6bd4b151698ce529e | [
"MIT"
] | permissive | haileibroad/SamplyzeR | 787f0f87332e4680bb2452e7929d9542ac2a0112 | fdd9fe66cfd50bdcfd87ed257e11c18f2eb46ca2 | refs/heads/master | 2021-05-15T12:35:45.215497 | 2017-10-26T18:02:26 | 2017-10-26T18:02:26 | 108,458,811 | 0 | 0 | null | 2017-10-26T19:53:21 | 2017-10-26T19:53:21 | null | UTF-8 | R | false | false | 1,744 | r | S3Method.R | #' Save an object to a specific format
#'
#' Save an object to tsv, RDS or excel format
#'
#' @param object
#'
#' @export
save <-function(object, ...) UseMethod('save')
#' Write sample dataset to tsv, RDS or excel files with S3 method save.
#'
#' @param object sample dataset
#' @param tsv path and output name of tsv file
#' @param RDS path and output name of RDS file
#' @param xls path and output name of excel file
#'
#' @export
save.sampleDataset <- function(object, RDS = NULL, tsv = NULL, xls = NULL) {
if (is.null(RDS) & is.null(tsv) & is.null(xls)) {
stop("Please specify at least one format (RDS, tsv or xls) for output")
}
if(!is.null(tsv)) {
write.table(object$df, file = tsv, sep = '\t', row.names = F, quote = F)
}
if (!is.null(RDS)) {
saveRDS(object, file = RDS)
}
if (!is.null(xls)) {
WriteXLS::WriteXLS(object$df, ExcelFileName = xls)
}
}
#' Update index of sample dataset by different annotation categories
#'
#' @param object sample dataset
#' @param by sort by which category
#' @return an sample dataset object with updated index
#'
#' @export
sort.sampleDataset <- function(object, by) {
byCol = which(names(object$df) == by)
object$df = object$df[order(object$df[ ,byCol], na.last = T),]
object$df$index = 1:dim(object$df)[1]
return(object)
}
#' Show dimensions of sample dataset object
#'
#' @param object sample data set object
#' @return a vector of rows and columns of the data frame of sample data set
#'
#' @export
dim.sampleDataset <- function(object){
dim(object$df)
}
#' @export
print.sampleDataset <- function(object) {
cat("\nClass: SampleDataset\n",
"Samples: ", dim(object)[1], "\n",
"Attributes:", attributes(object)$names, "\n\n"
)
}
|
2e73de4959f8d8fba9091ffd3fc1f52435d2bb06 | ebf6c0331b9d77a3b1ee3190d34be377a322000c | /R/pcrsim-package.r | 5d55ffd635b7060321f1bcfec6e28b8cceee6b0d | [] | no_license | cran/pcrsim | 64296cbaa1262d142987f40ebb6710c6cb144618 | dac5397ab9fa077915800be68c409459d180ee68 | refs/heads/master | 2021-01-17T11:20:07.490671 | 2017-03-17T23:30:37 | 2017-03-17T23:30:37 | 17,719,217 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,433 | r | pcrsim-package.r | ###############################################################################
#' Forensic DNA process simulator.
#'
#' PCRsim is a package to simulate the forensic DNA process. The function \code{pcrsim}
#' opens up a graphical user interface which allow the user to enter parameters
#' required for the simulation. Once calibrated the program can potentially
#' be used to: reduce the laboratory work needed to validate new STR kits,
#' create samples for educational purposes, help develop methods for
#' interpretation of DNA evidence, etc.
#'
#' This is a first version which is still experimental and under development.
#'
#' Areas in need of more research are better calibration and more correct
#' scaling to peak heights over a range of input amounts. The current
#' implementation is built to mimic the biological processes as closely as
#' possible and are not suitable for simulation of large number of samples
#' due to performance.
#'
#' @title Simulation of the Forensic DNA process
#' @docType package
#' @name pcrsim-package
#' @author Oskar Hansson \email{oskar.hansson@@fhi.no}
#' @keywords package
#' @references Gill, Peter, James Curran, and Keith Elliot.
#' \\u0022 A Graphical Simulation Model of the Entire DNA Process Associated with
#' the Analysis of Short Tandem Repeat Loci\\u0022
#' Nucleic Acids Research 33, no. 2 (2005): 632-643. doi:10.1093/nar/gki205.
#'
NULL |
070a7e957b1f852773afa4dd8ead3165cd97f420 | 70b973af1466108afbb476e62e5672b1fb495f94 | /seir-model/plotlib.R | 3afdc8d98104229bd9bf90897ab74aea5140b026 | [] | no_license | openmodels/coronaclimate | dbac851a4a7b4d51891f69c7e9e43d691e34ecf1 | 2909520af378cc9b38db09c295f4c451a3be2c00 | refs/heads/master | 2022-11-07T07:19:59.006186 | 2022-11-03T01:58:19 | 2022-11-03T01:58:19 | 249,415,990 | 0 | 0 | null | 2020-03-30T19:39:06 | 2020-03-23T11:46:24 | Python | UTF-8 | R | false | false | 2,159 | r | plotlib.R | labelmap <- list('mobility_slope'="Mobility Adjustment",
'alpha'="Gradual Adjustment Rate",
'invsigma'="Incubation Period (days)",
'invgamma'="Infectious Period (days)",
'invkappa'="Pre-Testing Period (days)",
'invtheta'="Reporting Delay (days)",
'logbeta'="Log Transmission Rate",
'omega'="Realised Reporting Rate",
'deathrate'="Death Rate",
'deathomegaplus'="Extra Record of Deaths",
'deathlearning'="Death Learning Rate",
'portion_early'="Portion Reported Early",
'e.t2m'="Air Temperature Trans.",
'e.tp'="Total Precipitation Trans.",
'e.r'="Relative Humidity Trans.",
'e.absh'="Absolute Humidity Trans.",
'e.ssrd'="Solar Radiation Trans.",
'e.utci'="Thermal Discomfort Trans.",
'o.t2m'="Air Temperature Detect",
'o.tp'="Total Precipitation Detect",
'o.r'="Relative Humidity Detect",
'o.absh'="Absolute Humidity Detect",
'o.ssrd'="Solar Radiation Detect",
'o.utci'="Thermal Discomfort Detect",
'error'="Model Error",
'logomega'="Log Reporting Rate",
'eein'="Exposed Imports",
'rhat'="Bayesian convergence")
paramorder <- c('alpha', 'invgamma', 'invsigma', 'invkappa', 'invtheta',
'mobility_slope', 'omega', 'portion_early',
'deathrate', 'deathomegaplus', 'deathlearning',
'logbeta', 'logomega', 'eein',
'e.absh', 'e.r', 'e.t2m', 'e.tp', 'e.ssrd', 'e.utci',
'o.absh', 'o.r', 'o.t2m', 'o.tp', 'o.ssrd', 'o.utci', 'error', 'rhat')
## Prefer to use this, since we've messed up with factor params before
get.param.labels <- function(params) {
param.labels <- sapply(params, function(param) labelmap[[as.character(param)]])
factor(param.labels, levels=rev(sapply(paramorder, function(param) labelmap[[param]])))
}
|
bd6bcf5e4a687a9f42d878f4e7efbfeb7a8f9694 | 1eb7487a6380572f6fbece1498acfa507f9a0662 | /R/co_methylation_step1.R | 083077fd5f4070312001c8cfa9f745cc735e03bc | [] | no_license | Gavin-Yinld/coMethy | 41c82f2a8f41183e82a2f91727bc675b6d073986 | 66745fc4f20141517c889fd2b57e720240cf4053 | refs/heads/master | 2020-05-17T07:35:55.236667 | 2019-08-23T09:16:01 | 2019-08-23T09:16:01 | 183,583,856 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,557 | r | co_methylation_step1.R | co_methylation_step1 <- function(csm_ml_matrix){
options(stringsAsFactors=F)
set.seed(123)
data <- as.matrix(csm_ml_matrix)
km.res <- kmeans(data, 3, nstart = 25)
pdf("parameter.pdf")
par(mfrow = c(3,2),mar=c(3,5,2,2));
for(i in 1:3)
{
require(WGCNA)
wgcna.data <- t(data[which(km.res$cluster==i),])
powers = c(c(1:10), seq(from = 12, to=30, by=2))
###Call the network topology analysis function
sft = pickSoftThreshold(wgcna.data, powerVector = powers, verbose = 5,networkType="signed")
####Plot the results:
cex1 = 0.9;
#######Scale-free topology fit index as a function of the soft-thresholding power
plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
xlab="Soft Threshold (power)",ylab="Scale Free Topology \n Model Fit,signed R^2",type="n",
main = paste("Scale independence"));
text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
labels=powers,cex=cex1,col="red");
############this line corresponds to using an R^2 cut-off of h
abline(h=0.80,col="red")
#######Mean connectivity as a function of the soft-thresholding power
plot(sft$fitIndices[,1], sft$fitIndices[,5],
xlab="Soft Threshold (power)",ylab="Mean Connectivity", type="n",
main = paste("Mean connectivity"))
text(sft$fitIndices[,1], sft$fitIndices[,5], labels=powers, cex=cex1,col="red")
#save(sft,file=paste0("kmeans_",i,"sft.Rdata"))
}
dev.off()
return(km.res)
}
###########################################################################
|
b33b3621e08d392ce59ec2092874d74ea9504116 | 30c5ed7d7cd44195dd4ce0d61a607579563f4133 | /man/setcover.Rd | f9b5c42b7c2b873586ed7cdfb1af037708d31eb9 | [] | no_license | cran/adagio | f08103c03dc491c09b2639e94a35339d90f5f36b | 8b46ee3aac3d6e84eb153209c5e9399b09808126 | refs/heads/master | 2022-11-02T19:54:59.661507 | 2022-10-03T12:40:02 | 2022-10-03T12:40:02 | 17,694,245 | 4 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,438 | rd | setcover.Rd | \name{setcover}
\alias{setcover}
\title{
Set cover problem
}
\description{
Solves the Set Cover problem as an integer linear program.
}
\usage{
setcover(Sets, weights)
}
\arguments{
\item{Sets}{matrix of 0s and 1s, each line defining a subset.}
\item{weights}{numerical weights for each subset.}
}
\details{
The Set Cover problems attempts to find in subsets (of a 'universe')
a minimal set of subsets that still covers the whole set.
Each line of the matrix \code{Sets} defines a characteristic function of
a subset. It is required that each element of the universe is contained
in at least one of these subsets.
The problem is treated as an Integer Linear Program (ILP) and solved
with the \code{lp} solver in \code{lpSolve}.
}
\value{
Returns a list with components \code{sets}, giving the indices of subsets,
and \code{objective}, the sum of weights of subsets present in the solution.
}
\references{
See the Wikipedia article on the "set cover problem".
}
\seealso{
\code{\link{knapsack}}
}
\examples{
# Define 12 subsets of universe {1, ..., 10}.
set.seed(7*11*13)
A <- matrix(sample(c(0,1), prob = c(0.8,0.2), size = 120, replace =TRUE),
nrow = 12, ncol = 10)
sol <- setcover(Sets = A, weights = rep(1, 12))
sol
## $sets
## [1] 1 2 9 12
## $no.sets
##[1] 4
# all universe elements are covered:
colSums(A[sol$sets, ])
## [1] 1 1 2 1 1 1 2 1 1 2
}
\keyword{ discrete-optimization }
|
7ca8a71d110d861361d5e289786c2fd7e9c73c5f | 3b62ffa02efef29b8bbaa9041d74a1ee72b4807a | /R/rhrIsopleths.R | 7b4dbe2216e872e5a1be144a96245f200f94d6d6 | [] | no_license | jmsigner/rhr | 52bdb94af6a02c7b10408a1dce549aff4d100709 | 7b8d1b2dbf984082aa543fe54b1fef31a7853995 | refs/heads/master | 2021-01-17T09:42:32.243262 | 2020-06-22T14:24:00 | 2020-06-22T14:24:00 | 24,332,931 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 582 | r | rhrIsopleths.R | ##' Isopleths of Home Range Estimate
##'
##' Function to retrieve isopleths of a home range estimate.
##'
##' Probabilistic estimators take (i.e. kernel density estimates) take
##' an additional argument, \code{levels}, that determines which isopleth are
##' returned.
##'
##' @template RhrEst
##' @param ... see details.
##' @return \code{SpatialPolygonsDataFrame}
##' @export
rhrIsopleths <- function (x, ...) {
UseMethod ("rhrIsopleths", x)
}
##' @export
rhrIsopleths.default <- function (x , ...) {
paste0 ("rhrIsopleths is not defined for object of class", class(x))
}
|
f90cd9a6f7cab7fee34ea4c92c27cfb67cd38b5c | 633be0e519a645e5828993070ec60192ec29ad46 | /DMC-MBN18/dmc/models/LNR-SS/dists.R | 035e4aaa656487a38e9ca656c7ff8f7bed5f53be | [] | no_license | StevenM1/summerschool_mbn_2018 | 5200f81a34d3025dee1ff8c9137a115585e39373 | b8f0e0f37b606b6bb7a161b7692f3f58455bf4dc | refs/heads/master | 2020-03-24T11:56:12.571909 | 2018-08-01T14:35:04 | 2018-08-01T14:35:04 | 142,698,961 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 19,315 | r | dists.R | ####################### LNR stop signal
####################### LNR n-choice ----
rlnr <- function (n, meanlog, sdlog, t0, st0 = 0)
# Race among n_acc accumulators, mealnlog and sdlog can be n_acc length
# vectors or n_acc x n matrices. t0 can be
# a) a scalar, b) a vector of length number of accumulators or
# c) a matrix with 1 row per accumulator, when start times differ on each trial
# st0, range of non-decison time variability, must be a scalar, as the same
# variability is assumed in a common encoding/production stage
{
n_acc <- ifelse(is.null(dim(meanlog)),length(meanlog),dim(meanlog)[1])
dt <- matrix(rlnorm(n = n*n_acc, meanlog = meanlog, sdlog = sdlog),
nrow = n_acc) + t0
winner <- apply(dt,2,which.min)
if (st0[1]==0) data.frame(RT=dt[cbind(winner,1:n)],R=winner) else
data.frame(RT=dt[cbind(winner,1:n)]+runif(n,0,st0[1]),R=winner)
}
n1PDFfixedt0.lnr=function(dt,meanlog,sdlog)
# Generates defective PDF for responses first among n_acc accumulator at
# dt (decison time), a matrix with one row for each accumulator (allowing for
# different start times per accumulator)
{
n_acc <- ifelse(is.null(dim(meanlog)),length(meanlog),dim(meanlog)[1])
if (!is.matrix(meanlog)) meanlog <- matrix(rep(meanlog,dim(dt)[2]),nrow=n_acc)
if (!is.matrix(sdlog)) sdlog <- matrix(rep( sdlog,dim(dt)[2]),nrow=n_acc)
# winner
dt[1,] <- dlnorm(dt[1,],meanlog[1,],sdlog[1,])
# loosers
if (dim(meanlog)[1]>1) for (i in 2:dim(meanlog)[1])
dt[1,] <- dt[1,]*plnorm(dt[i,],meanlog[i,],sdlog[i,],lower.tail=FALSE)
dt[1,]
}
####################### LNR stop signal ----
# NB1: no st0
# NB2: TRIALS effect on meanlog
rlnrss <- function (n, meanlog, sdlog, t0, t0sg, tf=0, gf=0, ts = 0,
SSD=Inf, TRIALS = NA, staircase=NA)
# Race among length(meanlog) accumulators (if meanlog a vector),
# or dim(meanlog)[1] (if a matrix), first of which is a stop accumulator.
# Acts the same as rlnr except NA returned for RT when winner = 1.
# Optional SSD argument can be used to adjust start time for first
# accumulator. SSD can be a scalar or vector length n; output has an SSD column
# For trials with winning first accumulator RT and R set to NA.
# tf = trigger failure probability, gf = go failure probability
# If any !is.na in staircase runs a staircase
# t0 is a scalar with the standard interpritaiton for GO accumulators.
# Definition of t0sg (a non-negative scalar) depends on whether response
# production time is assumed to be ballistic or not.
# If it is ballistic t0sg = stop encoding - go encoding time + t0
# If if it is NOT ballistic, t0sg = stop encoding
# IN BOTH CASES t0sg < 0 IS NOT ALLOWED
# ts = slope of slowing (speeding if negative) over TRIALS, meanlog - ts*TRIALS
# This has a linear effect on mean and sd
{
if ( t0sg < 0 ) stop("t0sg cannot be less than zero")
if ( length(SSD)==1 ) SSD <- rep(SSD,n)
if ( any(is.na(SSD)) || length(SSD) != n )
stop("SSD cannot have NAs and must be a scalar or same length as n")
n_acc <- ifelse(is.null(dim(meanlog)),length(meanlog),dim(meanlog)[1])
# t0sg -> sg for stop accumulator, relative to 0 for go accumulators
t0S <- matrix(rep(c(t0sg-t0[1],rep(0,n_acc-1)),length.out=n*n_acc),nrow=n_acc)
if (!is.matrix(meanlog)) meanlog <- matrix(rep(meanlog,n),nrow=n_acc)
if (!is.matrix(sdlog)) sdlog <- matrix(rep(sdlog,n),nrow=n_acc)
if ( !any(is.na(TRIALS)) ) {
if (length(TRIALS)!=n)
stop("TRIALS must have length n")
meanlog[-1,] <- meanlog[-1,] + rep(ts*TRIALS,each=n_acc-1)
}
if ( gf > 0 ) # Setup for GO failure
is.gf <- as.logical(rbinom(length(SSD),1,gf)) else
is.gf <- logical(length(SSD))
if ( all(!is.finite(SSD)) ) { # ALL GO
out <- rlnr(n,meanlog[-1,,drop=FALSE],sdlog[-1,,drop=FALSE],t0)
out$R <- out$R+1
} else { # SOME STOP
if ( any(is.na(staircase)) ) { # STOP fixed SSD
# add SSD to stop accumulator
t0S[1,] <- t0S[1,] + SSD
out <- rlnr(n,meanlog,sdlog,t0S)
if ( tf>0 ) {
is.tf <- logical(length(SSD))
is.tf[is.finite(SSD)][as.logical(rbinom(sum(is.finite(SSD)),1,tf))] <- TRUE
if ( any(is.tf) ) {
out[is.tf,] <- rlnr(sum(is.tf),meanlog[-1,is.tf,drop=FALSE],
sdlog[-1,is.tf,drop=FALSE],t0=0)
out[is.tf,"R"] <- out[is.tf,"R"]+1
}
}
} else { # STOP, staircase
if ( !is.numeric(staircase) | length(staircase)!=1 )
stop("Staircase must be a numeric vector of length 1 specifying the step.")
SSDi <- SSD[is.finite(SSD)][1] # begining SSD
dt <- matrix(rlnorm(n = n*n_acc, meanlog = meanlog, sdlog = sdlog),
nrow = n_acc) + t0S
# Setup
winner <- numeric(n)
for ( i in c(1:n) ) {
if ( !is.finite(SSD[i]) ) # not staircase
dt[1,i] <- dt[1,i] + SSD[i] else
dt[1,i] <- dt[1,i] + SSDi # staircase
if ( runif(1)<tf ) # Trigger failure
winner[i] <- which.min(dt[2:n_acc,i])+1 else
winner[i] <- which.min(dt[,i])
if (is.gf[i]) winner[i] <- 1
if ( is.finite(SSD[i]) ) { # update staircase
SSD[i] <- SSDi
if ( winner[i]==1 )
SSDi <- SSDi + staircase else
SSDi <- SSDi - staircase
if (SSDi<1e-10) SSDi <- 0
}
}
out <- data.frame(RT=dt[cbind(winner,1:n)],R=winner)
}
out$RT <- out$RT + t0 # Add t0 for go responses
}
out[out$R==1,"RT"] <- NA
if (gf > 0) {
out$RT[is.gf] <- NA
out$R[is.gf] <- 1
}
if ( any(is.na(TRIALS)) ) cbind.data.frame(out,SSD=SSD) else
cbind.data.frame(out,SSD=SSD,TRIALS=TRIALS)
}
n1PDF.lnrss <- function(rt,meanlog,sdlog,t0,t0sg,tf=0,gf=0,ts=0,
SSD=Inf,TRIALS=NA,Si)
# Same as n1PDF.lnr except SSD is either a scalar or vector of length(rt)
# stop accumulator must have name "NR". SSD is subtracted stop accumulator time
# and dt=NA done by integration.
#
# tf= probabiliy of trigger failure, where
# L = trigger fail & respond + trigger and respond + trigger and no-response
# = tf*L(N-1)+(1-tf)[L(N)+p(S)],
# L(N-1) = choice race likelihood (no stop accumulator),
# L(N) = full N unit race likelihood given they did respond,
# p(S) probability of stop winning
#
# gf = probabiliy of go failure.
# On go trials: L = go fail (so no response) + go and L above
# L = gf + (1-gf)*[tf*L(N-1)+(1-tf)[L(N)+p(S)]] or similarly
# L = [ p(non-response) ] + [ p(response) ]
# = [ gf + (1-gf)(1-tf)p(S) ] + [ (1-gf){(tf*Ln(n-1) + (1-tf)*L(N))} ]
#
# NB:rt is NOT decision time, but rather full RT as t0 has to be passed
# in order to include properly in cases where RT is NA (i.e., sucessful stop)
#
# Definition of t0sg (a non-negative scalar) depends on whether response
# production time is assumed to be ballistic or not.
# If it is ballistic t0sg = stop encoding - go encoding time + t0
# If if it is NOT ballistic, t0sg = stop encoding
# IN BOTH CASES t0sg < 0 IS NOT ALLOWED (zero likelihood returned)
{
stopfn <- function(t,meanlogj,sdlogj,t0,t0sg,SSD,Si)
{
# d = s-g = tD-t0(GO), then add SSDstart time to get
# start time go - start time stop, i.e., finishing time advantage for GO
t0S <- rep(0,length(meanlogj)) # Set relative to stop accumulator finish time
t0S[-Si] <- t0S[-Si]+t0sg-t0+SSD
# subtracting min(t0S) keeps all times positive
dt <- matrix(rep(t,each=length(meanlogj)),nrow=length(meanlogj))+t0S-min(t0S)
i <- c(Si,c(1:length(meanlogj))[-Si])
n1PDFfixedt0.lnr(dt[i,,drop=FALSE],meanlogj[i],sdlogj[i])
}
# NOTE: t0 is not subtracted when making dt but passed to handle RT=NA case
# Bad t0sg
if (t0sg<0) return(rep(0,length(rt)))
if ( length(SSD)==1 ) SSD <- rep(SSD,length(rt))
if (length(SSD) != length(rt))
stop("SSD must be a scalar or same length as rt")
n_acc <- ifelse(is.null(dim(meanlog)),length(meanlog),dim(meanlog)[1])
rt <- matrix(rep(rt,each=n_acc),nrow=n_acc)
is.stop <- is.na(rt[1,])
if (!is.matrix(meanlog)) meanlog <- matrix(rep(meanlog,dim(rt)[2]),nrow=n_acc)
if (!is.matrix(sdlog)) sdlog <- matrix(rep(sdlog,dim(rt)[2]),nrow=n_acc)
if ( any(is.na(TRIALS)) | ts == 0 ) {
p <- SSD[is.stop]
pj <- c(1:sum(is.stop))[!duplicated(p)] # index of unique SSD
} else {
meanlog[-Si,] <- meanlog[-Si,] + ts*TRIALS
p <- apply(
rbind(meanlog[,is.stop,drop=FALSE],SSD[is.stop]),
2,paste,collapse="")
pj <- c(1:sum(is.stop))[!duplicated(p)] # index of unique p and SSD
}
if ( any(!is.stop) )
{
rt[Si,!is.stop] <- rt[Si,!is.stop] - t0sg - SSD[!is.stop]
rt[-Si,!is.stop] <- rt[-Si,!is.stop]-t0
if ( tf > 0 )
{
rt[1,!is.stop] <- (1-gf)*(
tf*n1PDFfixedt0.lnr(rt[-Si,!is.stop,drop=FALSE],
meanlog[-Si,!is.stop,drop=FALSE],sdlog[-Si,!is.stop,drop=FALSE]) +
(1-tf)*n1PDFfixedt0.lnr(rt[,!is.stop,drop=FALSE],
meanlog[,!is.stop,drop=FALSE],sdlog[,!is.stop,drop=FALSE])
)
} else
rt[1,!is.stop] <- (1-gf)*n1PDFfixedt0.lnr(rt[,!is.stop,drop=FALSE],
meanlog[,!is.stop,drop=FALSE],sdlog[,!is.stop,drop=FALSE])
}
if ( any(is.stop) ) for (j in pj) {
# tmp <- ifelse(!is.finite(SSD[j]),0,
# try(integrate(f=stopfn,lower=0,upper=Inf,meanlogj=meanlog[,j],
# sdlogj=sdlog[,j],t0=t0,t0sg=t0sg,SSD=SSD[j],Si=Si,
# NoBallistic = FALSE)$value,silent=TRUE))
# if (!is.numeric(tmp)) tmp <- 0
if ( !is.finite(SSD[j]) ) tmp <- 0 else
tmp <- my.integrate(f=stopfn,lower=0,meanlogj=meanlog[,j],
sdlogj=sdlog[,j],t0=t0,t0sg=t0sg,SSD=SSD[j],Si=Si)
rt[1,is.stop][p %in% p[j]] <- gf +(1-gf)*(1-tf)*tmp
}
rt[1,]
}
# VERY EXTENSIVE TESTING WITH Two different SSDs
{
# # ########### TWO ACCUMULATOR CASE
#
# n=1e5
# meanlog=c(.75,.75); sdlog=c(.5,1)
# SSD = rep(c(1,10)/10,each=n/2)
#
# # Run one of the follwing two lines
# do.trials=FALSE
# do.trials = TRUE # requires very differnet plotting check, can be SLOW!
#
# #### RUN ONE OF THE FOLLOWING THREE LINES, all assume .2s go Ter
# t0=.2; t0sg= 0 # minimum possible value of t0sg, stop encoding .2 less than go
# t0=.2; t0sg=.2 # equal stop and go enconding times
# t0=.2; t0sg=.4 # stop .2 slower than go
#
# ### RUN ONE OF THE FOLLOWING FOUR LINES
# # Without trigger failure or go failure
# tf=0; gf=0
# # With trigger failure, no go failure
# tf=.1;gf=0
# # Without trigger failure, with go failure
# tf=0; gf=.1
# # With trigger failure and go failure
# tf=.1;gf=.1
#
# if (do.trials) {
# ts=.5; TRIALS=log10(seq(1,10,length.out=n)) # 1..10 natural so 0-1 on log
# TRIALS <- as.vector(t(matrix(TRIALS,nrow=2))) # interleave SSDs
# # Plot slowing in GO (usually nice and linear, up to smooting overfitting)
# sim.go <- rlnrss(n=n,meanlog,sdlog,t0,t0sg,tf=tf,gf=gf,TRIALS=TRIALS,ts=ts)
# is.in <- !is.na(sim.go$RT) # in case go failure
# plot(smooth.spline(TRIALS[is.in],sim.go$RT[is.in]),ylab="Smooth",xlab="TRIALS",type="l")
# } else {TRIALS=NA;ts=0}
#
# # Simulate stop trials
# sim <- rlnrss(n=n,meanlog,sdlog,t0,t0sg,SSD=SSD,tf=tf,gf=gf,TRIALS=TRIALS,ts=ts)
#
# # Plot densities
# par(mfrow=c(1,2))
# dns1 <- plot.cell.density(sim[sim$SSD==.1,],xlim=c(0,7),save.density=TRUE,main="SSD=.1")
# dns2 <- plot.cell.density(sim[sim$SSD!=.1,],xlim=c(0,7),save.density=TRUE,main="SSD=1")
# x1c <- dns1$'2'$x; x2c <- dns2$'2'$x
#
# # Signal respond RT
# dat <- sim; dat <- dat[!is.na(dat$RT),]; dat$R <- factor(as.character(dat$R))
# round(tapply(dat$RT,dat[,c("R","SSD")],mean),2)
#
# if (do.trials) {
# tmp <- n1PDF.lnrss(sim$RT[!is.na(sim$RT)],meanlog[2:1],sdlog[2:1],t0,t0sg,
# ts=ts,TRIALS=TRIALS[!is.na(sim$RT)],SSD=SSD[!is.na(sim$RT)],Si=2,tf=tf,gf=gf)
# par(mfrow=c(1,2))
# # red=black?
# plot(x1c,dns1$'2'$y,pch=".",main="SSD=.1",ylab="Density",xlab="RT")
# lines(smooth.spline(sim$RT[!is.na(sim$RT) & SSD==.1],
# tmp[c(SSD==.1)[!is.na(sim$RT)]]),col="red")
# # red=black?
# plot(x2c,dns2$'2'$y,pch=".",main="SSD=1",ylab="Density",xlab="RT")
# lines(smooth.spline(sim$RT[!is.na(sim$RT) & SSD==1],
# tmp[c(SSD==1)[!is.na(sim$RT)]]),col="red")
# print(tapply(is.na(sim$RT),sim$SSD,mean)) # empirical
# tmp <- n1PDF.lnrss(rep(NA,n),meanlog,sdlog,t0,t0sg,SSD=SSD,Si=1,tf=tf,gf=gf,ts=ts,TRIALS=TRIALS)
# print(mean(tmp[SSD==.1]))
# print(mean(tmp[SSD==1]))
# plot(TRIALS,tmp,pch=".",xlab="TRIALS",ylab="p(NA)",ylim=c(0,1))
# lines(smooth.spline(TRIALS[SSD==.1],as.numeric(is.na(sim$RT)[SSD==.1])),col="red")
# lines(smooth.spline(TRIALS[SSD==1],as.numeric(is.na(sim$RT)[SSD==1])),col="red")
# } else {
# # Save simulated densities
# r1 <- c(2,1)
# d.r1 <- n1PDF.lnrss(rt=c(x1c,x2c),meanlog[r1],sdlog[r1],t0,t0sg,
# SSD=c(rep(.1,length(x1c)),rep(1,length(x2c))),Si=2,tf=tf,gf=gf)
# # Plot simulated (black) and theoretical (red) densities
# par(mfrow=c(1,2))
# # red=black?
# plot(x1c,dns1$'2'$y,type="l",main="SSD=.1",ylab="Density",xlab="RT",
# ylim=c(0,max(dns1$'2'$y)))
# lines(x1c,d.r1[1:length(x1c)],col="red")
# # red=black?
# plot(x2c,dns2$'2'$y,type="l",main="SSD=1",ylab="Density",xlab="RT",
# ylim=c(0,max(dns2$'2'$y)))
# lines(x2c,d.r1[(length(x2c)+1):(2*length(x2c))],col="red")
#
# # p(Stop check)
# print(tapply(is.na(sim$RT),sim$SSD,mean)) # empirical
# print(n1PDF.lnrss(NA,meanlog,sdlog,t0,t0sg,SSD=.1,Si=1,tf=tf,gf=gf))
# print(n1PDF.lnrss(NA,meanlog,sdlog,t0,t0sg,SSD=1,Si=1,tf=tf,gf=gf))
# }
#
#
#
# ########### THREE ACCUMULATOR CASE
#
# n=1e5
# meanlog=c(.75,.75,1); sdlog=c(.5,1,1)
# SSD = rep(c(1,10)/10,each=n/2)
#
# do.trials=FALSE
# do.trials = TRUE # requires very differnet plotting check, can be SLOW!
#
# #### RUN ONE OF THE FOLLOWING THREE LINES, all assume .2s go Ter
# t0=.2; t0sg= 0 # minimum possible value of t0sg, stop encoding .2 less than go
# t0=.2; t0sg=.2 # equal stop and go enconding times
# t0=.2; t0sg=.4 # stop .2 slower than go
#
# ### RUN ONE OF THE FOLLOWING FOUR LINES
# # Without trigger failure or go failure
# tf=0; gf=0
# # With trigger failure, no go failure
# tf=.1;gf=0
# # Without trigger failure, with go failure
# tf=0; gf=.1
# # With trigger failure and go failure
# tf=.1;gf=.1
#
# if (do.trials) {
# ts=.5; TRIALS=log10(seq(1,10,length.out=n)) # 1..10 natural so 0-1 on log
# TRIALS <- as.vector(t(matrix(TRIALS,nrow=2))) # interleave SSDs
# # Plot slowing in GO (usually nice and linear, up to smooting overfitting)
# sim.go <- rlnrss(n=n,meanlog,sdlog,t0,t0sg,tf=tf,gf=gf,TRIALS=TRIALS,ts=ts)
# is.in <- !is.na(sim.go$RT) # in case go failure
# plot(smooth.spline(TRIALS[is.in],sim.go$RT[is.in]),ylab="Smooth",xlab="TRIALS",type="l")
# } else {TRIALS=NA;ts=0}
#
# # Simulate stop trials
# sim <- rlnrss(n=n,meanlog,sdlog,t0,t0sg,SSD=SSD,tf=tf,gf=gf,TRIALS=TRIALS,ts=ts)
#
# par(mfrow=c(1,2))
# dns1 <- plot.cell.density(sim[sim$SSD==.1,],xlim=c(0,7),save.density=TRUE,main="SSD=.1")
# dns2 <- plot.cell.density(sim[sim$SSD!=.1,],xlim=c(0,7),save.density=TRUE,main="SSD=1")
# x1c <- dns1$'2'$x; x2c <- dns2$'2'$x
# x1e <- dns1$'3'$x; x2e <- dns2$'3'$x
#
# # Signal respond RT
# dat <- sim; dat <- dat[!is.na(dat$RT),]; dat$R <- factor(as.character(dat$R))
# round(tapply(dat$RT,dat[,c("R","SSD")],mean),2)
#
# if (do.trials) {
# r1 <- c(2,1,3)
# is.in1 <- !is.na(sim$RT) & sim$R==2
# d.r1 <- n1PDF.lnrss(sim$RT[is.in1],meanlog[r1],ts=ts,TRIALS=TRIALS[is.in1],
# sdlog=sdlog[r1],t0=t0,t0sg=t0sg,SSD=SSD[is.in1],Si=2,tf=tf,gf=gf)
# r2 <- c(3,1,2)
# is.in2 <- !is.na(sim$RT) & sim$R==3
# d.r2 <- n1PDF.lnrss(sim$RT[is.in2],meanlog[r2],ts=ts,TRIALS=TRIALS[is.in2],
# sdlog=sdlog[r2],t0=t0,t0sg=t0sg,SSD=SSD[is.in2],Si=2,tf=tf,gf=gf)
# par(mfrow=c(1,3))
# # red=black?
# plot(x1c,dns1$'2'$y,pch=".",main="SSD=.1",ylab="Density",xlab="RT",type="l")
# lines(x1e,dns1$'3'$y,lty=2)
# lines(smooth.spline(sim$RT[is.in1 & sim$SSD==.1],
# d.r1[c(sim$SSD==.1)[is.in1]]),col="red")
# lines(smooth.spline(sim$RT[is.in2 & sim$SSD==.1],d.r2[c(sim$SSD==.1)[is.in2]]),
# lty=2,col="red")
# # red=black?
# plot(x2c,dns2$'2'$y,pch=".",main="SSD=1",ylab="Density",xlab="RT",type="l")
# lines(x2e,dns2$'3'$y,lty=2)
# lines(smooth.spline(sim$RT[is.in1 & sim$SSD==1],
# d.r1[c(sim$SSD==1)[is.in1]]),col="red")
# lines(smooth.spline(sim$RT[is.in2 & sim$SSD==1],
# d.r2[c(sim$SSD==1)[is.in2]]),col="red",lty=2)
#
# print(tapply(is.na(sim$RT),sim$SSD,mean)) # empirical
# tmp <- n1PDF.lnrss(rep(NA,n),meanlog,sdlog,t0,t0sg,SSD=SSD,Si=1,tf=tf,gf=gf,ts=ts,TRIALS=TRIALS)
# print(mean(tmp[SSD==.1]))
# print(mean(tmp[SSD==1]))
# plot(TRIALS,tmp,pch=".",xlab="TRIALS",ylab="p(NA)",ylim=c(0,1))
# lines(smooth.spline(TRIALS[SSD==.1],as.numeric(is.na(sim$RT)[SSD==.1])),col="red")
# lines(smooth.spline(TRIALS[SSD==1],as.numeric(is.na(sim$RT)[SSD==1])),col="red")
# } else {
# # Save simulated densities
# r1 <- c(2,1,3)
# d.r1 <- n1PDF.lnrss(rt=c(x1c,x2c),meanlog[r1],sdlog[r1],t0,t0sg,
# SSD=c(rep(.1,length(x1c)),rep(1,length(x2c))),Si=2,tf=tf,gf=gf)
# r2 <- c(3,1,2)
# d.r2 <- n1PDF.lnrss(rt=c(x1e,x2e),meanlog[r2],sdlog[r2],t0,t0sg,
# SSD=c(rep(.1,length(x1e)),rep(1,length(x2e))),Si=2,tf=tf,gf=gf)
# # Plot simulated (black) and theoretical (red) densities
# par(mfrow=c(1,2))
# # red=black?
# plot(x1c,dns1$'2'$y,type="l",main="SSD=.1",ylab="Density",xlab="RT",
# ylim=c(0,max(dns1$'2'$y)))
# lines(x1c,d.r1[1:length(x1c)],col="red")
# lines(x1e,dns1$'3'$y,lty=2)
# lines(x1e,d.r2[1:length(x1e)],col="red",lty=2)
# # red=black?
# plot(x2c,dns2$'2'$y,type="l",main="SSD=1",ylab="Density",xlab="RT",
# ylim=c(0,max(dns2$'2'$y)))
# lines(x2c,d.r1[(length(x2c)+1):(2*length(x2c))],col="red")
# lines(x2e,dns2$'3'$y,lty=2)
# lines(x2e,d.r2[(length(x2e)+1):(2*length(x2e))],col="red",lty=2)
#
# # p(Stop check)
# print(tapply(is.na(sim$RT),sim$SSD,mean)) # empirical
# print(n1PDF.lnrss(NA,meanlog,sdlog,t0,t0sg,SSD=.1,Si=1,tf=tf,gf=gf))
# print(n1PDF.lnrss(NA,meanlog,sdlog,t0,t0sg,SSD=1,Si=1,tf=tf,gf=gf))
# }
}
|
262d99ab098da8d9e75939d3ee22b5059ae648b6 | f49cfda960b70907ee90ae9ba5fab016a5b70861 | /server.R | cf058aaeb7effe3082c3736755e24fb6a5b58aff | [] | no_license | Semooooo/Web-Application | d412363b79379b768b3f0a05a978fbacebb9f80b | e4bf1304ff9141c2207a0b524ceff81887c0ddd6 | refs/heads/main | 2023-07-24T23:41:25.299964 | 2021-09-06T23:17:28 | 2021-09-06T23:17:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,591 | r | server.R | library(shiny)
library(readxl)
library(dplyr)
library(ggplot2)
library(DT)
library(shinydashboard)
library(shinydashboardPlus)
library(shinyWidgets)
library(psych)
library(corrplot)
library(htmltools)
library(GGally)
library(stats)
library(plotly)
library(ggpubr)
library(htmlwidgets)
library(shinythemes)
# Define server for application for Data Visualization
server <- function(input, output, session) {
# Get the upload file
myData <- reactive({
inFile <- input$file1
if (is.null(inFile)) return(NULL)
if (input$fileType_Input == "1") {
myData<-read.csv(inFile$datapath, header =
TRUE,stringsAsFactors = FALSE)
} else {
myData<-read_xlsx(inFile$datapath)
}
})
output$contents <- DT::renderDataTable({
DT::datatable(myData())
})
observe({
data <- myData()
updateSelectInput(session, 'y', choices = names(data))
})
#gets the y variable name, will be used to change the plot legends
yVarName <- reactive({
input$y
})
observe({
data <- myData()
updateSelectInput(session, 'x', choices = names(data))
})
#gets the x variable name, will be used to change the plot legends
xVarName <- reactive({
input$x
})
#################################
observe({
data <- myData()
updateSelectInput(session, 'Y', choices = names(data))
})
#gets the Y variable name, will be used to change the plot legends
YVarName <- reactive({
input$Y
})
observe({
data <- myData()
updateSelectInput(session, 'X', choices = names(data))
})
#gets the X variable name, will be used to change the plot legends
XVarName <- reactive({
input$X
})
# draw a checkbox input variables of uploaded file for xvariables..
output$xvariables <- renderUI({
df <- myData()
x<-colnames(df)
pickerInput(inputId = 'xvariable',
label = 'Select x-axis variable',
choices = c(x[1:length(x)]), selected=x[2],
options = list(`style` = "btn-info"),
multiple = TRUE)
})
# draw a checkbox input variables of uploaded file for xvariables..
output$yvariables <- renderUI({
df <- myData()
y<-colnames(df)
pickerInput(inputId = 'yvariable',
label = 'Select y-axis variable',
choices = c(y[1:length(y)]), selected=y[1],
options = list(`style` = "btn-info"),
multiple = FALSE)
})
output$Refresh1 <- renderText({
toString(format(Sys.Date(), format = "%d %b %Y"))
})
output$summary <- renderPrint({
df <- myData()
df <- df[,input$select_variable]
describeBy(df)
})
# This has not been placed anywhere; there is no uiOutput with id of checkbox in your ui code
output$sumcheckbox <- renderUI({
df <- myData()
checkboxGroupInput("select_variable", "Select Feature variables for Summary:",
names(df), selected = names(df))
})
# This has not been placed anywhere; there is no uiOutput with id of select in your ui code
output$select <- renderUI({
df <- myData()
selectInput("variable", "Variable:",names(df))
})
output$checkbox <- renderUI({
df <- myData()
checkboxGroupInput("select_var", "Select Feature variables:",
names(df), selected = names(df))
})
#Draw a Intrective histogram for Given data variables.
output$Plothist <- renderPlotly({
df <- myData()
df <- df[,input$variable]
# draw the histogram with the specified number of bins
plot_ly(x = ~df,
type = "histogram", nbinsx = 18,
histnorm = "probability",
marker = list(color = viridis::viridis_pal(option = "C", direction = -1)(18))) %>%
layout(title = "Histogram of Data",
yaxis = list(title = "Frequency",
zeroline = FALSE)
)
})
# for Barchart
output$plot <- renderPlot({
df <- myData()
ggplot(df, aes_string(x = input$x, y = input$y))+
geom_bar(stat = "identity", fill="blue", nbinsx=15)
})
# For Scatter plot and Boxplot
output$plot1 <- renderPlot({
df <- myData()
p <- ggplot(df, mapping = aes_string(x = input$x, y = input$y))
if (input[["plot_type"]] == "scatter plot")
{
p + geom_point() + stat_smooth(method = "lm", col = "blue")
}
else
{
p + geom_boxplot()
}
})
# For Correlation matrix and Correlation plot..
output$Correlation <- renderPrint({
df <- myData()
df <- df[,input$select_var]
round(cor(df),4)
})
output$Correlationplot <- renderPlot({
df <- myData()
df <- df[,input$select_var]
theme_lato <- theme_minimal(base_size=8, base_family="Lato Light")
ggpairs(df, lower = list(continuous = function(...)
ggally_smooth(..., colour="darkgreen", alpha = 0.3, size=0.4) + theme_lato),
diag = list(continuous = function(...)
ggally_barDiag(..., fill="purple") + theme_lato),
) +
theme(
strip.background = element_blank(),
strip.text = element_text(size=8, family="Lato Light"),
axis.line = element_line(colour = "grey"),
panel.grid.minor = element_blank(),
)
})
#For Simple and Multiple linear Regression model..
lmModel <- reactive({
df <- myData()
x <- input$xvariable
y <- input$yvariable
x <- as.numeric(df[[as.name(input$xvariable)]])
y <- as.numeric(df[[as.name(input$yvariable)]])
current_formula <- paste0(input$yvariable, " ~ ", paste0(input$xvariable, collapse = " + "))
current_formula <- as.formula(current_formula)
model <- lm(current_formula, data = df, na.action=na.exclude)
return(model)
})
output$lmSummary <- renderPrint({
req(lmModel())
summary(lmModel())
})
output$diagnosticPlot <- renderPlot({
req(lmModel())
par(mfrow = c(2,2))
plot(lmModel())
})
output$report <- downloadHandler(
# For PDF output, change this to "report.pdf"
filename = "report.pdf",
content = function(file) {
# Copy the report file to a temporary directory before processing it, in
# case we don't have write permissions to the current working dir (which
# can happen when deployed).
tempReport <- file.path(myData(), "report.Rmd")
file.copy("report.Rmd", tempReport, overwrite = TRUE)
})
} |
ccbdf63a38fc09dff1c5541f403329963213153a | bf8b1a069668712281a433a35d213cf1b20b29a3 | /OrderDataFrame.R | 99b0f0c5c9c23c76d6090e7fe96fe71e8b430214 | [] | no_license | wmbeam02/Assignment3 | 65c25483aa498814f4dc2c4acd8ce52505b1f7d5 | abff9955f0cf9fe6c88892cfae4af2c7eb42c74d | refs/heads/master | 2021-01-10T02:19:03.417788 | 2015-05-31T02:53:18 | 2015-05-31T02:53:18 | 36,444,892 | 0 | 0 | null | 2015-05-28T15:04:40 | 2015-05-28T14:42:06 | R | UTF-8 | R | false | false | 1,975 | r | OrderDataFrame.R | ## A function to sort a dataframe first by State then to order the data in lowest occurrence to highest with the names of the
## hospitals alphabetic relationship used to determine ties.
rankhospital=function(state, outcome, num="best") {
FileName="outcome-of-care-measures.csv"
data=read.csv(FileName, colClasses="character", na.strings="Not Available")
CorrectInput=c("heart attack", "heart failure", "pneumonia")
state=toupper(state)
outcome=tolower(outcome)
if (!state %in% data$State) {
stop("Invalid State")
}
if (!outcome %in% CorrectInput) {
stop("Invalid Cause")
}
## Was planning on using this <match> functionality to get the <ColumnNameSub> to enter into the <order> function below but it didn't work (<order>, not <match>)
# ColumnName=c("Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack", "Hospital.30.Day.Death..Mortality..Rates.from.Heart.Failure", "Hospital.30.Day.Death..Mortality..Rates.from.Pneumonia")
# ColumnNameSub=ColumnName[match(outcome, CorrectInput)]
# View(ColumnNameSub)
StateSub=data[data$State == state, ]
# View(StateSub)
if (outcome == "heart attack") {
RankedData=StateSub[order((as.numeric(StateSub[, "Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack"])), StateSub[ "Hospital.Name"], decreasing=FALSE, na.last=NA), ]
} else if (outcome == "heart failure"){
RankedData=StateSub[order((as.numeric(StateSub[, "Hospital.30.Day.Death..Mortality..Rates.from.Heart.Failure"])), StateSub[ "Hospital.Name"], decreasing=FALSE, na.last=NA), ]
} else {
RankedData=StateSub[order((as.numeric(StateSub[,"Hospital.30.Day.Death..Mortality..Rates.from.Pneumonia"])), StateSub[ "Hospital.Name"], decreasing=FALSE, na.last=NA), ]
}
View(RankedData)
if(num == "best"){
num=1
}
if (num == "worst"){
num=nrow(RankedData)
}
if (is.numeric(num) & num>nrow(RankedData)){
return(NULL)
}
Ranked=RankedData[num,"Hospital.Name"]
return(Ranked)
}
|
a30bad79eced9ae47e0ed6f69b0c9d1397f7a84b | 0122ae5c5fe5bd1ffa25184f70d525aea956cdef | /score.r | 035fbb4854318cf6bf765ac717e3abc5361d7a0a | [] | no_license | TBKelley/KAGGLE-WISE2014 | ddfc141397bc9db69ba2fac3e4f103c8d6f07135 | f4832c95cae57713468aaa8a2c2fca4f105a66c4 | refs/heads/master | 2021-01-10T19:31:07.250474 | 2014-07-04T12:34:17 | 2014-07-04T12:34:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 879 | r | score.r | score <- function(score.table) {
if ("FALSE" %in% rownames(score.table) && "FALSE" %in% colnames(score.table)) {
TN <- score.table["FALSE","FALSE"]
} else {
TN <- 0
}
if ("FALSE" %in% rownames(score.table) && "TRUE" %in% colnames(score.table)) {
FN <- score.table["FALSE","TRUE"]
} else {
FN <- 0
}
if ("TRUE" %in% rownames(score.table) && "FALSE" %in% colnames(score.table)) {
FP <- score.table["TRUE","FALSE"]
} else {
FP <- 0
}
if ("TRUE" %in% rownames(score.table) && "TRUE" %in% colnames(score.table)) {
TP <- score.table["TRUE","TRUE"]
} else {
TP <- 0
}
predicted <- ifelse((TP + FP) == 0, 1, TP /(TP + FP))
recall <- ifelse((TP + FN) == 0, 1, TP/(TP + FN))
F1 <- 2 * predicted * recall /(predicted + recall)
F1
}
|
4030053b564e9c3a8fb2917da4b30eac2fd22b7c | 41003e2f32d2d45720f4f5e6eb37b0a121923779 | /simulation/method_functions.R | 48558140aa5fbefb24c93fc5c9f653e9c3411327 | [] | no_license | GreenwoodLab/ggmix | d4a3de1a4d6740520fbbacec81d0de277646d2fa | 96fba3925a1f9a2a2d31918be9c30cbbb296f69b | refs/heads/master | 2020-04-02T10:40:01.978342 | 2018-06-28T14:17:37 | 2018-06-28T14:17:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,882 | r | method_functions.R | ## @knitr methods
source("/mnt/GREENWOOD_BACKUP/home/sahir.bhatnagar/ggmix/simulation/packages.R")
source("/mnt/GREENWOOD_BACKUP/home/sahir.bhatnagar/ggmix/simulation/functions.R")
# lasso <- new_method("lasso", "Lasso",
# method = function(model, draw) {
# fitglmnet <- cv.glmnet(x = model$x, y = draw, alpha = 1, standardize = F)
# list(beta = coef(fitglmnet, s = "lambda.min")[-1,,drop=F],
# yhat = predict(fitglmnet, newx = model$x, s = "lambda.min"),
# nonzero = coef(fitglmnet)[nonzeroCoef(coef(fitglmnet)),,drop=F],
# nonzero_names = setdiff(rownames(coef(fitglmnet)[nonzeroCoef(coef(fitglmnet)),,drop=F]),c("(Intercept)")),
# y = draw)
# })
lasso <- new_method("lasso", "lasso",
method = function(model, draw) {
fitglmnet <- cv.glmnet(x = model$x_lasso, y = draw, alpha = 1, standardize = T,
penalty.factor = c(rep(1, ncol(model$x)), rep(0,10)))
model_error <- l2norm(model$mu -
model$x %*% coef(fitglmnet, s = "lambda.min")[2:(ncol(model$x)+1),,drop=F])
list(beta = coef(fitglmnet, s = "lambda.min")[-1,,drop=F],
model_error = model_error,
eta = NA,
sigma2 = NA,
yhat = predict(fitglmnet, newx = model$x_lasso, s = "lambda.min"),
nonzero = coef(fitglmnet, s = "lambda.min")[nonzeroCoef(coef(fitglmnet, s = "lambda.min")),,drop=F],
nonzero_names = setdiff(rownames(coef(fitglmnet, s = "lambda.min")[nonzeroCoef(coef(fitglmnet, s = "lambda.min")),,drop=F]),c("(Intercept)")),
y = draw)
})
ggmix <- new_method("ggmix", "ggmix",
method = function(model, draw) {
fit <- penfam(x = model$x,
y = draw,
phi = model$kin,
thresh_glmnet = 1e-10,
epsilon = 1e-5,
fdev = 1e-7,
alpha = 1,
tol.kkt = 1e-3,
nlambda = 100,
# an = log(log(model$n)) * log(model$n),
# an = log(log(1000)),
an = log(length(draw)),
# lambda_min_ratio = ifelse(model$n < model$p, 0.01, 0.001),
lambda_min_ratio = 0.05,
eta_init = 0.5,
maxit = 100)
model_error <- l2norm(model$mu -
model$x %*% coef(fit, s = fit$lambda_min)[2:(ncol(model$x)+1),,drop=F])
list(beta = fit$beta[,fit$lambda_min,drop=F], #this doesnt have intercept and is a 1-col matrix
model_error = model_error,
nonzero = predict(fit, type = "nonzero", s = fit$lambda_min),
nonzero_names = setdiff(rownames(predict(fit, type = "nonzero", s = fit$lambda_min)), c("(Intercept)","eta","sigma2")),
yhat = fit$predicted[,fit$lambda_min],
eta = fit$eta[,fit$lambda_min],
sigma2 = fit$sigma2[,fit$lambda_min],
y = draw
)
})
twostep <- new_method("twostep", "two step",
method = function(model, draw) {
# pheno_dat <- data.frame(Y = draw, id = rownames(model$kin))
# fit_lme <- coxme::lmekin(Y ~ 1 + (1|id), data = pheno_dat, varlist = model$kin)
# newy <- residuals(fit_lme)
# fitglmnet <- glmnet::cv.glmnet(x = model$x, y = newy, standardize = F, alpha = 1)
# pheno_dat <- data.frame(Y = draw, id = rownames(model$kin))
x1 <- cbind(rep(1, nrow(model$x)))
fit_lme <- gaston::lmm.aireml(draw, x1, K = model$kin)
gaston_resid <- draw - (fit_lme$BLUP_omega + fit_lme$BLUP_beta)
fitglmnet <- glmnet::cv.glmnet(x = model$x, y = gaston_resid,
standardize = T, alpha = 1, intercept = T)
model_error <- l2norm(model$mu -
model$x %*% coef(fitglmnet, s = "lambda.min")[2:(ncol(model$x)+1),,drop=F])
list(beta = coef(fitglmnet, s = "lambda.min")[-1,,drop=F],
yhat = predict(fitglmnet, newx = model$x, s = "lambda.min"),
nonzero = coef(fitglmnet, s = "lambda.min")[nonzeroCoef(coef(fitglmnet, s = "lambda.min")),,drop=F],
nonzero_names = setdiff(rownames(coef(fitglmnet, s = "lambda.min")[nonzeroCoef(coef(fitglmnet, s = "lambda.min")),,drop=F]),c("(Intercept)")),
model_error = model_error,
eta = NA,
sigma2 = NA,
y = draw)
})
|
491f6fcb02cb9282e6708752ade0b27e6c0a8766 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/PPRL/examples/DeterministicLinkage.Rd.R | 8c9ca1cf14dd4c94fc7dfad61836d6efedd92722 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 920 | r | DeterministicLinkage.Rd.R | library(PPRL)
### Name: DeterministicLinkage
### Title: Deterministic Record Linkage
### Aliases: DeterministicLinkage 'Record Linkage'
### ** Examples
# load test data
testFile <- file.path(path.package("PPRL"), "extdata/testdata.csv")
testData <- read.csv(testFile, head = FALSE, sep = "\t",
colClasses = "character")
# define year of birth (V3) as blocking variable
bl <- SelectBlockingFunction("V3", "V3", method = "exact")
# Select first name and last name as linking variables,
# to be linked using the soundex phonetic (first name)
# and exact matching (last name)
l1 <- SelectSimilarityFunction("V7", "V7", method = "Soundex")
l2 <- SelectSimilarityFunction("V8", "V8", method = "exact")
# Link the data as specified in bl and l1/l2
# (in this small example data is linked to itself)
res <- DeterministicLinkage(testData$V1, testData,
testData$V1, testData, similarity = c(l1, l2), blocking = bl)
|
2e94bf8891e462098ba5fc68b473c32c2cfd2c59 | a225f9e6f0a2f10e9222b717549c8324cb4f0dc4 | /lib/makeIcon.R | 88ed7e5548af8ee2b26eab7919454260de39bcd0 | [] | no_license | Bingjiling/ShinyAPP-Rats-in-NYC | eeed1eb6990034d069554fdaff6684400bd5ad32 | 8edcdbc71a692d26599a100caa23a72d5fa62194 | refs/heads/master | 2021-01-18T12:18:44.791579 | 2016-02-27T03:25:10 | 2016-02-27T03:25:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,350 | r | makeIcon.R | size = 10
icon1 <- makeIcon(
iconUrl = "Hamburger.gif",
iconWidth = size, iconHeight = size
)
icon2 <- makeIcon(
iconUrl = "https://www.emojibase.com/resources/img/emojis/apple/x1f1ee-1f1f9.png.pagespeed.ic.SxTX2CImcp.png",
iconWidth = size, iconHeight = size
)
icon3 <- makeIcon(
iconUrl = "http://emojipedia-us.s3.amazonaws.com/cache/a1/5d/a15d18cf274f1701b4162c3876dd11e4.png",
iconWidth = size, iconHeight = size
)
icon4 <- makeIcon(
iconUrl = "https://www.emojibase.com/resources/img/emojis/hangouts/1f1e8-1f1f3.png",
iconWidth = size, iconHeight = size
)
icon5 <- makeIcon(
iconUrl = "http://pix.iemoji.com/images/emoji/apple/ios-9/256/hamburger.png",
iconWidth = size, iconHeight = size
)
icon6 <- makeIcon(
iconUrl = "http://pix.iemoji.com/images/emoji/apple/ios-9/256/hot-beverage.png",
iconWidth = size, iconHeight = size
)
icon7 <- makeIcon(
iconUrl = "http://3z489t2p9kbdv4il24as7q51.wpengine.netdna-cdn.com/wp-content/uploads/2015/10/pizza-emjoi.png",
iconWidth = size, iconHeight = size
)
icon8 <- makeIcon(
iconUrl = "http://emojipedia-us.s3.amazonaws.com/cache/76/3f/763f6bb87378b2c3e26571561b235e81.png",
iconWidth = size, iconHeight = size
)
icons = c(icon1,icon2,icon3,icon4,icon5,icon6,icon7,icon8)
|
14cd1e28447c722249056f3d545a66c5de6e02ef | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/PepSAVIms/examples/msDat.Rd.R | 0acc703c5c2825048333fe9b81f92ef50a7a7e79 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,065 | r | msDat.Rd.R | library(PepSAVIms)
### Name: msDat
### Title: Constructor for class 'msDat'
### Aliases: msDat
### ** Examples
# Load mass spectrometry data
data(mass_spec)
# Convert mass_spec from a data.frame to an msDat object
ms <- msDat(mass_spec = mass_spec,
mtoz = "m/z",
charge = "Charge",
ms_inten = c(paste0("_", 11:43), "_47"))
# Dimension of the data
dim(ms)
# Print the first few rows and columns
ms[1:5, 1:2]
# Let's change the fraction names to something more concise
colnames(ms) <- c(paste0("frac", 11:43), "frac47")
# Print the first few rows and columns with the new fraction names
ms[1:5, 1:8]
# Suppose there are some m/z levels that we wish to remove
ms <- ms[-c(2, 4), ]
# Print the first few rows and columns after removing rows 2 and 4
ms[1:5, 1:8]
# Suppose that there was an instrumentation error and that we need to change
# some values
ms[1, paste0("frac", 12:17)] <- c(55, 57, 62, 66, 71, 79)
# Print the first few rows and columns after changing some of the values in
# the first row
ms[1:5, 1:10]
|
980b40a1b7cbc39bfb8bd2eeb2eab5d600097da3 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/Ecfun/examples/logVarCor.Rd.R | 0d498761ae5c0ba9626259ecf0a2949404607160 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,725 | r | logVarCor.Rd.R | library(Ecfun)
### Name: logVarCor
### Title: Log-diagonal reprentation of a variance matrix
### Aliases: logVarCor
### Keywords: multivariate
### ** Examples
##
## 1. Trivial 1 x 1 matrix
##
# 1.1. convert vector to "matrix"
mat1 <- logVarCor(1)
# check
## Don't show:
stopifnot(
## End(Don't show)
all.equal(mat1, matrix(exp(1), 1))
## Don't show:
)
## End(Don't show)
# 1.2. Convert 1 x 1 matrix to vector
lVCd1 <- logVarCor(diag(1))
# check
lVCd1. <- 0
attr(lVCd1., 'corr') <- numeric(0)
## Don't show:
stopifnot(
## End(Don't show)
all.equal(lVCd1, lVCd1.)
## Don't show:
)
## End(Don't show)
##
## 2. simple 2 x 2 matrix
##
# 2.1. convert 1:2 into a matrix
lVC2 <- logVarCor(1:2)
# check
lVC2. <- diag(exp(1:2))
## Don't show:
stopifnot(
## End(Don't show)
all.equal(lVC2, lVC2.)
## Don't show:
)
## End(Don't show)
# 2.2. Convert a matrix into a vector
lVC2d <- logVarCor(diag(1:2))
# check
lVC2d. <- log(1:2)
attr(lVC2d., 'corr') <- 0
## Don't show:
stopifnot(
## End(Don't show)
all.equal(lVC2d, lVC2d.)
## Don't show:
)
## End(Don't show)
##
## 3. 3-d covariance matrix with nonzero correlations
##
# 3.1. Create matrix
(ex3 <- tcrossprod(matrix(c(rep(1,3), 0:2), 3)))
dimnames(ex3) <- list(letters[1:3], letters[1:3])
# 3.2. Convert to vector
(Ex3 <- logVarCor(ex3))
# check
Ex3. <- log(c(1, 2, 5))
names(Ex3.) <- letters[1:3]
attr(Ex3., 'corr') <- c(1/sqrt(2), 1/sqrt(5), 3/sqrt(10))
## Don't show:
stopifnot(
## End(Don't show)
all.equal(Ex3, Ex3.)
## Don't show:
)
## End(Don't show)
# 3.3. Convert back to a matrix
Ex3.2 <- logVarCor(Ex3)
# check
## Don't show:
stopifnot(
## End(Don't show)
all.equal(ex3, Ex3.2)
## Don't show:
)
## End(Don't show)
|
d459befca9d09de6d337cc94a0a776882928aa88 | 862bee845b055a3cfb7c0ab5ac1c277ed35f438f | /library/ggthemes/examples/ex-theme_pander.R | 7c5229b72d07bbadecaa22c4babc7ec12f96708d | [
"MIT"
] | permissive | mayousif/Turbidity-Cleaner | a4862f54ca47305cfa47d6fcfa048e1573d6a2b7 | e756d9359f1215a4c9e6ad993eaccbc3fb6fe924 | refs/heads/master | 2022-05-09T07:32:55.856365 | 2022-04-22T20:05:10 | 2022-04-22T20:05:10 | 205,734,717 | 2 | 2 | null | 2020-11-02T21:31:19 | 2019-09-01T21:31:43 | HTML | UTF-8 | R | false | false | 428 | r | ex-theme_pander.R | require("ggplot2")
if (require("pander")) {
p <- ggplot(mtcars, aes(x = mpg, y = wt)) +
geom_point()
p + theme_pander()
panderOptions("graph.grid.color", "red")
p + theme_pander()
p <- ggplot(mtcars, aes(wt, mpg, colour = factor(cyl))) +
geom_point()
p + theme_pander() + scale_color_pander()
ggplot(mpg, aes(x = class, fill = drv)) +
geom_bar() +
scale_fill_pander() +
theme_pander()
}
|
ac82022ed86025f45e7c0649a132f78b1c7d51a2 | 07bc1ed7d93eae1e2c43519d0afce17e33dfe8fb | /PAF_calculation_article_github.R | 79edc2207913cb31daf086fa8b89d44795f364cd | [] | no_license | mulderac91/R-STECO157-spatialanalysis | 48046a68a2bf06b6e3e25ce75f71622c754268dc | 199e02172f548b69244a4c7e37a1e94c4a6e5d38 | refs/heads/master | 2022-04-26T19:28:34.550359 | 2020-04-30T11:43:23 | 2020-04-30T11:43:23 | 260,139,922 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,735 | r | PAF_calculation_article_github.R | #
# Init ----
#
# Load packages
library(tidyverse)
library(INLA)
#
# Data ----
#
# Run script STEC and animal numbers first
#
# Augment data ----
#
tmp.data.summer <- tmp.data.summer %>% select(-geometry)
ex.data.aug <- bind_rows(
# Augmentation idicator: 0 = original data, 1 = augmented data
bind_cols(tmp.data.summer, tibble(augmented = rep(0, nrow(tmp.data.summer)))),
bind_cols(tmp.data.summer, tibble(augmented = rep(1, nrow(tmp.data.summer))))) %>%
mutate(
# Set exposed to reference value
Kleine_Herkauwers_totaal = if_else(augmented == 1, 0, Kleine_Herkauwers_totaal),
# Set exposed.cat to reference category
#exposed.cat = if_else(augmented == 1, factor(levels(exposed.cat)[1], levels = levels(exposed.cat)), exposed.cat),
# Set outcome to NA
cases = if_else(augmented == 1, NA_real_, cases)) %>%
ungroup
ex.data.aug
#
# Fit models ----
#
# Fit Poisson regression model with INLA
fit.inla <- inla(
formula = cases ~ sex + agecat + log2(Pluimvee_totaal + 1) + log2(Varkens_totaal + 1) +
log2(Rundvee_totaal + 1) + log2(Kleine_Herkauwers_totaal + 1) +
f(hex.car, model = "besag",
hyper = list(prec = list(prior = "loggamma", param = c(1, 0.1))),
graph = hex.nb) +
f(hex.iid, model = "iid",
hyper = list(prec = list(prior = "loggamma", param = c(1, 0.1)))),
E = population,
family = "poisson",
data = ex.data.aug,
control.inla = list(int.strategy = "eb"),
control.predictor = list(compute = TRUE),
# Enable posterior sampling
control.compute = list(config = TRUE))
#
# PAF
#
# Calculate predictions for both the original and augmented data
# for both GLM and INLA models
ex.data.aug <- ex.data.aug %>%
mutate(
# Predictions with INLA
pred.inla = population*exp(fit.inla$summary.linear.predictor[, "mean"]))
# PAF is then given by
pred.data <- ex.data.aug %>%
group_by(augmented) %>%
summarize(
pred.inla = sum(pred.inla))
PAF.inla <- pred.data %>% pull(pred.inla)
PAF.inla <- (PAF.inla[1] - PAF.inla[2])/PAF.inla[1]
PAF.inla
post.inla <- inla.posterior.sample(n = 10000, result = fit.inla)
linpred <- sapply(X = post.inla, FUN = function(x) {
x.latent <- x$latent
x.latent[x.latent %>% rownames %>% str_detect(pattern = "Predictor"), ]
})
PAF <- rep(0, ncol(linpred))
for (i in 1:ncol(linpred)) {
ex.data.aug <- ex.data.aug %>%
mutate(
# Predictions with INLA
pred.inla = population*exp(linpred[, i]))
# PAF is then given by
pred.data <- ex.data.aug %>%
group_by(augmented) %>%
summarize(
pred.inla = sum(pred.inla))
PAF.inla <- pred.data %>% pull(pred.inla)
PAF.inla <- (PAF.inla[1] - PAF.inla[2])/PAF.inla[1]
PAF[i] <- PAF.inla
}
quantile(PAF, c(0.5, 0.025, 0.975))
|
4f3d95fb7d71df2927ae9bb960c3a28a9a9b8e1d | 30d561f20092f3549e0e2edb74fa2a4b8189d388 | /man/edad_educ.Rd | 3638651e6943ba9050271bcd89d1160e87c51064 | [] | no_license | arcruz0/paqueteadp | 11b134c25cb12303352848260d916491720d5e58 | 44a8c7733b71bee322cba4aa12e68068652e0338 | refs/heads/master | 2021-06-13T09:40:27.350756 | 2021-04-02T22:08:14 | 2021-04-02T22:08:14 | 175,672,056 | 3 | 3 | null | null | null | null | UTF-8 | R | false | true | 646 | rd | edad_educ.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data_files.R
\docType{data}
\name{edad_educ}
\alias{edad_educ}
\title{Edad y educación como confounders}
\description{
Datos simulados, en los que la edad y la educación son confounders para el tratamiento de usar redes de mosquitos. Creados por Andrew Heiss.
Datos de los estados de Brasil, compilados por Freire (2018)
}
\references{
Freire, D. (2018). Evaluating the effect of homicide prevention strategies in são paulo, brazil: A synthetic control approach. Latin American Research Review, 53(2), 231. \url{https://doi.org/10.25222/larr.334}
}
\keyword{data}
|
5f8163933c748d20d768b0f86e5a20e0fdf5b473 | fc5e180eef8ec24d8fe2396794bb317fbb8b1a42 | /R/whatNWIS.R | 1573b3f6930246d12c87ff7cfe187e06dafecefb | [
"LicenseRef-scancode-warranty-disclaimer",
"LicenseRef-scancode-public-domain-disclaimer"
] | permissive | ldecicco-USGS/USGSwsDataRetrieval | cd6c3d7be7439e9a96b6aa3c88cd3c0ed210c88e | 507d4088860915778af2db0c16c8b8a63f622b3a | refs/heads/master | 2020-03-30T08:50:42.163419 | 2015-02-13T18:59:31 | 2015-02-13T18:59:31 | 30,770,882 | 0 | 0 | null | 2015-02-13T18:57:39 | 2015-02-13T18:57:39 | null | UTF-8 | R | false | false | 5,003 | r | whatNWIS.R | #' @title Data Inventory
#'
#' @description Gets a description of unit-value, daily-value, or instantaneous
#'water-quality data are available for a USGS station and the
#'beginning and ending dates of each parameter.
#'
#' @details The parameter group codes and corresponding NWIS descriptions
#'\tabular{ll}{
#'Code \tab Description\cr
#'INF \tab Information \cr
#'PHY \tab Physical \cr
#'INM \tab Inorganics, Major, Metals (major cations) \cr
#'INN \tab Inorganics, Major, Non-metals (major anions) \cr
#'NUT \tab Nutrient \cr
#'MBI \tab Microbiological \cr
#'BIO \tab Biological \cr
#'IMN \tab Inorganics, Minor, Non-metals \cr
#'IMM \tab Inorganics, Minor, Metals \cr
#'TOX \tab Toxicity \cr
#'OPE \tab Organics, pesticide \cr
#'OPC \tab Organics, PCBs \cr
#'OOT \tab Organics, other \cr
#'RAD \tab Radiochemical \cr
#'SED \tab Sediment \cr
#'POP \tab Population/community \cr
#'}
#' @param site a single USGS station identifier as a character string.
#' @param param.groups a character vector indicating the parameter group
#'codes to retrun. If blank, then return all parameters. If "ALL,"
#'then return only the summary of all constituents. Otherwise any combination of
#'"INF," "PHY," "INM," "INN," "NUT," "MBI," "BIO," "IMM," "IMN," "TOX,"
#'"OPE," "OPC," "OOT," "RAD," "SED", and "POP." See \bold{Details} for
#'a decription of the parameter group codes.
#' @return A data frame containing these columns:
#'site_no, the USGS station identifier;
#'parm_cd, the parameter code; description, a desciption of the parameter code;
#'begin_date, the earliest date available for the parameter code data;
#'end_date, the latest date available for the parameter code data; and
#'count_nu, the number of values available for the parameter code.
#',EndDate, pCode, and name.
#' @keywords DataIO
#' @examples
#' \dontrun{
#'# These examples require an internet connection to run
#'UVavailable <- whatNWISuv("04027000")
#'}
#' @name whatNWIS
#' @rdname whatNWIS
#' @export whatNWISuv
whatNWISuv <- function(site) {
## Coding history:
## 2014Sep09 DLLorenz merge whatUV with getDataAvailability
##
if(missing(site))
stop("site is required")
myurl <- paste("http://waterservices.usgs.gov/nwis/site?format=rdb&seriesCatalogOutput=true&sites=",site,sep = "")
retval <- importRDB(myurl)
## Filter for service requested (uv) and remove unnecessary columns
retval <- retval[retval$data_type_cd %in% "uv", c("parm_cd", "site_no", "stat_cd", "dd_nu", "loc_web_ds", "medium_grp_cd",
"parm_grp_cd", "srs_id", "access_cd", "begin_date",
"end_date", "count_nu")]
## Merge with pcode info to get descriptions
pCodes <- retval$parm_cd
pcodeINFO <- parameterCdFile[parameterCdFile$parameter_cd %in% pCodes, c("parameter_cd", "srsname", "parameter_units")]
retval <- merge(pcodeINFO,retval,by.x="parameter_cd", by.y="parm_cd", all.y=TRUE)
return(retval)
}
#' @rdname whatNWIS
#' @export whatNWISdv
whatNWISdv <- function(site) {
## Coding history:
## 2014Sep09 DLLorenz merge whatUV with getDataAvailability
##
if(missing(site))
stop("site is required")
myurl <- paste("http://waterservices.usgs.gov/nwis/site?format=rdb&seriesCatalogOutput=true&sites=",site,sep = "")
retval <- importRDB(myurl)
## Filter for service requested (uv) and remove unnecessary columns
retval <- retval[retval$data_type_cd %in% "dv", c("parm_cd", "site_no", "stat_cd", "dd_nu", "loc_web_ds", "medium_grp_cd",
"parm_grp_cd", "srs_id", "access_cd", "begin_date",
"end_date", "count_nu")]
## Merge with pcode info to get descriptions
pCodes <- retval$parm_cd
pcodeINFO <- parameterCdFile[parameterCdFile$parameter_cd %in% pCodes, c("parameter_cd", "srsname", "parameter_units")]
retval <- merge(pcodeINFO,retval,by.x="parameter_cd", by.y="parm_cd", all.y=TRUE)
return(retval)
}
#' @rdname whatNWIS
#' @export whatNWISqw
whatNWISqw <- function(site, param.groups="") {
## Coding history:
## 2014Sep09 DLLorenz merge whatUV with getDataAvailability
##
if(missing(site))
stop("site is required")
myurl <- paste("http://waterservices.usgs.gov/nwis/site?format=rdb&seriesCatalogOutput=true&sites=",site,sep = "")
retval <- importRDB(myurl)
## Filter for service requested (qw) and remove unnecessary columns
retval <- retval[retval$data_type_cd %in% "qw", c("parm_cd", "site_no", "stat_cd", "dd_nu", "loc_web_ds", "medium_grp_cd",
"parm_grp_cd", "srs_id", "access_cd", "begin_date",
"end_date", "count_nu")]
## Merge with pcode info to get descriptions
pCodes <- retval$parm_cd
pcodeINFO <- parameterCdFile[parameterCdFile$parameter_cd %in% pCodes, c("parameter_cd", "srsname", "parameter_units")]
retval <- merge(pcodeINFO,retval,by.x="parameter_cd", by.y="parm_cd", all.y=TRUE)
## Filter for parameter groups
if(param.groups[1L] != "")
retval <- retval[retval$parm_grp_cd %in% param.groups, ]
return(retval)
}
|
ef8e53ade373ab8ba221b077b3d2f81b33ba0b49 | 05965f92ec94ea32dcf6677f7345f6b0789265ae | /R/kobo_to_xlsform.R | 43d4aab24a6d092d01c045ce2dad52f654faf3b2 | [] | no_license | DamienSeite/koboloadeR | 3187286e6351b24c9a43f3a0938e4bdfe20b2681 | 5da3c04531cf415fbca40950b52e86e547578635 | refs/heads/gh-pages | 2020-03-28T17:30:24.576746 | 2018-09-14T14:05:35 | 2018-09-14T14:05:35 | 131,474,775 | 0 | 0 | null | 2018-04-29T07:29:05 | 2018-04-29T07:29:05 | null | UTF-8 | R | false | false | 3,561 | r | kobo_to_xlsform.R | #' @name kobo_to_xlsform
#' @rdname kobo_to_xlsform
#' @title Generate xlsfrom skeleton from a dataframe
#'
#' @description Creates and save a xlsform skeleton from a data.frames in your data folder
#' The form.xls will be saved in the data folder of your project.
#' The generated xlsfrom will need to be manually edited to configure your analysis
#'
#' Note that this function only works with \code{data.frames}. The function
#' will throw an error for any other object types.
#'
#' @param n number of levels for a factor to be considered as a text
#'
#'
#'
#'
#' @author Edouard Legoupil
#'
#' @export
#' @examples
#' data(iris)
#' str(iris)
#' kobo_to_xlsform(iris)
kobo_to_xlsform <- function(df,
n=100) {
stopifnot(is.data.frame(df))
# df <- data.df
## str(df)
# n = 10
df[sapply(df, is.labelled)] <- lapply(df[sapply(df, is.labelled)], as.factor)
## build survey sheet
survey <- data.frame( type = rep(as.character(NA), ncol(df)),
name = names(df),
label = names(df),
chapter = rep(as.character(NA), ncol(df)),
disaggregation = rep(as.character(NA), ncol(df)),
correlate = rep(as.character(NA), ncol(df)),
variable = rep(as.character(NA), ncol(df)),
sensitive = rep(as.character(NA), ncol(df)),
anonymise = rep(as.character(NA), ncol(df)),
stringsAsFactors = FALSE)
## Fill survey type
for(i in seq_along(df)) {
#i <-12
#cat(i)
if(is.factor(df[,i])) {
survey[i,]$type <- paste0('select_one ', as.character(names(df[i])), '_choices')
} else {
survey[i,]$type <- class(df[,i])[1]
}
}
## build choices sheet
choices <- data.frame(list_name = as.character(NA),
name = as.character(NA),
label = as.character(NA),
order = as.integer(NA),
stringsAsFactors = FALSE)
## Loop around variables to build choices based on factor levels
for(i in seq_along(df)) {
#i <-2
if(is.factor(df[,i])) {
cat(paste0("Factor: ",i,"\n"))
frame <- as.data.frame((levels(df[,i])))
if (nrow(frame)!=0 & nrow(frame)<100 ){
for(j in 1:nrow(frame)) {
# j <- 1
choices1 <- data.frame(list_name = as.character(NA),
name = as.character(NA),
label = as.character(NA),
order = as.integer(NA),
stringsAsFactors = FALSE)
cat(paste0("Inserting level: ",j,"\n"))
choices1[j,]$list_name <- paste0( as.character(names(df[i])), '_choices')
choices1[j,]$name <- as.character(frame[j, ])
choices1[j,]$label <- as.character(frame[j,])
choices1[j,]$order <- j
choices <- rbind(choices, choices1)
}
rm(choices1)
} else {cat("Too many choices to consider it as a factor\n")}
###
} else {cat("This is not a factor \n")}
}
# install.packages("WriteXLS")
library(WriteXLS)
WriteXLS("survey", "data/form-from-data.xls", AdjWidth = TRUE, BoldHeaderRow = TRUE, AutoFilter = TRUE, FreezeRow = 1)
#WriteXLS("choices", "data/form.xls", AdjWidth = TRUE, BoldHeaderRow = TRUE, AutoFilter = TRUE, FreezeRow = 1)
library(XLConnect)
writeWorksheetToFile(file = "data/form-from-data.xls", data = choices, sheet = "choices")
}
|
f844f7df03c50588a60b817035ce3cca3941df0c | c0693698db7a132954d748ee3b4c7e156e12475f | /preprocess/8_nat_fig.R | 70cf064610d6480d1e2f71cc52aeaa5233f9aeb2 | [] | no_license | Flowminder/FDFA_01 | cd527b2ed444da8d13eaf9aea58c8b8992f1e524 | 505792796b5cb4c88bc8f49518f87148976b1cf0 | refs/heads/master | 2023-04-10T04:51:56.346936 | 2021-04-15T10:58:10 | 2021-04-15T10:58:10 | 186,587,717 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,009 | r | 8_nat_fig.R | # national figure summaries ####
EM_int=tbl(mig_db,"EM_int")
EM_nat=tbl(mig_db,"EM_nat")
IM_int=tbl(mig_db,"IM_int")
IM_nat=tbl(mig_db,"IM_nat")
admin_names=tbl(mig_db,"admin_names")%>%
collect(n=Inf)
COUNTRIES=EM_int%>%
distinct(ISO)%>%
collect(n=Inf)
COUNTRIES=COUNTRIES$ISO
df=data.frame()
country_clicked="AFG"
for(country_clicked in COUNTRIES){
# EM_int number & % #####
EM_int_mig_data=EM_int%>%
filter(ISO==country_clicked)%>%
group_by(sex)%>%
summarise(move=sum(move))%>%
collect()
EM_int_mig_all=EM_int_mig_data%>%
filter(sex=="all")
EM_int_mig_female=EM_int_mig_data%>%
filter(sex=="F")
if(dim(EM_int_mig_female)[1]==0){
EM_int_mig_female=data.frame(sex="F",
move=NA)
}
EM_int_mig_male=EM_int_mig_data%>%
filter(sex=="M")
EM_int_mig_female_perc=EM_int_mig_female$move/EM_int_mig_all$move
EM_int_mig_male_perc=EM_int_mig_male$move/EM_int_mig_all$move
# EM_nat number & % #####
EM_nat_mig_data=EM_nat%>%
filter(ISO==country_clicked)%>%
group_by(sex)%>%
summarise(move=sum(move))%>%
collect()
EM_nat_mig_all=EM_nat_mig_data%>%
filter(sex=="all")
EM_nat_mig_female=EM_nat_mig_data%>%
filter(sex=="F")
EM_nat_mig_male=EM_nat_mig_data%>%
filter(sex=="M")
EM_nat_mig_female_perc=EM_nat_mig_female$move/EM_nat_mig_all$move
EM_nat_mig_male_perc=EM_nat_mig_male$move/EM_nat_mig_all$move
# IM_int number & % #####
IM_int_mig_data=IM_int%>%
filter(ISO==country_clicked)%>%
group_by(sex)%>%
summarise(move=sum(move))%>%
collect()
IM_int_mig_all=IM_int_mig_data%>%
filter(sex=="all")
IM_int_mig_female=IM_int_mig_data%>%
filter(sex=="F")
IM_int_mig_male=IM_int_mig_data%>%
filter(sex=="M")
IM_int_mig_female_perc=IM_int_mig_female$move/IM_int_mig_all$move
IM_int_mig_male_perc=IM_int_mig_male$move/IM_int_mig_all$move
# IM_nat number & % #####
IM_nat_mig_data=IM_nat%>%
filter(ISO==country_clicked)%>%
group_by(sex)%>%
summarise(move=sum(move))%>%
collect()
IM_nat_mig_all=IM_nat_mig_data%>%
filter(sex=="all")
IM_nat_mig_female=IM_nat_mig_data%>%
filter(sex=="F")
IM_nat_mig_male=IM_nat_mig_data%>%
filter(sex=="M")
IM_nat_mig_female_perc=IM_nat_mig_female$move/IM_nat_mig_all$move
IM_nat_mig_male_perc=IM_nat_mig_male$move/IM_nat_mig_all$move
# top destination countries of international emigration #####
data_dest_all=tbl(mig_db,"GLOBAL_mig_int_X_50_all_country")
top_destination_all=data_dest_all%>%
filter(ISOI==country_clicked)%>%
collect()%>%
group_by(ISOJ)%>%
summarise(move=sum(pred_seed1))%>%
arrange(desc(move))%>%
top_n(3,move)%>%
mutate(total=EM_int_mig_all$move,
perc=move/total)%>%
left_join(admin_names%>%
distinct(ISO,COUNTRY_NAME),
by=c("ISOJ"="ISO"))
data_dest_female=tbl(mig_db,"GLOBAL_mig_int_X_50_F_country")
top_destination_female=data_dest_female%>%
filter(ISOI==country_clicked)%>%
collect()%>%
group_by(ISOJ)%>%
summarise(move=sum(pred_seed1))%>%
arrange(desc(move))%>%
top_n(3,move)%>%
mutate(total=EM_int_mig_female$move,
perc=move/total)%>%
left_join(admin_names%>%
distinct(ISO,COUNTRY_NAME),
by=c("ISOJ"="ISO"))
data_dest_male=tbl(mig_db,"GLOBAL_mig_int_X_50_M_country")
top_destination_male=data_dest_male%>%
filter(ISOI==country_clicked)%>%
collect()%>%
group_by(ISOJ)%>%
summarise(move=sum(pred_seed1))%>%
arrange(desc(move))%>%
top_n(3,move)%>%
mutate(total=EM_int_mig_male$move,
perc=move/total)%>%
left_join(admin_names%>%
distinct(ISO,COUNTRY_NAME),
by=c("ISOJ"="ISO"))
# top source countries of international immigration ####
data_source_all=tbl(mig_db,"GLOBAL_mig_int_M_50_all_country")
top_source_all=data_source_all%>%
filter(ISOJ==country_clicked)%>%
collect()%>%
group_by(ISOI)%>%
summarise(move=sum(pred_seed1))%>%
arrange(desc(move))%>%
top_n(3,move)%>%
mutate(total=IM_int_mig_all$move,
perc=move/total)%>%
left_join(admin_names%>%
distinct(ISO,COUNTRY_NAME),
by=c("ISOI"="ISO"))
data_source_female=tbl(mig_db,"GLOBAL_mig_int_M_50_F_country")
top_source_female=data_source_female%>%
filter(ISOJ==country_clicked)%>%
collect()%>%
group_by(ISOI)%>%
summarise(move=sum(pred_seed1))%>%
arrange(desc(move))%>%
top_n(3,move)%>%
mutate(total=IM_int_mig_female$move,
perc=move/total)%>%
left_join(admin_names%>%
distinct(ISO,COUNTRY_NAME),
by=c("ISOI"="ISO"))
data_source_male=tbl(mig_db,"GLOBAL_mig_int_M_50_M_country")
top_source_male=data_source_male%>%
filter(ISOJ==country_clicked)%>%
collect()%>%
group_by(ISOI)%>%
summarise(move=sum(pred_seed1))%>%
arrange(desc(move))%>%
top_n(3,move)%>%
mutate(total=IM_int_mig_male$move,
perc=move/total)%>%
left_join(admin_names%>%
distinct(ISO,COUNTRY_NAME),
by=c("ISOI"="ISO"))
# top destination admin of internal emigration #####
data_dest_all=tbl(mig_db,"GLOBAL_mig_nat_X_50_all_admin")
top_destination_all_nat=data_dest_all%>%
filter(ISOI==country_clicked)%>%
collect()%>%
group_by(JOIN_ID_J)%>%
summarise(move=sum(sex_nat))%>%
arrange(desc(move))%>%
top_n(3,move)%>%
mutate(total=EM_nat_mig_all$move,
perc=move/total)%>%
left_join(admin_names%>%
filter(ISO==country_clicked)%>%
distinct(JOIN_ID,COUNTRY_NAME,ADMIN_NAME),
by=c("JOIN_ID_J"="JOIN_ID"))
data_dest_female=tbl(mig_db,"GLOBAL_mig_nat_X_50_F_admin")
top_destination_female_nat=data_dest_female%>%
filter(ISOI==country_clicked)%>%
collect()%>%
group_by(JOIN_ID_J)%>%
summarise(move=sum(sex_nat))%>%
arrange(desc(move))%>%
top_n(3,move)%>%
mutate(total=EM_nat_mig_female$move,
perc=move/total)%>%
left_join(admin_names%>%
filter(ISO==country_clicked)%>%
distinct(JOIN_ID,COUNTRY_NAME,ADMIN_NAME),
by=c("JOIN_ID_J"="JOIN_ID"))
data_dest_male=tbl(mig_db,"GLOBAL_mig_nat_X_50_M_admin")
top_destination_male_nat=data_dest_male%>%
filter(ISOI==country_clicked)%>%
collect()%>%
group_by(JOIN_ID_J)%>%
summarise(move=sum(sex_nat))%>%
arrange(desc(move))%>%
top_n(3,move)%>%
mutate(total=EM_nat_mig_male$move,
perc=move/total)%>%
left_join(admin_names%>%
filter(ISO==country_clicked)%>%
distinct(JOIN_ID,COUNTRY_NAME,ADMIN_NAME),
by=c("JOIN_ID_J"="JOIN_ID"))
# top source admin of internal immigration #####
data_source_all=tbl(mig_db,"GLOBAL_mig_nat_M_50_all_admin")
top_source_all_nat=data_source_all%>%
filter(ISOI==country_clicked)%>%
collect()%>%
group_by(JOIN_ID_I)%>%
summarise(move=sum(sex_nat))%>%
arrange(desc(move))%>%
top_n(3,move)%>%
mutate(total=EM_nat_mig_all$move,
perc=move/total)%>%
left_join(admin_names%>%
filter(ISO==country_clicked)%>%
distinct(JOIN_ID,COUNTRY_NAME,ADMIN_NAME),
by=c("JOIN_ID_I"="JOIN_ID"))
data_source_female=tbl(mig_db,"GLOBAL_mig_nat_M_50_F_admin")
top_source_female_nat=data_source_female%>%
filter(ISOI==country_clicked)%>%
collect()%>%
group_by(JOIN_ID_I)%>%
summarise(move=sum(sex_nat))%>%
arrange(desc(move))%>%
top_n(3,move)%>%
mutate(total=EM_nat_mig_female$move,
perc=move/total)%>%
left_join(admin_names%>%
filter(ISO==country_clicked)%>%
distinct(JOIN_ID,COUNTRY_NAME,ADMIN_NAME),
by=c("JOIN_ID_I"="JOIN_ID"))
data_source_male=tbl(mig_db,"GLOBAL_mig_nat_M_50_M_admin")
top_source_male_nat=data_source_male%>%
filter(ISOI==country_clicked)%>%
collect()%>%
group_by(JOIN_ID_I)%>%
summarise(move=sum(sex_nat))%>%
arrange(desc(move))%>%
top_n(3,move)%>%
mutate(total=EM_nat_mig_male$move,
perc=move/total)%>%
left_join(admin_names%>%
filter(ISO==country_clicked)%>%
distinct(JOIN_ID,COUNTRY_NAME,ADMIN_NAME),
by=c("JOIN_ID_I"="JOIN_ID"))
# correct missing variables #####
variables=c("EM_int_mig_all",
"EM_int_mig_female",
"EM_int_mig_male",
"EM_int_mig_female_perc",
"EM_int_mig_male_perc",
"IM_int_mig_all",
"IM_int_mig_female",
"IM_int_mig_male",
"IM_int_mig_female_perc",
"IM_int_mig_male_perc",
"EM_nat_mig_all",
"EM_nat_mig_female_perc",
"EM_nat_mig_male_perc",
"top_destination_all","top_destination_female","top_destination_male",
"top_source_all","top_source_female","top_source_male",
"top_destination_all_nat","top_destination_female_nat","top_destination_male_nat",
"top_source_all_nat","top_source_female_nat","top_source_male_nat")
for(VAR in variables){
var_test=get(VAR)
var_test=as.data.frame(var_test)
if(nrow(var_test)==0){
NAMES=names(var_test)
var_test=data.frame(data.frame(t(array(NA,ncol(var_test)))))
names(var_test)=NAMES
assign(VAR,
var_test)
}
}
# data frame #####
nat_fig=data.frame(ISO=c(country_clicked,country_clicked,country_clicked),
sex=c("all","F","M"),
int_number_X=c(EM_int_mig_all$move,EM_int_mig_female$move,EM_int_mig_male$move),
int_percent_X=c(1,EM_int_mig_female_perc,EM_int_mig_male_perc),
int_number_M=c(IM_int_mig_all$move,IM_int_mig_female$move,IM_int_mig_male$move),
int_percent_M=unlist(c(1,IM_int_mig_female_perc,IM_int_mig_male_perc)),
nat_number=c(EM_nat_mig_all$move,EM_nat_mig_female$move,EM_nat_mig_male$move),
nat_percent=c(1,EM_nat_mig_female_perc,EM_nat_mig_male_perc),
int_top_dest_1_name=c(top_destination_all$COUNTRY_NAME[1],top_destination_female$COUNTRY_NAME[1],top_destination_male$COUNTRY_NAME[1]),
int_top_dest_1_number=c(top_destination_all$move[1],top_destination_female$move[1],top_destination_male$move[1]),
int_top_dest_1_percent=c(top_destination_all$perc[1],top_destination_female$perc[1],top_destination_male$perc[1]),
int_top_dest_2_name=c(top_destination_all$COUNTRY_NAME[2],top_destination_female$COUNTRY_NAME[2],top_destination_male$COUNTRY_NAME[2]),
int_top_dest_2_number=c(top_destination_all$move[2],top_destination_female$move[2],top_destination_male$move[2]),
int_top_dest_2_percent=c(top_destination_all$perc[2],top_destination_female$perc[2],top_destination_male$perc[2]),
int_top_dest_3_name=c(top_destination_all$COUNTRY_NAME[3],top_destination_female$COUNTRY_NAME[3],top_destination_male$COUNTRY_NAME[3]),
int_top_dest_3_number=c(top_destination_all$move[3],top_destination_female$move[3],top_destination_male$move[3]),
int_top_dest_3_percent=c(top_destination_all$perc[3],top_destination_female$perc[3],top_destination_male$perc[3]),
int_top_source_1_name=c(top_source_all$COUNTRY_NAME[1],top_source_female$COUNTRY_NAME[1],top_source_male$COUNTRY_NAME[1]),
int_top_source_1_number=c(top_source_all$move[1],top_source_female$move[1],top_source_male$move[1]),
int_top_source_1_percent=c(top_source_all$perc[1],top_source_female$perc[1],top_source_male$perc[1]),
int_top_source_2_name=c(top_source_all$COUNTRY_NAME[2],top_source_female$COUNTRY_NAME[2],top_source_male$COUNTRY_NAME[2]),
int_top_source_2_number=c(top_source_all$move[2],top_source_female$move[2],top_source_male$move[2]),
int_top_source_2_percent=c(top_source_all$perc[2],top_source_female$perc[2],top_source_male$perc[2]),
int_top_source_3_name=c(top_source_all$COUNTRY_NAME[3],top_source_female$COUNTRY_NAME[3],top_source_male$COUNTRY_NAME[3]),
int_top_source_3_number=c(top_source_all$move[3],top_source_female$move[3],top_source_male$move[3]),
int_top_source_3_percent=c(top_source_all$perc[3],top_source_female$perc[3],top_source_male$perc[3]),
nat_top_dest_1_name=c(top_destination_all_nat$ADMIN_NAME[1],top_destination_female_nat$ADMIN_NAME[1],top_destination_male_nat$ADMIN_NAME[1]),
nat_top_dest_1_number=c(top_destination_all_nat$move[1],top_destination_female_nat$move[1],top_destination_male_nat$move[1]),
nat_top_dest_1_percent=c(top_destination_all_nat$perc[1],top_destination_female_nat$perc[1],top_destination_male_nat$perc[1]),
nat_top_dest_2_name=c(top_destination_all_nat$ADMIN_NAME[2],top_destination_female_nat$ADMIN_NAME[2],top_destination_male_nat$ADMIN_NAME[2]),
nat_top_dest_2_number=c(top_destination_all_nat$move[2],top_destination_female_nat$move[2],top_destination_male_nat$move[2]),
nat_top_dest_2_percent=c(top_destination_all_nat$perc[2],top_destination_female_nat$perc[2],top_destination_male_nat$perc[2]),
nat_top_dest_3_name=c(top_destination_all_nat$ADMIN_NAME[3],top_destination_female_nat$ADMIN_NAME[3],top_destination_male_nat$ADMIN_NAME[3]),
nat_top_dest_3_number=c(top_destination_all_nat$move[3],top_destination_female_nat$move[3],top_destination_male_nat$move[3]),
nat_top_dest_3_percent=c(top_destination_all_nat$perc[3],top_destination_female_nat$perc[3],top_destination_male_nat$perc[3]),
nat_top_source_1_name=c(top_source_all_nat$ADMIN_NAME[1],top_source_female_nat$ADMIN_NAME[1],top_source_male_nat$ADMIN_NAME[1]),
nat_top_source_1_number=c(top_source_all_nat$move[1],top_source_female_nat$move[1],top_source_male_nat$move[1]),
nat_top_source_1_percent=c(top_source_all_nat$perc[1],top_source_female_nat$perc[1],top_source_male_nat$perc[1]),
nat_top_source_2_name=c(top_source_all_nat$ADMIN_NAME[2],top_source_female_nat$ADMIN_NAME[2],top_source_male_nat$ADMIN_NAME[2]),
nat_top_source_2_number=c(top_source_all_nat$move[2],top_source_female_nat$move[2],top_source_male_nat$move[2]),
nat_top_source_2_percent=c(top_source_all_nat$perc[2],top_source_female_nat$perc[2],top_source_male_nat$perc[2]),
nat_top_source_3_name=c(top_source_all_nat$ADMIN_NAME[3],top_source_female_nat$ADMIN_NAME[3],top_source_male_nat$ADMIN_NAME[3]),
nat_top_source_3_number=c(top_source_all_nat$move[3],top_source_female_nat$move[3],top_source_male_nat$move[3]),
nat_top_source_3_percent=c(top_source_all_nat$perc[3],top_source_female_nat$perc[3],top_source_male_nat$perc[3])
)
if(country_clicked=="AFG"){
clean_names=names(nat_fig)
}
df=df%>%
bind_rows(nat_fig%>%
select(clean_names))
print(country_clicked)
}
# copy to DB #####
copy_to(mig_db,
df,
name="nat_fig",
temporary = FALSE,
indexes = list("ISO","sex"),
overwrite = T)
|
19ae717c54dc3e11b4a4aa7e421b0492a2d44e29 | 19b01a47e88f43766314a6e83c4bae13c2595ef9 | /R/target.R | 1bdfcee0bcdfaceafc1034354723a59ba5171cb3 | [
"MIT"
] | permissive | iandennismiller/i0 | 1db805eaa0802e4f34adb42c99f8539686a52ad2 | 36183c6a5d7e4be4fc2552baeb114bd692e194b3 | refs/heads/master | 2016-09-06T05:44:39.901985 | 2013-10-28T17:37:38 | 2013-10-28T17:37:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,891 | r | target.R | #' Create a target object.
#'
#' @param x input model function specification
#' @return target object
#' @keywords character
#' @export
target <- function(x, ...) UseMethod("target")
######
# generic handlers
#' Print a zero-targeted model
#'
#' @param x a zero-targeted model
#' @export
print.target <- function(x, ...)
{
print(x$estimates)
}
#' Get mean and stderr information about a zero-targeted model
#'
#' @param object a zero-targeted model
#' @export
summary.target <- function(object, ...)
{
res = list(summary = summarize(object))
class(res) <- "summary.target"
res
}
#' Print a zero-targeted summary
#'
#' @param x a zero-targeted summary object
#' @export
print.summary.target <- function(x, ...)
{
print(x$summary)
}
#' Plot a zero-targeted model +/- 1 standard deviation.
#'
#' @param x a zero-targeted model object
#' @param raw boolean indicating whether to plot raw values; defaults to FALSE
#' (i.e. print scaled rather than raw values)
#' @export
plot.target <- function(x, raw=F, ...) {
display = summarize(x)
if (raw) {
display = transform_raw(display, x$family)
}
else {
display$ymins = display$means - display$ci
display$ymaxs = display$means + display$ci
}
ggplot(display, aes(x=dim2, y=means, group=dim1)) +
geom_line(aes(linetype=dim1), size=1) +
geom_point(size=3, fill="white") +
scale_x_discrete(labels=c(paste("low", x$terms$d2_name), paste("high", x$terms$d2_name))) +
scale_linetype(labels=c(paste("high", x$terms$d1_name), paste("low", x$terms$d1_name))) +
coord_cartesian(xlim=c(0.8, 2.2)) +
xlab(NULL)+
ylab(x$terms$dv_name) +
theme_bw() +
geom_errorbar(aes(ymin=ymins, ymax=ymaxs), alpha=0.3, width=.05) +
labs(title = paste(x$terms$dv_name, " ~ '", x$terms$d1_name, "' and '", x$terms$d2_name, "'", sep="")) +
theme(legend.title=element_blank()) +
theme(legend.position=c(.2, .9)) +
theme(legend.text = element_text(size = 12)) +
theme(axis.text.x = element_text(size = 14)) +
theme(axis.text.y = element_text(size = 14))
}
#################
# private methods
# Unpack an S3 formula to figure out the name of the DV and IVs
#
# @param formula model specification
# @return target object
unpack_formula <- function(formula) {
terms = names(attr(terms(formula), "factors")[,1])
list(
dv_name = terms[1],
d1_name = terms[2],
d2_name = terms[3]
)
}
# undocumented
#
transform_raw <- function(display, family) {
if (family == "poisson") {
b0 = display$means - display$ci
display$ymins = exp(b0)
b0 = display$means + display$ci
display$ymaxs = exp(b0)
b0 = display$means
display$means = exp(b0)
}
else if (family == "binomial") {
b0 = display$means - display$ci
display$ymins = exp(b0) / (1 + exp(b0))
b0 = display$means + display$ci
display$ymaxs = exp(b0) / (1 + exp(b0))
b0 = display$means
display$means = exp(b0) / (1 + exp(b0))
}
else {
display$ymins = display$means - display$ci
display$ymaxs = display$means + display$ci
}
display
}
# undocumented
#
summarize <- function(x, ...) {
display = data.frame(
dim1 = c('low', 'low', 'high', 'high'),
dim2 = c('low', 'high', 'low', 'high'),
means = c(
x$estimates$low$low$mean,
x$estimates$low$high$mean,
x$estimates$high$low$mean,
x$estimates$high$high$mean
),
ci = c(
x$estimates$low$low$stderr,
x$estimates$low$high$stderr,
x$estimates$high$low$stderr,
x$estimates$high$high$stderr
)
)
display$dim2 = factor(display$dim2, levels=c('low', 'high'))
display
}
# undocumented
#
calc_zero_target <- function(terms, data) {
# zt means "zero targeted"
zt = data.frame(
#ids = seq(from=1, to=dim(data)[1]),
dv = data[[terms$dv_name]],
d1_high = data[[terms$d1_name]] - sd(data[[terms$d1_name]], na.rm=T),
d1_low = data[[terms$d1_name]] + sd(data[[terms$d1_name]], na.rm=T),
d2_high = data[[terms$d2_name]] - sd(data[[terms$d2_name]], na.rm=T),
d2_low = data[[terms$d2_name]] + sd(data[[terms$d2_name]], na.rm=T)
)
data.frame(data, zt)
}
# undocumented
#
gen_formulas <- function(formula_str, terms) {
f_low_low = str_replace_all(formula_str, terms$d1_name, "d1_low")
f_low_low = str_replace_all(f_low_low, terms$d2_name, "d2_low")
f_low_high = str_replace_all(formula_str, terms$d1_name, "d1_low")
f_low_high = str_replace_all(f_low_high, terms$d2_name, "d2_high")
f_high_low = str_replace_all(formula_str, terms$d1_name, "d1_high")
f_high_low = str_replace_all(f_high_low, terms$d2_name, "d2_low")
f_high_high = str_replace_all(formula_str, terms$d1_name, "d1_high")
f_high_high = str_replace_all(f_high_high, terms$d2_name, "d2_high")
list(
low_low = as.formula(f_low_low),
low_high = as.formula(f_low_high),
high_low = as.formula(f_high_low),
high_high = as.formula(f_high_high)
)
}
# undocumented
#
calc_estimates <- function(f, zt, mlm, family) {
if (mlm) {
fn = calc_lmer
}
else {
fn = calc_lm
}
list(
low_low = fn(f$low_low, zt, family),
low_high = fn(f$low_high, zt, family),
high_low = fn(f$high_low, zt, family),
high_high = fn(f$high_high, zt, family)
)
}
# undocumented
#
unpack_estimates <- function(estimate) {
list(
low = list(
low = list(
mean = estimate[["low_low"]][["mean"]],
stderr = estimate[["low_low"]][["stderr"]]
),
high = list(
mean = estimate[["low_high"]][["mean"]],
stderr = estimate[["low_high"]][["stderr"]]
)
),
high = list(
low = list(
mean = estimate[["high_low"]][["mean"]],
stderr = estimate[["high_low"]][["stderr"]]
),
high = list(
mean = estimate[["high_high"]][["mean"]],
stderr = estimate[["high_high"]][["stderr"]]
)
)
)
}
# undocumented
#
calc_lm <- function(spec, data, family) {
model = glm(spec, data, na.action="na.exclude", family=family)
list(
model = model,
mean = summary(model)$coefficients[1,1],
stderr = summary(model)$coefficients[1,2]
)
}
# undocumented
#
calc_lmer <- function(spec, data, family) {
model = lmer(spec, data, na.action="na.exclude", family=family)
list(
model = model,
mean = attr(model, 'fixef')[[1]],
stderr = attr(summary(model), "coefs")[[1,2]]
)
}
################
# public methods
#' Create a target object.
#'
#' @param x input model function specification
#' y data frame
#' @return target object
#' @keywords character
#' @export
#' @examples
#' data = data.frame(y = c(0, 1, 2), x1 = c(2, 4, 6), x2 = c(3, 6, 9))
#' t = target(y ~ x1 * x2, data=data)
#' t$point
target.formula <- function(
formula,
data=list(),
family="gaussian",
mlm=FALSE,
...)
{
terms = unpack_formula(formula)
f_str = as.character(formula)
formula_str = paste(f_str[2], f_str[1], f_str[3])
std_formulas = gen_formulas(formula_str, terms)
zt = calc_zero_target(terms, data)
models = calc_estimates(std_formulas, zt, mlm, family)
obj = list(
formula = formula,
call = match.call(),
terms = terms,
zero_targeted = zt,
models = models,
family = family,
estimates = unpack_estimates(models)
)
class(obj) = "target"
obj
}
|
68a92eaf40de4273d90ed95aff0a45f152aa19f9 | 6fa24cca5ba1dc15f9f206beb97c260786efe732 | /研討會_202307/script/old/8.1-glmm.R | 953a197585f553160cbc98d088f3d7c122ab7686 | [] | no_license | wetinhsu/Macaca-population-trend | fe14e5dfa6290b4a982175feae290053c60446b9 | a0e738ec817ae70b8ea042eeb7b64df4c9b5cb10 | refs/heads/master | 2023-07-19T12:38:45.198058 | 2023-07-11T01:13:59 | 2023-07-11T01:13:59 | 190,343,813 | 0 | 0 | null | 2019-06-05T07:07:49 | 2019-06-05T07:07:49 | null | UTF-8 | R | false | false | 3,174 | r | 8.1-glmm.R | library(tidyverse)
library(readxl)
library(writexl)
library(here)
here::here()
site_list <-
read_xlsx("./研討會_202307/data/refer/分層隨機取樣的樣區清單 _20221031.xlsx",
sheet = "樣區表") %>%
.[,7] %>%
setNames(., "site_list")
BBS_Monkey_1521_GLMM <-
read.csv("./研討會_202307/data/clean/BBS_Monkey_1521_0214.csv") %>%
reshape2::melt(id = "site", variable.name ="year", value.name = "count") %>%
mutate(year = str_remove_all(year, "X"))
Ch5_matrix_line_site <-
read_xlsx("./研討會_202307/data/refer/Dali/Ch5_matrix_line_site.xlsx")
M.data <- read_excel(here("./研討會_202307/data/clean/for analysis_1521_v2.xlsx"),
sheet=1) %>%
mutate_at(c("Year", "Survey", "Point", "Macaca_sur",
"Month", "Day", "Altitude", "julian.D"), as.numeric) %>%
filter(Site_N %in% site_list$`site_list`) %>%
filter(!(Year %in% 2020 & Survey %in% 3)) %>%
filter(analysis=="Y") %>%
mutate(transect = paste0(Site_N, "-", Point))
#---------------------------------
Df <-
M.data %>%
select(-(Macaca_dist:analysis))%>%
left_join(Ch5_matrix_line_site, by = c("transect" = "site")) %>%
filter(! is.na(Shannon))%>%
group_split(Year) %>%
map(., function(x){
sample(1:nrow(x), size = nrow(x[x$Macaca_sur %in% 1,]), replace = F) %>%
slice(x, .) %>%
mutate(Macaca_sur = 0)
}) %>%
bind_rows() %>%
bind_rows(
M.data %>%
select(-(Macaca_dist:analysis))%>%
left_join(Ch5_matrix_line_site, by = c("transect" = "site")) %>%
filter(! is.na(Shannon))%>%
filter( Macaca_sur != 0)
) %>%
select(transect,Site_N,Year, Macaca_sur, elevation,edge,P_forest,P_farmland,Shannon,edge_length) %>%
mutate(Year = Year %>% as.numeric(.) %>% as.integer(.))%>%
mutate_at(c("elevation","edge","P_forest",
"P_farmland", "Shannon", "edge_length"),scale) %>%
data.frame(.)
#----------------------------------
library(car)
vif(lm( Macaca_sur ~ Year+ elevation+ edge + P_forest + P_farmland +
Shannon , Df))
#-------------------------
library(ggcorrplot)
corr <-
Df %>%
filter(!is.na(edge)) %>%
select(Year,elevation,edge,P_forest,P_farmland,Shannon) %>%
cor(.) %>%
round(1)
ggcorrplot(corr,
# method = "circle",
hc.order = TRUE,
type = "lower",
lab = TRUE) #數字為correlation coefficient
#----------------------------------------------
library(glmmTMB)
model2 <- glmmTMB(
Macaca_sur ~ elevation+ edge + P_forest + P_farmland +
Shannon + (1|Year)+(1|transect) ,
Df,
family = binomial(link = "logit") )
summary(model2)
write.csv(Df,"./研討會_202307/data/clean/Df_0222.csv",
row.names = F)
Df <- read.csv("./研討會_202307/data/clean/Df_0222.csv" )%>%
mutate_at(c("elevation","edge","P_forest",
"P_farmland", "Shannon", "edge_length"),scale)
#--------------------
ggplot(Df, aes(x = P_farmland, y = Macaca_sur))+
geom_point(pch = 1)+
geom_smooth(method = "glm",
method.args = list(family = "binomial"),
se = F)
|
4817684a4cd60b24e91410f6bbca1bf3447d4ec2 | add0e24035b41dcb36aefd0156519221f85e8750 | /Simul_fun.r | f3490e538d97d1e9710f27c91f06d76a94e0f2bc | [] | no_license | lsaravia/LogisticStochastic | 831f4b4fdf3092a34b10e3a31c2c2263750e3078 | 2f2e1d5fa2089eff9571e5a4f7014403c6c0ead6 | refs/heads/master | 2020-04-15T04:37:31.117855 | 2016-11-14T19:36:48 | 2016-11-14T19:36:48 | 73,739,724 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,004 | r | Simul_fun.r |
# Simulacion estocastica para modelo logistico
#
# Simulacion estocastica para modelo logistico
# con fluctuación ambiental en r (1+a cos(w t))
#
STO_logistic_ef <- function(x,pars,times)
{
# Setup an array to store results: time, N
#
ltimes <- length(times)
output <- array(dim=c(ltimes,2),dimnames=list(NULL,names(x)))
t <- x[1]
stopifnot(t<=times[1])
# loop until either k > maxstep or
#
output[1,] <-x
k <- 2
while (k <= ltimes) {
while (t < times[k]) {
x <- logistic_ef_onestep(x,pars)
t <- x[1]
}
while (t >= times[k] && k <= ltimes) {
output[k,] <- x
k <- k+1
}
}
as.data.frame(output)
}
# Logistica con fluctuación ambiental en r - Simulacion de Eventos
#
logistic_ef_onestep<- function(x,pars){
p<-as.list(pars)
# 2nd element is the population
#
N<-x[2]
B<- p$r*(1+p$a*cos(p$omega*x[1])) # Birth rate
if(B<0) B<-0
R<- B+p$s*N
event_rate<- B/R
if(N>0){
if(runif(1) <=event_rate ){
N<-N+1
} else {
N<-N-1
}
}
# Exponential random number
tau<-rexp(n=1,rate=R)
c(x[1]+tau,N)
}
# Ecuación logistica determinista con fluctuaciones ambientales
#
logistic_ef_det<-function(t,State,Pars){
with(as.list(c(State, Pars)), {
dP <- N*(r*(1+a *cos(omega*t))-s*N)
return(list(c(dP)))
})
}
# Estimacion de parametros usando Aproximate Bayesian computation
#
#
estima_ABC <- function(dat,p,time,dlim,sim=1000)
{
da <- data.frame(matrix(, nrow = time, ncol = 2))
names(da) <- c("r","s")
j <-1
lendat<-length(dat)
for(i in 1:sim){
# calcula parametros
#
r<- runif(1,p$r[1],p$r[2])
s <- runif(1,p$s[1],p$s[2])
nini <- round(r/s)
# Corre el modelo
out <- STO_simul(c(0,nini),c(r=r,s=s),1:time)
# selecciona la ultima parte
out <- out[(time-lendat+1):time,2]
dis <- sum((dat-out)^2)
if(dis <= dlim){
da[j,]<-c(r,s)
j <- j + 1
}
}
return(na.omit(da))
}
|
e93b298fde37584159b521ebdf57c649d0642c1d | fb9259f3098f3e02aadbb12dcfa36e1e8f022623 | /R/wtd_mwu.R | 03a3fb373a23a33cc31616184df4446190f66df3 | [] | no_license | strengejacke/sjstats | 4786c05635f33a19211c1adaf801b664f093ce38 | 6f78eb27c779b2a833452ec20baa017042d274a0 | refs/heads/master | 2023-03-15T19:25:15.651996 | 2022-11-19T20:13:55 | 2022-11-19T20:13:55 | 59,321,248 | 186 | 21 | null | 2023-03-08T16:25:18 | 2016-05-20T19:33:23 | R | UTF-8 | R | false | false | 1,675 | r | wtd_mwu.R | #' @rdname weighted_sd
#' @export
weighted_mannwhitney <- function(data, ...) {
UseMethod("weighted_mannwhitney")
}
#' @importFrom dplyr select
#' @rdname weighted_sd
#' @export
weighted_mannwhitney.default <- function(data, x, grp, weights, ...) {
x.name <- deparse(substitute(x))
g.name <- deparse(substitute(grp))
w.name <- deparse(substitute(weights))
# create string with variable names
vars <- c(x.name, g.name, w.name)
# get data
dat <- suppressMessages(dplyr::select(data, !! vars))
dat <- na.omit(dat)
weighted_mannwhitney_helper(dat)
}
#' @importFrom dplyr select
#' @rdname weighted_sd
#' @export
weighted_mannwhitney.formula <- function(formula, data, ...) {
vars <- all.vars(formula)
# get data
dat <- suppressMessages(dplyr::select(data, !! vars))
dat <- na.omit(dat)
weighted_mannwhitney_helper(dat)
}
weighted_mannwhitney_helper <- function(dat, vars) {
# check if pkg survey is available
if (!requireNamespace("survey", quietly = TRUE)) {
stop("Package `survey` needed to for this function to work. Please install it.", call. = FALSE)
}
x.name <- colnames(dat)[1]
group.name <- colnames(dat)[2]
colnames(dat) <- c("x", "g", "w")
if (dplyr::n_distinct(dat$g, na.rm = TRUE) > 2) {
m <- "Weighted Kruskal-Wallis test"
method <- "KruskalWallis"
} else {
m <- "Weighted Mann-Whitney-U test"
method <- "wilcoxon"
}
design <- survey::svydesign(ids = ~0, data = dat, weights = ~w)
mw <- survey::svyranktest(formula = x ~ g, design, test = method)
attr(mw, "x.name") <- x.name
attr(mw, "group.name") <- group.name
class(mw) <- c("sj_wmwu", "list")
mw$method <- m
mw
}
|
0bf7bccd59becbac484a54bd848e55f41a8bc58f | 3038b772a273a2be8aca317104ceaa12ed7c76ec | /code/Data_CleanPrep.R | 6e14058e2523b57a8222c5a9cd8b8bd7c4028465 | [] | no_license | ArmanMadani/Stat-133-Project | 54a7f6ecbb7b4ddf12e63646cf5e12361212e036 | 7bc245ed9996f4b7f9fe7453e12cdaff4785e3a1 | refs/heads/master | 2016-09-01T10:43:01.452617 | 2015-12-14T01:22:13 | 2015-12-14T01:22:13 | 47,475,530 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,192 | r | Data_CleanPrep.R | # Data Cleaning and Preparation from .html files to .csv files
library(XML)
# Data Cleaning for Champions.html
nba_champions <- readHTMLTable('rawdata/champions.html')
nba_champions <- as.data.frame(nba_champions[[2]])
nba_champions[] <- lapply(nba_champions, as.character)
colnames(nba_champions) <- c('Year', 'Champion', 'Score', 'Opponent')
nba_champions <- nba_champions[-1, ]
write.csv(nba_champions, 'data/champions.csv')
# Getting the statistics for the champions
champion_stats <- c()
nba_champions$Champion[nba_champions$Year==1995]
for (i in 1:21) {
file.path <- paste0('data/leagues_NBA_', 1994 + i, '_team.csv')
files <- read.csv(file.path)
champion_stats[[i]] <-
files[c("Team", "FGA", "FGP", "X3PA", "X3PP", "years")]
}
for (i in 1:21){
years <- 1994 + i
champion <- nba_champions$Champion[nba_champions$Year==years]
year_data <- champion_stats[[i]]
year_data$Team <- gsub('\\*', '', as.character(year_data$Team))
champion_stats[[i]] <-
year_data[year_data$Team == champion | year_data$Team == "Average", ]
}
all_champs <- do.call(rbind, champion_stats)
all_champs <- all_champs[-seq(from = 2, to = nrow(all_champs), by = 2L), ]
write.csv(all_champs, file = "data/champ_data.csv")
# Drafted Players' Heights
to_inches <- function(height) {
h <- strsplit(as.character(height), "[^0-9]")
h <- grep("[0-9]", h[[1]], value = TRUE)
ft <- as.numeric(h[1])
inch <- as.numeric(h[2])
return(ft * 12 + inch)
}
players <- readHTMLTable('rawdata/player_heights1995.html')
players <- as.data.frame(players[[9]])
players["Height"] <- unlist(lapply(players["Height"][[1]], to_inches))
players["Year"] = 1995
players["Average_Height"] = mean(players$Height, na.rm = TRUE)
players["Average_Weight"] = mean(as.numeric(levels(players$Weight)),
na.rm = TRUE)
players <- players[c("Name", "Height", "Weight",
"Average_Height", "Average_Weight", "Year")]
for (i in 1996:2015) {
p <- readHTMLTable(paste0('rawdata/player_heights', i, '.html'))
p <- as.data.frame(p[[9]])
p["Height"] <- unlist(lapply(p["Height"][[1]], to_inches))
p["Year"] = i
p["Average_Height"] = mean(p$Height, na.rm = TRUE)
p["Average_Weight"] = mean(as.numeric(levels(p$Weight)), na.rm = TRUE)
p <- p[c("Name", "Height", "Weight",
"Average_Height", "Average_Weight", "Year")]
players <- rbind(players, p)
}
write.csv(players, file = "data/players.csv")
# Cleaning data for champion rosters
champ <- readHTMLTable('rawdata/champion_stats1995.html')
champ <- as.data.frame(champ[[1]])
champ["Ht"] <- unlist(lapply(champ["Ht"][[1]], to_inches))
champ["Year"] = 1995
champ["Average_Height"] = mean(champ$Ht, na.rm = TRUE)
champ["Average_Weight"] = mean(as.numeric(levels(champ$Wt)), na.rm = TRUE)
champ <- champ[c("Player", "Pos", "Ht", "Wt", "Average_Height",
"Average_Weight", "Year")]
for (i in 1996:2015) {
ch <- readHTMLTable(paste0('rawdata/champion_stats', i, '.html'))
ch <- as.data.frame(ch[[1]])
ch["Ht"] <- unlist(lapply(ch["Ht"][[1]], to_inches))
ch["Year"] = i
ch["Average_Height"] = mean(ch$Ht, na.rm = TRUE)
ch["Average_Weight"] = mean(as.numeric(levels(ch$Wt)), na.rm = TRUE)
ch <- ch[c("Player", "Pos", "Ht", "Wt", "Average_Height", "Average_Weight",
"Year")]
champ <- rbind(champ, ch)
}
write.csv(champ, file = "data/roster.csv")
# Case Study Player: Stephen Curry
steph_curry <- readHTMLTable('rawdata/steph_curry.html')
steph_curry <- as.data.frame(steph_curry[[11]])
steph_curry["Height w/o Shoes"] <-
unlist(lapply(steph_curry["Height w/o Shoes"], to_inches))
steph_curry["Height w/shoes"] <-
unlist(lapply(steph_curry["Height w/shoes"], to_inches))
steph_curry["Wingspan"] <-
unlist(lapply(steph_curry["Wingspan"], to_inches))
steph_curry["Standing Reach"] <-
unlist(lapply(steph_curry["Standing Reach"], to_inches))
write.csv(steph_curry, 'data/steph_curry.csv')
steph_curry_stats <- readHTMLTable('rawdata/steph_curry_career.html')
steph_curry_stats <- steph_curry_stats$per_game
write.csv(steph_curry_stats, 'data/steph_curry_stats.csv')
# Case Study Player: Draymond Green
draymond_green <- readHTMLTable('rawdata/draymond_green.html')
draymond_green <- as.data.frame(draymond_green[[11]])
draymond_green["Height w/o Shoes"] <-
unlist(lapply(draymond_green["Height w/o Shoes"], to_inches))
draymond_green["Height w/shoes"] <-
unlist(lapply(draymond_green["Height w/shoes"], to_inches))
draymond_green["Wingspan"] <-
unlist(lapply(draymond_green["Wingspan"], to_inches))
draymond_green["Standing Reach"] <-
unlist(lapply(draymond_green["Standing Reach"], to_inches))
write.csv(draymond_green, 'data/draymond_green.csv')
draymond_green_stats <- readHTMLTable('rawdata/draymond_green.html')
draymond_green_stats <- as.data.frame(draymond_green_stats[[12]])
write.csv(draymond_green_stats, 'data/draymond_green_stats.csv')
# Case Study Player: Tim Duncan
tim_duncan <- readHTMLTable('rawdata/tim_duncan.html')
tim_duncan <- as.data.frame(tim_duncan[[11]])
tim_duncan["Height w/o Shoes"] <-
unlist(lapply(tim_duncan["Height w/o Shoes"], to_inches))
tim_duncan["Height w/shoes"] <-
unlist(lapply(tim_duncan["Height w/shoes"], to_inches))
tim_duncan["Wingspan"] <- unlist(lapply(tim_duncan["Wingspan"], to_inches))
tim_duncan["Standing Reach"] <-
unlist(lapply(tim_duncan["Standing Reach"], to_inches))
write.csv(tim_duncan, 'data/tim_duncan.csv')
tim_duncan_stats <- readHTMLTable('rawdata/tim_duncan.html')
tim_duncan_stats <- as.data.frame(tim_duncan_stats[[12]])
write.csv(tim_duncan_stats, 'data/tim_duncan_stats.csv')
# Case Study Player: Shaq
shaq <- readHTMLTable('rawdata/shaq.html')
shaq <- as.data.frame(shaq[[11]])
shaq["Height w/o Shoes"] <- unlist(lapply(shaq["Height w/o Shoes"], to_inches))
shaq["Height w/shoes"] <- unlist(lapply(shaq["Height w/shoes"], to_inches))
shaq["Wingspan"] <- unlist(lapply(shaq["Wingspan"], to_inches))
shaq["Standing Reach"] <- unlist(lapply(shaq["Standing Reach"], to_inches))
write.csv(shaq, 'data/shaq.csv')
shaq_stats <- readHTMLTable('rawdata/shaq.html')
shaq_stats <- as.data.frame(shaq_stats[[12]])
write.csv(shaq_stats, 'data/shaq_stats.csv')
|
7fb982e1c16c249433f83529ad9ea9d468fbb7f3 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/Rfssa/examples/ftsplot.Rd.R | c33ff1408bfd99086374aac459a5ddd52cecc26e | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 679 | r | ftsplot.Rd.R | library(Rfssa)
### Name: ftsplot
### Title: Functional Time Series Plots
### Aliases: ftsplot
### ** Examples
data("Callcenter")
library(fda)
D <- matrix(sqrt(Callcenter$calls),nrow = 240)
N <- ncol(D)
time <- 1:30
K <- nrow(D)
u <- seq(0,K,length.out =K)
d <- 22 #Optimal Number of basises
basis <- create.bspline.basis(c(min(u),max(u)),d)
Ysmooth <- smooth.basis(u,D,basis)
Y <- Ysmooth$fd
par(mar=c(2,1,2,2),mfrow=c(1,3))
ftsplot(u,time,Y[1:30],space = 0.4,type=1,ylab = "",xlab = "Day",main = "Typ1=1")
ftsplot(u,time,Y[1:30],space = 0.4,type=2,ylab = "",xlab = "Day",main = "Typ1=2")
ftsplot(u,time,Y[1:30],space = 0.4,type=3,ylab = "",xlab = "Day",main = "Typ1=3")
|
5cc7ddbb67ed7455ee2fcac0ade66864132983fd | 4420e96700b4f0f4d4b005d8afb46f07277393f4 | /sread2.R | 9716fe1edc8eacf03b7f59a568dab9cb7661aa80 | [] | no_license | k2hrt/theo1 | 0df2a6dfcf4c3788d9eb9289f09514fbebfd17a1 | dbf52c47d8dfb9e4f23eee1b4479390687433e64 | refs/heads/master | 2022-11-13T11:14:58.192956 | 2020-07-11T00:02:38 | 2020-07-11T00:02:38 | 276,737,195 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 780 | r | sread2.R | sread2<-function(x,r,tau0=1)
{
# Get size of phase data array
N=length(x)
# Get # data frame rows
n=nrow(r)
# Allocate # analysis points array
num<-1:n
# Fill # analysis points array
# ith row of tau column is r[i,1]
for(i in 1:n){
af=(r[i,1]/0.75)/tau0
num[i]=((N-af)/2)*af
}
# Create output data frame
# This is Stable32 Theo1 Read format
# DF column not included
# Add num column to data frame
r<-cbind(r,num)
# Re-arrange data frame columns
# Now: tau,l,t,u,num
# Want: tau,num,t,l,u
r<-r[c(1,5,3,2,4)]
# Write output to file
# Note: Filename is hard-coded
write.table(r, "C:\\Data\\sigma.tau", row.names=F, col.names=F)
# Return output data frame
invisible(r)
}
|
6b7f86bd8c2e06411b19ae5c20b878746c86992d | ba1edf30bca6e023562e4aed21c0ca009d22f431 | /db/man/dbfile.insert.Rd | 943f28b9c98c6043d1a812e8cbb9e8653fe32101 | [
"NCSA",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | rgknox/pecan | 79f080e77637dfb974ebb29313b5c63d9a53228e | 5b608849dccb4f9c3a3fb8804e8f95d7bf1e4d4e | refs/heads/master | 2020-12-27T20:38:35.429777 | 2014-05-06T13:42:52 | 2014-05-06T13:42:52 | 19,548,870 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 784 | rd | dbfile.insert.Rd | \name{dbfile.insert}
\alias{dbfile.insert}
\title{Insert file into tables}
\usage{
dbfile.insert(filename, type, id, con, hostname = fqdn())
}
\arguments{
\item{filename}{the name of the file to be inserted}
\item{con}{database connection object}
\item{hostname}{the name of the host where the file is
stored, this will default to the name of the current
machine}
\item{params}{database connection information}
}
\value{
data.frame with the id, filename and pathname of the file
that is written
}
\description{
Function to insert a file into the dbfiles table
}
\details{
This will write into the dbfiles and machines the
required data to store the file
}
\examples{
\dontrun{
dbfile.insert('somefile.txt', 'Input', 7, dbcon)
}
}
\author{
Rob Kooper
}
|
17a0ee61723f79ed67a1159bc4785b60fdbc1e58 | 77f228315f25d2d5d3101a3f954fc6b1364fee59 | /R/weight.R | 4bab1eaad382c4584ca0629c0be0170f78d078bd | [] | no_license | TobieSurette/gulf.data | 5fbe678b64d10d9f50fed501d958c65f70d79fcf | 7e9796e47c90645e399775dadb1344e7f51a13b0 | refs/heads/master | 2023-09-03T23:53:10.733496 | 2023-08-29T18:45:20 | 2023-08-29T18:45:20 | 253,640,181 | 2 | 1 | null | 2022-06-01T12:33:15 | 2020-04-06T23:36:23 | R | UTF-8 | R | false | false | 11,211 | r | weight.R | #' Individual or Catch Weight
#'
#' @description Returns an estimate of the weight of individual organisms for a specified length.
#' Estimated weights of sample subsets can also be calculated.
#'
#' This function makes use of a set of allometric length-weight coefficients to
#' estimate the weight of an organism given its length. The species must be
#' specified and the sex may be specified for dimorphic species.
#'
#' If \code{year} is specified, then survey data is loaded and the length-weight
#' coefficients are calculated directly.
#'
#' @param x A numerical vector of organism lengths, or alternatively, a frequency table or a
#' data frame of length-frequencies as produced by \code{\link{freq}}. The presence
#' of \sQuote{species}, \sQuote{sex} or \sQuote{year} fields in the data frame will
#' be passed onto the corresponding function arguments.
#'
#' @param species Species code.
#'
#' @param sex Numerical code specifying sex.
#'
#' @param coefficients A two-element numerical vector specifying the \code{a} and \code{b}
#' coefficients, respectively. The the \sQuote{a} coefficient is assumed
#' to be on the log-10 scale and the units in grams.
#'
#' @param units Units of the weight vector to be returned. It may be either in grams
#' (\code{units = "g"}) or kilograms (\code{units = "kg"}).
#'
#' @param year Survey years to use as data when calculating the length-weight coefficients.
#' If left unspecified, a table of default values are used.
#'
#' @param ... Futher arguments.
#'
#' @param category A character string specifying a snow crab or crustacean category for syntax.
#'
#' @param by Character string(s) specifying the fields by which to group the estimated weights.
#'
#' @param probability Logical value specifying whether maturity values are to be filled in with
#' probabilities when morphometric values are unavailable. In this case, the
#' numbers returned may be non-integer values.
#'
#' @return Returns a numerical vector the same size as \code{length} containing the expected weight
#' of an organism for a given length measurement.
#'
#' @examples
#' # Weights for Atlantic cod:
#' weight(0:100, species = 10)
#'
#' # Weights for female white hake:
#' weight(0:100, species = 12, sex = 2)
#'
#' # Weights for female white hake based on 2010 September survey data:
#' weight(0:100, species = 12, sex = 2, year = 2010)
#'
#' # Weights for white hake based on pooled sexes and data from 2010-2013 September surveys:
#' weight(0:100, species = 12, sex = 2, year = 2010:2013)
#'
#' # Transform length-frequencies to weight-length:
#' x <- read.gulf(year = 2014, species = 40, card = "len")
#'
#' # Simple example:
#' f <- freq(x, scale = TRUE) # Pooled length-frequencies.
#' weight(f, species = 40)
#'
#' # Length-frequencies by stratum and sex:
#' f <- freq(x, scale = TRUE, by = c("species", "stratum", "sex"))
#' weight(f)
#'
#' # Length-frequencies by stratum and sex, use RV 2014 length-eight coefficients:
#' f <- freq(x, scale = TRUE, by = c("species", "stratum", "sex", "year"))
#' weight(f)
#'
#' # Load 2010 snow crab data:
#' x <- read.scsbio(year = 2012)
#'
#' # Calculate weight for each crab:
#' weight(x)
#'
#' # Calculate weights by tow:
#' weight(x, by = "tow.number")
#'
#' # Calculate total weights by day:
#' weight(x, by = c("year", "month", "day"), category = c("TM", "TMM", "TMSC12", "TMSC345"))
#' @export
weight <- function(x, ...) UseMethod("weight")
#' @describeIn weight Default weight function.
#' @export
weight.default <- function(x, species, sex, coefficients, units = "kg", ...){
# Parse 'units' argument:
units <- match.arg(tolower(units), c("grams", "kg", "kilograms"))
if (units == "kg") units <- "kilograms"
# Parse 'species' argument:
if (!("species" %in% tolower(names(x)))){
if (missing(species) & missing(coefficients)) stop("'species' or 'coefficients' must be specified.")
if (!missing(species) & !is.data.frame(x)){
if (length(species) == 1) species <- rep(species, length(x))
if (length(species) != length(x)) stop("'x' and 'species' have incompatible lengths.")
}
}
# Parse 'sex' argument:
if (!missing(sex) & !is.data.frame(x)){
if (length(sex) == 1) sex <- rep(sex, length(x))
if (length(sex) != length(x)) stop("'x' and 'sex' have incompatible lengths.")
}
# Input 'x' is a table or named vector of length-frequencies:
if (is.numeric(x) & is.table(x) | (is.null(nrow(x)) & !is.null(names(x)))){
# 'x' is a frequency vector:
if (length(grep("^[0-9]+$", names(x))) == length(x)){
f <- x
x <- as.numeric(names(f))
v <- f * weight(x, species, sex, coefficients, units, ...)
names(v) <- names(f)
return(v)
}
}
# Input 'x' are length-frequencies in a data frame:
if (is.data.frame(x)){
# Extract frequency matrix:
fvars <- names(x)[grep("^[ 0-9]+$", names(x))]
vars <- setdiff(names(x), fvars)
temp <- x[vars]
names(x) <- tolower(names(x))
# Check that frequency variables are numeric:
if (length(fvars) > 0){
flag <- TRUE
for (i in 1:length(fvars)) flag <- flag & is.numeric(x[, fvars[i]])
if (flag){
f <- x[fvars]
if ("sex" %in% names(x)) sex <- as.vector(repvec(x$sex, ncol = length(fvars)))
if ("species" %in% names(x)) species <- as.vector(repvec(x$species, ncol = length(fvars)))
x <- repvec(as.numeric(fvars), nrow = nrow(x))
d <- dim(x)
x <- as.vector(x)
if (!("year" %in% names(list(...))) & ("year" %in% names(temp))){
v <- weight(x, species, sex, coefficients, units, year = unique(temp$year), ...)
}else{
v <- weight(x, species, sex, coefficients, units, ...)
}
dim(v) <- d
v <- f * v
v <- cbind(temp, v)
return(v)
}
}
}
# Loop over species:
if (length(unique(species)) > 1){
species.list <- unique(species)
v <- rep(NA, length(x))
for (i in 1:length(species.list)){
index <- species == species.list[i]
if (missing(sex)){
v[index] <- weight.default(x[index], species = species[index], units = "g", ...)
}else{
v[index] <- weight.default(x[index], species = species[index], sex = sex[index], units = "g", ...)
}
}
}else{
# Fetch length-weight coefficients:
if (!missing(coefficients)){
if (is.data.frame(coefficients)){
if (!all(c("a", "b") %in% names(coefficients)))
stop("'a' and 'b' must be column names if the length-weight coefficients are specified as a data frame.")
if (nrow(coefficients) == 1){
coefficients <- c(coefficients$a, coefficients$b)
}else{
stop("'coefficients' must be a two-element numeric vector.")
}
}
if (is.numeric(coefficients) & length(coefficients)){
beta <- data.frame(a = coefficients[1], b = coefficients[2])
}else{
stop("'coefficients' must be a two-element numeric vector.")
}
}else{
if (missing(sex)){
beta <- length.weight(units = "g", log10 = TRUE, species = unique(species), ...)
}else{
by <- "sex"
if ("year" %in% names(list(...))) by <- c("year", by)
beta <- length.weight(units = "g", log10 = TRUE, species = unique(species), sex = unique(sex), by = by, ...)
}
}
# Calculate weights:
if (is.null(beta)) stop("Corresponding length-weight coefficients were not found.")
if (nrow(beta) == 1){
v <- (10^beta$a) * exp(beta$b * log(x))
}else{
# Match entries to corresponding length-weight coefficients:
res <- data.frame(x = x, species = species)
if (!missing(sex)) res$sex <- sex
index <- match(res[setdiff(names(res), "x")], beta)
v <- (10^beta$a[index]) * exp(beta$b[index] * log(x))
}
}
# Convert weight to proper units:
if (units == "kilograms") v <- v / 1000
return(v)
}
#' @describeIn weight Weight function for \code{scsbio} objects.
#' @export
weight.scsbio <- function(x, category, by, as.hard.shelled, units = "g", ...){
# Parse input arguments:
units <- match.arg(tolower(units), c("g", "kg", "grams", "kilograms", "tons", "tonnes", "mt", "t"))
if (units %in% c("tons", "tonnes", "mt")) units <- "t"
if (units == "kilograms") units <- "kg"
if (units == "grams") units <- "g"
# Parse 'as.hard.shelled' argument:
if (!missing(as.hard.shelled)){
if (!is.logical(as.hard.shelled)) stop("'as.hard.shelled' must be a logical value.")
if (as.hard.shelled) x$shell.condition <- 3
}else{
as.hard.shelled <- FALSE
}
if (missing(category) & missing(by)){
# Initialize weight vector:
w <- rep(0, dim(x)[1])
# New adult:
w <- w + is.category(x, "TMMSC12", ...) * exp(-9.399 + 3.315 * log(x$carapace.width))
# New adolescent:
w <- w + is.category(x, "TMISC12", ...) * exp(-10.154 + 3.453 * log(x$carapace.width))
# Intermediate adult:
w <- w + is.category(x, "TMMSC345", ...) * exp(-8.230136 + 3.098 * log(x$carapace.width))
# Intermediate adolescent:
w <- w + is.category(x, "TMISC345", ...) * exp(-7.512 + 2.899 * log(x$carapace.width))
# Immature females:
w <- w + is.category(x, "TFI", ...) * exp(-7.275 + 2.804 * log(x$carapace.width))
# Mature females:
w <- w + is.category(x, "TFM", ...) * exp(-7.162 + 2.816 * log(x$carapace.width))
}else{
# Define indicator vector of category membership:
if (!is.null(category)){
n <- is.category(x, category = category, ...) + 1 - 1
n <- as.data.frame(n)
names(n) <- category
}else{
n <- matrix(rep(1, dim(x)[1]))
dimnames(n) <- list(NULL, "n")
n <- as.data.frame(n)
}
w <- n * repvec(weight(x, ...), ncol = dim(n)[2])
# Return indicator vector if 'by' is NULL:
if (is.null(by)) return(w)
# Sum within 'by' groupings:
w <- stats::aggregate(w, by = x[by], sum, na.rm = TRUE)
}
# Weight unit conversion:
if (units == "kg") w <- w / 1000
if (units == "t") w <- w / 1000000
return(w)
}
#' @describeIn weight Weight function for \code{scobs} objects.
#' @export
weight.scobs <- function(x, ...){
# Buffer variables:
if (!("chela.height" %in% names(x))) x$chela.height <- x$chela.height.right
if (!("gonad.colour" %in% names(x))) x$gonad.colour <- NA
if (!("egg.colour" %in% names(x))) x$egg.colour <- NA
if (!("eggs.remaining" %in% names(x))) x$eggs.remaining <- NA
x$chela.height <- chela.height(x)
w <- weight.scsbio(x, ...)
return(w)
}
|
e136e65f8ef97f811196ac9f60c23d3bd1b331df | 9036de5c22b81a4c27adb2f25dcc4dbf0faec193 | /rhdf5_api.R | 60a8e46b0447716ef799d475dc4859728f20e019 | [] | no_license | vjcitn/rhdf5_api | afc7f0bf9045fce142488738f4222e0ebf83bb6c | d62020382635ddd7d17b4c8b240242f7210e6958 | refs/heads/master | 2020-03-21T16:28:41.020471 | 2018-06-26T17:44:59 | 2018-06-26T17:44:59 | 138,772,319 | 0 | 0 | null | 2018-06-26T17:40:43 | 2018-06-26T17:40:42 | null | UTF-8 | R | false | false | 3,198 | r | rhdf5_api.R | #' Function to create a file on hdf server
#' @param url character string with http server url
#' @note \url{"http://170.223.248.164:7248"}
#' @param domain character string with domain name to be created
#' @return r http response object
#' @export
putDomain = function(url, domain){
require(httr)
password = Sys.getenv("password")
username = Sys.getenv("username")
auth <- authenticate(username, password, type="basic")
r = PUT(url=url, config=auth, add_headers(host=domain))
r
}
#' Function to create a dataset in a file with extensible dimensions
#' @param url character string with http server url
#' @note \url{"http://170.223.248.164:7248/datasets"}
#' @param domain character string with domain name to be created
#' @param type character string with dataset type like H5T_IEEE_F32LE
#' @param shape integer array with initial dimensions of dataset
#' @param maxdims integer array with maximum extent of each dimension or 0 for unlimited dimension
#' @return r http response object
#' @export
postDataset = function(url, domain, type, shape, maxdims){
require(httr)
password = Sys.getenv("password")
username = Sys.getenv("username")
auth <- authenticate(username, password, type="basic")
args <- list(type=type, shape=shape, maxdims=maxdims)
r = POST(url=url, body=args, config=auth, add_headers(host=domain), encode="json")
r
}
#To grab UUID of dataset created :
#r = GET(url="http://170.223.248.164:7248/datasets",add_headers(host=domain))
#content(r)$datasets
#' Function to modify dataset shape
#' @param url character string with http server url
#' @note \url{"http://170.223.248.164:7248/datasets/dsetuuid/shape"}
#' @param domain character string with domain name to be created
#' @param newshape integer array with new dataset new dataset shape (works for a resizeable dataset)
#' @return r http response object
modifyShape = function(url, domain, newshape){
require(httr)
password = Sys.getenv("password")
username = Sys.getenv("username")
auth <- authenticate(username, password, type="basic")
args <- list(shape=newshape)
# PUT shape to resize the dataset
r = PUT(url=url, body=args, config=auth, add_headers(host=domain), encode="json")
r
}
#' Function to insert values into dataset
#' @param url character string with http server url
#' @note \url{"http://170.223.248.164:7248/datasets/dsetuuid/value"}
#' @param domain character string with domain name to be created
#' @param value list with values to be inserted into dataset
#' @param start (optional)integer/integer array with starting coordinate of selection to be updated
#' @param stop (optional)integer/integer array with ending coordinate of selection to be updated
#' @return r http response object
putValue = function(url, domain, value, start, stop){
require(httr)
password = Sys.getenv("password")
username = Sys.getenv("username")
auth <- authenticate(username, password, type="basic")
# to update or add data to dataset, value must be a list
if(missing(start)){
args <- list(value=value)
}
else{
args <- list(value=value, start=start, stop=stop)
}
r = PUT(url=url, body=args, config=auth, add_headers(host=domain), encode="json")
r
}
|
886b918211f848a97d17d3fc4d5a2a7f33d8f509 | 69adc2fa8e3765b7323021ea99f83231eda5d79d | /covid19.R | f822fc2c8ae77d250c2639bcd40180c5fa768f0c | [] | no_license | mathstudent97/COVID-19 | 246cc9a6213f2e6dd3da80e93043b0604e682b24 | 7e9d162ef75dad012a9c9bfc387bbf316dcdb76e | refs/heads/master | 2022-11-08T19:31:26.568861 | 2020-06-30T20:34:37 | 2020-06-30T20:34:37 | 275,473,577 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,820 | r | covid19.R | rm(list=ls()) # removes all variables stored previously
library(Hmisc) # need this for 'describe' function
covid19_dataset <- read.csv("C:/Users/maullonv/Downloads/datasets_494724_994000_COVID19_line_list_data (2).csv")
#####################################
### Short summary of the data set ###
#####################################
describe(covid19_dataset) # from Hmisc packg
# Shows some informative things regarding the dataset
# i.e. 27 vars and total of 1085 observations, missing and distinct values,
# max and min etc.
# Notice the data set is messy
# Example: The 'death' variable shows that there are 14 distinct values
# opposed to 2
# The reason for this is b/c the entries either shows a '1', '0', or
# date of the death
# '0' if one didn't die; '1' if one died
# So now, will proceed with cleaning up data as this is inconsistent and difficult to
# work with
#####################
## Clean the Data ###
#####################
# Cleaning the data ('death' column)
covid19_dataset$death_dummy <- as.integer(covid19_dataset$death != 0)
# Will create a new column called 'death_dummy'
# IOW if the data/ entry within the death column is '0', then the person died
# if it's not i.e. '1' then they died
# using 'unique(...)', it shows the values are only '0', '1'
# now the death col is clean
# Now, can CALCULATE the DEATH RATE
sum(covid19_dataset$death_dummy) / nrow(covid19_dataset)
# data shows a death rate of about 5.8%
###########
### AGE ###
###########
# According to research, it is more likely that
# the person that dies from covid-19 is older than
# a person that surivives covid-19
# Can we prove that this claim is correct using our data?
# CLAIM: people who tested positive for covid and died, are older
# than those who tested positive and survived
dead = subset(covid19_dataset, death_dummy == 1)
alive = subset(covid19_dataset, death_dummy == 0)
# So we have: 63 covid deaths and 1022 covid survivors
# Now will calculate the mean age of both groups (those dead and alive)
mean(dead$age, na.rm = TRUE)
mean(alive$age, na.rm = TRUE)
# The initial output is 'NA', indicating missing values
# Looking at the data, there are some entries that appear as 'NA'
# The reason behind Rs output as 'NA', indicates R does not
# know how to interpret such missing values
# So, will include 'na.rm = TRUE' into the code
# This will just ignore all those values that appear as 'NA'
# So now, the output shows:
# The average age of one with covid and DIED is 68-69 years old
# The average age of one with covid and SURVIVED is 48 years old
# There is a 20 year gap between the mean ages
# But is this statistically significant?
# Will now use the t-test to check
t.test(alive$age, dead$age,
aleternative="two.sided", conf.level = 0.95)
# Shows there is a 95% chance that the difference between a person
# who is alive and dead in age is from 24 yrs to 16-17 (this is the conf int)
# So an average, a person who is alive is much much younger
# Try with 99%
t.test(alive$age, dead$age,
aleternative="two.sided", conf.level = 0.99)
# Got around the same values
# Now, look at the p-value: 2.2e-16
# This is the probability that from the sample we randomly
# got such an extreme result, so this is basically 0
# So there is a 0% chance that the ages of the 2 populations
# (dead and alive) are the same (reject null)
# RECALL: normaly, if p-value is < 0.05, we reject null
# hypothesis
# CONCLUDE:our result IS STATISTICALLY SIGNIFICANT!
# So, the people who die from Covid-19 are indeed
# older than the people who survive covid-19
##############
### Gender ###
##############
# Question: Are women more likely to die due to Covid compared
# to men? Or is it vice-versa?
# Claim/ Hypothesis: Gender has no effect
# Similar to testing the claim regarding age and Covid
# Will make two subsets
women = subset(covid19_dataset, gender == "female")
men = subset(covid19_dataset, gender == "male")
# Shows there are 520 males and 382 females
mean(women$death_dummy, na.rm = TRUE)
# Shows women have a covid death rate of 3.7%
mean(men$death_dummy, na.rm = TRUE)
# Shows men have a covid death rate of 8.5%
# This is a pretty LARGE discrepancy
# Is this STATISTICALLY SIGNIFICANT?
t.test(women$death_dummy, men$death_dummy,
aleternative="two.sided", conf.level = 0.99)
# See that the means are the same
# and that with 99% confidence, men have from .78% to 8.8%
# higher fatality rates than woman
# With p-value: 0.002105 < 0.05 so reject null
# And thus IS SIGNIFICANT
# Therefore mens higher death rate represents the population
# and thus, gender does have an effect
|
f384e8b127a8ab422b7ce1a9bc19307e3da3ef70 | 3458aa7285e020e9e47fcf7b69600c807ead81bd | /setup_data.R | 2b02980dd1775763e29ac2b50f622b5f21345712 | [] | no_license | luoq/asap-sas | 04037980cb570f8fd942943b05ef762bf3c82ef8 | 24035950792746fc9a29c8c2f9b472d0f552c6af | refs/heads/master | 2021-01-10T19:59:42.197864 | 2012-11-18T02:27:30 | 2012-11-18T02:27:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,070 | r | setup_data.R | require(tm)
require(parallel)
train_set <- read.delim("../data/train_rel_2.tsv",stringsAsFactors=FALSE)
public_test_set <- read.delim("../data/public_leaderboard_rel_2.tsv",stringsAsFactors=FALSE)
numberOfEssaySet <- 10
Set=vector(mode="list",length=numberOfEssaySet)
Set <- mclapply(1:numberOfEssaySet,function(k){
within(list(),{
essay_set <- k
id <- with(train_set,Id[EssaySet==k])
corpus <- Corpus(VectorSource(with(train_set,EssayText[EssaySet==k])))
corpus.public <- Corpus(VectorSource(with(public_test_set,EssayText[EssaySet==k])))
id.public <- with(public_test_set,Id[EssaySet==k])
y <- with(train_set,Score1[EssaySet==k])
# simple_feature <- extract.simpleFeatrure(corpus)
# simple_feature.public <- extract.simpleFeatrure(corpus.public)
dtm <- as.Matrix(get_dtm(corpus,ngram=3,minDoc=floor(0.005*length(y)),maxDoc=floor(0.80*length(y))))
terms <- colnames(dtm)
ngram <- sapply(terms,wordNumber)
dtm.public <- as.Matrix(get_dtm(corpus.public,dictionary=terms,ngram=3))
})
})
save(Set,file="data.RData")
|
3398fe185f21c01b1d7602eabcbe16890a236053 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/spatstat/examples/transect.im.Rd.R | ada361ee2861e8d9492d1d43da6daac5dc8b6e98 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 317 | r | transect.im.Rd.R | library(spatstat)
### Name: transect.im
### Title: Pixel Values Along a Transect
### Aliases: transect.im
### Keywords: spatial manip iplot
### ** Examples
Z <- density(redwood)
plot(transect.im(Z))
## Not run:
##D if(FALSE) {
##D plot(transect.im(Z, click=TRUE))
##D }
##D
## End(Not run)
|
1d2a9637a41917c21f3cd79a1b93f9c6bea2b169 | f89c21bd47eaa88e29bef9bc3474d887486ecbc3 | /plot1.R | 1be6a032cba410ec9907713702f85aaadfd5ae31 | [] | no_license | davidmede/ExData_Plotting1 | ba2ffdf0f729355aa1e0608388a36ce18f3576e5 | d4bd705015c309ca6159faf20c779162793f06be | refs/heads/master | 2022-10-17T04:31:26.198173 | 2020-06-19T16:52:30 | 2020-06-19T16:52:30 | 273,086,053 | 0 | 0 | null | 2020-06-19T14:57:54 | 2020-06-17T21:56:46 | R | UTF-8 | R | false | false | 681 | r | plot1.R | dataset<-read.table("household_power_consumption.txt",header = TRUE,sep = ";",na.strings = "?")
str(dataset)
library(dplyr)
library(tidyr)
date_filter<-c("1/2/2007", "2/2/2007")
dataset_filter<-filter(dataset, Date %in% date_filter)
data_united<-unite(dataset_filter,col = "date_time", Date:Time,sep = "-")
data_united$date_time<-gsub("(/|:)","-",data_united$date_time)
library(lubridate)
Sys.setlocale("LC_TIME", "English")
data_united$date_time<-dmy_hms(data_united$date_time)
png("plot1.png", height=480,width = 480)
with(data_united,hist(Global_active_power,col="red", main = "Global Active Power",
xlab = "Global Active Power (Killowatts)"))
dev.off()
|
fb32681cf872aedafa95e6447969585209289c5e | f7b618d50b3f7777f7cdc8b55cdf4d9b08839262 | /Lab2.R | 937b88fec189e20cdb8925000712f4faf9fa33fa | [
"MIT"
] | permissive | 5x/TweetsDataAnalytics | d717b9643aba54a3ca821cd7a66129c8d3ad2094 | 73b39be9c6af270bfe2b5782be827a892370e05c | refs/heads/master | 2020-07-11T21:51:17.018529 | 2019-08-27T08:08:11 | 2019-08-27T08:08:11 | 204,651,081 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,310 | r | Lab2.R | ### ---------------------------------------------------------------------------
### Dependencies
### ---------------------------------------------------------------------------
dependencies <- c(
"twitteR",
"ROAuth",
"SnowballC",
"data.table",
"tm",
"ggplot2",
"graph",
"topicmodels",
"wordcloud",
"Rgraphviz"
)
dependency_loader <- function(dependencies) {
for (index in 1:length(dependencies)) {
dependency_package_name <- dependencies[index]
if (!require(dependency_package_name, character.only = TRUE)) {
install.packages(dependency_package_name, dependencies = TRUE)
library(dependency_package_name)
}
}
}
dependency_loader(dependencies)
### ---------------------------------------------------------------------------
### Constants
### ---------------------------------------------------------------------------
TWITTER_USER_NAME <- "nodejs"
TWITTER_MAX_NUMBER_OF_TWEETS <- 3200
TWITTER_CONSUMER_KEY <- "__USE_U_TWITTER_CONSUMER_KEY__"
TWITTER_CONSUMER_SECRET <- "__USE_U_TWITTER_CONSUMER_SECRET__"
TWITTER_ACCESS_TOKEN <- "__USE_U_TWITTER_ACCESS_TOKEN__"
TWITTER_ACCESS_SECRER <- "__USE_U_TWITTER_ACCESS_SECRER__"
TWEETS_FILE_PATH <- paste("./", TWITTER_USER_NAME, ".rds", sep = "")
WORKING_DIRECTORY <- getwd()
# Codes: http://www.loc.gov/standards/iso639-2/php/code_list.php
LANGUAGE_CODE <- "en"
WORDS_TO_ANALISE <- c("app", "team")
CUSTOM_STOP_WORDS <- c("can", "us")
STOP_WORDS <- c(stopwords("english"), stopwords("russian"), CUSTOM_STOP_WORDS)
HEAD_FIRST_TWEETS_COUNT <- 3
NUMBER_OF_TOP_WORDS <- 60
ASSIC_LOWER_CORRELATION_LIMITS = 0.2
FREQ_LOWER_BOUND <- 16
FREQ_MIN_VALUE <- 5
CLUSTERANALYSE_SPARSE_WEIGHT <- 0.95
CLUSTERANALYSE_CLUSTERS_NUMBER <- 5
TOPICMODEL_TOPIC_COUNT <- 10
POPICMODEL_TOPIC_TERMS_COUNT <- 5
### ---------------------------------------------------------------------------
### Scraping
###
### @see https://developer.twitter.com/en/docs/tweets/timelines/api-reference/get-statuses-user_timeline
### @see http://geoffjentry.hexdump.org/twitteR.pdf
### ---------------------------------------------------------------------------
# Make an API Calls to the Twitter, only one time. After tweets saves on file,
# and used from the local store.
if (!file.exists(TWEETS_FILE_PATH)) {
# Authorize a Twitter resource by OAuth protocol.
setup_twitter_oauth(
TWITTER_CONSUMER_KEY,
TWITTER_CONSUMER_SECRET,
TWITTER_ACCESS_TOKEN,
TWITTER_ACCESS_SECRER)
# Scraping tweets.
tweets <- userTimeline(TWITTER_USER_NAME,
n = TWITTER_MAX_NUMBER_OF_TWEETS)
# Save tweets to *.rds file.
saveRDS(tweets, file = TWEETS_FILE_PATH)
}
### ---------------------------------------------------------------------------
### Loading from local store
### ---------------------------------------------------------------------------
# Check for existing file with tweets.
if (!file.exists(TWEETS_FILE_PATH)) {
stop(paste("File doesn`t exist in work directory!\n",
"File path[", TWEETS_FILE_PATH, "],\n",
"Working directory path [", WORKING_DIRECTORY, "]."))
}
# Load tweets from local file store.
tweets <- readRDS(TWEETS_FILE_PATH)
# Getting real numbers of tweets loaded to memory.
tweets.length <- length(tweets)
print(paste("Number of downloaded tweets: ", tweets.length))
# Show example of tweets to standard out.
print(paste("Fist ", HEAD_FIRST_TWEETS_COUNT, "tweets:"))
for (index in 1:HEAD_FIRST_TWEETS_COUNT) {
cat(paste("[Tweet#", index, "] ", tweets[[index]]$text, "\n", sep = ""))
}
# Convert TwitteR Lists To Data.Frames
tweets.data_frame <- twListToDF(tweets)
### ---------------------------------------------------------------------------
### Text Cleaning
### ---------------------------------------------------------------------------
# Build a corpus, and specify the source.
# A vector source interprets each element of the vector as a document.
# https://cran.r-project.org/web/packages/tm/tm.pdf#52
documents <- Corpus(VectorSource(tweets.data_frame$text))
# Remove unsupported encode chars(broken UTF-pairs, etc).
documents <- tm_map(documents, function (x) {
x <- gsub("\n", "", x)
return(iconv(x, 'UTF-8', 'UTF-8', sub = ""))
})
# Transform characters to lowercase.
documents <- tm_map(documents, content_transformer(tolower))
# Remove punctuation symbols.
documents <- tm_map(documents, removePunctuation)
# Remove numeric values.
documents <- tm_map(documents, removeNumbers)
# Remove URLs links.
# @see: http://www.rdatamining.com/books/rdm/faq/removeurlsfromtext
removeURL <- function(x) gsub("http[^[:space:]]*", "", x)
documents <- tm_map(documents, content_transformer(removeURL))
# Remove stopwords.
documents <- tm_map(documents, removeWords, STOP_WORDS)
# Stem words in a text document using Porter's stemming algorithm.
documents <- tm_map(documents, function(x) {
return(stemDocument(x, language = LANGUAGE_CODE))
})
# Show the first N documents (tweets)
print(paste("Example of tweets after text cleaning: ",
HEAD_FIRST_TWEETS_COUNT))
for (index in 1:HEAD_FIRST_TWEETS_COUNT) {
cat(paste("[Tweet#", index, "] ", documents[[index]], "\n", sep = ""))
}
### ---------------------------------------------------------------------------
### Freq&Assoc Analise.
### ---------------------------------------------------------------------------
# Constructs coerces to a term-document matrix.
termDocMatrix <- TermDocumentMatrix(documents,
control = list(wordLengths = c(1, Inf),
tolower = FALSE))
# Show term-document matrix.
print(termDocMatrix)
# Association with selected words(terms).
associatedWeights <- findAssocs(termDocMatrix,
terms = WORDS_TO_ANALISE,
corlimit = ASSIC_LOWER_CORRELATION_LIMITS)
# Show association weights.
print(associatedWeights)
# Creating Term/Freq data frame.
freq.terms <- findFreqTerms(termDocMatrix, lowfreq = FREQ_LOWER_BOUND)
term.freq <- rowSums(as.matrix(termDocMatrix))
term.freq <- subset(term.freq, term.freq >= FREQ_MIN_VALUE)
termFreqDataFrame <- data.frame(term = names(term.freq), freq = term.freq)
# Sort Term/Freq data frame by freq.
termFreqDataFrame <- termFreqDataFrame[order(-termFreqDataFrame$freq),]
topTerm <- head(termFreqDataFrame, NUMBER_OF_TOP_WORDS)
# Build plot with top freq words.
ggplot(topTerm, aes(x = reorder(term, -freq), y = freq)) +
geom_bar(stat = "identity", fill = "orange") +
xlab("Terms") + ylab("Count") +
ggtitle("Histogram: Most freq words in tweets") +
coord_flip()
### ---------------------------------------------------------------------------
### Word cloud.
### ---------------------------------------------------------------------------
# Calculate the frequency of words and sort it by frequency.
word.freq <- sort(rowSums(as.matrix(termDocMatrix)), decreasing = TRUE)
# Build WordCloud plot.
wordcloud(
words = names(word.freq),
freq = word.freq,
min.freq = FREQ_MIN_VALUE,
random.order = FALSE)
### ---------------------------------------------------------------------------
### Clustering.
### ---------------------------------------------------------------------------
# Remove sparse terms.
clearedTermDocMatrix <- removeSparseTerms(
termDocMatrix,
sparse = CLUSTERANALYSE_SPARSE_WEIGHT)
# Transform to matrix.
termMatrix <- as.matrix(clearedTermDocMatrix)
# Cluster terms.
distMatrix <- dist(scale(termMatrix))
# Hierarchical cluster analysis on a set of dissimilarities and methods
# for analyzing it.
# @see: http://stat.ethz.ch/R-manual/R-devel/library/stats/html/hclust.html
hCluster <- hclust(distMatrix, method = "ward.D")
# Build cluster dendrogram.
plot(hCluster)
rect.hclust(
hCluster,
k = CLUSTERANALYSE_CLUSTERS_NUMBER,
border = 1:CLUSTERANALYSE_CLUSTERS_NUMBER)
# Perform k-means clustering on a data matrix.
kmeansResult <- kmeans(t(termMatrix), CLUSTERANALYSE_CLUSTERS_NUMBER)
# Round cluster matrix centers, and show it to standart out.
round(kmeansResult$centers, digits = 4)
### ---------------------------------------------------------------------------
### Topic model
### ---------------------------------------------------------------------------
# Constructs document-term matrix.
docTermMatrix <- as.DocumentTermMatrix(termDocMatrix)
# Latent Dirichlet allocation by N-topics.
lda <- LDA(docTermMatrix, k = TOPICMODEL_TOPIC_COUNT)
# Buid terms by every topic.
term <- terms(lda, POPICMODEL_TOPIC_TERMS_COUNT)
term <- apply(term, 2, paste, collapse = ", ")
# Show terms by evry topic.
print(term)
# Named topic list.
topic <- topics(lda, 1)
# Create data frame: Created date / Topic.
topics <- data.frame(date = as.IDate(tweets.data_frame$created), topic)
# Build topic model plot. Usage term on timeline.
qplot(
date,
..count..,
data = topics,
geom = "density",
main = "Timeline usage a terms(words) on the tweets",
fill = term[topic])
|
495c4c1e36388a188adf9e0dc7ba5c7e534f423e | 00071d2723782db228a267a287a9cd314ca12dec | /code/supp_crossover_duration/crossdur_res.R | b0da3963d138f04b8f8372780b75d3d537feb790 | [] | no_license | fintzij/ve_placebo_crossover | d12cbb8914256572a2ec8ed2f137074e42987266 | deef82f6d9a3a1712ee132cf72b90b6f64aef368 | refs/heads/main | 2023-02-16T10:19:46.827837 | 2021-01-13T16:59:56 | 2021-01-13T16:59:56 | 325,616,572 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,158 | r | crossdur_res.R | library(knitr)
library(kableExtra)
library(here)
library(tidyverse)
library(rsimsum)
setwd(here("jon_stuff/cross_dur_2yr"))
sim_settings <-
expand.grid(ve = c("waning", "constant"),
crossdur = c(2, 8))
pars <- expand.grid(ve = c("waning", "constant"),
param = c("intcpt", "slope"),
crossdur = c(2,8),
model = c("linear", "pspline"),
simulation = 1:1e4)
ests <- expand.grid(ve = c("waning", "constant"),
crossdur = c(2,8),
time = c(0, 0.5, 1, 1.5, 2),
model = c("const", "linear", "pspline"),
simulation = 1:1e4)
par_list <- vector("list", nrow(sim_settings))
ve_list <- vector("list", nrow(sim_settings))
decay_list <- vector("list", nrow(sim_settings))
t_cross_list <- vector("list", nrow(sim_settings))
n_cross_list <- vector("list", nrow(sim_settings))
for(s in seq_len(nrow(sim_settings))) {
filename =
paste0("cross_sim_",
sim_settings$ve[s], "_",
sim_settings$crossdur[s], ".Rds")
sim_res = readRDS(filename)
# get n_cross and t_cross
n_cross_list[[s]] <- sapply(sim_res, "[[", "n_cross")
t_cross_list[[s]] <- sapply(sim_res, "[[", "t_cross")
# subset to only the times and models of interest
ve_sub =
do.call(rbind, lapply(sim_res,
function(x)
subset(x$VE_ests,
time %in% c(0, 0.5, 1, 1.5, 2) &
model %in% c("const", "linear", "pspline"))))
decay_sub =
do.call(rbind,
lapply(sim_res,
function(x)
subset(x$VE_decays,
time %in% c(0.5, 1, 1.5, 2) &
model %in%
c("const", "linear", "pspline"))))
par_sub =
do.call(rbind,
lapply(sim_res,
function(x)
subset(x$par_ests,
model %in% c("linear", "pspline"))))
par_sub$param = c("intcpt", "slope")
par_sub$simulation = as.numeric(par_sub$simulation)
par_sub$truth = as.numeric(par_sub$truth)
par_sub$est = as.numeric(par_sub$est)
par_sub$var = as.numeric(par_sub$var)
inds = which(ests$ve == sim_settings$ve[s] &
ests$crossdur == sim_settings$crossdur[s])
par_inds = which(pars$ve == sim_settings$ve[s] &
pars$crossdur == sim_settings$crossdur[s])
# merge
ve_list[[s]] <-
left_join(ests[inds,], ve_sub,
by = c("time", "model", "simulation"))
decay_list[[s]] <-
left_join(ests[inds,], decay_sub,
by = c("time", "model", "simulation"))
par_list[[s]] <-
left_join(pars[par_inds,], par_sub,
by = c("param", "model", "simulation"))
}
# combine the results
res_ve = do.call(rbind, ve_list)
res_decay = do.call(rbind, decay_list)
res_pars = do.call(rbind, par_list)
# summarize crossover times and event counts
cross_stats =
data.frame(
sim_settings,
n_cross_mean = sapply(n_cross_list, mean, na.rm = T),
n_cross_sd = sapply(n_cross_list, sd, na.rm = T),
t_cross_mean = sapply(t_cross_list, mean, na.rm = T),
t_cross_mean = sapply(t_cross_list, sd, na.rm = T)
)
# summarize
ve_sum =
res_ve %>%
group_by(ve, crossdur, time, model) %>%
summarize(emp_var = var(est),
covg = mean(truth > (est - 1.96 * sqrt(var)) &
truth < (est + 1.96 * sqrt(var))))
decay_sum =
res_decay %>%
group_by(ve, crossdur, time, model) %>%
summarize(emp_var = var(est),
covg = mean(truth > (est - 1.96 * sqrt(var)) &
truth < (est + 1.96 * sqrt(var))))
par_sum =
res_pars %>%
group_by(ve, crossdur, model, param) %>%
summarize(emp_var = var(est), # sum((est - truth)^2) / (n()-1),
covg = mean(truth > (est - 1.96 * sqrt(var)) &
truth < (est + 1.96 * sqrt(var))))
# subset to get the true data generating mechanism and the pspline
ve_sum =
ve_sum %>%
filter(model == "pspline" | model == "linear") %>%
arrange(ve, crossdur, model, time) %>%
ungroup()
decay_sum =
decay_sum %>%
filter(model == "pspline" | model == "linear") %>%
arrange(ve, crossdur, model, time) %>%
ungroup()
ve_sum =
ve_sum[,c("ve", "crossdur", "model", "time", "emp_var", "covg")]
decay_sum =
decay_sum[,c("ve", "crossdur", "model", "time", "emp_var", "covg")]
res_sum = full_join(ve_sum, decay_sum, c("ve", "crossdur", "model", "time"))
# recode some of the variables
res_sum =
res_sum %>%
mutate(model =
case_when(model == "pspline" ~ "P-spline",
TRUE ~ "log-linear"),
crossdur =
case_when(crossdur == 2 ~ "Two week crossover",
crossdur == 8 ~ "Two month crossover"))
par_sum =
par_sum %>%
mutate(model =
case_when(model == "pspline" ~ "P-spline",
TRUE ~ "log-linear"),
crossdur =
case_when(crossdur == 2 ~ "Two week crossover",
crossdur == 8 ~ "Two month crossover"),
param =
case_when(param == "intcpt" ~ "Intercept",
TRUE ~ "Linear trend"))
res_sum[,5:8] = round(res_sum[,5:8], digits = 3)
par_sum[,5:6] = round(par_sum[,5:6], digits = 3)
# pivot par_sum wider
par_sum =
par_sum %>%
pivot_wider(names_from = c("param"), values_from = c("emp_var", "covg"))
par_sum = par_sum[,c(1:3,4,6,5,7)]
# generate tables
res_tab <-
res_sum %>%
arrange(desc(ve), desc(crossdur), model) %>%
select(2:8)
res_tab$crossdur[-seq(1,40,by=10)] = " "
res_tab$model[-seq(1,40,by=5)] = " "
res_tab %>%
knitr::kable(format = "latex", booktabs = T) %>%
kable_styling() %>%
add_header_above(c(" " = 2, "$VE(t)$" = 2, "$\\Delta VE(t)$" = 2)) %>%
pack_rows("Vaccine efficacy constant at 75%", 1, 20) %>%
pack_rows("Vaccine efficacy wanes from 85% to 35% over 1.5 years", 21, 40)
# par table
partab <-
par_sum %>%
arrange(desc(ve), desc(crossdur)) %>%
pivot_wider(names_from = c("param"), values_from = c("emp_var", "covg"))
partab$crossdur[seq(2,8,by=2)] = " "
partab[,c(3,4,6,5,7)] %>%
knitr::kable(format = "latex", booktabs = T) %>%
kable_styling() %>%
add_header_above(c(" " = 1,
"Intercept" = 2, "Linear Trend" = 2)) %>%
pack_rows("Vaccine efficacy constant at 75%", 1, 4) %>%
pack_rows("Vaccine efficacy wanes from 85% to 35% over 1.5 years", 5,8) %>%
pack_rows("Two week crossover", 1, 2) %>%
pack_rows("Two month crossover", 3, 4) %>%
pack_rows("Two week crossover", 5, 6) %>%
pack_rows("Two month crossover", 7, 8)
|
c6afd347aead3a326564816a7cfe256a6edd5b36 | 5e5235afb06284eec78be054f8c39ee1f9edbdac | /data-raw/friedman1.R | 305d3cfc3b33d007dc3e5ee847c94e4e61195e9c | [] | no_license | enriquegit/ssr | c9a368263a4d5d7a7f5d1412c96e33676a3b97ff | bbf935ecac4486f303312e9cf56cea147d684c73 | refs/heads/master | 2020-07-04T04:22:45.855889 | 2019-09-05T12:14:10 | 2019-09-05T12:14:10 | 202,154,442 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 260 | r | friedman1.R | # Script used to generate the friedman1 dataset using the tgp library.
library(tgp)
set.seed(1234)
friedman1 <- friedman.1.data(n = 1000)
friedman1 <- friedman1[,-11]
friedman1 <- scale_zero_one(friedman1)
usethis::use_data(friedman1, overwrite = TRUE)
|
5ade773042de856b862e90f309c48581b3eb90cd | 992a8fd483f1b800f3ccac44692a3dd3cef1217c | /Rstudy/genomicsRange.R | 1711c7913feb91348c46acf1966007c705f5b3ef | [] | no_license | xinshuaiqi/My_Scripts | c776444db3c1f083824edd7cc9a3fd732764b869 | ff9d5e38d1c2a96d116e2026a88639df0f8298d2 | refs/heads/master | 2020-03-17T02:44:40.183425 | 2018-10-29T16:07:29 | 2018-10-29T16:07:29 | 133,203,411 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,130 | r | genomicsRange.R | # Genomics Range data
library(IRanges)
rng <- IRanges(start=4, end=13)
rng
IRanges(start=4, width=3)
IRanges(end=5, width=5)
x <- IRanges(start=c(4, 7, 2, 20), end=c(13, 7, 5, 23))
names(x) <- letters[1:4]
x
class(x) # IRanges
start(x)
names(x)
range(x) # the span of the ranges
head(x)
x[2:3]# subset
x[start(x)<5] #
# range operation
# add or minus from both side
x <- IRanges(start=c(40, 80), end=c(67, 114))
x + 4L
x -10L
# restrict on one side
y <- IRanges(start=c(4, 6, 10, 12), width=13)
restrict(y, 5, 10)
# creates ranges width positions upstream of the ranges passed to it.
flank(x, width=7)
flank(x, width=7, start=FALSE)
reduce(x) # all the covered region
gap(x) # all the uncovered region
intersect(a, b)
setdff(a,b)
setdff(b,a)
union(b, a)
# Pairwise version
psetdiff(), pinter,
sect(), punion(), and pgap().
findOverlaps(qry, sbj)
distanceToNearest(qry, sbj)
distance(qry, sbj)
# Run length encoding
x <- as.integer(c(4, 4, 4, 3, 3, 2, 1, 1, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4))
xrle <- Rle(x)
xrle
as.vector(xrle) # ==x
summary(xrle)
runLength(xrle)
runValue(xrle)
library(GenomicRanges)
gr <- GRanges(seqname=c("chr1", "chr1", "chr2", "chr3"),
ranges=IRanges(start=5:8, width=10),
strand=c("+", "-", "-", "+"))
gr
ranges(gr)
seqnames(gr)
length(gr)
names(gr)<-letters[1:length(gr)]
gr
# sequence data
# fastQ
# count seq number
bioawk -cfastx 'END{print NR}' untreated1_chr4.fq
Lowercase bases are often used to indicate soft masked repeats or low complexity
sequences (by programs like RepeatMasker and Tandem Repeats Finder).
Repeats and low-complexity sequences may also be hard masked, where nucleotides
are replaced with N (or sometimes an X).
Name ASCII character range Offset Quality score type Quality score range
Sanger, Illumina (versions 1.8 onward) 33-126 33 PHRED 0-93
Solexa, early Illumina (before 1.3) 59-126 64 Solexa 5-62
Illumina (versions 1.3-1.7) 64-126 64 PHRED 0-62
# in python conver to 0 - 93
phred = [ord(b)-33 for b in qual]
P = 10-Q/10
Q = -10 log10P
[10**(-q/10) for q in phred]
When working with sequencing data, you should always
. Be aware of your sequencing technology's error distributions and limitations
(e.g., whether it's affected by GC content)
. Consider how this might impact your analyses
seqtk is a general-purpose sequence toolkit written by Heng Li that contains
a subcommand for trimming low-quality bases off the end of sequences
# code
sickle se -f untreated1_chr4.fq -t sanger -o untreated1_chr4_sickle.fq
seqtk trimfq untreated1_chr4.fq > untreated1_chr4_trimfq.fq
library(qrqc)
readfq # by Heng Li
# index
samtools faidx Mus_musculus.GRCm38.75.dna.chromosome.8.fa
# extract a specific region
samtools faidx Mus_musculus.GRCm38.75.dna.chromosome.8.fa 8:123407082-123410744
## SAM file head
@SQ
SN: seq names
LN: seq length
@RG read group
SM: sample information
PL: sequencing platform:PacBio, Illumina
@PG mapping program
VN: version
CL: cmd used
# view header
samtools view -H celegans.bam
# Alignment section
Query seq names | Bitwise Flag | Ref seq name | Position | mapping quality | CIGAR | RNEXT/ PNEXT |
Template length | SEQ | Quality
# bitwise flag
# true/false properties about an alignment
samtools flags 147
0x93 147 PAIRED,PROPER_PAIR,REVERSE,READ2
# CIGAR flag
# matches/mismatches, insertions, deletions, soft or hard clipped, and so on.
# Soft clipping is when only part of the query sequence is aligned to the reference
# hard-clipped regions are not present in the sequence stored in the SAM field SEQ.
# mapping quality
a mapping quality of 20 translates to a 10(20/-10) = 1%
chance the alignment is incorrect.
# sam to bam
samtools view -b celegans.sam > celegans_copy.bam
samtools view -h celegans.bam > celegans_copy.sam
# Index
## sort
samtools sort celegans_unsorted.bam celegans_sorted
# more memory and more CPUs
samtools sort -m 4G -@ 2 celegans_unsorted.bam celegans_sorted
## index
samtools index celegans_sorted.bam
# Extracting alignments from a region with samtools view
samtools view NA12891_CEU_sample.bam 1:215906469-215906652 | head -n 3
samtools view -b NA12891_CEU_sample.bam 1:215906469-215906652 > USH2A_sample_alns.bam
# from BEd extract regions
samtools view -L USH2A_exons.bed NA12891_CEU_sample.bam | head -n 3
# samtools view also has options for filtering alignments based on bitwise flags, mapping quality, read group.
samtools flags
samtools flags unmap
samtools flags 69
samtools flags READ1,PROPER_PAIR
samtools view -f 4 NA12891_CEU_sample.bam | head -n 3
samtools view -f 66 NA12891_CEU_sample.bam | head -n 3
# -F
# not match !=
samtools view -F 4 NA12891_CEU_sample.bam | head -n 3
# check counts
samtools view -F 6 NA12891_CEU_sample.bam | wc -
samtools view -F 7 NA12891_CEU_sample.bam | wc -l
samtools view -F 6 -f 1 NA12891_CEU_sample.bam | wc -l
# samtools mpileup call SNP
samtools mpileup --no-BAQ --region 1:215906528-215906567 \
--fasta-ref human_g1k_v37.fasta NA12891_CEU_sample.bam
|
e6369207741459c71e71698c908db63c88526a42 | 89c089c0203592b1acc9691cb8bbeae1454cd9ac | /src/run_adaptive.R | f1031f9540588c2573ee1b9b5ef4eccb51aa9220 | [] | no_license | kallus/sass | 789e4ce47e8652a84724b6ed68d32dccdf0db394 | b5138f3870d826948101840aad2d650a9a3d9f74 | refs/heads/master | 2022-11-09T11:14:36.298017 | 2020-06-23T08:21:32 | 2020-06-23T08:21:32 | 258,504,983 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,161 | r | run_adaptive.R | source('src/sass/sass.R')
source('src/sass/smoothing.R')
source('src/experiment/generator.R')
source('src/experiment/caching.R')
source('src/experiment/contenders.R')
source('src/experiment/real.R')
source('src/experiment/scores.R')
B <- 100
lambda_ratio <- 0.1
to_w <- function(theta, u=0.1, v=0.3) {
omega = as.matrix(theta)*v
diag(omega) = abs(min(eigen(omega)$values)) + 0.1 + u
stats::cov2cor(solve(omega))
}
pcor <- function(corr) {
P <- solve(corr)
Pii <- diag(P) %*% matrix(1, 1, ncol(P))
-P / sqrt(Pii * t(Pii))
}
# run contending methods on data files
cor_rmse_sassa <- c()
cor_rmse_glasso <- c()
pcor_rmse_sassa <- c()
pcor_rmse_glasso <- c()
for (datafile in list.files(path='cache/data/')) {
if (!startsWith(datafile, 'cluster')) {
next
}
print(datafile)
net <- readRDS(paste('cache/data', datafile, sep='/'))
n <- nrow(net$data)
lambda_max <- cached(c('find_lambda_max', datafile), net$data)
set.seed(1)
subsamples <- subsample_observations(n, B)
networks <- cached(c('subsample_neighsel', datafile),
net$data, lambda_ratio*lambda_max, B, subsamples)
stabsel_freq <- cached(c('selection_freq', datafile), networks)
threshold <- cached(c('FDR_threshold', 'stabsel', datafile),
stabsel_freq, FDR=0.1, length(networks))
stabsel_net <- cached(c('threshold_frequencies', 'stabsel', datafile),
stabsel_freq, threshold)
sassa_freq <- stabsel_freq
sassa_net <- stabsel_net
comm_counts <- cached(c('consensus_module_counts', 'sass', datafile), networks, B)
comm_threshold <- cached(c('smooth_inner_minima', 'sass', datafile),
uptri(comm_counts), 0, B)
if (length(comm_threshold) != 1) {
comm_threshold <- Inf
}
cmask <- comm_counts > comm_threshold
if (any(cmask)) {
inside_min <- cached(c('smooth_inner_minima', 'sass', 'inside', datafile),
2*B*uptri(stabsel_freq)[uptri(cmask)], 0, 2*B)
if (length(inside_min) == 1) {
between_min <- cached(c('best_smooth_min', 'sass', 'between', datafile),
2*B*uptri(stabsel_freq)[uptri(!cmask)], 0, 2*B)
if (!is.na(between_min) && inside_min < between_min) {
sassa_freq <- cached(c('adapt_frequencies_mask2', 'sass', datafile),
stabsel_freq, inside_min, between_min, cmask, B)
}
}
}
sassa_wwi <- cached(c('sass_adaptive_strength', 'sassa', datafile),
net$data, lambda_max, sassa_freq, stabsel_net)
glasso_wwi <- cached(c('glasso_strength', datafile), net$data, lambda_max,
stabsel_net)
true_wwi <- list()
true_wwi$w=to_w(net$theta)
true_wwi$wi=solve(true_wwi$w)
# compare partial correlations and/or correlations
# RMSE
cor_rmse_sassa <- c(cor_rmse_sassa,
sqrt(mean(c(true_wwi$w - cov2cor(sassa_wwi$w))^2)))
cor_rmse_glasso <- c(cor_rmse_glasso,
sqrt(mean(c(true_wwi$w - cov2cor(glasso_wwi$w))^2)))
pcor_rmse_sassa <- c(pcor_rmse_sassa,
sqrt(mean(c(pcor(true_wwi$w) - pcor(cov2cor(sassa_wwi$w)))^2)))
pcor_rmse_glasso <- c(pcor_rmse_glasso,
sqrt(mean(c(pcor(true_wwi$w) - pcor(cov2cor(glasso_wwi$w)))^2)))
}
write.table(cbind(cor_rmse_sassa, cor_rmse_glasso, pcor_rmse_sassa,
pcor_rmse_glasso), 'cache/glasso_sassa_cmp.tsv')
|
7600b1827198c89112739fbc6093a6a7cecabfbe | 1d481ceeca7236ce5464c5fafd397fea7e193126 | /man/survRM2-package.Rd | f8fa0f6b925136d2d15e0a0675d6c10917dc0e56 | [] | no_license | cran/survRM2 | d90db8bc397c9fa86db0d22231334a5abb748822 | 044089fcdd77b46e754cd18ac6d7f21498ce5e73 | refs/heads/master | 2022-07-27T15:04:02.632607 | 2022-06-14T02:50:02 | 2022-06-14T02:50:02 | 30,389,008 | 3 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,698 | rd | survRM2-package.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/survRM2-package.R
\docType{package}
\name{survRM2-package}
\alias{survRM2-package}
\title{Comparing Restricted Mean Survival Time}
\description{
Performs two-sample comparisons using the restricted mean survival time (RMST) as a summary measure of the survival time distribution.
Three kinds of between-group contrast metrics (i.e., the difference in RMST, the ratio of RMST and the ratio of the restricted mean time lost (RMTL)) are computed.
The package has a function to perform an ANCOVA-type covariate adjustment as well as unadjusted analyses for those measures.
}
\examples{
#--- sample data ---#
D=rmst2.sample.data()
time=D$time
status=D$status
arm=D$arm
tau=NULL
x=D[,c(4,6,7)]
#--- without covariates ----
a=rmst2(time, status, arm, tau=10)
print(a)
plot(a, xlab="Years", ylab="Probability", density=60)
#--- with covariates ----
a=rmst2(time, status, arm, tau=10, covariates=x)
print(a)
}
\references{
Uno H, Claggett B, Tian L, Inoue E, Gallo P, Miyata T, Schrag D,
Takeuchi M, Uyama Y, Zhao L, Skali H, Solomon S, Jacobus S, HughesM,
Packer M, Wei LJ. Moving beyond the hazard ratio in quantifying the between-group difference in survival analysis.
Journal of clinical Oncology 2014, 32, 2380-2385. doi:10.1200/JCO.2014.55.2208.
Tian L, Zhao L, Wei LJ. Predicting the restricted mean event time with the subject's baseline covariates in survival analysis. Biostatistics 2014, 15, 222-233. doi:10.1093/biostatistics/kxt050.
}
\seealso{
survival
}
\author{
Hajime Uno, Lu Tian, Miki Horiguchi, Angel Cronin, Chakib Battioui, James Bell
Maintainer: Hajime Uno <huno@jimmy.harvard.edu>
}
\keyword{survival}
|
ea250a79fec66bb6fca2ae7cb815c20da6b85655 | c29a2534fb4e5224d49d3fed5e23f6c86987d055 | /man/wgcna_get_module_genes_by_sign.Rd | 367d0956316143040b60bd1aeeb1f64bf6959646 | [] | no_license | ddeweerd/MODifieRDev | 8c1ae2cd35c297a5394671e05d3198b6f3b6fcf8 | 5660de4df282b57cd2da20e8fe493e438019b759 | refs/heads/Devel | 2020-03-28T18:37:56.271549 | 2019-11-07T10:45:09 | 2019-11-07T10:45:09 | 148,896,901 | 4 | 0 | null | 2019-08-20T14:14:35 | 2018-09-15T11:42:06 | R | UTF-8 | R | false | true | 839 | rd | wgcna_get_module_genes_by_sign.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wcgna.R
\name{wgcna_get_module_genes_by_sign}
\alias{wgcna_get_module_genes_by_sign}
\title{Split WGCNA module in module containing only positive or negative correlation}
\usage{
wgcna_get_module_genes_by_sign(wgcna_module, mode)
}
\arguments{
\item{wgcna_module}{Module object that has been produced by \code{wgcna} function}
\item{mode}{Character. "p" or "positive" for positive correlation, "n" or "negative"
for negative correlation.}
}
\value{
\code{wgcna_module} object
}
\description{
Split WGCNA module in module containing only positive or negative correlation
}
\details{
The functions returns a new \code{wgcna} module object that only contains positively or
negatively correlated colors
}
\seealso{
\code{\link{wgcna}}
}
\author{
Dirk de Weerd
}
|
95e8466c5adbf8da5e46aeaaf11a736e780e8dd6 | 97e4dfbf87eedd1ca0d32434215f17c50845e8c7 | /R/Tsq_chart.R | 26e387f863d362cc1c2041281d926ff802c53cd0 | [
"MIT"
] | permissive | cil0834/LAGG4793 | 73486aec788e2420161071323d8c932ef5498f8c | 5f60b208b81523d80f162527fb09783b6c579867 | refs/heads/master | 2023-04-10T17:47:41.482964 | 2021-04-22T22:36:58 | 2021-04-22T22:36:58 | 351,226,788 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,244 | r | Tsq_chart.R | #' Tsq_chart
#'
#'@description This function takes in data and plots a T-squared plot
#'
#' @param data a dataframe
#'
#' @export
#'
#' @examples
#' data = LAGG4793::project_data
#' Tsq_chart(data)
Tsq_chart = function(data){ # a dataframe
n = dim(data)[1]
p = dim(data)[2]
m = colMeans(data)
co_i = solve(stats::cov(data))
ucll = stats::qchisq(0.05, p, lower.tail = FALSE)
uclu = stats::qchisq(0.01, p, lower.tail = FALSE)
m_vec = as.matrix(data)
tsqs = c()
for(k in 1:n){
tsq = t(m_vec[k,]-m)%*%co_i%*%(m_vec[k,] - m)
tsqs = round(c(tsqs, tsq),2)
}
observation = 1:n
df = data.frame("observation" = observation, "tsqs" = tsqs)
g = ggplot2::ggplot(data = df, ggplot2::aes(x=observation, y = tsqs)) +
ggplot2::geom_point() +
ggplot2::geom_hline(yintercept=ucll, linetype="dashed") +
ggplot2::geom_hline(yintercept=uclu) +
ggplot2::geom_text(x=max(observation)*.75, y=stats::qchisq(0.04, p, lower.tail = FALSE), label=paste("95% limit")) +
ggplot2::geom_text(x=max(observation)*.75, y=stats::qchisq(0.0075, p, lower.tail = FALSE), label=paste("99% limit")) +
ggplot2::ylim(0, stats::qchisq(0.007, p, lower.tail = FALSE)) +
ggplot2::labs(title = "T-squared Plot")
print(g)
}
|
18b0e68ce959705821bcd69e4adfef800f880cb1 | 2879f5558c0a317ff1ca28f4fed69388304b69b6 | /DB_mon/server.R | 436e6e55ae2d87005d28c19d2514c086fdb0e81c | [] | no_license | liyuan97/R-SDSS | d2724b6d1ed136f3d981b1908bedd923fdd900f3 | a91f3741f0a5140411bbf642dbb660f8cc78c620 | refs/heads/master | 2020-03-11T23:02:52.219447 | 2018-05-20T13:48:35 | 2018-05-20T13:48:35 | 130,310,727 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,109 | r | server.R | library(shiny)
library(DBI)
library(ROracle)
drv <- dbDriver("Oracle")
con <- dbConnect(drv, user = "system", password="a", db="srcdb")
qDF <- dbSendQuery(con, "select file_name,bytes from dba_data_files")
dDF <- fetch(qDF)
qDept <- dbSendQuery(con, "select * from scott.dept")
dDept <- fetch(qDept)
qSalGrd <- dbSendQuery(con, "select * from scott.SALGRADE")
dSalGrd <- fetch(qSalGrd)
# Define server logic required to summarize and view the selected dataset
shinyServer(function(input, output) {
# Return the requested dataset
datasetInput <- reactive(function() {
switch(input$dataset,
"Datafiles" = dDF,
"dept" = dDept,
"salgrd" = dSalGrd)
})
# Generate a summary of the dataset
# output$summary <- reactivePrint(function() {
# dataset <- datasetInput()
# summary(dataset)
# })
# Show the first "n" observations
output$view <- reactiveTable(function() {
#head(datasetInput())
datasetInput()
})
output$main_plot <- reactivePlot(function() {
barplot(dDF$BYTES, cex.names=c(dDF$FILE_NAME), horiz=TRUE)
})
})
|
0ee8accf3fd1a17a01ccf63b497760318015af2f | e0e7425d5632f5b003a003c96fabc8663b3d1daf | /inst/doc/cvcrand.R | 50b8c0dfc8190f0a2cb98d14da62b8c6e254944e | [] | no_license | cran/cvcrand | b58bf12903bb343f4e903a30d5872cae06cfa05a | 81aba6e3091d51101aa00b3f03abc22ce2d25e89 | refs/heads/master | 2022-04-18T15:52:02.362306 | 2020-04-13T18:00:02 | 2020-04-13T18:00:02 | 112,377,460 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,065 | r | cvcrand.R | ## ----start,echo=FALSE,results="hide"------------------------------------------
library(cvcrand)
## ---- echo=FALSE, results='asis'----------------------------------------------
knitr::kable(Dickinson_design[ , 1:6])
## ---- echo=FALSE, results='asis'----------------------------------------------
knitr::kable(Dickinson_design[ , 7:11])
## ----cvrall, fig.keep="all", fig.width = 7, fig.height=4----------------------
Design_result <- cvrall(clustername = Dickinson_design$county,
balancemetric = "l2",
x = data.frame(Dickinson_design[ , c("location", "inciis",
"uptodateonimmunizations", "hispanic", "incomecat")]),
ntotal_cluster = 16,
ntrt_cluster = 8,
categorical = c("location", "incomecat"),
###### Option to save the constrained space #####
# savedata = "dickinson_constrained.csv",
bhist = TRUE,
cutoff = 0.1,
seed = 12345)
## ----set-options1, echo=FALSE, fig.keep="all", fig.width = 7, fig.height=4------------------------
options(width = 100)
## ---- fig.keep="all", fig.width = 7, fig.height=4-------------------------------------------------
# the balance metric used
Design_result$balancemetric
# the allocation scheme from constrained randomization
Design_result$allocation
# the histogram of the balance score with respect to the balance metric
Design_result$bscores
# the statement about how many clusters to be randomized to the intervention and the control arms respectively
Design_result$assignment_message
# the statement about how to get the whole randomization space to use in constrained randomization
Design_result$scheme_message
# the statement about the cutoff in the constrained space
Design_result$cutoff_message
# the statement about the selected scheme from constrained randomization
Design_result$choice_message
# the data frame containing the allocation scheme, the clustername as well as the original data frame of covariates
Design_result$data_CR
# the descriptive statistics for all the variables by the two arms from the selected scheme
Design_result$baseline_table
# the cluster pair descriptive, which is useful for valid randomization check
Design_result$cluster_coin_des
# the overall allocation summary
Design_result$overall_allocations
## ----cvrallst1, fig.keep="all", fig.width = 7, fig.height=4---------------------------------------
# Stratification on location, with constrained randomization on other specified covariates
Design_stratified_result1 <- cvrall(clustername = Dickinson_design$county,
balancemetric = "l2",
x = data.frame(Dickinson_design[ , c("location", "inciis",
"uptodateonimmunizations",
"hispanic", "incomecat")]),
ntotal_cluster = 16,
ntrt_cluster = 8,
categorical = c("location", "incomecat"),
weights = c(1000, 1, 1, 1, 1),
cutoff = 0.1,
seed = 12345)
## ---- fig.keep="all", fig.width = 7, fig.height=4-------------------------------------------------
Design_stratified_result1$baseline_table
## ----cvrallst2, fig.keep="all", fig.width = 7, fig.height=4---------------------------------------
# An alternative and equivalent way to stratify on location
Design_stratified_result2 <- cvrall(clustername = Dickinson_design$county,
balancemetric = "l2",
x = data.frame(Dickinson_design[ , c("location", "inciis",
"uptodateonimmunizations",
"hispanic", "incomecat")]),
ntotal_cluster = 16,
ntrt_cluster = 8,
categorical = c("location", "incomecat"),
stratify = "location",
cutoff = 0.1,
seed = 12345,
check_validity = TRUE)
## ---- fig.keep="all", fig.width = 7, fig.height=4-------------------------------------------------
Design_stratified_result2$baseline_table
## ----cvrcov, fig.keep="all", fig.width = 7, fig.height=4------------------------------------------
# change the categorical variable of interest to have numeric representation
Dickinson_design_numeric <- Dickinson_design
Dickinson_design_numeric$location = (Dickinson_design$location == "Rural") * 1
Design_cov_result <- cvrcov(clustername = Dickinson_design_numeric$county,
x = data.frame(Dickinson_design_numeric[ , c("location", "inciis",
"uptodateonimmunizations",
"hispanic", "income")]),
ntotal_cluster = 16,
ntrt_cluster = 8,
constraints = c("s5", "mf.5", "any", "any", "mf0.4"),
categorical = c("location"),
###### Option to save the constrained space #####
# savedata = "dickinson_cov_constrained.csv",
seed = 12345,
check_validity = TRUE)
## ----set-options2, echo=FALSE, fig.keep="all", fig.width = 7, fig.height=4------------------------
options(width = 100)
## ---- fig.keep="all", fig.width = 7, fig.height=4-------------------------------------------------
# the allocation scheme from constrained randomization
Design_cov_result$allocation
# the statement about how many clusters to be randomized to the intervention and the control arms respectively
Design_cov_result$assignment_message
# the statement about how to get the whole randomization space to use in constrained randomization
Design_cov_result$scheme_message
# the data frame containing the allocation scheme, the clustername as well as the original data frame of covariates
Design_cov_result$data_CR
# the descriptive statistics for all the variables by the two arms from the selected scheme
Design_cov_result$baseline_table
# the cluster pair descriptive, which is useful for valid randomization check
Design_cov_result$cluster_coin_des
# the overall allocation summary
Design_cov_result$overall_allocations
## ---- echo=FALSE, results='asis'------------------------------------------------------------------
knitr::kable(head(Dickinson_outcome, 10))
## ----cptest, fig.keep="all", fig.width = 7, fig.height=4------------------------------------------
Analysis_result <- cptest(outcome = Dickinson_outcome$outcome,
clustername = Dickinson_outcome$county,
z = data.frame(Dickinson_outcome[ , c("location", "inciis",
"uptodateonimmunizations", "hispanic", "incomecat")]),
cspacedatname = system.file("dickinson_constrained.csv", package = "cvcrand"),
outcometype = "binary",
categorical = c("location","incomecat"))
## ----cptestre, fig.keep="all", fig.width = 7, fig.height=4----------------------------------------
Analysis_result
## ----info, results='markup', echo=FALSE-----------------------------------------------------------
sessionInfo()
|
e6451dc393ced820fb46a7518659b7c517a21aec | e3007f1c87c734f576bcbd7083dce72ec9f59e4b | /R/convert_motifs.R | 5accf49f3e97266c8f633e70fceeb91fda1beb2a | [] | no_license | bjmt/universalmotif | 99b0dfe9c9afea6f9a5a0db596c88c264aa194b3 | ca0445479eda1043758a62aeb3b6239fbfa144ac | refs/heads/master | 2023-07-20T05:51:55.219814 | 2023-07-11T14:17:23 | 2023-07-11T14:17:23 | 82,811,804 | 17 | 16 | null | 2022-05-30T09:27:26 | 2017-02-22T14:10:28 | R | UTF-8 | R | false | false | 25,023 | r | convert_motifs.R | #' Convert motif class.
#'
#' Allows for easy transfer of motif information between different classes as
#' defined by other Bioconductor packages. This function is also used by
#' nearly all other functions in this package, so any motifs of a compatible
#' class can be used without needing to be converted beforehand.
#'
#' @param motifs Single motif object or list. See details.
#' @param class `character(1)` Desired motif class. Input as
#' 'package-class'. If left empty, defaults to
#' 'universalmotif-universalmotif'. (See details.)
#'
#' @return Single motif object or list.
#'
#' @details
#' ## Input
#' The following packge-class combinations can be used as input:
#' * MotifDb-MotifList
#' * TFBSTools-PFMatrix
#' * TFBSTools-PWMatrix
#' * TFBSTools-ICMatrix
#' * TFBSTools-PFMatrixList
#' * TFBSTools-PWMatrixList
#' * TFBSTools-ICMatrixList
#' * TFBSTools-TFFMFirst
#' * seqLogo-pwm
#' * motifStack-pcm
#' * motifStack-pfm
#' * PWMEnrich-PWM
#' * motifRG-Motif
#' * universalmotif-universalmotif
#' * matrix
#'
#' ## Output
#' The following package-class combinations can be output:
#' * MotifDb-MotifList
#' * TFBSTools-PFMatrix
#' * TFBSTools-PWMatrix
#' * TFBSTools-ICMatrix
#' * TFBSTools-TFFMFirst
#' * seqLogo-pwm
#' * motifStack-pcm
#' * motifStack-pfm
#' * PWMEnrich-PWM
#' * Biostrings-PWM (\code{type = 'log2prob'})
#' * rGADEM-motif
#' * universalmotif-universalmotif (the default, no need to specify this)
#'
#' Note: MotifDb-MotifList output was a later addition to [convert_motifs()].
#' As a result, to stay consistent with previous behaviour most functions
#' will always convert MotifDb-MotifList objects to a list of `universalmotif`
#' motifs, even if other formats would be simply returned as is (e.g. for
#' other formats, [filter_motifs()] will return the input format; for
#' MotifDb-MotifList, a list of `universalmotif` objects will be returned).
#'
#' @examples
#' # Convert from universalmotif:
#' jaspar <- read_jaspar(system.file("extdata", "jaspar.txt",
#' package = "universalmotif"))
#' if (requireNamespace("motifStack", quietly = TRUE)) {
#' jaspar.motifstack.pfm <- convert_motifs(jaspar, "motifStack-pfm")
#' }
#'
#' # Convert from another class to universalmotif:
#' if (requireNamespace("TFBSTools", quietly = TRUE)) {
#' library(TFBSTools)
#' data(MA0003.2)
#' motif <- convert_motifs(MA0003.2)
#'
#' # Convert from another class to another class
#' if (requireNamespace("PWMEnrich", quietly = TRUE)) {
#' motif <- convert_motifs(MA0003.2, "PWMEnrich-PWM")
#' }
#'
#' # The 'convert_motifs' function is embedded in the rest of the universalmotif
#' # functions: non-universalmotif class motifs can be used
#' MA0003.2.trimmed <- trim_motifs(MA0003.2)
#' # Note: if the motif object going in has information that the
#' # 'universalmotif' class can't hold, it will be lost
#' }
#'
#' @references
#'
#' Bembom O (2018). *seqLogo: Sequence logos for DNA sequence
#' alignments*. R package version 1.46.0.
#'
#' Droit A, Gottardo R, Robertson G, Li L (2014). *rGADEM: de novo
#' motif discovery*. R package version 2.28.0.
#'
#' Mercier E, Gottardo R (2014). *MotIV: Motif Identification and
#' Validation*. R package version 1.36.0.
#'
#' Ou J, Wolfe SA, Brodsky MH, Zhu LJ (2018). “motifStack for the
#' analysis of transcription factor binding site evolution.” *Nature
#' Methods*, **15**, 8-9. doi: 10.1038/nmeth.4555.
#'
#' Shannon P, Richards M (2018). *MotifDb: An Annotated Collection of
#' Protein-DNA Binding Sequence Motifs*. R package version 1.22.0.
#'
#' Stojnic R, Diez D (2015). *PWMEnrich: PWM enrichment analysis*. R
#' package version 4.16.0.
#'
#' Tan G, Lenhard B (2016). “TFBSTools: an R/Bioconductor package for
#' transcription factor binding site analysis.” *Bioinformatics*,
#' **32**, 1555-1556. doi: 10.1093/bioinformatics/btw024.
#'
#' Yao Z (2012). *motifRG: A package for discriminative motif
#' discovery, designed for high throughput sequencing dataset*. R
#' package version 1.24.0.
#'
#' @author Benjamin Jean-Marie Tremblay, \email{benjamin.tremblay@@uwaterloo.ca}
#' @include universalmotif-class.R
#' @export
setGeneric("convert_motifs", function(motifs,
class = "universalmotif-universalmotif")
standardGeneric("convert_motifs"))
#' @describeIn convert_motifs Generate an error to remind users to run
#' [to_list()] instead of using the column from [to_df()] directly.
#' @export
setMethod("convert_motifs", signature(motifs = "AsIs"),
definition = function(motifs, class) {
stop(wmsg(
"If you are providing the `motif` column from the `data.frame` output of ",
"to_df(), then please use to_list() instead. If this error ",
"message does not apply to you, then remove the `AsIs` class attribute ",
"and try again (`class(mylist) <- NULL`)."
))
})
#' @describeIn convert_motifs Convert a list of motifs.
#' @export
setMethod("convert_motifs", signature(motifs = "list"),
definition = function(motifs, class) {
motifs <- unlist(motifs)
if (!length(motifs)) stop("Input is an empty list")
mot_classes <- unique(vapply(motifs, function(x) class(x), character(1)))
if (length(mot_classes) == 1) {
classin <- strsplit(class, "-", fixed = TRUE)[[1]][2]
if (mot_classes == classin) return(motifs)
}
if (class == "MotifDb-MotifList") {
motifs <- lapply(motifs, function(x) convert_motifs(x))
motifs <- convert_to_motifdb_motiflist(motifs)
} else {
motifs <- lapply(motifs, function(x) convert_motifs(x, class = class))
}
motifs
})
#' @describeIn convert_motifs Convert a \linkS4class{universalmotif} object.
#' @export
setMethod("convert_motifs", signature(motifs = "universalmotif"),
definition = function(motifs, class) {
out_class <- strsplit(class, "-", fixed = TRUE)[[1]][2]
out_class_pkg <- strsplit(class, "-", fixed = TRUE)[[1]][1]
switch(out_class_pkg,
"universalmotif" = {
validObject_universalmotif(motifs)
motifs
},
"TFBSTools" = {
if (out_class %in% c("PFMatrix", "PWMatrix", "ICMatrix"))
convert_to_tfbstools_matrix(motifs, out_class)
else if (out_class == "TFFMFirst")
convert_to_tfbstools_tffmfirst(motifs)
else
stop("unknown 'class'")
},
"seqLogo" = {
if (out_class == "pwm")
convert_to_seqlogo_pwm(motifs)
else
stop("unknown 'class'")
},
"motifStack" = {
switch(out_class,
"pcm" = convert_to_motifstack_pcm(motifs),
"pfm" = convert_to_motifstack_pfm(motifs),
stop("unknown 'class'"))
},
"PWMEnrich" = {
if (out_class == "PWM")
convert_to_pwmenrich_pwm(motifs)
else
stop("unknown 'class'")
},
"Biostrings" = {
if (out_class == "PWM")
convert_to_biostrings_pwm(motifs)
else
stop("unknown 'class'")
},
"rGADEM" = {
if (out_class == "motif")
convert_to_rgadem_motif(motifs)
else
stop("unknown 'class'")
},
"MotifDb" = {
if (out_class == "MotifList")
convert_to_motifdb_motiflist(motifs)
else
stop("unknown 'class'")
},
stop("unknown 'class'")
)
})
convert_to_tfbstools_matrix <- function(motifs, out_class) {
motifs <- convert_type(motifs, "PCM")
bkg <- motifs["bkg"][DNA_BASES]
# names(bkg) <- DNA_BASES
extrainfo <- motifs["extrainfo"]
if (length(motifs["altname"]) == 0) {
motifs["altname"] <- ""
}
strand <- motifs["strand"]
if (strand %in% c("+-", "-+")) {
strand <- "*"
}
if (requireNamespace("TFBSTools", quietly = TRUE)) {
motifs <- TFBSTools::PFMatrix(name = motifs["name"],
ID = motifs["altname"],
strand = strand, bg = bkg,
profileMatrix = motifs["motif"])
switch(out_class,
"PFMatrix" = {
motifs <- motifs
},
"PWMatrix" = {
motifs <- TFBSTools::toPWM(motifs, type = "log2probratio",
pseudocounts = 1, bg = bkg)
},
"ICMatrix" = {
motifs <- TFBSTools::toICM(motifs, pseudocounts = 1, bg = bkg)
}
)
} else {
stop("package 'TFBSTools' is not installed")
}
if (length(extrainfo) > 0) {
motifs@tags <- as.list(extrainfo)
}
motifs
}
convert_to_tfbstools_tffmfirst <- function(motifs) {
motifs <- convert_type(motifs, "PPM")
if (!"2" %in% names(motifs@multifreq)) {
stop("cannot convert without filled multifreq slot")
}
if (motifs["alphabet"] != "DNA") stop("alphabet must be DNA")
bkg <- motifs["bkg"][DNA_BASES]
emission <- list(length = ncol(motifs@multifreq[["2"]]) + 1)
emission[[1]] <- bkg
transition <- matrix(rep(0, (ncol(motifs@multifreq$"2") + 1) *
ncol(motifs@multifreq$"2")),
nrow = ncol(motifs@multifreq$"2") + 1,
ncol = ncol(motifs@multifreq$"2"))
for (i in seq_len(ncol(motifs@multifreq$"2"))) {
emission[[i + 1]] <- motifs@multifreq$"2"[, i]
transition[i, i] <- 1
}
names(emission) <- rep("state", length(emission))
transition[nrow(transition), 1] <- 1
transition[2, 1:2] <- c(0.95, 0.05)
colnames(transition) <- seq_len(ncol(transition))
rownames(transition) <- c(0, seq_len(ncol(transition)))
if (length(motifs@altname) < 1) motifs@altname <- "Unknown"
strand <- motifs@strand
if (strand %in% c("+-", "-+")) strand <- "*"
family <- motifs@family
if (length(family) < 1) family <- "Unknown"
if (requireNamespace("TFBSTools", quietly = TRUE)) {
motifs <- TFBSTools::TFFMFirst(ID = motifs@altname,
name = motifs@name,
strand = strand, type = "First",
bg = bkg, matrixClass = family,
profileMatrix = motifs@motif,
tags = as.list(motifs@extrainfo),
emission = emission, transition = transition)
} else {
stop("package 'TFBSTools' is not installed")
}
motifs
}
convert_to_seqlogo_pwm <- function(motifs) {
motifs <- convert_type(motifs, "PPM")
if (requireNamespace("seqLogo", quietly = TRUE)) {
motifs <- seqLogo::makePWM(motifs@motif)
} else {
stop("'seqLogo' package not installed")
}
motifs
}
convert_to_motifdb_motiflist <- function(motifs) {
if (!is.list(motifs)) motifs <- list(motifs)
if (requireNamespace("MotifDb", quietly = TRUE)) {
motiflist_class <- getClass("MotifList", where = "MotifDb")
motifs <- convert_type(motifs, "PPM")
m <- lapply(motifs, function(x) x["motif"])
for (i in seq_along(m)) {
colnames(m[[i]]) <- seq_len(ncol(m[[i]]))
}
m_names <- vapply(motifs, function(x) x["name"], character(1))
m_altnames <- vapply(motifs, function(x) {
x <- x["altname"]
if (!length(x)) "<ID:unknown>"
else x
}, character(1))
m_family <- vapply(motifs, function(x) {
x <- x["family"]
if (!length(x)) "<family:unknown>"
else x
}, character(1))
m_organisms <- vapply(motifs, function(x) {
x <- x["organism"]
if (!length(x)) "<organism:unknown>"
else x
}, character(1))
m_nsites <- as.numeric(sapply(motifs, function(x) {
x <- x["nsites"]
if (!length(x)) NA_real_
else x
}))
if (any(duplicated(m_names))) {
stop("MotifDb-MotifList cannot be created from motifs with duplicated 'name' slots",
call. = FALSE)
}
names(m) <- m_names
extras <- c("dataSource", "geneSymbol", "geneId", "proteinId", "proteinType",
"bindingSequence", "bindingDomain", "experimentType", "pubmedID")
meta <- DataFrame(
providerName = m_names,
providerId = m_altnames,
dataSource = "<dataSource:unknown>",
geneSymbol = NA_character_,
geneId = NA_character_,
proteinId = NA_character_,
proteinIdType = NA_character_,
organism = m_organisms,
sequenceCount = m_nsites,
bindingSequence = NA_character_,
bindingDomain = NA_character_,
tfFamily = m_family,
experimentType = NA_character_,
pubmedID = NA_character_
)
for (i in seq_along(m)) {
m_ex_i <- motifs[[i]]["extrainfo"]
for (j in seq_along(extras)) {
m_ex_i_j <- m_ex_i[extras[j]]
if (!is.na(m_ex_i_j)) {
meta[[extras[j]]][i] <- m_ex_i_j
}
}
}
assoctab <- data.frame(
motif = m_names,
tf.gene = NA_character_,
tf.ensg = NA_character_
)
motifs <- new(motiflist_class, listData = m,
elementMetadata = meta,
manuallyCuratedGeneMotifAssociationTable = assoctab)
} else {
stop("'MotifDb' package not installed")
}
motifs
}
convert_to_motifstack_pcm <- function(motifs) {
if (requireNamespace("motifStack", quietly = TRUE)) {
pcm_class <- getClass("pcm", where = "motifStack")
motifs <- convert_type(motifs, "PCM")
motifs <- new(pcm_class, mat = motifs@motif,
name = motifs@name,
alphabet = motifs@alphabet,
background = motifs@bkg[DNA_BASES])
} else {
stop("'motifStack' package not installed")
}
colnames(motifs@mat) <- seq_len(ncol(motifs@mat))
names(motifs@background) <- rownames(motifs@mat)
motifs
}
convert_to_motifstack_pfm <- function(motifs) {
if (requireNamespace("motifStack", quietly = TRUE)) {
pfm_class <- getClass("pfm", where = "motifStack")
motifs <- convert_type(motifs, "PPM")
motifs <- new(pfm_class, mat = motifs@motif,
name = motifs@name,
alphabet = motifs@alphabet,
background = motifs@bkg[DNA_BASES])
} else {
stop("'motifStack' package not installed")
}
colnames(motifs@mat) <- seq_len(ncol(motifs@mat))
names(motifs@background) <- rownames(motifs@mat)
motifs
}
convert_to_pwmenrich_pwm <- function(motifs) {
if (requireNamespace("PWMEnrich", quietly = TRUE)) {
motifs <- convert_type(motifs, "PCM")
PWM_class <- getClass("PWM", where = "PWMEnrich")
bio_mat <- matrix(as.integer(motifs@motif), byrow = FALSE,
nrow = 4)
rownames(bio_mat) <- DNA_BASES
bio_priors <- motifs@bkg[DNA_BASES]
bio_mat <- PWMEnrich::PFMtoPWM(bio_mat, type = "log2probratio",
prior.params = bio_priors,
pseudo.count = motifs@pseudocount)
motifs <- new(PWM_class, name = motifs["name"],
pfm = motifs@motif,
prior.params = bio_priors,
pwm = bio_mat$pwm)
} else {
stop("package 'PWMEnrich' not installed")
}
motifs
}
convert_to_biostrings_pwm <- function(motifs) {
motifs <- convert_type(motifs, "PCM")
bio_mat <- matrix(as.integer(motifs["motif"]), byrow = FALSE,
nrow = 4)
rownames(bio_mat) <- DNA_BASES
bio_priors <- motifs@bkg[DNA_BASES]
motifs <- PWM(x = bio_mat, type = "log2probratio",
prior.params = bio_priors)
motifs
}
convert_to_rgadem_motif <- function(motifs) {
if (requireNamespace("rGADEM", quietly = FALSE)) {
motifs <- convert_type(motifs, "PPM")
rGADEM_motif_class <- getClass("motif", where = "rGADEM")
motifs <- new(rGADEM_motif_class, pwm = motifs@motif,
name = motifs@name,
consensus = motifs@consensus)
} else {
stop("'rGADEM' package not installed")
}
motifs
}
#' @describeIn convert_motifs Convert MotifList motifs. (\pkg{MotifDb})
#' @export
setMethod("convert_motifs", signature(motifs = "MotifList"),
definition = function(motifs, class) {
x <- motifs
altname <- x@elementMetadata@listData$providerName
name <- x@elementMetadata@listData$geneSymbol
family <- x@elementMetadata@listData$tfFamily
organism <- x@elementMetadata@listData$organism
motif <- x@listData
nsites <- as.numeric(x@elementMetadata@listData$sequenceCount)
dataSource <- x@elementMetadata@listData$dataSource
motifdb_fun <- function(i) {
mot <- universalmotif_cpp(altname = altname[i], name = name[i],
family = family[i], organism = organism[i],
motif = motif[[i]], alphabet = "DNA",
type = "PPM", nsites = nsites[i],
extrainfo = c(dataSource = dataSource[i]))
validObject_universalmotif(mot)
mot
}
motifs_out <- lapply(seq_len(length(x)), motifdb_fun)
convert_motifs(motifs_out, class = class)
})
#' @describeIn convert_motifs Convert TFFMFirst motifs. (\pkg{TFBSTools})
#' @export
setMethod("convert_motifs", signature(motifs = "TFFMFirst"),
definition = function(motifs, class) {
if (requireNamespace("TFBSTools", quietly = TRUE)) {
difreq <- motifs@emission[-1]
difreq <- do.call(c, difreq)
difreq <- matrix(difreq, nrow = 16) / 4
rownames(difreq) <- DNA_DI
colnames(difreq) <- seq_len(ncol(difreq))
mot <- universalmotif_cpp(name = motifs@name, altname = motifs@ID,
strand = motifs@strand, bkg = motifs@bg,
motif = TFBSTools::getPosProb(motifs))
mot@multifreq <- list("2" = difreq)
} else {
stop("package 'TFBSTools' is not installed")
}
convert_motifs(mot, class = class)
})
#' @describeIn convert_motifs Convert PFMatrix motifs. (\pkg{TFBSTools})
#' @export
setMethod("convert_motifs", signature(motifs = "PFMatrix"),
definition = function(motifs, class) {
if (all(names(motifs@bg) %in% DNA_BASES)) {
alphabet <- "DNA"
} else alphabet <- "RNA"
extrainfo <- motifs@tags
if (length(extrainfo) > 0) {
extrainfo <- unlist(extrainfo)
} else {
extrainfo <- character()
}
nsites <- sum(motifs@profileMatrix[, 1])
motifs <- universalmotif_cpp(name = motifs@name, altname = motifs@ID,
family = paste0(motifs@tags$family, collapse = " / "),
nsites = nsites,
organism = paste0(motifs@tags$species,
collapse = "/"),
motif = motifs@profileMatrix,
alphabet = alphabet, type = "PCM",
bkg = motifs@bg,
strand = collapse_cpp(motifs@strand),
extrainfo = extrainfo)
validObject_universalmotif(motifs)
convert_motifs(motifs, class = class)
})
#' @describeIn convert_motifs Convert PWMatrix motifs. (\pkg{TFBSTools})
setMethod("convert_motifs", signature(motifs = "PWMatrix"),
definition = function(motifs, class) {
if (all(names(motifs@bg) %in% DNA_BASES)) {
alphabet <- "DNA"
} else alphabet <- "RNA"
extrainfo <- motifs@tags
if (length(extrainfo) > 0) {
extrainfo <- unlist(extrainfo)
} else {
extrainfo <- character()
}
motifs <- universalmotif_cpp(name = motifs@name, altname = motifs@ID,
family = paste0(motifs@tags$family, collapse = " / "),
organism = paste0(motifs@tags$species,
collapse = "/"),
motif = motifs@profileMatrix,
alphabet = alphabet, type = "PWM",
bkg = motifs@bg,
strand = collapse_cpp(motifs@strand),
extrainfo = extrainfo)
validObject_universalmotif(motifs)
convert_motifs(motifs, class = class)
})
#' @describeIn convert_motifs Convert ICMatrix motifs. (\pkg{TFBSTools})
setMethod("convert_motifs", signature(motifs = "ICMatrix"),
definition = function(motifs, class) {
if (all(names(motifs@bg) %in% DNA_BASES)) {
alphabet <- "DNA"
} else alphabet <- "RNA"
extrainfo <- motifs@tags
if (length(extrainfo) > 0) {
extrainfo <- unlist(extrainfo)
} else {
extrainfo <- character()
}
motifs <- universalmotif_cpp(name = motifs@name, altname = motifs@ID,
family = paste0(motifs@tags$family, collapse = " / "),
organism = paste0(motifs@tags$species,
collapse = "/"),
motif = motifs@profileMatrix,
alphabet = alphabet, type = "ICM",
bkg = motifs@bg,
strand = collapse_cpp(motifs@strand),
extrainfo = extrainfo)
validObject_universalmotif(motifs)
convert_motifs(motifs, class = class)
})
#' @describeIn convert_motifs Convert XMatrixList motifs. (\pkg{TFBSTools})
#' @export
setMethod("convert_motifs", signature(motifs = "XMatrixList"),
definition = function(motifs, class) {
motif_num <- length(motifs@listData)
motifs_out <- lapply(seq_len(motif_num),
function(i) motifs@listData[[i]])
motif_names <- unlist(lapply(seq_len(motif_num),
function(i) motifs@listData[[i]]@name))
motifs <- lapply(motifs_out, convert_motifs)
convert_motifs(motifs, class = class)
})
#' @describeIn convert_motifs Convert pwm motifs. (\pkg{seqLogo})
#' @export
setMethod("convert_motifs", signature(motifs = "pwm"),
definition = function(motifs, class) {
motifs <- universalmotif_cpp(motif = motifs@pwm, type = "PPM",
alphabet = motifs@alphabet)
validObject_universalmotif(motifs)
convert_motifs(motifs, class = class)
})
#' @describeIn convert_motifs Convert pcm motifs. (\pkg{motifStack})
#' @export
setMethod("convert_motifs", signature(motifs = "pcm"),
definition = function(motifs, class) {
motifs <- universalmotif_cpp(name = motifs@name, motif = motifs@mat,
nsites = unique(colSums(motifs@mat))[1],
alphabet = motifs@alphabet,
bkg = motifs@background,
type = "PCM")
validObject_universalmotif(motifs)
convert_motifs(motifs, class = class)
})
#' @describeIn convert_motifs Convert pfm motifs. (\pkg{motifStack})
#' @export
setMethod("convert_motifs", signature(motifs = "pfm"),
definition = function(motifs, class) {
motifs <- universalmotif_cpp(name = motifs@name, motif = motifs@mat,
alphabet = motifs@alphabet,
bkg = motifs@background,
type = "PPM")
validObject_universalmotif(motifs)
convert_motifs(motifs, class = class)
})
#' @describeIn convert_motifs Convert PWM motifs. (\pkg{PWMEnrich})
#' @export
setMethod("convert_motifs", signature(motifs = "PWM"),
definition = function(motifs, class) {
if (all(names(motifs@pwm) %in% DNA_BASES)) {
alphabet <- "DNA"
} else alphabet <- "RNA"
motifs <- universalmotif_cpp(name = motifs@name, motif = motifs@pwm,
type = "PWM", alphabet = alphabet,
bkg = motifs@prior.params,
altname = motifs@id)
validObject_universalmotif(motifs)
convert_motifs(motifs, class = class)
})
#' @describeIn convert_motifs Convert Motif motifs. (\pkg{motifRG})
#' @export
setMethod("convert_motifs", signature(motifs = "Motif"),
definition = function(motifs, class) {
motifs <- universalmotif_cpp(name = motifs@pattern,
nsites = sum(motifs@count),
alphabet = "DNA",
type = "PCM",
extrainfo = c(score = motifs@score),
strand = paste(unique(motifs@match$match.strand),
collapse = ""),
motif <- create_motif(input = DNAStringSet(motifs@match$pattern))@motif)
validObject_universalmotif(motifs)
convert_motifs(motifs, class = class)
})
#' @describeIn convert_motifs Create motif from matrices.
#' @export
setMethod("convert_motifs", signature(motifs = "matrix"),
definition = function(motifs, class) {
motifs <- create_motif(motifs)
convert_motifs(motifs, class = class)
})
# @describeIn convert_motifs Convert non-\linkS4class{universalmotif} class motifs.
# @export
# setMethod("convert_motifs", signature(motifs = "ANY"),
# definition = function(motifs, class) {
# success <- FALSE
# ## convert to universalmotif
# in_class <- class(motifs)[1]
# in_class_pkg <- attributes(class(motifs))$package
# if (paste(in_class_pkg, in_class, sep = "-") == class) {
# return(motifs)
# }
# paste(in_class_pkg)
# paste(in_class)
# if (!success) stop("unrecognized class")
# ## convert to desired class
# motifs <- convert_motifs(motifs, class = class)
# motifs
# })
|
7468c4ec52b187f40673eeab1cf21f32cb933103 | 5b9559fbe07d61b8dc15bb5f701d9853e021f34f | /inst/scripts/outside.R | 08c87e1daff699692aa0dda4161b22854e4a7393 | [
"MIT"
] | permissive | adamkucharski/covidhm | ef1f3625a38000e919dc848222dcf94ec59afe02 | 0306b3a1e9940acdd4dc3ad8470a07fa67a1f702 | refs/heads/master | 2023-03-29T06:47:24.725622 | 2023-03-23T12:44:16 | 2023-03-23T12:44:16 | 563,936,939 | 0 | 0 | NOASSERTION | 2022-11-09T16:41:47 | 2022-11-09T16:41:46 | null | UTF-8 | R | false | false | 859 | r | outside.R | ###########################################
#SIMULATE OUTSIDE INFECTION
###########################################
rm(list=ls())
library(covidhm)
library(dplyr)
library(purrr)
library(tidyr)
source("inst/scripts/default_params.R")
future::plan("multiprocess")
# Simulate scenarios ------------------------------------------------------
intervention = c("nothing","primary_quarantine","secondary_quarantine")
outside = c(0.0001,0.001,0.005,0.01)
scenarios <- expand_grid(intervention,outside)
res <- scenarios %>%
mutate(results = map2(intervention,outside, ~ scenario_sim2(scenario = .x,
outside = .y,
distancing = 0,
testing = FALSE)))
saveRDS(res,"data-raw/outside.rds")
|
d39ec2f55c45529432fe20c3667743e93934c2da | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/jstable/examples/coxExp.Rd.R | d4f8b029bb899f461bf6804a59005c7d2db9ded1 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 297 | r | coxExp.Rd.R | library(jstable)
### Name: coxExp
### Title: coxExp: transform the unit of coefficients in cox model(internal
### function)
### Aliases: coxExp
### ** Examples
library(coxme)
fit <- coxme(Surv(time, status) ~ ph.ecog + age + (1|inst), lung)
jstable:::coxExp(jstable:::coxmeTable(fit))
|
29e14b1c489fb940d7ecbcca99d77d9d1ce2c537 | 62189fdb2b397f2b2050f885c4e9c7cac4045446 | /scripts/prims_field_viz/www/scripts/load_packages.R | 90167bd31c2ac2ab98a5c92c426bf9bb054bb71f | [] | no_license | ECO2ZTS/prims | 9fde52dfdf6e6260dbbd8c60cd2bea3467b9928c | 0740633804e230279e0b49981d71d67882199753 | refs/heads/master | 2022-04-04T01:17:36.281249 | 2020-01-28T08:06:13 | 2020-01-28T08:06:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 907 | r | load_packages.R | ########################################
# include all the needed packages here #
packages <- function(x){
x <- as.character(match.call()[[2]])
if (!require(x,character.only=TRUE)){
install.packages(pkgs=x,repos="http://cran.r-project.org")
require(x,character.only=TRUE)
}
}
## Packages for geospatial data handling
# packages(raster)
# packages(rgeos)
# packages(rgdal)
# packages(Formula)
## Packages for Shiny
packages(shiny)
packages(shinydashboard)
packages(shinyFiles)
packages(lubridate)
# packages(snow)
# packages(htmltools)
# packages(devtools)
# packages(RCurl)
## Packages for data table handling
# packages(xtable)
# packages(DT)
# packages(dismo)
packages(stringr)
packages(dplyr)
## Packages for graphics and interactive maps
packages(ggplot2)
# packages(leaflet)
# packages(RColorBrewer)
## Packages for BFAST
#packages(bfastSpatial)
#packages(parallel)
#packages(ncdf4) |
4990c7b26df014b84f6cafbe40ee6568ea4c7423 | cb9e345318bd0f0502a459984fb00cb647b3ec2c | /MCD12Q2C6/MCD12Q2C6_sample.R | 440e5860083008775fe070360585b8725c157b81 | [] | no_license | jm-gray/pixel-forge | f3e70fdf8cd1ea2c834305a907893d8deb0e8dcc | be3f4b3e00f53f1df933e878a63a80e75c497c81 | refs/heads/master | 2021-07-15T06:05:46.746375 | 2021-06-29T13:36:14 | 2021-06-29T13:36:14 | 61,559,574 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 9,451 | r | MCD12Q2C6_sample.R | library(raster)
library(parallel)
library(data.table)
library(argparse)
#--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
GetSDS <- function(file_path, sds=NULL){
# gets the SDS names for MCD12Q2 EOS HDFs
# valid sds options: Greenup, MidGreenup, Peak, Senescence, MidGreendown, Dormancy, EVI_Minimum, EVI_Amplitude, NumCycles, QA_Detailed, QA_Overall
# if sds is not provided, filenames for all SDS are returned
all_sds <- c("NumCycles", "Greenup", "MidGreenup", "Maturity", "Peak", "Senescence", "MidGreendown", "Dormancy", "EVI_Minimum", "EVI_Amplitude", "EVI_Area", "QA_Overall", "QA_Detailed")
if(is.null(sds)){
return(paste("HDF4_EOS:EOS_GRID:\"", file_path, "\":MCD12Q2:", all_sds, sep = ""))
}else{
return(paste("HDF4_EOS:EOS_GRID:\"", file_path, "\":MCD12Q2:", sds, sep = ""))
}
}
#--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
GetSDSQ1 <- function(file_path, sds="LC_Type1"){
# returns the proper EOS-HDF name for a particular MCD12Q1 C6 SDS
return(paste("HDF4_EOS:EOS_GRID:\"", file_path, "\":MCD12Q1:", sds, sep = ""))
}
#--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
GetMetricSample <- function(metric, in_file, pt_sp, LC_DIR="/rsstu/users/j/jmgray2/SEAL/MCD12Q1"){
# extract the values from in_file, corresponding to the phenometric metric, at the locations in pt_sp
# return as a long form data.table with: tile, cell#, x, y, lat, lon, metric, lc, year, and value columns
file_year <- as.integer(gsub(".*A([0-9]{4})001.*$", "\\1", basename(in_file)))
file_tile <- gsub(".*(h[0-9]{2}v[0-9]{2}).*$", "\\1", basename(in_file))
# extract values from MCD12Q2 data
s <- stack(GetSDS(in_file, metric))
vals <- extract(s, pt_sp)
# get the MCD12Q1 landcover
lc_in_file <- dir(file.path(LC_DIR, 2016, "001"), pattern=file_tile, full=T)
lc_r <- raster(GetSDSQ1(lc_in_file))
lc_v <- extract(lc_r, pt_sp)
# get the cell numbers and lat/lon of sample locations
cell_nums <- cellFromXY(s, pt_sp)
proj4_latlon <- "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"
pt_sp_latlon <- spTransform(pt_sp, CRS(proj4_latlon))
if(ncol(vals) > 1){
DTcycle1 <- data.table(tile=file_tile, cell_num=cell_nums, x=coordinates(pt_sp)[, 1], y=coordinates(pt_sp)[, 2], lat=coordinates(pt_sp_latlon)[, 2], lon=coordinates(pt_sp_latlon)[, 1], igbp=lc_v, phenometric=paste(metric, "1", sep=""), year=file_year, value=vals[, 1])
DTcycle2 <- data.table(tile=file_tile, cell_num=cell_nums, x=coordinates(pt_sp)[, 1], y=coordinates(pt_sp)[, 2], lat=coordinates(pt_sp_latlon)[, 2], lon=coordinates(pt_sp_latlon)[, 1], igbp=lc_v, phenometric=paste(metric, "2", sep=""), year=file_year, value=vals[, 2])
DTreturn <- rbind(DTcycle1, DTcycle2)
}else{
DTreturn <- data.table(tile=file_tile, cell_num=cell_nums, x=coordinates(pt_sp)[, 1], y=coordinates(pt_sp)[, 2], lat=coordinates(pt_sp_latlon)[, 2], lon=coordinates(pt_sp_latlon)[, 1], igbp=lc_v, phenometric=metric, year=file_year, value=vals[, 1])
}
return(DTreturn)
}
#--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
GetSample <- function(in_file, pt_sp, metrics=NULL){
# apply GetMetricSample() over multiple phenometrics for a particular file and return as an aggregated data.table
if(is.null(metrics)) metrics <- c("NumCycles", "Greenup", "MidGreenup", "Maturity", "Peak", "Senescence", "MidGreendown", "Dormancy", "EVI_Minimum", "EVI_Amplitude", "EVI_Area", "QA_Detailed", "QA_Overall")
system.time(all_mets <- lapply(metrics, GetMetricSample, in_file=in_file, pt_sp=pt_sp))
DT_all_mets <- do.call(rbind, all_mets)
return(DT_all_mets)
}
#--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
# let's do it...
arg_parser <- ArgumentParser()
arg_parser$add_argument("-tile", type="character") # tile to process
args <- arg_parser$parse_args()
tile <- args$tile
# define in/out directories and the sample size
inca_dir <- "/rsstu/users/j/jmgray2/SEAL/INCA/INCAglobaloutput"
output_dir <- "/rsstu/users/j/jmgray2/SEAL/INCA/GlobalPhenoSample"
data_dir <- "/rsstu/users/j/jmgray2/SEAL/INCA/MCD12Q2C6/MCD12Q2"
sample_frac <- 0.01
NUM_SAMPLES <- round(2400^2 * sample_frac)
# Define a sample for this tile by getting the INCA file and choosing from non-NA values
# this will undersample in deserts I guess?
proj4_modis_sin <- "+proj=sinu +R=6371007.181 +nadgrids=@null +wktext +unit=m"
proj4_latlon <- "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs "
inca_file <- dir(inca_dir, pattern=paste(tile, ".*Peak", sep=""), full=T)
inca_r <- raster(inca_file)
inca_sample <- sampleRandom(inca_r, size=NUM_SAMPLES, cells=T, na.rm=T)
xy <- xyFromCell(inca_r, inca_sample[, 1])
xy_sp <- SpatialPoints(xy, CRS(proj4_modis_sin))
xy_sp_latlon <- spTransform(xy_sp, CRS(proj4_latlon))
# Extract values across all years and all phenometrics at the sample locations
# create a large long form data.table of the results
# gather and sort input files
in_files <- dir(data_dir, patt=paste("MCD12.*", tile, ".*hdf$", sep=""), full=T, rec=T)
in_years <- as.integer(gsub(".*A([0-9]{4})001.*$", "\\1", basename(in_files))) # get data years from file name
in_files <- in_files[order(in_years)]
in_years <- sort(in_years)
# create a cluster, use individual threads to fully process a single year (MCD12Q2 HDF file)
cl <- makeCluster(8)
clusterExport(cl, c("GetSample", "GetSDS", "GetMetricSample", "GetSDSQ1"))
clusterEvalQ(cl, {library(raster); library(data.table)})
system.time(all_years <- parLapply(cl, in_files, GetSample, pt_sp=xy_sp))
# rbind all the output into one large data.table for this tile and write to disk
DTfinal <- do.call(rbind, all_years)
out_file <- file.path(output_dir, paste("GlobalPhenoSample_", tile, ".Rdata", sep=""))
save(DTfinal, file=out_file)
#-------------------------------
# check if we need to resubmit
# allRdata <- dir("/rsstu/users/j/jmgray2/SEAL/INCA/GlobalPhenoSample", pattern="*.Rdata")
# all_tiles <- c("h08v04", "h09v04", "h10v04", "h11v04", "h12v04", "h13v04", "h08v05", "h09v05", "h10v05", "h11v05", "h12v05", "h08v06", "h09v06", "h10v06", "h00v08", "h00v09", "h00v10", "h01v08", "h01v09", "h01v10", "h01v11", "h02v06", "h02v08", "h02v09", "h02v10", "h02v11", "h03v06", "h03v07", "h03v09", "h03v10", "h03v11", "h04v09", "h04v10", "h04v11", "h05v10", "h05v11", "h05v13", "h06v03", "h06v11", "h07v03", "h07v05", "h07v06", "h07v07", "h08v03", "h08v07", "h08v08", "h08v09", "h09v02", "h09v03", "h09v07", "h09v08", "h09v09", "h10v02", "h10v03", "h10v07", "h10v08", "h10v09", "h10v10", "h10v11", "h11v02", "h11v03", "h11v06", "h11v07", "h11v08", "h11v09", "h11v10", "h11v11", "h11v12", "h12v01", "h12v02", "h12v03", "h12v07", "h12v08", "h12v09", "h12v10", "h12v11", "h12v12", "h12v13", "h13v01", "h13v02", "h13v03", "h13v08", "h13v09", "h13v10", "h13v11", "h13v12", "h13v13", "h13v14", "h14v01", "h14v02", "h14v03", "h14v04", "h14v09", "h14v10", "h14v11", "h14v14", "h14v16", "h14v17", "h15v01", "h15v02", "h15v03", "h15v05", "h15v07", "h15v11", "h15v14", "h15v15", "h15v16", "h15v17", "h16v00", "h16v01", "h16v02", "h16v05", "h16v06", "h16v07", "h16v08", "h16v09", "h16v12", "h16v14", "h16v16", "h16v17", "h17v00", "h17v01", "h17v02", "h17v03", "h17v04", "h17v05", "h17v06", "h17v07", "h17v08", "h17v10", "h17v12", "h17v13", "h17v15", "h17v16", "h17v17", "h18v00", "h18v01", "h18v02", "h18v03", "h18v04", "h18v05", "h18v06", "h18v07", "h18v08", "h18v09", "h18v14", "h18v15", "h18v16", "h18v17", "h19v00", "h19v01", "h19v02", "h19v03", "h19v04", "h19v05", "h19v06", "h19v07", "h19v08", "h19v09", "h19v10", "h19v11", "h19v12", "h19v15", "h19v16", "h19v17", "h20v01", "h20v02", "h20v03", "h20v04", "h20v05", "h20v06", "h20v07", "h20v08", "h20v09", "h20v10", "h20v11", "h20v12", "h20v13", "h20v15", "h20v16", "h20v17", "h21v01", "h21v02", "h21v03", "h21v04", "h21v05", "h21v06", "h21v07", "h21v08", "h21v09", "h21v10", "h21v11", "h21v13", "h21v15", "h21v16", "h21v17", "h22v01", "h22v02", "h22v03", "h22v04", "h22v05", "h22v06", "h22v07", "h22v08", "h22v09", "h22v10", "h22v11", "h22v13", "h22v14", "h22v15", "h22v16", "h23v01", "h23v02", "h23v03", "h23v04", "h23v05", "h23v06", "h23v07", "h23v08", "h23v09", "h23v10", "h23v11", "h23v15", "h23v16", "h24v02", "h24v03", "h24v04", "h24v05", "h24v06", "h24v07", "h24v12", "h24v15", "h25v02", "h25v03", "h25v04", "h25v05", "h25v06", "h25v07", "h25v08", "h25v09", "h26v02", "h26v03", "h26v04", "h26v05", "h26v06", "h26v07", "h26v08", "h27v03", "h27v04", "h27v05", "h27v06", "h27v07", "h27v08", "h27v09", "h27v10", "h27v11", "h27v12", "h27v14", "h28v03", "h28v04", "h28v05", "h28v06", "h28v07", "h28v08", "h28v09", "h28v10", "h28v11", "h28v12", "h28v13", "h28v14", "h29v03", "h29v05", "h29v06", "h29v07", "h29v08", "h29v09", "h29v10", "h29v11", "h29v12", "h29v13", "h30v05", "h30v06", "h30v07", "h30v08", "h30v09", "h30v10", "h30v11", "h30v12", "h30v13", "h31v06", "h31v07", "h31v08", "h31v09", "h31v10", "h31v11", "h31v12", "h31v13", "h32v07", "h32v08", "h32v09", "h32v10", "h32v11", "h32v12", "h33v07", "h33v08", "h33v09", "h33v10", "h33v11", "h34v07", "h34v08", "h34v09", "h34v10", "h35v08", "h35v09", "h35v10")
# allRdata_tiles <- gsub(".*(h[0-9]{2}v[0-9]{2}).*","\\1", allRdata)
# resub_tiles <- all_tiles[!c(all_tiles %in% allRdata_tiles)]
|
a54dc682934a895bbdb48991fcdf88a2d8eb2e4f | 6292a37c62159e1ec96200c61fd5e5bd0ce03c2e | /dsia_demo_codes/ch0607.R | c4853a8d098c85252234203e88ee279f577bf270 | [] | no_license | jtlai0921/AEL018600_codes | 5270eb9bc11acc653f1ba24ff1f6eee340625eb5 | 1e4267ea82b75a2c9d57f92edfbc5e8f5a429dbf | refs/heads/master | 2020-12-18T18:07:52.593639 | 2020-01-22T01:55:00 | 2020-01-22T01:55:00 | 235,479,335 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 67 | r | ch0607.R | arr <- c(11, 12, 13, 14, 15)
arr[3] <- 87 # 將 13 更換為 87
arr |
426465e197a42645ebb3d260de0d2ba331e35fe1 | 4c43ba1d8985c586bf213fcb6258dca5cb1a6202 | /code/elliptical_shiny.R | 5e896d7d563cc0699e6b3fb933981749d0766fab | [] | no_license | kkemper/data_dashboard | d1996108b20ab1d67fcd2c54ba695c57c2ddafa7 | deda7ff2296ff5320097c5653b4a7f5f1fb41bfa | refs/heads/master | 2022-03-16T22:29:18.793162 | 2019-08-19T16:59:16 | 2019-08-19T16:59:16 | 184,115,561 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 291 | r | elliptical_shiny.R | library(shiny)
library(airtabler)
library(dplyr)
Sys.setenv("AIRTABLE_API_KEY"="<Your API key") #example key**************
airtable <- airtabler::airtable("<base key>", "<Tab/sheet name>")
ui <- fluidPage(
)
server <- function(input, output) {}
shinyApp(ui = ui, server = server) |
44a0005ee10469e70bfad21ed27bbe94ba793d3f | 2d7b0674a00f24f3e5b0744b2e5290798fa798cc | /IronBrain/OldVersion/FerricChloride+Cytotoxic_WST-1_logistic.R | ebe939f0270c44e938da02b1e5e33bc7e408b350 | [] | no_license | KJKwon/Lab_project | 48081fabe860781a00b9d70686b3ca4c334243a9 | f60232df2977a5ec222ffa37f500304b360ffd31 | refs/heads/master | 2022-09-02T12:42:33.897989 | 2022-06-15T07:52:42 | 2022-06-15T07:52:42 | 69,477,714 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,677 | r | FerricChloride+Cytotoxic_WST-1_logistic.R | library(ggplot2)
library(reshape2)
library(minpack.lm)
library(drc)
#Iron related gene error bar
tbl = read.table('SH-SY5Y_IronToxicityTest_FeCl2_24h_WST-1_results.csv',sep='\t',row.names = 1, header = TRUE)
tbl = apply(t(tbl),1,function(x){(x/tbl[,1])*100})
val.Mean = apply(tbl,2,mean)
val.Mean = c(as.numeric(val.Mean))
#Calculate standard error
val.se = apply(tbl,2,function(x){sd(x)/sqrt(length(rownames(tbl)))})
val.se = c(as.numeric(val.se))
tbl.dot = tbl
#Melt table for ggplot2 plotting
tbl.dotplot = melt(tbl.dot)
tbl.dotplot$variable = rep(c(0,100,200,500,1000,2000,5000,10000), times = 1, each = length(rownames(tbl)))
#Synchronize plot name to dotplot variable name
tbl.new = t(data.frame(val.Mean))
tbl.plot = melt(tbl.new)
tbl.plot$variable = c(0,100,200,500,1000,2000,5000,10000)
tbl.plot$se = val.se
tbl.plot = tbl.plot[,3:5]
colnames(tbl.plot) = c('variable', 'value', 'se')
p = ggplot(tbl.plot,aes(x = log10(value+1), y = variable)) + geom_line() + geom_point(aes(x = log10(variable+1) , y = value), tbl.dotplot)+
geom_errorbar(aes(ymin = variable - se, ymax = variable + se))+
theme(text = element_text(size = 15), legend.title = element_blank())+ xlab('log(FeCl2) uM') + ylab('Absorbance(450nm-690nm)')
plot(p)
##Test_set
p = ggplot() + geom_point(aes(x = log10(variable+1), y = value),tbl.dotplot)+
# geom_smooth(data= tbl.dotplot, aes(x = log10(variable+1), y = value), method = 'nls' ,
# formula = y ~ f(x,xmid,scal), method.args = list(start=c(xmid = 3.4910, scal = -0.6797)),se = FALSE) +
geom_smooth(data = tbl.dotplot, aes(x = log10(variable+1), y = value), method = 'auto', se = F)+
geom_errorbar(aes(x = log10(value+1), ymin = variable - se, ymax = variable + se), tbl.plot)+
#panel design part
theme(text = element_text(size = 15), legend.title = element_blank(), panel.background = element_blank(),
panel.border = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(size = 1, colour = 'black'),
axis.text.x = element_text(colour = 'black', size = 15), axis.text.y = element_text(colour = 'black', size = 15))+
xlab('log(FeCl2) uM') + ylab('% WST-1 Absorbance\n(compared to control)') + scale_y_continuous(breaks=c(0,25,50,75,100),
limits = c(0,100))+
annotation_logticks(sides = 'b')
plot(p)
##geom_point = scatter_plot, geom_errorbar = error_bar
p = ggplot() + geom_point(aes(x = log10(variable+1), y = value),tbl.dotplot)+
geom_smooth(data= tbl.dotplot, aes(x = log10(variable+1), y = value), method = 'nls' ,
formula = y ~ SSlogis(x, Asym, xmid, scal), se = FALSE) +
geom_errorbar(aes(x = log10(value+1), ymin = variable - se, ymax = variable + se), tbl.plot)+
#panel design part
theme(text = element_text(size = 15), legend.title = element_blank(), panel.background = element_blank(),
panel.border = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(size = 1, colour = 'black'),
axis.text.x = element_text(colour = 'black', size = 15), axis.text.y = element_text(colour = 'black', size = 15))+
xlab('log(FeCl2) uM') + ylab('% WST-1 Absorbance\n(compared to control)') + scale_y_continuous(breaks=c(0,25,50,75,100,125),
limits = c(0,125))+
annotation_logticks(sides = 'b')
#scale_y_continuous(breaks = seq(0,125,25))
plot(p)
ggsave(file = "200401_Iron_treat_suction_SH-SY5Y_WST-1_original.pdf", plot = last_plot(), width = 8, height = 6, units = c("in"),
device = pdf())
dev.off()
|
e6cc865481a34ed126855badac8863330c34aeef | 50dd2db2c197a76edfca7f5bb96439b36bfbc750 | /R/mdev_distance_function(MDEV_DISTANCE).R | 9dc09b78c8b67cb288e4b64fbececf16ca30d343 | [] | no_license | absuag/dreaweRTools | 1dbe6f00b8828bbbc84e21f687d794a3314e4506 | 583835604b3318d43ae75e36c73b0c71f2b926be | refs/heads/master | 2022-04-09T23:52:11.174939 | 2020-03-12T12:16:53 | 2020-03-12T12:16:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 411 | r | mdev_distance_function(MDEV_DISTANCE).R | #' Mean deviation distance
#'
#' Function to calculate mean deviation distances
#' Output: A vector containing mean deviation distances from each point in given vector
#'
#' @param attr A vector of numeric values
#'
#'
#' @return
#' @export
#'
#' @examples
#' MDEV_DISTANCE()
MDEV_DISTANCE <- function(attr){
tmpMean <- mean(attr)
tmpDev <- MDEV(attr)
ret <- (attr - tmpMean)/tmpDev
return(ret)
}
|
be3c2b1438309453d625ced3551eafc00aaedaa3 | ce55955fd4189ada9e35696d5cb7589e18eea266 | /data-raw/prepare-data.R | 423d16d3ca2eb5cc854941207939853ec67d41e1 | [
"MIT"
] | permissive | laurafdeza/phon | 1adec6d4b3a744b521048564c28bcca3b13ff10a | 2e169a9f9ac3f0253f45d2e1b6be008715bdbb1d | refs/heads/master | 2020-04-29T20:47:11.062654 | 2018-12-09T12:55:54 | 2018-12-09T12:55:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,456 | r | prepare-data.R |
suppressPackageStartupMessages({
library(dplyr)
library(purrr)
})
# This package uses the CMU Pronouncing Dictionary.
# See:
# http://www.speech.cs.cmu.edu/cgi-bin/cmudict
# http://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict/
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Raw cmu dictionary
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
cmu_raw <- readLines(here::here("data-raw", "cmudict-0.7b.txt"), encoding = 'UTF-8')
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Remove the cruft at the top of the file
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
cmu_raw <- cmu_raw[-(1:56)]
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# parse into usable R structure - a matrix of characters
# - column 1: the words
# - column 2: string representing the ARPABET phonetic codes
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
cmu_mat <- stringr::str_split(cmu_raw, "\\s+", n=2L, simplify = TRUE)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Convert the first column into a list of lowercase words.
# Some words are included multiple times to indicate alternate pronunciations.
# e.g. if 'xxx' has 2 pronunciations, the first is 'xxx' and the second
# is 'xxx(1)'.
# Going to remove all the "(N)" suffixes from the words
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
cmu_words <- cmu_mat[,1]
cmu_words[35418] <- 'DJ'
cmu_words <- stringr::str_to_lower(gsub("\\(.*?\\)", "", cmu_words))
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# A single string oh phonemes per word
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
cmu_phons_orig <- cmu_mat[,2]
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Internal data representation
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
cmudict <- setNames(cmu_phons_orig, cmu_words)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Save all the internal data
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
usethis::use_data(cmudict, internal = FALSE, overwrite = TRUE, compress = 'xz')
file.size(here::here("data/cmudict.rda"))
|
699264698a5a2756666b8bdb58e01371ef84e437 | 232c8b0213342e9e973ec8ffb695743759ee89b3 | /man/make.refFn.Rd | 2808846466a300003a7e81a60a92a34fb908fe0a | [] | no_license | uyedaj/bayou | 304c98ba9516fb91688b345fb33c9a41765d06cd | b623758bf7b08900e2cd60c9247c2650b564d06b | refs/heads/master | 2021-07-05T03:02:21.376172 | 2021-05-10T14:51:11 | 2021-05-10T14:51:11 | 21,963,529 | 19 | 10 | null | 2019-11-06T18:58:40 | 2014-07-18T01:29:03 | HTML | UTF-8 | R | false | true | 1,698 | rd | make.refFn.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bayou-steppingstone.R
\name{make.refFn}
\alias{make.refFn}
\title{Make a reference function in bayou}
\usage{
make.refFn(chain, model, priorFn, burnin = 0.3, plot = TRUE)
}
\arguments{
\item{chain}{An mcmc chain produced by \code{bayou.mcmc()} and loaded with \code{load.bayou()}}
\item{model}{A string specifying the model ("OU", "QG", "OUrepar") or a model parameter list}
\item{priorFn}{The prior function used to generate the mcmc chain}
\item{burnin}{The proportion of the mcmc chain to be discarded when generating the reference function}
\item{plot}{Logical indicating whether or not a plot should be created}
}
\value{
Returns a reference function of class "refFn" that takes a parameter list and returns the log density
given the reference distribution. If \code{plot=TRUE}, a plot is produced showing the density of variable parameters
and the fitted distribution from the reference function (in red).
}
\description{
This function generates a reference function from a mcmc chain for use in marginal likelihood
estimation.
}
\details{
Distributions are fit to each mcmc chain and the best-fitting distribution is chosen as
the reference distribution for that parameter using the method of Fan et al. (2011). For positive
continuous parameters \code{alpha, sigma^2, halflife, Vy, w2, Ne}, Log-normal, exponential, gamma and weibull
distributions are fit. For continuous distributions \code{theta}, Normal, Cauchy and Logistic distributions
are fit. For discrete distributions, \code{k}, negative binomial, poisson and geometric distributions are fit.
Best-fitting distributions are determined by AIC.
}
|
d60b8470cc7fea0b82ba311537c3cf6c4929f78f | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/madrat/examples/fingerprint.Rd.R | 674b4826bc13cd1e24d39803d43d8e5da821a1ce | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 171 | r | fingerprint.Rd.R | library(madrat)
### Name: fingerprint
### Title: Tool: fingerprint
### Aliases: fingerprint
### ** Examples
## Not run:
##D fingerprint(".",ls,c)
## End(Not run)
|
ebf03b4b7ab21f9349143497caed705447291b62 | 089f28e1e03609dcd9fac4065e26d11d3359a0f3 | /Work/Cleaning_Week2.R | b993025de0662c9d2b3d6b6f9c884da86ec79e16 | [] | no_license | themp731/datasciencecoursera | 8371f124617588205e7daa4089bab49af4d61907 | c363749ef94eda0f6507ebc10f1cfb1ef4638fe0 | refs/heads/master | 2020-04-09T19:16:52.921598 | 2016-10-10T19:35:33 | 2016-10-10T19:35:33 | 62,075,087 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,425 | r | Cleaning_Week2.R | "This is connecting to a mySql DB"
library("RMySQL")
ucscDb <- dbConnect(MySQL(),user="genome",
host="genome-mysql.cse.ucsc.edu")
result <- dbGetQuery(ucscDb,"show databases;");
dbDisconnect(ucscDb)
result
"The result is all the databases"
#We're interested in hg19
hg19 <- dbConnect(MySQL(), user="genome", db = "hg19",
host = "genome-mysql.cse.ucsc.edu")
allTables <- dbListTables(hg19)
length(allTables)
#Getting dimensions of a table takes us to get fields form a table
dbListFields(hg19, "affyU133Plus2")
dbGetQuery(hg19, "select count(*) from affyU133Plus2")
#Returning the data we want
affyData <- dbReadTable(hg19, "affyU133Plus2")
query <- dbSendQuery(hg19, "select * from affyU133Plus2 where misMatches between 1 and 3")
affyMis <- fetch(query)
quantile(affyMis$misMatches)
#You need to clear your queries when you're done
dbDisconnect(hg19)
"HDF5 databases"
#This is useful for downloading tons of large;
#hierarchical data sets.
"Reading Data from the Web"
#This is good web scraping etc.
# getting open a connection
con <- url("http://scholar.google.com/citations?user=HI-I6C0AAAAJ&hl=en")
#reading lines
htmlCode = readLines(con)
# but this is kind of annoying, so we can move it to read in XML
library(XML)
url <- "http://scholar.google.com/citations?user=HI-I6C0AAAAJ&hl=en"
html <- htmlTreeParse(url, useInternalNodes = TRUE)
close(con)
#Or using the get Comand |
e01c8eeac678fc73ea930593703bc0f07cdfe81e | 1a058815dc84cf41ed87efc68feba6ddbf511b92 | /diets.R | eaa30f92618b9067ba0d648ad30ab0c4419f3be4 | [
"MIT"
] | permissive | JPGibert/Foodweb_thermal_asymmetries | f1e1ee241ead4eb473190284aa2da2134814ec43 | 76a7629b979b080a82a2c1155fc69bdbd4872528 | refs/heads/master | 2023-04-14T03:34:18.282452 | 2022-05-17T15:03:25 | 2022-05-17T15:03:25 | 246,418,052 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,855 | r | diets.R | library(tidyverse)
#mammal mortality data
#mammal diet data
mammal_diet0 <- read_tsv('/Users/jgradym/Google Drive/Gibert Paper/Data/Diet/Mammals/doi_10.5061_dryad.6cd0v__v1/MammalDIET_v1.0.txt')
mammal_diet1 <- mammal_diet0 %>%
mutate(Species = paste(Genus, Species, sep = "_")) %>%
select(Species, TrophicLevel)
unique(mammal_diet1$TrophicLevel)
mammal_diet1$trophic_level <- NA
mammal_diet1$trophic_level[mammal_diet1$TrophicLevel == "Herbivore"] <- 2
mammal_diet1$trophic_level[mammal_diet1$TrophicLevel == "Carnivore"] <- 3
mammal_diet1$trophic_level[mammal_diet1$TrophicLevel == "Omnivore"] <- 2.5
mammal_diet1$trophic_level[mammal_diet1$TrophicLevel == "NotAssigned"] <- NA
mammal_diet1
#combine with mortality
mortality0 <- read_csv(file.path(gdrive_path,'McCoy_mortality_updated.csv'))
mammal_mort0 <- mortality0 %>% filter(Group == "Mammal")
mammal_mort <- left_join(mammal_mort0, mammal_diet1, by = "Species")
mammal_mort$Genus <- word(mammal_mort$Species, 1, sep = "_")
missing_mamm <- mammal_mort[is.na(mammal_mort$trophic_level),]
missing_mamm_gen <- word(missing_mamm$Species, 1, sep = "_")
unique(missing_mamm_gen )
#manual fix
mammal_mort$trophic_level[mammal_mort$Species == "Vulpes_lagopus"] <- 3
mammal_mort$trophic_level[mammal_mort$Genus == "Arctocephalus"] <- 4
mammal_mort$trophic_level[mammal_mort$Genus == "Balaena"] <- 3
mammal_mort$trophic_level[mammal_mort$Genus == "Balaenoptera"] <- 3
mammal_mort$trophic_level[mammal_mort$Genus == "Berardius"] <- 4.5
mammal_mort$trophic_level[mammal_mort$Genus == "Sapajus"] <- 2.5
mammal_mort$trophic_level[mammal_mort$Genus == "Callorhinus"] <- 4
mammal_mort$trophic_level[mammal_mort$Genus == "Cephalorhynchus"] <- 4
mammal_mort$trophic_level[mammal_mort$Genus == "Cystophora"] <- 4
mammal_mort$trophic_level[mammal_mort$Genus == "Delphinapterus"] <- 4
mammal_mort$trophic_level[mammal_mort$Genus == "Delphinus"] <- 4
mammal_mort$trophic_level[mammal_mort$Genus == "Dugong"] <- 2
mammal_mort$trophic_level[mammal_mort$Genus == "Erignathus"] <- 3.5
mammal_mort$trophic_level[mammal_mort$Genus == "Eschrichtius"] <- 3
mammal_mort$trophic_level[mammal_mort$Genus == "Eumetopias"] <- 4
mammal_mort$trophic_level[mammal_mort$Genus == "Halichoerus"] <- 3
mammal_mort$trophic_level[mammal_mort$Genus == "Hydrurga"] <- 4.5
mammal_mort$trophic_level[mammal_mort$Genus == "Hyperoodon"] <- 4.5
mammal_mort$trophic_level[mammal_mort$Genus == "Lagenorhynchus"] <- 4
mammal_mort$trophic_level[mammal_mort$Genus == "Lobodon"] <- 3
mammal_mort$trophic_level[mammal_mort$Genus == "Megaptera"] <- 3
mammal_mort$trophic_level[mammal_mort$Genus == "Mirounga"] <- 4.5
mammal_mort$trophic_level[mammal_mort$Genus == "Monodon"] <- 4
mammal_mort$trophic_level[mammal_mort$Genus == "Neophocaena"] <- 4
mammal_mort$trophic_level[mammal_mort$Genus == "Odobenus"] <- 3
mammal_mort$trophic_level[mammal_mort$Genus == "Orcinus"] <- 4.5
mammal_mort$trophic_level[mammal_mort$Genus == "Otaria"] <- 4
mammal_mort$trophic_level[mammal_mort$Genus == "Pusa"] <- 4
mammal_mort$trophic_level[mammal_mort$Genus == "Peponocephala"] <- 4
mammal_mort$trophic_level[mammal_mort$Genus == "Histriophoca"] <- 4
mammal_mort$trophic_level[mammal_mort$Genus == "Pagophilus"] <- 4
mammal_mort$trophic_level[mammal_mort$Genus == "Phoca"] <- 4
mammal_mort$trophic_level[mammal_mort$Genus == "Phocoena"] <- 4
mammal_mort$trophic_level[mammal_mort$Genus == "Phocoenoides"] <- 4
mammal_mort$trophic_level[mammal_mort$Genus == "Physeter"] <- 5
mammal_mort$trophic_level[mammal_mort$Genus == "Pontoporia"] <- 4
mammal_mort$trophic_level[mammal_mort$Genus == "Pseudorca"] <- 4.5
mammal_mort$trophic_level[mammal_mort$Genus == "Leontocebus"] <- 2.5
mammal_mort$trophic_level[mammal_mort$Genus == "Stenella"] <- 4
mammal_mort$trophic_level[mammal_mort$Genus == "Trichechus"] <- 2
mammal_mort$trophic_level[mammal_mort$Genus == "Tursiops"] <- 4
mammal_mort$trophic_level[mammal_mort$Genus == "Zalophus"] <- 4
mammal_mort[is.na(mammal_mort$trophic_level),]
#---- birds
bird_diet0 <- read_csv('/Users/jgradym/Google Drive/Gibert Paper/Data/Diet/Birds/41559_2019_1070_MOESM3_ESM.csv')
unique(bird_diet0$TrophicLevel)
bird_diet1 <- bird_diet0 %>%
rename(Species = Binomial) %>%
select(Species, TrophicLevel)
bird_diet1$trophic_level <- NA
bird_diet1$trophic_level[bird_diet1$TrophicLevel == "Herbivore"] <- 2
bird_diet1$trophic_level[bird_diet1$TrophicLevel == "Carnivore"] <- 3
bird_diet1$trophic_level[bird_diet1$TrophicLevel == "Omnivore"] <- 2.5
bird_diet1$trophic_level[bird_diet1$TrophicLevel == "Scavenger"] <- 2
#combine
bird_mort0 <- mortality0 %>% filter(Group == "Bird")
bird_mort <- left_join(bird_mort0 , bird_diet1, by = "Species")
bird_mort
bird_mort[is.na(bird_mort$trophic_level),]
#-------- invertebrates
invert_mort0 <- mortality0 %>% filter(Group == "Invertebrate")
invert_genera <- as_tibble(unique(word(invert_mort0$Species), 1, sep = "_"))
invert_genera <- invert_genera %>%
rename(genera = value)
write_csv(invert_genera, '~/Desktop/invert_genera.csv')
invert_diet <- read_csv('/Users/jgradym/Google Drive/Gibert Paper/Data/Diet/invert_diet.csv')
invert_mort0$genus <- word(invert_mort0$Species, 1, sep = " ")
invert_mort <- left_join(invert_mort0, invert_diet, by= "genus")
#fish
fish_mort0 <- mortality0 %>% filter(Group == "Fish") #234
fish_genera <- unique(word(fish_mort0$Species, 1, sep = " "))
fish_spp <- fish_mort0$Species
write.csv(fish_genera, "~/Desktop/fish_genera.csv")
write.csv(fish_spp, "~/Desktop/fish_spp.csv")
library(rfishbase)
library(taxize)
fish0 <- species_list(Class = "Actinopterygii")
fish1 <- fish0[fish0 %in% fish_spp]
missing_fish <- fish_mort0 %>%
filter(Species %nin% fish0)
#write_csv(missing_fish, "~/Desktop/missing_fish.csv")
#manual fixes
fish_mort1 <- fish_mort0
fish_mort1$Species[fish_mort1$Species == "Clupea pallassii"] <- "Clupea pallasii"
fish_mort1$Species[fish_mort1$Species == "Restrelliger kanagurta"] <- "Rastrelliger kanagurta"
fish_mort1$Species[fish_mort1$Species == "Restrelliger neglectus"] <- "Rastrelliger neglectus"
fish_mort1$Species[fish_mort1$Species == "Lethrinops longispinis"] <- "Lethrinops longipinnis"
fish_mort1$Species[fish_mort1$Species == "Pseudopeneus maculatus"] <- "Pseudupeneus maculatus"
fish_mort1$Species[fish_mort1$Species == "Sebastes paucispinus"] <- "Sebastes paucispinis"
fish_mort1$Species[fish_mort1$Species == "Bathylagus milleri"] <- "Pseudobathylagus milleri"
fish_mort1$Species[fish_mort1$Species == "Blennius pholis"] <- "Lipophrys pholis"
fish_mort1$Species[fish_mort1$Species == "Clupea pallasii"] <- "Clupea pallasii pallasii"
fish_mort1$Species[fish_mort1$Species == "Cynoglossus macrolepidus"] <- "Cynoglossus arel"
fish_mort1$Species[fish_mort1$Species == "Cynolebias adloffi"] <- "Austrolebias adloffi"
fish_mort1$Species[fish_mort1$Species == "Engraulis encrasicholus"] <- "Engraulis encrasicolus"
fish_mort1$Species[fish_mort1$Species == "Gadus minimus"] <- "Raniceps raninus"
fish_mort1$Species[fish_mort1$Species == "Gadus minutus"] <- "Trisopterus minutus"
fish_mort1$Species[fish_mort1$Species == "Gadus minitus"] <- "Trisopterus minutus"
fish_mort1$Species[fish_mort1$Species == "Haemulon plumieri"] <- "Haemulon plumierii"
fish_mort1$Species[fish_mort1$Species == "Haplochromis anaphyrmus"] <- "Mylochromis anaphyrmus"
fish_mort1$Species[fish_mort1$Species == "Haplochromis mloto"] <- "Copadichromis mloto"
fish_mort1$Species[fish_mort1$Species == "Lampanyctus regalis"] <- "Nannobrachium regale"
fish_mort1$Species[fish_mort1$Species == "Leiognathus splendens"] <- "Eubleekeria splendens"
fish_mort1$Species[fish_mort1$Species == "Leucichthys artedi"] <- "Coregonus artedi"
fish_mort1$Species[fish_mort1$Species == "Leucichthys sardinella"] <- "Coregonus sardinella"
fish_mort1$Species[fish_mort1$Species == "Lithrinus enigmaticus"] <- "Lethrinus enigmaticus"
fish_mort1$Species[fish_mort1$Species == "Merluccius gayi"] <- "Merluccius gayi gayi"
fish_mort1$Species[fish_mort1$Species == "Nemipterus bleekeri"] <- "Nemipterus bipunctatus"
fish_mort1$Species[fish_mort1$Species == "Nemipterus delagoe"] <- "Nemipterus bipunctatus"
fish_mort1$Species[fish_mort1$Species == "Nemipterus tolu"] <- "Nemipterus peronii"
fish_mort1$Species[fish_mort1$Species == "Pneumatophorus japonicus"] <- "Scomber japonicus"
fish_mort1$Species[fish_mort1$Species == "Pseudosciaena diacanthus"] <- "Protonibea diacanthus"
fish_mort1$Species[fish_mort1$Species == "Rastrelliger neglectus"] <- "Rastrelliger brachysoma"
fish_mort1$Species[fish_mort1$Species == "Sardinops caerrula"] <- "Sardinops sagax"
fish_mort1$Species[fish_mort1$Species == "Sardinops melanosticta"] <- "Sardinops sagax "
fish_mort1$Species[fish_mort1$Species == "Sebastes dalli"] <- "Sebastes dallii"
fish_mort1$Species[fish_mort1$Species == "Sebastes jorani"] <- "Sebastes jordani"
fish_mort1$Species[fish_mort1$Species == "Sebastes paucipinis"] <- "Sebastes paucispinis"
fish_mort1$Species[fish_mort1$Species == "Sebastes ruberrinus"] <- "Sebastes ruberrimus"
fish_mort1$Species[fish_mort1$Species == "Soela vulgaris"] <- "Solea solea"
fish_mort1$Species[fish_mort1$Species == "Stizostedion canadensis"] <- "Sander canadensis"
fish_mort1$Species[fish_mort1$Species == "Thunnus germo"] <- "Thunnus alalunga"
fish_mort1$Species[fish_mort1$Species == "Thunnus alaunga"] <- "Thunnus alalunga"
fish_mort1$Species[fish_mort1$Species == "Thunnus macoyi"] <- "Thunnus maccoyii"
fish_mort1$Species[fish_mort1$Species == "Tracharus japonicus"] <- "Trachurus japonicus"
fish_mort1$Species[fish_mort1$Species == "Tilapia esculenta"] <- "Oreochromis esculentus"
fish_mort1$Species[fish_mort1$Species == "Cheilodactylus macropterus"] <- "Nemadactylus macropterus"
fish_mort1$Species[fish_mort1$Species == "Cynolebias bellottii"] <- "Austrolebias bellottii"
fish_mort1$Species[fish_mort1$Species == "Cynoscion macdonaldi"] <- "Totoaba macdonaldi"
fish_mort1$Species[fish_mort1$Species == "Cynoscion nobilis"] <- "Atractoscion nobilis"
fish_mort1$Species[fish_mort1$Species == "Cynolebias wolterstarfii"] <- "Austrolebias wolterstorffi"
fish_mort1$Species[fish_mort1$Species == "Sardinops sagax "] <- "Sardinops sagax"
fish_mort1$Species[fish_mort1$Species == "Acipsnser fulvescens"] <- "Acipenser fulvescens"
fish_mort1$Species[fish_mort1$Species == "Aphinius fasciatus"] <- "Aphanius fasciatus"
fish_mort1$Species[fish_mort1$Species == "Centengraulis mysticetus"] <- "Cetengraulis mysticetus"
fish_mort1$Species[fish_mort1$Species == "Cololabis aira"] <- "Cololabis saira"
fish_mort1$Species[fish_mort1$Species == "Coryphaennoides acrolepis"] <- "Coryphaenoides acrolepis"
fish_mort1$Species[fish_mort1$Species == "Chelodactylus macropterus"] <- "Nemadactylus macropterus"
fish_mort1$Species[fish_mort1$Species == "Pseudoupeneus macularus"] <- "Pseudupeneus maculatus"
fish_mort2 <- fish_mort1
fish_mort2$TL <- estimate(fish_mort2$Species)$Troph
fish_mort2$Species[is.na(fish_mort2$TL)]
#combine mortality
fish_mort_fin <- fish_mort2 %>%
rename(trophic_level = TL)
fish_mort_fin$type <- NA
fish_mort_fin$TrophicLevel <- NA
invert_mort$genus <- NULL
invert_mort$TrophicLevel <- NA
invert_mort$TrophicLevel <- as.character(invert_mort$TrophicLevel )
mammal_mort$Genus <- NULL
mammal_mort <- mammal_mort %>%
rename(TrophicLevel = trophic_level)
endo_mort <- rbind(mammal_mort, bird_mort)
endo_mort$type <- NA
mort_TL <- rbind(endo_mort, invert_mort, fish_mort_fin)
write_csv(mort_TL, "~/Desktop/mortality_TL.csv" )
#attack rates
attack <- read_csv(file.path(gdrive_path, 'Lietal_oikos_2017_data.csv')) %>%
select(predator.ana.group, predator.species, predator.mass.mg, temperature.degree.celcius, attack.rate, everything() ) %>%
arrange(predator.ana.group, predator.species, attack.rate) %>%
mutate(index = 1:451)
attack_verts <- attack %>%
filter(predator.ana.group == "vertebrate") %>%
select(predator.species) %>%
rename(Species = predator.species)
attack_verts$TL <- estimate(attack_verts$Species)$Troph
missing_attack_vert <- unique(attack_verts$Species[is.na( attack_verts$TL)])
missing_attack_vert
attack_verts$Species[attack_verts$Species == "Brachydanio rerio"] <- "Danio rerio"
attack_verts$Species[attack_verts$Species == "Perca fluviatilis"] <- "Perca fluviatilis"
attack_verts$TL <- estimate(attack_verts$Species)$Troph
attack_verts$pred_type <- NA
attack_verts$pred_type <- as.character(attack_verts$pred_type)
attack_verts_TL <- attack_verts %>%
rename(predator.species = Species)
#Attack invertebrates
attack_inverts <- attack %>%
filter(predator.ana.group == "invertebrate") %>%
select(predator.species)
attack_invert_spp <- unique(attack_inverts$Species)
write.csv(unique(attack_inverts$Species), "~/Desktop/attack_invert_spp_new.csv")
attack_invert_TL0 <- read_csv('~/Desktop/attack_invert_spp.csv') %>%
rename(predator.species = Species)
attack_invert_TL <- left_join(attack_inverts, attack_invert_TL0 , by = "predator.species")
attack_TL0 <- as_tibble(rbind(attack_invert_TL, attack_verts_TL)) %>%
mutate(index = 1:451)
# Together
attack_TL <- left_join(attack, attack_TL0, by = "index") %>%
select(TL, predator.ana.group, pred_type, everything())
write_csv(attack_TL, "~/Desktop/attack_TL.csv" )
mort_Ea <- mortality %>%
filter(temp_range_genus >= 5, n_genus >= 5) %>%
nest(-Genus) %>% # the group variable
mutate(
fit = map(data, ~ lm(log(mass_corr_mortality) ~ one_kT , data = .x)), #this is a regression (y = attack.rate, x = one_kT), but you could adapt to whatever
tidied = map(fit, tidy)
) %>%
unnest(tidied)
attack_Ea <- mortality %>%
filter(temp_range_genus >= 5, n_genus >= 5) %>%
nest(-Genus) %>% # the group variable
mutate(
fit = map(data, ~ lm(log(mass_corr_mortality) ~ one_kT , data = .x)), #this is a regression (y = attack.rate, x = one_kT), but you could adapt to whatever
tidied = map(fit, tidy)
) %>%
unnest(tidied)
|
7509fb0963ac018dcce01b346228aa218b8fdd49 | 3b337766cdaa82787e0e1a188c0bbb84dae96e41 | /MechaCar_Challenge.RScript.R | e3a7926666e9b279b8688164c143a797a889fb58 | [] | no_license | ConorMcGrew/MechaCar_Statistical_Analysis | 80aa7d1474ed17fa2647a0814881919dbdf9103b | da874cbd452976b6ce3f4ad6d24ba2b8286cd5ca | refs/heads/main | 2023-06-06T23:30:48.665143 | 2021-06-28T03:43:07 | 2021-06-28T03:43:07 | 380,807,570 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,069 | r | MechaCar_Challenge.RScript.R | library(dplyr)
MPGdataframe <- read.csv(file='MechaCar_mpg.csv', stringsAsFactors = FALSE)
lm(mpg ~ vehicle_length + vehicle_weight + spoiler_angle + ground_clearance + AWD, data=MPGdataframe)
summary(lm(mpg ~ vehicle_length + vehicle_weight + spoiler_angle + ground_clearance + AWD, data=MPGdataframe))
SuspensionTable <- read.csv(file='Suspension_Coil.csv', stringsAsFactors = TRUE)
SuspensionTableSummary <- SuspensionTable %>% summarize(PSI_Mean = mean(PSI), PSI_Median = median(PSI), PSI_var = var(PSI), PSI_sd = sd(PSI))
lot_summary <- SuspensionTable %>% group_by(Manufacturing_Lot) %>% summarize(PSI_Mean = mean(PSI), PSI_Median = median(PSI), PSI_var = var(PSI), PSI_sd = sd(PSI))
t.test((SuspensionTable$PSI),mu=1500)
Lot1_Data <- subset(SuspensionTable, SuspensionTable$Manufacturing_Lot == "Lot1")
Lot2_Data <- subset(SuspensionTable, SuspensionTable$Manufacturing_Lot == "Lot2")
Lot3_Data <- subset(SuspensionTable, SuspensionTable$Manufacturing_Lot == "Lot3")
t.test(Lot1_Data$PSI, mu=1500)
t.test(Lot2_Data$PSI, mu=1500)
t.test(Lot3_Data$PSI, mu=1500)
|
438c5c501ba4a4cc7e41191ea2f8fb6396cd843c | 8e865dd13098b885dd99ff8377ec1adb09f6b4a1 | /AJB_scripts_github/filter_and_compare/subtree_functions.R | 2590fe72ea310c4702be58e786cd96592766336e | [] | no_license | annethomas/veronica_phylo | 157b7e9b699121d72fd718ed89f96e7ff386f202 | db5faabf16d5997acb7eee93544522a675c8e6c8 | refs/heads/main | 2023-05-31T07:57:58.379739 | 2021-06-17T21:13:39 | 2021-06-17T21:13:39 | 308,403,666 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,350 | r | subtree_functions.R | library(dplyr)
getDescWrapper=function(tree,node,root){
tree$tip.label[phytools::getDescendants(tree,node)[which(phytools::getDescendants(tree,node)<root)]]
}
## this is used in function, could add as argument
genbank_species=unlist(read.table(file.path(compare_dir,"genbank_compare_species.txt"),stringsAsFactors = FALSE))
add_node_group=function(tree,stat_table,info_table,group_name,group=NULL,subgroup=FALSE){
## checks and setup
if(!is.null(group_name) & is.null(group)){
#retrieve group species from info_table
if(group_name %in% info_table$hebe_group){
group=dplyr::filter(info_table, hebe_group==group_name & Species %in% genbank_species) %>% select("Species") %>% unlist()
} else if(group_name %in% info_table$hebe_group_detail){
group=dplyr::filter(info_table, hebe_group_detail==group_name & Species %in% genbank_species) %>% select("Species") %>% unlist()
} else{
print(unique(info_table$hebe_group))
print(unique(info_table$hebe_group_detail))
stop("please provide group_name present in info_table$hebe_group or info_table$hebe_group_detail")
}
} else if(is.null(group)){
stop("Please provide group (vector) or group name")
} else{
if(any(!group %in% genbank_species)){
print(group[which(!group %in% genbank_species)])
stop("group includes species not in genbank_species")
}
print("using provided group species")
}
## proceed
print("species in target group:")
print(group)
mrca=getMRCA(tree,group)
print(paste("mrca for",group_name,"is",mrca))
if(length(getDescWrapper(tree,mrca,80))==length(group)){
print("monophyletic")
nodes=c(mrca,getDescendants(tree,mrca))
print(nodes)
# if(!all(c("node","group") %in% names(stat_table))){
# stop("please provide stat table with 'node' and 'group' columns")
# }
if(!"group" %in% names(stat_table)){
#stop("please provide stat table with 'node' and 'group' columns")
print("adding group column")
stat_table$group=rep(NA,nrow(stat_table))
}
if(!"mrca" %in% names(stat_table)){
#stop("please provide stat table with 'node' and 'group' columns")
print("adding mrca column")
stat_table$mrca=rep(FALSE,nrow(stat_table))
}
if(subgroup){
if(!"subgroup" %in% names(stat_table)){
#stop("please provide stat table with 'node' and 'group' columns")
print("adding subgroup column")
stat_table$subgroup=rep(NA,nrow(stat_table))
}
if(!"subgroup_mrca" %in% names(stat_table)){
#stop("please provide stat table with 'node' and 'group' columns")
print("adding subgroup_mrca column")
stat_table$subgroup_mrca=rep(FALSE,nrow(stat_table))
}
stat_table[which(stat_table$node %in% nodes),"subgroup"]=group_name
stat_table[which(stat_table$node == mrca),"subgroup_mrca"]=TRUE
} else{
stat_table[which(stat_table$node %in% nodes),"group"]=group_name
stat_table[which(stat_table$node == mrca),"mrca"]=TRUE
}
return(stat_table)
} else{
print("paraphyletic species not in group:")
print(setdiff(getDescWrapper(tree,mrca,80),group))
stop("paraphyletic, please examine and break up group")
}
} |
aec4f8fa67dfb338b955ed0f862fa1c7aa032929 | 93cb634ab72a5456783b369e71b433678cfce7c7 | /etl.R | abf33e504fb912a7cdda6d47b153dbde5765f71d | [] | no_license | thatcher/ExData_PeerAssessment2 | f8ad8005746011c1bf958fd06e01aae83b23e500 | 437fc622406f3bb406005c411cbf7982ce9680f3 | refs/heads/master | 2021-01-19T00:46:44.696464 | 2015-03-22T17:07:55 | 2015-03-22T17:07:55 | 32,684,361 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,275 | r | etl.R | # Homework assignment for Coursera exdata-011
# Week 3
# etl.R
#
# Basic routines for fetching the data from the web, read into R,
# transform any fields we need to clean/manipulate, and load
# the required subset for exploratory analysis.
#
# Extract will only download the data if it cant find the file locally or
# is called with force=TRUE
#
# Tranform ensures the extract has been run will only load/manipulate/slice
# the source data if the data slice does not exist on disk.
#
# Load ensures the transform has been performed and reads in and returns
# just the serialized slice.
#
# All of the operations can be called with refresh=TRUE to force the step
# to be re-performed, even if there data exists on disk.
#
# Chris Thatcher
library(data.table)
library(lubridate)
NEI_DATA_URL = "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
NEI_SCC_FILE = "data/Source_Classification_Code.rds"
NEI_PM25_FILE = "data/summarySCC_PM25.rds"
NEI_PM25_BY_YEAR_FILE = "data/pm25_by_year.csv"
NEI_PM25_BALTIMORE_FILE = "data/pm25_baltimore.csv"
NEI_PM25_BALTIMORE_VEHICLE_FILE = "data/pm25_baltimore_vehicle.csv"
NEI_PM25_BALTIMORE_VEHICLE_BY_YEAR_FILE = "data/pm25_baltimore_vehicle_by_year.csv"
NEI_PM25_BALTIMORE_BY_YEAR_FILE = "data/pm25_baltimore_by_year.csv"
NEI_PM25_LOS_ANGELES_FILE = "data/pm25_los_angeles.csv"
NEI_PM25_LOS_ANGELES_VEHICLE_FILE = "data/pm25_los_angeles_vehicle.csv"
NEI_PM25_LOS_ANGELES_VEHICLE_BY_YEAR_FILE = "data/pm25_los_angeles_vehicle_by_year.csv"
NEI_PM25_LOS_ANGELES_BY_YEAR_FILE = "data/pm25_los_angeles_by_year.csv"
NEI_PM25_COAL_FILE = "data/pm25_coal.csv"
NEI_PM25_COAL_BY_YEAR_FILE = "data/pm25_coal_by_year.csv"
# The following globals will be exported from etl.extract
# NEI_SCC
# NEI_PM25
etl.extract = function(refresh=FALSE){
# Make sure we have the data to work with locally, otherwise go get it.
if( refresh || !file.exists(NEI_PM25_FILE) ){
message("Extracting data from url.")
data_zip = "data/temp.zip"
if("Windows" == Sys.info()["sysname"])
download.file(NEI_DATA_URL, destfile=data_zip)
else
download.file(NEI_DATA_URL, destfile=data_zip, method="curl")
unzip(data_zip, exdir='data')
file.remove(data_zip)
}
if(!exists('NEI_SCC')){
message('Reading NEI SCC codes.')
NEI_SCC <<- readRDS(NEI_SCC_FILE)
message('Complete.')
}
if(!exists('NEI_PM25')){
message('Reading NEI PM25 data.')
NEI_PM25 <<- readRDS(NEI_PM25_FILE)
NEI_PM25$type = as.factor(NEI_PM25$type)
message('Complete.')
}
}
# The following globals will be exported from etl.transform
# NEI_PM25_BY_YEAR
# NEI_PM25_BALTIMORE
# NEI_PM25_BALTIMORE_VEHICLE
# NEI_PM25_BALTIMORE_BY_YEAR
# NEI_PM25_BALTIMORE_VEHICLE_BY_YEAR
# NEI_PM25_LOS_ANGELES
# NEI_PM25_LOS_ANGELES_VEHICLE
# NEI_PM25_LOS_ANGELES_BY_YEAR
# NEI_PM25_LOS_ANGELES_VEHICLE_BY_YEAR
# NEI_PM25_COAL
# NEI_PM25_COAL_BY_YEAR
etl.transform = function(refresh=FALSE){
# loads the raw source data set
if(refresh || !file.exists(NEI_PM25_FILE)){
etl.extract(refresh=refresh)
}
# ensure the raw data is in scope
if(!exists('NEI_DATA') || !exists('SCC_CODES')){
etl.extract(refresh=refresh)
}
# Summarize the data for fine particulate matter by year,
if(!exists('NEI_PM25_BY_YEAR') || !file.exists(NEI_EPMI25_BY_YEAR_FILE)){
message('Calculating PM25 by year.')
NEI_PM25_BY_YEAR <<- aggregate(
Emissions ~ year,
data=NEI_PM25,
FUN=sum
)
write.csv(
NEI_PM25_BY_YEAR,
file=NEI_EPMI25_BY_YEAR_FILE,
row.names=FALSE
)
}
# Subset the data for fine particulate matter for Baltimore,
if(!exists('NEI_PM25_BALTIMORE') || !file.exists(NEI_PM25_BALTIMORE_FILE)){
message('Subsetting PM25 by fips 24510 (Baltimore).')
NEI_PM25_BALTIMORE <<- subset(NEI_PM25, fips == "24510")
write.csv(
NEI_PM25_BALTIMORE,
file=NEI_PM25_BALTIMORE_FILE,
row.names=FALSE
)
}
# Summarize the data for fine particulate matter for Baltimore,
if(!exists('NEI_PM25_BALTIMORE_BY_YEAR') || !file.exists(NEI_PM25_BALTIMORE_BY_YEAR_FILE)){
message('Calculating Baltimore PM25 by year.')
NEI_PM25_BALTIMORE_BY_YEAR <<- aggregate(
Emissions ~ year,
data=NEI_PM25_BALTIMORE,
FUN=sum
)
write.csv(
NEI_PM25_BALTIMORE_BY_YEAR,
file=NEI_PM25_BALTIMORE_BY_YEAR_FILE,
row.names=FALSE
)
}
# Subset the data for vehicle emissions from baltimore
if(!exists('NEI_PM25_BALTIMORE_VEHICLE') || !file.exists(NEI_PM25_BALTIMORE_VEHICLE_FILE)){
message('Subsetting PM25 by "Mobile Sources" in SCC.Level.One.')
vehicle_sources = subset(NEI_SCC, grepl("Mobile Sources", NEI_SCC$SCC.Level.One))
NEI_PM25_BALTIMORE_VEHICLE <<- subset(NEI_PM25_BALTIMORE, SCC %in% vehicle_sources$SCC)
write.csv(
NEI_PM25_BALTIMORE_VEHICLE,
file=NEI_PM25_BALTIMORE_VEHICLE_FILE,
row.names=FALSE
)
}
# Summarize the data for vehicle emissions for Baltimore,
if(!exists('NEI_PM25_BALTIMORE_VEHICLE_BY_YEAR') || !file.exists(NEI_PM25_BALTIMORE_VEHICLE_BY_YEAR_FILE)){
message('Calculating Baltimore vehicle PM25 by year.')
NEI_PM25_BALTIMORE_VEHICLE_BY_YEAR <<- aggregate(
Emissions ~ year,
data=NEI_PM25_BALTIMORE_VEHICLE,
FUN=sum
)
write.csv(
NEI_PM25_BALTIMORE_VEHICLE_BY_YEAR,
file=NEI_PM25_BALTIMORE_VEHICLE_BY_YEAR_FILE,
row.names=FALSE
)
}
# Subset the data for fine particulate matter for Los Angeles,
if(!exists('NEI_PM25_LOS_ANGELES') || !file.exists(NEI_PM25_LOS_ANGELES_FILE)){
message('Subsetting PM25 by fips 06037 (Los Angeles).')
NEI_PM25_LOS_ANGELES <<- subset(NEI_PM25, fips == "06037")
write.csv(
NEI_PM25_LOS_ANGELES,
file=NEI_PM25_LOS_ANGELES_FILE,
row.names=FALSE
)
}
# Summarize the data for fine particulate matter for Los Angeles,
if(!exists('NEI_PM25_LOS_ANGELES_BY_YEAR') || !file.exists(NEI_PM25_LOS_ANGELES_BY_YEAR_FILE)){
message('Calculating Los Angeles PM25 by year.')
NEI_PM25_LOS_ANGELES_BY_YEAR <<- aggregate(
Emissions ~ year,
data=NEI_PM25_LOS_ANGELES,
FUN=sum
)
write.csv(
NEI_PM25_LOS_ANGELES_BY_YEAR,
file=NEI_PM25_LOS_ANGELES_BY_YEAR_FILE,
row.names=FALSE
)
}
# Subset the data for vehicle emissions from Los Angeles
if(!exists('NEI_PM25_LOS_ANGELES_VEHICLE') || !file.exists(NEI_PM25_LOS_ANGELES_VEHICLE_FILE)){
message('Subsetting PM25 by "Mobile Sources" in SCC.Level.One.')
vehicle_sources = subset(NEI_SCC, grepl("Mobile Sources", NEI_SCC$SCC.Level.One))
NEI_PM25_LOS_ANGELES_VEHICLE <<- subset(NEI_PM25_LOS_ANGELES, SCC %in% vehicle_sources$SCC)
write.csv(
NEI_PM25_LOS_ANGELES_VEHICLE,
file=NEI_PM25_LOS_ANGELES_VEHICLE_FILE,
row.names=FALSE
)
}
# Summarize the data for vehicle emissions for Los Angeles,
if(!exists('NEI_PM25_LOS_ANGELES_VEHICLE_BY_YEAR') || !file.exists(NEI_PM25_LOS_ANGELES_VEHICLE_BY_YEAR_FILE)){
message('Calculating Los Angeles vehicle PM25 by year.')
NEI_PM25_LOS_ANGELES_VEHICLE_BY_YEAR <<- aggregate(
Emissions ~ year,
data=NEI_PM25_LOS_ANGELES_VEHICLE,
FUN=sum
)
write.csv(
NEI_PM25_LOS_ANGELES_VEHICLE_BY_YEAR,
file=NEI_PM25_LOS_ANGELES_VEHICLE_BY_YEAR_FILE,
row.names=FALSE
)
}
# Subset the data for fine particulate matter from coal sources,
if(!exists('NEI_PM25_COAL') || !file.exists(NEI_PM25_COAL_FILE)){
message('Subsetting PM25 by "Coal" in Energy Industry Sector.')
coal_sectors = subset(NEI_SCC, grepl("Coal", NEI_SCC$EI.Sector))
NEI_PM25_COAL <<- subset(NEI_PM25, SCC %in% coal_sectors$SCC)
write.csv(
NEI_PM25_COAL,
file=NEI_PM25_COAL_FILE,
row.names=FALSE
)
}
# Summarize the data for fine particulate matter from coal sources.
if(!exists('NEI_PM25_COAL_BY_YEAR') || !file.exists(NEI_PM25_COAL_BY_YEAR_FILE)){
message('Calculating PM25 from Coal by year.')
NEI_PM25_COAL_BY_YEAR <<- aggregate(
Emissions ~ year,
data=NEI_PM25_COAL,
FUN=sum
)
write.csv(
NEI_PM25_COAL_BY_YEAR,
file=NEI_PM25_COAL_BY_YEAR_FILE,
row.names=FALSE
)
}
}
etl.load = function(data, refresh=FALSE){
# loads the data slice we need for our plot exploration
etl.transform(refresh=refresh)
if( 'pm25' == data ){
return(NEI_PM25)
}
if( 'pm25_by_year' == data ){
return(NEI_PM25_BY_YEAR)
}
if( 'pm25_baltimore' == data ){
return(NEI_PM25_BALTIMORE)
}
if( 'pm25_baltimore_vehicle' == data ){
return(NEI_PM25_BALTIMORE_VEHICLE)
}
if( 'pm25_baltimore_vehicle_by_year' == data ){
return(NEI_PM25_BALTIMORE_VEHICLE_BY_YEAR)
}
if( 'pm25_baltimore_by_year' == data ){
return(NEI_PM25_BALTIMORE_BY_YEAR)
}
if( 'pm25_los_angeles' == data ){
return(NEI_PM25_LOS_ANGELES)
}
if( 'pm25_los_angeles_vehicle' == data ){
return(NEI_PM25_LOS_ANGELES_VEHICLE)
}
if( 'pm25_los_angeles_vehicle_by_year' == data ){
return(NEI_PM25_LOS_ANGELES_VEHICLE_BY_YEAR)
}
if( 'pm25_los_angeles_by_year' == data ){
return(NEI_PM25_LOS_ANGELES_BY_YEAR)
}
if( 'pm25_coal' == data ){
return(NEI_PM25_COAL)
}
if( 'pm25_coal_by_year' == data ){
return(NEI_PM25_COAL_BY_YEAR)
}
}
|
afd31e00287fb1c5ea1f185c93af0972c4920b26 | 19449fbd87dad541e001ad7898ba30af0c839c27 | /Kristina_Motue_Week8_Part2_Task3.R | e4d6ae39754f6a7cec7c827bf3e5b545ed92d433 | [] | no_license | KristinaMotue/LearningWeek8 | 7d0a3837c9c1b51124d728feae38a574d00ef70f | 7938d3921233d6c57951eaf97ce7f4484a40ccea | refs/heads/main | 2023-03-30T04:39:39.244195 | 2021-03-29T21:03:59 | 2021-03-29T21:03:59 | 352,783,262 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 119 | r | Kristina_Motue_Week8_Part2_Task3.R | library(ggplot2)
x <- 1:20
y <- x^2
qplot(x,y, xlab = "x", ylab = "x^2", geom=c("point", "line"), color=I("Pink"))
|
f204deb707f8e25916b75c70a1e44c6cf4dfd171 | 951ac92c5e14a43947292208fa7a5ec7b113f752 | /man/simVARmodel.Rd | 806785ace9440adcfbf1ef2cf08b3b02c8d9a56d | [] | no_license | Allisterh/VARshrink-1 | 91c9054ae38111ce242553e73e8c03718b7725cf | 2eb484246de70a9e4389357b2f7284b27caf56a3 | refs/heads/master | 2022-04-06T20:39:50.159056 | 2019-10-09T14:10:03 | 2019-10-09T14:10:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,160 | rd | simVARmodel.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/simVARmodel.R
\name{simVARmodel}
\alias{simVARmodel}
\title{Generate multivariate time series data using the given VAR model}
\usage{
simVARmodel(numT, model, burnin = 0)
}
\arguments{
\item{numT}{Number of observed time points, T.}
\item{model}{A list object with Coef, Sigma, dof;
Coef is a list with A and c; A is a list object of K-by-K coefficient
matrices and c is a length-K vector.
Sigma is a K-by-K scale matrix and
dof is a degree of freedom for multivariate t-distribution for noise.}
\item{burnin}{Number of initial points which are not included in the final values.}
}
\value{
A numT-by-K matrix
}
\description{
Generate a multivariate time series data set using the given VAR model.
}
\details{
First, it creates (p+burnin+numT x K) data, then
it remove the first (p+burnin) vectors.
Finally, it returns (numT x K) data.
}
\examples{
myCoef <- list(A = list(matrix(c(0.5, 0, 0, 0.5), 2, 2)), c = c(0.2, 0.7))
myModel <- list(Coef = myCoef, Sigma = diag(0.1^2, 2), dof = Inf)
simVARmodel(numT = 100, model = myModel, burnin = 10)
}
|
f494eeaa9a9ab7e8cdbc5ea1d8b3cb53357e2bf2 | 6f4796e1757c4e4f7bccd7207d18c87e6f1e4e6b | /R/datprep.R | dd0d3abb5e0539d75aaa0c1490c1af319bc64355 | [] | no_license | bjcochrane/TeachingPopGen | 12dd96517bb0b005615235c5b567490eb0a41adc | 033510f77f81fb1b2d668f34f682b857958c5cda | refs/heads/master | 2021-11-30T21:05:55.295372 | 2021-11-08T18:02:53 | 2021-11-08T18:02:53 | 20,026,473 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 420 | r | datprep.R | ## Function to extract genotype numbers only from raw HapMap files
## min sets the limit for eliminating snps with fewer than n represenatatives in any genotype class.
datprep <-function(dat,min=5){
genos <-cbind(dat$V13,dat$V16,dat$V19)
genos <-data.frame(genos)
rownames(genos) <-rownames(dat)
colnames(genos) <-c("AA","Aa","aa")
genos.sub <-genos[genos[,1]>min&genos[,2]>min&genos[,3]>min,]
genos.sub
} |
9ee3e78d603d35a0257b67a306292c13d33e7680 | 188dbd6c2e199912b833fa14414b9668b3a97863 | /scripts/05b_plot_ManhattanPlots.R | 65707c428ed4f462b3cc0a47e4ae935ffd0f240e | [] | no_license | JasonMares63/Polygenic-Score-Portability | 5ec9f611358246c9257c720cb3432bce30d93fc2 | 75ee51e2c5272d515902cbda20725be6ae4a6f50 | refs/heads/main | 2023-05-11T05:46:31.779941 | 2021-06-04T14:17:57 | 2021-06-04T14:17:57 | 358,634,609 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,490 | r | 05b_plot_ManhattanPlots.R | .libPaths("/rigel/mfplab/users/jm4454/rpackages/")
suppressPackageStartupMessages(suppressWarnings(library(tcltk)))
options(bitmapType='cairo')
library("qqman",lib="/rigel/mfplab/users/jm4454/rpackages/")
library("dplyr",lib="/rigel/mfplab/users/jm4454/rpackages/")
phenotypes <- c("Basophil", "BMI", "DBP", "Eosinophil", "Hb", "Height", "Ht", "Lymphocyte",
"MCH", "MCHC","MCV", "Monocyte", "Neutrophil", "Platelet", "RBC", "SBP","WBC")
for (i in 1:length(phenotypes){
chr1 <- paste0("data/gwas_results/",phenotypes[i],".chr1.",phenotypes[i],".glm.linear")
results_as <- read.csv(chr1,sep="\t")
#Bind linear regression output together of all autosomes
for (j in 2:22){
chr_bind <- paste0("data/gwas_results/",phenotypes[i],".chr",j,".",phenotypes[i],".glm.linear")
results_bind <- read.csv(chr_bind,sep="\t")
results_as <- bind_rows(results_as,results_bind)
}
results_as <- results_as[!is.na(results_as$P),]
# Manhattan Plot
png(paste0("img/",phenotypes[i],"_linear_manhattan.png"))
manhattan(results_as,chr="X.CHROM",bp="POS",p="P",snp="ID",
main = paste0("Manhattan plot: ",phenotypes[i]),
col = c("blue4", "orange3"), suggestiveline=T, genomewideline=T, cex=0.4)
dev.off()
#QQ Plot of P-values
png(paste0("img/",phenotypes[i],"_linear_qqplot.png"))
qq(results_as$P, main = paste0("Q-Q plot of GWAS p-values for ",phenotypes[i]),
xlim = c(0, 7), ylim = c(0, 12), pch = 18, col = "blue4", cex = 1.5, las = 1)
dev.off()
}
|
3c3ca5038e9d32c30d7083f1608ffd57dec9b876 | 99c837f0915c852a948947c3e69202de87525224 | /analyses/rankings/processingDatasets/addAlignments.R | 6e7521d29a671f51126e0e132f2df76bffa64100 | [] | no_license | crisprVerse/crisprVersePaper | 77ec43419f9f43c7edd9d28796e91e1ac2a9b1cd | b6e4356b6377c6c69a3d8e61c976372f496d1854 | refs/heads/master | 2023-04-10T10:55:32.970539 | 2022-10-20T17:01:41 | 2022-10-20T17:01:41 | 534,335,360 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,753 | r | addAlignments.R | library(crisprDesign)
library(crisprDesignGne)
gs <- readRDS("../processingRankings/crisprverse/crisprverse.results.rds")
cols <- c("ensembl_id", "spacer", "percentGC", "polyT",
"n0", "n0_c", "n1", "n1_c", "n2", "n2_c", "n3", "n3_c",
"hasSNP")
newCols <- c("percentGC", "polyT", "hasSNP",
"n0", "n0_c", "n1", "n1_c",
"n2", "n2_c", "n3", "n3_c")
gs <- gs[,cols]
gs$name20 <- paste0(gs$ensembl_id, "_", gs$spacer)
gs$name19 <- paste0(gs$ensembl_id, "_", substr(gs$spacer,2,20))
load("objects/rankings_achilles.rda")
wh <- match(rankings_achilles$name, gs$name20)
rankings_achilles <- cbind(rankings_achilles, gs[wh, newCols])
save(rankings_achilles, file="objectsFinal/rankings_achilles.rda")
load("objects/rankings_achilles_neg.rda")
wh <- match(rankings_achilles_neg$name, gs$name20)
rankings_achilles_neg <- cbind(rankings_achilles_neg, gs[wh, newCols])
save(rankings_achilles_neg, file="objectsFinal/rankings_achilles_neg.rda")
load("objects/rankings_sabatini.rda")
wh <- match(rankings_sabatini$name, gs$name20)
rankings_sabatini <- cbind(rankings_sabatini, gs[wh, newCols])
save(rankings_sabatini, file="objectsFinal/rankings_sabatini.rda")
load("objects/rankings_sabatini_neg.rda")
wh <- match(rankings_sabatini_neg$name, gs$name20)
rankings_sabatini_neg <- cbind(rankings_sabatini_neg, gs[wh, newCols])
save(rankings_sabatini_neg, file="objectsFinal/rankings_sabatini_neg.rda")
load("objects/rankings_toronto.rda")
wh <- match(rankings_toronto$name, gs$name20)
rankings_toronto <- cbind(rankings_toronto, gs[wh, newCols])
save(rankings_toronto, file="objectsFinal/rankings_toronto.rda")
load("objects/rankings_toronto_neg.rda")
wh <- match(rankings_toronto_neg$name, gs$name20)
rankings_toronto_neg <- cbind(rankings_toronto_neg, gs[wh, newCols])
save(rankings_toronto_neg, file="objectsFinal/rankings_toronto_neg.rda")
load("objects/rankings_toronto3.rda")
wh <- match(rankings_toronto3$name, gs$name20)
rankings_toronto3 <- cbind(rankings_toronto3, gs[wh, newCols])
save(rankings_toronto3, file="objectsFinal/rankings_toronto3.rda")
load("objects/rankings_toronto3_neg.rda")
wh <- match(rankings_toronto3_neg$name, gs$name20)
rankings_toronto3_neg <- cbind(rankings_toronto3_neg, gs[wh, newCols])
save(rankings_toronto3_neg, file="objectsFinal/rankings_toronto3_neg.rda")
load("objects/rankings_yusa.rda")
wh <- match(rankings_yusa$name, gs$name19)
rankings_yusa <- cbind(rankings_yusa, gs[wh, newCols])
save(rankings_yusa, file="objectsFinal/rankings_yusa.rda")
load("objects/rankings_yusa_neg.rda")
wh <- match(rankings_yusa_neg$name, gs$name19)
rankings_yusa_neg <- cbind(rankings_yusa_neg, gs[wh, newCols])
save(rankings_yusa_neg, file="objectsFinal/rankings_yusa_neg.rda")
|
9a8cb3014fe3533c88b17992f2bcdf239fba416c | 0a90eb1dcd39493a432098a6ff58f97231ef75d1 | /verificarCiudad.R | a45eace86bbdb19a5d456d82e3ac8d9ce7298a40 | [] | no_license | FerDoranNie/geodataLimpiezaSismo | b426162b08b30958cc3ce4cbe2a6589599f10442 | 3295b44ed620eded5e912f6cbca149dd08766678 | refs/heads/master | 2021-07-04T19:07:30.194507 | 2017-09-25T23:11:24 | 2017-09-25T23:11:24 | 104,497,405 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,451 | r | verificarCiudad.R | ####################################
#Creado por Fernando Dorantes Nieto
# <(°)
# ( >)"
# /|
####################################
library(magrittr)
c("data.table", "ggmap", "dplyr", "tidyr",
"lubridate", "geonames", "readxl") %>%
sapply(require, character.only=T)
setwd("~/ReposDesarollo/csvGeoData/")
setwd("~/ReposDesarrollo/geodataLimpiezaSismo/")
geocodeQueryCheck()
# Agregar el nombre de usuario de geonames
options(geonamesUsername="ferbase10")
#options(geonamesUsername="tu User Name") #####TIENES QUE PONER TU NOMBRE DE USUARIO DE GEONAMES
#####BASE DE DATOS DEL SERVICIO POSTAL MEXICANO PARA LA CIUDAD DE MÉXICO
codigos <- read.csv("CodigosPostalesCiudadMexico.csv", header = T,
stringsAsFactors = F)
codigos2 <- codigos %>%
select(Código.Postal, Municipio) %>%
data.table %>%
.[, Código.Postal := as.numeric(Código.Postal)]
names(codigos2)<- c("cp", "ciudad_municipio" )
# Funciones ---------------------------------------------------------------
###Usarse en casos excepcionales
codigoPostal <- function(postal){
postal <- as.numeric(postal)
busqueda <- GNpostalCodeLookup(postalcode=postal)
busqueda <- busqueda %>%
filter(countryCode=="MX")
print(busqueda)
#print(busqueda$placeName)
postal <- as.character(postal)
postal <-ifelse(nchar(postal)<4, paste0("0", postal), postal)
Y <-data.frame(Verificado = busqueda$adminName2,
cp = postal)
return(unique(Y))
}
archivos <- list.files("verificadosTotal",
all.files = T, full.names = T, pattern = "*.csv",
recursive = T)
#archivos <- archivos[-grep("Chiapas", archivos)]
lapply(archivos, function(X){
file <- X
file <- gsub(".*/","", file)
file <- paste0("verificadosVuelta2Total/", file)
X <- read.csv(X, header = T,
stringsAsFactors = F)
X1 <- X %>%
filter(ciudad_municipio_Verificado!= "Ciudad de México") %>%
data.frame
X2 <- X %>%
filter(ciudad_municipio_Verificado == "Ciudad de México")
X2 <- merge(X2, codigos2, by.x="cp_Verificado", by.y="cp") %>%
data.table %>%
.[, ciudad_municipio_Verificado := ciudad_municipio ] %>%
select(one_of(names(X2))) %>%
data.frame
X <- rbind(X1, X2)
X <- data.frame(X)
X %>%
write.csv(file, row.names=F)
})
|
a65844092dca75c037e7242fc9f882a2ed7aaf50 | 3799e008da31a5ca77b297ece995e5d2a4f74e20 | /R/combineMolIon.R | 555fba298ae0bbefac4f8f0afdba174c37eb2610 | [] | no_license | rsilvabioinfo/ProbMetab | 047f1ec7fff6d08aac2a97687af80c9b6d1cd39d | 8311928cc5d18c6a215e8b48f8c2b4d82ea5df2a | refs/heads/master | 2021-01-17T11:16:22.797086 | 2017-04-11T16:32:37 | 2017-04-11T16:32:37 | 16,100,752 | 2 | 2 | null | 2016-04-26T16:57:39 | 2014-01-21T11:32:18 | R | UTF-8 | R | false | false | 7,213 | r | combineMolIon.R | #' combineMolIon
#'
#' This function combines ion annotations in different acquisition modes. It operates
#' in two main modes, combining individual annotations given by get.annot
#' function, using the retention time and mass/charge windows provided by the user
#' or extracting annotations from a peak table provided by CAMERA's combinexsAnnos
#' function.
#' @param antPOS positive annotation list given by get.annot.
#' @param antNEG negative annotation list given by get.annot.
#' @param peaklist given by CAMERA's combinexsAnnos function. If this option
#' is chosen the user has to set the acquisition mode to the same as
#' in CAMERA's function, and provide the respective object for downstream analysis.
#' @param cameraobj xsAnnotate object for downstream analysis.
#' @param polarity the same CAMERA's function acquisition mode.
#' @param rtwin retention time window to annotate a peak as present
#' in both acquisition modes.
#' @param mzwin mass to charge ratio window to annotate a peak as present
#' in both acquisition modes.
#' @return a list with a matrix of possible molecular ions with a
#' trace of their annotation and the respective xsAnnotate object.
#'
#' @export
combineMolIon <- function(antPOS, antNEG, peaklist=NULL, cameraobj=NULL, polarity=NULL, rtwin=5, mzwin = 0.05) {
if(!is.null(peaklist)) {
peakidx <- which(peaklist[,"isotopes"]!="" | peaklist[,"adduct"]!="")
antPOS3 <- peaklist[peakidx, c("mz", "rt", "isotopes", "adduct")]
isoidx <- which(antPOS3$isotopes!="")
iso <- antPOS3$isotopes[antPOS3$isotopes!=""]
iso <- as.numeric(sapply(iso, function(x) sub("^\\[(\\d+)\\].+", "\\1", x)))
charge <- (sapply(antPOS3$isotopes[antPOS3$isotopes!=""][order(iso)], function(x) sub(".+(\\d)\\+|\\-$", "\\1", x)))
charge <- suppressWarnings(as.numeric(charge) )
charge[is.na(charge)] <- 1
niso <- (sapply(antPOS3$isotopes[antPOS3$isotopes!=""][order(iso)], function(x) sub(".+(\\[M.*\\]).+", "\\1", x)))
niso <- sub(".+(\\d).+", "\\1", niso)
niso <- suppressWarnings(as.numeric(niso))
niso[is.na(niso)] <- 0
preAnt <- cbind(iso[order(iso)], charge, niso, antPOS3[isoidx[order(iso)], c("mz", "rt", "isotopes")])
molIon <- data.frame(mass=numeric(nrow(preAnt)), retentionTime=numeric(nrow(preAnt)), isotope=numeric(nrow(preAnt)), adduct=numeric(nrow(preAnt)), trace=numeric(nrow(preAnt)))
if(polarity=="pos") {
molIon$mass <- preAnt$mz*preAnt$charge - (1.007276 * preAnt$charge)
}
if(polarity=="neg") {
molIon$mass <- preAnt$mz*preAnt$charge + (1.007276 * preAnt$charge)
}
molIon$retentionTime <- preAnt$rt
molIon$isotope <- preAnt$niso
molIon$adduct <- 0
molIon$trace <- as.numeric(rownames(antPOS3[isoidx[order(iso)],]))
molIon$comb <- polarity
addidx <- which(antPOS3$adduct!="")
v0 <- c(0,0)
for(i in 1:length(addidx)) {
v1 <- suppressWarnings(as.numeric(strsplit(antPOS3$adduct[addidx][i], " ")[[1]]))
v0 <- rbind(v0, cbind(i, v1[-which(is.na(v1))]))
}
v0 <- v0[-1,]
rnames <- as.numeric(rownames(antPOS3[addidx,]))
rnames <-rnames[v0[,1]]
molIon2 <- data.frame(mass=numeric(length(rnames)), retentionTime=numeric(length(rnames)), isotope=numeric(length(rnames)), adduct=numeric(length(rnames)), trace=numeric(length(rnames)))
molIon2$mass <- v0[,2]
molIon2$retentionTime <- peaklist[rnames, "rt"]
molIon2$isotope <- 0
molIon2$adduct <- 1:length(rnames)
molIon2$trace <- rnames
molIon2$comb <- polarity
molIon2$comb[which(peaklist[molIon2$trace, ncol(peaklist)]!="")] <- "both"
molIon=rbind(molIon, molIon2)
molIon$pcgroup <- peaklist[as.numeric(sapply(molIon[,"trace"], function(x) strsplit(as.character(x), ";")[[1]][1])), "pcgroup"]
if(sum(duplicated(molIon[,1:2]))) molIon <- molIon[-which(duplicated(molIon[,1:2])),]
antComb <- list(molIon=molIon, cameraobj=cameraobj)
return(antComb)
}
vidx <- c("","");
nvidx <- c("","");
pvidx <- c("","");
for(i in 1:nrow(antNEG$molIon)) {
idx <- which(antPOS$molIon[,1] > (antNEG$molIon[i,1]-mzwin)
& antPOS$molIon[,1] < (antNEG$molIon[i,1]+mzwin)
& antPOS$molIon[,2] > (antNEG$molIon[i,2]-rtwin)
& antPOS$molIon[,2] < (antNEG$molIon[i,2]+rtwin)
)
if(length(idx)){
for(k in 1:length(idx)) {
# Possible cases
# same isotopic distribution - discard one
# adduct convergence - discard one
# treated as isotope in one, and adduct in another
# if the adduct is equal the 12C peak it is discarded
# else keep the adduct and the 13C peak
if(antNEG$molIon[i,3] != antPOS$molIon[idx[k],3]) {
vidx <- rbind(vidx, c(i, idx[k]))
next
}
if(antNEG$molIon[i,4] == 0 & antPOS$molIon[idx[k],4]!=0) {
pvidx <- rbind(pvidx, c(i, idx[k]))
next
}
if(antNEG$molIon[i,4] != 0 & antPOS$molIon[idx[k],4]==0) {
nvidx <- rbind(nvidx, c(i, idx[k]))
next
}
if(antNEG$molIon[i,4] != 0 & antPOS$molIon[idx[k],4]!=0) {
nvidx <- rbind(nvidx, c(i, idx[k]))
next
}
if(antNEG$molIon[i,4] == 0 & antPOS$molIon[idx[k],4]==0) {
nvidx <- rbind(nvidx, c(i, idx[k]))
next
}
vidx <- rbind(vidx, c(i, idx[k]))
}
}
}
# for debugging only
if(!is.null(dim(vidx))) vidx <- vidx[-1,]
if(!is.null(dim(nvidx))) nvidx <- nvidx[-1,]
if(!is.null(dim(pvidx))) pvidx <- pvidx[-1,]
antNEG$molIon$ind <- 0
id1 <- which(antNEG$molIon[,3]==1)
antNEG$molIon$ind[id1] <- 1:length(id1)
id1 <- which(antNEG$molIon[,3]==1)-1
antNEG$molIon$ind[id1] <- 1:length(id1)
#antNEG2 <- antNEG$molIon[-as.numeric(vidx[,1]),]
antNEG$molIon$comb <- "neg"
if(!is.null(dim(pvidx))) antNEG$molIon$comb[as.numeric(pvidx[,1])] <- "both"
if(!is.null(dim(nvidx))) {
antNEG2 <- as.data.frame(antNEG$molIon[-as.numeric(nvidx[,1]),])
}
else {
antNEG2 <- as.data.frame(antNEG$molIon)
}
err1 <- setdiff(which(antNEG2[,3]==1),
which(antNEG2[,3]==0 & antNEG2[,4]==0)+1
)
if(length(err1)) antNEG2 <- as.data.frame(antNEG2[-err1,])
err2 <- setdiff(
which(antNEG2[,3]==0 & antNEG2[,4]==0),
which(antNEG2[,3]==1) -1
)
if(length(err2)) antNEG2 <- as.data.frame(antNEG2[-err2,])
sn1 <- unique(antNEG2$ind)
sn1 <- sn1[-which(unique(antNEG2$ind)==0)]
snListNeg <- lapply(antNEG$snList, function(x) x[sn1])
antPOS$molIon$ind <- 0
id1 <- which(antPOS$molIon[,3]==1)
antPOS$molIon$ind[id1] <- 1:length(id1)
id1 <- which(antPOS$molIon[,3]==1)-1
antPOS$molIon$ind[id1] <- 1:length(id1)
antPOS$molIon$comb <- "pos"
if(!is.null(dim(nvidx))) antPOS$molIon$comb[as.numeric(nvidx[,2])] <- "both"
if(!is.null(dim(pvidx))) {
antPOS2 <- as.data.frame(antPOS$molIon[-as.numeric(pvidx[,2]),])
}
else {
antPOS2 <- as.data.frame(antPOS$molIon)
}
err <- setdiff(which(antPOS2[,3]==1),
which(antPOS2[,3]==0 & antPOS2[,4]==0)+1
)
if(length(err)) antPOS2 <- as.data.frame(antPOS2[-err,])
sn1 <- unique(antPOS2$ind)
sn1 <- sn1[-which(unique(antPOS2$ind)==0)]
snListPos <- lapply(antPOS$snList, function(x) x[sn1])
#vpos <- unlist(sapply(vidx[,2], function(x) strsplit(x, ";")[[1]]))
#antPOS2$comb[as.numeric(vpos)] <- "both"
antComb <- list(molIon=rbind(antPOS2, antNEG2), antPOS=antPOS, antNEG=antNEG,
neg=antNEG$cameraobj, pos=antPOS$cameraobj,
snListPos=snListPos, snListNeg=snListNeg
)
}
|
bdea4b22c938fb007037424b5d7192871b32f34b | 5c82f476674236af1d1566c2e6d89ae7eb3a24a2 | /Additional_models/C5_building_v1.R | b18397ad93e77f235f1904ca63d98bc6b11e21e6 | [] | no_license | francescaprata/Indoor-Positioning_WiFi-Fingerprinting | ae0a7812a25b152892dcc7d460f8f8c7e9063bd7 | 487302044714e1d510263ea7253625a4084686df | refs/heads/master | 2020-04-27T16:48:58.955044 | 2019-03-26T16:14:46 | 2019-03-26T16:14:46 | 174,494,320 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,496 | r | C5_building_v1.R | ###########################################
# Name: Francesca Prata #
# Project: WiFi Locationing #
# Script: Predicting building ID with C.5 #
# Date: 5 March 2019 #
# Version: 1 #
###########################################
#Retrieving the necessary scripts
source(file = "2.PreProcess_v2.R")
##########################################################
# TRAINING DATA #
##########################################################
#############
# MODELLING #
#############
#Checking that BUILDINGID and FLOOR are both factors
Wifi_TrainSetNOZero$BUILDINGID <- as.factor(Wifi_TrainSetNOZero$BUILDINGID)
Wifi_TrainSetNOZero$FLOOR <- as.factor(Wifi_TrainSetNOZero$FLOOR)
#Data partition
set.seed(123)
indexTrain <- createDataPartition(y = Wifi_TrainSetNOZero$BUILDINGID,
p = .4,
list = FALSE)
training <- Wifi_TrainSetNOZero[indexTrain,]
testing <- Wifi_TrainSetNOZero[-indexTrain,]
#Predicting BUILDINGID by only using the WAPs
training <- select(training, -FLOOR, -SPACEID, -RELATIVEPOSITION,
-USERID, -PHONEID, -TIMESTAMP, -LONGITUDE, - LATITUDE)
#Setting cross validation parameters
fitcontrolC5 <- trainControl(method = "repeatedcv",
number = 10,
repeats = 1,
preProc = c("center", "scale", "range"),
verboseIter = TRUE)
#Training C5.0 model
set.seed(123)
C5FitBuilding <- train(BUILDINGID~.,
training,
method = "C5.0",
metric = "Accuracy",
tuneLength = 1,
trControl = fitcontrolC5)
#Predicting the BUILDING ID from the training data
predBUILD_C5 <- predict(C5FitBuilding, newdata = testing)
#Creating a new column with the predictions
testing$predBUILD_C5 <- predBUILD_C5
#Checking the metrics
confusionMatrix(testing$predBUILD_C5, testing$BUILDINGID)
############################################################
# VALIDATION DATA #
############################################################
#Predicting BUILDINGID from the validation data
predBUILD_C5 <- predict(C5FitBuilding, Wifi_ValidationSetNOZero)
plot(predBUILD_C5)
#Creating a new column with the predictions
Wifi_ValidationSetNOZero$predBUILD_C5 <- predBUILD_C5
#Checking that BUILDINGID and predBUILD_C5 are both factors
Wifi_ValidationSetNOZero$predBUILD_C5 <- as.factor(Wifi_ValidationSetNOZero$predBUILD_C5)
Wifi_ValidationSetNOZero$BUILDINGID <- as.factor(Wifi_ValidationSetNOZero$BUILDINGID)
#Checking the metrics
confusionMatrix(Wifi_ValidationSetNOZero$predBUILD_C5, Wifi_ValidationSetNOZero$BUILDINGID)
#Adding column with errors to the dataframe
Wifi_ValidationSetNOZero <- mutate(Wifi_ValidationSetNOZero, errorsBUILD = predBUILD_C5 - BUILDINGID)
#Storing the predicted values, actual values and errors in a tibble
resultsBUILDINGID <- tibble(.rows = 1111)
#Adding FLOOR and its prediction to the tibble
resultsBUILDINGID$predBUILD_C5 <- predBUILD_C5
resultsBUILDINGID$BUILDINGID <-Wifi_ValidationSetNOZero$BUILDINGID
#Storing the file
saveRDS(resultsBUILDINGID, file = "resultsBUILDC5(V1).rds") |
a47047391832c7d086421f6d744a513721f2e952 | a4d3c14135bb1e3cf25fd0d0176fecc6accdb9eb | /R/intersectionint.R | 8ac71fd6d5df97f71ebac21ed1265775e3c686d0 | [] | no_license | sdestercke/Belief-R-Package | 2d2ca9677547d302f3e46ac7c9beb59717063257 | 646fd2c73764e4af151bcfc26743c35000de8df0 | refs/heads/master | 2020-05-16T23:14:44.728977 | 2012-08-03T10:24:27 | 2012-08-03T10:24:27 | 3,516,891 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 237 | r | intersectionint.R | 'intersectionint'=function(interval,c){
#internal - used by SMCgen
#
inf=2*c-1
sup=2*c
bound_inf=max(interval[inf])
bound_sup=min(interval[sup])
if(bound_inf>bound_sup){
bound_inf=0
bound_sup=0
}
return(c(bound_inf,bound_sup))
} |
d683c6b0541a5bbbcdeba30f88a342584f2e3116 | 8e233535ad72cc5068a87b7b3844463a3309d432 | /2_Porcentaje_y_Enriquecimiento/Enriquecimiento_funcional.R | d8ebc2e358056988f5bd75234d0899b34e632c12 | [] | no_license | rbarreror/Epigenetics_ChromHMM | 9c7e030a2194b0aea0588b8bfa1d1cc1d1cbc8a0 | 97dcf36634f1ec592ae4981c434fedb1dd2aa6d5 | refs/heads/master | 2021-05-24T12:41:00.541480 | 2020-04-06T19:58:22 | 2020-04-06T19:58:22 | 253,566,385 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,498 | r | Enriquecimiento_funcional.R | """
Name: Rafael Barrero Rodríguez
Date: 2020-03-11
Description: En este script recogemos y realizamos el enriquecimiento funcional
a partir de los distintos ficheros BED con los intervalos genómicos donde se
encuentra nuestro estado.
"""
root_folder <- paste0("/home/rafael/Master_UAM/Transcriptomica_RegulacionGenomica_Epigenomica/",
"3_Regulacion_Genomica_Epigenomica/Trabajo_Polycomb/anotation/",
"enriquecimiento_funcional/")
setwd(root_folder)
library (clusterProfiler); packageDescription ("clusterProfiler", fields = "Version")
library(topGO)
library(org.Hs.eg.db)
library("FGNet")
library("AnnotationDbi")
library("KEGGprofile") # If not installed, we’ll use pathview instead (hopefully it will install)
library("pathview") # If not installed, then go to KEGG Mapper (web page https://www.genome.jp/kegg/tool/map_pathway2.html)
library("gProfileR") # If not installed (web page instead https://biit.cs.ut.ee/gprofiler/)
#########################################
# ALL SEGMENTS (segments_collapsed)
#########################################
# Empezamos realizando el análisis con todos los segmentos.
gene_table <- read.table("segments_collapsed/gene_table.tsv", header = TRUE, sep = "\t")
gene_table <- dplyr::distinct(gene_table)
"""
Lo primero que haremos será ver los distintos tipos de genes en los que se
encuentra (o en los que aparece asociado nuestro estado)
"""
gene_type <- as.data.frame(table(gene_table$Gene.type), stringsAsFactors = FALSE)
# Pie Chart with Percentages
slices <- gene_type$Freq[gene_type$Freq > 1000]
lbls <- gene_type$Var1[gene_type$Freq > 1000]
# Estamos desconsiderando elementos que tienen menos de 1000 ocurrencias
slices <- c(slices, sum(gene_type$Freq)-sum(slices))
lbls <- c(lbls, "Others")
pct <- round(slices/sum(gene_type$Freq)*100)
lbls <- paste(lbls, pct) # add percents to labels
lbls <- paste(lbls,"%",sep="") # ad % to labels
pie(slices,labels = lbls, col=rainbow(length(lbls)),
main="Gene Type Distribution")
"""
A continuación haremos el enriquecimiento funcional usando solo los genes del
tipo protein_coding
"""
# Tomamos los gene_symbols
gene_symbols <- as.character(gene_table$Gene.name[gene_table$Gene.type == "protein_coding"])
gene_symbols <- levels(factor(gene_symbols[!is.na(gene_symbols)]))
# Tomamos ENTREZ ID
gene_entrez_id <- as.character(gene_table$EntrezGene.ID[gene_table$Gene.type == "protein_coding"])
gene_entrez_id <- levels(factor(gene_entrez_id[!is.na(gene_entrez_id)]))
# Tomamos ENSEMBL ID
gene_ensembl_id <- as.character(gene_table$Gene.stable.ID[gene_table$Gene.type == "protein_coding"])
gene_ensembl_id <- levels(factor(gene_ensembl_id[!is.na(gene_ensembl_id)]))
# Usamos enrichGO para enriquecer en GO
ego1 <- enrichGO(gene = gene_symbols,
OrgDb = org.Hs.eg.db,
keyType = 'SYMBOL',
ont = "BP",
pAdjustMethod = "BH",
pvalueCutoff = 0.01,
qvalueCutoff = 0.05)
results <- ego1@result
dotplot(ego1, showCategory=15)
# enrichMap(ego1, vertex.label.cex=1.2, layout=igraph::layout.kamada.kawai)
# cnetplot(ego1)
# par(cex = 0.65)
# plotGOgraph(ego1, firstSigNodes = 5)
"""
Los términos GO que aparecen más enriquecidos son los asociados con la activación
de la respuesta inmune mediada por neutrófilos. Recordemos que nuestro estado
epigenético se asociaba a genes que estaban preparados para expresarse cuando
fuera necesario. Por ello, estos resultados son coherentes, al menos parcialmente,
teniendo en cuenta que nuestras células de partida eran células del sistema
inmune, en concreto, monocitos. Puede ser que muchos de los genes implicados
en la respuesta por neutrófilos también participen en la activación de monocitos.
"""
# A contniación haremos enriquecimiento de KEGG PATHWAYS
geneLabels <- unlist(as.list(org.Hs.egSYMBOL))
geneList <- geneLabels[which(geneLabels %in% gene_symbols)]
kegg_enrich <- enrichKEGG(gene = names(geneList),
organism = "hsa",
keyType = "kegg",
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
universe,
minGSSize = 10,
maxGSSize = 500,
qvalueCutoff = 0.2,
use_internal_data = FALSE)
kegg_enrich_df <- kegg_enrich@result
dotplot(kegg_enrich, showCategory=10)
dir.create("./segments_collapsed/KEGG_Profile")
setwd("./segments_collapsed/KEGG_Profile")
list_variantspergene <- setNames(rep(NA, length(gene_ensembl_id)), gene_ensembl_id)
#procesos_kegg <-levels(ENSGenes$pathway) # 6 pathways
procesos_kegg <- kegg_enrich_df$ID[c(2,3,5,8,9)]
for (keggNumber in procesos_kegg) {
plotKegg(paste0("hsa",keggNumber), geneExpr=list_variantspergene, geneIDtype="ENSEMBL")
}
setwd(root_folder)
write.table(results[1:15, -c(8,9)], file = "go_results.tsv", row.names = FALSE,
col.names = TRUE, quote = FALSE, sep = "\t")
write.table(kegg_enrich_df[1:15, -c(8,9)], file = "kegg_results.tsv", row.names = FALSE,
col.names = TRUE, quote = FALSE, sep = "\t")
|
6dfddebaeb951c775cd99bb2f477eb8a2c640f0f | 0c61299c0bfab751bfb5b5eac3f58ee2eae2e4b0 | /Daphnia/Juveniles/set_up_fex.R | d93b8c3df4586fec1b11105952ce1b7e73248c6e | [] | no_license | jwerba14/Species-Traits | aa2b383ce0494bc6081dff0be879fc68ed24e9c2 | 242673c2ec6166d4537e8994d00a09477fea3f79 | refs/heads/master | 2022-10-13T10:57:54.711688 | 2020-06-12T01:57:21 | 2020-06-12T01:57:21 | 105,941,598 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,311 | r | set_up_fex.R | ## juvenile daphnia feeding and excretion
source("../../transfer_functions.R")
source("../../Graphing_Set_Up.R")
library(tidyverse)
### hmm some problems with cc change-- need to double check
rdatj <- read.csv("Small_Daph_Feeding.csv")
## missing data for treatments 6 and 7, if I can get back into lab can find missing data...for now drop
names(rdatj)[names(rdatj)=="Rep.."] <- "Rep"
rdatj <- rdatj %>%
filter(!is.na(Chl_Time_Diff))
dim(rdatj)
cont <- rdatj %>%
mutate(chl_time_diff_h = Chl_Time_Diff/60, nh4_time_diff_h = Nh4_Time_Dif/60 ) %>%
filter(Control.Y.N == "Y") %>%
mutate(chl_diff =((Chl.1-Chl.2)/chl_time_diff_h), nh4_diff= ((Nh4.1-Nh4.2)/nh4_time_diff_h)) %>% ## change per hour
group_by(Treatment) %>%
summarize(mean_chl = mean(chl_diff, na.rm = T), mean_nh4 = mean(nh4_diff,na.rm = T)) ## onr row in treatment 3 is all NAs..??
dim(cont)
## add mean control avg back to main dataframe
dat <- left_join(rdatj,cont)
dim(dat)
## need to add in diff from control bc if positive algae grew so indiv actually ate more if neg indiv ate less???
dat <- dat %>%
filter(Control.Y.N == "N") %>%
mutate(chl_time_diff_h = (Chl_Time_Diff/60),nh4_time_diff_h = (Nh4_Time_Dif/60)) %>%
mutate(chl_diff = (Chl.1-Chl.2)/chl_time_diff_h, nh4_diff = (Nh4.1-Nh4.2)/nh4_time_diff_h) %>%
mutate(chl_diff_cc = (chl_diff-mean_chl)/Num_Daphnia, nh4_diff_cc = (nh4_diff-mean_nh4)/Num_Daphnia)
dim(dat)
dat1 <- dat %>% filter(nh4_diff_cc > -5) %>% ## remove one weird measurement
dplyr::select(Rep, Treatment,Chl.1,Nh4.1, chl_diff_cc,nh4_diff_cc, Num_Daphnia)
#ggplot(dat1, aes(Chl.1,chl_diff_cc)) + geom_point()
mod_lm <- lm(data = dat1, chl_diff_cc ~ -1+Chl.1)
#newpred <- sat_fun(k= seq(1,100,1), a=960892 ,b =1339121459)
newdata = data.frame(Chl.1 = seq(1,25,0.1))
newpred1 <- as.data.frame(predict(mod_lm, newdata = newdata, interval = "confidence"))
newdata$chl_diff_cc <- newpred1$fit
newdata$lwr <- newpred1$lwr
newdata$upr <- newpred1$upr
j_feed_g <- ggplot(data = dat1, aes(Chl.1, chl_diff_cc)) + geom_point() +
geom_line(data = newdata) +
geom_ribbon(data = newdata, aes(ymin = lwr, ymax= upr),alpha = 0.3) +
xlab("Chlorphyll a (ug/L)") +
ylab(str_wrap("Change in Chlorophyll a/Juvenile Daphnia/Day", width = 25)) +
ggtitle("LS: Linear Fit")
print(j_feed_g)
|
91425afc3bfa761e491b1cb2fcbb6c265f9ddf80 | a2d73f27df156aaf155ce81f474e38026ab350d0 | /2_Logistic_regression_and_nonparametric_methods.R | d754a3a196161c889665f983929cdd084029cda0 | [] | no_license | vonOrso/Stepic_524_Basics_of_statistics_Part_2 | f2a0e40cacbb27f4d7de737018c6d85fd2584a09 | 9cb1c93d58732b77f67516b05a1b322bd410aa03 | refs/heads/main | 2023-02-11T17:22:51.923159 | 2021-01-04T21:34:27 | 2021-01-04T21:34:27 | 324,353,295 | 9 | 1 | null | null | null | null | UTF-8 | R | false | false | 265 | r | 2_Logistic_regression_and_nonparametric_methods.R | # 2.1.3
# log(30/70)
# 2.1.4
# exp(-1)/(1+exp(-1))
# 2.1.5
# log((24/41)/(17/41))
# 2.2.4
# 50*(exp(-0.8472979)/(1+exp(-0.8472979)))
# 2.3.1
# exp(1.1243)+exp(-2.4778)
# 2.3.3
# (197/64)/(93/360)
# 2.5.2
# exp(-1.15+0.8+2.13-0.17)/(1+exp(-1.15+0.8+2.13-0.17)) |
79bf5b7b78bce981896f2990afd646aaf8c21159 | b67d0705c87c4ac2fc6a0d0eb2b00b5cde91e770 | /assignments/ch2_jakob.R | 177e23b37d0efd70f64dad1c5cbd7a086cf77e8a | [] | no_license | ahulman/Assignment1 | b607ac60c82b817bb42575ce814ff4a4c31a6f86 | 4d938e167d0aa9bf17899d6cb02641bf45b6347e | refs/heads/master | 2020-04-06T05:48:39.808159 | 2017-07-05T10:17:34 | 2017-07-05T10:17:34 | 82,927,709 | 0 | 2 | null | 2017-03-03T13:04:46 | 2017-02-23T13:02:19 | R | UTF-8 | R | false | false | 4,742 | r | ch2_jakob.R | ###Creates SimCohort
simCohort <- function(N,S){
set.seed(S)
###Jakob
id <- c(1:N)
#creates a sex variable for first 600 and appends last 400
sex <- rep.int(0,N*6/10)
sex <- append(sex, rep.int(1,N*4/10))
#creates dataframe from SEX and ID vectors
dataContinuous = data.frame("sex" = sex, "id" = id)
dataContinuous$age <- runif(N, min = 40, max = 70)
dataContinuous$bmi <- ifelse(dataContinuous$sex==0, 21 + 0.1*dataContinuous$age + rnorm(N, 0 , 2), 20 + 0.15*dataContinuous$age + rnorm(N, 0 , 2.5))
###OMAR
dataContinuous$bmi <- ifelse(dataContinuous$sex==0, 21 + 0.1*dataContinuous$age + rnorm(N, 0 , 2), 20 + 0.15*dataContinuous$age + rnorm(N, 0 , 2.5))
###OMAR
#Output data frame
dataCategorical = data.frame(sex, id)
#Generate ethnicity
dataCategorical$ethnic <-rbinom(N, 1, 0.05)
#Generate smoking status
smoke <- c(0, 1, 2)
dataCategorical$smoke <- ifelse(dataCategorical$sex == 0, sample(smoke, N, replace = TRUE, prob = c(0.5, 0.3, 0.2)), sample(smoke, N, replace = TRUE, prob = c(0.6, 0.3, 0.1)))
dataCategorical$smoke <- ifelse(dataCategorical$sex == 0, sample(smoke, N, replace = TRUE, prob = c(0.5, 0.3, 0.2)), sample(smoke, N, replace = TRUE, prob = c(0.6, 0.3, 0.1)))
dataCategorical$smoke <- factor (dataCategorical$smoke,
levels = c(0, 1, 2),
labels = c("never", "ex", "current"))
###Analysis
total <- merge(dataContinuous,dataCategorical, by=c("id","sex"))
#change numeric storage type to factor
total$ethnic <- factor (total$ethnic,
levels = c(0, 1),
labels = c("non-white", "white"))
total$sex <- factor (total$sex,
levels = c(0, 1),
labels = c("male", "female"))
return(total)
}
#creates sample cohorts
simCohort(20000,1)
cohort1 <- simCohort(100, 123)
cohort2 <- simCohort(10000, 987)
#plots associations to examine
#plots associations
plot(cohort1$age,cohort1$bmi)
plot(cohort1$sex,cohort1$bmi)
plot(cohort2$age,cohort2$bmi)
plot(cohort2$sex,cohort2$bmi)
# cohort1 -----------------------------------------------------------------
#creates linear models with single explanatory variable
bmi.mod <- lm(bmi ~ age, cohort1)
plot(cohort1$age,cohort1$bmi)
mean.bmi <- mean(cohort1$bmi)
abline(bmi.mod, col="red")
abline(h=mean.bmi, col="blue")
summary(bmi.mod)
bmi.mod <- lm(bmi ~ sex, cohort1)
bmi.mod <- lm(bmi ~ age, cohort2)
plot(cohort2$age,cohort2$bmi)
mean.bmi <- mean(cohort2$bmi)
abline(bmi.mod, col="red")
abline(h=mean.bmi, col="blue")
summary(bmi.mod)
#blue line indicate null-hypothesis, positiv slope indicates age affects BMI
##this plot indicates sex influences BMI
plot(cohort2$sex,cohort2$bmi)
##linear models with multiple explanatory variables
#plots the BMI by explanatory variables from cohort1
coplot(bmi~age|sex,cohort1)
#This plot indicates that sex affects the intersection of BMI, let's examine the slope for men and women:
summary(lm(bmi~age, sex == "male", data=cohort1))
summary(lm(bmi~age, sex == "female", data=cohort1))
#from the coeefficiens it appears that for men an increase of 1 year increases BMI with 0,09 abd for women it's 0,11
#The intersects are different indicating a difference in bmi at the same age
# Cohort2----
#plots the BMI by explanatory variables from cohort2
coplot(bmi~age|sex,cohort2)
#This plot indicates that sex affects atleast the deviation, let's examine the slope for men and women:
summary(lm(bmi~age, sex == "male", data=cohort2))
summary(lm(bmi~age, sex == "female", data=cohort2))
## the second cohort a closer to the true slope value, representing a a larger sample size. It is also
#noted a larger standard error for women
#multiple linear regression, notice the difference in slope and intersection. Cohort2 converges towards the true value
#of bmi at year 40 (know from the equation)
bmiMultiRegression <- lm(bmi~age*sex, cohort1)
bmiMultiRegression
plot(cohort1$age, fitted(bmiMultiRegression))
bmiMultiRegression <- lm(bmi~age*sex, cohort2)
bmiMultiRegression
plot(cohort2$age, fitted(bmiMultiRegression))
#notice that for females, the age effect is increased by 0,05 per year. Being female however reduces BMI with 0,9
#creates linear models
bmi.mod <- lm(formula = bmi ~ age, cohort1)
bmi.mod
bmi.mod <- lm(formula = bmi ~ sex, cohort1)
bmi.mod
plot(bmi.mod)
|
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