pierreqi/r_code_50k · Datasets at Hugging Face

6534f9570e23c9e042378ee079d2608f8be4b6d2

66fcd73c639c308b030f834ab3ece0725cfd712c

/SRC/Consolidado/Comparacion_TCGA.R

5b17700c7dbaaecd40c22ea19ab0c6e16ae6ea55

[]

no_license

chrismazzeo/Tesis_Marcadores_Glicoinmunologicos

ae871350a259afb7c08cde70d76e262bddd49e4e

2626a75ceedb2c59531fe39f02f5590ac8ebea85

refs/heads/master

2023-06-30T13:54:01.223662

2021-08-03T03:28:47

286,357,178

0

null

UTF-8

R

false

2,792

r

Comparacion_TCGA.R

#con este hacemos todos los heatmas lindos library(readr) library(writexl) library(RColorBrewer) library(pheatmap) #dataset <- read_delim("/Volumes/Externo/Google Drive/Bioinformática/PFI/Tesina/Tesis Final/Resultados Finales/Consolidación/Tablas/FinalMergeLimpia_comparativa_ratones2.csv", ";", escape_double = FALSE, trim_ws = TRUE) #dataset <- read_delim("Results/Tablas/FinalMergeLimpia_humanos.csv", ";", escape_double = FALSE, trim_ws = TRUE) dataset <- read_delim("~/Desktop/shares CCRAC/Todos los modelos-Tabla 1.csv", ";", escape_double = FALSE, trim_ws = TRUE) View(dataset) pdfOutput = "ratones_All_modelos.pdf" dataFrameOutput= "resultRatonesModelos.csv" minLog2FC = 2 dataset = as.data.frame(dataset) heatmap.breakUp = 10 heatmap.breakDown = 7 heatmap.colorUp = colorRampPalette((brewer.pal(n = 8, name = "Reds")))(heatmap.breakUp) heatmap.colorDown = colorRampPalette(rev(brewer.pal(n = 8, name = "Blues")))(heatmap.breakDown) heatmap.colorZero = "white" heatmap.colorNa = "white" heatmap.colors=c( heatmap.colorDown,heatmap.colorZero,heatmap.colorUp) a = dataset if (filter){ #lo usamos para filtrar filas que no cumplan con el log2fc #removemos filas que no tengan lo2fC > minLog2FC a = dataset[,3:dim(dataset)[2]] heatmap.breaks = unique(c( seq(min(a, na.rm = TRUE),-1, length=heatmap.breakDown), 0, rev(seq(max(a, na.rm = TRUE),1, length=heatmap.breakUp)) )) a[abs(a) < minLog2FC] = NA a = dataset[rowSums(is.na(a)) != ncol(a), ] #remuevo filas con todo NA a[a==0] = NA } family.colors = settings.graphics.colors[3:(length(levels(as.factor(a$Family)))+2)] names(family.colors) = levels(as.factor(a$Family)) annotationColor = list( Family = family.colors) annotationRow =data.frame(Family = a$Family) rownames(annotationRow) = a$Genes matrix = a[3:dim(a)[2]] rownames(matrix) = a$Genes #separación por familia de enzimas rowGaps.freq = as.data.frame(table(a$Family)) rowGaps.freq = rowGaps.freq$Freq rowGaps = 0 for (i in 1:length(rowGaps.freq)){ rowGaps = rowGaps+ rowGaps.freq[i] rowGaps.freq[i] = rowGaps } rowGaps.freq = rowGaps.freq[1:(length(rowGaps.freq)-1)] pheatmap(matrix, cellheight = 10,cellwidth = 30, treeheight_row = 45, treeheight_col = 15, annotation_row = annotationRow, annotation_colors = annotationColor, na_col ="white", display_numbers = FALSE, # gaps_row = rowGaps.freq, fontsize_row = 8, fontsize_number = 4, cluster_rows = FALSE, cluster_cols = FALSE, show_rownames = TRUE, show_colnames = TRUE, filename = paste0("./../",pdfOutput) ) HELPER_SAVE_DATA_FRAME(a, paste0("./../",dataFrameOutput))

ef26f529adecff7f73ef2d1fd7e29de315dc7d92

b76f9a09a87ba50a6d71a55e3c45abfe5850b5c7

/man/add_waypoints.Rd

0175e8e241d908cc2d3e6f7e799f0a944d0f8c64

[]

no_license

dweemx/dynwrap

963fa4029a103bfefa6a5de5cdb24df5754bbc3f

dcbe65a6e661ae3ba46e571fbeabc3b52699b6ef

refs/heads/master

2020-04-15T18:01:27.853035

2018-12-17T15:59:49

null

0

null

UTF-8

R

false

true

648

rd

add_waypoints.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/wrap_add_waypoints.R \name{add_waypoints} \alias{add_waypoints} \title{Add or create waypoints to a trajectory} \usage{ add_waypoints(trajectory, n_waypoints = 100, resolution = sum(trajectory$milestone_network$length)/n_waypoints) } \arguments{ \item{trajectory}{Wrapper with trajectory} \item{n_waypoints}{The number of waypoints} \item{resolution}{The resolution of the waypoints, measured in the same units as the lengths of the milestone network edges, will be automatically computed using n_waypoints} } \description{ Add or create waypoints to a trajectory }

a4f7f2df3d4a055d7ecc253c19369cc6f4739de0

2c2ebb391be90b61b86cd7c8182a88c3bbeddb20

/get_data.R

970a026c464224d4c09aa2f4b25a29e0220c510a

[]

no_license

langcomp/bicknell_levy_rayner

46110777d4713d4966b2b31f523129ed40436170

b883ef03378897d85e4cdb934b09b4440bd6f7f8

refs/heads/master

2021-05-09T15:51:07.954036

2020-02-07T20:56:29

119,101,675

2

1

null

UTF-8

R

false

8,563

r

get_data.R

## combined analysis of Yosemite, YosDos, and YosDosReplication library(Hmisc) library(dplyr) source("et-admin/et-admin.R") source("etAnalyze/R/etAnalyze.R") subj_excl <- readRDS("subj_excl.rds") df_exclusions <- readRDS("df_exclusions.rds") df.pretarget.length <- readRDS("df.pretarget.length.rds") get.all.data <- function(path, has.sub, # has subgaze info? expt) { ## get all the data df.ffix <- read.subj.by.item('FFIX.TXT', path) df.single <- read.subj.by.item('SINGLE.TXT', path) df.gzd <- read.subj.by.item('GZD.TXT', path) df.ro <- read.subj.by.item('REGOUT.TXT', path) df.nfix <- read.subj.by.item('NFIX.TXT', path) df.lpos <- read.subj.by.item('LPOS.TXT', path) df.launch <- read.subj.by.item('LAUNCH.TXT', path) df.ffix.pre <- read.subj.by.item('FFIXPRE.TXT', path) df.single.pre <- read.subj.by.item('SINGPRE.TXT', path) df.gzd.pre <- read.subj.by.item('GZDPRE.TXT', path) df.ro.pre <- read.subj.by.item('REGOPRE.TXT', path) df.nfix.pre <- read.subj.by.item('NFIXPRE.TXT', path) df.lpos.pre <- read.subj.by.item('LPOSPRE.TXT', path) df.launch.pre <- read.subj.by.item('LNCHPRE.TXT', path) if (has.sub) { df.gzd.sb <- read.subj.by.item('GZDSB.TXT', path) df.nfix.sb <- read.subj.by.item('NFIXSB.TXT', path) } else { df.gzd.sb <- df.gzd df.gzd.sb[, 4:ncol(df.gzd.sb)] <- 0 df.nfix.sb <- df.nfix df.nfix.sb[, 4:ncol(df.nfix.sb)] <- 0 } ## make sure they align check.alignment <- function(df) { identical(df.ffix[, c("subj", "item", "cond")], df[, c("subj", "item", "cond")]) } stopifnot(check.alignment(df.single) & check.alignment(df.gzd) & check.alignment(df.ro) & check.alignment(df.nfix) & check.alignment(df.lpos) & check.alignment(df.launch) & check.alignment(df.ffix.pre) & check.alignment(df.single.pre) & check.alignment(df.gzd.pre) & check.alignment(df.ro.pre) & check.alignment(df.nfix.pre) & check.alignment(df.lpos.pre) & check.alignment(df.launch.pre) & check.alignment(df.gzd.sb) & check.alignment(df.nfix.sb)) ## combine df.all <- df.ffix[, c("subj", "item", "cond")] %>% mutate(expt = expt) for (pre.label in c("", ".pre")) { for (measure.name in c("ffix", "single", "gzd", 'ro', 'nfix', 'lpos', 'launch')) { for (region.num in seq(2,4)) { source.df <- eval(parse(text=paste0("df.", measure.name, pre.label))) df.all[, paste0(measure.name, region.num, pre.label)] <- source.df[, region.num+3] } } } df.all$gzd3.sb <- df.gzd.sb$region2 df.all$nfix3.sb <- df.nfix.sb$region2 return(df.all) } df.yos <- get.all.data('Yosemite/Analysis/', F, 'yos') df.yd <- get.all.data('YosDos/Analysis/', T, 'yd') df.ydr <- get.all.data('YosDosReplication/Analysis/', T, 'ydr') df.lmer <- bind_rows(df.yos, df.yd, df.ydr) ## add condition names conds <- c("None", "Right", "None", "Left") # correspondence to numbers 1-4 df.lmer <- df.lmer %>% filter(cond != 0) %>% mutate(cond = factor(conds[cond], levels = c("None", "Right", "Left"))) ## create new variables df.lmer <- df.lmer %>% mutate(gzd3c = as.integer(gzd3 - gzd3.sb), nfix3c = nfix3 - nfix3.sb, single3c = ifelse(nfix3c == 1, gzd3c, NA), ffix3c = ifelse(nfix3c == 0, NA, ffix3.pre), # only true for the data we analyze skip3c = (nfix3c == 0), skip2.pre = (nfix2.pre == 0), skip3.pre = (nfix3.pre == 0), refix3 = (nfix3 > 1), refix3.pre = (nfix3.pre > 1), refix3c = (nfix3c > 1), lpos3c = ifelse(cond == "None", lpos3.pre, NA), lpos3c = ifelse(cond == "Right", lpos3.pre - 3, lpos3c), lpos3c = ifelse(cond == "Left", lpos3.pre + 3, lpos3c), single2.pre = ifelse(skip2.pre | single2.pre == 0, NA, single2.pre), ffix2.pre = ifelse(skip2.pre | ffix2.pre == 0, NA, ffix2.pre), gzd2.pre = ifelse(skip2.pre | gzd2.pre == 0, NA, gzd2.pre)) stopifnot(with(df.lmer %>% filter(cond != "Left"), identical(gzd3c, gzd3) & identical(nfix3c, nfix3))) ## When eyedry excludes trials for having too long of a duration, ## it just makes them a zero. This causes a problem for ## calculating gzd3c, so we exclude these cases across the board. df.lmer <- df.lmer %>% filter(!(gzd3==0 & nfix3 > 0) & !(gzd3.sb==0 & nfix3.sb > 0)) ## remove extraneous dependent measures that aren't meaningful anyway df.lmer <- df.lmer %>% select(subj, item, cond, expt, ffix2.pre, single2.pre, gzd2.pre, ro2.pre, nfix2.pre, lpos2.pre, launch2.pre, launch3.pre, gzd3c, nfix3c, single3c, ffix3c, skip3c, skip2.pre, skip3.pre, refix3c, lpos3.pre, lpos3c, nfix3.sb, lpos3) # keep these two just for a sanity check later df.lmer <- df.lmer %>% ## exclude subjects left_join(subj_excl, by=c("expt", "subj")) %>% filter(to_kick == F) %>% select(-c(to_kick)) %>% ## we're only analyzing cases where target is fixated both pre- and post-shift filter(!skip3c) %>% filter(skip3.pre == F) %>% # would have skipped target filter(launch3.pre != -1) %>% # b/c we exclude skips, these are problems (e.g., short fixations) filter(lpos3.pre != -1) %>% # would-be skips according to lpos mutate(throwoff = (((cond == "Right") & (lpos3.pre < 4)) | ((cond == "Left") & (lpos3.pre > 4)))) %>% filter(throwoff == F) %>% filter(lpos3.pre != 0) %>% # display change triggered without landing on word ## get rid of problematic cases left_join(df_exclusions, by=c("expt", "subj", "item")) nrow(df.lmer %>% filter(!good)) / nrow(df.lmer) df.lmer <- df.lmer %>% filter(good == T) # exclude blinks, long fixations, and display change problems ## sanity checks stopifnot(with(df.lmer, (min(gzd2.pre, na.rm = TRUE) >= 80) & (min(single3c, na.rm = TRUE) >= 80) & (min(ffix2.pre, na.rm = TRUE) >= 80) & (min(single2.pre, na.rm = TRUE) >= 80) & (min(gzd3c) >= 80) )) stopifnot(with(df.lmer, (cond=="None" & lpos3c == lpos3) | (cond=="Right" & lpos3c == lpos3) | (cond=="Left" & nfix3.sb == 0 & lpos3c == lpos3) | (cond=="Left" & nfix3.sb > 0 & lpos3c > lpos3))) ## outlier removal remove.outliers <- function(x, width=3) { x[x == 0] <- NA z <- scale(x) x[abs(z)>width] <- NA return(x) } df.lmer <- df.lmer %>% mutate(subj = paste(expt, subj)) %>% group_by(subj) %>% mutate(gzd3c = remove.outliers(gzd3c), single3c = remove.outliers(single3c), ffix2.pre = remove.outliers(ffix2.pre), ffix3c = remove.outliers(ffix3c), single2.pre = remove.outliers(single2.pre), gzd2.pre = remove.outliers(gzd2.pre)) %>% ungroup() # how many gzd3c datapoints were excluded? sum(is.na(df.lmer$gzd3c)) / nrow(df.lmer) # add pretarget length df.lmer <- df.lmer %>% left_join(df.pretarget.length, by=c("item")) %>% mutate(front.half.pretarget = ifelse(pretarget.length == 3, lpos2.pre > 1, NA), front.half.pretarget = ifelse(pretarget.length == 4, lpos2.pre > 2, front.half.pretarget)) # exclude very far launch sites with little data (this only affects the full dataset analysis. the subset already restricts launch site tightly) old_size <- nrow(df.lmer) table(df.lmer$launch3.pre) # exclude launch sites with fewer than 20 instances -> keeping 1-11 df.lmer <- df.lmer %>% filter(launch3.pre > 0, # this is a single case launch3.pre < 12) 1-nrow(df.lmer)/old_size # excluding 0.8% # (optionally) analyze tight dataset to rule out mislocated refixations, etc. # change full_dataset to TRUE this out to generate analyses / figures for the full dataset without these restrictions full_dataset <- TRUE if (!full_dataset) { cat("Dataset size before excluding multiple fixations of pretarget and fixations on back half of pretarget: ") cat(nrow(df.lmer), "\n") df.lmer <- df.lmer %>% filter(!skip2.pre, launch2.pre != -1, # b/c we exclude skips, these are problems (e.g., short fixations) ro2.pre == 0, nfix2.pre == 1, front.half.pretarget) cat("Dataset size after those exclusions: ") cat(nrow(df.lmer), '\n') } saveRDS(df.lmer, file = "df.lmer.rds")

af829690870b3469f1f4e4cbf74dc1d5b01bed5d

30662119ab3ef017ec94458f58ed9ee03bceb169

/Plot3.R

45d7ce47cd9b73fd1e133dc5c073aa8763b34606

[]

no_license

abinashi-prakash/Git

52ae0ed2d079e1cb46656b844d340ef939175308

30a56224123680b2e988141bf3e589c9156b4fb7

refs/heads/master

2020-04-27T09:12:29.739020

2019-05-30T05:16:17

174,205,016

0

null

UTF-8

R

false

934

r

Plot3.R

# Read the file powerdata<-read.table("household_power_consumption.txt",header = TRUE, sep= ";", na.strings = "?") #Convert Date powerdata$Date <- as.Date(powerdata$Date, "%d/%m/%Y") #Select the data in the date range powerdata2 <- subset(powerdata,Date >= as.Date("2007-2-1") & Date <= as.Date("2007-2-2")) # Combine Date and Time in a single variable powerdata2$DateTime<-paste(powerdata2$Date, powerdata2$Time) #Convert date time powerdata2$DateTime <- as.POSIXct(powerdata2$DateTime) ## Create Plot 3 with(powerdata2, { plot(Sub_metering_1~DateTime, type="l", ylab="Global Active Power (kilowatts)", xlab="") lines(Sub_metering_2~DateTime,col='Red') lines(Sub_metering_3~DateTime,col='Blue') }) legend("topright", col=c("black", "red", "blue"), lwd=c(1,1,1), c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3")) ## Saving to file dev.copy(png, file="Plot3.png", height=480, width=480) dev.off()

55a008e720c3151652bb299662b51b8624d46273

47248e6d22eb023dbfa7be13025e7b5697676d3b

/R/two_class_sim.R

6441ddb79e6d7b747ffee24c1f46955ce859636f

[]

no_license

markhwhiteii/stat-comp

9b096676beac874146c81a8e0f7c4b37286a8cbe

a76ab0eb1a472f0f3dcc7e9a5e2c5d271626af2d

refs/heads/master

2021-08-23T06:42:49.525748

2017-12-03T23:58:47

105,834,980

2

1

null

UTF-8

R

false

1,645

r

two_class_sim.R

two_class_sim <- function(n, intercept, linearVars, noiseVars, corrVars, minoritySize) { sigma <- matrix(c(2, 1.3, 1.3, 2), 2, 2) tmpData <- data.frame(MASS::mvrnorm(n = n, c(0, 0), sigma)) names(tmpData) <- paste("TwoFactor", 1:2, sep = "") tmpData <- cbind(tmpData, matrix(rnorm(n * linearVars), ncol = linearVars)) colnames(tmpData)[(1:linearVars) + 2] <- paste( "Linear", gsub(" ", "0", format(1:linearVars)), sep = "" ) tmpData$Nonlinear1 <- runif(n, min = -1) tmpData <- cbind(tmpData, matrix(runif(n * 2), ncol = 2)) colnames(tmpData)[(ncol(tmpData) - 1):ncol(tmpData)] <- paste( "Nonlinear", 2:3, sep = "" ) tmpData <- as.data.frame(tmpData) p <- ncol(tmpData) tmpData <- cbind(tmpData, matrix(rnorm(n * noiseVars), ncol = noiseVars)) colnames(tmpData)[(p + 1):ncol(tmpData)] <- paste( "Noise", gsub(" ", "0", format(1:noiseVars)), sep = "" ) lp <- intercept - 4 * tmpData$TwoFactor1 + 4 * tmpData$TwoFactor2 + 2 * tmpData$TwoFactor1 * tmpData$TwoFactor2 + (tmpData$Nonlinear1^3) + 2 * exp(-6 * (tmpData$Nonlinear1 - 0.3)^2) + 2 * sin(pi * tmpData$Nonlinear2 * tmpData$Nonlinear3) lin <- seq(10, 1, length = linearVars)/4 lin <- lin * rep(c(-1, 1), floor(linearVars) + 1)[1:linearVars] for (i in seq(along = lin)) lp <- lp + tmpData[, i + 3] * lin[i] prob <- binomial()$linkinv(lp) prob <- prob + (minoritySize - sort(prob)[(1 - minoritySize) * length(sort(prob))]) tmpData$Class <- ifelse(prob <= runif(n), "Class1", "Class2") tmpData$Class <- factor(tmpData$Class, levels = c("Class1", "Class2")) tmpData }

c916508abe0e51a2fea892dc5df43f012f8f38f5

4bb8fd8247d242a6ef4d1f71c05b5a31d10c860b

/man/install_packages.Rd

d0fd950571bcd496a276f246285f1f1017836339

[ "MIT" ]

permissive

isabella232/crancache

95827afe1eb9e58b782b0cc591e6e38d23b1334b

7ea4e479bdf780adadd1bd421a5ca23e5f951697

refs/heads/master

2022-12-20T18:44:31.170172

2020-03-07T09:57:11

null

0

null

UTF-8

R

false

true

4,392

rd

install_packages.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/install-packages.R \name{install_packages} \alias{install_packages} \title{Install Packages from Repositories or Local Files, with Caching} \usage{ install_packages(pkgs, lib, repos = getOption("repos"), contriburl = contrib.url(repos, type), method, available = NULL, destdir = NULL, dependencies = NA, type = getOption("pkgType"), ...) } \arguments{ \item{pkgs}{character vector of the names of packages whose current versions should be downloaded from the repositories. If \code{repos = NULL}, a character vector of file paths. These can be source directories or archives or binary package archive files (as created by \command{R CMD build --binary}). (\code{http://} and \code{file://} URLs are also accepted and the files will be downloaded and installed from local copies.) On a CRAN build of \R for macOS these can be \file{.tgz} files containing binary package archives. Tilde-expansion will be done on file paths. If this is missing or a zero-length character vector, a listbox of available packages is presented where possible in an interactive \R session.} \item{lib}{ character vector giving the library directories where to install the packages. Recycled as needed. If missing, defaults to the first element of \code{\link{.libPaths}()}. } \item{repos}{ character vector, the base URL(s) of the repositories to use, e.g., the URL of a CRAN mirror such as \code{"https://cloud.r-project.org"}. For more details on supported URL schemes see \code{\link{url}}. Can be \code{NULL} to install from local files, directories or URLs: this will be inferred by extension from \code{pkgs} if of length one. } \item{contriburl}{ URL(s) of the contrib sections of the repositories. Use this argument if your repository mirror is incomplete, e.g., because you burned only the \file{contrib} section on a CD, or only have binary packages. Overrides argument \code{repos}. Incompatible with \code{type = "both"}. } \item{method}{ download method, see \code{\link{download.file}}. Unused if a non-\code{NULL} \code{available} is supplied. } \item{available}{ a matrix as returned by \code{\link{available.packages}} listing packages available at the repositories, or \code{NULL} when the function makes an internal call to \code{available.packages}. Incompatible with \code{type = "both"}. } \item{destdir}{ directory where downloaded packages are stored. If it is \code{NULL} (the default) a subdirectory \code{downloaded_packages} of the session temporary directory will be used (and the files will be deleted at the end of the session). } \item{dependencies}{logical indicating whether to also install uninstalled packages which these packages depend on/link to/import/suggest (and so on recursively). Not used if \code{repos = NULL}. Can also be a character vector, a subset of \code{c("Depends", "Imports", "LinkingTo", "Suggests", "Enhances")}. Only supported if \code{lib} is of length one (or missing), so it is unambiguous where to install the dependent packages. If this is not the case it is ignored, with a warning. The default, \code{NA}, means \code{c("Depends", "Imports", "LinkingTo")}. \code{TRUE} means to use \code{c("Depends", "Imports", "LinkingTo", "Suggests")} for \code{pkgs} and \code{c("Depends", "Imports", "LinkingTo")} for added dependencies: this installs all the packages needed to run \code{pkgs}, their examples, tests and vignettes (if the package author specified them correctly). In all of these, \code{"LinkingTo"} is omitted for binary packages. } \item{type}{character, indicating the type of package to download and install. Will be \code{"source"} except on Windows and some macOS builds: see the section on \sQuote{Binary packages} for those. } \item{...}{additional arguments are passed to \code{\link[utils:install.packages]{utils::install.packages()}}.} } \description{ Install Packages from Repositories or Local Files, with Caching } \seealso{ Other caching package management functions: \code{\link{available_packages}}, \code{\link{download_packages}}, \code{\link{update_packages}} } \concept{caching package management functions}

90fe77c456c0f31f4fc05f76ebf4e03307d548e6

8d8d1d24986dce6b8a56ed8bcb71ada4b4eeb2bd

/man/starwars.Rd

0fe5c06f6c2d33b1f53c2855dee68e4249292ca1

[ "MIT" ]

permissive

schochastics/networkdata

edaed94b788dcd925f55ae07f8a2d8b58d45ae8e

535987d074d35206b6804e9c90dbfa4b50768632

refs/heads/master

2023-01-07T07:20:41.475574

2023-01-05T18:54:17

226,346,857

142

17

null

UTF-8

R

false

true

398

rd

starwars.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data-misc.R \docType{data} \name{starwars} \alias{starwars} \title{Star Wars Episode 1-7} \format{ list of igraph objects } \source{ Data downloaded from https://github.com/evelinag/StarWars-social-network } \usage{ starwars } \description{ Scene Co-occurrence of Star Wars Characters (Episode 1-7) } \keyword{datasets}

46f08a6f667c02eba1256854d36b806fbecad86f

19cd3e2856b30c6e8d4fb6b2608122367551cd8f

/man/taxonomy.Rd

33c79c69f96dc0b02854df55d72e9b62bb2c7781

[]

no_license

brendanf/phylotax

3b03469a9dadba6c6d8e4e44830e8a22989703aa

77fd100e26b9320a04955b32e59d7c24fa1e60e3

refs/heads/master

2021-08-07T09:03:47.573616

2021-03-03T07:15:19

245,027,006

0

null

UTF-8

R

false

true

3,098

rd

taxonomy.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/taxonomy.R \name{taxonomy} \alias{taxonomy} \alias{taxonomy_dada2} \alias{taxonomy_sintax} \alias{taxonomy_idtaxa} \title{Assign taxonomy to nucleotide sequences} \usage{ taxonomy(seq, reference, method, min_confidence = 50, multithread = FALSE, ...) taxonomy_dada2( seq, reference, multithread = FALSE, min_confidence, tryRC = FALSE, outputBootstraps = TRUE, verbose = TRUE, ... ) taxonomy_sintax( seq, reference, min_confidence = NULL, multithread = FALSE, exec = NULL, ... ) taxonomy_idtaxa( seq, reference, multithread = FALSE, strand = "top", min_confidence = 40, ... ) } \arguments{ \item{seq}{(`character`` vector or something that can be coerced to one, or a matrix with sequences as the column names ) Sequences to assign taxonomy} \item{reference}{(`character`` string giving a path to a file or the result from \code{\link[DECIPHER:LearnTaxa]{DECIPHER::LearnTaxa()}}/\code{\link[=train_idtaxa]{train_idtaxa()}}) An appropriately formatted reference database (see Details).} \item{method}{(`character`` string) taxonomy assignment method. Currently accepted values are "dada2", "sintax", and "idtaxa".} \item{min_confidence}{(`integer`` between 0 and 100) The minimum confidence to report results.} \item{multithread}{(\code{integer} scalar) the number of processors to use for assignment.} \item{...}{additional arguments for methods} \item{tryRC}{(\code{logical} scalar) passed on to \code{dada2::assignTaxonomy()}} \item{outputBootstraps}{(\code{logical} scalar) passed on to \code{dada2::assignTaxonomy()}} \item{verbose}{(\code{logical} scalar) passed on to \code{dada2::assignTaxonomy()}} \item{exec}{(\code{character} string) name of the executable to use for SINTAX search. The default is "vsearch", but "usearch" should also work. In either case, the executable should be installed and on the system path.} \item{strand}{(\code{character} string) passed on to \code{DECIPHER::IdTaxa()}} } \value{ raw results of the taxonomy assignment, of various types depending on \code{method}. } \description{ This method uses a common interface to call primary taxonomic assignment algorithms (i.e., those which assign taxonomy based on a taxonomically classified reference sequence database, but not based on the results of other algorithms) from other R packages or external programs. } \section{Return types}{ \itemize{ \item \code{taxonomy_dada2} and \code{taxonomy(..., method = "dada2")} return a \code{list} with elements "tax" and "boot", as \link[dada2:assignTaxonomy]{dada2::assignTaxonomy}. \item \code{taxonomy_sintax} and \code{taxonomy(..., method = "sintax")} return a \code{data.frame} with columns "label", "hit", "strand", and, if \code{min_confidence} is not given, "c12n" (short for "classification"). \item \code{taxonomy_idtaxa} and \code{taxonomy(..., method = "idtaxa")} gives an S4 object of classes "Taxa" and "Train". Any of these can be passed to \link{taxtable} to get a uniform format suitable for use in \link{phylotax}. } }

5d737f047b3eef11753d2da9297a6ebb7f36ffeb

09c8994f16ce0065502d36d64eb78c2c8a7e8875

/asd-predictors-results.R

e7c30f69c8bdc24edd9f8d45abead93f2dad1fe1

[]

no_license

hyeyeon-hwang/machine-learning-asd-predictors

bd7f32e9506ff16020699d5f4b2e997f646cedfa

c244ecf016e44f5e6c00fe15e6ac4b2d186a8f47

refs/heads/master

2020-03-29T20:18:35.823797

2019-12-05T17:23:50

150,305,496

2

null

UTF-8

R

false

18,686

r

asd-predictors-results.R

source("asd-predictors.R") library(knitr) library(kableExtra) # Random Forest Model Results --------------------------------------------- rDmrResult <- runFunctions(rDmr, p = 0.8, pos = "Rett") dDmrResult <- runFunctions(dDmr, p = 0.8, pos = "Dup15q") aDmrResult <- runFunctions(aDmr, p = 0.8, pos = "ASD") pDmrResult <- runFunctions(pDmr, p = 0.8, pos = "idiopathic_autism") # pDmrCBResult <- runFunctions(pDmrCB) # confusion matrix tables using kabble cmTable <- function(dmrResult) { dmrResult$confMat$table %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), font_size = 15) %>% add_header_above(header = c("Confusion Matrix" = 3), align = "c") } cmTable(rDmrResult) cmTable(dDmrResult) cmTable(aDmrResult) cmTable(pDmrResult) # predicted probabilities tables using kabble cmProb <- function(dmrResult) { dmrResult$probPreds %>% as.tibble() %>% add_column("Sample" = c(1:nrow(dmrResult$probPreds)), .before = 1) %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), font_size = 15) %>% add_header_above(header = c("Predicted Probabilities" = 3), align = "c") } cmProb(rDmrResult) cmProb(dDmrResult) cmProb(aDmrResult) cmProb(pDmrResult) # summary of results function sumRes <- function(dmrResult, caption) { a <- paste("Model", dmrResult$rfModel$modelInfo$label, sep = ": ") b <- paste("Disorder", dmrResult$confMat$positive, sep = ": ") c <- paste(a, b, sep = ", ") ntrain <- dim(dmrResult$rfModel$trainingData)[1] ntest <- nrow(dmrResult$probPreds) npred <- dim(dmrResult$rfModel$trainingData)[2] - 1 sumTable <- tibble(Measure = as.character(), Value = as.numeric()) %>% add_row(Measure = "Number of Samples in Training Data", Value = round(ntrain)) %>% add_row(Measure = "Number of Samples in Testing Data", Value = ntest) %>% add_row(Measure = "Number of Predictors", Value = npred) %>% add_row(Measure = "Accuracy", Value = dmrResult$confMat$overall["Accuracy"]) %>% add_row(Measure = "Kappa", Value = dmrResult$confMat$overall["Kappa"]) %>% add_row(Measure = "Accuracy P Value (Acc > NIR)", Value = dmrResult$confMat$overall["AccuracyPValue"]) %>% add_row(Measure = "Sensitivity", Value = dmrResult$confMat$byClass["Sensitivity"]) %>% add_row(Measure = "Specificity", Value = dmrResult$confMat$byClass["Specificity"]) %>% add_row(Measure = "Positive Predictive Values", Value = dmrResult$confMat$byClass["Pos Pred Value"]) %>% add_row(Measure = "Negative Predictive Values", Value = dmrResult$confMat$byClass["Neg Pred Value"]) %>% kable(caption = paste(c, caption, sep = " - ")) %>% kable_styling() %>% column_spec(1:2, color = "black") %>% add_header_above(header = c("Summarized results from classification algorithm" = 2), align = "c") return(sumTable) } rf_rett_table <- sumRes(rDmrResult, caption = "") rf_rett_table rf_dup_table <- sumRes(dDmrResult, caption = "") rf_dup_table rf_asd_table <- sumRes(aDmrResult, caption = "") rf_asd_table rf_plac_table <- sumRes(pDmrResult, caption = "") rf_plac_table # Feature Selection - Remove highly correlated variables ------------------ # run after removing highly correlated variables rDmr_noHC_90 <- removeHighCor(rDmr, cutoffValue = 0.90) #10 samples, 2884 predictors, acc = 0.925, 1 rDmr_noHC_80 <- removeHighCor(rDmr, cutoffValue = 0.80) #10 samples, 460 predictors, acc = 0.2027, 0 rDmr_noHC_70 <- removeHighCor(rDmr, cutoffValue = 0.70) dDmr_noHC_90 <- removeHighCor(dDmr, cutoffValue = 0.90) #8 samples, 1196 predictors, acc = 0.769, 0.5 dDmr_noHC_80 <- removeHighCor(dDmr, cutoffValue = 0.80) #8 samples, 116 predictors, acc = 0.0694, 0 dDmr_noHC_70 <- removeHighCor(dDmr, cutoffValue = 0.70) aDmr_noHC_90 <- removeHighCor(aDmr, cutoffValue = 0.90) #22 samples, 470 predictors, acc = 0.849, 1 aDmr_noHC_80 <- removeHighCor(aDmr, cutoffValue = 0.80) #22 samples, 443 predictors, acc = 0.87, 1 aDmr_noHC_70 <- removeHighCor(aDmr, cutoffValue = 0.70) pDmr_noHC_90 <- removeHighCor(pDmr, cutoffValue = 0.90) pDmr_noHC_80 <- removeHighCor(pDmr, cutoffValue = 0.80) pDmr_noHC_70 <- removeHighCor(pDmr, cutoffValue = 0.70) pDmr_noHC_75 <- removeHighCor(pDmr, cutoffValue = 0.75) pDmr_noHC_79 <- removeHighCor(pDmr, cutoffValue = 0.79) pDmr_noHC_60 <- removeHighCor(pDmr, cutoffValue = 0.60) # random forest models on data after removing highly correlated variables rDmrResult_noHC_90 <- runFunctions(rDmr_noHC_90, p = 0.8, pos = "Rett") rDmrResult_noHC_80 <- runFunctions(rDmr_noHC_80, p = 0.8, pos = "Rett") rDmrResult_noHC_70 <- runFunctions(rDmr_noHC_70, p = 0.8, pos = "Rett") dDmrResult_noHC_90 <- runFunctions(dDmr_noHC_90, p = 0.8, pos = "Dup15q") dDmrResult_noHC_80 <- runFunctions(dDmr_noHC_80, p = 0.8, pos = "Dup15q") dDmrResult_noHC_70 <- runFunctions(dDmr_noHC_70, p = 0.8, pos = "Dup15q") aDmrResult_noHC_90 <- runFunctions(aDmr_noHC_90, p = 0.8, pos = "ASD") aDmrResult_noHC_80 <- runFunctions(aDmr_noHC_80, p = 0.8, pos = "ASD") aDmrResult_noHC_70 <- runFunctions(aDmr_noHC_70, p = 0.8, pos = "ASD") # error, no predictors for 0.90, 0.80 cutoffs pDmrResult_noHC_90 <- runFunctions(pDmr_noHC_90, p = 0.8, pos = "idiopathic_autism") #no predictors pDmrResult_noHC_80 <- runFunctions(pDmr_noHC_80, p = 0.8, pos = "idiopathic_autism") #no predictors pDmrResult_noHC_70 <- runFunctions(pDmr_noHC_70, p = 0.8, pos = "idiopathic_autism") # less 1, 286 predictors pDmrResult_noHC_75 <- runFunctions(pDmr_noHC_75, p = 0.8, pos = "idiopathic_autism") # less 1, 286 predictors pDmrResult_noHC_79 <- runFunctions(pDmr_noHC_79, p = 0.8, pos = "idiopathic_autism") # less 1, 286 predictors pDmrResult_noHC_60 <- runFunctions(pDmr_noHC_60, p = 0.8, pos = "idiopathic_autism") # 270 predictors # generate results table rf_rett_hc90 <- sumRes(rDmrResult_noHC_90, caption = "0.90 highly correlated variables removed") rf_rett_hc80 <- sumRes(rDmrResult_noHC_80, caption = "0.80 highly correlated variables removed") rf_rett_hc70 <- sumRes(rDmrResult_noHC_70, caption = "0.70 highly correlated variables removed") rf_rett_hc90 rf_rett_hc80 rf_rett_hc70 rf_dup_hc90 <- sumRes(dDmrResult_noHC_90, caption = "0.90 highly correlated variables removed") rf_dup_hc80 <- sumRes(dDmrResult_noHC_80, caption = "0.80 highly correlated variables removed") rf_dup_hc70 <- sumRes(dDmrResult_noHC_70, caption = "0.70 highly correlated variables removed") rf_dup_hc90 rf_dup_hc80 rf_dup_hc70 rf_asd_hc90 <- sumRes(aDmrResult_noHC_90, caption = "0.90 highly correlated variables removed") rf_asd_hc80 <- sumRes(aDmrResult_noHC_80, caption = "0.80 highly correlated variables removed") rf_asd_hc70 <- sumRes(aDmrResult_noHC_70, caption = "0.70 highly correlated variables removed") rf_asd_hc90 rf_asd_hc80 rf_asd_hc70 rf_plac_hc70 <- sumRes(pDmrResult_noHC_70, caption = "0.70 to 0.79 highly correlated variables removed") rf_plac_hc60 <- sumRes(pDmrResult_noHC_60, caption = "0.60 highly correlated variables removed") rf_plac_hc70 rf_plac_hc60 # Neural Network Model Results -------------------------------------------- NNsumRes <- function(dmrResult, caption) { a <- paste("Model", dmrResult$nnModel$modelInfo$label, sep = ": ") b <- paste("Disorder", dmrResult$confMat$positive, sep = ": ") c <- paste(a, b, sep = ", ") ntrain <- dim(dmrResult$nnModel$trainingData)[1] ntest <- nrow(dmrResult$probPreds) npred <- dim(dmrResult$nnModel$trainingData)[2] - 1 sumTable <- tibble(Measure = as.character(), Value = as.numeric()) %>% add_row(Measure = "Number of Samples in Training Data", Value = round(ntrain)) %>% add_row(Measure = "Number of Samples in Testing Data", Value = ntest) %>% add_row(Measure = "Number of Predictors", Value = npred) %>% add_row(Measure = "Accuracy", Value = dmrResult$confMat$overall["Accuracy"]) %>% add_row(Measure = "Kappa", Value = dmrResult$confMat$overall["Kappa"]) %>% add_row(Measure = "Accuracy P Value (Acc > NIR)", Value = dmrResult$confMat$overall["AccuracyPValue"]) %>% add_row(Measure = "Sensitivity", Value = dmrResult$confMat$byClass["Sensitivity"]) %>% add_row(Measure = "Specificity", Value = dmrResult$confMat$byClass["Specificity"]) %>% add_row(Measure = "Positive Predictive Values", Value = dmrResult$confMat$byClass["Pos Pred Value"]) %>% add_row(Measure = "Negative Predictive Values", Value = dmrResult$confMat$byClass["Neg Pred Value"]) %>% kable(caption = paste(c, caption, sep = " - ")) %>% kable_styling() %>% column_spec(1:2, color = "black") %>% add_header_above(header = c("Summarized results from classification algorithm" = 2), align = "c") return(sumTable) } NNrunFunctions(rDmr_noHC_90, p = 0.8, pos = "Rett") # didn't converge, too many weights warnings nn_rDmrResult_noHC_80 <- NNrunFunctions(rDmr_noHC_80, p = 0.8, pos = "Rett") # converged, too many weights warnings nn_rDmrResult_noHC_70 <- NNrunFunctions(rDmr_noHC_70, p = 0.8, pos = "Rett") # worked nn_rett_hc80 <- NNsumRes(nn_rDmrResult_noHC_80, caption = "0.80 highly correlated variables removed, too many weights warnings" ) nn_rett_hc70 <- NNsumRes(nn_rDmrResult_noHC_70, caption = "0.70 highly correlated variables removed" ) nn_rett_hc80 nn_rett_hc70 nn_dDmrResult_noHC_90 <- NNrunFunctions(dDmr_noHC_90, p = 0.8, pos = "Dup15q") # didn't converge, too many weights warnings nn_dDmrResult_noHC_80 <- NNrunFunctions(dDmr_noHC_80, p = 0.8, pos = "Dup15q") # converged, error, subscript out of bounds nn_dDmrResult_noHC_70 <- NNrunFunctions(dDmr_noHC_70, p = 0.8, pos = "Dup15q") # converged, error, subscript out of bounds nn_aDmrResult_noHC_90 <- NNrunFunctions(aDmr_noHC_90, p = 0.8, pos = "ASD") # converged, error, subscript, weights warnings nn_aDmrResult_noHC_80 <- NNrunFunctions(aDmr_noHC_80, p = 0.8, pos = "ASD") # converged, error, subscript, weights warnings nn_aDmrResult_noHC_70 <- NNrunFunctions(aDmr_noHC_70, p = 0.8, pos = "ASD") # converged, error, subscript, weights warnings nn_pDmrResult_noHC_70 <- NNrunFunctions(pDmr_noHC_70, p = 0.8, pos = "idiopathic_autism") # converged, error, subscript, weights warnings nn_pDmrResult_noHC_60 <- NNrunFunctions(pDmr_noHC_60, p = 0.8, pos = "idiopathic_autism") # converged, error, subscript, weights warnings nn_rett_hc80 <- NNsumRes(nn_rDmrResult_noHC_80, caption = "0.80 highly correlated variables removed, too many weights warnings") nn_rett_hc70 <- NNsumRes(nn_rDmrResult_noHC_70, caption = "0.70 highly correlated variables removed") nn_rett_hc80 nn_rett_hc70 nn_dup_hc80 <- NNsumRes(nn_dDmrResult_noHC_80, caption = "0.80 highly correlated variables removed") nn_dup_hc70 <- NNsumRes(nn_dDmrResult_noHC_70, caption = "0.70 highly correlated variables removed") nn_dup_hc80 nn_dup_hc70 nn_asd_hc90 <- NNsumRes(nn_aDmrResult_noHC_80, caption = "0.90 highly correlated variables removed, too many weights warnings") nn_asd_hc80 <- NNsumRes(nn_aDmrResult_noHC_80, caption = "0.80 highly correlated variables removed, too many weights warnings") nn_asd_hc70 <- NNsumRes(nn_aDmrResult_noHC_70, caption = "0.70 highly correlated variables removed, too many weights warnings") nn_asd_hc90 nn_asd_hc80 nn_asd_hc70 nn_plac_hc70 <- NNsumRes(nn_pDmrResult_noHC_70, caption = "0.70 highly correlated variables removed, too many weights warnings") nn_plac_hc60 <- NNsumRes(nn_pDmrResult_noHC_60, caption = "0.60 highly correlated variables removed, too many weights warnings") nn_plac_hc70 nn_plac_hc60 # ran for more than 10 min # Error: Stopping # In addition: There were 50 or more warnings (use warnings() to see the first 50) # > warnings() # Warning messages: # 1: model fit failed for Fold1.Rep01: size=1, decay=0e+00 Error in nnet.default(x, y, w, entropy = TRUE, ...) : # too many (4644) weights NNrunFunctions(rDmr, p = 0.8, pos = "Rett") # warnings, too many weights, data set too large NNrunFunctions(rDmr_vi$sixty) # 10 samples, 28 predictors, size = 1, decay = 0.1, acc = 1, 1 NNrunFunctions(rDmr_vi$seventy) # 10 samples, 7 predictors, size = 1, decay = 0.1, acc = 1, 1 NNrunFunctions(rDmr_vi$eighty) # 10 samples, 7 predictors, size = 1, decay = 0.1, acc = 1, 1 NNrunFunctions(rDmr_vi$ninety) # 10 samples, 4 predictors, size = 1, decay = 0.1, acc = 1, 1 NNrunFunctions(dDmr_vi$sixty) # 8 samples, 32 predictors, size = 1, decay = 0.1, acc = 1, 1 NNrunFunctions(dDmr_vi$seventy) # 8 samples, 9 predictors, size = 1, decay = 0.1, acc = 1, 0.5 NNrunFunctions(dDmr_vi$eighty) # 8 samples, 9 predictors, size = 1, decay = 0.1, acc = 1, 0.5 NNrunFunctions(dDmr_vi$ninety) # 8 samples, 6 predictors, size = 1, decay = 0.1, acc = 1, 0.5 NNrunFunctions(aDmr_vi$sixty) # 22 samples, 4 predictors, size = 3, decay = 0.1, acc = 0.9083, 0.6 NNrunFunctions(aDmr_vi$seventy) # 22 samples, 1 predictors, size = 5, decay = 0.85059, acc = 1, 1 NNrunFunctions(aDmr_vi$eighty) # 22 samples, 1 predictors, size = 5, decay = 0.8505952, acc = 1, 1 NNrunFunctions(aDmr_vi$ninety) # 22 samples, 1 predictors, size = 1, decay = 0.8505952, acc = 1, 1 # Feature Selection - Variable Importance --------------------------------- # run after selecting important variables cutoff_vi <- c(60, 70, 80, 90) rDmr_vi <- list() rDmr_vi$sixty <- selectImpVar(rDmr, rDmrResult$rfModel, cutoffValue = 60) rDmr_vi$seventy <- selectImpVar(rDmr, rDmrResult$rfModel, cutoffValue = 70) # accuracy: 1 -> 0.5 rDmr_vi$eighty <- selectImpVar(rDmr, rDmrResult$rfModel, cutoffValue = 80) # accuracy: 1 -> 0.5 rDmr_vi$ninety <- selectImpVar(rDmr, rDmrResult$rfModel, cutoffValue = 90) dDmr_vi <- list() dDmr_vi$sixty <- selectImpVar(dDmr, dDmrResult$rfModel, cutoffValue = 60) dDmr_vi$seventy <- selectImpVar(dDmr,dDmrResult$rfModel, cutoffValue = 70) # accuracy: 1 -> 1 dDmr_vi$eighty <- selectImpVar(dDmr, dDmrResult$rfModel, cutoffValue = 80) # accuracy: 1 -> 1 dDmr_vi$ninety <- selectImpVar(dDmr, dDmrResult$rfModel, cutoffValue = 90) aDmr_vi <- list() aDmr_vi$sixty <- selectImpVar(aDmr, aDmrResult$rfModel, cutoffValue = 60) aDmr_vi$seventy <- selectImpVar(aDmr,aDmrResult$rfModel, cutoffValue = 70) # accuracy: 0.8 -> 1 aDmr_vi$eighty <- selectImpVar(aDmr, aDmrResult$rfModel, cutoffValue = 80) # accuracy: 0.8 -> 1 aDmr_vi$ninety <- selectImpVar(aDmr, aDmrResult$rfModel, cutoffValue = 90) pDmr_vi <- list() pDmr_vi$sixty <- selectImpVar(pDmr, pDmrResult$rfModel, cutoffValue = 60) pDmr_vi$seventy <- selectImpVar(pDmr,pDmrResult$rfModel, cutoffValue = 70) # accuracy: 0.8 -> 1 pDmr_vi$eighty <- selectImpVar(pDmr, pDmrResult$rfModel, cutoffValue = 80) # accuracy: 0.8 -> 1 pDmr_vi$ninety <- selectImpVar(pDmr, pDmrResult$rfModel, cutoffValue = 90) rf_rDmrResult_vi_60 <- runFunctions(rDmr_vi$sixty, p = 0.8, pos = "Rett") rf_rDmrResult_vi_70 <- runFunctions(rDmr_vi$seventy, p = 0.8, pos = "Rett") rf_rDmrResult_vi_80 <- runFunctions(rDmr_vi$eighty, p = 0.8, pos = "Rett") # 1 predictor rf_rDmrResult_vi_90 <- runFunctions(rDmr_vi$ninety, p = 0.8, pos = "Rett") # 1 predictor rf_dDmrResult_vi_60 <- runFunctions(dDmr_vi$sixty, p = 0.8, pos = "Dup15q") rf_dDmrResult_vi_70 <- runFunctions(dDmr_vi$seventy, p = 0.8, pos = "Dup15q") rf_dDmrResult_vi_80 <- runFunctions(dDmr_vi$eighty, p = 0.8, pos = "Dup15q") rf_dDmrResult_vi_90 <- runFunctions(dDmr_vi$ninety, p = 0.8, pos = "Dup15q") # 1 predictor rf_aDmrResult_vi_60 <- runFunctions(aDmr_vi$sixty, p = 0.8, pos = "ASD") # 1 predictor rf_aDmrResult_vi_70 <- runFunctions(aDmr_vi$seventy, p = 0.8, pos = "ASD") # 1 predictor rf_aDmrResult_vi_80 <- runFunctions(aDmr_vi$eighty, p = 0.8, pos = "ASD") # 1 predictor rf_aDmrResult_vi_90 <- runFunctions(aDmr_vi$ninety, p = 0.8, pos = "ASD") # 1 predictor rf_pDmrResult_vi_60 <- runFunctions(pDmr_vi$sixty, p = 0.8, pos = "idiopathic_autism") rf_pDmrResult_vi_70 <- runFunctions(pDmr_vi$seventy, p = 0.8, pos = "idiopathic_autism") rf_pDmrResult_vi_80 <- runFunctions(pDmr_vi$eighty, p = 0.8, pos = "idiopathic_autism") rf_pDmrResult_vi_90 <- runFunctions(pDmr_vi$ninety, p = 0.8, pos = "idiopathic_autism") # 1 predictor vi60pred <- colnames(rDmr_vi$sixty[-1]) rf_rett_vi60 <- sumRes(rf_rDmrResult_vi_60, caption = "variable importance 60") rf_rett_vi70 <- sumRes(rf_rDmrResult_vi_70, caption = "variable importance 70") rf_rett_vi80 <- sumRes(rf_rDmrResult_vi_80, caption = "variable importance 80") rf_rett_vi90 <- sumRes(rf_rDmrResult_vi_90, caption = "variable importance 90") rf_rett_vi60 rf_rett_vi70 rf_rett_vi80 rf_rett_vi90 rf_dup_vi60 <- sumRes(rf_dDmrResult_vi_60, caption = "variable importance 60") rf_dup_vi70 <- sumRes(rf_dDmrResult_vi_70, caption = "variable importance 70") rf_dup_vi80 <- sumRes(rf_dDmrResult_vi_80, caption = "variable importance 80") rf_dup_vi90 <- sumRes(rf_dDmrResult_vi_90, caption = "variable importance 90") rf_dup_vi60 rf_dup_vi70 rf_dup_vi80 rf_dup_vi90 rf_asd_vi60 <- sumRes(rf_aDmrResult_vi_60, caption = "variable importance 60") rf_asd_vi70 <- sumRes(rf_aDmrResult_vi_70, caption = "variable importance 70") rf_asd_vi80 <- sumRes(rf_aDmrResult_vi_80, caption = "variable importance 80") rf_asd_vi90 <- sumRes(rf_aDmrResult_vi_90, caption = "variable importance 90") rf_asd_vi60 rf_asd_vi70 rf_asd_vi80 rf_asd_vi90 rf_plac_vi60 <- sumRes(rf_pDmrResult_vi_60, caption = "variable importance 60") rf_plac_vi70 <- sumRes(rf_pDmrResult_vi_70, caption = "variable importance 70") rf_plac_vi80 <- sumRes(rf_pDmrResult_vi_80, caption = "variable importance 80") rf_plac_vi90 <- sumRes(rf_pDmrResult_vi_90, caption = "variable importance 90") rf_plac_vi60 rf_plac_vi70 rf_plac_vi80 rf_plac_vi90 rDmr_vi_Result <- lapply(rDmr_vi, runFunctions) dDmr_vi_Result <- lapply(dDmr_vi, runFunctions) # ∨ warnings() 50: In randomForest.default(x, y, mtry = param$mtry, ...) :invalid mtry: reset to within valid range aDmr_vi_Result <- lapply(aDmr_vi, runFunctions) # aDmrResult_vi <- runFunctions(aDmr_vi$sixty), no error # aDmrResult_vi <- runFunctions(aDmr_vi$seventy), eighty, ninety -> only 1 predictor # mtry warning: Tuning parameter 'mtry' was held constant at a value of 2 # Compare models and accuracy before and after variable importance rDmrResult$rfModel$results$Accuracy rDmrResult$confMat$overall["Accuracy"] # 28 predictors, accuracy = 1 rDmr_vi_Result$sixty$rfModel$results$Accuracy rDmr_vi_Result$sixty$confMat$overall["Accuracy"] # 7 predictors, accuracy = 1 rDmr_vi_Result$seventy$rfModel$results$Accuracy rDmr_vi_Result$seventy$confMat$overall["Accuracy"] # 7 predictors, accuracy = 1 rDmr_vi_Result$eighty$rfModel$results$Accuracy rDmr_vi_Result$eighty$confMat$overall["Accuracy"] # 4 predictors, accuracy = 1 rDmr_vi_Result$ninety$rfModel$results$Accuracy rDmr_vi_Result$ninety$confMat$overall["Accuracy"] # ROC curve, variable importance

73d2317de75fddefc4a6c6e983b9aa34f3af3889

12e3d5f8618bbc113e6f039b7346fc5d723015c9

/Stats_II/Class2/Class 2 - In Class Project.R

ae4a59e4efa5745a872635a75346306fbb6d6eec

[]

no_license

raschroeder/R-Coursework

4af2ded6e9af2c0c64697dcc796a12e508f38ae4

1e9800b00f84cb4092c956d9910a710729b9aff3

refs/heads/master

2020-04-05T12:44:28.824912

2019-02-06T15:59:07

156,878,511

0

null

UTF-8

R

false

4,098

r

Class 2 - In Class Project.R

########################################################## ######## Class 2 - In class Assignment ################### ########################################################## #Load Libraries you will need library(car) #graph data library(ppcor) #part corr #Set working directory (you need to change this) setwd('/Users/rachel/Box\ Sync/R\ Coursework/Stats_II/Class2') # Import this data file #### Note: Dataset give to me by Amanda Roy Class2.Data<-read.csv('Class2InClassData.csv') head(Class2.Data) ################ Variables of the day # idnum = ID of subject # gender = 1 = male, 2 = female [nominal] # age = age of person # Health = from 0 to 10 (most healthy today) [we are going to pretend its interval] # Yrsmar = Number of years Married [interval] ############################################################### # Question: Is the longer you are married the healthier you are, but we need to control for age!! ################################################################ ################################ ########### Task 1: ########### ################################ ## calcuate the semipartial and partial correlation #zero-order correlation between Health and Yrsmar Corr.Result.1<-cor.test(Class2.Data$Health, Class2.Data$Yrsmar, method = c("pearson")) Corr.Result.1 # Control for Age (SP) CorrSP.1<-spcor.test(Class2.Data$Health, Class2.Data$Yrsmar, Class2.Data$age) CorrSP.1 # Control for Age (P) CorrP.1<-pcor.test(Class2.Data$Health, Class2.Data$Yrsmar, Class2.Data$age) CorrP.1 #overall relationship didn't change much - there is no relationship between years and health when you've controlled for age ################################ ########### Task 2: ########### ################################ # Run the regression, Health~Yrsmar and a second regression controling for age. M.Model.1<-lm(Health~Yrsmar, data = Class2.Data) M.Model.2<-lm(Health~Yrsmar+age, data = Class2.Data) M.Model.1 M.Model.2 ## Now control for age # remember to control, you can residualize age form Yrsmar. # Then rerun your regression using the residualized Yrsmar # Also plot health by residualized Yrsmar Class2.Data$Age.from.Yrsmar<-residuals(lm(Yrsmar~age, Class2.Data)) M.Model.3<-lm(Health~Age.from.Yrsmar, data = Class2.Data) summary(M.Model.3) scatterplot(Health~Age.from.Yrsmar,Class2.Data, smoother=FALSE) #The relationship is gone now - the estimate is now only 0.008 ################################ ########### Task 3: ########### ################################ # you can just enter both Yrsmar and age into one lm model! M.Model.2<-lm(Health~Yrsmar+age, data = Class2.Data) summary(M.Model.2) CorrSP.1<-spcor.test(Class2.Data$Health, Class2.Data$Yrsmar, Class2.Data$age) CorrSP.1$estimate^2 CorrSP.2<-spcor.test(Class2.Data$Health, Class2.Data$age, Class2.Data$Yrsmar) CorrSP.2$estimate^2 ################################ ########### Task 4: ########### ################################ #Does years of marriage have anything to do with health once we control for age? NO # Why? ################################ ########### Task 5: ########### ################################ # Let do the whole process again but seperarly for males and females Class2.Males<-subset(Class2.Data, gender==1) Class2.Females<-subset(Class2.Data, gender==2) #zero-order corelation between Health and yearsmar # Males Corr.Result.2<-cor.test(Class2.Males$Health, Class2.Males$Yrsmar, method = c("pearson")) Corr.Result.2 # Females Corr.Result.3<-cor.test(Class2.Females$Health, Class2.Females$Yrsmar, method = c("pearson")) Corr.Result.3 # Control for Age (SP) # Males # Females # Run 2 LM Models: Yrsmar+age # Males # Females # Bootstrap the .95BCa CI for each gender library(boot) # Males # Females ################################ ########### Task 6: ########### ################################ # 1. Does the bootstraping match the conclusions from the pvalues for each gender model? # 2. Compare the coef from each gender and explain in plain english what the results suggest

2f1748a5042e4f172daf89df16dc1b177e073300

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/magic/examples/latin.Rd.R

4e869296f0dd4fab300221505244116ceaf3868d

[]

no_license

surayaaramli/typeRrh

d257ac8905c49123f4ccd4e377ee3dfc84d1636c

66e6996f31961bc8b9aafe1a6a6098327b66bf71

refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

null

0

null

UTF-8

R

false

700

r

latin.Rd.R

library(magic) ### Name: latin ### Title: Random latin squares ### Aliases: latin incidence is.incidence is.incidence.improper unincidence ### inc_to_inc another_latin another_incidence rlatin ### Keywords: array ### ** Examples rlatin(5) rlatin(n=2, size=4, burnin=10) # An example that allows one to optimize an objective function # [here f()] over latin squares: gr <- function(x){ another_latin(matrix(x,7,7)) } set.seed(0) index <- sample(49,20) f <- function(x){ sum(x[index])} jj <- optim(par=as.vector(latin(7)), fn=f, gr=gr, method="SANN", control=list(maxit=10)) best_latin <- matrix(jj$par,7,7) print(best_latin) print(f(best_latin)) #compare starting value: f(circulant(7))

f65de342aca34f96706e2f7beb8b4401612833ca

7ecbd1e903a5ac6781fd401f1ce6ffc2ef6323a5

/demo/example2.R

42f43288078f9b1267be728b04a1cb96d7cdbff0

[]

no_license

jtilly/knitroR

84db064b0053d3bd8c8ecfdc47f82c83bcb63d3c

3a1bdb8bff7aab287b22f7af23b1053b01c89ab8

refs/heads/master

2021-01-18T22:48:12.248719

2016-11-09T17:07:37

28,303,235

3

2

null

2017-11-17T19:00:49

2014-12-21T15:09:11

R

UTF-8

R

false

1,426

r

example2.R

# Example with two inequality constraints and one upper bound # # min 100 (x2 - x1^2)^2 + (1 - x1)^2 # s.t. x1 x2 >= 1 # x1 + x2^2 >= 0 # x1 <= 0.5 # # The standard start point (-2, 1) usually converges to the standard # minimum at (0.5, 2.0), with final objective = 306.5. # Sometimes the solver converges to another local minimum # at (-0.79212, -1.26243), with final objective = 360.4. # # The problem comes from Hock and Schittkowski, HS15 library(knitroR) # define the objective function objFun = function(x) { 100*(x[2] - x[1]^2)^2 + (1-x[1])^2 } # define the inequality constraint c_inequality = function(x) { return( c( 1- x[1] * x[2], -x[1] - x[2]^2) ) } ub = c(0.5, 1e20); # define starting values x0 = c(-2, 1) results1 = knitro(objFun=objFun, c_inequality = c_inequality, ub=ub, x0=x0, options="options.opt") # define objective function gradient objGrad = function(x) { grad = vector(mode="numeric", length=2) grad[1] = (-400.0 * (x[2] - x[1]^2) * x[1]) - (2.0 * (1.0 - x[1])) grad[2] = 200.0 * (x[2] - x[1]^2) return(grad) } # define the jacobian jac = function(x) { jac = matrix(0,nrow=2,ncol=2) jac[1,1] = -x[2] jac[1,2] = -x[1] jac[2,1] = -1.0 jac[2,2] = -2.0 * x[2] return(jac) } results2 = knitro(objFun=objFun, c_inequality = c_inequality, objGrad=objGrad, jac=jac, ub=ub, x0=x0, options="options.opt")

494288944fca2df9cb3943763aeb4aa47e2efbc1

034d0b59cc9a5c36c47d28bf8e3adb0c523bb8a2

/StitchingDays.R

dbc8e1fd6a10a484aae3e8bf5ee5c351ba8f916e

[]

no_license

abhie19/IN-IU

7c1ddb07803e1e3b4b18f03d652955464e651220

79562b016f8c01e1be657135589a860316670005

refs/heads/master

2021-01-19T04:14:54.047206

2016-07-16T02:39:29

63,461,790

0

null

UTF-8

R

false

2,071

r

StitchingDays.R

day1 = read.csv("/Users/Abhishek/Downloads/01.csv", header = FALSE, stringsAsFactors=FALSE) colnames(day1) <- c("start_time", "end_time" ,"some1", "s_ip", "d_ip", "s_port", "d_port", "protocol", "flag", "some2", "some3", "flows", "size", "some4", "some5", "some6", "some7", "some8", "some9") sapply(day1,class) day2 = read.csv("/Users/Abhishek/Downloads/02.csv", header = FALSE, stringsAsFactors=FALSE) colnames(day2) <- c("start_time", "end_time" ,"some1", "s_ip", "d_ip", "s_port", "d_port", "protocol", "flag", "some2", "some3", "flows", "size", "some4", "some5", "some6", "some7", "some8", "some9") # See data type #sapply(day1,class) # sapply(day2,class) day1$start_time <- strptime(x = as.character( day1$start_time ), format = "%Y-%m-%d %H:%M:%S") day1$end_time <- strptime(x = as.character( day1$end_time ), format = "%Y-%m-%d %H:%M:%S") day2$start_time <- strptime(x = as.character( day2$start_time ), format = "%Y-%m-%d %H:%M:%S") day2$end_time <- strptime(x = as.character( day2$end_time ), format = "%Y-%m-%d %H:%M:%S") day1_filtered <- day1[day1$end_time>"2015-12-01 23:55:00 EST",] day2_filtered <- day2[(day2$start_time>"2015-12-01 23:55:00 EST" & day2$start_time<"2015-12-01 23:59:59 EST") ,] count = 0 for(i in 1:nrow(day1_filtered)) { day1_s_ip <- day1_filtered[i,'s_ip'] day1_d_ip <- day1_filtered[i,'d_ip'] day1_s_port <- day1_filtered[i,'s_port'] day1_d_port <- day1_filtered[i,'d_port'] day1_protocol <- day1_filtered[i,'protocol'] for(j in 1:nrow(day2_filtered)){ day2_s_ip <- day2_filtered[j,'s_ip'] day2_d_ip <- day2_filtered[j,'d_ip'] day2_s_port <- day2_filtered[j,'s_port'] day2_d_port <- day2_filtered[j,'d_port'] day2_protocol <- day2_filtered[j,'protocol'] if((day1_s_ip == day2_s_ip) & (day1_d_ip == day2_d_ip) & (day1_s_port == day2_s_port) & (day1_d_port==day2_d_port) & (day1_protocol==day2_protocol)){ print(count) count=count+1 print("stichable flow found") print(day1_filtered[i,]) print(day2_filtered[j,]) } } }

2db8232e28a99c110442ebafa251971cf23022b0

47b6c88ef300b4c1dc3298bbdefd5b98637fd1d2

/man/dfp_createCustomFieldOptions.Rd

234e356dbecc069313e3b59676327a0ce36e4889

[]

no_license

StevenMMortimer/rdfp

cff34ff3a2b078a03c75e43d943b226b6ca72bba

e967f137d7605b754b53a07d41069f4e5fd209dc

refs/heads/main

2022-11-18T11:40:26.848090

2019-06-05T23:54:54

46,183,233

6

2

null

UTF-8

R

false

true

1,458

rd

dfp_createCustomFieldOptions.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/CustomFieldService.R \name{dfp_createCustomFieldOptions} \alias{dfp_createCustomFieldOptions} \title{CustomFieldService} \usage{ dfp_createCustomFieldOptions(request_data, as_df = TRUE, verbose = FALSE) } \arguments{ \item{request_data}{a \code{list} or \code{data.frame} of data elements to be formatted for a SOAP request (XML format, but passed as character string)} \item{as_df}{a boolean indicating whether to attempt to parse the result into a \code{data.frame}} \item{verbose}{a boolean indicating whether to print the service URL and POSTed XML} } \value{ a \code{data.frame} or \code{list} containing all the elements of a createCustomFieldOptionsResponse } \description{ Provides methods for the creation and management of CustomField objects. } \details{ createCustomFieldOptions Creates new CustomFieldOption objects. The following fields are required: \itemize{ \item{CustomFieldOption displayName} \item{CustomFieldOption customFieldId} } } \examples{ \dontrun{ request_data <- data.frame(customFieldId=rep(dfp_createCustomFields_result$id, 3), displayName=c('Morning', 'Afternoon', 'Evening')) result <- dfp_createCustomFieldOptions(request_data) } } \seealso{ \href{https://developers.google.com/ad-manager/api/reference/v201905/CustomFieldService#createCustomFieldOptions}{Google Documentation for createCustomFieldOptions} }

3d1bcb1076c2e88b3f1902451fd81c90d78025e1

4a5a1cb13d1e7a780e4eebe75398133542a916f4

/man/kmerFractions.Rd

b66f27195017f3b4750bfea891d2923ea1564f75

[]

no_license

Malarkey73/fastqc

fc596e6b9d2625252cd39e7cc4d79437c60bfa57

126b2726fbec7d8104e6e46258899b261e9f2815

refs/heads/master

2016-08-06T04:28:26.422108

2013-06-12T14:44:31

10,216,956

2

3

null

UTF-8

R

false

978

rd

kmerFractions.Rd

\name{kmerFractions} \alias{kmerFractions} \title{Over Represented kmers(5)} \description{ Sometimes short read data can contain biased or artefactual repetitive DNA sequence - sometimes also called low complexity. You can spot such repetitive atrefacts by screening for over-represented kmers (k=5). } \details{ The expected fraction of a kmer of 5 bases can be estimated from the overall base (A/T/G/C) composition. Ignoring N there are 1024 possible kmers. The fastqc function calculates the actual and expected base fraction for each. Then for this function there is a second argument \code{frac}. If the raito of actual kmers to expected kmers > \code{frac} then it is returned in the results table. The results table is sorted lexicographically i.e. AAAAA to TTTTT. \preformatted{% } } \author{Stephen Henderson s.henderson@ucl.ac.uk} \examples{ \dontrun{ test2=fastqc("test2.fastq") kmerfractions(test2) } } \keyword{fastq} \keyword{genomics}

87ac6dcf7c4dd474ce3057ba9118772c19b41c49

9079a7b85bc2f35002d0b7b9b526c41cd184cf28

/dv_finalproject_rabinbhattarai/01 Data/map.R

1ca9ab10dfb596a4e6ddf9a37c46782cd6ae69d0

[]

no_license

JN9765/JPN

5e4f26984fbc0345902e6b5c90ca0de9d66512e3

f054ef563733619d43f640e0a71e6c8ad52a9c3c

refs/heads/master

2021-04-29T09:51:18.771368

2016-12-30T03:07:59

77,654,892

0

null

UTF-8

R

false

855

r

map.R

m <- leaflet() m <- addTiles(m) m <- addMarkers(m, lng= c(-97.7243, -97.7037, -97.6949, -97.733, -97.6958, -97.7585, -97.6949, -97.662, -97.6943) , lat=c(30.2257, 30.2764, 30.3583, 30.2382, 30.3588, 30.2325, 30.3583, 30.2851, 30.3282),popup=c("The birthplace of R","another coordinate")) m

222c6bd68854704c629c9f985ab0dd253e37e785

f61064bb7d0013f111123206b230482514141d9e

/man/sis_csmc_tp.Rd

1ef5d5cde93b2201fc20adee1b98dd54fdf8a6ad

[]

no_license

nianqiaoju/agents

6e6cd331d36f0603b9442994e08797effae43fcc

bcdab14b85122a7a0d63838bf38f77666ce882d1

refs/heads/main

2023-08-17T05:10:49.800553

2021-02-18T23:01:47

332,890,396

3

0

null

UTF-8

R

false

true

1,787

rd

sis_csmc_tp.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sis_csmc_tp.R \name{sis_csmc_tp} \alias{sis_csmc_tp} \title{controlled SMC sampler for SIS model with population-level observations} \usage{ sis_csmc_tp(y, model_config, particle_config) } \arguments{ \item{y}{a vector of length (T+1)} \item{model_config}{a list containing: \itemize{ \item 'N': size of the population \item 'alpha0' : initial infection probability \item 'lambda': infection rate \item 'gamma': recovery rate \item 'adjacency_matrix_b': network structure \item 'rho': reporting rate \item 'policy': a policy resulting from approximate dynamic programming \item }} \item{particle_config}{a list containing: \itemize{ \item 'num_particles' : number of particles for the bootstrap particle filter. MUST HAVE! \item 'ess_threshold' : if effective sample size drops below the threshold, then perform a resample step. ess_threshold = 1 means resampling at every step. MUST HAVE! \item 'save_particles': binary \item 'clock' : binary, default to FALSE. If clock = TRUE, then we will use a stopwatch to document its Sys.time() \item 'save_genealogy': binary \item 'verbose': \item 'exact': binary, if FALSE, use translated Poisson and MCMC. MUST HAVE! }} } \value{ A list containing \itemize{ \item 'log_final_likelihood' : \item 'particles': [N, (T+1), num_particles] array storing the particles: \item 'eves' : the unique ancesters of each particle in the genealogy structure, if save_genealogy = TRUE; \item 'ess' : effective sample size at each step \item 'runtime' : elapse time at each step, if clock = TRUE; \item 'totaltime' : elapse time of running the whole particle filter, if clock = TRUE; } } \description{ controlled SMC sampler for SIS model with population-level observations }

ebb736e6c8c99080f023aa2d8c5f1a034a70c89b

7755d1332586784e58b9c67e2029bad94b93fd00

/clhs1.R

f4ef87a72dbcf0b2905cb55493b95b0978f531f5

[]

no_license

brendo1001/clhs_addition

9c45d9217075448e80705629ec6ad88d1fa2bd99

5f1b638536ddd3a81be03a87e14bb663c61f7f88

refs/heads/master

2020-05-01T05:14:20.744262

2019-03-24T10:59:49

177,296,490

0

null

UTF-8

R

false

3,412

r

clhs1.R

# Conditioned Latin Hypercube sampling # Checking how the clhs R function handles existing sample data # What does 'include' do # Checking outputs with coobs map to detemine whether new samples go to areas of low environmental coverage # created: 15.3.2019 setwd("Z:/Dropbox/2019/rmuddles/clhs_addtion") # data frame of covariate data tempD<- readRDS("tempD.rds") # Libraries library(raster);library(sp); library(rgdal); library(clhs) #rasterise covariate data s1<- stack() for (i in 4:ncol(tempD)){ r1<- rasterFromXYZ(tempD[,c(1,2,i)]) names(r1)<- names(tempD[i]) s1<- stack(s1,r1) } s1 #tabulate (as i want the cell number) tempD <- data.frame(cellNos = seq(1:ncell(s1))) vals <- as.data.frame(getValues(s1)) tempD<- cbind(tempD, vals) tempD <- tempD[complete.cases(tempD), ] cellNos <- c(tempD$cellNos) gXY <- data.frame(xyFromCell(s1, cellNos, spatial = FALSE)) tempD<- cbind(gXY, tempD) str(tempD) #rasterise again s1<- stack() for (i in 3:ncol(tempD)){ r1<- rasterFromXYZ(tempD[,c(1,2,i)]) names(r1)<- names(tempD[i]) s1<- stack(s1,r1) } s1 #Point data (the exisitng sample point data) dat<- read.table("HunterValley_SiteObsAll.txt", header = T,sep = ",") # existing soil point data #extract covariate data at points coordinates(dat)<- ~ X +Y DSM_data<- raster::extract(s1,dat, sp= 1, method = "simple") #extract dat<- as.data.frame(DSM_data) dat<- dat[complete.cases(dat),] str(dat) #remove the grid points where there is point data tempD<- tempD[-which(tempD$cellNos %in% dat$cellNos),] str(tempD) ## combine grid data with the observed data str(dat) dat.sub<- dat[,c(2,3,6:13)] names(dat.sub)[1:2]<- c("x", "y") tempD.new<- rbind(dat.sub, tempD) tempD.new$type<- NA tempD.new$type[1:nrow(dat.sub)]<- "orig" tempD.new$type[(nrow(dat.sub)+1):nrow(tempD.new)]<- "possibles" ## clhs sampling with fixed obs and add an extra 100 sites # note usage of the include parameter names(tempD.new) res <- clhs(tempD.new[,c(4:10)], size = nrow(dat.sub) + 100, iter = 10000, progress = TRUE, simple = TRUE, include = c(1:nrow(dat.sub))) res saveRDS(res, file = "clhs_res.rds") # get the selected data dat.sel<- tempD.new[res,] ## coobs raster surface r1<- raster("sampleNos.tif") plot(r1) # extract coobs dat coordinates(dat.sel)<- ~ x + y DSM_data2<- extract(r1,dat.sel, sp= 1, method = "simple") # Doing some summary statistics between the raster grid values and the sample sites for both original and addtional data # original sample sites dat1<- DSM_data2[101:432, ] sum(dat1$sampleNos >= 0 & dat1$sampleNos <= 5) / nrow(dat1) # very low coobs sum(dat1$sampleNos > 5 & dat1$sampleNos <= 10) / nrow(dat1) # low coobs sum(dat1$sampleNos > 10 & dat1$sampleNos <= 20) / nrow(dat1) # moderate coobs sum(dat1$sampleNos > 20 & dat1$sampleNos <= 40) / nrow(dat1) # high coobs sum(dat1$sampleNos > 40) / nrow(dat1) # quite high coobs # additional data as selected by clhs dat2<- DSM_data2[1:100, ] sum(dat2$sampleNos >= 0 & dat2$sampleNos <= 5) / nrow(dat2) sum(dat2$sampleNos > 5 & dat2$sampleNos <= 10) / nrow(dat2) sum(dat2$sampleNos > 10 & dat2$sampleNos <= 20) / nrow(dat2) sum(dat2$sampleNos > 20 & dat2$sampleNos <= 40) / nrow(dat2) sum(dat2$sampleNos > 40) / nrow(dat2) save.image("clhs_samp.RData") #save R session

4aa0c086fd2655473766aa71df91f21aede220ed

6e5b5fb1a944818e7cd9171037ee507247c25ba0

/pulse3d.R

6734163a140fd8e7087f618d83bb3f6f7d127e64

[]

no_license

adrienne-marshall/solute_transport

7b7f811c73a69281daedb0549f6a217d24246cf2

f812edcc97782c12210e2313d54589a158d6e758

refs/heads/master

2021-08-10T16:08:14.104577

2017-11-12T19:33:48

110,461,495

0

1

null

UTF-8

R

false

1,799

r

pulse3d.R

#Function to model concentration of a solute at L and time t based on a pulse input. #Should maybe output a vector of concentrations at different times. pulse3d <- function(pulse_conc, pulse_time, max_val = 0.1, time_limits = c(0, 100), L_limits = c(0, 100), v = 2, R = 1, D = 0.5, value_limit = TRUE){ #L is 30 cm, v is 2 cm/day, R is 1 (dimensionless). #For testing: # pulse_conc = 0.5; D = 0.5; pulse_time = 2 require(tidyverse) require(viridis) require(ggthemes) time_range <- seq(time_limits[1], time_limits[2], length.out = 100) L_range <- seq(L_limits[1], L_limits[2], length.out = 100) output <- expand.grid(time_range, L_range) names(output) <- c("time", "length") #Jury et al's solution to solute pulse transport (eq 196): #Should boundaries be adjusted? A <- pulse_conc*pulse_time #think about units: (g/g)*sec? output <- output %>% mutate(p1 = A*length*sqrt(R)/(2*sqrt(pi*D*(time^3)))) %>% mutate(p2 = exp(-((R*length - v*time)^2)/(4*R*D*time))) %>% mutate(c_l_t = p1*p2) %>% dplyr::select(-p1, -p2) output <- output %>% mutate(exceeded_max = ifelse(c_l_t > max_val, 1, 0)) p <- ggplot(output, aes(x = length, y = time, fill = c_l_t)) + geom_raster() + scale_fill_viridis() + theme_few() + scale_x_continuous(expand = c(0,0)) + scale_y_continuous(expand = c(0,0)) + labs(x = "Distance (cm)", y = "Time (sec)", fill = "Solute \nconcentration") + theme(panel.border = element_blank()) if(value_limit == TRUE){ p <- p + geom_contour(aes(x = length, y= time, z = exceeded_max), color = "white", size = 0.5) } return(p) }

09f839a1ac73ebcc845bcb94971d6e3bfe2d03f7

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/nullabor/examples/reg_dist.Rd.R

1af1f064074f715cc14b4bbc612fcbcf10e90715

[]

no_license

surayaaramli/typeRrh

d257ac8905c49123f4ccd4e377ee3dfc84d1636c

66e6996f31961bc8b9aafe1a6a6098327b66bf71

refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

null

0

null

UTF-8

R

false

210

r

reg_dist.Rd.R

library(nullabor) ### Name: reg_dist ### Title: Distance based on the regression parameters ### Aliases: reg_dist ### ** Examples with(mtcars, reg_dist(data.frame(wt, mpg), data.frame(sample(wt), mpg)))

73bb3ddde3838685d5fe61d1a2f34436395a4875

033f1b856609297a46fc60b57ea69b5ec2cad818

/man/bestbeforemaxdd.Rd

a474e345eb134b76489a8aaf489d858d1fd0c4ba

[ "MIT" ]

permissive

pluspku/bestbeforemaxdd

d57f8eb7e8c21f2d51be10b28ba4d03ffbd2d5bb

5901cd6c18896619f8afd67c9740e3312e248158

refs/heads/master

2021-01-10T04:37:19.008390

2015-12-11T22:16:12

47,852,937

0

null

UTF-8

R

false

true

559

rd

bestbeforemaxdd.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/bestbeforemaxdd.R \name{bestbeforemaxdd} \alias{bestbeforemaxdd} \title{Function to calculate the difference before meet the first max drawdown} \usage{ bestbeforemaxdd(x, threshold, long = TRUE) } \arguments{ \item{x}{data series, should be a vector} \item{threshold}{the threshold to determine max drawdown} \item{long}{is long or short?} } \description{ Function to calculate the difference before meet the first max drawdown } \examples{ bestbeforemaxdd() } \keyword{maxdd}

3856057e7092366c596a82a0a382a2475168d4fe

b9c73533135d8a3350cff8c38e3604b02683ea40

/ECT2_hyperTRIBE_model_data.R

6234a6bc5cbd04ddf804276f7f8b74dcd4a02cf9

[]

no_license

sarah-ku/targets_arabidopsis

415f4d08e6d308826a03133293362be9e1c5b6a5

ad524fd57073b569320998bb79ecc66d433b37c7

refs/heads/master

2023-08-15T06:07:57.404890

2021-10-28T19:03:41

312,227,154

0

null

UTF-8

R

false

3,612

r

ECT2_hyperTRIBE_model_data.R

setwd("/binf-isilon/alab/projects/ECT2_TC/hyperTRIBE") library(RNAeditR) ################################### for modelling dat_shoots <- read.table("./pipeline/output/baseCounts_shoots_hyperTRIBE.txt",header=F) dat_roots <- read.table("./pipeline/output/baseCounts_roots_hyperTRIBE.txt",header=F) dim(dat_shoots) dim(dat_roots) locsGR_shoots <- GRanges(Rle(dat_shoots$V1),IRanges(dat_shoots$V2,width=1),ref=dat_shoots$V3,names=paste(dat_shoots$V1,dat_shoots$V2,sep="_")) locsGR_roots <- GRanges(Rle(dat_roots$V1),IRanges(dat_roots$V2,width=1),ref=dat_roots$V3,names=paste(dat_roots$V1,dat_roots$V2,sep="_")) samp.names_roots <- c("E2T_Rc11","E2T_Rc12","E2T_Rc13","E2T_Rc14","E2T_Rc15", "E2T_Re6","E2T_Re7","E2T_Re8","E2T_Re9","E2T_Re10", "E2T_Rt1", "E2T_Rt2", "E2T_Rt3" ,"E2T_Rt4" ,"E2T_Rt5") samp.names_shoots <- c("E2T_Sc11","E2T_Sc12","E2T_Sc13","E2T_Sc14","E2T_Sc15", "E2T_Se6", "E2T_Se7", "E2T_Se8" , "E2T_Se9", "E2T_Se10", "E2T_St1", "E2T_St2", "E2T_St3" ,"E2T_St4" ,"E2T_Se5") data_list_roots <- extractCountData(dat_roots,samp.names_roots,strand=F) data_list_shoots <- extractCountData(dat_shoots,samp.names_shoots,strand=F) #check data is populated for all samples lapply(data_list_roots,nrow) lapply(data_list_shoots,nrow) #for the roots we remove Re8 since it's got very low coverage (indicating a problem with the sample) data_list_roots <- data_list_roots[-which(names(data_list_roots)=="E2T_Re8")] #now produce one design vector per experiment (total of 4 for the roots and the shoots, single and triple mutants combinations) design_vector_roots_single <- c(E2T_Rc11 = "control", E2T_Rc12 = "control", E2T_Rc13 = "control", E2T_Rc14 = "control", E2T_Rc15 = "control", E2T_Re6 = "treat", E2T_Re7 = "treat", E2T_Re9 = "treat", E2T_Re10 = "treat") table(design_vector_roots_single) design_vector_roots_triple <- c(E2T_Rc11 = "control", E2T_Rc12 = "control", E2T_Rc13 = "control", E2T_Rc14 = "control", E2T_Rc15 = "control", E2T_Rt1 = "treat", E2T_Rt2 = "treat", E2T_Rt3 = "treat", E2T_Rt4 = "treat", E2T_Rt5 = "treat") table(design_vector_roots_triple) design_vector_shoots_single <- c(E2T_Sc11 = "control", E2T_Sc12 = "control", E2T_Sc13 = "control", E2T_Sc14 = "control", E2T_Sc15 = "control", E2T_Se6 = "treat", E2T_Se7 = "treat", E2T_Se8 = "treat", E2T_Se9 = "treat",E2T_Se10 = "treat") table(design_vector_shoots_single) design_vector_shoots_triple <- c(E2T_Sc11 = "control", E2T_Sc12 = "control", E2T_Sc13 = "control", E2T_Sc14 = "control", E2T_Sc15 = "control", E2T_Se5 = "treat", E2T_St1 = "treat", E2T_St2 = "treat", E2T_St4 = "treat", E2T_St3 = "treat") table(design_vector_shoots_triple) design_vector_roots_single_triple <- c(E2T_Re6 = "control", E2T_Re7 = "control", E2T_Re9 = "control", E2T_Re10 = "control", E2T_Rt1 = "treat", E2T_Rt2 = "treat", E2T_Rt3 = "treat", E2T_Rt4 = "treat", E2T_Rt5 = "treat") table(design_vector_roots_single_triple) design_vector_shoots_single_triple <- c( E2T_Se6 = "control", E2T_Se7 = "control", E2T_Se8 = "control", E2T_Se9 = "control",E2T_Se10 = "control", E2T_Se5 = "treat",E2T_St1 = "treat", E2T_St2 = "treat", E2T_St4 = "treat", E2T_St3 = "treat") table(design_vector_shoots_single_triple)

bfaa5c6d99ca08d366bff94c057f88923a64cbd0

dd256026b874e4d4109fda14b1f1e906ada8468c

/man/agg_g2.Rd

379e9069465ca49fc1e01ae55694bbf3236ce38f

[]

no_license

cran/MAd

8a90077b46752bf4b8995c231ace3f86d62f75d2

2ac236b36e9e0826240e936cf158326773c78e2d

refs/heads/master

2022-09-09T16:17:36.767905

2022-08-06T21:40:02

17,680,460

0

null

UTF-8

R

false

181

rd

agg_g2.Rd

\name{agg_g2} \alias{agg_g2} \title{Internal anRpackage objects} \description{Internal anRpackage objects.} \details{These are not to be called by the user.} \keyword{internal}

ea9c1f4f16259c9019768224dce6959979eef25a

d884a45b9b3055b668c83b5acc2936be9107769e

/R/ebpm_gamma_mixture2.R

f6531c2c6d7eab985ae4daf6b606ede01f4e52bb

[]

no_license

stephenslab/ebpm

c9526a3a1a8ea4ce2ecd74cf3fef8bf6d84abd54

aa3a957698c153c008ea42c6c99e64e0f5682aec

refs/heads/master

2021-07-14T20:18:42.225297

2021-07-08T19:45:28

210,671,798

1

2

null

UTF-8

R

false

5,074

r

ebpm_gamma_mixture2.R

#' @title Empirical Bayes Poisson Mean with Mixture of Gamma as Prior (still in development) #' @description Uses Empirical Bayes to fit the model \deqn{x_j | \lambda_j ~ Poi(s_j \lambda_j)} with \deqn{lambda_j ~ g()} #' with Mixture of Gamma: \deqn{g() = sum_k pi_k gamma(shape = a_k, rate = b_k)} #' @import mixsqp #' @details The model is fit in 2 stages: i) estimate \eqn{g} by maximum likelihood (over pi_k) #' ii) Compute posterior distributions for \eqn{\lambda_j} given \eqn{x_j,\hat{g}}. #' @param x A vector of Poisson observations. #' @param s A vector of scaling factors for Poisson observations: the model is \eqn{y[j]~Pois(s[j]*lambda[j])}. #' @param shape A vector specifying the shapes used in gamma mixtures #' @param scale A vector specifying the scales used in gamma mixtures #' @param g_init The prior distribution \eqn{g}, of the class \code{gammamix}. Usually this is left #' unspecified (\code{NULL}) and estimated from the data. However, it can be #' used in conjuction with \code{fix_g = TRUE} to fix the prior (useful, for #' example, to do computations with the "true" \eqn{g} in simulations). If #' \code{g_init} is specified but \code{fix_g = FALSE}, \code{g_init} #' specifies the initial value of \eqn{g} used during optimization. #' #' @param fix_g If \code{TRUE}, fix the prior \eqn{g} at \code{g_init} instead #' of estimating it. #' #' @param m multiple coefficient when selectig grid, so the b_k is of the form {low*m^{k-1}}; must be greater than 1; default is 2 #' @param control A list of control parameters to be passed to the optimization function. `mixsqp` is used. #' #' @return A list containing elements: #' \describe{ #' \item{\code{posterior}}{A data frame of summary results (posterior #' means, and posterior log mean).} #' \item{\code{fitted_g}}{The fitted prior \eqn{\hat{g}}, of class \code{gammamix}} #' \item{\code{log_likelihood}}{The optimal log likelihood attained #' \eqn{L(\hat{g})}.} #' } #' @examples #' beta = c(rep(0,50),rexp(50)) #' x = rpois(100,beta) # simulate Poisson observations #' s = replicate(100,1) #' m = 2 #' out = ebpm::ebpm_gamma_mixture(x,s) #' #' @export ## compute ebpm_gamma_mixture problem ebpm_gamma_mixture2 <- function(x,s, grid = NULL, g_init = NULL, fix_g = FALSE, control_select_grid = NULL, control = NULL){ ## a quick fix when all `x` are 0 if(max(x) == 0){ stop("all x are 0") ## TODO replace with sth like gamma(0, ..) } if(length(s) == 1){s = replicate(length(x),s)} if(is.null(control)){control = mixsqp_control_defaults()} if(is.null(control_select_grid)){ ## TODO } if(is.null(g_init)){ fix_g = FALSE ## then automatically unfix g if specified so if(is.null(grid)){ params_ = c(list(x = x, s = s), control_select_grid) grid = do.call(select_grid2, params_) } g_init = grid2gammamix(grid, pi = NULL) } grid = gammamix2grid(g_init) ## make sure g_init and grid are consistent tmp <- compute_L_from_grid(x,s,grid) L = tmp$L l_rowmax = tmp$l_rowmax ## compute weight pi if(!fix_g){ ## need to estimate g_hat fit <- mixsqp(L, x0 = g_init$pi, control = control) w = fit$x w = w/sum(w) ## seems that some times pi does not sum to one } else{w = g_init$pi} fitted_g = grid2gammamix(grid, w) log_likelihood = sum(log(exp(l_rowmax) * L %*% w)) posterior = compute.posterior.gammamix(x,s,fitted_g, L) return(list(fitted_g = fitted_g, posterior = posterior,log_likelihood = log_likelihood)) } select_grid2 <- function(x, s, mus = NULL , vars = NULL, k = 2){ if(is.null(mus)){ if(is.null(k)){ stop("need to provide k in select_grid2") } mus = as.vector(kmeans(x, centers = k, nstart = 100, iter.max = 100)$centers) } if(is.null(vars)){vars = 10^seq(-5,5,1)} grid = construct_grid(mus, vars) return(grid) } construct_grid <- function(mus, vars){ M = length(mus) D = length(vars) a = c() b = c() for(m in 1:M){ for(d in 1:D){ b_ = mus[m]/vars[d] a = c(a, b_ * mus[m]) b = c(b, b_) } } return(list(a = a, b = b)) } compute_L_from_grid <- function(x,s,grid){ ## TODO: need to consider numerical issue later return( compute_L(x = x,s = s,a = grid$a, b = grid$b) ) } grid2gammamix <- function(grid, pi = NULL){ n = length(grid$a) if(is.null(pi)){pi = replicate(n, 1/n)} return( gammamix(pi = pi, shape = grid$a, scale = 1/grid$b) ) } gammamix2grid <- function(g){ return(list(a = g$shape, b = 1/g$scale)) } #' @export compute.posterior.gammamix compute.posterior.gammamix <- function(x,s,g, L){ a = g$shape b = 1/g$scale if(is.null(L)){L = compute_L(x = x,s = s,a = g$shape, b = 1/g$scale)$L} cpm = outer(x,a, "+")/outer(s, b, "+") Pi_tilde = t(t(L) * g$pi) Pi_tilde = Pi_tilde/rowSums(Pi_tilde) lam_pm = rowSums(Pi_tilde * cpm) c_log_pm = digamma(outer(x,a, "+")) - log(outer(s, b, "+")) lam_log_pm = rowSums(Pi_tilde * c_log_pm) posterior = data.frame(mean = lam_pm, mean_log = lam_log_pm) return(posterior) }

c22ab48b75f3ffec650e10d655f9f2481f220b69

e131207354f2565b45b6c70d438cea8d5c26bce5

/cachematrix.R

d8a9ee0a5f07852b9ed6426e96978732885be96f

[]

no_license

danoot/ProgrammingAssignment2

54bbbe35d903231b9ddc3f830b4707590dfb9a57

aeb194d382e6346a1fb684ee6c0c2a2cfadbc0b1

refs/heads/master

2020-12-14T08:50:01.990012

2014-10-27T00:45:20

null

0

null

UTF-8

R

false

1,447

r

cachematrix.R

## Put comments here that give an overall description of what your ## functions do ## This function will take a matrix, and return a list, which will contain: ## A function to set the matrix value ## A function to get the matrix values ## A function to set the inverse of the matrix ## A function to get the inverse of the matrix makeCacheMatrix <- function(x = matrix()) { inverse <- NULL set <- function(y) { x <<- y #this should set x, in the outer context, to y. inverse <<- NULL #set inverse to NULL because we might not need to bother. } get <- function() x #returns x (original matrix) setInverse <- function(aMatrix) inverse <<- aMatrix #push aMatrix into inverse getInverse <- function() inverse # returns inverse of x, calculated on set() list(set = set, get = get, getInverse = getInverse, setInverse = setInverse) #return the list of functions } ## The function below takes the output of makeCacheMatrix, and then: ## Checks if there is an inverse already ## if there is, returns it ## else, calculates it, then sets it, then returns it. cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' inverse <- x$getInverse() if(!is.null(inverse)){ message("getting cached inverse") return(inverse) } matrix <- x$get() #load the matrix in inverse <- solve(matrix) #invert it x$setInverse(inverse) #save it for future use inverse #return it }

955f4805e4e1bbcef0000a6c95a0b3bb56d1e217

b9e15be28c0915e70a4798a0acb34ebf9bb71b8d

/5.1.4.R

1351ff74b92350522f5d2105e90b9f4068fd1a35

[]

no_license

ItsRRM97/Suicides-in-India

8c9e1f006e4eeee5170d507c41287bb0d7558140

cafde4ea3613d0b7d428ff7f79ea8e284783bba7

refs/heads/master

2021-09-10T17:36:47.887438

2018-03-30T06:45:39

103,838,259

2

0

null

UTF-8

R

false

1,106

r

5.1.4.R

# age wise social of suicide ageSocial <- function(Dataset) { social <- vector() # Null Vector Dataset$Type_code <- as.character(Dataset$Type_code) Dataset$Type <- as.character(Dataset$Type) for(i in 1:236583) { if(Dataset$Type_code[i] == 'Social_Status') { social<-c(social,Dataset$Type[i]) } } social <- unique(social) social <- sort(social) age <- c(sort(unique(as.character((Dataset$Age_group))))) mat <- matrix(0, nrow = length(age), ncol = length(social)) dimnames(mat) <- list(age,social) #making a legend for readability #legend <- data.frame(list(1:length(social)),social) for(i in 1:236583) { if(Dataset$Total[i] != 0 && Dataset$Type_code[i] == 'Social_Status') { for(j in 1:length(age)) { if(Dataset$Age_group[i] == age[j]) { for(k in 1:length(social)) { if(Dataset$Type[i] == social[k]) { mat[j,k] = mat[j,k] + Dataset$Total[i] } } } } } } write.table(mat,"output/5.1.4.csv", row.names = TRUE, col.name = TRUE, sep = ",") #print(legend) }

14b3b1032f0ab34ad1b08bf3f5c11875295b3209

7d61a07208b1425ba7e54a8ad6143cfa4f827d35

/Week 7/ggplot2 facets and Panel Layout Designs/Post Confident Class Maps/NP_Confident Class Comparison map.r

2c2ec5a5712f4d64f5c5735681e961dc65704462

[]

no_license

ceharvs/Statistical-Graphics

27557777f4c55cdf20142ac6c93879f322239875

ff6812147bbda0ee7137700a4b935de7b1a724ed

refs/heads/master

2016-09-06T13:28:26.241644

2014-12-10T04:56:14

23,636,565

1

0

null

UTF-8

R

false

4,956

r

NP_Confident Class Comparison map.r

File NP_Confident Class Comparison Map.r By Daniel B. Carr Copyright 2010, 2011, 2012, 2013 Due The pdf file. Prototype script Labels on left rather the right Color variants: gray states white borders Reference variant: compared to VA Uses NP_NAEP_MergedDat.csv or VA_NAEP_MergedDat.csv stateVBorders.csv nationVBorders.csv This will work for Virginia if you change the prefix below and hand edit the top title in Section 5 to say Virginia instead of National Public. (With a little more time this could be automated) If other states are of interest you can get the data. 2011 data is available. ## Run from here to end of file # 1.read the data, select variables, set output prefix <- "NP_" # "VA_" fin <- paste(prefix,"NAEP_MergedDat.csv",sep='') tmpDat <- read.csv(fin, row.names=1) colnames(tmpDat) pdfFile <- paste(prefix, "Confident Comparison Class Map.pdf",sep='') # pdfFile <- NULL mapDat <- tmpDat[, c(1, 3, 7)] refRegion = "NP" if(prefix!="NP_"){ stId <- substring(prefix,1,2) refRegion <- stId i <- which(row.names(mapDat)==stId) mapDat[i,] <- c(2,2,2) } # 2. read Region boundary files set map xy limits stateVBorders <- read.csv('stateVBorders.csv', row.names=NULL, header=T, as.is=TRUE) statePolyId <- stateVBorders$st[is.na(stateVBorders$x)] nationVBorders <- read.csv('nationVBorders.csv', blank.lines.skip=F, row.names=NULL, header=T) names(stateVBorders) # st= state ids, x, y polygon coordinates names(nationVBorders) # # note na.rm removes missing values rx <- range(stateVBorders$x, na.rm=T) ry <- range(stateVBorders$y, na.rm=T) rx <- mean(rx)+ diff(rx)*1.06*c(-.5, .5) ry <- mean(ry)+ diff(ry)*1.06*c(-.5, .5) # 3. Define panel Layout_____________________________________ width=9.7 height=7.1 if(!is.null(pdfFile)){ pdf(width=width, height=height, file=pdfFile) } else { windows(width=width, height=height) } topMar= 1 bottomMar <- .6 leftMar <- .7 rightMar <- 0 borders=rep(.2, 4) nc <- 3 nr <- 3 panels <- panelLayout(nrow=nr, ncol=nc, borders=borders, topMar=topMar, bottomMar=bottomMar, leftMar=leftMar, rightMar=rightMar) # 4. Draw Maps stateName <- row.names(mapDat) myGray <- rgb(.5, .5, .5) myColor <- c("#AF5FFF", "#D0D0D0", "#40D040") for(i in 1:nr){ for(j in 1:nc){ panelSelect(panels, i, j) panelScale(rx, ry) panelFill(col="#D0FFFF") # panel fill panelOutline(col="gray") polygon(stateVBorders$x, stateVBorders$y, col="white", border="gray", lwd=1) # fill and outline panCol <- myColor[mapDat[, 3]] fore <- 4-i==mapDat[, 2] & j==mapDat[, 1] if(any(fore)){ foreNam <- stateName[fore] goodBnd <- !is.na(match(stateVBorders$st, foreNam)) goodPolyId <- !is.na(match(statePolyId, foreNam)) subs <- match(statePolyId[goodPolyId], stateName) polyCol <- myColor[mapDat[subs, 3]] # plot states in gray with white outlines polygon(stateVBorders$x[goodBnd], stateVBorders$y[goodBnd], col=polyCol, border="black", lwd=1) # fill and outline } } } # highlight reference state if any goodBnd = stateVBorders$st==refRegion if(any(goodBnd){ panelSelect(panels,2,2) panelScale(rx,ry) polygon(stateVBorders$x[goodBnd], stateVBorders$y[goodBnd], col=polyCol,border="black",lwd=2) # fill and outline } 5. Labeling # top___________________________________________________ panelSelect(panels, mar="top") panelScale(inches=TRUE) # Coordinate experiments (could get from panel Layout) xl <- 2.88 + leftMar xr <- 5.73 + leftMar xm <- mean(c(xl, xr)) topCenX <- c(xl, xm, xr) #points(topCenX, rep(.08, 3), pch=21, bg='red', cex=1.25) text(xm, .59, "Reading Average Scores: Grade 8, 2009", cex=1.12) text(xm, .9, "States Compared to National Public", adj=.5, cex=1.2) sep <- .67 topRectX <- c(xl+sep, xm, xr-sep) text(topRectX, rep(.38, 3), c("Below", "Similar To", "Above"), adj=.5, cex=1.12) wid <- c(.27, .40, .27) rect(topRectX-wid, rep(.08, 3), topRectX+wid, rep(.27, 3), col=myColor) # bot__________________________________________________ panelSelect(panels, mar="bottom") panelScale(inches=TRUE) botX <- apply(panels$coltabs[2:4, ], 1, mean) text(botX, rep(.48, 3), c("Below", "Similar To", "Above"), adj=.5, cex=1.12) text(botX[2], .18, "Mathematics Average Scores: Grade 4, 2009", adj=.5, cex=1.12) # left______________________________________________ panelSelect(panels, mar="left") panelScale(inches=TRUE) leftY <- rev(apply(panels$rowtabs[2:4, ], 1, mean)) text(rep(.53, 3), leftY, srt=90, c("Below", "Similar To", "Above"), adj=.5, cex=1.12) text(rep(.23, 3), leftY[2], srt=90, "Mathematics Average Scores: Grade 8, 2009", adj=.5, cex=1.12) if(!is.null(pdfFile))dev.off()

338234315825c1d8575bbb56d0b82669abb9f56b

690c3c3e583094011d339d20a819b0fbe11a2bf8

/output_analysis.R

b50edcf2b8eb86902be8e9d49683a35e4ee9ead5

[]

no_license

AllisonVincent/StarFM-code

a0f907e2931460b7867600bd1566cb39a600338b

eac755b6ef61af5d1925b3b65d02269c846e79e1

refs/heads/master

2021-06-17T15:02:43.013841

2021-04-20T17:19:42

194,706,294

0

null

UTF-8

R

false

2,864

r

output_analysis.R

### This script is for viewing and acquiring basic information about individual layers of Landsat, MODIS, and STARFM data library(raster) landsat<- brick('./landsat.tif') modis<- brick('./mod_.tif') data<- brick('./starfm_East_fusion.tif') ## starfm data ## To find the fraction of data available, first set pixels with data to 1, then convert all non-data values for each raster layer to NA ## First, do this for STARFM results for (i in 1:nlayers(data)) { # Use the raster Which() function for speed: masked <- Which(data[[i]] > -11111 , cells=TRUE) data[[i]][masked] <- 1 } for (i in 1:nlayers(data)) { # Use the raster Which() function for speed: masked <- Which(data[[i]] == -11111, cells=TRUE) data[[i]][masked] <- NA } ## Compute the data fraction for each layer ## total number of pixels in raster area<- ncol(data) * nrow(data) n <- nlayers(data) data_frac<- rep(NA, n) ## create an empty raster with the required number of layers to fill in via the loop below: for (i in 1:nlayers(data)) { layer<- as.vector(data[[i]], mode = 'numeric') good_data<- sum(layer, na.rm = TRUE) ## find the sum, or the number of pixels with data, for each layer frac<- good_data/area ## calculate the fraction of pixels with data for each layer data_frac[[i]]<- frac } starfm_df<- data.frame("STARFM" = data_frac) ## convert above results into a data frame to write to a .csv table file #write.table(starfm_df, "./starfm_data.csv", row.names = FALSE) ################## Repeat the above, but for Landsat for (i in 1:nlayers(landsat)) { # Use the raster Which() function for speed: masked <- Which(landsat[[i]] == 0 , cells=TRUE) landsat[[i]][masked] <- NA } for (i in 1:nlayers(landsat)) { # Use the raster Which() function for speed: masked <- Which(landsat[[i]] != 0 , cells=TRUE) landsat[[i]][masked] <- 1 } ## Compute the data fraction for each landsat layer area<- ncol(landsat) * nrow(landsat) n <- nlayers(landsat) land_frac<- rep(NA, n) for (i in 1:nlayers(landsat)) { layer<- as.vector(landsat[[i]], mode = 'numeric') good_data<- sum(layer, na.rm = TRUE) frac<- good_data/area land_frac[[i]]<- frac } ################## Now do the same for modis data for (i in 1:nlayers(modis)) { # Use the raster Which() function for speed: masked <- Which(modis[[i]] != 0 , cells=TRUE) ## no data values for MODIS area already set to NA modis[[i]][masked] <- 1 } ## Compute the data fraction for each layer area<- ncol(modis) * nrow(modis) n <- nlayers(modis) modis_frac<- rep(NA, n) for (i in 1:nlayers(modis)) { layer<- as.vector(modis[[i]], mode = 'numeric') good_data<- sum(layer, na.rm = TRUE) frac<- good_data/area modis_frac[[i]]<- frac } ## Put all the above data into a single dataframe df<- data.frame("Landsat" = land_frac, "Modis" = modis_frac, "STARFM" = data_frac)

12df01a2706afe67a50eb7dc59a807826fadd925

46fd3e7df135ee7f9c939bf48481beafb3f08abf

/inst/apps/brapi/mw_studytypes.R

2a0026d35eb2f2c8d80ac2bd0dc94fd7c487ef61

[]

no_license

CIP-RIU/brapiTS

40f4106727e4bde4baddc13c34c1ae99c2b2bfdd

e4c7074d9226941f4187426db0f582e65660eed6

refs/heads/master

2020-07-23T03:16:54.359229

2017-09-15T16:56:10

78,644,303

1

2

null

UTF-8

R

false

2,003

r

mw_studytypes.R

studyTypes_data = tryCatch({ res <- read.csv(system.file("apps/brapi/data/studyTypes.csv", package = "brapiTS"), stringsAsFactors = FALSE) }, error = function(e) { NULL } ) studyTypes_list = function(page = 0, pageSize = 100){ if(is.null(studyTypes_data)) return(NULL) # paging here after filtering pg = paging(studyTypes_data, page, pageSize) studyTypes_data <- studyTypes_data[pg$recStart:pg$recEnd, ] n = nrow(studyTypes_data) out = list(n) for(i in 1:n){ out[[i]] <- as.list(studyTypes_data[i, ]) #out[[i]]$datatypes = list(safe_split(out[[i]]$datatypes, ";")) #out[[i]]$methods = list(safe_split(out[[i]]$methods, ";")) } attr(out, "pagination") = pg$pagination out } studyTypes = list( metadata = list( pagination = list( pageSize = 10, currentPage = 0, totalCount = nrow(studyTypes_data), totalPages = 1 ), status = list(), datafiles = list() ), result = list(data = studyTypes_list()) ) process_studyTypes <- function(req, res, err){ prms <- names(req$params) page = ifelse('page' %in% prms, as.integer(req$params$page), 0) pageSize = ifelse('pageSize' %in% prms, as.integer(req$params$pageSize), 100) studyTypes$result$data = studyTypes_list(page, pageSize) studyTypes$metadata$pagination = attr(studyTypes$result$data, "pagination") if(is.null(studyTypes$result$data)){ res$set_status(404) studyTypes$metadata <- brapi_status(100, "No matching results!") } res$set_header("Access-Control-Allow-Methods", "GET") res$json(studyTypes) } mw_studytypes <<- collector() %>% get("/brapi/v1/studyTypes[/]?", function(req, res, err){ process_studyTypes(req, res, err) }) %>% put("/brapi/v1/studyTypes[/]?", function(req, res, err){ res$set_status(405) }) %>% post("/brapi/v1/studyTypes[/]?", function(req, res, err){ res$set_status(405) }) %>% delete("/brapi/v1/studyTypes[/]?", function(req, res, err){ res$set_status(405) })

3db96bf567f9b16ab42c6a4061f43e0b442cd466

1cbdfc9dae2fb81522cfad64ce4bc10f7db63b4a

/plot3.R

4c6b9f634535fbb243d3923ea2d28f88ef79aa23

[]

no_license

cstaats32/ExData_Plotting1

00f097de1b0022a6a0b1592fa0651127b611285e

493dac165dccf36dbe97dfe180ceae4eddb60c41

refs/heads/master

2021-01-09T06:42:32.320212

2017-02-06T02:30:50

81,039,344

0

null

2017-02-06T02:21:05

2017-02-06T02:21:04

null

UTF-8

R

false

1,963

r

plot3.R

setwd("~/Cathy/Coursera R Programming/Exploratory Data Analysis") ## read in the dataset poweruse <- read.table("household_power_consumption.txt", sep=";", header = TRUE, colClasses = c("character","character", "numeric","numeric","numeric", "numeric","numeric","numeric","numeric"), na.strings=c("?")) ## Install dplyr library library(dplyr) ## Convert Date to a Date class datev <- as.Date(poweruse$Date, "%d/%m/%Y") ## Combine time and date characters and convert to a POSIXct variable datetime <- as.POSIXct(paste(poweruse$Date, poweruse$Time), "%d/%m/%Y %H:%M:%S", tz="GMT") ## Construct a dataframe consisting of Date, timestamp, Sub_metering_1, Sub_metering_2, ## , Sub_metering_3 poweruse2 <- data.frame("Date" = datev, "timestamp" = datetime, "Sub_metering_1" = poweruse$Sub_metering_1, "Sub_metering_2" = poweruse$Sub_metering_2, "Sub_metering_3" = poweruse$Sub_metering_3) ## Filter based on desired dates poweruse2day <- filter(poweruse2, Date >= as.Date("2007-02-01") & Date <= as.Date("2007-02-02")) ## Open png device and create file in working directory png(filename = "plot3.png", width = 480, height = 480) ## Set up plot area plot(poweruse2day$timestamp, poweruse2day$Sub_metering_1, pch = NA, xlab="", ylab="Energy sub metering") ## Add line2 to chart for each of the variables lines(x=poweruse2day$timestamp, y=poweruse2day$Sub_metering_1) lines(x=poweruse2day$timestamp, y=poweruse2day$Sub_metering_2, col="red") lines(x=poweruse2day$timestamp, y=poweruse2day$Sub_metering_3, col="blue") legend("topright", lty = 1, col = c("black","red","blue"), legend = c("Sub_metering_1","Sub_metering_2","Sub_metering_3")) ## Close graphics device dev.off()

083e6678301797ef5858b11afeadb172b8e93389

d2945a5842efe71d476535cfdf3b32fb07a378c8

/man/get_genes.Rd

8a754bf44b24f1f9af588b3234036ef1f59f805d

[]

no_license

TheJacksonLaboratory/mousegwas

5f41001d57360b15eb1d197b6c356db34cfb7b0d

da23e1b918e1fd88252e305e821cc4545c069de7

refs/heads/master

2021-11-27T00:08:12.226207

2021-09-23T12:01:12

236,527,353

1

2

null

2021-09-23T11:58:39

2020-01-27T15:49:58

R

UTF-8

R

false

true

552

rd

get_genes.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/get_genes.R \name{get_genes} \alias{get_genes} \title{Retrieve genes from biomaRt and return the intersecting genes} \usage{ get_genes(snps = NULL, dist = 1e+06, attempts = 5, annot = NULL) } \arguments{ \item{snps}{A list of SNPs with chr, ps columns. if null returns genes only} \item{dist}{Distance of genes from SNPs} \item{attempts}{Maximal number of attempts to try biomaRt} } \value{ } \description{ Retrieve genes from biomaRt and return the intersecting genes }

fd16785050dc4aad72db83c067101cfd753e6402

7bd6b1d50f19113cce4a62cfcfa7d7d88d21c93e

/Assignment-6/3.R

9dfd6907ef4df0e15b6985902c5a8ad9ca15c4f4

[]

no_license

PedroINCA/Brasil_2019

e5ce80aab69cecc3cfbdbae409887b32463ee683

fbe00f6fdb19a8f3deac46349a5295048e50710e

refs/heads/master

2020-07-07T00:25:39.358351

2019-08-23T01:40:23

203,184,836

0

null

UTF-8

R

false

610

r

3.R

#Please write a function called containsAnyOfTheseKeys that accepts two arguments: 1) a list variable and 2) a vector variable. #Your function should return a Boolean (logical) value that indicates whether the list contains any key in the specified vector. #Hint: one way to do this is with the intersect function. x = list(dog="puppy",cat="kitten",horse="foal",pig="piglet",bear="cub",alligator="hatchling") y = c("dog","cat","horse","pig","bear","alligator") names(x) intersect(names(x),y) containsAnyOfTheseKeys = function(x,y) {return(any(intersect(names(x),y)))} containsAnyOfTheseKeys(x,y)

efbc381b4c249bc0886649cada2735d42639ff36

5137f6f49055a6d75b96f1b1b0c30b055636e44e

/man/run_test_applications.Rd

c6d8ec779309c9f72ffe6bae0eefd56696b969a8

[]

no_license

cran/rODE

d23abb178718e5d79aa6eba3591f398f485ca201

42190459c2b840012a6277018668051f2987ef43

refs/heads/master

2021-01-22T10:28:29.418155

2017-11-10T03:17:51

92,644,502

2

0

null

UTF-8

R

false

true

262

rd

run_test_applications.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{run_test_applications} \alias{run_test_applications} \title{run_test_applications} \usage{ run_test_applications() } \description{ Run test all the examples }

aac12ac005931f1c18d5b82997191a171e0b8203

335a31a8cd4afb48fb29867b560b0084b8f31142

/server.R

6779e15931bb15442986e435b78aecd29d822d1b

[]

no_license

sangtani/DataProducts

a3eec4c83d5802a837bae2078a5e2d29055a2076

f28e0a8f4240e1be9b3b8c149f9ba0207758e049

refs/heads/master

2021-01-10T03:57:52.707239

2015-11-22T16:50:09

46,667,447

0

null

UTF-8

R

false

727

r

server.R

library(shiny) library(caret) library(rpart) library(lattice) library(ggplot2) library(e1071) data(iris) inTrain <- createDataPartition(y=iris$Species, p=0.7,list=FALSE) training <- iris[inTrain,] testing <- iris[-inTrain,] modFit <- train(Species ~ ., data=training, method="rpart") shinyServer( function(input,output) { output$PredictionValue <- renderText({ inputData <- data.frame(input$SLength, input$SWidth, input$PLength, input$PWidth) names(inputData) <- c("Sepal.Length","Sepal.Width","Petal.Length","Petal.Width") output$inputValues <- renderTable(inputData) pred <- predict(modFit, newdata=inputData) outputMessage <- as.character(pred) outputMessage}) } )

ea68d5558578fa2d45383447ca3a1515eedb9606

40cc7a64fc13bba3f193b2257350c70c7a62952b

/Ex(8).R

15c54353e39484e26142a450779e7d62ed768cde

[]

no_license

NorthCL/-R-

0c021d613adfe3d05007d6a55d9b37dd53ac6b5d

59c42e2de6d81552b847e8e19a2d6c267731ecfc

refs/heads/main

2023-04-19T11:31:57.051577

2021-05-12T20:38:31

304,287,802

0

null

UTF-8

R

false

611

r

Ex(8).R

#а - мат ожидание x #b - среднеквадратическое отклонение x #c - мат ожидание e #d - среднеквадратическое отклонение е #n - кол-во чисел Function.NC <- function(a, b, c, d, n) { set.seed(30) x <- rnorm(n , mean = a, sd = b) e <- rnorm(n, c, d) y <- 100 - 6*x + e layout(matrix(c(1,2,2,1,2,2,4,3,3),nrow = 3,byrow = T)) boxplot(y,pch = 20,cex = 1,col = "red") plot(x,y,pch = 20,cex = 2,col = "red") boxplot(x,pch = 20,cex = 1,horizontal = T,col = "red") } Function.NC (8, 2, 0, 15, 300)

55c2a7613f54b842a09df7c129127a38c88e6b1b

4d20e5e7a209d0c77cd598259010bee26980c50f

/r_code.R

f387b2ac17bfba50f4a1b29b3f5939ede1c52966

[]

no_license

luehkenecology/extract_e_obs_gridded_dataset

d13df0baea4cd31e12c5505cb0692437105fa663

4a91257feb909bfcd63ecf282edb2bbde91d907a

refs/heads/master

2021-01-17T17:28:42.636569

2016-06-28T11:31:48

62,133,864

0

1

null

UTF-8

R

false

2,531

r

r_code.R

################################################### # clear memory ################################################### rm(list = ls()) #============================================================ # set working directory #============================================================ RPROJ <- list(PROJHOME = normalizePath(getwd())) attach(RPROJ) rm(RPROJ) setwd(PROJHOME) ################################################### # load libraries ################################################### library(plyr) library(ggplot2) library(scales) # to access breaks/formatting functions require(lubridate) library(zoo) require(fields) library(raster) library(maptools) library(spatstat) library(raster) library(ncdf) library(RNetCDF) library(ncdf.tools) library(fields) library(colorRamps) library(rworldmap) # function to convert *.nc to raster temp_func <- function(nc, start_dataset = '1950-01-01', year_start, day_start = "-01-01", year_end = year_start, day_end = "-12-31", extent_v = 0, var = "tg"){ # Time A1<-paste(year_start, day_start, sep = "") A2<-paste(year_end, day_end, sep = "") time.s=as.POSIXct(A1,tz='UTC') time.e=as.POSIXct(A2,tz='UTC') tseq=seq(time.s, time.e, by='24 hours') times=as.POSIXct(nc$dim$time$vals*86400, origin=start_dataset, tz='UTC') t1=which(times==time.s) tfull1=which(times==time.s) t2=which(times==time.e) tfull2=which(times==time.e) dt = t2-t1+1 afi<-get.var.ncdf(nc, var,start=c(1, 1, t1), count=c(-1, -1, dt)) lon <- nc$dim$longitude$vals lat <- nc$dim$latitude$vals TEST <- lapply(1:dt, function(x) m <- t((afi[,,x]))) TEST1 <- lapply(TEST, function(x) x[nrow(x):1,]) TEST2 <- lapply(TEST1, function(x) raster(x,xmn=min(lon),xmx=max(lon),ymn=min(lat),ymx=max(lat))) # crop the raster if an extent is present if(sum(extent_v) > 0 | sum(extent_v) < 0){ TEST3<-lapply(TEST2, function(x) crop(x, extent_v)) brick(unlist(TEST3)) } else{ brick(unlist(TEST2)) } } #============================================================ # loop through years to convert *.nc to raster #============================================================ for(i in 1950:2015){ # convert *.nc to raster data <- temp_func(open.ncdf("data/tg_0.25deg_reg_v13.0.nc"), year_start = i) # save raster writeRaster(data, paste("output/mean_temperature_europe_", i, ".grd", sep = ""), overwrite = T) }

8c58c00ae2a387cc08c69a0e294895c5ebf2286b

d467300a1edd4f18630caba9653acd54e11651bf

/Class_Code/Class_1/makestereo.r

f2056206b35abfd5a083b4563df887e0f90d204c

[]

no_license

ZixinNie/Introduction_to_Data_Science

63e4645b77dfd9b04570761a7933ca412694d445

5163ed6f9c2c613425fe3ce1b10133b2c8f1310a

refs/heads/master

2020-04-18T03:24:16.431261

2019-02-06T13:50:17

167,197,283

0

null

UTF-8

R

false

1,038

r

makestereo.r

require(lattice) make.Stereo <- function(Z, Groups, Screen1=list(z = 20, x = -70, y = 3), Screen2=list(z = 20, x = -70, y = 0), Main="Stereo", asp="Equal", Xlab="X", Ylab="Y",Zlab="Z", pch=16) { Z <- data.frame(Z) dimnames(Z) <- list(1:dim(Z)[1], c("Z1", "Z2", "Z3")) if (asp == "Equal") { X.range <- c(min(apply(Z,2,range)[1,]), max(apply(Z,2,range)[2,])) Y.range <- X.range Z.range <- X.range } else { X.range <- range(Z[,1]) Y.range <- range(Z[,2]) Z.range <- range(Z[,3]) } print(cloud(Z3 ~ Z1 * Z2, data = Z, pch=pch, cex = .8, perspective = FALSE, groups=Groups, subpanel = panel.superpose, main = Main, screen = Screen1, xlim=X.range,ylim=Y.range,zlim=Z.range, xlab=Xlab, ylab=Ylab, zlab=Zlab), split = c(1,1,2,1), more = TRUE) print(cloud(Z3 ~ Z1 * Z2, data = Z, pch=pch, cex = .8, perspective = FALSE, groups=Groups, subpanel = panel.superpose, main = Main, screen = Screen2, xlim=X.range,ylim=Y.range,zlim=Z.range, xlab=Xlab, ylab=Ylab, zlab=Zlab), split = c(2,1,2,1)) }

a50c26d9ac214a4b75d69f21a966a886ccdd0cc1

0e949f187763332b63439b811f7870c0cb959e67

/Plot4.R

ca4a0e889e8201c711012fdbe63e04f11f3a1bad

[]

no_license

MariaJose97-22/ExData_Plotting1

b1110e0c3fe9a26ae8403f0b9e8cc09f29502f0f

6aac1a411fea4ff1407e76e33c1a1a78b07af94f

refs/heads/master

2022-11-23T16:42:10.169661

2020-08-03T03:28:01

284,590,822

0

1

null

2020-08-03T09:49:49

2020-08-03T03:06:57

R

UTF-8

R

false

1,572

r

Plot4.R

setwd("C:/Users/Maria Jose Figueroa/Desktop") power_consumption<-read.table("./ExData_Plotting1/household_power_consumption.txt", skip=1,sep=";") names(power_consumption)<- c("Date","Time","Global_active_power","Global_reactive_power","Voltage","Global_intensity","Sub_metering_1","Sub_metering_2","Sub_metering_3") subset_data<-subset(power_consumption,power_consumption$Date=="1/2/2007"|power_consumption$Date=="2/2/2007") subset_data$Date<-as.Date(subset_data$Date,format = "%d%m%Y") subset_data$Time<-strptime(subset_data$Time,format = "%H:%M:%S") subset_data[1:1440,"Time"] <- format(subset_data[1:1440,"Time"],"2007-02-01 %H:%M:%S") subset_data[1441:2880,"Time"] <- format(subset_data[1441:2880,"Time"],"2007-02-02 %H:%M:%S") png(filename = "./ExData_Plotting1/Plot4.png") layout(matrix(c(1:4), nrow=2, byrow=FALSE)) plot(subset_data$Time,as.numeric(subset_data$Global_active_power),type="l",xlab="",ylab="Global Active Power (kilowatts)") plot(subset_data$Time,subset_data$Sub_metering_1,type="l", xlab="", ylab="Energy sub metering") with (subset_data, lines(subset_data$Time,subset_data$Sub_metering_2,col="red")) with (subset_data, lines(subset_data$Time,subset_data$Sub_metering_3,col="blue")) legend("topright",lty = 1, col=c("black","red","blue"),legend = c("Sub_metering_1","Sub_metering_2","Sub_metering_3")) plot(subset_data$Time,as.numeric(subset_data$Voltage),type="l",xlab="Date time",ylab="Voltage") plot(subset_data$Time,as.numeric(subset_data$Global_reactive_power),type="l",xlab="Date time",ylab="Global Reactive Power") dev.off()

c318cd738f8b83e5a02692d79c2960898f5c160b

ab67cc16f40aeb69c1551c8e907d97abfd144212

/R/scrape.R

754c5ed75e135c1e7c8d1c335aff0eaa3b2c73c4

[ "MIT" ]

permissive

news-r/papers

6c315f423b923eb14e0d67ea7f37555e2f449678

2e94fea7cacd1f511e4ed30072b5957b7e6fb771

refs/heads/master

2020-05-31T18:01:57.481746

2020-02-23T19:42:56

190,424,523

3

0

null

UTF-8

R

false

2,390

r

scrape.R

#' Regions #' #' Get the list of regions available. #' #' @import rvest #' @import polite #' #' @examples regions <- get_regions() #' #' @export get_regions <- function() { session <- bow(BASE_URL, force = TRUE, user_agent = get_user_agent()) result <- scrape(session) %>% html_node(".cList") %>% html_nodes("li") region <- html_text(result) link <- result %>% html_nodes("a") %>% html_attr("href") %>% paste0(BASE_URL, .) df <- tibble::tibble( region = region, link = link ) cat(crayon::blue(cli::symbol$info), nrow(df), "Countries\n") regions <- .construct_countries(df) invisible(regions) } #' @export print.regions <- function(x, ...){ print(x[["region"]]) } #' User Agent #' #' Get and set \code{user-agent} for subsequent calls. #' #' @param agent Agent name. #' #' @name user-agent #' @export get_user_agent <- function(){ ua <- Sys.getenv("PAPERS_USER_AGENT") if(nchar(ua) <= 0) ua <- "papers-r-package" return(ua) } #' @rdname user-agent #' @export set_user_agent <- function(agent){ if(missing(agent)) stop("Missing agent", call. = FALSE) Sys.setenv("PAPERS_USER_AGENT" = agent) } #' Papers #' #' Get papers from a certain region. #' #' @param data Dataset as returned by \code{\link{get_regions}}. #' @param region Name of the region \emph{or} index of region in \code{data}. #' #' @examples #' regions <- get_regions() #' get_papers(regions, "Belgium") #' get_papers(regions, 15) # 15th country in the regions data.frame #' #' @name get_papers #' @export get_papers <- function(data, region) UseMethod("get_papers") #' @rdname get_papers #' @method get_papers regions #' @export get_papers.regions <- function(data, region){ if(missing(region)) stop("Missing region", call. = FALSE) if(inherits(region, "character")) subset <- data[data[["region"]] == region,] else subset <- data[region, ] url <- subset[["link"]] region <- subset[["region"]] session <- bow(url, force = TRUE, user_agent = get_user_agent()) result <- scrape(session) %>% html_node(".cList") %>% html_nodes("li") newspaper <- html_text(result) %>% gsub("\$.+\$", "", .) %>% trimws() link <- result %>% html_nodes("a") %>% html_attr("href") df <- tibble::tibble( newspaper = newspaper, link = link ) df[["region"]] <- region return(df) }

1ac4b023a1f3593005f26a8ad0370eaea9d6955d

c045d3a278f9e394cfe812e02cba316c9aeae0fb

/man/twn.Rd

a0bbb0fb452a949fa612ba3d961fbf8d976b3001

[ "MIT" ]

permissive

RedTent/twn

67198ef20852342b681a51000e20811ebdcabb8a

ddcedabf99124d72ad2a963b7fbf7678880fcdf9

refs/heads/master

2023-06-23T06:00:57.839342

2023-06-20T09:33:12

242,074,205

0

NOASSERTION

2021-03-29T19:33:26

2020-02-21T06:54:10

R

UTF-8

R

false

true

633

rd

twn.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ZZ_package_description.R \docType{package} \name{twn} \alias{twn} \title{Een package voor de TWN-lijst} \description{ De bedoeling van 'twn' is om de TWN-lijst beschikbaar te maken in R en om er makkelijk mee te kunnen werken. De package biedt diverse functies die het eenvoudig maken om informatie van een taxon op te vragen. Zie ook: \itemize{ \item \link{twn_lijst} en \link{twn_statuscodes} \item \link{twn_info} \item \code{\link[=match_parent]{match_parent()}} } Voor meer informatie zie de package website: \url{https://redtent.github.io/twn/} }

09d8c39dcfe02e739f8b1e9ca8e815b9a3ad9db9

32725711b519cdbd3cfa57faea2d21e4de10a92a

/R/test-script.R

98633ccb5489e17016cf49d77f528d4c377bf2f7

[]

no_license

koopmans-michaela/mcs-qsar

74e02d58801a0c6f555615043b9d2e4defd5b377

ad0005404af8bc1c946929deb45efc96fb949722

refs/heads/master

2021-05-14T08:42:09.881356

2018-02-05T18:40:21

116,306,688

0

null

UTF-8

R

false

120

r

test-script.R

library(acepack) library(testthat) library(jsonlite) #here's a comment #testing for john and jay #learn committ and push

c283085e03888182859f7fcdfe1bcc659410ff15

3d9f5d23f0c4b933d433acdb7aebf801ae2aa653

/Hospitals_per_capita_by_Province.R

834e2b2a8f31e9bb742e620762dcb378a4a6f110

[]

no_license

bzhang1945/vizathonsubmission

c29c2cae65b6f18b9a73f77e99222309270ef079

f17043a74c98dbe90e87491f0ff24c10d74be411

refs/heads/main

2023-06-26T09:57:32.522352

2021-08-01T15:53:29

null

0

null

UTF-8

R

false

1,029

r

Hospitals_per_capita_by_Province.R

library(ggplot2) library(tidyverse) Canadian_province <- c("AB", "BC", "MB", "NB", "NL", "NS", "NT","NU", "ON","PE", "QC","SK","YT") Hospitals_per_capita <- c(0.00007461243237, 0.0002895378824, 0.0001549674677, 0.00007927598015, 0.0001844600125, 0.00019909153, 0.0001550868486, 0.00007612860659, 0.0002529953655, 0.0002189977412, 0.0002750717589, 0.0004012446218, 0.0009243458476) data <- data_frame(Canadian_province, Hospitals_per_capita) ggplot(data, aes(x = Canadian_province, y = Hospitals_per_capita)) + geom_bar(fill = "steelblue", stat = 'identity') + ggtitle("Hospitals Per Capita By Province") + xlab("Canadian Provinces") + ylab("Hospitals Per Capita")+ theme_minimal() + theme(plot.background = element_rect(fill = "lightblue")) + theme(plot.title = element_text(face = "bold", colour = "steelblue")) + theme(axis.text = element_text(color = "steelblue", size = 12)) + theme(axis.title = element_text(face = "bold", color = "steelblue")) + theme(panel.background = element_rect(fill = "linen"))

8faf0442d0f05d026b7ac003d9ef71180d810e8b

38116111ccbbb1c4580d8e8c5ac3f9775e1fa384

/man/calculateDiscreteDiscreteMI_Entropy.Rd

2b27068c8fb942a402365ea1069d093db12de7bb

[ "MIT" ]

permissive

terminological/tidy-info-stats

6c1e37684eeac8d765384b773a23f0488eb7b467

1b1f19a718edb44c7178943c322b45fd1e3c93b1

refs/heads/master

2022-11-30T08:16:46.311945

2022-11-18T20:37:21

232,600,275

0

1

null

UTF-8

R

false

true

1,137

rd

calculateDiscreteDiscreteMI_Entropy.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tidyDiscreteDiscreteMI.R \name{calculateDiscreteDiscreteMI_Entropy} \alias{calculateDiscreteDiscreteMI_Entropy} \title{calculate mutual information between a discrete value (X) and a discrete value (Y) using estimates of entropy} \usage{ calculateDiscreteDiscreteMI_Entropy( df, groupXVars, groupYVars, entropyMethod = "Grassberger", ... ) } \arguments{ \item{df}{- may be grouped, in which case the grouping is interpreted as different types of discrete variable} \item{groupXVars}{- the column of the discrete value (X) quoted by vars(...)} \item{groupYVars}{- the column of the discrete value (Y)} \item{entropyMethod}{- the method used to calculate the entropy (see ?tidyinfostats::calculateDiscreteEntropy) - defaults to "Grassberger"} } \value{ a dataframe containing the disctinct values of the groups of df, and for each group a mutual information column (I). If df was not grouped this will be a single entry } \description{ calculate mutual information between a discrete value (X) and a discrete value (Y) using estimates of entropy }

ec54e653dcc9bf40aea688a0c48c424fb394112e

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/sjmisc/examples/merge_imputations.Rd.R

a620f78ba39d3218a15c754461a7ca97e75e7923

[]

no_license

surayaaramli/typeRrh

d257ac8905c49123f4ccd4e377ee3dfc84d1636c

66e6996f31961bc8b9aafe1a6a6098327b66bf71

refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

null

0

null

UTF-8

R

false

495

r

merge_imputations.Rd.R

library(sjmisc) ### Name: merge_imputations ### Title: Merges multiple imputed data frames into a single data frame ### Aliases: merge_imputations ### ** Examples library(mice) imp <- mice(nhanes) # return data frame with imputed variables merge_imputations(nhanes, imp) # append imputed variables to original data frame merge_imputations(nhanes, imp, nhanes) # show summary of quality of merging imputations merge_imputations(nhanes, imp, summary = "dens", filter = c("chl", "hyp"))

357472be7ca58c06914d8d647191c5cc5d65deab

8e4353a1dc52d42267cd6e8c1d39d5e94c0b1b99

/r/registration_gui.R

9bed7c8650a5841bae13a0d7ce91b77c91eb4268

[ "Apache-2.0" ]

permissive

SimpleITK/ISBI2018_TUTORIAL

f6b7ff1dadb736274c6987627fdae7574f6e2e58

7e8f255bb9d0aca9bebd8f4091d7d867b791d1ac

refs/heads/master

2022-11-08T13:19:44.807531

2022-11-01T13:53:10

125,414,481

27

18

Apache-2.0

2018-03-27T14:16:44

2018-03-15T19:08:16

Jupyter Notebook

UTF-8

R

false

6,119

r

registration_gui.R

library(ggplot2) # Labels used in the POPI dataset popi_body_label <- 0 popi_air_label <- 1 popi_lung_label <- 2 # Callback invoked when the StartEvent happens, sets up our new data. # Functions ending in _jn are for use with Jupyter notebooks, as the display # behaviour is a bit different. # Note that we could use a special plotting environment instead of the global # environment, but we may want to use the metrics elsewhere and they are easier to # access if they are global start_plot <- function() { #global empty vectors (via assignment operator) metric_values <<- c() multires_iterations <<- c() } end_plot_jn <- function() { Multi <- rep(NA, length(metric_values)) Multi[multires_iterations] <- "M" DDF <- data.frame(IterationNumber=1:length(metric_values), MetricValue=metric_values, MultiresIteration=Multi) DDFM <- subset(DDF, !is.na(MultiresIteration)) pl <- ggplot(DDF, aes(x=IterationNumber, y=MetricValue)) + geom_line() + geom_point(data=DDFM, aes(colour=MultiresIteration)) + theme(legend.position="none") print(pl) rm(metric_values, pos = ".GlobalEnv") rm(multires_iterations, pos = ".GlobalEnv") } # Callback invoked when the IterationEvent happens, update our data and display new figure. # Note that this won't appear as an animation in R studio, but you can use the arrows to cycle # through plots plot_values <- function(registration_method) { metric_values <<- c(metric_values, registration_method$GetMetricValue()) Multi <- rep(NA, length(metric_values)) Multi[multires_iterations] <- "M" DDF <- data.frame(IterationNumber=1:length(metric_values), MetricValue=metric_values, MultiresIteration=Multi) DDFM <- subset(DDF, !is.na(MultiresIteration)) pl <- ggplot(DDF, aes(x=IterationNumber, y=MetricValue)) + geom_line() + theme(legend.position="none") if(nrow(DDFM) > 1) { pl <- pl + geom_point(data=DDFM, aes(colour=MultiresIteration)) } print(pl) dev.flush() Sys.sleep(0) } # Use this one inside a notebook plot_values_jn <- function(registration_method) { ## No point attempting to plot every one in a notebook metric_values <<- c(metric_values, registration_method$GetMetricValue()) } # Callback invoked when the sitkMultiResolutionIterationEvent happens, update the index into the # metric_values list. update_multires_iterations <- function() { multires_iterations <<- c(multires_iterations, length(metric_values)+1) } # # Get a coronal slice with overlaid contour of the mask for the specific slice index in all temporal images. # temporal_coronal_with_overlay <- function(coronal_slice_index, images, masks, label, window_min, window_max) { # Extract the 2D images and masks. slices <- lapply(images, function(img, slc) img[,slc,], slc=coronal_slice_index) slice_masks <- lapply(masks, function(msk, slc, lbl) msk[,slc,]==lbl , slc=coronal_slice_index, lbl=label) # Resample the image (linear interpolation) and mask (nearest neighbor interpolation) into an isotropic grid, # required for display. original_spacing <- slices[[1]]$GetSpacing() original_size <- slices[[1]]$GetSize() min_spacing <- min(original_spacing) new_spacing <- c(min_spacing, min_spacing) new_size <- c(as.integer(round(original_size[1]*(original_spacing[1]/min_spacing))), as.integer(round(original_size[2]*(original_spacing[2]/min_spacing)))) resampled_slices <- lapply(slices, function(slc, sz, spc) Resample(slc, sz, Transform(), "sitkLinear", slc$GetOrigin(), spc, slc$GetDirection(), 0.0, slc$GetPixelID()), sz=new_size, spc=new_spacing) resampled_slice_masks <- lapply(slice_masks, function(msk, sz, spc) Resample(msk, sz, Transform(), "sitkNearestNeighbor", msk$GetOrigin(), spc, msk$GetDirection(), 0.0, msk$GetPixelID()), sz=new_size, spc=new_spacing) # Create the overlay: cast the mask to expected label pixel type, and do the same for the image after # window-level, accounting for the high dynamic range of the CT. overlaid_slices <- mapply( function(slc, msk, win_min, win_max) LabelMapContourOverlay(Cast(msk, "sitkLabelUInt8"), Cast(IntensityWindowing(slc, windowMinimum=win_min, windowMaximum=win_max), "sitkUInt8"), opacity = 1, c(0,0), c(2,2)), resampled_slices, resampled_slice_masks, win_min=window_min, win_max=window_max) # Create the temporal slice, 3D volume representing 2D coronal+time temporal_image <- Image(c(overlaid_slices[[1]]$GetSize(), length(overlaid_slices)), overlaid_slices[[1]]$GetPixelID()) # Two subtle points: (1) to paste the 2D slice into the 3D volume we need to make it a 3D slice (JoinSeries), # (2) the Paste function uses SimpleITK indexing, requiring the seq()-1. invisible(mapply(function(slice, index) temporal_image<<- Paste(temporal_image, JoinSeries(slice), c(slice$GetSize(),1), c(0,0,0), c(0,0,index)), overlaid_slices, seq(length(overlaid_slices))-1)) return(temporal_image) }

367063fd58baaf0e39fde6c99af7db7c9499b9da

ba49eb475d4fcd6d61655270ec34fe829657494e

/man/elsc.Rd

63f22f19170cdc4187546ff723a263831db042e1

[]

no_license

cran/Bios2cor

f3b630684862d4f48e7137361e39094358336f63

50b2948bfdd3888e6593015247419ce1f5b58d8a

refs/heads/master

2022-07-31T18:21:28.244917

2022-07-08T08:25:23

101,306,648

1

0

null

UTF-8

R

false

2,688

rd

elsc.Rd

\name{elsc} \Rdversion{1.1} \alias{elsc} \title{ Explicit Likelihood of Subset Covariation (ELSC) function } \description{ Calculates a score based on rigorous statistics of correlation/covariation in a perturbation-based algorithm. It measures how many possible subsets of size n would have the composition found in column j in the subset alignment defined by the perturbation in column i, and in the ideal subset (i.e., in a subset with the amino acid distribution equal to the total alignment). } \usage{ elsc(align, gap_ratio = 0.2) } \arguments{ \item{align}{ An object of class 'align' created by the \code{\link{import.msf}} or the \code{\link{import.fasta}} function from a sequence alignment } \item{gap_ratio}{ Numeric value between 0 and 1 indicating the maximal gap ratio at a given position in the MSA for this position to be taken into account. Default is 0.2, positions with more than 20 percent of gaps will not be taken into account in the analysis. When gap_ratio is 1 or close to 1, only positions with at least 1 aa are taken into account (positions with only gaps are excluded). } } \details{ The ELSC score at position [i,j] has been computed with the following formula : \deqn{ELSC(i,j) = -ln\prod_{y}^{ } \frac{{{N_{y(j)}}\choose{n_{y(j)}}}}{{{N_{y(j)}}\choose{m_{y(j)}}}}} As a reminder, a binomial coefficient \eqn{{N}\choose{k}} is computed as follow : \deqn{{{N}\choose{k}} = \frac{N!}{k!(N-k)!}} where : \itemize{ \item {\eqn{N_{y(j)}}} {is the number of residues y at position j in the total (unperturbed) sequence alignment} \item {\eqn{n_{y(j)}}} {is the number of residues y at position j in the subset alignment defined by the perturbation in column i} \item {\eqn{m_{y(j)}}} {is the number of residues y at position j in the ideal subset (i.e., in a subset with the amino acid distribution equal to the total alignment)} } } \value{ A list of two elements which are numeric matrices containing the ELSC scores and Z-scores for each pair of elements. } \author{ Madeline DENIAUD and Marie CHABBERT } \references{ Dekker JP, Fodor A, Aldrich RW, Yellen G. A perturbation-bqsed method for calculating explicit likelihood of evolutionary covariance in multiple sequence alignements. Bioinformatics 2004;20:1565-1572. } \examples{ #Importing MSA file align <- import.msf(system.file("msa/toy_align.msf", package = "Bios2cor")) #Creating correlation object with ELSC method for positions with gap ratio < 0.1 elsc <- elsc(align, gap_ratio = 0.1) #Creating correlation object with ELSC method for positions with gap_ratio < 0.2 (Default) #elsc <- elsc(align) }

7de17c63a02f19eb43fa13e3f0b8b904d331d13c

1a826358019cd1bc4082a44099bbc9fb7034aa86

/OCRPractice.R

7a208e81601c1b897b693d408fdc55ea86b5cf2a

[]

no_license

sheharyarakhtar/Digit_Recognition

86dc367c85c75017886e5e8b7fbc3a96c512a578

3a36c0f12f8bdb05b45919fe75bcf69f21391ed5

refs/heads/main

2023-04-10T23:34:04.284972

2021-04-14T21:37:38

357,990,667

1

0

null

UTF-8

R

false

2,837

r

OCRPractice.R

library(keras) #Import dataset mnist for training and testing mnist <- dataset_mnist() #2 lists pf 2. First list first and second part contains training and label and second for test str(mnist) #Seperate them to variable trainx <- mnist$train$x trainy <- mnist$train$y testx <- mnist$test$x testy <- mnist$test$y table(trainy) table(testy) #plot images par(mfrow = c(3,3)) for(i in 1:9) plot(as.raster(trainx[i,,], max=255)) par(mfrow = c(1,1)) trainx[5,,] hist(trainx[5,,]) trainy #FIVE a <- c(1,12,36,48,66,101,133,139,146) par(mfrow = c(3,3)) for(i in a) plot(as.raster(trainx[i,,], max = 255)) par(mfrow = c(1,1)) #reshape and rescale ##reshape linearise the matrix into one long row of 784 variables. 784 =28x28pixels trainx <- array_reshape(trainx, c(nrow(trainx), 784)) str(trainx) testx <- array_reshape(testx, c(nrow(testx), 784)) str(testx) #Normalise the values by dividing them by 255, which the maximum value of each variable trainx <- trainx/255 testx <- testx/255 #One hot encoding ##Forms a matrix array from a given array trainy <- to_categorical(trainy, 10) testy <- to_categorical(testy, 10) head(trainy) #Model model <- keras_model_sequential() model %>% layer_dense(units = 512, activation = 'relu', input_shape = c(784)) %>% layer_dropout(rate = 0.4) %>% layer_dense(units = 256, activation = 'relu') %>% layer_dropout(rate = 0.3) %>% layer_dense(units = 128, activation = 'relu') %>% layer_dropout(rate = 0.2) %>% layer_dense(units = 10, activation = 'softmax') summary(model) #Compile model %>% compile(loss = 'categorical_crossentropy', optimizer = optimizer_rmsprop(), metrics = 'accuracy') #Fit model history <- model %>% fit(trainx, trainy, epochs = 12, batch_size = 32, validation_split = 0.2) plot(history) ##Evaluation of model model %>% evaluate(testx, testy) pred <- model %>% predict_classes(testx) table(Predicted = pred, Actual = mnist$test$y) prob <- model %>% predict_proba(testx) cbind(prob, Predicted_class = pred, Actual = mnist$test$y)[1:5,] library(EBImage) temp = list.files(pattern = '*.png') mypic <- list() for(i in 1:length(temp))mypic[[i]] <- readImage(temp[[i]]) par(mfrow = c(3,2)) for(i in 1:length(temp))plot(mypic[[i]]) par(mfrow = c(1,1)) #527680 #convert to greyscale for(i in 1:length(temp))colorMode(mypic[[1]]) <- Grayscale #make them white/blackbackground for(i in 1:length(temp))mypic[[i]] <- 1-mypic[[i]] #Resize the image for(i in 1:length(temp))mypic[[i]] <- resize(mypic[[i]], 28, 28) #Reshape the pictures for(i in 1:length(temp))mypic[[i]] <- array_reshape(mypic[[i]], c(28,28,3)) str(mypic) new <- NULL for (i in 1:length(temp))new <- rbind(new, mypic[[i]]) newx <- new[,1:784] newy <- c(5,2,7,6,8,0) ##model prediction pred <- model %>% predict_classes(newx) pred

5ce70df38c101f1830d625c919507672b45df5c9

df3b3e2cff3f789a3e91e561d7e3121603d51546

/man/superbData.Rd

6d63f85545f8ea0811c72d9f3f046de1c074b309

[]

no_license

humanfactors/superb

39218b3458d8d8d834b844412a22b9a4bf5a8746

cdb7a903d84c2a83d4a4c7c94a97a2d4bc2221a4

refs/heads/master

2023-07-16T21:47:00.530298

2021-09-04T16:52:48

null

0

null

UTF-8

R

false

true

3,453

rd

superbData.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/superbData.R \name{superbData} \alias{superbData} \title{Obtain summary statistics with correct error bars.} \usage{ superbData( data, BSFactors = NULL, WSFactors = NULL, WSDesign = "fullfactorial", factorOrder = NULL, variables, statistic = "mean", errorbar = "CI", gamma = 0.95, adjustments = list(purpose = "single", popSize = Inf, decorrelation = "none", samplingDesign = "SRS"), preprocessfct = NULL, postprocessfct = NULL, clusterColumn = "" ) } \arguments{ \item{data}{Dataframe in wide format} \item{BSFactors}{The name of the columns containing the between-subject factor(s)} \item{WSFactors}{The name of the within-subject factor(s)} \item{WSDesign}{the within-subject design if not a full factorial design (default "fullfactorial")} \item{factorOrder}{Order of factors as shown in the graph (x axis, groups, horizontal panels, vertical panels)} \item{variables}{The dependent variable(s)} \item{statistic}{The summary statistic function to use} \item{errorbar}{The function that computes the error bar. Should be "CI" or "SE" or any function name. Defaults to "CI"} \item{gamma}{The coverage factor; necessary when errorbar == "CI". Default is 0.95.} \item{adjustments}{List of adjustments as described below. Default is \code{adjustments = list(purpose = "single", popSize = Inf, decorrelation = "none", samplingDesign = "SRS")}} \item{preprocessfct}{is a transform (or vector of) to be performed first on data matrix of each group} \item{postprocessfct}{is a transform (or vector of)} \item{clusterColumn}{used in conjunction with samplingDesign = "CRS", indicates which column contains the cluster membership} } \value{ a list with (1) the summary statistics in summaryStatistics (2) the raw data in long format in rawData (using numeric levels for repeated-measure variables). } \description{ The function \code{suberbData()} computes standard error or confidence interval for various descriptive statistics under various designs, sampling schemes, population size and purposes, according to the \code{suberb} framework. See \insertCite{cgh21}{superb} for more. } \details{ The possible adjustements are the following \itemize{ \item popsize: Size of the population under study. Defaults to Inf \item purpose: The purpose of the comparisons. Defaults to "single". Can be "single", "difference", or "tryon". \item decorrelation: Decorrelation method for repeated measure designs. Chooses among the methods "CM", "LM", "CA" or "none". Defaults to "none". \item samplingDesign: Sampling method to obtain the sample. implemented sampling is "SRS" (Simple Randomize Sampling) and "CRS" (Cluster-Randomized Sampling). } } \examples{ # Basic example using a built-in dataframe as data; # by default, the mean is computed and the error bar are 95\% confidence intervals # (it also produces a $rawData dataframe, not shown here) res <- superbData(ToothGrowth, BSFactors = c("dose", "supp"), variables = "len") res$summaryStatistics # Example introducing adjustments for pairwise comparisons # and assuming that the whole population is limited to 200 persons res <- superbData(ToothGrowth, BSFactors = c("dose", "supp"), variables = "len", statistic = "median", errorbar = "CI", gamma = .80, adjustments = list( purpose = "difference", popSize = 200) ) res$summaryStatistics } \references{ \insertAllCited{} }

3c209b647b775b7d0353fdf7e8f6e75ee5acb445

66955ee1c32bd7cc6fff3fbbd893558c19d7358c

/Data Prep Classification and Prediction Analyis v6.r

11227594e40e02d2ed5e31a7d2a269e16d13b36d

[ "MIT" ]

permissive

ericsgagnon/psustat897groupproject

84445c6187fd6e50164d435c2013060189055226

6d3e6fc5ce028a616f97469a8c4b5b6e94dd18a7

refs/heads/master

2021-01-01T20:21:52.704474

2017-08-07T02:45:55

98,821,979

1

0

null

UTF-8

R

false

22,444

r

Data Prep Classification and Prediction Analyis v6.r

# DATA SETUP charity = read.csv("E:/STAT 897/Group Project/charity.csv") charity.t = charity charity.t$avhv = log(charity.t$avhv) charity.t$agif = log(charity.t$agif) charity.t$inca = log(charity.t$inca) charity.t$incm = log(charity.t$incm) charity.t$lgif = log(charity.t$lgif) charity.t$rgif = log(charity.t$rgif) charity.t$tgif = log(charity.t$tgif) charity.t$tlag = log(charity.t$tlag) data.train = charity.t[charity$part=="train",] x.train = data.train[,2:21] c.train = data.train[,22] # donr n.train.c = length(c.train) # 3984 y.train = data.train[c.train==1,23] # damt for observations with donr=1 # x.train.mat = model.matrix(damt~., data.train[c.train==1,2:23])[,-22] n.train.y = length(y.train) # 1995 data.valid = charity.t[charity$part=="valid",] x.valid = data.valid[,2:21] c.valid = data.valid[,22] # donr n.valid.c = length(c.valid) # 2018 y.valid = data.valid[c.valid==1,23] # damt for observations with donr=1 #x.valid.mat = model.matrix(damt~., data.valid[c.valid==1,2:23])[,-22] n.valid.y = length(y.valid) # 999 data.test = charity.t[charity$part=="test",] n.test = dim(data.test)[1] # 2007 x.test = data.test[,2:21] x.train.mean = apply(x.train, 2, mean) x.train.sd = apply(x.train, 2, sd) x.train.std = t((t(x.train)-x.train.mean)/x.train.sd) # standardize to have zero mean and unit sd # x.train.mat.mean = apply(x.train.mat, 2, mean) # x.train.mat.sd = apply(x.train.mat, 2, sd) # x.train.mat.std = t((t(x.train.mat)-x.train.mat.mean)/x.train.mat.sd) # standardize to have zero mean and unit sd apply(x.train.std, 2, mean) # check zero mean apply(x.train.std, 2, sd) # check unit sd data.train.std.c = data.frame(x.train.std, donr=c.train) # to classify donr data.train.std.y = data.frame(x.train.std[c.train==1,], damt=y.train) # to predict damt when donr=1 x.valid.std = t((t(x.valid)-x.train.mean)/x.train.sd) # standardize using training mean and sd # x.valid.mat.std = t((t(x.valid.mat)-x.train.mat.mean)/x.train.mat.sd) # standardize to have zero mean and unit sd data.valid.std.c = data.frame(x.valid.std, donr=c.valid) # to classify donr data.valid.std.y = data.frame(x.valid.std[c.valid==1,], damt=y.valid) # to predict damt when donr=1 x.test.std = t((t(x.test)-x.train.mean)/x.train.sd) # standardize using training mean and sd data.test.std = data.frame(x.test.std) train.dat = data.frame(x = x.train.std, y = as.factor(data.train.std.c$donr)) valid.dat = data.frame(x = x.valid.std, y = as.factor(data.valid.std.c$donr)) #LIBRARIES library(MASS) library(tree) library(randomForest) library(gbm) library(e1071) library(lars) library(leaps) library(glmnet) library(pls) library(splines) library(gam) library(ISLR) library(magrittr) library(stringr) library(lubridate) library(dplyr) library(ggplot2) library(ggthemes) #EXPLORATORY DATA ANALYSIS # Helper Functions density_plots <- function( df , main = 'Density Plots') { df %>% gather( key = measure , value = value ) %>% ggplot( aes( value , color = measure , group = measure )) + geom_density() + facet_wrap( ~measure , scales = 'free' ) + ggtitle( label = main ) + theme_gdocs() + theme( plot.title = element_text( face = 'bold' , size = rel(1.5) , hjust = .5 ) ) } ## EDA Visuals ################################################################ p <- list() data <- charity %>% tbl_df cfg.predictors <- c( "reg1" , "reg2" , "reg3" , "reg4" , "home" , "chld" , "hinc" , "genf" , "wrat" , "avhv" , "incm" , "inca" , "plow" , "npro" , "tgif" , "lgif" , "rgif" , "tdon" , "tlag" , "agif" ) # raw data density plots p$density_raw <- { data %>% filter( part == 'train' ) %>% dplyr::select( avhv , npro , tgif , lgif , rgif , agif , tdon , tlag , incm , inca ) %>% density_plots( 'Density Plots of Raw Data' ) } # log-transformed density plots p$density_logtransformed <- { data %>% filter( part == 'train' ) %>% dplyr::select( avhv , npro , tgif , lgif , rgif , agif , tdon , tlag , incm , inca ) %>% mutate_all( log ) %>% density_plots( 'Density Plots of Log-Transformed Data' ) } # correlation plot p$correlations <- { data %>% filter( part == 'train' ) %>% select( cfg.predictors ) %>% select( -matches('reg') ) %>% cor() %>% round( 3 ) %>% ggcorrplot( ggtheme = theme_gdocs , legend.title = 'r' , hc.order = T , colors = c('red' , 'white' , 'green') , show.diag = F , outline.color = 'black' , lab = T , lab_col = 'black' , lab_size = rel(2.5) , tl.cex = 13 ) + ggtitle( 'Predictor Correlations' ) + theme( axis.ticks = element_blank() , panel.grid.major = element_blank() , panel.grid.minor = element_blank() , axis.text = element_text(face = 'bold', size = rel(3.5) , hjust = 0 ) , plot.title = element_text( face = 'bold' , size = rel(1.5) , hjust = .5 ) ) } # predictors to damt - previously identified predictors are log-transformed first p$predstodamt <- { data %>% filter( part == 'train' ) %>% filter( damt > 0 ) %>% mutate_at( vars( avhv , tgif , lgif , rgif , agif , tlag , incm , inca ) , log ) %>% select( cfg.predictors , damt , -matches('reg') , -home , -genf ) %>% gather( predictor , value , -damt ) %>% ggplot( aes( value , damt , color = predictor , group = predictor ) ) + geom_point() + geom_density2d( color = 'gray' ) + geom_smooth( method = 'loess' , color = 'black') + facet_wrap( ~ predictor , scales = 'free' ) + theme_gdocs() + theme( legend.position = 'none' ) } # save plots to png p %>% names %>% lapply( function(x){ ggsave( filename = paste0( 'plot-' , x , '.png' ) , plot = p[[x]] , path = './output' ) }) #CLASSIFICATION MODELS: LOGIT, LDA, TREES-BASED, AND SVM #LOGISTIC REGRESSION model.logit <- glm(y ~ x.reg1 + x.reg2 + x.reg3 + x.reg4 + x.home + x.chld + x.hinc + x.genf + x.wrat + x.avhv + x.incm + x.inca + x.plow + x.npro + x.tgif + x.lgif + x.rgif + x.tdon + x.tlag + x.agif, train.dat, family=binomial("logit")) post.valid.logit = predict(model.logit, valid.dat, type="response") # n.valid.c post probs profit.logit = cumsum(14.5*c.valid[order(post.valid.logit, decreasing=T)]-2) plot(profit.logit) # see how profits change as more mailings are made n.mail.valid = which.max(profit.logit) # number of mailings that maximizes profits c(n.mail.valid, max(profit.logit)) # report number of mailings and maximum profit cutoff.logit = sort(post.valid.logit, decreasing=T)[n.mail.valid+1] # set cutoff based on n.mail.valid chat.valid.logit = ifelse(post.valid.logit>cutoff.logit, 1, 0) # mail to everyone above the cutoff logit.table = table(chat.valid.logit, c.valid) # classification table logit.mail = sum(logit.table[2,]) logit.mail.tp = logit.table[2,2] logit.error = (logit.table[2,1]+logit.table[1,2])/2018 logit.profit = 14.5*logit.mail.tp - 2*logit.mail logit.error #validation error rate: 0.2215 logit.mail #total mailings: 1,406 logit.profit #total profit: $11,383.50 #LDA Model Base model.ldaB = lda(y ~ x.reg1 + x.reg2 + x.reg3 + x.reg4 + x.home + x.chld + x.hinc + x.genf + x.wrat + x.avhv + x.incm + x.inca + x.plow + x.npro + x.tgif + x.lgif + x.rgif + x.tdon + x.tlag + x.agif, train.dat) # include additional terms on the fly using I() post.valid.ldaB = predict(model.ldaB, valid.dat)$posterior[,2] # n.valid.c post probs profit.ldaB = cumsum(14.5*c.valid[order(post.valid.ldaB, decreasing=T)]-2) plot(profit.ldaB) # see how profits change as more mailings are made n.mail.valid = which.max(profit.ldaB) # number of mailings that maximizes profits c(n.mail.valid, max(profit.ldaB)) # report number of mailings and maximum profit cutoff.ldaB = sort(post.valid.ldaB, decreasing=T)[n.mail.valid+1] # set cutoff based on n.mail.valid chat.valid.ldaB = ifelse(post.valid.ldaB>cutoff.ldaB, 1, 0) # mail to everyone above the cutoff ldaB.table = table(chat.valid.ldaB, c.valid) # classification table ldaB.mail = sum(ldaB.table[2,]) ldaB.mail.tp = ldaB.table[2,2] ldaB.error = (ldaB.table[2,1]+ldaB.table[1,2])/2018 ldaB.profit = 14.5*ldaB.mail.tp - 2*ldaB.mail ldaB.error #validation error rate: 0.2235 ldaB.mail #total mailings: 1,406 ldaB.profit #total profit: $11,354.50 #LDA Model Base + Quad (hinc, chld, tdon, tlag, wrat, inca, npro, tgif) model.ldaF = lda(y ~ x.reg1 + x.reg2 + x.reg3 + x.reg4 + x.home + x.chld + x.hinc + x.genf + x.wrat + x.avhv + x.plow + x.npro + x.tgif + x.tdon + x.tlag + x.agif + I(x.hinc^2) + I(x.chld^2) + I(x.tdon^2) + I(x.tlag^2) + I(x.wrat^2) + I(x.npro^2) + I(x.tgif^2), train.dat) # include additional terms on the fly using I() post.valid.ldaF = predict(model.ldaF, valid.dat)$posterior[,2] # n.valid.c post probs profit.ldaF = cumsum(14.5*c.valid[order(post.valid.ldaF, decreasing=T)]-2) plot(profit.ldaF) # see how profits change as more mailings are made n.mail.valid = which.max(profit.ldaF) # number of mailings that maximizes profits c(n.mail.valid, max(profit.ldaF)) # report number of mailings and maximum profit cutoff.ldaF = sort(post.valid.ldaF, decreasing=T)[n.mail.valid+1] # set cutoff based on n.mail.valid chat.valid.ldaF = ifelse(post.valid.ldaF>cutoff.ldaF, 1, 0) # mail to everyone above the cutoff ldaF.table = table(chat.valid.ldaF, c.valid) # classification table ldaF.mail = sum(ldaF.table[2,]) ldaF.mail.tp = ldaF.table[2,2] ldaF.error = (ldaF.table[2,1]+ldaF.table[1,2])/2018 ldaF.profit = 14.5*ldaF.mail.tp - 2*ldaF.mail ldaF.error #validation error rate: 0.1457 ldaF.mail #total mailings: 1,267 ldaF.profit #total profit: $11,763.00 #CLASSIFICATION TREE, BASE MODEL model.treeB=tree(y~.,train.dat) summary(model.treeB) model.treeB tree.pred=predict(model.treeB,valid.dat,type="class") treeB.table = table(tree.pred,valid.dat$y) treeB.mail = sum(treeB.table[2,]) treeB.mail.tp = treeB.table[2,2] treeB.error = (treeB.table[2,1]+treeB.table[1,2])/2018 treeB.profit = 14.5*treeB.mail.tp - 2*treeB.mail treeB.error #validation error rate: 0.1516 treeB.mail #total mailings: 1,165 treeB.profit #total profit: $11,140.50 #CLASSIFICATION TREE, RANDOM FORESTS MODEL #THE BELOW LOOP TO DISCOVER WHICH SEED RETURNS MAXIMUM PROFIT (SET.SEED(53) RETURNED MAXIMUM) # mat = matrix(, ncol=4) # for(i in 1:100){ # iter = i # set.seed(i) # model.rf = randomForest(y~., train.dat, mtry = 4, importance = TRUE) # summary(model.rf) # model.rf # tree.pred=predict(model.rf, valid.dat, type = "class") # rf.table = table(tree.pred, valid.dat$y) # rf.mail = sum(rf.table[2,]) # rf.mail.tp = rf.table[2,2] # rf.error = (rf.table[2,1]+rf.table[1,2])/2018 # rf.profit = 14.5*rf.mail.tp - 2*rf.mail # vec = c(iter, rf.error, rf.mail, rf.profit) # mat = rbind(mat, vec) # # print(i) # # print(rf.error) #validation error rate: # # print(rf.mail) #total mailings: # # print(rf.profit) #total profit: $ # } set.seed(53) model.rf = randomForest(y~., train.dat, mtry = 4, importance = TRUE) summary(model.rf) model.rf tree.pred=predict(model.rf,valid.dat,type="class") rf.table = table(tree.pred,valid.dat$y) rf.mail = sum(rf.table[2,]) rf.mail.tp = rf.table[2,2] rf.error = (rf.table[2,1]+rf.table[1,2])/2018 rf.profit = 14.5*rf.mail.tp - 2*rf.mail rf.error #validation error rate: 0.1070 rf.mail #total mailings: 1,063 rf.profit #total profit: $11,257.50 #SVM CLASSIFICATION MODEL svm.train = svm(y ~ x.reg1 + x.reg2 + x.reg3 + x.reg4 + x.home + x.chld + x.hinc + x.genf + x.wrat + x.avhv + x.plow + x.npro + x.tgif + x.tdon + x.tlag + x.agif + I(x.hinc^2) + I(x.chld^2) + I(x.tdon^2) + I(x.tlag^2) + I(x.wrat^2) + I(x.npro^2) + I(x.tgif^2), data = train.dat, kernel = "linear", cost=10,scale = FALSE) summary(svm.train) set.seed(53) tune.charity = tune(svm, y ~ x.reg1 + x.reg2 + x.reg3 + x.reg4 + x.home + x.chld + x.hinc + x.genf + x.wrat + x.avhv + x.plow + x.npro + x.tgif + x.tdon + x.tlag + x.agif + I(x.hinc^2) + I(x.chld^2) + I(x.tdon^2) + I(x.tlag^2) + I(x.wrat^2) + I(x.npro^2) + I(x.tgif^2), data = train.dat, kernel = "linear", ranges = list(cost = c(0.001, 0.005, 0.01, 0.05, 0.1, 1, 5, 10))) summary(tune.charity) bestmod = tune.charity$best.model summary(bestmod) svm.pred = predict(bestmod, valid.dat) svm.table = table(predict = svm.pred, truth = valid.dat$y) svm.mail = sum(svm.table[2,]) svm.mail.tp = svm.table[2,2] svm.error = (svm.table[2,1]+svm.table[1,2])/2018 svm.profit = 14.5*svm.mail.tp - 2*svm.mail svm.error #validation error rate: 0.1115 svm.mail #total mailings: 1,072 svm.profit #total profit: $11,239.50 # PREDICTION MODELING # LEAST SQUARES REGRESSION #LEAST SQUARES MODEL (LM) model.ls3 = lm(damt ~ reg1 + reg2 + reg3 + reg4 + chld + hinc + wrat + I(wrat^2) + I(wrat^3) + I(wrat^4) + avhv + plow + npro + tgif + tdon + tlag + I(tlag^2) + agif, data.train.std.y) pred.valid.ls3 = predict(model.ls3, newdata = data.valid.std.y) # validation predictions ls3.mse = mean((y.valid - pred.valid.ls3)^2) # mean prediction error ls3.se = sd((y.valid - pred.valid.ls3)^2)/sqrt(n.valid.y) # std error ls3.mse ls3.se #BEST SUBSETS (BSS) bss.train.fit = regsubsets(damt ~ reg1 + reg2 + reg3 + reg4 + chld + hinc + wrat + I(wrat^2) + I(wrat^3) + I(wrat^4) + avhv + plow + npro + tgif + tdon + tlag + I(tlag^2) + agif, data=data.train.std.y, nvmax=17) which.min(summary(bss.train.fit)$bic) coef(bss.train.fit,which.min(summary(bss.train.fit)$bic)) predict.regsubsets = function(object, newdata, id, ...){ form = as.formula(object$call[[2]]) mat = model.matrix(form, newdata) coefi = coef(object, id = id) xvars = names(coefi) mat[, xvars]%*%coefi } bss.pred = predict.regsubsets(bss.train.fit, data.valid.std.y, which.min(summary(bss.train.fit)$bic)) bss.mse = mean((y.valid - bss.pred)^2) # mean prediction error bss.se = sd((y.valid - bss.pred)^2)/sqrt(n.valid.y) # std error bss.mse bss.se #BEST SUBSETS 10-FOLD VALIDATION (BSSF) k=10 set.seed(53) bssf.folds = sample(1:k,nrow(data.train.std.y),replace=TRUE) bssf.cv.errors = matrix(NA, k, 10, dimnames=list(NULL, paste(1:10))) for (j in 1:k){ bssf.train.fit=regsubsets(damt ~ reg1 + reg2 + reg3 + reg4 + chld + hinc + wrat + I(wrat^2) + I(wrat^3) + I(wrat^4) + avhv + plow + npro + tgif + tdon + tlag + I(tlag^2) + agif, data=data.train.std.y[bssf.folds!=j,], nvmax=17) for (i in 1:10){ bssf.pred = predict(bssf.train.fit, data.train.std.y[bssf.folds==j,], id=i) bssf.cv.errors[j, i] = mean((bssf.pred - data.train.std.y$damt[bssf.folds==j])^2) } } bssf.mcv.errors = apply(bssf.cv.errors, 2, mean) which.min(bssf.mcv.errors) #Returns the number of variables in the Cross-Validation Selected model coef(bssf.train.fit, which.min(bssf.mcv.errors)) #Returns the coefficients for the CV selected model bssf.pred = predict.regsubsets(bssf.train.fit, data.valid.std.y, which.min(bssf.mcv.errors)) #Predicts y using the CV Selected model bssf.mse = mean((y.valid - bssf.pred)^2) #Calculates test MSE for the CV Selected model bssf.se = sd((y.valid - bssf.pred)^2)/sqrt(n.valid.y) # std error bssf.mse bssf.se #RIDGE REGRESSION (RR) #make x matrix and y from data.train.std.y rr.train.x = model.matrix(damt ~ reg1 + reg2 + reg3 + reg4 + chld + hinc + wrat + I(wrat^2) + I(wrat^3) + I(wrat^4) + avhv + plow + npro + tgif + tdon + tlag + I(tlag^2) + agif, data.train.std.y)[,-21] rr.train.y = data.train.std.y$damt rr.valid.x = model.matrix(damt ~ reg1 + reg2 + reg3 + reg4 + chld + hinc + wrat + I(wrat^2) + I(wrat^3) + I(wrat^4) + avhv + plow + npro + tgif + tdon + tlag + I(tlag^2) + agif, data.valid.std.y)[,-21] rr.valid.y = data.valid.std.y$damt set.seed(53) rr.cv.out = cv.glmnet(rr.train.x, rr.train.y, alpha=0) rr.bestlam = rr.cv.out$lambda.min rr.bestlam rr.train.fit = glmnet(rr.train.x, rr.train.y, alpha=0, lambda=rr.bestlam) coef(rr.train.fit)[, 1] rr.pred = predict(rr.train.fit, s=rr.bestlam, newx=rr.valid.x) rr.mse = mean((y.valid - rr.pred)^2) # mean prediction error rr.se = sd((y.valid - rr.pred)^2)/sqrt(n.valid.y) # std error rr.mse rr.se #LASSO set.seed(53) lasso.cv.out = cv.glmnet(rr.train.x, rr.train.y, alpha=1) lasso.bestlam = lasso.cv.out$lambda.min lasso.bestlam lasso.train.fit = glmnet(rr.train.x, rr.train.y, alpha=1, lambda=lasso.bestlam) coef(lasso.train.fit)[, 1] lasso.pred = predict(lasso.train.fit, s=lasso.bestlam, newx=rr.valid.x) lasso.mse = mean((y.valid - lasso.pred)^2) lasso.se = sd((y.valid - lasso.pred)^2)/sqrt(n.valid.y) # std error lasso.mse lasso.se #PRINCIPAL COMPONANTS set.seed(53) pcr.train.fit = pcr(damt ~ reg1 + reg2 + reg3 + reg4 + chld + hinc + wrat + I(wrat^2) + I(wrat^3) + I(wrat^4) + avhv + plow + npro + tgif + tdon + tlag + I(tlag^2) + agif, data=data.train.std.y, scale=TRUE, validation="CV") validationplot(pcr.train.fit,val.type="MSEP") #PCR (M=10) pcr.pred = predict(pcr.train.fit, data.valid.std.y, ncomp=10) pcr.mse = mean((y.valid - pcr.pred)^2) # mean prediction error pcr.se = sd((y.valid - pcr.pred)^2)/sqrt(n.valid.y) # std error pcr.mse pcr.se #PCR (M=13) pcr.pred = predict(pcr.train.fit, data.valid.std.y, ncomp=13) pcr.mse = mean((y.valid - pcr.pred)^2) # mean prediction error pcr.se = sd((y.valid - pcr.pred)^2)/sqrt(n.valid.y) # std error pcr.mse pcr.se #PCR (M=16) pcr.pred = predict(pcr.train.fit, data.valid.std.y, ncomp=16) pcr.mse = mean((y.valid - pcr.pred)^2) # mean prediction error pcr.se = sd((y.valid - pcr.pred)^2)/sqrt(n.valid.y) # std error pcr.mse pcr.se #PARTIAL LEAST SQUARES set.seed(53) pls.train.fit = plsr(damt ~ reg1 + reg2 + reg3 + reg4 + chld + hinc + wrat + I(wrat^2) + I(wrat^3) + I(wrat^4) + avhv + plow + npro + tgif + tdon + tlag + I(tlag^2) + agif, data=data.train.std.y, scale=TRUE, validation="CV") validationplot(pls.train.fit,val.type="MSEP") #PLS (M=3) pls.pred = predict(pls.train.fit, data.valid.std.y, ncomp=3) pls.mse = mean((y.valid - pls.pred)^2) # mean prediction error pls.se = sd((y.valid - pls.pred)^2)/sqrt(n.valid.y) # std error pls.mse pls.se #PLS (M=7) pls.pred = predict(pls.train.fit, data.valid.std.y, ncomp=7) pls.mse = mean((y.valid - pls.pred)^2) # mean prediction error pls.se = sd((y.valid - pls.pred)^2)/sqrt(n.valid.y) # std error pls.mse pls.se #PLS (M=14) pls.pred = predict(pls.train.fit, data.valid.std.y, ncomp=14) pls.mse = mean((y.valid - pls.pred)^2) # mean prediction error pls.se = sd((y.valid - pls.pred)^2)/sqrt(n.valid.y) # std error pls.mse pls.se #GAM #GAM - removed correlated variables gam.train.fit3 = gam(damt ~ reg1 + reg2 + reg3 + reg4 + s(chld,3) + s(hinc, 3) + poly(wrat, 3) + s(avhv, 3) + s(plow, 4) + s(npro, 3) + s(tgif, 4) + s(tdon, 3) + poly(tlag, 2) + s(agif, 4), data=data.train.std.y) gam.pred = predict(gam.train.fit3, data.valid.std.y) gam.mse = mean((y.valid - gam.pred)^2) # mean prediction error gam.se = sd((y.valid - gam.pred)^2)/sqrt(n.valid.y) # std error gam.mse gam.se #MODEL LDAF FINAL CLASSIFICATION MODEL model.ldaF = lda(donr ~ reg1 + reg2 + reg3 + reg4 + home + chld + hinc + genf + wrat + avhv + plow + npro + tgif + tdon + tlag + agif + I(hinc^2) + I(chld^2) + I(tdon^2) + I(tlag^2) + I(wrat^2) + I(npro^2) + I(tgif^2), data.train.std.c) # include additional terms on the fly using I() n.mail.valid = which.max(profit.ldaF) tr.rate = .1 # typical response rate is .1 vr.rate = .5 # whereas validation response rate is .5 adj.test.1 = (n.mail.valid/n.valid.c)/(vr.rate/tr.rate) # adjustment for mail yes adj.test.0 = ((n.valid.c - n.mail.valid)/n.valid.c)/((1 - vr.rate)/(1 - tr.rate)) # adjustment for mail no adj.test = adj.test.1/(adj.test.1+adj.test.0) # scale into a proportion n.mail.test = round(n.test*adj.test, 0) # calculate number of mailings for test set post.test = predict(model.ldaF, data.test.std)$posterior[,2] # post probs for test data cutoff.test = sort(post.test, decreasing=T)[n.mail.test+1] # set cutoff based on n.mail.test chat.test = ifelse(post.test>cutoff.test, 1, 0) # mail to everyone above the cutoff table(chat.test) #GAM1 FINAL PREDICTION MODEL yhat.test = predict(gam.train.fit3, newdata = data.test.std) # test predictions #OUTPUT ip = data.frame(chat=chat.test, yhat=yhat.test) # data frame with two variables: chat and yhat write.csv(ip, file="E:/STAT 897/Group Project/Report/Report Elements/ip.csv", row.names=FALSE) # use group member initials for file name

d7b5bc79cab5aba56dec4e658055a5d0dd8900f6

13694ccdbebfa834e371d38baf6c649b48b92c35

/man/baltimore_map.Rd

b50520c6e9f5c243ebaf0c2325e0e3c053c0bc3b

[]

no_license

heike/cityshapes

56c1d16696af1bc0e47fdeb1bb67cc15e9c7a05d

75b1c327dd4067ec68c86b0ae736c68eef019db1

refs/heads/master

2020-03-09T16:11:46.264511

2018-04-10T05:39:29

128,878,324

1

0

null

UTF-8

R

false

true

870

rd

baltimore_map.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} \name{baltimore_map} \alias{baltimore_map} \title{Shape files for the neighborhoods of Baltimore} \format{A data frame with 4732 rows and 6 variables: \describe{ \item{OBJECTID}{identifier for each region} \item{long}{geographic longitude} \item{lat}{geographic latitude} \item{group}{grouping variable for the polygon} \item{order}{order of the rows} \item{region}{name of the region} }} \usage{ baltimore_map } \description{ A dataset consisting of shapefiles describing the neighborhoods of Baltimore. The shapefiles of the neighborhoods is available from the Baltimore City Open GIS Data \url{http://gis-baltimore.opendata.arcgis.com/} } \examples{ data(baltimore_map) ggplot(baltimore_map, aes(x = long, y = lat, group = group)) + geom_path() } \keyword{datasets}

5c07dc429fd1896b4c8e082d9c8daed77dc00c58

29585dff702209dd446c0ab52ceea046c58e384e

/cna/R/cna.r

0486c507742007cc0b4de8f64018181640ddb2b7

[]

no_license

ingted/R-Examples

825440ce468ce608c4d73e2af4c0a0213b81c0fe

d0917dbaf698cb8bc0789db0c3ab07453016eab9

refs/heads/master

2020-04-14T12:29:22.336088

2016-07-21T14:01:14

null

0

null

UTF-8

R

false

9,424

r

cna.r

cna <- function(x, ordering = NULL, strict = FALSE, con = 1, cov = 1, notcols = NULL, maxstep = 5, suff.only = FALSE, what="mac") { if (inherits(x, "truthTab")) {tt <- x} else {tt <- truthTab(x)} if ((! is.null(notcols)) && notcols == "all") { colnames(tt)<-chartr("qwertzuiopasdfghjklyxcvbnmQWERTZUIOPASDFGHJKLYXCVBNM","QWERTZUIOPASDFGHJKLYXCVBNMqwertzuiopasdfghjklyxcvbnm",colnames(tt)) for (i in 1:nrow(tt)) { for (j in 1:ncol(tt)) { if (tt[i,j]==0) {tt[i,j]<-1} else if (tt[i,j]==1) {tt[i,j]<-0} } } } else if (! is.null(notcols)) { for (i in 1:length(notcols)) { colnumber<-which( colnames(tt)==notcols[i]) colnames(tt)[colnumber]<-chartr("qwertzuiopasdfghjklyxcvbnmQWERTZUIOPASDFGHJKLYXCVBNM","QWERTZUIOPASDFGHJKLYXCVBNMqwertzuiopasdfghjklyxcvbnm",colnames(tt)[colnumber]) for (j in 1:nrow(tt)) { if (tt[j,colnumber]==0) {tt[j,colnumber]<-1} else if (tt[j,colnumber]==1) {tt[j,colnumber]<-0} } } } # if (inherits(x, "truthTab")) {x <- tt} x <-tt cl <- match.call() if (nrow(tt) <= 1) stop("Truth table must have at least two rows.") if (ncol(tt) < 2 || ncol(tt) > 26) stop("Truth table must have between 2 and 26 columns.") check.ordering(ordering, tt) if (maxstep < 1) suff.only <- FALSE f <- as.vector(attr(tt, "n")) sol <- vector("list", length(tt)) effect.names <- colnames(tt) names(sol) <- effect.names tt.logi <- as.data.frame(lapply(tt, "mode<-", "logical")) for (zname in effect.names){ z <- tt.logi[[zname]] xz <- tt.logi[potential.effects(tt, zname, ordering, strict = strict)] if (ncol(xz) == 0) next # Identify and minimize sufficient conditions # ------------------------------------------- xsuff <- sufficient(xz, z, f, con = con) # initiate list of minimally sufficient conditions min.suff.conditions <- vector("list", ncol(xz)) consist <- vector("list", ncol(xz)) step <- 1L if (nrow(xsuff) > 0) repeat{ if (step >= ncol(xz)){ min.suff.conditions[[step]] <- xsuff consist[[step]] <- attr(xsuff, "consistency") break } # reduce sufficient conditions from previous step by removing one component reduced <- lapply(seq_along(xsuff), function(i) xsuff[!is.na(xsuff[i]), -i, drop = FALSE]) reduced.df <- do.call(Rbind, reduced) reduced.unique <- unique(reduced.df) # Eliminate duplicates # determine the reduced conditions that are sufficient xsuff.new <- sufficient(xz, z, f, cond = reduced.unique, con = con, nms = names(xz)) # identify mimally suffcient conditions among the sufficient conditions from the previous step row.origin <- unlist(lapply(xsuff, function(x) which(!is.na(x))), use.names = FALSE) combinations.grouped <- split(as.integer(combine(reduced.df)), row.origin) is.min.suff <- sapply(combinations.grouped, function(x) !any(x %in% attr(xsuff.new, "which.sufficient"))) min.suff <- xsuff[is.min.suff, , drop = FALSE] # add to list min.suff.conditions[[step]] <- min.suff consist[[step]] <- attr(xsuff, "consistency")[is.min.suff] if (nrow(xsuff.new) == 0) break step <- step + 1L xsuff <- xsuff.new } minSuff <- do.call(rbind, min.suff.conditions) if (length(minSuff) == 0) next vc <- verify.conditions(minSuff, xz) msc <- data.frame(condition = label.conditions(minSuff), consistency = unlist(consist), coverage = apply(vc, 2, coverage, z, f), stringsAsFactors = FALSE) msc <- msc[order(-with(msc, consistency * coverage), msc$condition), ] sol[[zname]] <- list(msc = msc) # Identify and minimize necessary conditions # ------------------------------------------- # initiate list of atomic solution formulas if (!necessary(minSuff, xz, z, f, cov = cov) || suff.only) next cc <- vc[z, , drop = FALSE] fz <- f[z] sum.fz <- sum(fz) min.nec.conditions <- cov.min.nec <- cons.min.nec <- list() step <- 1L repeat{ CondInd <- t(combn(seq_len(ncol(cc)), step)) # eliminate conditions that can not be minimally necessary for (i in seq_along(min.nec.conditions)){ elimCond <- contains(CondInd, min.nec.conditions[[i]]) CondInd <- CondInd[!elimCond, , drop = FALSE] if (nrow(CondInd) == 0) break } cover <- apply(CondInd, 1, function(x) sum(fz[apply(cc[, x, drop = FALSE], 1, any)]) / sum.fz) nec <- which(cover >= cov) # consistency of necessary conditions necConds <- apply(CondInd[nec, , drop = FALSE], 1, function(nci) minSuff[nci, , drop = FALSE]) cons.necConds <- sapply(necConds, function(nc){ logvect <- apply(verify.conditions(nc, xz), 1, any) consistency(logvect, z, f) }) # check whether necessary conditions are also sufficient if (length(nec) && con < 1){ also.consistent <- cons.necConds >= con nec <- nec[also.consistent] cons.necConds <- cons.necConds[also.consistent] } if (length(nec)){ min.nec.conditions <- c(min.nec.conditions, list(CondInd[nec, , drop = FALSE])) cov.min.nec <- c(cov.min.nec, list(cover[nec])) cons.min.nec <- c(cons.min.nec, list(cons.necConds)) } if (step >= maxstep || step >= ncol(cc)) break step <- step + 1L } if (length(min.nec.conditions) > 0){ lbls <- label.conditions(minSuff) asf0 <- lapply(min.nec.conditions, function(x) apply(x, 1, function(r) paste(sort(lbls[r]), collapse = " + "))) sol.frame <- data.frame(condition = unlist(asf0), consistency = unlist(cons.min.nec), coverage = unlist(cov.min.nec), stringsAsFactors = FALSE) sol.frame <- sol.frame[order(-with(sol.frame, consistency * coverage), sol.frame$condition), ] rownames(sol.frame) <- NULL sol[[c(zname, "asf")]] <- sol.frame } } out <- structure(list(), class = "cna") out$call <- cl out$x <- x out$ordering <- ordering out$truthTab <- tt # names(sol) <- toupper(names(sol)) out$solution <- sol out$what <- what return(out) } # print method for class cna print.cna <- function(x, what=x$what , digits = 3, nsolutions = 5, row.names = FALSE, show.cases=FALSE, ...){ cat("--- Coincidence Analysis (CNA) ---\n") # cat("\nFunction call:\n", deparse(x$call), "\n", sep = "") what <- tolower(what) if (what == "all") whatl <- rep(TRUE, 4) else whatl <- !is.na(match(c("t", "m", "a", "c"), unlist(strsplit(what, "")))) names(whatl) <- c("t", "m", "a", "c") if (whatl["t"]){ cat("\nTruth table:\n") if (show.cases==TRUE) {print(x$truthTab,show.cases=TRUE)} else {print(x$truthTab)} } if (!is.null(x$ordering)) cat("\nCausal ordering", if(!is.null(x$call$strict) && eval(x$call$strict)) " (strict)", ":\n", do.call(paste, c(lapply(x$ordering, paste, collapse = ", "), sep = " < ")), "\n", sep = "") else cat("\nFactors:", paste(names(x$truthTab), collapse = ", "), "\n") if (whatl["m"]){ msc.df <- msc(x) cat("\nMinimally sufficient conditions:\n", "--------------------------------", sep = "") if (nrow(msc.df) == 0) cat("\n*none*\n") else for (msc1 in split(msc.df, msc.df$outcome)){ cat("\nOutcome ", msc1$outcome[1], ":\n", sep = "") if (short <- ((nsol <- nrow(msc1)) > nsolutions)) msc1 <- msc1[seq_len(nsolutions), , drop = FALSE] print(msc1[c("condition", "consistency", "coverage")], digits = digits, row.names = row.names, ...) if (short) cat(" ... (total no. of conditions: ", nsol, ")\n", sep = "") } } if (any(whatl[c("a", "c")])) asf.df <- asf(x) if (whatl["a"]){ cat("\nAtomic solution formulas:\n", "-------------------------", sep = "") if (nrow(asf.df) == 0) cat("\n*none*\n") else for (asf1 in split(asf.df, asf.df$outcome)){ cat("\nOutcome ", asf1$outcome[1], ":\n", sep = "") if (short <- ((nsol <- nrow(asf1)) > nsolutions)) asf1 <- asf1[seq_len(nsolutions), , drop = FALSE] print(asf1[c("condition", "consistency", "coverage")], digits = digits, row.names = row.names, ...) if (short) cat(" ... (total no. of formulas: ", nsol, ")\n", sep = "") } } if (whatl["c"]){ csf1 <- csf(asfx = asf.df) cat("\nComplex solution formulas:\n", "--------------------------\n", sep = "") if (nrow(csf1) == 0) cat("*none*\n") else { if (short <- ((nsol <- nrow(csf1)) > nsolutions)) csf1 <- csf1[seq_len(nsolutions), , drop = FALSE] print(csf1, digits = digits, row.names = row.names, ...) if (short) cat(" ... (total no. of formulas: ", nsol, ")\n", sep = "") } } invisible(x) }

fa59d7b8697ccb5e08543160c4e733e8fcaadfa8

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/docopulae/examples/nint_space.Rd.R

efc7f4273f35d36bfc4c1d7446accad0c708f3c0

[]

no_license

surayaaramli/typeRrh

d257ac8905c49123f4ccd4e377ee3dfc84d1636c

66e6996f31961bc8b9aafe1a6a6098327b66bf71

refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

null

0

null

UTF-8

R

false

407

r

nint_space.Rd.R

library(docopulae) ### Name: nint_space ### Title: Space ### Aliases: nint_space ### ** Examples s = nint_space(nint_gridDim(seq(1, 3, 0.9)), nint_scatDim(seq(2, 5, 0.8)), nint_intvDim(-Inf, Inf), nint_funcDim(function(x) nint_intvDim(0, x[1])), list(nint_gridDim(c(0, 10)), list(nint_intvDim(1, 7))) ) s

ab68ca5282c4f02160ecbc9327aacb490acb7f72

a1da88a19d3025b77df3edc4b3bcb55a90925ac7

/content/post/old/corona.R

a95dde01ae5f74bbb0f7e5f3d910f1a33771e928

[]

no_license

tormodb/academic_blog

d43e0b0d522d82ecd2ae7c71f9e36ffe6e40c07f

bf7a40f638a75af78a6e7bfac323d20effa0d1c4

refs/heads/master

2023-06-22T23:04:25.086287

2023-06-14T13:09:21

229,732,379

1

null

UTF-8

R

false

4,065

r

corona.R

library(tidyverse) library(rvest) library(xml2) today <- format(Sys.time(), "%a %b %d %X %Y") day <- unlist(strsplit(today, " "))[1] day <- recode(day, Mon = "mandag", Tue = "tirsdag", Wed = "onsdag", Thur = "torsdag", Fri = "fredag", Sat = "lordag", Sun = "sondag") mth <- unlist(strsplit(today, " "))[2] mth <- recode(mth, Mar = "mars", Apr = "april") date <- unlist(strsplit(today, " "))[3] year <- unlist(strsplit(today, " "))[5] url3 <- paste0("https://www.fhi.no/nyheter/2020/status-koronasmitte-", day, "-", date, ".-", mth, "-", year) url_res3 <- xml2::read_html(url3) frafhi <- url_res3 %>% html_nodes(".textual-block") %>% map_df(~{ tibble( # postal = html_node(.x, "span") %>% html_text(trim=TRUE), fhi = html_nodes(.x, "ul > li") %>% html_text(trim=TRUE) ) }) innlagt_fhi <- frafhi$fhi[1] %>% str_match_all("[0-9]+") %>% unlist %>% as.numeric intensiv_fhi <- frafhi$fhi[4] %>% str_match_all("[0-9]+") %>% unlist %>% as.numeric innl_num <- word(innlagt_fhi)[1] # innl_num <- as.numeric(paste0(innl_num[1], innl_num[2])) intens_num <- as.numeric(word(intensiv_fhi)[3]) # month <- fulldate <- format(Sys.time(), "%d.%m.%Y") fullmonth <- format(Sys.time(), "%m") fullyear <- format(Sys.time(), "%Y") infint_fhi <- as.data.frame(cbind(date, fullmonth, fullyear, innl_num, intens_num, fulldate)) write.table(infint_fhi, "/Users/st06810/Dropbox/UiB/blog/content/post/infint_fhi.csv", dec=".", quote=FALSE, append=T, sep="\t", col.names=FALSE) url2 <- paste0("https://www.helsedirektoratet.no/nyheter/oversikt-over-innlagte-pasienter-med-covid-19-per-", format(Sys.Date(), "%d"),".mars") url_res2 <- xml2::read_html(url2) url_res2 %>% html_text() hdir <- url_res2 %>% html_nodes(".b-article-intro__intro") %>% html_text() day_hdir <- unlist(strsplit(hdir, " "))[3] day_hdir <- gsub('[[:punct:] ]+','', day_hdir) mth_hdir <- unlist(strsplit(hdir, " "))[4] mth_hdir <- ifelse(mth_hdir=="mars", "03", ifelse(mth_hdir=="april", "04", ifelse(mth_hdir=="mai", "05", ifels(mth_hdir=="juni", "06", NA)))) n_innl_hdir <- unlist(strsplit(hdir, " "))[6] n_int_hdir <- unlist(strsplit(hdir, " "))[15] # This function is supposed to replace NULL values with missing values in case the script is used # before the website is updated. Not working properly yet... if (is.null(day_hdir) && is.null(day_hdir && is.null(mth_hdir) && is.null(n_innl_hdir) && is.null(n_int_hdir))){ day_hdir <- NA mth_hdir <- NA n_innl_hdir <- NA n_int_hdir <- NA return(day_hdir) return(mth_hdir) return(n_innl_hdir) return(n_int_hdir) } # day_hdir # mth_hdir # n_innl_hdir # n_int_hdir year_hdir <- 2020 date <- (paste(day_hdir, mth_hdir, year_hdir, sep = ".")) innl_date <- as.data.frame(cbind(day_hdir, mth_hdir, year_hdir, n_innl_hdir, n_int_hdir, date)) write.table(innl_date, "/Users/st06810/Dropbox/UiB/blog/content/post/innl_date.csv", dec=".", quote=FALSE, append=T, sep="\t", col.names=FALSE) url <- "https://www.fhi.no/sv/smittsomme-sykdommer/corona/" url_res <- xml2::read_html(url) n_inf <- url_res %>% html_nodes(".fhi-key-figure-number") %>% .[[2]] %>% html_text() n_inf <- as.numeric(n_inf) date_txt <- url_res %>% html_nodes(".fhi-key-figure-desc") %>% .[[2]] %>% html_text() day_tmp <- unlist(strsplit(date_txt, " "))[7] mth_tmp <- unlist(strsplit(date_txt, " "))[8] day <- gsub('[[:punct:] ]+','',day_tmp) day <- as.numeric(day) mth <- ifelse(mth_tmp=="mars", "03", ifelse(mth_tmp=="april", "04", ifelse(mth_tmp=="mai", "05", ifels(mth_tmp=="juni", "06", NA)))) year <- 2020 date <- (paste(day, mth, year, sep = ".")) mth <- as.numeric(mth) inf_date <- as.data.frame(cbind(day, mth, year, n_inf, date, day_tmp, mth_tmp)) write.table(inf_date, "/Users/st06810/Dropbox/UiB/blog/content/post/inf_date.csv", dec=".", quote=FALSE, append=T, sep="\t", col.names=FALSE)

b9c90eeebd3b012fa865f124214901f98f34fccb

bc12a6667b3d98685b5e3cc4abbfb186b969d968

/R/utils_update_grants_db.R

67267613ffa95f671e6a195c2d8e8c65176c2ece

[ "MIT" ]

permissive

include-dcc/pub-include-r

16d28c5c5931f6f4427d44191d7b1a59cd3c2f31

8074939a074ccbb32f277c092120ce7303e882f3

refs/heads/main

2023-06-17T08:55:19.614567

2021-07-22T00:59:01

384,605,785

0

null

UTF-8

R

false

1,484

r

utils_update_grants_db.R

#' update_grants_db #' #' @description A utils function #' @importFrom magrittr %>% #' @return The return value, if any, from executing the utility. #' #' @noRd update_grants_db <- function() { .fetch_grants_table() %>% .filter_grants() %>% .format_grants() } .fetch_grants_table <- function() { sheets_id <- "17dWU0Vh_fR0Kg_tBsCAYfGBt5UdacnzZr3mlUzPay3g" googlesheets4::read_sheet( sheets_id, col_types = "cccccciDccicciiccDDc?iccicicddicccccccDDiciiiciiicc" ) } .filter_grants <- function(grants_tbl) { grants_tbl %>% filter(`INCLUDE Project?` == "Yes") } .format_grants <- function(grants_tbl) { grants_tbl %>% dplyr::mutate( grant_regex = stringr::str_glue("(?<={Activity})\\w+"), `Grant Number` = stringr::str_extract(`Project Number`, grant_regex) ) %>% dplyr::select(`Project Title`, `Administering IC`, `Application ID`, `Award Notice Date`, FOA, `Project Number`, `Grant Number`, Type, Activity, IC, `Serial Number`, `Project Start Date`, `Project End Date`, `Contact PI Person ID`, `Contact PI / Project Leader`, `Organization Name`, `Organization ID (IPF)`) %>% dplyr::distinct() %>% janitor::clean_names() }

d6779eea7f6cd3acef6f5e9e9c05bd2cb29b0434

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/SimInf/examples/plot.Rd.R

7904e17f4422dcf38e070dec2f29ff4f04ce6364

[]

no_license

surayaaramli/typeRrh

d257ac8905c49123f4ccd4e377ee3dfc84d1636c

66e6996f31961bc8b9aafe1a6a6098327b66bf71

refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

null

0

null

UTF-8

R

false

1,440

r

plot.Rd.R

library(SimInf) ### Name: plot,SimInf_model-method ### Title: Display the outcome from a simulated trajectory ### Aliases: plot,SimInf_model-method ### ** Examples ## Not run: ##D ## Create an 'SIR' model with 100 nodes and initialise ##D ## it with 990 susceptible individuals and 10 infected ##D ## individuals in each node. Run the model over 100 days. ##D model <- SIR(u0 = data.frame(S = rep(990, 100), ##D I = rep(10, 100), ##D R = rep(0, 100)), ##D tspan = 1:100, ##D beta = 0.16, ##D gamma = 0.077) ##D ##D ## Run the model and save the result. ##D result <- run(model) ##D ##D ## Plot the median and interquartile range of the number ##D ## of susceptible, infected and recovered individuals. ##D plot(result) ##D ##D ## Plot the median and the middle 95% quantile range of the ##D ## number of susceptible, infected and recovered individuals. ##D plot(result, range = 0.95) ##D ##D ## Plot the median and interquartile range of the number ##D ## of infected individuals. ##D plot(result, compartments = "I") ##D ##D ## Plot the number of susceptible, infected ##D ## and recovered individuals in the first ##D ## three nodes. ##D plot(result, node = 1:3, range = FALSE) ##D ##D ## Plot the number of infected individuals in the first node. ##D plot(result, compartments = "I", node = 1, range = FALSE) ## End(Not run)

9f951474bdce4d39dbc8021330fa87884d2fa2b4

1827dfb1a8868ec2cfb71fa68e15e2c0f301407d

/R/internals.R

d8e5e57b9525e34e2f7516e3145bad118934f3f9

[]

no_license

pssguy/rwunderground

4541546bcc38f93deca6b90880d67c8b10237248

d86429c111e948bd5a97dbb302e90a59c04f75a8

refs/heads/master

2021-07-13T01:56:52.887387

2017-10-19T16:41:48

107,566,640

0

null

2017-10-19T15:44:39

null

UTF-8

R

false

3,627

r

internals.R

##### # Internal pacakage functions for URL handling and data.frame formatting ##### #' Base URL for wunderground API #' #' @return base wunderground URL #' base_url <- function() { return("http://api.wunderground.com/api") } #' Build wunderground request URL #' #' @param key wunderground API key #' @param request_type request type TODO::list all request_types #' @param date Date, only applicable for history requests #' @param location location set by set_location #' build_url <- function(key = get_api_key(), request_type, date, location) { location <- paste0(location, ".json") # check if request_type supports adding in a date if (!is.null(date) & !(request_type %in% c("history", "planner"))) { warning("Ignoring date as it is not used in this request.") } else if (!is.null(date) & (request_type %in% c("history", "planner"))) { request_type <- paste(request_type, date, sep = "_") } URL <- paste(base_url(), key, request_type, "q", location, sep = "/") return(URL) } #' Detect and stop for any wunderground request errors #' #' @param httr_parsed_req httr request object #' stop_for_error <- function(httr_parsed_req) { if (is.null(httr_parsed_req$response)) { stop("Unknown error: Server failed to provide response status") } if (is.null(httr_parsed_req$response$error)) { return(invisible(TRUE)) } else { type <- httr_parsed_req$response$error$type description <- httr_parsed_req$response$error$description stop(paste0("Error from server:: ", type, " - ", description)) } } #' wunderground api requests #' #' @param request_type Request type TODO::list all types #' @param location locations set of set_location #' @param date Date, only applicable for history requests #' @param key wunderground API key #' @param message if TRUE print out requested #' @return httr request object #' wunderground_request <- function(request_type, location, date = NULL, key = get_api_key(), message = TRUE) { URL <- build_url( key = key, request_type = request_type, date = date, location = location ) if (request_type == "currenthurricane") URL <- gsub("/q", "", URL) req <- httr::GET(URL) httr::stop_for_status(req) parsed_req <- httr::content(req, type = "application/json") if (message) { print(paste0("Requesting: ", URL)) } parsed_req } #' Processes data.frames and replaces wunderground's -9999/-999 to NAs #' #' @param df the data.frame to process #' @return data.frame with correctly encoded NAs #' encode_NA <- function(df) { df[df == -9999] <- NA df[df == -999] <- NA df[df == -99] <- NA df } #' as.numeric with special handling for length 0 (NULL) objects #' #' @param x the object to cast as numeric #' @return value of type double #' as.numeric.nonempty <- function(x) { ifelse(length(x)>0, as.numeric(x), NA_real_) } #' return object, or NA for length 0 (NULL) objects #' #' @param x the object to cast as numeric #' @return value of type double #' nonempty <- function(x) { ifelse(length(x)>0, x, NA) } #' Check if a variable exists for a PWS. If not set the value to -9999 #' #' @param x the value to check #' @param class a character given the desired class for the variable measurement_exists <- function(x, class = "numeric") { val <- ifelse(is.null(x), -9999, x) do.call(paste0("as.", class), list(val)) }

524d8b9a3f329d4d2b0b08901744055a89e55526

2a0d1fc07d673b8c7cf07c6596dac2630ae3fd7c

/scripts/individualmodelTMB.R

f3098a9965fdd5ece6bdc7c0682fa48dd246ee07

[]

no_license

stelmacm/WNS

aef0320ac63d789590cc229e7f24e2fe248c036d

a455ce91c547c8174444d8fb811b4236c2ae38f3

refs/heads/master

2022-04-29T11:23:05.577810

2022-03-04T14:31:02

241,282,675

0

null

UTF-8

R

false

1,604

r

individualmodelTMB.R

#This will be the R portion of the model TMB #Staring off with individual level because I dont know how to carry lists into TMB source("modeltesting/creatingfunctiondf.R") #This is what would be needed to run in compile("scripts/fullmodelTMB2.cpp") dyn.load(dynlib("scripts/fullmodelTMB")) set.seed(123) #SM = list of matrices that are shared users per year #dist = matrix distances between 2 counties (counties in alphabetical order) #countyincidence= list of matrices(technically vectors) that contain county incidence per year #Dim = number of counties in matrices #number of years = years including 2006 which gets removed in program #unifectedcounty = uninfected counties (2006 removed) #yearindicator = vector of factored years for the mixed model(2006 and years a county is infected are removed) #countyindicator = vector of factored counties for the mixed model data <- list(SM = bigsharedusers, dist = orderedmat, countyincidence = countylist, dim = 548, numberofyears = 13, uninfectedcounty = uninfectedcountylist, yearindicator = years, countyindicator = counties, incidence = incidence) #d = scaling param #theta = azzalini power #rho = proportion of shared users compared to distance matrix #a = offset parameter (so that log 0 doesn't happen) parameters <- list(d = 10, theta = 1.5, rho = .5, a = 0.001, Random_vectorone= random_vec, Random_vectortwo = random_vec2) obj <- MakeADFun(data, parameters, DLL="individualmodelTMB") obj$hessian <- TRUE opt <- do.call("optim", obj) opt opt$hessian ## <-- FD hessian from optim obj$he() ## <-- Analytical hessian sdreport(obj)

1cd42db6770567d852b1547833f9a84208b7de91

191d276ade533e816e9db403e766f01cf0894315

/man/wilcox_test.Rd

ed8582b0e039b990f1b978aafd2c957303a01223

[]

no_license

gitronald/htester

b5132778c4b7aa6f09bb351782e08206bcad222a

7aac3653461ec2d1928dbad3cee4f6daa389fd73

refs/heads/master

2021-01-16T21:46:08.742779

2016-07-23T01:36:10

62,968,156

0

null

UTF-8

R

false

true

792

rd

wilcox_test.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/wilcox_test.R \name{wilcox_test} \alias{wilcox_test} \title{Wilcoxon Rank Sum and Signed Rank Tests} \usage{ wilcox_test(var1, var2, round = TRUE, ...) } \arguments{ \item{var1}{a vector to correlate with var2} \item{var2}{a vector to correlate with var1} \item{round}{logical, whether or not to round the results} \item{method}{the correlation method to use, either pearson, spearman, or kendall} } \value{ Returns a data.frame version of the standard htest output. See \code{ks.test} for additional arguments and further detail } \description{ A wrapper for \code{\link[stats]{wilcox.test}} that returns htest data in a data.frame rather than a list } \examples{ wilcox_test(rnorm(10, 5), rnorm(10, 5)) }

81a0906d7cbd2c441b7614798edc4535690b585e

ff708723a712c8e3cba19dc783031be098cd0c3a

/04_Model.Testing/02_Jackknife_hake_all_stages_ROMS_PPC_for_GitHub.R

115b1223e95327c3511efed41d5f909eb7886eaf

[]

no_license

pacific-hake/recruitment-index

430f66e8e4b55175421ed5b9c896cfecf25e73c4

ac05ffe31b7e51124b2846c30e9b2dde7c27fcf3

refs/heads/main

2023-04-27T05:01:45.670796

2023-04-20T04:23:11

2023-04-20T04:25:27

366,157,268

0

null

UTF-8

R

false

2,464

r

02_Jackknife_hake_all_stages_ROMS_PPC_for_GitHub.R

# jackknife the best fit model. drop one year of data and re-fit best-fit model # source(paste0(main,'/00_BestModelInfo.R')) MainFile = "/Users/cdvestfals/Desktop/Hake_Analysis_for_GitHub" setwd(paste0(MainFile,'/04_Model.Testing/02_jackknife')) library(MuMIn) ######### need to set parms here if I in parms list ################### # rename data data_1 = df0 # current = getwd() # FileDir = paste0(current,'/Standardized') # dir.create(FileDir) # setwd(FileDir) for(z in 1:2){ if(z==1){data_1 = df0; suffix = 'raw'} if(z==2){ # set to run standardized results std = apply(data_1[,parms2],2, FUN=scale) data_1[,parms2] = std suffix = "standardized" } # get fitted model for comparison #### # add to file below ### fm = lm(form_best, data=data_1) sfm = summary(fm) fmcoef = t(data.frame(sfm$coefficients[,1])) fitted.model = data.frame(cbind( sfm$r.squared, sfm$adj.r.squared, sfm$fstatistic[1], sfm$fstatistic[2], sfm$fstatistic[3], fmcoef)) for(k in 1:nrow(data_1)) {# refit model 30 times dropping one year each time # drop one datum and run data_2 = data_1[-k,] print(k) fit = lm(form_best, data=data_2, na.action = na.fail) s1 = summary(fit) Coeffs = t(data.frame(s1$coefficients[,1])) r1 = data.frame(cbind(s1$r.squared, s1$adj.r.squared,s1$fstatistic[1],s1$fstatistic[2],s1$fstatistic[3], Coeffs)) if(k == 1){results = r1}else{results = rbind(results,r1)} } # end k loop colnames(results)[1:6] = c('r2','adjr2','F','df1','df2', 'Intercept') results$p = 1-pf(results$F,results$df1, results$df2) write.table(results,paste0('R_jackknife_hake_all_stages_ROMS_PPC_',suffix,'.csv'), sep=',',col.names = TRUE, row.names = FALSE) # uses same code as bootstrap version, but not bootstrping # get mean and 95% CLs mn = apply(results,2,mean) md = apply(results,2,median) # quantile function qt = function(x){quantile(x,c(0.025,0.975))} ci = apply(results,2, qt) boot.stats = rbind(mn,md, ci) fitted.model$p = NA colnames(fitted.model) <- colnames(boot.stats) fitted.model$p = 1-pf(fitted.model$F,fitted.model$df1, fitted.model$df2) boot.stats2 = data.frame(rbind(fitted.model,boot.stats)) x = rownames(boot.stats2) x[1] <- "fitted model" boot.results <- cbind(x,boot.stats2) boot.final = boot.results #rbind(boot.results,CL2) write.table(boot.final,paste0('R_jackknife_stats_all_stages_ROMS_PPC_',suffix,'.csv'), sep=',',col.names = TRUE, row.names = FALSE) boot.final } setwd(MainFile)

0e72cab68593c59444e8312cf65db2d24faedeea

992a8fd483f1b800f3ccac44692a3dd3cef1217c

/Project_bioinformatics/Bra.WGT.paper/SweeD.annotation/2unique.SweeD.R

b1e70acac25a890acc32ed40ccd516152b255f72

[]

no_license

xinshuaiqi/My_Scripts

c776444db3c1f083824edd7cc9a3fd732764b869

ff9d5e38d1c2a96d116e2026a88639df0f8298d2

refs/heads/master

2020-03-17T02:44:40.183425

2018-10-29T16:07:29

133,203,411

3

1

null

UTF-8

R

false

1,891

r

2unique.SweeD.R

getwd() setwd("C:/Users/qxs/Desktop") ch<-read.table("145ch.chr1-10.outlier_genes.anno",sep="_") ch nrow(ch) ch[,2] length(ch[,2]) Uch=unique(ch[,2]) length(unique(ch[,2])) write.table(unique(ch[,2]), "145ch.chr1-10.outlier_genes.anno.unique", quote=F,row.names=F,col.names=F) pk<-read.table("145pk.chr1-10.outlier_genes.anno",sep="_") pk nrow(pk) pk[,2] length(pk[,2]) Upk=unique(pk[,2]) length(unique(pk[,2])) write.table(unique(pk[,2]), "145pk.chr1-10.outlier_genes.anno.unique", quote=F,row.names=F,col.names=F) tril<-read.table("145tril.chr1-10.outlier_genes.anno",sep="_") tril nrow(tril) tril[,2] length(tril[,2]) Utril=unique(tril[,2]) length(unique(tril[,2])) write.table(unique(tril[,2]), "145tril.chr1-10.outlier_genes.anno.unique", quote=F,row.names=F,col.names=F) sylv<-read.table("145sylv.chr1-10.outlier_genes.anno",sep="_") sylv nrow(sylv) sylv[,2] length(sylv[,2]) Usylv=unique(sylv[,2]) length(unique(sylv[,2])) write.table(unique(sylv[,2]), "145sylv.chr1-10.outlier_genes.anno.unique", quote=F,row.names=F,col.names=F) EUCA<-read.table("EUCA.chr1-10.outlier_genes.anno",sep="_") EUCA nrow(EUCA) EUCA[,2] length(EUCA[,2]) UEUCA=unique(EUCA[,2]) length(unique(EUCA[,2])) write.table(unique(EUCA[,2]), "EUCA.chr1-10.outlier_genes.anno.unique", quote=F,row.names=F,col.names=F) #Venn library(gplots) venn(list(Upk,Uch,Utril,Usylv,UEUCA)) venn(list(Upk,Uch,Utril,Usylv,UEUCA)) # REF: https://cran.r-project.org/web/packages/gplots/vignettes/venn.pdf U4=c(as.character(Uch),as.character(Upk),as.character(Utril),as.character(Usylv)) # 5669 U4=unique(U4) # 4674 write.table(unique(U4), "U4.chr1-10.outlier_genes.anno.unique", quote=F,row.names=F,col.names=F) UEUCA=as.character(UEUCA) #2058 venn(list(Upk,Uch,Utril,Usylv))

5de4062e4f790df58b5937628744d59066736c10

f37b0e4bd5854edd93c0acea3d99761469d52247

/analysis/data_infrastructures/use-bigrquery/example-select.r

267b3145896df9252bcdc9037c4ae9f3c558c4be

[]

no_license

metacommunities/metacommunities

a80cbba48025c7f5dbbd7519771352cf2845d31e

242de413ea47224f1abf372d794d61aa40f582bf

refs/heads/master

2020-04-05T02:04:52.294053

2018-01-17T15:26:18

10,381,192

1

0

null

UTF-8

R

false

329

r

example-select.r

library(bigrquery) library(Rook) library(tools) library(brew) library(rjson) billing_project <- "237471208995" sql <- " SELECT repo, repolinkDistinctQuestions AS question, repolinkAnswers AS answers FROM repos_linked_to_from_SO " dat <- query_exec("metacommunities", "github_explore", sql, billing=billing_project)

018f5f46900a6f9994db5fd8626d6db8e58ce1f2

157b2dc0d0b7c98ece7fd04759ca43c8638707c0

/_fnc/tidy_summary.R

2216a9348e67151905e077d4d9535b6bf4532606

[]

no_license

eugejoh/PAHO_dengue

14d9c5d9b273c5975fc9bd4a6ec551ee3858a512

c4016f828fcd81edfd4b341c881d86caf4c69f97

refs/heads/master

2020-03-09T08:19:43.096503

2018-09-19T23:17:40

128,686,621

1

0

null

UTF-8

R

false

1,146

r

tidy_summary.R

#' Tidy Summary Statistics #' #' This function outputs a tidy data frame of summary statistics, utilizing the base summary functions #' `mean`, `median`, `min`, `max`, `quantile`, `sd`. This function selects only columns that `is.numeric = TRUE`. #' This function ignores missing values `na.rm = TRUE`. #' #' @param input a `data.frame` object #' #' @return a `data.frame` object with input variables as the first column and respective summary statistics are columns #' @export #' #' @examples #' #' data(airquality) #' tidy_summary(airquality) #' tidy_summary <- function(input) { input %>% select_if(is.numeric) %>% summarise_all(funs( min = min(., na.rm = TRUE), q25 = quantile(., 0.25, na.rm = TRUE), mean = mean(., na.rm = TRUE), median = median(., na.rm = TRUE), q75 = quantile(., 0.75, na.rm = TRUE), max = max(., na.rm = TRUE), sd = sd(., na.rm = TRUE), missing = sum(is.na(.)) )) %>% gather(stat, val) %>% separate(stat, into = c("var", "stat"), sep = "_") %>% spread(stat, val) %>% select(variable = var, min, q25, mean, median, q75, max, sd, missing) }

18fbbf9fc4afca9d95e04484c90a0d92b962de0c

fa703db3c7f0621c2c2656d47aec2364eb51a6d8

/4_ss21/r/P01-1.R

ecda29a3821195dbcf50dde6dfc5c371531d339c

[]

no_license

JosuaKugler/uni

f49b8e0d246d031c0feb81705f763859446f9b0f

7f6ae93a5ef180554c463b624ea79e5fbc485d31

refs/heads/master

2023-08-15T01:57:53.927548

2023-07-22T10:42:37

247,952,933

0

null

UTF-8

R

false

132

r

P01-1.R

# Christian Merten, Josua Kugler x <- c(1.3, .5, 42, -8e-5) x[1:4] <- x[4:1] x[x < 1] <- -2 sum(x^2) q <- 1:100 q[q %%2 == 0] <- 0

766abd13a2d7ca121f1bb04b225ab82d62acc700

40c196ea90dbab156db44d9053688d58a90b2800

/01_source/03_facebook.R

e04aceec47e87f136df89d8dcdd771b410a44265

[]

no_license

robcortesl/social_media

678b04ffb3e8678adc0ccdfaddc53e83fb5aca4b

945dde13ba21d026cf3682b313e06a2d06d3cf5e

refs/heads/master

2021-07-24T07:00:51.228101

2017-10-31T21:37:53

null

0

null

UTF-8

R

false

780

r

03_facebook.R

# Facebook install.packages("Rfacebook") library(Rfacebook) # a traves de token temporal (dar de alta a cuales servicios puede acceder) token <- "EAACEdEose0cBAB5NouC14dtF13Dv93oWG3RX3eEyZCHslrPGnB5Ly9ZA6LzZB07req3gaU0nZCnsqNIR2xKrA14NpfOaZBUz6kdcbTlcy0byyseMZCR1BiDMFuZCvKMMjnXM4wXZC417tYki8Xl8JroPVQvv1qgljFo1KZBJmtLZCrLV8VlrjxP8tVGLHVSIL91HUiPJltlFueZCwZDZD" me <- getUsers("me", token, private_info=TRUE) me getUsers("barackobama", token, private_info=TRUE) getUsers(c("barackobama", "donaldtrump"), token) myfriends <- getFriends(token, simplify = TRUE) myfriends my_friends <- getFriends(token, simplify = FALSE) my_friends my_friends_info <- getUsers(my_friends$id, token, private_info = TRUE) my_friends_info mat <- getNetwork(token, format = "adj.matrix") mat

58df2b5b449c7d24bf52fc4082482000c7feec19

3e9f1994f0c73173fc9f7123d4c0096cae76cb15

/Generate numbers which follow normal distribution.R

760da700ba77a9398f3211bb4d1ec960767cf104

[]

no_license

SAUVIK/Start-R-1

093a13651e379c0b105a1a83327ab4f253fd54a3

b9cce6bf85e3b1149731bfe2ba143553cba09f9a

refs/heads/master

2021-01-23T17:37:56.736073

2017-09-07T18:15:15

102,770,413

1

0

null

UTF-8

R

false

224

r

Generate numbers which follow normal distribution.R

#Generate numbers which follow a normal distribution noraml_distribution_numbers <- rnorm(1000) # default mean and standard deviation os 0 and 1 respectively mean(noraml_distribution_numbers) sd(noraml_distribution_numbers)

96a380cdda18347c333208edc76f9cd5cdd14af4

9a430b05c1e8cd124be0d0323b796d5527bc605c

/wsim.io/man/read_dimension_values.Rd

9042272236c50efe8eff519bac634aaed1537ae1

[ "Apache-2.0" ]

permissive

isciences/wsim

20bd8c83c588624f5ebd8f61ee5d9d8b5c1261e6

a690138d84872dcd853d2248aebe5c05987487c2

refs/heads/master

2023-08-22T15:56:46.936967

2023-06-07T16:35:16

135,628,518

8

0

null

UTF-8

R

false

true

741

rd

read_dimension_values.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/read_dimension_values.R \name{read_dimension_values} \alias{read_dimension_values} \title{Get the values of the dimensions associated with a variable in a netCDF file} \usage{ read_dimension_values(vardef, exclude.dims = NULL, exclude.degenerate = FALSE) } \arguments{ \item{vardef}{filename/variable to check} \item{exclude.dims}{dimensions to ignore in output} \item{exclude.degenerate}{if \code{TRUE}, do not return dimensions having length <= 1 in output} } \value{ a list with names representing dimension names and values representing dimension values } \description{ Get the values of the dimensions associated with a variable in a netCDF file }

11e8c249ed3336686b9fd756a3112e62a50a06de

3ef4654c876937535cd5a19f3a3b6b9796e1a7c5

/global.r

877da28f81f852883e7e6940907c2921b1776b17

[]

no_license

yifanStat/metrics_app

3c6a60b9432c18f6878d11fd52c09de38eb93514

847b6e423ead7d0151db78ad3e55cb729d1b4a4d

refs/heads/master

2021-07-17T00:15:30.290567

2017-10-23T04:19:35

107,930,504

0

null

UTF-8

R

false

150

r

global.r

library(shiny) source("src/shine_d.r", echo = FALSE, max.deparse.length = 1000L) source("src/shine_h.r", echo = FALSE, max.deparse.length = 1000L)

3fa091d28f9c037a094ccab4a5d4a9a43e057e3f

cff4412c81ece904c62c68df4b081583a8f17270

/Linear Model Selection and Prediction.R

bc9c5ae77860d54329bd3563b8d8950345999dbe

[]

no_license

ArvindPawar08/Intermediate-Analytics

a78b20d341d20604c9da6bcc21d2e53bc9787f82

227ddb375d7c7fdc20b4b8cd20d679564118e3b5

refs/heads/master

2020-05-05T10:04:27.940109

2019-04-07T07:00:55

179,929,240

0

null

UTF-8

R

false

3,791

r

Linear Model Selection and Prediction.R

#part1 setwd("C:/Users/Arvind/Desktop/Intermediate Analytics/Week 3") a3 <- read.csv("assignment3.csv") a3 set.seed(12345) train <- floor(0.75*nrow(a3)) train_ind <-sample(seq_len(nrow(a3)),size = train) trainset <- a3[train_ind, ] testset <- a3[-train_ind, ] dim(trainset) dim(testset) names(a3) Model <- lm(EP~wind+pressure+humidity+visability+FFMC+DMC+DC+ISI, data = trainset)#build the regression model for EP summary(Model) Model<- lm(EP~wind+pressure+humidity+visability+FFMC+DMC+ISI, data = trainset)#removed DC summary(Model) Model<- lm(EP~wind+pressure+humidity+visability+FFMC+DMC, data = trainset)#removed ISI summary(Model) Model<- lm(EP~wind+pressure+visability+FFMC+DMC, data = trainset)#removed humidity summary(Model) Model<- lm(EP~wind+pressure+FFMC+DMC, data = trainset)#removed visability summary(Model) Model<- lm(EP~wind+pressure+DMC, data = trainset)#removed FFMC summary(Model) Model<- lm(EP~pressure+DMC, data = trainset)#removed wind summary(Model) Reduced_Model<- lm(EP~pressure, data = trainset)#removed DMC summary(Model) anova(Reduced_Model, Model) install.packages("leaps") library(leaps) predict_model<-predict(Model,newdata = testset) head(predict_model)#using saturated model Observed_Values <- testset$EP head(Observed_Values) #calculate the the R_Squared Value and RMSE for Model SSE<-sum((Observed_Values-predict_model)^2) SST<-sum((Observed_Values-mean(Observed_Values))^2) r_squared<-1-SSE/SST r_squared RMSE<-sqrt(mean((Observed_Values-predict_model)^2)) RMSE RSS <- sum((predict_model - Observed_Values)^2) RSS MAE <- mean(abs(Observed_Values - predict_model)) MAE #predict reduced model predict_reduced<- predict(Reduced_Model, newdata=testset) head(predict_reduced) #calculate the the R_Squared Value and RMSE for Model SSE1<-sum((Observed_Values-predict_reduced)^2) SST1<-sum((Observed_Values-mean(Observed_Values))^2) r_squared1<-1-SSE1/SST1 r_squared1 RMSE1<-sqrt(mean((Observed_Values-predict_reduced)^2)) RMSE1 RSS1 <- sum((predict_reduced - Observed_Values)^2) RSS1 MAE1 <- mean(abs(Observed_Values - predict_reduced)) MAE1 #install.packages("ISLR") #require(caTools) install.packages("ISLR") library(ISLR) fit_model <- lm(EP~., data = a3) Step_AIC <- step(fit_model, direction = "backward") #part2 #a x_train<-model.matrix(EP~.,trainset)[,-1] y_train<-trainset$EP x_test<-model.matrix(EP~.,testset)[,-1] y_test<-testset$EP head(x_train) head(y_train) head(x_test) head(y_test) #b install.packages("glmnet") library(glmnet) Lasso_model<-glmnet(x_train,y_train,alpha=1,nlambda = 100,lambda.min.ratio = 0.001) plot(Lasso_model,xvar = "lambda") Cross_Validation<-cv.glmnet(x_train,y_train,nlambda = 100,alpha=0.5,lambda.min.ratio=0.001) plot(Cross_Validation) best_lambda<-Cross_Validation$lambda.min predict_y<-predict(Lasso_model, s = best_lambda, newx = x_test) #c Observe_y<-y_test rss <- sum((predict_y - Observe_y) ^ 2) ## residual sum of squares tss <- sum((Observe_y - mean(Observe_y)) ^ 2) ## total sum of squares rsq <- 1 - rss/tss rsq #part 3 install.packages("pls") library(pls) set.seed (1000) pcr_model <- pcr(EP~., data = a3, scale = TRUE, validation = "CV") summary(pcr_model) ##RMSEP validationplot(pcr_model,val.type = c("RMSEP")) ##MSEP validationplot(pcr_model,val.type = c("MSEP")) ##R2 validationplot(pcr_model,val.type = c("R2")) ##5 comm pcr_model_train<-pcr(EP~.,data=trainset,scale=TRUE,validation='CV') pcr_pred <- predict(pcr_model, testset, ncomp = 5) mean((pcr_pred - testset$EP)^2) summary(pcr_pred) lrm <- lm(EP~., data = trainset) lrm_pred <- predict(lrm, testset) mean((lrm_pred - testset$EP)^2)

8e27b0455382e3b79f6dd601997ce479eb2c2ea2

d9d213281d875a47089f1a0114919eeceedf4a60

/Figure S1/Figure_S1_plot_gray_value_all_time_points.R

e03525f4716590f4c8416c62b781e32895aa6852

[]

no_license

hibberd-lab/Xiong_High-light-response-of-the-rice-bundle-sheath

6845c4eac2300e8621cd7cd738cda95b2d91906a

221b6b7a9dfd87b67d6325ea77a3efb3db1d8d96

refs/heads/main

2023-03-31T08:04:41.543999

2021-04-15T08:20:10

306,381,513

0

null

UTF-8

R

false

1,772

r

Figure_S1_plot_gray_value_all_time_points.R

all1 <- read_csv("Gray value.csv") head(all1) all1$Distance<-as.numeric(all1$Distance) all1$time<-factor(all1$time,levels = c("0 min","5 min","10 min","15 min","20 min","30 min","40 min","50min","60 min")) all1$Treatment<-factor(all1$Treatment,levels = c("high light","0 min")) str(all1) celltype_position <- read_csv("celltype_position.csv") head(celltype_position) celltype_position$time <- factor(celltype_position$time,levels = c("0 min","5 min","10 min","15 min","20 min","30 min","40 min","50min","60 min")) celltype_position$Treatment<-factor(celltype_position$Treatment,levels = c("high light","0 min")) celltype_position$'Cell type' <- factor(celltype_position$'Cell type',levels=c("M","BSS")) str(celltype_position) head(celltype_position) pd <- position_dodge(0.05) # move them .05 to the left and right ggplot(all1, aes(x=Distance, y=mean)) + geom_rect(aes(x=NULL, y=NULL, xmin = start, xmax = end, fill = celltype_position$'Cell type'), ymin = -Inf, ymax =Inf, data = celltype_position) + scale_fill_manual(values=alpha(c("green","brown"),0.1))+ geom_errorbar(aes(ymin=mean-se, ymax=mean + se),colour="black", width=.1, position=pd) + geom_line(aes(colour=Treatment),position=pd,alpha=1, size=0.5) + facet_wrap(~time, ncol=4)+ xlab("Distance (??m)")+ ylab("Gray value")+ xlim(0,140)+ ylim(0,160)+ theme_bw() + theme(strip.background = element_rect(colour = "black", fill = "white"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x =element_text(size = 9,colour = "black"), axis.text.y =element_text(size = 9,colour = "black"), aspect.ratio = 1, strip.text.x = element_text(size = 9,colour = "black") )

a784b73d2cb2bf22311b41f1e1567a97ad5e1872

7021962b10e1fa20e48a99807b85e422bee02cec

/Plot1.R

542619889e876d0d4bfa743a0970f1ecfe6853b2

[]

no_license

pbarry129/ExData_Plotting1-1

271090b688105ce53066377e48c53327e0ce6632

47d203e5742bb7b46347d5fa392088a4bd49f5db

refs/heads/master

2020-06-13T17:22:35.808014

2016-12-05T01:36:28

75,575,966

0

null

2016-12-05T01:07:49

2016-12-05T01:07:48

null

UTF-8

R

false

904

r

Plot1.R

#Read in the power consumption data my_power_data<-read.csv("household_power_consumption.txt", sep=";") # Create date string my_power_dates<-paste(as(my_power_data$Date, "character"), as(my_power_data$Time, "character")) # Put date string in Date format POS_dates<-strptime(my_power_dates, "%d/%m/%Y %T", tz="") # Add Dates to data power_data <- cbind(my_power_data, POS_dates) # Get start and end dates start_date <-strptime("31/01/2007 23:59:59","%d/%m/%Y %T", tz="") end_date <- strptime("02/02/2007 23:59:59","%d/%m/%Y %T", tz="") # Subset the data two_days <- subset(power_data, power_data$POS_dates > start_date & power_data$POS_dates<end_date) hist(as.numeric(two_days$Global_active_power), main="Global Active Power", xlab = "Global Active Power (kilowatt)", col="red") dev.copy(png, file="plot1.png", width=480, height=480, units="px") dev.off()

625df374735a2600cc9eede5821522bf68d0cb4e

2631efe4c86afda92bff9141bf09ec1b359da511

/man/topgenes.Rd

3ad7449e2c39b5dd8c3b94c5fae1280b5204dd51

[]

no_license

Oshlack/AllSorts_v1

d4e753805a04fc8e2d80ad8cfcdd540aa9e39c37

529f6f9c42266c74a3de87531e3e01847d8ca8ff

refs/heads/master

2023-02-12T07:00:28.985590

2021-01-18T03:35:06

76,910,901

0

null

UTF-8

R

false

251

rd

topgenes.Rd

\name{topgenes} \docType{data} \alias{topgenes} \title{Random Forest Classification genes} \description{ A list of genes required by the random forest classifier. } \usage{topgenes} \format{A character vector of 20 genes required for classification.}

8a221b7840510f7ef824040794d90a110956226f

73cdd09cf558fa34e0799043837afdbbea61dcbd

/R/exportfuns.R

c041f134a0036e7a7a5f000cf705a092acd6edd3

[]

no_license

epi-chen/bioage

1561974f28b2b2fc48dc8c3017c2204e7df0b31b

c67e377984b370c82b244131f7ccd166a9ecf0b1

refs/heads/master

2021-09-16T19:16:54.732472

2018-06-23T19:46:58

null

0

null

UTF-8

R

false

41

r

exportfuns.R

#these are for exporting output to stata

a799817c5a33e18d0f2647f05d4ab8074b228657

b779522919291bcc1a2c05e6034394fd82858fc9

/skript_EDA_2017.R

fd41a6bd7b2f653af0d1e8dcb425012a37825f92

[]

no_license

Kobzol/statistics

e63b419d01cd3e248901825169cce116e65d98b0

56637eb4b69d24d7268b244b88ff9c15177a39eb

refs/heads/master

2021-03-30T17:36:04.324846

2017-03-27T18:31:39

86,370,452

0

null

WINDOWS-1250

R

false

21,539

r

skript_EDA_2017.R

####################################################################################### ################ Preprocesing dat a explorační analýza ################################ ############### Adéla Vrtková, Martina Litschmannová ################################## ####################################################################################### ## Máme data, a co dál? # 1. Spustíme potřebné balíčky, které obsahují další statistické funkce # 2. Nastavíme pracovní adresář, odkud importujeme data, popř. kam chceme ukládat výstupy # 3. Importujeme data (z pracovního adresáře, z internetu) # 4. Pre-processing -> a) Podíváme se na data # b) uložíme si data ve více formátech (každá funkce má "raději" jiný formát) # 5. Analýza kvalitativních proměnných # 6. Analýza kvantitativních proměnných # 7. Identifikace a rozhodnutí o vyloučení/ponechání odlehlých pozorování ####################################################################################### ## 1. Jak nainstalovat a spustit rozšiřující balíček funkcí? ########################## # Instalování balíčku install.packages("openxlsx") # Načtení balíčku (nutno opakovat při každém novém spuštění Rka, vhodné mít na začátku skriptu) library(openxlsx) ####################################################################################### ## 2. Kde se ukládají generované výstupy, nastavení pracovního adresáře ############### # Výpis pracovního adresáře getwd() # Nastavení pracovního adresáře -> do uvozovek, celou cestu setwd("C:/Users/lit40/.rkward") ####################################################################################### ## 3. Načtení datového souboru ######################################################## # základní funkce - read.table, read.csv, read.csv2, ... # záleží hlavně na formátu souboru (.txt, .csv), na tzv. oddělovači jednotlivých hodnot, desetinné čárce/tečce # Načtení a uložení datového souboru ve formátu csv2 z lokálního disku do datového rámce data data=read.csv2(file="C:/Martina/STA1/DATA/aku.csv") # Načtení a uložení datového souboru ve formátu csv2 z internetu do datového rámce data data=read.csv2(file="http://am-nas.vsb.cz/lit40/DATA/aku.csv") # Načtení a uložení datového souboru ve formátu xlsx z lokálního disku do datového rámce data - používáme funkci z balíčku XLConnect, který jsme v úvodu rozbalili install.packages("openxlsx") library(openxlsx) data=readWorkbook("C:/Users/lit40/Desktop/aku.xlsx", sheet=1, # číslo listu (defaultní hodnota 1) colNames=TRUE, # informace, zda v prvním řádku je hlavička s názvy sloupců (defaultní hodnota TRUE) startRow = 4, # na kterém řádku má načítání začít (není-li tento parametr zadán, začíná načítání na řádku 1) cols = 2:9) # které sloupce se mají načíst (není-li tento parametr zadán, načítají se všechny neprázdné sloupce) colnames(data)=c("A5","B5","C5","D5","A100","B100","C100","D100") # nebo install.packages("XLConnect") library(XLConnect) wb=loadWorkbook("C:/Martina/STA1/DATA/aku.xlsx") data=readWorksheet(wb, sheet="Data", header=TRUE, startRow = 4, startCol=2) colnames(data)=c("A5","B5","C5","D5","A100","B100","C100","D100") # Načtení a uložení datového souboru ve formátu xlsx z internetu (pomocí balíčku XLConnect) # do datového rámce data (komplikovanější, doporučujeme raději si stáhnout xlsx soubor na lokální disk) tmp = tempfile(fileext = ".xlsx") download.file(url = "http://am-nas.vsb.cz/lit40/DATA/aku.xlsx", destfile = tmp, mode="wb") wb=loadWorkbook(tmp) data=readWorksheet(wb,sheet="Data",header=TRUE,startRow = 4,startCol=2) colnames(data)=c("A5","B5","C5","D5","A100","B100","C100","D100") ####################################################################################### ## 4. Pre-processing dat ############################################################## # Výpis datového souboru data # Zobrazení prvních šesti řádků head(data) # Zobrazení posledních šesti řádků tail(data) # Zobrazení 10. řádku data[10,] # Zobrazení 3. sloupce data[,3] # nebo (víme-li, jak se jmenuje proměnná zapsána ve 3. sloupci) data[["C5"]] # nebo data$C5 # Uložení prvního a pátého sloupce dat. rámce data do dat. rámce pokus pokus=data[,c(1,5)] # Uložení prvních 4 sloupců dat. rámce data do dat. rámce data5 data5=data[,c(1:4)] #nebo data5=data[,-c(5:8)] ## Pozn. při ukládání dat mysleme na přehlednost v názvech, data5 obsahují kapacity akumulátorů všech výrobců po 5ti cyklech ## Převod dat do standardního datového formátu data5S=reshape(data[,1:4], # část datového rámce, která bude převáděna do std. datového formátu direction="long", # parametr určující tzv. "long" nebo "wide" formát varying=c("A5","B5","C5","D5"), # názvy proměnných, které mají být zařazeny do sloupce hodnot v.names="kap5", # pojmenování sloupce hodnot times=c("A","B","C","D"), # varianty proměnné, která přiřazuje identifikátory jednotlivým hodnotám proměnné kap5 timevar="vyrobce") # pojmenování proměnné obsahující identifikátory proměnné kap5 # převedení proměnné data5S$vyrobce na typ factor data5S$vyrobce=as.factor(data5S$vyrobce) # odstranění nadbytečné proměnné id z datového rámce data5S data5S=data5S[,-3] # odstranění NA z datového rámce data5S data5S=na.omit(data5S) ## Převod párových dat do standardního datového formátu dataS=reshape(data, direction="long", varying=list(c("A5","B5","C5","D5"), c("A100","B100","C100","D100")), v.names=c("kap5","kap100"), times=c("A","B","C","D"), timevar="vyrobce") # převedení proměnné dataS$vyrobce na typ factor dataS$vyrobce=as.factor(dataS$vyrobce) # odstranění nadbytečné proměnné id z datového rámce dataS dataS=dataS[,-4] # odstranění NA z datového rámce dataS dataS=na.omit(dataS) # Definování nové proměnné v stávajícím datovém rámci dataS$pokles=dataS$kap5-dataS$kap100 ## Vytvoření samostatných proměnných a5=dataS$kap5[dataS$vyrobce=="A"] b5=dataS$kap5[dataS$vyrobce=="B"] c5=dataS$kap5[dataS$vyrobce=="C"] d5=dataS$kap5[dataS$vyrobce=="D"] a100=dataS$kap100[dataS$vyrobce=="A"] b100=dataS$kap100[dataS$vyrobce=="B"] c100=dataS$kap100[dataS$vyrobce=="C"] d100=dataS$kap100[dataS$vyrobce=="D"] pokles.a=dataS$pokles[dataS$vyrobce=="A"] pokles.b=dataS$pokles[dataS$vyrobce=="B"] pokles.c=dataS$pokles[dataS$vyrobce=="C"] pokles.d=dataS$pokles[dataS$vyrobce=="D"] ### Poznámky pro zopakování principu grafiky v R ###################################### # základem jsou tzv. high-level funkce, které vytvoří graf (tj. otevřou grafické oknou a vykreslí dle zadaných parametrů) # na ně navazují tzv. low-level funkce, které něco do aktviního grafického okna přidají, samy o sobě neotevřou nové # př. low-level funkcí - např. abline, points, lines, legend, title, axis ... které přidají přímku, body, legendu... # tzn. před použitím "low-level" funkce je potřeba, volat "high-level" funkci (např. plot, boxplot, hist, barplot, pie,...) # další grafické parametry naleznete v nápovědě # nebo např. zde http://www.statmethods.net/advgraphs/parameters.html # nebo zde https://flowingdata.com/2015/03/17/r-cheat-sheet-for-graphical-parameters/ # nebo http://bcb.dfci.harvard.edu/~aedin/courses/BiocDec2011/2.Plotting.pdf ## Barvy v R # http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf # https://www.nceas.ucsb.edu/~frazier/RSpatialGuides/colorPaletteCheatsheet.pdf ####################################################################################### ## 5. Explorační analýza a vizualizace kategoriální proměnné ########################## ## Výpočet četostí #################################################################### cetnosti=table(dataS$vyrobce) cetnosti # výpis # Výpočet relativních četností - 2 ekvivalentní způsoby rel.cetnosti=100*cetnosti/sum(cetnosti) rel.cetnosti # výpis rel.cetnosti2=prop.table(cetnosti)*100 # Zaokrouhlení relativních četností (%) na 1 desetinné místo rel.cetnosti=round(rel.cetnosti,digits=1) rel.cetnosti # výpis # Pozor na zaokrouhlovací chybu!! rel.cetnosti[4]=100-sum(rel.cetnosti[1:3]) rel.cetnosti # výpis # Sloučení četností a relativních četností do tabulky četností tabulka=cbind(cetnosti,rel.cetnosti) # cbind() ... sloučení sloupců tabulka # výpis # Přejmenování názvů sloupců v tabulce četností colnames(tabulka)=c("četnost","rel.četnost (%)") tabulka # Uložení tabulky do csv souboru pro export do MS Excel write.csv2(tabulka,file="tabulka.csv") # Kde je tabulka uložena? getwd() ## Výsečový (koláčový) graf - angl. piechart ########################################## cetnosti=table(dataS$vyrobce) # v tuto chvíli není nutno používat, četnosti jsme již spočetli a uložili do proměnné cetnosti výše pie(cetnosti) # Zabarvení grafu pie(cetnosti, col=c("red","green","yellow","blue")) pie(cetnosti, col=heat.colors(4)) # Přidání názvu grafu a popisků pie(cetnosti, col=heat.colors(4), main="Zastoupení výrobců ve výběru", labels=c("Výrobce A","Výrobce B","Výrobce C","Výrobce D")) pie(cetnosti, col=heat.colors(4), main="Zastoupení výrobců ve výběru", labels=paste("Výrobce",names(cetnosti),"\n",cetnosti)) # funkce paste() umožňuje sloučit textové řetězce a hodnoty proměnných, symbol "\n" tvoří nový řádek v textu # rel. četnosti byly spočteny a uloženy do proměnné rel.cetnosti výše pie(cetnosti, col=heat.colors(4), main="Zastoupení výrobců ve výběru", labels=paste("Výrobce",names(cetnosti),"\n",cetnosti,";",rel.cetnosti,"%")) # Pro zájemce - balíček plotrix a funkce pie3D vytvoří 3D koláčový graf ## Sloupcový graf - angl. barplot ##################################################### cetnosti=table(data5S$vyrobce) # v tuto chvíli není nutno používat, četnosti jsme již spočetli a uložili do proměnné cetnosti výše barplot(cetnosti) # Změna barev, přidání názvu barplot(cetnosti, col=heat.colors(4), main="Zastoupení výrobců ve výběru", space=0.6) # parametr space vytvoří mezeru mezi sloupci # Přidání dalších popisků a legendy barplot(cetnosti, col=heat.colors(4), horiz=TRUE, # horizontální orientace grafu border=FALSE, # nevykresluje čáru kolem sloupečků main="Zastoupení výrobců ve výběru", names.arg=paste("Výrobce",names(cetnosti))) legend("topright", paste("Výrobce",names(cetnosti)), col=heat.colors(4), fill=heat.colors(4), border=FALSE, bty="n") # Přidání absolutních a relativních četností k odpovídajícím sloupcům bp = barplot(cetnosti, col=heat.colors(4), main="Zastoupení výrobců ve výběru", names.arg=paste("Výrobce",names(cetnosti))) text(bp,cetnosti,cetnosti) bp = barplot(cetnosti, col=heat.colors(4), main="Zastoupení výrobců ve výběru", names.arg=paste("Výrobce",names(cetnosti))) text(bp, cetnosti,paste(cetnosti,";",rel.cetnosti,"%"), pos=1) # parametr pos udává, kde bude text uveden vzhledem k dané pozici (1 = pod, 2 = vlevo, 3 = nad, 4 = vpravo) ## Jak graf uložit? ################################################################## # Zjištění aktivního okna, nastavení aktivního okna - tj. který graf chceme uložit? dev.cur() dev.set(2) # Uložení obrázku ve formátu pdf (výška a šířka jsou uvedeny v palcích (inch), 1inch=2,54cm) dev.print(device=pdf,file="barplot.pdf",width=6.5,height=5) # Zavření grafického okna dev.off() # Kam se obrázek uložil? getwd() ####################################################################################### #####Pie charts are a very bad way of displaying information.########################## ##The eye is good at judging linear measures and bad at judging relative areas.######## ##A bar chart or dot chart is a preferable way of displaying this type of data. ####### ####################################################################################### ####################################################################################### ## 6. Explorační analýza a vizualizace kvantitativní proměnné ######################### ## Popisná statistika ################################################################# summary(dataS$kap5) # Výpočet průměru jedné proměnné mean(dataS$kap5) mean(a5) # Pozor na chybějící hodnoty mean(data$C5) mean(data$C5,na.rm=TRUE) mean(na.omit(data$C5)) # Výpočet mediánu jedné proměnné quantile(dataS$kap5,probs=0.5) quantile(a5,probs=0.5) # Určení rozsahu length(na.omit(dataS$kap5)) ####################################################################################### ## funkce, které umožní aplikaci vybrané funkce na sloupce datového rámce, na hodnoty vektoru apod. x=1:4 # - lapply - na vstup aplikuje zvolenou funkci, vrátí list (seznam) hodnot lapply(x,sqrt) # - sapply - na vstup aplikuje volenou funkci, vrátí vektor hodnot sapply(x,sqrt) # - vapply - stejné jako sapply, akorát s parametrem navíc, který specifikuje výstup vapply(x,sqrt,numeric(1)) # - tapply - lze aplikovat na vektor, jemuž je přiřazen faktor rozlišující hodnoty do skupin # - použijeme dále ####################################################################################### # Výpočet průměrů kapacit po 5 cyklech dle výrobců tapply(data5S$kap5, data5S$vyrobce, mean) # Výpočet mediánu kapacit po 5 cyklech dle výrobců tapply(data5S$kap5, data5S$vyrobce, quantile, probs=0.5) # Obdobně lze určit další charakteristiky. # Pozor! Funkce pro výpočet šikmosti (skewness) a špičatosti (kurtosis) nejsou součástí základního R, najdete je v balíčku moments install.packages("moments") library(moments) # další charakteristiky -> var(), sd(), min(), max(), skewness(), kurtosis() ## Krabicový graf - angl. boxplot ##################################################### boxplot(b5) # Úprava grafu boxplot(b5, main="Kapacita po 5 cyklech (mAh)", xlab="Výrobce B", ylab="kapacita (mAh)") # Zabarvení boxplotu, zobrazení průměru boxplot(b5, main="Kapacita po 5 cyklech (mAh)", xlab="Výrobce B", ylab="kapacita (mAh)", col="grey") points(1, mean(b5,na.rm=TRUE), pch=3) # Horizontální boxplot boxplot(b5, main="Kapacita po 5 cyklech (mAh), výrobce B", horizontal=TRUE, xlab="kapacita (mAh)") # Další parametry boxplot(b5, main="Kapacita po 5 cyklech (mAh)", xlab="Výrobce B", ylab="kapacita (mAh)", col="grey", outline=FALSE, # nezobrazí odlehlá pozorování boxwex=0.5) # změní šířku krabice na 1/2 # Přidání přímky y=mean(b5) abline(h=mean(b5,na.rm=TRUE), col="red",lty=2) # Funkci boxplot lze využít nejen k vykreslení grafu boxplot(b5,plot=FALSE) ## Vícenásobný krabicový graf - angl. multiple boxplot ################################ boxplot(data5) boxplot(data5, boxwex=0.5, col="grey") boxplot(data5, boxwex=c(0.5,0.5,1.5,1.5), col=c("red","yellow","grey","blue")) boxplot(data5, boxwex=c(0.5,0.5,1.5,1.5), col=terrain.colors(4)) abline(h=1900,col="red",lty=2) # Mezera v boxplotu - myšlenka - třetí pozice bude vynechána boxplot(data5, at=c(1,2,4,5)) # Boxplot konstruován z dat ve standartním datovém formátu - s využitím funkce split pom=split(data5S$kap5, data5S$vyrobce) #do proměnné pom jsou uložena data ve formátu list, tj. seznam proměnných boxplot(pom) # Pořadí krabic dle přání s využitím funkce list pom=list(a5, b5, c5, d5) boxplot(pom) pom=list(d5, c5, b5, a5) boxplot(pom) ## Histogram ########################################################################## hist(a5) hist(a5,breaks=10) # Co dělají různé hodnoty parametru breaks s grafem? hist(a5, main="Histogram pro kapacitu akumulátorů po 5 cyklech, výrobce A", xlab="kapacita (mAh)", ylab="f(x)", col="blue", border="grey", labels=TRUE) # přidá absolutní četnosti daných kategorií ve formě popisků hist(a5, main="Histogram pro kapacitu akumulátorů po 5 cyklech, výrobce A", xlab="kapacita (mAh)", ylab="f(x)", col="blue", border="grey", labels=TRUE, # přidá absolutní četnosti daných kategorií ve formě popisků freq=FALSE) # změna měřítka na ose y --> f(x) lines(density(a5)) # připojí graf odhadu hustoty pravděpodobnosti # Generování hustoty normálního rozdělení xfit=seq(min(a5), max(a5), length=40) yfit=dnorm(xfit, mean=mean(a5), sd=sd(a5)) lines(xfit, yfit, col="black", lwd=2) ## QQ-graf - aneb grafický nástroj pro posouzení normality ############################ qqnorm(a5) qqline(a5) ####################################################################################### ## Více grafů do jednoho obrázku -> funkce layout nebo par ############################ ## Podívejte se na možnosti kombinování grafů - http://www.statmethods.net/advgraphs/layout.html ## Kombinace histogramu a boxplotu #################################################### pom=layout(mat = matrix(1:8,2,4, byrow=FALSE), height = c(2.5,1)) layout.show(pom) par(oma=c(2,2,3,2),mar=c(2,2,3,2)) hist(a5, main="Výrobce A", xlab="kapacita (mAh) po 5 cyklech", ylab="četnost", ylim=c(0,32), xlim=c(1730,2040)) boxplot(a5, horizontal=TRUE, ylim=c(1700,2040), boxwex=1.5) hist(b5, main="Výrobce B", xlab="kapacita (mAh) po 5 cyklech", ylab="četnost", ylim=c(0,32), xlim=c(1730,2040)) boxplot(b5, horizontal=TRUE, ylim=c(1700,2040), boxwex=1.5) hist(c5, main="Výrobce C", xlab="kapacita (mAh) po 5 cyklech", ylab="četnost", ylim=c(0,32), xlim=c(1730,2040)) boxplot(c5, horizontal=TRUE, ylim=c(1700,2040), boxwex=1.5) hist(d5, main="Výrobce D", xlab="kapacita (mAh) po 5 cyklech", ylab="četnost", ylim=c(0,32), xlim=c(1730,2040)) boxplot(d5, horizontal=TRUE, ylim=c(1700,2040), boxwex=1.5) mtext("Histogramy a boxploty pro jednotlivé výrobce po 5 cyklech", cex = 1.5, outer=TRUE, side=3) # Pro pokročilé - pomocí for-cyklu pom=layout(mat = matrix(1:8,2,4, byrow=FALSE), height = c(2.5,1)) layout.show(pom) par(oma=c(2,2,3,2), mar=c(2,2,3,2)) for (i in 1:4){ hist(data5[,i], main=paste("Výrobce",colnames(data5)[i]), xlab="kapacita (mAh) po 5 cyklech", ylab="četnost", xlim=c(min(data5,na.rm=TRUE), max(data5,na.rm=TRUE)), ylim=c(0,32)) boxplot(data5[,i], horizontal=TRUE, ylim=c(min(data5,na.rm=TRUE), max(data5,na.rm=TRUE)), boxwex=1.5) } mtext("Histogramy a boxploty pro jednotlivé výrobce po 5 cyklech", cex = 1.5, outer=TRUE, side=3) ## Kombinace histogramu a QQ-plotu #################################################### pom=layout(mat = matrix(1:8,2,4, byrow=FALSE), height = c(2,1.5)) layout.show(pom) par(oma=c(2,2,3,2), mar=c(2,2,3,2)) for (i in 1:4){ hist(data5[,i], main=paste("Výrobce",colnames(data5)[i]), xlab="kapacita (mAh) po 5 cyklech", ylab="četnost", xlim=c(min(data5,na.rm=TRUE), max(data5,na.rm=TRUE)), ylim=c(0,0.037), freq=FALSE) lines(density(data5[,i], na.rm=TRUE)) xfit=seq(min(data5[,i], na.rm=TRUE), max(data5[,i], na.rm=TRUE), length=40) yfit=dnorm(xfit, mean=mean(data5[,i], na.rm=TRUE), sd=sd(data5[,i], na.rm=TRUE)) lines(xfit, yfit, col="blue", lty=2) qqnorm(data5[,i]) qqline(data5[,i]) } mtext("Histogramy a QQ-ploty pro jednotlivé výrobce po 5 cyklech", cex = 1.5, outer=TRUE, side=3) ####################################################################################### ## 7. Identifikace odlehlých pozorování (a jejich odstranění z dat. rámce)############# ## S individuálním posouzením, jak naložit s odlehlými hodnotami ###################### # Seřázení datového rámce aku.st podle proměnných aku.st$vyrobce a data.st$kap5 data5S=data5S[with(data5S, order(data5S$vyrobce,data5S$kap5)),] # V RkWardu lze pro seřazení dat použít záložku Data / Sort data # Zápis proměnné do nového sloupce, v němž budeme odstraňovat vybraná odlehlá pozorování data5S$kap5.bez=data5S$kap5 # Následně lze pohodlně "ručně" odstranit (popř. opravit) vybraná odlehlá pozorování # Sofistikovanější metody -> např. vnitřní hradby, mnohorozměrná detekce odlehlých hodnot...

c8e6348d6b28caddf95147aeb30a875483dc6edf

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/datadr/examples/convert.Rd.R

6825df9b4d8631b2c73986563402387192faf462

[]

no_license

surayaaramli/typeRrh

d257ac8905c49123f4ccd4e377ee3dfc84d1636c

66e6996f31961bc8b9aafe1a6a6098327b66bf71

refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

null

0

null

UTF-8

R

false

262

r

convert.Rd.R

library(datadr) ### Name: convert ### Title: Convert 'ddo' / 'ddf' Objects ### Aliases: convert ### ** Examples d <- divide(iris, by = "Species") # convert in-memory ddf to one stored on disk dl <- convert(d, localDiskConn(tempfile(), autoYes = TRUE)) dl

73379c322f349dde606049efa7ea3f77e089ebaf

9bdef83f28b070321ba27709d2c7ec028474b5c3

/R/packages/package.creation.R

bcf8ffd6ab3ab0e3aa9e4e6c4f5b03f8ef145c1e

[]

no_license

antagomir/scripts

8e39ce00521792aca1a8169bfda0fc744d78c285

c0833f15c9ae35b1fd8b215e050d51475862846f

refs/heads/master

2023-08-10T13:33:30.093782

2023-05-29T08:19:56

7,307,443

10

15

null

2023-07-19T12:36:45

2012-12-24T13:17:03

HTML

UTF-8

R

false

2,981

r

package.creation.R

replace.description.field <- function (version.name, field, entry) { # Replaces given field in the DESCRIPTION file # define DESCRIPTION file based on version.name f <- paste(version.name,"/DESCRIPTION",sep="") # Read DESCRIPTION file lins <- readLines(f) # search the line that contains the field field.line <- grep(field,lins) # Replace the line lins[[field.line]] <- paste(field,"",entry,sep="") # Write new Rd file contents writeLines(lins,file(f)) } replace.rd.fields <- function(rdfile, fills) { # Replace the given fields (in fills) in a man/*.Rd file # fills is a list that lists the replacement texts for the fields # fills <- list(author = "N.N", reference = "RandomJournal", ...) # Read the Rd file rdlines <- readLines(rdfile) # Go through specified fields for (field in names(fills)) { # If replacment is given for the field.. if (!is.na(fills[[field]])) { # Replace the given field with replacement text rdlines <- replace.rd.field(rdlines, field, replacement = fills[[field]]) } } # Write new Rd file contents writeLines(rdlines,file(rdfile)) } replace.rd.field <- function(rdlines, field, replacement) { # Replace annotation field in a man/*.Rd file # Detect starting line for the field sind <- grep(paste(field,"\\{",sep=""),rdlines) # Proceed only if the field is present in the file if (length(sind)>0) { # start line sline <- rdlines[[sind]] # Detect last line of the field eind <- sind while (length(grep("}", rdlines[[eind]]))==0) { eind <- eind+1 } # Set new field on the first line of old field rdlines[[sind]] <- paste("\\",field,"{",replacement,"}",sep="") # remove the rest of the old field if (eind>sind) { rdlines <- rdlines[-((sind+1):eind)] } } else {} rdlines } set.keywords <- function (rdfile, keywords) { # Read the Rd file rdlines <- readLines(rdfile) # detect keyword lines inds <- grep("keyword\\{",rdlines) # store file lines before and after keywords after.keywords <- rdlines[-seq(max(inds))] before.keywords <- rdlines[seq(min(inds)-1)] # write keyword lines keywordlines <- vector(mode="character") for (i in 1:length(keywords)) { keywordlines[[i]] <- paste("\\keyword{",keywords[[i]],"}",sep="") } # add the parts rdlines <- c(before.keywords, keywordlines, after.keywords) # Write new Rd file contents writeLines(rdlines,file(rdfile)) } remove.help.lines <- function (rdfile, helplines) { # Remove specified (help) lines from the Rd files # Read the Rd file rdlines <- readLines(rdfile) remove.idx <- c() for (lin in helplines) { remove.idx <- c(remove.idx, grep(lin, rdlines)) } if (length(remove.idx)>0) { rdlines <- rdlines[-remove.idx] # Write new Rd file contents writeLines(rdlines,file(rdfile)) } else {} }

71bc643423256db4b3ab9a2b49a35cd62cf85b31

0eb87c697d21c87fe6ffdeb731033ea17e92a327

/man/grid_spatialpoints.Rd

c59287814e5a3c435d8cfc95af66d9d0ea95c643

[]

no_license

Grelot/rgeogendiv

e4566fcb965a07db5a655a95744eb20221f893ce

d261bcea4d12a70d40ce3efd058b0908744a3c27

refs/heads/master

2023-02-16T18:41:09.436973

2020-12-17T16:26:31

300,564,835

0

null

UTF-8

R

false

true

918

rd

grid_spatialpoints.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/grid_spatialpoints.R \name{grid_spatialpoints} \alias{grid_spatialpoints} \title{Build a map grid spatialpoints} \usage{ grid_spatialpoints( siteSize = 260000, projectionCRS = "+proj=cea +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +datum=WGS84 +ellps=WGS84 +units=m +no_defs" ) } \arguments{ \item{siteSize}{size of each cell of the grid in meter} \item{projectionCRS}{Interface class to the PROJ projection and transformation system. Default value is "+proj=cea +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +datum=WGS84 +ellps=WGS84 +units=m +no_defs"} } \value{ a spatialpoint object of all sites of the grid into the projection `projectionCRS` given as argument } \description{ The function download from NaturalEarth database a worldmap raster. A grid is built from this raster. Then the list of centroids of each site of the grid is returned. }

98348fa9e5648a988db50027e9a7742470468d7d

80ebdb34963c9d62e9d70bad0dae43673c40f0e8

/server.R

0c0a1cb3615f02adb2c13ec23b40f7fd7c4171a3

[ "MIT" ]

permissive

AdamSpannbauer/shiny_groom_proposal

8d4d61b5b369e178d8194965eafedbd5582dcb80

9df9c512ccf0f0d091a99fca14f3f9882f40cb23

refs/heads/master

2020-04-12T00:22:50.920568

2018-12-18T21:38:10

162,198,075

1

0

null

UTF-8

R

false

1,931

r

server.R

shinyServer(function(input, output, session) { # get groomsman id from url query (returns FALSE if invalid/missing) groomsman_id = reactive({ query = parseQueryString(session$clientData$url_search) gm_id = query[['gm_id']] if (is.null(gm_id)) gm_id = FALSE if (!(gm_id %in% groomsman_ids)) gm_id = FALSE gm_id }) # if valid groomsman id display beer gif, response buttons, & groomposal gif # if invalid groomsman id display message to go away output$beer_gif = renderUI({ if (groomsman_id() == FALSE) return(unknown_id_response) p( img(src='wide_pbr_spill.gif', width='40%') ) }) output$goomposal_gif = renderUI({ req(groomsman_id(), sum(input$yes_button, input$no_button) == 0) p( img(src=glue('groomposal_gifs/{groomsman_id()}_groomposal.gif'), width='50%') ) }) output$response_buttons = renderUI({ req(groomsman_id(), sum(input$yes_button, input$no_button) == 0) p( actionGroupButtons(inputIds = c('yes_button', 'no_button'), labels = c('yes', 'no'), direction = 'horizontal', status='danger') ) }) output$button_press_response = renderUI({ req(groomsman_id(), sum(input$yes_button, input$no_button) > 0) email_subject = glue('{groomsman_id()} groomposal response') if (input$yes_button > 0) { send_email(subject = email_subject, message_text = 'yes', stop_on_fail = FALSE) response_gif = img(src='reaction_gifs/happy_michael_scott.gif', width='40%') } else { send_email(subject = email_subject, message_text = 'no', stop_on_fail = FALSE) response_gif = img(src='reaction_gifs/bummed_michael_scott.gif', width='30%') } p(response_gif) }) })

88c556f232a55684bc9c3de8c03460b041701572

1fb167f75fac5fd1438cc442d1e21296c68fe277

/combine_data.R

6e6a2edc04ed5a48b4b843f4f72a6abc9eb048be

[]

no_license

BrianDarmitzel/INFO-201-Group-Project

8cf4d3f7b56f0a61d9e0a8d37ee6a90e315a084b

6e9eeddaab914b61a1b5b8765d8ce20e94663a6c

refs/heads/master

2020-08-31T06:42:21.936296

2019-12-05T06:02:30

218,626,398

0

null

UTF-8

R

false

4,049

r

combine_data.R

library("dplyr") library("stringr") library("plotly") # load in filtered data set emissions_data <- read.csv(unz("data/filtered_datasets.zip", "filtered_datasets/emissions_data.csv")) fuel_economy_data <- read.csv( unz("data/filtered_datasets.zip", "filtered_datasets/vehicles_individual_data.csv")) # convert values to upper case fuel_economy_data$make <- str_to_upper( fuel_economy_data$make) fuel_economy_data$model <- str_to_upper( fuel_economy_data$model) emissions_data$Represented.Test.Vehicle.Model <- str_to_upper( emissions_data$Represented.Test.Vehicle.Model) # rename a few columns emissions_data <- rename(emissions_data, "Vehicle Manufacturer" = Represented.Test.Vehicle.Make) fuel_economy_data <- rename(fuel_economy_data, "Vehicle Manufacturer" = make) emissions_data <- rename(emissions_data, "Vehicle Model" = Represented.Test.Vehicle.Model) fuel_economy_data <- rename(fuel_economy_data, "Vehicle Model" = model) brands1 <- emissions_data %>% group_by(`Vehicle Manufacturer`) %>% summarize(average_emission_emitted = sum(Emission_Emitted) / n()) brands2 <- fuel_economy_data %>% group_by(`Vehicle Manufacturer`) %>% summarize(`Average city MPG` = sum(Average.city.MPG) / n(), `Average highway MPG` = sum(Average.highway.MPG) / n(), `Combined MPG` = sum(Combined.MPG) / n(), `Annual gas Consumption in Barrels` = sum(Annual.gas.Consumption.in.Barrels) / n(), `Tailpipe Emissions in g/mi` = sum(Tailpipe.Emissions.in.g.mi) / n(), `Annual Fuel Cost` = sum(Annual.Fuel.Cost) / n(), `Cost Savings for Gas over 5 Years` = sum(Cost.Savings.for.Gas.over.5.Years) / n()) all_brands <- merge(x = brands1, y = brands2, by = "Vehicle Manufacturer") all_cars <- merge(x = emissions_data, y = fuel_economy_data, by = c("Vehicle Manufacturer", "Vehicle Model")) %>% rename("Average Emissions Emitted" = Emission_Emitted, "Average city MPG" = Average.city.MPG, "Average highway MPG" = Average.highway.MPG, "Combined MPG" = Combined.MPG, "Annual gas Consumption in Barrels" = Annual.gas.Consumption.in.Barrels, "Tailpipe Emissions in g/mi" = Tailpipe.Emissions.in.g.mi, "Annual Fuel Cost" = Annual.Fuel.Cost, "Cost Savings for Gas over 5 Years" = Cost.Savings.for.Gas.over.5.Years) %>% select(-Number.of.Models.in.Data) averages <- lapply(all_cars[3:10], mean) graph_ranking <- function(car_model, column) { cars_avg <- all_cars %>% mutate(result = all_cars[[column]] - averages[[column]]) data <- cars_avg %>% filter(`Vehicle Model` == car_model) %>% select(result) color_map <- c() if (data$result < 0) { if (is.element(column, c("Average city MPG", "Average highway MPG", "Combined MPG", "Cost Savings for Gas over 5 Years"))) { color_map[colnames(data)] <- c("data" = "red") } else { color_map[colnames(data)] <- c("data" = "blue") } } else { if (is.element(column, c("Average city MPG", "Average highway MPG", "Combined MPG", "Cost Savings for Gas over 5 Years"))) { color_map[colnames(data)] <- c("data" = "blue") } else { color_map[colnames(data)] <- c("data" = "red") } } plot_ly() %>% add_bars( x = column, y = as.vector(data$result, mode = "numeric"), marker = list(color = color_map[[colnames(data)]]), text = round(as.vector(data$result, mode = "numeric"), 1), hoverinfo = "message", textposition = "auto" ) %>% layout( yaxis = list(range = c(min(cars_avg[["result"]]), max(cars_avg[["result"]]))) ) }

c122c438af228805083f06f5424b53a7149ffb66

0a5cf23a3ef5fb9f36a9051ec59d1215ee62a99c

/RScripts/DataVisualization.R

914ac74f1d30c477acd5c9f4d9c6e00ca3eb1c59

[]

no_license

phanisrikar93/cricScoreR

23ec98a5ad17c6e7712ce8ecd43c6df8430c55ed

b2d18d8c4f4d79e18484b07e8c9e25c03d834314

refs/heads/master

2021-01-19T02:39:15.435212

2016-07-20T16:57:53

63,249,022

0

1

null

2016-07-20T16:57:54

2016-07-13T13:40:13

R

UTF-8

R

false

1,462

r

DataVisualization.R

require(lattice) #To read data file into frame. FinalDataFrame = read.csv("C:\\Users\\admin\\Downloads\\Use_Case_Dhruv\\BindIPLData.csv") #Summary of RunsScored column of FinalData Frame. Summary1 = summary(FinalDataFrame$RunsScored) Summary1 #Standard deviation of RunsScored column of FinalDataFrame. sd1 = sd(FinalDataFrame$RunsScored) sd1 #Summary of Extras column. Summary2 = summary(FinalDataFrame$Extras) Summary2 #Standard deviation of Extras column. sd2 = sd(FinalDataFrame$Extras) sd2 FinalDataFrame$Over = factor(trunc(FinalDataFrame$Over)) #Histogram of RunsScored with Over as a factor variable. histogram(~RunsScored | Over,data = FinalDataFrame,type = "count") #Histogram of Runs Scored with Team as a factor variable. histogram(~RunsScored | Team,data = FinalDataFrame,type = "percent") #Histogram of Runs Scored with StrikeBatsman as a factor variable. histogram(~RunsScored | StrikeBatsman,data = FinalDataFrame,type = "count") #Histogram of Runs Scored with Bowler as a factor variable. histogram(~RunsScored | Bowler,data = FinalDataFrame,type = "count") FinalDataFrame$RunsScored = factor(FinalDataFrame$RunsScored) histogram(~RunsScored,data = FinalDataFrame,type = "count") histogram(~RunsScored,data = FinalDataFrame,type = "percent") #To find correalation between Extras and Runs Scored. correalation = cov(FinalDataFrame$RunsScored,FinalDataFrame$Extras)/(var(FinalDataFrame$RunsScored)*var(FinalDataFrame$Extras)) correalation

4ca0b7ee9f2b701404c8568f3a9e287031c1c05d

c7c5813adee3d966baced00501b4f7d15ecc3e4c

/man/Rboot.Rd

ab9b5be4a9611233c16910948cae8ab2b53cd2ec

[]

no_license

E-Caron/slm

8f181ce1a03526843f1b4ea1b647186b67145edc

a80d9765fda9e29fa3af78c990fea3931199d0f2

refs/heads/master

2020-06-06T09:43:52.667197

2020-01-08T19:08:17

192,704,653

2

0

null

UTF-8

R

false

true

1,414

rd

Rboot.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/auxiliary-fun.R \name{Rboot} \alias{Rboot} \title{Risk estimation for a tapered covariance matrix estimator via bootstrap method} \usage{ Rboot(epsilon, treshold, block_size, block_n, model_max, kernel_fonc) } \arguments{ \item{epsilon}{an univariate process.} \item{treshold}{number of estimated autocovariance terms that we consider for the estimation of the covariance matrix.} \item{block_size}{the size of the bootstrap blocks. \code{block_size} must be greater than \code{model_max}.} \item{block_n}{blocks number used for the bootstrap.} \item{model_max}{the maximal dimension, that is the maximal number of terms available to estimate the covariance matrix.} \item{kernel_fonc}{the kernel to use. The user can define his own kernel and put it in the argument.} } \value{ This function returns a list with: \item{risk}{for one treshold, the value of the estimated risk.} \item{SE}{the standard-error due to the bootstrap.} } \description{ This function computes an estimation of the risk for the tapered covariance matrix estimator of a process via a bootstrap method, for a specified treshold and a specified kernel. } \references{ E. Caron, J. Dedecker and B. Michel (2019). Linear regression with stationary errors: the R package slm. \emph{arXiv preprint arXiv:1906.06583}. \url{https://arxiv.org/abs/1906.06583}. }

73333ab5b9efff43160c08a9094cd68ab8e26426

1dd840b99146dbdd57e3b267d6243d695354bdd0

/DTF_Create.R

3d31bbcb8946136a212c30fdfad21e0491634276

[]

no_license

diardelavega/TotalPrediction

654e6ca7d4d6f1df3153dd94ab31c7a0790e738b

464cafdc346c419ba58d7c34ac7ff82ebfddb4ff

refs/heads/master

2020-04-12T08:52:37.906304

2017-03-04T17:22:23

61,332,137

0

null

UTF-8

R

false

13,382

r

DTF_Create.R

#page to be used for loading /7 initiating all the data for the creation of DTF obj os<-Sys.info()["sysname"]; # find the operating system runAll<- function(trPaths,dtfKind, fld=10, betNoBet='nobet',fff='f5'){ exit <- "DTF_END_OK"; #trPaths is a vector with all the tr paths of the competitions # dtfKind is a vector with the kind of dtf that we want to create {h,s,p1,p2,ht,ft} print(paste("trPaths :",trPaths)); print(paste("dtfKind :",dtfKind)); # -0 load the dtf-crfv-estimation files (head,score,1p,2p,ht,ft) library(methods); print("methods"); libLoader(); print("libLoader"); DTFLoader(); print("DTFLoader"); predAtt_Loader(); print("predAtt_Loader"); print("@ DataStructLoader"); dataStructLoader(); print("dataStructLoader"); dtfExit <- tryCatch({ #os<-Sys.info()["sysname"]; log <- "/home/user/BastData/R_LOG";# initial log file path is for linux o-systems if(grepl("win",tolower(os))){ log <- "C:/BastData/R_LOG"; } print("@ log-> funcs loaded"); write(paste0("DTF..."," ",dtfKind), file = log, ncolumns = 10, append = T, sep = ",") for(path in trPaths){ write(c("\t", path), file = log, ncolumns = 10, append = T, sep = ",") # -1 create datasets to work with dtf <<- read.csv(path); ndtf <<- diffFunc(); dirNam<- dirmaker(path);# create the folder of the competiton where the dtf object files will be stored # -2 start the object that will hold the pred data CREATION tryCatch({ if("h" %in% dtfKind){ print("\t ------: HEAD"); if(!ishead(dirNam)){ # if file doesnt exzist headCrfvInit(fld,betNoBet,fff); # ret :hDtf calculate headmaker(dirNam) # store write("\t HEAD", file = log, ncolumns = 10, append = T, sep = ",") }} }) tryCatch({ print("\t ------: SCORE"); if("s" %in% dtfKind){ if(!isscore(dirNam)){ scoreCrfvInit(fld,betNoBet,fff); # ret :csDtf scoremaker(dirNam) write("\t SCORE", file = log, ncolumns = 10, append = T, sep = ",") }} }) tryCatch({ if("ft" %in% dtfKind){ print("\t ------: FT"); if(!isft(dirNam)){ totFtCrfvInit(fld,betNoBet,fff); # ret :tftDtf ftmaker(dirNam); write("\t FT", file = log, ncolumns = 10, append = T, sep = ",") }} }) # tryCatch({ # if(mean(dtf$t1AvgHtScoreIn)<=0){ # write("mean(dtf$t1AvgHtScoreIn)<=0", file = log, ncolumns = 10, append = T, sep = ",") # # average of ht scores is 0 or less (-1) -> no real ht results # # so skip the ht dtf objects # next; # } # }) tryCatch({ if("p1" %in% dtfKind){ print("\t ------: P1"); if(!isp1(dirNam)){ p1CrfvInit(fld,betNoBet,fff); # ret :p1Dtf p1maker(dirNam); write("\t P1", file = log, ncolumns = 10, append = T, sep = ",") }} }) tryCatch({ if("p2" %in% dtfKind){ print("\t ------: P2"); if(!isp2(dirNam)){ p2CrfvInit(fld,betNoBet,fff); # ret :p2Dtf p2maker(dirNam); write("\t P2", file = log, ncolumns = 10, append = T, sep = ",") }} }) tryCatch({ if("ht" %in% dtfKind){ print("\t ------: HT"); if(!isht(dirNam)){ totHtCrfvInit(fld,betNoBet,fff); # ret :thtDtf htmaker(dirNam); write("\t HT", file = log, ncolumns = 10, append = T, sep = ",") }} }) } return(exit); }, error = function(err) { # error handler picks up where error was generated #print(paste("MY_ERROR: ",err)); write(paste("\t MY_ERROR: ",err), file = log, ncolumns = 10, append = T, sep = ",") exit <- "DTF_ERR_END"; return(exit); }, finally = { # in case of error save whatever can be saved #fileMaker(path); # create folder/subfolders & save the dtfs }) # END tryCatch return(dtfExit); } diffFunc <- function(){ t1adoe <- dtf$t1AtackIn - abs(dtf$t2DefenseOut) # because defence is negative nr t2adoe <- abs(dtf$t1DefenseIn) - dtf$t2AtackOut t1e <- dtf$t1Atack - abs(dtf$t2Defense) # because defence is negative nr t2e <- dtf$t2Atack - abs(dtf$t1Defense) #---------- datk <- dtf$t1Atack-dtf$t2Atack datkin <- dtf$t1AtackIn-dtf$t2AtackIn datkout <- dtf$t1AtackOut-dtf$t2AtackOut ddef <- dtf$t1Defense-dtf$t2Defense ddefin <- dtf$t1DefenseIn-dtf$t2DefenseIn ddefout <- dtf$t1DefenseOut-dtf$t2DefenseOut doav_ht <- dtf$t1AvgHtScoreIn-dtf$t2AvgHtScoreOut doav_ft <- dtf$t1AvgFtScoreIn-dtf$t2AvgFtScoreOut #---------- dav_htin <- dtf$t1AvgHtScoreIn-dtf$t2AvgHtScoreIn dav_htout <- dtf$t1AvgHtScoreOut-dtf$t2AvgHtScoreOut dav_ftin <- dtf$t1AvgFtScoreIn-dtf$t2AvgFtScoreIn dav_ftout <- dtf$t1AvgFtScoreOut-dtf$t2AvgFtScoreOut owd <- dtf$t1WinsIn-dtf$t2WinsOut odd <- dtf$t1DrawsIn- dtf$t2DrawsOut old <- dtf$t1LosesIn - dtf$t2LosesOut #---------- dwin <- dtf$t1WinsIn-dtf$t2WinsIn dwout <- dtf$t1WinsOut-dtf$t2WinsOut ddin <- dtf$t1DrawsIn-dtf$t2DrawsIn ddout <- dtf$t1DrawsOut-dtf$t2DrawsOut dlin <- dtf$t1LosesIn-dtf$t2LosesIn dlout <- dtf$t1LosesOut-dtf$t2LosesOut pd <- dtf$t1Points-dtf$t2Points fd <- dtf$t1Form-dtf$t2Form mfd1<-c() mfd2<-c() for(i in 1:dim(dtf)[1]){mfd1[i] <- mean(dtf[i,13],dtf[i,14],dtf[i,15],dtf[i,16])} for(i in 1:dim(dtf)[1]){mfd2[i] <- mean(dtf[i,39],dtf[i,40],dtf[i,41],dtf[i,42])} #owd <- dtf$t1WinsIn-dtf$t2WinsOut #odd <- dtf$t1DrawsIn- dtf$t2DrawsOut #old <- dtf$t1LosesIn - dtf$t2LosesOut #----------dtf data dtf$mfd1 <<-mfd1 dtf$mfd2 <<-mfd2 dtf$odd <<- odd dtf$old <<- old dtf$owd <<-owd #ttdf$mfd1 <-mfd1 #ttdf$mfd2 <-mfd2 #ttdf$odd <- odd #tdf$old<- old #tdf$owd <-owd #---------------- f1d <- dtf[,13]-dtf[,39] f2d <- dtf[,14]-dtf[,40] f3d <- dtf[,15]-dtf[,41] f4d <- dtf[,16]-dtf[,42] #-------------- ndf <- data.frame( mfd1,mfd2,pd,fd, # f1d,f2d,f3d,f4d, t1adoe,t2adoe,t1e,t2e, owd,odd,old, dwin,dwout,ddin,ddout,dlin,dlout, datk,datkin,datkout,ddef,ddefin,ddefout, doav_ht,doav_ft, dav_htin,dav_htout,dav_ftin,dav_ftout ) ndf$week <- dtf$week ndf$headOutcome <-dtf$headOutcome ndf$scoreOutcome<-dtf$scoreOutcome ndf$ht1pOutcome <-dtf$ht1pOutcome ndf$ht2pOutcome <-dtf$ht2pOutcome ndf$ggOutcome <-dtf$ggOutcome ndf$totHtScore <- dtf$totHtScore ndf$totFtScore <-dtf$totFtScore ndf$t1 <-dtf$t1 ndf$t2<-dtf$t2 ndf$bet_1<-dtf$bet_1 ndf$bet_X<-dtf$bet_X ndf$bet_2<-dtf$bet_2 ndf$bet_O<-dtf$bet_O ndf$bet_U<-dtf$bet_U ndf$t1Classification<-dtf$t1Classification ndf$t2Classification<-dtf$t2Classification ndf$mfd <- ndf$mfd1-ndf$mfd2 ndf$t1Form <- dtf$t1Form ndf$t2Form <- dtf$t2Form ndf$f1d <- f1d ndf$f2d <- f2d ndf$f3d <- f3d ndf$f4d <- f4d # rm(datk,datkin,datkout,ddef,ddefin,ddefout,doav_ht,doav_ft,dav_htin,dav_htout, # dav_ftin,dav_ftout, owd,odd,old,dwin,dwout,ddin,ddout,dlin,dlout,pd,fd,mfd1,mfd2,f1d,f2d,f3d,f4d, # t1adoe,t2adoe,t1e,t2e ) return(ndf); } fileMaker <- function(file_path){ # file_path <- patha fileName<- gsub("Pred/Data","DTF",file_path); fileName<- gsub("__Data",".dtf.RData",fileName); pathSegment <- strsplit(fileName,"/")[[1]]; dirName <- paste0(pathSegment[1:length(pathSegment)-1],collapse = "/") if(!dir.exists(dirName)){ dir.create(dirName,recursive = T,mode = 753) } if(dir.exists(dirName)){ #create a new file and sotre the dtf objs created for the competition save(hDtf,csDtf,p1Dtf,p2Dtf,tftDtf,thtDtf,file=fileName); } print(fileName); } dirmaker<- function(trPath){ dirName <- gsub("Pred/Data","DTF",trPath); dirName <- gsub("__Data","",dirName); if(!dir.exists(dirName)){ dir.create(dirName,recursive = T,mode = 753); # print(dirName); } return(dirName); } ishead<-function(dirPath){ headFile<- paste0(dirPath,"/head.dtf.RData"); if(file.exists(headFile)){ return (TRUE); } return(FALSE); } headmaker<- function(dirPath){ headFile<- paste0(dirPath,"/head.dtf.RData"); save(hDtf,file=headFile); } isscore<-function(dirPath){ scoreFile<- paste0(dirPath,"/score.dtf.RData"); if(file.exists(scoreFile)){ return (TRUE); } return(FALSE); } scoremaker<- function(dirPath){ scoreFile<- paste0(dirPath,"/score.dtf.RData"); save(csDtf,file=scoreFile); } isp1<-function(dirPath){ p1File<- paste0(dirPath,"/p1.dtf.RData"); if(file.exists(p1File)){ return (TRUE); } return(FALSE); } p1maker<- function(dirPath){ p1File<- paste0(dirPath,"/p1.dtf.RData"); save(p1Dtf,file=p1File); } isp2<-function(dirPath){ p2File<- paste0(dirPath,"/p2.dtf.RData"); if(file.exists(p2File)){ return (TRUE); } return(FALSE); } p2maker<- function(dirPath){ p2File<- paste0(dirPath,"/p2.dtf.RData"); save(p2Dtf,file=p2File); } isht<-function(dirPath){ htFile<- paste0(dirPath,"/ht.dtf.RData"); if(file.exists(htFile)){ return (TRUE); } return(FALSE); } htmaker<- function(dirPath){ htFile<- paste0(dirPath,"/ht.dtf.RData"); save(thtDtf,file=htFile); } isft<-function(dirPath){ ftFile<- paste0(dirPath,"/ft.dtf.RData"); if(file.exists(ftFile)){ return (TRUE); } return(FALSE); } ftmaker<- function(dirPath){ ftFile<- paste0(dirPath,"/ft.dtf.RData"); save(tftDtf,file=ftFile); } remover <- function(){ # aparently we dont need to use remove afterall, we are not saving the entire workspace just the DTF objs # remove all unwanted vars, save only the dtf objs rm(dtf,ndf,DTFLoader,predAtt_Loader,diffFunc); # DTFRemover(); dataStructRemover(); predAtt_Remover(); libRemover(); } libLoader <- function(){ library(plyr) library(e1071) #svm library(C50) library(randomForest) library(ipred) library(RWeka) library(rpart) library(tree) } libRemover <- function(){ detach(package:plyr, unload=TRUE) detach(package:e1071, unload=TRUE) detach(package:C50, unload=TRUE) detach(package:randomForest, unload=TRUE) detach(package:ipred, unload=TRUE) detach(package:RWeka, unload=TRUE) detach(package:rpart, unload=TRUE) detach(package:tree, unload=TRUE) } DTFLoader <- function(){ base<-"/home/user/Git"; #initial base directory is for linux if(grepl("win",tolower(os))){ base <- "C:"; } source(paste0(base,"/TotalPrediction/HeadCrfvEstimation.R")); source(paste0(base,"/TotalPrediction/ScoreCrfvEstimation.R")); source(paste0(base,"/TotalPrediction/P1CrfvEstimation.R")); source(paste0(base,"/TotalPrediction/P2CrfvEstimation.R")); source(paste0(base,"/TotalPrediction/totHtScoreCrfvEstimation.R")); source(paste0(base,"/TotalPrediction/totFtScoreCrfvEstimation.R")); } DTFRemover <- function(){ rm(headCrfvInit,headPredFunc,headTreBestChoser,headCrfv); rm(scoreCrfvInit,scorePredFunc,scoreTreBestChoser,scoreCrfv); rm(p2CrfvInit,p2PredFunc,p2TreBestChoser,p2Crfv); rm(p1CrfvInit,p1PredFunc,p1TreBestChoser,p1Crfv); rm(fulltotFtBet,fullTotFtNoBet,differencedTotFtBet,differencedTotFtNoBet) rm(totHtCrfvInit,totHtPredFunc,totHtTreBestChoser,totHtCrfv); } predAtt_Loader <- function(){ base<-"/home/user/Git"; #initial base directory is for linux if(grepl("win",tolower(os))){ base <- "C:"; } source(paste0(base,"/TotalPrediction/Head_AttPredDataset.R")); source(paste0(base,"/TotalPrediction/Score_AttPredDataset.R")); source(paste0(base,"/TotalPrediction/P1_AttPredDataset.R")); source(paste0(base,"/TotalPrediction/P2_AttPredDataset.R")); source(paste0(base,"/TotalPrediction/totFt_AttPredDataset.R")); source(paste0(base,"/TotalPrediction/totHt_AttPredDataset.R")); } predAtt_Remover <- function(){ rm(fullHeadBet,fullHeadNoBet,differencedHeadBet,differencedHeadNoBet) rm(fullScoreBet,fullScoreNoBet,differencedScoreBet,differencedScoreNoBet) rm(full2pBet,full2pNoBet,differenced2pBet,differenced2pNoBet) rm(full1pBet,full1pNoBet,differenced1pBet,differenced1pNoBet) rm(totFtCrfvInit,totFtPredFunc,totFtTreBestChoser,totFtCrfv) rm(fullTotHtBet,fullTotHtNoBet,differencedTotHtBet,differencedTotHtNoBet); } dataStructLoader <- function(){ base<-"/home/user/Git"; #initial base directory is for linux if(grepl("win",tolower(os))){ base <- "C:"; } # the file with the description of the structure of the DTF obj source(paste0(base,"/TotalPrediction/dataStructure.R")); } dataStructRemover <- function(){ rm(Instance,AlgoData,CleanScoreDtf,CleanHeadDtf,Clean2pDtf,Clean1pDtf,CleanTotFtDtf,CleanTotHtDtf) rm(modelFunc,attDtsFunc,scoreResultCount,headResultCount,p2ResultCount,p1ResultCount,totFtResultCount,totHtResultCount); } test <- function(v, vec){ base<-"/home/user/Git"; #initial base directory is for linux if(grepl("win",tolower(os))){ base <- "C:"; } dir_nam <- paste0(base,'/ff1/ff2/ff3/ff5'); fil_nam <- paste(dir_nam,'marioFile.mar',sep="/"); dir.create(dir_nam,recursive = T,mode = 753) for(i in 1:v){ write(i,fil_nam,append=TRUE); } for(i in 1:length(vec)){ write(vec[i],fil_nam,append=TRUE); } return(v+4); } #---------------------------------------------------- #---------------------------------------------------- #----------------------------------------------------

2fa9516d586ad18b5c5faf49c31ae5dd6d792710

56ea0cd71b0982ca04835a6e7179f00ab1ff94d8

/DutchessStarter.R

60af26b48047e9d1503214698e929ac2ddffc634

[]

no_license

alizzo/Traffic_Data_HVHackathon

bb4f617e9819c06669dfef91c25d36d13e32f3a2

5d91ec7968078e66e8a9a231f87b36ea53b47767

refs/heads/main

2023-09-01T21:53:49.524240

2021-10-30T19:25:08

422,969,908

0

null

UTF-8

R

false

2,826

r

DutchessStarter.R

# Load Libraries library(tidyverse) library(sf) library(leaflet) library(viridis) # Read in the Shape File municipalities <- read_sf("./dutchesscounty4326/muni_boundaries-polygon.shp") # Read in the geospatial data for Dutchess County Fire & EMS Stations. fire_ems <- read_csv("fire_ems_stations-point.csv") %>% st_as_sf(coords = c("X", "Y"), crs = 4326, agr = "field") # Read in the geospatial data for Dutchess County Police Stations. police <- read_csv("police_stations.csv") %>% st_as_sf(coords = c("X", "Y"), crs = 4326, agr = "field") # Transform the data capwords <- function(s) { cap <- function(s) paste(toupper(substring(s, 1, 1)), {tolower(substring(s, 2))}, sep = "", collapse = " ") sapply(strsplit(s, split = " "), cap, USE.NAMES = !is.null(names(s))) } # Municipalities dataset transformation to proper projection system (4326 or WGS 84) municipalities_clean <- municipalities %>% st_transform(4326) %>% mutate(municipalities_label = paste0('Location: ', capwords(NAME))) fire_ems_clean <- fire_ems %>% rename(ANA = `ANA`) %>% mutate(popup_label = paste(paste0('City: ', MCN, ''), paste0('Address: ', AAD), sep = ' ')) police_clean <- police %>% mutate(popup_label = paste(paste0('Dept.: ', DEPARTMENT, ''), paste0('Address: ', LOCATION), sep = ' ')) # Build the Map Using Leaflet fire_ems_clean$MCN <- factor(sample.int(5L, nrow(fire_ems_clean), TRUE)) factpal <- colorFactor(topo.colors(5), fire_ems_clean$MCN) map <-leaflet(fire_ems) %>% addTiles(group="Dutchess County") %>% addPolygons(data = municipalities_clean, color = 'white', weight = 1.5, opacity = 1, fillColor = 'black', fillOpacity = .8, highlightOptions = highlightOptions(color = "#FFF1BE", weight = 5), popup = ~municipalities_label) %>% addCircleMarkers(data = fire_ems_clean, popup = ~popup_label, stroke = F, radius = 4, fillColor = ~factpal(MCN), fillOpacity = 1, group = "Fire-EMS") %>% addMarkers(data = police_clean, popup = ~popup_label, group = "Police") %>% addLayersControl( baseGroups = c("Dutchess County"), overlayGroups = c("Fire-EMS", "Police"), options=layersControlOptions(collapsed=FALSE)) map

74fa2efe453a83b8096ab45ba9e8de7595e0f224

bfde25295f3330b108ba93d5f90e71382627c639

/A2_submission.R

823ed92a98cfaf131aee71613f4fb8c2617e40b2

[]

no_license

jgow68/CDA2

84630ec4ab76c339980e7fb3287acce3b2559cf6

6a18358dee4a37a4d743326cbd176f6d98f5937a

refs/heads/master

2021-01-22T22:20:19.133256

2017-03-30T06:20:27

85,531,396

0

null

UTF-8

R

false

11,724

r

A2_submission.R

# Q1 ---------------------------------------------------------------------- dat = data.frame(interview=c("no", "yes"), cases=c(195,46), controls=c(979,370)) dat = data.frame(med_status=c("no", "yes"), cases=c(979,195), controls=c(370,46)) # cases is non-participation dat # Q1 - use med status as predictor ---------------------------------------- fm = glm(cbind(cases,controls) ~ med_status, family=binomial, data=dat) summary(fm) # med status coef is significant anova(fm, test="Chisq") # med status coef is significant, there is a relationship between participation and medical aid exp(coef(fm)[2]) # odds of non-participation from mothers having med status is 1.602 higher than non med-status # calculate CI of log odds, then transform back lor_CI = coef(fm)[2] + c(-1,1)*qnorm(0.975)*sqrt(diag(vcov(fm))[2]) exp(lor_CI) # CI does not include 1, med-status is a risk factor for participation dat_white = data.frame(med_status=c("no", "yes"), cases=c(104,22), controls=c(10,2)) dat_black = data.frame(med_status=c("no", "yes"), cases=c(91,957), controls=c(36,368)) (fm_white = glm(cbind(cases,controls) ~ med_status, family=binomial, data=dat_white)) (fm_black = glm(cbind(cases,controls) ~ med_status, family=binomial, data=dat_black)) # both shows that med-status doesnt affect participation anova(fm_white, test="Chisq") anova(fm_black, test="Chisq") # participation may have higher dependence on black / white race instead on med_status dat_race = data.frame(race=c("black", "white"), cases=c(1048, 126), controls=c(404, 12)) (fm_race = glm(cbind(cases,controls) ~ race, family=binomial, data=dat_race)) anova(fm_race, test="Chisq") # zero residual deviance, coef race is significant dat_race_nomed = data.frame(race=c("black", "white"), cases=c(957, 22), controls=c(368, 22)) (fm_race_nomed = glm(cbind(cases,controls) ~ race, family=binomial, data=dat_race_nomed)) anova(fm_race_nomed, test="Chisq") dat_race_med = data.frame(race=c("black", "white"), cases=c(91, 104), controls=c(36, 10)) (fm_race_med = glm(cbind(cases,controls) ~ race, family=binomial, data=dat_race_med)) anova(fm_race_med, test="Chisq") # Q1 - initial use interview as predictor --------------------------------- fm = glm(cbind(cases,controls) ~ interview, family=binomial, data=dat) summary(fm) # interview coef is significant anova(fm, test="Chisq") # interview coef is significant, there is a relationship between participation and medical aid exp(coef(fm)[2]) # odds of having medical aid from interviewed mothers is 0.624 times the odds of not interviewed # calculate CI of log odds, then transform back lor_CI = coef(fm)[2] + c(-1,1)*qnorm(0.975)*sqrt(diag(vcov(fm))[2]) exp(lor_CI) # CI does not include 1, participation is a risk factor for medical aid involvment plot(cases/(cases+controls) ~ interview, data=dat) plot(predict(fm)) dat_white = data.frame(interview=c("no", "yes"), cases=c(104,10), controls=c(22,2)) dat_black = data.frame(interview=c("no", "yes"), cases=c(91,36), controls=c(957,368)) (fm_white = glm(cbind(cases,controls) ~ interview, family=binomial, data=dat_white)) (fm_black = glm(cbind(cases,controls) ~ interview, family=binomial, data=dat_black)) # both shows that participation doesnt affect the medical aid status anova(fm_white, test="Chisq") anova(fm_black, test="Chisq") # med aid may have higher dependence on black / white race instead on participation dat_race = data.frame(race=c("black", "yes"), cases=c(127, 114), controls=c(1325, 24)) (fm_race = glm(cbind(cases,controls) ~ race, family=binomial, data=dat_race)) anova(fm_race, test="Chisq") # zero residual deviance # Q2 ---------------------------------------------------------------------- data_set2 = read.csv("task2.csv", header=T) #show the tas2.csv layout str(data_set2) # Q2a --------------------------------------------------------------------- # smoking and family as response, rest as explanatory variables # min model is (family, smoking, race:sex:age) fm = glm(Count ~ (.)^5, data_set2, family=poisson()) summary(fm) drop1(fm, test="Chisq") fm = update(fm, .~. -Family:Race:Sex:Age:Smoking_I) # drop 5 way interaction (p-value 0.05644) drop1(fm, test="Chisq") # drop Family:Race:Sex:Age, p-value=0.7923 fm = update(fm, .~. -Family:Race:Sex:Age) drop1(fm, test="Chisq") # drop Family:Race:Sex:Smoking_I, p-value 0.5966 fm = update(fm, .~. -Family:Race:Sex:Smoking_I) drop1(fm, test="Chisq") # drop Family:Race:Sex, p-value 0.7459 fm = update(fm, .~. -Family:Race:Sex) drop1(fm, test="Chisq") # drop ace:Sex:Age:Smoking_I, p-value 0.61798 fm = update(fm, .~. -Race:Sex:Age:Smoking_I) drop1(fm, test="Chisq") # can't drop Race:Sex:Age, drop Race:Sex:Smoking_I, pva=0.57454 fm = update(fm, .~. -Race:Sex:Smoking_I) drop1(fm, test="Chisq") # can't drop Race:Sex:Age, drop Family:Sex:Age:Smoking_I, pva=0.34809 fm = update(fm, .~. -Family:Sex:Age:Smoking_I) drop1(fm, test="Chisq") # can't drop Race:Sex:Age, drop Sex:Age:Smoking_I , pva=0.81672 fm = update(fm, .~. -Sex:Age:Smoking_I) drop1(fm, test="Chisq") # can't drop Race:Sex:Age, drop Family:Sex:Age , pva=0.29404 fm = update(fm, .~. -Family:Sex:Age) drop1(fm, test="Chisq") # can't drop Race:Sex:Age, drop Family:Sex:Smoking_I , pva=0.12656 fm = update(fm, .~. -Family:Sex:Smoking_I) drop1(fm, test="Chisq") # can't drop Race:Sex:Age, drop Family:Sex , pva=0.46289 fm = update(fm, .~. -Family:Sex) drop1(fm, test="Chisq") # can't drop Race:Sex:Age, drop Sex:Smoking_I, pva=0.1887 fm = update(fm, .~. -Sex:Smoking_I) drop1(fm, test="Chisq") # can't drop Race:Sex:Age, drop Family:Race:Age:Smoking_I, pva=0.0847 fm = update(fm, .~. -Family:Race:Age:Smoking_I) drop1(fm, test="Chisq") # can't drop Race:Sex:Age, drop Family:Age:Smoking_I, pva=0.95417 fm = update(fm, .~. -Family:Age:Smoking_I) drop1(fm, test="Chisq") # can't drop Race:Sex:Age, drop Family:Race:Age , pva=0.8473 fm = update(fm, .~. -Family:Race:Age ) drop1(fm, test="Chisq") # can't drop Race:Sex:Age, drop Race:Age:Smoking_I , pva=0.2882 fm = update(fm, .~. -Race:Age:Smoking_I ) drop1(fm, test="Chisq") # can't drop Race:Sex:Age, drop Family:Age , pva=0.26258 fm = update(fm, .~. -Family:Age) drop1(fm, test="Chisq") # can't drop Race:Sex:Age, drop Family:Race:Smoking_I, pva=0.05113 fm = update(fm, .~. -Family:Race:Smoking_I) drop1(fm, test="Chisq") # can't drop Race:Sex:Age, drop Race:Smoking_I, pva=0.450285 fm = update(fm, .~. -Race:Smoking_I) drop1(fm, test="Chisq") # can't drop Race:Sex:Age, cant drop any more # Final model: Count ~ Family + Race + Sex + Age + Smoking_I + Family:Race + # Family:Smoking_I + Race:Sex + Race:Age + Sex:Age + Age:Smoking_I + Race:Sex:Age pchisq(fm$deviance, fm$df.residual, lower.tail=F) # pval 0.334 reject H0, model is adequate # state the conditional independence structure in the selected model # Q2b --------------------------------------------------------------------- # smoking as response, rest as explanatory variables # min model is (smoking, family:race:sex:age) fm = glm(Count ~ (.)^5, data_set2, family=poisson()) summary(fm) drop1(fm, test="Chisq") fm = update(fm, .~. -Family:Race:Sex:Age:Smoking_I) # drop 5 way interaction (p-value 0.05644) drop1(fm, test="Chisq") # cannot drop Family:Race:Sex:Age , drop Family:Race:Sex:Smoking_I, pval 0.6502 fm = update(fm, .~. -Family:Race:Sex:Smoking_I) drop1(fm, test="Chisq") # cannot drop Family:Race:Sex:Age , drop Race:Sex:Age:Smoking_I, pval 0.64504 fm = update(fm, .~. -Race:Sex:Age:Smoking_I) drop1(fm, test="Chisq") # cannot drop Family:Race:Sex:Age , drop Race:Sex:Smoking_I , pval 0.53462 fm = update(fm, .~. -Race:Sex:Smoking_I ) drop1(fm, test="Chisq") # cannot drop Family:Race:Sex:Age , drop Family:Sex:Age:Smoking_I , pval 0.36547 fm = update(fm, .~. -Family:Sex:Age:Smoking_I) drop1(fm, test="Chisq") # cannot drop Family:Race:Sex:Age , drop Sex:Age:Smoking_I , pval 0.79750 fm = update(fm, .~. -Sex:Age:Smoking_I) drop1(fm, test="Chisq") # cannot drop Family:Race:Sex:Age , drop Family:Sex:Smoking_I, pval 0.10118 fm = update(fm, .~. -Family:Sex:Smoking_I) drop1(fm, test="Chisq") # cannot drop Family:Race:Sex:Age , drop Sex:Smoking_I , pval 0.1994 fm = update(fm, .~. -Sex:Smoking_I ) drop1(fm, test="Chisq") # cannot drop Family:Race:Sex:Age , drop Family:Race:Age:Smoking_I, pval 0.0847 fm = update(fm, .~. -Family:Race:Age:Smoking_I) drop1(fm, test="Chisq") # cannot drop Family:Race:Sex:Age , drop Family:Age:Smoking_I, pval 0.95417 fm = update(fm, .~. -Family:Age:Smoking_I) drop1(fm, test="Chisq") # cannot drop Family:Race:Sex:Age , drop Race:Age:Smoking_I , pval 0.2845 fm = update(fm, .~. -Race:Age:Smoking_I) drop1(fm, test="Chisq") # cannot drop Family:Race:Sex:Age , drop Family:Race:Smoking_I, pval 0.05025 fm = update(fm, .~. -Family:Race:Smoking_I) drop1(fm, test="Chisq") # cannot drop Family:Race:Sex:Age , drop Race:Smoking_I , pval 0.46187 fm = update(fm, .~. -Race:Smoking_I ) drop1(fm, test="Chisq") # cannot drop Family:Race:Sex:Age , drop Age:Smoking_I , pval 0.05157 fm = update(fm, .~. -Age:Smoking_I) drop1(fm, test="Chisq") # cannot drop Family:Race:Sex:Age , cant drop any other predictors summary(fm) pchisq(fm$deviance, fm$df.residual, lower.tail=F) # pval 0.079 reject H0, model is adequate # residuals test library(boot) fm.diag = glm.diag(fm) round(ftable(xtabs(fm.diag$rp ~ Family + Race + Sex + Age + Smoking_I, data=data_set2)),2) # overest smokers from mother family, black, female, age<13 # underest smokers from mother family, black, female, age>13 cbind(data_set2$Count,fitted(fm)) # check the fitted values against data # Count ~ Family + Race + Sex + Age + Smoking_I + Family:Race + # Family:Sex + Family:Age + Family:Smoking_I + Race:Sex + Race:Age + Sex:Age + Family:Race:Sex + # Family:Race:Age + Family:Sex:Age + Race:Sex:Age + Family:Race:Sex:Age tmp = xtabs(Count ~ Family + Race + Sex + Age + Smoking_I, data=data_set2) tmp ftable(tmp) Race_Smoking = apply(tmp, c("Race", "Smoking_I"), sum) Family_Smoking = apply(tmp, c("Family", "Smoking_I"), sum) Sex_Smoking = apply(tmp, c("Sex", "Smoking_I"), sum) Age_Smoking = apply(tmp, c("Age", "Smoking_I"), sum) library(vcd) or_race_smoking = oddsratio(Race_Smoking, log=F) # odds of white kids smoking is 0.9894 times the odds of black kids or_family_smoking = oddsratio(Family_Smoking, log=F) # odds of children from family with mother only to smoke is 1.7467 times the odds of children from family with both parents or_sex_smoking = oddsratio(Sex_Smoking, log=F) # odds of Male smoking is 0.8322 times the odds of Females?? SURPRISING!! or_age_smoking = oddsratio(Age_Smoking, log=F) # odds of children >=13 smoking is 1.496 times the odds of childre <12 # sample size dominated by both parents, white confint(or_race_smoking) # CI incl. 1 confint(or_family_smoking) # CI do not incl. 1 confint(or_sex_smoking) # CI incl. 1 confint(or_age_smoking) # CI do not incl. 1 # family and age significantly affects smoking patterns at 95% confidence level # state the logit model equivalent to the selected loglinear model # prepare the logit data set dat.logit = cbind(expand.grid(A=levels(data_set2$Age), S=levels(data_set2$Sex),# need to relevel Sex, default set Female as first level R=levels(data_set2$Race), F=levels(data_set2$Family)), SN=data_set2$Count[data_set2$Smoking_I=="none"], SS=data_set2$Count[data_set2$Smoking_I=="some"]) data_set2$Sex = relevel(data_set2$Sex, "Male") dat.logit fm.logit = glm(cbind(SN, SS) ~ F+R+S+A, dat.logit, family=binomial) fm.logit$deviance; fm$deviance summary(fm.logit)$call;summary(fm)$call # logit much simpler than log linear update(fm.logit, .~. -A-R-S, dat.logit )$deviance; fm$deviance # matched log linear deviance

6b73ad3a1e1cc5a75fa2003406c68c7a11aeaec4

8b1d00ae218ab6b4c300083fb179bbc3d441e221

/scripts/topGO_vignetteTrial.R

f0b921b2cce9449de5b65572defe60bf79654420

[]

no_license

davetgerrard/LiverProteins

79d1aa8ee265b27c6b161b22e189a4cba891dca6

d99c0adab3c71579151d72eb7a5ebb65921209d9

refs/heads/master

2021-01-01T17:58:07.123517

2011-08-05T15:12:10

1,941,622

0

null

UTF-8

R

false

10,976

r

topGO_vignetteTrial.R

######################################### # # # Dave Gerrard # # University of Manchester # # 2011 # # # ######################################### library(topGO) library(ALL) data(ALL) data(geneList) affyLib <- paste(annotation(ALL), "db", sep = ".") library(package = affyLib, character.only = TRUE) sum(topDiffGenes(geneList)) sampleGOdata <- new("topGOdata", description = "Simple session", ontology = "BP", allGenes = geneList, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.db, affyLib = affyLib) sampleGOdata resultFisher <- runTest(sampleGOdata, algorithm = "classic", statistic = "fisher") resultFisher resultKS <- runTest(sampleGOdata, algorithm = "classic", statistic = "ks") resultKS.elim <- runTest(sampleGOdata, algorithm = "elim", statistic = "ks") allRes <- GenTable(sampleGOdata, classicFisher = resultFisher, classicKS = resultKS, elimKS = resultKS.elim, orderBy = "elimKS", ranksOf = "classicFisher", topNodes = 10) pValue.classic <- score(resultKS) pValue.elim <- score(resultKS.elim)[names(pValue.classic)] gstat <- termStat(sampleGOdata, names(pValue.classic)) gSize <- gstat$Annotated/max(gstat$Annotated) * 4 gCol <- colMap(gstat$Significant) colMap <- function(x) { .col <- rep(rev(heat.colors(length(unique(x)))), time = table(x)) return(.col[match(1:length(x), order(x))]) } plot(pValue.classic, pValue.elim, xlab = "p-value classic", ylab = "p-value elim", pch = 19, cex = gSize, col = gCol) gCol <- colMap(gstat$Significant) plot(pValue.classic, pValue.elim, xlab = "p-value classic", ylab = "p-value elim", pch = 19, cex = gSize, col = gCol) BPterms <- ls(GOBPTerm) head(BPterms) library(genefilter) selProbes <- genefilter(ALL, filterfun(pOverA(0.2, log2(100)), function(x) (IQR(x) > 0.25))) eset <- ALL[selProbes, ] selProbes eset geneID2GO <- readMappings(file="C:/Users/dave/Documents/R/win-library/2.11/topGO/examples/ensembl2go.map") ## replaced the file name here geneID2GO str(head(geneID2GO)) GO2geneID <- inverseList(geneID2GO) str(head(GO2geneID)) geneNames <- names(geneID2GO) head(geneNames) myInterestingGenes <- sample(geneNames, length(geneNames)/10) geneList <- factor(as.integer(geneNames %in% myInterestingGenes)) names(geneList) <- geneNames str(geneList) GOdata <- new("topGOdata", ontology = "MF", allGenes = geneList, annot = annFUN.gene2GO, gene2GO = geneID2GO) GOdata y <- as.integer(sapply(eset$BT, function(x) return(substr(x, 1, 1) == "T"))) table(y) y geneList <- getPvalues(exprs(eset), classlabel = y, alternative = "greater") geneList topDiffGenes <- function(allScore) { return(allScore < 0.01) } x <- topDiffGenes(geneList) sum(x) GOdata <- new("topGOdata", description = "GO analysis of ALL data; B-cell vs T-cell", ontology = "BP", allGenes = geneList, geneSel = topDiffGenes, annot = annFUN.db, nodeSize = 5, affyLib = affyLib) #############more detritus #######THIS TRIAL SECTION WORKS library(topGO) library(org.Hs.eg.db) go2entrez <- annFUN.org("BP", mapping = "org.Hs.eg.db", ID = "Entrez") allGenes <- unique(unlist(go2entrez )) someGenes <- sample(allGenes,400) geneList <- 1:length(someGenes) names(geneList) <- someGenes GOdata <- new("topGOdata", description = "Liver proteins data set", ontology = "BP", allGenes = geneList, geneSelectionFun = topDiffGenes, nodeSize = 5, annot = annFUN.org, mapping = "org.Hs.eg.db", ID = "Entrez") test.stat <- new("classicScore", testStatistic = GOKSTest, name = "KS tests") resultKS <- getSigGroups(GOdata, test.stat) test.stat <- new("elimScore", testStatistic = GOKSTest, name = "Fisher test", cutOff = 0.01) resultElim <- getSigGroups(GOdata, test.stat) allRes <- GenTable(GOdata, KS = resultKS , elim=resultElim, orderBy = "elim", ranksOf = "KS", topNodes=20) ################# #prot_list <- list() prot_list <- unique(protGoMap$DB_Object_ID) listGoPerProt <- function(dataFrame,thisProt) { dataFrame$GO_ID[which(dataFrame$DB_Object_ID == thisProt)] } prot2go <- lapply(prot_list,FUN = function (x) listGoPerProt(protGoMap,x)) names(prot2go) <- prot_list go2prot <- inverseList(prot2go) prot2go <- inverseList(go2prot) #topGoToProt <- annFUN.GO2genes("BP", feasibleGenes = NULL, go2prot) #topGoProtToGo.BP <- annFUN.gene2GO("BP", feasibleGenes = NULL, prot2go ) allGenes <- unique(names(prot2go )) someGenes <- sample(allGenes,400) geneList <- 1:length(someGenes) names(geneList) <- someGenes #geneList <- 1:length(prot_list) #names(geneList) <- prot_list GOdata <- new("topGOdata", description = "Liver proteins data set", ontology = "BP", allGenes = geneList, geneSelectionFun = topDiffGenes, nodeSize = 5, annot = annFUN.GO2genes, GO2genes=go2prot ) test.stat <- new("classicScore", testStatistic = GOKSTest, name = "KS tests") resultKS <- getSigGroups(GOdata, test.stat) test.stat <- new("elimScore", testStatistic = GOKSTest, name = "Fisher test", cutOff = 0.01) resultElim <- getSigGroups(GOdata, test.stat) allRes <- GenTable(GOdata, KS = resultKS , elim=resultElim, orderBy = "elim", ranksOf = "KS", topNodes=20) ######DEVELOPMENT sampleGOdata <- new("topGOdata", description = "Simple session", ontology = "BP", allGenes = geneList, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.db, affyLib = affyLib) library(org.Hs.eg.db) # not sure if this is already loaded. raw.ent2up <- org.Hs.egUNIPROT # Get the entrez gene IDs that are mapped to a Uniprot ID mapped_genes <- mappedkeys(raw.ent2up) # Convert to a list ent2up <- as.list(raw.ent2up[mapped_genes]) #ent2up [1:5] #inverseList(ent2up) ## could use this to get entrez IDs from Uniprot? up2entrez <- inverseList(ent2up) up2entrez[[proteinByLiverSample$spAccession]] up2entrez[proteinByLiverSample$spAccession] ## some proteins have multiple gene identifiers. go2entrez <- annFUN.org("BP", mapping = "org.Hs.eg.db", ID = "Entrez") head(go2entrez) en2go <- inverseList(go2entrez) ## BPterms <- ls(GOBPTerm) # loaded with topGO GOdata <- new("topGOdata", ontology = "MF", allGenes = geneList, annot = annFUN.gene2GO, gene2GO = geneID2GO) y <- as.integer(sapply(eset$BT, function(x) return(substr(x, 1, 1) == "T"))) allGenes <- unique(unlist(go2entrez )) #myInterestedGenes <- sample(allGenes, 500) #geneList <- factor(as.integer(allGenes #names(geneList) <- allGenes ################# From Using GO GOTERM$"GO:0019083" GOBPANCESTOR$"GO:0019083" GOBPPARENTS$"GO:0019083" GOMFCHILDREN$"GO:0019083" GOTERM$"GO:0019084" GOTERM$"GO:0019085" GOTERM$"GO:0006350" GOTERM$"GO:0019080" GOTERM$"GO:0000003" GOBPPARENTS$"GO:0019084" GOTERM$"GO:0009299" GOTERM$"GO:0019083" #######THIS TRIAL SECTION WORKS library(topGO) library(org.Hs.eg.db) go2entrez <- annFUN.org("BP", mapping = "org.Hs.eg.db", ID = "Entrez") allGenes <- unique(unlist(go2entrez )) topDiffGenes <- function(allScore) { return(allScore > 1) } someGenes <- sample(allGenes,400) geneList <- 1:length(someGenes) names(geneList) <- someGenes GOdata <- new("topGOdata", description = "Liver proteins data set", ontology = "BP", allGenes = geneList, geneSelectionFun = topDiffGenes, nodeSize = 5, annot = annFUN.org, mapping = "org.Hs.eg.db", ID = "Entrez") test.stat <- new("classicScore", testStatistic = GOKSTest, name = "KS tests") resultKS <- getSigGroups(GOdata, test.stat) test.stat <- new("elimScore", testStatistic = GOKSTest, name = "Fisher test", cutOff = 0.01) resultElim <- getSigGroups(GOdata, test.stat) allRes <- GenTable(GOdata, KS = resultKS , elim=resultElim, orderBy = "elim", ranksOf = "KS", topNodes=20) #goID <- allRes[1, "GO.ID"] #print(showGroupDensity(GOdata, goID, ranks = TRUE)) ## doesn't work unless Bioconductor annotated chip. #showSigOfNodes(GOdata, score(resultKS), firstSigNodes = 5, useInfo = "all") #showSigOfNodes(GOdata, score(resultWeight), firstSigNodes = 5, useInfo = "def") #printGraph(GOdata, resultKS, firstSigNodes = 5, fn.prefix = "tGO", useInfo = "all", pdfSW = TRUE) #printGraph(GOdata, resultElim, firstSigNodes = 5, fn.prefix = "tGO", useInfo = "def", pdfSW = TRUE) #####END OF TRIAL SECTION ##############from the vignette library(topGO) library(ALL) data(ALL) data(geneList) affyLib <- paste(annotation(ALL), "db", sep = ".") library(package = affyLib, character.only = TRUE) sum(topDiffGenes(geneList)) sampleGOdata <- new("topGOdata", description = "Simple session", ontology = "BP", allGenes = geneList, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.db, affyLib = affyLib) sampleGOdata resultFisher <- runTest(sampleGOdata, algorithm = "classic", statistic = "fisher") resultFisher resultKS <- runTest(sampleGOdata, algorithm = "classic", statistic = "ks") resultKS.elim <- runTest(sampleGOdata, algorithm = "elim", statistic = "ks") allRes <- GenTable(sampleGOdata, classicFisher = resultFisher, classicKS = resultKS, elimKS = resultKS.elim, orderBy = "elimKS", ranksOf = "classicFisher", topNodes = 10) pValue.classic <- score(resultKS) pValue.elim <- score(resultKS.elim)[names(pValue.classic)] gstat <- termStat(sampleGOdata, names(pValue.classic)) gSize <- gstat$Annotated/max(gstat$Annotated) * 4 gCol <- colMap(gstat$Significant) colMap <- function(x) { .col <- rep(rev(heat.colors(length(unique(x)))), time = table(x)) return(.col[match(1:length(x), order(x))]) } plot(pValue.classic, pValue.elim, xlab = "p-value classic", ylab = "p-value elim", pch = 19, cex = gSize, col = gCol) gCol <- colMap(gstat$Significant) plot(pValue.classic, pValue.elim, xlab = "p-value classic", ylab = "p-value elim", pch = 19, cex = gSize, col = gCol) BPterms <- ls(GOBPTerm) head(BPterms) library(genefilter) selProbes <- genefilter(ALL, filterfun(pOverA(0.2, log2(100)), function(x) (IQR(x) > 0.25))) eset <- ALL[selProbes, ] selProbes eset geneID2GO <- readMappings(file="C:/Users/dave/Documents/R/win-library/2.11/topGO/examples/ensembl2go.map") ## replaced the file name here geneID2GO str(head(geneID2GO)) GO2geneID <- inverseList(geneID2GO) str(head(GO2geneID)) geneNames <- names(geneID2GO) head(geneNames) myInterestingGenes <- sample(geneNames, length(geneNames)/10) geneList <- factor(as.integer(geneNames %in% myInterestingGenes)) names(geneList) <- geneNames str(geneList) GOdata <- new("topGOdata", ontology = "MF", allGenes = geneList, annot = annFUN.gene2GO, gene2GO = geneID2GO) GOdata y <- as.integer(sapply(eset$BT, function(x) return(substr(x, 1, 1) == "T"))) table(y) y geneList <- getPvalues(exprs(eset), classlabel = y, alternative = "greater") geneList topDiffGenes <- function(allScore) { return(allScore < 0.01) } x <- topDiffGenes(geneList) sum(x) GOdata <- new("topGOdata", description = "GO analysis of ALL data; B-cell vs T-cell", ontology = "BP", allGenes = geneList, geneSel = topDiffGenes, annot = annFUN.db, nodeSize = 5, affyLib = affyLib)

b063efeaeca24f59b0cd0f142f50d067bbb23248

aa103303a64aac3a17160833d8136cd6a7bd21a4

/LinearAlgebra/Unit_01/vectors.R

5cf3d6339f621f19e74c782d034708e6f431e69e

[]

no_license

anhnguyendepocen/MSDS-Supervised-Learning

4e9ed791d686eae2bb7519a563ad05f9b5c98b5d

67dc32d564bdcd498137620f1efbbf2445a87364

refs/heads/master

2022-02-22T08:08:05.342690

2019-10-11T23:08:32

null

0

null

UTF-8

R

false

63

r

vectors.R

m <- matrix(c(1, 2, -1, 3, -4, 5, 1, -8, 7), nrow = 3) det(m)

63c063b794ab78a9ccb77b1ce78a2720bf542650

7dd0f3d19b98750e34d2dfa62533cbd50fa18db5

/plot5.R

132937d2abedd8ebc32979857bb41048e6541510

[]

no_license

gregorypierce/datascience-exdata-courseproject2

e551f29fb5ee9b87ffd24526efcf03ef569a39ec

4445d60d26a6b30c37fa6f5f6e168ff0b3da9e56

refs/heads/master

2016-09-01T20:51:24.448542

2015-07-25T05:37:54

39,669,223

0

null

UTF-8

R

false

1,090

r

plot5.R

library(ggplot2) ## set the project Directory projectDirectory <- "/projects/datascience/exploraratorydata/courseproject2" dataDirectory <- paste0( projectDirectory,"/data") setwd( projectDirectory ) ## This first line will likely take a few seconds. Be patient! NEI <- readRDS( paste0( dataDirectory, "/summarySCC_PM25.rds" ) ) SCC <- readRDS( paste0( dataDirectory, "/Source_Classification_Code.rds") ) ## Parse out a logical vector of the vehicle data vehicles <- grepl("vehicle", SCC$SCC.Level.Two, ignore.case = TRUE) vehiclesSCC <- SCC[vehicles,]$SCC vehiclesNEI <- NEI[NEI$SCC %in% vehiclesSCC,] ## Extract the data specifically for baltimore baltimoreNEI <- vehiclesNEI[vehiclesNEI$fips==24510,] baltimoreNEIPlot <- ggplot(baltimoreNEI, aes(factor(year), Emissions)) + geom_bar( stat = "identity", fill="grey") + theme_bw() + guides(fill=FALSE) + labs( x="Year", y=expression("Total PM2.5 Emissions (Tons)")) + labs( title=expression("PM2.5 Motor Vehicle Source Emissions from 1999 - 2008 (Baltimore)")) print( baltimoreNEIPlot ) dev.copy(png, 'plot5.png') dev.off()

0abead7ffa99418df8fc1dfbc532d93b0649eeff

97e3baa62b35f2db23dcc7f386ed73cd384f2805

/inst/app/app.R

d2dbc951cbdf95b988aebd26bf6b3eb7496089f0

[]

no_license

conservation-decisions/smsPOMDP

a62c9294fed81fcecc4782ac440eb90a299bca44

48b6ed71bdc7b2cb968dc36cd8b2f18f0e48b466

refs/heads/master

2021-06-25T22:23:31.827056

2020-10-27T08:56:07

161,746,931

7

0

null

UTF-8

R

false

36,169

r

app.R

source("helper.R") ## UI #### ui <- shinydashboard::dashboardPage( title = "POMDP solver: When to stop managing or surveying cryptic threatened species ?", # HEADER ############################# shinydashboard::dashboardHeader( title = "smsPOMDP", shiny::tags$li( a( strong("Building an app"), href = "guidance.pdf", height = 40, title = "", target = "_blank" ), class = "dropdown" ), shiny::tags$li( a( strong("ABOUT smsPOMDP"), height = 40, href = "https://github.com/conservation-decisions/smsPOMDP", title = "", target = "_blank" ), class = "dropdown" ) ), # SIDEBAR ##################### shinydashboard::dashboardSidebar(disable = TRUE), # BODY ############################## shinydashboard::dashboardBody( shiny::tags$head(shiny::tags$style(shiny::HTML(".shiny-output-error-validation {color: red; font-size: large; font-weight: bold;}"))), shiny::fluidRow( #POMDP PARAMETERS #### shiny::tags$div(class = "another-box", id = "primariy2", shinydashboard::box( title = "POMDP parameters", width = 3, solidHeader = TRUE, status = "primary", # Probabilities #### shiny::h3("Probabilities"), bsplus::shinyInput_label_embed( shiny::numericInput('p0', 'Local probability of persistence (if survey or stop)', min = 0, max = 1, value = 0.9), bsplus::bs_attach_modal(bsplus::shiny_iconlink(),id_modal = "modal_p0") ), bsplus::shinyInput_label_embed( shiny::numericInput('pm', 'Local probability of persistence (if manage)', min = 0, max = 1, value = 0.94184), bsplus::bs_attach_modal(bsplus::shiny_iconlink(), id_modal = "modal_pm") ), bsplus::shinyInput_label_embed( shiny::numericInput('d0', 'Local probability of detection (if stop)', min = 0, max = 1, value = 0.01), bsplus::bs_attach_modal( bsplus::shiny_iconlink(), id_modal = "modal_d0") ), bsplus::shinyInput_label_embed( shiny::numericInput('dm', 'Local probability of detection (if manage)', min = 0, max = 1, value = 0.01), bsplus::bs_attach_modal(bsplus::shiny_iconlink(), id_modal = "modal_dm") ), bsplus::shinyInput_label_embed( shiny::numericInput('ds', 'Local probability of detection (if survey)', min = 0, max = 1, value = 0.78193), bsplus::bs_attach_modal( bsplus::shiny_iconlink(), id_modal = "modal_ds") ), # Costs #### shiny::h3("Costs"), bsplus::shinyInput_label_embed( shiny::numericInput('V', 'Estimated economic value of the species ($/yr)', value = 175.133), bsplus::bs_attach_modal(bsplus::shiny_iconlink(), id_modal = "modal_V") ), bsplus::shinyInput_label_embed( shiny::numericInput('Cm', 'Estimated cost of managing ($/yr)', value = 18.784), bsplus::bs_attach_modal( bsplus::shiny_iconlink(), id_modal = "modal_Cm") ), bsplus::shinyInput_label_embed( shiny::numericInput('Cs', 'Estimated cost of surveying ($/yr)', min = 0, value = 10.840), bsplus::bs_attach_modal( bsplus::shiny_iconlink() , id_modal = "modal_Cs") ), # Case studies #### shiny::h3("Case studies"), bsplus::shinyInput_label_embed( shiny::selectInput("case_study", "Select case study", choices = c("Sumatran tiger", "Expensive management", "Detection in management")), bsplus::bs_attach_modal( bsplus::shiny_iconlink(), id_modal = "modal_case_study") ), shiny::actionButton("reload", "Reload parameters") ), shiny::tags$style(shiny::HTML(" #primariy2 .box.box-solid.box-primary>.box-header { color:#fff; background:#666666 } .box.box-solid.box-primary { border-bottom-color:#666666; border-left-color:#666666; border-right-color:#666666; border-top-color:#666666; } ")) ), # Plot parameters #### shinydashboard::box(width = 3, bsplus::shinyInput_label_embed( shiny::numericInput("initial_belief", "Initial belief state", value = 1, min = 0, max = 1), bsplus::bs_attach_modal( bsplus::shiny_iconlink(), id_modal = "modal_initial_belief") ) ), shinydashboard::box(width = 3, bsplus::shinyInput_label_embed( shiny::numericInput('Tmanage', "Duration of past data (time steps)", value = 5, min = 0), bsplus::bs_attach_modal( bsplus::shiny_iconlink(), id_modal = "modal_Tmanage") ) ), shinydashboard::box(width = 3, bsplus::shinyInput_label_embed( shiny::numericInput('Tsim', "Duration of simulation (time steps)", value = 10, min = 0, max = 20), bsplus::bs_attach_modal( bsplus::shiny_iconlink(), id_modal = "modal_Tsim") ) ), # plots#### shinydashboard::box(width = 9, bsplus::bs_attach_modal(bsplus::shiny_iconlink(), id_modal = "modal_gif"), "Choose actions performed in the past", plotly::plotlyOutput("plot_actions", height = "350px")), shinydashboard::box(width = 9, "Choose observations following the actions", plotly::plotlyOutput("plot_observations", height = "300px")), shinydashboard::box(width = 9, "Explore discounted expected rewards over time", plotly::plotlyOutput("plot_reward", height = "300px")) # add modals #### , modal_p0 , modal_pm , modal_d0 , modal_dm , modal_ds , modal_V , modal_Cm , modal_Cs , modal_initial_belief , modal_Tmanage , modal_Tsim , modal_case_study , modal_gif # activate tooltips, popovers, and MathJax #### , bsplus::use_bs_tooltip() , bsplus::use_bs_popover() , shiny::withMathJax() ) ) ) # SERVER ############################### server <- function(input, output, session){ #Inputs ##### p0 <- shiny::reactive({ shiny::validate(shiny::need(input$p0 >=0 & input$p0 <=1 , "Please select local probability of persistence (if survey or stop) between 0 and 1") ) input$p0 }) pm <- shiny::reactive({ shiny::validate( shiny::need(input$pm >=0 & input$pm<=1 , "Please select local probability of persistence (if manage) between 0 and 1") ) input$pm }) d0 <- shiny::reactive({ shiny::validate( shiny::need(input$d0 >=0 & input$d0 <=1 , "Please select local probability of detection (if stop) between 0 and 1") ) input$d0 }) dm <- shiny::reactive({ shiny::validate( shiny::need(input$dm >=0 & input$dm <=1 , "Please select local probability of detection (if manage) between 0 and 1") ) input$dm }) ds <- shiny::reactive({ shiny::validate( shiny::need(input$ds >=0 & input$ds <=1 , "Please select local probability of detection (if survey) between 0 and 1") ) input$ds }) V <- shiny::reactive({ shiny::validate( shiny::need(input$V >=0 , "Please select estimated economic value of the species ($/yr) positive") ) input$V }) Cm <- shiny::reactive({ shiny::validate( shiny::need(input$Cm >=0, "Please select estimated cost of managing ($/yr) positive") ) input$Cm }) Cs <- shiny::reactive({ shiny::validate( shiny::need(input$Cs >=0, "Please select estimated cost of survey ($/yr) positive") ) input$Cs }) Tmanage <- shiny::reactive({ shiny::validate( shiny::need(input$Tmanage >=0, "Please select horizon of past management positive") ) input$Tmanage }) init_belief <- shiny::reactive({ shiny::validate( shiny::need(input$initial_belief >=0 & input$initial_belief <=1 , "Please select initial belief state (extant) between 0 and 1") ) c(input$initial_belief, 1-input$initial_belief) }) #initial belief state Tsim <- shiny::reactive({ shiny::validate( shiny::need(input$Tsim >=0, "Please select a positive duration of simulation") ) input$Tsim }) Tplot <- shiny::reactive({ shiny::validate( shiny::need(input$Tsim >=0, "Please select a positive duration of simulation") ) max(10, input$Tsim) }) # Treat inputs ##### input_past <- shiny::reactiveValues( data_actions=c(), actions = c(), data_observations = c(), observations = c(), belief_extant = isolate({matrix(init_belief(), ncol = 2)}), rewards = c() ) data_action_reactive <- shiny::reactive({ return(input_past$data_actions) }) data_observation_reactive <- shiny::reactive({ return(input_past$data_observations) }) actions_past <- shiny::reactive({input_past$actions}) observations_past <- shiny::reactive({input_past$observations}) # change Tmanage #### shiny::observeEvent(Tmanage(), { if (input$Tmanage == 0){ input_past$data_actions <- c() input_past$actions <- c() input_past$data_observations <- c() input_past$observations <- c() input_past$belief_extant <- matrix(init_belief(), ncol = 2) return() } if (is.null(input_past$data_actions)) { actions <- c("Stop","Survey","Manage") time_steps <- seq_len(Tmanage()) tab <- expand.grid(actions, time_steps) tab$color <- "Off" names(tab) <- c("action", "step", "color") input_past$data_actions <- tab } else { data <- input_past$data_actions Tmax <- max(data$step) diff <- Tmanage()-Tmax if (diff > 0){ actions <- c("Stop","Survey","Manage") time_steps <- seq(Tmax+1,Tmanage()) tab <- expand.grid(actions, time_steps) tab$color <- "Off" names(tab) <- c("action", "step", "color") input_past$data_actions <- rbind(data, tab) } else { input_past$data_actions <- data[which(data$step <= Tmanage()),] } } if (is.null(input_past$data_observations)) { obs <- c("Not seen","Seen") time_steps <- seq_len(Tmanage()) tab2 <- expand.grid(obs, time_steps) tab2$color <- "Off" names(tab2) <- c("obs", "step", "color") input_past$data_observations <- tab2 } else { data <- input_past$data_observations Tmax <- max(data$step) diff <- Tmanage()-Tmax if (diff > 0){ obs <- c("Not seen","Seen") time_steps <- seq(Tmax+1,Tmanage()) tab <- expand.grid(obs, time_steps) tab$color <- "Off" names(tab) <- c("obs", "step", "color") input_past$data_observations <- rbind(data, tab) } else { input_past$data_observations <- data[which(data$step <= Tmanage()),] } } if ( (Tmanage()-length(input_past$actions)) < 0){ input_past$actions <- input_past$actions[seq_len(Tmanage())] } if ( (Tmanage()-length(input_past$observations)) < 0){ input_past$observations <- input_past$observations[seq_len(Tmanage())] } }) # click on action plot #### shiny::observeEvent(plotly::event_data("plotly_click", source = "A"),{ d <- plotly::event_data("plotly_click", source = "A") if (is.null(d)){ return() } isolate({ x <- d$x + Tmanage() +1 y <- d$y tab <-input_past$data_actions tab[which(tab$step == x),]$color <- "Off" tab[which((tab$step == x)&(tab$action == y)),]$color <- "On" input_past$data_actions <- tab input_past$actions[x] <-d$y }) }) # click on observation plot #### shiny::observeEvent(plotly::event_data("plotly_click", source = "O"),{ d <- plotly::event_data("plotly_click", source = "O") if (is.null(d)){ return() } isolate({ x <- d$x + Tmanage() +1 y <- d$y tab <-input_past$data_observations tab[which(tab$step == x),]$color <- "Off" tab[which((tab$step == x)&(tab$obs == y)),]$color <- "On" input_past$data_observations <- tab input_past$observations[x] <-d$y }) }) # beliefs #### #reactive list of beliefs in the past, matrix beliefs <- shiny::reactive({ if (length(observations_past()) != length(actions_past()) |any(is.na(observations_past())) |any(is.na(actions_past())) ){ return(input_past$belief_extant) } else { input_past$belief_extant <- smsPOMDP::compute_belief_list(p0(), pm(), d0(), dm(), ds(), V(), Cm(), Cs(),init_belief(), actions_past(), observations_past()) return(input_past$belief_extant) } }) #reactive vector of current belief current_belief <- shiny::reactive({ b <- beliefs() return(b[nrow(b),]) # input_past$belief_extant[nrow(input_past$belief_extant), ] }) # rewards #### #reactive vector of rewards rewards <- shiny::reactive({ if (length(observations_past()) != length(actions_past()) |any(is.na(observations_past())) |any(is.na(actions_past())) ){ return(input_past$rewards) } else { input_past$rewards <- smsPOMDP::reward_belief(p0(), pm(), d0(), dm(), ds(), V(), Cm(), Cs(),beliefs(), actions_past()) return(input_past$rewards) } }) #reactive datasets of simulations #### data_sim <- shiny::reactive({smsPOMDP::simulations_tab(p0(), pm(), d0(), dm(), ds(), V(), Cm(), Cs(), current_belief(), Tsim())}) data_sim_ref <- shiny::reactive({smsPOMDP::simulations_tab(p0(), pm(), d0(), dm(), ds(), V(), Cm(), Cs(), init_belief(), Tmanage()+Tsim())}) # Plots #### #optimal solution plot #### optimal_solution <- shiny::reactive({ log_dir <- tempdir() infile <- paste0(log_dir, "/optimal_sol.png") png(infile, width = 1280, height = 720, units = "px") smsPOMDP::graph(p0(), pm(), d0(), dm(), ds(), V(), Cm(), Cs(), current_belief(), size = 2) dev.off() png::readPNG(infile) }) #actions plot #### output$plot_actions <- plotly::renderPlotly({ xaxis <- list( title = "Time steps", autotick = FALSE, ticks = "outside", dtick = 1, range = c(-Tmanage()-1, Tplot()+2) ) yaxis <- list(type = "category", categoryorder ="array", categoryarray = c("Stop", "Survey", "Manage"), title = "Actions") if (Tmanage() >0){ if (length(unique(data_action_reactive()$color))==1){colors_palette = c("grey")} else {colors_palette = c("grey","red")} if ((length(observations_past()) == length(actions_past())) & (length(observations_past()) == Tmanage())){ plotly::plot_ly( height = 350, source = "A") %>% plotly::add_trace(x=~step-Tmanage()-1, y=~action, hoverinfo="text",text=~action, data = data_action_reactive(), type = 'scatter', mode = 'markers', marker = list(size = 20, opacity = 0.8) , color = ~color, colors = colors_palette, showlegend = FALSE) %>% plotly::layout( xaxis = xaxis,yaxis=yaxis, showlegend = TRUE , images = list( source = plotly::raster2uri(as.raster(optimal_solution())), x = (Tmanage()+1)/(Tmanage()+Tplot()), y = 0.5, sizex = (Tplot()-1)/(Tmanage()+Tplot()), sizey = 0.9, xref = "paper", yref = "paper", xanchor = "left", yanchor = "middle" ) , legend = list(orientation = 'h', y = 1.1) , margin = list(l=100, r=100) ) } else { plotly::plot_ly( height = 350, source = "A") %>% plotly::add_trace(x=~step-Tmanage()-1, y=~action, hoverinfo="text",text=~action, data = data_action_reactive(), type = 'scatter', mode = 'markers', marker = list(size = 20, opacity = 0.8) , color = ~color, colors = colors_palette, showlegend = FALSE) %>% plotly::layout( xaxis = xaxis,yaxis=yaxis, showlegend = FALSE , margin = list(l=100, r=100) ) } } else if (Tmanage()==0){ xaxis <- list( title = "Time steps", autotick = FALSE, ticks = "outside", dtick = 1, range = c(-Tmanage()-1, Tplot() + 2) ) yaxis <- list(type = "category", categoryorder ="array", categoryarray = c("Stop", "Survey", "Manage"), visible = FALSE) plotly::plot_ly( height = 350, source = "A") %>% plotly::layout( xaxis = xaxis,yaxis=yaxis, showlegend = TRUE , images = list( source = plotly::raster2uri(as.raster(optimal_solution())), x = (Tmanage()+1)/(Tmanage()+Tplot()), y = 0.5, sizex = (Tplot()-1)/(Tmanage()+Tplot()), sizey = 0.9, xref = "paper", yref = "paper", xanchor = "left", yanchor = "middle" ) , legend = list(orientation = 'h', y = 1.1) , margin = list(l=100, r=100) ) } }) #observations plot #### output$plot_observations<- plotly::renderPlotly({ xaxis <- list( title = "Time steps", autotick = FALSE, ticks = "outside", dtick = 1, range = c(-Tmanage()-1, Tplot() + 2) ) yaxis <- list( type = "category", categoryorder ="array", categoryarray = c("Not seen", "Seen"), title = "Observations" ) ay <- list( overlaying = "y", side = "right", title = "b(extant)", range = c(-0.3, 1.3) ) if (Tmanage()>0){ b <- beliefs() extant <- c(b[,1]) xtime <- seq(1,length(extant))-Tmanage()-1 if (length(unique(data_observation_reactive()$color))==1){colors_palette = c("grey")} else {colors_palette = c("grey","red")} if ((length(observations_past()) == length(actions_past())) & (length(observations_past()) == Tmanage())){ plotly::plot_ly( height = 300, source = "O") %>% plotly::add_trace(x=~step-Tmanage()-1, y=~obs, hoverinfo="text",text=~obs, data = data_observation_reactive(), type = 'scatter', mode = 'markers', marker = list(size = 20, opacity = 0.8) , color= ~color, colors = colors_palette, showlegend = FALSE) %>% #simulations from present plotly::add_trace(x=seq(0, Tsim()), y=~mean_belief, name = "User's optimal future trajectory b(extant)", data=data_sim(), yaxis = "y2", type = 'scatter', mode = 'lines+markers', visible = TRUE, line = list(color = "green"), marker =list(color = "green"), colors = "green") %>% plotly::add_ribbons(x=seq(0, Tsim()), ymin=~low_belief, ymax=~up_belief, name="User's optimal future trajectory b(extant) 95%", line=list(color="green", opacity=0.4, width=0), data = data_sim(), yaxis = "y2", visible = TRUE, fillcolor ="rgba(0,255,0,0.2)", showlegend = FALSE) %>% #current belief plotly::add_trace(x=xtime, y=extant, name = "User's current b(extant)", visible = TRUE, yaxis = "y2", type = 'scatter', mode = 'lines+markers', showlegend = TRUE, line = list(color = "red"), marker =list(color = "red"), colors = "red") %>% #simulations from reference point plotly::add_trace(x=seq(-Tmanage(), Tsim()), y=~mean_belief, name = "Optimal trajectory b(extant)", data=data_sim_ref(), yaxis = "y2", type = 'scatter', mode = 'lines+markers', visible = TRUE, line = list(color = "blue"), marker =list(color = "blue"), colors="blue") %>% plotly::add_ribbons(x=seq(-Tmanage(), Tsim()), ymin=~low_belief, ymax=~up_belief, name="Optimal trajectory b(extant) 95%", line=list(color="blue", opacity=0.4, width=0), data = data_sim_ref(), yaxis = "y2", visible = TRUE, showlegend = FALSE, fillcolor ="rgba(0,0,255,0.2)") %>% plotly::layout( xaxis = xaxis, yaxis=yaxis, yaxis2 = ay, legend = list(orientation = 'h', # y = -0.3, x=0) , margin = list(l=100, r=100) ) } else { plotly::plot_ly( height = 300, source = "O") %>% plotly::add_trace(x=~step-Tmanage()-1, y=~obs, hoverinfo="text",text=~obs, data = data_observation_reactive(), type = 'scatter', mode = 'markers', marker = list(size = 20, opacity = 0.8) , color= ~color, colors = colors_palette, showlegend = FALSE) %>% #current belief plotly::add_trace(x=xtime, y=extant, name = "User's current b(extant)", visible = TRUE, yaxis = "y2", type = 'scatter', mode = 'lines+markers', showlegend = TRUE, line = list(color = "red"), marker =list(color = "red"), colors = "red" ) %>% #simulations from reference point plotly::add_trace(x=seq(-Tmanage(), Tsim()), y=~mean_belief, name = "Optimal trajectory b(extant)", data=data_sim_ref(), yaxis = "y2", type = 'scatter', mode = 'lines+markers', visible = TRUE, line = list(color = "blue"), marker =list(color = "blue"), colors = "blue") %>% plotly::add_ribbons(x=seq(-Tmanage(), Tsim()), ymin=~low_belief, ymax=~up_belief, name="Optimal trajectory b(extant) 95%", line=list(color="blue", opacity=0.4, width=0), data = data_sim_ref(), yaxis = "y2", fillcolor = "rgba(0,0,255,0.2)", visible = TRUE, showlegend = FALSE) %>% plotly::layout( xaxis = xaxis, yaxis=yaxis, yaxis2 = ay, legend = list(orientation = 'h', # y = -0.3, x=0) , margin = list(l=100, r=100) ) } } else { plotly::plot_ly( height = 300, source = "O") %>% #simulations from reference point plotly::add_trace(x=seq(-Tmanage(), Tsim()), y=~mean_belief, name = "Optimal trajectory b(extant)", data=data_sim_ref(), yaxis = "y2", type = 'scatter', mode = 'lines+markers', visible = TRUE, line = list(color = "blue"), marker =list(color = "blue"), colors="blue") %>% plotly::add_ribbons(x=seq(-Tmanage(), Tsim()), ymin=~low_belief, ymax=~up_belief, name="Optimal trajectory b(extant) 95%", line=list(color="blue", opacity=0.4, width=0), data = data_sim_ref(), yaxis = "y2", visible = TRUE, showlegend = FALSE, fillcolor ="rgba(0,0,255,0.2)") %>% plotly::layout( xaxis = xaxis, yaxis=yaxis, yaxis2 = ay, legend = list(orientation = 'h', # y = -0.3, x=0) , margin = list(l=100, r=100) ) } }) # rewards plot #### output$plot_reward <- plotly::renderPlotly({ if (Tmanage()>0){ if (length(rewards())>=1){ xtime <- seq(1,length(rewards()))-Tmanage()-1 xaxis <- list( autotick = FALSE, title = "Time steps", ticks = "outside", dtick = 1, range = c(-Tmanage()-1, Tplot() + 2) ) yaxis <-list( title = "Expected reward" ) if ((length(observations_past()) == length(actions_past())) & (length(observations_past()) == Tmanage())){ plotly::plot_ly(height = 300, source = "R") %>% #simulations from present plotly::add_lines(x=seq(0, Tsim()), y=~mean_reward, data=data_sim(), name = "User's optimal future expected reward", visible = TRUE, line = list(color = "green"), marker =list(color = "green"), colors="green") %>% plotly::add_ribbons(x=seq(0, Tsim()), ymin=~low_reward, ymax=~up_reward, name="Reward 95%", data = data_sim(), line=list(color="green", opacity=0.4, width=0), visible = TRUE, showlegend = FALSE, fillcolor ="rgba(0,255,0,0.2)") %>% #current instant reward plotly::add_trace(x=c(xtime, 0), y=c(rewards(), data_sim()$mean_reward[1]), name = "User's current expected reward", visible = TRUE, type = 'scatter', mode = 'lines+markers', showlegend = TRUE, line = list(color = "red"), marker =list(color = "red"), colors = "red") %>% #simulations from reference point plotly::add_lines(x=seq(-Tmanage(), Tsim()), y=~mean_reward, data=data_sim_ref(), name = "Optimal expected reward", line = list(color = "blue"), marker =list(color = "blue"), colors="blue") %>% plotly::add_ribbons(x=seq(-Tmanage(), Tsim()), ymin=~low_reward, ymax=~up_reward, name="Ref reward 95%", data = data_sim_ref(), line=list(color="blue", opacity=0.4, width=0), visible = TRUE, showlegend = FALSE, fillcolor ="rgba(0,0,255,0.2)") %>% plotly::layout( xaxis = xaxis, yaxis=yaxis, showlegend = TRUE, legend = list(orientation = 'h' #, y = -0.4 ) , margin = list(l=100, r=100) ) } else { plotly::plot_ly(height = 300, source = "R") %>% #current instant reward plotly::add_trace(x=xtime, y=rewards(), name = "User's current expected reward", visible = TRUE, type = 'scatter', mode = 'lines+markers', showlegend = TRUE, line = list(color = "red"), marker =list(color = "red"), colors = "red") %>% #simulations from reference point plotly::add_lines(x=seq(-Tmanage(), Tsim()), y=~mean_reward, data=data_sim_ref(), name = "Optimal expected reward", line = list(color = "blue"), marker =list(color = "blue"), colors="blue") %>% plotly::add_ribbons(x=seq(-Tmanage(), Tsim()), ymin=~low_reward, ymax=~up_reward, name="Ref reward 95%", data = data_sim_ref(), line=list(color="blue", opacity=0.4, width=0), visible = TRUE, showlegend = FALSE, fillcolor ="rgba(0,0,255,0.2)") %>% plotly::layout( xaxis = xaxis, yaxis=yaxis, showlegend = TRUE, legend = list(orientation = 'h' #, y = -0.4 ) , margin = list(l=100, r=100)) } } else { xaxis <- list( autotick = FALSE, title = "Time steps", ticks = "outside", dtick = 1, range = c(-Tmanage()-1, Tplot() + 2) ) yaxis <-list( title = "Expected reward" ) plotly::plot_ly(height = 300, source = "R") %>% #simulations from reference point plotly::add_lines(x=seq(-Tmanage(), Tsim()), y=~mean_reward, data=data_sim_ref(), name = "Optimal expected reward", line = list(color = "blue"), marker =list(color = "blue"), colors="blue") %>% plotly::add_ribbons(x=seq(-Tmanage(), Tsim()), ymin=~low_reward, ymax=~up_reward, name="Ref reward 95%", data = data_sim_ref(), line=list(color="blue", opacity=0.4, width=0), visible = TRUE, showlegend = FALSE, fillcolor ="rgba(0,0,255,0.2)") %>% plotly::layout( xaxis = xaxis, yaxis=yaxis, showlegend = TRUE, legend = list(orientation = 'h' #, y = -0.4 ) , margin = list(l=100, r=100) ) } } else { xaxis <- list( autotick = FALSE, title = "Time steps", ticks = "outside", dtick = 1, range = c(-Tmanage()-1, Tplot() + 2) ) yaxis <-list( title = "Expected reward" ) plotly::plot_ly(height = 300, source = "R") %>% #simulations from reference point plotly::add_lines(x=seq(-Tmanage(), Tsim()), y=~mean_reward, data=data_sim_ref(), name = "Optimal expected reward", line = list(color = "blue"), marker =list(color = "blue"), colors="blue") %>% plotly::add_ribbons(x=seq(-Tmanage(), Tsim()), ymin=~low_reward, ymax=~up_reward, name="Ref reward 95%", data = data_sim_ref(), line=list(color="blue", opacity=0.4, width=0), visible = TRUE, showlegend = FALSE, fillcolor ="rgba(0,0,255,0.2)") %>% plotly::layout( xaxis = xaxis, yaxis=yaxis, showlegend = TRUE, legend = list(orientation = 'h' #, y = -0.4 ) , margin = list(l=100, r=100) ) } }) # reload values depending on the case of study ################ shiny::observeEvent(input$reload, { if (input$case_study == "Sumatran tiger"){ shiny::updateNumericInput(session, 'p0', value = 0.9) shiny::updateNumericInput(session, 'pm', value = 0.94184) shiny::updateNumericInput(session, 'd0', value = 0.01) shiny::updateNumericInput(session, 'dm', value = 0.01) shiny::updateNumericInput(session, 'ds', value = 0.78193) shiny::updateNumericInput(session, 'V', value = 175.133) shiny::updateNumericInput(session, 'Cm', value = 18.784) shiny::updateNumericInput(session, 'Cs', value = 10.840) } else if (input$case_study == "Expensive management"){ shiny::updateNumericInput(session, 'p0', value = 0.9) shiny::updateNumericInput(session, 'pm', value = 0.94184) shiny::updateNumericInput(session, 'd0', value = 0.01) shiny::updateNumericInput(session, 'dm', value = 0.01) shiny::updateNumericInput(session, 'ds', value = 0.78193) shiny::updateNumericInput(session, 'V', value = 200) shiny::updateNumericInput(session, 'Cm', value = 50) shiny::updateNumericInput(session, 'Cs', value = 25) } else if (input$case_study == "Detection in management"){ shiny::updateNumericInput(session, 'p0', value = 0.9) shiny::updateNumericInput(session, 'pm', value = 0.94184) shiny::updateNumericInput(session, 'd0', value = 0.01) shiny::updateNumericInput(session, 'dm', value = 0.5) shiny::updateNumericInput(session, 'ds', value = 0.78193) shiny::updateNumericInput(session, 'V', value = 175.133) shiny::updateNumericInput(session, 'Cm', value = 18.784) shiny::updateNumericInput(session, 'Cs', value = 10.840) } }) } shiny::shinyApp(ui, server)

cc6f94771e418cbfc5308c03ff0490c11592ee4c

f936ecec924cd1a5f430dd01e2540767a7df29e8

/R-ML/prepocx.R

8d317e79ca21f4f9cccfb76bef4897819e4b8cab

[]

no_license

PiscatorX/Project-Roger-Dodger

b4359d79e7234e8385b4d6115b14b2a6524e888e

e402876c7aa3c6e92036fcb8dfbba872887e5e33

refs/heads/master

2023-07-29T12:22:00.861078

2021-09-09T07:23:32

292,900,383

0

null

UTF-8

R

false

5,597

r

prepocx.R

library(tidyverse) library(magrittr) library(ggplot2) library(ggpubr) library(dplyr) zeolite = read.table("zeolitex_final.tsv", sep = "\t", header = T) colnames(zeolite) %>% data.frame() zeolite <- zeolite %>% mutate_all(na_if,"") ##################### Multicollinearity Analysis ############################### #http://www.sthda.com/english/articles/39-regression-model-diagnostics/160-multicollinearity-essentials-and-vif-in-r/ #A VIF value that exceeds 5 or 10 indicates a problematic amount of collinearity (James et al. 2014) #James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2014. An Introduction to Statistical Learning: With Applications in R. Springer Publishing Company, Incorporated. set.seed(1) # [1] "Adsorbent" "SA" "Vmicro" "Vmeso" "pore.size" # [6] "Si_Al" "Ag" "Ce" "Cu" "Ni" # [11] "Zn" "La" "Cs" "Pd" "Nd" # [16] "adsorbate" "C_start" "solvent" "Batch_Dynamic" "Oil_adsorbent_ratio" # [21] "Temp" "Capacity" zeolite_subset <- zeolite %>% select(Adsorbent, Capacity) %>% #,Vmicro,Vmeso,pore.size,Si_Al,C_start,solvent,Oil_adsorbent_ratio,Temp) filter #C_start,solvent,Oil_adsorbent_ratio,Temp,Capacity training_samples <- zeolite_subset %>% sample_frac(size = 0.8) zeolite_subset$Adsorbent <- gsub('_', '-', zeolite_subset$Adsorbent) simone1 <- lm(Adsorbent ~ Capacity, data = zeolite_subset) summary(training_samples) head(training_samples) colnames(training_samples) nrow(training_samples) #training_samples %>% as_tibble() %>% View() test_data <- setdiff(zeolite_subset, training_samples,Vmeso) model1 <- lm(Capacity ~ ., data = training_samples) factor(training_samples$Adsorbent) levels(test_data$Adsorbent) predictions <- model1 %>% predict(test_data) # ################################################################################ # # N <- nrow(zeolite) # # missing <- zeolite %>% summarise_all(funs(100*sum(is.na(.))/N)) %>% data.frame() %>% round(2) # # #Inspect missing data # t(missing) # # #write to file to keep a record # write.table(missing, "zeolite.miss", sep="\t", row.names = F, quote = F) # # #Adsorbetcounts # Adsorbent_analysis <-zeolite %>% group_by(Adsorbent) %>% summarise(Count=n()) %>% arrange(desc(Count)) # # p <- ggbarplot(Adsorbent_analysis, x = "Adsorbent", y = "Count", # fill = "Count", # sort.by.groups = FALSE, # sort.val = "asc") # # ggpar(p, x.text.angle = 45) # # Adsorbent_analysis %>% filter(Count<=2) %>% arrange(Adsorbent) %>% data.frame() # #Singletone maybe typos # #eg Ag-Y vs AgY # #What about CsY could it be CeY # # #Missing hyphens may be an issue # #Count duplicates when hyphen are removed # # gsub("-", "", Adsorbent_analysis$Adsorbent) %>% # as.tibble() %>% # group_by(value) %>% # summarise(count=n()) %>% # filter(count != 1) %>% # data.frame() # # #These below need to be checked # # 1 AgY 2 # # 2 CeY 2 # # 3 CuY 2 # # 4 HY 2 # # 5 NaY 2 # # 6 NiCeY 2 # # 7 NiY 2 # # #Surface area # ggdotchart(data = zeolite %>% filter(!is.na(SA)), # x = "Adsorbent", # y = "SA", # color = "Adsorbent", # sorting = "descending", # ggtheme = theme_pubr()) + # theme(legend.position = "none") # # # SA <- zeolite %>% filter(!is.na(SA)) # # # ggplot(data=SA, # aes(x = SA)) + # geom_density(aes(y = ..count..)) + # geom_vline(aes(xintercept = mean(SA)), # linetype = "dashed", size = 0.6) + # ylab("Density") + # theme_pubr() # # # # # # ggviolin(data = SA, # y = "SA", # fill = "light blue", # palette = c("#00AFBB", "#E7B800", "#FC4E07"), # add = "boxplot") # # iqr <- summary(SA$SA) # # SA %>% filter(SA < 500 | SA > 700) %>% arrange(desc(SA)) # # # SA_fit <- lm(Capacity ~ SA, data = SA) # # # summary(SA_fit) # # # ggscatter(SA, x = "SA", # y = "Capacity", # add = "reg.line", # conf.int = T) + # stat_cor(label.x = 450, label.y = 60) + # stat_regline_equation(label.x = 450, label.y = 65) # # # ############################ Vmicro ############################################ # # # Vmicro <- zeolite %>% filter(!is.na(Vmicro)) # # # # ggviolin(data = SA, # y = "Vmicro", # fill = "light blue", # palette = c("#00AFBB", "#E7B800", "#FC4E07"), # add = "boxplot") # # # ggplot(Vmicro) + # geom_density(aes(x = Vmicro)) + # theme_pubr() # # # colnames(Vmicro) # # ggplot(Vmicro) + # geom_point(aes(x = Vmicro, y = Capacity, color = Adsorbent)) + # theme_pubr() # # # # # ggscatter(SA, x = "Vmicro", # y = "Capacity", # add = "reg.line", # conf.int = T) + # stat_cor() + # stat_regline_equation(label.x = 0.35) # # # # # # # # # #

9073daeca9c5c8be3e2fce570f8abd55e69d6fe6

552d16746aeb43a11a7801f4ee94d55289ced977

/function_practice.R

a6d85a5c436e54e28fabf58ed70a5c38c994812a

[]

no_license

saurabb2297/datasciencecoursera

2304e672acb4b579998b0fedcccbe9905140903e

1ea24067c60025a1585d91b339e2add5c047dd92

refs/heads/master

2022-09-28T16:34:08.348076

2020-06-06T06:37:09

264,628,775

0

null

UTF-8

R

false

445

r

function_practice.R

add2 <- function(a,b){ a + b } above <- function(x,n=10){ use <- x>n x[use] } #calculating mean of each column of dataframe #removeNA used to deal with missing values columnmean <- function(x,removeNA = TRUE){ nc <- ncol(x) #number of columns in df means <- numeric(nc) #empty vector of length number of col to store mean of each column for (i in 1:nc){ means[i] <- mean(x[,i],na.rm = removeNA) } means #to return mean }

fe3e66ebc6c28313dc6709a07d5320685a4d7c16

8f2aa4469495a4983a669e44b038f97ce8dda877

/plot1.R

0d480b2536c114402cf6b7145373222a42e546ac

[]

no_license

Juan-Yi/ExData_Plotting1

7deded198a75c617067f7a7ba2f9a8d304b431a1

eda76a3966ea986e529ff4bce2beee1516d280bc

refs/heads/master

2021-07-17T16:40:21.206514

2017-10-25T00:34:09

107,892,587

0

null

2017-10-22T18:39:51

null

UTF-8

R

false

383

r

plot1.R

power<-read.table("household_power_consumption.txt", sep = ";", header = TRUE) power$Global_active_power<-as.numeric(as.character(power$Global_active_power)) power2day<- power[power$Date %in% c("1/2/2007","2/2/2007") ,] hist(power2day$Global_active_power, xlab = "Global Active Power(kilowatts)", ylab = "Frequency", main = "Global Active Power", col = "red", )

288a07fb2e9add0f7add78b2d188644e74695b6d

e3d9fdf5a0720b59afa52f62abef64c08b00e8e9

/users/Faith/Evenness/analysis/mixed_models/Abundance_Calcluate_Evenness.R

adae4646585dbd1301368e5f0c04c304469d5041

[]

no_license

bioTIMEHub/bioTIME

4d5f960b137b4040de32426ff1c447a256147c38

e44a750827cae059f825dcc3c93ba6a0b7ae2a91

refs/heads/master

2021-06-08T09:53:41.387274

2021-03-30T12:42:53

92,727,984

5

4

null

UTF-8

R

false

2,362

r

Abundance_Calcluate_Evenness.R

setwd("C:\\Users\\faj\\Documents\\OneDrive for Business\\research\\ch3 - metaanalysis\\data") AbData <- read.csv("AbudnaceOnceRarefy.csv") library(vegan) library(reshape2) #code to calculate evenness change #---------------------------------------------------------------- head(AbData) names(AbData)[2] <- "abundance" #select data to practice on AbData10 <- AbData[AbData$Study_ID == 10,] AbData10_y1 <- AbData10[AbData10$Year == AbData10$Year[1],] #sum abundance for each species evennessData <- aggregate(AbData10_y1$abundance, list(AbData10_y1$Species_Identity), sum) H <- diversity(evennessData[,2], index = "simpson")#Simpson index J <- H/specnumber(evennessData[,2])#calculate Pileu'e evenness #calculate evenness each year in dataset 10 using a loop EvenData10 <- data.frame(unique(AbData10$Year)) #make a dataframe to imupt data names(EvenData10) <- "Year" EvenData10$Evenness <- 0 for (y in unique(AbData10$Year)){ AbData10_y <- AbData10[AbData10$Year == y,] #select data for approprate year evennessData_y <- aggregate(AbData10_y$abundance, list(AbData10_y$Species_Identity), sum) #how many individuals of each species H <- diversity(evennessData_y[,2])#Shannon index J <- H/log(specnumber(evennessData_y[,2]))#calculate Pileu'e evenness EvenData10$Evenness[EvenData10$Year == y] <- J } #make code to loop through all data #--------------------------------------------------- EvenChange <- list() i <- 1 for(s in unique(AbData$Study_ID)){ AbData_s <- AbData[AbData$Study_ID == s,] EvenData_s <- data.frame(unique(AbData_s$Year)) #make a dataframe to imupt data names(EvenData_s) <- "Year" EvenData_s$Evenness <- 0 EvenData_s$Study_ID <- s for (y in unique(AbData_s$Year)){ AbData_s_y<- AbData_s[AbData_s$Year == y,] #select data for approprate year evennessData_y <- aggregate(AbData_s_y$abundance, list(AbData_s_y$Species_Identity), sum) #how many individuals of each species H <- diversity(evennessData_y[,2])#Shannon index J <- H/log(specnumber(evennessData_y[,2]))#calculate Pileu'e evenness EvenData_s$Evenness[EvenData_s$Year == y] <- J } EvenChange[[i]] <- EvenData_s i <- i +1 } evennessAbData <- do.call(rbind, EvenChange) write.csv(evennessAbData, "evennessAbundance.csv")

bdcd73a5219db5cdf1066063bdb08a66dc0afe70

0ca71d16a83d6861cab501c19a62a664fdbca559

/ui.R

fdc4cc433502d98655fd80ed3003fb1fc367b043

[]

no_license

checkmyschool/data-portal_shiny

6c27c5a1a898aac0d26fb3722a53238d67ab67d8

611dc59569671beb0d61dcd7b2552d6ea578765d

refs/heads/master

2020-08-02T04:42:19.081241

2019-10-14T02:16:40

211,238,061

1

0

null

UTF-8

R

false

34,781

r

ui.R

shinyUI(navbarPage(h5("CheckMySchool Data Portal", style = "color: #ffffff;"), theme = "styles.css", tabPanel(h5("Welcome", style = "color: #ffffff;"), mainPanel(width = 11, column(10, offset = 1, h1("CheckMySchool School Neediness Index", align = "center"), h5("This School Neediness Index Map identifies which among the 44,751 public elementary and secondary schools in the country are in need of resources. The index consists of seven variables grouped into three categories: accessibility (remoteness, percentage of students receiving conditional cash transfers); amenities (water access, internet access, electricity access); and classroom condition (student-teacher ratio, student-classroom ratio). It made use of DepEd data from the Enhanced Basic Education Information System (E-BEIS), the National School Building Inventory conducted by the Education Facilities Division (EFD), and the Remoteness Index developed by the School Effectiveness Division (SED).", align = "center"), h5("Heather Baier and Angela Yost worked on this map and the study on School Neediness Index. They came to the Philippines last May to July 2018 as Summer Fellows of William & Mary’s Global Research Institute.", align = "center"), h1(" "), hr(), h1(" "), h4(tags$b("School Neediness Index Variables and Definitions"), align = "center"), div(tableOutput("variables_table"), align = "center"), div(img(src='all_logos.png', height = 550, width = 1000), align = "center"), hr() ) ) ), #navbarMenu(h5("About", style = "color: #ffffff;"), tabPanel(h5("User Guide", style = "color: #ffffff;"), h3(strong("School Neediness Index Map"), align = "center"), h5("The first tab of the CMS Data Portal houses a map of every public schools in the Philippines with filters available to choose which schools you would like to display.", align = "center"), h5("Select your desired school pararameters using the selecters and slider inputs on the left to see schools on the map that fit your desired specifications.", align = "center"), div(img(src='tutorial1.png', height = 500, width = 900), align = "center"), h1(" "), h3(strong("Data Explorer"), align = "center"), h5("The second tab of the CMS Data Portal, the Data Explorer, works much like the School Neediness Index Map, except the data is displayed in a table instead of a map.", align = "center"), h5("Click on the empty box below a column name to choose the observation you would like to filter for and the table will adjust itself accordingly. For drop down menus, you can filter for multiple variables. Click on the numbered boxes at the bottom right of the page to see the next observations fitting your desired criteria.", align = "center"), div(img(src='tutorial2.png', height = 500, width = 900), align = "center"), h1(" "), h3(strong("School Profiles"), align = "center"), h5("The third tab of the CMS Data Portal, School Profiles, allows you to choose an individual school to see its respective data.", align = "center"), h5("Choose a school region in the right-side panel and continue to filter for School District, Divisions and Name to find you desired school. The table on the top left shows the School Neediness Index data for the chosen school. The histogram on the top right shows the distribution of the selected variable. The table on the bottom left shows the basic data for each school. The pie chart on the bottom right shows the gender distribution of the selected school.", align = "center"), div(img(src='tutorial3.png', height = 500, width = 900), align = "center"), h1(" ") ), #tabPanel(h5("Methodology", style = "color: #000000;"), #h2("Methodology heeeerrrreeee") #) #), # tabPanel("About the School Neediness Index", # # column(10, offset = 1, # # h1("School Neediness Index Methodology", align = "center"), # # h1(" "), # # h5("The variables in the School Neediness Index were determined based on focus group and # individual discussions with school teachers and principals in Guimaras and Rizal.", align = "center"), # # h1(" "), # # hr(), # # fluidRow( # # column(10, offset = 1, # # tableOutput("variables_table") # # ) # # ), # # hr() # # ) # # ), tabPanel(h5("School Neediness Index Map", style = "color: #ffffff;"), sidebarLayout( sidebarPanel( selectInput('region_map', "School Region", choices = unique(all_data$region)), selectInput('year_map', "School Year", choices = c(2015, 2016, 2017), selected = 2015), sliderInput("shi_score_map", "School Neediness Index Score", 0, max(all_data$shi_score), value = range(0, max(all_data$shi_score)), step = 0.1), sliderInput("stratio_map", "Student Teacher Ratio", min(all_data$student_teacher_ratio), max(all_data$student_teacher_ratio), value = range(all_data$student_teacher_ratio), step = 1), sliderInput("scratio_map", "Student Classroom Ratio", min(all_data$student_classroom_ratio), max(all_data$student_classroom_ratio), value = range(all_data$student_classroom_ratio), step = 1), selectInput('water_map', "Access to Water", choices = c("Yes", "No"), multiple = TRUE, selected = c("Yes", "No")), selectInput('internet_map', "Access to Internet", choices = c("Yes", "No"), multiple = TRUE, selected = c("Yes", "No")), selectInput('elec_map', "Access to Electricity", choices = c("Yes", "No"), multiple = TRUE, selected = c("Yes", "No")), sliderInput("ri_map", "Remoteness Index", min(all_data$remoteness_index, na.rm = TRUE), max(all_data$remoteness_index, na.rm = TRUE), value = range(all_data$remoteness_index, na.rm = TRUE), step = 100), sliderInput("cct_map", "Percentage of Student's Recieving CCT's", min(all_data$cct_percentage, na.rm = TRUE), max(all_data$cct_percentage, na.rm = TRUE), value = range(all_data$cct_percentage, na.rm = TRUE), step = 10) ), mainPanel( leafletOutput("map", width = '1150px', height = '850px') ) ) ), tabPanel(h5("Data Explorer", style = "color: #ffffff;"), DT::dataTableOutput("timeseries_table") ), tabPanel(h5("School Profiles", style = "color: #ffffff;"), sidebarLayout( sidebarPanel(width = 3, selectInput('region_profile', "Select Region", choices = c("Select Region" = "", sort(unique(as.character(all_data$region))))), conditionalPanel("input.region_profile", selectInput('division_profile', "Select Division", choices = c("All Divisions" = "") ) ), conditionalPanel("input.division_profile", selectInput('district_profile', "Select District", choices = c("All Districts" = "") ) ), conditionalPanel("input.district_profile", selectInput('school_profile', "Select School", choices = c("All Schools" = "") ) ), hr(), helpText("Select a school to see its resources and classroom conditions and how it stacks up to national averages."), leafletOutput('school_select_map') ), mainPanel( tabsetPanel( tabPanel("School Year 2015 - 2016", h1(" "), fluidRow( column(5, style = "background-color: #DCDCDC; border-radius: 5px; height: 500px; height: 700px", div(h4(tags$u("School Neediness Index Data")), align = "center"), tableOutput("snitable_profile_2015") ), column(1, " "), column(5, style = "background-color: #DCDCDC; border-radius: 2px; height: 700px", div(h4(tags$u("Basic School Data")), align ="center"),#, style="color:red"), tableOutput("p_table2_2015") ), column(1) ), hr(), fluidRow( column(5, #style = "border: 2px solid #DCDCDC; border-radius: 2px; height: 500px;", div(h4(tags$u("Male to Female Student Ratio")), align ="center"), plotOutput("distPie_2015") ),#columnrowclose column(1, h1(" ")), column(5, #style = "border: 2px solid #DCDCDC; border-radius: 2px; height: 500px;", div(h4(tags$u("Distribution of Students with Disabilities")), align ="center"), highchartOutput("pwdChart_2015") )#columnrowclose ), hr()#fluidrowclose ),#mainpanelrowclose tabPanel("School Year 2016 - 2017", h1(" "), fluidRow( column(5, style = "background-color: #DCDCDC; border-radius: 5px; height: 700px", div(h4(tags$u("School Neediness Index Data")), align = "center"), tableOutput("snitable_profile_2016") ), column(1, " "), column(5, style = "background-color: #DCDCDC; border-radius: 2px; height: 700px", div(h4(tags$u("Basic School Data")), align ="center"),#, style="color:red"), tableOutput("p_table2_2016") ), column(1) ), hr(), fluidRow( column(5, #style = "border: 2px solid #DCDCDC; border-radius: 2px; height: 500px;", div(h4(tags$u("Male to Female Student Ratio")), align ="center"), plotOutput("distPie_2016") ),#columnrowclose column(1, h1(" ")), column(5, #style = "border: 2px solid #DCDCDC; border-radius: 2px; height: 500px;", div(h4(tags$u("Distribution of Students with Disabilities")), align ="center"), highchartOutput("pwdChart_2016") )#columnrowclose ), hr()#fluidrowclose ), tabPanel("School Year 2017 - 2018", fluidRow( column(5, style = "background-color: #DCDCDC; border-radius: 5px; height: 700px", div(h4(tags$u("School Neediness Index Data")), align = "center"), tableOutput("snitable_profile_2017") ), column(1, " "), column(5, style = "background-color: #DCDCDC; border-radius: 2px; height: 700px", div(h4(tags$u("Basic School Data")), align ="center"),#, style="color:red"), tableOutput("p_table2_2017") ), column(1) ), hr(), fluidRow( column(5, #style = "border: 2px solid #DCDCDC; border-radius: 2px; height: 500px;", div(h4(tags$u("Male to Female Student Ratio")), align ="center"), plotOutput("distPie_2017") ),#columnrowclose column(1, h1(" ")), column(5, #style = "border: 2px solid #DCDCDC; border-radius: 2px; height: 500px;", div(h4(tags$u("Distribution of Students with Disabilities")), align ="center"), highchartOutput("pwdChart_2017") )#columnrowclose ), hr()#fluidrowclose ) )#sidebarlayoutclose ) ) ), tabPanel(h5("Data Set Builder", style = "color: #ffffff;"), fluidRow( column(width = 4, offset = 1, style = "background-color: #DCDCDC; border-radius: 2px;", h3("Choose Columns"), checkboxGroupInput('columns', label = 'Choose Columns to include in CSV', choices = c('Remoteness Index' = 'remoteness_index', "Total Number of Learners Receiving CCT's" = 'total_recieving_cct', "Percentage of Students Recieving CCT's" = 'cct_percentage', 'Water Access' = 'original_water_boolean', 'Internet Access' = 'original_internet_boolean', 'Electricty Access' = 'original_electricity_boolean', 'Total Number of Learners with Gender Distribution' = 'total_enrollment', "Total Number of Learners With Disability" = 'pwds')), conditionalPanel("input.columns.indexOf('Total Number of Learners With Disability') != -1", checkboxGroupInput('pwd_breakdown', label = NULL, choices = c('Difficulty Seeing Manifestation' = 'ds_total', 'Cerebral Palsy' = 'cp_total', "Difficulty Communicating Manifestation" = 'dcm_total', "Difficulty Remembering, Concentrating, Paying Attention and Understanding based on Manifestation" = 'drcpau_total', "Difficulty Hearing Manifestation" = 'dh_total', "Autism Spectral Disorder" = 'autism_total', "Difficulty Walking, Climbing and Grasping" = 'wcg_total', "Emotional-Behavioral Disorder" = 'eb_total', "Hearing Impairment" = 'hi_total', "Intellectual Impairment" = 'id_total', "Learning Impairment" = 'li_total', "Multiple Disabilities" = 'md_total', "Orthopedic/Physical Disorder" = 'pd_total', "Special Health Problem/Chronic Illness" = 'shp_total', "Speech Disorder" = 'speech_total', "Visual Impairment Disorder" = 'vi_total', "Intellectual Impairment" = 'ii_total', "Orthopedic/Physical Disorder" = 'p_total')) ) ), column(width = 2, h1(" ") ), column(width = 4, style = "background-color: #DCDCDC; border-radius: 2px;", h3("Choose Rows"), radioButtons('FilterGeo', label = 'Choose geographic breakdown to filter data', choices = c('School Region', 'School Province', 'School Division', 'School District', 'School Municipality') ), conditionalPanel("input.FilterGeo == 'School Region'", selectInput('QueryRegion', "Choose School Regions", choices = unique(all_data$region), multiple = TRUE) ), conditionalPanel("input.FilterGeo == 'School Province'", selectInput('QueryProvince', "Choose School Provinces", choices = unique(all_data$province), multiple = TRUE) ), conditionalPanel("input.FilterGeo == 'School Division'", selectInput('QueryDivision', "Choose School Divisions", choices = unique(all_data$division), multiple = TRUE) ), conditionalPanel("input.FilterGeo == 'School District'", selectInput('QueryDistrict', "Choose School Districts", choices = unique(all_data$district), multiple = TRUE) ), conditionalPanel("input.FilterGeo == 'School Municipality'", selectInput('QueryMunicipality', "Choose School Municipalities", choices = unique(all_data$municipality), multiple = TRUE) ), div(downloadButton('QueryBuilder', h4('Download CSV')), align = 'center') ) ), hr(), div(tags$u(h3("Data Set Preview")), align = 'center'), h1(" "), DT::dataTableOutput("QueryTablePreview") ) ) )

3d824809116ada929e6be510d1e4b318644aaac0

d892409b67c45508a018c4a3d46d490310ddd06e

/Codigoredes.R

1d13c50fda2c184c450e94220716a032b4f83f07

[]

no_license

camilavalenciarod/TrabajoFinal

e57c7a3616cdd5d7648e36fba51f4d6bf2605e6c

c759e543e7a09881181cfc3726f306dc97dc78bc

refs/heads/master

2020-07-06T02:17:54.070106

2016-11-25T06:31:27

74,062,893

0

null

ISO-8859-1

R

false

14,889

r

Codigoredes.R

install.packages('igraph') install.packages('network') install.packages('sna') install.packages('ndtv') install.packages('visNetwork') library(igraph) library(network) library(sna) library(ndtv) library(visNetwork) base_1 <- read.csv("C:/Users/camila.valencia/Desktop/Efectos Pares/red1.csv", sep=";") base_2 <- read.csv("C:/Users/camila.valencia/Desktop/Efectos Pares/red2.csv", sep=";") base_3 <- read.csv("C:/Users/camila.valencia/Desktop/Efectos Pares/red3.csv", sep=";") base_4 <- read.csv("C:/Users/camila.valencia/Desktop/Efectos Pares/red4.csv", sep=";") base_5 <- read.csv("C:/Users/camila.valencia/Desktop/Efectos Pares/red5.csv", sep=";") base_6 <- read.csv("C:/Users/camila.valencia/Desktop/Efectos Pares/red6.csv", sep=";") Atributos2 <- read.csv ("C:/Users/camila.valencia/Desktop/Efectos Pares/Atributos2.csv", sep=";") #La red como una matriz network_1 <- as.matrix(base_1) #Crea el grafico de la matriz con el cual se pueden hacer todo tipo de analisis g_1 <- graph.adjacency(network_1) #Betweenness es (b_1 <- betweenness(g_1, directed = FALSE)) #closeness es (c_1 <- closeness(g_1, mode = "out")) #degree es (d_1 <- degree(g_1, mode = "out")) #Hace un objeto en formato red para que red_1 <-as.network.matrix(network_1) #grafica la red plot.network(red_1) title(main = "Red de estudio fuera de clase (min 1 x semana)") ## Adjuntar la matriz de la primera base m=as.matrix(base_1) net=graph.adjacency(m,mode="undirected",weighted=NULL,diag=FALSE) #Importar los atributos de los individuos V(net)$promedio=as.character(Atributos2$nota3[match(V(net)$name,Atributos2$Ind)]) # This code says to create a vertex attribute called "Sex" by extracting the value of the column "Sex" in the attributes file when the Bird ID number matches the vertex name. V(net)$promedio V(net)$color=V(net)$promedio V(net)$color=gsub("1","red",V(net)$color) V(net)$color=gsub("2","yellow",V(net)$color) V(net)$color=gsub("3","green",V(net)$color) plot.igraph(net,vertex.label=NA,layout=layout.fruchterman.reingold, vertex.size=3) V(net)$size=degree(net)*0.5 plot.igraph(net,vertex.label=NA,layout=layout.fruchterman.reingold) title(main = "Red de estudio fuera de clase (min 1 x semana)") l <- legend( "bottomright", inset = .02, cex = 1, bty = "n", legend = c("Bajo","Medio","Alto") , title="Desempeño académico", pt.bg = c("red", "yellow", "green") , pch = c(21,21,21)) V(net)$Sex=as.character(Atributos2$Genero[match(V(net)$name,Atributos2$Ind)]) #Crea el atributo genero haciendo match en las dos bases por el Ind V(net)$Sex # Este imprime el atributo V(net)$color=V(net)$Sex #Asigna el atributo genero al color de los vertices V(net)$color=gsub("0","purple",V(net)$color) #Mujeres seran moradas V(net)$color=gsub("1","lightgreen",V(net)$color) #Hombres seran verdes V(net)$size=degree(net)*0.5 #Hace la grafica segun el grado de conectividad plot.igraph(net,vertex.label=NA,layout=layout.fruchterman.reingold) title(main = "Red de estudio fuera de clase (min 1 x semana)") l <- legend( "bottomright", inset = .02, cex = 1, bty = "n", legend = c("Mujeres","Hombres") , title="Genero", pt.bg = c("purple", "lightgreen") , pch = 21) V(net)$estrato=as.character(Atributos2$Estrato[match(V(net)$name,Atributos2$Ind)]) # This code says to create a vertex attribute called "Sex" by extracting the value of the column "Sex" in the attributes file when the Bird ID number matches the vertex name. V(net)$estrato V(net)$color=V(net)$estrato V(net)$color=gsub("1","darkblue",V(net)$color) V(net)$color=gsub("2","darkblue",V(net)$color) V(net)$color=gsub("3","blue1",V(net)$color) V(net)$color=gsub("4","blue1",V(net)$color) V(net)$color=gsub("5","lightblue",V(net)$color) V(net)$color=gsub("6","lightblue",V(net)$color) plot.igraph(net,vertex.label=NA,layout=layout.fruchterman.reingold, vertex.size=3) V(net)$size=degree(net)*0.5 plot.igraph(net,vertex.label=NA,layout=layout.fruchterman.reingold) title(main = "Red de estudio fuera de clase (min 1 x semana)") l <- legend( "bottomright", inset = .02, cex = 1, bty = "n", legend = c("1 y 2","3 y 4","5 y 6") , title="Estratos Socioeconómicos", pt.bg = c("darkblue", "blue1", "lightblue") , pch = c(21,21,21)) V(net)$pilo=as.character(Atributos2$pilopaga[match(V(net)$name,Atributos2$Ind)]) V(net)$color=V(net)$pilo V(net)$color=gsub("0","red",V(net)$color) V(net)$color=gsub("1","darkred",V(net)$color) plot.igraph(net,vertex.label=NA,layout=layout.fruchterman.reingold, vertex.size=3) V(net)$size=degree(net)*0.5 plot.igraph(net,vertex.label=NA,layout=layout.fruchterman.reingold) title(main = "Red de estudio fuera de clase (min 1 x semana)") l <- legend( "bottomright", inset = .02, cex = 1, bty = "n", legend = c("Si","No") , title="Beneficiario ser pilo paga", pt.bg = c("darkred", "red") , pch = 21) V(net)$programa=as.character(Atributos2$programa[match(V(net)$name,Atributos2$Ind)]) V(net)$color=V(net)$programa V(net)$color=gsub("0","green",V(net)$color) #Programa de Finanzas V(net)$color=gsub("1","blue",V(net)$color) #Programa de economia plot.igraph(net,vertex.label=NA,layout=layout.fruchterman.reingold, vertex.size=3) V(net)$size=degree(net)*0.5 plot.igraph(net,vertex.label=NA,layout=layout.fruchterman.reingold) title(main = "Red de estudio fuera de clase (min 1 x semana)") l <- legend( "bottomright", inset = .02, cex = 1, bty = "n", legend = c("Finanzas y Comercio Internacional","Economía") , title="Programa Academico", pt.bg = c("green", "blue") , pch = 21) ##Ahora trabajare con la red de actividades sociales network_2 <- as.matrix(base_2) g_2 <- graph.adjacency(network_2) (b_2 <- betweenness(g_2, directed = FALSE)) (c_2 <- closeness(g_2, mode = "out")) (d_2 <- degree(g_2, mode = "out")) red_2 <-as.network.matrix(network_2) plot.network(red_2) title(main = "Red de planes sociales (min 1 x semana)") m1=as.matrix(base_2) net1=graph.adjacency(m1,mode="undirected",weighted=NULL,diag=FALSE) #Importar los atributos de los individuos V(net1)$promedio=as.character(Atributos2$nota3[match(V(net1)$name,Atributos2$Ind)]) # This code says to create a vertex attribute called "Sex" by extracting the value of the column "Sex" in the attributes file when the Bird ID number matches the vertex name. V(net1)$promedio V(net1)$color=V(net1)$promedio V(net1)$color=gsub("1","red",V(net1)$color) V(net1)$color=gsub("2","yellow",V(net1)$color) V(net1)$color=gsub("3","green",V(net1)$color) plot.igraph(net1,vertex.label=NA,layout=layout.fruchterman.reingold, vertex.size=3) title(main = "Red de actividades sociales (min 1 x semana)") l <- legend( "bottomright", inset = .02, cex = 1, bty = "n", legend = c("Bajo","Medio","Alto") , title="Desempeño académico", pt.bg = c("red", "yellow", "green") , pch = c(21,21,21)) V(net1)$Sex1=as.character(Atributos2$Genero[match(V(net1)$name,Atributos2$Ind)]) #Crea el atributo genero haciendo match en las dos bases por el Ind V(net1)$Sex1 # Este imprime el atributo V(net1)$color=V(net1)$Sex1 #Asigna el atributo genero al color de los vertices V(net1)$color=gsub("0","purple",V(net1)$color) #Mujeres seran moradas V(net1)$color=gsub("1","lightgreen",V(net1)$color) #Hombres seran verdes plot.igraph(net1,vertex.label=NA,layout=layout.fruchterman.reingold, vertex.size=3) V(net1)$size=degree(net1)* 0.5 #Hace la grafica segun el grado de conectividad title(main = "Red de actividades sociales (min 1 x semana)") l <- legend( "bottomright", inset = .02, cex = 1, bty = "n", legend = c("Mujeres","Hombres") , title="Genero", pt.bg = c("purple", "lightgreen") , pch = 21) V(net1)$estrato=as.character(Atributos2$Estrato[match(V(net1)$name,Atributos2$Ind)]) V(net1)$estrato V(net1)$color=V(net1)$estrato V(net1)$color=gsub("1","darkblue",V(net1)$color) V(net1)$color=gsub("2","darkblue",V(net1)$color) V(net1)$color=gsub("3","blue1",V(net1)$color) V(net1)$color=gsub("4","blue1",V(net1)$color) V(net1)$color=gsub("5","lightblue",V(net1)$color) V(net1)$color=gsub("6","lightblue",V(net1)$color) plot.igraph(net1,vertex.label=NA,layout=layout.fruchterman.reingold, vertex.size=3) title(main = "Red de actividades sociales (min 1 x semana)") l <- legend( "bottomright", inset = .02, cex = 1, bty = "n", legend = c("1 y 2","3 y 4","5 y 6") , title="Estratos Socioeconómicos", pt.bg = c("darkblue", "blue1", "lightblue") , pch = c(21,21,21)) V(net1)$pilo=as.character(Atributos2$pilopaga[match(V(net1)$name,Atributos2$Ind)]) V(net1)$color=V(net1)$pilo V(net1)$color=gsub("0","red",V(net1)$color) V(net1)$color=gsub("1","darkred",V(net1)$color) plot.igraph(net1,vertex.label=NA,layout=layout.fruchterman.reingold, vertex.size=3) title(main = "Red de actividades sociales (min 1 x semana)") l <- legend( "bottomright", inset = .02, cex = 1, bty = "n", legend = c("Si","No") , title="Beneficiario ser pilo paga", pt.bg = c("darkred", "red") , pch = 21) V(net1)$programa=as.character(Atributos2$programa[match(V(net1)$name,Atributos2$Ind)]) V(net1)$color=V(net1)$programa V(net1)$color=gsub("0","green",V(net1)$color) #Programa de Finanzas V(net1)$color=gsub("1","blue",V(net1)$color) #Programa de economia plot.igraph(net1,vertex.label=NA,layout=layout.fruchterman.reingold, vertex.size=3) title(main = "Red de actividades sociales (min 1 x semana)") l <- legend( "bottomright", inset = .02, cex = 1, bty = "n", legend = c("Finanzas y Comercio Internacional","Economía") , title="Programa Academico", pt.bg = c("green", "blue") , pch = 21) ##Ahora trabajare con la red de actividades deportivas network_3 <- as.matrix(base_3) g_3 <- graph.adjacency(network_3) (b_3 <- betweenness(g_3, directed = FALSE)) (c_3 <- closeness(g_3, mode = "out")) (d_3 <- degree(g_3, mode = "out")) red_3 <-as.network.matrix(network_3) plot.network(red_3) title(main = "Red de actividades deportivas") m2=as.matrix(base_3) net2=graph.adjacency(m2,mode="undirected",weighted=NULL,diag=FALSE) #Importar los atributos de los individuos V(net2)$promedio=as.character(Atributos2$nota3[match(V(net2)$name,Atributos2$Ind)]) # This code says to create a vertex attribute called "Sex" by extracting the value of the column "Sex" in the attributes file when the Bird ID number matches the vertex name. V(net2)$promedio V(net2)$color=V(net2)$promedio V(net2)$color=gsub("1","red",V(net2)$color) V(net2)$color=gsub("2","yellow",V(net2)$color) V(net2)$color=gsub("3","green",V(net2)$color) plot.igraph(net2,vertex.label=NA,layout=layout.fruchterman.reingold, vertex.size=3) title(main = "Red de actividades deportivas (min 1 x semana)") l <- legend( "bottomright", inset = .02, cex = 1, bty = "n", legend = c("Bajo","Medio","Alto") , title="Desempeño académico", pt.bg = c("red", "yellow", "green") , pch = c(21,21,21)) V(net2)$Sex=as.character(Atributos2$Genero[match(V(net2)$name,Atributos2$Ind)]) #Crea el atributo genero haciendo match en las dos bases por el Ind V(net2)$Sex # Este imprime el atributo V(net2)$color=V(net2)$Sex #Asigna el atributo genero al color de los vertices V(net2)$color=gsub("0","purple",V(net2)$color) #Mujeres seran moradas V(net2)$color=gsub("1","lightgreen",V(net2)$color) #Hombres seran verdes plot.igraph(net2,vertex.label=NA,layout=layout.fruchterman.reingold, vertex.size=3) title(main = "Red de actividades deportivas (min 1 x semana)") l <- legend( "bottomright", inset = .02, cex = 1, bty = "n", legend = c("Mujeres","Hombres") , title="Genero", pt.bg = c("purple", "lightgreen") , pch = 21) V(net2)$estrato=as.character(Atributos2$Estrato[match(V(net2)$name,Atributos2$Ind)]) V(net2)$estrato V(net2)$color=V(net2)$estrato V(net2)$color=gsub("1","darkblue",V(net2)$color) V(net2)$color=gsub("2","darkblue",V(net2)$color) V(net2)$color=gsub("3","blue1",V(net2)$color) V(net2)$color=gsub("4","blue1",V(net2)$color) V(net2)$color=gsub("5","lightblue",V(net2)$color) V(net2)$color=gsub("6","lightblue",V(net2)$color) plot.igraph(net2,vertex.label=NA,layout=layout.fruchterman.reingold, vertex.size=3) title(main = "Red de actividades sociales (min 1 x semana)") l <- legend( "bottomright", inset = .02, cex = 1, bty = "n", legend = c("1 y 2","3 y 4","5 y 6") , title="Estratos Socioeconómicos", pt.bg = c("darkblue", "blue1", "lightblue") , pch = c(21,21,21)) V(net2)$pilo=as.character(Atributos2$pilopaga[match(V(net2)$name,Atributos2$Ind)]) V(net2)$color=V(net2)$pilo V(net2)$color=gsub("0","red",V(net2)$color) V(net2)$color=gsub("1","darkred",V(net2)$color) plot.igraph(net2,vertex.label=NA,layout=layout.fruchterman.reingold, vertex.size=3) title(main = "Red de actividades sociales (min 1 x semana)") l <- legend( "bottomright", inset = .02, cex = 1, bty = "n", legend = c("Si","No") , title="Beneficiario ser pilo paga", pt.bg = c("darkred", "red") , pch = 21) V(net2)$programa=as.character(Atributos2$programa[match(V(net2)$name,Atributos2$Ind)]) V(net2)$color=V(net2)$programa V(net2)$color=gsub("0","green",V(net2)$color) #Programa de Finanzas V(net2)$color=gsub("1","blue",V(net2)$color) #Programa de economia plot.igraph(net2,vertex.label=NA,layout=layout.fruchterman.reingold, vertex.size=3) title(main = "Red de actividades sociales (min 1 x semana)") l <- legend( "bottomright", inset = .02, cex = 1, bty = "n", legend = c("Finanzas y Comercio Internacional","Economía") , title="Programa Academico", pt.bg = c("green", "blue") , pch = 21) #Descripcion de las otras redes network_4 <- as.matrix(base_4) g_4 <- graph.adjacency(network_4) (b_4 <- betweenness(g_4, directed = FALSE)) (c_4 <- closeness(g_4, mode = "out")) (d_4 <- degree(g_4, mode = "out")) red_4 <-as.network.matrix(network_4) plot.network(red_4) title(main = "Red de consejos personales") network_5 <- as.matrix(base_5) g_5 <- graph.adjacency(network_5) (b_5 <- betweenness(g_5, directed = FALSE)) (c_5 <- closeness(g_5, mode = "out")) (d_5 <- degree(g_5, mode = "out")) red_5 <-as.network.matrix(network_5) plot.network(red_5) title(main = "Conocidos antes de entrar a la UR") network_6 <- as.matrix(base_6) g_6 <- graph.adjacency(network_6) (b_6 <- betweenness(g_6, directed = FALSE)) (c_6 <- closeness(g_6, mode = "out")) (d_6 <- degree(g_6, mode = "out")) red_6 <-as.network.matrix(network_6) plot.network(red_6) title(main = "Red de los considerados lideres")

966622ae1e99faf232577e90507ce08560d76fba

d072433fb4facac496d0337b4cf22b5e00cf6853

/man/flowers.Rd

1da9fea70267d0ca79f5b4510677fc7a36344d24

[]

no_license

cran/asuR

3e3d745182d938acba825b723fc09b14cc16dbaa

578f3e0693d29fb8fd3056d52fa8913d5039cc79

refs/heads/master

2020-12-24T15:05:44.493897

2007-06-01T00:00:00

null

0

null

UTF-8

R

false

1,538

rd

flowers.Rd

\name{flowers} \alias{flowers} \docType{data} \title{Flower} \description{ A data set with the dry mass of all flowers and the dry mass of the total plant from 20 species growing at high and 20 species growing at low altitude. } \usage{data(flowers)} \format{ A data frame with 40 observations on the following 3 variables. \describe{ \item{\code{alt}}{a factor with levels \code{high} \code{low}} \item{\code{flower}}{a numeric vector; dry mass of flowers in mg} \item{\code{total}}{a numeric vector; dry mass of total plant in mg} } } \details{ The data sets shows a random subsample of the original data set. } \source{ Fabbro, T. & Koerner, Ch. (2004): \emph{Altitudinal differences in flower traits and reproductive allocation.} FLORA 199, 70-81. Fabbro, Koerner () } \examples{ data(flowers) % ## having a look at the data % # xyplot(log(flower) ~ log(total)|alt, data=flower, % # panel=function(x,y,...){ % # panel.xyplot(x,y,...) % # panel.lmline(x,y,...) % # }) ## a model with two intercepts and two slopes # m1 <-lm(log(flower) ~ alt/log(total) -1, data=flower) # ## a model with two intercepts and one slope # altdiff <- rbind("high-low"=c(1,-1)) # m2 <- lm(log(flower) ~ alt + log(total), data=flower, # contrasts=list(alt=mycontr(contr=altdiff))) # ## are separate slopes needed? # anova(m1, m2) # conclusion: # ## no difference in slopes but difference in intercept ## for interpretation also test whether the slope is one! } \keyword{datasets}

10875d8efde3b62455a44c76cc72b209c15c0897

52a4315886671fb197e31d4f3e1fd665767814a8

/cachematrix.R

f4a394fa62d5a98d29c8da427572f6f1cc8e560f

[]

no_license

julthida/ProgrammingAssignment2-master

d646d41c7287bb8410d215f80366f29d80c18bf6

62b9a5c7ce1304216fc2f0bda633e6080f879d29

refs/heads/master

2020-12-20T15:20:29.750768

2020-01-28T06:30:13

236,121,145

0

null

UTF-8

R

false

1,024

r

cachematrix.R

## Caching the Inverse of a Matrix ## Below are two functions that are used to create a ## special matrix and caches its inverse ## creates the list for cachesolve makeCacheMatrix <-function(x=Matrix()){ inverse<<-NULL setmatrix<-function(y){ x<<-y inverse<<-NULL } getmatrix<-function() x setinverse<-function(x) { inverse2<-matrix(nrow=ncol(x),ncol=nrow(x)) for(i in seq(1,nrow(x),by=1)){ inverse2[i,]<-x[,i] } inverse<<-inverse2 } getinverse<-function(){ return(inverse) } cache<<-(list(set=setmatrix,get=getmatrix,setinv=setinverse,getinv=getinverse)) } ## computes the inverse of the special "matrix" returned by `makeCacheMatrix` ## retrieve the inverse from the cache if the inverse already been calculated cacheSolve <- function(x) { inverse<-cache$getinv() if(!is.null(inverse)){ message("getting cached data") return(inverse) } data<-cache$get() inverse<-cache$setinv(data) cache$setinv(inverse) return(inverse) }

498a273d144b017676e5bee06b6a11f127904072

002929791137054e4f3557cd1411a65ef7cad74b

/R/isEmpty.R

f31db6ee212f3c213d039bd559bace06d8e3503f

[ "MIT" ]

permissive

jhagberg/nprcgenekeepr

42b453e3d7b25607b5f39fe70cd2f47bda1e4b82

41a57f65f7084eccd8f73be75da431f094688c7b

refs/heads/master

2023-03-04T07:57:40.896714

2023-02-27T09:43:07

301,739,629

0

NOASSERTION

2023-02-27T09:43:08

2020-10-06T13:40:28

null

UTF-8

R

false

299

r

isEmpty.R

#' Is vector empty or all NA values. #' ## Copyright(c) 2017-2020 R. Mark Sharp ## This file is part of nprcgenekeepr #' #' @return \code{TRUE} if x is a zero-length vector else \code{FALSE}. #' #' @param x vector of any type. isEmpty <- function(x) { x <- x[!is.na(x)] return(length(x) == 0) }

1cca483f6b110481df00925c30a4a5a50a910e3e

d2eda24acceb35dc11263d2fa47421c812c8f9f6

/R testing/Smoothing.R

588ae17fb56aeff1d8ba01c3591949de3d40bfe2

[]

no_license

tbrycekelly/TheSource

3ddfb6d5df7eef119a6333a6a02dcddad6fb51f0

461d97f6a259b18a29b62d9f7bce99eed5c175b5

refs/heads/master

2023-08-24T05:05:11.773442

2023-08-12T20:23:51

209,631,718

5

1

null

UTF-8

R

false

2,008

r

Smoothing.R

grid = array(0, dim = c(15,16)) ## Add Corners grid[1,1] = 114 grid[1,16] = 121 grid[15,1] = 116 grid[15,16] = 121 delta.j1 = (grid[15,1] - grid[1,1]) / 14 delta.j6 = (grid[15,16] - grid[1,16]) / 14 ## Fill in first and last column for (i in c(1:15)) { grid[i,1] = grid[1,1] + delta.j1 * (i-1) grid[i,16] = grid[1,16] + delta.j6 * (i-1) } ## Fill in all values for (i in c(1:15)) { delta = (grid[i,16] - grid[i,1]) / 15 for (j in 2:15) { grid[i,j] = grid[i,j-1] + delta } } smooth.gaussian = function(x, sd = 0.2, n = 5) { stencil = array(1, dim = rep(n, 2)) for (i in 1:n) { stencil[i,] = stencil[i,] * dnorm(c(1:n), (n+1)/2, sd) } for (i in 1:n) { stencil[,i] = stencil[,i] * dnorm(c(1:n), (n+1)/2, sd) } stencil = stencil / sum(stencil, na.rm = T) new = x buffer = (n + 1) / 2 for (i in buffer:(dim(x)[1] - buffer)) { for (j in buffer:(dim(x)[2] - buffer)) { new[i,j] = sum(stencil * x[(i-buffer+1):(i+buffer-1), (j-buffer+1):(j+buffer-1)]) } } new } original = array(runif(40000, -1, 1) > 0, dim = c(250,160)) plot.image(z = original) test = smooth.gaussian(original, sd = 0.1, n = 9) plot.image(z = test) poc = read.satellite('C:/Data/Satellite/A20022492002256.L3m_8D_POC_poc_4km.nc', lon = c(-180, -140), lat = c(40, 80)) str(poc$field) poc.smooth = smooth.gaussian(poc$field[[1]], 0.2, 3) map = make.map.nga() add.map.layer(map, poc$lon, poc$lat, poc$field[[1]], pal = 'parula', zlim = c(0, 1e3)) add.map.layer(map, poc$lon, poc$lat, poc.smooth, pal = 'parula', zlim = c(0, 1e3)) redraw.map(map) frrf = load.frrf('C:/Users/Tom Kelly/Desktop/FRRF/CTG-Act2Run/Auto-saved FLC data files/20220308/') plot(frrf[[1]]$A$E, frrf[[1]]$A$JPII, type = 'l', lwd = 3, yaxs = 'i', xaxs = 'i', xlim = c(0,700), xlab = 'E') lines(frrf[[2]]$A$E, frrf[[2]]$A$JPII, lwd = 3, col = 'dark green') lines(frrf[[3]]$A$E, frrf[[3]]$A$JPII, lwd = 3, col = 'dark green')

82fb82dd36f91bf1f2bbd87dfbfe48bd84060fb3

102c6eb2165121a04e4e57e7cf651d6d8d41c0e8

/Final Project/bank_branches/app.R

0f0684201d541359fa58550b5b63e761d702cebd

[]

no_license

Jagdish16/CUNY_DATA_608

df2ec2b4cad594e6b7e0a88a345fb3a073fdea5b

6ee3d6b496f4d1ef85fd1e906cefebfd345cc361

refs/heads/master

2023-01-24T14:35:02.841799

2020-12-13T03:12:17

293,076,162

0

null

2020-09-05T12:57:47

2020-09-05T12:57:46

null

UTF-8

R

false

3,379

r

app.R

# # This is a Shiny web application. You can run the application by clicking # the 'Run App' button above. # Find out more about building applications with Shiny here: # http://shiny.rstudio.com/ # # Name of application = bank_branches library(shiny) library(dplyr) library(ggplot2) library(rsconnect) library(plotly) library(kableExtra) library(readr) library(leaflet) library(tidyverse) library(maps) #rsconnect::deployApp('~/ShinyApps/banks') # Load the Bank Branches data for different years all2020<-read_csv('ALL_2020.csv') # Make a list of columns of interest cols<-c('ASSET', 'CHARTER', 'CITYBR', 'CNTRYNA', 'DEPDOM', 'DEPSUM', 'DEPSUMBR', 'NAMEBR', 'NAMEFULL', 'REGAGNT', 'RSSDID', 'SIMS_LATITUDE', 'SIMS_LONGITUDE', 'SPECDESC', 'SPECGRP', 'STALPBR', 'STNAMEBR', 'UNINUMBR', 'YEAR', 'ZIPBR') # Filter the data frame to select only the columns of interest all2020<-all2020[ ,which((names(all2020) %in% cols)==TRUE)] # Rename the columns to more intuitive names all2020<-all2020%>%rename(state_code=STALPBR, region=STNAMEBR, total_assets=ASSET, city=CITYBR, domestic_deposits=DEPDOM, total_deposits=DEPSUM, branch_deposits=DEPSUMBR, branch=NAMEBR, bank_name=NAMEFULL,regulator=REGAGNT, specialization=SPECDESC, branch_id=RSSDID,zipcode=ZIPBR, location_id=UNINUMBR, spec_grp=SPECGRP, branch_lat=SIMS_LATITUDE, branch_lon=SIMS_LONGITUDE) # Convert the region column to lower case to enable merging all2020$region<-tolower(all2020$region) # Create a new dataframe with top banks by branch count shortlist<-all2020%>%group_by(bank_name)%>%summarise(n=n())%>%arrange(desc(n))%>%top_n(5) # Create a new dataframe for just the top banks by branch count top5_2020<-all2020[all2020$bank_name %in% shortlist$bank_name,] # Create a dataframe with the bank short names short<-read.table(file="bank_short_names.txt",header=TRUE,sep="|") # Merge the dataframes so that the bank short name is part of the top bank dataset top5_2020<-merge(top5_2020,short,by='bank_name') # Filter for Bank of America branches bankam_branches_2020<-top5_2020%>%filter(bank=="Bank of America") # Create icon based on Bank of America logo bankamIcon<-icons("bankam.png",iconWidth=8,iconHeight=8) # Define UI for application ui <- fluidPage( # Application title titlePanel("Bank of America Branches By State for 2020"), # Sidebar with a selection for State sidebarLayout( sidebarPanel( #selectInput('bank',"Select Bank",top15$bank%>%unique,selected=NULL, multiple=FALSE,selectize=TRUE), selectInput('state',"Select State",bankam_branches_2020$state_code%>%unique,selected=NULL,multiple=FALSE,selectize=TRUE), ), # Show a spatial density map for selected bank and state mainPanel( leafletOutput("densityMap",width="100%",height=400) ) ) ) server<-shinyServer(function(input,output,session) #server <- function(input, output) { # Create a new dataset by filtering branches by state only bank_state<-reactive({bankam_branches_2020%>%filter(state_code==input$state)}) output$densityMap<-renderLeaflet({ leaflet()%>%addProviderTiles("CartoDB.Positron")%>%addMarkers(data=bank_state(),lng=~branch_lon,lat=~branch_lat,icon=bankamIcon,label=~branch,popup=~paste("Deposits = $",branch_deposits)) }) } ) # Run the application shinyApp(ui = ui, server = server)

a4808015bc8bf19c9e7613b9e68fdf4a0b890f7a

0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb

/cran/paws.end.user.computing/man/appstream_delete_stack.Rd

ae0637391f0d738a829f56d40fb34ba649b46edd

[ "Apache-2.0" ]

permissive

paws-r/paws

196d42a2b9aca0e551a51ea5e6f34daca739591b

a689da2aee079391e100060524f6b973130f4e40

refs/heads/main

2023-08-18T00:33:48.538539

2023-08-09T09:31:24

154,419,943

293

45

NOASSERTION

2023-09-14T15:31:32

2018-10-24T01:28:47

R

UTF-8

R

false

true

665

rd

appstream_delete_stack.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/appstream_operations.R \name{appstream_delete_stack} \alias{appstream_delete_stack} \title{Deletes the specified stack} \usage{ appstream_delete_stack(Name) } \arguments{ \item{Name}{[required] The name of the stack.} } \description{ Deletes the specified stack. After the stack is deleted, the application streaming environment provided by the stack is no longer available to users. Also, any reservations made for application streaming sessions for the stack are released. See \url{https://www.paws-r-sdk.com/docs/appstream_delete_stack/} for full documentation. } \keyword{internal}

74a34e80916da916e08f5cff55ba54e35b023617

facd83d2c6378682421bb7902191f1f49fa1836f

/R/cbk.periodic.R

6ecc288344f6a9769068682aec935989fddc6705

[]

no_license

misasa/chelyabinsk

cbb9e3acdaaefb3254d01d38c42e404164dfa2d1

495e8bab926934467a3a7fd7c74bb1be69d2f094

refs/heads/master

2021-06-28T09:11:19.766488

2020-11-20T00:26:38

69,550,402

0

2

null

2019-06-21T10:49:18

2016-09-29T09:04:01

R

UTF-8

R

false

1,431

r

cbk.periodic.R

#' @title Return properties of elements from a periotic-table #' #' @description Return properties of elements from a periotic-table. #' Specify property of your concern otherwise this return dataframe #' of periodic table. #' #' @param property A name of PROPERTY that is one of 'atomicnumber', #' 'volatility', or 'compatibility' #' @return A numeric vector of element property with label or #' dataframe of periodic-table #' @export #' @importFrom utils read.csv #' @examples #' cbk.periodic() #' cbk.periodic("atomicnumber") #' cbk.periodic("volatility") #' cbk.periodic("compatibility") cbk.periodic <- function(property=NULL){ ## EXAMPLES ## cbk.periodic("atomicnumber") ## H Li Be B C N F Na Mg Al Si P S Cl K Ca Ti Cr Mn Fe Ni Rb Sr Y Zr Nb ## 1 3 4 5 6 7 9 11 12 13 14 15 16 17 19 20 22 24 25 26 28 37 38 39 40 41 ## In Cs Ba La Ce Pr Nd Sm Eu Gd Tb Dy Ho Er Tm Yb Lu Hf Ta Tl Pb Bi Th U ## 49 55 56 57 58 59 60 62 63 64 65 66 67 68 69 70 71 72 73 81 82 83 90 92 ## csvfile <- cbk.path("periodic-table.csv") ## message(sprintf("The csvfile is located at |%s|.",csvfile)) ## foo <- read.csv(csvfile,header=T,row.names=1) foo <- cbk.read.dflame(cbk.path("periodic-dflame0.csv"),verbose=FALSE) if(!is.null(property)){ bar <- foo[,property] names(bar) <- rownames(foo) out <- sort(bar) } else { out <- foo } return(out) }