pierreqi/r_code_50k · Datasets at Hugging Face

da1c32131ca39777aae69fcae686ac3e954c1f2f

e92f5c95c8c17f4b2dc8d754fd212fd4fcc4c8b4

/data-raw/oraciones.R

6580a98f31a8ed8acf92a59583c20cc194dae5dd

[ "CC0-1.0", "LicenseRef-scancode-unknown-license-reference" ]

permissive

cienciadedatos/datos

de1cab523ce46ed1b1d86dbb4f947ba9e58ff432

6008b75bfc68e2e8332fe92c37f5ec59361fa4f4

refs/heads/main

2023-07-20T12:01:18.032448

2023-07-17T12:50:37

2023-07-17T15:10:53

140,963,726

37

34

CC0-1.0

2023-07-17T12:52:14

2018-07-14T17:07:44

R

UTF-8

R

false

3,200

r

oraciones.R

# From `dput(datos::oraciones)` (245b4af) # Googling "latin small letter a with acute" and so on # a "\u00e1" # e "\u00e9" # E "\u00c9" # i "\u00ed" # o "\u00f3" # u "\u00fa" # # n \u00f1 oraciones <- c( "Las casas est\u00e1n construidas de ladrillos de arcilla roja.", "La caja fue arrojada al lado del cami\u00f3n estacionado.", "El domingo es la mejor parte de la semana.", "Agrega a la cuenta de la tienda hasta el \u00faltimo centavo.", "Nueve hombres fueron contratados para excavar las ruinas.", "Pega la hoja en el fondo azul oscuro.", "Instalaron azulejos verdes en la cocina.", "Si arrojo la taza azul al suelo se romper\u00e1.", "Dos peces azules nadaban en el tanque.", "El ancho camino brillaba bajo el calor del sol.", "Una voluta de nube flotaba en el aire azul.", "Las hojas se vuelven de color marr\u00f3n y amarillo en el oto\u00f1o.", "\U00c9l orden\u00f3 tarta de melocot\u00f3n con helado.", # FIXME "La mancha en el papel secante fue hecha por la tinta verde.", "Hab\u00eda barro salpicado en la parte delantera de su camisa blanca.", "El coj\u00edn del sof\u00e1 es de color rojo y de peso ligero.", "El cielo de la ma\u00f1ana era claro y azul brillante.", "El m\u00e9dico lo cur\u00f3 con estas dos pastillas.", "Pod\u00edan re\u00edr a pesar de que estaban tristes.", "El tercer acto era aburrido y cans\u00f3 a los actores.", "Una grulla azul es una ave zancuda y alta.", "Los cables expuestos deben mantenerse cubiertos.", "El choque ocurri\u00f3 cerca del banco en la calle principal.", "La l\u00e1mpara brillaba con una llama verde y continua.", "El pr\u00edncipe orden\u00f3 que le cortaran la cabeza.", "La planta creci\u00f3 grande y verde en la ventana.", "El lazo p\u00farpura ten\u00eda diez a\u00f1os.", "Recu\u00e9state y rel\u00e1jate en la hierba fresca y verde.", "El lago brillaba bajo el sol c\u00e1lido y rojo.", "Marca el lugar con un cartel pintado de rojo.", "El humo sal\u00eda de cada grieta.", "La cubierta del sof\u00e1 y las cortinas de la sala eran azules.", "Ofreci\u00f3 evidencia a trav\u00e9s de tres gr\u00e1ficos.", "Un hombre con un su\u00e9ter azul se sent\u00f3 en el escritorio.", "El sorbo de t\u00e9 revive a su amigo cansado.", "Una gruesa capa de pintura negra cubr\u00eda todo.", "Dibuja el gr\u00e1fico con l\u00edneas negras gruesas.", "La peque\u00f1a l\u00e1mpara de ne\u00f3n de color rojo se apag\u00f3.", "Pinta los encajes en la pared de color verde opaco.", "Despi\u00e9rtate y lev\u00e1ntate, camina hacia el verde exterior.", "La luz verde en la caja marr\u00f3n parpadeaba.", "Puso su \u00faltimo cartucho en la pistola y dispar\u00f3.", "El carnero asust\u00f3 a los ni\u00f1os de la escuela.", "Corta una delgada l\u00e1mina de la almohadilla amarilla.", "Llovieron centavos de todos lados.", "El cielo en el oeste se ti\u00f1e de rojo anaranjado.", "El granizo repiqueteaba en la hierba marr\u00f3n quemada.", "La gran manzana roja cay\u00f3 al suelo.", "El olor de la primavera hace que los corazones j\u00f3venes salten.", "Cada palabra y cada frase que habla es cierta." ) use_data(oraciones, overwrite = TRUE)

1e0cc38d24e84ef9d55d6db4bad81026c02387ac

d8abe01116e043544cbb198ad054c7969da022eb

/readyData.R

f1791f959466e85fc1d2382137a3c3f727e775ae

[]

no_license

rpsoft/POPMI

0b9478f1dc3f647c240acfb4280bf75f76e9e261

6acfa0ecf9aeb40357c40317a0afe899b86bd764

refs/heads/master

2023-01-21T22:54:24.921825

2020-12-04T23:42:46

318,381,158

0

1

null

UTF-8

R

false

8,600

r

readyData.R

library(tidyverse) library(plumber) library(future) options(future.globals.maxSize= 891289600) joint_probs <- readRDS("data/joint/joint_probs.rds") joint_cvd_pop <- read_csv("data/pops/simPop2.cvd.cvd.500.14nov20.csv") joint_mi_pop <- read_csv("data/pops/simPop2.mi.mi.500.12nov20.csv") cvd = readRDS(file = 'data/cond/cond_prob_cvd.rds') mi = readRDS(file = 'data/cond/cond_prob_mi.rds') cond_probs <- list( cvd = cvd, mi = mi ) weighted_risk_cvd <- read_csv("data/joint/weighted_risk_cvd.csv") weighted_risk_mi <- read_csv("data/joint/weighted_risk_mi.csv") # weighted_risk_cvd %>% filter( year_cat = "(1989,1994]") %>% arrange(event_1,event_2,event_3,event_4 ) %>% View initials <- function ( terms ){ paste0(unlist(lapply ( terms, function (x){ substring(x, 1, 1) })), collapse = "") } filterWeightedMSMData <- function( source, year_cat_in ){ if ( source == "mi" ){ source_data = weighted_risk_cvd } else { source_data = weighted_risk_mi } source_data <- source_data %>% filter(year_cat == year_cat_in) %>% mutate( wr = round(wr*100,3) ) %>% mutate( probability = wr ) %>% mutate( event_0 = source ) %>% mutate( event_id = "fourth_evnt") %>% mutate( event_id = ifelse(is.na(event_4), "thrd_evnt",event_id)) %>% mutate( event_id = ifelse(is.na(event_3), "scnd_evnt",event_id)) %>% mutate( event_id = ifelse(is.na(event_2), "frst_evnt",event_id)) source_data <- source_data %>% select( colnames(source_data) %>% sort() ) source_data <- source_data %>% rowwise %>% mutate( eventSequence = paste0( c(event_0, event_1, event_2, event_3, event_4), collapse = ",")) %>% mutate ( eventSequence = str_replace_all(eventSequence,",NA", "")) first_ev <- source_data %>% filter( event_id == "frst_evnt") %>% mutate( source = event_0, target=paste0("(",initials(c(event_0)),") ",event_1)) second_ev <- source_data %>% filter( event_id == "scnd_evnt") %>% mutate( source = paste0("(",initials(c(event_0)),") ",event_1), target= paste0("(",initials(c(event_0,event_1)),") ",event_2)) third_ev <- source_data %>% filter( event_id == "thrd_evnt") %>% mutate( source = paste0("(",initials(c(event_0,event_1)),") ",event_2), target=paste0("(",initials(c(event_0,event_1,event_2)),") ",event_3)) fourth_ev <- source_data %>% filter( event_id == "fourth_evnt") %>% mutate( source = paste0("(",initials(c(event_0,event_1,event_2)),") ",event_3), target=paste0("(",initials(c(event_0,event_1,event_2,event_3)),") ",event_4)) #paste0("(4) ",event_4)) all_transitions <- first_ev %>% rbind(second_ev) %>% rbind(third_ev) %>% rbind(fourth_ev) filtered <- source_data %>% select(eventSequence,event_0:event_4,wr) %>% arrange(desc(event_1),desc(event_2),desc(event_3),desc(event_4)) %>% rowwise %>% mutate( ss = str_split(eventSequence, ",") ) root <- unlist(lapply(filtered$ss, function(x){ ss <- x[!is.na(x)] unlist(paste0(x[1:(length(x)-1)], collapse = ",")) })) values <- unlist(lapply(root, function(x) { sum((filtered %>% filter( str_detect(eventSequence,x) ))$wr) })) transitions <- (tibble ( eventSequence = root, eventSequence2 = root, values = values) %>% distinct) %>% separate(eventSequence2, c( "event_0", "event_1", "event_2", "event_3", "event_4"), ",") transitions <- transitions %>% mutate( year_cat = year_cat_in, wr = values, probability = values, event_id="aggr") transitions <- transitions %>% mutate( event_id = "fourth_evnt") %>% mutate( event_id = ifelse(is.na(event_4), "thrd_evnt",event_id)) %>% mutate( event_id = ifelse(is.na(event_3), "scnd_evnt",event_id)) %>% mutate( event_id = ifelse(is.na(event_2), "frst_evnt",event_id)) %>% rowwise() first_ev <- transitions %>% filter( event_id == "frst_evnt") %>% mutate( source = event_0, target=paste0("(",initials(c(event_0)),") ",event_1)) %>% ungroup() second_ev <- transitions %>% filter( event_id == "scnd_evnt") %>% mutate( source = paste0("(",initials(c(event_0)),") ",event_1), target= paste0("(",initials(c(event_0,event_1)),") ",event_2)) %>% ungroup() third_ev <- transitions %>% filter( event_id == "thrd_evnt") %>% mutate( source = paste0("(",initials(c(event_0,event_1)),") ",event_2), target=paste0("(",initials(c(event_0,event_1,event_2)),") ",event_3)) %>% ungroup() fourth_ev <- transitions %>% filter( event_id == "fourth_evnt") %>% mutate( source = paste0("(",initials(c(event_0,event_1,event_2)),") ",event_3), target=paste0("(",initials(c(event_0,event_1,event_2,event_3)),") ",event_4)) %>% ungroup() aggr_transitions <- first_ev %>% rbind(second_ev) %>% rbind(third_ev) %>% rbind(fourth_ev) aggr_transitions <- aggr_transitions %>% select(all_transitions %>% colnames) all_transitions <- all_transitions %>% rbind (aggr_transitions) %>% ungroup all_transitions <- all_transitions %>% arrange(desc(event_0),desc(event_1),desc(event_2),desc(event_3),desc(event_4)) %>% mutate( target = ifelse(is.na(event_1), source , target), source = ifelse(is.na(event_1), NA , source), event_id = ifelse(is.na(event_1), "root_evnt" , event_id) ) %>% distinct } filterMSMData <- function( source, sex_in, simd, hf_in, cerebro_or_mi, year_cat_in, age_in ){ if ( source == "mi" ){ source_data = cond_probs$mi source_pop = joint_mi_pop ids <- (source_pop %>% filter( sex == sex_in ) %>% filter(simd_2009 == simd) %>% filter(cerebrovasc == cerebro_or_mi) %>% filter(hf == hf_in) %>% filter(year_cat == year_cat_in)) } else { source_data = cond_probs$cvd source_pop = joint_cvd_pop ids <- (source_pop %>% filter( sex == sex_in ) %>% filter(simd_2009 == simd) %>% filter(mi == cerebro_or_mi) %>% filter(hf == hf_in) %>% filter(year_cat == year_cat_in)) } sel_id <- (ids %>% mutate( dff = abs(age-age_in) ) %>% arrange(dff))$id[1] pdata <- source_data %>% ungroup %>% filter(pid == sel_id) %>% mutate( event_0 = source) #%>% filter(event_1 == "bleeding") pdata <- pdata %>% mutate( event_1 = as.character(event_1)) %>% mutate( event_2 = as.character(event_2)) %>% mutate( event_3 = as.character(event_3)) %>% mutate( event_4 = as.character(event_4)) pdata <- pdata %>% mutate( event_1 = as.character(ifelse( is.na(event_1),"",event_1 ) )) %>% mutate( event_2 = as.character(ifelse( is.na(event_2),"",event_2 ) )) %>% mutate( event_3 = as.character(ifelse( is.na(event_3),"",event_3 ) )) %>% mutate( event_4 = as.character(ifelse( is.na(event_4),"",event_4 ) )) pdata <- pdata %>% mutate( path = paste0(event_0,"_",event_1,"_",event_2,"_",event_3,"_",event_4)) pdata <- pdata %>% mutate( path = str_replace(path,"_*$", "") ) first_ev <- pdata %>% filter( event_id == "frst_evnt") %>% mutate( source = event_0, target=paste0(event_0,"_",event_1)) second_ev <- pdata %>% filter( event_id == "scnd_evnt") %>% mutate( source = paste0(event_0,"_",event_1), target=paste0(event_0,"_",event_1,"_",event_2)) third_ev <- pdata %>% filter( event_id == "thrd_evnt") %>% mutate( source = paste0(event_0,"_",event_1,"_",event_2), target=paste0(event_0,"_",event_1,"_",event_2,"_",event_3)) fourth_ev <- pdata %>% filter( event_id == "fourth_evnt") %>% mutate( source = paste0(event_0,"_",event_1,"_",event_2,"_",event_3), target=paste0(event_0,"_",event_1,"_",event_2,"_",event_3,"_",event_4)) all_transitions <- first_ev %>% rbind(second_ev) %>% rbind(third_ev) %>% rbind(fourth_ev) %>% mutate( source = str_replace(source,"_*$", ""), target = str_replace(target,"_*$", "") ) all_transitions <- all_transitions %>% filter ( cond_prob != 1) labels <- c(all_transitions$source, all_transitions$target) %>% unique() all_transitions_n <- all_transitions %>% rowwise() %>% mutate( source_n = which(labels == source)[1], target_n = which(labels == target)[1] ) labels <- tibble(target=labels) %>% left_join(all_transitions_n %>% select(target,cond_prob)) %>% mutate( ss = str_split(target,"_") ) %>% rowwise %>% mutate( labels = paste0(ss[length(ss)], ifelse(is.na(cond_prob) , "" ,paste0(" (", round(cond_prob*100,2),"%)"))) ) all_transitions_n <- all_transitions_n %>% select( all_transitions_n %>% colnames() %>% sort() ) data <- list( data = all_transitions_n, labels = labels$labels ) # browser() return(data) } # filterMSMData("mi", 1, 3, 1, 0, "(1989,1994]", 50) $data %>% View

ed4ed99c003d7bf798d16dfb4c64aff6e06123da

f78c451bc1d7892e6684f92413b20515fddc8936

/R/get.adjacency.norm.R

e40e1d41d392acf1abf74fbc7a473793e724fc03

[]

no_license

alexjcornish/DiseaseCellTypes

3336f0bd93406ddad021c1ee2d6c467cc834080f

ea5d62898549eacf653f3c388d86ceb4af05eef0

refs/heads/master

2021-01-19T08:46:26.242215

2015-09-02T09:03:56

27,534,586

2

0

null

UTF-8

R

false

1,049

r

get.adjacency.norm.R

get.adjacency.norm <- function( g, edge.attr ) { # compute a column-normalised adjacency matrix to be used to compute the RWR distance # function outputs a row for each vertex in g and a column for each vertex in g # a sparse matrix is always output # g should be an undirected igraph object and edge.attr a edge attribute containing weights # if edge.attr is NULL, then it is assumed that all edge weights equal 1 # in order to speed up the function, numbers rather than names are used to refer to vertices # this function is designed to be as fast as possible, and therefore inputs are not checked weights <- if (is.null(edge.attr)) 1 else get.edge.attribute(g, edge.attr) el <- cbind(get.edgelist(g, names=F), weights) # el: edgelist with weights el <- rbind(el, el[el[, 1] != el[, 2], c(2,1,3)]) weights.colsum <- sapply(split(el[, 3], el[, 1]), sum) el[, 3] <- el[, 3] / weights.colsum[el[, 1]] sparseMatrix(dims=rep(vcount(g), 2), i=el[, 2], j=el[, 1], x=el[, 3], symmetric=FALSE) }

e3bc4c84f106e0a4d317f6a1a70da5aa5b4fe702

364c91054852403d0f124b7349e030b4bda407a1

/dev/matchingAlgo/correlation.R

b3d18f5135e9cb448129fa8c5e4df982fd25f9ec

[]

no_license

sanathkumarbs/gradscout

d54e5ff7b6388dc3240c80fda5a130123e910735

9a8aca269aa10ceaadff5aa8d30064d7dc97e15b

refs/heads/master

2021-01-11T19:05:11.470698

2017-05-31T05:25:26

79,312,168

2

1

null

2017-04-06T01:33:21

2017-01-18T06:51:58

HTML

UTF-8

R

false

432

r

correlation.R

library(data.table) #loading data.table library in my R workspace library(ggplot2) #loading ggplot2 library in my R workspace library(corrplot) #loading corrplot library in my R workspace GS <- data.table(University.Quantitative.Data) #loading dataset University.Quantitative.Data summary(GS) #summarizing the dataframe gs <- cor(GS) #generating the correlation values corrplot(gs, method="number") #creating the correlation matrix

2de49ffb9505c322c6599d4b8e24e54dc46fd5fc

af656e348cf17aaaa54ee2ec3fe83b5e7a927b39

/Challenge_3/makeAdmixturePlot.R

604ef5db538446daf0dd7e8e7688ae45fbd9e54d

[]

no_license

sivasubramanics/Julich_PhD

891322a13c9d2c8e010bb386a814f56f2f4d2efd

bfb04e13ad5416ff9cfddabe36cea248b64fd68d

refs/heads/master

2023-04-05T21:42:58.635800

2021-04-11T19:51:10

356,939,155

0

null

UTF-8

R

false

1,565

r

makeAdmixturePlot.R

#! /usr/bin/env Rscript # title :makeAdmixturePlot.R # description :This script will generate Plot for Admixture Qmatrix. # author :c.s.sivasubramani@gmail.com # date :11042021 # version :0.1 # usage :Rscript makeAdmixturePlot.R # notes : This script expects additional Utility source code for processing. # ============================================================================== # Load the POPS utilities source("POPSutilities.r") # Reading Admixture Q matrix Qmatrix = read.table("maize.3.Q") # Reading geo coord file (2 columns separated with comma) coord = read.table("Maize.coord") # Output PNG File png("AdmixtureClusters.png") # Initial geo ploting plot(coord, pch = 19, xlab="Longitude", ylab = "Latitude") map(add = T, interior = F, col = "grey80") asc.raster = "RasterMaps/North_America.asc" # Defining grid for ancestory grid=createGridFromAsciiRaster(asc.raster) constraints=getConstraintsFromAsciiRaster(asc.raster,cell_value_min=0) show.key = function(cluster=1,colorGradientsList=lColorGradients){ ncolors=length(colorGradientsList[[cluster]]) barplot(matrix(rep(1/10,10)),col=colorGradientsList[[cluster]][(ncolors-9):ncolors],main=paste("Cluster",cluster))} layout(matrix(c(rep(1,6),2,3,4), 3, 3, byrow = FALSE), widths=c(3,1), respect = F) par(ps = 22) maps(matrix = Qmatrix, coord, grid, constraints, method = "max", main = "Ancestry coefficients", xlab = "Longitude", ylab = "Latitude") par(ps = 16) # Adding Cluster legends for(k in 1:3){show.key(k)} dev.off()

f7b12813a994b9e550ca8db3bd4fcab7e0c1d0cb

4a0426ecc2d49c81f990d3ddc34b401f2893694f

/R/grad_descent.R

cedd78cc517b3924fc95039af1d5a152b38ccb35

[]

no_license

Yannuo10/bis557

2d90922e5a05b9d3b5dae4e88cbfc666a670c867

913885913db2691871102904f2fcb3b24664a9f0

refs/heads/master

2023-02-01T05:43:09.501516

2020-12-19T05:05:45

296,765,930

0

null

UTF-8

R

false

976

r

grad_descent.R

#' @title grad_descent() function #' @description another way to build a linear model. gradient descent for ordinary least squares #' @param form a formula; #' @param data a data frame used for the function; #' @param contrasts a list of contrasts for factor variables #' @param itr number of iteration #' @examples #' data(iris) #' fit_linear_model <- grad_descent(Sepal.Length ~ ., iris, contrasts = list(Species = "contr.sum")) #' @export grad_descent <- function(form, data, contrasts = NULL, itr = 1e6){ df <- model.frame(form, data) if (is.null(contrasts)) { X <- model.matrix(form, df) } else (X <- model.matrix(form, df, contrasts.arg=contrasts)) y_name <- as.character(form)[2] Y <- matrix(df[, y_name], ncol=1) beta <- matrix(rep(1, length(colnames(X))), nrow=length(colnames(X))) L <- 0.0001 for (i in 1:itr){ pd <- (-2)* t(X) %*% Y + 2 * t(X) %*% X %*% beta beta <- beta - L * pd } ret <- list(coefficients=beta) ret }

94d1163f0aca3a00ef6a67019fba111375e3a30a

d57bfd5bbefab86d21ed46b4e15f1d489c61bcbc

/man/shypo.Rd

85cf2b4cd036976b9601be4e12c852bdec068766

[]

no_license

cran/smovie

553d3d6441a762a4b538699c47d028ad4e5c995a

9a05f94188335a1b79a98bdfa4011bbcbc3033e8

refs/heads/master

2021-11-24T16:55:00.478591

2021-10-31T04:30:02

123,954,735

0

null

UTF-8

R

false

true

4,538

rd

shypo.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/simple_hypotheses.R \name{shypo} \alias{shypo} \title{Testing simple hypotheses} \usage{ shypo( mu0 = 0, sd = 6, eff = sd, n = 10, a = mu0 + eff/2, target_alpha = 0.05, target_beta = 0.1, panel_plot = TRUE, hscale = NA, vscale = hscale, delta_n = 1, delta_a = sd/(10 * sqrt(n)), delta_eff = sd, delta_mu0 = 1, delta_sd = 1 ) } \arguments{ \item{mu0}{A numeric scalar. The value of \eqn{\mu} under the null hypothesis H0 with which to start the movie.} \item{sd}{A positive numeric scalar. The (common) standard deviation \eqn{\sigma} of the normal distributions of the data under the two hypotheses.} \item{eff}{A numeric scalar. The \emph{effect size}. The amount by which the value of \eqn{\mu} under the alternative hypothesis is greater than the value \code{mu0} under the null hypothesis. That is, \code{mu1} = \code{eff} + \code{mu0}. \code{eff} must be non-negative.} \item{n}{A positive integer scalar. The sample size with which to start the movie.} \item{a}{A numeric scalar. The critical value of the test with which to start the movie. H0 is rejected if the sample mean is greater than \code{a}.} \item{target_alpha}{A numeric scalar in (0,1). The target value of the type I error to be achieved by setting \code{a} and/or \code{n} if the user asks for this using a radio button.} \item{target_beta}{A numeric scalar in (0,1). The target value of the type II error to be achieved by setting \code{a} and/or \code{n} if the user asks for this using a radio button.} \item{panel_plot}{A logical parameter that determines whether the plot is placed inside the panel (\code{TRUE}) or in the standard graphics window (\code{FALSE}). If the plot is to be placed inside the panel then the tkrplot library is required.} \item{hscale, vscale}{Numeric scalars. Scaling parameters for the size of the plot when \code{panel_plot = TRUE}. The default values are 1.4 on Unix platforms and 2 on Windows platforms.} \item{delta_mu0, delta_eff, delta_a, delta_n, delta_sd}{Numeric scalars. The respective amounts by which the values of \code{mu0, eff, a, n} and \code{sd} are increased (or decreased) after one click of the + (or -) button in the parameter window.} } \value{ Nothing is returned, only the animation is produced. } \description{ A movie to illustrate statistical concepts involved in the testing of one simple hypothesis against another. The example used is a random sample from a normal distribution whose variance is assumed to be known. The simple hypotheses relate to the value of the mean \eqn{\mu}. } \details{ The movie is based on two plots. The top plot shows the (normal) probability density functions of the sample mean under the null hypothesis H0 (mean \code{mu0}) and the alternative hypothesis H1 (mean \code{mu1}, where \code{mu1} > \code{mu0}), with the values of \code{mu0} and \code{mu1} indicated by vertical dashed lines. H0 is rejected if the sample mean exceeds the critical value \code{a}, which is indicated by a vertical black line. The bottom plot shows how the probabilities of making a type I or type II error (alpha and beta respectively) depend on the value of \code{a}, by plotting these probabilities against \code{a}. A parameter window enables the user to change the values of \code{n}, \code{a}, \code{mu0}, \code{eff} = \code{mu1} - \code{mu0} or \code{sd} by clicking the +/- buttons. Radio buttons can be used either to: \itemize{ \item{}{set \code{a} to achieve the target type I error probability \code{target_alpha}, based on the current value of \code{n};} \item{}{set \code{a} and (integer) \code{n} to achieve (or better) the respective target type I and type II error probabilities of \code{target_alpha} and \code{target_beta}.} } If \code{eff = 0} then a plot will be produced even though this case is not practically meaningful. In the "set a and n to achieve target alpha and beta" case, the plot will be the same as the case "set a and n by hand" case. } \examples{ # 1. Change a (for fixed n) to achieve alpha = 0.05 # 2. Change a and n to achieve alpha <= 0.05 and beta <= 0.1 shypo(mu0 = 0, eff = 5, n = 16, a = 2.3, delta_a = 0.01) } \seealso{ \code{\link{movies}}: a user-friendly menu panel. \code{\link{smovie}}: general information about smovie. }

e89f0df18a1a456eb3ca87c09a2e7a877630a555

958cdec963f4e7ed6f2e7f264613809898ce2971

/scripts/make_mutspec.R

826152b13e0bc7ce8e8d8846fadba16361dd6a07

[ "MIT" ]

permissive

merckey/mc3_icgc_variant_pipeline

642f671a2a0e6d0d4fe93170bfff4d6a7014de17

b01eb4b9c678467e29b899c2ed602d37d41575dc

refs/heads/master

2020-11-24T01:22:25.745657

2019-10-22T17:56:49

null

0

null

UTF-8

R

false

6,861

r

make_mutspec.R

library(data.table) library(UpSetR) library(dplyr) library(ggplot2) library(reshape2) library(RColorBrewer) library(scales) library(gplots) #DEVELOPMENT ONLY###### #args <- c("processed_data/exome.broadbed.gaf.maf","processed_data/genome_broadbed.gaf.maf", "figures/mc3_mutspec.pdf","figures/pcawg_mutspec.pdf","processed_data/mutspecNotes.txt") args = commandArgs(trailingOnly=TRUE) if (length(args)!=6) { stop("Full.tsv and output should be supplied (input file).n", call.=FALSE) } mc3 <- fread("processed_data/exome.broadbed.gaf.maf") mc3 <- mc3[which(!grepl("oxog",mc3$V105)),] pcawg <- fread("processed_data/genome_broadbed.gaf.maf",header=F,sep="\t",na.strings="NA",colClasses=list(character=c(17,33,35))) smap = fread("/diskmnt/Projects/ICGC_MC3/ID_Mapping/MC3_PCAWG_Matched.ids.v3.txt") ctypes = fread("/diskmnt/Projects/ICGC_MC3/ID_Mapping/PCA.sample.cancer.txt",header=F) #Deal with PCAWG pmaf <- merge(pcawg,smap,by.x="V12",by.y="tcga_pcawg_aliquot_id") pmaf$char12 = substr(pmaf$mc3_exome_barcode,1,12) pmafc <- merge(pmaf,ctypes,by.x="char12",by.y="V1") mutspsamp <- pmafc %>% group_by(char12) %>% tally() pmafc$tstv <- paste(pmafc$V7,">",pmafc$V9,sep="") nonhypers <- mutspsamp[which(mutspsamp$n < 1500),]$char12 pmafc <- pmafc[which(pmafc$char12 %in% nonhypers),] #DEAL with MC3 mc3$char12 = substr(mc3$V12,1,12) mc3c <- merge(mc3,ctypes,by.x="char12",by.y="V1") mutspsamp <- mc3c %>% group_by(V12) %>% tally() mc3c$tstv <- paste(mc3c$V7,">",mc3c$V9,sep="") mc3c <- mc3c[which(mc3c$char12 %in% nonhypers),] #The the tally that I need. good = c("A>C","A>G","A>T","C>A","C>G","C>T") mytstv <- pmafc[which(pmafc$tstv %in% good),] pcawg_tstv <- data.frame(mytstv %>% group_by(V2.y,tstv) %>% tally()) mytstv <- mc3c[which(mc3c$tstv %in% good),] mc3_tstv <- data.frame(mytstv %>% group_by(V2.y,tstv) %>% tally()) totest <- merge(mc3_tstv,pcawg_tstv,by=c("V2.y","tstv")) cancers <- unique(totest$V2.y) #Cancer type level. OUT <- NULL TOPLOT <- NULL for(i in cancers){ CAN <- totest[which(totest$V2.y == i),] CAN$V2.y <- NULL row.names(CAN) = CAN$tstv CAN$tstv <- NULL tCAN <- t(CAN) cnt <- rowSums(tCAN) tCAN[2,] = tCAN[2,]*(cnt[1]/cnt[2]) #this scales the counts to make sure we differences in shape are not due to sample count differences. out <- chisq.test(tCAN) # contrib <- 100*out$residuals^2/out$statistic contrib <- 100*out$residuals^2 pf <- data.frame("Cancer"=i,t(contrib[1,])) TOPLOT <- rbind(TOPLOT,pf) df <- data.frame("Cancer"=i,"stat"=out$statistic,"pval"=out$p.value) OUT <- rbind(OUT,df) } row.names(TOPLOT) <- TOPLOT$Cancer TOPLOT$Cancer <- NULL pdf("figures/balloon.mutSpec.pdf",height=6,width=6,useDingbats=F) balloonplot(t(as.table(as.matrix(TOPLOT))), main ="ChiSq residuals^2 contribution", xlab ="", ylab="",label = FALSE, show.margins = FALSE) dev.off() #Look into a couple cancer typs to get numbers i = "KICH" ##############################################################################################################3 #Now I want to do this with the unique calls #Dont forget to remove hypermutators full <- fread("/diskmnt/Projects/ICGC_MC3/mc3_icgc_variant_pipeline/output/full_cleaned.tsv",sep="\t", header=TRUE, na.strings="NA",colClasses=list(character=c(128,135,139))) ctypes = fread("/diskmnt/Projects/ICGC_MC3/ID_Mapping/PCA.sample.cancer.txt",header=F) full$match = ifelse(!is.na(full$Chromosome) & !is.na(full$"Chromosome:1"),1,0) full$PCAWG_only = ifelse(is.na(full$Chromosome) & !is.na(full$"Chromosome:1"),1,0) full$MC3_only = ifelse(is.na(full$"Chromosome:1") & !is.na(full$Chromosome),1,0) fullm <- full[which(full$MC3_only == 1 | full$match == 1),] fullp <- full[which(full$PCAWG_only == 1 | full$match == 1),] #DEAL with PCAWG fullp$char12 = substr(fullp$mc3_exome_barcode,1,12) pull <- merge(fullp, ctypes, by.x="char12",by.y="V1") mutspsamp <- pull %>% group_by(char12) %>% tally() nonhypers <- mutspsamp[which(mutspsamp$n < 1500),]$char12 #This repesnets the 95% ile pull$tstv <- paste(pull$"Reference_Allele:1",">",pull$"Tumor_Seq_Allele2:1",sep="") pull <- pull[which(pull$char12 %in% nonhypers),] #DEAL with MC3 fullm$char12 = substr(fullm$mc3_exome_barcode,1,12) mull <- merge(fullm, ctypes, by.x="char12",by.y="V1") mutspsamp <- mull %>% group_by(char12) %>% tally() mull$tstv <- paste(mull$"Reference_Allele",">",mull$"Tumor_Seq_Allele2",sep="") mull <- mull[which(pull$char12 %in% nonhypers),] good = c("A>C","A>G","A>T","C>A","C>G","C>T") #get counts for these pull_tt = pull[which(pull$tstv %in% good),] pcnt = data.frame(pull_tt %>% group_by(V2,tstv) %>% tally()) mull_tt = mull[which(mull$tstv %in% good),] mcnt = data.frame(mull_tt %>% group_by(V2,tstv) %>% tally()) #Pull together totull <- merge(mcnt,pcnt,by=c("V2","tstv"),all=T) totull[is.na(totull)] <- 0 #Cancer type level. cancers <- unique(totull$V2) OUTULL <- NULL TOPLOTULL <- NULL for(i in cancers){ CAN <- totull[which(totull$V2 == i),] CAN$V2 <- NULL row.names(CAN) = CAN$tstv CAN$tstv <- NULL tCAN <- t(CAN) cnt <- rowSums(tCAN) tCAN[2,] = tCAN[2,]*(cnt[1]/cnt[2]) #this scales the counts to make sure we differences in shape are not due to sample count differences. out <- chisq.test(tCAN) # contrib <- 100*out$residuals^2/out$statistic contrib <- 100*out$residuals^2 pf <- data.frame("Cancer"=i,t(contrib[1,])) TOPLOTULL <- rbind(TOPLOTULL,pf) df <- data.frame("Cancer"=i,"stat"=out$statistic,"pval"=out$p.value) OUTULL <- rbind(OUTULL,df) } @SOMETHING IS GOING WRONG HERE! I DON'T' KNOW WHAT.... #####################################################################################################################33 #IT Also looks like there may be some problemsome cancertypes from the information above. COAD,KICH,LIHC,LUAD,LUSC,OV,READ,STAD #PCAWG sample level mytstv <- pmafc[which(pmafc$tstv %in% good),] pcawg_tstv_s <- data.frame(mytstv %>% group_by(char12,V2.y,tstv) %>% tally()) #MC3 samples level mytstv <- mc3c[which(mc3c$tstv %in% good),] mc3_tstv_s <- data.frame(mytstv %>% group_by(char12,V2.y,tstv) %>% tally()) totest_s <- merge(mc3_tstv_s,pcawg_tstv_s,by=c("char12","V2.y","tstv"),all=T) otest_s[is.na(totest_s)] <- 0 samps <- unique(totest_s$char12) SAMPS = NULL for(i in samps){ S <- totest_s[which(totest_s$char12 == i),] row.names(S) = S$tstv code = unique(paste(S$char12,"_",S$V2.y,sep="")) S$V2.y <- NULL S$tstv <- NULL S$char12 <- NULL tS <- t(S) print(code) print(tS) out <- chisq.test(tS) df <- data.frame("ID"=code,"stat"=out$statistic,"pval"=out$p.value) SAMPS <- rbind(SAMPS,df) } #This is a list of significant differences and the individual level, samps = c("TCGA-A6-2683","TCGA-AF-2689","TCGA-BL-A13J","TCGA-AG-3885","TCGA-49-4486","TCGA-44-2659","TCGA-KN-8418")

00d30285617f3f666aa35dc8918582da19576195

2444432aa75e2a6340420a44ac91d88f204537ce

/man/gibbs_sampler.Rd

f97544ef340bfd57e2faa8ad56d9bf2684dc0a46

[]

no_license

guhjy/bcfbma

03bbe6aafdce9cff39722f817b90c9b9b0116e4b

fe7911143a30c49d919d7048f7646856ec8a8f82

refs/heads/master

2020-08-08T14:48:06.976746

2019-10-07T11:27:19

null

0

null

UTF-8

R

false

true

537

rd

gibbs_sampler.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/RcppExports.R \name{gibbs_sampler} \alias{gibbs_sampler} \title{Obtain draws from gibbs sampler} \usage{ gibbs_sampler(overall_sum_trees_mu, overall_sum_trees_tau, overall_sum_mat_mu, overall_sum_mat_tau, y, BIC_weights, num_iter, burnin, num_obs, a_mu, a_tau, sigma, mu_mu_mu, mu_mu_tau, nu, lambda, resids_mu, resids_tau, z, test_data, test_pihat, z_test, include_pi2, num_propscores, num_test_obs) } \description{ Obtain draws from gibbs sampler }

6d3fa7c034391717ca15faecb51e42e8a30ff9c0

7f2f9299ced47b047fd38491c5a49992afd3fd0e

/run_analysis.R

2a5e484988b262c2162cc5e73f581c474b07269e

[]

no_license

hamish222/Coursera-Course-3

edf381b5492d59e65578eb9f17bd91e22b4eaf55

e811968f641732935c117146c54c40bb142ec49d

refs/heads/master

2021-01-10T11:33:53.029442

2016-02-29T02:46:07

52,757,817

0

null

UTF-8

R

false

2,580

r

run_analysis.R

## Set the working directory. ## setwd("C:\\Users\\Hamish\\Documents\\Educational Materials\\Coursera Data Science\\Course 3\\") ## Use source("run_analysis.R") at the command line to run this script . ## Available at https://github.com/hamish222/Coursera-Course-3/tree/master. ## Part 1 # Load test data. testdata <- read.table("UCI HAR Dataset\\test\\X_test.txt",header=TRUE) # Load training data. traindata <- read.table("UCI HAR Dataset\\train\\X_train.txt",header=TRUE) # Load and extract the variable names for the columns in testdata and traindata. variableNames <- read.table("UCI HAR Dataset\\features.txt",header=FALSE) variableNames <- variableNames[,2] # Limit attention to those variables that are means or standard deviations. variableIndices <- c(grep("mean",variableNames),grep("std",variableNames)) variableNames <- variableNames[variableIndices] # Extract the corresponding data from testdata and traindata. testdata <- testdata[,variableIndices] traindata <- traindata[,variableIndices] # Re-name the variables for the test and training data sets. names(testdata) <- variableNames names(traindata) <- variableNames # Combine the avg/std data from the test and training data. alldata <- rbind(testdata,traindata) # Load and combine the activity data for the test and training data. testactivities <- read.table("UCI HAR Dataset\\test\\y_test.txt",header=TRUE) trainactivities <- read.table("UCI HAR Dataset\\train\\y_train.txt",header=TRUE) names(testactivities) <- "activity" names(trainactivities) <- "activity" activities <- rbind(testactivities ,trainactivities) # Load and combine the subject data for the test and training data. testsubjects <- read.table("UCI HAR Dataset\\test\\subject_test.txt",header=TRUE) trainsubjects <- read.table("UCI HAR Dataset\\train\\subject_train.txt",header=TRUE) names(testsubjects ) <- "subject" names(trainsubjects ) <- "subject" subjects <- rbind(testsubjects,trainsubjects) # Prepend the subject and activity columns to alldata. alldata <- cbind(subjects, activities, alldata) # Write the new data to a file. write.table(alldata,"Course3ProjectCombinedData.txt", row.names=FALSE) ## Part 2 # Use aggregate to compute the means for each activity and subject. summary <- aggregate(alldata, by=list(alldata$activity,alldata$subject), FUN=mean,na.rm=TRUE) # Remove the two columns that aggregate introduces. summary$Group.1 <- NULL summary$Group.2 <- NULL # Write the summary means to a file. write.table(summary,"Course3ProjectSummaryData.txt", row.names=FALSE)

b0da84074d860f45426abe91edd87de46a8a0e58

73cbe254a53b69f07c32721a28be92970f25f758

/load_project_data.R

c4b38b09ea745906e28e20bfecf91c20df30d092

[]

no_license

pradhyu/ExData_Plotting1

cd4d207696bf6d0acba2684dfd99f4034752270d

3cdf115c040e86e5c8349af1e8cf4cead033028e

refs/heads/master

2020-12-31T03:03:58.626195

2016-05-08T15:05:51

56,355,812

0

null

2016-04-15T23:43:10

null

UTF-8

R

false

1,365

r

load_project_data.R

# current working directory should be the repo which has the R files for this project library(httr) data.Url<- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip" # create data directory if it doesn't exists. data.folder <- "data" if(!file.exists(data.folder)){ dir.create(data.folder) } data.zip <- paste(getwd(), "/", data.folder,"/household_power_consumption.zip", sep = "") if(!file.exists(data.zip)){ download.file(data.Url, data.zip, method="curl", mode="wb") } data.txt<- paste(getwd(),"/", data.folder, "/household_power_consumption.txt", sep = "") if(!file.exists(data.txt)){ unzip(data.zip, list = FALSE, overwrite = FALSE, exdir = data.folder) } data.summaryFile<- paste(getwd(), "/", data.folder, "/data_summary.rds", sep = "") if(!file.exists(data.summaryFile)){ data.loaded <- read.table(data.txt, header=TRUE, sep=";", colClasses=c("character", "character", rep("numeric",7)), na="?") data.loaded$Time <- strptime(paste(data.loaded$Date, data.loaded$Time), "%d/%m/%Y %H:%M:%S") data.loaded$Date <- as.Date(data.loaded$Date, "%d/%m/%Y") data.dateRange <- as.Date(c("2007-02-01", "2007-02-02"), "%Y-%m-%d") data.loaded <- subset(data.loaded, Date %in% data.dateRange) saveRDS(data.loaded, data.summaryFile) } else { data <- "data/data_summary.rds" data.loaded <- readRDS(data) }

6e790314905189f5143c15c6120e87aa836a7219

f010762b3f997669ba84738fcec95e8cb5b23f60

/allele_freqs/distance_from_H.r

ee1cdbc6f3931c5f6f8d6f38174553fedf5f17b1

[]

no_license

nriddiford/Investigating-structural-variation-in-cancer-genomes

7f9d26ebfad3449adee1fe6ac40ef24ca7b133b3

51d208f8b5e245027b31aa1e6aad4e61815e4dcb

refs/heads/master

2021-09-05T10:17:07.740646

2018-01-26T10:14:21

72,754,078

1

0

null

UTF-8

R

false

2,740

r

distance_from_H.r

library(plotly) library(ggplot2) library(scales) #setwd("/Users/Nick/iCloud/Desktop/snp_data/data") setwd("/Users/Nick_curie/Desktop/script_test/snp_data") path = getwd() file.names <- dir(path, pattern =".txt") for(i in 1:length(file.names)){ cat("Processing", file.names[i]) snps<-read.delim(file.names[i], header = T) parts<-strsplit(file.names[i], '[.]')[[1]] sample<-parts[1] p <- ggplot(data = snps, aes(x = position, y = freq, colour = type ), show.legend = FALSE) p <- p + stat_smooth(aes(fill = factor(type)), size = 0.25, alpha = 0.15, show.legend = FALSE) p <- p + geom_line() p <- p + scale_y_continuous(breaks = seq(0, 100, by = 25)) p <- p + facet_wrap(~chrom, scale="free_x") p <- p + ggtitle( paste( sample ) ) outfile <- paste(sample, '_', "allele_freqs", '.pdf', sep='') ggsave(outfile, scale = 0.9) } library(plotly) library(ggplot2) library(scales) setwd("/Users/Nick_curie/Desktop/script_test/snp_data") file<-'A512R21.dist_from_h.txt' snps<-read.delim(file, header = T) parts<-strsplit(file, '[.]')[[1]] sample<-parts[1] # chrom2plot<-"2R" # chromosome<-subset(snps, snps$chrom == chrom2plot ) # single chrom # ggplot p <- ggplot(data = chromosome, aes(x = position, y = dist_ratio), show.legend = FALSE) p <- p + stat_smooth(aes(fill = dist_ratio), size = 0.25, alpha = 0.15, show.legend = FALSE) p <- p + geom_point(aes(colour = -dist_ratio, text = paste("count: ", count )), show.legend = FALSE) # g <- g + scale_y_continuous(breaks = seq(0, 50, by = 10)) p <- p + ggtitle( paste( sample, chrom2plot ) ) p # # plotly g <- ggplot(data = chromosome, aes(x = position, y = dist_ratio), show.legend = FALSE) g <- g + stat_smooth(aes(fill = dist_ratio), size = 0.25, alpha = 0.15, show.legend = FALSE) g <- g + geom_point(aes(colour = -dist_ratio, text = paste("count: ", count )), show.legend = FALSE) # g <- g + scale_y_continuous(breaks = seq(0, 50, by = 10)) g <- g + ggtitle( paste( sample, chrom2plot ) ) ggplotly(g) # all chroms p <- ggplot(data = snps, aes(x = position, y = dist_ratio), show.legend = FALSE) p <- p + stat_smooth(aes(fill = dist_ratio), size = 0.25, alpha = 0.15, show.legend = FALSE) p <- p + geom_point(aes(colour = -dist_ratio, text = paste("count: ", count )), show.legend = FALSE) p <- p + facet_wrap(~chrom, scale="free_x") p <- p + ggtitle( paste( sample ) ) p # plotly g <- ggplot(data = snps, aes(x = position, y = freq, colour = type ), show.legend = FALSE) g <- g + stat_smooth(aes(fill = factor(type)), size = 0.25, alpha = 0.15, show.legend = FALSE) g <- g + geom_line() g <- g + scale_y_continuous(breaks = seq(0, 100, by = 25)) g <- g + facet_wrap(~chrom, scale="free_x") g <- g + ggtitle( paste( sample ) ) ggplotly(p) p p

1e1b43fe43275698afc6902cbf8f097c02842797

37c17847af9fff4d45272b81560cdbb71f7a9135

/Kurstag4_rskript.R

c0dd462a09ac56dfbbdba688eb3e96181f2bea0e

[]

no_license

LSBurchardt/Kurs-Statistiksoftware_R

1df617adee82a603e4fa3f73131b80f5ddd93b6c

6afa75454caef92d620c6cf2c8bbf13dbe2789c1

refs/heads/master

2020-05-24T10:09:24.695575

2020-05-04T14:15:04

187,221,640

0

null

UTF-8

R

false

6,990

r

Kurstag4_rskript.R

################################################################# #Statistiksoftware R #Dozentin: Lara S. Burchardt, l.s.burchardt@gmx.de # #24.05/25.05 #21.06/22.06 # ################################################################ #4. Kurstag # 00: Pakete laden --------------------------------------------------------------- library(tidyverse) #includes for example ggplot2, dplyr library(psych) library(cluster) library(NbClust) library(plotly) library(nycflights13) library(shiny) # 01: Daten einlesen -------------------------------------------------------------- #Lade den Datensatz "Erstis" #Lade den Datensatz "sub.df.timeseries.data" (liegt in Stine) # 02: Fehler beheben--------------------------------------------------------------- # Führe den Code aus und verstehe und behebe (wenn möglich) die Fehler. #1 data<-mtcars plot(data$mpg, Data$qsec) #2 data.2 <- iris plot(Sepal.Length,Sepal.Width) #3 mean(iris$Species) #4 M <- cor(mtcars) corrplot(M, method = "circles") #5 output <- vector[] #leerer Vektor wird erstellt for x in c(1:10) # Sequenz wird definiert output[x] <- x + (x+1) # was soll getan werden? #6 ggplot(erstis, (x= Alter, y= zuf.inh.1))+ geom_point(color= "skyblue", shape = "square") geom_smooth(method = 'l m', se = FALSE) #7 erstis$alter[193] <- 78 #8 mean.column <- mean(erstis[1:191,57], na.rm = TRUE) #9 mean <- mean(erstis$alter) if (mean > 12) {print("c is greater than 12")} #10 letters.mat <- matrix(NA, nrow = 5, ncol = 5) for (m in 1:6) { for (n in 1:5){letters.mat[m,n] <- (letters[n])} } print(letters.mat) # 03: Text Mining library(tm) library(pdftools) library(wordcloud) file.location <- "Wahlprogramme/fdp.pdf" txt <- pdf_text(file.location) #Corpus erstellen txt_corpus <- Corpus(VectorSource(txt)) # Corpus aufreinigen txt_corpus <- tm_map(txt_corpus, tolower) #alles klein geschrieben txt_corpus <- tm_map(txt_corpus, removePunctuation) #Interpunktion entfernt txt_corpus <- tm_map(txt_corpus, stripWhitespace) # Leerzeichen löschen #Stop Wörter entfernen txt_corpus <- tm_map(txt_corpus, removeWords, stopwords("de")) # Inhalt des Corpus angucken txt_corpus$content # Dokument-TErm-Matrix erstellen dtm <- DocumentTermMatrix(txt_corpus) dtm <- as.matrix(dtm) number.occurences <- colSums(dtm) number.occurences <- sort(number.occurences, decreasing = TRUE) #plot WordCloud wordcloud(head(names(number.occurences),30), head(number.occurences, 30), scale = c(2,1)) # Erstelle anhand der erhaltenen Wordcloud einen Vektor mit weiteren Stopwörtern und entferne auch diese. # Lass die Analyse erneut laufen und dir eine neue Wordcloud ausgeben. # 04: Plotly ------------------------------------------------------- # Daten laden: "Daten/timeseries_example.rds" a <- list(title = "Date", showticklabels = TRUE) g<- list( title = "Temperature [deg C]", showticklabels = TRUE) p2<-plot_ly(sub.df.timeseries.data, name = "Mean Temperature", showlegend = FALSE) %>% add_trace(x= ~sub.df.timeseries.data$date_mean, y= ~sub.df.timeseries.data$Tem_mean,type= 'scatter', mode= 'markers', marker = list(symbol= "circle", size=4, color="grey"), #error_y= ~list(type = "data", array=df.sub$Tem_sd,color= "grey"), hoverinfo = 'text', text = ~paste(' Mean Temp', ' Date', sub.df.timeseries.data$date_mean, ' Mean: ', round(sub.df.timeseries.data$Tem_mean, digits= 2) , ' Max: ', round(sub.df.timeseries.data$Tem_max, digits = 2), ' Min: ', round(sub.df.timeseries.data$Tem_min, digits = 2))) %>% layout(xaxis = a, yaxis = g, autosize = FALSE) # Baue einen interaktiven Plot für einen Datensatz und eine Variablenkombination deiner Wahl # 05: Shiny ----------------------------------------------------------- #https://shiny.rstudio.com/ # Öffne eine neue Datei im File Tab. Wähle dafür die Option "Shiny Web App..." # Wie ist die App aufgebaut? Welche Teile sind zu erkennen und was macht die App? # Probiert ein wenig herum, z.B: fügt einen weiteren Slider hinzu, erzeugt ein zweites Histogram mit # den Daten aus dem zweiten Slider (der Datensatz "faithful" der verwendet wird, enthält eine zweite Variable) # 06: R Markdown ------------------------------------------------------- # Oeffnet ein neues File und wählt die Option "R Markdown". Füllt die notwendigen Felder aus und erstellt das Dokument. --- title: "R Kurs" author: "Lara S. Burchardt" date: "20 Juni 2019" output: word_document --- # Text wird einfach eingegeben. Ein Code Chunk wird mit ``` gestartet und beendet. # Nach dem Start steht mindestens {r} hier können auch weitere Optionen eingefügt werden, der Chunk kann benannt werden # und es kann angegeben werden, ob man den R Code im Dokument sehen soll (default bzw. include = TRUE) oder nicht (include = FALSE) ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ``` ## R Markdown This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>. When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this: ```{r cars} summary(cars) ``` ## Including Plots You can also embed plots, for example: # Soll das Output gezeigt werden, aber nicht der Code, müssen die Optionen include = TRUE (default, kann also weggelassen werden) und echo = FALSE gesetzt werden. ```{r pressure, echo=FALSE} plot(pressure) ``` Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot. # Erstellt ein R Markdown Dokument. Stellt euch hier ein Dokument zusammen, dass übersichtlich die Lerninhalte des Kurses darstellt. # Erklärungen, Codeschnippsel und Beispielplots oder Tabellen z.B zum: # 1) laden und speichern von Daten, # 2) Datenformate, wie sie erstellt werden und wie das Output aussieht # 3) Hilfe in R # 4) ein Scatterplot in ggplot # 5) ein Boxplot in ggplot # 6) ein Histogram # 7) weitere verwendete oder fuer euch wichtige Plots # 8) Datenmanagment (filter, select, group_bym summarize_at, summarize_all, etc.) # 9) Haufigkeitstabellen # 10) lineare Regression # 11) Korrelation # 12) t test und ANOVA # 13) for loops # 14) if Abfragen # 15) PCA und Faktoranalyse # 16) Clusteranalyse # 17) Hinweise zur Behebung von Fehlern # 18) weitere Möglichkeiten in R (textmining, plotly, shiny) # Beendet dieses Dokument als zweite Hausaufgabe. Das Dokument ist eure Hilfestellung für weitere Arbeiten in R # und sollte deshalb so detailiert sein, wie ihr es gerne hättet und die Themen beinhalten, die für euch am wichtigsten sind. # Abgabe: klären wir im Kurs.

c429eecdb1fc75700def1cd15ff6bc1959194c02

6ac9cab2e20f60a877a88a7e85998240a1a13b32

/exercises/EDA/CHIS.R

a2416e24f42b101d6a32087746c6d552848d2a6b

[]

no_license

ankit-sharma90/SpringboardDSCourse

365b632efc1d44c94f0cf040a573d96be1f35c78

a5cad3b0639055becda04b2ddb464c315f43bf95

refs/heads/master

2023-03-01T19:46:47.188840

2021-02-09T19:30:37

null

0

null

UTF-8

R

false

711

r

CHIS.R

# Plot so far p # Position for labels on y axis (don't change) index <- DF_all$xmax == max(DF_all$xmax) DF_all$yposn <- DF_all$ymin[index] + (DF_all$ymax[index] - DF_all$ymin[index])/2 # Plot 1: geom_text for BMI (i.e. the fill axis) p1 <- p %+% DF_all + geom_text(aes(x = max(xmax), y = yposn, label = FILL), size = 3, hjust = 1, show.legend = FALSE) p1 # Plot 2: Position for labels on x axis DF_all$xposn <- DF_all$xmin + (DF_all$xmax - DF_all$xmin)/2 # geom_text for ages (i.e. the x axis) p1 %+% DF_all + geom_text(aes(x = xposn, label = X), y = 1, angle = 90, size = 3, hjust = 1, show.legend = FALSE)

143960dfc4a0c1a87d5546c3bbf514cc640d05d0

1b13ba6821b4d9780cf8faceccb465c9e1255b08

/R/Scripts/get_saker.R

835eb91e0662f952b4da86dee588d442a42e8e5a

[]

no_license

emanlapponi/storting

7213ad63f3a5868dc2e0f686780e393545b34b16

b043d54ca25b4956c120580267e9b227c88f0fd0

refs/heads/master

2021-01-19T03:00:20.578031

2017-06-19T11:50:26

54,019,041

2

1

null

UTF-8

R

false

552

r

get_saker.R

rm(list = ls());gc();cat("\014") library(dplyr); library(pbmcapply) # Loading data ###### dagsorden <- read.csv2("./Data/dagsorden.csv", stringsAsFactors = FALSE) url_base <- "https://data.stortinget.no/eksport/sak?sakid=" path <- "/media/martin/Data/saker_raw/" done <- sort(gsub("[^0-9]", "", list.files(path))) sak_ids <- sort(as.character(unique(dagsorden$sak_id))) sak_ids <- sak_ids[which((sak_ids %in% done)==FALSE)] for(i in sak_ids){ system(paste0("wget -O ", path, "sak_", i, ".xml ", url_base, i, " ")) Sys.sleep(abs(2+rnorm(1))) }

767fd649d538c14085381fe0f7acccf3947691c1

276bd8306aeb9fc90c5a63f57ef21407a59dddd1

/plot6.R

9fad0d44b182c385a35cfc3fde45a9eeae2f28d7

[]

no_license

MichaelSzczepaniak/ParticulateMatterStudy1999to2008

cbcd61fc4d62994903d2b217843e5e4c2dc4bacc

2003f8f8d6acad8edcf05bcf835d5544aa93d117

refs/heads/master

2016-09-06T00:55:18.122313

2015-08-23T23:16:48

40,325,000

0

null

UTF-8

R

false

6,018

r

plot6.R

## ## Generates a 1 x 2 panel plot of the PM25 motor vehicle emissions ## normalized by 1999 Emissions levels for Baltimore city and Los Angeles ## county from 1999 to 2008. For details see: ## https://github.com/MichaelSzczepaniak/ParticulateMatterStudy1999to2008 ## ## The criteria used for determine which records are considered ## "motor vehicle sources" is any record with an EI.Sector field value that ## contains the string "mobile" followed by the string "road" ignoring case. ## ## Sources should be subsetted to only include those that are common in each ## of the measurement years (1999, 2002, 2005, and 2008). Subsetting was not ## done here in order to allow the ON-ROAD measurement to show up in the plot ## as advised in this forum thread: ## https://class.coursera.org/exdata-031/forum/thread?thread_id=132 ## library(dplyr) library(ggplot2) ## Load function that normalizes the NEI dataframe by grabbing records with ## sources that are common across the years 1999, 2002, 2005, 2008. ## This function is not being called because when it is, all ON-ROAD sources ## get dropped due to these sources not existing in 2008. I leave the code in ## because I believe it is the more correct way to do the analysis, but from ## the discussion boards, no one else seems to be doing it this way so I don't ## want to cause any confusion when peer review time comes around. normalizeNEI <- function(nei) { nei1999 <- filter(nei, year == 1999) scc1999 <- unique(nei1999$SCC) nei2002 <- filter(nei, year == 2002) scc2002 <- unique(nei2002$SCC) nei2005 <- filter(nei, year == 2005) scc2005 <- unique(nei2005$SCC) nei2008 <- filter(nei, year == 2008) scc2008 <- unique(nei2008$SCC) sccCommon <- intersect(scc1999, scc2002) sccCommon <- intersect(sccCommon, scc2005) sccCommon <- intersect(sccCommon, scc2008) # sccCommon contains only the sources common to all 4 time periods normalizedNEI <- filter(nei, SCC %in% sccCommon) return(normalizedNEI) } getNeiSummary <- function(file = "summarySCC_PM25.rds") { # save time if function has been executed already and NEI is in workspace if(!exists("NEI")) { NEI <- readRDS("summarySCC_PM25.rds") } NEI <- readRDS("summarySCC_PM25.rds") sourceClasses <- readRDS("Source_Classification_Code.rds") # get indices of motor vehicle sources as described above motorVehicleIndices <- grep("(mobile)(.*)(road)", sourceClasses$EI.Sector, ignore.case=T) sccValues <- sourceClasses$SCC[motorVehicleIndices] # get the Baltimore and Los Angeles data and group them by year allMotor <- filter(NEI, SCC %in% sccValues) motorBalt <- filter(allMotor, fips == "24510") motorLA <- filter(allMotor, fips == "06037") motorBaltByYear <- group_by(motorBalt, year) motorLAByYear <- group_by(motorLA, year) # free some memory # rm(NEI) rm(allMotor) rm(motorBalt) rm(motorLA) # total the Baltimore emissions and add nomalized emissions column emissionsBalt <- summarise(motorBaltByYear, Total_Emissions = sum(Emissions, na.rm = TRUE)) emissionsBalt <- mutate(emissionsBalt, City = "Baltimore") emissionsBalt <- emissionsBalt[, c(1, 3, 2)] # make Total_Emissions last col # add Baltimore normalized emissions column emissions1999 <- emissionsBalt$Total_Emissions[1] # emissionsBalt <- mutate(emissionsBalt, # Normalized_Emissions = (Total_Emissions /emissions1999)) emissionsBalt <- mutate(emissionsBalt, Normalized_Emissions = (Total_Emissions)) # total the Los Angeles emissions and add nomalized emissions column emissionsLA <- summarize(motorLAByYear, Total_Emissions = sum(Emissions, na.rm = TRUE)) emissionsLA <- mutate(emissionsLA, City = "Los Angeles") emissionsLA <- emissionsLA[, c(1, 3, 2)] # make Total_Emissions last col # add normalized LA emissions column emissions1999 <- emissionsLA$Total_Emissions[1] # emissionsLA <- mutate(emissionsLA, # Normalized_Emissions = (Total_Emissions /emissions1999)) emissionsLA <- mutate(emissionsLA, Normalized_Emissions = (Total_Emissions)) # combine the Baltimore and LA data so we can plot them together emissionsCombined <- rbind(emissionsBalt, emissionsLA) emissionsCombined <- rename(emissionsCombined, Year = year) emissionsCombined <- arrange(emissionsCombined, desc(City), Year) emissionsCombined <- mutate(emissionsCombined, City = factor(City)) return(emissionsCombined) } createPanelPlots6 <- function(file = "plot6.png", width = 720, height = 500, units = "px") { emissionsCombined <- getNeiSummary() png(file = file, width = width, height = height, units = units) g <- ggplot(emissionsCombined, aes(x = Year, y = Normalized_Emissions, shape = City, group = City)) plot <- g + geom_point(size = 4) #plot <- plot + geom_smooth(size=1, linetype=3, method="lm", se=FALSE) # most folks in forum don't like this, so comment out # plot <- plot + facet_grid(. ~ City) + geom_line() plot <- plot + facet_wrap(~ City, nrow = 1, ncol = 2, scales = "free") plot <- plot + geom_line() plot <- plot + ggtitle(" Motor Vehicle Emissions 1999 to 2008: Baltimore vs. LA") plot <- plot + coord_cartesian(xlim=c(1998, 2009)) plot <- plot + scale_x_continuous(breaks=seq(1999, 2008, 3)) # plot <- plot + scale_y_continuous(breaks=c(seq(0.2, 1.2, 0.2))) plot <- plot + labs(y = expression(PM[2.5] * "Emissions (in tons)")) # make the fonts a bigger so everything is more readable plot <- plot + theme(text = element_text(size=16), axis.text.x = element_text(angle=90, vjust=1)) plot <- plot + theme(legend.position="none") print(plot) dev.off() } createPanelPlots6()

91dab45f8d450fe4427138bc47a70ed87d32fef7

0e0fb77df671d38ab424ac14d9fc91b7fd0d1318

/R/rrna.R

f716aefae920c571b183b1aa7e071eb1381e0fb4

[]

no_license

giraola/taxxo

7d30f52474d6113e288dfa42297365c0e5ecd5ef

ba9a33a357b6267e11857659b28156f7540d92b1

refs/heads/master

2021-01-13T16:56:52.251231

2017-09-13T20:58:41

77,538,782

0

1

null

UTF-8

R

false

8,753

r

rrna.R

#' rrna #' #' Outputs a individual rRNA gene sequences and a multiple sequence alignment. #' @param path is the full path to where genomes in fasta format are placed. #' @param pattern a pattern (like '.fasta') for recognizing the genome files. #' @param outdir a name for the output directory that will be created for results. #' @param subunit takes '16S' (default) or '23S'. #' @param multiple takes a logical (default, FALSE) for managing multiple copies of rRNA genes. If more than one copy is found, FALSE will return one random copy while TRUE will return all of them. #' @param distance takes a logical (default, TRUE) indicating if a distance matrix is calculated and outputed or not. #' @param phylogeny takes a logical (default, TRUE) indicating if a Neighbor-Joining phylogeny is constructed or not. #' @param kingdom takes 'bacteria' (default) or 'archaea'. #' @param align takes a logical (default, TRUE) indicating if sequence alignment is performed. #' @keywords rRNA 16S 23S #' @export #' @examples #' rrna(align=T,kingdom='bacteria',multiple=F,outdir='./test_genomes/rrna_result',path='./test_genomes/',pattern='.fna',subunit='16S',phylogeny=TRUE) rrna<-function(path, pattern, outdir='rrna_out', subunit='16S', kingdom='bacteria', align=TRUE, multiple=FALSE, distance=TRUE, phylogeny=TRUE, ...) { # Options # options(getClass.msg=FALSE,warn=1) gw<-getwd() barrnap<-paste(system.file('barrnap',package='taxxo'),'/common/bin/barrnap',sep='') # Dependencies # suppressMessages(require(seqinr,quietly=T)) #suppressMessages(require(msa,quietly=T)) suppressMessages(require(phangorn,quietly=T)) # Check input # if (kingdom=='bacteria'){ king<-'bac' } else if (kingdom=='archaea'){ king<-'arc' } else { stop("ERROR: Parameter kingdom must be 'archaea' or 'bacteria'") } if (!subunit%in%c('16S','23S','both')){ stop("ERROR: Parameter subunit must be '16S', '23S' or 'both'") } # Get genomes # flist<-list.files(path=path,pattern=pattern,full.names=T) fnams<-list.files(path=path,pattern=pattern) outfiles<-paste(gsub(pattern,'',fnams),'.',subunit,'.fasta',sep='') # Create output directory # system(paste('mkdir',outdir)) system('touch rrna.err') # Perform rRNA search with barrnap # for (f in 1:length(flist)){ outgff<-paste(gsub('.fasta','',fnams[f]),'.tmp.gff',sep='') cmd<-paste(barrnap,' --kingdom ',king,' ',flist[f],' > ',outgff,sep='') system(cmd,ignore.stderr=T) genome<-read.fasta(flist[f]) sequen<-lapply(getSequence(genome),toupper) snames2<-gsub('>','',system(paste("grep '>' ",flist[f],sep=''),intern=T)) snames<-unlist(lapply(snames2,function(x){strsplit(x,' ')[[1]][1]})) if (file.info(outgff)$size<=16){ mssg<-paste('No ',subunit,' gene found in genome ',fnams[f],sep='') cat(mssg,file='rrna.err',append=T,sep='\n') warning(mssg) } else { gff<-read.table(outgff,sep='\t',skip=1) sun<-as.vector(gff[,9]) if (subunit=='16S'){ grp<-grep(subunit,sun) if (length(grp)==0){ mssg<-paste('No ',subunit,' gene found in genome ',fnams[f],sep='') cat(mssg,file='rrna.err',append=T,sep='\n') warning(mssg) } else if (length(grp)==1){ ini<-gff[grp,4] fin<-gff[grp,5] std<-gff[grp,7] nam<-as.vector(gff[grp,1]) contig<-which(snames==nam) if (std=='+'){ gene<-sequen[[contig]][ini:fin] } else if (std=='-'){ gene<-toupper(rev(comp(sequen[[contig]][ini:fin]))) } write.fasta(gene,names=gsub('.fasta','',outfiles[f]),file=outfiles[f]) system(paste('mv *.16S.fasta',outdir)) } else if (length(grp)>1){ if (multiple==F){ ini<-gff[grp[1],4] fin<-gff[grp[1],5] std<-gff[grp[1],7] nam<-as.vector(gff[grp[1],1]) contig<-which(snames==nam) if (std=='+'){ gene<-sequen[[contig]][ini:fin] } else if (std=='-'){ gene<-toupper(rev(comp(sequen[[contig]][ini:fin]))) } write.fasta(gene,names=gsub('.fasta','',outfiles[f]),file=outfiles[f]) system(paste('mv *.16S.fasta',outdir)) } else if (multiple==T){ gff2<-gff[grp,] ini<-gff2[,4] fin<-gff2[,5] std<-as.vector(gff2[,7]) nam<-as.vector(gff2[,1]) for (g in 1:length(grp)){ contig<-which(snames==nam[g]) if (as.vector(as.vector(std[g]))=='+'){ gene<-sequen[[contig]][ini[g]:fin[g]] } else { gene<-toupper(rev(comp(sequen[[contig]][ini[g]:fin[g]]))) } write.fasta(gene,names=gsub('fasta',g,outfiles[f]),file=outfiles[f],open='a') } system(paste('mv *.16S.fasta',outdir)) } } } else if (subunit=='23S'){ grp<-grep(subunit,sun) if (length(grp)==0){ mssg<-paste('No ',subunit,' gene found in genome ',fnams[f],sep='') cat(mssg,file='rrna.err',append=T,sep='\n') warning(mssg) } else if (length(grp)==1){ ini<-gff[grp,4] fin<-gff[grp,5] std<-gff[grp,7] nam<-as.vector(gff[grp,1]) contig<-which(snames==nam) if (std=='+'){ gene<-sequen[[contig]][ini:fin] } else if (std=='-'){ gene<-toupper(rev(comp(sequen[[contig]][ini:fin]))) } write.fasta(gene,names=gsub('.fasta','',outfiles[f]),file=outfiles[f]) system(paste('mv *.23S.fasta',outdir)) } else if (length(grp)>1){ if (multiple==F){ ini<-gff[grp[1],4] fin<-gff[grp[1],5] std<-gff[grp[1],7] nam<-as.vector(gff[grp[1],1]) contig<-which(snames==nam) if (std=='+'){ gene<-sequen[[contig]][ini:fin] } else if (std=='-'){ gene<-toupper(rev(comp(sequen[[contig]][ini:fin]))) } write.fasta(gene,names=gsub('.fasta','',outfiles[f]),file=outfiles[f]) system(paste('mv *.23S.fasta',outdir)) } else if (multiple==T){ gff2<-gff[grp,] ini<-gff2[grp,4] fin<-gff2[grp,5] std<-as.vector(gff2[grp,7]) nam<-as.vector(gff2[grp,1]) for (g in 1:length(grp)){ contig<-which(snames==nam[g]) if (as.vector(std[g])=='+'){ gene<-sequen[[contig]][ini[g]:fin[g]] } else { gene<-toupper(rev(comp(sequen[[contig]][ini[g]:fin[g]]))) } write.fasta(gene,names=gsub('fasta',g,outfiles[f]),file=outfiles[f],open='a') } system(paste('mv *.23S.fasta',outdir)) } } } else { stop('Parameter subunit must be "16S" or "23S"') } } } system('rm -rf *.tmp.gff') system(paste('mv rrna.err',outdir)) # Align sequences # if (align==TRUE){ setwd(outdir) if (subunit=='16S'){ system('cat *.16S.fasta > all.16S.fasta') #namali<-gsub('>','',system("grep '>' all.16S.fasta",intern=T)) cmd<-paste(clustalo, ' -i all.16S.fasta -o alignment.16S.fasta ', '--threads ',proc, ' --output-order=input-order', sep='') system(cmd) #aux<-capture.output( #alignment<-msa(inputSeqs='all.16S.fasta',method='ClustalOmega',type='dna',order='input')) #aliconver<-msaConvert(alignment,type='seqinr::alignment') #seqs<-lapply(aliconver$seq,s2c) #write.fasta(seqs,names=namali,file='alignment.16S.fasta') } else if (subunit=='23S'){ system('cat *.23S.fasta > all.23S.fasta') cmd<-paste(clustalo, ' -i all.23S.fasta -o alignment.23S.fasta ', '--threads ',proc, ' --output-order=input-order', sep='') system(cmd) #namali<-gsub('>','',system("grep '>' all.23S.fasta",intern=T)) #aux<-capture.output( #alignment<-msa(inputSeqs='all.23S.fasta',method='ClustalO',type='dna',order='input')) #aliconver<-msaConvert(alignment,type='seqinr::alignment') #seqs<-lapply(aliconver$seq,s2c) #write.fasta(seqs,names=namali,file='alignment.23S.fasta') } } # Distance matrix # if (distance==TRUE){ aliconver$nam<-namali dista1<-dist.alignment(aliconver) dista2<-round(dista1^2,digits=3) dnam<-paste('distance_matrix_',subunit,sep='') assign(dnam,dista2) save(list=dnam,file=paste(dnam,'Rdata',sep='.')) } # Phylogeny # if (phylogeny==TRUE){ phydat<-msaConvert(alignment,'phangorn::phyDat') distma<-dist.ml(phydat,model='F81') tre<-NJ(distma) write.tree(tre,file=paste('NJ.',subunit,'.tree.nwk',sep='')) } setwd(gw) }

b40583aa54a76098aceb2afcf544acfe396d207a

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/GPareto/examples/crit_EMI.Rd.R

3684e95d533f0d9c2904d965e8c8eb7414b9a100

[]

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

1,503

r

crit_EMI.Rd.R

library(GPareto) ### Name: crit_EMI ### Title: Expected Maximin Improvement with m objectives ### Aliases: crit_EMI ### ** Examples #--------------------------------------------------------------------------- # Expected Maximin Improvement surface associated with the "P1" problem at a 15 points design #--------------------------------------------------------------------------- set.seed(25468) library(DiceDesign) n_var <- 2 f_name <- "P1" n.grid <- 21 test.grid <- expand.grid(seq(0, 1, length.out = n.grid), seq(0, 1, length.out = n.grid)) n_appr <- 15 design.grid <- round(maximinESE_LHS(lhsDesign(n_appr, n_var, seed = 42)$design)$design, 1) response.grid <- t(apply(design.grid, 1, f_name)) Front_Pareto <- t(nondominated_points(t(response.grid))) mf1 <- km(~., design = design.grid, response = response.grid[,1]) mf2 <- km(~., design = design.grid, response = response.grid[,2]) EMI_grid <- apply(test.grid, 1, crit_EMI, model = list(mf1, mf2), paretoFront = Front_Pareto, critcontrol = list(nb_samp = 20)) filled.contour(seq(0, 1, length.out = n.grid), seq(0, 1, length.out = n.grid), nlevels = 50, matrix(EMI_grid, nrow = n.grid), main = "Expected Maximin Improvement", xlab = expression(x[1]), ylab = expression(x[2]), color = terrain.colors, plot.axes = {axis(1); axis(2); points(design.grid[,1], design.grid[,2], pch = 21, bg = "white") } )

bd2d858fa76beb70204eadb68c2cd4963c9c713c

5bd4b82811be11bcf9dd855e871ce8a77af7442f

/gap/man/LD22.Rd

a8a1d539c814cd8bbce44a375db108611577b273

[]

no_license

jinghuazhao/R

a1de5df9edd46e53b9dc90090dec0bd06ee10c52

8269532031fd57097674a9539493d418a342907c

refs/heads/master

2023-08-27T07:14:59.397913

2023-08-21T16:35:51

61,349,892

10

8

null

2022-11-24T11:25:51

2016-06-17T06:11:36

R

UTF-8

R

false

true

1,505

rd

LD22.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/2ld.R \name{LD22} \alias{LD22} \title{LD statistics for two diallelic markers} \usage{ LD22(h, n) } \arguments{ \item{h}{a vector of haplotype frequencies.} \item{n}{number of haplotypes.} } \value{ The returned value is a list containing: \itemize{ \item h the original haplotype frequency vector. \item n the number of haplotypes. \item D the linkage disequilibrium parameter. \item VarD the variance of D. \item Dmax the maximum of D. \item VarDmax the variance of Dmax. \item Dprime the scaled disequilibrium parameter. \item VarDprime the variance of Dprime. \item x2 the Chi-squared statistic. \item lor the log(OR) statistic. \item vlor the var(log(OR)) statistic. } } \description{ LD statistics for two diallelic markers } \details{ It is possible to perform permutation test of \eqn{r^2} by re-ordering the genotype through R's sample function, obtaining the haplotype frequencies by \code{\link{gc.em}} or \code{\link{genecounting}}, supplying the estimated haplotype frequencies to the current function and record x2, and comparing the observed x2 and that from the replicates. } \note{ extracted from 2ld.c, worked 28/6/03, tables are symmetric do not fix, see kbyl below } \examples{ \dontrun{ h <- c(0.442356,0.291532,0.245794,0.020319) n <- 481*2 t <- LD22(h,n) } } \references{ \insertRef{zabetian03}{gap} \insertRef{zapata97}{gap} } \seealso{ \code{\link{LDkl}} } \author{ Jing Hua Zhao } \keyword{models}

e95c0a9ee76cf6d7f958b0def87cd518906e8209

68fbefe55a62483086fb54bb516b069cef3229f9

/global.R

fbb481469092bb713ec78142b823e3867f7f1336

[]

no_license

Argaadya/shiny_covid

f9ee81527662d5fbb77a11761520882403fa01a9

a2afc1acee1ec54beb343989e3b67975756549dc

refs/heads/master

2022-12-19T02:31:40.571583

2020-09-18T08:05:06

292,202,042

0

1

null

UTF-8

R

false

2,309

r

global.R

library(shiny) library(shinydashboard) library(dashboardthemes) # data wrangling library(dplyr) library(tidyr) library(lubridate) # visualization library(ggplot2) library(plotly) library(scales) library(glue) library(leaflet) # Import Data from John Hopkins University case_confirmed <- read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv", check.names = F) case_recover <- read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv", check.names = F) case_death <- read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv", check.names = F) # Data Cleansing ## Active Cases case_confirmed <- case_confirmed %>% pivot_longer(-c(`Province/State`, `Country/Region`, Lat, Long), names_to = "Date", values_to = "Case") %>% mutate( Date = mdy(Date) ) ## Recovery case_recover <- case_recover %>% pivot_longer(-c(`Province/State`, `Country/Region`, Lat, Long), names_to = "Date", values_to = "Recover") %>% mutate( Date = mdy(Date) ) ## Death case_death <- case_death %>% pivot_longer(-c(`Province/State`, `Country/Region`, Lat, Long), names_to = "Date", values_to = "Death") %>% mutate( Date = mdy(Date) ) ## Merge/Join data.frame covid <- case_confirmed %>% left_join(case_recover) %>% left_join(case_death) %>% mutate( cfr = Death/Case ) %>% rename(State = `Province/State`, Country = `Country/Region` ) %>% filter(!(Country %in% c("Diamond Princess", "MS Zaandam"))) # Data from the latest date only covid_update <- covid %>% filter(Date == max(Date)) # Theme for Visualization theme_algo <- theme( panel.background = element_rect(fill = "white"), panel.grid.major = element_line(colour = "gray80"), panel.grid.minor = element_blank(), plot.title = element_text(family = "serif", size = 18) )

9fb815b392a4149ec01eae8948a33671f9bd846e

e6366e79d87ff74e54e73776690265b49c0c74fa

/cachematrix.R

3968c54afeaad9fcaa28ffb804be1057179d9303

[]

no_license

Nicbars/ProgrammingAssignment2

c4d90aa0b4ed40b027155263fbae05574e4fad08

0db75fc440504381290a6302aea09620a97d964b

refs/heads/master

2023-08-24T07:52:59.726355

2021-10-27T13:02:43

414,578,042

0

null

UTF-8

R

false

820

r

cachematrix.R

## The goal in this repository is to write functions ## using "makeCacheMatrix" to get the inverse of the matrix makeCacheMatrix <- function(x = matrix()) { inv <- NULL set <- function(y) { x <<- y inv <<- NULL } get <- function() x setmatrixinverse <- function(inverse) m <<- inverse getmatrixinverse <- function() m list(set = set, get = get, setmatrixinverse = setmatrixinverse, getmatrixinverse = getmatrixinverse) ## Use cacheSolve to compute the inverse of the matrix from the makeCacheMatrix ## When the inverse is computed, cachesolve will get the inverse from the cache cacheSolve <- function(x, ...) { inv <- x$getinv() if(!is.null(inv)) { message("getting cached result") return(inv) } data <- x$get() inv <- solve(data, ...) x$setinv(inv) inv }

528a853c46888690c6531c63c3242b605d5f10ba

40418557df079d403dcbbd0840711e6a924cafda

/deepsolar.R

35400e19b1e04d8caf99c6e65290cf5bc1c1a889

[]

no_license

Angel-GM/deepsolar

4041ef9ab99559fffcd43fe0d659dcd93d15aa5d

e5802c8b697c71dd0a797af376a062b7b5c11364

refs/heads/master

2022-08-03T08:27:19.942354

2020-05-19T10:04:26

265,210,632

1

0

null

UTF-8

R

false

12,258

r

deepsolar.R

library(dplyr) library(glmnet) library(randomForest) library(ggplot2) library(reshape2) library(gridExtra) library(cowplot) solar = read.csv('deepsolar_ny.csv' ) set.seed(0) # Excluding targer var and other nonfeature vars. X = solar %>% select(-solar_system_count,-county, -state) %>% data.matrix() y = solar$solar_system_count %>% as.vector() # Imputting n/a with means for(i in 1:ncol(X)){ X[is.na(X[,i]), i] <- mean(X[,i], na.rm = TRUE) } mu = as.vector(apply(X, 2, 'mean')) sd = as.vector(apply(X, 2, 'sd')) X.orig = X for (i in c(1:n)){ X[i,] = (X[i,] - mu)/sd } X = X[,-1] p = dim(X)[2] n = dim(X)[1] # test train split, 80%/20% n.train = floor(.8*n) n.test = n-n.train M = 100 Rsq.test.rf = rep(0,M) # rf= randomForest Rsq.train.rf = rep(0,M) Rsq.test.en = rep(0,M) #en = elastic net Rsq.train.en = rep(0,M) Rsq.test.ls = rep(0,M) #ls = lasso Rsq.train.ls = rep(0,M) Rsq.test.rg = rep(0,M) #rg = ridge Rsq.train.rg = rep(0,M) for (m in c(1:M)) { shuffled_indexes = sample(n) train = shuffled_indexes[1:n.train] test = shuffled_indexes[(1+n.train):n] X.train = X[train, ] y.train = y[train] X.test = X[test, ] y.test = y[test] # fit elastic-net and calculate and record the train and test R squares cv.fit.en = cv.glmnet(X.train, y.train, alpha = .5, nfolds = 10) fit = glmnet(X.train, y.train, alpha = .5, lambda = cv.fit.en$lambda.min) y.train.hat = predict(fit, newx = X.train, type = "response") y.test.hat = predict(fit, newx = X.test, type = "response") Rsq.test.en[m] = 1-mean((y.test - y.test.hat)^2)/mean((y - mean(y))^2) Rsq.train.en[m] = 1-mean((y.train - y.train.hat)^2)/mean((y - mean(y))^2) # fit lasso and calculate and record the train and test R squares cv.fit.ls = cv.glmnet(X.train, y.train, alpha = 1, nfolds = 10) fit = glmnet(X.train, y.train, alpha = 1, lambda = cv.fit.ls$lambda.min) y.train.hat = predict(fit, newx = X.train, type = "response") y.test.hat = predict(fit, newx = X.test, type = "response") Rsq.test.ls[m] = 1-mean((y.test - y.test.hat)^2)/mean((y - mean(y))^2) Rsq.train.ls[m] = 1-mean((y.train - y.train.hat)^2)/mean((y - mean(y))^2) # fit ridge and calculate and record the train and test R squares cv.fit.rg = cv.glmnet(X.train, y.train, alpha = 0, nfolds = 10) fit = glmnet(X.train, y.train, alpha = 0, lambda = cv.fit.rg$lambda.min) y.train.hat = predict(fit, newx = X.train, type = "response") y.test.hat = predict(fit, newx = X.test, type = "response") Rsq.test.rg[m] = 1-mean((y.test - y.test.hat)^2)/mean((y - mean(y))^2) Rsq.train.rg[m] = 1-mean((y.train - y.train.hat)^2)/mean((y - mean(y))^2) # fit RF and calculate and record the train and test R squares rf = randomForest(X.train, y.train, mtry = sqrt(p), importance = TRUE) y.test.hat = predict(rf, X.test) y.train.hat = predict(rf, X.train) Rsq.test.rf[m] = 1-mean((y.test - y.test.hat)^2)/mean((y - mean(y))^2) Rsq.train.rf[m] = 1-mean((y.train - y.train.hat)^2)/mean((y - mean(y))^2) # cat(sprintf("m=%3.f| Rsq.test.rf=%.2f, Rsq.test.en=%.2f| Rsq.train.rf=%.2f, Rsq.train.en=%.2f| \n", m, Rsq.test.rf[m], Rsq.test.en[m], Rsq.train.rf[m], Rsq.train.en[m])) } # Part b, box plots of rsq testplot = ggplot(melt(Rsq.test), aes(x=Var2, y=value)) + geom_boxplot() + scale_y_continuous(limits = c(.4,1)) + labs(title='Test R^2', x='Method', y="R^2") trainplot = ggplot(melt(Rsq.train), aes(x=Var2, y=value)) + geom_boxplot() + scale_y_continuous(limits = c(.4,1)) + labs(title='Train R^2', x='Method', y="R^2") grid.arrange(testplot, trainplot , nrow=1) # Part c, 10fold CV curves plot(cv.fit.rg, sub = 'Ridge') plot(cv.fit.en, sub = 'Elastic Net') plot(cv.fit.ls, sub = 'Lasso') # Bootstrapping bootstrapSamples = 100 beta.rf.bs = matrix(0, nrow = p, ncol = bootstrapSamples) beta.en.bs = matrix(0, nrow = p, ncol = bootstrapSamples) beta.ls.bs = matrix(0, nrow = p, ncol = bootstrapSamples) beta.rg.bs = matrix(0, nrow = p, ncol = bootstrapSamples) for (m in 1:bootstrapSamples){ bs_indexes = sample(n, replace=T) X.bs = X[bs_indexes, ] y.bs = y[bs_indexes] # fit bs rf rf = randomForest(X.bs, y.bs, mtry = sqrt(p), importance = TRUE) beta.rf.bs[,m] = as.vector(rf$importance[,1]) # fit bs en a = 0.5 # elastic-net cv.fit = cv.glmnet(X.bs, y.bs, alpha = a, nfolds = 10) fit = glmnet(X.bs, y.bs, alpha = a, lambda = cv.fit$lambda.min) beta.en.bs[,m] = as.vector(fit$beta) # fit bs ls b = 1 # lasso cv.fit = cv.glmnet(X.bs, y.bs, alpha = b, nfolds = 10) fit = glmnet(X.bs, y.bs, alpha = b, lambda = cv.fit$lambda.min) beta.ls.bs[,m] = as.vector(fit$beta) # fit bs rg c = 0 # rg cv.fit = cv.glmnet(X.bs, y.bs, alpha = c, nfolds = 10) fit = glmnet(X.bs, y.bs, alpha = c, lambda = cv.fit$lambda.min) beta.rg.bs[,m] = as.vector(fit$beta) cat(sprintf("Bootstrap Sample %3.f \n", m)) } # calculate bootstrapped standard errors / alternatively you could use qunatiles to find upper and lower bounds rf.bs.sd = apply(beta.rf.bs, 1, "sd") en.bs.sd = apply(beta.en.bs, 1, "sd") ls.bs.sd = apply(beta.ls.bs, 1, "sd") rg.bs.sd = apply(beta.rg.bs, 1, "sd") # fit rf to the whole data rf = randomForest(X, y, mtry = sqrt(p), importance = TRUE) # fit en to the whole data a=0.5 # elastic-net cv.fit.en = cv.glmnet(X, y, alpha = a, nfolds = 10) fit.en = glmnet(X, y, alpha = a, lambda = cv.fit.en$lambda.min) # fit ls to the whole data b=1 # lasso cv.fit.ls = cv.glmnet(X, y, alpha = b, nfolds = 10) fit.ls = glmnet(X, y, alpha = b, lambda = cv.fit.ls$lambda.min) # fit rg to the whole data c=0 # ridge cv.fit.rg = cv.glmnet(X, y, alpha = c, nfolds = 10) fit.rg = glmnet(X, y, alpha = c, lambda = cv.fit.rg$lambda.min) betaS.rf = data.frame(names(X[1,]), as.vector(rf$importance[,1]), 2*rf.bs.sd) colnames(betaS.rf) = c( "feature", "value", "err") betaS.en = data.frame(names(X[1,]), as.vector(fit.en$beta), 2*en.bs.sd) colnames(betaS.en) = c( "feature", "value", "err") betaS.ls = data.frame(names(X[1,]), as.vector(fit.ls$beta), 2*ls.bs.sd) colnames(betaS.ls) = c( "feature", "value", "err") betaS.rg = data.frame(names(X[1,]), as.vector(fit.rg$beta), 2*rg.bs.sd) colnames(betaS.rg) = c( "feature", "value", "err") rfPlot = ggplot(betaS.rf, aes(x=feature, y=value)) + geom_bar(stat = "identity", fill="white", colour="black") + geom_errorbar(aes(ymin=value-err, ymax=value+err), width=.2) enPlot = ggplot(betaS.en, aes(x=feature, y=value)) + geom_bar(stat = "identity", fill="white", colour="black") + geom_errorbar(aes(ymin=value-err, ymax=value+err), width=.2) lsPlot = ggplot(betaS.ls, aes(x=feature, y=value)) + geom_bar(stat = "identity", fill="white", colour="black") + geom_errorbar(aes(ymin=value-err, ymax=value+err), width=.2) rgPlot = ggplot(betaS.rg, aes(x=feature, y=value)) + geom_bar(stat = "identity", fill="white", colour="black") + geom_errorbar(aes(ymin=value-err, ymax=value+err), width=.2) grid.arrange(rfPlot, enPlot, lsPlot, rgPlot, nrow = 4) # we need to change the order of factor levels by specifying the order explicitly. betaS.rf$feature = factor(betaS.rf$feature, levels = betaS.rf$feature[order(betaS.rf$value, decreasing = F)]) betaS.en$feature = factor(betaS.en$feature, levels = betaS.rf$feature[order(betaS.rf$value, decreasing = F)]) betaS.ls$feature = factor(betaS.ls$feature, levels = betaS.rf$feature[order(betaS.rf$value, decreasing = F)]) betaS.rg$feature = factor(betaS.rg$feature, levels = betaS.rf$feature[order(betaS.rf$value, decreasing = F)]) rfPlot = ggplot(betaS.rf, aes(x=feature, y=value)) + geom_bar(stat = "identity", fill="white", colour="black") + geom_errorbar(aes(ymin=value-err, ymax=value+err), width=.2) + scale_x_discrete(name="Feature Number") + coord_flip() +labs(title = 'Random Forest', y = "Importance") + theme( axis.text.y = element_text(size = 6)) enPlot = ggplot(betaS.en, aes(x=feature, y=value)) + geom_bar(stat = "identity", fill="white", colour="black") + geom_errorbar(aes(ymin=value-err, ymax=value+err), width=.2) + coord_flip() +labs(title = 'Elastic Net', y ='Importance') + scale_x_discrete(limits=rev(topN)) + theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) lsPlot = ggplot(betaS.ls, aes(x=feature, y=value)) + geom_bar(stat = "identity", fill="white", colour="black") + geom_errorbar(aes(ymin=value-err, ymax=value+err), width=.2) + coord_flip() +labs(title = 'Lasso', y ='Importance') + scale_x_discrete(limits=rev(topN)) + theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) rgPlot = ggplot(betaS.rg, aes(x=feature, y=value)) + geom_bar(stat = "identity", fill="white", colour="black") + geom_errorbar(aes(ymin=value-err, ymax=value+err), width=.2) + coord_flip() +labs(title = 'Ridge', y ='Importance') + scale_x_discrete(limits=rev(topN)) + theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) plot_grid(rfPlot, rgPlot, enPlot, lsPlot, align = "h", nrow = 1, rel_widths = c(.28, .24, .24, .24)) ## Residual plots # fit rf to the whole data rf = randomForest(X, y, mtry = sqrt(p), importance = TRUE) # fit en to the whole data a=0.5 # elastic-net cv.fit.en = cv.glmnet(X, y, alpha = a, nfolds = 10) fit.en = glmnet(X, y, alpha = a, lambda = cv.fit.en$lambda.min) # fit ls to the whole data b=1 # lasso cv.fit.ls = cv.glmnet(X, y, alpha = b, nfolds = 10) fit.ls = glmnet(X, y, alpha = b, lambda = cv.fit.ls$lambda.min) # fit rg to the whole data c=0 # ridge cv.fit.rg = cv.glmnet(X, y, alpha = c, nfolds = 10) fit.rg = glmnet(X, y, alpha = c, lambda = cv.fit.rg$lambda.min) rf.res = (y - rf$predicted) rg.res = (y - (X %*% fit.rg$beta)) en.res = (y - (X %*% fit.en$beta)) ls.res = (y - (X %*% fit.ls$beta)) residuals_all = cbind(rf.res, rg.res, en.res, ls.res) %>% as.matrix() colnames(residuals_all) = c("Random Forest", "Ridge" , "Elastic Net", "Lasso") # Violin plot of residuals ggplot(melt(residuals_all), aes(x=Var2, y=value)) + geom_violin() + scale_y_continuous() + labs(title='Residuals of All Methods', x='Method', y="Residuals") + stat_summary(fun.data=mean_sdl,mult=1, geom="crossbar", color="black")

ff34d55868bf21519055c0c31d39a9b9ed95820e

356a111876127015ba131f1ff4891901016f1ad9

/STATISTICAL_INFERENCE/ScrptR_examples_sampling distribution.R

b12991f103e379c9dcb27ff2456e0f193ce97ff1

[]

no_license

htnani/IBSTATB

0deb7c07b6f64861732e717c1ab0455dcc13efe0

bd92145ed906f88db33240fe75de6da92767ca7b

refs/heads/master

2020-04-01T20:08:21.206493

2017-09-12T13:52:33

null

0

null

UTF-8

R

false

1,343

r

ScrptR_examples_sampling distribution.R

library(lattice) # Example: Sampling distribution of the variance install.packages("lattice") library(lattice) n<-5 vec.var<-c(rep(0,500)) for(i in 1:500) {x<-rnorm(n,mean=5,sd=2) vec.var[i]<-var(x)*(n-1)/(2^2)} qqmath(vec.var,distribution=function(p)qchisq(p,n-1),main=paste('Q-QPlot Chi-Square,n=',n) ,xlab="Theoretical quantiles",ylab="Empirical quantiles") ############################################# n<-15 vec.var<-c(rep(0,500)) for(i in 1:500) {x<-rnorm(n,mean=5,sd=2) vec.var[i]<-var(x)*(n-1)/(2^2)} qqmath(vec.var,distribution=function(p)qchisq(p,n-1),main=paste('Q-QPlot Chi-Square,n=',n) ,xlab="Theoretical quantiles",ylab="Empirical quantiles") ############################################# n<-30 vec.var<-c(rep(0,500)) for(i in 1:500) {x<-rnorm(n,mean=5,sd=2) vec.var[i]<-var(x)*(n-1)/(2^2)} qqmath(vec.var,distribution=function(p)qchisq(p,n-1),main=paste('Q-QPlot Chi-Square,n=',n) ,xlab="Theoretical quantiles",ylab="Empirical quantiles") ############################################# n<-60 vec.var<-c(rep(0,500)) for(i in 1:500) {x<-rnorm(n,mean=5,sd=2) vec.var[i]<-var(x)*(n-1)/(2^2)} qqmath(vec.var,distribution=function(p)qchisq(p,n-1),main=paste('Q-QPlot Chi-Square,n=',n) ,xlab="Theoretical quantiles",ylab="Empirical quantiles")

b251fc8ed150128be83e1a198b4bd2f67ed6b2e1

126053dbd6fae0697106d6c6ef9d33430e27848e

/Basics of Language/whileloop.R

b9bf394d67c9acbce08c00b90ee19f7d289175ed

[]

no_license

777shipra/R-Beginners

7cff5c02b7fe61bfcba9667f69cd894d992ee1f0

cee55837a0fec006544a4d5aa01e3c7fc31efe8b

refs/heads/master

2020-03-31T02:14:03.206678

2018-10-20T13:52:58

151,814,057

0

null

UTF-8

R

false

268

r

whileloop.R

#<-------------------Structure-----------> while(condition){ statements } #<-----------------Examples-------------------> while(TRUE){ print("hello") } #press esc for terminating infinite loop counter<-1 while (counter<12){ print(counter) counter<-counter+1 }

38855280e8ca8cd8fd43b5b49cc6162b5ca7ea6b

266fd6d604513251119fc891f4b389fca9b73ed0

/R/theme.R

70deb8bb49707f84c5e545c6cb4c9acc59f1a1c5

[]

no_license

christopherkenny/ei

58b511984556d4cf71d56e56333c3a83eae0d7b2

c28b8eb59e6a2ce5fb1cbedacc770b478b1a6a06

refs/heads/master

2023-09-01T11:35:47.665557

2021-09-09T16:28:13

400,650,628

0

null

2021-09-09T16:28:14

2021-08-27T22:39:11

R

UTF-8

R

false

118

r

theme.R

theme_ei <- function() { ggplot2::theme_bw() + ggplot2::theme(text = ggplot2::element_text(family = "Times")) }

771767a40483dbbbfb451b4d9362b02645f06fb2

e21e28dcbe64bff4766b3b8607ceb48e7d8727ac

/run_analysis.R

e57d39780250557c0ef1d1a54f2a810c307fb2d6

[]

no_license

darrenredmond/getting_cleaning_data_project

29187877a748b19bf0a6e5f0494750f9c544a9b6

9b1694fe8cca6d16f66e7a5bf00c38982df4b17b

refs/heads/master

2021-01-11T09:15:59.258612

2016-12-22T23:43:58

77,176,413

0

null

UTF-8

R

false

2,706

r

run_analysis.R

# set the working directory. setwd("~/dev/coursera/johns_hopkins/getting_cleaning_data_project") # read in the feature feature_data <- read.table('UCI HAR Dataset/features.txt') # read in the activity data activity_data <- read.table('UCI HAR Dataset/activity_labels.txt') names(activity_data) <- c('y', 'activity') # read in the test data x_test_data <- read.table('UCI HAR Dataset/test/X_test.txt') y_test_data <- read.table('UCI HAR Dataset/test/y_test.txt') names(y_test_data)[1] <- 'y' subject_test_data <- read.table('UCI HAR Dataset/test/subject_test.txt') names(subject_test_data)[1] <- 'subject' # read in the train data x_train_data <- read.table('UCI HAR Dataset/train/X_train.txt') # 4. Appropriately labels the data set with descriptive variable names. # set the names of x to be the features names(x_test_data) <- feature_data$V2 names(x_train_data) <- feature_data$V2 y_train_data <- read.table('UCI HAR Dataset/train/y_train.txt') names(y_train_data)[1] <- 'y' subject_train_data <- read.table('UCI HAR Dataset/train/subject_train.txt') names(subject_train_data)[1] <- 'subject' # combine the columns for test and train data test_data <- cbind(x_test_data, y_test_data, subject_test_data) train_data <- cbind(x_train_data, y_train_data, subject_train_data) # 1. Merges the training and the test sets to create one data set. # combine the row data from test and train data sets full_data <- rbind(test_data, train_data) # play with extracting mean and std column names. has_mean <- function(v) { grepl('mean', v) } has_std <- function(v) { grepl('std', v) } valid_column <- function(v) { has_mean(v) || has_std(v) } matching_columns <- Filter(valid_column, names(full_data)) names(full_data) # create the mean and standard deviation data variables for all columns with mean/std in it. mean_std_data <- full_data[,grepl("mean", colnames(full_data)) | grepl("std", colnames(full_data))] # 2. Extracts only the measurements on the mean and standard deviation for each measurement. mean_std_matrix <- rbind(apply(full_data, 2, mean), apply(full_data, 2, sd)) rownames(mean_std_matrix) <- c('mean', 'sd') mean_std_matrix # 3. Uses descriptive activity names to name the activities in the data set names(full_data) full_data <- mutate(full_data, activity_id = y) full_data_activity <- merge(full_data, activity_data, by.x="y", by.y="y") head(full_data_activity) # 5. From the data set in step 4, creates a second, independent tidy data set with the average of each variable # for each activity and each subject. grouped_average_data <- full_data_activity %>% group_by(subject, activity) %>% summarise_each(funs(mean(., na.rm=TRUE))) View(grouped_average_data)

f3792ac5613dac656a9cd82cb0946c1a1abf87c9

7db7ca4aea3b8d0557248beb1b4f559e6eb10809

/R/onLoad.R

4ee90abfe5cda01ae36d205cacd8e42cc67dcf61

[]

no_license

tera-insights/gtBase

aabbbb7ec58bf1beb75879decf35832e628874d9

bfbc7459923456895ab99143a5020bdfdf536720

refs/heads/master

2020-05-22T00:03:44.268580

2017-06-06T14:42:37

25,822,663

3

0

null

UTF-8

R

false

1,869

r

onLoad.R

.onAttach <- function(libname, pkgname) { grokit <<- new.env() ## A list of alias names that have been generated. It is kept in the form of ## type -> vector pairs. Tame should be the type of name being generated, such ## as waypoint, input, output, etc. Each vector should be named and numeric, ## where a given name is mapped to the number of times it has been used. Each ## time it is used, its mapped value is increment so that the generated names ## are name_1, name_2, name_3, and so forth. Names should avoid ending in an ## underscore followed by a number to avoid clashing. grokit$names <- list() grokit$alias <- list() ## A list of name -> expressions of inputs. grokit$expressions <- list() ## The set of Grokit PHP/C++ libraries to load for queries being ran. grokit$libraries <- c("base", "statistics") grokit$tasks <- list() ## A character vector of alias names in the order of which they were created. grokit$waypoints <- character() ## A character vector of unique output names. grokit$outputs <- character() grokit$cluster <- list() ## Reading the various schema grokit$schemas <- get.schema() ## These are used for the testing interface grokit$tests <- character() ## Plug-ins are functions that automatically alter the query plan. ## See `plugins` for more information. grokit$plugins <- list() for (class in c("Filter", "Generated", "GF", "GI", "GIST", "GLA", "Load", "Join")) for (stage in c("before", "after")) grokit$plugins[[class]][[stage]] <- list() } .onDetach <- function(libpath) { rm(grokit, envir = .GlobalEnv) } .reset <- function() { grokit$alias <- list() grokit$expressions <- list() grokit$libraries <- c("base", "statistics") grokit$tasks <- list() grokit$waypoints <- character() grokit$outputs <- character() grokit$cluster <- list() }

f720ed14a161634e7899b47a588cf6fcedaf83f8

81780d7000220293b9cecb54d4def069faa7d649

/R/define_observation_periods.R

6d1dd7badf034a1b625b376b466f4406159a730b

[ "Apache-2.0" ]

permissive

pwatrick/DrugRepurposingToolKit

8ef405a602e6e100306365e3c9acf9d4cd56bc2a

8c0f8c26013b8efec5c89afb68f182e98794bc3c

refs/heads/main

2023-04-18T10:57:15.211297

2022-08-09T11:14:26

352,338,175

2

5

null

UTF-8

R

false

1,710

r

define_observation_periods.R

#' Define baseline and treatment periods #' #' #' @description #' \code{define_observation_periods} creates baseline and treatment period columns #' #' @details #' This function defines the baseline and treatment period columns from #' clinical data from electronic health record (EHR) databases that have #' adopted the Observational Medical Outcomes Partnership (OMOP) #' Common Data Model (CDM) format. #' #' Baseline period = "start_date" to "first_{drug}_exposure". #' Treatment period = ("first_{drug}_exposure" + 30 days) to "last_{drug}_exposure". #' If treatment period > 12 months, then treatment period = #' ("first_{drug}_exposure" + 30 days) to "end_date". #' #' Updated: 2021-03-27 #' #' #' @param .data A tibble #' @export define_observation_periods <- function(.data, ...) { #Calculate number of days between "first_drug_exposure" and "last_drug_exposure" .data$drug_length <- lubridate::interval(.data$first_drug_exposure, .data$last_drug_exposure) .data$drug_length <- lubridate::time_length(.data$drug_length, "day") #Remove patients with "drug_length" less than 30 days .data <- .data %>% dplyr::filter(drug_length >= 30) #If "last_drug_exposure" occurs before "end_date", set "final_end_date" to "last_drug_exposure", else set to "end_date" .data <- .data %>% dplyr::mutate( final_end_date = dplyr::if_else(last_drug_exposure <= end_date, last_drug_exposure, end_date) ) #Get final columns of interest .data <- .data %>% dplyr::select(person_id, start_date, first_drug_exposure, final_end_date) %>% dplyr::distinct() }

dbe2763c3124167f889d994021ba7bc8cee893ff

c5e0e4f054c443978b44e5d78bdf670b131164a9

/Recreate_geneLenDataBase.R

1bf2020e4a5076510f9ebe37dbd5963a1bcc88ec

[]

no_license

Quarkins/goseq_dev

82defe34641dc3c34c0fab0594589bbcf862a6c7

af0906a6350b9ab6101a71cb26dc7cc5fdbec060

refs/heads/master

2021-01-10T16:08:26.360434

2016-03-09T21:10:24

53,532,080

0

null

UTF-8

R

false

651

r

Recreate_geneLenDataBase.R

################################### # Description: A little script to reproduce the dataset used for geneLenDataBase # Author: Anthony Hawkins # Date last mdoified: 07/03/16 #################################### source("downloadLengthfromUCSC.R") library(limma) #Read in file with common tables common = read.table("myfiles.txt",stringsAsFactors = FALSE) tmp = strsplit2(common[,1],"[.]") common_df = data.frame(Genome=tmp[,1], track =tmp[,2]) #Make the function which produces an RData with gene length info make_data<-function(x){ downloadLengthfromUCSC(x[[1]],x[[2]]) } #Now create the files apply(common_df,1,make_data) #common_df

b74cf0d00966e05b5b3323453f74fb47120a16c9

7be2f6044afbbf654bebca5caa7a7e34b0e75d42

/R/random_paths.R

bbd34a134e14311380ab49ad60c63fd56a1eae7e

[ "MIT" ]

permissive

KWB-R/kwb.pathdict

b6228fd423dbc6de33094e997644e0a6ed86dd33

b107485e18408a1bfb0346c785ce28d90fa6b087

refs/heads/master

2022-06-16T01:50:12.815755

2020-01-10T13:53:33

175,963,892

0

MIT

2019-03-17T17:17:06

2019-03-16T11:33:35

R

UTF-8

R

false

2,907

r

random_paths.R

# random_paths ----------------------------------------------------------------- #' Create Random File Paths Using English Words #' #' @param max_depth maximum path depth #' @param min_chars least number of characters per folder or file name #' @param max_elements maximum number of elements (files or subfolders) in a #' folder #' @param depth_to_leaf_weight function that calculates a weight from the given #' path depth. The weight is used to increase the probability of a folder #' element to be a file and not a subdirectory. By default the weight is #' calculated by 1.2^depth, i.e. for a folder in depth 10 it is about six #' times (1.2^10 = 6.19) more probable of its elements to be files instead of #' subfolders #' @export #' @examples #' # Make this example reproducible #' set.seed(12059) #' #' # Create random paths #' paths <- kwb.pathdict::random_paths(max_depth = 5) #' #' # Show the random paths #' paths #' #' # Frequency of path depths #' table(lengths(kwb.file::split_paths(paths))) #' random_paths <- function( max_depth = 5, min_chars = 5, max_elements = 10, depth_to_leaf_weight = function(depth) 1.2^depth ) { random_paths_(max_depth, min_chars, max_elements, depth_to_leaf_weight) } # random_paths_ ---------------------------------------------------------------- #' @keywords internal random_paths_ <- function( max_depth = 5, min_chars = 5, max_elements = 10, depth_to_leaf_weight = function(depth) 1.2^depth, depth = 0, leaf = FALSE, debug_depth = 0 ) { if (leaf || depth == max_depth) { return(random_filenames(min_chars, size = 1)) } parent <- sample(english_words(min_chars), 1) (n_elements <- sample(max_elements, 1)) prob <- c(depth_to_leaf_weight(depth), 1) is_leaf <- sample(c(TRUE, FALSE), n_elements, replace = TRUE, prob = prob) paste0(parent, "/", unlist(lapply(seq_along(is_leaf), function(i) { kwb.utils::catIf( depth < debug_depth, paste(rep(" ", depth), collapse = ""), "Creating node ", i, "/", length(is_leaf), "\n" ) random_paths_( max_depth = max_depth, min_chars = min_chars, max_elements = max_elements, depth_to_leaf_weight = depth_to_leaf_weight, depth = depth + 1, leaf = is_leaf[i] ) }))) } # random_filenames ------------------------------------------------------------- random_filenames <- function(min_chars = 4, max_elements = 10, size = NULL) { words <- english_words(min_chars) extensions <- c("pdf", "doc", "xls", "R", "png", "jpg") size <- kwb.utils::defaultIfNULL(size, sample(max_elements, 1)) paste0(sample(words, size), ".", sample(extensions, size, replace = TRUE)) } # english_words ---------------------------------------------------------------- english_words <- function(min_chars = 0) { words <- qdapDictionaries::Fry_1000 words <- gsub("'", "_", words) words[nchar(words) >= min_chars] }

6b00c425a0da7c7468916bbce4c5b133a324167a

0a906cf8b1b7da2aea87de958e3662870df49727

/grattan/inst/testfiles/IncomeTax/libFuzzer_IncomeTax/IncomeTax_valgrind_files/1610052252-test.R

9752b5c3abfa932d61b1cbbf83fa0e46a8cbd536

[]

no_license

akhikolla/updated-only-Issues

a85c887f0e1aae8a8dc358717d55b21678d04660

7d74489dfc7ddfec3955ae7891f15e920cad2e0c

refs/heads/master

2023-04-13T08:22:15.699449

2021-04-21T16:25:35

360,232,775

0

null

UTF-8

R

false

635

r

1610052252-test.R

testlist <- list(rates = numeric(0), thresholds = c(-2.30331110816477e-156, -2.30331110816477e-156, NaN, 2.81199605989981e-312, NaN, -8.22918610319053e+303, 4.46108959687689e-140, 3.60297094497336e-306, -1.06556334613796e-314, 9.7020880290895e-310, -1.72138794739967e-296, 2.81199605863994e-312, NaN, -8.22918610319053e+303, 0, -1.40444775900538e+306, NaN, 7.2911220195564e-304, 1.39067116124321e-309, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), x = numeric(0)) result <- do.call(grattan::IncomeTax,testlist) str(result)

ac06f3325611e3160189dba0cd28d652910e9940

41ba1d5e55d42ae9acc5ef3a25b964ef6762d066

/man/wind.dl_2.Rd

bee65aed0d8237bcd6b8904539c964a00f503780

[]

no_license

jabiologo/rWind

81c4b8b92435c7eded44f08e8e12f5cda91b8f3b

5df183cd7776ec9370c857e57afe3de2b48d1d48

refs/heads/master

2022-02-08T04:15:08.014617

2022-01-24T14:56:45

99,371,080

26

12

null

2021-12-21T16:09:14

2017-08-04T18:46:25

R

UTF-8

R

false

true

3,388

rd

wind.dl_2.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/wind_functions2.R \name{wind.dl_2} \alias{wind.dl_2} \alias{[[.rWind_series} \title{Wind-data download} \usage{ wind.dl_2(time, lon1, lon2, lat1, lat2, type = "read-data", trace = 1) \method{[[}{rWind_series}(x, i, exact = TRUE) } \arguments{ \item{time}{a scalar or vector of POSIXt or Date objects or an character which can transformed into those, see example below. There are currently these options at the GFS database for the hours: 00:00 - 03:00 - 06:00 - 09:00 - 12:00 - 15:00 - 18:00 - 21:00 (UTC) (TO).} \item{lon1}{Western longitude} \item{lon2}{Eastern longitude} \item{lat1}{Southern latitude} \item{lat2}{Northern latitude} \item{type}{Output type. "read-data" is selected by default, creating an R object. If you choose "csv", wind.dl create a a CSV file in your work directory named "wind_yyyy_mm_dd_tt.csv".} \item{trace}{if trace = 1 (by default) track downloaded files} \item{x}{object from which to extract element(s).} \item{i}{indices specifying elements to extract.} \item{exact}{Controls possible partial matching (not used yet).} } \value{ an object of class \code{rWind_series} or .csv file/s with U and V vector components and wind direction and speed for each coordinate in the study area defined by lon1/lon2 and lat1/lat2. } \description{ wind.dl_2 downloads time-series wind data from the Global Forecast System (GFS) of the USA's National Weather Service (NWS) (https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast). Wind data are taken from NOAA/NCEP Global Forecast System (GFS) Atmospheric Model collection. Geospatial resolution is 0.5 degrees (approximately 50 km), and wind is calculated for Earth surface, at 10 m. More metadata information: http://oos.soest.hawaii.edu/erddap/info/NCEP_Global_Best/index.html } \details{ To get the same format as wind.dl, you should run \code{tidy} function from wind.dl_2 output. The output type is determined by type="csv" or type="read-data". If type="csv" is selected, the function creates a "wind_yyyy_mm_dd_tt.csv" file that is downloaded at the work directory. If type="read-data" is selected, an \code{rWind_series} object is created. } \note{ wind.dl_2 requires two dates that represent the boundaries of the time lapse to download wind series data. U and V vector components allow you to create wind averages or tendencies for each coordinate at the study area. Longitude coordinates are provided by GFS dataset in 0/360 notation and transformed internally into -180/180. "dir" denotes where the wind/sea current is going (toward), not from where is coming. } \examples{ # Download wind for Iberian Peninsula region at 2015, February 12, 00:00 \dontrun{ wind.dl_2("2018/3/15 9:00:00", -10, 5, 35, 45) library(lubridate) dt <- seq(ymd_hms(paste(2018, 1, 1, 00, 00, 00, sep = "-")), ymd_hms(paste(2018, 1, 2, 21, 00, 00, sep = "-")), by = "3 hours" ) ww <- wind.dl_2(dt, -10, 5, 35, 45) tidy(ww) } } \references{ http://www.digital-geography.com/cloud-gis-getting-weather-data/#.WDOWmbV1DCL http://oos.soest.hawaii.edu/erddap/griddap/NCEP_Global_Best.graph } \seealso{ \code{\link{wind.mean}}, \code{\link{wind2raster}}, \code{\link{wind.dl}}, \code{\link{as_datetime}}, \code{\link{as.POSIXct}} } \author{ Javier Fernández-López (jflopez.bio@gmail.com) } \keyword{~gfs} \keyword{~wind}

a9233cf6fd9330737ec5963a2f78dde609773464

1b7e5fae2c969553d2d0a8c4e01e09f295595704

/plot2.R

1da8392baa244b131796e97936863ae9b3db8ed5

[]

no_license

kmaratus/ExData_Plotting1

d839f573a5b0ef199cf61f5226e0d20fbd91294e

fa27d0d81fb44deb97463a018abc18b32bb02e54

refs/heads/master

2021-01-12T06:19:44.609703

2016-12-25T22:49:52

77,341,737

0

null

2016-12-25T20:27:59

2016-12-25T20:27:58

null

UTF-8

R

false

1,044

r

plot2.R

##read data header into R filename<-"household_power_consumption.txt" header<-read.table(filename,header=TRUE,sep=";",na.strings = "?",nrow=1) columnnames<-names(header) ncolumn<-length(columnnames) ##read first column to find start and end date number rows data<-read.table(filename,header=TRUE,sep=";",na.strings = "?",colClasses = c(NA,rep("NULL",ncolumn-1))) dataDate<-as.Date(data$Date,"%d/%m/%Y") nstart<-which.max(dataDate=="2007-02-01") nend<-which.max(dataDate=="2007-02-03")-1 ##read data into R for the given dates data<-read.table(filename,header=TRUE,sep=";",na.strings = "?",col.names = columnnames,skip = nstart-1,nrows = nend-nstart+1) ##make a plot and save it into PNG file png(filename = "plot2.png",width = 480,height = 480,bg="transparent") ##combine date and time into one variable DateTime<-as.POSIXct(paste(data$Date,as.character(data$Time)),format="%d/%m/%Y %H:%M:%S") plot(DateTime,data$Global_active_power,type="n",xlab="",ylab="Global Active Power (kilowatts)") lines(DateTime,data$Global_active_power) dev.off()

52a7b99868a9c6d91266e61f940ed4127397310d

61c07605275b7d4d407a40c3bd754b2e686c4be3

/fechas y tiempo.R

46c8cc3e91cd12f500ddf2f77d37f91fdd200694

[]

no_license

sauc117/Programacion_Actuarial_lll

419b06660d301b0eadcc6b996425fa6c1cb78e9a

d176b02d635505f11a312baab9fa946ea37c185e

refs/heads/master

2021-09-14T23:12:45.966516

2018-05-22T01:37:36

119,411,262

0

null

WINDOWS-1250

R

false

431

r

fechas y tiempo.R

x <- as.Date("1970-01-01") x unclass(x) unclass(as.Date("1970-01-02")) #POSIXct es un entero muy grande, util para guardar datos en un data frame #POSIXlt es como una lista y guarda un conjunto de informacion util como dia # de la semana,dia del aoń, dia del mes, etc. weekdays(as.Date("1998-07-23")) weekdays(as.Date("1998-09-24")) weekdays(as.Date("1998-09-24")) a <- as.POSIXct("1998-09-24") b <- as.POSIXlt("1998-09-24")

1564a682be664f1a6d8bd3a97b954e7f87a79adc

86920ac8061f5b29b86733ec332469bf83761202

/rethinking/rethinking_var_sd_test.R

ddff2ea6d93c04847abac34e4ff9e796e95ce4a1

[]

no_license

jebyrnes/temp_kelp_change

64b5c54dd58213d51b356017e2512867ef156e5e

b8891d25038ffab02410e5afd4b81d0f0c4d4cea

refs/heads/master

2021-10-11T14:11:31.283204

2021-10-04T19:53:09

51,555,573

0

null

2021-10-04T19:53:10

2016-02-11T23:36:10

R

UTF-8

R

false

815

r

rethinking_var_sd_test.R

library(rethinking) make_data <- function(slope =1, int = 1, sd_e = 5, group=1, x=1:20){ ret <- data.frame(x=x) ret <- within(ret, { y <- rnorm(length(x), int + slope*x, sd_e) group <- group }) ret } set.seed(31415) my_df <- rbind(make_data(), make_data(slope=5, sd_e=15, group=2), make_data(slope=3, sd_e=8, group=3)) ggplot2::qplot(x, y, color=group, data=my_df) mod <- alist( #likelihood y ~ dnorm(y_loc, sd_loc), # likelihood #model y_loc <- a[group] + b[group]*x, sd_loc <- sd_e[group], #priors a[group] ~ dnorm(0,10), b[group] ~ dnorm(0,10), sd_e[group] ~ dcauchy(0,1) #prior for study level errors ) fit <- map2stan(mod, data=my_df, constraints=list(sd_e="lower=0") , start=list(sd_e=rep(1,3)))

e89666f3d7a451f325d2e8c7dad5765e09e93270

4f88e0337d3374fcf643717e41c8ac3d76f9709a

/R/search.R

6ca8951bb5db551a90c2bd155c6525e2328d1a9d

[]

no_license

att/rcloud.solr

bc1283b2f50a6e68832b5132518803228bffa3e3

af3d5d9f3d7d6b7bc1c26a7e03afee13a8ad457e

refs/heads/develop

2022-03-26T23:26:05.358352

2019-12-20T16:04:15

80,539,919

4

7

null

2018-02-15T14:18:53

2017-01-31T16:43:43

R

UTF-8

R

false

2,712

r

search.R

#' Search RCloud Notebooks #' #' Main search function exposed as an OCAP to the client. #' #' @param query Search string #' @param all_sources Logical: Search single or multiple solr instances? #' @param sortby Passed to solr for sorting #' @param orderby Passed to solr for sorting #' @param start Passed to solr #' @param pagesize Passed to solr #' @param max_pages Sets the size of search #' @param group.limit Passed to solr. Controls how many cells to highlight for each notebook hit. #' @param hl.fragsize How many characters to return with the highlighting #' #' @return Search response after parsing #' @export #' rcloud.search <- function(query, all_sources = FALSE, sortby = "score", orderby = "desc", start = 0, pagesize = 10, max_pages = 20, group.limit = 4, hl.fragsize = 60) { .SC$search( query = query, all_sources = all_sources, sortby = sortby, orderby = orderby, start = start, pagesize = pagesize, max_pages = max_pages, group.limit = group.limit, hl.fragsize = hl.fragsize ) } #' Passthrough Notebook Search #' #' On description and optionally user. This does minimal processing server side to increase speed. #' #' @param description search string to match against description. Fuzzy matching and wildcarding is used. #' @param user optional to specify a user #' @inheritParams rcloud.search #' #' @return Search result direct from solr with no parsing #' @export #' #' @examples #' \dontrun{ #' rcloud.search.description("test", user = "juninho") #' } rcloud.search.description <- function(description, user = NULL, start = 0, pagesize = 100, sortby = "description", orderby = "desc") { url <- rcloud.support:::getConf("solr.url") if (is.null(url)) stop("solr is not enabled") user <- if (!is.null(user) && user!="") paste0(" AND user: ", user, "*~") else " " descSplit <- unlist(strsplit(description, split = c(" |\\+|\\\\|/"))) description <- paste0(descSplit, "*~", collapse = " AND ") query <- paste0("description: (", description, ")", user, " AND doc_type:notebook") solr.query <- list( q = query, start = start, rows = pagesize, indent = "true", fl = "description,id,user,updated_at,starcount", sort = paste(sortby, orderby) ) # pass it straight back no post-processing .solr.get( solr.url = url, query = solr.query, solr.auth.user = rcloud.support:::getConf("solr.auth.user"), solr.auth.pwd = rcloud.support:::getConf("solr.auth.pwd") ) }

007cb0d2e6492f12e4ba9a52b0005bea87489e34

4c6647e21d6015b2729bc4c2f6e4c9e0ee8a13ab

/man/dictHomerMotifs.Rd

dc31ebeac5c205b7b710695b433fb1921bc7f053

[]

no_license

mireia-bioinfo/maRge

67cfd91807e605fce540d4c4f96e55b80e179446

99f5823cfb61fe0b0d5700a473f067817fa87595

refs/heads/master

2021-06-24T18:00:24.226520

2017-08-31T10:23:23

94,434,545

1

0

null

UTF-8

R

false

true

766

rd

dictHomerMotifs.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/dictHomerMotifs.R \docType{data} \name{dictHomerMotifs} \alias{dictHomerMotifs} \title{Dictionary of Homer Motifs} \format{A data frame with 53940 rows and 10 variables: \describe{ \item{Motif.Name}{Name of the motif, as outputed by HOMER.} \item{Motif.Symbol}{Section of the Motif.Name including the symbol.} \item{external_gene_name}{Standarized gene symbol.} \item{ensembl_gene_id}{ENSEMBL gene id.} }} \source{ \url{HOMER} } \usage{ dictHomerMotifs } \description{ A dataset containing the names of the motifs used by HOMER and their corresponding symbols and ensembl_gene_id. Useful when trying to intersect HOMER motif data with transcription data.#' } \keyword{datasets}

96c5bfd31bb7d219d2e3eb1c5406432bcafd1bac

826cc17cd51ccbceeb0b33ee23cab81ccee3932f

/tests/testthat/test-plot_indel_contexts.R

0bde8c251427d56f02412eb0e7bcd3b6afd32c84

[ "MIT" ]

permissive

UMCUGenetics/MutationalPatterns

9b9d38a7ab69d7e29d8900f11fa9fb7ef328cfb9

ca9caf0d0ba3cd1e13cb909009dc5b3b27b84631

refs/heads/master

2023-04-14T23:28:50.852559

2022-11-22T11:37:17

53,409,261

86

37

MIT

2022-11-22T11:37:18

2016-03-08T12:10:11

R

UTF-8

R

false

678

r

test-plot_indel_contexts.R

context("test-plot_indel_contexts") ## Get indel counts indel_counts <- readRDS(system.file("states/blood_indel_counts.rds", package = "MutationalPatterns" )) ## Plot contexts output <- plot_indel_contexts(indel_counts) output_same_y <- plot_indel_contexts(indel_counts, same_y = TRUE) output_extra_labels <- plot_indel_contexts(indel_counts, extra_labels = TRUE) output_condensed <- plot_indel_contexts(indel_counts, condensed = TRUE) test_that("Output has correct class", { expect_true(inherits(output, c("gg"))) expect_true(inherits(output_same_y, c("gg"))) expect_true(inherits(output_extra_labels, c("gg"))) expect_true(inherits(output_condensed, c("gg"))) })

5875d40220aab9f14c8448aa399ae30b0dc8607f

44cf65e7ab4c487535d8ba91086b66b0b9523af6

/data/Newspapers/2000.04.29.editorial.23713.0325.r

63f2024791d2aaa1cf18455d55f5efe4a1365eaa

[]

no_license

narcis96/decrypting-alpha

f14a746ca47088ec3182d610bfb68d0d4d3b504e

5c665107017922d0f74106c13d097bfca0516e66

refs/heads/master

2021-08-22T07:27:31.764027

2017-11-29T12:00:20

111,142,761

0

1

null

UTF-8

R

false

4,252

r

2000.04.29.editorial.23713.0325.r

exista o traditie ca de sarbatori oamenii sa isi trimita felicitari . cu timpul , s - au adaugat si mici atentii . De cind au aparut privatizatii , acestia trimit urarile de bine insotite de o ciocolata , de o cutie cu sucuri , de o sticla cu vin , de o sticla cu sampanie , de un pachet cu cafea . uneori si obiecte promotionale . are sau nu haz acest tip de urare , numai Dumnezeu stie ! cu siguranta ca nu este insa condamnabil . dar de aici si pina la spaga de sarbatori , pina la peschesul primit de functionarul public cu ocazia Craciunului sau a Pastilor e totusi cale lunga . din pacate , la redactie nu avem atit de multe teleobiective de mare putere pentru a supraveghea toate casele demnitarilor sau ale inaltilor functionari din institutiile statului . asa ca , in loc de pinda , echipa de redactori ai " Evenimentului zilei " a recurs la o provocare . cite doi , insotiti de un fotograf , reporterii nostri au pretins ca sint membri de partid , consilieri , petenti , simpatizanti , oameni care au o belea si vor sa foloseasca prilejul sarbatorilor ori pentru a - si arata recunostinta , ori pentru a - si intari sansele de rezolvare a problemei . si au batut pe la porti cu plocoane . socul a fost enorm ! cu citeva mici exceptii , alesii poporului au primit peschesurile cu o naturalete paralizanta . Se purtau de parca ar fi clipit sau ar fi respirat . un miel de la un simpatizant al partidului e mai banal decit un " buna ziua " . Cartonul cu sticle de vin , damigenele , mieii si alte produse ale agriculturii noastre in tranzitie devenisera elemente banale de peisaj . Miercuri , joi si vineri , la institutiile statului erau convoaie de masini venite din provincie , toate incarcate cu plocoane . unora le - am facut o surpriza enorma . chiar le - am pus in plicuri memorii cu diverse plingeri adresate redactiei noastre . sa speram ca se vor indura sa miste macar un deget pentru rezolvarea lor . operatiunea MOV ( miel , oua , vin ) ne - a mai aratat ceva . parlamentarii si functionarii publici primesc pomana cu o anume inconstienta . nu se intreaba de unde vin cadourile , nu se gindesc ca ar putea fi imbolnaviti sau chiar otraviti . le iau de - a valma si nu - si fac vreo problema . unul dintre redactorii nostri chiar exclama , ii puteam otravi pe toti ! dar cea mai complicata problema ramine cea legata de morala . absolut nici unul nu s - a intrebat de ce sa primeasca un plocon ! e de la sine inteles ca ne aflam in fata unui reflex bine consolidat . in Romania , asemenea " cadouri " se dau chiar din anii de dinaintea comunismului . sint in firea lucrurilor de multa vreme . intre rude , ploconul e chiar o traditie . dar ca reflex la functionarul de stat e cu totul altceva . vrem , nu vrem , ne aflam in fata unei forme discrete de coruptie . nimeni n - o mai incrimineaza . de la miel si vin se trece usor la televizor color , la masina si apartament . sa nu uitam ca , intr - o tara civilizata , un om si - a ratat cariera politica fiindca nu a platit taxele pentru femeia de serviciu care i - a facut curat in casa . la noi , toti sintem revoltati de coruptia care ne inconjoara , dar dam si primim un miel ca si cum am da sau am primi un pix . ba , am ajuns in situatia ca un biet functionar sau un cetatean care ajunge la o " marime " sa se simta prost ca se duce cu mina goala . adeseori ni se intimpla ca la redactie sa vina cititori care ne cer sprijinul si care considera ca ar trebui sa ne gratuleze cu un cadou . si asta face parte tot dintr - o mentalitate balcanica , dintr - un mod de a fi cu reguli care se topesc in nereguli . si de acolo incep aranjamentele , combinatiile , eludarea legilor si favoritismele . daca in aceste relatii marunte se poate aranja orice , daca functionarul public poate primi cu portbagajul fara ca macar sa clipeasca inseamna ca toate celelalte lucruri mari si importante nu sint cladite decit pe nisip miscator . in loc de mari intrebari despre lume , despre viata , moarte si inviere , in aceste zile ne - am putea intreba despre duplicitate . pe de o parte , toti sintem de acord ca vrem un climat social mai curat , dar cind e sa traim intr - un asemenea spirit , ne coplesesc apucaturi de oameni mici si repede trecatori .

f9d0a586cb18c340a9fc92297003f271b59e4d30

29585dff702209dd446c0ab52ceea046c58e384e

/lifecontingencies/inst/doc/introToLifecontingencies.R

203d7f281c35a8d9a3e1b035d4911f097e037f6e

[]

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

7,198

r

introToLifecontingencies.R

## ----setup, include=FALSE------------------------------------------------ library(knitr) rm(list=ls()) ## ----load, echo=TRUE, warning=FALSE, message=FALSE----------------------- library(lifecontingencies) #load the package ## ----finmat1, echo=TRUE, warning=FALSE, message=FALSE-------------------- capitals <- c(-1000,200,500,700) times <- c(0,1,2,5) #calculate a present value presentValue(cashFlows=capitals, timeIds=times, interestRates=0.03) ## ----finmat2, echo=TRUE, warning=FALSE, message=FALSE-------------------- ann1 <- annuity(i=0.03, n=5, k=1, type="immediate") ann2 <- annuity(i=0.03, n=5, k=12, type="due") c(ann1,ann2) ## ----finmat3, echo=TRUE, warning=FALSE, message=FALSE-------------------- bondPrice<-5*annuity(i=0.03,n=10)+100*1.03^-10 bondPrice ## ----demo1--------------------------------------------------------------- #create an demo lifetable xDemo<-seq(from=0,to=5,by=1) lxDemo<-c(100,95,90,60,30,5) lifetableDemo<-new("lifetable",x=xDemo, lx=lxDemo,name="Demo") ## ----demo2--------------------------------------------------------------- data(demoIta) #using the internal Italian LT data set lxIPS55M <- with(demoIta, IPS55M) #performing some fixings pos2Remove <- which(lxIPS55M %in% c(0,NA)) lxIPS55M <-lxIPS55M[-pos2Remove] xIPS55M <-seq(0,length(lxIPS55M)-1,1) #creating the table ips55M <- new("lifetable",x=xIPS55M, lx=lxIPS55M,name="IPS 55 Males") ## ----demo3, tidy=TRUE---------------------------------------------------- #decrements between age 65 and 70 dxt(ips55M, x=65, t = 5) #probabilities of death between age 80 and 85 qxt(ips55M, x=80, t=2) #expected curtate lifetime exn(ips55M, x=65) ## ----createacttable, tidy=FALSE------------------------------------------ #creates a new actuarial table ips55Act<-new("actuarialtable", x=ips55M@x,lx=ips55M@lx, interest=0.02,name="IPS55M") ## ----pureend, tidy=FALSE------------------------------------------------- #compute APV APV=50e3*Exn(actuarialtable = ips55Act,x=30,n=35) #compute Premium P=APV/axn(actuarialtable = ips55Act,x=30,n=35) c(APV,P) ## ----endowmentcalcs, tidy=FALSE------------------------------------------ #defining the ranges interest.range<-seq(from=0.015, to=0.035,by=0.001) term.range<-seq(from=20, to=40,by=1) #computing APV sensitivities apv.interest.sensitivity<-sapply(interest.range, FUN = "AExn",actuarialtable=ips55Act,x=30,n=30) apv.term.sensitivity<-sapply(term.range,FUN = "AExn", actuarialtable=ips55Act,x=30) ## ----endowmentplot, tidy=FALSE, echo=FALSE,fig.width=5, fig.height=5,fig.align='center'---- par(mfrow=c(1,2)) plot(x=interest.range, y=apv.interest.sensitivity,type="l",xlab="interest rate",ylab="APV",main="APV by Interest Rate") plot(x=term.range, y=apv.term.sensitivity,type="l",xlab="term",ylab="APV",main="APV by term") ## ----reserves, tidy=FALSE------------------------------------------------ #compute the APV and premium APV=100e3*Axn(actuarialtable = ips55Act,x=25,n=40) P=APV/axn(actuarialtable = ips55Act,x=25,n=40) #define a reserve function reserveFunction<-function(t) 100e3*Axn(actuarialtable = ips55Act,x=25+t,n=40-t) - P *axn(actuarialtable = ips55Act,x=25+t,n=40-t) reserve<-sapply(0:40,reserveFunction) ## ----reserves2, tidy=FALSE, echo=FALSE, fig.align='center'--------------- plot(x=0:40,y=reserve,main="Reserve", xlab="Policy Age",ylab="Reserve outstanding",type="l") ## ----AEXn1 , tidy=FALSE, size="small"------------------------------------ #analyzing and Endowment of 100K on x=40, n=25 #compute APV APV=AExn(actuarialtable = ips55Act,x=40,n=25) #sampling AEXnDistr<-rLifeContingencies(n=10e3, lifecontingency = "AExn",x = 40, t=25,object = ips55Act) ## ----AExn1, tidy=FALSE, size="small"------------------------------------- #assess if the expected value match the theoretical one t.test(x=AEXnDistr,mu = APV) ## ----AEXn2, tidy=FALSE, echo=FALSE--------------------------------------- hist(AEXnDistr, main="Endowment Actuarial Value Distribution", probability = TRUE, col="steelblue") ## ----leecarter01, tidy=FALSE, include=FALSE, results='hide'-------------- #library(demography) #italy.demo<-hmd.mx("ITA", username="spedicato_giorgio@yahoo.it", password="mortality") ## ----leecarter0, tidy=FALSE, warning=FALSE, message=FALSE---------------- #load the package and the italian tables library(demography) #italyDemo<-hmd.mx("ITA", username="yourUN", #password="yourPW") load(file="mortalityDatasets.RData") #load the dataset ## ----leecarter1, tidy=FALSE, warning=FALSE------------------------------- #calibrate lee carter italy.leecarter<-lca(data=italyDemo,series="total", max.age=103,adjust = "none") #perform modeling of kt series kt.model<-auto.arima(italy.leecarter$kt) #projecting the kt kt.forecast<-forecast(kt.model,h=100) ## ----leecarter2, tidy=FALSE, size='tiny'--------------------------------- #indexing the kt kt.full<-ts(union(italy.leecarter$kt, kt.forecast$mean), start=1872) #getting and defining the life tables matrix mortalityTable<-exp(italy.leecarter$ax +italy.leecarter$bx%*%t(kt.full)) rownames(mortalityTable)<-seq(from=0, to=103) colnames(mortalityTable)<-seq(from=1872, to=1872+dim(mortalityTable)[2]-1) ## ----leecarter2plot, tidy=FALSE, echo=FALSE------------------------------ plot.ts(kt.full, main="historical and projected KT",xlab="year", ylab="kt",col="steelblue") abline(v=2009,col="darkred",lwd=2.5) ## ----leecarter3, tidy=FALSE---------------------------------------------- getCohortQx<-function(yearOfBirth) { colIndex<-which(colnames(mortalityTable) ==yearOfBirth) #identify #the column corresponding to the cohort #definex the probabilities from which #the projection is to be taken maxLength<-min(nrow(mortalityTable)-1, ncol(mortalityTable)-colIndex) qxOut<-numeric(maxLength+1) for(i in 0:maxLength) qxOut[i+1]<-mortalityTable[i+1,colIndex+i] #fix: we add a fictional omega age where #death probability = 1 qxOut<-c(qxOut,1) return(qxOut) } ## ----leecarter4, tidy=FALSE, size='scriptsize'--------------------------- #generate the life tables qx1920<-getCohortQx(yearOfBirth = 1920) lt1920<-probs2lifetable(probs=qx1920,type="qx", name="Table 1920") at1920<-new("actuarialtable",x=lt1920@x, lx=lt1920@lx,interest=0.015) qx1950<-getCohortQx(yearOfBirth = 1950) lt1950<-probs2lifetable(probs=qx1950, type="qx",name="Table 1950") at1950<-new("actuarialtable",x=lt1950@x, lx=lt1950@lx,interest=0.015) qx1980<-getCohortQx(yearOfBirth = 1980) lt1980<-probs2lifetable(probs=qx1980, type="qx",name="Table 1980") at1980<-new("actuarialtable",x=lt1980@x, lx=lt1980@lx,interest=0.015) ## ----leecarter5, tidy=FALSE, echo=TRUE----------------------------------- cat("Results for 1920 cohort","\n") c(exn(at1920,x=65),axn(at1920,x=65)) cat("Results for 1950 cohort","\n") c(exn(at1950,x=65),axn(at1950,x=65)) cat("Results for 1980 cohort","\n") c(exn(at1980,x=65),axn(at1980,x=65))

ae45bd86a774a50fb8363bf1bb699c9e40171b6d

fb3ab9e1dd466ec076b5dcba19fe4419a1116656

/Day 1/1.3.R

4478001182c60a13ecf9fce267c9e6fbf8d1317f

[]

no_license

eElec/DALab

f4e67a034467286e5d7ee12f6dcbf7ac18b8c77a

33c1ca7a5e91beded8c6b76264f8342295b0fbe4

refs/heads/master

2023-02-16T14:38:45.665532

2021-01-14T08:15:23

329,549,595

0

null

UTF-8

R

false

134

r

1.3.R

roll=1806188 name="Adrish Aditya" branch="IT" print("Roll: ") print(roll) print("Name: ") print(name) print("Branch: ") print(branch)

5ee64d20fd48dae2855e76043c79a473a2564313

de6c16a929ddc7b6d5e212203d62d5e8151d3037

/Tutorial4_31.R

2459efb9b9ae7e68fb46846a8db9e377f3796f00

[]

no_license

arthurnovello/r_course

cea600f4c08b726f73d332703bc30be6bdb2d99d

8acfebdbf6ac660a71c0553fd17c5827a8066174

refs/heads/master

2021-05-05T05:32:50.772137

2018-02-03T14:22:42

118,685,522

0

null

UTF-8

R

false

262

r

Tutorial4_31.R

# ------------ Operations with matrices ------------ Games rownames(Games) colnames(Games) Games["LeBronJames", "2012"] FieldGoals FieldGoals/Games round(FieldGoals/Games, 1) round(MinutesPlayed/Games) MinutesPlayed round(FieldGoalAttempts/FieldGoals, 3)

969bfce72a95d1b58340d15d8b5afa3c394d8b90

e434c2f582adcc781e9fd0b7183899aba48e5dde

/scripts/prepdata/ausplots_sites.R

d8315ea4b916df36efa828f58ce4a50a6dcab0ce

[ "MIT" ]

permissive

rubysaltbush/flowering-period-climate

fa87156deebc524ea89fd26dc04776b6d603ecfd

b384e5f95df81dbf40f4d2f4f86dd268ea925a4b

refs/heads/main

2023-04-17T20:07:30.215796

2022-05-12T06:32:51

389,864,707

0

null

UTF-8

R

false

260

r

ausplots_sites.R

# using ausplotsR package access site data from AusPlots and cache csv # requires internet connection ausplots_sites <- cache_csv("data_cache/ausplots_sites.csv", function() { get_ausplots(site_info = TRUE, veg.vouchers = FALSE, veg.PI = FALSE)$site.info })

bb2df86fe73174367710b11a0abb70666016b59f

b50cbb8ef475fbed1f4f808b619ed63297aa8de4

/PacketMetrics/Tests.R

940bf17d01846e9f9d122f4d39254dcdef2f00e1

[]

no_license

jac50/FinalYearProject

49a7826814cd10996ed16e50aed479e92bf1bfb5

fbc314d39083fb1442f03c39c7092925f11e34b3

refs/heads/master

2021-01-21T12:39:37.414929

2014-05-05T18:18:58

null

0

null

UTF-8

R

false

1,112

r

Tests.R

# Tests for RunHead - Sample Size < 0 - Sample Size Too Big - Sample Size non number - Directory not valid - Original File not found # Tests for createArguments - argparse not compatible or not found ? # Tests for initLogger - Input Tests: + Quiet - TRUE Verbose - FALSE . Expected Result: Set to Quiet Mode (lvl 30) + Quiet - TRUE Verbose - TRUE Expected Result: throw error. set to normal mode + Quiet - FALSE Verbose - FALSE . Expected Result: set to normal mode + Quiet - FALSE Verbose - TRUE . Expected Result: Set to Verbose mode (lvl 10) + Quiet - NOT LOGICAL Verbose - TRUE Expected Result: Set to Verbose mode (lvl 10) + Quiet - TRUE Verbose - NOT LOGICAL : Expected Result: Set to Quiet Mode (lvl 30) + Quiet - NOT LOGICAL Verbose NOT LOGICAL - Expected Result: throw error. set to normal mode - Other Tests: + Directory does not exist + Directory cannot be created # Tests for ParseFileName - Input Tests: - Other Tests: # Tests for readFile - Input Tests: - Other Tests: # Tests for purgeData - Input Tests: - Other Tests: # Tests for ParseFileName - Input Tests: - Other Tests:

088e47dda78471f97b996f2b437d1ff91895afc5

8ad7f68413ee9a4ec8bd868bcb3566755833b56a

/generar_tipo_cambio_trimestral_anual.r

b4d7e1ee3eb38b850e64e9eeaec4bac7a275e028

[]

no_license

droper/scriptsthesis

124ce67c7083f022e21063a4135af797503a12aa

e16f8721b2ff5e60dff08445bd9eb3b3677df121

refs/heads/master

2021-01-25T08:48:12.036983

2015-06-13T01:51:43

37,352,260

0

null

UTF-8

R

false

2,233

r

generar_tipo_cambio_trimestral_anual.r

# Directorios origen de la data DATA_TIPO_CAMBIO = "/home/pedro/univs/doctorado/tesis/tesis/material tesis/data_economica/" TIPO_CAMBIO_CSV = "tipo_cambio.csv" TIPO_CAMBIO_TRIM_CSV = "tipo_cambio_trimestral.csv" TIPO_CAMBIO_ANUAL_CSV = "tipo_cambio_anual.csv" # Se lee un archivo de información financiera tipo_cambio = read.csv(file=paste(DATA_TIPO_CAMBIO, TIPO_CAMBIO_CSV, sep=""), header=TRUE, sep=",", colClasses = c("character", "numeric")) tc_trim = numeric(0) per_trim = character(0) anno = 1992 i = 3 per = 1 # Se generan los promedios trimestrales while (i <= nrow(tipo_cambio)){ promedio = (tipo_cambio[["TC"]][i] + tipo_cambio[["TC"]][i-1] + tipo_cambio[["TC"]][i-2])/3 tc_trim = c(tc_trim, promedio) per_trim = c(per_trim, paste(anno,'-',per, sep="" )) i = i + 3 # Si periodo es igual a cuatro, lo regresamos a uno # y aumentamos en uno el año if (per == 4){ per = 1 anno = anno + 1 } else { per = per + 1 } } tc_anual = numeric(0) per_anual = character(0) anno = 1992 i = 12 # Se generan los promedios anuales while (i <= nrow(tipo_cambio)){ promedio = (tipo_cambio[["TC"]][i] + tipo_cambio[["TC"]][i-1] + tipo_cambio[["TC"]][i-2] + tipo_cambio[["TC"]][i-3] + tipo_cambio[["TC"]][i-4] + tipo_cambio[["TC"]][i-5] + tipo_cambio[["TC"]][i-6] + tipo_cambio[["TC"]][i-7] + tipo_cambio[["TC"]][i-8] + tipo_cambio[["TC"]][i-9] + tipo_cambio[["TC"]][i-10] + tipo_cambio[["TC"]][i-11])/12 tc_anual = c(tc_anual, promedio) per_anual = c(per_anual, anno) i = i + 12 anno = anno + 1 } # Se generan los promedios trimestrales del tipo de cambio juntando los vectores tipo_cambio_trim = data.frame(per_trim, tc_trim) # Se generan los promedios anuales del tipo de cambio juntando los vectores tipo_cambio_anual = data.frame(per_anual, tc_anual) # Se escribe el data frame de datos para las pruebas en un archivo cuyo prefijo es # pruebas write.csv(tipo_cambio_trim, file=paste(DATA_TIPO_CAMBIO, TIPO_CAMBIO_TRIM_CSV, sep=""), sep=",", row.names=FALSE) write.csv(tipo_cambio_anual, file=paste(DATA_TIPO_CAMBIO, TIPO_CAMBIO_ANUAL_CSV, sep=""), sep=",", row.names=FALSE)

437e7cba4e4420da82efebc66c8eab847977e801

68562f46424bf312d5fe070990243ae03ed1454e

/R/binarizeSNPs.R

6ac42179eea1795a977fc4b2242080d8f589b9f1

[ "Apache-2.0" ]

permissive

ANTsX/ANTsR

edb12114bc3d143c59ebd3947301de705ec51b63

8deb4d897fdb295a0213ca59e3bf1846f62ce99a

refs/heads/master

2023-06-24T14:48:05.362501

2023-06-24T11:15:10

5,782,626

86

32

Apache-2.0

2023-06-17T12:15:50

2012-09-12T16:28:03

R

UTF-8

R

false

836

r

binarizeSNPs.R

.binarizeSNPs <- function(snps) { if (nargs() == 0) { print("Usage: x_b<-.binarizeSNPs( x ) ") return(1) } nrep <- 2 binsnps <- (matrix(rep(NA, nrow(snps) * ncol(snps) * nrep), nrow = nrow(snps), ncol = ncol(snps) * nrep)) binsnpsdf <- data.frame(matrix(rep(NA, nrow(snps) * ncol(snps) * nrep), nrow = nrow(snps), ncol = ncol(snps) * nrep)) inds1 <- seq(1, (ncol(binsnps)), by = 2) inds2 <- inds1 + 1 binsnps[, inds1] <- snps binsnps[, inds2] <- snps ww <- (binsnps[, inds1] == 2) binsnps[, inds1][ww] <- 0 binsnps[, inds1][!ww] <- 1 ww <- (binsnps[, inds2] == 1) binsnps[, inds2][ww] <- 0 binsnps[, inds2][!ww] <- 1 osnps <- data.frame(binsnps) names(osnps)[inds1] <- paste(names(snps), ".1", sep = "") names(osnps)[inds2] <- paste(names(snps), ".2", sep = "") return(osnps) }

fa22f2d7609387ab14bbc30fddad121aed0dd640

55af8a760323f6a8f5e3542e404f23e87ebbbdca

/render_rmd.R

d813da084e8aa63f1e5195cc52fea229c4f0bbbf

[]

no_license

petermeissner/ecprwsmt16adcr

79b8227f762d4960abe2a3417e76fc48032af28d

03dc54c2cfa34eb5804bfec2ea29b9e24ee40ff7

refs/heads/master

2021-01-10T05:33:22.315542

2016-03-06T13:08:27

53,254,841

1

0

null

UTF-8

R

false

1,010

r

render_rmd.R

#!/usr/bin/env Rscript # necessary package library(stringr) # extracting argument file <- commandArgs()[grep("--args", commandArgs())+1] # worker-function render_rmd <- function(file, try=FALSE){ if( length(file) == 0 ) return("nothing to do") if( length(file) > 1 ){ lapply(file, render_rmd, try=try ) }else{ file_r <- str_replace(file, ".Rmd", ".R") if( try==TRUE ){ try(rmarkdown::render(file, "all", encoding="UTF-8") ) try( knitr::purl(input=file, output=file_r) ) }else{ rmarkdown::render(file, "all", encoding="UTF-8") knitr::purl(input=file, output=file_r) } } } # doing-duty-to-do if( length(file) == 0 ){ rmd <- list.files("course", pattern = ".Rmd$", recursive = TRUE, full.names = TRUE) pdf <- str_replace(rmd, "Rmd$", "pdf") rmd_mtime <- file.info(rmd)$mtime pdf_mtime <- file.info(pdf)$mtime iffer <- is.na(rmd_mtime > pdf_mtime) | rmd_mtime > pdf_mtime render_rmd(rmd[iffer], try=FALSE) }else{ render_rmd(file) }

22c40c554ed648a5fd46fa5f3e087f124c24d2f9

f33eb8d7112fcbc3ca78e0fa1eeb613364bc430c

/R/Orify.R

ae105c00f3eb08c63d06d65e53e357e6b1840abc

[]

no_license

paulhendricks/functools

d49d084b903735d95ec3624186d026cc1afc07fd

63eb95d538864a953f13cb612359dfa9a8e365d5

refs/heads/master

2021-01-10T05:25:45.035861

2017-07-01T20:22:57

36,077,427

13

1

null

UTF-8

R

false

827

r

Orify.R

#' Predicate function operator that creates new predicate functions linked by the || operator. #' #' @param ... n functions to apply in order from left to right #' @return A predicate function linked by the || operator. #' @family predicate function operators #' @seealso \code{\link{Andify}} to create new predicate functions linked by the && operator. #' @examples #' # Examples #' is_character_or_factor <- Orify(is.character, is.factor) #' is_character_or_factor(letters) # TRUE #' is_character_or_factor(factor(state.abb)) # TRUE #' is_character_or_factor(1:100) # FALSE #' @export Orify <- function(...) { fs <- lapply(list(...), match.fun) first <- fs[[1]] rest <- fs[-1] function(...) { out <- first(...) for (f in rest) { if (out) return(TRUE) out <- `||`(out, f(...)) } out } }

c4c210ac3b755d8f9d55802c231c65142bbe466a

b8be9f57f05c5e279ff38ff1d0bb7efeb26b4308

/man/consumption_vis.Rd

ab7b91724b4ed28185329ba57d2c6a0670c63da3

[]

no_license

Ozeidi/water

aa96f8e451b008f0224da000e8e389a6df492273

9aa62d17b217d00346fac920e2d02c66798c2786

refs/heads/master

2020-06-17T08:02:26.012796

2019-07-08T16:36:39

195,854,634

0

null

UTF-8

R

false

true

1,167

rd

consumption_vis.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/01.Visualisation.R \name{consumption_vis} \alias{consumption_vis} \title{consumption_vis} \usage{ consumption_vis(df, contracts_list = NA, scale = "discrete", fixed_scale = TRUE, file_name = "p", path = getwd(), output_format = c("png", "pdf")) } \arguments{ \item{df}{xts object of the meter consumption} \item{contracts_list}{list of contracts to be investigated} \item{scale}{type of scale used for the heatmap, continues or discrete} \item{fixed_scale}{Boolean, to indicate whether the scale should be fixed for all the plots or change dyanmically per plot. We recommend keeping the scale fixed to enable comparison over different contracts. Default is TRUE.} \item{file_name}{name of the file to be used for outputing the plotting results} \item{path}{name of the path where plots will be outputed} \item{output_format}{png or pdf format} } \value{ pdf of png output with the results } \description{ Generate diagnostic plots to analyse the consumption patterns } \examples{ \dontrun{consumption_vis(contract_lst, contract_list= NA, scale= 'discrete', fixed_scale= TRUE)} }

4631c101222cfa8da3090c1ffd88888769477fff

b2318f37b16834b822fd946b0586737cfeb45090

/Clean data EC & PS - no join.R

55913d067dc87567eae6f2cc7d30e3707b65e72d

[]

no_license

philipjxg/Scrape-Goats

fa0d89abbf54c656f480ab518f48d3d9a062d31c

0a5dc61aa70431773f9c4d84a378fcfaea962f9c

refs/heads/master

2020-04-06T06:59:42.681078

2016-08-23T13:41:39

65,821,193

0

2

null

2016-08-19T13:55:25

2016-08-16T13:08:25

null

UTF-8

R

false

7,025

r

Clean data EC & PS - no join.R

library("readr") library("purrr") library("dplyr") library("tidyr") library("ggplot2") library("stringr") library("plyr") ########################################################################################## ##### Political Science ################################################################## ########################################################################################## raw.data.PS=read.csv("C:\\Users\\PhilipJunXian\\OneDrive\\Polit\\Master\\Social Data Science\\Opgave\\staku.csv") ######################################################### ### Clean the data for NA og hyperlink data.PS = raw.data.PS %>% filter( !is.na(o_kar)==TRUE) %>% select(-X, -id, -o_gns, -r_gns, -hyperlink) ######################################################### ### clean Ej mødt, Ikke bestået, Syg ### create aggregated grade variable data.PS$s_kar = ifelse(is.na(data.PS$r_kar)==TRUE,data.PS$o_kar, data.PS$o_kar + data.PS$r_kar ) data.PS$kar.m = as.numeric(levels(data.PS$kar))[data.PS$kar] data.PS=data.PS %>% filter(!is.na(kar.m)==TRUE) data.PS$kar.m = NULL ######################################################### ### create semester and year column data.PS = data.PS %>% separate(sem, c("semester", "year"), 7) data.PS = as.data.frame(lapply(data.PS,function(x) if(is.character(x)|is.factor(x)) gsub("Winter-","Fall",x) else x)) data.PS = as.data.frame(lapply(data.PS,function(x) if(is.character(x)|is.factor(x)) gsub("Summer-","Spring",x) else x)) data.PS$semester = ifelse(is.na(data.PS$blok)==TRUE, as.character(data.PS$semester), "Summer" ) data.PS$blok = NULL ######################################################### ### correct grades and numeric variables as.numeric(data.PS$o_kar) as.numeric(data.PS$r_kar) as.numeric(data.PS$s_kar) revalue(data.PS$kar, c("00" = "0", "02" = "2", "-3" = "-3")) -> data.PS$kar data.PS$kar = data.PS$kar %>% as.character() %>% as.numeric() ######################################################### ######################################################### #### preparing data frame for plotting graph # select variables data.PS.graf = data.PS %>% select(year, semester, name, kar, s_kar) data.PS.graf$product = data.PS.graf$s_kar * data.PS.graf$kar data.PS.graf1 = aggregate(data.PS.graf$product, by=list(data.PS.graf$year, data.PS.graf$semester), FUN=sum, na.rm=TRUE) colnames(data.PS.graf1)[3] = "sum.product" data.PS.graf2 = aggregate(data.PS.graf$s_kar, by=list(data.PS.graf$year,data.PS.graf$semester), FUN=sum, na.rm=TRUE) colnames(data.PS.graf2)[3] = "count" data.PS.graf.plot = left_join(data.PS.graf1, data.PS.graf2) colnames(data.PS.graf.plot)[1] = "year" colnames(data.PS.graf.plot)[2] = "semester" data.PS.graf.plot$average = data.PS.graf.plot$sum.product / data.PS.graf.plot$count ### HUSK - fjern summer: summer 2014 og 2015 5 - Ikke retvisende: 1 og hhv. 5 fag # data.PS.graf.plot1 = data.PS.graf.plot[!(data.PS.graf.plot$semester=="Summer" ),] ##### Political Science SLUT ############################################################# ########################################################################################## ##### Economics ########################################################################## ########################################################################################## raw.data.EC=read.csv("C:\\Users\\PhilipJunXian\\OneDrive\\Polit\\Master\\Social Data Science\\Opgave\\polit.csv") ######################################################### ### Clean the data for NA og hyperlink data.EC = raw.data.EC %>% filter( !is.na(o_kar)==TRUE) %>% select(-X, -id, -o_gns, -r_gns, -hyperlink) ######################################################### ### clean Ej mødt, Ikke bestået, Syg ### create aggregated grade variable data.EC$s_kar = ifelse(is.na(data.EC$r_kar)==TRUE,data.EC$o_kar, data.EC$o_kar + data.EC$r_kar ) data.EC$kar.m = as.numeric(levels(data.EC$kar))[data.EC$kar] data.EC=data.EC %>% filter(!is.na(kar.m)==TRUE) data.EC$kar.m = NULL ######################################################### ### create semester and year column data.EC = data.EC %>% separate(sem, c("semester", "year"), 7) data.EC = as.data.frame(lapply(data.EC,function(x) if(is.character(x)|is.factor(x)) gsub("Winter-","Fall",x) else x)) data.EC = as.data.frame(lapply(data.EC,function(x) if(is.character(x)|is.factor(x)) gsub("Summer-","Spring",x) else x)) data.EC$semester = ifelse(is.na(data.EC$blok)==TRUE, as.character(data.EC$semester), "Summer" ) data.EC$blok = NULL ######################################################### ### correct grades and numeric variables as.numeric(data.EC$o_kar) as.numeric(data.EC$r_kar) as.numeric(data.EC$s_kar) revalue(data.EC$kar, c("00" = "0", "02" = "2", "-3" = "-3")) -> data.EC$kar data.EC$kar = data.EC$kar %>% as.character() %>% as.numeric() ######################################################### ######################################################### #### preparing data frame for plotting graph # select variables data.EC.graf = data.EC %>% select(year, semester, name, kar, s_kar) data.EC.graf$product = data.EC.graf$s_kar * data.EC.graf$kar data.EC.graf1 = aggregate(data.EC.graf$product, by=list(data.EC.graf$year, data.EC.graf$semester), FUN=sum, na.rm=TRUE) colnames(data.EC.graf1)[3] = "sum.product" data.EC.graf2 = aggregate(data.EC.graf$s_kar, by=list(data.EC.graf$year,data.EC.graf$semester), FUN=sum, na.rm=TRUE) colnames(data.EC.graf2)[3] = "count" data.EC.graf.plot = left_join(data.EC.graf1, data.EC.graf2) colnames(data.EC.graf.plot)[1] = "year" colnames(data.EC.graf.plot)[2] = "semester" data.EC.graf.plot$average = data.EC.graf.plot$sum.product / data.EC.graf.plot$count ### HUSK - fjern 2011, spring - ej retvisende: 1 fag ### HUSK - fjern 2016, summer - endnu ej realiserede karaktere: 2 fag # data.EC.graf.plot1 = data.EC.graf.plot[!(data.EC.graf.plot$year=="2011" & data.EC.graf.plot$semester=="Spring"),] # data.EC.graf.plot1 = data.EC.graf.plot1[!(data.EC.graf.plot1$year=="2016" & data.EC.graf.plot1$semester=="Summer"),] ##### Economics SLUT ##################################################################### ########################################################################################## ########################################################################################## ########################################################################################## ### Combining the EC and PS data frames data.EC$study = "Economics" data.PS$study = "Political Science" data.Comb = rbind(data.EC, data.PS) # data.EC.graf.plot$study = "Economics" # data.PS.graf.plot$study = "Political Science" # # data.Comb.graf.plot = rbind(data.EC.graf.plot, data.PS.graf.plot) ###

1899476b1578f3bb9bf2a6da96d3393121618918

f0b63c76d1221a7a859b2692327bbc809b9015f9

/AES_R/AES+SHA1_sign.R

779e3a1331b58a6d4045ece0d385805c7f5f9279

[]

no_license

K-subin/R_Cipher

df5aed824da80cb513cb5f0fdd0d248f2cbeb729

7e479eb348a08217d02200fbe8f454551dfbb7a3

refs/heads/main

2023-02-19T21:38:44.854572

2021-01-18T09:06:54

312,599,342

0

null

UTF-8

R

false

893

r

AES+SHA1_sign.R

#========================================= # AES (using digest built-in function) #========================================= library("digest") msg_chr <- "Hello! Our Team name is 4. This is LEA Encrypt !" msg_raw <- charToRaw(msg_chr) msg_raw #AES Algoritm Setup aes_key <- as.raw(0:15) aes <- AES(aes_key, mode = "ECB") #Call AES Encrypt cipher_raw <- aes$encrypt(msg_raw) #Generate HMAC Tag hmac_key <- as.raw(16:31) hmac_tag <- hmac(hmac_key, msg_chr, "sha1") hmac_tag ### ciphertext packet = ( cipher_raw, hmac_tag ) ---> [Bob] aes_key_bob <- as.raw(0:15) aes_bob <- AES(aes_key_bob, mode = "ECB") msg_bob_raw <- aes_bob$decrypt(cipher_raw, raw=TRUE) msg_bob_chr <- rawToChar(msg_bob_raw) msg_bob_chr hmac_bob_key <- as.raw(16:31) hmac_bob_tag <- hmac(hmac_bob_key, msg_bob_chr, "sha1") hmac_bob_tag stopifnot(identical(hmac_tag, hmac_bob_tag))

486f335a5b546b8b985a5665625f62a0a72ecb3d

dcc2229cce6a8737f3a8d8e367547a365fcf066f

/man/remove_edges.Rd

68a99d1546cdb7940a7fd951531b7147f5fbea11

[ "Apache-2.0" ]

permissive

synalogik-mike/arango-driver

335090183c67f0f6ca9d7f84fc540072a1f58a97

488e8e128ea94131f276a20b1ee605d232ad171d

refs/heads/master

2022-12-25T04:18:20.366591

2019-07-08T06:58:42

295,026,977

0

null

UTF-8

R

false

true

527

rd

remove_edges.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/arango_graph.R \name{remove_edges} \alias{remove_edges} \title{Edges removal} \usage{ remove_edges(.graph, listOfEdges) } \arguments{ \item{.graph}{the graph affected by the deletion} \item{listOfEdges}{a list of lists containing edges information} } \value{ the ArangoGraph object of the structure affected by the change } \description{ Removes a collection of elements from the given graph. } \author{ Gabriele Galatolo, g.galatolo(at)kode.srl }

95a0165aa344c94125bc5b35abef4e50d8819dcf

9bf1f830525f8aa8b2f56a95d9f2bf762d773dd3

/run_analysis.R

f2e9a321f99038d839f66acc3398a34673099b5d

[]

no_license

tynesjo/getcleandata

0443d08d91beb99f2c18e98d929bb81af358a036

77da5ff8e36845b29d7c259b817b993fec15ae6e

refs/heads/master

2021-01-19T09:57:30.654519

2015-06-21T22:54:56

37,828,433

0

null

UTF-8

R

false

3,316

r

run_analysis.R

# Script to Acquire and Organize Wearable Tech Machine Learning Data # ============================================================================== # Settings. # ------------------------------------------------------------------------------ url <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip" dat_dir <- "./dat" # Folder in which to save the data. zip_file <- "dat.zip" # Name of the zip file once downloaded. colnames_dictionary <- c( # Translations for column names "Mag"= "Magnitude", "^t"="time", "^f"="frequency", "Acc"="Accelerometer", "Gyro"="Gyroscope", "BodyBody"="Body" ) # Packages. # ------------------------------------------------------------------------------ require(magrittr) # To use forward-pipes ("%>%" etc.). require(plyr) # To create the tidy data set. # Step 1: Acquire and Combine the Data. # ------------------------------------------------------------------------------ DataGetUnzip <- . %>% { # Downloads and unzips the data file. # Create data directory if not existing. if(!file.exists(dat_dir)) {dir.create(dat_dir)} # Dowload data file if not existing (use "curl" method to handle https) dest <- file.path(dat_dir, zip_file) if(!file.exists(dest)) {download.file(url=url, destfile=dest, method="curl")} # Unzip data file. unzip(zipfile=dest, exdir=dat_dir) # Unzip. } ReadT <- function(sub_dir, file, path=file.path(dat_dir, "UCI HAR Dataset")) { # Reads a .txt file given a subdirectory path and file name using read.table file.path(path, sub_dir, paste0(file, ".txt")) %>% read.table(header=FALSE, stringsAsFactors=FALSE) } feat_names <- ReadT(".", "features")$V2 # Feature names from file. d <- cbind( # Combine the data into one data frame. # Subject data (combined training and testing). rbind(ReadT("train", "subject_train"), ReadT("test", "subject_test")), # Activity data (combined training and testing) rbind(ReadT("test", "y_test"), ReadT("train", "y_train")), # Features data. rbind(ReadT("test", "X_test"), ReadT("train", "X_train")) ) %T>% # Finally assign column names. {colnames(.) <- c("subject", "activity", feat_names)} # Step 2: Extract only standard deviation and mean related features. # ------------------------------------------------------------------------------ d %<>% {.[, c(1:2, grep("mean\$|std", colnames(.)))]} # Step 3: Appropriately label the activities using factors. # ------------------------------------------------------------------------------ activity_factors <- ReadT(".", "activity_labels") d$activity %<>% factor(levels=activity_factors[,1], labels=activity_factors[,2]) # Step 4: Make Column Names # ------------------------------------------------------------------------------ v <- colnames_dictionary for(i in 1:length(v)) { colnames(d) %<>% gsub(names(v)[[i]], v[[i]] %>% unname, .) } # Clean up the column names. {colnames(d) %<>% gsub("-", "_", .) %>% gsub("\\(\$", "", .)} rm(v) # Step 5: Create a Tidy Data Set. # ------------------------------------------------------------------------------ TidyCreate <- . %>% { # Creates a tidy data set from "d" and saves as CSV file. aggregate(. ~ activity + subject, d, mean) %>% {.[order(.$subject, .$activity),]} %>% write.table("tidy_data.txt", row.names=F) }

d28d5c75b49e8a9254685d119a92336855c169d2

ca2802548f8a961ca6e0fe57d7906f912eb3f221

/man/order_matchdata.Rd

b68ac5a0c884fa93a39ef30f82c4d2ef76a4589c

[]

no_license

O1sims/FootballStats

4dca2ebb135922f5ca3beafa7c3206022faea490

266d476f5f15d57960f8715d33a766f8fa091daa

refs/heads/master

2023-06-08T08:48:39.646688

2021-06-29T09:07:44

381,303,716

0

null

UTF-8

R

false

true

670

rd

order_matchdata.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/classify_utils.R \name{order_matchdata} \alias{order_matchdata} \title{Order Match Dataset} \usage{ order_matchdata(matchData, formatter = "\%d.\%m.\%Y") } \arguments{ \item{matchData}{A data frame that contains rows of single matches that have been played between two teams.} \item{formatter}{A string that defines how the dates coming in should look, i.e. allow for any kind of standard date formats.} } \value{ A data frame that has been ordered by date. } \description{ A function that takes an arbitrary data frame consisting of match data and orders it by date in ascending order. }

089e416e07582b550d272428c648c8e12515782a

d6a831acab0f09e1292de90f33cbad86fe6ab0e9

/code/6_R2R_R1_grouped.R

ee9b00d823bea3ff6dbf86f8dbe36e3769b009c0

[ "CC0-1.0" ]

permissive

yangclaraliu/covid_vac_africa

1a42f79205ab543d172fbafd13f56ab896a24428

5400d14f43cfdd73251621c4de0107ec73509a8c

refs/heads/main

2023-04-10T20:06:44.570204

2023-02-07T21:36:14

426,681,664

0

null

UTF-8

R

false

14,552

r

6_R2R_R1_grouped.R

merge_novac_grouped <- function(tmp){ require(magrittr) tmp$pfizer$non_fatal %<>% left_join(res_novac_grouped$pfizer[[1]] %>% rename(novac = value), by = c("name", "population", "group","year")) tmp$pfizer$fatal %<>% left_join(res_novac_grouped$pfizer[[2]], by = c("name", "population", "group", "year")) tmp$az$non_fatal %<>% left_join(res_novac_grouped$az[[1]] %>% rename(novac = value), by = c("name", "population", "group","year")) tmp$az$fatal %<>% left_join(res_novac_grouped$az[[2]], by = c("name", "population", "group", "year")) return(tmp) } # load data params_grid_az_70_grouped <- readRDS("~/GitHub/covid_vac_africa/data/intermediate/params_grid_az_70_grouped.rds") params_grid_pfizer_70_grouped <- readRDS("~/GitHub/covid_vac_africa/data/intermediate/params_grid_pfizer_70_grouped.rds") res_novac_grouped <- readRDS("~/GitHub/covid_vac_africa/data/intermediate/res_novac_grouped.rds") # organise results res_grouped <- organise_outcomes(tmp_pfizer = params_grid_pfizer_70_grouped, tmp_az = params_grid_az_70_grouped) res_grouped_merged <- merge_novac_grouped(res_grouped) # write tables input_fatal_grouped <- list() res_grouped_merged$pfizer$fatal %>% mutate(Type = "pfizer") %>% bind_rows(res_grouped_merged$az$fatal %>% mutate(Type = "az")) %>% left_join(fitted_table[,c("loc", "iso3c")] %>% rename(population = loc), by = c("population", "iso3c") ) %>% rename(epi_id = scenario_id, country = iso3c, age = group, deaths = value) %>% mutate(age = factor(age, levels = unique(res_grouped_merged$pfizer$fatal$group), labels = 1:16), age = as.numeric(age)) %>% ungroup -> input_fatal_grouped[["all"]] c(input_fatal_grouped, input_fatal_grouped$all %>% dplyr::select(epi_id, country, year, age, deaths, Type) %>% group_by(Type) %>% group_split() %>% setNames(c("az","pfizer")) %>% map(dplyr::select, -Type) %>% map(data.table)) -> input_fatal_grouped input_fatal_grouped$all %>% dplyr::select(epi_id, country, year, age, novac) %>% rename(deaths = novac) %>% distinct() %>% data.table() -> input_fatal_grouped[["novac"]] input_non_fatal_grouped <- list() res_grouped_merged$pfizer$non_fatal %>% mutate(Type = "pfizer") %>% bind_rows(res_grouped_merged$az$non_fatal %>% mutate(Type = "az")) %>% filter(!grepl("_p_", name)) %>% dplyr::select(-novac) %>% pivot_wider(names_from = name, values_from = value) %>% rename(epi_id = scenario_id, country = iso3c, icu = critical_i_all, non_icu = severe_i_all) %>% ungroup() -> input_non_fatal_grouped[["all"]] c(input_non_fatal_grouped, input_non_fatal_grouped$all %>% dplyr::select(epi_id, country, group, year, cases, non_icu, icu, Type) %>% group_by(Type) %>% group_split() %>% setNames(c("az","pfizer")) %>% map(dplyr::select, -Type)%>% map(data.table)) -> input_non_fatal_grouped res_grouped_merged$pfizer$non_fatal %>% mutate(Type = "pfizer") %>% bind_rows(res_grouped_merged$az$non_fatal %>% mutate(Type = "az")) %>% filter(!grepl("_p_", name)) %>% dplyr::select(-value, -Type) %>% distinct() %>% pivot_wider(names_from = name, values_from = novac) %>% rename(epi_id = scenario_id, country = iso3c, icu = critical_i_all, non_icu = severe_i_all) %>% ungroup() %>% data.table() -> input_non_fatal_grouped[["novac"]] input_non_fatal_grouped$novac |> dplyr::select(colnames(input_non_fatal_grouped$az)) -> input_non_fatal_grouped$novac # #testing # epi_deaths = input_fatal_grouped$az # epi_cases = input_non_fatal_grouped$az # econ_scens = econ_scens[1,] # LT = UNLT # POP = UNPOP # GDPPC = GDPPC # GNIPC = GNIPC # pre calculate life expectancy as now we only care about one type of economic # scenario dLE <- vector("list", length = nrow(econ_scens)) for (s in 1:nrow(econ_scens)){ # calculate discount life expectancy for given discount rate, smr and country dLE[[s]] <- cov_dLE( r = econ_scens[[s,"discount_rate"]], smr = econ_scens[[s,"smr"]], selectCountries = unique(epi_deaths[,country]), LT = UNLT, POP = UNPOP ) dLE[[s]][, econ_id := s] } dLE <- rbindlist(dLE) # update_functions cov_econ_outcomes_grouped <- function( epi_deaths, # data table of age-specific deaths epi_cases, # data table of cases, non_icu and icu admission (not age-specific) econ_scens, # econ scenarios specifying discount rate, smr LT, # UNWPP life tables POP, # UNWPP population estimates GDPPC, # World Bank country GDP per capita 2020 GNIPC # World Bank country GNI per capita 2020 ){ require(data.table) # merge deaths and vsl data vsl <- cov_VSL(GNIPC = GNIPC) ylls <- vsl[ # merge deaths and vsl data epi_deaths, on = .(country == country) ] # merge gdp per capita data ylls <- GDPPC[ ylls, on = .(country == country) ] # merge dLE data and calculated ylls, vsl and hc first_year <- 2021 # min(epi_deaths$year) # reference year for discounting ylls <- dLE[ ylls, on = .(country = country, AgeBand = age), allow.cartesian = TRUE ][ , .( ylls = sum(deaths * d_LEx * 1 / (1 + econ_scens[[s,"discount_rate"]])^(year - first_year) ), vsl = sum(deaths * vsl), human_capital = sum(d_LEx * GDPPC_2020_USD), GDPPC_2020_USD = mean(GDPPC_2020_USD) ), by = .(epi_id, econ_id, country, AgeBand) # collapse age bands ] # ylds based on number of cases / hospitalizations unit_ylds <- cov_unit_ylds() first_year <- 2021 ylds <- epi_cases[ , as.list(econ_scens[,c("econ_id","discount_rate")]), # combine with different econ scenarios by=epi_cases ][ , .( ylds = sum( ( (cases * unit_ylds$per_case) + (non_icu * unit_ylds$per_non_icu_case) + (icu * unit_ylds$per_icu_case) ) * 1 / (1 + discount_rate)^(year - first_year) # discounting ) ), by = .(epi_id, econ_id, country, group) ] ylls |> mutate(group = factor(AgeBand, levels = 1:16, labels = unique(ylds$group))) |> dplyr::select(-AgeBand) -> ylls # merge ylls with ylds and calculate dalys out <- ylds[ ylls, on = .(epi_id, econ_id, country, group) ][, dalys := ylls + ylds] return(out) } cov_econ_outcomes_grouped( epi_deaths = input_fatal_grouped$az, epi_cases = input_non_fatal_grouped$az, econ_scens = econ_scens[1,], LT = UNLT, POP = UNPOP, GDPPC = GDPPC, GNIPC = GNIPC) -> out_az_grouped cov_econ_outcomes_grouped( epi_deaths = input_fatal_grouped$pfizer, epi_cases = input_non_fatal_grouped$pfizer, econ_scens = econ_scens[1,], LT = UNLT, POP = UNPOP, GDPPC = GDPPC, GNIPC = GNIPC) -> out_pfizer_grouped cov_econ_outcomes_grouped( epi_deaths = input_fatal_grouped$novac, epi_cases = input_non_fatal_grouped$novac, econ_scens = econ_scens[1,], LT = UNLT, POP = UNPOP, GDPPC = GDPPC, GNIPC = GNIPC) -> out_novac_grouped # when medium is better than fast pop_bycountry <- pop |> group_by(iso3c) |> summarise(tot = sum(m,f)*1000) |> filter(iso3c %in% fitted_table$iso3c) ICER$az_03 |> filter(econ_id == 1) |> mutate(Type = "az") |> bind_rows(ICER$pf_03 |> filter(econ_id == 1) |> mutate(Type = "pf")) |> dplyr::select(date_start, ICER_scaled, iso3c, scenario, Type) |> left_join(pop_bycountry, by = "iso3c") |> mutate(ICER_cat = case_when(ICER_scaled < 0.1 ~ 1, ICER_scaled >= 0.1 & ICER_scaled < 0.3 ~ 2, ICER_scaled >= 0.3 & ICER_scaled < 0.5 ~ 3, ICER_scaled >= 0.5 & ICER_scaled < 1 ~ 4, ICER_scaled >= 1 ~ 5)) |> # select(-ICER_scaled) |> dplyr::select(-ICER_cat) |> mutate(ICER_scaled = round(ICER_scaled, 2)) |> pivot_wider(names_from = scenario, values_from = ICER_scaled) |> mutate(check = case_when(medium < fast ~ "1", medium == fast ~ "2", medium > fast ~ "3")) -> bar_met # plot results out_az_grouped |> left_join(ms_scenarios |> rownames_to_column(var = "epi_id"), by = "epi_id") |> mutate(Type = "az") |> bind_rows(out_pfizer_grouped |> left_join(ms_scenarios |> rownames_to_column(var = "epi_id"), by = "epi_id") |> mutate(Type = "pf")) |> mutate(date_start = ymd(date_start), older = if_else(group %in% c("60-64", "65-69", "70-74", "75+"), T, F)) |> group_by(epi_id, country, scenario, date_start, older, Type) |> summarise(dalys = sum(dalys)) |> group_by(epi_id, country, scenario, date_start, Type) |> mutate(dalys_tot = sum(dalys), dalys_prop = dalys/dalys_tot, scenario = factor(scenario, levels = c("slow", "medium", "fast"))) |> rename(iso3c = country) |> right_join(bar_met[,c("date_start", "iso3c", "check", "Type")], by = c("date_start", "iso3c", "Type")) |> filter(older == T, scenario != "slow") |> ungroup() |> dplyr::select(-dalys, -dalys_tot, -epi_id) |> pivot_wider(names_from = scenario, values_from = dalys_prop) |> data.table() -> data_test t1 <- t.test(data_test[check == "1" & Type == "az", ]$fast, data_test[check == "1" & Type == "az", ]$medium, paired = T, alternative = "two.sided") t2 <- t.test(data_test[check == "3" & Type == "az", ]$fast, data_test[check == "3" & Type == "az", ]$medium, paired = T, alternative = "two.sided") t3 <- t.test(data_test[check == "1" & Type == "pf", ]$fast, data_test[check == "1" & Type == "pf", ]$medium, paired = T, alternative = "two.sided") t4 <- t.test(data_test[check == "3" & Type == "pf", ]$fast, data_test[check == "3" & Type == "pf", ]$medium, paired = T, alternative = "two.sided") data.frame(mean = c(t1$estimate, t2$estimate, t3$estimate, t4$estimate), LL = c(t1$conf.int[1], t2$conf.int[1], t3$conf.int[1], t4$conf.int[1]), UL = c(t1$conf.int[2], t2$conf.int[2], t3$conf.int[2], t4$conf.int[2]), lab = c("ICER_medium < ICER_fast", "ICER_medium > ICER_fast"), Type = c("Viral vector vaccine", "Viral vector vaccine", "mRNA vaccine", "mRNA vaccine")) |> ggplot() + geom_point(aes(x = lab, y = mean), size = 3) + geom_segment(aes(x = lab, xend = lab, y = LL, yend = UL), size = 1.5) + theme_bw() + custom_theme + labs(x = "", y = expression("paDALY"["fast, 60+"] - "paDALY"["medium, 60+"])) + scale_x_discrete(labels = parse(text = c("ICER[medium] < ICER[fast]", "ICER[medium] > ICER[fast]")))+ facet_wrap(~Type, ncol = 1, scales = "free") ggsave("figs/R2R_R1/ICER_grouped.png", width = 8, height = 12) out_az_grouped |> left_join(ms_scenarios |> rownames_to_column(var = "epi_id"), by = "epi_id") |> mutate(Type = "az") |> bind_rows(out_pfizer_grouped |> left_join(ms_scenarios |> rownames_to_column(var = "epi_id"), by = "epi_id") |> mutate(Type = "pf")) |> mutate(date_start = ymd(date_start), older = if_else(group %in% c("60-64", "65-69", "70-74", "75+"), T, F)) |> group_by(epi_id, country, scenario, date_start, older, Type) |> summarise(dalys = sum(dalys)) |> group_by(epi_id, country, scenario, date_start, Type) |> mutate(dalys_tot = sum(dalys), dalys_prop = dalys/dalys_tot, scenario = factor(scenario, levels = c("slow", "medium", "fast"))) |> rename(iso3c = country) |> ungroup() |> filter(older == T) |> dplyr::select(Type, iso3c, scenario, dalys_prop, date_start) |> pivot_wider(names_from = scenario, values_from = dalys_prop) |> mutate(fast = round(fast, 2), medium = round(medium, 2), diff_paDALYs = case_when(fast > medium ~ "paDALYs: fast > medium", fast < medium ~ "paDALYs: fast < medium", fast == medium ~ "paDALYs: fast == medium")) |> right_join(bar_met[,c("date_start", "iso3c", "check", "Type")], by = c("date_start", "iso3c", "Type")) |> rename(diff_ICERs = check) |> mutate(diff_ICERs = case_when(diff_ICERs == 1 ~ "ICER: fast > medium", diff_ICERs == 2 ~ "ICER: fast == medium", diff_ICERs == 3 ~ "ICER: fast < medium")) |> group_by(diff_paDALYs, diff_ICERs, Type) |> tally() -> byTable byTable |> filter(Type == "pf") |> filter(diff_ICERs != "ICER: fast == medium", diff_paDALYs != "paDALYs: fast == medium") |> pivot_wider(names_from = diff_ICERs, values_from = n) %>% # replace(is.na(.), 0) |> ungroup() %>% as.data.frame() %>% set_rownames(.[,1]) |> dplyr::select(3:4) |> as.matrix() |> oddsratio() byTable |> filter(Type == "az") |> filter(diff_ICERs != "ICER: fast == medium", diff_paDALYs != "paDALYs: fast == medium") |> pivot_wider(names_from = diff_ICERs, values_from = n) |> ungroup() %>% as.data.frame() %>% set_rownames(.[,1]) |> dplyr::select(3:4) |> as.matrix() |> oddsratio()

2de291c4a2a267cebdc9a493946511a679c539f5

1ec5ef3b45a275980230031720f98d7878dc5e97

/plot1.R

34dd47890a78e95f41720823c2d4af8c7b7876b8

[]

no_license

courseraRR/ExData_Plotting1

67bd55e62590d9b3c3e1459c056ecffb9bd8353b

99eff103c9cfc1910f515b8371aea7b14ba4a487

refs/heads/master

2021-01-14T14:27:52.038785

2014-05-10T22:57:11

null

0

null

UTF-8

R

false

1,029

r

plot1.R

#' Script to plot Global Active Power frequence dist #if necessary download and unzip the data file to current directory if(!file.exists("household_power_consumption.txt")){ require(utils) download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip", "household_power_consumption.zip", method="curl") # download unzip("household_power_consumption.zip" ) } #set the path to the data file data.file.name<-"./household_power_consumption.txt" #read the file df<-read.csv(data.file.name, sep=";", stringsAsFactors=FALSE, na.strings="?") #subset to the desired dates df<-subset(df, df$Date=="1/2/2007"| df$Date=="2/2/2007") #for convenience, assign global active power to a variable called gap gap<-df$Global_active_power #turn on the graphics device png("./plot1.png") #do the plot hist(gap, breaks=seq(floor(min(gap)), ceiling(max(gap)), by=.5), col="red", main="Global Active Power", xlab="Global Active Power (kilowattts)") #turn of the graphics device dev.off()

26357137a3044d1b596438ccc3a05af195fff500

d3a83275ed27bf445f2d6ddf967f3c3013dac89e

/NCAA/project/src/features/FeatureManipulation.R

16187949a3ec01f36fbc8239fd63ade604511cad

[ "MIT" ]

permissive

paridhi1603/NCAA-Prediction

e67cd657f3bada155f3e0afa6b2cb4e0664247f4

8236c74831641df7e311d6c680b7f15c28970478

refs/heads/main

2023-06-19T04:11:39.745347

2021-07-15T20:43:38

379,400,771

0

null

2021-06-25T18:42:13

2021-06-22T21:06:30

R

UTF-8

R

false

6,553

r

FeatureManipulation.R

rm(list = ls()) library(data.table) library(caret) library(reshape2) library(tidyr) test <- fread('./project/volume/data/raw/MSampleSubmissionStage2.csv') season <- fread('./project/volume/data/raw/MRegularSeasonDetailedResults.csv') tourney <- fread('./project/volume/data/raw/MNCAATourneyDetailedResults.csv') ranks <- fread('./project/volume/data/raw/MMasseyOrdinals.csv') All_Games_Table <- rbind(season,tourney) W_stats <- All_Games_Table[,.(Season, DayNum, WTeamID, WScore, WFGM, WFGA, WFGM3, WFGA3, WFTM, WFTA ,WOR ,WDR , WAst, WTO, WStl, WBlk ,WPF)] L_stats <- All_Games_Table[,.(Season, DayNum, LTeamID, LScore, LFGM, LFGA, LFGM3, LFGA3, LFTM, LFTA ,LOR ,LDR , LAst, LTO, LStl, LBlk ,LPF)] colnames(W_stats) <- c("Season", "DayNum", "TeamID", "Score", "FGM", "FGA", "FGM3", "FGA3", "FTM", "FTA" ,"OR" ,"DR" , "Ast", "TO", "Stl", "Blk" ,"PF") colnames(L_stats) <- c("Season", "DayNum", "TeamID", "Score", "FGM", "FGA", "FGM3", "FGA3", "FTM", "FTA" ,"OR" ,"DR" , "Ast", "TO", "Stl", "Blk" ,"PF") master_stats <- rbind(W_stats,L_stats) stats_by_day <- NULL for (i in 1:max(master_stats$DayNum)) { sub_master_stats <- master_stats[DayNum < i] team_stats_by_day <- data.table::dcast(setDT(sub_master_stats), TeamID+Season~., mean , value.var = c("FGM", "Score", "FGA", "FTM", "FTA", "FGA3", "FTM", "FTA" ,"OR" ,"DR" , "Ast", "TO", "Stl", "Blk" ,"PF")) team_stats_by_day$DayNum <- i stats_by_day <- rbind(stats_by_day, team_stats_by_day) } stats_by_day$TeamID <- as.character(stats_by_day$TeamID) #- Clean test #test <- data.table(matrix(unlist(strsplit(test$id,"_")),ncol=2,byrow=T)) #setnames(test,c("V1","V2"),c("team_1","team_2")) test<- data.table(matrix(unlist(strsplit(test$ID,"_")),ncol=3,byrow=T)) setnames(test,c("V1","V2","V3"),c("Season","team_1","team_2")) #test$DayNum<-max(d2$Season==2021,d2$DayNum)+1 test$DayNum<-max(tourney$Season == 2021,tourney$DayNum) #test$DayNum <- max(season[Season == 2021,DayNum]) + 1 test$Result <- 0.5 #- initializing train train <- rbind(season,tourney) train <- train[,.(WTeamID,LTeamID,Season,DayNum)] setnames(train,c("WTeamID","LTeamID"),c("team_1","team_2")) train$Result <- 1 #- make master data file master <- rbind(train,test) #- ensure my team ids are characters master$team_1 <- as.character(master$team_1) master$team_2 <- as.character(master$team_2) master$Season <- as.integer(master$Season) temp <- merge(master, stats_by_day, by.x = c("team_1", "Season", "DayNum"), by.y = c("TeamID", "Season", "DayNum"), all.x = T) master <- merge(temp, stats_by_day, by.x = c("team_2", "Season", "DayNum"), by.y = c("TeamID", "Season", "DayNum"), all.x = T) master$FGMdif<- master$FGM.x-master$FGM.y master$Scoredif<- master$Score.x-master$Score.y master$FGAdif<- master$FGA.x-master$FGA.y master$FTMdif<- master$FTM.x-master$FTM.y master$FTAdif<- master$FTA.x-master$FTA.y master$ORdif<- master$OR.x-master$OR.y master$FGA3dif<- master$FGA3.x-master$FGA3.y master$DRdif<- master$DR.x-master$DR.y master$Astdif<- master$Ast.x-master$Ast.y master$TOdif<- master$TO.x-master$TO.y master$Stldif<- master$Stl.x-master$Stl.y master$Blkdif<- master$Blk.x-master$Blk.y master$PFdif<- master$PF.x-master$PF.y master<-na.omit(master) #- teams' rank often change the day of a game so don't want to use the 'future' # values. we ofset them by one. ranks$Season<- as.integer(ranks$Season) ranks$DayNum <- ranks$RankingDayNum+1 #- you should optimize the following by creating a list of the systems and #- creating a loop to add them into the table #- following is the list of the five systems you should use. Your mission, should # you decide to accept it is to turn the big chunk of code starting at line #48ish # into a for loop to incorporate the five rankings which_system <- c("POM","SAG","MOR","DOK") master$Season<-as.integer(master$Season) #- start here #- subset the ranks table for (i in (which_system)) { #subset_ranks<- ranks[SystemName == which_system][,.(Season,DayNum,TeamID,OrdinalRank)] one_rank <- ranks[SystemName == i][,.(Season,DayNum,TeamID,OrdinalRank)] #- prep and join into the first team setnames(one_rank,"TeamID","team_1") one_rank$team_1 <- as.character(one_rank$team_1) setkey(master,Season,team_1,DayNum) setkey(one_rank,Season,team_1,DayNum) #- join here master <- one_rank[master,roll=T] setnames(master,"OrdinalRank","team_1_rank") #- prep and merge into the second team setnames(one_rank,"team_1","team_2") setkey(master,Season,team_2,DayNum) setkey(one_rank,Season,team_2,DayNum) master <- one_rank[master,roll=T] setnames(master,"OrdinalRank","team_2_rank") #subtract the rankings for a new variable master$rank_dif <- master$team_2_rank-master$team_1_rank master$team_1_rank <- NULL master$team_2_rank <- NULL setnames(master,"rank_dif",paste0(i,"_dif")) # end here } ####INITIAL TAABLES CODE STARTS HERE #- clean up the data master <- master[order(Season,DayNum)] #- get rid of id variables and nas ( you should keep the ids, Season and Day) master <- master[,.(team_1,team_2,POM_dif, SAG_dif, MOR_dif, DOK_dif, Scoredif, FTMdif, FTAdif, PFdif, Blkdif, Stldif, TOdif, Astdif, DRdif, FGA3dif, ORdif, Result)] master <- master[!is.na(master$POM_dif)] #master <- master[!is.na(master$PIG_dif)] master <- master[!is.na(master$SAG_dif)] master <- master[!is.na(master$MOR_dif)] master <- master[!is.na(master$DOK_dif)] # #add all #split back into train and test test <- master[Result == 0.5] train <- master[Result == 1] #- divide so I have losses rand_inx <- sample(1:nrow(train),nrow(train)*0.5) train_a <- train[rand_inx,] train_b <- train[!rand_inx,] #- train_b will encode the loses train_b$Result <- 0 train_b$POM_dif <- train_b$POM_dif*-1 #train_b$PIG_dif <- train_b$PIG_dif*-1 train_b$SAG_dif <- train_b$SAG_dif*-1 train_b$MOR_dif <- train_b$MOR_dif*-1 train_b$DOK_dif <- train_b$DOK_dif*-1 #train_b$FGMdif<- NULL train_b$Scoredif<- train_b$Scoredif*-1 #train_b$FGAdif<- NULL train_b$FTMdif<- train_b$FTMdif*-1 train_b$FTAdif<- train_b$FTAdif*-1 train_b$ORdif<- train_b$ORdif*-1 train_b$FGA3dif<- train_b$FGA3dif*-1 train_b$DRdif<- train_b$DRdif*-1 train_b$Astdif<- train_b$Astdif*-1 train_b$TOdif<- train_b$TOdif*-1 train_b$Stldif<- train_b$Stldif*-1 train_b$Blkdif<- train_b$Blkdif*-1 train_b$PFdif<- train_b$PFdif*-1 setnames(train_b,c("team_1","team_2"),c("team_2","team_1")) train <- rbind(train_a,train_b) fwrite(test,'./project/volume/data/interim/test.csv') fwrite(train,'./project/volume/data/interim/train.csv')

1976180af646ccad917ce81201e753be6cbaecfe

a5d4010a57f0703d915652d6ff08191b654e1d3a

/cachematrix.R.bak

1d6faef628e4e53e31903d6fe27089b4c5d6ebfa

[]

no_license

githubds/ProgrammingAssignment2

be92bd8267b6f2b8221aefd40421e11020faef74

a7ea2551aad88c2ab9334b4ec99c045a0f0ab515

refs/heads/master

2021-04-09T13:50:15.071525

2015-03-19T07:56:25

32,507,008

0

null

2015-03-19T07:28:21

2015-03-19T07:28:20

null

UTF-8

R

false

1,208

bak

cachematrix.R.bak

## Put comments here that give an overall description of what your ## functions do ## Write a short comment describing this function ##makeCacheMatrix ## input param--1. a square matrix; returns a list of functions ## this function creates an enviornment and initalizes x_inv variable ## enclosing functions ## set --assigns to variables in parent env ## get --returns the input parameter of the parent function ## setInv --sets value to parent env variable x_inv ## getInv --gets the parent env variable x_inv makeCacheMatrix <- function(x = matrix()) { x_inv<-NULL set<- function(y){ x<<-y x_inv<<-NULL } get<-function() x setInv<- function(inv) x_inv<<-inv getInv<- function() x_inv list( set=set ,get=get ,setInv=setInv ,getInv=getInv ) } ## cacheSolve ## inputparam --function; returns the inverse of square matrix ## first it checks if the inverse is available in cache; if not then ## it calculates inverse and stores that in the cache cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' m <- x$getInv(); if (!is.null(m)){ print("getting cached data...") return (m) } z<-x$get() m<-solve(z) x$setInv(m) m }

3702b57e3e0a1c43ab3993059ae842dc45329206

0969a8a76b2361bd1301b61503392debe2e454e3

/Rproject/archive/R/GR_clustering_1.R

7fe19dbb3ad33ce8a3e0c8940ff7fc0eacc75a5c

[]

no_license

daliagachc/GR_chc

9a85b133919863769e9d655c2e7c63fa3676177a

c40d7a28e18f1cea4c940af44151e48c8926bf55

refs/heads/master

2020-06-01T04:00:06.653784

2019-06-13T05:33:04

190,622,843

0

null

UTF-8

R

false

2,015

r

GR_clustering_1.R

#GR_clustering minR=0.8 wd=event_GR_wide %>% mutate(R_00_03=NULL,GR_00_03=NULL,GR_total=NULL,R_total=NULL) wd=wd %>% mutate(GR_03_07=ifelse(R_03_07>=minR,GR_03_07,NA), GR_07_20=ifelse(R_07_20>=minR,GR_07_20,NA), GR_20_80=ifelse(R_20_80>=minR,GR_20_80,NA), special=NULL) wd=na.omit(wd) mask=npfevent_size_frame %>% select(ion,eventID) %>% filter(ion=="negative") mask=unique(mask) wd=merge(wd,mask,by="eventID") wd=subset(wd,ion="negative") %>% select(eventID,GR_03_07, GR_07_20,GR_20_80) #creating the normalization factor for every day wd_normfac=wd %>% dplyr::group_by(eventID) %>% dplyr::summarise(normfac=sqrt(GR_03_07^2+GR_07_20^2+GR_20_80^2)) #combining the two and creating a normalized column wd_merged=merge(wd,wd_normfac, by="eventID") wd_merged$GR_03_07=wd_merged$GR_03_07/wd_merged$normfac wd_merged$GR_07_20=wd_merged$GR_07_20/wd_merged$normfac wd_merged$GR_20_80=wd_merged$GR_20_80/wd_merged$normfac toCluster=wd_merged[,1:4] #plotting the "cluster cuantity selection" plot wssplot(toCluster[-1]) set.seed(1234) nc <- NbClust(toCluster[-1], min.nc=2, max.nc=7, method="kmeans") table(nc$Best.n[1,]) #doing the cluster fit set.seed(1234) fit.km <- kmeans(toCluster[-1], 2, nstart=25) #3 fit.km$size fit.km$centers #which days are assigned to which cluster df_clustered=data.frame(eventID=wd$eventID, clus=fit.km$cluster) #plotting cluster centers test=aggregate(na.omit(df_wide[-1]), by=list(cluster=fit.km$cluster), mean) test_long=gather(data=test, key=hour,value=value, as.character(0:23)) test_long$hour=as.numeric(test_long$hour) test_long$cluster=as.factor(test_long$cluster) # #creating the normalization factor for every day wd_normfac=wd %>% dplyr::group_by(eventID) %>% dplyr::summarise(normfac=sqrt(GR_3_7^2+GR_7_20^2)) #combining the two and creating a normalized column wd_merged=merge(wd,wd_normfac, by="eventID") workData_merged$TGM_norm=workData_merged$TGM/workData_merged$normfac

f9512951782f6e50339a93406e8ee6cde4f46059

d810f1775139ce2fc0392d860fdbb85637aec357

/data-raw/loadExampleData.R

b0da87b1301c67eb5ea2b70508f84fd5007725ab

[]

no_license

SusannaPagni/knitauthors

bc509a0ac406d8909d3d42c59967cf79d60574a0

15f87cf98f561cc097d47f463905b2ca5b4ab35b

refs/heads/master

2023-05-31T14:36:02.528889

2016-06-16T15:18:32

null

0

null

UTF-8

R

false

108

r

loadExampleData.R

exampleInput <- read.delim("data-raw/authors.tab", sep="\t",) devtools::use_data(exampleInput, overwrite=T)

0c1851a84c145e68e3d2f09665c4314eb46d8f39

40cf390f349297ad4b12cdcef20577b0ff2d0340

/R/system.R

f83843b724a307b231c79e7095034591ac58d7e2

[]

no_license

omegahat/RTimingTools

2fa1742fb5ddf3f6f4d1555a2ebe50dec4be16b0

cfcdf8dae2b2303a93cc0cbdb3618145ee682fad

refs/heads/master

2016-09-06T00:45:11.588514

2012-02-22T01:54:38

null

0

null

UTF-8

R

false

473

r

system.R

# Alternatives: # C code http://gwenole.beauchesne.info/projects/cpuinfo/ cpuInfo.Linux = function() { txt = system("cat /proc/cpuinfo", intern = TRUE) m = read.dcf(textConnection(txt)) colnames(m) = gsub("\\s*$", "", colnames(m)) structure( list(mhz = m[,"cpu MHz"], cache = m[, 'cache size'], cores = m[, 'cpu cores'], model = m[, 'model name'], vendor = m[, 'vendor_id']), class = c("LinuxCPUInfo", "CPUInfo")) }

8e13aa69fc62ceb0c0ea10db2b77c5644f4294e6

88e00382da404a7929f346ad82818d5986d3c667

/run_analysis.R

e7d23feba37e10dbac4574f88b39ab8e0bdb92ba

[]

no_license

jjc0965/getting_and_cleaning_data

0d4b49203213fdba3f87b46f4a3551b1f5ba87ec

48c5abe5b930cca5b04d181462f700020919bc01

refs/heads/master

2021-01-10T22:13:45.460664

2015-03-20T17:18:53

32,487,780

0

null

UTF-8

R

false

3,083

r

run_analysis.R

## 1) READS DATA INTO R activity_labels<-read.table("UCI HAR Dataset/activity_labels.txt") ## Imports the different types of activities into R features<-read.table("UCI HAR Dataset/features.txt") ## Reads the features data (i.e., "tBodyAcc-mean()-X") into R train_set<-read.table("UCI HAR Dataset/train/X_train.txt") ## Reads X-train data (training set) into R train_labels<-read.table("UCI HAR Dataset/train/y_train.txt") ## Reads training labels (1-6) into R train_subject<-read.table("UCI HAR Dataset/train/subject_train.txt") ## Reads subject who performed the training activity (1-30) into R test_set<-read.table("UCI HAR Dataset/test/X_test.txt") ## Reads X-test data (test set) into R test_labels<-read.table("UCI HAR Dataset/test/y_test.txt") ## Reads test labels (1-6) into R test_subject<-read.table("UCI HAR Dataset/test/subject_test.txt") ## Reads subject who performed the test activity (1-30) into R ## 2) COMBINES TRAIN AND TEST DATA combined_set<-rbind(train_set, test_set) ## Combines train and test sets combined_labels<-rbind(train_labels, test_labels) ## Combines train and test labels combined_subject<-rbind(train_subject, test_subject) ## Combines train and test subjects ## 3) EXTRACTS ONLY FEATURES WHICH ANALYZE MEANS OR STANDARD DEVIATIONS features_mean_only<-grep("-mean()", as.character(features$V2), fixed=TRUE) ## A vector of integers - selects only the features data containing "mean". ## Setting fixed=TRUE does not allow () to be interpreted as a metacharacter. I am eliminating all fields with "meanFreq", by doing this, as these are not truly means features_std_only<-grep("-std()", as.character(features$V2), fixed=TRUE) ## A vector of integers - selects only the features data containing "std" - same rationale as above features_mean_or_std<-sort(c(features_mean_only, features_std_only)) ## A vector of integers - selects only the features data containing either "mean" or "std" ## 4) CREATES FINAL WORKING DATA SET - NAMED "set" set<-cbind(combined_subject, combined_labels, combined_set[features_mean_or_std]) ## the set we will be working with with columns combined and only means and stds included - short name for this reason ## 5) CREATION OF TIDY DATA SET tidydata<-aggregate(set[3:68], by=set[1:2], mean) ## Gets aggregate of data by test subject ID and activity colnames(tidydata)<-c("Subject.ID", "Activity", as.character(features[features_mean_or_std,2])) ## Labels all columns tidydata<-tidydata[with(tidydata, order(Subject.ID, Activity)), ] ## Puts the data in order by "Subject.ID and "Activity" rownames(tidydata)<-NULL tidydata[,"Activity"]<-activity_labels[tidydata[,"Activity"],2] ## Puts text of activity in column 2 (labeled "Activity") of the output ## 6) WRITES TABLE TO "tidydata.txt" write.table(tidydata, file="tidydata.txt", sep="\t") ## Writes the data to a file named "tidydata.txt", not incorporating row.names ## 7) READS DATA FROM "tidydata.txt" BACK TO "data_final" data_final<-read.table("tidydata.txt") ##Reads the "tidydata.txt" file back into R View(data_final)

79c8be389c05b3de40e7532082060e836f541bda

8572f31d31d0ea012b1b5625e6efb9b944df113b

/2020/week13/R/analysis.R

4fc7cac3cb9714e06f4c2f9fb845c51306e883f7

[]

no_license

larsbrenta/tidyt_jkaupp

0bf5572c230ad4d2d8bc40fd83d0803c8e541681

7ce8ae0df2f58b62794a45c90405b5aa561fcc93

refs/heads/master

2023-06-12T17:24:52.896620

2021-07-06T16:23:03

null

0

null

UTF-8

R

false

2,209

r

analysis.R

library(tidyverse) library(here) library(jkmisc) library(janitor) library(waffle) library(glue) library(scales) tbi_age <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-24/tbi_age.csv') injury_mechanisms <- set_names(unique(tbi_age$injury_mechanism), c("Motor Vehicle", "Struck By/Against", "Struck By/Against", "Other/Unknown", "Self-Harm", "Assault", "Other/Unknown")) age_groups <- c("0-4", "5-14", "15-24", "25-34", "35-44", "45-54", "55-64", "65-74", "75+") injury_labels <- c("Motor Vehicle", "Self-Harm", "Assault", "Other/Unknown", "Struck By/Against") tbi_age_pruned <- tbi_age %>% filter(age_group != "0-17", age_group != "Total") %>% mutate(injury_mechanism = fct_recode(injury_mechanism, !!!injury_mechanisms), age_group = factor(age_group, age_groups)) %>% count(age_group, injury_mechanism, wt = number_est) %>% mutate(per_capita = n/1000) pal <- c(RColorBrewer::brewer.pal(6, "Greys")[c(-1, -6)],"#048ba8") %>% set_names(injury_labels) titles <- imap(pal, ~highlight_text(.y, .x, 'b')) plot <- ggplot(tbi_age_pruned) + geom_waffle(aes(fill = fct_rev(injury_mechanism), values = per_capita), n_rows = 10, flip = TRUE, size = 0.1, color = "white", show.legend = FALSE) + facet_wrap(~age_group, nrow = 1, strip.position = "bottom") + scale_x_discrete() + scale_y_continuous(labels = function(x) glue("{x * 10}K"), expand = c(0,0), limits = c(0, 50)) + coord_equal() + scale_fill_manual(values = pal) + labs(title = "Traumatic Brain Injuries Peak in Late Teens/Early Adults and again in Seniors.", subtitle = glue("{paste0(flatten_chr(titles[-4:-5]), collapse = ', ')} and {titles[4]} all pose risks for brain injury, but accidents classified as {titles$`Struck By/Against`} are the greatest risk across all ages."), caption = "**Data**: CDC | **Graphic**: @jakekaupp") + theme_jk(grid = FALSE, ticks = TRUE, markdown = TRUE) + theme(strip.text.x = element_text(hjust = 0.5, size = 9)) ggsave(here("2020", "week13", "tw13_plot.png"), plot, width = 10, height = 6, dev = ragg::agg_png())

cb0ae5875366a8dfc90dde5631b50510262135c9

79eb7c6958b247770731ee20a5d9be525d8f5ed0

/exercises/concept/lasagna/lasagna.R

cafa7c4b4a395a03ff0320574eda074fcbc09a21

[ "CC-BY-SA-4.0", "CC-BY-3.0", "CC-BY-4.0", "MIT" ]

permissive

exercism/r

345781f314b8a66be047abd889238cba2630a20c

566676cca76019e3e6a602f8f4d8086c54a51e1e

refs/heads/main

2023-08-03T09:30:59.027153

2023-07-28T00:18:31

24,401,761

22

37

MIT

2023-09-05T11:19:45

2014-09-24T05:22:10

R

UTF-8

R

false

227

r

lasagna.R

# TODO: define the 'expected_minutes_in_oven()' function # TODO: define the 'remaining_time_in_minutes()' function # TODO: define the 'prep_time_in_minutes()' function # TODO: define the 'elapsed_time_in_minutes()' function

8382313196b1b54d0f5076f9b1f9a03e0e24119f

f3244f77367f735f30833a5b6e2b69321a28dfb5

/man/chronique.figure.preferendums.Rd

873d67e00d71c48d8d38b934859fbdffdf555950

[]

no_license

jbfagotfede39/aquatools

3f36367668c6848ddd53950708222fd79f0e3b7a

9c12f80919790ec3d0c1ee7f495e9c15cc2c9652

refs/heads/master

2023-08-03T09:10:17.314655

2023-07-25T08:32:45

46,334,983

0

1

null

UTF-8

R

false

true

2,055

rd

chronique.figure.preferendums.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/chronique.figure.preferendums.R \name{chronique.figure.preferendums} \alias{chronique.figure.preferendums} \title{Vues des préférendums thermiques des especes piscicoles} \usage{ chronique.figure.preferendums( staderecherche = c("Adulte", "Reproduction", "Embryon", "Larve", "Juvénile", "Tous stades"), tmm30j = 0, liste_especes = c("Toutes espèces", "TRF", "CHA", "LPP", "VAI", "LOF", "OBR", "EPI", "BLE", "BLN", "CHE", "APR", "GOU", "HOT", "TOX", "BAF", "LOT", "SPI", "VAN", "EPT", "BOU", "BRO", "PER", "GAR", "ABL", "CAS", "GRE", "CCO", "SAN", "TAN", "BRB", "BRE", "PES", "ROT", "BBG", "PCH", "SIL"), titre = "", save = F, projet = NA_character_, format = ".png" ) } \arguments{ \item{staderecherche}{Stade de vie recherché : \code{Adulte} (par défaut), \code{Reproduction}, \code{Embryon}, \code{Larve}, \code{Juvénile} ou \code{Tous stades}} \item{tmm30j}{Valeur de Tmm30j à représenter : 0 par défaut (aucun affichage)} \item{liste_especes}{Liste des espèces à afficher : toutes par défaut. Les espèces sont affichées dans l'ordre de saisie.} \item{titre}{Titre du graphique (vide par défaut)} \item{save}{Si \code{FALSE} (par défault), n'enregistre pas les figures. Si \code{TRUE}, les enregistre.} \item{projet}{Nom du projet} \item{format}{Défini le format d'enregistrement (par défaut .png)} } \description{ Cette fonction permet de représenter les préférendums thermiques des espèces piscicoles, avec éventuellement la Tmm30j observée } \examples{ chronique.figure.preferendums() chronique.figure.preferendums("Reproduction") chronique.figure.preferendums("Tous stades", tmm30j = 17.4) chronique.figure.preferendums("Toutes espèces", tmm30j = 17.4) chronique.figure.preferendums("Adulte", tmm30j = 17.4, "TRF,CHA,LOG,VAL") chronique.figure.preferendums("Adulte", tmm30j = 17.4, liste_especes) chronique.figure.preferendums("Adulte", tmm30j = 17.4, .$chsta_sprep) } \keyword{chronique} \keyword{poissons}

549457eb0d4ad9e300e7b89ad061bb1521a05f90

5f684a2c4d0360faf50fe055c1147af80527c6cb

/2020/2020-week38/kids.R

a114660755e392caa7419a3cc5d76e477001fb65

[ "MIT" ]

permissive

gkaramanis/tidytuesday

5e553f895e0a038e4ab4d484ee4ea0505eebd6d5

dbdada3c6cf022243f2c3058363e0ef3394bd618

refs/heads/master

2023-08-03T12:16:30.875503

2023-08-02T18:18:21

174,157,655

630

117

MIT

2020-12-27T21:41:00

2019-03-06T14:11:15

R

UTF-8

R

false

2,676

r

kids.R

library(tidyverse) library(geofacet) library(colorspace) kids <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-15/kids.csv') states_abbr <- read_csv(here::here("2020-week38", "data", "us-states-abbr-AP.csv")) %>% select(state = title_case, ap_style) libraries <- kids %>% filter(variable == "lib") %>% filter(year == 1997 | year == 2016) %>% mutate(inf_adj_perchild = inf_adj_perchild * 1000) %>% pivot_wider(id_cols = state, names_from = year, names_prefix = "year_", values_from = inf_adj_perchild) %>% mutate(diff = year_2016 - year_1997) %>% left_join(states_abbr) f1 <- "Proxima Nova" f1b <- "Proxima Nova Bold" f1m <- "Proxima Nova Medium" f2c <- "IBM Plex Sans Condensed" ggplot(libraries) + # Point and arrow geom_point(aes(x = 0, y = year_1997, color = ifelse(diff > 0, "grey97", "grey20")), size = 1.5) + geom_segment(aes(x = 0, xend = 1, y = year_1997, yend = year_2016, color = ifelse(diff > 0, "grey97", "grey20")), arrow = arrow(length = unit(0.2, "cm")), size = 0.75) + # State label geom_text(aes(x = -0.3, y = 350, label = ap_style), stat = "unique", hjust = 0, color = "grey95", family = f1b, size = 3.5) + # 1997 value label geom_text(aes(x = 0, y = year_1997 - 90, label = round(year_1997)), hjust = 0.5, family = f2c, size = 3, color = darken("#7E95A9", 0.3)) + # 2016 value label geom_text(aes(x = 1.1, y = year_2016 - 90, label = round(year_2016)), hjust = 0.5, family = f2c, size = 3, color = darken("#7E95A9", 0.4)) + # Scales, facet, labs # scale_color_gradient2(low = "red", mid = "grey75", high = "grey97") + scale_color_identity() + facet_geo(vars(state)) + labs( title = "Change in public spending on libraries from 1997 to 2016", subtitle = "Dollars spent per child, adjusted for inflation", caption = "Source: Urban Institute | Graphic: Georgios Karamanis" ) + coord_cartesian(clip = "off", expand = FALSE) + # Theme theme_void() + theme( legend.position = "none", plot.background = element_rect(fill = "#7E95A9", color = NA), plot.margin = margin(20, 30, 15, 30), panel.spacing.x = unit(1.5, "lines"), panel.spacing.y = unit(1, "lines"), strip.text = element_blank(), plot.title = element_text(size = 16, family = f1b, hjust = 0.5, margin = margin(10, 0, 0, 0)), plot.subtitle = element_text(size = 14, family = f1, hjust = 0.5, margin = margin(5, 0, 25, 0)), plot.caption = element_text(family = f1, hjust = 1, margin = margin(20, 0, 0, 0)) ) ggsave(here::here("temp", paste0("kids-", format(Sys.time(), "%Y%m%d_%H%M%S"), ".png")), dpi = 320, width = 9, height = 8)

c1f6c7a51e6ded825bb4aba0c952a80bd936e58b

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/paleotree/examples/unitLengthTree.Rd.R

6f3e08da1ff6edfa38e9db59fea375d971d913e8

[]

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

226

r

unitLengthTree.Rd.R

library(paleotree) ### Name: unitLengthTree ### Title: Scale Tree to Unit-Length ### Aliases: unitLengthTree ### ** Examples set.seed(444) tree <- rtree(10) layout(1:2) plot(tree) plot(unitLengthTree(tree)) layout(1)

077dfe6aa8ffb31a98739987875d0a271ec147d0

5be5469a64c3c6a26f7bbda0d6b2d996755396f3

/plot4.R

2ef901a5ac350959b839530371919be93a6fb37d

[]

no_license

Slak60/ExData_Plotting1

c29efd984b0f2c6c0809dbe72e26d5666ff8a986

5bde35c4d3a6152fac2cfbdc5233b0d6be2dbe16

refs/heads/master

2022-10-26T15:39:45.398905

2020-06-16T17:46:18

272,774,053

0

null

2020-06-16T17:43:19

2020-06-16T17:43:18

null

UTF-8

R

false

1,442

r

plot4.R

temp <- tempfile() ## create a temp file to download the .zip file download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip", temp)##download zip file power_consumption <- unzip(temp)## unzip file ## read the .txt file mydata<- read.table("./household_power_consumption.txt", header=TRUE, sep=";",na.strings="?",stringsAsFactors = FALSE) library(lubridate) mydf <- transform(subset(mydata, Date=="1/2/2007"| Date =="2/2/2007"), Date=format(as.Date(Date, format="%d/%m/%Y"),"%Y-%m-%d"), Time=format(strptime(Time, format="%H:%M:%S"),"%H:%M:%S")) mydf$DateTime <- as.POSIXct(paste(mydf$Date,mydf$Time)) png(file="Plot4.png",width=480,height=480) my.par <- par(mfrow=c(2,2)) plot(mydf$DateTime,mydf$Global_active_power,type="l",xlab="",ylab="Global Active Power(kilowatts)") plot(mydf$DateTime,mydf$Voltage,type="l",xlab="datetime",ylab="Voltage") plot(mydf$DateTime,mydf$Sub_metering_1,type="l",col="black",xlab="",ylab="Energy sub metering") lines(mydf$DateTime,mydf$Sub_metering_2, col="red") lines(mydf$DateTime,mydf$Sub_metering_3, col="blue") legend("topright", legend=c("Sub_metering_1","Sub_metering_2","Sub_metering_3"), col=c("black","red","blue"), pch=c("-","-","-"), lty=c(1,1,1),lwd=c(2,2,2), ncol=1) plot(mydf$DateTime,mydf$Global_reactive_power,type="l",xlab="datetime",ylab="Global_reactive_power") par(my.par) dev.off()

da70d1e7b8d0ec2a97e6021621b0b0341da77bc3

7224813a0f5d032aed634a637328e87030dd070d

/R/prepare_models.R

6ab767f8d08309f6a8de7971b7d64cd6d54d3705

[ "MIT" ]

permissive

ethanwhite/portalcasting

3d164472a54e5ae746a668cc848e813e4b795eaf

03bd42b5a01536a9178e9aeb47046847acda9984

refs/heads/master

2023-04-30T20:59:54.178447

2022-06-01T00:13:25

157,772,725

0

MIT

2018-11-15T21:06:31

null

UTF-8

R

false

7,968

r

prepare_models.R

#' @title Read and Write Model Control Lists #' #' @description Input/output functions for model control lists. #' #' @param quiet \code{logical} indicator controlling if messages are printed. #' #' @param main \code{character} value of the name of the main component of the directory tree. #' #' @param models \code{character} vector of name(s) of model(s) to include. #' #' @param settings \code{list} of controls for the directory, with defaults set in \code{\link{directory_settings}}. #' #' @param new_model_controls \code{list} of controls for any new models (not in the prefab models) listed in \code{models} that are to be added to the control list and file. #' #' @return \code{list} of \code{models}' control \code{list}s, \code{\link[base]{invisible}}-ly for \code{write_model_controls}. #' #' @name read and write model controls #' #' @export #' read_model_controls <- function (main = ".", settings = directory_settings()) { read_yaml(file.path(main, settings$subs$models, settings$files$model_controls)) } #' @rdname read-and-write-model-controls #' #' @export #' model_controls <- function (main = ".", models = prefab_models(), settings = directory_settings()) { nmodels <- length(models) if (nmodels == 1) { read_model_controls(main = main, settings = settings)[[models]] } else if (nmodels > 1) { read_model_controls(main = main, settings = settings)[models] } } #' @rdname read-and-write-model-controls #' #' @export #' write_model_controls <- function (main = ".", new_model_controls = NULL, models = prefab_models(), settings = directory_settings(), quiet = FALSE) { model_controls <- prefab_model_controls() nmodels <- length(model_controls) nnew_models <- length(new_model_controls) if (nnew_models > 0) { for (i in 1:nnew_models) { model_controls <- update_list(model_controls, x = new_model_controls[[i]]) names(model_controls)[nmodels + i] <- names(new_model_controls)[i] } } write_yaml(x = model_controls, file = file.path(main, settings$subs$models, settings$files$model_controls)) invisible(model_controls) } #' @title Write Model Function Script into Directory #' #' @description Writes a model's function as a script into the defined directory for use in forecasting. \cr \cr \code{model} can be input as a \code{character} string, symbol (backquoted name), or \code{function}, as \code{\link{match.fun}} #' #' @param main \code{character} value of the name of the main component of the directory tree. #' #' @param model \code{character} name of a model function, the \code{function} itself, or its symbol (backquoted name). #' #' @param settings \code{list} of controls for the directory, with defaults set in \code{\link{directory_settings}} that should generally not need to be altered. #' #' @param quiet \code{logical} indicator if progress messages should be quieted. #' #' @param verbose \code{logical} indicator of whether or not to print out all of the information (and thus just the tidy messages). #' #' @param datasets \code{character} vector of dataset names for the model. #' #' @return \code{write_mode} \code{\link{write}}s the model script out and returns \code{NULL}, \code{\link[base]{invisible}}-ly.. \cr \cr #' \code{model_template}: \code{character}-valued text for a model script to be housed in the model directory. \cr \cr #' \code{control_list_arg}: \code{character}-valued text for part of a model script. \cr \cr #' #' @examples #' \donttest{ #' create_dir() #' write_model("AutoArima") #' model_template() #' control_list_arg(runjags_control(nchains = 3), "runjags_control") #' } #' #' @export #' write_model <- function (main = ".", model = NULL, settings = directory_settings(), quiet = FALSE, verbose = FALSE) { return_if_null(model) control_model <- tryCatch(prefab_model_controls()[[model]], error = function(x){NULL}) datasets <- control_model$datasets if (is.null(datasets)) { messageq(" ~datasets = NULL for ", model, quiet = quiet) datasets <- prefab_datasets() } model_file <- paste0(model, ".R") mod_path <- file.path(main, settings$subs$models, model_file) mod_template <- model_template(main = main, model = model, datasets = prefab_datasets(), settings = directory_settings(), quiet = FALSE, verbose = FALSE) if (file.exists(mod_path) & settings$overwrite) { write(mod_template, mod_path) messageq(" -", ifelse(verbose, "Updating ", ""), model, quiet = quiet) } else if (!file.exists(mod_path)) { write(mod_template, mod_path) messageq(" -", ifelse(verbose, "Adding ", ""), model, quiet = quiet) } invisible() } #' @rdname write_model #' #' @export #' model_template <- function (main = ".", model = NULL, datasets = NULL, settings = directory_settings(), quiet = FALSE, verbose = FALSE) { return_if_null(model) control_model <- tryCatch(prefab_model_controls()[[model]], error = function(x){NULL}) datasets <- control_model$datasets nds <- length(datasets) return_if_null(datasets) main_arg <- paste0(', main = "', main, '"') quiet_arg <- paste0(', quiet = ', quiet) verbose_arg <- paste0(', verbose = ', verbose) ds_args <- paste0('dataset = "', datasets, '"') settings_arg <- paste0(', settings = directory_settings()') additional_args <- NULL nadditional_args <- length(control_model$args) if (nadditional_args > 0) { for (i in 1:nadditional_args) { additional_args <- paste0(additional_args, ", ", names(control_model$args)[i], " = ", control_model$args[i]) } } out <- NULL for(i in 1:nds){ resp <- paste0('cast_', datasets[i]) model_args <- paste0(ds_args[i], main_arg, settings_arg, quiet_arg, verbose_arg, additional_args) model_fun <- paste0(model, '(', model_args, ');') model_line <- paste0(resp, ' <- ', model_fun) save_args <- paste0(resp, main_arg, settings_arg, quiet_arg) save_fun <- paste0('save_cast_output(', save_args, ');') save_line <- save_fun newout <- c(model_line, save_line) out <- c(out, newout) } out } #' @title Create a covariate model list #' #' @description Convenience function for creating covariate model \code{list}s. #' #' @param model \code{character} name for covariate models. Currently only \code{"pevGARCH"} is supported. #' #' @return \code{list} of covariate model structures. #' #' @examples #' covariate_models() #' #' @export #' covariate_models <- function (model = "pevGARCH") { out <- NULL if (model == "pevGARCH") { out <- list(c("maxtemp", "meantemp", "precipitation", "ndvi"), c("maxtemp", "mintemp", "precipitation", "ndvi"), c("mintemp", "maxtemp", "meantemp", "precipitation"), c("precipitation", "ndvi"), c("mintemp", "ndvi"), c("mintemp"), c("maxtemp"), c("meantemp"), c("precipitation"), c("ndvi"), c(NULL)) } out }

97f82e3e771548ea9f0b5473c01595758aa6a308

6d70e865c21e483f74dd6085fa0cb01f91b04729

/man/cytoband.col.Rd

26db63cc4c09130c04a06b9ca905c5122d1db63a

[ "MIT" ]

permissive

dgabbe/circlize

8eb53ec62425033b46cb9ca652e7c5cc2dec4f61

a01d0015e1ccd60f6ff2f7a8d568c32f873063f4

refs/heads/master

2022-07-17T08:37:49.032202

2020-11-23T18:07:17

232,622,670

0

NOASSERTION

2020-01-08T17:42:35

2020-01-08T17:42:34

null

UTF-8

R

false

473

rd

cytoband.col.Rd

\name{cytoband.col} \alias{cytoband.col} \title{ Assign colors to cytogenetic band (hg19) according to the Giemsa stain results } \description{ Assign colors to cytogenetic band (hg19) according to the Giemsa stain results } \usage{ cytoband.col(x) } \arguments{ \item{x}{A vector containing the Giemsa stain results } } \details{ The color theme is from \url{http://circos.ca/tutorials/course/slides/session-2.pdf,} page 42. } \examples{ # There is no example NULL }

4c9f7b32004dd9d96bccc94607c51a0aff00b5fd

29ce175254b27a361714074b99fe5644ca2c3158

/man/data_wings.Rd

6da340dbafae16e4ca6602eae629919495ea9238

[]

no_license

raz1/Momocs

148903d3f05428e72d7cef8ede46c0bba7f80f04

09a817bb0720d87969d48dd9e0f16516e042e13e

refs/heads/master

2021-01-17T18:09:55.724182

2014-08-15T14:49:17

null

0

null

UTF-8

R

false

425

rd

data_wings.Rd

% Generated by roxygen2 (4.0.0): do not edit by hand \docType{data} \name{wings} \alias{wings} \title{Data: Landmarks coordinates of mosquito wings} \format{A \link{Ldk} object containing 18 (x; y) landmarks from 127 mosquito wings, from} \source{ Rohlf and Slice 1990 and \url{http://life.bio.sunysb.edu/morph/data/RohlfSlice1990Mosq.nts} } \description{ Data: Landmarks coordinates of mosquito wings } \keyword{Datasets}

6151107fd63e57ca653739bbdb8ad939a4c0c4df

c3e7dcf4ac8688519084710e5eb6ec7c557dbfdf

/examples/FH.frac.cal_example.R

ada132007c1fb71cea44b0b96cb35774ee2b16c1

[]

no_license

lilywang1988/IAfrac

e24df17c15d314cc98257740435236138a154277

2c9e5329174937c0a418b93d2cc6f467f1dd20f1

refs/heads/master

2021-06-24T19:29:02.958609

2021-03-07T07:59:48

202,036,400

1

null

2021-03-07T07:59:48

2019-08-13T01:16:18

R

UTF-8

R

false

2,036

r

FH.frac.cal_example.R

# Examples for FH.frac.cal # install.packages("devtools") # library(devtools) # install_github("keaven/nphsim") library(nphsim) eps<-2 # delayed effect p<-0.5 #treatment assignment b<-30 # an intrinsic parameter to decide the number of intervals per time unit tau<- 18 # end of the study R<-14 # accrual period [0,R] omega<- tau-R lambda<-log(2)/6 # control group risk hazard theta<-0.7 # hazard ratio lambda.trt<- lambda*theta #hazard after the change point for the treatment arm rho<- 0 # parameter for the weights gamma<-1 #parameter for the weights alpha<-0.025 #type 1 error beta<-0.1 #type 2 error # First we decide the sample size: size_FH <- sample.size_FH(eps,p,b,tau,omega,lambda,lambda.trt,rho, gamma,alpha,beta) n_FH <-size_FH$n n_event_FH<-size_FH$n_event accrual.rt<-n_FH/R # the needed arrual rate #Generate data accordingly, use eta=1e-5 to inhibit censoring data_temp <- nphsim(nsim=1,lambdaC=lambda, lambdaE = c(lambda,lambda.trt), ssC=ceiling(n_FH*(1-p)),intervals = c(eps),ssE=ceiling(n_FH*p), gamma=accrual.rt, R=R, eta=1e-5, fixEnrollTime = TRUE)$simd # Example 1 for FH.frac.cal: Set trimmed=F and work on the crude dataset from nphsim() inf_frac_vec1<-FH.frac.cal(data_temp,c(10,15,18),I_denom,rho,gamma,trimmed=F) inf_frac_vec1 # Example 2 for FH.frac.cal: First trim the data before inputting into FH.frac.cal() setting trimmed=T, and obtain the whole spectrum. I_denom<-I.1(rho, gamma,lambda,theta,eps,R,p,t.star=tau)*n_FH tau.star=21 #in case the ratio=1 when t>tau #Trim the data data_temp2 <-data.trim(tau.star,data_temp) t_seq <- seq(0.1,tau.star,0.1) # the time series to check the information fraction inf_frac_vec2<-FH.frac.cal(data_temp2,t_seq,I_denom,rho,gamma,trimmed=T) # WLRT at the interim interim_index<- which.min(abs(inf_frac_vec2-0.6)) interim_time<-t_seq[interim_index] interim_frac<-inf_frac_vec2[interim_index] # WLRT at the final final_index<- which.min(abs(inf_frac_vec2-1)) final_time<-t_seq[final_index] final_frac<-inf_frac_vec2[final_index]

0ec3c71d4f411425302d2ff37c4fdbd74a554c09

9f63b0971eda98e020bb8ba3edeba5d3332cda16

/scripts/create_inflow_files_sunapee/MergeFlowNutrients_Sunapee_Make1Inflow.R

d36b7810fb347d2f77c2e7d65102bdd2f794acb1

[]

no_license

tadhg-moore/Sunapee-GLM

c2664404660712267587ee1a95d3a6ae615345af

083ff8ef04cdba3d05aa5999ea51dad3ba6ccc4a

refs/heads/master

2023-05-08T14:03:19.041890

2021-06-01T20:53:47

null

0

null

UTF-8

R

false

11,143

r

MergeFlowNutrients_Sunapee_Make1Inflow.R

rm(list=ls()) #options(scipen = 999) nuts<-read.csv("./data/individual_inflows/TN_TP_Inflow_conc_fracNP_boot_2021-03-16.csv") files<-list.files("./data/individual_inflows/", pattern = "*totalinflow_temp_2021-03-17.csv") alldata <- do.call(rbind,lapply(paste0("./data/individual_inflows/", files), read.csv)) alldata$date<-as.character(alldata$date) alldata$date<-as.POSIXct(alldata$date, format="%Y-%m-%d") alldata$year<-as.POSIXct(alldata$date, format="%Y") library(dplyr) i505<-alldata[which(alldata$stream_id==505),] i505<- subset(i505, select=c("date", "modelinflow_m3ps", "ModStreamTemp_degC")) names(i505)[names(i505)==c("date", "modelinflow_m3ps", "ModStreamTemp_degC")] <- c("time","FLOW505","TEMP505") nut505<-subset(nuts,select=c("V1","i505_TP","i505_TN")) names(nut505)[names(nut505)==c("V1", "i505_TP","i505_TN")] <- c("time","TP505","TN505") nut505$time<-as.character(nut505$time) nut505$time<-as.POSIXct(nut505$time, format="%Y-%m-%d") nut505$time<-as.POSIXct(nut505$time, format="%Y") i505 <- i505 %>% left_join(nut505) i790<-alldata[which(alldata$stream_id==790),] i790<- subset(i790, select=c("date", "modelinflow_m3ps", "ModStreamTemp_degC")) names(i790)[names(i790)==c("date", "modelinflow_m3ps", "ModStreamTemp_degC")] <- c("time","FLOW790","TEMP790") nut790<-subset(nuts,select=c("V1","i790_TP","i790_TN")) names(nut790)[names(nut790)==c("V1", "i790_TP","i790_TN")] <- c("time","TP790","TN790") nut790$time<-as.character(nut790$time) nut790$time<-as.POSIXct(nut790$time, format="%Y-%m-%d") nut790$time<-as.POSIXct(nut790$time, format="%Y") i790 <- i790 %>% left_join(nut790) i830<-alldata[which(alldata$stream_id==830),] i830<- subset(i830, select=c("date", "modelinflow_m3ps", "ModStreamTemp_degC")) names(i830)[names(i830)==c("date", "modelinflow_m3ps", "ModStreamTemp_degC")] <- c("time","FLOW830","TEMP830") nut830<-subset(nuts,select=c("V1","i830_TP","i830_TN")) names(nut830)[names(nut830)==c("V1", "i830_TP","i830_TN")] <- c("time","TP830","TN830") nut830$time<-as.character(nut830$time) nut830$time<-as.POSIXct(nut830$time, format="%Y-%m-%d") nut830$time<-as.POSIXct(nut830$time, format="%Y") i830 <- i830 %>% left_join(nut830) i788<-alldata[which(alldata$stream_id==788),] i788<- subset(i788, select=c("date", "modelinflow_m3ps", "ModStreamTemp_degC")) names(i788)[names(i788)==c("date", "modelinflow_m3ps", "ModStreamTemp_degC")] <- c("time","FLOW788","TEMP788") nut788<-subset(nuts,select=c("V1","i788_TP","i788_TN")) names(nut788)[names(nut788)==c("V1", "i788_TP","i788_TN")] <- c("time","TP788","TN788") nut788$time<-as.character(nut788$time) nut788$time<-as.POSIXct(nut788$time, format="%Y-%m-%d") nut788$time<-as.POSIXct(nut788$time, format="%Y") i788 <- i788 %>% left_join(nut788) i510<-alldata[which(alldata$stream_id==510),] i510<- subset(i510, select=c("date", "modelinflow_m3ps", "ModStreamTemp_degC")) names(i510)[names(i510)==c("date", "modelinflow_m3ps", "ModStreamTemp_degC")] <- c("time","FLOW510","TEMP510") nut510<-subset(nuts,select=c("V1","i510_TP","i510_TN")) names(nut510)[names(nut510)==c("V1", "i510_TP","i510_TN")] <- c("time","TP510","TN510") nut510$time<-as.character(nut510$time) nut510$time<-as.POSIXct(nut510$time, format="%Y-%m-%d") nut510$time<-as.POSIXct(nut510$time, format="%Y") i510 <- i510 %>% left_join(nut510) i540<-alldata[which(alldata$stream_id==540),] i540<- subset(i540, select=c("date", "modelinflow_m3ps", "ModStreamTemp_degC")) names(i540)[names(i540)==c("date", "modelinflow_m3ps", "ModStreamTemp_degC")] <- c("time","FLOW540","TEMP540") nut540<-subset(nuts,select=c("V1","i540_TP","i540_TN")) names(nut540)[names(nut540)==c("V1", "i540_TP","i540_TN")] <- c("time","TP540","TN540") nut540$time<-as.character(nut540$time) nut540$time<-as.POSIXct(nut540$time, format="%Y-%m-%d") nut540$time<-as.POSIXct(nut540$time, format="%Y") i540 <- i540 %>% left_join(nut540) # i800<-alldata[which(alldata$stream_id==800),] i800<- subset(i800, select=c("date", "modelinflow_m3ps", "ModStreamTemp_degC")) names(i800)[names(i800)==c("date", "modelinflow_m3ps", "ModStreamTemp_degC")] <- c("time","FLOW800","TEMP800") nut800<-subset(nuts,select=c("V1","i800_TP","i800_TN")) names(nut800)[names(nut800)==c("V1", "i800_TP","i800_TN")] <- c("time","TP800","TN800") nut800$time<-as.character(nut800$time) nut800$time<-as.POSIXct(nut800$time, format="%Y-%m-%d") nut800$time<-as.POSIXct(nut800$time, format="%Y") i800 <- i800 %>% left_join(nut800) i835<-alldata[which(alldata$stream_id==835),] i835<- subset(i835, select=c("date", "modelinflow_m3ps", "ModStreamTemp_degC")) names(i835)[names(i835)==c("date", "modelinflow_m3ps", "ModStreamTemp_degC")] <- c("time","FLOW835","TEMP835") nut835<-subset(nuts,select=c("V1","i835_TP","i835_TN")) names(nut835)[names(nut835)==c("V1", "i835_TP","i835_TN")] <- c("time","TP835","TN835") nut835$time<-as.character(nut835$time) nut835$time<-as.POSIXct(nut835$time, format="%Y-%m-%d") nut835$time<-as.POSIXct(nut835$time, format="%Y") i835 <- i835 %>% left_join(nut835) i805<-alldata[which(alldata$stream_id==805),] i805<- subset(i805, select=c("date", "modelinflow_m3ps", "ModStreamTemp_degC")) names(i805)[names(i805)==c("date", "modelinflow_m3ps", "ModStreamTemp_degC")] <- c("time","FLOW805","TEMP805") nut805<-subset(nuts,select=c("V1","i805_TP","i805_TN")) names(nut805)[names(nut805)==c("V1", "i805_TP","i805_TN")] <- c("time","TP805","TN805") nut805$time<-as.character(nut805$time) nut805$time<-as.POSIXct(nut805$time, format="%Y-%m-%d") nut805$time<-as.POSIXct(nut805$time, format="%Y") i805 <- i805 %>% left_join(nut805) i665<-alldata[which(alldata$stream_id==665),] i665<- subset(i665, select=c("date", "modelinflow_m3ps", "ModStreamTemp_degC")) names(i665)[names(i665)==c("date", "modelinflow_m3ps", "ModStreamTemp_degC")] <- c("time","FLOW665","TEMP665") nut665<-subset(nuts,select=c("V1","i670_TP","i670_TN")) names(nut665)[names(nut665)==c("V1", "i670_TP","i670_TN")] <- c("time","TP665","TN665") nut665$time<-as.character(nut665$time) nut665$time<-as.POSIXct(nut665$time, format="%Y-%m-%d") nut665$time<-as.POSIXct(nut665$time, format="%Y") i665 <- i665 %>% left_join(nut665) i760<-alldata[which(alldata$stream_id==760),] i760<- subset(i760, select=c("date", "modelinflow_m3ps", "ModStreamTemp_degC")) names(i760)[names(i760)==c("date", "modelinflow_m3ps", "ModStreamTemp_degC")] <- c("time","FLOW760","TEMP760") nut760<-subset(nuts,select=c("V1","i760_TP","i760_TN")) names(nut760)[names(nut760)==c("V1", "i760_TP","i760_TN")] <- c("time","TP760","TN760") nut760$time<-as.character(nut760$time) nut760$time<-as.POSIXct(nut760$time, format="%Y-%m-%d") nut760$time<-as.POSIXct(nut760$time, format="%Y") i760 <- i760 %>% left_join(nut760) nuts<-nuts%>%mutate(aveTP=(i505_TP+i790_TP+i830_TP+i788_TP+i510_TP+i540_TP+i800_TP+ i835_TP+i805_TP+i670_TP+i760_TP)/11) nuts<-nuts%>%mutate(aveTN=(i505_TN+i790_TN+i830_TN+i788_TN+i510_TN+i540_TN+i800_TN+ i835_TN+i805_TN+i670_TN+i760_TN)/11) test<-subset(nuts,select=c("V1","aveTP","aveTN")) iUNG<-alldata[which(alldata$stream_id=="ung"),] iUNG<- subset(iUNG, select=c("date", "modelinflow_m3ps", "ModStreamTemp_degC")) names(iUNG)[names(iUNG)==c("date", "modelinflow_m3ps", "ModStreamTemp_degC")] <- c("time","FLOWung","TEMPung") #nutUNG<-test #!!!14June19 testing how to reduce nutrient concentration for overall inflow concentration being ~3x epi concentration nutUNG<-subset(nuts,select=c("V1","i505_TP","i505_TN")) #names(nutUNG)[names(nutUNG)==c("V1","aveTP","aveTN")] <- c("time","TPung","TNung") names(nutUNG)[names(nutUNG)==c("V1","i505_TP","i505_TN")] <- c("time","TPung","TNung") nutUNG$time<-as.character(nutUNG$time) nutUNG$time<-as.POSIXct(nutUNG$time, format="%Y-%m-%d") nutUNG$time<-as.POSIXct(nutUNG$time, format="%Y") iUNG <- iUNG %>% left_join(nutUNG) iBAL<-alldata[which(alldata$stream_id=="bal"),] iBAL<- subset(iBAL, select=c("date", "modelinflow_m3ps", "ModStreamTemp_degC")) names(iBAL)[names(iBAL)==c("date", "modelinflow_m3ps", "ModStreamTemp_degC")] <- c("time","FLOWBAL","TEMPBAL") nutBAL<-test names(nutBAL)[names(nutBAL)==c("V1","aveTP","aveTN")] <- c("time","TPBAL","TNBAL") nutBAL$time<-as.character(nutBAL$time) nutBAL$TPBAL<-0.0001 nutBAL$TNBAL<-0.0001 nutBAL$time<-as.POSIXct(nutBAL$time, format="%Y-%m-%d") nutBAL$time<-as.POSIXct(nutBAL$time, format="%Y") iBAL <- iBAL %>% left_join(nutBAL) iALL <- iUNG %>% left_join(iBAL)%>%left_join(i505)%>% left_join(i790) %>% left_join(i830) %>% left_join(i788) %>% left_join(i510) %>% left_join(i540) %>% left_join(i800) %>% left_join(i835) %>% left_join(i805)%>% left_join(i665)%>% left_join(i760) iALL<-iALL%>%mutate(totFLOW=FLOWung+FLOWBAL+FLOW505+FLOW790+FLOW830+FLOW788+FLOW510+FLOW540+FLOW800+ FLOW835+FLOW805+FLOW665+FLOW760) iALL<-iALL%>%mutate(vwsTP=(TPung*FLOWung/totFLOW)+(TPBAL*FLOWBAL/totFLOW)+(TP505*FLOW505/totFLOW)+(TP790*FLOW790/totFLOW)+ (TP830*FLOW830/totFLOW)+(TP788*FLOW788/totFLOW)+(TP510*FLOW510/totFLOW)+ (TP540*FLOW540/totFLOW)+(TP800*FLOW800/totFLOW)+ (TP835*FLOW835/totFLOW)+ (TP805*FLOW805/totFLOW)+(TP665*FLOW665/totFLOW)+(TP760*FLOW760/totFLOW)) iALL<-iALL%>%mutate(vwsTN=(TNung*FLOWung/totFLOW)+(TNBAL*FLOWBAL/totFLOW)+(TN505*FLOW505/totFLOW)+(TN790*FLOW790/totFLOW)+ (TN830*FLOW830/totFLOW)+(TN788*FLOW788/totFLOW)+(TN510*FLOW510/totFLOW)+ (TN540*FLOW540/totFLOW)+(TN800*FLOW800/totFLOW)+ (TN835*FLOW835/totFLOW)+ (TN805*FLOW805/totFLOW)+(TN665*FLOW665/totFLOW)+(TN760*FLOW760/totFLOW)) iALL<-iALL%>%mutate(vwsTEMP=(TEMPung*FLOWung/totFLOW)+(TEMPBAL*FLOWBAL/totFLOW)+(TEMP505*FLOW505/totFLOW)+(TEMP790*FLOW790/totFLOW)+ (TEMP830*FLOW830/totFLOW)+(TEMP788*FLOW788/totFLOW)+(TEMP510*FLOW510/totFLOW)+ (TEMP540*FLOW540/totFLOW)+(TEMP800*FLOW800/totFLOW)+ (TEMP835*FLOW835/totFLOW)+ (TEMP805*FLOW805/totFLOW)+(TEMP665*FLOW665/totFLOW)+(TEMP760*FLOW760/totFLOW)) iALL<-iALL%>%mutate(SALT=0) oneINFLOW<-subset(iALL,select=c("time","totFLOW","SALT","vwsTEMP","vwsTP","vwsTN")) names(oneINFLOW)[names(oneINFLOW)==c("time","totFLOW","SALT","vwsTEMP","vwsTP","vwsTN")] <- c("time","FLOW","SALT","TEMP","TP","TN") test<-oneINFLOW[which(oneINFLOW$time>"2004-12-26"),] test<-test[which(test$time<"2011-01-01"),] mean(test$TP,na.rm=TRUE)*31/1000 mean(test$TN,na.rm=TRUE)*14/1000 oneINFLOW<-oneINFLOW%>%mutate(OGM_doc=125)%>%mutate(OGM_poc=12.5)%>%mutate(OGM_don=0.4*TN)%>% mutate(NIT_nit=0.1*TN)%>%mutate(NIT_amm=0.1*TN)%>%mutate(OGM_pon=0.4*TN)%>% mutate(PHS_frp=0.0295*TP)%>%mutate(OGM_dop=0.1435*TP)%>%mutate(OGM_pop=0.327*TP)%>% mutate(PHS_frp_ads=0.5*TP) oneINFLOW<-subset(oneINFLOW,select=c("time","FLOW","SALT","TEMP","OGM_doc","OGM_poc", "OGM_don","NIT_nit","NIT_amm","OGM_pon","PHS_frp","OGM_dop", "OGM_pop","PHS_frp_ads")) write.csv(oneINFLOW,paste0("./data/oneInflow", Sys.Date(), ".csv"), row.names = FALSE, quote = FALSE)

48702b58e038be4cadb781cc8cc20d8b98872234

6df5c96f99b722b3cd5cd0b1f44f122072fad24b

/code/Unchecked/function_ah2sp.R

ca876b3f37224bcd443585e7c88e61317679e41d

[ "MIT" ]

permissive

Otoliths/Floristic-Kingdoms

95d2cc6caaa25200eee3427819bf5c37f2394fc1

7cb66545dffac44150f292dea482a6ed99213dda

refs/heads/main

2023-04-09T01:12:42.893686

2021-04-22T19:17:01

null

0

null

UTF-8

R

false

5,520

r

function_ah2sp.R

ah2sp <- function(x, increment=360, rnd=10, proj4string=CRS(as.character(NA)),tol=1e-4) { if (!inherits(x, "ahull")) { stop("x needs to be an ahull class object") } # Extract the edges from the ahull object as a dataframe xdf <- as.data.frame(x$arcs) #correct for possible arc order strangeness (Pascal Title addition 29 Nov 2013) k <- 1 xdf <- cbind(xdf, flip = rep(FALSE, nrow(xdf))) repeat{ if (is.na(xdf[k+1, 'end1'])) { break } #cat(k, '\n') if (xdf[k,'end2'] == xdf[k+1,'end1']) { #cat('case 1\n') k <- k + 1 } else if (xdf[k,'end2'] != xdf[k+1,'end1'] & !xdf[k,'end2'] %in% xdf$end1[k+1:nrow(xdf)] & !xdf[k,'end2'] %in% xdf$end2[k+1:nrow(xdf)]) { #cat('case 2\n') k <- k + 1 } else if (xdf[k,'end2'] != xdf[k+1,'end1'] & xdf[k,'end2'] %in% xdf$end1[k+1:nrow(xdf)] & !xdf[k,'end2'] %in% xdf$end2[k+1:nrow(xdf)]) { #cat('case 3\n') m <- which(xdf$end1[k+1:nrow(xdf)] == xdf[k,'end2']) + k xdf <- rbind(xdf[1:k,],xdf[m,],xdf[setdiff((k+1):nrow(xdf),m),]) } else if (xdf[k,'end2'] != xdf[k+1,'end1'] & !xdf[k,'end2'] %in% xdf$end1[k+1:nrow(xdf)] & xdf[k,'end2'] %in% xdf$end2[k+1:nrow(xdf)]) { #cat('case 4\n') m <- which(xdf$end2[k+1:nrow(xdf)] == xdf[k,'end2']) + k tmp1 <- xdf[m,'end1'] tmp2 <- xdf[m,'end2'] xdf[m,'end1'] <- tmp2 xdf[m,'end2'] <- tmp1 xdf[m,'flip'] <- TRUE xdf <- rbind(xdf[1:k,], xdf[m,], xdf[setdiff((k+1):nrow(xdf), m),]) } else { k <- k + 1 } } # Remove all cases where the coordinates are all the same xdf <- subset(xdf, xdf$r > 0) res <- NULL if (nrow(xdf) > 0) { # Convert each arc to a line segment linesj <- list() prevx <- NULL prevy <- NULL j <- 1 for(i in 1:nrow(xdf)) { rowi <- xdf[i,] v <- c(rowi$v.x, rowi$v.y) theta <- rowi$theta r <- rowi$r cc <- c(rowi$c1, rowi$c2) # Arcs need to be redefined as strings of points. Work out the number of points to allocate in this arc segment. ipoints <- 2 + round(increment * (rowi$theta / 2), 0) # Calculate coordinates from arc() description for ipoints along the arc. angles <- alphahull::anglesArc(v, theta) if (rowi['flip'] == TRUE){ angles <- rev(angles) } seqang <- seq(angles[1], angles[2], length = ipoints) x <- round(cc[1] + r * cos(seqang),rnd) y <- round(cc[2] + r * sin(seqang),rnd) # Check for line segments that should be joined up and combine their coordinates if (is.null(prevx)) { prevx <- x prevy <- y # added numerical precision fix (Pascal Title Dec 9 2013) } else if ((x[1] == round(prevx[length(prevx)],rnd) | abs(x[1] - prevx[length(prevx)]) < tol) && (y[1] == round(prevy[length(prevy)],rnd) | abs(y[1] - prevy[length(prevy)]) < tol)) { if (i == nrow(xdf)){ #We have got to the end of the dataset prevx <- append(prevx ,x[2:ipoints]) prevy <- append(prevy, y[2:ipoints]) prevx[length(prevx)] <- prevx[1] prevy[length(prevy)] <- prevy[1] coordsj <- cbind(prevx,prevy) colnames(coordsj) <- NULL # Build as Line and then Lines class linej <- Line(coordsj) linesj[[j]] <- Lines(linej, ID = as.character(j)) } else { prevx <- append(prevx, x[2:ipoints]) prevy <- append(prevy, y[2:ipoints]) } } else { # We have got to the end of a set of lines, and there are several such sets, so convert the whole of this one to a line segment and reset. prevx[length(prevx)] <- prevx[1] prevy[length(prevy)] <- prevy[1] coordsj <- cbind(prevx,prevy) colnames(coordsj)<-NULL # Build as Line and then Lines class linej <- Line(coordsj) linesj[[j]] <- Lines(linej, ID = as.character(j)) j <- j + 1 prevx <- NULL prevy <- NULL } } #Drop lines that will not produce adequate polygons (Pascal Title addition 9 Dec 2013) badLines <- vector() for (i in 1:length(linesj)){ if (nrow(linesj[[i]]@Lines[[1]]@coords) < 4){ badLines <- c(badLines,i) } } if (length(badLines) > 0){linesj <- linesj[-badLines]} # Promote to SpatialLines lspl <- SpatialLines(linesj) # Convert lines to polygons # Pull out Lines slot and check which lines have start and end points that are the same lns <- slot(lspl, "lines") polys <- sapply(lns, function(x) { crds <- slot(slot(x, "Lines")[[1]], "coords") identical(crds[1, ], crds[nrow(crds), ]) }) # Select those that do and convert to SpatialPolygons polyssl <- lspl[polys] list_of_Lines <- slot(polyssl, "lines") sppolys <- SpatialPolygons(list(Polygons(lapply(list_of_Lines, function(x) { Polygon(slot(slot(x, "Lines")[[1]], "coords")) }), ID = "1")), proj4string=proj4string) # Create a set of ids in a dataframe, then promote to SpatialPolygonsDataFrame hid <- sapply(slot(sppolys, "polygons"), function(x) slot(x, "ID")) areas <- sapply(slot(sppolys, "polygons"), function(x) slot(x, "area")) df <- data.frame(hid,areas) names(df) <- c("HID","Area") rownames(df) <- df$HID res <- SpatialPolygonsDataFrame(sppolys, data=df) res <- res[which(res@data$Area > 0),] } return(res) }

f4e97f28e8d553b76578df0fc86efb42552c5d8d

028d02e227415930b1e42ac363a98e7ecba8a493

/Text_Word_Vec_Analysis.R

ead4922bdf1f99b7a5eef1da041b97daa31e2f9a

[]

no_license

SaraJKerr/Letters_1916_Internship

8dc3f7dba97013a4f916ab5eac4693e0ec12a52c

90a6f1f70f13791154a76be7b6d4129a7999b672

refs/heads/master

2021-01-24T11:18:29.122999

2017-07-05T15:40:49

70,236,628

0

null

UTF-8

R

false

10,041

r

Text_Word_Vec_Analysis.R

################################################################################ # File-Name: Text_Word_Vec_Analysis.R # # Date: 1 September 2016 # # Author: Sara J Kerr # # ORCID:orcid.org/0000-0002-2322-1178 # # Purpose: Word2Vec creation, analysis and visualisation # # Based on: https://github.com/bmschmidt/wordVectors/tree/master/R # # http://www.codeproject.com/Tips/788739/Visualization-of-High- # # Dimensional-Data-using-t-SNE # # Matthew L. Jockers (2014) Text Analysis with R # # https://eight2late.wordpress.com/2015/12/02/a-gentle-introduction- # # to-network-graphs-using-r-and-gephi/ # # http://kateto.net/networks-r-igraph # # # # Data Used: Plain text file of combined texts # # Packages Used: wordVectors, tsne, Rtsne, magrittr, ggplot2, ggrepel, # # stringi # # Input: folder of plain text files # # Output: VSMs, csv files, wordlists, t-SNE plots, html network graphs # # Last Updated: 8th May 2017 # ################################################################################ # w2v_train uses train_word2vec and allows several variables to be used # to create 4 VSM, one based on the default settings, one based on Baroni, Dinu # and Kruszewski's (2014) suggestions for predictive models, and one with a larger # window and negative samples also per Baroni et al. # train_word2vec takes several parameters - an input prepared .txt file, an # output file, vectors are the number of dimensions the default is 100, and # window is the number of words either side of the context word, by default # the function uses skip-gram this can be changed by including cbow = 1 # text <- "Processed_Files/Letters_corpus.txt" w2v_train <- function(text) { train_word2vec(text, output= paste0(config_results_folderpath, "/W2V/Let_default.bin"), threads = 2, vectors = 100, window = 12, negative_samples = 5) train_word2vec(text, output = paste0(config_results_folderpath, "/W2V/Let_win5.bin"), threads = 2, vectors = 400, window = 5, negative_samples = 10) train_word2vec(text, output = paste0(config_results_folderpath, "/W2V/Let_win2.bin"), threads = 2, vectors = 300, window = 2, negative_samples = 10) train_word2vec(text, output = paste0(config_results_folderpath, "/W2V/Let_win15.bin"), threads = 2, vectors = 300, window = 15, negative_samples = 10) } ################################################################################ # text_kwic takes in a .txt file checks whether a target word is present and, # if so, creates a dataframe with text name and keyword in context and saves it. text_kwic <- function(files, input, word, context) { corpus <- make_word_list(files, input) context <- as.numeric(context) keyword <- tolower(word) result <- NULL # create the KWIC readout for (i in 1:length(corpus)) { hits <- which(corpus[[i]] == keyword) let <- files[i] if(length(hits) > 0){ for(j in 1:length(hits)) { start <- hits[j] - context if(start < 1) { start <- 1 } end <- hits[j] + context myrow <- cbind(let, hits[j], paste(corpus[[i]][start: (hits[j] -1)], collapse = " "), paste(corpus[[i]][hits[j]], collapse = " "), paste(corpus[[i]][(hits[j] +1): end], collapse = " ")) result <- rbind(result, myrow) } } else { z <- paste0(let, " YOUR KEYWORD WAS NOT FOUND\n") cat(z) } } colnames(result) <- c("file", "position", "left", "keyword", "right") write.csv(result, paste0(config_results_folderpath, "/KWIC/", word, "_", context, ".csv")) cat("Your results have been saved") } # Function used within text_kwic make_word_list <- function(files, input.dir) { # create an empty list for the results word_list <- list() # read in the files and process them for(i in 1:length(files)) { text <- scan(paste(input.dir, files[i], sep = "/"), what = "character", sep = "\n") text <- paste(text, collapse = " ") text_lower <- tolower(text) text_words <- strsplit(text_lower, "\\W") text_words <- unlist(text_words) text_words <- text_words[which(text_words != "")] word_list[[files[i]]] <- text_words } return(word_list) } ################################################################################ # w2v_analysis2 analyses a chosen term in a vector space model # The function takes 6 arguments: # vsm - a vector space model # words - a character vector of focus words # seed - an integer # path - the path to the folder you want files saved to # ref_name - the reference name for the exported files # num - the number of nearest words you wish to examine # The function will create a vector which is the average of the words input and # will output a wordlist of the n nearest words, a csv of the words and their # positions, and a plot of the 2D reduction of the vector space # model using the Barnes-Hut implementation of t-SNE. The points for each word # are marked in red so the labels can be moved by ggrepel for ease of reading. # An HTML network graph for the chosen word will also be created # set.seed is used to ensure replicability w2v_analysis2 <- function(vsm, words, seed, path, ref_name, num) { # Set the seed if (!missing(seed)) set.seed(seed) # Identify the nearest 10 words to the average vector of search terms ten <- nearest_to(vsm, vsm[[words]]) # Identify the nearest n words to the average vector of search terms and # save as a .txt file main <- nearest_to(vsm, vsm[[words]], num) wordlist <- names(main) filepath <- paste0(path, ref_name) write(wordlist, paste0(filepath, ".txt")) # Create a subset vector space model new_model <- vsm[[wordlist, average = F]] # Run Rtsne to reduce new VSM to 2D (Barnes-Hut) reduction <- Rtsne(as.matrix(new_model), dims = 2, initial_dims = 50, perplexity = 30, theta = 0.5, check_duplicates = F, pca = F, max_iter = 1000, verbose = F, is_distance = F, Y_init = NULL) # Extract Y (positions for plot) as a dataframe and add row names df <- as.data.frame(reduction$Y) rows <- rownames(new_model) rownames(df) <- rows # Save dataframe as .csv file write.csv(df, paste0(filepath, ".csv")) # Create t-SNE plot and save as jpeg ggplot(df) + geom_point(aes(x = V1, y = V2), color = "red") + geom_text_repel(aes(x = V1, y = V2, label = rownames(df), size = 8)) + xlab("Dimension 1") + ylab("Dimension 2 ") + theme_bw(base_size = 16) + theme(legend.position = "none") + ggtitle(paste0("2D reduction of VSM ", ref_name, " using t_SNE")) ggsave(paste0(ref_name, ".jpeg"), path = path, width = 24, height = 18, dpi = 100) # Create a network plot of the words sim <- cosineSimilarity(new_model, new_model) %>% round(2) # convert those below threshold to 0 sim[sim < max(sim)/2] <- 0 g <- graph_from_incidence_matrix(sim) # Create a graph object edges <- get.edgelist(g) # Name columns colnames(edges) <- c("from", "to") g2 <- graph(edges = edges) g2 <- simplify(g2) # removes loops # Community detection based on greedy optimization of modularity cfg <- cluster_fast_greedy(as.undirected(g2)) V(g2)$community <- cfg$membership pal2 <- rainbow(33, alpha = 0.7) V(g2)$color <- pal2[V(g2)$community] data <- toVisNetworkData(g2) visNetwork(nodes = data$nodes, edges = data$edges, main = "Network of Letters Clustered by Fast Greedy") %>% visOptions(highlightNearest = T, selectedBy = "community") %>% visSave(paste0(ref_name, ".html")) new_list <- list("Ten nearest" = ten, "Status" = "Analysis Complete") return(new_list) }

4d99c4dc7320850f835fdc5d2048c33f3cf769a6

ebbe08d58a57ae2e9d308a12df500e1e0ef8d098

/wgk/AllSample.R

8e854561f81e0c315d71ed54a7d1dde4c55b5b64

[]

no_license

Drizzle-Zhang/bioinformatics

a20b8b01e3c6807a9b6b605394b400daf1a848a3

9a24fc1107d42ac4e2bc37b1c866324b766c4a86

refs/heads/master

2022-02-19T15:57:43.723344

2022-02-14T02:32:47

171,384,799

2

0

null

UTF-8

R

false

16,291

r

AllSample.R

setwd('/home/zy/my_git/bioinformatics/wgk') .libPaths('/home/yzj/R/x86_64-pc-linux-gnu-library/4.0') library(Seurat) library(ggplot2) require("RColorBrewer") file.all <- '/home/yzj/JingMA_NEW/res/Harmony/ALL/RDS/PBMC_harmony.RDS' seurat.all <- readRDS(file.all) path.data <- '/home/disk/drizzle/wgk/data/AllSample_2_merge/' DimPlot(seurat.all, group.by = "batch") status <- rep('0', length(seurat.all$batch)) status[seurat.all$batch %in% c('C1', 'C2', 'C3', 'C4', 'C5')] <- 'Normal' status[seurat.all$batch %in% c('M1', 'M2', 'M3')] <- 'Microtia' seurat.all$status <- status DimPlot(seurat.all, group.by = "status") seurat.all <- FindNeighbors(seurat.all, reduction = "pca", dims = 1:100) seurat.all <- FindClusters(seurat.all, resolution = 3) DimPlot(seurat.all, group.by = "RNA_snn_res.3", label = T) FeaturePlot(seurat.all, features = c('ID3', 'HES1', 'COL1A1', 'CYTL1')) FeaturePlot(seurat.all, features = c('DCN', 'STC1', 'COL9A3', 'FRZB')) # unknown 23 28 FeaturePlot(seurat.all, features = c('IFIT1', 'OAS3', 'XAF1', 'OAS1')) # neuron 27 FeaturePlot(seurat.all, features = c('PLP1', 'SLITRK6', 'ANK3', 'CIT')) # commu stromal 10 FeaturePlot(seurat.all, features = c('CFD', 'APOE', 'VCAN', 'CSF3')) # matrix stromal 3 FeaturePlot(seurat.all, features = c('MMP10', 'COCH', 'OGN', 'COMP')) # stromal stem cell 22 FeaturePlot(seurat.all, features = c('HES1', 'ID3', 'COL1A1', 'LUM')) # Chondral stem cell 9 21 FeaturePlot(seurat.all, features = c('HES1', 'ID3', 'CYTL1', 'COL2A1')) # Transitional chondrocyte 13 17 FeaturePlot(seurat.all, features = c('HES1', 'ID3', 'CYTL1', 'COL2A1')) # Chondrocyte1 1 2 5 8 12 14 15 26 FeaturePlot(seurat.all, features = c('COL1A1', 'COL1A2', 'COL2A1'), ncol=3) # Chondrocyte2 0 4 6 7 11 16 FeaturePlot(seurat.all, features = c('STC1')) # filter seurat.all.filter <- subset(seurat.all, subset = RNA_snn_res.2 %in% setdiff(unique(seurat.all$RNA_snn_res.2), c(23, 27, 28))) seurat.all.filter <- FindNeighbors(seurat.all.filter, reduction = "pca", dims = 1:100) seurat.all.filter <- FindClusters(seurat.all.filter, resolution = 1.5) DimPlot(seurat.all.filter, group.by = "RNA_snn_res.2", label = T) cluster_3 <- seurat.all$RNA_snn_res.3 celltypes <- rep('_', length(cluster_3)) celltypes[cluster_3 %in% c(22, 25)] <- 'Stromal cell1' celltypes[cluster_3 %in% c(20, 21, 29)] <- 'Stromal cell2' celltypes[cluster_3 %in% c(27)] <- 'Stromal stem cell' celltypes[cluster_3 %in% c(18, 23, 33, 35)] <- 'Chondral stem cell' celltypes[cluster_3 %in% c(12, 16, 25, 9, 18)] <- 'Transitional chondrocyte' celltypes[cluster_3 %in% c(10, 16, 1, 5, 15, 12, 17, 4, 6, 13, 8)] <- 'Chondrocyte1' celltypes[cluster_3 %in% c(0, 3, 7, 11, 14, 9, 19, 26, 30, 38)] <- 'Chondrocyte2' celltypes[cluster_3 %in% c(28, 36, 24)] <- 'Perivascular cell' celltypes[cluster_3 %in% c(32, 37, 41)] <- 'Endothelial cell' celltypes[cluster_3 %in% c(39)] <- 'Immune cell' seurat.all$celltype_3 <- celltypes DimPlot(seurat.all, group.by = "celltype_3", label = T) # cell type cluster_2 <- seurat.all.filter$RNA_snn_res.2 celltypes <- rep('_', length(cluster_2)) celltypes[cluster_2 %in% c(10)] <- 'Stromal cell1' celltypes[cluster_2 %in% c(3)] <- 'Stromal cell2' celltypes[cluster_2 %in% c(22)] <- 'Stromal stem cell' celltypes[cluster_2 %in% c(9, 21)] <- 'Chondral stem cell' celltypes[cluster_2 %in% c(13, 17, 14, 5)] <- 'Transitional chondrocyte' celltypes[cluster_2 %in% c(1, 2, 5, 8, 12, 14, 15, 26)] <- 'Chondrocyte1' celltypes[cluster_2 %in% c(0, 4, 6, 7, 11, 16)] <- 'Chondrocyte2' celltypes[cluster_2 %in% c(19, 20, 24)] <- 'Perivascular cell' celltypes[cluster_2 %in% c(18)] <- 'Endothelial cell' celltypes[cluster_2 %in% c(25)] <- 'Immune cell' table(seurat.all.filter$batch, celltypes)/as.vector(table(seurat.all.filter$batch)) seurat.all.filter$celltype <- celltypes DimPlot(seurat.all.filter, group.by = "celltype", label = T) file.merge_2 <- paste0(path.data, 'seurat_celltype.Rdata') saveRDS(seurat.all.filter, file.merge_2) seurat.all.filter <- readRDS(file.merge_2) # cell marker clusters <- unique(seurat.all.filter$celltype) list.marker.all <- list() for (cluster in clusters) { sub.markers <- FindMarkers(seurat.all.filter, ident.1 = cluster, group.by = 'celltype', logfc.threshold = 0.25, min.diff.pct = 0.05, only.pos = T) list.marker.all[[cluster]] <- sub.markers } # surface marker file.surface <- '/home/disk/drizzle/wgk/public_data/surface_marker_wlab.txt' df.surface <- read.delim2(file.surface) surface.genes <- unique(toupper(df.surface$ENTREZ.gene.symbol)) # chon and stroma lineage marker genes seurat.CS <- subset(seurat.all.filter, subset = celltype %in% c('Stromal cell1', 'Stromal cell2', 'Stromal stem cell', 'Chondral stem cell', 'Transitional chondrocyte', 'Chondrocyte1', 'Chondrocyte2')) clusters <- unique(seurat.CS$celltype) list.marker <- list() for (cluster in clusters) { sub.markers <- FindMarkers(seurat.CS, ident.1 = cluster, group.by = 'celltype', logfc.threshold = 0.3, min.diff.pct = 0, only.pos = T) list.marker[[cluster]] <- sub.markers } View(list.marker$`Chondral stem cell`) View(list.marker$`Stromal stem cell`) intersect(rownames(list.marker$`Chondral stem cell`), surface.genes) intersect(rownames(list.marker$`Stromal stem cell`), surface.genes) genes.chon <- c("CD83", "TSPAN6", 'ENPP1', 'ITM2B', 'CD99', 'TSPAN4', 'SCARA3', 'BOC', 'PTPRZ1', 'MXRA8', 'FGFR3', 'AQP1', 'CNTFR', 'CADM1', 'SLC29A1', 'LRP1', 'CD46', 'A2M', 'ITGA10') genes.stroma <- c("GPNMB", "LRRC32", 'ABCA8', 'SCARA5', 'THY1', 'SLC2A3', 'MXRA8', 'PDGFRB', 'LEPR', 'SLC38A2', 'ANK2', 'PCDH9', 'CLEC2B', 'GPRC5A', 'PRNP', 'SLC20A1', 'CERCAM', 'BOC', 'FAP', 'PLP2', 'CD276', 'AQP1', 'ABCA1', 'ITM2B', 'BMPR2', 'TSPAN4', 'SGCE', 'CD83', 'EMP1', 'SLC19A2', 'F3', 'TMEM2', 'FCGRT', 'LRP1', 'IL1R1') genes.CS <- unique(c(genes.chon, genes.stroma)) celltypes <- unique(seurat.all.filter$celltype) df.bubble <- data.frame(stringsAsFactors = F) for (cell in celltypes) { sub.seurat <- subset(seurat.all.filter, subset = celltype == cell) sub.mat <- sub.seurat@assays$RNA@data for (gene in genes.CS) { vec.exp <- sub.mat[gene,] mean.exp <- mean(vec.exp) prop.exp <- sum(vec.exp != 0)/length(vec.exp) df.bubble <- rbind(df.bubble, data.frame(Cell = cell, Gene = gene, MeanExp = mean.exp, Prop = prop.exp)) } } mat.exp <- reshape2::dcast(df.bubble, Cell ~ Gene, value.var = 'MeanExp') row.names(mat.exp) <- mat.exp$Cell mat.exp$Cell <- NULL mat.exp.scale <- scale(mat.exp) df.exp.scale <- reshape2::melt(mat.exp.scale) names(df.exp.scale) <- c('Cell', 'Gene', 'ScaleExp') df.bubble <- merge(df.bubble, df.exp.scale, by = c('Cell', 'Gene')) df.bubble$Cell <- factor(df.bubble$Cell, levels = c('Stromal cell1', 'Stromal cell2', 'Stromal stem cell', 'Chondral stem cell', 'Transitional chondrocyte', 'Chondrocyte1', 'Chondrocyte2','Perivascular cell', 'Endothelial cell', 'Immune cell')) plot.bubble <- ggplot(data = df.bubble, aes(x = Gene, y = Cell, size = Prop, color = ScaleExp)) + geom_point(fill = 'cornsilk') + scale_colour_gradientn( colours = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(100)) + # facet_grid( ~ Status) + labs(x = 'Gene', y = 'Cell Type', color = 'Scaled expression', size = 'Proportion') + theme(panel.background = element_rect(color = 'gray', fill = 'transparent'), axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) ggsave(plot = plot.bubble, path = path.data, filename = 'marker_surface.png', height = 15, width = 35, units = 'cm') # no filter # cell type cluster_2 <- seurat.all$RNA_snn_res.2 celltypes <- as.character(cluster_2) celltypes[cluster_2 %in% c(10)] <- 'Stromal cell1' celltypes[cluster_2 %in% c(3)] <- 'Stromal cell2' celltypes[cluster_2 %in% c(22)] <- 'Stromal stem cell' celltypes[cluster_2 %in% c(9, 21)] <- 'Chondral stem cell' celltypes[cluster_2 %in% c(13, 17)] <- 'Transitional chondrocyte' celltypes[cluster_2 %in% c(1, 2, 5, 8, 12, 14, 15, 26)] <- 'Chondrocyte1' celltypes[cluster_2 %in% c(0, 4, 6, 7, 11, 16)] <- 'Chondrocyte2' celltypes[cluster_2 %in% c(19, 20, 24)] <- 'Perivascular cell' celltypes[cluster_2 %in% c(18)] <- 'Endothelial cell' celltypes[cluster_2 %in% c(25)] <- 'Immune cell' # table(seurat.all$batch, celltypes)/as.vector(table(seurat.all$batch)) seurat.all$celltype <- celltypes DimPlot(seurat.all, group.by = "celltype", label = T) celltypes <- unique(seurat.all$celltype) df.bubble <- data.frame(stringsAsFactors = F) for (cell in celltypes) { sub.seurat <- subset(seurat.all, subset = celltype == cell) sub.mat <- sub.seurat@assays$RNA@data for (gene in genes.CS) { vec.exp <- sub.mat[gene,] mean.exp <- mean(vec.exp) prop.exp <- sum(vec.exp != 0)/length(vec.exp) df.bubble <- rbind(df.bubble, data.frame(Cell = cell, Gene = gene, MeanExp = mean.exp, Prop = prop.exp)) } } mat.exp <- reshape2::dcast(df.bubble, Cell ~ Gene, value.var = 'MeanExp') row.names(mat.exp) <- mat.exp$Cell mat.exp$Cell <- NULL mat.exp.scale <- scale(mat.exp) df.exp.scale <- reshape2::melt(mat.exp.scale) names(df.exp.scale) <- c('Cell', 'Gene', 'ScaleExp') df.bubble <- merge(df.bubble, df.exp.scale, by = c('Cell', 'Gene')) df.bubble$Cell <- factor(df.bubble$Cell, levels = c('Stromal cell1', 'Stromal cell2', 'Stromal stem cell', 'Chondral stem cell', 'Transitional chondrocyte', 'Chondrocyte1', 'Chondrocyte2','Perivascular cell', 'Endothelial cell', 'Immune cell', '23', '27', '28')) plot.bubble <- ggplot(data = df.bubble, aes(x = Gene, y = Cell, size = Prop, color = ScaleExp)) + geom_point(fill = 'cornsilk') + scale_colour_gradientn( colours = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(100)) + # facet_grid( ~ Status) + labs(x = 'Gene', y = 'Cell Type', color = 'Scaled expression', size = 'Proportion') + theme(panel.background = element_rect(color = 'gray', fill = 'transparent'), axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) ggsave(plot = plot.bubble, path = path.data, filename = 'marker_surface_no_filter.png', height = 18, width = 35, units = 'cm') # sort df.chon <- df.bubble[df.bubble$Cell == 'Chondral stem cell', ] df.bubble$Gene <- factor(df.bubble$Gene, levels = df.chon[order(df.chon$ScaleExp, decreasing = T), 'Gene']) plot.bubble <- ggplot(data = df.bubble, aes(x = Gene, y = Cell, size = Prop, color = ScaleExp)) + geom_point(fill = 'cornsilk') + scale_colour_gradientn( colours = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(100)) + # facet_grid( ~ Status) + labs(x = 'Gene', y = 'Cell Type', color = 'Scaled expression', size = 'Proportion') + theme(panel.background = element_rect(color = 'gray', fill = 'transparent'), axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) ggsave(plot = plot.bubble, path = path.data, filename = 'marker_surface_no_filter_chon.png', height = 18, width = 35, units = 'cm') df.stroma <- df.bubble[df.bubble$Cell == 'Stromal stem cell', ] df.bubble$Gene <- factor(df.bubble$Gene, levels = df.stroma[order(df.stroma$ScaleExp, decreasing = T), 'Gene']) plot.bubble <- ggplot(data = df.bubble, aes(x = Gene, y = Cell, size = Prop, color = ScaleExp)) + geom_point(fill = 'cornsilk') + scale_colour_gradientn( colours = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(100)) + # facet_grid( ~ Status) + labs(x = 'Gene', y = 'Cell Type', color = 'Scaled expression', size = 'Proportion') + theme(panel.background = element_rect(color = 'gray', fill = 'transparent'), axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) ggsave(plot = plot.bubble, path = path.data, filename = 'marker_surface_no_filter_stroma.png', height = 18, width = 35, units = 'cm') # violin plot library(ggplot2) marker.genes <- c('APOE', 'VCAN', 'OGN', 'COCH', 'COL1A1', 'LUM', 'HES1', 'ID3', 'SOX9', 'CYTL1', 'COL2A1', 'COL9A2', 'ACAN', 'ACTA2', 'PDGFRB', 'CDH5', 'IL1B') # marker.genes <- c('CFD', 'APOE', 'VCAN', 'MMP10', 'COCH', # 'ASPN', 'OGN') df.gene <- data.frame(stringsAsFactors = F) for (gene in marker.genes) { df.sub <- data.frame(expvalue = seurat.all.filter@assays$RNA@data[gene,], gene = rep(gene, ncol(seurat.all.filter@assays$RNA@data)), celltype = seurat.all.filter$celltype) df.gene <- rbind(df.gene, df.sub) } df.plot <- df.gene df.plot$gene <- factor(df.gene$gene, levels = marker.genes) df.plot$celltype <- factor(df.gene$celltype, levels = c('Stromal cell1', 'Stromal cell2', 'Stromal stem cell', 'Chondral stem cell', 'Transitional chondrocyte', 'Chondrocyte1', 'Chondrocyte2','Perivascular cell', 'Endothelial cell', 'Immune cell')) plot.vln <- ggplot(data = df.plot, aes(x = gene, y = expvalue, color = gene, fill = gene)) + geom_violin(trim = T, scale = 'width') + facet_grid( ~ celltype) + theme_classic() + coord_flip() + stat_summary(fun= mean, geom = "point", shape = 23, size = 2, color = "black") + labs(x = 'Gene', y = 'Expression Level') + theme(legend.position = 'none') ggsave(plot = plot.vln, path = path.data, filename = 'Vln.png', height = 15, width = 35, units = 'cm') # prop seurat.4 <- subset(seurat.all, subset = RNA_snn_res.0.6 == 4) table(seurat.4$batch)/table(seurat.all$batch) df.plot <- data.frame(sample = names(table(seurat.4$batch)), prop = as.numeric(table(seurat.4$batch)/table(seurat.all$batch))) ggplot(df.plot, aes(x = sample, y = prop)) + geom_bar(stat = 'identity') seurat.5 <- subset(seurat.all, subset = RNA_snn_res.0.6 == 5) table(seurat.5$batch) seurat.9 <- subset(seurat.all, subset = RNA_snn_res.0.6 == 9) table(seurat.9$batch) seurat.11 <- subset(seurat.all, subset = RNA_snn_res.2 == 11) DimPlot(seurat.11, group.by = 'RNA_snn_res.2') seurat.0 <- subset(seurat.all, subset = RNA_snn_res.2 == 0) DimPlot(seurat.0, group.by = 'RNA_snn_res.2') seurat.M3 <- subset(seurat.all, subset = batch == 'M3') FeaturePlot(seurat.M3, features = c('HES1', 'ID3', 'COL1A1', 'CYTL1')) seurat.M1 <- subset(seurat.all, subset = batch == 'M1') FeaturePlot(seurat.M1, features = c('HES1', 'ID3', 'COL1A1', 'CYTL1')) FeaturePlot(seurat.M1, features = c('TNF')) seurat.C4 <- subset(seurat.all, subset = batch == 'C4') FeaturePlot(seurat.C4, features = c('HES1', 'ID3', 'COL1A1', 'CYTL1')) seurat.C6 <- subset(seurat.all, subset = batch == 'C6') FeaturePlot(seurat.C6, features = c('HES1', 'ID3', 'COL1A1', 'CYTL1')) FeaturePlot(seurat.C6, features = c('TNF')) seurat.C1 <- subset(seurat.all, subset = batch == 'C1') FeaturePlot(seurat.C1, features = c('TNF', 'TNFSF10')) (table(seurat.4$batch) + table(seurat.5$batch))/table(seurat.all$batch) FeaturePlot(seurat.first, features = c('FRZB', 'CTGF', 'SERPINA1', "SCRG1", 'COL9A3', 'FGFBP2'), ncol = 3)

ccbdb91734d68709d8a30504765c9851f988d1a4

4059f892c51304bb8af43d90105da02f49fa263a

/storetrackeR/global.R

85b779590152d0448dc79927d0434204ce5bcd83

[]

no_license

martinSchneiderEoda/storetracker

49fbc350feed0f2c2744a94d0dd6f142b6d4fbe9

47982c735c450d25224ab04369ae6ad502aac75b

refs/heads/master

2021-04-09T07:31:04.518342

2020-03-22T17:24:56

248,850,995

0

1

null

2020-03-21T13:02:13

2020-03-20T20:59:21

R

UTF-8

R

false

915

r

global.R

# libraries --------------------------------------------------------------- library(shiny) library(shinyMobile) library(leaflet) library(DT) library(DBI) library(lubridate) library(geosphere) library(dplyr) library(ggplot2) library(tidyr) library(stringr) library(geoloc) library(revgeo) library(osmdata) library(sf) library(dbplyr) # source ------------------------------------------------------------------------- source("storeFunctions.R") source("uiFunctions.R") con <- dbConnect(RSQLite::SQLite(), "storeTrackeDB.sqlite") # ------------------------------------------------------------------------- # replace with db product_choices <- tbl(con, "Products") %>% pull(ID) names(product_choices) <- tbl(con, "Products") %>% pull(Name) stores <- tbl(con, "Supermarket") %>% pull(ID) names(stores) <- tbl(con, "Supermarket") %>% pull(Name) store_choices <- tbl(con, "Supermarket") %>% pull(Name)

693c1721b79ca3ccac10a24e60e6fc89c5277309

11bd69f4cc83ac6ea62eb3118072fc22c667cf76

/correlation.R

45c2fedb473f655efeee1b9a6d6990b834c3af5d

[]

no_license

VarSriv/Firefight

c482c11638e15e733fdf39f394050781a7184149

02936d2bf66e321f2ac542ba5a255f72d2a70519

refs/heads/master

2021-08-16T01:20:25.976253

2017-11-18T14:48:13

111,216,984

0

null

2017-11-18T15:37:14

2017-11-18T15:37:13

null

UTF-8

R

false

922

r

correlation.R

library('RSQLite') conn <- dbConnect(SQLite(), 'C:/Users/User/Downloads/FPA_FOD_20170508.sqlite') # pull the fires table into RAM fires <- dbReadTable(conn,"Fires") # disconnect from db dbDisconnect(conn) ctrl <- fires[is.na(fires$CONT_DATE) == FALSE,"CONT_DATE"]-fires[is.na(fires$CONT_DATE) == FALSE,"DISCOVERY_DATE"] cor(ctrl, fires[is.na(fires$CONT_DATE) == FALSE,"FIRE_SIZE"]) plot(fires[is.na(fires$CONT_DATE) == FALSE,"FIRE_SIZE"],ctrl,xlim = c(0,40000),ylim = c(0,10000)) df <- as.data.frame(cbind(fires[is.na(fires$CONT_DATE) == FALSE,"FIRE_SIZE"],ctrl)) set.seed(123) train_index <- sample(c(TRUE, FALSE), nrow(df), replace = TRUE, prob = c(0.8, 0.2)) test_index <- !train_index # Create x/y, train/test data x_train <- df[train_index,] x_test <- df[test_index,] model <- lm(ctrl~V1,x_train) summary(model) p <- predict(model,x_test) error <- mean((abs(p-x_test$ctrl))/p)

3756f79772e703349f7f5fb5c8546be644de60e5

4d66d82231c7f9218538ca62fd15f366b2b2d8a2

/src/analysis/calculate_k_from_hall_eq.R

9353d79b83d8d73c7336d0688a90ce197521537f

[]

no_license

amcarter/nhc_50yl_sandbox

78f12f2ee3175f165f0e12fe2c567526d476c585

3cd8a25fa8279b88c80be04c2a42a730e274dcdb

refs/heads/master

2023-06-24T13:02:50.673995

2021-07-23T04:01:11

null

0

null

UTF-8

R

false

6,978

r

calculate_k_from_hall_eq.R

# Calculate k values for NHC based on the equation in Hall 1970 # Appendix # Equation 4 # k2 = 5.026 * V ^ 0.969 * R(ft) ^ -1.673 at 20 C (day-1, per volume) # Hall approximates R, hydraulic radius as Depth # k2(T) = k2(T=20) * 1.0241^(T-20) # k (g/m3/d) = 2.3 * k2 * DO.sat / 24hr # K (g/m2/d) = k * d library(streamMetabolizer) # get Hall K values #### ##Convert KO2 to K600 calc_k210_from_d_v <- function(d_f, v_fs){ k2 = 5.026 * v_fs ^ 0.969 * d_f ^ -1.673 return(k2) } calc_k_from_k210 <- function(k2, temp, DOsat = NULL, airP = 101.3){ if(is.null(DOsat)){ DOsat <- calc_DO_sat(temp, airP * 10)} k = k2 * 1.024^(temp - 20) * DOsat * 2.3 / 24 return(k) } calc_K2_from_K00 <- function(K600, temp){ sa = 1568 sb = -86.04 sc = 2.142 sd = -0.0216 se = -0.5 K2 = K600 * ((sa + sb * temp + sc * temp^2 + sd * temp^3)/600) ^ se return(K2) } calc_K600_from_k210 <- function(k210, temp){ sa = 1568 sb = -86.04 sc = 2.142 sd = -0.0216 se = -0.5 K2 = k210 * 2.3 K600 = K2 / ((sa + sb * temp + sc * temp^2 + sd * temp^3)/600) ^ se return(K600) } # load hall data hallk <- read_csv("hall_50yl/code/data/hall/hall_tableA2_k_morphology.csv") hallk <- hallk %>% mutate(k2_day_vol = K2_perday_perarea / depth_m, K600 = calc_K600_from_k210(k2_day_vol, 20), v_ms = ((k2_day_vol / (5.026 * (depth_m * 3.28) ^ -1.673)) ^ (1/0.969))/3.28) write_csv(hallk,"hall_50yl/code/data/hall/hall_tableA2_k_morphology_extended.csv") # get measured K #### # ar_k <- read_csv("data/estimated_k_values.csv") # d = depth (m), v = velocity (m/s), DO.sat = DO at saturation (mg/L), t = temp C # setwd("C:/Users/Alice Carter/Dropbox (Duke Bio_Ea)/projects/NHC_2019_metabolism/data") # nhc <- read_csv("metabolism/processed/NHC.csv") %>% # mutate(DateTime_EST = force_tz(DateTime_EST, tz = "EST"), # date = as.Date(DateTime_EST)) %>% # filter(date == as.Date("2017-06-27")) %>% # select(DO.obs, temp.water, level_m, depth, discharge) # unhc <- read_csv("metabolism/processed/NHC.csv") %>% # mutate(DateTime_EST = force_tz(DateTime_EST, tz = "EST"), # date = as.Date(DateTime_EST)) %>% # filter(date == as.Date("2017-07-11")) %>% # select(DO.obs, temp.water, level_m, depth, discharge) # # summary(nhc) # nhc_k <- ar_k %>% # filter(site =="NHC") %>% # mutate(depth = .23, # discharge = .14, # watertemp = 22, # K600 = K600fromO2(watertemp, k_md/depth)) # # summary(unhc) # unhc_k <- ar_k %>% # filter(site =="UNHC") %>% # mutate(depth = .14, # discharge = .02, # watertemp = 25, # K600 = K600fromO2(watertemp, k_md/depth)) # # ar_k <- bind_rows(nhc_k, unhc_k) # write_csv(ar_k, "gas_data/measured_k_Ar_releases.csv") # calc k from Hall 1970 equation #### # load processed site data files # Calculate K600 for sites #### # # # this width needs to be replaced with actual data from longitudinal surveys # widths <- read_csv("C:/Users/Alice Carter/Dropbox (Duke Bio_Ea)/projects/NHC_2019_metabolism/data/siteData/NHCsite_metadata.csv") %>% # select(site = sitecode, width_m = width_mar_m) %>% # slice(1:7) # ZQ <- read_csv("rating_curves/depth_discharge_relationship_LM1953.csv") %>% # rename(site = sitename) %>% # left_join(widths) %>% # mutate(cv_ms = 1/c_m/width_m) # ZQ$cv_ms[ZQ$site %in% c("NHC", "UNHC")] <- .194 # This loop needs to be stepped through for each individual site. kk <- data.frame() par(mfrow = c(2,1)) for(site in sites$sitecode){ dat <- read_csv(paste0("NHC_2019_metabolism/data/metabolism/processed/", site, ".csv"), guess_max = 10000) dat <- dat %>% group_by(date = as.Date(with_tz(DateTime_UTC, tz = "EST"))) %>% select(date, discharge, depth, avg_velocity, DO.obs, DO.sat, temp_C = temp.water) %>% summarize_all(mean, na.rm = T) %>% mutate(k210_vol = calc_k210_from_d_v(depth * 3.28, avg_velocity * 3.28), k_gm3hr = calc_k_from_k210(k210_vol, temp_C, DO.sat), K600 = calc_K600_from_k210(k210_vol, temp_C)) # plot(dat$depth, dat$avg_velocity, pch = 20, main = site, # col = "grey50", ylim = c(0,1)) # points(hallk$depth_m, hallk$v_ms, col = 2, pch = 20) # # plot(dat$depth, dat$K600, pch = 20, main = site, col = "grey50", # ylim = c(0,20)) # points(hallk$depth_m, hallk$K600, col = 2, pch = 20) dat$site = site kk <- bind_rows(kk, dat) } # as.tibble(kk) %>% # ggplot(aes(depth, K600, color= site)) + # geom_point() write_csv(kk, "NHC_2019_metabolism/data/siteData/KQ_hall_prior_from_equation_daily.csv") # generate K/Q nodes for SM for each site year: # kq <- data.frame() # par(mfrow = c(1,1)) # for(site in unique(kk$site)){ # dat <- kk %>% # filter(site == !!site) # plot(dat$discharge, dat$K600, log = "xy", main = site) # m <- lm(log(K600) ~ log(discharge), dat) # mm <- summary(m)$coefficients[,1] # Qrng <- range(log(dat$discharge), na.rm = T) # delta = 2 # n = 6 # while(delta > 1){ # n = n + 1 # delta <- (Qrng[2]-Qrng[1])/n # } # Qnodes <- seq(Qrng[1] + delta/2, by = delta, length.out = n) # lnK600 <- mm[1] + mm[2] * Qnodes # points(exp(Qnodes), exp(lnK600), col = 2, pch = 19) # nodes <- data.frame(site = site, # lnQ = Qnodes, # lnK600 = lnK600) # kq <- bind_rows(nodes, kq) # } # # write_csv(kq, "siteData/KQ_hall_prior_from_equation.csv") # # # Previous attempt #### # # Hall used the numbers from stream morphology for his calculations, so I will too # hallQ <- log(range(hall_k$discharge_m3s)) # # #get the range of all Q's # # q <- read_csv("metabolism/processed/NHC.csv") %>% # # select(discharge) # # Q <- log(range(q, na.rm = T)) # # Q <- seq(-9, 5) # # comb <- hall_k %>% # mutate(logQ = log(discharge_m3s)) %>% # select(K600, logQ) %>% # full_join(data.frame(nodes = Q, # logQ = Q)) %>% # arrange(logQ) # # comb$K600[which(comb$logQ <= hallQ[1])] <- hall_k$K600[1] # comb$K600[which(comb$logQ >= hallQ[2])] <- # hall_k$K600[nrow(hall_k)] # # comb_k <- transform(comb, K600 = na.approx(K600, logQ, na.rm = F)) # # plot(comb_k$logQ, comb_k$K600) # points(comb_k$nodes, comb_k$K600, col = "red", pch = 19) # write_csv(comb_k, "siteData/KQ_hall_prior.csv") # # # 11/19/2020 # # didn't finish here the plan was to use the hall data that already was # # calculated for stream morphology, but that is problematic for a few reasons: # # 1. The range is smaller than that of our data # # 2. the hall rating curve doesn't necesarily apply to this depth # # # # Another thing to try is calculating the k from my own data, this is # # a rabbit hole though because the depth is all fucky from using calc_depth # # my plan is to use the level data to modify these depths, but I will need # # to clean it up a lot. I could also use this to get something like velocity # # as well.

a2c225d68d548fb0782b3c9a6c794e6720528937

96e82189064bacbc7d032ff4937332902cda649e

/skeleton.R

576a78fbc399372fd509d7f38f7589f6f0fa8542

[]

no_license

tonyshenyy/Stat-133

87b07274c18200f7d8e83ce30b97ebb9ef2cb4c6

d98c3b4e99041dd9e9261967507551948918172d

refs/heads/master

2020-05-24T12:37:19.021817

2015-08-15T20:17:32

40,556,295

0

null

UTF-8

R

false

1,412

r

skeleton.R

# ============================================================================= # Skeleton.R # Description: This file contains all the commands to create folders and files # as well as commands to download resource files. # Name: Yiyang Shen and Caylie Marie Connelly # ============================================================================= # commands create folders code, rawdata, data, resources, report and images dir.create('./code') dir.create('./rawdata') dir.create('./data') dir.create('./resources') dir.create('./report') dir.create('./images') # command to create file README.md file.create('./README.md') # command to download the raw data files # For storms.csv and tracks.csv download.file( 'ftp://eclipse.ncdc.noaa.gov/pub/ibtracs/v03r06/wmo/hurdat_format/basin/Basin.NA.ibtracs_hurdat.v03r06.hdat', './rawdata/Basin.NA.ibtracs_hurdat.v03r06.hdat') download.file( 'ftp://eclipse.ncdc.noaa.gov/pub/ibtracs/v03r06/wmo/hurdat_format/basin/Basin.EP.ibtracs_hurdat.v03r06.hdat', './rawdata/Basin.EP.ibtracs_hurdat.v03r06.hdat') # For Visualization download.file('ftp://eclipse.ncdc.noaa.gov/pub/ibtracs/v03r06/wmo/csv/basin/Basin.EP.ibtracs_wmo.v03r06.csv', './rawdata/Basin.EP.ibtracs_wmo.v03r06.csv') download.file('ftp://eclipse.ncdc.noaa.gov/pub/ibtracs/v03r06/wmo/csv/basin/Basin.NA.ibtracs_wmo.v03r06.csv', './rawdata/Basin.NA.ibtracs_wmo.v03r06.csv')

e9be02c5ffef95bcc17ca60332f95a43b16eeee6

6b49bd4cd7728144ba7d2acf2f0c40d347fdac26

/Final.Project.Script.R

ea14ddb7349dd3783d5a5f380f623a70d98c7ae4

[]

no_license

sbordena/BD_final_assignment

ac25c044224a440b8e6e2d40293078cca35afd73

3ec0a8cca37910e67cfa4dc41c7c3ffbbd17efa0

refs/heads/master

2021-01-10T04:08:55.038725

2016-03-02T19:04:29

52,987,815

0

1

null

2016-03-05T20:22:48

2016-03-02T18:53:35

R

UTF-8

R

false

7,078

r

Final.Project.Script.R

## Final Project Script ## Load libraries library(tree) library(rpart) library(randomForest) library(gbm) library(data.table) library(caret) library(doParallel) library(glmnet) library(e1071) library(pROC) library(curl) library(dismo) library(distrom) library(gamlr) library(dplyr) library(Matrix) ## Set Working Directory setwd("C:/Users/Chingono/Documents/Big Data Course") ## Get Functions source("https://raw.githubusercontent.com/ChicagoBoothML/HelpR/master/lift.R") helpr_repo_raw_url <- 'https://raw.githubusercontent.com/ChicagoBoothML/HelpR/master' source(file.path(helpr_repo_raw_url, 'EvaluationMetrics.R')) source("naref.R") source("fdr.R") source("roc.R") source("deviance.R") ##Parallel Computing # set randomizer's seed set.seed(28) # Parallel Computation Setup cl <- makeCluster(detectCores()-2) # create a compute cluster using all CPU cores except 2 clusterEvalQ(cl, library(foreach)) registerDoParallel(cl) # register this cluster ###load data data=read.csv("equities_data.csv") data <- data[complete.cases(data),] ### To read the quarterly data, install the "foreign" and "readstata13" packages ### and un-comment the following: # library(foreign) # library(readstata13) # quarterly_data <- read.dta("Quarterly_Fundamentals_2.17.16.dta") # monthly_returns <- read.dta("Monthly Returns 2.17.16.dta") ## Create factors among explanatory variables data$size_index <- as.factor(data$size_index) data$pyreturn_index <- as.factor(data$pyreturn_index) data$value_index <- as.factor(data$value_index) data$profit_index <- as.factor(data$profit_index) data$pb_index <- as.factor(data$pb_index) data$pyreturn_below_median <- as.factor(data$pyreturn_below_median) data$asset_turnover <- as.factor(data$asset_turnover) data$debtpy_debt <- as.factor(data$debtpy_debt) data$sharespy_shares <- as.factor(data$sharespy_shares) #split data into train, validate, and test set.seed(28) n=nrow(data) n1=floor(0.6*n) n2=floor(0.2*n) n3=n-n1-n2 ii=sample(1:n,n) trainDf=data[ii[1:n1],] valDf=data[ii[n1+1:n2],] testDf=data[ii[n1+n2+1:n3],] ###check that samples are balanced in terms of y (Future Debt Paydown) table(trainDf$y) table(valDf$y) table(testDf$y) ## ~42% of companies in each sample pay down debt over the next year ### Logistic Regression ### ###fit simple logit model lgfit = glm(y~.,data=trainDf[,-27:-28],family=binomial) # exclude next 1 year return and portfolio year (column 27 & 28) summary(lgfit) pv <- coef(summary(lgfit))[,4] #grab p-values length(pv[pv<=0.05]) # 36 coefficients appear to be significant at the 5% level ## Prediction with simple logit model p_logit <- predict(lgfit, newdata=valDf, type="response") D <- deviance(y=valDf$y, pred=p_logit, family="binomial") ybar <- mean(valDf$y==1) # marginal prob(y==1) D0 <- deviance(y=valDf$y, pred=ybar, family="binomial") ## OOS R-squared is 5.23% 1 - D/D0 ## Plot a couple of Variables that appear to be related to y (Future Debt Paydown) ## We see that: ## 1a. Most companies have low leverage (Long Term Debt/Enterprsie Value) ## 1b. There is a monotonic decrease in frequency of observations as LT Debt/EV increases ## 2. On average, companies that did NOT pay down debt last year have higher LT Debt/Assets ## 3. On average, companies that DID pay down debt last year have higher Gross Profitability hist(trainDf$lt_debt_ev, main="Long Term Debt / Enterprise Value", xlab="LT Debt / EV") boxplot(debt_assets~debtpy_debt, data=trainDf, ylim=c(0,1), main="LT Debt / Assets by Prior Debt Paydown", xlab="Prior Debt Paydown", ylab="LT Debt / Assets") boxplot(gprofit_assets~debtpy_debt, data=trainDf, ylim=c(-0.5,1), main="Gross Profitability by Prior Debt Paydown", xlab="Prior Debt Paydown", ylab="Gross Profit / Assets") ## FDR COntrol ## Plot the p-values hist(pv,col=8, main="Histogram of P-Values") # We see a big spike at zero, indicating more p-values near zero than one # would expect if they all came from the null. There is signal in the data. # FDR Cut at 0.01: alpha <- fdr_cut(pv,q=.01,plotit=TRUE) alpha ## alpha cutoff is 0.001070771 signif <- which(pv <= alpha) ## which are significant length(signif) ## 26 coefficients (out of the original 36) are significant after FDR control ###fit logit model with interactions x_train <- trainDf[,-26:-28] x_train_sp <- sparse.model.matrix(~ .^2, data=x_train)[,-1] #This takes a while! x_val <- valDf[,-26:-28] x_val_sp <- sparse.model.matrix(~ .^2, data=x_val)[,-1] x_test <- testDf[,-26:-28] x_test_sp <- sparse.model.matrix(~ .^2, data=x_test)[,-1] ## Lasso & CV Regression (both models take a few minutes to run) y_train <- trainDf$y lasso_reg <- gamlr(x_train_sp,y_train, family="binomial", lmr=1e-4) plot(lasso_reg) lasso.cv <- cv.gamlr(x_train_sp,y_train, family="binomial", lmr=1e-4, verb=TRUE) plot(lasso.cv) # CV OOS R-Squared is higher than the simple GLM model (1- lasso.cv$cvm[lasso.cv$seg.1se]/lasso.cv$cvm[1]) # 1se rule = 6.70% (1- lasso.cv$cvm[lasso.cv$seg.min]/lasso.cv$cvm[1]) # min rule = 6.81% ### Set up Deviance Loss Function to get OOS R2 of Lasso Regression### ###deviance loss function lossf = function(y,phat,wht=0.0000001) { #y should be 0/1 #wht shrinks probs in phat towards .5, don't log 0! if(is.factor(y)) y = as.numeric(y)-1 phat = (1-wht)*phat + wht*.5 py = ifelse(y==1,phat,1-phat) return(-2*sum(log(py))) } # Lasso reg OOS R-squared p_lasso_reg <- predict(lasso_reg, newdata=x_val_sp, type="response") phat_lasso = matrix(p_lasso_reg,ncol=1) D <- lossf(y=valDf$y, phat=phat_lasso) ybar <- mean(valDf$y==1) # marginal prob(y==1) D0 <- deviance(y=valDf$y, pred=ybar, family="binomial") ## Lasso Reg's OOS R-squared is 6.69% (also higher than simple logit) 1 - D/D0 ### Principal Component Analysis ### ## Turn Factors to numeric to do PCA x_train$size_index <- as.numeric(x_train$size_index) x_train$pyreturn_index <- as.numeric(x_train$pyreturn_index) x_train$value_index <- as.numeric(x_train$value_index) x_train$profit_index <- as.numeric(x_train$profit_index) x_train$pb_index <- as.numeric(x_train$pb_index) x_train$pyreturn_below_median <- as.numeric(x_train$pyreturn_below_median) x_train$asset_turnover <- as.numeric(x_train$asset_turnover) x_train$debtpy_debt <- as.numeric(x_train$debtpy_debt) x_train$sharespy_shares <- as.numeric(x_train$sharespy_shares) pca_train <- prcomp(x_train, scale=TRUE) z_train <- predict(pca_train) ## plot and interpret plot(pca_train, main="") mtext(side=1, "Equity PCs", line=1, font=2) round(pca_train$rotation[,1:4],1) ## PC1 captures the size factor (see loadings on big stocks) ## PC2 captures low leverage, large growth stocks with positive momentum ## PC3 capturs low leverage, small growth stocks with negative momentum ## PC4 captures value stocks with high profitability save.image() ```{r} stopCluster(cl) # shut down the parallel computing cluster ```

2bb69888fe535e2c5a9389cd43abafd51329f25b

1623d0f14bec8e9813b75a29e4dd549462000ab2

/man/getXMLFromWebService.Rd

c7e482d72c87c298092b17f9e2450820c2faf82d

[ "MIT" ]

permissive

jbracher/epi_forecasting_DE

e410827f10b64fb7ea59a6bad093d4f75f79af1e

4f88974beb2b2a3c84f54982553f780f6d91b193

refs/heads/master

2023-09-04T12:13:14.788907

2021-10-27T08:11:18

239,568,850

0

null

UTF-8

R

false

true

432

rd

getXMLFromWebService.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/survstat_webservice.R \name{getXMLFromWebService} \alias{getXMLFromWebService} \title{method to recieve the xml from the WebService} \usage{ getXMLFromWebService(body_, service_) } \arguments{ \item{body_}{the xml request} \item{service_}{the WebService method} } \value{ something XMLish } \description{ method to recieve the xml from the WebService }

71c4facaf6af9377d7d507a1e3bbbb750d26fa6a

b77b91dd5ee0f13a73c6225fabc7e588b953842b

/11_run_all_analyze_plot_scripts.R

63648746f94bcf27152b638604fdd54a39e32e7b

[ "MIT" ]

permissive

ksamuk/gene_flow_linkage

a1264979e28b61f09808f864d5fa6c75568147b0

6182c3d591a362407e624b3ba87403a307315f2d

refs/heads/master

2021-01-18T09:18:02.904770

2017-04-02T16:51:40

47,041,898

1

0

null

UTF-8

R

false

834

r

11_run_all_analyze_plot_scripts.R

################################################################################ # This script performs the following functions: # 1. Plots all the raw figures for the manuscript # 3. Performs permutations on fitted model ouput # 2. Creates all the supplementary data files ################################################################################ library("dplyr") # this generates warnings; they are safe to ignore (mostly ggplot reporting NAs being removed) list.files(pattern = "plot", full.names = TRUE) %>% grep("RUN", ., invert = TRUE, value = TRUE) %>% sapply(source) list.files(pattern = "analyze", full.names = TRUE) %>% grep("RUN", ., invert = TRUE, value = TRUE) %>% sapply(source) list.files(pattern = "prepare", full.names = TRUE) %>% grep("RUN", ., invert = TRUE, value = TRUE) %>% sapply(source)

ce1e31ecfc4da45d10d656cb9d6aa0dfc1d65852

d9b3b9a18b1151bd5613d5302afa32ef92a5b219

/share/R/CI.R

aedcbfd8ec64933c61c38f266ddae7a453e7f571

[ "MIT" ]

permissive

Rbbt-Workflows/combination_index

f56f3019b69ecc6355f1950768e4b0b7febed6af

abb72987f105ae1073c6e68e06ca6c669d168636

refs/heads/master

2022-06-27T21:57:57.586452

2022-06-20T07:50:40

51,080,222

4

1

null

UTF-8

R

false

17,531

r

CI.R

rbbt.require('drc') rbbt.require('ggplot2') CI.eff_ratio = function(x){ return(x / (1-x)); } CI.misc.seq <- function(){ res = seq(0, 0.1, length.out=50) res = c(res, seq(0.1, 0.9, length.out=50)) res = c(res, seq(0.9, 1, length.out=50)) return(res) } CI.misc.log_seq <- function(center.point){ res = c(center.point) for (i in seq(1,5,by=0.5)) { n = center.point * i res = c(res, n) n = center.point * 2^i res = c(res, n) n = center.point * 4^i res = c(res, n) n = center.point * 8^i res = c(res, n) n = center.point * 16^i res = c(res, n) n = center.point * 32^i res = c(res, n) n = center.point * 64^i res = c(res, n) n = center.point / i res = c(res, n) n = center.point / 2^i res = c(res, n) n = center.point / 4^i res = c(res, n) n = center.point / 8^i res = c(res, n) n = center.point / 16^i res = c(res, n) n = center.point / 32^i res = c(res, n) n = center.point / 64^i res = c(res, n) } for (i in seq(0,100, by=1)) { res = c(res, i) } return(sort(res)) } CI.add_curve = function(m_1, m_2, dm_1, dm_2, d_1, d_2){ additive.levels = CI.misc.seq() additive.doses = sapply(additive.levels, function(level){ ratio = CI.eff_ratio(level); t1 = d_1/(dm_1*(ratio^(1/m_1))) t2 = d_2/(dm_2*(ratio^(1/m_2))) (d_1+d_2)/(t1 + t2) }) data.add = data.frame(Dose=additive.doses, Response=additive.levels) return(data.add) } CI.add_curve.bliss = function(m_1, m_2, dm_1, dm_2, d_1, d_2){ additive.levels = CI.misc.seq() additive.doses = sapply(additive.levels, function(level){ ratio = CI.eff_ratio(level); t1 = d_1/(dm_1*(ratio^(1/m_1))) t2 = d_2/(dm_2*(ratio^(1/m_2))) (d_1+d_2)/(t1 + t2) }) data.add = data.frame(Dose=additive.doses, Response=additive.levels) return(data.add) } CI.me_curve = function(m, dm, center_dose=NA){ if (is.na(center_dose)) center_dose = dm doses.me = c(CI.misc.log_seq(center_dose), CI.misc.seq()) if (is.null(m) || is.na(m)){ data.me = data.frame(Dose=doses.me, Response=rep(NA, length(doses.me))) }else{ response_ratios.me = sapply(doses.me, function(d){ (d / dm)^m }); responses.me = sapply(response_ratios.me, function(ratio){ ratio / (1+ratio) }); data.me = data.frame(Dose=doses.me, Response=responses.me) } return(data.me) } CI.least_squares.fix_log <- function(value){ res = exp(value)/(1+exp(value)) res[res>1] = 1 return(res) } CI.predict_line <- function(modelfile, doses, least_squares=FALSE, invert=FALSE,level=0.90){ data.drc = data.frame(Dose=doses); if (!is.null(modelfile)){ model = rbbt.model.load(modelfile) if (least_squares){ data.drc$Response = predict(model, data.drc) data.drc$Response = CI.least_squares.fix_log(data.drc$Response) tryCatch({ data.drc$Response.upr = predict(model, data.frame(Dose=data.drc$Dose), interval="confidence", level=level)[,'upr']; data.drc$Response.lwr = predict(model, data.frame(Dose=data.drc$Dose), interval="confidence", level=level)[,'lwr']; data.drc$Response.upr = CI.least_squares.fix_log(data.drc$Response.upr) data.drc$Response.lwr = CI.least_squares.fix_log(data.drc$Response.lwr) }) }else{ data.drc$Response = predict(model, data.drc) tryCatch({ data.drc$Response.upr = predict(model, data.frame(Dose=data.drc$Dose), interval="confidence", level=level)[,'Upper']; data.drc$Response.lwr = predict(model, data.frame(Dose=data.drc$Dose), interval="confidence", level=level)[,'Lower']; }) } if (invert){ data.drc$Response = 1 - data.drc$Response tryCatch({ data.drc$Response.upr = 1 - data.drc$Response.upr data.drc$Response.lwr = 1 - data.drc$Response.lwr }) } }else{ return(NULL) } return(data.drc) } CI.subset_data <- function(data, min, max){ data = subset(data, data$Response <= 1) data = subset(data, data$Dose <= max) data = subset(data, data$Dose >= min) return(data) } #{{{ PLOTS }}}# CI.plot_fit <- function(m, dm, data, data.me_points=NULL, modelfile=NULL, least_squares=FALSE, invert=FALSE, random.samples=NULL){ data.me = CI.me_curve(m, dm) max = max(data$Dose) min = min(data$Dose) data.me = subset(data.me, data.me$Response <= 1) data.me = subset(data.me, data.me$Dose <= max) data.me = subset(data.me, data.me$Dose >= min) data.drc = CI.predict_line(modelfile, data.me$Dose, least_squares, invert) if (is.null(data.drc)){ data.drc = data.me } min.response=min(c(0,data.me$Response, data.drc$Response, data.me_points$Response)) max.response=max(c(1,data.me$Response, data.drc$Response, data.me_points$Response)) min.dose = min(data.me_points$Dose) max.dose = max(data.me_points$Dose) if (least_squares){ plot = ggplot(aes(x=Dose, y=log(Response/(1-Response))), data=data) #+ xlim(log(c(min.dose, max.dose))) + ylim(c(1,-1)) }else{ plot = ggplot(aes(x=Dose, y=Response), data=data) + ylim(c(min.response,max.response)) #+ xlim(log(c(min.dose, max.dose))) } if(sum(!is.nan(data.drc$Response.upr)) > 0 && ! least_squares){ plot = plot + geom_ribbon(data=data.drc, aes(ymin=Response.lwr, ymax=Response.upr),col='blue',fill='blue',alpha=0.2, cex=0.1) } if (!is.null(random.samples) && length(random.samples)>0){ for (i in seq(0,length(random.samples)/2)){ m.s = random.samples[2*i+1] dm.s = random.samples[2*i+2] data.me.s = CI.me_curve(m.s, dm.s) data.me.s = CI.subset_data(data.me.s, min, max) plot = plot + geom_line(data=data.me.s, col='cyan', cex=2, linetype='solid', alpha=0.2) } } plot = plot + scale_x_log10() + annotation_logticks(side='b') + geom_line(data=data.me, col='blue', cex=2) + geom_line(data=data.drc, col='blue', linetype='dotted',cex=2) + geom_point(cex=5) + geom_point(data=data.me_points, col='blue',cex=7, shape=18) + geom_point(data=data.me_points, col='white',cex=4, shape=18) return(plot) } CI.plot_combination <- function(blue_m, blue_dm, blue_dose, red_m, red_dm, red_dose, response, blue_data, red_data, data.blue_me_points, data.red_me_points, blue.modelfile = NULL, red.modelfile=NULL, least_squares=FALSE, blue.invert=FALSE, red.invert=FALSE, fix_ratio=FALSE, more_doses = NULL, more_responses = NULL, blue.random.samples=NULL, red.random.samples=NULL, blue.fit_dose = NULL, red.fit_dose = NULL){ data.blue_me = CI.me_curve(blue_m, blue_dm) data.red_me = CI.me_curve(red_m, red_dm) max = max(c(blue_data$Dose, red_data$Dose)) min = min(c(blue_data$Dose, red_data$Dose)) data.blue_me = subset(data.blue_me, data.blue_me$Response <= 1) data.blue_me = subset(data.blue_me, data.blue_me$Dose <= max) data.blue_me = subset(data.blue_me, data.blue_me$Dose >= min) data.red_me = subset(data.red_me, data.red_me$Response <= 1) data.red_me = subset(data.red_me, data.red_me$Dose <= max) data.red_me = subset(data.red_me, data.red_me$Dose >= min) if (fix_ratio){ blue_ratio = (blue_dose + red_dose)/blue_dose red_ratio = (blue_dose + red_dose)/red_dose }else{ blue_ratio = red_ratio = 1 } data.add = CI.add_curve(blue_m, red_m, blue_dm, red_dm, blue_dose, red_dose) data.blue_drc = CI.predict_line(blue.modelfile, data.add$Dose/blue_ratio, least_squares, blue.invert) data.red_drc = CI.predict_line(red.modelfile, data.add$Dose/red_ratio, least_squares, red.invert) if (is.null(data.blue_drc)){ data.blue_drc = data.blue_me } if (is.null(data.red_drc)){ data.red_drc = data.red_me } blue_data$Dose = blue_data$Dose * blue_ratio data.blue_me$Dose = data.blue_me$Dose * blue_ratio data.blue_drc$Dose = data.blue_drc$Dose * blue_ratio data.blue_me_points$Dose = data.blue_me_points$Dose * blue_ratio red_data$Dose = red_data$Dose * red_ratio data.red_me$Dose = data.red_me$Dose * red_ratio data.red_drc$Dose = data.red_drc$Dose * red_ratio data.red_me_points$Dose = data.red_me_points$Dose * red_ratio min.response=min(c(0,response, data.blue_me_points$Response, data.blue_drc$Response)) max.response=max(c(1,response, data.blue_me_points$Response, data.blue_drc$Response)) min.response=min(c(min.response, data.red_me_points$Response, data.red_drc$Response)) max.response=max(c(max.response, data.red_me_points$Response, data.red_drc$Response)) if (!is.null(more_responses)){ min.response=min(c(min.response, more_responses)) max.response=max(c(max.response, more_responses)) } max.response = min(c(1, max.response)) min.response = max(c(0, min.response)) all.doses = c(data.blue_me$Dose, data.red_me$Dose, more_doses) min.dose = min(all.doses) max.dose = max(all.doses) data.blue_drc = CI.subset_data(data.blue_drc, min.dose, max.dose) data.red_drc = CI.subset_data(data.red_drc, min.dose, max.dose) data.blue_me = CI.subset_data(data.blue_me, min.dose, max.dose) data.red_me = CI.subset_data(data.red_me, min.dose, max.dose) data.add = CI.subset_data(data.add, min.dose, max.dose) plot = ggplot(aes(x=as.numeric(Dose), y=as.numeric(Response)), data=blue_data) if (!is.null(more_responses)){ len = min(c(length(more_doses), length(more_responses))) md=more_doses[1:len] me=more_responses[1:len] plot = plot + geom_smooth(aes(x=Dose, y=Response), data=data.frame(Dose=md, Response=me), linetype='dashed', col='black', level=0.95, se=FALSE) } if (!is.null(blue.random.samples) && !is.null(red.random.samples)){ max = min(length(blue.random.samples), length(red.random.samples)) for (i in seq(0,max/2)){ m.blue.s = blue.random.samples[2*i+1] dm.blue.s = blue.random.samples[2*i+2] m.red.s = red.random.samples[2*i+1] dm.red.s = red.random.samples[2*i+2] data.add.s = CI.add_curve(m.blue.s, m.red.s, dm.blue.s, dm.red.s, blue_dose, red_dose) data.add.s = CI.subset_data(data.add.s, min.dose, max.dose) plot = plot + geom_line(data=data.add.s, col='cyan', cex=2, linetype='solid', alpha=0.2) } } plot = plot + xlim(min.dose, max.dose) + ylim(min.response, max.response) + scale_x_log10() + annotation_logticks(side='b') + xlab("Dose") + ylab("Response") + geom_point(data=blue_data, col='blue',cex=3,alpha=0.8) + geom_point(data=red_data, col='red',cex=3,alpha=0.8) + geom_line(data=data.blue_me, col='blue', cex=2,alpha=0.8) + geom_line(data=data.red_me, col='red', cex=2,alpha=0.8) + geom_line(data=data.blue_drc, linetype='dashed', col='blue', cex=1,alpha=0.8) + geom_line(data=data.red_drc, linetype='dashed', col='red', cex=1,alpha=0.8) + geom_line(data=data.add, col='black', cex=2,alpha=0.8) + geom_point(x=log10(blue_dose + red_dose), y=response, col='black',cex=5,alpha=0.8) + geom_point(data=data.blue_me_points, col='blue', shape = 18, cex=7) + geom_point(data=data.blue_me_points, col='white', shape = 18, cex=4) + geom_point(data=data.red_me_points, col='red', shape = 18, cex=7) + geom_point(data=data.red_me_points, col='white', shape = 18, cex=4) if (!is.null(more_responses)){ for (i in seq(1, len)){ plot = plot + geom_point(x=log10(more_doses[i]), y=more_responses[i], col='black', cex=2, alpha=0.4) } } if (!is.null(blue.fit_dose)){ plot = plot + geom_vline(x=blue.fit_dose*blue_ratio, col='blue', cex=1, linetype='dotted') plot = plot + geom_vline(x=red.fit_dose*red_ratio, col='red', cex=1, linetype='dotted') } return(plot) } CI.plot_combination.bliss <- function(blue_dose, red_dose, response, blue_data, red_data, bliss_data, fix_ratio=FALSE, more_doses = NULL, more_responses = NULL, all_bliss_data = NULL){ max = max(c(blue_data$Dose, red_data$Dose)) min = min(c(blue_data$Dose, red_data$Dose)) if (fix_ratio){ blue_ratio = (blue_dose + red_dose)/blue_dose red_ratio = (blue_dose + red_dose)/red_dose }else{ blue_ratio = red_ratio = 1 } blue_data$Dose = blue_data$Dose * blue_ratio red_data$Dose = red_data$Dose * red_ratio min.response=min(as.numeric(c(0,response, blue_data$Response, red_data$Response, bliss_data$Response))) max.response=max(as.numeric(c(1,response, blue_data$Response, red_data$Response, bliss_data$Response))) if (!is.null(more_responses)){ min.response=min(c(min.response, more_responses)) max.response=max(c(max.response, more_responses)) } max.response = min(c(1.2, max.response)) min.response = max(c(-0.2, min.response)) all.doses = c(more_doses) min.dose = min(all.doses) max.dose = max(all.doses) plot = ggplot(aes(x=as.numeric(Dose), y=as.numeric(Response)), data=blue_data) + xlim(min.dose, max.dose) + ylim(min.response, max.response) str(max.response) str(min.response) if (!is.null(more_responses)){ len = min(c(length(more_doses), length(more_responses))) md=more_doses[1:len] me=more_responses[1:len] plot = plot + geom_smooth(aes(x=Dose, y=Response), data=data.frame(Dose=md, Response=me), linetype='dashed', col='black', method="loess", level=0.95, se=FALSE) } alpha = 0.8 plot = plot + scale_x_log10() + annotation_logticks(side='b') + xlab("Dose") + ylab("Value") + geom_point(data=blue_data, col='blue',cex=3,alpha=alpha) + geom_point(data=red_data, col='red',cex=3,alpha=alpha) + geom_point(data=bliss_data, col='purple',cex=3,alpha=alpha) + geom_smooth(data=bliss_data, linetype='dashed', col='purple',method="loess", level=0.95, se=FALSE) + geom_point(x=log10(blue_dose + red_dose), y=response, col='black',cex=5,alpha=alpha) if (!is.null(all_bliss_data)){ plot = plot + geom_point(data=all_bliss_data, col='purple', cex=5, alpha=alpha/2) } if (!is.null(more_responses)){ for (i in seq(1, len)){ plot = plot + geom_point(x=log10(more_doses[i]), y=more_responses[i], col='black', cex=2, alpha=0.4) } } return(plot) } CI.plot_combination.hsa <- function(blue_dose, red_dose, response, blue_data, red_data, hsa_data, fix_ratio=FALSE, more_doses = NULL, more_responses = NULL){ max = max(c(blue_data$Dose, red_data$Dose)) min = min(c(blue_data$Dose, red_data$Dose)) if (fix_ratio){ blue_ratio = (blue_dose + red_dose)/blue_dose red_ratio = (blue_dose + red_dose)/red_dose }else{ blue_ratio = red_ratio = 1 } blue_data$Dose = blue_data$Dose * blue_ratio red_data$Dose = red_data$Dose * red_ratio min.response=min(as.numeric(c(0,response, blue_data$Response, red_data$Response, hsa_data$Response))) max.response=max(as.numeric(c(1,response, blue_data$Response, red_data$Response, hsa_data$Response))) if (!is.null(more_responses)){ min.response=min(c(min.response, more_responses)) max.response=max(c(max.response, more_responses)) } max.response = min(c(1.2, max.response)) min.response = max(c(-0.2, min.response)) all.doses = c(more_doses) min.dose = min(all.doses) max.dose = max(all.doses) plot = ggplot(aes(x=as.numeric(Dose), y=as.numeric(Response)), data=blue_data) + xlim(min.dose, max.dose) + ylim(min.response, max.response) str(max.response) str(min.response) if (!is.null(more_responses)){ len = min(c(length(more_doses), length(more_responses))) md=more_doses[1:len] me=more_responses[1:len] plot = plot + geom_smooth(aes(x=Dose, y=Response), data=data.frame(Dose=md, Response=me), linetype='dashed', col='black', method="loess", level=0.95, se=FALSE) } plot = plot + scale_x_log10() + annotation_logticks(side='b') + xlab("Dose") + ylab("Value") + geom_point(data=blue_data, col='blue',cex=3,alpha=0.8) + geom_point(data=red_data, col='red',cex=3,alpha=0.8) + geom_point(data=hsa_data, col='purple',cex=3,alpha=0.8) + geom_smooth(data=hsa_data, linetype='dashed', col='purple',method="loess", level=0.95, se=FALSE) + geom_point(x=log10(blue_dose + red_dose), y=response, col='black',cex=5,alpha=0.8) if (!is.null(more_responses)){ for (i in seq(1, len)){ plot = plot + geom_point(x=log10(more_doses[i]), y=more_responses[i], col='black', cex=2, alpha=0.4) } } return(plot) }

abe1647784377f4664eb8aab678a19e55106126c

5065f337e3ffb1793b855e6b2264dfde4467fb97

/[archive from before 11-2018]/R code/05 code split plot variety nitrogen [Splitplot 2Way ANOVA].R

fe59169043a3f6e6cf146c2b99475940165ad82a

[]

no_license

Reicharf/R-SAS.Introductory.Courses

faed787ec5c0eaa76d55dc476e6142f27c24ce6f

69ec001f8961d5f1c4d1387f80249daa4cbc159b

refs/heads/master

2022-06-16T23:21:33.677994

2020-04-26T13:50:08

null

0

null

UTF-8

R

false

3,498

r

05 code split plot variety nitrogen [Splitplot 2Way ANOVA].R

rm(list=ls()) setwd("D:/Hohenheim/R-SAS.Introductory.Courses/Datasets") library(data.table) dt <- fread("05 split plot variety nitrogen.txt") # directly import as data.table format via fread() dt$Block <- as.factor(dt$Block) dt$N <- as.factor(dt$N) dt$Var <- as.factor(dt$Var) dt$Yield <- as.numeric(dt$Yield) # Split Plot Design # Two-Way ANOVA ### Split Plot Design # When some factors (independent variables) are difficult # or impossible to change in your experiment, a completely # randomized design isn't possible. The result is a # split-plot design, which has a mixture of hard to # randomize (or hard-to-change) and easy-to-randomize # (or easy-to-change) factors. The hard-to-change factors # are implemented first, followed by the easier-to-change factors. # boxplots for first impression boxplot(data=dt, Yield ~ N + Var, las=2) boxplot(data=dt, Yield ~ N , las=2) boxplot(data=dt, Yield ~ Var , las=2) # In a split-plot design, the (incomplete) mainplots should # be taken as a "random effect". As a general principle, # each randomization units needs to be represented by a random effect, # so each randomization unit has its own error term. # Since we then have random and fixed effects # in one model, we are fitting a "mixed model". # In R the most common packages for that are "lme4", "nlme", "asreml-R" and "sommer". # If you use lme4, always load the lmerTest package, too #install.packages("lme4") #install.packages("lmerTest") library(lme4) library(lmerTest) # Fit general linear mixed model ################################# # Treatment effects: Variety, Fertilizer and their interaction # Design effects: Block and mainplot(=random effect) # Step 1: Check F-Test of ANOVA and perform backwards elimination # Step 2: Compare adjusted means per level mod <- lmer(data = dt, formula = Yield ~ N + Var + N:Var + Block + (1|Block:N)) # Note: In this example, Block*N identifies the incomplete blocks (=main plots) within each complete block. # To read more about this example, see p. 59 of Prof. Piepho's lecture notes for "Mixed models for metric data" anova(mod) # Interaction effect significant - final model # plot(mod) # residual plot 1 # qqnorm(resid(mod)); qqline(resid(mod)) # residual plot 2 mod # Basic results summary(mod) # More detailed results # get adj. means for Variety effect and compare library(emmeans) # get means and comparisons means <- emmeans(mod, pairwise ~ N | Var, adjust = "tukey") # to get t-test: adjust="none" # Note that N | Var gets pairwise N comparisons for each # Variety separately. You can use N:Var instead to get all # pairwise comparisons. means # look at means and comparisons means$emmeans # look at means means$contrasts # look at comparions output <- CLD(means$emmeans, details=T, Letters = letters) output # this data format is not good for ggplot output <- as.data.table(output$emmeans) # reformatting into one table output # this is better # plot adjusted means #install.packages("ggplot2") library(ggplot2) p <- ggplot(data=output, aes(x=N)) p <- p + geom_bar(aes(y=emmean), stat="identity", width=0.8) p <- p + geom_errorbar(aes(ymin=emmean-SE, ymax=emmean+SE), width=0.4) p <- p + geom_text(aes(y=emmean+1500, label=.group)) p <- p + facet_wrap(~Var) # one per variety p # show plot # save ggplot as file into your working directory ggsave("test.jpeg", width = 20, height = 10, units = "cm")

2ed038142a7a5b785cc838a42d708fe49b2521e5

1ed93f1491f02dc355dbbe0cbe5e6931134f7a7d

/R/pcorstarsl.R

a12d6367ccf5147697ce7e8f7cbafd105cd9ff02

[]

no_license

GiulioCostantini/markerIndex

b346ed40f13fa385d4e232687eda01116a33e02e

febb6fba8f538ee48698085f3f3a810d4ccc0e23

refs/heads/master

2021-07-13T02:28:41.310778

2021-02-23T11:31:20

237,241,394

0

null

UTF-8

R

false

1,487

r

pcorstarsl.R

# adapted from here to include partial correlation matrix # https://github.com/kyuni22/ksmv/blob/master/functions/corstarsl.R pcorstarsl <- function(pcm, n, np, digits = 2, full = FALSE){ # pcm = partial correlation matrix # n = sample size # np = number of variables partialled out if(is.null(rownames(pcm))) rownames(pcm) <- 1:nrow(pcm) if(is.null(colnames(pcm))) colnames(pcm) <- 1:ncol(pcm) R <- pcm p <- corr.p(R, n-np, adjust = "none")$p ## define notions for significance levels; spacing is important. mystars <- ifelse(p < .001, "***", ifelse(p < .01, "** ", ifelse(p < .05, "* ", ifelse(p < .1, "+ ", " ")))) ## trunctuate the matrix that holds the correlations to two decimal R <- format(round(R, digits)) R <- apply(R, 2, str_replace, "0.", ".") ## build a new matrix that includes the correlations with their apropriate stars Rnew <- matrix(paste(R, mystars, sep=""), ncol=ncol(pcm)) diag(Rnew) <- paste(diag(R), " ", sep="") rownames(Rnew) <- colnames(pcm) colnames(Rnew) <- paste(colnames(pcm), "", sep="") ## remove upper triangle Rnew <- as.matrix(Rnew) Rnew[upper.tri(Rnew, diag = TRUE)] <- "" Rnew <- as.data.frame(Rnew) ## remove last column and return the matrix (which is now a data frame) Rnew <- cbind(Rnew[1:length(Rnew)-1]) if(full) out <- list("Rformatted" = Rnew, "R" = pcm, "p" = p) else out <- Rnew out }

9556fd0f837fd6be7dff163b3a22579d5a4017e1

1d0465c228a1e8fd4e1c271fbde2262bf24ccb59

/Data Analysis in R. Part 2/1.4.1.r

ce225f2591e02ade103fea98a362afc2ea8dd46e

[]

no_license

framirov/R-course

536ab92111a2a54c0160f82fab960ed8047877a9

a01439fa0b1e530385c303bedf7f44af239a6683

refs/heads/main

2023-01-27T20:38:50.428334

2020-12-04T08:26:30

312,372,536

0

null

UTF-8

R

false

81

r

1.4.1.r

positive_sum <- function(x){ lapply(x, function(x) sum(x[x>0], na.rm = T)) }

bc9a57b404d34bd11caf8f915c4978d54b13afff

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/kzs/examples/kzs.2d.Rd.R

8708e337f47e21858b995ed665b27f59471de060

[]

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

1,501

r

kzs.2d.Rd.R

library(kzs) ### Name: kzs.2d ### Title: Spatial Kolmogorov-Zurbenko Spline ### Aliases: kzs.2d ### Keywords: smooth ts nonparametric ### ** Examples # EXAMPLE - Estimating the Sinc function in the interval (-3pi, 3pi) # Load the LATTICE package # Gridded data for X = (x1, x2) input variables x1 <- seq(-3*pi, 3*pi, length = 60) x2 <- x1 df <- expand.grid(x1 = x1, x2 = x2) # Apply the Sinc function to the (x1, x2) coordinates df$z <- sin(sqrt(df$x1^2 + df$x2^2)) / sqrt(df$x1^2 + df$x2^2) df$z[is.na(df$z)] <- 1 # Any point outside the circle of radius 3pi is set to 0. This provides # a better picture of the outcome solely for the purposes of this example. dst <- sqrt((df$x1 - 0)^2 + (df$x2 - 0)^2) df$dist <- dst df$z[df$dist > 3*pi] <- 0 # Add noise to distort the signal ez <- rnorm(length(df$z), mean = 0, sd = 1) * 1/4 df$zn <- ez + df$z ### (1) 3D plot of the signal to be estimated by kzs.2d() wireframe(z ~ x1 * x2, df, main = "Signal to be estimated", drape = TRUE, colorkey = TRUE, scales = list(arrows = FALSE)) ### (2) 3D plot of the signal buried in noise wireframe(zn ~ x1 * x2, df, main = "Signal buried in noise", drape = TRUE, colorkey = TRUE, scales = list(arrows = FALSE)) ### (3) Execute kzs.2d() # kzs.2d() may take time to run; k = 1 iteration is used here, but k = 2 # will provide a smoother outcome. sw <- c(1, 1) sc <- c(0.2, 0.2) kzs.2d(y = df[,5], x = df[,1:2], smooth = sw, scale = sc, k = 1, edges = TRUE, plot = TRUE)

058aefb10cca6d9d609a98adc8fe1247b543112f

761d9b095dd97a9fe0aa25db0966087cea34963d

/Section IV - Distance, Knn, Cross Validation, and Generative Models, Part 3 Generative Models.R

e1e536b19406826ab0cedf773a79fb6b69ee84ec

[]

no_license

mike-tex/machine-learning

776e478ab2b01f837be762751985212e3e67a796

c76c177a7d5550002dde5faa23a03471145966c9

refs/heads/master

2022-11-17T04:26:32.101227

2020-07-09T21:39:07

271,375,389

0

null

UTF-8

R

false

13,918

r

Section IV - Distance, Knn, Cross Validation, and Generative Models, Part 3 Generative Models.R

## Section 4: Distance, Knn, Cross-validation, ## and Generative Models ## 4.3: Generative Models ## Generative Models ## Naive Bayes # Generating train and test set library(dslabs) library(tidyverse) library("caret") data("heights") y <- heights$height set.seed(2, sample.kind = "Rounding") test_index <- createDataPartition(y, times = 1, p = 0.5, list = FALSE) train_set <- heights %>% slice(-test_index) test_set <- heights %>% slice(test_index) # Estimating averages and standard deviations params <- train_set %>% group_by(sex) %>% summarize(avg = mean(height), sd = sd(height)) params # Estimating the prevalence pi <- train_set %>% summarize(pi=mean(sex=="Female")) %>% pull(pi) pi # Getting an actual rule x <- test_set$height f0 <- dnorm(x, params$avg[2], params$sd[2]) f1 <- dnorm(x, params$avg[1], params$sd[1]) p_hat_bayes <- f1*pi / (f1*pi + f0*(1 - pi)) ## Controlling Prevalence # Computing sensitivity y_hat_bayes <- ifelse(p_hat_bayes > 0.5, "Female", "Male") sensitivity(data = factor(y_hat_bayes), reference = factor(test_set$sex)) # Computing specificity specificity(data = factor(y_hat_bayes), reference = factor(test_set$sex)) # Changing the cutoff of the decision rule p_hat_bayes_unbiased <- f1 * 0.5 / (f1 * 0.5 + f0 * (1 - 0.5)) y_hat_bayes_unbiased <- ifelse(p_hat_bayes_unbiased > 0.5, "Female", "Male") sensitivity(data = factor(y_hat_bayes_unbiased), reference = factor(test_set$sex)) specificity(data = factor(y_hat_bayes_unbiased), reference = factor(test_set$sex)) # Draw plot qplot(x, p_hat_bayes_unbiased, geom = "line") + geom_hline(yintercept = 0.5, lty = 2) + geom_vline(xintercept = 67, lty = 2) ## qda and lda # QDA # Quadratic discriminant analysis (QDA) # Load data data("mnist_27") # Estimate parameters from the data params <- mnist_27$train %>% group_by(y) %>% summarize(avg_1 = mean(x_1), avg_2 = mean(x_2), sd_1 = sd(x_1), sd_2 = sd(x_2), r = cor(x_1, x_2)) # Contour plots mnist_27$train %>% mutate(y = factor(y)) %>% ggplot(aes(x_1, x_2, fill = y, color = y)) + geom_point(show.legend = FALSE) + stat_ellipse(type="norm", lwd = 1.5) # Fit model library(caret) train_qda <- train(y ~., method = "qda", data = mnist_27$train) # Obtain predictors and accuracy y_hat <- predict(train_qda, mnist_27$test) confusionMatrix(data = y_hat, reference = mnist_27$test$y)$overall["Accuracy"] # Draw separate plots for 2s and 7s mnist_27$train %>% mutate(y = factor(y)) %>% ggplot(aes(x_1, x_2, fill = y, color = y)) + geom_point(show.legend = FALSE) + stat_ellipse(type="norm") + facet_wrap(~y) # LDA - linear discriminant analysis params <- mnist_27$train %>% group_by(y) %>% summarize(avg_1 = mean(x_1), avg_2 = mean(x_2), sd_1 = sd(x_1), sd_2 = sd(x_2), r = cor(x_1, x_2)) params <- params %>% mutate(sd_1 = mean(sd_1), sd_2 = mean(sd_2), r = mean(r)) train_lda <- train(y ~., method = "lda", data = mnist_27$train) y_hat <- predict(train_lda, mnist_27$test) confusionMatrix(data = y_hat, reference = mnist_27$test$y)$overall["Accuracy"] ## Case Study: More than Three Classes if(!exists("mnist"))mnist <- read_mnist() # set.seed(3456) set.seed(3456, sample.kind="Rounding") # in R 3.6 or later index_127 <- sample(which(mnist$train$labels %in% c(1,2,7)), 2000) y <- mnist$train$labels[index_127] x <- mnist$train$images[index_127,] index_train <- createDataPartition(y, p=0.8, list = FALSE) # get the quadrants # temporary object to help figure out the quadrants row_column <- expand.grid(row=1:28, col=1:28) upper_left_ind <- which(row_column$col <= 14 & row_column$row <= 14) lower_right_ind <- which(row_column$col > 14 & row_column$row > 14) # binarize the values. Above 200 is ink, below is no ink x <- x > 200 # cbind proportion of pixels in upper right quadrant # and proportion of pixels in lower right quadrant x <- cbind(rowSums(x[ ,upper_left_ind])/rowSums(x), rowSums(x[ ,lower_right_ind])/rowSums(x)) train_set <- data.frame(y = factor(y[index_train]), x_1 = x[index_train,1], x_2 = x[index_train,2]) test_set <- data.frame(y = factor(y[-index_train]), x_1 = x[-index_train,1], x_2 = x[-index_train,2]) train_set %>% ggplot(aes(x_1, x_2, color=y)) + geom_point() train_qda <- train(y ~ ., method = "qda", data = train_set) predict(train_qda, test_set, type = "prob") %>% head() predict(train_qda, test_set) %>% head() confusionMatrix(predict(train_qda, test_set), test_set$y)$table confusionMatrix(predict(train_qda, test_set), test_set$y)$overall["Accuracy"] train_lda <- train(y ~ ., method = "lda", data = train_set) confusionMatrix(predict(train_lda, test_set), test_set$y)$overall["Accuracy"] train_knn <- train(y ~ ., method = "knn", tuneGrid = data.frame(k = seq(15, 51, 2)), data = train_set) confusionMatrix(predict(train_knn, test_set), test_set$y)$overall["Accuracy"] train_set %>% mutate(y = factor(y)) %>% ggplot(aes(x_1, x_2, fill = y, color=y)) + geom_point(show.legend = FALSE) + stat_ellipse(type="norm") ## Comprehension Check: Generative Models # Q1 # Create a dataset of samples from just cerebellum # and hippocampus, two parts of the brain, # and a predictor matrix with 10 randomly selected columns # using the following code: library(dslabs) library(caret) library(tidyverse) data("tissue_gene_expression") # set.seed(1993) #if using R 3.6 or later set.seed(1993, sample.kind="Rounding") ind <- which(tissue_gene_expression$y %in% c("cerebellum", "hippocampus")) y <- droplevels(tissue_gene_expression$y[ind]) x <- tissue_gene_expression$x[ind, ] x <- x[, sample(ncol(x), 10)] # Use the train() function to estimate the accuracy of LDA. # For this question, use the version of x and y created # with the code above: do not split them or tissue_gene_expression # into training and test sets (understand this can lead # to overfitting). Report the accuracy from the train() # results (do not make predictions). train(y ~ x, method = "lda", data = tibble(x = x, y = y)) # What is the accuracy? # Accuracy # 0.8707879 ## YES!!! ## Explanation from the web site # The following code can be used to estimate # the accuracy of the LDA: fit_lda <- train(x, y, method = "lda") fit_lda$results["Accuracy"] # Q2 # In this case, LDA fits two 10-dimensional # normal distributions. Look at the fitted model by looking # at the finalModel component of the result of train(). # Notice there is a component called means that includes # the estimated means of both distributions. Plot # the mean vectors against each other and determine # which predictors (genes) appear to be driving the algorithm. # # Which TWO genes appear to be driving the algorithm # (i.e. the two genes with the highest means)? fit_lda$finalModel # RAB1B # OAZ2 # Explanation from the web site # The following code can be used to make the plot: t(fit_lda$finalModel$means) %>% data.frame() %>% mutate(predictor_name = rownames(.)) %>% ggplot(aes(cerebellum, hippocampus, label = predictor_name)) + geom_point() + geom_text() + geom_abline() # Q3 # Repeat the exercise in Q1 with QDA. # Create a dataset of samples from just cerebellum # and hippocampus, two parts of the brain, # and a predictor matrix with 10 randomly selected columns # using the following code: cat("\014") rm(list = ls()) library(dslabs) library(caret) library(dplyr) data("tissue_gene_expression") # set.seed(1993) # set.seed(1993, sample.kind="Rounding") # if using R 3.6 or later ind <- which(tissue_gene_expression$y %in% c("cerebellum", "hippocampus")) y <- droplevels(tissue_gene_expression$y[ind]) x <- tissue_gene_expression$x[ind, ] x <- x[, sample(ncol(x), 10)] # Use the train() function to estimate the accuracy of QDA. # For this question, use the entire tissue_gene_expression # dataset: do not split it into training and test sets # (understand this can lead to overfitting). fit_qda <- train(x, y, method = "qda") fit_qda$results["Accuracy"] # What is the accuracy? # Accuracy # 0.8147954 # Explanation # The following code can be used to estimate # the accuracy of QDA: fit_qda <- train(x, y, method = "qda") fit_qda$results["Accuracy"] ## Q4 # Which TWO genes drive the algorithm when using QDA # instead of LDA (i.e. the two genes with the highest means)? fit_qda$finalModel t(fit_qda$finalModel$means) %>% data.frame() %>% mutate(predictor_name = rownames(.)) %>% ggplot(aes(cerebellum, hippocampus, label = predictor_name)) + geom_point() + geom_text() + geom_abline() # RAB1B # OAZ2 # Explanation from the web site t(fit_qda$finalModel$means) %>% data.frame() %>% mutate(predictor_name = rownames(.)) %>% ggplot(aes(cerebellum, hippocampus, label = predictor_name)) + geom_point() + geom_text() + geom_abline() ## Q5 # One thing we saw in the previous plots is that # the values of the predictors correlate in both groups: # some predictors are low in both groups and others high # in both groups. The mean value of each predictor found # in colMeans(x) is not informative or useful for prediction # and often for purposes of interpretation, it is useful # to center or scale each column. This can be achieved # with the preProcess argument in train(). Re-run LDA # with preProcess = "center". Note that accuracy does not change, # but it is now easier to identify the predictors # that differ more between groups than based on # the plot made in Q2. cat("\014") ## clear the console rm(list = ls()) library(dslabs) library(caret) library(dplyr) data("tissue_gene_expression") # set.seed(1993) # set.seed(1993, sample.kind="Rounding") # if using R 3.6 or later ind <- which(tissue_gene_expression$y %in% c("cerebellum", "hippocampus")) y <- droplevels(tissue_gene_expression$y[ind]) x <- tissue_gene_expression$x[ind, ] x <- x[, sample(ncol(x), 10)] fit_lda_pp <- train(x, y, method = "lda", preProcess = "center") fit_lda_pp$results["Accuracy"] fit_lda$finalModel library(dplyr) # Q2 plot t(fit_lda_pp$finalModel$means) %>% data.frame() %>% mutate(predictor_name = rownames(.)) %>% ggplot(aes(cerebellum, hippocampus, label = predictor_name)) + geom_point() + geom_text() + geom_abline() t(fit_lda_pp$finalModel$means) %>% data.frame() %>% mutate(predictor_name = rownames(.)) %>% ggplot(aes(cerebellum, hippocampus, color = predictor_name)) + geom_point() + geom_abline() # NickBova: I plotted predictor_name on the x axis # and (scaled) means on the y t(fit_lda_pp$finalModel$means) %>% data.frame() %>% mutate(tissue_names = names(.)) mutate(predictor_name = rownames(.)) %>% ggplot(aes(x = predictor_name, label = tissue)) + geom_point() + geom_text() + geom_abline() varImp(fit_lda_pp) temp1 <- fit_lda$finalModel$means %>% data.frame() %>% t() temp1 temp2 <- as.data.frame(temp1) temp2 temp3 <- as_tibble(temp2) rowtemp <- rownames(temp2) temp3 <- as_tibble(temp2, rownames = "rowtemp") temp3 temp4 <-temp3 %>% mutate(avg = (hippocampus + cerebellum) / 2, delta = abs(hippocampus - cerebellum), hippo_error = abs(hippocampus - mean(hippocampus)), cere_error = abs(cerebellum - mean(cerebellum))) temp4 arrange(temp4, abs(avg)) temp4 %>% arrange(abs(hippocampus), abs(cerebellum), abs(avg)) temp4 %>% arrange(cere_error, hippo_error) # # Which TWO genes drive the algorithm after performing # the scaling? # PLCB1 ## wrong # RAB1B ## correct # above partially correct # RAB1B ## correct # OAZ2 ## right! but it showed wrong when I selected it! # Explanation # The following code can be used to make the plot to evaluate which genes are driving the algorithm after scaling: fit_lda <- train(x, y, method = "lda", preProcess = "center") fit_lda$results["Accuracy"] t(fit_lda$finalModel$means) %>% data.frame() %>% mutate(predictor_name = rownames(.)) %>% ggplot(aes(predictor_name, hippocampus)) + geom_point() + coord_flip() # You can see that it is different genes driving # the algorithm now. This is because the predictor # means change. # # In the previous exercises we saw that both LDA # and QDA approaches worked well. For further exploration # of the data, you can plot the predictor values # for the two genes with the largest differences between # the two groups in a scatter plot to see how they appear # to follow a bivariate distribution as assumed # by the LDA and QDA approaches, coloring the points # by the outcome, using the following code: d <- apply(fit_lda$finalModel$means, 2, diff) ind <- order(abs(d), decreasing = TRUE)[1:2] plot(x[, ind], col = y) ## Q6 # Now we are going to increase the complexity # of the challenge slightly. Repeat the LDA analysis # from Q5 but using all tissue types. Use the following # code to create your dataset: library(dslabs) library(caret) data("tissue_gene_expression") # set.seed(1993) # set.seed(1993, sample.kind="Rounding") # if using R 3.6 or later y <- tissue_gene_expression$y x <- tissue_gene_expression$x x <- x[, sample(ncol(x), 10)] fit_lda <- train(x, y, method = "lda", preProcess = "center") fit_lda$results["Accuracy"] # What is the accuracy using LDA? # Accuracy # 1 0.8194837 ## YES, correct ## Explanation from the website: # The following code can be used to obtain # the accuracy of the LDA: fit_lda <- train(x, y, method = "lda", preProcess = c("center")) fit_lda$results["Accuracy"]

8d76ab192d61c71612974b4f7e97ef4d7e8f01d2

30ad23d2b71cb8a59866fda53676f1dfe6b99cc0

/Prediction.R

a825abe2bc2957714eeb7b629ff6d1a06bb187b3

[ "MIT" ]

permissive

kaswani29/RestaurantRevenuePrediction

35e1b0fb9a305b177272b5a6741c594e136005df

25d7c9dbeb308f2bf8fd4f5c9eb95139693d06f2

refs/heads/master

2021-06-09T13:22:36.364427

2016-11-10T01:29:16

null

0

null

UTF-8

R

false

6,783

r

Prediction.R

library(caret) library(doParallel) set.seed(54321) # 5/8/2015 #Revenue Prediction competition #To download data visit: # https://www.kaggle.com/c/restaurant-revenue-prediction/data # Data Processing --------------------------------------------------------- #download the files from train <- read.csv("train.csv") test <- read.csv("test.csv") n.train <- nrow(train) test$revenue <- 1 ##Converting into single dataframe myData <- rbind(train, test) myData <- myData[,-1] rm(train, test) #normalize normalize <- function(x){ return((x-min(x))/(max(x)-min(x)))} #Tranform Time myData$Open.Date <- as.POSIXlt("04/30/2015", format="%m/%d/%Y") - as.POSIXlt(myData$Open.Date, format="%m/%d/%Y") myData$Open.Date<- normalize(as.numeric(myData$Open.Date)) # summary(myData$Open.Date) ##Variable wise transformation city<- data.frame(table(myData$City[1:137])) # View(city[!city$Freq==0,]) #Consolidating Cities myData$City <- as.character(myData$City) myData$City[myData$City.Group == "Other"] <- "Other" myData$City[myData$City == unique(myData$City)[4]] <- unique(myData$City)[2] myData$City <- as.factor(myData$City) #Consolidate Types myData$Type <- as.character(myData$Type) myData$Type[myData$Type=="DT"] <- "IL" myData$Type[myData$Type=="MB"] <- "FC" myData$Type <- as.factor(myData$Type) value<- read.csv("values.csv") new1<-cbind(myData,clustering=value) myData$clustering<- value[,1] #########################################################################3 #Checking which columns are factor one at a time checkfactor<- function(y){ x<- myData x[,y]<- as.factor(x[,y]) # str(x) # fitControl <- trainControl(method = "repeatedcv", number = 10, ## repeated ten times repeats = 5) #2304291 set.seed(54321) model1 <- train(revenue~., data=x[1:n.train,],trControl = fitControl,method = "rf", importance=TRUE) return (min(model1$results$RMSE)) } a<- c(5,9,10,11,12,13:16,18:29,34:41) b<- c(6:8,17,30:33) fac_rmse1<- sapply(b,checkfactor) summary(myData$P12) vec<- c(5,9,10,11,12,13:16,18:29,34:41,6:8,17,30:33) fac<- c(fac_rmse,fac_rmse1) outp<- data.frame(cbind(fac,vec)) outp$flag<- output$fac<2304291 outp$score<- 2304291 factors<- outp$vec[output$flag] #8 9 12 16 17 18 21 23 27 29 33 40 factors<- sort(factors) numeral<- c(1,outp$vec[!outp$flag]) numeral<- sort(c(numeral)) numeral<-C(1,5,6,7,10,11,13,14,15,19,20,22,24,25,26,28,30,31,32,34,35,36,37,38,39,41) str(myData[,numeral]) ######factors################# factors<- c(8,9, 12 ,16, 17, 18, 21, 23, 27, 29, 33, 40,43) myData[,c(2,3,4,factors)]<- lapply(myData[,c(2,3,4,factors)],factor) str(myData) ############################################# #Preprocess numeral<-c(1,5,6,7,10,11,13,14,15,19,20,22,24,25,26,28,30,31,32,34,35,36,37,38,39,41) #Normalizing data by transformation preProcValues <- preProcess(myData[1:n.train,numeral], method = "BoxCox") myData <- predict(preProcValues, myData) # myData$revenue <- log(myData$revenue) summary(preProcValues) # View(myData[1:n.train,]) ######Cluster Analysis###########33 d <- dist(myData[,c(1,5:7,10:11,13:15,19,20,22,24,25,28,30:32,34:39,41)], method = "euclidean") hc <- hclust(d) plot(hc,labels=myData[1:n.train,4]) rect.hclust(hc,k=3) myData$deg<-as.factor(cutree(hc, k=3)) table(deg) str(myData) # Model prepration -------------------------------------------------------- # # #run model in parallel ####################Random Forest##############################3 cl <- makeCluster(detectCores()) registerDoParallel(cl) set.seed(54321) # Control Parameters fitControl <- trainControl(method = "repeatedcv", number = 10, ## repeated ten times repeats = 7) set.seed(54321) model_rf <- train(revenue~., data=myData[1:n.train,],trControl = fitControl,method = "rf", importance=TRUE) model_rf x<-model1$finalModel x importance <- varImp(model1, scale=FALSE) importance #############################other model####################### # SVM with grid search set.seed(54321) # Control Parameters fitControl <- trainControl(method = "repeatedcv", number = 10, ## repeated ten times repeats = 7) #c("P5","P10","P13","P17","P20","P21","P28","P29","P36","P2","P23","revenue") # #8 9 12 16 17 18 21 23 27 29 33 40 #8, ##16, 17,18,9,13 set.seed(54321) model_radial<- train(revenue~.,data=myData[1:n.train,c(numeral,42,43,16,18)],trControl = fitControl,method = "svmRadial", tuneGrid = expand.grid(.sigma=c(.05),.C=c(seq(.88)))) model_radial$results$RMSE model_radial rm(model_radial) #Polynomial kernel svm # set.seed(54321) # model_poly<- train(revenue~.,data=myData[1:n.train,c(numeral,42)],trControl = fitControl,method = "svmPoly", # tuneGrid = expand.grid(.degree=c(2),.scale= (seq(0.01,.1,.01),.C=c(seq(.1,1,.05)))) # ) # model_poly # degree = 2, scale = 0.01 and C = 0.25. rm(model) ###########Feature Selection############### ##Genetic Algorithm for kernel ptm <- proc.time() ga_ctrl <- gafsControl(functions = rfGA, method = "repeatedcv", number = 6, repeats = 5, allowParallel = T, genParallel = T) rf_ga <- gafs(x = myData[1:n.train,1:41], y = myData[1:n.train,42], iters = 150, popSize = 100, gafsControl = ga_ctrl) rf_ga proc.time() - ptm plot(rf_ga) + theme_bw() summary() #####################rfe######################################### ##Recursive feature selection control <- rfeControl(functions = rfFuncs, method = "repeatedcv", verbose = FALSE, returnResamp = "final", number = 10, repeats = 10, allowParallel = TRUE) subsets <- c(5:25) ref1<- rfe(x = myData[1:n.train,c(1,3:41)], y = myData[1:n.train,42], rfeControl = control, sizes = subsets) ref1 #############################AFter GA and RFE ################################ # Results of GA and RFE cols<- c("P5","P10","P13","P17","P20","P21","P28","P29","P36","P2","P23","revenue") gaData<-myData[,cols] gaData[,fac] <- lapply(gaData[,fac], factor) str(gaData) # Prediction -------------------------------------------------------------- df_yhat_test <- predict(model_rf,myData[138:nrow(myData),]) df_yhat_test1 <- predict(model_radial,myData[138:nrow(myData),]) df_yhat_ensemble<- (.7*df_yhat_test + .3*df_yhat_test1) output<- cbind("Id"= c(0:99999),"Prediction"= (df_yhat_ensemble)) write.csv(output,"df_yhat_ensemble.csv",row.names=FALSE,quote=FALSE)

c6f29c9dfa16b32b441206a999c80a345a4d2c2f

eb836d0d03b1f50f8cd7524cbeaf0b839a58c774

/hw1/hw1_prob3_try2.R

6e485ef789ece88e074a513b1c18149455ac18a5

[]

no_license

nquist/R-Class

af65be1dd2a5bd8132c7dd4a91308448e15e32bc

45d57366cabc89a0e284cbd494231b545852fafa

refs/heads/master

2020-04-22T07:32:20.946866

2019-09-05T19:32:19

170,219,810

0

null

UTF-8

R

false

180

r

hw1_prob3_try2.R

# Regrade: 5/5 # Excellent! print("This is an edit to the code for a second try.") rm(list = ls()) print("Hello world!") samp <- rnorm(100, mean = 10, sd = 3) print(mean(samp))

edfd3dbccfad754c74bcee6733eb19eec4479b48

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/codemetar/examples/write_codemeta.Rd.R

345c57861cef2724fe95ca38138544f1e6210638

[]

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

220

r

write_codemeta.Rd.R

library(codemetar) ### Name: write_codemeta ### Title: write_codemeta ### Aliases: write_codemeta ### ** Examples ## No test: write_codemeta("codemetar", path = "example_codemetar_codemeta.json") ## End(No test)

0e0063a56f1718c4891e4ac985487b105c3a9459

cbdeccd83cc91da4b9da37e858617dc6ab4c5a41

/man/simplexample-package.Rd

05f5acc08043fc1232a47ee95fcf6a7e43b8213e

[]

no_license

Sleepingwell/SimpleCInterfaceRPackage

223fc644034da791af227a8bf918b4bdb91f6943

2045651a6167467337465a9da220ee1a9047266a

refs/heads/master

2021-01-01T19:10:32.677467

2012-08-16T15:41:51

null

0

null

UTF-8

R

false

746

rd

simplexample-package.Rd

\name{simplexample-package} \alias{simplexample-package} \alias{simplexample} \docType{package} \title{Demonstrate calls to a .C, .Call and .External functions.} \description{ A package that shows how one might use the .C .Call and .External interfaces for calculating a dot product. } \details{ \tabular{ll}{ Package: \tab simplexample\cr Type: \tab Package\cr Version: \tab 1.0\cr Date: \tab 2012-08-16\cr License: \tab What license is it under?\cr } The only function in this package is \code{dotproduct} which calculate the dot product of its arguments using each of the calling conventions. } \author{ Simon Knapp Maintainer: Simon Knapp <simon.knapp@verusol.com> } \examples{ x <- rnorm(10) y <- rnorm(10) dp <- dotproduct(x, y) }

5baaa41318979bd0466e47615b2fc6ab9f6aaa6d

ec843d0167f14b8f70faf6d9223b94fd6e1f3491

/Programi/cor_analisys_number_of_days_to_peak_deaths_and_proportion_of_infected.R

8a4732650255998dbc7255facb391eed8f9b3fe6

[]

no_license

KalcMatej99/Seminarska-VS-Covid-19

c6757885d16d50c6a0f6ad2999cdfe45df1de05d

e592a3142afa96660d6938e69385fb74e169ba92

refs/heads/master

2022-12-19T11:08:59.724253

2020-09-10T10:58:43

281,406,020

0

null

UTF-8

R

false

807

r

cor_analisys_number_of_days_to_peak_deaths_and_proportion_of_infected.R

library(pracma) fileDB <- './../Podatki/db.csv' db<- read.csv(fileDB, header=TRUE, sep=",") dataFrameDates <- data.frame(date=db$Date_of_peak_of_deaths,tx_start=db$Date_of_first_death) dataFrameDates$date_diff <- as.Date(as.character(dataFrameDates$date), format="%Y-%m-%d")- as.Date(as.character(dataFrameDates$tx_start), format="%Y-%m-%d") N <- as.numeric(dataFrameDates$date_diff) P <- 100 * db$Infected_to_peak/db$Population plot(N,P, main = "Vpliv števila dni do vrha prvega vala mrtvih na delež okuženih", xlab = "Število dni do vrha prvega vala mrtvih", ylab = "Delež okuženih") r <- cor(N, P, method = "pearson") s <- cor(N, P, method = "spearman") rtest <- cor.test(N,P, method = "pearson") stest <- cor.test(N,P, method = "spearman")

3ce1291b4abcdf0cb3c5cda8adf1968478436f35

a6ecdfb7028b10fadff7fdc9fc7110c51814761c

/day4/lineplots.R

c398df70ed97fff033b14d702c63bb00a46596b2

[]

no_license

C-MOOR/jhu-intersession-2017

7ac21ecc3c1d0e20b2ae86e3396515b20651b80a

7610aff9b622ba697fe43f49b221769a3c14493d

refs/heads/master

2020-04-01T18:25:00.840344

2018-10-17T18:52:12

153,491,047

0

null

UTF-8

R

false

1,326

r

lineplots.R

selected_genes <- c(2:5) library( "DESeq" ) # Use DESeq to normalize the read counts for each sample so they can be averaged gut_metadata <- read.table( "/home/intro-rna/2017/midgut.tsv", header=TRUE ) cds <- newCountDataSetFromHTSeqCount( gut_metadata, directory="/home/intro-rna/2017" ) cds <- estimateSizeFactors( cds ) # make a data.frame of read counts in the correct orientation for the aggregate function gene_counts <- data.frame(t(counts(cds, normalized=TRUE)[selected_genes,])) # make a new data.frame of the mean count for each region mean_counts <- aggregate(gene_counts,by=list(gut_metadata$condition), mean) # scale counts so they can be plotted on one graph scaled_counts <- apply(mean_counts[,-1], 2, function(x) x/max(x)) plot(mean_counts[,2], type='l', xaxt='n', col="red", ylim=c(0,2000)) lines(mean_counts[,3], type='l', xaxt='n', col="green") lines(mean_counts[,4], type='l', xaxt='n', col="blue") lines(mean_counts[,5], type='l', xaxt='n', col="purple") axis( 1, at=1:10, labels=mean_counts$Group.1) plot(scaled_counts[,1], type='l', xaxt='n', col="red", ylim=c(0.0, 1.0)) lines(scaled_counts[,2], type='l', xaxt='n', col="green") lines(scaled_counts[,3], type='l', xaxt='n', col="blue") lines(scaled_counts[,4], type='l', xaxt='n', col="purple") axis( 1, at=1:10, labels=mean_counts$Group.1)

3a035d0055c1c7c0339a86c75d4a80054a8eba7a

88c0b70954bd568717719b01b20d090c475e29c5

/plot1.R

84167f97e51d908b536f75dd18b1ebf4d75cadaf

[]

no_license

fish515/ExData_Plotting1

fc249ec6051c7455cb4ec465b9591a3ab4e0c99b

bcd349fc6daf0941a65acb8c07ce6442b5189081

refs/heads/master

2020-12-26T21:37:52.770221

2016-03-07T07:02:01

53,297,145

0

null

2016-03-07T05:02:47

null

UTF-8

R

false

334

r

plot1.R

data<-read.table("household_power_consumption.txt",header=TRUE,sep=";",na.strings = "?") subsetdata<- data[data$Date %in% c("1/2/2007","2/2/2007") ,] png("plot1.png", width=480, height=480) hist(subsetdata$Global_active_power,col="red",xlab="Global Active Power(kilowatts)",ylab = "Frequency",main="Global Active Power") dev.off()

12d11fa23cf66cef9fb7a2febef858930d730ba6

23e87af56a0d581c32532fadb463913b55d3deb5

/首日基線/predict.R

fe20814b24f2b5170861f973f5308e33453e1a98

[]

no_license

ji1ai1/201907-AT

15fb66ccc4840db6546929892e5dd41dfafaf294

22d13287a9b6d9967849021ebf55a2f4eeb7ac23

refs/heads/master

2023-01-14T02:47:04.882158

2020-11-07T16:04:18

232,135,787

5

1

null

UTF-8

R

false

917

r

predict.R

#R version 3.6.1 (2019-07-05) -- "Action of the Toes" #R package data.table 1.12.2 #输入： # Antai_AE_round2_item_attr_20190813.csv # Antai_AE_round2_test_20190813.csv # Antai_AE_round2_train_20190813.csv #输出： # result.csv #0.1606 library(data.table) Testing = fread("Antai_AE_round1_test_20190626.csv") Testing$si = as.double(as.POSIXct(Testing$create_order_time)) - as.double(as.POSIXct("2018-09-01")) Items = Testing[, .(item_nrecords = .N), .(item_id)][order(-item_nrecords)] Prediction = Testing[, .(score = sum(1 / (si / 86400) ** 64)), .(buyer_admin_id, item_id)] Prediction = Prediction[order(-score)] Prediction = Prediction[, .(item_id = unique(c(item_id, Items$item_id[1:30]))[1:30]), .(buyer_admin_id)] Prediction = Prediction[, .(prediction_string = paste(item_id, collapse ="," )), .(buyer_admin_id)] write.table(Prediction, "result.csv", sep = ",", quote = F, row.names = F, col.names = F)

f74151c6b0768028646f95cda33b06c5c41a3add

accc3c168b0f9d6973b44a9a19211899b69384a6

/fcbmmo/R/transform_log.R

5a049a63069c91117a24aafb4c0a854057afccf9

[]

no_license

fcbny/fcbmmo

f54d77a34ff3fcd99a15b5176beab553629159c5

6bdc4e1742a973fd55c72d4354ea9dc4f0424589

refs/heads/master

2021-01-10T19:47:10.368161

2014-10-31T18:54:54

null

0

null

UTF-8

R

false

98

r

transform_log.R

transform_log <- function(x,base=exp(1),add=0) { r <- log((x + add),base=base) return(r) }

a05a337e78fe6895b9ea77bf243e107f32520cc1

92e828aeed0eb4203cd1890fbdcee430d6cc9773

/src/API_pathways_KEGG.R

d1a056da12cb5abbb383cd8d7cb1e1d5bfbf7c5b

[ "MIT" ]

permissive

dy-lin/stat540-project

46d9ee4f4638dbe933bdb607f3f8232e872a6847

20f040d9399ab5b9df0341b317e0f22eefafe5e8

refs/heads/master

2022-11-18T03:27:53.049420

2020-07-19T22:47:39

259,114,940

0

1

null

UTF-8

R

false

3,879

r

API_pathways_KEGG.R

# Script for performance of pathway enrichment analysis in KEGG using PathDIP portal (Rahmati et al., 2016) API # For more information: http://ophid.utoronto.ca/pathDIP/API.jsp library(httr) library(tidyverse) library(here) # const values url <- "http://ophid.utoronto.ca/pathDIP/Http_API" searchOnGenesymbols <- function(IDs, component, sources) { parameters <- list( typeChoice = "Gene Symbol", IDs = IDs, TableName = component, DataSet = sources ) # ... send http POST res <- POST(url, body = parameters, encode = "form", verbose()) } # make results-map as keyword - value makeMap <- function(res) { ENTRY_DEL = "\001" KEY_DEL = "\002" response = content(res, "text") arr = unlist(strsplit(response, ENTRY_DEL, fixed = TRUE)) list_map <- list("") vec_map_names <- c(""); for (str in arr) { arrKeyValue = unlist(strsplit(str, KEY_DEL, fixed = TRUE)); if (length(arrKeyValue) > 1) { list_map[length(list_map) + 1] <- arrKeyValue[2] vec_map_names[length(vec_map_names) + 1] <- arrKeyValue[1] } } names(list_map) <- vec_map_names list_map } ##################################### # Example of search on Gene Symbols # ##################################### # Gene Symbols # - Comma delimited. # - Mind case. genes <- read.csv(here("results","final","primary_topGenes_limma2.csv")) IDs <- toString(genes$gene) # Data component # - Uncomment the only one of those five: # component <- "Literature curated (core) pathway memberships" component <- "Extended pathway associations. Protein interaction set: Experimentally detected PPIsMinimum confidence level for predicted associations: 0.99" # component <- "Extended pathway associations. Protein interaction set: Experimentally detected PPIsMinimum confidence level for predicted associations: 0.95" # component <- "Extended pathway associations. Protein interaction set: Experimentally detected and computationally predicted PPIs (full IID)Minimum confidence level for predicted associations: 0.99" # component <- "Extended pathway associations. Protein interaction set: Experimentally detected and computationally predicted PPIs (full IID)Minimum confidence level for predicted associations: 0.95" # Data sources # - Use some or all of those: # BioCarta, EHMN, HumanCyc, INOH, IPAVS, KEGG, NetPath, OntoCancro, PharmGKB, PID, RB - Pathways, Reactome, stke, systems - biology.org, Signalink, SIGNOR, SMPDB, Spike, UniProt_Pathways, WikiPathways # - Comma delimited. # - Mind exact spelling. #sources <- "BioCarta,EHMN,HumanCyc,INOH,IPAVS,KEGG,NetPath,OntoCancro,PharmGKB,PID,RB-Pathways,Reactome,stke,systems-biology.org,Signalink,SIGNOR,SMPDB,Spike,UniProt_Pathways,WikiPathways"; # Soureces specifically set to KEGG for our pathway enrichment analysis sources<- "KEGG" res <- searchOnGenesymbols(IDs, component, sources) responseCode = status_code(res) if (responseCode != 200) { cat("Error: Response Code : ", responseCode, "\r\n") } else { list_map <- makeMap(res) # print results cat("\r\n", "Search on Uniprot IDs:", "\r\n") cat("Generated at: ", unlist(list_map["GeneratedAt"]), "\r\n") cat("IDs: ", unlist(list_map["IDs"]), "\r\n") cat("DataComponent: ", unlist(list_map["TableName"]), "\r\n") cat("Sources: ", unlist(list_map["DataSet"]), "\r\n") cat("\r\n", "Summary size: ", unlist(list_map["SummarySize"]), "\r\n") sm <- unlist(list_map["Summary"]) cat("Summary: \r\n", sm, "\r\n") # formatted as tab - delimited spreadsheet cat("\r\n", "Details size: ", unlist(list_map["DetailsSize"]), "\r\n") dl <- unlist(list_map["Details"]) cat("Details: \r\n", dl, "\r\n") # formatted as tab - delimited spreadsheet df.Summary <- read_tsv(sm) df.Details <- read_tsv(dl) write.csv(df.Details,here("results","final","pathway_KEGG_primary_genes")) }

a7a65fc736fbb1742dbb2ead45c2517b2db66549

3d0787d24620c700303ecdc4453325b6235c0e5d

/01-variables_constants_operators/arithmetic.R

507c7fbe77d819c80c71e52f662c494ddd87aab0

[]

no_license

lincolnbrito/r-examples

2244e0160944f65c276a58c9e97b5a4ead8923b4

ab75c88576026ee839befb0f9acccf52ec6f2e4e

refs/heads/master

2020-03-08T10:55:10.518422

2018-04-05T03:21:00

128,084,946

1

0

null

UTF-8

R

false

190

r

arithmetic.R

" + addition - subtraction * multiplication / division ^ exponent %% modulus (remainder from division) %/% integer division " x <- 5 y <- 16 x+y x-y x*y y%/%x y%%x y^x