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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d00fe34b591160c30a5e62021b65b004b9180378 | 697c87e65f7c3a12fdea8141c2859f9da044841f | /R/aimerMethods.R | 3fde8c248ad4e05cc8459748c60c7c2d650cdc69 | [] | no_license | Lei-D/aimer | 944278394d280cacba3d109205c8802c2043f1d5 | b7650af34f9661e1dbfffe9e2b40ecb719d49bea | refs/heads/master | 2021-07-19T11:54:29.280976 | 2017-10-27T15:50:59 | 2017-10-27T15:50:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,028 | r | aimerMethods.R | #'predicts new values for an aimer model.
#'
#'@param object required, a model generated by the aimer function.
#'@param newdata required, a new data matrix to predict with.
#'@param ... additional arguments, currently ignored.
#'
#'@return predicted value vector of length nrow(newdata).
#'
#'@export
predict.aimer <- f... |
709ccc2d97653a5ba92d9d4f299fc11c86ccf960 | 4a8209a0137e308f4a96ad0baede74f79803b384 | /R/Project.R | 7381a0471260149c1e05b942a45ff357677ab962 | [
"MIT"
] | permissive | david-yunbae/Project | 52e96297b6f3a0457727a6da4fcca5337a034dab | 2a04bd5088c1cf149623e1cde5f445a439ab91f1 | refs/heads/main | 2023-08-28T18:15:50.252789 | 2021-10-28T23:02:20 | 2021-10-28T23:02:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,386 | r | Project.R | packages <- c("tidyverse", "knitr", "rmarkdown","roxygen2","testthat","usethis","devtools","ggplot2","ggrepel","stats","kableExtra","bookdown","shiny","patchwork","dplyr","broom")
lapply(packages, library, character.only=TRUE)
#' Assumption
#'
#' @description This function outputs the 3 graphs of assumption for the li... |
a4c1087ed21cedafa72cbdf3cb2f34bbc217e292 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/RSDA/examples/sym.kmeans.Rd.R | 9c7b4f3784107559ca6124db9dc3f65181bb39b2 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 187 | r | sym.kmeans.Rd.R | library(RSDA)
### Name: sym.kmeans
### Title: Symbolic k-Means
### Aliases: sym.kmeans
### Keywords: Kmeans Symbolic
### ** Examples
data(oils)
sk<-sym.kmeans(oils,k=3)
sk$cluster
|
5e5d33320b97da7c500becab519de981d051ca41 | 4642e25457ecfdaf564ec83133084715de71818a | /DQ Functions/summarise-changes.R | 0ba1cc9a09f4da48251ff2d1671c66dc4e96038d | [] | no_license | DasOakster/wilko-data-quality | 4639743b654e79c7db982f1ad19549f94a680d41 | c65642bc3a4fd87055e2edeced1b7092dc5a4486 | refs/heads/master | 2021-09-13T03:01:59.193182 | 2018-04-12T14:26:08 | 2018-04-12T14:26:08 | 123,016,410 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,372 | r | summarise-changes.R |
count.changes <- function(psa1) {
library(dplyr)
psa1.dir <- paste("D:/OneDrive/Work Files/Wilko/Data Cleanse/",psa1,"/Uploads/",sep="")
update.age.data <<- read.csv(paste(psa1.dir,psa1,"_Updates_Age.csv",sep = ""),header = TRUE,stringsAsFactors = FALSE)
update.assembly.data <<- read.csv(paste(ps... |
9bb382b36a186f9e452d22d612270717130b7121 | 0d3f737aa66620cd3c9cdd5d09b11340873ff099 | /run_analysis.R | 7726929f8956756d307d1d1403f809685e38ba6b | [] | no_license | tjdurrant/Data_Cleaning_Assignment | c6a4076f83a309d1b033e569c4c86676e5b69442 | 9610cb2ea4b641f2fa05750445948f297cfbbc63 | refs/heads/master | 2021-01-22T22:09:16.622064 | 2017-03-19T23:21:42 | 2017-03-19T23:21:42 | 85,513,158 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,002 | r | run_analysis.R | library(dplyr)
#Read in features and activites data
features <- read.table("./features.txt")
activity_labels <- read.table("./activity_labels.txt")
#Read in training data
x_train <- read.table("./x_train.txt")
y_train <- read.table("./y_train.txt")
subject_train <- read.table("./subject_train.txt")
#Read in test dat... |
c34a74d648ae975bfa23a3d6b2d9e4c7e5b553a5 | 78a12d66cf2c127fdd2c368b7b251972cbc0330d | /code.R | 624fe0790d0e9759928b0508d8677b8908df0af1 | [] | no_license | jqyangDaz/Daz | b3d3095cd0a48dcca060cef0c6b4391ef3f72206 | 43de7196431cdd0ae1d694d4a228c0c45ba96c24 | refs/heads/master | 2020-03-16T21:41:02.617593 | 2018-05-11T08:10:56 | 2018-05-11T08:10:56 | 133,009,907 | 1 | 0 | null | null | null | null | GB18030 | R | false | false | 7,116 | r | code.R | library(plotrix)
library(grid)
library(vcd)
library(tseries)
#导入所需的包
homi<-read.csv('e:/database.csv')
#录入数据
nrow(homi)
#查看数据行数
length(homi)
#查看数据列数
View(homi)
#预览数据
attach(homi)
bar1<-table(Month... |
37f6cb4cde2cba89d93364960ad0366321fe2a7e | 99e62a6f0161cc9de03bf066ae7d27e9cf25c179 | /sc_preprocess.R | e4e3da05a63da525aaec9e249b4fa17d501f6fda | [] | no_license | ardadurmaz/trajectory-sc | 09431f8e1037a3548916b1d5df8e04854422a56e | 6164659b7b415115a4aacf90991ca38e8b754e84 | refs/heads/master | 2020-04-17T15:11:18.370858 | 2019-01-20T17:30:12 | 2019-01-20T17:30:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,503 | r | sc_preprocess.R | library(Matrix)
library(cellrangerRkit)
library(ggplot2)
library(scran)
library(scater)
library(edgeR)
library(limma)
## Read Data ##
temp.path <- '~/SingleCellData/single_cell_analysis/Marusyk_Joint3Aggr_ALK_redo'
genome <- 'GRCh38'
count <- load_cellranger_matrix(temp.path)
aggr.data <- read.csv('~/SingleCellData/si... |
6eaf6ec142fe7b5d8cac6583dacbf196c1bc0673 | 76f353cb3366684c4708b36662c44f86204381d2 | /script/DANMPUT.R | 17c8d38314255d2aa07db99449ca094521a6815e | [] | no_license | barbarian1803/saham_analisis | cdd431499348db370072eaec71490a6bbb2ff590 | b5c33108faf16e8b4fcda9e0ef9f9a8a9b08c16e | refs/heads/master | 2021-01-24T08:09:07.733311 | 2016-11-21T05:38:17 | 2016-11-21T05:38:17 | 70,199,529 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 184 | r | DANMPUT.R | DANMPUT <- read.table("RD/DANMPUT.csv",header=FALSE,quote="",sep="\t")
colnames(DANMPUT) <- c("date","price")
summary(DANMPUT$price)
DANMPUT[duplicated(DANMPUT$date),]
rm(DANMPUT_rev)
|
c0453a013dbea734608437d85644e4eadf80a419 | cef3b5e2588a7377281a8f627a552350059ca68b | /cran/paws.game.development/man/gamelift_start_matchmaking.Rd | 2d58a972f016a45ef5bbaf276e751ebfe5e7d7e7 | [
"Apache-2.0"
] | permissive | sanchezvivi/paws | b1dc786a9229e0105f0f128d5516c46673cb1cb5 | 2f5d3f15bf991dcaa6a4870ed314eb7c4b096d05 | refs/heads/main | 2023-02-16T11:18:31.772786 | 2021-01-17T23:50:41 | 2021-01-17T23:50:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,588 | rd | gamelift_start_matchmaking.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gamelift_operations.R
\name{gamelift_start_matchmaking}
\alias{gamelift_start_matchmaking}
\title{Uses FlexMatch to create a game match for a group of players based on
custom matchmaking rules}
\usage{
gamelift_start_matchmaking(TicketId, Con... |
453571b12e34505bee05f29aa647616ba55e6ac1 | 7b00b082bb032398858c863965607bf47c2e6513 | /artigo_pairs_def.R | 427436c5972b347d847c53129c138a8173444a69 | [] | no_license | lemuelemos/Pairs_trading | b9f79978caebc2891767d4b10e6924a762269b89 | 95b52fc80cac9c092ca2671964fa4478ad02ac3c | refs/heads/master | 2021-04-27T11:26:28.688792 | 2019-04-01T03:01:40 | 2019-04-01T03:01:40 | 122,562,684 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,068 | r | artigo_pairs_def.R | library(doParallel)
library(partialCI)
library(readxl)
library(xts)
library(stringr)
library(dplyr)
library(plyr)
library(timeSeries)
library(Rcpp)
##### Import data and cleaning NA's
#source('cpp_codes.R')
sourceCpp("cpp_codes.cpp")
ibrx_2007_2018 <- read_excel("ibrx last price 2007 até 2018_2.xlsx") #### Reading the... |
ee2499ee634b668899d58ee3276c10a51944c3f3 | a520b1ed88904ce8090687cec3284111a871ac7a | /StatisticalRethinking/Exercise_Ch5.R | 878a86601afcbfb5d29f5508f45824260b681a63 | [] | no_license | EijiGorilla/R-Statistics | b59cb499cb24a383043f58f415a5b4d03bee863a | e900a786dcf80b21eeab29fefb9bed409d4907d0 | refs/heads/master | 2020-04-18T11:52:19.386254 | 2019-01-25T08:50:24 | 2019-01-25T08:50:24 | 167,515,532 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 22,059 | r | Exercise_Ch5.R | library(rethinking)
data("WaffleDivorce")
d=WaffleDivorce
head(d)
op=par(mfrow=c(2,2))
d$MedianAgeMarriage.s=(d$MedianAgeMarriage-mean(d$MedianAgeMarriage))/sd(d$MedianAgeMarriage)
plot(Divorce~MedianAgeMarriage.s,data=d,col=col.alpha(rangi2,0.5))
#fit model
m5.1=map(
alist(
Divorce~dnorm(mu,sigma... |
9e7df1607075a39ffd944df777f1f3f635649e3c | 9802eec0484e38be543320d3b40cf6993ac27e47 | /tests/testthat.R | a20dd2811727f84397cf0dc71ce80d4a5a094d4b | [] | no_license | ijlyttle/datadict | 31e861930f5ade672ceca6501e49952a2981ba85 | 34abba8bd50e3db0f42e01a059dbe7dc4047b9ef | refs/heads/master | 2021-01-22T16:25:19.994574 | 2014-08-11T13:08:22 | 2014-08-11T13:08:22 | 22,684,355 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 59 | r | testthat.R | library(testthat)
library(datadict)
test_check("datadict") |
eee75dcd86db61a01ca8108fb61d59e06a8d0b0b | 2c058880d02be07bd9ee7a9e273752ff930075f8 | /cachematrix.R | 616cd8f96f26841a9cf9b959145c7f7c0adc7e94 | [] | no_license | preisacher/ExData_Plotting1 | 9ce612087070a83193ca8276de2e417719c1cc39 | a50d49f52900b5460c5a953724b709ad4eb07a4c | refs/heads/master | 2020-12-25T03:00:49.840150 | 2016-01-26T13:45:57 | 2016-01-26T13:45:57 | 42,243,338 | 0 | 0 | null | 2015-09-10T12:34:22 | 2015-09-10T12:34:22 | null | UTF-8 | R | false | false | 884 | r | cachematrix.R | ## These functions are very closely immitating the examples we
## were given using vectors. All I really did was to change from
## using Vectors to a Matrix.
##
## The makeCacheMatrix returns a list of functions that can be used to return
## its inverse ... solve.
makeCacheMatrix <- function(x = matrix()) {
m<-NU... |
0938fb7d7514875d826e229be67b19534b5a87ea | e4a073e4785f3415e46907c302da09680cd064ff | /cutting_floor/email.R | fe81fcb40b80ca15789e19d518cff4a8515371bb | [] | no_license | sandeepgangarapu/COVID-DAILY-STATSLETTER | e15b280a1da3bea32ab18968728106bf1bbaf6b4 | 5cfd065e31e9309f1ed33b7f319128ab3458a2eb | refs/heads/master | 2021-05-17T06:48:37.217485 | 2020-04-03T11:42:16 | 2020-04-03T11:42:16 | 250,682,036 | 0 | 2 | null | 2020-04-05T08:19:04 | 2020-03-28T00:39:54 | Python | UTF-8 | R | false | false | 607 | r | email.R | library(gmailr)
library(googlesheets4)
email_list = read_sheet("https://docs.google.com/spreadsheets/d/1HI0llUuuVHRTSnMacgnIVzQmYgZGUckQ2cRMhPTA7QM/")
setwd("G:\\My Drive\\Projects\\COVID")
#gm_auth_configure(path = "G:\\My Drive\\Projects\\COVID\\credentials.json")
html_msg <- gm_mime() %>%
gm_bcc(email_list$emai... |
afadcd52356a814b133a03023f1d2b0d6878fdda | d0a7960cae8a536457425921ae8913516a24a824 | /scripts/res_themes.R | 04dd86d87c0465d4c34f223b075bc7dc0424b3e5 | [
"MIT"
] | permissive | dampierch/herv | 6ab3cb82c2b103d0335c51c4cd84eb80656b6e29 | 9f1ce0e676977b6c8d25fdf446c0807826b80bea | refs/heads/master | 2023-03-09T02:01:52.970776 | 2021-02-23T19:40:00 | 2021-02-23T19:40:00 | 284,400,012 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,153 | r | res_themes.R | ## to be sourced in results.R
ggp_theme_default <- theme(
panel.background=element_rect(fill="white"),
panel.grid.major=element_line(color="white"),
panel.grid.minor=element_line(color="white"),
plot.margin=margin(t=1, r=1, b=1, l=1, unit="lines"),
plot.title=element_text(size=12, face="bold", hju... |
51522325be929343b492c194fccd8d7949c8dda8 | 3156a7a4033706a8db408fa52d8694d41cc36078 | /R/multigraph.R | 6f54c7a18c5cc6968c9cfc896ac338d5631b3585 | [] | no_license | cran/netCoin | d38db4ec39ea0049c475749c3f3b53a88e2a8a51 | b81ebd9501cf53c5ef2770a715a6e072ef265ac6 | refs/heads/master | 2023-04-02T22:45:46.863944 | 2023-03-23T21:40:02 | 2023-03-23T21:40:02 | 77,029,038 | 3 | 2 | null | null | null | null | UTF-8 | R | false | false | 812 | r | multigraph.R | multigraphCreate <- function(..., mode = c("default","parallel","frame"),
mfrow = c(1,2),
frame = 0, speed = 50, loop = FALSE, lineplots = NULL,
dir = NULL, show = FALSE){
diraux <- NULL
if(!is.null(dir) && !identical(show,TRUE)){
diraux <- dir
}
mode <- substr(mode[1],1,1)
if(mode=="p"... |
d0ab966e7a657eaf632a9cede4ece1c6f4f5fd14 | dcb76ed15a952660c4546916f8ef9e111f496884 | /R/machine-ls.R | 0971b6226912587c637300f81ef221be52354b3a | [] | no_license | DavisVaughan/machinegun | b8bea1d56d278a6a28cc4daf37e67ddd9426c6fd | 128898cead1c4dfc23785aa0a7ad45bc5817a3dd | refs/heads/master | 2020-03-26T10:16:07.139337 | 2018-08-16T02:32:36 | 2018-08-16T02:32:36 | 144,789,195 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 112 | r | machine-ls.R | #' List all docker-machine instances
#'
#' @export
machine_ls <- function(...) {
machine_command("ls", ...)
}
|
5950f52a372220f54d585f3d8b50b1c2ab223cd8 | 7c47b764ee111e5ef3f0b3e3aa4b284dd6cda770 | /man/create_spp_occurence_raster_list.Rd | 8bae1cdeaa96439786fd73419e086126531f4e4f | [
"MIT"
] | permissive | Vizzuality/vspt | 845476a6fe1556015b59392003d4966e4d963051 | c5d927ea031519733af4d60516d239a1b0c2b438 | refs/heads/master | 2021-07-12T13:25:19.277790 | 2020-10-28T15:34:19 | 2020-10-28T15:34:19 | 216,042,807 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 700 | rd | create_spp_occurence_raster_list.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/create_spp_occurence_raster_list.R
\name{create_spp_occurence_raster_list}
\alias{create_spp_occurence_raster_list}
\title{Create spp presence-absence raster(s)}
\usage{
create_spp_occurence_raster_list(spp_occurence_sfc_list, r, land_mask)
}... |
bb289de82567ae04fc8421575ae05b9d1c8881ef | f5ad30ec784cccd03371242a63b99f265bf98dc5 | /R/sprintf-ca-function.R | 74c0cd96b744ee564e44c59f8bbe32eb4a69bf9f | [] | no_license | claus-e-andersen/clanTools | cf93ec1b81b38277f90eaab7901a8f82b2ffa847 | dbbca750aba52b08c99d43a2a49a05810e2d0630 | refs/heads/master | 2021-07-06T09:22:18.006123 | 2020-10-17T04:20:21 | 2020-10-17T04:20:21 | 22,555,505 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,804 | r | sprintf-ca-function.R | #' @title Print number to fit certain format. An alternative is used if this requires too many characters.
#' @description
#' This is a variation of sprintf, that checks if the formated result is too long.
#' This can happen, for example, with electrometer output, where normal results are
#' 0.004 pC and then an ov... |
312a0903d5e2ec8d659a09fe65735b2788bbb61f | 28d02ce963df64a6802d003f40a5fb4a1e987b5f | /10.27_PseudotimeClassification/virtualtrend.R | 02685fe1cd071c520bd8385a026eae1883ec299d | [] | no_license | Zhouzhiling/R | b31586a13a0e3e2557bb519ef73d0fc9b6e5c87b | 6d46af4117b6c8f5b013d0c02b517a84cfb756d8 | refs/heads/master | 2021-05-16T10:19:46.904628 | 2017-10-27T15:57:28 | 2017-10-27T15:57:28 | 104,708,872 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,945 | r | virtualtrend.R | rm(list=ls())
src <- read.table(file.choose(),head=T,sep="\t")
src
src <- src[,order(src[1,])]
#setwd("C:/Users/37908/Desktop/")
#jpeg(file="1027.jpg",width=20000,height=10000)
#plot(src[1,])
#dev.off()
src_trim <-as.matrix(src[,-rep(1:26)])
low_quant <- ceiling(ncol(src_trim) / 3)
high_quant <- ceiling(n... |
c149b7aeabe98d312a4945a97e2c51b25813619a | 1cc4e9886e3bc4614c8105d48b7e98e209948cf5 | /Rscripts/functions.R | 0971e222cbdadbd8e7d1077b4eb09ad3ea81c777 | [] | no_license | mikaells/Find-pBGCs | 55108bb349346e856f31ca424dc4d7773d75fb15 | f59e7b25bdc94592735733076634ecf3b491770b | refs/heads/master | 2023-07-09T07:43:39.796862 | 2021-08-16T18:55:02 | 2021-08-16T18:55:02 | 299,373,323 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,515 | r | functions.R | color.gradient <- function(x, colors=c("grey90","red"), colsteps=100) {
return( colorRampPalette(colors) (colsteps) [ findInterval(x, seq(min(x),max(x), length.out=colsteps)) ] )
}
layout.by.attr <- function(graph, wc, cluster.strength=1,layout=layout.auto) {
g <- graph.edgelist(get.edgelist(graph)) # cre... |
286b1c08937ceab6355320d2442a97456e92294e | 0137840c620668a93987f291f2d527793dae28f6 | /scripts/tutorial.R | 003b9e91322f0b9522a5dc6cffeb75bbca49b663 | [] | no_license | justinmillar/trevor-strava | 9271c87246585b49d497bd55b8e2b1fedc07e53f | 58dfe478d53d96f9dfa3fcf124844f0d510ee41a | refs/heads/master | 2021-05-05T07:07:53.019964 | 2018-01-26T21:38:51 | 2018-01-26T21:38:51 | 118,854,663 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,657 | r | tutorial.R | library(strava)
library(tidyverse)
data <- process_data("data/")
# Facets ----
p1 <- plot_facets(data)
ggsave("plots/facets01.png", p1, width = 20, height = 20, units = "cm")
# Activity map ----
p2 <- plot_map(data, lon_min = -87, lon_max = -82, lat_min = 42, lat_max = 45.5)
ggsave("plots/map01.png", p2, width = 20,... |
258450d4384da9d9ac34e5c568fe2d3f0f00367e | 6f9ab236999fff566b0ed76f6fc2146d63e3f7f1 | /rotations/man/plot.Rd | 861277a4d7ca2c382073a30aabf92ca5ed2f01c3 | [
"MIT"
] | permissive | stanfill/rotationsC | d4733140b6e40c61b2d9312474c1a8786f1974fb | 66722f095a68d81e506c29cfac7d4a8a69e664cf | refs/heads/master | 2022-07-22T01:17:54.917073 | 2022-06-24T21:27:24 | 2022-06-24T21:27:24 | 9,582,475 | 0 | 3 | null | 2021-03-11T21:43:23 | 2013-04-21T16:51:07 | C++ | UTF-8 | R | false | true | 2,835 | rd | plot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot.R
\name{plot}
\alias{plot}
\alias{plot.SO3}
\alias{plot.Q4}
\title{Visualizing random rotations}
\usage{
\method{plot}{SO3}(
x,
center = mean(x),
col = 1,
to_range = FALSE,
show_estimates = NULL,
label_points = NULL,
mean_r... |
08f9c592f8d465d692cd625850d8818e065c5cfb | 1204d0e3c990b77ae922038254354c3b660b0cab | /Graphs/2D_and.R | 92c34dce36dd5f767b682b162847667892137ad8 | [] | no_license | HussamHallak/Soundex_Arabic_Names | 56174b370c84566245ef3397338d2a5bd6f11812 | 142cf7cd4672c8a84eec09c382fcaf22d1a530b6 | refs/heads/main | 2023-02-04T19:07:54.994422 | 2020-12-27T00:42:15 | 2020-12-27T00:42:15 | 324,659,651 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 868 | r | 2D_and.R | # Create a grouped barplot and add a legend
Libindic_Soundex <- c(P=0.961, R=0.802, TNR=0.999, F=0.874, ACC=0.996, BA=0.901)
Jellyfish_Soundex <- c(P=0.963, R=0.833, TNR=0.999, F=0.894, ACC=0.997, BA=0.917)
Jellyfish_Match_Rating <- c(P=0.988, R=0.657, TNR=0.999, F=0.789, ACC=0.995, BA=0.828)
Fuzzy_NYSIIS <- c(P=0.982,... |
ef4c261c544b8fe70aec91ce21bb2f2a42758291 | 5a9d99c0266ced580e9e02bf3839acb2fb8ef49d | /man/add_starting_trees_to_xml.Rd | dbcaee9925b17e702a8dd7b3bf1571cf99ff310a | [] | no_license | emvolz-phylodynamics/sarscov2Rutils | 1555fe2512057f86ec12f5d192b912121c7b4781 | 6747443599fb391a2f21c5de019d11058dec7e86 | refs/heads/sarscov2Rutils | 2023-04-18T23:54:43.415890 | 2020-11-05T12:02:56 | 2020-11-05T12:02:56 | 266,338,998 | 12 | 5 | null | 2021-02-02T19:39:22 | 2020-05-23T13:09:29 | R | UTF-8 | R | false | true | 1,037 | rd | add_starting_trees_to_xml.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/starttree1.R
\name{add_starting_trees_to_xml}
\alias{add_starting_trees_to_xml}
\title{Make starting trees, insert into beast xml and create ML tree plot}
\usage{
add_starting_trees_to_xml(
xmlfn,
fastafn,
plotout = NULL,
regionDemes ... |
4c7903ff5a122868df61324446e9a61649b85f47 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/hash/examples/del.Rd.R | d650b7bf4fcea782d94af6346fa264dc9889a334 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 315 | r | del.Rd.R | library(hash)
### Name: del
### Title: Remove key-value pair(s) from a hash
### Aliases: del del-methods del,ANY,hash-method delete delete-methods
### delete,ANY,hash-method
### Keywords: methods data manip
### ** Examples
h <- hash( letters, 1:26 )
h # 26 elements
del( "a", h )
h # 25 elements
|
26cf5ba907ef3adec892dbb3b624b4c324d513fc | bbb9d380e3e7af973f69661b0e7693b2fea73da6 | /man/occupations.Rd | 7b8a8abdcd4edd8b19e27dd875a139493c1d3597 | [] | no_license | cran/skillsengineeR | 55c7fa9a46e3bb588a927ddc6630bd10b039902c | a8eacbc9ab7b28f8f62ff974369d8e6ad4425b2d | refs/heads/master | 2023-03-19T06:57:25.246922 | 2021-03-04T08:50:06 | 2021-03-04T08:50:06 | 344,525,243 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 655 | rd | occupations.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_functions.R
\name{occupations}
\alias{occupations}
\title{Pull the full list of occupations}
\usage{
occupations(token, handle_status = "warn", response_raw = FALSE)
}
\arguments{
\item{token}{Authorization token obtained from \code{get_a... |
3f06a9035cb1cb6aeb8377bf67a2e6cf03705754 | bbc76ad786e92e25ec734ab20d6bcc0e51c1bd9d | /man/dtw.Rd | 5ab1f10fd82aa2056af869104bc61c13f46152bc | [] | no_license | akinori-ito/DTW | fda888b339c4318069e3cad150545a7f908b2bff | 6fcc339763fda4bb8837ecfdefda0203f165932e | refs/heads/master | 2020-04-02T03:43:05.556146 | 2016-06-07T00:38:53 | 2016-06-07T00:38:53 | 60,339,033 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 585 | rd | dtw.Rd | \name{dtw}
\alias{dtw}
\title{Dynamic Time Warping}
\usage{
dtw(x,y,window=100)
}
\description{
dtw calculates correspondence of rows of two matrices x and y using dynamic time warping algorithm.
}
\details{
Variables x and y should be matrices having the same length of columns. The variable window denotes the calcula... |
e71831d208ffcc3ee0936b7488c5b7506b29bad1 | 28f02ab412dcfed615601f747c6d28e654c444f1 | /technical_analysis-master/michele/code_quantmod/04_miller01.R | 84126287d0f78e8221caf2f63159fdf3e991f142 | [
"MIT"
] | permissive | karansoneja/DataScience | a7b2a09cd4ca20cd8d51d7fed4f2196ce2d0de84 | 883343eebbb27664e55a89cc9d42bc842ed1a3f7 | refs/heads/master | 2022-12-27T12:25:04.192150 | 2020-09-30T17:19:59 | 2020-09-30T17:19:59 | 215,329,005 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,228 | r | 04_miller01.R | ## Miller's code (https://ntguardian.wordpress.com/2017/04/03/introduction-stock-market-data-r-2/)
# library(quantmod)
library(IKTrading)
## ===== get data ==========
start <- as.Date("2010-01-01")
end <- as.Date("2016-10-01")
# Let's get Apple stock data; Apple's ticker symbol is AAPL. We use the quantmod function... |
9ec281bd3c50c693bb9eba082b954c1aca4ff41e | d8be1dd889f673d8636c02139c4f5fbfa4200fcd | /tests/testthat/test_bind_schema_to_entity_request.R | 4576a21487c6d909d1b6ae8c0207c3efe07fa229 | [] | no_license | thomasyu888/synr-sdk-client | c03a23ff852a819f3dab1ee103f8caa7f7d16ec0 | a090a5e062ca41ee68ee719ac30b041a0b2a9809 | refs/heads/main | 2023-02-26T03:10:25.802797 | 2021-02-02T15:10:28 | 2021-02-02T15:10:28 | 333,743,854 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 874 | r | test_bind_schema_to_entity_request.R | # Automatically generated by openapi-generator (https://openapi-generator.tech)
# Please update as you see appropriate
context("Test BindSchemaToEntityRequest")
model.instance <- BindSchemaToEntityRequest$new()
test_that("entityId", {
# tests for the property `entityId` (character)
# The ID of the the entity.
... |
edf8d812654d5e4ff45af08c2c1e54c77dc5f399 | 43937991b4969b915f03c247d13cf00a5d81ae3e | /R/plot_calls.R | 16b397d19da61a21abef103a41c3cd8d5e7edcde | [] | no_license | mjwestgate/circleplot | 51f2e84acd590553e611ece0b2eb59c817bc34a0 | 52d705dc050f74ec75a5a528ed981a9c5a1c772f | refs/heads/master | 2020-12-09T23:34:15.721674 | 2017-07-04T08:50:57 | 2017-07-04T08:50:57 | 24,923,479 | 10 | 0 | null | 2016-09-09T03:35:04 | 2014-10-08T03:47:28 | R | UTF-8 | R | false | false | 5,001 | r | plot_calls.R | # plot functions - called by user
# function to draw a figure, if supplied with a data.frame or matrix
circleplot<-function(
input, # a distance matrix (class 'dist') or square matrix (class matrix)
cluster=TRUE, # should points be rearranged using hclust? Defaults to TRUE
reduce=FALSE, # should nodes with no conn... |
e8782ff1256b0c96bac3f19070a5222c371bf053 | c5344271e392c529d9245ce7fdfe723678edd580 | /Simulations/Fig_4_Analysis.R | 6c2a6f7cdca696b4e25686bb43316f07ff0da5f2 | [] | no_license | njacobs627/Pan99_IVGs_Spatial_Structure | 41cfb0a61e174e7ccd2f4d166b31432ffdf756a7 | a1d82fb50b0ebe4105476e4a20b328ecd0e4c6ae | refs/heads/master | 2020-05-23T07:22:00.328932 | 2019-06-08T13:51:18 | 2019-06-08T13:51:18 | 186,676,634 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 22,919 | r | Fig_4_Analysis.R | require(tidyverse)
require(dplyr)
Proj.Home = "/Users/Nate/Box Sync/Lowen_Lab/Data_Notebook/Documentation" #For Nate's Macbook Pro
Data.Path = file.path(Proj.Home,"Simulation_Test_Results")
setwd(Data.Path)
# Analyze ----
Fig4 = read.csv(file = file.path(Data.Path,"Fig4_Data_SHORT.csv"))
Fig4.Peak = Fig4 %>%
group... |
72c25df60fe1fb7031a8b3670b78f97eeb918236 | 44692f59da65ef2a635b7c721a2f2e9a8ada38bd | /R/MS_SSM_plots.R | 1d7ea2e78a780a0951ab95c2d3028b2fa319c8ea | [] | no_license | vtrijoulet/MS_SSM | d35a807a7305c8b2ea504ad9149fc2104e1050ae | 672535011415f0e2fe1825b76b809501028f9642 | refs/heads/master | 2020-04-22T21:11:46.074173 | 2019-09-30T11:56:13 | 2019-09-30T11:56:13 | 170,665,801 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,513 | r | MS_SSM_plots.R | years<-min(data$year1):data$lastyear
Y1<-min(data$year1)
sp.names<-attr(data,"sp_names")
z.stat <- 1.96
name.folder<-paste(paste(sp.names,collapse ="_"),"_",Y1,"-",data$lastyear,"_rec",data$recruit_model,
"_M",data$M_model,"_err-rec",data$process_rec,"_err-survi",data$process_survival,
... |
1eb1e4be825f17e3a1aa009b6f5ca5bd7c2c4dc6 | 787a278be967f58fced754b31722033c127b600e | /6_three_sp_consortia.R | adada694efe21852f2bcf22a05cd51ff525b75bb | [
"MIT"
] | permissive | djbajic/structure-function-bacilli | 17cd654ba4c8409a244b0bd140fcd10a8cddb701 | 16769943b58d20926c9cb0e8486126847cb5b4fa | refs/heads/master | 2020-09-01T01:20:52.291192 | 2019-11-11T03:32:31 | 2019-11-11T03:32:31 | 218,840,107 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 891 | r | 6_three_sp_consortia.R | source('functions.R')
map <- fread('data/stepwise_map_PA.csv')
strfun <- fread('data/structure_function_PA.csv')
strains <- c( 'C', 'E', 'M', 'P', 'S', 'T')
# .. epistasis in pairs as a function of single background strains (3-body)
d <- map[bg %in% strains | is.na(bg)]
d[, bg := ifelse(is.na(bg), '-', bg)]
d[, bg :=... |
f227191a4c89510c8e1e01db1c646ab0017f957d | 4c44593583577dc94b74f6102b23e90b11d62de9 | /BIEN Scripts/ext.clade.sister.r | 81155a417c6b98053ad2c0e7c0e5131f91867bd8 | [] | no_license | redakc/kerkhofflab | 7334bfd89de5877b37e52bc9b07a321b45156b5f | 425ed6100137d48c5cbc07db75af4ca2cc8df52c | refs/heads/master | 2020-04-05T22:49:01.500457 | 2017-03-27T18:45:49 | 2017-03-27T18:45:49 | 52,403,262 | 0 | 0 | null | 2017-03-27T18:45:50 | 2016-02-24T00:58:13 | R | UTF-8 | R | false | false | 412 | r | ext.clade.sister.r | #Function to extract clade that includes all desired tips plus the
#clade sister to the mrca of those tips
ext.clade.sister=function(phy, tips){
require(ape)
require(geiger)
require(phytools)
mrca.focal=getMRCA(phy, tips)
mrca.sister=getSisters(phy, mrca.focal)
mrca.both=getMRCA(phy, c(tips(phy,mrca.focal)... |
70231340a9a4d134fd2b7bdee4988c315aba9105 | 328eaeda3c51826f13d93859ce1b58e298e34f08 | /Scripts/Data Organization.R | ba42075c733b9b1cc29e08d065ceb390d24c3c76 | [] | no_license | s-gibson/ASA-NFL-book | 237ce5ad62f9dba1774bc8e8836d81de8a55b914 | d0519c816d93aa7ec4460ce4be3207692f6af8e6 | refs/heads/master | 2021-01-25T09:38:01.826688 | 2017-08-30T12:13:36 | 2017-08-30T12:13:36 | 93,863,763 | 8 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,750 | r | Data Organization.R | ########################
## ASA NFL DFS book ##
## Data Oganization ##
## Stewart Gibson ##
## 6/10/17 ##
########################
## Import data of weekly scores, O/U's
OU.2016 <- read.csv("data/2016_DonBest_VegasData_NFL_Week.csv")
OU.2012_2015 <- read.csv("data/2012_2015_Final_DonbestData_NFL.csv... |
0795b5816f7caf177dcfbcd449aa34691e8a0d7d | ba65e258d288b8ccd8011313e6b4c0522f802e93 | /man/logtransform.Rd | 9ce618e836a47e75ef8efbbc511e8c9feea0d7c2 | [] | no_license | cran/doebioresearch | 3572acba107f48228ea6db867d9c8639e29829ce | fd141323443a017ae973f373c9be3e9c7f25d0ce | refs/heads/master | 2022-11-18T14:03:19.248583 | 2020-07-08T11:20:03 | 2020-07-08T11:20:03 | 278,226,971 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 933 | rd | logtransform.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/logtransform.R
\name{logtransform}
\alias{logtransform}
\title{Log transformation of the numeric vector}
\usage{
logtransform(numeric.vector)
}
\arguments{
\item{numeric.vector}{data vector to be transformed}
}
\value{
A list of
\itemize{
\... |
c32f5f2865dd05ab590cc974b0569f488864fc95 | e2d06410af5af94eff00bffd230fb5c689daaba1 | /R/users.R | 8fcfd3383b75aac9712bcc34ff24564c7d56d5a3 | [] | no_license | MarkEdmondson1234/gtmR | b99855895768c2b7dd3eee33881a89adcdc20770 | 644684285808ecf02a59fa31830802d4b1435d28 | refs/heads/master | 2021-01-10T16:22:00.092511 | 2020-12-05T21:32:24 | 2020-12-05T21:32:24 | 55,138,326 | 6 | 3 | null | 2020-12-05T21:32:25 | 2016-03-31T09:37:49 | R | UTF-8 | R | false | false | 717 | r | users.R | #' GTM users list
#'
#' Downloads all users that have access to the account
#'
#' @param accountId Add your GTM account ID
#' @export
#'
gtm_list_users <- function(accountId){
acc_url <- "https://www.googleapis.com/tagmanager/v2/accounts"
user_url <- paste(acc_url, "/",accountId,"/user_permissions", sep = "")
f_p... |
892db8af0dcfb43f65106aa161b1d11a37e6902f | e8c17cc82dd543dc61d633aacd1fde248868db99 | /scripts/R/session2/simple_database_queries.R | 2bd84bcc8d049f679a41766d0ba9defb9c10e956 | [] | no_license | H4estu/COVID19-Data-Exploration | 7d19eb30229b71c6118117769fb7ce1909f985c2 | f549e629fe15f61ac0c4e83406af4ed860604f31 | refs/heads/master | 2022-03-08T03:42:53.456581 | 2022-02-23T19:06:46 | 2022-02-23T19:06:46 | 250,889,937 | 0 | 2 | null | 2020-06-01T22:21:42 | 2020-03-28T20:44:37 | HTML | UTF-8 | R | false | false | 913 | r | simple_database_queries.R | library(data.table)
library(sf)
library(RPostgreSQL)
library(magrittr)
# -------- Access the COVID-19 Database --------- #
source(file.path(git.path,'Code/config_files/db_config.R'))
con <- db_connect.fn()
# ----------------------------------------------- #
report_data <- dbGetQuery(con, 'SELECT * FROM covid_data.du... |
ddb1b14993d8748f81f64216c173630e8f476b1e | 9fed66e4b6496d9f2428c8cb0054d255b3bb3c90 | /plot2.R | bccc425720f30a263da3521d807160590374cde4 | [] | no_license | catblue/ExData_Plotting1 | 6312dad8f9d9a6c9b8fc8e414c06994e5e86411a | 2bac4fe1ca9c6c1616f3e8ab986d574ab4e9a356 | refs/heads/master | 2020-12-26T02:11:11.058585 | 2014-05-11T17:13:45 | 2014-05-11T17:13:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,542 | r | plot2.R | #downloading and unziping data
if(!file.exists("./data/household_power_consumption.txt")){
if(!file.exists("./data")){dir.create("./data")}
fileUrl <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip?accessType=DOWNLOAD"
download.file(fileUrl, destfile="./exdata-data-househol... |
b0c7138849192921ae2ef20802f546afe70c1e0d | 2d590228a8b39b5bf747825d3fe303e430124066 | /man/sensitivity.Rd | 89e11a8baf099f24162dc36da6b4e1a160ab31f5 | [] | no_license | cran/gripp | b33cbb359f68bdeedb9dcef9476923ed68f56c7e | 8fedae6b2871b53e9a61af45a107720390a7f741 | refs/heads/master | 2020-06-23T10:12:44.184493 | 2019-08-24T04:30:02 | 2019-08-24T04:30:02 | 198,593,773 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,511 | rd | sensitivity.Rd | \name{sensitivity}
\alias{sensitivity}
\title{Sensitivity matrix calculator}
\usage{sensitivity(parm_s)}
\arguments{
\item{parm_s}{Set of values to be considered as parameters for the Direct Problem solution}
}
\value{A matrix with the derivative of the function that represents the Direct
Problem for each par... |
80f4968cfe695048d1bf4e32b493250958de963b | 70f7231b9f8c041c21abc4abaedfaa5b2a6bd107 | /depth_split.R | 323c8e1542d4a6736c696e7a3b1666ff6b7ba0e9 | [] | no_license | Kikiliuz/depth_split | 07f41f9a84c6f177e567b82d0b5660020bf0d275 | 4ae58b5335cc4709e24914cb75e30294b7212aa5 | refs/heads/master | 2020-03-21T05:42:34.265900 | 2018-06-21T13:44:50 | 2018-06-21T13:44:50 | 138,173,631 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 637 | r | depth_split.R |
library('dplyr')
set.seed(1)
x<-round(runif(min=100,max=10000,n=1231))
y<-runif(min=0,max=1,n=1231)
data<-data.frame(cbind(x,y))
depth_split = function(data, k, variable) {
n = nrow(data) # 获取data的行数
data = arrange(data, variable) # 对data进行排序
depth_list=rep(0, n) # 创建新行，初始化为0
for(i in c(1:n)) {
depth_... |
c1f97beb7e622887a55a3afd7d7926ef52591500 | f76dcb5082462f759a63ff46d41c4acc2dbe5a93 | /man/rws_read_init.Rd | 8d6ed90adf3f213229d78f078a2880e03ee79497 | [
"MIT"
] | permissive | poissonconsulting/readwritesqlite | ba7ae2d6c814eb4880f94ee3e0ee77793a12436a | db961138ad98b957b70b3e4f257ab8c9c317b8e2 | refs/heads/main | 2022-11-02T22:30:43.073748 | 2022-10-16T23:09:15 | 2022-10-16T23:09:15 | 158,617,726 | 39 | 1 | NOASSERTION | 2022-09-28T12:29:35 | 2018-11-21T23:26:36 | R | UTF-8 | R | false | true | 568 | rd | rws_read_init.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/init.R
\name{rws_read_init}
\alias{rws_read_init}
\alias{rws_read_sqlite_init}
\title{Read Initialization Data table from a SQLite Database}
\usage{
rws_read_init(conn)
}
\arguments{
\item{conn}{A \linkS4class{SQLiteConnection} to a database.... |
cf019fc12b77515d05074adcab187c4af9078b45 | e6b97f595b589e38238287d617605753b20511cf | /workflow.R | 2ffb9a74aa28acbe63161c55dc1ddbc57d104fdb | [] | no_license | peggylind/SyntheticDataSet | c3bd0f8cce17fe32f4341ac49fd5c98680c3d010 | b2cdf2581d64504e9af4d5af2c2c199ea4908d57 | refs/heads/master | 2020-04-05T22:17:47.762325 | 2019-07-18T00:36:56 | 2019-07-18T00:36:56 | 157,251,390 | 0 | 0 | null | 2018-11-12T17:34:16 | 2018-11-12T17:34:15 | null | UTF-8 | R | false | false | 1,167 | r | workflow.R | # Workflow controller for SAM creation
source("BaseScripts/basesam.R")
#set working directory to where all the data folders can be found
housingDataDirectory <- "~/University Of Houston/Price, Daniel M - Social Network Hypergraphs/HCAD/2015/"
censusDataDirectory <- "~/University Of Houston/Price, Daniel M - Social Net... |
121f60b8bc82be2a14ed46868b42dd206a6fe88c | a04716b6fd12d346a07b26c85d3cb3ea995def54 | /a1.R | e167e22260636ba5614089c0cddebe570b345c28 | [] | no_license | mjherz/kt1 | 991fcfedffbda25a2788148d7aaec3a0071337f4 | e6a2592aa60df06596b03ccff22e5850e1bc1632 | refs/heads/master | 2016-09-05T12:43:45.241676 | 2013-06-25T18:46:36 | 2013-06-25T18:46:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,441 | r | a1.R | set.seed(6001)
train1<-read.csv("train.csv")
test1<-read.csv("test.csv")
gcm<-read.csv("genderclassmodel.csv")
gm<-read.csv("gendermodel.csv")
names(train1)
dim(train1)
sapply(train1,class)
train1$pclass<-as.factor(train1$pclass)
train1$survfact<-as.factor(train1$survived)
#develop cabin group
train1$cabgr<-as.facto... |
80cf90b140d1c5cee4a463861963a0b4e8e8fb9a | 40b8c18de5170436038973ed1478e9817a4270f8 | /scripts/03_linear_regression_training_data.R | dba08d10bd85e0efd31bab4ef7388945004ef0e3 | [] | no_license | desval/ResazurinMIC | a97eb7e12a8407e8af27cfb7de77d3fc60c269fe | 19b6aac10a24280eec77139c36e7192e280468c5 | refs/heads/master | 2021-09-15T16:56:18.775159 | 2017-07-10T10:46:04 | 2017-07-10T10:46:04 | 79,265,781 | 0 | 0 | null | 2017-01-17T19:54:13 | 2017-01-17T19:54:13 | null | UTF-8 | R | false | false | 4,807 | r | 03_linear_regression_training_data.R |
# Description
## ----------------------------------------------------------------------------------------------------------------------------
# In this file we fit a linear regression to the training data and plot the model
# Load dependencies and set options
## -------------------------------------------------------... |
d97f26345dd556b3af2c247fe54e40cf00da083e | 749df9b8dd733b3c63dbea598540ffc7a97d8c84 | /Supplementaryscript2.r | bef7758c7c2745d94323df0e7bb0bb71db9b5964 | [] | no_license | thalescherubino/RTqPCR | 4845dd030d4f28c2a64e552935fd52da663c1d7c | ca205737b4b9edf56b4f6a7f040934895d014649 | refs/heads/main | 2023-04-08T05:40:42.966678 | 2021-04-23T16:29:41 | 2021-04-23T16:29:41 | 360,313,870 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,454 | r | Supplementaryscript2.r | # Supplementaryscript2.r
############################
#Traditional method-DeltaCT#
############################
#first calculate the mean between both technical replicates
deltaCtData <- RTqPCRdata[seq(1,nrow(RTqPCRdata),2),]
deltaCtData$ct <- colMeans(matrix(RTqPCRdata$ct,nrow=2),na.rm=T)
#Opt - Wat
target.opt <-... |
5c6641a06f79b0be82b10516160e6f9171238bda | 09847d5e16938d783a1a8a37b576d5840dd7e5f7 | /tests/predict.R | 063999b0337b332bb914c32a4e5c1231642d6e69 | [] | no_license | cran/aster | b6dc91ebf539df904d55f4cacd584a6ea5ff27a1 | 61fc3cd89e7f6776279333b534652356c7059f89 | refs/heads/master | 2021-07-04T00:20:22.476008 | 2021-06-13T03:40:32 | 2021-06-13T03:40:32 | 17,694,509 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,986 | r | predict.R |
library(aster)
# needed because of the change in R function "sample" in R-devel
suppressWarnings(RNGversion("3.5.2"))
set.seed(42)
nind <- 25
vars <- c("l2", "l3", "f2", "f3", "h2", "h3")
pred <- c(0, 1, 1, 2, 3, 4)
fam <- c(1, 1, 1, 1, 3, 3)
length(pred) == length(fam)
nnode <- length(pred)
theta <- m... |
6c4a97f7f670a2e4a5aa03decb6a0a8c8bdcd7d4 | 325902f26f8df4914f3931d6a3a5c5af12b975b1 | /R scripts/Protein_examples_zoom.R | 98add3d4ae4e9ab296e4dbbb99e713dcc412aef3 | [] | no_license | Rappsilber-Laboratory/ProteomeHD | 5e9ca415a0dac31ef46972eeff018547b9ee8aeb | 2ee6d87110b9d4932af0d106927eb289dfbce321 | refs/heads/master | 2020-04-28T22:22:08.913323 | 2019-11-05T12:24:45 | 2019-11-05T12:24:45 | 175,614,695 | 8 | 0 | null | 2019-03-14T12:10:33 | 2019-03-14T12:10:32 | null | UTF-8 | R | false | false | 7,099 | r | Protein_examples_zoom.R | ## In this script we analyse the distribution of four example proteins in the coregulation map: An uncharacterised microprotein,
## an uncharacterised protein without coregulation partners above threshold and two multifunctional proteins
# Load the required libraries
library(data.table); library(ggplot2); library(grid... |
32161ff44f3983f7a36ff985cefdbb4e7c1ce135 | 104e81350a792f38d9522857cfbad03836ef7683 | /Week1/htmlDownload.R | ed292b98f6ace500fae02bd44930f705fcdd6c3f | [] | no_license | jtantongco/Coursera_GetCleanData | bcf184d13e0e245b204b784871330f616b3086b4 | 080ff4bd328942d0d0d68043b52cb5fc48fd653a | refs/heads/master | 2021-01-01T15:36:18.433559 | 2014-08-24T09:22:21 | 2014-08-24T09:22:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 287 | r | htmlDownload.R | library(XML)
fileUrl <- "http://espn.go.com/nfl/team/_/name/bal/baltimore-ravens"
doc <- htmlTreeParse(fileUrl, useInternal=TRUE)
scores <- xpathSApply(doc, "//li[@class='score']", xmlValue) #seems to return empty
teams <- xpathSApply(doc,"//li[@class='team-name']", xmlValue) #is good |
38ba394aa46c2540bd30a5189a91dfe1f69920e5 | afe39a330e68856413be87018519f7119dde6508 | /R/create_resource.R | 48cb36254855e2251a580e1d4dbfcbeeb5f5df8b | [] | no_license | jchrom/trelloR | 4a142222c34d480b25b7f3fd75614c6af1cf66eb | cca04eb70bf5060a7c7f858fa9911bd8c68e2089 | refs/heads/master | 2023-08-31T06:33:57.940993 | 2023-08-27T18:09:07 | 2023-08-27T18:09:07 | 34,352,839 | 40 | 12 | null | 2017-01-21T19:47:02 | 2015-04-21T21:20:56 | R | UTF-8 | R | false | false | 2,484 | r | create_resource.R | #' Create Resources
#'
#' Create resources via Trello API.
#'
#' See [Trello API reference](https://developer.atlassian.com/cloud/trello/rest)
#' for more info about what elements can be included in POST request body.
#'
#' @param resource Model name, eg. `"card"`.
#' @param id Model id.
#' @param path Path.
#' @param ... |
17289ae45f927b9fc86c7967a20242ecdcb704bd | de8336641d9f4ee5295a9e42fff7b4f742281338 | /tests/testthat/test-pick_UScode.R | f114cfecf5ca8f883688c165ed88b8ebbc260e00 | [
"MIT"
] | permissive | sjmarks/Birdr | 44b8e2abe13ccacdf32073e757121f4e3853ebe5 | 2183cbb28272d6b96f0635c21987fd6d06e471c6 | refs/heads/master | 2022-06-07T03:04:35.841992 | 2020-05-04T04:14:29 | 2020-05-04T04:14:29 | 260,333,483 | 2 | 2 | MIT | 2020-05-04T04:14:30 | 2020-04-30T22:40:53 | R | UTF-8 | R | false | false | 286 | r | test-pick_UScode.R | test_that("pickUSCode returns proper name and code", {
correct_result <- tidyr::tibble(code = "US-CA-079", name = "San Luis Obispo")
my_result <- pick_UScode(state = "California", county = "San Luis Obispo", ebirdkey = "rqksong3qcbm")
expect_equal(my_result, correct_result)
})
|
3c5a60b9d187b32c6037ebeea5ef336cb962c8b7 | 53e56475273a36deeb2d1f133947f531e4e3871a | /proj_1/sampleMultinomial.R | 8fc3017cee09f6168b8a512ed0f3f3ff58f3b428 | [] | no_license | Marvedog/Tma4300-Kode | 34af409101da8471be4853f4ec07ed7d986a23bb | d8af15888f6078030a9834fecddb30fec7b3fc9d | refs/heads/master | 2021-05-12T06:52:51.580650 | 2018-02-09T09:01:25 | 2018-02-09T09:01:25 | 117,229,525 | 0 | 1 | null | 2018-02-02T07:48:04 | 2018-01-12T10:45:57 | R | UTF-8 | R | false | false | 641 | r | sampleMultinomial.R | # -------------- Multinomial sampling -----------------#
# Input:
# p : Sorted list of probabilities summing to unity.
# N : Number of draws
# Output:
# out: vector of draws corresponding to each interval
sampleMultinomial <- function(p, N) {
len_p <- length(p)
# Compute cumulative sum
cumSum <- matrix(0,l... |
623e52504b377e12623dc0325c265163f598f73b | 9c4fd75b5fa36683a7f72598d5b7ffb3e155fa74 | /man/dput_levels.Rd | 7a9c6bdf5a40d17ca612a47c8a7919e50376a577 | [] | no_license | gridl/thinkr | 3c291d6ec16c83c32f596064493f4178977f7714 | 24f85da8c65e77b0f36cd80b7d40389e0ba7b92a | refs/heads/master | 2020-03-23T03:17:16.909851 | 2018-06-21T04:15:31 | 2018-06-21T04:15:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 402 | rd | dput_levels.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/levels_to_vec.R
\name{dput_levels}
\alias{dput_levels}
\title{return R instruction to create levels}
\usage{
dput_levels(vec)
}
\arguments{
\item{vec}{a factor or character vector}
}
\value{
a R instruction
}
\description{
retu... |
b001b7f7bd00b59a34c1d09f2ca8e184ee209281 | 50291a5d7e7652f8dcfbfb718906f4ba0b8964e7 | /ui.R | d83c90417c1a28dc44a4dc38546d54532c49755a | [] | no_license | bborgesr/reactnb | e9f94f00951299519f7968d61e48a75d345d7506 | 0083b79c560ecc1dd6b38221fe0bbcc75ad48b72 | refs/heads/master | 2021-08-22T16:53:56.972462 | 2017-11-30T17:56:32 | 2017-11-30T17:56:32 | 119,948,057 | 1 | 0 | null | 2018-02-02T07:28:43 | 2018-02-02T07:28:43 | null | UTF-8 | R | false | false | 682 | r | ui.R | shinyUI(basicPage(
tags$link(rel='stylesheet', type='text/css', href='lib/jqueryui/css/ui-lightness/jquery-ui-1.10.3.custom.css'),
tags$script(src='lib/jqueryui/js/jquery-ui-1.10.3.custom.js'),
tags$script(src='lib/jquery.ui.touch-punch.min.js'),
tags$script(src='reactnb.js'),
tags$link(rel='stylesheet', type... |
63d5e1962e11107df6c82eec599dbacb6856038c | f7a0f3cbeefdc01fc0f172a47359c0c4610c95a7 | /code_active/sim_analysis_looping_info_1.R | 93157ed8c287ec91e07d04bb3d727e609354f40a | [] | no_license | EESI/exploring_thematic_structure | 65e77efbb56fea646a9f165eaa94f955f68259ff | 06f7ea096c31dbb63b09fc117ee22411e52ab60e | refs/heads/master | 2020-08-25T03:30:04.253394 | 2019-10-23T03:02:18 | 2019-10-23T03:02:18 | 216,955,082 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 30,832 | r | sim_analysis_looping_info_1.R | jsd <- function(p,q){
m <- .5*(p+q)
sqrt(.5*(sum(p*log(p/m)) + sum(q*log(q/m))))
}
entropy <- function(x){
x <- x[x!=0]
f <- x/sum(x)
-sum(f * log2(f))
}
norm10 <- function(x) (x-min(x))/(max(x)-min(x))
bcd <- function(x,y) sum(abs(x-y)/sum(x+y))
N_OTU <- 500
N_SAMP <- 100
SC_P <- c(.1,.25,.50,.75)
SC_N <- 1... |
5f142a713c53ea4a45d0ff3b11740b18bc6cd06d | 79aa26d2d18b78dce81bb799bedd74d0ac0c47d9 | /man/select2.Rd | feb18f7845b33ae038d1fa18458d4ca5db1d69dc | [] | no_license | MadsQE/minidplyr | 729fe7d5409959a21d7fb08cb5381d6d7ab2606f | dac9172150bccd7e796eadffaba1a2ab382e3e3f | refs/heads/main | 2023-07-16T21:13:46.823292 | 2021-08-26T09:34:53 | 2021-08-26T09:34:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 373 | rd | select2.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/select.R
\name{select2}
\alias{select2}
\title{Select some columns}
\usage{
select2(df, names_or_ind)
}
\arguments{
\item{df}{A data frame}
\item{names_or_ind}{A vector of column names or indices}
}
\value{
An other data frame, with the subs... |
82edd76f78571c515f71a3f5f42cda08d79a104e | 447418d950c976e5dd9ead3722399f927e29fb7c | /Factors.R | 6c2b6d9f332a0f392b549aeb1d9f53ee509fc89b | [] | no_license | archits14/datasciencecoursera | 9678c4ade7410dc7f11554ab5753aa1c57647c0a | 7b08290838f390f377608170f0600c76ba96f4f8 | refs/heads/master | 2022-12-14T07:57:49.002406 | 2020-09-05T03:24:25 | 2020-09-05T03:24:25 | 291,470,543 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 178 | r | Factors.R | temps <- c('cold','med','hot','hot','hot','cold','med')
summary(temps)
fact.temp <- factor(temps, ordered = TRUE, levels = c('cold','med','hot'))
fact.temp
summary(fact.temp)
|
20548dbb89198b87b644e2f7923604fbd009265a | 94da0f2f5d7fce4f84d4a540dd0edfed09b264e4 | /R/sumZero.R | ae7c1704d04f97aa87e9d8d723d64d0734bef29a | [] | no_license | karirogg/SirKR | 6f02cd2f80cf29ef38b600cbfd54f79ba315a6ef | f88039ef5c7e918a08d2d20d02d64df26ffe3059 | refs/heads/master | 2021-05-29T15:49:41.948932 | 2015-04-11T00:02:46 | 2015-04-11T00:02:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 812 | r | sumZero.R | #' Sum of Vector Elements
#'
#' Takes in a numeric vector and returns the sum of it. If at least one entry is numeric, then the function returns 0. If no entry is a value, then it returns NA.
#' @param x A numeric vector to find the sum of
#' @return The sum of the vector x
#' @examples
#' x <- c(NA,2)
#' sumZero(x) ... |
581ba27e784b1b41178c61d555e590d6b7da6cf3 | 4d6a64ab3bc0813e6888d58eeff8a809739f826b | /FishBase/R/fb_ration.R | 7f66b730d034167a7b103c9637f4f03c71da5f50 | [
"Apache-2.0"
] | permissive | cornejotux/FishBaseR | 2d54075078238c89887ed8356b878c6f1cb7b900 | 72fbabbda485583fbcf06fb5502593e0f6e7fe7a | refs/heads/master | 2022-12-25T18:03:43.153346 | 2020-09-29T20:13:50 | 2020-09-29T20:13:50 | 299,727,130 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,443 | r | fb_ration.R | fb_ration <-
function(idFB = NA, Genus = NA, Species = NA, server = 'http://www.fishbase.tw/')
{
require("XML")
require("stringr")
require('RCurl')
if (is.na(idFB))
{
ids <- fb_ids(Genus=Genus, Species=Species, server = server)
idFB <- ids$idFB
StockCode <- ids$StockCode
}
if(!is.na(idFB))
{
url <- p... |
fdd7796b725ecd7c5fdcaa5581783410bf2f3eae | 47bb25ddbf692279a80e28ecaadeffbfd28973d5 | /tests/testthat/test-advent.R | 8717155e334ef55f02b7034791b574fa09d79234 | [] | no_license | MatMoore/adventofcode2019 | d7c28cd7c9650d8ba9472506b4d71a5e82035964 | 95db2b43dd9b3647b93ef0e75d337a1e1df1ecae | refs/heads/master | 2020-09-22T14:33:08.996773 | 2019-12-06T22:00:47 | 2019-12-06T22:00:47 | 225,240,509 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,574 | r | test-advent.R | context("day 1")
test_that("day 1 part 1 returns the correct answer", {
answer <- day_01_part1()
expect_equal(answer, 3456641)
})
test_that("day 1 part 2 returns the correct answer", {
answer <- day_01_part2()
expect_equal(answer, 5182078)
})
context("day 2")
test_that("day 2 part 1 returns the correct answer... |
4fe6f29010b516c529ecc9fca961e0a47471158b | 0fc8ad550144e4e96c9aae06f83e2be7aaef5a48 | /R/SmallNR.R | b631ce64079c7d0c78b8f9e55c350adc9854599e | [] | no_license | nroming/NRmisc | f3a3072bfbe257b65f00005948d68d37635624f8 | 41b375758997310288a99a562f5bd89a01cb844e | refs/heads/master | 2021-01-22T02:39:57.560916 | 2015-03-05T10:55:49 | 2015-03-05T10:55:49 | 25,294,408 | 0 | 2 | null | 2015-02-03T15:52:10 | 2014-10-16T09:03:22 | R | UTF-8 | R | false | false | 490 | r | SmallNR.R | #' Return n-smallest value of a vector of numbers
#'
#' @author Niklas Roming
#' @param x A vector (numeric or integer)
#' @param n Which value to return (e.g. n=2 => second-smallest)
#' @return The n-smallest value of x
#' @export
SmallNR <- function(x, n){
# this function returns the second smallest value of a vec... |
1af80c4abeb254be90f906eab515ac24a9046cf1 | ee547df8ce469ca54c111a61edb66b3c6123b588 | /man/SVMModel.Rd | 08a851ba76aa33db4166b861e1dba0a2100628df | [] | no_license | brian-j-smith/MachineShop | b4d6ee3d3e900ac0733ea0a663c54ba74cc17800 | 3599d9af6f58faff2f423520193d3029637f7bc5 | refs/heads/master | 2023-04-07T00:49:41.675201 | 2023-03-21T14:23:23 | 2023-03-21T14:23:23 | 150,329,909 | 63 | 12 | null | 2018-11-18T23:55:38 | 2018-09-25T21:12:07 | R | UTF-8 | R | false | true | 3,518 | rd | SVMModel.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ML_SVMModel.R
\name{SVMModel}
\alias{SVMModel}
\alias{SVMANOVAModel}
\alias{SVMBesselModel}
\alias{SVMLaplaceModel}
\alias{SVMLinearModel}
\alias{SVMPolyModel}
\alias{SVMRadialModel}
\alias{SVMSplineModel}
\alias{SVMTanhModel}
\title{Support ... |
7eae27afc03859369c64a562ea1c95aed2e1630f | 79457aaae83a0b3914a38874c10907440e0dfc61 | /man/gdalcubes_options.Rd | 3753ecc5b24dc7169976e380dc6125c8204054d9 | [] | permissive | appelmar/gdalcubes | be9786b36fbe4e25a5c0245968634f57a40752ad | 2134f769454e147660e7a73c61afa14219de20b4 | refs/heads/master | 2023-08-07T20:56:02.442579 | 2023-07-25T06:36:46 | 2023-07-25T06:36:46 | 148,130,790 | 74 | 7 | MIT | 2023-03-23T19:56:08 | 2018-09-10T09:25:01 | C++ | UTF-8 | R | false | true | 3,083 | rd | gdalcubes_options.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/config.R
\name{gdalcubes_options}
\alias{gdalcubes_options}
\title{Set or read global options of the gdalcubes package}
\usage{
gdalcubes_options(
...,
parallel,
ncdf_compression_level,
debug,
cache,
ncdf_write_bounds,
use_overv... |
7359924195940ed7e5e7ef88fcb7e92e512daf60 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/netgsa/examples/NetGSA.Rd.R | 4a55b66b2865863e633000fa57df9f37261cc1e3 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 357 | r | NetGSA.Rd.R | library(netgsa)
### Name: NetGSA
### Title: Network-based Gene Set Analysis
### Aliases: NetGSA
### ** Examples
set.seed(1)
## NetGSA with directed networks
## NetGSA with undirected networks
data(netgsaex2)
A = netgsaex2$A
B = netgsaex2$B
x = netgsaex2$x
y = netgsaex2$y
# -Not-run-
# fit = NetGSA(A, x, y, B, l... |
026150d9880f35f653a89fae7a4750f8ebf7abda | ee2553a8d0160ce6b0410007cdda86e71fd5ad3d | /JHigh HW 3/HW 3 Problem 4/HW 4 Problem 4.R | 11558cbef4784f74e35bd0ae928ee035df77a19b | [] | no_license | joehigh/EN.553.732 | bd4b958acbcbc7cf91b5c0d120add6ca2994b6c9 | fc277a288418c3ad577f28c6733e56b3e07fd416 | refs/heads/master | 2022-04-27T01:41:00.268173 | 2020-04-29T09:24:44 | 2020-04-29T09:24:44 | 255,237,001 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,167 | r | HW 4 Problem 4.R | #Problem4
#part a
glucose = read.table("glucose.dat", header = FALSE);
data=as.matrix(glucose)
data=as.numeric(data)
hist(data,breaks=seq(50,200,5),freq=FALSE,main="Problem 4 Part a")
lines(density(data))
#part c
y=data
set.seed(123)
n=length(y)
iter=10000
a=1
b=1
mu0=120
tau0.sq=200
sigma0.sq=1000
nu0=10
x=matrix(0... |
bec35d2c7f7335148b8eb60880d63bf84884fcc7 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/Devore7/examples/ex07.58.Rd.R | a0e5200c0d1e07d27b90a9fc173e1b20f8fd2014 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 160 | r | ex07.58.Rd.R | library(Devore7)
### Name: ex07.58
### Title: R Data set: ex07.58
### Aliases: ex07.58
### Keywords: datasets
### ** Examples
data(ex07.58)
str(ex07.58)
|
94819142fd6c9795a92bd2ddfc7542259883d733 | 060c6a303098ef689c43ea0feff68c68272477e9 | /kinomeAnalysis/kinome_baseline_data_munging.R | 12fd6dbdfd110b218cba0a1ef5b52d55405c2070 | [] | no_license | Sage-Bionetworks/Synodos_NF2 | e1c004191de438d6efa2d565f7d1c1e36a90efaa | 1506b57c74469439e81fe8afbc6de9add681c57c | refs/heads/master | 2022-12-20T16:20:09.620615 | 2022-12-14T22:53:52 | 2022-12-14T22:53:52 | 20,036,276 | 2 | 0 | null | 2014-05-28T18:09:16 | 2014-05-21T20:29:18 | R | UTF-8 | R | false | false | 4,448 | r | kinome_baseline_data_munging.R | library(synapseClient)
library("gdata")
library("tidyr")
library("dplyr")
library("reshape2")
require("parallel")
library("plyr")
library("doMC")
library("gdata")
registerDoMC(4)
synapseLogin()
#schwannoma baseline data
baseline_schwannoma_human_synid <- "syn4214458"
baseline_schwannoma_human <- synGet(baseline_schwa... |
7cb4806da6638c720a9dc80285c58ee74060e83f | 75778fec111d3c10b1dca261dd7dddd47c3a4bc8 | /script_data_analysis.R | b591f73e2c54cc36f1e1dd21413c49989a59861b | [] | no_license | veren4/Protein_Prediction_II_plots | 6ed5591b13580422b340e038c852f7e48db487cd | 9e59d9b77baa408c2cc490ff45098f18197faaf1 | refs/heads/master | 2022-11-23T06:50:37.937935 | 2019-11-28T11:28:32 | 2019-11-28T11:28:32 | 279,658,912 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,136 | r | script_data_analysis.R | #
# density plot of positions of NLSs/ NESs in the protein sequences
#
# in fasta file: find out absolute lengths of proteins
library(tidyverse)
my_protein_sequences = seqinr::read.fasta(file = "C:/Users/Verena/1_Studium/03_Aufbaustudium_Informatik/Protein Prediction II/Exercise/data/ns/nes_nls.fasta",
... |
e576868601a4dd8b191f700bc3b84a42622bc402 | 18e8822e6cce16631058ecfd14906bbb1580aa66 | /R/BITFAM_scATAC.R | 536e2ce15f87955d9762087b92ab584f7a490118 | [] | no_license | jaleesr/BITFAM | fe443d9b0bc23016526e483e918cfe38bd069913 | b604014c40329b3f737d4a152d44114a60c518b1 | refs/heads/master | 2023-02-21T11:10:50.972711 | 2023-02-13T13:50:50 | 2023-02-13T13:50:50 | 310,433,311 | 27 | 18 | null | null | null | null | UTF-8 | R | false | false | 340 | r | BITFAM_scATAC.R | #' Extract the genes that have scATAC-seq peaks on their promoter regions
#'
#' @param scATAC_obj A preprocessed Seurat object of scATAC-seq data.
#' @return the genes that have scATAC-seq peaks on their promoter regions
#' @export
#' @import rstan
#' @import Seurat
BITFAM_scATAC <- function(scATAC_obj){
return(rowna... |
312d64ea0082a5d031500741fdb5457733e9d543 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/bamlss/examples/boost.Rd.R | a17b2ba42017f1f0414018c6214b03e6a653c4db | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,784 | r | boost.Rd.R | library(bamlss)
### Name: boost
### Title: Boosting BAMLSS
### Aliases: boost boostm boost.summary boost.plot print.boost.summary
### plot.boost.summary boost.frame
### Keywords: regression
### ** Examples
## Not run:
##D ## Simulate data.
##D set.seed(123)
##D d <- GAMart()
##D
##D ## Estimate model.
##D f <- ... |
a3e3b07172666b137d96f2e19211870f68d2a0cd | d69f4337b05a04eafa938a070eb824ef0cb517e3 | /bin/plotAME.R | fe36766cbafec4dd41738be6e31bf27931b38bdf | [] | no_license | biomystery/atacMotif | 2d7b0212c0bc4c4f6f38f49b09e4a063bf3dc384 | e68d54bf00036a648ea6da62c19c3ab308e834f8 | refs/heads/master | 2022-02-24T01:07:02.357139 | 2019-10-09T06:59:25 | 2019-10-09T06:59:25 | 138,637,073 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,498 | r | plotAME.R |
source("./bin/aux_funs.R")
tfclass <- readRDS("./db/tfclass.rds")
# input parameters --------------------------------------------------------
ame_path <- "./test/ame_2kbg_all.res.txt"
th <- 0.1
# ame’s output ------------------------------------------------------------
ame_res <- read.table(ame_path,
... |
c7b3fb11b7d158cf807bfa1824220d8a0e786490 | ba0d52a9447cc2cedcaacafd8349fc50a32363b5 | /R/prepBoxplotHealthData.R | 591ef4c4c3ae0449e86e21cfd8bd5626e302bff3 | [
"CC0-1.0"
] | permissive | robschick/tangled | 49590a754531b8e50294abb4d86fcd9cc85d037c | e4c0e49fa87802dd39fba01dc4fba5cef25e7b31 | refs/heads/master | 2023-04-07T19:24:43.838552 | 2022-05-04T19:11:30 | 2022-05-04T19:11:30 | 33,547,111 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,416 | r | prepBoxplotHealthData.R | #' Prepare data for making the boxplots of health during entanglement
#'
#' The goal of this is to make the box plots of the health during the entanglement window.
#' This uses two data frames: (tangRepro and tangNonRepro) which are stored
#' in the package structure. The function also used the unimpacted reference
... |
d0f182d7d28df9cf4c8b8e3d4abb480cb1ecfff3 | 35ae390a7e9df0e77e3a247588424efc50cc1220 | /plot5.R | 3f66f6e6fd2754bff4e6d38e0fc1a6889ff2b0b4 | [] | no_license | tomashaber/ExData_Plotting2 | b27ab507953b2bdcf839d6d091519cd8af354d37 | 45530013fd608609e6398f36ea2b1d1d4f0f3c7b | refs/heads/master | 2021-01-10T02:39:12.454755 | 2015-11-22T20:47:22 | 2015-11-22T20:47:22 | 46,672,432 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 701 | r | plot5.R | library(ggplot2)
library(dplyr)
wd <- getwd()
setwd("2")
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
v <- grepl("vehicle", SCC$SCC.Level.Two, ignore.case=TRUE)
vSCC <- SCC[v,]$SCC
vNEI <- NEI[NEI$SCC %in% vSCC,]
data <- filter(vNEI, vNEI$fips=="24510")
png("plot5.png", w... |
7963efa405f6168f029bd29157d0f2db4b106237 | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/7896_0/rinput.R | 37bb1153bd7289eddc308772f2c26435eaaa0ffd | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 135 | r | rinput.R | library(ape)
testtree <- read.tree("7896_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="7896_0_unrooted.txt") |
0b277d8b7671e0d05988444ae511cc6af4e9e3eb | 231e93f8115c71e8a4ef643ca51524f041f3f57d | /man/bold_stats.Rd | 24c896be1678e57f1317cbe01be011ec3594f3f8 | [
"MIT"
] | permissive | GhostsOfHiroshima/bold | 88c7d6551f3ef978e89986f945438e0fc1c9dcfb | 82de5cfc8971e0b59eb661eab76f7f96f9858f78 | refs/heads/master | 2020-06-11T14:00:14.434375 | 2019-06-26T02:56:00 | 2019-06-26T02:56:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,663 | rd | bold_stats.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bold_stats.R
\name{bold_stats}
\alias{bold_stats}
\title{Get BOLD stats}
\usage{
bold_stats(taxon = NULL, ids = NULL, bin = NULL, container = NULL,
institutions = NULL, researchers = NULL, geo = NULL,
dataType = "drill_down", response = F... |
ea43da1a58f6e7a167c472346f0601653e222693 | e2baae0f2cfe109900b67fbdfc0080eb2aa854b5 | /man/khclust_euc.Rd | 3d63bb058a3b4e04df4f7d909384dafa1435cca9 | [] | no_license | HBPMedical/CCC | 5eb3411c420e13a14529858b1b848208ca4416d7 | 32a4b64e07e60c229c86f33286aa43742ba6f23c | refs/heads/master | 2021-09-07T19:07:00.290850 | 2018-02-27T14:35:46 | 2018-02-27T14:35:46 | 69,594,420 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 883 | rd | khclust_euc.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functions.R
\name{khclust_euc}
\alias{khclust_euc}
\title{Title Gap statistics for hclust Euclidean}
\usage{
khclust_euc(x, K.max, B, verbose, plot.num.clus)
}
\arguments{
\item{x}{data matrix}
\item{K.max}{positive integer speci... |
865887324e48c77dd0f66476cea169a51e71366d | 34dd9a3a2313d0b4484ea3bf32e65175cdd718eb | /man/GSA.read.gmt.Rd | c52078044c44448138b1a24ea5868e3405e7d927 | [] | no_license | cran/GSA | 6afc50cd79249bb0051596d7ef82a2e7513b97af | 43634ce04a75fcc52023419eb6225524bacf71e0 | refs/heads/master | 2022-05-13T06:39:05.299449 | 2022-03-19T13:42:01 | 2022-03-19T13:42:01 | 17,679,483 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,656 | rd | GSA.read.gmt.Rd | \name{GSA.read.gmt}
\alias{GSA.read.gmt}
\title{Read in a gene set collection from a .gmt file}
\description{
Read in a gene set collection from a .gmt file
}
\usage{
GSA.read.gmt(filename)
}
\arguments{
\item{filename}{The name of a file to read data values from. Should be
a tab-separated text file, with one row p... |
c7d394c6398719f97b6e6feb8fcbdef2209ab4fc | 3124eae2c2cc624306b83f945f0f0730841798ce | /man/itakura.dist.Rd | 57caebc88cd639902ea6d8dae5c69d8713fe69b4 | [] | no_license | cran/seewave | 7841703a7f1cf237ce67e84f0f5b1dba877b1dff | 1f0b3d5688151141129368c17f826ccedcb4ad96 | refs/heads/master | 2023-08-08T11:45:55.093227 | 2023-07-16T05:50:02 | 2023-07-16T06:34:59 | 17,699,546 | 18 | 12 | null | 2023-01-27T12:57:14 | 2014-03-13T06:15:47 | R | UTF-8 | R | false | false | 2,399 | rd | itakura.dist.Rd | \name{itakura.dist}
\alias{itakura.dist}
\title{Itakuro-Saito distance}
\description{Compare two distributions (e.g. two frequency spectra) by
computing the Itakuro-Saito distance}
\usage{itakura.dist(spec1, spec2, scale=FALSE)}
\arguments{
\item{spec1}{any distribution, especially a spectrum obtained with \... |
1024d7186c5da32c9916729369d608d93ebbb518 | f884d7bcd31c81582960a7d8f820e3d3f4cf6953 | /man/formatSettings.Rd | aa055446faf970e99612b2044615e309f75709e0 | [
"MIT"
] | permissive | rtmtemp/kwb.monitoring | bc716dfb18f489dc4d1040f39359fc781d362ff2 | 6aefc1d076c4cfa8ffbdd1abf8f143f7deb8b9e5 | refs/heads/master | 2022-11-06T23:51:03.604207 | 2019-02-14T01:04:28 | 2019-02-14T01:04:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 552 | rd | formatSettings.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/info.R
\name{formatSettings}
\alias{formatSettings}
\title{Format Settings}
\usage{
formatSettings(settings, settingNames = names(settings),
do.stop = FALSE)
}
\arguments{
\item{settings}{list of settings}
\item{settingNames}{names of the ... |
e7ad0d3d60a6a427f7e38abe2b57bf2d05bae028 | 2407dd0c4ecba24555a73ca84640330fa3e754e4 | /R/gonogo.R | 0cfec4e3b84f89acce74285634d08c4d53919c3b | [] | no_license | brian-lau/Rexpneuro | c6f7cf7cc8ede7f41145b4d9b389620aa8e30103 | 0caf9b72c2108ce50f7b7e1ebd8d43aea3a6f949 | refs/heads/master | 2023-07-03T00:44:58.757667 | 2020-11-23T17:06:31 | 2020-11-23T17:06:31 | 308,354,517 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,956 | r | gonogo.R | #' @export
read_eventide <- function(fname = NULL,
name = NULL,
basedir = getwd(),
start_date = "30012017", # daymonthyear
end_date = "30012021", # daymonthyear
min_trials = 1,
... |
2327910847afe871f74f18386c2304382a3b4cbb | 487a34c5ace2b1a60229c5403335de734616561e | /1d2-basicstats.R | 9eb57d44dfb473d21d7e2d1708b617ea0d7f9575 | [] | no_license | hhenoida/dataanalytics | 39d261a288f90c97effc0358d49fd2ffb8566578 | c563272f7890a0731fbb9e24e5ff6309ea8586ee | refs/heads/master | 2020-03-31T19:50:12.135620 | 2018-10-29T12:40:39 | 2018-10-29T12:40:39 | 152,513,808 | 259 | 15 | null | null | null | null | UTF-8 | R | false | false | 620 | r | 1d2-basicstats.R | # Basic Stats
x = ceiling(rnorm(10000, mean=60, sd=20))
mean(x)
median(x)
#there is no mode function for mode stats
table(x)
sort(table(x), decreasing=T)
#mode
library(modeest)
mlv(x,method='shorth')
#quantile
quantile(x)
quantile(x,seq(.1,1,by=.1)) #decile
quantile(x,seq(.01,1,by=.01)) #percentile
library(e1071) ... |
e0ee1e3f4b7b94112dbde3108138803580450774 | 7f02263a680124a9b6fed6e709013034be0dc2e8 | /SciDataEpi2020/functions/plot_plus.r | 996d8e1545e100e85fbcc929c1474de820715f1f | [] | no_license | Hindrance/EpiSciData2020 | a8fa07e67a240a81d76391b614175593369e2810 | b271bb99bd793992fea7f41fe46ef1a981e33e61 | refs/heads/master | 2022-11-10T14:53:32.093301 | 2020-06-23T11:21:55 | 2020-06-23T11:21:55 | 266,233,813 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,609 | r | plot_plus.r | #############################################################
# PLOTS: POINTS PLUS DENSITIES - by Juliette (and Vince :D)
#############################################################
# __
# _.-~ )
# _..--~~~~,' ,-/ _
# .... |
bf4204c4eeaceb96894c8cd6933899f93d83f7d6 | c981caf103a3540f7964e6c41a56ca34d67732c4 | /R/mice.impute.eap.R | 162326db22d762e9d3ab3ff88d0136c95af7e900 | [] | no_license | alexanderrobitzsch/miceadds | 8285b8c98c2563c2c04209d74af6432ce94340ee | faab4efffa36230335bfb1603078da2253d29566 | refs/heads/master | 2023-03-07T02:53:26.480028 | 2023-03-01T16:26:31 | 2023-03-01T16:26:31 | 95,305,394 | 17 | 2 | null | 2018-05-31T11:41:51 | 2017-06-24T15:16:57 | R | UTF-8 | R | false | false | 415 | r | mice.impute.eap.R | ## File Name: mice.impute.eap.R
## File Version: 2.07
mice.impute.eap <- function (y, ry, x, eap, ...)
{
pos <- parent.frame(n=1)
res <- mice_imputation_get_states(pos=pos)
vname <- res$vname
newstate <- res$newstate
M.scale <- eap[[ vname ]][[ "M" ]]
SE.scale <- eap[[ vname ]][[ "SE" ]]
N ... |
c7878052270c641731c821593ef8bdced6675f93 | 0a906cf8b1b7da2aea87de958e3662870df49727 | /grattan/inst/testfiles/anyOutside/libFuzzer_anyOutside/anyOutside_valgrind_files/1610386845-test.R | 72fd159b80b0d971b048ad0e03215b200edd1218 | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 597 | r | 1610386845-test.R | testlist <- list(a = -774646785L, b = 385877056L, x = c(-623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623191334L, -623247288L, ... |
2d06eb0cf5a285db35ba08d3c9f9ba8ec5dd3719 | 8c4c1dd8db80357e6d0fed7adafe9a4a371c2801 | /plot3.R | 5c242439e09ef484895cce3691ac147703735b73 | [] | no_license | tj---/ExData_Plotting1 | 694b50bf42721088a904e673b9296eab70695ba0 | c402485d9a8c4b8343560d965af2b0b090c0b4da | refs/heads/master | 2021-01-22T18:42:55.226052 | 2015-06-04T20:24:24 | 2015-06-04T20:24:24 | 36,820,921 | 0 | 0 | null | 2015-06-03T18:00:33 | 2015-06-03T18:00:31 | null | UTF-8 | R | false | false | 480 | r | plot3.R | source("util.R")
data <- load_data() # Load the relevant data from the file
png(file = "plot3.png", width = 480, height = 480)
plot(data$DateTime, data$Sub_metering_1, type = "l", xlab = "", ylab = "Energy sub metering")
lines(data$DateTime, data$Sub_metering_2, col = "red")
lines(data$DateTime, data$Sub_metering_3, ... |
7a5b8863f39d34673b1ca152d584e82619025bc7 | 80fbecdc50f0f580336f32ee3160a8ebf16b83ff | /run_analysis.R | d5fded9ec1ec5172fb677cd7eecc5d6c8b79a491 | [] | no_license | hpirespt/Getting-and-Cleaning-Data | b22a6089fe58e30d03ac47e3308ff3ec436cd1b1 | b257e40f26369916beb6bfcf27b50e38d2a3be8c | refs/heads/master | 2022-04-15T00:44:33.517701 | 2020-04-11T14:21:09 | 2020-04-11T14:21:09 | 254,882,900 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,279 | r | run_analysis.R | run_analysis<-function(){
library(dplyr)
#path definitions
pathfeatures<-".\\data\\UCI HAR Dataset\\features.txt"
pathactivities<-".\\data\\UCI HAR Dataset\\activity_labels.txt"
pathtestX<-".\\data\\UCI HAR Dataset\\test\\X_test.txt"
pathtesty<-".\\data\\UCI HAR Dataset\\test\\... |
ff27a56b2bb45cdc2ed34799304a405dbe00d7a2 | 439a1a4a95ea2a915c20b12aa49d083d28be5e72 | /visualization/phylogenetic_analysis.R | 9d70dae12ca54855371dfc4dce0307b39e61d837 | [] | no_license | davidgllund/ARG_analysis_scripts | 611b4b1efa8976a4b5ef37b7b8d67a1c4dbacb63 | 7f1388ab8fa48951534813c850ae62222deebd5a | refs/heads/master | 2023-05-26T23:36:37.365107 | 2021-06-01T12:48:36 | 2021-06-01T12:48:36 | 337,497,994 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,939 | r | phylogenetic_analysis.R | # Import packages
library("ggplot2")
library("ggtree")
# Import files with the ids of the tips in the tree, the phylum corresponding to each tip, and the names of the tips
ids<-read.table('headers.txt')
phylum<-read.table("phylum.txt")
names<-read.table("names.txt")
# Combine input files to a data.frame cont... |
f40af827692e9ca06ffa8200f43d5a7e565f6f4b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/homals/examples/roskam.Rd.R | 02f0d5e43c2a8cd5890554f51451c51eada5d962 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 151 | r | roskam.Rd.R | library(homals)
### Name: roskam
### Title: Roskam dataset
### Aliases: roskam
### Keywords: datasets
### ** Examples
data(roskam)
roskam
|
ffc4c5049094d1363bd378f35a3c953a306cddfa | 06efc31a33d3ea3cd3b645c582bacda5373badd4 | /CeTrAn/other_codes/F1000master.R | 40f389f19284d25c8ec8552dfab6e5b9352baaf6 | [
"MIT"
] | permissive | Marlouck/CeTrAn | 081b4d1d42417f67fab1c085a1d0c78068ffc95d | e4883361de3fa21ba9f6f2a33f38850953f4fe57 | refs/heads/master | 2020-04-17T18:09:14.563943 | 2019-01-10T09:23:57 | 2019-01-10T09:23:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,426 | r | F1000master.R | ### code for figure 4 in the F1000 paper
REBOOT =F#if true: all data are recalculated
only_one_additional_group=F #if true, groups selected such that only the group given will be analysed with the 5 initial groups.
#Additional_group = "data entered by the user!"
#indicate analysis variables:
g_duration_slider = 10 ... |
e8db6fb2cfe01d30355d0fcd8127286b61deb8d1 | 4faa70a753a7192dd0eaff0191e2c21bb2997185 | /wgmeans.R | 43cc11c5e241f529fbaf0cbd982d3a81647c1c97 | [] | no_license | SaptarshiC98/-WG--means | 7b85909c69754ef889692cb761e57086af3f3de9 | 0c9910947f4aa4e0804a2c65e100f60e7c8abac8 | refs/heads/master | 2021-09-23T23:30:50.254010 | 2018-09-29T04:25:28 | 2018-09-29T04:25:28 | 108,992,157 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,456 | r | wgmeans.R | sq.euc.dist= function(x1, x2) sum((x1 - x2) ^ 2)
wt.euc.dist.sq=function(x1,x2,w){
p=(x1-x2)^2
p=w*p
return(sum(p))
}
vec.wt.euc.dist.sq=function(x1,x2,w){
p=(x1-x2)^2
p=w*p
return(p)
}
library(nortest)
sq.euc.dist= function(x1, x2) sum((x1 - x2) ^ 2)
k.means= function(X,M,w,tmax){
X... |
f2b28d086d320fe8832badefc0f85b398ceaf2ee | 43e6a86a838a2f4bb21059190640551dd1fc4dc9 | /R/plotMethy.R | ebd74b1319290932867a1f276eb510047e2aab97 | [] | no_license | jianhong/RRBSeq | 3dc6e2e0bacc0d8bbda49d4a6ea907d21b522f63 | e7f648897a588b705aaef559402e0eb2e2f28a63 | refs/heads/master | 2021-01-25T08:42:39.259480 | 2017-03-30T14:16:32 | 2017-03-30T14:16:32 | 35,629,158 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,386 | r | plotMethy.R | plotMethy <- function(data, txdb, orgdb, range, gene,
meanCutoff=c(totalC=0, methyC=0, ratio=0.05),
...){
if(missing(txdb) || missing(orgdb) || missing(data)){
stop("data, txdb and orgdb are required")
}
if(missing(range) && missing(gene)){
stop("... |
ecce800734040ab9f42831e9f9e55ea96dde411a | 5b430612262f3de20da50ee86c29dc71d4477edb | /IDU_Incidence_Maps/app.R | 8e6c329c84e8f15a5fb5c3e89bfc45a708b98398 | [] | no_license | ErichDenk/Team_AMIE_670_Project | 9d9ea7c788d5467a04992e65685c4a722287a636 | 1b7a8a79b8c803e2ffb1c95bb688aec01d3a5938 | refs/heads/master | 2020-04-20T17:12:48.138619 | 2019-04-30T00:56:42 | 2019-04-30T00:56:42 | 168,982,286 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 4,045 | r | app.R | #
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
require(urbnmapr)
require(tidyverse)
require(shiny)
require(viridis)
require(here)
incidenceData <- read.csv(here... |
297db95253655b0ae42e632bd42b54717c3e7a26 | 573edac85effdda60291c96f568af4bcf36833a5 | /man/compute_fixed_coef.Rd | 8daacbf88cae558df14169b49067fd1c29ba83f5 | [] | no_license | L-Ippel/SEMA | d71835566c17df707896bcd9ef32960c71b2c43a | 1d0e3a48c855df704cad18c7ab6bb73d08bd4efa | refs/heads/master | 2021-06-03T18:26:07.776301 | 2018-08-06T11:32:00 | 2018-08-06T11:32:00 | 38,366,963 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 824 | rd | compute_fixed_coef.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Mstep.R
\name{compute_fixed_coef}
\alias{compute_fixed_coef}
\title{compute_fixed_coef computes the coefficients of the fixed effects,
see from raudenbush and bryk (2002) Hierarchial linear models, 2nd edition,
EQUATION 14.10.}
\usage{
co... |
c36e1d4889fdda0db66fcfbbe2a0b4d6425cba71 | 7bb21189354bf72b2e8aeeb9f0e4340e69ed2913 | /man/plot.Planes.Rd | b57a6bc745e1a17eb4f26aa02f567fde81b733fc | [] | no_license | elvanceyhan/pcds | 16371849188f98138933afd2e68a46167f674923 | 00331843a0670e7cd9a62b7bca70df06d4629212 | refs/heads/master | 2023-07-02T10:03:48.702073 | 2023-06-16T15:50:46 | 2023-06-16T15:50:46 | 218,353,699 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,621 | rd | plot.Planes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ClassFunctions.R
\name{plot.Planes}
\alias{plot.Planes}
\title{Plot a \code{Planes} \code{object}}
\usage{
\method{plot}{Planes}(
x,
x.grid.size = 10,
y.grid.size = 10,
xlab = "x",
ylab = "y",
zlab = "z",
phi = 40,
theta = 40,... |
aa88882fa026ac3669c58c0342b8e2c12e9a75d2 | fd84077ffbb3f1662c0d4d1c06116a55893b6201 | /tests/testthat/test-main.R | 8848e2182de860ecf4a2137092be322f7d496ffb | [] | no_license | hamedbh/popstats | c9e46849e83934129b0d6b98bbb4ebb8a8da05d0 | 18da0cd52e7dfe710f245cca4545d430c837f3da | refs/heads/master | 2020-08-12T00:47:12.069260 | 2019-10-19T07:34:11 | 2019-10-19T07:34:11 | 214,659,002 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,447 | r | test-main.R | context("Results with non-numeric values")
test_that("Character type throw errors", {
expect_error(pop_var(letters))
expect_error(pop_sd(letters))
})
test_that("Factor type gives warning", {
expect_warning(pop_var(factor(1:5)))
expect_warning(pop_sd(factor(1:5)))
})
obj_pop_var_factor <- suppressWarn... |
3fbfa16c51aa88b7ab28d930049c3f79c958c666 | f30cc1c33978ca5a708a7e0a493403ea88550160 | /R/hxsurf.R | a7f9cc045fb90c27b31bf819036434de4abf7d17 | [] | no_license | natverse/nat | 044384a04a17fd0c9d895e14979ce43e43a283ba | 1d161fa463086a2d03e7db3d2a55cf4d653dcc1b | refs/heads/master | 2023-08-30T21:34:36.623787 | 2023-08-25T07:23:44 | 2023-08-26T19:02:50 | 15,578,625 | 35 | 10 | null | 2023-01-28T19:03:03 | 2014-01-02T07:54:01 | R | UTF-8 | R | false | false | 30,998 | r | hxsurf.R | #' Read Amira surface (aka HxSurface or HyperSurface) files into hxsurf object
#'
#' @details Note that when \code{RegionChoice="both"} or
#' \code{RegionChoice=c("Inner", "Outer")} both polygons in inner and outer
#' regions will be added to named regions. To understand the significance of
#' this, consider two ... |
3fb514477eb39cc62fe0edacda199229c82097fd | efeba9f5aff2e7afbf96a57e0baf62a8fb1a3b94 | /Part2/Stage3-Structured data/ex.3-4/practice1.R | 6462171e7e8b8e1ec01b715f0310cf45c653e86f | [] | no_license | psm9619/R_Data_Analysis | b1db04295607b5b0811eb2151ce5378a812b2aa3 | b6b8186a582174533ab41a68aeab77bdcf0ea854 | refs/heads/master | 2020-05-29T13:27:26.350660 | 2019-10-10T01:07:53 | 2019-10-10T01:07:53 | 189,161,472 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 966 | r | practice1.R | library(reshape)
library(ggplot2)
library(dplyr)
data1 <- read.csv("연도별요양기관별보험청구건수_2001_2013.csv")
data2 <- read.csv("연도별요양기관별보험청구건수_2001_2013_세로.csv")
data2
row.names(data2) <- data2[,1]
data2
data2[1] <- NULL
func <- function(x) {
x/100000
}
data3 <- apply(data2, 2, func)
plot (data3[,1], xlab="", ylab="",
... |
eeb17a82aa5df82b78992448ae73539c028732b9 | 1a6f7dc7e39fd02f390ad6f058874367a1119742 | /R/occurrencesLessThan.R | b42a91d3a053a45b53954de76e827d3f496af64a | [] | no_license | cran/inverseRegex | f881d95cec7bdcd073a3779f876dec0dc78657d6 | 69f0cd3ec678a6babd4878ffc3e73ceb0f2e37b6 | refs/heads/master | 2022-11-09T02:02:21.757705 | 2022-10-23T15:25:07 | 2022-10-23T15:25:07 | 210,402,220 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,210 | r | occurrencesLessThan.R | ##' Identifies Infrequent inverseRegex Patterns in an R Object.
##'
##' Calls \code{inverseRegex} on the input object and identifies values that
##' occur infrequently.
##'
##' @param x Object to analyse for infrequent regex patterns.
##' @param fraction Fraction of the R object size; regex patterns that occur less
#... |
1e8b1d2d9d3a37fdeb392411f0762d0adf265806 | 908b54c4546885f4a165606117b3f1e06d2bc1dc | /tests/testthat/test_z_transform.R | c3daa8c238d7c77dc0b3f2b0cc8006f0a822bd02 | [
"MIT"
] | permissive | jeremymcrae/cifer | 406de54640189e4d0135bceecedf06ed7a59915e | 328114b73c76183d98e7a222bbcb281b66ad9de3 | refs/heads/master | 2021-01-17T07:18:46.317264 | 2016-07-08T12:52:45 | 2016-07-08T12:52:45 | 23,546,083 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,516 | r | test_z_transform.R |
library(cifer)
library(testthat)
context("Z-transform data")
test_that("get_l2r_z_scores output is correct", {
# construct a dataframe for two samples, one of which will have a Z
# transformed mean close to -0.707106, the other close to 0.707106, and
# trio members with Z transformed values close to 2.12... |
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