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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
94bfd1349c31fdf3787e2285c11035d819245c43 | 49b3470e94000265077bb930eb3374fbe0d34d3a | /vignettes/rolling_statistics.R | 935ec4d255cdadb8855e72a4fe8ed1e837502644 | [] | no_license | lozanof/HighFreq | 9cddc77c03456de26687b0ee3aaca56ec2b76c3e | da68b7b886217b70913dd4a31382ad85d77af6f5 | refs/heads/master | 2022-05-29T05:48:50.004919 | 2020-05-03T20:30:09 | 2020-05-03T20:30:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,759 | r | rolling_statistics.R | ## ----eval=FALSE----------------------------------------------------------
# # load HighFreq to load SPY data
# library(HighFreq)
# # rolling average prices
# look_back <- 10
# prices_rolling <- rutils::roll_sum(Cl(HighFreq::SPY), look_back=look_back)/look_back
# colnames(prices_rolling) <- "SPY.Prices"
# chart... |
7a8f951cd5739273280fdc974f007ee78cfce19b | 6c12225069086e6c544199652ef147c7d7c2e5ba | /refresh_Update_DB2.R | 41199e7209e4d384f1d18a1ce18cda6fcd6fe95c | [] | no_license | maquins/ewars_dashboard | 237eed007b758b588c7f51a7d11acf006c359461 | edb8c94b7d2d5162d445c0e32e564cac74ccbc9a | refs/heads/master | 2023-06-08T04:26:12.012375 | 2021-06-24T14:38:43 | 2021-06-24T14:38:43 | 287,126,031 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,925 | r | refresh_Update_DB2.R | # contact: sewemaquins@gmail.com
#options(shiny.reactlog=FALSE)
#list11<-list(x=2,y=3)
#list11[[c(1:2)]][1]
print("Running refresh DB script........")
print(paste("district is: ",input$district_dB2))
#print(names(dat_ou$df))
p<-as.numeric(input$district_dB2)
covariates_d<-c("Cases","Population",as.character(str_split... |
fb8eaff998ef4b82caad23567608aa20040aa635 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/CoopGame/examples/shapleyShubikIndex.Rd.R | 5a78993d50c1bbf1bf885abf4ea6c8630e6290bf | [] | 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 | 401 | r | shapleyShubikIndex.Rd.R | library(CoopGame)
### Name: shapleyShubikIndex
### Title: Compute Shapley-Shubik index
### Aliases: shapleyShubikIndex
### ** Examples
library(CoopGame)
shapleyShubikIndex(v=c(0,0,0,0,1,0,1))
## No test:
#Example from Stach (2011):
library(CoopGame)
v=weightedVotingGameVector(n=4,q=50,w=c(10,10,20,30))
shapleyShu... |
eb2602e63fb972b25e7b7e0a82c74f32c3c2c556 | 8f4db2544a30e207d2d7a8dcde9b232cba571b1d | /server.R | 53c33b3297740e4ed41927f99f08e406f19093f3 | [] | no_license | ktdrv/NameExplorer | 1911e9c88b0a440ff0c24894a049ff56fc69c45a | a6d36282f63a56279e378ea7e3e979bc6caabd1d | refs/heads/master | 2020-05-20T00:10:12.452567 | 2014-06-23T22:39:21 | 2014-06-23T22:39:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,469 | r | server.R | library(plyr)
library(ggplot2)
library(shiny)
filenames <- list.files(path="names", pattern="yob[[:digit:]]+.txt", full.names=T)
data <- ldply(.data=filenames,
.fun=function (fname) {
t <- read.csv(file=fname, header=F, sep=",", col.names=c('Name', 'Sex', 'Count'), colClasses=c("characte... |
01e48ad8e3b7af30e03541a366dcd983efa95696 | 3f6dd3134f16de2f08aa6ec52e772d7e5c5422c0 | /man/n_dist.ChiSquare.Rd | 2cbd9e62bb80d2952fa457735d8f12d4b9bef37f | [
"MIT"
] | permissive | imbi-heidelberg/blindrecalc | 1ee7045d042f20b2d3392753838fb41a41a2017f | b6df80d1ff7b9605fbb6ee1adc38a7aac32f08e5 | refs/heads/master | 2022-11-24T15:18:40.995647 | 2022-11-22T14:30:31 | 2022-11-22T14:30:31 | 228,565,854 | 8 | 2 | NOASSERTION | 2022-11-22T14:30:33 | 2019-12-17T08:10:34 | R | UTF-8 | R | false | true | 1,859 | rd | n_dist.ChiSquare.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ChiSquare.R
\name{n_dist,ChiSquare-method}
\alias{n_dist,ChiSquare-method}
\title{Distribution of the Sample Size}
\usage{
\S4method{n_dist}{ChiSquare}(
design,
n1,
nuisance,
summary = TRUE,
plot = FALSE,
allocation = c("exact", "... |
f64c6c154ec9f4d3a5c9b34d81e4dc737eac3bc5 | 48f35d86ca1ab91a8b6cb99d42ff099e64cb7ef9 | /practice.R | b87296b1c3917b25ae139c9c2334e036e36f76c9 | [] | no_license | anastasia-lubinets/practice | 1882ee622039c824a4aec4efd34650e7c532969e | 7597c070302a8a9f1d869f75f4254d7650ccf17e | refs/heads/master | 2020-12-28T14:51:05.334093 | 2020-02-12T05:58:11 | 2020-02-12T05:58:11 | 238,377,266 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,656 | r | practice.R | ## Practice the following problem
# What is the sum of the first 1000 positive integers?
sum(1:1000)
#1. Use the function c to create a vector with the average high temperatures in January for
#Beijing, Lagos, Paris, Rio de Janeiro, San Juan, and Toronto, which are 35, 88, 42, 84, 81,
#and 30 degrees Fahrenheit. C... |
2497d2d650482d5903caaa61db3c30459c63e19a | 0a906cf8b1b7da2aea87de958e3662870df49727 | /diffrprojects/inst/testfiles/dist_mat_absolute/libFuzzer_dist_mat_absolute/dist_mat_absolute_valgrind_files/1609963183-test.R | 32f4e12193b25d84edae1448d3f4d4b633649ffa | [] | 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 | 711 | r | 1609963183-test.R | testlist <- list(x = 704641023L, y = c(-11776948L, -702873809L, -445680667L, 1919251292L, 1651471657L, 677605230L, -2745809L, -1L, -256L, 115L, 0L, -1L, 0L, 1929379840L, 16776986L, 805250559L, -451142628L, 471604252L, 471604252L, 471604252L, 471604252L, 471604252L, 471652095L, NA, -10497L, 692857302L, 31L, NA, -167... |
884481d99523242cbb9f91a8e63f69e0105b8e8e | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/miceadds/examples/Reval.Rd.R | 9fc9a4a3609aff4d8a99a4ca70547be9e30770a2 | [] | 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 | 524 | r | Reval.Rd.R | library(miceadds)
### Name: Reval
### Title: R Utilities: Evaluates a String as an Expression in R
### Aliases: Reval Revalpr Revalprstr Revalpr_round Revalpr_maxabs
### Keywords: R utilities
### ** Examples
# This function is simply a shortage function
# See the definition of this function:
Reval <- function( Rstr... |
6325f4de2e2a86c243bb1cd2c51c36f2df7c7c91 | 570b70266d3a2a857f9476dd112487bf32136c70 | /ui.R | a49a1f0f6130a1819028af5ddfc483d73e2d3d69 | [] | no_license | gregtozzi/IrisClassifier | 03b9d33d91fdd95a8ae4fbfeaa5a6fe9a45185e2 | 89a04b1b5552db8cf4ddb14da9e78de3c3615c33 | refs/heads/master | 2021-12-10T20:44:54.228442 | 2021-11-24T19:41:09 | 2021-11-24T19:41:09 | 29,459,137 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,329 | r | ui.R | library(shiny)
library(rCharts)
#shinyUI(fluidPage(theme = "css/cosmo.css",
shinyUI(fluidPage(
# Application title
titlePanel("Iris Classifier"),
fluidRow(
column(12,
h4("Overview"))),
fluidRow(
column(6... |
560da25887266f937cff8822f9f8115c19259880 | 267aa85b975d3348c5557505298364d6f1b5b7f4 | /inst/shiny/shiny_ui/ui_main_constraints.R | bfe180b4089f4b8267626365ae55790c3b965b9a | [] | no_license | matdoering/openPrimeRui | d2a6dad1038ddbe8e3541ccba6a2172271ccaf09 | 9b3f330bffff554986733cc85d2d40f7f8e2953f | refs/heads/master | 2021-01-19T19:40:13.861823 | 2020-08-14T07:52:24 | 2020-08-14T07:52:24 | 101,199,926 | 3 | 3 | null | null | null | null | UTF-8 | R | false | false | 7,538 | r | ui_main_constraints.R | ########
# Constraints main panel in UI
#######
tabPanel("Constraints",
value = "constraints_view",
icon = icon("resize-small", lib = "glyphicon"),
br(),
selectizeInput("selected_constraint_result", # select a constraint result to display
"Selected result",
choices = list(
... |
59881f6df29bf5edf023882ba37228de304bf5d5 | 128025338a34d4751aa16adc2109c57d97b0da3f | /R/JS.counts.R | 466f6238a0cc4eeb28eec7a7530704c113e37a02 | [] | no_license | MurrayEfford/openCR | e2de3810d9582406d598d0637fa4dd864468b961 | 949c39e3c65be41be859499fb7d3c9645e951629 | refs/heads/master | 2023-07-21T22:28:22.287511 | 2023-07-11T08:52:45 | 2023-07-11T08:52:45 | 116,920,401 | 2 | 0 | null | 2021-12-02T20:16:51 | 2018-01-10T06:57:54 | R | UTF-8 | R | false | false | 1,645 | r | JS.counts.R | ###############################################################################
# JS.counts.R
## 2018-04-20 openCR 1.2.0
## 2021-04-18 stratified
###############################################################################
JS.counts <- function(object, primary.only = TRUE, stratified = FALSE) {
if (stratified) ... |
af728e860c253c5371e5f4135044b3cd3e29367d | 7cceb23f6b1518bc032ac983ca3eeaa0726fd2da | /man/DuffyNGS_Annotation.Rd | e48b35717ee6058ae05b90cdec632a90d6e16d78 | [] | no_license | sturkarslan/DuffyNGS | 309b07351bc08d6cb0bd6defe59a8c0c00be22df | 661bde0bda2cec95203b241a6f8560fe14f0c607 | refs/heads/master | 2020-05-26T19:21:58.422462 | 2014-11-26T18:08:27 | 2014-11-26T18:08:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,523 | rd | DuffyNGS_Annotation.Rd | \name{DuffyNGS_Annotation}
\title{
Annotation File of Sample-Specific Settings
}
\description{
The Annotation file defines the SampleID and several sample-specific settings for
each dataset to be processed. See \code{\link{DuffyNGS_Options}} for processing
settings that are not specific to each sample. Each ... |
c6d3546e13ed6cd6b57279e5560fa1fd0e28f16c | 3b0cfdef3d8eac5720642bf78e2f5c031bdb188c | /howtomakeurllist.R | 5661f4520852a5a1540adf3d55737950dc2701b9 | [] | no_license | williamhickman/test | ccc3df0d0fb36eeaada71d12406ca53f4a15d47c | dbd64c93d4cc80c6e1f80fb237318d8ec0e2127d | refs/heads/main | 2023-08-04T05:03:04.169995 | 2021-09-16T17:17:49 | 2021-09-16T17:17:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 890 | r | howtomakeurllist.R | #making a list of URL's (article titles are from Excel)
library(readxl)
# load data
testurls <- read_xl("~/Desktop/Fall 2021/testurls.xlsx")
library(tidyverse)
# get rid of spaces and replace with +
testurls<-
testurls%>%
mutate(url2=str_replace_all(url2," ","+"))
# put everything into one unified url
testurl... |
638cc65c16784b4de5fd61a751559dd91020a4b9 | 55ec4a0425e1219cf7cd1504b1efd78d60f2a32d | /EDA.R | 05e958d22587dd7043bda3330c75ee2d3c7a935c | [
"MIT"
] | permissive | nihalij2/CU_churn | 13763c2c140dcbed80981cf3869b08f74acf5b3e | c212c56963d38262601d5fde3f5773651692337a | refs/heads/master | 2020-04-03T13:07:21.067552 | 2018-10-29T00:29:19 | 2018-10-29T00:29:19 | 155,274,423 | 0 | 0 | null | 2018-10-29T20:10:13 | 2018-10-29T20:10:12 | null | UTF-8 | R | false | false | 2,490 | r | EDA.R |
library("lubridate")
library("zipcode")
member = read.csv("Member Dataset.csv")
#creating age groups
member$Age_bin = ifelse(member$Age < 14, "Kids", ifelse(member$Age < 22, "Teens",
ifelse(member$Age<35, "Millenials",
... |
2c2aefac152ad27c5094f7681a84fce877b03e12 | a837471a5b02854fa291f72338ea709a81b9f44e | /man/plotTime.Rd | 1036f299b601eecbd608264b8a1c09bcdcbe4ad5 | [
"Artistic-2.0"
] | permissive | bvieth/powsimR | 4dab889467c698d65b66a65608183f6e49b2203d | d9e49ace330214513761e4be37396e4afed96e86 | refs/heads/master | 2023-08-14T14:52:40.322693 | 2023-07-31T12:14:42 | 2023-07-31T12:14:42 | 88,427,309 | 102 | 27 | Artistic-2.0 | 2021-06-28T07:22:53 | 2017-04-16T16:22:12 | R | UTF-8 | R | false | true | 2,588 | rd | plotTime.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Plot.R
\name{plotTime}
\alias{plotTime}
\title{Visualize computational time}
\usage{
plotTime(evalRes, Table=TRUE, Annot=TRUE)
}
\arguments{
\item{evalRes}{The output of \code{\link{evaluateSim}}.}
\item{Table}{A logical vector. If \code{TRU... |
598bb24e2c07c632f950b1a698a47acd75c7a22c | 487be43c960999e4b0eb56eb8c574922c5eb350e | /test.R | c7f9e3f8157f26478e2fd73588f4fda041b5bcbd | [] | no_license | river-fish/tandemQueue | 5076d8262ebdc7648d3ce10b39f54255b45ee2d6 | a4011c81a93d4bf9de7dbbdb2f3770884d89012a | refs/heads/master | 2021-06-05T17:17:35.590327 | 2016-11-02T18:38:58 | 2016-11-02T18:38:58 | 72,096,814 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 80 | r | test.R | x = rep(1,10)
y = 1:10
plot(x, y, xlim=c(-10, 10))
z = x + y
plot(z)
plot(x, z) |
63a388e432b7c72ed50e40f9f2a04db811f54abd | 02f053ce70b065724d4a02619fb402adcc0ec997 | /analysis/boot/boot890.R | 34ba2aa891fe65a0d9c761ae575d13afc2c3c674 | [] | no_license | patperry/interaction-proc | 27950482929240bba55c7d0f2f8c5235d770feea | cf8dfd6b5e1d0684bc1e67e012bf8b8a3e2225a4 | refs/heads/master | 2021-01-01T06:11:47.125853 | 2012-12-04T20:01:42 | 2012-12-04T20:01:42 | 673,564 | 1 | 3 | null | null | null | null | UTF-8 | R | false | false | 3,766 | r | boot890.R | seed <- 890
log.wt <- -13.968555670627097
penalty <- 2.8115950178536287e-8
intervals.send <- c()
intervals.recv <- c(56, 112, 225, 450, 900, 1800, 3600, 7200, 14400, 28800, 57600, 115200, 230400, 460800, 921600, 1843200, 3686400, 7372800, 14745600, 29491200, 58982400)
dev.null <- 358759.0022669336
df.null <- 35567
dev.... |
cd7035f671d2460e1f54962be99ad072220ae999 | eebabe55f31dab6c9e1a435bde3be0468d7d73ce | /man/getBezierAdj4Arrw.Rd | debb3c45cfee4ecd695701de80b34db494357c4a | [] | no_license | gforge/Gmisc | 279763beb0bab02d75b6e44c6a7f8d5a79249a25 | 968d4edfd84453c33c483d5a1f1f0825fae98c8e | refs/heads/master | 2023-08-31T09:30:16.367537 | 2023-08-25T21:55:45 | 2023-08-25T21:55:45 | 10,400,448 | 43 | 16 | null | 2022-01-03T18:23:20 | 2013-05-31T08:01:23 | R | UTF-8 | R | false | true | 611 | rd | getBezierAdj4Arrw.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bezier_private_functions.R
\name{getBezierAdj4Arrw}
\alias{getBezierAdj4Arrw}
\title{Gets the bezier points adjusted for an arrow}
\usage{
getBezierAdj4Arrw(x, y, arrow_length, length_out = 100)
}
\arguments{
\item{x}{The x start and end poin... |
b7776273662c5a302fa0c6050c9e19cc26586d76 | 693d88d479f96e91be7607de520875861f3f6e4d | /man/propcattlecheck.Rd | 9a0a242c3df51cb4077be7f0f05970fb71141fb3 | [] | no_license | vijaydairyf/DMMongoDB | c4239c144f3357856177855e2fa82baf72bd34bf | 920bbbbaed086df6d271be6c47dd2f4bcbe4341a | refs/heads/master | 2020-12-05T19:20:18.297176 | 2019-12-18T21:44:41 | 2019-12-18T21:44:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,299 | rd | propcattlecheck.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/propcattlecheck.R
\name{propcattlecheck}
\alias{propcattlecheck}
\title{Check cattle access to ALMS accross whole property}
\usage{
propcattlecheck(property, days = NULL, username = NULL,
password = NULL)
}
\arguments{
\item{property}{this ... |
52e7f259d28c0be7acbfb922388e91de21d848cf | 74d3ccdbeeee691888e89073039b47a9b737d78f | /tests/OandaMachineLearningHiddenMarkovModel.R | 689ffdfc17ff724a4f7bc0e537a4a5986d5cd9b7 | [
"MIT"
] | permissive | elephann/RQuantTrader | e5f8813eb880ce05cf997f01b9732cfaa57b995f | 067c715c036a5d86596b8589d617ec795a8dc3c1 | refs/heads/master | 2022-03-21T17:35:51.078741 | 2019-09-07T13:18:40 | 2019-09-07T13:18:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,953 | r | OandaMachineLearningHiddenMarkovModel.R | library('depmixS4')
# library('quantmod')
set.seed(1)
loadQuantPackages()
Cur1 = 'AUD'
Cur2 = 'USD'
PRICE.OA = prepareForexOandaPrices(oanda.count = 500, Cur1 = Cur1, Cur2 = Cur2, oanda.granularity = 'M15')
price.oa = PRICE.OA$OA.MID
oa.ret = ROC(Cl(price.oa), n = 1, type = 'discrete')[-1]; names(oa.ret) = 'target'
... |
27344ecead9118e31b7387fde278834439a2332b | 3608fad236c6d1485761ee60456546c260b52eab | /doc/data/lch-eps/lch-pf/make-pf-plot-data.r | 428a7c9c7249395fdbaf6810f82db62ef28cfbc9 | [] | no_license | kreuvf/phd-public | ae4cf4e70ec04b823540dccd4e6d6ba6ee281904 | 10a1245b93b631371ffda9c9e3a9ba399f01c1d5 | refs/heads/master | 2022-04-25T02:03:13.717890 | 2020-04-25T19:23:17 | 2020-04-25T20:10:50 | 258,830,856 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 18,956 | r | make-pf-plot-data.r | # # # # # # # # # # # # # # # # # # # #
# Generate graphs of LCHF0
# LCH-PF, two figures, each with four y-axes
# # # # # # # # # # # # # # # # # # # #
library(splitstackshape) # cSplit
library(lubridate) # as.duration
library(plyr) # round_any, join
library(zoo) # rollapply
library(readxl) # read_excel
# # # # # #... |
e6c09a6396ce83665c6c492632b9ea44a0d61994 | 1d8b2cd0581dd0874bd68815e3650466f2deddca | /Sec3L18/Vector_index_element_selection.R | 3d655318b9f7d84cfe51c22ffdc3dfdc7a7cb2f8 | [] | no_license | whosivan/R-BootCamp-ML- | 4cd616f38111c59f6c7b1c4d41c8af7d61a6d56b | a6a572c63e2981821631218f4f88af00a1891fbe | refs/heads/master | 2021-01-23T14:15:40.454305 | 2017-12-20T05:50:26 | 2017-12-20T05:50:26 | 102,680,343 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 322 | r | Vector_index_element_selection.R | x <- c(1,123, 534, 13, 4) #combine
y <- seq(201, 250, 11) #sequence
z <- rep("Hi!", 3) #replicate
w <- c("a", "b", "c", "d", "e")
w
w[1]
w[2]
w[3]
w[-1]
w[-3]
w[c(1, 3, 5)]
w[c(-2, -4)]
w[-3:-5]
w[1:2]
w[7]
w[-100]
w[-800]
w[1:9]
w[5:9]
#----
a <- c("a", "b", "c")
b <- c(1, 2, 3)
a
b
toString(b)
a + b
is.character(b... |
bc292ec0bb4e3a0665b6004087183a139986ca04 | 24c6301ee0b35cf45faea2dc246ed2ad45818efc | /SFAUR-TEC_ENERO_2017.R | bd21a8841ce66b00aedcbc4f35a8f54104484d68 | [] | no_license | maikelonu/Research_SFASUR-TEC_River_Analysis_Model | 3baded960ab7a1421e4615037d5d3887fa3a1b95 | a923f436847ecb6129e4064eeba0bbdc80894ee0 | refs/heads/master | 2020-09-26T10:04:22.105150 | 2019-12-06T02:54:37 | 2019-12-06T02:54:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 60,492 | r | SFAUR-TEC_ENERO_2017.R | # Streamflow and Flood Analysis Using R (SFAUR-TEC)
# Instituto Tecnologico de Costa Rica (www.tec.ac.cr)
# Maikel Mendez-M (mamendez@itcr.ac.cr);(maikel.mendez@gmail.com)
# Luis Alexander Calvo-V (lcalvo@itcr.ac.cr);(lualcava.sa@gmail.com)
# This script is structured in R (www.r-project.org)
# General purpose: Ge... |
cc8370ad46cd9428067ba33972435fab3b022085 | 7796666bf5bfd050ca3e393ef0357f17f0528e31 | /plot-3D.R | 793fe9de3241cd9921ea1a398500af48629c2cb1 | [] | no_license | clarajegousse/fcm | f16588ee928cb1eac49f7687b09f6fc0fedd693e | d9b65b4058dd962792cb9cf42ee04347c060e066 | refs/heads/master | 2020-03-20T00:32:06.080963 | 2018-06-15T11:26:57 | 2018-06-15T11:26:57 | 137,047,501 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,462 | r | plot-3D.R | ###LOAD PACKAGES
library(flowCore)
library(ggcyto)
library(stringr)
library(car)
library(rgl)
###VARIABLES
Folder.path <- '/Users/bland/Desktop/Flow-cytometry_data/Input/' #Path of the folder containing the FCS Files
csv.path <- '/Users/bland/Desktop/Flow-cytometry_data/Output/Dataframe/' #Path of th... |
feab48e8800e668f70de8f1ec73deb328cd2f99a | 40db507664c28975eeaaea556307b476381edf25 | /shiny/server.R | cc53e4c1243bc4dcc68cdab01dc243a37d31925c | [] | no_license | fstrueb/tools | 982d35092ac65840b0a60b0da88f52a1d4a852ac | d4547b00a7d98bbc924034e4c77419b32a5a00c9 | refs/heads/master | 2020-05-24T15:56:17.015405 | 2017-07-11T16:20:50 | 2017-07-11T16:20:50 | 84,855,178 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,547 | r | server.R | library(shiny)
library(dplyr)
library(rtracklayer)
library(DT)
library(shinyjs)
path = getwd()
source('../R_functions/plotPromoter.R')
source('../R_functions/makeRangeOfInterest.R')
source('../R_functions/scanRangeForTFBS.R')
source('../R_functions/siteSetToDataFrame.R')
source('../R_functions/unlistJASPAR.R')
source(... |
b1f3b90f8e7d7bf39c18be2a24c83c3e024fd197 | 762ff19db84f778a9135269cc8c0ce62f06bc0b7 | /R/pathos.R | 78a8552311810b62aa70e4cffeedce014da1e4ce | [] | no_license | Trackage/pathos | d8ddb99bf68b17620ea04021fd6db93d21fdf9d3 | 915649b3a9fc5286f0388aa8dffaaf4887dab378 | refs/heads/master | 2021-06-19T15:26:56.930296 | 2017-07-19T22:56:27 | 2017-07-19T22:56:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,213 | r | pathos.R | #' Plot segments and arrows
#'
#' Add segments or arrows to plot from prepare_segments.
#'
#' Input is a data frame with `x0`, `y0`, `x`, `y` columns as per [segments]
#' @param d data frame
#' @param ... arguments passed to [segments()]
plot_segments <- function(d, ...) {
segments(d$x0, d$y0, d$x1, d$y1, ...)
}
#'
#... |
24894a2682bba3b94f601cee7a9f4882608cef18 | 605c9ab0818959b65d00f6c89efe296e9ec8a0f6 | /01_diversity_analyses/bayesian_modeling/defining_strategies.R | 612543a3cb386f1a0cd9b22f3eba35f2058c3325 | [] | no_license | nyu-cdsc/diversity | c5d1246911fe3fcc89a27a346a357a8c98064e8b | 16355ce74a043cf2655c7042d441edcfe0b08854 | refs/heads/master | 2020-03-10T15:55:35.031602 | 2019-06-24T17:03:30 | 2019-06-24T17:03:30 | 129,461,256 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,589 | r | defining_strategies.R | ## Defining the hypothesis space
# This creates a matrix for each strategy (assigning probabilities to how a likely a choice would be if the person adopted a particular strategy).
# These individual matrices are compiled into a single matrix that has all possible selections given experimenter choice.
# This will be ... |
5b4d97a5e79e2265385514bf70168e6339192a9a | 15107b515d45e60c7ea59cfcb63b758984c52272 | /R/private_createEstimateArray.R | 831e166b5d8653e4bfc092c38adb2c33aa2bb598 | [] | no_license | gforge/forestplot | e00e700b727758c30a530d077168d26b86c63f4b | b26b33561d2664933fc7a9b8258e26dffa8fe2e5 | refs/heads/master | 2023-09-01T12:16:44.558022 | 2023-08-27T19:52:18 | 2023-08-27T19:52:18 | 28,350,997 | 38 | 16 | null | 2022-11-23T20:55:20 | 2014-12-22T17:53:03 | R | UTF-8 | R | false | false | 2,100 | r | private_createEstimateArray.R | createEstimateArray <- function(labeltext, lower, upper, mean) {
if (missing(lower) &&
missing(upper) &&
missing(mean)) {
if (missing(labeltext)) {
stop(
"You need to provide the labeltext or",
" the mean/lower/upper arguments"
)
}
mean <- labeltext
labeltext <... |
e04aa44edcea222ae547d207d6bd955c1bd5e50e | 3b41c8c6913935a01a19cdf17136078582bf17bc | /R/temp_till_9am.R | d3daeb3dcec1026b75e861f9e6afc26676de6531 | [] | no_license | pkezich/SolarPVUWA | 4a62db6a74dc2c7a2c9af2d135a934db988f986e | cadd4005ac16876ef1418408adba3abb62d2a652 | refs/heads/master | 2021-01-17T05:54:23.910629 | 2015-06-03T12:42:17 | 2015-06-03T12:42:17 | 36,012,715 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 782 | r | temp_till_9am.R | # The function temp_till_9am is used to temperatures for time up to and including 9am. With the function
# temp_after_9am used for the later times. The main function extracts the mean max and min temperatures from
# the closest BOM weather station and uses the first to functions to extract the hourly temperatures.
#' ... |
bf60e814b8b03955f2c4b12ea4b7bb953a2120ce | 091ef5c6d171eac17282a656f98c02aa0eec3727 | /R/bdiagMat.R | 26a7690917e685463c993c750c47f2710e8b703b | [] | no_license | mikewlcheung/metasem | 079a7168a525950768024d7cf45e6496deb5c5b6 | c5daae1ece3f1c9d9bd2cac1f752a89b7ed9a043 | refs/heads/master | 2023-01-21T23:36:42.082583 | 2023-01-08T01:27:06 | 2023-01-08T01:27:06 | 33,676,124 | 31 | 11 | null | 2021-07-11T11:37:21 | 2015-04-09T15:14:44 | R | UTF-8 | R | false | false | 721 | r | bdiagMat.R | bdiagMat <- function(x){
if(!is.list(x)) stop("\"x\" must be a list.")
n <- length(x)
if(n==0) return(NULL)
x <- lapply(x, function(y) if(length(y)) as.matrix(y) else stop("Zero-length component in x"))
d <- array(unlist(lapply(x, dim)), c(2, n))
rr <- d[1,]
cc <- d[2,]
rsum <- sum(rr)
csum <- sum(cc)... |
05bf4ddf686cd6cc999e30104cbf9af30ddb145c | f2a1cebe0c88195da10e3ad8f3184e6738bab6a8 | /Simulation/getcumulativecounts.R | caf4776441183568a417d9e888a8d13db42e5769 | [] | no_license | als23/identifying_and_responding_to_outlier_demand_in_revenue_management | 229ac85e0e723d0c2b352d0c186eeda0b16fde4a | bba9b8197a432bee65ea8fcf5fbc9d78d9b36726 | refs/heads/main | 2023-08-15T08:14:40.903841 | 2021-09-24T22:45:46 | 2021-09-24T22:45:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 88 | r | getcumulativecounts.R | getcumulativecounts <- function(data){
df <- t(apply(data, 1, cumsum))
return(df)
}
|
e704b99bea415bf0d784ba705319927ffbe4626f | cfaec47464084ffa17bf1732ff7c87e5372084a4 | /Stats/Scripts/sexplot/sexage.R | c8f2403d6fff1b6eb28a6c120ac5a547df90aec8 | [] | no_license | pushpendra42/AcademicProjects | f972150b898338a4aa4a370a8147938ccc233974 | 723789fdd9050fd9c9468204ca0cc4d7c62c5d36 | refs/heads/main | 2023-01-30T12:25:27.093261 | 2020-12-17T08:21:29 | 2020-12-17T08:21:29 | 321,918,449 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 787 | r | sexage.R | ######## SEXPLOT ###########
library(ggplot2)
sexdata <- data.frame(
Sex = c("Male", "Female"),
value = c(10,5)
)
plot1 <- ggplot(sexdata, aes(x="", y=value, fill=Sex))+
geom_bar(width = 1, stat = "identity") +
coord_polar("y", start=0) +
scale_fill_manual(values=c("#42b6f4", "#f7a0c9")) +
labs(t... |
4a547c7cd986b0e4b97ab3ab6856024e7bd58a3e | 92a2fef26ccc8b48fc40a9d38d1a36c2233aaa65 | /R/Ch9/9.2RegularizationMethods.R | 597971b6d01fa7a95293e6556d264898994e6acb | [] | no_license | happyrabbit/DataScientistR | a48eb2b9b08d315de2545d650a7b2cbfaf4e3003 | 2e32e045af051ec6b1a38e2e7c8895552440088a | refs/heads/master | 2020-12-10T04:55:22.800805 | 2020-06-23T21:46:59 | 2020-06-23T21:46:59 | 89,527,935 | 18 | 7 | null | null | null | null | UTF-8 | R | false | false | 3,077 | r | 9.2RegularizationMethods.R | #############
### 岭回归
#############
dat<-read.csv("https://raw.githubusercontent.com/happyrabbit/DataScientistR/master/Data/SegData.csv")
# 对数据进行一些清理，删除错误的样本观测，消费金额不能为负数
dat<-subset(dat,store_exp>0 & online_exp>0)
# 将10个问卷调查变量当作自变量
trainx<-dat[,grep("Q",names(dat))]
# 将实体店消费量和在线消费之和当作应变量
# 得到总消费量=实体店消费+在线消费
trainy<-da... |
a431cc6f135692ea87727dbedf08bd74561af3b8 | 7f72ac13d08fa64bfd8ac00f44784fef6060fec3 | /RGtk2/man/cairoScaledFontSetUserData.Rd | 730188ff907256f3b84f3969598ea4d70d357ea5 | [] | no_license | lawremi/RGtk2 | d2412ccedf2d2bc12888618b42486f7e9cceee43 | eb315232f75c3bed73bae9584510018293ba6b83 | refs/heads/master | 2023-03-05T01:13:14.484107 | 2023-02-25T15:19:06 | 2023-02-25T15:20:41 | 2,554,865 | 14 | 9 | null | 2023-02-06T21:28:56 | 2011-10-11T11:50:22 | R | UTF-8 | R | false | false | 911 | rd | cairoScaledFontSetUserData.Rd | \alias{cairoScaledFontSetUserData}
\name{cairoScaledFontSetUserData}
\title{cairoScaledFontSetUserData}
\description{Attach user data to \code{scaled.font}. To remove user data from a surface,
call this function with the key that was used to set it and \code{NULL}
for \code{data}.}
\usage{cairoScaledFontSetUserData(sc... |
384591656fb5c64dce640e0414cf6c5f52a96b87 | 1144fe71d1be1db8481d0f32cd1e7b637bc4cded | /src/eda.R | cb8ee00b786d18f9974fd8bfb26dbcb482b713b7 | [] | no_license | 2ndFloorStuff/CompetitionIndexer | b999a68dbb7a7c696a323ff07cf6faf32bbec71f | ef58777b32fbd6fd51f832dfc8138fce671da343 | refs/heads/master | 2020-05-17T11:48:03.448173 | 2014-04-15T02:11:10 | 2014-04-15T02:11:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 31,785 | r | eda.R |
R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution... |
500e7f9b1f0cc89e7afeb3dc34344772337989ef | c8ca4a1e1229c0b431887a230644a83685b62cf0 | /2 R Programming/ProgrammingAssignment2/cachematrix.R | 89a3445fb18d318a1c8d366c7267861aea397af8 | [] | no_license | cabetodc/datasciencecoursera | 0b633cbe6bc372760ec870dc23b86d1e0abb3d53 | 2d7faf533a47a8a6bc6a881fb0c852aaef4e3733 | refs/heads/master | 2021-01-10T19:40:19.783581 | 2015-04-10T23:20:58 | 2015-04-10T23:20:58 | 31,678,657 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,893 | r | cachematrix.R | ######################################
## Caching the Inverse of a Matrix ##
#####################################
## The following functions create a matrix that cache and
## compute the inverse of a square matrix with the "solve" function,
## assuming that the matrix supplied is always invertible.
## A. makeCacheM... |
662331c779142212974ae539b6ecf1054ea6d23b | f2ca5431d921b1189a6ebaacd88aef3a9a1a1820 | /R/LearnerClustMiniBatchKMeans.R | dd0ed9103224da227a3587e2d1624641a6dc5454 | [] | no_license | mlr-org/mlr3cluster | 44747d2b4fae9170b5ea20704cccfdad777f198f | 161aee5e75aa299bea29617020339768a8d9a75c | refs/heads/main | 2023-06-22T09:58:51.455583 | 2023-06-15T22:32:15 | 2023-06-15T22:32:15 | 157,852,274 | 15 | 7 | null | 2023-03-10T01:08:56 | 2018-11-16T10:32:38 | R | UTF-8 | R | false | false | 4,073 | r | LearnerClustMiniBatchKMeans.R | #' @title Mini Batch K-Means Clustering Learner
#'
#' @name mlr_learners_clust.MBatchKMeans
#' @include LearnerClust.R
#' @include aaa.R
#'
#' @description
#' A [LearnerClust] for mini batch k-means clustering implemented in [ClusterR::MiniBatchKmeans()].
#' [ClusterR::MiniBatchKmeans()] doesn't have a default value fo... |
7976fbf603c3267c963439ddb83838bb840a36ec | cf0a40ab0fb3b8f4a88f0710004900dbd3c3c1e9 | /airbnb_trial.r | 662e44bbc36b3d6712d0fc0cab185e13e3cb1a8e | [] | no_license | codingfinance/misc | 0da96fc4254bf6217d9b57c341028946ccddda52 | 2ec36a86c249089b0a95934736599f9b286d8b60 | refs/heads/master | 2022-05-25T01:56:10.142156 | 2022-03-27T23:37:55 | 2022-03-27T23:37:55 | 248,232,775 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 226 | r | airbnb_trial.r | library(tidyquant)
library(tidyverse)
library(rvest)
my_url <- read_html("https://www.airbnb.com/s?query=milan&checkin=2020-04-16&checkout=2020-04-20")
text <- my_url %>%
html_nodes("div._8ssblpx") %>%
html_text()
text
|
70b4c290a8174b5ba0c7a5179e5f86f7bae64fd7 | 2a815dbd7c1e19628740238018a4a0f0c131af3b | /R/zomotu.R | d3dea90b3d42c8cf7c92393d95d5de382c252080 | [] | no_license | yewei369/clotu | 227c470f19c1f6dacb068dfce2a1cac513d2cdaf | 6b51bfc54a456c2149ef3fb915d148460d9afef2 | refs/heads/main | 2023-06-09T15:59:48.628243 | 2021-07-01T10:18:34 | 2021-07-01T10:18:34 | 337,001,765 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 992 | r | zomotu.R | #' Zombie OTUs
#'
#' Find zombie OTUs, and preprocess the dataset through deleting zombie OTUs and zombie observations
#'
#' @param data, matrix of OTU data
#' @param thr, threshold for zombie OTU
#' @param del, when T, delete zombie OTUs
#' @return a list of preprocessed dataset, reference OTUs
#' @examples
#' da<-s... |
c2b410cf423c2f8012012e1388aae3e1b2201b60 | 5ec349b9abfcaaecf5916f27cad0180f5e21659a | /R/slider.match.R | 1560e618354c14c37df4c7429fa4dbd153a49c9f | [] | no_license | way2joy/LearnEDA | 322b4abecc9df2927d9842e387c5b6e6d8a8a0be | e823e1ba9a174d4f2d89effe163ea9443c32e0ea | refs/heads/master | 2021-01-24T04:43:24.272946 | 2015-08-28T23:27:18 | 2015-08-28T23:27:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 483 | r | slider.match.R | slider.match <- function(x){
power.plot <- function(power, x){
power <- ifelse(round(power, 2)==0, 1e-04, power)
reexpressed <- mtrans(x, power)
xlb <- "Reexpressed Data"
tit <- paste("Power =", round(power, 1), ", d =",
round(hinkley(reexpressed), 2))
boxplot(data.frame(raw = x... |
6d43cfd09cbbf54bbd4b040091e12797eba40a2b | 2b79549048707707667fbabc464cd35ed0579278 | /R/SMITE.R | 3941fed5b2a7efaba15b6d0e383fa896f5eee85d | [] | no_license | GreallyLab/SMITE | 3e49f7e1927445ed895b63ada2d953eeb0fcfcf9 | 1536ac38c699be0d548c1702a64d6d77bac4be4d | refs/heads/master | 2021-01-24T02:07:06.610389 | 2017-03-27T12:23:30 | 2017-03-27T12:23:30 | 39,521,031 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 62,625 | r | SMITE.R | ##SMITE_1_0_2 10/9/2015
##internal function to perform a Stouffer's test for p value combination
setMethod(
f="stoufferTest",
signature="vector",
definition=function(pvalues, weights)
{
if(is.null(weights)){
weights <- rep(1, length(pvalues))/length(pvalues)
}
Zi ... |
62fc3714d5dde6fc9a08fe0acdb11690256e1391 | d89cb07a30dac2581efc14fa2b53841f476a4e5a | /DSM_book/rcode/intro2R/P3_Intro_R_2017_Part3.R | de712c644fccbcd7f1908e008c3e5722eb2926f8 | [] | no_license | brendo1001/brendo1001.github.io | 27da7ffdc543edc3512710fa9c9e00f1c2452f0a | ac78ed31430f838f51954351e7808b7c69d84476 | refs/heads/master | 2023-08-08T00:25:13.576182 | 2023-07-31T15:37:28 | 2023-07-31T15:37:28 | 72,090,350 | 3 | 0 | null | 2020-12-02T11:15:22 | 2016-10-27T09:01:07 | HTML | UTF-8 | R | false | false | 4,050 | r | P3_Intro_R_2017_Part3.R | ## R literacy: Part 3
##########################################################################################################################
## Data frames, data import, and data export
## ----echo=TRUE, tidy=TRUE,cache=FALSE,eval=TRUE, background='white'------
dat<- data.frame(profile_id= c("Chromosol","Vertos... |
ba9a5dd65cd7ccf4ead65edac7810bc3b3d943d4 | 010cdc330fbbd95423de66a264d13853606477a5 | /man/sdists.center.align.Rd | 7f842cc206d02651c72ad6241b5b57e7a9229d86 | [] | no_license | cran/cba | 1cd3ed427fded73783bf98ca346dddcd62baaf2a | d16e229b75fa69ff5b5484176bf5f6428073837c | refs/heads/master | 2022-12-23T00:51:52.160556 | 2022-12-07T08:48:43 | 2022-12-07T08:48:43 | 17,694,994 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,971 | rd | sdists.center.align.Rd | \name{sdists.center.align}
\alias{sdists.center.align}
\title{Align Sequences to a Center}
\description{
Find a global alignment of a collection of sequences using the
center-star-tree heuristic.
}
\usage{
sdists.center.align(x, center, method = "ow", weight = c(1, 1, 0, 2),
exclude = c(NA, NaN... |
1a9fad2918121bce9833def1b0f8514ee1a13675 | 888bc7297bce0524768099a664dbb8cdb9ce6795 | /R/class.R | 4ed404951cbc6aff678615e5719efc75d05e5a6d | [
"MIT"
] | permissive | chinhungtseng/wtotariffcrawler | b9da6814b5c1f47ff9ed5d5d7b5d06bb41935b86 | db8f4f8a85c897ea4fd69e94f12b7a5eb398ebd3 | refs/heads/master | 2020-12-08T14:28:51.613865 | 2020-01-14T02:50:01 | 2020-01-14T02:50:01 | 233,005,191 | 0 | 0 | NOASSERTION | 2020-01-13T05:16:56 | 2020-01-10T08:49:54 | R | UTF-8 | R | false | false | 2,970 | r | class.R | #' new_wto_crawler
#'
#' @param .verbose TRUE or FALSE
#'
#' @return list
#' @export
new_wto_crawler <- function(.verbose = FALSE, .proxy = FALSE) {
ATTEMPTS <- 0
MAXTRY <- 10
while (ATTEMPTS < MAXTRY) {
ATTEMPTS <- ATTEMPTS + 1
tryCatch({
url <- "http://db2.wtocenter.org.tw/tariff/Search_byHSCode... |
175491d553a7db064859657c3bf8617765ae807a | 13bb1694b07014883228eaadfe29ca3774133006 | /27-umap-tsne-plot-coloring.R | 34649048fc9e00551488b9b253b8eff5b958b6f6 | [] | no_license | DevkotaLab/ha-et-al-2020-cell | 0b8278ff040bf98ca8961ef73f5d9c752ba0013e | f5c5a724d5a89058b5d2b9cfac312222f15009c4 | refs/heads/master | 2022-12-08T15:52:27.632998 | 2020-09-14T19:35:08 | 2020-09-14T19:35:08 | 241,246,468 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,069 | r | 27-umap-tsne-plot-coloring.R | ## Generate UMAP and tSNE plots.
## Updated 2020-06-26.
##
## Improve consistency of colors in figures, per Suzanne's request:
## - Control samples: purple
## - Crohn's samples: orange
##
## Need to set the factor levels in UMAP/tSNE plots for consistency.
source("_setup.R")
cellranger_all <- readRDS(file.path(
"... |
a2cbc364552c65c46f773202cf2a5a43f4d0638a | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /meteor/inst/testfiles/ET0_ThornthwaiteWilmott/AFL_ET0_ThornthwaiteWilmott/ET0_ThornthwaiteWilmott_valgrind_files/1615831070-test.R | 51ec842b4accb2d579043b78ac7eb2a80382da75 | [] | no_license | akhikolla/updatedatatype-list3 | 536d4e126d14ffb84bb655b8551ed5bc9b16d2c5 | d1505cabc5bea8badb599bf1ed44efad5306636c | refs/heads/master | 2023-03-25T09:44:15.112369 | 2021-03-20T15:57:10 | 2021-03-20T15:57:10 | 349,770,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 575 | r | 1615831070-test.R | testlist <- list(doy = numeric(0), latitude = numeric(0), temp = c(1.67534972842843e-305, -8.94553002689695e-210, -1.56500839841835e-209, -1.5871349126006e-151, -5.81641510220318e-224, 7.60646408195443e-311, -1.07091123094137e+34, -1.82219451443966e+307, -7.76664403038719e-292, -1.49198822262059e-154, 1.25786901112... |
1314bceddf548bc7f52d19271dd33e7a1ad0ca8d | 882a43935f3353a94c5d67e13f8308c04f9152f9 | /0260_time.R | 9bf07ea6ef972adb70d13cd307304e846e7f333c | [] | no_license | akicho8/learn_R | a1711c6cd5f07b004b9dbccae6d681b8148c19ec | 8f0977dfe8da05d179d265a5805304b4ecbebf08 | refs/heads/master | 2021-01-10T20:39:29.459947 | 2013-06-03T15:38:05 | 2013-06-03T15:39:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 119 | r | 0260_time.R | t <- proc.time() # 現在時間取得
Sys.sleep(0.000000001) # 指定秒待つ
proc.time() - t
|
53fb14b48ef44f43b53d1b30cac1d8a1545d9140 | 10fbd1788ed37fd0c61403f40e8233853bc00cfc | /R/1DGPfunctions.R | 8d824c4e466441ac3ca0f07688df0214282ceb9b | [] | no_license | lmmontoya/SL.ODTR | 50dafaa45376dc1f7da74816585ce77047e03a02 | 9ffe0a3021f7c248f59f038a2f82fab135887da2 | refs/heads/master | 2023-03-06T12:48:47.929749 | 2023-02-20T22:14:59 | 2023-02-20T22:14:59 | 214,280,056 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 26,193 | r | 1DGPfunctions.R | #' @name QAW_null
#' @aliases QAW_null
#' @title Simulate with null
#' @description Generate QAW according to null
#'
#' @param W Data frame of observed baseline covariates
#' @param A Vector of treatment
#'
#' @return conditional mean of Y given A and W
#'
#' @export
#'
QAW_null = function(A, W) {
W1 = W$W1
W2 =... |
71b685977e856129ba2d0b67bdaf277d5ece483e | 9836c46617b7dc42d87fe9cf57dd00edda056089 | /man/quarticity_rqq.Rd | 027702380de348550fc8d14b7abf54893756c1c5 | [] | no_license | cran/PortfolioEffectEstim | 577086a2d5e858caa0258f6a2c4e27afa9bea286 | ea23b2129d544b500187e69dec5990351164a248 | refs/heads/master | 2020-04-06T21:07:08.344365 | 2016-09-17T19:54:52 | 2016-09-17T19:54:52 | 48,086,033 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,405 | rd | quarticity_rqq.Rd | \name{quarticity_rqq}
\alias{quarticity_rqq}
\title{Realized Quadpower Quarticity}
\usage{quarticity_rqq(estimator)
}
\arguments{
\item{estimator}{Vector of (time, price) observations for market asset when external market data is used.}
}
\description{
Realized Quadpower Quarticity (RQQ) is an asymptotica... |
4263569e29f81bc99628540c581a2adddc4dc748 | b12e833ec41c29d263f5060653da17e2d6ae7401 | /app.R | a6281781ae16b35f3105ccae12a071ee9541b478 | [] | no_license | daddyprasad5/capstoneshiny | d14e4ddbe5d72e6a8a73504908c24c673eede4b7 | 666e0600958d4dda35f43fdf09bea777fe4cfb06 | refs/heads/master | 2021-01-12T06:21:57.832681 | 2016-12-27T17:40:44 | 2016-12-27T17:40:44 | 77,347,762 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,911 | r | app.R | library(shiny)
jscode <- '
$(function() {
var $els = $("[data-proxy-click]");
$.each(
$els,
function(idx, el) {
var $el = $(el);
var $proxy = $("#" + $el.data("proxyCl... |
9e22125057273d639354680b83152318848b0c4e | a6e50afb0bfd2b4994d4bae77bfaa56a7534c2ef | /scripts/plot/HE_plot_15P.R | 46bf57e102a5c269a45f70d3589cfc3d1579784b | [] | no_license | Yuzi-00/starch-degradation | 827336e5dd98286bd454147cf98046aadfd5abb2 | 9cf1e650803dd524c766d15cea72a66b78f20260 | refs/heads/master | 2021-07-11T13:16:38.323117 | 2020-12-07T19:44:29 | 2020-12-07T19:44:29 | 225,079,205 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,899 | r | HE_plot_15P.R |
library(tidyverse)
# ** plots are generated using the dataset with outliers replaced **
# ** scatter plot of the HE **
# import the dataset
data_15P_cal_HE_outlier_replaced <- read_csv("data/tidydata/data_15P_cal_HE_outlier_replaced.csv") %>%
mutate(... |
ad99c225b869a7b0d1687b92e2df45a01efe5517 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.internet.of.things/man/iot1clickprojects_list_projects.Rd | 23a17d20ae4a972da0301b750514dbd1a92c39da | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | true | 1,114 | rd | iot1clickprojects_list_projects.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/iot1clickprojects_operations.R
\name{iot1clickprojects_list_projects}
\alias{iot1clickprojects_list_projects}
\title{Lists the AWS IoT 1-Click project(s) associated with your AWS account
and region}
\usage{
iot1clickprojects_list_projects(nex... |
8bde298e9659cf3326b0aab695c2bff1a6dc58fa | 303a19806df04acf4049c40be3eab4ef22ff09f2 | /Computational_Statistics_and_Stochastic_Optimization/Simulation_Algorithms_and_Hypothesis_Testing.R | 580348a8199e265232b7340c7491cf2c1e4f5faf | [] | no_license | marmix96/Data-Science-Machine-Learning | 2dccf08ce03474f7b117d951464a57f9dd5bf9ac | a112cf9ba2273ca2cfa04230b8cd52af99441929 | refs/heads/master | 2023-06-12T09:13:45.852792 | 2021-07-04T15:49:25 | 2021-07-04T15:49:25 | 382,886,340 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,752 | r | Simulation_Algorithms_and_Hypothesis_Testing.R | #askisi_2a
set.seed(03400100)
f = function(x) {return (exp(x) / (exp(3) - 1) )}
F_inv = function(x) {return (log(1 + x*(exp(3) - 1)))}
u = runif(1000)
x = F_inv(u)
hist(x, prob = T, xlab='X', ylim = c(0, 1.2), xlim = c(0, 3), main='Inversion Sampling')
curve(f , from = 0, to = 3, lwd=2, xlab = "", yl... |
3737086b9276bb26b9cb125dc4f822c5ff09681b | 24fcc7a9446871f5affbc82d3ae1ed20d6a7c8aa | /R/data.R | 6ec11a382f114767e531810f6b806ba2f92ee240 | [
"MIT"
] | permissive | mrc-ide/malariasimulation | 3188657f6ff9da4ea35646189d0bd75d6e35aa52 | 397a7b7efe90958dd01f97110a1d16c71d041f33 | refs/heads/master | 2023-08-23T11:29:10.050424 | 2023-07-03T15:58:32 | 2023-07-03T15:58:32 | 233,609,741 | 10 | 10 | NOASSERTION | 2023-08-17T15:48:41 | 2020-01-13T14:06:17 | R | UTF-8 | R | false | false | 263 | r | data.R | #' Parameter draws
#'
#' 1000 draws from the joint posterior fit from
#'
#' @format ## `parameter_draws`
#' A list of lists of length 1000, each level contains a list of drawn parameters
#'
#' @source <https://www.nature.com/articles/ncomms4136>
"parameter_draws" |
b538f2aac268409aadea0e38091cb4de1343503c | 1a3a98a94d54bd72c6cba0dfbe72d013141fbc51 | /man/declare-package.Rd | e3b65231e4f72a42d835ffc7d595d82b75fad05f | [] | no_license | kcf-jackson/declare | 2c86fbf488b64a72107d4f196c46b4fca96e061b | 5c63ee97c64fb9b1d4ba7fa6eed0bb531e29f62d | refs/heads/master | 2020-04-15T04:28:03.207712 | 2019-01-10T12:12:49 | 2019-01-10T12:12:49 | 164,384,476 | 7 | 0 | null | null | null | null | UTF-8 | R | false | true | 511 | rd | declare-package.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/declare.R
\docType{package}
\name{declare-package}
\alias{declare}
\alias{declare-package}
\title{declare: Optional type annotation}
\description{
Allows user to add type annotation to a function via comments.
The package uses a simple me... |
3f35e915ddbb1393b2d3e0a421f25eb8e276c48c | ebee9629abd81143610a6352288ceb2296d111ac | /man/pcf_directional.Rd | 5d52d0a06b18104ed72729ba6338f34e9a1ce2ef | [] | no_license | antiphon/Kdirectional | 76de70805b4537a5aff0636486eb387cb64069b0 | 98ab63c3491f1497d6fae8b7b096ddd58afc4b29 | refs/heads/master | 2023-02-26T02:19:41.235132 | 2023-02-12T13:07:11 | 2023-02-12T13:07:11 | 37,183,574 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,247 | rd | pcf_directional.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pcf_directional.R
\name{pcf_directional}
\alias{pcf_directional}
\title{Directed pcf function}
\usage{
pcf_directional(x, u, epsilon, r, ..., cylindrical = FALSE)
}
\arguments{
\item{x}{pp, spatstat's ppp-object, or a coordinate matrix, or a ... |
aff331f6ee6e9f2f9bf3edb051ff67369714a854 | efd0d6bec42aa38c1e62b6eecd5b1f4234581ec2 | /man/time_n.Rd | eaa126b2d726a28df2871334bcf13a360af11823 | [] | no_license | jlegewie/bife | 5bdabfd799f8075ea1f55f60002d3d427d45c2c5 | 2206789a3a8bc157fbd7c8d10aca6243d626dc1d | refs/heads/master | 2020-04-06T16:52:13.632534 | 2018-11-15T02:03:50 | 2018-11-15T02:03:50 | 157,637,657 | 0 | 0 | null | 2018-11-15T02:01:55 | 2018-11-15T02:01:55 | null | UTF-8 | R | false | true | 340 | rd | time_n.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/time_n.R
\docType{data}
\name{time_n}
\alias{time_n}
\title{Computation time with varying N}
\format{A named matrix with 10 rows and 4 columns.}
\usage{
time_n
}
\description{
Results reported in the vignette.
}
\seealso{
\code{\link{bife}}
}... |
64ee3e63e0ba2d9d1736614173db9d14d902b6ac | 876f5f4de8ccb50bb67a28af9d731f8d94d9988e | /R/coenoclinerutil.R | a75559588dc7850f1cf3c68e86c4dddb399e8800 | [] | no_license | jarioksa/GO | f9e763b5e3b0e89ada51d588de71ff5f71040a0a | 42e5f65902d48dfe702127a8b5ef5572405ab380 | refs/heads/master | 2016-09-16T15:07:10.117729 | 2015-08-12T08:23:28 | 2015-08-12T08:23:28 | 32,721,902 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,516 | r | coenoclinerutil.R | #' Utility Functions for 'coenocliner' Package
#'
#' Functions to automated simulation routines using \pkg{coenocliner}
#' package.
#'
#' @author Jari Oksanen
#'
#' @examples
#' require(coenocliner) || stop("examples need 'coenocliner' package")
#' ## small simulation
#' nsim <- 10
#' npoints <- 50
#' ## generate a se... |
2a8008a3e18aa8bd99a95dbb378d3965c922dd3d | 957b2233b78ce98dbce9fe10b676cee213da0d18 | /superlda-dev/man/get_top_keywords.Rd | 39356fd12f0f13f74d1daf9eb19c96a4793ff034 | [] | no_license | traviscoan/politics_in_sermons | 646d0a69a3f8afd111832fa0e5d245e52de3e20b | eebe45252f39f3ac69f761ff55891a99bf8359c7 | refs/heads/master | 2020-11-27T01:26:22.360047 | 2020-07-09T19:36:32 | 2020-07-09T19:36:32 | 278,427,604 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 681 | rd | get_top_keywords.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utility.R
\name{get_top_keywords}
\alias{get_top_keywords}
\title{Return the most probable tokens for each label}
\usage{
get_top_keywords(word_probs, slda_obj, n = 10)
}
\arguments{
\item{word_probs}{Word (or token) probability matrix from a... |
3074cb6f12718e23a31ac82a22b3087fd7f4285d | cc1ef8247b36e600a795ab7d2662085712806f01 | /prophet_R.R | fa5779176119680a875b03e831289ba230243163 | [] | no_license | fyin-stats/prophet_time_series | 9f6ed1bb3aa27082498f578c6efd8b17b3b35053 | 83cc8aac0447dc1f48fd93dc697a7e73f75f0f5f | refs/heads/main | 2023-03-14T18:46:50.396013 | 2021-03-01T05:09:13 | 2021-03-01T05:09:13 | 343,233,567 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 18,270 | r | prophet_R.R | ##################################
##################################
ipak <- function(pkg){
new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
if (length(new.pkg))
install.packages(new.pkg, dependencies = TRUE)
try(sapply(pkg, require, character.only = TRUE), silent = TRUE)
}
packages <-... |
e2468ed626b006ac8db81175a571d4b95a0d72f0 | 957c8d51a391ee5813d99fdd17087aa5fa986f39 | /Barcode_analysis.R | 75ba84daf480c0ac6cf70f3b9c2e1efc1466700f | [] | no_license | minjdott/Barcode_Analysis | 5d87fd75e71a4f44250e4d51e021dc302d94f03e | 4bd5b33d79730ceac66c30e9e5eb3c328ce837e4 | refs/heads/master | 2020-04-19T20:52:37.532451 | 2019-02-01T04:48:29 | 2019-02-01T04:48:29 | 168,426,713 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,126 | r | Barcode_analysis.R | ##starting with a loop
#making a list that contains all the files from "dat"
library(sangerseqR)
list.names<-list.files(path = path.expand("./Data"),pattern=".ab1$")
#the list "list.names" contain all the names of the file that end with "ab1"
list.sequence<-list()
#making a list that will contain all my files
setwd... |
f739ba1da3884510614772fd73efe2474c090eb5 | fae5be729442ae4d10cecf38eff2c27e56347be9 | /spmutils/R/spm_stan.r | 9a3bc51b6d43bb241209dc09c8f492c10a571b7b | [
"MIT"
] | permissive | mlpeck/spmutils | 80f38697c71ef41df33d95539c6b568c21212b1d | 455cc47fc636691ce35ea5372d84861603c13166 | refs/heads/master | 2023-04-27T22:56:48.688934 | 2023-04-16T22:38:14 | 2023-04-16T22:38:14 | 159,246,982 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 24,471 | r | spm_stan.r | stanfit_one <- function(gdat, dz, nnfits, which.spax,
prep_data = prep_data_mod,
init_opt = init_opt_mod,
init_sampler = init_sampler_mod,
stan_model=NULL,
stan_file="spm_dust_mod_simpl.stan", stan_fi... |
530367198dc931fb21330f0bc816562ca423aede | f4a3019d8055d06939abcf952f451f9a328af6a8 | /self_test.R | d8268cb00ae01d050b62b775ef83c7b4787d16fb | [] | no_license | COMP-1433/HousingPricePredictionRCode | 048bbb02ea65cf9cfc676f125cce19b03934ca67 | 0c18c372bf863bdfbf268ea7b4aa56b9244b707c | refs/heads/main | 2023-04-23T19:04:14.561183 | 2021-05-02T14:27:01 | 2021-05-02T14:27:01 | 363,667,635 | 0 | 0 | null | null | null | null | GB18030 | R | false | false | 6,839 | r | self_test.R | # import data
train<- read.csv("train.csv")
test<- read.csv("test.csv")
train<-subset(train,select = -c(Utilities))
test<-subset(test,select = -c(Utilities))
train
#typeof(train)
#typeof(test)
train_char <- train[,sapply(train,is.character)]
#train_char
#the character data in train
train_int <- train[,sapply(tra... |
3fc3cff8a6a146aaf05967fce350e5a2ab55fe65 | b432642a0d72020ee0ee22ecbfdc534721dfce6c | /R/count_votes.R | dd6b2f1d2e049f9806e99ac08761c69f81d91216 | [] | no_license | hanase/vote | 33efd6f18ec925164c807573d67f2fe1b2eaa1de | c37db10367d4c433f1bf1add9580efe9def11a13 | refs/heads/master | 2022-02-12T16:30:36.366764 | 2022-02-04T21:11:45 | 2022-02-04T21:11:45 | 82,612,543 | 2 | 1 | null | 2021-05-28T18:32:18 | 2017-02-20T23:15:03 | R | UTF-8 | R | false | false | 1,211 | r | count_votes.R | count.votes <- function(votes, method=c("auto", "plurality", "approval", "stv", "score",
"condorcet", "tworound.runoff"),
fsep='\t', ...) {
# Main function for counting votes.
# If method is "auto" it determines the right method depending on the
# number of ... |
2d5f1ab34ce26008cc8b987c882df2413d9f7db0 | 1554efd5de247b393039460b2905d1dbb00136bb | /forest.r | 7e9243e897a99cb8bac847aa9bf998c4588a3d82 | [] | no_license | daoleen/RAir | b0b71884f5470be7e83c0cf574e4ac30c929e461 | 323e70cc772dcaaa4fc1fa67db75aeeb924f6b8f | refs/heads/master | 2021-01-10T20:35:35.185053 | 2014-06-09T11:54:26 | 2014-06-09T11:54:26 | 20,642,702 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,313 | r | forest.r | require(ggplot2)
require(randomForest)
data <- read.csv(file="GreenwichLondonAirQuality.csv", header=TRUE)
#clearing data
#dataFail <- (data[is.na(data$Value),]) # failed data
data <- data[complete.cases(data$Value),]
data <- data[complete.cases(data$ReadingDateTime),]
# add the vibras in future
# Map
NO <- data[da... |
8e3a4baadceaa4b0f9b46ae5ad99c98f11956471 | bc2ab255d93e0652fbb7bf72bdf91aee0d212d07 | /buyer_ratio.R | 87ec6a069a49efdcd3e3a923c77e5a943c79f6de | [] | no_license | Humza-Wani/Hypothesis_Testing-WIth_R | 46754cc787d5c2aea4972e110f7c199dd1e86491 | 4a849a9f6041ce009cf779e441626f78285ff1a0 | refs/heads/main | 2023-06-14T13:41:11.999722 | 2021-07-05T02:21:15 | 2021-07-05T02:21:15 | 382,989,784 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,342 | r | buyer_ratio.R | data1 <- read.csv(file.choose())
View(data1)
#EDA on data
plot(data1)
class(data1)
data2 <- as.matrix(data1)
View(data2)
barplot(data2,col = c('red','green'))
legend("topleft",c("Males","Females"),fill = c("red","green"))
#prop.test can be used for testing the null that
#the proportions (probabilities of s... |
49d1bcefb128a5fc912211990958884b97e57416 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/RCzechia/tests/test-1-data-structures.R | 91795a35f1f1387393380079f6b2817fe6c5768d | [] | 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 | 4,908 | r | test-1-data-structures.R | library(dplyr)
library(httr)
library(sf)
context("republika")
expect_that(is.data.frame(republika()), is_true())
expect_that(is.data.frame(republika("low")), is_true())
expect_that(is.data.frame(republika("high")), is_true())
expect_that(inherits(republika(), "sf"), is_true())
expect_that(inherits(republika... |
66be1e28270d2b116c0dd17a7d5b27e35f9100db | 3c9651873db8b92697e71d5d6da96bf489efaf3f | /convexhull.R | 93b046517238752984e2e1a88f2f433ac6dae1c8 | [] | no_license | pieterminnaar/ProgrammingAssignment2 | 0c566a66aff5d203aea73334998d9d692f45ade9 | b15d691f19cda35ae76418409138ed9a94ec0d3a | refs/heads/master | 2021-01-18T01:44:59.440436 | 2014-04-26T20:16:59 | 2014-04-26T20:16:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 761 | r | convexhull.R | library(sp)
library(rgeos)
# Make up some 'banana shaped' points
mypts=cbind(runif(100),runif(100))
keep=4*(mypts[,1]-0.5)**2 + 0.2 > mypts[,2]
mypts=mypts[keep,]
# Coerce to SpatialPointsDataframe
mypts2=SpatialPointsDataFrame(mypts,data=data.frame(NA*mypts[,1]),
match.ID=F)
# Now take a buffer that covers up all t... |
7bd51837242ec773f4fcb24a68f9d1de729aa04a | 0e6c5b0fc0ee861539be1e32b0fe2d031a832066 | /scripts/data_processing/2_calc_field_predictors.R | 277bb912257e6c15008e27410f10f6d32e5c5349 | [] | no_license | limnoliver/EOF_SW1 | 945b7c1aa702a53232d37f3d7fe348a9d382de95 | 44f6fa95c659b54d3e507878a19ab3cd2fcce707 | refs/heads/master | 2020-06-13T16:33:49.262282 | 2019-07-01T16:59:52 | 2019-07-01T16:59:52 | 194,711,822 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,682 | r | 2_calc_field_predictors.R | # calculate days since field action variables
# e.g., days since planing corn, alfalfa
# days since last fertilizer/manure application
# use the field activity sheet and storm start dates to calculate
storm_filename <- file.path('data_cached', paste0(site, '_prepped_WQbystorm.csv'))
storms <- read.csv(storm_filename, s... |
7a9b0d8d2cb1ff81e8f5e778449f7fb77111104a | 643248857926aa16523e6b941cbe73e1bf9cf2c8 | /Temp/kemaaans.R | 27e89327b3892436258e61c2a38643fe33967e0e | [] | no_license | ksrikanthcnc/Data-Mining | 004135123e6c6d83d0a84bf99f38c4764f598bf0 | 1fdc62de42f8fb80e0dd2f645737317f5cfdb9fe | refs/heads/master | 2020-03-16T17:56:06.725758 | 2019-05-23T13:58:15 | 2019-05-23T13:58:15 | 132,852,902 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,740 | r | kemaaans.R | fold <- trainControl(method="repeatedcv", number = 10,repeats = 1,
verboseIter = TRUE,
savePredictions = TRUE
)
ratio <- trainControl(number = 5,
verboseIter = TRUE,
savePredictions = TRUE,
)
#cat("--------------------MODELS---------... |
f41bba66f5cf2f398fb4032f0e74487f61d3512a | 602980a2b335336d9bac17d1a924ddc690449691 | /man/startStandalone.Rd | 965a799ca73ea93be50324949417ca5eeb33fe4e | [] | no_license | epiviz/epivizr-release | 55211407cb8bf781ce8c5706479299d81ad3a5f8 | 798b350442b74334fdf3ac834c65a878e0d436e0 | refs/heads/master | 2021-01-13T01:40:53.888146 | 2015-06-09T17:08:33 | 2015-06-09T17:08:33 | 18,971,179 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,848 | rd | startStandalone.Rd | \name{startStandalone}
\alias{startStandalone}
\title{
Start the standalone epiviz interface
}
\description{
Create an epiviz session manager for the epiviz web application served
from the localhost.
}
\usage{
startStandalone(geneInfo = NULL, geneInfoName = "", seqinfo = NULL, chr = NULL, start = NULL, end = NULL, sta... |
a9de8cf787cf52fee1c13924d285657b7a03bd98 | 664f7a10e3478c9ce542eebdfa67dbdc2858e24f | /man/create_predictions.Rd | be7f58d6476d0ab9cab615db8566527e91e027d1 | [] | no_license | aranryan/arfredpack | 79f245a1d05fc88f0e0f579b22537978b7bc3e36 | 48036be5561f16151e19ea88badbeec7fe63a348 | refs/heads/master | 2020-05-27T21:09:36.222136 | 2017-03-02T15:24:36 | 2017-03-02T15:24:36 | 83,603,037 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 533 | rd | create_predictions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/create_predictions.R
\name{create_predictions}
\alias{create_predictions}
\title{Create predictions using make_predictions}
\usage{
create_predictions(working_df, df_use, area_todo)
}
\arguments{
\item{area_todo}{}
}
\description{
This takes ... |
ee9c90bde8e255580c66f02f53e3ee839b7ee569 | 23a8bfd3eff73d21afd1f50bd82e7bdb6e8358c8 | /ggplot2.R | 9e89e75425fa36cdf8e4a06cbca7f433147e1742 | [] | no_license | michbur/PADR | 0f1233350601c22d4aec8dc45945f0acd06772ee | 01389ab38f6a7c8a15ab23c22a2f4974ef513f13 | refs/heads/master | 2021-05-16T03:47:11.321262 | 2017-10-03T10:29:16 | 2017-10-03T10:29:16 | 105,617,472 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,795 | r | ggplot2.R | # https://github.com/michbur/PADR/blob/master/ggplot2.R
final_dat <- melt(dat, variable.name = "medium") %>%
mutate(medium = sapply(strsplit(as.character(medium), "_"), first),
value = ifelse(value < 0, 0, value)) %>%
group_by(active, strain, medium) %>%
summarise(value = median(value)) %>%
inner_... |
d2d97a58bfa4e7dced7a2529d0ce65a8b439fa66 | 7cff8ac1004df750d94468f594bcc71553e3dcec | /man/vol_salable.Rd | f80857387c944204938aac5d08af69eb9c362cb3 | [] | no_license | Forest-Economics-Goettingen/woodValuationDE | afad2be6e484e3d2e3af17ab1c16088bcb90c59a | 44973dc02dc5d8bc1dcd1a84f07b00fd1c96e43a | refs/heads/master | 2023-04-09T08:08:30.982932 | 2023-03-23T08:40:34 | 2023-03-23T08:40:34 | 473,502,255 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,646 | rd | vol_salable.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/vol_salable.R
\name{vol_salable}
\alias{vol_salable}
\title{Relative share in the volume over bark that is salable}
\usage{
vol_salable(
diameter.q,
species,
value.level = 2,
logging.method = "combined",
species.code.type = "en",
... |
92b26f958cdac6241a4536295fcc90bf12405bd8 | 18813ee8ff46b9e3f7f3c0fc6eb1657b77323398 | /5,练习.R | a42d05adaabbfd4e0c5b5c364e86be0ddc0d734d | [] | no_license | cj2030010002-xgk/2030010002 | 4032c89353df56e782e68df04b49616d7cd44020 | 7325a6f0232c2374bdc2e6c72338470f2067f7d0 | refs/heads/main | 2023-01-04T02:27:09.579575 | 2020-11-07T06:04:36 | 2020-11-07T06:04:36 | 309,053,657 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,754 | r | 5,练习.R | (a_list <- list(
c(1,1,2,5,14,42),
month.abb,
matrix(c(3,-8,1,-3),nrow = 2),
asin
))
names(a_list) <- c("catalan","months","involutary","arcsin")
a_list
(main_list <- list(
middle_list = list(
element_in_middle_list = diag(3),
inner_list = list(
element_in_inner_list = pi^(1:4),
... |
4494ee9f762cdc61baf7607a1e28cbeaa8b68e70 | b6daeeeb4d312f77ee39941545f46534219d3a51 | /bachproef/scripts/test_rq1.R | 5472a92e4fede2024b6f4c63488e0ff4b60198cc | [] | no_license | JakobLierman/bachelorproef-hogent-1920 | e2358930174ec71e3e448343659ae89241e24465 | 3fd2e4dfdbb6a87fd581d6135ccba92b79a2230d | refs/heads/master | 2022-10-22T04:05:10.335514 | 2020-06-12T10:04:29 | 2020-06-12T10:04:29 | 226,527,749 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,353 | r | test_rq1.R | # Data
dataSet <- read.csv(file = "dataset.csv", sep = ";")
dataSet$times_mean <- (dataSet$settings_task + dataSet$new_task + dataSet$add_task + dataSet$delete_task + dataSet$calculator_task + dataSet$add_task_repeat) / 6
subSetWithoutOnboarding <- subset(dataSet, onboarding_elements == FALSE, select = -c(onboarding_... |
0de12fed00d633059ddb8c23dc2427026f33a65c | 3c9a1b88e0adccecc0a0570838c7e2616b164d14 | /man/createRelationship.Rd | ad910ecefb3b2936b2d6eabae2ce51103748b991 | [] | no_license | pavang/R-Neo4J | 36e662a522bb72c5b811c9de8ac8dc294d8b0114 | 0a8c8e35bfc03abf7692c60e639e068aea5ee69d | refs/heads/master | 2021-01-19T18:00:57.893768 | 2014-03-19T01:41:02 | 2014-03-19T01:41:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,422 | rd | createRelationship.Rd | \name{createRelationship}
\alias{createRelationship}
\title{This function creates a (directed) relationship between to nodes on the Neo4j graph DB specified by the handle.}
\usage{
createRelationship(start, end, type, properties = NULL, handle)
}
\arguments{
\item{start}{Numeric.The node at the bottom of the
relati... |
b1822704f3e6192927bee3c0f8b688af13cdbaec | 0855a3e9aad865ddcbbc02d62b7b86a329ac5814 | /Restaurant_R/restaurant_outliers.R | d0102f7c4fab16e2893948592400f361ab5ba303 | [
"MIT"
] | permissive | Cat-n-Dog/follow-m | 87bb2a37f581dc2de8e160add6dc755acd8d10d5 | f6a52ac0dab0e315a2b32e1553f7d0ab18f0fad5 | refs/heads/master | 2016-09-06T09:24:59.525711 | 2015-05-05T04:44:07 | 2015-05-05T04:44:07 | 32,771,706 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 700 | r | restaurant_outliers.R | library('ggplot2')
library('lubridate')
library('dplyr')
library('tree')
library('randomForest')
library('gbm')
library('caret')
train_df <- read.csv(file = 'train.csv')
train_df$Open.Date <- lubridate::mdy(train_df$Open.Date)
train_df <- train_df %>%
dplyr::mutate(Open.Year = year(Open.Date), Open.Month = month(Ope... |
96af317e55c6c520f2457af25538489e8aba8f50 | 82504d5f1ed4be1ed3b09e90661c550bab24220e | /scripts/generate_figure.R | 96a212f3dd711fd879535a89410a120d3a69bf76 | [] | no_license | mbi2gs/its_blast_demo | 1b883c4db21d6aa5a572b46bf3c6ae783cdf9619 | f68ff51cfbbee98553fd6f3322297a0004457b0b | refs/heads/master | 2020-08-30T14:35:58.176888 | 2019-11-20T17:10:36 | 2019-11-20T17:10:36 | 218,410,699 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 403 | r | generate_figure.R | library(tidyverse)
genus_df = read_delim('results/summary_genus_counts.tsv', delim='\t')
ggplot(genus_df, aes(x=reorder(genus, -`0`), y=`0`)) +
geom_bar(stat='identity', aes(fill=genus)) +
coord_polar() +
scale_y_log10() +
theme_minimal() +
theme(axis.title=element_blank(),
axis.ticks = element_... |
fdb16268e3bab462fc3a3ea2ecb05fd7bcecb50d | 71fabf8fb017e64d9a0fc0bbde44204b893e2735 | /DataScience/pset1/HW1.R | e8fc8f5ca1f47b273e436ae2b1015cfa18fb15b8 | [] | no_license | wconrad9/r | 08e026d4b3a873a111a233a8cd8e56733ca71119 | 4b279ea6f6f4b8d22366a2c09e982285d101cbb7 | refs/heads/main | 2023-01-01T14:04:16.185130 | 2020-10-18T16:16:30 | 2020-10-18T16:16:30 | 305,004,306 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,642 | r | HW1.R | install.packages("openintro")
library(openintro)
library(tidyverse)
marioKart
#to answer question 1, we find that the least expensive set with one wheel was 33 dollars
marioKart %>%
group_by(wheels) %>%
filter(wheels > 0) %>%
arrange(totalPr)
#question 2: UPS 3 day ground had the smallest shipping range at $... |
fcd9cddf71ebbe637e78c18cde02a6cc9af0933f | 765ace978763d7e7d0ad63112565605100fec15a | /data handling/Day 1_ study_.R | f50328f5503c3f1700b3e3e3cc4d2dc62f5b6f21 | [] | no_license | ykiseong303/DataHandlingStudy | 56017387b56fd9e434209c0278bcb310c250ece2 | 507179a24523c7fe961241f273d838b021d4d896 | refs/heads/master | 2020-12-28T18:54:25.116276 | 2020-02-21T14:42:01 | 2020-02-21T14:42:01 | 238,449,808 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,598 | r | Day 1_ study_.R | #벡터 생성
vector1 <- c(1,2,3)
vector1
#벡터의 구조 확인
str(vector1)
#숫자 1과 벡터c(1)의 비교
# > 결과 true
item <- 1
vec_item <-c(1)
identical(item,vec_item)
# 벡터의 타입 확인
mode(vector1)
# 범주형 데이터
과일 <- c("사과","오렌지","딸기","딸기","사과","오렌지","사과","오렌지")
과일_범주 <- factor(과일)
print(과일_범주)
str(과일_범주)
# 범주형 데이터를 문자 벡터로 변환... |
6bf1868b87c9bf40da7b725c4f7c67592d813e59 | 29585dff702209dd446c0ab52ceea046c58e384e | /grouped/R/lL.gaussian.R | 80ff3cf641708ef1fc54a0c5c50be3188504cedf | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 157 | r | lL.gaussian.R | "lL.gaussian" <-
function(params){
sigma <- params[p]
mu <- c(X %*% params[-p])
-sum(log(pnorm(qb, mu, sigma) - pnorm(qa, mu, sigma)))
}
|
b9f61bc9d45818e749d6fa0dc9f5fd387ef355b8 | 6312f6e7e2e22bb7cb7580b0b92c0a6bbeeb5627 | /wltr_new/wltr.git/joinTrainAndLabel.R | c7d8ebecec1de046b054eea730758a1ceef05ef9 | [] | no_license | babyang/wltr | 20708cee2661b9c6ae8b67bdf43343dfbeadac84 | 9a9a76d474aebf3fc350b9cdcf5734328b11be60 | refs/heads/master | 2020-05-17T02:40:21.406024 | 2014-12-02T09:30:23 | 2014-12-02T09:30:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 488 | r | joinTrainAndLabel.R |
readDataFromCSV <- function(filename) {
return(read.table(file=filename,
sep=",",
header=TRUE))
}
joinTrainAndLabel <- function(args) {
trainFilename <- args[1]
labelFilename <- args[2]
sampleFilename <- args[3]
trainData <- readDataFromCSV(trainFilename)
labelData <- readDataFromCSV(labelFilename)
... |
473a9eec8d5fe0bbffd2eb565770bd7dbfa589bf | fdd5f9373bd535dda8575a681fbd5be7c8e3af1a | /R/fillna.R | 70db49d9c2cafc63f7a56655f1ba93433e47150e | [
"MIT"
] | permissive | jakesherman/jakemisc | 941f46cd724cc15610c9dc9acb19bd6b973bc6eb | 13dab160b6776b813c9768fd66654332741b5de1 | refs/heads/master | 2016-09-15T03:41:46.207665 | 2016-05-05T21:29:49 | 2016-05-05T21:29:49 | 27,505,697 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 10,393 | r | fillna.R | ## ============================================================================
##
## fillna() - inspired by pandas.fillna()
##
## ============================================================================
# Method dispatch for fillna
fillna <- function(data, ..., inplace = FALSE) UseMethod("fillna")
# Data.table m... |
83de715a466b1bac78fe1f842631515c80b143ba | e8f6138f6bee3e95bea0255ec447226b4ef4b5f7 | /EnhancedSpiral.js/rscripts/server.R | 4403cb37fac9eb0f3335d068cf63ad28a4420f4b | [] | no_license | timkonieczny/LekagulChallenge | 4253c457d4ac0f85089ced3ef222ebda3738a9de | 84b06bb8ed9f804c1428500f57c69f11631fd8f2 | refs/heads/master | 2020-06-15T09:14:36.200528 | 2017-08-09T06:56:44 | 2017-08-09T06:56:44 | 94,133,182 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,086 | r | server.R | require(Rook)
require(xts)
require(rjson)
find.freq <- function(x)
{
n <- length(x)
spec <- spec.ar(c(x),plot=FALSE)
if(max(spec$spec)>10) # Arbitrary threshold chosen by trial and error.
{
period <- round(1/spec$freq[which.max(spec$spec)])
if(period==Inf) # Find next local maximum
{
j <- whi... |
45ebc45bcee44d7690e03923d12b2dcc8b7e6b50 | 93d426e1a913d462a7969c84feae89cf59fef34e | /R/utils-globalVar.R | 5a04c40d18138c48e7b7d5de6c435aff7f15b305 | [] | no_license | cran/polypharmacy | 2c14ad772d1ccd01118b24d602388a2366fc7057 | 0f9cc8c7ffe7849356d176ece3f09b8f30c9b1b1 | refs/heads/master | 2023-06-21T22:59:22.184540 | 2021-07-12T08:30:02 | 2021-07-12T08:30:02 | 385,299,314 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 489 | r | utils-globalVar.R | ### Just to be sure that R CMD CHECK has no NOTE messages
### -> problem with data.table package
globalVariables(sort(unique(c(
".",
"by_hosp", "by_hospit",
"diff_grace", "drug_code", "drug_duration", "duration_ajust",
"grace_per",
"hosp", "hosp_date",
"i_D_s_", "id", "ids", "is_present",
"nd... |
8070801f5d0c0837f1c5daac7b0335da7f4a6226 | 67c40f58937f1ab0ee425eb026eaeb82b1ef52c3 | /stretcher_to_zoo.R | c6f1d63650891e8279b22cf55f987712c0568913 | [] | no_license | katherinetanaka/scripts | 1de668fd8e8b856f6d48bee8fdf798e869d63f87 | 2a995f3325697456d7764eb88b02984cf4b67ca4 | refs/heads/master | 2021-01-01T15:32:48.559127 | 2017-10-11T14:58:20 | 2017-10-11T14:58:20 | 97,637,368 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,168 | r | stretcher_to_zoo.R | *###############################################################################
#### Multiple alignment to variant base call -- pAsa4 ##########################
# Katherine Tanaka, May 12th 2015 ##
#### Attribution ####
# This script is part of the following publication:
# Tanaka KH, Vincent AT, Trudel MV, Paquet VE,... |
c7388c5d8102e255c7a74ea0576ae0ccc6d31808 | b85cb92935407d40d03405ea09a7f96d005c1954 | /scripts/0_1_extract_covariates.R | 17c645190de4d58db2bef049b8ab4bcaa29723c0 | [] | no_license | enerhiya/Spatio-Temporal-Cross-Covariance-Functions-under-the-Lagrangian-Framework | 0cccffd7a98d13e4f4c7353d9c42e923ae34dbdd | 5084f24d9b89c9bff2794b0575a44d7ea0ccaf54 | refs/heads/master | 2021-06-18T19:50:38.829233 | 2021-02-17T17:09:46 | 2021-02-17T17:09:46 | 177,747,457 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,188 | r | 0_1_extract_covariates.R |
directory <- '/home/salvanmo/Desktop/'
root <- paste(directory, 'Spatio-Temporal-Cross-Covariance-Functions-under-the-Lagrangian-Framework/', sep = '')
source(file = paste(root, "Functions/load_packages.R",sep=''))
source(file = paste(root, "Functions/auxiliary_functions.R",sep=''))
saudi<- map("world", "Saudi", fi... |
99ed0d15ae35a29497cb93988b62b03bc396a4d6 | 7255055a2d8552621a5839aac9c0f45761ceabf6 | /GitHub/monografia/Scripts/CalculateCompanyTechAn.R | 3a0097c8add261d68a788315c0f8bc5e566e6abe | [] | no_license | camurca1/monografia | e78072ee7520fd417df0d38629ba0655667abee0 | ae3df56c192fa94f435ea50b2e439ab61056ae2a | refs/heads/master | 2023-08-06T05:56:47.305072 | 2021-08-10T20:53:11 | 2021-08-10T20:53:11 | 373,916,678 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,587 | r | CalculateCompanyTechAn.R | # Escrito por: Alexandre Camurça Silva de Souza
# Ambiente RStudio Desktop 1.4.1717 "Juliet Rose"
# Etapa 5 - Calcular indicadores técnicos
# limpar memória e desativar notação científica
rm(list = ls())
options(scipen = 999)
#### Gereciamento de pacotes ####
# informar os pacotes que serao utilizados no script
pa... |
e4cc19edf12bb8e1c60f788f5e891693e0d8745a | ce02b8bec171866b782e9fa700ab86863a6e002f | /R/Aoptrcd.maeT.R | ffb5ec8db59455d37abf3ee3b8c29022a5d13292 | [] | no_license | cran/optrcdmaeAT | f8d66c044adda02648e07c7846d75e48fb34821e | 20d1a5e82d018ba60a0839dd720f168c5f0f2f73 | refs/heads/master | 2021-01-19T13:05:39.050377 | 2017-04-12T13:24:01 | 2017-04-12T13:24:01 | 88,062,965 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,276 | r | Aoptrcd.maeT.R | #Subsection 2.1: Function for search of A-optimal or near-optimal row-column designs
# SubSubsection 2.1.1 (Function for construction of A-optimal row-column designs using treatment exchange algorithm)
Aoptrcd.maeT<-function(trt.N,col.N,theta,nrep,itr.cvrgval) {
#House keeping
del.1<-matrix(1000,trt.N,3)
de... |
0f651f9bd2fd5913800f07288c26882466df9aaf | 6eed4337c1a918c2e615198699b8271ac8d25ffc | /R_basics/3Rloop.R | 3642437685b212d2b6e1b76f72c2da20185b4568 | [] | no_license | Niks056/R_basics | b1653d6d0cb0d6f31033fa1c822a513272c5d43d | 67fb11246ebb5757a0f3d19543361bae23586064 | refs/heads/master | 2022-12-12T17:18:54.513429 | 2020-09-10T10:00:21 | 2020-09-10T10:00:21 | 294,370,966 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,179 | r | 3Rloop.R | #for loop
x <- c(5,8,10,15)
for (val in x){
print (val)
}
numbers<- c(6,5,3,8,4)
n=0
for (val in numbers) {
n=n+val
print(paste('TotalAmount:',n))
}
rollno <- c(1,2,3,4,5)
marks <- c(70,40,55,35,80)
n=length(marks)
result<-vector()
count<-1
for (val in marks) {
if(val>=50)
{
... |
62267595e091f08cc7002f370fee7c701fb5be10 | f94fdac9ef22ded8a95e6cc3758795314c2f9814 | /man/ORDERS.Rd | 87f2320b8e2827758607861e7d7d800a1b1f8cc4 | [] | no_license | shawngiese/classic.models | 0d4be31582b4552931d145cd5a40f16345aa672a | 56c1290a278748a3796e1a9d96f86fe5a25cd591 | refs/heads/master | 2021-06-10T21:17:18.368100 | 2020-11-24T20:34:14 | 2020-11-24T20:34:14 | 100,576,465 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,020 | rd | ORDERS.Rd | \name{ORDERS}
\alias{ORDERS}
\docType{data}
\title{Order data}
\description{
This data describes the information about each order, the order status, comments by sales, and the dates of order events such as the order date.
}
\usage{data("ORDERS")}
\format{
A data frame with 326 observations on the following 7 variabl... |
4cea227d1e7a86c3f25b9c467875389c28776387 | 7ccbc70f0348ce89aa72e28296e47d751f8a1938 | /rebuild/ConvertToClioInfraLayout.R | 0f9c321d2533f42a0ba981df2bfb60fa19f45a2f | [
"MIT"
] | permissive | CLARIAH/wp4-clioinfra | 0ccdb61ffe7799442d1cce8c65ab72cf565c384c | 52a9f16c299d4286123b6ee16d29bed3afdaa630 | refs/heads/master | 2022-09-26T14:22:28.060810 | 2022-09-09T13:45:06 | 2022-09-09T13:45:06 | 83,451,537 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 43,697 | r | ConvertToClioInfraLayout.R | options( java.parameters = "-Xmx10g")
library(rJava)
library(WriteXLS)
library(xlsx)
library(data.table)
library(tcltk)
library(countrycode)
setwd(dirname(rstudioapi::getSourceEditorContext()$path))
source('hwlfunctions.R')
AddColsForMaddision <- F
ISO2plus <- read.xlsx('../UPDATE 20210315/ISO_Codes_2_3.xlsx',sheet... |
890ab1ff655f45ed45111cdcaf8b212d3e67c0d2 | 3e2422c3547f959c36bf69ad38007bcc1b958120 | /CensusData/top_five_cities_deaths.R | 1dc87c791d1a226d1c1d184a160429df4971d32c | [] | no_license | mewsha/r-examples | e8a93a1941b532daa7370a2e6640c8ea515f945d | 6aff24957e3ca854b2547a486568c1fef9ca73a7 | refs/heads/master | 2020-06-27T11:57:36.829250 | 2019-08-13T17:33:57 | 2019-08-13T17:33:57 | 199,948,477 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,891 | r | top_five_cities_deaths.R | #' Census Data Script
#' Author: Jessica Diehl
#' Data Source: https://www.census.gov/data/datasets/time-series/demo/
#' popest/2010s-total-metro-and-micro-statistical-areas.html
#' Style Guide: https://style.tidyverse.org/index.html
#' Load Dependencies ---------------------------------
options(scipen=999)
... |
8c1d4e3ca9e18f15488267bc3f1dcac6be4abb16 | ec9725a7107f32f5bfff68fe6832ad0d5e1942f5 | /session11.R | fa06e1dc89d71314be943639a9f3835216825bf3 | [] | no_license | leezasantos/readmission_analysis | 4b284aa2ece74a36b6b8465defce07741be04c8e | 7cb5d33561a938168211771b3a75e2abeabe0089 | refs/heads/main | 2023-04-10T11:22:32.087149 | 2021-04-20T03:08:15 | 2021-04-20T03:08:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,654 | r | session11.R | # installing packages
install.packages("dplyr")
install.packages("ggplot2")
install.packages("tidyr")
install.packages("PASWR2")
# load libraries
library(dplyr) # data cleaning
library(ggplot2) # data visuals
library(tidyr) # data visuals
library(PASWR2) # z test
# load data, data was converted to csv
df... |
04469f32ea64b56c815b5933691cf7a6a358e795 | f2a7091926c21c42b6daf8cfc2eef7682a4cc338 | /scripts/07-figure-2.R | adf05d035bdde8d4ba7b6fc8e28a9592d8fd82ef | [] | no_license | mneunhoe/wahlkreis_vorhersage | b17d7d4d6da6054714bd86e6d61a94eb09c7f6aa | 4b7e50c426544446043e80dbd45f7b0d97024340 | refs/heads/master | 2023-01-14T21:49:47.482057 | 2020-11-17T12:44:18 | 2020-11-17T12:44:18 | 180,787,479 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,052 | r | 07-figure-2.R | ###########################
# Plot win probabilities on maps
###########################
old_par <- par()
nsim <- 500
mu_nsim <- 25
election <- 2017
cutoff <- 2
res_co_el <- readRDS("../processed-data/final_res_09_17.RDS")
res_list <- res_co_el[[paste0(election)]][[paste0(cutoff)]]
winner <-
matrix(
unlist(... |
8f32f3e4628ae8ae7cfc5b180a76ad73d4e9aa2e | b9b14fdf597bcfc1f400b4e51aafadb2d7b10acf | /src_old_2017/remove_dups_nhanes_table_description.R | f35309c062021e93870bc0f1f6ae9b68c930a9d1 | [] | no_license | chiragjp/nhanes_scraper | b625770008b8adb89f330701ee28c192485bab23 | 75ea624045b9e8869e23dcc4056e5eeda5170241 | refs/heads/master | 2021-01-21T15:03:28.868170 | 2020-06-02T15:14:27 | 2020-06-02T15:14:27 | 29,565,384 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 835 | r | remove_dups_nhanes_table_description.R | ## remove dups, ugh
source('db_connect.R')
sql <- "select table_name, count(table_name) from nhanes_table_description group by table_name"
dupTable <- dbGetQuery(con, sql)
duplicates <- dupTable[dupTable[, 2] > 1, ]
sql <- 'select * from nhanes_table_description'
allTables <- dbGetQuery(con, sql)
dupTables <- allTab... |
605e57d6968761bc45a2d49feb978cd42eef73b0 | 416550c21c0e3f49ae34ef843b4c352910c3c2f9 | /man/MsDataSet-class.Rd | c6783b3b9f10e8ce8a7cb8fd89329eb8a80a66c8 | [] | no_license | thomasp85/MSsary | 34dc8e93fd13a33ba6f78598626bb134d6cb151c | bf182b67b072256c4ff16b8c72678109f899ecc5 | refs/heads/master | 2021-01-22T12:12:39.641522 | 2015-01-26T11:44:40 | 2015-01-26T11:44:40 | 25,297,627 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 305 | rd | MsDataSet-class.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/MsDataSet.R
\docType{class}
\name{MsDataSet-class}
\alias{MsDataSet-class}
\title{Store and operate on sets of samples}
\description{
This object handles multiple MsData sets and facilitates batch analysis on
these.
}
|
fa45b0bfc5edd866efafe1267bb12448d1ee3140 | d7800c86c6f8a41bce1be6bd736339d7590da999 | /R/dkim.R | 179d96de9bbc29f8d9d27aee79fc3e8ab8fc4907 | [] | no_license | gyepi/aws.ses | e2974f8a8633b20dd15b5850c7275784c45fc81d | 0db7875425d7089de595b9b9af0f5dd15c498380 | refs/heads/master | 2020-05-07T10:49:31.816279 | 2019-04-09T19:56:08 | 2019-04-09T19:56:08 | 180,434,185 | 0 | 1 | null | 2019-04-09T19:16:32 | 2019-04-09T19:16:31 | null | UTF-8 | R | false | false | 1,316 | r | dkim.R | #' @rdname dkim
#' @title DKIM
#' @description Manage DKIM
#' @template identity
#' @template dots
#' @examples
#' \dontrun{
#' verify_dkim("example.com")
#' get_dkim("me@example.com")
#' set_dkim("me@example.com", TRUE)
#' get_dkim("me@example.com")
#' }
#' @export
get_dkim <- function(identity, ...) {
query <- l... |
85f50bea59bcae23721eb245159ba3f7d6545247 | 2a631ebe0119ecb1ac2ef2ff3b270b06a05755ee | /R/R-class/c4/mynote_chapter4.R | c1a7385a4359fdede8f50ef99c4fdde2a8d56e7e | [] | no_license | WangLiuying/R-in-SOE | 1642ebe8b5745cfee16f2e594e40a1dce4f79d7e | bf582e2f1018720ce6f7fae2e7fe5bff36f3f0b9 | refs/heads/master | 2021-01-21T06:25:06.456610 | 2017-05-09T00:17:58 | 2017-05-09T00:17:58 | 82,865,560 | 0 | 0 | null | 2017-02-23T00:13:35 | 2017-02-23T00:13:35 | null | UTF-8 | R | false | false | 3,967 | r | mynote_chapter4.R | ##mynote 2017-3-28
iTotal <- 0
for(i in 1:100)
{
iTotal <- iTotal + i
}
cat("Sum of 1-100:",iTotal,"\n",sep="")
szSymbols <- c("MSFT","GOOG","AAPL","INTL","ORCL","SYMC")
for(SymbolName in szSymbols)
{
cat(SymbolName,"\n",sep="")
}
x <- matrix(1:6, 2, 3)
ifelse(x >= 0, sqrt(x), NA)
ccc <- c("b","QQ","a","A","bb"... |
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