blob_id stringlengths 40 40 | directory_id stringlengths 40 40 | path stringlengths 2 327 | content_id stringlengths 40 40 | detected_licenses listlengths 0 91 | license_type stringclasses 2
values | repo_name stringlengths 5 134 | snapshot_id stringlengths 40 40 | revision_id stringlengths 40 40 | branch_name stringclasses 46
values | visit_date timestamp[us]date 2016-08-02 22:44:29 2023-09-06 08:39:28 | revision_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | committer_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | github_id int64 19.4k 671M ⌀ | star_events_count int64 0 40k | fork_events_count int64 0 32.4k | gha_license_id stringclasses 14
values | gha_event_created_at timestamp[us]date 2012-06-21 16:39:19 2023-09-14 21:52:42 ⌀ | gha_created_at timestamp[us]date 2008-05-25 01:21:32 2023-06-28 13:19:12 ⌀ | gha_language stringclasses 60
values | src_encoding stringclasses 24
values | language stringclasses 1
value | is_vendor bool 2
classes | is_generated bool 2
classes | length_bytes int64 7 9.18M | extension stringclasses 20
values | filename stringlengths 1 141 | content stringlengths 7 9.18M |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e41cb125fc0050e0e5f313b58d1a6b08b9d175f8 | fa1cac5372cac6deda38a026e6ffe296a22fd186 | /demo.R | 3f508a68cf7bca009b1e6b105e90ee8013fe5a98 | [] | no_license | sVujke/inequality_measures_r_py | 2752322e5a0c6142a61f6a5727eea88b520a8860 | 59c51603571c531b35362b60a3eee914d4c63526 | refs/heads/master | 2021-01-20T09:45:56.281558 | 2017-05-11T16:22:53 | 2017-05-11T16:22:53 | 90,283,711 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,248 | r | demo.R | library(ineq)
data_set <- read.csv('users_dataset.csv', TRUE, ",")
head(data_set)
income <- data_set$income
paste("gini:", ineq(income,type = "Gini"), sep=" ")
#print("gini: ",ineq(data_set$income,type = "Gini")
#plot(Lc(AirPassengers))
gini <- ineq(income,type = "Gini")
atkinson0 <- Atkinson(income, parameter = 0, na... |
153e5dc647c8702b5ace082ffe2af85e23f050f2 | 5f89f3a68e52f8cfc59d001efce81e2357aedb6b | /code_dtwcp/3-split-aki.R | 626b05d3bed8674b2daa67d3708e7c820c0eb1c7 | [] | no_license | andreaczhang/DTW-CP | b399aa601e96956114a0443d0ab7d0f4a068450c | 5c908643f62f7a0662cb1ded40ce787f79c45191 | refs/heads/master | 2022-11-14T23:05:46.832435 | 2020-07-14T13:16:03 | 2020-07-14T13:16:03 | 278,315,923 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,342 | r | 3-split-aki.R | # keep spliting, now AKI cohort
# the difference is only that I sample 350/150 control/cases
# 50 random sets for each day
library(magrittr)
library(purrr)
library(dplyr)
library(tictoc)
# library(caret)
#### data
dataPath_rev_aki <- 'path/to/data/aki/'
aki_dinfo <- read.csv(paste0(dataPath_rev_aki, 'cohortInfo_... |
5260ad1a17362010859690a3b9fed244db569631 | 07f566e07ed138e6d1db5dfbbfaf977be87c91ad | /01_hello_world.R | bcc17ebffe99daf49cf0ca94059eb1c1c3fd367f | [
"MIT"
] | permissive | eshanmherath/r-in-data-science | 3bef91450f2041ce09cca5ce1faf105afb02032c | 7aa4bdff396c7325413190cc437cd834a0516b4c | refs/heads/master | 2021-09-03T21:54:35.242628 | 2018-01-12T09:37:37 | 2018-01-12T09:37:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 80 | r | 01_hello_world.R | # R Programming Hello World
hello.world <- "Hello World"
print(hello.world)
|
251faaae16c6ae0668c55fd513c36d93e0da987d | f54a2ac4d0cf19f638b700d87f3c42ddf9807f86 | /Xinyu Leng homework3-3.R | ab5839e774200c652a30955d486dbe4a0c4acb14 | [] | no_license | XinyuLeng/Datamining_Homework3 | ad79662d81843fa974741cacc04db49068fbab29 | f6e2d40c8006bd0684bc8d2c9f06b767d9c1dbbb | refs/heads/main | 2023-04-03T22:28:13.103887 | 2021-04-12T08:36:59 | 2021-04-12T08:36:59 | 357,117,613 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,590 | r | Xinyu Leng homework3-3.R | # \section{Problem 3:Predictive model building: California housing}
install.packages("ggmap")
install.packages("leaps")
library(ggplot2)
library(tidyverse)
library(ggmap)
library(leaps)
library(scales)
CAhousing <- read.csv("C:/Users/Administrator/Desktop/CAhousing.csv", stringsAsFactors=TRUE)
summary(CAhousin... |
da66dc8dcbb7645c966f5e21d4cf801d7d43e231 | 04825f1f6b860bb08287c13dda6d5f8b7587f987 | /plot2.R | 5bec83b88dbe5469c6600ebb443a808fafd57bfa | [] | no_license | ClauMorgado/ExData_Plotting1 | 4520d053df38176b9f46ea577ff477c9f4d924ad | fbc9a3daf927321c8e90ffe18346f7dbd2e98e87 | refs/heads/master | 2021-01-14T13:57:38.171620 | 2015-04-11T11:10:21 | 2015-04-11T11:10:21 | 33,732,437 | 0 | 0 | null | 2015-04-10T14:22:06 | 2015-04-10T14:22:05 | null | UTF-8 | R | false | false | 449 | r | plot2.R | data <- read.table("household_power_consumption.txt", sep=";", header = TRUE, stringsAsFactors = FALSE)
gap <- subset(data, Date == "1/2/2007" | Date == "2/2/2007")
gap$DateTime <- strptime(paste(gap$Date, gap$Time, sep=" "), "%d/%m/%Y %H:%M:%S")
png("plot2.png", width=480, height=480)
Sys.setlocale("LC_ALL","C")
... |
3fb3ea9bcf8570bfd1bc37fe5fbbf802895ed9ef | f7e9d03142cf38c239eed63b2e7cc41a45456398 | /RESULTS_tableS4.R | 21f6978ebd7db845290db2d30428f90f952d8f0f | [] | no_license | bozenne/Article-lvm-small-sample-inference | 1e67c5f7de9e00c29b74c9b2c0e6b50d06004e3d | 8290c2452a247bd5d58f7636363e1e9e7b68e436 | refs/heads/master | 2022-01-29T03:37:23.136079 | 2022-01-18T09:20:27 | 2022-01-18T09:20:27 | 166,855,261 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,844 | r | RESULTS_tableS4.R | path.data2 <- "./data2/"
## * package
library(data.table)
source("FCT.R") ## get function fitStudent
if(FALSE){ ## sanity check
set.seed(10)
dt.test <- rbind(data.table(name = "rt", estimate.MLcorrected = rt(1e3, df = 3), se.MLcorrected = 1, df.MLcorrected = 3),
data.table(name = "rnorm"... |
29a7d08075b18af4d2cccb4a67ac955607e790e2 | 53a0e1eebcf10e6d661865624227e06774bc30f4 | /man/readControls.Rd | 8f6c9da10e98830fae29b846f260cc1496c18250 | [] | no_license | graumannlab/readat | 447416ce15be432e1349db57896edca165b973d1 | 34de431d478dac37d91bd87d1efdab7d43362da8 | refs/heads/master | 2020-09-10T01:02:53.251425 | 2020-05-14T12:24:44 | 2020-05-14T12:24:44 | 67,595,712 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,382 | rd | readControls.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/read-sample-submision-input-files.R
\name{readControls}
\alias{readControls}
\title{Read SomaLogic Sample Submission Controls File}
\usage{
readControls(file = "controls.csv")
}
\arguments{
\item{file}{A string denoting the path to an input C... |
e55ebe23df06ac0f88cd5fb59c91ace529d37192 | de3f9765bc5085aa11c508b32a1a8565cf2a8739 | /DAM1_reader.R | cafcc0695bacf77ee92f2abad9160485cc75665c | [] | no_license | nli8888/Circadian_Rhythm | 4922bdc87369a36a517738a3c5ad2eb8bd2b5ef2 | 922e6e5248ccd4a44d5c3a23f7a8b85b457e08bf | refs/heads/master | 2021-01-19T22:52:42.547952 | 2017-06-27T07:43:24 | 2017-06-27T07:43:24 | 88,878,910 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,314 | r | DAM1_reader.R | library(rethomics)
#time_format can either be "hr"/"min"/"sec"
#time_to_round_to in seconds
DAM1_single_reader = function(file,
#file_format = "DAM1",
time_format = "min",
time_to_round_to = rethomics::hours(1), #aka hour in seconds
#num_of_d... |
78e4c9749370936e5f490e8a0db883163202873c | 6f59eb1098f964b0611ffa60127759eb11f8d968 | /src/R/PlotDiscriminateErrorFunctions.R | f2d5d10c245c23c88b67927c1cc9c52c3dbbdd6a | [] | no_license | evolvedmicrobe/cafe-quality | f1403725ea11f771a70b5dea27cf3febaad09c0f | c54e2eb629cb89bf44acd48a497745ee4b04a4a7 | refs/heads/master | 2021-01-15T13:34:25.551557 | 2015-04-02T16:37:56 | 2015-04-02T16:40:53 | 25,172,010 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 531 | r | PlotDiscriminateErrorFunctions.R |
x = seq(-13.8, 4.5, .01)
y = -log(1+exp(x))
plot(x,y)
y2 = 1 / (1 + exp(-x))
plot(x, y2)
n = length(x)
y3 = rep(1,n)
y3[x<0] = 0
xs = c(x,x,x)
ys = c(-y,y2,y3)
group = c(rep("Log-Sigmoid (CCS)", n), rep("Logistic (Quiver/MCE)", n), rep("Empirical Error Rate",n))
d = data.frame(xs,ys,group)
ggplot(d, aes(x=xs, y ... |
c3b944c8e33467e89b38de9f6fa323815d63e240 | e41839aeffcce1a9c8be1983f47633eabdce4d76 | /P1/Codigo/ParteB.R | 118af9421461256a54cc2d180c4993d913107de9 | [] | no_license | byKakayo/analise_reco_padroes | ca491e0550fe985bb57f6ed47f15ffea23e921cd | 3b921e102de10a8726a19e69be6b4ab9881fb796 | refs/heads/master | 2022-12-18T23:36:48.780445 | 2020-09-18T20:48:48 | 2020-09-18T20:48:48 | 261,283,263 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 1,531 | r | ParteB.R | #AUTOMATO PROBABILÍSTICO
#Chama arquivo em R com as funcoes utilizadas
source("functions.R")
#Nós do automato
nos <-c("0", "1", "2", "3", "4", "5")
#Probabilidade de transicao
dt <-c(0.9, 0.882, 0 , 0 , 0 , 0.01 ,
0.1, 0.098, 0 , 0 , 0 , 0 ,
0 , 0.02 , 0.2, 0.194, 0 , 0 ,
... |
556dbf4e92599b571c8a3e68ebc1baf5bbd20d52 | 21cf09a1aad63021ca2788fea19b83d26c389ef3 | /marathon/marathon_dashboard/server.R | 10efb49700ca963424366731cdb081ffad6c7083 | [] | no_license | AntoniaLovjer/NYC-Marathon-EDA | 8d51e8a7fd655c2d841e3b4cee3ff7bbfd3db8f9 | 3d9f7a4f53ea572357750d0753daa0e357f32be8 | refs/heads/master | 2020-04-15T00:37:55.337738 | 2018-12-13T16:30:52 | 2018-12-13T16:30:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,040 | r | server.R | # create the server functions for the dashboard
server <- function(input, output) {
# Read Data
marathon <- read_csv('marathon.csv')
source('aux_functions.R')
# Read inputs
location <- reactive({input$loc})
state <- reactive({input$state})
input_year <- reactive({input$input_year})
country <- reactive(... |
d97c2d4a6f4820528b788eb48fc6461f82807be7 | 90832012541b9c048a75ca29ebc88b806370f097 | /data-raw/populate.R | 5cf36f547192a7a4fb1e8cb6f33752846cbea019 | [] | no_license | beanumber/etllahman | b00197630d20887f1a8dc8a65a95fac5bcaeb8a0 | c79a0be77fc44600c6d7c98304bccd0c2ad8f85f | refs/heads/master | 2020-03-11T14:44:30.408132 | 2018-04-18T20:04:52 | 2018-04-18T20:04:52 | 130,063,432 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 167 | r | populate.R |
library(etllahman)
db <- src_mysql_cnf(dbname = "lahman2016")
bdb <- etl("etllahman", db = db, dir = "~/dumps/lahman")
bdb %>%
etl_init()
bdb %>%
etl_update()
|
3a9f38998b9e125102db6953d7de5ad59250ad0f | 8f8d61d286054a9b4ea299216ddf2e6f0ffe4221 | /lDopaEI/man/convertUnits.Rd | ae20a1a3098f24cd0ab84f26721c69a7a5eb85a4 | [
"MIT"
] | permissive | MonteShaffer/L-Dopa_EI | fcf9e6c703694bac4c4da4ea4fae1d60db30e644 | d5ceca2be18efe8313ee2b08f8659fcb0e81d26c | refs/heads/master | 2021-07-11T01:20:08.645760 | 2017-10-07T20:37:54 | 2017-10-07T20:37:54 | 105,927,990 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 530 | rd | convertUnits.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/integrateMe.R
\name{convertUnits}
\alias{convertUnits}
\title{Convert Units (from gravity to m/s^2)}
\usage{
convertUnits(x, from = "g", to = "m/s^2")
}
\arguments{
\item{x}{numeric vector}
\item{from}{units to change from}
\item{to}{units ... |
15eed6a99f8b0eb7358ac38e6f18567efd7b286f | 2b2908fb7a492b3f801f23c780a35cef8d5b0d59 | /WangJun/apriorTest3.R | d630967cf2f411c1062fa98a967d806a532ab401 | [] | no_license | yaoran2000/MyWork | 957129dfa4d587d0ad32f99d9f5c0ace1c36282a | c952e97654a9086cf53ab7dcaef56260540ffdd2 | refs/heads/master | 2020-04-06T04:44:31.844899 | 2017-04-25T06:15:52 | 2017-04-25T06:15:52 | 82,897,882 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 912 | r | apriorTest3.R | library(Matrix)
library(arules)
library(arulesViz)
library(grid)
library(arulesViz)
require('RPostgreSQL')
pw <- {"Admin1234$"}
drv <- dbDriver("PostgreSQL")
con <- dbConnect(drv, dbname = "mht",host = "localhost", port = 5432,user = "etl", password = pw)
rm(pw)
df_bill <- dbGetQuery(con,"select cast(t1.billid as tex... |
5bf49489b86276cf89e66e2744ebb7368a73f31a | 07f9ba53c35091bb55094a0244a2701056bd3323 | /man/player_profile.Rd | 9ed9018cbd1bca1e8245a3f04646f10d29423c33 | [] | no_license | MrDAndersen/mfl2R | cd1b9e4ed479c4cc7595a4cb5f8a5fd01deed4e7 | fa3496dbffaf7a55e2b990709e75a9122f8ae5a9 | refs/heads/master | 2022-12-12T15:53:21.890433 | 2022-11-30T15:08:19 | 2022-11-30T15:08:19 | 249,209,957 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 421 | rd | player_profile.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/endpoint.R
\name{player_profile}
\alias{player_profile}
\title{Player Profile}
\usage{
player_profile(player_id)
}
\arguments{
\item{player_id}{A single player ID or vector of multiple player IDs to get information on}
}
\description{
Returns... |
a9e5db5fa28d456ce9371fd639f8666a983c7f9f | a17cf22be2304c96d267fc1b68db7b7279c4a293 | /man/getCurrentTarget.Rd | 873fb6233b146a5675fefb9a51310fbbd166939d | [] | no_license | robertdouglasmorrison/DuffyTools | 25fea20c17b4025e204f6adf56c29b5c0bcdf58f | 35a16dfc3894f6bc69525f60647594c3028eaf93 | refs/heads/master | 2023-06-23T10:09:25.713117 | 2023-06-15T18:09:21 | 2023-06-15T18:09:21 | 156,292,164 | 6 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,021 | rd | getCurrentTarget.Rd | \name{getCurrentTarget}
\alias{getAllTargets}
\alias{getCurrentTarget}
\alias{getCurrentTargetSpecies}
\alias{getCurrentTargetFilePrefix}
\title{
Get Current Target
}
\description{
Get details about the current target of organism(s)
}
\usage{
getAllTargets()
getCurrentTarget()
getCurrentTargetSpecies()
getCurrentTarget... |
7caba2bfffba12282210d4c66a72dd13db52b395 | 23b6db906b59256e600320113aba459a5123fe05 | /man/launch_app.Rd | 7688e9afdac1582710eb4211ea077a1aacceef0d | [] | no_license | uk-gov-mirror/datasciencecampus.gRoot | d00486a737239ac1314ac8474b66a232da77316c | 353d2437bb13d3e48cd9c67e9bf0072616dd22c5 | refs/heads/master | 2021-09-23T09:59:54.027508 | 2018-09-21T13:00:42 | 2018-09-21T13:00:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 327 | rd | launch_app.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/launch_app.R
\name{launch_app}
\alias{launch_app}
\title{launch_app}
\usage{
launch_app()
}
\value{
launch the gReeneRy app in internet browser window
}
\description{
Visualise and interact with urban forest data of Cardiff
}
\author{
Joe Pes... |
6a25bd401e115b35acb8ee0f15f6f6b81305b4cc | d1e988ee94288a547f32063d500fdecd4e6b0bc1 | /Assignment3/E3.R | ac2ba5acd5046756e455844bb5831e9792523699 | [] | no_license | HaraldBrolin/Big_data_exercises | 1859c4dd1615e4c01fa4d0b3cbf0e412d0c6e33e | d4b884b394c5daf96a53b602b4889dbb7d53f9ab | refs/heads/master | 2021-10-09T13:04:05.233130 | 2017-12-14T08:43:02 | 2017-12-14T08:43:02 | 112,359,555 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 960 | r | E3.R | #-- Som perceptroner, kan testa utan training
rm(list = ls())
library(caret); library(mlbench); library(adabag); library(plyr); library(fastAdaboost)
load("HWD_train_data.RData")
set.seed(2017)
df_numbers <- sample(data_train)
rm(data_train)
id_train <- sample(1:dim(df_numbers)[1], 0.8*dim(df_numbers)[1])
train_data ... |
42ed82ce698c086f78fd3c8251c0995e3d71ce57 | 564b61f29747117c49fe061e8225e0cee485208a | /run_analysis.R | 426f659317278fb59524d9f9b15f3aa9f41c402d | [] | no_license | rajeshtwn/getandcleandata | 2d78898b526dcf3f50a258ce4f12008496bce3ef | 30ce783f98590c95e892fbe78749db03e05ee3b2 | refs/heads/master | 2021-01-10T05:39:19.447149 | 2016-03-10T09:35:27 | 2016-03-10T09:35:27 | 52,954,339 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,258 | r | run_analysis.R | run_analysis <- function() {
features = read.table(file="./UCI HAR Dataset/features.txt")
activity_labels = read.table(file="./UCI HAR Dataset/activity_labels.txt")
train_data <- read.table(file="./UCI HAR Dataset/train/X_train.txt")
test_data <- read.table(file="./UCI HAR Dataset/test/X_test.txt")
tcombined_data... |
ef5fd14883ab222d4635f75d1549f36fea325ad2 | 9d68fb722ba7a4fab7f959d3bf145b2f4ff44a8c | /tests/testthat/test-rsample-time_series_cv.R | e35ad3ecec9a8619fcf3ee17f367adb04c768e23 | [] | no_license | business-science/timetk | 29d2e7251082522d7d0683dae635402c532cba0b | 086f46824f8c53bfde83f709f964ae5d6b544a5a | refs/heads/master | 2023-08-04T19:50:20.594218 | 2023-03-30T12:32:01 | 2023-03-30T12:32:01 | 87,742,539 | 518 | 91 | null | 2023-05-11T15:54:47 | 2017-04-09T22:11:02 | R | UTF-8 | R | false | false | 4,695 | r | test-rsample-time_series_cv.R | context("TEST TIME SERIES CV")
# SINGLE TIME SERIES ----
m750 <- m4_monthly %>% filter(id == "M750") %>% arrange(desc(date))
resample_spec <- time_series_cv(data = m750,
initial = "6 years",
assess = "24 months",
... |
4f7e9a047b96ee32017f51ab177704d014600f32 | 5788453c664275919c952748a3f232359c5ea363 | /R/eurusds.R | 5647112f42f59e50f82be568ea719447df796ccc | [] | no_license | anb133/EurUsd | b857e9f624b689f4dce0686d470fb55335b5e645 | bdaec6f069327e0545ed148926c06706aa4d524e | refs/heads/master | 2020-05-29T16:43:39.416886 | 2019-05-29T16:24:41 | 2019-05-29T16:24:41 | 189,256,508 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,046 | r | eurusds.R | #' Get EUR-USD exchange rates
#'
#' The function gets EUR-USD exchange rates for the provided time period.
#'
#' @param date_from date. Formatted as yyyy-mm-dd
#' @param date_to date. Formatted as yyyy-mm-dd
#' @return data.table
#' @export
#'
#' @importFrom httr GET content
#' @importFrom logger log_debug
#' @importFr... |
9e1c93ab51d7c19758c40cd6991ac0a274627497 | 8bfee4afaa66bfbe790484ba48b3fe1f16ac2865 | /Code/bayesmpp_aux.R | 8bada7c8152ac5292cfec87a07c3e387859b9f7b | [] | no_license | AdrianaPAS90/Tesis_APAS | 71fecb250419d6607a2ad0b361cf51ae18f007ad | 3b135e5c4cd19f87c22e242c7d5bb68788737017 | refs/heads/master | 2021-01-12T09:51:24.772547 | 2018-06-29T18:11:39 | 2018-06-29T18:11:39 | 76,280,756 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,009 | r | bayesmpp_aux.R | ###----------------------------------------------------------------------
bayesmpp <- function(alpha_0 =2,beta_0 = 0.3, d, c, n, M){
# datos - arreglo de nx3 (col1-individuo, col2-duraciones, col3-costos)
# M - numero de simulacion del gibbs sampler
#
# Inicio-Repositorios
# Parametros
alpha_d_rep <- ar... |
7d1146009a085f2b029e7e6f80cffe99fcdb09f8 | 83676931d003fc9bb6821509279deb057d669ba3 | /data-raw/all_family_tree-3funds.R | 83f9475d9c8407866f92735f97edca5a34ad3d7d | [] | no_license | LunaSare/phunding | c70a6fd65314650cfd52471347df1aa7415e0a5d | 4ea97eebea39c820d0ad0c0e24700c86ab2182d2 | refs/heads/master | 2020-04-02T05:30:26.245639 | 2019-02-21T16:18:41 | 2019-02-21T16:18:41 | 154,083,205 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 695 | r | all_family_tree-3funds.R |
gg <- match(tolower(fam_tree_brlen$ott_ids), tolower(names(fam_funds$funds)))
# just to check which ott_ids are not in the output dated tree
gg <- gg[!is.na(gg)]
funds <- fam_funds$funds[gg] # the tips that have funding
length(funds) == length(fam_funds$funds) # if TRUE all funded taxa are in the tips of the tree
no_... |
49c4014b014c53e4132160e74445684870cf04c8 | ba6e98c2d1a1b4d7de382624632b5b442101898f | /Startup.R | 9c75a46edf5274e112b83b7491bfd77741103df0 | [] | no_license | amart90-UI/CriteriaScripts | 41d74534a30b63249ddc717c30c3aba83e87b184 | 36c2a8d6cbe24386c942c15c4c6b34957c859437 | refs/heads/master | 2020-03-23T01:08:25.057385 | 2018-07-19T19:06:31 | 2018-07-19T19:06:31 | 140,902,464 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,685 | r | Startup.R | #set up WD
setwd("C:/Users/PyroGeo/Refugia/Ranking") # school computer
#setwd("D:/Refugia/Ranking") # home computer
#setwd("C:/Users/Anthony/Refugia/Ranking") # laptop
#load libraries
library(sp)
library(rgeos)
library(raster)
library(rgdal)
library(matrixStats)
library(plyr)
library(gdistance)
library(dismo)
# Load ... |
ce836bea139274ce606136639e5211703fbd6efa | dd116ab1aa141a5c6128dd3c94eeed549c4ca983 | /man/remove_unused_jamie.Rd | cd1e921516f48a8638eac78d062553ec7002ce24 | [
"MIT"
] | permissive | pahanc/malariasimulation | 56aecf1f7f3057d6cb631a32aeda226e25f45946 | 48aea12df28d519bec16e2791d175b70ad8c7ecc | refs/heads/master | 2023-06-30T10:35:59.220798 | 2021-08-02T11:14:52 | 2021-08-02T11:14:52 | 351,858,799 | 0 | 0 | NOASSERTION | 2021-08-02T11:56:14 | 2021-03-26T17:19:21 | C++ | UTF-8 | R | false | true | 416 | rd | remove_unused_jamie.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/compatibility.R
\name{remove_unused_jamie}
\alias{remove_unused_jamie}
\title{remove parameter keys from jamie's format that are not used
in this IBM}
\usage{
remove_unused_jamie(params)
}
\arguments{
\item{params}{with keys in the jamie's fo... |
7e6eea37650c8739a76556f27678e2a849adbf94 | 5390aac150a93ba4762983b3eaaf1ec58c3e78d8 | /source/asm_Functions/asm_fragConsistencyChecker.R | f5a73283d80d4bd0625b7422b399ced1357de4fc | [
"NIST-PD"
] | permissive | asm3-nist/DART-MS-DBB | 144a44de34e71d3dcf4c19a2317d4f9766cbba41 | 6189fd3b894f69de49113ed13afd6a7c650f74d2 | refs/heads/master | 2023-04-10T22:33:33.820668 | 2021-05-03T20:10:16 | 2021-05-03T20:10:16 | 292,061,121 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 622 | r | asm_fragConsistencyChecker.R | asm_fragConsistencyChecker <- function(x){
nspectra = length(x)
metric1 = numeric(nspectra) # "center of mass" in each spectra. should decrease across energies
metric2 = numeric(nspectra) # max m/z.. too much spectral noise to be good as currently implemented.. do not return
for(i i... |
dcd43544a09f7cbad2df6fb6263f162dfc401668 | d5331ec752b979e7c0b7edb81536875f6400a97f | /man/ISRaD.extra.Cstocks.Rd | 2965e4838dd3290d63162d9fc5f4a7a7f2dde67d | [] | no_license | AuHau/ISRaD | 81ef5005951ad1dfb0bd72d34d2aea0ebd257f9b | 4e69adfdfadd521d932898a0b01f436bd2328e8e | refs/heads/master | 2020-04-18T01:23:27.646220 | 2019-04-04T16:25:29 | 2019-04-04T16:25:29 | 167,116,873 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 641 | rd | ISRaD.extra.Cstocks.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ISRaD.extra.Cstocks.R
\name{ISRaD.extra.Cstocks}
\alias{ISRaD.extra.Cstocks}
\title{ISRaD.extra.Cstocks}
\usage{
ISRaD.extra.Cstocks(database)
}
\arguments{
\item{database}{ISRaD dataset object.}
}
\value{
returns ISRaD_data object with fille... |
65eb9a7ff66ef9bc4675dc38e8aed2aaac8f6af1 | 112638177c6bf7820395552772b9f606b457b4c8 | /R/ICP-fitters.R | 6c2a5b186320e52db8d3fc56ae8b598cc1d9caa4 | [] | no_license | JoaoSantinha/ICPSurv | 36abb23bd9c605e6faf0fa46181f1bd4f2d02738 | 9b1b369e5ef70f88490608bb5f642cb659a2d22d | refs/heads/master | 2022-04-14T08:23:26.401308 | 2020-02-22T16:02:38 | 2020-02-22T16:02:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,567 | r | ICP-fitters.R | #' Internal regression functions
#'
#' The \code{fit_model} function is a generic function meant for internal use in
#' the \code{ICPSurv} package, and as such it is not exported to the namespace.
#'
#' The \code{fit_model.X} and \code{fit_nonparam_model.X} functions are internal
#' fitter functions. They are usually a... |
58d333abaa71b5737a671b506524d4fd787eb103 | 5e3f359317825ac9034f50c21746b1cab14bb554 | /Z_otherScripts/econJobforum.R | 8e3293fc239bd8d0c221008320fa96914c6ff109 | [
"MIT"
] | permissive | rpplayground/GSERM_TextMining | 961239625a027d6329a960e5eb7bbf3c8d5c154a | 49fccfe1877db8171f8cd357e06be49a0640b817 | refs/heads/master | 2020-09-08T13:13:54.360494 | 2019-09-09T15:25:11 | 2019-09-09T15:25:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,589 | r | econJobforum.R | #' Title: Obtain text from econonics job forum
#' Purpose: Learn some basic scraping
#' Author: Ted Kwartler
#' email: ehk116@gmail.com
#' License: GPL>=3
#' Date: 2019-5-12
# Lib
library(rvest)
library(purrr)
# Init
pg <- read_html('https://www.econjobrumors.com/') #https://www.econjobrumors.com/page/2
maxPg <- pg ... |
4f2fe3ea570effc0d85fb039466b400fbecdf940 | d2c892e59bb876e2205ad6ca9acb3e904aaeab5b | /code/R/SIMLR_Estimate_Number_of_Clusters.R | 5fd26f5096a64aac31a2956b1fd4731909f899f7 | [] | no_license | yuqimiao/multiomics-SIMLR | 088738b77a7e0441a41e0d6b14137c2e1aefa8a7 | bedd32e5e5ddafad2844803d901e5075c716203a | refs/heads/master | 2023-03-09T10:18:04.289011 | 2021-02-20T04:40:39 | 2021-02-20T04:40:39 | 293,400,493 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,526 | r | SIMLR_Estimate_Number_of_Clusters.R | # Estimates the number of clusters by means of two huristics
"SIMLR_Estimate_Number_of_Clusters" = function( X, NUMC = 2:5, cores.ratio = 1 ) {
D_Kernels = multiple.kernel.numc(t(X),cores.ratio)
distX = array(0,c(dim(D_Kernels[[1]])[1],dim(D_Kernels[[1]])[2]))
for (i in 1:length(D_Kernels)) {
distX... |
825f85bd8efb54459c50bc272bc8380dd1c391ce | 05d95ea2a571a5d502bbfe78f0c8f7e24cb5db80 | /inst/scripts/make-metadata_19Q3.R | 8808a98cb9ee10a091873b2b62645bdd82b03c4f | [] | no_license | back-kom/depmap | aa365a3e3e7da2f0efa9998d65ca9cbcdf013b45 | 548dac61536dae83a97a7e41032d36232713338e | refs/heads/master | 2022-07-14T14:40:41.075830 | 2020-04-17T15:40:09 | 2020-04-17T15:40:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,145 | r | make-metadata_19Q3.R | ### =========================================================================
### depmap metadata 19Q3 data
### -------------------------------------------------------------------------
### this script generates the metadata_19Q3.csv
## note: this script assumes that your current directory is depmap/inst/scripts/
met... |
cf844a8fa4fd9d97995b27b5dda82fec13a429a7 | a0108f3ebcea5379c09976db1d7c22cf426aa738 | /Chapter 11 - Introduction to Moderation/chap11 - analyzing data.R | 8ec3825d9591e6751827b103f59f58a5f5009b7d | [
"MIT"
] | permissive | ajb254/BehavioralDataAnalysis | 8562f1a4d4da8c9a26960d031da003a490f16e1a | accc4b523f868d0d74ab003a1a9e698d9abe1449 | refs/heads/master | 2023-04-26T21:38:45.541798 | 2021-05-30T08:14:22 | 2021-05-30T08:14:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,985 | r | chap11 - analyzing data.R | #################################
##### This script analyzes the data used in chapter 11,
##### Introduction to Moderation
#################################
##### Setup #####
# Common libraries
suppressMessages(suppressWarnings(library(tidyverse)))
library(boot) #Required for Bootstrap simulations
library(rstudioapi... |
968443c8d8866bee97c3da186789a19650f06b80 | a84a1f8417a7076e07d279eb91f436819802ad86 | /clustering/routine_data_preparation.r | 0df5c61cd074d459b56b82e46bd7a002ec3b1a12 | [] | no_license | mlukasik/spines | 1fbb6648663459f29be60529ec88fb56069c82cc | a2897ee35b5498a456bc53224e86dec0c7aa03ed | refs/heads/master | 2021-01-10T20:44:33.523480 | 2016-03-30T08:44:03 | 2016-03-30T08:44:03 | 40,179,987 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,153 | r | routine_data_preparation.r | # Routine that loads and prepares data for global typology generation.
source(file="loading.r")
source(file="pca.r")
print("DATA LOADING AND (PCA) TRANSFORMING")
if (!(exists("test.data.file"))){
test.data.file = train.data.file
}
# Print parameters
print(paste("train.data.file =", train.data.file))
print(paste("... |
4bebb4e0abbbbbaadb14143a3d84f04a565287f0 | 948eacbfad766b6345859b18fd9fcf969d57efe5 | /Discovering Behaviors with Unsupervised Learning/kmeans_steps.R | e52da8c5aed9d850e7e5f327605a2b7d330e325c | [] | no_license | enriquegit/behavior-code | 5d795de076f0adb4d17ab5cc8219e2c799d5b133 | 1c5063c223653a4d1375e992082d95c76751a731 | refs/heads/master | 2023-05-04T21:19:45.758564 | 2021-05-13T13:47:45 | 2021-05-13T13:47:45 | 297,452,697 | 8 | 4 | null | null | null | null | UTF-8 | R | false | false | 3,318 | r | kmeans_steps.R | # This script demonstrates k-means implementation.
source(file.path("..","auxiliary_functions","globals.R"))
source(file.path("..","auxiliary_functions","functions.R"))
# Prepare students mental health dataset.
# Load students mental health behavior dataset.
dataset <- read.csv(file.path(datasets_path,"students_menta... |
2c9a68df1c85bc40a72e7c9761215be31d9953fb | 645ba07f53773e9e4fa2b1462dd46293d3405d76 | /plot4.R | fb8171b77ca777276c2c81abfe9f85e05e895313 | [] | no_license | PanBartosz/ExData_Plotting1 | 98ba59423a52ae64e4296d1d3845362072ca0bb7 | 1b80a464af7cce8041e48c641222365b2c1cbff9 | refs/heads/master | 2021-01-24T22:36:41.308242 | 2015-06-07T19:15:21 | 2015-06-07T19:15:21 | 37,027,941 | 0 | 0 | null | 2015-06-07T18:49:15 | 2015-06-07T18:49:14 | null | UTF-8 | R | false | false | 1,072 | r | plot4.R | data <- read.table("household_power_consumption.txt", sep = ";", header = TRUE)
d1 <- subset(data, Date == "1/2/2007"| Date == "2/2/2007")
d1$Date <- paste(d1$Date, d1$Time)
d1$Date <- strptime(d1$Date, format = "%d/%m/%Y %H:%M:%S")
par(mfcol = c(2,2))
plot(d1$Date, as.numeric(as.vector(d1$Global_active_power)), type="... |
846769ac8b0f2eeb6b24fca2e0006465a0fc10f6 | a2170e32571a703e4acf2f2dcc0880044866b20c | /man/Timezone.Rd | 1b0d6611f9b48eb08230e16737d206f0cc209ee4 | [
"MIT"
] | permissive | grepinsight/lookr | de7cfd7e5870fc2f2296771be5c6ba64d1994f39 | c8a5e2f0a55bbfc3ce056974f09ade9187359951 | refs/heads/master | 2020-10-01T03:55:06.264726 | 2020-09-09T22:07:10 | 2020-09-09T22:07:10 | 227,449,023 | 1 | 0 | MIT | 2019-12-11T19:57:46 | 2019-12-11T19:57:45 | null | UTF-8 | R | false | true | 2,676 | rd | Timezone.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Timezone.r
\name{Timezone}
\alias{Timezone}
\title{Timezone Class}
\description{
Timezone Class
Timezone Class
}
\section{Public fields}{
\if{html}{\out{<div class="r6-fields">}}
\describe{
\item{\code{value}}{}
\item{\code{label}}{}
\item... |
03be97a9efd50b9a4e49cdae6b2071b11e12d4f9 | c7ea655b5fa1a26c34beb3449a86f3aa32dd5e1f | /simple.R | a51be7050cae3aa2de7679e77379e3a1dac98e93 | [] | no_license | tdhock/datatable-foverlaps | 0255f2be539020abe52fac2c9a4136a238150574 | fd202ea93a1cb67c11c783d40f287f8b8041b36b | refs/heads/master | 2021-01-22T04:48:17.153576 | 2015-02-12T19:00:54 | 2015-02-12T19:00:54 | 30,076,054 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,174 | r | simple.R | works_with_R("3.1.2",
GenomicRanges="1.18.4",
data.table="1.9.4")
bedGraph <- data.table(chrom="chr1",
chromStart=as.integer(c(0, 200)),
chromEnd=as.integer(c(200, 300)),
coverage=as.integer(c(1, 2)))
bg.gr <- with(bedGraph,... |
bca8e8ca1b38a12f0767a8cce636217863e3f6fc | 234a13f1c58ed3e724b54943c1a2ed6e12d77c50 | /Course Project 1 Setup.R | 860e903b92640541e55e8d5d8d4abf0690044b51 | [] | no_license | kward34/RepData_PeerAssessment1 | f01a2e1c9356f2ae9514eeb1e84b15772a4b4320 | 9b466be6bd98ae61426f0bd597617f822d4a0747 | refs/heads/master | 2020-12-02T07:46:31.961414 | 2017-07-10T03:26:54 | 2017-07-10T03:26:54 | 96,724,492 | 0 | 0 | null | 2017-07-10T01:43:49 | 2017-07-10T01:43:48 | null | UTF-8 | R | false | false | 383 | r | Course Project 1 Setup.R | setwd("~/Documents/Documents/Important Files/Hopkins/Data Science/Reproducable Research/Week 2")
if(!file.exists("./projectdata")){dir.create("./projectdata")}
fileUrl <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip"
download.file(fileUrl,destfile="./projectdata/projectDataset.zip")
unzip(zi... |
dd9554df0899327983275540a9adac71eea0b328 | cc28ab14a36ec63a828b840ea2f99a32c9dc5fdc | /man/smoking.Rd | 5fc9baed271d166521ad9a028fec8f408b1eb198 | [] | no_license | cran/HSAUR3 | 7dfea10c9c3c91784ed0644bcfdda0ae91adef8b | 02c2e7664afeb6d33c3b4bf3e28748b4a5b885aa | refs/heads/master | 2023-04-28T01:39:02.583604 | 2023-04-15T06:10:02 | 2023-04-15T06:10:02 | 18,895,724 | 5 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,118 | rd | smoking.Rd | \name{smoking}
\alias{smoking}
\docType{data}
\title{ Nicotine Gum and Smoking Cessation }
\description{
Data from a meta-analysis on nicotine gum and smoking cessation
}
\usage{data("smoking")}
\format{
A data frame with 26 observations (studies) on the following 4 variables.
\describe{
\item{\code{qt}}{th... |
b4672fce52031c9bee7df9ca6d7713b92289a966 | 38915da347869e164d9485f9bbd394fe56d2fcb0 | /1_1MFScondist_jp/ui_Chi.R | 76c63f28d34a1d518ae299c4d0e38d5206c02551 | [
"MIT"
] | permissive | mephas/mephas_web | 24df65c5bdbf1e65c91523f4bfd120abae03e409 | 197e99828d6b9a6a3c1d11b2fc404c9631103ec0 | refs/heads/master | 2023-07-21T14:29:37.597163 | 2023-07-13T03:32:32 | 2023-07-13T03:32:32 | 161,571,944 | 8 | 3 | null | null | null | null | UTF-8 | R | false | false | 4,516 | r | ui_Chi.R | #****************************************************************************************************************************************************1.6. chi
sidebarLayout(
sidebarPanel(
h4(tags$b("Step 1. データソースを選ぶ")),
p("数式ベース、シミュレーションベース、又は、ユーザのデータベース"),
#Select Src
selectInput(
"InputSrc_x", "選択肢",
... |
466cef28cb282dd124d1203542c0c7bcd0b7afbf | e15b2830959991a75a8635d6958c691c2bd52974 | /R/single.R | 3defbcdd9d9db55e690d145c61daf55661e1e3ea | [] | no_license | CHOIJUNGWON/big_data_web | 93a5afb46b473ecf2c3fa952d57986b882efc4b5 | f41cd7db738ccfa724eceff3b02da304ff61713b | refs/heads/master | 2021-05-08T17:30:00.879909 | 2018-03-02T04:12:54 | 2018-03-02T04:12:54 | 119,476,942 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 768 | r | single.R | mpg<-as.data.frame(ggplot2::mpg)
head(mpg)
library(ggplot2)
displ1<-mpg%>%filter(displ<=4)
displ2<-mpg%>%filter(displ>=5)
mean(displ1$hwy)
mean(displ2$hwy)
cty1<-mpg%>%filter(manufacturer=='audi')
cty2<-mpg%>%filter(manufacturer=='toyota')
mean(cty1$cty)
mean(cty2$cty)
hwy1<-mpg%>%filter(manufacturer=='chevrolet'|manuf... |
127c24934078d47bfbeabb743d3495da402ec79e | eb03c1bd9aeb0d0d9fc76dd8b4faec4d94c9a97c | /run_analysis.R | 27b9c54caf28f477930b50b7b0ead232170c7907 | [] | no_license | RobinGeurts3007/gettingandcleaningdata | c9567f8f7613e23f346ae2976965ba72e50802de | e7bd48aa5daa3bf97e902f142ce34ffc686f826f | refs/heads/master | 2020-04-05T04:41:01.476646 | 2018-11-16T14:09:29 | 2018-11-16T14:09:29 | 156,561,696 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,115 | r | run_analysis.R | run_analysis <- function() {
library(tidyr)
library(dplyr)
setwd("UCI HAR Dataset")
## 1.1 read train data and labels
setwd("train")
xtrain <- read.table("X_train.txt") ##data
ytrain <- read.table("Y_train.txt") ##labels
subtrain <- read.... |
8698040a4e915c2682452277e3fe1e9657cb4ab1 | 6b769ade12829c97f7aa9930265418bede361967 | /man/Table7_12.Rd | f494b4eb395fe5bd7536aa4b71443d5b367f9654 | [] | no_license | brunoruas2/gujarati | 67b579a4fde34ae1d57f4a4fd44e5142285663c1 | a532f2735f9fa8d7cd6958e0fc54e14720052fd4 | refs/heads/master | 2023-04-07T01:15:45.919702 | 2021-04-04T15:10:58 | 2021-04-04T15:10:58 | 284,496,240 | 14 | 6 | null | null | null | null | UTF-8 | R | false | true | 1,195 | rd | Table7_12.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/table7_12.R
\docType{data}
\name{Table7_12}
\alias{Table7_12}
\title{Table 7_12}
\format{
\itemize{
\item \strong{Year}
\item \strong{C: }real consumption expenditures in billions of chained 1996 dollars.
\item \strong{Yd: } real personal dis... |
8af663f77614f891a89d981b6bc1b3928a904c6f | 4beebbe7c247266d9f182f8a6d05a6e51b319376 | /45.MakeKeysLN.r | edcdefa3021270278bc54a7c060891b4dcdfe75f | [] | no_license | IRRDDv45/Scripts | 471708895fe7af7c0f206bdc54512e300ebc20c3 | d82bd200bc2bc25a605211240bcd7251f3f9fc32 | refs/heads/master | 2016-08-05T02:39:17.858253 | 2013-09-12T14:41:47 | 2013-09-12T14:41:47 | 12,785,755 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,938 | r | 45.MakeKeysLN.r | 1 # 45.MakeKeys
2 #
3 # source("45.MakeKeys.r",echo=TRUE)
4 #
5 rm(list=ls())
6
7 #library(knitr)
8 library(gdata)
9 library(rms)
10 library(gdata)
11 library(gmodels)
12 library(RMySQL)
13 library(Hmisc)
14
15 load("../Data/mihow.RDa... |
f3ad3fa832a7bc2309b022233ecbaae830d0eb44 | dc5cb16b7ca29ba8a214b92c4af47521dbe4a81b | /figures/Rplot2.r | 01042184e3e3de55e915937b060771343d4a6d67 | [] | no_license | d2i2k/RepData_PeerAssessment1 | 160286ff2371bd32067bdad95f4babfc4362f8a6 | b103f7c67993ee7530c0f9eaec845ffb1b045241 | refs/heads/master | 2021-01-20T23:16:43.376919 | 2015-04-06T22:19:27 | 2015-04-06T22:19:27 | 22,814,618 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 416 | r | Rplot2.r | setwd("C:/Users/d2i2k/tmp/RepData_PeerAssessment1")
ActivityData <- read.csv("activity.csv", header=TRUE)
y <- tapply(ActivityData$steps,INDEX=ActivityData$interval,FUN=mean,na.rm=TRUE)
x <- tapply(ActivityData$interval,INDEX=ActivityData$interval,FUN=mean,na.rm=TRUE)
xy <- cbind(x,y)
plot(xy,type="l",main="Time Serie... |
bcf64acf079550dfd01be92188a2bcb8e6564296 | eea1603cbc55b36ebb1093c9fc96e8adf5652f88 | /man/bindTables.Rd | a0a31e352ae291c5a7964b07dfa0d073a515e839 | [] | no_license | lcmercado/AutoAnalysis | 954bcda9a7de59e73f99dd0edbcbd2dd59ecca9a | 41f40a6817ce29844fc80e1082ee9cab2eea7288 | refs/heads/master | 2021-04-03T01:46:57.624551 | 2018-09-10T10:42:38 | 2018-09-10T10:42:38 | 124,944,551 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 868 | rd | bindTables.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bindTables.R
\name{bindTables}
\alias{bindTables}
\title{Bind compatible data tables that have a similar name}
\usage{
bindTables(x)
}
\arguments{
\item{x}{a string pattern that is also included in all the data frames that have to be binded.}... |
534cd9093c1807ecf6529de07f9ea66447408f5d | 732913cad1b98cfa04839d4bd46aa2b6f7272a33 | /R/view_section_spreadsheet.R | df152c4f6fd6a75f3a8ddbd1da2e0fb2f4833705 | [] | no_license | KerrLab/introbiocure | 8c3029bf010c070ca119708cad448752591d3dad | db9ad5039bfb3a7690acb065aa04d89c83d01569 | refs/heads/master | 2020-06-24T06:13:10.351793 | 2017-09-22T22:54:50 | 2017-09-22T22:54:50 | 96,922,212 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,037 | r | view_section_spreadsheet.R | #' Open the Google Sheet for a BIO 180/200 section
#'
#' @inheritParams build_section_spreadsheet_title
#'
#' @export
#'
#' @examples
#' \dontrun{
#' view_section_spreadsheet(course = 180, year = 2017, quarter = "AU", section = "C")
#' }
view_section_spreadsheet <- function(course, year, quarter, section) {
section... |
45918a64b56c416d27987aac001be8da6aa54ae9 | 1712ed440489db168071b533ff8e79a1ede57df7 | /man/occupancyData.Rd | 64e7528a29f3fcfdaa14e996c4d423dab1be7ff9 | [] | no_license | dsjohnson/stocc | 2947fc1f52e54e6e1747485fc3d905e7078bebc2 | 584c24792bb3c6083de274fab839a3f713c4adf0 | refs/heads/master | 2022-10-21T01:42:42.469014 | 2022-10-05T18:52:47 | 2022-10-05T18:52:47 | 14,853,161 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,247 | rd | occupancyData.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/stocc-package.R
\docType{data}
\name{occupancyData}
\alias{occupancyData}
\title{Simulated occupancy for the 40 x 40 study area.}
\format{
A data frame with 1600 observations on the following 5 variables.
\describe{
\item{site}{Site labels} ... |
cbcdaa4f6b70c9f7653af35550ab1126c61ff08d | 9477487b28fcc311b706d51943b609baf0f9d589 | /SynthData/decode_DDC.R | 2d71773ab2ecd3d580585f1b5501139c9bf7b68f | [] | no_license | bbujfalussy/tSeq | 2a7cf19c0df97b1bc3499579e14039b11e68c67a | 6d131540ba45e56b46f07aba1d0cc211114d0a6d | refs/heads/main | 2022-10-30T15:57:48.113938 | 2022-10-18T16:43:16 | 2022-10-18T16:43:16 | 553,709,039 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,652 | r | decode_DDC.R | # DDC decoding using standard ratemaps
dir.create('./SimFigs/Fig5/', showW=F)
## decoding the spikes
outfile <- paste('decode_estR_Tmax', Tmax, '_Ncells', N.cells, '_', code, '_spPth', spPth, reg, '_DDC.RData', sep='')
if (outfile %in% list.files('./SimFigs/Fig5/')) {
ddcfile <- paste('./SimFigs/Fig5/', outfile, ... |
52cc2f1796bd07de6c25a6c70183f6e09547b50b | 4d92414b5f6ac4461ece23e8ac14c792190596e6 | /challenge_6_data no 8/intPrev_bootstrap.R | 9c613f5fcbc4019eeafbce340ab971eac6bf329e | [] | no_license | vmolchan/CodeDataScience | 6f0a28e89332926bdcd7c0a09f412cb815d46bf5 | 3ba3e9e0d27869f14e6ea7a16261e3939ce89402 | refs/heads/master | 2022-02-19T11:32:36.840168 | 2019-07-24T21:02:07 | 2019-07-24T21:02:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,545 | r | intPrev_bootstrap.R | n <- 20
sigma <- 0.05
x <- seq(0, 1, length.out = n)
X <- cbind(rep(1,n), x)
beta <- matrix(c(1, 0.5), ncol = 1)
set.seed(0)
y <- X%*%beta + rnorm(n, mean = 0, sd = sigma)
plot(x, y)
abline(a = beta[1], b = beta[2], lty = "dotted")
df <- data.frame(x = X, y = y)
m <- lm(y~x, data = df)
abline(m, col = "blue")
nNew ... |
19140356f7bf7273e0f5b6c4bd0f253dd0791a97 | 1c92699b1f59e73440756844f52c9728f49d6e11 | /R/draw.barplot2D.R | 1dbe41a2d288f91022feb4d93f25cd35da1485ab | [] | no_license | marchtaylor/mapplots | 4dfca74282facc317f37f0b2d59d56a24ff6a98e | 0cf8125ccb7d0cb966ebee73c04912880677d96d | refs/heads/master | 2020-06-03T21:36:15.016473 | 2018-05-22T10:25:28 | 2018-05-22T10:25:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,395 | r | draw.barplot2D.R | draw.barplot2D <-
function(x,y,z,width,height,scale=F,col=NULL,col.frame='black',lwd.frame=1,silent=TRUE,...){
nx <- length(x)
nz <- dim(z)[2]
if (is.null(col))
col <- c("#737373", "#F15A60", "#7BC36A", "#599BD3", "#F9A75B", "#9E67AB", "#CE7058", "#D77FB4")
col <- rep(col, length.out = nz)
if(le... |
e252f1d3b88fbcda00badae24b9c8ddd8f60bb67 | e87785dc6f078afb4445d50af697c827b81df38d | /Modulo_III/R_Modulo_III.R | 0ced88293f5f7bb220b22deb0960bde75c0f8711 | [
"MIT"
] | permissive | rodrigosantana/Curso_R_Intro_UNIVALI_2015 | aa9f6d9145e6878d29c7314eeec0b585678a4ac3 | 029265058a3544a8ca8fba9dd3f6bbe5ae3d98b1 | refs/heads/master | 2020-12-24T15:04:42.676811 | 2015-07-16T00:45:03 | 2015-07-16T00:45:03 | 39,032,112 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,535 | r | R_Modulo_III.R |
## @knitr setup
# smaller font size for chunks
opts_chunk$set(size = "small",
prompt = TRUE,
comment = NA,
tidy = FALSE,
cache = TRUE,
fig.align = "center",
fig.width = 5,
fig.height = 5)
thm <- knit_theme$get("bea... |
05e5f0971d5819fdba85260af753c7ef7b8c443b | e8d24ceceb6c57171a61d0ee14e83d9b910266cb | /utils.R | 56e40e99116a19cfb56fde553e701698ac0c74f2 | [] | no_license | adamsqi/srd | a706fb7abb8773636f4c925eba6e8b530b227026 | e53dfbd67826c8ee8f64f40621f0f7f6bf68bbf8 | refs/heads/master | 2022-01-05T00:36:40.857360 | 2019-04-29T16:20:51 | 2019-04-29T16:20:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 108 | r | utils.R | col_to_factor <- function(data, colname){
data[colname] <- as.factor(data[colname])
return(data)
} |
d944defad96c78ecce2846b808e1d83be74d2f46 | a498124c89ff88c7a587f180be8c632a79530246 | /man/cache-management.Rd | e74d1662d809012b1963a7afe5224a8c24737c29 | [] | no_license | billdenney/httpcache | 550d7509ddc1e7c5d205282f0b73bb5b6d8ab539 | 1f7148d9f85cd335f3a4fe4c35646c82682d2de2 | refs/heads/master | 2020-03-28T04:35:40.151144 | 2018-08-27T05:03:51 | 2018-08-27T05:03:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 461 | rd | cache-management.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cache.R
\name{cache-management}
\alias{cache-management}
\alias{cacheOn}
\alias{cacheOff}
\alias{clearCache}
\alias{cacheOff}
\alias{clearCache}
\title{Manage the HTTP cache}
\usage{
cacheOn()
cacheOff()
clearCache()
}
\value{
Nothing. Func... |
20e6b1d73ca16aa6ac9be30e873da34197c4fdd8 | 35416468434ede48f9886955b7d0ddb0e9c089a5 | /1.Import&PreprocessingGALAXY_v3.R | a33546aa2bc9adb472e87725981f2e9c4d00f4fc | [] | no_license | francescaprata/Sensitivity-Analysis | 9dcdf890be1ffa349a7e2c43f19fc4b69feb8dd6 | 9d6212313fe38e1a4189c459994c2405cfe8f033 | refs/heads/master | 2020-05-03T02:08:01.755003 | 2019-04-03T07:31:43 | 2019-04-03T07:31:43 | 178,359,005 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,393 | r | 1.Import&PreprocessingGALAXY_v3.R | ###############################
# Name: Francesca Prata #
# Project: Sentiment Analysis #
# Script: Galaxy set-up #
# Date: 30 March 2019 #
# Version: 3 #
###############################
#Loading required packages
if(!require(pacman))install.packages("pacman")
pacman::p... |
d06198f7578ab69f745ea3d04ba5a06f6af53b7b | cd3bad7fb562ad5f0333728c261d23ed0661e8bf | /modelo_transporte/07_capacity_into_merged.R | 59e7f24033c94bb75033fe7d1308d2e835a14925 | [] | no_license | Joaobazzo/Master-thesis-scripts | 6bfeff05d6567ad5a11a6f8047229943d7b61aea | effe21e025993844209bf4af3cbdb7eeadbe34b1 | refs/heads/master | 2021-01-02T12:57:01.458385 | 2020-04-01T21:55:54 | 2020-04-01T21:55:54 | 196,636,722 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,531 | r | 07_capacity_into_merged.R | #
#
# CAPACITY FIELD ADDED TO INTERSECTION
#
# soma ideas for calibrating the results
# https://stats.stackexchange.com/questions/171154/fitting-known-equation-to-data/171191?newreg=00e24e0321294bc0aeefda68c3ff1fe3i
#
# -----------------------------------
rm(list=ls())
setwd("L:/# DIRUR #/ASMEQ/bosistas/joao... |
04d293306f9519abc9e05df39fa3b8118792b5d2 | 46a23d8ffb23dd4cd845864e8183cba216bc8d68 | /app.R | cebf8af519f25a99413210f7afa624c1f9ae3645 | [] | no_license | DavidBarke/weibulltools-app | 84099f30c1027ed808f9905ec90d0529aeb46565 | 2fe0a3a793231da1539767d81d9e22773598f386 | refs/heads/main | 2023-04-12T06:34:38.306466 | 2021-04-20T14:01:54 | 2021-04-20T14:01:54 | 329,618,724 | 6 | 0 | null | 2021-02-09T09:22:14 | 2021-01-14T13:08:39 | R | UTF-8 | R | false | false | 4,662 | r | app.R | library(shiny)
library(shinymeta)
library(shinyjs)
library(shinycssloaders)
library(bs4Dash)
library(dplyr)
library(purrr)
library(DT)
library(R.utils)
library(weibulltools)
library(xml2)
ui_server <- function(source_to_globalenv = FALSE) {
# If source_to_global_env all sourced functions get added to the global
... |
f572a51f5c31b8672d6461244e84652ee1e9c9fa | 8bef64009c1256ace384188118ec8611429256ed | /R/RcppExports.R | ac6b7ac3050214eccdd538577805fd560008467e | [] | no_license | NathanWycoff/iPLSV | 3f78cde543aff868dca4303dc9bf19c5055906b3 | cfc4ffe6884f005fc076312b45cf0f3d4cc211a5 | refs/heads/master | 2020-03-13T00:03:06.642527 | 2018-06-28T22:04:05 | 2018-06-28T22:04:05 | 130,879,295 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 784 | r | RcppExports.R | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
softmaxC <- function(x) {
.Call(`_iplsv_softmaxC`, x)
}
exp_nlpostC <- function(Z_exp, PHI, THETA, PSI, docs, Ns, eta, gamma, beta) {
.Call(`_iplsv_exp_nlpostC`, Z_exp, PHI, THETA, PSI... |
ba51be5b34f37fb95ba0ab7bcce8e7fce6ba200e | 8035a3d05fc5ab5c38c753f4e96d3e50e35915ed | /man/imputeBLOQ.Rd | a2b5f9df739f208ada97f86db96844d71a7a99c3 | [] | no_license | cran/BLOQ | 95de339375c26aae6ae5fedf075f4aa2dc7cab02 | bacaf5984daa7972b3f5f32918354242a3d14aa7 | refs/heads/master | 2021-07-12T00:15:04.413710 | 2020-06-07T17:30:06 | 2020-06-07T17:30:06 | 145,910,986 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,994 | rd | imputeBLOQ.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/imputeBLOQ.R
\encoding{UTF-8}
\name{imputeBLOQ}
\alias{imputeBLOQ}
\title{impute BLOQ's with various methods}
\usage{
imputeBLOQ(inputData, LOQ, imputationMethod, progressPrint = FALSE, ...)
}
\arguments{
\item{inputData}{numeric matrix or da... |
dcf0039b5fbfabccda10d70a52887ffbe5661a55 | a76e8d681bb9e4480b89a4136ddc9661c016536d | /R/utils.R | 001d3d883c1a59cb7c55ac2fecae427eb6cd0e37 | [] | no_license | voronoys/fairsplit | 1f85adbb4c6f56ad8d03bb848fb654bd373ad42b | 5e9ef665efae1503501c7d03a5c1ed1e646ccd97 | refs/heads/master | 2023-05-02T02:14:31.027989 | 2021-05-13T22:32:12 | 2021-05-13T22:32:12 | 354,666,400 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 18,738 | r | utils.R | #' @title Cosine weighted distance matrix
#'
#' @param x matrix to calculate columns distance
#' @param weights Weights for each attribute in the dataset
#'
#' @return Cosine distance matrix
cosine_sim <- function(X_mat, i, j, w) {
x <- X_mat[i, ]
y <- X_mat[j, ]
out <- sum(w*x*y)/sqrt(sum(w*x^2)*sum(w*y^2)... |
63b177e9c475fa65e4bb973b6595f0d4e955167a | ac362ad0dc04a7014c064d5d652128f6a3b954fe | /man/zscore.Rd | e63b817ebf5f8b5659c8a96fbbd822093f8d306b | [
"MIT"
] | permissive | hauselin/hausekeep | 4078ed0d25d22da83fee6b3dfc15cd6c7bae752c | 3e1560814e953f67fd407701868eb833c30e0a1d | refs/heads/master | 2023-01-20T15:23:23.711015 | 2023-01-19T02:16:08 | 2023-01-19T02:16:08 | 168,783,741 | 9 | 2 | null | null | null | null | UTF-8 | R | false | true | 577 | rd | zscore.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/zscore.R
\name{zscore}
\alias{zscore}
\title{Compute z-score}
\usage{
zscore(x, n = 0)
}
\arguments{
\item{x}{A vector}
\item{n}{use n or n-1 in the denominator when computing SD (default 0 for n; use -1 for n = -1)}
}
\value{
A vector
}
\de... |
cc6a6a135abcbfe8b4524eaefdc9869036b68c6b | ca33b1708cdf025a6579fa7c3d4241e0c78052ef | /app/health_analysis.R | c03ef45a64cbdb589bedce9ece6e722a43cb9e48 | [] | no_license | matthgray/health_care_spending | 0497a0ff709d58e979d851df400a1f74554ee861 | 2cbf252d1d76970dd2d27979dd93bb4e22fa7fba | refs/heads/master | 2023-02-26T22:44:34.471633 | 2021-01-27T16:12:04 | 2021-01-27T16:12:04 | 323,480,923 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,582 | r | health_analysis.R | if (!require("pacman")) install.packages("pacman")
library("tidyverse")
# Load contributed packages with pacman
pacman::p_load(pacman, party, rio, tidyverse)
library(ggplot2)
library(dplyr)
library("growthrates")
health_spending <- read_csv('CleanData.csv')
if (!require("pacman")) install.packages("pacman")
# pacman ... |
579455575e62065f0ccfa7e96ae80a6b77ebea16 | ba2b092b91f207ae91c269587b8210ede808ecce | /R/matchChromosomes.R | c6a9cf370e8f435b7caf50ec52fc8a8a264978dc | [
"Apache-2.0"
] | permissive | fursham-h/factR | cf12691985dfad6bb67e58e09b602895d1a5012e | 99ef8f40bf109b8a718bc51a3699578c2b3b6c4a | refs/heads/master | 2023-08-18T13:46:27.265640 | 2023-08-02T16:38:24 | 2023-08-02T16:38:24 | 230,322,841 | 2 | 0 | Apache-2.0 | 2021-04-30T12:01:08 | 2019-12-26T20:14:29 | R | UTF-8 | R | false | false | 2,106 | r | matchChromosomes.R | #' Match seqlevels of input GRanges to reference GRanges or BioString objects
#'
#' @description
#' A convenient wrapper to match seqlevels of a query GRanges object to a
#' reference object that contain seqlevels information. Reference can be a
#' GRanges, GRangesList, BioString or DNAString object. Seqlevels which ... |
6bf7e3b241f2902b70470d42a022897e070b6077 | 44143d0c480e1cabf87f2c44909afe2aa85bd67c | /man/compute.QDiD.Rd | e4cb7ca8ccea19747dc48ddcbf79fe23f4ca8f14 | [] | no_license | bcallaway11/qte | c383e991a3969e9e50e30477e701ee11c500d574 | 09830e766b7f9e28643928e9a170f73d9c4c0bcf | refs/heads/master | 2023-08-30T21:43:34.342973 | 2023-08-15T21:37:43 | 2023-08-15T21:37:43 | 19,584,525 | 8 | 5 | null | null | null | null | UTF-8 | R | false | true | 413 | rd | compute.QDiD.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/QDiD.R
\name{compute.QDiD}
\alias{compute.QDiD}
\title{Quantile Difference in Differences}
\usage{
compute.QDiD(qp)
}
\arguments{
\item{qp}{QTEparams object containing the parameters passed to QDiD}
}
\value{
QTE object
}
\description{
\code{... |
d200e0512643c7011c751305ea23c6bdb13cd802 | 288da8178b59a864653cf71d3aab544484d55ff9 | /R/ap_parse_page.R | a46feebefc12ef588da33b9f8e153cbb11f3ca02 | [
"MIT"
] | permissive | patperu/albopop | 430ab153ace929b8ced90a7cea2706194c70029a | 012d447a03307882fb466269cd8494da1fabdea5 | refs/heads/master | 2021-01-10T05:24:01.723465 | 2016-02-25T18:59:35 | 2016-02-25T18:59:35 | 52,151,601 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,192 | r | ap_parse_page.R | #' Fetch the data.
#'
#' This scrapes the 'Albo Pretorio' data
#' @param url A URL
#' @param link_url The URL for the links, if NULL same as `url`
#' @param site character, sets the input, currently four sites are supported: "JCityGov", "mapweb", "saga", "studiok"
#' @return A dataframe
#' @export
ap_parse_page <- func... |
44a02c7800ad238b4669781f5e9652da089cc83e | 222ddcb4176c06aa122588179cb5395652653d2d | /archive/weighted_update.R | 36a1c778b1e7a929ec0ed1cabab129a6e0e1aa96 | [
"MIT"
] | permissive | ck37/acic-tmle-2018 | f0b1bc9732b10edfa4809f6d5e1729b2e8c338b8 | 471bcdf1e46bea804d62a1c4e1a1d92ef47ff32d | refs/heads/master | 2022-01-08T14:37:52.497376 | 2019-01-23T20:09:11 | 2019-01-23T20:09:11 | 98,351,394 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,573 | r | weighted_update.R | # Takes initdata, dataframe or list containing elements: A, Y, Q, g
update <- function(initdata) {
n = length(initdata$A)
# Create ``clever covariate'', which is 0 for treated units. This will only be used to update Q01.
H0W = with(initdata, - g / (1 - g))
HAW = with(initdata, ifelse(A == 1, 0, H0W))
... |
74fa7da9cdcf56736392a86eb3c8514024b0c2a3 | b0cad7c3a4ca1a0be8462e31ce0ccce61dbcdfff | /cachematrix.R | c88a63d591cae4c59bafa62fb6fa64b81afed11d | [] | no_license | JeremiShane/ProgrammingAssignment2 | 4be257e4fd2b6b13247949cdc7c95900aebfa407 | 92e9f6524d599ff861873beab0ec4f1c3de2fbaf | refs/heads/master | 2021-01-02T08:30:46.459008 | 2017-08-02T20:52:17 | 2017-08-02T20:52:17 | 99,015,060 | 0 | 0 | null | 2017-08-01T15:17:11 | 2017-08-01T15:17:11 | null | UTF-8 | R | false | false | 1,270 | r | cachematrix.R | # Caching the Inverse of a Matrix
## assignment is to write a pair of functions that cache the inverse of a matrix
## 1. makeCacheMatrix creates a special "matrix" object that can cache its inverse
## 2. cacheSolve computes the inverse of the special "matrix" object, or retrieves the cached inverse
##
### the inverse ... |
8f944d23ef597bba7bd63ac72be724f4074eb31d | 955a3a610c2586acd6aebef7d962ad5add461e86 | /example05.R | 7323a254de17a8e5f18a2bc2ec8f903aba70f160 | [] | no_license | aboyd20/Social-Network-Analysis | e073df4abfe0401479bdbae76f8fa6646a355325 | c821086b421650f5f0cd4f5258a72e8dfc6c54b4 | refs/heads/master | 2022-12-13T19:09:51.039921 | 2020-09-12T21:42:17 | 2020-09-12T21:42:17 | 295,028,561 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,058 | r | example05.R | # Example 5
## Working with CUG testing
# ---- C1 ----
library("ggplot2")
library("GGally")
library("mcclust")
library("sna")
# Must load other packages first
library("sand")
# ---- C2 ----
#path <- "C:/Users/rburke/Dropbox/2016_cFall/csc495DB/example/ex5"
path <- "/Volumes/BOYDDEPAUL/Fall Quarter 2018/DSC 480/Week... |
f7cb6c0a2f70c65f3edbff0165bc3abd10b81677 | f12112c0024d3c5c84f9d7ecad8fbf36aec48a73 | /man/generateTwoCircles.Rd | 45253484e5c4964b06022b6d9babbbcbc933d01f | [] | no_license | biocq/RSSL | edb8d33f2f35ab36a935f91884e3413761c5dad3 | ca33dbfe5bda775be2ed6e6cac59d18fb063d6d5 | refs/heads/master | 2020-12-28T19:08:02.797890 | 2016-08-10T22:03:19 | 2016-08-10T22:03:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 416 | rd | generateTwoCircles.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/GenerateSSLData.R
\name{generateTwoCircles}
\alias{generateTwoCircles}
\title{Generate data from 2 circles}
\usage{
generateTwoCircles(n = 100, noise_var = 0.2)
}
\arguments{
\item{n}{integer; Number of examples to generate}
\item{noise_var}... |
455a2687a2eac2c466cdbdc34c02e19e13f6d204 | eb67b6ceb2458478bc7b1e45b42a8112297eb3f7 | /Hypothesis Tests Scripts/New_AGG_REGION_EDA_FOR_MATLAB.R | a8db02f8b5c930ed925c44b27c8fdeba9c3384e2 | [] | no_license | JKG114/2020-Final-Project | c526758a644d687113f2a9ded90a69414ead5347 | f6392ab3aa0f5faf56e8d0cd40595663aab65f6d | refs/heads/master | 2020-03-15T03:36:24.589191 | 2018-05-19T18:11:30 | 2018-05-19T18:11:30 | 131,946,438 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 9,593 | r | New_AGG_REGION_EDA_FOR_MATLAB.R | library(ggplot2)
library(ggcorrplot)
library(corrplot)
library(dplyr)
#This is very similar to code where we exported csvs for matlab that contained census tract level
#demographic statistics and the races of those killed by police at the census tract level, only now
#we export 8 csvs where each csv has the same censu... |
6c6436742593209b6c81aea177987d79acb135ea | 5c350a667a10e0b0fa13e8af82b4d269471a95e1 | /RProgramming.R | 588f769782466f1ed2e1aa2aac359c08a457a145 | [] | no_license | mpitman85/courseraRProgramming | fa84c71f712e342d52ea84bb659e5eb16b6bc8a4 | 2833fce2fae8a525761aec3caae932ebd3b5af25 | refs/heads/master | 2021-01-20T07:19:14.567411 | 2017-03-29T14:40:08 | 2017-03-29T14:40:08 | 82,600,646 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,475 | r | RProgramming.R | #File for testing out and learning code for R Programming Coursera course in the Data Science Specialization
##Quiz Week 1
setwd("~/Dropbox/Projects/Classes/Coursera/DataScienceSpecialization/RProgramming/courseraRProgramming")
data<-read.csv("hw1_data.csv")
names(data)
data[1:2,]
dim(data)
tail(data)
data[152:153,]
d... |
ff67f2f2b0bd13bd4155e5c7e3f9f61d7667d8a7 | f96c492e9ac0db5dcd30a2ac3455ff6cd83ac0f1 | /man/plotBg.Rd | 07971f71366796016cbbe80fe41f8b68abbfdba1 | [] | no_license | openanalytics/poissontris | 66421066a42007caaedca882ca67860f6e07b9f7 | cf83ec1e09d3f47407faaafac6aa6e35e58ea932 | refs/heads/master | 2020-12-02T19:36:25.519219 | 2017-09-18T11:20:51 | 2017-09-18T11:20:51 | 96,363,523 | 11 | 4 | null | null | null | null | UTF-8 | R | false | false | 405 | rd | plotBg.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/plottingFunctions.R
\name{plotBg}
\alias{plotBg}
\title{Plot the background list object}
\usage{
plotBg(bg)
}
\arguments{
\item{bg}{list containing x, y and color values}
}
\value{
none (objects plotted to current device)
... |
617c8ed5bdad13d4b1d367b5f91fcddbbb7960b9 | a027e58d29261e3069442596451966e736eff115 | /man/Mutation.Rd | d267462978dc6b25e9bfff471965930f9c01c820 | [] | no_license | weitinging/MinNetRank | b2e24e6bc583df3aa51a6c7e6e2e9334d1e36a6e | 553fc83ab28fd2ad4f8d166b89a178c49a811dff | refs/heads/master | 2020-09-25T11:19:43.374851 | 2019-12-28T08:34:01 | 2019-12-28T08:34:01 | 225,994,650 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 586 | rd | Mutation.Rd | \name{Mutation}
\alias{Mutation}
\title{
geting the gene mutation score for each individual sample
}
\description{
SNP is the mutation matrix. If sample k has the gene i mutation, the value =1. We canculate the normalized matrix of SNP by column.
}
\usage{
Mutation(SNP, Network)
}
\arguments{
\item{SNP}{t... |
7f9fde88b679aab139406528c81dc9c600695ddc | f9554c2ce8d6ebe6dd7bb135c1420a7ca142886d | /explore_data/plotting_week_1/plot3.R | bb75ed27d133a4426f0751d3bb92a9bd3007f2f4 | [] | no_license | tomMulholland/datasciencecoursera | 0294ad17ef72595e2f31b60668adb0b09dfac3c1 | 6001eebffb2747e96a214f7e78fd9a81ad88757b | refs/heads/master | 2020-05-17T00:08:54.898804 | 2014-08-07T18:45:20 | 2014-08-07T18:45:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,140 | r | plot3.R | # A plot of global active power in different areas of the house
# Open the file
png(filename = "plot3.png", width = 480, height = 480)
# Column names for the data
col_names = c("Date", "Time", "Global_active_power", "Global_reactive_power",
"Voltage", "Global_intensity", "Sub_metering_1",
... |
8b8525f24ee200d9938dd3588e0dd0a544195c0f | f431e82a9f18a6875df9260b4d141b58b4247cea | /Listas/lista_1.R | 71d44690f242b4df60ae6fcc4e1397d0820519b1 | [] | no_license | taisbellini/analise-multivariada | fb0ab81d786791a107dbce56d967ae24d7ea4246 | b84a342ef1b8fcc3dc87f0bf4a72f28d8d4fe03e | refs/heads/main | 2023-02-19T07:03:33.827886 | 2021-01-20T12:36:48 | 2021-01-20T12:36:48 | 303,694,024 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,264 | r | lista_1.R | ### Lista 01 ###
#### Cap 1 ####
#### 1.12 ####
if (!require(mvtnorm)) install.packages("mvtnorm")
library(mvtnorm)
dist = function(P) {
return(max(abs(P[1]), abs(P[2])))
}
#a)
P = c(-3, 4)
P_dist = dist(P)
paste("Distance of P to the origin: " , P_dist)
#b)
# Os pontos que terao distancia 1 da origem sao os ... |
94eb0db35bc23facfaa192d85eb392cc82ab092a | cc2ee7cda1080631699c80372640a15eb7ffd03c | /man/crossBoundCumProb.Rd | fd30e48a2dae4088ecdbd87b9582c640d2dae7d2 | [] | no_license | mjuraska/seqDesign | 2d11a6ea8e1dbabbbdf57aaf8dd4485f462fddc1 | 23965224e807e5512f3a6c75c008ab02f490e9e5 | refs/heads/master | 2022-12-11T22:06:58.018005 | 2022-12-08T20:18:27 | 2022-12-08T20:18:27 | 176,831,673 | 2 | 1 | null | null | null | null | UTF-8 | R | false | true | 3,392 | rd | crossBoundCumProb.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/postMonitoring.R
\name{crossBoundCumProb}
\alias{crossBoundCumProb}
\title{Estimate cumulative probabilities of crossing an efficacy or non-efficacy boundary in an event-driven 2-arm trial design}
\usage{
crossBoundCumProb(
boundType = c("e... |
bc580249a3e76b36a902c77459ee973bc7a25ea1 | 42abe0fef0d12287d170fd2445864f9fb9aec74b | /R/dump.R | 1d69a2e049ff5a1737287408d6c5314ac3a27ab1 | [] | no_license | natverse/neuprintr | 45bf13ea5f351c7088ad0e9de36a737342e070f3 | 1bf392fb896710d64e1b03cdddb38ea05a54f5d1 | refs/heads/master | 2023-08-26T15:19:05.332544 | 2023-08-16T03:32:48 | 2023-08-16T03:32:48 | 176,496,271 | 3 | 2 | null | 2023-09-07T19:59:04 | 2019-03-19T11:22:52 | R | UTF-8 | R | false | false | 5,481 | r | dump.R | #' @title Download data from neuprint for specified bodyids / a specified ROI
#'
#' @description Download neuron morphology and connectivity data to a specified directory as .csv and .rda files
#' @inheritParams neuprint_read_neurons
#' @inheritParams neuprint_bodies_in_ROI
#' @inheritParams neuprint_connection_table
... |
948d8fe9f605a66ad8c436c99daee3165b10ff19 | d104de65c0df3546de0eb0952e1344fa59245a2d | /R/pace_converter_functions.R | 06254d4c13d1f0d1d3357c08a7ca78b65fad14d8 | [] | no_license | alegerosa/runnR | 6e515f64be2ec2ff5e5526cb4504602423d4d2ac | 9972834d390f5666c4eb9b2b1ae560f794061af5 | refs/heads/master | 2020-12-20T14:58:50.656950 | 2020-02-17T04:19:08 | 2020-02-17T04:19:33 | 236,115,044 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,100 | r | pace_converter_functions.R | #' Pace km to pace mi, text
#'
#' This function converts paces from minutes per kilometer to minutes per mile, taking strings as inputs and returning strings as outputs
#' @param pace_km_text A string or vector of strings with pace in minutes per kilometer, where the minutes are separated from the seconds by a colon.
#... |
6d5d03bdea61bbe1ffd03e5e911275dc0e2732d3 | fcaaf7ba8ec7e21883394ad57f3fb544f4dd63dc | /Cap09/13-Distr-Normal.R | f0eafa4bef01e5796c09cb162069a5f9f9d543ed | [] | no_license | GasparPSousa/BigDataAnalytics-R-Azure | f3226150461496c0d78781bfd8fe3b5bb5237199 | aeeb060f32f8846ea80f6bc4631d0f07d21cbf1e | refs/heads/main | 2023-05-14T23:57:15.302363 | 2021-06-06T14:04:48 | 2021-06-06T14:04:48 | 357,303,863 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,943 | r | 13-Distr-Normal.R | # Distribuição Normal
# Configurando o diretório de trabalho
# Coloque entre aspas o diretório de trabalho que você está usando no seu computador
# Não use diretórios com espaço no nome
setwd("~/Cursos/DSA/FCD/BigDataRAzure/Cap09")
# Para saber qual diretório estou trabalhando
getwd()
# Lista de pacotes base carreg... |
99ce2f7a3c70d6e0c5a0062a6f2223c777f9e9b6 | f33bc23a2cf454b908ec5129cd91f202cfaa8893 | /tests/testthat/test-query.R | 4f7513b15a55b6385ba8fc3c8984114a80d34258 | [
"MIT"
] | permissive | ibartomeus/traitbaser | 7e86da6ec2fbee813e57fb3fcf51cc243be849e5 | cae4a35e6ab8c7a3cb45e24f0ecfc7b1fe46b73f | refs/heads/master | 2021-04-29T03:18:35.979292 | 2020-04-14T08:20:32 | 2020-04-14T08:20:32 | 78,012,880 | 0 | 0 | null | 2017-01-04T12:17:13 | 2017-01-04T12:17:12 | null | UTF-8 | R | false | false | 1,338 | r | test-query.R | context("query")
test_that("query species", {
cnx <- connect("http://www.traitbase.info", "demo", "1234")
resource <- resource(cnx, "species")
out <- query(resource)
expect_equal(TRUE, length(out) > -1)
expect_equal(8, ncol(out))
})
test_that("query species limit=1", {
cnx <- connect("http://www.traitbase... |
1195ea09f1de5896dc09391cd7d10676d6ab8e53 | b8a44a643675919a6ce53e077c1e6ed84e4ade99 | /code/settings_to_formula.R | 10d366f99c5b6d7c4e6d6c4054561d40afc0b72a | [] | no_license | bernard-liew/2020_stairs_biomech | 30f17ac50c0b8e0fedd1d3c7b6e6684e723c2dc9 | 5e478f092385b4455faafbe904d19bb39c9742b4 | refs/heads/master | 2023-04-09T18:14:08.766217 | 2021-02-25T11:23:40 | 2021-02-25T11:23:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,298 | r | settings_to_formula.R | settings_to_formula <- function(
k_cycle,
k_age,
k_speed,
k_cycle_age_1,
k_cycle_age_2,
k_cycle_speed_1,
k_cycle_speed_2,
k_age_speed_1,
k_age_speed_2,
k_cycle_age_speed_1,
k_cycle_age_speed_2,
k_cycle_age_speed_3,
k_cycle_ht_1,
k_cycle_ht_2,
k_ht,
k_strlen,
k_cycle_strlen_1,
k_cycl... |
892979ae6fb5b58cf0cccb72d260819691bb8aea | 784526035645204d053fe5e911b94df76cdd76ef | /git.R | 06ff586f2f09b870bc8ecdb174aae95484c13926 | [] | no_license | parinfuture/nlp | 3b798082ec362d3713c6f62b70497ff8510f642a | f873138f73cf3c684eb80a6a6d6d0ece51bb8656 | refs/heads/master | 2021-01-22T23:16:49.064871 | 2017-03-20T20:46:55 | 2017-03-20T20:46:55 | 85,624,636 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,196 | r | git.R | # This R environment comes with all of CRAN preinstalled, as well as many other helpful packages
# The environment is defined by the kaggle/rstats docker image: https://github.com/kaggle/docker-rstats
# For example, here's several helpful packages to load in
library(ggplot2) # Data visualization
library(readr) # CSV ... |
811b4e93962f0fb0261d1fa604f58467195151ae | 43d60aeb722f2803c028273e3b142a4a2f7b9d54 | /女子100米预测.R | 12d4fa1cbde862de4ce947c4cae5354a2bf16dc2 | [] | no_license | jianchongsu/R | cc531346b27200365762a1fe6b38f97eef9a3928 | 6377e27d828e075157c6b1ade687c4265c2c5433 | refs/heads/master | 2016-09-06T10:04:29.651118 | 2012-08-09T16:49:48 | 2012-08-09T16:49:48 | null | 0 | 0 | null | null | null | null | GB18030 | R | false | false | 1,254 | r | 女子100米预测.R | library(XML)
library(drc)
surl="http://www.databaseolympics.com/sport/sportevent.htm?sp=ATH&enum=700"
#url <- "http://www.databaseolympics.com/sport/sportevent.htm?enum=110&sp=ATH"
sdata <- readHTMLTable(readLines(surl), which=2, header=TRUE)
#golddata <- subset(data, Medal %in% "GOLD")
#或者可以采用我这个也可以
gold=sdata[which... |
d1c8663a7736e53cac48b9c3937bfec729fe3ea0 | 59209c2327ffc2e64514a81cc0c2565facdbb780 | /R/spacetime_bisquare.R | 6db8427b771a21b063daee03e8d1af1a5cfcd82f | [] | no_license | holans/ST-COS | 22db522b5a53b90b0bcd2661458239eb0226615a | a20c61cc19fb03919a3e6273c275af199018f60c | refs/heads/master | 2021-03-27T08:33:24.973767 | 2020-06-03T14:36:03 | 2020-06-03T14:36:03 | 70,738,620 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,386 | r | spacetime_bisquare.R | #' Space-Time Bisquare Basis
#'
#' @description
#' Space-time bisquare basis on point data.
#'
#' @param dom Space-time points \eqn{(\bm{u}_1,v_1), \ldots, (\bm{u}_n,v_n)}
#' to evaluate. See "Details".
#' @param knots Spatio-temporal knots
#' \eqn{(\bm{c}_1,g_1), \ldots, (\bm{c}_r,g_r)}
#' for the basis. See "Detail... |
286d5dce1f948734cf01db187387aed6fb96c3d4 | 59abcf8d840cb4681de35fc5342184e5f0302c95 | /examples/acetest.r | 9ecae728f0eaafd240b473e8c07c196dc4b67d93 | [
"MIT"
] | permissive | skranz/shinyAce2 | 9863c8ce102e96f6b13e53d3f586c754fb099629 | 80cffdb254e8526c30563a7957605e6b94a33ee8 | refs/heads/master | 2016-09-05T16:26:48.552943 | 2014-08-13T14:48:02 | 2014-08-13T14:48:02 | 22,919,242 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 694 | r | acetest.r | library(shiny)
library(shinyAce)
library(restorepoint)
keys = list(runLineKey="Ctrl-Enter", helpKey="F1", runKey="Ctrl-R|Ctrl-Alt-R", hintKey="Ctrl-H")
ui = bootstrapPage(
aceEditor("ace",value="text1\nline2", height=100,
keyId=keys, showLineNumbers=FALSE,highlightActiveLine=FALSE)
,aceEditor("ace2",va... |
870ba52e2b06745c0b2f174cc078b50c6a0b8782 | 1318b29d7b0f212ebe1a87145a13ee563ea094d8 | /man/ANOVA.Repeat.Measure.Rd | 91e9e434151de7f713875978bd04e25e0082963a | [] | no_license | cran/TrialSize | 73c3ff9086760334fa83d4608c111bb0ea32765b | 314e951e9d33786b6a2883f7cd483984cb611243 | refs/heads/master | 2021-06-04T14:24:45.564491 | 2020-07-06T20:40:03 | 2020-07-06T20:40:03 | 17,693,970 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,033 | rd | ANOVA.Repeat.Measure.Rd | \name{ANOVA.Repeat.Measure}
\alias{ANOVA.Repeat.Measure}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
ANOVA with Repeat Measures
}
\description{
The study has multiple assessments in a parallel-group clinical trial. \eqn{\alpha_i} is the fixed effect for the ith treatment \eqn{\sum \a... |
a9f90dd3acf41f00476ab2e12bc5f3ddea6f2c31 | f90115826c0234fbdfe7f580e6654925aa98e6b2 | /R-source/corr.R | 37683625829d5dba262a588e89e01b26db74b283 | [] | no_license | mizma2k/FX-Git | 9272485edc4f7dfb4f12d3a4fbcaa74d9e2896d9 | b9629452419f30c4cade872928c2dcf119096b64 | refs/heads/master | 2022-12-30T23:11:03.398272 | 2020-09-09T03:40:11 | 2020-09-09T03:40:11 | null | 0 | 0 | null | null | null | null | WINDOWS-1252 | R | false | false | 7,285 | r | corr.R | #|------------------------------------------------------------------------------------------|
#| corr.R |
#| Copyright © 2012, Dennis Lee |
#| Assert Question ... |
5cbda6d34e360652ddd4a8a64497e6f45585618b | 4e149b593bac0ab3d59b0663f3f7464aa3f71d41 | /Commute_RouteExploring_Public.R | f4d25b318062ff3327361fd3a68888692d94381c | [] | no_license | ZacharyST/CommuteTimes | 856713b6c745c7efe30bc0ad4ccc206ca22b4b3a | a561ce3401a454feeeca3b908c2fd75e0c68eeee | refs/heads/master | 2021-01-19T17:07:26.293221 | 2017-04-14T22:44:38 | 2017-04-14T22:44:38 | 88,305,502 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,818 | r | Commute_RouteExploring_Public.R | #!usr/bin/Rscript
############
# LIBRARIES
############
library(googleway)
library(ggplot2)
############
# FUNCTIONS
############
processDirections <- function(origin, destination, mode){
current_time <- Sys.time() + 1
api_key <- 'oops_almost_put_it_in_here'
result <- google_directions(origin=origin, destination... |
79ba26cb91d50a56af2c5b941557ae016c0031fb | f90fec7b801f45ff649ceb7262d8a1058339a26b | /Megadetector accuracy/ena24_analysis.R | ae511b67b685b50545354f314a0d94d009423290 | [] | no_license | NaomiMcWilliam/ZSL-Internship | f27de445051f042afe2caed9f7c3b45dbede890d | 394140045529288eccba39c0e8f5e76da2d8f14b | refs/heads/main | 2023-07-16T08:35:21.131212 | 2021-09-02T00:58:22 | 2021-09-02T00:58:22 | 389,102,048 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,900 | r | ena24_analysis.R | library(jsonlite)
library(rjson)
library(tidyverse)
library(glue)
data <- fromJSON(file = "../ENA24/ena24_metadata.json")
#there are 9676 images in total in the dataset
#some of these have humans in, and have been emitted from the images
#so the metadata does not match up with the megadetector, as there are less image... |
8710d59ac6370090b17eef379a2765dcbac2e73e | 7f72ac13d08fa64bfd8ac00f44784fef6060fec3 | /RGtk2/man/pangoContextGetMatrix.Rd | 0b4b296a8bd1cc9f8964fc4852faee5ea5f31124 | [] | 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 | 589 | rd | pangoContextGetMatrix.Rd | \alias{pangoContextGetMatrix}
\name{pangoContextGetMatrix}
\title{pangoContextGetMatrix}
\description{Gets the transformation matrix that will be applied when
rendering with this context. See \code{\link{pangoContextSetMatrix}}.}
\usage{pangoContextGetMatrix(object)}
\arguments{\item{\verb{object}}{[\code{\link{PangoCo... |
46898ac55c27dc8fb9689265e0568e6e09b2fb40 | aeec49bf59ba42b433054c48e7dfd0f935fcf5cb | /R/S3_predict.R | 7e50dc914f844ca736976b269dd93685730c7075 | [] | no_license | bowers-illinois-edu/estimatr | 60a214f1a1466a41a59f80f77fbe1c16a229988d | 63d29278c38aebc8147b2f62191c407025fe0993 | refs/heads/master | 2020-03-18T21:56:49.436686 | 2018-05-29T15:17:26 | 2018-05-29T15:17:26 | 135,314,710 | 0 | 0 | null | 2018-05-29T15:17:27 | 2018-05-29T15:15:17 | R | UTF-8 | R | false | false | 7,611 | r | S3_predict.R | #' Predict method for \code{lm_robust} object
#'
#' @param object an object of class 'lm_robust'
#' @param newdata a data frame in which to look for variables with which to predict
#' @param se.fit logical. Whether standard errors are required, default = FALSE
#' @param interval type of interval calculation. Can be abb... |
53d74f5b02c85ac6d20af7ecc334242bf71d536c | 4846b5b3748b6724d7c379dae7572e9fa90a798d | /R/processCapRout.R | 0c1810d69edb77b361ff8fdd95af1ae23f811700 | [] | no_license | vbusa1/nearBynding | d225bcbdb1541b65c3f01604a1affd8ff51b068a | 9ccf2b0e7fec87c426cf37fe45077d67abef210a | refs/heads/master | 2023-04-07T19:01:47.323219 | 2021-07-30T17:39:58 | 2021-07-30T17:39:58 | 278,680,217 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,970 | r | processCapRout.R | #' @title processCapRout
#'
#' @description Creates context-separated bedGraph files of CapR output for
#' genome and transcriptome alignments.
#'
#' @param CapR_outfile Name of CapR output file. Required
#' @param output_prefix Prefix string to be appended to all output files.
#' Required.
#' @param chrom_size Name of... |
9a0d9fbd46d60f2b68bb37abf30896bf9ca8a8c1 | 99b5eff4ec20e62f531f7f1aa9429ce311fbac6b | /R/mod_optimization.R | db384edb7c90c6e2035ca7c3fe0aeb6abadc6ad3 | [] | no_license | Binary117736/RNAdecay | 1e84079da8b54be04224b0ded528a7bb6edfbf72 | 5eef364514cb76f59ac8af1718b3bd0fd9b125ec | refs/heads/master | 2023-07-10T22:43:08.887802 | 2020-04-20T15:54:52 | 2020-04-20T15:54:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,552 | r | mod_optimization.R | ################################################################################
#' model optimization for fitting exponential decay models to normalized data
#'
#' The mod_optimization function finds the estimates of model parameters by
#' maximum likelihood, for a single gene on a specified list of models, and
#' ... |
60222a309319540ad799347aa6f1527465f213c2 | febb10d22db400a4a9e48d17933759623f4fd7c1 | /DM/lengthen.R | c978fdf3537369de61bc6135029a8db4abe98f90 | [] | no_license | RCanDo/RPacks | 8f2692c1622afdc64e5e70297efc8ce726fb89ca | 05643971c3cea56f9eb076bbfac442f1f2480a92 | refs/heads/master | 2021-05-16T16:18:23.881625 | 2021-02-06T07:49:15 | 2021-02-06T07:49:15 | 119,801,561 | 0 | 0 | null | null | null | null | WINDOWS-1252 | R | false | false | 3,592 | r | lengthen.R | ## ---------------------------------------------------------------------------------------------------------------------•°
## FUNCTIONS HERE
## lengthen()
## trim()
## adjust()
##
## DEPENDENCIES
## none
## -------------------------------------------------------------------------------------------------------------... |
5caac502f83cb8cdd73776c9a19da440effdca20 | 72d7aa9ad93c5a8400a2ed6ab88afda72acdefd3 | /Chip_diff/step9_DEG_chip_gene.R | 12a69192a0365bd719dbecaa54eb5261d6bf8ef3 | [] | no_license | ZhaoChen96/colorectal-cancer | 256f2a1c918c038aa68abf3602641632b2386a50 | 9eed8c37298afa4b4b5c124253158950b525afaf | refs/heads/master | 2022-11-21T20:08:15.752283 | 2020-07-21T01:50:09 | 2020-07-21T01:50:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 25,005 | r | step9_DEG_chip_gene.R | #--------------install package-----------------------------------
install_biomaRt = function (){
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("biomaRt", version = "3.8")
}
#--------------analysis-------------------------------------------
library("... |
d48505ed397c04f1344cffcad31fb0c4a30fa86f | 4f49d7b50d6b3f1e6bb9abc964630bd40641fa82 | /import_data.R | 23570d5f1a9b5899520013ed4b8ec117ed8c6cd7 | [] | no_license | cfia-data-science/OGP-Summit-Hackathon-Sommet-PGO | ff15ba84c3ccff6229afbe3cc91dade4c1f5cadf | ab4a92f549c6c74e5f982df4614fc228400632ac | refs/heads/master | 2020-05-28T05:10:25.796017 | 2019-05-29T19:39:44 | 2019-05-29T19:39:44 | 188,889,726 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 269 | r | import_data.R | dataUN_export <- read.csv("./ogp_data/us_trademerchtotal_export.csv", head = TRUE, check.names = FALSE, sep = ",", quote = "\"")
dataUN_export_share <- read.csv("./ogp_data/us_trademerchtotal_export_share.csv", head = TRUE, check.names = FALSE, sep = ",", quote = "\"") |
f3860cf6d052bba989dd6fb107b5e0b114375b9d | c55294b3f89a774f7600a7c594c796fe059fc99e | /Older scripts for models/n_prox_1m.R | 0a106c594c1d9a1d6454082aa83eac348346627f | [] | no_license | ardenice916/Spider_monkey_stats | 1e571f1bd83b35f24f76c9475740766996afb98a | c6674ad820a045c2224c6cead389ba5de1dc7d53 | refs/heads/master | 2020-03-21T10:35:11.686763 | 2019-08-10T18:08:19 | 2019-08-10T18:08:19 | 138,459,523 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,806 | r | n_prox_1m.R | #COMPLETE ANALYSIS OF 2016 WILDTRACKS DATA, FEBRUARY 2018
#set working directory
setwd("~/Spider_monkey_stats")
#install packages
install.packages("readxl")
install.packages("readr")
install.packages("dplyr")
install.packages("tidyr")
install.packages("nortest")
install.packages("lattice")
install.packages("nlme")
i... |
53c317e4fd385e6e323be846f4586ab93b0fbab7 | 0fe99bf08805179f25232de3cfacd28ee2a12ed4 | /rmd/functions/cv_da.R | 3f326afe36bd20b6d7b9aebd6939391a36de2f4a | [] | no_license | jerry-ye-xu/stat3014_nutm3004_major_project | 7f716fe3ca946aae25d6e1c344a55708b0adf31e | a4c8ac1c250e42786088368882e3b653e9187878 | refs/heads/master | 2020-03-28T10:09:58.611234 | 2018-10-26T12:57:58 | 2018-10-26T12:57:58 | 148,087,496 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,186 | r | cv_da.R | library(MASS) # Discriminant analysis
source("./functions/cv.R")
cv_da = function(X,y,method=c("lda","qda"),V,seed=NA)
{
# Set the seed
if (!is.na(seed)) {
set.seed(seed)
}
# Set n
n = length(y)
# Split the data up into V folds
cvSets <- cvFolds(n, V)
# Loop through each fold and calculat... |
42efe7f4f10e76d2a57bd2b18e32b07a61f5d368 | b5a685ab79de211822dc5216d4ce63bd040f2b48 | /man/redisHGet.Rd | 9bec12afa88a979cef2d1b00b8b2756a75de745a | [
"Apache-2.0"
] | permissive | mcroiban/rredis | c0e99712b1478821cda8bc3b3dd5029982bcf8f6 | 51ac7c4f9c065b262751f55851cb30344388e6b2 | refs/heads/master | 2020-12-25T13:51:07.129996 | 2013-03-19T12:01:19 | 2013-03-19T12:01:19 | 10,570,879 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 793 | rd | redisHGet.Rd | \name{redisHGet}
\alias{redisHGet}
\title{Retrieve a hased value from Redis.}
\description{Retrieve a value identified by a key and field
from the Redis database.
}
\usage{
redisHGet(key, field, raw = FALSE)
}
\arguments{
\item{key}{
A key name.
}
\item{field}{
A field name.
}
\item{raw}{
Set \code{raw=TRUE} to s... |
12525e4abdd4e595b5d29f08181bbb27ecd1a807 | 176930f8e95c1571446fb8031b92c913a81ae020 | /2020/week_25/week25_black_population.R | 6c5b5825e856169201a5b085365bc6cfaef6b070 | [] | no_license | inkyscope/TidyTuesday-1 | 656489c99d7d0f8f4e609ea6fc8ab73838e772da | 7e86f5f795882c64a1d7ab0b357c34abc50fd4dc | refs/heads/master | 2023-02-26T11:51:11.313834 | 2021-02-03T21:43:42 | 2021-02-03T21:43:42 | 193,203,574 | 1 | 0 | null | 2019-06-22T07:10:23 | 2019-06-22T07:10:23 | null | UTF-8 | R | false | false | 1,539 | r | week25_black_population.R | library(tidyverse)
census <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-06-16/census.csv')
data_graph <- census %>%
filter(region == "USA Total") %>%
pivot_longer(cols = c("black_free", "black_slaves"),
names_to = "black_",
values_... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.