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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
974ebb715659c071e7de1d64b33a2e549e033841 | ebb359239eaa04c8e02ecdd0a32b260737c7c1d0 | /R/shared_functions.R | 465deb108373f63fe4dd950ac92a128d4a7128f9 | [] | no_license | Halvee/rvatk | ffd9cd200f5f63090a2e877683569f75fdd42c16 | dea66eba71b52f5397e4c9684a588e4285412e2a | refs/heads/master | 2021-01-12T08:10:12.717410 | 2018-04-06T18:11:15 | 2018-04-06T18:11:15 | 76,492,599 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,045 | r | shared_functions.R | # GLOBAL VARS
kGeneNameCol <- "Gene Name"
kVariantIdCol<- "Variant ID"
kSampleNameCol <- "Sample Name"
kGenotypeCol <- "Genotype"
kLvgQualvarCols <- c(kGeneNameCol, kVariantIdCol, kSampleNameCol)
kCphtGeneNameCol <- "Gene"
kCasectrlAllColnames <- c("name","method","alternative")
kCasectrlFetColnames <- c(kCasectrlAllCo... |
a6a9341769eaf9920195a3bfd8c9b00b4d6dea4e | 4c8bc2a54b8126162adb7ded82fe92c14ef78236 | /USArrests.R.R | 7b55d46fbd559cb7de1c9bf63e97ebe1cf4d6b2b | [] | no_license | fall2018-wallace/snehab_dataviz | f77fdab1836c6fb7bb99d14a5733574ad210a14e | c7469d222b55206595bdabc2543a0daf017eb734 | refs/heads/master | 2020-04-02T18:32:59.593654 | 2018-10-25T17:23:55 | 2018-10-25T17:23:55 | 154,704,506 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 79 | r | USArrests.R.R |
arrests<-data.frame(USArrests)
arrests$stateNames<-rownames(arrests)
arrests
|
5d6fcb5ea8191f553995d0a30a0662e2d16d5c16 | 32902f92d3a16b13b1790ffdb8b2d553d08d5a12 | /Code/LimpiezaTweets.R | 0b5552e190c046a4bdc6320387163b01ff6386d6 | [] | no_license | jnm733/Andotter | d0b70af2ea40c22a2b339f121ad74b6d5d67495d | fa055d93f9b0fe62760ee849ba1d212e5efa1750 | refs/heads/master | 2020-12-25T15:08:41.163460 | 2016-09-11T19:49:00 | 2016-09-11T19:49:00 | 66,074,411 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,556 | r | LimpiezaTweets.R | library(stringr)
#Función para convertir en minuscula
catch.error = function(x)
{
# let us create a missing value for test purpose
y = NA
# Try to catch that error (NA) we just created
catch_error = tryCatch(tolower(x), error=function(e) e)
# if not an error
if (!inherits(catch_error, "error"))
y = tol... |
5ffc976cc1013de311169f540ce180863e43ee92 | 29585dff702209dd446c0ab52ceea046c58e384e | /coala/tests/testthat/test-tools.R | 254642c65c565ce73d5e45b26b7f0cf0021d620d | [] | 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 | 165 | r | test-tools.R | context("Tools")
test_that("it checks for packages", {
skip_on_cran()
expect_true(require_package("coala"))
expect_error(require_package("2l3ihjrpaiwhf"))
})
|
6137ce2ccc4e84a69ccdf2e90f9ed33f7077d12d | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /corpustools/man/tCorpus-cash-set_special.Rd | 32e6c10b453a5150f873d6f4a7f14314aec9ead8 | [] | no_license | akhikolla/InformationHouse | 4e45b11df18dee47519e917fcf0a869a77661fce | c0daab1e3f2827fd08aa5c31127fadae3f001948 | refs/heads/master | 2023-02-12T19:00:20.752555 | 2020-12-31T20:59:23 | 2020-12-31T20:59:23 | 325,589,503 | 9 | 2 | null | null | null | null | UTF-8 | R | false | true | 1,073 | rd | tCorpus-cash-set_special.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/documentation_methods.r
\name{tCorpus$set_special}
\alias{tCorpus$set_special}
\alias{set_special}
\title{Designate column as columns with special meaning (token, lemma, POS, relation, parent)}
\arguments{
\item{token}{Name of the column that... |
d8853cb09ba59590ec7e28962c1df89bb1397ea6 | dacc730b25c72f29be816010e7aa67ae36f51f0a | /Store_Item_Demand_Forecasting_Challenge/src/models/arima.R | 6e8c63b8ba6bb9b33aa7f3f57ff7fb4812167fd7 | [] | no_license | Mattias99/Kaggle | 7601981db30810401da285374275772d45681708 | 8d1d76a36b0fe6081ccada61ffed09c9f29df354 | refs/heads/master | 2020-03-23T03:56:15.257100 | 2019-03-21T09:02:34 | 2019-03-21T09:02:34 | 125,730,361 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,743 | r | arima.R | # ARIMA
# Evaluation of data on store = 1, item = 1
# Determine ACF and PACF
# Non stationary
train_one$sales %T>%
acf(main = "Orginal Time-Serie") %>%
pacf(main = "Orginal Time-Serie")
# Transformation with non-season length
train_one$sales %>% diff(lag = 1) %T>%
acf(main = "One-Diff Time-Serie") %>%
pacf... |
309335f2f311be75cf9bb51578f80db48237504d | 13a5d2deae8247072c637d3437be888027c66c87 | /11-15/opgave11_4.R | cbb84596e17f584b7313fcf92aaadfeb05162149 | [] | no_license | wildered/R_code | 26a1f74f90eac709cc474dd4022cb7316755b49c | 1bc440fefe7e2a181ba0e8da315aca3650e8ee91 | refs/heads/master | 2021-01-12T14:40:14.790768 | 2016-10-26T20:04:54 | 2016-10-26T20:04:54 | 72,039,912 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 381 | r | opgave11_4.R | n <- 20
x <- rgamma(n, 3, 1)
hist(x, probability = TRUE)
count <- 10
for (v in c(1/2, 3/4, 1, 5/4, 6/4, 2)){
d <- density(x, adjust=v)
lines(d, col=count)
count <- count + 7
}
n <- 2000
x <- rgamma(n, 3, 1)
hist(x, probability = TRUE)
count <- 10
for (v in c( 1/4, 1/2, 3/4, 1, 5/4, 6/4, 2)){
d <- density(... |
14ce9d0af3f7931dc0f1f6b730ffb3d7ee875afa | 9ab05b7f8d8697fe99e6d4e7917fcb2b3234269c | /man/SiteRisksetsByStrata.Rd | 64132929c87c7432115e3ad0bce39b3c6cc9b65d | [] | no_license | kaz-yos/distributed | 87ba8da54be2379c06fe244f4f570db4555770d7 | 46e53316e7ed20bcb8617e238b1b776fbeb364e2 | refs/heads/master | 2021-05-05T17:31:45.076267 | 2018-06-27T14:37:17 | 2018-06-27T14:37:17 | 103,559,562 | 2 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,184 | rd | SiteRisksetsByStrata.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/02.ParepareDataWithinSites.R
\name{SiteRisksetsByStrata}
\alias{SiteRisksetsByStrata}
\title{Construct risk set data stratifying on a variable}
\usage{
SiteRisksetsByStrata(time = NULL, event, A, W = NULL, strata = NULL)
}
\arguments{
\item{t... |
13effb125e1ebd1851f8bd7b9787ca87c425c049 | 5aa7bc82cb400833a0b103d1d447ca9ac927aa39 | /additional_data/general_use/mmap/man/make.fixedwidth.Rd | 9c01540c50ce64ad81243d65c8acc64934dcbcef | [] | no_license | gearslaboratory/gears-singularity | f4c6cfa47c043a387316fd4463a1e434d1cfad4c | f77ca9a87d3e8bf647deb353dfdfc3aec525995e | refs/heads/master | 2021-05-13T14:23:30.228684 | 2021-02-18T18:29:39 | 2021-02-18T18:29:39 | 116,738,122 | 4 | 6 | null | 2018-07-11T13:04:41 | 2018-01-08T22:57:33 | C | UTF-8 | R | false | false | 1,534 | rd | make.fixedwidth.Rd | \name{make.fixedwidth}
\alias{make.fixedwidth}
\title{
Convert Character Vectors From Variable To Constant Width
}
\description{
Utility function to convert a vector of character strings
to one where each element has exactly \sQuote{width}-bytes.
}
\usage{
make.fixedwidth(x, width = NA, justify = c("left", "right"))
}
... |
e9a32fb66cb28d4b85bfb188f5044156c3758f00 | ab7d15d06ed92cd51cc383dc9e98ae2a8fa41eaa | /man/trav_reverse_edge.Rd | afbceba98bd29b5bd5d90631f714f698b90751b0 | [
"MIT"
] | permissive | rich-iannone/DiagrammeR | 14c46eb994eb8de90c50166a5d2d7e0668d3f7c5 | 218705d52d445c5d158a04abf8107b425ea40ce1 | refs/heads/main | 2023-08-18T10:32:30.784039 | 2023-05-19T16:33:47 | 2023-05-19T16:33:47 | 28,556,914 | 1,750 | 293 | NOASSERTION | 2023-07-10T20:46:28 | 2014-12-28T08:01:15 | R | UTF-8 | R | false | true | 2,632 | rd | trav_reverse_edge.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/trav_reverse_edge.R
\name{trav_reverse_edge}
\alias{trav_reverse_edge}
\title{Traverse to any reverse edges}
\usage{
trav_reverse_edge(graph, add_to_selection = FALSE)
}
\arguments{
\item{graph}{A graph object of class \code{dgr_graph}.}
\it... |
7eed2431a5dc7b7cd128b24a7591a5428a955176 | 8369681fc1c33fab4b7aca6e4514d4782271adbb | /R/HW5.R | 0174b37ffd94a1f6a79b52af73ebe5420c9a597d | [] | no_license | mshagena89/IAA-Code | e90772197abd4f24c78bf3e119158c6069abef7b | 7c64686fe37f3c60c092bfc22a8f4b2bc1d280db | refs/heads/master | 2021-01-01T15:30:31.566197 | 2014-12-09T22:47:23 | 2014-12-09T22:47:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,153 | r | HW5.R | #Solutions for Data Mining HW5
#variance-covariance Matrix
sigma = matrix(c(100, -40, -40, 25), nrow=2, ncol=2)
#calculate sigma inverse, used later in mahalanobis function
sigmaInv = solve(sigma)
#calculating Euclidean Distance between individual and Means
#distance between individual and non-defaulter
nondef <- m... |
74f0ad15de12994c59ced1630d80476c71a06288 | 62e8659296b80ffad17a90b30bc9aea688df485f | /experiments/test_exponential.r | 00fb4933e0bdadf0b0d59e6df86eeb886551ccbe | [] | no_license | matheushjs/dealing-with-popmin | b63bde3b63f895dad2300fb0bd2d9488c000902a | fb375ed9777128e35dfc4cb8b95840a721108380 | refs/heads/master | 2023-03-01T21:03:50.368606 | 2021-02-06T19:50:33 | 2021-02-06T19:50:33 | 259,324,432 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,828 | r | test_exponential.r | require(colorspace);
ALL.N = c(10, 25, 50, 75, 100, 200);
#ALL.N = 100*c(100, 200);
ITERATIONS = 200;
RATE = 1/3
plotMeans = NULL;
plotConf = NULL;
df = NULL;
for(N in ALL.N){
for(idx in 1:ITERATIONS){
min.median = qexp(1 - (1 - 0.5)**(1/N), rate=RATE);
# 1% and 5% quantiles of the minimum distribution
quan... |
a0d80c61fe5ef8bddb4698116fa73e3d8490ed4b | 56e22dd051b4ecc6bf96a7fa93d6dcecc74eebcb | /R/shortcuts.R | 301425dc4e383447108de0a8029612f500de4ffb | [
"MIT"
] | permissive | yjunechoe/hrbragg | 44337b14d1a23b3d3757ac5dae3c2899913ff01b | 8f8b1f098d02329632fff26a89083237fa8cc75f | refs/heads/master | 2023-06-14T13:32:21.889462 | 2021-07-10T12:36:19 | 2021-07-10T12:36:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 803 | r | shortcuts.R | #' Shortcut for `element_blank`
#' @return An S3 object of class element, rel, or margin.
#' @export
elb <- ggplot2::element_blank
#' Shortcut for `element_line`
#'
#' @param colour,size,linetype,lineend,color,arrow,inherit.blank
#' See [ggplot2::element_line()]
#' @return An S3 object of class element, rel, or... |
d633b34b0ccca0c469602d0468e0d5359f9f2d77 | 7f7c55fce129ce299358e22b4f82757d5bfb111e | /R/ApiKey.R | 836fb6032cf31b972eabcad63fea440a379f03d2 | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | RickPack/urlshorteneR | 32c43e797b7720d647947b23277d230c0de8b5a0 | 040c218e3670ed21ee44363f5edd580271759298 | refs/heads/master | 2020-04-28T00:47:33.950190 | 2019-03-10T17:50:24 | 2019-03-10T17:50:24 | 169,720,605 | 0 | 0 | Apache-2.0 | 2019-02-21T10:53:57 | 2019-02-08T10:48:18 | R | UTF-8 | R | false | false | 3,048 | r | ApiKey.R | .state <- new.env(parent = emptyenv())
globalVariables(c("bitly_token"))
# Bitly_api_version <- "v4"
# Isgd_api_version <- "v2019"
#' @title Assign API tokens using OAuth2.0
#'
#' @description You should register an application in order to get Client ID and Client Secret code.
#' For Bit.ly, go to \url{https://bitl... |
c87e16368607911cdac0295054860071af9e1c9f | 0284cda1023b82fcb23f46373fe8d406273494dd | /man/emHMM.Rd | d6b6614f7f4786ee467e656a60ebe835c1d7044a | [] | no_license | flyingxiang/CCRWvsLW | b206569dd094a795a391c1bda8f8e821bf221f5b | fdccfa229695f7d92ffd6a709cb524a43d25e013 | refs/heads/master | 2021-01-22T17:39:56.325678 | 2016-06-10T20:36:30 | 2016-06-10T20:36:30 | 65,390,605 | 1 | 0 | null | 2016-08-10T14:45:43 | 2016-08-10T14:45:42 | null | UTF-8 | R | false | false | 2,414 | rd | emHMM.Rd | \name{emHMM}
\alias{emHMM}
\alias{EMHMM}
\title{EM-algorithm to fit a hidden Markov model representing the CCRW
}
\description{
emHMM finds the maximum likelihood estimate for the parameters of the CCRW by fitting the hidden Markov model through an Expectation Maximization (EM) algorithm.
}
\usage{
emHMM(SL, TA, missL,... |
2701f222199004ea1374338ddae0a4d0d31fb0a8 | bf8458974cd7c5daa0882bba005d37b795aadeff | /man/independencetests.Rd | 3d3290c6c1bfede7a8115b8786b0072a12c09eed | [] | no_license | cran/SpatialNP | 67e43d86a7e00a1ca2f218ae09e57346329183a0 | 57669fbaf6c594659c25439fe7c7b530e0a2e96e | refs/heads/master | 2021-12-14T12:48:51.284359 | 2021-12-08T11:50:05 | 2021-12-08T11:50:05 | 17,693,745 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,953 | rd | independencetests.Rd | \name{Independence tests}
\alias{sr.indep.test}
\title{Multivariate test of independence based on spatial signs or
ranks}
\description{ Test of independence between two sets of
variables. Inference is based on the spatial signs of the
observations, symmetrized signs of the observations or spatial signed
ranks of the... |
071894307309259fd95c9ff0698192c21d4d1a8d | 894024e86cc9f5a0b95df606f80547fc78a59d1a | /R/gpuApply_Funcs.R | 06cce59d332cd8fa35bef12acddfcab3cdc86b0b | [] | no_license | Jiefei-Wang/gpuMagic | 08047002c398eb303deeddd702139d67529ce555 | 0d6f49dccff7b4826afc685aff1e25ec2032e8d4 | refs/heads/master | 2022-05-10T00:20:41.729900 | 2022-03-15T00:18:21 | 2022-03-15T00:18:21 | 151,973,314 | 11 | 2 | null | 2022-03-15T00:18:22 | 2018-10-07T18:40:04 | R | UTF-8 | R | false | false | 12,017 | r | gpuApply_Funcs.R |
saveGPUcode <- function(GPUcode) {
GPUcode_hash = GPUcode
GPUcode_hash$parms = NULL
for (i in seq_along(GPUcode_hash$varInfo)) {
if (is(GPUcode_hash$varInfo[[i]],"hash"))
GPUcode_hash$varInfo[[i]] = copy(GPUcode_hash$varInfo[[i]])
}
GPUcode_hash$parmsName = names(GPUcode$parms)... |
bce11c79f274b6cd6fc6987445e62f67cf449048 | bcc3bf661017041dfc14adc9be256996ce0fce56 | /2019-accountability/cdf_to_student_level.R | 2bc45cfdd37321f9a806c999b2829c4b1da8116b | [] | no_license | tnedu/accountability | 60bc867c76342bc43e66464150439b10360331e1 | 395c4d880d02cede1ff37ee3d4980046e0bcf783 | refs/heads/master | 2021-11-02T03:30:30.545850 | 2019-10-23T18:39:11 | 2019-10-23T18:39:11 | 42,124,295 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,560 | r | cdf_to_student_level.R | library(acct)
library(tidyverse)
## TODO: School Numbers for 964/964 and 970/970
msaa <- read_csv("N:/ORP_accountability/data/2019_cdf/2019_msaa_cdf.csv") %>%
filter(!(reporting_status %in% c("WDR", "NLE"))) %>%
mutate(
test = "MSAA",
semester = "Spring",
special_ed = 1L,
perfor... |
4416ec54ef21e9db290050bb8d4f0be873730e98 | 5dc064f8f0df5f9dc0251dd789617402bb648ece | /functions.R | 6648acdc6ae220ce49b719881618ec5ed67248da | [] | no_license | fbetteo/dm-HyadesCluster | 192fb42c6493dc062f090c1150ee7721636b5a06 | 15b8f5fdb73056471c37c9ad018042440d222e43 | refs/heads/master | 2020-04-03T13:45:44.936591 | 2018-10-29T23:57:48 | 2018-10-29T23:57:48 | 155,297,021 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,535 | r | functions.R | source("libraries.R")
# operador para concatenar texto:
"%+%" <- function(a,b) paste(a,b,sep="")
# transformacion minmax
minmax <- function(x) (x-min(x))/(max(x)-min(x))
# saca n_rem outliers de data.frame
remove_outliers = function(df, n_rem) {
good_i = MASS::cov.rob(df, cor=F, quantile.used=nrow(df)-n_rem)$best
... |
e94889d65f3ec75a5982deaeb076a1f2563c512b | f2b85324be5786e64007d6569ee0f859cd4e7890 | /utils_plot_fcts.R | 618b42391610a18efc708d59c53acde7111fdd17 | [] | no_license | marzuf/Cancer_HiC_data_TAD_DA | 17313c4bd142b1d47b9c544e642cc248e7329dd6 | 67d62faba164678c7e4751f20131b34ecd4d11a5 | refs/heads/master | 2020-04-15T21:39:51.365591 | 2019-06-20T07:47:52 | 2019-06-20T07:47:52 | 165,041,352 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,685 | r | utils_plot_fcts.R |
printVar <- function(x){
cat(paste0(x, " = ", eval(parse(text=x)), "\n"))
}
plot_cumMatch <- function(dt, tomatch){
curr_match <- na.omit(dt[, tomatch])
xvect <- seq_len(max(curr_match))
yvect <- sapply(xvect, function(x){
sum(curr_match >= x)
})
plot(x = xvect,
y = yvect,
xlab = paste0... |
ad00c64dfbd9304cc6fc3842c6aca2b83589b7a1 | 9bf7e9b202b2b02d65ad8eed6346a50584392b1a | /R/NglWidget.R | 2c485b87198435e3b2c09e3cb813fce8a8079a4d | [] | no_license | paul-shannon/nglShinyS4 | 53be289f1148bf6711d2783a17461fc588175940 | a96fd5c79057ed05c3acf7be60d7c9aa623d31f3 | refs/heads/master | 2022-09-17T15:21:25.091519 | 2020-05-31T17:03:12 | 2020-05-31T17:03:12 | 267,874,245 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,117 | r | NglWidget.R | #' @import shiny
#' @import htmlwidgets
#' @import shinyjs
#' @importFrom methods new
#'
#' @title NglWidget
#------------------------------------------------------------------------------------------------------------------------
#' @name NglWidget-class
#' @rdname NglWidget-class
#' @aliases NglWidget
#'
## @import m... |
c02b971ae9cffbc7f3a81d4dc35f8e58de1c806c | 4cf1e9eb8178a3c30575f13c6e889b40d758a709 | /cachematrix.R | d59c7b320f6f4a27deca787b5f1e5ae0094e9b33 | [] | no_license | jgbarberena/ProgrammingAssignment2 | 84885975da2f7ee5e939718aeced352d44af8485 | ead374e6ce0bb71e91a1e6270abd6f8dd39da576 | refs/heads/master | 2021-01-09T08:08:40.071613 | 2014-11-18T20:08:01 | 2014-11-18T20:08:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,364 | r | cachematrix.R |
## This function creates a special type of matrix. It actually consists on a list of
## functions for manipulating a matrix. It has four fuunctions:
## The 'set' function sets the values of the matrix and NULL for the inverse, so each time a matrix is
## created or changed, it's inverse is reset t... |
cc691c8b1786c8296a5f38cdf7460476157ec6de | e6d0aa42a4e601e8acf1bb5838668e4f0c906f9e | /plot3.R | 1973c7800be86aec4c90e8745e550dd4198c410f | [] | no_license | DylanLennard/Coursera_JHU_EDA_Final_Project | 886d47dd4ff65fe522f44613a38838f70ce3de12 | ae0e842304c44a0f38c773770a7fb24d7ac0ab21 | refs/heads/master | 2021-01-12T14:08:46.897859 | 2016-10-05T14:07:46 | 2016-10-05T14:07:46 | 69,757,907 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 891 | r | plot3.R | ### plot3 ###
setwd("~/Desktop/Statistical_Programming/Coursera/Exploratory_Data_Analysis/EDA_Final_Project")
library("readr")
library("tidyverse")
library("data.table")
url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
fileNames <- c("summarySCC_PM25.rds", "Source_Classification_Code.rds")... |
95edaefafac0155884af99b7b1497368e75a37b2 | 4f038917144f89bcdb949346c92ae90782ab6f72 | /man/CONST_LOGIN_TYPES.Rd | 656f810b9cda438e42dbe7c9d795beb238ce0a51 | [
"MIT"
] | permissive | isabella232/rauth0 | a4df725b2eb1f0f351140e878e419ff44d9e862c | 07972e009ed95e1d7980de32ba8ef54b6ff97c5f | refs/heads/master | 2023-06-09T14:00:41.898109 | 2021-07-01T08:58:08 | 2021-07-01T08:58:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 376 | rd | CONST_LOGIN_TYPES.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/variables.R
\docType{data}
\name{CONST_LOGIN_TYPES}
\alias{CONST_LOGIN_TYPES}
\title{Login types}
\format{An object of class \code{character} of length 9.}
\usage{
CONST_LOGIN_TYPES
}
\description{
Types of events which count as active users ... |
79ec78f63f678cf895e9d907ce952d41dddc97fb | 0382fe6ebf899ce56eecfdb7f234a477e7c26800 | /scripts/CRSeafood Functions-example.R | 15555da00b1317eb2397c24746ac6d6cf837f3b6 | [] | no_license | fishmgt/fishmgt.github.io | a3eaa117bd6292f99c7fd22797720ee8befa2a0f | e0f595ed9aaa4d427138e9e3a03b6b78056c74e0 | refs/heads/master | 2016-08-11T05:30:05.804920 | 2016-03-29T01:25:50 | 2016-03-29T01:25:50 | 49,733,944 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,601 | r | CRSeafood Functions-example.R | #===================================================================
# Using optim in R to fit a logistic growth model to Costa Rican snapper
# 'catches.csv' -> gives regional (Guanacaste) catches between 1990 - 2013
# 'CPUE.csv' -> gives local catch per unit effort (number caught per trip) between 2007 - 2013
... |
b7a7d1ddc8eaaa271073efdd8ff174998b17a91f | aaf8222e2e7c1ca3480092387472ed539e79985a | /man/JiraTime.Rd | 5458731bc087c5af9338dc4b4d1b90772ce4d8e3 | [] | no_license | M3SOulu/MozillaApacheDataset-Rpackage | 57e7028f2d2ee9a6a672a9775f20bf40af9e4f4a | 3644dbd266325309be4bfdf1ac926ae8859ebd19 | refs/heads/master | 2022-06-23T11:56:58.580415 | 2022-06-20T11:03:39 | 2022-06-20T11:03:39 | 238,914,906 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 429 | rd | JiraTime.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/jira.R
\name{JiraTime}
\alias{JiraTime}
\title{Jira Time}
\usage{
JiraTime(table, name)
}
\arguments{
\item{table}{deta.frame containing raw time fields}
\item{name}{Name of the column in which to add the parsed time.}
}
\value{
The table wi... |
71c0ac0df78222c817e348985bdbbcfb59acaadc | 0a3111aa5626916b3517627c1f280f082a12db3d | /tests/test-get_aligns.R | 4c85bf93e53e9480da9d90c22d0d338724683020 | [] | no_license | naszim/homework-02 | 27c37c9310336dbea083206e31a578f6a3dc5acc | 5a8b541d5c61a416e8651cb6be460b50e6c11cc5 | refs/heads/master | 2020-05-25T14:44:16.684720 | 2017-03-15T20:01:24 | 2017-03-15T20:01:24 | 84,941,013 | 0 | 0 | null | 2017-03-14T11:24:20 | 2017-03-14T11:24:20 | null | UTF-8 | R | false | false | 825 | r | test-get_aligns.R | ##testthat behívása-------------------------------------------------------------
library(testthat)
##get_aligns 0 elemű vektort ad, ha az argumentumba számot írunk
test_that("get_aligns does not work with numeric argument",
{expect_identical(get_aligns(alignment = 5), character(0))
})
##get_aligns... |
0987b6dbc7938a0708b3648eb29a7c318cb16d9c | 3f6dd3134f16de2f08aa6ec52e772d7e5c5422c0 | /tests/testthat/test_chisquare_ndist.R | 7d34e3100a4e314a1661d335bb54fd7113fb8989 | [
"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 | false | 1,569 | r | test_chisquare_ndist.R | context("test n_dist for ChiSquare test")
test_that("error messages are thrown correctly", {
d1 <- setupChiSquare(alpha = 0.025, beta = 0.2, r = 1, delta = 0.1)
expect_error(n_dist(d1, n1 = 20, nuisance = 1.1, TRUE))
d2 <- setupChiSquare(alpha = 0.025, beta = 0.2, r = 2, delta = 0.1, n_max = 301)
expect_error... |
d7982db0c0227b190202d394a563078de93933dc | 222ddcb4176c06aa122588179cb5395652653d2d | /archive/simulations/TMLE_ATT.R | 14f0705decdf57a76247d254f90734bafd512832 | [
"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 | 4,508 | r | TMLE_ATT.R | # Susan Gruber and Mark van der Laan
# October 26, 2015
# Supplemental Materials for One-Step Targeted Minimum Loss-based Estimation
# Based on Universal Least Favorable One- Dimensional Submodels.
# R source code for
# - iterative TMLE for ATT parameter using one epsilon
# - one-Step TMLE for ATT parameter
# - Simu... |
6cac0ec5dcec030467c67ec34ef1375cd2eb675e | 82c0113017734464b1b8d92f27b255368969155a | /R/cellassign.R | 9a959cfb4c5f68a6e482f397d93b0c14ea125e6e | [
"Apache-2.0"
] | permissive | kieranrcampbell/cellassign | 030d16048b22ddd8b38f638a64e97f7f75e21af4 | ea5329e2f6e343a58e7e4ac60289c40f391140a2 | refs/heads/master | 2020-06-16T01:16:28.983066 | 2020-04-14T13:33:02 | 2020-04-14T13:33:02 | 195,441,954 | 1 | 0 | NOASSERTION | 2019-11-25T12:43:46 | 2019-07-05T16:51:28 | R | UTF-8 | R | false | false | 14,777 | r | cellassign.R | #' Annotate cells to cell types using cellassign
#'
#' Automatically annotate cells to known types based
#' on the expression patterns of
#' a priori known marker genes.
#'
#' @param exprs_obj Either a matrix representing gene
#' expression counts or a \code{SummarizedExperiment}.
#' See details.
#' @param marker_gene_... |
552f9733e54dddfc8a38622fd61163464e59ac58 | 20f36a6ec7a216b425ea26bdcac23314d846bfd8 | /Analysis/IWTomics/low_resolution/IWTomicsData_low_resolution.r | aa9f234fb1e9af5369d3637ba927243a6b61e1ba | [
"MIT"
] | permissive | makovalab-psu/L1_Project | 0ffd46c9544f20112c05dfba977ea69c51a08494 | d8a3ae36dbe65fc48a4cee7a3ccf27a594e0c039 | refs/heads/master | 2021-05-24T10:33:25.312392 | 2020-07-13T20:45:55 | 2020-07-13T20:45:55 | 253,520,011 | 1 | 2 | MIT | 2020-04-07T17:55:45 | 2020-04-06T14:21:06 | Jupyter Notebook | UTF-8 | R | false | false | 38,986 | r | IWTomicsData_low_resolution.r | setClassUnion(".listOrNULL",c("list","NULL"))
setClass("IWTomicsData",
slots=c(metadata="list",
regions="GRangesList",
alignment="character",
features="list",
length_features="list",
test=".listOrNULL"),
proto... |
21fb3561ccbbe0c622d150255d35faaf82a0b015 | 87760ba06690cf90166a879a88a09cd2e64f3417 | /man/recipe_helpers.Rd | c4493f0b5e3ed1886f54edfb08a1a842418b0cde | [
"MIT"
] | permissive | topepo/modeltime | 1189e5fe6c86ee3a70aec0f100387a495f8add5f | bff0b3784d1d8596aa80943b221eb621481534e1 | refs/heads/master | 2022-12-27T07:11:58.979836 | 2020-10-08T16:07:27 | 2020-10-08T16:07:27 | 289,933,114 | 1 | 0 | NOASSERTION | 2020-08-24T13:17:10 | 2020-08-24T13:17:10 | null | UTF-8 | R | false | true | 1,469 | rd | recipe_helpers.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dev-xregs.R
\name{recipe_helpers}
\alias{recipe_helpers}
\alias{juice_xreg_recipe}
\alias{bake_xreg_recipe}
\title{Developer Tools for processing XREGS (Regressors)}
\usage{
juice_xreg_recipe(recipe, format = c("tbl", "matrix"))
bake_xreg_re... |
532f64fb16cd73d983aa279f5446db7815f347e3 | 3ca84ee0818caa8ba642431fce356f523fc4a8d4 | /stackoverflow/anova.R | c4e8e1f6f891bb0935399d7d1e2d000b5d6c9c3f | [] | no_license | Zedseayou/reprexes | 0f15109ee57e2d4da5538c47a826ebd126bfbfd9 | a38a88acaff05bd7b9cd9c4ba3b77899b5f33b81 | refs/heads/master | 2021-05-03T10:57:11.405154 | 2019-05-22T23:29:32 | 2019-05-22T23:29:32 | 120,541,074 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 325 | r | anova.R | library(tidyverse)
tbl <- read_table2(
"id number value
1 1 2
1 2 1
1 3 4
2 1 4
2 2 3
2 3 4"
)
tbl %>%
group_by(number) %>%
do(anova(lm(value ~ number, data = .)))
library(broom)
tbl %>%
group_by(number) %>%
do(tidy(anova(lm(value ~ number, data = ... |
7a388cc05aeda87002ea418aff3743fa8a85a0df | ebef50b72699404ed4e523bdd702806b108f4fab | /man/ShowOptDesign.Rd | b5b57bc250ea0485a63ea0f11b5f2fdd1e41d9eb | [] | no_license | cran/hiPOD | a4691dc4f657132ee40dad821a47907aa189242d | 85448d9356e7a9451b846392b9eb8afdc333bf74 | refs/heads/master | 2021-01-22T17:47:39.699768 | 2012-04-27T00:00:00 | 2012-04-27T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,920 | rd | ShowOptDesign.Rd | \name{ShowOptDesign}
\alias{ShowOptDesign}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Print the top choices of designs
}
\description{
Show the top [num.designs] choices of valid designs.
}
\usage{
ShowOptDesign(opt.design.results, num.designs = 10)
}
%- maybe also 'usage' for other objects ... |
7c19873b45fbd2ba0e5de62dfea265d223ba3973 | f64fea318bda54ddf7a18aab6ea6683d2b2c94e1 | /exploratory/explore_3_viz_text.R | d264a57478d562faf41949d4b4bb10c6fbdeb721 | [] | no_license | SportsTribution/doing_data | 75faedc24fe467120cbb2e46892e98db219d2e54 | c728afee4d3cb4fdf7d25cf319cf220497e9eb87 | refs/heads/master | 2018-01-08T08:10:22.206196 | 2016-02-24T16:19:00 | 2016-02-24T16:19:00 | 52,455,390 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,629 | r | explore_3_viz_text.R | if (j==1){
DFfieldText <- paste(as.character(DFfac[,i]), collapse=" ")
DFfieldText <- Corpus(VectorSource(DFfieldText))
DFfieldText <- tm_map(DFfieldText,content_transformer(function(x) iconv(x, to='UTF-8', sub='byte')), mc.cores=1)
DFfieldText <- tm_map(DFfieldText, content_transformer(tolower))
DFfieldText... |
d2ac83d5ee3768b66b73a203ac8c3fe0afbe48b4 | 2e4f3592e872117efc1559b0219299ba7d6e840c | /tests/testthat.R | 1d1c6fe0d09c5424d912293b98f4d6b88ae98c09 | [
"MIT"
] | permissive | gl2668/whichState | 1d9134f1998af3764d987feed1894e5ed7015ab4 | afc5523e75cee28b191665c3a05c6700b3c46967 | refs/heads/master | 2020-08-22T19:53:40.592257 | 2019-11-25T19:36:00 | 2019-11-25T19:36:00 | 216,468,112 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 64 | r | testthat.R | library(testthat)
library(whichState)
test_check("whichState")
|
af4a9cf6061e487ba8bee242ac7691166fa1c480 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/VGAM/examples/genrayleigh.Rd.R | 936f43b940169fc477bbbb50a402776cb0787be2 | [] | 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 | 437 | r | genrayleigh.Rd.R | library(VGAM)
### Name: genrayleigh
### Title: Generalized Rayleigh Distribution Family Function
### Aliases: genrayleigh
### Keywords: models regression
### ** Examples
Scale <- exp(1); shape <- exp(1)
rdata <- data.frame(y = rgenray(n = 1000, scale = Scale, shape = shape))
fit <- vglm(y ~ 1, genrayleigh, data = r... |
a1ae3ff9d29df1957189bf3f3fe47ff753546a6e | 4df4f7d46f919516073166fa5f4f654bb54a1d02 | /man/pld.Rd | 5d938a3b7816da5b2c79156cf853efefbc6dea4c | [] | no_license | cran/polysat | feb78fd54904f91d909fbc9b9e35e84d067eab36 | c39682b0f45a3f889e44514a8629f4947210ed1f | refs/heads/master | 2022-09-08T14:09:00.861300 | 2022-08-23T13:10:02 | 2022-08-23T13:10:02 | 17,698,645 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,693 | rd | pld.Rd | \name{pld}
\alias{pld}
\alias{pld<-}
\alias{plCollapse}
\title{
Accessor, Replacement, and Manipulation Functions for \code{"ploidysuper"} Objects
}
\description{
\code{pld} accesses and replaces the \code{pld} slot of objects of
\code{"\linkS4class{ploidysuper}"} subclasses. \code{plCollapse} tests
whether... |
f8a7e2d82f85f38e7e3805be573a83294eb8afea | e1b685959f55e5556adda5e25520b15c2f338705 | /src/main/R/libyogiseq.R | d5af6e3cbd189d2ec7e6a83b4430fc81c9a79531 | [] | no_license | jweile/screen_pipeline | e5f13a771d7e6f374144447b0a10aed40142eff5 | 7e73babeec0a1e5f56d6cd7ac71b347a9769ae9d | refs/heads/master | 2020-09-24T08:04:12.539267 | 2017-03-27T17:43:36 | 2017-03-27T17:43:36 | 225,709,768 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 31,577 | r | libyogiseq.R |
#Function to turn string into character array
to.char.array <- function (str) sapply(1:nchar(str), function(i) substr(str,i,i))
char.at <- function(str,i) substr(str,i,i)
new.sequence <- function(sequence, qual=NULL, id=NULL) {
.seq <- sequence
.qual <- qual
.id <- id
toString <- function() {
.seq
}
getQua... |
8584cd4541163e061c014ea9cd83b00baa565a0a | 91484bf347364a04d7eab95629840cd6f2c9e823 | /ui.R | 718ff04108dfb785cb4bb65b6a713344a8de04ad | [] | no_license | matschmitz/NOMIREG | b7976242dcfd5a5da190cba3a1f81d82f1fc69da | 99a6c16ca265f37bb8790267e3a18ce6446570e9 | refs/heads/master | 2023-01-11T04:46:36.962730 | 2020-11-12T10:25:53 | 2020-11-12T10:25:53 | 312,241,387 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,261 | r | ui.R | library(shiny)
fluidPage(theme = "myCSS.css",
headerPanel(title = "", windowTitle = "Régression multiple nominale"),
h2("Régression multiple nominale"),
sidebarLayout(
sidebarPanel(width = 4,
matrixInput("codes", class = "numeric",
... |
a53143485a087a50e43577b106f67385c504beca | dd470dfb159767bb3d2fade6697e6f25568cbe0e | /R_Files/Chapter_17_Neural_Network.R | b64cbc1b82b9aa13c9e29277fffff39ee87f2548 | [
"MIT"
] | permissive | djdhiraj/Data_Science_In_R | 2011c3304f56fb9b96cc62ff84ca362d6e9ff134 | 4bf78a8542689c87f14072885a292e4762f390ac | refs/heads/master | 2020-04-02T04:22:40.700705 | 2019-02-09T12:45:25 | 2019-02-09T12:45:25 | 154,013,949 | 1 | 0 | MIT | 2018-12-04T13:00:10 | 2018-10-21T13:55:24 | R | UTF-8 | R | false | false | 1,284 | r | Chapter_17_Neural_Network.R | #install.packages("neuralnet")
library(neuralnet)
dim(infert)
View(infert)
nn<-neuralnet(case~age+parity+induced+spontaneous,data=infert,hidden=2,err.fct = "ce",
linear.output = FALSE)
nn
plot(nn)
nn$net.result
nn$weights
nn$results
nn$result.matrix
nn$covariate
infert$case
nn$net.result[[1]]
nn1<-ifelse... |
87ffb45f51a593c1391c11b95ceb4221d60d125a | ee2a149e5a84006dc3a3672d70eaf02bbec30b4b | /R/plot_simple_fit.R | e20facaa257a960f06ba1eb97dc2238018dc02e0 | [] | no_license | annube/Chifit | 57d55c31aa54d1880432693aa3f82517e10b4642 | a4381fef2ab6d8fc47bf5f4bc7d9e972aa01219e | refs/heads/master | 2021-01-01T17:17:31.136458 | 2013-08-14T19:59:41 | 2013-08-14T19:59:41 | 3,256,011 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 263 | r | plot_simple_fit.R |
## library(hadron)
plot_simple_fit <- function(data_x,data_y,data_dy,fitfn,file="simple_fit.pdf")
{
pdf(file=file)
plotwitherror(data_x,data_y,data_dy)
xs = seq(min(data_x),max(data_x),length.out=100)
ys = fitfn(xs)
lines(xs,ys)
dev.off()
}
|
e087f100429413e49d87db599338836c92ac9608 | 2bb1abc51dd9746776948143f91e2cfdd4463a7e | /R/plot.R | 7fbcca744283e7fdac9ebddf22276350a7e5a22f | [
"BSD-2-Clause"
] | permissive | parenthetical-e/boldR | 90c53f09fdca36098ed744380959680391e1eb4b | 8ae1b67cf1764a1a789d612fc7bf7ba0c1250980 | refs/heads/master | 2016-09-05T19:36:07.421610 | 2014-06-24T03:45:27 | 2014-06-24T03:45:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,678 | r | plot.R | library("ggplot2")
library("reshape2")
plot.bolddf.homogeneity <- function(bolddf, name, seperate_by=NA,
height=0, width=0, returnp=FALSE, title=NA){
if(! is.na(name)){ .pdf.device.setup(name, height, width) }
# else { dev.new(width=width, height=height) }
if (is.na(seperate_by)){
p <- gg... |
a4a62c2f4232743312eb96287f35d59f1167cf5c | 37cc63b9708638db1fd1e01c0b3e52013654986c | /analysis/Brendan/Gene Filter/gene_groups.R | 534bac67d333589fa98bfb58720723dbe1c68088 | [] | no_license | brendan4/sravandevanathan | e7082bd5892ccc5cf679132aaa06c88abe4c17fc | 00c54737e09ea29b517a2d008a420748af43d9b7 | refs/heads/master | 2022-03-26T08:02:45.299082 | 2019-12-13T21:09:21 | 2019-12-13T21:09:21 | 170,389,554 | 0 | 0 | null | 2019-02-12T20:52:38 | 2019-02-12T20:52:37 | null | UTF-8 | R | false | false | 6,394 | r | gene_groups.R |
min_nonzero = 1
gene.list <- c("MDM2","RPL11", "TP53", "GATA1", "PML", "MYC", "CDKN2A")
# two options: expressed genes and expressed trans datasets
filtered.data <- filter.genes(expressed.genes,gene.list) # expressed genes dataset
filtered.data <- filter.genes(expressed.trans,gene.list) # expressed trans dataset
#... |
bafcab8f473f287d49d8815f77796d4400c2a7f1 | 703ad20be09e009077863d8a571c3585f9e2a061 | /man/as_node.Rd | 338fe66898a3538d269a12922ae5a0a668f64955 | [
"MIT"
] | permissive | russHyde/s3tree | ccbfde0cd8451834ae3f41c3ae8c88d7b12281a0 | 711831f9c0736439bc3eb950643dc4c64d4249e8 | refs/heads/master | 2020-04-19T06:03:50.949064 | 2019-01-30T11:21:10 | 2019-01-30T11:21:10 | 168,007,301 | 0 | 0 | NOASSERTION | 2019-01-30T11:21:11 | 2019-01-28T17:45:36 | R | UTF-8 | R | false | true | 328 | rd | as_node.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/node.R
\name{as_node}
\alias{as_node}
\title{Convert a `list` into a `Node`}
\usage{
as_node(x)
}
\arguments{
\item{x}{A list of data. This must contain entries named
`name`, `parent` and `children`.}
}
\description{
Convert a `list` into a `... |
9c9815d949a644a91ea44c530ef35687065e87e2 | d03baca03096273c73c930b29e654836f0d3f0b8 | /man/PlotHist.Rd | d6416191fc68fa2fabcf64c0ff8ba936c16a2c39 | [] | no_license | ericschulz/rpawl | fb2235112126d988f1e5ec09d22915d6affa390c | 0e5ea45356644ffc79c6381ef002e5599242c676 | refs/heads/master | 2016-08-11T07:29:18.270112 | 2013-06-07T04:25:56 | 2013-06-07T04:25:56 | 43,148,457 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 732 | rd | PlotHist.Rd | \name{PlotHist}
\alias{PlotHist}
\title{Plot a histogram of one component of the chains}
\usage{
PlotHist(results, component)
}
\arguments{
\item{results}{Object of class \code{"list"}: either the
output of \code{\link{pawl}} or of
\code{\link{adaptiveMH}}.}
\item{component}{Object of class \code{"numeric"}:... |
dfa9c38cb07baf07b2bf101f7921b30575bdee0a | 7bf3503de3ccf77a0f57491b4bccfaa8aeff5b84 | /R Scripts/KNNAlgorithm.R | 65b3d1e7ffbaec904edb5303d84513dfddacb569 | [] | no_license | augaonkar/Yelp-Dataset-Analysis | dbb193a49e4b52d5b758ef8c54bfb1c7f26a32a0 | ad9234f9c652570b51bdaffcf86035945d3afab5 | refs/heads/master | 2021-01-21T10:04:59.189758 | 2017-02-28T01:27:25 | 2017-02-28T01:27:25 | 83,370,719 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,789 | r | KNNAlgorithm.R | library(rjson)
library(plyr)
library(dplyr)
library(ggplot2)
library(knitr)
library(glmnet)
library(googleVis)
library(DT)
library(scales)
library(varhandle)
install.packages("caret")
library(ISLR)
library(caret)
con <- file("C:/Users/yesha/OneDrive/Documents/yelp_academic_dataset_business.json", "r")
input <- readLi... |
5ed3e128c0db43544862ce08e5b2f73f512cba43 | 721d96cf611a8ee4ee224e6dae7c92a9faf180c0 | /man/mwStatsGrid.Rd | 9a84a8c6a9e361ca0a869175c876a075b2a148c0 | [] | no_license | cran/astrochron | 5b05df5f4f114cdf6df630cdfe0f649e5f45dbbf | c86d83297c0b75ce1ab1a026a25fe05e0e6cb3fe | refs/heads/master | 2023-09-01T06:57:37.447424 | 2023-08-26T13:40:02 | 2023-08-26T14:30:40 | 21,361,937 | 5 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,177 | rd | mwStatsGrid.Rd | \name{mwStatsGrid}
\alias{mwStatsGrid}
\title{'Dynamic window' moving average, median and variance of stratigraphic series, using evenly spaced spatial/temporal grid}
\description{
'Dynamic window' moving average, median and variance of stratigraphic series.
This routine adjusts the number of data points in the window... |
9f3d2d9ceff46932dd29b8a5d2121139d7569d62 | 14d2d4e7cacb355ed158ae9b55cab59444b8d400 | /R/studentGrowthPercentiles.R | 582ccf53ba08fcd0e985fe72c3db1f47b36ed89c | [] | no_license | shangyi/SGP.experiment | 8bfdcf988f54d94a6bb621b44b2dcc5b60dc36bc | 597bc09c8e4b5ec754acaca49c8056338980c9ba | refs/heads/master | 2016-09-09T20:41:22.051372 | 2014-05-09T21:48:57 | 2014-05-09T21:48:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 64,131 | r | studentGrowthPercentiles.R | ## experimental version for simexing aggregate sgp
`studentGrowthPercentiles` <-
function(panel.data, ## REQUIRED
sgp.labels, ## REQUIRED
panel.data.vnames,
additional.vnames.to.return=NULL,
grade.progression,
content_area.progression=NULL,
... |
29cf510cc14caf1d31364c191859e77d33fadbfa | 5127927036020b809569d9b57581518f13e4c548 | /10XGenomics/R_scripts/01_new_gtf.R | 0df611d50fa7217340d203c94fac70bbe43b9f21 | [
"MIT"
] | permissive | zamanianlab/Bmsinglecell-ms | f2abacefbe74feb3041df06076b71fc740ea8779 | ebcf3846212be1a97583a7777663910033a76484 | refs/heads/main | 2023-06-30T09:25:14.631569 | 2023-05-01T23:25:16 | 2023-05-01T23:25:16 | 528,939,348 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,820 | r | 01_new_gtf.R | # determining the ideal extension length for each gene for maximal mapping rates across the transcriptome.
# Need to increase vector memory to 100GB prior to beginning or you will get nothing done.
options(future.globals.maxSize = 8000 * 1024^2)
# install packages
install.packages("hdf5r")
install.packages("readbit... |
ddb270049c2ac30c3a3ad3b1368ce41521d59a16 | c555092c911699a657b961a007636208ddfa7b1b | /tests/testthat/test-stat-align.R | c727073daabd168a9a40d26c6a18803391a51c73 | [] | no_license | cran/ggplot2 | e724eda7c05dc8e0dc6bb1a8af7346a25908965c | e1b29e4025de863b86ae136594f51041b3b8ec0b | refs/heads/master | 2023-08-30T12:24:48.220095 | 2023-08-14T11:20:02 | 2023-08-14T12:45:10 | 17,696,391 | 3 | 3 | null | null | null | null | UTF-8 | R | false | false | 986 | r | test-stat-align.R | test_that("standard alignment works", {
df <- tibble::tribble(
~g, ~x, ~y,
"a", 1, 2,
"a", 3, 5,
"a", 5, 1,
"b", 2, 3,
"b", 4, 6,
"b", 6, 7
)
p <- ggplot(df, aes(x, y, fill = g)) + geom_area(color = "black")
expect_doppelganger("align two areas", p)
})
test_that("align... |
e4c7dd55383d52f7557132fcb400e2e9155b197b | b08b7e3160ae9947b6046123acad8f59152375c3 | /Programming Language Detection/Experiment-2/Dataset/Train/R/sum-of-squares.r | 4e0140025307fe5f7d6d5f6c5d11edf9b59fbd52 | [] | no_license | dlaststark/machine-learning-projects | efb0a28c664419275e87eb612c89054164fe1eb0 | eaa0c96d4d1c15934d63035b837636a6d11736e3 | refs/heads/master | 2022-12-06T08:36:09.867677 | 2022-11-20T13:17:25 | 2022-11-20T13:17:25 | 246,379,103 | 9 | 5 | null | null | null | null | UTF-8 | R | false | false | 41 | r | sum-of-squares.r | arr <- c(1,2,3,4,5)
result <- sum(arr^2)
|
deb01e4247b7d66bcfb7f14cff97ce9705c28456 | 4b76f1a19c6fc9a8a2263ddda140aff00ec3396a | /R/normal_rejection.R | 90eaa2a4032d5b4c7eb83e23ee21c78aa46903eb | [] | no_license | cran/tmvmixnorm | db90df7285039e701fde8ce91665fd133327313a | cbb7edbc4b3c255eea3ceeaea9e77947c6422ddd | refs/heads/master | 2021-06-27T21:36:10.016413 | 2020-09-18T17:00:02 | 2020-09-18T17:00:02 | 145,902,241 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 519 | r | normal_rejection.R | #' Normal rejection sampling
#'
#' \code{norm_rej} is used for normal rejection sampling.
#'
#' @param a lower bound
#' @param b upper bound
#'
#' @return \code{norm_rej} returns a list
#' \code{x}: sampled value; and
#' \code{acc}: total number of draw used.
#'
#' @examples
#' set.seed(1)
#' norm_rej(a=1,... |
56fb082457271bcf28cca6faac84161a7a592c59 | 2244c60d2e337787768123af843783825496e14a | /man/BlaAlt.Rd | 30f3a62e49d662c82631495b8f11508e3ba07e44 | [] | no_license | cran/pvrank | a46f30b5cfc4df678937f4e05ea9d6c3346b16a7 | c03170854caf8df1eabc7e831ee0f82deeae3db8 | refs/heads/master | 2020-12-09T09:09:57.472522 | 2018-05-17T07:38:40 | 2018-05-17T07:38:40 | 31,172,247 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,062 | rd | BlaAlt.Rd | \name{BlaAlt}
\alias{BlaAlt}
\docType{data}
\title{Pairs of measurements of forced expiratory volume}
\description{ A problem with the use of the correlation coefficient between the first and second measurements is that there is no reason to suppose that their order is important. If the order were important the measure... |
22b192431de25ab3c9f5ace8800c5ada20cbcd2b | f43db456bb3b51ab6bc71d8a4adf5752df699109 | /R/offenses_known.R | a569c0d0350f3830dfd5e37c8a08b8e3609c1ec9 | [] | no_license | sefabey/crime_data | 333ebf79d95a80af1ef4d452b136f19ad4115d3c | f1c2b8d77dd794782387c70d377d0ac49c7022da | refs/heads/master | 2020-09-10T16:13:02.647384 | 2019-10-27T01:40:40 | 2019-10-27T01:40:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,337 | r | offenses_known.R | source(here::here('R/crosswalk.R'))
source(here::here('R/utils/global_utils.R'))
source(here::here('R/make_sps/make_offenses_known_sps.R'))
source(here::here('R/utils/offenses_known_utils.R'))
crosswalk <- read_merge_crosswalks()
get_all_return_a_monthly(crosswalk)
offenses_known_yearly <- get_data_yearly("offenses_kn... |
53462820a7db31a9e4c6d85a7ff15cab50c0876c | 55c59b150b49de2123191bbd9e62cc5baed5c52b | /man/reg.mle.lda.Rd | 02e7bcd0ddb072d85ecae62da66e0d6f43ee8f05 | [] | no_license | RfastOfficial/Rfast2 | b45c43d627571f5f1c5bbf293454d92643853146 | 23c639c345d526ac05ce8b1613d9671975a8402b | refs/heads/master | 2023-08-08T01:15:34.684148 | 2023-07-21T10:48:12 | 2023-07-21T10:48:12 | 213,210,384 | 27 | 4 | null | 2023-01-20T11:23:56 | 2019-10-06T17:14:37 | C++ | UTF-8 | R | false | false | 1,391 | rd | reg.mle.lda.Rd | \name{Regularised maximum likelihood linear discriminant analysis}
\alias{reg.mle.lda}
\title{Regularised maximum likelihood linear discriminant analysis
}
\description{
Regularised maximum likelihood linear discriminant analysis.
}
\usage{
reg.mle.lda(xnew, x, ina, lambda)
}
\arguments{
\item{xnew}{
A numerical ve... |
80bebfd18099b6eabdb0de2ba60b6875c52b0409 | 407a7bb9c951051c93805b580056832f8d32d5bd | /src/R/init.R | 35e40d679b2570d2137e7dde3d41a6de6752e942 | [
"MIT"
] | permissive | qutang/stereotypy_ubicomp_14 | ea8bbfcb47f27fde42663bd168638152f3b85588 | a9c1d2e114d74d2b34f62e223811d4d480479559 | refs/heads/master | 2023-01-29T17:55:43.201258 | 2020-12-06T23:51:10 | 2020-12-06T23:51:10 | 319,152,029 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,917 | r | init.R | #!/usr/bin/env r
rm(list=ls())
### Set up root path
source("src/R/setRoot.R")
### Check libraries, if not install it ====
check_required_libraries <- function (package1, ...) {
# convert arguments to vector
packages <- c(package1, ...)
# start loop to determine if each package is installed
for(packa... |
18b76359380fca0b21e748e20a6d6f967a980aca | 595e5a281c84fbb3cc2f52f00b1c326af533019c | /man/pdf_doc.Rd | 2d132a5bfdcf4a81a2c3bec34a30edbf2d1961f8 | [] | no_license | jiang-hang/rwp | e23a3e0f17d48fdc58e2451caf15f7838854c22f | 2e0a91020fe23c117b9a380e9cec618e7ec2fd78 | refs/heads/master | 2021-01-10T09:07:53.192403 | 2016-10-16T20:09:33 | 2016-10-16T20:09:33 | 54,447,467 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 310 | rd | pdf_doc.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rwordpress.R
\name{pdf_doc}
\alias{pdf_doc}
\title{replacement of rmarkdown::pdf_document}
\usage{
pdf_doc(inputfile)
}
\arguments{
\item{inputfile}{value}
}
\value{
returndes
}
\description{
description
}
\examples{
x=c(1,2,3)
}
|
0eb501c897f57acae576b8e0b115419b2a1eeaa1 | 428dad6718179a377e250c40d0adf0c5070e7d51 | /functions/cv_analysis.R | b89dfc66f44619b9900dfec038ae1954a9f7cb60 | [] | no_license | michbur/malarial_signal_peptides | 5156ea01192768cc92c0580b0e7e7c9db860fa23 | aa6651cacfa39970963f60d605f59e185b115967 | refs/heads/master | 2020-04-10T03:56:10.127236 | 2016-09-28T04:43:41 | 2016-09-28T04:43:41 | 40,606,471 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,609 | r | cv_analysis.R | #' Get performance measures of cross-validation
#'
#' Get performance measures of cross-validation
#' @param folds cross-validation results (very large list)
#' @inheritParams calc_metrics
#'
#' @return a data frame of cross-validation performance measures
perf_rep <- function(folds, threshold = 0.5) {
do.call(rbind... |
53df24aaaa3c9282d818eb2206561fe54629d990 | 1f5590d3276d541e8a916bc6d589d6dd98562854 | /man/display.sym.table.Rd | 503c84312b3c8b7a7a5e6d6fbf6229e2e0b9de03 | [] | no_license | Frenchyy1/RSDA | 0e8c683e79c73c214b3487991ea3d6e2b8f8b621 | 60445a2749d8f009a748a158f89f53d022edb6f0 | refs/heads/master | 2020-04-14T14:27:57.888698 | 2018-10-10T19:36:39 | 2018-10-10T19:36:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,018 | rd | display.sym.table.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/display.sym.table.R
\name{display.sym.table}
\alias{display.sym.table}
\title{display.sym.table}
\usage{
display.sym.table(sym.data)
}
\arguments{
\item{sym.data}{Shoud be a Symbolic Data table that have been read with read.sym.table(...)}
}
... |
fafb9550046c81aee96a6c7fcf0dbe7c0bec34e5 | e635f968dc35fda610f15836a8e293bf4a7cce81 | /TheoreticalRSquared.R | 02677e703d9babb8c28571917045a91c0c1d552d | [] | no_license | ggazzola/DBC-RCPI | 40a0199ea46a11b22df36e693db1c481e46614de | 4ff6973a37ba82c2cef9b2368b368bc42480bbe4 | refs/heads/master | 2022-01-09T01:30:12.135012 | 2019-05-02T15:46:06 | 2019-05-02T15:46:06 | 117,899,884 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,883 | r | TheoreticalRSquared.R | TheoXYCorCov = function(beta, betaNoise, CovMat) {
# Returns vector theoXYCorVect of theoretical linear correlation between each of X_1, X_2, ..., X_p and Y,
# vector theoXYCovVect of theoretical covariance between each of X_1, X_2, ..., X_p and Y,
# and theoretical standard deviation of Y
# assuming:
# 1) Y = be... |
a545eeda1fb5cedb813b5ea7e8997812bc751400 | 51703d55be207df29decc17441c323be93b8adaf | /HW2/Solutions/8.R | 75b902087850db21882148e3f1988771c3bfb840 | [] | no_license | Mahbodmajid/DataAnalysis | b4ee16f39e91dff0bbeea058e92162f91f28d84c | 127e59a101d4847171fcb7728db38f4405a10e38 | refs/heads/master | 2021-06-10T02:04:52.153255 | 2019-11-25T00:58:18 | 2019-11-25T00:58:18 | 141,756,223 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 256 | r | 8.R | valid_mob <- mobile %>%
filter(!is.na(battery_mah), !is.na(weight))
ggplot(valid_mob, aes(x = battery_mah, y = weight)) +
geom_point(na.rm = T, size = 0.5)+
xlab("Battery (m A.h)")+
ylab("Weight (g)")
cor(valid_mob$battery_mah, valid_mob$weight)
|
8cbc9fe9faf171c97c4f59600056361c90ca4c98 | b7cb6c3b387515f1969278137899a158b75b79ae | /json/285.r | 75f044b9702d25ab24c709cc9774326e9ee368c7 | [] | no_license | rweekly/rweekly.org | 92d8528cde9336cfcf7dfd307de116c61ac73741 | 719d2bff2e16d716d200561384111655d772f829 | refs/heads/gh-pages | 2023-09-03T19:17:18.733983 | 2023-09-01T08:49:24 | 2023-09-01T08:49:24 | 59,336,738 | 676 | 559 | null | 2023-09-14T15:33:23 | 2016-05-21T02:03:54 | R | UTF-8 | R | false | false | 7,876 | r | 285.r | [
{
"title": "A Talk About Campaign Finance in Brazil",
"href": "https://feedproxy.google.com/~r/danielmarcelino/~3/qWUMB985JaA/"
},
{
"title": "Applied Statistical Theory: Quantile Regression",
"href": "https://mathewanalytics.com/2015/11/13/applied-statistical-theory-quantile-regression/"
},
... |
55ddbc34d2ba01b155f6543963aad317eb784403 | 14ea5102d208aad4d1983627f69bcfb65ce954eb | /getLiDAR.R | 5722d18c84e7f7a664b1d0bb2ef76bcaebe88ff0 | [] | no_license | barnabasharris/rspatial | e7d51eb67921cbd0bdff1e6de10121502186892c | 12467990f8d5c8b6a036d0f1fca8f1e75bd50b0d | refs/heads/master | 2023-01-24T12:03:46.090179 | 2020-12-11T20:17:07 | 2020-12-11T20:17:07 | 294,397,586 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,571 | r | getLiDAR.R | getLidar2 <- function(bufferedPoly,
whichProd="LIDAR Tiles DTM",
whichYears,
minSurvey = 2,
userDataDirRoot='tmp',
overwrite=T) {
if (is.null(bufferedPoly$tile50k_name_char)) {
bufferedPoly$tile50k_nam... |
90d015b94f95197ba78426f8a4e794346c9f15c3 | 26080c27d35e63e7b2ac501f65d3f606806b34a6 | /man/try_read_xml.Rd | ba397aa6e7ba6b04e7857592d3c59dbf7462692b | [
"MIT"
] | permissive | lawine90/datagokR | 1fb953a1a2ef91ee0300a3787c0903e3acf9695f | b4026238ab7b307c9d079f117c9412f3bbd12985 | refs/heads/master | 2021-07-03T23:33:50.207804 | 2020-09-23T07:46:11 | 2020-09-23T07:46:11 | 180,745,810 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 430 | rd | try_read_xml.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/try_read_xml.R
\name{try_read_xml}
\alias{try_read_xml}
\title{Try xml2::read_xml function at least 5 times If got errors, save error message.}
\usage{
try_read_xml(url, times = 5)
}
\arguments{
\item{url}{url want to read.}
\item{times}{try... |
6e0c208fab8f8b718865282dea3879c904bc6ee9 | 649bac45381b4bd84d39d6991327cfd056153b36 | /Visulation.R | d4e622ec0f73a730a4adad2d3bae25930c292adf | [
"Apache-2.0"
] | permissive | basakstuff/UFC-Machine-Learning-Project | 3b862a6a23a06703223afdf0eb35ffd4264225dc | 6a983994f9b32359d50b47c9fd6927444625d245 | refs/heads/main | 2023-06-11T05:53:48.640409 | 2021-06-30T14:46:04 | 2021-06-30T14:46:04 | 327,935,048 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,369 | r | Visulation.R | load.libraries <- c('data.table', 'testthat', 'gridExtra', 'corrplot', 'GGally',
'ggplot2', 'e1071', 'dplyr', 'Hmisc', 'tidyverse', 'funModeling',
'plotly','psych','rattle','caret','tree', 'rpart','magrittr',
'class','formattable','randomForest')
install.lib ... |
ebec70e83d9b93d85cd0f0155fa4089c6893b523 | 2036e35b23e6f77296a11523360a65a6e8252580 | /scripts/04_aoh_bat_spatial_data.R | 066b7f2d87494150762a355138457157a25e1447 | [
"CC0-1.0",
"MIT"
] | permissive | ecohealthalliance/sars_cov_risk | 6ee585cf788daf068226eb3efd78ea7dac4431ec | 9512e010d90502b96d86907e19943fe8bdcb5b7b | refs/heads/main | 2023-04-09T09:18:29.347436 | 2022-05-11T17:44:10 | 2022-05-11T17:44:10 | 398,210,464 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,898 | r | 04_aoh_bat_spatial_data.R | # code to create several spatial files that will be use in AOH analyses
source(here("R/prepSEAfiles.R"))
# habitat types global map
# the 1km res map is easier/faster to work with
# also the authors recommend working at the coarsened resolution
hab.ras <- raster(here("data-raw/iucn_habitatclassification_composite_1km... |
fb3e3b9722b7fac295dc949b34ade054e7fbe115 | 7f873c96ced1c3b57c378102febbdcda3bc8e0c4 | /statistics_economics/examples/contingency_table.R | cbd33136fab065c69b6cf447a09042ff1b7626cd | [] | no_license | IanMadlenya/teaching | ec71610301c2f23f4e38b0e73c7f052edd71c984 | 6ca5a296b07b5b42e80ff56070edd4d72a561a8f | refs/heads/master | 2020-03-14T03:32:16.376964 | 2018-04-25T14:09:56 | 2018-04-25T14:09:56 | 131,422,094 | 1 | 0 | null | 2018-04-28T15:38:38 | 2018-04-28T15:38:37 | null | UTF-8 | R | false | false | 932 | r | contingency_table.R | ## Example contingency table
# First generate some data simulating the relation between revolutions following military defeat
set.seed(42)
R<-rgamma(200,1,7) # Baseline risk of revolution
defeat<-rbinom(200,1,prob=.12) # Probability of military defeat
R<-R+.4*defeat+rnorm(200,.01,.001) # Adjust pro... |
1b1df2422338184c58205b51490870343c2616ec | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/hamlet/examples/mem.plotresid.Rd.R | 69b75d95f15c563f5c4ec8f4446656abfef99d8f | [] | 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 | 721 | r | mem.plotresid.Rd.R | library(hamlet)
### Name: mem.plotresid
### Title: Plot residuals of a mixed-effects model along with trend lines
### Aliases: mem.plotresid
### Keywords: regression
### ** Examples
data(vcaplong)
exdat <- vcaplong[vcaplong[,"Group"] %in% c("Vehicle", "ARN"),]
library(lme4)
f0 <- lmer(log2PSA ~ 1 + DrugWeek + (1 ... |
b21f704c4737995e572feadd0b82cc16da15262e | e4b9f3d8f5f45eeea21591455b5de77c1454548d | /teste1b.R | f66828df21267e494678e4431118197f34160233 | [] | no_license | JamesJnJ/analisededadosExerc01 | 1b11c1ea8dd1598f7b900cc11e230bbbc11a1066 | 0412bcf9d6fe911238c4c48a8932ebb12c8c26d4 | refs/heads/master | 2020-04-25T07:40:45.223481 | 2019-02-26T23:00:05 | 2019-02-26T23:00:05 | 172,621,601 | 0 | 0 | null | 2019-02-26T02:22:54 | 2019-02-26T02:22:53 | null | UTF-8 | R | false | false | 3,016 | r | teste1b.R | ########################################
# Teste 1B
# Nome(s): James Andrade Moreno Jr e Renann Camargo
########################################
#ATENÇÃO: Você precisa fazer o download do arquivo chustomer_churn.csv e
# deixá-lo na mesma pasta que o arquivo teste1b.R
# Depois, Fornecer o ca... |
b1fbf21e9ba6dc2dfaaa54231db25e0be9396494 | 9f0c7a45e1dae89638c46bf241f4a8ddf4a023d6 | /task/analysis/CompareAlgorithms.R | a21a8d388994e949f1b89600201c6cd8e792616c | [] | no_license | danielbrianbennett/jsbandit | 953b0738ff8496a162735086785dc821ee8028e1 | c1fc5ede9a27cb4c1659e29176ae17df822177e5 | refs/heads/master | 2021-01-10T12:47:40.075416 | 2018-09-14T16:54:16 | 2018-09-14T16:54:16 | 49,531,899 | 0 | 0 | null | 2016-01-22T03:41:03 | 2016-01-12T22:05:04 | JavaScript | UTF-8 | R | false | false | 11,645 | r | CompareAlgorithms.R | # load relevant packages
library(ggplot2)
library(boot)
# clear workspace
rm(list = ls())
# set version
version <- "v2point2" # either v2point2, v3, or v4
fileDir <- "~/Documents/Git/jsbandit/task/data/"
# load file
filename <- paste0(fileDir, "banditData_", version, ".RData")
load(filename)
# load list of filtered... |
4882e1a14f45f5d3c4ede772dc4fce07087620ab | a831455edcc95ffa62747cb06e166920c974afce | /analysis.R | c6cc02530a5169dcae882f30db877508391e7056 | [] | no_license | abentsui/hku-admission-2015 | 46a142e5bb47b3d2486642d19fd58b4514e8cb2c | b5c73460bc16bc167315a8fbc05c1494ea370545 | refs/heads/master | 2022-12-21T23:54:35.285796 | 2020-09-24T14:30:55 | 2020-09-24T14:30:55 | 298,302,335 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,272 | r | analysis.R | #Library
library(tidyverse)
library(pdftools)
library(ggplot2)
library(tidyr)
#Set directory to this project directory
#Step 1: Read the table from HKU report
path1 <- "data/HKU_2015_2016_UgReport.pdf"
txt1 <- pdf_text(path1)
txt1 <- txt1[[28]]
tab1 <- str_split(txt1, "\r\n")
tab1 <- tab1[[1]]
#As the header names ... |
6958f389e8015abf51fe9f4124bf0eaddfbbd492 | e5cbbdd1afba5eae30369a2ff7bfbcb42afcae29 | /clusters_data.R | f11249b46151bf786542675a4dcca379c6da4e5a | [] | no_license | JonathanJohann/EE364BFinalProject | 3ca76c2ff84a3c9e34045d35acb621a6c9002dbb | 09989a695d92367946be38489b1e1d382a2d904f | refs/heads/master | 2020-05-25T11:19:56.158116 | 2019-06-05T10:44:27 | 2019-06-05T10:44:27 | 187,777,660 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,951 | r | clusters_data.R |
library(ggplot2)
library(Matrix)
library(MASS)
source("l2_map.R")
dataset = "clusters"
generate_clusters <- function(mean_vals,n_per_cluster){
df <- c()
labels <- c()
p <- dim(mean_vals)[2]
for(i in 1:dim(mean_vals)[1]){
tmp <- MASS::mvrnorm(n_per_cluster,mu=mean_vals[i,],Sigma=diag(... |
ede1566e942cc4dcdf72ff9305dfb00caa60ad4c | 407c55b957ae702ce8f738498f9d0b8a8bf2b52f | /vis/smartphones/smartphones.r | 48880f56daf06c19e2a3336db0fd52d654964ea7 | [
"MIT"
] | permissive | edwardoughton/pytal | 0fc25072d14238a6796e5683b8c5fa7d85cc1239 | aebab40178f2f7f1599ec1efdac6def25f0834ba | refs/heads/master | 2022-07-11T02:43:21.762818 | 2022-02-18T15:49:17 | 2022-02-18T15:49:17 | 197,574,539 | 6 | 3 | MIT | 2021-08-11T11:56:51 | 2019-07-18T11:31:51 | Python | UTF-8 | R | false | false | 2,713 | r | smartphones.r | #Spectrum costs
library(tidyverse)
library(ggpubr)
#get folder directory
folder <- dirname(rstudioapi::getSourceEditorContext()$path)
folder_inputs = file.path(folder, "data_inputs")
files = list.files(path=folder_inputs, pattern="*.csv")
data <-
do.call("rbind",
lapply(files,
functio... |
c7e148075a20b5327e373d612462617d8a54ee8d | 3a6b47ec44a959bba312709cc1bee52ac3270092 | /Scrape Data.R | 5e8b61be5095efad50ba3238496b1a58b9c62371 | [] | no_license | bmewing/garthbrooks | f7c81dc0694f46625002d833b0cd6ced5b21a295 | 2eeba7cf7f4082c6fc07137c1fdfda320bdd4564 | refs/heads/master | 2021-01-18T13:10:36.402102 | 2017-02-02T14:00:04 | 2017-02-02T14:00:04 | 80,728,837 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,603 | r | Scrape Data.R | library(RSelenium)
library(rvest)
library(magrittr)
library(dplyr)
#helper functions for zip codes
source('Zip Codes/get all zip codes needed.R')
#It seems that brickseek uses a radius of 100 miles based on observation
#This function will generate a (conservative) grid of zip codes that provide complete coverage of t... |
d40931ec74e0fa7f8cad70b646874103df0c6f83 | a3c9774bfc4fae322e706fd83a4caf15775ed39a | /inst/doc/geex_intro.R | 3101676b955419434d341c10902fbf2f0bd59f98 | [] | no_license | BarkleyBG/geex | b3d2ba8bcb0f683d9784f3300449982e672977d6 | a70c010a72698aa2a9f88d746d4c9d75ceceecd3 | refs/heads/master | 2021-05-01T02:04:28.712327 | 2017-02-06T15:13:33 | 2017-02-06T15:13:33 | 79,879,625 | 0 | 0 | null | 2017-01-24T04:51:24 | 2017-01-24T04:51:24 | null | UTF-8 | R | false | false | 17,003 | r | geex_intro.R | ## ---- echo = FALSE, message = FALSE, warning=FALSE-----------------------
library(geex)
library(dplyr)
library(inferference)
library(sandwich)
library(xtable)
library(moments)
library(MASS)
library(knitr)
library(rprojroot)
# child.path <- normalizePath(paste0(find_package_root_file(), '/vignettes/examples/'))
opts_k... |
a5e0046070330a7520da9a24d0e8c9b06b57c3bf | d6d0ba8ddab5eb7cfd52781802fca21241611bb0 | /BulkRNASeq/ISB025_mapping.R | 01646db77114d528d56b59c6aebc72d067f1a3ca | [
"Apache-2.0"
] | permissive | cemalley/Jovanovic_methods | dc3a9de74230dec25868cdba17318469a77f0948 | 475728042d1bba1195c02c5bc58a9a862b4e0dec | refs/heads/main | 2023-08-23T16:13:00.563835 | 2021-10-12T15:51:19 | 2021-10-12T15:51:19 | 366,406,313 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,463 | r | ISB025_mapping.R | library(data.table)
library(readxl)
library(stringr)
setwd('/Volumes/ncatssctl/NGS_related/BulkRNA/ISB025/')
files <- as.data.table(readxl::read_xlsx('ISB025_lane_concatenating.xlsx', sheet=1))
samples <- unique(files$Sample)
# ~~~~~~~PC_siRNA~~~~~~~~-------------------
setwd('/Volumes/ncatssctl/NGS_related/BulkRNA/... |
1978fa225f4ea315bb95895131b55e705ffc971f | cf9e51a70485c84874479cc5e9797bd3352f5545 | /outLocate() function.R | 4a49682c3ea04bc118a79728e6c958e001799c34 | [] | no_license | GourabNath/DataCleaning | 7fb7acd70db903c672060870033c3f3c783f139f | c3a2040f44bcf84bec437b0d16e90bcfbdebc4af | refs/heads/master | 2021-01-10T16:42:19.592643 | 2016-04-18T19:34:53 | 2016-04-18T19:34:53 | 54,949,035 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,780 | r | outLocate() function.R | ## Locating Outliers:
## outLocate() function
##
## Outlier is definitely a matter of interest. Therefore it is required that we should know where exactly this outlier is located for a
## particular variable. This function will help to do so.
##
## The function outLocate takes two arguments -
## data - a data f... |
d08307eea6140e6e2961b52d4936d7247704cd0f | 4d3672136d43264176fe42ea42196f113532138d | /R/nsize.R | 1ba3c28c114041ac64bf32b1181d085e23ef8338 | [] | no_license | alanarnholt/BSDA | 43c851749a402c6fe73213c31d42c26fa968303e | 2098ae86a552d69e4af0287c8b1828f7fa0ee325 | refs/heads/master | 2022-06-10T10:52:15.879117 | 2022-05-14T23:58:15 | 2022-05-14T23:58:15 | 52,566,969 | 5 | 13 | null | 2017-07-27T02:06:33 | 2016-02-26T00:28:07 | R | UTF-8 | R | false | false | 2,827 | r | nsize.R | #' Required Sample Size
#'
#' Function to determine required sample size to be within a given margin of
#' error.
#'
#' Answer is based on a normal approximation when using type \code{"pi"}.
#'
#' @param b the desired bound.
#' @param sigma population standard deviation. Not required if using type
#' \code{"pi"}.
#'... |
d80b7e880476e033a81866db37208fe7be658e8d | 7179a563634bc94ecd2322777c91203ad2dc7245 | /app/ui.R | 6b4f9661264e5d390b4b000ed05876e5a357f542 | [] | no_license | hoangtv1899/DataScienceCapstone | 9a65d68b3ebf2239a7c99b6ad19b98bbc60fd0fc | edc3a8251517c244bd2d301bed5a4d37b778fa94 | refs/heads/master | 2021-09-01T12:20:12.644261 | 2017-12-27T00:20:15 | 2017-12-27T00:20:15 | 115,463,946 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,126 | r | ui.R |
library(shiny)
library(markdown)
# Define UI for application that draws a histogram
shinyUI(fluidPage(
# Application title
titlePanel("DATA SCIENCE CAPSTONE - PREDICTING NEXT WORD"),
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
helpText("Enter a ... |
0e4b3a058bc443ed5dd38a43dc9158d252531e7f | fc132d038f07aeba9c15ef544c11a3aca0bc6f43 | /findDepthOffset.R | d5d9e7a62537a3213f5231e191df915559bc62d7 | [] | no_license | sherrillmix/turtleSurvival | 071538ca0f0c3a483a049079c4a3d8ff4dcb3637 | f0d3eee8533c908bd1689b91bedd7c975adbb437 | refs/heads/master | 2021-01-10T03:47:56.967801 | 2016-11-22T14:49:40 | 2016-11-22T14:49:40 | 49,825,523 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,762 | r | findDepthOffset.R |
statusDepths<-statusData[!is.na(statusData$Depth)&statusData$deployDay>-5,]
depthRange<-range(statusDepths$Depth[!is.na(statusDepths$Depth)])
#NOTE generating plot and adjustment info in one step
pdf('out/depthOffset.pdf')
depthOffsets<-lapply(info$PTTID,function(ptt){
message(ptt)
thisInfo<-info[info$PTTID==ptt,]... |
2e1578ce6ab64127b14a645746e2e4ebbe2253b6 | 599e4da9d94e1f4970680a1389cb4e0b96adb544 | /Analysis/crump.r | e3735c90346f2b93ec39b504e2c23e9f3ed760d6 | [] | no_license | mcefalu/PPTA | 73ea053a46918481501c062536d9c72571bc8bb7 | 0834bd2ee87138321fdc00665c5113cb32c602d0 | refs/heads/master | 2017-12-01T01:28:02.296009 | 2017-10-25T18:53:53 | 2017-10-25T18:53:53 | 108,310,193 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 638 | r | crump.r | #####################################
## implementation of crumps method ##
#
# Y is the outcome
# A is the treatment
# PS is the propensity score
crump <- function(Y,A,PS){
PS2 = PS*(1-PS)
PStemp = PS*(PS<=.5) + (1-PS)*(PS>.5)
PStemp = sort(PStemp)
for (alpha in PStemp){
alpha2=alpha*(1-alpha)
... |
943e93092e7f558d2dd1e2b3179309aa8e62db02 | 1a9eeed43a99e1b01b61233d6013746b17e7286b | /scripts/plot2.R | abe96f4222ecaf5ad63fd014078f2c3ccc383087 | [] | no_license | srrussel74/ExData_Plotting1 | b46ad1787817d28ec1005ad5c51038cca9ac4343 | 177a2e8ab7bd25f3ae9130f56aa6b3c005f3cd4c | refs/heads/master | 2021-01-14T12:44:46.680762 | 2014-09-06T11:52:54 | 2014-09-06T11:52:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,141 | r | plot2.R | library(datasets)
#Load data with given options
data<-read.table("./household_power_consumption.txt", header=TRUE, sep=";");
#Convert class factor to class Date for col Date and to char for col Time
data$Date<-as.Date(data$Date,format="%d/%m/%Y");
#Subset data with 01 and 02 february 2007
subdata<-subset(data, Date... |
1b8b44a13d3558bd2ee7421947159ba3a1ebb98c | 5c55e61748fd4d288cf74692d438dd23a0b36cb2 | /exdata-012/project-2/plot4.R | f34cb2dbfe449ff56d016a9c9e9515dae7640a3e | [] | no_license | maxim5/r-stuff | aa807ddec61513bbb3af45951128a18c25e57a40 | 92f63307068cb92b59e23142e1147f0702aa4e46 | refs/heads/master | 2021-04-02T10:57:24.012000 | 2015-08-28T12:10:57 | 2015-08-28T12:10:57 | 248,266,917 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,300 | r | plot4.R | source("common.R")
sectors = unique(scc_mapping$EI.Sector)
coal_sectors = sectors[grep("Coal", sectors)]
coal_mapping = scc_mapping %>% filter(EI.Sector %in% coal_sectors)
coal_summary = inventory %>%
filter(SCC %in% coal_mapping$SCC) %>%
group_by(year) %>%
summarise(total... |
5e3837f93dff2a9fe0cdf9cfaefa82dbf2e85ca5 | 8d25028ef38f747bc7ee94ce571588dc70f6748f | /tests/testthat/test-discrete_by_quantile.R | 1c5c738773148b5f8fdecadeb218d1f0e44a3aaf | [
"MIT"
] | permissive | nhejazi/nima | 645130b400e24f25258a36ceed081f11376274a0 | b31aaeef4f9a44e725cdd3004ab4b4d8e3b52f76 | refs/heads/master | 2020-12-25T09:08:03.780186 | 2020-03-06T02:01:37 | 2020-03-06T02:01:37 | 52,858,693 | 1 | 3 | NOASSERTION | 2019-12-11T18:07:20 | 2016-03-01T07:53:36 | R | UTF-8 | R | false | false | 315 | r | test-discrete_by_quantile.R | context("Discretizing vectors by quantile")
test_that("range of discretization matches quantiles for numerics", {
expect_equal(range(discrete_by_quantile(rnorm(100))), c(1, 4))
})
test_that("discretization fails for non-numeric and non-factor inputs", {
expect_error(discrete_by_quantile(c("foo", "bar")))
})
|
7fa65204d2b3eb113507e6dfcfcc4e2614169081 | c5ed03ffbb6b7a6f298e48dda384343166483a47 | /Psoralen/sacCer3_analysis/linker_lengths.r | bd70af80302727054da2b384ef9d3002dd66230f | [] | no_license | HJeffery/Thesis_supplementary | d9ae1dc18de47823a9399285187e66de67936574 | b6af00c42c749da6bfcfc2ea149f1f3e534ac64c | refs/heads/master | 2023-04-21T18:27:21.935040 | 2021-05-16T17:11:41 | 2021-05-16T17:11:41 | 367,930,591 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,003 | r | linker_lengths.r | # Script to make a plot of linker sizes
# Written by Heather Jeffery
# 29th April 2020
library(ggplot2)
library(plyr)
linker_lengths <- read.csv("2009_Jiang_linker_sizes_sacCer3.csv", header = FALSE)
colnames(linker_lengths) <- c("Lengths")
summary <- count(linker_lengths, vars = "Lengths")
print(summary)
# Plot
p... |
1ae8f488da913eb92d80fc3bded0856ac7b2833c | f02e02d6d797a7da2879b04022d088024798187d | /Advanced R Programming/lab03/lab03_result/tests/testthat/test-my-test.R | 6dc8fd1b625a65162776d5b0f7392bac887d27d2 | [
"MIT"
] | permissive | lennartsc/MSc-Statistics-and-Machine-Learning | aa0d920955f12daf79c01d3233fc6381d5923672 | 57905f689db794ca9bfe4775859106942d80456a | refs/heads/master | 2023-05-26T23:48:08.461875 | 2020-07-07T10:16:57 | 2020-07-07T10:16:57 | 214,446,376 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,295 | r | test-my-test.R | context("test-my-test.R")
context("test-my-test.R")
test_that("package works", {
expect_equal(euclidean(100, 1000), 100)
expect_equal(euclidean(123612, 13892347912), 4)
expect_equal(euclidean(-100, 1000), 100)
expect_equal(dijkstra_man(wiki_graph, 1), c(0,7,9,20,20,11))
expect_equal(dijkstra_man(wiki_graph,3), c(9,10,... |
1758698b550fdceebbd95f76a7c60b42ac8ae32e | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/BAMMtools/examples/computeBayesFactors.Rd.R | 9a3f584af5bab01e47db378b72d875d25a12c9bb | [] | 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 | 252 | r | computeBayesFactors.Rd.R | library(BAMMtools)
### Name: computeBayesFactors
### Title: Compute Bayes Factors
### Aliases: computeBayesFactors
### Keywords: models
### ** Examples
data(mcmc.whales)
computeBayesFactors(mcmc.whales, expectedNumberOfShifts = 1, burnin = 0.1)
|
87427423ff861dfb1db408bcfbb92b7cef8af738 | a47ce30f5112b01d5ab3e790a1b51c910f3cf1c3 | /A_github/sources/authors/4648/Bagidis/BAGIDIS.dist.BD.r | 972f5d09d542f9d8d5b241a04eee49aeadaab53b | [] | no_license | Irbis3/crantasticScrapper | 6b6d7596344115343cfd934d3902b85fbfdd7295 | 7ec91721565ae7c9e2d0e098598ed86e29375567 | refs/heads/master | 2020-03-09T04:03:51.955742 | 2018-04-16T09:41:39 | 2018-04-16T09:41:39 | 128,578,890 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,633 | r | BAGIDIS.dist.BD.r |
BAGIDIS.dist.BD= function(Details1, Breakpoints1,
Details2, Breakpoints2,
p = 2,
wk=NULL,
Param=0.5){
#=======================================================================
# Selection des details et breakpoints des d... |
120f0e0c779bbe6fb711b048b1852db353de1c06 | f51ad1cfad4ef6c28062aae4b5a41c46c2bcac4e | /shiny.R | 41ed8a51011f471344a968847a35ff88e4f3bf0e | [] | no_license | sarasarasun/Rtemp | 03ccb05fbb1828723063ac3f2b930edb451564e3 | 11d0f5fc6bcdb3e6f24b5e71ef7a82abc2941ae8 | refs/heads/master | 2021-01-19T04:22:00.510882 | 2016-08-16T04:19:22 | 2016-08-16T04:19:22 | 65,777,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,343 | r | shiny.R | #R Code for Shiny Tutorial for Boston Data Con
#Load the dataset
#install.packages('ElemStatLearn') #loaded during session
library(ElemStatLearn)
data("SAheart")
?SAheart
names(SAheart)
summary(SAheart)
SAheart$chd<-factor(SAheart$chd)
#First install and load the package
#install.packages("shiny") # loaded during ses... |
0937fe4e71be0a28484d8a450b678b140e8722c7 | ae7b89639ac8befcfcb72b497482df70f853a897 | /man/wirecost.Rd | 8ce0c4dd54c35d6878ac286b0351826d64163582 | [] | no_license | Pralhad-Analyst/wireharness | 09a31772168f71e234ecc2e959be4301a83a1691 | b0dbbf89d2f62fb92ebb5c30a832c19a0ff222e1 | refs/heads/master | 2022-12-19T09:57:46.073626 | 2020-09-25T05:34:29 | 2020-09-25T05:34:29 | 294,894,888 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 474 | rd | wirecost.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wirecost.R
\name{wirecost}
\alias{wirecost}
\title{Calculate Raw Wire Cost}
\usage{
wirecost(data_gauge, wirelengthh, Awg)
}
\arguments{
\item{data_gauge}{data-set of gauge rates}
\item{wirelengthh}{numeric string which mentions the total wi... |
7c9fabe88445c47afbb8af39b02c553e8b67c7db | 2099a2b0f63f250e09f7cd7350ca45d212e2d364 | /DUC-Dataset/Summary_p100_R/D086.AP900802-0135.html.R | 5e68c1c137e1d6887a5df98ab3c1b989923bcf92 | [] | no_license | Angela7126/SLNSumEval | 3548301645264f9656b67dc807aec93b636778ef | b9e7157a735555861d2baf6c182e807e732a9dd6 | refs/heads/master | 2023-04-20T06:41:01.728968 | 2021-05-12T03:40:11 | 2021-05-12T03:40:11 | 366,429,744 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 910 | r | D086.AP900802-0135.html.R | <html>
<head>
<meta name="TextLength" content="SENT_NUM:6, WORD_NUM:103">
</head>
<body bgcolor="white">
<a href="#0" id="0">Iraq Invades Kuwait; U.S. Responds With Sanctions; Kuwait radio, 1st graf, a0571.</a>
<a href="#1" id="1">Foreigners reached by telephone said they watched from their windows or dived for cover a... |
d9b3d73d5f4eb9c6f8700f58d34306d6cc704661 | 92d54f598099f13f7150d8a6fbf39d14e7371ff4 | /R/default.R | 6edc61707244d1d77348955f6e53183aaeb4f13a | [
"MIT"
] | permissive | r-dbi/RPostgres | 3c44d9eabe682e866411b44095a4671cbad275af | 58a052b20f046c95723c332a0bb06fdb9ed362c4 | refs/heads/main | 2023-08-18T09:48:04.523198 | 2023-07-11T02:17:42 | 2023-07-11T02:17:42 | 28,823,976 | 230 | 66 | NOASSERTION | 2023-08-31T08:20:25 | 2015-01-05T17:43:02 | R | UTF-8 | R | false | false | 1,434 | r | default.R | #' Check if default database is available.
#'
#' RPostgres examples and tests connect to a default database via
#' `dbConnect(`[RPostgres::Postgres()]`)`. This function checks if that
#' database is available, and if not, displays an informative message.
#'
#' @param ... Additional arguments passed on to [dbConnect()]
... |
aa592c2dac22701defae84caf71599d9b0a9a64d | 1d4edd65cac511c7d9b559087f5ffa253562cf46 | /man/summary.Rd | 41e13c2ef9ca599ffc001ba581984798ad177109 | [] | no_license | TobieSurette/gulf.stats | 1f775524ad0a0fee5f6baba031fe957429163813 | 3f1497acdd292d2b9ae57e0d07689df8489db2fa | refs/heads/master | 2023-01-06T09:59:25.834213 | 2022-12-23T17:53:53 | 2022-12-23T17:53:53 | 253,641,836 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 461 | rd | summary.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/summary.R
\name{summary.ked}
\alias{summary.ked}
\title{Summary for Statistical Models}
\usage{
\method{summary}{ked}(x, polygon, ...)
}
\description{
Generate a summary for a statistical model.
}
\section{Methods (by class)}{
\itemize{
\item... |
4157c2cc102c984cfd03d07202811b45cadd74c4 | b7e3b2977a19a5ea95d832a54d0443fb8acae6f3 | /rasqualTools/man/tabixFetchGenes.Rd | 56c51adb4acf1caf6f1e65bd9b567c66de33f6c2 | [] | no_license | kauralasoo/rasqual | f29ad9e74795dc1dd07e11bed95e1785def31b96 | 05c3a1e38f75679cce5e0806640cfd250363eede | refs/heads/master | 2021-01-18T01:57:38.996084 | 2017-07-28T12:32:08 | 2017-07-28T12:32:08 | 53,346,349 | 9 | 5 | null | 2016-03-07T17:53:05 | 2016-03-07T17:53:05 | null | UTF-8 | R | false | true | 575 | rd | tabixFetchGenes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/import_export_files.R
\name{tabixFetchGenes}
\alias{tabixFetchGenes}
\title{Fetch particular genes from tabix indexed Rasqual output file.}
\usage{
tabixFetchGenes(gene_ranges, tabix_file)
}
\arguments{
\item{gene_ranges}{GRanges object with ... |
80c93ac3d581fd3c5b6e3c04606af31a99ab3f41 | 3eb3ddae9516b87d63e66c4cd4db49f1a878564b | /R Library/R/Tinn-R/sample/french/Tinn-R_exemple de script.r | 18c6586b617797d0023642f0d4fab03ef0e79bcb | [] | no_license | openefsa/C-TSEMM | 7903b2e73c5c202312a92af71962984471f6ff56 | 34da6db9f6bbfdd27687d3326f02812f5ed18549 | refs/heads/master | 2021-01-20T18:24:04.496167 | 2016-06-01T12:15:49 | 2016-06-01T12:15:49 | 60,173,311 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 2,767 | r | Tinn-R_exemple de script.r | #===================================//=========================================#
# Mail: <<< joseclaudio.faria@gmail.com >>>
# <<< phgrosjean@sciviews.org >>>
#
# Plus: <<< http://zoonek2.free.fr/UNIX/48_R/all.html >>>
#===================================//=========================================#
# help... |
86ffac3e4f1a1b3ae22692250b7c22c8daf27c9a | 97ae4842948e48a5843d7866f94efc44f099f3ae | /R/colorlegend.R | 3f74fa347a756b915d3afc4a2917c52b988408c5 | [
"MIT"
] | permissive | taiyun/corrplot | d60e54742baba0d25ac2e2ff2b1343dffc917d12 | 0497ad6717c2909fb1ed0c75ff6d269605107808 | refs/heads/master | 2023-07-05T14:51:00.802057 | 2022-08-31T04:54:03 | 2022-08-31T04:54:03 | 2,910,722 | 274 | 105 | NOASSERTION | 2022-08-31T04:54:04 | 2011-12-04T15:15:45 | R | UTF-8 | R | false | false | 3,362 | r | colorlegend.R | #' Draw color legend.
#'
#' @param colbar Vector, color of colbar.
#' @param labels Vector, numeric or character to be written.
#' @param at Numeric vector (quantile), the position to put labels. See examples
#' for details.
#' @param xlim See in \code{\link{plot}}
#' @param ylim See in \code{\link{plot}}
#' @param v... |
c987ea835722f7a6e25bfbbc3d2474a1331c421c | da74c7c306abb829fd272b5fd45191ad68fc9d67 | /ProgAssignment3-data/code/Best.R | dfbcc8e11583fa39bed3c27c09c9cb3473985768 | [] | no_license | JPaul23/datasciencecoursera | ab08ec88e1199d423436240cb0d19d7e236f0fc5 | 030f72580f8dfb176db806f3ad03423cbe714c39 | refs/heads/master | 2023-03-27T06:23:20.767729 | 2021-03-29T23:02:19 | 2021-03-29T23:02:19 | 342,956,831 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,279 | r | Best.R | best <- function(state, outcome){
data <- read.csv("outcome-of-care-measures.csv",
colClasses = 'character',header = TRUE)
dt <- as.data.frame(cbind(data[, 2], #Heart rate
data[, 7], #State
data[, 11], #Heart at... |
f941802b1d84029d04fd46db0ca0dd426e0c7dc8 | 6710ed3f613c15aa016796fb4acd26d7cb1ad6e1 | /man/barycenter_unbalanced_stabilized.Rd | 15cb08d63a91e0b8a408b8e0611179b439b439f5 | [] | no_license | shizhan666/ROT | defcd2b2586b45ca0bb2490fb540dc29936e6986 | 6f387b60643043d17d1302410b0b45ca508565c7 | refs/heads/main | 2023-02-13T16:07:31.943526 | 2021-01-12T08:18:23 | 2021-01-12T08:18:23 | 328,911,614 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,834 | rd | barycenter_unbalanced_stabilized.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{barycenter_unbalanced_stabilized}
\alias{barycenter_unbalanced_stabilized}
\title{unbalanced barycenter}
\usage{
barycenter_unbalanced_stabilized(
A,
M,
weights,
reg = 0.1,
reg_m = 1,
numItermax = 1000L,
stop... |
c5b4ab6fad84ebbd6f17f16af098315a94235552 | 0198bd016fc0867660639ff7b5979c088e42c6c7 | /man/Kommunegrense.Rd | d105dbcf31852e696f14f1aa2ac29c7edf93040a | [
"CC-BY-4.0"
] | permissive | hmalmedal/N5000 | 62f4f046ccbd28e8d3e5a05dacab8f755a6c5950 | 6b7e55001998e3ea05fcb26c564069d6862d7a63 | refs/heads/master | 2023-05-12T12:08:37.036517 | 2019-02-21T19:12:57 | 2023-05-01T08:11:06 | 171,929,948 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,848 | rd | Kommunegrense.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/datadoc.R
\docType{data}
\name{Kommunegrense}
\alias{Kommunegrense}
\title{Kommunegrense}
\format{
\if{html}{\out{<div class="sourceCode">}}\preformatted{Simple feature collection with 865 features and 3 fields
Geometry type: LINESTRING
Dimen... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.