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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
003fc7f1d0899c3b2970d7f90275834ccaed743e | 09f53cade40e8236d508b6014d3935623f75edf9 | /GO_hypergetric distribution/FDR0005_time/bar_graph_GO.R | 5f3158867653bdcf0a0890a0c2481d7872f25274 | [] | no_license | YKeito/iterations16 | 1e0c75bc838f21d94b0ee78dfcc495eef676e53a | c81624bf54154a2eed658b2837135b80c9c469c7 | refs/heads/master | 2022-12-18T05:05:10.574761 | 2020-09-28T05:26:43 | 2020-09-28T05:26:43 | 299,197,561 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,724 | r | bar_graph_GO.R | #####bar graph####
####CY15####
#subcluster1_CY15_1h
library(ggplot2)
#NumCY15_1h_subcluster
subcluster1_CY15_1h <- EnrichedGO_Summ_CY15_1h_FDR005[EnrichedGO_Summ_CY15_1h_FDR005$CluNum == 1, ]
subcluster1_CY15_1h <- subcluster1_CY15_1h[1:10, ]
g <- ggplot(
subcluster1_CY15_1h,
aes(
x = reorder(GO_term, enrichme... |
bd6b37fcd2bb45fcea8c07fef79b80d176d7c444 | 71af941bf29e9a831b95abb2e95a1440481bf0eb | /petstore.R | 29d9477ea099de953dfb2cb5cbeb31044f41e82d | [] | no_license | SaffraP/senior_project | f05fd9d3573ed154c7000cf5560294cac52e359d | 04cdcf52f755291472d95a631d84046764fe933f | refs/heads/master | 2022-11-18T13:32:11.707400 | 2020-07-17T04:40:11 | 2020-07-17T04:40:11 | 263,985,591 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 529 | r | petstore.R | library(tidyverse)
petstore <- read_csv("Data/petstore_data.csv")
sapply(petstore, mode)
sapply(petstore, class)
petstore[, c(1:3)] <- sapply(petstore[, c(1:3)], as.character)
petstore[, c(2:3)] <- sapply(petstore[, c(2:3)], as.numeric)
table(apply(petstore, MARGIN = 1, function(x) sum(is.na(x))))
# 0 = # of ro... |
7f1db281c1f5ba2453259aeb929e33dfe6d0f8e7 | 9765e867e097a4460608299af8f66883b0447e2c | /AllocateR/alloc_discrete_paper_graphs/fs_rev.R | 072d65828d36a47e6e9e2d7f313b4933d8ef95ef | [
"MIT"
] | permissive | DynamicProgramming-StructuralEstimation/PrjOptiSNW | eba9f1956297401df138487e98699761536ce662 | c65a989b36b4ceba6c99d7a54dad98724d304a9e | refs/heads/master | 2023-03-29T05:36:16.169607 | 2021-03-30T19:45:39 | 2021-03-30T19:45:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,785 | r | fs_rev.R | # Graphs where allocation lines from multiple planners are jointly compared.
# "Paper\Appendix_graphs_and_tables\Robustness_Tax_model_Actual_vs_feasible_c_allocation_round1_18-64_year-olds_by_income.png"
try(dev.off(dev.list()["RStudioGD"]),silent=TRUE)
try(dev.off(),silent=TRUE)
# Libraries
library(tidyverse)
librar... |
e31b415554dc5f7569ab6937f31c4659d600bf50 | acd69edac208c44604fa81b27c8687c288efdee2 | /R/model_selection.R | 87cc972d20be6356335b4c110e23347f868b7938 | [] | no_license | kcf-jackson/glmGraph | 483a2c92001d4f2fc41a8560b3f0c11cc40186a3 | 81c2a2ea59194d5cc828f4855d1660398a6a0a8a | refs/heads/master | 2021-01-19T08:47:17.748331 | 2017-05-03T15:00:21 | 2017-05-03T15:00:21 | 87,677,603 | 1 | 1 | null | 2017-05-03T15:00:22 | 2017-04-09T02:32:52 | R | UTF-8 | R | false | false | 6,707 | r | model_selection.R | #' Model selection for GLM graph
#' @param data0 dataframe; the data.
#' @param p Between 0 and 1; prior brief of how connected the graph is.
#' @param lambda Tuning parameter for gibbs sampler.
#' @param num_iter number of iterations for gibbs sampler.
#' @param graph_init characters string; one of "random", "correlat... |
b19345319c530d960e8f8d8fc949c99bfdadaec8 | 6ba2ac661a2c08cfd4f638d5060da01041602cd5 | /Rscript.R | bc502d8ebcbda16d3e9a1ea9784c1fd9116d8ca1 | [] | no_license | ameenkhan07/Temperature-Outliers-Analysis | b3cf8f4c967ac2bf4a26155bf4e20add6e9cfd5a | a90ef0c9f1ae305f997b47dcb576b0f37e035120 | refs/heads/master | 2020-04-11T13:55:24.679524 | 2017-11-05T12:16:05 | 2017-11-05T12:16:05 | 40,604,376 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,336 | r | Rscript.R |
data<-read.csv("esya.csv",header=TRUE,na.strings="NA",stringsAsFactors=FALSE)
station<-unique(data$station_name)
station_str_hot<-integer()
station_hot<-integer()
station_str_cold<-integer()
station_cold<-integer()
long<-integer()
lati<-integer()
for(st_name in station)
{
date_data<-data[data$stat... |
3747261624549c0cb907be8bee2a9453e1f7bb55 | 4296809eeea7449b42b1797fd0aaf00741307f6d | /tools/Scripts_Miscellaneous/0_SWSFTesting_HandRunCode.R | ae49ed793b27708e3d52bda1e758b797e1067b4b | [] | no_license | BrendenBe1/rSFSW2 | 1153a1cae12338adc28ea73a8d11fcc7cc651173 | d154c1e19b608d4789aa5b682ec9a7f598ba92db | refs/heads/master | 2020-05-24T16:31:15.451960 | 2017-02-23T09:20:58 | 2017-02-23T09:20:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,158 | r | 0_SWSFTesting_HandRunCode.R | workersN <- num_cores
swDataFromFiles <- sw_inputDataFromFiles(dir=dir.sw.in,files.in=swFilesIn) #This acts for the basis for all runs.
filebasename <- basename(swFiles_WeatherPrefix(swDataFromFiles))
#i = i_sim: consecutive number of seq.todo, i.e., counting the simulation runs
#i_xxx = the i_tr-row of xxx for the i... |
2416b1a122569647beeae8e7d19035a01aed1ab9 | 6585c4953346f131123537a00252f7021c647752 | /pollutantmean.R | a3caed9786b6d7f78a008d4905e3db03ebcf6c38 | [] | no_license | rjbeilstein/Personal | de32bacf900b88f8c68c3933db0fb962794701db | 1e93dd80ec954f8197b880c06a040adad174305e | refs/heads/master | 2021-01-19T09:44:05.047438 | 2015-03-10T13:51:28 | 2015-03-10T13:51:28 | 27,402,824 | 0 | 0 | null | 2014-12-12T15:11:25 | 2014-12-01T22:14:56 | Perl | UTF-8 | R | false | false | 1,759 | r | pollutantmean.R | pollutantmean <- function(directory, pollutant, id = 1:332) {
## Copyright (C) Bob Beilstein, 2015
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (a... |
d5255f09ebcbcde08287da71bfac65a172956cb4 | e657528bf984624f4762d0c006c516df4c570a4e | /man/create_experiment.Rd | b384201220325041a84b1b70e91a873159105e9c | [
"MIT"
] | permissive | sigopt/SigOptR | 95b0323a6c66ef7ba885a5fd32ef1fcd18afb7cc | d580444a7af5eebced232069eea9b3406a69ff29 | refs/heads/main | 2023-08-31T15:38:13.614967 | 2023-08-22T20:45:11 | 2023-08-22T20:45:11 | 52,380,994 | 15 | 7 | MIT | 2023-08-22T20:45:13 | 2016-02-23T18:14:31 | R | UTF-8 | R | false | true | 638 | rd | create_experiment.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sigopt_api_wrapper.R
\name{create_experiment}
\alias{create_experiment}
\title{Create an experiment}
\usage{
create_experiment(body)
}
\arguments{
\item{body}{POST body of create request}
}
\value{
experiment created by SigOpt
}
\description{... |
0223789ec39c92cb629dd60a03b43ce05d77c60a | f61153b739229133bcd5a9fc80f35bd9f10268cd | /Module 2/random.R | 2451a6dbe6d8bb7d447e3811c0565b9626648227 | [] | no_license | anitakurm/Cognitive-Modelling | 54863dc98a3f35ce90afaee7347766f8a414e22a | e0772e88fdcdd01cb9c2d1ee165d5a1c8d9b7f32 | refs/heads/master | 2021-01-05T15:42:11.270725 | 2020-03-23T00:52:59 | 2020-03-23T00:52:59 | 241,064,724 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 561 | r | random.R | random <- function(payoff, ntrials, theta) {
#bias towards option 1 (theta) and option 2
b <- c(theta, 1-theta)
#empty array to be filled
x <- array(0, c(ntrials))
#rewards array to be filled
r <- array(0, c(ntrials))
for (t in 1:ntrials) {
#agent that chooses randomly between options 1 and 2... |
c434ada474b4a99d7047fe0c77f0b0fb9fc1fc79 | 52bf004a1380cc1a35554d26055cf374165d8394 | /cachematrix.R | 8b52a8b6a08b9d850ddfcbef18e2cd53b7d418cd | [] | no_license | jennwv/ProgrammingAssignment2 | 7df5d7e15d997940f77ede1055a1a45b6c76543e | cd396b8d5f6c4a7b04060c570eb07f6d88850061 | refs/heads/master | 2021-01-18T02:06:54.267331 | 2015-01-21T21:18:03 | 2015-01-21T21:18:03 | 29,611,674 | 0 | 0 | null | 2015-01-21T20:38:34 | 2015-01-21T20:38:34 | null | UTF-8 | R | false | false | 904 | r | cachematrix.R | ## Create a new matrix object that allows caching of the inverse.
## Assumes that input is an invertible square matrix.
## Makes a new matrix object from a regular matrix.
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y) {
x <<- y
inv <<- NULL
}
get <- function() x
setinver... |
96c5efe76bbe7e495ad380d7eab2ed2a76415b5c | d7d8f1751a02d82b85fd62f7e0d7b64a6443299c | /plot_FST.r | d9d83819af971c59c28112f0475487a833fc762c | [
"MIT"
] | permissive | zhuochenbioinfo/PopGen | 5ac608daacec492dbed840f7897aea80b6df5666 | 181cb6a31fb61afa0ea27b7327d2db76ca6bcd0a | refs/heads/master | 2023-06-10T00:07:45.003109 | 2023-05-31T10:35:23 | 2023-05-31T10:35:23 | 101,312,312 | 7 | 4 | null | null | null | null | UTF-8 | R | false | false | 1,264 | r | plot_FST.r | library(ggplot2)
args <- commandArgs(TRUE)
file <- args[1]
outfile <- args[2]
data <- read.delim(file)
goodChrOrder <- paste("Chr", c(1:12), sep="")
data$CHROM <- factor(data$CHROM, levels=goodChrOrder)
# 获取显著性阈值
top5.value <- as.numeric(quantile(data$WEIGHTED_FST,prob=1-5/100))
top10.value <- as.numeric(quantile(d... |
3b62cd4c79872ea6fa2a71aab7387e1e45132066 | 9b202913ece1e2916e80c913693bc17c0adba768 | /R/fig8b.R | cdd627d7603c1c88e0922112ee6197e5812e6558 | [] | no_license | PMassicotte/cdoc | 5217750db3e1fdf699a1a8d5a26a8f1b83d52253 | fef25b848cb1ac1f2c7be171b290ad5d5fef7af7 | refs/heads/master | 2021-09-13T21:36:56.273815 | 2018-05-04T12:59:30 | 2018-05-04T12:59:30 | 80,427,518 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,782 | r | fig8b.R | rm(list = ls())
cdom_complete <- read_feather("dataset/clean/cdom_dataset.feather") %>%
filter(wavelength <= 500) %>%
filter(study_id != "nelson") %>% # Nelson is missing wl < 275
filter(study_id != "greenland_lakes") %>% # These had lamp problem at 360 nm
filter(study_id != "horsen") %>%
group_by(unique_... |
1a327fbd8860584b68ce6131258cf1b4f57d385a | 36badbb3ac98c190db813af2b03194c6bc90241f | /tests/testthat/test_notinteractive.R | a824133df27ad7f174773e9d8e5de5e61093e5ad | [
"MIT"
] | permissive | Chris1221/ggsource | d731aad7746dd90fc1e6d82a1e03b34007556364 | 6c2dc589de39f51cbf514488ad9428f963d688a5 | refs/heads/main | 2023-08-01T21:44:31.193665 | 2021-09-12T15:07:21 | 2021-09-12T15:07:21 | 397,310,117 | 2 | 0 | null | 2021-09-12T14:18:32 | 2021-08-17T15:43:28 | R | UTF-8 | R | false | false | 361 | r | test_notinteractive.R | library(ggplot2)
library(ggsource)
p = ggplot(mpg, aes(displ, hwy, colour = class)) +
geom_point()
tmp_path = tempfile(fileext = ".pdf")
test_path = "test"
test_that("ggsource runs okay",{
expect_error(ggsave(tmp_path, p, scriptname = test_path), NA)
expect_true(file.exists(tmp_path))
expect_equal(ggsource(t... |
5baf1e7534a0442348e8ba4ef7f566cba4dc93e6 | 5db643f963b6d8285a8632881708b4b2aade03d8 | /13_gseaSummaryTable.R | bd45da55560c706a7a31dcb6f89b981cec7d3725 | [
"MIT"
] | permissive | uhkniazi/BRC_SupernumeraryTooth_Gui_PID_19 | e325c2aab69da25277398c5380a2a242f6d8b218 | 5d1fb0f56b0bc107c5c297d8fc0f2a57563a2e31 | refs/heads/master | 2020-04-26T17:58:27.415030 | 2019-04-23T13:08:45 | 2019-04-23T13:08:45 | 173,730,457 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,410 | r | 13_gseaSummaryTable.R | # File: 13_gseaSummaryTable.R
# Auth: umar.niazi@kcl.ac.uk
# DESC: merge the gsea results for all contrasts in one table
# Date: 14/03/2019
lFiles = list.files('results/', pattern='*pathways_mSigDb_c2_*', full.names = T, ignore.case = F)
# load the files
ldfData = lapply(lFiles, function(x) read.csv(x, row.names=1))... |
69a81f0623cb6104c6754027674363fd2b8ca152 | 2742a72eef62bde91aeaaa2fffd21b6dc10387cc | /190713/190713_diamonds.R | 0f0eed02943bfe619b7357c93b19ef7dd593072f | [
"Apache-2.0"
] | permissive | gittykite/r-basic-viz | f3d9922dbab04bd90e3b0ccb04353616594dbf0d | 06941089d5ed29ab2a2eaf2ccab0a8c6f971f9f3 | refs/heads/master | 2022-03-02T08:46:54.439237 | 2019-07-27T07:34:04 | 2019-07-27T07:34:04 | 197,858,792 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,602 | r | 190713_diamonds.R | # mpg: cars
mpg
data(mpg)
str(mpg)
# diamonds
str(diamonds) # show headings
head(diamonds) # print heading data
head(diamonds, 10) # print heading data
tail(diamonds) # print tailing data
tail(diamonds, 3) # print tailing data
dim(diamonds) # row, col num
summary(diamonds)
# dplyr function (SQL func)
... |
c6ac7635da7e0d1a74ec8ec459a323cc6934d716 | 67a4ffc2937795fb803de60cd23a5d286ecd062c | /BIN/checkReported.R | 2f44c391eb378f7cd2fa21a27c5d156cd8dcc5cb | [] | no_license | TesiNicco/CentenAssoc | 7dd8c6638b5109d351cb31a7e0ed24bd174220d0 | 38740b8b5af29b7e270b73b840791c5bf61396a0 | refs/heads/master | 2021-07-13T16:55:56.643449 | 2020-11-12T14:44:34 | 2020-11-12T14:44:34 | 222,994,936 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,592 | r | checkReported.R | library(data.table)
# read input files
d <- fread("RESULTS/SINGLE_VARIANT/chrAll_reported_variants_assoc.txt", h=T, sep="\t")
ref <- fread("INPUTS_OTHER/GWAS_data_Timmers_cleaned.txt.gz", h=T)
###
# merge files together
d.my <- merge(d, ref, by.x="ID", by.y="SNP")
# initialize direction column
d.my$DIRECTION <- NA
... |
040e3f19e647bdf618d22a77f7adcfbd43539735 | 345091e8b1e418edb0e901d17856b2c1c3a86707 | /man/s_cross_p.Rd | 40c7db02e22f6c459328eafc7be5d068b19e5f09 | [] | no_license | zhaoy/zhaoy | 56e18ce7cd8090320128036e22d54b999ec06745 | e96086b8e7957e7430374962f4c877e8e3f3bc53 | refs/heads/master | 2022-09-04T03:06:49.812011 | 2022-08-11T21:45:06 | 2022-08-11T21:45:06 | 93,218,094 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 548 | rd | s_cross_p.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/s_cross_p.R
\name{s_cross_p}
\alias{s_cross_p}
\title{2- or 3-way frequency tables of percents}
\usage{
s_cross_p(x, ..., direction)
}
\arguments{
\item{x}{A data-frame.}
\item{...}{2 or 3 variables.}
\item{direction}{Direction in which to ... |
32bd15dfa6ec4a46dd45306e2e4cae2984b6951d | 0a906cf8b1b7da2aea87de958e3662870df49727 | /biwavelet/inst/testfiles/rcpp_row_quantile/libFuzzer_rcpp_row_quantile/rcpp_row_quantile_valgrind_files/1610556081-test.R | f7bcbc579512191eabf3559999600b5cd446e2a0 | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 259 | r | 1610556081-test.R | testlist <- list(data = structure(c(Inf, 1.72723536788899e-77, 7.06327441559897e-304, -Inf, NaN, 5.0841157981812e-74, 2.46690988593888e-308), .Dim = c(1L, 7L)), q = 1.39067124445999e-309)
result <- do.call(biwavelet:::rcpp_row_quantile,testlist)
str(result) |
51362d62ba27a73ec912b51a75b0e04c0d5b2702 | 5b1630bbb35bb556386d8bdd16ea6acc508b341d | /man/list2toml.Rd | df275a41f87dfd8d93f5a1851ebd634cb16c7e4b | [
"MIT"
] | permissive | bioDS/beter | ca7282d17ffcd188d50be02fc339dffe737261a6 | 45534d062ae67f36ea0a73514de11870f9d8ed9a | refs/heads/master | 2022-06-17T13:01:44.990966 | 2022-06-02T22:54:23 | 2022-06-02T22:54:23 | 224,336,560 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 892 | rd | list2toml.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/list2toml.r
\name{list2toml}
\alias{list2toml}
\title{list to toml}
\usage{
list2toml(x)
}
\arguments{
\item{x}{a named list}
}
\value{
a string vector representing individual lines of TOML representation of \code{x}.
}
\description{
Convert ... |
030f488bb7e301bd249c3df625245f6e58b8bde0 | 29585dff702209dd446c0ab52ceea046c58e384e | /MMMS/R/extract.list.R | 6656ac6085cdb96e2c9186f589937f25a80ef957 | [] | 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 | 189 | r | extract.list.R | extract.list <-
function(L, s1, s2=NULL) {
if(is.null(s2)) {
sapply(L,function(x) with(x,get(s1)))
} else {
sapply(L,function(x) with(with(x,get(s1)),get(s2)) )
}
}
|
d7e8bf5ab2a6b022853c5617b7c458d19a542ff3 | db0e77329139306d1a97d816929b406064d4c16a | /web/IntroToShiny/exercises/19-tags/app.R | 7e96af911779f25a759851d971451dbc4d66af01 | [] | no_license | lihm1/self-study | 2a106824024e3bfdf8e2df753a9981b703bd467c | 112ac12fd7d82181d756b4defb683e77d87f911a | refs/heads/master | 2023-04-30T13:18:40.141160 | 2021-05-18T16:00:45 | 2021-05-18T16:00:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 572 | r | app.R | library(shiny)
ui <- fluidPage(
sidebarLayout(
sidebarPanel(
sliderInput("num", "Choose a number", 1, 100, 50),
tags$img(height = 120, src = "shiny.png"),
tags$br(),
tags$em("Powered by "),
tags$a(href = "shiny.rstudio.com", "shiny")
),
mainPanel(
plotOutput("hist"),... |
4b98456877010091eb3508956656304ced35f652 | 55cf7bb541cff8116fe70a00834be893df322f39 | /man/aoristic.Rd | a9519c738383e2b7866a3d2b121b58c26bbf0694 | [] | no_license | georgekick/aoristic | d2b78a0c79305027f6c1c2bbe81c101d2499d0b5 | 3ec0f6efd7f5033317ec23494c91f04b09b2bf7c | refs/heads/master | 2021-01-01T16:44:44.832602 | 2015-01-10T10:33:36 | 2015-01-10T10:33:36 | 12,824,202 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 421 | rd | aoristic.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/aoristic-package.R
\docType{data}
\name{aoristic}
\alias{aoristic}
\title{Sample data of crime (df) and council district (spdf)}
\source{
City of Arlington
}
\usage{
data(aoristic)
}
\description{
Sample data of burglary incident data... |
54487704b652060985694ef0840e77f927bcfcbf | aaac559889d1968ee128d67460bcf4a4272e39fb | /figure/Plot 3.R | 3129b6b54cc448b97f720d9074f2665034917850 | [] | no_license | Omar-Ma/ExData_Plotting1 | 7a6f9cd928afe2f42ac50f6d0e9edc5e680b99a7 | 4bfad1eb25ea314250548c63f399a7424c03ef17 | refs/heads/master | 2021-01-09T07:02:54.416243 | 2014-10-12T23:04:21 | 2014-10-12T23:04:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 924 | r | Plot 3.R | da<-read.table("household_power_consumption.txt",sep=";",header=T)
da$Date1<-as.Date(da$Date,"%d/%m/%Y")
da1<-subset(da,Date1=="2007-02-01"|Date1=="2007-02-02")
da1$DateAndTime<-paste(da1$Date,da1$Time)
da1$DateAndTime<-strptime(da1$DateAndTime,"%d/%m/%Y %H:%M:%OS")
with(da1, plot(da1$DateAndTime,da1$Sub_metering_1,typ... |
95c1c16120da661b2d1164d2b89b7ea4fc42bbf3 | 221863c465c6629762198fa28b9d2585f04233be | /download-osm-data.R | 92d0a091ae6b0221d13d52d5ee74c8e189ee6205 | [] | no_license | Robinlovelace/20_Study | 3ce5b88a3090938f04a06be5a4958517eba96a96 | aa74aa076be20f221e0af3d1dfa057b9fdc2fb86 | refs/heads/master | 2020-12-26T08:27:42.755888 | 2016-03-30T18:27:18 | 2016-03-30T18:27:18 | 54,464,511 | 0 | 0 | null | 2016-03-22T09:58:11 | 2016-03-22T09:58:11 | null | UTF-8 | R | false | false | 378 | r | download-osm-data.R | # install.packages("osmplotr")
library(osmplotr)
library(tmap)
?osmplotr
b = bb("Leeds")
b = as.vector(b)
class(b)
h = extract_osm_objects(key = "highway", bbox = b, extra_pairs = c("maxspeed", "20"))
h60 = extract_osm_objects(key = "highway", bbox = b, extra_pairs = c("maxspeed", "60"))
summary(h)
h = h$obj
plot(h6... |
15459ad69ca23782455a7499d2c97c3637e04c93 | 42a37b1c9f47f345a0026923c549bc2e027c09f6 | /Thesis/thesisUtility.R | 342a924b4b93573396b1dc0958e0a0e0404c029b | [] | no_license | kianBlanchette/Thesis | c73fafb94a80e020949488ec0b059a0a1406adc8 | 79ea53c296da187ddf756d00782c35a4091b1da5 | refs/heads/master | 2023-03-07T16:08:41.079636 | 2023-02-20T00:15:32 | 2023-02-20T00:15:32 | 289,295,575 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,687 | r | thesisUtility.R | library(multitaper)
library(VGAM)
dyn.load("f95Lib.so")
dpssap <- function(V, maxdeg, alpha=0.75) {
# Sanity checks
stopifnot(is.matrix(V), is.numeric(maxdeg), maxdeg>=0)
N <- length(V[, 1])
K <- length(V[1, ])
P <- maxdeg + 1
timeArr <- 1:N
R <- matrix(data=0, nrow=N, ncol=P)
U <- matrix(data=0,... |
192473d56ca2b2bdd29d004689c6eeaa450a2780 | 0ff904b62106e21b9c47c902549d6e176e3224e9 | /air_pollution/getMeasures.R | 0fd20a2d8471f036df21516fbe8b45f6c4f9ad8a | [
"BSD-3-Clause"
] | permissive | HimesGroup/k12bioinformatics | a2829a1ca4cd765a676890f0249d1fe4cecfa723 | 527eec1316c36824774e27e76a1d58e9e1818b3a | refs/heads/master | 2021-11-29T01:03:18.455101 | 2021-11-09T19:51:53 | 2021-11-09T19:51:53 | 181,563,187 | 3 | 1 | BSD-3-Clause | 2021-11-09T19:51:53 | 2019-04-15T20:46:41 | R | UTF-8 | R | false | false | 3,302 | r | getMeasures.R |
library(pargasite)
library(dplyr)
library(data.table)
##Get data from downloaded daily EPA data https://aqs.epa.gov/aqsweb/airdata/download_files.html
daily_df <- read.csv("daily_88101_2017.csv",header=T)
states <- c("Pennsylvania","New York","Florida","Montana","California","New Mexico","Oregon","South Dakota")
cit... |
abd0d0ce811874f84b79ba46b268ac7b3aabc58e | 35e82ea9c3c886b99f444f59b81b5c1e4af1b405 | /man/analyze.lavaan.Rd | 6ea1a5a0016509c35efe1a984bd35355f07dffa7 | [
"MIT"
] | permissive | anhnguyendepocen/psycho.R | 5d68d8e00fc520d4fec999836a8480b5ba0e73c7 | 611e984742890e698c4e94b6965d917432e98348 | refs/heads/master | 2020-09-05T21:50:11.877771 | 2019-10-03T15:36:36 | 2019-10-03T15:36:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 892 | rd | analyze.lavaan.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/analyze.lavaan.R
\name{analyze.lavaan}
\alias{analyze.lavaan}
\title{Analyze lavaan SEM or CFA) objects.}
\usage{
\method{analyze}{lavaan}(x, CI = 95, standardize = FALSE, ...)
}
\arguments{
\item{x}{lavaan object.}
\item{CI}{Confidence inte... |
805030db60490c8957a2dec9490573a93528bb82 | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /gtfs2gps/R/zzz.R | adc96e65f102123d92f89489a03f177a1839fd14 | [] | 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 | false | 1,516 | r | zzz.R |
utils::globalVariables(c(".", "%>%", ":="))
.onLoad = function(lib, pkg) {
# Use GForce Optimisations in data.table operations
# details > https://jangorecki.gitlab.io/data.cube/library/data.table/html/datatable-optimize.html
options(datatable.optimize = Inf) # nocov
# set number of threads used in d... |
896e1f18a637fdb85bb1b7ab4a45b85a055b298a | 4bc2de391e8d28db03d49b9a69e5b9734356aa5a | /constructed data 's restructureing_for_mian_dataset_HOV lane_2.1.R | 8212ea472ba40631ce07e908d4f8299d750f0845 | [] | no_license | sadvaseb/Restructured-NGSIM-Dataset-for-surrounding-vehicles | d0cb2c1f6ed897aeaeacf73c656926e7e2b1ac45 | 9cb08914c4741448e799c090b8445ae42191ba02 | refs/heads/master | 2022-12-10T18:56:55.209579 | 2020-09-06T06:37:03 | 2020-09-06T06:37:03 | 293,001,377 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,702 | r | constructed data 's restructureing_for_mian_dataset_HOV lane_2.1.R | library(dplyr)
library(tidyr)
library(data.table)
library(corrplot)
library(reshape)
library(plyr)
library(reshape2)
library(readr)
library(tictoc)
tic()
setwd("C:/Users/X1/Documents")
# read csv file
data_v <- read.csv("C:/Users/X1/Dropbox/X2/2018/2018.02.02- RNN and clustering/reconstracted-I-80.csv")
# sort file ba... |
8e98a6329993ab34fdf2b10d9a6e46cf9a34ac26 | 32864d98d0a48323dce8a57bede4f84fc89b13dd | /blog/2015/may252015/unkown.R | 8bf75bcffdc9615dc763bb7760f75b29a0476055 | [] | no_license | darrkj/darrkj.github.io | 5666cd0d5ed75657fc59696aee0bd1dd8a7eefad | afdd9f7fbab2c0941b4a3ddfbbbc29fbafda4f08 | refs/heads/master | 2020-05-22T01:47:14.054218 | 2018-09-09T18:36:45 | 2018-09-09T18:36:45 | 24,395,464 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,274 | r | unkown.R | # msm (Jackson 2011) handles Multi-State Models for panel data;
install.packages('msm')
library(msm)
library(dplyr)
library(lubridate)
library(markovchain)
bike <- read.csv('2014-Q2-Trips-History-Data.csv')
bike %>% select(sdate = Start.date, edate = End.date,
sterm = Start.terminal, eterm = End.term... |
88a8a8ab040e07375cc6be4c019cee69d25c396f | 2b96dda01e284f5df6c5f749272249ff3b1c26cd | /ch03/ex3-1.r | 7fd21a0702d2af540ebe6f2ee755c0808831232f | [] | no_license | freebz/The-Art-of-R-Programming | f9a82b42fedebc5ef0f10bfd132bdcad27600d83 | 6e51856512b945455810b1ad686bae58f0779ff7 | refs/heads/master | 2021-06-05T03:27:00.519420 | 2016-08-12T05:01:24 | 2016-08-12T05:01:24 | 65,524,551 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 228 | r | ex3-1.r | # 3.1 행렬 만들기
y <- matrix(c(1,2,3,4),nrow=2,ncol=2)
y
y <- matrix(c(1,2,3,4),nrow=2)
y
y[,2]
y <- matrix(nrow=2,ncol=2)
y[1,1] <- 1
y[2,1] <- 2
y[1,2] <- 3
y[2,2] <- 4
y
m <- matrix(c(1,2,3,4,5,6),nrow=2,byrow=T)
m
|
b545f108fe8a14f584919f7233e289b4af805657 | f668e02a08ad08b4f5a5ca1ab7a2057e1a56e25f | /man/en_generation_inst_gen_cap_agg.Rd | 3c93a324e8d8a9cd42cf9967b5cbd192beef5f19 | [] | no_license | ktaranov/entsoeapi | b2e9d5b5d4966c74eacc9deae13eb54bf765ba57 | 0cf92df963949d8fa4df07b506de5fc3fbde2569 | refs/heads/master | 2022-12-23T15:11:28.282633 | 2020-09-24T21:11:44 | 2020-09-24T21:11:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 529 | rd | en_generation_inst_gen_cap_agg.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/en_generation.R
\name{en_generation_inst_gen_cap_agg}
\alias{en_generation_inst_gen_cap_agg}
\title{Get total load from Entsoe}
\usage{
en_generation_inst_gen_cap_agg(eic, year, security_token = NULL)
}
\arguments{
\item{eic}{Energy Identific... |
137feb1bc39012571102dad268275be536e93879 | a3e83ec811b3ab800e11bc3ad732d3e50583e0f0 | /scripts/06b.1_KCCA.extractclusters.R | 0e9c5e8b6cd899ea730118f31fcaa824a5def2cd | [] | no_license | aditiq/CSC-lupien | d007120a474c88f384f8356b2f52fc4a6b6a1f63 | a5c1a128cef421d302ae1941b828ddb79122de24 | refs/heads/master | 2021-05-02T00:39:37.467197 | 2019-01-03T22:45:51 | 2019-01-03T22:45:51 | 120,949,008 | 0 | 0 | null | 2018-02-09T19:56:53 | 2018-02-09T19:52:39 | null | UTF-8 | R | false | false | 12,899 | r | 06b.1_KCCA.extractclusters.R | #--------------------------------------------------------------------------------------------------------------------------------
#--------------------------------------------------------------------------------------------------------------------------------
# R-3.4.1
# mordor
# Objective : Extract clusters from KCCA
... |
be11ac8bc7cca2779a9221b2bd709f465bb3c212 | 5d92eb9380216f13890280561204d494dca6fc41 | /part2/20160423/Santander/xgboost.R | 9469ab71169dec22a9cd9e89c2771cf6f6d0395e | [] | no_license | mkhoin/analyticstool | 4d184e3fca01f6c0cb30e78cd78d66707a3cc5d3 | 6683ea6edb27940450be5f82aebef5423bfb603f | refs/heads/master | 2020-05-14T16:49:12.264450 | 2019-04-09T02:57:04 | 2019-04-09T02:57:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,780 | r | xgboost.R | # https://www.kaggle.com/zfturbo/santander-customer-satisfaction/xgb-lalala
install.packages("xgboost","Matrix")
library(xgboost)
library(Matrix)
set.seed(4321)
train <- read.csv("./train.csv")
test <- read.csv("./test.csv")
# train <- read.csv("../input/train.csv")
# test <- read.csv("../input/test.csv")
##### ... |
5717b579429c95e40580b6ef579c1189a3fef746 | 25393b604343ae34d5c27775cb2c10fd805bda62 | /R/zzz.R | 500a588cc7cd70a936bbc7ae8816c5363108da71 | [] | no_license | cran/staRt | e8530c433ac920e2dcf2b7682da3f001379cb393 | 06ba0e414b9b0f6d87276ffee3b5449233ceb87c | refs/heads/master | 2021-01-21T05:02:05.004723 | 2007-10-12T00:00:00 | 2007-10-12T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,166 | r | zzz.R | .onLoad<-function(libname,pkgname){
require("tools",quietly=TRUE)
}
.onAttach<-function(libname,pkgname){
#library.dynam(pkgname,pkgname,lib.loc=libname)
options(warn=-1)
cat("\n")
cat("---------------------------------------------------------------------------------\n")
pkg.info<-drop(read.dcf(fi... |
637669f6fac6feedf3e6e523626c3ac1f4e7dc1b | fc85f0333c547417034309ecb031e80c15e32717 | /19Decembre.R | 23d762214ad42ebe12c7ca3bbdc85eb4325b75c1 | [] | no_license | Evargalo/AdventOfCode2019 | 140d30c403586719b5b0268f0a1f1a0162dfc3dc | 098ecc4db020f4ead8139c396763321799f252c5 | refs/heads/master | 2020-09-26T14:40:29.187725 | 2020-01-20T14:37:25 | 2020-01-20T14:37:25 | 226,275,418 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,346 | r | 19Decembre.R | #########################
# Advent Of Code 2019 #
# 19 Decembre #
######################
require(dplyr)
require(stringr)
require(tidyr)
require(purrr)
require(purrrlyr)
require(ggformula)
source("intCode.R")
library(readr)
X19DecembreInput <- read_delim("19DecInput.txt", delim= ",",
... |
0368a2f5a41b2068fec50559898011ae006ac71f | 431f33c4b361ce49f70505f3bc923162eb92a062 | /man/IntLimResults-class.Rd | ba95dd4430b59fe80c776c232f4139a7c3199b61 | [] | no_license | Mathelab/IntLIM | 17559cdef93768831f6fe45d88783bda02d6017a | 17c3229139d02de2ded783221323c988222beed3 | refs/heads/master | 2022-07-31T03:37:56.881904 | 2022-07-06T11:04:23 | 2022-07-06T11:04:23 | 99,143,369 | 5 | 7 | null | 2018-09-05T21:27:30 | 2017-08-02T17:23:01 | R | UTF-8 | R | false | true | 846 | rd | IntLimResults-class.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AllClasses.R
\name{IntLimResults-class}
\alias{IntLimResults-class}
\title{IntLimResults class}
\description{
IntLimResults class
}
\section{Slots}{
\describe{
\item{\code{interaction.pvalues}}{matrix of interaction p-values}
\item{\code{in... |
e5a846b7224402a941e5b288a07593cafadb0cae | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/hdnom/examples/hdcox.mnet.Rd.R | 944c3dd5e5362a9455766b00fd1f5bdb10805d6e | [] | 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 | 839 | r | hdcox.mnet.Rd.R | library(hdnom)
### Name: hdcox.mnet
### Title: Mnet Model Selection for High-Dimensional Cox Models
### Aliases: hdcox.mnet
### ** Examples
library("survival")
library("rms")
# Load imputed SMART data; only use the first 120 samples
data("smart")
x = as.matrix(smart[, -c(1, 2)])[1:120, ]
time = smart$TEVENT[1:120]... |
ba9140919e9edf2d288091e556d99e1059e4abd2 | 24b2f1530846fa114bc5cc594b54e30c203302c0 | /man/ShotSeasonGraphPlayer.Rd | 02b677c8524cf010f80dd532d204ca74045bef8a | [] | no_license | cran/SpatialBall | 46000fd163a86d64dedaa19c3e145248111971bb | d8abd291458ab7ec32f5ca8157751bc1576a26f4 | refs/heads/master | 2021-08-30T15:19:42.441068 | 2017-12-18T11:28:54 | 2017-12-18T11:28:54 | 114,638,956 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,032 | rd | ShotSeasonGraphPlayer.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plots.R
\name{ShotSeasonGraphPlayer}
\alias{ShotSeasonGraphPlayer}
\title{Generates a shot chart for a given player}
\usage{
ShotSeasonGraphPlayer(Seasondata, player, quant = 0.4, type = "PPS")
}
\arguments{
\item{Seasondata}{a data frame wit... |
d758878fb6208dcb8f607737f2880ff49311a7d6 | 24066ef8e3f3c9631091279481eaffaa346b11df | /R/make_TMT_se.R | bff8b1d3884dca1b9cd8446a06f13a52dce77102 | [] | no_license | demar01/protrusionproteome | 5430c92ae08274f25bb55c2f55795b52fd08fc6d | df71e443d1bad93737564866bd50772b924204b6 | refs/heads/master | 2023-04-23T23:27:07.713419 | 2021-04-26T16:40:12 | 2021-04-26T16:40:12 | 329,885,253 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,871 | r | make_TMT_se.R | #' Data.frame to SummarizedExperiment object
#' conversion using file information and user input
#'
#' \code{make_TMT_se} creates a SummarizedExperiment object
#' based on protein table and user's input about experimental design.
#'
#' @param proteins_unique Data.frame,
#' Protein table with unique names annotated in t... |
d37418c4925fe5aa720481e5cf7b38233dbfc2fd | cdb0244a09bc5ca71faf116644ea60971970b83e | /lectures/07-Make-and-Makefiles/make_power_gender_dataset.R | aa7a666dc36c7b59620165b11da00ad1d9e80bfc | [] | no_license | akantuncch/datasci611 | c628be109f01e8784cb408210e7826c02ce9538a | 1ac1d0854e147ff79935c7dc7889934447213fc2 | refs/heads/main | 2023-09-04T12:31:08.183903 | 2021-10-27T18:43:01 | 2021-10-27T18:43:01 | 401,401,805 | 0 | 0 | null | 2021-08-30T15:54:35 | 2021-08-30T15:54:35 | null | UTF-8 | R | false | false | 766 | r | make_power_gender_dataset.R | library(tidyverse);
source("utils.R");
powers <- read_csv("derived_data/deduplicated_powers.csv");
genders <- read_csv("derived_data/gender_data.csv");
power_gender <- genders %>%
inner_join(powers, by=c("character","universe")) %>%
select(-url,-property_name) %>%
rename(gender=value) %>%
write_csv("... |
564d28d2898e7fb2de31f1f360827e9555a546dd | d2ac85674d6812fe3f606094bae82ea089659609 | /NCANDA/Scripts/growthrate_gamm.R | 667776e61771d6765cdda29032f888379f7064eb | [] | no_license | LabNeuroCogDevel/R03Behavioral | 2a98e71917b1f35a4affe08298e32f9100df3b93 | f743b316ac00aa3381eb72ae08c47b3c87891ebf | refs/heads/master | 2020-09-23T07:19:38.313210 | 2019-12-05T22:19:06 | 2019-12-05T22:19:06 | 225,437,014 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,681 | r | growthrate_gamm.R | ttor<-function(ts,nobs){
rfromt<-sqrt((t^2)/(t^2+nobs))
}
gamm_growthrate<-function(m, agevar, idvar = NULL, n.iterations = 10000, qnt = c(0.025,
0.975))
{
simdiff <- sim_diff1_from_gamm(m, agevar, idvar, n.iterations = n.iterations)... |
a7774e6d898200a39b9040f8f1ce462f0e6ba047 | ddc949cee0c036248e373a2c75b8631ec37c57a8 | /Paired_Analysis.R | 5d6051016b7040ef147b9d726a726d7496104c39 | [] | no_license | soulj/Dunnetal2015 | 963fc7dff5afefdbd3e8171525dbda294e8a2267 | 0deebe41e4bfccc8cc7599db7c4030a8c0591b9b | refs/heads/master | 2021-01-06T20:37:21.946061 | 2015-09-07T09:55:07 | 2015-09-07T09:55:07 | 42,040,110 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 27,239 | r | Paired_Analysis.R | #script for analysis of paired OA damaged vs undamaged data
#Define the working directory
setwd("~/Testing123/PairedOAAnalysis/")
#Do you want to visualise the networks in cytoscape?
visualise = FALSE
#load the required libraries
library("DESeq2")
library("gdata")
library("data.table")
library("lattice")
library("R... |
91a19a0dab27c98649cd07fb772d78533cefd0ba | 97fcf2ba0b4dc336ebeef80388f73d811e5a0047 | /models/mod/mod.R | 8415aa60fc419c083a0114ded60d29cbefd30883 | [] | no_license | karenenes/selectiveharvesting | 6d2b6f81fb6bd52c3dd7e72ab248e97b05ea0f1b | 43946422b1428264ad9b6ca648561ce06bc6e16f | refs/heads/master | 2022-11-01T11:52:21.174126 | 2022-10-22T23:36:23 | 2022-10-22T23:36:23 | 239,534,499 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 692 | r | mod.R | library(R6)
##
#
##
MOD <- R6Class("MOD",
public = list(
##
# Initializes the model instance.
##
initialize = function()
{},
##
#
##
isValid = function()
{
return (TRUE)
},
##
... |
dc0adaa420d093b26c97aa6169ba5c9162eefdac | fb4ee97814efccd540d909a1a19cec8c170646fd | /R/19_0_category_reduction_of_NOT_downloaded.R | 807cb4ea6f9dca916de88c25bf10e3fc1b8c055f | [] | no_license | potockan/Text-clustering-basing-on-string-metrics | 6ba1ac23f5d29a2cf59e8ea57f7ea43985dc614e | babdf79db3a0ab875cc8641160fe41533e3ec6e3 | refs/heads/master | 2021-01-15T17:46:10.962750 | 2016-05-16T10:52:52 | 2016-05-16T10:52:52 | 25,786,834 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,527 | r | 19_0_category_reduction_of_NOT_downloaded.R |
library(rvest)
library(dplyr)
library(stringi)
library(Hmisc)
library(RSQLite)
library(compiler)
prepare_string <- cmpfun(function(str) {
stri_paste("'", stri_replace_all_fixed(str, "'", "''"), "'")
})
expand_tree <- function(link, level) {
#print(link)
#stri_sub(link, 1, 1) <- stri_trans_toupper(stri_sub(... |
26d23a07bb296aab504ddd594fc2b421e22f147b | 49928aacb833073fe225ae2cff02f32423e4da34 | /man/eskin.Rd | 83ba64945ba4fd0df0e25d2137be102d76bbe842 | [] | no_license | cran/nomclust | a30d00e74894e06fe4a063dadf696143a53aac48 | 63d7ad4664c5fe23ebb8bf1b961ae421b0329229 | refs/heads/master | 2023-08-31T05:09:52.306488 | 2023-08-18T10:12:38 | 2023-08-18T11:30:56 | 36,813,240 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,041 | rd | eskin.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/eskin.R
\name{eskin}
\alias{eskin}
\title{Eskin (ES) Measure}
\usage{
eskin(data, var.weights = NULL)
}
\arguments{
\item{data}{A data.frame or a matrix with cases in rows and variables in columns.}
\item{var.weights}{A numeric vector settin... |
29f1d40a2b92dd769417f6c039d892afde83e650 | d93922bca2e78e91d24e9fb93bdec867bdfe657c | /StatisticalDataMining R codes/Assignment1/Problem 1.R | 78e915c0f490189b6c2b9b399dfb109649696ed6 | [] | no_license | freyagenesis/Fall2018-codes | 9476d1f67815a75b7cd97121fca2d926278a5bee | e49b02f835cf39b7c172e4759351f6088005a5fe | refs/heads/master | 2020-04-13T21:39:26.875265 | 2018-12-29T00:37:20 | 2018-12-29T00:37:20 | 163,460,866 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,163 | r | Problem 1.R | ##################################################################
# Problem 1
##################################################################
# install some packages
#install.packages("DAAG")
#install.packages("lattice")
#install.packages("MASS")
#install.packages("plyr")
library("DAAG")
library("lattice")
librar... |
e08a2b327ae8432de326a8a32d99606752a92cce | 0efa8ba6159e0a5aecea978bb45d26b458f53bc5 | /r/code-snippets/probability.R | 23f542fea1b55a2dec9e96992360f34ed43feb37 | [
"MIT"
] | permissive | patrickbucher/docs | 53368a42736a7b551a651b4b4579c078085ab243 | a0c7516173f9dee1e94bd41376ef6b2a70ed8a3c | refs/heads/master | 2023-09-01T03:45:05.728392 | 2023-08-29T16:03:23 | 2023-08-29T16:03:23 | 71,139,618 | 14 | 1 | null | 2016-11-17T07:59:43 | 2016-10-17T13:06:57 | TeX | UTF-8 | R | false | false | 1,647 | r | probability.R | game.outcomes <- c(-4, 0, 1, 8) # loose 4, gain 0, 1 or 8
game.probs <- c(0.32, 0.48, 0.15, 0.05)
game.cumprobs <- cumsum(game.probs)
# probability distribution
barplot(game.probs, ylim = c(0, 0.5), names.arg = game.outcomes, space = 0,
xlab = "x", ylab = "Pr(X = x)", main = "Probabilities")
# cumulative pro... |
d48ea4c1e515dd502c87140fa06f4703b01a4d4e | 5459d7af208e247c081930c04978dde5b788a90c | /WordnetVerb.r | bf787e2be343f811c7cbe829592ab407692b5fa7 | [] | no_license | Smullle/FinalYearThesis | 9c49ae53ba4105595864c1eec1a78600f5536366 | a409624d512d3c63b3c946704abd3ff316032206 | refs/heads/master | 2022-05-22T09:30:19.497299 | 2020-04-13T20:23:29 | 2020-04-13T20:23:29 | 255,603,525 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 34,929 | r | WordnetVerb.r | # System usage
# s2v
s2v_fist_first <- c(0.43291122,0.6673107,1.0,1.0,0.44664878,0.34282508,0.16701001,1.0,1.0,0.2744395,1.0,0.4215669,1.0,1.0,1.0,0.39310393,1.0,0.26562777,0.3074523,0.21201238,1.0,1.0,0.16701001,1.0,1.0,0.7070686,1.0,1.0,0.51232535,1.0,1.0,1.0,0.4071389,0.3902227,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.1661317... |
bcb32b3fc8f4316a5a55ca673529b4b75bcd8793 | a176626eb55b6525d5a41e2079537f2ef51d4dc7 | /Uni/Projects/code/P020.Temprature_NE_MIA/archive/RSOE_review_analysis_V2.r | 385f428c5992d1e0c1ba90f1e109d60970d61ef3 | [] | no_license | zeltak/org | 82d696b30c7013e95262ad55f839998d0280b72b | d279a80198a1dbf7758c9dd56339e8a5b5555ff2 | refs/heads/master | 2021-01-21T04:27:34.752197 | 2016-04-16T04:27:57 | 2016-04-16T04:27:57 | 18,008,592 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 9,502 | r | RSOE_review_analysis_V2.r | library(lme4)
library(reshape)
library(foreign)
library(ggplot2)
library(plyr)
library(data.table)
library(reshape2)
library(Hmisc)
library(mgcv)
library(gdata)
#create CV table
mod1table <- data.frame(type=character(17), R2=numeric(17),Bias=numeric(17),RMSPE=numeric(17))
#name columns
mod1table$type <- c("WV", "VA"... |
203443a8bf746aadc63631d82a842cd2d8f5aec4 | 5ba7be90516c93d2224001914f726ab05d23e713 | /rpackage/bohemia/man/voronoi.Rd | 478c0cb30d8a9b99bcc7f58b2d2f48bfa706ae03 | [
"MIT"
] | permissive | bikeforsale/bohemia | cbb3ddcc92d084ec6d98986f299ea08dc2324228 | 251a4ff737b5c6e7452eeb845ccfae11e48587c9 | refs/heads/master | 2022-09-05T07:35:00.972380 | 2020-05-02T20:30:11 | 2020-05-02T20:30:11 | 265,606,212 | 0 | 0 | NOASSERTION | 2020-05-20T15:21:57 | 2020-05-20T15:21:56 | null | UTF-8 | R | false | true | 575 | rd | voronoi.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/voronoi.R
\name{voronoi}
\alias{voronoi}
\title{Create voronoi tiles}
\usage{
voronoi(shp, poly = NULL)
}
\arguments{
\item{shp}{A spatial points dataframe with the following columns in the data slot: lng, lat, id (the id being the repeated a... |
c037f6559f62aa628d83860ad0eca9db6eb6883d | 7cc6c1874fe2ea4193bc812e70adc01859aa6c83 | /travis/travis_prep.R | e9d8aa29c5dbb68160bd88c4ae08c6b068ec06fb | [
"BSD-2-Clause"
] | permissive | daniel0128/receptormarker | c0fe2d3429182d05a56c6bfe3317732c0953e2cd | 6b343ccd60f05ffcd494e8c636eccb06a9c04152 | refs/heads/master | 2020-12-31T02:02:30.028813 | 2015-12-12T00:54:20 | 2015-12-12T00:54:20 | 45,644,690 | 0 | 0 | null | 2015-11-05T22:55:38 | 2015-11-05T22:55:38 | null | UTF-8 | R | false | false | 108 | r | travis_prep.R | devtools::install_github('jimhester/lintr')
source("http://bioconductor.org/biocLite.R")
biocLite("muscle")
|
f5422b30a8ec1726f8234a0d9a7036226ac34b9b | da8dae69e597072bc616936d1d72a96f65e4efa0 | /code/oldversions/v4_20190329/shiny/options.R | f1ea19a42ef10c820f56a65a07e1f964aab69a5e | [] | no_license | UCL/provis | 71e82c383cd9414840e57c2a2867826d6b4ee3e6 | 86a287c7bc705d4aeffb9bbcf96747e97e6d688b | refs/heads/master | 2020-08-01T04:08:20.198284 | 2019-11-08T12:09:43 | 2019-11-08T12:09:43 | 210,310,151 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,521 | r | options.R | # Options for UI inputs
# Select regions using "selectInput"
region_options<-as.list(c(1:11))
names(region_options)<-c("Ca-MK-Ox","Cornwall and Devon",
"East Midlands","East of England",
"London","North East","North West",
"Sout East","South We... |
b06b22ba3ebbf070e720feb48abd9a96a4686118 | f7983f4b74cb9fcd6154007f3b11b34b9ad11ede | /bayarea/scripts/household_assignment.R | 014134f5bb41714413c84bb2f1bbbba6668d5ab1 | [] | no_license | psrc/urbansim | 7d2fe615d7c22db839c283304067fce746fdb512 | c392d15b35aa1d47bbc185ed76314f8e6dd9f92f | refs/heads/master | 2023-06-09T16:55:50.415783 | 2023-05-11T19:24:34 | 2023-05-11T19:24:34 | 19,719,383 | 4 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,806 | r | household_assignment.R | ##this R script assigns households to buildings according to matched zone_id, tenure, and building_type_id
##author(s): Yujiang Mou
setwd("/Users/yujiangmou/Desktop/updating hh_table")
load("hhtable_newbuilding.rda")
load("building_newbuilding.rda")
save(sub.bds,file="building_newbuilding.rda")
library("for... |
8fdf7b3f9b983984dda28402706509f3f4af1aca | dc1ead4b62bba2a151b894d33dc16d76d12856e2 | /TheFeetMaze/demos/main TheFeetMaze.R | b776200b50044404d760f7401dba568d550c9d1f | [] | no_license | pabloirigo/IS_RProjects | 6343e968523155164e4ef8d41dfbbe68f7588c35 | e6576d713641038c09495c6dc8be3d458e10e3ab | refs/heads/master | 2020-04-28T15:38:32.411954 | 2019-05-20T11:58:47 | 2019-05-20T11:58:47 | 175,378,835 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,441 | r | main TheFeetMaze.R | # =======================================================================
# Names:Luis Urrechaga, Pablo Irigoyen
# Group Number: B
# Assignment:TheFeetMaze
# Date:
# =======================================================================
# 1. Be sure to include, with this template, any necessary files
# for exec... |
a4ddd50344e9b730b569546137c987e4d5585172 | a864a155a4ac597349fdc6a0d0b7618e695f1f2e | /man/spatialCoords-set-SpatialExperiment-method.Rd | 3d23adcc0fd2a9b13f5ce5e0bdd0dc9e70f936e4 | [] | no_license | celsomilne/SpatialExperiment | eb80936e938596ced34514c5a7af4b30e18e7658 | ae1da7d1eaeeed6bee5492b0cb08a7e05ac544f2 | refs/heads/master | 2022-12-02T06:22:56.199378 | 2020-07-31T00:52:29 | 2020-07-31T00:52:29 | 280,007,348 | 0 | 0 | null | 2020-07-31T00:52:30 | 2020-07-15T23:49:43 | null | UTF-8 | R | false | true | 813 | rd | spatialCoords-set-SpatialExperiment-method.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/SpatialExperimentMethods.R
\name{spatialCoords<-,SpatialExperiment-method}
\alias{spatialCoords<-,SpatialExperiment-method}
\alias{spatialCoords<-}
\title{spatialCoords-setter}
\usage{
\S4method{spatialCoords}{SpatialExperiment}(x) <- value
}... |
99443471a1b146eb35771003e9ce086726c9f88e | eb7ab30af415f747378906f0596f06ee3729a10c | /man/skriging.Rd | c896227a3e39bfe006530326a80b05d1ea1959e3 | [] | no_license | cran/SK | f6a33286e3e5b74ac7fa55cc3a9ae492774744b4 | 8d04a47caa3b2a613305b47323f7d33b6eac6f8e | refs/heads/master | 2020-03-17T14:33:28.229937 | 2018-05-16T13:17:01 | 2018-05-16T13:17:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,741 | rd | skriging.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/skriging.R
\name{skriging}
\alias{skriging}
\alias{print.skriging}
\alias{plot.skriging}
\title{Function for Segment-based Kriging models}
\usage{
skriging(formula, polyline = polyline, method = "srk",
lwd = "width", obspred = "obs1... |
18ed9be153ffe6e5ca57fb87d2cbeb3b21888cd0 | b9d8c0753c70ab28bc16249cf2c8ef3a9d2f0c7e | /data cleaning/week_3_quiz.R | cee4f94ff707001efe535db477476131a51802e1 | [] | no_license | sesh1729/datasciencecoursera | 8e26201a8ac07c9b0438cd5e974f5fbbfc191af5 | 3c8d65b7fba35c37bfea426ee504dd1225b84866 | refs/heads/master | 2021-01-17T12:45:54.903557 | 2016-11-07T07:16:49 | 2016-11-07T07:16:49 | 59,560,742 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,235 | r | week_3_quiz.R | #download.file(url='https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2Fss06hid.csv',destfile='idaho_housing.csv',method='curl')
idaho_housing_data<-read.csv(file='idaho_housing.csv')
households<-idaho_housing_data$ACR==3 & idaho_housing_data$AGS==6
which(households)
#download.file(url='https://d396qusza40orc.cloud... |
d9c5e24255999bd547334aaf24f1cee6220cd6b7 | 12e09fb76eac4d76ef25c4214d6a40128282818d | /R/stj_baixar_docs_pdf.R | 7b1ee5a04ffd06a2062e56d77f376e0f6d012fb2 | [
"MIT"
] | permissive | jjesusfilho/stj | 76d5676c447a9fb58e42f4bdec9a0761c0a43c0a | 2a4ccd87ba81197b4f929839830ca71135e15aea | refs/heads/master | 2023-06-22T03:30:11.940248 | 2023-06-20T16:55:25 | 2023-06-20T16:55:25 | 202,847,868 | 12 | 6 | null | null | null | null | UTF-8 | R | false | false | 2,165 | r | stj_baixar_docs_pdf.R | #' Baixa docs em pdf
#'
#' @param sequencial Sequencial do documento
#' @param registro Número do registro
#' @param data Data no formato dd/mm/aaaa
#' @param diretorio Diretório onde armazenar os pdfs
#'
#' @return pdf
#' @export
#'
#' @examples
#' \dontrun{
#' stj_baixar_docs_pdf("112282908", "20200165980","04/08/20... |
1414e48d5df5de3e341aef4d17a5f646a645d53c | a855bb0b9c803b79c756b581b35492f4571c1c67 | /Cleaning_data.R | 090ac539532f783d5066bc0379ae6c08683f8cad | [] | no_license | aellman13/NYT_Recipe_scrapy | 64d0da65eda98a9bd47a47d2d76731830de83b01 | 66a2622243309191cf850d53579120ec2b6e0a25 | refs/heads/master | 2020-05-19T02:52:02.974264 | 2019-06-19T21:24:24 | 2019-06-19T21:24:24 | 184,787,947 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 319 | r | Cleaning_data.R | library(tidyverse)
recipes = read_csv('NYT_recipes2.csv')
# Make new columns
recipes['index'] = 1:nrow(recipes)
recipes['time_in_minutes'] = 0
# Column to factor
col_to_factor = function(column_name, df){
df$column_ = forcats::as_factor(df$column_name)
}
col_to_factor(column_name = author, df = recipes)
|
769d27575fd006d493b18cf373fe6495009b7b37 | e86bc617d84f3c31be186460b53833eac1076658 | /man/repsample.Rd | 9d0db8da023fb98c081e05d53ae4c8b2c7a91c12 | [] | no_license | qkdrk777777/lotto | 7ba535c3b47d0c202d94d39cfc8c390158ebee10 | f9f61e1aa1a7755e55d23486017dfc2add2049be | refs/heads/master | 2021-05-08T19:39:52.598341 | 2018-03-17T08:43:55 | 2018-03-17T08:43:55 | 119,474,782 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 629 | rd | repsample.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/6 sampling2.R
\name{repsample}
\alias{repsample}
\title{sample2}
\usage{
repsample(n, count = 1, keep = NULL, drop = NULL, hold = NULL,
up = NULL, header = 10, write = F)
}
\arguments{
\item{n}{= The number of times the sample is pulled}
\... |
c0a88a67ceecbb76847bab75f7db03c0950ba480 | 4385010fdebcfd752eb8f53de5293c52df6ef838 | /2020/day03.R | db01414e1062856749f6f28c1412c5e59dcfb7df | [] | no_license | nickopotamus/advent-of-code | 53456b871a43f0b47d54d153b1ad439fc298ecb6 | 62009283e095ebdd6065ae709a1ec84a4ed59520 | refs/heads/main | 2023-01-31T18:59:51.747768 | 2020-12-10T17:32:17 | 2020-12-10T17:32:17 | 320,250,303 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 839 | r | day03.R | # Read input - 323 routes
input <- readLines('input03.txt')
## Part 1
# Moves 3 right, 1 down; returns position on each of the 323 rows
# (Loops at end of row)
positions <- (3 * (seq_along(input) - 1)) %% nchar(input) + 1
# Boolean if tree is encountered, then sums all trues
sum(substr(input, positions, positions) == ... |
55ab4ea90fd068493cb5c61e5f40166a014686db | 7e9d5d4490544d217ee3b291beb950c3e80c1428 | /man/summaryRedcapDqaResults.Rd | 8dbe414dba93d5364d1fe5e2d2363a80c34337ec | [] | no_license | lagvier/RedcapDqa | 5651ac1e2d4826ae86c7545842316db1652bb600 | f664129f62be61845f3fcf9a5a6caf6672942590 | refs/heads/master | 2021-01-23T19:59:32.473750 | 2016-11-22T14:02:29 | 2016-11-22T14:02:29 | 102,841,070 | 0 | 1 | null | 2017-09-08T08:58:17 | 2017-09-08T08:58:17 | null | UTF-8 | R | false | true | 334 | rd | summaryRedcapDqaResults.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Concordance.R
\name{summary.RedcapDqaResults}
\alias{summary.RedcapDqaResults}
\title{summary DQA Results}
\usage{
\method{summary}{RedcapDqaResults}(x, ...)
}
\description{
Summarize DQA Results
}
\details{
Generic summary method
}
\concept{... |
62df8cb177d74713dcd2c84a08d95cecd4a67466 | 3b26ab6bc88a47dfef383d4937558e4bd44da506 | /man/lookAtPair.Rd | 144c50942944365dde3de7b472731c1b58fc2522 | [
"MIT"
] | permissive | SMBaylis/fishSim | affafad3915dad24057895d1b0708bc53dd206bd | 2f98c4545780d4d42f63dd169fb9902c61d0c614 | refs/heads/master | 2021-08-02T18:07:06.651542 | 2021-07-23T06:17:11 | 2021-07-23T06:17:11 | 144,930,871 | 3 | 2 | MIT | 2021-02-15T01:28:04 | 2018-08-16T03:17:48 | R | UTF-8 | R | false | true | 1,421 | rd | lookAtPair.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fishSim_dev.R
\name{lookAtPair}
\alias{lookAtPair}
\title{show a readable relationship summary for a pair of relatives}
\usage{
lookAtPair(pair)
}
\arguments{
\item{pair}{a data.frame object, with structure identical to a row from findRelativ... |
d5f34fc48e4876097d195726cc12594c4cfef2b2 | ed53601b1b157a91398fc862cc0c1015bc115d44 | /R/Interplot_plot.R | bcdd2f5a4c262e5667073038c324a3bbeff7251b | [] | no_license | cran/interplot | ba1ff1b62f0e7bc83a6f504d5a109072e2d3f490 | 44eb5f233740c6b52cb328291e611803f489fd7f | refs/heads/master | 2021-07-20T00:35:20.610136 | 2021-02-18T05:20:07 | 2021-02-18T05:20:07 | 38,122,196 | 2 | 4 | null | null | null | null | UTF-8 | R | false | false | 13,156 | r | Interplot_plot.R | #' Plot Conditional Coefficients in Models with Interaction Terms
#'
#' Graph based on the data frame of statistics about the conditional effect of an interaction.
#'
#' @param m A model object including an interaction term, or, alternately, a data frame recording conditional coefficients. This data frame should ... |
fd11412635fda170bca782ad04f620ce924e94d8 | 397182bd2f6ed4e39dcc3f3d62e89807af03ac5a | /R/frontend-arcs.R | e53e986b08bc2a396953c7d8a3c419971c621a8c | [] | no_license | cran/bnlearn | e04a869228c237067fc09d64b8c65a360dada76a | dda6458daf2e0b25d3e87313f35f23d8a1c440c1 | refs/heads/master | 2023-05-14T14:21:07.744313 | 2023-04-29T09:30:02 | 2023-04-29T09:30:02 | 17,694,839 | 58 | 42 | null | 2020-01-03T23:49:19 | 2014-03-13T04:09:21 | C | UTF-8 | R | false | false | 4,861 | r | frontend-arcs.R |
# return the arcs in the graph.
arcs = function(x) {
# check x's class.
check.bn.or.fit(x)
if (is(x, "bn"))
x$arcs
else
fit2arcs(x)
}#ARCS
# rebuild the network structure using a new set fo arcs.
"arcs<-" = function(x, check.cycles = TRUE, check.illegal = TRUE, debug = FALSE,
value) {
# chec... |
a01719b86fa86617ca37ed4773beea159225d51e | 25be27365977838a5f716ab736e01acc93b2823e | /run_simulation.R | aee56259e07912263e66f947f353c8470c920b07 | [] | no_license | MWesselkamp/DomAdapt | ab45e2e97f46244b876dadcd14a203f03867d31a | fef2c1f57c31b9aa89c77854892eac4090545560 | refs/heads/master | 2023-03-27T02:32:02.975281 | 2021-03-29T14:49:56 | 2021-03-29T14:49:56 | 269,294,513 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,934 | r | run_simulation.R | #=================#
# run simulations #
#=================#
# In this file, the climate data to use and the number of simulations are specified.
# As well as the range of functions we are going to use below.
source("simulation_functions.R")
load("Rdata/fmT.Rdata")
load("Rdata/fmPAR.Rdata")
load("Rdata/fmVPD.Rdata")
l... |
759813e7d8b74188f64e5ddfced483c71ee7e7ac | ef3eb2afb71993519415ebd9919dc3854b22ae37 | /results/figure_4/format.R | 02b22f5e73ad00647aaa954497505db72889531f | [] | no_license | matthewkling/winds-of-change | 7a0fd897dcdebf2f3e566f1388393bfcad47a993 | beb690268b810f2c7f666fb22a06b89c6ac111fe | refs/heads/master | 2020-07-23T02:13:57.345121 | 2020-05-27T18:22:35 | 2020-05-27T18:22:35 | 267,161,663 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 747 | r | format.R |
# reformat the raw dataset, which is too large for github, contains many extraneous variables, and has obtuse variable names
library(tidyverse)
d <- read_csv("results/figure_4/p1_wnd10m_250km_inv.csv") %>%
select(x, y,
wind_fwd_windshed_size, wind_rev_windshed_size,
clim_fwd_windshed_... |
b7a397e1af25f18d4ec7d08e9b3c7b2a2d6364b9 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/spam/examples/lu.tri.Rd.R | 838c3d0cdd6d0dfc6369160679d7eda12c262b99 | [] | 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 | 342 | r | lu.tri.Rd.R | library(spam)
### Name: lower.tri
### Title: Lower and Upper Triangular Part of a Sparse Matrix
### Aliases: lower.tri upper.tri lower.tri.spam upper.tri.spam
### Keywords: array algebra
### ** Examples
smat <- spam( c( 1,2,0,3,0,0,0,4,5),3)
upper.tri( smat)
upper.tri( smat, diag=TRUE)
options(spam.trivalues=TRUE)... |
4260d5a1ff01ecbbb549513d73c69b189cb3440e | 8fdfd167bc1cee813ee5d49c6954f3ceed666529 | /NADIA_package/NADIA/R/PipeOpSample_B.R | f43fa2cc2992b512d613faa3ea1772d1456f8fa2 | [] | no_license | jjanborowka/EMMA | 53db6273b09d215721dcef9893c0917b7f4a37d9 | e93e1f4056e80e9ac06b54c9c12a057b2f63efc4 | refs/heads/master | 2023-02-04T10:37:02.366643 | 2020-12-22T10:04:42 | 2020-12-22T10:04:42 | 282,159,979 | 0 | 0 | null | 2020-11-26T13:35:48 | 2020-07-24T08:02:16 | HTML | UTF-8 | R | false | false | 2,415 | r | PipeOpSample_B.R | #' @title PipeOpSample_B
#'
#' @name PipeOpSample_B
#'
#' @description
#' Impute features by sampling from non-missing data in approach B (independently during the training and prediction phase).
#'
#' @section Input and Output Channels:
#' Input and output channels are inherited from \code{\link{PipeOpImpute}}.
#'
#' ... |
9936c811706f6d125b82666152bd77d03f946f89 | e0369cc936a3ee597ebee96c1eee92fb27f2ed65 | /WorkOnVariousDatasets/Automating Reviews in Medicine/trials.R | 8dc62b909398e773e660a854a13296d77490546d | [] | no_license | vihangp/MachineLearning | 0bc6cdbd0cbc75cdebdd2e2a8595925f745f0320 | 8c14a7daed9fed2c336e82a167425be89cf63d20 | refs/heads/master | 2021-06-11T04:43:39.024470 | 2017-01-29T17:36:34 | 2017-01-29T17:36:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,318 | r | trials.R | #1.1
trials = read.csv("clinical_trial.csv", stringsAsFactors = FALSE)
max(nchar(trials$abstract))
#1.2
nrow(trials[nchar(trials$abstract) == 0,])
#1.3
trials[which.min(nchar(trials$title)),'title']
#2.1
corpusTitle = Corpus(VectorSource(trials$title))
corpusTitle = tm_map(corpusTitle, tolower)
corpusTitle = tm_map(... |
f374afbe2d7d210db408fc628988745cc61bb4a3 | 73a165c11149f8ad0a3db8e4d7a36311485cee33 | /Scripty.R | ad3d130d3201807860c27f311385a21957e88d52 | [
"MIT"
] | permissive | christosto/FirstRepo | ab8c9e5aa2c15d03212c9a99e638d70d83d28e28 | 840b769ab0c2639fdd9de48b375e6eca0286b10f | refs/heads/master | 2020-07-09T03:55:13.532868 | 2019-08-29T20:37:07 | 2019-08-29T20:37:07 | 203,869,024 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 42 | r | Scripty.R | #sample script
A <- 1 + 1
B <- 1: 10
3*A
|
68a6be939b5c3ca7300694b7a7b84e36e9e7ab1b | 9796ce6157e5f00095da4c84c12f2f551b6b8a69 | /tests/testthat.R | 4f402c4c14500df4181f7b606d654282f953397f | [] | no_license | Suhani8884/Lab3A | 0c0a35df81b6ac38cfd471fd7e13ce7f0900c454 | 7c5c17dcf2786bb793707ff2ef1bbccdb4ef3a98 | refs/heads/master | 2022-12-14T03:07:43.070389 | 2020-09-13T20:53:27 | 2020-09-13T20:53:27 | 294,890,841 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 54 | r | testthat.R | library(testthat)
library(Lab3A)
test_check("Lab3A")
|
23c5715299c5ad95a490f274e8ad5fbf224b0d9d | ab918939f5de4246046ef24bc313e9e602d24792 | /trunk/oncotcap/sortOntologyFiles.R | 46e4d3bc7239bea14730520083c64c97912bee5f | [] | no_license | professorbeautiful/oncotcap | 3c3006ff9d9da5283ba8e77378dcaf74b7fb9e3f | 5025b8b921f26cd74076eb26241c7041a7bc8d34 | refs/heads/master | 2021-01-21T18:10:37.083536 | 2014-02-19T15:59:26 | 2014-02-19T15:59:26 | 15,146,293 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,627 | r | sortOntologyFiles.R | # USAGE: Rscript --vanilla sortOntologyFiles.R [FileChoice] [FolderChoice]
# Typically this will be run after saving the ontology, twice:
# Rscript --vanilla sortOntologyFiles.R pins .
# Rscript --vanilla sortOntologyFiles.R pont .
# in the directory "." where the files were saved.
# The unsorted files are backed up... |
191c524d56a44b42157e244fd1d585d4526532d8 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/HDtest/examples/wntest.Rd.R | 756e8c8674624b279708cc53b409676e82499937 | [] | 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 | 533 | r | wntest.Rd.R | library(HDtest)
### Name: wntest
### Title: Testing for multivariate or high dimensional white noise
### Aliases: wntest
### ** Examples
library(expm)
p = 15
n = 300
S1 = diag(1, p, p)
for(ii in c(1:p)){
for(jj in c(1:p)){
S1[ii, jj] = 0.995^(abs(ii-jj))
}
}
S11 = sqrtm(S1)
X = S11 %*% matrix(rt(n*p, df = 8), ncol ... |
0485aa8896f6360ed6e0cc2475c5f3f854c7b75e | c2ca52ff213c784ebb7e3e6f45ed9d148fb089cd | /BA/GetData/GetEmails-01.R | 880d6bb0ecd36384d03de65c95ad6093499b2872 | [
"MIT"
] | permissive | jeklen/notes | 93d3091ea2a14cf244004fe1a3a539018ae671d2 | 700498ce6577f83707c8d497ddef4b673b190e2a | refs/heads/master | 2021-06-12T09:36:41.024345 | 2017-01-22T02:13:22 | 2017-01-22T02:13:22 | 71,848,578 | 1 | 0 | null | null | null | null | GB18030 | R | false | false | 1,144 | r | GetEmails-01.R | library(stringr) # for the function str_match 调入stringr package
setwd("d:/BA/GetData") #设置工作目录
con <- file("Email.txt", "r") #读文本文件Email.txt
email_list <- NULL #email_list用来保存提取出来的Email，初始化为空。
pattern <- '[[:alnum:]_.-]+@[[:alnum:]_.-]+\\.[A-Za-z]+' #Email的正则表达式。
# a simplier RE: pattern <- '[[:alnum:].-_]+@[[:alnum:... |
98107fec19c80c1602eea6c45cd04296b4a06544 | 2595ffba6f364508c7189553c5e01db01fea1292 | /man/append_object.Rd | 957a5b584bdfad470cff70d54586de65075bc9e2 | [] | no_license | cran/tidytidbits | c16685e56ef0f807226b19f85c324b5d3b6dcccb | 629ef60315367a8923d68e7c0944c50549805d76 | refs/heads/master | 2022-04-30T02:44:50.836003 | 2022-03-16T21:30:06 | 2022-03-16T21:30:06 | 146,012,291 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,027 | rd | append_object.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/purrr-like.R
\name{append_object}
\alias{append_object}
\title{Appending in a pipe, never unlisting}
\usage{
append_object(x, .l, name = NULL)
}
\arguments{
\item{x}{Object to append. If the object is a list, then it is appended as-is, and no... |
357aba8c1c790ff29c0a1ebf1cc14869c53ed799 | 7e1ca878a6b0f0d17bbc81e76224b13cb70acb42 | /Repository/Supplementary Files/CreateExampleTrajectoriesGradual.R | a146323c7c539ff25fd4732771c6ff00be3b106e | [] | no_license | EvoNetHIV/Mittler-et-al-TasP-by-Age | 6edac9ae7b94dd96099816ac6689f8a10a4b710f | 408216556a3640968c8073a885bdfc9373c8fd26 | refs/heads/master | 2022-04-12T18:37:06.015449 | 2019-12-03T22:59:29 | 2019-12-03T22:59:29 | 107,707,867 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 24,934 | r | CreateExampleTrajectoriesGradual.R | #note, graphing routine starts line 163, need to change pathways below
re_read_data <- FALSE
par(mfrow=c(3,3))
legend_text_size <- 0.65
if (re_read_data == TRUE) {
run1_title <- "'Random'"
load("H:/TasP2/Exp20_Gradual_95_care_N10000/txt_opto_Exp20_paramsGradual_95_N10000_TasP60perc__random_prop0.2.RData")... |
9a11657bc5c461e67c8e6e72cab16f8612b173ba | 5b4f2711c63a5e49e792ab41be5e965820ff7c97 | /Indian Data/describeIndia.R | 861f1fa457b842ecc37c8a899a8117298fe119ec | [] | no_license | calbarber21/Hwang-Lab-Economic-Research | f09c7e7bbbf8de8a7e157a59213c9ab176eab5c2 | a2928a454db5bbc24439fa15f9f7b70dafc61ed0 | refs/heads/master | 2022-12-26T23:32:13.630993 | 2020-09-27T19:39:23 | 2020-09-27T19:39:23 | 295,228,415 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,632 | r | describeIndia.R |
describeIndia <- function(){
library(patchwork)
library(dplyr)
library(ggplot2)
file <- "WVS.csv"
WVS<- read.csv(file)
#the sex ratio variable can be changed to reflect urban, rural or overall population. Use the filter option to only take responses from those who live in rural areas. Rural area is defined by tow... |
78517c767781691620332f7fe7bd5291fd42a94e | 75f792fc99722753a76b6e428ba30a591f2d5368 | /data-raw/small_community.comp.R | 822748a2b0e1006b9ce5175e69d2ac92c71b7b98 | [
"MIT"
] | permissive | jtmccr1/HIVEr | 9739c7883ec6e8f1071a537517a62b7288f553cd | 61601afa9f173fe2ca033b0a0028b109f7b90de6 | refs/heads/master | 2021-09-20T03:25:25.257483 | 2018-08-02T15:15:31 | 2018-08-02T15:15:31 | 109,002,994 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 822 | r | small_community.comp.R |
small_isnv$HOUSE_ID <- 000000
get_freqs(c("HS1595","HS1563"),small_isnv)->small_dups
small_dups.comp<-polish_freq(small_dups,freq1,0.02)
extra<-small_dups.comp
extra$SPECID1<-"MH0000"
small_dups.comp$freq2[1]<-0
small_dups.comp<-rbind(small_dups.comp,extra)
small_dups.comp<-rbind(small_dups.comp,small_dups.comp)
sm... |
bab4571752dd01d9d455c284238106bd65a37889 | d20612d64c5cf45664ddb6a50dfbf05028c72158 | /R/data.R | b7fc911616a2a77b5c41d56fe3b0f053f81cf532 | [] | no_license | kuriwaki/ddi | 4f0a634c79c7f1d9b3528a7b2a3158c8216ddb4b | bbbc6772ef521b958033485f5fc30cb096e191b1 | refs/heads/master | 2021-07-16T09:56:51.972865 | 2020-05-09T01:04:05 | 2020-05-09T01:04:05 | 145,268,603 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,963 | r | data.R | #' 2016 General Election Results and Survey Estimates
#'
#' Donald Trump's voteshare in each U.S. state,
#' with survey estimates from the Cooperative Congressional Election Study
#' (pre-election wave). See Meng (2018) referenced below for more details.
#' We focus on unweighted estimates to capture the response patte... |
6050c43ab2d7e30a22afb5cba22903db06900669 | 3fc72b0734b728dcb57baf287b5b8e06f6e5b0b7 | /tools/archive/_valueParser.R | 17722ae553defa29384350502be5340a27555fb2 | [] | no_license | WengLab-InformaticsResearch/DQueST | a8e24a4f5bd050fa0f78c5c33f69a16b7b2c57e1 | f1fb9302ba06141c837bf27a686e614b55889cda | refs/heads/master | 2020-04-02T23:27:05.844799 | 2018-10-26T16:55:56 | 2018-10-26T16:55:56 | 154,867,944 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,191 | r | _valueParser.R | valueNormalize <- function(has_value){
if(is.na(has_value)){
return(NA)
}
has_value = tolower(has_value)
bigger_words = c("\\>","\\+","more","big","great","more","excess","high","better","exceed","old","long","over","at least")
smaller_words = c("\\<","\\-","less","small","low","worse","young","short")
... |
64500e06ad88a3eefa5317b6a1c2db83192eebd6 | b5a1bd1654ad240813aec25a16a236b198558095 | /Archive/paper/size2/bigsize1d.R | 0468152976d9a771a2d8f551d84b678a844bb64b | [] | no_license | trevor-harris/assimilation-cfr | af4d6125dff636f0edd338f0bd4fbbd57a176aca | 60a311dccafc4d524136e6400a41118a4ec2ff58 | refs/heads/master | 2021-03-22T00:23:45.788770 | 2020-06-18T19:06:09 | 2020-06-18T19:06:09 | 118,714,285 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,381 | r | bigsize1d.R |
library(reshape2)
library(tictoc)
library(future)
library(future.apply)
source("/home/trevorh2/assimilation-cfr/code/depth_tests.R")
source("/home/trevorh2/assimilation-cfr/code/depths.R")
source("/home/trevorh2/assimilation-cfr/code/simulation.R")
#### SIZE
set.seed(042696)
iter = 50
sims = 1000
ranges = c(10, 20... |
3654d5dca352a31830160c0d269b8c1bda35f61f | eb8e225361c5a640d23207735e88d87aa8477533 | /man/MeanDurUnder.Rd | 844f5e3b70164f6cc61455f2571b4fa527f769d0 | [] | no_license | cran/NHMSAR | 6ec23ee69d3398005f85e64293b32fe5f71b5375 | 6fbbfdd21700d5b6b95322844abdc93129cddc22 | refs/heads/master | 2022-03-03T06:41:29.892008 | 2022-02-09T06:40:11 | 2022-02-09T06:40:11 | 27,052,794 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,227 | rd | MeanDurUnder.Rd | \name{MeanDurUnder}
\alias{MeanDurUnder}
\title{
Mean Duration of sojourn under a treshold
}
\description{
Plot the mean duration of sojourn under thresholds for an observed time series and a simulated one with respect to teh empirical cumulative distribution function (cdf). Confidence intervals are plotted too.}
\us... |
c2fb7a7db9855b522d2c1874f73ad96aa49d72e6 | 343a4b66f68e7652d06dea9f74b01831b93e1c96 | /R/dados_estruturais/tabelas_locias_proximo.R | d895d7e48cd8b4390dc6a2ad437d5485e67d5972 | [] | no_license | Marcelo-Honorio/modelos_tratamento_de_dados | fd3f6324290734ddfed4b356addc8de9ea288845 | d0941880b09765a5fcdc1cf63f494ffac600db7b | refs/heads/master | 2022-05-28T16:17:23.525509 | 2020-05-02T20:11:18 | 2020-05-02T20:11:18 | 257,465,309 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,201 | r | tabelas_locias_proximo.R | rm(list = ls(all = T))
library(tidyverse)
library(stringr)
library(readr)
library(readxl)
library(lubridate)
library(formattable)
library(knitr)
library(kableExtra)
library(webshot)
setwd('C:/Users/marce/Documents/Emovel/Dados/avaliacao_ banco/locais_proximos')
locais <- read_excel('locais_prox_itau.xlsx')
locais <... |
a63e86c3a306ecedd16612083b3eef0944b462f8 | 60669f36c55c532bc9fad514a5eda9a0ce20c571 | /tests/testthat.R | de65f8bc8c5b7159d9e31db84a2b100cb2afbb32 | [] | no_license | bmewing/mgsubcpp | 99c856409842d16e21bc6917cc609e682090c18c | 331f5467db619b9b9e49cd6f5b80278ea885f72f | refs/heads/master | 2021-05-13T12:03:30.676221 | 2018-01-16T16:09:10 | 2018-01-16T16:09:10 | 117,150,106 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 60 | r | testthat.R | library(testthat)
library(mgsubcpp)
test_check("mgsubcpp")
|
c3a045b24fd3abfa2bb290a7f7fd6a0bf3b608ff | 794863d2e9e26424a04079a91c3a23063bdb4f8e | /R/VFEVD.R | d1aaaeef55453da6fc40df984d747e53a161b9e3 | [] | no_license | GabauerDavid/ConnectednessApproach | ef768e64e0bc458ad180bac6b667b3fe5662f01d | 0ca4799a2f5aa68fdd2c4a3e8a2e0e687d0a9b17 | refs/heads/main | 2023-08-09T07:23:45.002713 | 2023-07-27T22:57:04 | 2023-07-27T22:57:04 | 474,462,772 | 47 | 20 | null | 2023-03-12T04:22:26 | 2022-03-26T20:47:15 | R | UTF-8 | R | false | false | 3,076 | r | VFEVD.R |
#' @title Generalized volatility forecast error variance decomposition and volatility impulse response functions
#' @description This function provides the volatility impulse responses and the forecast error variance decomposition of DCC-GARCH models.
#' @param fit Fitted DCC-GARCH model
#' @param nfore H-step ahead f... |
54bdae68b09974308e282b56a5df7ae5513430ff | 936a5fd21e0eb2b92d8128b9ab1c04866d21e73a | /inst/examples/ames_glmnet.R | 3fd7c89ffc0075e8150c873502e1eba6064b0b22 | [
"MIT"
] | permissive | tidymodels/tune | 7a8dcc35196ac1be7f11ef26b12107e204f7c390 | 034b75a1b032bd29ceb70df8d4e3ce10e9fcc17b | refs/heads/main | 2023-09-04T07:23:00.149702 | 2023-08-24T12:37:39 | 2023-08-24T12:37:39 | 201,538,545 | 253 | 48 | NOASSERTION | 2023-09-08T21:18:10 | 2019-08-09T20:49:40 | R | UTF-8 | R | false | false | 1,673 | r | ames_glmnet.R | library(tidymodels)
library(tune)
library(AmesHousing)
# ------------------------------------------------------------------------------
ames <- make_ames()
# Make sure that you get the same random numbers
set.seed(4595)
data_split <- initial_split(ames, strata = "Sale_Price")
ames_train <- training(data_split)
set... |
2e1a9328da936d1c5b2a8751d7615a66bdb505e4 | 976f676cdd85b2dffd7405712c3ed70e48bfde0e | /02-add-log-odds-ranking.R | 4edf16e7deb735f690b87b75bb2f40b617f9c07b | [] | no_license | shuckle16/job-description-analytics | 493c24723d9b1927ae7c3b0fa84f0bf3424d83d2 | f424cbf6a84358ecc3ce975e4f1e535fbccfa0df | refs/heads/master | 2020-11-28T11:39:34.835242 | 2020-01-07T15:59:31 | 2020-01-07T15:59:31 | 229,801,248 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 789 | r | 02-add-log-odds-ranking.R | library(tidytext)
library(tidylo)
find_keywords2 <- function(job_description = "Accountants", n = 10) {
job_descs %>%
unnest_tokens(word, description) %>%
count(title, word) %>%
filter(
!(word %in% tidytext::stop_words$word)
) %>%
bind_log_odds(title, word, n) %>%
arrange(-log_od... |
fb17c20a275247c258a758f47adcbbe715190c28 | bbe0ba427d05eaeded9a920251d5c109b48d731d | /man/get_color.Rd | c29e3a2767f6b60b9de7dcb78986c47b17fa1d5c | [] | no_license | cran/rcolors | 77c9a96490bffb5a3a80a8d6d2ef5cbcc03dbd0d | e09ad9df5e846c2971b39b52a88c7e9962f6ae8c | refs/heads/master | 2023-04-22T11:09:19.959872 | 2021-04-23T09:40:02 | 2021-04-23T09:40:02 | 360,933,493 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,134 | rd | get_color.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_color.R
\name{get_color}
\alias{get_color}
\title{Get and interpolate colors}
\usage{
get_color(col, n = NULL, show = FALSE)
}
\arguments{
\item{col}{color name in the \code{rcolors} or a vector of colors}
\item{n}{Integer, de... |
9be91a684d4085c2a73b29ca1b1004e56b65c9f2 | 29585dff702209dd446c0ab52ceea046c58e384e | /UpSetR/R/Set.metadata.R | e6bfabac642504ab8ffbe115e8bc4b8f58f07dd6 | [] | 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 | 1,885 | r | Set.metadata.R | Make_set_metadata_plot <- function(set.metadata, set_names){
metadata <- as.data.frame(set.metadata$data)
num_of_att <- length(set.metadata$plots)
metadata_columns <- colnames(metadata)
metadata_columns[1] <- "sets"
names(metadata) <- metadata_columns
metadata <- metadata[which(metadata$sets %in% set_names)... |
fc10e2f107553dff9b15d304bc1f6a9ef087770b | a5db873e3ee26b7047fe601863a7831d147cf1a1 | /man/summariseValidTagsLGR.Rd | 55905b53d24ff0359b76428fae2891a4c9f40b73 | [
"CC0-1.0",
"CC-BY-4.0"
] | permissive | KevinSee/PITcleanr | ad10fa9e2a74cf84afb051750d5e611bbd8d7d87 | 1bd2c6106c65f310a2cb09f363009ab8507ffb7d | refs/heads/main | 2023-08-22T10:44:07.865356 | 2023-08-16T00:16:33 | 2023-08-16T00:16:33 | 276,694,561 | 4 | 8 | CC-BY-4.0 | 2023-09-11T20:04:01 | 2020-07-02T16:23:49 | R | UTF-8 | R | false | true | 584 | rd | summariseValidTagsLGR.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/summariseValidTagsLGR.R
\name{summariseValidTagsLGR}
\alias{summariseValidTagsLGR}
\title{Summarise Valid Tags}
\usage{
summariseValidTagsLGR(valid_df)
}
\arguments{
\item{valid_df}{dataframe of valid tags extracted from the trap database wit... |
84a94c5085e97246860311fa95b8e776ab5f70d3 | dfa6f9ae20dd7b548b25d917c016fc3d5780ae5c | /R/parse_beast_posterior.R | e3248af0f7a7af488ec06641a149f9babaa7b058 | [
"MIT"
] | permissive | venkataduvvuri/RBeast | 13de4b2a8a7fed45ed2f35d6046625a3e0603b9c | 1e9ba811a1ac328dc4ee9209bb0c68c19fa3e43e | refs/heads/master | 2022-04-27T11:04:19.878296 | 2019-04-10T21:50:12 | 2019-04-10T21:50:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 767 | r | parse_beast_posterior.R | #' Parses BEAST2 output files to a posterior
#' @param trees_filename name of the BEAST2 .trees output file
#' @param log_filename name of the BEAST2 .trees output file
#' @return a posterior
#' @export
#' @examples
#' trees_filename <- system.file(
#' "extdata", "beast2_example_output.trees", package = "RBeast"
#... |
09fa36a4560cdfdf31e7dcccc50c9970b3f2ffed | 7bae5569fd5509263b0cdd20fc1c6c14436410f9 | /packages/RNASeq/OFBIT/SCONE/SCONE_EX4.R | 63ced337370e90fb09af021865bebff465790797 | [] | no_license | cfbuenabadn/YosefCode2 | fe578ac0e9d0ff5ce724209dde1379acae6ab0ad | 35bd4e749301b728ad502d6327b88c01de71cbd3 | refs/heads/master | 2021-07-05T03:40:51.753643 | 2017-06-23T10:50:08 | 2017-06-23T10:50:08 | 105,191,082 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,813 | r | SCONE_EX4.R | load("/data/yosef/users/mbcole/WTP63/First/WTp63_counts_QCscores_someMetaD_GeneLists.rda")
rm(list = ls()[ls() != "HKlistE1"])
source("~/YosefCode/packages/RNASeq/OFBIT/SCONE/SCONE.R")
## ----- Standardize Quality Matrix -----
# Standardize quality metric
# q = quality metric matrix (columns = named features, rows = ... |
bbe7bb81b419f519be94486f6f4adac8f629c162 | e06965698053952f7f97c60349a590e42d08b633 | /man/assets.Rd | 7ac535e8495fa784aadd9ab6536c6ef184957f1f | [
"Apache-2.0"
] | permissive | kcf-jackson/sketch | a9940c89ed8183627914861a11893856b1c47429 | b597f01e540f35aab1f5ee2d3744f6f64c70c94d | refs/heads/master | 2022-11-01T03:28:32.088340 | 2022-10-23T14:22:05 | 2022-10-23T14:22:05 | 222,058,097 | 106 | 5 | NOASSERTION | 2022-10-23T14:22:07 | 2019-11-16T06:36:59 | HTML | UTF-8 | R | false | true | 688 | rd | assets.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/assets-loader.R
\name{assets}
\alias{assets}
\title{Process assets in headers}
\usage{
assets(file, ..., trace = FALSE)
}
\arguments{
\item{file}{Character string; the file path.}
\item{...}{(Optional) List of processors to pass to \link{con... |
10f4166122e667979fc38106f97ea6dc6d4aebb8 | 02b178b7ebb101940d6ede02b10c52dec501dcd6 | /snakemake/chiplike_analysis/workflow/scripts/t_vs_c_filter.R | 843c9909710c915e0f0e4c0d3caea7dab2e3ec38 | [
"MIT"
] | permissive | radio1988/bpipes | 21ea7c124f1bd962afe32644c445da3bb7a7d177 | 0aceb97070210c2361adb45ee0040b6aa5be771b | refs/heads/master | 2023-08-24T12:40:19.129216 | 2023-08-24T00:49:42 | 2023-08-24T00:49:42 | 140,731,030 | 3 | 2 | null | null | null | null | UTF-8 | R | false | false | 8,352 | r | t_vs_c_filter.R | # For PullDown vs Input Only
# output Any LFC_RAW > 0
# Use colSum(COUNT) as SizeFactor
# Pvalue not very useful, LFC_RAW good for this step
# todo: Each Treatment must > mean(IgG)?
library(DESeq2)
library(ashr)
library(WriteXLS)
readExcel <- function(fname){
df <- readxl::read_xlsx( fname, na='NA', sheet=1) # n... |
69aa1c18b6112b910811bb5a4a54afd1fad990b5 | 3abfaa6cf0b52c5ff44e9c0aa7d842f73e2d876e | /scripts/buoys-validator/resources/r/rmse.R | ae4403de1c03be6ac8ddd7fbf70aa9622334dbb6 | [] | no_license | rchailan/mirmidon-toolbox | 0c272c4f7b7fd3db1d2a2a975b8a4e86876bb677 | d116b1e0c8c7faaa4dd99144ea4965a1fa64fd03 | refs/heads/master | 2021-05-29T01:03:44.384387 | 2015-04-23T17:10:45 | 2015-04-23T17:10:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 827 | r | rmse.R | #remove nan row(s)
df.mix2<-df.mix[complete.cases(df.mix),]
#subselect time frame
#df.mix2<-df.mix2[df.mix2$date > as.POSIXct("2011-09-01 00:00:00") & df.mix2$date < as.POSIXct("2011-12-31 00:00:00"),]
#Plot error
hs.error<-(df.mix2$hs.modeled-df.mix2$hs.measured)
df<-data.frame(date=df.mix2$date,error=hs.error)
plin... |
df9d493d184186f868eeda4ee8626c6147f719c7 | b4e274d4513130bb3333d9f665233145a9ab8cb6 | /man/relAbForTimes.Rd | debf77f4e87cbe435249a248f52dc208b5a065f8 | [] | no_license | HegemanLab/ProteinTurnover | 14f4bb415342070a660425ba57f3e6d796ce4f16 | cc2f184ff508c78d4336bcfb2a838f19f12b564b | refs/heads/master | 2021-01-10T17:46:41.292492 | 2017-07-18T21:48:43 | 2017-07-18T21:48:43 | 50,138,014 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,111 | rd | relAbForTimes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/proportions2.R, R/run.R
\name{relAbForTimes}
\alias{relAbForTimes}
\alias{regressionPlot}
\alias{plot.RelAbTimes}
\alias{makeRelAb}
\title{Relative Abundance from Counts, for multiple time points}
\usage{
relAbForTimes(Count, Channel, RT, Tim... |
4eab660befddd713aec78010bb58a349d0c31b61 | 51f9af2d345c8a6a4bd219dfb715dda93f009f26 | /man/ref.Rd | a3619ae4f40feb6c57688cd9dd9c4f70ed676911 | [] | no_license | eeholmes/SardineForecast | 1e7449046aa4c750bfcc654b60a99e6288507752 | ead7a65dd5e222b72748e569e8e0b52ab03b5bf1 | refs/heads/master | 2021-07-20T04:26:55.048757 | 2021-07-16T02:59:47 | 2021-07-16T02:59:47 | 108,604,050 | 0 | 2 | null | null | null | null | UTF-8 | R | false | true | 1,185 | rd | ref.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ref.r
\name{ref}
\alias{ref}
\title{Create counter for figure and tables}
\usage{
ref(useName, pre = "", app = "")
}
\arguments{
\item{useName}{name of the label. Must be of the form type:name, like fig:spawners}
\item{pre}{tag to put in fr... |
5d43751c7b997a1dff0ad682995bc7fcf09e3c4b | 7e5c87886c6fcbf4fd7cad55aadb4de90ed377fb | /janata_hr.R | 9a776adc168f56ff313df9158dec84c7d8e53919 | [] | no_license | neek-ds/r-programming | 61748b8edb8b7db12b204756ac92db31801c5387 | 61c836240ecfaedce85af7c483a1232a09d78a55 | refs/heads/master | 2021-07-09T03:27:59.893768 | 2020-11-01T18:33:13 | 2020-11-01T18:33:13 | 207,118,926 | 0 | 0 | null | 2019-09-09T05:28:20 | 2019-09-08T13:45:13 | null | UTF-8 | R | false | false | 9,878 | r | janata_hr.R | # to predict the probability of an enrollee to look for a new job.
#***********Load the Dataset**********************************
hrdata = read.csv(file.choose(),header = T)
#checking the structure of the data
head(hrdata)
str(hrdata)
#copy the data to another variable
hr_train = hrdata
str(hr_train)
h... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.