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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
c8e64af6ab866aeac439146196afd9a618d6a6f4 | 809c790aebf2f784c358e396e79d7b3b9bfba815 | /r_code/master_reduced_form.R | 3a4029390b6769f931c8fb6c9c389905a398c934 | [] | no_license | aalexee/give_me_challenge | 8ecb05a6e61aea4a7bd8f9a730a770bfca268221 | 7f1034a5a8c330f6a31a2fe140eec3480cb157e0 | refs/heads/main | 2023-03-26T15:44:15.203098 | 2021-03-11T11:41:55 | 2021-03-11T11:41:55 | 345,990,469 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 28,634 | r | master_reduced_form.R | # Packages ----------------------------------------------------------------
library(tidyverse)
library(magrittr)
library(broom)
library(ggpubr)
library(ggsci)
library(plm)
library(lmtest)
library(sandwich)
library(margins)
library(ggeffects)
library(fixest)
library(lubridate)
library(stargazer)
# Functions ----... |
ba7ee06f6953e872a3bf502d546cad97d5f82df1 | dc7ae82a9ac699342701307aff96dfe753d9c455 | /01_scripts/Rscripts/subset_ind_coordinates.r | 8fbc6919e5e302c00d7a913b4db6e09cfc6388a9 | [] | no_license | clairemerot/angsd_pipeline | b30aca8a7c649a9bb2c903c9024c2e8da84263d8 | 27e284ddd7a578892d5cef7aba097c9befc42a6e | refs/heads/master | 2023-06-22T07:53:03.059492 | 2023-06-15T20:30:36 | 2023-06-15T20:30:36 | 138,884,266 | 18 | 10 | null | 2022-10-16T14:03:23 | 2018-06-27T13:18:43 | Shell | UTF-8 | R | false | false | 1,176 | r | subset_ind_coordinates.r | #this R script output a file with the column to keep in the beagle file.
argv <- commandArgs(T)
GROUP <- argv[1] #file with the list of bamfile from the subgroup
BAM_ALL <- argv[2] # file with all the bamfiles that was used to construct the whole beagle
library(dplyr)
BAM_beagle<-as.data.frame(read.table(BAM_ALL))... |
4ebafb5fdef7857605269a0773359dc567572a08 | 84304d9256e55242443143fcd78df223627ef15b | /man/conicMatrix.Rd | 54c115254d5dfd140c1153c752c60f15fb442324 | [] | no_license | cran/conics | 87674f8dfd4f2a9fc188259a2b64292c3dc1ac31 | 70ac06a0a404b423308728383174ad08abdbec44 | refs/heads/master | 2021-03-12T20:33:37.057711 | 2013-11-22T00:00:00 | 2013-11-22T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,194 | rd | conicMatrix.Rd | \name{conicMatrix}
\alias{conicMatrix}
\title{Matrix representing a conic}
\description{
Build a symmetric matrix representing a quadratic polynomial in two variables.
}
\usage{ conicMatrix(v)}
\arguments{
\item{v}{(\code{vector}) a 6-length vector containing the coefficients of a quadratic polynomial.}
}
\detai... |
cda05ed13191eff28f9d5ba9b180b8d04a7f5e43 | 2b5728585d67ad9f0210a21189459a1515faa72f | /man/importLimeSurveyData.Rd | 77fea8269a593cacc4fada94f753d981ca0e6c8c | [] | no_license | Matherion/userfriendlyscience | 9fb8dd5992dcc86b84ab81ca98d97b9b65cc5133 | 46acf718d692a42aeebdbe9a6e559a7a5cb50c77 | refs/heads/master | 2020-12-24T16:35:32.356423 | 2018-09-25T06:41:14 | 2018-09-25T06:41:14 | 49,939,242 | 15 | 9 | null | 2018-11-17T10:34:37 | 2016-01-19T08:50:54 | R | UTF-8 | R | false | false | 5,556 | rd | importLimeSurveyData.Rd | \name{importLimeSurveyData}
\alias{importLimeSurveyData}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
importLimeSurveyData
}
\description{
This function can be used to import files exported by LimeSurvey, a powerful
Open Source online survey application that can be used for, for example,
psych... |
da9cc68dc97a5fba27f30d0a3580e494081eaee9 | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/4270_1/rinput.R | d01244038b095c84c033eadeb3442678d899e46f | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 135 | r | rinput.R | library(ape)
testtree <- read.tree("4270_1.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="4270_1_unrooted.txt") |
2f2d165ee12066bc6d85c7e8c8540a4c4b374abb | ffc4849987a0c059ae306deb4747a83b015cc35c | /SensorFiles/initialize.R | 7901cc7017f8cd577d946b4732679769af17ffe0 | [] | no_license | AGarsha/presentation | f11b4526dde363918acaaaf133e05aba96548d77 | 02798b55a408f536ecc7d20544881e4e68802873 | refs/heads/master | 2020-12-25T10:14:28.133535 | 2016-02-13T00:39:26 | 2016-02-13T00:39:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,101 | r | initialize.R | f.initialize <- function(sdSystemAge = 7, sdBuilding = 7,
sdTemp = 7, sdNonLinear = 1, sampleFlag = FALSE,
downSampleTime = 10, fold = 1, type = "linearModel", transformFlag = TRUE ) {
#check install packages
pkgs = c("rms", "caret","survival","pec","prodlim","... |
c591847e25f8e3151cac48752dc5d774df7771b7 | 73794aa4e4d95d02b167c2cb3891e3d883df15c9 | /man/dies.ok.Rd | 37153faab296faeacdde5cdc6cb2ff539dd64d46 | [] | no_license | apomatix/AnalysisPageServer | 6283263e25011f01c59dcfdc1531647b557d143c | 1a89fd712a601d5df195175b2d8770a2fddfeda0 | refs/heads/master | 2021-09-17T21:58:34.357330 | 2018-07-05T19:11:05 | 2018-07-05T19:11:05 | 110,210,811 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 747 | rd | dies.ok.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/test.R
\name{dies.ok}
\alias{dies.ok}
\title{dies.ok}
\usage{
dies.ok(call, regex, testname)
}
\arguments{
\item{call}{An expression to evaluate}
\item{regex}{A regular expression to match the error against. If omitted then don't test the ex... |
c94a1121d1648c3e8e16d5d8ff76b74dba4d8000 | 3bfc998ab6d6e275f2ad1b2e6d23aa3aa36d22c3 | /R/stringdb.score.R | c13f7685d130ce9da9ec20a5ceb18d3f99402910 | [
"BSD-3-Clause"
] | permissive | unmtransinfo/metap | 93ecf33e6a02bb6bf7741221a5e23e198abafc7b | 159cfcdd6f96f82cdaf7d9fe7764b1e2136c366c | refs/heads/master | 2020-04-05T09:57:47.161855 | 2019-10-30T19:56:24 | 2019-10-30T19:56:24 | 156,782,128 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,292 | r | stringdb.score.R | #!/usr/bin/env Rscript
library(data.table)
library(RPostgreSQL)
conn <- dbConnect(PostgreSQL(), user = "oleg", host = "localhost", dbname = "metap")
protein <- dbGetQuery(conn, "select protein_id,stringdb_id from stringdb")
dbDisconnect(conn)
rm(conn)
setDT(protein)
download.file("https://stringdb-static.org/download... |
87c93427be61e264c1f274f84b3163308145c52c | c8d3eac72924cc8952e6bdf77497cd2c571194df | /fluodilution/man/proliferation.Rd | a9a528fe092013cfd3ba926abe970321fd9d226b | [
"MIT"
] | permissive | hchauvin/fluodilution | af57ec858aefe41ae4ad92377ba80ec699d8d2b4 | 1fd52c705edfd3a0951152511f3d1b54b8762f4a | refs/heads/master | 2021-04-03T08:19:23.059053 | 2019-01-11T16:05:03 | 2019-01-11T16:05:03 | 125,096,130 | 0 | 0 | MIT | 2019-09-24T21:52:03 | 2018-03-13T18:22:18 | R | UTF-8 | R | false | true | 5,653 | rd | proliferation.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/proliferation-.R, R/proliferation-branching.R,
% R/proliferation-cyton.R
\name{proliferation}
\alias{proliferation}
\alias{fd_proliferation_branching}
\alias{fd_proliferation_cyton}
\title{Implementation of the Cyton and branching models.}
... |
34ac7e112d45941e02c945d91290b5b9e1dbd1d0 | 54de529bed13c31f7eaa70d05831f9af562a4650 | /R/moustache.R | 082a4fff217ecc0fd6817e186766b2a7df8e4d97 | [
"MIT"
] | permissive | dtkaplan/checkr2 | 0f850351af1ec39f2294485c15dd70a821f9fc32 | c0e633873d0cab355d2c5f3073a01d1ac62f2605 | refs/heads/master | 2021-09-03T18:13:09.969279 | 2018-01-11T01:33:37 | 2018-01-11T01:33:37 | 113,483,746 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,908 | r | moustache.R | #' evaluate expressions in a string using a particular environment
#'
#' @param string a character string which presumably contains some moustaches
#' referring to objects found in the bindings environment.
#' @param bindings an environment or list in which the objects moustached in `string` are defined.
#' @examples
#... |
ea013ed684134aee80dc0cf04453e9d1f7353338 | 8a166837b22915d7597ed8fe81e1284cb96ead39 | /Mouse/Metatranscriptome/MultiOmics_Mouse_Metatranscriptome_GIT.r | cc94d36a6649c3c8f8fa0f01ed3fcb4dbccf6a64 | [] | no_license | segalmicrobiomelab/functional_microbiomics | 5a824070087d535cc0c06f29bee61fcd02c3ee78 | f4015ebf638b62676a5da3debdca16899f73bc03 | refs/heads/master | 2022-04-03T04:56:46.413545 | 2020-01-31T19:47:51 | 2020-01-31T19:47:51 | 208,831,180 | 7 | 1 | null | 2019-10-21T20:35:18 | 2019-09-16T15:14:45 | null | UTF-8 | R | false | false | 21,330 | r | MultiOmics_Mouse_Metatranscriptome_GIT.r | #Load Packages
library(DESeq2)
library(edgeR)
library(limma)
library(Glimma)
library(gplots)
library(RColorBrewer)
library(pheatmap)
library(ggplot2)
library(ggrepel)
library(pathfindR)
library(scales)
library(data.table)
library(fBasics)
library(forcats)
library(omu)
library(maptools)
library(phyloseq)
library(SpiecEa... |
5199dddc97aeab292067b5bdbe578e0fa4f2b458 | 5ec06dab1409d790496ce082dacb321392b32fe9 | /clients/r/generated/R/ComDayCqDamCoreImplJmxAssetMigrationMBeanImplProperties.r | 80cac3c415330aab3684a7d9e7a4a982bb481564 | [
"Apache-2.0"
] | permissive | shinesolutions/swagger-aem-osgi | e9d2385f44bee70e5bbdc0d577e99a9f2525266f | c2f6e076971d2592c1cbd3f70695c679e807396b | refs/heads/master | 2022-10-29T13:07:40.422092 | 2021-04-09T07:46:03 | 2021-04-09T07:46:03 | 190,217,155 | 3 | 3 | Apache-2.0 | 2022-10-05T03:26:20 | 2019-06-04T14:23:28 | null | UTF-8 | R | false | false | 2,487 | r | ComDayCqDamCoreImplJmxAssetMigrationMBeanImplProperties.r | # Adobe Experience Manager OSGI config (AEM) API
#
# Swagger AEM OSGI is an OpenAPI specification for Adobe Experience Manager (AEM) OSGI Configurations API
#
# OpenAPI spec version: 1.0.0-pre.0
# Contact: opensource@shinesolutions.com
# Generated by: https://openapi-generator.tech
#' ComDayCqDamCoreImplJmxAssetMigra... |
e341c7cb1cd8367b8d274bd40c6ccb9425e7c79d | be935ba1a1e91ba791c424437653b1adea81bbce | /R/write_files.R | 7a7aa1b55216e2ef47a7e18260ef255172149c6c | [] | no_license | sbpost/myco-project | 0f9b751164d7deaaf6ce49f071521cd39b456e8f | c6ec05975f9084167af8a49f3140eac17e17d833 | refs/heads/master | 2023-01-11T12:50:21.062987 | 2020-11-18T09:14:36 | 2020-11-18T09:14:36 | 313,873,903 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 727 | r | write_files.R | write_files <- function(
tbl_ls,
corp_path,
corp_filings_path,
activities_path,
addresses_path,
mortgages_path,
officers_path
) {
# This function just writes the files to .csv. Nothing fancy.
# Corp
write_csv(
tbl_ls$corp_tbl,
file = corp_path
)
# CorpFilings
write_csv(
t... |
c2dc9891cfcc15c79f01e13b2b4b8dcf115517c5 | 6c12225069086e6c544199652ef147c7d7c2e5ba | /spde tutorial functions.R | ada2e56e6232b7a5460a2c2fccfa9e8f012a91cc | [] | no_license | maquins/ewars_dashboard | 237eed007b758b588c7f51a7d11acf006c359461 | edb8c94b7d2d5162d445c0e32e564cac74ccbc9a | refs/heads/master | 2023-06-08T04:26:12.012375 | 2021-06-24T14:38:43 | 2021-06-24T14:38:43 | 287,126,031 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,463 | r | spde tutorial functions.R | ## http://jfly.iam.u-tokyo.ac.jp/color/
## source inla spde book
c7rgb <- rgb(c(80, 80, 90, 95, 35,0, 0)/100,
c(60, 40, 60, 90, 70, 45, 0)/100,
c(70, 0, 0, 25, 90, 70, 0)/100)
rMatern <- function(n, coords, kappa, variance, nu=1) {
m <- as.matrix(dist(coords))
m <- exp((1-nu)*log(2) + ... |
9222d0d26e695e13ba5d5f09c495d4d15b0f5793 | 58fb33dd28029b82fb688bc8ab537354a46b59c1 | /Q1.R | e37ccd91a5198f39095d43648e4c2657985d1101 | [] | no_license | sarveshsuresh/SamplingAssignment2 | 26c6b4ce7ddd6ed801f1bdc5b99921369c786268 | f6a972cedeeca4ae73a76fb99d3e05838189c736 | refs/heads/main | 2023-07-19T20:51:46.703324 | 2021-08-30T09:05:47 | 2021-08-30T09:05:47 | 401,261,807 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,859 | r | Q1.R | sam_size=c()
N=10
bm=c()
bv=c()
pm=c()
pv=c()
nm=c()
nv=c()
bn1m=c()
bn1v=c()
bn2m=c()
bn2v=c()
for(j in 1:100){
n=j*N
print(j)
for(i in 1:100){
sam_size=append(sam_size,n)
binom_sample=rbinom(n,12,0.25)
#binom_sample
binom_mean=mean(binom_sample)
binom_v... |
14cb397ae0d6056034788326eeaf32802f0d1f93 | 387f308b3c2283a1491028dae275c34001292aa4 | /src/session01/example1.1.R | 6f0e6bf697f85d71ebefc343e4b6adc8eae29a2d | [] | no_license | limves/MAIM | 2185c95b318beba2021701dc85fb28312109d8a9 | ba96e77874bbddafd55fa0aa9ea9b21ec65a0cf3 | refs/heads/master | 2020-04-17T06:43:48.632029 | 2016-08-23T04:02:57 | 2016-08-23T04:02:57 | 66,323,690 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,338 | r | example1.1.R | library(genalg)
library(ggplot2)
dataset <- data.frame(item = c("pocketknife", "beans", "potatoes", "unions",
"sleeping bag", "rope", "compass"), survivalpoints = c(10, 20, 15, 2, 30,
10, 30), weight = c(1, 5, 10, 1, 7, 5, 1))
weightlimit <- 20
chromosome = c(1, 0, 0, 1, 1, 0, 0)
dataset[chromosome == 1, ]
... |
2f78bbb725a2478617de98810e2bc846371387af | 928312a96a59805cea732721ffc366202559e007 | /R/build_ssgsea.R | 3dc7086c3088d121e00287c6858b5f29a2921768 | [] | no_license | pujana-lab/systematicBNR | 3c81f9b4818b5281d1b71ba98d1bdaf0f17f5ccb | fa44d30b07300e27dac8141d26a5421d70530fca | refs/heads/master | 2022-11-07T13:03:25.655488 | 2020-06-10T10:49:49 | 2020-06-10T10:49:49 | 269,133,821 | 1 | 0 | null | 2020-06-08T18:10:12 | 2020-06-03T16:04:51 | R | UTF-8 | R | false | false | 470 | r | build_ssgsea.R | #' Perform ssGSEA pipeline for related signatures in defined genome.
#'
#' Encapsulates ssGSEA pipeline at GSVA package
#'
#' @param genome Genome expression object in matrix format, with samples in columns and genes in rows.
#' @param signatures Signatures list object
#' @param ... GSVA ssgsea extra parameters
build_s... |
c54e18467776dc9f7c685b08635a677bc6ba532b | 78b8a059bca8dbb4dedce7699c9b41ea66b22647 | /Shiny app example 1.R | 9bd9b0c7a770394c0d27fdaf42704d04d50c7582 | [] | no_license | uwolanowska1/Advanced-Data-Analysis-course | 40ee13a1a4bfbf486227d98a2230cb346c71a9eb | 792465ec7cbf7284b68c94b1c00c497561475f10 | refs/heads/main | 2023-06-07T05:24:55.346704 | 2021-07-07T12:46:35 | 2021-07-07T12:46:35 | 381,996,808 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,926 | r | Shiny app example 1.R | library(shiny)
library(palmerpenguins)
library(ggplot2)
library(dplyr)
penguins <- palmerpenguins::penguins
colnames(penguins) <- c('species', 'islands', 'bill length mm',
'bill depth mm', 'flipper length mm', 'body mass g',
'sex', 'year')
ui <- fluidPage(
... |
03da88278167e19680f14468ba9bec7e1b385146 | d6ff90166d2f6fbacdb23b8062666b2e122b7ac9 | /data_preprocessing_template.R | 60cac2f1d7c66b4fbadf50d4ce348e9bbf1977e8 | [] | no_license | PSAB/pythonML1 | 5d1db5501c7f453ff1663a5e43eb8ae5e4459f55 | fb7a03343a533dfabbb1a3d7f6f5e670bed48106 | refs/heads/master | 2020-03-12T12:30:49.430382 | 2018-09-18T19:12:10 | 2018-09-18T19:12:10 | 130,620,009 | 0 | 0 | null | 2018-04-23T01:53:49 | 2018-04-23T00:34:43 | Python | UTF-8 | R | false | false | 737 | r | data_preprocessing_template.R | # Data Preprocessing Template
# Importing the dataset
dataset = read.csv('Data.csv')
# Taking care of missing data:
# Pointing to the column "Age":
# ifelse 3 values: condition, value you want to input if
#condition is true, and value you want to input if condition is
#false
dataset$Age = ifelse(is.na(dataset$Age),... |
c6b70d01eb1a1ce5a65665f4d37a34a7c4514878 | ca609a94fd8ab33cc6606b7b93f3b3ef201813fb | /2015/4.EDA-graphics/eda-graphics.R | 8c09cc9034b6877b7e74ca5f81348ddea0e910ee | [] | no_license | rajesh2win/datascience | fbc87def2a031f83ffceb4b8d7bbc31e8b2397b2 | 27aca9a6c6dcae3800fabdca4e3d76bd47d933e6 | refs/heads/master | 2021-01-20T21:06:12.488996 | 2017-08-01T04:39:07 | 2017-08-01T04:39:07 | 101,746,310 | 1 | 0 | null | 2017-08-29T09:53:49 | 2017-08-29T09:53:49 | null | UTF-8 | R | false | false | 392 | r | eda-graphics.R | library(ggplot2)
students=read.csv("E:/data analytics/datasets/students.csv")
dim(students)
summary(students$Sex)
table(students$Level)
with(students, table(Level))
ggplot(students, aes(x = BloodType)) + geom_bar()
with(students, table(Sex, Level))
ggplot(students, aes(x = Level, fill = BloodType)) + geom_bar(positio... |
493aa67f84d3eb058244cba2e06a2719e8a6430e | f604c8b18cd46043a1c5a52b8ae8e82d57ce0ed1 | /configure_template.R | c29222e0537699a6164abf9f31dde52dd1ed4ea0 | [
"Apache-2.0"
] | permissive | rstudio/distill | 0d720ffb8041efae435ac8e3e08beecbd6f9dee7 | ac5e3bf1dca2054a5bf61cfe81b59d7bdb5e3705 | refs/heads/main | 2023-09-01T03:47:43.142431 | 2023-08-28T16:44:23 | 2023-08-28T16:44:23 | 130,758,590 | 323 | 79 | Apache-2.0 | 2023-08-28T16:44:24 | 2018-04-23T21:24:50 | HTML | UTF-8 | R | false | false | 2,166 | r | configure_template.R | #!/usr/bin/env Rscript
library(git2r)
library(stringr)
library(readr)
path <- file.path(tempfile(pattern = "git2r-"), "template")
dir.create(path, recursive = TRUE)
repo <- clone("https://github.com/rstudio/template", branch = "radix", path)
system2("npm", args = c("--prefix", path, "install"))
system2("npm", args = c(... |
aa8e0ec5900137e864f1f000c729bc28315f2e61 | 4d6a64ab3bc0813e6888d58eeff8a809739f826b | /FishBase/man/fb_ecotox.Rd | f6a16ee30d20367cfec40fcc6d167711d70df35c | [
"Apache-2.0"
] | permissive | cornejotux/FishBaseR | 2d54075078238c89887ed8356b878c6f1cb7b900 | 72fbabbda485583fbcf06fb5502593e0f6e7fe7a | refs/heads/master | 2022-12-25T18:03:43.153346 | 2020-09-29T20:13:50 | 2020-09-29T20:13:50 | 299,727,130 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,225 | rd | fb_ecotox.Rd | \name{fb_ecotox}
\alias{fb_ecotox}
\title{
Obtain a list of CL50 by chemical used.
}
\description{
Download, as a table, the list of chemical and their CL50 for the specie. Read the HTMLwebpage, recognize the table and transform it into a Data.Frame object.
}
\usage{
fb_ecotox(idFB, Genus, Species, server)
}
\arguments... |
4bd02b91a0a8f1179e2f4460da3c544b44ae9c60 | af582f033de3521151b72fa326c4bf135a23c651 | /R/spectral-plots.R | f7aa332ead92dc8d5e5f8db3527ee5eb39e90a6f | [] | no_license | mbjoseph/hyperspec-beta | 51f8aac80f3505de7a1e959783d178b8b2a2ae0e | cf40c8059dc24c5775b0d345a8950776a744acd3 | refs/heads/master | 2020-04-06T06:59:12.945261 | 2017-10-02T22:39:49 | 2017-10-02T22:39:49 | 61,833,137 | 0 | 2 | null | 2016-07-13T16:00:55 | 2016-06-23T20:02:43 | HTML | UTF-8 | R | false | false | 4,769 | r | spectral-plots.R | library(tidyverse)
library(ggthemes)
d <- read_csv('data/neon_plants.csv') %>%
select(-X1, -starts_with('site'), -plotid,
-easting, -northing, -taxonid, -pointid, -individualdistance, -individualazimuth,
-starts_with('dbh'), - starts_with('basal'), -starts_with('canopy'),
-starts_with('... |
0420d6c05b66aed2f06ccc0a9741af7f08ae116c | 7759c70261a6f897d2595105f0b137e2ea2c883a | /man/whop.eg.selectOrganism.Rd | 78a108414055722e648e7b90dddef84e69956a22 | [] | no_license | cran/WhopGenome | b0b7ef46183496bf6ab8e3e2a0eb10c7d5ba84d7 | 81b6f858a5c964ea72870f36d787c1db28ce6b6b | refs/heads/master | 2022-02-04T05:14:35.344752 | 2017-03-13T16:10:56 | 2017-03-13T16:10:56 | 17,694,110 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 620 | rd | whop.eg.selectOrganism.Rd | \name{whop.eg.selectOrganism}
\alias{whop.eg.selectOrganism}
\title{
Select the organism to query with subsequent whop.eg calls and load the appropiate database(s).
}
\description{
Select the organism to query with subsequent whop.eg calls and load the appropiate database(s).
}
\usage{
whop.eg.selectOrganism(organismna... |
a4b67e6f6a1dae66551152fbca2e986ce7299511 | 574fba4068d17d46857e4aa3373fce5fab65c121 | /KYTJ.Web/R/Pro/9.4/9.4.R | 1239a0ae1beb91987382508c1d0f1b1cbbd5e55b | [] | no_license | cykb518hu/kytj | 96ad38d942e35257eceb7f09da047b5127f5c3d0 | da952767348304207c2c4f805490f7c0271ce506 | refs/heads/master | 2023-06-10T08:30:30.554410 | 2021-06-29T02:35:36 | 2021-06-29T02:35:36 | 349,309,075 | 0 | 0 | null | null | null | null | GB18030 | R | false | false | 18,437 | r | 9.4.R | args <- commandArgs()
rlib<-args[6] #libary path
if (is.na(rlib)) {output<-args[6]} else {output<-args[7]} #param path
print(output)
#output<-c('D:\\Works\\产品\\科研\\代码\\ScienceStatistics.Web\\Output\\1\\1_1\\636525834691779319')
setwd(output)
d<-read.csv("./data.csv")
p<-read.csv('./Parameters.csv')
a<-p
#参数设定
idata<-... |
9926f8098c673c01f547bd4589168598402c7188 | 7cd8e6ac8097d2ad5811eab2f3688ff22b0a0feb | /man/GetAnyXMLAttribute.Rd | 800ef62cbc7d3595f930ab15b6ed5d28bb2e1054 | [] | no_license | noahhl/r-google-analytics | 400e492011fd096448f7db677f6adaf81094f9f6 | 5c396e1bded0ef00a84c15f000f6fde37d45040f | refs/heads/master | 2016-08-04T15:04:37.911940 | 2011-03-23T15:21:06 | 2011-03-23T15:21:06 | 1,411,707 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 413 | rd | GetAnyXMLAttribute.Rd | \name{GetAnyXMLAttribute}
\alias{GetAnyXMLAttribute}
\title{Function to return the value attribute of the nodes of the parsed XML.}
\usage{GetAnyXMLAttribute(vNode, attr.name)}
\description{Function to return the value attribute of the nodes of the parsed XML.}
\value{The value contained with the XML node.}
\arguments{... |
bcb03246b1ffe6506160c80ee70daf52baed4d2e | 473736973c702e196327e702808fae70b5685c6a | /zeroSum/tests/testthat/test_costfunction_logistic.R | d24d86b38d0cd221faaa226c9eb6c8a8df623474 | [] | no_license | mshardel/zeroSum | c8a93429045d9b3f0063d915cac1efa80190da3d | efbd2e120d6f976b6b131b1bfadd75f2ef4b2c77 | refs/heads/master | 2020-03-22T11:55:25.428712 | 2018-05-24T14:06:31 | 2018-05-24T14:06:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,126 | r | test_costfunction_logistic.R | context("Testing logistic cost function")
test_that( "check whether the R and C logistic cost function yield the same result",{
library(glmnet)
set.seed(10)
## logistic regression test
x <- log2(exampleData$x)
P <- ncol(x)
N <- nrow(x)
y <- exampleData$ylogistic
alpha <- 0.5
lam... |
58d3efbbbb749f22f7a99fedcc8e93b8bf7e173d | 33a743d734d68adb0ac9b05d08ee6e3d2593f7ec | /ui.R | 2bb34e251313898904bed7abe1bba2b251d0641b | [] | no_license | leonaQ620/New-York-Tree | 0ead35b2f381c328dfcfae73c9b71085f3af216e | 6cdbc4396ee273821899e0e411be561a048dd894 | refs/heads/main | 2023-03-16T23:11:09.647997 | 2021-03-09T23:54:50 | 2021-03-09T23:54:50 | 346,171,928 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,908 | r | ui.R | #
# This is the user-interface definition of a Shiny web application. You can
# run the application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(ggplot2)
library(leaflet)
library(shinythemes)
# Define UI for appli... |
fab195f961aaeac0257edd33aa546961282f3955 | 262a347beae2643b62367b59bc45d2a96aa7dbfc | /man/update_segments_data.Rd | fd741943f3d2d9976003c6dcc2146c86141422bc | [] | no_license | BenioffOceanInitiative/whalesafe4r | 18ab7e7875808f2db91f42b62dea7e9eefc2f8f8 | ef4341c0622c9e32ccdd1d45ca3f5ca76b15effb | refs/heads/master | 2022-11-28T02:20:43.257741 | 2020-08-07T01:08:12 | 2020-08-07T01:08:12 | 162,746,601 | 0 | 1 | null | 2020-06-18T19:26:37 | 2018-12-21T18:39:01 | HTML | UTF-8 | R | false | true | 407 | rd | update_segments_data.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/update_ais.R
\name{update_segments_data}
\alias{update_segments_data}
\title{Update AIS Segments Database Table}
\usage{
update_segments_data(segs_data = NULL)
}
\arguments{
\item{ais_data}{}
}
\description{
Creates database connection and wr... |
56eca3d6fb11b23b54a9e185a1afbc02a391bc6f | 8f18bfbe67f75a7f7718c2f1eee031373ad35b97 | /tagPrediction/markov_chain.R | dcb967471249cc75783b03c56e46da2b4e48664f | [] | no_license | jhudsl/fitbit_donation | cbf69e11556cc990e10e97d8f20849fcabd18466 | e24c0e7d8aecb0d8646fedc76a18b40c4dc7f0ef | refs/heads/master | 2020-04-05T12:34:31.365718 | 2017-08-16T17:44:07 | 2017-08-16T17:44:07 | 95,149,868 | 0 | 0 | null | 2017-08-16T16:12:39 | 2017-06-22T19:33:40 | R | UTF-8 | R | false | false | 2,227 | r | markov_chain.R | # Attempting classification with markov chain model
library(here)
library(tidyverse)
library(lubridate)
rawData <- here::here("tagPrediction/raw_data.csv") %>% read_csv() %>% select(-X1)
tagData <- here::here("tagPrediction/activity_tags.csv") %>% read_csv() %>% select(-X1) %>% mutate(start = round(start/60), end = ro... |
d35770b51b8028610554f4ce1ebc843442ba58d7 | a11470a5ca9a46b6d723bfd4aa1c5f40838649d8 | /inTrees_wrapper.R | 31ceb24ae606165ff15f865702d1ae71cb4389eb | [] | no_license | julianhatwell/interpret_basics_auxr | 3c2f9393c291f2e3228e048de3e7d9810217b905 | 7564bf89c02374507ef37edce828311beece1347 | refs/heads/master | 2021-05-10T12:40:59.193357 | 2020-10-24T06:20:40 | 2020-10-24T06:20:40 | 118,448,401 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 705 | r | inTrees_wrapper.R | library(inTrees)
inTrees_wrapper <- function(X_train
, y_train
, model
, ntree) {
# extract rules
ruleExec0 <- extractRules(RF2List(model)
, X_train
, ntree = ntree) # hard limit ... |
ad5de7dfb37a102684656445c517176ec89d110b | 3841ba01f6675cac7278939803560ff990ad7318 | /analysis/data.R | fa1104f0700f7ae18b8f10df1a2cb82b3b44d021 | [
"MIT"
] | permissive | stormxuwz/SeabirdCode | 32a6210a1861e0091cd200a2c433a87451adfb54 | 943c38b0ef0272c04157700ee6ecc2e87f2c2aaa | refs/heads/master | 2021-08-25T10:29:49.251792 | 2019-03-30T16:22:27 | 2019-03-30T16:22:27 | 57,337,298 | 1 | 5 | null | 2017-12-18T07:58:21 | 2016-04-28T22:17:54 | Python | UTF-8 | R | false | false | 4,231 | r | data.R | require(RMySQL)
require(reshape2)
dbConfig <- list(dbname = "Seabird", username="root", password="XuWenzhaO", host="127.0.0.1")
sqlQuery <- function (sql) {
if(nchar(sql)<1){
stop("wrong sql")
}
conn <- dbConnect(MySQL(), dbname = dbConfig$dbname, username=dbConfig$username, password=dbConfig$password, ho... |
59f9bdc01940e44c7a2c056dc290cf64d0c4305e | 4bce1164c09a6a35646c5fb262c495c26d228224 | /R/print.R | 95e5ec2a3e3b9206de36516488e32295486a76c9 | [
"MIT"
] | permissive | USCbiostats/MethCon5 | 1d45c78f5bc044148fd39345a5fcb05753acf57b | 71aae030b648ebca0dbaa2d45385a14ea0ba7a29 | refs/heads/master | 2022-04-06T11:16:33.583390 | 2019-12-20T18:57:46 | 2019-12-20T18:57:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 214 | r | print.R | #' @export
print.methcon <- function(x, ...) {
cat("# Methcon object\n")
cat("# .id:", attr(x, ".id"), "\n")
cat("# .value:", attr(x, ".value"), "\n")
class(x) <- setdiff(class(x), "methcon")
print(x)
}
|
f4fac2c907a776755600ca7894c2e81623459417 | 9b6a37d925d0cc87800bdc017e4d331baf40ef50 | /clarite/modules/analyze/regression/r_code/ewas_r.R | 1f28800f587a9607f3d24c39da975c4fa839a20e | [
"BSD-3-Clause"
] | permissive | HallLab/clarite-python | 921b964894e57578a93abdc9a7394bfd30f937cb | 817ccad90e3773a2f2e85290ea6b2bcaf621bcf6 | refs/heads/master | 2023-07-09T09:05:17.399598 | 2023-07-03T17:47:42 | 2023-07-03T17:47:42 | 183,051,306 | 5 | 3 | BSD-3-Clause | 2021-07-19T14:04:27 | 2019-04-23T16:07:53 | Python | UTF-8 | R | false | false | 27,736 | r | ewas_r.R | library(survey)
# Catch errors from glm and similar, warning instead
warn_on_e <- function(var_name, e){
warning(paste0("NULL result for ", var_name, " due to: ", e), call=FALSE)
return(NULL)
}
# Quote variable names with backticks to account for special characters
quote_name <- function(s){paste0("`", s, "`")}
... |
75c11f6d5f7d0925bcc173072e6beb3c2514cdd5 | e6b2a1c8b46e86551ed2a0711aca72d8b9f361a1 | /homework/tf_answers/codigo/2_analisis.R | 94cc19a4ec006861c8073eb8090f8dd112afcc13 | [] | no_license | JosefaHernandez/dar_soc4001 | e597c2e7795dfaa2e7c1bc0b2950b9578c4e8b49 | a817a119ca0fdf11fe91b81965da4b7c51c2f4bc | refs/heads/master | 2023-07-25T06:07:50.099707 | 2021-08-25T18:30:48 | 2021-08-25T18:30:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,783 | r | 2_analisis.R |
# Tabla Descriptivos
# total país
name_file <- paste0(dirresultados, "tabla_pais.txt")
covid_comunas %>% select(Poblacion, edad, esc,ytotcorh, npers, `2020-06-12`,`2020-11-27`) %>%
as.data.frame() %>%
stargazer(summary.stat = c("n", "mean","median", "sd"),
covariate.labels = c("Población", "Edad", "Escolari... |
e2ca74ad7a0b0a632b280eebb2d156ee1c108ee0 | 8c4a74b0a344440a15a2edee5bb761bcd2dfcad9 | /man/testClosedBelow.Rd | dacbc37b7a9a56e16573efb36dab6609f2715552 | [
"MIT"
] | permissive | xoopR/set6 | 341950b7649629dc9594b9230710df5140679bf7 | e65ffeea48d30d687482f6706d0cb43b16ba3919 | refs/heads/main | 2023-05-22T22:46:30.493943 | 2022-08-27T17:20:08 | 2022-08-27T17:20:08 | 197,164,551 | 9 | 0 | NOASSERTION | 2021-11-16T15:02:05 | 2019-07-16T09:36:22 | R | UTF-8 | R | false | true | 1,016 | rd | testClosedBelow.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/assertions.R
\name{testClosedBelow}
\alias{testClosedBelow}
\alias{checkClosedBelow}
\alias{assertClosedBelow}
\title{assert/check/test/ClosedBelow}
\usage{
testClosedBelow(object, errormsg = "This is not a set closed below")
checkClosedBelo... |
e5ab26e532a89b3e0f7746afeb756457c1916ebf | 0ed9873bfbe30499aeaad03fbdef16204dec8296 | /Stocks and Bonds.R | 8381129244c659a5ee23295ce5d90b283c054be1 | [] | no_license | levineol/stats-r | 85c76dee0d323c7a35e5f237738d0e9192460c4f | 0aa1bd636cb0ed61de8334d2b7487668c224026f | refs/heads/master | 2020-04-07T10:52:52.672856 | 2018-11-19T23:37:45 | 2018-11-19T23:37:45 | 158,303,875 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 859 | r | Stocks and Bonds.R | source("http://jgscott.github.io/teaching/r/mvnorm/rbvnorm.R")
mu_stocks = 0.065
mu_bonds = 0.017
sd_stocks = 0.195
sd_bonds = 0.075
rho = -0.15
returns = rbvnorm(50, mu_stocks, mu_bonds, sd_stocks, sd_bonds, rho)
plot(returns)
Wealth = 10000
Horizon = 40
for(year in 1:Horizon) {
return_stocks = rnorm(1, mu_stock... |
0951110db2fd0248b15ed892f13572f00b478a53 | 897251074da9cac85a547b9e7f380ef06a0a8a98 | /Makefile.R | 486dd92d88be8b63803bb549096d9e40a5c1636f | [] | no_license | rongmastat/stat-627 | a09b43a61ba8ff6a134b8d2505a952c59f71893a | af7897c0c41172e64de4493f6c24ced685cf856e | refs/heads/master | 2021-06-26T21:09:56.565166 | 2016-11-29T13:54:32 | 2016-11-29T13:54:32 | 42,261,670 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 114 | r | Makefile.R | # One script to rule them all
source("00_download_data.R")
source("01_data_analysis.R")
source("02_make_plot.R")
|
535f6c8ed18c87ba6518e102c8631483ebe2d698 | 1aaffc3ad2c374ca5d308fecb92f9f832110d97c | /worksheets/recipe_examples/02_variable_recipe.r | 5853198e58540a64c75209ae83968168e92a08a8 | [] | no_license | bertozzivill/infx572_winter17 | ab3a234a16904faa328da4a82ec16e49c2873b3a | 87547966dadc480a3756a16f780e0fd4f99f68f3 | refs/heads/master | 2021-01-12T03:35:32.212280 | 2019-10-24T05:13:13 | 2019-10-24T05:13:13 | 78,232,320 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 788 | r | 02_variable_recipe.r | ##---------------------------------------------------------
## Blueberry muffin recipe (makes 12):
## 3 cups all-purpose flour
## 2 tablespoons sugar
## 1.5 tablespoons baking powder
## 0.5 teaspoon salt
## 3 eggs
## 0.5 cup butter
## 1 cup buttermilk
## 1 teaspoon vanilla extract
## 2 cups frozen blueberries
##-------... |
c19cffbcda8b3098c57a53563ba6a605542985bf | 8646d753247f7dddea6d73d81422b1ec742c9f5a | /WSGeometry/R/bin2d.R | 463ad22462ff723c7c3ab20b36c2844c96f596e4 | [] | no_license | akhikolla/updatedatatype-list4 | 24f5271c9d2807aca7dc8620b56eb96e740cd3c4 | 0a2a0a46e9fb6e066f30899c4cb3c016ba3a1504 | refs/heads/master | 2023-04-01T20:11:05.484102 | 2021-04-02T01:19:34 | 2021-04-02T01:19:34 | 352,124,197 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,238 | r | bin2d.R | #' Bin data onto a grid.
#' @description Bin data onto a equidistant grid in [0,1]^2.
#' @param data.pos A Mx2 matrix specifying the positions of the data measure.
#' @param data.weights A list of vectors of the same size as the number of rows in data.pos.
#' All entries in the vector must be non-negative and the ... |
86bba6a43b760d48eba033eaf2e43e18c6dea2f0 | b19114efbef9e22421753abd5dfa4520806718c3 | /R/cmd_add.R | 318c5268bb10e50b860ee65e988acb0a4662bd6f | [
"MIT"
] | permissive | mcomsa/ado | 3c2b041a0bd88a22c385ac3d77bad1686b147127 | d403f435eb3f4b5e9f58bfd8dad9df3a4b0eed92 | refs/heads/master | 2020-03-25T12:15:22.350946 | 2018-08-03T14:44:51 | 2018-08-03T14:44:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,563 | r | cmd_add.R | ## Add user-defined commands provided at runtime, rather than defined in this
## package's source.
ado_cmd_addCommand <-
function(context, expression, option_list=NULL)
{
if(context$debug_match_call)
return(match.call())
valid_opts <- c("env", "newname")
option_list <- validateOpts(option_list, va... |
357714ee2ff94ccda77b728df235c03e08b04a91 | 2b9965f115cfb6acc674070dfb974546cc1837cb | /inst/shiny_apps/FRAME/Data_trim.R | ca3c6a1c8c9b6288d66ce2463ccfcd4166df4680 | [] | no_license | tcarruth/FRAME | d378f8fc186f0527710879af9fb351ce3b38fa63 | 268e45f98fe2ab7f27e92be539e87cb78f2b54ca | refs/heads/master | 2021-04-15T16:59:05.658261 | 2019-04-18T14:52:43 | 2019-04-18T14:52:43 | 126,888,989 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,430 | r | Data_trim.R |
Data_trimer<-function(Data){
if(is.na(Data@LHYear)|is.null(Data@LHYear)|Data@LHYear==Data@Year[length(Data@Year)]){
message("Data could not be trimmed, make sure LHYear is less than max(Year)")
return(NA)
}else if(Data@LHYear>(Data@Year[length(Data@Year)]-3)){
return(NA)
}else{
DataT<-Data
... |
5aaa3295aa8d628c2270f8fd4db229e660d0e1b5 | 5a2137abc519deb2c19b19922926a48901d4be62 | /R/otus_correlation_cluster.r | cd93e1d4d49ffbdfb4a3adf37f008f81a53416cf | [] | no_license | markap/PandaPlayground | 92e9cf3a6548887cfef012ecf3fcfcfc0150ac2d | 921e0c857259384f2668107d125c89f7ed862d98 | refs/heads/master | 2021-01-19T03:01:26.073543 | 2014-03-29T18:48:48 | 2014-03-29T18:48:48 | 17,605,690 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 808 | r | otus_correlation_cluster.r | library(gplots)
otus = read.csv('../preprocessed/otus.csv', row.name=1)
cor_otus <- as.dist(1-cor(t(otus)))
h_cluster <- hclust(cor_otus)
X11()
#png("myplot.png")
plot(h_cluster)
savePlot("out.jpg", type="jpeg")
#dev.off()
message("Press Return To Continue")
invisible(readLines("stdin", n=1))
groups = cutree(h... |
8a6514f67a718e07f3eefe692557189c466bad5e | 91be8ed16a4daad36b22acdd172d75a6a1674693 | /run_analysis.R | f43d97397c7d9016ce3ceda9742c0324c81a29ff | [] | no_license | RaymondJiangkw/Getting_And_Cleaning_Data_Project | 3c9b9730f26c2a23f7b6e6ab56bc158ea122c0e6 | 4297b4b2fcb21b38698ae1fa83ab0b1e5af6c8cb | refs/heads/master | 2020-12-26T05:41:38.444208 | 2020-01-31T11:32:04 | 2020-01-31T11:32:04 | 237,404,664 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,775 | r | run_analysis.R | # Getting and Cleaning Data Project from @John Hopkins University
# Author: RaymondKevin
# Date: 2020-01-31
# Do Preparations and Get the Data
library(reshape2)
library(dplyr)
path = ".//src//UCI HAR Dataset/"
Features_Label <- read.table(file.path(path,"features.txt"),col.names = c("index","featureNames"))
Activity_L... |
55712595bbc39623706fd3012ac8ca156bba820f | 4910e6fcaa1556c8916dcd6031d2288201443d9d | /spPlotSampCourse/man/stdizePlots.Rd | d93e76f952a2e8481bd5b19f8dbb53a45ac93aed | [] | no_license | jayverhoef/spPlotSampCourse_package | 80c5537a08a9d7f4f08e17878493f3a65c5272e4 | 107608dcc29ce93c9926112781e955b46dfa6baa | refs/heads/master | 2021-01-20T11:00:20.312211 | 2013-06-05T13:03:23 | 2013-06-05T13:03:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 910 | rd | stdizePlots.Rd | \name{stdizePlots}
\alias{stdizePlots}
\title{Standardize the plot coordinates}
\usage{
stdizePlots(plots, xmean.fix, ymean.fix, xystdv.fix)
}
\arguments{
\item{plots}{as polygons of sp Class SpatialPolygons}
\item{xmean.fix}{mean for standardizing x coordinates}
\item{ymean.fix}{mean for standardizing y coor... |
df966b7cf18c8dc92edf47778d940a643fcc8275 | a53ca7e5df8e663bad100a0aa5bc47c74274ce58 | /R/PrzetwarzanieDanychUstrukturyzowanych/PD3/bitcoin_analysis/posts_questions_answers.R | b771762cb3af9cf24ddd35f1f5302d3141966b70 | [] | no_license | pawel99k/Studia | f4799e41b7468e9a2d3a9493c34fd283b2ee3ed5 | bfd378adcc64d2d47a5e44520edd631901792e6d | refs/heads/master | 2021-06-16T19:32:16.816899 | 2021-04-15T11:11:01 | 2021-04-15T11:11:01 | 191,762,389 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 787 | r | posts_questions_answers.R | source("xmluj.R")
xmluj("bitcoin.stackexchange.com/Posts.xml") -> Posts
Posts1 <- Posts[PostTypeId==1,]
Posts2 <- Posts[PostTypeId==2]
Posts1$ViewCount <- as.numeric(Posts1$ViewCount)
Posts2$ParentId <- as.numeric(Posts2$ParentId)
Posts2$Score <- as.numeric(Posts2$Score)
Posts1[order(Posts1$ViewCount, decreasing = ... |
71aff68397faab3c1d047076892151c48eeb6278 | ae3a01bcafd7b940c15d8edb9b5a4105655d5fe2 | /source_functions/ww_genetic_corr_start.R | 80d2c5eab89365042cce38e19905442604f0f1e4 | [] | no_license | harlydurbin/angus_hairshed | a5c29713c340f839e3fe6b6ae5f831b812555d11 | dc31e4d5bb69945ae41753f494896aacea272133 | refs/heads/master | 2023-03-02T01:42:41.507055 | 2021-02-12T19:00:08 | 2021-02-12T19:00:08 | 276,473,429 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,664 | r | ww_genetic_corr_start.R |
library(readr)
library(dplyr)
library(tidyr)
library(stringr)
library(tibble)
library(glue)
library(rlang)
library(lubridate)
library(magrittr)
library(purrr)
library(readxl)
library(tidylog)
source(here::here("source_functions/hair_weights.R"))
source(here::here("source_functions/three_gen.R"))
###... |
d880ed5f7a565101b544a86d00b7159d33d8dac9 | e120654c5c8380dcd8e993af2720446f2f05b075 | /plot4.R | 9ae4f5cd59792299156f3e927bb04b612267e080 | [] | no_license | robertbounds/ExData_Plotting1 | 971e95f9129d9a4d01844ee840197dc107a7078f | bab28786dbb613c061ee30c59bd177c3102a556f | refs/heads/master | 2021-01-20T19:45:44.348187 | 2014-06-07T17:27:48 | 2014-06-07T17:27:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,468 | r | plot4.R | ### This file, plot4.R, contains five functions:
### (1) GAPplot;
### (2) voltPlot;
### (3) subMeterPlot;
### (4) GRPplot;
### (5) plot4.
###
### functions (1)-(4):
### take required input parameters
### (data, dayVec, and DayIndexVec)
### to accordingly subset data, and create
### t... |
8bae12f0130e0508dca006fada4704e63e959fb9 | b471a4ea59cc3d74d57b8f35564c83e53b5b61f4 | /Project 1/DATA607_Project1_Full.R | 0082ef26877bec19220f25c47e6cb12382a2b42c | [] | no_license | Jennier2015/DATA-607 | 54f95107d89a6c61152e06184c4d6289111df8f4 | ed2722e6fefc481b7f7241d0ed57df1110494daf | refs/heads/master | 2021-01-13T11:17:19.723702 | 2017-03-24T04:02:49 | 2017-03-24T04:02:49 | 81,400,574 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,730 | r | DATA607_Project1_Full.R | # DATA 607 - Project 1 Chess Tournament Results
# Introduction
# In this project, you're given a text file with chess tournament results where the information has some structure. Your
# job is to create an R Markdown file that generates a .CSV file (that could for example be imported into a SQL database)
# with the fol... |
b383f49292224aba7809608c411f5619e44475f9 | 2072f3bd397d10e689da90535ad6774cbcd70840 | /model1_fixtime.R | 469ee9f7b1fe457210277f8c5e520d7448f8dc0b | [] | no_license | mam737/ParentalCurseScripts | 1f08e23aa39ce96a8c90aef8476b6cf2f8f5c3bd | 8f21755b9f1bd149abb9ac27b203ac58bc267894 | refs/heads/master | 2020-03-19T22:49:52.361412 | 2018-12-20T22:59:57 | 2018-12-20T22:59:57 | 136,983,156 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,296 | r | model1_fixtime.R | # Model 1: mito-auto Mother's curse - 11 Mar 2018
# Invasion and dynamics of Mother's curse mitochondrial type with autosomal restorer
# female genotypes AAM AaM aaM AAm Aam aam
# and fitnesses 1 1 1 1+sf 1+sf 1+sf
# male genotypes AAMb AaMb aaMb AAmb Aamb aamb
# and fitnesses 1 ... |
7199491396196b70d3390b1cd8de027eaa6ba062 | 6eddde9b74487719db12c51caefa7a788bcdf04a | /man/VIP.Rd | 7b19f60ecb96aed99ecdf46711b8d085fbaad517 | [] | no_license | uwadaira/plsropt | 79be7e7e91398b78ce4c662caed2cef81fcdd2c5 | b633eaa63257333bd7ee5f64d824e8101f1855c7 | refs/heads/master | 2020-04-12T01:46:42.868932 | 2017-08-15T07:49:58 | 2017-08-15T07:49:58 | 45,820,246 | 2 | 1 | null | 2016-03-30T05:52:14 | 2015-11-09T06:45:12 | R | UTF-8 | R | false | true | 432 | rd | VIP.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/VIP.R
\name{VIP}
\alias{VIP}
\title{Variable importance in projection for PLS regression}
\usage{
VIP(object)
}
\arguments{
\item{object}{a model object}
}
\value{
VIP values
}
\examples{
data(yarn)
yarn.pls <- plsr(density ~ NIR, 6, data = y... |
8526da30c2c448b675bc29e69e3a9cc8f7e531e5 | a867658310e4a922b2d1484ffa5f6f1d532c6ce0 | /grad/bioinformatics-and-genomes/part-2/project.R | c7ae6e19fdf3f258e3a505c37fcf32a002945fd4 | [
"MIT"
] | permissive | positivevaib/nyu-archive | d48174c81bd5ca0fbc5c370fc74cffbce83ecbaf | 6d6aa06bf0303dbb5918a0db4bdba4dad17c5d8a | refs/heads/master | 2023-03-02T04:08:28.096090 | 2021-02-14T06:15:07 | 2021-02-14T06:15:07 | 149,333,963 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,275 | r | project.R | # Load libraries
library(HMM)
library(seqinr)
library(ape)
library(phangorn)
# Q2
# Setup old HMM as discussed in class
states <- c("Exon", "5site", "Intron")
symbols <- c("A","C","G","T")
transProbs = matrix(c('EE'=0.9,'E5'=0.1,'EI'=0, '5E'=0, '55'=0, '5I'=1.0, 'IE'=0, 'I5'=0, 'II'=1.0), c(length(states), length(st... |
18f0fbb7158b7f65a386c368be2843fc2e8bb870 | ff13ebac5f03c26551ef3f508cc37a2033e89107 | /code/demo.R | d802ef5d4b25a9b0491a43d17f1586da581727f3 | [] | no_license | ejosymart/winLossArea | e4f212bfed34c45bb392907e71a776d430f76e96 | d70a06b2a9ca6878928b15086163c3451353c521 | refs/heads/master | 2020-05-02T11:05:31.066361 | 2020-04-09T10:36:57 | 2020-04-09T10:36:57 | 177,917,357 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 893 | r | demo.R | library(raster)
library(SDMTools)
source("code/joinRaster_functions.R")
#Junta raster y suma valores
JR <- joinRaster(files = c("data/Binario_Presente.asc",
"data/Binario_2050.asc",
"data/Binario_2100.asc"), newValue = 14)
JR
#Grafico
cols <- rev(ter... |
04b6359131bc51695c05f2bb19c0ed265c8e517e | b0b54c7b80ff22fbb7d3910c7f4eaa0fa5e970c1 | /check_top_ld.R | c202e6de5f9e666bf4cd91c5901d09160ff9df0e | [] | no_license | yumifoo/finemap | 7880e572fcf333fb1b458eeb65110aeb81dcc5ef | 9d3132c3683c2409ab8d5249e062bd8ceac387fc | refs/heads/master | 2020-09-24T18:33:45.759851 | 2019-12-11T12:08:11 | 2019-12-11T12:08:11 | 225,817,684 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,252 | r | check_top_ld.R | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
trait <- as.character(args[1])
chromosomes <- 1:23
need_merge <- c()
for(ii in 1:length(chromosomes)){
chr <- chromosomes[ii]
if(file.exists(sprintf("/fs/projects/ukbb/yu/BOLT_basicQT_agesq/%s/sex_combined/chr%d/ld/top_snp_sex_combined_win_1mb.ld",tra... |
7d4d93bb4f4bb2507e05ed036177ef1692719dc0 | c981caf103a3540f7964e6c41a56ca34d67732c4 | /R/plausible.value.draw.R | 9aa768d859ce633637a463dd51bac2f4516a8b13 | [] | no_license | alexanderrobitzsch/miceadds | 8285b8c98c2563c2c04209d74af6432ce94340ee | faab4efffa36230335bfb1603078da2253d29566 | refs/heads/master | 2023-03-07T02:53:26.480028 | 2023-03-01T16:26:31 | 2023-03-01T16:26:31 | 95,305,394 | 17 | 2 | null | 2018-05-31T11:41:51 | 2017-06-24T15:16:57 | R | UTF-8 | R | false | false | 1,853 | r | plausible.value.draw.R | ## File Name: plausible.value.draw.R
## File Version: 0.15
plausible.value.draw <- function( data, X, beta0, sig0, b=b,
a=rep(1, length(b) ), c=rep(0, length(b) ),
theta.list=seq(-5,5,len=40), pvdraw=1 )
{
# recode missings
y <- data
y[ is.na(data) ] <- 1
respind <- 1 - is.na(d... |
2d750999d1a461a331e6032328a7e48ab94397f8 | aa5daf106a59917da72a67aafe2901f2cc7882db | /scripts/5-transfer-to-s3.R | 3ad7df99d939abac05526ff53362c2647d304ec3 | [] | no_license | brittany-durkin/covid-neighborhood-job-analysis | 752574dd4e705456bd4c5132109b84a926fb84dc | b9dd0bdaa552c3a11147b8ab0197c69c385db903 | refs/heads/master | 2022-04-22T02:20:01.799767 | 2020-04-18T14:58:24 | 2020-04-18T14:58:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,739 | r | 5-transfer-to-s3.R | # Transfer necesssary files from local computer to S3
library(aws.s3)
library(tidyverse)
#----AWS Setup--------------------------------------
# read in AWS secret keys
secret_keys <- read_csv("data/raw-data/small/secret_keys.csv")
# set keys
key <- secret_keys$`Access key ID`
secret_key <- secret_keys$`Secret acces... |
3248b769ab8c60ff21a8434dae9a4f687624d98d | c0222d0bd4a9815fa66bf57bb6d817197c0d7558 | /Scripts/plots_for_prez.R | ad8c2f4b493f2d6ad667e9f801306dc83f3842d5 | [
"MIT"
] | permissive | achafetz/AIL | 6d2df590ab33b6a8cc92472acc55fabf81086a46 | 988b907f1ac6adf0f9f7779efe81fb53fff052dc | refs/heads/main | 2023-04-14T18:29:44.988692 | 2021-04-28T01:17:47 | 2021-04-28T01:17:47 | 360,957,412 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,066 | r | plots_for_prez.R | # PROJECT: AIL
# AUTHOR: A.Chafetz | USAID
# PURPOSE: charts for Sheperd Center Talk
# LICENSE: MIT
# DATE: 2021-04-23
# UPDATED: 2021-04-27
# DEPENDENCIES ------------------------------------------------------------
library(tidyverse)
library(glitr)
library(glamr)
library(extrafont)
library(sca... |
372112bc469c9be4f6132b6a08599fd4294d4a30 | 29de3b9256ca8eb59b74203c7392d29939f161b5 | /ui.r | 027aebb2e6d267e8a4eb7ebad96625fdc78302af | [] | no_license | mratliff/iris | 24db14d133e60dc4d9dc68c9f0627206234988ff | d078b8aaa0106e1c4c85e302bd951729e8404bda | refs/heads/master | 2020-12-30T14:56:03.842708 | 2015-01-08T20:55:13 | 2015-01-08T20:55:13 | 28,984,161 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 796 | r | ui.r | library(shiny)
shinyUI(pageWithSidebar(
headerPanel("Predict me an Iris!"),
sidebarPanel(
h3('Input Values'),
numericInput('slength', 'Sepal Length (4.3 to 7.9)', 5.8, min = 4.3, max = 7.9, step = .1),
numericInput('swidth', 'Sepal Width (2 to 4.4)', 3, min = 2, max = 4.4, step = .1),
numericInput... |
3c19848f40a299a342618e4ff9a3f9d24901fb02 | 1d7fb86444c74f5dcf6f0ff19b0c5b986f2b38eb | /R/test_run_as_long_as.R | 0e22d0a48f3fab2485eb53117ada666b451fef9c | [] | no_license | benjaminguinaudeau/hideR | da303a7be3139efa8cccf7c6ea5934e813d4d0cd | 39de37f596f828a9fd84b38cc562e0eb2e903d6e | refs/heads/master | 2020-05-14T18:33:32.105765 | 2019-12-19T15:24:03 | 2019-12-19T15:24:03 | 181,911,260 | 5 | 2 | null | 2019-12-19T15:08:44 | 2019-04-17T14:42:38 | R | UTF-8 | R | false | false | 215 | r | test_run_as_long_as.R | #' second_modulo
#' @export
second_modulo <- function(x) round(lubridate::second(Sys.time())) %% x
#' expr
#' @export
expr <- rlang::expr(map_lgl(1:3, ~{cat(".") ; Sys.sleep(1) ; return(second_modulo(5) == 0)}))
|
552758bb0e6288a4dc4c261724d2b51ff88b6269 | f5f88607d563579112b0b07503898e139e8f40be | /textanalysis/src/plot_generator.R | b4b2b3629fb4638548fc3ba2ac18ca468dab1a9e | [] | no_license | Costax/text_analysis | 9bc0f765e28337656a83d0de31333c310c10f8a6 | 5f98f9c81b59bd844169797ec859b0ff8963e0de | refs/heads/master | 2021-01-22T06:27:55.295159 | 2017-05-26T23:37:50 | 2017-05-26T23:37:50 | 92,555,843 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,168 | r | plot_generator.R | #
# Author: Adan Hirales Carbajal
# Email: adan.hirales@cetys.mx
#
plot_generator <- function( freq = 10 ) {
# Get command line arguments
args = commandArgs(trailingOnly=TRUE)
# Parse arguments (basename & dirname)
inFile <- args[1]
path <- args[2]
outFile <- basename(inFile)
outFile <- s... |
4e3d2462bda0efcb1d282ebac47b004d4fd29f5c | c5faa9a2e350978662624f73725eb7ee02c55cb0 | /man/RGData.Rd | 6244d2f5cd9ec2aa575deaf29b4cd11dbea3d79c | [] | no_license | HenrikBengtsson/aroma | 341cc51ddd8f9c111347207535bfe2a85ea7622a | c0314ea003fb1d99d0db7f314e86059502d175c6 | refs/heads/master | 2016-09-05T18:24:56.275671 | 2014-06-19T04:13:11 | 2014-06-19T04:13:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,230 | rd | RGData.Rd | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Do not modify this file since it was automatically generated from:
%
% RGData.R
%
% on Tue Jan 15 18:36:16 2008.
%
% by the Rdoc compiler part of the R.oo package.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
220ae470562ec1209d90e984e2626a45457c069d | 0b30a3f1d338e8e10c9192aa8b1709d5174aaa16 | /data/data_carpentry/generate-tidy-adaptation-data.R | 4f3a1228e4ff4146201d1e99b953de85289f6f5f | [] | no_license | mikoontz/ppp-adaptation | a5495a3817bfdb3683b10da82fcfa1eb436701a5 | 0133fc8935c0054b5d5d1112f28dcae605a9d59d | refs/heads/master | 2020-04-05T00:51:04.578014 | 2018-11-06T16:59:40 | 2018-11-06T16:59:40 | 156,412,890 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,782 | r | generate-tidy-adaptation-data.R | # Title: generate tidy adaptation data
#
# Author: Michael Koontz
# Email: mikoontz@gmail.com
#
# Date Created: 20150411
# Last Updated: 20150414
# This function takes the entered Tribolium flour beetle data from the "Eco-evolutionary consequences of multiple introductions" adaptation experiment (which is in long for... |
77ec9ab686a71ff7bcaf4010f884c1acb3aaecaa | 527160365d8a10036149ed196f5aa1ff04fe6f14 | /models/increase_in_sqft.R | ad263f736e46c497babf66baca73c5d8256a3da9 | [] | no_license | SumedhSankhe/DS5110Project | 4907c84510f5cb049f50343df9d28f7309a2139f | 5c8a62ab454f49b2a157b3dc8d72c2f3a95707c8 | refs/heads/master | 2020-04-20T10:16:11.715101 | 2017-12-11T00:51:09 | 2017-12-11T00:51:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,365 | r | increase_in_sqft.R | model.data <- read_csv("models/model_data.csv",
col_types = cols(Latitude = col_skip(),
Longitude = col_skip(),
STRUCTURE_CLASS = col_skip(),
X1 = col_skip(),
... |
eea5d5c00f031044f2a273c614efaf76306b7b1f | 52193a50c81771fb2186cc55e27a23d7e07cc9b8 | /R/GetSongsArtistsID.R | 615dc1d41cc2c9899097db1c86f2fb1fcd15b73f | [] | no_license | epmrio/AutomatedGeniusR | 7258c8a7f8864301e59f72656c44db07ab4df6f2 | 1607f4ab9fe2fdbafb135dc179fcf0d8eb20478b | refs/heads/master | 2021-05-21T19:02:32.717739 | 2020-10-31T17:52:21 | 2020-10-31T17:52:21 | 252,762,970 | 0 | 0 | null | 2020-10-31T17:52:22 | 2020-04-03T14:56:02 | R | UTF-8 | R | false | false | 3,688 | r | GetSongsArtistsID.R | #' Scrap Artists and songs information from ID
#'
#' This function allow you to get the information aboute artists and songs from an artist ID from Genius API. You need to have package geniusr installed
#'
#' @param x A list of artists IDs
#'
#' @return A dataframe
#'
#' @examples
#'
#' \dontrun{
#'
#' ## Get a dataset... |
a427c86869197e841582f5c655f15bd6a23ecab1 | 69288158e4f6663f0828dc99d17db2aa3bda6284 | /scripts/analyses_R/old/positive_selection.R | b9c51ab580ab6b75b8edea35df192185dd60034d | [] | no_license | wbglizhizhong/DNA-methylation-signatures-of-duplicate-gene-evolution-in-angiosperms | 6ec00301db157100be21e83301d47e5af811464f | a76269d01277ddb0986f705fb7efef986d40981a | refs/heads/master | 2023-04-29T06:24:45.870065 | 2021-05-18T14:56:35 | 2021-05-18T14:56:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,350 | r | positive_selection.R | library(ggplot2)
library(scales)
species = c("Aduranensis","Aipaensis","Alyrata","Athaliana","Atrichopoda",
"Bdistachyon","Boleracea","Brapa","Bvulgaris","Cclementina","Cpapaya",
"Clanatus","Cmelo","Crubella","Csativus","Egrandis","Eguineensis",
"Esalsugineum","Fvesca","Fxananassa",... |
0379f042491da69bdbbbb730a8b1211d4d012f49 | 107f1f46755f354c35f66a11bcfca2d71ec088e6 | /Optimization/NelderMead.R | 35bb9b9f743de99d8f94ab787543ec829f9bbc19 | [] | no_license | AngelPone/SCalgorithm | a0db81da45014bbbd02ae046f6cbd2e8326e87c9 | 65538ccac1b32cafcd406e432ae942bbcce779eb | refs/heads/master | 2020-05-26T20:09:27.563866 | 2019-05-24T05:35:56 | 2019-05-24T05:35:56 | 188,358,678 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,498 | r | NelderMead.R | NelderMead <- function(func, ps, aim = 0, param = list(alpha = 1, gamma = 2, rho = 0.5), plot = FALSE, plot_bg = NULL){
#### Library Requirment####
library(dplyr)
library(animation)
#### Sub Functions ####
judge_1 <- function(ps, p, aim){
if(class(p) == "data.frame"){p <- as.vector(as.matrix(p))}
if(a... |
f0cdc5624a176e6943cb7153ffef99733f776a42 | 5bbe298693fd1f49b5367d3dff473108cf82770f | /permutation.R | b0680608071163a06c94f38292e985f59321eb14 | [] | no_license | percylinhai/stat545 | f61fcd129aad4706af8c58ddc87280092bea6bc0 | 13c40f85b90c7f69a8986fb98b9fb8d18f851d2a | refs/heads/master | 2020-05-27T01:03:46.953584 | 2019-05-24T14:05:46 | 2019-05-24T14:05:46 | 188,432,071 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,535 | r | permutation.R | library(ggplot2)
dat<-read.table("~/Study/study resources/STAT545/Project/Transcripts/Split_Transcripts/sample12.txt",h=T)
Trump<-dat%>%filter(Identity==0)
Clinton<-dat%>%filter(Identity==1)
null_dist_trump<-NULL
null_dist_clinton<-NULL
for (i in 1:1000){
b<-sample(Clinton$Broadness,1)
d<-sample(Clinton$demonstrative... |
6977e4dec1b5ff651f1d34ba5fc48f2b7902e968 | 01d5318b66d8b7fe8e9942e4c5171725c01f0400 | /ui.R | eba15c69c14c0758a57834538f4838162f7df853 | [] | no_license | sannpeterson/find-genes | c64ec396a4494d427a83dcee6f60d80d2c3b8fab | d9c3c20831141ca998c56727d7cb7ad9c76add86 | refs/heads/master | 2020-09-09T10:26:43.868815 | 2019-08-14T16:40:00 | 2019-08-14T16:40:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,033 | r | ui.R | ## app.R ##
library(shinydashboard)
library(shiny)
library(plotly)
library(DT)
jscode <- '
$(function() {
var $els = $("[data-proxy-click]");
$.each(
$els,
function(idx, el) {
var $el = $(el);
var $proxy = $("#" + $el.data("proxyClick"));
$el.keydown(function (e) {
if (e.keyCode == 13) {
$proxy.click();
}
});
}
);
});... |
f13264384027bd1285408a4b0dac5cc95f24417b | a407fbe5b374e7639c3608ebb420d9a62675a3ba | /man/car.Rd | 2ff79d71a4ecd1377bb04ec556be8ff511967d05 | [] | no_license | Barardo/FFTrees | c0528d0312d1db5c293ce8fe168ba6e17f484f64 | 2444c3b1c9228041d04eac4a6d9058807a3edea7 | refs/heads/master | 2023-04-16T01:05:01.451355 | 2021-04-28T16:03:44 | 2021-04-28T16:03:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 565 | rd | car.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/car_doc.R
\docType{data}
\name{car}
\alias{car}
\title{Car acceptability data}
\format{
A data frame containing 1728 rows and 7 columns
\describe{
\item{buying.price}{Numeric}
\item{maint.price}{Factor}
\item{doors}{Factor}
\item{pers... |
9ada50094f9dfba49830594739d7a77604cecbe3 | 00ac235cefd57ee0316cece5c5c3163d24f04809 | /4Neutal model-classlevel/vine-t1-C101-random/0Neutral model.R | 590125305a4eb697ca9f36a2a9f31bd1cacebc65 | [] | no_license | wangtingting0104/HolobionT-Dataset | d7cfa6d098cd25e51705b47c974fae2d1f12f95c | 04fcb2814e36d07260fec0a9a60de296dbc1f4bd | refs/heads/main | 2023-04-09T15:39:21.900658 | 2022-11-01T18:33:38 | 2022-11-01T18:33:38 | 560,533,697 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,782 | r | 0Neutral model.R |
library(dplyr)
source("Neutral model.r")
metacommunity = read.csv(file ="0vine-t1-c101-pool.csv" ,header=TRUE,sep=",",row.names = 1)
metacommunity[1:10,1:10]
metacommunity = t(metacommunity)
otu = read.csv(file ="0vine-t1-c101-otutable.csv" ,header=TRUE,sep=",",row.names = 1)
otu=t(otu)
design = read... |
307f933c57cbacd45e2de6c34277a2abde9025fe | e1466f0fb923fa17fb98d005d632d7deb8aba44f | /Risk Measures vol.2.R | fff06451ffa1129c42b958720dcd316c33a72b97 | [] | no_license | LuckyJoni/R_finance | 00b8aa6a7477b83ff9f9b8f7822394a6f2af1de6 | 80f51012c805fb28e7991c74f0e2790a12507886 | refs/heads/main | 2023-04-08T02:03:36.797100 | 2021-04-01T16:34:23 | 2021-04-01T16:34:23 | 351,757,413 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,640 | r | Risk Measures vol.2.R | #============================================================================
# RISK MEASURES
#============================================================================
# load libraries
library(PerformanceAnalytics)
library(quantmod)
library(mvtnorm)
library(mnormt)
library(MASS)... |
fae31e266f4a77a0a92c5d482eadd3660b1eff5c | c4297fe5e9fb1c3a959aa1e5e39f0568467880e5 | /Rproject_MW.R | 81be9968447adf9a4b61f299e2a0e349170749fd | [] | no_license | maccwinter/Octubre- | fa5b796f7cd1efb5bcdda48221ca3c1072c73092 | a2c6a0b781e57e68a318387e224ba5452f76218f | refs/heads/master | 2020-08-08T08:47:04.324282 | 2019-12-07T00:03:12 | 2019-12-07T00:03:12 | 213,796,831 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 94 | r | Rproject_MW.R | #R Project
library(tidyverse)
st <- read_csv('sampling_stations.csv')
head(st)
names(st)
|
ca0402e1014d0f6836c44e9d2d7b38df93f5c2fd | e98ef98470a5d3c99c97f90273e1208e63d2ccf2 | /setup.R | aec72aff8961b8e3c4d38da6b9729d8205120698 | [] | no_license | Sta523-Fa14/esmxy | d4003f8eedb903a354d535f4cc006831d0b7f958 | 64f97f95c22d7aa88227628a8b7033a37d54be9a | refs/heads/master | 2021-01-18T17:17:58.493088 | 2014-12-11T22:10:00 | 2014-12-11T22:10:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 505 | r | setup.R | toLoad=c('dplyr','stringr','rgdal', 'rgeos','rnoaa','RNetCDF',
'ggplot2','alphahull','igraph','prevR','gridExtra','usdm','nlme','MASS')
for(lib in toLoad){
if(!(lib %in% installed.packages()[,1])){
install.packages(lib, repos="http://cran.us.r-project.org") }
library(lib, character.only=TRUE)
}
di... |
eec6e272366068dc4109a8c6ec4b59aa1af4b830 | a3de160e7678a050597d8bff7527eda267e3b84f | /model/reg.pca.fn.R | 08fc1e7ab5095f78796c9b3aa3ee79a38cf17f0b | [
"MIT"
] | permissive | salmuz/rcep | 8bcd54c495773940b2e7e7bc43cae4f7970db106 | e68872ad00efc77be56aff52a438515a529bdee7 | refs/heads/master | 2021-01-21T12:58:26.955761 | 2017-05-19T12:59:43 | 2017-05-19T12:59:43 | 91,804,346 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,025 | r | reg.pca.fn.R | # library dependance for computing the first composante
reg.pca.regularization <- function(sample, nb.comp = 1, n.setting = 10,
cv.seed = 87, cv.k = 4, idx.y = 1, setting.max = 1,
n.iter = 200 , ...){
set.seed(cv.seed)
cv.segments <- cvsegments... |
e708dc470cc2a12b60b5e270f99766d5f17c2c2a | 719df495394b36568d5a426d24973cd43c784d33 | /calendar_plot.R | ed7f8c2c194f1e176883fbab9a89e32215e19882 | [] | no_license | RomanKyrychenko/library | dd4d5a1aacd87cbaad341d92c625f2641e24bce3 | f38cb1c5c05f75493b343ed0c77f73c8e6266213 | refs/heads/master | 2021-07-15T00:33:38.965753 | 2021-03-02T12:11:55 | 2021-03-02T12:11:55 | 77,147,372 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,784 | r | calendar_plot.R | sbd <- bigset %>% group_by(trans_date) %>% dplyr::summarise(am=sum(amount))
hh<- seq(as.Date("2015-09-01"), as.Date("2017-05-16"), by="days")
'%!in%' <- function(x,y)!('%in%'(x,y))
hh <- data_frame(
trans_date=hh[hh %!in% as.Date(sbd$trans_date+3600*3)],
am=NA
)
sbd <- rbind(sbd,hh)
sbd <- sbd %>%
mutate(year = a... |
8f2031408fe8b857e2209be77adb75bad74efc68 | 1fb4f8ed4a8822b970ffb145566add7a917e41d3 | /Data example/Data Example.R | bb6e253148102246fbec0ce33db659fdedf10413 | [] | no_license | fuweiboy1988/Gabriel-CV | 95591b80a3defdfdc2ad8427a5f416efeb6cb9ab | 0f0fca78c8f59e1a63ece32c7469a5de99118f4d | refs/heads/master | 2021-01-19T15:25:59.840680 | 2017-04-13T23:39:06 | 2017-04-13T23:39:06 | 88,214,143 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,515 | r | Data Example.R |
library("bcv")
library("devtools")
library("cluster")
library("e1071")
library("mclust")
library("MASS")
library("nnet")
load_all("../lib/fpc")
load_all("../lib/NbClust")
#source("../lib/NbClust.R")
source("../code/classify.R")
source("../code/cluster.R")
source(".... |
6f5123773c1d8a65bcfef43735db564510908ac5 | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/2675_0/rinput.R | 09c7f01b159eeb856b18161d65db0b2ba8629697 | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 135 | r | rinput.R | library(ape)
testtree <- read.tree("2675_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="2675_0_unrooted.txt") |
ca66b0bd42f24edfec43b80d8d08523ed92040a3 | b2fac096bb0d923dd26108a55da002df76cc6a48 | /eda.R | ef833409629d9a63ee247265a69daad9101548a9 | [] | no_license | lianna1016/three_pointers | 6f2d489905a9b05616398de63dc4a924ca30b20d | 47de54d3ac4c0cafd470ef9f9a64dad86cc63654 | refs/heads/master | 2022-03-16T11:25:33.052429 | 2019-12-12T21:10:05 | 2019-12-12T21:10:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,812 | r | eda.R | # Jess, Anna and Seth Project EDA
# 11/29/19
source('styleguide.R')
source('helpers.R')
source('packages.R')
source('cleaner.R')
# https://cran.r-project.org/web/packages/segmented/segmented.pdf
# Read in Clean DF
df.clean <- add_time("complete_data_clean.csv")
df.tourney <- add_time("tourney_data_clean.csv")
names(d... |
8831c882e03c493fcf679f8ef696cf0df7fe22c2 | 82e0ed3cb65ba4b68cd15557888a9e47ae7b79f9 | /plot3.R | c6fc4c586b06d13426410fbf7757a3846dd52fe5 | [] | no_license | Kraev-Anatol/ExData_Plotting1 | a19f3f732f2537af0ae3ac987f1071b3beaea4b2 | b0e87ad97f3e66a7457c3ceacbd873e7454c0a6b | refs/heads/master | 2020-05-25T20:31:24.288740 | 2019-05-22T06:40:00 | 2019-05-22T06:40:00 | 187,976,909 | 0 | 0 | null | 2019-05-22T06:31:41 | 2019-05-22T06:31:40 | null | UTF-8 | R | false | false | 1,025 | r | plot3.R | path <- getwd()
household_power <- data.table::fread(file.path(path, "exdata_data_household_power_consumption/household_power_consumption.txt"), na.strings = "?")
# Selection of the database for dates: 1/2/2007 and 2/2/2007
feb_power <- subset(household_power, household_power$Date == "1/2/2007" | household_power$Date... |
5f98fda02cefcfbd1f7b37c4a6c0fb0006faf1b1 | 0eb0513c136e513d6dd25a2d5cf12706ae1c6d74 | /plot1.R | 1f8663256ef393c59b9ea2dfb570d854e6d3b7b6 | [] | no_license | ycaesari/ExData_Plotting1 | 715cbf8388a94f708d34ea381ce2d16ee6bd884d | cdb4facc6bed1e14a11d0be6999c268dcee9e66f | refs/heads/master | 2021-01-22T12:38:39.344686 | 2014-11-09T20:34:53 | 2014-11-09T20:34:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 805 | r | plot1.R | # Create a png file - Global Active Power
# Read the data from file and filter out the relevant dates
chosen_days <- c("2007-02-01","2007-02-02")
consumption <- read.csv("household_power_consumption.txt", sep=";", header=TRUE)
consumption$Date <- as.Date(consumption$Date, "%d/%m/%Y")
consumptionOfDays <- consumption[c... |
e5375945f52857aa77f0c849c5dfba9ce4b968c9 | 842de311d4fe188f6bd1c384327dfa0fd71f6531 | /NIO - Stock Movement.R | 3654df81d09acfe6e55f318174bc90f382a03091 | [] | no_license | ltheod01/stock---time-series-analysis- | 8bc98c6d3e9c28acb83ccccd7788bf0729ca08c0 | c2c62e394e6ce7536997fd8f2bc8bc0dccc0de23 | refs/heads/main | 2023-06-03T19:23:51.865461 | 2021-06-21T15:14:30 | 2021-06-21T15:14:30 | 378,973,219 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,995 | r | NIO - Stock Movement.R | library(devtools)
library(tidyquant)
library(crypto)
library(ggplot2)
library(tseries)
library(zoo)
library(dplyr)
library(xts)
options("getSymbols.warning4.0"=FALSE)
options("getSymbols.yahoo.warning"=FALSE)
# ===============================================================================
#We first co... |
6517866ab6ad0408bb3314bb2ffc2f15e452883d | 237a5f9cc3fdb2fbe496273f43751e335881577d | /protein/toy_data/src/50_31_2019/05_30_2019.R | 8209dea8d82a7445fcdcf17ff255eecf4f09a65c | [] | no_license | popejonpaul/maxquant | 81c433eeb172b954b3c13e03fcdcf13a5dc2f589 | 279af6fff748f23c2d3e6a309278f9d7c6c56b3f | refs/heads/master | 2020-06-19T08:40:31.665210 | 2019-07-12T22:28:44 | 2019-07-12T22:28:44 | 196,645,939 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,289 | r | 05_30_2019.R | library(seqinr)
library(tidyverse)
library(gtools)
fasta <- read.fasta("toy_data/uniprot-proteome_UP000002494+reviewed_yes.fasta",
seqtype = "AA")
protein_groups <- read_tsv("toy_data/proteinGroups.txt", guess_max = 20000) %>%
{set_names(., gsub(" ", "_", names(.)))}
peptides <- read_... |
7c91eaa76083518e8148d80d232965a66af97ebb | 39d64d0beb81dfb22dde788ee62027a6604eed68 | /dendrograms.R | a5d8644326c7427a28e713c19d9817aa7aab4091 | [] | no_license | sdavison88/phylogenetictree_IMDB | 6c90c1007448e50940e468ed6925324f21e479fb | 9802a73001f21e57c4a6d465a89e12e8264cc13c | refs/heads/master | 2020-05-18T14:24:51.494798 | 2017-03-07T21:17:30 | 2017-03-07T21:17:30 | 84,246,917 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,629 | r | dendrograms.R | library(rJava)
library(tidyverse)
library(xlsx)
library(cluster)
library(ape)
library(dendextend)
library(dplyr)
library(tidyr)
library(tibble)
library(splitstackshape)
movie_metadata_csv <- read_csv("~/Downloads/movie_metadata.csv.zip")
library(ggplot2)
library(ggthemes)
library(party)
library(earth)
movies <- movie... |
6a0c5ed2c32c478d233c25573a4eae65b1f8b97b | ab272ba3abdc98ab1536dbd0e534e2f1d252092d | /speed.R | eae2eca552b891c3f4ccbd3f20d9e962ebab8220 | [] | no_license | hagr1dden/hagr1dden | 2a1538c4d6d7c8e792c565ca7099fa2d3ebefdca | c3ca954ac8eca8268cb3ea7e60b06dcb95da92ac | refs/heads/master | 2021-01-13T03:43:37.988188 | 2018-05-01T07:40:32 | 2018-05-01T07:40:32 | 77,278,658 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,255 | r | speed.R | library(trackeR)
library(data.table)
library(isofor)
library(ggmap)
library(e1071)
#testing
runDF <- readTCX(file = '/home/evgeny/My_Route(1).tcx', timezone = "GMT")
#runTr0 <- trackeRdata(runDF)
#runSummary <- summary(runTr0)
#plotRoute(runTr0, speed = FALSE)
#print(runSummary)
#plot(runSummary, group = c("total", "... |
ebcbc5eed68de902626820644121cb78877e203f | eeea10b971ed75bf87305d7b4163cf355eac1240 | /RF LOWESS sim/Simulations/SimPrac.R | 93b56bfda2f3eddd3e1ebcffc18565572994f0a2 | [] | no_license | AndrewjSage/RF-Robustness | 42e0caa6cc5c1f46031f6a3b77e33a56dc4fc83b | bace62de6a191832c1a9d19462c140686a15bf1b | refs/heads/master | 2022-11-21T12:09:04.041716 | 2020-07-24T04:52:51 | 2020-07-24T04:52:51 | 106,871,057 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,231 | r | SimPrac.R | setwd("/work/STAT/ajsage")
library(RFLOWESS)
library(parallel)
#Apply iteratively using parSapply
# Calculate the number of cores
no_cores <- detectCores() - 1
# Initiate cluster
cl <- makeCluster(no_cores)
clusterEvalQ(cl, {
library(RFLOWESS)
})
clusterSetRNGStream(cl, 03142018)
RL1 <- parSapply(cl=cl, X=1:5, si... |
a947b09127c120b889c24bc25bd571e6930294ab | 1716005639cc0dd03d482645a8c8239673a407f7 | /R/getPredictionVariables.R | 34a16fad98063cb33025811062db2f2e09cb633d | [
"LicenseRef-scancode-public-domain-disclaimer",
"LicenseRef-scancode-warranty-disclaimer"
] | permissive | jlthomps/surragateRegression | 23d0b0aed009185f2fce65712dd504ec76fe1f58 | 60085337c6bc035439ed24eb7f44a717b698bc40 | refs/heads/master | 2021-01-17T23:25:31.325136 | 2013-08-23T16:29:07 | 2013-08-23T16:29:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 695 | r | getPredictionVariables.R | #' getPredictVariables
#'
#' Returns a character vector of prediction variables.
#'
#'@param DTnames column names of DT dataframe
#'@return predictVariables string predict variables based on column headers
#'@keywords predict
#'@export
#'@examples
#' DTComplete <- DTComplete
#' UV <- UV
#' predictVars <- getPredictVar... |
522118d8eac74aedf9edbca447102ff3add76ee9 | d6875904f4d9fe89fe2536741f7b979e251323f9 | /FinalPtj/Brown-FinalProject-121519.R | ad1f4666526dfde9a80005b6f5852520125d1986 | [
"MIT"
] | permissive | diedrebrown/Fa19-Info640-WDB | adafb417a931ab0c41ffad4ff66e62d4997450c3 | 33876f32fbbaf25214b9e89b63886edc644580f0 | refs/heads/master | 2022-12-06T15:48:19.052522 | 2020-08-29T15:51:32 | 2020-08-29T15:51:32 | 205,041,411 | 0 | 0 | null | 2020-08-29T15:51:33 | 2019-08-28T23:33:51 | R | UTF-8 | R | false | false | 17,371 | r | Brown-FinalProject-121519.R | ####PROJECT TITLE AND CONTACT####
#Diedre Brown | dbrow207@pratt.edu
#INFO 640 Data Analysis | Pratt Institute
#Final Project
#Text Analysis of Lewis Carroll's Alice in Wonderland
#15 December 2019
####LOAD PACKAGES AND LIBRARIES####
#install.packages("tidyverse")
#install.packages("ggplot2")
#install.packages("ggthem... |
156907ccc8f9154bae7b1b4d321d5d27aac61e47 | b5efcab211ce6d512ef5d17c5715e767d5de2165 | /analysis/monteCarlo.R | 1be249605da3ca6345c045bac669a720affba29c | [] | no_license | MBrouns/Zipfs-Law-and-city-development | 152f5d83b735726068c574bc4b5703b32848812a | 63ab9c7cf4e1095cbaa4812925af11148f0b9b67 | refs/heads/master | 2021-01-02T08:14:27.496860 | 2015-02-15T15:13:27 | 2015-02-15T15:13:27 | 26,484,266 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,845 | r | monteCarlo.R | # This code is used to perform a monte carlo analysis on the zipf's law Netlogo model
# rf variable importance
# Analysis setup
storeAllValues <- TRUE
noOfReplications <- 10
runsToDo <- c(1:10)
runName <- "EV-testing-households"
seed <- 1338
nl.path <- "C:/Program Files (x86)/NetLogo 5.1.0"
model.path <- "C:/Users/Ma... |
ac6f64becbd566b281cfc6463e73a300ed67f672 | 5bd83f74cd2c7e88c0b56e25d3b9b415dcb18c06 | /man/hux_pretty_numbers.Rd | 1145178ddfd99d3a1f8ccfabea523c113bec7240 | [] | no_license | meerapatelmd/chariotViz | 123c04e6fc6b09b2ffdc9ef1eb9fa94d227ee846 | c45947a963b23f75237fe4417dd03b6f27c620d5 | refs/heads/master | 2023-07-19T21:44:18.089969 | 2021-09-04T17:30:09 | 2021-09-04T17:30:09 | 394,073,446 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 616 | rd | hux_pretty_numbers.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hux_pretty_numbers.R
\name{hux_pretty_numbers}
\alias{hux_pretty_numbers}
\title{FUNCTION_TITLE}
\usage{
hux_pretty_numbers(ht, ..., big.mark = ",", scientific = FALSE)
}
\arguments{
\item{ht}{PARAM_DESCRIPTION}
\item{...}{PARAM_DESCRIPTION}... |
6b63ab6d294eb43ed594f2ef00883cad5f6e3e30 | 763d01cdd0313411ea5b88c28dcf4ab7ad43d8f9 | /projects/33/bh-cleanup.R | c891fd334ec20ee46845e2856077ff09e4baf7af | [
"MIT"
] | permissive | NuriaQueralt/BioHackathon-projects-2020 | bbc82a25d7eeafb5bb264802480b7830d787ee7c | a52d2ccaa9883aa904f60ae772272ac1354396a8 | refs/heads/master | 2023-02-22T19:36:30.506935 | 2021-01-24T19:13:32 | 2021-01-24T19:13:32 | 292,899,379 | 0 | 0 | null | 2020-09-04T16:45:08 | 2020-09-04T16:45:08 | null | UTF-8 | R | false | false | 7,826 | r | bh-cleanup.R | #continue with result from bh-apicalls.R script
#or read the txt file with ENA sequences
#brpossibles = read_tsv("brpossibles.txt")
brpossibles = br3
#filter meisenburg and different, longer BR acronyms
brf = brpossibles %>%
filter(!grepl("Meisenburg",specimen_voucher),
!grepl("[A-Z]BR",specimen_v... |
ef96a12b03b2dc5172391f91d19950625f2f565b | 86e31fb088f45fe875977dee91fbeb7bdb819706 | /man/mkd_lm_results.Rd | 1c905dc5ee718877109c52a762f28f31c429a355 | [] | no_license | freuerde/puzzle | 74ab078008971735308453dfad443c7787b4321b | 8d07a59debdaf4c1f83193746e8732daa92088fa | refs/heads/master | 2022-04-28T22:20:32.194741 | 2022-03-11T05:51:15 | 2022-03-11T05:51:15 | 161,799,720 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,048 | rd | mkd_lm_results.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mkd_lm_results.R
\name{mkd_lm_results}
\alias{mkd_lm_results}
\title{Creates a markdown document showing the linear regression results}
\usage{
mkd_lm_results(
lm_list,
dat = NULL,
mkd_path = NULL,
open = FALSE,
overwrite = TRUE
)
}... |
8e54ae5a1b158e45b56e789ef24f8b519a52803e | d86dc658f9e948c83a2bd0015a9412916830bbdf | /binomial/man/bin_variable.Rd | ad9f72bb715fbc76bb5076d197c509341bf97fbc | [] | no_license | stat133-sp19/hw-stat133-kejunzhou123 | 40fe9b286b6edb964e5434f4f1455d65b4a3a625 | f32a357833025470f8a19a1a4b860e4aba52c8b7 | refs/heads/master | 2020-04-28T18:20:16.608993 | 2019-05-02T21:47:15 | 2019-05-02T21:47:15 | 175,475,255 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 480 | rd | bin_variable.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/binomial.R
\name{bin_variable}
\alias{bin_variable}
\title{bin_variable}
\usage{
bin_variable(trials, prob)
}
\arguments{
\item{trials}{trial numbers}
\item{prob}{success probability}
}
\value{
a list containing trials and probability
}
\des... |
1a0741b9bccfb747535834b99c1b7d1501341c28 | 214b0eafe04af176044ac6bf157fa56b2cfc08da | /R Programming/Week 1/Forms of the Extract Operator in R.R | 18122e5d2dd6c9654dfa64eaca935846758e7587 | [] | no_license | jonathanecm/Data-Science | 534be2fbbdf145f10f1ea785fa54d42ce6c8017b | bb7628e815bc1f29ffa796644c09809fc393d618 | refs/heads/master | 2021-04-09T08:13:34.501426 | 2016-07-09T20:57:33 | 2016-07-09T20:57:33 | 60,739,408 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,015 | r | Forms of the Extract Operator in R.R | # Forms of the Extract Operator in R
## Extracting elements form a vector.
### Extractor ([]) used to extract content from vectors, lists, or data frames.
####Using [] with direct refering of elemets.
x <- 16:30 # vector definition
x[13:15] # Extracting the last thre elements (13 to 15) from the vector.
#### Usi... |
58507bc7755da29bd2d1fe4b6e10da25f3735bd1 | de33a793a74752ddb4baa657f59869d9c56161e0 | /R/shiny_vector_filter_numeric_few.R | 5ad9800b198f244e52fd30007f8c22e019825deb | [
"MIT",
"Artistic-2.0"
] | permissive | MayaGans/IDEAFilter | e2eb8aa1f1779e7cfd2bf0a36c83269a40577627 | c81cc1f988bd74e3588f60946c2fcb39ba2f5d04 | refs/heads/master | 2022-11-07T19:56:18.293838 | 2020-06-02T03:14:22 | 2020-06-02T03:14:22 | 251,663,396 | 2 | 2 | NOASSERTION | 2021-08-07T15:07:46 | 2020-03-31T16:27:25 | R | UTF-8 | R | false | false | 3,014 | r | shiny_vector_filter_numeric_few.R | #' A vector filter for numeric variables with only a few choices
#'
#' @param input requisite shiny module field specifying incoming ui input
#' reactiveValues
#' @param output requisite shiny module field capturing output for the shiny
#' data filter ui
#' @param session requisite shiny module field containing the... |
1bf967e4b055b1100fa883b6924624ab0e9921ca | ef3651d556d9f397eeb978b0f36934a7443b7cbe | /Project 2 final code.R | 4d9ccbe4313fd5bc689bde487b48a82a1f9bda75 | [] | no_license | meet-chauhan/R-analytics---Customer-Contract-Renewal-Analysis | 4c893a2ac14775ad72ef5760fd1816a73e615d19 | a0b832df07e70eb818da52744b93b74dece5edca | refs/heads/master | 2020-05-14T15:16:49.222804 | 2019-04-17T08:37:17 | 2019-04-17T08:37:17 | 181,849,628 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 9,158 | r | Project 2 final code.R | library(parallel)
setwd("C:/Users/meetr/Desktop/Fall 2018/R Analytics INSY 5392/project2")
d<-read.csv("final_data.csv")
#d<-d1[sample(nrow(d1), 15000), ]
#t = d$t
#number_of_Cases = d$number_of_Cases
#number_of_escalation = d$number_of_escalation
#number_of_response_missed = d$number_of_response_missed... |
1a79076ee2d509eb0cc2ff1ca0d9572e3f6edd6e | 84c27ec545e7a5e9448d95c0676b882317fafd7c | /R/MxSE.R | 44768306e9004bc06a60a2f91b3aaac32aa2ca00 | [] | no_license | OpenMx/OpenMx | ac58c848b4ce63079c79ccad13f972d81c90d348 | cbe1c3207453b92efc96b4fc37205cbe231dda27 | refs/heads/master | 2023-08-24T11:01:53.655345 | 2023-08-20T20:30:35 | 2023-08-20T20:30:35 | 4,393,940 | 86 | 50 | null | 2023-09-01T01:57:08 | 2012-05-21T13:38:34 | R | UTF-8 | R | false | false | 8,298 | r | MxSE.R | # Copyright 2007-2020 by the individuals mentioned in the source code history
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
# ... |
89c6bba3997a3151d6fc5d17f97ad5cd05b9dbaa | 93d3f810a4169d7bd993641e6f776af1616dd79e | /utils/r_tidy_utils/Docker/bin/combine_tabular_files.R | ea7526708317ffe88f58aed56944115e5b349637 | [] | no_license | CRI-iAtlas/iatlas-workflows | 699c3b897e580a391e9700b48b91b64e2a03eb55 | d94151d3aaadee96b52f27e4ce84692c0366fe75 | refs/heads/develop | 2023-05-24T11:08:56.155005 | 2023-05-23T15:21:58 | 2023-05-23T15:21:58 | 156,773,901 | 2 | 4 | null | 2023-05-22T21:19:12 | 2018-11-08T21:54:27 | Common Workflow Language | UTF-8 | R | false | false | 864 | r | combine_tabular_files.R | library(argparse)
library(magrittr)
library(purrr)
library(readr)
library(dplyr)
parser = ArgumentParser(description = "Combine multiple tabular files into one.")
parser$add_argument(
"-f",
"--files",
type = "character",
nargs = "+",
required = TRUE,
help = "array of tabular files to combine")... |
96ac0f7db154d142705f006cc4de282e56b131da | 8724910530b7c5d927ed5019738545c3208a93e6 | /shinyUI(pageWithSidebar(.R | e9e81ec507fe4a09cf3b693cba5cde48861bbf0a | [] | no_license | saraabi/DevelopingDataProducts | 51907d407b4c8756579aa459ce1e7546f25c103e | ca3300bca40196e979b85fb9df53f9068b249185 | refs/heads/master | 2016-09-06T17:11:40.811317 | 2015-07-16T00:16:20 | 2015-07-16T00:16:20 | 39,167,358 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 326 | r | shinyUI(pageWithSidebar(.R | library(shiny)
shinyUI(pageWithSidebar(
headerPanel("Is this number prime?"),
sidebarPanel(
numericInput('input1', 'Input any integer between 2 and 10,000', 0, min = 0, max = 10000, step = 1)
),
mainPanel(
h2('You entered'), verbatimTextOutput("oid1"),
h2('This number is '), verbatimTextOutput("primeorno")
)... |
274ba9e60149a60d3690b6d772ebf842dde8fca7 | 4bc5ec512acba81ae288b2fe67b5c63558b51646 | /inst/test/code/select-3.R | 15f7a4955a9d0a4a4e57ddf53c10e3f7d9fb5745 | [
"MIT"
] | permissive | eshaimran/mario | 5e54c3e28e9a25cb23b946315e4e1d80dceb243e | 8ba99dbe607e322212493ded7ebdb172994f5206 | refs/heads/main | 2023-08-29T02:25:20.231413 | 2021-11-06T00:21:25 | 2021-11-06T00:21:25 | 425,271,666 | 0 | 0 | NOASSERTION | 2021-11-06T14:58:56 | 2021-11-06T14:58:55 | null | UTF-8 | R | false | false | 145 | r | select-3.R | library(dplyr)
mt <- mtcars %>%
slice(1:5) %>%
select(mpg, cyl, disp, hp)
mt %>%
select(-cyl, mpg, hp, disp) %>%
select(mpg, hp, disp)
|
86ed5ba50e584153a8e9aeb26d5afb9a877e3d33 | c2f842c35192068e91ec37e55b3ae17fef589ca0 | /plot3.R | 76950d28b3e63b42039db7e90b619962014bd1bb | [] | no_license | behnam8011/ExData_Plotting1 | 9600803f1503778719c364394669e794cdae76b1 | 238d537c80350f96580eb26571d98c355737080a | refs/heads/master | 2021-01-17T23:18:09.564134 | 2016-03-05T05:01:52 | 2016-03-05T05:01:52 | 53,150,023 | 0 | 0 | null | 2016-03-04T16:42:51 | 2016-03-04T16:42:51 | null | UTF-8 | R | false | false | 1,171 | r | plot3.R | setwd("C:/Users/Behnam/Documents/Ben/Analytics Training/Data-Science-Coursera/Exploratory-Data-Analysis/Week-1/")
df <- read.table(file = "./household_power_consumption.txt", sep = ";", header = TRUE, stringsAsFactors = FALSE, nrows = 10)
colclass <- sapply(df,class)
df <- read.table(file = "./household_power_consump... |
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