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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
188c6f08f11c4ddcbcc8b7ec3f4c14e9399b9b20 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/morse/examples/ppc.reproFitTT.Rd.R | d48338d4beec6965d67f8be9b1f4e8bee4038d5f | [] | 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 | 561 | r | ppc.reproFitTT.Rd.R | library(morse)
### Name: ppc.reproFitTT
### Title: Posterior predictive check plot for 'reproFitTT' objects
### Aliases: ppc.reproFitTT
### ** Examples
# (1) Load the data
data(cadmium1)
# (2) Create an object of class "reproData"
dataset <- reproData(cadmium1)
## Not run:
##D # (3) Run the reproFitTT function ... |
d72ae9150e0844d8f197957857cc55be2f6ef845 | 38b161f15f0d6ca60e386ffbb1e37f91806d944a | /man/pptx_content_dimensions.Rd | 9b9547c1d3d0acd97575ab3b5a7d87f2da4c0731 | [
"MIT"
] | permissive | MusculusMus/pptxtemplates | 51c871077023ec5afaa77481e4ba8496502b933c | fab50ccdc9ec1caa5494dd307e5781397b057b4e | refs/heads/master | 2023-07-02T18:54:10.146636 | 2021-08-07T22:40:12 | 2021-08-07T22:40:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 395 | rd | pptx_content_dimensions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pptx_content_dimensions.R
\name{pptx_content_dimensions}
\alias{pptx_content_dimensions}
\title{Extract Content Dimension from pptx document}
\usage{
pptx_content_dimensions(file)
}
\arguments{
\item{file}{Filepath to pptx document}
}
\value{... |
7916d909bab6c03b12996cd21aadd0d0394d4de0 | e1434311fdd51d20e15eb3ae26aa261e712b58f3 | /man/coreOTUModuleUI.Rd | 42e332a82c57416843975c9fd0aa465253b38b5a | [] | no_license | tseanlu/PathoStat | 9b88c1ebd0d893e8581e5d664999ceb32d3880f1 | f346fbaff12faba2e2f603c7019be51a4bb56815 | refs/heads/master | 2021-04-28T08:10:40.365224 | 2018-03-06T18:23:26 | 2018-03-06T18:23:26 | 117,146,929 | 1 | 0 | null | 2018-01-11T19:59:05 | 2018-01-11T19:59:05 | null | UTF-8 | R | false | true | 1,124 | rd | coreOTUModuleUI.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/coreOTUModule.R
\name{coreOTUModuleUI}
\alias{coreOTUModuleUI}
\title{UI function for Core OTU Module}
\usage{
coreOTUModuleUI(id, label = "Core OTUs")
}
\arguments{
\item{id}{Namespace for module}
\item{label}{Tab label}
}
\value{
A \code{\... |
6f5575b7065db076d9a23187c05b2bd7e2341dd1 | b4210f37c0f781a5caf535700d58f937e4cb1a99 | /server.R | 0b241dbb2fffff321f8d0672d40a257191d7bdfd | [] | no_license | michaelpboyle/DevDataProducts | 7bfcedbbbcded8aec20ac688033cb7ef5536ef7f | 1dd9340b1bef92a189f67c1f0d8cccefc5b18630 | refs/heads/master | 2020-04-06T04:34:01.974414 | 2015-07-12T02:11:09 | 2015-07-12T02:11:09 | 38,947,007 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,258 | r | server.R | ## Load "Shiny" package
library(shiny)
## Load "cars" dataset
data(cars)
## Initialize "Shiny Server"
shinyServer(
function(input,output) {
## Render output objects for use in User Interface.
## Render plot, subsetting speeds from user input.
## Add mean and median lines, and legend
... |
173dfe97182672b070cbf6bd0c60106a3c919cb3 | ceeff04aac64c2f6d3a4b54c3c4af9eabf08efb3 | /man/getPosterior.Rd | ba8775033b1eac5a1b2aa220a7d51d2336da4dbc | [
"MIT"
] | permissive | suleimank/bmsr | d1c7a0b616ed06fb52975c71de9e7f960336d5fc | e69a764b3cf9f7076e28169f10ba22f49cfadde8 | refs/heads/master | 2021-07-02T14:46:51.027450 | 2021-03-07T02:32:52 | 2021-03-07T02:32:52 | 344,309,622 | 0 | 0 | MIT | 2021-03-05T22:48:58 | 2021-03-04T01:08:31 | C++ | UTF-8 | R | false | true | 472 | rd | getPosterior.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bmsr.R
\name{getPosterior}
\alias{getPosterior}
\title{baseline function to get posterior}
\usage{
getPosterior(file = NULL, out)
}
\arguments{
\item{file}{is the stan file name containig the stan code.}
\item{out}{is trained STAN model.}
}
... |
77a7373d48a95385720736b963e0669cf4ace0c8 | ee7a448d0cdfe0478a82ba713e1cc6a5c9e0db4a | /03_analysis/02_main-analyses/99_run-all/02_run_demographics.R | 17fde754a69301331bced9b5245d008e93f97e6d | [
"CC-BY-4.0"
] | permissive | gpwilliams/levenik | ce46b00534b564280ff4f1023e69c2082d842344 | 2eeeb6a6a96849e408f1152ac3391b6caaffedf5 | refs/heads/master | 2021-08-06T08:39:03.528594 | 2021-07-28T15:46:01 | 2021-07-28T15:46:01 | 165,866,366 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 262 | r | 02_run_demographics.R | # Run Demographics ----
# Prepares data and saves demographic output.
message("Preparing data.")
# run and save demographics to a list
source_files(data_preparation_source_files)
# save demographics
saveRDS(
demographics,
file = demographics_output_path
) |
0348eebd8918fdf2696149dae7de79d6567cd71a | 9ba2a529bb7f60b0841e5236bedbcbcab3fd69ea | /preprocess_1.R | 204c3b3a55c00c7893833b8dc53d289655039db6 | [] | no_license | effat/csc-591 | 0504d0bd911b80bf36698328630468565d750540 | f3cb24d9dfe9690eb3c683cf4f6bb085f3cd610e | refs/heads/master | 2021-01-20T10:32:17.417844 | 2019-11-19T22:39:50 | 2019-11-19T22:39:50 | 101,643,671 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,943 | r | preprocess_1.R | library(zoo)
working_dir<-"\\afs\\unity.ncsu.edu\\users\\e\\efarhan\\csc-591"
### .txt files downloaded from Datashop
file1<-"student_problem.txt"
file2<-"student_step.txt"
### concatenate path location to filename
input_stdProb<-paste(working_dir,"\\",file1, sep="")
input_stdStep<-paste(working_dir,"\\"... |
b0abb9639adf7ce3eb54ad6759a1994602677ca4 | b00ec2c60f0eb44104ad1ccf22f8794e5082fdc0 | /paper-1.R | bca02bc3a44fcf2b458fb72984fa3d7a21c634ca | [
"MIT"
] | permissive | dwwood1981/NOAA-migration | 77224370ed3547ae962f68e8f4ef9b8c3fde5807 | e5aa0dc59639ba4e1d86de7bc51bdd530a12cf2a | refs/heads/master | 2021-04-28T21:31:50.567555 | 2016-09-04T21:21:57 | 2016-09-04T21:21:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 277 | r | paper-1.R | # ---- Start --------------------------------------------------------------
net_migration <- readRDS("1-Organization/Migration/netmigration.rds")
noaa_event <- readRDS("0-Data/NOAA/events.rds")
# ---- Manipulation -------------------------------------------------------
|
8de53dd0c4cc404f97073f18e4c3da061647d770 | be77b7f49c0e3abbd0da77dc419e88d66a8492a7 | /man/cluster_map.Rd | 7fd6ad62e8ae5aed2dc6672ae82a4b3244ff2449 | [] | no_license | kaneplusplus/basket | daba90e9a16ab116845143654dd3a9c7551c1257 | a665c37a79a193dbc8af33fb40f750b12abefaf4 | refs/heads/master | 2023-07-19T00:32:23.013485 | 2023-07-17T15:42:01 | 2023-07-17T15:42:01 | 152,809,160 | 6 | 2 | null | 2020-02-06T03:35:32 | 2018-10-12T21:24:35 | R | UTF-8 | R | false | true | 770 | rd | cluster_map.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/accessors.r
\name{cluster_map}
\alias{cluster_map}
\title{Get the Clusterwise Maximum A Posteriori Probability Matrix}
\usage{
cluster_map(x)
}
\arguments{
\item{x}{either an exchangeability model or basket object.}
}
\description{
MEM analys... |
b878bc70c010ba5bca03dc7f5f5f1d4079c5999f | 64a3cb5a0bb2b325e98a3785f630b9e710b7cf95 | /Cases/IV National City Bank/code scaffold.R | 9690b4da7a72275b73db156ebe418822423061fd | [] | no_license | shorton278/Harvard-Data-Mining | 9d471b8969fa1e5a765175de0da8d411f13966de | e994f4461039418c02be97872fb9b4941cccc9c9 | refs/heads/master | 2022-12-08T18:34:40.545750 | 2020-09-04T19:07:04 | 2020-09-04T19:07:04 | 295,265,592 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,747 | r | code scaffold.R | #' Case II Supplemental
#' TK
#' 4-30
# Libs
library(dplyr)
library(vtreat)
library(caret)
# Wd
setwd("/cloud/project/cases/National City Bank/training")
# Raw data, need to add others
currentData <- read.csv('CurrentCustomerMktgResults.csv')
newDataSource <- read.csv('householdVehicleData.csv')
# Perform a join... |
366f90d313b35383353acf2b0d1b45b9c339d300 | 966ea3714f1b7d3d63bb3c38b4d59d8c06814f92 | /04_spring_2015_project/project_submission/lab4-utils.R | 091ba2457b8d2f3aa1234c51213457229c541b34 | [] | no_license | alexherseg/analytics_projects | 96066a8fc1d347e1f1ead3652cc55b594facf83f | cf65494a03d6665755bd056d13b3f677673f51a3 | refs/heads/master | 2020-07-03T03:12:22.462425 | 2016-12-04T23:28:33 | 2016-12-04T23:28:33 | 74,202,732 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 445 | r | lab4-utils.R | # Lab 4 Utilities
# Plot GA networks
# Just a regular plot, but with men blue and women pink
# Assumes "sex" attribute
gaplot <- function(gr, names=TRUE)
{
nlist <- rep("", vcount(ga.gr))
if (names)
{
nlist <- V(gr)$vertex.names
}
plot(gr, vertex.color=c("#8888FF","pink")[1+(V(gr)$sex=="F")],
verte... |
f4a49ba8a1b3dc4b1103cc32fe7ea3adf8d40b71 | af901bc01d668ecd411549625208b07024df3ffd | /man/is_dictionaryish.Rd | 96ba7adb6ffbd24f398b9e186cacd5f2ee5674a3 | [
"MIT",
"BSD-2-Clause"
] | permissive | r-lib/rlang | 2784186a4dafb2fde7357c79514b3761803d0e66 | c55f6027928d3104ed449e591e8a225fcaf55e13 | refs/heads/main | 2023-09-06T03:23:47.522921 | 2023-06-07T17:01:51 | 2023-06-07T17:01:51 | 73,098,312 | 355 | 128 | NOASSERTION | 2023-08-31T13:11:13 | 2016-11-07T16:28:57 | R | UTF-8 | R | false | true | 349 | rd | is_dictionaryish.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/attr.R
\name{is_dictionaryish}
\alias{is_dictionaryish}
\title{Is a vector uniquely named?}
\usage{
is_dictionaryish(x)
}
\arguments{
\item{x}{A vector.}
}
\description{
Like \code{\link[=is_named]{is_named()}} but also checks that names are ... |
e4de7f376feb8f448c3544307e7f1080dfde8580 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/rstackdeque/examples/without_front.rdeque.Rd.R | 2f6afd12b480de99eead972917acaa430a7a48e3 | [] | 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 | 346 | r | without_front.rdeque.Rd.R | library(rstackdeque)
### Name: without_front.rdeque
### Title: Return a version of an rdeque without the front element
### Aliases: without_front.rdeque
### ** Examples
d <- rdeque()
d <- insert_front(d, "a")
d <- insert_front(d, "b")
d <- insert_front(d, "c")
d2 <- without_front(d)
print(d2)
d3 <- without_front(... |
b71787e428fc6b3142716fc5e8357c1d5a94037e | afb8f2a156447f5bedcb3b2b31931ab2f72d4396 | /venn_exemple.R | 623c34c9823d5e5735b83ae5df2cbc982b132f5a | [] | no_license | ESHAMATHUR/Summer_KEYS | 5b479505903abd2365443a225cf0f10283378166 | 87567db86919d78d7748c92b582b55186c008407 | refs/heads/master | 2022-11-07T00:02:19.542565 | 2020-07-08T21:52:08 | 2020-07-08T21:52:08 | 275,210,860 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,079 | r | venn_exemple.R | library(dplyr)
library(tidyverse)
library(stringr)
library(VennDiagram)
library(RColorBrewer)
#+++++++++++++++++++++++++
# Import data
# and cleanup
#+++++++++++++++++++++++++
file <- "../Raw_Data/comparing_vibrant_virsorter.csv"
comp_data <- read_csv(file)
#+++++++++++++++++++++++++
# Venn diagram
# VirSorter/Vibran... |
efffb84943eb0608359384ea55754eb018b70d40 | d164e285eda74b1463decaa356756c23215534ee | /R/act_raw.R | 96cf7477ffeffe28117cfb2341f5db6d54fccd2f | [] | no_license | duju211/heartbeats | 3ad97088498dcb4d125b8deece448f69485c42d3 | 4c0fbbff3e28861f6b021e983e23ee7f094ea0db | refs/heads/master | 2023-05-04T17:29:12.688272 | 2021-05-23T12:50:44 | 2021-05-23T12:50:44 | 324,873,907 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 239 | r | act_raw.R | act_raw <- function() {
board_register_github(repo = "duju211/strava_act", branch = "master")
df_act <- pin_get("df_act", board = "github")
board_disconnect("github")
df_act %>%
filter(has_heartrate) %>%
clean_names()
}
|
9c4b0938e28409d01e38201648992805e04ffc1b | c698dbe65e7522a7e4103d8e92bde5bcef7734a4 | /app.R | 5af390a9b59308e950e2b7d1f7757de674d908c7 | [] | no_license | fionazhang94/Dissertation | 96c669c77fa2e5e61bb37c9c0071355f4b83140e | 2541a831e616afe59314c384f91ca7c5dc558f28 | refs/heads/master | 2020-06-12T14:19:37.282955 | 2019-08-21T14:35:11 | 2019-08-21T14:35:11 | 194,327,234 | 0 | 0 | null | 2019-07-30T19:31:57 | 2019-06-28T19:56:53 | Jupyter Notebook | UTF-8 | R | false | false | 1,444 | r | app.R | library(shiny)
library(dplyr)
library(broom)
library(readr)
# https://www.kaggle.com/uciml/pima-indians-diabetes-database/downloads/pima-indians-diabetes-database.zip/1
diabetes <- read_csv("diabetes.csv")
ui <- shinyUI(
pageWithSidebar(
headerPanel('diabetes k-means clustering'),
sidebarPanel(
selectI... |
283c7b99c342d3c4d4ad8b8b8d53960c05dd0eb4 | a061885a00a2c99e6c830c9f98cc5594b6aed1da | /plot2.R | 61af93cc84ff8e95bebeb1f7553f3c43ecc78806 | [] | no_license | chamathka25/ExData_Plotting1 | f9b5a12090762dc8daf2cbc042b9919e1c88ee83 | 3d92f586073d5e3bd8ac89d79e7a9a06883a127d | refs/heads/master | 2022-11-22T20:03:02.042958 | 2020-07-23T08:13:27 | 2020-07-23T08:13:27 | 281,833,247 | 0 | 0 | null | 2020-07-23T02:44:38 | 2020-07-23T02:44:37 | null | UTF-8 | R | false | false | 581 | r | plot2.R | mydata=read.table("./household_power_consumption.txt",sep=";",header = T)
data=subset(mydata,mydata$Date=="1/2/2007" | mydata$Date=="2/2/2007")
View(data)
names(data)
data$Date=as.Date(data$Date,format ="%d/%m/%Y")
data$Time=strptime(data$Time,format ="%H:%M:%S")
dim(data)
data[1:1440,"Time"]<-format(data[1:1440,"Time... |
b2bd308bbf21bae1e7b39bc2fe433f5aafe926c3 | 570abc2b93f05cbce92d95f6b9bffbe48708bb6c | /R_source/p_j.R | 4521d2219bfbb982a64389a44e3c48fd5fbeef98 | [] | no_license | Danhisco/artigo_mestrado | ed871edab0e89e28a5569668a670ea43d3f548e3 | 1ff761608ea7312c533ef216b51fd67fb8268b00 | refs/heads/master | 2023-02-03T00:15:42.720220 | 2023-01-30T02:13:25 | 2023-01-30T02:13:25 | 121,779,367 | 0 | 1 | null | 2018-11-01T11:05:29 | 2018-02-16T17:28:00 | HTML | UTF-8 | R | false | false | 1,066 | r | p_j.R | library(ggplot2)
library(gridExtra)
load("/home/danilo/Documents/dissertacao/dados/resultados_DaniloPMori.Rdata")
pdf(file="~/Desktop/p_J.pdf")
par(mfrow=c(1,2))
hist(df_resultados$N, # N
col="chartreuse4",
border="black",
prob = TRUE,
xlab = "indivíduos",
main = "J",
ylim=c(0, 7.748e-... |
df0a9b9d2bc278e233a5f792ca83b30d8587af89 | 29e74b9b3a5a3228d5de27090f5b0e6728d77ee7 | /70207_Lab7.R | 1657d43a686939999dcfd037802beabf41a1743c | [] | no_license | typark99/TeachingLab_ProbabilityStatistics | c6e9f64122d809d51ace55b3618330ee8e9cef2d | 9bd8d2397c73bfa73916a1304aaebdc310eb1334 | refs/heads/master | 2022-11-22T11:26:39.672726 | 2020-07-26T15:57:23 | 2020-07-26T15:57:23 | 282,683,309 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,205 | r | 70207_Lab7.R | #####################################
# File: Lab7.R
# Author: Taeyong Park
# Summary: Comparing Three or More Means
######################################
#####################
#
# Population Means
#
#####################
# Chemitech compares three methods used to produce filtration systems.
# To this end, Chemite... |
77ee8da649283c2f9f08620c5a00d9b23f8387e2 | 094f81c31a3cfd560b24280e476d5af4fb52b9e3 | /R/findviolation.R | cbd1f1ec5fdf5a95b82c97a23e6a58e304d9cfd2 | [
"MIT"
] | permissive | PJOssenbruggen/Basic | 6c2343dcb135cb364d059160925ded5cb43b5455 | 1885fa40d3318cc554b4dd80154b263baef19ac4 | refs/heads/master | 2021-01-25T11:57:19.583401 | 2019-01-04T13:03:32 | 2019-01-04T13:03:32 | 123,449,454 | 0 | 0 | null | 2018-03-05T12:26:55 | 2018-03-01T14:56:48 | R | UTF-8 | R | false | false | 1,512 | r | findviolation.R | #' \code{findviolation} determines if a zone violates the safe headway rule.
#'
#' @return \code{findviolation} fills the cell of the \code{dfcrit} table.
#' @param tstart start time, a number
#' @param tend end time, a number
#' @param tend.0 end time for over the long time range, a number
#' @param df1 leading vehicl... |
a28bdd6a007768be78439117762210edfc5ac4e4 | 29585dff702209dd446c0ab52ceea046c58e384e | /rtypeform/tests/testthat/test_get_all_typeforms.R | 784f3834216617d3a5b67777637ef1b20e61562e | [] | 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 | 130 | r | test_get_all_typeforms.R | test_that("Testing get_all_typeforms", {
skip_on_cran()
typeforms = get_all_typeforms()
expect_equal(ncol(typeforms), 2)
}
) |
32da3b6aa796ab350d3833b3ffe02e1e8dfff191 | 97bdb95da4059299469a8aea98c8ab10762993e0 | /R/nvd-class.R | 5ee7c7c36b638c8005dc55e1c56f7986ebec364c | [] | no_license | cran/nevada | 1924beaa3985652ae21777e067859d07df7b6c3b | b6724e527026e31fda1e5e56ec967effa184673f | refs/heads/master | 2023-08-17T22:30:18.581173 | 2021-09-25T05:40:02 | 2021-09-25T05:40:02 | 410,309,689 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,044 | r | nvd-class.R | #' Network-Valued Data Constructor
#'
#' This is the constructor for objects of class \code{nvd}.
#'
#' @param model A string specifying the model to be used for sampling networks
#' (current choices are: \code{"sbm"}, \code{"k_regular"}, \code{"gnp"},
#' \code{"smallworld"}, \code{"pa"}, \code{"poisson"} and \code... |
1597b529a0ed888f1751fdc7d175ef116cc7090a | 6464efbccd76256c3fb97fa4e50efb5d480b7c8c | /paws/man/configservice_put_organization_config_rule.Rd | b0e8395ec7d68f15b5dee9b20dd6b02c5de7a2d4 | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | johnnytommy/paws | 019b410ad8d4218199eb7349eb1844864bd45119 | a371a5f2207b534cf60735e693c809bd33ce3ccf | refs/heads/master | 2020-09-14T23:09:23.848860 | 2020-04-06T21:49:17 | 2020-04-06T21:49:17 | 223,286,996 | 1 | 0 | NOASSERTION | 2019-11-22T00:29:10 | 2019-11-21T23:56:19 | null | UTF-8 | R | false | true | 3,426 | rd | configservice_put_organization_config_rule.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/configservice_operations.R
\name{configservice_put_organization_config_rule}
\alias{configservice_put_organization_config_rule}
\title{Adds or updates organization config rule for your entire organization
evaluating whether your AWS resources... |
e94f56b1c1f22fbae5e0d0b33ccade2816492eb6 | 6d83839f46b1a625e71507442030f7d281a4218c | /inst/developer/function_ideas/thoughts on a quick string syntax.R | f4a3486b63fdf6d02b2e99af4e55f03e1444855c | [] | no_license | guhjy/umx | 06be5268bdeca6d2edbe7c72a1d720dd1f02445f | 4e1f524d909696625c82dc2f4045c8d964c5f4d5 | refs/heads/master | 2020-04-08T14:23:15.782049 | 2018-11-27T21:28:00 | 2018-11-27T21:28:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 862 | r | thoughts on a quick string syntax.R | HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
fit <- cfa(HS.model,
data = HolzingerSwineford1939,
group = "school")
summary(fit)
To convert lavaan to OpenMx
1. replace "=~" with "->"
2. add the black-box elements "visual <-... |
2a088a303054c5325033fc587f0585bb3c1ee995 | 88931c8cf916f9e8bacd99c65c1442e21e34e903 | /scripts/six_mnase_heatmaps.R | c09a0b5c0905bbf4c2638729c2518ba3e9f43147 | [] | no_license | james-chuang/dissertation | cdb91652f9842da5ae75d72f2600c11dceb78721 | b44d9a88cd934c1862b415b5a3961afc8ce78ec6 | refs/heads/master | 2020-06-14T07:03:10.846181 | 2019-07-29T21:18:32 | 2019-07-29T21:18:32 | 194,939,682 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,804 | r | six_mnase_heatmaps.R |
main = function(theme_spec,
netseq_data, mnase_data, quant_data, annotation_path,
fig_width, fig_height, assay,
pdf_out){
source(theme_spec)
library(cowplot)
sample_ids = c("WT-37C-1", "spt6-1004-37C-1", "spt6-1004-37C-2")
max_length = 1
mnase_cutoff... |
4952009335f5b3cfaf1acc63a0bd9add7c3c5b36 | dc3114f71d124f090616e1475d7a2f0f3cc58e67 | /study1_analysis.R | 9b60a53d975694802edb7416090100d9c80f3b78 | [] | no_license | stevenfelix/R_Code_Samples | c2e30461c9e8bb51502b190c12b389da3d7e2634 | e61ef57ebd359a995f6a8cfd0adef042bc5fc6d5 | refs/heads/master | 2021-01-16T19:36:48.316588 | 2017-08-13T14:17:50 | 2017-08-13T14:17:50 | 100,180,809 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,153 | r | study1_analysis.R | # Sample analyses from an MTurk survey study
# Steven Felix
#
# Description: These are excerpts from my script used to produce analyses for a manuscript.
#
rm(list = ls())
search()
# packages ----------------------------------------------------------------
library(effects)
library(psych)
library(car)
library(dplyr... |
457c2141739d908e82ea83290e3a3473ad4d4294 | 7b7c570b30d6d7a0e9b904c7cb378cfb0d0f0e07 | /mlflow/R/mlflow/R/model-crate.R | 56689eb1cfc310929bff06159c9aa0fc28d41fce | [
"Apache-2.0"
] | permissive | mlflow/mlflow | ca97bfbbf32f8e59f454e428f5e46eb3d34d062f | 37298ffafcd34002352d01d579d4524790544267 | refs/heads/master | 2023-09-01T13:15:53.902815 | 2023-09-01T09:00:42 | 2023-09-01T09:00:42 | 136,202,695 | 14,102 | 3,748 | Apache-2.0 | 2023-09-14T21:52:42 | 2018-06-05T16:05:58 | Python | UTF-8 | R | false | false | 738 | r | model-crate.R | #' @rdname mlflow_save_model
#' @export
mlflow_save_model.crate <- function(model, path, model_spec=list(), ...) {
if (dir.exists(path)) unlink(path, recursive = TRUE)
dir.create(path)
serialized <- serialize(model, NULL)
saveRDS(
serialized,
file.path(path, "crate.bin")
)
model_spec$flavors <- a... |
79ce96ec02d44ea07d1947a96e5e5fad7a9d1c3f | 71cc117affb4cf023ee3c807a0a23846b18b7469 | /R/scf_scaffold.R | 375aae8f95239b8d843786c9a7adc97f31dddb46 | [
"MIT"
] | permissive | petermeissner/scaffold | 479d8bc164df9a29e6b038f3249b104253449a96 | 0e130d16400dbf75da3005fd688d5cf72e6dcd66 | refs/heads/master | 2020-06-09T02:56:43.628044 | 2020-02-19T21:19:37 | 2020-02-19T21:19:37 | 193,357,276 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 898 | r | scf_scaffold.R | #' general scaffolding function
#'
#' @param scf path to scaffold from
#' @param path_to path to scaffold to
#' @param package the package to look for the scaffolding
#'
#' @export
#'
#' @examples \dontrun{
#'
#' scf_scaffold("shiny_material", path_to = ".")
#'
#' }
#'
scf_scaffold <-
function(scf, pat... |
1e19d98f6dfcef6bcc258a09da7aa9797daa73f4 | 819f05d261404015a3e95649a83975622b145c16 | /4.11.R | 1bd156d3d026b1280e6d92c3ffe3978c180b8c9d | [] | no_license | fengzenggithub/R-Simulation-by-Ross | 1036a4d6bbdbafaf09f0b3023e3f0bb05e7c0076 | 3d9fd8cfaffbb7ff23f9a377294d07703faaa474 | refs/heads/master | 2023-03-16T06:50:05.439459 | 2017-03-05T04:53:44 | 2017-03-05T04:53:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,546 | r | 4.11.R | # This problem is good because it shows that intuitively plausible solutions
# may not work!
n = 10
k = 4
r = 3
N = 100000
# The most efficient way would be to generate a random element from {1, ..., k},
# and then generate a subset of size r-1 from the rest of the elements...
# This does not work as in fact this wou... |
a1a20206fbeb62e21a2b803ec0087de2c2b43b22 | 44e7ec2d56b0d973f35f0a372beba2460d1f93d6 | /man/power_lm_app.Rd | 6653f07f495a3d3a41f450a93de5c4812b690370 | [
"CC-BY-4.0"
] | permissive | biostats-r/biostats.tutorials | 02471032f76acdbf34caef5839cb60bcebe0c6f6 | 6bafcf0f2a5f811c23ef557cdce577b6ee4f5a27 | refs/heads/main | 2023-08-29T16:41:01.340658 | 2021-10-12T13:21:00 | 2021-10-12T13:21:00 | 301,679,132 | 1 | 2 | CC-BY-4.0 | 2023-09-05T15:39:49 | 2020-10-06T09:40:13 | R | UTF-8 | R | false | true | 237 | rd | power_lm_app.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/power_lm_app.R
\name{power_lm_app}
\alias{power_lm_app}
\title{Simulations of power in lm}
\usage{
power_lm_app()
}
\description{
Simulations of power in lm
}
|
684558e7a6df6cb45aa52655aa3d811fcf1458f6 | c148b02c89314ebbf164a4daac5f9d406985148e | /man/JointRegBC.default.Rd | df4041de844678c129b2f7fd21ee0a6ce7ab5dfa | [] | no_license | cran/JointRegBC | 8b02625a240cdbc4c2a8909dc7dfc09cb8019456 | 997655e70a18e6e995218f9123c06d896aa1c34b | refs/heads/master | 2016-09-06T01:35:49.434863 | 2013-06-13T00:00:00 | 2013-06-13T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,786 | rd | JointRegBC.default.Rd | \name{JointRegBC.default}
\alias{JointRegBC.default}
\title{Joint Modelling of Mixed Correlated Binary and
Continuous Responses : A Latent Variable Approach.}
\description{
A joint regression model for mixed correlated binary and continuous responses is presented. In this model binary response can be dependent on ... |
badcda8516c00ed3ddd3230fb4cc0c75e0c82ca5 | 20eb3d806fd691a1c7300d3c2695214bafa0674c | /data/download.R | 8bd64459eadf09b667e913de658a99713e5df40a | [] | no_license | chl781/-Global-Historical-Climatology-Time-Series-Analysis | 5e93cb9efc5e9ecab0f70274924480b1cde40cf5 | 1175b67a39617d26b70a485f8ca91dffdc1e0c92 | refs/heads/master | 2020-09-28T06:30:35.839136 | 2019-12-11T05:17:37 | 2019-12-11T05:17:37 | 226,712,969 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 211 | r | download.R | a=read.csv("best50.csv")
a$names<-as.character(a$names)
library(HelpersMG)
for(i in 1:50){
wget(paste0("https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/access/",a$names[i],".csv"))
} |
4a16e408ff0018460866e0c9c8d1694884e6d9a6 | a5d82208381cf0c1a03e9a07379a2c4a9f719995 | /R/load.pathway.definition.R | 0f4c6a73fb0b3bfb0def25299a4661e843c14ef9 | [
"MIT"
] | permissive | yfyang86/ARTP3 | f5eefab5e6457918f40074daa992f8b5d4c1115f | 51bef5f4ef30511eb1be477989d659489a1b25a2 | refs/heads/master | 2020-12-28T20:09:32.514738 | 2015-11-02T20:10:25 | 2015-11-02T20:10:25 | 45,478,605 | 0 | 0 | null | 2015-11-03T16:13:22 | 2015-11-03T16:13:22 | null | UTF-8 | R | false | false | 1,804 | r | load.pathway.definition.R |
load.pathway.definition <- function(pathway, options){
msg <- paste("Loading definition of pathway:", date())
if(options$print) message(msg)
if(is.character(pathway)){
tmp <- try(pd <- read.table(pathway, header = TRUE, as.is = TRUE), silent = TRUE)
if(error.try(tmp)){
msg <- paste0("Cannot l... |
23116fad4586deac392a4c9000cb048f1c56b09b | f1798de3f2c8d3df5bbbb9b21c9fd3b603ae720c | /accessory_scripts/duplicate_participant_xref_w_dbs.r | 7810fcb1e856c8339fb26e6c21b46bb15991d5da | [] | no_license | genomicsengland/af_letter_distribution_dataset | 5625d2c0188364882a45482aefd553dc6e959481 | 86c4497647262da216f9be1bcbb595b4a38ba985 | refs/heads/master | 2023-05-29T03:14:29.924984 | 2021-06-11T15:44:57 | 2021-06-11T15:44:57 | 371,753,029 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,491 | r | duplicate_participant_xref_w_dbs.r | #-- check that duplicate participants are genuine duplicates
rm(list = objects())
options(stringsAsFactors = FALSE,
scipen = 200)
library(wrangleR)
library(tidyverse)
library(DBI)
p <- getprofile("indx_con")
metrics_con <- dbConnect(RPostgres::Postgres(),
dbname = "metrics",
host = p$h... |
7330bc5ef2e26632d8fd6c3a6f9c0555a26ecd0c | c87286e29bf62160dc5869913ecef6eceedb8ee9 | /Mehr Objekt Erkennung/ui.R | 03ad206da1b7e07b843f83fd97884628e65bd901 | [] | no_license | hjynick/Man-ver-Erkennung | 8f7a8b090c71150de147df5de50bc1326ff9b191 | 2f44b85a55aa721a09ab93934a7ae0aa00c955f7 | refs/heads/master | 2020-04-04T14:55:35.956832 | 2018-11-04T17:11:37 | 2018-11-04T17:11:37 | 156,018,554 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,716 | r | ui.R | library(shiny)
library(shinydashboard)
shinyUI(
##fluidPage(
##titlePanel("Manoever Erkennung"),
# titlePanel("Uploading Files"),
#
# # Sidebar layout with input and output definitions ----
# sidebarLayout(
#
# # Sidebar panel for inputs ----
# sidebarPanel(
#
# # Input: Select... |
eabaf69bf696c44dc39769ff172b229a0f14e5e3 | 0790b99473d0c669412e6c5d8d81765e11a23fc5 | /code/demographics/education.R | c1909bd83ae52006b485cdc19af83eaba01b9de1 | [] | no_license | katiemlyon/nvs-report | cf3d4227a88d3dd05bda0b9addadaa3468004f86 | 972b2ad2e182fd5b1abb99d623991364ab8763c9 | refs/heads/master | 2020-04-30T10:51:38.900385 | 2019-04-12T16:04:39 | 2019-04-12T16:04:39 | 176,787,597 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,541 | r | education.R | library(ggplot2)
#load functions
source("code/functions/calc_pct.R")
source("code/functions/round_df.R")
#read data
nvs2018 <- read.csv("data/nvs2018.csv")
###########################################
# Education
###########################################
str(nvs2018$SCHOOL)
range(nvs2018$SCHOOL, na.rm=TRUE)
table(... |
f7cce0a5fb2e8be06d7de281eaeca2426d75609b | 5056a6153e4ed04845843e9fd368601d081b797f | /R/barcode_count.R | 4f8148243b8d7431772d3900fe7362ef4e485f0d | [] | no_license | jessievb/RAID | ae68a3ed63edd26783ec417e5b48354e5a8f375f | cf417d21c1b7ea8b88848019f6ca71d867df748c | refs/heads/master | 2020-03-20T21:27:59.269054 | 2018-06-18T12:52:24 | 2018-06-18T12:52:24 | 137,742,215 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,325 | r | barcode_count.R | #' Count the number of unique UMI per Barcode_1 and Barcode_2 columns.
#' The data table that should be used is the iSeq_UMI_count table, which
#' contains all the unique (collapsed) UMI,
#' barcode_1 and barcode_2 information. The column row_occurence_count can be
#' ignored.
#' @export
raid_barcode_count <- function... |
edf088ad2555bbb909baf1b24f20060a9320852a | 3bb80cde674096b52fde6d957b2a7b9c32a399ba | /section02/conditionals.R | 7bee7f2ecec2fc538c7b243ef2c684e703cd0382 | [] | no_license | AmundsenJunior/r-programming-udemy | 107e45d603a1e91febd3b300ea089e36e1b6ca42 | b903a165941dbfd2ed9d56d1977639d6b42d5410 | refs/heads/master | 2020-03-14T13:50:12.054901 | 2018-04-30T20:03:06 | 2018-04-30T20:03:06 | 131,640,981 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 278 | r | conditionals.R | rm(list=ls())
# set of randomly generated numbers within the normal distribution
# can include mean and std dev as 2nd & 3rd args
x <- rnorm(1)
x
if (x > 1) {
answer <- "greater than 1"
} else if (x >= -1) {
answer <- "between -1 and 1"
else {
answer <- "less than -1"
} |
cff49bbf5796da529784bf98c7bd7596b3374008 | 2eb9544eb511850ea2a7bb40c4159015b53fa87e | /server.R | 167662f215f8d924e5c71fa656d4665eed3cc4f1 | [] | no_license | pmPartch/HorseColicShiny | ea4cc34316192a4bf56a0eca67f13453ca9f0c07 | 660497a2b302725d59b85013b902d56c5b559a28 | refs/heads/master | 2021-01-10T06:19:39.197238 | 2015-11-22T05:38:11 | 2015-11-22T05:38:11 | 46,648,002 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,838 | r | server.R |
# This is the server logic for a Shiny web application.
# You can find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com
#
library(shiny)
library(randomForest)
#model load
modelFit <- readRDS("rfMin.rds") #only wish to load this once per app startup (not with the page is refreshed)... |
908858740ad5bdde9ad5e988c3922b34f23db353 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1+A1/Database/Miller-Marin/fpu/fpu-10Xh-correct02-nonuniform-depth-15/fpu-10Xh-correct02-nonuniform-depth-15.R | f50bf1d1a51b201e2b5a5605ac94ae2272509532 | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 94 | r | fpu-10Xh-correct02-nonuniform-depth-15.R | 43fa82174ead4f88f12fa27cebf7ce0a fpu-10Xh-correct02-nonuniform-depth-15.qdimacs 412577 1101179 |
98776bada73d5a5375df677d30baed00bc3d8206 | 4cc92a349885a505896de9056887465f5db40c76 | /code/NC13Huddle.R | 3419260339b01cd48464e695a55385a6616a440a | [] | no_license | guanjiahui/Social-Network_rhesus-macaques | cdaba33cbc333c00e67963e7dffd2c622d66e333 | aee1f7583c168ca17a9c83bc0659d1e645cb96e6 | refs/heads/master | 2020-04-18T14:39:45.612811 | 2019-01-25T18:46:08 | 2019-01-25T18:46:08 | 167,594,532 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,145 | r | NC13Huddle.R | #############
#process the data
NC13_RU1_Huddling_Matrix <- read.csv("~/Dropbox/Research/SNH_health profile data for Fushing-selected/NC13_RU1_Huddling_Matrix.csv", header=FALSE)
NC13HuddleR1=as.matrix(NC13_RU1_Huddling_Matrix[-1,-1])
colnames(NC13HuddleR1)=NC13_RU1_Huddling_Matrix[-1,1]
rownames(NC13HuddleR1)=NC13_R... |
83f6a09761af6c9aec1c6707e0e9b8ec9ff80459 | 52e4526a947689c1c63453e1a6b4a6c1e20513c4 | /man/fsOrder.Rd | b15dd65c3221b18df79654cab0d2e53d8285a9d7 | [] | no_license | cran/robustfa | eeb6dbed14e04edb0c44f6cd697bb732dc57c7b1 | c1951b364fadb7f15a62ce471979c34e12fe3b67 | refs/heads/master | 2023-05-06T21:28:30.116250 | 2023-04-16T13:40:02 | 2023-04-16T13:40:02 | 17,699,300 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,158 | rd | fsOrder.Rd | \name{fsOrder}
\alias{fsOrder}
\title{
Compute the Ordered Factor Scores
}
\description{
Compute the ordered factor scores according to the first/second/third... column of the original factor scores.
}
\usage{
fsOrder(factorScores)
}
\arguments{
\item{factorScores}{
The original factor scores.
}
}
... |
90855dfcbba43cd815ffba2e040096ad852b7877 | 8521eda607ce938b257845af1b25f106e86c9f2a | /Shiny Sample Sizes/ShinySampleSizesDocumentation/References/R Worksheets/Examples Worksheet.R | 46059ce818157043334894fb6c8c9c3dacd575bd | [] | no_license | mattpartridge/ShinySampleSizes | 1e6ee5dcecb5db706b4f275571fa299fcbcbcee9 | 65ae8016f3ef2017e40d70e38d0de141b67b4dd5 | refs/heads/master | 2021-01-10T07:29:12.635874 | 2017-10-29T14:09:15 | 2017-10-29T14:09:15 | 49,987,528 | 0 | 1 | null | 2016-10-16T19:08:43 | 2016-01-19T22:26:30 | R | UTF-8 | R | false | false | 2,278 | r | Examples Worksheet.R | # Data Prep
library(survival)
## FL
fl = flchain
fl$kl = fl$kappa/fl$lambda
fl$kl.med = as.factor(ifelse(fl$kl < median(fl$kl, na.rm = T), "Low", "High"))
fl$kl.2575 = as.factor(ifelse(fl$kl <= quantile(fl$kl)[2], "<25th", ifelse(fl$kl >= quantile(fl$kl)[4], ">75th", NA)))
fl$mgus = as.factor(fl$mgus)
fl$death ... |
7835c2f4f332d7c9195f4b3d251f9ed907fd67cb | 8e3e9d61fbe2640f2b7bcd128647dd36dc6b60e6 | /man/celda_G.Rd | 4f7c9dc3f841e71115e510622e65e15eb4608aef | [
"GPL-2.0-only",
"MIT"
] | permissive | AndrewGr12/celda | bcf115559635d8c7447f38642e0d9be78abd5a69 | 8ac2eeaa64f3204c45bca63732276573717381b2 | refs/heads/master | 2020-03-25T03:20:27.937760 | 2018-07-24T22:38:00 | 2018-07-24T22:38:00 | 143,336,504 | 0 | 0 | MIT | 2018-08-02T19:26:39 | 2018-08-02T19:26:38 | null | UTF-8 | R | false | true | 2,111 | rd | celda_G.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/celda_G.R
\name{celda_G}
\alias{celda_G}
\title{celda Gene Clustering Model}
\usage{
celda_G(counts, L, beta = 1, delta = 1, gamma = 1, stop.iter = 10,
max.iter = 200, split.on.iter = 10, split.on.last = TRUE,
count.checksum = NULL, seed ... |
e9c065cbeef8aa9a94d2ba7f63bb41722c5a3672 | f4778157f1298955553edd0b216ea87b2618ba46 | /man/ks.heatmap.Rd | ecf02a5b93062f2062c3bbabc9bbc3536c6cb768 | [] | no_license | mgkaszkowiak/miRNAselector | 1ca865c1cc045e74e64c94c479c500d6b629ef1a | a9baf719168b0ee652ce2a977cfa3bda3a61a374 | refs/heads/master | 2022-04-21T23:13:21.007912 | 2020-04-20T22:57:22 | 2020-04-20T22:57:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 661 | rd | ks.heatmap.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ks.heatmap.R
\name{ks.heatmap}
\alias{ks.heatmap}
\title{ks.heatmap}
\usage{
ks.heatmap(
x = trainx[, 1:10],
rlab = data.frame(Batch = dane$Batch, Class = dane$Class),
zscore = F
)
}
\arguments{
\item{x}{Matrix of log-transformed TPM-no... |
17f318a6cf72f47db4d100f0846022c0ca7fb38f | 0a906cf8b1b7da2aea87de958e3662870df49727 | /distr6/inst/testfiles/C_EmpiricalMVPdf/libFuzzer_C_EmpiricalMVPdf/C_EmpiricalMVPdf_valgrind_files/1610035521-test.R | 6cbc10be016d970224eae210e765cfa93692bcde | [] | 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 | 552 | r | 1610035521-test.R | testlist <- list(data = structure(0, .Dim = c(1L, 1L)), x = structure(c(1.51067888575209e-314, 0, 2.90905852271326e-319, 1.1125369292536e-307, 7.2911220195564e-304, 8.48798319399909e-314, 3.20506244267395e-310, 0, 0, 2.12276966337746e-313, 8.81442565517813e-280, 0, 0, 1.72085029849862e-260, 0, 0, 0, 0, 0, 0, 0, 0, ... |
a4c6462602c6747e1b27a080b5d3511634e40552 | fa0a5cbe982bee427d9f8c46018a849154d3d290 | /U.S. Green Vehicle Outlook/code/summary_table.R | 269fa6cd6f382f72a3434040467a48e54208faa7 | [] | no_license | chengz51/ClassProjects | b16975639a96b83b72edcc13070935f442c9c110 | c31f3ab61ec863139127f5f22ec237993bc92c76 | refs/heads/main | 2023-03-28T02:01:55.086671 | 2021-03-26T17:19:20 | 2021-03-26T17:19:20 | 351,322,638 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,489 | r | summary_table.R | library(dplyr)
source("scripts/population_function.R")
#( group_by function already used in population_function)
# load in all the datasets
ev_num_data <- read.csv("data/SupplyData.csv", stringsAsFactors = FALSE)
ghg_emisson <- read.csv("data/us-ghg-emissions_fig-1.csv",
stringsAsFactors = FALSE
)
gas_prices <- read.... |
1e22cefc1a7b9ae316a862724652de3efca705c7 | 24e3d5250f2b8a56b90810451b97a3073f186b71 | /scripts/week_4_class_code.R | 31c752c43662eaf52077b953031797c3869b799d | [] | no_license | gge-ucd/r-davis-in-class-jasgre | 6c93bfda5a14dfbecdd5b239d5dd99cc57c93d7e | 79bc92b30471db173f5fbc0bbaeeb09066d438e3 | refs/heads/master | 2020-04-17T15:47:29.387532 | 2019-05-27T03:28:45 | 2019-05-27T03:28:45 | 166,713,352 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,446 | r | week_4_class_code.R | # week 4 class code
download.file(url = "https://ndownloader.figshare.com/files/2292169", destfile = "data/portal_data_joined.csv")
surveys <- read.csv(file = "data/portal_data_joined.csv")
# ways to look at large dataframes
head(surveys) # shows data in all columns in top 6 rows
str(surveys)
dim(surveys) # returns... |
8d520f446362fd146894ff0fc3a4f17ca4cab062 | 3da61e0097d852d2202dbf5e6c9b3fbfe8fb9b92 | /assignment1/complete.R | 773d93e23bd4876dde2a6c9f9bef500654bcf7a5 | [] | no_license | veerakumarnice/rprog-033 | 2381d90ed18bd5305a92bb0e2e74cddeba19503f | 815e51e22c605b1a298a688168d1f22e5a7d2ef9 | refs/heads/master | 2021-01-10T02:10:18.169246 | 2015-10-31T09:53:49 | 2015-10-31T09:53:49 | 44,557,218 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 413 | r | complete.R | complete <- function(directory, id = 1:332) {
string <- sprintf("%03d.csv",id)
files <- paste(directory,string,sep ="/")
nobs <- NULL
for (file in files ) {
content <- read.csv(file)
need <- complete.cases(content$sulfate, content$nitrate)
... |
7b7084583830cb76459f75b820771da2ee5e3500 | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /resemble/man/get_predictions.Rd | 7897c795c84920f1fc820fc8b31ec4af2a9ca2a8 | [] | no_license | akhikolla/TestedPackages-NoIssues | be46c49c0836b3f0cf60e247087089868adf7a62 | eb8d498cc132def615c090941bc172e17fdce267 | refs/heads/master | 2023-03-01T09:10:17.227119 | 2021-01-25T19:44:44 | 2021-01-25T19:44:44 | 332,027,727 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 801 | rd | get_predictions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_predictions.R
\name{get_predictions}
\alias{get_predictions}
\title{Extract predictions from an object of class \code{mbl}}
\usage{
get_predictions(object)
}
\arguments{
\item{object}{an object of class \code{mbl} as returned by ... |
52e327d86efa433b1273999f073d2400ca48492f | 2a82c473bccb19ba59bbdd0696f35043b9cfcfa2 | /release.R | 10c1908d1f945d44c96e0bb5e799be3f6f0237a0 | [
"MIT"
] | permissive | wkostelecki/ezplot | daab0c404af67977f4bc6daa02798c6737dd3dc0 | 3422e7cd110f960d63da1fd9fc3203423fbe2b3d | refs/heads/master | 2023-06-23T06:56:16.773219 | 2023-06-17T05:23:51 | 2023-06-17T05:23:51 | 39,913,514 | 6 | 0 | NOASSERTION | 2020-11-15T14:05:06 | 2015-07-29T20:01:32 | R | UTF-8 | R | false | false | 982 | r | release.R | library(magrittr)
library(glue)
packages = setdiff(c("tidyr", "testthat", "devtools", "DT", "git2r",
"devtools", "spelling", "rhub", "patchwork"),
installed.packages())
install.packages(packages)
old.packages()
update.packages(ask = FALSE)
covr = covr::package_coverage()
covr::... |
1402d83a228f644c44ca44b1b21711d359ce6ca6 | 6964d8eb7cf8f9ed5abd612f6c2f0756877bca04 | /R/embed_plot.R | e5ed65211ac5adb04d2aba7666a650eb155e63d5 | [
"Unlicense"
] | permissive | s-fleck/hammr | 7a6805acc2f897c380b3f40d4e9112900646006d | b8fd5fa9d67698bc4c46ef48d079b0948a036387 | refs/heads/master | 2023-07-20T11:56:32.005037 | 2023-07-10T07:32:59 | 2023-07-10T07:32:59 | 119,056,265 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,370 | r | embed_plot.R | #' Embed plot in html document
#'
#' Generates a png file from a plot and encodes it as a base64 string
#' encode it as a base64 string, and wraps that string in an html `<img>` tag.
#'
#' @param x a function that plots something or a ggplot object
#' @param img Logical. If `TRUE` result will be wrapped in an img tag
#... |
4e1ae3990301df7364821fadf75b915e0a149320 | 0f3df7f607d83626fd4bee17acf3a771c2734f6c | /cachematrix.R | 6026f3e389a1029123a64fabcaaeeaebfc1aa4c7 | [] | no_license | saganot/ProgrammingAssignment2 | 3cba9f3c04093da31469af7fe649a7cbe22016a1 | ac03dbeb95f3968a20448a51cdb90b8c560a50a8 | refs/heads/master | 2021-04-28T23:07:12.173717 | 2016-12-31T13:52:01 | 2016-12-31T13:52:01 | 77,740,851 | 0 | 0 | null | 2016-12-31T13:14:24 | 2016-12-31T13:14:23 | null | UTF-8 | R | false | false | 1,389 | r | cachematrix.R | ## These two functions allow for the inverse of a matrix to be calculated once
## and cached, then read back from the cache whenever needed, so as to avoid
## costly recalculations.
## makeCacheMatrix creates a special "matrix" that is actually a list of functions.
## The purpose is to store the matrix in the global v... |
fa55fdc7f58bebd5f9249d44c6058df47fd670f4 | cfb444f0995fce5f55e784d1e832852a55d8f744 | /man/rnorm_pre.Rd | 23ef082ce81e093bccc367d81119cec6fe394434 | [
"MIT"
] | permissive | debruine/faux | 3a9dfc44da66e245a7b807220dd7e7d4ecfa1317 | f2be305bdc6e68658207b4ad1cdcd2d4baa1abb4 | refs/heads/master | 2023-07-19T18:28:54.258681 | 2023-07-07T16:59:24 | 2023-07-07T16:59:24 | 163,506,566 | 87 | 15 | NOASSERTION | 2023-01-30T10:09:37 | 2018-12-29T11:43:04 | R | UTF-8 | R | false | true | 967 | rd | rnorm_pre.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rnorm_pre.R
\name{rnorm_pre}
\alias{rnorm_pre}
\title{Make a normal vector correlated to existing vectors}
\usage{
rnorm_pre(x, mu = 0, sd = 1, r = 0, empirical = FALSE, threshold = 1e-12)
}
\arguments{
\item{x}{the existing vector or data ta... |
7487f5314f4de28fb468cad8c6c6728d669249bf | 330a27c197664d05c9592b21d0bf2682b6ae094a | /MLOpsMonitoring/man/create_agg_prix_qty.Rd | ed581d3992bf4e1b5aa5fb402e0c6b0080808583 | [] | no_license | datastorm-open/demo_webinar_mlops | 091140efe460c98b85789b8c70c11f81bd37e371 | 1dbd231478b84939460294464131b525b3d015f4 | refs/heads/master | 2023-08-03T10:34:59.647526 | 2023-07-20T09:21:35 | 2023-07-20T09:21:35 | 321,422,579 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 644 | rd | create_agg_prix_qty.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dataset.R
\name{create_agg_prix_qty}
\alias{create_agg_prix_qty}
\title{First part of features computing. Compute the average basket and other features.}
\usage{
create_agg_prix_qty(sub_data_agg, all_customers)
}
\arguments{
\item{sub_data_ag... |
ee55ff5167cc857bf8ce014cca5db3c7faa953e1 | d48a6be6d855db72443aa767d680e13596e2a180 | /RMark/R/import.chdata.R | 34855472882fe813e26df80a52a1ea268438ec1a | [] | no_license | jlaake/RMark | f77e79d6051f1abfd57832fd60f7b63540a42ab9 | 7505aefe594a24e8c5f2a9b0b8ac11ffbdb8a62d | refs/heads/master | 2023-06-26T21:29:27.942346 | 2023-06-25T16:35:43 | 2023-06-25T16:35:43 | 2,009,580 | 17 | 15 | null | 2019-01-10T17:17:11 | 2011-07-06T23:44:02 | R | UTF-8 | R | false | false | 6,449 | r | import.chdata.R | #' Import capture-recapture data sets from space or tab-delimited files
#'
#' A relatively flexible function to import capture history data sets that
#' include a capture (encounter) history read in as a character string and an
#' arbitrary number of user specified covariates for the analysis.
#'
#' This functi... |
8a629acef8c1287c3618460f6e26ab9fad5bb573 | 811de088d25e921b066b1074942860f63df4ec3e | /ui.R | c5139ae0a30d4add6e5922421cef3806d24b1557 | [] | no_license | cwhite1026/FBICrimeStats | 2ac1364ce15899715b12008441e6b72d4b15c6b4 | 4c84f9ae214fa83720fa29aca031f9807d429f58 | refs/heads/master | 2021-01-10T23:05:04.701508 | 2016-10-09T19:08:31 | 2016-10-09T19:08:31 | 70,424,653 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,773 | 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)
# We want a main page with plots, plus tabs that show the data tables
# a... |
1a81c37c6abd90ba7e94c268402f543ad1feff1b | 7864effa271e722a9f83105b3ee10e57b675e062 | /plot4.R | 0dbd388b260945aba90e615600394bd181d730b4 | [] | no_license | bwperlstein/Exploratory-Data-Course-Project-2 | e88cfdc8fdde609de0b5a31ab6dbae28443f4f43 | 94bb809a062432a4ccd0cd6c9b80c5a39e668bd9 | refs/heads/master | 2020-08-08T07:23:04.103381 | 2019-10-08T23:46:34 | 2019-10-08T23:46:34 | 213,777,243 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,447 | r | plot4.R | chgSCCtoCharacter <- function(dftemp) {
dftemp <- as_tibble(dftemp)
dftemp$SCC <- as.character(dftemp$SCC)
return(dftemp)
}
yrSelect <- function(df, year1) {
yrSelect.df <- subset(df, df$year == year1)
return(yrSelect.df)
}
getMedian <- function(dftemp) {
me... |
f924ac354430d8c31a65e32ca987c4e93801fef8 | 4b0cff5e09efd41994db11d589ef3069266ccce4 | /man/oapply.Rd | 7461ae62dbf44d8bf75d5e9a3b50cbcaf66d782e | [] | no_license | cran/Jmisc | 7d43070011ebd9b56327ca8704dacbeeb5e84c2c | 0b141061bedc22bc9c7e7b6fa97dde67066f06a9 | refs/heads/master | 2022-07-13T05:12:17.641540 | 2022-06-22T04:53:25 | 2022-06-22T04:53:25 | 17,680,105 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 744 | rd | oapply.Rd | \name{oapply}
\alias{oapply}
\title{Outer apply}
\usage{
oapply(X, Y, FUN, switch_order = FALSE, ...)
}
\arguments{
\item{X}{first argument to \code{FUN}}
\item{Y}{second argument to \code{FUN}}
\item{FUN}{a function to apply. See mapply}
\item{switch_order}{Switch the order of \code{X} and
\code{Y} in exp... |
6b6899feb0e3b519c334e91261c319c8c9a7584a | c76d70620a863a0d1e2613d00276cd1a50831b8b | /train_TrCASAVA_script.R | ca967011896b8726469f2d63e291663a69a70b7a | [] | no_license | zhanglabtools/CASAVA | 7bf299241918bf930d6ada2b0c3654b75cd18022 | f84e0327aec0bb6405a971530b7091e3c486d982 | refs/heads/master | 2023-02-04T05:56:11.757994 | 2020-12-26T09:39:41 | 2020-12-26T09:39:41 | 280,851,154 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,957 | r | train_TrCASAVA_script.R | ################################################################################
# last time modified: 2020/12/26
# This script is used to train TrCASAVA models in our experiment.
# The inputs file have been uploaded to https://zenodo.org/record/4365899#.X-b3CdgzaUk.
# See the folder /feature/ in 03_Disease_and_TrC... |
e89d32ebfe6cef50caa282bf9f41dfcea5bbd751 | 81de910c2709361dff0f83cdf087a47ef4e31818 | /figures/Figure3/11_counts_bonf.R | e6c5eef7cee315e065959f9366a1ec241bdcf931 | [] | no_license | fl-yu/singlecell_bloodtraits | 1fdfb4edeac23a3763199f377a7115e9541fa5df | 1da2a246b6f1ad13e8bfcbd424080f8ea5917c86 | refs/heads/master | 2022-02-05T10:02:17.033827 | 2019-07-09T22:38:39 | 2019-07-09T22:38:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,139 | r | 11_counts_bonf.R | library(dplyr)
df <- readRDS("allEnrichments-df.rds")
# Equivalent statistics
p = 0.0001736111
Z = 3.75
chisq = 14.1
allstats <- data.frame(
Yes = c(sum(df$lineageSpecific& df$ldscore_pvalue < p), sum(df$lineageSpecific& df$chromVAR_pvalue < p),
sum(df$lineageSpecific& df$gchromVAR_pvalue < p), sum(d... |
b344ddaff2f27029d09c063c153633778a608456 | 48516682819308cb1ebc92edce2c1c144744cc2f | /scripts_for_HM_ancestral_tests/0_plate_arrangementsDAS.R | cefb2fc8dfe519602da0287e6e385db4851df1ad | [] | no_license | Landrylab/Gene_duplication_2019 | 139c8b2ca0af251cfe4398ab8d11191ef0c7346b | af5cacc44768bcccb952ba9ce27ba7ddb707b008 | refs/heads/master | 2020-05-02T07:44:58.301178 | 2019-03-26T15:48:36 | 2019-03-26T15:48:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,747 | r | 0_plate_arrangementsDAS.R | #install.packages("tidyr")
#install.packages("reshape")
library(tidyr)
library(reshape)
library(dplyr)
library(xlsx)
rm(list=ls())
setwd("C:/Users/Diana Ascencio/Dropbox/Project_HeteroHomodimers/pca/array_files/")
dhfr12 <- read.xlsx("DIAS_dest_array_12_20180920.xls",sheetIndex = 1,startRow = 2,stringsAsFactors... |
916f6d23cce14fef0e2d558ba5585a391334f180 | b820ecdf40c5982d4f30b26afce5bd448fde50c7 | /R/get_recent_spread.R | d2628a0af1fd4762dc9da9afa7ef518472c06a97 | [] | no_license | mstei4176/krakenR | 88d0e7db3fe9641ea386732fcc8342df8faff40a | 7b7405a9b549b6669703c0916735ec9bf6136d1d | refs/heads/master | 2021-04-28T13:30:03.223158 | 2017-12-10T19:57:16 | 2017-12-10T19:57:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 260 | r | get_recent_spread.R | # https://api.kraken.com/0/public/Spread
#
get_recent_spread <- function(pair = "XBTEUR") {
base_url <- "https://api.kraken.com/0/public/Spread"
url <- paste0(base_url, "?", "pair=", pair)
spread_out <- jsonlite::fromJSON(url)
return(spread_out)
}
|
8d09e23e0b2dc9c1b9de0918b862c691aa5e6bb8 | 4a7718b5618d75bdcfb3fb71324569d0d11ac749 | /R/lFC_in_time.R | 5aaa61d26cbfade8835f28826b5d3225d2a65b55 | [] | no_license | EwaMarek/FindReference | 859676f1744ea2333714634fd420d6b91b367956 | eedc8c80809b6f3e4439999bac4cb09ec2b228f2 | refs/heads/master | 2018-08-01T08:39:05.326467 | 2018-06-02T17:05:06 | 2018-06-02T17:05:06 | 104,148,930 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,146 | r | lFC_in_time.R | # na wejscie: symbole genow do wyrysowania, EntrezID wszystkich genow wystepujacych na mikromacierzach, info o wszytkich probkach IR, FC_data,
# ranking, skala: linear, log, czy dotyczy miRNA
# na wyjscie: wykres
#' @title Plot fold change in time
#'
#' @param genes_to_valid Character vector with gene symb... |
693754154abd9913126653e5ca9ca1d7393a1b84 | f6c00e4ca190a03309d606553c84a2606fda4582 | /supplementary_info/prisma_diagram.R | e57edaefaa40d93f2abef46d8fe7daec01e4e8a9 | [] | no_license | mslein/therm_var_meta_analysis | 516e9771c64f714ceb42ada9e9cd7f5fea077724 | 88ca128db82c0dea2e6897918636b9cb12545e47 | refs/heads/main | 2023-04-19T00:15:44.205995 | 2023-01-16T22:48:50 | 2023-01-16T22:48:50 | 467,272,388 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,422 | r | prisma_diagram.R | pacman::p_load(tidyverse)
prisma <- read_csv("litsearch_subgroups14sept21 copy.csv") %>%
mutate(notes_tidy = case_when(notes %in% c("background", "review",
"review/synthesis", "book") ~ "reviews/background",
notes %in% c("mod... |
f273d947534d41bba682eba5c673ebd1fe0a9886 | e0f1cbfce20607ae9d771a65aeed59b2d9fc7a5b | /mod-metr.R | f32220b31542e01ef9f52e0107ebd8b61dd022ff | [] | no_license | DrRoad/demo-shiny-modules | 2a39d6dde57390114a25dba52d48b3f1ef112f0a | 12d9632a462b5c42a06f19698f20fe025efcbcb1 | refs/heads/master | 2022-11-20T04:39:07.458452 | 2020-07-26T17:25:50 | 2020-07-26T17:25:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 608 | r | mod-metr.R | # metric module ----
metric_ui <- function(id) {
fluidRow(
text_ui(NS(id, "metric")),
plot_ui(NS(id, "metric"))
)
}
metric_server <- function(id, df, vbl, threshhold) {
moduleServer(id, function(input, output, session) {
text_server("metric", df, vbl, threshhold)
plot_server("metric... |
8e9c49654977e642fb2b007786746ccbaa2d4a45 | c9c6aed13ac8d59c59dd1df61a8a9a497c7b16a9 | /eco_model.r | 4b36b44c5d83ad34c921a14111cfa7c4a9a68de2 | [
"MIT"
] | permissive | mpdannenberg/geog-4470 | a795e4b9493bc67b798cdc4d4aad870062a853df | 93bcc6a42ac52ec1d0c39cae274926994dfabefa | refs/heads/master | 2020-12-10T16:29:43.893652 | 2020-06-23T14:54:41 | 2020-06-23T14:54:41 | 233,647,868 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,608 | r | eco_model.r | ## Functions for ecological model
date2doy <- function(yr, mo, dy){
yr <- as.character(yr)
mo <- as.character(mo)
dy <- as.character(dy)
dt <- paste(c(yr,'-',mo,'-',dy), collapse='')
doy <- strftime(dt, format='%j')
return(doy)
}
define_global_variables <- function(){
# Site constants
PAR2SWIR <... |
9305c05290d1dd00658d0c05d9c253fd96464ecd | 857c1cead6e3e79a0f1e425eccfb58bfa8d37f6a | /plot2.R | e6003bc5b5140156297c538eadfb20ef88a206cf | [] | no_license | tijanatadic/ExData_Plotting1 | e7d6cdcab3fd922194a2fc1433fffac15cd3ee37 | e2efaa6c9de7c71cfc5501f1a0973a741598aa62 | refs/heads/master | 2020-12-03T00:22:41.133298 | 2017-07-06T09:45:03 | 2017-07-06T09:45:03 | 96,023,032 | 0 | 0 | null | 2017-07-02T12:50:16 | 2017-07-02T12:50:16 | null | UTF-8 | R | false | false | 728 | r | plot2.R | #Plot 2
#Read subset of the data, only for dates 1/2/2007 and 1/2/2007
fileName<-"./C4/household_power_consumption.txt"
data<-read.table(fileName, na.strings=c("?", "NA"), sep=";", skip=grep("1/2/2007", readLines(fileName, ok=TRUE)), nrow=2879)
data<-na.omit(data)
#assign coumn names for data frame
names<-c("... |
5f1284e44b29ac5e6a1ce8588e18db669178a921 | c4d995e188be8b3059352fa006ca18e87d81bf0e | /man/supunsup_clean.Rd | a9c29a51dd73e1da4192b301876eecc9f8f84718 | [
"MIT"
] | permissive | kleinschmidt/phonetic-sup-unsup | 1adc867e3deb38515e0a22800b69156f3dee398b | 5c51177e61d7be67942a0657d6ce22b60956aab4 | refs/heads/master | 2021-01-17T07:04:08.934062 | 2017-02-01T16:00:49 | 2017-02-01T16:00:49 | 32,366,435 | 3 | 1 | null | null | null | null | UTF-8 | R | false | true | 459 | rd | supunsup_clean.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{supunsup_clean}
\alias{supunsup_clean}
\title{Non-excluded assignments (Expt. 1)}
\format{An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 82362 rows and 27 columns.}
\usage{
supu... |
d05ab9a490fa637c6752b86bbd0fb376ea2284a0 | caeb8764dabd4d0ed17d37e7486ad7e3d714b04e | /R/sym_symbol.R | 33e7706e6aad3bb36bc8832bdab49f0893d18029 | [] | no_license | cran/caracas | 579b5cefd2b7a2db85e691d140f2215f4275c3bc | 42b3bf8eb37ddf5b6adde28b33fdb3359ecc4f11 | refs/heads/master | 2023-08-16T18:15:53.677578 | 2023-08-11T13:13:47 | 2023-08-11T15:30:42 | 236,567,703 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,960 | r | sym_symbol.R | TXT_NOT_CARACAS_SYMBOL <- paste0("must be a caracas_symbol, ",
"e.g. constructed by symbol() ",
"followed by elementary operations")
PATTERN_PYHTON_VARIABLE <- "[a-zA-Z]+[a-zA-Z0-9_]*"
stopifnot_symbol <- function(x) {
if (!inherits(x, "caracas_sym... |
46b473cd1b43657955c73392066a08eedaff4975 | c7557b0ee7435b1a74e62b42161a18302934cca9 | /src/generate_comb_pair_exprs.r | 51d5636318bdcd3a64f68f98a3f563bfb0c811b8 | [] | no_license | tmorikuicr/spresso | cb86165cef34a55d3fdefd20e8bacea4beffa01c | 4f1609f272f7e9307814abe9a91225f7a7ec0ae0 | refs/heads/master | 2020-05-07T11:49:00.749320 | 2019-07-02T01:39:03 | 2019-07-02T01:39:03 | 180,476,675 | 4 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,843 | r | generate_comb_pair_exprs.r | library(getopt, quietly=T)
#======================================
# Options
#======================================
spec <- matrix(c(
"help", "h", 0, "logical", "show this help",
"inptable", "t", 2, "character", "[required] input som result table",
"cutoff", "c", 1, "numeric", "[required] cutoff of th... |
c9662d7f63f36e47cbec5d2f7cb6fa8f84cd547b | c78ea793abf6c910d92dde56a48d0c880e5ba174 | /samples/GSD1329/01_experimenting.R | e01aabfcb7e031d8b1adf316940ba7988eeec456 | [] | no_license | bdomokos74/Snippets | 9d10c29f2a99396ecf988cf79cd1d87303221596 | 3c2ce5e8f13802a768738830ca28884ba1975e0c | refs/heads/master | 2021-01-20T07:50:59.805027 | 2013-11-05T22:01:53 | 2013-11-05T22:01:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,196 | r | 01_experimenting.R | library(affy)
dat <- read.table("data/brca.csv", sep=",", h=T)
dat$Title <- NULL
ma.data <- ReadAffy(filenames=paste("data/array/", dat$Samples, ".CEL", sep=""))
sample.names <- dat$Tumor
colnames(exprs(ma.data)) <- sample.names
e <- exprs(ma.data)
dim(e)
gnames <- geneNames(ma.data)
image(ma.data)
boxplot(ma.data... |
74fd0774dbcfbc085599edee1d87dfcef4d32792 | 00e91ed4130c104fccf7f220a2784d25b1b2b128 | /man/barycenter.Rd | 6ea7d627d67f85e5b8e3aea7ad528518722c514b | [] | no_license | cran/centiserve | 9ff59d7a5ef3d4a068d6fbc9c969d259e99ad11e | ebc2eb58ab4d8fbb995b7d056127b9cf71b3b31e | refs/heads/master | 2021-01-01T05:55:06.643964 | 2017-07-15T08:34:41 | 2017-07-15T08:34:41 | 97,306,257 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,284 | rd | barycenter.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{barycenter}
\alias{barycenter}
\title{Find the barycenter centrality score}
\usage{
barycenter(graph, vids = V(graph), mode = c("all", "out", "in"),
weights = NULL)
}
\arguments{
\item{graph}{The input graph as igraph object}
\item{vids}{Vertex se... |
0094be3dcedc6763c2d4617087f858ce193d9dc1 | 10125f1600835b777a6a51b3198a4907d4452643 | /R/causationT2.R | bd9d31416c2d27ebb700df5cdf83391841669bd5 | [
"MIT"
] | permissive | jyfeather/LASSO-BN | d3c87faa498f33287852a3d3dfba2ad53356141a | a20a3b2da25396ee976e5f59eac93bf6b062ce33 | refs/heads/master | 2020-05-18T11:21:59.179394 | 2015-09-15T22:50:45 | 2015-09-15T22:50:45 | 25,669,694 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,166 | r | causationT2.R | # It is simple: for each x_i, you just build a regression model using PA(x_i) as the predictors,
# then calculate the residual vector, then use hypothesis testing (t-test?) to test
# whether or not the mean of the residual is zero. If yes, then x_i is not a rout cause variable.
# Otherwise, it is a root cause variab... |
33f60af95931545d3387802d7d26a36be9590b9a | bf39f0007def1af90f4522f3ddfd4fbcc384ef6c | /process_kdd.R | e09ec11413f884cde341eae4290da9e1a334d310 | [] | no_license | bibudhlahiri/learning | 13d2deddd4f2021db9915c3d7df10f103d262777 | da7ae4b542f9dae0f5d47b76aa5aa0591c9c88b3 | refs/heads/master | 2021-01-17T01:27:16.773005 | 2017-11-26T05:00:32 | 2017-11-26T05:00:32 | 23,405,407 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,978 | r | process_kdd.R | library(data.table)
load_kdd_data <- function()
{
filename <-
"C:\\Users\\blahiri\\kdd_cup_for_SAx\\kddcup.data.corrected"
data_corr <- fread(filename, header = FALSE, sep = ",", stringsAsFactors = FALSE, showProgress = TRUE,
colClasses = c("numeric", "character", "character", "... |
db074a87c2ee2f1efc5e759c32b91cd5971c716e | 1175c37b33404586c7a55fef15b91cfe9c3c48f4 | /man/make_filenames.Rd | 6a71752d8976cfdf357d02c57b5b6584ad1e63af | [] | no_license | rmsharp/renameSurgerySheets | 189c4fe6e457b56402cef49a3542144c0414eaa9 | ea3f2a08b067022b6890225b4e4a119764edd99a | refs/heads/master | 2021-09-17T20:21:04.473638 | 2018-07-05T00:48:15 | 2018-07-05T00:48:15 | 100,298,194 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 488 | rd | make_filenames.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/make_filenames.R
\name{make_filenames}
\alias{make_filenames}
\title{Make new sequenced filenames from full path names}
\usage{
make_filenames(file_list)
}
\arguments{
\item{file_list}{character vector of one or more having the path and basen... |
19cb77fc789c40db3b1bda9371a7c62a5e51d6a9 | 3208008eabc8c851c6bf6fa063dffa2c58bc8815 | /plot1.R | 26a38a1b74c4c7f3ab799ce407ce6efe3c7fe09b | [] | no_license | tothzoltan81/ExData_Plotting1 | 0fdcf99aa444f520da2c8dd6c69637de8b8d1505 | c3b6c1d3957cbe1ec1c1eb004fdfa607c4c2da9c | refs/heads/master | 2021-01-23T15:16:01.866430 | 2014-12-07T21:00:38 | 2014-12-07T21:00:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 535 | r | plot1.R | setwd("d:/R_files/CoURSERA")
data_household<-read.csv('household_power_consumption.txt',header=T, sep=";", dec=".", na.strings=c("?"))
data_household2<-data_household[66637:69517,]
datetime<-as.POSIXct(paste(data_household2$Date,data_household2$Time), format="%d/%m/%Y %H:%M:%S")
data_household3<-cbind(data_household2,d... |
33d8b6487f8688e350c59451887283932e694bc5 | 7f717941a38a9efd8f18da5ca8deb774c74f7b9a | /package/ashr/R/ashr-package.r | 8700c3e962e7fb67990d92a5ec3cf2802febeedc | [] | no_license | daichaoxing/ash | 0f7ceada274b1c59e757c6f366fc0f1d4b6fbec2 | 67524524e626150773716fb6ac22df3c0815b154 | refs/heads/master | 2020-12-24T09:01:06.222347 | 2015-02-10T03:11:27 | 2015-02-10T03:11:27 | 28,334,024 | 1 | 0 | null | 2014-12-22T14:53:14 | 2014-12-22T10:16:14 | R | UTF-8 | R | false | false | 50 | r | ashr-package.r | #' ashr
#'
#' @name ashr
#' @docType package
NULL
|
112c39556ed22b98ca2993469986e171483b4ea7 | 9876f2d87cbd128c440450d0e500e1c77d1fbe81 | /usable_demo.R | a6c7c07e7a9d013450c8f101c07a9f5eaab8f3ae | [] | no_license | gesturestudios/rPi_weather_station | 6e3d29bde6602633bec347f001c8fc8f67623c7d | 5542ce01d6b57ec84bbed95020e9ff9af420779b | refs/heads/master | 2021-06-08T15:59:31.119084 | 2020-05-09T19:03:22 | 2020-05-09T19:03:22 | 96,737,289 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,486 | r | usable_demo.R | # import libraries----------
library(shiny)
library(shinydashboard)
library(plotly)
library(ggplot2)
library(RMySQL)
library(dplyr)
library(reshape2)
library(gsheet)
hexcolor <- function(x){
if (x>100) {result = "#9E0142"}
else if (x>90) {result = "#9E0142"}
else if (x>80) {result = "#D62F27"}
else if (x>70) {... |
68a25cb008b2d774f9b77fecbddc40c667c28b6a | a2b58771d7acd6ab467d0f59da8dd7fdf7ac5ec2 | /R/trim_outlier.R | a6e4e8672c786b814c100aa5932a3eedf3d13423 | [] | no_license | Schwenk-Lab/BAf-R_package | 9849f64f965ef35d76f7f99ead23fd03fb698db6 | 18f67d3d4c2d21843330a97f36baea0d4571f2dd | refs/heads/master | 2022-06-28T00:22:13.155061 | 2020-05-12T08:59:31 | 2020-05-12T08:59:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,520 | r | trim_outlier.R | # -----------------------------------------------------------------------------#
#' Trim outlier out by Robust PCA
#'
#' Trim outlier out based on the orthogonal and score distances computed by
#' robust principal components analysis (PCA). After log-transformation, like
#' ordinary PCA, the values are scaled, but us... |
25427864d34c65008ce38c6869fbc1f96143d53e | c36783569c701ad176286bb1c57a7ff2559e3c56 | /In_class/Sept18_inclass.R | e839b28535e860db2c112ee4b1807f15702cdb42 | [] | no_license | AndrewUpdegrove/DataAnalytics2020_Andrew_Updegrove | a5eb0816f27081d372c6dc5f80c87e7c46109658 | 43438aefc518db3a4d9b1d161d76597f9ce264c0 | refs/heads/master | 2023-01-31T20:29:44.564365 | 2020-12-14T15:38:24 | 2020-12-14T15:38:24 | 292,866,954 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 383 | r | Sept18_inclass.R | multivariate <- read.csv(file.choose(), header=T)
attach(multivariate)
names(multivariate)
multivariate
#Scatterplots
plot(Income,Immigrant, main="Scatterplot")
plot(Immigrant,Homeowners)
#fitting Linear Models
mm = lm(Homeowners ~ Immigrant)
mm
plot(Immigrant, Homeowners)
abline(mm)
abline(mm, col="G... |
f27536f64bd914df681a9f3f26379b72b884cf83 | d86268c2fdd4195208c3fd5aecab31c324af7bca | /omd/man/fill_na.Rd | 38d89bc6e2bff7bac23147d245605ad48b81c247 | [] | no_license | bio-datascience/omd | 0e2edc61e86c135383b5d4bf29c14c95af026f5f | 5f2f532dfe077388f7911cc7999622c4b6a3f8b8 | refs/heads/master | 2023-08-28T21:44:27.488641 | 2021-11-02T15:25:02 | 2021-11-02T15:25:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 329 | rd | fill_na.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/helpers.R
\name{fill_na}
\alias{fill_na}
\title{Fill in NA from surrounding values.}
\usage{
fill_na(mat)
}
\arguments{
\item{mat}{A matrix with possibly missing values}
}
\value{
The same object.
}
\description{
Fill in NA from surrounding v... |
fe19085df818edb7ad42519d58cd6257178b6deb | 8912afe2f91c01683ec4230630dfc87595f3cf4c | /Happiness Challenge/Happiness.R | fc7905266da25bb21ee14c25f7c00d00c8c10856 | [] | no_license | dsouzarc/data101 | 70c757ce6e88408d68fab7773711bb0c3b2a3c02 | 9e0b759f1989a34328d785ddef696236c3ca3604 | refs/heads/master | 2021-01-10T12:17:36.765944 | 2016-02-27T20:37:37 | 2016-02-27T20:37:37 | 50,802,547 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,542 | r | Happiness.R | happiness <- read.csv('Happiness Challenge/Happiness.csv')
#Country happiness
countryHappiness <- tapply(happiness$HAPPINESS, happiness$COUNTRY, mean)
sortedHappiness <- sort(countryHappiness)
happinessLength <- length(sortedHappiness)
#Various default variable
yLimit <- c(0, 10)
yLabel = "Average Happiness"
imageDir... |
c704c4fd537b3f68eafe52d4ded6cbef1a427021 | cef9e0516578ddc6fc2f1184b8f4850408887c0d | /R/coxphSeries.R | 691035bdb2e6d3940666887195e5eef58b01565a | [] | no_license | tagteam/Publish | 62cad3b48930743d19b5246ce2f8aeb7343ebe98 | b027ce49f882ee051118247997f0a931cedcd46e | refs/heads/master | 2023-01-28T04:10:25.237750 | 2023-01-17T14:21:03 | 2023-01-17T14:21:03 | 32,985,684 | 17 | 4 | null | null | null | null | UTF-8 | R | false | false | 2,318 | r | coxphSeries.R | ##' Run a series of Cox regression analyses for a list of predictor variables
##' and summarize the results in a table.
##' The Cox models can be adjusted for a fixed set of covariates
##'
##' This function runs on \code{coxph} from the survival package.
##' @title Run a series of Cox regression models
##' @param fo... |
81e0f1a9f8fb0351ddb109e4e33703e74243e359 | 4c65edc1964eaf7cf648e9ec8300166d46a5eb92 | /Stop/app.R | 796a6c45391fc8cfa27bd99e421901d0891a48b2 | [] | no_license | josephYen0829/dataffffff | 66e8de506a48c70e2e51c5de7cc727276b844c73 | 851ea8e4488deaf0a2cc2824bacc878b997131f0 | refs/heads/master | 2020-03-23T04:54:03.466934 | 2019-09-27T05:50:17 | 2019-09-27T05:50:17 | 141,111,863 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 103,183 | r | app.R |
# setwd("~/PIITSStop Project/Shiny Dashboard/Data")
############################################################
# Load libraries and depended scripts
############################################################
dependencies <- c("shinydashboard", "leaflet", "DT", "shiny", "readxl", "plotly",
... |
a040b7ff6dae1cd87b41eeb3247fd99895ea911a | 2cbe58f7cd1af3d7cccfda6adfdc24bb4b5dce82 | /election1.R | f7a6f8659f96c774e9e2ea10d5b8aff0330a9a7f | [] | no_license | gitcnk/Git-class-demo | 80a731611576bba847fbb8c17cfd4f3da91d0a2b | aeac81250de3dfd117d8d531cc044bc5c15a70b8 | refs/heads/master | 2023-08-12T13:47:52.382695 | 2021-10-14T09:13:15 | 2021-10-14T09:13:15 | 291,781,172 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,669 | r | election1.R | library(dplyr)
FL <- read.csv('https://raw.githubusercontent.com/gitcnk/Data/master/ElectionData/Florida_before2016.csv')
FL_edu <- FL %>%
select(no_hs_diploma,
hs_diploma,
associates_degree,
bachelors_degree,
above_bachelors_... |
72ebb44b16210cb4176f941ff1ea251d9949c620 | a9e2d4666f1b55c5c70ffece61a49d1c2cf17478 | /random.R | 0cb4e6041d26d56bbc156eea69af8405b04eaaed | [] | no_license | cforter/wine_experiment | 7f56264050761374d57176db58408dce0ea05288 | 36c0f846caadf25ae671f7ce9e470853a388c75e | refs/heads/master | 2019-01-02T04:55:15.902442 | 2015-05-01T17:52:16 | 2015-05-01T17:52:16 | 31,523,285 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 164 | r | random.R | assignment <- c(rep(1,2), rep(0,2))
assignment.random <- sample(assignment, 4)
names <- c("Tyler", "Tony", "Vedant")
df <- cbind(names, assignment.random)
View(df)
|
3ae65ff9272da955d2f1c38e65c2e2a95aee4068 | 3bb9a36aaed141664f5533cc8ce36c11c6df09b1 | /src/munge.R | 04070960f21f3b213202cebb897a1127ca7ac835 | [] | no_license | tereom/HMM | 0dc4c05d1792b7bc060e094b8b4222fc9355492e | d038497ac9d5c175c59b3bb4db5bb19914783708 | refs/heads/master | 2016-09-05T14:15:50.505639 | 2014-11-20T16:05:21 | 2014-11-20T16:05:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,800 | r | munge.R | library(lubridate)
library(stringr)
library(raster)
library(plyr)
library(dplyr)
library(tidyr)
library(Hmisc)
library(ggplot2)
library(fda)
paths <- list.files(path = "../data/gpp", pattern = "\\PsnNet_1km.tif$",
full.names = TRUE)
## Aislaremos una región del mapa
image_1 <- raster(paths[1])
plo... |
a822b5232a7604374dd69a5768462f6ef2c03213 | 34eec9275ed1b4106e0aca5af87ce33e99a4f782 | /Exploratory Data Analysis/Week4 Project/Plot5.R | 4088f5f00177a5211bfe4a023a5069a4a0797e2e | [] | no_license | dexterwang/DataScienceJohnHopkinsUni | eb6bb7fc4fdadd7c31b0b2415125b9228c3eea17 | 8182450515a49317ec4034bce7bbc61b8461431a | refs/heads/master | 2020-12-24T20:24:30.050685 | 2016-05-25T17:29:42 | 2016-05-25T17:29:42 | 57,115,505 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,630 | r | Plot5.R | setwd("C:/D/R/Exploratory Data Analysis/week4 project")
if (!require("ggplot2")) {
install.packages("ggplot2")
}
library(ggplot2)
# read data from source file
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
# by searching Source Classification Code file,
# Short.Name (the... |
ddcfba770366bb048ec0f6c8d73c05500407c674 | 4af1baeb8bd7ca845beb983fcf7c662ab5df6d7e | /Finance/S&P Analysis - All Stock.R | d7a6b578535503d7e1f0031817b68632f04f7682 | [] | no_license | santiagovama/R | 54a52cebae1d36ddbabb3080205fbf2fe8b7b956 | c12cd9b9de4e7a8888386c20ce64a1a481327766 | refs/heads/master | 2023-08-17T06:27:32.434463 | 2021-10-02T15:01:05 | 2021-10-02T15:01:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,464 | r | S&P Analysis - All Stock.R |
# http://www.mattdancho.com/investments/2016/10/23/SP500_Analysis.html
library(quantmod) # get stock prices; useful stock analysis functions
library(xts) # working with extensible time series
library(rvest) # web scraping
library(tidyverse) # ggplot2, purrr, dplyr, tidyr, readr, tibble
library(... |
c1647fcb999d41715b4c10f8eb4184c73be62f73 | 7737cd699104d2a88668f1defa564ef63c52628e | /workout03/binomial/man/bin_kurtosis.Rd | 513547f01f8c493d940588c802022bec27875530 | [] | no_license | stat133-sp19/hw-stat133-yousufhusain | a7a920554e5b93992c535152f8c4c5566121b6b9 | d8ddc6f8b6c3dddc75a4e295f915ad3f92dfaf1e | refs/heads/master | 2020-04-28T20:47:21.487400 | 2019-05-04T03:32:54 | 2019-05-04T03:32:54 | 175,556,678 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 384 | rd | bin_kurtosis.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/All Functions.R
\name{bin_kurtosis}
\alias{bin_kurtosis}
\title{bin_kurtosis}
\usage{
bin_kurtosis(trials, prob)
}
\arguments{
\item{trials}{number of trials (numeric)}
\item{prob}{probabiltiy value (numeric)}
}
\value{
kurtosis
}
\descripti... |
0d84f7fc0dbbae8bd83d7d60c0585a0ef8bda147 | 6197cec08c2f1d9d0e4d0f14c179e38d8705cbf1 | /man/get_ancestry_matrix.Rd | 6a5cf41b4dd043d58092ddf9d03bd91cf701382e | [] | no_license | dgrtwo/GSEAMA | a63ab3db90027228ae3b83b0800570cfb14e20e6 | 54913f20b83444aa4f8292f0a14152a111ac24a7 | refs/heads/master | 2021-05-16T02:39:32.695142 | 2018-05-11T17:57:26 | 2018-05-11T17:57:26 | 23,238,459 | 12 | 9 | null | null | null | null | UTF-8 | R | false | true | 746 | rd | get_ancestry_matrix.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/GOMembershipMatrix.R
\name{get_ancestry_matrix}
\alias{get_ancestry_matrix}
\title{Build an offspring matrix of GO terms}
\usage{
get_ancestry_matrix(terms, ontology = c("BP", "MF", "CC"),
type = "OFFSPRING", upward = TRUE, tbl = FALSE)
}
\... |
f2fe8030bb34d65958f0d2d7b5dc2796962cdad6 | 3bd97ff19bc6cb8db2f20df43cf6f1bc9a3aba82 | /scratch.R | a95f2575e6a019abd9afa6219ca8d82c0621f4da | [] | no_license | bpafoshizle/RepData_PeerAssessment1 | e4204b116302664dc3295a2ae3313af4242952e0 | 55cced4d29aaedeb60f55a63063d9f358903029f | refs/heads/master | 2021-01-17T12:12:47.944910 | 2015-02-09T05:00:11 | 2015-02-09T05:00:11 | 30,498,091 | 0 | 0 | null | 2015-02-08T16:58:46 | 2015-02-08T16:58:46 | null | UTF-8 | R | false | false | 894 | r | scratch.R | readZipCSV <- function(zipFilePath, ...){
# Function extracts a csv file in a zip file assuming the same name.
# Get the file name without extension
fileNameNoExt = gsub(pattern = "(.*)\\..*$", "\\1", basename(zipFilePath))
read.csv(unz(zipFilePath, paste(fileNameNoExt, ".csv", sep="")))
}
activity = readZ... |
dfe3abcfc69338e2f0120826661beb6b2db6d476 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.developer.tools/man/drs_describe_jobs.Rd | cd11002d80723dce48890edcf71cc8139c2e3084 | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | true | 1,009 | rd | drs_describe_jobs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/drs_operations.R
\name{drs_describe_jobs}
\alias{drs_describe_jobs}
\title{Returns a list of Jobs}
\usage{
drs_describe_jobs(filters = NULL, maxResults = NULL, nextToken = NULL)
}
\arguments{
\item{filters}{A set of filters by which to return... |
76aba84135d247ae3de8c99ca4c54e98f869f0fb | 7c30f05e1c8e2bf6e5ce2c2a44257d3478fd1427 | /twitter sentiment.r | d331fc8f4142b6c6591c79633b0b57c6de177845 | [] | no_license | Rishisai/rishi2mshanker | 8dca9557a757018e27d92fc2c1c483e49bb1de48 | b27aceb6f78faefd4b54686ebe86ebf84f868a7e | refs/heads/master | 2021-01-22T18:33:01.373610 | 2018-05-06T17:34:13 | 2018-05-06T17:34:13 | 102,409,624 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,169 | r | twitter sentiment.r | data = twitter_sanders_apple2
data$class = NULL
str(data)
is.na(data)
data$text = gsub("&", "", data$text)
data$text = gsub("(RT|via)((?:\\b\\W*@\\w+)+)", "", data$text)
data$text = gsub("@\\w+", "", data$text)
data$text = gsub("[[:punct:]]", "", data$text)
data$text = gsub("[[:digit:]]", "", data$text)
dat... |
33c1e044294eeb13e88ff9f04581812822fdfe4e | 9e06252e613edcefaa4d7c569a3f18ab4ede85e4 | /R/Officials.getByOfficeTypeState.R | d9af2595c04257ae1ded6d504cc3c249f2219038 | [] | no_license | umatter/pvsR | b9be083c1224a96fdbc817b2c2749b9763284e7d | 9ab57a5a67c0bbf9e0342ea37e14ea496d180df4 | refs/heads/master | 2021-01-19T08:41:25.275771 | 2021-01-05T06:38:13 | 2021-01-05T06:38:13 | 87,662,576 | 1 | 3 | null | 2021-01-05T06:33:50 | 2017-04-08T20:34:59 | R | UTF-8 | R | false | false | 3,535 | r | Officials.getByOfficeTypeState.R | ##' Get a list of officials according to office type and state
##'
##' This function is a wrapper for the Officials.getByOfficeTypeState() method of the PVS API Officials class which grabs a list of officials according to the office type and state they represent. The function sends a request with this method to the PV... |
7395a51fbcea35f97759675bb0be46bff03346c2 | 7ee660ae88243581367005cc35f2e5be95d5b284 | /inst/unitTests/test_combine.R | 24b110f8d688d92a53fbc3185540c3975eef8164 | [] | no_license | dbnunes23/bsseq | 1de16429c41ff2257c18dab3657f144e18b20d35 | 98a0ecfaa56d25e77012aabdd45ec6ab19979b48 | refs/heads/master | 2021-01-25T08:01:15.903678 | 2017-04-11T12:44:05 | 2017-04-11T12:44:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,924 | r | test_combine.R | checkBSseqAssaysIdentical <- function(x, y) {
stopifnot(is(x, "BSseq") && is(y, "BSseq"))
assay_names <- c("M", "Cov", "coef", "se.coef")
check_identical <- vapply(assay_names, function(an) {
if (!is.null(getBSseq(x, an))) {
identical(as.array(getBSseq(x, an)), as.array(getBSseq(y, an)))... |
8ce5ab4badc7a394572c69f4696cd3c4728f5cef | 9300d3ad75869869d214d6bdfd390b02f5f3c137 | /exercise_3/Exercise3.r | 6ea234fb2099df14e32c1e6b6bd21d23941cb1f6 | [] | no_license | KouXou/R_Exercises | 11cb1d006253dcc1927cd856e97c6bd2ae879fa0 | 5840aa9e297d3209b5b0c0f9dd239a9a712d1ec4 | refs/heads/master | 2022-04-11T01:19:02.968963 | 2020-03-23T18:36:29 | 2020-03-23T18:36:29 | 230,941,794 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,590 | r | Exercise3.r | library(car)
library(leaps)
# Function to read Data
read_data <- function(file_path) {
d <- read.table(file_path, header = TRUE, dec = ".", row.names = 1)
return(d)
}
# Function to plot Data
plot_data <- function(in_data) {
pairs(in_data)
summary(in_data)
}
ozone_data <- read_data('./ozone.txt')
head(ozone... |
b9827dba0570d17aa118f5d779c081733a51bf30 | c2d9ded06bb67d510a0797f8bf22b24532f80f0d | /03_Humidity_Correction.R | 8e68812dcdde9c5da771bfebae49782e10244557 | [] | no_license | Neys-Code/Masterarbeit | 2093a34c68f6d7b2043aa9d60725ff9bf8ac114a | 7fa32a3f0a73381c74d3e66c3f5dbc5a664ffb1b | refs/heads/main | 2022-12-24T23:51:35.473151 | 2020-10-11T21:03:15 | 2020-10-11T21:03:15 | 303,211,670 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,044 | r | 03_Humidity_Correction.R | ########################################################
#
#Humidity correction
#
########################################################
#run script 2 first !!!!!!
liste3<-c()
# best parameter for every sensor and correction formula
# create variables
parameter.PM10<-c()
corr.PM10<-c()
parameter.PM25<... |
7a56167283914c77d1832c1f1055216d0a4fcc4e | 97a26457d27e5976d7f7a716b795999ba9b03de5 | /lab2a.R | 1f6e10fa9cd715674d32cc1c178aa7a433fee207 | [] | no_license | agdosne/uuadvstatcomp | 015681766f8ab258f31dc4a38a26c9bf2ddffa69 | eb3a87fa2422e346413215d8e2b4e546819c805f | refs/heads/master | 2021-01-16T23:22:54.313582 | 2015-12-04T09:45:48 | 2015-12-04T09:45:48 | 43,811,883 | 0 | 0 | null | 2015-10-07T11:47:00 | 2015-10-07T11:47:00 | null | UTF-8 | R | false | false | 4,745 | r | lab2a.R | #####################################################
### Advanced Statistical Computing Course
### Lab 2
### Winter 2015
### Report by Anne-Gaelle Dosne
#####################################################
### Optimization
myfun <- function(x) { # create a univariate function
(x - 3)**2 + 2 * (x - ... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.