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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1100a73dc6bbcd1d249bd3edabcaa67299995e71 | c78170677c97a0f8e258bf651f2bd55068274cb8 | /analysis/3_msg_parameters/var_cost.R | d8720c114fb8ebfe7e937c61911c54ba1db390d7 | [] | no_license | junukitashepard/message_trade | a013b18f5a66021cc052b32b37b9ceb6ba57b5c1 | be07af7b88a9f45425f5b933279bef617b7b4faa | refs/heads/master | 2022-07-23T23:18:46.670202 | 2022-07-14T17:30:53 | 2022-07-14T17:30:53 | 190,220,147 | 1 | 4 | null | null | null | null | UTF-8 | R | false | false | 5,331 | r | var_cost.R | ####################################
# Build parameters: var_cost #
####################################
# You must run 2_regress files before compiling parameter!
input_reg <- paste0(wd, "output/analysis/regress")
# Import regression file
paths <- readRDS(file.path(input_reg, 'var_cost_from_reg.rds'))
isid('paths', c... |
62c42c8bf5eafd303d055dd20b871909f3398ba4 | 6656318be29e1b39b5ab20d6872a27dd9af923b2 | /man/toDD.Rd | 7842d714fc0f430970c0545b81f4a1ce131087bf | [] | no_license | mdfrias/downscaleR | 4ceb27793d2a119860d18ed5bc48b90a02705250 | a841f836eccae7ba749030b3a65b997745906a92 | refs/heads/master | 2021-01-16T22:53:43.767577 | 2015-08-18T09:19:39 | 2015-08-18T09:19:39 | 31,306,761 | 0 | 1 | null | 2015-02-25T09:42:57 | 2015-02-25T09:42:57 | null | UTF-8 | R | false | false | 684 | rd | toDD.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/toDD.R
\name{toDD}
\alias{toDD}
\title{6-hourly to daily data aggregator}
\usage{
toDD(NDarray, dimNamesRef, dailyAggr)
}
\arguments{
\item{NDarray}{A N-dimensional array, as returned by \sQuote{readDataSlice}}
\item{dimNamesRefRef}{... |
2af1c6c08407227471efec69d46d00d11238777f | 6589b6692169bc5e2aef8b4b960dc85b644f0c20 | /utility_theory_and_binary_output_of_choice.R | 703818eaaff16e64fd049e82c84113d6c5d7f56c | [] | no_license | dimkon97/hello-world | 30d0a65f641599a389d6ce8be8e0218d9ddac466 | 37cc5e9d4c83acaca2d27f0000f36b6ced142138 | refs/heads/master | 2021-01-25T10:55:45.596169 | 2017-06-27T21:20:54 | 2017-06-27T21:20:54 | 93,891,298 | 0 | 1 | null | 2018-10-31T17:50:46 | 2017-06-09T19:38:53 | Matlab | UTF-8 | R | false | false | 1,698 | r | utility_theory_and_binary_output_of_choice.R | #Initialize gambles in a dataframe
gambles <- data.frame(safe_1 = 100, safe_2 = 80,
risky_1 = 190, risky_2 = 5,
p_1 = seq(0.1,1,0.1))
gambles$p_2 = 1-gambles$p_1
# exp_val <- function(alpha,gamma){
exp_val_2 <- function(alpha, gamma, gambles){
gambles$wp_1 <- exp(-... |
77e4f939d2c68fd57ebfc3379888441a5532ecac | ab7d15d06ed92cd51cc383dc9e98ae2a8fa41eaa | /man/get_leverage_centrality.Rd | 6a1c6b94e0fcdbdaf60600a6a2641332fa0bf7d5 | [
"MIT"
] | permissive | rich-iannone/DiagrammeR | 14c46eb994eb8de90c50166a5d2d7e0668d3f7c5 | 218705d52d445c5d158a04abf8107b425ea40ce1 | refs/heads/main | 2023-08-18T10:32:30.784039 | 2023-05-19T16:33:47 | 2023-05-19T16:33:47 | 28,556,914 | 1,750 | 293 | NOASSERTION | 2023-07-10T20:46:28 | 2014-12-28T08:01:15 | R | UTF-8 | R | false | true | 1,391 | rd | get_leverage_centrality.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_leverage_centrality.R
\name{get_leverage_centrality}
\alias{get_leverage_centrality}
\title{Get leverage centrality}
\usage{
get_leverage_centrality(graph)
}
\arguments{
\item{graph}{A graph object of class \code{dgr_graph}.}
}
\value{
A ... |
30eaa2f110ab05e88d228702eff18804a5637486 | 29ced85982f8f7739f6b4df28f042c2299456549 | /BasicFilters/SpdFilt.R | b77427882b6b0fafcb06c58d07265236b9b16ce9 | [] | no_license | ATLAS-HUJI/R | f5a056f5b9e82b277a2c1f41ad3c9f746e585fe1 | d74b5d21c7b8e70e42620633159bad6e887b391b | refs/heads/master | 2021-01-24T02:15:51.298877 | 2018-10-16T13:11:48 | 2018-10-16T13:11:48 | 122,840,774 | 4 | 3 | null | null | null | null | UTF-8 | R | false | false | 631 | r | SpdFilt.R | #speed based filter by Ingo Schiffner 2017
#calculates speed between consecutive localizations and filters out segments exceeding spd_lim
#assumes x and y coordinates are given in a projected format in meters and time(t) given as ATLASTimeStamp(ms)
#returns a data.frame containing filtered x,y and time(t)
SpdFilt <... |
e14a09c84e88e76807c880546dc4d880e76b12a6 | 346ca394e5d9f64ee6cc9741f285f603c903c727 | /Shiny_connect.R | 69008bcb814273b798823d0efc7a222b58fe5668 | [] | no_license | DavidykZhao/Comparative_pol_measurement_project | 845825cfe3911b31e256bb106a8972eda2279d98 | f0595449d1e9e47672389e9b2f0fe512fed259ed | refs/heads/master | 2020-04-23T20:09:13.510100 | 2020-04-03T21:13:47 | 2020-04-03T21:13:47 | 171,429,870 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 288 | r | Shiny_connect.R | install.packages('rsconnect')
library(rsconnect)
rsconnect::setAccountInfo(name='yikai-zhao',
token='12389A9B53A08CC115D3FD54D4F123F3',
secret='')
rsconnect::deployApp('/Users/zhaoyikai/Comparative_pol_measurement_project/Shiny_app')
|
7355243e212a204594180e0497f608737b8d3a06 | ce2d6ec9fd987c5b6325eaf7271534ace6010152 | /QualityControl.R | 3c265239a4d2fba88f34554caedcfbaaabb44d9f | [
"MIT"
] | permissive | Chenmengpin/CoA-scRNAseq-pipeline | 3005b6c58a690080ab55bcc8b787e36013f5f10f | 89b211d33dae1f669fea4a08de13bce5cd98bd00 | refs/heads/master | 2020-09-18T18:55:56.472782 | 2018-12-12T05:50:21 | 2018-12-12T05:50:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,465 | r | QualityControl.R | # Do quality control on a cell and gene level
# Cell-level quality control
CellQC <- function(q_array, m_array, id, qc_m_array, original_q_array) {
geneset_size <- rowSums(q_array != 0)
gene_qc <- geneset_size > 1500
gene_m_array <- m_array[gene_qc == TRUE,] # these need to be separated so there can be a separat... |
d0cd6ae3538847fcfe394032d3fc3a2efb252f14 | f18a02daf3f78f763962f1b7165b7bfa4525cdb2 | /Random number mean.R | 52c7b21701c29d020b80c8ee9ffb2e06733544b1 | [] | no_license | rashmigangadharaiah/Statistical-Analysis-using-R | e1d8c393a113c946dde16b4d795599bf9348cf61 | d3e635d8a697c40731498e0928ce174d43d80668 | refs/heads/master | 2021-10-08T07:59:13.618425 | 2018-03-28T04:50:10 | 2018-03-28T04:50:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 156 | r | Random number mean.R | n<-10000
count<-0
for (i in rnorm(n)){
if (-1<i & i<1){
count<-count+1
}
}
answer<-count/n
answer
x<- rnorm(5)
for (i in x){
print (i)} |
50e96d1e43ea4fe20cdd1b12161cc200c82af861 | 2e4ca04aaff834b3e0a8177d1ebb6f86c057e674 | /man/sortData.Rd | cdd6c8e29d46c733c072f45c33f216cf68a9a9ea | [] | no_license | crtahlin/medplot | 60691530bc273a056143883c15f451d966b1e009 | 1c8365ca0c79459170b2108ac12fe2db982303ba | refs/heads/master | 2021-01-19T07:34:35.405634 | 2016-01-26T06:04:10 | 2016-01-26T06:04:10 | 7,378,840 | 4 | 2 | null | 2015-01-22T09:48:42 | 2012-12-30T17:59:19 | R | UTF-8 | R | false | false | 718 | rd | sortData.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{sortData}
\alias{sortData}
\title{Sort results data}
\usage{
sortData(data, sortMethod = "BEA", nUNITS, DATES.COLUMN.FIRST,
DATES.COLUMN.LAST, TEST.RESULT.LEVELS)
}
\description{
Function that sorts the data according to a criterion. Not to
be called directly... |
2f32045b147b7e9483a917ea254c56b0fbe4f52d | 0c0aad04a8a20651d11ace04fb47167798cbad7f | /VFGA_DESAFIO2.R | 3ca768f02d8d6d8b796473dcf1157abce2a8bb20 | [] | no_license | Vero-arte/VFGA_DESAFIO2 | de281cf9b645914e98f011b3b683e896a235151b | 641a8ee59d283e3edfdf41eb8309a51151859e27 | refs/heads/master | 2022-12-25T12:08:16.877458 | 2020-09-24T03:27:10 | 2020-09-24T03:27:10 | 298,157,863 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,400 | r | VFGA_DESAFIO2.R | #
#Hecho con gusto por Veronica F. Garcia Arteaga (UAEH)
#
# LABORATORIO - Desafio 2
#
#cargar datos
#
#
#
#
# cargar libreria ggplot2
library(ggplot2)
# grafica de puntos con colores LEYvd
ggplot(data = gender,
mapping = aes(x=STATE,
y=LEYvd,
color= STATE)) +
geom_po... |
aedd42d4411cf23d09b29665076558b747dab96e | 1c6c63233fbd72e06573c114dbfc881aecde130d | /code/analysis/compare.ROC.R | d4ce2f115294c1b4451971da50d75ba7319633fe | [] | no_license | Minzhe/geneck.ena | 2f361a4754865262ebf9539363860c13eb360369 | 498f886a36136c22b7b1ddb1f86d53af236a27dc | refs/heads/master | 2021-01-01T18:53:48.235866 | 2017-12-20T18:09:09 | 2017-12-20T18:09:09 | 98,461,856 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,169 | r | compare.ROC.R | ############################################################
### Compare.ROC.R ###
############################################################
setwd("/project/bioinformatics/Xiao_lab/s418336/projects/geneck.ena")
suppressMessages(library(argparse))
source("code/analysis/compare.... |
9d2fc1a2621c375e988554c61e530edcdd77ca83 | b01c5ad3ea739749059c4ee94a4734349a5f71ed | /R/biom_survey_map_compespecie.R | 001704c8068ca18922953dd9bbe2c654ffb1cab4 | [] | no_license | PabloMBooster/fenix | 278e7f1d26241528a2c60001372c47b033c5b41e | 3f19c4ce0108d33d7813eedc6b1c07c5f9557d2c | refs/heads/master | 2023-08-03T14:40:33.426599 | 2023-07-31T21:45:30 | 2023-07-31T21:45:30 | 76,564,342 | 3 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,245 | r | biom_survey_map_compespecie.R | biom_survey_map_compespecie = function(baseDat, outFolder = ".", outFile = "MapPieComposicionSp.png", xLim = c(-83, -70), yLim = c(-20, -3), Pch = 16,
Cols = rainbow(6), CexPoint = 0.9, widthFig = 700, heightFig = 820,
spLabels = NULL, Add = F, save = F, portImpor... |
cba7f1184d1263695da2ea117392ee3eca3fb7fb | d5e64b2499f6a4ae18dff2c15894caf91bd41fc7 | /R/spheroid_dist.R | 4adf33c6a3f049293c9cbac1e986887efd9bfa06 | [
"MIT"
] | permissive | bczernecki/climate | cbe81b80335d3126ad943726c8d3185805900462 | 9c168a6a58854c374cd4c7b13b23cba28adeb7e2 | refs/heads/master | 2023-04-14T03:02:26.083700 | 2023-04-01T13:48:48 | 2023-04-01T13:48:48 | 197,452,909 | 66 | 23 | NOASSERTION | 2023-04-01T13:48:50 | 2019-07-17T19:49:40 | R | UTF-8 | R | false | false | 1,099 | r | spheroid_dist.R | #' Distance between two points on a spheroid
#'
#' Calculate the distance between two points on the surface of a spheroid
#' using Vincenty's formula. This function can be used when GIS libraries
#' for calculating distance are not available.
#'
#' @param p1 coordinates of the first point in decimal degrees (LON, LAT... |
0805acbd532702cf37599ac8fdb801b4302ed7ce | 3396f4a2a342489c200ac10c09cdff4e6706d6d4 | /Teleconnections/dataPrep/NLDAS_to_Met_Hourly.R | 78f9473b265f4e651b5abf59f07151a5a30b52c3 | [] | no_license | CareyLabVT/MacrosystemsEDDIE | ab0b38fa49cffdb71d1638b639c601b1c17ec5dc | 7f8cf883075eb332121bbae68a8c3a450d41d9c1 | refs/heads/master | 2023-06-07T04:43:13.636680 | 2023-05-26T13:52:03 | 2023-05-26T13:52:03 | 91,721,847 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,309 | r | NLDAS_to_Met_Hourly.R | ### Format NLDAS output into GLM-friendly format
pacman::p_load(tidyverse, lubridate)
options(scipen=999)
LakeName = 'Prairie Lake'
source <- paste('C:/Users/KJF/Desktop/R/MacrosystemsEDDIE/Teleconnections/dataPrep/NLDAS/',LakeName, '/',sep='')
glm_dir <- paste('C:/Users/KJF/Desktop/R/MacrosystemsEDDIE/Teleconnections... |
17f8d978aa28d2f2688956d5959ca3073a067cfb | b09958d658d683d351c30630bc6c9dacac825c42 | /analysis/data_selection.R | 8e4c6dfdcb336812c1bd5f5d795b890278869a6f | [
"MIT"
] | permissive | opensafely/comparative-booster | e5e45a66c25315dc7c09492945d658fc7b9e330f | bca54292baa80e967187ca28988d4897ae88aedc | refs/heads/main | 2023-08-23T10:44:15.655135 | 2023-03-01T15:29:21 | 2023-03-01T15:29:21 | 481,140,039 | 0 | 0 | MIT | 2022-11-28T16:21:42 | 2022-04-13T08:48:40 | R | UTF-8 | R | false | false | 10,057 | r | data_selection.R |
# # # # # # # # # # # # # # # # # # # # #
# Purpose: import processed data and filter out people who are excluded from the main analysis
# outputs:
# - inclusion/exclusions flowchart data (up to matching step)
# # # # # # # # # # # # # # # # # # # # #
# Preliminaries ----
## Import libraries ----
library('tidyverse... |
5dda57c1a58f10781f4da962493a2ca6a2c1152c | 2e1f19f01e19a1acf2465d24fc3954263e281b52 | /man/get_quarter_office_expenses_house_member.Rd | 910d998ef2e942e97c8f4acd6ad559713f452163 | [] | no_license | DavytJ/ProPublicaR | ebdc03ac0bc30efa6933aaa62316fa3fcbf98b00 | e9fe623ffc063665581238c3196f78bb32b08b77 | refs/heads/master | 2020-03-19T08:33:55.239980 | 2018-10-30T16:10:25 | 2018-10-30T16:17:09 | 136,215,002 | 0 | 0 | null | 2018-06-05T17:56:19 | 2018-06-05T17:56:19 | null | UTF-8 | R | false | true | 1,248 | rd | get_quarter_office_expenses_house_member.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_quarter_office_expenses_house_member.R
\name{get_quarter_office_expenses_house_member}
\alias{get_quarter_office_expenses_house_member}
\title{Get Quarterly Office Expenses by a Specific House Member}
\usage{
get_quarter_office_expenses_h... |
ae24878780b7735fea0e0414f088e6838e4ae69d | bae57f27c447250ef182abe8c6d12e13aea24ba2 | /R/data-directory.R | d9b4d544cdf7a57d87e40d63d5e9cdf782d5b662 | [] | no_license | denalitherapeutics/archs4 | a254680554856fdcb18a81a3f0fc6f71d045fd46 | be3aa0e5b7eb3321223d5f63ef193d77a6f9a2fc | refs/heads/master | 2021-04-15T16:54:55.559093 | 2018-09-19T21:54:21 | 2018-09-19T21:54:21 | 126,898,823 | 10 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,516 | r | data-directory.R | # These are lower-level functions that support "the health" of the local
# datadir that is used to store the data required to drive a
# LocalArchs4Repository
#' Initialize a local datadir to act as an ARCHS4 data datadir
#'
#' @details
#' A local datadir needs to be created and initialized (wth a `meta.yaml`
#' file),... |
ad0760f4b42fea66b46365d3cd27563506c35e05 | 81e3ecad25fd6fc01370f665d284ec1fe52f494e | /VARFIMA_lobato.R | 71e92c63f2741ef9bf1b3e8c9377813201fd9b72 | [] | no_license | booleanboo/VEGARMA | 2eddf961ba6fb9daeeae198245c5cfe7d7a5b5b5 | 0bf9d71b558c2f2ad133f225e4ad8ecea7a8197d | refs/heads/main | 2023-04-23T13:47:58.626154 | 2021-04-08T01:08:41 | 2021-04-08T01:08:41 | 349,892,474 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,349 | r | VARFIMA_lobato.R | library(LongMemoryTS)
library(MTS)
library(FKF)
library(orthopolynom)
library(MASS)
library(longmemo)
library(beyondWhittle)
library(forecast)
library(nlme)
library(msos)
theta1<-vector()
theta1[1]<-1
theta1[2]<-0.2
for(j in 3:1000)
{
theta1[j]=theta1[j-1]*(j-2+0.2)/(j-1)
}
theta2<-vector()... |
46a967d48d0604f58db7f253f3e91ab0f04046fc | 4c51ece15418ab7523df7b5dffc55c0f5c2a2c6c | /verify_gRNA_matrix.R | 5fef335b184ef2bc31636f7b2a7aebaa1f589fed | [] | no_license | scarlettcanny0629/import-gasperini-2019 | 482e7b59feee8082c756376f81f31592ab43bfd0 | 98531a1466532b08f9687e69194e7e77db542e24 | refs/heads/main | 2023-06-28T04:53:11.901772 | 2021-08-06T16:13:25 | 2021-08-06T16:13:25 | 388,646,676 | 0 | 0 | null | 2021-07-23T01:42:02 | 2021-07-23T01:42:02 | null | UTF-8 | R | false | false | 2,581 | r | verify_gRNA_matrix.R | # test correctness of gRNA matrix
require(dplyr)
# set directories
gasp_offsite <- .get_config_path("LOCAL_GASPERINI_2019_DATA_DIR")
intermediate_data_dir <- paste0(gasp_offsite, "intermediate/")
raw_data_dir <- paste0(gasp_offsite, "raw/")
# load gRNA barcodes, count matrix, and indicator matrix
gRNA_barcodes_in_use... |
8d178d88a412780d4699ceda225438b75d035873 | 26f722da50b82b98bf8c730a14e1e4bc886021af | /man/pca_time.Rd | f05492bd69cb622725a57890b93b2053b95688fa | [] | no_license | panders225/mvstats | 0633db302a7a40b64a35833ad972a02799e782fc | 74c01ac74cb55e0c6bedc50a813e59806c6c0240 | refs/heads/master | 2021-05-15T07:13:11.407638 | 2017-11-20T22:06:47 | 2017-11-20T22:06:47 | 111,461,151 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 497 | rd | pca_time.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pca_time.R
\name{pca_time}
\alias{pca_time}
\title{Producing PCA greatest hits}
\usage{
pca_time(x)
}
\arguments{
\item{x}{a matrix or dataframe object}
}
\description{
Produce a complete table of PCA loadings,
and a biplot of the first two p... |
567f3c67588c39272fc9926c51091650f45a6b9b | bc63aeafff31bb14fbc429a85de4d85078573d39 | /ppp/ppp.R | 83d77645f4c0276abc84dd8a382685e53dfe9688 | [] | no_license | nmmarquez/re_simulations | e5c0c286fc574809bda58f1bd082d267f9a74597 | db25cbea4a512c71e3d90eabece7cd721ef1e424 | refs/heads/master | 2021-01-17T17:58:51.040721 | 2020-08-07T21:32:41 | 2020-08-07T21:32:41 | 70,098,697 | 6 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,333 | r | ppp.R | rm(list=ls())
pacman::p_load(INLA, ggplot2, data.table, lattice, arm, dplyr, TMB, ar.matrix)
set.seed(124)
mesh2DF <- function(x){
M <- length(proj$x)
DT <- data.table(x=rep(proj$x, M), y=rep(proj$y, each=M),
obs=c(inla.mesh.project(proj, field=x)))
DT
}
n <- 1000
loc <- matrix(runif... |
dbe1f99535fa1c5482e8255e0c24d563652cb380 | b93f14b970fe61ed7ffa4592654a027adc19b3fc | /man/make_filename.Rd | 32e9aa04e5a7a88a8ddb022bec2c75943078b714 | [] | no_license | yuriygdv/farsfunctions | 1093001e2349400a18c2c800f158d88b090058fd | 0560cc087832a01c45e4014d8ecb3e6a837d31f0 | refs/heads/master | 2021-04-30T03:58:30.600541 | 2018-02-14T15:30:02 | 2018-02-14T15:30:02 | 121,523,457 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 693 | rd | make_filename.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fars_functions.R
\name{make_filename}
\alias{make_filename}
\title{Make a filename in the format used for FARS data files}
\usage{
make_filename(year)
}
\arguments{
\item{year}{A year in the numeric format}
}
\value{
This function returns a c... |
1eba737a61e993a5c98f5830bf50346a15b530b4 | c0594b6c8ad34662469cb3369cda7bbbf959ae69 | /man/parse_d20200423_SANCHEZ-CANETE.Rd | fb53aefd0efa7c0f48c0dd793ed61e684a6863e8 | [
"CC-BY-4.0"
] | permissive | bgctw/cosore | 289902beaf105f91faf2428c3869ac6bba64007f | 444f7c5ae50750ec7f91564d6ab573a8dc2ed692 | refs/heads/master | 2022-10-22T13:48:37.449033 | 2020-06-17T05:46:19 | 2020-06-17T05:46:19 | 269,271,512 | 0 | 0 | CC-BY-4.0 | 2020-06-17T05:46:20 | 2020-06-04T05:47:05 | null | UTF-8 | R | false | true | 477 | rd | parse_d20200423_SANCHEZ-CANETE.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/parse-others.R
\name{parse_d20200423_SANCHEZ-CANETE}
\alias{parse_d20200423_SANCHEZ-CANETE}
\title{Parse a custom file from d20200423_SANCHEZ-CANETE}
\usage{
`parse_d20200423_SANCHEZ-CANETE`(path)
}
\arguments{
\item{path}{Data directory path... |
4166325fb2032ca97b1bc8cab11b86483e5e0681 | e573bc7fd968068a52a5144a3854d184bbe4cda8 | /Recommended/boot/man/glm.diag.Rd | 3d5b172bc5b1231e84d62763e68a5ce8029402ec | [] | no_license | lukaszdaniel/ivory | ef2a0f5fe2bc87952bf4471aa79f1bca193d56f9 | 0a50f94ce645c17cb1caa6aa1ecdd493e9195ca0 | refs/heads/master | 2021-11-18T17:15:11.773836 | 2021-10-13T21:07:24 | 2021-10-13T21:07:24 | 32,650,353 | 5 | 1 | null | 2018-03-26T14:59:37 | 2015-03-21T21:18:11 | R | UTF-8 | R | false | false | 1,482 | rd | glm.diag.Rd | \name{glm.diag}
\alias{glm.diag}
\title{
Generalized Linear Model Diagnostics
}
\description{
Calculates jackknife deviance residuals, standardized deviance residuals,
standardized Pearson residuals, approximate Cook statistic, leverage and
estimated dispersion.
}
\usage{
glm.diag(glmfit)
}
\arguments{
\item{glmfit}{
... |
f7af66f6a489bc5e9720506bf0b4cbdef5a99abc | d07beaab6703de4a9b901138ad3f6609e49eb1b4 | /glove_app/server.R | 4e5656945298e0bb698ae8b7d850a822437e4760 | [] | no_license | lsempe77/NLP-and-digital-humanities | 1a6eb9965e66cda1a9cd651ac97db24150d48bf8 | f18456e5b93485200bc9254f381b808c21ec01cd | refs/heads/master | 2023-04-10T04:03:35.546030 | 2021-02-22T14:13:51 | 2021-02-22T14:13:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,482 | r | server.R | options(shiny.maxRequestSize=30*1024^2)
# Import libraries that are needed for processing in this module.
library(shiny)
library(dplyr)
library(data.table)
library(R.utils)
set.seed(42)
normalize = function(m, norm = c("l1", "l2", "none")) {
stopifnot(inherits(m, "matrix") || inherits(m, "sparseMatrix"))
norm ... |
ea34ad02f6647d180e9c887d629e8641878732ab | e5bd337550aa219533eb9039952d35d72bd97497 | /man/birthrate.Rd | 5c09ac9654eb5cdf79b1b83910de2385f4843ecb | [] | no_license | nxskok/d29data | 246c31081f30c34c83b9d0a5ea66b1f17d0faf0f | 3dece19d3d3101b9349c446af0870a4aa8bc4f92 | refs/heads/master | 2021-01-10T05:48:31.573769 | 2015-12-27T15:12:06 | 2015-12-27T15:12:06 | 48,649,759 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 720 | rd | birthrate.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/docco.R
\docType{data}
\name{birthrate}
\alias{birthrate}
\title{Vital statistics by country}
\format{A data frame with 97 rows and 4 variables:
\describe{
\item{birth}{Birth rate (units unspecified)}
\item{death}{Death rate}
\item{infa... |
449e088f555cbe58e9e4b65691108e7c36566f39 | 603ef4d458ae15590178a3bb83e41597bcbc0447 | /man/format_date.Rd | d98559702dbd6822ec9a1629a013e6698368159b | [] | no_license | ntncmch/myRtoolbox | 8dace3f0d29e19670624e6e3c948ba6d0fa38cec | 8ec2a6bc2e7dd33fb23d7f4b2c6cf2d95ca5ef8d | refs/heads/master | 2020-05-14T14:14:34.465272 | 2014-09-22T13:17:47 | 2014-09-22T13:17:47 | 21,052,420 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,531 | rd | format_date.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{format_date}
\alias{format_date}
\title{Format date variables in a data frame}
\usage{
format_date(df_2format, pattern = "date", orders = "dmy", year_max = NULL,
as_date = FALSE, excel = FALSE)
}
\arguments{
\item{df_2format}{A data frame}
\item{pattern}{Vec... |
927d244099838510baaeed3827a9da3240a15a04 | d0653d0ab1e14a079f7e9c33f133d5d43a9d003e | /week4/islr_logistic_reg_lab.R | d1d747ae25ed47db2888df0b1615bd5e61a39eb5 | [] | no_license | Brendafried/coursework | 18e404791d79f97b478071fb589965dd54c72888 | ce13e9510b5e798e3c7a24a0a69aa230cd97f74a | refs/heads/master | 2020-06-03T03:18:52.333079 | 2019-07-08T03:06:46 | 2019-07-08T03:06:46 | 191,413,241 | 0 | 0 | null | 2019-06-11T16:50:09 | 2019-06-11T16:50:09 | null | UTF-8 | R | false | false | 1,356 | r | islr_logistic_reg_lab.R | library(ISLR)
names(Smarket)
dim(Smarket)
summary(Smarket)
cor(Smarket)
cor(Smarket[, -9])
attach(Smarket)
plot(Volume)
glm.fits = glm(Direction ~ Lag1 + Lag2 + Lag3 + Lag4 + Lag5 + Volume, family = binomial, data = Smarket)
summary(glm.fits)
coef(glm.fits)
summary(glm.fits)$coef[, -4]
glm.probs = predict(glm.fits, t... |
14c89ae984640603f3ecea92b85b8b58f7f9ffbf | a99956a3b217e9c87daa50b3854e431c88beee0b | /plot3.R | d9b8c153b4ed67240172fcf5fac4e3e9cb487ece | [] | no_license | cktc4b/ExData_Plotting1 | fbf3b24d0701cff2aab421960261666ac893734e | ea5c1690522ee5f1182fdf010258e218e9f91dbf | refs/heads/master | 2021-01-16T18:48:30.743068 | 2014-08-09T16:09:24 | 2014-08-09T16:09:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,368 | r | plot3.R | ##Identify file location and download
url<-"https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(url, "HPC.zip")
##Unzip the file
unzip("HPC.zip")
##Load the data from the unzipped file
hpc<-read.table("household_power_consumption.txt", header=TRUE, sep=";")
##Convert ... |
433f846fe082b5247a03becc9f7321de58715f3f | 384c3dbc571be91c6f743d1427dec00f13e0d8ae | /r/kernels/panda1023-svm-example-for-titanic/script/svm-example-for-titanic.R | 729c948c2ef88c0ac7024745e1170d10163676c3 | [] | no_license | helenaK/trustworthy-titanic | b9acdd8ca94f2fa3f7eb965596eed4a62821b21e | ade0e487820cf38974561da2403ebe0da9de8bc6 | refs/heads/master | 2022-12-09T20:56:30.700809 | 2020-09-10T14:22:24 | 2020-09-10T14:22:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,457 | r | svm-example-for-titanic.R |
# This script trains a Random Forest model based on the data,
# saves a sample submission, and plots the relative importance
# of the variables in making predictions
# I made some changes to add more models including
library(ggplot2)
library("e1071")
set.seed(1)
train <- read.csv("../input/train.csv", s... |
7a72cd778a6ecdae8ec973c7020f102337495e69 | f8eb5031901516071f5ff3738f0b4e198fe3f381 | /SentimentAnalysis/main.R | f1c3558683f93a0101ce3fdfe1d2226911ed7fc2 | [] | no_license | jordanatlas/HLML | 0d255ffd17c2888420c27cf35a0ce082ccb615d1 | 723b78ea2f1464176b17511190560a2a95ae00c2 | refs/heads/master | 2016-09-06T03:11:24.884141 | 2014-06-20T21:20:01 | 2014-06-20T21:20:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,976 | r | main.R | # required packages
require("plyr") # package to compute counts in aggregates
require("nnet") # multinomial models package
# constants
inputTestData <- "test.tsv"
inputTrainingData <- "train.tsv"
outputTestData <- "test_output.csv"
outputTestDataKaggle <- "test_output_kaggle.csv"
outputTestDataWeka <- "test_output_we... |
7d1ecb63ad9cc87aff5a8bb26cea96ae4406c4ec | f099279224e672b76e7696650b8faa72e112ac88 | /OM/model_base.R | efbeb2e2eb0b9bcafd9c3d5d2e25ee3ba2354627 | [] | no_license | iotcwpm/SWO | 4d6559992774a400e600079c1a6116e4e0ff346c | a01312eb57231788b3018703cfcba8091ba1f8db | refs/heads/main | 2022-03-06T22:49:57.645612 | 2022-03-03T20:21:14 | 2022-03-03T20:21:14 | 75,840,159 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,178 | r | model_base.R | # model_base.R - Runs and diagnostics for the base case SS3 model
# SWO/OM/model_base.R
# Copyright Iago MOSQUEIRA (WMR), 2020
# Author: Iago MOSQUEIRA (WMR) <iago.mosqueira@wur.nl>
# Modified: Daniela Rosa (IPMA)
# Distributed under the terms of the EUPL-1.2
library(ss3om)
library(ss3diags)
library(icesTAF)
# SET... |
4ab3fa3bd1497aa173732267a82b3021f11ea636 | d14bcd4679f0ffa43df5267a82544f098095f1d1 | /R/groupm.mleprobplot.R | e3862df0e863a22717b7369bd59831de269ff86e | [] | no_license | anhnguyendepocen/SMRD | 9e52aa72a5abe5274f9a8546475639d11f058c0d | c54fa017afca7f20255291c6363194673bc2435a | refs/heads/master | 2022-12-15T12:29:11.165234 | 2020-09-10T13:23:59 | 2020-09-10T13:23:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 18,703 | r | groupm.mleprobplot.R | #' Title
#'
#' @param data.ld
#' @param distribution
#' @param formula
#' @param group.var
#' @param xlab
#' @param ylab
#' @param conf.level
#' @param xlim
#' @param ylim
#' @param relationship
#' @param power
#' @param dump
#' @param mle.intervals
#' @param cex
#' @param grids
#' @param slope.axis
#' ... |
3bd986ce8be11796a8222abe59b4e7df1615e04b | 57f883e1a1b8031b09f884f6e1b1d57f0a24681a | /data-raw/tx-rates.R | 928e10b3e8ae817b8afcf20933855ebc10912f12 | [
"CC0-1.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | rnabioco/practical-data-analysis | 704edbf97df3b6834263d98287db54f1e663135c | 676e05830a1a65bd5d978f124cc120b2954c527f | refs/heads/master | 2022-07-20T19:23:08.576961 | 2022-07-07T00:17:50 | 2022-07-07T00:17:50 | 105,456,301 | 7 | 2 | NOASSERTION | 2019-12-10T22:37:30 | 2017-10-01T16:32:15 | R | UTF-8 | R | false | false | 644 | r | tx-rates.R |
download.file("ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE56nnn/GSE56977/suppl/GSE56977_rates_gene.tx.txt.gz",
"tx_rates.txt.gz")
dat <- read_tsv("tx_rates.txt.gz", skip = 1, col_names = F)
col_ids <- c(
"gene",
"quantile"
)
other_col_ids <- paste("rate at", seq(0, 180, by = 15))
new_col_ids <- c(... |
dd4d4bec44f89b7b35b4bbdabc2dcf58d4a40670 | 5b520b6461fc479ab03932eda8443804355a574f | /dummy_data_maker.R | 5a673e5f932ca7bf9b729a6d97266022ed4afc23 | [] | no_license | qgeissmann/r_workshops | 12ecf4f601eb07a565165b1fa73b61c598918310 | 5a43d08fbcfc328e3f382fa06ea59489061a1d5e | refs/heads/master | 2020-09-03T03:25:53.505275 | 2019-11-03T23:13:04 | 2019-11-03T23:13:04 | 219,373,966 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,386 | r | dummy_data_maker.R | rm(list=ls())
set.seed(1)
library( data.table)
N_PROTO <- 30
date <- as.Date("2019-03-01") + 1:N_PROTO * 7
site <- LETTERS[1:6]
trap_id <- 1:4
dt <- data.table(expand.grid(date=date,site=site,trap_id=trap_id),key=c("date","site"))
trend_pest <- cumsum(rnorm(N_PROTO))
trend_pred <- cumsum(rnorm(N_PROTO))
trend_para <... |
9c478ef9441007df9a797415eb727a3e4a4d5490 | 882445fe44bbd012c82d7c72633d67f2b7c0306c | /tests/testthat/helper.R | 184ebbc523b8416057328749554ede25947cce48 | [] | no_license | Crunch-io/crunchgeo | 941b9453b7ef471198a70e231d10b7ded173d15b | 671afa96f7f2e6959b6916d2c308a8ffc5c80e81 | refs/heads/master | 2021-01-01T16:59:32.791673 | 2017-08-07T20:12:05 | 2017-08-07T20:12:05 | 97,972,639 | 0 | 0 | null | 2020-01-10T18:03:35 | 2017-07-21T17:39:15 | R | UTF-8 | R | false | false | 265 | r | helper.R | Sys.setlocale("LC_COLLATE", "C") ## What CRAN does; affects sort order
set.seed(999) ## To ensure that tests that involve randomness are reproducible
options(warn=1)
# grab the crunch package's test framework
source(system.file("crunch-test.R", package="crunch"))
|
ff1f6e5e896596e3b7e609c1686b96450dd9d62d | 2327d0bc2cc45a5504c39109846e0f4cba266606 | /QID-3203-SFEcomplogreturns/SFEcomplogreturns.R | a11524ba62c1b9cc1b4bdbfca00d6eb90aaf3b82 | [] | no_license | QuantLet/SFE | 3d98a33cfcdc533210856c7618c32a78e111a6ce | d25a728a4371538eae982f44ea811b5b93328828 | refs/heads/master | 2022-06-15T13:35:17.387252 | 2022-06-08T01:22:00 | 2022-06-08T01:22:00 | 72,103,182 | 12 | 32 | null | 2022-01-30T18:58:21 | 2016-10-27T11:50:43 | R | UTF-8 | R | false | false | 1,635 | r | SFEcomplogreturns.R |
# clear variables and close windows
rm(list = ls(all = TRUE))
graphics.off()
# load the data
x1 = read.table("FTSElevel(03.01.00-30.10.06).txt")
x2 = read.table("BAYERlevel(03.01.00-30.10.06).txt")
x3 = read.table("SIEMENSlevel(03.01.00-30.10.06).txt")
x4 = read.table("VWlevel(03.01.00-30.10.06).txt")
# calculating ... |
812f00b2600db0d802eba13092da4dc8498d66f4 | 9a27ad5e99fe494437b23043e6220c4846325d30 | /r/man/print.RFCDE.Rd | 47656c0e93506d18bfdff20f64c735b50818eb70 | [] | no_license | zhangc927/RFCDE | cbea299adf0de1c738e8b6c15fd2a7da2295a150 | b388c4ff4eb60c59a8e5a4ff7518d4212e15c6f8 | refs/heads/master | 2023-05-26T14:30:48.945105 | 2021-06-10T20:28:45 | 2021-06-10T20:28:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 329 | rd | print.RFCDE.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RFCDE.R
\name{print.RFCDE}
\alias{print.RFCDE}
\title{Print method for RFCDE objects}
\usage{
\method{print}{RFCDE}(x, ...)
}
\arguments{
\item{x}{A RFCDE object.}
\item{...}{Other arguments to print}
}
\description{
Print method for RFCDE o... |
5eaba32b268eae3f7552d523c40ae0359c966785 | c29240b00e31dca6300b6c051d69d61b53e05c1a | /man/hill_rarefaction.Rd | 16c0906846749c4ffb46d9f13124b31b1a574ac5 | [] | no_license | metabaRfactory/metabaR | b514c595c7bc8977afaf301312170ccd996c3733 | 23f3c8e3de9a08ca2e679949c29c0e4cd7c52282 | refs/heads/master | 2023-04-15T10:38:36.294253 | 2023-01-17T08:28:14 | 2023-01-17T08:28:14 | 165,215,140 | 14 | 2 | null | 2023-01-17T15:33:53 | 2019-01-11T09:18:24 | HTML | UTF-8 | R | false | true | 4,029 | rd | hill_rarefaction.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hill_rarefaction.R
\name{hill_rarefaction}
\alias{hill_rarefaction}
\alias{gghill_rarefaction}
\title{Generating rarefaction curves using Hill numbers}
\usage{
hill_rarefaction(metabarlist, nboot = 10, nsteps = 10)
gghill_rarefaction(hill_ra... |
5a3346df9b9a4e34daabc6a58afca7702fa3d8fc | 2d4523c043b19c3118071d3f9946b5a7a74d62f3 | /tests/testthat/test-makeStandardTable.R | 59267b2e027a39888ecc3fc555b0c8721f2179e8 | [
"MIT",
"GPL-3.0-only"
] | permissive | Rapporteket/NORIC | 6d87df439a204354b73157684ca1a7abe18cbedb | 515d8f014d489c9170203dfc40844497f7fb4f63 | refs/heads/master | 2023-09-01T02:12:43.940714 | 2023-06-29T13:45:33 | 2023-06-29T13:45:33 | 40,961,904 | 1 | 1 | MIT | 2023-09-06T10:20:00 | 2015-08-18T09:05:45 | R | UTF-8 | R | false | false | 599 | r | test-makeStandardTable.R | test_that("function returns kable objects", {
expect_true("kableExtra" %in% class(mst(tab = mtcars[1:10, ],
type = "html")))
expect_true("knitr_kable" %in% class(mst(tab = mtcars[1:10, ], lsd = TRUE)))
expect_true("knitr_kable" %in% class(mst(tab = mtcars[1:10, ],
... |
c048e89dbf4f47d1181a396855762ebc40e10c12 | a8e8000b370d54c2f6a097ee59876827f4daafbe | /9.4/code.R | a2f507dcbafebf7076276257e96e92294db3da07 | [] | no_license | weidaoming/R | 142be073ebdf097740ae5a02a7e75308a06e30d1 | 5048ca1d46025ba41d03b00049a17b309e8dfedc | refs/heads/master | 2021-07-12T10:47:27.552074 | 2017-10-18T07:09:09 | 2017-10-18T07:09:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 106 | r | code.R | x<-c(1,9,2,8,3,9,4,5,7,6)
#均值
mean(x)
#中位数
median(x)
#方差
var(x)
#标准差
sd(x)
summary(x)
|
1fd693ce9b6fbf2a09ced23d74a237e9ee02f5b2 | c9506e3bcfa0f3568eaac03772c02c2386840ede | /Aaron's General Workspace/Template1clusts.r | 76ede0e80f5caddf2ac7d5debd8ae1f221a4f372 | [] | no_license | Adamyazori/EDA-Project | d374e0517a116298c8f670f34c003dcd98562bb9 | 088cf2e43c7cf98de12980fec340ecbd5d687944 | refs/heads/master | 2023-03-20T10:11:40.589122 | 2013-05-14T19:40:49 | 2013-05-14T19:40:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,495 | r | Template1clusts.r | ##########################################################
###################### CODE SECTION #######################
##########################################################
template.type <- 1
##########################################################
#AF CODE
roundpval<-function(pv){
rpv<-formatC(p... |
c61f91a68c6fe62d0aa1b987c07007fb3ae9021c | 941bcfc6469da42eec98fd10ad1f3da4236ec697 | /R/track_bearing_to.R | 6fc46f0c4150018871d8313b0b36a5e5a554d16c | [] | no_license | cran/traipse | 29c3fd65e98f65049da98b1d878512bfdd93940f | 01635fd40512f2144e1ce712e0f5912143214e49 | refs/heads/master | 2022-10-22T02:59:19.828085 | 2022-10-10T06:40:02 | 2022-10-10T06:40:02 | 236,953,410 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,717 | r | track_bearing_to.R | #' Track bearing to location/s
#'
#' Calculate geodesic bearing to a location or locations based on longitude,
#' latitude (from) input vectors and longitude, latitude (to) input vectors. The
#' unit of bearing is degrees. The *to* values may be a single value or
#' individual to each *from* location.
#'
#' No missing ... |
651c24e0d8d641ea750a76124d4ce84a41c1150a | f298a1e000324a52cc70d682e2c5ef7e210b795d | /R/method.R | 5268277e847bc85eb407de90fb81c58c65e0f463 | [] | no_license | botam2/test_list | 90d23f7de85fec497903102632cb4c8d41d270c4 | 7660d9fbb6810b71efd88dcbd3e3a11ff091a2b6 | refs/heads/main | 2023-07-11T17:13:08.658935 | 2021-08-25T19:16:51 | 2021-08-25T19:16:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,725 | r | method.R | #' Set of function to zonal statistic
#' @param x image of type Image o Image Collection
#'
#' @param y region of type Feacture o FeatureCollection
#'
#' @param by a limit of pass
#'
#' median
#' @import rgee
# Functions for extract the mean of pixels of a rasterdata
# ee.Reducer.count()
ee_count <- function(x, y, by ... |
9ff85560b2791b3547a8f8749dec5350568ddd35 | 928683a31caed13e0ffea6eb32180cf29d77a74b | /man/read.digraph.Rd | 715ef13d1968ee0fc7da3bbac6c2caba9e66d0a0 | [] | no_license | SWotherspoon/QPress | 30fca8e4bba04bcdcf1559ee68187fbbafc42e15 | 699306e24d588c1b8254ba876b95d5608de87dc2 | refs/heads/master | 2022-10-04T00:37:17.928502 | 2022-09-20T05:31:10 | 2022-09-20T05:31:10 | 9,925,502 | 4 | 7 | null | 2019-06-12T21:27:17 | 2013-05-08T01:13:43 | R | UTF-8 | R | false | true | 2,201 | rd | read.digraph.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/text.R
\name{read.digraph}
\alias{read.digraph}
\alias{parse.digraph}
\alias{deparse.digraph}
\alias{write.digraph}
\title{Text Representations of Models}
\usage{
read.digraph(file, labels = NULL)
parse.digraph(lines, labels = NULL)
deparse... |
9c678293502c724851242827a4064070d2981bb4 | 36e4ecc719de97e498af4a1f7d2b3faeb220884a | /man/ques_invalidOptions.Rd | 253fb2ccfb31c4a132617851d73cc2d383362332 | [] | no_license | takewiki/nscspkg | 388fd23a3cfb0353e5669d535bd161aecade1640 | 259614ab9474c1f4a366db00eda80309bf7f82da | refs/heads/master | 2021-07-17T20:10:37.632378 | 2020-10-02T07:32:44 | 2020-10-02T07:32:44 | 217,025,321 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 350 | rd | ques_invalidOptions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/07_ques_multiA.R
\name{ques_invalidOptions}
\alias{ques_invalidOptions}
\title{增加辅助无效功能}
\usage{
ques_invalidOptions(data)
}
\arguments{
\item{data}{数据}
}
\value{
返回值
}
\description{
增加辅助无效功能
}
\examples{
ques_invalidOptions();
}
|
75a183cd539f7287e009328aa99bdb1bb73007bf | caf49f80f93709b63c5dd4f39b89dc65c5658639 | /demo_14_creating_documents/LaTeX_from_R/Code/House_Price_Reg.R | d9b9fc84409ee5f3ff13d745c98398299fc8c0f2 | [] | no_license | LeeMorinUCF/QMB6358F20 | 20cbdf9bd5a263b1863391bb4feb41584a9f18be | 7970330c26d25810eae277935a47eaddbebd8e73 | refs/heads/master | 2023-03-30T14:58:14.808107 | 2021-04-04T20:32:51 | 2021-04-04T20:32:51 | 288,279,006 | 7 | 10 | null | null | null | null | UTF-8 | R | false | false | 12,888 | r | House_Price_Reg.R | ##################################################
#
# QMB 6358: Software Tools for Business Analytics
#
# OLS Regression Demo
# Regression with Data from Spreadsheet
#
# Lealand Morin, Ph.D.
# Assistant Professor
# Department of Economics
# College of Business Administration
# University of Central Florida
#
# October... |
2bcefd12a26aac47f178dcfdc81bc5a275edd716 | 82f971819e9730c97b63fe69bca43c6e3c5b30fe | /09-2. 성별에 따른 월급 차이.R | 7395c85ea58858531a009706bec745051e9ce9ec | [] | no_license | xoyeon/Doit_R | 1b762a690d4bca24e5184357d0b54ea19cd615ea | bd87b9b5497ef423eca6c8650197a2f5b8747f7a | refs/heads/main | 2023-08-21T10:24:41.601246 | 2021-10-22T01:43:39 | 2021-10-22T01:43:39 | 404,265,867 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,745 | r | 09-2. 성별에 따른 월급 차이.R | # 데이터 불러오기
raw_welfare <- read.spss(file = 'Koweps_hpc10_2015_beta1.sav', to.data.frame = T, reencode='utf-8')
# 복사본 만들기
Welfare <- raw_welfare
# 데이터 검토하기
head(Welfare)
tail(Welfare)
View(Welfare)
dim(Welfare)
str(Welfare)
summary(Welfare)
# 변수명 바꾸기
Welfare <- rename(Welfare,
sex = ... |
a4824aa79644815714888d30f8e0541caa190567 | 54f9314cf3a933b39ae1316e1d1e78a21f7b8b56 | /tests/testthat/helper-AlpacaforR.R | 24925f8b038b288599312b1365f46d287ff680a4 | [] | no_license | tanho63/AlpacaforR | 1b36bcd44a188bd73c908223708bfbdd78820196 | d23df32fd337185c413dbeed8383bb0221a84034 | refs/heads/master | 2023-06-05T11:36:21.782384 | 2020-12-31T18:12:34 | 2020-12-31T18:12:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,629 | r | helper-AlpacaforR.R | library("vcr")
if (basename(getwd()) == "AlpacaforR") {
vcr::vcr_log_file("tests/testthat/vcr.log")
invisible(vcr::vcr_configure(dir = "tests/testthat/vcr", log = TRUE, log_opts = list(file = "tests/testthat/vcr.log"), write_disk_path = "tests/testthat/vcr"))
} else {
vcr::vcr_log_file("vcr/vcr.log")
invisible(... |
4d201c67fa526f19201af1fec3e531fa894f554b | b0af05775cdeadd5941664062b4bff7005fc0927 | /population_data/ISTATdataScript.R | 6213fc0e0039787790cf06b43b03b806525cb290 | [] | no_license | timmmerlan/fossgis_project | a967197a79ff4138c9fd3a1457dd3740765d5423 | f919d9d8f7bc2bce7a2c7861bce6bb9c3c908b90 | refs/heads/master | 2020-12-06T08:43:40.870424 | 2020-04-19T19:43:20 | 2020-04-19T19:43:20 | 232,413,289 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,122 | r | ISTATdataScript.R | # script for cleaning the "raw" table of the population data from ISTAT
rm(list = ls())
setwd("C:/Users/jnlpu/Documents/Studium/Geographie/5. Semester/FOSSGIS/Abschlussprojekt")
# load table
TabelleUnbereinigt <- read.csv(file = "Censimento_2011_Indicatori_famiglie_per_Comuni_nella_regione_SICILIA.csv",
... |
d0a8e0758e50392dd76a81a93a9dcada727d84dc | 0c9257f066b92c904af7bf891fefde48a97da6cc | /Script 8.R | a75555e29c45f596bc02d59ab196ed8ee2cb57fa | [] | no_license | homayoun1990/R-Basic | 5270079e1799706d4ab0d1604998a4cfc65693aa | 45011fa36dfdb4b615b677917856b67cca5a57e8 | refs/heads/master | 2020-04-05T22:25:13.777470 | 2018-11-12T18:50:08 | 2018-11-12T18:50:08 | 157,255,827 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,065 | r | Script 8.R | ## loading the datasets library although the package will automaticlly
## loads when R starts
library(datasets)
data(package = "datasets") # lists the datasets in a package
str(sleep) # shows the structure of an object
head(sleep) ## return some of the rows inside sleep datasets
help(sleep) ## access help abou... |
9f1536a3111bcb66ade5f33bf8d31d6c1432f5ac | f3e914e8a3ccb1c4d73555321e3eaf52b59f52e0 | /R/3.4-course.R | 9eacc272622e26a71e7246f1540d2d05ddc4bffa | [] | no_license | youjia36313/learn_R | 08be35ebc032839e8c25466c63ae5a0292069855 | 674de3d09e0e7dfec2d3e164ffab98e0c40ca597 | refs/heads/master | 2020-09-15T19:39:00.136679 | 2019-11-23T06:37:41 | 2019-11-23T06:37:41 | 223,541,846 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 444 | r | 3.4-course.R | df <- read.csv("RentData.csv")
#df[1:5,]
#mean(df$Rent.money)
#mean(df$Area)
#median(df$Rent.money)
#median(df$Area)
#there is no mode in R
#table(df$Room.layout)
#table(df$Structure)
x <- df$Rent.money
var(x)
#there is no varp/sample variance in R.only var/unbiased sample variance
varp <-function(x){var(x)*(length(x)-... |
ec16d4007c84a3cc86c6adbc8b79febaffaed3da | 688185e8e8df9b6e3c4a31fc2d43064f460665f1 | /man/convert_txtCollection.Rd | ef2467722746a66630813539d58d415ca7806e44 | [] | no_license | IPS-LMU/emuR | 4b084971c56e4fed9032e40999eeeacfeb4896e8 | eb703f23c8295c76952aa786d149c67a7b2df9b2 | refs/heads/master | 2023-06-09T03:51:37.328416 | 2023-05-26T11:17:13 | 2023-05-26T11:17:13 | 21,941,175 | 17 | 22 | null | 2023-05-29T12:35:55 | 2014-07-17T12:32:58 | R | UTF-8 | R | false | true | 1,601 | rd | convert_txtCollection.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/emuR-convert_txtCollection.R
\name{convert_txtCollection}
\alias{convert_txtCollection}
\title{Converts a collection of audio files and plain text transcriptions into an emuDB}
\usage{
convert_txtCollection(
dbName,
sourceDir,
targetDir... |
b997d6ec16cf45d177eb0664c641add0fc8aba2f | c0befdac32dd86f06994c71eb80cab99cb3e5c6a | /man/agedotoliths.Rd | 1f1c023b88e8e45c6257e2b06b8ac5573947c4f1 | [] | no_license | aaronmberger-nwfsc/hakedataUSA | 8180602ae01a47f85ad0a6166341db687e5c2fcb | f6ee60568885f670a559502e1728b00f5d90ed5b | refs/heads/master | 2023-02-11T17:09:25.867759 | 2021-01-07T05:37:34 | 2021-01-07T05:52:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 686 | rd | agedotoliths.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/agedotoliths.R
\name{agedotoliths}
\alias{agedotoliths}
\title{Summary of NORPAC Otoliths by Year}
\usage{
agedotoliths(agedata)
}
\arguments{
\item{agedata}{A data frame of NORPAC ages, often called atsea.ages.}
}
\value{
A data frame of pro... |
cfdfb3e879c77e9efe4444b6d46b8e8f35833747 | e2f16485aa15699c8f8f0784215c081b497c2647 | /gisssurface/server.R | 70d99c5d555b51584180393def4e673c6c24322e | [] | no_license | Unsa15120/shinyproject | f0f1a90712f9d706526fbf48b51f8d4eb08c6227 | cad5edf1382c370e26cd3a14ed0438401bf98bd6 | refs/heads/master | 2020-07-10T06:25:48.413964 | 2019-08-25T21:28:26 | 2019-08-25T21:28:26 | 204,192,265 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,925 | r | server.R | library(shiny)
library(ggplot2)
library(plotly)
library(shinythemes)
# Downloading and Cleaning Data
#Data 1 = Global
globalData<- read.csv("global.csv",header = FALSE,sep = ",",skip = 3,na.strings = "***")
cnames <-readLines("global.csv",2)
cnames<-strsplit(cnames,",",fixed = TRUE)
names(globalData) <- cnames[[2]]
d... |
9ff02f377d3d767d7a87e9a993e984aef6ed8b4f | 3693150470d1dce04403f8dad6b2ef0b092d6020 | /R/FacetMuiPlotresultBar.R | 29b5698d1bd0346064ff8c1929aa2c2348fc3ef4 | [] | no_license | Jayoel/EasyAovWlxPlot | f77b11098be7fe8ffcf61e628d96632f78789731 | 46cad6119526193a3cdd260c22c1f434cd4f530f | refs/heads/master | 2021-05-17T23:09:54.761864 | 2020-02-26T03:13:24 | 2020-02-26T03:13:24 | 250,995,058 | 2 | 0 | null | 2020-03-29T09:24:17 | 2020-03-29T09:24:16 | null | UTF-8 | R | false | false | 3,937 | r | FacetMuiPlotresultBar.R | # \item{data}{输入数据框，第一列为样本编号，第二列为分组，注意分组标签必须设定为group，第三列以后就是测定或者收集的指标了}
#
# \item{num}{代表您想要进行统计的列,这里可以输入多个列，只需要指定列号即可：例如：num = c(4:6)}
#
# \item{sig_show}{代表差异展示方式；sig_show ="abc"是使用字母表示;sig_show ="line"是使用连线和星号表示；如果是NA，那么就不显示显著性结果}
#
# \item{result}{代表显著性差异分析结果，是一个数据框，每一列是显著性标记字母,MuiKwWlx}
# \item{ncol}{代表分面展示每一行放几张图... |
0acbef9c362fc3bcd587204d651b94755f74eb45 | 73eec22a33e4f2f08a61cc3e5c8c5a2883009d73 | /man/num.samples.Rd | 6a8b96913b84b85fdef9f2b5c9121023a4ad260a | [] | no_license | itsrainingdata/sparsebnUtils | 958ec179724d75728dfd03a40bbde718f68cc0cc | a762b74dda916956d16e2654463736e55b57be0b | refs/heads/master | 2020-04-06T06:36:08.237032 | 2019-05-29T11:04:07 | 2019-05-29T11:04:07 | 50,886,867 | 3 | 2 | null | 2017-04-10T22:45:30 | 2016-02-02T02:05:24 | R | UTF-8 | R | false | true | 991 | rd | num.samples.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/s3-generics.R, R/s3-sparsebnData.R,
% R/s3-sparsebnFit.R, R/s3-sparsebnPath.R
\name{num.samples}
\alias{num.samples}
\alias{num.samples.sparsebnData}
\alias{num.samples.sparsebnFit}
\alias{num.samples.sparsebnPath}
\title{num.samples}
\usag... |
05534ab248d93eb54787b7f53dbf876da362122c | 0dbd60b634c090f2153f21f945fb306495a67df6 | /R/ROMS_COBALT/call_make_ROMS_files.R | 9b7e6589717fd6ae7aea02b0b6e8af48af5916eb | [] | no_license | wechuli/large-pr | afa8ec8535dd3917f4f05476aa54ddac7a5e9741 | 5dea2a26fb71e9f996fd0b3ab6b069a31a44a43f | refs/heads/master | 2022-12-11T14:00:18.003421 | 2020-09-14T14:17:54 | 2020-09-14T14:17:54 | 295,407,336 | 0 | 0 | null | 2020-09-14T14:17:55 | 2020-09-14T12:20:52 | TypeScript | UTF-8 | R | false | false | 3,125 | r | call_make_ROMS_files.R |
source(here::here('R','make_ROMS_files_2.R'))
# source('C:/Users/joseph.caracappa/Documents/GitHub/neus-atlantis/R/make_ROMS_files_new_levels.R')
dir.names = 1981:1983
# dir.names = 2010:2014
# ellapsed.t = list()
for(yr in 1:length(dir.names)){
# for(yr in 1:length(dir.names)){
if(!dir.names[yr] %in% dir('D:/Out... |
794570a81f650f1218b7f42215480a877eddfc47 | 0055c9911455887f73d902cb934bd32969208410 | /dfest_2 step.R | 40df1c279e9a02ce4fb125c0f2fa52b692be8d3a | [] | no_license | christinaschang/2018-Datafest-Munich | e5e05efe39cd8c60b0cb5ab27c5ea1e9e4de256e | 614fdaf15fb53615436d4190fe7a3ade3a0153c4 | refs/heads/master | 2021-04-15T14:47:12.132172 | 2018-04-20T14:35:01 | 2018-04-20T14:35:01 | 126,637,914 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,021 | r | dfest_2 step.R | library(readr)
library(tidyverse)
library(ggplot2)
library(plotly)
library(RColorBrewer)
library(lubridate)
library(rworldmap)
library(scales)
setwd("/Users/Berlin/Desktop/datafest2018_data_and_documentation/data")
conn1 <- read_csv("conn_posix.csv")
View(conn1)
# Tiles by weekday and hour
conn1$weekday <- wday(co... |
4b47ac4730384ebfd467f92101c7706d08403435 | 11923a0d573d8a87f5b7a16d443722367594b223 | /AMI_code/explain/scfa_mol/scfa_explain_af.R | afca8f4968323a398f0631299a1fbc78d2df06e3 | [] | no_license | BioLcl/AMI | ed6a932d72ac836984e8dacf72ea3048aa0fbd6b | 295e3c1b4b4ce6b90ec4f56313e6606279c0e72b | refs/heads/main | 2023-02-24T03:14:16.987204 | 2021-01-24T09:55:36 | 2021-01-24T09:55:36 | 332,409,641 | 0 | 0 | null | null | null | null | GB18030 | R | false | false | 5,421 | r | scfa_explain_af.R | library(psych)
int <- function(x){
qnorm((rank(x,na.last="keep")-0.5)/sum(!is.na(x)))
}
setwd("D:/FangCloudV2/Zheng lab/Zheng lab共享资料/刘成林/ACS/ACS_code/explain/scfa_mol")
### all group explain
meta<-read.delim("scfa_mol_ratio.txt", row.names = 1, sep = '\t', stringsAsFactors = FALSE, check.names = FALSE)
meta<-as.d... |
1c69bce8d4be30cc6c2f15275fddc8a500b7ff5d | 0d40400d131f04630afba060bdab5c80f6b4df87 | /Anova_video_problems.R | f3357db8f3c495bd6574a0b8cb632f386e3481b3 | [] | no_license | tpaarth/R_code | 5d6239fc485587e9000884fd690c96bc824375ff | a8937bf8c601330ab293651977480fc9c019bade | refs/heads/master | 2021-06-22T16:59:10.392499 | 2021-03-28T11:03:46 | 2021-03-28T11:03:46 | 203,936,265 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,814 | r | Anova_video_problems.R | ##One Factor ANOVA
setwd("C:/Users/paart/Documents/PGP_BABI/R_Programming/datasets")
library(plyr)
library(ggplot2)
library(lattice)
library(MASS)
golf_data <- read.csv('Golfball.csv',header = T)
attach(golf_data)
golf_data
model <- aov(Distance~Design, data = golf_data)
summary(model)
print(summary(mode... |
67da2bb7a8314dc9e58d8ba6c2a0382f0fea8672 | f4a081e3696cc3737cef833bbe36e6cbba0b4022 | /man/cov.wml.Rd | 6cbcd6fbeee8e3a84e6ee10b96c9d6334d32f05e | [] | no_license | cran/fpc | 5ba4ad1e5d8bd50009060ce009161df3a765421f | f16ec459c931722a5354788e23325931ad835e8f | refs/heads/master | 2023-01-09T20:22:03.277537 | 2023-01-06T23:20:13 | 2023-01-06T23:20:13 | 17,696,125 | 10 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,257 | rd | cov.wml.Rd | \name{cov.wml}
\alias{cov.wml}
%- Also NEED an `\alias' for EACH other topic documented here.
\title{Weighted Covariance Matrices (Maximum Likelihood)}
\description{
Returns a list containing estimates of the weighted covariance
matrix and the mean of the data, and optionally of the (weighted)
correlatio... |
1c52b5543a1d0a28c8ccd3a770e92286816d5f92 | 7f72ac13d08fa64bfd8ac00f44784fef6060fec3 | /RGtk2/man/gtkEntrySetIconFromPixbuf.Rd | 964ae03c08e71b91dab1918492eaea0bf706b912 | [] | no_license | lawremi/RGtk2 | d2412ccedf2d2bc12888618b42486f7e9cceee43 | eb315232f75c3bed73bae9584510018293ba6b83 | refs/heads/master | 2023-03-05T01:13:14.484107 | 2023-02-25T15:19:06 | 2023-02-25T15:20:41 | 2,554,865 | 14 | 9 | null | 2023-02-06T21:28:56 | 2011-10-11T11:50:22 | R | UTF-8 | R | false | false | 618 | rd | gtkEntrySetIconFromPixbuf.Rd | \alias{gtkEntrySetIconFromPixbuf}
\name{gtkEntrySetIconFromPixbuf}
\title{gtkEntrySetIconFromPixbuf}
\description{Sets the icon shown in the specified position using a pixbuf.}
\usage{gtkEntrySetIconFromPixbuf(object, icon.pos, pixbuf = NULL)}
\arguments{
\item{\verb{object}}{a \code{\link{GtkEntry}}}
\item{\verb{icon.... |
8aee5cd2c100bc651e55c6c45ee89af3f55c2450 | af325890e4442dc45c2c9316400c3eb7fad9e107 | /R/geary.R | cfb5e408fdf388da9594be6ade92615b8317282f | [] | no_license | cran/moments | 57475456b7c3a7130490b2e3f985658a013a4df0 | 724bcfd8a214f24872cda0d8a8a46bcb0e846b64 | refs/heads/master | 2022-05-21T10:55:18.637220 | 2022-05-02T12:01:55 | 2022-05-02T12:01:55 | 17,697,626 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 398 | r | geary.R | "geary" <-
function (x, na.rm = FALSE)
{
if (is.matrix(x))
apply(x, 2, geary, na.rm = na.rm)
else if (is.vector(x)) {
if (na.rm) x <- x[!is.na(x)]
n <- length(x)
rho <- sqrt(sum((x-mean(x))^2)/n);
tau <- sum(abs(x-mean(x)))/n;
tau/rho
}
else if (is.data.frame(x))
sappl... |
0f7dbcc326c99780511513769d2ac9ae33082dbb | 92719b80937aa4aaa47865285e390c015012b5a4 | /man/get.basis.Rd | 346e4538157439f7c6413fe80bba8976fcba5c3d | [] | no_license | cran/lpSolveAPI | a3702d143aa3c9027e09ab4db49ba6917f9cf96c | 0ebfc5cabed2946fe5b2b3dfc31c7d5b793335b1 | refs/heads/master | 2022-11-06T04:22:42.063480 | 2022-10-20T16:12:46 | 2022-10-20T16:12:46 | 17,697,189 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 826 | rd | get.basis.Rd | \name{get.basis}
\alias{get.basis}
\title{Get Basis}
\description{
Retrieve the basis from a solved lpSolve linear program model object.
}
\usage{
get.basis(lprec, nonbasic = FALSE)
}
\arguments{
\item{lprec}{an lpSolve linear program model object.}
\item{nonbasic}{a logical value. If \code{TRUE}, the nonbasic ... |
c621be6b9f55a6be83bc640c0c700622fc9c4e82 | 3593fdf70b57effc2abff5004209220aac2c7f41 | /R/Stats.R | 0e9cd5d11991628ab76fa13b6407caf1ff48eb36 | [] | no_license | ShunHasegawa/WTC_IEM | dcc00054709c59acf226044c5aa3ddcc09b6da16 | 3ffb6c0f306ac366e61d6a2e5de02c26da30501d | refs/heads/master | 2016-09-06T10:46:05.650853 | 2015-08-31T21:06:30 | 2015-08-31T21:06:30 | 20,788,741 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,489 | r | Stats.R | #####################################
# merge soil moisture and temp data #
#####################################
load("Data/WTC_soilMoistTemp_Chamber_DailySummary.RData")
# restructure
names(soilChmSmry)[4] <- "probe"
SoilChMlt <- melt(soilChmSmry, id = c("Date", "Chamber", "temp", "probe"))
SoilCh <- cast(So... |
3577de30015ae35184bae57c8629e968eabecf9f | a5170d90be2827eb50b62fa9ee31a4515f914616 | /Assignment 4/src/Question2.R | f0110c9a11b205325c5278533711fade2cbda22e | [] | no_license | ayeshabhimdi/Machine-Learning | c2aa4de5b58fcd53e79c3ca6fb4f8f93d18f6b67 | bfa7c6ea693d9d0470bb1ea5e6ca22081464aa93 | refs/heads/master | 2021-10-11T06:29:12.971455 | 2016-07-01T15:10:34 | 2016-07-01T15:10:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,182 | r | Question2.R | data=read.csv('C:/ML/Assignment 3/a3barebones/susysubset.csv')
mydata<- data[sample(1:nrow(data),2700,replace = FALSE),]
# Split Data set
sub <- sample(nrow(mydata),floor(nrow(mydata) * 0.75))
# Spliting Training and Test Data in ratio 3:1
training <- mydata[sub, ]
testing <- mydata[-sub, ]
# X train and Y train... |
380d3389aa7cb3084bff946d85199034a4ada14d | c8a3e165e3c142337578e818947ca1da1262fb3f | /Churn.r | f68aab0f7f49bb46a69c19173df52d861aa3b060 | [] | no_license | AdityaKanungo/Customer-Churn-Reduction | d1333723e1d0dcddb0ea10301b05a488d7480612 | f3fa23e9c0584da8ec93e12fc661f64e9038dde1 | refs/heads/master | 2020-03-18T20:54:52.539019 | 2018-05-29T07:27:15 | 2018-05-29T07:27:15 | 135,246,869 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,412 | r | Churn.r | rm(list=ls())
setwd("C:/Users/BATMAN/Desktop/1st project working copy")
getwd()
#------------------------------
library(corrplot)
library(DMwR)
library(e1071)
library(caret)
library(class)
library(C50)
#------------------------------
# Load train data
train_df = read.csv("C:/Users/BATMAN/Desktop/1st project working co... |
d2d8dd533ecf7e6b377241925768c2b0e2f03dbb | 348e226ce8f69f44eb678234ce10bec52d0a4a66 | /man/wine.Rd | 316dede379cbffa207227bd896536741223e78c4 | [] | no_license | cran/datasetsICR | 846765e801298cea340c64626f0c5ac5d7799344 | 3c7779f10d7ebc8e12cc6d7501bf26464381a0cb | refs/heads/master | 2022-09-11T08:35:05.163141 | 2020-06-04T10:40:10 | 2020-06-04T10:40:10 | 269,666,425 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,319 | rd | wine.Rd | \name{wine}
\alias{wine}
\docType{data}
\title{
wine dataset}
\description{
Chemical analysis of wines grown in the same region in Italy but derived from 3 different cultivars.
}
\usage{data(wine)}
\format{
A data.frame with 178 rows on 14 variables (including 1 classification variable).
}
\details{
The dataset include... |
ece3c104ce2c22895b53b1b0a945c5c1f5efe2fa | 018556e178f4aa3af1b4dacbcf9cb6be3142c162 | /first_round.R | cb0fbbe6f5954ca7801de2fcbd4ac25b67592974 | [] | no_license | eugenern/spurs-prospects-stats | 54a75376a6b7e712a5871a67b698e079ce13211a | 1c280a58fcd5679da23a0954986385651e4ad0a6 | refs/heads/master | 2020-04-29T22:25:25.468880 | 2019-03-24T15:26:38 | 2019-03-24T15:26:38 | 176,447,518 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,330 | r | first_round.R | library(magick)
fig <- image_graph(width = 1920, height = 1080, res = 96)
# incomplete stats; will need updating
lw.stats <-
c(21.8, 8.8, 20, 1.5, 4.3, 2.7, 3.4, 0.9, 3, 3.8, 2.3, 1.5, 0.6, 1.9, 2.5)
lw.shooting <- c(43.9, 35.8, 81.1, 50.8)
dw.stats <-
c(25.7,
9.2,
20.3,
2.6,
7.7,
... |
922ebce18958dacb6ecaf13d8a3d498b6a9eb3ff | 0851f0cb3cd0d4ab242fb228aa1f6a1d7b0b0ac1 | /R/lit_docker.R | ffdd7eb9c362d79019afa47ceb4ac15224633767 | [] | no_license | nbarsch/pineium | 21e5eec45082dfae12a14a29b65814ef71121241 | 7946911dde2b838f080190c42cee9a449ee184b2 | refs/heads/master | 2022-08-12T19:24:51.738308 | 2022-07-08T09:27:22 | 2022-07-08T09:27:22 | 211,971,778 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,575 | r | lit_docker.R | #' lit_docker()
#'
#' Primary selenium browser launcher
#' @param browser chrome or firefox
#' @param port port number
#' @param headless MUST BE =TRUE FOR USING DOCKER
#' @export
lit_docker <-function(port=4445,browser="chrome",headless=TRUE){
if(headless==FALSE){
print("NOTE: WHEN USING DOCKER YOU ARE REQUIRED... |
c8d288a27252cdf7dbda21dc8ceb4ca7f3975716 | 6329d08d30f8bff1ec7c9b75d59e32e708df047e | /tests/testthat.R | 54960609006b567f0e62831f6be10810c300fe7a | [] | no_license | vjcitn/pogos | 44289491dc9ff7827541d2fab8fa217a63025bf2 | b9541ec71ff8c8b012dedc8c9b3d1398e6380198 | refs/heads/master | 2023-02-13T01:22:59.602385 | 2023-02-05T20:37:19 | 2023-02-05T20:37:19 | 103,438,764 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 54 | r | testthat.R | library(testthat)
library(pogos)
test_check("pogos")
|
a75358b1c0b8415be5e7a5003adf1c676b528691 | ab5089dfb654aa5dd230b93d46686506426c05af | /man/r2d3.Rd | a2a69267d24c1420da368699426bd1b115b78a2a | [
"BSD-3-Clause"
] | permissive | rstudio/r2d3 | a6c5cc3d4f5da06819c8ea41e4a559f8a60ec7b2 | becfb81989c7fabfe79dee2dde999190025d4ba3 | refs/heads/main | 2023-08-21T20:27:09.447322 | 2021-11-18T21:31:50 | 2021-11-18T21:31:50 | 126,084,978 | 489 | 121 | NOASSERTION | 2022-02-25T17:45:41 | 2018-03-20T21:31:01 | R | UTF-8 | R | false | true | 2,264 | rd | r2d3.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/render.R
\name{r2d3}
\alias{r2d3}
\title{D3 visualization}
\usage{
r2d3(
data,
script,
css = "auto",
dependencies = NULL,
options = NULL,
d3_version = c("6", "5", "4", "3"),
container = "svg",
elementId = NULL,
width = NULL,... |
c283c03168e766c3b7e9fe5ca9a8cc7edf0e0393 | 84b0b8e4ad2fd017ea7e14b5689fa32662140345 | /example_installPackage.R | 7734e2d551d1a6648bca5922237e9010cae45b77 | [] | no_license | gddickinson/R_code | 23e2b0713f942370f7c7cfc32a88a75626302a93 | 8393a94ee37eacd013118743a70c7b506c66d235 | refs/heads/master | 2021-11-25T22:07:21.861994 | 2021-11-23T03:07:15 | 2021-11-23T03:07:15 | 56,209,636 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 94 | r | example_installPackage.R | install.packages("rafalib")
library(rafalib)
install.packages("swirl")
library(swirl)
swirl() |
096e36e6fe7521fc0ab70e2388c8b2d20071ad79 | 6e96ceacd5a6d4f66fc982f512a527732d1d3f38 | /R/DBI-object.R | 1347364251e784652b97e1e1e608fcf6d709eec2 | [] | no_license | carlganz/pool | 334ea11eee9043688cc6ec7e26d1dd88fdc38c28 | 3073b629ddd5cb35561134d34ffd5f57a2f314fe | refs/heads/master | 2021-01-02T23:02:28.348880 | 2017-07-04T15:10:16 | 2017-07-04T15:10:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,101 | r | DBI-object.R | #' @include DBI.R
NULL
#' DBIObject methods.
#'
#' Pool object wrappers around DBIObject methods. See
#' \code{\link[DBI]{dbDataType}}, \code{\link[DBI]{dbGetInfo}}
#' and \code{\link[DBI]{dbIsValid}} for the original
#' documentation.
#'
#' @name DBI-object
NULL
#' @param dbObj,obj,... See \code{\link[DBI]{dbDataTyp... |
cb18a2471c64cfcad869c2947fdbbd94b5eb6755 | 403f786c7c85fa551326d1e077bc895fea26e7c9 | /man/py_eval.Rd | f52e49710c509dc9d221bb4d40b12ab1ab118d41 | [
"Apache-2.0"
] | permissive | rstudio/reticulate | 81528f898d3a8938433d2d6723cedc22bab06ecb | 083552cefe51fe61441679870349b6c757d6ab48 | refs/heads/main | 2023-08-22T01:41:52.850907 | 2023-08-21T16:19:42 | 2023-08-21T16:19:42 | 81,120,794 | 1,672 | 399 | Apache-2.0 | 2023-09-13T20:35:47 | 2017-02-06T18:59:46 | R | UTF-8 | R | false | true | 1,348 | rd | py_eval.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/python.R
\name{py_eval}
\alias{py_eval}
\title{Evaluate a Python Expression}
\usage{
py_eval(code, convert = TRUE)
}
\arguments{
\item{code}{A single Python expression.}
\item{convert}{Boolean; automatically convert Python objects to R?}
}
\... |
aaefcc7d04a65352a90d6cfa715ae8cb196cde2e | 13be69f45af55cb89ea2605fc9d72d5eb1985c94 | /R/util.r | 54f695fe82fed7da7c1dc7c66af9afa4aa890aeb | [] | no_license | AdamWongCH/corpustools | 2e63ee8c650600f806d52fa4f62b51cdae44e226 | 971606c053aa852f9a41ae771f308d7d3b2e00d8 | refs/heads/master | 2020-08-29T03:22:17.642090 | 2017-05-22T06:50:13 | 2017-05-22T06:50:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,125 | r | util.r | verbose_counter <- function(n, i=0, ticks=10){
function() {
i <<- i + 1
if (i %% ticks == 0) message(cat(i, ' / ', n, '\n'))
}
}
verbose_sum_counter <- function(n, i=0){
function(add) {
i <<- i + add
message(cat(i, ' / ', n, '\n'))
}
}
fast_dummy_factor <- function(x) { ## if , still return a ... |
02cc2d349b3ec7c3fcc74019a8468e59b19ee737 | 0f9fa909a1a2175302f2c8eb405482791145ee74 | /man/export.Rd | a8206690759ce455dad28df98a1e0590db214a2f | [] | no_license | jasenfinch/metaboMisc | 21942aac4a41043b35bfe36cb26f6d79031fc9a6 | 36d6630c151e29fadb687a77f5b946c80293029c | refs/heads/master | 2023-08-08T13:19:36.745124 | 2023-07-21T16:49:37 | 2023-07-21T16:49:37 | 144,775,967 | 0 | 0 | null | 2023-07-21T16:49:38 | 2018-08-14T21:56:31 | R | UTF-8 | R | false | true | 5,819 | rd | export.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/export.R
\name{exportData}
\alias{exportData}
\alias{exportData,Binalysis-method}
\alias{exportData,MetaboProfile-method}
\alias{exportData,AnalysisData-method}
\alias{exportData,Analysis-method}
\alias{exportData,Assignment-method}
\alias{ex... |
4d68648122c1f60a8307b380ab6b813d7b8002d8 | e09d229dd1ad18879fb051e4cb7d97c1475f49aa | /man/trackr_timepoint.Rd | 752c05b6def6a4ff4fdda576c97fb327a51ddc19 | [
"MIT"
] | permissive | hamishgibbs/rtrackr | 15bc922c8f8dfb765ee5b5da80df66b84eb16b16 | 2a353b73f8507e96c71c32c1ea557cfc04f9c0b2 | refs/heads/master | 2022-11-11T17:35:52.513669 | 2020-06-20T12:19:33 | 2020-06-20T12:19:33 | 271,510,902 | 1 | 0 | NOASSERTION | 2020-06-12T14:45:06 | 2020-06-11T09:54:51 | R | UTF-8 | R | false | true | 943 | rd | trackr_timepoint.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/trackr_timepoint.R
\name{trackr_timepoint}
\alias{trackr_timepoint}
\title{trackr_timepoint}
\usage{
trackr_timepoint(
dataframe,
trackr_dir = NULL,
timepoint_message = NULL,
log_data = TRUE,
suppress_success = FALSE
)
}
\arguments{... |
5fb360e3947ea0a5bc68d46ccbf232752a1984b7 | 8dd89b265cfbb974f40a4b0a5727fe8f0ecc8e5b | /man/download_healthdata_dailyrevision.Rd | 3a95ff85f487a845294b994ac4d409cb719e37f4 | [] | no_license | reichlab/covidData | aa5fbadd032d1bb937011fac84cb53bb87660d7d | c306b3f6a3f5f37922723661101540065d7fd0c0 | refs/heads/master | 2023-08-19T07:25:44.765179 | 2023-08-19T00:24:22 | 2023-08-19T00:24:22 | 277,713,248 | 9 | 10 | null | 2022-11-09T20:27:43 | 2020-07-07T04:06:58 | R | UTF-8 | R | false | true | 788 | rd | download_healthdata_dailyrevision.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/healthdata_download.R
\name{download_healthdata_dailyrevision}
\alias{download_healthdata_dailyrevision}
\title{Download daily revision data at a specific issue date}
\usage{
download_healthdata_dailyrevision(issue_date, healthdata_dailyrevis... |
e2184f92117c51049abde38aae229d24490f75fd | 0a23144af0f50b7039909476279c3e95aff27f32 | /library/gpboost/function/interpret/gpb.plot.importance.R | 9d3b72ed0f60e3900c18998402fd89e66005abdc | [] | no_license | delta0726/r-hierarchical_model | 5d9fed43df7a1e214c7c9277f13759620ae0d5fc | 050d813fa1d1dc2a35929cd162c8da896280ca9e | refs/heads/master | 2023-04-11T07:09:26.989051 | 2021-04-07T14:33:22 | 2021-04-07T14:33:22 | 349,565,116 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,123 | r | gpb.plot.importance.R | # ***********************************************************************************************
# Function : gpb.plot.importance
# Objective : 計算された特徴の重要度を棒グラフとしてプロット
# Created by: Owner
# Created on: 2021/03/28
# URL : https://www.rdocumentation.org/packages/gpboost/versions/0.5.0/topics/gpb.plot.importance
#... |
0b1dda128b022922340ead5490f887db14d6953b | 36c06c757ad713d2ae124d64c9372e7d6a5c4a42 | /man/gradient.Rd | 6a3b8079e83f00e402ca0a50ea5fdff64c4b169a | [] | no_license | torekleppe/RAutoDiff | 45044cef10a0fb953e4cfe86b70a7fc1f8d50728 | 28568fb1132cd7449d906adcd9fc1c9379a0c5b9 | refs/heads/master | 2023-04-15T13:36:35.871089 | 2021-04-23T06:44:54 | 2021-04-23T06:44:54 | 351,431,449 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 683 | rd | gradient.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rad.R
\name{gradient}
\alias{gradient}
\title{Get gradient from scalar AD type}
\usage{
gradient(y)
}
\arguments{
\item{x}{An ADtype or AD_matrix type (overloaded also for numeric and matrix types)}
}
\value{
The gradient of the (scalar) AD t... |
19512efae792bfba62d7781f3258c7f94ef50287 | 9ad4b4acb8bd2b54fd7b82526df75c595bc614f7 | /misc/PB T Cell Proportion.R | 1c8536ad1bdc1b4dba42b880a5b0bb436fad569a | [] | no_license | sylvia-science/Ghobrial_EloRD | f27d2ff20bb5bbb90aa6c3a1d789c625540fbc42 | 041da78479433ab73335b09ed69bfdf6982e7acc | refs/heads/master | 2023-03-31T14:46:27.999296 | 2021-04-02T15:09:49 | 2021-04-02T15:09:49 | 301,811,184 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,285 | r | PB T Cell Proportion.R | # Get mean num of PB T cells and mean proportion
# Remove junk cells and take only baseline PBMCs
data_harmony_run_label_remove = data_harmony_run_label[,data_harmony_run_label$`Sample Type` == 'PBMC']
data_harmony_run_label_remove = data_harmony_run_label_remove[,!(Idents(data_harmony_run_label_remove) %in% c('Remove... |
c0d3bbadd7896ee86305dd5c8443faf666b25840 | 9ee9957c4aa96ec14f64009fa3cb81ce21739e9b | /R/functions/import.data.R | d945c9e367c497276187d7c0c66772fc6c089b4e | [] | no_license | andrebrujah/ElsaPredictiveModeling | 5461c24433ffaecfbd56a6d39c41776b9d2dd2e1 | e1bb5fb9cc95959de372a9a0975832d09f961d77 | refs/heads/master | 2021-01-17T06:50:29.955937 | 2016-08-01T03:41:03 | 2016-08-01T03:41:03 | 50,808,885 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,496 | r | import.data.R | ############################################################################
####################### functions/import.data.R ############################
#
# Funções para importação dos dados. A definição de quais variáveis são
# categóricas ou numéricas é feita através de arquivos externos contendo os
# nomes das va... |
86203bf603dec0c2ee1e0ab24cf20c9667257bac | c28e41f60c74442d9fd22c5067f427b6f3828f14 | /funs/fun.grep.R | 31fa36f234db2175198c4d34e7e81de79d9b6cfc | [
"MIT"
] | permissive | elifesciences-publications/CNApp | e832496b9bb2653581a9bd3e78fc25e215618afb | 2974581a508b2db5bae35dc76ba275ba14e570c6 | refs/heads/master | 2020-12-19T04:24:48.985593 | 2020-01-17T20:39:51 | 2020-01-17T20:39:51 | 235,619,834 | 0 | 0 | null | 2020-01-22T16:53:36 | 2020-01-22T16:53:35 | null | UTF-8 | R | false | false | 330 | r | fun.grep.R |
fun_grep <- function(x, z){
# HINT: Use it as in 'apply' object-data
# 'x' is a numeric matrix
# 'y' is a vector of terms to be counted
e_vector <- rep(NA, length(z))
for (i in 1:length(z)){
term <- z[i]
n_term <- length(which(x==term))
e_vector[i] <- n_term
}
ans <- as.numeric(e_vector)
names(ans) <- z
ans... |
efaa51b88986825897c2609196847f7759bdf5f4 | 84af651242bc11422fed9a5b0e064f2bc4f8a4f5 | /man/klmer.Rd | 8ccb91fb023d509e211f4d35a275ad8004eccb74 | [] | no_license | YangLeeeee/RSPPlme4 | f923061f8b9961c21caf36b0341223337c988c0c | 810ac6fe01360d8715e1fc77edff1d711757bab5 | refs/heads/master | 2022-06-18T04:27:58.289732 | 2018-03-15T01:06:35 | 2018-03-15T01:06:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,302 | rd | klmer.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/klmer.R
\name{klmer}
\alias{klmer}
\title{function to fit mixed effects model to k functions at a set distance}
\usage{
klmer(formula, k, data, weights, na.action = "na.omit", ...)
}
\arguments{
\item{formula}{A one-sided formula describing t... |
f1dea7d04ac1a9da73469a35e6e1d516b7aa89d6 | aeb8ac419da1d200d4f9a34aec30a895a98ee973 | /03_R_Codes/08_NLP/02_Naive_Baye's_Classification/01_NaiveBayes.R | 600131905157345e503dd7047b832cd40ae33459 | [] | no_license | wenki1990/Data_Science_1 | 61327bd26396075b6170d31ca241f52ae689761b | 96396cfcb5f7ef124952323a6e5f1798b93c4d06 | refs/heads/master | 2021-06-12T07:59:39.396661 | 2021-03-12T14:14:36 | 2021-03-12T14:14:36 | 153,059,233 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,384 | r | 01_NaiveBayes.R | setwd('F:\\Library\\Analytics Path\\02-R\\02-R DataSets\\Sentiment Analysis and Navie Baye')
sms_raw<-read.csv('sms_spam.csv')
head(sms_raw)
View(sms_raw)
sms_raw$type<-as.factor(sms_raw$type)
str(sms_raw)
library(tm)
library(NLP)
sms_corpus<-Corpus(VectorSource(sms_raw$text))
clean_corpus<-tm_map(... |
3d7856a93082a26510f59114adf6adcc1dfbb7bd | 8faa2869f1496461af2180dfb496e0887fa7f722 | /man/selectScenes.Rd | ecfa3077daf555e3c89a0d6e289cd7de3cb5bc07 | [] | no_license | yangxhcaf/timeSyncR | ea1cd34c48fe4bf32709215ae3e14e883b6fd945 | 14bfc1e3aec929a0b29e2fc102f29fe75d926c8f | refs/heads/master | 2020-07-01T15:24:46.632996 | 2017-10-25T19:56:25 | 2017-10-25T19:56:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,001 | rd | selectScenes.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/selectScenes.R
\name{selectScenes}
\alias{selectScenes}
\title{Select scenes from directory}
\usage{
selectScenes(x, targ, padding = NULL, verbose = TRUE)
}
\arguments{
\item{x}{The object whose extent will be checked. It can be a ras... |
8600842aa91626b6932c274eb476851748a569f9 | 78b8fee81df8d494a8e890837ff81afa4552ac8b | /ensemble/man/optParams.Rd | 1aaf561a14786db54bdbe67de8af646c5a303d2a | [] | no_license | joshbrowning2358/Ensemble_Building_Code | 0dffef930d68146561e3e37e3833d5d3985f61a8 | 7b7d536f64081737717c05b274d8f5e915a67149 | refs/heads/master | 2023-02-17T18:32:22.888144 | 2014-09-19T15:18:44 | 2014-09-19T15:18:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,128 | rd | optParams.Rd | \name{optParams}
\alias{optParams}
\title{
Optimize model parameters
}
\description{
This function is designed to optimize the tuning parameters to a particular data mining model by building many models. Note that it may be extremely slow, but should give good estimates for the optimal tuning parameters (by trying man... |
e7ae58fc822e225af0864f3019029aabe86b89f7 | 759392dff9b5f70c9424d87f6552f52a5a122534 | /man/RToCausataNames.Rd | 577dadcba781069343d03497e46d8b1f392a2866 | [] | no_license | meantrix/Causata | fd88ffa3e6d6a8f6b54f9be4758e1243a9434027 | 62271eb1445a6b92efcc5d151a762cddc2690e06 | refs/heads/master | 2022-02-24T07:04:06.178043 | 2013-07-18T00:00:00 | 2013-07-18T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,662 | rd | RToCausataNames.Rd |
\name{RToCausataNames}
\alias{RToCausataNames}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Converts R-friendly causata column names to the corresponding Causata system name
}
\description{
Converts R-friendly causata column names to the corresponding Causata system name
}
\usage{
RToCausataN... |
414d4bb98fd24c58bf17a06257edb0d019c146fb | db377b98ae482c97a225d8532ffedff88010aabb | /man/makeSMOTEWrapper.Rd | 66a407892b9573a91d12c1bdfb4f3c406bba54f4 | [
"BSD-2-Clause"
] | permissive | JiaHaobo/mlr | d0a568480d6495c506c2dc72bd89618281fed3ce | 17d7eac68433b5e37bc4c118d1a9056c5e4cc497 | refs/heads/master | 2021-01-19T11:20:27.365236 | 2017-04-11T15:27:00 | 2017-04-11T15:27:00 | 87,954,613 | 1 | 0 | null | 2017-04-11T16:10:10 | 2017-04-11T16:10:10 | null | UTF-8 | R | false | true | 2,777 | rd | makeSMOTEWrapper.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/SMOTEWrapper.R
\name{makeSMOTEWrapper}
\alias{makeSMOTEWrapper}
\title{Fuse learner with SMOTE oversampling for imbalancy correction in binary classification.}
\usage{
makeSMOTEWrapper(learner, sw.rate = 1, sw.nn = 5L, sw.standardize = TRUE,
... |
58cfad6fe37c0791446e98ef4de7c96dc43b9760 | d0981a02d8ae7974f0f6013fc08c6e74445969d4 | /lang/R/R/Interface.R | 2b88e3801bd90e3bb23b7d5f2fd7c56a63774587 | [
"CC-BY-4.0"
] | permissive | airr-community/airr-standards | 7c115e1c9ea926d5bdb8389bcf1e7f4a10632817 | a98d307a190fc03143fbf2d3d20966d647da28f8 | refs/heads/master | 2023-09-01T08:58:22.038847 | 2023-08-29T20:03:47 | 2023-08-29T20:03:47 | 100,383,740 | 37 | 23 | CC-BY-4.0 | 2023-08-28T16:30:49 | 2017-08-15T14:06:27 | Python | UTF-8 | R | false | false | 26,067 | r | Interface.R | #### Read TSV ####
#' Read AIRR tabular data
#'
#' \code{read_tabular} reads a tab-delimited (TSV) file containing tabular AIRR records.
#'
#' @param file input file path.
#' @param base starting index for positional fields in the input file.
#' If \code{"1"}, then these fiel... |
2bab137fe3152f34abca1063ea01d9350af4070b | 58724d750895403a1b0c94cfbc0fad061c77670b | /pkg/retistruct/man/ReconstructedCountSet.Rd | 99fc36526c40be52aa54c09e70b6ea784adb5336 | [] | no_license | ZeitgeberH/retistruct | 8052826b91d5321f3eb42a88062b6688bac1afbd | 0f7ca278dc57fef6c25fc75370a49753399ffb8c | refs/heads/master | 2020-07-01T09:41:10.305635 | 2019-07-31T15:33:52 | 2019-07-31T15:34:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 688 | rd | ReconstructedCountSet.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ReconstructedCountSet.R
\docType{data}
\name{ReconstructedCountSet}
\alias{ReconstructedCountSet}
\title{ReconstructedCountSet class}
\format{An object of class \code{R6ClassGenerator} of length 24.}
\usage{
ReconstructedCountSet
}
\value{
An... |
0a15117e4ed38cf7db4d49791b57db3da9d020f5 | 3b26ab6bc88a47dfef383d4937558e4bd44da506 | /man/quickin.Rd | 0e2ca87a8c3376859d57952b27391bc4f4973aaa | [
"MIT"
] | permissive | SMBaylis/fishSim | affafad3915dad24057895d1b0708bc53dd206bd | 2f98c4545780d4d42f63dd169fb9902c61d0c614 | refs/heads/master | 2021-08-02T18:07:06.651542 | 2021-07-23T06:17:11 | 2021-07-23T06:17:11 | 144,930,871 | 3 | 2 | MIT | 2021-02-15T01:28:04 | 2018-08-16T03:17:48 | R | UTF-8 | R | false | true | 1,365 | rd | quickin.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fishSim_dev.R
\name{quickin}
\alias{quickin}
\title{Quick lookup of CKMR-relevant relationships}
\usage{
quickin(inds, max_gen = 2)
}
\arguments{
\item{inds}{an 'indiv' matrix, as from 'mort()', with some
individuals marked as 'captured'}
\i... |
f49b09216fd8a0deb90a3090c7ac9b055e3129d0 | c16e93230eef744aef141adcb6620c45ab50a721 | /multibandsBFAST/man/valitable.Rd | f742b1f80268acf549a050267a23e1dd32087bff | [] | no_license | mengluchu/multibandsBFAST | 0ccae505f8e64884ace80a6d35d3e8508459c3e9 | eaefc4074d31f1febb41394c52c0b2e4d0ce9409 | refs/heads/master | 2020-12-25T14:59:08.706074 | 2017-03-29T10:16:56 | 2017-03-29T10:16:56 | 66,471,940 | 1 | 1 | null | 2016-10-21T11:03:47 | 2016-08-24T14:38:30 | R | UTF-8 | R | false | true | 639 | rd | valitable.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/validationsimple.R
\name{valitable}
\alias{valitable}
\title{validation}
\usage{
valitable(cx2, oridensetime, oritemplate, EarlyDateIsCommission = T, totalp,
nofchange, colmWith = 2)
}
\arguments{
\item{cx2}{validation chart, dataframe with... |
0b189e14fa8383eab479af0a8efb497516b95a7b | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /robmixglm/man/print.outlierTest.Rd | cd210d5bd96d00aabd42fd6ad81f5c2d8b934c04 | [] | no_license | akhikolla/InformationHouse | 4e45b11df18dee47519e917fcf0a869a77661fce | c0daab1e3f2827fd08aa5c31127fadae3f001948 | refs/heads/master | 2023-02-12T19:00:20.752555 | 2020-12-31T20:59:23 | 2020-12-31T20:59:23 | 325,589,503 | 9 | 2 | null | null | null | null | UTF-8 | R | false | false | 576 | rd | print.outlierTest.Rd | \name{print.outlierTest}
\alias{print.outlierTest}
\alias{summary.outlierTest}
\alias{print.summary.outlierTest}
\title{Print an outlierTest object}
\description{
Print an outlierTest object.
}
\usage{
\method{print}{outlierTest}(x, \ldots)
}
\arguments{
\item{x}{outlierTest object}
\item{\ldots}{further arguments... |
a70b98de0f6c71879c4ae6577bacb9e8f62700d7 | b94bde90fdb3e38483293d906c0b5f0669af647e | /simsem/R/tagHeaders-methods.R | 114e6419e6e1aecde26161c8aedb9da81bca2542 | [] | no_license | pairach/simsem | c2da13f31af4b8ed986647320090bbd9edc0c400 | 8194f63851ed0c0dbd447726988b0a58619ec43a | refs/heads/master | 2020-12-25T01:50:53.664082 | 2012-05-29T21:38:06 | 2012-05-29T21:38:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,066 | r | tagHeaders-methods.R | # tagHeaders: This element will add names in each element of a vector or will add row and columns names of a matrix with variable or factor names
setMethod("tagHeaders", signature = "VirtualRSet", definition = function(object) {
ny <- NULL
nx <- NULL
nk <- NULL
ne <- NULL
modelType <- object... |
3dda7d18f04feaae4f6408ed23ac471e874f3577 | f2ecedf2b1a39abc178ba6e31f6fb7e1f28f0e99 | /man/ap_textplot.Rd | 882746c19435840d92bac9339d00c1aa169a4652 | [] | no_license | cekehe/rappp | 02ae1bee6b6112b210ea85d29cefe606178afbe7 | 256d983ff1e07d5635446f9b3a0d62fca9858729 | refs/heads/master | 2022-06-07T11:54:44.324202 | 2022-05-16T13:37:43 | 2022-05-16T13:37:43 | 192,409,000 | 0 | 3 | null | 2022-05-16T13:37:50 | 2019-06-17T19:47:53 | R | UTF-8 | R | false | true | 851 | rd | ap_textplot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ap_textplot.R
\name{ap_textplot}
\alias{ap_textplot}
\title{Display text information in a graphics plot.}
\usage{
ap_textplot(object, halign = "center", valign = "center", cex, cmar = 1.5, ...)
}
\description{
This function displays text outp... |
836f62fa2d1da347e5b2494f10a4c0655aa52e36 | 16886bf71c969197ecdeb76f5bdfbf8291ac4410 | /R/mapping.R | 7409a99b67db77b017970a29e2590edbed3cdecc | [] | no_license | mapping-elections/mappingelections | 298d4838172e14bc3fa241f35424d95f606985cd | 5841ddf796572f142f8e103bb7b98705aaebb585 | refs/heads/master | 2021-01-23T12:32:37.432586 | 2019-07-23T14:59:33 | 2019-07-23T14:59:33 | 93,166,371 | 0 | 0 | null | 2018-07-26T21:40:50 | 2017-06-02T13:07:19 | R | UTF-8 | R | false | false | 16,826 | r | mapping.R | #' Map elections data
#'
#' @param data An \code{sf} object with elections data returned by
#' \code{\link{join_to_spatial}}.
#' @param congress The number of the Congress. If \code{NULL}, it will be
#' guessed from the data.
#' @param projection If not provided, then the best state plane projection will
#' be gu... |
2cd63a325c5d48dc9b870bf937fb948d1314d5b6 | 4630a28100fbb60d6dbaf71540c0547346760bc3 | /R/utilities.R | 680719424871503d5e3852390371ece46f39c932 | [] | no_license | Bioconductor/BiocManager | e202aa74fb2db70cbfed2295958c88d416209d3f | 125d50a723caaea36d3c27d241f78f7d96e2a3d7 | refs/heads/devel | 2023-09-01T01:22:18.656330 | 2023-08-21T20:11:04 | 2023-08-21T20:11:04 | 33,965,307 | 74 | 23 | null | 2023-09-08T13:39:13 | 2015-04-15T01:04:01 | R | UTF-8 | R | false | false | 2,794 | r | utilities.R | .is_CRAN_check <-
function()
{
!interactive() && ("CheckExEnv" %in% search())
}
.is_character <-
function(x, na.ok = FALSE, zchar = FALSE)
{
is.character(x) &&
(na.ok || all(!is.na(x))) &&
(zchar || all(nzchar(x)))
}
.is_scalar_character <- function(x, na.ok = FALSE, zchar = FALSE)
... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.