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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
68e703a2a617d143a2fa272bb5b4d0a1e7a157af | 34a8fb816af4e9c1d1872708e7da2bee2655cbea | /man/N_model.Rd | 26da738a6ca039b179fb35a32b2199b86d140328 | [] | no_license | AgronomiaR/seedreg | 1352db5486133703d041298edc2d2d4f75daed5e | 7b80724cf113252d9551ef4ef5501ef1de439ae2 | refs/heads/main | 2023-05-05T00:07:10.674164 | 2021-05-18T01:35:43 | 2021-05-18T01:35:43 | 339,866,638 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,215 | rd | N_model.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/N_model.R
\name{N_model}
\alias{N_model}
\title{Analysis: graph for not significant trend}
\usage{
N_model(
trat,
resp,
ylab = "Germination (\%)",
error = "SE",
xlab = expression("Temperature ("^"o" * "C)"),
theme = theme_classic(... |
c63a5ca14f6f71e5715e42f6bf2e52aa53c2ca4a | 753e3ba2b9c0cf41ed6fc6fb1c6d583af7b017ed | /service/paws.cloudsearchdomain/man/search.Rd | 89f573ee59a0960c4f3137616f024bead32dd003 | [
"Apache-2.0"
] | permissive | CR-Mercado/paws | 9b3902370f752fe84d818c1cda9f4344d9e06a48 | cabc7c3ab02a7a75fe1ac91f6fa256ce13d14983 | refs/heads/master | 2020-04-24T06:52:44.839393 | 2019-02-17T18:18:20 | 2019-02-17T18:18:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 15,768 | rd | search.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/paws.cloudsearchdomain_operations.R
\name{search}
\alias{search}
\title{Retrieves a list of documents that match the specified search criteria}
\usage{
search(cursor = NULL, expr = NULL, facet = NULL,
filterQuery = NULL, highlight = NULL, p... |
b14142764fb47e80858e32a8c7520631d5a966ee | 183d605c11aa8526e3420c91f2a402d98a8cd355 | /R/lhs_getpairs.r | 81ead7c90074fdfd13070e4883b153a2ef66f571 | [] | no_license | CenterForPeaceAndSecurityStudies/MeasuringLandscape | aa732d3be364d5097a20174ae5f4b2b7a86bb417 | 06c46f2af800b5d6d0c3c8e5e95e1f747410bbe7 | refs/heads/master | 2021-09-09T22:42:01.403895 | 2018-03-20T03:14:39 | 2018-03-20T03:14:39 | 125,955,143 | 1 | 0 | null | 2018-03-20T03:25:40 | 2018-03-20T03:25:40 | null | UTF-8 | R | false | false | 2,039 | r | lhs_getpairs.r | # These functions involve retrieving string suggestions based on locality sensitive hashing
#
#
#
#
# library(textreuse)
# library(LSHR)
lhs_getpairs <- function(strings, grams, bands_number=400, rows_per_band=5, mc.cores = parallel::detectCores()) {
pairs <- LSHR:::get_similar_pairs_cosine(
grams,
bands_n... |
af87fbe0393277c97587fc401baba97775e9d529 | 5325176ee2337407e603d274e384d7baaf348588 | /R/aussim_df.R | 7965dc4cbff08157add101968b3122313da1f15d | [] | no_license | xxzhiyouwo/PensionAge | 8af1b42f76b7ed59a7fb4c40862db1e998a09d34 | 05601719b2bc5cfab68437581ac6e77767c6756a | refs/heads/master | 2023-04-17T07:32:11.388956 | 2021-05-05T07:38:11 | 2021-05-05T07:38:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 626 | r | aussim_df.R | make_aussim_df <- function(aus.sim, maxyear = 2050) {
# Combine simulation results into one tibble
dimnames(aus.sim[["male"]]) <- dimnames(aus.sim[["female"]]) <- list(
Age = dimnames(aus.sim[[1]])[[1]],
Year = as.numeric(dimnames(aus.sim[[1]])[[2]]) + 1,
Rep = dimnames(aus.sim[[1]])[[3]]
)
aussim_d... |
41a86c7fe45e699e1f479c760b194cf5a4b5a53e | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.machine.learning/man/comprehend_start_events_detection_job.Rd | 2d6d324e72df4d16c2dccfa9565e4de01291a2a2 | [
"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 | 1,672 | rd | comprehend_start_events_detection_job.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/comprehend_operations.R
\name{comprehend_start_events_detection_job}
\alias{comprehend_start_events_detection_job}
\title{Starts an asynchronous event detection job for a collection of documents}
\usage{
comprehend_start_events_detection_job(... |
4ee4873fc09cb2e927d0c86f83515597278987fd | 512f85e00d2bf57117cf336f6b1951bdaf2dd3ea | /man/data.Rd | 1ff3f91b073f4066f71f844a392ffe3102acf291 | [] | no_license | giabaio/survHE | a6ee6394000c9920ae97ea7c052d47d0ab241a62 | 17c8d5ba9761e38c577d13506b13cfb43bfb49e0 | refs/heads/main | 2023-05-24T18:23:30.529957 | 2023-05-23T11:11:24 | 2023-05-23T11:11:24 | 75,872,554 | 40 | 23 | null | 2023-02-09T17:33:28 | 2016-12-07T20:21:33 | C++ | UTF-8 | R | false | true | 1,062 | rd | data.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{data}
\alias{data}
\title{A fictional survival trial.}
\format{
A data frame with 367 rows and 8 variables:
\describe{
\item{ID_patient}{The individual level identifier}
\item{time}{The observed time at which the e... |
40c0101493d1be61bfe320cd5d050d9dd18209d7 | 6817390c7d7ca2b221dd9bdfb23b03ed08494c00 | /R/fct_loadStage.R | e350a201e3a9b2a120f970bc2a1d60a457554bbe | [] | no_license | nvelden/shinyNGLVieweR | 4e038154924ee38e39196a0aa0a8fef3174e4d07 | 96f4dd7f25532346b6af5a6f34c72388d502eda2 | refs/heads/master | 2023-07-03T07:08:29.154554 | 2021-08-18T14:09:39 | 2021-08-18T14:09:39 | 366,112,797 | 7 | 3 | null | null | null | null | UTF-8 | R | false | false | 781 | r | fct_loadStage.R | #' Load stage from a .ngl file
#'
#' @description
#' Load stage from a .ngl file
#'
#' @param NGLVieweR NGLVIeweR object.
#' @param stage data.frame of selections loaded from .ngl file.
#'
#' @import NGLVieweR
#' @export
loadStage <- function(NGLVieweR, stage) {
viewer <- NGLVieweR
if (!is.null(stage)) {
viewer... |
59212873297c2bfff633ab9f3f3a904257d904dc | 0a906cf8b1b7da2aea87de958e3662870df49727 | /grattan/inst/testfiles/anyOutside/libFuzzer_anyOutside/anyOutside_valgrind_files/1610130943-test.R | da8bd6131fd18b8c8cdcd04e68d6b447bc813861 | [] | 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 | 735 | r | 1610130943-test.R | testlist <- list(a = -171L, b = -256L, x = c(-1L, -16383233L, -16320513L, 505085951L, 16777471L, 63996L, -114883070L, -63753L, -54785L, 1845557756L, -109874144L, 1685026146L, 1818569827L, 1862271015L, 704612488L, -2004318072L, -2004318072L, -2004318072L, -2004318072L, -2004353024L, 0L, 0L, 30L, 452984576L, 0L, 1677... |
83de342faa1df68f354f3238e29c3630a1cd5817 | 5f3198e36d7c42b0ed15e1e364a7bc3b3e00652e | /man/reviewNeuronsMoreFrames.Rd | d003563fa00709ab09a4e24e0b40fdc9d2f6f803 | [] | no_license | cran/scalpel | f6a2eeca848d9f793810754400de0059ccaa5bda | 5ed5b98cfd326688c8bdabfdd8d487a83e807768 | refs/heads/master | 2021-06-26T13:05:04.209432 | 2021-02-03T04:30:02 | 2021-02-03T04:30:02 | 84,911,225 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,264 | rd | reviewNeuronsMoreFrames.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/SCALPEL_reviewNeurons.R
\name{reviewNeuronsMoreFrames}
\alias{reviewNeuronsMoreFrames}
\title{Save additional frames for manually classifying the identified neurons from SCALPEL.}
\usage{
reviewNeuronsMoreFrames(scalpelOutput, neuronSet, numF... |
4f71d9fc4d81908da53d73b0ec4d692240084476 | 7a74bf857ab0db8ef9d174e1b8932a26d6880eac | /scripts/06_matching.R | 819d97f5c535eb7c110daed7072b03e1dfca453b | [] | no_license | fghjorth/vkme16 | 47669c1753d30f75cac7f38428210d7ee59d77a8 | fe7966d9a1e3861c0f2be7964038a8f331acf619 | refs/heads/master | 2020-04-05T18:57:25.570505 | 2016-12-13T13:28:56 | 2016-12-13T13:28:56 | 51,516,886 | 3 | 5 | null | 2016-11-21T10:04:24 | 2016-02-11T13:37:09 | R | UTF-8 | R | false | false | 1,446 | r | 06_matching.R | setwd("~/GitHub/vkme16/")
require(haven)
require(stargazer)
require(cem) #coarsened exact matching
require(dplyr)
#indlæs data
ld<-read_dta("data/6_laddlenz.dta")
#bivariat model
ols1<-lm(vote_l_97~tolabor,data=ld)
summary(ols1)
#model med kontroller (jf Ladd/Lenz s 402)
ols2<-lm(vote_l_97~tolabor+vote_l_92+vote_c_... |
e55284dfab31d5ebb8e2ef88a47da8524113ae7d | f6b5afc27bdcb335263f0b97c5a9d5c2b65d0e9a | /R/eA1c.R | 849d223eb7646cdec4ed2430ce7db40b75c80145 | [] | no_license | marhenriq/cgmquantify | ea2a69e27f6aaa62f6e71e3a90f425741ad77b14 | 797f4b59a3a97a8801e54de71a2e659d635cf464 | refs/heads/main | 2023-03-01T18:26:11.997739 | 2021-02-10T22:04:06 | 2021-02-10T22:04:06 | 331,686,450 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 470 | r | eA1c.R | #' Compute Estimated A1c
#'
#' This function computes the estimated A1c, according to
#' the American Diabetes Association calculator
#'
#' @param df Data frame read through readfile
#' @return A numeric value representing eA1c
#' @examples
#' mydatafile <- system.file("extdata", "my_data_file.csv", package = "cgmquant... |
d15818ee20a601e4aced298acfb0dc3d55766314 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/Opt5PL/examples/Deff.Rd.R | 20d786678635f41900ec25ae7e312e00b21d389c | [] | 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 | 579 | r | Deff.Rd.R | library(Opt5PL)
### Name: Deff
### Title: Obtaining D-efficiency for estimating model parameters
### Aliases: Deff
### ** Examples
##Under the 5PL model with the parameter values
##T5=(30000,0.5,800,0.5,2) and the dose range [1.95,32000],
##find the D-efficiency of the broad range design.
##The broad range design... |
655ab5d69be6d41a5236d28caeea08343f58ac09 | fc8a4b06b96c26619d2d9178a6b6f15f87dcb5b0 | /analysis.R | 1c937760a91bae8943b094a684ec1693e47a7cc4 | [] | no_license | chopley/timHuntSentimentAnalysis | f41b859970d92aeee6cea3f1c7856e262576d519 | df7fb978c962aa2a386a8d7c86712a92a768b6d7 | refs/heads/master | 2021-01-23T06:44:38.166388 | 2015-07-28T11:10:20 | 2015-07-28T11:10:20 | 38,750,164 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,907 | r | analysis.R | require(twitter)
require(sentiment)
require(plyr)
require(ggplot2)
require(wordcloud)
require(RColorBrewer)
require(tm)
#get a list of words that have emotions attached to them
wordList<-read.csv2('AFINN/AFINN-111.txt',sep='\t')
filenameList<-c('2015-06-08_2015-06-09_timhunt','2015-06-09_2015-06-10_timhunt','2015-06-... |
1586e89a3cc1c375174fd35dfe973488cba3e398 | 70c2171ef10f71b24c218a27bf4276b3c8779f61 | /armpipeline_r/ARM/improvement.R | fd917d45bccb53764d56fe8f5299e09f913d5fd2 | [] | no_license | DataAnalyticsinStudentHands/RPipeLine | 295b873e0510727113f77bc86af31d8d95ee06c4 | 13c59e9e5a11222e22bacd3f73d392a4da0331dd | refs/heads/master | 2021-01-13T17:06:58.535140 | 2017-01-26T19:21:40 | 2017-01-26T19:21:40 | 69,997,633 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,415 | r | improvement.R | improvement <- function(rules, t = 0.1) {
# This function determines whether the OR of a rule is interesting or if it just
# a consequence of a parent rule. The goal is to eliminate redundant rules with a
# Occam's Razor strategy
# Exctract interesting data from rules structure
i <- rules@lhs@data@i ... |
23bde576f1c596f123930163716701f007185cdd | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/condmixt/examples/gpd.mme.Rd.R | c9297e66bcf1804deb3aafdfcb37a948beee9739 | [] | 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 | 242 | r | gpd.mme.Rd.R | library(condmixt)
### Name: gpd.mme
### Title: Moment Estimator for the Generalized and the Hybrid Pareto
### Distribution
### Aliases: gpd.mme hpareto.mme
### ** Examples
r<-rhpareto(1000,0.1,0,1,trunc=FALSE)
hpareto.mme(r,p=0.991)
|
b771ee8203ee667198079980d917570a909ac7cc | f6dcb066042632979fc5ccdd6aa7d796d3191003 | /Problem Sets/Student Submissions/Problem Set 3/1830531/e3_q1_1830531.R | 5238da24c8506636c5ef1f7d02bdf9f4cdbd8d3d | [] | no_license | NikoStein/ADS19 | 45301bcd68d851053399621dd8a0be784e1cc899 | 90f2439c6de8569f8a69983e0a605fd94e2e9f0a | refs/heads/master | 2020-05-19T19:10:12.411585 | 2020-03-12T00:02:14 | 2020-03-12T00:02:14 | 185,165,255 | 0 | 4 | null | 2019-08-06T06:16:30 | 2019-05-06T09:26:01 | HTML | UTF-8 | R | false | false | 5,027 | r | e3_q1_1830531.R | # Problem Set 3
# Question 1
library(tidyverse)
library(readr)
library(RColorBrewer)
# 1 a)
data <- read_csv2("https://www.bundeswahlleiter.de/dam/jcr/5441f564-1f29-4971-9ae2-b8860c1724d1/ew19_kerg2.csv",
skip = 9)
# consider only big parties
Parteien <- c("CDU","CSU", "GRÜNE", "SPD", "... |
750e90251e0fe5b9161236de582cad50c43cdbaf | ed823b6da656fb94805c7ff74dfd7b921c5624c9 | /man/mm10.chromosomes.Rd | 0add80fa00ef86353b24258e1707189f0af19896 | [] | no_license | vallotlab/ChromSCape | cbde454c903445706e75b27aade45a7a68db5986 | 382eac1015cd7f67e448124faf5a917f4c973aa1 | refs/heads/master | 2023-03-15T20:18:37.915065 | 2023-03-13T16:46:50 | 2023-03-13T16:46:50 | 191,729,569 | 11 | 5 | null | 2019-07-03T13:06:05 | 2019-06-13T09:10:39 | R | UTF-8 | R | false | true | 600 | rd | mm10.chromosomes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{mm10.chromosomes}
\alias{mm10.chromosomes}
\title{Data.frame of chromosome length - mm10}
\format{
mm10.chromosomes - a data frame with 24 rows and 3 variables:
\describe{
\item{chr}{Chromosome - character}
\item{s... |
9a1797d69d370181d96af293ce94b58ceefb8294 | 2e731f06724220b65c2357d6ce825cf8648fdd30 | /BayesMRA/inst/testfiles/rmvn_arma_scalar/libFuzzer_rmvn_arma_scalar/rmvn_arma_scalar_valgrind_files/1612725998-test.R | e2a7616dfbec4061333fad9b4145d7c75c84cf7b | [] | no_license | akhikolla/updatedatatype-list1 | 6bdca217d940327d3ad42144b964d0aa7b7f5d25 | 3c69a987b90f1adb52899c37b23e43ae82f9856a | refs/heads/master | 2023-03-19T11:41:13.361220 | 2021-03-20T15:40:18 | 2021-03-20T15:40:18 | 349,763,120 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 132 | r | 1612725998-test.R | testlist <- list(a = 2405990150192.13, b = 4.94065645841247e-324)
result <- do.call(BayesMRA::rmvn_arma_scalar,testlist)
str(result) |
cf120c14daff0aee5769c9d37d282feb965cdda8 | 2dd4f5b6b22ce1de32fa6e86e9b787e456c81209 | /[P21_08] Project Code.R | 6467c9ee959e9e4c6d72c3093eb9606d9cffd040 | [] | no_license | ethanduncan65/20-Years-Later | d1e3a0324d56278b9fda982edb9e53f0a77434d0 | 703dd9cc5775bbc27ff2074dad44bc4a67269059 | refs/heads/main | 2023-08-13T05:47:50.360927 | 2021-10-06T21:33:13 | 2021-10-06T21:33:13 | 399,227,890 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,073 | r | [P21_08] Project Code.R | #install package for cv.glm()
library(boot)
# load the data
df = read.csv('MLB Team Stats 2015-2019.csv', header = TRUE)
# reduce data to variables of interest
df_playoff = df[-c(3:6, 8:9, 13, 15:20, 27)]
# quick EDA
pairs(df_playoff) ### {win_pct vs. run_diff}
# plot made_playoffs against likely predictors
par(mfrow... |
c91e09bc6c807572e163a40165c57d9df3723423 | 06aa50fc00e7c7ebbdec19450f531222a23aa0d7 | /man/gradrate.Rd | 97d934cc61bc46e61b993991542f1637f7c08752 | [
"MIT"
] | permissive | djliden/youthhealthr | 5c4074d90956a1bf287c67c26124e71f3a9a427f | a1c2958a5d46d77b81fb1c01b81e53efa8941ee9 | refs/heads/master | 2023-03-17T06:05:39.024302 | 2021-03-16T22:15:38 | 2021-03-16T22:15:38 | 305,777,929 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 586 | rd | gradrate.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/datadoc.R
\docType{data}
\name{gradrate}
\alias{gradrate}
\title{2015-2019 4-year graduation rates in Nevada}
\format{
a tibble with 40 rows and 3 columns:
\describe{
\item{Group:}{Race/Ethnicity}
\item{rate:}{4-year graduation rate}
\item{ye... |
395600229a21dcd5471c15de8a4662a52d241fc6 | 165cddf1b6eb420642e7ab6035bbb5d8817ceea9 | /plot4.R | ceb085ebef464355dac6ead2536e104fef897dd0 | [] | no_license | guschini/ExData_Plotting1 | 0ac3715c3abe0765655d6e30f86dd977fcabf118 | e72130a442d88d412ea0245ffeb7209133cf1a50 | refs/heads/master | 2021-01-21T06:19:21.110426 | 2017-02-26T20:21:21 | 2017-02-26T20:21:21 | 83,214,281 | 0 | 0 | null | 2017-02-26T14:21:53 | 2017-02-26T14:21:53 | null | UTF-8 | R | false | false | 457 | r | plot4.R | source("read_data.R")
do_plot <- function(data = get_data()){
png(filename="plot4.png", width = 480, height = 480)
par(mfrow = c(2, 2))
source("plot2.R")
do_plot(data)
with(data, plot(data$TimeTime, data$Voltage, xlab = "datetime", ylab = "Voltage", type="l"))
source("plot3.R")
do_plot(data)
... |
df8029ba709b861a759742720a597232ca05073b | 13c1757aa797082f47245f0a725ad8ccdcb639fe | /man/plot.kendall_fit.Rd | 6df090d54e30579c00761c352c37e671e6d64a2c | [
"MIT"
] | permissive | mstaniak/kendallRandomPackage | dfe043554ecbf537fcc103dbb01a1754a981eca5 | 6f7a9728f53272d6538ee306ec47698b7382f4eb | refs/heads/master | 2020-05-22T08:38:23.750713 | 2019-08-26T12:17:05 | 2019-08-26T12:17:05 | 84,684,409 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 479 | rd | plot.kendall_fit.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/stable_kendall_fit.R
\name{plot.kendall_fit}
\alias{plot.kendall_fit}
\title{QQ-plot for the result of fitting stable Kendall distribtion.}
\usage{
\method{plot}{kendall_fit}(x, ...)
}
\arguments{
\item{x}{List returned by fit_separate or fit... |
6a685beee78123622d5df02748f5994f751a37f1 | ab8d121437f155ea43c86818c728940ac66b5e67 | /ui.R | 82f269a1a16364cc291f4dc6aebe8555e93bf6e1 | [] | no_license | andrewbaxter439/Name_that_country | 4b5175077fc12420b4549a90fa55586be1d200b3 | a37750f4be5d9de61f7f0fc7433da4ebceb015fa | refs/heads/master | 2020-07-04T08:47:25.141111 | 2019-08-13T22:53:29 | 2019-08-13T22:53:29 | 202,227,862 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,294 | r | ui.R | ui <- fluidPage(
tags$head(tags$style("#c_typing{color: blue;
font-size: 40px;
font-style: bold;
}
#c_title{color: black;
font-size: 40px;
... |
699b69d122cd73b29a8f24dccef865a79c12074e | 22fa1f93d4008dfb544d6f850f43334d839692f6 | /analyses/zarchive/models_stan_nsc.R | e886780c8ef8bb1e956c343140c67beea07a654e | [] | no_license | cchambe12/nscradiocarbon | c27f6b4be0b5523b1a32a5cecd009117685603e3 | 6bb9694d9f8a494f73e48225f114a2bb4ee63a5e | refs/heads/master | 2020-08-14T01:24:32.660625 | 2020-06-05T14:09:46 | 2020-06-05T14:09:46 | 215,071,988 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,547 | r | models_stan_nsc.R | ### 14 October 2019 - Cat
## Now with real data...
### Main Question:
# This is really what I want to show -
# that concentrations vary seasonally A LOT in shallower increments, and only a little in deeper increments.
## And this varies between diffuse and ring-porous wood anatomies
## Main Issues with data:
# Rig... |
1d8d990a6d3ed00dcee76abf276559536804f39c | 034397bd13dbcff5a3e77ab0480df9eb33468e64 | /MovieLens.R | b4ffeea061694e81fce981e151d42dc524ab1473 | [] | no_license | ksapaev/MovieLens | 59a40e341c0f1e623bb1c222d43d5f0fb9da1eab | 9c77f991e21910beec552b7606211dfb273981c7 | refs/heads/master | 2020-12-04T21:58:06.711201 | 2020-01-05T12:28:00 | 2020-01-05T12:28:00 | 231,913,657 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,882 | r | MovieLens.R | ### Author: Khushnud Sapaev
### Project: MovieLens Project
### Course: HarvardX: PH125.9x - Capstone Project
### GitHub: https://github.com/ksapaev/
###############################
### Dataset and Preparation ###
###############################
# Create edx set and validation set
# Note: this process c... |
599c8d2a172b43f9636ae6d4c3d6bda87e04b7db | 43bc17bf2e2ec22df7fac309c416d1788e7c7d83 | /supervised-learning-in-r-classification/naive-bayes-prediction.R | 20de19936c0c01e39f7c06a312cef37f871aaf4a | [] | no_license | EvanKaeding/datacamp | 178d1574d6acfd3d77f37e9156cc3abbe95a5937 | 747cd437361da88b4690499ab293c9d1845c7f1e | refs/heads/master | 2020-04-04T18:03:35.472557 | 2019-04-07T00:15:39 | 2019-04-07T00:15:39 | 156,148,026 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,318 | r | naive-bayes-prediction.R | # Naive Bayes predictions
download.file(url = "https://assets.datacamp.com/production/course_2906/datasets/locations.csv",
destfile = "bretts-location.csv", method = "curl")
where9am <- read.csv(file = "bretts-location.csv", stringsAsFactors = TRUE)
# Doing a basic predicated prediction
# ... |
1e04b652a5051b791eb46ff3a91dc388c82d165a | b856d3b1d21207a026b7f756e1540cde1cc55b0f | /man/get_superclasses_information.Rd | f9890241503d507945f67ffe2b9c67a71f999642 | [] | no_license | robertzk/refclass | 9629beb904f351697b3a43696811912bc5c4f27d | 8b403c36dd24f9b60b654d8d8926d476b924bd84 | refs/heads/master | 2021-01-11T11:10:07.721148 | 2015-10-04T15:58:44 | 2015-10-04T15:58:44 | 24,520,672 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,079 | rd | get_superclasses_information.Rd | % Generated by roxygen2 (4.0.1): do not edit by hand
\name{get_superclasses_information}
\alias{get_superclasses_information}
\title{Get superclass information.}
\usage{
get_superclasses_information(contains, where)
}
\arguments{
\item{contains}{character. A character vector of super class names.}
\item{where}{environ... |
7440523449bcde8770b0461cbeb6b0e8c0068795 | 214bd9de4719c2131e95f4331c9eaf9d03c4b378 | /scripts/utilityScripts/runDnDsCv.R | 038b965b5f1d172e519a0d650263fb51b78e362e | [] | no_license | ndfriedman/evolution_of_hypermutation | ff721eab19adbd1bbca99b0b390fdc347585ccbb | 28999f742cedc059931217e0fae082c0f4a7c2f8 | refs/heads/master | 2023-01-04T02:48:11.022869 | 2020-10-16T15:22:42 | 2020-10-16T15:22:42 | 294,239,633 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,204 | r | runDnDsCv.R | #written by noah friedman, help from craig to run
#a template for R scripts for plotting
library(ggplot2)
library(grid)
require(cowplot)
library(egg)
library(dplyr)
library(data.table); setDTthreads(6)
library(dndscv)
library(data.table)
library(ggrepel)
library(plyr)
fix_maf_cols <- function(maf){
maf <- data.tabl... |
deeb7cecd3448ac3d9e2a2d296f7e3489dcef28e | 05083015cff89d8cbb53df35eeadbaa43fe3d2f3 | /Assignment2/pollutantmean.R | d4f6a6f8c4e2cf2608f94bbc6bd7b85bef033ab8 | [] | no_license | AviB18/DataScienceCoursera | bdc2067563f98b5bf5a63bedae6fae201729b591 | c2571e645a4e692af1265f92c30081cbe7da66be | refs/heads/master | 2020-05-19T12:46:05.731944 | 2014-10-26T13:16:52 | 2014-10-26T13:16:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 391 | r | pollutantmean.R | pollutantmean <- function(directory, pollutant, id = 1:332) {
cwd <- getwd()
setwd(directory)
fileList = paste0(formatC(id, width = 3, flag = "0"), ".csv")
data = lapply(fileList, read.csv)
polList <- list()
for(d in data) {
polList <- append(polList, d[[pollutant]])
}
polVec ... |
e2e7c421fd7884ee274959fc8e5faf49723a1839 | 88e2e55f7ac29695ed214b7d52bb85a0bbf658e8 | /scripts/05_tibble_adatbeolvasas.R | a4cf91df8610a75708edfa1be66fb9dbb614fdb9 | [] | no_license | bpolner/introduction_to_R | 391cc9fc83acd4a937dc741e895170abf50f9732 | e801e2e03b4fa6d315c6eeb2427522846ded9b9d | refs/heads/master | 2022-10-28T18:03:31.763798 | 2022-10-24T08:46:45 | 2022-10-24T08:46:45 | 168,689,525 | 4 | 5 | null | 2021-03-05T20:18:23 | 2019-02-01T11:44:37 | HTML | UTF-8 | R | false | false | 14,898 | r | 05_tibble_adatbeolvasas.R | # A tibble. Fájlok olvasása és írása.
library(tidyverse)
# 1. A tibble ----------------------------------------------------------------
# A tibble csomaggal lehet őket kezelni - ez is benne van a tidyverse szupercsomagban
# data.frame-ből tibble:
iris
class(iris)
as_tibble(iris)
# Vektorokbol tibble
tibble(... |
374e88f2c3cbb5b9f865277a09f4040ceb8bc254 | b2eca9ace5716ca74d66980cd89b3baf8805597c | /hierarchical_CleanedData.R | b9e62878e45a9fb3d1adf5ec0544a133d985f8d6 | [] | no_license | AlexandrosPetropoulos/BankMarketing | c00fc4ea271f9abd6abf330fac3e22fc58e97e63 | e5f185205bf52eb1ff14f83b71237a3223b20907 | refs/heads/main | 2023-01-30T14:38:14.507393 | 2020-12-18T13:22:10 | 2020-12-18T13:22:10 | 322,601,082 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,073 | r | hierarchical_CleanedData.R | # Clear plots
if(!is.null(dev.list())) dev.off()
# Clean workspace
rm(list=ls())
# Clear console
cat("\014")
# Set working directory
setwd("~/Big_Data")
# Load libraries (mporei na ksemine kapoia apo tis dokimes kai tora na min xreiazetai)
# entoli gia install packages install.packages("caret")
#library(rpart) #DEC... |
d35afe3130be0484307dfcca1ec385d027a4df51 | b2757d8cca182148d664e55a5d29aa1b82e453e5 | /script/rml/instacard/tools.R | 1af239232868b24d6ffa698644d8e1a74a159443 | [] | no_license | genpack/tutorials | 99c5efb6b0c81a6ffeda0878a43ad13b1987ceeb | c1fe717272f06b81ca7a4855a0b874e98fde4c76 | refs/heads/master | 2023-06-10T21:18:27.491378 | 2023-06-06T00:15:14 | 2023-06-06T00:15:14 | 163,214,555 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,774 | r | tools.R | ## Tools for instacard project:
# Version: 2
# Product Within-Cluster Features Added:
next_last = function(v){
if(length(v) < 2) return(-1) else return(v[length(v) - 1])
}
uncumsum = function(v){
nn = length(v)
if(nn < 2) return(-1) else return(c(v[1], v[-1] - v[-nn]))
}
## input dataset must contain all orde... |
f5c2d72e05cc9975d77189f34e06d4f354b5f78f | 9b090643f95d8ca5f91b9e7beab6fb040f0dd7bd | /Set 1 excercise.R | c8d9d37fc673e71420485ed29d309771ce630b91 | [] | no_license | smrutisanchita/Hypothesis-Testing-Statistics-Using-R | dfea3af45e41474519efb8c9edc419dd090ed836 | 4115b177c944fbbe3ce3432b4dde172c0729c4b0 | refs/heads/main | 2023-03-05T11:23:34.026884 | 2021-02-18T09:39:28 | 2021-02-18T09:39:28 | 339,997,132 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,080 | r | Set 1 excercise.R | #Set 1 1 Sample Z- test
##########################
#Question 1
1 Sample Z- test
---------------
#ho: u=130
#h1: u!=130
n=9
mu0=130
x_bar=131.08
sigma=1.5
alpha=0.01
z=(x_bar-mu0)/(sigma/sqrt(n))
#two tail test
qnorm(alpha/2)
# as z is not in CR , we will NOT reject Ho
----------------------------... |
ae65c3a13a98689f1b665f28c7d0a7845434fad7 | 1916f798d0b2c2ec56a84b3bbe17911bd8f3f1c0 | /loadData.R | 7a55c2aa25ef7254be0d19d936e6128d7fe45f55 | [] | no_license | potterrr/Expl_data_Analysis_proj2_Plt4 | cc38dfb33f91e05a6758ed3de4081e7465d8ed20 | a1113956effb3346099957e844b2282bf75fcded | refs/heads/master | 2020-12-26T03:12:17.043474 | 2015-11-23T22:33:18 | 2015-11-23T22:33:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,281 | r | loadData.R | ## Download extract and load data into R
# check if complete unziped data is pressent allready if not download and unzip
if (!file.exists("./input/summarySCC_PM25.rds") |
!file.exists("./input/Source_Classification_Code.rds")) {
# creat directory for data
if (!dir.exists("./input")) {
dir.creat... |
509fceaae0ea7c8a63a6044f6566b433792f62d8 | 7c40a7f4735e15b9156fd999fc729924830b5c6b | /R/varpart_helper_funs.R | 2c0ee81a2ccb01555ee2bdeeb63e9dd7ccca7c33 | [
"MIT"
] | permissive | jannes-m/2020-enso-tdf | 0e720bbe1daf32fae324df8e4fa83166f1be06e9 | f230d75cab7c56ecbbe6426b4d631e28b30bed58 | refs/heads/master | 2022-12-22T16:53:45.170945 | 2020-09-20T21:37:11 | 2020-09-20T21:37:11 | 286,574,235 | 0 | 0 | MIT | 2020-09-24T15:15:13 | 2020-08-10T20:39:24 | R | UTF-8 | R | false | false | 6,465 | r | varpart_helper_funs.R | #' @title Function retrieves the DCA scores of the first two axes and plots them
#' against two environmental tables
#' @details Function applies a DCA, retrieves the scores of the first axes and
#' plots the scores against the environmental variables.
#' @param londo Plot-species matrix with an id column named \co... |
487a4f0afc10e659308acad3e1eca7cb005941e1 | 81d787d69aa856acb59a7498f6a6f6b659832b5f | /ui.R | b18900961dfd89607d9e5e3918d5e953dedf96bd | [] | no_license | colinking/dataAnalyticsFinalProject | e2f0ccca664bbb837eb804f4036df9681371e40d | 76e79876cc3e8514d077bf7d7012da4b75269926 | refs/heads/master | 2016-08-04T14:43:37.472879 | 2015-01-25T16:51:22 | 2015-01-25T16:51:22 | 29,820,519 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,636 | r | ui.R | #install.packages('devtools')
#devtools::install_github('shiny-incubator', 'rstudio')
library(shinyIncubator)
shinyUI(
pageWithSidebar(
headerPanel("Stock Market Simulator"),
sidebarPanel(
h4("Simulator Controls"),
actionButton('playGame', 'Play Simulator', icon = icon("play")),
br(),br(),
... |
66bab61c0ec969043b95a0bb219e6fafb66cf966 | 4d9808b08204d65194923a6a975c98604f5a6746 | /R/EnrichmentScore.R | 1f7875ec1d7d5a0f0ad9ea8382c0df496812ab15 | [
"Apache-2.0"
] | permissive | hanjunwei-lab/MiRSEA | 41b21f9dcf24e513017a3b100e2cd2411406ff8a | 2914d2e41ba742589d80e91c0559cc3e83e3692b | refs/heads/master | 2022-11-08T07:45:54.054691 | 2020-06-30T13:18:48 | 2020-06-30T13:18:48 | 275,349,916 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,001 | r | EnrichmentScore.R | ########################################################################
##get enrichment score for each miR set
EnrichmentScore <- function(miR.list, miR.set, weighted.score.type = 1,
correl.vector = NULL) {
tag.indicator <- sign(match(miR.list, miR.set, nomatch=0)) # notice that the sign is 0 (no tag) or 1... |
f26a174406ca642e81a29e09d3a814694a26f52e | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/FRESA.CAD/R/jaccard.r | 98d45b26497e4cc09ba2ca262d88e5c127765693 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,482 | r | jaccard.r | #' returns the jaccard index matrix of two labeled sets
#'
#' jaccard = intersection/union
#' @param clustersA cluster labels of set
#' @param clustersB second cluster labels of the same set
#' @return Returns the jaccard matrix of the labeled set
#' @examples
#' @importFrom
#' @export
jaccardMatrix <- fun... |
c5facb3f4a9747200f33978b874f0c9e41fa5d42 | 771c05fa7b58f8f2dab7938da389e9e72b3cf3d4 | /Rvasp/man/poscar.extractlayers.Rd | 7a55eba1fc7df7d732e417b414ea1ef6843ec864 | [
"MIT"
] | permissive | gokhansurucu/Rvasp | 56a75b10daa606768791935530bd108204d92f4f | 8983440a96ca8acf017f47af8dbfd3f32faaad22 | refs/heads/master | 2020-04-08T21:14:33.155967 | 2014-03-14T20:08:59 | 2014-03-14T20:08:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 704 | rd | poscar.extractlayers.Rd | \name{poscar.extractlayers}
\alias{poscar.extractlayers}
\title{Extracts atoms of object of class poscar}
\usage{
poscar.extractlayers(poscar, layer, layers,
vacuum = c(0, 0, 0), center = T)
}
\arguments{
\item{poscar}{object of class poscar}
\item{layer}{indices of layers which will be extracted}
\item{l... |
49907ef73eb753799e44fbb1d0494b3d11bb7845 | 1d95131d65ea71dfa5d3bbe40886577b3d97879c | /getDataStockPrices.R | 22aa406c5a1f9ecd083aa41e7b56f38b82619065 | [] | no_license | agranado/LSTM_timeseries | e3597de9e0efba19e242c47eedeae9ff7086c1aa | 2c77644ae89b6b4145b732b25fabbbe3b3ab66a1 | refs/heads/main | 2023-02-27T05:47:13.356337 | 2021-02-06T20:51:05 | 2021-02-06T20:51:05 | 336,632,354 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 622 | r | getDataStockPrices.R | library(BatchGetSymbols)
# we get data from 2000 to include the dot.com recovery
first.date <- '2000-01-01'
# Until today
last.date <- Sys.Date()
freq.data <- 'daily'
symbols = c('MSFT','NVDA', 'AMZN', 'GOOGL','AAPL', '^IXIC')
aa =BatchGetSymbols(symbols, first.date = first.date, last.date = last.date... |
930756f26e4329800b89f94425410b06102c6fca | 1233bd68fa715c898ea416f1945235bd1ee341ac | /scripts/particle_analysis_1.R | 8c338cd0a677e586e87fb441e970efcb8e957066 | [] | no_license | grapp1/mb_sensitivity | 98a3ef97e989b99f945e452b2859efb77c0a05fe | 783531044cd8877a21e32803543a0eb8bd4d8453 | refs/heads/master | 2021-06-14T06:36:09.843400 | 2020-09-01T00:56:39 | 2020-09-01T00:56:39 | 254,479,809 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,904 | r | particle_analysis_1.R | # -------------------------------------------------------------------------------------------------
# particle_analysis_1.R
# EcoSLIM analysis script
# to generate saturated age PDFs and variance plots for six scenarios
# -------------------------------------------------------------------------------------------------
... |
871ddc803f9e42a09e39104d16a58a9e3f77366c | 994012ddc5c85019df5101b7c4d4f824fb9d02af | /Ch.7/GelmanHill2007_Ch7Question1_IRC.R | b33421e4b79213550b6a733e2c35cb6ec6d1b0d1 | [] | no_license | donahuem/Gelman_Hill_readinggroup | 655c9d7c54606286472d44cce9bd6a3777c728ca | 8562c2b55071ca61e0623abc27a5d812d721b8ad | refs/heads/master | 2020-12-12T22:57:12.594422 | 2017-02-10T22:43:43 | 2017-02-10T22:47:19 | 50,400,514 | 3 | 3 | null | null | null | null | UTF-8 | R | false | false | 1,665 | r | GelmanHill2007_Ch7Question1_IRC.R | #Gelman & Hill - Hierarchical Models
#Chapter 7 Questions
#Question 1
# Discrete probability simulation: suppose that a basketball player has a 60%
# chance of making a shot, and he keeps taking shots until he misses two in a
# row. Also assume his shots are independent (so that each shot has 60% probability
# ... |
676b62b19e3afb457a934ee600becc764fa6c253 | c8e71af48d925c34d1cb9f4dad262c970e8968d5 | /man/InsuranceVote.Rd | 2404cdba85b59c48ca6cb21fbfd33c18ea707f87 | [
"MIT"
] | permissive | tessington/qsci381 | 43c7cd323ab64cf28ba738be35779157c93e62cf | b981f0bd345b250d42ff5f1c0609e5e61f5911f7 | refs/heads/master | 2022-12-24T20:56:56.045374 | 2020-09-24T20:50:29 | 2020-09-24T20:50:29 | 284,817,926 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,589 | rd | InsuranceVote.Rd | \name{InsuranceVote}
\alias{InsuranceVote}
\docType{data}
\title{Congressional Votes on a Health Insurance Bill}
\description{
Congressional votes on an ObamaCare health insurance bill in 2009
}
\format{
A dataset with 435 observations on the following 9 variables.
\tabular{rl}{
\code{Party} \tab {Party affilia... |
d53c5ebf92164ef6f9c33fd827905256ca94aec7 | 57641b8222ba9f6c4dad5080326630bfb85b592c | /man/SplitSentences.Rd | 82a1a093598bff8a0656eeea24c9bcaddcba3b86 | [] | no_license | M3SOulu/EmoticonFindeR | 28ac13272d7bf3298423b8462a22af117e3748e9 | fb99960b6f2ddaab85b1ef7a2715a8c757658134 | refs/heads/master | 2022-06-28T07:36:24.370149 | 2022-06-20T10:45:55 | 2022-06-20T10:45:55 | 192,537,114 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 494 | rd | SplitSentences.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sentences.R
\name{SplitSentences}
\alias{SplitSentences}
\title{Split sentences}
\usage{
SplitSentences(text, nclusters = parallel::detectCores())
}
\arguments{
\item{text}{Text to split into sentences.}
\item{nclusters}{Number of clusters t... |
427e3cbe2cf3a030ebec598681b81ad76c12a91a | 45c1672d4885fb27d9210e688f1861ab5d7407fc | /randomize_network/load_links_file_format.R | dc2e9462831d346589700636cb90b05f85691bd5 | [] | no_license | seoanezonjic/sys_bio_lab_scripts | b518ff0a1b57fe3a4abd6b81192977d8a61fcfa0 | 77d57fd70deafb39d85576c2d9f7fc619fdd30eb | refs/heads/master | 2023-05-30T00:16:54.032085 | 2023-05-22T12:32:40 | 2023-05-22T12:32:40 | 216,526,241 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,114 | r | load_links_file_format.R | #' @author Fernando Moreno Jabato <jabato@uma.com>
#' @description function to load specific links file formarts
#' @RVersion 3.4.2
load_links_file_format <- function(file,sep="\t",header=F){
#' Method to load a table of relationships loaded in a specific format into a given file.
#' After load, info is parsed an... |
304883c63c7b33349b2ac0681c35e87242e2c1d2 | 29585dff702209dd446c0ab52ceea046c58e384e | /gaussDiff/R/gaussDiff.R | c783b0d7203aef4ecb5db9f02d176c497976c27c | [] | 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 | 4,686 | r | gaussDiff.R | ######################################################
### gaussDiv.R
### implement multiple divergence measures to compare normal
### pdfs, i.e. similarity and dissimilarity measures
### Henning Rust Paris 18/02/09
### all the implemented similarity and dissimilarity measures
### are described ... |
25a332c666c406be64b96f846c523f30d32ac916 | ca19c130932d2ff7f3680ec42bc23e830c0e5d8b | /examples/SVWQR.R | fe783671e999b1a0fbd865dca95b83510ee87ac3 | [] | no_license | PedroBSB/mlRFinance | 1765a3c0993b06cb2f4248a6d2dbc6a236066b88 | af5cc4c99f3ec7d90a123ce4c402f69b2a358fd8 | refs/heads/master | 2021-03-27T12:00:35.227497 | 2017-12-05T18:35:52 | 2017-12-05T18:35:52 | 71,817,274 | 8 | 21 | null | 2017-12-05T18:35:53 | 2016-10-24T18:09:36 | C++ | UTF-8 | R | false | false | 898 | r | SVWQR.R | #Page 160 - Small
library(mlRFinance)
A<-matrix(c(1,2,5,6),nrow=4,ncol=1)
d<-c(-1,-1,+1,-1)
rank<-order(d)
svm2<- CSVWQR(d,rank, A, 50,0.5 ,0.5, "Polynomial", c(2,1))
svm2
PredictedCSVRL1(svm2, A, A)
R2PredictedCSVRL1(svm2, A)
#Habilita o pacote quantmod
library(quantmod)
#Cria um novo ambiente para armazenar os da... |
07176d2fccf94d3c73a7bc69ae0bfbe33c4c5744 | afa19b675487da30bd0f0d45d29d9b2d220f68b0 | /man/genomic_ranges_reduce.Rd | f144293b8ea6cdfdd35644fe344960d589522ab3 | [] | no_license | rkirk822/rnaseq | fc930785525d0f02ed02cdebedd311afb31e2bbf | 31b5a460058af5055a9cf2f63841e295805b992d | refs/heads/master | 2021-09-17T09:21:48.623668 | 2018-06-29T21:13:20 | 2018-06-29T21:13:20 | 121,541,806 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 920 | rd | genomic_ranges_reduce.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/genomic_ranges_reduce.R
\name{genomic_ranges_reduce}
\alias{genomic_ranges_reduce}
\title{Reduce genomic ranges to set of non-overlapping ranges}
\usage{
genomic_ranges_reduce(inFile, outFile = NULL)
}
\arguments{
\item{inFile}{String - Name ... |
495ad532f829e54eb519eed24b4189205a7e9436 | 496449f594ea62e002bd0b8d7d8e960d3a354e09 | /Covid_analysis.R | 9124159ef98aa1e95aa3208ccce177d56ecf9125 | [] | no_license | lanchett/Covid19 | b12f965cc60aef7afddc19dd3f0bbd6be9a18407 | fa4cb1389036cc59783059485c436639a1ab447c | refs/heads/master | 2021-04-02T19:24:22.079848 | 2020-03-18T18:40:50 | 2020-03-18T18:40:50 | 248,311,798 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,192 | r | Covid_analysis.R | library(tidyverse)
library(gganimate)
# Load data ---------------------------------------------------------------
data <- read.csv("http://cowid.netlify.com/data/full_data.csv")
data <-
data %>% replace_na(.,
list(
new_cases = 0,
new_deaths = 0... |
d158a20e81b7864cc2742506117294c5626f9340 | aa95ae81839d50fed3bf1009d894a12387cb6dd9 | /DES_Koen/function UDR.R | cdcea2e9461572c2a9943fbb4a0fcf8183b7b3dd | [] | no_license | cfbalmaceda/2020-DARTH-Advanced-Workshop | 5c05c3e077249fb04734480892fb3e9db6e50d24 | 802a14cc82fb545fc574cb02fdd794b62b9fd299 | refs/heads/master | 2021-05-19T12:29:56.982966 | 2020-04-03T13:47:49 | 2020-04-03T13:47:49 | 251,699,488 | 0 | 0 | null | 2020-03-31T18:43:55 | 2020-03-31T18:43:54 | null | UTF-8 | R | false | false | 4,046 | r | function UDR.R | #########################################################################################################
##
## UDR Function
##
## Supplementary to the manuschipt:
##
## Comparing strategies for modeling competing risks in discrete event simulations:
## a simulation study and illustration in colorectal c... |
f8c89482da8f44788748445d9b3882f5805281c3 | 6d37064b9751e41020f1836b060054ce8b94bd56 | /R/utils_text.R | bf9f84e16d20f9f283b4c4298d521d286c0dcb2f | [
"MIT"
] | permissive | Chenyz03/pointblank | 78c2a7fdbb48fba8798f6008fcd44a9301021cbc | 51b4fd1a35b8d1368668171ef189e36d038096b5 | refs/heads/master | 2023-01-07T23:22:34.262264 | 2020-05-08T17:52:27 | 2020-05-08T17:52:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,471 | r | utils_text.R | reporting_languages <- c("en", "fr", "de", "it", "es")
#
# Text for autobriefs
#
precondition_text <-
c(
"en" = "Precondition applied",
"fr" = "Condition pr\u00E9alable",
"de" = "Voraussetzung angewendet",
"it" = "Prerequisito applicato",
"es" = "Condici\u00F3n previa aplicada"
)
column_comp... |
922d14cdd04f2239bd12bc91f52f0b4bdeff09b9 | 4fa10361f4cb3a7e01618acd898db278ae9d3546 | /METABRIC/METABRIC.Heatmap.CC.RNASeq.WHX.R | 7252528636e55fb22f22d4438dd6e600fefa01d8 | [] | no_license | dudious/QCRI-SIDRA-ICR | 2e8005e518a51ffba8a7c02adbf4ceb3206af3a2 | c38d69b7eb523cb6f5e869d8a2220a13abeb4944 | refs/heads/master | 2021-04-18T23:35:44.960473 | 2019-05-22T09:04:38 | 2019-05-22T09:04:38 | 32,503,747 | 7 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,113 | r | METABRIC.Heatmap.CC.RNASeq.WHX.R | #################################################################
###
### This Script Plots Heatmaps based on
### Consensus Clustering grouping of RNASeq Data
### from METABRIC
###
### Input data :
### ./3 ANALISYS/CLUSTERING/RNAseq/...
### Data is saved :
### NO DATA
### Figures are saved :
### ./4 FIGURES/Heatmaps... |
60c15c0bcc58a444b8861f07e595af74abdc9d3c | 34914a35bd83b5a587bab647e6e0fc892a119dd5 | /Gillespie_Algorithm.R | 26e8949913a73094813d97f03b390292ecf4f9ef | [] | no_license | martind-hub/Gillespie-Algorithm | 293f0e06243e048d2a268332865435d332393071 | a3e8e1b8044e4a1e263bc2a2c89e8c5b07643b56 | refs/heads/main | 2023-07-14T08:39:46.069253 | 2021-08-30T18:08:38 | 2021-08-30T18:08:38 | 401,440,365 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,917 | r | Gillespie_Algorithm.R | # import the necessarily libraries
library(ggplot2)
#################################################################################################
#### Gillespie algorithm ####
#################################################################################################
# define the function to output the resu... |
aa5ef65937ec825285e36f901f6e3de86c29758e | 266bca1ee343ddc3870dfd80a3acd12be6c6fc6d | /Summary.R | 37e8c77d01ff7d401ca77c897d8e837fae3da0d8 | [] | no_license | MichaelStickels/Educational_Justice_Visualization_Project | be184a26995a1f929dd10674409d38ec452596b6 | 74a12339e8f8b190ba0f9c057845b44c0f2d7ab4 | refs/heads/main | 2023-03-25T01:54:52.862810 | 2021-03-14T20:21:00 | 2021-03-14T20:21:00 | 332,898,047 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,598 | r | Summary.R | # File to calculate summary information about our dataframes
# Load in libraries
library(stringr)
library(tidyr)
library(dplyr)
# Load in tables
neighborhoods <- read.csv("https://raw.githubusercontent.com/MichaelStickels/Educational_Justice_Visualization_Project/main/Data/NCES%20Data/School%20Neighborhood%20Poverty%... |
cbdbb3d8f9e7a1b9e6cc2665a27b727d1e706297 | 3d1a05335aa4c19461591d7d5895a17c43124208 | /ANOVA/boxplot_haricot_dbca.R | 67c1f1ee1b45467f83a770a4c18fbbfc180316f3 | [] | no_license | rodney-davermann/FDSEG-CH-Statistiques-g-n-rales- | 8cc0f3b58fa40b9df98924a1230b3b48fb2dc14c | f718b10f7afc4a8ca6e57e6c31b3aaaac3955e3d | refs/heads/master | 2021-01-01T06:50:44.409330 | 2015-10-02T03:13:07 | 2015-10-02T03:13:07 | 42,944,380 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 253 | r | boxplot_haricot_dbca.R |
# Read the data
library(xlsx)
haricot<-read.xlsx("C://R_Training//anova_haricot_dbca.xls", sheetIndex=1,header=TRUE)
# Boxplot for All combined blocks
boxplot(Rendement~Variete, col=c("green","yellow","red"), vertical=T, main="Rendement par Variete") |
ddbc98a9bb4dc6f1470e907c53d4dc56803954e2 | 506ba5bd7966e619d0c8df79cc640375b1c6c96f | /FAOSTAT/R/FAOSTAT-package.R | 2602754f1c7e2d4f9385826cc3a6e2120b0ca44c | [] | no_license | sebastian-c/FAOSTATpackage | 7627982608001a43eb5f0faf6a5d25235292047e | c4dd5f4a7c38eced35fbb94580b0aba6921dd809 | refs/heads/master | 2023-04-09T00:52:13.050968 | 2017-01-30T16:20:02 | 2017-01-30T16:20:02 | 96,107,563 | 0 | 0 | null | 2017-07-03T12:17:42 | 2017-07-03T12:17:42 | null | UTF-8 | R | false | false | 442 | r | FAOSTAT-package.R | ##' Search and extract data from FAOSTAT database of the Food and
##' Agricultural Organization of the United Nations.
##'
##' @name FAOSTAT-package
##' @docType package
##' @title Search and extract data from FAOSTAT database of the Food and
##' Agricultural Organization of the United Nations.
##' @author Mic... |
1d1c0dc50abbd87740722e294540e2587aba31ef | fac1fe26e6b58e32c3797ef206d803a301403f2b | /inst/run_analysis.R | 9c673ec581e58d9654db17dbbb4e5760409291be | [
"WTFPL"
] | permissive | n0542344/coolmlproject | ded3afc3f27e7fdbc4b0d0597b411fb00120712b | 2a903cdff8699ea8123b73b36cadd6cdcdf24b2c | refs/heads/master | 2022-06-28T08:02:15.255459 | 2020-05-13T07:40:34 | 2020-05-13T07:40:34 | 263,478,738 | 0 | 0 | WTFPL | 2020-05-12T23:50:01 | 2020-05-12T23:50:00 | null | UTF-8 | R | false | false | 1,306 | r | run_analysis.R | ## load (and, if not present, install) necessary packages
if (!require("pacman")) install.packages("pacman"); library(pacman)
pacman::p_load(coolmlproject,
dplyr,
drake,
parsnip,
dials,
tune)
# pacman::p_load(tidymodels)
plan <- drake_plan... |
17076b981f8acc01f4d667a3ae3c54bd051f77ac | 56b32941415e9abe063d6e52754b665bf95c8d6a | /R-Portable/App/R-Portable/library/igraph/tests/test_count.multiple.R | e1bbf372265452aad0d0af357138ee207f1a3a64 | [
"LicenseRef-scancode-unknown-license-reference",
"GPL-2.0-only",
"LicenseRef-scancode-warranty-disclaimer",
"LicenseRef-scancode-newlib-historical",
"GPL-2.0-or-later",
"MIT"
] | permissive | voltek62/seo-viz-install | 37ed82a014fc36e192d9a5e5aed7bd45327c8ff3 | e7c63f4e2e4acebc1556912887ecd6a12b4458a0 | refs/heads/master | 2020-05-23T08:59:32.933837 | 2017-03-12T22:00:01 | 2017-03-12T22:00:01 | 84,758,190 | 1 | 0 | MIT | 2019-10-13T20:51:49 | 2017-03-12T21:20:14 | C++ | UTF-8 | R | false | false | 1,285 | r | test_count.multiple.R |
context("count_multiple")
test_that("count_multiple works", {
library(igraph)
set.seed(42)
g <- barabasi.game(10, m=3, algorithm="bag")
im <- which_multiple(g)
cm <- count_multiple(g)
expect_that(im, equals(c(FALSE, TRUE, TRUE, FALSE, TRUE, TRUE,
FALSE, FALSE, FALSE, FALSE, FAL... |
98139761f6d82853684155f899d14b9ff650c0d9 | 37ac806c710d7d9c81e5d55900c65bd11283407f | /man/get_head.Rd | a2ebb2f6f4bbd4ed74fc74b2727bd0d8b7941abe | [
"MIT"
] | permissive | dirkschumacher/transduceR | 14dc05afb931041b9819cf99dceb7cdb760378f7 | 3ac0bc2b3e119bda274f36f7c8318fe4f987b14a | refs/heads/master | 2020-09-12T22:21:36.069370 | 2016-09-19T17:29:19 | 2016-09-19T17:29:19 | 35,349,106 | 35 | 0 | null | 2016-09-19T17:29:20 | 2015-05-09T23:17:38 | R | UTF-8 | R | false | true | 304 | rd | get_head.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sequence.R
\name{get_head}
\alias{get_head}
\title{Returns the first element of a collection.}
\usage{
get_head(sequence)
}
\arguments{
\item{sequence}{a sequence}
}
\description{
Returns the first element of a collection.
}
|
be2b2a9fd64d7bbef9fdac288028ff1b9e4b6f7d | 33f510378be81a8840c5ad39d20456454d98386c | /pkg/randtoolbox/tests/test-runifInterface.R | d39374f27fc8425bf33efcb8a8b0491be521dd8a | [] | no_license | sstoeckl/rmetrics | 41ebe1d0be6ec19cac02dbe2501551f0e1416698 | dd766277b5891415c514039ac2da0351d86b7c8b | refs/heads/master | 2020-03-31T17:26:51.225928 | 2018-10-10T12:57:30 | 2018-10-10T12:57:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,386 | r | test-runifInterface.R | library(randtoolbox)
RNGkind()
#see e.g. https://en.wikipedia.org/wiki/Linear_congruential_generator
#Park Miller congruential generator
set.generator(name="congruRand", mod=2^31-1, mult=16807, incr=0, seed=12345)
get.description()
runif(5)
setSeed(12345)
congruRand(5, dim=1, mod=2^31-1, mult=16807, incr=0)
RNGkind()... |
9234532fa4f8118004d5baaa5933703095d6441e | b786c156f778c28a2f78ed979923d144390beedd | /figure scripts/trophicTransferFAbarplot.R | a6378b7562b9281fc4b012cdb83334111056c08f | [] | no_license | pkelly13/bugEmergenceProject | fc905b521b1a7d7ee3f4e7c702d5eb8d773f2970 | 57facae093a5aba1f5a2ddca7647113306e4341f | refs/heads/master | 2020-04-16T12:54:33.023047 | 2014-08-26T13:16:12 | 2014-08-26T13:16:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,563 | r | trophicTransferFAbarplot.R | #Bar plot figure of either absolute concnetration of fatty acids, or % fatty acids depending on how much of the tsGrowth_and_PCAplots script is run. Source the scrip to run % of total FAs
#PTK 11 June 2014
#set working directory
setwd('~/bugEmergenceProject')
#source script
source('tsGrowth_and_PCAplots.R')
#save plo... |
7e8b536564b72dffb41312013b53f9fecc36778d | 69630a75fb71b75a1abd21f74b8a811533f7cab4 | /man/total_sales.Rd | d5c9ec7d1b4a7cb56faf1672d3ae8a7f3a867f60 | [] | no_license | codeclan/CodeClanData | aa0cd21aea3390d3629ab7ebbd53543623d92941 | e3965c596a0439e125b4643412bd434a54266da8 | refs/heads/master | 2023-03-16T06:52:38.769843 | 2023-03-10T09:02:43 | 2023-03-10T09:02:43 | 190,196,169 | 5 | 8 | null | 2019-08-03T10:46:43 | 2019-06-04T12:24:19 | R | UTF-8 | R | false | true | 386 | rd | total_sales.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/documentation.R
\docType{data}
\name{total_sales}
\alias{total_sales}
\title{Total sales}
\format{
A data frame
}
\usage{
total_sales
}
\description{
An invented dataset with total sales across 7 branches.
}
\details{
A monthly breakdown of t... |
8675eccb8e38598ba64781cbcb49ef793f0a22a4 | 0a4a2054c304ac1aa473a0c29c6eb9df7fc26275 | /scripts/locus-multispecies-ch-tables.R | 2f00b676447c07bbde057768fbec90bdd55dfe75 | [] | no_license | willbradshaw/thesis | 1039cb1cee95b4159de2cea502a4f2130a91c626 | ad7e65ec88bdb72f2f560b0f409cf8c38d8239c6 | refs/heads/master | 2022-11-17T08:20:04.157046 | 2020-06-27T17:55:59 | 2020-06-27T17:55:59 | 250,043,773 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,485 | r | locus-multispecies-ch-tables.R | ###############################################################################
## FIGURE ##
## Multispecies C-region maps ##
###############################################################################
... |
eb63df9ced6058d61409966dc22ee993971f5f4d | 5c618b59cc2ac45e48c05bb24d2e56be4e27077c | /models/contamination/paper/code/Step4_mask_contamination_hindcasts.R | a15eb0d4e33b5aaa5b0bfd362a508642a529ab0f | [] | no_license | cfree14/dungeness | fefcd5e256e0f8fe4721fbd1b627942e74704b5b | 76054741b1209078e92ce2cc543620023900ab6d | refs/heads/master | 2023-08-29T04:43:00.107409 | 2023-08-08T18:21:41 | 2023-08-08T18:21:41 | 189,051,316 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,549 | r | Step4_mask_contamination_hindcasts.R |
# Clear workspace
rm(list = ls())
# Setup
################################################################################
# Packages
library(sf)
library(zoo)
library(caret)
library(raster)
library(tidyverse)
library(tidymodels)
library(lubridate)
# Directories
datadir <- "models/contamination/paper/data"
outputdi... |
02e0de6b737d2f5448612c1fcf44b27572efef0c | cc2e4b5a9a57396a5b3d32100c92dbc630c59dde | /Part1/4회/분석 결과 정리와 공유.R | e985f5dd6c5051a7f53a543cebbcaeb6daa803a5 | [] | no_license | hellogurney/RTong | d63530f0fe282ae86276cf399dd4182240e4f0f1 | 2ccbb5740326d85616f4c2216b023fe4e059cf8b | refs/heads/master | 2021-04-09T16:34:42.622912 | 2018-09-14T07:40:01 | 2018-09-14T07:40:01 | 125,618,783 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 441 | r | 분석 결과 정리와 공유.R | #### 분석 결과 정리와 공유 ####
# 따라하며 배우는 데이터 과학 12장 PPT: https://goo.gl/mzAGqT
#### Markdown (마크다운) ####
# 마크다운이란 ? : https://goo.gl/fD10fA
# 마크다운 문법 : https://goo.gl/hz7Dt1
# 마크다운 연습 : http://markdownlivepreview.com/
#### R Markdown ####
# cheat sheet : https://goo.gl/uUHJq6
# ex : https://rpubs.com/jmhome/datatype_analysi... |
c7cc1db3d0120d034f7889f9f869db714e461cef | 09c564464713ca4de570a098810f197a0da0edd8 | /man/option_list.Rd | a6e0f9fcd1cc5789d55e5e7f0adbf85258d93d45 | [] | no_license | thigm85/RBi | 40ccc7caed9cc1ac70e8591f58a626504b2691e8 | d4781208f109fb6792c37627df9cc61da258602d | refs/heads/master | 2020-03-15T07:54:37.032268 | 2018-06-11T19:03:54 | 2018-06-11T19:03:54 | 132,039,973 | 0 | 0 | null | 2018-06-11T18:55:52 | 2018-05-03T19:21:50 | R | UTF-8 | R | false | true | 414 | rd | option_list.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/util_option_list.R
\name{option_list}
\alias{option_list}
\title{Convert string to option list}
\usage{
option_list(...)
}
\arguments{
\item{...}{any number of strings to convert}
}
\value{
option list
}
\description{
This function is used to... |
933f1fffe4d4733eeebc5378b898f189b0d7a14a | 8cdda276bac8b6681385b03fb902f115ad4acae2 | /R/hypoexpRuinprob.R | bb51ff3395eb3b36736c55ff0cad8340cedaad8d | [] | no_license | cran/sdprisk | 4fd9eb860a6f07ed4772af181915836b35f8fea8 | 8a30ea3963f7ab74ac21fbe3a2c152f9e805839c | refs/heads/master | 2021-05-15T02:17:04.336187 | 2019-04-29T19:50:03 | 2019-04-29T19:50:03 | 17,699,519 | 0 | 1 | null | 2015-04-23T18:15:01 | 2014-03-13T06:15:04 | R | UTF-8 | R | false | false | 2,139 | r | hypoexpRuinprob.R | hypoexpRuinprob <- function(process) {
stopifnot(is.hypoexp(process[['claims']]))
mypoly.factors <- PolynomF::as_polylist(lapply(X = process[[c('claims', 'hypoexp', 'rates')]],
FUN = function(arg) {
c(ar... |
63e490da4190647319e71eee967b70e2b522d8f8 | 12127d8a553d4b8fbe638c2297420d5995bb7d51 | /R/data.R | 60fe2eca8d3702b1014b0478269878c024af33b7 | [
"CC-BY-4.0"
] | permissive | stefanocoretta/coretta2019eng | 01b55ab6efc9a44adda59f4ee95d632ec7b3b508 | 39c5eda16b2419040d79f1419b3a4321531075a1 | refs/heads/master | 2021-07-20T08:05:38.688629 | 2020-05-29T07:32:38 | 2020-05-29T07:32:38 | 175,707,282 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,780 | r | data.R | #' Data on vowel, closure, and release to release duration in English.
#'
#' A dataset with durational measurements from 15 speakers of Mancunian English.
#'
#' @docType data
#' @format A tibble with 1800 observations and 21 variables.
#' \describe{
#' \item{\code{speaker}}{The speaker's ID.}
#' \item{\code{sentenc... |
7b599346ed7bda3c2ced61f9379f14a471d30af0 | 3b4b536a3b002e9f6db2e4e98edaa0e7bc699ce5 | /_site/R/blue_lightnings.r | b334d9af92db6be859647bbe588c91c909876262 | [] | no_license | verticales/verticales.github.io | 7b81480998e7bd3e3d73a87489bf07cb4d89d363 | 005f34ccd164879055dc9c2f8bf0401f0765e68e | refs/heads/master | 2021-01-10T02:00:16.008585 | 2015-11-03T23:41:47 | 2015-11-03T23:41:47 | 45,355,332 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,213 | r | blue_lightnings.r | # ========================================================================
# Blue Lightnings
# ========================================================================
# size
n <- 500
# generations
gens <- 50
# colors
blues <- hsv(h = 0.65, s = runif(20, 0.8, 1), v = 1,
alpha = runif(20, 0.6, 0.9))
white... |
a253cc27d2c3db13dfdf69c28ad249c0af0eb0bc | ff467a97a5465d23b6abb8ec5ff088a231e5e288 | /exercises/01-flights.R | dc8ac2af289e3ae7a66f09a72863fbc66ec6c392 | [] | no_license | qualityland/Grolemund_Reproducible_Research | 68b07b6bdeca006b398b31f13b5363cdc9dce3fe | 75c3706820d2f95ce4be01bcfd8089a990a1851d | refs/heads/master | 2023-04-09T23:45:36.517006 | 2021-04-26T06:28:57 | 2021-04-26T06:28:57 | 257,597,478 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,691 | r | 01-flights.R | # QUESTION: Which NYC airport has the longest delays for a given day of the week?
# If you do nto have one or more of the packages used below, please install them
# by connecting to the internet, opening an R session and running this command (without the #):
# install.packages(c("nycflights13", "dplyr", "ggplot2", "lu... |
48717fbc9ada9c13f05ca934b32a593809af7839 | 29585dff702209dd446c0ab52ceea046c58e384e | /EMbC/R/functions.R | 1f5517d2010c9a48329af1bd3626051bb3b14724 | [] | 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 | 10,004 | r | functions.R | # The EMbC Package for R
#
# Copyright 2013, 2014, 2015 Joan Garriga <jgarriga@ceab.csic.es>, Aitana Oltra <aoltra@ceab.csic.es>, John R.B. Palmer <johnrbpalmer@gmail.com>, Frederic Bartumeus <fbartu@ceab.csic.es>
#
# EMbC is free software: you can redistribute it and/or modify it under the terms of the GNU General Pub... |
719ae9797fe4ff776fe9a699a776762101a2836f | 0afc025181a55a24b1bf22c9a950f5332dba7864 | /R/mpersonalized_cv.R | d814bfcfba39c99474e01af5a7411b5d97026bbc | [] | no_license | chenshengkuang/mpersonalized | 10a9de0ccc37dc3e50395d8b2cc5dd5be6c153fb | 949b5a69370df42541edc2e089db1825e4e77a7f | refs/heads/master | 2021-10-25T14:24:07.586521 | 2019-04-04T16:19:59 | 2019-04-04T16:19:59 | 106,764,230 | 2 | 1 | null | 2019-04-04T16:20:00 | 2017-10-13T01:42:51 | R | UTF-8 | R | false | false | 29,325 | r | mpersonalized_cv.R | #' @title Cross Validation for \code{mpersonalized}
#'
#' @description This function implments \code{mpersonalized} and use cross validatation to tune penalty parameter.
#' The optimal penalty parameter is selected by minimizing \deqn{\sum_{i=1}^{n_k}\frac{|\hat{C}_k(X_{i})|}{\sum_{i=1}^{n_k}|\hat{C}_k(X_{i})|}\bigl ... |
a3e9a7f46b7614d5ad4ebe110367a3531a45f715 | c9555ae7694cf75e459a4b7026c1a003033dd83b | /wareHouse/website_warehouse/pruet/hydro/global.R | 4b33437a85caa6535e04be1a778d06c9db9110de | [
"MIT"
] | permissive | YutingYao/Ag | 4285fa9d1c942448ed072200e798dc5f51fd9f29 | fe73916f6e549ff6cc20bfed96a583e3919ac115 | refs/heads/master | 2023-06-17T21:58:14.849291 | 2021-07-20T17:28:30 | 2021-07-20T17:28:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,083 | r | global.R | ### Packages ###
require(shiny)
require(shinydashboard)
require(purrr)
require(tidyr)
require(dplyr)
require(tibble)
require(ggplot2)
require(leaflet)
require(stringr)
require(lubridate)
require(rgdal)
### User Defined Functions ###
source("functions/read_RDS.R") ## read RDS data files
source("funct... |
b35d05d21b17d375489ea7ee796da32ac5b8f5d4 | 22903e55d4c63712f23e345604df7629865d7671 | /data-raw/catmap164/run.R | 606c74cee420c7a6332a33090db2da9a7d45848d | [] | no_license | tpq/catmap | 864954b17f691e684e85ff9a7b214d27974a3b8f | 79462202636c209a00efbebedb318a3b96ce6d0a | refs/heads/master | 2021-01-20T00:16:25.473873 | 2017-06-29T00:24:16 | 2017-06-29T00:24:16 | 89,104,691 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 367 | r | run.R | # Name the working directory where your file is located:
setwd("./data-raw/catmap164")
library(catmap)
data(catmapdata)
# Build cm.obj from file location
cm.obj <- catmap(catmapdata, 0.95, TRUE)
# Call catmap functions
catmap.forest(cm.obj, TRUE, TRUE)
catmap.sense(cm.obj, TRUE, TRUE, TRUE)
catmap.cumulative(cm.obj,... |
6f1717845bf0ff594ef763c9af9d5c1ead40ab67 | 156811aac95d26f45fa74d249f416e32254fb4eb | /Machine learning KNN.R | 36e6e14abc61d5874356204d34266f7387f0f69e | [] | no_license | wangrenfeng0/SMU-Data-Science | 652fe6e37541cd9ab42881229bcfb1aeab3bbb03 | cc7ff4b0ee248dae7e515af4f59390ba6297d7d5 | refs/heads/master | 2023-01-07T11:40:30.836318 | 2020-10-21T03:26:04 | 2020-10-21T03:26:04 | 288,824,372 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,709 | r | Machine learning KNN.R | # Unit 6 KNN and K-Means
```{r}
#install package and load library
#install.packages("class")
#install.packages("caret")
#install.packages("e1071")
library(class)
library(caret)
library(e1071)
library(ggplot2)
library(dplyr)
library(magrittr)
# Simple Example Credit Rating as a Function of income and debt
dfTrain = data... |
eb4cd8181a91e975d82114eec6336eab31a400b5 | 787ca972a55f78a582999cc016f61275d225b701 | /plot4.R | 9d5f32a08aaa8ac83a6200ed17ba3bf1a55d193b | [] | no_license | qcbit/ExData_Plotting1 | 2397ee5b8a4defb30c1df7c0e63c3356d56eccef | 4e3884c696034597d31126a0d9e6aee99686d98b | refs/heads/master | 2021-01-18T02:42:25.661909 | 2014-10-08T21:46:08 | 2014-10-08T21:46:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,015 | r | plot4.R | library(lubridate)
d1<-read.table("household_power_consumption.txt", na.strings = "?", header=TRUE, sep=";")
d2<-within(d1, Datetime<-as.POSIXct(paste(Date, Time), format="%d/%m/%Y %T"))
d3<-subset(d2, year(Datetime) == 2007 & month(Datetime) == 2 & day(Datetime) >= 1 & day(Datetime) <= 2)
par(mfcol=c(2,2))
#plot 1
p... |
29e195227840f8124ddc09b783c530c927e96a84 | 2a002aa01c0bfa6041298b5f8b7fb017c6277501 | /man/uhi_stats.Rd | 01e83a72ff1710f89f262bdbf9efc394db2ff2fc | [] | no_license | RichardLemoine/LSTtools | 185fdd168eb0ccf80fb28a7de4894e6d926d1dda | 4cd5a3b90d954c7f3b7dc99449268f7d4e94f6f7 | refs/heads/master | 2022-12-29T02:40:06.582598 | 2020-10-15T14:13:24 | 2020-10-15T14:13:24 | 285,254,084 | 7 | 3 | null | null | null | null | UTF-8 | R | false | true | 1,888 | rd | uhi_stats.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/UHI_indicators.R
\name{uhi_stats}
\alias{uhi_stats}
\title{uhi_stats}
\usage{
uhi_stats(x, y, id = NULL)
}
\arguments{
\item{x}{LST raster layer.}
\item{y}{Raster indicating spatial limits (e.g., land cover).}
\item{id}{Numeric: ID of the s... |
6c3c40980fe4bb2331f43a984642c2d05f4a2dad | 151afcb214c56140170f56a6a956912b38173cb0 | /Challenge.R | ffeb4e5e21765cdf5b8d629de0a2f2ab97df978b | [] | no_license | alblaine/text-analysis-using-r | 69066b866a3db60587678c5b30590921b3ebe80d | af0c64e0b7488aa1b0697a4df296c985e124a86f | refs/heads/main | 2023-08-02T20:45:26.984805 | 2021-09-12T17:34:06 | 2021-09-12T17:34:06 | 405,696,766 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 659 | r | Challenge.R | # Challenge Activity:
# Use the Restaurant_Reviews.tsv dataset and do some analysis
rest_reviews <- readtext('Restaurant_Reviews.tsv')
# I. Sentiment Analysis (see Activity 3)
# 1. What review has the most positive terms?
# 2. What review has the most negative terms?
# II. Collocations (see Activity 6)
# 3. What t... |
4700772d16cf19f7df0987dca3d94aaa4825bbd0 | 0a99dd8ff6bcec8f5735fac4922d55cf24444902 | /cachematrix.R | 450d4f72f7a4b827b9bf4b44d87bf743d81012ac | [] | no_license | leabuton/ProgrammingAssignment2 | 917863afb6fdebe41eec18c898c0f49308744776 | add4c3dd85744e40fed7e757b2bc93e479a99c66 | refs/heads/master | 2020-05-29T11:07:35.140073 | 2015-07-25T11:33:51 | 2015-07-25T11:33:51 | 39,681,998 | 0 | 0 | null | 2015-07-25T11:22:22 | 2015-07-25T11:22:22 | null | UTF-8 | R | false | false | 4,891 | r | cachematrix.R | ## This script is part of a Coursera course assignment. The course is
## "R Programming", the assignment is "Programming Assignment 2".
## In this script:
## makeCacheMatrix : A function that allows to cache the inverse of a matrix.
## cacheSolve: A function that allows to compare a matrix to a cached one and
## ... |
a21d30fb230a1ca10e7530b31c029434662f4fb1 | 0078cb5b8c3a36e68f1801c1f6e129d149935ad8 | /2016 fraud score script_update.R | 77094836c1df7c2ed1f57e080d0da013061c045f | [] | no_license | colejharvey/mexico_russia_fraud | 90f6a4ccdb1b64e522bd8499f51d6cf70992e663 | f46cf051cc3c2d7e9a85a39229f20ef6e949e2ea | refs/heads/master | 2021-06-23T00:43:01.465306 | 2021-02-07T17:12:35 | 2021-02-07T17:12:35 | 190,785,454 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,007 | r | 2016 fraud score script_update.R | ###Script for doing all chi square tests in one go
##Presidential elections
rm(list=ls())
russia2016 <- read.csv("C:/Users/Cole/Documents/Research topics literature/All Russian election data/2016 Russia election data full ids.csv")
####Set up ones-digits if necessary##
urdigit<-(russia2016$united.russia)%%10
kprfdigit... |
8eddf82f7dc84288567d091041e3901bb0ab5d62 | fe254ef6be0bd316d41b6796ef28f1c9e1d5551e | /R/ternaryDiagLines.R | dbb0caad88e816fa2a863641f200a002b6e0f088 | [] | no_license | matthias-da/robCompositions | 89b26d1242b5370d78ceb5b99f3792f0b406289f | a8da6576a50b5bac4446310d7b0e7c109307ddd8 | refs/heads/master | 2023-09-02T15:49:40.315508 | 2023-08-23T12:54:36 | 2023-08-23T12:54:36 | 14,552,562 | 8 | 6 | null | 2019-12-12T15:20:57 | 2013-11-20T09:44:25 | C++ | UTF-8 | R | false | false | 225 | r | ternaryDiagLines.R | ternaryDiagLines <- function(x, ...){
s <- rowSums(x)
if (any(s <= 0))
stop("rowSums of the input data x must be positive.")
x <- x/s
top <- sqrt(3)/2
xp <- x[, 2] + x[, 3]/2
yp <- x[, 3] * top
lines(xp, yp, ...)
}
|
ca84c859dfdb2f805eac4c6813e2fdcf0c1f47a4 | 86e18632193c38dacb08b4226d3938f09c04c2e8 | /Pararrel export script.R | 6035b43ef4cae23d9f7c990a6bb67741661a252f | [] | no_license | chreehan/Dissertation-Code | 542e9cf7f934cc535c8b95e380b5d9ff5393d8e6 | 84c21fe67fa24ade1dc5451c84112877ce2da34b | refs/heads/master | 2021-05-29T16:02:49.796887 | 2015-10-12T05:15:01 | 2015-10-12T05:15:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,474 | r | Pararrel export script.R | load("workspaceImage.RData")
install.packages("tsDyn")
install.packages("dplyr")
install.packages("data.table")
install.packages("urca")
library(tsDyn)
library(dplyr)
library(data.table)
library(urca)
mastertable1 = data.table(CNHCoefm = 1:2000000, CNYCoefm = 1:2000000, CnhTstat = 1:2000000,
... |
6b320988aa215d3fdb0089a13fefa62f3e306ab0 | 67df5e7f56b5458ea7b003dbb927bd467129da0e | /Stare_rzeczy/Wykres_spend_RD/skrypt_spend_RD.r | 0b04524202cf2b4ca98d8addbb9c2f1abf487a35 | [] | no_license | arctickey/TWD_01 | 248591e38f4bd37caf727f93cd989b0771390bcb | caaa103e6dd05cf7d6c5dc29ca0511ae4de3bac8 | refs/heads/master | 2020-08-21T15:27:23.142596 | 2020-03-31T07:07:45 | 2020-03-31T07:07:45 | 240,883,627 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,766 | r | skrypt_spend_RD.r | library(dplyr)
library(haven)
library(intsvy)
library(countrycode)
#Wczytanie ramki danych z wynikami
#dane <- haven::read_sas("~/Programowanie/TWD_01/cy6_ms_cmb_stu_qqq.sas7bdat")
#Wczytanie danych o współczynniku liczba uczniów na nauczyciela (pochodzenie danych OECD)
rd_data <- read.csv("Wykres_spend_RD/Spend_on_... |
58fa027f0e2c7086ee0361e5d3ee696c3baa55d9 | 304a22604d0db86b3b218a4b8c0263f1294911b7 | /R/Parse_Tables.R | be582446668b7395df54e9776da5b6ad7c0081f3 | [] | no_license | hansthompson/pdfHarvester | 3ec9e0dcfaa57a97e85c45eca91bf2f7df31f4bd | 3522125974a08f6c6296cfa56487115fd7e0d278 | refs/heads/master | 2021-01-13T02:18:01.192974 | 2014-01-25T17:18:49 | 2014-01-25T17:18:49 | 16,054,577 | 11 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,971 | r | Parse_Tables.R | #' @export
Parse_Tables <- function(project) {
require(png)
folders <- list.dirs(project, recursive = FALSE)
for(z in seq(folders)) {
filefolder <- folders[z]
filevector <- list.files(paste(filefolder, "/LowQuality", sep = ""), pattern = ".png", full.names = T)
if(is.na(filevector)) {next()}
nfile... |
bdf35f74fdc0bb4f28e4829c39a4628c6bd2ee11 | 6300606517c0dcaae4dce093a8366eea953deb37 | /2015/solutionsR/day04.R | a8ff2b394ba9042efb23fb065389706711e965d9 | [] | no_license | akulumbeg/adventofcode | e5b5f8e509d240279ce0b4daf7325a48a4cbf9fc | 71d2a329beb4dd42d7e9dd6f544aa0c8fbc343cd | refs/heads/master | 2022-07-28T16:59:30.147837 | 2022-07-11T14:46:15 | 2022-07-11T14:46:15 | 220,464,721 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,404 | r | day04.R | # Day 4 - Part 1 ----------------------------------------------------------
input <- readLines("2015/data/day04.txt", warn = F) # load the data
input <- paste0(input, 1:250000) # 250000 is a wild guess
# I have to break my rule of not using external packages
# because I cannot code an md5 generator from scratch
ins... |
f63c7b0de41c66e28c2c67f3324e13c90f28157f | 9ad4b4acb8bd2b54fd7b82526df75c595bc614f7 | /Cleaning/SeuratNorm.R | b9421b94bde559adb772d1622a4424ef4ae58671 | [] | 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 | 10,053 | r | SeuratNorm.R | library(Seurat)
library(dplyr)
library(ggplot2)
library(cowplot)
library(Matrix)
library(resample )
library(readxl)
source('/home/sujwary/Desktop/scRNA/Code/Functions.R')
source('/home/sujwary/Desktop/scRNA/Code/Plot_func.R')
filename_sampleParam <- paste0('/home/sujwary/Desktop/scRNA/Data/sample','_parameters.xlsx')... |
9901ea9fb2dd1e4e7cf196fd92d88389fc4ab35f | 9b013a34b3f89d3e09ad16766e574a05ad30cd28 | /run_analysis.R | 00f33f478125925ded8aa6526e83230f6b1775ce | [] | no_license | ErikGiezen/Getting-cleanig-data | d3e121e594da006a07fcdf8b158fb994b118d04a | 5f34442bc727a0ef9dae7a1de3a96d17d76c7318 | refs/heads/master | 2021-01-17T06:24:32.234836 | 2016-07-24T20:53:11 | 2016-07-24T20:53:11 | 63,984,286 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,948 | r | run_analysis.R | ## 1. Merges the training and the test sets to create one data set.
# read txt files into data frames with read.table
features <- read.table("features.txt", header = FALSE)
labels = read.table("activity_labels.txt", header=FALSE)
train_subject <- read.table("subject_train.txt", header = FALSE)
train_x <- read.table("X... |
71d7540b0c0eacda476f755b746854b1732f2d2a | 33499d17a57e1c11fd2929599beef530fe9397f9 | /run_analysis.R | c6b4fb57e00049da48f45c4c155c7638cefe9759 | [] | no_license | klatz/GettingCleaningData | 877cea425bfb89bfe358d7a99ef4a50b781725ab | e7ece098c714723ce0b6fa985c0db4bd3223bd67 | refs/heads/master | 2021-01-01T19:16:20.882248 | 2014-06-26T19:23:54 | 2014-06-26T19:23:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,198 | r | run_analysis.R | #YOU NEED plyr & reshape2 package
library(plyr)
library(reshape2)
#STEP 1
#read training dataset
#subject:who y:activity
sub_train <- read.csv("UCI HAR Dataset/train/subject_train.txt",header=FALSE)
X_train <- read.csv("UCI HAR Dataset/train/X_train.txt",sep="",header=FALSE)
y_train <- read.csv("UCI HAR... |
6f2344c90d8961e94c626e68560bb0c6b303e1ac | 1a668e43e81a0722ae887f3a34f1da7013952a45 | /Phylogenetic_tree.R | 0e40246faec9dc3208577bbde1c6948b99aa63c1 | [] | no_license | xingzhis/A-dual-eigen-analysis-on-canid-genomes | b03b4fa06c8933d4000954e07aa31eaa6f458d33 | e4ae77ba6988885d2cba52badfa686f65e690d86 | refs/heads/master | 2022-07-03T04:38:35.040861 | 2020-05-11T09:02:14 | 2020-05-11T09:02:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,572 | r | Phylogenetic_tree.R | library(ape)
snp.tree <- read.tree("snphylo.output.ml.tree")
#plot(snp.tree)
#library(tidytree)
# to map
library(phytools)
demographic.data <- read.csv('~/demographic.csv')
demographic.data.valid <- demographic.data[!(is.na(demographic.data$Latitude)|is.na(demographic.data$Longitude)),]
lat.long <- data.frame(
la... |
f32612fb2e7a586230f4ee2d84e22c9d26038418 | 107775eb06b9233f0abdf86cf7ff8f85ab069f1f | /test.R | d25a77c697d18660942bc94e4e9b1294b4c7e66f | [] | no_license | jt1800/project1 | b7386ea4311e636dfe5edf3d1bf1e510016a5008 | 41e3c6d1f70777ffd43cc3f8851d859c55788862 | refs/heads/master | 2021-05-16T06:02:15.730150 | 2017-09-12T21:47:45 | 2017-09-12T21:47:45 | 103,324,416 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 250 | r | test.R |
collatz = function(x = 1,plot=FALSE){
nums <- c(x)
repeat{
if (x%%2 != 0){
x <- 3*x + 1
} else{
x <- x%/%2
}
nums <- append(nums,x)
if (x <= 1) break
}
if (plot) plot(1:length(nums),nums)
return(nums)
}
|
047f9b0b8d87ccf8dfe4b3da3e43d4717ec47874 | 3fdb12a1fe34aca6b96aa9047df4593404a5fc52 | /transmodel.pub.R | c7ed0cfcc2ed51950b17794ad1044bbf03fe7f8b | [] | no_license | carnegie-dpb/bartonlab-modeling | 06c90e10df8fc37973a02db41f2c882bc8ceedfd | 7d875f16f675bf94fc04a360ae8f6855d4642619 | refs/heads/master | 2021-01-22T03:48:47.674881 | 2018-04-18T22:29:04 | 2018-04-18T22:29:04 | 81,460,423 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,519 | r | transmodel.pub.R | ##
## plot linear transcription model for a direct target in one condition, custom made for publication (no colors, etc.)
##
source("rhoc.R")
source("rhon.R")
source("rhop.R")
source("Rsquared.R")
transmodel.pub = function(rhon0=1, rhoc0=20, nu=10, gamman=0.7, rhop0=1, etap=1, gammap=1, dataTimes, dataValues, dataLab... |
5e9d02229d782767d259e11064dee731d97b7e58 | bebe94f1d0b3a30f12ad309a6629737ae706fb19 | /R/read_rds.R | f3f1e9f51a2429fa5c3a569210f846c8f9b01660 | [] | no_license | BHGC/bhgc.wx | d19956db738f841cda62ef3e176c7a3c54ab1709 | a4b28cfabf4ec2107799dda868601c7d3cdbdfb3 | refs/heads/master | 2022-03-24T21:49:31.627577 | 2020-10-11T04:20:45 | 2020-10-11T04:20:45 | 156,008,704 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,265 | r | read_rds.R | #' Robustly Reads an RDS File
#'
#' @param pathname RDS file to read.
#'
#' @param \ldots (optional) Additional arguments passed to [base::readRDS()].
#'
#' @return The \R object read.
#'
#' @details
#' Uses [base::readRDS] internally but gives a more informative error message
#' on failure.
#'
#' @importFrom utils fil... |
8bb1a0a15f3e3925f568c2162474922f5bc62d72 | 2921619274fa59a739d5722e5e4fa75151126d6b | /R/data_katastralni_uzemi.R | 32eb8acf3ede85b0511188d0bb4cf4f6b719ef5a | [
"MIT"
] | permissive | JanCaha/CzechData | 494f8ce3a6587abc06f63f0e1afd664dcd189916 | ebe77c0ad444ba7e2b6eb4f9ce290e8852d9564c | refs/heads/master | 2022-12-21T08:38:28.376988 | 2021-12-14T17:03:44 | 2021-12-14T17:03:44 | 166,657,541 | 8 | 2 | MIT | 2022-12-20T19:07:16 | 2019-01-20T12:17:14 | R | UTF-8 | R | false | false | 868 | r | data_katastralni_uzemi.R | #' data.frame of all cadastral territories in Czech Republic
#'
#' A dataset containing the names and other attributes of all 13,078
#' cadastral territories in Czech Republic. The codes (every column with string kod in
#' name) are treated as character strings even though that some of them are numbers. These codes,
... |
fcb3833a9701a0d81fd27bd2738152b65bb7bf20 | b08b7e3160ae9947b6046123acad8f59152375c3 | /Programming Language Detection/Experiment-2/Dataset/Train/R/knapsack-problem-bounded-2.r | 29e29a86c09d41be6f8c610116b563023dccd07f | [] | no_license | dlaststark/machine-learning-projects | efb0a28c664419275e87eb612c89054164fe1eb0 | eaa0c96d4d1c15934d63035b837636a6d11736e3 | refs/heads/master | 2022-12-06T08:36:09.867677 | 2022-11-20T13:17:25 | 2022-11-20T13:17:25 | 246,379,103 | 9 | 5 | null | null | null | null | UTF-8 | R | false | false | 709 | r | knapsack-problem-bounded-2.r | library(rgenoud)
fitness= function(x= rep(1, nrow(task_table))){
total_value= sum(task_table$value * x)
total_weight= sum(task_table$weight * x)
ifelse(total_weight <= 400, total_value, 400-total_weight)
}
allowed= matrix(c(rep(0, nrow(task_table)), task_table$pieces), ncol = 2)
set.seed(42)
evolution= genoud(f... |
eabdf6a56a6d600f2f538d688403852b71fb27f8 | 3f3a0717863ea89ed1c1ebf692f13aaf54a2acc9 | /man/estimatesTable.Rd | 6d2096c202f9812b94f0b54c87f055c9836f986f | [] | no_license | cardiomoon/semMediation | 55cf39159f43589b6d58e05e2a788bc5de387a30 | ffe9338ae0a64efd604bf5e11439c12e43967681 | refs/heads/master | 2018-12-29T09:24:48.533628 | 2018-12-23T14:22:02 | 2018-12-23T14:22:02 | 77,782,004 | 2 | 2 | null | null | null | null | UTF-8 | R | false | true | 1,013 | rd | estimatesTable.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CorPlot.R
\name{estimatesTable}
\alias{estimatesTable}
\title{convert parameterEstimates to data.frame}
\usage{
estimatesTable(fit, latent = TRUE, regression = TRUE,
mediation = FALSE, covar = FALSE, ci = FALSE,
standardized = TRUE, digit... |
7748a5c8e94efaea05a49bf54fde90f789524de8 | 59d501a829468e393db33cc38a192c1ed154f8ef | /man/qqnormsim.Rd | a716c84ccd02d88821ca604683cded755fcdece7 | [] | no_license | aaronbaggett/labs4316 | 9687d80b2db2a73a80478bd343b75111c8821510 | e467139cd2d14c0b11561db4a2146e7d969bbbce | refs/heads/master | 2020-04-21T08:39:50.833663 | 2019-09-17T18:27:27 | 2019-09-17T18:27:27 | 169,426,048 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 647 | rd | qqnormsim.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/qqnormsim.R
\name{qqnormsim}
\alias{qqnormsim}
\title{Generate simulated QQ plots}
\usage{
qqnormsim(sample, data)
}
\arguments{
\item{sample}{the variable to be plotted.}
\item{data}{data frame to use.}
}
\value{
A 3 x 3 grid of qqplots.
}
... |
e78e6b54f7cbaa9100953c1e344f3152d1ca1fcb | cd4d27b44a869bd1751b9e8dd34f126f48ec57e7 | /tm_examples.R | 6e4e361731abaecce8992f0004ffb1fdfeabd0ff | [] | no_license | shuckle16/tm_examples | 9dafcf7ef6876eebc9fc348de76c11b96725e601 | 4c2f0c9d6a5d6ab81d91c4c0e55f54858ac80e7d | refs/heads/master | 2021-01-15T15:44:16.685273 | 2016-10-05T01:42:04 | 2016-10-05T01:42:04 | 49,775,559 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,621 | r | tm_examples.R | #install.packages('tm')
library(tm)
sci.electronics <- Corpus(DirSource("sci.electronics")) # A corpus with 981 text documents
talk.religion.misc <- Corpus(DirSource("talk.religion.misc")) # A corpus with 628 text documents
############################
#### Text Data Cleaning ####
############################
# c... |
c389ac7d485befe3d9069d97561c7f34595d0b07 | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /MESS/man/soccer.Rd | 2b87e0091421f0b31033da43b826d30de3129ba4 | [] | no_license | akhikolla/InformationHouse | 4e45b11df18dee47519e917fcf0a869a77661fce | c0daab1e3f2827fd08aa5c31127fadae3f001948 | refs/heads/master | 2023-02-12T19:00:20.752555 | 2020-12-31T20:59:23 | 2020-12-31T20:59:23 | 325,589,503 | 9 | 2 | null | null | null | null | UTF-8 | R | false | true | 1,119 | rd | soccer.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MESS-package.R
\docType{data}
\name{soccer}
\alias{soccer}
\title{Danish national soccer players}
\format{
A data frame with 805 observations on the following 5 variables.
\describe{ \item{name}{a factor with names of the players}
\item{DoB}{... |
df9ed9d0547502a66e4e83ea2ed3c95b95d77348 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/NMOF/examples/TA.info.Rd.R | faa57bae7cc0ecf0b41bfe2b375b7635c1c98ded | [] | 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 | 1,663 | r | TA.info.Rd.R | library(NMOF)
### Name: TA.info
### Title: Threshold-Accepting Information
### Aliases: TA.info
### ** Examples
### MINIMAL EXAMPLE for TAopt
## objective function evaluates to a constant
fun <- function(x)
0
## neighbourhood function does not even change the solution,
## but it reports information
nb <- func... |
0b5c3c47cd3474a36bef8e47a7d2261f4e576b4a | 9216e41e93c0224b2a44a1cc5227986031abcdaa | /global.R | 364d170ca9ec415b75e169d078360dd241ca8e03 | [] | no_license | bridgecrew-perf7/Shiny_deploy | 90e768b6a19405a1afd5b2284aea955b0c606c7d | abeb1858a754489874986db097b68aad348cd579 | refs/heads/main | 2023-06-06T07:32:54.454133 | 2021-06-11T06:08:32 | 2021-06-11T06:08:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 437 | r | global.R | # Plot libraries
# --------------------
library(ggplot2)
library(ggthemes)
library(plotly)
# Data manipulation libraries
# --------------------
library(tidyverse)
library(dplyr)
library(reshape2)
# Shiny Libraries
# ---------------
library(shiny)
library(shinyglide)
library(shinyjs)
library(shinydashboard)
library(bs... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.