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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6534f9570e23c9e042378ee079d2608f8be4b6d2 | 66fcd73c639c308b030f834ab3ece0725cfd712c | /SRC/Consolidado/Comparacion_TCGA.R | 5b17700c7dbaaecd40c22ea19ab0c6e16ae6ea55 | [] | no_license | chrismazzeo/Tesis_Marcadores_Glicoinmunologicos | ae871350a259afb7c08cde70d76e262bddd49e4e | 2626a75ceedb2c59531fe39f02f5590ac8ebea85 | refs/heads/master | 2023-06-30T13:54:01.223662 | 2021-08-03T03:28:47 | 2021-08-03T03:28:47 | 286,357,178 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,792 | r | Comparacion_TCGA.R | #con este hacemos todos los heatmas lindos
library(readr)
library(writexl)
library(RColorBrewer)
library(pheatmap)
#dataset <- read_delim("/Volumes/Externo/Google Drive/Bioinformática/PFI/Tesina/Tesis Final/Resultados Finales/Consolidación/Tablas/FinalMergeLimpia_comparativa_ratones2.csv", ";", escape_double = FAL... |
ef26f529adecff7f73ef2d1fd7e29de315dc7d92 | b76f9a09a87ba50a6d71a55e3c45abfe5850b5c7 | /man/add_waypoints.Rd | 0175e8e241d908cc2d3e6f7e799f0a944d0f8c64 | [] | no_license | dweemx/dynwrap | 963fa4029a103bfefa6a5de5cdb24df5754bbc3f | dcbe65a6e661ae3ba46e571fbeabc3b52699b6ef | refs/heads/master | 2020-04-15T18:01:27.853035 | 2018-12-17T15:59:49 | 2018-12-17T15:59:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 648 | rd | add_waypoints.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wrap_add_waypoints.R
\name{add_waypoints}
\alias{add_waypoints}
\title{Add or create waypoints to a trajectory}
\usage{
add_waypoints(trajectory, n_waypoints = 100,
resolution = sum(trajectory$milestone_network$length)/n_waypoints)
}
\argum... |
a4f7f2df3d4a055d7ecc253c19369cc6f4739de0 | 2c2ebb391be90b61b86cd7c8182a88c3bbeddb20 | /get_data.R | 970a026c464224d4c09aa2f4b25a29e0220c510a | [] | no_license | langcomp/bicknell_levy_rayner | 46110777d4713d4966b2b31f523129ed40436170 | b883ef03378897d85e4cdb934b09b4440bd6f7f8 | refs/heads/master | 2021-05-09T15:51:07.954036 | 2020-02-07T20:56:29 | 2020-02-07T20:56:29 | 119,101,675 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,563 | r | get_data.R | ## combined analysis of Yosemite, YosDos, and YosDosReplication
library(Hmisc)
library(dplyr)
source("et-admin/et-admin.R")
source("etAnalyze/R/etAnalyze.R")
subj_excl <- readRDS("subj_excl.rds")
df_exclusions <- readRDS("df_exclusions.rds")
df.pretarget.length <- readRDS("df.pretarget.length.rds")
get.all.data <- ... |
af829690870b3469f1f4e4cbf74dc1d5b01bed5d | 30662119ab3ef017ec94458f58ed9ee03bceb169 | /Plot3.R | 45d7ce47cd9b73fd1e133dc5c073aa8763b34606 | [] | no_license | abinashi-prakash/Git | 52ae0ed2d079e1cb46656b844d340ef939175308 | 30a56224123680b2e988141bf3e589c9156b4fb7 | refs/heads/master | 2020-04-27T09:12:29.739020 | 2019-05-30T05:16:17 | 2019-05-30T05:16:17 | 174,205,016 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 934 | r | Plot3.R | # Read the file
powerdata<-read.table("household_power_consumption.txt",header = TRUE, sep= ";", na.strings = "?")
#Convert Date
powerdata$Date <- as.Date(powerdata$Date, "%d/%m/%Y")
#Select the data in the date range
powerdata2 <- subset(powerdata,Date >= as.Date("2007-2-1") & Date <= as.Date("2007-2-2"))
# Combine... |
55a008e720c3151652bb299662b51b8624d46273 | 47248e6d22eb023dbfa7be13025e7b5697676d3b | /R/two_class_sim.R | 6441ddb79e6d7b747ffee24c1f46955ce859636f | [] | no_license | markhwhiteii/stat-comp | 9b096676beac874146c81a8e0f7c4b37286a8cbe | a76ab0eb1a472f0f3dcc7e9a5e2c5d271626af2d | refs/heads/master | 2021-08-23T06:42:49.525748 | 2017-12-03T23:58:47 | 2017-12-03T23:58:47 | 105,834,980 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,645 | r | two_class_sim.R | two_class_sim <- function(n, intercept, linearVars, noiseVars,
corrVars, minoritySize) {
sigma <- matrix(c(2, 1.3, 1.3, 2), 2, 2)
tmpData <- data.frame(MASS::mvrnorm(n = n, c(0, 0), sigma))
names(tmpData) <- paste("TwoFactor", 1:2, sep = "")
tmpData <- cbind(tmpData, matrix(rnor... |
c916508abe0e51a2fea892dc5df43f012f8f38f5 | 4bb8fd8247d242a6ef4d1f71c05b5a31d10c860b | /man/install_packages.Rd | d0fd950571bcd496a276f246285f1f1017836339 | [
"MIT"
] | permissive | isabella232/crancache | 95827afe1eb9e58b782b0cc591e6e38d23b1334b | 7ea4e479bdf780adadd1bd421a5ca23e5f951697 | refs/heads/master | 2022-12-20T18:44:31.170172 | 2020-03-07T09:57:11 | 2020-03-07T09:57:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 4,392 | rd | install_packages.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/install-packages.R
\name{install_packages}
\alias{install_packages}
\title{Install Packages from Repositories or Local Files, with Caching}
\usage{
install_packages(pkgs, lib, repos = getOption("repos"),
contriburl = contrib.url(repos, type... |
90fe77c456c0f31f4fc05f76ebf4e03307d548e6 | 8d8d1d24986dce6b8a56ed8bcb71ada4b4eeb2bd | /man/starwars.Rd | 0fe5c06f6c2d33b1f53c2855dee68e4249292ca1 | [
"MIT"
] | permissive | schochastics/networkdata | edaed94b788dcd925f55ae07f8a2d8b58d45ae8e | 535987d074d35206b6804e9c90dbfa4b50768632 | refs/heads/master | 2023-01-07T07:20:41.475574 | 2023-01-05T18:54:17 | 2023-01-05T18:54:17 | 226,346,857 | 142 | 17 | null | null | null | null | UTF-8 | R | false | true | 398 | rd | starwars.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data-misc.R
\docType{data}
\name{starwars}
\alias{starwars}
\title{Star Wars Episode 1-7}
\format{
list of igraph objects
}
\source{
Data downloaded from https://github.com/evelinag/StarWars-social-network
}
\usage{
starwars
}
\description{
S... |
46f08a6f667c02eba1256854d36b806fbecad86f | 19cd3e2856b30c6e8d4fb6b2608122367551cd8f | /man/taxonomy.Rd | 33c79c69f96dc0b02854df55d72e9b62bb2c7781 | [] | no_license | brendanf/phylotax | 3b03469a9dadba6c6d8e4e44830e8a22989703aa | 77fd100e26b9320a04955b32e59d7c24fa1e60e3 | refs/heads/master | 2021-08-07T09:03:47.573616 | 2021-03-03T07:15:19 | 2021-03-03T07:15:19 | 245,027,006 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,098 | rd | taxonomy.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/taxonomy.R
\name{taxonomy}
\alias{taxonomy}
\alias{taxonomy_dada2}
\alias{taxonomy_sintax}
\alias{taxonomy_idtaxa}
\title{Assign taxonomy to nucleotide sequences}
\usage{
taxonomy(seq, reference, method, min_confidence = 50, multithread = FAL... |
5d737f047b3eef11753d2da9297a6ebb7f36ffeb | 09c8994f16ce0065502d36d64eb78c2c8a7e8875 | /asd-predictors-results.R | e7c30f69c8bdc24edd9f8d45abead93f2dad1fe1 | [] | no_license | hyeyeon-hwang/machine-learning-asd-predictors | bd7f32e9506ff16020699d5f4b2e997f646cedfa | c244ecf016e44f5e6c00fe15e6ac4b2d186a8f47 | refs/heads/master | 2020-03-29T20:18:35.823797 | 2019-12-05T17:23:50 | 2019-12-05T17:23:50 | 150,305,496 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 18,686 | r | asd-predictors-results.R | source("asd-predictors.R")
library(knitr)
library(kableExtra)
# Random Forest Model Results ---------------------------------------------
rDmrResult <- runFunctions(rDmr, p = 0.8, pos = "Rett")
dDmrResult <- runFunctions(dDmr, p = 0.8, pos = "Dup15q")
aDmrResult <- runFunctions(aDmr, p = 0.8, pos = "ASD")
pDmrResult <... |
73d2317de75fddefc4a6c6e983b9aa34f3af3889 | 12e3d5f8618bbc113e6f039b7346fc5d723015c9 | /Stats_II/Class2/Class 2 - In Class Project.R | ae4a59e4efa5745a872635a75346306fbb6d6eec | [] | no_license | raschroeder/R-Coursework | 4af2ded6e9af2c0c64697dcc796a12e508f38ae4 | 1e9800b00f84cb4092c956d9910a710729b9aff3 | refs/heads/master | 2020-04-05T12:44:28.824912 | 2019-02-06T15:59:07 | 2019-02-06T15:59:07 | 156,878,511 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,098 | r | Class 2 - In Class Project.R | ##########################################################
######## Class 2 - In class Assignment ###################
##########################################################
#Load Libraries you will need
library(car) #graph data
library(ppcor) #part corr
#Set working directory (you need to change this)
setwd('/Use... |
2f1748a5042e4f172daf89df16dc1b177e073300 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/magic/examples/latin.Rd.R | 4e869296f0dd4fab300221505244116ceaf3868d | [] | 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 | 700 | r | latin.Rd.R | library(magic)
### Name: latin
### Title: Random latin squares
### Aliases: latin incidence is.incidence is.incidence.improper unincidence
### inc_to_inc another_latin another_incidence rlatin
### Keywords: array
### ** Examples
rlatin(5)
rlatin(n=2, size=4, burnin=10)
# An example that allows one to optimize a... |
f65de342aca34f96706e2f7beb8b4401612833ca | 7ecbd1e903a5ac6781fd401f1ce6ffc2ef6323a5 | /demo/example2.R | 42f43288078f9b1267be728b04a1cb96d7cdbff0 | [] | no_license | jtilly/knitroR | 84db064b0053d3bd8c8ecfdc47f82c83bcb63d3c | 3a1bdb8bff7aab287b22f7af23b1053b01c89ab8 | refs/heads/master | 2021-01-18T22:48:12.248719 | 2016-11-09T17:07:37 | 2016-11-09T17:07:37 | 28,303,235 | 3 | 2 | null | 2017-11-17T19:00:49 | 2014-12-21T15:09:11 | R | UTF-8 | R | false | false | 1,426 | r | example2.R | # Example with two inequality constraints and one upper bound
#
# min 100 (x2 - x1^2)^2 + (1 - x1)^2
# s.t. x1 x2 >= 1
# x1 + x2^2 >= 0
# x1 <= 0.5
#
# The standard start point (-2, 1) usually converges to the standard
# minimum at (0.5, 2.0), with final objective = 306.5.
# Sometimes the solver c... |
494288944fca2df9cb3943763aeb4aa47e2efbc1 | 034d0b59cc9a5c36c47d28bf8e3adb0c523bb8a2 | /StitchingDays.R | dbc8e1fd6a10a484aae3e8bf5ee5c351ba8f916e | [] | no_license | abhie19/IN-IU | 7c1ddb07803e1e3b4b18f03d652955464e651220 | 79562b016f8c01e1be657135589a860316670005 | refs/heads/master | 2021-01-19T04:14:54.047206 | 2016-07-16T02:39:29 | 2016-07-16T02:39:29 | 63,461,790 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,071 | r | StitchingDays.R | day1 = read.csv("/Users/Abhishek/Downloads/01.csv", header = FALSE, stringsAsFactors=FALSE)
colnames(day1) <- c("start_time", "end_time" ,"some1", "s_ip", "d_ip", "s_port", "d_port", "protocol", "flag", "some2", "some3", "flows", "size", "some4", "some5", "some6", "some7", "some8", "some9")
sapply(day1,class)
day2 = r... |
2db8232e28a99c110442ebafa251971cf23022b0 | 47b6c88ef300b4c1dc3298bbdefd5b98637fd1d2 | /man/dfp_createCustomFieldOptions.Rd | 234e356dbecc069313e3b59676327a0ce36e4889 | [] | no_license | StevenMMortimer/rdfp | cff34ff3a2b078a03c75e43d943b226b6ca72bba | e967f137d7605b754b53a07d41069f4e5fd209dc | refs/heads/main | 2022-11-18T11:40:26.848090 | 2019-06-05T23:54:54 | 2019-06-05T23:54:54 | 46,183,233 | 6 | 2 | null | null | null | null | UTF-8 | R | false | true | 1,458 | rd | dfp_createCustomFieldOptions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CustomFieldService.R
\name{dfp_createCustomFieldOptions}
\alias{dfp_createCustomFieldOptions}
\title{CustomFieldService}
\usage{
dfp_createCustomFieldOptions(request_data, as_df = TRUE,
verbose = FALSE)
}
\arguments{
\item{request_data}{a \... |
3d1bcb1076c2e88b3f1902451fd81c90d78025e1 | 4a5a1cb13d1e7a780e4eebe75398133542a916f4 | /man/kmerFractions.Rd | b66f27195017f3b4750bfea891d2923ea1564f75 | [] | no_license | Malarkey73/fastqc | fc596e6b9d2625252cd39e7cc4d79437c60bfa57 | 126b2726fbec7d8104e6e46258899b261e9f2815 | refs/heads/master | 2016-08-06T04:28:26.422108 | 2013-06-12T14:44:31 | 2013-06-12T14:44:31 | 10,216,956 | 2 | 3 | null | null | null | null | UTF-8 | R | false | false | 978 | rd | kmerFractions.Rd | \name{kmerFractions}
\alias{kmerFractions}
\title{Over Represented kmers(5)}
\description{
Sometimes short read data can contain biased or artefactual repetitive DNA sequence - sometimes also called low complexity. You can spot such repetitive atrefacts by screening for over-represented kmers (k=5).
}
\details{
The... |
87ac6dcf7c4dd474ce3057ba9118772c19b41c49 | 9079a7b85bc2f35002d0b7b9b526c41cd184cf28 | /dv_finalproject_rabinbhattarai/01 Data/map.R | 1ca9ab10dfb596a4e6ddf9a37c46782cd6ae69d0 | [] | no_license | JN9765/JPN | 5e4f26984fbc0345902e6b5c90ca0de9d66512e3 | f054ef563733619d43f640e0a71e6c8ad52a9c3c | refs/heads/master | 2021-04-29T09:51:18.771368 | 2016-12-30T03:07:59 | 2016-12-30T03:07:59 | 77,654,892 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 855 | r | map.R |
m <- leaflet()
m <- addTiles(m)
m <- addMarkers(m, lng= c(-97.7243,
-97.7037,
-97.6949,
-97.733,
-97.6958,
-97.7585,
-97.6949,
-97.662,
... |
222c6bd68854704c629c9f985ab0dd253e37e785 | f61064bb7d0013f111123206b230482514141d9e | /man/sis_csmc_tp.Rd | 1ef5d5cde93b2201fc20adee1b98dd54fdf8a6ad | [] | no_license | nianqiaoju/agents | 6e6cd331d36f0603b9442994e08797effae43fcc | bcdab14b85122a7a0d63838bf38f77666ce882d1 | refs/heads/main | 2023-08-17T05:10:49.800553 | 2021-02-18T23:01:47 | 2021-02-18T23:01:47 | 332,890,396 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,787 | rd | sis_csmc_tp.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sis_csmc_tp.R
\name{sis_csmc_tp}
\alias{sis_csmc_tp}
\title{controlled SMC sampler for SIS model with population-level observations}
\usage{
sis_csmc_tp(y, model_config, particle_config)
}
\arguments{
\item{y}{a vector of length (T+1)}
\item... |
ebb736e6c8c99080f023aa2d8c5f1a034a70c89b | 7755d1332586784e58b9c67e2029bad94b93fd00 | /clhs1.R | f4ef87a72dbcf0b2905cb55493b95b0978f531f5 | [] | no_license | brendo1001/clhs_addition | 9c45d9217075448e80705629ec6ad88d1fa2bd99 | 5f1b638536ddd3a81be03a87e14bb663c61f7f88 | refs/heads/master | 2020-05-01T05:14:20.744262 | 2019-03-24T10:59:49 | 2019-03-24T10:59:49 | 177,296,490 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,412 | r | clhs1.R | # Conditioned Latin Hypercube sampling
# Checking how the clhs R function handles existing sample data
# What does 'include' do
# Checking outputs with coobs map to detemine whether new samples go to areas of low environmental coverage
# created: 15.3.2019
setwd("Z:/Dropbox/2019/rmuddles/clhs_addtion")
#... |
4aa0c086fd2655473766aa71df91f21aede220ed | 6e5b5fb1a944818e7cd9171037ee507247c25ba0 | /pulse3d.R | 6734163a140fd8e7087f618d83bb3f6f7d127e64 | [] | no_license | adrienne-marshall/solute_transport | 7b7f811c73a69281daedb0549f6a217d24246cf2 | f812edcc97782c12210e2313d54589a158d6e758 | refs/heads/master | 2021-08-10T16:08:14.104577 | 2017-11-12T19:33:48 | 2017-11-12T19:33:48 | 110,461,495 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,799 | r | pulse3d.R |
#Function to model concentration of a solute at L and time t based on a pulse input.
#Should maybe output a vector of concentrations at different times.
pulse3d <- function(pulse_conc,
pulse_time,
max_val = 0.1,
time_limits = c(0, 100),
L_limi... |
09f839a1ac73ebcc845bcb94971d6e3bfe2d03f7 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/nullabor/examples/reg_dist.Rd.R | 1af1f064074f715cc14b4bbc612fcbcf10e90715 | [] | 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 | 210 | r | reg_dist.Rd.R | library(nullabor)
### Name: reg_dist
### Title: Distance based on the regression parameters
### Aliases: reg_dist
### ** Examples
with(mtcars, reg_dist(data.frame(wt, mpg), data.frame(sample(wt), mpg)))
|
73bb3ddde3838685d5fe61d1a2f34436395a4875 | 033f1b856609297a46fc60b57ea69b5ec2cad818 | /man/bestbeforemaxdd.Rd | a474e345eb134b76489a8aaf489d858d1fd0c4ba | [
"MIT"
] | permissive | pluspku/bestbeforemaxdd | d57f8eb7e8c21f2d51be10b28ba4d03ffbd2d5bb | 5901cd6c18896619f8afd67c9740e3312e248158 | refs/heads/master | 2021-01-10T04:37:19.008390 | 2015-12-11T22:16:12 | 2015-12-11T22:16:12 | 47,852,937 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 559 | rd | bestbeforemaxdd.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bestbeforemaxdd.R
\name{bestbeforemaxdd}
\alias{bestbeforemaxdd}
\title{Function to calculate the difference before meet the first max drawdown}
\usage{
bestbeforemaxdd(x, threshold, long = TRUE)
}
\arguments{
\item{x}{data series, should be ... |
3856057e7092366c596a82a0a382a2475168d4fe | b9c73533135d8a3350cff8c38e3604b02683ea40 | /ECT2_hyperTRIBE_model_data.R | 6234a6bc5cbd04ddf804276f7f8b74dcd4a02cf9 | [] | no_license | sarah-ku/targets_arabidopsis | 415f4d08e6d308826a03133293362be9e1c5b6a5 | ad524fd57073b569320998bb79ecc66d433b37c7 | refs/heads/master | 2023-08-15T06:07:57.404890 | 2021-10-28T19:03:41 | 2021-10-28T19:03:41 | 312,227,154 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,612 | r | ECT2_hyperTRIBE_model_data.R | setwd("/binf-isilon/alab/projects/ECT2_TC/hyperTRIBE")
library(RNAeditR)
################################### for modelling
dat_shoots <- read.table("./pipeline/output/baseCounts_shoots_hyperTRIBE.txt",header=F)
dat_roots <- read.table("./pipeline/output/baseCounts_roots_hyperTRIBE.txt",header=F)
dim(dat_shoots)
dim(da... |
bfaa5c6d99ca08d366bff94c057f88923a64cbd0 | dd256026b874e4d4109fda14b1f1e906ada8468c | /man/agg_g2.Rd | 379e9069465ca49fc1e01ae55694bbf3236ce38f | [] | no_license | cran/MAd | 8a90077b46752bf4b8995c231ace3f86d62f75d2 | 2ac236b36e9e0826240e936cf158326773c78e2d | refs/heads/master | 2022-09-09T16:17:36.767905 | 2022-08-06T21:40:02 | 2022-08-06T21:40:02 | 17,680,460 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 181 | rd | agg_g2.Rd | \name{agg_g2}
\alias{agg_g2}
\title{Internal anRpackage objects}
\description{Internal anRpackage objects.}
\details{These are not to be called by the user.}
\keyword{internal} |
ea9c1f4f16259c9019768224dce6959979eef25a | d884a45b9b3055b668c83b5acc2936be9107769e | /R/ebpm_gamma_mixture2.R | f6531c2c6d7eab985ae4daf6b606ede01f4e52bb | [] | no_license | stephenslab/ebpm | c9526a3a1a8ea4ce2ecd74cf3fef8bf6d84abd54 | aa3a957698c153c008ea42c6c99e64e0f5682aec | refs/heads/master | 2021-07-14T20:18:42.225297 | 2021-07-08T19:45:28 | 2021-07-08T19:45:28 | 210,671,798 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 5,074 | r | ebpm_gamma_mixture2.R | #' @title Empirical Bayes Poisson Mean with Mixture of Gamma as Prior (still in development)
#' @description Uses Empirical Bayes to fit the model \deqn{x_j | \lambda_j ~ Poi(s_j \lambda_j)} with \deqn{lambda_j ~ g()}
#' with Mixture of Gamma: \deqn{g() = sum_k pi_k gamma(shape = a_k, rate = b_k)}
#' @import mixsqp
... |
c22ab48b75f3ffec650e10d655f9f2481f220b69 | e131207354f2565b45b6c70d438cea8d5c26bce5 | /cachematrix.R | d8a9ee0a5f07852b9ed6426e96978732885be96f | [] | no_license | danoot/ProgrammingAssignment2 | 54bbbe35d903231b9ddc3f830b4707590dfb9a57 | aeb194d382e6346a1fb684ee6c0c2a2cfadbc0b1 | refs/heads/master | 2020-12-14T08:50:01.990012 | 2014-10-27T00:45:20 | 2014-10-27T00:45:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,447 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## This function will take a matrix, and return a list, which will contain:
## A function to set the matrix value
## A function to get the matrix values
## A function to set the inverse of the matrix
## A function to get the inverse of ... |
955f4805e4e1bbcef0000a6c95a0b3bb56d1e217 | b9e15be28c0915e70a4798a0acb34ebf9bb71b8d | /5.1.4.R | 1351ff74b92350522f5d2105e90b9f4068fd1a35 | [] | no_license | ItsRRM97/Suicides-in-India | 8c9e1f006e4eeee5170d507c41287bb0d7558140 | cafde4ea3613d0b7d428ff7f79ea8e284783bba7 | refs/heads/master | 2021-09-10T17:36:47.887438 | 2018-03-30T06:45:39 | 2018-03-30T06:45:39 | 103,838,259 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,106 | r | 5.1.4.R | # age wise social of suicide
ageSocial <- function(Dataset) {
social <- vector() # Null Vector
Dataset$Type_code <- as.character(Dataset$Type_code)
Dataset$Type <- as.character(Dataset$Type)
for(i in 1:236583) {
if(Dataset$Type_code[i] == 'Social_Status') {
social<-c(social,Dataset$Type[i])
}
... |
14b3b1032f0ab34ad1b08bf3f5c11875295b3209 | 7d61a07208b1425ba7e54a8ad6143cfa4f827d35 | /Week 7/ggplot2 facets and Panel Layout Designs/Post Confident Class Maps/NP_Confident Class Comparison map.r | 2c2ec5a5712f4d64f5c5735681e961dc65704462 | [] | no_license | ceharvs/Statistical-Graphics | 27557777f4c55cdf20142ac6c93879f322239875 | ff6812147bbda0ee7137700a4b935de7b1a724ed | refs/heads/master | 2016-09-06T13:28:26.241644 | 2014-12-10T04:56:14 | 2014-12-10T04:56:14 | 23,636,565 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,956 | r | NP_Confident Class Comparison map.r | File NP_Confident Class Comparison Map.r
By Daniel B. Carr
Copyright 2010, 2011, 2012, 2013
Due The pdf file.
Prototype script
Labels on left rather the right
Color variants: gray states
white borders
Ref... |
338234315825c1d8575bbb56d0b82669abb9f56b | 690c3c3e583094011d339d20a819b0fbe11a2bf8 | /output_analysis.R | b50edcf2b8eb86902be8e9d49683a35e4ee9ead5 | [] | no_license | AllisonVincent/StarFM-code | a0f907e2931460b7867600bd1566cb39a600338b | eac755b6ef61af5d1925b3b65d02269c846e79e1 | refs/heads/master | 2021-06-17T15:02:43.013841 | 2021-04-20T17:19:42 | 2021-04-20T17:19:42 | 194,706,294 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,864 | r | output_analysis.R | ### This script is for viewing and acquiring basic information about individual layers of Landsat, MODIS, and STARFM data
library(raster)
landsat<- brick('./landsat.tif')
modis<- brick('./mod_.tif')
data<- brick('./starfm_East_fusion.tif') ## starfm data
## To find the fraction of data available, first set pixel... |
12df01a2706afe67a50eb7dc59a807826fadd925 | 46fd3e7df135ee7f9c939bf48481beafb3f08abf | /inst/apps/brapi/mw_studytypes.R | 2a0026d35eb2f2c8d80ac2bd0dc94fd7c487ef61 | [] | no_license | CIP-RIU/brapiTS | 40f4106727e4bde4baddc13c34c1ae99c2b2bfdd | e4c7074d9226941f4187426db0f582e65660eed6 | refs/heads/master | 2020-07-23T03:16:54.359229 | 2017-09-15T16:56:10 | 2017-09-15T16:56:10 | 78,644,303 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,003 | r | mw_studytypes.R |
studyTypes_data = tryCatch({
res <- read.csv(system.file("apps/brapi/data/studyTypes.csv", package = "brapiTS"),
stringsAsFactors = FALSE)
}, error = function(e) {
NULL
}
)
studyTypes_list = function(page = 0, pageSize = 100){
if(is.null(studyTypes_data)) return(NULL)
# paging here after fi... |
3db96bf567f9b16ab42c6a4061f43e0b442cd466 | 1cbdfc9dae2fb81522cfad64ce4bc10f7db63b4a | /plot3.R | 4c6b9f634535fbb243d3923ea2d28f88ef79aa23 | [] | no_license | cstaats32/ExData_Plotting1 | 00f097de1b0022a6a0b1592fa0651127b611285e | 493dac165dccf36dbe97dfe180ceae4eddb60c41 | refs/heads/master | 2021-01-09T06:42:32.320212 | 2017-02-06T02:30:50 | 2017-02-06T02:30:50 | 81,039,344 | 0 | 0 | null | 2017-02-06T02:21:05 | 2017-02-06T02:21:04 | null | UTF-8 | R | false | false | 1,963 | r | plot3.R |
setwd("~/Cathy/Coursera R Programming/Exploratory Data Analysis")
## read in the dataset
poweruse <- read.table("household_power_consumption.txt", sep=";", header = TRUE,
colClasses = c("character","character",
"numeric","numeric","numeric",
... |
083e6678301797ef5858b11afeadb172b8e93389 | d2945a5842efe71d476535cfdf3b32fb07a378c8 | /man/get_genes.Rd | 8a754bf44b24f1f9af588b3234036ef1f59f805d | [] | no_license | TheJacksonLaboratory/mousegwas | 5f41001d57360b15eb1d197b6c356db34cfb7b0d | da23e1b918e1fd88252e305e821cc4545c069de7 | refs/heads/master | 2021-11-27T00:08:12.226207 | 2021-09-23T12:01:12 | 2021-09-23T12:01:12 | 236,527,353 | 1 | 2 | null | 2021-09-23T11:58:39 | 2020-01-27T15:49:58 | R | UTF-8 | R | false | true | 552 | rd | get_genes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_genes.R
\name{get_genes}
\alias{get_genes}
\title{Retrieve genes from biomaRt and return the intersecting genes}
\usage{
get_genes(snps = NULL, dist = 1e+06, attempts = 5, annot = NULL)
}
\arguments{
\item{snps}{A list of SNPs with chr, p... |
fd16785050dc4aad72db83c067101cfd753e6402 | 7bd6b1d50f19113cce4a62cfcfa7d7d88d21c93e | /Assignment-6/3.R | 9dfd6907ef4df0e15b6985902c5a8ad9ca15c4f4 | [] | no_license | PedroINCA/Brasil_2019 | e5ce80aab69cecc3cfbdbae409887b32463ee683 | fbe00f6fdb19a8f3deac46349a5295048e50710e | refs/heads/master | 2020-07-07T00:25:39.358351 | 2019-08-23T01:40:23 | 2019-08-23T01:40:23 | 203,184,836 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 610 | r | 3.R | #Please write a function called containsAnyOfTheseKeys that accepts two arguments: 1) a list variable and 2) a vector variable.
#Your function should return a Boolean (logical) value that indicates whether the list contains any key in the specified vector.
#Hint: one way to do this is with the intersect function.
... |
efbc381b4c249bc0886649cada2735d42639ff36 | 5137f6f49055a6d75b96f1b1b0c30b055636e44e | /man/run_test_applications.Rd | c6d8ec779309c9f72ffe6bae0eefd56696b969a8 | [] | no_license | cran/rODE | d23abb178718e5d79aa6eba3591f398f485ca201 | 42190459c2b840012a6277018668051f2987ef43 | refs/heads/master | 2021-01-22T10:28:29.418155 | 2017-11-10T03:17:51 | 2017-11-10T03:17:51 | 92,644,502 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 262 | rd | run_test_applications.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{run_test_applications}
\alias{run_test_applications}
\title{run_test_applications}
\usage{
run_test_applications()
}
\description{
Run test all the examples
}
|
aac12ac005931f1c18d5b82997191a171e0b8203 | 335a31a8cd4afb48fb29867b560b0084b8f31142 | /server.R | 6779e15931bb15442986e435b78aecd29d822d1b | [] | no_license | sangtani/DataProducts | a3eec4c83d5802a837bae2078a5e2d29055a2076 | f28e0a8f4240e1be9b3b8c149f9ba0207758e049 | refs/heads/master | 2021-01-10T03:57:52.707239 | 2015-11-22T16:50:09 | 2015-11-22T16:50:09 | 46,667,447 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 727 | r | server.R | library(shiny)
library(caret)
library(rpart)
library(lattice)
library(ggplot2)
library(e1071)
data(iris)
inTrain <- createDataPartition(y=iris$Species, p=0.7,list=FALSE)
training <- iris[inTrain,]
testing <- iris[-inTrain,]
modFit <- train(Species ~ ., data=training, method="rpart")
shinyServer(
function(input,o... |
ea68d5558578fa2d45383447ca3a1515eedb9606 | 40cc7a64fc13bba3f193b2257350c70c7a62952b | /Ex(8).R | 15c54353e39484e26142a450779e7d62ed768cde | [] | no_license | NorthCL/-R- | 0c021d613adfe3d05007d6a55d9b37dd53ac6b5d | 59c42e2de6d81552b847e8e19a2d6c267731ecfc | refs/heads/main | 2023-04-19T11:31:57.051577 | 2021-05-12T20:38:31 | 2021-05-12T20:38:31 | 304,287,802 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 611 | r | Ex(8).R | #а - мат ожидание x
#b - среднеквадратическое отклонение x
#c - мат ожидание e
#d - среднеквадратическое отклонение е
#n - кол-во чисел
Function.NC <- function(a, b, c, d, n)
{
set.seed(30)
x <- rnorm(n , mean = a, sd = b)
e <- rnorm(n, c, d)
y <- 100 - 6*x + e
layout(matrix(c(1,2,2,1,2,2,4,3,3),n... |
55c2a7613f54b842a09df7c129127a38c88e6b1b | 4d20e5e7a209d0c77cd598259010bee26980c50f | /r_code.R | f387b2ac17bfba50f4a1b29b3f5939ede1c52966 | [] | no_license | luehkenecology/extract_e_obs_gridded_dataset | d13df0baea4cd31e12c5505cb0692437105fa663 | 4a91257feb909bfcd63ecf282edb2bbde91d907a | refs/heads/master | 2021-01-17T17:28:42.636569 | 2016-06-28T11:31:48 | 2016-06-28T11:31:48 | 62,133,864 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,531 | r | r_code.R | ###################################################
# clear memory
###################################################
rm(list = ls())
#============================================================
# set working directory
#============================================================
RPROJ <- list(PROJHOME = normalizePa... |
8c58c00ae2a387cc08c69a0e294895c5ebf2286b | d467300a1edd4f18630caba9653acd54e11651bf | /Class_Code/Class_1/makestereo.r | f2056206b35abfd5a083b4563df887e0f90d204c | [] | no_license | ZixinNie/Introduction_to_Data_Science | 63e4645b77dfd9b04570761a7933ca412694d445 | 5163ed6f9c2c613425fe3ce1b10133b2c8f1310a | refs/heads/master | 2020-04-18T03:24:16.431261 | 2019-02-06T13:50:17 | 2019-02-06T13:50:17 | 167,197,283 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,038 | r | makestereo.r | require(lattice)
make.Stereo <- function(Z, Groups, Screen1=list(z = 20, x = -70, y = 3), Screen2=list(z = 20, x = -70, y = 0), Main="Stereo", asp="Equal", Xlab="X", Ylab="Y",Zlab="Z", pch=16) {
Z <- data.frame(Z)
dimnames(Z) <- list(1:dim(Z)[1], c("Z1", "Z2", "Z3"))
if (asp == "Equal") {
X.range <- c(mi... |
a50c26d9ac214a4b75d69f21a966a886ccdd0cc1 | 0e949f187763332b63439b811f7870c0cb959e67 | /Plot4.R | ca4a0e889e8201c711012fdbe63e04f11f3a1bad | [] | no_license | MariaJose97-22/ExData_Plotting1 | b1110e0c3fe9a26ae8403f0b9e8cc09f29502f0f | 6aac1a411fea4ff1407e76e33c1a1a78b07af94f | refs/heads/master | 2022-11-23T16:42:10.169661 | 2020-08-03T03:28:01 | 2020-08-03T03:28:01 | 284,590,822 | 0 | 1 | null | 2020-08-03T09:49:49 | 2020-08-03T03:06:57 | R | UTF-8 | R | false | false | 1,572 | r | Plot4.R | setwd("C:/Users/Maria Jose Figueroa/Desktop")
power_consumption<-read.table("./ExData_Plotting1/household_power_consumption.txt", skip=1,sep=";")
names(power_consumption)<- c("Date","Time","Global_active_power","Global_reactive_power","Voltage","Global_intensity","Sub_metering_1","Sub_metering_2","Sub_metering_3")
sub... |
c318cd738f8b83e5a02692d79c2960898f5c160b | ab67cc16f40aeb69c1551c8e907d97abfd144212 | /R/scrape.R | 754c5ed75e135c1e7c8d1c335aff0eaa3b2c73c4 | [
"MIT"
] | permissive | news-r/papers | 6c315f423b923eb14e0d67ea7f37555e2f449678 | 2e94fea7cacd1f511e4ed30072b5957b7e6fb771 | refs/heads/master | 2020-05-31T18:01:57.481746 | 2020-02-23T19:42:56 | 2020-02-23T19:42:56 | 190,424,523 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,390 | r | scrape.R | #' Regions
#'
#' Get the list of regions available.
#'
#' @import rvest
#' @import polite
#'
#' @examples regions <- get_regions()
#'
#' @export
get_regions <- function() {
session <- bow(BASE_URL, force = TRUE, user_agent = get_user_agent())
result <- scrape(session) %>%
html_node(".cList") %>%
html_n... |
1ac4b023a1f3593005f26a8ad0370eaea9d6955d | c045d3a278f9e394cfe812e02cba316c9aeae0fb | /man/twn.Rd | a0bbb0fb452a949fa612ba3d961fbf8d976b3001 | [
"MIT"
] | permissive | RedTent/twn | 67198ef20852342b681a51000e20811ebdcabb8a | ddcedabf99124d72ad2a963b7fbf7678880fcdf9 | refs/heads/master | 2023-06-23T06:00:57.839342 | 2023-06-20T09:33:12 | 2023-06-20T09:33:12 | 242,074,205 | 0 | 0 | NOASSERTION | 2021-03-29T19:33:26 | 2020-02-21T06:54:10 | R | UTF-8 | R | false | true | 633 | rd | twn.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ZZ_package_description.R
\docType{package}
\name{twn}
\alias{twn}
\title{Een package voor de TWN-lijst}
\description{
De bedoeling van 'twn' is om de TWN-lijst beschikbaar te maken in R en om er makkelijk mee te kunnen werken.
De package bied... |
09d8c39dcfe02e739f8b1e9ca8e815b9a3ad9db9 | 32725711b519cdbd3cfa57faea2d21e4de10a92a | /R/test-script.R | 98633ccb5489e17016cf49d77f528d4c377bf2f7 | [] | no_license | koopmans-michaela/mcs-qsar | 74e02d58801a0c6f555615043b9d2e4defd5b377 | ad0005404af8bc1c946929deb45efc96fb949722 | refs/heads/master | 2021-05-14T08:42:09.881356 | 2018-02-05T18:40:21 | 2018-02-05T18:40:21 | 116,306,688 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 120 | r | test-script.R | library(acepack)
library(testthat)
library(jsonlite)
#here's a comment
#testing for john and jay
#learn committ and push |
c283085e03888182859f7fcdfe1bcc659410ff15 | 3d9f5d23f0c4b933d433acdb7aebf801ae2aa653 | /Hospitals_per_capita_by_Province.R | 834e2b2a8f31e9bb742e620762dcb378a4a6f110 | [] | no_license | bzhang1945/vizathonsubmission | c29c2cae65b6f18b9a73f77e99222309270ef079 | f17043a74c98dbe90e87491f0ff24c10d74be411 | refs/heads/main | 2023-06-26T09:57:32.522352 | 2021-08-01T15:53:29 | 2021-08-01T15:53:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,029 | r | Hospitals_per_capita_by_Province.R | library(ggplot2)
library(tidyverse)
Canadian_province <- c("AB", "BC", "MB", "NB", "NL", "NS", "NT","NU", "ON","PE", "QC","SK","YT")
Hospitals_per_capita <- c(0.00007461243237, 0.0002895378824, 0.0001549674677, 0.00007927598015, 0.0001844600125, 0.00019909153, 0.0001550868486, 0.00007612860659, 0.0002529953655, 0.... |
8faf0442d0f05d026b7ac003d9ef71180d810e8b | 38116111ccbbb1c4580d8e8c5ac3f9775e1fa384 | /man/calculateDiscreteDiscreteMI_Entropy.Rd | 2b27068c8fb942a402365ea1069d093db12de7bb | [
"MIT"
] | permissive | terminological/tidy-info-stats | 6c1e37684eeac8d765384b773a23f0488eb7b467 | 1b1f19a718edb44c7178943c322b45fd1e3c93b1 | refs/heads/master | 2022-11-30T08:16:46.311945 | 2022-11-18T20:37:21 | 2022-11-18T20:37:21 | 232,600,275 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,137 | rd | calculateDiscreteDiscreteMI_Entropy.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tidyDiscreteDiscreteMI.R
\name{calculateDiscreteDiscreteMI_Entropy}
\alias{calculateDiscreteDiscreteMI_Entropy}
\title{calculate mutual information between a discrete value (X) and a discrete value (Y) using estimates of entropy}
\usage{
calc... |
ec54e653dcc9bf40aea688a0c48c424fb394112e | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/sjmisc/examples/merge_imputations.Rd.R | a620f78ba39d3218a15c754461a7ca97e75e7923 | [] | 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 | 495 | r | merge_imputations.Rd.R | library(sjmisc)
### Name: merge_imputations
### Title: Merges multiple imputed data frames into a single data frame
### Aliases: merge_imputations
### ** Examples
library(mice)
imp <- mice(nhanes)
# return data frame with imputed variables
merge_imputations(nhanes, imp)
# append imputed variables to original data... |
357472be7ca58c06914d8d647191c5cc5d65deab | 8e4353a1dc52d42267cd6e8c1d39d5e94c0b1b99 | /r/registration_gui.R | 9bed7c8650a5841bae13a0d7ce91b77c91eb4268 | [
"Apache-2.0"
] | permissive | SimpleITK/ISBI2018_TUTORIAL | f6b7ff1dadb736274c6987627fdae7574f6e2e58 | 7e8f255bb9d0aca9bebd8f4091d7d867b791d1ac | refs/heads/master | 2022-11-08T13:19:44.807531 | 2022-11-01T13:53:10 | 2022-11-01T13:53:10 | 125,414,481 | 27 | 18 | Apache-2.0 | 2018-03-27T14:16:44 | 2018-03-15T19:08:16 | Jupyter Notebook | UTF-8 | R | false | false | 6,119 | r | registration_gui.R | library(ggplot2)
# Labels used in the POPI dataset
popi_body_label <- 0
popi_air_label <- 1
popi_lung_label <- 2
# Callback invoked when the StartEvent happens, sets up our new data.
# Functions ending in _jn are for use with Jupyter notebooks, as the display
# behaviour is a bit different.
# Note that we could use ... |
367063fd58baaf0e39fde6c99af7db7c9499b9da | ba49eb475d4fcd6d61655270ec34fe829657494e | /man/elsc.Rd | 63f22f19170cdc4187546ff723a263831db042e1 | [] | no_license | cran/Bios2cor | f3b630684862d4f48e7137361e39094358336f63 | 50b2948bfdd3888e6593015247419ce1f5b58d8a | refs/heads/master | 2022-07-31T18:21:28.244917 | 2022-07-08T08:25:23 | 2022-07-08T08:25:23 | 101,306,648 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,688 | rd | elsc.Rd | \name{elsc}
\Rdversion{1.1}
\alias{elsc}
\title{
Explicit Likelihood of Subset Covariation (ELSC) function
}
\description{
Calculates a score based on rigorous statistics of correlation/covariation in a perturbation-based algorithm. It measures how many possible subsets of size n would have the composition fou... |
7de17c63a02f19eb43fa13e3f0b8b904d331d13c | 1a826358019cd1bc4082a44099bbc9fb7034aa86 | /OCRPractice.R | 7a208e81601c1b897b693d408fdc55ea86b5cf2a | [] | no_license | sheharyarakhtar/Digit_Recognition | 86dc367c85c75017886e5e8b7fbc3a96c512a578 | 3a36c0f12f8bdb05b45919fe75bcf69f21391ed5 | refs/heads/main | 2023-04-10T23:34:04.284972 | 2021-04-14T21:37:38 | 2021-04-14T21:37:38 | 357,990,667 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,837 | r | OCRPractice.R | library(keras)
#Import dataset mnist for training and testing
mnist <- dataset_mnist()
#2 lists pf 2. First list first and second part contains training and label and second for test
str(mnist)
#Seperate them to variable
trainx <- mnist$train$x
trainy <- mnist$train$y
testx <- mnist$test$x
testy <- mnist$test$y
ta... |
5ce70df38c101f1830d625c919507672b45df5c9 | df3b3e2cff3f789a3e91e561d7e3121603d51546 | /man/superbData.Rd | 6d63f85545f8ea0811c72d9f3f046de1c074b309 | [] | no_license | humanfactors/superb | 39218b3458d8d8d834b844412a22b9a4bf5a8746 | cdb7a903d84c2a83d4a4c7c94a97a2d4bc2221a4 | refs/heads/master | 2023-07-16T21:47:00.530298 | 2021-09-04T16:52:48 | 2021-09-04T16:52:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,453 | rd | superbData.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/superbData.R
\name{superbData}
\alias{superbData}
\title{Obtain summary statistics with correct error bars.}
\usage{
superbData(
data,
BSFactors = NULL,
WSFactors = NULL,
WSDesign = "fullfactorial",
factorOrder = NULL,
variables,
... |
3c209b647b775b7d0353fdf7e8f6e75ee5acb445 | 66955ee1c32bd7cc6fff3fbbd893558c19d7358c | /Data Prep Classification and Prediction Analyis v6.r | 11227594e40e02d2ed5e31a7d2a269e16d13b36d | [
"MIT"
] | permissive | ericsgagnon/psustat897groupproject | 84445c6187fd6e50164d435c2013060189055226 | 6d3e6fc5ce028a616f97469a8c4b5b6e94dd18a7 | refs/heads/master | 2021-01-01T20:21:52.704474 | 2017-08-07T02:45:55 | 2017-08-07T02:45:55 | 98,821,979 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 22,444 | r | Data Prep Classification and Prediction Analyis v6.r | # DATA SETUP
charity = read.csv("E:/STAT 897/Group Project/charity.csv")
charity.t = charity
charity.t$avhv = log(charity.t$avhv)
charity.t$agif = log(charity.t$agif)
charity.t$inca = log(charity.t$inca)
charity.t$incm = log(charity.t$incm)
charity.t$lgif = log(charity.t$lgif)
charity.t$rgif = log(cha... |
d7b5bc79cab5aba56dec4e658055a5d0dd8900f6 | 13694ccdbebfa834e371d38baf6c649b48b92c35 | /man/baltimore_map.Rd | b50520c6e9f5c243ebaf0c2325e0e3c053c0bc3b | [] | no_license | heike/cityshapes | 56c1d16696af1bc0e47fdeb1bb67cc15e9c7a05d | 75b1c327dd4067ec68c86b0ae736c68eef019db1 | refs/heads/master | 2020-03-09T16:11:46.264511 | 2018-04-10T05:39:29 | 2018-04-10T05:39:29 | 128,878,324 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 870 | rd | baltimore_map.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{baltimore_map}
\alias{baltimore_map}
\title{Shape files for the neighborhoods of Baltimore}
\format{A data frame with 4732 rows and 6 variables:
\describe{
\item{OBJECTID}{identifier for each region}
\item{long}{ge... |
5c07dc429fd1896b4c8e082d9c8daed77dc00c58 | 29585dff702209dd446c0ab52ceea046c58e384e | /cna/R/cna.r | 0486c507742007cc0b4de8f64018181640ddb2b7 | [] | 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 | 9,424 | r | cna.r | cna <- function(x, ordering = NULL, strict = FALSE, con = 1, cov = 1, notcols = NULL, maxstep = 5,
suff.only = FALSE, what="mac") {
if (inherits(x, "truthTab")) {tt <- x}
else {tt <- truthTab(x)}
if ((! is.null(notcols)) && notcols == "all")
{
colnames(tt)<-chartr("qwertzuiopasdfghjkl... |
fa59d7b8697ccb5e08543160c4e733e8fcaadfa8 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/docopulae/examples/nint_space.Rd.R | efc7f4273f35d36bfc4c1d7446accad0c708f3c0 | [] | 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 | 407 | r | nint_space.Rd.R | library(docopulae)
### Name: nint_space
### Title: Space
### Aliases: nint_space
### ** Examples
s = nint_space(nint_gridDim(seq(1, 3, 0.9)),
nint_scatDim(seq(2, 5, 0.8)),
nint_intvDim(-Inf, Inf),
nint_funcDim(function(x) nint_intvDim(0, x[1])),
list(nint_... |
ab68ca5282c4f02160ecbc9327aacb490acb7f72 | a1da88a19d3025b77df3edc4b3bcb55a90925ac7 | /content/post/old/corona.R | a95dde01ae5f74bbb0f7e5f3d910f1a33771e928 | [] | no_license | tormodb/academic_blog | d43e0b0d522d82ecd2ae7c71f9e36ffe6e40c07f | bf7a40f638a75af78a6e7bfac323d20effa0d1c4 | refs/heads/master | 2023-06-22T23:04:25.086287 | 2023-06-14T13:09:21 | 2023-06-14T13:09:21 | 229,732,379 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,065 | r | corona.R | library(tidyverse)
library(rvest)
library(xml2)
today <- format(Sys.time(), "%a %b %d %X %Y")
day <- unlist(strsplit(today, " "))[1]
day <- recode(day,
Mon = "mandag",
Tue = "tirsdag",
Wed = "onsdag",
Thur = "torsdag",
Fri = "fredag",
Sat = "lordag",
Sun = "so... |
b9c90eeebd3b012fa865f124214901f98f34fccb | bc12a6667b3d98685b5e3cc4abbfb186b969d968 | /R/utils_update_grants_db.R | 67267613ffa95f671e6a195c2d8e8c65176c2ece | [
"MIT"
] | permissive | include-dcc/pub-include-r | 16d28c5c5931f6f4427d44191d7b1a59cd3c2f31 | 8074939a074ccbb32f277c092120ce7303e882f3 | refs/heads/main | 2023-06-17T08:55:19.614567 | 2021-07-22T00:59:01 | 2021-07-22T00:59:01 | 384,605,785 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,484 | r | utils_update_grants_db.R | #' update_grants_db
#'
#' @description A utils function
#' @importFrom magrittr %>%
#' @return The return value, if any, from executing the utility.
#'
#' @noRd
update_grants_db <- function() {
.fetch_grants_table() %>%
.filter_grants() %>%
.format_grants()
}
.fetch_grants_table <- function() {
sheets_... |
d6779eea7f6cd3acef6f5e9e9c05bd2cb29b0434 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/SimInf/examples/plot.Rd.R | 7904e17f4422dcf38e070dec2f29ff4f04ce6364 | [] | 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 | 1,440 | r | plot.Rd.R | library(SimInf)
### Name: plot,SimInf_model-method
### Title: Display the outcome from a simulated trajectory
### Aliases: plot,SimInf_model-method
### ** Examples
## Not run:
##D ## Create an 'SIR' model with 100 nodes and initialise
##D ## it with 990 susceptible individuals and 10 infected
##D ## individuals in... |
9f951474bdce4d39dbc8021330fa87884d2fa2b4 | 1827dfb1a8868ec2cfb71fa68e15e2c0f301407d | /R/internals.R | d8e5e57b9525e34e2f7516e3145bad118934f3f9 | [] | no_license | pssguy/rwunderground | 4541546bcc38f93deca6b90880d67c8b10237248 | d86429c111e948bd5a97dbb302e90a59c04f75a8 | refs/heads/master | 2021-07-13T01:56:52.887387 | 2017-10-19T16:41:48 | 2017-10-19T16:41:48 | 107,566,640 | 0 | 0 | null | 2017-10-19T15:44:39 | 2017-10-19T15:44:39 | null | UTF-8 | R | false | false | 3,627 | r | internals.R | #####
# Internal pacakage functions for URL handling and data.frame formatting
#####
#' Base URL for wunderground API
#'
#' @return base wunderground URL
#'
base_url <- function() {
return("http://api.wunderground.com/api")
}
#' Build wunderground request URL
#'
#' @param key wunderground API key
#' @param request_... |
524d8b9a3f329d4d2b0b08901744055a89e55526 | 2a0d1fc07d673b8c7cf07c6596dac2630ae3fd7c | /scripts/individualmodelTMB.R | f3098a9965fdd5ece6bdc7c0682fa48dd246ee07 | [] | no_license | stelmacm/WNS | aef0320ac63d789590cc229e7f24e2fe248c036d | a455ce91c547c8174444d8fb811b4236c2ae38f3 | refs/heads/master | 2022-04-29T11:23:05.577810 | 2022-03-04T14:31:02 | 2022-03-04T14:31:02 | 241,282,675 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,604 | r | individualmodelTMB.R | #This will be the R portion of the model TMB
#Staring off with individual level because I dont know how to carry lists into TMB
source("modeltesting/creatingfunctiondf.R")
#This is what would be needed to run in
compile("scripts/fullmodelTMB2.cpp")
dyn.load(dynlib("scripts/fullmodelTMB"))
set.seed(123)
#SM = list of ma... |
1cd42db6770567d852b1547833f9a84208b7de91 | 191d276ade533e816e9db403e766f01cf0894315 | /man/wilcox_test.Rd | ed8582b0e039b990f1b978aafd2c957303a01223 | [] | no_license | gitronald/htester | b5132778c4b7aa6f09bb351782e08206bcad222a | 7aac3653461ec2d1928dbad3cee4f6daa389fd73 | refs/heads/master | 2021-01-16T21:46:08.742779 | 2016-07-23T01:36:10 | 2016-07-23T01:36:10 | 62,968,156 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 792 | rd | wilcox_test.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wilcox_test.R
\name{wilcox_test}
\alias{wilcox_test}
\title{Wilcoxon Rank Sum and Signed Rank Tests}
\usage{
wilcox_test(var1, var2, round = TRUE, ...)
}
\arguments{
\item{var1}{a vector to correlate with var2}
\item{var2}{a vector to correl... |
81a0906d7cbd2c441b7614798edc4535690b585e | ff708723a712c8e3cba19dc783031be098cd0c3a | /04_Model.Testing/02_Jackknife_hake_all_stages_ROMS_PPC_for_GitHub.R | 115b1223e95327c3511efed41d5f909eb7886eaf | [] | no_license | pacific-hake/recruitment-index | 430f66e8e4b55175421ed5b9c896cfecf25e73c4 | ac05ffe31b7e51124b2846c30e9b2dde7c27fcf3 | refs/heads/main | 2023-04-27T05:01:45.670796 | 2023-04-20T04:23:11 | 2023-04-20T04:25:27 | 366,157,268 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,464 | r | 02_Jackknife_hake_all_stages_ROMS_PPC_for_GitHub.R | # jackknife the best fit model. drop one year of data and re-fit best-fit model
# source(paste0(main,'/00_BestModelInfo.R'))
MainFile = "/Users/cdvestfals/Desktop/Hake_Analysis_for_GitHub"
setwd(paste0(MainFile,'/04_Model.Testing/02_jackknife'))
library(MuMIn)
######### need to set parms here if I in parms list ###... |
0e72cab68593c59444e8312cf65db2d24faedeea | 992a8fd483f1b800f3ccac44692a3dd3cef1217c | /Project_bioinformatics/Bra.WGT.paper/SweeD.annotation/2unique.SweeD.R | b1e70acac25a890acc32ed40ccd516152b255f72 | [] | no_license | xinshuaiqi/My_Scripts | c776444db3c1f083824edd7cc9a3fd732764b869 | ff9d5e38d1c2a96d116e2026a88639df0f8298d2 | refs/heads/master | 2020-03-17T02:44:40.183425 | 2018-10-29T16:07:29 | 2018-10-29T16:07:29 | 133,203,411 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,891 | r | 2unique.SweeD.R | getwd()
setwd("C:/Users/qxs/Desktop")
ch<-read.table("145ch.chr1-10.outlier_genes.anno",sep="_")
ch
nrow(ch)
ch[,2]
length(ch[,2])
Uch=unique(ch[,2])
length(unique(ch[,2]))
write.table(unique(ch[,2]),
"145ch.chr1-10.outlier_genes.anno.unique",
quote=F,row.names=F,col.names=F)
pk<-read.table("1... |
5de4062e4f790df58b5937628744d59066736c10 | f37b0e4bd5854edd93c0acea3d99761469d52247 | /analysis/data_infrastructures/use-bigrquery/example-select.r | 267b3145896df9252bcdc9037c4ae9f3c558c4be | [] | no_license | metacommunities/metacommunities | a80cbba48025c7f5dbbd7519771352cf2845d31e | 242de413ea47224f1abf372d794d61aa40f582bf | refs/heads/master | 2020-04-05T02:04:52.294053 | 2018-01-17T15:26:18 | 2018-01-17T15:26:18 | 10,381,192 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 329 | r | example-select.r | library(bigrquery)
library(Rook)
library(tools)
library(brew)
library(rjson)
billing_project <- "237471208995"
sql <-
"
SELECT repo, repolinkDistinctQuestions AS question, repolinkAnswers AS answers
FROM repos_linked_to_from_SO
"
dat <- query_exec("metacommunities", "github_explore", sql,
billing=billing_p... |
018f5f46900a6f9994db5fd8626d6db8e58ce1f2 | 157b2dc0d0b7c98ece7fd04759ca43c8638707c0 | /_fnc/tidy_summary.R | 2216a9348e67151905e077d4d9535b6bf4532606 | [] | no_license | eugejoh/PAHO_dengue | 14d9c5d9b273c5975fc9bd4a6ec551ee3858a512 | c4016f828fcd81edfd4b341c881d86caf4c69f97 | refs/heads/master | 2020-03-09T08:19:43.096503 | 2018-09-19T23:17:40 | 2018-09-19T23:17:40 | 128,686,621 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,146 | r | tidy_summary.R | #' Tidy Summary Statistics
#'
#' This function outputs a tidy data frame of summary statistics, utilizing the base summary functions
#' `mean`, `median`, `min`, `max`, `quantile`, `sd`. This function selects only columns that `is.numeric = TRUE`.
#' This function ignores missing values `na.rm = TRUE`.
#'
#' @param inp... |
18fbbf9fc4afca9d95e04484c90a0d92b962de0c | fa703db3c7f0621c2c2656d47aec2364eb51a6d8 | /4_ss21/r/P01-1.R | ecda29a3821195dbcf50dde6dfc5c371531d339c | [] | no_license | JosuaKugler/uni | f49b8e0d246d031c0feb81705f763859446f9b0f | 7f6ae93a5ef180554c463b624ea79e5fbc485d31 | refs/heads/master | 2023-08-15T01:57:53.927548 | 2023-07-22T10:42:37 | 2023-07-22T10:42:37 | 247,952,933 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 132 | r | P01-1.R | # Christian Merten, Josua Kugler
x <- c(1.3, .5, 42, -8e-5)
x[1:4] <- x[4:1]
x[x < 1] <- -2
sum(x^2)
q <- 1:100
q[q %%2 == 0] <- 0
|
766abd13a2d7ca121f1bb04b225ab82d62acc700 | 40c196ea90dbab156db44d9053688d58a90b2800 | /01_source/03_facebook.R | e04aceec47e87f136df89d8dcdd771b410a44265 | [] | no_license | robcortesl/social_media | 678b04ffb3e8678adc0ccdfaddc53e83fb5aca4b | 945dde13ba21d026cf3682b313e06a2d06d3cf5e | refs/heads/master | 2021-07-24T07:00:51.228101 | 2017-10-31T21:37:53 | 2017-10-31T21:37:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 780 | r | 03_facebook.R | # Facebook
install.packages("Rfacebook")
library(Rfacebook)
# a traves de token temporal (dar de alta a cuales servicios puede acceder)
token <- "EAACEdEose0cBAB5NouC14dtF13Dv93oWG3RX3eEyZCHslrPGnB5Ly9ZA6LzZB07req3gaU0nZCnsqNIR2xKrA14NpfOaZBUz6kdcbTlcy0byyseMZCR1BiDMFuZCvKMMjnXM4wXZC417tYki8Xl8JroPVQvv1qgljFo1KZBJmtL... |
58df2b5b449c7d24bf52fc4082482000c7feec19 | 3e9f1994f0c73173fc9f7123d4c0096cae76cb15 | /Generate numbers which follow normal distribution.R | 760da700ba77a9398f3211bb4d1ec960767cf104 | [] | no_license | SAUVIK/Start-R-1 | 093a13651e379c0b105a1a83327ab4f253fd54a3 | b9cce6bf85e3b1149731bfe2ba143553cba09f9a | refs/heads/master | 2021-01-23T17:37:56.736073 | 2017-09-07T18:15:15 | 2017-09-07T18:15:15 | 102,770,413 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 224 | r | Generate numbers which follow normal distribution.R | #Generate numbers which follow a normal distribution
noraml_distribution_numbers <- rnorm(1000) # default mean and standard deviation os 0 and 1 respectively
mean(noraml_distribution_numbers)
sd(noraml_distribution_numbers)
|
96a380cdda18347c333208edc76f9cd5cdd14af4 | 9a430b05c1e8cd124be0d0323b796d5527bc605c | /wsim.io/man/read_dimension_values.Rd | 9042272236c50efe8eff519bac634aaed1537ae1 | [
"Apache-2.0"
] | permissive | isciences/wsim | 20bd8c83c588624f5ebd8f61ee5d9d8b5c1261e6 | a690138d84872dcd853d2248aebe5c05987487c2 | refs/heads/master | 2023-08-22T15:56:46.936967 | 2023-06-07T16:35:16 | 2023-06-07T16:35:16 | 135,628,518 | 8 | 0 | null | null | null | null | UTF-8 | R | false | true | 741 | rd | read_dimension_values.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/read_dimension_values.R
\name{read_dimension_values}
\alias{read_dimension_values}
\title{Get the values of the dimensions associated with a variable in a netCDF file}
\usage{
read_dimension_values(vardef, exclude.dims = NULL, exclude.degener... |
11e8c249ed3336686b9fd756a3112e62a50a06de | 3ef4654c876937535cd5a19f3a3b6b9796e1a7c5 | /global.r | 877da28f81f852883e7e6940907c2921b1776b17 | [] | no_license | yifanStat/metrics_app | 3c6a60b9432c18f6878d11fd52c09de38eb93514 | 847b6e423ead7d0151db78ad3e55cb729d1b4a4d | refs/heads/master | 2021-07-17T00:15:30.290567 | 2017-10-23T04:19:35 | 2017-10-23T04:19:35 | 107,930,504 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 150 | r | global.r | library(shiny)
source("src/shine_d.r", echo = FALSE, max.deparse.length = 1000L)
source("src/shine_h.r", echo = FALSE, max.deparse.length = 1000L)
|
3fa091d28f9c037a094ccab4a5d4a9a43e057e3f | cff4412c81ece904c62c68df4b081583a8f17270 | /Linear Model Selection and Prediction.R | bc9c5ae77860d54329bd3563b8d8950345999dbe | [] | no_license | ArvindPawar08/Intermediate-Analytics | a78b20d341d20604c9da6bcc21d2e53bc9787f82 | 227ddb375d7c7fdc20b4b8cd20d679564118e3b5 | refs/heads/master | 2020-05-05T10:04:27.940109 | 2019-04-07T07:00:55 | 2019-04-07T07:00:55 | 179,929,240 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,791 | r | Linear Model Selection and Prediction.R | #part1
setwd("C:/Users/Arvind/Desktop/Intermediate Analytics/Week 3")
a3 <- read.csv("assignment3.csv")
a3
set.seed(12345)
train <- floor(0.75*nrow(a3))
train_ind <-sample(seq_len(nrow(a3)),size = train)
trainset <- a3[train_ind, ]
testset <- a3[-train_ind, ]
dim(trainset)
dim(testset)
names(a3)
Mod... |
8e27b0455382e3b79f6dd601997ce479eb2c2ea2 | d9d213281d875a47089f1a0114919eeceedf4a60 | /Figure S1/Figure_S1_plot_gray_value_all_time_points.R | e03525f4716590f4c8416c62b781e32895aa6852 | [] | no_license | hibberd-lab/Xiong_High-light-response-of-the-rice-bundle-sheath | 6845c4eac2300e8621cd7cd738cda95b2d91906a | 221b6b7a9dfd87b67d6325ea77a3efb3db1d8d96 | refs/heads/main | 2023-03-31T08:04:41.543999 | 2021-04-15T08:20:10 | 2021-04-15T08:20:10 | 306,381,513 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,772 | r | Figure_S1_plot_gray_value_all_time_points.R | all1 <- read_csv("Gray value.csv")
head(all1)
all1$Distance<-as.numeric(all1$Distance)
all1$time<-factor(all1$time,levels = c("0 min","5 min","10 min","15 min","20 min","30 min","40 min","50min","60 min"))
all1$Treatment<-factor(all1$Treatment,levels = c("high light","0 min"))
str(all1)
celltype_position <- read_csv(... |
a784b73d2cb2bf22311b41f1e1567a97ad5e1872 | 7021962b10e1fa20e48a99807b85e422bee02cec | /Plot1.R | 542619889e876d0d4bfa743a0970f1ecfe6853b2 | [] | no_license | pbarry129/ExData_Plotting1-1 | 271090b688105ce53066377e48c53327e0ce6632 | 47d203e5742bb7b46347d5fa392088a4bd49f5db | refs/heads/master | 2020-06-13T17:22:35.808014 | 2016-12-05T01:36:28 | 2016-12-05T01:36:28 | 75,575,966 | 0 | 0 | null | 2016-12-05T01:07:49 | 2016-12-05T01:07:48 | null | UTF-8 | R | false | false | 904 | r | Plot1.R | #Read in the power consumption data
my_power_data<-read.csv("household_power_consumption.txt", sep=";")
# Create date string
my_power_dates<-paste(as(my_power_data$Date, "character"), as(my_power_data$Time, "character"))
# Put date string in Date format
POS_dates<-strptime(my_power_dates, "%d/%m/%Y %T", tz="... |
625df374735a2600cc9eede5821522bf68d0cb4e | 2631efe4c86afda92bff9141bf09ec1b359da511 | /man/topgenes.Rd | 3ad7449e2c39b5dd8c3b94c5fae1280b5204dd51 | [] | no_license | Oshlack/AllSorts_v1 | d4e753805a04fc8e2d80ad8cfcdd540aa9e39c37 | 529f6f9c42266c74a3de87531e3e01847d8ca8ff | refs/heads/master | 2023-02-12T07:00:28.985590 | 2021-01-18T03:35:06 | 2021-01-18T03:35:06 | 76,910,901 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 251 | rd | topgenes.Rd | \name{topgenes}
\docType{data}
\alias{topgenes}
\title{Random Forest Classification genes}
\description{
A list of genes required by the random forest classifier.
}
\usage{topgenes}
\format{A character vector of 20 genes required for classification.}
|
8a221b7840510f7ef824040794d90a110956226f | 73cdd09cf558fa34e0799043837afdbbea61dcbd | /R/exportfuns.R | c041f134a0036e7a7a5f000cf705a092acd6edd3 | [] | no_license | epi-chen/bioage | 1561974f28b2b2fc48dc8c3017c2204e7df0b31b | c67e377984b370c82b244131f7ccd166a9ecf0b1 | refs/heads/master | 2021-09-16T19:16:54.732472 | 2018-06-23T19:46:58 | 2018-06-23T19:46:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 41 | r | exportfuns.R | #these are for exporting output to stata
|
a799817c5a33e18d0f2647f05d4ab8074b228657 | b779522919291bcc1a2c05e6034394fd82858fc9 | /skript_EDA_2017.R | fd41a6bd7b2f653af0d1e8dcb425012a37825f92 | [] | no_license | Kobzol/statistics | e63b419d01cd3e248901825169cce116e65d98b0 | 56637eb4b69d24d7268b244b88ff9c15177a39eb | refs/heads/master | 2021-03-30T17:36:04.324846 | 2017-03-27T18:31:39 | 2017-03-27T18:31:39 | 86,370,452 | 0 | 0 | null | null | null | null | WINDOWS-1250 | R | false | false | 21,539 | r | skript_EDA_2017.R | #######################################################################################
################ Preprocesing dat a explorační analýza ################################
############### Adéla Vrtková, Martina Litschmannová ##################################
########################################################... |
c8e6348d6b28caddf95147aeb30a875483dc6edf | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/datadr/examples/convert.Rd.R | 6825df9b4d8631b2c73986563402387192faf462 | [] | 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 | 262 | r | convert.Rd.R | library(datadr)
### Name: convert
### Title: Convert 'ddo' / 'ddf' Objects
### Aliases: convert
### ** Examples
d <- divide(iris, by = "Species")
# convert in-memory ddf to one stored on disk
dl <- convert(d, localDiskConn(tempfile(), autoYes = TRUE))
dl
|
73379c322f349dde606049efa7ea3f77e089ebaf | 9bdef83f28b070321ba27709d2c7ec028474b5c3 | /R/packages/package.creation.R | bcf8ffd6ab3ab0e3aa9e4e6c4f5b03f8ef145c1e | [] | no_license | antagomir/scripts | 8e39ce00521792aca1a8169bfda0fc744d78c285 | c0833f15c9ae35b1fd8b215e050d51475862846f | refs/heads/master | 2023-08-10T13:33:30.093782 | 2023-05-29T08:19:56 | 2023-05-29T08:19:56 | 7,307,443 | 10 | 15 | null | 2023-07-19T12:36:45 | 2012-12-24T13:17:03 | HTML | UTF-8 | R | false | false | 2,981 | r | package.creation.R |
replace.description.field <- function (version.name, field, entry) {
# Replaces given field in the DESCRIPTION file
# define DESCRIPTION file based on version.name
f <- paste(version.name,"/DESCRIPTION",sep="")
# Read DESCRIPTION file
lins <- readLines(f)
# search the line that contains the f... |
71bc643423256db4b3ab9a2b49a35cd62cf85b31 | 0eb87c697d21c87fe6ffdeb731033ea17e92a327 | /man/grid_spatialpoints.Rd | c59287814e5a3c435d8cfc95af66d9d0ea95c643 | [] | no_license | Grelot/rgeogendiv | e4566fcb965a07db5a655a95744eb20221f893ce | d261bcea4d12a70d40ce3efd058b0908744a3c27 | refs/heads/master | 2023-02-16T18:41:09.436973 | 2020-12-17T16:26:31 | 2020-12-17T16:26:31 | 300,564,835 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 918 | rd | grid_spatialpoints.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/grid_spatialpoints.R
\name{grid_spatialpoints}
\alias{grid_spatialpoints}
\title{Build a map grid spatialpoints}
\usage{
grid_spatialpoints(
siteSize = 260000,
projectionCRS = "+proj=cea +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +datum=WGS84 ... |
98348fa9e5648a988db50027e9a7742470468d7d | 80ebdb34963c9d62e9d70bad0dae43673c40f0e8 | /server.R | 0c0a1cb3615f02adb2c13ec23b40f7fd7c4171a3 | [
"MIT"
] | permissive | AdamSpannbauer/shiny_groom_proposal | 8d4d61b5b369e178d8194965eafedbd5582dcb80 | 9df9c512ccf0f0d091a99fca14f3f9882f40cb23 | refs/heads/master | 2020-04-12T00:22:50.920568 | 2018-12-18T21:38:10 | 2018-12-18T21:38:10 | 162,198,075 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,931 | r | server.R | shinyServer(function(input, output, session) {
# get groomsman id from url query (returns FALSE if invalid/missing)
groomsman_id = reactive({
query = parseQueryString(session$clientData$url_search)
gm_id = query[['gm_id']]
if (is.null(gm_id)) gm_id = FALSE
if (!(gm_id %in% groomsman_ids)) gm... |
88c556f232a55684bc9c3de8c03460b041701572 | 1fb167f75fac5fd1438cc442d1e21296c68fe277 | /combine_data.R | 6e6a2edc04ed5a48b4b843f4f72a6abc9eb048be | [] | no_license | BrianDarmitzel/INFO-201-Group-Project | 8cf4d3f7b56f0a61d9e0a8d37ee6a90e315a084b | 6e9eeddaab914b61a1b5b8765d8ce20e94663a6c | refs/heads/master | 2020-08-31T06:42:21.936296 | 2019-12-05T06:02:30 | 2019-12-05T06:02:30 | 218,626,398 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,049 | r | combine_data.R | library("dplyr")
library("stringr")
library("plotly")
# load in filtered data set
emissions_data <- read.csv(unz("data/filtered_datasets.zip",
"filtered_datasets/emissions_data.csv"))
fuel_economy_data <- read.csv(
unz("data/filtered_datasets.zip",
"filtered_datasets/vehicles_individua... |
c122c438af228805083f06f5424b53a7149ffb66 | 0a5cf23a3ef5fb9f36a9051ec59d1215ee62a99c | /RScripts/DataVisualization.R | 914ac74f1d30c477acd5c9f4d9c6e00ca3eb1c59 | [] | no_license | phanisrikar93/cricScoreR | 23ec98a5ad17c6e7712ce8ecd43c6df8430c55ed | b2d18d8c4f4d79e18484b07e8c9e25c03d834314 | refs/heads/master | 2021-01-19T02:39:15.435212 | 2016-07-20T16:57:53 | 2016-07-20T16:57:53 | 63,249,022 | 0 | 1 | null | 2016-07-20T16:57:54 | 2016-07-13T13:40:13 | R | UTF-8 | R | false | false | 1,462 | r | DataVisualization.R | require(lattice)
#To read data file into frame.
FinalDataFrame = read.csv("C:\\Users\\admin\\Downloads\\Use_Case_Dhruv\\BindIPLData.csv")
#Summary of RunsScored column of FinalData Frame.
Summary1 = summary(FinalDataFrame$RunsScored)
Summary1
#Standard deviation of RunsScored column of FinalDataFrame.
sd1 = sd(FinalDa... |
4ca0b7ee9f2b701404c8568f3a9e287031c1c05d | c7c5813adee3d966baced00501b4f7d15ecc3e4c | /man/Rboot.Rd | ab9b5be4a9611233c16910948cae8ab2b53cd2ec | [] | no_license | E-Caron/slm | 8f181ce1a03526843f1b4ea1b647186b67145edc | a80d9765fda9e29fa3af78c990fea3931199d0f2 | refs/heads/master | 2020-06-06T09:43:52.667197 | 2020-01-08T19:08:17 | 2020-01-08T19:08:17 | 192,704,653 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,414 | rd | Rboot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/auxiliary-fun.R
\name{Rboot}
\alias{Rboot}
\title{Risk estimation for a tapered covariance matrix estimator via bootstrap method}
\usage{
Rboot(epsilon, treshold, block_size, block_n, model_max, kernel_fonc)
}
\arguments{
\item{epsilon}{an un... |
73333ab5b9efff43160c08a9094cd68ab8e26426 | 1dd840b99146dbdd57e3b267d6243d695354bdd0 | /DTF_Create.R | 3d31bbcb8946136a212c30fdfad21e0491634276 | [] | no_license | diardelavega/TotalPrediction | 654e6ca7d4d6f1df3153dd94ab31c7a0790e738b | 464cafdc346c419ba58d7c34ac7ff82ebfddb4ff | refs/heads/master | 2020-04-12T08:52:37.906304 | 2017-03-04T17:22:23 | 2017-03-04T17:22:23 | 61,332,137 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,382 | r | DTF_Create.R | #page to be used for loading /7 initiating all the data for the creation of DTF obj
os<-Sys.info()["sysname"]; # find the operating system
runAll<- function(trPaths,dtfKind, fld=10, betNoBet='nobet',fff='f5'){
exit <- "DTF_END_OK";
#trPaths is a vector with all the tr paths of the competitions
# dtfKind is a v... |
2fa9516d586ad18b5c5faf49c31ae5dd6d792710 | 56ea0cd71b0982ca04835a6e7179f00ab1ff94d8 | /DutchessStarter.R | 60af26b48047e9d1503214698e929ac2ddffc634 | [] | no_license | alizzo/Traffic_Data_HVHackathon | bb4f617e9819c06669dfef91c25d36d13e32f3a2 | 5d91ec7968078e66e8a9a231f87b36ea53b47767 | refs/heads/main | 2023-09-01T21:53:49.524240 | 2021-10-30T19:25:08 | 2021-10-30T19:25:08 | 422,969,908 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,826 | r | DutchessStarter.R | # Load Libraries
library(tidyverse)
library(sf)
library(leaflet)
library(viridis)
# Read in the Shape File
municipalities <- read_sf("./dutchesscounty4326/muni_boundaries-polygon.shp")
# Read in the geospatial data for Dutchess County Fire & EMS Stations.
fire_ems <- read_csv("fire_ems_stations-point.csv") ... |
74fa2efe453a83b8096ab45ba9e8de7595e0f224 | bfde25295f3330b108ba93d5f90e71382627c639 | /A2_submission.R | 823ed92a98cfaf131aee71613f4fb8c2617e40b2 | [] | no_license | jgow68/CDA2 | 84630ec4ab76c339980e7fb3287acce3b2559cf6 | 6a18358dee4a37a4d743326cbd176f6d98f5937a | refs/heads/master | 2021-01-22T22:20:19.133256 | 2017-03-30T06:20:27 | 2017-03-30T06:20:27 | 85,531,396 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,724 | r | A2_submission.R |
# Q1 ----------------------------------------------------------------------
dat = data.frame(interview=c("no", "yes"), cases=c(195,46), controls=c(979,370))
dat = data.frame(med_status=c("no", "yes"), cases=c(979,195), controls=c(370,46)) # cases is non-participation
dat
# Q1 - use med status as predictor ---------... |
6b73ad3a1e1cc5a75fa2003406c68c7a11aeaec4 | 8b1d00ae218ab6b4c300083fb179bbc3d441e221 | /scripts/topGO_vignetteTrial.R | f0b921b2cce9449de5b65572defe60bf79654420 | [] | no_license | davetgerrard/LiverProteins | 79d1aa8ee265b27c6b161b22e189a4cba891dca6 | d99c0adab3c71579151d72eb7a5ebb65921209d9 | refs/heads/master | 2021-01-01T17:58:07.123517 | 2011-08-05T15:12:10 | 2011-08-05T15:12:10 | 1,941,622 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,976 | r | topGO_vignetteTrial.R |
#########################################
# #
# Dave Gerrard #
# University of Manchester #
# 2011 #
# #
#########################################
library(topGO)
library(ALL)
data(ALL)
data(geneList)
affyLib <- paste(annotation(ALL), "db", sep = ".")
library(package = affyLib, character.only = TRUE)
sum... |
b063efeaeca24f59b0cd0f142f50d067bbb23248 | aa103303a64aac3a17160833d8136cd6a7bd21a4 | /LinearAlgebra/Unit_01/vectors.R | 5cf3d6339f621f19e74c782d034708e6f431e69e | [] | no_license | anhnguyendepocen/MSDS-Supervised-Learning | 4e9ed791d686eae2bb7519a563ad05f9b5c98b5d | 67dc32d564bdcd498137620f1efbbf2445a87364 | refs/heads/master | 2022-02-22T08:08:05.342690 | 2019-10-11T23:08:32 | 2019-10-11T23:08:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 63 | r | vectors.R | m <- matrix(c(1, 2, -1, 3, -4, 5, 1, -8, 7), nrow = 3)
det(m)
|
63c063b794ab78a9ccb77b1ce78a2720bf542650 | 7dd0f3d19b98750e34d2dfa62533cbd50fa18db5 | /plot5.R | 132937d2abedd8ebc32979857bb41048e6541510 | [] | no_license | gregorypierce/datascience-exdata-courseproject2 | e551f29fb5ee9b87ffd24526efcf03ef569a39ec | 4445d60d26a6b30c37fa6f5f6e168ff0b3da9e56 | refs/heads/master | 2016-09-01T20:51:24.448542 | 2015-07-25T05:37:54 | 2015-07-25T05:37:54 | 39,669,223 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,090 | r | plot5.R | library(ggplot2)
## set the project Directory
projectDirectory <- "/projects/datascience/exploraratorydata/courseproject2"
dataDirectory <- paste0( projectDirectory,"/data")
setwd( projectDirectory )
## This first line will likely take a few seconds. Be patient!
NEI <- readRDS( paste0( dataDirectory, "/summarySCC_PM2... |
0abead7ffa99418df8fc1dfbc532d93b0649eeff | 97e3baa62b35f2db23dcc7f386ed73cd384f2805 | /inst/app/app.R | d2dbc951cbdf95b988aebd26bf6b3eb7496089f0 | [] | no_license | conservation-decisions/smsPOMDP | a62c9294fed81fcecc4782ac440eb90a299bca44 | 48b6ed71bdc7b2cb968dc36cd8b2f18f0e48b466 | refs/heads/master | 2021-06-25T22:23:31.827056 | 2020-10-27T08:56:07 | 2020-10-27T08:56:07 | 161,746,931 | 7 | 0 | null | null | null | null | UTF-8 | R | false | false | 36,169 | r | app.R | source("helper.R")
## UI ####
ui <- shinydashboard::dashboardPage(
title = "POMDP solver: When to stop managing or surveying cryptic threatened species ?",
# HEADER #############################
shinydashboard::dashboardHeader(
title = "smsPOMDP",
shiny::tags$li(
a(
strong("Building an a... |
cc6f94771e418cbfc5308c03ff0490c11592ee4c | f936ecec924cd1a5f430dd01e2540767a7df29e8 | /R-ML/prepocx.R | 8d317e79ca21f4f9cccfb76bef4897819e4b8cab | [] | no_license | PiscatorX/Project-Roger-Dodger | b4359d79e7234e8385b4d6115b14b2a6524e888e | e402876c7aa3c6e92036fcb8dfbba872887e5e33 | refs/heads/master | 2023-07-29T12:22:00.861078 | 2021-09-09T07:23:32 | 2021-09-09T07:23:32 | 292,900,383 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,597 | r | prepocx.R | library(tidyverse)
library(magrittr)
library(ggplot2)
library(ggpubr)
library(dplyr)
zeolite = read.table("zeolitex_final.tsv", sep = "\t", header = T)
colnames(zeolite) %>% data.frame()
zeolite <- zeolite %>% mutate_all(na_if,"")
##################### Multicollinearity Analysis #################... |
9073daeca9c5c8be3e2fce570f8abd55e69d6fe6 | 552d16746aeb43a11a7801f4ee94d55289ced977 | /function_practice.R | a6d85a5c436e54e28fabf58ed70a5c38c994812a | [] | no_license | saurabb2297/datasciencecoursera | 2304e672acb4b579998b0fedcccbe9905140903e | 1ea24067c60025a1585d91b339e2add5c047dd92 | refs/heads/master | 2022-09-28T16:34:08.348076 | 2020-06-06T06:37:09 | 2020-06-06T06:37:09 | 264,628,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 445 | r | function_practice.R | add2 <- function(a,b){
a + b
}
above <- function(x,n=10){
use <- x>n
x[use]
}
#calculating mean of each column of dataframe
#removeNA used to deal with missing values
columnmean <- function(x,removeNA = TRUE){
nc <- ncol(x) #number of columns in df
means <- numeric(nc) #empty vector of length number of co... |
fe3e66ebc6c28313dc6709a07d5320685a4d7c16 | 8f2aa4469495a4983a669e44b038f97ce8dda877 | /plot1.R | 0d480b2536c114402cf6b7145373222a42e546ac | [] | no_license | Juan-Yi/ExData_Plotting1 | 7deded198a75c617067f7a7ba2f9a8d304b431a1 | eda76a3966ea986e529ff4bce2beee1516d280bc | refs/heads/master | 2021-07-17T16:40:21.206514 | 2017-10-25T00:34:09 | 2017-10-25T00:34:09 | 107,892,587 | 0 | 0 | null | 2017-10-22T18:39:51 | 2017-10-22T18:39:51 | null | UTF-8 | R | false | false | 383 | r | plot1.R | power<-read.table("household_power_consumption.txt", sep = ";", header = TRUE)
power$Global_active_power<-as.numeric(as.character(power$Global_active_power))
power2day<- power[power$Date %in% c("1/2/2007","2/2/2007") ,]
hist(power2day$Global_active_power,
xlab = "Global Active Power(kilowatts)",
ylab = "F... |
288a07fb2e9add0f7add78b2d188644e74695b6d | e3d9fdf5a0720b59afa52f62abef64c08b00e8e9 | /users/Faith/Evenness/analysis/mixed_models/Abundance_Calcluate_Evenness.R | adae4646585dbd1301368e5f0c04c304469d5041 | [] | no_license | bioTIMEHub/bioTIME | 4d5f960b137b4040de32426ff1c447a256147c38 | e44a750827cae059f825dcc3c93ba6a0b7ae2a91 | refs/heads/master | 2021-06-08T09:53:41.387274 | 2021-03-30T12:42:53 | 2021-03-30T12:42:53 | 92,727,984 | 5 | 4 | null | null | null | null | UTF-8 | R | false | false | 2,362 | r | Abundance_Calcluate_Evenness.R | setwd("C:\\Users\\faj\\Documents\\OneDrive for Business\\research\\ch3 - metaanalysis\\data")
AbData <- read.csv("AbudnaceOnceRarefy.csv")
library(vegan)
library(reshape2)
#code to calculate evenness change
#----------------------------------------------------------------
head(AbData)
names(AbData)[2] <- "ab... |
bdcd73a5219db5cdf1066063bdb08a66dc0afe70 | 0ca71d16a83d6861cab501c19a62a664fdbca559 | /ui.R | fdc4cc433502d98655fd80ed3003fb1fc367b043 | [] | no_license | checkmyschool/data-portal_shiny | 6c27c5a1a898aac0d26fb3722a53238d67ab67d8 | 611dc59569671beb0d61dcd7b2552d6ea578765d | refs/heads/master | 2020-08-02T04:42:19.081241 | 2019-10-14T02:16:40 | 2019-10-14T02:16:40 | 211,238,061 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 34,781 | r | ui.R | shinyUI(navbarPage(h5("CheckMySchool Data Portal", style = "color: #ffffff;"), theme = "styles.css",
tabPanel(h5("Welcome", style = "color: #ffffff;"),
mainPanel(width = 11,
... |
3d824809116ada929e6be510d1e4b318644aaac0 | d892409b67c45508a018c4a3d46d490310ddd06e | /Codigoredes.R | 1d13c50fda2c184c450e94220716a032b4f83f07 | [] | no_license | camilavalenciarod/TrabajoFinal | e57c7a3616cdd5d7648e36fba51f4d6bf2605e6c | c759e543e7a09881181cfc3726f306dc97dc78bc | refs/heads/master | 2020-07-06T02:17:54.070106 | 2016-11-25T06:31:27 | 2016-11-25T06:31:27 | 74,062,893 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 14,889 | r | Codigoredes.R | install.packages('igraph')
install.packages('network')
install.packages('sna')
install.packages('ndtv')
install.packages('visNetwork')
library(igraph)
library(network)
library(sna)
library(ndtv)
library(visNetwork)
base_1 <- read.csv("C:/Users/camila.valencia/Desktop/Efectos Pares/red1.csv", sep=";")
ba... |
966622ae1e99faf232577e90507ce08560d76fba | d072433fb4facac496d0337b4cf22b5e00cf6853 | /man/flowers.Rd | 1da9fea70267d0ca79f5b4510677fc7a36344d24 | [] | no_license | cran/asuR | 3e3d745182d938acba825b723fc09b14cc16dbaa | 578f3e0693d29fb8fd3056d52fa8913d5039cc79 | refs/heads/master | 2020-12-24T15:05:44.493897 | 2007-06-01T00:00:00 | 2007-06-01T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,538 | rd | flowers.Rd | \name{flowers}
\alias{flowers}
\docType{data}
\title{Flower}
\description{
A data set with the dry mass of all flowers and the dry mass of the
total plant from 20 species growing at high and 20 species growing at low altitude.
}
\usage{data(flowers)}
\format{
A data frame with 40 observations on the following 3 vari... |
10875d8efde3b62455a44c76cc72b209c15c0897 | 52a4315886671fb197e31d4f3e1fd665767814a8 | /cachematrix.R | f4a394fa62d5a98d29c8da427572f6f1cc8e560f | [] | no_license | julthida/ProgrammingAssignment2-master | d646d41c7287bb8410d215f80366f29d80c18bf6 | 62b9a5c7ce1304216fc2f0bda633e6080f879d29 | refs/heads/master | 2020-12-20T15:20:29.750768 | 2020-01-28T06:30:13 | 2020-01-28T06:30:13 | 236,121,145 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,024 | r | cachematrix.R | ## Caching the Inverse of a Matrix
## Below are two functions that are used to create a
## special matrix and caches its inverse
## creates the list for cachesolve
makeCacheMatrix <-function(x=Matrix()){
inverse<<-NULL
setmatrix<-function(y){
x<<-y
inverse<<-NULL
}
getmatrix<-function() x
s... |
498a273d144b017676e5bee06b6a11f127904072 | 002929791137054e4f3557cd1411a65ef7cad74b | /R/isEmpty.R | f31db6ee212f3c213d039bd559bace06d8e3503f | [
"MIT"
] | permissive | jhagberg/nprcgenekeepr | 42b453e3d7b25607b5f39fe70cd2f47bda1e4b82 | 41a57f65f7084eccd8f73be75da431f094688c7b | refs/heads/master | 2023-03-04T07:57:40.896714 | 2023-02-27T09:43:07 | 2023-02-27T09:43:07 | 301,739,629 | 0 | 0 | NOASSERTION | 2023-02-27T09:43:08 | 2020-10-06T13:40:28 | null | UTF-8 | R | false | false | 299 | r | isEmpty.R | #' Is vector empty or all NA values.
#'
## Copyright(c) 2017-2020 R. Mark Sharp
## This file is part of nprcgenekeepr
#'
#' @return \code{TRUE} if x is a zero-length vector else \code{FALSE}.
#'
#' @param x vector of any type.
isEmpty <- function(x) {
x <- x[!is.na(x)]
return(length(x) == 0)
}
|
1cca483f6b110481df00925c30a4a5a50a910e3e | d2eda24acceb35dc11263d2fa47421c812c8f9f6 | /R testing/Smoothing.R | 588ae17fb56aeff1d8ba01c3591949de3d40bfe2 | [] | no_license | tbrycekelly/TheSource | 3ddfb6d5df7eef119a6333a6a02dcddad6fb51f0 | 461d97f6a259b18a29b62d9f7bce99eed5c175b5 | refs/heads/master | 2023-08-24T05:05:11.773442 | 2023-08-12T20:23:51 | 2023-08-12T20:23:51 | 209,631,718 | 5 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,008 | r | Smoothing.R |
grid = array(0, dim = c(15,16))
## Add Corners
grid[1,1] = 114
grid[1,16] = 121
grid[15,1] = 116
grid[15,16] = 121
delta.j1 = (grid[15,1] - grid[1,1]) / 14
delta.j6 = (grid[15,16] - grid[1,16]) / 14
## Fill in first and last column
for (i in c(1:15)) {
grid[i,1] = grid[1,1] + delta.j1 * (i-1)
grid[i,16] = gri... |
82fb82dd36f91bf1f2bbd87dfbfe48bd84060fb3 | 102c6eb2165121a04e4e57e7cf651d6d8d41c0e8 | /Final Project/bank_branches/app.R | 0f0684201d541359fa58550b5b63e761d702cebd | [] | no_license | Jagdish16/CUNY_DATA_608 | df2ec2b4cad594e6b7e0a88a345fb3a073fdea5b | 6ee3d6b496f4d1ef85fd1e906cefebfd345cc361 | refs/heads/master | 2023-01-24T14:35:02.841799 | 2020-12-13T03:12:17 | 2020-12-13T03:12:17 | 293,076,162 | 0 | 0 | null | 2020-09-05T12:57:47 | 2020-09-05T12:57:46 | null | UTF-8 | R | false | false | 3,379 | r | app.R | #
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
# Find out more about building applications with Shiny here:
# http://shiny.rstudio.com/
#
# Name of application = bank_branches
library(shiny)
library(dplyr)
library(ggplot2)
library(rsconnect)
library(plotly... |
a4808015bc8bf19c9e7613b9e68fdf4a0b890f7a | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.end.user.computing/man/appstream_delete_stack.Rd | ae0637391f0d738a829f56d40fb34ba649b46edd | [
"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 | 665 | rd | appstream_delete_stack.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/appstream_operations.R
\name{appstream_delete_stack}
\alias{appstream_delete_stack}
\title{Deletes the specified stack}
\usage{
appstream_delete_stack(Name)
}
\arguments{
\item{Name}{[required] The name of the stack.}
}
\description{
Deletes ... |
74a34e80916da916e08f5cff55ba54e35b023617 | facd83d2c6378682421bb7902191f1f49fa1836f | /R/cbk.periodic.R | 6ecc288344f6a9769068682aec935989fddc6705 | [] | no_license | misasa/chelyabinsk | cbb9e3acdaaefb3254d01d38c42e404164dfa2d1 | 495e8bab926934467a3a7fd7c74bb1be69d2f094 | refs/heads/master | 2021-06-28T09:11:19.766488 | 2020-11-20T00:26:38 | 2020-11-20T00:26:38 | 69,550,402 | 0 | 2 | null | 2019-06-21T10:49:18 | 2016-09-29T09:04:01 | R | UTF-8 | R | false | false | 1,431 | r | cbk.periodic.R | #' @title Return properties of elements from a periotic-table
#'
#' @description Return properties of elements from a periotic-table.
#' Specify property of your concern otherwise this return dataframe
#' of periodic table.
#'
#' @param property A name of PROPERTY that is one of 'atomicnumber',
#' 'volatility', ... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.