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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0e99b3383d46e9047400b74985c4db6e967fc27f | a90254b94137497077573b358e47dcfa35ceaa05 | /man/rlr_type.Rd | 90830c25a973e1c1dc0ca7482f4a29537d6ba2c9 | [] | no_license | cran/regressoR | 07277cd527fcda07dbd3d06983b4a867a1409e4f | 2e687cea7d2019b61ae2f513ca350cab596a7d3f | refs/heads/master | 2023-07-05T22:32:40.063059 | 2023-06-29T15:40:02 | 2023-06-29T15:40:02 | 209,471,676 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 465 | rd | rlr_type.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mod_penalized_Regression_utils.R
\name{rlr_type}
\alias{rlr_type}
\title{rlr_type}
\usage{
rlr_type(alpha_rlr = 0)
}
\arguments{
\item{alpha_rlr}{the penalty is defined as alpha=1 is the lasso penalty, and alpha=0 the ridge penalty.}
}
\descr... |
ad52e513b3b4cdfccfbdc75c0d879fbe1f851abc | d0c3bfcf62364de23de01b55ac97792bfe9116cd | /USG_Collaboration_Plots_160705.r | caa0154d8462c1166d8ed1992d60d42b20c9d983 | [] | no_license | rungec/USG-Infrastructure-sharing | d3c4d6167d52af4b8f903e42139941aff4022a6f | 6b344151c29fe464ac85b586978df7f2cb6d8d23 | refs/heads/master | 2021-01-12T11:52:10.422522 | 2017-05-30T23:48:44 | 2017-05-30T23:48:44 | 69,593,658 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 32,957 | r | USG_Collaboration_Plots_160705.r | #Redo of figures following reviewer comments #Round 2
#NOTE I looked at changing how homogenous impacts are calculated but didn't end up using this in the analysis
scen1Dir <- "Y:/Data/GPEM_Postdoc/1_USG_Collaboration/Analysis/tables/final costs/1_high_finalcosts_161221.csv"
minegroupingsDir <- "Y:/Data/GPEM_Postd... |
986cc6925d21eb853d589678d7c2928bf3b66df7 | 1f547ef48e642eff3847aa3fc6c137a90e5540e1 | /KNN/Zoo_sol.R | 9f4ed2168cafec80aaecba78789c80e24d74dd08 | [] | no_license | prateek-gitcode/Data-Science-practice-problems | 2f5a8af5f15f72b2d1625e883a779f9ea4c902d4 | 7d7709eb383e7e93a27ca311dc051d736dd531da | refs/heads/master | 2023-04-19T04:49:22.852158 | 2021-05-10T10:08:14 | 2021-05-10T10:08:14 | 365,997,624 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 807 | r | Zoo_sol.R | #Importing the Dataset
library(readr)
zoo_data <- read.csv(file.choose())
zoo_data <- zoo_data[-1]
#EDA
summary(zoo_data)
#Splitting the data into training set and test set
library(caTools)
split <- sample.split(zoo_data$type,SplitRatio = 0.75)
training_set <- subset(zoo_data,split==TRUE)
test_set <- subset(zoo_data,... |
af078ada0e38fe5a76e54a80ce32b068091fd53e | a43a5f942ebf81cbc2bda2b8b5413efdedb03ed8 | /tests/testthat/test-espn_ratings_fpi.R | f48f6b53d5e17c7ab45fcda4fa306dfb718f5806 | [
"MIT"
] | permissive | Engy-22/cfbfastR | 7d6775943c8124c532c36728dec5cc7aee9ad4f5 | 92ebfdd0fb4a70bcb9f3cc2d11f3a61d863d9743 | refs/heads/master | 2023-08-30T11:08:09.332930 | 2021-10-26T17:34:33 | 2021-10-26T17:34:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 537 | r | test-espn_ratings_fpi.R | # context("ESPN FPI Ratings")
#
# cols <- c(
# "year", "team_id", "name", "abbr",
# "fpi", "fpi_rk", "trend", "proj_w", "proj_l", "win_out",
# "win_6", "win_div", "playoff", "nc_game", "nc_win",
# "win_conf", "w", "l", "t"
# )
#
# test_that("ESPN FPI Ratings", {
# skip_on_cran()
# x <- espn_rat... |
8b7da2e72d838a9221d0e20bd5ca7027a9329991 | a53ea6b185a697cd57e54b5e8285ae2aa3fd5cb1 | /R_scripts_to_makePLOTS/CoveragePLOTs.R | cb90d45ab7f80ff28e7d5c1454965d555e259258 | [] | no_license | kovimallik/Cas9Enrichment | 6f21003654542cd99f1cacde1f808aa403689c71 | cb2be67b165d0d98c1f8f84979c21ccb489dfe47 | refs/heads/master | 2022-04-17T06:28:01.137279 | 2020-02-10T19:03:27 | 2020-02-10T19:03:27 | 414,004,831 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,452 | r | CoveragePLOTs.R | #!/usr/env Rscript
# this script takes in an input bam alignment file
# and a bed file containing a region of interest
# output is a saved pdf file with coverage at that locus
#install.packages("tidyverse")
#install.packages("cowplot")
library(GenomicRanges)
library(GenomicAlignments)
library(tidyverse)
library(... |
cd10dcd1f35622131f475b039f31017209409ee4 | a47ce30f5112b01d5ab3e790a1b51c910f3cf1c3 | /A_github/sources/authors/2353/TBSSurvival/dist.error.r | 17e9d5f1957e357edd07cfddf29842a1137e790d | [] | no_license | Irbis3/crantasticScrapper | 6b6d7596344115343cfd934d3902b85fbfdd7295 | 7ec91721565ae7c9e2d0e098598ed86e29375567 | refs/heads/master | 2020-03-09T04:03:51.955742 | 2018-04-16T09:41:39 | 2018-04-16T09:41:39 | 128,578,890 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,681 | r | dist.error.r | # TBSSurvival package for R (http://www.R-project.org)
# Copyright (C) 2012-2013 Adriano Polpo, Cassio de Campos, Debajyoti Sinha
# Jianchang Lin and Stuart Lipsitz.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License a... |
fab04f40aa698b96a18f9711232d6447e3c64830 | 87760ba06690cf90166a879a88a09cd2e64f3417 | /tests/testthat/test-algo-prophet_reg.R | 1354ae256ad71c28e002e0698637ded082798b4f | [
"MIT"
] | permissive | topepo/modeltime | 1189e5fe6c86ee3a70aec0f100387a495f8add5f | bff0b3784d1d8596aa80943b221eb621481534e1 | refs/heads/master | 2022-12-27T07:11:58.979836 | 2020-10-08T16:07:27 | 2020-10-08T16:07:27 | 289,933,114 | 1 | 0 | NOASSERTION | 2020-08-24T13:17:10 | 2020-08-24T13:17:10 | null | UTF-8 | R | false | false | 9,777 | r | test-algo-prophet_reg.R | # ---- STANDARD ARIMA ----
context("TEST prophet_reg: prophet")
# SETUP ----
# Data
m750 <- m4_monthly %>% filter(id == "M750")
# Split Data 80/20
splits <- initial_time_split(m750, prop = 0.8)
# Model Spec
model_spec <- prophet_reg(
growth = 'linear',
changepoint_num = 10,
c... |
04118df50e5aa3a626c17d41ceab8953554c8372 | 0a906cf8b1b7da2aea87de958e3662870df49727 | /grattan/inst/testfiles/IncomeTax/libFuzzer_IncomeTax/IncomeTax_valgrind_files/1610383197-test.R | c873a4d4b93833354a8117aa7cc8afd0c1d8026f | [] | 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 | 482 | r | 1610383197-test.R | testlist <- list(rates = numeric(0), thresholds = NaN, x = c(NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.78105030004262e-309, 1.28683596660321e-167, 0, 0, 0, 0, 0, 0, 0, 3.92660099062145e-310, NaN, 1.25986739689518e-321, 0, 0, 0, 0, 0, 0, 0, NaN, 1.06150300700353e-313, NaN, NaN, NaN... |
8d24764392d5b18ede3fac63d8bcf7bbed927650 | 25f62747b4306d1436ebb394ac443729aae24181 | /man/updateData.Rd | 43ce32a5a7ad8e3fa224ccdb4b840ba0e37d4309 | [] | no_license | takewiki/DTedit | 79b987a135c82533d5207d234b148e5be08195e3 | 4551a951347cd131799974053d8f1c6c42d49cf5 | refs/heads/master | 2021-07-07T20:48:31.151739 | 2020-10-02T06:57:09 | 2020-10-02T06:57:09 | 193,644,846 | 1 | 0 | null | 2019-06-25T06:06:22 | 2019-06-25T06:06:22 | null | UTF-8 | R | false | true | 360 | rd | updateData.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/util.R
\name{updateData}
\alias{updateData}
\title{更新数据}
\usage{
updateData(proxy, data, ...)
}
\arguments{
\item{proxy}{代理对象，支持更新}
\item{data}{数据}
\item{...}{其他选英}
}
\value{
返回值
}
\description{
更新数据
}
\examples{
updateData()
}
|
a31bd92142b942caa6a57ca688d22ccb1825b9b6 | 0f24c8bfc4257f25e6397d627d95103b8fba028c | /lib/gsea_helpers.R | 71a18aa5266c00ed80b33972c43b314af111c1b9 | [] | no_license | BrownellLab/UISO_code | 10c9c068d99dd1e982810ba19fb21d42d9b30d73 | f81c34ea81c585e628cca1e23ad08cf9e0ea8454 | refs/heads/master | 2021-01-16T00:03:57.743454 | 2014-12-10T20:15:02 | 2014-12-10T20:15:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,061 | r | gsea_helpers.R | # From https://gist.github.com/kdaily/7806586
# Convert an ExpressionSet into a GCT file for GSEA or IGV.
eset2gct <- function(eset, filename) {
numsamples <- ncol(exprs(eset))
numprobes <- nrow(exprs(eset))
row1.dummy <- rep("", numsamples + 1)
row2.dummy <- rep("", numsamples)
cat(c(paste(c("#1.2", row1... |
b38d9931008d39a4258cfed6df1adb2fb14e1526 | fddeb9bdb530fbaa7ad37b23f667c31bf2ba5f1d | /man/protcomp.Rd | 404ed06e96d11e80ee8f1525bcabf89982a35055 | [] | no_license | cran/canprot | 405873c9be0becd7ac8edbbc29207eae31a3bd13 | bf69c4c70a56f5d36bc17d7034b9a482b2841eba | refs/heads/master | 2022-02-05T21:19:46.105674 | 2022-01-17T07:22:51 | 2022-01-17T07:22:51 | 94,257,433 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,234 | rd | protcomp.Rd | \encoding{UTF-8}
\name{protcomp}
\alias{protcomp}
\title{Amino Acid Compositions}
\description{
Get amino acid compositions of proteins.
}
\usage{
protcomp(uniprot = NULL, aa = NULL, aa_file = NULL)
}
\arguments{
\item{uniprot}{character, UniProt IDs of proteins}
\item{aa}{data frame, amino acid compositions}... |
44a22a5a978daac46aab9992d4694d942f452edd | bac3ad65c587f5d96816789e68eefcc2c53a761d | /man/measure_access.Rd | 55072739511cfb2e0e75bfbb1413e9acf45cd14b | [] | no_license | franc703/minnccaccess | a4eef2cb96f60abc39cfa9751c317b47b83238f5 | 19d4edf95c6c2f5e042540ae90f320a0998e09c4 | refs/heads/main | 2023-05-31T05:27:35.413098 | 2021-07-10T10:13:12 | 2021-07-10T10:13:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 903 | rd | measure_access.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/measure_access.R
\name{measure_access}
\alias{measure_access}
\title{Measure child care access}
\usage{
measure_access(
method = "hexagon",
geography = "census-block-group",
geo_year = 2010,
acs_year = 2019,
hex_side = 1,
radius =... |
d44f8be73f6241819a88e9447f44b122da7ee15b | c18980f3afc6b564344de9a52b9cf0d3972fa618 | /R/ggplot2_small_map_data.R | 4b07fd20e75ca24be838d240fe54e2f444d7d6a0 | [] | no_license | jhnwllr/gbifapi | 949b42e28749e034b67dc393694f88c12e9a05d6 | 010347732fcde62cf645078bdfbbecfaeb13fa5b | refs/heads/master | 2022-04-26T23:23:19.966690 | 2022-04-26T12:01:32 | 2022-04-26T12:01:32 | 153,618,081 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 541 | r | ggplot2_small_map_data.R |
# reduces the size of ggplot2::map_data for plotting with svg
ggplot2_small_map_data = function(dTolerance=1.1) {
countries = ggplot2::map_data("world") %>%
sf::st_as_sf(coords = c("long", "lat")) %>%
group_by(group) %>%
summarize(do_union=FALSE) %>%
sf::st_cast("POLYGON") %>%
ungroup() %>%
mutate(geomet... |
da191fe1709588ab80770201520fad08dbfc4379 | c19dfad6d86f3dd6b8d5bacbdd0ab7c055962fa5 | /tests/testthat/test-lmertest.R | aad1620cccf272b73be828519cd41e270d082110 | [] | no_license | bbolker/broom.mixed | e0ff763ade4df0ebecd6fd7332866cfe0e20e46f | 3ba939b400fbec61e0aa1860a57a09c046df2d20 | refs/heads/main | 2023-08-17T16:42:40.148968 | 2023-08-04T15:21:49 | 2023-08-04T15:21:49 | 113,072,861 | 221 | 33 | null | 2023-05-19T14:16:36 | 2017-12-04T17:24:08 | R | UTF-8 | R | false | false | 1,662 | r | test-lmertest.R | stopifnot(require("testthat"), require("broom.mixed"))
## test lmerTest
if (require(lmerTest, quietly = TRUE)) {
test_that("testing lmerTest p-values", {
lmm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
td <- tidy(lmm1, "fixed")
expect_equal(td$df, c(17, 17), tolerance=1e-3)
check_tidy(td... |
9af4876f623653b4ace170434277edca824e1a4b | 8229099d8000ee0e905971f4c5464d78bc8e2634 | /man/all_na.Rd | 2aed97c87ed5607443566b828118fa17d041819f | [] | no_license | alecplotkin/alpacage | 1ea46f7e83e42cbc40dc0d286ef38c10a537d64b | 84a1b16f70bea14e771c284e4c88a705d202ac6a | refs/heads/main | 2023-01-23T15:52:51.453014 | 2020-11-22T05:21:04 | 2020-11-22T05:21:04 | 314,960,928 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 519 | rd | all_na.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/all_na.R
\name{all_na}
\alias{all_na}
\title{A function to return TRUE for columns of a data frame
which are all NA.}
\usage{
all_na(df)
}
\arguments{
\item{df}{A data frame.}
}
\value{
A logical vector of length ncol(df), that is TRUE only ... |
315cea782518f801741997e89b60d64b15857b32 | a83dafea71d80e3c8a2f06e93007b03de7355254 | /functions.R | 61d15887c9f70f1eb11f3a1beabebb8a26049537 | [] | no_license | gtsitsiridis/lung_aging_atlas | 3717387f7eb7bb7ea16298cc5fa8e9b5d04b24d6 | d6b7723fe723abe46ce358cf1f95adbba10f9162 | refs/heads/master | 2020-03-20T02:24:48.727852 | 2018-06-20T10:30:05 | 2018-06-20T10:30:05 | 137,112,175 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,969 | r | functions.R | getMarkersTable <- function(cell_type = "Alveolar_macrophage") {
dt <-
markers_table[cluster == cell_type,-c(which(colnames(markers_table) == "cluster")), with =
F]
dt <- cbind(gene = dt$gene, dt[, 1:5])
dt
}
# Volcano plot
plot_volcano <- function(de_table=copy(gene_de_table),... |
d8727b1d683133dae8a0788bf0bb3117cbf52e3a | ef01bab1215f822fe415021c73c2b915fdd787ba | /02_survival_models/helper_functions.R | 8c57933d0133145856cca018c94557fe5da18b7c | [] | no_license | nvkov/MA_Code | b076512473cf463e617ed7b24d6553a7ee733155 | 8c996d3fdbbdd1b1b84a46f84584e3b749f89ec3 | refs/heads/master | 2021-01-17T02:48:40.082306 | 2016-09-25T18:53:53 | 2016-09-25T18:53:53 | 58,817,375 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 790 | r | helper_functions.R | #helper functions
##' @S3method predictSurvProb rpart
predictSurvProb.rpart <- function(object,newdata,times,train.data,...){
-# require(rpart)
- ## require(rms)
learndat <- train.data
nclass <- length(unique(object$where))
learndat$rpartFactor <- factor(predict(object,newdata=train.data,...))
... |
ec242a3a578eb46bd337c06fe04f76eef137f79c | b4182374ee423938631aef113d503ef65bc45080 | /cachematrix.R | fc6ac524e354cf78dfcffa1a6318aaa9791ee532 | [] | no_license | DavideDelVecchio/ProgrammingAssignment2 | e97db5df0a83b0d3a23eee5c71c15d0017e01666 | be70d99f65081adf88822c2dd07d3503381339b7 | refs/heads/master | 2020-12-14T09:43:21.452810 | 2014-12-20T11:07:25 | 2014-12-20T11:07:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,125 | r | cachematrix.R | ## The makeCacheMatrix constructs a suitable object that can store the value
## of both the direct and cached inverse.It should be used in combination with cacheSolve
## that, when called, before perforimg the computation of the inverse checks for the cached variable
## if not found computes the inverse and caches it ... |
f605ce829cd9918e4bae67d8f650f29477d39b0d | 712561e4275220a6fe93c6b0e204df41ba169a85 | /plot3.R | c2bdfb209f0395c0377c3652c82725a7a657d5a3 | [] | no_license | urvog/ExData_Plotting1 | 3b8ae73e8783203ab45942e202e6df90d27dec31 | 422ea4617ba3f65c56c2612948d56bd18e12eb69 | refs/heads/master | 2021-01-12T13:59:21.384589 | 2016-03-27T01:08:09 | 2016-03-27T01:08:09 | 54,789,335 | 0 | 0 | null | 2016-03-26T16:55:22 | 2016-03-26T16:55:22 | null | UTF-8 | R | false | false | 990 | r | plot3.R | ## Plot 3
## The file household_power_consumption.txt must be in the same directory
data_raw<-read.table("household_power_consumption.txt", header = TRUE, sep = ";", na.strings = "?")
##Changing column type to date with as.Date() function and subsetting with date interval needed
data_raw$Date<-as.Date(data_raw$Date, f... |
5decf24fb76d373921eb19fb61b4832aa5452b79 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/tadaatoolbox/examples/tadaa_pairwise_tukey.Rd.R | e8b7c52c58dc06f86b1ec4e709ce39080b4062b9 | [] | 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 | 283 | r | tadaa_pairwise_tukey.Rd.R | library(tadaatoolbox)
### Name: tadaa_pairwise_tukey
### Title: Tukey HSD pairwise comparisons
### Aliases: tadaa_pairwise_tukey
### ** Examples
tadaa_pairwise_tukey(data = ngo, deutsch, jahrgang, geschl)
tadaa_pairwise_tukey(data = ngo, deutsch, jahrgang, print = "console")
|
d945d4be769f7af0e975b11ab4bed306728bc6ef | 2404b054351c57922bd54fa6230ee9046f1be961 | /Factor Analysis/bibliometrix/bibliometrix_cocit_matrix.R | bb43790a2f51ae270171972c1c96bee53c0c7275 | [] | no_license | andrelmfsantos/R_Scripts | 2840aca19a8038d2a5f4896d1e1a0880c21a8085 | cf5d95c1dcd2a0ee7954320133e5ab8eab90cc08 | refs/heads/master | 2021-03-22T08:00:08.483741 | 2020-03-07T13:20:12 | 2020-03-07T13:20:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,507 | r | bibliometrix_cocit_matrix.R | library(bibliometrix)
# se for usar direto da Scopus
D <- readFiles("~/Google Drive/Academic/Orientações/Jefferson da Costa/RSL/BibTeX_Scopus/158_stakeholder.bib")
M <- convert2df(D, format = "bibtex", dbsource = "scopus")
write.csv2(M, "Bruto_Bibliometrix.csv")
# import the clean dataset to R
M <- read.csv2("Bruto... |
19b7b64e4b49122b6894938c2d805b90656c69db | d6716ad11768a252ca4d9b88172937f24b904329 | /R/jumpoints.R | 8ac9893d4f423ee2b63eedf958c841e8e95f0f63 | [] | no_license | cran/cumSeg | 5651f714af39aa5cb090a92e42c35ab368476d2a | 67cf77a9f02f991f197a2e6084d72aa678af7e70 | refs/heads/master | 2023-04-08T08:40:01.063844 | 2020-07-17T08:10:02 | 2020-07-17T08:10:02 | 17,695,344 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 13,580 | r | jumpoints.R | jumpoints <-function(y, x, k=min(30,round(length(y)/10)), output="2",
psi=NULL, round=TRUE, control = fit.control(), selection=sel.control(), ...) {
#jump-point models
#y: the response; x the explanatory (if missing index integers are assumed)
#psi: the starting values for the breakpoints. If NULL k quantiles ... |
6c1673b514b7234d1be90a6c2eeb83d23dbd7d73 | bc13a38c71f9d95bf5bc1bbaadbcd4b0d4d412c6 | /00_packageLoad.R | c6bd3c76b8176fb10047d6f607b7f67ecddf2bea | [] | no_license | nturaga/RNAseq_workflow | 6660296ed0557944f21b54ea59ce1bc9e6903b0c | 0f0a4b2f35d44b1ad81a8d9601937d0165f62441 | refs/heads/master | 2021-03-24T13:52:25.089808 | 2014-03-21T17:06:49 | 2014-03-21T17:06:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 439 | r | 00_packageLoad.R | # Nitesh Turaga
# TCGA- Expression-Gene UNC Agilent analysis
# Set path
my.path = "~/TestRun/TCGA-Expression-Gene/"
setwd(my.path)
# Install packages required
packageList = c("EDASeq","edgeR","DESeq","BitSeq","Rsubread","easyRNASeq","goseq","DSS")
library(BiocInstaller)
biocLite(packageList)
# Load packages
require... |
7f9aa56de6bd7d17188e83193ca01a2338737871 | c878d39a4dd0a0d30015f7b520336fd8949ac13d | /tests/testutils.R | 0967838d747b253e42790b5d29a249c3692710f5 | [] | no_license | patdab90/etric | 45df5ac863ecdb8222cd7041fd25500231ea2f66 | 5a32a008c707f7a0596c30b254f3c69b3c0ca2ea | refs/heads/master | 2021-01-10T20:43:03.921425 | 2015-02-09T10:06:52 | 2015-02-09T10:06:52 | 22,640,557 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 825 | r | testutils.R | library(stringr) # for tests cases
constraintsToString <- function(lhs, dir, rhs){
res <- matrix("", nrow=nrow(lhs), ncol=1, dimnames=list(rownames(lhs)))
for(j in 1:nrow(lhs)){
for(i in 1:ncol(lhs)){
if(lhs[j,i] != 0){
if(lhs[j,i] > 0){
sign <- "+"
if(res[j,] == "") {
... |
fcbc0e0ee39bbdaf57471e4cba3949501a9a1893 | 41bd4616c0ed105a58e82ac69def9c74767948f9 | /configure_Newbler.R | b6a345d3ca6ee8412befec0399d13164d80bac1d | [
"Artistic-2.0"
] | permissive | liangdp1984/GRC_Scripts | 0385409d00283a1d0c47f0c0c3b9e340b4ce0aa1 | b5619db7475c7d94dbd871cc556bac656bb086dd | refs/heads/master | 2023-07-04T15:12:01.374105 | 2015-08-31T16:57:10 | 2015-08-31T16:57:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,996 | r | configure_Newbler.R | #!/usr/bin/env Rscript
suppressPackageStartupMessages(library("optparse"))
# specify our desired options in a list
# by default OptionParser will add an help option equivalent to
# make_option(c("-h", "--help"), action="store_true", default=FALSE,
# help="Show this help message and exit")
option_list <- list(
make_... |
1414a71d4dec3ca5de3a21287fabeceaf8549e93 | f7546999748d00b74db8551ed65e02cc564f7f4f | /man/getSegPurity.Rd | a1e4154779a956ad3a463dca7f5fd75aea550fe6 | [] | no_license | cran/CHAT | b5887ac9eb97d1deace91cd638477e5d7bf56319 | 1819354a80335e6d92384002b90899b70c60719f | refs/heads/master | 2021-01-19T08:15:18.756721 | 2014-02-10T00:00:00 | 2014-02-10T00:00:00 | 19,303,604 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,357 | rd | getSegPurity.Rd | \name{getSegPurity}
\alias{getSegPurity}
\title{
Segment-specific AGP inference by sample
}
\description{
This function implements the sAGP inference algorithm, by placing each of the data point onto a BAF-LRR plot. AGP inference must be done in prior.
}
\usage{
getSegPurity(seg.dat, oo, AGP = 1, type = 1, para, rm.thr... |
ab8170e58b95bc7530b74a2f76304e231c8473cb | d1fafdc9f199bac28aa5265ba5927d7b77277e11 | /data_analysis/exotic grass simulation.R | f53ea2593d756bf2d0b7ebaa83f81874fcac9527 | [] | no_license | laurenmh/sToration-vernal-pools | d979b33becb1c86ecbde1cdc4c020bc09a347c98 | 344606cc6c0e90fd0d21514e16cac287197b00f4 | refs/heads/master | 2022-12-04T16:57:51.637103 | 2022-11-27T22:24:01 | 2022-11-27T22:24:01 | 213,767,447 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 26,327 | r | exotic grass simulation.R | #####################################################
#Would adaptive management improve LACO populations?#
#####################################################
#Goal: Simulate exotic grasses (EG) removal to promote LACO persistence
#Step 1. Simulate EG removal
#Step 2. Average the growth rates of LACO over time for ... |
5e21d5de7b31993985f06060ce57c35b262e28f0 | a06ad0b5797e82bde9ae94c15216aaddc654f214 | /R/calculate_n_and_p.R | 39f029594bbdc8e70fa1e174e93c9fd4274fc948 | [
"Artistic-2.0"
] | permissive | khemlalnirmalkar/HMP16SData | 5fb436a4aa6638ed1ec07e0f267f7a68bea9e8d9 | 11795265b43cb5eea1c1de57fd6a9936fe54577a | refs/heads/master | 2023-09-02T18:12:38.092116 | 2021-10-26T16:29:57 | 2021-10-26T16:29:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 171 | r | calculate_n_and_p.R | #' @keywords internal
#'
#' @importFrom dplyr full_join
calculate_n_and_p <- function(x) {
y <- calculate_n(x)
z <- calculate_p(x)
full_join(y, z, by = "x")
}
|
355b9aed506732a8e3b2b35153fcdc8fa08ee80e | 0b6983d442055421658983158179a563b745256b | /DataWranglingEx1.R | e29022eaf85ee94c0d5c6ba6f881f10be41805a5 | [] | no_license | roblwallace/DataWranglingEx1 | 702240ed017de10bb08b4f49335b774a44ac0dc7 | b1624da1ca9afcbc39466d7a3340035a60cdb510 | refs/heads/master | 2020-09-12T07:05:20.031017 | 2016-09-07T23:08:25 | 2016-09-07T23:08:25 | 67,648,061 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,380 | r | DataWranglingEx1.R | # Data Wrangling Excercise 1: Basic Data Maniplulation
# Rob Wallace, roblwallace@gmail.com
# 5-SEPT-2016
#
# [X] 0. Load the data in RStudio
# [X] 1. Clean Up Brand Names
# [X] 2. Separate product code and number
# [X] 3. Add product categories
# [X] 4. Add full address for geocoding
# [X] 5. Create dummy variables fo... |
63be56ca98b669559815f728ce591e3b479d4eb4 | f2d6a9cae53ab792d7cc5aac1967e40b2cd623c2 | /R/term.R | 66d99898b7aac8125b506a0676c6997bb6323a1f | [] | no_license | stevencarlislewalker/setup | 071c04dc37917e472d5791388819c7e131ca3bdd | 9d00d77586f7e5f477ad3186fa94f3057f205c59 | refs/heads/master | 2021-01-21T11:08:28.265796 | 2015-02-17T13:41:37 | 2015-02-17T13:41:37 | 28,967,449 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 321 | r | term.R | ##' Open bash terminal in current directory
##'
##' @export
term <- function() system("bash")
##' Relative directory name
##'
##' @param dir directory, character string
##' @export
reldir <- function(dir) {
if(missing(dir)) dir <- getwd()
dirSplit <- strsplit(getwd(), "/")[[1]]
dirSplit[length(dirSplit)]
}... |
b6a6ba121ae935d15a8bc17e47497d4e0529cfce | b4640a579976061201bf896505d65f4bab5569b2 | /tokeniser_demo.R | 8f3fab89b70ca7fe261aa04c5b5825670e033145 | [] | no_license | strategist922/IDA-MOOC-Data-Exploratory-Cleaning | 871f7afbdb88a148eda7c2f745adc54fb72a4725 | ffd944795cc535f5fb3960ddf4c44d9392e8e07c | refs/heads/master | 2021-05-30T18:49:04.796051 | 2015-12-20T11:59:40 | 2015-12-20T11:59:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 870 | r | tokeniser_demo.R | library(tm)
sample = c('There is “something” going on right now, like right… now. He’s there.',
'There is "something" going on right now, like right... now. He\'s there.')
corpus = VCorpus(VectorSource(sample), readerControl = list(reader = readPlain))
# NLP's Bigram Tokeniser
NLPBigramTokenizer <- funct... |
d09717bab1ca00292ef06ca3de5fcbea51adf21f | 726a92a53407406654d5498be6170ad86b02502d | /Scripts/Variograms.R | b10cc03187301c353654b1b2b215f36f7a4ccd5f | [] | no_license | dansmi-hub/SomersetLevels | 416e7a18b61f31174a318e388e230bbaa248b636 | 6df06d0ec4f90d0b67031c5ffb448d41765633e5 | refs/heads/master | 2023-03-05T18:31:26.897957 | 2021-02-11T12:38:14 | 2021-02-11T12:38:14 | 269,067,962 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,019 | r | Variograms.R | # Semivariogram
library(dplyr)
library(geoR)
library(gstat)
library(moments)
library(raster)
library(cowplot)
data <- readRDS("Data/hmscdata.rds")
data <- as.data.frame(data)
data <- na.omit(data)
# Columns 7, 8, 9, 10 are the mosquitoes we care about
cols = 7:10
# Assign coords using sp
spdata <- data
coordinates... |
14e259a4ae194768a5d1c87345c5b545b72a88bd | cb1edbd312fe5583702e8567e1aa6e32e103d300 | /man/markChanges.Rd | 91673b859e2f9c282d74792b5c2d2980beaa111a | [] | no_license | cran/phytools | e8cb2ddac5592a9c27a0036df4599649a393717a | 910fa95b3f5f1619c85ac420bd07286a3fe8cfcf | refs/heads/master | 2023-07-22T15:18:46.363446 | 2023-07-14T20:00:02 | 2023-07-14T21:30:43 | 17,698,535 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,722 | rd | markChanges.Rd | \name{markChanges}
\alias{markChanges}
\title{Add marked changes to a plotted tree with mapped discrete character}
\usage{
markChanges(tree, colors=NULL, cex=1, lwd=2, plot=TRUE)
}
\arguments{
\item{tree}{an object of class \code{"simmap"}.}
\item{colors}{a named vector of colors used to plot the stochastical... |
f85c895c077c4814ffd0eaba871bd7024d764ed7 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/pbdNCDF4/examples/nc_open.Rd.R | 9bff0bb47aae76a56bff92f43974829ea4372cdc | [] | 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 | 1,269 | r | nc_open.Rd.R | library(pbdNCDF4)
### Name: nc_open
### Title: Open a netCDF File
### Aliases: nc_open
### Keywords: utilities
### ** Examples
## Not run:
##D # Define an integer dimension
##D dimState <- ncdim_def( "StateNo", "count", 1:50 )
##D
##D # Make an integer variable. Note that an integer variable can have
##D # a do... |
6799d42a1be12a10b5b1463f38c20ff244aad126 | d75b7bc015b47d94254bcc9334ba15972d3ec9a1 | /1. FIRST YEAR/Introduction to Computing/Exercices_Laura/exercici36b.R | e0fbb394720ea248079e8c248c0b0b0858f303b6 | [] | no_license | laurajuliamelis/BachelorDegree_Statistics | a0dcfec518ef70d4510936685672933c54dcee80 | 2294e3f417833a4f3cdc60141b549b50098d2cb1 | refs/heads/master | 2022-04-22T23:55:29.102206 | 2020-04-22T14:14:23 | 2020-04-22T14:14:23 | 257,890,534 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 134 | r | exercici36b.R | n <- scan(n=1, what=numeric(), quiet=TRUE)
i <- 2
while ( i < n ) {
if ( i %% 2 == 0){
cat(i, "\n")
}
i <- i + 1
}
|
60b4e4fe77e79cb424adf8ce4013079ca410958a | c0f710dd706e7fea94b09aee9f35caf1103cf4be | /LinearReression.R | d03002d09515e59162d44e97703160ca8721b9ee | [] | no_license | bhawneshdipu/Rshiny | d47d2921027d6ac70cba1411b54c82162a145a9b | ec7331e69c131962d77776547c3da0247724e1ba | refs/heads/master | 2020-03-07T04:41:04.865737 | 2018-04-07T19:07:46 | 2018-04-07T19:07:46 | 127,273,391 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,585 | r | LinearReression.R | ## Lib.R ##
library(DT)
library(readr)
library(plotly)
library(stringr)
library(DataLoader)
library(forecast)
library(tseries)
library(zoo)
library(xts)
library(fts)
library(MASS)
library(caret)
library(e1071)
library(dplyr)
library(h2o) # Awesome ML Library
library(timetk) # Toolkit for working with time se... |
f3e8afa0c78394ae9ec657129b9abb294c8587dd | 965b3a75a2597de3cc534c83b1b61847f2c4f71d | /VEST/script.R | e41cb5b1e21b92ffc741f2311a81cb5d7c6f84d4 | [] | no_license | muchuVishal/analysis | 71615a2a02c36b546ea40a3f2728731e57ad367e | 2b68c2ece2110584a0181920d67a8088016f8ad2 | refs/heads/master | 2021-01-19T03:57:09.147270 | 2016-06-28T08:01:26 | 2016-06-28T08:01:26 | 50,667,911 | 0 | 0 | null | 2016-06-28T08:01:27 | 2016-01-29T14:37:02 | R | UTF-8 | R | false | false | 2,751 | r | script.R | library("RMySQL")
library("reshape2")
source("CodonMap.R")
#***************** General-purpose functions ********************
#****************************************************************
# Connect to database
mydb <- dbConnect(dbDriver("MySQL"), user = "rousniakl", password = "rousn!@k1", dbname = "vest_snvbox", ... |
08a7b34b844e9e5973ee39cfd34d531c72442173 | 5c8787bcb1bc2a75295db85d57a87d4a3ac33ad1 | /man/show_handers.Rd | 9d7a82cf2fed7f16d219db3ce71cf3787bd714ac | [
"MIT"
] | permissive | zcm2403/ngstk | 3f00d9f8fdf87fd714d5375b73a1ea28de4d9770 | 07bdc274c6eef90726d4f0255d364271b984fe4e | refs/heads/master | 2021-08-19T07:27:34.839632 | 2017-11-24T15:39:26 | 2017-11-25T06:57:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,015 | rd | show_handers.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hander.R
\name{show_handers}
\alias{show_handers}
\title{Function to show all avaliabe handler function}
\usage{
show_handers(hander_lib = "default_handers", show_all_funs = TRUE,
show_code = NULL, show_description = FALSE,
hander_confg_f... |
67184175ad4916774361bfca027ecb6491dcc2eb | 8531cb0526ca547b2ecd1f0a86683c4d3328577b | /Code/Process_large_raster_f.R | 3fe1ad5d4b0f6ad04192e9e954948a17d5a33b28 | [] | no_license | shaohuizhang/Global-to-local-GLOBIOM | 8b1c5042d58a5dfc03e4515d3bafefa033977ec7 | 85687084068bdf05081cbb868f063de3d65289a0 | refs/heads/master | 2020-03-21T07:40:36.927153 | 2018-03-07T15:04:11 | 2018-03-07T15:04:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,260 | r | Process_large_raster_f.R | # Function to remove values from very large raster where simple raster operations give memory problems
# https://cran.r-project.org/web/packages/raster/vignettes/functions.pdf
# For progress bar:
# https://www.r-bloggers.com/all-in-on-r%e2%81%b4-progress-bars-on-first-post/?utm_source=feedburner&utm_medium=email&utm_ca... |
f624f07713dc8a7d90a6c02313d450bfbe8f3f84 | 676961fa8be3aee524385850133b2d442f6914e5 | /stateCluster.R | 14e020906b9a7a5dc0a53e1c6687b39abc68ed38 | [] | no_license | richshaw/stateCluster | 462d50363d5c16f785cbb03d9499a515897ce978 | 2232fd9296be2ad34cf64666361f2d22fc689789 | refs/heads/master | 2021-01-19T19:37:09.441917 | 2015-03-03T02:22:47 | 2015-03-03T02:22:47 | 31,573,733 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,322 | r | stateCluster.R |
# Load data
data <- read.csv(file="censusStateClean.csv", header=T, sep=",", row.names=1)
# Scale data because using different units
data2 <- data.frame(scale(data))
# data3 <- data[,-c(1)]
# data2 <- data.frame(scale(data3))
# Run PCA
pc <- princomp(data2)
loadings(pc)
pc <- prcomp(data2)
# Choose number of principa... |
d112190869fef770650a4e527e7a36369158e960 | 127ebda60a4dc1bb143083fe7a73e009c74472e2 | /R/sample_data.R | 96ee08fb403a261f4571d2f5d187fa8ebcf25502 | [] | no_license | telvis07/kaggle-melbourne-university-seizure-prediction | 1b29d703ec0820f1dc5e4f9bcaf38c81c294b3d9 | 53ef57415b9e8491cf04e0f771f2dacbbd234dd6 | refs/heads/master | 2021-03-27T14:46:33.146597 | 2017-02-07T14:09:54 | 2017-02-07T14:09:54 | 73,083,918 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,336 | r | sample_data.R | library(dplyr)
sample_data <- function(trainset, n_neg_samples=0, n_pos_samples=0) {
# make sure we don't subsample rows with all NULLs
trainset <- trainset[rowSums(is.na(trainset)) == 0,]
# separate pos and negative class
neg_samples <- trainset[trainset$target == "interictal",]
pos_samples <- trainset[t... |
8c6d157b55f9089adda714d5e0969f1769f6b908 | 81f518e29b4cac7cd61ea8e2c895d4f7edfd209b | /R/cor_smooth.R | 8cf00a6eb591172fadb4873c1c993bf9c9bb2162 | [] | no_license | cran/correlation | 5e2d691df07edb5aa69aba35f3780e19ffefcf57 | db4fd0ce345a0dcee08de2a9c1810f79b5575b63 | refs/heads/master | 2023-04-13T04:06:37.689083 | 2023-04-06T08:23:26 | 2023-04-06T08:23:26 | 247,916,232 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,732 | r | cor_smooth.R | #' Smooth a non-positive definite correlation matrix to make it positive definite
#'
#' Make correlations positive definite using `psych::cor.smooth`. If smoothing
#' is done, inferential statistics (*p*-values, confidence intervals, etc.) are
#' removed, as they are no longer valid.
#'
#' @param x A correlation ... |
c8ce517f6195bd8df11fb2e9b71a4ddc76163a2e | 6ce79966b1b89de1a6d6eb29cea945188c18652c | /R/algorithms__MHPropWithKStepNewton.R | 7f74fca38ff45d2f4e1728e702452e34b211f0b9 | [] | no_license | feng-li/movingknots | d3041a0998f0873459814a09e413c714fff700c6 | 5f921070e4cd160a831c5191255f88dd7d4c850c | refs/heads/master | 2021-06-10T00:18:57.172246 | 2021-03-22T05:56:44 | 2021-03-22T05:56:44 | 145,708,629 | 4 | 3 | null | null | null | null | UTF-8 | R | false | false | 6,204 | r | algorithms__MHPropWithKStepNewton.R | #' Metropolis–Hastings algorithm with K-step Newton method for the spline model.
#'
#' Details are available in the paper.
#' @param param.cur NA
#' @param gradhess.fun.name NA
#' @param logpost.fun.name NA
#' @param nNewtonStep NA
#' @param Params NA
#' @param hessMethod NA
#' @param Y NA
#' @param x0 NA
#' @param cal... |
f641072d2154f617cca3863534b0ed3d3f7ffa88 | ad6932226ea17dbf9d0c09ba3fd465b639f27bda | /vignettes/read-gse-matrix-file.r | cc9bd2b3d2fe22eb9120016713f1fb6b4c485293 | [] | no_license | perishky/meffonym | d00e0ccf417c1bababe2cb5cafb4bc91e1ee4f9a | ddf7f11e0831bdba486b5e49b80b508890affb39 | refs/heads/master | 2022-12-27T13:36:48.988965 | 2022-12-15T17:09:35 | 2022-12-15T17:09:35 | 200,882,117 | 5 | 0 | null | 2022-12-01T18:50:55 | 2019-08-06T15:52:04 | R | UTF-8 | R | false | false | 1,144 | r | read-gse-matrix-file.r | read.gse.matrix.file <- function(filename) {
dat <- readLines(filename)
nseries <- sum(grepl("^!Series_", dat))
nsamples <- sum(grepl("^!Sample_", dat))
ndata <- length(dat) - match("!series_matrix_table_begin", dat) - 2
con <- file(filename, "r")
header <- read.table(con, sep="\t", header=F, nr... |
cc4b8c44f306d75ceb02925e7128896b388e2ae2 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/lmtest/examples/resettest.Rd.R | 978e585ca4b9ca56b8289c0f0f4e4a5e1cf13e13 | [] | 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 | 287 | r | resettest.Rd.R | library(lmtest)
### Name: resettest
### Title: RESET Test
### Aliases: resettest reset
### Keywords: htest
### ** Examples
x <- c(1:30)
y1 <- 1 + x + x^2 + rnorm(30)
y2 <- 1 + x + rnorm(30)
resettest(y1 ~ x, power=2, type="regressor")
resettest(y2 ~ x, power=2, type="regressor")
|
5b66b81f19cf805b918f1a403690d73ce442c416 | e696c7eb91d2e2bcc299c01958dc29113d9c25d0 | /tests/testthat/test_h5create.R | f34ca346cf5389404fc4dfe29f99f9e24f7d40a3 | [] | no_license | grimbough/archive-rhdf5 | 06b8ad682f88352f56a357c2a01e316c51fbae28 | 8acc2f9744382ade564fb6bc7b5ded1a7b2aafc7 | refs/heads/master | 2021-06-25T12:54:21.208183 | 2017-08-24T14:44:45 | 2017-08-24T14:44:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,102 | r | test_h5create.R | library(rhdf5)
############################################################
context("h5createFile")
############################################################
## output file name
h5File <- tempfile(pattern = "ex_save", fileext = ".h5")
if(file.exists(h5File))
file.remove(h5File)
test_that("Default arguments", ... |
b6be02e0b2e5baebbe3ee4cd939c96859d330412 | d3344fe3ff4009239d415facd12e8cddef7aa089 | /seer_script.R | 5b8468b8ec073f063fb50d4f04b0e49f9788daa2 | [] | no_license | datasciencebirds/seer1 | 3710d4e274162c7624d8b93b9e01748e82db04ba | 25b148e06edf32f9a56ad7e7fa6a0c1febd386ec | refs/heads/master | 2020-05-01T05:58:23.695265 | 2019-03-23T17:20:31 | 2019-03-23T17:20:31 | 177,317,549 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,890 | r | seer_script.R | setwd(dir = "C:/Drive/Workstation/data-science/seer-workspace/paper1/")
#install.packages("caret")
#install.packages("ellipse")
#install.packages("mlbench")
#library("caret")
#library(mlbench)
filename <- "data/RESPIR_PROCESSED_DATA_min224.csv"
dataset <- read.csv(filename, header = FALSE)
colnames(dataset) <- c("Pa... |
94faa25671ce7d0b32cce593f95ca041a30c1a4d | 011f465997737cb91ac4ac32ee4b4611e8b6d0f3 | /qc_integration.R | 6e4e1f64285f041fbc1824894b6605fdf1f2cae9 | [] | no_license | puweilin/scRNAseq_PTC | f8ffb65bf7e4b8e20dec655c94184da85eaa8bb1 | d6b1229386e53b8e4cc059c49b747c85e83c2a57 | refs/heads/master | 2023-07-30T23:26:48.464808 | 2021-09-18T04:55:49 | 2021-09-18T04:55:49 | 407,757,645 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 36,926 | r | qc_integration.R | library(plyr)
library(dplyr)
library(sctransform)
library(Seurat)
library(ggplot2)
library(ggsci)
library(readr)
library(readxl)
library(DoubletFinder)
q = theme_classic() +
theme(panel.border = element_blank(),
axis.line.x = element_line(size = 0.5, linetype = "solid", colour = "black"),
axis.line... |
5d49a0c97f2977546e82c180bf86d485d3083e5d | 49d6ea16082a529fb78af5fb63cca6b75820ca3f | /Avocado_Visualizations/Avocado_Visualizations.R | ddb2b504ffd668091b162c508019b94e8ff14c5e | [] | no_license | Monica-Kulkarni/Avocado-pricing-forecast | a44d3260f722fc62481bd694dc9fdab1a8cc4ace | 8c886a466836848dd0b1d31f83efc474eafc1e6d | refs/heads/master | 2020-06-18T22:35:37.030777 | 2019-07-14T00:28:02 | 2019-07-14T00:28:02 | 196,477,398 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,313 | r | Avocado_Visualizations.R | setwd("C:/Users/Monica Kulkarni/Downloads/avocado.csv")
df <- read.csv("avocado.csv")
original_df <- df
levels(df$type)
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(skimr))
suppressPackageStartupMessages(library(GGally))
suppressPackageStartupMessages(library(viri... |
15ee8a16f222133eab6a9b09308aa9a6e711c495 | 1bb525ea09cc4dee92174a1c20719b8ced46aafd | /man/helloworld.Rd | 4695623e3571924a9738db05b29091bdc9d4c0af | [] | no_license | rflodin/testpkg | d1fcc217425c342862912042cb27765f4aec8a2a | 67984f5ba6e9eb865510d013782270e88bf42281 | refs/heads/master | 2022-11-29T23:06:52.807031 | 2020-07-28T23:20:12 | 2020-07-28T23:20:12 | 283,348,514 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 287 | rd | helloworld.Rd | \name{helloworld}
\alias{helloworld}
\title{
A helloworld Function
}
\description{
The basic helloworld.
}
\usage{
helloworld(x)
}
\arguments{
\item{x}{
a character string.
}
}
\value{
a character string, "hello world" plus the value of \code{x}.
}
\examples{
helloworld("Seattle!")
}
|
dfa6c121bdb5e12db92a7d2a4f66b3815df73537 | 7b2cacf99fe488c001d09b6a51eac439bdfa5272 | /analysis/sliding_windows/regions_summary.plot.R | 923c5892e4686440eca310987bbb92f170cc6337 | [
"LicenseRef-scancode-unknown-license-reference",
"MIT"
] | permissive | talkowski-lab/rCNV2 | d4fc066478db96322b7aa062f4ece268098b9de3 | 7e97d4c1562372a6edd7f67cdf36d4167da216f8 | refs/heads/master | 2023-04-11T08:48:40.884027 | 2023-01-25T15:59:13 | 2023-01-25T15:59:13 | 178,399,375 | 14 | 4 | MIT | 2022-03-16T16:42:46 | 2019-03-29T12:13:54 | R | UTF-8 | R | false | false | 8,673 | r | regions_summary.plot.R | #!/usr/bin/env Rscript
######################
# rCNV Project #
######################
# Copyright (c) 2019 Ryan L. Collins and the Talkowski Laboratory
# Distributed under terms of the MIT License (see LICENSE)
# Contact: Ryan L. Collins <rlcollins@g.harvard.edu>
# Plot summary schematics for final segments fr... |
52b1616f40ef77a6e6195b95158618d82ddf67fd | 9f77863a8d6916ea52ff17f49e7c4901ea84f547 | /Shiny_Application/ui.R | 7a73e4a0b4edd05f0d2e660438078bc224205184 | [] | no_license | meowjiang/repoDB | 210ef68a3f93e80f39dfb2175965c8d56ee960d7 | cd4edec67e57df1d2a5f552e30a6960a38d79432 | refs/heads/master | 2020-05-07T22:26:09.058728 | 2017-07-28T17:22:56 | 2017-07-28T17:22:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,278 | r | ui.R | ########
# Load #
########
## Libraries
library(shiny)
library(DT)
#################
# UI Definition #
#################
shinyUI(fluidPage(
## Header
headerPanel('',
tags$head(
tags$img(src="logo.png", height="80px", width='275px',
... |
0fadbccc32772ece7d41a97fbf875fcfe42c7733 | 125d5252f02b02e5a3fa302c782c64b0199473c0 | /Z_Backup.R | 3dcbee082f7cc759883a33291f1e63fd1da1768a | [] | no_license | NicolasGoeller/ADS_Project_NicoNoah | e904b765e328434f6e43b33cf98a0bcd20c84b97 | 5b92e688a6dcbd05181c3674d77b30a070d2308a | refs/heads/master | 2020-04-06T11:33:30.515972 | 2019-01-31T17:57:16 | 2019-01-31T17:57:16 | 157,422,024 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,366 | r | Z_Backup.R | ### Stuff we have coded, but we couldn't implement in current workstream
## 4. Construct index for social capial
## Draw on initial EVS_2008 to obtain proxies for social capital
# Our own social capital index with questionable intercorrelatedness:
EVS_2008 %<>% within({
imp_fam <- v2 #importnace of family (=ve... |
c929c7c2dce1457d11961bf417a1e81db9314986 | 01d80c2cd1edae9956e67ae585b822d6014d4d96 | /lab2/parzenWindow.R | 0ce600c16ec12fff92fe7d356fda092e6d644875 | [] | no_license | alexlapiy/ML0 | 4e61c1e1e42f6f9f767bf1c7202f0013d87f15b6 | e7235abb5a7e0bfc13e3faace121fa418c69d607 | refs/heads/master | 2020-07-19T03:59:40.105683 | 2019-12-23T05:45:34 | 2019-12-23T05:45:34 | 206,370,343 | 0 | 0 | null | 2019-10-02T13:53:41 | 2019-09-04T17:02:57 | null | UTF-8 | R | false | false | 3,914 | r | parzenWindow.R | euclidDist <- function(u, v) {
sqrt(sum((u - v)^2))
}
sortObjectsByDist <- function(xl, u) {
l <- dim(xl)[1]
n <- dim(xl)[2] - 1
# формируем матрицу расстояний состоящую из индекса и расстояния евклида из выборки для некоторой точки
distances <- matrix(NA, l, 2)
for (i in 1:l) {
distances[i, ] <- c(i, ... |
6c8d7fe3842c2a8543ed9820ece709244f54258e | 0e76443b6de1312c8d3988d2538263db0cd7385b | /分析及画图/ggplot_中文.R | 25d48f5de9de395b44003190cbdd33540fbf0bb5 | [] | no_license | mrzhangqjankun/R-code-for-myself | 0c34c9ed90016c18f149948f84503643f0f893b7 | 56f387b2e3b56f8ee4e8d83fcb1afda3d79088de | refs/heads/master | 2022-12-30T08:56:58.880007 | 2020-10-23T03:20:17 | 2020-10-23T03:20:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,312 | r | ggplot_中文.R | ##2019.3.14
#ggplot出图显示中文
#https://mp.weixin.qq.com/s/ITKP6zlkbXGKeCWnNMP3Bw
install.packages('Cairo')
library("Cairo")
?Cairo
#example
ggsave("geo_Fus_wilt.pdf", p1, width = 12, height =8 , device = cairo_pdf, family = "Song")
# 字体选择链接：
# 新细明体, PMingLiU ,
# 细明体, MingLiU,
# ... |
3f949ce3f4665828027cca674ab826fd7ed5a106 | c7d5fa4a80cf89aeb6e17159a0953ad90ad1f4dc | /man/batchconvert.Rd | 3723f9bb16f0923f27ca2ed08d41a7a85ff8917a | [] | no_license | cran/PopGenKit | 4ac75f13f1f189d006d2fe95550eecb8854d1011 | 57588283dc44a661993babce5570e2bec9a3945b | refs/heads/master | 2021-01-18T08:07:28.105429 | 2011-07-21T00:00:00 | 2011-07-21T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,308 | rd | batchconvert.Rd | \name{batchconvert}
\alias{batchconvert}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Convert all Genepop files in dir to Arlequin format
%% ~~function to do ... ~~
}
\description{This function converts all Genepop files (extension .gen) in the working directory to Arlequin files (extens... |
bbb62850494fa69276643fe220d9449000090eb4 | ff85b91ac7e2ef583c56e7f3dae6ca56897a9cd7 | /man/inputGFF3OutputGenbank.Rd | 0417eee1e60bc70f23bf60c10e15f7c1b6cb38c6 | [
"MIT"
] | permissive | Tasu/EpDB2UG | a8d5a3ea82c5410a85576f03c05c40050ef28dbc | ed4b83026440262f222e8c9493999d6e4d073da8 | refs/heads/master | 2021-01-20T20:53:16.212945 | 2016-08-20T19:06:28 | 2016-08-20T19:06:28 | 63,458,667 | 0 | 0 | null | 2016-08-20T19:06:28 | 2016-07-16T00:53:11 | R | UTF-8 | R | false | true | 410 | rd | inputGFF3OutputGenbank.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/main.R
\name{inputGFF3OutputGenbank}
\alias{inputGFF3OutputGenbank}
\title{inputGFF3OutputGenbank}
\usage{
inputGFF3OutputGenbank(toxoDBGFF)
}
\arguments{
\item{toxoDBGFF}{input file path}
}
\description{
genbank file will be made.
output gen... |
9416d832fef8c81feebbbe6c264f21e36ff557b0 | 2d34708b03cdf802018f17d0ba150df6772b6897 | /googledrivev2.auto/man/permissions.update.Rd | b1b7a5b48b96b5e84f9e428d05f753020a7e44d8 | [
"MIT"
] | permissive | GVersteeg/autoGoogleAPI | 8b3dda19fae2f012e11b3a18a330a4d0da474921 | f4850822230ef2f5552c9a5f42e397d9ae027a18 | refs/heads/master | 2020-09-28T20:20:58.023495 | 2017-03-05T19:50:39 | 2017-03-05T19:50:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,512 | rd | permissions.update.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/drive_functions.R
\name{permissions.update}
\alias{permissions.update}
\title{Updates a permission.}
\usage{
permissions.update(Permission, fileId, permissionId, removeExpiration = NULL,
supportsTeamDrives = NULL, transferOwnership = NULL)
... |
2c2a47b340f50f4b30366efb184069f9fa5d3e1e | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/extremefit/examples/cox.adapt.Rd.R | f325afa7d443669542cc4de69e3eb817c2cc607a | [] | 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 | 410 | r | cox.adapt.Rd.R | library(extremefit)
### Name: cox.adapt
### Title: Compute the extreme quantile procedure for Cox model
### Aliases: cox.adapt
### ** Examples
library(survival)
data(bladder)
X <- bladder2$stop-bladder2$start
Z <- as.matrix(bladder2[, c(2:4, 8)])
delta <- bladder2$event
ord <- order(X)
X <- X[ord]
Z <- Z[ord,]
d... |
a6461dfbe752902e07a007b35b80563fe2076153 | 76283a39fcc37ae4ad222c2753f3509022fe36f2 | /scripts/selection_analysis.R | af1f6789be325413f8ae7be42861493aad749646 | [] | no_license | ljljolinq1010/Adaptive-evolution-in-late-development-and-adult | 015035644f981bad623ec0aa2ffce3b37d5859fc | 9ce53bb72082f5dfdaa78bca9f935eab8f82f8a4 | refs/heads/master | 2021-05-12T14:24:03.356937 | 2018-01-10T14:05:54 | 2018-01-10T14:05:54 | 116,954,623 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 22,052 | r | selection_analysis.R | library("ggplot2")
library("reshape2")
library("RColorBrewer")
######* module analysis *#####
moduletest<-function(module,selectome,orgName) {
if(orgName=="D.melanogaster") {
earlyM<-selectome[selectome$Ensembl.Gene.ID%in%module$Early.embryo,]
middleM<-selectome[selectome$Ensembl.Gene.ID%in%module$Middle.... |
61ab1f246b710ba79074a8887568a00575ea40bc | 78a663673b1fc7cba571b2de0f44e0de4959abce | /freesurfer/flag_outliers.R | c5b3dd5c7ef0713d05cc8bff8aa563561b134c2f | [] | no_license | PennBBL/conte | 5da93edc2c65ee4951ba934bb2a8e3ea6e155ff7 | cb1838ea7406b274ee7355094d6a6eda790c0c0c | refs/heads/master | 2021-01-20T17:39:46.855146 | 2017-06-14T20:08:27 | 2017-06-14T20:08:27 | 90,881,167 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 9,139 | r | flag_outliers.R | # this is a subscript of QA.sh that should run at the end of the script.
# The other scripts called by QA.sh create some csvs with thickness, volume,
# surface area, and curvature. This script flags all of these based 2sd outliers.
# The measures that are flagged are based on comments here:
# http://saturn/wiki/index.p... |
a015b12d061a0f32f389f612161f3c8c8b8dec33 | 6d73e84867b990ee17e885e1975e8eb905025837 | /Descarga_de_datos.R | c3e6c17d3f3afac794a1b9edbb693cf6576a37c6 | [] | no_license | Juliansrami99/dashboard | 577ada18b1d2a87a7b305406da00dbf73588c494 | 8876f42b7a7e99e6c6bf62680a6add67e0c6866c | refs/heads/master | 2020-09-15T12:20:57.241524 | 2019-11-22T16:27:46 | 2019-11-22T16:27:46 | 223,443,019 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,347 | r | Descarga_de_datos.R | library(timeSeries)
library(PerformanceAnalytics)
library(FRAPO)
library(fPortfolio)
library(quantmod)
library(dplyr)
convertir<-function(tabla){
dias=as.data.frame(index(tabla))
nueva=as.data.frame(tabla)
nueva=nueva[,-c(1,2,3,5,6)]
nueva=as.data.frame(nueva)
y=cbind(dias,nueva)
y=as.data.frame(y)
colna... |
4e9489af1632e2bada92918bc8aeb031d36f3a69 | 27b6a72a64a83ad6e3f48f9b8836be7aae3fd163 | /rcp.4c.rangerSubs.R | 5d00f824e802f6752150d0ee1373b97c235ef97c | [] | no_license | richardparrow/GSwag | 4815991f90c66f923c9d6b90963f514d03c2c086 | a3eda1671fce583824cf065a8cef603b1da84049 | refs/heads/master | 2020-03-28T21:46:29.933019 | 2018-09-26T16:12:28 | 2018-09-26T16:12:28 | 149,181,539 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,968 | r | rcp.4c.rangerSubs.R | # libraries
library(ranger)
library(dplyr)
# loading
load("modelMatrixTrain_ranger.RData")
load("modelMatrixTest_ranger.RData")
# Random Forest with ranger!
# model 1: num.trees = 1000; mtry = 100 > sqrt(p) --- 6h (num.threads = 6)
print(system.time(
rfOne <- ranger(dependent.variable.name = "transactionReve... |
014741be90556917c5de40265ec47af08eb6062f | 2ef6132cab8f2ece5e522c9f26c54cac3410673f | /server.R | d2a9a8f7e2c75c668c8b2320be46bb5d8a7b7a77 | [] | no_license | Nilesh1978/Developing-Data-Products | 4c0705824eba340c1791d3d76577c9b610b0c417 | fea955d3207da71ecca9a6db9b551e53eb4f5036 | refs/heads/master | 2020-12-25T05:06:48.977438 | 2015-07-26T19:04:19 | 2015-07-26T19:04:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 686 | r | server.R | # Developing Data Products Project
library(shiny)
server = function(input, output, session) ({
output$gimg <- renderUI({
if (input$gender== "male")
{
img(src = "male.png", height = (as.integer(input$height)*10/7), width = (as.integer(input$weight)*10/7))
}
else
{
img(src = "female.png", height = as.integ... |
3285c5ccfd3662200e024fd3fe5fba14531516f0 | 32835c0a1fc6b04bdead155d22a3c3ec3f43a126 | /datasets/R/plotsim_aggupset.R | 61d114ab50eeb6e90961eade9f14da32f936c031 | [
"MIT",
"CC-BY-NC-ND-4.0"
] | permissive | pkimes/benchmark-fdr | a95784d38d273f05c593fa8037af478dd0082073 | 18c1e412afbbbbfa742a3b4c124c9e1611d5a4e4 | refs/heads/master | 2021-01-19T12:51:31.142996 | 2019-04-16T23:59:14 | 2019-04-16T23:59:14 | 100,814,197 | 15 | 7 | MIT | 2018-09-13T22:19:09 | 2017-08-19T18:37:27 | Jupyter Notebook | UTF-8 | R | false | false | 8,522 | r | plotsim_aggupset.R | #' Aggregated UpSet Plot
#'
#' Generate an UpSet plot showing the (rounded) median overlap between methods
#' across a collection of SummarizedBenchmark objects, e.g. corresponding
#' to simulation replicates.
#'
#' @param res list of SummarizedBenchmark objects to be combined in the plot.
#' @param alpha significance... |
4e26042e0802d6d5fa95ab01fd03ab656050132e | 802681f4028c1645678c9de8f24e5cb29e78e2a5 | /R/EpiMutations.R | 07775f0d50141e1cf2b4ef8975089ec666ad2c60 | [
"MIT"
] | permissive | das2000sidd/EpiMutations | 936efe5670a5daf55aead548acdc8f1c0375d151 | 2fa076cf0cf9511dab0ec8ef02377f44d3d15ac1 | refs/heads/master | 2023-02-17T08:29:46.238154 | 2020-11-26T14:52:31 | 2020-11-26T14:52:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,019 | r | EpiMutations.R | #' @export
EpiMutations<-function(diseases, num.cpgs = 10, pValue.cutoff = 0.01,
cutoff =0.1, outlier.score = 0.5,
nsamp = "deterministic",method = "manova")
{
#Correct parameter verification
if(is.null(diseases))
{
stop("'Diseases' parameter must be intro... |
50332d95a91486da7f3507a0de8e0547d63f4a20 | bd1912018a7de9cb7509f54224c4dc206417127d | /R/saturation.R | 096f27c6df347226a6f08bae80ba128787efaa69 | [] | no_license | cran/secrdesign | 2eba2706579c539d47c5f29ea1d8fffc0562b278 | 5076a1d0f4aa141312135aef2c6f0d36a681851c | refs/heads/master | 2023-03-16T21:18:52.407592 | 2023-03-10T18:30:02 | 2023-03-10T18:30:02 | 19,372,578 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,438 | r | saturation.R | ###############################################################################
## package 'secrdesign'
## saturation.R
## 2017-11-02
###############################################################################
saturation <- function (traps, mask, detectpar, detectfn =
c('HHN', 'HHR'... |
81a81a6207215209652748e44821760744269489 | 48f7530f80150a8b9c71434cfdde178d57ad0590 | /Ch3_LinearRegression/NonLinear.R | 6df6bfbeca6e9f5d80d05dfd9a9110b54b2383d4 | [] | no_license | ssh352/Statistical_Learning | e3f9650bfacfd0c477a6d1cf74b809ce7307a293 | 5d8c4674ae39728a64ab2374d7b9ea5d94b6bd1c | refs/heads/master | 2021-09-15T11:26:15.676956 | 2018-05-31T11:58:07 | 2018-05-31T11:58:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 724 | r | NonLinear.R | library(MASS) # needed for Boston data
attach(Boston)
# x^2 in R must be wrapped using I(), ==> I(X^2)
lm.fit1 = lm(medv ~ lstat + I(lstat^2))
# Investigate the quadratic term
summary(lm.fit1)
# low p-value suggests quadratic term improves model
lm.fit = lm(medv ~ lstat)
anova(lm.fit, lm.fit1)
# F-stat = 1... |
fbd25eea6d3980f57f892174a9d8e40f7eccbd41 | b56b5285c96be24eca2705a44c923ad73dbbb96d | /create_randomdata_R.R | 4f3b52bb6e7d8660caa38f76d0f80b2f66a8900b | [] | no_license | Juliecho0101/WaterWaste_Project2019 | febf0343c3c0b80d8f10e6b36b28f23d33fb7918 | cfad781992e7c1fb619297e2ad7d0a16db1e68a9 | refs/heads/master | 2020-08-01T00:29:45.871127 | 2019-11-11T06:03:48 | 2019-11-11T06:03:48 | 210,800,117 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,177 | r | create_randomdata_R.R | # 일정한 시간간격 생성하여 컬럼별 난수데이터 생성하기
data1 <- as.Date("2019-08-01")
#z1 <- seq.POSIXt(as.POSIXct(data1), as.POSIXct(data1+7), by = "1 min")
df1 <- data.frame(GET_DATE =seq.POSIXt(as.POSIXct(data1), as.POSIXct(data1+7), by = "1 min"),
VC_VAL = rtruncnorm(n=10081, a=100, b=300, mean=220, sd=20),
... |
f77dc590b26454be7391a9a6a9a810a9e20ce3c9 | c6007faac6dca68f2e1a450af02a267b01d9349f | /man/add_headers.Rd | a567fcc28092a94e5046d6bff30282a5399380b2 | [] | no_license | vbonhomme/Momosaics | aeffe332dc4c6510b1c10fc0caf07a03f01e72a3 | 595d62186092fce1d5daabc017f81aabb50dd25f | refs/heads/master | 2021-05-05T13:50:36.253260 | 2018-01-23T12:25:21 | 2018-01-23T12:25:21 | 118,444,235 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 723 | rd | add_headers.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/add_layers.R
\name{add_headers}
\alias{add_headers}
\title{Add headers to groups on a mosaic plot}
\usage{
add_headers(df, column = "f", cex, ...)
}
\arguments{
\item{df}{\code{mosaic_df}}
\item{column}{(\code{f} by default) name or position... |
b7d99d28b74a9c4d153a61967c94ba61096dc207 | 9cb318be063dec190b8045464beb40ab2dbef9cf | /ui.R | b6bb10611a48ef2789fc73a36000bbb96401f8d8 | [] | no_license | apeco2020/ShinyApplicationAndReproduciblePitch | ecd7b1c847c8e47842c0e15c5751acae5e0d8801 | 19c7a3dcebcb96aa4c75d57b46803b5ce9e4b934 | refs/heads/master | 2021-04-09T20:35:54.593614 | 2018-09-17T17:35:25 | 2018-09-17T17:35:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 992 | r | ui.R | # ui.R
# Richard A. Lent
# Monday, September 17, 2018 at 11:05 AM
library(shiny)
shinyUI(fluidPage(
titlePanel("Draw a Graph with the Iris Data"),
sidebarLayout(
sidebarPanel(
isolate(
selectInput("variable", "Select variable",
choices = c("Sepal.Length", "Sepal.Width",... |
946846b11e7b782966f3901a2d8c41efa59c03e5 | 385a0d901a0cfe86912da704d7d907e7e8f38b82 | /zV_Past/Past0/z_Backup_HM/003HedgeMaster/Final0_OutputPriceMatrix.R | 5365749edad9668f4a8e295302baff40d3f3d87c | [] | no_license | junyitt/HM2016 | 2bfe1244d7c8bd54d7289ba30616058e409e3cfc | 750f3784de35f05da5b584b7f1704903ab294646 | refs/heads/master | 2020-04-11T06:28:34.904849 | 2016-11-12T20:05:38 | 2016-11-12T20:05:38 | 68,070,936 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,017 | r | Final0_OutputPriceMatrix.R |
library(dplyr)
###INPUT
source("~/003HedgeMaster/0f_pricing.R")
source("~/003HedgeMaster/0meta_pricing.R")
#########################
##Price of underlying - 8
#########################
#########
# assetn <- names(mu)
seed <- c(5,6,8,91,11)
S0 <- c(3.3,4,1000,240,2000)
assetlist <- list()
# for(i in 1:length(seedn... |
0b695786321a62cd61ad4565b6a0144f183deee4 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1/Database/Tentrup/ltl2aig-comp/load_full_3_comp5_REAL.unsat/load_full_3_comp5_REAL.unsat.R | f7047152118404a1bacadc4f15adf8dfaa2068d8 | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 82 | r | load_full_3_comp5_REAL.unsat.R | 64fa8ad4bfe0f62126ed8d3c09496748 load_full_3_comp5_REAL.unsat.qdimacs 30111 101864 |
12ef403687e0956f816988697c9e994038c8415e | e2a5cdf2dcbd788ac7c091897b5a027a809c302a | /R/snowColors.R | c3b404f82652969e4326ebc8c937edfe6c3e0ac9 | [] | no_license | lindbrook/cholera | 3d20a0b76f9f347d7df3eae158bc8a357639d607 | 71daf0de6bb3fbf7b5383ddd187d67e4916cdc51 | refs/heads/master | 2023-09-01T01:44:16.249497 | 2023-09-01T00:32:33 | 2023-09-01T00:32:33 | 67,840,885 | 138 | 13 | null | 2023-09-14T21:36:08 | 2016-09-10T00:19:31 | R | UTF-8 | R | false | false | 765 | r | snowColors.R | #' Create a set of colors for pump neighborhoods.
#'
#' Uses \code{RColorBrewer::brewer.pal()}.
#' @param vestry Logical. \code{TRUE} uses the 14 pumps in the Vestry Report. \code{FALSE} uses the original 13.
#' @return A character vector of colors.
#' @note Built with 'RColorBrewer' package.
#' @export
snowColors <- ... |
1f0f7196f9f4b2431d7f70169a7c4efe15f11d4d | 76434d63930c563cb9bab7d263df2c80da04cb6f | /R/mixnorm.R | 5b3c7948fc01941489704041f7c6e2b5f54bf35c | [] | no_license | cran/bda | 45de77f9d513cbeea00fc34120308f1d37dd2fd0 | b7cc310ed8ce18c2327f99647f024727e28e59dd | refs/heads/master | 2023-06-22T14:56:20.682683 | 2023-06-18T21:40:09 | 2023-06-18T21:40:09 | 17,694,669 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,381 | r | mixnorm.R |
.dmnorm <- function(x,p,mean,sd){
k <- length(p)
res <- 0
for(i in 1:k){
res <- res + p[i] * dnorm(x,mean[i],sd[i])
}
res
}
dmnorm <- function(x,p,mean,sd){
if(missing(p)) p <- 1
if(missing(mean)) mean <- 0
if(missing(sd)) sd <- 1
ndim <- length(p)
if(length(mean) != ndim | length(sd) != ndim)... |
930416ef0488fb45943d2f2e83e345b5ffdc7283 | ed7e9bd07bf58346c5b4dd4325275c3b9607f948 | /Shiny_Project/server.R | 3f1c240315f3374b965786a81a79254e6021286e | [] | no_license | kristinteves/GooglePlay2018 | 44ca22e9ee06c8bda1c81ddcf40d94f1276af169 | c2b930300e660327d0d5d22466ceb75371949b7f | refs/heads/master | 2022-12-26T19:43:11.063708 | 2020-10-08T06:16:51 | 2020-10-08T06:16:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,909 | r | server.R | library(DT)
library(shiny)
library(googleVis)
library(tidyverse)
library(dplyr)
library(ggplot2)
library(ggcorrplot)
shinyServer(function(input,output){
# Background Tab
output$totalapps <- renderInfoBox({
totalapps = length(df$App)
totalappstate = infoBox("Total Apps", totalapps, icon = icon("google-p... |
27ff80a7faff06ad615975caf9f30f827ef122a4 | b313ba13c1156ccb088c4de6327a794117adc4cc | /December2019/IBSsimvsobs | 7846f9efb9ac389bf6a8ed06da505098aac406c0 | [] | no_license | kbkubow/DaphniaPulex20162017Sequencing | 50c921f3c3e8f077d49ccb3417daa76fb4dde698 | c662b0900cc87a64ec43e765246c750be0830f77 | refs/heads/master | 2021-08-20T10:09:59.483110 | 2021-06-10T20:04:31 | 2021-06-10T20:04:31 | 182,109,481 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 22,739 | IBSsimvsobs | #!/usr/bin/env Rscript
### libraries
library(gdsfmt)
library(SNPRelate)
library(data.table)
library(ggplot2)
library(foreach)
library(lattice)
library(tidyr)
library(SeqArray)
library(cowplot)
### Load Observed IBS file
inputobsfiles <- list.files(path="/scratch/kbb7sh/Daphnia/MappingDecember2019/... | |
de39bc84971e85bfa59efb89d806885679e7d64c | a6e57b6e4c6011af4bcfec2c6233e184fde36493 | /TD3/Ex3-b.R | 981f431953003c4d4de83660857e43da95274930 | [] | no_license | Philtesting/Exercice-Language-R | db40ca41ece01bda6bda49f942ad3f40aa8b726e | 98ef1ede8f36577c966dbe251d0d5847e9993c10 | refs/heads/master | 2020-12-15T16:36:35.801112 | 2020-01-20T19:30:22 | 2020-01-20T19:30:22 | 235,181,478 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 256 | r | Ex3-b.R | rm(list = ls(all = TRUE))
p = 20
N =c()
L =c()
for (n in 1:p){
i = floor(runif(1, min= 1, max = n))
M <- matrix(data = (i + i - 1)^(-1), nrow = i, ncol=i)^n
N = c(N, det(M))
L = c(L, log(det(M)))
}
{
plot(1:p, N, "l")
plot(1:p, L, "l")
}
|
9ae6a4346807f648b1fb43968e541c4e70991986 | 374a98e903856d2c5bf7f04a7a23361f5d79949b | /R/RcppExports.R | 5a5092e3deaccc795b1feb28b064abb558885601 | [] | no_license | hheiling/glmmPen | c60a1217c3ddf0e31494cf59635714b9c45f36f2 | d24a0992ccf3df156113f3756703652e95f77ce1 | refs/heads/master | 2023-07-21T05:08:25.893420 | 2023-07-18T16:44:42 | 2023-07-18T16:44:42 | 187,713,641 | 3 | 2 | null | 2023-09-08T22:00:19 | 2019-05-20T21:08:49 | R | UTF-8 | R | false | false | 3,479 | r | RcppExports.R | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
grp_CD_XZ_FA_step <- function(y, X, Z, group, pBigMat, J_f, dims, beta, offset, step_size, sig_g, family, link, init, phi, X_group, K, penalty, params, trace) {
.Call('_glmmPen_grp_CD_XZ_FA... |
6bf87c8c4ba4f287f22ac2cc03b7885ad4704199 | a730692eff417c0c25b716d72000dc7e486b8c91 | /HRAnalytics.R | 11d7d80dc9fbde553358f91c1b9436e0afb456b7 | [] | no_license | sayonti/HR-Analytics | 21e0cd26223867cc6a4ece8b3d93611a9202e226 | 2f1adb85f10980ca2e5cfa4066009da011ede743 | refs/heads/master | 2021-01-23T01:02:06.618474 | 2017-03-22T20:52:47 | 2017-03-22T20:52:47 | 85,867,435 | 0 | 0 | null | 2017-03-22T19:23:21 | 2017-03-22T19:23:21 | null | UTF-8 | R | false | false | 3,635 | r | HRAnalytics.R | #install.packages('gsheet')
library(gsheet)
origData <- as.data.frame(gsheet2tbl('https://docs.google.com/spreadsheets/d/19-Zv4KiYXw20Dmtj97BfcE6Cri4paA2lnALa6H3w7pc/edit#gid=205206323'))
employeeID <- c(1:dim(origData)[1])
HRData<- as.data.frame(cbind(employeeID, origData))
colnames(HRData)[1] <- c('employee_ID')
name... |
79a0dc872cd5553ed4c3333e312eee4c7a5ab32a | 29ecbdc56a470141afdf02077628a5b3cd6a12f7 | /CustomFunctions/PlotPeaks.R | 7a442938dfa265c32cdc8e9b51d3124fc8f969fa | [
"MIT"
] | permissive | gretchunkim/NEAT | d8fd333abbeb10974abdc30fb663e8848928b967 | 10864f1924fc25bb2e847c69e5a08f1e2aae6dbe | refs/heads/master | 2021-05-29T17:09:02.517936 | 2015-10-18T03:51:59 | 2015-10-18T03:51:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,067 | r | PlotPeaks.R | #------------------------------------------------------------
# Libraries
source("~/PepsRscripts/RScripts/PepsFunctions/RectPlotter.R")
#------------------------------------------------------------
# PlotPeaks takes a matrix of peaks of form chr - start - end - val
# Plots peaks. If no value is provided, height of pe... |
382eb959aa62899d151c88aa4b75d4e862190635 | d75b7bc015b47d94254bcc9334ba15972d3ec9a1 | /1. FIRST YEAR/Introduction to Computing/Exercices_Laura/exercici11.R | b568437676c1eb53a80413c1d2610f7499969487 | [] | no_license | laurajuliamelis/BachelorDegree_Statistics | a0dcfec518ef70d4510936685672933c54dcee80 | 2294e3f417833a4f3cdc60141b549b50098d2cb1 | refs/heads/master | 2022-04-22T23:55:29.102206 | 2020-04-22T14:14:23 | 2020-04-22T14:14:23 | 257,890,534 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 162 | r | exercici11.R | cat("Escriu tres nombres enters:","\n")
x <- scan(n=1, quiet=TRUE)
y <- scan(n=1, quiet=TRUE)
z <- scan(n=1, quiet=TRUE)
cat(x>y && y>z || x<y && y<z, "\n") |
6d56fc2123b8a4ecd5bf904a07ef6d932246fe30 | fd2a324a9505ed29e6136a06216edce999fa97a1 | /man/plot.NMixPredCDFMarg.Rd | f0e8a448608dcac1d8982268d8a61f49538e4ecf | [] | no_license | cran/mixAK | 995c88ac9b1f70ab2dac51b4fc1347b9b1356eed | adc4c2229d8ad3573e560fd598158e53e5d1da76 | refs/heads/master | 2022-09-27T10:45:02.953514 | 2022-09-19T13:46:13 | 2022-09-19T13:46:13 | 17,697,529 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,073 | rd | plot.NMixPredCDFMarg.Rd | \name{plot.NMixPredCDFMarg}
\alias{plot.NMixPredCDFMarg}
\title{
Plot computed marginal predictive cumulative distribution functions
}
\description{
This is a basic plotting tool to visualize computed marginal
cumulative distribution functions, see \code{\link{NMixPredCDFMarg}}.
}
\usage{
\method{plot}{NMixPredCD... |
ac344bbbe6e8931f23666f1aa8fb8d8fcbb60038 | bae192fc279f36e7f05df7afd7b395beb32fe357 | /graph/CY151620_131224h_NumDEGs.R | 4caf91667675efbea9e16621c6edfdb00e52ff25 | [] | no_license | YKeito/CY_eachtime | fdcb07b2a0f0ba84af310bba4b1e3cba24bfa661 | 7480e1f34f062884ff7e06d4f67d035517c6c33d | refs/heads/master | 2022-12-21T23:34:38.744444 | 2020-09-28T05:19:22 | 2020-09-28T05:19:22 | 299,196,070 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,998 | r | CY151620_131224h_NumDEGs.R | ####FDR005####
#up
CY15_1h_FDR005_up <- sum(allRNASeq$CY15_1h[allRNASeq$CY15_1h_q_value < 0.05] > 0)
CY15_3h_FDR005_up <- sum(allRNASeq$CY15_3h[allRNASeq$CY15_3h_q_value < 0.05] > 0)
CY15_12h_FDR005_up <- sum(allRNASeq$CY15_12h[allRNASeq$CY15_12h_q_value < 0.05] > 0)
CY15_24h_FDR005_up <- sum(allRNASeq$CY15_24h[allRNAS... |
ba92c8bda664c248cb82e9da10a543e8d68a331a | 3657383536fd9efb61367977254be08dae26a623 | /Models/Ranger/model-mlr-ranger-learn-curve.R | 206e338c2cb79125bfb47e5f987a1ad7dc3f35f4 | [] | no_license | kevinkr/kaggle-house-prices | 54b176be43f383b7cd35a2c6bdfa3948f7980459 | da985ba1331735042b8dd0dcce2ec0f430d2ac8c | refs/heads/master | 2021-01-12T08:29:28.389256 | 2017-02-10T15:26:47 | 2017-02-10T15:26:47 | 76,594,754 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,705 | r | model-mlr-ranger-learn-curve.R | # Kaggle House Prices 12-21-16
# MLR Ranger
library(mlbench)
library(ranger)
library(Metrics)
library(mlr)
makeRLearner.regr.ranger = function() {
makeRLearnerRegr(
cl = "regr.ranger",
package = "ranger",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "num.trees", lower = 1L, default = 500L... |
7688138ee0c5836839ce8093a33fe634b99ba7c8 | 5cfe0376f8e6d47b8c47525c4a23b39563ccee29 | /Week_01.Introduction.To.Statistics/Rsession_week_01.plotting.R | 87aae64ba28d10fb2badb4fef94bf12233ba8c86 | [] | no_license | sophiemaichau/DataScience | 206421f08eb03c41d1822ba14e57498fae3a68de | f83c0bbf636b9a57b8000648b2c88395e10a28c6 | refs/heads/master | 2021-05-07T14:46:50.715951 | 2018-01-22T10:16:29 | 2018-01-22T10:16:29 | 109,861,591 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,225 | r | Rsession_week_01.plotting.R | #### Introduction ####
#
# Datascience in Bioinformatics (an extended version of an earlier course called LEARNING FROM GENOME DATA I
#
# The questions and exercises are identified as # Q:
#### This is a section header ####
# Section headers make it a lot easier to navigate your script
# Please check the drop down m... |
915b44457c2392e80f701b36c125c48c426d54b3 | 915625d842373876ed2f4579eea3ca7d67d6de6d | /cachematrix.R | 0585cf14647001ec29894a09ca26c3c8182a9685 | [] | no_license | malinos/ProgrammingAssignment2 | da0814d72e528425695a4a0a91322001b04c6a77 | 607639ce2bf6a41109883bcaf1dd77c0dfab65ff | refs/heads/master | 2021-01-16T18:03:36.684782 | 2015-07-23T16:07:08 | 2015-07-23T16:07:08 | 39,566,459 | 0 | 0 | null | 2015-07-23T12:36:45 | 2015-07-23T12:36:45 | null | UTF-8 | R | false | false | 1,406 | r | cachematrix.R | ## Matrix inversion is usually a costly computation
## and there may be some benefit to caching the inverse of a matrix
## rather than compute it repeatedly.
## This is the ProgrammingAssignment2 with the pair of function:
## 1.makeCacheMatrix: creates a special "matrix" object that can cache its inverse.
## 2.cach... |
5f04cb1d77ccbd08dcf6c352e94734841d53431c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/wheatmap/examples/WColumnBind.Rd.R | 86eb48ed42a6f59731b14897c3f8b1b9dcd2f57f | [] | 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 | 292 | r | WColumnBind.Rd.R | library(wheatmap)
### Name: WColumnBind
### Title: column bind non-overlapping objects
### Aliases: WColumnBind
### ** Examples
WHeatmap(matrix(rnorm(2000),nrow=40),name='a') +
WHeatmap(matrix(rnorm(30), nrow=3), RightOf(),name='b') +
WColorBarH(1:10, TopOf(WColumnBind('a','b')))
|
e7bcaa2079831e4bb86180a482f7549ea52e5a3b | 988b1f6a93ff7ee36c1017b4a1ba42e1f273aba2 | /shiny_app/mriqception_app/app.R | 6827f3c4a89ee42946cc168dda5a9b0e0eb2fd32 | [] | no_license | crewalsh/mriqception | 15f239d5c5483e61b695d5c90bb0356dc0aa1491 | cc34d0376f81d50a313de301d0dd4013a45e1a24 | refs/heads/master | 2022-06-14T18:26:31.950752 | 2022-05-19T00:40:11 | 2022-05-19T00:40:11 | 200,738,461 | 0 | 1 | null | 2019-08-05T22:40:35 | 2019-08-05T22:40:35 | null | UTF-8 | R | false | false | 15,090 | 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/
#
library(shiny)
library(reshape2)
library(plotly)
library(jsonlite)
`%notin%` <- Negate(`%in%`)
#source("~/Docume... |
4ce5479f112c53ba1c3290afc623ee0a6c4cd59e | 85130399796efd779e40efa389aaffc885b0dcaf | /Rbasics.R | 5fbbe12fc96d7c64bd984dde2ffcfd547506ed51 | [] | no_license | victorfeagins/WhatisR | 6d3aba4552afb75885a23c6af8cc4f9706b67b38 | acdf1a238b14c06ee20f2b020f3d54d11bfc7207 | refs/heads/master | 2023-07-24T17:21:59.606323 | 2021-09-03T22:20:57 | 2021-09-03T22:20:57 | 402,834,955 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,722 | r | Rbasics.R |
# R Basics ----
# R is all about objects
#Let's learn the essential Data types
## Numeric ----
#Arithmetic Operators
50 + 10 #Add
50 - 10 #Subtract
50 / 10 #Divide
50 * 10 #Multiply
2 ** 3 #Exponent
### Numeric Functions ----
#f(x) = y
round(50.70) #Round to nearest whole number
sqrt(4) #Taking the square root... |
9063d9f8d7fd6066e4317d064781e0497bd90508 | 23db4d2af33d272ea56eac407399c90c4b2b682d | /script_02.R | 954c8075a9402edb6ab75978d08b760ebf346f1a | [] | no_license | bdemeshev/r_socio_hse | 5ce6a1263f949b0d225a3972d0bff890cb4a8496 | 17f16afb6df6a5f32cfec79437ec017071a48073 | refs/heads/master | 2021-01-22T20:21:42.919836 | 2017-09-20T13:10:49 | 2017-09-20T13:10:49 | 85,315,335 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,107 | r | script_02.R | library(tidyverse) # обработка данных, графики
library(forcats) # для работы с факторными переменными
library(lubridate) # для работы с датами/временем
library(stringr) # для работы с текстовыми переменными
library(reshape2) # преобразование длинные <-> широкие
library(readr) # чтение файлов
# install = инсталлировать... |
f8930b741cd8992e0821e4d201a7970a2438f999 | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/6468_0/rinput.R | cbaaf164297baac62ab0316813a008bf42c1dd9c | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 135 | r | rinput.R | library(ape)
testtree <- read.tree("6468_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="6468_0_unrooted.txt") |
a19a8cb3fb770f9f57996ec3936023cd9a350767 | 498e7df01e78657277b23d81d7b07ab431def4fb | /share_sim_hosp_sderr.R | 5da368f388cee7e8a893c2832cd5cfcc4b6583eb | [] | no_license | kralljr/share_medicare | de5be725529fd00b42ab8aaf6edd31b91731a16e | 17aac20ee28e70e5cc93e71d4b11ce5e3f5ec2a5 | refs/heads/master | 2021-01-17T07:40:19.613158 | 2016-07-15T15:53:38 | 2016-07-15T15:53:38 | 18,215,156 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,272 | r | share_sim_hosp_sderr.R | ### File to simulate mortality effects
rm(list = ls())
args <- commandArgs(TRUE)
sim <- as.numeric(args[[1]])
err <- as.numeric(args[[2]])
print(c(sim, err))
#load others
library(share)
library(handles)
library(sharesim)
#load data
data(keeps, cms, sds, vec, names)
# new combo of sources
load("~/SHARE/shar... |
2bc2df21f9a370efa2c5755240431bae49d42157 | 9bcbf545552e7a8ead8478e6b3550c6a7a2f17cb | /man/read_data.Rd | 40f45274fbce251724a3977ed8371dc176ee474b | [] | no_license | bgulbis/BGTools | 5f77e9f277e986907f38cdeefbb4c42b5614420e | e31d098e7c21459f04c291bd712ae802a8d6945e | refs/heads/master | 2021-01-21T04:43:20.281983 | 2016-06-29T12:16:08 | 2016-06-29T12:16:08 | 47,362,878 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,037 | rd | read_data.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/read_data.R
\name{read_data}
\alias{read_data}
\title{Read and join data from multiple csv files}
\usage{
read_data(data.dir, file.name, base = FALSE, check.distinct = TRUE)
}
\arguments{
\item{data.dir}{A character string with the name of th... |
eb7a21f31d2bf5ee3547c712dd5a9d5988c3a70d | 60de7eab71406c75aaef944f53d0098c80148a43 | /sandbox/Ineq_Visuals.R | bfcf9574a5cd2dac3b455f3012df9b4614ba2684 | [] | no_license | ds-civic-data/sd-pdx-sea | 5f9bb3bc020e6146f291a8af7a176558a6ef7063 | 8dcb176ae14b322ede777bb244dad7052b747695 | refs/heads/master | 2020-03-06T21:00:13.381244 | 2018-05-10T23:58:01 | 2018-05-10T23:58:01 | 127,067,873 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 688 | r | Ineq_Visuals.R | ######################################################################################
## Title: Counting Migration by State (PUMS) and other exploration
## Author: Josephine Baker
## Created: 04/22/2018
##########################################
library(tidyverse)
library(readr)
library(ineq)
library(ggplot2)
source... |
4e08967a83633dd296325acaaa602bb8749a8b66 | 953e84446de8d060683c87669f2c62350939ed5f | /code/16S/deprecated/after_dada2_make_otu_table.R | 6ff7aa2ea164b21c8b52461260e56b8d77a9cab8 | [] | no_license | tkarasov/pathodopsis | 29e23821c33ac727421158d9e40b277a09b8b8ca | c5d486ac02b1f0b2ff525439d3bf2d3843231e4d | refs/heads/master | 2023-08-25T02:31:53.053463 | 2023-08-15T21:37:10 | 2023-08-15T21:37:10 | 172,746,993 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,080 | r | after_dada2_make_otu_table.R | #library(phyloseq)
library(dada2)
library(dplyr)
library(tidyverse)
library(fossil)
#library(msa)
#library(DECIPHER)
library(genefilter)
library(phangorn)
library("RColorBrewer")
library(gplots)
library(sjstats)
library(nlme)
#path="/Users/tkarasov/work_main"
#choose whether working on home computer or at work
path... |
07c777bbd57903f2ae2bfa0948b1192637e9026c | b21bdfcee70b2e4bce5dc7cf3d43c0940763fc5c | /inst/www/templates/script.R | 567d2f0b8e5c9645cf82803e0ee87d2771ede6e5 | [
"MIT"
] | permissive | dreamRs/addinit | f0a7858c4f805337093b315b32bca89bbdc10279 | c8d4bd1986b79a4c51eb1939b7af78c2dbb1425c | refs/heads/master | 2021-12-14T22:10:15.411235 | 2021-12-11T19:05:59 | 2021-12-11T19:05:59 | 96,358,956 | 57 | 6 | null | 2018-03-15T12:08:06 | 2017-07-05T20:33:28 | R | UTF-8 | R | false | false | 239 | r | script.R |
# ------------------------------------------------------------------------
#
# Title : {{title}}
# By : {{author}}
# Date : {{date}}
#
# ------------------------------------------------------------------------
{{packages}}
|
e402e41806dddf1ab3efc0d1b2893c0652b8ae9b | c846f88fbdd6d56f50949ee86bdd63a1de5e3dc2 | /04_Abril/5 Poblacion_CM_Supermercado_Cuidalela/Conexion_access.R | c4b5602e36c85606164ec7092c1257891d388240 | [] | no_license | edgaruio/A-Requerimiento-2020 | 4bf0752436d1a06bea6a74ba97f654e9047b696a | c48dcdf9c557a45a12530047e54fc268c44ee5ff | refs/heads/master | 2022-11-27T07:35:28.647661 | 2020-08-03T13:40:45 | 2020-08-03T13:40:45 | 276,465,243 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 658 | r | Conexion_access.R | # XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
# Data tablas Conversion ----
# XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
channel <- odbcDriverConnect(
"Driver={Microsoft Access Driver (*.mdb, *.accdb)};DBQ=//bogak08beimrodc/bi/Tabla_conv... |
49ab8a320ed1e79bd4f6426d01282f65f25c6314 | b058c9a53f23c2dcbd801b89b82b435237096941 | /Descriptive Statistics.R | 0c2246f15eeeadc371b989dd76216cb994c4e71d | [] | no_license | adiganesh93/Customer-Retention-System | 8c53f9dde9e482de677e42d46a39a96832a307d1 | ba233839e07ffa0e4845c6d9ae38bb85510d9c06 | refs/heads/master | 2020-05-20T17:12:33.615914 | 2019-05-09T00:43:20 | 2019-05-09T00:43:20 | 185,682,822 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,367 | r | Descriptive Statistics.R | #package
library(ggplot2)
library(MASS)
#read data
df <- read.csv("C:/Users/Aditya/Desktop/IST-687/Satisfaction Survey.csv")
#clean missing value
df$Departure.Delay.in.Minutes[which(is.na(df$Departure.Delay.in.Minutes) & (df$Flight.cancelled == 'Yes'))] <- 0
df$Arrival.Delay.in.Minutes[which(is.na(df$Arrival... |
13d1a7fdecde90d5b8003043e0e30e316783fe09 | 95ddb283bc126d83c683cecbf9b874521e08da98 | /man/s3faInit.Rd | 414a7f8f14e995ee314f6b00550f691bb14f07ba | [] | no_license | aciobanusebi/s2fa | 05770bcc5c23d8524beaa5cf71d343d0ab452b26 | e3f0dd9770e0d3843948c6d039504531d575d752 | refs/heads/master | 2021-09-07T21:38:38.465399 | 2021-08-04T15:25:44 | 2021-08-04T15:25:44 | 192,394,527 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,450 | rd | s3faInit.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/s3faInit.R
\name{s3faInit}
\alias{s3faInit}
\title{Generate initial parameters for EM/S3FA}
\usage{
s3faInit(X_t_supervised, Z_t_supervised, X_t_unsupervised, type = "fa",
checkArgs = TRUE)
}
\arguments{
\item{X_t_supervised}{trai... |
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