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84d84ae7efebe7f094087ec080c0c2b2d1dadc6d | 4ed6dfdbac828314df254c82b9dab547d7167a79 | /04.ExploratoryDataAnalysis/video_lectures/week3.video01.HierarchicalClusteringPart1.v1.R | f5d239d7530f4d751a5a0c6edc85612e9f3540b8 | [] | no_license | minw2828/datasciencecoursera | beb40d1c29fc81755a7b1f37fc5559d450b5a9e0 | e1b1d5d0c660bc434b1968f65c52987fa1394ddb | refs/heads/master | 2021-03-22T00:15:15.147227 | 2015-08-21T07:55:10 | 2015-08-21T07:55:10 | 35,082,087 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 242 | r | week3.video01.HierarchicalClusteringPart1.v1.R | ## Hierarchical Clustering (part 1)
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53443702bc3b6756f4f89ce2b68b9fb05296b3a2 | 461c3b43ec1490872ecdb2e8fa87ab4526f895fa | /Quinton_WGD_2020/Dependency/Analysis/2018.12.20/20181220_CERES_analysis.R | 3f5e124abc01d863a3def67b9a805f213d424c66 | [
"MIT"
] | permissive | campbio/Manuscripts | 8f49d01e5190810a12226e9feb1c6d048f238d43 | 23bde921d7a9c7b1d3c99cbc395de3d6081654d7 | refs/heads/master | 2022-05-16T23:32:54.780949 | 2022-04-04T19:30:01 | 2022-04-04T19:30:01 | 210,632,134 | 12 | 10 | MIT | 2022-04-02T02:57:55 | 2019-09-24T15:08:11 | HTML | UTF-8 | R | false | false | 4,998 | r | 20181220_CERES_analysis.R | source("~/GIT/utilities/R/lm_utils.R")
source("~/GIT/utilities/R/mut_utils.R")
library(stringr)
ceres.17q2 <- read.table(gzfile("../../Data/gene_effect_17Q2.csv.gz"), header = T, sep = ",", row.names=1, check.names=F)
ceres.18q3 <- t(read.table(gzfile("../../Data/gene_effect_18Q3.csv.gz"), header = T, sep = ",", row.n... |
352bb6736a341cef1aa1f4ca706ca54f489d908b | db48edb75b5d4d79acb6d0b491e8515c6c5e2d9a | /man/mesaDev.Rd | e691c21788bf3189e348a86d2f813ff05286a721 | [
"MIT"
] | permissive | tunelipt/rwmesa | 9a164c96b54c87be428969f721ec8ec12ffd4e37 | 579465f190bd4b3bd858821c00f3019779540841 | refs/heads/master | 2020-07-27T13:54:10.802381 | 2019-09-17T19:09:29 | 2019-09-17T19:09:29 | 209,114,111 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 600 | rd | mesaDev.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wmesaclient.R
\name{mesaDev}
\alias{mesaDev}
\title{Criar conexão com a mesa giratório do túnel de vento do IPT}
\usage{
mesaDev(url = "localhost", port = 9596)
}
\arguments{
\item{url}{String com URL do servidor XML-RPC}
\item{port}{Inteiro... |
3754be5ce66bf3eee92a2ac8efcbdeb7ff29c1bb | 35107538d0ab4c8bcac7804d54ed33c4db4a8754 | /Enemble_test_codes/2_CrossValidation and bagging.r | bbe7e86d9b50b99f9b5dcfa532cad0053368efb2 | [] | no_license | shikharsgit/Ensemble-Selection | 7b68277be3c4899e7ebfe605a9df12d9972fdfd5 | 5bb4b2e5aede43a6a570883c49fe465de59ca64b | refs/heads/master | 2021-03-16T06:52:42.583673 | 2017-07-23T21:19:14 | 2017-07-23T21:19:14 | 94,370,372 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,935 | r | 2_CrossValidation and bagging.r |
rm(list=ls())
user=(Sys.info()[6])
Desktop=paste("C:/Users/",user,"/Desktop/",sep="")
setwd(Desktop)
dir.create(paste(Desktop,"/MEMS",sep=""))
dir.create(paste(Desktop,"/MEMS/S6/NIC",sep=""))
dir.create(paste(Desktop,"/MEMS/S6/NIC/Datasets",sep=""))
home=paste(Desktop,"MEMS/S6/NIC/Datasets/",sep="")
setwd(home)
d... |
24b0787c86c75b25510de0350210a371994032b2 | 6d2265c1d24df25711f4796261b9fb9f36ff8bc6 | /MSOD_syn_deleteJobs.R | 3009458deb25fc392c0ae7a4f0128ee0ab181e1e | [] | no_license | yujunnokia/MSOD | 0a0721d3885057d938488b6a89f4a2cee2748163 | 29efd2b8dcda1b02f0a4234c94646443b28a5b3b | refs/heads/master | 2020-05-28T10:15:49.236682 | 2014-01-22T17:41:29 | 2014-01-22T17:41:29 | 16,145,811 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 393 | r | MSOD_syn_deleteJobs.R | #! /usr/bin/env Rscript
datasets <- c("syn","syn-I","syn-NL") #c("syn","syn-I","syn-NL","syn-I-NL")
indices <- 1:15
models <- c("MSODVL") # c("TRUE","OD","ODLP","MSODTRUE","MSOD")
for (dataset in datasets) {
for (index in indices) {
for (model in models) {
job <- paste("S.",model,".",index, ... |
013fde112bb984be66371163020b21d7b251c878 | fd91fd81027df91f03e29138b26e2a1b6e31e054 | /man/PhyDat2Morphy.Rd | bb0b009c79b91dc0c791a4c96f66c5f2cf5fd212 | [] | no_license | gitter-badger/TreeSearch | 77fa06b36d691f942c8ef578f35f3e005cc2f13e | 5a95195211d980baa6db29260bf929a12c5bf707 | refs/heads/master | 2022-04-20T07:40:33.050434 | 2020-04-16T13:47:57 | 2020-04-16T13:47:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,556 | rd | PhyDat2Morphy.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mpl_morphy_objects.R
\name{PhyDat2Morphy}
\alias{PhyDat2Morphy}
\title{Initialize a Morphy Object from a \code{phyDat} object}
\usage{
PhyDat2Morphy(phy)
}
\arguments{
\item{phy}{An object of class \code{\link{phyDat}}.}
}
\value{
A pointer t... |
4a88282325714905a38b2a70312e4378bbf08602 | 9bc17a169325375bc993b540d2ad0f0810ca0e76 | /man/Formula1.Rd | 76eba1564bbdbb684a8ceb208b886add490f5bd1 | [] | no_license | alanarnholt/PASWR | 335b960db32232a19d08560938d26f168e43b0d6 | f11b56cff44d32c3683e29e15988b6a37ba8bfd4 | refs/heads/master | 2022-06-16T11:34:24.098378 | 2022-05-14T22:56:11 | 2022-05-14T22:56:11 | 52,523,116 | 2 | 1 | null | null | null | null | UTF-8 | R | false | true | 685 | rd | Formula1.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/PASWR-package.R
\docType{data}
\name{Formula1}
\alias{Formula1}
\title{Pit Stop Times}
\format{
A data frame with 10 observations on the following 3 variables:
\describe{
\item{Race}{number corresponding to a race site}
\item{Team1}{pit stop... |
943ec7ad191ad74114fc5e5f4a314558672dc7d1 | 352090e86c783a1edd02f4c8634137f456e92ce3 | /man/rptgam.Rd | c73f463cda9621b753b6efcf4a2a39db6aec7085 | [] | no_license | elipickh/rptGam | 140e56494efa16cb08606704886417f509341556 | 365d4247caee9c4fa25d5e880ac749b98a34491b | refs/heads/master | 2020-08-05T19:07:09.376862 | 2019-11-20T13:32:43 | 2019-11-20T13:32:43 | 212,670,006 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 12,678 | rd | rptgam.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rptgam.R
\name{rptgam}
\alias{rptgam}
\title{Repeatability estimates for random effect GAM models}
\usage{
rptgam(
formula = NULL,
data = NULL,
gamObj = NULL,
rterms = NULL,
gam_pars = NULL,
bam_pars = NULL,
nboot = 0,
boot_ty... |
d0b8fa2aebdeea15e05b5caa28fc572480df0d1d | e8443eddb0560a39855bb69c0f7d1cdc54397173 | /static/stat572/notes/Code_gprior.R | 8ddbeba1b0870702c2afd5748071a5dacaa14b61 | [
"CC-BY-4.0"
] | permissive | UrbanStudy/stat2019_website | 68b6f530e0e18d60fb24cd9092ab80f7939a26e4 | 9d41d5caf4ece4c62bf0c301eb8c90429b17ce7d | refs/heads/master | 2021-06-17T01:30:06.363299 | 2021-02-01T04:12:55 | 2021-02-01T04:12:55 | 148,949,001 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,324 | r | Code_gprior.R | #Function to generate draws from the joint density of beta and sigma.sq
lm.gprior <- function(y,X,g=dim(X)[1],nu0=1,
s20=try(summary(lm(y~-1+X))$sigma^2,silent=TRUE),
S=1000){
n<-dim(X)[1] ; p<-dim(X)[2]
Hg<- (g/(g+1)) * X%*%solve(t(X)%*%X)%*%t(X)
SSRg<- t(y)%... |
6886b87f98b2a49f23f4e3a92466dd642d2a32b0 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/js/examples/coffee_compile.Rd.R | 7ba030f9a590717f788787392e32fca206cc63e6 | [] | 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 | 374 | r | coffee_compile.Rd.R | library(js)
### Name: coffee_compile
### Title: Coffee Script
### Aliases: coffee_compile coffee
### ** Examples
# Hello world
coffee_compile("square = (x) -> x * x")
coffee_compile("square = (x) -> x * x", bare = TRUE)
# Simple script
demo <- readLines(system.file("example/demo.coffee", package = "js"))
js <- cof... |
293cf06338c85f063193067ba9e440740cb8e7fb | 764c655327e373a61091591d760f4ceeb00fe9e7 | /bin/setup | ecd8a69f159ee51b3c778c3c3cfb391d802cd3a4 | [] | no_license | nelsonmestevao/beautiful-cli | f89bc53226085dc9c1f21daa21236c6700835b46 | 82fb0d954c8e091e5890533d7dcabc24aadc7122 | refs/heads/main | 2023-02-20T04:00:31.805725 | 2020-12-21T21:45:06 | 2020-12-21T21:45:06 | 332,502,941 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 120 | setup | #!/usr/bin/env Rscript
if (!requireNamespace("renv", quietly = TRUE)){
install.packages("renv")
}
renv::restore()
| |
de7c48e9e16c16be91cf3643730274b7cbb0aed4 | 6bca977d67101a6274457ca850517ee41cf06c45 | /plot_functions/plot.meth.nek2.R | 5503c88e8072c8350f07b7f9ccc7a66564e7cfa6 | [] | no_license | AAlhendi1707/preinvasive | bedcf1f1eca93ab9ae4b44bf32e4d0f9947a1fad | e683fa79ad76d0784437eba4b267fb165b7c9ae4 | refs/heads/master | 2022-01-06T18:25:52.919615 | 2019-01-18T09:39:42 | 2019-01-18T09:39:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 400 | r | plot.meth.nek2.R | ########################################################################################################
# Methylation of NEK2-associated probe by group
########################################################################################################
plot.meth.nek2 <- function(filename){
pdf(filename)
plo... |
1e9558bdc03791b5c9c8016e416b9ea0dd5c2452 | 27c8c8337342e22d3e638d9738ca6499243bc86b | /R/statistic-viper.R | 0cd07251cf3070c5ed4a8bb77e28383b45ada355 | [] | no_license | Eirinits/decoupleR | 1f578ef44dd3a81496e276058fb3c6eca7d6608d | 3926381bc63362a7ec7cb1b32b40a85f1f9a9cd1 | refs/heads/master | 2023-06-03T01:35:56.461380 | 2021-05-25T18:57:17 | 2021-05-25T18:57:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,924 | r | statistic-viper.R | #' VIPER wrapper
#'
#' This function is a convenient wrapper for the [viper::viper()] function.
#'
#' @inheritParams .decoupler_mat_format
#' @inheritParams .decoupler_network_format
#' @inheritDotParams viper::viper -eset -regulon -minsize
#'
#' @return A long format tibble of the enrichment scores for each tf
#' acr... |
676f28a312982a8707f270f8b9521a083d171115 | 14c304c74e251ea09cb40abfb106623e45cb336b | /R/ddo_ddf_spark.R | db36a4cf52ce964090b6fef3dd738c561c2da01e | [
"BSD-3-Clause"
] | permissive | krseibel/datadr | d4ac9ecd42234729cef7d68f603497f7d089727a | b8ad0bdfd207a2f311d56209f4ff254f55c7fdec | refs/heads/master | 2020-12-28T02:04:32.139688 | 2014-11-05T17:37:42 | 2014-11-05T17:37:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,275 | r | ddo_ddf_spark.R | ## Methods for object of class "kvSparkData" - key-value pairs as Spark RDDs
#' @export
ddoInit.sparkDataConn <- function(obj, ...) {
structure(list(), class="kvSparkData")
}
#' @export
ddoInitConn.sparkDataConn <- function(obj, ...) {
obj
}
#' @export
requiredObjAttrs.kvSparkData <- function(obj) {
list(
... |
d5b78dda633475adda81f937247203d9eb6d453c | fc9b8c83a7ec01667bbfeca205c70d4ca24bcd20 | /sentiment analysis.R | d887099a35aaee73ea53cf01eebae2d98ec1832e | [] | no_license | hannahng97/State-of-the-Union-Analysis | f91fe3d9b5e8f9518dc262710341a01e6cfb9fc4 | 7875acd54f74ff6c9679534e757a7f4c4bc4f811 | refs/heads/master | 2020-03-08T17:57:41.926403 | 2018-04-06T04:06:28 | 2018-04-06T04:06:28 | 128,282,746 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,089 | r | sentiment analysis.R | library(readtext)
library(tidytext)
library(dplyr)
library(stringr)
library(tidyr)
library(ggplot2)
library(wordcloud)
library(reshape2)
# read in text
jfk <- readtext("JFK first state of the union.txt") %>%
unnest_tokens(word, text)
obama <- readtext("obama first state of the union.txt") %>%
unnest_tokens(word... |
fa822ae00c6f41d15cb33c8c793756bdefaf6722 | 80e2a96a6a3e47dabc5ef98139c9526adb9cad4c | /models/annotation_stratification.R | 0c25e4cc17843c7ff8d028f079f4e9af7d36c3c1 | [] | no_license | jonvw28/arabidopsis_cytosine_methylation | eb6c78fa0f669c7ec83009da1907b898d162886e | c615245da6e4bbf09f061b37dd6d30bcfa2b83b1 | refs/heads/master | 2020-12-24T07:31:01.364139 | 2016-08-17T09:24:16 | 2016-08-17T09:24:16 | 57,891,780 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,325 | r | annotation_stratification.R | At_tiles.data <- readRDS("data_complete.RData")
#
#LIBRARIES
#
source('functions_read_in_first.R')
#
if(!require("dplyr")){
install.packages("dplyr")
}
library(dplyr)
#
# Select Relevant Data
#
tags <- which.index(At_tiles.data,c('relative_meth','cytosinesCountCG',
'cytosin... |
28a39e9a7abbefa2516fff8afb45080db7ce1de9 | 6ba8e14f902e2d7d4d4189d58f32d2df6d3a4672 | /man/greycol.Rd | 90d334bc30b7c199093179f6ae3b24e816fe4a25 | [] | no_license | cran/shape | 7bf191a0810cbea1a1683741ced89c40358b9a51 | 3641dca21ed3ee20673ba28a8ea8b8281b087fbd | refs/heads/master | 2021-08-07T05:07:49.981244 | 2021-05-19T06:20:03 | 2021-05-19T06:20:03 | 17,699,636 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 856 | rd | greycol.Rd | \name{greycol}
\alias{greycol}
\alias{graycol}
\title{
white-black color palette
}
\description{
Creates a vector of (n) contiguous colors from white/grey to black
}
\usage{
greycol(n = 100, interval = c(0.0, 0.7))
}
\arguments{
\item{n }{number of colors.
}
\item{interval }{interval *to* where to interpol... |
999acc36dac8774fc24b45bb54257b09ffa16b5a | 5677446a232a94486df697870454ba6d97ef223d | /pca.R | e0b09676548566b40f2b0eb2767094dceae9b66d | [] | no_license | Mikemeat/north-american-octo-spice | 20e8b62aef35226f9f9b46bdb6db058b278e710e | 13546464cf5b9f809de7163180a8e05b926b19fa | refs/heads/master | 2021-01-01T05:39:44.037264 | 2014-06-13T09:48:20 | 2014-06-13T09:48:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,089 | r | pca.R | #pca example
#replicate this in your own adventure traveller
Price <- c(6,7,6,5,7,6,5,6,3,1,2,5,2,3,1,2)
Software <- c(5,3,4,7,7,4,7,5,5,3,6,7,4,5,6,3)
Aesthetics <- c(3,2,4,1,5,2,2,4,6,7,6,7,5,6,5,7)
Brand <- c(4,2,5,3,5,3,1,4,7,5,7,6,6,5,5,7)
data <- data.frame(Price, Software, Aesthetics, Brand)
#two type... |
845be7367ef5af8f4f76bab0396a052860bdd1d4 | afe9b94df6f6a3211ace68b127f57ca38a1965af | /tests/testthat/test-updateGeneralSettings.R | d680f3a3e2cc08990453ca0d86b4379d60accdfd | [] | no_license | datastorm-open/antaresEditObject | d10e1f80cdcb4749a82b575ba037ddb642c183fb | 49739939a8a4e4857db94031b5e76a81ddb03f7c | refs/heads/master | 2021-07-21T14:38:29.878961 | 2017-10-31T08:41:54 | 2017-10-31T08:41:54 | 106,667,353 | 1 | 0 | null | 2017-10-12T08:42:54 | 2017-10-12T08:42:54 | null | UTF-8 | R | false | false | 599 | r | test-updateGeneralSettings.R | #Copyright © 2016 RTE Réseau de transport d’électricité
context("Function updateGeneralSettings")
# Setup study -------------------------------------------------------------
path <- tempdir()
# Unzip the study
setup_study(path, sourcedir)
# set simulation path in mode input
opts <- antaresRead::setSimulationPath... |
3ce64873902f7cf92a81c3c48e984786f7fe9f90 | f7b4ea0419535fde79825ec8790bc535405954a0 | /man/loopit_2D3D.Rd | 3813c7afb9a7f0fc8f25766aa9db0417e8020c7d | [] | no_license | janjansen86/ptrackr | 9d4f844a9b1986c6e23cb1a85b6f1e06d762cb88 | 72d4f48284251b90c5ea9b072e3bb215e10d8358 | refs/heads/master | 2020-04-15T15:54:58.397067 | 2017-11-17T06:01:49 | 2017-11-17T06:01:49 | 51,494,252 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 5,455 | rd | loopit_2D3D.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/loopit_2D3D.R
\name{loopit_2D3D}
\alias{loopit_2D3D}
\title{Loopit 2D/3D}
\usage{
loopit_2D3D(pts_seeded, romsobject, roms_slices = 1, start_slice = 1,
domain = "2D", trajectories = FALSE, speed, runtime = 10,
looping_time = 0.25, sedimen... |
b5251f749495b8efdce7c7d6f5d3443cbb2b1ab8 | 7346258bfcf9d1602c45e8452e224eb64aa2cc19 | /a_team/cmt_semantic_analysis.r | 4f5bf97de69c85005f1321351b785bca120531f6 | [] | no_license | kmangyo/Kleague_Data | f84f6ff88e0d9e1bd6913f1d251cdc3c3937bafc | 374168f2ebe3b185f25737314c749e5847c8f449 | refs/heads/master | 2021-01-21T06:14:43.033660 | 2017-11-13T12:05:45 | 2017-11-13T12:05:45 | 47,014,809 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,439 | r | cmt_semantic_analysis.r | library(RSelenium)
library(rvest)
library(plyr)
library(dplyr)
library(reshape2)
library(stringi)
library(stringr)
library(ggplot2)
# comments data is from http://sports.news.naver.com/gameCenter/textRelayFootball.nhn?category=amatch&tab=player_stats&gameId=20171010A01A001618
# in mac terminal: docker run -d -p 4445:... |
07c2247aee3f58850934ee42801bca2044eaf2c2 | 3afd8f34493e45d70b28f46b4be8686214c996d3 | /R/generate_lag_props.R | 10d6cdf0ef7bf56ba8d6ab8759e76109bd39ef3a | [] | no_license | gcgibson/2018-2019-cdc-flu-contest | 8feb2fabe2bbb564777e19a3496cffa35bc63708 | fadf216cd05769dc339e0e04dd1b2c9543ccc114 | refs/heads/master | 2021-07-12T11:10:29.639525 | 2019-03-26T17:26:17 | 2019-03-26T17:26:17 | 152,277,720 | 0 | 0 | null | 2018-11-30T18:55:51 | 2018-10-09T15:41:53 | R | UTF-8 | R | false | false | 5,651 | r | generate_lag_props.R | download_backfill_data <- function(){
library(plyr) # for rbind.fill
library(dplyr)
source("https://raw.githubusercontent.com/cmu-delphi/delphi-epidata/master/src/client/delphi_epidata.R")
# Fetch data
all_obs <- lapply(c("nat", paste0("hhs", 1:10)),
function(region_val) {
... |
ab4b76740aec7c88530e92c9b59960b333cfd61e | d6f4c96bf2e19e52181f1bcdc260cb0c3cf77181 | /man/fars_summarize_years.Rd | 84e6d6636007bad8db7d463e50a0bd32ce8822ea | [] | no_license | drsmd23/Fars | bd5f0fac26f93ec1fdff8164412b60a3cdf924eb | 30a0228c80fe491139e18661952dab8a5b427c26 | refs/heads/master | 2021-01-19T17:10:56.023992 | 2017-04-15T02:39:24 | 2017-04-15T02:39:24 | 88,312,817 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,708 | rd | fars_summarize_years.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fars_functions.R
\name{fars_summarize_years}
\alias{fars_summarize_years}
\title{Summarize multiple years of motor vehicle crash data}
\usage{
fars_summarize_years(years)
}
\arguments{
\item{years}{A string or integer vector giving the years ... |
44c1c91171d6fdcaef1197b2e165b59558e49708 | d0da3117f0fda250bd1299b505013546f3e208d9 | /script/chart_year_sexualMinority.R | ebbe318c3b182adb4bd03793cbc68ef37ff5a0dc | [] | no_license | MaggieTsang/INFO201_Final_Project | bd02ba3bc986176b7a3029b57349bc219803ec7a | 871c60efe338f97cb9735325b9efd3c3abea3f24 | refs/heads/master | 2021-01-20T18:08:10.904964 | 2017-06-02T05:59:25 | 2017-06-02T05:59:25 | 90,907,920 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,014 | r | chart_year_sexualMinority.R | # Views trends between year and the appearance frequency of sexual minorities
library(dplyr)
library(plotly)
# Uses a line graph to view trendlines
YearSexuality <- function(dataset) {
# Get graphing summary
plot.summary <- GSMtable(dataset)
colnames(plot.summary) <- c("YEAR", "str_freq", "gsm_freq")
# Cre... |
031787c31d988d2ae8cfa084e2bbee2a59dd4c1b | ee769cd2ee8a012416dd156b3344e39670e55c6c | /0507_ShinyApp.R | a202f190c75930b98c978e0645b4c0dc541a486f | [] | no_license | vank-stats/STS347-Spring2020 | a09fcf371e55a5d283a46487ec3a4a89d4c89791 | d542a46be722e3356769eba6abead1eec020dd9a | refs/heads/master | 2020-12-23T06:51:06.117628 | 2020-05-08T01:00:47 | 2020-05-08T01:00:47 | 237,073,958 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,995 | r | 0507_ShinyApp.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/
#
# In the preamble, we will load any packages we want to use and possibly also
# any functions or data sets that we... |
ec9221f23579ebe292357045e4245bef6da4efcf | 3fb693303788a999f8e93d6e0323f68ff87a0f85 | /run_analysis.R | 7839983505c8728a131bd91505508684192e4683 | [] | no_license | Rakudajin/Clean_Data_Project | 5b09890089853fd594cc271e42874932d4c684fa | d1f7c4619f8fa07969c97860bc9338ed98531a48 | refs/heads/master | 2020-03-29T18:27:49.509928 | 2014-11-23T15:21:54 | 2014-11-23T15:21:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,490 | r | run_analysis.R |
# Load labels
feat_labs = read.table("UCI HAR Dataset/features.txt")
act_labs = read.table("UCI HAR Dataset/activity_labels.txt")
# Load train
data.sources_train = list.files("UCI HAR Dataset/train/",
pattern="*.txt$", full.names=TRUE, ignore.case=TRUE)
sapply(data.sources_train, read.table... |
2378d03b861a441a5859b0e56cd9d86e750fa178 | 69cc68dd79f328f4f628a06923be8fef20a8c46f | /script/rollingonly.R | 81a6a964f7b913e26fb8c5e2d92fb1cb3aa6d0d7 | [] | no_license | cw-NaoyaHieda/ToS_Garch | 365f332e4a9084117348dcc5a8670dd0d43e046a | daab49bee282df5aafb84ffc8af18899de497467 | refs/heads/master | 2021-09-17T15:43:41.978556 | 2018-07-03T11:21:39 | 2018-07-03T11:21:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,529 | r | rollingonly.R | # 重点サンプリングの結果がなんか違うのでやり直してみる
IS.fa_pre <- function(par){
f <- function(x, theta, par){
exp(theta*x)*dfas2(x, mu=par[1], sigma=par[2],
lambda=par[3], delta=par[4])
}
#weightを計算するためにMを計算する．99%,97.5%,95%をまとめて行う
M1 <- integrate(f, -30, 30, theta=theta.val1, par=par)$value
M25 <- int... |
9e064bcb712a26ade902d9fb8803128e7a1ec3f7 | 6a4b1fdeed411b03b17664114647746f88feeead | /Functions/draw_samples.R | 57778f3f2bafb2d9a92bfa0c3532dc584ecf5ebb | [] | no_license | glenmcgee/hospODS | 8b1162663670c5baf785af7efe731fa984971f24 | 19886dd09901296f16e2ed866db98a3efd1ba7e7 | refs/heads/master | 2021-04-15T11:32:24.961170 | 2019-09-09T02:56:15 | 2019-09-09T02:56:15 | 126,397,317 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,010 | r | draw_samples.R | ####################################################################
# #
# Sampling Functions #
# #
############################################... |
fe89f6c06d3147e339808bb12ef0d3cfc4183e4b | b186dcdf7e429997ea11c9e8cfc22077c060e489 | /scripts/analysis/functions/fd_stan_main.R | 1d4468bc40f72507b077d1fe1ed6432a234fd2db | [] | no_license | behinger/fixdur | 841599cbd231052dbc77ed0213d9a95c0d7faa1e | 8f6f4a8837b4ca1dd0dfcf9a96fddccc568e51cf | refs/heads/master | 2021-03-27T19:08:37.449607 | 2017-09-25T10:41:58 | 2017-09-25T10:41:58 | 68,902,529 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,706 | r | fd_stan_main.R | fd_stan_main <- function(mres,name,nchains=6,niter=500,rerun=F,is_on_grid=F){
if(is_on_grid){
rerun=T
nchains=1
name = paste0(name, 'grid_',paste0(sample(c(0:9, letters, LETTERS),10,replace=TRUE),collapse=''))
}
name = paste0(name,'.RData')
library(rstan)
dir = '../../cache/stanfit/'
dir.create(... |
17fc05c72cfb3855066809e2bfa857aa3aff2770 | 567d8f2a240cd3b7f3899b3fd5e3bd9328b2b895 | /R/demoplot.R | 7976a855254f07f0a99f495b7a3ee78783a3e0de | [] | no_license | cran/colorspace | ec9123555d9a820c2a2c639b94d28734563df6e0 | fadb043aeb85048a0a2b9daddbb258002d0a4dfc | refs/heads/master | 2023-01-24T18:54:37.214672 | 2023-01-23T10:40:02 | 2023-01-23T10:40:02 | 17,695,192 | 7 | 3 | null | 2017-08-29T03:43:34 | 2014-03-13T04:18:48 | R | UTF-8 | R | false | false | 8,711 | r | demoplot.R | #' Color Palette Demonstration Plot
#'
#' Demonstration of color palettes in various kinds of statistical graphics.
#'
#' To demonstrate how different kinds of color palettes work in different
#' kinds of statistical displays, \code{demoplot} provides a simple convenience
#' interface to some base graphics with (most... |
fce984c44279b5cf62401f0c7fd2722e680455ac | fecd3f8d27df0861bac6a1fd8306d5cbd0ce554a | /Homework 7.R | 3a4307526482b247882da8fa8fed25f61f289965 | [] | no_license | paschalmj/Bayesian-Albatross-Model | c26cef3c04ae80ec16691d122686d6db2833b121 | e522728a51a062c28b540928b88279393283174a | refs/heads/master | 2020-05-17T05:58:34.069055 | 2019-04-26T03:20:17 | 2019-04-26T03:20:17 | 183,549,270 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,568 | r | Homework 7.R | set.seed(123) #make sure it's reproducable
#PART 1
alb.data = read.csv("STAL data.csv", header=T) #breeding pair data
#Calculates NLL assuming exponential growth
#Input: growth rate (r), initial pop (N0), additional variance (CVadd), real data (alb.data)
#Return: NLL
getNLL = function(r, N0, CVadd, alb.data) ... |
836f31a1e2e8f6cba749fce479be0fb6fa6be8d3 | 448a1ff0d4b3d7029b5df32c0a09f32a338c8b65 | /Lab Week 9-3.R | 7cfcf30529c03adfc933276660a8d03c42d954b2 | [] | no_license | LouisJBooth/R | ccdca481d1e5ef78aa18e346046f42163b4ee982 | 6820bf082a3562a74a259ad7688771a2c68e1325 | refs/heads/master | 2020-04-17T15:50:57.087680 | 2019-01-20T23:24:59 | 2019-01-20T23:24:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 762 | r | Lab Week 9-3.R | pdf(file="half sphere.pdf")
x <- matrix(rep(seq(-5, 5, length= 100),100),100,100)
y <- t(x)
f <- function(x, y) { r <- sqrt((25-x^2-y^2)*(25-x^2+-y^2>0)) }
z <- f(x,y)
z[z==0] <- NA
persp(x[,1], y[1,], z, theta = 0, phi = 0, expand = 1, col = "red", xlab="x", ylab="y", zlab="z")
contour(z)
image(z)
contour(z, add=TRUE... |
c81c2d438b63c73cd2940f3c3182b16bfa130bf6 | 46239fbdbd5af227b892b6cb5ec2782657cf86cb | /week2/lessons/code1.R | 2dd101de17ef04c835535fdb652efb8dcd8939bc | [] | no_license | c91403/mitx15071x | e9d112d50794da857b9b3ee4425440016abafbf5 | 929d0694d1e93c5967288ae129ea359871d5d1f6 | refs/heads/master | 2020-05-17T05:44:32.797381 | 2014-03-21T20:49:14 | 2014-03-21T20:49:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,324 | r | code1.R | wine = read.csv("wine.csv")
str(wine)
# 1 variable linear regression, price vs agst
model1 = lm(Price ~ AGST, data=wine)
summary(model1)
# adjusted r-squared = adjust on independent variables, R-squared will decrease if you add a bad var
model1$residuals
# calculate the SSE
SSE = sum(model1$residuals ^ 2)
SSE
... |
6ad0f90faddd08f29034bffe1bf648fbf74d82ce | 614f6596ff7ba85e5fb2abf006c12d8e69e6c650 | /R/methods.R | 7e1840774c44876407e2c03e7043785d6199a94b | [] | no_license | sheng-liu/SeqFrame | 09651fb03dc3f55438bb77fce923041689ed92cf | d22650c4dc48d86f4aa22b4c2718cec1f84e00d7 | refs/heads/master | 2016-09-06T13:49:44.903793 | 2014-12-31T11:16:07 | 2014-12-31T11:16:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,980 | r | methods.R | # SeqFrame - methods
#
#
###############################################################################
## so add a id column (the row.numbers), use it subset annotation, transfer to
## grangs and open genome browser todo: remove or hide id columne as row names
## in flowCore
## ------------------------------------... |
92ebe27e93280389c14b7700d30d1b3f269748cd | 172c131b7456c76b300d5c7213528b281617db8d | /man/sSpec-class.Rd | 7b108ae289c62490252f662c251a6e834f78edb3 | [] | no_license | cran/momentfit | 7b23f8d5ed63e32cc4ba368277a106acc4dc8618 | b7c29f7d8b6f4c3fa0ea5356409b654ede8c8df7 | refs/heads/master | 2023-06-08T23:42:57.900725 | 2023-06-05T14:20:02 | 2023-06-05T14:20:02 | 237,930,018 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 889 | rd | sSpec-class.Rd | \name{sSpec-class}
\docType{class}
\alias{sSpec-class}
\title{Class \code{"sSpec"}}
\description{
A class to store the specifications of the kernel used to smooth moment
conditions.
}
\section{Objects from the Class}{
Objects can be created by calls of the form \code{new("sSpec", ...)}.
It is created by \code{\link{k... |
97c361144c3524060531d48079de65090a7a53e6 | 8283ef54fe2d4e743bdb50e54f44cf8068df021d | /R/helpers.R | d80e7f69406c532247cdb4b5d7ada51cc9eed7fe | [] | no_license | daroczig/PWA | 181ec3a98cdf1a191cfce16659febdad6eb81590 | 234912efcc2d9aaa7ff1bd910713df60a023f6dd | refs/heads/master | 2016-08-07T02:50:32.643363 | 2014-09-14T12:04:13 | 2014-09-14T12:04:13 | 24,019,354 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 264 | r | helpers.R | #' Outlier detection function
#' @param x numeric vector
#' @param z standardized threshold
#' @return vector index of outliers
#' @export
#' @examples
#' out(runif(10), 0.9)
out <- function (x, z = 0.7)
which(abs(scale(x, scale = TRUE, center = TRUE)) >= z)
|
0b227b6f392d16188736a4a8846190e8d92d4164 | b188cae574794bfc9b2cb74f8ef89912c92c7cfe | /label_examples/label_spots/label_checker.R | 020c4f3b65c104a6691a8f900c4cfa6a408ed59d | [
"MIT"
] | permissive | dinhnhobao/urops | e1dff94de828b420f7fd1df1b07b95de59b6cffe | cc25d02830f907040eaad741e426b7b961186bd2 | refs/heads/master | 2022-09-08T23:17:56.570128 | 2018-12-07T06:38:37 | 2018-12-07T06:38:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,263 | r | label_checker.R | library(imager)
library(stringr)
# Script that, given a directory of pictures and the date entitling their
# labels .csv file, plots the pictures and their labels side-by-side.
# Before running this script, set the working directory as the location of this
# file.
pictures_directory <- "pictures_to_label/"
labels_dir... |
21eccbb21d94643fff36915f2a227436c7760762 | b43a223cbf97422a2a26e6004f55500713ae9df1 | /server.R | 732d5476173589fa8296af3980860bf71816b65a | [] | no_license | eriklindquist/bfastspatial | 1c8d44d89e2b3db7095c057a46e35764ac07ad7f | d5a8d875c80afadca168973cc1ef2a90ddd974ac | refs/heads/master | 2020-03-18T07:02:56.039397 | 2018-05-09T11:30:36 | 2018-05-09T11:30:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,575 | r | server.R | ####################################################################################
####### BFAST
####### SEPAL shiny application
####### FAO Open Foris SEPAL project
####### remi.dannunzio@fao.org - yelena.finegold@fao.org
####################################################################################
#########... |
c2454b882148e73ba81b9435398ab2d0fb9a22b1 | 338cfd3efe0cc943d2e6b58becf7432ced163ab2 | /02Mastering Machine Learning with R/ch2linear_regression/i2snake_fit.R | b3d874bce249963fdee64834a96fb36ecdca41ad | [] | no_license | greatabel/RStudy | e1b82574f1a2f1c3b00b12d21f2a50b65386b0db | 47646c73a51ec9642ade8774c60f5b1b950e2521 | refs/heads/master | 2023-08-20T17:07:34.952572 | 2023-08-07T13:22:04 | 2023-08-07T13:22:04 | 112,172,144 | 6 | 4 | null | null | null | null | UTF-8 | R | false | false | 245 | r | i2snake_fit.R | library(alr3)
data(snake)
names(snake) <- c('content', 'yield')
attach(snake)
yield.fit <- lm(yield ~ content)
summary(yield.fit)
plot(content, yield)
abline(yield.fit, lwd=3, col='red')
par(mfrow = c(2,2))
plot(yield.fit)
qqPlot(yield.fit) |
5aa71237cc1693fd62b55c60d9e52ec2ff94a905 | d0abe38ea4cd9a88321437cd4d922d2affc91f46 | /JDE_Shubert3_JUL_07_2020.R | 77e0b3e2fd3e50d6b5b62a0b33c8859ef450dafb | [] | no_license | ucfilho/Raianars_R_language_July_2020 | 33c50e82ebd1cef15cecaf9e49e86f7ac11cfb6b | 155ea5e629bcf6c9ac09f556dd4d0a12bffdd9c8 | refs/heads/master | 2022-11-18T13:33:21.320916 | 2020-07-09T22:18:54 | 2020-07-09T22:18:54 | 277,663,167 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 658 | r | JDE_Shubert3_JUL_07_2020.R | library('DEoptimR')
Shubert3= function(x)
{
Num=length(x)
fun=0
for (i in 1:Num)
{
for (j in 1:5) { fun = fun+ j * sin(((j + 1) * x[ i]) + j) }
}
return(fun)
}
# Global Minimum: 0 , domain=[-10,10]
dim=30
RUNS=50
ITE=2000
NPAR=100
Bounds=10
Y=0;X=0
for(i in 1:RUNS)
{
JDE_R=JDEoptim(rep(-... |
1e49cd2725c8e42b471b4c5be5be4dcf83c0db14 | 26c213b0b8a7720d4447660073503f661bcb7a46 | /final project synthetic data.R | 144c038baf5f251d1affb1d1e321439b0ce5b38b | [] | no_license | brianpclare/SynetheticMedicareData | 38ea45ff0f62452a80dd13375bdef14a18174328 | 4e4600724bc6fa6065d0b07434fad5515d7533a3 | refs/heads/master | 2020-03-09T21:53:08.599409 | 2018-04-20T20:57:20 | 2018-04-20T20:57:20 | 129,021,185 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,091 | r | final project synthetic data.R |
# Synthetic Medicare Claims Data - not included in Github repo, over file size limit
# Outpatient Claims - 154 MB file
# https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/SynPUFs/DESample01.html
# Variable Guide
# https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadab... |
027d66ff1261ccd24a70dbf71876cd82840230c9 | 7c5cf1fa0ca132146f7325f1af17b5e515d1adb1 | /man/dot-addSurvivalPredictions.Rd | 3518a109958170708ba1c89f941250a8bc61a39b | [] | no_license | jspaezp/MSstats | 00f2deebdfd502f7e07e4d3f00c5518212ceb03a | 76af4698ca93c904bb8eae694dcf794f56e472ed | refs/heads/master | 2023-04-29T19:11:17.476073 | 2023-03-27T09:49:46 | 2023-03-27T09:49:46 | 93,803,624 | 0 | 0 | null | 2018-05-29T02:11:38 | 2017-06-09T00:43:26 | R | UTF-8 | R | false | true | 409 | rd | dot-addSurvivalPredictions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils_imputation.R
\name{.addSurvivalPredictions}
\alias{.addSurvivalPredictions}
\title{Get predicted values from a survival model}
\usage{
.addSurvivalPredictions(input)
}
\arguments{
\item{input}{data.table}
}
\value{
numeric vector of pre... |
68381950f74624cd323383c07f3991d4ba5b0538 | 5e88cabd66814e2edc394548f6c7d76c6511b41e | /man/ConfRatio.Rd | 67d8c432d205c25feebf6386c8b06403dff81dab | [
"MIT"
] | permissive | EarthSystemDiagnostics/paleospec | ba7125c62946eba4302e1aaf20e1f7170262809d | bf2086b9d4adb5c657af3863d15745a730f9b146 | refs/heads/master | 2023-09-01T07:23:35.955702 | 2023-06-18T15:18:16 | 2023-06-18T15:18:16 | 223,199,924 | 0 | 0 | NOASSERTION | 2023-06-18T15:18:18 | 2019-11-21T15:02:33 | R | UTF-8 | R | false | true | 568 | rd | ConfRatio.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ConfRatio.R
\name{ConfRatio}
\alias{ConfRatio}
\title{Confidence Interval of ratios}
\usage{
ConfRatio(varratio, df.1, df.2, pval = 0.1)
}
\arguments{
\item{varratio}{a variance ratio}
\item{df.1}{degree of freedom of denominator}
\item{df.... |
0cef39e44e771a05d52c9fd5743d8db8055a934a | 053f4cf013243c844b2c7728438d4d6c314149dc | /R/placeholder.r | 50a85853df8445d3342152b075d4b29ce5e398df | [] | no_license | LeverageData/RTutor | e4269dbc509920449a1c549305ae920310c1bc2a | 81a67b29f02d66a2ac44624383b2f052a7692e09 | refs/heads/master | 2023-01-13T12:26:13.610354 | 2020-11-11T17:07:14 | 2020-11-11T17:07:14 | 257,319,587 | 1 | 0 | null | 2020-04-20T18:04:05 | 2020-04-20T15:12:59 | null | UTF-8 | R | false | false | 237 | r | placeholder.r | get.placeholder = function(ps=get.ps()) {
ph = ps$rps$placeholder
if (is.null(ph)) return("___")
ph
}
has.call.placeholder = function(call) {
if (!is.character(call)) {
call = deparse1(call)
}
has.substr(call,".PH_._")
} |
4c276dfbb42698e2695587292a2f04730876c8c9 | 0bbaef2c499561083f1239b2ea5c95245b111d60 | /man/workspace_sync.Rd | 2f8b0b85f056ff3b0aee8d5d7d8d9676c871a164 | [] | no_license | vh-d/languageserver | 81f8313cd5836dd977c33d1df3480aea39f97ed1 | 70279af46677906ce1a191e1aa35b752f5af75a0 | refs/heads/master | 2020-03-07T10:13:15.696417 | 2018-03-21T04:32:32 | 2018-03-21T04:32:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 348 | rd | workspace_sync.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/workspace.R
\name{workspace_sync}
\alias{workspace_sync}
\title{Determine workspace information for a given file}
\usage{
workspace_sync(uri, document)
}
\arguments{
\item{uri}{the file path}
\item{document}{the content of the file}
}
\descr... |
45a3387e38e61f75ee1285e0e2a5dfe3dbcbb6a7 | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /cenROC/man/RocFun.Rd | 6dbc38ca0f978cba814bb60fcdaeec564cacb333 | [] | no_license | akhikolla/InformationHouse | 4e45b11df18dee47519e917fcf0a869a77661fce | c0daab1e3f2827fd08aa5c31127fadae3f001948 | refs/heads/master | 2023-02-12T19:00:20.752555 | 2020-12-31T20:59:23 | 2020-12-31T20:59:23 | 325,589,503 | 9 | 2 | null | null | null | null | UTF-8 | R | false | true | 1,336 | rd | RocFun.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/zzzz.R
\name{RocFun}
\alias{RocFun}
\title{ROC estimation function}
\usage{
RocFun(U, D, M, bw = "NR", method, ktype)
}
\arguments{
\item{U}{The vector of grid points where the ROC curve is estimated.}
\item{D}{The event indicator.}
\item{M... |
e9f0ddd56ae71b21431c3c72b3f8c66de9bbb74a | bf2f36573a114860dcedd02e16acab2391ef9c07 | /development.R | 4d0326b5c4164caee40b45b035eabf1cefcc60a7 | [] | no_license | romangerlach/rBExIS | f8f3b9c195ecb65fa0fc8f31e1a289d7e02c8b75 | 389bd1df142aa9ffa6486fcadef7648990b25204 | refs/heads/master | 2021-01-18T02:56:42.806188 | 2016-06-10T06:52:55 | 2016-06-10T06:52:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 550 | r | development.R | # This is a development helper file. It hosts calls to help with the
# documentation and package checking and to try out new functions.
# load the devtools package
require("devtools")
# load/reload the bexis package functions
load_all("rBExIS")
# check package for consistency
check("rBExIS")
# Here start functions ... |
1faa9973b05608ad0bf48557027d8202a182b28a | 40b05699e3dea47eedd27bb4d03b065eaffec42a | /cachematrix.R | 49b2669a641db79fa17112245bb95136f34f58dd | [] | no_license | andylytics/ProgrammingAssignment2 | a6f81da9498070e263504bf79df3868014597b0b | bc0cb772360d430fb6a18c3d81779bd059796d09 | refs/heads/master | 2021-01-12T21:45:07.417180 | 2014-04-26T02:27:49 | 2014-04-26T02:27:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,185 | r | cachematrix.R | ### Assignment 2, Coursera R Programming, 2014-04-25
## These two functions are intended to compute and cache the inverse of a supplied square matrix.
## Caching can improve the speed of a script or program by preventing the need to perform a
## computation redundantly
## the makeCacheMatrix function creates a list o... |
058cb7ad068049725fc2cc89b7a327ff4420b0c1 | a30f11c8b9ebc0cedd06c23237ced83ef3abc32c | /Scripts/00_Functions.R | 021b83b827651375c2b1ca68f12b888ec53b0375 | [
"MIT"
] | permissive | avaimar/social_cohesion | 00770a182434fcd08d31d17e5c923bd5f0fe1181 | 7296a0cfa9510008232a0744b89ff3b8e6e2c765 | refs/heads/main | 2023-05-07T04:06:58.219528 | 2021-06-05T01:09:50 | 2021-06-05T01:09:50 | 366,936,146 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,316 | r | 00_Functions.R | generate_X_Y_W_C <- function(outcome, covariates) {
# _______________________________________________________
# Generates the X, Y, W and cluster components for a given outcome
# and a set of covariates by eliminating missing observations
# from the SC_Data dataset. Assumes the treatment column is
# named 't... |
a3cb1effe726553c9f6ea50e853e09449fe03ef1 | 6f447146bd1a7ef9a17cb4fbcbd89682c64a7733 | /man/cell2nb.Rd | bdf23e88c66dfde1d03c748ecc300abf0dcb3cc4 | [] | no_license | zhanglibin123kib/spdep | cf28eb2fe0e5f1e3f22a0e936cef61d7ce923a95 | bc16d2c867224ad695638eeb67c964fb39cc9448 | refs/heads/master | 2023-03-18T07:56:39.431747 | 2021-03-05T09:09:26 | 2021-03-05T09:09:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,405 | rd | cell2nb.Rd | % Copyright 2001 by Roger S. Bivand
\name{cell2nb}
\alias{cell2nb}
\alias{mrc2vi}
\alias{rookcell}
\alias{queencell}
\alias{vi2mrc}
\title{Generate neighbours list for grid cells}
\description{
The function generates a list of neighbours for a grid of cells. Helper
functions are used to convert to and from the vector ... |
a47fb5e2d2874b1538453ca6d3b842dc2a59c848 | 0a7ae4e2439d60a6e0f0cc3cb5cdb94225e37211 | /R/plot.toswp.R | 0a5484b5a59caa2b90e8f19210d5b2dc2b23f92f | [] | no_license | cran/BootWPTOS | 2a348a545eddda918cf4e3d69683a20dc8afbf2e | 345d143c842b942dcaffcfaf4edf4fdbaeeb40c6 | refs/heads/master | 2022-06-12T06:16:20.567081 | 2022-05-20T11:40:02 | 2022-05-20T11:40:02 | 62,325,638 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,867 | r | plot.toswp.R | plot.toswp <-
function (x, sub = NULL, xlab = "Time", arrow.length = 0.05,
verbose = FALSE, ...)
{
object <- x
x <- x$x
if (is.null(sub))
sub <- paste("Packet:", paste("(", object$level, ",", object$index,")", sep=""), "Using Bonferonni:", object$nreject,
" rejected.")
#
... |
d08fe904c5baf7c14598d8dae861cd71ad013f53 | 6629f51c9381de154ff736fe296781abe18ec0c2 | /shiny/HCT116graph/global.R | 137b55ba14a630b7c4dbebdbaf6beb82948f390d | [] | no_license | leeju-umich/Park_et_al_Cell_Reports_2020 | f10a7e897bacda2644a75185751a51980d738f06 | 4c4585c9bd3382ad9074fa24b893ae10171d7e0e | refs/heads/master | 2023-01-01T14:25:34.620153 | 2020-10-26T17:04:44 | 2020-10-26T17:04:44 | 281,723,115 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,256 | r | global.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(ggplot2)
library(Seurat)
library(stringr)
library(dplyr)
library (gtools)
li... |
d3804510acc3cc0631a512bef7f53c47ad49b110 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/kwb.hantush/examples/hantushDistancesBaseProps.Rd.R | c0fdc5700e7bda5ddda4e3c97406ba7f28bae3a8 | [] | 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 | 1,008 | r | hantushDistancesBaseProps.Rd.R | library(kwb.hantush)
### Name: hantushDistancesBaseProps
### Title: Hantush distances & base properties: allows input of vector of
### x,y coordinates and also a vector for one of the base properties
### Aliases: hantushDistancesBaseProps
### ** Examples
baseProps <- baseProperties( time = 2^(0:6),
... |
088f9a84e8d2bd96cea55e526ebcab5be745189e | 9e29face83aae3213bbc6a156caab03b4fd2bf07 | /R/plot.modgam.R | f3ed553b47e7a0f124a14bfa4cb433592912b5bc | [] | no_license | cran/MapGAM | 1a17f31700f02462c95ce14e384aff40695851a5 | 4d4cb56e3ec5abeeac7831503d7170fb9d0f398b | refs/heads/master | 2023-07-22T15:30:56.523947 | 2023-07-15T11:00:02 | 2023-07-15T12:49:47 | 17,680,825 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,309 | r | plot.modgam.R | #***********************************************************************************
#
# Maps Predicted Values and Clusters for modgam Objects
# Copyright (C) 2016, The University of California, Irvine
#
# This library is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Publi... |
080fb4795cdc519022f6122c1567222dd019e7ee | 0a906cf8b1b7da2aea87de958e3662870df49727 | /diffrprojects/inst/testfiles/dist_mat_absolute/libFuzzer_dist_mat_absolute/dist_mat_absolute_valgrind_files/1609962324-test.R | 1d0c442989fdb51fb2e7a69dfe379e251cb4a9e1 | [] | 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 | 1,233 | r | 1609962324-test.R | testlist <- list(x = c(-8399334L, 794034134L, NA, -54529L, -180L, 1291845631L, -1L, -1L, -1L, -1L, -1L, -16711681L, -1L, -687865865L, -2097153L, -1L, -65536L, 0L, -1L, -393258L, -2049L, -536871026L, -1L, -10726L, 803602431L, -1L, -10497L, -1L, -1L, -1L, -1L, -1L, -1L, -15060993L, 673513472L, 15728639L, -1L, -1L, -1... |
516291e2fea08a48a64fa16776f626a1fd1008bd | d9cb21e1111781770e9d9a48938d65c2662faab8 | /R/removeRowsAndColumns.R | df4888d0f4c68ae89ac617d7b918075b1fa4cca1 | [] | no_license | Displayr/flipStandardCharts | 2f30f8571160f263bd64f4ebaae5fc24cbe91815 | d70f790a95906db1969689fe571918367cc4aaaa | refs/heads/master | 2023-06-22T07:38:34.602645 | 2023-06-13T22:59:26 | 2023-06-13T22:59:26 | 56,812,768 | 5 | 5 | null | 2023-06-13T22:59:27 | 2016-04-21T23:57:16 | R | UTF-8 | R | false | false | 711 | r | removeRowsAndColumns.R | removeRowsAndColumns <- function(chart.matrix, rows.to.ignore = "", cols.to.ignore = "")
{
## Get the individual headings from the input
remove.rows <- as.vector(sapply(strsplit(rows.to.ignore, ","), function(x) gsub("^\\s+|\\s+$", "", x)))
remove.cols <- as.vector(sapply(strsplit(cols.to.ignore, ","), func... |
3ca9f5ff75e519375d686e90e02af5083b339961 | 2d1f1df7d7d47e6981160a806c69cbe472936cfb | /R/jet.colors.R | d5283298447ab578179aa36fe311cedd3afca326 | [
"Apache-2.0"
] | permissive | anlopezl/predictiveModeling | 113420e5aadc27025c062c4b32e420457230ee31 | da1e5b299936733b2885ed01b03c99f2ca14ed18 | refs/heads/master | 2021-01-21T09:38:32.983257 | 2013-02-11T17:13:30 | 2013-02-11T17:13:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,365 | r | jet.colors.R | library(RColorBrewer)
jet.colors <- colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan", "#7FFF7F",
"yellow", "#FF7F00", "red", "#7F0000"))
redblue.colors <- colorRampPalette(c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7",
"#F7F7F7", "#D1E5F0", "#92C5DE", "#4393C3", "#2166AC", "#053061"))
r... |
b2b9a2edf50142b42dff72648de2fe9842fa07c8 | fca72c9df63447447a30579c128c769908de8ef7 | /3.R | 840420aeca6791032c1cfbe7440025f0de1f165c | [] | no_license | zhtmr/r | 7ec8f1d783910ba76a875fdee86168d07cafbc8d | 43e00da9222fa413d5fc8f489329eb3c40537c44 | refs/heads/main | 2023-01-25T05:05:41.023216 | 2020-11-23T11:20:50 | 2020-11-23T11:20:50 | 307,611,682 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,830 | r | 3.R | library(ggplot2)
# x축 displ, y축 hwy로 지정해 배경 생성
ggplot(data=mpg,aes(x=displ,y=hwy,color=drv))+
# 배경에 산점도 추가
geom_point(
# 점 크기 지정
size=5
)+
#범위 지정
xlim(3,6)+
ylim(10,30)+
stat_smooth()
head(mpg)
ggplot(data=mpg,aes(x=cty,y=hwy,color=drv))+
geom_point()+
stat_smooth()
str(midwest)
ggplot(da... |
0e410d8765ff50f95b6147773bb6e19e028daa7b | a8c42539f00da72b7b4fdcf6939efb6f9e8b55f2 | /vignettes/find_chr_variables_download_data.R | e191d868021bc615a466bffb4212ec58f95ea200 | [] | no_license | uva-bi-sdad/dc_county_health_rankings | 1a5eb551d95f408744f90d52c7d36dc30d5e032a | c8be12638f8bab805697244b6625149cf952738a | refs/heads/main | 2023-08-27T13:29:57.008263 | 2021-10-28T21:55:54 | 2021-10-28T21:55:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 330 | r | find_chr_variables_download_data.R | ## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)
## ----load_column_definitions--------------------------------------------------
#load("data/county.hlth.rnks.columns.RData")
data(county.hlth.rnks.columns.RData, package = "dc.chr.preventable.hospitaliz... |
6bdbc92b05616449fa29ca451eaa572f8dab7fe8 | 9ef33787b83ad56a11d50dd43f477ff22eee5bcb | /R/stat_marimekko.R | 13e0760215d8be68f2f6732c4a7382845de4422c | [] | no_license | cran/ggTimeSeries | 23c34bf61319ccd65ca49d9321c421fdd5e6a91b | 7826f0133c9894c496bb41ee04af1e92a999376d | refs/heads/master | 2022-02-06T05:16:51.826879 | 2022-01-23T15:22:42 | 2022-01-23T15:22:42 | 147,195,681 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,051 | r | stat_marimekko.R | #' Marimekkofy
#'
#' @param data dataframe
#' @param xbucket x value
#' @param ybucket y value
#' @param weight weight value
#'
#' @return df
#' @export
Marimekkofy <- function(data,
xbucket = "xbucket",
ybucket = "ybucket",
weight = NULL) {
xmax... |
1b881e6f17d35c47d09664386e3dc6260e6ea443 | d232d8214f9d216546c45ba5818acf45201200ec | /.Rproj.user/70A32BCB/sources/s-2ABB0572/084633B8-contents | 7ef3b52606c51e8ee8edd5fef0de9e312bf381aa | [] | no_license | Keniajin/alacohol_karuitha | 1d47d090fedefb16234a1419c3631a2dfec660a1 | 4d42199082405e846fc8654aca4bec9dcf05b094 | refs/heads/master | 2022-11-23T13:41:08.828982 | 2020-07-30T09:42:27 | 2020-07-30T09:42:27 | 283,726,653 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,498 | 084633B8-contents | ##THE BEER DEBATE PROJECT ----
# Objective- to get and clean data on beer ratings ----
# Set working directory ----
setwd("C:\\Users\\John Karuitha\\OneDrive - University of Witwatersrand\\Documents\\My Thesis\\Karuitha and Ojah Data\\r_training\\beer_debate")
save.image("C:/Users/John Karuitha/OneDrive - University of... | |
2fe07911617aa5ca478991b58e9164990b2a3cff | 110f1df5d85bd38a51b2ebba882636c35ce3dc3e | /model_fitting.R | c48ca2584b17ac1ec9614c866fd154e22a9859eb | [] | no_license | xdu071/quant_skills_1 | 372d0f9c8fde18c2241edc283d3b36eb05c9632c | 2b81682384df6b288783f3137da142d4f8e6b688 | refs/heads/main | 2023-09-03T12:00:38.738687 | 2021-11-08T17:20:21 | 2021-11-08T17:20:21 | 425,875,072 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,051 | r | model_fitting.R | # Model fitting and Generalized Linear Model
# This file contains a tutorial material for fitting models and constructing Generalized Linear Model
# Edited by David Du
# Nov-08-2021
# Note: model fit refers to investigating the R-squard adj value
# Note: AIC -> Akaike Information Criterion -> refers to the likelihoo... |
2bcf56930f97b107b560259e04282961e14232c1 | 898f5b96a57ead23a7a71ffe503455a6e9aa4d1d | /complete.R | 6aaa89e965c497b8c40a7c3e6c83aed75a02da52 | [] | no_license | BearyTatsumaki/datasciencecoursera | e5c8ba32a5465b483c9510077491e2bb77a4c219 | d490d0c4b0f1933e767af797579b70c2d12cb20d | refs/heads/master | 2022-11-20T18:50:28.139630 | 2020-07-27T14:52:47 | 2020-07-27T14:52:47 | 280,234,060 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 887 | r | complete.R | complete <- function(directory, id = 1:332)
{
data <- c()
idframe <- c()
#for loop takes x in an array, not just a value
for(x in id)
{
#formatC adds the excess 0's in front of the id value
file <- paste(getwd(), "/", directory, "/", formatC(x, 2,flag = 0), ".csv", ... |
22b716719e3de806240f2cb3a473da40aca80631 | 9ecd686648f3f0c1eb2c640719da8b133da1cf24 | /R/affil_df.R | b5b2f6511a00c448124376f5f2709e6aaaccb4ac | [] | no_license | muschellij2/rscopus | 55e65a3283d2df25d7611a4ace3f98bd426f4de4 | bf144768698aaf48cb376bfaf626b01b87a70f73 | refs/heads/master | 2022-01-02T15:18:19.295209 | 2021-12-18T00:37:22 | 2021-12-18T00:37:22 | 44,287,888 | 65 | 18 | null | 2021-12-18T00:23:52 | 2015-10-15T02:06:02 | R | UTF-8 | R | false | false | 2,032 | r | affil_df.R |
#' @title Search Affiliation Content on SCOPUS
#'
#' @description Searches SCOPUS to get information about documents on an affiliation
#' @param affil_id Affiliation ID number.
#' @param affil_name affiliation name. Disregarded if \code{affil_id} is
#' specified
#' @param verbose Print diagnostic messages
#' @param a... |
523a6b3e36f7bdae7356db08f48f60c6c7e15666 | a2a1d0eec6173e66040071d696f556215603d593 | /man/A000108.Rd | 3c7e397da99bb5165a132241f8f68f02d3e851cd | [] | no_license | cran/Zseq | 08ffaf47a36c16d95c294d97b0eac6f6d4d31732 | 1dfdcf0c142076df3ff5337a10af4d570a86cefa | refs/heads/master | 2022-09-28T02:32:44.076755 | 2022-09-07T06:50:18 | 2022-09-07T06:50:18 | 108,447,611 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 757 | rd | A000108.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Catalan.R
\name{Catalan}
\alias{Catalan}
\alias{A000108}
\alias{Segner}
\title{Catalan numbers}
\usage{
Catalan(n, gmp = TRUE)
}
\arguments{
\item{n}{the number of first \code{n} entries from the sequence.}
\item{gmp}{a logical; \code{TRUE} ... |
679c5104ee5e86432244d19733ea7790378e4bc0 | 9dd3e7638423ca7e8c52875f8b43445177f69054 | /PaperData/Sect. 4.5/GenerateGraph.R | 5c98aa351dafcbfe9e35693741c76e302b7b108e | [] | no_license | douglaspasqualin/Bench-20-STM | c9d191a4bce6708be03046d2c05330947f9a5895 | a2c0df9ad88a683fb08abddeb60f670e52bb5a8e | refs/heads/master | 2023-03-05T19:49:33.080419 | 2021-02-17T17:32:26 | 2021-02-17T17:32:26 | 299,409,095 | 0 | 0 | null | 2020-10-06T22:20:07 | 2020-09-28T19:18:27 | C | UTF-8 | R | false | false | 1,022 | r | GenerateGraph.R | library(ggplot2)
csv <- read.table("mseDynamic.txt", sep = ",", header = TRUE)
#remove with ID zero (no MSE, first matrix collected)
table <- subset(csv, id != 0)
yticks <- c(0, 250, 500, 750, 1000, 1250, 1500)
graph <-
ggplot(data = table, aes(x = id, y = mse, group = app)) +
theme_bw() +
theme(
legend.p... |
d364f2044fa6653b7adcb579a902816ce982944e | 79f51a99ceac749670589464fb6b9bc78fe2e95c | /StepE.R | 3700b1f046d81b2ca618df758cb27d9693e2af05 | [] | no_license | fall2018-saltz/ananth_gv_svm | a26c1a2ca191978c0c87906351b7e9378f921129 | 898879df3cff94ac99db2199e6cc9f1e49cd5d9c | refs/heads/master | 2020-04-06T16:40:05.583231 | 2018-11-24T07:32:36 | 2018-11-24T07:32:36 | 157,629,189 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,765 | r | StepE.R |
###################################################################
#Part D: Predict Values in the Test Data and Create a Confusion Matrix
# 8. Use the predict( ) function to validate the model against test data. Assuming that you put the output from the ksvm( ) call into svmOutput and that your test data set is in a... |
999a46d405c00321b487e086a1335cf9db2f2179 | 046d88173798e32f626ae598547496da898d49f5 | /R/shannon.R | eb09537bab1d08af21d9244bbe78fccf13730f63 | [] | no_license | wisekh6/epihet | 6199b38362200bd3f2826d716fecb653176a679b | 9cff2f91a1af377df712dbe6c3cbb5748e1bf860 | refs/heads/master | 2023-02-06T06:53:28.256472 | 2020-12-21T15:26:58 | 2020-12-21T15:26:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 334 | r | shannon.R | #' @title Shannon Entropy
#'
#' @description
#' Calculates the Shannon entropy value
#'
#' @param p A vector of epiallele probabilities
#' @return The Shannon entropy value
#' @examples
#' a<-c(rep(0,13),60.86960,0.00000,39.1304)
#' shannon(a)
#' @export
shannon <- function(p) {
p1 <- p[p > 0]
sum(-p1/100 * l... |
407b3189357f53468782062d54577fd6781db723 | 37a70a2a8c84f353d45cd678f059cbe5446d5346 | /day8_1/nba_eda.R | 0b9d565ccdba19a98fa7a2b89b6d862799038037 | [] | no_license | jee0nnii/DataITGirlsR | a27f7ce1c3f90765366f120ff85cd7f2cee60e8c | cc6e7b3f2d30c690a41e4ca5a165d32de47d3c3f | refs/heads/master | 2021-03-19T16:49:01.412022 | 2017-11-06T15:21:56 | 2017-11-06T15:21:56 | 109,706,695 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,129 | r | nba_eda.R | # NBA 데이터를이용해, 슛확률(Spoint)에대해회귀분석을시행해보자.
nba <-read.csv('NBA.csv')
head(nba)
library(GGally)
plot(nba)
str(nba)
for (i in 1:dim(nba)[2]){
print(summary(nba[i]))
}
test <- subset(nba, select=c(2:11))
ggpairs(test)
colnames(nba)
# [1] "Name" "height" "games" "minutes" "age" "point"
# [7] "... |
dc9295b01173e65ca451f694123366e4b0a50af1 | 7c9c2e23420a68537b2988ea08f25ac16db42a9c | /facets/facet_map.R | 66678464e29b874047951e4858f82173a9a7d756 | [] | no_license | VinayArora404219/TN_COVID | eede36a1ccb8617c3f0d36199366b7e2082467bb | a9dd22a17eafa9e3511b1db658c04a83ef1ebfd0 | refs/heads/master | 2022-11-26T18:23:37.449134 | 2020-08-06T03:10:56 | 2020-08-06T03:10:56 | 285,459,297 | 0 | 0 | null | 2020-08-06T03:03:17 | 2020-08-06T03:03:16 | null | UTF-8 | R | false | false | 3,378 | r | facet_map.R | ### Make a facet map of US states with the number of new cases over time
library(tidyverse)
library(tidycensus)
library(cowplot)
library(TTR)
library(geofacet)
library(broom)
### Pull cases data from the NY Times repo below
###
### https://github.com/nytimes/covid-19-data
###
spreadsheet <-
"../Datasets/nytimes/cov... |
be5b0f3cc13155f1fa12c82b5cf8129486aeeb23 | 05087895b6d9efba31e9b68a361fe72f3e046492 | /portfolio/analysis/3.3-plot-within-strategy-effects.R | 0831d0d4abb52f9e997c8f7f3a641997efe481cd | [] | no_license | NCEAS/pfx-commercial | a37a6644ee60af90bfc72f45987fbe665d0ca347 | 910304637a7be3c3b151972a8cb65a2423914bed | refs/heads/master | 2020-04-04T07:18:28.148662 | 2017-09-02T14:46:48 | 2017-09-02T14:46:48 | 45,410,050 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,751 | r | 3.3-plot-within-strategy-effects.R | library(dplyr)
library(ggplot2)
library(viridis)
library(ggrepel)
load("portfolio/data-generated/diff-dat-stan.rda")
load("portfolio/data-generated/m.rda")
devtools::load_all("pfxr")
b <- broom::tidyMCMC(m, conf.int = T, estimate.method = "median",
conf.level = 0.5, conf.method = "quantile")
g1 <- b[grepl("coef_g1"... |
fb011adef8c89b1e804648d18c9648b3e294f5f5 | 1c9991dd85472c1265e1ba0c1c4591337887a908 | /ProbTable.R | 0b7e5eb5c97e19ae588da628a2fd87cfedd3952c | [] | no_license | Jonplussed/PCC-MTH243 | 26ad5dac24931158f95a8ef7d99425112211c780 | ebd387ec5c1e2650b263208a58ed7b3934b61c5b | refs/heads/master | 2023-05-29T10:38:42.851898 | 2021-06-08T13:43:23 | 2021-06-08T13:51:26 | 357,639,088 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 744 | r | ProbTable.R | # A table of possible values and their probabilities, from which we can derive
# the mean (expected value), variance, and standard deviation.
ProbTable <- setRefClass("ProbTable",
fields = list(
frame = "data.frame",
mean = "numeric",
var = "numeric",
stdev = "numeric"
),
methods = list(
i... |
d59a537f63ba900d3627b78a072c12d855b1c40e | 325f4cc05aca5072febcfea5d67934e30526beb2 | /teaching/Applied Statistics and Data Analysis/section-10-11-11am.R | b4262b72d5d2a024c0c8a5215c5f11ab1c253984 | [] | no_license | CongM/CongM.github.io | 5e293bd7a71c21708acf2f09e138e593b1424e74 | f6aa745fa3bcc35ae2fe388cd75fd536faa8a674 | refs/heads/master | 2023-07-25T14:58:40.359155 | 2023-07-09T06:48:23 | 2023-07-09T06:48:23 | 153,951,666 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,923 | r | section-10-11-11am.R | ###################################################
#### Examples in Faraway Chapter 6
####
#### Cong Mu
#### 10/11/2019
###################################################
library(faraway)
library(ggplot2)
library(lmtest)
#### Example in Chapter 6
## Constant variance
data(savings)
head(savings)
lmod <- lm(sr ~ p... |
b40e845cc3c753860cac6649a6442d2ae5df03a2 | 1563a87464e27bba0c67cd3eb5ee3e7fa93b3254 | /R/client.R | 572bac679184d0f8e3de6f99259c5726482afeff | [] | no_license | XiangdongGu/flaskrpy | 22a432c6f3f1e70b5e0a426703e5c447e4698f7d | 591f043ef54e9b75cbba1568ae85ef82fc2ae237 | refs/heads/master | 2021-01-19T08:36:30.031496 | 2017-04-09T15:57:40 | 2017-04-09T15:57:40 | 87,653,022 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,644 | r | client.R | #' Make an API call
#'
#' @param model model name
#' @param func function name
#' @param req request data, typically a list or data
#' @param host the host of the API server
#' @export
#' @examples
#' \dontrun{
#' api_run("iris", "pred", iris)
#' }
#'
api_call <- function(model, func, req, host = "http://127.0.0.1:5000... |
3745c1c60e6a8657416085c69281b723799fd693 | ceb3918a00d69ea84b6a0057cf84da1ccb736c7c | /man/auto_bio.Rd | 6975a5ea647eed37dff507380536b635bbceb1ed | [] | no_license | zsmith27/CHESSIE | 3006d6f7b4b49f1bf846837d597fd31c5d87996b | 785192be00e1b4713fa00238b93996f8d365f9f2 | refs/heads/master | 2020-05-25T22:14:07.599940 | 2018-08-20T16:09:42 | 2018-08-20T16:09:42 | 84,974,122 | 1 | 2 | null | null | null | null | UTF-8 | R | false | true | 497 | rd | auto_bio.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/auto.R
\name{auto_bio}
\alias{auto_bio}
\title{Automate Spatial Classification}
\usage{
auto_bio(my.data, index_res, bioregion = NULL)
}
\arguments{
\item{my.data}{=}
\item{index_res}{= Spatial Resolution (i.e., "BASIN", "COAST", "INLAND", o... |
c91fe62d3875cd2598ad6b1cbd27131a5476953e | e19c30ce934823c012664af5ed6a07beaa3f9e4a | /pspecter_container/Server/Export/ExportVISPTM.R | 8adda6e0706cca863cc6388e9e4dec9f03fc8d85 | [] | no_license | rjea/PSpecteR | 2d543ce80908b4cd58005a301eba970f80e0c88e | d7842034b0f691f8e287a60e246c3497c56ae260 | refs/heads/master | 2023-04-17T23:38:58.727261 | 2021-04-21T17:07:30 | 2021-04-21T17:07:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,487 | r | ExportVISPTM.R | ## David Degnan, Pacific Northwest National Labs
## Last Updated: 2020_09_24
# DESCRIPTION: This contains the modal for exporting the FRAG csv.
list(
# Get all figures
getAllVisPTMFigs <- function(VisPTM) {
# Initiate List to hold all Vis PTM Figures
AllVisPTMFigs <- list()
# Start progress bar for each... |
0a949e7e60a121e2a672cde8605c6c9cb0e39ea6 | 5cbcbd04710d05301b3dab4b5a9bb1e3f22d9512 | /man/sim.plot.Rd | ff3e6eb7497ad4412c6178c1433e9055278fa593 | [] | no_license | cran/iAdapt | da44d081922a287678c846174f4ee40055bab2f0 | 48db1d3f478589faf75146e9e39605f5be3f5b77 | refs/heads/master | 2021-08-11T19:41:55.152661 | 2021-08-06T03:50:09 | 2021-08-06T03:50:09 | 236,613,273 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,594 | rd | sim.plot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sim.plot.R
\name{sim.plot}
\alias{sim.plot}
\title{Generate plots for estimated percent allocation and response per dose.}
\usage{
sim.plot(sims)
}
\arguments{
\item{sims}{output from sim.trials}
}
\value{
Error plots of estimated (1) percent... |
dd195637757988b7cf71ea60d8d46b858ab06097 | 222b9a96e0767baa47e525ae680612b6550a3e21 | /data/repo.R | 604d60d18aa8b2a3b8ec42ea07a8341f4b3e6377 | [] | no_license | nwant/cs4500 | 494cb5c86463e15aaa5d0a8a50567b5f3988e900 | 567c99cbadb9b6fcb562b438bc1516f9dc5f87aa | refs/heads/master | 2021-03-27T16:59:49.745573 | 2019-01-16T21:47:41 | 2019-01-16T21:47:41 | 70,825,084 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,531 | r | repo.R | #------------------------------
# repo.R
# This file returns dataframes related to the CSV's that are imported into the functions
##############################################################################################
library("plyr")
library("stringr")
library("zoo")
#============================
# get... |
077ea622a50318c43ddf91fd8f68d6be827f8041 | 23d0908daec85c6efbd7f02a458fbe9e98c3e10a | /run_analysis.R | ba7d1ec60b56a969e9c7b68039171afa9c69491f | [] | no_license | janmunich/Getting-and-Cleaning-Data-Course-Project | 9f959dfee466e7ba80971928ead0a101bb913d31 | f2a27561e321f825c7819fb499ebce5fef5d242d | refs/heads/master | 2021-01-13T06:04:24.549167 | 2017-06-22T08:54:16 | 2017-06-22T08:54:16 | 95,093,667 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,834 | r | run_analysis.R |
library(tidyverse)
# create x,y and subject data sets
X_test <- read.table( "./test/X_test.txt", sep = "", quote = "\"'", skip = 0, stringsAsFactors = FALSE)
y_test <- read.table( "./test/y_test.txt", sep = "", quote = "\"'", skip = 0, stringsAsFactors = FALSE)
subject_test <- read.table( "./test/subject_... |
e4c083b4e61408f38863de0dbc3899f481ee45d6 | 953226613115cf91fdda04d6f8d873a9960b7461 | /data_project.R | 716c5210b038806c42efa04a987e1bc854a7c1ab | [] | no_license | jw2020c/DataAnalyticsSpring2020 | e5b38f660744ace787844d366afdc3dee0fb3613 | a3e9683859555fb2736f1380ebe0e0ab98cb97b6 | refs/heads/master | 2020-12-19T19:21:56.077392 | 2020-05-04T23:52:11 | 2020-05-04T23:52:11 | 235,828,229 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,358 | r | data_project.R | setwd("C:/Users/23721/Downloads/DAta analytics")
data <- read.csv("Traffic_Crashes_-_Vehicles.csv")
levels(data$MODEL)
apply(data,2,function(x) print(class(x)))
data$CRASH_DATE<-as.POSIXct(data$CRASH_DATE,format="%m/%d/%Y %I:%M:%S %p", tz="UTC")
data <- data[,-c(1,2,3,5,7,8,9)]
#reform the year
library(lub... |
847c05a1cb02e84cb88cc20c341c27595b42637e | 8efa4abbf80541dee202d9211bec2d71991519da | /ch_13/ch_13.R | 1d1ff33b33608293890662e172ecc3b94513c8d1 | [] | no_license | kimjunho12/R_BigData | ad07009c7e9b919f0321b84655758791004cb3ab | fdff2da689a31a6bbe38d448c52f7decc0730fee | refs/heads/master | 2023-06-09T10:42:01.070830 | 2021-06-30T01:53:43 | 2021-06-30T01:53:43 | 361,130,167 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,218 | r | ch_13.R | # 일표본
# 01. 데이터 불러오기
RTD = read.csv('../data/Ch09.RTD.csv', header = T)
head(RTD)
RTD = round(RTD, digits = 2)
head(RTD)
# 02. 기술통계량 확인
attach(RTD)
library(psych)
describe(RTD)
# 03. 그래프 그리기
rpar = par(no.readonly = T) # 디폴트 par 값을 미리 할당
par(mfrow = c(1, 2))
boxplot(weight)
hist(
weight,
breaks = 10,
c... |
39d351969bd0af8195fbda8e8a572c6b6e96bfb2 | 3f453706bbd71babb3e1f5ae9a2dfeddd3bf0be6 | /kaggle_crime_pega.R | d9506f37d98e61b3425f6c6c206d8411ba3c6d4e | [] | no_license | davisincubator/kaggle_sf_crime | 4cbe91e45d5ab497121faf9434fca368a3ccac35 | 3ecea38e025f94c89e8550e7845ae0926db3e6a3 | refs/heads/master | 2021-01-10T12:50:41.504272 | 2016-02-20T03:37:54 | 2016-02-20T03:37:54 | 52,128,036 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,035 | r | kaggle_crime_pega.R | #####--------------------------#####
##### Kaggle SF crime #####
#####--------------------------#####
dir()
dat <- read.csv("train.csv")
head(dat)
table(dat$Category)
which(dat$Category == "TREA")
dat[which(dat$Category == "ASSAULT"), ]$Descript
######################################################
# plot ... |
22e9d53d0ec8e1e4981390cad583c8009d6e08ab | eed89847372fac4e0409b2847b751823d8f7b2f8 | /show_code.R | 9e2b3d6b62286ec4f5f74ff3a7a03055663ab600 | [] | no_license | vogon-poetic/math-comp | 32c7f66b7ab6598dec7a2e990a6237cd6723a854 | d89b4c477e685887e8d3188b601302b62dfd6411 | refs/heads/master | 2021-03-19T13:06:03.401082 | 2018-05-03T18:51:10 | 2018-05-03T18:51:10 | 119,073,695 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 364 | r | show_code.R |
##Question 2) check if a five-digit number n is divisible by 9
n <- as.numeric(readline("enter a positive int (n): "))
n_copy <- n
digits <- numeric(5)
for (i in 1:5) {
digits[i] <- n %% 10
n <- n %/% 10
}
if (sum(digits) %% 9 == 0) {
cat(sprintf("%d is divisible by 9\n", n_copy))
} else {
cat(sprin... |
e55718a7a158c876b09bbb2cd8133a069cb4b10d | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /meteor/inst/testfiles/E_Penman/libFuzzer_E_Penman/E_Penman_valgrind_files/1612738520-test.R | f5e50847e0dedd344787e0f7a34b573a6d30112f | [] | no_license | akhikolla/updatedatatype-list3 | 536d4e126d14ffb84bb655b8551ed5bc9b16d2c5 | d1505cabc5bea8badb599bf1ed44efad5306636c | refs/heads/master | 2023-03-25T09:44:15.112369 | 2021-03-20T15:57:10 | 2021-03-20T15:57:10 | 349,770,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 619 | r | 1612738520-test.R | testlist <- list(Rext = numeric(0), Rs = numeric(0), Z = numeric(0), alpha = numeric(0), atmp = numeric(0), relh = numeric(0), temp = c(5.37986976831671e+228, 3.07839226128608e+169, 3.62462043001606e+228, 5.43226404014686e-312, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... |
4ed4d624d2835706f6e71dae01754416edb77f97 | 48b62187c14ecf1d404043747a90705982a60f61 | /Text Mining/text.R | 34b663187e3328a1b50399b3da5c2e548f79f7a7 | [] | no_license | nndark/R | 7bb017a9565c90e6db60f947447918ab48d9a38e | 1711dce3c0a4fb1c81a5996bef02e427049c914c | refs/heads/master | 2021-02-17T00:14:09.091496 | 2020-12-22T13:59:04 | 2020-12-22T13:59:04 | 245,055,221 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 607 | r | text.R |
#============
# Text mining
#============
# Text 에서 인사이트를 찾는 좋은 분석 방법이다
# 앱 평가, 인기 검색어, 연설문, 소설 등 다양한 텍스트 형태의 파일을 분석할 수 있다
#
#========
# Library
#========
Sys.setenv(JAVA_HOME = 'C:/Program Files/Jave/jre1.8.0_251') # Java Home 위치 설정 필요
library(KoNLP) # 한국어 분석
library(worldcloud) # 워드클라우드
library()
#==========... |
ac22747fa70c9b4280f3e920b2ab5bdc4e56acd0 | 331daade012f87484e435d4e8397122a45d10dae | /R/p.lin.lang.R | b1ab625f0315a271105cb3220e796a70c0a9c6d1 | [] | no_license | stela2502/Rscexv | 9f8cd15b6a1b27056d1ef592c4737e33f4ec459f | 81c3d6df48152a3cccd85eead6fd82918b97733f | refs/heads/master | 2022-07-26T15:29:37.035102 | 2022-07-06T15:59:55 | 2022-07-06T15:59:55 | 54,368,831 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,634 | r | p.lin.lang.R | #' @name p.lin.lang
#' @aliases p.lin.lang,Rscexv-method
#' @rdname p.lin.lang-methods
#' @docType methods
#' @description Calculates the linear hypothesis p value.
#' @param x data matrix
#' @param groups.n the amount of groups
#' @param clus the grouping vector
#' @param n the amount of boot strap operations default=... |
6538410ae461be85cd6fe9be858c896d1e4de2eb | 30cbe00473560ef95ee135d0f624f9768b04a384 | /01_retrieve-data.R | c53f73c89e9274634aef4af2a7b49e627cb5cc1c | [
"Apache-2.0"
] | permissive | mcrpc/human-service-transportation | ccadedceaa67a842d90406747e8d06394028726e | 4029b12e55031fe9beac3e3e11e9eb1484fe0b96 | refs/heads/master | 2021-07-07T03:10:50.133265 | 2020-08-26T21:13:00 | 2020-08-26T21:13:00 | 225,943,989 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 10,091 | r | 01_retrieve-data.R | # initialize local variables ----------------------------------------------
acsSurvey <- "acs5"
censusYear <- 2010
columnTypeList <- readr::cols(
GEOID = col_character()
)
# load data ---------------------------------------------------------------
# for Illinois counties, Illinois tracts, and HSTP region 6 block gr... |
1b2bf73c86c3efe0bb1fc9b85264f66a95abad58 | 8b21d97a1a4adc9ac9df962f762cbc820f6b6ab5 | /R/gbr.R | 19f5ef5619491e6988ba737aa38d31cc69c85889 | [] | no_license | rileym/AfricaSoil | 79b16f21ae4dd2d47dde5405661b6b514c166331 | c0dec6c33d0f48d1c903f92b46035dd24a6f0154 | refs/heads/master | 2016-09-06T17:41:47.630754 | 2015-03-05T23:14:10 | 2015-03-05T23:14:10 | 30,996,962 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,119 | r | gbr.R | # GBM Regression
library("gbm")
library('caret')
source(file = './helpers.R')
##
## Load Data, Clean, & Split
##
setwd('/Users/rileymatthews/Projects/Africa Soil')
full.df <- read.csv(file = './Data/training.csv', header = TRUE, row.names = 'PIDN')
full.test.df <- read.csv(file = './Data/sorted_test.csv', header = T... |
a1d6daa2c96dc80e013d9791b854306147427634 | 89ce7e266948ac66630549c288357d21245895e9 | /Day_3/Day_3_part2.R | f666abc39cab8b19785fb1c71e77dd3777712fa6 | [] | no_license | Emma-Buckley/Biostats-2021 | 9c45616b2a462e41d083cb25e3f4bd27a2b5ee86 | 56d1d94086f9f4e374622f7b26427e51bb1b3044 | refs/heads/master | 2023-04-09T08:54:14.977528 | 2021-04-24T13:44:46 | 2021-04-24T13:44:46 | 359,395,287 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,981 | r | Day_3_part2.R | #Emma Buckley
#21 April 2021
#Correlations
#Day 3
install.packages("ggpubr")
install.packages("corrplot")
# Load libraries #activate the packages
library(tidyverse)
library(ggpubr)
library(corrplot)
# Load data
ecklonia <- read_csv("~/Biostatistics/Second part of R/Biostats-2021/data/ecklonia.csv")
#Removing cate... |
55058bff15d832d1f79a0be6b669f4b37099d490 | 6a28ba69be875841ddc9e71ca6af5956110efcb2 | /Probability_And_Statistics_For_Engineering_And_The_Sciences_by_Jay_L_Devore/CH12/EX12.12/Ex12_12.R | 892ff45e10642756a5b7cfbec6637cd512b668c4 | [] | permissive | FOSSEE/R_TBC_Uploads | 1ea929010b46babb1842b3efe0ed34be0deea3c0 | 8ab94daf80307aee399c246682cb79ccf6e9c282 | refs/heads/master | 2023-04-15T04:36:13.331525 | 2023-03-15T18:39:42 | 2023-03-15T18:39:42 | 212,745,783 | 0 | 3 | MIT | 2019-10-04T06:57:33 | 2019-10-04T05:57:19 | null | UTF-8 | R | false | false | 426 | r | Ex12_12.R | #Ex12.12, Page 496
#Answers may vary slightly due to rounding off of values
x<-c(42.2,42.6,43.3,43.5,43.7,44.1,44.9,45.3,45.7,45.7,45.9,46.0,46.2,46.2,46.8,46.8,47.1,47.2)
y<-c(44,44,44,45,45,46,46,46,47,48,48,48,47,48,48,49,49,49)
data1<-data.frame(x,y)
model<-lm(y~x,data=data1)
cat("Regression model:\n")
p... |
639ee79b720413c2d21b5ce49f889e3cba6bc2f8 | 7f72ac13d08fa64bfd8ac00f44784fef6060fec3 | /RGtk2/man/gdkEventGetScreen.Rd | 2535960c6a0fe15f2390e6a687003c1b6dd04036 | [] | no_license | lawremi/RGtk2 | d2412ccedf2d2bc12888618b42486f7e9cceee43 | eb315232f75c3bed73bae9584510018293ba6b83 | refs/heads/master | 2023-03-05T01:13:14.484107 | 2023-02-25T15:19:06 | 2023-02-25T15:20:41 | 2,554,865 | 14 | 9 | null | 2023-02-06T21:28:56 | 2011-10-11T11:50:22 | R | UTF-8 | R | false | false | 663 | rd | gdkEventGetScreen.Rd | \alias{gdkEventGetScreen}
\name{gdkEventGetScreen}
\title{gdkEventGetScreen}
\description{Returns the screen for the event. The screen is
typically the screen for \code{event->any.window}, but
for events such as mouse events, it is the screen
where the pointer was when the event occurs -
that is, the screen which has t... |
99d36ebf4e05b773fd45c8a2b5cc656bce7cc27d | af63178518502c1b2392eff4cad496784ba5bd30 | /Lab1.R | 721a7328e7c98cc75872be160c808a7ee851ddd3 | [] | no_license | emilyOberH/R-language | a98fb82094c816635361cb7388cb96eaac4dda4b | 934ce7e4f6360a950eea91ac7b9cd837edacbcac | refs/heads/master | 2020-08-05T18:40:06.066731 | 2019-12-09T17:41:33 | 2019-12-09T17:41:33 | 212,659,957 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 264 | r | Lab1.R | Kate <-c(22,18,23,20,16,20)
Lucy <-c(32,18,24,18,20,16)
Kate_price = 12
Lucy_price = 15
Kate_profit = sum(Kate * Kate_price)
Lucy_profit = sum(Lucy * Lucy_price)
Combined_profit = Kate_profit + Lucy_profit
Kate_profit
Lucy_profit
Combined_profit
|
fbf78bf2f558e1626275c8a541bd0efbdea3da5e | 79e46fb854004c24a020e326b2dd06a1044bdce6 | /Fonctions_1.0/5AFC_Fonction.R | 36d3084ec7476dedcd2d3218ba1614f522767f0e | [] | no_license | floriancafiero/Motifs | d0a1c4b6ac96ae48a0cfb00d0e6e9292df2a1263 | 561111860fb8f6fce369aba1dd26f3f0485afdf2 | refs/heads/master | 2023-05-23T01:38:20.803320 | 2021-06-09T20:56:21 | 2021-06-09T20:56:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,466 | r | 5AFC_Fonction.R | ## Stage - Legallois ##
## Fonction AFC ##
## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
# path = "~/Dropbox/2019-2020/Stage/Corpus_Retour_au_texte/"
# csv = "Corpus_motifs.csv" (sortie du script de regex)
# une_oeuvre = "Rigodon.cnr" (si choix d'affichage d'une seule oeuvre, entrez le nom... |
302cb377f1a012e27c2159ee68781e798ccadf09 | ba185ddcf7dbf23cc0f2b6e6adff5d6c6839fa82 | /cgp_mut_inf.R | 05449bbdee46d1108d743c6c5a3bfd497674bfe6 | [] | no_license | LOBUTO/CANCER.GENOMICS | 56cbee294c50d17d4f2dde203a1bb806123a0404 | d1bb3be22cb7ff763ff24b4247a283a8de56cbe7 | refs/heads/master | 2020-04-04T04:05:57.122690 | 2019-01-03T16:17:45 | 2019-01-03T16:17:45 | 26,308,455 | 0 | 3 | null | null | null | null | UTF-8 | R | false | false | 1,790 | r | cgp_mut_inf.R | #cgp_mut_inf.R
library(data.table)
library(reshape2)
library(parallel)
# This script will be to get essential mutations only
# Load file
cgp_mut <- fread("/tigress/zamalloa/OBJECTS/mutations.txt", select = c(1,4,7))
cgp_exp <- readRDS("/tigress/zamalloa/CGP_FILES/083016_cgp_exp.rds")
# Process effect of each mu... |
eb3458ae94bf53647b87599c4fd925393abf39df | edd6a9dd0f4ddb95b5c7f43226b775aa83b12dca | /LinReg/man/linreg-class.Rd | 0430ac3b5ca4a7c17e5fbbd9d5f588375f25108c | [
"MIT"
] | permissive | aleka769/A94Lab4 | 0d63a254c9af704f19863165db67ccce0053f98d | b77bb436222adf12bb762deaaaa5a7da0020b4e4 | refs/heads/master | 2020-03-28T22:45:48.557157 | 2018-09-24T09:23:21 | 2018-09-24T09:23:21 | 149,256,127 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 800 | rd | linreg-class.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/linreg_class2.R
\docType{class}
\name{linreg-class}
\alias{linreg-class}
\alias{linreg}
\title{RC type object to represent linreg-data.}
\description{
linreg object holds data and methods for data calculated in \code{linreg()}-function.
}
\se... |
10a09b4daf37bf08efb578a660bae28352ca9915 | 24e9286f61183294a131b1dac53343091294fb41 | /Bagging/Classification/bagging_class_test.R | 81b2f3b9c88d3222869b9f7d434e882995927f42 | [] | no_license | saikatmondal15/Stock-Price-Prediction-of-Indian-Banking-Sector-Using-Machine-Learning-Tecniques | 11ee7b4a592a926c7ca5a5661262269bce033f51 | a872f05fadb982d5b1a87bc973a343ad29e217e3 | refs/heads/master | 2023-04-21T14:34:10.313190 | 2021-05-14T12:00:03 | 2021-05-14T12:00:03 | 367,329,095 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,062 | r | bagging_class_test.R | library(ipred)
training <- read.csv("/home/saikat/Documents/2020/Project/Dataset_ML_Cla.csv")
training <- training[0:2610,]
summary(training)
tail(training)
test <- read.csv("/home/saikat/Documents/2020/Project/Dataset_ML_Cla_test.csv")
test <- test[0:259,]
training$close_norm <- as.factor(training$close_norm)
set.see... |
d0322c5a91fc728fe9ad68b7da7fe00ed4264842 | 93f8e4312d4de70c0fe8012d0cf3e0f56451982d | /ta_opcost_curve.rsx | f32f00052455630fec684dea841388f87ac518a5 | [] | no_license | alfanugraha/lumens_scripts | c312ce23e690c3e561c34443071befa5f0269649 | dba6a7ad48bc21aaeb9f021f4cc354d72b144840 | refs/heads/master | 2023-07-27T19:16:14.244002 | 2019-07-16T10:44:01 | 2019-07-16T10:44:01 | 103,492,658 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,983 | rsx | ta_opcost_curve.rsx | ##TA-PostgreSQL=group
##NPV=file
##ques_c_db=string
##cost_threshold=number 2
##statusoutput=output table
# library(pander)
# library(knitr)
# library(markdown)
library(rasterVis)
library(reshape2)
library(plyr)
library(lattice)
library(latticeExtra)
library(RColorBrewer)
library(hexbin)
library(grid)
library(ggplot2)... |
895f673b08a4978c8b459c185b3aa649fd8366c0 | b02cf92ccfac713628c653aff2cf0d8057a622d8 | /code/04_clean_general_payment_data.R | 2797de3f92eff7e774b337db3bc4874cad6c15a8 | [] | no_license | anhnguyendepocen/unl-stat850 | 80992ab85cf816c588642aa35cf9cbc413024fc4 | c597eeb5f75a7c6a3e332d0db31697a5010cd8e7 | refs/heads/master | 2023-06-18T16:22:15.405094 | 2021-07-15T17:43:54 | 2021-07-15T17:43:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 885 | r | 04_clean_general_payment_data.R | # Clean up General Payment Data
library(tidyverse)
gpd <- read_csv("data/General_Payment_Data_Full.csv", guess_max = 36000)
gpd2 <- gpd %>%
select(-matches("Teaching_Hospital"), -ends_with(c("2", "3", "4", "5")),
-Form_of_Payment_or_Transfer_of_Value,
-Covered_Recipient_Type, -Physician_Primary_Typ... |
0e856c5bc103cea15db0f1ac79c486b61ff07c85 | bd986e1216c71b4efcddf1c1a835030e524be04a | /man/searchFeatures.Rd | c2f6e0a89a1b185aed9a1a8bbc0ee049d1abae37 | [] | no_license | labbcb/GA4GHclient | 43ac3a6b4bd9ab802ddff20bfc57ec0c2871c44c | ec3a6efba8c3e8698b467620dccf441d8419e335 | refs/heads/master | 2021-01-19T07:28:36.178878 | 2017-10-30T16:54:30 | 2017-10-30T16:54:30 | 68,452,125 | 4 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,260 | rd | searchFeatures.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/searchFeatures.R
\name{searchFeatures}
\alias{searchFeatures}
\title{searchFeatures function}
\usage{
searchFeatures(host, featureSetId, name = NA_character_,
geneSymbol = NA_character_, parentId = NA_character_,
referenceName = NA_charac... |
9e31f9d08e0d5d6e735383b0a6b739eb99bb08ab | 6a490bfbbe969cfd282cea5e1dd9b6b9523445d9 | /man/AgNode-class.Rd | eeb770f20a568860f85444a7f4318ae3cd4860ff | [] | no_license | kasperdanielhansen/Rgraphviz | 5a8d7b6f80948d1914c38e27a1806b4c8f02fa3d | 2c5057bb982db79840c40c0f47b37af7d7887be2 | refs/heads/master | 2023-08-14T22:31:31.342702 | 2022-10-28T17:07:11 | 2022-10-28T17:07:11 | 17,160,926 | 8 | 8 | null | 2023-07-21T14:32:16 | 2014-02-25T04:02:14 | C | UTF-8 | R | false | false | 3,672 | rd | AgNode-class.Rd | \name{AgNode-class}
\docType{class}
\alias{AgNode-class}
\alias{AgNode}
\alias{AgNode<-}
\alias{color}
\alias{fillcolor}
\alias{name}
\alias{shape}
\alias{getNodeRW}
\alias{getNodeLW}
\alias{getNodeCenter}
\alias{getRadiusDiv}
\alias{getNodeHeight}
\alias{style}
\alias{style,AgNode-method}
\alias{color,AgNode-method}
\... |
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