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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
34b610674482a7e7b16f5476969d94d2d54cea35 | cbec21b1f1959bdfa922b3eec5706432d3d89bdc | /Examples/AD-MA_examples.R | 3110f6275cfb7c0da89d35e133ee988537f124cb | [] | no_license | MichailBelias/meta-analysis | b3a28b11ca2eda505bb35d08181b1413fdb6f047 | 605fc3516a8c36276907d0ee696b387af80868bf | refs/heads/master | 2022-02-13T16:34:23.824998 | 2019-02-25T12:26:22 | 2019-02-25T12:26:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,689 | r | AD-MA_examples.R | ## ----echo=F, warning=FALSE, message=FALSE--------------------------------
library(knitr)
opts_chunk$set(fig.width=9, fig.height=6, fig.path='Figs/',
echo=F, warning=FALSE, message=FALSE, fig.pos = "H", comment = "")
## ------------------------------------------------------------------------
# install.packag... |
69bf7f19d197d84c02b95e0e14553ff8ffb95b60 | 7d4211b87ce623e242e5fe4fe2379a45c6d48160 | /database_script.r | f4501f11c20017ed34bcb5210aaacfe33a1e60f8 | [] | no_license | erichoffman1217/mls_data | 858b7babb8af2b96f4b242772c6db0aa22618946 | 4a0a8e8834a5619a862376548ae48532ecd2581c | refs/heads/master | 2020-04-08T21:29:17.622078 | 2019-01-23T04:06:05 | 2019-01-23T04:06:05 | 159,746,109 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,004 | r | database_script.r | library(tidyverse)
library(readxl)
team_db <- read_delim("database/raw/team_db.txt",
"|", escape_double = FALSE,trim_ws = TRUE)
game_db <- read_delim("database/raw/game_db.txt",
"|", escape_double = FALSE,trim_ws = TRUE)
players_db <- read_delim("database/raw/players_db.tx... |
04910cb6f853a834b66380b11eeae3192029df0a | 5a72adedad0a87f05d38c1d7949ab5cf2cb8e6a8 | /run_model_IRT.R | 0364a66a2f55fe6689c92f6db413f9e57f13329b | [] | no_license | saudiwin/arab_tweets | c81907de0e1b5f6aeaca45006dda62732b96bab4 | b0334835b2a5a39e096dc315d6ce5c076e18f888 | refs/heads/master | 2021-05-02T16:59:04.737350 | 2020-09-23T08:14:14 | 2020-09-23T08:14:14 | 72,550,558 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,361 | r | run_model_IRT.R | # Loading data
require(dplyr)
require(tidyr)
require(RSQLite)
require(rstan)
require(bayesplot)
require(ggplot2)
require(readr)
require(forcats)
require(googledrive)
#Load in codings
# elite_coding <- read_csv('data/Coding Comparison - Sheet1.csv') %>%
# mutate(final_code=coalesce(`Dana Coding`,
# ... |
5b39e470427455eef14d9150fc5b9daa40f1693d | 6ad8c187f7576bd9e0e480cbee41ddbc7e47ad39 | /man/tidy_levels_labels.Rd | 0afe80281f76dfb58cd3ddcdc127511eddb61dd4 | [] | no_license | mllg/pixiedust | 8b946d3ffed58a73965672d0fc4499904aec7f0b | 176d4426ab56cde9240b7ec64e50f475a3747d85 | refs/heads/master | 2020-03-09T07:56:22.288426 | 2018-04-08T19:57:23 | 2018-04-08T19:57:23 | 128,676,688 | 1 | 0 | null | 2018-04-08T19:54:41 | 2018-04-08T19:54:41 | null | UTF-8 | R | false | true | 6,865 | rd | tidy_levels_labels.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tidy_levels_labels.R
\name{tidy_levels_labels}
\alias{tidy_levels_labels}
\title{Term and Level Descriptions for \code{pixiedust} Tables}
\usage{
tidy_levels_labels(object, descriptors = "term", numeric_level = c("term",
"term_plain", "labe... |
e843094c14f1e7e4656416da8a7d41e59eceef7d | af901bc01d668ecd411549625208b07024df3ffd | /R/standalone-downstream-deps.R | 44e7848a6c90d45d7b985f97b845ba4d19813e00 | [
"MIT",
"BSD-2-Clause"
] | permissive | r-lib/rlang | 2784186a4dafb2fde7357c79514b3761803d0e66 | c55f6027928d3104ed449e591e8a225fcaf55e13 | refs/heads/main | 2023-09-06T03:23:47.522921 | 2023-06-07T17:01:51 | 2023-06-07T17:01:51 | 73,098,312 | 355 | 128 | NOASSERTION | 2023-08-31T13:11:13 | 2016-11-07T16:28:57 | R | UTF-8 | R | false | false | 9,358 | r | standalone-downstream-deps.R | # ---
# repo: r-lib/rlang
# file: standalone-downstream-deps.R
# last-updated: 2022-01-19
# license: https://unlicense.org
# ---
#
# No dependencies but uses rlang and pak if available. In interactive
# sessions the user is prompted to update outdated packages. If they
# choose no, they are informed about the global op... |
adac7ade802a25aeadba64c29a749ef2482ae12e | 7ad3ffcfb001733227962a2aeacc00657d30350f | /inst/resources/scripts/book/data_management.r | c5dc454eecc5beec63caafd729b289582df69e6c | [] | no_license | cran/FAwR | b70f10a5ada58a3da4a56464d86534eb1a59fbb0 | 9917873167c1a0109136e772024009c7e81131ab | refs/heads/master | 2021-06-02T13:37:48.157146 | 2020-11-09T04:20:02 | 2020-11-09T04:20:02 | 17,679,114 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 25,208 | r | data_management.r | ### R code from vignette source 'data_management.rnw'
###################################################
### code chunk number 1: Setup
###################################################
options(repos="http://cran.r-project.org")
if(!require(Hmisc, quietly=TRUE)) install.packages("Hmisc")
if(!require(lattice, quietl... |
32ee2bc161976ae3c0d2d2ebfcd78557ce3495d6 | 36c253c5be0a91b937b1fa09908cdf33a94f8e87 | /Crime_Rate.R | ea9ab0dc1fffe8281bf8b17748b817172da987c4 | [] | no_license | aliasgerovs/Crime-Rate | 407b5e0563257a020e423333e358b0c21e6a7484 | d61a5c8fc7ace53d5f43be4ec306fcbe549a37b1 | refs/heads/main | 2023-04-26T15:46:59.616797 | 2021-05-16T18:19:21 | 2021-05-16T18:19:21 | 367,954,906 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,011 | r | Crime_Rate.R |
#Case Study Week 7
# Imporing libraries & dataset
library(tidyverse)
library(data.table)
library(rstudioapi)
library(recipes)
library(caret)
library(skimr)
library(purrr)
library(inspectdf)
library(mice)
library(graphics)
library(Hmisc)
library(glue)
library(highcharter)
library(plotly)
library(h... |
35d4546eb43f1fe430b6407f84e3017794759d34 | 22f6f25b97b0bf425f365f43511ddfea5e02ff42 | /CompletitudDiaria.R | b533fb0c6592e82f3090eb4e0091b855a7a193c9 | [] | no_license | JuanAlvarezVazquez/TFM | f4da74710d67dc80592707c3373e0e87b09fe5ff | 5ecde6acb959eb3738384e91212087b6baf0162e | refs/heads/master | 2020-07-19T23:12:43.682477 | 2019-09-05T10:00:33 | 2019-09-05T10:00:33 | 206,529,501 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,763 | r | CompletitudDiaria.R | #=============Cargamos librerias y archivo de inspeccion=================
library(tidyverse)
library(dplyr)
library(lubridate)
library(ggrepel)
registroLingotes<-read_csv2("Datos/hist_inspec.csv", col_names = c("Horno","Fecha","Aleacion","Formato","Colada","Lingotera","Id_Especialista","Especialista","Id_Mando",... |
03b13fec7659c80ab29bdec2ecca624d95dc8e5e | 0e2859631b841ccdaf020dbdcdd42c5193b77192 | /tests/testthat/test-helpers.R | 5f6ecf83ef8f504f80b86829ccfaa444e8613b1d | [
"Apache-2.0"
] | permissive | bcgov/rems | 4f41ccf7588e38bc15e88ed1d6880505eac644cf | 85cbbca54aff440909d9751e7c387adb8fbbba63 | refs/heads/master | 2023-08-17T07:49:23.354561 | 2023-08-10T16:55:46 | 2023-08-10T16:55:46 | 63,990,524 | 20 | 6 | Apache-2.0 | 2023-08-10T16:55:47 | 2016-07-23T00:03:38 | R | UTF-8 | R | false | false | 445 | r | test-helpers.R | context("testing helpers")
test_that("checking correct number EMS_IDs and REQ_IDs of lt_lake_sites and lt_lake_req()",
{
expect_length(lt_lake_sites(), 74)
expect_length(lt_lake_req(), 654)
}
)
test_that("checking data type of lt_lake_sites() and lt_lake_req()",
... |
f2a4d6595d29d6aef1fbe64b7ab1420bfd3766fc | dba7646b74a68ef18375cd5c0ca3233ba1684ea1 | /manuscript/figure_spearman_heatmaps/simpleHeatmapExample.R | d6920a132d3f8bc9b04317745a6c3a81ebcd7e12 | [] | no_license | jmig5776/targetscore | 130dfe6aec7e6437e2f9ae6e3d075e5a871097e8 | 83e6550446abea571f78bfe39aeb7e4fde894c98 | refs/heads/master | 2023-02-06T14:17:23.311838 | 2020-12-18T13:36:08 | 2020-12-18T13:36:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 643 | r | simpleHeatmapExample.R | library(ggplot2)
library(reshape2)
library(readr)
mydata <- mtcars[, c(1,3,4,5,6,7)]
cormat <- round(cor(mydata),2)
melted_cormat <- melt(cormat, na.rm=TRUE)
# Heatmap
p <- ggplot(data = melted_cormat, aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "blue", high = "red", ... |
1773e0bade9bbda4eb00ef23f060f3ea3e53083b | 3fca57c782031174c3ddcbb5fac094811b77f840 | /hugo_summarise_data.Rd | 6de5b71d4c1a750a865513486d65e5c203f13bcc | [] | no_license | woznicak/hugo | f82f67f3e669246be79172f0e79812c52f3401ec | af3fb40a267d2ac33636d9e5f9cf72646f92503a | refs/heads/master | 2021-09-15T23:51:48.591332 | 2018-06-13T05:48:03 | 2018-06-13T05:48:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 374 | rd | hugo_summarise_data.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hugo_summarise_data.R
\name{hugo_summarise_data}
\alias{hugo_summarise_data}
\title{Title}
\usage{
hugo_summarise_data(data, earlier_settings = TRUE, output = "pdf",
replace = TRUE, mode = "check", report_title = NULL)
}
\arguments... |
4aed7e00474c0cef4cdae936716a1697312028e2 | 913d325204484c4311e1d8dba5d6c455e8aa1703 | /make_national_tables.r | 10b69d86508af4ae13e70a61dad1086f9e4aa635 | [] | no_license | f-edwards/ai_an_transitions | 0e0dd38724f92e750e4461ba39c724abf3d82cd5 | 455733b5d74893cb6f77fcc0b093d0ff872d7004 | refs/heads/master | 2023-04-03T18:23:31.044963 | 2023-03-22T12:56:57 | 2023-03-22T12:56:57 | 228,456,286 | 2 | 0 | null | 2020-12-08T20:42:55 | 2019-12-16T19:07:18 | HTML | UTF-8 | R | false | false | 4,874 | r | make_national_tables.r | ###################################################
### age-specific National tables
###################################################
### make national tables for each outcome
## investigation
inv_nat_tab<-list()
race_id<-unique(ncands_inv$race_ethn)
imps<-unique(ncands_inv$.imp)
index<-1
for(i in 1:length(imps))... |
3688ceeb912414af634406e3f9cec15ca85989cd | e699d84bd076c5ef9c31ac0a4f3caa5fb21464eb | /man/phylo.betapart.core.rd | 2538bb495dec240ffbb1bacc8e18849e1f5d64b1 | [] | no_license | cran/betapart | e28acb1b8a3c80f7163453b3ecfc2a0e7699d6a1 | 5a785771c8f59f1ac14ee0cf789e7f22723711cf | refs/heads/master | 2023-03-15T21:59:30.702820 | 2023-03-13T16:10:15 | 2023-03-13T16:10:15 | 17,694,697 | 1 | 4 | null | null | null | null | UTF-8 | R | false | false | 3,295 | rd | phylo.betapart.core.rd | \encoding{utf8}
\name{phylo.betapart.core}
\alias{phylo.betapart.core}
\title{
Core calculations of phylogenetic dissimilarities metrics
}
\description{
Computes the basic quantities needed for computing the multiple-site phylogenetic beta diversity measures
and pairwise phylogenetic dissimilarity matrices.
... |
85f7a6cbb14c6cb46679d302c4958a5891be06c7 | 0bddcf275b7786d95ee6d96b5a0740c8b58eb958 | /PELfun 2.R | 0b9672c4ed71eb918ea73f166d7e737239246b6f | [] | no_license | sheng-ying/PEL-logit | 3f83d68bfd315baafc4fd914e0eed6806dc7e331 | e5e6f21a3d8162de6ad3e356e7d074646a2c637e | refs/heads/master | 2023-02-12T14:48:38.863766 | 2021-01-06T04:54:35 | 2021-01-06T04:54:35 | 246,386,630 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,493 | r | PELfun 2.R | library(Matrix)
library(foreach)
library(mvtnorm)
library(glmnet)
library(BB)
#=============================================================================#
# the extended PEL_alpha estimator that accounts for population heterogeneity
# b: paramter of interest
# a: parameter in the density ratio model
# eta: Lagran... |
009b5aa16e8d74b4db53ed8302e91bdf3fd36529 | 41f079ce35208231a5b4dd3f6f89218d9436d6fb | /hw/rlab_1.R | 3bc823552bb0eae9f754fb4194ff9dbf73b3f242 | [] | no_license | dalsgit/897 | 838a8c278480a97d49d359932e8090b8500f2479 | 7caf877e70351bbadc08f927c0f0971a4a8b57a2 | refs/heads/master | 2021-01-21T22:14:31.415897 | 2017-11-27T23:20:24 | 2017-11-27T23:20:24 | 102,136,843 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 615 | r | rlab_1.R | install.packages("rmarkdown")
install.packages("ISLR")
library(ISLR)
x=c(1,2,3,3)
summary(x)
set.seed(3)
y=rnorm(100)
summary(y)
?plot
x3=matrix(1:12,3)
x3
x3[,2]
fix(Auto)
attach (Auto)
cylinders =as.factor (cylinders )
plot(cylinders , mpg)
#install.packages("doBy")
library(doBy)
summaryBy(mpg ~ cylinders, data... |
3ef9ea4f9751c65db29c0cf6b9eb089e950697bf | bc664d2c4810c04a398cbb461ce8727a68391499 | /R_scripts.R | a1e8bbeb471bd9165f6aab9e7028588ebc3986b9 | [] | no_license | JingChen1114/RepData_PeerAssessment1 | f3206de03dd7b25019db7f3c9bc8bb85e02c408c | 6a488cd26df5bc794e0bb12ec534620ca4e323b1 | refs/heads/master | 2020-12-03T09:00:55.331898 | 2020-01-02T15:56:40 | 2020-01-02T15:56:40 | 231,264,628 | 0 | 0 | null | 2020-01-01T21:13:43 | 2020-01-01T21:13:42 | null | UTF-8 | R | false | false | 3,617 | r | R_scripts.R | ###############################################
# R_scripts.R
# DESCRIPTION: script for Reproducible Research
# Course Project1
###############################################
library(data.table)
library(dplyr)
library(ggplot2)
#Loading and processing data
unzip("activity.zip")
activity <- read.csv("activi... |
c6f1a28e687a6dc366c95cc3ef0d1dc157a2bc59 | b215ed7c605e5750f748b4dd2276e4cebf588318 | /R/grab_rt_live.R | 53e9fd8fe5e6a338ee6a5f89cda09aa406ca5cab | [] | no_license | LaurenHarriman/CalCAT | 43b79fc874ea4ca218687069e302395a094b6e9b | 6c974ee984b2b31e89fd6fd62283ddc225b6eade | refs/heads/master | 2022-11-05T19:19:40.366508 | 2020-06-25T19:45:16 | 2020-06-25T19:45:16 | 274,999,811 | 1 | 0 | null | 2020-06-25T19:35:03 | 2020-06-25T19:35:02 | null | UTF-8 | R | false | false | 625 | r | grab_rt_live.R |
grab_rt_live <- function(State = state_name, ST = state_abbrv){
url <- "https://d14wlfuexuxgcm.cloudfront.net/covid/rt.csv"
if ( as.character(url_file_exists(url)[1]) == "TRUE" ) {
rt_live <- read.csv("https://d14wlfuexuxgcm.cloudfront.net/covid/rt.csv") %>%
filter(region == ST) %>%
mutate(dat... |
5d6b971e566d0c47b5b7827e1da8dece7d022fbe | 8b879b032d3ec61b5d389d3deec855e5b5522b81 | /tmp/mosdepth.R | c49c321df0a356ee2d05b9167820ba9bfadd6c8b | [] | no_license | msubirana/ergWgsTools | 5756e5a42c8ec6c090a2be11f6548eefca2ced3c | f692050f574ed78b9b2e425d3b84f686a6103b2e | refs/heads/master | 2021-03-29T06:05:10.588693 | 2020-06-03T15:10:31 | 2020-06-03T15:10:31 | 247,924,755 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 380 | r | mosdepth.R | source('/imppc/labs/lplab/share/marc/repos/ergWgsTools/tmp/variables.R')
library(devtools)
devtools::load_all('/imppc/labs/lplab/share/marc/repos/ergWgsTools')
threads <- parallel::detectCores()
args <- commandArgs(trailingOnly = TRUE)
inputFile <- args[1]
outPath <- args[2]
threads <- args[3]
mosdepth(inputFile=inp... |
009c101da124bbdcaf0878af5406ba93095965bd | b6f61d4ab4b273d985a5a2ac0cb3aaad74ae4dad | /cachematrix.R | ea56592472c2ea7e8c9ec319f0b63b5357a0e862 | [] | no_license | lautier/1st_repo | e6729d99c4e5171d6779ab45898a753c658d707d | 46833b1183ad8442b2bbcfe5111acdc6de826bde | refs/heads/master | 2021-01-16T23:06:37.733362 | 2015-11-07T18:43:43 | 2015-11-07T18:43:43 | 40,971,192 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,691 | r | cachematrix.R | ## Functions defined below are aimed at caching the inverse
## of a matrix instead of computing it multiple times,
## hence making the computations quicker.
## The matrix has to be square and invertible.
##makeCacheMatrix creates a matrix object, which can be used
##for caching the inverse of a matrix.
makeCacheMat... |
0f10c87d35a5f728cc7ef2c39783664380f4ecb3 | b2d7e379ec3d409c3c097df88805ed68ecae085d | /ksvm_mais.R | b3f48dd4f00079b30dd5569d2e348a14468243d4 | [] | no_license | renluqin/MachineLearningProjects | 96d57e6917037528d1c38a777688252d61f47b98 | 92b62908ac09df0991160bf7ba074e1658df3324 | refs/heads/master | 2022-12-04T09:22:23.532201 | 2020-08-27T21:09:47 | 2020-08-27T21:09:47 | 290,880,093 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 731 | r | ksvm_mais.R | mais <- read.csv(file = "mais_train.csv", header = TRUE,na.strings="?",row.names=NULL)
head(mais)
mais <- subset(mais, select = -X )
#mais <- as.data.frame(scale(mais))
library(e1071)
#install.packages("kernlab")
library(kernlab)
K <- 10
folds = sample(1:K,nrow(mais),replace=TRUE)
CV <- matrix(data=0,nrow = 10... |
071627e6ef4ce789db05a43be29f0f1a74e4b7bb | 735d13ef3b0a2f7c640951c3c26944aabaa5908f | /R/info_summary.R | 26f845cacf7654a1e0c110718417e28feb10ff91 | [] | no_license | cran/DIFtree | c6f2582a868e36dc12a514a428a8c4b205bbb929 | a1dfeb6c9e89a078c543da504698939992578700 | refs/heads/master | 2020-12-25T17:13:07.031869 | 2020-06-05T08:30:03 | 2020-06-05T08:30:03 | 34,724,799 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,459 | r | info_summary.R | info_summary <-
function(splits,
item,
model,
type){
if(model==2 & type==2){
dif_items <- unique(c(splits[[1]][,"item"],splits[[2]][,"item"]))
} else{
dif_items <- unique(splits[,"item"])
}
dif <- ifelse(item %i... |
73202a83bb79c95f83f10071745b2e3563e7f06c | daa4e8cf09f8b0a7437c72e8400c901798cf5102 | /man/subset_models.Rd | 742946f420ec58959c397f4dbb240d0d4913d6eb | [] | no_license | jacobbien/simulator | cbab7e91945b9a6dbe9b309a256f41e8ef1a6a30 | 3d325ec78d48c57cbdc16d1455c2ffcc85ae8bb1 | refs/heads/master | 2023-05-26T15:26:59.755742 | 2023-02-02T07:53:44 | 2023-02-02T07:53:44 | 62,213,043 | 51 | 13 | null | 2023-05-19T04:46:04 | 2016-06-29T09:21:45 | R | UTF-8 | R | false | true | 553 | rd | subset_models.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/model-class.R
\name{subset_models}
\alias{subset_models}
\title{Subset Models}
\usage{
subset_models(m, ...)
}
\arguments{
\item{m}{list of \code{\linkS4class{Model}} objects}
\item{...}{logical expression involving parameters of Models. Fo... |
03ab0ceddbe597c6d8f709356a6ac66a88bc4c5c | b7457a6e39c6f2d9e0d54d0ba19fb013517a11bf | /man/load_install_packages.Rd | 5d19d703cbf84c4ba429180f36d6f1b5ad33aa91 | [
"MIT"
] | permissive | stevenndungu/quickr | 57522cb1d2bb2e34e9823f81a03e4f78ff0d06f3 | 3d30c85d7bced4550a40ac1b8fcde81add83694a | refs/heads/master | 2022-05-27T22:52:15.251104 | 2020-05-01T09:32:16 | 2020-05-01T09:32:16 | 228,349,778 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 535 | rd | load_install_packages.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/install_load_packages.R
\name{load_install_packages}
\alias{load_install_packages}
\title{A function that loads installed packages and loads and installs those not installed.}
\usage{
load_install_packages(x)
}
\arguments{
\item{x}{A vector... |
ff6db7b142dc3856b18ad0406a7592be977ed248 | 527f5efb3c31800f50b725c851fd0e86a239bb33 | /data-mining-iti8730_assignment-3/DegreeCentrality_DegreePrestige_NodeGregariousness.R | b0e5a36b7938b85194fbcd1e909b8459ca488418 | [
"MIT"
] | permissive | shwetasuran/Data_Mining_Assignments | 778f10429e1a071c7cd81362cf4ace65fd6ace56 | af8bd0b30da0ae364309bf9d329ef5bad7c9d59d | refs/heads/master | 2023-09-04T20:38:28.610053 | 2021-11-14T17:09:49 | 2021-11-14T17:09:49 | 427,987,947 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,270 | r | DegreeCentrality_DegreePrestige_NodeGregariousness.R | # Degree Centrality - Degree Prestige &Node Gregariousness
rm(list = ls())
graphics.off()
library("igraph")
Edges <- read.csv("Dataset1-Media-Example-EDGES.csv")
Nodes <- read.csv("Dataset1-Media-Example-NODES.csv")
NodeCount = nrow(Nodes)
EdgeCount = nrow(Edges)
NodeName = Nodes$media
NodeWeight = c(Nodes$audien... |
433524ef3a1b0c44875e5637c4c3c469a7ecbf65 | 51250726e0ce12a81f75572be193d0b6742554cf | /man/p_base.Rd | 9da5d613eaa9e3f11b558b82ae764fe89f679b96 | [] | no_license | dpastoor/pacman | 6ead1b9913e7d2a6b018fc2e6390fd2d86ff4673 | 3b4c2c7f47f2d7faf7563f7b76a92953da47f884 | refs/heads/master | 2021-01-23T22:01:25.517006 | 2014-11-04T00:00:09 | 2014-11-04T00:00:09 | 26,233,734 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 831 | rd | p_base.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{p_base}
\alias{p_base}
\title{Base Install Packages}
\usage{
p_base(base.only = TRUE, open = FALSE, basemarker = "***")
}
\arguments{
\item{base.only}{logical. If \code{TRUE} a character vector of only base
install packages is returned.}
\item{open}{logical. ... |
cb3a89c6d0a9c91da0274f7e015816f6cd92906c | 67a81b3866bcf28cb8471f923004cb53c48990b7 | /man/scglrTheme.Rd | 7654e8b51d4f84431ed2bf253eed50d8538d95ab | [] | no_license | cran/SCGLR | b446421e879170dc7d80fab35f4a73e2fd5b934a | f3777ab03ac360c000915777863a0c91aa500e21 | refs/heads/master | 2021-01-17T13:03:49.640138 | 2018-09-28T08:30:03 | 2018-09-28T08:30:03 | 17,693,491 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,123 | rd | scglrTheme.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/theme.r
\name{scglrTheme}
\alias{scglrTheme}
\title{Function that fits the theme model}
\usage{
scglrTheme(formula, data, H, family, size = NULL, weights = NULL,
offset = NULL, subset = NULL, na.action = na.omit, crit = list(),
me... |
3e6d8b24b2812da03f4f64189b211a860ae21a20 | 81dc5d5cbbf5335e1d951374c04a73937a3a9766 | /11_gene_content_analysis/function_make_pcoaTable.R | 1dde12ffd54b1d31f9d7fef5d421635d577db617 | [] | no_license | LebeerLab/caseiGroup_mSystems_pipeline | 19f90be776fd47c98cb561724644560d7169cc77 | 78f013f861d28080d1ed11c4ceb236752381cafe | refs/heads/master | 2021-01-01T18:44:31.406047 | 2018-05-14T09:37:43 | 2018-05-14T09:37:43 | 98,420,988 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 426 | r | function_make_pcoaTable.R |
make_pcoaTable = function(T_genome_orthogroup) {
T_genome_orthogroup %>%
select(orthogroup, genome, ngenes) %>%
spread(key = orthogroup, value = ngenes, fill = 0) %>%
remove_rownames() %>%
column_to_rownames(var = "genome") %>%
as.matrix() %>%
vegdist(method = "bray") %>%
cmd... |
fec328ea3dbead5215eab08691466bdce6232a95 | 10873a4e41464f753732b28ba9425cda5520f850 | /emulator/res/045.r | d4d82af524aecaae29165e89511e9a365845956b | [] | no_license | uatach/mc861-nes | 3b3e11bb6876ca47b319acd25d7714b7c7bb9069 | 9583086364ab5866c7104408bb154bec6f3e164c | refs/heads/master | 2020-08-02T12:47:28.823972 | 2019-10-04T20:53:15 | 2019-10-04T20:53:15 | 211,356,279 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,816 | r | 045.r | | pc = 0xc002 | a = 0x00 | x = 0x00 | y = 0x00 | sp = 0x01fd | p[NV-BDIZC] = 00110110 |
| pc = 0xc003 | a = 0x00 | x = 0x00 | y = 0x00 | sp = 0x0100 | p[NV-BDIZC] = 00110110 |
| pc = 0xc005 | a = 0x00 | x = 0x42 | y = 0x00 | sp = 0x0100 | p[NV-BDIZC] = 00110100 |
| pc = 0xc006 | a = 0x00 | x = 0x00 | y = 0x00 | sp = 0x... |
36dd963556895f76d9d8a4b9a1234620b35f84a7 | 675b6e49d198bb4156860333f24b0247d3beed01 | /server.R | 01fd80bf426371ca5d34ac907e40bd5f1e937ea8 | [] | no_license | geohemex/DDP | eee0d26625977d50ecc780ac48f9c13574124394 | 2d3d7bc6a5359f1cefc47b986cccdbd9ec033da9 | refs/heads/master | 2021-01-10T23:04:30.421013 | 2016-10-09T21:16:48 | 2016-10-09T21:16:48 | 70,425,974 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 728 | r | server.R | # library(shiny)
# library(dplyr)
# setwd("C:\\Users\\Geovanni\\Desktop\\Prueba")
# Data<-read.csv("total.csv", skip=1,header=F)
# setwd("C:\\Users\\Geovanni\\Desktop\\Data Products\\Prueba")
# colnames(Data)<-c("Hour", "Node_ID","LMP","Ener_comp","Lo_comp","Cong_comp", "Date", "NADA")
# Data$Node_ID<-NULL
# Data$NADA<... |
42a0183e945d5efc2f42a2233b73baf128d3c9c1 | 2fa7055a4bbb879ad8a47440a5311e2cbd58eb8b | /IV.Genomic_analysis/Scripts/A.MethRNAseq.R | 01e2f0dafa2983116d73f3b534a42476826b0de9 | [
"BSD-3-Clause"
] | permissive | Christensen-Lab-Dartmouth/brca_lowstage_DMGRs | d6332bd8a8eee885ed4c5e888aab00269c6577aa | 1b1a00d00cefea380d2bb1cbd1589e570fd8fdbd | refs/heads/master | 2020-04-06T04:36:32.312410 | 2017-09-14T14:31:05 | 2017-09-14T14:31:05 | 45,754,471 | 4 | 7 | null | 2017-03-10T19:29:38 | 2015-11-07T21:03:23 | R | UTF-8 | R | false | false | 6,282 | r | A.MethRNAseq.R | #####################################################################
# ~~~~~~~~~~~~~~~~~~
# Tumor subtype and cell type independent DNA methylation alterations
# associated with stage progression in invasive breast carcinoma
# ~~~~~~~~~~~~~~~~~~
# Way, G., Johnson, K., Christensen, B. 2015
#
# Examine whether... |
01e067d59bcbdc7b82ba2b23010da239e123d48e | cb325ebdbfcc6ae43fd4d6e916acb2a90720d812 | /man/draw_plots.Rd | dc618185f24d0c9d8adc6baa0094a6b4f8c81d2b | [] | no_license | resplab/voiPeermodels | 798a3096b3cd495bc789bb206a0ff01343b9d826 | 2b9c04b2ebcc9d40a51ed52fe1ba5f335261f64d | refs/heads/master | 2023-07-14T13:55:57.830478 | 2021-08-17T16:44:02 | 2021-08-17T16:44:02 | 394,715,302 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 394 | rd | draw_plots.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/1client_lib.R
\name{draw_plots}
\alias{draw_plots}
\title{draws plots generated by the model in R Session}
\usage{
draw_plots(plot_number = NULL)
}
\arguments{
\item{plot_number}{the number of the plot to be rendered}
}
\value{
graphical obje... |
1ba67629a681d77aa7edd7f177600c0b023a156f | 6e32987e92e9074939fea0d76f103b6a29df7f1f | /googleidentitytoolkitv2.auto/man/GoogleCloudIdentitytoolkitV2MfaTotpSignInRequestInfo.Rd | fbe756b6526d6ff361193d76523a551c4675b49a | [] | no_license | justinjm/autoGoogleAPI | a8158acd9d5fa33eeafd9150079f66e7ae5f0668 | 6a26a543271916329606e5dbd42d11d8a1602aca | refs/heads/master | 2023-09-03T02:00:51.433755 | 2023-08-09T21:29:35 | 2023-08-09T21:29:35 | 183,957,898 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 803 | rd | GoogleCloudIdentitytoolkitV2MfaTotpSignInRequestInfo.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/identitytoolkit_objects.R
\name{GoogleCloudIdentitytoolkitV2MfaTotpSignInRequestInfo}
\alias{GoogleCloudIdentitytoolkitV2MfaTotpSignInRequestInfo}
\title{GoogleCloudIdentitytoolkitV2MfaTotpSignInRequestInfo Object}
\usage{
GoogleCloudIdentity... |
1fc767c9baa36b7c47cc66e6c8311e81c0dbc463 | 9ba2359e8c4217607ba39d176fc841a5c01351af | /tests/testthat/test_Mbin_noint.R | 399b3a0e8c4720b51e120eb812b16082a148944b | [] | no_license | shiandy/causalMediation | 69e011cdc292db95d256850d24aa26cf169b96c7 | 2c301a871d64333d67e001c2f2990f7bdb61597c | refs/heads/master | 2021-01-22T01:58:27.284583 | 2017-01-15T05:44:26 | 2017-01-15T05:44:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,946 | r | test_Mbin_noint.R | #
# # df <- read.csv("data/Mbin_noint_data.txt", sep = " ")
# #df <- read.csv("data/Mbin_noint_data_10000.txt", sep = " ")
#
# df <- read.csv("Mbin_noint_data_10000.txt", sep = " ")
#
# ##----- Bootstrap - Y_cont_noint
#
# set.seed(1234)
# s_boot_Y_cont_noint_M_bin <- causalMediation(data = df,
# ... |
836265536d19fcf7a7b7a6e48a24f30af7bc0a54 | a71b7fe35d652d86f136823cd1801eb51d902839 | /glucose.R | 342ceafe3efb85bb73b6bd56cfe61b52944f8feb | [] | no_license | StaThin/data | 9efd602022db768b927c3338e5ce7483f57e3469 | d7f6c6b5d4df140527c269b032bb3b0be45ceeeb | refs/heads/master | 2023-03-29T18:40:09.694794 | 2023-03-15T09:32:42 | 2023-03-15T09:32:42 | 29,299,462 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,126 | r | glucose.R | "glucose" <-
structure(list(Y = c(6.8, 9.9, 8.3, 10.9, 7, 8.8, 11.5, 9.9,
9.5, 7, 13.5, 7.4, 10.8, 10.2, 6.2, 6.8, 8.3, 8.3, 8.3, 13.2,
9, 12.9, 10, 6.9, 11.5, 10.2, 9.1, 9.7, 9.2, 8.8, 7.3, 5.9, 7.4,
8.9, 6.4, 10.4, 9, 14.1, 11.7, 10.5, 9.2, 11.2, 10, 10.8, 7.6,
8, 9.2, 7.4, 6.8, 8.5, 11, 11, 5.4, 8.7, 13, 10.2, 8... |
88b084f138587c51497799b39dee1adea3cf9edb | 989ad7379c164a5a3b42fbc31c4d1915a1e9595a | /prog/plot.R | a2c46e14b4ccbeb963ad226ff8be1c15cd01d57b | [
"Apache-2.0"
] | permissive | kenoshiro/AIM-CCU | 6d7f42885333c58277824c9037c2cfb7eeceadba | 1ffc4f6b378cd1c4b25848d1e1a715e844564b78 | refs/heads/main | 2023-08-03T10:52:22.303131 | 2023-07-22T07:03:32 | 2023-07-22T07:03:32 | 518,424,576 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 113,272 | r | plot.R | mytheme <- list()
mytheme$set1 <- theme_bw()+theme(plot.title=element_text(size=9.5),
panel.grid=element_blank(),
panel.border=element_blank(),
axis.line=element_line(color='black',size=.3),
... |
7bca2ee0fe6677380a41d20aab7986360e963cfc | db1a7b8867acc251e5765d0a307890f21ecf233a | /HW2/tmp/working/q3_1.r | 0c834d48cf2a20df24d4cb9adb5ae60c8548f633 | [] | no_license | DeepeshLall/BayesianAnalysis | c2011be5543c7d94e331744ec23bb442f666d7db | a4a1be952d807101259e2356c6636cc2fb345670 | refs/heads/master | 2023-03-21T01:33:27.702735 | 2021-03-19T16:09:13 | 2021-03-19T16:09:13 | 349,481,755 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,191 | r | q3_1.r | library(readxl)
library(plyr)
library(dplyr)
library(modeest)
library(truncnorm)
library(invgamma)
library(psych)
noOfData = 1240
# Reading the data into y and X from .Rdata file created by q2.r
q2_result <- read.table("~/Desktop/working/q2_result.Rdata", quote="\"", comment.char="")
Y <- q2_result[,1]
X <- q2_result... |
3a0a1f4a0b45f3b4b97f4efa332e00f0704c0561 | 6322ce6b8a91ce012868bf40dd9ff28999ba2819 | /A_Developing_a_reference_set/functions/required.funcs.r | 5c5f775191dfed649767003b07002ff30ea2e062 | [
"MIT"
] | permissive | ices-eg/wg_WGNEPS | 35d81c310e9c0e972c604fc20f03e37139234e46 | a00af2764a97133bdf6f87f46e0ab27f42b83e92 | refs/heads/master | 2022-02-08T01:27:45.092876 | 2021-11-18T17:20:36 | 2021-11-18T17:20:36 | 120,731,989 | 0 | 2 | MIT | 2022-01-25T17:37:58 | 2018-02-08T08:19:22 | R | UTF-8 | R | false | false | 9,618 | r | required.funcs.r | #tapply.ID is rather like aggregate, but I wrote it before I discovered "aggregate" and actually it does a few more things!
#tapply.ID takes a data frame, a vector of the names of the data column, a vector of the factors to apply across, the name of the function to apply (i.e. sum) and the name of the new variable
#we ... |
3992f0ad856cb320dfe64346e508b643be6974c6 | e1cbbf8791b0ac6d40f6d5b397785560105441d9 | /man/cdfgam.Rd | 359bbb3d79ad0be4bb3663f56b665f9bf73ea3a4 | [] | no_license | wasquith/lmomco | 96a783dc88b67017a315e51da3326dfc8af0c831 | 8d7cc8497702536f162d7114a4b0a4ad88f72048 | refs/heads/master | 2023-09-02T07:48:53.169644 | 2023-08-30T02:40:09 | 2023-08-30T02:40:09 | 108,880,810 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,470 | rd | cdfgam.Rd | \name{cdfgam}
\alias{cdfgam}
\title{Cumulative Distribution Function of the Gamma Distribution}
\description{
This function computes the cumulative probability or nonexceedance probability of the Gamma distribution given parameters (\eqn{\alpha} and \eqn{\beta}) computed by \code{\link{pargam}}. The cumulative distrib... |
489cc8cc4e21ef335de9513eec0771285f954e91 | b621dc04edbca760936fbdd9ea6725484a24bf85 | /interactive_heatmap_dotplot/interactive_heatmap_dotplot.R | b8b3d58ff12bbcd9c72e73591d61e2e935e7e8f5 | [] | no_license | DoruMP/Fast-data-portals-for-scRNAseq-data | 416a0f5d88c1ab41c9e894482ffe974cd9d0ea4b | b256d0d9d9386662bd2a8de6b2ee5c4906984fba | refs/heads/master | 2020-05-14T05:22:41.243501 | 2019-05-14T13:48:05 | 2019-05-14T13:48:05 | 181,701,003 | 14 | 2 | null | null | null | null | UTF-8 | R | false | false | 4,797 | r | interactive_heatmap_dotplot.R | library(Seurat)
library(methods)
python.addr = 'python3.6'
args = commandArgs(trailingOnly=T)
options_file = args[1]
options_fobj = file(options_file, 'r')
options_fields = readLines(options_fobj)
close(options_fobj)
file_name = options_fields[1]
set.ident = options_fields[2]
output_folder = options_fi... |
d3389ab72480657c3c49a60b0594d19b27467d82 | ab7db0dbd19eb599fae18f11b8767a8862c3eff9 | /cachematrix.R | 9d26de8c9461d8fc43b2b868d393a71ce4d8725d | [] | no_license | tripah/ProgrammingAssignment2 | cfe33ad947d998f553ddfc744adf8a30ba727d69 | 3731f2473ffe10152e7b6cc484594f3c05dfdde2 | refs/heads/master | 2021-01-14T14:23:39.394517 | 2014-11-23T19:46:52 | 2014-11-23T19:46:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,262 | r | cachematrix.R | ## Overall description of what your
## functions do:
## 1. caching a matrix and it's inverse: methods for setting and getting both objects
## 2. calculate the inverse if not already done and setting the inverse "inverse-x".
## this function
## first: it assigns a matrix to x and sets the object "inverse" to NULL
## t... |
55894743d68c7d0fbc3e54acdf6b5ed4a14f7711 | d6302bdd07645e0da8ad4430a261d3ebe2149435 | /man/bipartitionShi.Rd | da34cb147b379250128bcc68b2176fff645a087f | [] | no_license | cran/RclusTool | 3a8fec24edeaedee42ef0f255f32dfc4be107dfe | 7ed428f6c896889a9b291a279e1e82f8f6d9cd3b | refs/heads/master | 2022-09-04T15:47:33.547991 | 2022-08-29T07:40:08 | 2022-08-29T07:40:08 | 236,879,246 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,341 | rd | bipartitionShi.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/codeSpectral.R
\name{bipartitionShi}
\alias{bipartitionShi}
\title{Spectral clustering}
\usage{
bipartitionShi(sim)
}
\arguments{
\item{sim}{similarity matrix.}
}
\value{
The function returns a list containing:
\item{label}{vector of labels.}... |
63ac89897d763983ed88cbb1fe948753ba60590d | e1d7a92b1a0f23f62c683b71a82681ab5f65ab59 | /plot4.R | d617cfcf20de3233db8ca7bfead428813787456d | [] | no_license | luisben/ExData_Plotting1 | 2e19bbd85efd3b5fda2fcce7c5b4a1502f527845 | d763493bd4497127bfbeedb031fc57146646cf0c | refs/heads/master | 2021-01-15T21:14:46.100563 | 2014-08-09T17:11:40 | 2014-08-09T17:11:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 988 | r | plot4.R | source("dataLoad.r")
prev_locale <- Sys.getlocale(category = "LC_ALL")
Sys.setlocale(category = "LC_ALL", locale = "C")
png("myplots/plot4.png",width = 480, height = 480, units = "px")
#set mfcol
par(mfcol = c(2,2))
#first plot
with(pw_data,plot(timestamp,Global_active_power,type = "l",xlab="",ylab="Global Active Power... |
e5c0cac6eac7123f7bbcfee7b5a866d0b062bf7c | cc45e66be835f29864d3bf2ac39bd2983f6a05c4 | /Dumbbell_Chart.R | 3423d5ccbd8f437c7539809ea9c34b15717b0fb7 | [
"MIT"
] | permissive | paulinelemenkova/R-11-Dumbbell-Charts | aaa7524b3cbf1a82577ce88c36daf6619d33e16e | ce35b048f2d00b1f86e65f092c264787081d88af | refs/heads/master | 2020-03-19T11:34:30.723816 | 2019-06-17T13:09:15 | 2019-06-17T13:09:15 | 136,462,641 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,992 | r | Dumbbell_Chart.R | library(ggplot2) # используем данные библиотеки
library(ggalt) # используем данные библиотеки
# ЧАСТЬ-1. формируем исходный датафрейм.
# шаг-1. вчитываем таблицу. делаем из нее исходный датафрейм.
MorDF <- read.csv("Morphology.csv", header=TRUE, sep = ",")
head(MorDF)
summary(MorDF)
# ЧАСТЬ-2. строим график Дамббел... |
62135879cf7cdc14a243df8c316216e96b6f6b09 | 0ca78ef5a8670fbdab55409eecda579cec2baf68 | /DM/mlMDS.R | 452289e170dd56eab4fd5e8a4dc6f7fb614d7e43 | [] | no_license | zhurui1351/RSTOCK_TRAIL | ab83fdef790778a1e792d08a876522ef13a872e6 | 2396c512c8df81a931ea3ca0c925c151363a2652 | refs/heads/master | 2021-01-23T09:01:36.814253 | 2019-05-17T15:26:19 | 2019-05-17T15:26:19 | 23,482,375 | 9 | 4 | null | null | null | null | UTF-8 | R | false | false | 7,051 | r | mlMDS.R | #https://github.com/johnmyleswhite/ML_for_Hackers/blob/master/09-MDS/chapter09.R
library('foreign')
library('ggplot2')
set.seed(851982) # To make sure results are consistent
ex.matrix <- matrix(sample(c(-1, 0, 1), 24, replace = TRUE),
nrow = 4,
ncol = 6)
row.names(ex.matrix) <- ... |
d14645fa1b78b0d47e49dee7f7f138c15c94ebbf | a257667eb2a709200f5b9664e63c6c974c051a67 | /code/stan-plots.R | b6f654437ea4c5f42e94b8dec0b3d1e251a8319f | [] | no_license | chiangwe/covid19-forecast-evals | ef892145304c9defaf61bc21a09154f047d97545 | 6a964c8ad60cefc14ca2b12b23cd2c4095e4246a | refs/heads/main | 2023-03-02T04:53:42.808052 | 2021-02-08T03:30:30 | 2021-02-08T03:30:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,233 | r | stan-plots.R | stan_fit_nb <- readRDS("../paper-inputs/20200927-stan-fit-scores-negbin.rds")
launch_shinystan(stan_fit_nb)
pp_samples <- posterior_predict(stan_fit_nb)
dim(pp_samples)
scored_models_df_pred$pp1 <- pp_samples[1,]
scored_models_df_pred$pp2 <- pp_samples[143,]
scored_models_df_pred$pp3 <- pp_samples[1430,]
scored_models... |
3a9dbd3b3fe5fb9e6a079b00f8ef3175ac647e6d | 8ac1fa003fd7be97fb170bea5d92e3008cf6e09b | /R/sql_insert.r | 95d655725114b6e617c7df9e8bbc5fe3516dd296 | [] | no_license | swish-climate-impact-assessment/swishdbtools | 28271f06aef8b85740c8e2630c6c84eff55ee95d | acae288c23a9fb74e8daaddc3c319777d06e765e | refs/heads/master | 2020-08-25T03:19:32.312877 | 2020-05-15T07:12:08 | 2020-05-15T07:12:08 | 7,141,752 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,244 | r | sql_insert.r | ################################################################
# name:sqlquery_insert
sqlquery_insert <- function(channel, variables="*",
from_schema = 'public', from_table,
where=NA, limit=1, eval = FALSE)
{
# assume ch exists
exists <- pgListTables(channe... |
d92ed94688849f68146360c5152b4b3b6b335773 | 50d9adf0b0ff309013c2c963aa8220ea2bcf6339 | /get_required_dataset.R | 288fca58f6a6dd2d958d1b8ea442c235bb3dbb9a | [] | no_license | ravikanth1979/Data-Analysis-and-Visualization | 343542334d63aaeaf6f5fb3539606b38b6ddcbb7 | 9abf5287cf9d9b5434bd2f4fabde8e88f9836cc8 | refs/heads/master | 2020-06-21T20:58:34.293576 | 2019-07-18T09:04:12 | 2019-07-18T09:04:12 | 197,550,988 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,758 | r | get_required_dataset.R |
get_lpi_dataset <- function(dataset){
lpi_indicators = c("LP.LPI.CUST.XQ",
"LP.LPI.INFR.XQ",
"LP.LPI.ITRN.XQ",
"LP.LPI.LOGS.XQ",
"LP.LPI.OVRL.XQ",
"LP.LPI.TIME.XQ",
... |
9644261333be2dcd002543d4271c19e9b7d48092 | bd43b64c52566ff9a18c12608728bcac15468877 | /man/pnls.Rd | 55f2a30bc90cddceaef51b4d8657b679932b26bd | [] | no_license | cran/nlsr | 09cef8d9d081ce911b5ca77f34ceb1f7465f33c4 | b1f05b425dfa0570fde55543026a09ffa3cbd2ca | refs/heads/master | 2023-05-25T21:49:56.874260 | 2023-05-10T14:50:02 | 2023-05-10T14:50:02 | 81,798,223 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 275 | rd | pnls.Rd | \name{pnls}
\alias{pnls}
\title{pnls}
\usage{
pnls(x)
}
\arguments{
\item{x}{an nls() result object from nls() or nlsLM()}
}
\value{
none
}
\description{
Compact display of specified \code{nls} object \code{x}
}
\author{
J C Nash 2014-7-16, 2023-5-8 nashjc _at_ uottawa.ca
}
|
7172ca187689d403223dce14acfd34d960590fe3 | 56e14c3b514a0af4d8967cee1171b96bcab0c27c | /Munge.R | 25bdf23e1fed13de182f23d0a47cee949ffcd8a0 | [] | no_license | rgknight/ma-enrollment-analysis | cbe149648cecbb67c0b48d956a993f9599a58c67 | 9db8fbee64b209d2aecec94ee65bfbb7526c0933 | refs/heads/master | 2020-03-28T15:49:06.492494 | 2015-05-08T12:49:49 | 2015-05-08T12:49:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,137 | r | Munge.R | # Calculate the number of students enrolled in Boston by Grade
require(dplyr)
require(tidyr)
require(stringr)
options(stringsAsFactors=F)
setwd('C:/Users/rknight/Documents/GitHub/ma-doe-data')
# School Files
cleanup <- function(thisone){
ifiles <- list.files(path= "data/enrollmentbygrade", pattern=paste(thisone))... |
80e06ca0da393d89685fabfe8c43222949e441e7 | 53ff4594cd6256d1d222adbab1b96ceb5391cada | /kallisto_deseq2/Fig1D_heatmaps_proteostasis.R | 55e8d3b19ecd382e36789e01383c4b05ff985445 | [] | no_license | brunetlab/Leeman_et_al_2017 | 1803a443bfffb6b8c23b659134db8e2871686617 | 8d8e9b071f2c06dfb0332e67ee3eb3a693eb29d8 | refs/heads/master | 2021-09-05T04:11:58.001606 | 2018-01-24T04:06:33 | 2018-01-24T04:06:33 | 113,623,951 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,204 | r | Fig1D_heatmaps_proteostasis.R | ## Make three (small) heatmaps that are attached underneath each other in Adobe Illustrator: proteasome, lysosome, unfolded protein binding.
library(Biobase)
library(pheatmap)
library(RColorBrewer)
library(Hmisc)
load("Data/eset_vsd_replicate_corrected.RData")
eset <- eset[, c(21:24, 17:20, 34:35, 31:33,
... |
216d66024437ce60d42640f29a1228aaf2376a85 | 0dca3db7f3534c43d1dce59a5156680621164ebc | /Project 9.R | 82885c752cf46b78bbc7915c335c4c892513fbe6 | [] | no_license | cRistiancec/Credit-Risk-Modeling-ARIMA-Time-Series-Forecasting-ADF-Test | cfd95624ca07428bf70b37404537e230023704a3 | c1f85e51d8a55ad8d4076e78e198a60f4b56cef4 | refs/heads/main | 2023-01-01T19:13:13.193764 | 2020-10-28T13:06:15 | 2020-10-28T13:06:15 | 397,710,802 | 1 | 0 | null | 2021-08-18T19:15:36 | 2021-08-18T19:15:36 | null | UTF-8 | R | false | false | 25,133 | r | Project 9.R | getwd()
setwd("C:/Users/HP/Documents/R Dataset")
rm(list = ls())
library(DataExplorer)
library(ggplot2)
library(readxl)
library(Hmisc)
library(naniar)
library(nFactors)
library(psych)
train <- read_excel("GL-raw-data.xlsx")
summary(train)
str(train)
names(train)
describe(train)
dim(train)
#ref... |
6773cea45ed0ced719fa05b268a7c5b56c9aa36e | dc0d8d8ee472e4623b4b53097d220f80176d7ee6 | /app.R | fe956e026a7d0effdbcdb08546e0ae2adb010b62 | [] | no_license | rodserr/trading-ML | 75aa8eea1431375c6bb460becab5dd46171c0954 | 43d5f47b1fe703a5f0e6418e3ed50041da718887 | refs/heads/master | 2020-04-17T13:41:16.271881 | 2019-05-20T15:05:50 | 2019-05-20T15:05:50 | 166,625,875 | 0 | 0 | null | 2019-05-20T15:05:51 | 2019-01-20T05:15:43 | TeX | UTF-8 | R | false | false | 8,568 | r | app.R | # libraries----
library(shiny)
library(shinydashboard)
library(lubridate)
library(magrittr)
library(plotly)
library(tidyverse)
library(PerformanceAnalytics)
library(TTR)
library(reshape2)
library(dbplyr)
library(RPostgres)
library(gridExtra)
library(caret)
library(factoextra)
library(purrr)
library(xtable)
library(rsco... |
baceb0301b572365443486b4c3e9e9ef64819cf2 | 3803548786efc94f399326c9d6e5066842538c37 | /Definitive test July 16.R | a6b7a31b9a347a9b85b5f040196ffa4e82bc3f5b | [] | no_license | JiayiQin/AshEcotox | 97dd6c46c5e20724385a49801330cf8b9371dba6 | c66f04879a17d989d40973821302221c3279219b | refs/heads/master | 2021-01-21T17:31:58.426600 | 2018-03-12T11:34:41 | 2018-03-12T11:34:41 | 91,962,944 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 23,590 | r | Definitive test July 16.R | PersonResiduals<-residuals(LinerRegression,"pearson")
#Point out the working folder
setwd ("/Users/JiayiQin/Dropbox/ASHBACK/BIOS/single-species test")
#Import datafile
SUM <- read.table( file = "Briefly summary.csv", header=TRUE, sep =",", dec =".")
SUM
summary (SUM)
#############################Foulum-soil Definitive... |
b6cfecf74f3625d10652be8ea47fa090d88f6514 | c9ce69cc331f06c751e6bc58bd43131335869bc2 | /Fig_1_State_of_photosynthetic.R | 12eff616fa44465d92581a3a3868690750e623f9 | [] | no_license | lkulmala/Hari-et-al | 730652fa80fc2f9c59f25eded166d005bc278fd0 | 59f55bdd1abf1ed869c074913dfe92c0cec24cc9 | refs/heads/master | 2021-01-23T04:19:31.314233 | 2017-03-25T20:29:00 | 2017-03-25T20:29:00 | 86,183,947 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 819 | r | Fig_1_State_of_photosynthetic.R |
setwd('input/')
datafile<-read.table("Fig_1_State_of_photosynthetic.txt", header=TRUE)
S2011<-datafile$X2011
S2012<-datafile$X2012
S2013<-datafile$X2013
S2014<-datafile$X2014
days<-seq(as.Date("2014-04-01"), as.Date("2014-10-16"), by="days", format="%m-%d-%Y")
xlimits<-c(as.Date("2014-03-25", sep=""),as.Date("2014-... |
5fef4d8cb1258dc3a959e4846dcde8ab143564b7 | 768b26832ce21ac800f547bcf1e282b5f9e691a0 | /Shiny/Recommendation/ui.R | 3903602d4a499c669ed9408e560b6e014c8d3a48 | [
"Apache-2.0"
] | permissive | dautroc1/Recommendation-system | 2526aa809f311846e47d66307a6d0b06545b2aa8 | 087ef82598b9cd0be482d7b91bcd9964e37587c8 | refs/heads/main | 2023-02-16T03:22:14.157505 | 2021-01-10T02:07:17 | 2021-01-10T02:07:17 | 323,897,635 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 707 | r | ui.R | shinyUI(fluidPage(
# Application title
titlePanel("Anime recommendation"),
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
h2("Instruction"),
h5("1. Input user id to the box"),
h5("2. Press enter and the recommendation w... |
d6650a98a7ba7da443442389fac08afbe414f6da | 528e2696dbc7ad1ac2fa899dc2f862ca4116e21b | /map-suspected-reinfections.R | 3bd1465dd2729c6c25ccbcc00245f6d92b1adafa | [
"MIT"
] | permissive | jmcastagnetto/bnonews-reinfection-covid19 | 45ea1cca6880e58cad710747be9db7283529361e | e21ba69e5a2ecc1b6ceb558ba513859eb0bb2bed | refs/heads/main | 2023-02-24T11:16:54.610871 | 2021-02-01T18:02:52 | 2021-02-01T18:02:52 | 321,224,874 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,198 | r | map-suspected-reinfections.R | library(tidyverse)
library(tmap)
data(World)
raw <-read_csv(
"data/covid19_suspected_reinfections.csv",
na = c("", "-", "NA", "N/A")
)
suspected <- raw %>%
group_by(
country, iso3c
) %>%
summarise(
Suspected = sum(cases, na.rm = TRUE)
)
n_suspected <- sum(suspected$Suspected, na.rm = TRUE)
date_ra... |
c5fac2f58329e066fafd4be7c51550859dcde2b9 | d52bc56044d2a90407f1c08048a4d6a88ce39193 | /man/imports_put_archive.Rd | efa72c34658955971b7a75964975f407a920ddf7 | [
"BSD-3-Clause"
] | permissive | wlattner/civis-r | 71e15c650ff99e41b7028d32ccaa9c3df54e1f36 | d794680c8b155c6302dc9d41b500f719e3a8c16c | refs/heads/master | 2021-06-25T17:16:54.733530 | 2017-08-21T23:19:18 | 2017-08-21T23:19:18 | 100,273,729 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 4,240 | rd | imports_put_archive.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generated_client.R
\name{imports_put_archive}
\alias{imports_put_archive}
\title{Update the archive status of this object}
\usage{
imports_put_archive(id, status)
}
\arguments{
\item{id}{integer required. The ID of the object.}
\item{status}... |
94c0064d2efe151727c82a0421bc7d2b182b80ba | b5547ead9c9590de07e4d661ba08605dc335329b | /ggplot.R | ee9d4c2fbe953066c90cfb32cf51a12a5ab594fc | [] | no_license | shivani02/FinancialModelling-Rprograms | aa4accd3f1124adeda54a7f6aabe31eb69166c6f | 9d04880caa1a87b9c6abfacc41390661504e3559 | refs/heads/master | 2020-06-08T09:34:22.923598 | 2015-05-09T19:58:31 | 2015-05-09T19:58:31 | 35,126,582 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 941 | r | ggplot.R | # call price function
callprice<-function(x,t,T,r,sigma,K)
{
d2<- (log(x/K) +(r-0.5*sigma^2)*(T-t))/(sigma*sqrt(T-t))
d1<-d2+sigma*sqrt(T-t)
x*pnorm(d1)-K*exp(-r*(T-t))*pnorm(d2)
}
#implied volatility function
ImpliedVolatility<-function(x,t,T,r,K,ObsPrice)
{
sigma<-seq(0.001,10,by=0.0001)
length(sigma)
C<-callpric... |
ba04f48f70df6a11fca51c4b1f105271ea83e3bf | 5feca36689ab072f63447022f577c0b5dcdcd214 | /man/pmcode_99329.Rd | c76e96e9773cbed956710f2f2dff5a4446953d89 | [
"CC0-1.0",
"LicenseRef-scancode-warranty-disclaimer",
"LicenseRef-scancode-public-domain-disclaimer"
] | permissive | iembry-USGS/ie2miscdata | dfc205ab712ff2b6adf1249fde4477104f9d6b20 | 142bffa6f678eb8d7cadc59669e8429d45da19c9 | refs/heads/master | 2021-01-21T04:35:30.514811 | 2016-07-16T06:55:22 | 2016-07-16T06:55:22 | 49,751,904 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 807 | rd | pmcode_99329.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pmcode_99329.R
\docType{data}
\name{pmcode_99329}
\alias{pmcode_99329}
\title{99329 Coliphage, somatic, E. coli C-host, 2-stepenrichment presence/absence per 1 liter}
\format{A data frame with 2 rows and 3 variables:
\describe{
\item{Paramete... |
6d979cdf97edb2570ff28bd2a32f1c15e4b8188f | 603ef4d458ae15590178a3bb83e41597bcbc0447 | /R/summarize.r | 21a640d9b85d497e4ad29deaf16bfbb8234e6316 | [] | no_license | ntncmch/myRtoolbox | 8dace3f0d29e19670624e6e3c948ba6d0fa38cec | 8ec2a6bc2e7dd33fb23d7f4b2c6cf2d95ca5ef8d | refs/heads/master | 2020-05-14T14:14:34.465272 | 2014-09-22T13:17:47 | 2014-09-22T13:17:47 | 21,052,420 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,679 | r | summarize.r | #'Basic accounting for NA values
#'
#'All these functions performs the same as the basic version (same name without \code{_na} at the end) but differ when all elements are \code{NA}. In this case, \code{NA} is returned in the same format as \code{x}, which is not always the case when the basic function is used with \co... |
902d13b17610bc1b2efac578f20daca67fd986dd | 2c5a3c1b0ca9b746ca3657e811466ab9017be59f | /Gotham_Cabs/code/garbage_code/Linear_Regession/Script1_data_inspection.R | 3d06bd5609add881d755cf6295d043cd1d24d80e | [] | no_license | ccirelli2/ML_Final_Project_2019 | 5982263cdd2f7ef4818ae9b976dd7525f7dcdc0d | 02b3df31f6a253ac0270ef27545eb46cfac51792 | refs/heads/master | 2020-05-15T13:28:49.872082 | 2019-05-02T20:02:34 | 2019-05-02T20:02:34 | 182,301,101 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,840 | r | Script1_data_inspection.R | # DATA INSPECTION
'Content:
1.) Average Duration: By month, day and route.
Plot: Bar
Plot: Boxplot.
Observations:
Duration: See plot. It appears that the majority are
Month: Only... |
08ed8cc586396a859a09feeae58b4bc68e5b4599 | 6e0cdf0db71decd74e246cd8a2f5bbd4f4bef076 | /R/graphs.R | 1e10f2460e8a8a5755b677c5fc5dcbb64f597c7b | [
"MIT"
] | permissive | jandraor/SEIR_cohorts_calibration | d88e2d7a56fd3ea8789d7565eeed73ff7dc254ee | 2da19c9f7866769700d13976a0fcbe9019f5b6b8 | refs/heads/master | 2021-08-28T21:01:30.566173 | 2021-08-16T12:34:56 | 2021-08-16T12:34:56 | 246,302,611 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,547 | r | graphs.R | draw_multiple_densities <- function(densities_df, stats_df,
x_pos, text_size = 5) {
g <- ggplot(densities_df, aes(x = x, y = y)) +
geom_line() +
geom_ribbon(aes(ymin = 0, ymax = y, fill = area)) +
facet_wrap(~ param, scales = "free") +
scale_fill_manual(values = c(N... |
34e9668689b3fd7d48ade1e6c337f52ebfe09ff6 | 8188db98bf6f785fdd8f52a4ff341927b13a02aa | /man/readExposome.Rd | c2c7e1b5f0f8a48bc8f714498220979f351a0bea | [
"MIT"
] | permissive | isglobal-brge/rexposome | a931ee6ac44044652dc40dbaf9752d6478b8f833 | 0bb431c5d01bd4f2112205c8e6f4d46478d037e5 | refs/heads/master | 2023-06-09T13:23:34.657993 | 2023-01-26T15:05:53 | 2023-01-26T15:05:53 | 79,567,386 | 4 | 2 | null | null | null | null | UTF-8 | R | false | true | 3,768 | rd | readExposome.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/readExposome.R
\name{readExposome}
\alias{readExposome}
\title{Creation of an ExposomeSet from files}
\usage{
readExposome(
exposures,
description,
phenotype,
sep = ",",
na.strings = c("NA", "-", "?", " ", ""),
exposur... |
ce98e0951660d7a39235a0d30fc3a3b9a9e1ce6b | fe04211f605d0ab8b5404accee6cfc725ad4bc53 | /imp/saver/code/01_normalize_libsize.R | c8914ea92b2e72745eab9b836660a9af9d3c39c3 | [] | no_license | Winnie09/COVID_integrative | 7707403d4d1dbced66704c4b6176d93785d8df0f | c024b999bd30c17f584000e16c2f96eb923db2c1 | refs/heads/master | 2023-02-27T17:23:05.152064 | 2021-02-08T04:00:14 | 2021-02-08T04:00:14 | 304,786,205 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 458 | r | 01_normalize_libsize.R | library(Matrix)
library(parallel)
setwd('/dcl02/hongkai/data/covid/data/200916/')
rdir <- '/dcl02/hongkai/data/whou/covid/imp/data/norm/'
pbmc <- readRDS('pbmc.rds')
ap <- sub(':.*', '', colnames(pbmc))
libsize <- colSums(pbmc)
libsize <- libsize/median(libsize)
nn <- sapply(unique(ap), function(p){
print(p)
tmp ... |
e5f83aa235291c61ed905631d549b7d07a70f6de | a5f53a0276eed01abb9cab59b24206b167e0f086 | /man/readMetaInformation.Rd | b834d5e0ab245f34bbb56e71beaf25021d9222a0 | [] | no_license | gridl/timeseriesdb | 7c0eebde52e522eb86034aafb96a3242079ddb5e | 0b0528fc2a1414eee777550506804cc0e3468dd7 | refs/heads/master | 2022-02-06T02:58:45.205568 | 2019-07-08T07:46:07 | 2019-07-08T07:46:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,557 | rd | readMetaInformation.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/readMetaInformation.R
\name{readMetaInformation}
\alias{readMetaInformation}
\title{Read Meta Information from a Time Series Database}
\usage{
readMetaInformation(con, series, locale = "de",
tbl_localized = "meta_data_localized",
tbl_unlo... |
0ab3c25e424cbc9ac55df00b1bef3a7d9067fa08 | 36529e0093254e9aa33f02f39d191d6201a2f826 | /PQE-input/Masking up extreme floods in 70s, Gingera/masking_two_floods_in_70s.R | 0a12fbe2f227f40051c4a6d7c448ca937d8db38c | [] | no_license | LynnSeo/Sensitivity-Analysis | 09a07d39f5011de3e130166cd1a2a4a6fbc73414 | e8c763c4553aa56cad0ecbce8799d844dfda67fc | refs/heads/master | 2021-01-23T07:34:52.522742 | 2017-06-19T05:17:11 | 2017-06-19T05:17:11 | 86,503,370 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,451 | r | masking_two_floods_in_70s.R | #########exact data######Calibration of Sensitive Parameters Only
library('zoo')
library('hydromad')
library('xts')
library('stringr')
#set up working directory
wd='C:/UserData/seol/Sensitivity Analyses/Sacramento/Calibration considering SA/Calibration of Sensitive Parameters only/'
setwd(wd)
##############... |
b97a40821cc8669164a6dbbf6c2f14c32f24469e | 4050c25b8aa1bd07808af59300bf8058c7890949 | /Scripts/HornwortsLiverworts/LiverwortDiversity.R | 6f0847f9af521faa39b481b9c1d993b9772d469e | [] | no_license | KerkhoffLab/Bryophytes | 6faf03b9f73be24eeff7017f092451e824ac15ca | 9bb7a8e4c0fa5a9f16e4dbfc937b643da0a69ab4 | refs/heads/master | 2021-07-23T17:12:46.284440 | 2021-07-22T16:26:02 | 2021-07-22T16:26:02 | 193,754,056 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,334 | r | LiverwortDiversity.R | #Adapted from HLDiversity.R and MossDiversity.R
#Kathryn Dawdy, Summer 2020
#Load data
BryophytePresence <- readRDS("Data/BryophytePresence.rds")
#Subset liverwort data
LiverwortPresence <- subset(BryophytePresence, BryophytePresence$Group=="Liverworts")
#Create occurrence by cell matrix by reshaping dataframe, then... |
537423d89e2fff543f5656737457c3c5dfcc001d | 27c8c8337342e22d3e638d9738ca6499243bc86b | /man/pivot_wider_profile.Rd | 900aa68e51528ee753b3cdc9680cc4be40c83cef | [] | no_license | Eirinits/decoupleR | 1f578ef44dd3a81496e276058fb3c6eca7d6608d | 3926381bc63362a7ec7cb1b32b40a85f1f9a9cd1 | refs/heads/master | 2023-06-03T01:35:56.461380 | 2021-05-25T18:57:17 | 2021-05-25T18:57:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,061 | rd | pivot_wider_profile.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils-profiles.R
\name{pivot_wider_profile}
\alias{pivot_wider_profile}
\title{Pivot a data frame to wider and convert it to matrix}
\usage{
pivot_wider_profile(
data,
id_cols,
names_from,
values_from,
values_fill = NA,
to_matrix ... |
8b7cb4470f28d9b10b062a751b430fd0988fcda1 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/tabr/vignettes/tabr-engraving.R | eb1529490282227b7a98151ef98f7db9ffa6b0b5 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,929 | r | tabr-engraving.R | ## ----setup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE, comment = "#>", message = FALSE, warning = FALSE, error = FALSE, tidy = TRUE, out.width = "100%"
)
## ----args----------------------------------------------------------------
library(tabr)
args(... |
9104c0e34f5a0491470559dffc4e98dcf4cacb08 | cd3312ba777c36aaca541a6223addbdae5be7932 | /scripts/stepthrough/step_2_calibration/s2.1_calibrate_ler_models.R | a3d1bf5f8fd1fd535355fe3961b4a06142c25eb4 | [] | no_license | jacob8776/sunapee_LER_projections | 2aa96061fcfd8b354569cd86b85d42509274b109 | dcd3a757487cbb773c93f134bedc9881d3ba185d | refs/heads/main | 2023-04-30T21:28:24.512696 | 2022-07-22T15:15:00 | 2022-07-22T15:15:00 | 376,087,592 | 0 | 1 | null | 2022-08-08T19:18:39 | 2021-06-11T16:46:55 | HTML | UTF-8 | R | false | false | 5,109 | r | s2.1_calibrate_ler_models.R | Sys.setenv(TZ = "UTC")
# remotes::install_github("tadhg-moore/LakeEnsemblR", ref = "flare")
# remotes::install_github("tadhg-moore/gotmtools", ref = "yaml")
# remotes::install_github("tadhg-moore/LakeEnsemblR", ref = "flare")
# remotes::install_github("aemon-j/gotmtools", ref = "yaml", force = TRUE)
# devtools::instal... |
3edb31d3ff6afd5d909ddaa18a9b21c27d3a6b0c | b43e19276f5a9f498d3c04b7ab8d25373c9089f1 | /cachematrix.R | e4dcc86b62e266a9db8602fe0b6482c3f30ac0a0 | [] | no_license | kushla/ProgrammingAssignment2 | 1577a8efc63ee9bceb24c7b0f36b04e5d2d4e9f7 | 3a1c234b450be2391bb747aa2fa3c4ca9b2ac7fc | refs/heads/master | 2021-08-23T04:50:46.964477 | 2017-12-03T12:05:13 | 2017-12-03T12:05:13 | 112,907,669 | 0 | 0 | null | 2017-12-03T07:06:46 | 2017-12-03T07:06:45 | null | UTF-8 | R | false | false | 1,923 | r | cachematrix.R | ## This function creates an R object that stores a matrix and its inverse
## Initialize function name and set default value (empty matrix) to a formal argument x
makeCacheMatrix <- function(x = matrix()) {
## Assign free argument s (result of the inverse) to NULL
s <- NULL
## Function "... |
c3adfd5b8c8680120e08742ef570deefe6b0c5f1 | 62da952d6afb59390d3e164081498503abd50fa9 | /detectRUNS/man/heteroZygotTest.Rd | d41b638546a403bda2191207d79b038cbec22fa0 | [] | no_license | bioinformatics-ptp/detectRUNS | 1739c908310c696296920b8e570f3e00a42c0ecf | e383906cf14a4597980b44830c0df7e9de4b82ef | refs/heads/master | 2023-06-08T13:44:30.383026 | 2022-03-22T16:59:17 | 2022-03-22T16:59:17 | 61,555,187 | 8 | 3 | null | 2023-05-25T15:13:32 | 2016-06-20T14:44:24 | R | UTF-8 | R | false | true | 1,049 | rd | heteroZygotTest.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/funktionen.R
\name{heteroZygotTest}
\alias{heteroZygotTest}
\title{Function to check whether a window is (loosely) heterozygous or not}
\usage{
heteroZygotTest(x, gaps, maxHom, maxMiss, maxGap, i, windowSize)
}
\arguments{
\item{x}{vector of ... |
ac7ed40e3ebec52198643555d4d97f6378c36c00 | d9e676cf47f9f50080538d9e8d8dbce5c2456a3d | /mcmcplots/R/as.mcmc.rjags.R | 4401220e8f526009078d426259780310410a56d9 | [] | no_license | sumtxt/mcmcplots | 33da3e490994b633e599a40550b2d693292a6ad7 | bc13fd645c653f48bf7bcd6cd17505ac0f146dee | refs/heads/master | 2020-12-29T19:04:02.966002 | 2014-05-14T12:46:45 | 2014-05-14T12:46:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 56 | r | as.mcmc.rjags.R | as.mcmc.rjags <- function(x) as.mcmc.bugs(x$BUGSoutput)
|
81492890d7e82a659f01a029920a66ea01aa14a8 | 8af539b0b62a0c7838347fc8f766621ade9d7028 | /Project2/final_code.R | 3e799910fef648edb7124c421f83d34ce880f81e | [] | no_license | alexaoh/stochmod | a4f16418cd151c5fa25c881c5d54fb8226b1d6b9 | f15c2b1d390e4beaa9e1f98e3aa3c62877265fe6 | refs/heads/master | 2023-01-23T10:35:07.021417 | 2020-11-17T14:52:17 | 2020-11-17T14:52:17 | 290,275,470 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,116 | r | final_code.R | #Project 2 Stochastic Modelling
#Problem 1: Modelling the common cold
#Constants
alpha = 0.1
lambda = 1/100
mu_L = 1/7
mu_H = 1/20
simulate_markov <- function(state0, tEnd, plot) {
time_infected = 0
time_between_Ih = 0
time_between_Ih_list <- c()
if (plot) {
plot(NULL, NULL,
xlim = c(0, tE... |
2170da8d2868e03aeb074074ad56b71b78b75239 | 598a2f6059a264cb10a609cb65a8048086015e67 | /offline-advetising-campaigns-efficiency/Moscow Best model.R | 42eb3643762a16a436b302a268cf0aa95e25a975 | [] | no_license | alexey-nikolaev/code-examples | eb5142bcf3b9d19fff6c4badeda77278fc3251de | efd0f4a19aea0e388e024da8f211258458aa95fd | refs/heads/master | 2021-05-09T18:50:35.500699 | 2018-01-27T19:46:31 | 2018-01-27T19:46:31 | 119,175,976 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,978 | r | Moscow Best model.R | # https://www.periscopedata.com/app/wheely/115382/Advertising-Efficiency
library(readr)
data <- read_csv('https://app.periscopedata.com/api/wheely/chart/csv/8dbe6edf-0ee5-60c4-6bef-9b30eaa48935/189541')
library(CausalImpact) # based on BSTS
library(bsts) # BSTS
library(forecast) # auto.ARIMA
data$mm <- format(data$dd... |
a367fdce06e87b8a2b1d709b693c40714c24df65 | 5535aebd21d291783f77aa8d35e127d66022acb9 | /man/get_col.Rd | 939038d64ce4897f4586c4c0ff11b83668f84489 | [] | no_license | arturochian/gspreadr | 41cb4e276a703ba7bfdf3006153cc08793aa5b99 | e7b0b0a155bc2d1b8e3aa858fee79143530a5dad | refs/heads/master | 2020-12-11T03:21:03.254666 | 2015-01-13T00:14:38 | 2015-01-13T00:14:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 473 | rd | get_col.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{get_col}
\alias{get_col}
\title{Get all values in a column.}
\usage{
get_col(ws, col)
}
\arguments{
\item{ws}{worksheet object}
\item{col}{column number or letter (case insensitive)}
}
\value{
A data frame.
}
\description{
Get all values in a column.
}
\seeals... |
ec558b355b74e4fc9e6d86ef355e0aec844dec8c | b6312d8298f60b08b040b51ad1d66f8f3b6627a5 | /R/multiQQPlot_function.R | 17003ee787bd17775fd554dfaaf295be1dd15b1d | [] | no_license | hutchisonjtw/JNCCTools | 36ced580cb7feb91cf310684220451843996bb16 | 48242eac43c37d16b50aa50504dd4ca7f02c4551 | refs/heads/master | 2021-01-10T10:13:43.298389 | 2017-03-22T14:25:45 | 2017-03-22T14:25:45 | 54,502,489 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,069 | r | multiQQPlot_function.R | #' multiQQPlot
#'
#' Plots quantile-quantile plots for multiple probability distributions on one set of axes.
#'
#' @param x Numeric vector of data values to be plotted to be plotted as a histogram.
#' @param main Title for the plot. Default value is \code{"QQ plot of sample data against likely distributions"}.
#' @par... |
91ba8b17bd4402ece80857f92658f8b742608c8b | b2de870cc0a65b07724b3201970c2973b19d688b | /man/formattable.Rd | af97846aa13af853c355002b304d0c50c3fa3b00 | [
"MIT"
] | permissive | renkun-ken/formattable | 81ca995467a44f79d979ba64371a6b79b26540d6 | 66f69944ef869156a4362dd332cc0b931fc157cd | refs/heads/master | 2023-07-02T02:45:22.643478 | 2023-03-23T03:04:07 | 2023-03-23T03:07:00 | 33,297,795 | 720 | 102 | NOASSERTION | 2022-10-20T23:02:01 | 2015-04-02T08:22:11 | R | UTF-8 | R | false | true | 640 | rd | formattable.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/formattable.R
\name{formattable}
\alias{formattable}
\title{Generic function to create formattable object}
\usage{
formattable(x, ...)
}
\arguments{
\item{x}{an object.}
\item{...}{arguments to be passed to methods.}
}
\value{
a \code{format... |
6804d9831317572f4176ee2baa80340516919d42 | 430ca7d8ba944d18d20349ad40dd81f36756c04e | /multiple comparison_sample code.R | f9049137a53c9ac82262683b698e94d5cd38bd66 | [] | no_license | NxNiki/multidimensional_age_prediction | 9a45bbccb9275b99e288b70e1966c7156bcd2feb | dcf42175937d8033d3ec20f10de5f27cdc89e80f | refs/heads/master | 2022-08-08T04:39:18.947106 | 2022-08-02T04:04:13 | 2022-08-02T04:04:13 | 236,085,971 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 304 | r | multiple comparison_sample code.R | p.values = read.csv()
p <- c(0.015, 0.036, 0.048, 0.052, 0.023 )
p.bon = p.adjust(p, "bonferroni")
p.adjust(p, "holm")
p.adjust(p, "hochberg")
p.adjust(p, "hommel")
x = rnorm(10)
y = .1*x+rnorm(mean = 10, sd = 2)
plot(x,y)
data = data.frame(x=x, y=y)
mod = lm(y~x, data)
summary(mod) |
3601291239e08f44f60ac99e26b13df04dd06bab | e365698941fa20641c21c5dea00dcd2ebc853223 | /ProgAssig1/plot4.R | f9e6f8abc3df59d9bf38509201822d1213067c72 | [] | no_license | danidelacuesta/ExData_Plotting1 | bed545807b8297398cea9cf122fdeb3d0fc48092 | 182b1f1d09e6da3e34408e2edef3b31d8d8d4d1f | refs/heads/master | 2021-01-20T23:40:59.934005 | 2015-03-09T01:38:33 | 2015-03-09T01:38:33 | 31,868,490 | 0 | 0 | null | 2015-03-08T22:32:48 | 2015-03-08T22:32:47 | null | UTF-8 | R | false | false | 1,220 | r | plot4.R | plot4 <- function() {
library(lubridate)
data <- fread(".//household_power_consumption.txt",skip=65000,nrows=5000)
data <- data[data$V1=="1/2/2007"|data$V1=="2/2/2007"]
data$Date_Combined <- dmy(data$V1)+hms(data$V2)
par(mfrow=c(2,2))
{
#graph1
... |
c801e182b7719ae5f58bb84a23d41f235ecd21ec | af883594be37bf9b58d4d11c05ac685bb0919652 | /R/ex16_지도시각화.R | a52f91a361baa7c4114770f6345b8fa5143a03f5 | [] | no_license | handaeho/lab_R | 7593574af1dc345c1f98f92c219c3af3d411d493 | c735aaf2cb004254bf57637ec059ee8344c3f4f9 | refs/heads/master | 2020-11-26T08:40:18.922074 | 2019-12-19T09:17:59 | 2019-12-19T09:17:59 | 229,017,638 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,312 | r | ex16_지도시각화.R | # 지도 시각화를 해보자
# 지도위에 통계 값 표시하기
# 패키지 설치 & 로드
# ggplot2::map_data()함수가 지도 데이터를 처리하기 위해 필요한 패키지
install.packages("maps")
install.packages("ggplot2")
install.packages("mapproj") # ggplot2::coord_map()함수가 사용하는 패키지
library(ggplot2)
# 동북아시아 지도 만들기
# 지역 정보를 가지고 있는 데이터 프레임 만들기
asia_map = map_data(map = "world",
... |
fa899ca0e37ed923b085ef5b758826ef53560e15 | 244393a89b3f8a836ee5afdd2ec9c91f5e52a6cd | /Visualization/case_study_trends_in_world_health.R | ff4962bd0fe8a50f1204984cf2ab4646a0932015 | [] | no_license | mjchenko/R_for_Data_Science | c33e470bb7b054ba5255df99aa06f60c2940976d | a2d228b738400a80fa2ab6fbf9df7af40a2ad83e | refs/heads/main | 2023-02-01T13:39:57.324999 | 2020-12-18T20:27:20 | 2020-12-18T20:27:20 | 322,691,962 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 513 | r | case_study_trends_in_world_health.R | library(dslabs)
data(gapminder)
head(gapminder)
gapminder %>%
filter(year == 2015 & country %in% c("Sri Lanka", "Turkey", "Poland",
"South Korea", "Malaysia", "Russia",
"Pakistan", "Vietnam", "Thailand",
... |
45de45e85a4bdcc148bb6dd352e97dccece0d32e | cbffba8db095c390010b0c73039ffae2154fbc52 | /scripts/data_preprocessing.R | c6e1e51896b0dc6fcc45a32a01ebafcfc2447270 | [] | no_license | caramirezal/hcv_scDataIntegration | b005f283cf72e76c893ea02c6eeb6023bf45b3cd | 6bb008ac8b36ea4ae3683881b036a4e77f7f6d86 | refs/heads/master | 2020-12-22T07:14:26.169716 | 2020-03-16T17:24:48 | 2020-03-16T17:24:48 | 236,708,218 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,798 | r | data_preprocessing.R | ## Data preprocessing of HCV project data
library(Seurat)
library(Signac)
library(tidyverse)
library(liger)
## Input: A seurat object
## Performs normalization, scaling, pca and umap
## Output: A processed seurat object
st_workflow <- function(
seurat_object,
n_features = 3000,
n_pca_dims = 15... |
3c4fe08d1c87bdfe90a8b9c9f9680c26bda0c2c4 | 2abf0a9ba6ca20f8599feee37ce84e5749e41a40 | /man/dist_ij_k.Rd | 07369943b304b31ae6c87cb7f86e3d3166d58de1 | [] | no_license | talegari/bigdist | 40635eae08fa3696932c701762ebeca050717e92 | a7f8365f40cd8e61a4a471cfd15699b00dfd8b38 | refs/heads/master | 2021-06-11T23:37:10.341795 | 2021-04-05T06:24:14 | 2021-04-05T06:24:14 | 146,413,978 | 4 | 2 | null | 2021-04-05T06:24:14 | 2018-08-28T08:10:14 | R | UTF-8 | R | false | true | 452 | rd | dist_ij_k.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dist_index_converters.R
\name{dist_ij_k}
\alias{dist_ij_k}
\title{Vectorized version of dist_ij_k_}
\usage{
dist_ij_k(i, j, size)
}
\arguments{
\item{i}{row indexes}
\item{j}{column indexes}
\item{size}{value of size attribute of the dist o... |
3edeb4e5bea7e00989fbdc7185fd824b0b68ce7d | 0b4dece4d948400f501bdd830c3001c498ef69df | /Code TP3.R | 220812f3836627a2188410d39dd74aa42c06422b | [] | no_license | ghatfan99/StatDescrptive | a54e9be7e5a09a7b18bd71e4b07aa7636156fe9d | 3c760b6da587bc6861f0cce2fe87a8a6538da67e | refs/heads/master | 2021-07-06T21:21:42.129732 | 2020-12-13T16:52:25 | 2020-12-13T16:52:25 | 210,345,607 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 979 | r | Code TP3.R | data=read.csv("diplome_sexe.csv",header =TRUE,sep=";")
dim(data)
head(data)
diplome=data$Diplome
sexe=data$Sexe
levels(diplome)
table(diplome)
length(which(diplome=="Licence"))
diplome_sexe=table(diplome,sexe)
chisq.test(diplome_sexe)
qchisq(0.95,2) # valeur critique, on peut l'obtenir dans le tableau fo... |
22d4afb6113a6cf52b8fbee489f380f0cd6ea033 | 02bfe46647db874539d5c41cfd5521046c382717 | /Codes for running simulated data.R | 72972419a87a11fb7565b8bb41cd707ce5796aef | [] | no_license | zaq0718/A-Two-decision-Unfolding-Tree-Model | 2bdbbc46cdafbfc64b08aa10faf78037441d47f3 | aec269ba262695ae2d8fd3308970f8df18bb0e68 | refs/heads/master | 2021-06-21T04:24:30.332000 | 2020-12-14T13:03:49 | 2020-12-14T13:03:49 | 146,249,940 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,320 | r | Codes for running simulated data.R | library(R2WinBUGS)
GGUMTree <- function () {
for (i in 1:I) {
theta[i,1:2] ~ dmnorm(mu[1:2], I_cov[1:2,1:2])
for (j in 1:J) {
# Process I
num.p1[i,j,1] <- 1
num.p1[i,j,2] <- exp(alpha[j]*(1*(theta[i,1]-delta[j]) - tau[j]))
num.p1[i,j,3] <- exp(alpha[j]*(2*(theta[i,1]-delt... |
874ea0735a7ae1490283aa66dbaefc95a782b2d4 | 839302e3d94ffc434f1f3106782b31498105dabd | /DataScienceFromAtoZ/Script/Script/Part_2/R_Demo_4_subscript_filter.R | 255cf34815c10003f93a049554fdea8e09c06ca1 | [] | no_license | bcafferky/shared | cb084a457b39e591ef30beb932b3341a861f81ed | c1eb1b8d5172a8ffe7407241af0115fe7fdb5b85 | refs/heads/master | 2023-08-19T07:42:24.604452 | 2023-08-16T12:10:08 | 2023-08-16T12:10:08 | 96,798,774 | 384 | 312 | null | 2023-05-22T15:16:37 | 2017-07-10T16:29:03 | Jupyter Notebook | UTF-8 | R | false | false | 677 | r | R_Demo_4_subscript_filter.R | # R is all about arrays
myvect <- 1:10
myvect
myvect > 3 # Returns logicals or booleans
myvect[myvect > 3] # Returns elements
myvectbool <- myvect > 3
myvectbool
yourvect <- 20:30
yourvect
# Now let's extract from yourvect using the boolean array from myvect
yourvect[myvectbool]
# Wait...Reverse that...
yourv... |
1ec35c41492f7fc45957639d83eaf760842f8e59 | 29585dff702209dd446c0ab52ceea046c58e384e | /Luminescence/R/internals_RLum.R | 87fd4ac383881b8fb7c298475911fe1fa0b620dd | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 726 | r | internals_RLum.R | ####################################################################################################
## INTERNAL HELPER FUNCTIONS ##
####################################################################################################
#' Create unique... |
8d10247c715a09a3ace97e2b6bc2e82344e859f4 | 9d4f4c06a13bf23ea65f5b2969ab7a03e050efe7 | /man/popmid.Rd | 3733b680c6c1ad12d7cac1fc80a1b24c857ed279 | [] | no_license | m-allik/SocEpi | 817fc7ba1dbc0ea8c0aa6e4c15c047d35821288a | aa90c4110061bd3feb7c510a11abc05ae936aeab | refs/heads/master | 2021-07-14T05:00:54.159205 | 2020-05-18T08:12:37 | 2020-05-18T08:12:37 | 139,704,200 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 577 | rd | popmid.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/popmid_fun.R
\name{popmid}
\alias{popmid}
\title{Population midpoints}
\usage{
popmid(x)
}
\arguments{
\item{x}{Vector of population distribution that sums to 1.}
}
\value{
A vector of population midpoints.
}
\description{
\code{popmid} calcu... |
5b0341896a4d599c52a319b1ead6dcfc5de2bc9d | 3b9dac7e9b3806989c11dec24bee44b203060e36 | /time_series/final_proj/report_graphics.R | 562ccba7f276306cff0c58f1366aec61f59e74c7 | [] | no_license | dhbrand/iaa_fall2_hw | d07bb9c3de6ebd0ef3e4253f35dd1d81c6f0b977 | 26acf1fb8a03c99183a9adf0b6f3254b36d49e7a | refs/heads/master | 2020-04-25T05:25:53.430686 | 2019-02-25T16:37:10 | 2019-02-25T16:37:10 | 172,542,914 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,632 | r | report_graphics.R | library(haven)
library(tidyverse)
library(forecast)
library(lubridate)
fcast <- read_sas("data/forecast.sas7bdat")
str(fcast)
tail(fcast)
fc_ts <- ts(fcast$FORECAST)
timeset <- seq(ymd_h("2016-06-01 0", tz = "est"), ymd_h("2018-06-12 23", tz = "est"), "hours") %>%
tibble %>%
select(date = 1)
fcast <- bind_co... |
09ca49042cda41dd5fc5bc22254b19d4179535c8 | 69162f720f226384a4f48c8ab5f72af856ff8e6d | /src/train_test_split.R | 44c62950497a0fa25a500ed515a1fa47878ce38b | [] | no_license | SoftServeSAG/aws_anomaly_detection | 4899fdeb93218fa6f50cda190152b5e2211981ad | ca5c4d924032efe770eaaf238e721fb3d305d58d | refs/heads/master | 2021-09-13T09:40:48.016445 | 2018-04-28T02:00:33 | 2018-04-28T02:00:33 | 107,226,234 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,171 | r | train_test_split.R | require(xts)
require(lubridate)
require(dplyr)
require(dygraphs)
#Data Preprocessing Functionality: Train-test split
train_test_split_time_series <- function(data, split_data=0.75){
# Function splitting time-series data in train and test, plot time-series data
#
# Input:
# data - xts object with time... |
0f1a8877cd3b508b535a25e9b750862a2fbba236 | cc8fce695e24ab817e2e1646725c8e05423dcdbe | /textMining/dataFetcher.R | 0f09a1b1b637b1995373bb2cda721b80d28efb4d | [] | no_license | Regateiro/kdd | afbeb33d37372d24d3bf86b391248b8e2dd99e52 | ba88dcc2cdee2b048a4486380312c1eaef108433 | refs/heads/master | 2021-01-10T15:29:35.307639 | 2015-12-16T12:23:52 | 2015-12-16T12:23:52 | 43,708,804 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,269 | r | dataFetcher.R | #install.packages("httr")
library(httr)
#URL and API keys for Import.IO movie list
url = "https://api.import.io/store/data/71e12311-6ce3-436e-8b5d-9c366a87276d/_query?input/webpage/url=http%3A%2F%2Fwww.imdb.com%2Fsearch%2Ftitle%3Flanguages%3Den%257C1%26num_votes%3D10000%2C%26sort%3Duser_rating%2Cdesc%26start%3D"
url2 ... |
238c011337d41f5e90d68d357343fab41d1de5b9 | 85e9720bd0a467ee1425d64a93fdd8f75128dbe7 | /Caso2/mejor.R | 15ab5f0f90b2001a228a040b6ef3fa44f9a54fdc | [] | no_license | FranciscoGarCar/Programacion_Actuarial_III_OT16 | d648f3a521e3ac80fe6fa5ae022902a931346b1c | 6e32d177bd17dc821b279542c89ac13310cea641 | refs/heads/master | 2020-04-17T15:19:53.818794 | 2016-11-07T05:46:00 | 2016-11-07T05:46:00 | 66,847,613 | 0 | 0 | null | null | null | null | ISO-8859-2 | R | false | false | 2,991 | r | mejor.R | outcome <- read.csv("outcome-of-care-measures.csv", colClasses = "character")
head(outcome)
ncol(outcome)
colnames(outcome)
names(outcome)
outcome[, 11] <- as.numeric(outcome[, 11])
##
hist(outcome[, 11])
mejor<- function (estado,resultado){
#LECTURA DE DATOS
getwd()
setwd("~/GitHub/Programacion... |
ff29e506c5d3493b6352b7aff38906e83344fb77 | a19555ba297495802404e97e710c6c007c5e4f43 | /man/Alg_RVB1.Rd | 5cdef1b4492ef31997876488e44ead5712312c7a | [
"MIT"
] | permissive | hrnasif/rvb | 2fd8568277ae440cb04088f65f5fcb36eba73b5b | fdaada9c5b77613a6734253244dea3fc6145f47b | refs/heads/main | 2023-05-19T13:50:31.426629 | 2021-06-10T05:06:59 | 2021-06-10T05:06:59 | 365,866,425 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 883 | rd | Alg_RVB1.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Alg_RVB1.R
\name{Alg_RVB1}
\alias{Alg_RVB1}
\title{RVB1 Algorithm implementation}
\usage{
Alg_RVB1(y, X, Z, Wprior, etahat, model, m = 1)
}
\arguments{
\item{y}{List. Responses per cluster}
\item{X}{List. Covariates per cluster for fixed eff... |
9be0556d00055e8f725310533e4eae494820c82d | f5199fc56c1a4e0f2a28c8eceb8f8f8955101e87 | /Into bayesian data analysis.R | e5f4cc8849e73cde7ceef6568cad4d63899fcb61 | [] | no_license | mshasan/BayesianDataAnalysis | 4940a507d77e5b79ce67259b9678e2d1a8ad0cfe | e6e320b78076f94000c516b6fef63bfd99978ccb | refs/heads/master | 2021-01-20T08:40:27.025570 | 2017-05-03T17:49:53 | 2017-05-03T17:49:53 | 90,175,198 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 819 | r | Into bayesian data analysis.R | ## Problem 2 ----------------
library(ggplot2)
library(grid)
library(gridExtra)
x <- seq(0, 1, length = 100)
a <- dbeta(x, .5, .5)
b <- dbeta(x, 10.2, 1.5)
c <- dbeta(x, 1.5, 10.2)
d <- dbeta(x, 100, 62)
p1 <- qplot(x,a, main="Beta (0.5, 0.5)",geom="line")
p2 <- qplot(x,b, main="Beta (10.2, 1.5)",geom="line")
p3 <... |
3622c3a5cc0e62447d05daa64f1eb2d9f54b8787 | 5d0bc9fa9c48a468d115e9930f5eac66a0764789 | /inst/snippets/Exploration2.2.10.R | d8d319dc377cad186302e279313ffc706bf0d381 | [] | no_license | rpruim/ISIwithR | a48aac902c9a25b857d2fd9c81cb2fc0eb0e848e | 7703172a2d854516348267c87319ace046508eef | refs/heads/master | 2020-04-15T20:36:55.171770 | 2015-05-21T09:20:21 | 2015-05-21T09:20:21 | 21,158,247 | 5 | 2 | null | null | null | null | UTF-8 | R | false | false | 53 | r | Exploration2.2.10.R | histogram(~ sleepHrs, data = SleepTimes, nint = 15)
|
04e81101465031de5f9f6c5d204bdf1fd91a98b6 | 39f4df1f5c2faadbdf366d65ede30aa5edba3497 | /man/reverse.Rd | 20423c770f5c2f6ba650eb8df459e156d99880e7 | [] | no_license | cran/kutils | a19c69b6730548aa849ca841291d95de92dd3863 | 53ada7e4308f456a0a109955ecfd9122f6263aba | refs/heads/master | 2023-07-10T17:11:29.010416 | 2023-06-26T21:40:02 | 2023-06-26T21:40:02 | 77,183,151 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 2,123 | rd | reverse.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/factors.R
\name{reverse}
\alias{reverse}
\title{Reverse the levels in a factor}
\usage{
reverse(x, eol = c("Skip", "DNP"))
}
\arguments{
\item{x}{a factor variable}
\item{eol}{values to be kept at the end of the list. Does not
accept regular... |
fcfb6b5fbd52de76e3f0ef6df2c302980733ad05 | 64ba42a2cb4b5ee3c4aac24362cf01e6a88c40f8 | /man/dot-get_trait_individuals_values.Rd | df88c3ea0e1442ff498a633babeb013ab6ccf8e7 | [] | no_license | gdauby/bdd_plots_central_africa | 8ac5bbb87429aa7d2e02b873f57d96d228dd1680 | 467d7f31aebd5b2caa39b44428d45ac88d2c26e4 | refs/heads/master | 2023-06-23T00:49:19.408360 | 2023-06-09T13:58:21 | 2023-06-09T13:58:21 | 165,248,727 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,145 | rd | dot-get_trait_individuals_values.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functions_manip_db.R
\name{.get_trait_individuals_values}
\alias{.get_trait_individuals_values}
\title{Internal function}
\usage{
.get_trait_individuals_values(
traits,
src_individuals = NULL,
id_individuals = NULL,
ids_plot = NULL,
... |
fa59f861eec86b6879ea053c3eafabe31ccbeaf2 | e5f9833167d902326e1404180d759c62038f7507 | /run_analysis.R | 0725943e30898cc047214500d6e071e5b40afbd7 | [] | no_license | werderhg/Data-Cleaning-Course-Project | 5ae326de4d99fff75cd57f51778b7c3cdae42fa3 | 47b8ec840736ba29527e9a3a4af9a35c70d47377 | refs/heads/master | 2021-01-25T10:43:37.456222 | 2014-12-20T10:39:17 | 2014-12-20T10:39:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,102 | r | run_analysis.R |
library(downloader)
url <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download(url, "dataset.zip", mode="wb",method = "curl")
unzip("dataset.zip")
unlink(url)
### START
#set my working directory to the location where the files are unzipped.
# Merges the training ... |
3eb4eaba9f09dc43026d426dcdb335368f3cc04b | fe24de2e850eb944394cdd2be8e3eb976a1d5e23 | /run_analysis.R | 72e546fdb6b5a036d032ef98d5cecb8120f133b1 | [] | no_license | Tennlin/Getting-and-Clean-Data-project | ca91696244c08e14d66dfd7a4541f2d763f8dfe2 | 4e0d364a1aeaae57e81d315a533264ec842284a8 | refs/heads/master | 2021-01-10T15:12:38.017318 | 2015-11-22T17:21:54 | 2015-11-22T17:21:54 | 46,670,380 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,056 | r | run_analysis.R | run_analysis <- function(){
x_train <- read.table("X_train.txt",sep = "")
y_train <- read.table("y_train.txt",sep = "")
sub_train <- read.table("subject_train.txt",sep = "")
x_test <- read.table("X_test.txt",sep = "")
y_test <- read.table("y_test.txt",sep = "")
sub_test <- read.table("subject_test.txt",sep ... |
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