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4e223b1f5b7326d4d81346a3b24954f23fbffeae | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /terra/man/time.Rd | fcad8722c54b0cb64ad785a636049f2f895dcf04 | [] | no_license | akhikolla/TestedPackages-NoIssues | be46c49c0836b3f0cf60e247087089868adf7a62 | eb8d498cc132def615c090941bc172e17fdce267 | refs/heads/master | 2023-03-01T09:10:17.227119 | 2021-01-25T19:44:44 | 2021-01-25T19:44:44 | 332,027,727 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 712 | rd | time.Rd | \name{time}
\alias{time}
\alias{time<-}
\alias{time,SpatRaster-method}
\alias{time<-,SpatRaster-method}
\title{time of SpatRaster layers}
\description{
Get or set the time of the layers of a SpatRaster. Experimental. Currently only Date's allowed.
}
\usage{
\S4method{time}{SpatRaster}(x, ...)
\... |
bd2737d4a78e583d69aa2dbb0d1c93f3a252ca15 | adb6cc1648bb33b06e73be9dd35112d6f639b2db | /plot2.R | 3d002f73d99ec4961c1f390aade368ba6024e315 | [] | no_license | ohjho/ExData_Plotting1 | f5ccdaed99d27a4e86e9ced7a92a58735503472f | 5a730c71728c644b9f1451ca5333f1c81e47b1a8 | refs/heads/master | 2021-05-31T13:22:23.532957 | 2016-04-18T09:52:20 | 2016-04-18T09:52:20 | 56,209,764 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 824 | r | plot2.R | # Loading and cleaning Data
fname <- "household_power_consumption.txt"
if (!file.exists(fname)){
message(fname, " not found. Exiting script...")
stop("See information on the dataset from README.md or download here: https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip")
}
raw <- read... |
a83dec7bbc49c79dd3644db244321f3c33d1732c | 63f78bd8589218f2b0317158acbeeec3c3c43340 | /R/TIMEDEC/TIMEDEC.R | 565de35c977e334152d5b84de08e12a3ac3685c0 | [] | no_license | Decision-Neuroscience-Lab/boPro | f462d94e9800ff6fb2df3ae3e73a55731c943b24 | 8e0f9dca1ea6999d9e6ca9cdacf2fbd45db0a53a | refs/heads/master | 2020-05-27T21:15:41.627997 | 2017-03-02T06:51:03 | 2017-03-02T06:51:03 | 83,645,323 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 11,175 | r | TIMEDEC.R | # TIMEDEC
## Bowen J Fung, 2015
# Repeated measures for feedback manipulation
feedMan <- read.csv("~/Documents/R/TIMEDEC/feedbackMan.csv")
# Subset to only ECG data
feedMan <- subset(feedMan, id < 121)
require(PMCMR)
h <- kruskal.test(k-k2 ~ as.factor(condition), data = feedMan)
posthoc.kruskal.nemenyi.test(k-k2 ~ as.... |
97793633733242c523763bb1762471f8f016a425 | f2d61b91feef89fa7523e2fedd8a3a0461ef0cba | /R/wp/wp_film.R | fcbade88e2c08661a9dad6973df7c6edd26431d6 | [] | no_license | MarcinKosinski/trigeR5 | c94f49476ecdf09da8277d5e81e7b2966b5642ce | 1711e9b43a13d2b0073bd6e2cfbe967671dba427 | refs/heads/master | 2021-01-19T21:11:38.640114 | 2017-05-13T10:12:03 | 2017-05-13T10:12:03 | 88,619,642 | 4 | 11 | null | 2017-05-12T21:34:36 | 2017-04-18T11:56:39 | JavaScript | UTF-8 | R | false | false | 1,941 | r | wp_film.R | db <- dbConnect(drv = SQLite(), dbname = "data/wp.db")
#### FILM ####
adress <- "http://film.wp.pl/"
adresses <- adress %>%
read_html() %>%
html_nodes(css = "._1lXcfrU") %>%
html_text %>%
tolower() %>%
gsub("[[:space:][:punct:]]", "", .) %>%
chartr("ąćęłńóśźż", "acelnoszz", .) %>%
paste0(adress, .)
adress... |
3f3b27619ecbb722e4389d242d16a05076c1c851 | 4f8a077dc78236d66b3b81569990f7eddb3d45c3 | /h2o-r/tests/testdir_demos/runit_demo_glrm_walking_gait.R | 3cfdf82c41cbddf73bca102ee978f3e3c6d02166 | [
"Apache-2.0"
] | permissive | konor/h2o-3 | 22b8e7c0e64597d18693f34a06079f242826cd92 | 77b27109c84c4739f9f1b7a3078f8992beefc813 | refs/heads/master | 2021-01-14T11:20:29.772798 | 2015-10-11T01:56:36 | 2015-10-11T01:56:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,001 | r | runit_demo_glrm_walking_gait.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source('../h2o-runit.R')
# Connect to a cluster
# Set this to True if you want to fetch the data directly from S3.
# This is useful if your cluster is running in EC2.
data_source_is_s3 = F
locate_source <- function(s) {
if (data_source_is_s3)
... |
920434b2ed55d90f8bdb9a65a29f2de6e5a0d3b0 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/LOGICOIL/examples/LOGICOILfit.Rd.R | 0d9f51f439ea4959a0c4a08ad8653c8ad1d58216 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 272 | r | LOGICOILfit.Rd.R | library(LOGICOIL)
### Name: LOGICOILfit
### Title: Fit of the multinomial log-linear model obtained from the
### LOGICOIL training dataset.
### Aliases: LOGICOILfit
### Keywords: datasets
### ** Examples
data(LOGICOILfit)
names(LOGICOILfit)
LOGICOILfit$coefnames
|
4aa0c34af98e037f8aa3f71b30cb3e7c8511af40 | e09d229dd1ad18879fb051e4cb7d97c1475f49aa | /R/trace_backwards.R | bf1e8c2586a0df22a1815e13dbeaf1e4efc35061 | [
"MIT"
] | permissive | hamishgibbs/rtrackr | 15bc922c8f8dfb765ee5b5da80df66b84eb16b16 | 2a353b73f8507e96c71c32c1ea557cfc04f9c0b2 | refs/heads/master | 2022-11-11T17:35:52.513669 | 2020-06-20T12:19:33 | 2020-06-20T12:19:33 | 271,510,902 | 1 | 0 | NOASSERTION | 2020-06-12T14:45:06 | 2020-06-11T09:54:51 | R | UTF-8 | R | false | false | 756 | r | trace_backwards.R | # trace_backwards
#
# @description recursively traverse a log file tree to identify parent nodes of a given trackr_id
#
# @param target_id string, a trackr_id
#
# @return list, the trackr_ids of parent record(s)
trace_backwards <- function(target_id){
parent_id <- target_id
parents <- list()
i = 1
wh... |
93e8075f55939bbee2fdec4ec001999ac8560c58 | 150ddbd54cf97ddf83f614e956f9f7133e9778c0 | /R/avg.R | 9d445f48f1bf7469c5aabf1231792268eb849743 | [
"CC-BY-4.0"
] | permissive | debruine/webmorphR | 1119fd3bdca5be4049e8793075b409b7caa61aad | f46a9c8e1f1b5ecd89e8ca68bb6378f83f2e41cb | refs/heads/master | 2023-04-14T22:37:58.281172 | 2022-08-14T12:26:57 | 2022-08-14T12:26:57 | 357,819,230 | 6 | 4 | CC-BY-4.0 | 2023-02-23T04:56:01 | 2021-04-14T07:47:17 | R | UTF-8 | R | false | false | 3,361 | r | avg.R | #' Average Images
#'
#' Create an average from a list of delineated stimuli.
#'
#' @details
#'
#' ### Normalisation options
#'
#' * none: averages will have all coordinates as the mathematical average of the coordinates in the component templates
#' * twopoint: all images are first aligned to the 2 alignment point... |
22317d7d5f468b9f01d76e6704ee1951d1cae712 | 8327aedc9fca9c1d5f11c160d440ecc082fb915d | /man/per.Rd | 5da25bae9fca76698a7ee2c4106a0dca850b47d7 | [] | no_license | SESjo/SES | f741a26e9e819eca8f37fab71c095a4310f14ed3 | e0eb9a13f1846832db58fe246c45f107743dff49 | refs/heads/master | 2020-05-17T14:41:01.774764 | 2014-04-17T09:48:14 | 2014-04-17T09:48:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 953 | rd | per.Rd | \name{per}
\alias{per}
\title{Decompose an atomic vector to its successive values and their length.}
\usage{
per(x, idx = FALSE)
}
\arguments{
\item{x}{The atomic vector to examine.}
\item{idx}{Should the indexes (start and end) of
homogeneous sequences be returned as well ?}
}
\value{
A data frame with values a... |
cd19b39a4fba632862748005ab8d96566406ab65 | 532d0fe3ec396c2898574944a66e3e43d750b4b9 | /IslandOfLostScripts/cm_v1.R | 5ff985c3469167341077bb883d39186d1d36d3fa | [] | no_license | myellen/MF850_Computational_Finance_Final | 6d91653abe90d48a84df5eca08ed178bab2870fe | 7410931178707089e286867f9af49d7aee7f6434 | refs/heads/master | 2020-06-11T13:02:40.224997 | 2016-12-19T10:37:40 | 2016-12-19T10:37:40 | 75,659,108 | 0 | 1 | null | 2016-12-19T09:52:48 | 2016-12-05T19:32:11 | R | UTF-8 | R | false | false | 4,576 | r | cm_v1.R | <<<<<<< HEAD
## Load the data
mydata<-read.csv(file="mf850-finalproject-data.csv", header=TRUE, sep=",")
=======
library(glmnet)
##Read Test Data
mytestdata<-read.csv(file="/Users/leighm888/Desktop/Test_set_v2.csv", header=TRUE, sep=",")
#turn categorical variables into factors
date<-mytestdata[,1]
RETMONTH_SPX<-mytes... |
e190f56c61f4acf90546e56ed6023e1ffdf188b6 | 521fa790f4faa0d25d617bc40604a6bcbfa7e324 | /code/define_couples_mutate.R | fe46761676b4ad69f7265e4c98f9e5e896dc0e93 | [] | no_license | CedricBezy/stat_sante_git | deaced07b3c21e6161611b2165ef432d66076120 | 7177aab8f1106fae54472de5779f631d419af2f5 | refs/heads/master | 2021-05-15T04:56:03.235445 | 2018-02-02T16:16:35 | 2018-02-02T16:16:35 | 118,431,016 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,227 | r | define_couples_mutate.R | #---------------------------------------------------
# Cedric Bezy
# 25 / 01 / 2018
# Projet Stat Sante
#---------------------------------------------------
rm(list = ls())
library(dplyr)
library(tibble)
load('stat_sante_copy/data/couples_init.RData')
##==================================================
# Functions... |
e6889f727ba31e1f51f915a237ad0f85e7df5c6f | fd2bf6d71e00c84e16814fa8fc41c35d52e0752b | /plot-fdcs-w-err-bar-Function.R | bce8e78f76a615eb66cbe63b91f22aef6cb5fc3f | [] | no_license | BTDangelo/Function-Archive | b9a6a5538e2dd56043b60d0ad7fe58286fb5ec9a | 20cf00f19a5999ac4c6fab01f4cf68288fccd1b4 | refs/heads/master | 2020-12-02T18:04:59.106707 | 2017-08-09T20:05:06 | 2017-08-09T20:05:06 | 96,469,222 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,202 | r | plot-fdcs-w-err-bar-Function.R |
## BTD - Function to create flow duration curve with error bars
flow.d.c <- function(chr.dir.main, chr.dir.flow.data, chr.dir.figures, chr.eb) {
## chr.dir.main - path to main workspace
## chr.dir.flow.data - path to flow ... |
2e3f36a0154a3a1e3bfdb82fd57e387901848f06 | c542082c439cf134c109d1b12605aedabe2ca082 | /R/playground/old_attempts/predict_2_kernels.R | 352660db0e77206d43255c8fe66b7495a449ade5 | [] | no_license | NathanWycoff/GPArcLength | f811702d0bcb3bd3e447c8c9ab8611d1830936f7 | 4b84fb390dd21cf3487f53b5709ef9a9c8b1eadd | refs/heads/master | 2021-05-06T04:14:31.562516 | 2018-01-27T17:08:44 | 2018-01-27T17:08:44 | 114,920,616 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,840 | r | predict_2_kernels.R | #!/usr/bin/Rscript
# predict_2_kernels.r Author "Nathan Wycoff <nathanbrwycoff@gmail.com>" Date 01.08.2018
require(mds.methods)
source('../../lib/some_gp_funcs.R')
## Seeing what predictions looked like on the nonsmooth multiscale GP
######### Generate some data with specified weirdness
set.seed(1234)
n <- 50
p <- ... |
94f41207c4622ee8e480ac0a3ed1836e8871b16d | fc00987cf8ddb7ee81fd7865cfd8f272a7f4a101 | /R/by-game-parsers.r | d0fa28d8eff979a579a49739c450c7b029a8d40e | [
"MIT"
] | permissive | zamorarr/msf | 72bcaed4569b2c4f3bca05940965285b4c0c3fd4 | d84327bd04a15efbd36e918646d82458bf61a280 | refs/heads/master | 2018-10-06T20:16:27.831851 | 2018-06-22T14:16:11 | 2018-06-22T14:16:11 | 116,212,412 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,596 | r | by-game-parsers.r | #' Parse box scores
#'
#' @param json content from response
#' @export
#' @examples
#' \dontrun{
#' resp <- game_boxscore("nfl", "20170917-ARI-IND", season = "2017-2018-regular")
#' resp <- game_boxscore("nhl", "20171114-BUF-PIT", season = "2017-2018-regular")
#' parse_boxscore(resp$content)
#' }
parse_boxscore <- func... |
e63ba045123abac07392c7175a229257ba14b35f | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/intrinsicDimension/examples/M_rozza.rd.R | a6eabda7b791ecdb0970acf93228058dc0cf14d5 | [] | 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 | 430 | r | M_rozza.rd.R | library(intrinsicDimension)
### Name: M_rozza
### Title: Manifolds from Rozza et al. (2012)
### Aliases: m14Manifold m15Manifold
### Keywords: datagen
### ** Examples
datap <- m14Manifold(800)
par(mfrow = c(1, 3))
plot(datap[,1], datap[,3])
plot(datap[,2], datap[,3])
plot(datap[,1], datap[,2])
datap <- m15Manifold(... |
01bccb36b6bafff6eaa8ea497431ab4d0f8c0ef1 | 030b6b645e227da9a2be3b812c7846499e3bf65a | /hai.r | 662fad51300f6badbd1465c165b9f482239f16cc | [] | no_license | endft/kmmi_r | 7b055db7722ad1367939a25bf5e3b6a313bd068a | 0e502a94c23c6c4093012a4e58b44e295cc1a587 | refs/heads/main | 2023-06-30T04:45:12.580868 | 2021-08-09T03:31:27 | 2021-08-09T03:31:27 | 393,987,945 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15 | r | hai.r | teks1 ="haiiii" |
ac906c1a8ada194f07d84769d9b81e641b5d3b92 | b4c24634b5f5d84a23405e1339bd03b065ebc62c | /R/derivatives_basic.R | fbd16f4499b8ad73c542c3f7c4f792d95aa8eda3 | [
"MIT"
] | permissive | minghao2016/diseq | 0f6f41ae1d3c258d7ce3df264c2137a2857e12ec | 035c7d54f3c3fbe07fbb7255bf61f6a9c565f228 | refs/heads/master | 2023-02-28T00:06:13.168971 | 2021-01-26T17:22:02 | 2021-01-26T17:22:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,701 | r | derivatives_basic.R | #' @include system_basic.R
setGeneric("partial_beta_d_of_loglh", function(object) {
standardGeneric("partial_beta_d_of_loglh")
})
setMethod("partial_beta_d_of_loglh", signature(object = "system_basic"), function(object) {
# nolint start
c(((object@supply@psi * object@rho1 / (object@demand@sigma * object@supply... |
802f61fe60a78fe20cb8c95e509e0f037f89e141 | ffe095c7f1411c8cc009fcf09bc2392e7f739455 | /tests/testthat.R | c4afda67212410b0052f0570b85991d4bb01b060 | [
"MIT"
] | permissive | atusy/swiper | da4fce37abbcf06bc3420ef4726b200fe066b389 | d930167a705e3409bcb1ebca1718481c14844823 | refs/heads/master | 2023-06-08T08:49:25.606844 | 2021-07-01T16:23:54 | 2021-07-01T16:23:54 | 376,047,555 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 56 | r | testthat.R | library(testthat)
library(swiper)
test_check("swiper")
|
e8862a8472346c49665b1688981a70961dfdc7a0 | 9c79f8d1e89ee5adf7b93115ccc741d3303404f1 | /InteractiveMaps/Trojborg/Outs.R | 08c423ecced7361684d62533c5f8a3f4ae6a5307 | [] | no_license | derek-corcoran-barrios/derek-corcoran-barrios.github.io | e1631feef111cfc9bc693df1853e02818435071a | ccb8f21c053fd41559082eb58ccb7f64cc7fcf86 | refs/heads/master | 2023-07-17T13:11:43.739914 | 2023-07-03T07:24:21 | 2023-07-03T07:24:21 | 107,616,762 | 33 | 33 | null | 2020-06-18T19:25:50 | 2017-10-20T01:23:44 | HTML | UTF-8 | R | false | false | 4,953 | r | Outs.R | library(tidyverse)
library(sf)
library(raster)
library(spThin)
Trojborg_Raster <- read_rds("TrojborgRaster.rds")
Trojborg_Raster <- Trojborg_Raster[[1]] %>% projectRaster(crs ="+proj=longlat +datum=WGS84 +no_defs")
Trojborg <- read_sf("GroupsTrojborg.shp") %>%
st_transform(crs = "+proj=longlat +datum=WGS84 +no_de... |
d75ffd79a260959cfade2c8c0e26fc0a967725ca | 8633d09805e0c6cd67765865d2dd8708e400b057 | /scripts/excess_deaths_script.R | fef4313808a5dd2892bb4eb907208b78748774d4 | [
"MIT",
"CC-BY-4.0"
] | permissive | nnutter/covid-19-excess-deaths-tracker | 0f51a258841fbc8664e90181d2560f43f1c2bf59 | f8933ac749fe175f3078c8a8e4ac10d7575bcea8 | refs/heads/master | 2022-12-14T01:20:52.751827 | 2020-09-09T16:04:18 | 2020-09-09T16:04:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,558 | r | excess_deaths_script.R | # Step 1: import libraries and data ---------------------------------------
# Import libraries
library(tidyverse)
library(readxl)
library(data.table)
library(lubridate)
options(scipen=999)
# Import data
austria_weekly_deaths <- fread("output-data/historical-deaths/austria_weekly_deaths.csv")
belgium_weekly_deaths <- ... |
40d006c456321f8ecae7ad008c7d225985cd8143 | 2b36bf4a6b6ec05db94f6fa23076cd27843ff747 | /scripts/IRAIL_data_exploration_020117.R | 01c5436066032e9d9cbf80bd3509632cebbeae7d | [] | no_license | simonkassel/IRAIL | 9227f1a307221e4793df2387d7277654d6793eff | fd37b45343efd652b4cefcb2ca9f3e107b8cf336 | refs/heads/master | 2021-01-25T06:55:12.596187 | 2017-04-18T19:35:06 | 2017-04-18T19:35:06 | 80,666,443 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,530 | r | IRAIL_data_exploration_020117.R | # INTRO -------------------------------------------------------------------
# Explore dataset through visualization and summary statistics
# Simon Kassel
# Created: 1 Feb 17
# load helper functions
source("https://raw.githubusercontent.com/simonkassel/IRAIL/master/scripts/IRAIL_helper_functions_032317.R")
# load ... |
38b33ccb9acc2b85d68f4e24fe52ade94e2520b2 | 8ad3594325900e5a4715ca4405cd765bc9958158 | /statistical-inference/goodness-of-fit/exercise-06.r | 627e5be831eecf8a7f16f9492745c6a269e17943 | [
"Apache-2.0"
] | permissive | garciparedes/r-examples | 22806859c7c147a6d503f1b1223a5168b6fa9d76 | 0e0e18439ad859f97eafb27c5e7f77d33da28bc6 | refs/heads/master | 2021-01-25T16:59:16.020983 | 2019-05-21T10:26:27 | 2019-05-21T10:26:27 | 102,385,669 | 1 | 0 | Apache-2.0 | 2018-05-24T07:38:21 | 2017-09-04T17:27:27 | Jupyter Notebook | UTF-8 | R | false | false | 809 | r | exercise-06.r | ## Author: Sergio García Prado
## Title: Statistical Inference - Goodness of Fit - Exercise 06
rm(list = ls())
observed <- c(442, 38, 514, 6)
(k <- length(observed))
# 4
(n <- sum(observed))
# 1000
EspectedProbabilities <- function(p) {
c(0.5 * p, 0.5 * (1 - p), 0.5 * p ^ 2 + p * (1 - p), 0.5 * (1 - p) ^ 2)
}
L... |
27bc7bcfbab83d8bdc522e01eb67dae55f5b41d5 | c87eac12aee2d5403410e925baf8b4e5ec295475 | /play_sequence_generators/test_prebuilt_lstm.R | ad1c5f68d17f2ecd92dded9df5c20b616522ccc7 | [
"Apache-2.0"
] | permissive | cxd/text_dnn_experiments | 9cbc7274b4c760f801d5074229be2bdfb3ef64cc | 4e57ca2db4151ba3796583abd0ed3bf2feaf8356 | refs/heads/master | 2021-06-14T07:13:36.161787 | 2021-03-11T10:14:00 | 2021-03-11T10:14:00 | 158,998,082 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,153 | r | test_prebuilt_lstm.R |
library(keras)
library(stringr)
source("lib/init.R")
source("lib/prepare_squad_data.R")
source("lib/read_glove.R")
source("lib/lstm_sequence_learner.R")
# Setup environment
cfg <- init(getwd())
prebuilt <- "test/bri-data-01/model3.h5"
modelTest <- load_model_hdf5(prebuilt, compile=TRUE)
path <- get_file(
"nietz... |
ce2b19b78239e96c46b52f94cd4c74e1bb220a22 | 510734b2e6f1fe4110177aa90e647739764b737d | /man/rename_states.Rd | c182fa72aaf765d4240b19977c15cdb03b785e0c | [] | no_license | helske/KFAS | 4be85a2db7c33c9c1e7c95d66f0fa26ccdd6b764 | e183590a08cce796763451a023e6714a52ce83fe | refs/heads/master | 2023-03-11T03:07:50.040079 | 2023-02-06T15:12:09 | 2023-02-06T15:12:09 | 18,439,915 | 56 | 19 | null | 2016-06-11T12:28:15 | 2014-04-04T13:32:14 | R | UTF-8 | R | false | true | 1,085 | rd | rename_states.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rename_states.R
\name{rename_states}
\alias{rename_states}
\title{Rename the States of SSModel Object}
\usage{
rename_states(model, state_names)
}
\arguments{
\item{model}{Object of class SSModel}
\item{state_names}{Character vector giving n... |
dcaf315d43d018e823c86dffd72da8ecef1e09c9 | 6ef05ff1b841edfeea7a2e54e055d03a450b7469 | /R/get.adjacency.matrix.R | 92168ae72a4fc991cead750a124dd0cc5984ad07 | [] | no_license | cran/SIMMS | c0527ec2c154495a8dc3fb18237f47a5b808f999 | 7e3d61a1757bc01b3df19576814d381855388b74 | refs/heads/master | 2022-05-02T23:05:38.840427 | 2022-04-24T13:50:05 | 2022-04-24T13:50:05 | 17,693,536 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,161 | r | get.adjacency.matrix.R | #' A utility function to convert tab delimited networks file into adjacency
#' matrices
#'
#' A utility function to convert tab-delimited networks file into adjacency
#' matrices
#'
#'
#' @param subnets.file A tab-delimited file containing networks. New networks
#' start with a new line with '#' at the begining of n... |
7c9992b712d51d5f381601b43c026bcd357d4c6c | c05e0de22f5699d1c2b2921480be68c8e8b8943f | /man/tab_caption.Rd | afc368c9dce30835382afd96867ca9d83cc85577 | [
"MIT"
] | permissive | rstudio/gt | 36ed1a3d5d9a1717dfe71ed61e5c005bc17e0dce | c73eeceaa8494180eaf2f0ad981056c53659409b | refs/heads/master | 2023-09-04T06:58:18.903630 | 2023-09-01T02:06:05 | 2023-09-01T02:06:05 | 126,038,547 | 1,812 | 225 | NOASSERTION | 2023-09-08T00:21:34 | 2018-03-20T15:18:51 | R | UTF-8 | R | false | true | 2,404 | rd | tab_caption.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tab_create_modify.R
\name{tab_caption}
\alias{tab_caption}
\title{Add a table caption}
\usage{
tab_caption(data, caption)
}
\arguments{
\item{data}{\emph{The gt table data object}
\verb{obj:<gt_tbl>} // \strong{required}
This is the \strong... |
c80fc7ec62cd2ab4453e7f7563d75269d5ea1b37 | c221bac282063ef7c50923eb6ae422b81bda8af8 | /GoodnessOfFit.R | db69ad564883a27503814aa42a23941c0b9a13a9 | [] | no_license | daviddwlee84/StatisticInference | 60481b9747371817e641244b4f80824b4223dabd | d02e3d33fd8e91a31f5bbcdd8d5bf990939981d4 | refs/heads/master | 2021-01-25T11:28:21.971166 | 2017-07-02T02:28:26 | 2017-07-02T02:28:26 | 93,928,780 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 536 | r | GoodnessOfFit.R | # Goodness of fit
source("GoodnessOfFitFunctions.R")
alpha = as.numeric(readline("Significance level(in %): "))
alpha = alpha/100
dist <- menu(c("Multinomial Distribution", "Normal Distribution", "Poisson Distribution", "Binomial Distribution"), title="Select the distribution of the Sample Statistic")
if(dist == ... |
b1c549fbb6df3d6e66c9a825242cb9c4dc74ad90 | b844fc764deff4c305d5a5499f78266f2ec817e9 | /man/fitted.mylm.Rd | b19e30afabeff13c30cef2f0826c1ec953335cbf | [] | no_license | jenper/mylm | 0aa7c1e7498a35dc9225e26dc2f75f35ab5a808a | 785316d117b822c2edf9b63ff5d1c1e7f7902c22 | refs/heads/main | 2023-08-03T10:49:26.281973 | 2021-07-22T01:41:54 | 2021-07-22T01:41:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 332 | rd | fitted.mylm.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/funcs.R
\name{fitted.mylm}
\alias{fitted.mylm}
\title{Fitted values}
\usage{
\method{fitted}{mylm}(object, ...)
}
\arguments{
\item{object}{object of class "mylm"}
\item{...}{additional arguments to be passed to methods}
}
\description{
Fitt... |
2fd056d93f11620a97d4d17b37a0c7a7f86d41e1 | 530753dfb8c6b2db7d32e1de9b69e9c04df3c501 | /cachematrix.R | 9635c8e3259c98683846de0ace6851e79df3ecf8 | [] | no_license | khomyuk/ProgrammingAssignment2 | 45f3617ff88fb4d422e0dbf5447849dcd54fb10c | a8859a26b5236b7c905580322d8ca04647c9b10f | refs/heads/master | 2021-01-15T11:08:17.863618 | 2015-12-27T23:10:12 | 2015-12-27T23:10:12 | 48,662,006 | 0 | 0 | null | 2015-12-27T22:00:18 | 2015-12-27T22:00:15 | null | UTF-8 | R | false | false | 886 | r | cachematrix.R | ## The function makeCacheMatrix allows you to create an oblect of a matrix that can cache its inverse.
makeCacheMatrix <- function(x = matrix()) {
inverted_matrix <- NULL
set <- function(y) {
x <<- y
inverted_matrix <<- NULL
}
get <- function() x
setinv <- function(inv) inverted_matrix <<- inv
get... |
049fb55dec8a56e5625293dbd9a742810115419c | 3cb0fcdaa83cb2bc60aef905d229c00ac4440243 | /R/ImmuneSpace.R | 946854d4f37f169e3af928d9b276672cdc863068 | [] | no_license | jfrelinger/ImmuneSpaceR | 45325d9841391dd6a7992d550ab777e5bff11b15 | deefe0813ffd0b101b10c5c63fd549c21443a4e9 | refs/heads/master | 2021-01-21T19:13:20.752212 | 2014-11-19T21:29:58 | 2014-11-19T21:29:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 28,650 | r | ImmuneSpace.R | #'@docType package
#'@title A Thin Wrapper Around ImmuneSpace.
#'@description ImmuneSpaceR provides a convenient API for accessing data sets within the ImmuneSpace database.
#'
#'@details Uses the Rlabkey package to connect to ImmuneSpace. Implements caching, and convenient methods for accessing data sets.
#'
#'@name I... |
9877c4ee0f911e1f708d069ab0dc8db6e9c5515e | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/rhnerm/examples/cmseRHNERM.Rd.R | 28ee8deb135c6ada587bd81229cc12899b5455b7 | [] | 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 | 688 | r | cmseRHNERM.Rd.R | library(rhnerm)
### Name: cmseRHNERM
### Title: Conditional mean squared error estimation of the empirical Bayes
### estimators under random heteroscedastic nested error regression
### models
### Aliases: cmseRHNERM
### ** Examples
#generate data
set.seed(1234)
beta=c(1,1); la=1; tau=c(8,4)
m=20; ni=rep(3,m); N... |
c37675c91c6ce937c750378a3b3c81a02eaed72b | 87a5d63aa52e25dfb121b4283c6dc935a6fa4c87 | /R_Cointegration Case/S_ECM_wADF.R | 017fbf722c1e08d96e8e7fb825bf8318d59f5592 | [] | no_license | AnthonyGachuru/cqf-1 | fca9834a95bf24d1753fedb4813390d70c630a4c | 8d66227755dff201b671a25cf45408ce7527bcfb | refs/heads/master | 2021-05-21T22:07:47.254035 | 2019-10-29T06:59:41 | 2019-10-29T06:59:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,320 | r | S_ECM_wADF.R | ######################################################################
# 2015. Richard Diamond. Quieries to r.diamond@cqf.com #
# Models are specified and validated but any use is at your own risk #
######################################################################
# ECM IMPLEMENTATION (two variables... |
3667fa6be414754330b9a087e7808b68d0c40b71 | 00c98a4502e7a0670813325a408e16d1c7da4139 | /man/dorem_no_link_func.Rd | 708f948a4482f29d7caf6719a71070f05627e5e7 | [
"MIT"
] | permissive | mladenjovanovic/dorem | 7f192c94080d511bef5a2244205a7a4e06198de2 | 573377ac7740b8e5190bf92d5f023bf06e8cc277 | refs/heads/master | 2023-04-11T07:09:31.504879 | 2022-07-18T19:03:29 | 2022-07-18T19:03:29 | 256,017,605 | 7 | 3 | MIT | 2020-08-30T17:04:29 | 2020-04-15T19:32:52 | R | UTF-8 | R | false | true | 365 | rd | dorem_no_link_func.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dorem-control.R
\name{dorem_no_link_func}
\alias{dorem_no_link_func}
\title{Default link function}
\usage{
dorem_no_link_func(x)
}
\arguments{
\item{x}{Numeric vector}
}
\value{
Numeric vector
}
\description{
By default there is no link funct... |
1969ba4540326c5579608bd503a131f3c1b9d227 | 4d77b035d6cbb2b2ba6111b63298f87f3279d778 | /run_analysis.R | 55e7cad356754fa1532c36d6d1e56b698b3ae0cb | [] | no_license | xnoamix/GettingAndCleaningDataCourseProject | 799af7e73dd55e094b618405082b84ab623eadec | 575421e36d66d447ca9e202dedb746d924943bc2 | refs/heads/master | 2021-01-17T09:31:54.078234 | 2014-11-23T13:53:32 | 2014-11-23T13:53:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,019 | r | run_analysis.R | library(dplyr)
unzip("getdata_projectfiles_UCI HAR Dataset.zip")
## Loading all the different files belong to the training and test sets
features <- read.table("UCI HAR Dataset/features.txt")
test_data <- read.table("UCI HAR Dataset/test/X_test.txt")
subject_test <- read.table("UCI HAR Dataset/test/subject_test.txt"... |
b87101f2a7e258c35bfe56ef04b2034e77644255 | d3a4319a66f8b86051c127c28d32619a256de156 | /R/onload.R | ff5872961b2830ccd3fa0b52bf2cbdcfe9a19c60 | [
"MIT"
] | permissive | SCAR/sohungry | 11fbfbc14bb740754047ae5146f6c137f2ba913a | fd33d2c05fb7f2a8d51bb341ee30497f6098bab2 | refs/heads/master | 2023-04-22T07:14:44.234956 | 2023-04-03T23:21:38 | 2023-04-03T23:21:38 | 78,815,311 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,388 | r | onload.R | .onLoad <- function(libname, pkgname) {
## populate the options slot
this_options <- list(
doi_file = "scar_diet_energetics_doi.txt",
sources_table = "ecology.dbo.scar_references",
sources_file = "scar_sources.csv",
energetics_table = "ecology.dbo.scar_energetics",
energe... |
a73a202d42e7ea843f350e4972bfa9d3702eef5c | 72721f21b5e6bd7802f88fefda30dc77cd602a64 | /FDR_control/simulated_dames.R | bf2fa1936edf02fb9c13df32f4abdab4d1b402d9 | [] | no_license | markrobinsonuzh/allele_specificity_paper | 9effc5cad41159c7473743449968c7313a2369e6 | 4bb4cc7c7270f1a7a063e8a5482a0f9b84b89bc8 | refs/heads/master | 2021-03-19T13:12:32.965571 | 2020-02-14T12:24:46 | 2020-02-14T12:24:46 | 49,428,747 | 2 | 1 | null | 2020-02-14T12:24:47 | 2016-01-11T13:40:19 | R | UTF-8 | R | false | false | 13,133 | r | simulated_dames.R | #!/usr/bin/env Rscript
# chmod +x
# run as [R < scriptName.R --no-save]
#########################################################################################
# Benchmark p-val assigment strategies with simulations
#
# TBS-seq data CRCs Vs Norm
#
#
# Stephany Orjuela, May 2019
#####################################... |
b62341d9eaeace251dfe76fef1142c5f51055895 | b2d46260f641db68780b0899f41661cb52413b43 | /survival_gene_list_tcga.R | 3b6b9202489ae9a4d462ea63e2949ec4b91a3d3d | [] | no_license | bio-liucheng/brca-singlecell | 53dd13a82f8fba4411edcc247d81227a9883cdc4 | 98d63348e6daad001f8d0b9f3aea7ae5d483834e | refs/heads/main | 2023-04-12T06:21:37.666495 | 2021-12-14T11:24:55 | 2021-12-14T11:24:55 | 423,029,266 | 1 | 0 | null | null | null | null | WINDOWS-1252 | R | false | false | 6,196 | r | survival_gene_list_tcga.R | library(survival)
library(survminer)
library(survMisc)
gene_list = c("GPR157")
setwd("G:/scRNA-seq/LC/TCGA/BRCA")
options(stringsAsFactors = F)
clin <- read.delim("BRCA_clinicalMatrix")
expr <- read.delim("HiSeqV2.gz")
surv <- read.delim("BRCA_survival.txt.gz")
rownames(expr) <- expr[,1]
expr <- expr[,-1]... |
cb77c1de7f55a5cccccd59e541872cf5989d100d | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/snipEM/R/sclust.R | 5e322f8dcfcd1d390596939589e055b1e947216d | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,906 | r | sclust.R | .eigenConst <- function(Sev, p, lambda=12){
moptv <- 999999
if( Sev[p] > 0 & Sev[1]/Sev[p] < lambda){
moptv <- sum(log(Sev) + 1)
return(Sev)
} else{
for(i in 1:p){
cm <- Sev[i]
if( cm > 0){
m <- cm/lambda
Sevm <- pmin( pmax( Sev, m ), cm)
moptv_tmp <- sum(log(Sevm) + Sev/Sevm)
if( moptv_t... |
4d2b36c3695aec153a87f78e677ba58f09869d22 | 9969b02c26fa5388ac971b8212c761c6abf98efb | /inst/helperCode/find_gaps.r | 39c21f8ea9cbe3a1b1f644ecd6cba6752e6f000c | [] | no_license | tmcd82070/CAMP_RST | 0cccd7d20c8c72d45fca31833c78cd2829afc169 | eca3e894c19936edb26575aca125e795ab21d99f | refs/heads/master | 2022-05-10T13:33:20.464702 | 2022-04-05T21:05:35 | 2022-04-05T21:05:35 | 10,950,738 | 0 | 0 | null | 2017-05-19T20:42:56 | 2013-06-25T21:24:52 | R | UTF-8 | R | false | false | 7,672 | r | find_gaps.r | find_gaps <- function( river, site, taxon, min.date, max.date ){
# river <- "American River"
# site <- 57000
# taxon <- 161980
# min.date <- '1980-01-01'
# max.date <- '2016-03-02'
if(river == ''){
db.file <- db.file1
} else if(river == 'Sacramento River'){
db.file <- db.file2
} else if(rive... |
76a87887d72473969097e79efdb7f4dc761dd6e0 | 77dc1bb37706ca78aec3efa42b0e4e39c9aab257 | /R/RcppExports.R | b13a9348a40ed2113e915ddc59f6bbec8d44cba4 | [] | no_license | yjzeng017/StatComp20088 | 82ee555adda1140cca32667ad7c57b464f59d9aa | 15219b56be63e117ff21cfd6fe68304b3259f05b | refs/heads/master | 2023-02-02T16:25:54.334536 | 2020-12-20T12:33:42 | 2020-12-20T12:33:42 | 323,021,375 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 673 | r | RcppExports.R | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
#' @title Random walk
#' @description Random walk with Metropolis sampling method using Rcpp
#' @param sigma variance
#' @param x0 initial value
#' @param N sample size
#' @return a random sam... |
2ecd71938696966f9a496b4e7883e36e8a29372b | 323d3dcc710c658eeffd9e95878530f720efad42 | /2-Regression/1. Simple Linear Regression/SimpleLinearRegression.R | 1273ef260b0b89bab343ced44f33dc91521bda70 | [] | no_license | ahorsager/MLFoundations | 35eb98bbf86c57d4606e6760e8ec6f10d192d85e | 9a6b5848202ebb21f307f38e518ec9e360f63c7a | refs/heads/master | 2020-04-11T01:11:30.260798 | 2019-01-15T01:31:55 | 2019-01-15T01:31:55 | 161,408,250 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,549 | r | SimpleLinearRegression.R | # SimpleLinearRegression.R
# A template for a simple linear regression model
# Author: Alan Horsager
# Created: 12-DEC-2018
# **DATA PREPROCESSING**
# Importing the data set
setwd("/Users/horsager/Dropbox/projects/analytics/MLTraining/2-Regression/Simple Linear Regression")
DataSet = read.csv('SalaryData.csv')
# **SP... |
a59325be06c7b1a48d1bec440125968585d62e43 | d2ca86d0aa2e84b14b0d455ded547df90b1a7bc1 | /plot1.R | fea142390474772b9ccda2b58659320db590f66c | [] | no_license | adoroszlai/ExData_Plotting1 | e5622bf069b8aa2e138a362a596e0377c34ce3ef | ac1e9481a8fcb62ffa02d5a99210b4816c5f3777 | refs/heads/master | 2021-01-18T11:08:33.306316 | 2014-06-03T17:40:32 | 2014-06-03T17:40:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 201 | r | plot1.R | source('clean_data.R')
# plot 1
png('plot1.png', width = 480, height = 480)
hist(df$global_active_power, main = "Global Active Power", xlab = "Global Active Power (kilowatts)", col = "red")
dev.off()
|
08d80b9ed21597dd15f87c3e38be2ee33b8d3fa0 | 6b95e88fd11aff60e778c90ef75e75383a965c0c | /Q1.R | 1ff42b01eda80588f0c332c416209856ddeefaa6 | [] | no_license | bishal839/AP_LAB8 | 2de69d3c7ef804a222793ddfe652ee4d447b889b | e8a87d00e36853e5c4119885d24ab018c6d56f8c | refs/heads/master | 2020-05-03T08:59:32.315312 | 2019-03-30T10:29:43 | 2019-03-30T10:29:43 | 178,541,736 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 638 | r | Q1.R | x=read.csv("student.csv")
print(paste("\nquestion 1\n"))
y<-max(x$Percent)
print(subset(x,(Percent==y)))
print(paste("\nquestion 2\n"))
z<-subset(x,(Branch=="cse"))
print(subset(z,(Percent>=80)))
print(paste("\nquestion 3\n"))
print(subset(x,as.Date(DOA)>(as.Date("2016/07/01"))))
print(paste("Question... |
672f2b3b7cef92d57004aea0bbcb769786c37aa9 | d13a597f0dca27d35d63991e887a19dc3e5354c4 | /R/packages.R | 363c6ae1f9b543d93a1ba98bd910eafbd162a083 | [
"Apache-2.0"
] | permissive | NewGraphEnvironment/backupr | 9de69ffc286450a33fc807ac74673aa391fab54b | 0230245296bc59557f307f56eac910e22d789a2c | refs/heads/main | 2023-03-30T13:07:19.789039 | 2021-03-28T22:47:23 | 2021-03-28T22:47:23 | 346,763,301 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 85 | r | packages.R | pacman::p_load(
tidyverse,
RPostgres,
RPostgreSQL,
DBI,
sf,
data.table
)
|
f0e397809ee482545fe27f95e8dacf202e5aa2ea | 3f47892735e42d094e31341f6b306424cf9d36d2 | /R/feed.extract.R | 93c554c6bdbffb2a85c259af48dabd79b0e43de7 | [] | no_license | lovetoken/feed | f562c900a760a44344f25bb23d3baf138d64a7e5 | 52dc2f9f577ae26756c579c0611a2f184fe537ff | refs/heads/master | 2020-03-25T01:02:11.830724 | 2018-08-02T10:07:46 | 2018-08-02T10:07:46 | 143,218,782 | 1 | 0 | null | 2018-08-01T23:37:09 | 2018-08-01T23:37:09 | null | UTF-8 | R | false | false | 807 | r | feed.extract.R | #' feed.extract
#'
#' This function extract and re-combine the list from feed.info().
#' @param url A URL that you want to scraping.
#' @param n A number of list from feed.info().
#' @keywords feed, feedipedia
#' @export
#' @import rvest
#' @import dplyr
#' @examples
#' feed.extract("https://www.feedipedia.org/node/556... |
618933237507ac6651cdffe043c3380b1919a47a | ee360f07fd7a202207aec4a26cfc68ba3d053bc5 | /analysis/utils.R | d539c3071d8b6ec15738a9254f9bee1834741d0a | [] | no_license | timole/usage | bb3715d903022d33b4028f472908db5c43b7c2f8 | a06fc4349c6b968b6020b2be8065761a42322464 | refs/heads/master | 2021-01-10T19:41:36.064006 | 2015-05-07T10:05:59 | 2015-05-07T10:05:59 | 33,768,302 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,205 | r | utils.R | library("kohonen")
library("rjson")
classifierToXY <- function(somMap, c) {
col <- (c - 1) %% somMap$grid$xdim + 1
row <- somMap$grid$ydim - ( floor( (c - 1) / somMap$grid$xdim))
return(list(x = col - 1, y = row - 1))
}
getSomItemLocations <- function(somMap) {
lapply(somMap$unit.classif, function(c) { return... |
637cf985e944cb990ad33d23d196c287fb5587bd | 26dfe6af409cb36c6aa723dd92e53d793420632f | /long_term_trials/code/data_soil_carbon.R | 3995a9ecd19e6ba14e6ed2212bb9b8cd5a0fe189 | [] | no_license | cwreed/SHI | d1a6f8050eaf384473dc82a2f02c13662dd5196d | 88e874faec7568eaf62501faf3bb039080b3f102 | refs/heads/master | 2021-06-27T22:39:21.269491 | 2021-01-21T21:07:21 | 2021-01-21T21:07:21 | 201,963,976 | 0 | 0 | null | 2020-04-28T17:58:22 | 2019-08-12T16:04:30 | R | UTF-8 | R | false | false | 7,092 | r | data_soil_carbon.R | source("code/libraries.R")
d.carbon.raw <- read.xlsx('data/Long_term_yield _data.xlsx', sheet = 'Carbon')
d.carbon <- d.carbon.raw[,-20]
names(d.carbon)[1:5] <- c("Paper",
"DOI",
"Study_name",
"Years_of_study",
"Ye... |
4a758677541eba70396a48644b72220b47cda9d3 | b4dac3475d3c9d6f56b5cc24b80f904cf24400b5 | /r_course_phd/all_R_script_files1/simple_function.R | 9e3eab7e6a0a20bc4c511468717f8a17aac003fc | [] | no_license | rohitfarmer/learning | 8aecdeddfa82bddf59be4ee9005e6783df4a4010 | f0550cb9bc91287f3fbcee13d63fb182462ee920 | refs/heads/master | 2020-03-13T00:43:24.836300 | 2019-10-06T21:20:10 | 2019-10-06T21:20:10 | 130,882,198 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 65 | r | simple_function.R | add <- function(arg1, arg2) {
result <- arg1 + arg2
result
} |
c1b40a7a1dfa71e429b934fe33b59146660b3d40 | ef290d0ed8111815f8d83054a80f79f34e2b82ce | /Alderaan.R | 1842f13cb5d02dc4ef541be63cf8784da75c3e6a | [] | no_license | Venkatagutha/Web-APIs-in-R. | 14ec0451d2f3ac92b2e00a4d649c0c6724039a2a | 1b475563b4e3166872dbc00b160aa1d2a7f272cc | refs/heads/master | 2020-03-22T12:31:39.423708 | 2018-07-12T03:50:14 | 2018-07-12T03:50:14 | 140,045,321 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 549 | r | Alderaan.R | install.packages("httr")
install.packages("jsonlite")
install.packages("magrittr")
library(httr)
library(jsonlite)
library(magrittr)
# With the help of method GET, get the
#data for planet Alderaan in StarWorlds
alderaan<-GET("http://swapi.co/api/planets/",
query = list(search="alderaan"))
alderaan$sta... |
9f3d2c0965a4a03cb9b954a303eaefd4cab9ffab | 7aa6036ba7caf7ca08c6e341814ada363838ad39 | /Ch04/4_2_Condition.R | 6d2efdc2059ead34a039be13034f8b59ae3ed482 | [] | no_license | kimhalyn/R | 1db9ee75fa944f66fee63cf9abc33f94be82b296 | ca67137b14d1e14650f859ced5dfbd9e6670c0cf | refs/heads/master | 2023-06-09T17:17:24.121110 | 2021-07-01T15:11:34 | 2021-07-01T15:11:34 | 330,568,605 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,810 | r | 4_2_Condition.R | # 날짜 : 2021/01/19
# 이름 : 김하린
#내용 : Ch04.제어문과 함수 - 조건문 교재 p110
#교재 p110 실습 - if() 사용하기
x <- 50;y <- 4;z <- x * y
if(x * y >= 40) {
cat("x * y의 결과는 40 이상입니다.\n")
cat("x * y = ", z)
}else{
cat("x * y의 결과는 40 미만입니다. x * y = ",z, "\n")
}
#교재 p110 실습 - if() 사용으로 입력된 점수의 학점 구하기
score <- scan()
result <- "노력" #결과 초기값 설... |
e8dd351fa7fe437e55b7010faff998dc0812fe26 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/VTrack/examples/PointsCircuitous_crocs.Rd.R | e1e707477fb175add4cb413cdca25eb47546a626 | [] | 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 | 696 | r | PointsCircuitous_crocs.Rd.R | library(VTrack)
### Name: PointsCircuitous_crocs
### Title: Points File Containing VR2 Locations on the Wenlock River in
### 2008 with Waypoints Connecting Receivers
### Aliases: PointsCircuitous_crocs
### Keywords: datasets
### ** Examples
# Load the points file for the Wenlock River
data(PointsCircuitous_crocs)... |
e62ecf9fa74ff3a725b37268de28c8f5f2c1da85 | 66f8711bc942a1bc635a6deea253e9a49c718094 | /man/romanToArabic.Rd | afccb3474947a903ada699c05917d38b32775986 | [
"MIT"
] | permissive | seanrsilver/novnet | bd179476c48a8dd809757c60488dde7193a4145b | 85107cfbbabc68c603134db5b5fc8bbf9219624b | refs/heads/master | 2020-06-05T18:20:58.057024 | 2019-06-18T14:29:45 | 2019-06-18T14:29:45 | 192,495,039 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 490 | rd | romanToArabic.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/romanToArabic.R
\name{romanToArabic}
\alias{romanToArabic}
\title{Roman Numeral Conversion}
\usage{
romanToArabic(filename)
}
\arguments{
\item{filename}{File name as character string, i.e. "Crusoe".}
}
\description{
This function converts ro... |
ac435ccee6822aa1ed7324dac50670fc2e95d0a2 | a010c9aaf3a0e87e289f6fc9aa232ebf80b15116 | /Code_fraud_project_12_new.R | 64f0720ba3b4f13f2daa18637ddd9b391e08e9f0 | [] | no_license | jmunich/Fraud-detection | a0d6d07841453f7ff1e498b26f6088a583c3cc95 | dd0b8e7e9b6e98206c403a9ef99d4e745dfca1e0 | refs/heads/master | 2020-05-05T04:09:17.420473 | 2019-04-05T14:55:49 | 2019-04-05T14:55:49 | 179,699,613 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,056 | r | Code_fraud_project_12_new.R | ###Create corpus
library(caret)
library(readtext)
library(quanteda)
library(spacyr)
library(igraph)
library(text2vec)
library(reshape2)
library(gtools)
library(lexicon)
library(gdata)
library(pROC)
source("Get_new_text.R")
###Clean data
##From British to American
toks<-tokens(tot_corpus)
vocabulary<-r... |
91bda5082d8724b25f8049415845500ea23afa50 | 0886d094611c5e514a3366482ae2238a7b7a3e4b | /man/pmwright1.Rd | ac0f4ec7203f5abf08e717b6f4eecda08e3cc2ba | [] | no_license | cran/MWright | 8ac3118ce26d91ded51c14f1ba709b2e28711e12 | 828384ab72439cdbc6bf53d1385ea72fb59204dc | refs/heads/master | 2020-06-30T00:49:44.347860 | 2019-08-07T22:00:05 | 2019-08-07T22:00:05 | 200,671,449 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,574 | rd | pmwright1.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/distn1side.R
\name{pmwright1}
\alias{pmwright1}
\title{Distribution function for one-sided M-Wright distribution}
\usage{
pmwright1(alp, sc, upper)
}
\arguments{
\item{alp}{point estimate for shape parameter alpha.}
\item{sc}{poin... |
b1a7a9887c90a42bc8f152513629c95ed21703b5 | 1c74d653f86b446a9cd87435ce3920977e2cb109 | /packages/av/test.R | 338ef472ee47953b7503858c788e553ce5b0966a | [
"Apache-2.0"
] | permissive | rstudio/shinyapps-package-dependencies | f1742d5cddf267d06bb895f97169eb29243edf44 | 8d73ce05438f49368b887de7ae00ff9d2681df38 | refs/heads/master | 2023-07-22T08:53:56.108670 | 2023-07-12T13:58:58 | 2023-07-12T13:58:58 | 22,746,486 | 81 | 76 | NOASSERTION | 2023-07-12T13:59:00 | 2014-08-08T04:57:26 | R | UTF-8 | R | false | false | 498 | r | test.R | options(download.file.method="curl")
install.packages("av", repos="https://cran.rstudio.com")
# from av_demo
output = tempfile(fileext = ".mp4")
av::av_demo(output = output)
stopifnot(file.exists(output))
output = tempfile(fileext = ".mkv")
av::av_demo(output = output)
stopifnot(file.exists(output))
output = tempfil... |
18881877321fe312d54ecc102f4551d41a56580e | 229f163de91efd1d38909e1f4d24aac4741c92f6 | /PRSmodels/lassosum.R | be9c6d8fe04e79d7972cc1fc5667a8836f5c9e68 | [] | no_license | daiqile96/OTTERS | 531433cdaeb9e6dbe35eb1ba92977c55974154ae | 8e0bb1f1c9c1065a05ecf9f88a53d76a828a76cd | refs/heads/main | 2023-09-01T17:37:25.069311 | 2023-08-09T14:41:34 | 2023-08-09T14:41:34 | 468,945,830 | 14 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,470 | r | lassosum.R | #!/usr/bin/env Rscript
###################################################################
# Import packages needed
library(data.table)
library(lassosum)
###############################################################
# parse input arguments
Sys.setlocale("LC_ALL", "C")
options(stringsAsFactors = F)
## Collect argum... |
c7b7b8fdf40c2819a9862b84085b3f5065490ae7 | 4c14bcc37fa428673536b87083afb734866f947c | /man/series.Rd | aa5e156f41bd3b263686e7f1eb45d14f60bb8d13 | [] | no_license | RobinHankin/ResistorArray | 9c06802cb867eb3c40014ae5552ae8b8420411d1 | fe8588cc44b3c5afd91033efd768ce9846860087 | refs/heads/master | 2021-09-28T17:31:52.431138 | 2021-09-18T22:25:41 | 2021-09-18T22:25:41 | 168,077,182 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 985 | rd | series.Rd | \name{series}
\alias{series}
\title{Conductance matrix for resistors in series}
\description{
Conductance matrix for resistors of arbitrary resistance in series
}
\usage{
series(x)
}
\arguments{
\item{x}{The resistances of the resistors.}
}
\details{
\strong{Note:} if \code{length(x)=n}, the function returns a
co... |
0324b1faab7b06bba5491ad27f965f412e339b6f | 7c6017497f50d6e068f4ad18d70c1acb119c391a | /cachematrix.R | f624dbd3d66412954f093fce55f0a2aa13613c01 | [] | no_license | johnfossella/ProgrammingAssignment2 | f9af7a77993b85aa20ff2cded7493cbce2249bb8 | 296e196254e9102e3d5068cd31a18f51c48a5111 | refs/heads/master | 2021-01-16T23:02:03.406020 | 2015-09-21T23:02:52 | 2015-09-21T23:02:52 | 42,868,290 | 0 | 0 | null | 2015-09-21T13:37:13 | 2015-09-21T13:37:12 | null | UTF-8 | R | false | false | 901 | r | cachematrix.R |
## For an invertable matrix this script creates
## a list of functions sets and gets the matrix
## and also sets and gets the inverse of the matrix.
## These are used by the cacheSolve script.
makeCacheMatrix <- function(x = matrix()) {
inv = NULL
set = function(y) {
x <<- y
inv <<- NULL
... |
374dad41da5ae7ab808091d4e44529211951f91a | 3866452efa0b4bc18eb3e560106c6c4d7951f07c | /man/step.Rd | 718f723789145c7f017a0943177ff56c65f597e0 | [] | no_license | cran/relax | 39b419a84a1271e725aa01748fd5152fcd454212 | 9032feb8f608664c9fb145fd62ef11fa83fe998f | refs/heads/master | 2020-04-14T23:23:20.277277 | 2014-03-10T00:00:00 | 2014-03-10T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 789 | rd | step.Rd | \name{step}
\alias{step}
\title{ modified version of step for relax }
\description{Select a formula-based model by AIC.}
\usage{
step(object, scope, scale = 0, direction = c("both", "backward", "forward"),
trace = 1, keep = NULL, steps = 1000, k = 2, ...)
}
\arguments{
\item{object}{ model }
\item{scope}{ rang... |
13990779e4ec34828f6c370b52fd6871aa7d8b90 | 03bf43d695db86fb8203e5186a8b3ce12d92d9aa | /tarea0/problema6.R | 723c14e4c172a7c19ac38c18ae22027d8cfbccfb | [] | no_license | joseaznar/simulacion | e01fc9bace916cb445641190c7583cda87d0d8f1 | 525484c4616fb8e0fde3483ca70a7f1f93dc1ee2 | refs/heads/master | 2021-09-06T06:02:24.358856 | 2018-02-03T00:40:24 | 2018-02-03T00:40:24 | 119,995,028 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 218 | r | problema6.R | # problema 6
# primero definimos la ecuación
eq = function(x){exp(-x*x)/(1+x*x)}
# graficamos la función entre 0 y 10
curve(eq, from=0, to=10)
# ahora integramos entre 0 e infinito
integrate(eq, lower=0, upper=Inf) |
8e1871d2afb46c6c782a6ddb153cf6bf2a992b46 | 5e1560b3a996ed2f56a74f32dc987a8e60e405f3 | /R/tolerance.eigen.R | f0e12877f856cc4724c5124cde17d1c92211bd04 | [] | no_license | diogo-almeida/GSVD | 19803dde129b18cb0492d3e211e037e33efb7d31 | 90cb90497daf1efd38e1a3ab85439407f16ff46e | refs/heads/master | 2020-12-18T15:34:15.768461 | 2019-11-14T21:28:24 | 2019-11-14T21:28:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,999 | r | tolerance.eigen.R | #' @export
#'
#' @title \code{tolerance.eigen}: An eigenvalue decomposition to truncate potentially spurious (near machine precision) components.
#'
#' @description \code{tolerance.eigen} eliminates likely spurious components: any eigenvalue (squared singular value) below a tolerance level is elminated.
#' The (like... |
b75f1a115ed69306fd9141173a7784bb4a50912d | d80d2f9e911820898bb21bc9e2e2c7d10e8cfa59 | /R/prompt-git.R | 918088dae97a9c6187ed55b6c2a0ceb45ec03a38 | [] | no_license | Robinlovelace/prompt | d0d8f43d4459f2d8e67ed433e55cdf88ff28d07a | 950124035700126412df8f5bc78cb583ee0555f6 | refs/heads/master | 2020-04-09T22:37:33.908096 | 2018-09-11T21:50:52 | 2018-09-11T21:51:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,681 | r | prompt-git.R |
#' An example 'git' prompt
#'
#' It shows the current branch, whether there are
#' commits to push or pull to the default remote,
#' and whether the working directory is dirty.
#'
#' @param ... Unused.
#'
#' @family example prompts
#' @export
#' @examples
#' \dontrun{
#' set_prompt(prompt_git)
#' }
prompt_git <- fu... |
c8708248a3c9286ee6acb1d41a9556c777fbc3f2 | 109734b597c2d760725a1a050174a5d11b3c1a9b | /man/diameter.owin.Rd | cad0b6deaad18918e2a9ccb4160dd51a0d1680b0 | [] | no_license | rubak/spatstat | c293e16b17cfeba3e1a24cd971b313c47ad89906 | 93e54a8fd8276c9a17123466638c271a8690d12c | refs/heads/master | 2020-12-07T00:54:32.178710 | 2020-11-06T22:51:20 | 2020-11-06T22:51:20 | 44,497,738 | 2 | 0 | null | 2020-11-06T22:51:21 | 2015-10-18T21:40:26 | R | UTF-8 | R | false | false | 1,076 | rd | diameter.owin.Rd | \name{diameter.owin}
\alias{diameter.owin}
\title{Diameter of a Window}
\description{
Computes the diameter of a window.
}
\usage{
\method{diameter}{owin}(x)
}
\arguments{
\item{x}{
A window whose diameter will be computed.
}
}
\value{
The numerical value of the diameter of the window.
}
\details{
Thi... |
e0a49b14aeaa0b04e94e3dadfd813d0d10d0543c | 03d20ec52ea429d2bffdefa849044ab6d0ad7481 | /03_stop_frisk/scripts/shiny/server.R | 53321fcac699cc40920f5e00eae34ce5662d3954 | [] | no_license | GWarrenn/dc_data | 3f679b28aa02f1cec7b9e887d66087d44ed40d7c | 15b358d77210644dcdd908ef05d6e95930fbf62e | refs/heads/master | 2021-11-17T07:36:55.337352 | 2021-09-30T21:44:56 | 2021-09-30T21:44:56 | 98,127,130 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,263 | r | server.R | library(shiny)
library(DT)
shinyServer(function(input, output) {
filedata <- read.csv("sf_nbh_summary.csv")
format_cols <- c("Black.Diff","Hispanic.Latino.Diff","Juvenile.Diff","White.Diff")
numeric_cols <- c("Black.stop_and_frisk","Black.census","Black.Diff",
"Hispanic.Latino.stop_and_fr... |
1a21e4fca73fddb89742615632cb67512929cca6 | 9d4e1ec7dd4128c99360e98b05de206661f3f130 | /stoke_boost.R | d76d0f0fcffad9ffcdbfb1b7483876242c17740e | [] | no_license | coderjones/stroke_prediction | df6afb8fc5681ea46e050a22a07456b6445cb89e | be5a1dbea2436c068b313dbf39d471e8df84591e | refs/heads/main | 2023-06-08T05:17:47.580765 | 2021-06-27T20:07:01 | 2021-06-27T20:07:01 | 367,732,010 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,059 | r | stoke_boost.R | # Using gbm for gradient boosting classification
library(tidyverse)
library(fastDummies)
library(rsample)
library(gbm)
# set working directory
setwd("/Users/jeremiahhamilton/code/stroke_prediction")
# read in data
df <- read.csv("healthcare-dataset-stroke-data.csv")
df <- dummy_cols(df, select_columns = c('smoking_... |
9a9a6b4f313264e2921ba1a55a9e113ffc3df5ed | 22da09a9095cbd13d25edff454c1b32972357ffc | /man/animate_series.Rd | 152d9861ff8020765cc6fd1c31658782afaab7a4 | [] | no_license | ThoDah/rabbiTS-1 | f49f516b72f4805b5d7ddb7899eb412ff6757e3d | ef7427619aeb5b6b174751dc1067b2cfe6d3d6f4 | refs/heads/master | 2020-03-17T18:03:51.299496 | 2018-05-17T13:02:27 | 2018-05-17T13:02:27 | 133,813,294 | 0 | 0 | null | 2018-05-17T12:55:20 | 2018-05-17T12:55:19 | null | UTF-8 | R | false | true | 1,246 | rd | animate_series.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/animate_series.R
\name{animate_series}
\alias{animate_series}
\title{Animate a time series of rasters}
\usage{
animate_series(r, dates, breaks, param = NULL, param.name = "Parameter",
file.name = tempfile(fileext = ".gif"), ...)
}
\argument... |
a28b6d45d9b576b7d7fd1b692ab7c6579bce7ddf | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/rnr/tests/test-solve.R | 7991f85e7dbd1be1bb33531a853d989d70bf0c16 | [] | 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 | 640 | r | test-solve.R | context("sensitize")
test_that("solve_closed() finds correct value for atomic vector", {
ps <- seq(0, 1, 0.01)
delta <- 2
theta <- 1
for (p in ps) {
lhs <- 1 - (p*inv_logit(theta) + (1 - p)*inv_logit(theta + delta))
generated <- solve_closed(p, delta, lhs)
expect_equal(generated, theta)
}
})
te... |
41ec86a3d14cb16a89dd7b1ca80144522889526f | e8524f6a0301d922ec18d6d017cfb223c9eceee4 | /data-portraits/andy-challenge-02.R | fb515bf21f170f303fc060dbaf3c245ba255fd24 | [] | no_license | melodyaltschuler/tidytuesday | eff7713ff1cc36a9ea382954159e9f315f0c4691 | 6514ec95de2cc366be1b340050218819430963c4 | refs/heads/master | 2023-04-12T22:38:52.281026 | 2021-04-22T22:42:03 | 2021-04-22T22:42:03 | 296,454,761 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,524 | r | andy-challenge-02.R | ## DATA PORTRAITS - CHALLENGE 2
## ICD TIDY TUESDAY
## MARCH 2021
# Load library
library(tidyverse)
library(showtext) #To use googlefonts
library(patchwork)
# Import data
conjugal <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-02-16/conjugal.csv')
# Convert to lo... |
7a3dbdf4368c303fb0cafbd899712af6cf5ca0d4 | 3530fb409502ac4e55bfcf053daadf14573e5b08 | /q14.R | ad2626bf18668adc582fa78232b0bf762f8c8ec9 | [] | no_license | nathandarmawan/rprog_quiz_week1 | fa0ee5b46ea0ceb56f808739aacdb9eb095f2db6 | 2f239f96a8cdd683b5f9df23b7adb7998670b98d | refs/heads/master | 2021-01-19T05:36:32.364798 | 2014-10-13T06:36:16 | 2014-10-13T06:36:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 277 | r | q14.R | ## Q 14
## Extract the last 2 rows of the data frame
## and print them to the console.
## What does the output look like?
## Reading Data
setwd("D:/GitHub/rprog_quiz_week1")
data <- read.csv("hw1_data.csv")
## Show the last 2 rows of the data frame
## Use tail()
tail(data,2) |
3e397af706756bbde6d44b2845e1aed5af54847c | 4a4cae45a127183fa4c58bd75737fb0980bd8bfd | /required_packages.R | 91af782f392747c0430f8a9eeb697b1c7a3121c1 | [
"MIT"
] | permissive | klintkanopka/nn_workshop | 7394a97a80cddb9a5a91e73e754f931b46884fde | bd2d857af0ebc98b83bebff1b266da6a6082cec0 | refs/heads/master | 2020-05-23T10:23:38.903759 | 2019-05-15T06:43:03 | 2019-05-15T06:43:03 | 186,719,123 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 59 | r | required_packages.R | install.packages("tidyverse")
install.packages("neuralnet") |
ec74b4bff2900a44e9be9a2956db480c1f4fa0de | 5395cdc191ff5a30d1c59e68ca0f95a288892c8b | /man/M_el_mat.Rd | a5090a55249ad8698b59c2e7044182532cd445bc | [] | no_license | nielsjdewinter/ShellTrace | fe16bb69b8981211bd24ef120627fc38d283db66 | 34dd076d72bb0812f251c986b1aad04b6849261b | refs/heads/master | 2021-07-23T13:37:20.750368 | 2017-11-02T08:57:26 | 2017-11-02T08:57:26 | 105,881,428 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,852 | rd | M_el_mat.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/M_el_mat.R
\docType{data}
\name{M_el_mat}
\alias{M_el_mat}
\title{Matrix of modelled mass accumulation rates per trace element}
\format{A data frame with 5 rows and 24 variables:
\describe{
\item{C}{Mass accumulation of C in subincrement}
... |
14990f5ce2aee8d498ee1d7e83b7972396f7a8be | caa9387f050ded3c5f1b9879eb1935a29f7db8ce | /code.R | b5ead6e513166d9c633d1f8d1dc107ec8d13ef4f | [] | no_license | joebrew/map_plos | b4a3a901d7d1a4bc62aabbc75b2bca1c5d1f49b2 | b1380ae0eb1efbda6a162396f4f3f21b2c195187 | refs/heads/master | 2021-01-10T09:54:17.699613 | 2016-03-08T10:40:53 | 2016-03-08T10:40:53 | 52,874,937 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,688 | r | code.R | library(rgdal)
library(dplyr)
library(raster)
library(readxl)
library(RColorBrewer)
require(maptools)
library(ggrepel) # for avoiding overlapping labels in ggplot2
library(ggthemes)
##### Read in shapefiles
# arruamento <- readOGR('data/spatial/', 'arruamento')
bairros_e_zonas <- readOGR('data/spatial/', 'Bairros_e_Z... |
8fd04719dd11319c33c7835ca12d0729516e6622 | 021fa1134701528153dab7dd4c24ed145d15af06 | /Template.R | 12fd9c8bdf56d63a4b4b84502051c42697854615 | [] | no_license | a30123/R_Handy | 924802172fdd1dab1f965f5f90c029ebe992db26 | 059abe4df07bbcd1c76d02eef2befe16b04f3c59 | refs/heads/master | 2021-01-19T12:36:20.663018 | 2015-07-05T11:42:35 | 2015-07-05T11:42:35 | 38,416,196 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,051 | r | Template.R | #### created date:
#### last modified date:
#### author:A30123
#### description:
#########################################################################################################
### ##### ##### ##### ############### # ### ### ### ################
### ######### ######## ... |
8f8aae35fe32f7dfb78e8093d74f73109b2add4c | f99326be507c62c63b91a45ec3246aa1b3a55f30 | /RandForest.R | c1207b7e7049b4d78f82df18c5eacbef2d1169d9 | [] | no_license | sherryxhu/wesad | 3fa6b5190bef1a517bde46e0c6e38f8e51f088d8 | 1695af914b638a6f809e17599e4fc22b0c9bc524 | refs/heads/master | 2022-11-08T22:25:54.747305 | 2020-06-20T17:09:51 | 2020-06-20T17:09:51 | 272,251,557 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,934 | r | RandForest.R | # install packages
install.packages('nnet', repos = "http://cran.us.r-project.org")
install.packages('tidyverse', repos = "http://cran.us.r-project.org")
install.packages('dplyr', repos = "http://cran.us.r-project.org")
install.packages('arm', repos = "http://cran.us.r-project.org")
install.packages('plyr', repos = "ht... |
d0b6c777b1310244f3d4289feb19afc95893b080 | 8629ad85edfb2293280f0820c27c933739bebc5a | /submissions/01_r4ds-data-transformation-hl2da.R | 957b9a18a885c03769a0ed618c8810c46f2369ee | [] | no_license | GCOM7140/r4ds-exercises | ce94ac4f3a4a7c5d3038db76ae54cacbad6ad22d | a5fefe1bfcca6ae0d4d231a4b3e2222cb963ce17 | refs/heads/master | 2021-05-01T15:03:27.315763 | 2019-07-29T22:31:46 | 2019-07-29T22:31:46 | 121,028,562 | 1 | 1 | null | 2018-04-11T13:10:41 | 2018-02-10T15:43:46 | HTML | UTF-8 | R | false | false | 2,628 | r | 01_r4ds-data-transformation-hl2da.R | <<<<<<< HEAD
library(tidyverse)
library(nycflights13)
# Question1
# How many flights flew into LAX
filter(flights, dest == "LAX")
nrow(filter(flights, dest == "LAX"))
flights %>%
filter(dest =="LAX") %>%
nrow()
#HOW many flights flew out of LAX
flights %>%
filter(origin =="LAX") %>%
nrow()
#How many flig... |
43a8f805d63f9f365ca2e07dc37044c60d710f2e | db9a558fe2273bcaa88d5c0c47633857766492fa | /Chapter 1 Updated.R | e361f67523eb5d09107ab6fb4da1d49038e70bfd | [] | no_license | uvonpunkfarm/que | e0058061a286107e6b638aa487ff902ff1b0a4ea | fe375fd65a12e363d7a96ed118b49b560a6a9158 | refs/heads/master | 2020-12-26T20:25:45.186586 | 2020-02-09T21:33:09 | 2020-02-09T21:33:09 | 237,631,400 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 900 | r | Chapter 1 Updated.R | x <- c(5,10,15,20,25,30,35,40)
x
sum(x)
mean(x)
x
x
y <- seq(5,40,13)
y
z <- seq(2,6,2)
z
hypoteneuse <- function(a,b){
hyp <- sqrt(a^2+b^2)
return(hyp)
}
Raptors <-c("Lowry", "DeRozan", "Bosh", "Kawhi")
Long ass number <- 5:200
quadrifecta <- c(1,2,3,4)
repeated_quadrifecta <- rep(quadrifecta,5)
repeated_quadr... |
73496ffcce43e5246e8427b8aef8c5fa932e4f80 | 57d6bac4eae56c4efcddcd212eedf47eaafa142d | /practical_machine_learning/project_scratchpad_june.R | 32089cc6daadb6a0fbe87ceed69ad435a88f3aeb | [] | no_license | sdevine188/coursera_code | 14e8ef7e74e02c50fca76636f7cb4ed7f47af6bf | f2069acec6a247746c178ee706bd85fa4d550479 | refs/heads/master | 2021-01-25T06:40:03.171925 | 2016-01-25T16:15:19 | 2016-01-25T16:15:19 | 31,866,660 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,404 | r | project_scratchpad_june.R | # read in data
setwd("C:/Users/Steve/Desktop/Coursera/Practical Machine Learning")
full_training <- read.csv("pml-training.csv")
full_testing <- read.csv("pml-testing.csv")
# split full_training into training and testing
in_train <- createDataPartition(full_training$classe, p = .7, list = FALSE)
training <- full_train... |
94c011b1344d5fcf6d08962b94adfacb2c030402 | 7fb8caee598f0d71598f3f022d9552c6b9b862f6 | /sentiment.r | 9c9bd112e6128bb878f132d98261ec30c23d7a6b | [] | no_license | Nivas138/Predicting-Social-Nexus | 6525f6d8a68de5775ff02f08ee20e8876386d051 | 3077bd25a22e0600a2e4879dca82f593e52e1e3d | refs/heads/master | 2020-04-13T22:26:56.306650 | 2019-03-20T08:27:48 | 2019-03-20T08:27:48 | 163,479,473 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,600 | r | sentiment.r | setwd('C:\\Users\\Nivas\\Documents\\')
getwd()
install.packages("ggplot2")
install.packages("tm")
install.packages("wordcloud")
install.packages("syuzhet")
?tm
library(ggplot2)
library(tm)
library(wordcloud)
library(syuzhet)
texts = readLines("chat1.txt")
print(texts)
docs = Corpus(VectorSource(tex... |
7894c5bcf78e793c74074f125630c74445c6b2e9 | da4c8b3a0201143378037766966603e8d5e4598d | /plot1.R | f98d286f62a24e738e771bb829fda6bfdfd765f6 | [] | no_license | hmoralesos/ExData_Plotting1 | 1b0e9e089b19c7a699acd3959097c2053a8723d6 | 3dc08ee4931e6dc1ca57722ca593ecafc4931f99 | refs/heads/master | 2021-01-17T20:24:59.835872 | 2016-08-15T19:11:38 | 2016-08-15T19:11:38 | 65,756,123 | 0 | 0 | null | 2016-08-15T18:43:22 | 2016-08-15T18:43:19 | null | UTF-8 | R | false | false | 1,257 | r | plot1.R | ################################################################################
# Read complete dataset #
################################################################################
dataset<-read.table("household_power_consumption.txt",header=TRUE,sep=";")
h... |
779d56e0eac0986e7a411fe7ff6dc307e14c419e | 714e7c6736a2e3d8fd07634427c4a8bb3cef2d61 | /man/plot_avg_dot.Rd | 9c313e4ce776c5e8b33c5b7c4d2e58312fc89a51 | [
"MIT"
] | permissive | flaneuse/llamar | da7cb58a03b2adbffb6b2fe2e57f3ffeede98afb | ea46e2a9fcb72be872518a51a4550390b952772b | refs/heads/master | 2021-01-18T00:10:00.797724 | 2017-10-24T13:41:21 | 2017-10-24T13:41:21 | 48,335,371 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 3,444 | rd | plot_avg_dot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_avg_dot.R
\name{plot_avg_dot}
\alias{plot_avg_dot}
\title{Plot a dot plot after averaging the values}
\usage{
plot_avg_dot(df, by_var = "region", value_var = "avg", incl_x_axis = TRUE,
x_label = NULL, x_limits = NULL, x_breaks = waiver... |
8de5be52896a330d9c1aa8306d1d06d4cd921b38 | 109681dbabeb2ba82dc1ef895a28d40f03033ccb | /man/ontologyLogPage-methods.Rd | 16bed29bf67ea25f428bbb7494345174f15e23f4 | [] | no_license | frenkiboy/cellexalvrR | 97cc210f47c0fcff998704200adfcd549ffabbcf | cf9ab9e8c5fd519d0db2dd98b7ccbc84812cba77 | refs/heads/master | 2020-12-22T11:00:05.728789 | 2020-01-28T14:49:13 | 2020-01-28T14:49:13 | 236,758,585 | 0 | 0 | null | 2020-01-28T14:47:26 | 2020-01-28T14:47:25 | null | UTF-8 | R | false | true | 842 | rd | ontologyLogPage-methods.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ontologyLogPage.R
\docType{methods}
\name{ontologyLogPage}
\alias{ontologyLogPage}
\alias{ontologyLogPage,cellexalvrR-method}
\title{description of function ontologyLogPage}
\usage{
ontologyLogPage(cellexalObj, genes, grouping = NULL, ontolog... |
bd7440d20b5875b1e8ca57116b8ff91c92b9d6da | 58f4573bc3e9efbc14ff9ebbf089231c246cf066 | /man/inlineModel.Rd | 895753bad6ac628462b0402af0c548a93d1eddea | [] | no_license | Anathawa/mlxR | 1a4ec2f277076bd13525f0c1d912ede3d20cb1cc | 7e05119b78b47c8b19126de07c084e7d267c4baf | refs/heads/master | 2021-01-19T09:17:35.765267 | 2017-04-05T18:00:39 | 2017-04-05T18:00:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 311 | rd | inlineModel.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/inlineModel.R
\name{inlineModel}
\alias{inlineModel}
\title{inline model}
\usage{
inlineModel(str, filename = NULL)
}
\arguments{
\item{str}{model}
\item{filename}{where to write the temporary model}
}
\description{
inline model
}
|
fc1a2cb14010fca4dbefa9f9828c2477cb486e0c | f25c5405790cf17a2b6e78b4ef58654810c8bb7b | /R/piechart.R | 32fff8197d5b8c9a0f0e970e21c6e576b6017c2e | [] | no_license | moturoa/shintodashboard | 15ad881ea4c72549b616a3021852a0db8c25f6fd | 80385da221d370a563eb1cfe8946964acfacfe15 | refs/heads/master | 2023-05-31T07:05:11.026309 | 2021-06-28T12:55:32 | 2021-06-28T12:55:32 | 312,505,839 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,174 | r | piechart.R | # #' @importFrom waffle geom_waffle
# #' @importFrom waffle theme_enhance_waffle
piechart <- function(data,
xvar,
yvar,
xlab = NULL,
ylab = NULL,
glab = NULL,
type = "Pie",
... |
d81082b025b15834cce818b8dc6c2de6f4c8c166 | d8da5c909feddfa679dceb2aa79da8483b607ada | /models/stacked_model/stacked_model_draft.R | 560a31eb4f1ed8da430a84ee0a719d79f3245241 | [] | no_license | edouardArgenson/house_prices | 9af379fba6378a123227239c1e56a0fb991bac5f | 6c348f2d02359ba2790ae9f3e23dc287aabb2d9a | refs/heads/master | 2021-01-20T08:41:40.694968 | 2017-05-03T18:25:11 | 2017-05-03T18:25:11 | 70,399,835 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,188 | r | stacked_model_draft.R |
library('lattice')
library('ggplot2')
library('caret')
library('data.table')
library('Metrics')
library('MASS')
library('e1071')
library('kernlab')
library('gbm')
library('survival')
library('splines')
library('parallel')
library('plyr')
train = fread('~/kaggle/house_prices/data/train.csv',
colClasses=c... |
71768805f8dbbd444da86772f916489c730959db | 2e3c6f281490f908608c19e1841fdfdbeb081c21 | /stuff.R | 7f200f2a7cf1c26a52d07300b38499777f693b79 | [] | no_license | bdeonovic/binsum1 | ad81ccf90be3097c6a929946f92855c9a68aa5a9 | a9f501e4aeaf5dc49fdd9c88153392ef029e71c9 | refs/heads/master | 2020-04-25T06:54:55.202443 | 2017-07-13T19:50:04 | 2017-07-13T19:50:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,228 | r | stuff.R | # stuff
# Stirling2 is (n,m) where m is upstairs in brackets
# ie first parameter greater than second
Stirling2(4,2)
choose(4,2)
clji(6,5,6)
ans=data.frame()
for (l in 1:6) {
for (j in 1:l) {
for (i in j:l) {
ans=rbind(ans,c(l=l,j=j,i=i,clji=clji(l,j,i)))
}
}
}
ans
kolmo(c(2,3),c(0.2,0.1),5,0.1)... |
a17bc682a2aaf5955f8b7fb31007ccfe1b03f84d | fea181071db54de2be82d3d669e7c8048400a84b | /Assignment 2.R | fedf153c277ada871baffe2cbabdab1ccbcf8d00 | [] | no_license | CoHae/First-Repository-USF-R-Class | 16c5e03205588d3a7a9ccce7b2eccd2ee2be04d4 | 997d21c8ac8a7e7941c52eff618237e2000dc03a | refs/heads/master | 2020-12-10T17:27:53.385416 | 2020-04-04T16:25:23 | 2020-04-04T16:25:23 | 233,659,658 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 459 | r | Assignment 2.R | # original/faulty Assignment 2
# it shows that neither "assignment" nor "someData" exist
assignment2 <- c(16, 18, 14, 22, 27, 17, 19, 17, 17, 22, 20, 22)
myMean <- function(assignment2) { return(sum(assignment)/length(someData)) }
myMean(assignment2)
someData
# corrected code for Assignment 2
assignment2 <- c(16, 18, 1... |
e23ecb72cacc054bf9e5641b7ae0dd0d3c6e95cf | 8f7320c10f2c5fc8475753dc5256d1a66067e15c | /rkeops/man/ternaryop.LazyTensor.Rd | 79e4fd52c367bfd2900deb4aabd33ec5edb75a31 | [
"MIT"
] | permissive | getkeops/keops | 947a5409710379893c6c7a46d0a256133a6d8aff | 52ed22a7fbbcf4bd02dbdf5dc2b00bf79cceddf5 | refs/heads/main | 2023-08-25T12:44:22.092925 | 2023-08-09T13:33:58 | 2023-08-09T13:33:58 | 182,054,091 | 910 | 69 | MIT | 2023-09-03T20:35:44 | 2019-04-18T09:04:07 | Python | UTF-8 | R | false | true | 1,865 | rd | ternaryop.LazyTensor.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lazytensor_preprocess.R
\name{ternaryop.LazyTensor}
\alias{ternaryop.LazyTensor}
\title{Build a ternary operation}
\usage{
ternaryop.LazyTensor(x, y, z, opstr, dim_check_type = "sameor1", dim_res = NA)
}
\arguments{
\item{x}{A \code{LazyTenso... |
cea2b0ede00b21627fbb3d5d5ce963f294a20af9 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/traitdataform/examples/cast.traitdata.Rd.R | 38ed9c56d63859476c8762bceb44e44f7d3824cf | [] | 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 | 1,056 | r | cast.traitdata.Rd.R | library(traitdataform)
### Name: cast.traitdata
### Title: Cast long-table trait data into wide-table format
### Aliases: cast.traitdata
### ** Examples
pulldata("arthropodtraits")
head(arthropodtraits)
dataset3 <- as.traitdata(arthropodtraits,
taxa = "SpeciesID",
... |
2dd02c35be9e211e0f4a5cf903af2c9e32509371 | 8823b744fa8328268704c81fcbd23644cc65a271 | /R/simulate_data.R | 78db8dcabc88f0e74b7ad67d418d1d3e611cc3e5 | [
"MIT"
] | permissive | zosob/RiskAssessment | 53c1b21f499fd2e82d1ab56096dbfbf2f8a81c0b | 412a4c4d1f89fbe011e185f26b4f22f379bbb23b | refs/heads/master | 2022-01-26T20:17:22.056667 | 2019-07-18T09:05:10 | 2019-07-18T09:05:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,522 | r | simulate_data.R | #' @title Simulate Data
#' @description Function that simulate complete data, incomplete data and also approximate the top event probability distribution by simulation.
#' The data is a matrix type in R with the number of rows equal to the number of observation and the number
#' of columns equal to the number... |
c4910451f175881fe1f6a54d71053db446ae261f | 8b2a91990c0d78af91ccae2939985bf0a1eed858 | /part3.R | 31de6151b9b8e28e0729e8ba0ceb0d75e6f27020 | [] | no_license | i94u/Lab1_603410031 | ceaae5ff38196e0dbfb4e90947396454bff11250 | bacf8a73c6b774c49184163c2f9d106e4878f5c4 | refs/heads/master | 2021-01-10T10:13:02.200693 | 2015-09-24T04:08:19 | 2015-09-24T04:08:19 | 43,038,216 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 480 | r | part3.R | Wingcrd <- c(59, 55, 53.5, 55, 52.5, 57.5, 53, 55)
mean(Wingcrd); median(Wingcrd); min(Wingcrd); max(Wingcrd)
Tarsus <- c(22.3, 19.7, 20.8, 20.3, 20.8, 21.5, 20.6, 21.5)
mean(Tarsus); median(Tarsus); min(Tarsus); max(Tarsus)
Head <- c(31.2, 30.4, 30.6, 30.3, 30.3, 30.8, 32.5, NA)
mean(Head, na.rm = TRUE); median(Head... |
2ec01c7a58c12b7dd8eeda96b25b00e19e2a29a3 | f3dffcb0cd531bb61c12e68e38dc8b4d6192d4c0 | /plot1.R | 6f40d08b4a102e0ac2533150615045388f6e0f26 | [] | no_license | joekieffer/EDA---PM2.5 | e9d8e81f9beae6d5702005ece013e520de81e846 | be9d5a210fc1997c033b1bae7df56a5686d1e89e | refs/heads/master | 2021-01-10T21:11:11.771063 | 2014-10-26T18:44:19 | 2014-10-26T18:44:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 314 | r | plot1.R | #importing data
source('Class_Project2.R')
#data manipulation
totalE <- aggregate(Emissions ~ year, NEI, sum)
#printing of plot
png(file="plot1.png", bg="white")
barplot(height=totalE$Emissions, names.arg=totalE$year, ylab="Total emissions", xlab="Years",main="Total fine particulate matter emission")
dev.off() |
d765ec52059a123c4e9060ee9fcaccd7f36d9684 | 37cbbbbfc95eda55dc99f5637b39dba59bbddc6a | /tests/testthat/test_dCModel.R | c27c32194d952f86407f1137f31098f20f7538b4 | [
"MIT"
] | permissive | djinnome/rtedem | f2f080e1eabfc01b3e448ac610aee16c6d12b547 | 7a3232d46410e9f29b42209165a990ce1e0bb934 | refs/heads/master | 2021-01-12T13:17:07.189860 | 2016-09-24T16:11:10 | 2016-09-24T16:11:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,705 | r | test_dCModel.R | # Testing code for the RCMIP5 'dCModel.R' script
# Uses the testthat package
# See http://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf
library(testthat)
# To run this code:
# source("R/dCModel.R")
# library(testthat)
# test_file("tests/testthat/test_dCModel.R")
context("dCModel")
test_that... |
6531935d91b9eb1d701f9f649be1fa229e34fc0c | 95a0aaef3033adc33dee58dc00742c526bf67f95 | /RProgramming/Assignment2/cachematrix.R | b7636cfb42d96aac18a5ddec4242eaaa191c7520 | [
"MIT"
] | permissive | skyguy94/datasciencecoursera | 9add9f8e6df3e0837e8817f99ad2bfd4a8f91d39 | 236892664c25f65f8f45c1290aa84d137e1e890d | refs/heads/master | 2021-01-10T04:11:51.147895 | 2015-08-23T22:27:33 | 2015-08-23T22:27:33 | 36,816,880 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,094 | r | cachematrix.R | ## This code attempts to optimize the handling the computation of inverse
## matrices by caching the result of previous computations in memory
## and checking that cache before computing an existing result.
## This function creates a special "matrix" object that
## can cache its inverse.
makeCacheMatrix <- function(... |
d428d73de4c8da9a9f63d56a80d58cbe81d3440c | 2bee25fa7cd8961eed2336183284b768a575e4d6 | /R/plot.samplesize.R | 3a729796b1f713f556560230b0ec927ed5476412 | [] | no_license | annaheath/EVSI | d2a6adb3a15b4013d3e7a82de815e51a016377a3 | 11accaca10816c0eb0cc32cdb0ae74829ed41c01 | refs/heads/master | 2022-07-18T02:00:08.761837 | 2022-06-24T13:02:55 | 2022-06-24T13:02:55 | 102,010,781 | 8 | 1 | null | 2019-03-21T20:17:17 | 2017-08-31T14:40:52 | R | UTF-8 | R | false | false | 3,468 | r | plot.samplesize.R | ##plot.samplesize###########################################################
plot.samplesize <- function(evsi,wtp=NULL,pos=c("bottomright"),CI=NULL){
##'Calculating the EVSI for a specific WTP giving the uncertainty bands across the different
##'samples sizes
##INPUTS
##'@param evsi Output of the comp.evsi.N fu... |
c4a83149e6d22334ebe3636808b34810dba0863a | af982fba9c4fab24bf06e810720de721a7c43bd2 | /data-raw/weather data/make weather data.R | 5735741262d425469607cfc692bd0c3f57f3cff2 | [
"Apache-2.0"
] | permissive | Schmidtpk/Covid | 398dcc70bc12d91bb588939eedd3e7248c547e23 | 0efcfe093be2b8b66799930185b580bee1dfe529 | refs/heads/master | 2021-04-11T09:36:33.532750 | 2020-04-25T08:27:00 | 2020-04-25T08:27:00 | 249,008,243 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,079 | r | make weather data.R | library(httr)
vars <- c("cloud","tMax","tMin","precip","humidity","wind")
df <- NULL
for(var.cur in vars)
{
df.cur <-as_tibble(read.csv(
text=as.character(
GET(
paste0("https://raw.githubusercontent.com/imantsm/COVID-19/master/csv/",
var.cur,
".csv")),
header=T)))... |
b3d2ca6ad251c578d9327d6ce91a605c579e7fee | a55c6e1f121a7114d238437cb3ff7002e31c4d42 | /tests/testthat/test-overscope.R | 9a596f5a45f3b70744022303f5afc4ba9e656aee | [
"MIT"
] | permissive | mohamedndiaye/dplyr | 93af925b8144d462cbeb3d94ff5ca9b0e9c94b99 | 12e76215b01cea302d26d600a17549d5019026d3 | refs/heads/master | 2020-03-06T16:02:24.867469 | 2017-04-28T14:28:47 | 2017-04-28T14:28:47 | 126,966,112 | 1 | 1 | null | 2018-03-27T09:56:37 | 2018-03-27T09:56:36 | null | UTF-8 | R | false | false | 309 | r | test-overscope.R | context("overscope")
test_that(".data has strict matching semantics (#2591)", {
expect_error(
data_frame(a = 1) %>% mutate(c = .data$b),
"Column `b`: not found in data"
)
expect_error(
data_frame(a = 1:3) %>% group_by(a) %>% mutate(c = .data$b),
"Column `b`: not found in data"
)
})
|
aa53f30a8b749cced8084d6fe6f5fb375650623e | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/igraph/examples/vertex_attr.Rd.R | c3a9c580aabad670b314007a92f5b0edc3b7179a | [] | 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 | 337 | r | vertex_attr.Rd.R | library(igraph)
### Name: vertex_attr
### Title: Query vertex attributes of a graph
### Aliases: vertex_attr get.vertex.attribute vertex.attributes
### ** Examples
g <- make_ring(10) %>%
set_vertex_attr("color", value = "red") %>%
set_vertex_attr("label", value = letters[1:10])
vertex_attr(g, "label")
vertex_at... |
8a0ccce6475661429a346bdbad4b25a2ea5ad341 | 1462094b01791141a5e21727aec8c15c205ee28f | /ui.R | dff75b82ecaf1903385d93694a3146a9d3c2ce49 | [] | no_license | dcarvalho/analise_dados_abertos | f668b58d007f8b316236c5d81aa048863d738237 | c2b2cf6c9be48fa88111e4461720dd4db122e255 | refs/heads/master | 2020-04-15T11:49:49.358424 | 2019-04-03T17:59:10 | 2019-04-03T17:59:10 | 164,646,914 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,077 | r | ui.R | library(shiny)
library(DT)
library(plotly)
d<- as.POSIXlt(Sys.Date())
data_fim<-d
d$year<-d$year-5
data_inicio<-as.Date(d)
shinyUI(
fluidPage(style = "text-align:center;",
# Application title
titlePanel("Matriz de Informação Social - Dados Abertos"),
fluidRow(style = "background-color:lightgray;text-al... |
9d56c21c3bab72a41eb6987f6dfe53b8f96b1acf | e2f262ced6cc36bebd9ff8e142ca082f8904d8a2 | /R/uscb_acs_5ye.R | c94aaec453f421ca14f2655ff94f8df234bdb343 | [
"MIT"
] | permissive | josesamos/geogenr | 408949b2801df306ac8e0c209a09c737c11fbae3 | 448e4c09e052da5d34e2539fd5ad2735c4cbc8fc | refs/heads/master | 2023-01-22T02:00:39.706397 | 2020-11-19T11:32:37 | 2020-11-19T11:32:37 | 303,603,346 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,061 | r | uscb_acs_5ye.R | #' `uscb_acs_5ye` S3 class
#'
#' Internal low-level constructor that creates new objects with the correct
#' structure.
#'
#' @param folder A string.
#'
#' @importFrom magrittr %>%
#' @name %>%
#'
#' @return A `uscb_acs_5ye` object.
#'
#' @keywords internal
new_uscb_acs_5ye <- function(folder = "") {
years <- 2010:2... |
fb0a0a40d6f321941c78f0743c60a7e762585498 | 9ee587651e82c3efdf58036364c197829ffa57e1 | /Chapter1_FineScaleAcousticSurvey/nmds_birds_v.laptopt.R | ef6f8da4081bbd470eefc41f87aea477d837cbd7 | [
"Apache-2.0"
] | permissive | QutEcoacoustics/spatial-acoustics | 7f0fd2af6663200ab529a2f8979eec56a0bf2e40 | 5e8eaba29576a59f85220c8013d0b083ddb70592 | refs/heads/master | 2023-04-15T09:50:44.063038 | 2023-03-14T23:36:36 | 2023-03-14T23:36:36 | 222,621,976 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 16,105 | r | nmds_birds_v.laptopt.R | #NMDS#
#Marina Scarpelli#
#07.01.2020#
rm(list = ls())
library(tidyverse)
library(ggplot2)
library(stringi)
library(car)
library(data.table)
library(MuMIn)
library(plotly)
#Reading and preparing the data ####
getDataPath <- function (...) {
return(file.path("C:/Users/scarp/OneDrive - Queensland University of Techn... |
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