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a58236235df0fb3c5287e69b5f92387cff31567b | 64238f37c5af76a0bb82ccb5765b243447fe545f | /ProjetoVisualiza/BusinessLogic.R | efea0a9600d9a7c685b1e98b353999e6a6ea08cd | [] | no_license | Tocchetto/nwtapp | 898331f4a302fc6d375f9ab03f80e1b1a127441f | d1de23b2cf453b59d6883dec49cd30053119e5ce | refs/heads/master | 2021-01-20T08:26:43.538704 | 2017-10-27T12:50:07 | 2017-10-27T12:50:07 | 92,299,129 | 0 | 1 | null | 2017-05-24T14:05:57 | 2017-05-24T14:05:57 | null | UTF-8 | R | false | false | 6,309 | r | BusinessLogic.R | getSuffix <- function(variable){
if(variable == "CAPE")return(" J/kg")
if(variable == "CLSF" || variable == "GHFL" || variable == "CSSF" || variable == "OCES" || variable == "OCIS" ||
variable == "OLES" || variable == "OLIS")return(" W/m2")
if(variable == "RNSG" || variable == "RNOF" || variable == "EVTP"... |
0586cfeeaf2f8e45f7a6b61ba3582d99203d3387 | 6cd15fd0e072741b5db8284ca20bf6534e495a20 | /R/data_error_b.R | 098b92fa95b38356a06fc620652b6f986319b80c | [
"MIT"
] | permissive | renands/RMLPCA | fffbd18c502e2e3ccfafaa4be677159877cb831b | 039d34002fe4b98688869184e5139a3b842bfa00 | refs/heads/master | 2023-05-09T07:34:03.769415 | 2021-05-31T19:22:13 | 2021-05-31T19:22:13 | 273,766,066 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 579 | r | data_error_b.R | #' Errors generated for mlpca_b model
#'
#' A dataset where each column contain values from a normal density with mean
#' = 0 and standard deviation from 0.2 to 1, the standard deviations differs in
#' the column. The main ideia is described in figure 3 on Wentzell, P. D.
#' "Other topics in soft-modeling: maximum like... |
b6003236e3ebfeb3d9635a94ae2667e21c7aa66b | 6fc77d31ad1688033d6dd9830d3c531760a6aabf | /tests/testthat/test-prediction-missing-years.R | dd1f640cbb92efd80fc1248cd6fdc4f507557157 | [] | no_license | pbs-assess/sdmTMB | ba24efb807680f28fdfa9a27a2a775b1817b49c8 | 6aa4e8a7847318f81e91a0bfb6c85001db07d0da | refs/heads/main | 2023-09-03T17:06:22.517565 | 2023-08-18T20:54:48 | 2023-08-18T20:54:48 | 149,399,567 | 133 | 12 | null | 2023-05-11T18:43:58 | 2018-09-19T05:59:53 | R | UTF-8 | R | false | false | 674 | r | test-prediction-missing-years.R | test_that("Prediction works with missing time", {
skip_on_cran()
skip_if_not_installed("INLA")
fit <- sdmTMB(
density ~ 1,
data = pcod_2011, mesh = pcod_mesh_2011, time = "year",
family = tweedie(link = "log")
)
nd <- pcod_2011[pcod_2011$year %in% c(2013, 2017), ]
p1 <- predict(fit, newdata = nd... |
d5832307534199924256c708a922b760db9142e5 | 38d166ede31183e2121388be0f66fe9d7ac4e93a | /man/phyloseq_coverage.Rd | 69446872cc81f88046beaf6011d2199a29687f7e | [
"MIT"
] | permissive | vmikk/metagMisc | a01151347b620745b278265700e503dc74669af5 | 310b1a40951de46348084e150d7471ed66feb0c8 | refs/heads/master | 2023-08-31T08:41:27.684905 | 2023-08-28T10:09:50 | 2023-08-28T10:09:50 | 76,531,351 | 38 | 12 | MIT | 2019-07-29T06:12:12 | 2016-12-15T06:40:05 | R | UTF-8 | R | false | true | 1,584 | rd | phyloseq_coverage.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/phyloseq_coverage.R
\name{phyloseq_coverage}
\alias{phyloseq_coverage}
\title{Estimate the observed abundance-based sample coverage for phyloseq object}
\usage{
phyloseq_coverage(physeq, correct_singletons = FALSE, add_attr = T)
}
\arguments{... |
e9500d1f97b0e8896c88492266e797e398a627ad | 5baf5ec86241518b59f6e3fa33721ef6322baa6b | /Construction/stream_tributary_locations.R | 1c38a6b4d74df2edd323c7f91e892d02c45c258d | [
"CC-BY-4.0"
] | permissive | CCheCastaldo/MSHMicroMet | a4539d9759416b0f717a9951d54d3ea195c629f0 | b4bc7852f6bdaa58b8671979dcb7986c5c298bb3 | refs/heads/main | 2023-04-19T05:32:13.644932 | 2022-12-21T21:26:04 | 2022-12-21T21:26:04 | 561,350,602 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 356 | r | stream_tributary_locations.R | # geolocate all stream hobo installations
stream_locations_df <-
read_excel("AccessMigration/StreamTributaryLocations.xlsx") %>%
mutate(easting = round(easting), northing = round(northing)) %>%
mutate(terrestrial = FALSE) %>%
dplyr::select(survey_id_legacy,
site_id,
site_description,
terrestrial,
... |
84600b8c6c086cc1b55c688d9e1f89830fc4b1d8 | 156140b80fd46aa214fed8d06407ecf9220066c5 | /cumulative-cases-animation.R | 39edc1f763655557680b481882b6a10abeb30998 | [] | no_license | glaswasser/animated-running-corona-bar-plot | 4a0298c614d09e55677d55611ad82294508dfaa5 | 9d77cb3d647ed5ee67a9f95b161456bf8571033b | refs/heads/master | 2023-03-14T19:17:45.178581 | 2021-03-14T21:53:22 | 2021-03-14T21:53:22 | 256,786,881 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,834 | r | cumulative-cases-animation.R | library(shiny)
library(nCov2019)
library(plotly)
library(tidyverse)
library(gganimate)
## DESCRIPTION:
# a running corona-barchart
#### LOAD DATA ####
y <- load_nCov2019(lang = 'en', source='github')
# get global data:
d = y['global']
# FORMAT DATA
formatted <- d %>%
group_by(time) %>% #group by the date
mut... |
55e6ea357573460d0c409444b0af0b09d4a3c7d9 | 62415c1e371e7377e4a4582e2ed12411fc1db754 | /man/ca_food_group.Rd | f28bfe0dbc07d6a56b0eeb69b88c4cfe1bfdf2c0 | [] | no_license | yihanwu/CAnutrients | aff218519191cdc33b42a8b68773da76eddaa16c | 67ac440f2292618f5b98faa7286fd1212b779599 | refs/heads/master | 2020-04-27T11:34:47.164720 | 2019-03-07T08:18:34 | 2019-03-07T08:18:34 | 174,300,566 | 2 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,340 | rd | ca_food_group.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CAnutrients.R
\docType{data}
\name{ca_food_group}
\alias{ca_food_group}
\title{Food group dataset}
\format{a \code{tbl_df} with 23 observations of the following 4 fields:
\describe{
\item{FoodGroupID}{Sequential number generated by the databa... |
093bf46913d561edd910337475eff973ee597e66 | 5bd4b82811be11bcf9dd855e871ce8a77af7442f | /gap/R/hap.score.R | 252bc89c96dbbb927ba490b5fd907387f9e6d942 | [] | no_license | jinghuazhao/R | a1de5df9edd46e53b9dc90090dec0bd06ee10c52 | 8269532031fd57097674a9539493d418a342907c | refs/heads/master | 2023-08-27T07:14:59.397913 | 2023-08-21T16:35:51 | 2023-08-21T16:35:51 | 61,349,892 | 10 | 8 | null | 2022-11-24T11:25:51 | 2016-06-17T06:11:36 | R | UTF-8 | R | false | false | 18,814 | r | hap.score.R | #' Score statistics for association of traits with haplotypes
#'
#' @param y Vector of trait values. For trait.type = "binomial", y must have values of 1 for event, 0 for no event.
#' @param geno Matrix of alleles, such that each locus has a pair of adjacent columns of alleles, and the order of columns corresponds... |
21a606fee04010515035544677f5fcc7c5206f2d | 963b75306674956433ce16a562816de9bd4f9393 | /man/persist.match.Rd | b9d595bf84b21df9e4a6d8c7334686cff5356180 | [] | no_license | cran/LogicForest | cd68de6f2bfa89b6cfc9fe49410b0ce20fa94f29 | 0619287ba164198feec015682d110a1bcdce58da | refs/heads/master | 2021-01-20T04:32:33.071237 | 2014-09-18T00:00:00 | 2014-09-18T00:00:00 | 17,717,763 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 432 | rd | persist.match.Rd | \name{persist.match}
\alias{persist.match}
\title{Internal Logic Forest Functions
}
\description{Internal function called by \code{\link{persistence.prep}}. Not intended to be used independently of this function.
}
\details{ Generates a list of all subset matches for variables or variable interactions in a log... |
cd8380ff6dd2a7e1daceae1fee1a930a3001b53b | e2626d6d02b8533b9e3bc5ba2aa86afffe7a1758 | /Script.R | 92068af5e1825b1e8129f5864ac4c3c15cfda008 | [] | no_license | KHHaugen/Rclub | 0bcd6b274caafda61cb3aedbe5decdba8316225c | 450a99a023ecc91180424521feebcd06be4c411b | refs/heads/master | 2020-05-17T08:30:39.401614 | 2019-04-26T10:21:30 | 2019-04-26T10:21:30 | 183,608,052 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,877 | r | Script.R | ## Spatial models: R club 26/04 2019 Kristian H. Haugen
# Packages ----------------------------------------------------------------
library(pacman)
p_load(data.table, sp, spdep, ggplot2)
# Loading data ----------------------------------------------------------------
data <- fread('https://raw.githubusercontent.c... |
fd80e198e4dd9f63825e70a0294777200d4f48c7 | 1f9039e664ab3bb9df978a5ad05d60b69ca7095d | /FLUXNET_Gs_PMinv.R | 62cdcdf0f523f99b661f1f9b44dfdf31cb8a53da | [] | no_license | l5d1l5/Gs_FLUXNET2015 | 5dd8b093f0167aee81739e174f97d5d1ac412ed8 | 8afce8aba86ca35f7fe20a6f275ee686474cc198 | refs/heads/master | 2022-03-14T02:59:31.187417 | 2018-03-11T04:26:24 | 2018-03-11T04:26:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,605 | r | FLUXNET_Gs_PMinv.R | # Calculate hourly surface conductance
#####
rm(list = ls())
library(lubridate)
###############
# define functions
###############
# slope of the Ta-VPD curve (KPa K-1)
delta_fun <- function(Ta){
# input - air temperature, (C)
# output - slope of T-es curve (KPa*K-1)
h2osat_fun <- function(x){
h2o... |
b9468a85da22520bd99495d60bd75cd9fcf38302 | 1499dd7ea7db28073afe87446e84442de9e7ad87 | /R/new_project.R | 342da9ab0d232f5fb56ca0d2fef28729a006aec2 | [] | no_license | sal-medstats/ctru | 16b21c7f187c4589e2487733dbba3706e913ff4e | 971c7c7da6e9463776af6f681fe2aac97c1239be | refs/heads/master | 2020-06-13T11:46:19.314895 | 2019-07-01T14:41:29 | 2019-07-01T14:41:29 | 194,643,154 | 0 | 0 | null | 2019-07-01T14:41:01 | 2019-07-01T09:33:45 | R | UTF-8 | R | false | false | 1,208 | r | new_project.R | #' Setup a new project directory
#'
#' @description Setup a project directory for a CTRU study.
#'
#' @details
#'
#' Creates a default project directory.
#'
#'
#' @param path Absolute or relative path to create the directory structure.
#'
#' @export
new_project<- function(path = '',
...){
#... |
dc5348bd9da5b499a9101730d738c9c2c1a39c9e | 007bb51eb8faec440faa73b8bfeb8749e5cb9faf | /Cropyields.R | 5a065b7124f1f896d194f55f015ac3242fec7f13 | [] | no_license | DanMungai1/TidyTuesday | 3dad84b1ec974bef17ce95ae0d6080710dda8c91 | 843c310d15ca7f0667831270a2d4a5c0f897b614 | refs/heads/master | 2023-01-22T16:46:41.992464 | 2020-11-17T18:19:09 | 2020-11-17T18:19:09 | 282,913,158 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,061 | r | Cropyields.R | #Load Packages
library(tidyverse)
library(ggthemes)
##Loading Data
tuesdata <- tidytuesdayR::tt_load(2020, week = 36)
key_crop_yields <- tuesdata$key_crop_yields
fertilizer <- tuesdata$cereal_crop_yield_vs_fertilizer_application
Kenya <- key_crop_yields %>%
pivot_longer(cols = 4:14, names_to = "produce", ... |
f42c212ebdba3066c57b53b34296a76417b80bc4 | bff934b8e0f51eadce86bcb5c19597bce927480c | /tests/testthat/helper-set-token.R | 2592c4c3ffab4a1dd6e285584a5cc51057a8f10d | [] | no_license | richierocks/yelp | 84436adc4e9a85b035a01a8543d489cdb9b71d9c | 7b9bce77d12ad9bb7af2919477560e6a877367f6 | refs/heads/master | 2021-05-08T09:47:07.291829 | 2018-10-16T00:21:15 | 2018-10-16T00:21:15 | 106,317,783 | 6 | 4 | null | 2023-02-06T10:57:55 | 2017-10-09T18:04:36 | R | UTF-8 | R | false | false | 333 | r | helper-set-token.R | set_token <- function() {
old_token <- Sys.getenv("YELP_ACCESS_TOKEN", NA)
if(is.na(old_token)) {
Sys.setenv(
YELP_ACCESS_TOKEN = readRDS(test_path("sample_yelp_access_token.rds"))
)
}
invisible(old_token)
}
unset_token <- function(token) {
if(nzchar(token)) {
Sys.setenv(YELP_ACCESS_TOKEN =... |
9ec5d478891738ef97ea3f41dda3a0caf4ac8316 | 62c7a28fcf9a9bb29368b2a733ed86819dbae7a8 | /Ex9.R | 93a87b779713262f322bf7171d350ec76cee9d75 | [] | no_license | andrewmackinn/Biocomp-Fall2018-181102-Exercise9 | 4a6b7c6dde85b161fbe7fce0aa1f90e7f4b27c9a | db71a61fdfc8646ba2bfc4044f1ee6281c6cd6fc | refs/heads/master | 2020-04-04T11:14:29.816797 | 2018-11-09T14:28:01 | 2018-11-09T14:28:01 | 155,883,631 | 0 | 0 | null | 2018-11-02T15:15:23 | 2018-11-02T15:15:22 | null | UTF-8 | R | false | false | 1,105 | r | Ex9.R | # online dataset, comparison of budged and domestic gross
#reading dataframe
data = read.csv("moviedata.csv", header = TRUE, stringsAsFactors= FALSE )
head(data)
#load the needed packages
library(ggplot2)
library(gridBase)
library(gridExtra)
#get data into the correct forms
data$Budget = as.numeric(as.character(data$Bu... |
ded54e016757145035c7db1183cf83bbb114c89f | 9522e1c12bb96df3e1889017e465cc3220e75ea3 | /R/RandomUnifArray-class.R | 5591e03a382a4f41ab5beea2b0f0eac3bdf8b23c | [] | no_license | LTLA/DelayedRandomArray | 66b577d28a964d8f387172715a37b4f1785b3205 | a44a55818dd26cf0c792b3696d6f565eea15e60f | refs/heads/master | 2023-04-22T13:04:59.328711 | 2021-04-29T20:30:03 | 2021-04-29T20:30:03 | 357,088,359 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,439 | r | RandomUnifArray-class.R | #' DelayedArray of random uniform values
#'
#' A \linkS4class{DelayedArray} subclass that performs on-the-fly sampling of uniformly distributed values.
#'
#' @param dim Integer vector of positive length, specifying the dimensions of the array.
#' @param min,max Numeric vector used as \code{min} and \code{max}, respecti... |
c4f27f6634461c31dbd6ea01a09f12fdb040a102 | af419fb17048e90038745b883ec30e1ee23ac5ff | /code/retrieving_data.R | 6933c4518fae9b14be5967419c516771ec2b4b8c | [] | no_license | vfuentesc/HW8_Choropleth_Map | 53ffa3430a886969e79b1e44892a9553e93d0473 | 91ae1cfe0b3f294a78f76e9c745f2ea6c47129b3 | refs/heads/main | 2023-04-21T23:38:04.329508 | 2021-05-21T07:10:53 | 2021-05-21T07:10:53 | 369,435,114 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,646 | r | retrieving_data.R | ###################################
### README ########################
###################################
# The following code retrieves:
## (1) 2020 Presidential elections results by county
## (2) Employed/unemployed people by county by month for 2019-2020 years
rm(list=ls())
# Loading Libraries
library(tidyvers... |
be999aa808297f9532d63a53420dbdabdf097d7f | a0414d8a9e187737f236f262148c3d721e11c600 | /man/busca_fuzzy.Rd | 5e5eba089503daacce4d668bb5e71beff294914f | [] | no_license | courtsbr/JurisMiner | 695fb93b58754ef94ee834cac7b1a4ec754da9e1 | d94b3cbd9575bb1833fee5b4c8ed44624a969f1d | refs/heads/master | 2023-06-22T21:57:27.847425 | 2023-06-21T11:36:51 | 2023-06-21T11:36:51 | 91,446,216 | 24 | 10 | null | null | null | null | UTF-8 | R | false | true | 634 | rd | busca_fuzzy.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/busca_fuzzy.R
\name{busca_fuzzy}
\alias{busca_fuzzy}
\title{Procura a palavra ou frase do segundo vetor que melhor
se aproxima do primeiro. Particularmente útil para
comparar nomes de municípios.}
\usage{
busca_fuzzy(x, y)
}
\argument... |
aa0f7483c94bb11670a9e349b62af4c00ce3fbc1 | 339cff1cc63bd09839168835c0190fefbd46db77 | /R/Veterinarian_figs.R | e8ef59afc08e9860cbe662a95065dafa898300ee | [] | no_license | cmzambranat/sci_adv_pandemics | 4370c6a90fba91a87bfad24cd28b97db64d389e2 | 4ccb8b728ebf058ab6cba2d0a8118b35e79d3fb7 | refs/heads/main | 2023-05-28T17:27:57.049683 | 2021-06-17T07:30:40 | 2021-06-17T07:30:40 | 377,739,503 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,793 | r | Veterinarian_figs.R | library(here)
library(tidyverse)
library(readxl)
library(lmodel2)
library(scales)
library(ggrepel)
library(countrycode)
library(styler)
# Global options
theme_set(
theme_bw(base_size = 18)
)
# read data
# World Bank data country classification
wb_class = read_xls(here("data/worldbank_classification.xls"), sheet = ... |
af08570d6e7f20983a5453227e3d02b8f0e96822 | cc88465c29f245ff7f499a295ca62c1898fafd1e | /final_project_fxn.R | 630e3e84fc2bb6ca7e7a4d690bdfe7f02e9bd512 | [] | no_license | alextsfox/LICOR_6400-Cleaner | 5cc885c4973dd4881662f739baa6809427ede079 | dd0fbd0f69c067c35af96c2c495e6eb2a2f60087 | refs/heads/master | 2022-03-27T12:03:31.394134 | 2020-01-14T19:34:05 | 2020-01-14T19:34:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,222 | r | final_project_fxn.R | #LC takes IRGA data and removes all non-data rows. It also creates a Comment column that stores data taken after a Remark
LC<-function(dat){
#Renames the columns
names(dat)<- as.character(unlist(dat[min(which(dat[,1] == "Obs")),]))
#creates a comments column
dat$comment<-NA
dat<-dat[c(ncol(dat),1:(n... |
a753e75c5bd230fe21c77d89c2c455efc5868737 | dc5d0864d557e6113fe49a2c6e7318dd290cfd36 | /man/bfsl_control.Rd | 2ee5b7cd262c96c0933405965b02d1685ddb0117 | [
"MIT"
] | permissive | pasturm/bfsl | dd56af17394edee8d6c22a796dee9d00b6f22b7b | 9096b1e682b4c007ee7bebf090f5a6797246e606 | refs/heads/master | 2022-09-11T22:34:49.292487 | 2022-08-26T13:44:03 | 2022-08-26T13:44:03 | 159,489,543 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 713 | rd | bfsl_control.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bfsl.R
\name{bfsl_control}
\alias{bfsl_control}
\title{Controls the Iterations in the bfsl Algorithm}
\usage{
bfsl_control(tol = 1e-10, maxit = 100)
}
\arguments{
\item{tol}{A positive numeric value specifying the tolerance level for the
conv... |
3014fa8360d2b7d39a30407e891e5336aba4ab60 | 590bf5c505c4ece3d1dfcfcc2ecc1a6313edf1ef | /R/ConcatActions_m0.R | 3d22942c1d4bbbdca2eae152f95ba416c6668612 | [] | no_license | cran/LOGAN | 509208570af1d762d243fd96e169341aa92e14b0 | da705973772d32e74a6a6bc783f21aca80a1e038 | refs/heads/master | 2022-11-02T19:42:09.787980 | 2022-10-25T07:47:56 | 2022-10-25T07:47:56 | 184,891,067 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,524 | r | ConcatActions_m0.R | #' Concatenate events
#'
#' This function allows you to concatenate event actions from diferent variables
#' in a unique vector.
#'
#' @param data A \code{matrix} or \code{data.frame} where the concatenated
#' events are
#' @param concat.events a vector where all the events are listed. Each element
#' of this vecto... |
8d5eea783e1417678ffc3c0f1d50535452f94d1f | 55ba4622941c73a4f1002f9c2b57bf46b614aa3d | /code/Global_analysis/Create_boxplots.R | a199dd88a9cccaa976f3dcdb97e3df7d1c8ed93c | [
"MIT"
] | permissive | malihhhh/CUIMC-NYP_COVID_autopsy_lung | 2089693eeb0bd08973026578b285a8a16f266ec0 | bf6fc4add36095c7bdc12b6e6ede33d768530bb7 | refs/heads/main | 2023-04-24T00:46:49.969287 | 2021-05-03T14:42:08 | 2021-05-03T14:42:08 | 548,264,345 | 1 | 0 | MIT | 2022-10-09T08:02:03 | 2022-10-09T08:02:02 | null | UTF-8 | R | false | false | 20,356 | r | Create_boxplots.R | #!/usr/bin/env Rscript
### title: Generate boxplots of cell type frequencies, grouped by either disease
### status, or sex of samples author: Yiping Wang date: 02/08/2021
consistentcolors = colors <- c("#006E82", "#AA0A3C", "#8214A0", "#00A0FA", "#FA5078",
"#005AC8", "#CC79A7", "#FAE6BE", "#0072B2", "#A0FA82", "... |
3cb543deaeb91464bd9f11f1490c2175216482fb | 32d1f1150418649d7a6593a0fdb264ca44cafd4d | /Sample_MeTWASCode.R | 012af0bd5c825fa9464ffe688e59765178935c10 | [] | no_license | bhattacharya-a-bt/mostwas_suppdata | 38b633b8cd615e30befbcd7af678cdad117999a1 | 2da3f38f2730d3f655ccce281281d257ffa91686 | refs/heads/master | 2021-06-14T01:14:12.145427 | 2020-11-30T16:10:19 | 2020-11-30T16:10:19 | 254,466,476 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,998 | r | Sample_MeTWASCode.R | ### Define a vector geneList with the genes you wish to train
tempFolder = paste0('temp',i,'/')
if (dir.exists(tempFolder)){
system(paste0('rm -r ',tempFolder))
}
if (!dir.exists(tempFolder)){
dir.create(tempFolder)
}
require(bigsnpr)
require(data.table)
require(MOSTWAS)
snpObj = snp_attach(snp_readBed('T... |
4d15573a1e40db8043ad730579ed51f033c15e75 | f545016bc144f83e5501557ec30933b81e0e3526 | /scripts/test.R | 1b53a858c48aca7032a4c6ab0a21faa19f8adf2d | [] | no_license | Zirys1/who-intervenes-how | 257510720345c7c4169223ec90ae1a6c407dfd17 | 2e87bdb3b19bb6d8c36c41dcbfc427df05e55fcd | refs/heads/master | 2021-01-24T23:40:52.246568 | 2016-08-18T15:56:28 | 2016-08-18T15:56:28 | 68,587,422 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,630 | r | test.R | library(mgcfv)
FindIt(model.treat = Donation ~ RecvsDefD, model.main = ~ ReactanceM,
model.int = ~ ReactanceM,
data = df, type = "continuous",
treat.type = "single")
summary(lm(Dist ~ NosvsSomeD*RecvsDefD * ReactanceM, df))
###################################################
## Example 2: Trea... |
806081163bb8a4d0b3c2c2f166338b726c37c380 | 673468682a91337871c7ef62f941f71f9bc01521 | /Curso_Fundamentos_R/Scripts/Sesion7-FundamentosR.R | 38eb543488298be7a32491dcc0bfb0dfc3e7a6e4 | [] | no_license | MiguelAngelderobles/Curso-R | 3bdf16649fca1d214fe4ec0dc8eacb767eb3db00 | ecd62668c6b45a38dfc27191b54f0997abb5116d | refs/heads/main | 2023-05-31T06:45:25.861449 | 2021-06-13T20:41:35 | 2021-06-13T20:41:35 | 376,636,357 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,839 | r | Sesion7-FundamentosR.R | "
title: Fundamentos R desde 0
author: Veronica J. Gomez Catunta
Sesión: Número 7
"
# Importar Datos ----------------------------------------------------------
# Cargar paquete readr
# Nota.- No necesitamos instalar porque la instalacion se realizo en la sesión 6
library(readr)
#trim_ws = Deben recortarse los espac... |
45f2716f736fd34e4115c9bdc56587b5dbea288e | 4a4cbd2a6b4af7c337f6e754e8f89c2aa06efd66 | /Recuit_Simule_Mesim4.R | f2ebc009677acb038c6632d0cfdb7becc64b8570 | [] | no_license | Skoomy/MonteCarlo-Simulation-with-R-Mesim- | 6ec58fc9a6bd1ad4941dc410dce9c119347eb6f3 | e24a531e988764f896bdb753b3838394e49391ad | refs/heads/master | 2021-10-25T02:35:36.039309 | 2021-10-13T14:09:20 | 2021-10-13T14:09:20 | 56,853,095 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 914 | r | Recuit_Simule_Mesim4.R |
###Recuit simulé In R
f<-function(x){
return (5/49*(x-10)^2+4)
}
p=ggplot(data.frame(x=c(0, 2)), aes(x)) + stat_function(fun=f)
recuit<-function(f,x0,Ti,Tf,ampli,alpha,Max_etat,Iter)
{
l=vector(,Iter)
x=xopt=x0;
fx=fxopt=f(x0);
Tmp=Ti;
ess= 0;
while(Tmp>Tf&&xopt<3){
etat=0;
for(i in... |
f83d1e1b5f484a61ea3d76a49082950697089f50 | 29585dff702209dd446c0ab52ceea046c58e384e | /predictmeans/R/covariatemeans.R | 8aace5065af43b789117d7513fdbdef77dd46cb4 | [] | 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 | 8,446 | r | covariatemeans.R | covariatemeans <- function (model, modelterm=NULL, covariate, level=0.05, Df=NULL, trans=NULL,
responsen=NULL, trillis=TRUE, plotord=NULL, mtitle=NULL, ci=TRUE, point=TRUE, jitterv=0, newwd=TRUE) {
if (is.null(modelterm) || modelterm%in%c("NULL", "")) {
modelterm <- covariate
... |
c92ed0e050222db51a3dafae61538edde6f62090 | 1056f60631d647cc54525da3aaae774114b2d81c | /R/blocklist.r | 91529b83ff11e7349d9c4274b73af981227085b1 | [] | no_license | hrbrmstr/blocklist | f897df33c102e2556dfbec600c7005aa2a7970d0 | 089aad5779abe521bda38c2659ea120d69918adf | refs/heads/master | 2021-01-10T10:08:35.962525 | 2016-04-04T02:45:25 | 2016-04-04T02:45:25 | 55,381,067 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,175 | r | blocklist.r | S_GET <- purrr::safely(GET)
#' Query <blocklist.de> API for IPs blocked in since a period in time, optionally
#' filtering by service
#'
#' @param since either a UNIX timestamp (e.g. \code{1459736686}), a string in "\code{HH:MM}"
#' format or a time difference in seconds (e..g \code{3600}). The API will
#' ... |
11d0f39497241905c4f2d14aa591e3d250a5dc18 | 8dfdf4263c8b372a9c728a8f482db9f429e38183 | /Twitter_data_extraction.R | 7397adf79e3f16f5ff638db830b14dbd086bcacf | [] | no_license | prayashbarua/TwitterDataWebScrapper | 54ef9691a4c72051e94497e925e3992204376430 | ff896df0800a6f49fc8a9540abeb7929abb0b4fb | refs/heads/master | 2020-03-27T03:29:53.732724 | 2018-09-14T00:19:57 | 2018-09-14T00:19:57 | 145,868,046 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,984 | r | Twitter_data_extraction.R | install.packages("twitteR")
install.packages("httr")
install.packages("ROAuth")
install.packages("tm")
install.packages("tmap")
install.packages("rtweet")
library('httr')
library("devtools")
library("twitteR")
library("ROAuth")
library("tm")
library("tmap")
library("rtweet")
library("plyr")
#oauth_endpoints("twitter")
... |
847fc21ba4895123f973a9b73a1074ef31d9c775 | d794c2c6908c9b95607fe3ae445b035f58e24355 | /man/lambda.Rd | 904e711c4a09f8286ed4d7183b9c7773873ef273 | [] | no_license | robertzk/magrittr | e2224b4045822f7dcbbe888eddec4dd449376080 | b463edff3e753118b863a7d5c4aefa7ebd575324 | refs/heads/master | 2021-01-18T12:18:16.911394 | 2015-02-13T00:22:27 | 2015-02-13T00:22:27 | 30,732,033 | 0 | 0 | null | 2015-02-13T00:06:41 | 2015-02-13T00:06:41 | null | UTF-8 | R | false | false | 1,173 | rd | lambda.Rd | % Generated by roxygen2 (4.0.1): do not edit by hand
\name{lambda}
\alias{l}
\alias{lambda}
\title{Shorthand notation for anonymous/lambda functions
in magrittr pipelines.}
\usage{
lambda(expr)
l(expr)
}
\arguments{
\item{expr}{A special kind of expression for the anonymous function.
The syntax is \code{symbol ~ expre... |
5ed700330858836d9364b6822b8402cdc3ae0427 | cba3e90d5af37d408f2f05824769fba9b16e2ec2 | /cursos/ACT11302/datosycodigo/ACT11302_151204.R | 8185f23f3957466a368f228a5cea09c588d7d2ec | [] | no_license | ramja/jcmartinezovando.github.io | 5e301ce152d18dbb52f409f7a45018d07ef65900 | c4351d78a8a700062dfd0cfbad5c2587857a26b3 | refs/heads/master | 2020-04-15T06:38:03.210665 | 2016-01-14T07:45:34 | 2016-01-14T07:45:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,973 | r | ACT11302_151204.R | #
# ACT-11302: Calculo Actuarial III
#
# Autor: Juan Carlos Martinez Ovando
# Email: juan.martinez.ovando@itam.mx
#
# Este codigo fue probado en R v.3.1.2
#
rm(list = ls())
## Main:
# install.packages("ggplot")
## Masked:
install.packages("actuar")
install.packages("fExtremes")
ins... |
bcf0e4c9ecd986780de0c6a50afa26e00afcc87f | e4ad2398aa4b2d308ba0ec11803d58e36bba43d5 | /R/qcs.cpn.r | 39d63783810f0e1e1c1fc2c665237261ae11c101 | [] | no_license | mflores72000/qcr | 2204b2810a24a91bee75ef68094feaf6198746bd | 4b07dcc8bdc2293ed0504d438e835b9562746612 | refs/heads/main | 2023-06-08T04:46:35.286754 | 2023-05-30T16:06:10 | 2023-05-30T16:06:10 | 387,871,922 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,655 | r | qcs.cpn.r | #-----------------------------------------------------------------------------#
# #
# QUALITY CONTROL STATISTICS IN R #
# #
... |
b9804d76299e3682eb6689971703f2bcd18876b8 | d5facf2eb1940a5ef24399017845e17ca172ebf3 | /man/writeRaster2.Rd | 36e31a84fc649cd13cfc948a846c0453bd9a325b | [] | no_license | ailich/mytools | 3970d0254b4bc9b7bb23b2918f99ec7e966ddbbe | 2e8b244974483df793ae000d8a44f8904e44bc9a | refs/heads/master | 2023-01-13T23:41:50.623083 | 2022-12-29T17:56:54 | 2022-12-29T17:56:54 | 117,773,800 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 767 | rd | writeRaster2.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/writeRaster2.R
\name{writeRaster2}
\alias{writeRaster2}
\title{Modification of writeRaster that can delete auxillary files}
\usage{
writeRaster2(x, filename, overwrite = TRUE, ...)
}
\arguments{
\item{x}{Raster* object}
\item{filename}{Outpu... |
cc21859d0200a8d0d1004f6fb4ba0804343b228d | e32541a3498bc9618c21d8322fb5da16c466cc69 | /retiring/doregression/Samples/Sparklines.R | 3ae86f7115153b83862dd811f477871fc39f91f8 | [] | no_license | StefanoPicozzi/r-base | 96b580ef7f6480b8eaaa76ba1b3f80ac562149f1 | 06946b23d9c4a40e238015d2c28ba6cd0978b2b0 | refs/heads/master | 2021-01-10T15:46:13.631314 | 2016-01-03T19:51:06 | 2016-01-03T19:51:06 | 48,297,497 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,250 | r | Sparklines.R | library("lattice")
library("MASS")
#library("YaleToolkit")
ppi <- 300
noDays <- 62
noWeeks <- 10
dayOfWeek <- c(
"1-Th", "Fr", "Sa", "Su", "Mo", "Tu",
"2-We", "Th", "Fr", "Sa", "Su", "Mo", "Tu",
"3-We", "Th", "Fr", "Sa", "Su", "Mo", "Tu",
"4-We", "Th", "Fr", "Sa", "Su", "Mo", "Tu",
"5-We", "Th", "Fr", "Sa",... |
2c707f92c4bc87e32e641bae38cde1ef876a7ed9 | 9e6c6d3ea78d408a6746fcdeca6ff0d3a8a3308c | /man/convert_et.Rd | 14a4683526a9e21a173d6b33c8539d3a6fc20ed5 | [] | no_license | stineb/rbeni | 36f28d38f58301d2af24255e9d63fe5ac6809ebe | 2f9d26d0a286c550cb90ee9d30a1f2b6c3b112f6 | refs/heads/master | 2023-02-18T22:18:52.856980 | 2023-02-16T17:29:09 | 2023-02-16T17:29:09 | 167,402,490 | 3 | 6 | null | 2020-09-25T09:35:32 | 2019-01-24T16:49:15 | R | UTF-8 | R | false | true | 939 | rd | convert_et.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/convert_et.R
\name{convert_et}
\alias{convert_et}
\title{Convert evapotranspiration to mm}
\usage{
convert_et(et_e, tc, elv = 0, return_df = FALSE)
}
\arguments{
\item{et_e}{A numeric value or vector specifying vapotranspiration in energy uni... |
8a8f630703468b10e18a3110154c98eed47537e1 | 2e627e0abf7f01c48fddc9f7aaf46183574541df | /PBStools/man/imputeRate.Rd | 428657c90f6efee0d049849fb64038f4bf366b03 | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | pbs-software/pbs-tools | 30b245fd4d3fb20d67ba243bc6614dc38bc03af7 | 2110992d3b760a2995aa7ce0c36fcf938a3d2f4e | refs/heads/master | 2023-07-20T04:24:53.315152 | 2023-07-06T17:33:01 | 2023-07-06T17:33:01 | 37,491,664 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,581 | rd | imputeRate.Rd | \name{imputeRate}
\alias{imputeRate}
\title{Impute Rate of Return for an Investment}
\description{
Impute the rate of return for an investment that experiences
regular or irregular contributions and/or withdrawals.
}
\usage{
imputeRate(qtName="Ex03_Portfolio", dbName="Examples",
AID=1, pathN=2, hnam=... |
c0335214567dd60212865df8b9a54cf15d116faa | 4307ddbb84c4973aaa728ddebbd4e4b0b1537096 | /R/data.R | e83e310bccf80e5b6e627892048ac5e2034ac3fa | [] | no_license | ms609/TreeDistData | 8157ab67959ea813c39d98176d6a02480c8a9199 | 15d4901e6bbd639c590c1f8875742db08c0c3f15 | refs/heads/master | 2021-06-13T19:52:13.475387 | 2021-05-18T09:24:20 | 2021-05-18T09:24:20 | 196,380,775 | 0 | 1 | null | 2021-05-06T10:19:13 | 2019-07-11T11:23:58 | R | UTF-8 | R | false | false | 13,006 | r | data.R | #' Bullseye test results
#'
#' Implementation and results of a 'Bullseye' test, after that proposed by
#' Kuhner and Yamato (2015).
#'
#' @format
#'
#' `bullseyeTrees` is a list with four elements, named `5 leaves`, `10 leaves`,
#' `20 leaves` and `50 leaves`.
#' Each element contains 1\ifelse{html}{ }{,}000 trees... |
5c4ea3386dc6cee336eb1f1dde801a069de4b05f | 67986f8ae2ee39d53fa028c8646505c7f0a7d6c3 | /run_analysis.R | c0efc9e30970766666d0f466942f231a72d9b049 | [] | no_license | M1chael50/getdata-016_CourceProject | 54bdc7a52ce4bc1926317665381540cce61a6224 | 355d1d0a75b0582a0b3b56bc0e50cfcc9e914e52 | refs/heads/master | 2021-01-13T02:19:35.850916 | 2014-12-21T18:15:00 | 2014-12-21T18:15:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,213 | r | run_analysis.R | # run_anaysis.R an R script file to download prepare and save data
#creata data folder if not already present
if(!file.exists("data")){dir.create("data")}
#Download data file
fileUrl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
localZipFile <- "./data/getdata_projectfil... |
34ffcb48a0dd1ba626466232529710feec658edb | 8e1e0255ef2796e9ab2b636f16c846a157046457 | /rgl_texture.R | e85ff2ce6fbc17cf1a99953110dec04e8b6fa8b9 | [] | no_license | r-gris/grisexamples | f4f966b87442b99f185ea9eebc49866edae8677f | 1e8ab7f6f4a9c9094406e0607c9c6fa8cf82b1e5 | refs/heads/master | 2020-04-13T23:22:22.981563 | 2016-08-04T14:16:46 | 2016-08-04T14:16:46 | 50,912,125 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,685 | r | rgl_texture.R |
## quad index template
p4 <- function(xp, nc) {
(xp + c(0, 0, rep(nc, 2)))[c(1, 2, 4, 3)]
}
## offset pairs from a vector
prs <- function(x) {
cbind(head(x, -1), tail(x, -1))
}
## pixel corners from a raster
edgesXY <- function(x) {
coordinates(shift(
extend(x,
extent(xmin(x), xmax(x) + res(x)... |
241f743a9f49745376ef20c66f086c148018df8f | 4e3b9d7a25a61763cebc660778f8c673a94ac6ac | /man/scores.Rd | 626a61d8e354baa0a25bb9dccd8fb3f9c6ac1aa8 | [] | no_license | paupuigdevall/GenomicScores | d59afb72181642776bcfc6527e7c148f67c4bd97 | dbd072fbcdb9aafd6d49c8104ecd474e2bd777fe | refs/heads/master | 2021-01-17T22:07:05.786095 | 2017-03-14T11:14:51 | 2017-03-14T11:14:51 | 84,188,155 | 0 | 0 | null | 2017-03-07T10:48:58 | 2017-03-07T10:48:58 | null | UTF-8 | R | false | false | 2,779 | rd | scores.Rd | \name{scores}
\alias{scores,GScores,GRanges-method}
\alias{scores}
\alias{availableGScores}
\alias{getGScores}
\title{Accessing genomic scores}
\description{
Functions to access genomic scores through \code{GScores} objects.
}
\usage{
availableGSscores()
getGScores(x)
\S4method{scores}{GScores,GRanges}(object, gpos,... |
8f855769ffd5f99a85a11791ffc3029a410383eb | 19554efd8681305208e7299d8565318779e10240 | /server.R | 0963c909115441a0378ed9f1e67b5cdeae1879e7 | [] | no_license | JAngstenberger/DevDataProd | 3a9496fdad2d8a575cd03bce5b9d7fe3705a2301 | de4998a3b6a536e7fa6e991759a076f5e90db114 | refs/heads/master | 2016-08-12T20:54:23.948012 | 2015-09-26T11:36:10 | 2015-09-26T11:36:10 | 43,202,253 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,210 | r | server.R |
# This is the server logic for a Shiny web application.
# You can find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com
#
library(shiny)
# Plotting
library(ggplot2)
library(rCharts)
library(ggvis)
# Data processing libraries
library(data.table)
library(reshape2)
library(dplyr)
# ... |
4df590d48bbad94e3502908bf5f585ba944aa69f | 597a5c9f177db6f86f7c0e28dcae18052159fc8e | /man/grGeneAnnot.Rd | 81413f966c04efa1f8c5644291a6667fca939516 | [] | no_license | demuellae/muRtools | 241e3d1bdc25ada69c54d4b088980433bc7ea15d | 74db0ac00c56bd39d95a44b52e99bbe03c22d871 | refs/heads/master | 2023-06-21T15:07:03.928229 | 2023-06-20T08:49:25 | 2023-06-20T08:49:25 | 18,805,524 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 978 | rd | grGeneAnnot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/genomicRegions.R
\name{grGeneAnnot}
\alias{grGeneAnnot}
\title{grGeneAnnot}
\usage{
grGeneAnnot(
gr,
rsdb,
geneSetName = "genes_protein_coding",
geneSetCollection = "Gencode",
maxDist = 1e+05
)
}
\arguments{
\item{gr}{\code{GRanges}... |
158113ab7085ba6c24f088e2f3f78e6d823125b5 | 30a4a06543abd1183da998acb931e98f40918088 | /plot4.R | 81cec57ea9101cbf6b4b896423b13a2d0e9d7c6b | [] | no_license | srujanrouthu/ExData_Plotting1 | a1912d65b1d44b400e7162e12a460e044d952f1e | 0f339a787dcd37f44f4e0c91ae405698233ccce2 | refs/heads/master | 2021-01-18T12:45:59.188424 | 2014-08-10T14:44:11 | 2014-08-10T14:44:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,238 | r | plot4.R | data <- read.table("./household_power_consumption.txt", sep = ";", header = TRUE);
DatTim <- paste(data$Date, data$Time);
DT <- strptime(DatTim, "%d/%m/%Y %H:%M:%S");
data <- cbind(DT, data);
for (i in 4:9) data[, i] <- as.numeric(as.character(data[, i]));
subdata <- subset(data, DT >= "2007-02-01 00:00:00" & DT <= "20... |
92e29f2056a65bad3e06968da10048669f10ff29 | dd5a2a40de26efc49bf9daaa0c5a7bf36e17371d | /old-R-scripts/test_data.R | 1b8e36433e9f6049b7a735d9dab2468baf4d1a68 | [] | no_license | tuh8888/MSPrep | fa0e21e7ef6c22b054721e15d8f8ed0ea0155451 | f93dff1dcd8ffe7cfc98648166348f0deff6a112 | refs/heads/master | 2022-01-21T23:47:08.193216 | 2022-01-06T19:21:40 | 2022-01-06T19:21:40 | 152,809,547 | 0 | 0 | null | 2018-10-12T21:29:42 | 2018-10-12T21:29:42 | null | UTF-8 | R | false | false | 5,937 | r | test_data.R | #' Object exported from readdata() function
#'
#' Object exported from the readdata function. Contains clinical data and
#' summarized data.
#'
#' @docType data
#' @format
#' A data frame with 53940 rows and 10 variables:
#' The format is:
#' List of 3
#' $ sum_data1: num [1:9, 1:2654] 0 24885 23820 20... |
8cdff991530c951bd008d1c073f12ddfb3903551 | e8cb3a9a7d6a0df08c625a917a0caeaaf4b1bb79 | /testing/test-script.R | 498a215ff59e8229e9cb74f71a8f34f6fdd4cd1f | [
"MIT"
] | permissive | ralhei/pyRserve | 48793970fa22cf3624425a5ab4248a26eb2f27fd | c3d0a731bb393e15b2fe8a768389b0aeb53991fc | refs/heads/master | 2023-08-08T11:16:23.987841 | 2023-07-27T19:19:26 | 2023-07-27T19:19:26 | 10,841,606 | 44 | 15 | NOASSERTION | 2023-07-27T19:19:27 | 2013-06-21T11:53:36 | Python | UTF-8 | R | false | false | 520 | r | test-script.R | # Test file for Ralph with plot returned as raw file
#
# Author: yanabr
###############################################################################
rm(list=ls())
graphics.off()
pid <- Sys.getpid()
## some dummy data
x <- sort(rnorm(100))
y <- 2*x+rnorm(100,0,0.5)
## model
model <- lm(y~x)
filename <- paste('pl... |
7126152a4b62b9818f5133505ff7e20c8ccbc035 | e50e8103d40f6860c7e7644513e2488d8753993b | /R/EvalPass.R | f30de0bf22aac34dd37c16e1dc1f426422e0f8c4 | [] | no_license | JeremyTate/ridership | 544368d67bbd28f0abf72e3a5b321e53eb3d4ca8 | 43521b765d1452fe1c6a9a20076ee90fdf1719dc | refs/heads/master | 2020-04-28T06:57:33.887203 | 2019-03-14T02:29:41 | 2019-03-14T02:29:41 | 175,076,267 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,045 | r | EvalPass.R | #' Evaluate passengers according to cutoff values
#'
#' A function to calculate whether or not a single observation is a passenger
#' based on specified cutoff values
#'
#' @param obs A single observation from a wifi dataset that is to be classified
#' as a passenger or not.
#' @param pass.mod A random forest model to ... |
4e244b33945dee0e39ee5e6807fb45ff7ff94be9 | 8e6a647d5e70419c34434ec78b381949d1dc0964 | /README.rd | 7c663a9551140044fb25dca78f1a34e4263b3f07 | [] | no_license | acme/ruby-data-page | 3e9f4ee8594efe160d8c9e2575db9a3790bdcc6a | 4d9c0b0e4c939b2fd0c03dcd1a30b5d7589872ca | refs/heads/master | 2020-05-27T12:58:35.007388 | 2011-08-25T13:13:02 | 2011-08-25T13:13:02 | 2,267,974 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,203 | rd | README.rd | =begin
index:Ej
= Data/Page: Help when paging through sets of results
Last Modified: 2005-05-22 00:28:04
--
When searching through large amounts of data, it is often the case
that a result set is returned that is larger than we want to display
on one page. This results in wanting to page through various pages of
da... |
4bca44878dc7160c745da7da12e680bc7dc8a515 | 8c02ea3f035ea3988105a71d67b243ac351ad5eb | /r_studio/print_integers.R | c08da5e358e896c9fb65e52cc80eb3668f2a7c7f | [] | no_license | sudharsaanj001/st2195_assignment_1 | f2090fd27bacb233d382cddc339dbe376a9a3dd1 | 88fd9628bd564f8d2f512c5747364113d6ddc0e0 | refs/heads/main | 2023-08-05T11:17:04.458033 | 2021-09-25T12:58:06 | 2021-09-25T12:58:06 | 409,173,419 | 2 | 3 | null | 2021-09-25T12:56:26 | 2021-09-22T11:16:26 | HTML | UTF-8 | R | false | false | 125 | r | print_integers.R | ## R script that prints all integers between 1 and 10 including 1 and 10 using a for loop.
for(i in 1:10){
print(i)}
|
dbe119d26828698f800dd3421bc3aca589649813 | 7dbd125a0a3d3b400c0b1fe0e8db71f270a8e93b | /Models/Reproduction/phase.R | 0e52be744ff00d559fa903e1ae7ce09ac4950154 | [] | no_license | JusteRaimbault/MediationEcotox | 6cf343f3bee156a79e163bcbd1b60a5c4ac62c0a | bbba2e1e25581821056bdd91b3246a2de16aea07 | refs/heads/master | 2021-01-18T22:07:15.638069 | 2019-09-17T09:00:26 | 2019-09-17T09:00:26 | 44,667,567 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,092 | r | phase.R |
# Phase space / Liapounov phase diagram for Prey Predator model
setwd(paste0(Sys.getenv('CS_HOME'),'/MediationEcotox/Results/PreyPredator'))
library(dplyr)
library(ggplot2)
source('functions.R')
resfiles = list.files(path="split")
# parameter def
for(sheepGain in c(20,60,100)){for(wolfGain in c(20,60,100)){for(sh... |
395a1365aecd6df1d0a7780098998024637396a9 | 31bd220a932ce88bbabc6aa7a2819e48008dde19 | /man/factorize.Rd | f927a5ad71f29df7b700b20d6d62552620c7c27f | [] | no_license | cran/DoE.base | 6fa51fb944ba3f841ad35302c60e0a7ab3da6902 | ecad38c67623b041c29252cb7608e54035e0567d | refs/heads/master | 2023-05-12T04:24:46.333540 | 2023-05-08T15:20:07 | 2023-05-08T15:20:07 | 17,691,739 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,404 | rd | factorize.Rd | \name{factorize}
\alias{factorize.factor}
\alias{factorize.design}
\alias{factorize.data.frame}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Factorize integer numbers and factors
}
\description{
Methods to factorize integer numbers into primes or factors
into pseudo factors with int... |
a87efc6d667bba3ffd607a2b88a8e4c7c798d69c | 9d0f2ba4463891d59f7e13983e70e20f5eb18bfa | /R/heterogeneity_stats.R | cc8c8984c24265228bbf674a50d1a6725c2e47bf | [
"MIT"
] | permissive | donaldRwilliams/blsmeta | 9a336f0fb78880c99101cbcc4699e22a0931918f | 8e9cdcc3c6b66a88b2052c5e9973c66893e65d5b | refs/heads/main | 2023-06-16T04:12:32.857648 | 2021-07-11T17:34:47 | 2021-07-11T17:34:47 | 357,192,752 | 8 | 1 | NOASSERTION | 2021-06-13T14:44:33 | 2021-04-12T12:53:50 | R | UTF-8 | R | false | false | 3,877 | r | heterogeneity_stats.R | #' @title Credible Intervals for Heterogeneity Statistics
#'
#' @description Compute credible intervals for between-study variance,
#' between-study standard deviation, I2, and H2. This
#' function mimics that of **metafor**, but for credible
#' and not confidence intervals, as ... |
5d9925c769f0f9e616e23ec455cc837e630b7b26 | 9440c39a8e9e1cde67c704fa30ea82178421370f | /notebooks/190-CNV-PCAs/CNV_PCA.r | 05a7af6fa6f6b60a998b1bb5b0d29af5b7d69c7c | [
"CC-BY-4.0"
] | permissive | alimanfoo/ag1000g-phase2-data-paper | 238c0f467a7599261f144ad8a2de7364949215cc | 05f29d2e1c55467be898e5b8848fc7a5e515e649 | refs/heads/master | 2020-03-16T22:47:49.937764 | 2019-10-02T21:40:19 | 2019-10-02T21:40:19 | 133,052,828 | 1 | 0 | null | 2018-05-11T14:52:03 | 2018-05-11T14:52:03 | null | UTF-8 | R | false | false | 10,614 | r | CNV_PCA.r | # First get presence / absence data for the CNVs
cyp6.pa.calls <- read.table('../CNV_stats/tables_for_phase2_paper/cyp6aap.csv', header = T, row.names = 1)
colnames(cyp6.pa.calls) <- paste('Cyp6', colnames(cyp6.pa.calls), sep = '_')
cyp9k1.pa.calls <- read.table('../CNV_stats/tables_for_phase2_paper/cyp9k1.csv', header... |
8e60153e05ff0b90df26876776b2b88012d0c41d | 996b1f638557f3168caf0a8953d9bb17d04cacd9 | /plot1.R | badfccf7b8ac000404561ec6feb94a74ec7c5f2b | [] | no_license | rkrsathya/ExData_Plotting1 | 1fed5b1968bb3145f4b7fad91da72c9aad0e15fa | 6457223ca27ec70502445cd55e2b1a980ea6853e | refs/heads/master | 2020-05-23T08:16:50.775665 | 2016-10-09T00:31:25 | 2016-10-09T00:31:25 | 70,237,351 | 0 | 0 | null | 2016-10-07T10:36:01 | 2016-10-07T10:36:01 | null | UTF-8 | R | false | false | 809 | r | plot1.R | ## Create a barplot graph for global active power with frequency
# Load the csv data
commData<- read.csv2("household_power_consumption.txt")
commData$modifiedDate <- as.Date(commData$Date,"%d/%m/%Y")
# Create subset of data only first 2 days of february 2007
origData <- subset(commData,commData$modifiedDate>... |
6b947092bfa5f63430f8eabbce206d185a2eafe3 | 5ac3bb2d932d370ae510ea9383e2b243eb93b6a8 | /R/R_Day2.R | 9237ba23634433f6f9573b3787338584f9565ff6 | [] | no_license | Kingkong92/TIL | 637fb55fa56ae67ba08f07db7bf2b3676b280841 | 3cf134404d1904445c3a29abbc302a675b702493 | refs/heads/master | 2020-09-25T13:15:47.938430 | 2020-06-18T00:19:58 | 2020-06-18T00:19:58 | 226,011,113 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,429 | r | R_Day2.R | exam<-read.csv("r데이터분석_Data/Data/csv_exam.csv")
exam
library(dplyr)
#summarise function
exam %>% summarise(mean_math=mean(math))
View(exam)
#mean(math), sd(), IQR(), max,min,sum
#group_by. 그룹별 분류 연산 가능.
exam %>%
group_by(class) %>%
summarise(mean_math=mean(math))
exam %>%
group_by(class) %>%
summarise... |
82b574ec598ed698eec3bd73a930511d4f3f0c4e | 5eac4dceb44bff203fc70f2c40dbdf11b0fa84f2 | /doc/getting_started_rvcetools.R | c3a20c96da4749793d45a5ec29404236ca36dc54 | [] | no_license | pvrqualitasag/rvcetools | a50841c74224a4dc4b239cb49c5a97899e857760 | e3bdc284c604ab643e7429ac6b85e234bd3d24db | refs/heads/master | 2021-12-30T00:27:03.715802 | 2021-12-21T16:26:10 | 2021-12-21T16:26:10 | 211,788,325 | 0 | 1 | null | 2020-04-28T06:38:37 | 2019-09-30T06:15:07 | R | UTF-8 | R | false | false | 1,998 | r | getting_started_rvcetools.R | ## ---- include = FALSE----------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup, eval=TRUE----------------------------------------------------
library(rvcetools)
## ------------------------------------------------------------------------
(s_input <... |
f3a138b186f34562945e6bcfcebbd55155f6c63a | 5db6bb7d143c6d6e53e85c89c1ad5c207adae3d2 | /SecondSEM/DataMining/lab3/lab3.R | df39186301e921e631793b964887ca1a8be6a43a | [] | no_license | alperenkara/smada | 541c6431b3fe17d1017ef35d9c2fd5912081e5e1 | 2e7f10d25f2b2977b6371ec196498dfb835fc5b1 | refs/heads/master | 2021-04-15T14:08:53.009186 | 2019-01-24T22:53:36 | 2019-01-24T22:53:36 | 126,248,443 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 45 | r | lab3.R | # Data Mining Lab 3
# 24/10/2018
# Alperen
|
7f399ea58e3a93b7393973011cfbab12e3e6f85b | f6aeb9fcaae4dc01c7ebc9504810dc5ccb20630a | /behave/DERS_DTS.R | 3816a090c5a79909f32e403e13f04f9fe49469ad | [] | no_license | LabNeuroCogDevel/7TBrainMech_scripts | e28468b895e1845c676bb4c3188719f248fd1988 | 1089f64ee73841cabae40d88a9913dacd761ed9e | refs/heads/master | 2023-08-30T17:10:31.737956 | 2023-08-18T17:40:04 | 2023-08-18T17:40:04 | 160,859,221 | 4 | 1 | null | 2019-04-16T15:10:27 | 2018-12-07T18:09:20 | MATLAB | UTF-8 | R | false | false | 6,518 | r | DERS_DTS.R | #!/usr/bin/env Rscript
suppressPackageStartupMessages({library(dplyr); library(tidyr); library(glue)})
# 20220804WF - init
# Difficulties in emotion regulation scale (DERS)
# depenends on 000_getQualtrics.R writing selfreport.csv files
DERS_QUESTIONS <- c(
"I am clear about my feelings.", "I pay attention to how I... |
0ef034900cebe17fea21db705bc88d0073e19172 | dbbf242e80ec855cd8e1c4c122356aa0ba74080d | /man/summarize_short.integer.Rd | 3a17926a8e49f6fb900615442e35d3c9b52e7575 | [
"MIT"
] | permissive | LenaNoel/visR | f736dda7dead7b11b16bbc9fc3b27fb20c7efc1f | 469f940ebd6c0b1245fa2324d0f8c031fd6b3fce | refs/heads/main | 2023-08-28T17:34:54.927186 | 2021-06-15T15:25:41 | 2021-06-15T15:25:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 497 | rd | summarize_short.integer.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils_table.R
\name{summarize_short.integer}
\alias{summarize_short.integer}
\title{Create variable summary for integer variables}
\usage{
\method{summarize_short}{integer}(x)
}
\arguments{
\item{x}{an object of class "integer"}
}
\value{
Sho... |
7518ca3ddd452f45c7669b33670a25584fb24474 | b721796fa801f363e375beec789b096cadb9b10b | /man/rmetaSMOTE.Rd | 4c15ba6f7193d89c5f70ccd604c4ee8f57593303 | [
"MIT"
] | permissive | rusher321/rmeta | fdb0cc0a7eb2f69ae8f08cf99b14fc416734c53e | a00a2fe458c88e6b8171b7ad3fbfa931ca118db8 | refs/heads/master | 2022-05-13T01:46:44.924444 | 2022-03-13T08:13:17 | 2022-03-13T08:13:17 | 199,239,908 | 6 | 2 | null | null | null | null | UTF-8 | R | false | true | 289 | rd | rmetaSMOTE.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/modelMeta.R
\name{rmetaSMOTE}
\alias{rmetaSMOTE}
\title{rmetaSMOTE}
\usage{
rmetaSMOTE(form, data, perc.over = 200, k = 5, learner = NULL, ...)
}
\arguments{
\item{...}{}
}
\value{
}
\description{
rmetaSMOTE
}
|
f1827585cf50f87f0397f8ed06cd1cebac7f3751 | 85e2f39e88f95ebb89a9d02af3711c052c91f385 | /Correlation_covariance(fun).R | eb6a15991309d8d244f6c568e5034300bf87baa7 | [] | no_license | Eustrain/Genetics-correlation | d86cf99af045bcc1bd823e71d840a721dd977564 | 12c1dc3e5f8f17e36f8ece1dc67ab62c6f86b3ce | refs/heads/master | 2020-06-05T02:12:26.416290 | 2019-06-25T02:26:48 | 2019-06-25T02:26:48 | 192,277,371 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,299 | r | Correlation_covariance(fun).R | library(lme4)
data <- read.csv("data.csv",head=T)
names(data)
Traits <- c("ears" ,"len" , "weight" ,"yield" )
co_gp(Traits,data$Parents,data$rep,data)
co_gp<- function(Traits,Entry,Rep,data){
traits <- Traits
geno<-as.factor(Entry)
rep <-as.factor(Rep)
###########################################
nrep <-... |
f2c5143b3d2cff81e2599dbffea006c3ac46a220 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/not/examples/random.intervals.Rd.R | 64c4c36b88d1791f71e0bf09de3a7cba1c5e6e9d | [] | 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 | 225 | r | random.intervals.Rd.R | library(not)
### Name: random.intervals
### Title: Generate random intervals
### Aliases: random.intervals
### ** Examples
#*** draw 100 intervals with the endpoints in 1,...,100
intervals <- random.intervals(50, 100)
|
19995d9a0d98ce15176de6480c85b2556383822d | 89fe6de0f06778887600d7bd5d369b04cb5bab61 | /man/int_flip.Rd | c2fa4de15f6cc6420963d810891c7d286736bc93 | [] | no_license | Poissonfish/lubridate | b3326e3e3a10384e50db32c5ba46f04c00cf8063 | 398c64ed4ee549feb3e93fa99f705e7d9ad09d80 | refs/heads/master | 2020-04-05T23:13:10.787535 | 2016-02-19T08:40:23 | 2016-02-19T08:40:23 | 52,164,196 | 1 | 0 | null | 2016-02-20T16:54:53 | 2016-02-20T16:54:52 | null | UTF-8 | R | false | true | 718 | rd | int_flip.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/intervals.r
\name{int_flip}
\alias{int_flip}
\title{Flip the direction of an interval}
\usage{
int_flip(int)
}
\arguments{
\item{int}{An interval object}
}
\value{
An interval object
}
\description{
Reverses the order of the start date and en... |
f72c9126c57c46b5a48efdd35cc72129578f5012 | ed46dd8d36d63b8c5b691b5501670518499acd1c | /Task 3 Dealing with Numbers Operation.R | db68f37fbc1f186979c1f82e947aa64bc54b2f91 | [] | no_license | ThistleAna/learn-R | 4578a78306b72d3e6d5b75d799adebce61a4d9e0 | e10a8235ff08735abac8aae58c28d0df1122a980 | refs/heads/main | 2023-03-25T18:23:41.886926 | 2021-03-23T17:19:42 | 2021-03-23T17:19:42 | 350,791,158 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 335 | r | Task 3 Dealing with Numbers Operation.R | # Write a R program to create a sequence of numbers
# from 20 to 50 and find the mean of numbers from 20
# to 60 and sum of numbers from 51 to 91.
print("Print number sequence from 20 to 50.")
print(seq(20,50))
print("Print the mean of numbers from 20 to 60")
print(mean(20:60))
print("Sum of numbers from 51 to 91")
pri... |
0ca26e8e707613604680316dbc98b16197abbb88 | f8b1d3258c2927f59a4d59cb19cf62157cc835e1 | /tests/testthat/apps/MIQ_en_num-items-8/app.R | 3d0877062a5e360119935fd407df4f84200e5ccc | [
"MIT"
] | permissive | ViolaPsch/MIQ | 71c3f094abb0b8da1f3fae7c56f178f3f7c4bfbf | 95b15b33422ecd374dc010777d9f2012fac98662 | refs/heads/master | 2022-12-25T09:58:59.311073 | 2020-09-23T14:58:18 | 2020-09-23T14:58:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 64 | r | app.R | library(psychTestR)
library(MIQ)
MIQ_standalone(num_items = 8)
|
60ff7ff013ca5a7fc19c708c37edbb53b9095d5d | ec2b9803a923d928751c76bbf1c31227928bffc9 | /R/samples.sample.R | da71ba965dec005304f3eee08d1231ffd0dc6d79 | [] | no_license | cran/BRugs | a5106711a3f8d3fa0adb91465df235e0f27a1b18 | acafa2035e6ef39e566085026eeabf67cd1361cd | refs/heads/master | 2023-05-27T11:23:45.986896 | 2023-05-15T05:52:29 | 2023-05-15T05:52:29 | 17,677,954 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,103 | r | samples.sample.R | "samplesSample" <-
function(node)
# Get stored sample for single component of OpenBUGS name
{
if(samplesGetFirstChain() > samplesGetLastChain())
stop("Number of first chain is larger than last chain!")
if(length(node) != 1)
stop("Exactly one scalar node must be given.")
sM <- sampl... |
e235f654e3853d8c54ff739114190df76c4db6ec | 2d34708b03cdf802018f17d0ba150df6772b6897 | /googledrivev3.auto/man/team.delete.Rd | bb30808d7a8d198b15e82f13808658797fe5baa4 | [
"MIT"
] | permissive | GVersteeg/autoGoogleAPI | 8b3dda19fae2f012e11b3a18a330a4d0da474921 | f4850822230ef2f5552c9a5f42e397d9ae027a18 | refs/heads/master | 2020-09-28T20:20:58.023495 | 2017-03-05T19:50:39 | 2017-03-05T19:50:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 840 | rd | team.delete.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/drive_functions.R
\name{team.delete}
\alias{team.delete}
\title{Permanently deletes a Team Drive for which the user is an organizer. The Team Drive cannot contain any untrashed items.}
\usage{
team.delete(teamDriveId)
}
\arguments{
\item{team... |
6d6e23f356b9d2f588530ee812284eaa6ebf8073 | 90b1d6a6c3bbf2a83b94b72b4867c94f8bb2aab5 | /run_analysis.R | 8bf8ee025c18ca7e3ae11a07ce472315c8f82ac3 | [] | no_license | jcasaboza/run_analysis.r | 249cb970da3c4b52c0817ddb4de9981a5486232f | 033f8f01a6078dbf27f14691bb55d77593acd56e | refs/heads/main | 2022-12-25T12:56:36.077427 | 2020-10-10T01:17:55 | 2020-10-10T01:17:55 | 302,150,933 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,068 | r | run_analysis.R | #Im using library dplyr
library(dplyr)
#now im going to download and prepare the data
currdir <- "./data"
if(!dir.exists("./data")) dir.create("./data")
setwd(currdir)
downloadurl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
zipfile <- "UCI HAR Dataset.zip"
dow... |
0484b83e77804fb8f60d210dfb6bf32735cbb705 | cfc494fb498c1c61870253df3e99d209cbcd821b | /Summary Stats, Hyp Testing, Regression R Code - 1.R | 4c86cd706ac9feabd77f0de6df98474ef6df2e5e | [] | no_license | yangzh6598/R | 3a9cba7421cecee136f3b2e75499ac17396e2d15 | 6c70b2bd3734a794cf2eac97ed5b9bd4a2152d26 | refs/heads/master | 2021-01-20T15:39:00.367264 | 2017-05-09T20:17:08 | 2017-05-09T20:17:08 | 90,787,952 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,606 | r | Summary Stats, Hyp Testing, Regression R Code - 1.R |
##### Summary statistics and visualization
mydata=read.csv(file=file.choose()) #Brings pop up box to select .csv data file
mydata #Displays all data
attach(mydata) #Makes columns accessible by name
head(mydata) #Displays column names and first 6 data points
nrow(mydata) #Reports number of observations (data ... |
bd1913fec1f7f56d1dafc3c9ce77965c08234210 | 29585dff702209dd446c0ab52ceea046c58e384e | /koRpus/R/FOG.R | 8d0fc730ee3f5dc8d94f684f2274e2afa3ecc983 | [] | 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 | 3,001 | r | FOG.R | # Copyright 2010-2014 Meik Michalke <meik.michalke@hhu.de>
#
# This file is part of the R package koRpus.
#
# koRpus is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at yo... |
2f2ae3262f65b804be7ed5039e53d4fb75cc820c | be348ef72c01bd46481b14a9f9df770b46c25f72 | /Skellam.R | 16027810689d3afbdcfc221653f278b1d161df75 | [] | no_license | cardsbettor/OddsEngine | b0ad1d16bf02e54da316240ec825ecc48c7ecd58 | dcda80a365a96bf1602f2ac19119c57f93007ccc | refs/heads/main | 2023-02-10T00:13:42.766742 | 2021-01-01T21:18:05 | 2021-01-01T21:18:05 | 321,608,874 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 229 | r | Skellam.R | Skellam <- function(diff,mu1,mu2){
#Probability a Poisson dist. with lambda = mu1 exceeds Poisson dist. with lambda = mu2 by diff or more.
k = 0
for (i in diff:10){
k <- k + skellam::dskellam(i,mu1,mu2)
}
k
}
|
24025ddb7b6964d248eb85d59202b85431e6149e | 5ab3adef6c2a9e4e13b0b127dfe71c7446269a87 | /r_walkthrough.r | 53adeaf9506f1da7da3ed4a78d1bb09e443fd1e3 | [] | no_license | analyticsPierce/times-series-forecasting | e61059fb8cdaadb1cdc17966e24dd0efe210b52e | ea5bd73ad240440238a3cec293e69c1e29eaadb4 | refs/heads/master | 2021-01-01T17:22:05.240964 | 2013-10-06T20:27:30 | 2013-10-06T20:27:30 | 13,368,602 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,794 | r | r_walkthrough.r | # libraries - load these before you get started
library(ggplot2)
library(Hmisc)
library(forecast)
library(RMySQL)
# load data
# this is a basic file, notice it loads from a url
# this will create a dataframe. read about these. I use dataframes almost all the time. these are like a table in a database.
timeSeries <- r... |
629d91588f161d231971213ab33fa170e7be8445 | 98f1d2f59230bdc06be9be1a7c88de458115ae92 | /Data.R | 260053be4c2e631dbc73cd1f887cecb149bcb808 | [] | no_license | aimod62/DDP-Final-Project | f099e2f134a932fc0cc507d2b66fe82103ffc6c3 | 71d20515629cfc42e2cdaf26e1060ba2e304fb45 | refs/heads/master | 2021-01-19T00:28:08.567901 | 2017-04-05T02:54:15 | 2017-04-05T02:54:15 | 87,173,037 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,599 | r | Data.R | library(dplyr)
library(reshape2)
library(ggplot2)
library("dygraphs")
library(plotly)
library(shiny)
library(maps)
#Extracting and Cleaning the Data.
#Source:World Bank - Data Bank
#Loading the Raw Data
data1<- read.csv("~/DDP_Finalproject/57b46c40-8cd0-44f0-bb12-b47778bb9861_Data.csv", stringsAsFact... |
ff0119a13324cdb49f26622bfa64efc17ae90e15 | 6c11c5e9d7c83793a48cf2ce0a4e15160aeb6c54 | /R/c.R | 9a87d3711e2986f2f6789a7c694f0750b529ef27 | [] | no_license | earowang/vctrs | ff6704de55bb6ca9448471de0ec749ef43c0b7f1 | 671dd82c6af02a2cdc5b3b26795f521dfa199609 | refs/heads/master | 2020-05-28T09:41:58.097869 | 2020-05-04T17:11:05 | 2020-05-04T17:11:05 | 188,959,149 | 0 | 0 | null | 2019-05-28T05:21:43 | 2019-05-28T05:21:42 | null | UTF-8 | R | false | false | 1,790 | r | c.R | #' Combine many vectors into one vector
#'
#' Combine all arguments into a new vector of common type.
#'
#' @section Invariants:
#' * `vec_size(vec_c(x, y)) == vec_size(x) + vec_size(y)`
#' * `vec_ptype(vec_c(x, y)) == vec_ptype_common(x, y)`.
#'
#' @param ... Vectors to coerce.
#' @param .name_repair How to repair nam... |
ab665ba138d1afdc4a6e5eca8d9ac31f5629bd36 | 4980082f584caa193954b2268589998718b01a10 | /app.R | 2d7a2c8fbfa8a65c80caf5075f1de285056fe503 | [] | no_license | jingwang24/PowerSS-shiny-app | c223bba059b70f9cac7ee43d56f59deae89a2a1d | 9536d0065fe56e05c221865277091db2ce54df41 | refs/heads/master | 2020-03-23T05:39:41.551270 | 2018-07-16T15:38:14 | 2018-07-16T15:38:14 | 141,158,899 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,825 | r | app.R |
library(shiny)
library(dplyr)
library(tidyr)
library(ggplot2)
library(exact2x2)
library(shinythemes)
library(DT)
fet <- function(a,b,c,d) {round(fisher.test(matrix(c(a,b,c,d), byrow=T, nrow=2))$p.value,3)}
n1<-n2<-16
matrix_out<- matrix("NA",n1+1,n2+1)
for (i in 0:n1){
for (j in 0:n2)... |
550d90bb09147a3942753b1c6f2e215de2ac471c | c59d908f1c76f552f18eb074ca4e03bb189a50de | /Models/NLP_TextMining/NLP.R | 228f8e801b43ed20154dcd246196ff2d58e0d88b | [] | no_license | ZellW/MachineLearning | c64a20d9a53699d78b71da08c9f96ede7b8cabcf | d5c751c1d23f425db86e2410c4f9be7577bf3cf3 | refs/heads/master | 2021-04-26T23:12:35.957795 | 2020-12-14T13:40:38 | 2020-12-14T13:40:38 | 123,948,478 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 11,257 | r | NLP.R | library(cleanNLP)
library(sotu)
library(dplyr)
library(tokenizers)
library(reticulate)
reticulate::use_python("../anaconda3/envs/NLP2/")
data(sotu_text)
data(sotu_meta)
txt <- c("You're invited to join Yext on Tuesday, April 23rd, in a private suite",
"The Charlotte Knights take on the Toledo Mud Hens",
... |
332065cd113d449ae8902d7de6c14ea7017365cc | 1888b813183a0924e0cce3f466f0b16b268bc090 | /stats/chisq.test.R | 317fe1ad7aa7771c1ab745c09cd8aa7c90d0a66c | [] | no_license | abreschi/utils | ea61091e53fa67ee504675c54025d4dc1efda201 | 30c8497eef35967315e75c15d573093ad79c1df0 | refs/heads/master | 2021-01-22T20:19:36.973179 | 2019-04-09T20:21:18 | 2019-04-09T20:21:18 | 85,313,317 | 0 | 4 | null | 2017-06-23T12:51:20 | 2017-03-17T13:15:41 | Python | UTF-8 | R | false | false | 431 | r | chisq.test.R | #!/usr/bin/env Rscript
cat("USAGE: script.R setAB setA setB total\n")
args = commandArgs(TRUE)
args = as.list(as.double(args))
# Given the contingency matrix
# _
# | A | A
# --|---------
# B | a | b
# _ |---------
# B | c | d
#
a=args[[1]]
b=args[[2]]-a
c=args[[3]]-a
d=args[[4]]-a-b-c
M = matrix(c(a,b... |
810b23e9b652b95175af389b39cc89016e5e766d | 22d395b282768b359dff330261add3fbb29961c8 | /man/smhiAPI.Rd | c63a19a79747eb031ac5f8ff1c7e975b2a410750 | [] | no_license | Antpe404/SMHI_API | 40c9cfa39c2b71b2c947a03b84089e66a725eb74 | f64f4eb5f0297dfd6513177dc90621801c0d1966 | refs/heads/master | 2020-05-29T08:46:22.218753 | 2016-09-30T14:08:43 | 2016-09-30T14:08:43 | 69,466,113 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 359 | rd | smhiAPI.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/smhiAPI.R
\docType{package}
\name{smhiAPI}
\alias{smhiAPI}
\alias{smhiAPI-package}
\title{Access the SMHI API via R}
\description{
This package uses XML and JSON to access the SMHI API.
The package includes four functions, which will help you... |
41556746264216778048f5b611cec611d43888d6 | 22f761644fa84c4fe0086e3a013fd1f636e2ae0c | /inst/doc/Financial_and_non_financial.R | 6f0c054fa96d751a0bae4ed763f481c53437ae03 | [] | no_license | cran/bizdays | a8fe606fd516f02231f0e6e42377f32747a76892 | fc0512ebbae7cbb9d8b26829ca35004fc3cf9f3d | refs/heads/master | 2023-01-25T04:01:52.148067 | 2023-01-20T16:40:06 | 2023-01-20T16:40:06 | 17,694,808 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,362 | r | Financial_and_non_financial.R | ## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)
## ----message=FALSE, warning=FALSE---------------------------------------------
library(bizdays)
create.calendar(name = "example1", weekdays = c("saturday", "sunday"), start.date = "2017-01-24", ... |
8c7315c6696a469a64a24dea18a55f75e1ac44bd | 7e7bb7bfdf62c24b7fecf78f5247d28839728710 | /Leading Educators/Leading Educator Rand.R | 96bdc9ef1371baae5750c79ef782c517344c9868 | [] | no_license | kippchicago/Data_Analysis | 1ad042d24c7a1e11e364f39c694692f5829363a4 | 8854db83e5c60bc7941654d22cbe6b9c63613a7f | refs/heads/master | 2022-04-09T10:10:43.355762 | 2020-02-20T18:03:40 | 2020-02-20T18:03:40 | 5,903,341 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,100 | r | Leading Educator Rand.R | library(silounloadr)
library(tidyverse)
library(lubridate)
schools <- data_frame(schoolid = c(78102, 7810, 400146, 4001462, 400163, 4001802, 400180, 4001632),
schoolname = c("Ascend Primary", "Ascend Middle", "Academy Chicago","Academy Chicago Primary", "Bloom", "One Primary", "One Academy", "Blo... |
261fa7e1a07f832672c10952441414bf546f1432 | 2601a446ea97ca9b54438d1bbb9197289a142f68 | /plot3.R | b0700ae2780b71770138387f120ae5617079f0dc | [] | no_license | srinivasksh/Exploratory-Data-Analysis_1 | 1bc4d98616bd288360540a692d10fb6708c54cf2 | 489051b14cf1b63c3432455858db2d52ae7f7d82 | refs/heads/master | 2021-01-10T03:42:34.906160 | 2016-01-10T11:14:29 | 2016-01-10T11:14:29 | 49,363,391 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,416 | r | plot3.R | ## Read Power consumption data delimited by ; into a table
power_raw <- read.table("household_power_consumption.txt", sep = ";", header=TRUE)
## Covert values in Date column into Date format
power_raw$Date <- as.Date(power_raw$Date, "%d/%m/%Y")
## Filter data confined to 01-Feb-2007 and 02-Feb-2007
power <- subset(po... |
50572438a3641ec5090d6309711f7fc792f1e3a9 | 5dd8887301eb53cfb5ffab58a6f339349ca20919 | /plot3.r | 133f77ae446ae34e1b9c6f1cadbcda614163c6c3 | [] | no_license | andresalvarez/ExData_Plotting1 | 5dd3634308ca37d2dcfe40083cf6de6ca0436e2f | fa0bd408721b4a0074499fb0ab166d314f0c026d | refs/heads/master | 2020-12-11T07:44:23.823682 | 2016-05-15T19:59:19 | 2016-05-15T19:59:26 | 58,814,170 | 0 | 0 | null | 2016-05-14T15:08:08 | 2016-05-14T15:08:08 | null | UTF-8 | R | false | false | 1,296 | r | plot3.r | library(data.table)
setwd("E:\\DataScience Specialization\\course4\\A1\\ExData_Plotting1")
DT <- fread("./household_power_consumption.txt",
select = c("Date","Time","Global_active_power","Global_reactive_power","Voltage","Global_intensity","Sub_metering_1","Sub_metering_2","Sub_metering_3"),
c... |
e6c437bf53a3d66557bf4ec4aa1276475d695642 | 6d9c67637ffc0876311953250e2de397beaddccf | /Licence_agreement/I_accept/PCModel1350/PCModel/3.00/Models/PCDitch/2.13.16/PCShell/scripts/R_system/3161/PCShell.r | 1b09fe4eeb082674bc8beb1354184bc7fbb8195c | [] | no_license | RedTent/PCModel | 7ed7aa95503bdd2b531929d05c44ec082d8b2562 | f98f62e15f1975f80c835fb616b36223b33c5d00 | refs/heads/master | 2023-04-18T10:14:26.302989 | 2020-08-28T09:52:50 | 2021-05-06T08:20:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,026 | r | PCShell.r | # **************************************************************************
# --------------------------------------------------------------------------
# PCSHELL (compiling cpp modelcode) (Main Program)
# author: Luuk van Gerven (april 2012)
# used libraries: ggplot2, deSolve, RODBC
# ----------------------------... |
90450aed5f701d5134461411129ca77205817cb3 | 9d13550ab15bee71e95326a1513d4b62c62197b0 | /R/annotation.R | 1edc25318d24268f3c16289a3504261ee3bdae63 | [] | no_license | cran/genoPlotR | 8612232d2992605511e71b74bced5378841f66d0 | 3887f91ed718b7df935c11d1a84e9276f4f6b01c | refs/heads/master | 2021-07-21T15:01:58.200076 | 2021-01-07T14:00:02 | 2021-01-07T14:00:02 | 17,696,331 | 4 | 5 | null | null | null | null | UTF-8 | R | false | false | 1,855 | r | annotation.R | ################################
# Annotation class and methods
################################
# annotation is a set of text and one or two positions for each
# if one, other must be NA
annotation <- function(x1, x2=NA, text, rot=0, col="black"){
if (missing(x1) | missing(text)) stop("Args x1 and text must be provi... |
9618b0bf5e22ba6c65fa6c2adcc1e4918e500ec9 | cd49ddae495f695493c2ff1b692cf69a52a4cb38 | /sucesohi_tipomuhi_vic_boxplot.R | ca3ddf94f4635eeb2a1128df2363eb89c70142a1 | [
"CC0-1.0"
] | permissive | AdrianGonzalezDS/bookdown-demo-master | 9ef1f28c5d1812d1998f9c9e4661a46292b015ac | 1303ecb471c9a95696515c05ba6347b9257e425b | refs/heads/main | 2023-08-31T19:34:48.435682 | 2021-10-14T18:26:59 | 2021-10-14T18:26:59 | 413,385,635 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 3,423 | r | sucesohi_tipomuhi_vic_boxplot.R | library(devtools)
library(ggplot2)
library(tidyverse) #requerido para la funcion gather
library(bbplot) #requerido para bbc style
library(plyr) #requerido para hacer gr?ficos de pir?mide
library(dplyr) #requerido para usar la funcion mutate
library(tidyr) #requerido para usar la funcion gather
library(stringr)#requerid... |
ced107ec35344d5e76c51fccd4ce7d3aa2f506da | 75db022357f0aaff30d419c13eafb9dddfce885a | /R/getFccir.r | 0635bc6e3448ecb886022c4c1a4c2bbca5a6c588 | [] | no_license | LobsterScience/bio.lobster | d4c553f0f55f561bb9f9cd4fac52c585e9cd16f8 | b2af955291cb70c2d994e58fd99d68c6d7907181 | refs/heads/master | 2023-09-01T00:12:23.064363 | 2023-08-23T16:34:12 | 2023-08-23T16:34:12 | 60,636,005 | 11 | 5 | null | 2017-01-20T14:35:09 | 2016-06-07T18:18:28 | R | UTF-8 | R | false | false | 2,079 | r | getFccir.r | #' @export
getFccir=function(p){
load(file.path(project.datadirectory("bio.lobster"),"data","exploitationccir.rdata"))
load(file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledBinomialModels33.rdata'))
r33=ouBin[,c("LFA","Yr","ERfm")]
load(file.path(project.datadirectory('bio.lobste... |
7fa4a2561d1f11447408ea2fce4c57fcbf56b768 | ce6d5c499e05126f0184d466549eba617ef5dd20 | /man/call_fn.Rd | 6fc4e7081d9278832713bcb90c0a177a16bee943 | [
"BSD-2-Clause",
"MIT"
] | permissive | cderv/rlang | e929db50350600be716cb6243a99109bca7d4019 | aa5cdf174cc977faa98a0b8582472a1468d9f98d | refs/heads/master | 2023-08-01T00:44:14.020173 | 2021-09-13T15:16:21 | 2021-09-13T15:16:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 788 | rd | call_fn.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/call.R
\name{call_fn}
\alias{call_fn}
\title{Extract function from a call}
\usage{
call_fn(call, env = caller_env())
}
\arguments{
\item{call}{Can be a call or a quosure that wraps a call.}
\item{env}{The environment where to find the defini... |
11060d94fe28d87b964fde2f31a3989ea617d43c | ca5f11d0358ab203d9468659c1306d1b186eb206 | /unused/regression.test.R | 628cf8b60737da781a1bf4e1c1883f1e40915d87 | [] | no_license | deepankardatta/blandr | 75b3a30b2d961fd3c7b12824ab035943f8c01208 | 4d5b1a43536cd1fd9021ff5b1736a7534bc14072 | refs/heads/v.0.5.3-development | 2021-12-14T12:45:38.472889 | 2020-03-28T07:15:04 | 2020-03-28T07:15:04 | 95,990,424 | 15 | 9 | null | 2021-12-06T01:33:16 | 2017-07-01T22:25:47 | R | UTF-8 | R | false | false | 640 | r | regression.test.R | statistics.results <- blandr.statistics( giavarina$Method.A , giavarina$Method.B )
# Passed data to the blandr.plot.limits function to
plot.limits <- blandr.plot.limits( statistics.results )
# Pass data to the blandr.ggplot function to use ggplot2 graphics system
ba.plot <- blandr.ggplot( statistics.results , plot.li... |
591c54ff21d5d1d7e43f11dccc6b667c509056b6 | 2d6b65aa4308ee002e27992fcb97f1945052d85b | /contact-pred-master/step1.R | 9be9e1897954dc61ef0685323cc59fe5179afdea | [] | no_license | red333/ccm_int | aec607c75769008351e66d783cbb425df0211967 | 4d0eeaf4ba2ee8b214bd4a7c572cc5eaf7677108 | refs/heads/master | 2021-04-27T14:47:18.997046 | 2018-04-04T21:16:24 | 2018-04-04T21:16:24 | 122,458,596 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,121 | r | step1.R | #!/usr/bin/Rscript
#assign gap binary and interface binary to the MSA
#
#myfasta_Align <- "PYRB_1ekxA.alignment.fasta"
step1 <- function(myfasta_Align){
interfacelib = read.table("vi.table")
a = read.fasta(myfasta_Align,rm.dup = T, to.upper = T, to.dash = T)
id = a$id
ali = a$ali
alignmatrix = as.data.f... |
f3ae23e7cbf03544e13b76294e4b05acac8c4c35 | 49ac865d0a7739438342dff7fc6a2bcf18112994 | /R/fepsfrontieR.R | 964eedc3dd3969182ef2c13c9f6ec93df9c8eca1 | [] | no_license | oliverdippel/fepsfrontieR | 20163f3c4346a05b2722646f7ba2a75a1898b3b3 | df789877103bb8d40f2ee686a4e7abaedae689cb | refs/heads/master | 2020-03-25T06:41:57.293032 | 2018-08-04T08:30:04 | 2018-08-04T08:30:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 742 | r | fepsfrontieR.R | #' fepsfrontieR: A package for estimation of fixed-effect panel stochastic
#' frontier models by within-model transformation as proposed by Wang & Ho.
#' As proposed, Maximum Likelihood Estimation using nlminb().
#' Confidence Intervals are provided using the Hessian Matrix or
#' (time intensive) Bootstrapping to calcu... |
b1b52de297f7be5e6589d9ee1b0d7f08ea15795e | ba14c315f4ed435384c5b48185a5707dcf1ce093 | /ui.R | 518b7097ef1281b0562dbc889d03be10790a39f7 | [] | no_license | antgers/Project_AquaMiner_Periodic | 0e318e381f1e244ba6858407f22d8900a78d7f6f | 7e81781d607e83833e1bd2fd60f93bd5995b8497 | refs/heads/master | 2021-01-17T19:20:10.620858 | 2016-10-23T20:57:37 | 2016-10-23T20:57:37 | 71,663,263 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 24,428 | r | ui.R | ### Version Periodic (Sampling To Sampling) Datasets
#
# Created: 30/08/2016
# Last Modified: 23/10/2016
#
# Author: Gerasimos Antzoulatos (i2s)
#
source("helpers.R")
source("SidebarUi.R")
#----------------------------------------------------
#
shinyUI(
navbarPage( theme = "bootstrap.css",
img(src... |
9872823c8e4049bdbda9061c37c2f6c21cb30d0a | a6e8109fee1b8cae226b8d84faaf5a97772dc8e7 | /R/shinyApps/googleMapQuestion1/ui.R | bce2565ff974b92f84a9eb9a0398ccf000435d01 | [
"MIT"
] | permissive | ati-ozgur/stackoverflowQuestions | 2f92be880e975d7c96b7e216dc1a211d836de7a2 | 647a79f6f55bb8e04d79721082017ca381cc70f3 | refs/heads/master | 2020-05-30T18:15:04.873739 | 2017-12-22T12:06:50 | 2017-12-22T12:06:50 | 27,180,389 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 167 | r | ui.R | library(plotGoogleMaps)
library(shiny)
shinyUI(fluidPage(
pageWithSidebar(
headerPanel('Map'),
sidebarPanel(""),
mainPanel(uiOutput('mymap'))
)
))
|
3c88126e13edb6ad6758da287a4856ea07b40e6a | 4bb7250f73d464865f5bbcd59767d3fa3daac269 | /R/linmod.default.R | d3e1081d31ddad7f91a1219eb3cfd7cac39baf98 | [] | no_license | rcastaneda2/sfunction3 | 640981979a18048e443add7f841e8cbf58f305d4 | 1f9b386a0de6bc777ae29f3f0ca44e82509dd0db | refs/heads/master | 2021-01-10T05:01:19.326827 | 2016-02-08T23:18:33 | 2016-02-08T23:18:33 | 49,517,555 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 244 | r | linmod.default.R | linmod.default <-
function(x, y,...){
x<-as.matrix(x)
y<-as.matrix(y)
est<-linmodEst(x,y)
est$fitted.values <- as.vector(x %*% est$coefficients)
est$residuals <- y - est$fitted.values
est$call <- match.call()
class(est) <- "linmod"
est
}
|
bdce48d828e03bfdaf7fab642040a98780a1388b | 8e8a1c5373df9b08f91f72dfea837955b32d4304 | /man/dim-GGobiData-ok.rd | d626daab227fcb03898d849c076a7cb04f8decdb | [] | no_license | cran/rggobi | 0e7b0a9c4e81e52863eef3dcfe525a6f6d5955f8 | b0a379b06ceef0903b97b0c364192c7df441fe24 | refs/heads/master | 2021-01-10T19:43:46.669608 | 2018-07-07T15:20:03 | 2018-07-07T15:20:03 | 17,699,210 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 347 | rd | dim-GGobiData-ok.rd | \name{dim.GGobiData}
\alias{dim.GGobiData}
\title{GGobiData dimensions}
\author{Hadley Wickham <h.wickham@gmail.com>}
\description{
Retrieve the dimension of a GGobiData
}
\usage{\S3method{dim}{GGobiData}(x)}
\arguments{
\item{x}{dataset}
}
\examples{
if (interactive()) {
g <- ggobi(mtcars)
dim(g[1])}}
\keyword{att... |
603f132ce613b4f52c2d639d980173b46b11ef0e | 01eb2bcd3640a9ead4c4bde834814cff000f4ecb | /dPCR_plot2.R | a695c15625968b1c5da8bedf2ecd1fff4c55b763 | [] | no_license | devSJR/dpcReport_USER | d96178144580aaeb15d2eef2d10363d794192f35 | a443e68ec9d6968a77394393e133eed2aa96ff07 | refs/heads/master | 2020-12-14T07:27:39.445438 | 2017-06-27T12:23:11 | 2017-06-27T12:23:11 | 95,555,484 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,295 | r | dPCR_plot2.R | library(dplyr)
library(ggplot2)
all_lines <- readLines("./data/digital_PCR.txt")
all_abstr <- all_lines[grep("^[0-9]+\\. [A-Z]", all_lines)]
potential_abstr <- grep("^[0-9]+\\. [A-Z]", all_lines)
abstr_id <- as.numeric(sapply(strsplit(all_lines[potential_abstr], ". ", fixed = TRUE), first))
abstr_status <- c(TRUE, s... |
540f042311e27c9b81e2a2bd092b96f6a95a4937 | 0fbc58702c39addfa7949391d92533922dcf9d49 | /inst/examples/lattice-iris-hist.R | d5766142b5d250377648c48d8094b5a1978972a0 | [] | no_license | yihui/MSG | d3d353514464f962a0d987efd8cf32ed50ac901a | 8693859ef41139a43e32aeec33ab2af700037f82 | refs/heads/master | 2021-11-29T08:12:02.820072 | 2021-08-15T17:14:36 | 2021-08-15T17:14:36 | 1,333,662 | 30 | 12 | null | 2021-08-15T17:14:37 | 2011-02-06T05:42:53 | R | UTF-8 | R | false | false | 198 | r | lattice-iris-hist.R | # 三种鸢尾花各自的花萼长度直方图
library(lattice)
print(
histogram(~ Sepal.Length | Species, layout = c(3, 1), data = iris,
xlab = "花萼长度", ylab = "百分数")
)
|
012516a5d8f4e2418213e99eed5974338eda5274 | 1f634cc9a938438cd575ffdc308ff3e94beae27e | /bankr/R/Account.R | dcc40cf92ca067b3a75625b75fe208666996a68a | [
"MIT"
] | permissive | fort-w2021/r6-ex-muskuloes | 9a28079120f7152407697a5cf207ec2471aabc0d | c511a553daad3377ba9e37ec766019fadfdf2147 | refs/heads/main | 2023-03-07T14:06:35.251600 | 2021-02-22T18:05:05 | 2021-02-22T18:05:05 | 326,735,725 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,339 | r | Account.R | # Adding a field TransactionLog would require that the
# $clone method does a deep copy of the class to prevent errors
# as $clone itself isn't recursive by default.
#' @title Account Class
#'
#' @description
#' This base class for account objects.
#'
#' @importFrom R6 R6Class
#' @importFrom checkmate assert_number
#'... |
705cb1c837a969da99e40a03fea7fa299db530d4 | 1ff3a51b463c951aa02ef40a89c5a884c94f9516 | /man/overlaidKernelDensityPlot.Rd | 1a29569d3bb13c93e147ef81809a054ba1f62d6d | [] | no_license | cran/fit.models | 3a250a89603637cfd2296b4cf25f6bcc8e38eda6 | 2548545703702dbc11c8a2b9ceda8da77777386e | refs/heads/master | 2021-01-10T01:00:23.547075 | 2020-08-02T13:30:02 | 2020-08-02T13:30:02 | 17,696,066 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 594 | rd | overlaidKernelDensityPlot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/overlaidKernelDensityPlot.R
\name{overlaidKernelDensityPlot}
\alias{overlaidKernelDensityPlot}
\title{Overlaid Kernel Density Estimate Plot}
\usage{
overlaidKernelDensityPlot(x, fun, ...)
}
\arguments{
\item{x}{a \code{fit.models} object.}
\... |
75e55f7ce4f44f4cd058d97609126f2005bf5102 | 5263483e619575a5238491ff0227577a319a5a57 | /O2_P7_DiagnosticCode_PR_M_MainDashDashCheck.R | 6affa35aeeed1eba7db838f720ea0b3d27f52a64 | [] | no_license | abulhassansheikh/O2_P7_DiagnosticCode | 62ad42ac00d68511f80fac42d608a5d63d6ad6ff | 1852094b8b43695cb1475fa18decef5825a3b48a | refs/heads/master | 2020-06-12T14:03:10.620098 | 2020-02-21T16:31:14 | 2020-02-21T16:31:14 | 194,322,540 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 875 | r | O2_P7_DiagnosticCode_PR_M_MainDashDashCheck.R | ################################################
#Load the list of Main Sheet folder Names
#Loaded prefix file
MainDashDashCheck <- function(){
REF.Prefix = read.csv("//192.168.2.32/Group/Data Team/Brand_Update_Location/5_R_Brand_Reference_Files/Brands_Prefix.csv", header = TRUE, row.names=NULL)
for (i in 1:nrow(RE... |
8ac8c7f0c1713478cddb814b4c9035e4e1dcd191 | f2a03d4bc7e2ff7a86d8b35a4e833e6d23df86c3 | /test_indicator_functions/ReadIndicatorParms.R | bcde8af2d198c7545a6160264645741dca322cb7 | [
"MIT"
] | permissive | NIVA-Denmark/ekostat | 51d639d2b70ea19f48ecd4428eb9f6fa775a2707 | 2254d7cb9ec52c96a2e26afad1db0cb6c00f6d6a | refs/heads/master | 2021-07-02T20:31:11.389288 | 2019-04-29T12:23:29 | 2019-04-29T12:23:29 | 170,341,805 | 0 | 0 | MIT | 2019-04-29T12:23:30 | 2019-02-12T15:28:55 | R | UTF-8 | R | false | false | 310 | r | ReadIndicatorParms.R | ReadParms_chla <- function() {
covparams_CumCover <- haven::read_sas("data/covparms_chla_test.sas7bdat")
parmest_CumCover <- haven::read_sas("data/parmest_chla_test.sas7bdat")
res <- list(
covparams_CumCover = covparams_CumCover,
parmest_CumCover = parmest_CumCover
)
return(res)
} |
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