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values | gha_event_created_at timestamp[us]date 2012-06-21 16:39:19 2023-09-14 21:52:42 ⌀ | gha_created_at timestamp[us]date 2008-05-25 01:21:32 2023-06-28 13:19:12 ⌀ | gha_language stringclasses 60
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values | filename stringlengths 1 141 | content stringlengths 7 9.18M |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
167cb2b092467d43f7e052544e6f2b464b4d54cb | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.security.identity/man/fms_list_discovered_resources.Rd | a022878ebd2cb7a70232199abb7768c80fc2d462 | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | true | 1,580 | rd | fms_list_discovered_resources.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fms_operations.R
\name{fms_list_discovered_resources}
\alias{fms_list_discovered_resources}
\title{Returns an array of resources in the organization's accounts that are
available to be associated with a resource set}
\usage{
fms_list_discover... |
8a24b5f15cd2af7a6de2fd6a369edd6930626e27 | d28e1990f31441eb2e776f4b468bc2af65316164 | /load_distribution.R | 4430df47990f9400e9f6ee043a8ee1e2e57dc24b | [] | no_license | balasubramaniansa/Representative-load-curves | af0215c8b052a4daa33e4e251172e3751cdb7a57 | 56910445cebca7df0143eee41abc595f20f81554 | refs/heads/main | 2023-07-15T18:38:40.150919 | 2021-09-01T16:34:19 | 2021-09-01T16:34:19 | 402,124,332 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,816 | r | load_distribution.R | #Jan-Feb DATA
start_day = 1
number_of_days = 1
block1_number_of_hours<-7
block2_number_of_hours<-6
block3_number_of_hours<-5
block4_number_of_hours<-3
block5_number_of_hours<-3
block1<-c(1,7)
block2<-c(8,13)
block3<-c(14,18)
block4<-c(19,21)
block5<-c(22,24)
jan_feb_data<-as.data.frame(seq(from=start_day... |
4ad824482270b07330c5bd32508ec0531bec88f2 | c916bd4e5574beceade8b369291955c9b8bf1a3c | /rankall.R | 7a3f51e4ea5689fb9df74195d7bff681f0265f00 | [] | no_license | vsunjeev93/R-programming-Assignment-3 | d4de253c5fa8773e10aced47559a399b9709a4ae | 3090d6ac17d37874ab13fec64ea14feda153f862 | refs/heads/master | 2020-03-30T22:50:55.898229 | 2018-10-05T06:40:38 | 2018-10-05T06:40:38 | 151,680,329 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,982 | r | rankall.R | rankall<-function(out,num='best'){outcome<-read.csv('outcome-of-care-measures.csv',colClasses='character')
sf<-split(outcome,outcome$State)
# function to order lists in ascending order of mortality rate
order_ha<-function(s){f<-as.data.frame(s)
ord_out1<-f[order(as.numeric(f$Hospital.30.Day.Death..Mortality..Ra... |
c4a8a7b3bfe9dc8ad44f210016dcfd834ff3a5db | a6590ff24de52a38946bd66a8a03e8212dc79fc1 | /book.r | d0bb894931f749f9639607eb37cdb4c158880f29 | [] | no_license | therao096/recommendation- | a4b4ce8d5aa0677d8e0cb9f7000744c54abd344c | eb76cc6940587e36bec48e9264437e06a944b3ff | refs/heads/master | 2022-11-06T07:11:50.015599 | 2020-06-20T12:11:29 | 2020-06-20T12:11:29 | 273,701,083 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 719 | r | book.r | setwd("F:\\EXCEL R\\ASSIGNMENTS\\RECCOMENDATION SYSTEM")
library(recommenderlab)
library(caTools)
library(Matrix)
books_data <- read.csv("book.csv")
View(books_data)
class(books_data)
str(books_data)
table(books_data$Book.Title)
hist(books_data$Book.Rating)
book_matrix <- as(books_data,'realRatingMatrix')
View(book_mat... |
bd6b1f395a81dfdb2819c67e936d07c1fbfbf15c | 6690e529b8ce8823febfac75e1696dfdb2d960a2 | /sev_score_fun.R | 10940746e3923b071c881a04a6a4b7d8089a933d | [] | no_license | jjwill2/sfcw_sed_exposure_analysis | a54285c2d30839bdd93238f00bdfe3ff55ec57c9 | 98aea7c89f1fd0e91a555c379c6dbf6791b23308 | refs/heads/main | 2023-01-23T23:41:06.992465 | 2020-11-24T00:42:21 | 2020-11-24T00:42:21 | 315,470,541 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,036 | r | sev_score_fun.R |
# function to run checks on data ----------------------------------------------------------------------
# write separate functions for each check, then combine into one master check function
# check1 - dataframe column names
check1 <-function(dataframe) {
df_colnames <-colnames(dataframe)
... |
3b129f75b17ea9291c6d55e16a277d18a64b2293 | 1267e61a042649f0640e773c6d588976ae69fc25 | /R/lasagna.R | de51765eeb35bb3a6b7d8ac0da4ab7dccbe829c6 | [] | no_license | rshiny/lasagnar | 168d98e176b275b0a12b353595c6741150bf0529 | 32ae55500fffcf17a1dd01a348ea19c204a168b4 | refs/heads/master | 2021-05-31T09:09:42.345502 | 2016-02-03T18:57:39 | 2016-02-03T18:57:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,899 | r | lasagna.R | #' @name lasagna
#' @title Lasagna Function
#' @description Generic Lasagna Function
#' @param X a matrix: each row a subject, each column a time/location across subjects; preferably rownames are the ids.
#' @param col colors. See "Escaping RGB Land" and the vignette for the colorspace package
#' for followable advice... |
16c934ea1c4027a8e98b0816cbb264ddf8e5102d | 09dce3d5ee039ab4e33a38724101a54db6ce3ad8 | /plot3.R | 1f57c5ffcf800e311665cc089a9933d90dd3d678 | [] | no_license | dekaufman/ExData_Plotting1 | 0c8ca5d88c6da0ba6ebdeff8c812f0f4d3e43e6b | 34db1cc3b50d65684f3aeaf074b0916263737ce5 | refs/heads/master | 2021-01-16T20:41:05.666823 | 2015-02-08T23:55:10 | 2015-02-08T23:55:10 | 30,505,541 | 0 | 0 | null | 2015-02-08T20:59:00 | 2015-02-08T20:58:59 | null | UTF-8 | R | false | false | 720 | r | plot3.R |
data <- read.table("household_power_consumption.txt", header = TRUE, sep = ";", col.names = c("dt", "tm", "active", "reactive", "vltge", "amps", "sub1Wh", "sub2Wh", "sub3wh"), na.strings = c("?"), nrows = 2100000)
dataTwoday <- subset(data, data$dt == "1/2/2007" | data$dt == "2/2/2007")
png(filename = "plot3.png", w... |
598b08f86aa70a486516fc70ac3f4afc55b23e39 | fc0d01525a10db22d9d8b2076bb3fb4a6d556df2 | /oldcode.R | b24d7cdad16d89b4d4907d4c5832a0e7eddf2bcc | [] | no_license | adderan/multitask | 11f9c9278c4fb787e17da4a39ccfc5c67c274692 | 12cbe5afdce2089eaa4fe59f3af3f5faa379330b | refs/heads/master | 2020-04-29T02:26:40.179606 | 2015-01-20T01:01:09 | 2015-01-20T01:01:09 | 15,994,453 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,857 | r | oldcode.R | #no longer used
run.ando <- function(X.all, y.egfr, y.sens) {
m <- length(y.egfr)
nproblems <- dim(X.all)[[2]] #number of prediction problems to use. The i-th prediction problem is the i-th sample in X.all
p <- dim(X.all)[[1]]
print(m)
X.list <- list()
y.list <- list()
#build list of X.all repeated m tim... |
b67778bcd4cc7796f349651bb6f602db26d23f25 | 71c0c61fad07794e9d12c603115323b72942382d | /plot2.R | a0088fba8be5c77c3bbd05256ff5a64c3471f4b0 | [] | no_license | PaddyPadmanaban/ExData_Plotting1 | 1639b28cd97d0a1ab62b0603f1b50934dbf1288d | 1b5ca3e01b8243c6ebeecc494e61d42cf1c311d1 | refs/heads/master | 2021-01-16T23:12:56.981480 | 2015-02-08T00:45:08 | 2015-02-08T00:45:08 | 30,226,967 | 1 | 0 | null | 2015-02-03T05:33:18 | 2015-02-03T05:33:18 | null | UTF-8 | R | false | false | 744 | r | plot2.R | ## Plot 2 Project 1 Exp Data Analysis
## Read the required data in (Only the 2 dates of interest)
sel1 <- read.csv.sql("C:/Users/Padma/Documents/Coursera/ExpDataAnalysis/household_power_consumption.txt", sql = "select * from file where (Date = '1/2/2007' or date = '2/2/2007') ", sep = ";")
## Set up the plot
plot... |
46e4e256e1332952d0e476bbce7419a40d51a1d6 | 79ff729ee201e0f283c4ff5c16f23ebedc8e07e1 | /plot4.R | 8b16aa95b1710a2a4752766263fa73edd9e4d6d6 | [] | no_license | karthik-anba/Exploratory_Data_Analysis | 46719de1f926fa5859e51e38c33bedacf0998e47 | 8c35588b0c5097daf2c4d876768f438ce1bf87b3 | refs/heads/master | 2021-05-28T01:16:21.030124 | 2014-11-23T21:15:08 | 2014-11-23T21:15:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,044 | r | plot4.R | ##Loading the data
data <- read.table(file.choose(), header = TRUE, sep = ";", na.strings= "?")
attach(data)
str((data))
y<-data[(Date == "1/2/2007" | Date == "2/2/2007"),]
str((y))
detach(data)
attach(y)
z <- paste(Date, Time)
y$DateTime <- strptime(z, "%d/%m/%Y %H:%M:%S")
head(y)
y$Date <- as.Date(y$Date, format = "%... |
aa6c7402a7ce8bc225bbf652837ee8823ffe112c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/gap/examples/qqunif.Rd.R | d0b12b800e5515891ec9b15dbf4c86045b066e5e | [] | 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 | 434 | r | qqunif.Rd.R | library(gap)
### Name: qqunif
### Title: Q-Q plot for uniformly distributed random variable
### Aliases: qqunif
### Keywords: hplot distribution univar
### ** Examples
## Not run:
##D # Q-Q Plot for 1000 U(0,1) r.v., marking those <= 1e-5
##D u_obs <- runif(1000)
##D r <- qqunif(u_obs,pch=21,bg="blue",bty="n")
##D... |
f034d96d844cb9fd6772c296d888081e0d6cb909 | 9c8962826b6125045ec4f93cab879bc26c9f9a59 | /mse/11b-rebuilding-recovery-target-2022.R | 0d1acfd16994fe637506f1b6ac74972b718d1565 | [] | no_license | pbs-assess/yelloweye-inside | c00c61e3764b48e17af3c10b7ddf7f5b5ff58d16 | 8ea4a374f3cee21e515a82627d87d44e67228814 | refs/heads/master | 2022-08-19T05:15:03.352305 | 2022-08-15T23:51:36 | 2022-08-15T23:51:36 | 219,026,971 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 16,306 | r | 11b-rebuilding-recovery-target-2022.R |
FRENCH <- FALSE
library(tidyverse)
library(rosettafish)
library(gfdlm)
if(FRENCH) {
options(french = TRUE)
}
sc <- readRDS("mse/om/ye-scenarios2.rds")
sc_french <- c(
"(1) Base",
"(2) Faibles prises",
"(3) Recrutement\népisodique",
"(4) Estimation de la\nsélectivité du RPFD",
"(A) Faible M ",
"(B) CV... |
19f41dd733c27abca4e37f2f57087ec7d55a6044 | ec44cf2c3f4316610e890bee2665cdb15cb0d986 | /Simulation/PracticasVectores_ii.R | 8d20529cf318dbea0c895176bd63361b7a7d1852 | [] | no_license | jr-98/R | c92959c244f3fa51a98f7859a275451675c076ce | 8164b6ccfdba4c9c5a58ba81bb76482d73066f55 | refs/heads/master | 2020-07-03T16:30:23.561059 | 2019-08-23T09:36:03 | 2019-08-23T09:36:03 | 201,968,926 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,062 | r | PracticasVectores_ii.R | x<-c(1,2,3,4,5)
min(x) #devuelve l valor menor del vector
mean(x)# devuelve la ?
median(x)# Devuelve la media
var(x)# devuelce la varianza
prod(x)# El producto de todas los valores del vector
cumprod(x)# devuelve un vectror que contiene la multiplicacion sicesiva de sus terminos
quantile(x)# Devuelve los cuartiles y s... |
17311ff268cfb5bb39700e1200ca2f52c0864955 | 64341466a2f01a718abece011226197d0585b393 | /tests/testthat/test-crosstalk.R | 7489ff72c0c4d6c24a0895b07865fb6756ac570c | [] | no_license | ggobi/ggally | 2aa29f340250005c084e1a913a94c69af685ccaa | d79b4efae51ae78bc3fc2d4c94504327169e1b42 | refs/heads/master | 2023-09-03T21:00:58.614094 | 2023-07-03T02:07:11 | 2023-07-03T02:07:11 | 1,384,794 | 558 | 124 | null | 2022-11-04T16:15:43 | 2011-02-19T00:18:46 | R | UTF-8 | R | false | false | 1,090 | r | test-crosstalk.R |
context("crosstalk")
test_that("crosstalk works with ggduo and ggpairs", {
skip_if_not_installed("crosstalk")
sd <- try(crosstalk::SharedData$new(iris[1:4]), silent = TRUE)
if (inherits(sd, "try-error")) {
skip("crosstalk data can not be initialized")
}
expect_silent({
pm <- ggpairs(sd)
})
... |
e464d5ec96a09e650cabad91d77085c63f24b6e0 | f77d4ae139d960f6138e29f4e5e9e39fcba528fb | /R_CODES/Previous/final R codes/5_1 compare ABC posterior to likelihood posterior.R | 390bbe19148efced57a670be7feade13e24f3336 | [] | no_license | zenabu-suboi/masters_project | fc80eb077af8e92bf81cda94952d4dec196bb263 | d865eb68e66d35c52229023d7aa83b78fd7518f4 | refs/heads/master | 2022-04-22T15:01:15.063667 | 2020-04-28T10:50:17 | 2020-04-28T10:50:17 | 179,095,292 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,971 | r | 5_1 compare ABC posterior to likelihood posterior.R | ### COMPARE ABC POSTERIOR TO LIKELIHOOD POSTERIOR
################ REJECTION ABC
### abc0.1
sum((ras.abc0.1.mat-lik.abc0.1.mat)^2)
### percentage overlap:
1-((sum(abs(ras.abc0.1.mat-lik.abc0.1.mat))+(2*(nreprej*0.1-sum(ras.abc0.1.mat))))/ (2*(nreprej*0.1)))
### abc0.01
sum((ras.abc0.01.mat-lik.abc0.01.mat)^2)
... |
87cf8d2128c5c1c7119d394b395411c70c73ecb5 | 4f98c8660757b8d163312336992cc68604f15df7 | /run_analysis.R | 00e10ec381147d32c0a7cfca9237b0d9fc070e05 | [] | no_license | rajithnair/Getting_Cleaning_Data | fb3741a9e092e1d66b3e42079d813795ab9d473e | 26deb16467b43ad702d93db0b1fb5000be06f345 | refs/heads/master | 2021-01-22T14:32:42.522422 | 2014-06-22T18:09:10 | 2014-06-22T18:09:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,258 | r | run_analysis.R | run_analysis<-function(){
### OBTAINING THE DATA FROM THE SOURCE
fileurl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
zipname <- "./data/getdata-projectfiles-UCI HAR Dataset.zip"
# Creating the data folder
if (!file.exists("data")) {
di... |
5ecf329a5af926d79ff96d87ce0404d7767c77b7 | bb17abdd113af9108aec487935cc86a0906d902e | /Shiny Datasets.R | 53a2a1e7db85882e29e9be0fcdc1d183ebf13c21 | [] | no_license | jn131/Spatial-Visualization-For-Public-Health-Intelligence | 3e12f02702565b052342800062fc0b6f64aa2036 | 7e9af770bdaeac4bdd85661b46619b1226d2bb14 | refs/heads/master | 2021-07-12T20:32:56.641813 | 2017-10-12T18:32:29 | 2017-10-12T18:32:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,152 | r | Shiny Datasets.R | #.......................
# Shiny Datasets
# Jessica Needleman
#.......................
## df
df <- deaths_county %>% dplyr::rename(GEOID10 = GeoIDBlkGr)
# race_shiny
race_original <- c("African American","Chinese","Filipino","Hawaiian","Japanese","Native American","Other","Other Asian","Unknown","White")
race_new <-... |
af1294c12d2ffc129b92080bc505903c38865eef | 1686456163a1cf0d04a4c8e22cae63e1566f13cf | /tree.R | 487e4717071a6b22af454e1f5e055c4e233b16ca | [] | no_license | yaomisun/R-Modelling | 66beddd5004ec847a89b8d45ba4df28bff31411b | b6a38c9dbde658367f7386f46b7edebf3779c8ce | refs/heads/main | 2023-01-08T17:08:35.823558 | 2020-11-10T17:58:38 | 2020-11-10T17:58:38 | 311,426,759 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,946 | r | tree.R |
#read data
myData=read.table("uscrime.txt", stringsAsFactors=FALSE, header=TRUE)
#myData
library(tree)
Ctree<-tree(Crime~.,data=myData)
summary(Ctree)
#visualize
plot(Ctree)
text(Ctree)
Ctree$frame
Ctree$where
#predict
PredTree<-predict(Ctree)
PredTree
plot(PredTree,myData$Crime)
#computes the root mean squared er... |
9881a18f313bb159166c92e294136e6c32cd638a | 2ce2a707b6805971283454659d014e74a545ab60 | /R/surg2rec.R | f0babd0ddfc491f12145a7f644e82a717831f952 | [] | no_license | UCCC/McLeod | 6843ae4f6d0b0264ced9de21a82438fc0c362d2d | 5368d34d5a8efc9360f7b955558a1f09c85fe735 | refs/heads/master | 2020-03-11T16:17:04.119880 | 2018-07-19T20:59:12 | 2018-07-19T20:59:12 | 130,112,059 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 291 | r | surg2rec.R | #' surg2rec: The goal of surg2rec is to generate the tables requested by Lisa McLeod.
#'
#' The surg2rec package contains the function, .RMD files, and data files necessary for generating tables from CHORDS data for Surgery to Recovery project.
#'
#' @docType package
#' @name surg2rec
NULL
|
842d0b7c9ea5bda5c6f39ee4d18fdfd39e7727dc | 2ed70e68a904ddb39e489b79b7569a847919a8ae | /run_maxent_one_fold.R | 3d933abba0c2746ac652f4f7b35cb2a0e96a561d | [] | no_license | abbylute/Rock_glacier_modeling | 90fea79dfd9de11243ccfe251bbad06bfde4430e | 316aa762e17fb5d5f47d9b1fc7f837b08bee99ed | refs/heads/master | 2023-06-02T03:49:17.588779 | 2021-06-25T22:07:46 | 2021-06-25T22:07:46 | 308,749,557 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 407 | r | run_maxent_one_fold.R | s1 <- s2 %>% filter(fold==2)
bg.coords1 <- bg.coords[bgfold==2,]
bgfold1 <- bgfold[bgfold==2]
occ1 <- occ[s2$fold==2,]
bg11 <- rbind(occ1,bg.coords1)
p = rep(0,nrow(bg11))
p[1:nrow(occ1)] <- 1
bg111 <- bg11[,3:ncol(bg11)]
sb1 <- dismo::maxent(bg111,p,factors='lith')
saveRDS(sb1, file=paste0(outdir,'ENMeval_object.RD... |
e8e7cf2798c9029ef8f6dde9ea4e72c5ddcda1d8 | dc65db19ba8bcd45f1954599b8ff526b9d44573c | /ScripsCassava.R | 01b157772cb15374d8711fecef8c0af86971980e | [] | no_license | caosmax/gfsf_project | dd345be3e63b73eeee9edc8b9365f65e4f83fc22 | bbd770aa0411cdb45e43a009853368b7f4ed9b71 | refs/heads/master | 2021-01-19T10:06:38.217105 | 2017-04-07T18:11:12 | 2017-04-07T18:11:12 | 87,828,521 | 2 | 0 | null | 2017-04-10T15:40:46 | 2017-04-10T15:40:46 | null | ISO-8859-10 | R | false | false | 45,217 | r | ScripsCassava.R | ### preparacion de los datos para presentacion de yuca.
## Autor: CArlos Eduardo Gonzalez
## prep
#librerias------
library(XML)
library(treemap)
library(migest)
library(circlize)
library(rworldmap)
library(maptree) #maps of
library(ggplot2) #plot
library(dplyr) # reshape
library(raster) # procesar Raster
library(pl... |
46cdc7ba3161979bc417a8930aebff497bfe2a4b | 61999842a6809627d96b554267b51b8b2e563816 | /rankall.R | 25ded797a7dd948bfebc1f5524f0500f4ea03500 | [] | no_license | odonovdo/ProgrammingAssignment3 | 3c433b06dfd5f1e3f7be6a47aac275e3714264b1 | 216f6fb63e6c0c779962e60ddceef8565c3b7e01 | refs/heads/master | 2020-04-13T03:32:19.779077 | 2015-03-25T09:11:41 | 2015-03-25T09:11:41 | 32,801,307 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,852 | r | rankall.R | setwd("C:/Users/odonovad/datasciencecoursera/ProgrammingAssignment3")
rankall <- function(outcome,num= "best") {
File.in <- read.csv("outcome-of-care-measures.csv", colClasses = "character")
# remove dots from colunms
colnames(File.in) <- casefold(gsub("[.]"," ",colnames(File.in)))
# creat... |
064263e942446ac3f0ba69f04a9c8e2c25f576ce | fe3f2623101dac6ecf2bab6885d80fff02d31d9f | /assignment(Introduction_to_R_Programming)-shrutigupta.R | a976aeb7ee093ddedbd3d1079a353c8aa2404b39 | [] | no_license | shrutinikki/Data-Science | e4660db250b6b2ea2cd5b2246c5196217ef54de5 | 99e05e7e1a3877c8d0df1c05a9ea2213906bc03c | refs/heads/master | 2021-06-24T12:27:48.395402 | 2021-01-26T12:18:06 | 2021-01-26T12:18:06 | 199,778,432 | 0 | 0 | null | 2021-01-26T12:18:07 | 2019-07-31T04:27:35 | Jupyter Notebook | UTF-8 | R | false | false | 1,803 | r | assignment(Introduction_to_R_Programming)-shrutigupta.R | #Author: Shruti Nikhila Gupta
#Assignment 1: Introduction to R Programming
#Assignment Objective: Hands-on practice on diamond dataset using functions from dplyr package
#Questions
#Q1. Which color has maximum price/carat
#Q2. Which clarity has minimum price/carat
#Q3. Which cut has more missing values
#Q4.... |
e964415af31ae605899a1508366179a7dc688b5c | d769c2c28cdf8d1a8b1a825f18424c4ecd6e5ff1 | /man/summary.equivttest.Rd | 757839b7f4a7a403384e09a67aa9d0cd027be8e0 | [
"MIT"
] | permissive | jwbowers/equivtest | 8a025ef7b00b9e83283dae42f30a4d155ce9a7f0 | 5f881c57b559ec38792fa6c03842dcbfeabbf43a | refs/heads/master | 2022-01-08T14:57:16.591079 | 2019-07-03T02:01:57 | 2019-07-03T02:01:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 664 | rd | summary.equivttest.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/summary_equivttest.R
\name{summary.equivttest}
\alias{summary.equivttest}
\title{Equivalence t-Test Summary Function}
\usage{
\method{summary}{equivttest}(res)
}
\description{
\code{summary.equivttest} is a summary function for objects of the... |
e745749e5db6af7ee7db3e5115c464ba8061a41e | de7b5af9c415426ad5496ccf0b87f91b663cee29 | /man/checkNotDigitInDataframe.Rd | 57b8e0f90970c7640e6d4970ca002c5a92cf1164 | [
"MIT"
] | permissive | rgriffier/statsBordeaux | bb465057eab0b41e6d1515f02cfd13adb8179ac8 | ded4292fb43e2a959058267a1f707d25505c0b7d | refs/heads/master | 2021-09-06T15:02:07.420504 | 2021-08-03T13:56:13 | 2021-08-03T13:56:13 | 178,895,755 | 2 | 0 | null | 2020-09-28T14:14:45 | 2019-04-01T15:45:33 | R | UTF-8 | R | false | true | 1,053 | rd | checkNotDigitInDataframe.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/statistic_functions.R
\name{checkNotDigitInDataframe}
\alias{checkNotDigitInDataframe}
\title{Check if data are only digits}
\usage{
checkNotDigitInDataframe(data, returnError = FALSE, excelFormat = TRUE)
}
\arguments{
\item{data}{a data.fram... |
8547e65b1ed8b2dfe0307280e752c39a55b72095 | 1c591b580a42e90ba318675e3cebeabdfc06534a | /R/func__geoTempAnalyser__calcAllelicDiversityPerCountry.R | eb5b6a3d91ed1bfd4f44a250ae9e11a1b7506ad8 | [
"Apache-2.0"
] | permissive | wanyuac/GeneMates | 974d9a883d43ccd7602167204d8b3ff5bba6b74c | e808430b2cdd920f1b9abd8b6b59993fde8754a7 | refs/heads/master | 2022-08-31T13:00:42.583172 | 2022-08-08T10:01:02 | 2022-08-08T10:01:02 | 138,949,733 | 25 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,347 | r | func__geoTempAnalyser__calcAllelicDiversityPerCountry.R | #' @title Allelic diversity per country or any kind of regions
#'
#' @description Calculate Simpson's or Shannon's diversity index for alleles per
#' country or any other kind of geographic regions. Each strain must belong to
#' only a single country.
#'
#' @param sam A data frame whose first and second columns are str... |
3ca2481c82930cb54a2c64a935f293c2f6ee52e0 | 2e85b739e89e8be13c2325049016f63079f4db75 | /man/env.two.Rd | 5e15581c9ca90c92952a74603b2ca778e6f0ac56 | [] | no_license | cran/gma | 8c5028cea049bdfbdb5bb91dab63cc32ad0f2335 | eb424ae18c87f5d60ebf0cd8abe23b5ccd429cc2 | refs/heads/master | 2021-06-30T00:10:55.739931 | 2017-09-19T09:07:35 | 2017-09-19T09:07:35 | 104,059,236 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,351 | rd | env.two.Rd | \name{env.two}
\alias{env.two}
\docType{data}
\title{ Simulated two-level dataset
}
\description{ "env.two" is an R environment containing a data list generated from 50 subjects, and the parameter settings used to generate the data.
}
\usage{data("env.two")}
\format{ An R environment.
\describe{
\item{\code{data2}}{... |
9df0f5da9aeb7e4d0d84ab0a5ede084b4db12de6 | a2891ad5a64ec595f04b6b6dbc182e9b4604ad68 | /R/odd_even.R | b36d6152f440f0b82a56751c4a1d48d874bbae80 | [] | no_license | wyliehampson/lasagnashark | 38a0861dcfd490500452f23cb31b5b2af4988112 | 822b688eaece25c9e4e775e5576bb03b3a33ba40 | refs/heads/main | 2023-07-13T07:31:35.494289 | 2021-08-20T16:32:23 | 2021-08-20T16:32:23 | 395,761,721 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 343 | r | odd_even.R | #' Odd_Even
#'
#' Adds two numbers together and says whether the sum is odd or even
#'
#' @param num1 The first number
#' @param num2 The second number
#'
#' @return
#' @export
#'
#' @examples odd_even(10, 53)
odd_even <- function(num1, num2) {
sum <- num1 + num2
if ((sum %% 2) == 1) {
print("Odd")
} else {
... |
f498ae3a1be5195862dad3330b2fca2c8f97c5b7 | 318d4910b23bd2388d51f42201ac849e48de7581 | /cachematrix.R | 692990a45214663d384a99076e2ebec21ddfc9c4 | [] | no_license | BenMacrae/ProgrammingAssignment2 | b760e22b27853858d75adc09428e4b8556590484 | f9e6446154a2a39cca750517acb08bd61f8d8dad | refs/heads/master | 2021-04-29T14:07:36.567050 | 2018-02-16T16:30:49 | 2018-02-16T16:30:49 | 121,767,001 | 0 | 0 | null | 2018-02-16T15:34:26 | 2018-02-16T15:34:25 | null | UTF-8 | R | false | false | 1,429 | r | cachematrix.R | ## Here we define two functions which will allow a matrix inverse to be calculated
## and stored, such that subsequent calls to calculate the inverse will load the
## result from previous computation instead. This is a slightly modified version
## of the similar script taking means provided in the exercise text.
## Th... |
51431674666ec246f80f8849ca1a0c48491dbea2 | 0e8702748e6f94e40243ac5af83056e423cb5df5 | /Metadimensional tools/HepatocellularCarcinoma/SNF/HPC_SNFTool_Analysis_3Labels_UsingTop10Top5FilteredDataset.R | 372ed72a75076bc165360933a4001fc144d12b0f | [] | no_license | AtinaSat/Evaluation-of-integration-tools | 373aeec1669384371c51115d5dd35b966d95f05b | 2cdd6316bcb8e899ba57976c0ff5e040bc94cc68 | refs/heads/master | 2020-04-19T03:36:08.812575 | 2019-08-07T12:14:41 | 2019-08-07T12:14:41 | 167,938,938 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,657 | r | HPC_SNFTool_Analysis_3Labels_UsingTop10Top5FilteredDataset.R | ## HPC 3 labels SNFTool analysis using Top10Top5filtered
library(SNFtool)
library(tictoc)
load("HPC_Top10Top5Filtered_data.Rdata")
tic("SNF-HPC3Labels")
# Calculating distance matrices
dist1 <- as.matrix(dist(mrna))
dist2 <- as.matrix(dist(mirna))
dist3 <- as.matrix(dist(me))
# Calculating affinity matrics
W1 <- aff... |
4e515a32b3000c5d897681170405359c26f5ece1 | 4d3b6b633f8f5be05f320cd9a6ad5b94e39dfe16 | /javacode/target/classes/rscripts/generateModel.R | dc1cd9a2a20c0b6d61265d6d01309ea4277f7280 | [
"MIT"
] | permissive | sbelete/ReFa | 651bcd7bd3ba593363c5fa7499727fe716641039 | 3c271051e40f6fc4c5920436ebc12aeb9abc87d7 | refs/heads/main | 2021-07-15T14:17:38.749739 | 2017-10-20T18:24:04 | 2017-10-20T18:24:04 | 91,976,027 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 669 | r | generateModel.R | library(caret)
library(RSQLite)
library(DBI)
sqlite <- dbDriver("SQLite")
exampledb <- dbConnect(sqlite,wd)
dbListTables(exampledb)
dbGetQuery(exampledb, "SELECT * FROM main")
results <- dbSendQuery(exampledb, "SELECT * FROM main")
data = fetch(results,-1)
data = data[ ,c(2:8,13:15)]
data$real = as.factor(data$real... |
3ad2fe6ff170c53a23762ae523a43d4fb12c0a17 | 1d5f8ab25866b9cb4898be799bb700d260ef5b62 | /R/kernel_model.R | d18398fce519e2d93ec787212643b39ea27696b8 | [] | no_license | CollinErickson/GauPro | 20537d576a5a47308840ecbe080dabb2c244b96c | c12cfa14b5ac4e1506daec1baec27f75a2253f53 | refs/heads/master | 2023-04-16T08:42:18.177784 | 2023-04-12T00:59:27 | 2023-04-12T00:59:27 | 64,254,165 | 14 | 1 | null | null | null | null | UTF-8 | R | false | false | 147,791 | r | kernel_model.R | #' Gaussian process model with kernel
#'
#' @description
#' Class providing object with methods for fitting a GP model.
#' Allows for different kernel and trend functions to be used.
#' The object is an R6 object with many methods that can be called.
#'
#' `gpkm()` is equivalent to `GauPro_kernel_model$new()`, but is e... |
13defe2159ffe04150a3e90bb5dfa5e08e288988 | e979752c4498c5edf47791d8b7eaafb2730524bf | /sim20032009/figs/points/fixed/anualSeries.R | f2f9e131094c3845b40649e9c027de8fe41396e9 | [] | no_license | ClaudiaGEscribano/aod_and_PV | 0bf4d6c7398351aebdef7b9f9538246c5ee2bd49 | 77eaa4e454ce4ec4ec784795e2e89b8945bc1737 | refs/heads/master | 2021-03-27T15:41:09.158241 | 2018-12-10T14:58:39 | 2018-12-10T14:58:39 | 83,782,692 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,241 | r | anualSeries.R | ## Este script sive para representar las series temporales de la media anual de radiación en los distintos puntos donde están las estaciones de bsrn.
library(zoo)
## MODELOS
load("../../../calc/points/fixed/bsrn_fixedY_caer.Rdata")
load("../../../calc/points/fixed/bsrn_fixedY_cno.Rdata")
## SATELITE
load("../../.... |
7ae3499eebc4cc5207ea6f78ee515873334f8a74 | 0ca78ef5a8670fbdab55409eecda579cec2baf68 | /globaltool/data_handle/collectdata/winddata/forex/getFxWind.R | fa98ff50788e8c7c2bb78843256fe5bba43550d2 | [] | no_license | zhurui1351/RSTOCK_TRAIL | ab83fdef790778a1e792d08a876522ef13a872e6 | 2396c512c8df81a931ea3ca0c925c151363a2652 | refs/heads/master | 2021-01-23T09:01:36.814253 | 2019-05-17T15:26:19 | 2019-05-17T15:26:19 | 23,482,375 | 9 | 4 | null | null | null | null | UTF-8 | R | false | false | 281 | r | getFxWind.R | getFxdata_wind = function()
{
path='D:/data/collectdata/windata/forex/usdcny_d.csv'
today = as.character(as.Date(now()))
w_wsd_data<-w.wsd("USDCNY.EX","close","1995-01-01",today,"Fill=Previous;PriceAdj=F")
data = w_wsd_data$Data
write.csv(data,path,row.names=F,quote=F)
} |
802c7d267673a057cc81bee5c0c40b3789567fc1 | bb7eb91b3bb804dc4c479450fc6bef8143ef5619 | /get_random_english_tweets.R | ec0aaba8a1cff706abb0ab255cad382634b72ddb | [] | no_license | Octophi/covid_tweets | 624765d757089b0cab0ef9973f39861e5b089bc4 | b212d96cabe45f72cd34f58f543a7a4327f32d77 | refs/heads/master | 2023-02-19T04:22:24.142195 | 2021-01-09T00:30:42 | 2021-01-09T00:30:42 | 327,993,462 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,341 | r | get_random_english_tweets.R | # Gets sample of 4000 random English tweets from a particular day
# List of Things you might have to change, in order of importance
# - the filepath in filepath_prefix on line 10
# - the filepath for where you want to save your stuff on line
# - the seed in set.seed, if you're not getting enough tweets on line 34
# ... |
5f3fe800cbd45227710fe9cb1f01faaa44d23afd | e2f3fee3cb8f1abdee08724f0fe8a89b5756cfbe | /COSTdata/man/CEFAS2006cs.Rd | d6516cb0a359178711c23c922f168ca4ea00d12c | [] | no_license | BackupTheBerlios/cost-project | 1a88c928f4d99db583a95324b31d6a02d9bd20c9 | 4ab39d16c48f031ca46512545895cb17e5586139 | refs/heads/master | 2021-01-21T12:39:53.387734 | 2012-03-26T14:58:36 | 2012-03-26T14:58:36 | 40,071,425 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,800 | rd | CEFAS2006cs.Rd | \name{CEFAS2006cs}
\alias{CEFAS2006cs}
\alias{CEFAS2006ce}
\alias{CEFAS2006cl}
\docType{data}
\title{CEFAS sampling, landings and effort data}
\description{
CEFAS commercial sampling, landings and effort data in the COST data exchange format.
\cr Mixed spaecies data for 2006.
}
\usage{
data(CEFAS2006cs)
data(CEFAS2006... |
24dd0e12d96e0d597b17a3b7beff6e0f54301a4e | a5895f7ac0ad3f9e892faac18807278d41172bb5 | /Rscripts/trials/PdPy_Div_Assembly_Bacq0_HNFq1_int.R | b67d2c25cd6385ffc05386ea0ff1d14c5c438d49 | [] | no_license | OscarFHC/PdPy_Div | 9c149d9020446ff9e446321df9c4b246187dfe32 | a373aa152ade2bcba957b564e5c2a58f3b3794c9 | refs/heads/master | 2021-12-15T07:31:23.671225 | 2021-12-02T10:20:27 | 2021-12-02T10:20:27 | 202,524,018 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 41,776 | r | PdPy_Div_Assembly_Bacq0_HNFq1_int.R | ###############################################################################################
##### Loading packages ########################################################################
###############################################################################################
if (!require(rmarkdown)) {
ins... |
fa31c1c50efe0894f871e94fc967390cc158a7d0 | c892b24af15e4ca31a72137b3c9ab1db056dfdcc | /R/zzz.R | 44aba989b1e3ecff2fe2aa4d9746f95577201bd0 | [] | no_license | melimore86/nestR | 4ac72ca0856aac03cfa5117aaf66fbfe23ee25c4 | 51b094927fbca2b63e357bb71c9d35501c3af835 | refs/heads/master | 2020-08-05T03:59:57.996812 | 2019-08-20T15:58:16 | 2019-08-20T15:58:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 89 | r | zzz.R | .onAttach <- function(libname, pkgname) {
packageStartupMessage("Welcome to nestR!")
}
|
33d69ebec6ea6268441e31d749aff0294d24d50d | bbc3754d8900e36146bf80d3cc98f5c36c450cd2 | /man/ggplot_summary.Rd | 6f616057dd04936b83d4111abea20708fee59960 | [] | no_license | netterie/cantrance | e9782750337bffd0476b76bfed627df0dddf6c5f | a2b29ed44c6dfd50858be06ec03accb2264b85fa | refs/heads/master | 2021-01-25T07:34:45.788607 | 2015-08-26T02:51:45 | 2015-08-26T02:51:45 | 41,400,505 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 893 | rd | ggplot_summary.Rd | \name{ggplot_summary}
\alias{ggplot_summary}
\title{Plots survival results}
\description{Takes in a list of survival summaries to
plot using ggplot2 and graphs them as survival curves}
\usage{ggplot_summary(summ, estimate_column, covar.order = NULL, ys = c(0,
1), ybreaks = seq(0, 1, by = 0.1), graph.title = ... |
9c52f938379cd55bde90766eaaba24aabd992215 | fa1c8875e84c8ea311e839c0e55409745b21744a | /기말대비 연습문제 풀이.R | bf0aa95f6f18daf89f6e6afaeeb0d0289fa97fa9 | [] | no_license | HwayoungYoon/2019-2-Statistical-Programming-R | e187cdf023c9da3afb4e1b6e8f81ae6af49df361 | ab3726a778853df006bfbedb6283be5f67a7603a | refs/heads/master | 2021-02-12T20:32:35.525111 | 2020-03-03T12:18:43 | 2020-03-03T12:18:43 | 244,628,090 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,757 | r | 기말대비 연습문제 풀이.R | #6.5
dia=read.csv("C:/RR/c6no5.csv",header=T)
dia
attach(dia)
#6.5.a
cor(y1,y2)
#6.5.b
cor(x1,y2)
#6.5.c
cor.test(x1,y2)
#6.5.d
tdia=table(dia$y2>=90)
names(tdia)=c("y2>=90","y2<90")
pie(tdia)
#6.5.e
plot(y1,y2)
#6.5.f
plot(x1,y2)
#6.5.g
cor(dia)
#7.2
li=c(25,16,44,62,36,58,38)
#7.2.a
mean(li)
... |
de725dec28ebfd1729851d50db54ecf3a5b5ee2d | 2ba1c86ff2118b2df0b1dfb12f3a28ad7997385a | /EMMA200EMMAX+FarmCPU/longtowide.R | f936f931829ab7b43324cda223b3d49ad6b2610e | [] | no_license | cks2903/FromFabaBaseToGWAS | 6013403b9eb1d52b279ba9b055754e9b993ea1a2 | f147e86278b2a6688f693f6c25c288c44b9128d5 | refs/heads/main | 2022-12-30T05:46:58.943678 | 2020-10-22T14:12:02 | 2020-10-22T14:12:02 | 306,330,165 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,430 | r | longtowide.R | # a script to reshape the genotype data outputtet by mysql
# load libraries
library(tidyr)
# Load data
extremelylargetable <- read.csv("genotypes.txt",sep='\t',head=F)
colnames(extremelylargetable)=c("GenotypingDate","GenotypeCall","Genotyping_Comments","SNPIdentifier","ProbeSetID","Start","Strand","Gene","Annotation... |
db28ee90bb1594da1ad4986b607d21d87d4beebd | 3ce2834cbc34f99f3e4b9d062cb296ddb7e24227 | /LinearModelwithKFold.R | 2b93afd0f2c07b44da9a2a0a816cd8543313dde1 | [] | no_license | caitdreis/sys6018-competition-safe-driver | 7bf9781be312d4b926e92c9bbb73ddc5ff2da160 | b8fe426f71fb872ae863196e53b03ed1df8bc18f | refs/heads/master | 2021-05-08T07:16:03.041846 | 2017-11-04T01:45:03 | 2017-11-04T01:45:03 | 106,733,620 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,971 | r | LinearModelwithKFold.R | #Kaggle Linear Model
#--------------------- Working Directory and Read in Data
#set working directory & read in data
setwd("~/Dropbox (Personal)/Academic/University of Virginia/Data Science Institute/Fall 2017/SYS 6018/sys6018-competition-safe-driver-master")
train <- read.csv("train.csv")
#--------------------- Pack... |
79b30d96b74df868bd0eb572fe0736713a0eebdd | 97558a08d71b43814c2e8884d40cd13fed3efab3 | /man/conv_size.Rd | bd0ffe2a8df5a73dbc9108daf8395bebe7c4c1bf | [
"MIT"
] | permissive | sambold/bagR | 3cde66501497d3da08209d34d4cfdc19c9ea5a2f | eba539bd5aedcb766921a00183592d3d196d8f18 | refs/heads/master | 2020-09-25T08:25:55.671750 | 2020-09-08T20:45:36 | 2020-09-08T20:45:36 | 225,961,937 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 595 | rd | conv_size.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/conv_size.R
\name{conv_size}
\alias{conv_size}
\title{conv_size: konvertiert Fontsize von Theme in size-Einheit}
\usage{
conv_size(x)
}
\arguments{
\item{x}{Fontsize (analog to Theme), die in size-Einheit konvertiert werden soll}
}
\value{
ko... |
94cbe27778e000eddd32b5bb4e21037dcff0ac68 | f515db530e0b9879055ddb951afc1fcf510cafec | /R/get_pred.R | 27559b89c6f9e958a275877bc77838b5d3ed9737 | [
"MIT"
] | permissive | MiguelRodo/modutils | cc65a580c087a3427bb6790d170b0a6d403e631a | 6c3c34e18e58a84b3c60ecec010d12c1d14d1056 | refs/heads/master | 2023-08-17T10:07:20.197874 | 2021-10-04T08:40:12 | 2021-10-04T08:40:12 | 413,340,718 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,939 | r | get_pred.R | if(FALSE){
library(lme4)
library(merTools)
library(tibble)
library(magrittr)
library(tidyr)
library(ggplot2)
library(cowplot)
library(dplyr)
theme_set(theme_cowplot())
set.seed(2106); n_id <- 30
test_tbl <- tibble::tibble(id = rep(as.character(1:n_id), each = 2),
grp ... |
9c69bd19fac72d0b973e5e543af8ab464292811b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/BioGeoBEARS/examples/lrttest_on_summary_table.Rd.R | c0ded77f2502b557efd2d49cb40e00294797d5cc | [] | 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 | 199 | r | lrttest_on_summary_table.Rd.R | library(BioGeoBEARS)
### Name: lrttest_on_summary_table
### Title: Calculate Likelihood Ratio Test (LRT) results, and add to table
### Aliases: lrttest_on_summary_table
### ** Examples
test=1
|
71c7ea0cf77192e29439b70fb08ae6e918823e8e | d1668835b545c49d79c5e37b8b8132c82db14e70 | /Plot6.R | f36075655a1ea5fa30de4b9b36c00a49f287348d | [] | no_license | igembitsky/P2.5-Emissions | e62328c402b97d6a9be1c3282a9c8f6521dfcc5e | 3856fd1dd35c1462ab86c5ab08341d61c11f516a | refs/heads/master | 2020-04-13T09:50:42.052904 | 2015-02-21T04:56:28 | 2015-02-21T04:56:28 | 31,111,197 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,481 | r | Plot6.R | ## Set working directory
setwd("/Users/Igor/Coursera/ExploratoryAnalysis/2/")
## Read data
NEI <- readRDS("summarySCC_PM25.rds")
## Compare emissions from motor vehicle sources in Baltimore City with emissions
## from motor vehicle sources in Los Angeles County, California (fips == "06037").
## Which city has seen ... |
615e12650e32f9722dfafffa465473e609806e1e | 210683b5347b6f584b258f26c7d48ab51a518fe3 | /man/MatrixPaste.Rd | ab695a6e0fa304bf5a21de581aa2d271d8beda84 | [
"MIT"
] | permissive | statisticsnorway/SSBtools | 6b95eab7f46c1096cd7d6ee3f61d3898150d49d0 | aa2728571e0840e1965f3e7ed0f1984c818ca7a1 | refs/heads/master | 2023-06-24T02:48:17.178606 | 2023-06-23T08:05:58 | 2023-06-23T08:05:58 | 137,074,899 | 5 | 0 | Apache-2.0 | 2023-06-23T08:06:00 | 2018-06-12T13:21:36 | R | UTF-8 | R | false | true | 952 | rd | MatrixPaste.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MatrixPaste.R
\name{MatrixPaste}
\alias{MatrixPaste}
\alias{MatrixPaste1}
\title{Combining columns of a matrix}
\usage{
MatrixPaste(x, sep = "_", forceCharacter = FALSE, stringEmpty = " ")
MatrixPaste1(x, stringEmpty = "1")
}
\arguments{
\it... |
c7fbadde7738b96a285e459f10b651e93b27c23e | 57d62d29e15c2c4adaddedaa94dfdb07cf75dea7 | /R/prettyTable.R | 7effaecd8d848156209cafd6ba3446fcab7d2e83 | [] | no_license | cran/admisc | c604fc786939c73f9b369ae11e96966ed7c640ef | 448868ac6bffbff436f45ae43712cc2cadfe0373 | refs/heads/master | 2023-07-05T20:06:42.303908 | 2023-06-30T20:30:13 | 2023-06-30T20:30:13 | 183,607,219 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,297 | r | prettyTable.R | # Copyright (c) 2019 - 2023, Adrian Dusa
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, in whole or in part, are permitted provided that the
# following conditions are met:
# * Redistributions of source code must retain the above copyright
# noti... |
d45dc02d4a7071bf04e1b8c613b8b488bd0a8d46 | f6ef5e5c0722748229da8dc257e48650e27f7576 | /inst/shiny/ui.R | 013971cf9e67215dec918f67f24d23461cd1624b | [] | no_license | jemus42/gridsampler | 5bb6e1151c185f2638d434bad248ef939d9d50fc | a4a7440dd995524ac7765671b32771eaa6b5cf3c | refs/heads/master | 2020-05-20T12:58:03.642667 | 2016-08-21T16:35:24 | 2016-08-21T16:35:24 | 58,479,108 | 0 | 0 | null | 2016-05-10T17:03:09 | 2016-05-10T17:03:09 | null | UTF-8 | R | false | false | 10,219 | r | ui.R | #### Shiny UI definition ####
#### Intro Tour using boostrap-tour library (http://bootstraptour.com/) ####
# Add a tour to GUI to explain the panels and basis steps.
# The tour is defined in www/tour.js
header <- list(tags$script(src = "bootstrap-tour-0.10.3/js/bootstrap-tour.min.js"),
tags$link(rel ... |
633bf88803324c78586ca9a35787ee3d1eacbc5d | b873d459c9abb805ae9bcf70a012be294a5beb11 | /ClusterAnalysis_cereals.R | 22da549c1763b5f190f556d3e05dfc0605796adf | [] | no_license | suman083/ImarticusProject2 | f28e986ef599bc947ba5b590f5b1b179a49f1088 | 671510eef6983b2ce14af414776113bd1a320827 | refs/heads/master | 2023-02-17T18:39:26.125074 | 2021-01-17T17:12:55 | 2021-01-17T17:12:55 | 317,057,036 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,393 | r | ClusterAnalysis_cereals.R | cereals_data <- read.csv("G:/Suman/batch34/cluster analysis exercise/cereals_data.csv", row.names=1, stringsAsFactors=TRUE)
df<-cereals_data[,c(4,5,6,7)]
df
library(cluster)
library(factoextra)
dim(df)
df<-na.omit(df)
df<-scale(df)
df
#samplew of 15 rows
ss<-sample(1:77,15)
df1<-df[ss,]
df1
df1.scaled<-scale(df1)
df1.... |
5394e64cf196ad8d9b54ef2d7941b6ddec19b411 | 74e89fd67001e7268fb0f7fb55a1b47f76f7b84b | /man/hbbrPilotResp.Rd | 2a01a7c9f8bd346920ac9e62bdc15d98712a1d70 | [] | no_license | cran/hbbr | 49591e0deb23c4e495f518507f98fe02ec9531b5 | 39f252c403b158e0fe01fe29be92960d1beeb768 | refs/heads/master | 2020-08-27T22:26:12.258942 | 2019-10-25T07:20:02 | 2019-10-25T07:20:02 | 217,504,845 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,296 | rd | hbbrPilotResp.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hbbrPilotResponse.R
\docType{data}
\name{hbbrPilotResp}
\alias{hbbrPilotResp}
\title{A list consisting of pilot data and associated discrete choice design information for the HBBR model framework.}
\format{A list consisting of pilot response ... |
0230ca0e2a6459daa06a8f03b94dc3b651addd70 | 025083fb3f57193e94ea887ad8ee16dc41ac275e | /man/structure_control.Rd | 963f2853a6bf9b116f29557156ef3084fc6925f8 | [] | no_license | bridachristian/DataQualityCheckEuracAlpEnv | 891671f84e6036c60d7733bfecce3fc9dd50ddc8 | 8941a20caf657cbc66e7f38ef5d553665457feb8 | refs/heads/master | 2020-08-26T20:17:15.864913 | 2019-10-23T15:08:38 | 2019-10-23T15:08:38 | 112,318,390 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,383 | rd | structure_control.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/structure_control.R
\name{structure_control}
\alias{structure_control}
\title{This function check if in input file there is empty rows and if a row is shifted in the previous line}
\usage{
structure_control(FILE_PATH, FILE_NAME, DATETIME_HEAD... |
5e93301ca2b47d802edc583e40fd442ed19e2939 | 22ad2af5206643829c70b65a63b331891ed5af8a | /2019_Nature_Communications_Chloropicon_primus/Synteny/vsCHROMOSOMES/CONCATENATED/OLUCI.R | 54e7604e40ed5becc6c0aa2b475822afd2766cfe | [
"MIT"
] | permissive | PombertLab/Publication_scripts | ef93c8ede6be8ab3d57313ae65eca57478227a1b | 0cc4c36a5522525e4a9c7853a755e09af1c3b491 | refs/heads/master | 2023-06-22T11:13:38.866526 | 2023-06-14T15:02:45 | 2023-06-14T15:02:45 | 56,014,929 | 3 | 3 | null | null | null | null | UTF-8 | R | false | false | 4,049 | r | OLUCI.R | #!/usr/bin/Rscript
library(ComplexHeatmap)
library(RColorBrewer)
library(methods)
library(circlize)
colors <- colorRamp2(c(0, 20, 40, 60, 80, 100), c("white", "yellow", "lightblue", "blue", "magenta", "red"))
ht_global_opt(heatmap_row_names_gp = gpar(fontsize = 1, fontface = "italic"), heatmap_column_names_gp = gpar(fo... |
986dc2f9ad0c535a8f1d4a2378a56cd0e710ad05 | 401d698eee53b3829ad9bd02579b797270d50253 | /Analysis/Exp_1/Before_while_after.R | 338ad2f66f7340e37ddebc7eea36e3cd32c16b70 | [] | no_license | maxgotts/Rubenstein_Mpala | 2c98937afcdbdf8770b992eacc54a060dfb03af0 | 25ef37f35b0f6f265e29885e8c5a54bd2d4df714 | refs/heads/main | 2023-07-19T23:02:31.025276 | 2021-09-05T20:42:31 | 2021-09-05T20:42:31 | 403,402,682 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,050 | r | Before_while_after.R | rm(list=ls())
library(dplyr)
library(lubridate)
library(ggplot2)
source('~/Desktop/MPALA/mpala.R')
df <- filter(df, is.before(mdy(df$Date),ymd("2021-07-05")), !(Identifier%in%blacklist)) #Species%in%zebra.abbr
df <- duplicate.rows(df)
df$Latitude <- as.numeric(df$Latitude)
df$Longitude <- as.numeric(df$Longitude)
... |
14cec58226e0760ac0e6b36bd73c8402a1ae5ce2 | 0acd7d90e625234ef2d3b8fdbb505bd0d92fbef4 | /Module_5.R | 29871216f82f14b9d142e110e07b2ae02a30c9db | [] | no_license | geochemica/Working_Repository | 3e8e94010c9f12d786dc1240190c8e8ef075ad08 | f6a7f76e022e97ba6140581e1c0914fea9720efe | refs/heads/master | 2021-07-17T04:21:54.872771 | 2017-10-23T02:06:45 | 2017-10-23T02:06:45 | 103,066,126 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,296 | r | Module_5.R | rm(list=ls())
#I can't clear the variables#
f <- "~/Desktop/CPDS-1960-2014-reduced.txt"
d <- read.table(f, header = TRUE, sep = "\t", stringsAsFactors = FALSE)
d
head(d) # lists the first 6 lines of data
#It's saying that head(d): object 'd' not found
tail(d) # shows the last 6 lines of data
#It's saying that head(d)... |
40b918d3bbf07ca15d84fdf6a663098506ca1467 | 863f12248c5d33c96d06ce2c5fcd6a0f852e606f | /run_analysis.R | 0772829c239f32af24026044949b842eb4e86391 | [] | no_license | pedelin/Getting-And-Cleaning-Data-Course-Project | 0226123a0864a90bc06d042edb1fa2de68467b48 | d125cd8ae79316aaeb809083cbc92ad8a20a168f | refs/heads/master | 2021-01-18T08:12:05.841901 | 2016-09-15T11:29:31 | 2016-09-15T11:29:31 | 68,284,287 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,229 | r | run_analysis.R | library(data.table)
# File URL
fileUrl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
# File name
filename <- "getdata_projectfiles.zip"
# Download and unzip file if not in wd
if(!file.exists("UCI HAR Dataset")){
download.file(fileUrl,filename)
unzip(filename... |
49fd0e9d99663ee693d3f75b32c382d1b79487df | f501869c8aa8dee755092ce12ed5eeac31658374 | /AFC/AFD-bac-dataset.R | bf3fc42247e91dfcf8d9ab56b3a094414fe99606 | [
"MIT"
] | permissive | Vergil592/R | 7d8e65b05fca1646789aa8eb14504bda0b0f363e | 2c86c8b7fe32ed54a0286b0afa505d01c1ca4170 | refs/heads/master | 2020-03-19T22:00:55.625206 | 2018-11-09T13:28:05 | 2018-11-09T13:28:05 | 136,957,223 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 327 | r | AFD-bac-dataset.R | ## AFD
require("MASS")
data3 <- read.csv("bacdata.csv",header=TRUE, sep='\t')
dim(data3)
n <-sum(data3)
D1 = diag( rowSums(data3) )
D2 = diag( colSums(data3) )
M <- solve(D2)*n
VM = t(data3) %*% solve(D1) %*% as.matrix(data3) %*% solve(D2)
decompo_VM <- eigen(VM)
# Plot
afc.data3 <- corresp(data3,nf=2)
plot(afc... |
804784fd28f73affb6628d10b3b5d9769ab35c36 | af4bc29575bc35732de9f9dffe6e55a336467101 | /R/timeseries.R | a3518bdfd600bc979f6e83a1ebaa5ae8d3648bff | [] | no_license | tanvi-arora/timeseries | 8bc574ac3b2faa431ac566dd839d96830e04dd08 | 170e5a7c050a28533a86107d3b2c125482245c82 | refs/heads/master | 2020-03-24T12:49:43.425272 | 2018-07-31T04:08:20 | 2018-07-31T04:08:20 | 142,725,764 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,006 | r | timeseries.R | #################################
# Script name : timeseries.R
# Author : Tanvi Arora
# Date created : 2018/07/29
# Description : This script contains all code related to timeseries functions
#################################
##@knitr ses_maxtemp
# ses() or simple exponential smoothing
fit_ses <- se... |
a7d8c3247a6aae1ab4dd7d63a9105fb86f995484 | a0e6041a0b64d2b60cdc5ce6ec127b027309d788 | /data-raw/scraping.R | 355814e034a85b49c4a1a835971ae33f7c162adc | [] | no_license | davben/arvig | 88c8593d2bdfba159bca740471b1b19f6834e276 | 18786b90455dd7668713c5cf6c4083d96b07ee50 | refs/heads/master | 2020-12-28T14:47:53.406961 | 2018-06-26T13:47:16 | 2018-06-26T13:47:16 | 45,528,107 | 11 | 3 | null | null | null | null | UTF-8 | R | false | false | 4,631 | r | scraping.R | #' Scrape Events of Anti-Refugee Violence from "Mut gegen rechte Gewalt" Chronicle.
#'
#' This function is built only for one specific purpose:
#' Scraping the website \url{https://www.mut-gegen-rechte-gewalt.de/service/chronik-vorfaelle}
#' for all events a specified year (currently, 2015 and 2016). It retrieves the d... |
5dc1e64fb751b9f5406829eb002decef72f8ae7e | 4201e9b754760dc35fc0aeef9df5a8b9d801c47f | /bin/R-3.5.1/src/library/tools/R/check.R | 38bd2fa4691682e90dfd9d96663a9402dd21ce4a | [
"Artistic-2.0",
"GPL-2.0-or-later",
"LGPL-2.0-or-later",
"Artistic-1.0",
"CECILL-2.0",
"MIT",
"BSD-3-Clause",
"LicenseRef-scancode-free-unknown",
"GPL-1.0-or-later",
"GPL-2.0-only",
"BSL-1.0",
"BSD-2-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | lifebit-ai/exomedepth | cbe59cb7fcf2f9183d187f8d466c6620fb1a0c2e | 5a775ae5e2a247aeadc5208a34e8717c7855d080 | refs/heads/master | 2020-03-27T12:55:56.400581 | 2018-10-11T10:00:07 | 2018-10-11T10:00:07 | 146,578,924 | 0 | 0 | MIT | 2018-08-29T09:43:52 | 2018-08-29T09:43:51 | null | UTF-8 | R | false | false | 267,019 | r | check.R | # File src/library/tools/R/check.R
# Part of the R package, https://www.R-project.org
#
# Copyright (C) 1995-2018 The R Core Team
#
# This program 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 v... |
116a7b89f41ef073e0a1836aecfd17b0b4f881ea | 8218c5e8a362673882278bbf58e42ebcd937dc86 | /rfp4/S00-env.R | 6905019c40fb57b66ab8feec324da6f172988994 | [] | no_license | Sjan1/r-for-proteomics-tsl | 276fb78c84d82f89f1e5f17080c19c04bd2fa196 | 24a8e08ca3e00f8e73089755aaa00694d5d83a01 | refs/heads/master | 2021-05-04T13:58:42.792222 | 2020-05-11T18:40:21 | 2020-05-11T18:40:21 | 120,327,192 | 1 | 2 | null | 2018-02-05T16:00:33 | 2018-02-05T16:00:33 | null | UTF-8 | R | false | false | 454 | r | S00-env.R | #install.packages("BiocManager")
#BiocManager::install()
#install.packages("MSnbase")
#if(!requireNamespace("BiocManager",quietly = TRUE))
# install.packages("BiocManager")
#BiocManager::install("MSnbase", version = "3.8")
#source("http://www.bioconductor.org/biocLite.R")
#library("BiocInstaller")
#biocLite("MSnbase... |
c1790a5600a3a7661dea943825de88504c009d84 | 03c99906a94c70e9a13e7714aad996f461f339c1 | /man/rare_Rao.Rd | 67d9383e0c4e7cdc097830704a5f03986091313e | [] | no_license | cran/adiv | 6a111f6a1ef39fe302a2f882b9a9d04e7d652c04 | d65d6e0301e4611a94a91933299bff1fdc06d96b | refs/heads/master | 2022-10-28T08:07:33.352817 | 2022-10-06T12:40:04 | 2022-10-06T12:40:04 | 97,764,074 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,384 | rd | rare_Rao.Rd | \name{rare_Rao}
\alias{rare_Rao}
\title{
Functional Rarefaction for Species Abundance Data
}
\description{
The function \code{Rare_Rao} performs distance-based rarefaction curves using species abundance data. It finds the expected functional diversity (if functional distances between species are used) as a functi... |
bf8478b8804e5cbacfd3b00bfcd19c734133316b | c84970ea7163ace7b97839d529b0e171348fc35a | /plot1.R | f699e3114d45d5e16114dc868ad8df64e3db2354 | [] | no_license | JIsernhagen/ex_res_assgn_1 | 1e07b5a0bd44e960039ef60186739d8d671bdc47 | 0100a8bf36266c181badfb3f96c611e6ae55c23b | refs/heads/master | 2020-08-06T16:57:35.331912 | 2015-09-12T03:12:01 | 2015-09-12T03:12:01 | 42,342,313 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 925 | r | plot1.R | # "household_power_consumption.txt" must be unzipped in the working directory in order for this to function.
#read in the data, subset to the relevant dates, and transform/combine date and time into useful timestamp.
consumption <- read.table("household_power_consumption.txt", header = TRUE, sep=";", na.strings = "?", ... |
5e99105b30a94a81f84d1a1faee400cad33535b8 | bd238017a55ea6dc2a1674d8af58a781a7958094 | /cachematrix.R | fdec9289b27692ec39c5bbe0d0796e4fb9cb5e7a | [] | no_license | synchunt/ProgrammingAssignment2 | 65aa24445841d845299e09659fa6b3c0791d45da | 30b9f70e213ba7cbcc8f5085312e3e6dc24e6105 | refs/heads/master | 2022-07-08T11:18:34.472911 | 2020-05-10T20:17:25 | 2020-05-10T20:17:25 | 262,659,501 | 0 | 0 | null | 2020-05-09T21:09:53 | 2020-05-09T21:09:52 | null | UTF-8 | R | false | false | 1,252 | r | cachematrix.R | ## The first function is used mainly to get the matrix, which in reality
## is used to set and get the value of matrix and
## to then set and get the inverse of the matrix
makeCacheMatrix <- function(x = matrix()) {
inver <- NULL
setMat <- function(o){
x <<- o
inver <<-... |
93bc688653ab807c9913587039743b2a723b6b8b | 1bfe87671e72dcdfa187d9df747a3b4f8eae913a | /R/loadCrimedata.R | 1531bda247c4953b25992febb74ce8b630f625d6 | [] | no_license | ian-flores/mapPR | 592b5e925adf2a0a7ca25caaa6aa4f1827c0c430 | 8492574424790251b385933933eef9082cfe7f27 | refs/heads/master | 2020-04-05T15:57:05.666966 | 2018-11-14T02:40:09 | 2018-11-14T02:40:09 | 156,990,955 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 483 | r | loadCrimedata.R | library(tidyverse)
library(lubridate)
library(rio)
mapPR_crime <- function(.data_format = 'all_data', .intial_date = '2017-01-01', .final_date = '2017-12-01'){
.base_url <- 'https://github.com/OpenDataPR/PR_Crime_Data/raw/master/data/'
.url <- paste0(.base_url, .data_format, '.feather')
.data <- rio::imp... |
0f0b29aba3fd69e2a5c5283746d5c4e5ee1cf9b3 | c507b15b53be5ddfae81bee94b5332ddd9ef5886 | /code/site_setup/setup_my17_LOS_abundance.R | 23dbc398508aaea315b4de6a9a65f5148829afff | [] | no_license | ppgibson/ELH_AnnualReport | 3dfe7bbd685553dc5cd2e22014b4af16742048e9 | 704255af1404e4594933f179d18d6e34fab96f10 | refs/heads/master | 2022-11-05T23:31:04.906269 | 2020-06-24T23:52:44 | 2020-06-24T23:52:44 | 274,791,166 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,495 | r | setup_my17_LOS_abundance.R | ## MY 17 LOSTINE
## ANALYSIS OPTIONS FOR ABUNDANCE CALCULATION
# Current analysis run
my.cur <- 2017
my.txt <- "my17"
site.cur <- "LOS"
# Analysis options
include.INT.catch <- TRUE #Default = TRUE. Std ELH policy is to include catch numbers from INT days in simple abundance estimates.
use.prv.... |
8a4749574bc23a52dc6266f39bcbed922599ead7 | 251f6a072167f3de27c185dd766cbb5a8125d0ec | /Code/old/SF.Gibbs.Rerun.r | b98992f7a40adde74cda92ad63d90cf2d5def59f | [] | no_license | bellland/StaCC-Model | 6e62571268c7322d84f1b45d91aa553d92b04dd8 | 87e88339568bd0e084ee7cec505fd06ef60a30b2 | refs/heads/master | 2021-03-12T23:39:05.008912 | 2015-02-05T16:50:59 | 2015-02-05T16:50:59 | 17,839,579 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,882 | r | SF.Gibbs.Rerun.r | # Run Gibbs Sampler for Sap Flux Model
setwd('/Volumes/dbell9$/Sap Flux')
files <- list.files(pattern='TC_1_')
load(files[17])
for(g in 1:ng){
if(!SECTION){
tmp <- rescale()
specvars <- tmp$sv2
}
ptmp2 <- update_processpars() #process parameters
aspec <- ptmp2$a
blitespec <- ptmp2$bl... |
d19443c88532265b2873cc8b2150a17b06efdf60 | a286b67e469be262bfe0b40ca484ae270da6a1e5 | /TEMP/Time Series Attempts/time_series_plots.R | b890c6a959947fee8ebf44840c818cdd11ca3725 | [] | no_license | alexazhu/PHIGHT-COVID | da9c213c3d9b4a01f8327bf9e7615fa3025d71ec | 3258e9559335b4d144a0945d583cde57226ea309 | refs/heads/main | 2023-07-09T08:18:04.490684 | 2021-08-09T07:31:17 | 2021-08-09T07:31:17 | 338,371,542 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 9,311 | r | time_series_plots.R |
library(tidyverse)
require(scales)
source("step2_data_wrangle.R")
county_policy_wide$major_teaching <- factor(county_policy_wide$major_teaching,
levels = c("On Premises",
"Hybrid",
... |
7e161e29df3f2f41434b807025d996df16ec1ffb | 4b8781be7f6063044b8d5431521e374879ede23b | /R/handle_mash_results.R | 7fd8977384f47a86765b786a0bd26c287fdbbdcd | [] | no_license | salarshaaf/CDBNgenomics | 009561eeb5dcf1682265621c2c1fcd203c9814fe | e44ebb73f2a1f5868b23124f27f9b0334f269625 | refs/heads/master | 2022-04-11T23:46:30.901140 | 2020-04-09T16:04:21 | 2020-04-09T16:04:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,750 | r | handle_mash_results.R | # --- Get Results from Mash Object ---------
#' @title Get column names from a mash object
#'
#' @description This function extracts the column names from the local false
#' sign rate table of a mash object's results. This can tell you the condition
#' names or phenotype names used in the mash object. That can be usef... |
039d75d84fe1362969367bdedf8e3b3efb8e3129 | ec82f12dede5a701549578c41e963bd4cfc79617 | /man/assign-method.Rd | 4d03bd5ff0b630f69f2379263ec6a39c56819a06 | [] | no_license | impromptuRong/microbr | 43f7631d38e21802dc4a54ad9a9c28fb40da9bff | 98698ff50246e3454acb8576fc3722b535bcb665 | refs/heads/master | 2021-01-23T04:13:29.363142 | 2015-04-28T03:12:22 | 2015-04-28T03:12:22 | 34,646,779 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,500 | rd | assign-method.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/physet-class.R
\docType{methods}
\name{assign}
\alias{$<-,physet-method}
\alias{Snames<-}
\alias{Snames<-,physet-method}
\alias{Tnames<-}
\alias{Tnames<-,physet-method}
\alias{assign}
\alias{otu_table<-}
\alias{otu_table<-,physet-meth... |
fb5ea924b833af8ade5216e9995bc1676beb844d | 0b01338c904a2662bc925a5df0462ae36f2210c4 | /10_graphics_MA.R | 6ece2cb38b072be5b161bd9fbc5e742017b4f180 | [] | no_license | JMF47/recountNNLSpaper | 946980021d82e1b73eafab1fb69534e2c8d962fa | d251efaae1a20f2d96bf2f103445ab8a68905fc2 | refs/heads/master | 2021-09-14T12:20:29.791693 | 2018-05-13T17:22:10 | 2018-05-13T17:22:10 | 117,008,135 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,467 | r | 10_graphics_MA.R | ####################################################
### MA Plots (supplements)
####################################################
cs = 0.15
ids = c("37_1", "37_2", "50_1", "50_2", "75_1", "75_2", "100_1", "100_2", "150_1", "150_2")
for(id in ids){
scenario = str_split_fixed(id, "_", n=2)
rl = scenario[1... |
c36f62165b9822e435d58c663086ce5e52ae7fd2 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/munsell/examples/mnsl2hvc.Rd.R | 70025127c8eec87702d5abf31dc112e94c710e9a | [] | 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 | 205 | r | mnsl2hvc.Rd.R | library(munsell)
### Name: mnsl2hvc
### Title: Converts a Munsell colour to a hue, chroma and value triplet
### Aliases: mnsl2hvc
### ** Examples
mnsl2hvc("5PB 5/10")
hvc2mnsl(mnsl2hvc("5PB 5/10"))
|
74ff004f33c80e2b74bedf4b5e74b7b81ef88945 | bca18488d27f397819fc39434dd2209a71485ab5 | /Elasticsearch/estudio errores 3.R | 035c338c9d4393727ed57f0847182a87ccceba2a | [] | no_license | Xmael/R | c5819e8b7be9b3706b78de33494c376146c9248f | 88eba6349bdb71b26162785226e7e51ae6e3cc6f | refs/heads/master | 2021-01-19T13:29:50.465856 | 2016-03-10T08:30:48 | 2016-03-10T08:30:48 | 34,176,309 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,153 | r | estudio errores 3.R | library(elastic)
library(RJSONIO)
library(XML)
connect(es_base="http://52.28.187.57")
today=Sys.Date()-1
periodo=seq(today, length.out=7, by="-1 day") #Obtengo las fechas desde hoy hasta diez días atrás
periodo=format(periodo,"%Y.%m.%d")
library(data.table)
load("~/Trivago/hoteles(20.1.16).RData")
parsea=function(men... |
e6771d106a4ca46541021c4cb846db1a4fcf8fb5 | 278c4c50164dec711aa191b0b3dda4e8f3667112 | /R/utils.r | cdd51cd164c7937632b968281e66fc5a10cd5e6d | [] | no_license | tonyelhabr/tetext | ce3768c56d6093a7dc0a9e3aae2655b34d89ccc0 | ed55c5be8a7e2fb72dc58d186ae553864e203e31 | refs/heads/master | 2021-04-29T23:55:15.076607 | 2018-10-08T12:58:48 | 2018-10-08T12:58:48 | 121,565,058 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,967 | r | utils.r |
get_color_hex_inverse <- function(color) {
grDevices::rgb(t(255 - grDevices::col2rgb(color)), max = 255)
}
generate_named_dual_colors <-
function(color_main,
lab_main,
lab_other,
color_default = "grey50") {
if (!is.null(color_main) & !is.null(lab_main)) {
out <- stats:... |
54e410360c0d37cccf88bf4361209053295fd346 | 85dea3294b6a57d2b0a052486d4a52334d6b63c8 | /plot1.R | d644595ef40903507b825112e5da3b9efe72ff4c | [] | no_license | yanyangai/ExData_Plotting1 | ee79ec81c21e33600b3292aa5400193da094a97f | 129cb881f95ed8c08fefd21a997f0a457585d83a | refs/heads/master | 2020-12-31T06:22:13.873086 | 2015-11-08T22:07:58 | 2015-11-08T22:07:58 | 45,757,817 | 0 | 0 | null | 2015-11-07T22:42:01 | 2015-11-07T22:42:01 | null | UTF-8 | R | false | false | 689 | r | plot1.R | # read in data #
dat <- read.table("household_power_consumption.txt", header = TRUE, sep = ";", stringsAsFactors = FALSE, dec = ".")
dat2 <- dat
# format date and time variable #
dat2$datetime <- strptime(paste(dat2$Date, dat2$Time, sep = " "), "%d/%m/%Y %H:%M:%S")
# subset data #
dat2$Date <- as.Date(dat2$Date, for... |
2a120f87134dce2041f79b82615cb98342e65c7c | fbd79e39470342d1bdd91a90ed94cfba25c91e31 | /man/addValidation.Rd | 0cdccea3109892e0e8a4c242b32038d1bd472711 | [] | no_license | judyheewonlee/Nh3D2.0 | bcaa1b9e37d4dfbf389687a73efd2fc69e1a5aee | 05add8a9216ca11f37a451185fe8de96d9df9d81 | refs/heads/master | 2020-03-31T11:21:07.916071 | 2019-04-15T05:11:01 | 2019-04-15T05:11:01 | 152,173,299 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 759 | rd | addValidation.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/addValidation.R
\name{addValidation}
\alias{addValidation}
\title{addValidation.R}
\usage{
addValidation(xmlFile, pdbID, cathTable)
}
\arguments{
\item{xmlFile}{(vector) A XML file path that will be read}
\item{pdbID}{(string) The ID of the ... |
305ae5dde71e32176385128bb2c33fa87d413e62 | 9cc49351c9649dd0b4a84eef9c3d5a98ab983b2d | /analyses/frequencies_vs_recovery/plot.r | ce5bdbff5007d0b229dbfc42602ce67ac13061bc | [] | no_license | SimonGreenhill/northwind | 8a926436d58711cd0b87c0d1ef48467d4e934d60 | fa9e2e52bf3c2585a89a6c954eda183d6eb02812 | refs/heads/master | 2023-01-14T18:48:45.899610 | 2020-10-05T09:34:31 | 2020-10-05T09:34:31 | 197,758,170 | 0 | 0 | null | 2023-01-09T01:04:48 | 2019-07-19T11:04:09 | Python | UTF-8 | R | false | false | 3,638 | r | plot.r | library(ggplot2)
library(ggrepel)
library(viridis)
library(patchwork)
df <- read.delim('results.dat', header=TRUE)
# 1. Rank = how common it is cross-linguistically (= rank in Phoible)
# 2. Ni = number of illustrations listing the phoneme in inventory
# 3. Nt = number of illustrations attesting the phoneme in NWS tex... |
e4e88f07944805b7100d1ae68d3c512a933e09de | d2d215fd569ce28476a290f29d08d553c0996cdc | /rcodes/adhoc_analysis.R | bbaf779c1e097e9dca58eb4219769b083fefcf55 | [] | no_license | vikasFid/lda_pipeline | a079dde1101b74f0d02a7315e9ce584b6a602838 | fc8c9f6511a19b3a7cb3507e4510160acb867577 | refs/heads/master | 2021-07-03T15:15:36.485624 | 2020-08-25T09:07:12 | 2020-08-25T09:07:12 | 146,553,537 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,356 | r | adhoc_analysis.R | #to take command line arguments
args<-commandArgs(TRUE)
#free up memory from previous runs
gc()
library(tm)
library(dplyr)
library(topicmodels)
library(LDAvis)
library(tsne)
library(servr)
library(Matrix)
library(tidytext)
library(igraph)
library(sqldf)
#inputFile = args[1]
txt_input = read.csv('adhoc_chunked_output... |
e0d4013c8f2effa2d6b5fbdcfcb349cfdf053944 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ROCR/examples/ROCR.hiv.Rd.R | 13f5c789fbef70a05888a486d5e710356c38b0dc | [] | 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 | 863 | r | ROCR.hiv.Rd.R | library(ROCR)
### Name: ROCR.hiv
### Title: Data set: Support vector machines and neural networks applied to
### the prediction of HIV-1 coreceptor usage
### Aliases: ROCR.hiv
### Keywords: datasets
### ** Examples
data(ROCR.hiv)
attach(ROCR.hiv)
pred.svm <- prediction(hiv.svm$predictions, hiv.svm$labels)
perf.sv... |
efa013e16845a88132c384f98093b1034a281ee9 | 8509f3c69283492cb6f3866aafd4a508cd037441 | /grade_analysis.R | c88f994833bc92d4c06a315eff256118d1daacfa | [] | no_license | ollikiili/Text-mining-MSc-Thesis-project | 7cf1dc7e8a1f45bf3ed8d5499a81c6c61497d5d6 | 25dd2b1f168e61ec220e6553396a2316db222b8e | refs/heads/master | 2020-05-25T02:46:25.182930 | 2019-05-27T08:42:58 | 2019-05-27T08:42:58 | 187,586,149 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,271 | r | grade_analysis.R | setwd("Z:\\Documents")
options(stringsAsFactors=F)
Sys.setlocale('LC_ALL','C')
#apparently this file is not used so commented the load out
#load("TI_data_by_reviews_preprocessed")
load("tokenized_words")
load("tokenized_bigrams")
unigrams <- c("room", "staff", "check", "location", "casino", "pool", "bed"... |
fed109e08f738c6398de179086cbc33c2eb503e5 | c7d9640c5b23575db444ff2539187fbbb65e163f | /app/server.R | 4054286de9abdc5229a8799e26f4483fd65806ad | [] | no_license | datasketch/transparencia-2019 | 164e051fc77dd437dd8b11e84178ef1adda65243 | 86d4d876fa486928dcc8d0f0bf2769e35d5359e8 | refs/heads/master | 2020-05-18T19:49:29.301455 | 2019-07-03T20:52:30 | 2019-07-03T20:52:30 | 184,615,916 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 27,447 | r | server.R | shinyServer(function(input, output, session) {
# Datos para graficar
output$base_opts <- renderUI({
data_dis <- setNames(c("Territorios de concentración/consolidación", "informe II 2016-2018"), c("Territorios de paz", "Hechos 2016-2018"))
checkboxGroupInput("base_cliente", HTML("<div style='font-weight: ... |
02e1566216e8b23de5fbed797e5765e6719a2906 | 95299589f967b19ce37cf98d33455b182e43c5b0 | /scripts/util/__Util_TaskStateFunctions.R | 4511e01990b3f8a10c7345f2b69ed7ebfe63ba9e | [] | no_license | christokita/mixing-model | baec1217eacb98a5ba786211544888e3b9e2403b | bc291ee00db5edaf80d6fe09a2ee2e6abb7362d2 | refs/heads/master | 2021-07-14T09:56:21.520775 | 2020-06-11T19:16:15 | 2020-06-11T19:16:15 | 154,517,060 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,639 | r | __Util_TaskStateFunctions.R | ##################################################
#
# Task performance/state functions
#
##################################################
####################
# Set initial probabilities of performance
####################
initiateProbMatrix <- function(n, m) {
rowValues <- rep(1/m, m)
p_g <- matrix(data = rep(... |
4ac8a38e90da28113e1a1b66be3377ad9541f712 | c57488528ae6e1cefb608b2b84b4797e634771df | /Assignment 2/corr.R | 43ae35b7574a39d375483885b532c373875f630d | [] | no_license | riteshjjw/datasciencecoursera | 76820ae410832117bec623b89c338b7f2558f0a6 | d5cf27813c4570f27361751dfd40759f2afaa94d | refs/heads/master | 2022-11-22T00:14:48.757530 | 2020-07-17T13:38:00 | 2020-07-17T13:38:00 | 278,347,665 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 722 | r | corr.R | corr <- function(directory, threshold = 0){
source("complete.R")
files <- list.files(path = directory, full.names = TRUE)
completecases <- complete(directory)
abovethreshold <- completecases[completecases$nobs > threshold, 1]
correlation <- rep(NA, length(abovethreshold))
... |
6c39f32428ad7c56a49509ca527e159bbfe78e6c | 9e67c36a97d61e672e655ec20466da58bcad016f | /inst/examples/shiny-app/server.R | 2cad4dcdb47861a96b0de82452e8580955af3a1f | [] | no_license | arturochian/shinyjs | 82a7dc6475574798f444acdf621c3b0a3b7c31d8 | 207a60f1317e3d05dc266c787e29d1820fae08a9 | refs/heads/master | 2021-01-18T08:00:31.620561 | 2015-03-25T09:43:56 | 2015-03-25T09:43:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 454 | r | server.R | library(shiny)
library(shinyjs)
shinyServer(function(input, output, session) {
shinyjs::setShinyjsSession(session)
observe({
if (input$submitExpr == 0) {
return()
}
shinyjs::hide("error")
tryCatch(
isolate(
eval(parse(text = input$expr))
),
error = function(err) {... |
aaf708dab7d0ed01c758fb85004c2d9a1fbff909 | 19d3d74116db27808ef80693cf06cdbe484e3fa0 | /man/rwa_read.Rd | 1b4a0d887ee92524bad4d31b467ddb7a1258c95f | [] | no_license | JBGruber/rwhatsapp | b9c4f37b870d8b126df4aace09c21fb2ac1ea945 | 6c12c6bc36d346ae8feadb12dfe193fcdd057d5e | refs/heads/master | 2022-10-19T15:12:51.843400 | 2022-10-04T09:40:34 | 2022-10-04T09:40:34 | 147,067,905 | 99 | 18 | null | 2020-04-24T11:35:20 | 2018-09-02T08:26:21 | R | UTF-8 | R | false | true | 1,461 | rd | rwa_read.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rwhatsapp.R
\name{rwa_read}
\alias{rwa_read}
\title{Read WhatsApp history into R}
\usage{
rwa_read(x, tz = NULL, format = NULL, verbose = FALSE, encoding = "UTF-8", ...)
}
\arguments{
\item{x}{Path to a txt or zip file of a WhatsApp history o... |
732329cf24e52b4cb110431bde5e2956d6e5e0dc | ebb09f52b1ee12d8ae8d4c493e6f1079ee57868c | /ExploratoryDataAnalysis/Project2/plot2.R | 7cd4e9c1bd033a5ed0478fb211d1d8c81923898a | [] | no_license | r6brian/datasciencecoursera | a1723f812a34eee7094dfaa0bfde6c618b349d6c | 548944d3ba68d302160f05158fb90859bc4c8bae | refs/heads/master | 2021-01-19T10:29:54.605308 | 2015-08-23T20:00:04 | 2015-08-23T20:00:04 | 26,268,379 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 807 | r | plot2.R | # 2. Have total emissions from PM2.5 decreased in Baltimore City, Maryland from 1999 to 2008?
# Read data files
NEI <- readRDS("data/exdata-data-NEI_data/summarySCC_PM25.rds")
SCC <- readRDS("data/exdata-data-NEI_data/Source_Classification_Code.rds")
# Subset NEI data, select * rows for Baltimore(fip=24510).
baltimor... |
f253460c0e363aba005becd560c06613af9e691a | d4f6682af4f1189bed66bb21fe39d71f1382d555 | /tests/testthat/test-front-page-scaling.R | 9bfdf6736102beb0a22d28dabbf4dbfe6dd02e9b | [] | no_license | thechrelsres/sfthemes | dfa87760169a24a0ebfedf3fddec33f301197dbf | fcfa15dbed8dee69a45f9c7a4d831c136240945f | refs/heads/main | 2023-02-25T12:17:35.634990 | 2021-01-17T16:04:04 | 2021-01-17T16:04:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,667 | r | test-front-page-scaling.R | context("pkgdown-front-page")
test_that("front page figures showcasing the scaling have been generated...", {
testthat::skip_on_cran()
testthat::skip_on_appveyor()
testthat::skip_on_travis()
testthat::skip_on_os("windows")
library(ggplot2)
classes <- c("xSmall", "Small", "Medium", "Large", "x... |
0ddb7e20da8199f2e4e966e766ca0debdc7da6b2 | 056a9308784b7ba57b6c4685d298489b2529fb7e | /Chapter02/cluster_inc.R | 5965aa4d36adec755764e27b535e2563ef7c67fa | [
"MIT"
] | permissive | taecjee/R-Deep-Learning-Essentials-Second-Edition | 7227ae70aea25fca481c2ce137930d734e476866 | ae6edf846f6ed40e003894de2f2e92e5beb38171 | refs/heads/master | 2020-11-24T17:36:50.136884 | 2020-02-11T08:47:14 | 2020-02-11T08:47:14 | 228,276,353 | 0 | 0 | MIT | 2019-12-16T01:05:28 | 2019-12-16T01:05:28 | null | UTF-8 | R | false | false | 30 | r | cluster_inc.R | library(caret)
library(RSNNS)
|
b1571dffb3dedaa6b5394fd45e2db2a0453f8366 | 00c16d1609fea324f4cf31459d805215f6b654ee | /R/source_fatbloom.R | b42ff7ae0aff467c8bd35139b5b28d2a7fc6b44c | [] | no_license | dan2cil/tecTools | 73b1ecead88f6101cf060df9d5f7b296ddf3c400 | d38b47f6147b10e3b6b16dba414d3201112a1c6a | refs/heads/main | 2023-07-06T16:41:44.593467 | 2021-08-03T19:37:24 | 2021-08-03T19:37:24 | 392,432,341 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,947 | r | source_fatbloom.R | #' mosaicFB
#'
#' Simulate chocolate crystallization using Imogen Foubert model
#' of crystallization kinetics of cocoa butter
#'
#' @param data data frame of data
#' @param cluster field to use to clusterize data
#' @param filter data filter
#' @param depivot boolean, if T depivot data, if F none
#' ... |
09eb2081e7ac0a42591c865025cecc42d15eeea4 | 3b3168707c67aefbd85934bae572b6f686b479e0 | /R/plot_contacts.R | e99e17cae4a967fb7f3aa9abe8528f94352e3488 | [] | no_license | qenvio/hicvidere | e114869c279da21e5de178b7961432fbded5a191 | 65df8c6a2d5a9ba9d7e138da776afd3b47ab7f6e | refs/heads/master | 2021-01-10T04:44:26.737390 | 2016-01-25T15:56:53 | 2016-01-25T15:56:53 | 43,735,401 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,378 | r | plot_contacts.R | #' Plot a HiC map from a contact matrix.
#'
#' This function takes a contact matrix and plots a HiC map
#' @import magrittr
#' @import RColorBrewer
#' @param contacts Input contact matrix. Usually the output of \code{\link{get_contacts}}
#' @param rotate Do you want matrix to be rotated so the diagonal is from top-left... |
45bb34f8f304085fa967e711241c532f6796a4a9 | bbfcc35c6394e5cb99b3164c0c5b16ad6821ddec | /R/bt.tag.R | d2ae4274c8ada88c949663d4ef7d06b523a61657 | [
"MIT"
] | permissive | PhanstielLab/bedtoolsr | 41ec321d3c16a2f893a56182c2d01254bf386de9 | cce152f1ce653771d8d41431e0d12e4ef9c42193 | refs/heads/master | 2022-11-15T13:02:56.285838 | 2022-11-08T22:20:19 | 2022-11-08T22:20:19 | 151,143,796 | 31 | 5 | NOASSERTION | 2019-12-05T18:11:09 | 2018-10-01T19:08:56 | R | UTF-8 | R | false | false | 2,553 | r | bt.tag.R | #' Annotates a BAM file based on overlaps with multiple BED/GFF/VCF files
#' on the intervals in -i.
#'
#' @param i <BAM>
#' @param files FILE1 .. FILEn
#' @param s Require overlaps on the same strand. That is, only tag alignments that have the same
#' strand as a feature in the annotation file(s).
#'
#' @param S ... |
4681848836b54882578d077711a088b0812d81b3 | e66b402e019db06907a31c4c4efe04f498c9b22e | /cachematrix.R | 9efcb3f472062ef971549994402d90412cb881a0 | [] | no_license | codeforcalifornia/rprog-hw2 | 5c38b838d7491ec4e25cc9dc8d36d1c838305a41 | 2e46f38cf388d74aae2782f3b1d0ac613ee63213 | refs/heads/master | 2021-01-18T17:44:46.968994 | 2014-06-21T17:43:19 | 2014-06-21T17:43:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 991 | r | cachematrix.R | ## These functions create a special object that can cache the value of the
## inverse of a matrix if necessary
## Create an object that can hold a matrix value and optionally set a
## computed inverse. If the matrix value changes, invalidate the computed
## inverse.
makeCacheMatrix <- function(x = matrix()) {
de... |
bc7a4900d4b954b9d4eb531c3d5acb799ffea296 | 7d125cf7b30e9be0ef1f02e24ad13495b4481f4e | /src/Library/compareGeneExpDiffSubset.R | 6ef7d726be1c8e50fdc595326bec0be0c845b4ed | [] | no_license | DToxS/Differential-Comparison | 0616004e275cfa17d605505cecc6842a0baa4b2a | d6b3d4cc7c4ef2bdb21527655fb927c146453942 | refs/heads/master | 2022-04-06T22:12:37.767298 | 2020-02-27T22:19:56 | 2020-02-27T22:19:56 | 105,199,221 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,917 | r | compareGeneExpDiffSubset.R | # Perform a DEG comparison for a subset of samples between a pair of conditions.
# Append experiment info to a title text.
appendExprtInfo <- function(title_text, exprt_info)
{
if(!is.null(title_text) && !is.null(exprt_info))
{
if(!is.na(exprt_info["cell"])) title_text <- paste(title_text, "in", exprt_... |
83995e2f5a662f6b541c129eb266dfb740d37ed7 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/sBIC/R/MixtureModels.R | ee756004723dd1b887e85fc0e88d302e6579051e | [] | 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 | 2,964 | r | MixtureModels.R | #' @include ModelPoset.R
NULL
#' Linear collections of mixture models.
#'
#' An abstract class representing a collection of mixture models that are
#' linearly ordered by the number of components in the mixture. This class
#' should not be instantiated, just extended.
#'
#' @name MixtureModels
#' @usage MixtureModels(p... |
644ae5524d8da183a67a5ce26695e650238297de | 139e93dc5ad1f30938195671caf4aefce99f188d | /tests/testthat/test-map-if-at.R | 48307d99a5efd148a857e7bdbef1cdfdbafc0665 | [
"MIT"
] | permissive | tidyverse/purrr | 7b94592b1eb6f4e6db8d83fc307465ce7b65b520 | ac4f5a9b9ff2b5b36770c4c5e064547264544fd2 | refs/heads/main | 2023-08-28T01:39:40.614443 | 2023-08-10T14:13:52 | 2023-08-10T14:13:52 | 27,309,729 | 901 | 265 | NOASSERTION | 2023-09-03T11:49:30 | 2014-11-29T17:33:40 | R | UTF-8 | R | false | false | 1,087 | r | test-map-if-at.R | test_that("map_if() and map_at() always return a list", {
skip_if_not_installed("tibble")
df <- tibble::tibble(x = 1, y = "a")
expect_identical(map_if(df, is.character, ~"out"), list(x = 1, y = "out"))
expect_identical(map_at(df, 1, ~"out"), list(x = "out", y = "a"))
})
test_that("map_at() works with tidyselec... |
8e55677eff254603c8923e967e25be4ddcce35f7 | cd081c730b15c35eecb48d8524de182cb348162a | /app/ui/tab2_ui.R | 06efb7428f8ba7df32bfe43ac4b3848562355123 | [] | no_license | tonyk7440/shiny_template | 6a83099725b4dbb98922a79b8f34cfe2c7923e32 | d0eda95f7da9f19663c68a4bd28587842ce7a1bd | refs/heads/master | 2021-01-12T10:31:36.523649 | 2016-12-14T18:18:02 | 2016-12-14T18:18:02 | 76,471,000 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 49 | r | tab2_ui.R | tabItem("DataTwo",
titlePanel("Tab 2")
)
|
5f535f4efe02c8d4acc9f117aeedb0fa9acec746 | a9003002f4c54b1da0395aa68ad933a086d3973e | /FlashX-release/Rpkg/man/mean.Rd | 2c87e0c4b8e500ae0643f4e16d4b9b21a6c17a45 | [
"Apache-2.0"
] | permissive | rwang067/HistorySystem | ca45a31658c1881bdc5586a4779add981a818ba6 | ee06919c022d7d45b47d675d053fb261601bb310 | refs/heads/master | 2020-03-23T05:01:02.619671 | 2018-07-16T10:40:44 | 2018-07-16T10:40:44 | 141,119,561 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 486 | rd | mean.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/FlashR_base.R
\docType{methods}
\name{mean}
\alias{mean}
\alias{mean,fm-method}
\alias{mean,fmV-method}
\title{Arithmetic Mean}
\usage{
\S4method{mean}{fm}(x, ...)
\S4method{mean}{fmV}(x, ...)
}
\arguments{
\item{x}{A FlashR vector or matrix... |
f5f94e6bf29c8d29131d98b46f1f228c5a33e979 | 6034d565642a30876b7b7a025b74a31580c44613 | /R/p_value_kenward.R | 4650bdfb59156d82f310264b3a6879b5705e94a3 | [] | no_license | cran/parameters | a95beba8c8bd820a88b74ca407609cc08a62fcab | f19575ccdbbd303a1896a13d8b4b8210563cabfa | refs/heads/master | 2023-06-08T08:58:24.080762 | 2023-05-26T09:20:02 | 2023-05-26T09:20:02 | 211,083,154 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,982 | r | p_value_kenward.R | #' @title Kenward-Roger approximation for SEs, CIs and p-values
#' @name p_value_kenward
#'
#' @description An approximate F-test based on the Kenward-Roger (1997) approach.
#'
#' @param model A statistical model.
#' @param dof Degrees of Freedom.
#' @inheritParams ci.default
#'
#' @details Inferential statist... |
23e527d2713080d80611503b8aaab9180cb87d98 | 54a1d94786a6e8083c6d4d85b741a39a688c22d8 | /tests/testthat/test-UserMetrics.R | 6a03adbb1d73177bf4f5c97f4c16965da0fda172 | [
"Apache-2.0"
] | permissive | dmpe/urlshorteneR | dd75db112224bd4c0ab62bd7c0e364ce5d7d50b9 | c9276e932e76c4f12b6a51bd6348b58399ee9442 | refs/heads/master | 2022-09-08T16:37:50.278295 | 2022-08-20T10:41:04 | 2022-08-20T10:41:04 | 36,286,688 | 16 | 8 | Apache-2.0 | 2020-12-02T20:14:08 | 2015-05-26T09:46:00 | R | UTF-8 | R | false | false | 2,230 | r | test-UserMetrics.R | library(testthat)
library(httr)
library(jsonlite)
library(stringr)
library(urlshorteneR)
library(lubridate)
context("User Metrics")
test_that("will rollup the click counts to a referrer about a single Bitlink.", {
expect_gte(bitly_user_metrics_referring_domains(bitlink = "bit.ly/2EUGovW", size = 100)$units, -1)
u... |
ecf9c88d6359095b9df7620fcf43e3573ce8e18a | 7ca6b681c96e2445ca2e9d1bf9aa7b3bfd99e362 | /server.R | f051a57b3b545dbc2da293c0c70710698a0ce42a | [] | no_license | atishaysehgal/DataDashboardSI | b9898a7cf355d388571233f6c10c06da0625e24c | 9b195a64addae4f34f6f98cbbb6e474c14742701 | refs/heads/master | 2020-04-04T09:00:43.443578 | 2018-11-02T16:23:22 | 2018-11-02T16:23:22 | 155,803,224 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 457 | r | server.R | library(shiny)
library(ggplot2) # load ggplot
# Define server logic required to plot various variables against mpg
shinyServer(function(input, output) {
output$yearPlot <- renderPlot({
df <- data.frame(year=as.factor(insurance$year), County = as.factor(insurance$County), variable = insurance[[input$variable]... |
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