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3476e07ef8c391f7e11aaa3c6049d8137061a0fb | 6b46e9d05bbf0622728f1034174732003d80bdd7 | /R/group.R | 5ddfe7639ed2ecc01b3d46298d935c500c6d5df4 | [
"MIT"
] | permissive | gwb/RGroupFormation | 314c5f1b738512b734dd327fba5631fca697adb2 | f28dd330817efe7e4c71717dc8e96386f7fd643c | refs/heads/master | 2022-12-12T11:00:39.111760 | 2020-09-07T09:37:48 | 2020-09-07T09:37:48 | 287,511,525 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,876 | r | group.R |
##suppressMessages(library(purrr))
#' Generates a random permutation
#'
#' If \code{x} is an integer, then returns an element of the symmetric
#' group on \code{x} elements. If \code{x} is a vector, then generates a
#' permutation of the elements of \code{x}.
#'
#' @param x An integer or a vector.
#' @return A vec... |
d67afb7cba9635292c968160684ef4144a13fb12 | f96af69ed2cd74a7fcf70f0f63c40f7725fe5090 | /MonteShaffer/humanVerseWSU/humanVerseWSU/man/whichMin.Rd | c0936ad184b4ce4f5671d52f7806a7be34a652fe | [
"MIT"
] | permissive | sronchet/WSU_STATS419_2021 | 80aa40978698305123af917ed68b90f0ed5fff18 | e1def6982879596a93b2a88f8ddd319357aeee3e | refs/heads/main | 2023-03-25T09:20:26.697560 | 2021-03-15T17:28:06 | 2021-03-15T17:28:06 | 333,239,117 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 771 | rd | whichMin.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functions-vector.R
\name{whichMin}
\alias{whichMin}
\title{whichMin}
\usage{
whichMin(x)
}
\arguments{
\item{x}{numeric vector}
}
\value{
numeric vector that contains the indexes of *all* min elements, not just the *first*
}
\description{
beh... |
aa2198287739539b073eeb3bb1b0507683e8a424 | b61c793564f2197ea1f076cabc990f81baccec8f | /man/grepv.Rd | e40f3d1252da571f81dea6e1513ee980aac64808 | [
"MIT"
] | permissive | tkonopka/shrt | 46fabfcbfd3819a9016b412f1a7b91f4ba88c28b | eeef8bf50aee0412b5feff427c12ba2eec17332d | refs/heads/master | 2020-05-21T17:48:37.016989 | 2020-02-28T06:26:33 | 2020-02-28T06:26:33 | 60,825,097 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 629 | rd | grepv.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{grepv}
\alias{grepv}
\title{Pattern matching}
\usage{
grepv(pattern, x, value = T, ...)
}
\arguments{
\item{pattern}{character, pattern to look for}
\item{x}{character vector, object wherein to look for the pattern}
\item{valu... |
c560af1e09c5a92fa7e7e43b7d877ef540c04ee8 | 05ba1456015c848734180d4419c0875cae0e7d96 | /man/rowRanks.Rd | 64098e67405cbd0ffc4153cfdc9bcf17e3be898a | [] | no_license | bkmontgom/matrixStats | 31ccdfeacc33120e1b3905e7f7a19866d919342a | 43ed438a0114f67c3bd5174e75e15c6b2f07b7bd | refs/heads/master | 2020-05-15T18:35:13.436117 | 2019-05-04T22:03:29 | 2019-05-05T02:36:24 | 182,430,860 | 0 | 0 | null | 2019-04-20T16:47:29 | 2019-04-20T16:47:29 | null | UTF-8 | R | false | true | 3,332 | rd | rowRanks.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rowRanks.R
\name{rowRanks}
\alias{rowRanks}
\alias{colRanks}
\title{Gets the rank of the elements in each row (column) of a matrix}
\usage{
rowRanks(x, rows = NULL, cols = NULL, ties.method = c("max",
"average", "first", "last", "random", "... |
fbdfc8ab6b2c04e8fbc146ebc264c428bf59ede0 | a877efed473317cb676c7f7603aad960e658043e | /scr/rcourseday2.R | c0982e3dd9e58cfe67d95a981ca7b47b55528dce | [
"MIT"
] | permissive | nreigl/R.TTU_2018 | 6ef238d595f0f1a123660e7c6f2db74524fdc8af | aeed6aea45d09e4f307e55280fa94a322df72896 | refs/heads/master | 2021-05-08T23:37:03.744642 | 2018-03-19T10:47:56 | 2018-03-19T10:47:56 | 119,717,970 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 4,790 | r | rcourseday2.R | #' R course day 2/ Live coding
#' 10.Feb.2018
# Recap day 1
mydata <- 5
rm(mydata)
myvector <- c(3,5, 4.3, 6)
mean(gdp$date)
dataframe$newvariable <- NULL
?functionname
summary()
str()
head()
tail()
names(gdp)
#
piaac <- read.csv("http://www.ut.ee/~iseppo/piaacest.csv")
mean(piaac$earnhr, na.rm = T)
median(piaac$e... |
ced66bc52e50de26d4c471bb7f4a8d263a4be8d8 | 2ef76297c9731a56e2b00626c3a1679cccd5132e | /load.R | cce522de7fd2d1c7d4f9b800ba81c7c681ff61c3 | [] | no_license | vhy1967/YVY | 777155adf32482e81657cc102c09451237e554c4 | 79f4e37d3cc54af1a1c95578eb3b389acf2efdec | refs/heads/master | 2020-03-18T04:38:55.188954 | 2018-05-21T16:29:08 | 2018-05-21T16:29:08 | 134,298,356 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 897 | r | load.R | 'C:/Users/yuferov-vg/Desktop/YVY/processed/АнодиованиеРезы/3/основ/'
#fnames=list.files('C:/Users/yuferov-vg/Desktop/YVY/processed/АнодиованиеРезы/3/основ/',
fnames=list.files("C:/Users/yuferov-vg/Desktop/YVY/data/",
pattern = "Log-20171116-(\\d{6}).csv",full.names = T)
fnames=fnames[order(fnames)]
require(xts)
#fna... |
10a31c3172ea28fde4ffeff29fcc0b1a949d9e97 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/waved/examples/make.lidar.Rd.R | 8b4f9a29b94c2108cdd2890cb8a2d20412dc9b40 | [] | 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 | 184 | r | make.lidar.Rd.R | library(waved)
### Name: make.lidar
### Title: Make LIDAR signal
### Aliases: make.lidar
### Keywords: internal
### ** Examples
plot(seq(0,1,le=1000),make.lidar(1000),type='l')
|
58cb3dcefe28d92575edf3e4a2a08fe3b9b00146 | 0cd7388579dfbb382be628be2f38374b18783650 | /man/whately_2015.Rd | bd598bf028fa26e9476b3dba057f5e4569057617 | [] | no_license | beanumber/macleish | 411d8f6b7368fc8331b49ffb32c2dd9b9cb8ff38 | 4f345d0f6a03946cc9fd32d60cb59558925f996f | refs/heads/master | 2022-07-22T01:00:41.136122 | 2022-07-14T17:14:27 | 2022-07-14T17:14:27 | 45,202,012 | 2 | 6 | null | 2022-06-29T18:23:54 | 2015-10-29T18:13:58 | R | UTF-8 | R | false | true | 3,736 | rd | whately_2015.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{whately_2015}
\alias{whately_2015}
\alias{orchard_2015}
\title{Weather data from Macleish Field Stations}
\format{
For both, a data frame (\code{\link[dplyr:tbl_df]{dplyr::tbl_df()}}) with roughly 52,560 rows and 8... |
96d0f43ccfbf376a6c871626a7821bfee99de593 | b76879ca270a8d94a42ee2cf821ae24508d5a510 | /man/extract.prior.Rd | 7f02a5d91ca5caa29af6fe41ede162c76fa1b39c | [] | no_license | cran/DCL | ee11184e6a8b74515ea856e3bd27acda5488d861 | bd9aa3502f861a5c0ff0266730a438c7d0d7116a | refs/heads/master | 2022-05-17T09:38:59.010683 | 2022-05-05T15:40:02 | 2022-05-05T15:40:02 | 17,678,627 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,733 | rd | extract.prior.Rd | \name{extract.prior}
\alias{extract.prior}
\title{Extracting information about zero-claims and severity inflation
}
\description{A way of extracting information about zero-claims and severity development inflation through the DCL method applied to two counts triangles: number of payments and number of reported clai... |
70f7390e75298b7f13291f7e7d2ac3cd1e4857a3 | b64f494df1015a60619f8cddc70465f742652525 | /preprocess.R | c58a1bf4cb7439d22a52fdc5a02ab06c60aaabad | [] | no_license | hnyang1993/UmbrellaAcademy | 5b604e9caa0d998f580760fb3970138348cec22f | a287c4df9fd342fe93b2204645d693e66bc7cbb2 | refs/heads/master | 2020-05-02T15:31:57.398931 | 2019-04-26T01:32:42 | 2019-04-26T01:32:42 | 178,043,804 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,366 | r | preprocess.R | library(text2vec)
library(data.table)
library(magrittr)
library(stopwords)
library(SnowballC)
library(caret)
library(glmnet)
setwd("~/Desktop/BIOS 735/final_project/jigsaw-toxic-comment-classification-challenge")
sample <- fread("sample_submission.csv")
test <- fread("test.csv")
test_labels <- fread("tes... |
984ec54b1c80afda1f7c2318a22e398bfb151cdb | c0dcb97d778aada5336bdd129446f4b66a0c8a32 | /Crab.R | e2715db11f4d54e740df24eb6b7fa15175a401e1 | [] | no_license | OmarVillalobos/Crab | d936d658bbaa16807f9cba0ff675b0a44d06f9f5 | 000fad9e3727ddadd0b8b7b7bfe8e901d61ae57c | refs/heads/master | 2020-03-23T16:56:51.384839 | 2018-08-01T13:33:27 | 2018-08-01T13:33:27 | 141,834,826 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,563 | r | Crab.R | df <- read.csv("C:/Users/OmarVr/Downloads/small_train.data", sep = '\t',
header=T,encoding = 'UTF-8')
label <- read.csv("C:/Users/OmarVr/Downloads/small_train_upselling.labels", sep = '\t',
header = F, encoding = 'UTF-8')
new_data <- read.csv("C:/Users/OmarVr/Downloads/small_test.data"... |
9980d58e8a9b49a3bb2e13d96525549d01d9a6eb | 7cae64bf335cf9c08ffb5cdd7564b82f1ea2abd4 | /Multiple Linear Regression Class.R | 730a7917e419fc7594a391601c564402ba0b4af8 | [] | no_license | Shivanandrai/R-Code- | 89945897c875c8dedcec8b69867dd2ee5b29317e | 8716ea634f8bcf95680288fdc0153e4fa17f95b5 | refs/heads/master | 2022-11-15T22:46:06.076331 | 2020-07-02T09:48:34 | 2020-07-02T09:48:34 | 276,605,378 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,471 | r | Multiple Linear Regression Class.R | setwd("~/Desktop/ESCP/R Class")
Fueleff <-read.csv("1-FuelEfficiency.csv")
head(Fueleff)
plot(GPM~WT, Fueleff)
model_1= lm(GPM~., data=Fueleff)
summary(model_1)
Fueleff= Fueleff[-1]
model_1= lm(GPM~., data=Fueleff)
summary(model_1)
cor(Fueleff)
library(leaps)
X=Fueleff[ ,2:7]
Y=Fueleff[ ,1]
#new function. I use Ma... |
71bae1af1ccb319a0eceaa651ebbd92f52ff0e56 | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/4281_1/rinput.R | c010c739beb20f60af8ccfd0e23bd6be86021ce9 | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 135 | r | rinput.R | library(ape)
testtree <- read.tree("4281_1.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="4281_1_unrooted.txt") |
9de1844dd5a651f63ed149a6ef11256452aa976c | 5ac57449f8a0cfbc0e9c8f716ab0a578d8606806 | /man/pDcalc.Rd | c55dced881ff73a38cd3b1c1a1599e130432fb75 | [] | no_license | hugaped/MBNMAtime | bfb6913e25cacd148ed82de5456eb9c5d4f93eab | 04de8baa16bf1be4ad7010787a1feb9c7f1b84fd | refs/heads/master | 2023-06-09T01:23:14.240105 | 2023-06-01T12:51:48 | 2023-06-01T12:51:48 | 213,945,629 | 5 | 0 | null | null | null | null | UTF-8 | R | false | true | 4,021 | rd | pDcalc.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/run.functions.R
\name{pDcalc}
\alias{pDcalc}
\title{Calculate plugin pD from a JAGS model with univariate likelihood for studies
with repeated measurements}
\usage{
pDcalc(
obs1,
obs2,
fups = NULL,
narm,
NS,
theta.res... |
29f3cdb9cc66a32c3ae1001a460167bb97e80d1f | 7b25b67ba55aeb0dec69a3ec6d0ed48939bf944b | /R/getClusterTree.R | 47cd0be1f94d9dfc84dd1c67d67781612e513b34 | [] | no_license | rwoldford/trec | 4524c08406042f4fdfb7fc839f51691f162d6515 | 5cac232d997815956c767d9f12ec6abe2357dc1f | refs/heads/master | 2020-04-18T09:11:31.902264 | 2019-04-26T17:27:28 | 2019-04-26T17:27:28 | 167,425,596 | 1 | 1 | null | 2019-04-26T17:27:29 | 2019-01-24T19:37:44 | R | UTF-8 | R | false | false | 4,622 | r | getClusterTree.R | #' Transform all data structures into clusterTree
#'
#' @param x data structure output by some clustering method
#' (e.g. hclust, kmeans, dbscan, etc.)
#' @return a matrix providing the mapping
#' between data points and cluster id.
#' @examples
#' x <- kmeans(matrix(rnorm(100),nrow=50),centers=3)
#' getClusterTree(x... |
e6828f4683612f05ee327ba768651df4bd83dbdf | a58298d4d1afffcbf7de672cb99ad8ab7ff6b5c5 | /Scripts/depth_12.4G_VS_6.2G.R | aac5baaf5c35690a38cf2c59fa9300ffbb581d2f | [] | no_license | zhouyunyan/PIGC | b6edb9685d720bdd0145598fa34dccd1f750223e | cfceab8ad4cadab522d56106adb4b636dfbe24c6 | refs/heads/master | 2023-04-12T04:45:05.660394 | 2022-07-21T08:34:03 | 2022-07-21T08:34:03 | 295,356,035 | 18 | 15 | null | null | null | null | TIS-620 | R | false | false | 1,014 | r | depth_12.4G_VS_6.2G.R | ####Comparison of predicted gene number between high and low seqeuncing depth
table <- read.table("geneNum_12.4G_VS_6.2G.txt",header = T)
library(reshape2)
table_melt <-melt(table,id.vars = "ID")
table_melt$value <- table_melt$value/1000
table_melt$variable <- factor(table_melt$variable, levels = c("Base6.2G","B... |
02a5610d9d1c1699019d5bf41b267ea24c5b6ab5 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/glamlasso/R/glamlasso_RH.R | f762de858045df09c97c18dac2bdcc1542d2f992 | [] | 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 | 2,099 | r | glamlasso_RH.R | #
# Description of this R script:
# Rotated H-transform of an array A by a matrix M.
#
# Intended for use with R.
# Copyright (C) 2015 Adam Lund
#
# 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
# ... |
125cc0244cb0ce75d1f94e532c38978dff35dae4 | 53fbeea7ca52f3b474e79efe34b1bee3f9a52572 | /man/coerce-tbl_df.Rd | bdebb4cef4a34c53140ced88dc5046c7189c21bc | [
"MIT"
] | permissive | csu-xiao-an/transformer | 575aa7a59094e02006d44672c3fa5f79c085c757 | 04658a7d99973ac0d3d9b4484b0e599ecedcf936 | refs/heads/master | 2020-07-26T17:56:40.024193 | 2019-09-09T16:55:50 | 2019-09-09T16:55:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,598 | rd | coerce-tbl_df.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/as_tibble-methods.R, R/coerce-tbl_df-methods.R
\name{as_tibble}
\alias{as_tibble}
\alias{as_tibble.DataFrame}
\alias{as_tibble.IPosRanges}
\alias{as_tibble.GenomicRanges}
\alias{coerce-tbl_df}
\alias{coerce,data.frame,tbl_df-method}
\alias{co... |
2994378f961ffafaa25268f4a7a4f8ee2492d446 | d771ff12fe4ede6e33699704efa371a2f33cdfaa | /R/do.filter.R | aa360ae6cb29ce9ba44f9337127e9a7047807add | [
"MIT"
] | permissive | ImmuneDynamics/Spectre | aee033979ca6a032b49ede718792c72bc6491db5 | 250fe9ca3050a4d09b42d687fe3f8f9514a9b3bf | refs/heads/master | 2023-08-23T14:06:40.859152 | 2023-04-27T00:31:30 | 2023-04-27T00:31:30 | 306,186,694 | 52 | 17 | MIT | 2023-08-06T01:26:31 | 2020-10-22T01:07:51 | HTML | UTF-8 | R | false | false | 13,283 | r | do.filter.R | #' do.filter - filtering data.table using multiple match values
#'
#' This function allows filtering of a data.table using multiple match values -- all cells that contain any of the match values will be filtered, and provided in a new data.table.
#'
#' @param dat NO DEFAULT. A data.table
#' @param use.col DEFAULT = NUL... |
663c1f66553ddc4b9f7c9d9578ecc79828535073 | 1391c4c885aa13dbfc7e8ab54598787697c65662 | /Data Analytics/DataVisualizationPhase3.R | 2f9e8e69adb2fb6be71022df8a5fbf2a5812af02 | [] | no_license | salpoddar/Predicting-NewYork-Taxi-Fare | 86a7b497dd057f23e7c5dc1b3f0b657631a43eec | 575c2fefd5b8c1e79e1739f416f2c1d0cbd84b09 | refs/heads/master | 2023-04-02T06:56:35.378700 | 2021-04-12T20:27:15 | 2021-04-12T20:27:15 | 357,330,226 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,149 | r | DataVisualizationPhase3.R | #load the libraries
library(data.table) # Fast CSV read
library(ggplot2) #Visualisation
library(lubridate) #Date-Time extraction
library(dplyr) #Data wrangling
library(ranger) # Random forest
library(geosphere) #Dist
library(caret) #Cross vaildation
library(xgboost)
library(DataExplorer)
library(mlr)
library(... |
6d01e5648f5d7cab0103c56b44b90114117f6983 | 0606177a3914cc80d5caa4d7a98d7f9870d4cb40 | /code/10/9/question.R | 30ef3bd73fb34b92a85b1ff5d8378a1bebe3c2c6 | [] | no_license | jeffljx12/thesis-new | 624183776824eee36c3a38c0edb30aca6454012b | 7ffab51491bbccfb8f5b953a3fb41cc84f214ebf | refs/heads/master | 2020-04-07T19:08:32.966809 | 2019-03-10T16:36:45 | 2019-03-10T16:36:45 | 158,637,647 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,906 | r | question.R | library(nlme)
bigF= function(){
# build model
d3k2_model = function(time,beta0,beta1,beta2,beta3,beta4,beta5,
ra1_i,ra2_i,ra3_i,tau1,tau2,
covar1,covar2,covar3,covar4,covar5,
cov.coef1,cov.coef2,cov.coef3,cov.coef4,cov.coef5,env = parent.fram... |
a3f755197161849a4d5aa574b9bbae4de5222d5f | 8d542ee756ee30acb737f5497625308da92a9678 | /meta learners.r | 72e3a19b1977a0a9e602f4533d1d18bdc5c0adcb | [] | no_license | uint4/retail-sales-forecasting-with-meta-learning | 82e60c4f70b6af96fb046174f7c69184ef4071aa | c613f6e66ce9faa9b9266d3c2c703b331c0ac0ef | refs/heads/master | 2022-03-24T01:04:48.967757 | 2020-01-09T05:14:50 | 2020-01-09T05:14:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,166 | r | meta learners.r |
scale_mse <- function( scales ) { ### loss function
loss<-function(y_true, y_pred){
k_mean( (k_mean(k_square(y_pred-y_true),axis=-1,keepdims=TRUE)/scales))
}
loss
}
fcn_block<-function(input){ ### convonlutional blocks
squeeze_excite_block<-function(input){
filters = unlist(k_get_var... |
f389d990b16379a6a0aa9058cddba98a7642a587 | 2bb0ba7e4b29ea458cbb064411c7ef69ef47065d | /R/NonParametric.VUS.R | c03e27d8046d50abc347ac2fa499a623b646602d | [] | no_license | cran/DiagTest3Grp | a1b04922524129306eacb1d1643b0a7bc47f6df3 | 25099a0b5d0da160778aa6412021a2e2f1e01720 | refs/heads/master | 2021-01-23T08:43:47.100435 | 2014-02-20T00:00:00 | 2014-02-20T00:00:00 | 17,713,726 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,934 | r | NonParametric.VUS.R | NonParametric.VUS <- function(x,y,z,alpha=0.05,NBOOT=50,FisherZ=FALSE)
{
######################################################################################################################################################
########This function provides Nonparametric estimate on VUS =Pr(X1<=X2<=X3)
#####... |
76f517ec95eba28c3cd580638f093385832672fb | d3b774668f6e577cefdeea4dd2be1326ee4b5aee | /R/all_mailinglists.R | 776a333096b514cdc6ca782ac0f16d6e49a5b97e | [
"MIT"
] | permissive | ropensci/qualtRics | 50e68a3dd3f184ee14f19126bd7783b4b9bd61d1 | c721563fa2fcb734c1ad9c4d8ccd80bbefbed15d | refs/heads/main | 2023-08-31T01:00:05.366989 | 2023-06-23T18:55:13 | 2023-06-23T18:55:13 | 70,817,337 | 188 | 64 | NOASSERTION | 2023-09-07T19:38:56 | 2016-10-13T14:51:26 | R | UTF-8 | R | false | false | 859 | r | all_mailinglists.R | #' Retrieve a data frame of all mailing lists from Qualtrics
#'
#' @template retry-advice
#' @importFrom purrr map_df
#' @importFrom purrr flatten
#' @export
#'
#' @examples
#' \dontrun{
#' # Register your Qualtrics credentials if you haven't already
#' qualtrics_api_credentials(
#' api_key = "<YOUR-API-KEY>",
#' b... |
6a530b2815570c4eb3626b0ed55c78c3d62c4b37 | 86d4288e42815531c36804e95e3a58cef083090b | /run_analysis.R | 5359385150e0d75065da4e90f4fc292968113e78 | [] | no_license | shilpibhatnagar/Getting-cleaningdata | 2cb07afa0f8ec2e54fb93eaa2024b622e197b5ef | 69eb35c2f43727c9a7be825f081bcfa03e8c052f | refs/heads/master | 2021-01-12T01:53:53.659156 | 2017-01-10T10:00:33 | 2017-01-10T10:00:33 | 78,441,816 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,357 | r | run_analysis.R | #download the files and change the working directory in R Studio to the folder where the data files exist
#setwd("filepath")
# First we read all the data files for which we need to tidy the data.
#Training files
train_data_x <- read.table("./train/X_train.txt")
train_data_y <- read.table("./train/y_train.txt")
train_... |
4c4aa63abeccb5690ca4713738dfac0557e53790 | 1eb7bac365b579c4840e1489ed3c0171d1bd5e00 | /scripts/genieR.R | b5cfd5f1eb3e7346b789de5e7972f6025915bbcd | [] | no_license | asmmhossain/phlow | 80b853fd9b3aeca569d56882c72348437bdcf963 | 2fd76033b5412b1239f6af256ff20ee29bf7d237 | refs/heads/master | 2020-09-13T10:22:36.830570 | 2017-09-27T15:43:25 | 2017-09-27T15:43:25 | 13,800,552 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 988 | r | genieR.R | #!/usr/bin/env Rscript
args <- commandArgs(trailingOnly=T)
if(length(args) < 2)
{
stop('Usage: rtt2.R <tree> <outname>')
}
suppressPackageStartupMessages(require(ape))
suppressPackageStartupMessages(require(pander))
suppressPackageStartupMessages(require(genieR))
#suppressPackageStartupMessages(require(adephylo))
... |
8075bad8ab102375abf67c245d1f90b85dde0aa9 | 95fecb2a9ee2c90763b0baa04379a5f119546063 | /github_whatsappcode.R | 8165419648a69ce5b67709e997eee7ad9e5d1291 | [] | no_license | iPALVIKAS/whatsapp-chat-text-mining | 2db29ba946192788617f9eb0ace2371b6423f302 | 5482d3c10197fdbde970021bbaf4f23d9f4edfcd | refs/heads/master | 2021-01-20T22:19:06.211986 | 2016-07-27T11:15:53 | 2016-07-27T11:15:53 | 64,249,782 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,271 | r | github_whatsappcode.R | #Vikas Pal
#the data show the text minning and representation of it the form of word cloud
#loading data from the local folder
data <- read.csv('data.csv',header = FALSE)
#handling emoticons
data <- sapply(data,function(row) iconv(row, 'latin1', 'ASCII',sub = ''))
#calling text mining package
library(tm)
... |
97a8d61c01562ab66fd50be17b143e3b6d95827b | ddd18a4a1cc9911b3f1ec8e38165d5cdf3386fac | /303ar_age.R | 43d8174b33da77011035b3ed19db368347c88472 | [] | no_license | stewartli/auditworkpaper | 656e47fd7113170886ce59585a18d349bda0b5d2 | 3a4ee3fdeba67e58657435c5849fc5ae4d78a5ed | refs/heads/master | 2020-08-09T13:46:03.584327 | 2020-07-26T06:51:08 | 2020-07-26T06:51:08 | 214,100,102 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,316 | r | 303ar_age.R | source("001package.R")
source("202df_tb.R")
# reconcile sales to ar----
#+
df_join <- df_ar %>%
filter(subaccount == "Accounts Receivable") %>%
mutate(amt = debit,
amt = ifelse(amt == 0, -credit, amt)) %>%
group_by(num) %>%
full_join(df_ar %>%
filter(subaccount == "Revenue") %>% ... |
b5ae4fc98d03f1f4e638cef97332b62dbea08775 | cd181d1f57308093a0142a6f3630db006bda2c1d | /preliminary.R | ffcd5f357f8650bf992eb77d8b0615d0741675a4 | [] | no_license | CoMoS-SA/agriLOVE | 717df7d531faacdc361360f53613af93595716a0 | 91153495a29dd2cba938df64cf745daacf398b0f | refs/heads/main | 2023-08-26T20:09:25.028184 | 2021-10-13T15:32:58 | 2021-10-13T15:32:58 | 416,800,601 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,245 | r | preliminary.R | #### PRELIMINARIES
#Contains preliminary code chunks to be reproduced at the beginning of each time step
preliminary_f <- function(){
if(flag_suspend_ergodic==1){
if(t<ergodic_trans){
flag_auction <<- 0
flag_deforestation <<- 0
flag_land_abandon <<- ... |
c5f4a2e84e1920858c7e621b2b754c7fc517cedd | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /iBATCGH/man/Scenario2.Rd | 7f4d977136834079b8952e5e63cc1953dce61639 | [] | no_license | akhikolla/TestedPackages-NoIssues | be46c49c0836b3f0cf60e247087089868adf7a62 | eb8d498cc132def615c090941bc172e17fdce267 | refs/heads/master | 2023-03-01T09:10:17.227119 | 2021-01-25T19:44:44 | 2021-01-25T19:44:44 | 332,027,727 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,249 | rd | Scenario2.Rd | \name{Scenario2}
\alias{Scenario2}
\title{
Simulated data - Scenario 2
}
\description{
Simulates the data as described in the reference provided below (Scenario 2).
}
\usage{
Scenario2(sigmak = 0.1)
}
\arguments{
\item{sigmak}{
Standard deviation of the error term
}
}
\value{
Return a list made of the following item... |
58bbbf2db2c86b867fbc5edf8f4d2cde5682daa5 | 8b387d5ca95fab93d6e73cd2f14af4579c1828c9 | /man/tiler.Rd | 6cdcf4cf79ba1bc47d96721d9e00f8069d44ab9c | [] | no_license | swebb1/questplots | f13564cd617aedd92543b0f7b6bc7d33e174d9d4 | 70d04398f053e01c0041ecdf80212ae46b3ec2a3 | refs/heads/master | 2021-01-17T18:04:57.301061 | 2016-06-30T10:45:38 | 2016-06-30T10:45:38 | 62,303,322 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,212 | rd | tiler.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tiler.r
\name{tiler}
\alias{tiler}
\title{tiler function}
\usage{
tiler(t, x, xl = F, xs = 1, y, yl = F, ys = 1, z, zl = F, zs = 1,
bin = 1, min = -5, max = 5, xrange = c(-100, 100), yrange = c(-100,
100), func = "median", col = cols)
}
\... |
5b667106864d73d0521c9cc8dfd61d06af822dda | 17655e4899fc18dc366e4ea1a066a2b6b100a2b0 | /tests/testthat.R | 0ce499727e83c679d75fc270d6a2b72f9af920e5 | [] | no_license | mdsumner/rmdal0 | 704ba2a23e6b5b88f51bc968ff1d0b387ce4d324 | 37313a442ee327bad86bb348e52d2714560254fa | refs/heads/master | 2020-04-06T12:51:51.260753 | 2019-09-20T07:05:03 | 2019-09-20T07:05:03 | 157,473,804 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 56 | r | testthat.R | library(testthat)
library(rmdal0)
test_check("rmdal0")
|
4223a33170b44c1407b3ee59730f724c2e4dbde1 | 1c33c264f7b501d2d38c4f0fc0ea016d1fe6a7a2 | /man/computeAuc.Rd | 48896555f76e0346dfe8dd05f5ad9361f635d0b8 | [
"Apache-2.0"
] | permissive | OHDSI/PatientLevelPrediction | 348a0418bfcaac4a54f7fdf8d56593f1bca60119 | f6bda233eae96bae81b5745b46879c685d278e36 | refs/heads/main | 2023-08-16T22:31:30.383549 | 2023-08-16T12:34:22 | 2023-08-16T12:34:22 | 32,690,570 | 176 | 98 | null | 2023-09-07T14:58:48 | 2015-03-22T19:08:53 | HTML | UTF-8 | R | false | true | 600 | rd | computeAuc.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/EvaluationSummary.R
\name{computeAuc}
\alias{computeAuc}
\title{Compute the area under the ROC curve}
\usage{
computeAuc(prediction, confidenceInterval = FALSE)
}
\arguments{
\item{prediction}{A prediction object as generated using the
\code{... |
2af2bf5e13ed177c602579f0436d98c194a4276c | f7bdf02c5d15335f306ee45720570c0e00856e5d | /day1/day1_fish_exercises.r | 229bd904e4fc0a254fee9ace185cd7e2c68d147d | [] | no_license | rkbauer/R_Course_Basic_Statistics_for_Marine_Fishery_Biologists | e8171da5a1256dc53cc94f8c101b9ca6ea20edef | 79b691dcc956e7894274c11e09e7e162ce7b6322 | refs/heads/master | 2023-01-22T12:42:57.447146 | 2020-11-28T21:28:14 | 2020-11-28T21:28:14 | 263,082,981 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,550 | r | day1_fish_exercises.r | # fish exercise - day 1
# 1. Import "Fish.csv" (mind not available values!)
setwd('~/Dropbox/R_course/day1')
fish <- read.table("Fish.csv", header=T, sep=',', dec=".") # load dataframe
head(fish)
attach(fish)
# 2. Which was the maximum, which was the minimum size of each caught species?
ran... |
12d189dd5162a620c49f63e9aadd1984877cf90f | 4838ff30f4cf7fc1d6a268a1f28f122dea90d056 | /man/PopHumanAnalysis.Rd | 8d65d0d2cbb3ee9d454917857061c5d23445795d | [] | no_license | BGD-UAB/iMKT | 319ff35016ba54108fe1dc3ce64e90e623b733b0 | 1fb62426f850c4423c042f027fd9fb844f56304b | refs/heads/master | 2021-06-15T23:20:46.913981 | 2021-02-15T12:07:05 | 2021-02-15T12:07:05 | 144,316,815 | 8 | 2 | null | 2019-04-29T13:25:01 | 2018-08-10T17:51:58 | R | UTF-8 | R | false | true | 2,331 | rd | PopHumanAnalysis.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/PopHumanAnalysis.R
\name{PopHumanAnalysis}
\alias{PopHumanAnalysis}
\title{iMKT using PopHuman data}
\usage{
PopHumanAnalysis(
genes = c("gene1", "gene2", "..."),
pops = c("pop1", "pop2", "..."),
cutoff = 0.05,
recomb = TRUE/FALSE,
... |
62af94e697a62041523d91d8457944f479d36029 | f2badfafe4cbaa709547c18ff5abb1283a255390 | /kokoro.r | 0f04d0b6d1805343216ebca0e57e7b3f2694f089 | [] | no_license | satocos135/lecture2019shimane | 71bf893f49ba08b9ac5ba75984266d0eaeb30faa | 5483f5d4524bc9d488ab1acd2ef8121db24c01cc | refs/heads/master | 2020-06-19T03:11:29.705018 | 2019-07-24T07:27:45 | 2019-07-24T07:27:45 | 196,543,866 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,155 | r | kokoro.r |
# path = Sys.getenv('PATH')
# Sys.setenv(PATH= sub('MeCab', 'MeCab_sjis', path))
# Sys.getenv('PATH')
#Sys.getenv('PATH')
library('tidyverse')
library('RMeCab')
library('igraph')
getwd()
#setwd('../projects/lec')
setwd('../lecture2019/')
kokoro = read.delim('data/kokoro.tsv', header=T, sep='\t', stringsAsFactor=... |
6f62de3ff85a3df17b91794c73867687f4f6fcaf | 92081f34f55b066932c7490529b1d9b64cfc2fe6 | /server.r | 878bb93a47784656cde07e4defda55abf0faf207 | [] | no_license | dattashingate/Rshiny-Apps-radioButtons | 4c8dc2b94771a7f50f9b318226954020a0c4038d | f5a36021c8334fa1dc1f1c39885920069901472a | refs/heads/master | 2022-10-05T10:19:48.873848 | 2020-06-09T08:40:24 | 2020-06-09T08:40:24 | 270,953,210 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 220 | r | server.r | library(shiny)
shinyServer(
function(input,output)
{
output$name=renderText(input$name)
output$age=renderText(input$age)
output$addr=renderText(input$addr)
output$gender=renderText(input$gender)
}
) |
fe15ce43bf8753340209dd53dd7788908eb837cf | 2a374d65d81be09bc7bf9c6f5153aa239ad20c5b | /R/fetch.R | 3b0ce1913ed3ed279749b999d04bbeedbfa5c0bb | [] | no_license | KennethTM/windfetchR | e037dfb97a88b6691993883a8eed3a84388d68c3 | 60a3506f5658fe4aacff359e7c50a7771e71094d | refs/heads/main | 2023-02-17T23:06:22.307535 | 2021-01-20T11:35:27 | 2021-01-20T11:35:27 | 331,251,052 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 91 | r | fetch.R | #' @useDynLib windfetchR
#' @importFrom Rcpp evalCpp
#' @exportPattern "^[[:alpha:]]+"
NULL |
8b32c990456f0e8c288c7f8f9b5d6f6d85d801cc | 440ad9e927eee7e0080e05a602eada7b8ca645ac | /man/mirror2esnssite.Rd | 0fcc320d464215a965a45806ee4be7bbcab1f7ff | [] | no_license | jae0/SCtagging | 517de7d5ce6d58153af877d5eb7c828092707344 | bcf5e885bc932657da43643b367c91541f834408 | refs/heads/master | 2023-02-24T17:56:40.806931 | 2021-01-21T13:15:59 | 2021-01-21T13:15:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 380 | rd | mirror2esnssite.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/SCtagging.R
\name{mirror2esnssite}
\alias{mirror2esnssite}
\title{mirror2esnssite}
\usage{
mirror2esnssite(region = "ScotianShelf")
}
\value{
status message in case it was called by webpage
}
\description{
ESSENTIAL FUNCTION. Must call after ... |
e13054a1d03ea93a50e8ae950a79d55414c686f4 | 8d5679573c40ea3391c2c63f2ec794928b594b65 | /man/lines.TPCmsm.Rd | 66a8a58a6fcf2deb6a4a04f4cf04c53e28d44d30 | [] | no_license | cran/TPmsm | 1320002cef52b8c2205e07c75758561edc221ac9 | 6f54632266e86a7cd24948437da6bf42e2e44010 | refs/heads/master | 2023-04-06T09:37:29.701579 | 2023-01-13T19:30:02 | 2023-01-13T19:30:02 | 17,693,879 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,777 | rd | lines.TPCmsm.Rd | \encoding{UTF-8}
\name{lines.TPCmsm}
\alias{lines.TPCmsm}
\title{lines method for a TPCmsm object}
\description{lines method for an object of class \sQuote{TPCmsm}.}
\usage{\S3method{lines}{TPCmsm}(x, plot.type="t", tr.choice, col, lty, conf.int=FALSE,
ci.col, ci.lty, legend=FALSE, legend.pos, curvlab, legend.bty="n"... |
af8970157927bdf11a711f44561c37a9f8bc5e35 | 833201ed243c95e2702337630f4f67fd91a63c01 | /TA1_Admin_Info_Visualizaciones_Carlos.R | 4a7bce3af601494f7645940bdb86798dddbb7b6b | [] | no_license | OscarFloresP/Administracion | 150b2ca29947ef74b08e6c3924af1fd011007041 | d049c9b81713d07e0484a32f1e81eb6bb76e821e | refs/heads/main | 2023-04-18T05:20:25.542551 | 2021-05-06T03:10:22 | 2021-05-06T03:10:22 | 363,507,955 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,230 | r | TA1_Admin_Info_Visualizaciones_Carlos.R | #Cargar los datos
DFHoteles <- read.csv("hotel_bookings.csv")
View(DFHoteles)
#Inspeccionar los datos
nrow(DFHoteles)
ncol(DFHoteles)
colnames(DFHoteles)
str(DFHoteles)
summary(DFHoteles)
#Mis Visualizaciones
#Primer grafico
table(DFHoteles$arrival_date_year)
table(DFHoteles$arrival_date_month)
table(DFHot... |
c49942aa201042c697917e28470f8f522569d429 | 72e7b17d8bb90d293f8075a71abec37910831030 | /HouseholdPowerConsumption/plot2.R | 5d372fd361c1fdc4fa5e488fe507d2129e8fa501 | [] | no_license | kieranroberts/R-datascience | 2b70bdef780af48920a869f8bf87ed9fbef99758 | acac610dfc9ba9a75129c762d85136f666245405 | refs/heads/master | 2016-09-13T08:10:48.007527 | 2016-05-18T07:03:45 | 2016-05-18T07:03:45 | 58,977,950 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 813 | r | plot2.R | # Temporary change the locale
my_lc_time <- Sys.getlocale("LC_TIME")
Sys.setlocale("LC_TIME", "en_GB.UTF-8")
# Get the data
source("getData.R")
getData()
# read and extract relevant data
data <- read.table("./data/household_power_consumption.txt", header=TRUE, sep = ";", stringsAsFactors=FALSE)
subsetData <- subset(d... |
d09645c8ad7bb969312c7d9fbfad567add596c47 | a72c6d86ff34d7e0669cfd82c0629ff85ecbffc7 | /classifier.R | fe233ec1350089f7a21b0febf2ccf35267eb41db | [] | no_license | GCDigitalFellows/WebScraping | 86c2a7dc644d655354d420b339bbd31f597db740 | c69bff920a69b7cdecec6bc63110877bd647528f | refs/heads/master | 2020-04-05T18:55:32.493448 | 2015-05-20T21:25:45 | 2015-05-20T21:25:45 | 38,452,845 | 2 | 0 | null | 2015-07-02T19:32:33 | 2015-07-02T19:32:32 | null | UTF-8 | R | false | false | 956 | r | classifier.R | library(caret)
library(mlbench)
library(magrittr)
data(Sonar)
set.seed(107)
in_train <- createDataPartition(y = Sonar$Class, p = 0.75, list = FALSE)
training <- Sonar[in_train, ]
testing <- Sonar[-in_train, ]
ctrl <- trainControl(method = "repeatedcv",
repeats = 3,
classPr... |
c9f3776530e67b16fe39dd424f81fed4613cd261 | 9d3e3c3950c4101bc863a90e69606d7c7d03a4e9 | /codling_moth/code/drivers/pest_window_code/since_day_1/takes_long/d_extract_needed_info.R | 3801f997d0a1f1ce1f789c32fd02c3498e0dbfa0 | [
"MIT"
] | permissive | HNoorazar/Ag | ca6eb5a72ac7ea74e4fe982e70e148d5ad6c6fee | 24fea71e9740de7eb01782fa102ad79491257b58 | refs/heads/main | 2023-09-03T18:14:12.241300 | 2023-08-23T00:03:40 | 2023-08-23T00:03:40 | 146,382,473 | 3 | 6 | null | 2019-09-23T16:45:37 | 2018-08-28T02:44:37 | R | UTF-8 | R | false | false | 2,682 | r | d_extract_needed_info.R | #!/share/apps/R-3.2.2_gcc/bin/Rscript
#library(chron)
library(data.table)
data_dir = "/data/hydro/users/Hossein/codling_moth_new/local/processed/section_46_Pest/"
output_dir = "/data/hydro/users/Hossein/codling_moth_new/local/processed/section_46_Pest/"
name_pref = "combined_CMPOP_4_pest_rcp"
models = c("45.rds", "85.... |
955dca00ea98c4b3c9c9b66f1b83da7ba4e9864f | d590f2ad3d1352449b5085ab2ee2a9eca853486f | /I_Consent_Gazepoint_Project/scripts/TMSP.R | 58aa47c2af543224e9e81f02f4e976ee07daa19d | [] | no_license | jhthompson/cpsc-4120-gazepoint-project | 1afed31589001de6985a4984d36e188c4c6f8ec1 | 300cac949f2c6b262dd23e92d7f6bc58b89d2207 | refs/heads/master | 2020-08-18T22:54:23.532647 | 2019-12-01T21:45:18 | 2019-12-01T21:45:18 | 215,843,412 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 22,637 | r | TMSP.R |
################### IMPORTANT NOTICE #######################
# Below you'll find 4 functions:
# TransMatrix
# calculates transition matrix between AOIs - returns transtion matrix M,
# TransPlot
# plots tranistion matrix - returns TM plot with probabilities in each cell;
# TransEntropy
# ... |
50d3a687167ba4d783732922dde6f3f2c779c351 | de64950c44d13417e8ca44c1d25791d05544e867 | /Explore/density_test_train_plot.r | 1b716f023929d48bab04f5d216363285c02cb1da | [] | no_license | gumpu/kagglehiggs | c6bb393a1d1c48ed865b5f26ac881d7456e4ddab | 3b408c1c23ff6cd2b73698166ab45ab80b02fbda | refs/heads/master | 2021-01-21T01:59:50.427112 | 2014-06-22T18:52:13 | 2014-06-22T18:52:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 672 | r | density_test_train_plot.r | #=============================================================================
#
# This plots the density for the various
# input variables grouped according to
#
# test / trainingset
#
#=============================================================================
require(ggplot2)
load(file="../Processed/norm_datas... |
59f7d96d9fb5fd994883f2f35f70616c01eec875 | 7c3f2e88c0273324f42c5175795931c668631520 | /R/utils_io_fst.R | 53c1cbe337150101bb2cc7d8a0abc5a0a392407e | [] | no_license | dipterix/rave | ddf843b81d784a825acf8fe407c6169c52af5e3e | fe9e298fc9f740a70f96359d87987f8807fbd6f3 | refs/heads/master | 2020-06-26T19:52:26.927042 | 2019-07-30T22:47:36 | 2019-07-30T22:47:36 | 110,286,235 | 0 | 0 | null | 2018-06-20T03:18:47 | 2017-11-10T19:45:40 | R | UTF-8 | R | false | false | 4,925 | r | utils_io_fst.R | # fst IO
#' R6 class to load fst files
LazyFST <- R6::R6Class(
classname = 'LazyFST',
private = list(
file_path = NULL,
transpose = F,
meta = NULL,
dims = NULL,
data = NULL,
last_visited = NULL,
delayed = 3
),
public = list(
open = function(...){},
close = function(..., .rem... |
26fae9feb961f4bf006909396e0eec6c81f71981 | 28ba974b2647aaebf2e4cfe32103f63456414bf4 | /nonmem/190223_nonmem_process_sim.R | 3f784e457970cdc11b2702e4479678b863e04117 | [] | no_license | jhhughes256/nivo_sim | 87d1089638319df14e4ef9d16fa2271e66b20921 | 255e86a054469f05848fd64060212bde085f7680 | refs/heads/master | 2022-01-08T12:31:48.062569 | 2019-07-03T21:32:31 | 2019-07-03T21:32:31 | 168,279,237 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,805 | r | 190223_nonmem_process_sim.R | # Process NONMEM simulation .fit file from 190220_nonmem_pop.R dataset
# -----------------------------------------------------------------------------
# Aim is to replicate some of the plots from the paper
# Designed to be used with three separate simulation files
# - - - - - - - - - - - - - - - - - - - - - - - - - - ... |
490a612399eef91c4666059025e715cd87b17b5f | 06f362f76b1542bbdea12c34c0f239c9a624c877 | /man/addISCO.Rd | 72c425132d5d7c4fd6afcff0a72f57e457714da6 | [] | no_license | mi2-warsaw/PISAoccupations | 31412147943082c058d998618ac6c78c06c9caf7 | 0b817f09c5599b59390e58edab602453ac9b0fe4 | refs/heads/master | 2020-05-22T06:51:14.081893 | 2017-04-18T18:49:12 | 2017-04-18T18:49:12 | 63,240,717 | 2 | 1 | null | 2016-12-09T00:00:09 | 2016-07-13T11:32:33 | R | UTF-8 | R | false | true | 481 | rd | addISCO.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/importexport.R
\name{addISCO}
\alias{addISCO}
\title{Add columns with occupations and change format to long.}
\usage{
addISCO(dataSource, NAcodes)
}
\arguments{
\item{dataSource}{data frame returned by importFromTxt function.}
\item{NAcodes}... |
283404b8ad231838cb6fc7e4dba35152a1d2eeb5 | 8472d9c0fc2109172b688c2caf5bcb13d2d2881c | /R/EPIC.R | 482032622d85cf8a850f074a829a21f952e184a2 | [
"Apache-2.0"
] | permissive | changwn/DeconvoLib | eba2a2c0904c42ecc8cbbbb100e45f96951f2465 | bd0df2d4b56d7fd2e253eea565c5547a5b1d4272 | refs/heads/master | 2020-11-28T02:05:15.210339 | 2019-12-23T04:27:27 | 2019-12-23T04:27:27 | 228,144,540 | 0 | 0 | Apache-2.0 | 2019-12-23T00:15:20 | 2019-12-15T07:17:51 | R | UTF-8 | R | false | false | 11,112 | r | EPIC.R | # code from EPIC r package.
#signature_epic <- EPIC::TRef$refProfiles
#gene_epic <- EPIC::TRef$sigGenes
EPIC <- function (bulk, reference = NULL, mRNA_cell = NULL, mRNA_cell_sub = NULL,
sigGenes = NULL, scaleExprs = TRUE, withOtherCells = TRUE,
constrainedSum = TRUE, rangeBasedOptim = FALSE)
{
... |
5ce82e5461b130c650785c5588387b4f0dae6c34 | 92e597e4ffc9b52cfb6b512734fb10c255543d26 | /man/catIf.Rd | 7b0be3692dd43ea0173b2f15dc81985d96b06b5e | [
"MIT"
] | permissive | KWB-R/kwb.utils | 3b978dba2a86a01d3c11fee1fbcb965dd15a710d | 0930eaeb9303cd9359892c1403226a73060eed5b | refs/heads/master | 2023-05-12T15:26:14.529039 | 2023-04-21T04:28:29 | 2023-04-21T04:28:29 | 60,531,844 | 9 | 1 | MIT | 2023-04-21T04:28:30 | 2016-06-06T13:52:43 | R | UTF-8 | R | false | true | 328 | rd | catIf.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/io.R
\name{catIf}
\alias{catIf}
\title{Call cat If Condition Is Met}
\usage{
catIf(condition, ...)
}
\arguments{
\item{condition}{if TRUE, cat is called, else not}
\item{\dots}{arguments passed to cat}
}
\description{
Call cat If Condition I... |
693cb096b621b635506fafda7e255bd6cbeba106 | e7d5aaaa05ee4204f414e2adcdb73f93b8fa21fb | /code/analysis_default_vary_cb.R | 9342adb78b479c21b85c008e65e33fe80c696613 | [] | no_license | markusneumann/altruism_simulation | a7d5572f354bcc3ff27532d27c5fec0d51fdbed8 | f918ffe618e9105744fc22cf7eeacea5d5522712 | refs/heads/master | 2020-12-25T17:46:07.544751 | 2020-07-26T20:42:05 | 2020-07-26T20:42:05 | 61,062,722 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,199 | r | analysis_default_vary_cb.R | rm(list = ls()) #ensure that no hidden variables are loaded through .Rhistory or .Rsession
library(reshape2)
library(ggplot2)
N <- 300 #default is 300
nsim <- 100 #default is 100
network_type <- "WS"
vary <- "cb"
WS.nei <- 5 #default is 5
WS.p <- 0.2 #default is 0.1; 0.2 corresponds to Figure 4
#Note: the reason th... |
40d5d12ada3c1a8f33b518201ddbc66487c26c94 | 4a411afcafea626670dd79dddf8ce1f9771f761f | /l2/work.R | 1cd72350475443e980704fb230130e010fc210ef | [] | no_license | alfonsokim/machine-learning-class | 75402424b60e399044f4704709c021448f0dbc8d | 8ea97008e7303146df7e58634a6b0e93741484f1 | refs/heads/master | 2020-03-29T13:10:55.321689 | 2013-12-09T22:04:17 | 2013-12-09T22:04:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 100 | r | work.R | setwd("/Users/Alfonso/r-workspace/machine-learning/l2")
list.files()
library(shiny)
runApp(".")
|
ece313573b608990a66ce0291f60f1789dc16049 | fd570307c637f9101ab25a223356ec32dacbff0a | /src-local/specpr/src.specpr/crtp/window.r | e31adfbf45b57b4ef82f76d520d7a73ab13a80c2 | [] | no_license | ns-bak/tetracorder-tutorial | 3ab4dd14950eff0d63429291c648820fb14bb4cb | fd07c008100f6021c293ce3c1f69584cc35de98a | refs/heads/master | 2022-07-30T06:04:07.138507 | 2021-01-03T22:19:09 | 2021-01-03T22:49:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,333 | r | window.r | subroutine window(lbnd,ubnd,xmin,xmax,diff)
implicit integer*4 (i-q)
include "../common/hptrm"
include "../common/lundefs"
real*4 lbnd,ubnd,xmin,xmax,diff
character*1 escape
character*80 iopcon,outline
escape = char(27)
# RED Initialize to 0 the following 4 vars
x1 = 0
x2 = 0
y1 = 0
y2 = 0
icheck=0
#... |
666dff7d651bc499baf63b235b1679beb4d05d74 | 5a66a0950c91f0f093612bd6496e25cbd7cc5383 | /week1.R | 70c13ee0f0cf77e43d073f55f1e8465ab8baf8d5 | [] | no_license | hizkiafebianto/LearningR | 5be8590d991712b669cb2420a2e914c680276ac2 | aff531ac3672f51ec0d284e1ca13a66d5414cd70 | refs/heads/master | 2021-01-17T18:22:13.501647 | 2016-06-20T02:36:39 | 2016-06-20T02:36:39 | 60,750,757 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,810 | r | week1.R | #title :week1.R
#description :This is a practice file from week 1 lessons.
#author :Hizkia Febianto
#date :20170609
###Entering Input###
x<-1
print(x)
msg <- "Hello"
msg
#Printing a sequence
x <- 1:20 # a sequence of numbers from 1 to 20
x #Output: [1] 1 2 3 4 5 6 7 8 9 10 11... |
f51817749d9789b2ae867a748fbf9cb32016fbd0 | 05343067e6a3b7b66cdc58a1be5c3b0efc939b27 | /day02/2-ribbon.R | e8671778fdce135b67297b56fb18b61895ede6bb | [] | no_license | pdil/adventR | 1ebdbea943c21609f9d4e182e608536ada0a9ef7 | 8a18aae49f81dca608837b24a6a4cd68bc0c17b8 | refs/heads/master | 2020-01-23T21:50:02.372178 | 2016-01-21T03:10:29 | 2016-01-21T03:10:29 | 47,226,210 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 805 | r | 2-ribbon.R |
library(readr)
library(dplyr)
# read input from file
input <- read_lines("input.txt")
# separate each line into three separate numbers, convert to data frame
input_split <- input %>% lapply(strsplit, split = "x") %>% unlist %>%
matrix(ncol = 3, byrow = TRUE) %>% as.data.frame(stringsAsFactors = FALSE)
# coerce char... |
77dea56b922b2c75504409ac28ded2b7aaf5492c | 8691943a2547990118c85beefecdc43312371618 | /R/fplot.R | 30402e02e032a1f4d55bf6c50c118e6dd75263b0 | [] | no_license | Flavjack/GerminaR | f0d97cbf735520db702538601e2cf511527cf66c | 3535a1aea0729abe5ba79114386885d487f946f4 | refs/heads/master | 2022-09-04T10:21:15.391932 | 2022-05-29T16:19:10 | 2022-05-29T16:19:10 | 49,505,163 | 5 | 3 | null | null | null | null | UTF-8 | R | false | false | 10,041 | r | fplot.R | #' Plot line or bar graphic
#'
#' @description Function use the dtsm function for plot the results
#' @param data Output from ger_testcomp function
#' @param type Type of graphic. "bar" or "line"
#' @param x Axis x variable
#' @param y Axis y variable
#' @param group Group variable
#' @param ylab Title for the axis y
#... |
376977f5810d9471dd10730efbf543d134d6e2ec | 51aa41cd56fa4ab3e661a4787f23cb831e833787 | /tsEbolaplots.R | baad74a8647be1db82d19cb61463c90179deae4b | [] | no_license | dushoff/Ebola_sims | 9a01d563172a89727c04315140a0803ba6b17c5f | a2c33546d656d3d273bb75e19bfacf4894d6b179 | refs/heads/master | 2020-04-03T10:05:57.783894 | 2020-02-20T22:50:44 | 2020-02-20T22:50:44 | 63,194,488 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 337 | r | tsEbolaplots.R | library(ggplot2)
theme_set(theme_bw())
casePlot <- (
ggplot(sims, aes(x=day, y=cases))
+ geom_line(aes(color=Symptomatic), size=1.5)
)
print(casePlot+scale_y_log10())
print(casePlot)
infPlot <- (
ggplot(sims, aes(x=day, y=infections))
+ geom_line(aes(color=Symptomatic), size=1.5)
)
print(infPlot+scale_y_log10()... |
3c720afec3f8771884b09c7e734061101dcdaa72 | e2d3550de157dadc159be78eb151210e8c1a7dac | /man/casl_tsne.Rd | c4a26076518a4fced10cbf40dcda83930cbcabd5 | [] | no_license | statsmaths/casl | b40d812127802c8f07a7c6a94d26a018e71628c0 | 196caaff495cbd60648434424a20db472b5b4eda | refs/heads/master | 2020-03-28T07:03:32.596204 | 2018-11-18T13:20:59 | 2018-11-18T13:20:59 | 147,878,257 | 6 | 4 | null | null | null | null | UTF-8 | R | false | true | 809 | rd | casl_tsne.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ch09-dim.R
\name{casl_tsne}
\alias{casl_tsne}
\title{Compute t-SNE variance values.}
\usage{
casl_tsne(X, perplexity = 30, k = 2L, iter = 1000L, rho = 100)
}
\arguments{
\item{X}{A numeric data matrix.}
\item{perplexity}{Desired perplexity s... |
d41c1683dfcebbae9cb1785cd413974d2236f54d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/reliaR/examples/abic.logis.exp.Rd.R | 54dc4d6737df35ab762592ef193fb8a04507bd97 | [] | 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 | 557 | r | abic.logis.exp.Rd.R | library(reliaR)
### Name: abic.logis.exp
### Title: Akaike information criterion (AIC) and Bayesian information
### criterion (BIC) for Logistic-Exponential(LE) distribution
### Aliases: abic.logis.exp
### Keywords: models
### ** Examples
## Load data sets
data(bearings)
## Maximum Likelihood(ML) Estimates of alp... |
e46246b6a97e8b1bd5c4869f8830ebfad1b7860a | 9e6f54110611694c0a04fac0d37886192cc5a030 | /R/scale_tree.R | 1f6283954b6add79e320af84a5354f6f38386502 | [] | no_license | cran/bnpsd | acdeb759cdf1e725abc9a565889af4e4cbd00e5d | 273cdd47a5e73724bd72a0c2231644a4a0dfee19 | refs/heads/master | 2023-07-15T13:23:18.532331 | 2021-08-25T11:50:26 | 2021-08-25T11:50:26 | 117,667,984 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,656 | r | scale_tree.R | #' Scale a coancestry tree
#'
#' Scale by a scalar `factor` all the edges (`$edge.length`) of a `phylo` object from the `ape` package, including the root edge (`$root.edge`) if present, and additive edges (`$edge.length.add`, present in trees returned by [fit_tree()]).
#' Stops if any of the edges exceed 1 before or af... |
154dc708881b0338df42bb9a5214c0456b9b259e | 15c072f05f8670f072b7358c4d70774456fabd38 | /man/spark_options_box.Rd | 8b95770cf1d242cf189d67c9c263ddd1667bfaae | [] | no_license | mrhopko/DTHelper | 3fb0e73789d0c065f6a9780726b8271ba3c027b5 | d5fc3a14ecadfb2cf84932c835199ef0bec87cca | refs/heads/master | 2021-01-20T04:16:57.347415 | 2017-04-28T07:57:04 | 2017-04-28T07:57:04 | 89,670,626 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,041 | rd | spark_options_box.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sparkline_helper.R
\name{spark_options_box}
\alias{spark_options_box}
\title{Create sparkline options js to include in column call back}
\usage{
spark_options_box(raw = NULL, showOutliers = NULL, outlierIQR = NULL,
boxLineColor = NUL... |
2736ceb344b8de8a924ae2bcbe3a929e4a6a8966 | 051c93b817d1688caa689aefbd5ccc82006e616c | /Plot2.R | f80c177da74381f1fc5b6ba3d2bb2f78411ef038 | [] | no_license | 82andries/ExData_Plotting1 | f1f8288e3462e1584f7c7d4860b3ae0b2a6ab4c8 | 2146faf5dcc229e6ef7522bb3c840f5aad9c5ee2 | refs/heads/master | 2020-07-30T15:02:22.520013 | 2016-11-13T16:39:09 | 2016-11-13T16:39:09 | 73,627,821 | 0 | 0 | null | 2016-11-13T16:23:48 | 2016-11-13T16:23:47 | null | UTF-8 | R | false | false | 766 | r | Plot2.R | #Reading in the data and subsetting for the required dates
powerdata <- read.table("household_power_consumption.txt",header = TRUE, sep=";")
subpowerdata <- subset(powerdata, powerdata$Date=="1/2/2007" | powerdata$Date=="2/2/2007")
# Changing the data from factor to numeric
subpowerdata$Global_active_power <- as.... |
9eacd4b0a908a8a272633662a1d16c4cf15d5ea3 | 897d154e93f8c9c45c294cfe88986e7f0b178f51 | /집단 검정/단일 집단 평균 검정 (T-test).R | 36cc1e4eb753de0e19893f030324a4d7fb0ac60e | [] | no_license | freegray/R_training | b9a3d425673e5f201e6e7b2802950765f8c7441e | ed3e165931e47df003db0ec5d7d929dd08d6499e | refs/heads/master | 2023-04-12T11:32:41.292096 | 2021-05-21T15:44:40 | 2021-05-21T15:44:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,439 | r | 단일 집단 평균 검정 (T-test).R |
# 단일 집단 평균 검정(단일 표본 T 검정)
setwd("/Users/yuhayung/Desktop/coding/학원/Rtraining/dataset2")
data <- read.csv("one_sample.csv", header = T)
str(data) # 150
head(data)
x<- data$time
head(x)
summary(x)
mean(x)
mean(x,na.rm = T) # 데이터 정제
x1 <- na.omit(x) # na 데이터 (omit) 빼기
mean(x1)
# 정규분포 검정
# 귀무가설 - x의 데이터 분포는 정규분포이다... |
5bd58cfe61e675fc40183fc393a6f9c98d17bbaf | 498e7df01e78657277b23d81d7b07ab431def4fb | /east_share_season.R | d8a31b3e750aa19f626ad125ce22bd355081e5e8 | [] | no_license | kralljr/share_medicare | de5be725529fd00b42ab8aaf6edd31b91731a16e | 17aac20ee28e70e5cc93e71d4b11ce5e3f5ec2a5 | refs/heads/master | 2021-01-17T07:40:19.613158 | 2016-07-15T15:53:38 | 2016-07-15T15:53:38 | 18,215,156 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,436 | r | east_share_season.R | # File to estimate major sources in NE US
#Set directory
home.dir <- "~/Dropbox/SpatialFA/"
dir1 <- file.path(home.dir, "data")
paperdir <- file.path(home.dir, "paper_spatialfa/figs")
#Load my packages
library(share)
library(handles)
library(sharesim)
#load other packages
library(devtools)
library(RColorBrewer)
lib... |
591f8d54a53a3b6cd18263d63041ea61ca9337b5 | 33b8c52fc3fffcf88865e888ac8d3935afc80f0a | /datamass/Deszcz/rain_model_identification.R | d0e37aa610154ce0bcc3956dab7d025db8d94e70 | [
"Apache-2.0"
] | permissive | carldata/argo | f72082016471ad6d3df077d3625d44c9927bdb84 | dede10a172f6eacf10d49091321f5e4f00b997c0 | refs/heads/master | 2022-05-14T11:09:03.182686 | 2022-04-25T11:33:48 | 2022-04-25T11:33:48 | 116,822,122 | 1 | 0 | Apache-2.0 | 2018-02-15T09:11:36 | 2018-01-09T13:51:48 | Jupyter Notebook | UTF-8 | R | false | false | 2,928 | r | rain_model_identification.R | library("datetime")
new_probe_period<-30
load('rain_day.RData')
load('input_data.RData')
lambda <- 0.9
output_vector_length <- 2
input_vector_length <- 2
data_length <- output_vector_length+input_vector_length
### Clean model ###
params <- rep.int(0.1,data_length)
P <- diag(data_length)
### DATA ###
response_data <- ... |
ba7a805a1ec115a65ac2fc3246e53815b7eb8e8a | 14bc409b2f0a66e56d9b6d8a0774e187e48063d7 | /plot1.R | 6e18eaafbabdab9b2c4b90d42f4d82dddfd85b02 | [] | no_license | yumemi-hkamijo/exploratory_data_analysis_course_project2 | 6f8d5dbe6a22d826c461a30bed67ac781a266777 | c91624a5a16d4bf30448ca3c240c0e3b7f488456 | refs/heads/master | 2021-03-02T01:05:09.576569 | 2020-03-09T14:22:55 | 2020-03-09T14:22:55 | 245,825,731 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 567 | r | plot1.R | library('data.table')
NEI <- readRDS("data/exdata_data_NEI_data/summarySCC_PM25.rds")
SCC <- readRDS("data/exdata_data_NEI_data/Source_Classification_Code.rds")
# get total emissions of the years 1999, 2002, 2005, and 2008.
aggregated_total_by_year <- aggregate(Emissions ~ year, NEI, sum)
png('plot1.png')
barplot(he... |
3031e43dbfae43f9e07695f31c52c0cf75ed55de | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/synlik/R/I_cleanHessian.R | a57c54b71fbfa95e8dacebd73e1350dcd357c0dc | [] | 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 | 1,320 | r | I_cleanHessian.R | # Cleans the hessian used to get confidence intervals for the parameters
# Until the hessian is not positive definite, we take the smallest eigenvalues
# and increase them to a tolerance. We then invert this modified hessian and
# we remove the parameter with the highest variance from the hessian.
# We returned the re... |
867219471918a052fe39351c876c78dbd7d69c87 | ab2ca92926a046e6cb5ef6677f0aa99d0a18c037 | /Plot_04.R | 82bfa2a8347c95082245d6cb7a7f563efc294e33 | [] | no_license | AlexanderSobolevV/ExData_Plotting1 | ce645655af1d3be436b5f3cfd67a8e5f0076a178 | d17929bf913279d178c9949ede760e47860cfcc1 | refs/heads/master | 2021-08-27T21:31:56.812305 | 2017-12-10T12:11:11 | 2017-12-10T12:11:11 | 112,948,295 | 0 | 0 | null | 2017-12-03T17:28:47 | 2017-12-03T17:28:47 | null | UTF-8 | R | false | false | 1,331 | r | Plot_04.R | #optional area'
#setwd("/Users/i312190/Desktop/Data Science/UNIT4/WEEK1")
#optional area'
raw_data <- read.csv("household_power_consumption.txt", header = TRUE, sep = ';', na.strings = "?")
raw_data$Time <- paste(raw_data$Date, raw_data$Time)
raw_data$Date <- as.Date(raw_data$Date, format = "%d/%m/%Y")
new_data <- raw_... |
a5d00b2e45b4d774bd5a87e742dc8accc80ea763 | 76db567fe36a2d907cbf083bfe9a46b907aa3922 | /man/validate_metadata.Rd | 3659ef906dfce5ee2b0ebb2e3b4f4de4969e5b8d | [] | no_license | cran/sfarrow | 2aa1d0aeac4bfc2cb3c38ee3774ef038bfc49fc4 | 12d732edbebeee9f3072a762c8bcebf2a75fb9b6 | refs/heads/master | 2023-09-04T06:31:38.973965 | 2021-10-27T15:30:02 | 2021-10-27T15:30:02 | 379,600,748 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 412 | rd | validate_metadata.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/st_arrow.R
\name{validate_metadata}
\alias{validate_metadata}
\title{Basic checking of key geo metadata columns}
\usage{
validate_metadata(metadata)
}
\arguments{
\item{metadata}{list for geo metadata}
}
\value{
None. Throws an error and stop... |
c0ea236cd073cf2145c19375098819528dddecf9 | dded25b3cf087b2228c35a79d6bf21834455b932 | /Visualization_Wrangling_Reporting/Data Visualization/ggplot2_geometrics.R | 72c6fab0fe287723c4eb9856ba7dda7c26a834f6 | [] | no_license | PakistanAI/Data_Analytics_Certification | 927c6839c6f250ddab32093b5139a61fce9aa79b | 1a7ca14eb5d098ea13e05a3b877183c013cbc939 | refs/heads/master | 2020-04-18T22:02:18.649103 | 2019-04-11T15:38:48 | 2019-04-11T15:38:48 | 167,782,524 | 7 | 8 | null | null | null | null | UTF-8 | R | false | false | 2,175 | r | ggplot2_geometrics.R | # Shown in the viewer:
ggplot(mtcars, aes(x = cyl, y = wt)) +
geom_point()
# Solutions:
# 1 - With geom_jitter()
ggplot(mtcars, aes(x = cyl, y = wt)) +
geom_jitter()
# 2 - Set width in geom_jitter()
ggplot(mtcars, aes(x = cyl, y = wt)) +
geom_jitter(width=0.1)
# 3 - Set position = position_jitter() in geom_poi... |
0df44517eec019e7cd4b34856b1ee52e35f22fd9 | 02c27fc07ee76bf11d21c4cf59ae5d3b94194a3e | /man/Normal_ID.Rd | a15f8b3dcee2fc42c318e1fd8fa3dc6e4c0bf6c6 | [] | no_license | rnorouzian/BayesianforL2 | cd581e5d5bba2de6d12411aa26214dab85490deb | e97f4d7fccf3c1b9ee1619dec496584b0d22f505 | refs/heads/master | 2021-01-20T16:09:22.503864 | 2017-12-02T06:49:24 | 2017-12-02T06:49:24 | 90,819,012 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,471 | rd | Normal_ID.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Normal_ID.R
\name{Normal_ID}
\alias{Normal_ID}
\title{Normal Prior Distribution Identifier}
\usage{
Normal_ID(Low, High, Cover = NULL)
}
\arguments{
\item{Low}{researchers LOWEST value for the parameter.}
\item{High}{researchers HIGHEST valu... |
1d5e25b1bbf21a25c4e449eebfa7fb3f98889204 | 57fc9c6e10a2797b6aa852a5eb345df182a108eb | /tests/testthat/test-utils.R | 34039a5c80b4e4f1fbd2edc07a1aaca608ce3472 | [] | no_license | aammd/remake | d36a1181ca4d0a6cca2fcce6a6231d28cbee72c7 | 9becf862b2270f146e23d3b5f54373dab67cd333 | refs/heads/master | 2021-01-15T21:24:21.421773 | 2015-09-02T06:18:30 | 2015-09-02T06:18:30 | 31,632,581 | 2 | 0 | null | 2015-03-04T02:01:50 | 2015-03-04T02:01:50 | R | UTF-8 | R | false | false | 4,315 | r | test-utils.R | context("Utilities")
test_that("insert_at", {
y <- "value"
expect_that(insert_at(list(), y, 1), equals(list(y)))
expect_that(insert_at(character(0), y, 1), equals(y))
expect_that(insert_at(list(), y, 0), throws_error("Invalid position"))
expect_that(insert_at(list(), y, 2), throws_error("Invalid position"))... |
ceb645ea910e36cc0699dd3ef9059121a6201766 | 7747a3fdf0fdc57b767d8ed199b323afb4d491a2 | /R/sim_transmit.r | f7caa107dd90c2f61a96a5fe1a91256cdf2e25d3 | [] | no_license | ianjonsen/simsmolt | dcafaad041d6caa29cd573cd543dbeab7e14868a | 09c9a8b8132bedaa499dd71c5c2fc6e2439256eb | refs/heads/master | 2022-07-28T10:03:06.683400 | 2022-07-07T14:13:08 | 2022-07-07T14:13:08 | 155,731,825 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,775 | r | sim_transmit.r | #' @title Simulate telemetry transmitter signals along a path
#'
#' @description
#' `Modified from C. Holbrook by IDJ` Simulate tag signal transmission along a pre-defined path (x, y coords)
#' based on constant movement velocity, transmitter delay range, and duration
#' of signal.
#'
#' @param path A two-column d... |
6f075bdd3844f100338d6e557406034ba425117a | 07744eecf50ea11922ff44de44338e4d74604ae8 | /cross_validation.R | 33060d47a1337192c77faadab21488304caeac9c | [] | no_license | mateuscgc/enem | 21291413a6d2448ccdbe171d9b74e08adc3e26e3 | e1ae7ecaa54d5fd1fa63cff9e3149fe00131f22e | refs/heads/master | 2021-05-01T01:07:42.613914 | 2016-10-25T15:48:47 | 2016-11-28T13:10:35 | 68,045,503 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,951 | r | cross_validation.R | # Biblioteca com implementação do Naive Bayes
library(e1071)
same_class <- function(a, b) {
return ((a == 'P' && b == 'P') || (a != 'P' && b != 'P'))
}
dados <- read.table("./processed/processed.csv", sep=",", header=TRUE)
folds <- 1:10
dados$fold <- sample(folds, nrow(dados), replace = TRUE)
foldColNum <- grep(... |
f4ef08e4986899f62782d00aa6923cc6f84d113e | 38c9bdbe080e4ce9eac69231b6a7411bc99ae3a5 | /server.R | 35a5e42d192a380fd63ac26458749bf956d29dfb | [
"MIT"
] | permissive | ctlab/shinygam | 79c2a85971fc36abaa9ca960d67f412863c6fa21 | a9e26e376d692ee3de2d97001a748335a1bd9e3d | refs/heads/master | 2022-08-17T04:42:52.179887 | 2022-08-11T09:06:15 | 2022-08-11T09:06:15 | 29,388,290 | 10 | 4 | null | null | null | null | UTF-8 | R | false | false | 25,986 | r | server.R | library(shiny)
library(data.table)
library(igraph)
library(GAM)
library(GAM.db)
library(GAM.networks)
library(RCurl)
library(parallel)
#library(xlsx)
library(pryr)
library(logging)
addHandler(writeToFile, file="/dev/stderr", level='DEBUG')
"%o%" <- pryr::compose
options(shiny.error=traceback)
#options(shiny.trace=TRU... |
2f6c958851c41429925102a9f3abd9ae692964f3 | c2cd76dd228bd63faf5f4fa7864577ed8ee63085 | /R/src/src_kymo/GetFluoTimeSeries.R | 210ecad28e9dbd292ded6df469912f1774574309 | [] | no_license | destritux/CHUKNORRIS | 0f575602a1276e6dc8fca15b874669448b215183 | 9002e5b835a4f0b1bb70917a32c2839eae191ef5 | refs/heads/master | 2021-07-02T08:46:38.223962 | 2017-09-23T20:05:37 | 2017-09-23T20:05:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 286 | r | GetFluoTimeSeries.R | #-------------------------------------------------------------------------------
GetFluoTimeSeries <- function(kymo, ini.ind, avg.width){
return(apply(kymo[, ini.ind:(ini.ind + avg.width)], 1, median))
}
#------------------------------------------------------------------------------- |
0c1a63019a82f0859c3c2d94f523bd218c8707d3 | 1d71b1b06a24b54529fac458c8fe084ce7e3875b | /scripts/n4J_trait_DB_structure.R | 22932214ade0e3251d796ce257e3667689da00ec | [] | no_license | rjcmarkelz/BR_genome_DB | b76cf35d527ef33f07f733f5e4ce731c2913a110 | 637b4c0dd2fa5e78e8b7734f34526aeebbed967c | refs/heads/master | 2016-09-05T18:01:21.416560 | 2015-12-11T23:45:09 | 2015-12-11T23:45:09 | 29,374,873 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,741 | r | n4J_trait_DB_structure.R | install.packages("devtools")
devtools::install_github("nicolewhite/RNeo4j")
library(RNeo4j)
?startGraph
graph <- startGraph("http://localhost:7474/db/data/")
startGraph
?createNode
addConstraint(graph, "gene", "name")
gene1 <- createNode(graph, "gene", name = "Bra_10001")
gene2 <- createNode(graph, "gene", name = "... |
aae5f9bc7e2af1ea257f62ec6cdab815d4fd910f | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/TreeBUGS/examples/plotFit.Rd.R | 16d486380e11671347f3a3dbec98eed75d883fbf | [] | 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 | 373 | r | plotFit.Rd.R | library(TreeBUGS)
### Name: plotFit
### Title: Plot Posterior Predictive Mean Frequencies
### Aliases: plotFit
### ** Examples
## Not run:
##D # add posterior predictive samples to fitted model:
##D fittedModel$postpred$freq.pred <-
##D posteriorPredictive(fittedModel, M=1000)
##D
##D # plot model fit
##D pl... |
155471ba8e5278808459597e7dc6e04cff3e562e | 5781d284a1af118c558031848076df2dd090b37c | /man/aggreg_stratdata_in_harmonclasses.Rd | 6bb8b7ce7a055785279970aa3e21e26876622e93 | [
"MIT"
] | permissive | TabeaSonnenschein/GenSynthPop | 16b4a699bdd62cb691020bcc56a93b24b8b6e0bc | 08298e6577fcb3bbc8d3a6e9c3ba293f5458a09f | refs/heads/main | 2023-04-11T15:35:28.814404 | 2023-01-29T21:00:52 | 2023-01-29T21:00:52 | 594,842,211 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,518 | rd | aggreg_stratdata_in_harmonclasses.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data_preparation.R
\name{aggreg_stratdata_in_harmonclasses}
\alias{aggreg_stratdata_in_harmonclasses}
\title{Aggregating a stratified dataset into the newly added harmonised classes}
\usage{
aggreg_stratdata_in_harmonclasses(
df,
harmon_v... |
b6eebe689767fcea7ec7c59e090068fa7e53d2bd | 821c5b17dc28a8504950794023c92434b964c997 | /code/draft_scripts/merge-all.R | 84c13989e70ffdc21f721b81315f26bda2d5f451 | [] | no_license | scmcdonald/honorsthesis | 5a0d26b182b5790b90b1c3d8373889e62ed735fd | d3b26acce18c66e46970dbe5807c3b420f8ee616 | refs/heads/master | 2022-04-18T10:23:38.298822 | 2020-03-27T18:13:42 | 2020-03-27T18:13:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,672 | r | merge-all.R | library(sf)
library(ggplot2)
library(maptools)
library(dplyr)
library(stringr)
library(plyr)
library(zoo)
data$FIPS <- ifelse(nchar(data$FIPS) != 2 ,gsub(" ", "", paste("0",data$FIPS), fixed = TRUE), data$FIPS)
#colnames(data)[colnames(data) == "CNTLAFFI"] <- "PUBPRIVATE"
var <- read.csv("variables2.csv")
sector <... |
e9fcd77bb9786e5dd799caff81ff56587f00db8b | 162fab589748e453ab81eca414b8153fec0c592f | /R/user_input.R | d6d4aff6e6acbced7703213ab14873d6a016daaa | [] | no_license | TheWorkingBee/Scraper | 770bb2e8a66a9546b48ac9e186802741d5725000 | 2f594da5472fd1c3168012856a0159fdcc21341a | refs/heads/main | 2023-04-18T20:52:45.370007 | 2021-04-28T10:39:56 | 2021-04-28T10:39:56 | 362,377,056 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 711 | r | user_input.R | ## ---- user_input.R ----
#Date 2020-03-12
#Link to starting page for appartements that listed for sale on blocket and number of pages to be scraped
home_url <- "https://www.blocket.se"
start_url <- "https://www.blocket.se/annonser/hela_sverige/bostad/lagenheter?cg=3020"
n_pages <- 9
#selectors
table_div <- "KeyValTa... |
bef6d8b7f8b4148ad3a10ec63929deda5cbb5c7a | 3393efb272d29d743658810e7358e557c3f06be7 | /tests/mc_sim_eval_dtbs_glmm/b_run_mcsim.R | 06a3d59ea3a57db2cbbc4add51bc74703349c82f | [] | no_license | mikejacktzen/treeboot_double | 5ab6752f6dc7b65724e657bde927f406ad50d059 | 88a90a04b98627a7134ea283edf68d19b49a982c | refs/heads/master | 2023-02-21T23:11:15.109806 | 2021-01-15T20:44:09 | 2021-01-15T20:44:09 | 214,261,869 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,110 | r | b_run_mcsim.R | # Data Generating Processs to evaluate performance
# 1) list_param_rds
# batch submit num_mc_sim=1 iteration
# see how long it takes (hoffman will give you the .log file)
# keep track how many cores you used
# fake example: 8 cores takes 20 minutes for num_mc_sim=1
# Population
require(plyr)
require(dplyr)
require(... |
e00c41b75783a6d2f68888b56fe70790869d0539 | bdcaa34802008ad9d28a451ac13b1f4d31c853df | /server.R | 062454d44b9fcd30be0ef3f6f2a23af170eb1f01 | [] | no_license | deromed2000/presDDPcourse | 73601da83a5d4e7a463edc6b0fab5336a7fc42bb | d4a42c3b7ab565ba5b929a1ee72810dc931d2f81 | refs/heads/master | 2021-01-10T11:56:07.696976 | 2015-09-28T07:01:53 | 2015-09-28T07:01:53 | 43,248,395 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,863 | r | server.R | library(shiny)
library(ggplot2)
library(data.table)
library(maps)
library(rCharts)
library(reshape2)
library(markdown)
library(mapproj)
library(ISLR);library(randomForest); library(caret); data(Wage)
Wage1 <- subset(Wage,select=-c(logwage, region, sex, health))
WageP <- subset(Wage,select=-c(logwage, region, health))
p... |
7592d558d63e803e2b705edf9743c8420465ddea | b179d78ff4cfadbdfd8ec20a38fbb9914a076b1c | /data.table/example_reference.R | 25bd1c1bb8218fd3c10e2293a4b5bbc193d9cbe1 | [] | no_license | jbhender/CSCAR_Workshops | 1dd68b83a57a39db62125bc13881989d8e72b16f | 8fb89f09678fdc51a4b6a9d4e508ebdd01bcf199 | refs/heads/main | 2021-12-23T21:38:53.497557 | 2021-08-19T20:39:18 | 2021-08-19T20:39:18 | 189,452,495 | 0 | 3 | null | null | null | null | UTF-8 | R | false | false | 2,075 | r | example_reference.R | ## Quick examples of updating by reference using data.table
##
## These examples are more abstract than practical.
##
## Updated: May 30, 2019
## Author: James Henderson, PhD (CSCAR)
# libraries: ------------------------------------------------------------------
library(tidyverse); library(data.table)
# create a data... |
7693f9ec9681a5ff342daebe9b6d547dba3ce61a | 64452d8bbc144fdfc96df86c4582dc8537024608 | /man/share_secret.Rd | 5b5512a098c982a0443a775e99c33385665eec06 | [] | no_license | GreyZephyr/secret | 5dc5820063cae33f69a4272acfc463e1c4bc460c | c308ffadde5b326ac628f023887e36b950e5e356 | refs/heads/master | 2020-12-28T17:24:12.117873 | 2019-10-22T07:57:26 | 2019-10-22T07:57:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,932 | rd | share_secret.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/secrets.R
\name{share_secret}
\alias{share_secret}
\title{Share a secret among some users.}
\usage{
share_secret(name, users, key = local_key(), vault = NULL)
}
\arguments{
\item{name}{Name of the secret, a string that can contain alphanumeri... |
7bc1ecc37c70816b3fb61518f024b24548775af6 | fd869b374a6a819e26262f873b2ec6b1def14398 | /cache_matrix.R | 50c03e94dadaa1720fcda8340a82dfd9712d2da7 | [] | no_license | magicbunny1103/R-programming | d3e5de9999cd073a90aa70f12bfa3d3c3a1023e3 | 9b5b868865f922c29a497ee04dbd138b804850b6 | refs/heads/master | 2020-06-13T16:58:04.424836 | 2019-07-01T19:06:46 | 2019-07-01T19:06:46 | 194,722,855 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,293 | r | cache_matrix.R | #The first function, makeCacheMatrix creates a special "matrix", which is really a list containing a function to
#set the value of the matrix
#get the value of the matrix
#set the value of the invertible matrix
#get the value of the invertible matrix
makeCacheMatrix <- function(x = matrix()) {
inverse_matrix <- N... |
d2630ccb6295795bef1b2aac95dadf74b30217e4 | 3f00f7c81c6ed9bb50db182fa6652e26a062a5f1 | /man/consensus_LG_assignment.Rd | 9c4e3ad7ba369fcf1ddd05324daf838d68dbf05a | [] | no_license | cran/polymapR | c2c2130a476b2e1da85b1ac0d96cd330b4eb2b1a | ae247e7e8fb238f9fd8933d12e2d46ae04606b1e | refs/heads/master | 2023-03-19T23:30:05.960131 | 2023-03-13T16:20:02 | 2023-03-13T16:20:02 | 113,219,924 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,401 | rd | consensus_LG_assignment.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/exported_functions.R
\name{consensus_LG_assignment}
\alias{consensus_LG_assignment}
\title{Consensus LG assignment}
\usage{
consensus_LG_assignment(
P1_assigned,
P2_assigned,
LG_number,
ploidy,
consensus_file = NULL,
log = NULL
)
... |
9a67bfc7a9b781d83baa8d63aa66f500db2b3f17 | fd365694237edb699e53eef04f1c3c0ff649f3c8 | /man/opal.assign.Rd | 46ef3ed25738e1a103fd106f37e2d0f97681a5b8 | [] | no_license | obiba/opalr | f73a0eb0280bc768b47711d6a1a08ce0eded7ce1 | 5ca4936deae7e3410db5ee6a02df7994ff5fa336 | refs/heads/master | 2023-08-03T06:18:07.954481 | 2023-07-21T06:58:07 | 2023-07-21T06:58:07 | 166,788,279 | 3 | 3 | null | 2021-05-13T15:50:49 | 2019-01-21T09:45:41 | R | UTF-8 | R | false | true | 2,636 | rd | opal.assign.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/opal.assign.R
\name{opal.assign}
\alias{opal.assign}
\title{Data or expression assignment}
\usage{
opal.assign(
opal,
symbol,
value,
variables = NULL,
missings = FALSE,
identifiers = NULL,
id.name = NULL,
updated.name = NULL,
... |
40ef156343bd52ba6bef17061c85620b2ba719e2 | fa52fd0c2d9eda31ad27c0c9521bc8d0747316d4 | /5. Basic t-procedures/DA5_t_procedures.R | 44c7c0fd54c299a6ef031e556fa189ef5e9d1540 | [] | no_license | IvanHalim/r-statistics | ca0360755d1feeb524b3c1e05e13b9b494a488ff | 166725cc3f60f7a01c1bc6b58682100dc7f3a21b | refs/heads/master | 2020-05-18T15:49:56.702530 | 2019-05-02T02:36:33 | 2019-05-02T02:36:33 | 184,510,802 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,271 | r | DA5_t_procedures.R | ##########PART 1 START ############################################################################# # R code and explanation for data analysis 5 t procedures.
# Read in the microbeersW19.csv dataset
microbeers = read.csv(file.choose(), header = TRUE)
# gives variable names.
names(microbeers) # gives variable names.... |
35d7c606162313407d0f3e844eaed3449f0b8129 | 3feadcaf381e598f80367b9b94211a768c8b49b8 | /Part_A.R | 15ff9ec11d464692d6580b34dcfc225bf5582025 | [] | no_license | Frankiwy/Graph-Randomization | 4e77513fb1a9909ebe02255c5200b441e8af2586 | 488c5742b17baa39ed50f340215b23ad1cf12de7 | refs/heads/main | 2023-01-15T15:56:19.870676 | 2020-11-22T23:22:14 | 2020-11-22T23:22:14 | 310,292,218 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,403 | r | Part_A.R | library(igraph)
library(sdpt3r)
library(glue)
library(mc2d)
library(ggplot2)
library(knitr)
set.seed(161821)
ER_graph = erdos.renyi.game(10,p=1/3, type = 'gnp', directed=FALSE) # Erdős–Rényi
VS_graph = graph_from_literal(1--2, 1--4, 2--3, 4--3, 3--5, 5--6, 5--7) # the very small one
include_nodes <- c(2,3,5)
plot(V... |
56752522db1b68914693deb8ffd1cd66879423d7 | 5c2dcf913088ef4671fa2bd07f9fbcd4ad564e71 | /tests/testthat/test-print.R | dd46396677ed118e59709faaffab172aa4ad5b2f | [] | no_license | GRSEB9S/errors | 9c93724ddbcb2056eede1ac65f1242076afcc290 | 68d28a5dab9c69065d0d7a7f6adaa1eac7586304 | refs/heads/master | 2021-08-22T17:02:45.003708 | 2017-11-30T18:27:04 | 2017-11-30T18:27:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,439 | r | test-print.R | context("print")
test_that("error formatting works properly", {
x <- set_errors(rep(11111.22222, 8),
c(12345678, 1234.5678, 12.345678, 1.2345678,
.12345678, .012345678, .000012345678, .000000012345678))
expect_equal(capture.output(print(x)), c(
"errors: 1.234568e+07 1.234... |
c816a3a31db5d0a98a50e4b1ff1b5029dafb7d5d | 4c70c5d35ccf53e69e97240dc35ced615fa014eb | /www/R/plotEvolution.R | a24cf38db32cf3860069bc71be94e7ef164ccfc3 | [] | no_license | shroff254/shiny_windfarm | d331aa0710184d3405667957ec2563d7c49d0789 | 80dbaf09465b0195392296d6d19a16a6e7dea6b4 | refs/heads/master | 2022-01-07T00:58:43.365500 | 2019-05-30T14:34:19 | 2019-05-30T14:34:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,520 | r | plotEvolution.R | #' @title Plot the evolution of fitness values
#' @name plotEvolution
#' @description Plot the evolution of energy outputs and efficiency rates
#' over the whole generations. Plots min, mean and max values.
#' @export
#'
#' @importFrom graphics plot lines grid points par
#'
#' @param result The output matrix of \code{... |
ec7323989c493fc49eb22d3ac0439c7a48954eae | 9730cd65b21efb77e13821b22512498d6f6f903b | /Code/spat21_moz_data_import.R | 936333c961e91dec2ec4d025f883c3753c56abae | [] | no_license | sam-k/malaria-spat21-analysis | d7bd7f8133b340b71b5902848c732882576956dd | 5705fd2f424c71504e6e87b22e23fa9a3a648efa | refs/heads/master | 2020-04-13T22:04:32.161882 | 2019-08-25T04:48:11 | 2019-08-25T04:48:11 | 163,471,841 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,738 | r | spat21_moz_data_import.R | # ----------------------------------------- #
# Spat21 Data Set Importing #
# Mosquito Data #
# December 18, 2018 #
# K. Sumner, S. Kim #
# ----------------------------------------- #
#### ------------------ load packages -----... |
c18b462fc9ddaa0d3aa2be35bc25774aaac385f8 | 6582dfb79c42b7f8f7b8bddd77f1f9e99317e4a9 | /source-code/analysis.r | 50c63820faae2ed1ca3bf05f49e0bd84a69ee207 | [] | no_license | gtrdp/masters-thesis-guntur | d74bfdc0aeb284527bec94e5612dde9672ec1cf9 | 3432ace7684aba436797e4a0bb01936adbfee0c6 | refs/heads/master | 2020-12-07T11:45:23.303834 | 2017-02-06T14:34:16 | 2017-02-06T14:34:16 | 67,314,219 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,730 | r | analysis.r | all_global_data <- read.table("global-dump.txt",
header = TRUE,
sep="\t",
col.names=c("ap", "pr","au", "gt",
"rms", "pklv", "rssi", "snr", "loc"),
fill=FALSE,
strip.white=TRUE)
global_data <... |
be42810b91072cae02893ecd5f7912dc53f2ade6 | bc43cfc66bf4508f26682b1d9bf0bd29f219a3cc | /r_code/stab2.R | a76ea9677a258255184e824d299adc856b948a02 | [] | no_license | fernote7/thesis | 0a7b856477de9c5beeb2fedbd98af926d10ef719 | c79684f4a4c1443ddb79aaa89f151066b99b30cc | refs/heads/master | 2021-03-23T11:35:57.911382 | 2017-12-08T18:20:29 | 2017-12-08T18:20:29 | 81,347,227 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 463 | r | stab2.R | par(mfrow=c(1,2), xpd=FALSE, mar=c(2,4,2,2), oma=c(2,0,0,0))
z=10
plot(z, asp = 1, xlim = c(-2.5, 0), ylim = c(-1.5,1.5), axes = FALSE, ylab = "", xlab = "")
rect(-5,-5,2,2, density = 15, angle = 50)
draw.circle(-1, 0, 1, nv = 1000, border = NULL, lty = 1, lwd = 1, col = "white")
abline(h=0)
abline(v=0)
z=10
plot(z, a... |
1da2e4b966ccc60bce70d28ae7ef66fdce463d69 | efcd3c537262887632b7e9356f6ce095dc66335e | /R/preparingData.R | e00aacd0e409f87b6902f8df0e831a2cfa371296 | [] | no_license | ZarnackGroup/m6Aboost | e6785db5a99e1d189f4cab214d679bffa7ef3551 | 7797eff2fc60798294d93ff3b0f2178329dc79ce | refs/heads/master | 2023-06-12T16:51:16.310769 | 2021-07-02T08:06:45 | 2021-07-02T08:06:45 | 371,287,281 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,266 | r | preparingData.R | ## ============================================================================
## The Methods-preparingData for the GRanges objects
## ----------------------------------------------------------------------------
#' @import GenomicRanges
#' @import methods
#' @rawNamespace import(IRanges, except = c(collapse, slice, d... |
c58543eed7a288e1801a0639a46ecf42e90f326a | df0b491ed96c8726e2fe3930818deb23210ecb17 | /cachematrix.R | d3b095db13d82ad8d0bd2549994bd30fa12708a3 | [] | no_license | cvaruns/ProgrammingAssignment2 | e2ec59ac0dddc3bbe98c24519ef76600aaa1a90f | 4fb28b008324830e7be5448780452b526da54b06 | refs/heads/master | 2021-01-21T21:29:22.774397 | 2015-06-19T18:43:34 | 2015-06-19T18:43:34 | 37,738,580 | 0 | 0 | null | 2015-06-19T18:20:31 | 2015-06-19T18:20:31 | null | UTF-8 | R | false | false | 1,107 | r | cachematrix.R | ## The purpose of these functions is to create a cache matrix, store it and return the
## inverse of the cached matrix stored.
## makeCacheMatrix is the function used to take a 'square matrix' as an argument and create a cache matrix
makeCacheMatrix <- function(x = matrix()) ##Accepts only Square Matrix
{
i <- ... |
4ac14ed911a62c289991b76e9e4b05c82dfd77e0 | 4b49631324270db21fa4e01f7fea617ac773b791 | /elevation/download_elevation_files.R | 17bef57f7c1d9be742cc5eba2f4f5271e54391b3 | [] | no_license | mvanhala/geospatial_data_sources | c93106ddf22ad52ef3493b484138483c6ac9af40 | f8315df51498bf197b80134d20a73e344048ddbe | refs/heads/master | 2021-06-30T22:34:31.553697 | 2018-06-21T18:15:15 | 2018-06-21T18:15:15 | 137,977,202 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,516 | r | download_elevation_files.R |
library(sf)
library(dplyr)
library(purrr)
library(stringr)
library(readr)
library(parallel)
states <- read_sf("/mnt/data/boundaries/tl_2017_us_state") %>%
st_transform(4326)
system(
glue::glue(
"aws configure set aws_access_key_id {id}",
id = Sys.getenv("AWS_ACCESS_KEY_ID")
)
)
system(
glue::glue(
... |
b5281a6ce87629a1dca25c2b72e82cf48f8a1ea8 | b8058ad0e52402a536943b3007c69d0f3ce90333 | /EDA.R | 1753900179547f614d8203d6a9189c645b89a9ee | [] | no_license | pecu/EDA | 53ba8af1edd5cf5239ad6c673ab1e507d3929eff | 4adb792195ebd625453a125e7b3615d4ff930e48 | refs/heads/master | 2020-06-17T14:58:34.604278 | 2017-06-13T01:48:40 | 2017-06-13T01:48:40 | 94,157,053 | 1 | 1 | null | null | null | null | BIG5 | R | false | false | 1,787 | r | EDA.R | library(ggplot2)
library(dplyr)
raw = read.csv("Project(Part 1).csv",
header = TRUE, sep = ',')
# 統計敘述
result = summary(raw)
# 颱風修復工程代碼
ans <- filter(raw, raw$颱風 == "97年辛樂克及薔蜜") %>%
group_by(機關名稱, 工程代碼) %>%
summarise(price = sum(審議經費.千元.))
highPrice <- filter(ans, price > 50000... |
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