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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
55daadf25c726c067d242b88bcea19ff148786da | 0597d697bfd9062630ade080a81cbac05755f725 | /src/08_ts_decomposition.R | e362e47ba3aef55c4815761c44ed49f7f8d31345 | [] | no_license | ian-flores/suicidesPR | 6a0e9c4f08b4a6df713b79328ddfc83c82ef297d | 6a466aa3906b9361372fee68385f844b3a2bf801 | refs/heads/master | 2020-04-18T23:28:50.892880 | 2019-02-18T02:40:38 | 2019-02-18T02:40:38 | 167,822,498 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,077 | r | 08_ts_decomposition.R | library(fs)
library(lubridate)
library(tidyverse)
library(ggfortify)
data_files <- dir_ls('data/mortality_data_2000_2008/', type = 'file')
mortality <- map_df(data_files, read_csv, col_types = cols(.default = "c"))
ts_mortality <- mortality %>%
filter(typedeath == '2') %>%
select(yeardeath, monthdeath) %>%
... |
95c6d406efa2a53f1965991c597721619f3b1b2d | 289b70ac6d95d7f4585b1ac61439dfefd786fc77 | /man/fitch.Rd | 17157977d0407aa69f931db31bd1b6576bac4a14 | [] | no_license | syerramilli/R-sysid | f8ede18883a691e363b5ca3110c2583a5d7a426c | be2928b20b5f3e1230f292ea45166ae95cc71a23 | refs/heads/master | 2023-06-08T17:55:07.929065 | 2023-06-07T03:29:14 | 2023-06-07T03:29:14 | 29,390,663 | 3 | 2 | null | 2023-06-07T03:29:15 | 2015-01-17T12:38:48 | R | UTF-8 | R | false | true | 829 | rd | fitch.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/estpoly.R
\name{fitch}
\alias{fitch}
\title{Fit Characteristics}
\usage{
fitch(x)
}
\arguments{
\item{x}{the estimated model}
}
\value{
A list containing the following elements
\item{MSE}{Mean Square Error measure of how well the response of... |
062e3af004febdc7ce2c05f0ca15cf4fe536cc6c | a8300d09f99711d3f21e4d648ad84b2b84e188e1 | /man/surveySurvival.Rd | 7213cf9e5babefeac337c57263a5a9e35a34ba0a | [] | no_license | dougkinzey/Grym | aee89081fc67ace0881ab8c4760bdb6c4d516330 | e960444a4edd7d29388e2f10e7a5a78effc71a54 | refs/heads/master | 2023-02-28T00:32:22.981848 | 2020-11-04T14:56:52 | 2020-11-04T14:56:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,779 | rd | surveySurvival.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Grym.R
\name{surveySurvival}
\alias{surveySurvival}
\title{Survival to a survey period.}
\usage{
surveySurvival(yr, cls, s1, s2, Ms, M, Fs = 0, F = 0, rcls = 1)
}
\arguments{
\item{yr}{vector of survey year}
\item{cls}{vector of survey age c... |
2ff2506ae581e3d17abd8c9dd734fbc8d1d2304a | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1/Database/Jordan-Kaiser/reduction-finding-full-set-params-k1c3n4/query30_query05_1344n/query30_query05_1344n.R | 7be70544863458f898645e69753eea2d5de78acb | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 70 | r | query30_query05_1344n.R | f7646fd9a761932cc38b7c4791c991dc query30_query05_1344n.qdimacs 179 363 |
5f70b00b3fc8c38f4b50bddc16aebac71f3b71b5 | cee5203605e8a913f8c6316398b80a88e9cb1395 | /lecture_3/data_table_exercise_filled.r | 7bf5ff3657d341382dfa1e50baef9913bd456b3e | [] | no_license | bertozzivill/infx572_fall16 | 241a6e3cefd72a852ca3f9971f902ba3b1ed867e | 63ec645fc29dfcbe4c9d286d73054a063b2f10b7 | refs/heads/master | 2020-12-02T03:20:39.788879 | 2017-01-02T22:05:58 | 2017-01-02T22:05:58 | 67,311,693 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,533 | r | data_table_exercise_filled.r | ##############################################################################
## data.table exercise
## Author: Amelia Bertozzi-Villa
##############################################################################
library(data.table)
## Creating a data.table -----------------------------------------------------------... |
30ba26c88bde5564cdf0f5841a0c899b0d5ff491 | f5f4b24c2588379493f6383181853ab0fe11121b | /scripts/skills_extraction.R | 229c8574725d3c3d04164d9a2666903b28ac80f2 | [] | no_license | PPPeck313/team_tidy | 6889ab9e886197de9dcead6866be304e0afa8705 | e50c9c806a111d3397f77d6ec1cc59d2dba6e36a | refs/heads/main | 2023-08-19T13:32:36.978534 | 2021-10-21T02:03:35 | 2021-10-21T02:03:35 | 416,798,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,518 | r | skills_extraction.R | library(httr)
library(jsonlite)
library(tidyverse)
library(stringr)
get_token <- function(client_id, secret, scope){
url <- "https://auth.emsicloud.com/connect/token"
payload <- str_interp("client_id=${client_id}&client_secret=${secret}&grant_type=client_credentials&scope=${scope}")
encode <- "form"
respons... |
369a8828e31dffbedcc508d8a37e3bfb6d7b8a48 | 6e747b34010f0613b82b292887e883f5fd4ed912 | /plot1.R | 43dd13717e9cdea066440b235ad46ae89d3ee739 | [] | no_license | DDHGITHUB/ExData_Plotting1 | d3de5e4227374bfc4f00102cb4d75df0d85d6dfe | 72f5c72480f84dfab7978ef0768aa4285902dcaf | refs/heads/master | 2021-01-15T10:06:27.081234 | 2014-05-08T01:44:19 | 2014-05-08T01:44:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 594 | r | plot1.R | ## Read in table
DF <- read.table("household_power_consumption.txt", header=TRUE, as.is = T , sep=";")
## Convert Data field to Date format
DF$DD<-as.Date(DF$Date,"%d/%m/%Y")
## Filter only 2 days needed
DF.select = DF[(DF$DD == "2007-02-01" | DF$DD == "2007-02-02"),]
## make GAP field a number
DF.select$Global_ac... |
c47979e77c8aa583368eb820eb3cae401e8cd9c4 | 02dd84f04aa2c568440848c418b4a621c22bd07c | /data-raw/corn_110110.R | 016ec854cf9c1a16a77385d83e23d64f2d437cca | [] | no_license | BrunoProgramming/BBOToolkit | ba9aca6fa5c27c1daa912bf1816c1d9f021b8bb9 | 0029821f27c479231e7051ff3c28b5423022b6e0 | refs/heads/master | 2021-06-04T03:33:27.220592 | 2016-04-07T16:21:09 | 2016-04-07T16:21:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,282 | r | corn_110110.R |
corn_110110 <- read_fwf('data-raw/XCBT_C_FUT_110110.TXT', fwf_widths(c(8,6,8,1,3,1,4,5,7,1,7,1,1,1,1,1,1,2,1,1,1,1,1,6 ),
col_names = c("TradeDate", "TradeTime", "TradeSeq#", "SessionInd",
"TickerSym", "FOIInd", "DeliveryDate", "TrQuantity",
... |
08fb5c8d99d89bce9a525b8da7d3a6151379b23b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/nivm/tests/nicqTestChecks.R | 96e470f589814da0f8fe886cdb251adecd5b620e | [] | 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 | 692 | r | nicqTestChecks.R | library(nivm)
## following gives diff in prop outside CI.
## This is because ic!=ceiling(nc*q)
## Now gives warning
#x<-nicqTest(20,g=nimDiffOR,delta0=.1,q=.2,nc=200,nt=300,
# ic=round(600*.2),conf.int=TRUE)
#x
## check that it works without specifying ic
#x<-nicqTest(20,g=nimDiffOR,delta0=.1,q=.2,nc=200,
... |
0e5f7ad496b1c2e34345c6349abe37d5d64465a9 | b7f4e0760240e4d5030734ae7831808fdaa55367 | /plot4.R | 337dcdf2469d96ac3a58574e42494dd01bf8ddc3 | [] | no_license | henriqueineves/Data-Science-Coursera | 9556f273899d6da851ad7e5977bd956281c6387f | 3ed07ebcfd200299caf47071a70da801adde9029 | refs/heads/master | 2023-04-06T04:03:56.144180 | 2021-03-29T19:51:24 | 2021-03-29T19:51:24 | 274,762,850 | 0 | 0 | null | 2021-01-02T23:29:16 | 2020-06-24T20:26:55 | R | UTF-8 | R | false | false | 1,207 | r | plot4.R | ##Code for the second Peer Review assigments of course 4 Exploratory data analysis
#Loaging the packages:
library(zip); library(ggplot2)
#Download, unzip the file, openning the data:
if (!file.exists("NEI_data.zip")){
download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip",
... |
87064421a783caa20e32808b6559e54a239418a1 | 0d4afcc61512d15237ba9b509150326686e89ab0 | /R/write_xts.R | 28baceb06123458949f70a6f3851ae42cbe8a376 | [] | no_license | dleutnant/tsconvert | 4b86475a0c182bed145969806bcb9e4503ded55b | 3fcfba99a08de4f45517140ff961a98e0316b1a3 | refs/heads/master | 2021-07-07T05:14:52.089773 | 2016-09-22T14:58:20 | 2016-09-22T14:58:20 | 39,088,458 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,038 | r | write_xts.R | #' Writes xts objects to file
#' @title Write xts objects to file
#' @param xts The xts object to write.
#' @param file A connection, or a character string naming the file to write to.
#' If file is "", print to the standard output connection.
#' @param format The time format.
#' @param sep The field separator string... |
40ceb2dbeda8235201bbd839feb8c3b47356a05c | b58997475db8fa11755a77ba1a927309bbbf4f7e | /SIS.R | 5486254bf67c67f815d45d76f3b5e597d43a0a82 | [] | no_license | cnguyen351/chem160project2 | 19626382e9fb8091ff97eaa165ff174e3584ff94 | 3aae11e1d7966e3ffbb3114f1a85a2ec145c23a9 | refs/heads/main | 2023-01-25T00:53:21.212594 | 2020-11-11T23:17:08 | 2020-11-11T23:17:08 | 312,113,830 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,299 | r | SIS.R | alp.m <- 0.000006 #transmission rate:male person-1 day-1
alp.f <- 0.0000009 #transmission rate:female person-1 day-1
gam.m <- 0.05 #recovery rate:male day-1
gam.f <- 0.007 #recovery rate:female day-1
Sm <- 14000 #susceptible males
Sf <- 9000 #susceptible females
Im <- 1000 #infected males
If <- 1000 #infected f... |
badff4d8fe708a7c4348861818bd6eb7f83f647f | 96b13e6429f1177dab9628da532b687e91fc1c25 | /dfsf.R | 9cecdd3a1877b9df315c1cc7853e731d2139f43e | [
"MIT"
] | permissive | V-Yash/AIML_Lab | 13073bc5862ed241fa1fa65e14242777abe1a58b | 4c162fe5d8a67aef45a10a4623a55652fad7334d | refs/heads/main | 2023-02-10T19:47:56.191988 | 2021-01-10T07:11:07 | 2021-01-10T07:11:07 | 328,324,414 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 952 | r | dfsf.R | x=c(3,5,8,4,1)
if("8" %in% x){
print("Yes")
}
if("2" %in% x){
print("yes")
}else{
print("no")
}
num=8
if((num %% 2) == 0) {
print(paste(num,"is Even"))
} else {
print(paste(num,"is Odd"))
}
x=c("Yash Verma")
i=1
repeat
{
print(x)
i=i+1
if (i>5)
{
break
}
}
x=c("Yash Ver... |
b3f14c17ac269dee6e663d29f3c63e7b3e84b0eb | 9df772af4027f13cfc76fc212d65fa03d96e6b67 | /code/figures/Hummingbird Range Graph.R | 2c62f3529d23f398e9db1675acfe52db684bd9b5 | [] | no_license | austinspence/hbird_transplant | cc4c2d82024f739a8b2ca1af6dcdc283eb64053e | 32fc6d9c61d385ba81f3a499f3eaedf38381a816 | refs/heads/master | 2020-04-06T07:32:55.711493 | 2018-11-12T21:06:22 | 2018-11-12T21:06:22 | 157,276,381 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,242 | r | Hummingbird Range Graph.R | #### Experimental Design Figure --------------------
# Austin Spence
# October 12th, 2017
par(mfrow=c(1, 2))
#par(mfrow=c(1, 1))
### Range Plot
plot(c(1:5), 1:5, type = "n", ylab = "Elevation (m)", xlab = NA,
axes = FALSE,
main="Hummingbird Range
Along Sierra Nevada Mountain")
Axis(side=2, at = 1:5, labels=c(... |
3f854dd9281c9d06ea3c6d4600590bcaeb97d55e | ec9745615f10cf8aa8edd135d8d73d5fb8ba943d | /R/data_gen.R | 852ba8cd0ffb1896ef146695c0e2f899dc180083 | [] | no_license | liangyuanhu/CIMTx | 8720511ab8e9d492ee97ab4fb0cbb14e5256ded3 | 138ac444cb9c34b953015f0db44ef71b66859d8d | refs/heads/master | 2022-07-01T00:47:39.928182 | 2022-06-16T14:46:52 | 2022-06-16T14:46:52 | 252,886,194 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,328 | r | data_gen.R | #' Data generation function
#'
#'This function generates data to test different causal inference methods.
#' @param n total number of units for simulation
#' @param scenario simulation scenario 1 or scenario 2
#' @param ratio ratio of units in the treatment groups
#' @param overlap levels of covariate overlap: Please s... |
f272958fae02bff87288ae263120f895543b9904 | c36ef613cd20d36130b4a1ff351fcb5d76f6d63f | /R/PTMscape_main_functions.R | 4f2c8f0d176b9c06050c66e3df09759150da2419 | [] | no_license | ginnyintifa/PTMscape | 24b8913683f5a89769e939b455d0347186d5b87f | de86a471e92bbb954e30dedda71aa1395d908db0 | refs/heads/master | 2021-11-24T13:28:44.458778 | 2021-11-05T16:06:28 | 2021-11-05T16:06:28 | 117,210,690 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 60,812 | r | PTMscape_main_functions.R | ### do every thing without decoy
# pssm_generation ---------------------------------------------------------
pssm_generation = function(candidate_Rds_name,
center_position,
output_label)
{
candidate = readRDS(candidate_Rds_name)
pos_window <- candi... |
aa51f752f17a377dd65c831ae4a56faa7b0d655a | 0c91fa27c912ee29fac64e4a10fb34374ecef3a7 | /R/airfoil.R | 6b46a2a33646bfbca533e4b0b27dc22414c6a98e | [] | no_license | xmengju/RRBoost | d2069c3cbebe98455d29c2da18abb04daeaeb1b4 | b6e479ecd706fcb775916b367ad319e2429e17c2 | refs/heads/master | 2021-08-08T09:28:37.106335 | 2020-08-26T20:08:26 | 2020-08-26T20:08:26 | 215,244,704 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 674 | r | airfoil.R | #' Airfoil data
#'
#' Here goes a description of the data.
#'
#' Here goes a more detailed description of the data.
#' There are 1503 observations and 6 variables:
#' \code{y}, \code{frequency}, \code{angle}, \code{chord_length},
#' \code{velocity}, and \code{thickness}.
#'
#' @docType data
#'
#' @usage data(airfoil)
#... |
cc491932ca6592e488a3f81d1fbd63d1445a7863 | 78c179dd7e008050b2e4a25468ee87fde776760f | /Tutorials/week6/code/6.R | 9b0aacb412c940ba1a1760c107b71215c0824e5e | [] | no_license | shonil24/Applied-Analytics | f2d03aea5ba34f4b95d8f575f6f75caffba0c8ed | 700d9f2d696d613de6239e1e678524b6d0efd90b | refs/heads/master | 2023-06-02T05:35:03.564689 | 2021-06-21T18:28:32 | 2021-06-21T18:28:32 | 307,153,670 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,154 | r | 6.R | library(readr)
library(dplyr)
library(epitools)
ames <- read_csv("F:/MS/Sem 1/AA/week6/ames.csv")
population <- ames$area
sample <- sample(population, 60)
# Q1
summary(sample)
hist(sample)
# Every student will have different values and mean
# Q2
mean.ci <- function(x, conf = 0.95) {
alpha <- 1- conf
t_crit <-... |
a2346a7fa80110c06bf6845ab03acf54dc191b71 | 74de6acd13236646d5837771ced81315cd8c8f21 | /SVM.R | 1d99202f8b3e42634af5806495b510d0b4e1f873 | [] | no_license | amitshyamsukha/Machine-Learning | 8482d1a08db4e618fca95d9d11b5f263cde705f1 | 7543c9cd75a13025de36ae11d191ca19faa750ed | refs/heads/master | 2020-03-27T09:55:41.639376 | 2018-08-28T03:24:48 | 2018-08-28T03:24:48 | 146,383,168 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,720 | r | SVM.R |
library(kernlab)
library(readr)
library(caret)
setwd("C:/Amit/Upgrad/SVM_dataset/SVM Dataset1")
## Read the training data set
mnist_dataset <- read.csv("mnist_train.csv" , header = F)
###Structure of the dataset
str(mnist_dataset)
## Examine few records
head(mnist_dataset)
#Exploring the data
summary(mnist_dat... |
b60a0b3aba8da9219b0a88e734d4cd63a4007439 | c1d60db29ef427d263ff86ad5609deec4871da3e | /tests/testthat/testthat.R | 1bc609c26c39f5f84570331cb8e41b1346bb1439 | [] | no_license | dabrowskia/dspace | 46d15b978f88b680132b94ca88352c684ddf809d | 94ed23bc1221f4384a223d1bdce0f687dcd1508c | refs/heads/master | 2021-07-10T03:20:26.771048 | 2020-06-29T14:42:23 | 2020-06-29T14:42:23 | 139,340,321 | 4 | 1 | null | 2020-06-29T14:42:24 | 2018-07-01T15:29:04 | R | UTF-8 | R | false | false | 1,435 | r | testthat.R | context("ds_polygon")
test_that("regionalization of polygon data (ds_polygon)",
{
data("socioGrid")
socioGrid$class <- ds_polygon(socioGrid, k = 7,
disjoint = TRUE, plot = TRUE, explain = FALSE)
expect_equal(
head(socioGrid$class),
... |
60e844fdf7e98bc0c8e3cb3d19f70959550e9608 | 351a143adc1d7f9c5f424c0bf520667a41f5507d | /man/predict.coco.Rd | 853a731ed539fbdcc081c5e88fa09e91c6310222 | [
"MIT"
] | permissive | GreenwoodLab/BDcocolasso | ade5a59890410338e063fc971e2bedc2eef3aba8 | 29b3860ac5172737bc40d97b0091e129628ddb9e | refs/heads/master | 2021-11-11T02:26:15.690312 | 2021-11-01T00:10:27 | 2021-11-01T00:10:27 | 228,453,442 | 0 | 0 | NOASSERTION | 2020-04-10T03:32:39 | 2019-12-16T18:50:43 | null | UTF-8 | R | false | true | 1,597 | rd | predict.coco.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/methods.R
\name{predict.coco}
\alias{predict.coco}
\title{Make predictions from a coco object}
\usage{
\method{predict}{coco}(
object,
newx,
s = NULL,
lambda.pred = NULL,
type = c("response", "coefficients"),
...
)
}
\arguments{
\... |
22ae995e0d334103c97b6e8cb8f1696f2a7259c8 | b375db95fc50eee5368d5e8c6694d65658aaa88c | /ui.R | c12fa88b6f48be4f621fc9ccc2bbc7fa84b540bb | [] | no_license | rcomyn/Moneyball | 4167cca11c0a23cb792d77b7221390ef2681c604 | d3577841f580b32219df1473ac113b74b6a5a264 | refs/heads/master | 2021-01-10T05:01:22.674794 | 2015-09-24T00:34:14 | 2015-09-24T00:34:14 | 43,035,021 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,839 | r | ui.R | library(shiny)
fluidPage(
tags$head(
tags$style(HTML("
.shiny-text-output {
color:navy;
font-size:large;
font-weight:bold;
}
"))
),
headerPanel(
h1("MONEYBALL", style='color:navy')
),
h3("Estimating Runs... |
95556f769c9c346b28c25c393432ccc524e0a1a2 | 3e5c13f5544298f28d78e7d6251d39410e70165e | /man/pca.reg2.Rd | 29c5df613f3ee2ae7dc60be5bf2d90c43a889b68 | [] | no_license | branchlizard/ordiR | 1288f45b3a2a461a1c2b6f058892e64fd33d6718 | 1aef23e72f33af1aece18a2b0e601f36e8f95be5 | refs/heads/master | 2020-06-10T09:41:45.914977 | 2015-08-27T20:58:48 | 2015-08-27T20:58:48 | 75,973,727 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,378 | rd | pca.reg2.Rd | % Generated by roxygen2 (4.1.1.9000): do not edit by hand
% Please edit documentation in R/pca.reg2.R
\name{pca.reg2}
\alias{pca.reg2}
\title{Linear Regression on PCA objects 2}
\usage{
pca.reg2(pca.object, sp.name, group, plot = TRUE)
}
\arguments{
\item{pca.object}{PCA object from rda function (vegan).}
\item{sp.nam... |
7a8ee6273b4f79a4e1a5b5d2e052a7957088ea53 | 56a98c60765e9c2df99061666760285d1a492c29 | /srs-cran/src/models/arima/ArimaModelPrices.R | f96b7a3deadf2e9f6002e45d5113aef04361b1cf | [] | no_license | ajinkya-github/stocksimulation | 52263a7ab03031b9f426751e05871f70cdb0b871 | 1ffc092495534c58e42f3338e05fb31f58a611f2 | refs/heads/master | 2021-05-05T15:41:25.320326 | 2018-01-14T10:01:20 | 2018-01-14T10:01:20 | 117,318,030 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,198 | r | ArimaModelPrices.R | # TODO: Add comment
#
# Author: ajinkya
library(forecast)
arimaPricePrediction <- function(ret,predictionsignals)
{
winsize <- 30
x <- as.ts(ret$daily.returns)
start <- 1
end <- nrow(ret)
pred <- nrow(ret)
pred <-NULL
for(i in 1:nrow(ret)+1)
{
index <- start + winsize
window <- ret[start:index,]
x <- as.ts... |
cf7f805d295b60c79298314ae2a144c16c1cd906 | 786974da78a3df3cf58149a006153e5a22682fbb | /R/make_f_alldata.R | 7ec1455b77f8bb8447321d810a25f7408804ff91 | [] | no_license | emjosephs/qxtools | 9954405144ad03685f97ffc17312119d21953d1e | 96e7f94980d62efdbc401a1e0dd1670c21ab4d02 | refs/heads/master | 2021-04-28T08:14:36.354221 | 2018-03-06T23:04:21 | 2018-03-06T23:04:21 | 122,244,993 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 689 | r | make_f_alldata.R |
##makes a kinship matrix when there is no missing data. Takes frequency data, not # of copies. Later would be good to add a check for this. This function also drops an individual to deal with losing a degree of freedom when mean centering. The input table has rows as individuals, loci as columns.
make_f_alldata <- fu... |
8820b349a9da925173bc6e7b160c5c74b9de0b78 | c2e9d1608fcd5257a0600e1fb0ce5a9fd928ef64 | /Chapter5_knn_cv.R | ab62209ffe69890026ce628b2c2ea9bc7931a19a | [] | no_license | chandrabanerjee/StatisticalLearning | d7ecdaf2dc9c1049b3649c13349f00e78eb0010b | 07490f4abbc7289ba542783884714dc4a788ff5d | refs/heads/master | 2021-06-14T23:28:49.948307 | 2017-03-01T06:27:50 | 2017-03-01T06:27:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,218 | r | Chapter5_knn_cv.R | ## This script uses the caret package to estimate a cross validated KNN classifier using the Auto dataset.
library("caret")
library("ISLR")
library("ggplot2")
library("MASS")
library("class")
library("gridExtra")
library("doMC")
registerDoMC(cores = 4)
data(Auto)
View(Auto)
Auto$mpg01[Auto$mpg < median(Auto$mpg)] ... |
de9d45968450602bc747e965fc2d5cfaf9af2cf4 | 6c57bfb022993ec61d0af3593ce117a1efff6022 | /R/plot_functions.R | 70712ec8f4fdc56c26aca13301fb988dae3c1424 | [] | no_license | anuj-kapil/driveralertness1 | b090f3f4f1c087463b7159d3d1e85809b84e62ca | a6dab9b48e328d4d5413296eeed97dd22ae09889 | refs/heads/master | 2020-09-06T13:37:38.585699 | 2019-11-08T11:22:55 | 2019-11-08T11:22:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 891 | r | plot_functions.R | #' Side by Side Histogram and Box Plots
#'
#' @param dat The dataset
#' @param x The variable in the dataset
#'
#' @return nothing
#' @export
#'
#' @examples
#' nothing
hist_box_plots <- function(dat, x){
hist(dat[, x], main = paste0("Histogram of ",x), xlab=x, ylab="Frequency", col = "blue")
boxplot(dat[, x], main... |
9516bc1f9a0eb44cc748d4d105d1f61118c60f62 | 35dd79ae3daa40a05be19f14f26e0884dc639b36 | /manuscript/SuperExactTest/SuperExactTest.r | 6b57111f1c99a9d1a708f865f8c131517f0c8483 | [] | no_license | fazekasda/HSF1base | 816569b18e106324ff46f63ccc214a3251900dc4 | 76f558637a95a66308ec0ec62b347001378ad35f | refs/heads/master | 2021-06-22T10:48:24.273300 | 2020-12-18T11:43:19 | 2020-12-18T11:43:19 | 205,836,167 | 0 | 0 | null | 2021-03-26T00:45:53 | 2019-09-02T10:51:10 | HTML | UTF-8 | R | false | false | 1,222 | r | SuperExactTest.r | #install.packages("SuperExactTest")
#install.packages("openxlsx", dependencies = TRUE)
#install.packages("dplyr")
#install.packages("tidyverse")
library(SuperExactTest)
library("dplyr")
require(openxlsx)
library(tidyverse)
xlsx_sheet <- read.xlsx("Fig1_Venn_David.xlsx", sheet = 1, startRow = 1, colNames = FALSE)
cats ... |
7a683c009e43456ffb72435de1b44d161875ae4d | 9121a31d34a3ea5ec930a7ccf87b17f3591488d9 | /docs/articles/advanced_features.R | 68d6b5bc99926389265eb8c0e293173ba01800c0 | [
"MIT"
] | permissive | amirmasoudabdol/DeclareDesign | eafe1d2adf0b11083e52170e735d8378b48f2cfb | 239249f06687f092d291753349c0eb451783ef42 | refs/heads/master | 2020-03-13T13:15:57.346368 | 2018-04-21T19:13:14 | 2018-04-21T19:13:14 | 131,135,067 | 0 | 0 | null | 2018-04-26T09:48:22 | 2018-04-26T09:48:21 | null | UTF-8 | R | false | false | 3,646 | r | advanced_features.R | ## ----echo=FALSE, warning=FALSE, message=FALSE----------------------------
set.seed(42)
library(DeclareDesign)
options(digits=2)
my_population <-
declare_population(N = 1000,
income = rnorm(N),
age = sample(18:95, N, replace = TRUE))
pop <- my_population()
my_potential_outcomes <- declare_potential_outcomes(
... |
369d86b0153bcebb0aabd64e9ff7b237cec2d85c | 06616d126b280a447a75b4ceb300d61e1986170b | /R/bioclimvars.R | 083f146d8c2c9a5f9b2ff8ba58dc390502d433c2 | [] | no_license | ilyamaclean/climvars | 8cdfd691e5b3e498f6f1742dea8679cf4999c0f8 | de73840be2e731758526955b97244678ebc0260e | refs/heads/master | 2021-07-07T17:00:33.740062 | 2019-06-17T19:26:53 | 2019-06-17T19:26:53 | 178,940,000 | 1 | 5 | null | 2020-09-12T08:08:50 | 2019-04-01T20:15:23 | R | UTF-8 | R | false | false | 49,171 | r | bioclimvars.R | #' bio1: Calculates mean annual temperature
#'
#' @description `bio1` is used to calculated mean annual temperature.
#'
#' @param temps a vector of temperatures, normally for one year (see details).
#' @param tme a `POSIXlt` object representing the date and time of each `temps` value.
#' Ignored if method unspecified.
... |
62ff3aaedf96a2a30242e306b48f5ff774cc11d7 | ab79177ad95b0e89d70210a3478b91f98cdb6b30 | /man/event_term.Rd | 6573de626c3089bcdf1afa59366a2efe0d7fa418 | [] | no_license | bbuchsbaum/fmrireg | 93e69866fe8afb655596aa23c6f9e3ca4004a81c | 2dd004018b3b7997e70759fc1652c8d51e0398d7 | refs/heads/master | 2023-05-10T17:01:56.484913 | 2023-05-09T14:38:24 | 2023-05-09T14:38:24 | 18,412,463 | 6 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,649 | rd | event_term.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/event_vector.R
\name{event_term}
\alias{event_term}
\title{Create an event model term from a named list of variables.}
\usage{
event_term(evlist, onsets, blockids, durations = 1, subset = NULL)
}
\arguments{
\item{evlist}{A list of named vari... |
1375468a42425fa2cae8e645ddb2a29484c69486 | a574a2feb28729d20606f3ebf5c0414fa7ebdd49 | /man/as_search.Rd | 5c3a45f27c1fb350b61506fb6a0a158bb787ff74 | [
"MIT"
] | permissive | gvegayon/rtimes | feb8a2043388380eab432c62825fa13e80b47c45 | 0286a27bb3eb25952b1f566261479f0e86168056 | refs/heads/master | 2021-01-16T22:42:10.438650 | 2015-08-11T17:23:12 | 2015-08-11T17:23:12 | 41,016,002 | 1 | 0 | null | 2015-08-19T05:54:37 | 2015-08-19T05:54:37 | null | UTF-8 | R | false | false | 3,107 | rd | as_search.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/as_search.R
\name{as_search}
\alias{as_search}
\title{Search articles}
\usage{
as_search(q, fq = NULL, sort = NULL, begin_date = NULL, end_date = NULL,
key = NULL, fl = NULL, hl = FALSE, page = 0, facet_field = NULL,
facet_filter ... |
d50d8189a561339b73a5234a8c3247cbd8b975ad | 423aa882d6d4385c7163eaf28025a3966c8a03a6 | /avx2/simple/papi/plot.r | 88f174dc99a237e267014631423c5f6f062378f4 | [] | no_license | mittmann/streaming_loads | e1b05ac6d9483ae06cbc2ee0e49f7236159df50d | 399b093bf617b246cf63274af658f51ab10b5300 | refs/heads/master | 2021-04-02T23:07:17.373647 | 2020-07-24T18:46:48 | 2020-07-24T18:46:48 | 248,334,404 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 735 | r | plot.r | library(dplyr);
library(ggplot2);
df <- read.csv("saidas.11.05.2020/papiallrapl");
k <- df %>% select(counter,size,memtype,temp,opt,value) %>%
group_by(counter,size,memtype,temp,opt) %>% summarise(mean=mean(value), n=n(), sd=sd(value), se=sd/sqrt(n), ic=2.576*se)
write.csv(k, "pp.csv")
exit
... |
472f8543bdf6d58aca5880090cecffd1d7b5b3f6 | 9c438d7bd98ddaacd14705c10c8d3bb195c925a4 | /code/daejeon_linear.R | 3802144e981745d413599b31e249add6ba79f272 | [] | no_license | Hmiiing/daejeon_Call | f51ab8562c4902475be612259e792b1f598f934a | c5594ce2fb5d91bc0794b882aa65e88d7b4c2f8d | refs/heads/master | 2022-11-14T06:34:28.479321 | 2020-06-20T15:50:37 | 2020-06-20T15:50:37 | 273,735,354 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,326 | r | daejeon_linear.R | ######daegeon_modeling_linear regression#####
#예측값이 양수->log transform
####전체를이용한분석####
###original_rmse###
library(Metrics)
set.seed(2019)
sqrt(mean((predict(lm(mean(log(new_n))~1.,data=train),newdata = test)-log(test$new_n))^2))
library(Metrics)
rmse(predict(lm(mean(log(new_n))~1.,data=train),newdata = test),l... |
43e90b9b1dbff39d85a21888e6fb830b40f8c56a | d7ff71e8ffb07419aad458fb2114a752c5bf562c | /tests/testthat/serialize_tests/k2-another-in_file-out.R | 9f77f05f3d2d231cd74a597ac708afa36a1902af | [
"MIT"
] | permissive | r-lib/styler | 50dcfe2a0039bae686518959d14fa2d8a3c2a50b | ca400ad869c6bc69aacb2f18ec0ffae8a195f811 | refs/heads/main | 2023-08-24T20:27:37.511727 | 2023-08-22T13:27:51 | 2023-08-22T13:27:51 | 81,366,413 | 634 | 79 | NOASSERTION | 2023-09-11T08:24:43 | 2017-02-08T19:16:37 | R | UTF-8 | R | false | false | 56 | r | k2-another-in_file-out.R | call(1,
call2(call(3, 1, 2),
4))
|
afdf51b04cdaf73a5f6946e9a95cc67cd4c38fc0 | 0853134802bde59234f5b0bd49735b9b39042cfb | /Rsite/source/api/man/mx.symbol.gamma.Rd | 26a692c73e7f32acec8063ac24c36887b71c0a7b | [] | no_license | mli/new-docs | 2e19847787cc84ced61319d36e9d72ba5e811e8a | 5230b9c951fad5122e8f5219c4187ba18bfaf28f | refs/heads/master | 2020-04-02T03:10:47.474992 | 2019-06-27T00:59:05 | 2019-06-27T00:59:05 | 153,949,703 | 13 | 15 | null | 2019-07-25T21:33:13 | 2018-10-20T21:24:57 | R | UTF-8 | R | false | true | 539 | rd | mx.symbol.gamma.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mxnet_generated.R
\name{mx.symbol.gamma}
\alias{mx.symbol.gamma}
\title{gamma:Returns the gamma function (extension of the factorial function \
to the reals), computed element-wise on the input array.}
\usage{
mx.symbol.gamma(...)
}
\argument... |
b209d16e221d7e3c85c21d2fd7b78529cb9b9acb | 7217693dc00b148a48c6503f6fe4ec1d478f52e8 | /mr/process_mr_results.R | 46fcff5b8eda7f50a38e0786ec00ccb4d0d3d50b | [] | no_license | Eugene77777777/biomarkers | 8ac6250e1726c9233b43b393f42076b573a2e854 | 9e8dc2876f8e6785b509e0fce30f6e215421f45b | refs/heads/master | 2023-07-25T10:52:30.343209 | 2021-09-08T18:12:45 | 2021-09-08T18:12:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,375 | r | process_mr_results.R | # Our pipeline is largely based on
# https://academic.oup.com/ije/article/47/4/1264/5046668, Box 3
# Aka the Rücker model-selection framework
setwd("~/Desktop/rivaslab/biomarkers/resubmission1/")
library(data.table)
# read the trait name info
trait_names = fread("./mr_rg_traits.txt",
data.table = ... |
ffd940a4b9e041bed8a3774f124eafecf3e2c3e3 | 76a593d829b0d61806e3c5b5e144adcd6a1ab3e7 | /biobank_vs_loo_effects.R | bd72d61a73af3a4353da9cdb511af8de2fd601ed | [] | no_license | ktsuo/globalbiobankmeta-Asthma | a97a3e993780c263945512aacc18afdead1d53ee | 4682c4122eaf9aa833de1e05dca09324185ecaff | refs/heads/main | 2023-04-07T00:17:17.003412 | 2022-09-30T17:51:12 | 2022-09-30T17:51:12 | 543,720,743 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,817 | r | biobank_vs_loo_effects.R | ##############################################################
##### Biobanks VS. Leave-that-biobank-out meta-analyses ######
##############################################################
library(data.table)
library(ggplot2)
library(dplyr)
library(broom)
library(readxl)
library(ggrepel)
library(writexl)
library(tidyv... |
55790f4a5e9708469f88e2bfa6e7791939ecd4e3 | 48c952257ef4d414e822eee952fbd79bfa900d2b | /mult.R | ed88f6f620fbb1ce9dcacb85bbdd8268930e93d1 | [] | no_license | michel-briand/emath | 1c64e3630e1cc98c6ef940996061cc27ada1fe3c | 896185ed58b325d116762108e09adbe3559e189c | refs/heads/master | 2020-03-29T23:42:53.141295 | 2018-09-26T20:19:13 | 2018-09-26T20:19:13 | 150,485,364 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,550 | r | mult.R | #
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
#
# This application is a small workshop for mathematics.
# It is inspired from the Micmaths video (https://youtu.b... |
5439e9d3160fc501c674975d8a6a1cd2b7290186 | 015011d242b514c0a4925b859ebb7ae371351837 | /Rscripts/gff2gtf.R | cb839c7c7f7e4bf08d4ca896bba7d81c28adb240 | [
"MIT"
] | permissive | devxia/NativeRNAseqComplexTranscriptome | 763f93b014f66b0b7b1b70046594521e3bdeaf00 | 1ef939d7606527283b3db1855be5c775a635089c | refs/heads/master | 2022-02-28T18:59:15.892634 | 2019-10-29T19:13:54 | 2019-10-29T19:13:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 879 | r | gff2gtf.R | args <- (commandArgs(trailingOnly = TRUE))
for (i in 1:length(args)) {
eval(parse(text = args[[i]]))
}
print(gff)
print(gtf)
suppressPackageStartupMessages({
library(rtracklayer)
library(readr)
library(withr)
})
## Filter out exons that can not be handled by gffcompare
x <- readr::read_tsv(gff, col_names = F... |
c699ebdac44df080840ac108a8838f6f115897fa | 3eb24ba0d0a6b79c441cfd393ef035d0dce36b3f | /personal/baby_names.R | d638ccd8f425309fe264b1697fe878a90acc6112 | [] | no_license | mdgbeck/projects | 4e57c33e5919b4c77bedd27c3dd6a7ddf298a3ac | 6e8205dcc139a5d2335e35ba667f056e2f569e10 | refs/heads/master | 2022-05-30T02:10:34.793217 | 2022-05-18T18:38:41 | 2022-05-18T18:38:41 | 124,141,737 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,377 | r | baby_names.R |
library(tidyverse)
library(lubridate)
library(babynames)
library(mdgr)
library(scales)
names <- babynames %>%
filter(year >= 1950 &
sex == "F" &
n > 15) %>%
group_by(name) %>%
mutate(n_years = n_distinct(year),
prev = lag(prop),
change = prop - prev,
perc_chg ... |
e6c2fe8e8ee0423f282d73de3fc3212b4baa1713 | f78c451bc1d7892e6684f92413b20515fddc8936 | /R/add.missing.expression.R | d3a089430e0a323e51e13cba11e0b4ecb358fc30 | [] | no_license | alexjcornish/DiseaseCellTypes | 3336f0bd93406ddad021c1ee2d6c467cc834080f | ea5d62898549eacf653f3c388d86ceb4af05eef0 | refs/heads/master | 2021-01-19T08:46:26.242215 | 2015-09-02T09:03:56 | 2015-09-02T09:03:56 | 27,534,586 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 860 | r | add.missing.expression.R | add.missing.expression <- function(
expression,
genes,
score.missing
) {
# add genes in 'genes' missing in 'expression' to 'expression' with score 'score.missing'
# remove genes in 'expression' not represented in 'genes'
contexts <- colnames(expression)
n.contexts <- ncol(expression)
... |
19dc627e9408e01e2c25d447c0b7133820c677ac | f951a642ade060ee0c084b610faf33eddc7901e2 | /util/copy_high_performing_benchmark.R | 6f47d577b57daee22ec684a610298ef915774dd5 | [] | no_license | tadeze/ADMV | fc1ac13c7ff5c72f05f6aff3100565938243bd11 | fc4a0bf011f28355f5910c5d6d1f84cfed51a18c | refs/heads/master | 2022-01-15T12:56:25.767940 | 2019-05-06T23:55:05 | 2019-05-06T23:55:05 | 117,147,033 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,075 | r | copy_high_performing_benchmark.R |
pathdir="/nfs/guille/bugid/adams/meta_analysis/results_summaries/"
destination = "/nfs/guille/bugid/adams/ifTadesse/kddexperiment/group2/"
ff = list.files(pathdir)
aucs <- data.frame()
for (bench in ff){
if(bench=="new_all" || bench=="all"){
next
}
for(idx in 290:300){
#idx = 300
bench_name = paste0(pathdi... |
a9a6954c347eed7cd7bf38c53b7410a5b14e4490 | 0a9d14249e04d4daeb7ef2df3afb2db5e47d4551 | /man/custom_distance.Rd | 4299717257636d26a83fc24e4d9b693be7c569c6 | [] | no_license | vda-lab/stad | 5f61154db5e7be4527d221e52fd5a88f908f41a5 | f7ab25e6492c0360e2093e6f73bf005d087d56db | refs/heads/master | 2020-04-25T20:53:38.508275 | 2020-03-22T13:32:05 | 2020-03-22T13:32:05 | 173,063,712 | 5 | 2 | null | 2020-03-22T12:49:05 | 2019-02-28T07:31:07 | R | UTF-8 | R | false | true | 590 | rd | custom_distance.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/stad_with_lens.R
\name{custom_distance}
\alias{custom_distance}
\title{Custom distance}
\usage{
custom_distance(x, metric)
}
\arguments{
\item{x}{array variable. Dimension of the lens.}
\item{metric}{string or array defining the metrics supp... |
9f0c47952e7f03bc6b122c904c6846c6b11948d9 | f374f8e079698141cde195c14717f6e56a48087b | /4_split.r | ea57e4b2c01aacaccb56bb330f0049dbd3c76c9e | [] | no_license | DavidMoranPomes/census-profiling-and-income-prediction | d42601376c80569ec6444fbf475ed282d90dafdc | 68cd49e4fdffd550a54c845d91b45dfa6d60af69 | refs/heads/master | 2020-06-17T20:45:01.995050 | 2019-07-09T17:13:26 | 2019-07-09T17:13:26 | 196,048,435 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 226 | r | 4_split.r | load('data/adult.rds')
set.seed(1234)
train <- sample(nrow(adult), 0.5*nrow(adult))
set <- logical(nrow(adult))
set[train] <- TRUE
adult$Set <- factor(ifelse(set, 'Train', 'Test'))
save(adult, file='data/adult_split.rds')
|
76b441761aa261758a9e3e376c77ee4130b7076e | 95d43b808610f161c3e28b6e985ca2f5c319b754 | /Using R/Text Mining/2/2.R | 0a1ec624a6dbc57e7917790a9d9c92a189b0543b | [] | no_license | dharmesh-coder/Data-Science | 824b53a9b867e4eb268b18a5d03b1c8991491f83 | 913e6e1e2503cd2be94f458ce6add3afa2a63605 | refs/heads/master | 2023-04-19T20:58:39.355216 | 2021-05-15T17:00:51 | 2021-05-15T17:00:51 | 367,411,369 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 611 | r | 2.R | library(dplyr)
library(rvest)
library(NLP)
library(tm)
url <- "http://www.analytictech.com/mb021/mlk.htm"
data <- read_html(url)
text <- data %>% html_nodes("p") %>% html_text()
text
doc <- Corpus(VectorSource(text))
inspect(doc)
doc <- tm_map(doc,removeNumbers)
doc <- tm_map(doc,removeWords,stopwords("english"... |
879c01adf16e609b5216fd5a331a90621b662569 | 405c68ad20a0a48272b7fe53d85a841146fdc488 | /R/dvinesim.R | 44d292b98225ca9491b5deb82f35fa37cdfffd5d | [] | no_license | cran/CopulaREMADA | 1d65cac1c329c311f1133422b3f0748f618c85e6 | 2d861b35e51a117c2c8ec0407619b4b5da8f9d2d | refs/heads/master | 2022-08-20T08:58:43.777187 | 2022-08-07T15:10:04 | 2022-08-07T15:10:04 | 31,186,515 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 830 | r | dvinesim.R | dvinesim=function(nsim,param,qcond1,pcond1,tau2par1,qcond2,pcond2,tau2par2)
{ tau12=param[1]
tau23=param[2]
tau34=param[3]
tau13.2=param[4]
tau24.3=param[5]
tau14.23=param[6]
p = matrix(runif(nsim * 4), nsim, 4)
th=matrix(0,4,4)
th[1,2]=tau2par1(tau12)
th[1,3]=tau2par2(tau23)
th[1,4]=tau2par1(... |
08fec4879d5acdbbee49e87581985bd461da7ea3 | ca609a94fd8ab33cc6606b7b93f3b3ef201813fb | /2016-April/11-Optimization/3d-visulizations.R | 6794b8abb5418e00cb401e851e01be90d07b1e36 | [] | no_license | rajesh2win/datascience | fbc87def2a031f83ffceb4b8d7bbc31e8b2397b2 | 27aca9a6c6dcae3800fabdca4e3d76bd47d933e6 | refs/heads/master | 2021-01-20T21:06:12.488996 | 2017-08-01T04:39:07 | 2017-08-01T04:39:07 | 101,746,310 | 1 | 0 | null | 2017-08-29T09:53:49 | 2017-08-29T09:53:49 | null | UTF-8 | R | false | false | 579 | r | 3d-visulizations.R | library(plot3D)
fun = function(x,y) {
return(x+y)
}
x = seq(-2, 4, 0.5)
y = seq(-2, 4, 0.5)
f = outer(x,y,fun)
windows(width=50, height=60)
persp3D(x, y, f, xlab="x", ylab="y", zlab="f", theta = 30, phi = 10)
X11()
persp3D(x, y, f, xlab="x", ylab="y", zlab="f",color.palette = heat.colors, theta = 30, phi = 10, colk... |
94fcfdb992e5a10002efadc6f0e0248bc1ec24f4 | 2e42b670fdffe5bf4844cb73aec4ed4943aa1e36 | /man/shift_index.Rd | 82efd4e1fab0cbf8311a4d15dacc7a3ef5d776c0 | [] | no_license | pawelru/sudoku_r_game | 9f8ba3c4b4862b1a77dccef696439afba55c422a | fbe9a7c8f3ab7b42415e130a4a0faea8c99ebd94 | refs/heads/master | 2020-04-02T04:42:35.292097 | 2018-10-21T17:05:57 | 2018-10-21T17:05:57 | 154,030,365 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 362 | rd | shift_index.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/matrix.R
\name{shift_index}
\alias{shift_index}
\title{Move elements from tail to the head of vector}
\usage{
shift_index(x, n)
}
\arguments{
\item{x}{vecor to be shifted}
\item{n}{shift value}
}
\value{
shifted vector
}
\description{
Move e... |
69938ceac993f32f32276cab8aa94d8a6f588e16 | cbdc14ffeac8a3ea94cb4e27be81f97c121801ef | /Normal Contaminada - Poder Sinal.R | 4f98b31e6ad026179e10aa1617832efd4086b6f5 | [] | no_license | ArthurCarneiroLeao/Monte-Carlo-Sinal | c373e0f405253504062fa5d28413d58102e696e1 | 28c61e9c38fc8b7ad9d8db6f3915550a3a1ddb78 | refs/heads/master | 2020-04-02T16:16:37.471734 | 2018-11-20T19:59:02 | 2018-11-20T19:59:02 | 154,605,992 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 2,747 | r | Normal Contaminada - Poder Sinal.R | #NormalContaminada-Poder Sinal(5%)#
### usando o grau de contaminação 5% e lamb = 3
library(BSDA)
library(tidyverse)
delta <- 0.05
lambda <- 3
r<-1000
theta.test<-c(seq(-2,2,0.05))
M <- length(theta.test)
power <- numeric(M)
nobs<-c(5, 10, 30, 80) #Vetor Para tamanhos de amostras diferent... |
c55376daa9ff2fee7e891353fc4109f16d550e3a | 84a34111f811cc0aa836707d1c22aaece01f2ead | /Programming Assignment 3/rankhospital.R | 31b42453725e9960be5dc9d70dcb9c4e9d9fd3e7 | [] | no_license | JaMedina/DS_R_Programming | 8912cf2bfe30e9a32a3db887c5b4dbb7f32b9832 | 5220028e3efcdb213d0a108deaa18df4e9d09069 | refs/heads/master | 2020-05-14T11:28:50.485432 | 2014-04-30T18:44:19 | 2014-04-30T18:44:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,066 | r | rankhospital.R | rankhospital <- function(state, outcome, num="best"){
outcome_measures <- read.csv("outcome-of-care-measures.csv", colClasses = "character");
if(!state %in% outcome_measures$State){
stop("Invalid State.");
}
outcome_measures <- outcome_measures[outcome_measures$State==state,];
number_of_deaths <- ... |
785c2b046bf33ad64145c987ab3a41fe63bb0f06 | fd0e2346e6d3002ef95eb0f826b35cd6260aea10 | /man/adjust_Rsq.Rd | 4f56c03055ae11b08eeff2d429674976b504ded7 | [] | no_license | cran/configural | 91f24339a1c235245a8a7c2657db3ca13766944f | c192d23b7db98216dc4c07b9a282bb4f9a9d4a28 | refs/heads/master | 2021-06-19T14:45:02.052286 | 2021-01-18T20:30:03 | 2021-01-18T20:30:03 | 171,525,050 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,276 | rd | adjust_Rsq.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utility.R
\encoding{UTF-8}
\name{adjust_Rsq}
\alias{adjust_Rsq}
\title{Adjust a regression model R-squared for overfitting}
\usage{
adjust_Rsq(Rsq, n, p, adjust = c("fisher", "pop", "cv"))
}
\arguments{
\item{Rsq}{Observed model R-s... |
3ddea2d03c7da8a6815210960033569cc9494899 | 4f9a3ae52cfe45a839a7f293b764ea97e0a4438e | /Day2.R | 0c96c68cc4d674679fda442e7ac9aa84d1836001 | [] | no_license | eileenschaub/R_in_Git | 40c113c95a03ea7a889186486f990f1577a2b47d | 10e53daeac772432c99c4eff1e557247c74f3b11 | refs/heads/master | 2020-04-16T05:32:30.483587 | 2019-01-11T21:18:37 | 2019-01-11T21:18:37 | 165,310,074 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,171 | r | Day2.R | # Software Carpentry R Workshop - James's Code
#doing some programming!
number <- 37
if(number > 100) {
print("greater than 100")
}
# The above produces no visible result cos 37 isn't > 100.
number <- 37
if(number > 100) {
print("greater than 100")
} else {
print("less than or equal to 100")
}
#Some notation... |
7f748fa95c05e7506f93902546a9c97aab1f0e8f | dccbfbe9eae4f0ad67ee5f32b38f6b2caed616ff | /plot3.R | ffd4c2e7cc27041b3941e23f084a259887a6c36c | [] | no_license | andreaslowe/ExData_Plotting1 | 12bc4c35880f6e3887b440d4a6a9e535a7f21f35 | a74db3f346fcc26cd4045b5d37485ebcb86ec1fd | refs/heads/master | 2021-01-21T10:34:30.523257 | 2017-02-28T22:32:14 | 2017-02-28T22:32:14 | 83,455,846 | 0 | 0 | null | 2017-02-28T16:39:47 | 2017-02-28T16:39:47 | null | UTF-8 | R | false | false | 876 | r | plot3.R | #Exploratory Data Analysis Assignment 1
#working data from plot1.R and plot2.R is used
png(filename = "plot3.png", width = 480, height = 480) #open png device, set w x h in pixels (though 480 is typically default)
par(pty="s") #make plot square
#create plot and add submetering 1 line (default is black so don'... |
c1df7a26ef050267d3631835756b16bb20043553 | 67319c944a4e8b4da2733c16803dc23b1d94bb2d | /Tides and lunar data scrape code.R | c87a1777cb19fa7af5d91bb83773112e1d94b906 | [] | no_license | Plaladin/SnowyPlover_cycles | d50e0810c6fccfecb27e6319611e23151978b6dc | 4d364577a93ab8ee8cb257408d657e431f8b40d1 | refs/heads/master | 2021-01-20T14:39:08.988677 | 2018-06-12T15:01:55 | 2018-06-12T15:01:55 | 90,640,121 | 0 | 0 | null | 2017-05-08T14:53:06 | 2017-05-08T14:53:06 | null | UTF-8 | R | false | false | 11,200 | r | Tides and lunar data scrape code.R | # Scrape tide data
library(rvest)
#2006-2007 (2008 must be loaded seperately)
tides_06_07 <- lapply(paste0("http://tides.mobilegeographics.com/calendar/year/3689.html?y=",2006:2007, "&m=4&d=1"),
function(url){
url %>% read_html() %>%
html_nodes(... |
3fa3062766590ed6cf9215c9ab31588af97c30c4 | 169adce5d523299aaf1501d5cd3d45a64044c36e | /profile/man/write_rprof.Rd | 63bc66a366a0519d3c42cad610ac1a085fd1b667 | [] | no_license | r-prof/_meta | 2fd2d1872340496635d3cb4dd21618021637b9f7 | c0d7ebe085f4e88cd72c11bfb05feded8db2df88 | refs/heads/master | 2021-09-01T04:37:25.881122 | 2017-12-24T21:30:59 | 2017-12-24T21:30:59 | 115,286,849 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 447 | rd | write_rprof.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rprof-write.R
\name{write_rprof}
\alias{write_rprof}
\title{Write profiler data to an R profiler file}
\usage{
write_rprof(ds, path)
}
\arguments{
\item{ds}{Profiler data, see \code{\link[=validate_profile]{validate_profile()}}}
\item{path}{... |
12fdfbef84fd4d2b36cfeae9c33870858a639c57 | 187fccafa2ac14ca45fadad0d2ca395e34e1b0f3 | /Test file.R | fe39471b71737d1f3b9f8ef711dcc6f34a0347a3 | [] | no_license | ronmexico7811/Econ4670researchproject | 82dedd449f602e543d93196c02b834fff6348b1f | e801b7f111f4ddf890ed3c517d9cdc6860da0f2d | refs/heads/master | 2020-04-18T03:45:43.334354 | 2019-04-24T16:57:00 | 2019-04-24T16:57:00 | 167,212,276 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,484 | r | Test file.R | library(readr)
shelter_skim <- read_csv("Econ4670researchproject/shelter_skim.csv")
shelter_skim$TotalEnrollments = NULL
shelter_return <- subset(shelter_skim , StayedInShelterMoreThanOnce == 1)
shelter_no_return <- subset(shelter_skim , StayedInShelterMoreThanOnce == 0)
shelter_no <- shelter_no_return[1:328,]
shelter... |
abe08a9a53ac2aa83692e7106e1c42aa896c817d | 7e1b2b59a21d58ed8058df89a7b474b9bb4f3731 | /data/youtube datacleaner.R | 0a5a4e22f0f9aa72ed19e9227edc45fb5a3bc6ba | [] | no_license | yaowser/YoutubeTrending | 6c1fcec727bd7dad00b47548d90353a10853b192 | e85fa2355fe7531a56d14a79606faa08cd30b09a | refs/heads/master | 2021-04-15T15:03:05.208470 | 2018-04-26T12:31:40 | 2018-04-26T12:31:40 | 126,577,169 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,268 | r | youtube datacleaner.R | cat("\014")
options(warn=1)
require(survey)
require(dplyr)
require(lattice)
#read in, remove columns, from https://www.kaggle.com/datasnaek/youtube-new/data
setwd("C:/Users/Yao/Desktop/you")
youtubeRawUS <- read.csv(file="USvideos.csv", header=TRUE, sep=",")
youtubeRawUS$country <- rep("US",nrow(youtubeRawUS))
youtub... |
ffbe1a8b6a3123d5e974de0d42554f0876b2ce84 | 39986b2417af8bcdd6d64cc1441de82a7ae47b59 | /man/fit_nodk.Rd | 6818bafd337aef0c04831067254374c94fd53c07 | [
"MIT"
] | permissive | cran/guess | 3c5a9222e67fb1c32590e807f95a89796acd1589 | 6a4ead9edc2d00ed4d7259f2f799dfc3b0fb343d | refs/heads/master | 2016-08-11T15:21:04.016983 | 2016-02-08T23:44:06 | 2016-02-08T23:44:06 | 54,415,144 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 877 | rd | fit_nodk.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fit_nodk.R
\name{fit_nodk}
\alias{fit_nodk}
\title{Goodness of fit statistics for data without don't know}
\usage{
fit_nodk(pre_test, pst_test, g, est.param)
}
\arguments{
\item{pre_test}{data.frame carrying pre_test items}
\item{... |
2f8e129f9331c1c7072bdfa7e2c728a76259080c | ee90b400fc8d344c576198d1e58eafac51e5dc90 | /code/Exercicio8.R | 444c99e95a002159bbe0becd42ff751d8437d2a2 | [] | no_license | lucasfernog/data-science-exercises | c2a944fd98d31c818188c4fa77341160a6687ab5 | b07bde440561980d2c3383e83045ee163eaf3ca6 | refs/heads/master | 2020-03-31T14:32:16.507332 | 2018-10-09T18:17:20 | 2018-10-09T18:17:20 | 152,299,157 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 374 | r | Exercicio8.R | install.packages("gridExtra")
library(xlsx)
library(gridExtra)
library(grid)
ex8 <- read.xlsx("data/exercicio8.xls", sheetName = "Plan1")
ex8
tabela <- table(ex8$Altura.dos.pacientes)
tabela
barplot(tabela, ylab = "Frequencia", ylim = c(0,3), main = "'Distribuicao de frequencia'")
hist(ex8$Altura.dos.pacientes, mai... |
9fbde8cbe3b49becd6866607959386d9074d5014 | fe94391f87c4a5726cf7375155139edfd7b8fe1b | /dplyr_code.R | c53d1eda7a9280d687049df475d5ef62cb36c7b1 | [] | no_license | aofcrazy/bootcamp-data-science-learning | 497f75cefe434d75be4087e38f673dbddb89430f | 3aa9404bd58349eee8c21e2e5e189e9a69bb5e66 | refs/heads/master | 2023-05-02T13:56:43.288869 | 2021-05-20T15:26:34 | 2021-05-20T15:26:34 | 369,093,776 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,562 | r | dplyr_code.R | ## Data tranformation
library(tidyverse)
library(readxl)
# read data into R
student <- read_excel("scholarships.xlsx", 1)
address <- read_excel("scholarships.xlsx",2)
scholarships <- read_excel("scholarships.xlsx",3)
## VLOOKUP (Spreadsheet) == left_join (R)
## Mutating join (left, inner, right, full)
## data pipel... |
c5391e21e38babcdadb5cb8a18ad744e8eac4718 | 0d054649ad79bad9c5ecfb467a1afa4fd12a0a12 | /man/rpart_labels.Rd | 9a0d373a8ce371928b378506a78ff38ea7e14793 | [] | no_license | joey711/ggdendro | 101dee4ea46d418e58a59db7b8d3f5e24cfb12c1 | f18ca86f9370895256e74271b3f74107539747ce | refs/heads/master | 2020-12-25T06:02:54.945578 | 2012-02-02T15:09:40 | 2012-02-02T15:09:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 943 | rd | rpart_labels.Rd | \name{rpart_labels}
\alias{rpart_labels}
\title{Extract labels data frame from rpart object for plotting using ggplot.}
\usage{
rpart_labels(model, splits = TRUE, label, FUN = text,
all = FALSE, pretty = NULL,
digits = getOption("digits") - 3, use.n = FALSE,
fancy = FALSE, fwidth = 0.8, fheight = 0... |
b20afabc0f2ce722bbcd9593e09e0e85968d1c07 | cebf3c6700ff85f87c61de6d7f882880315eddd2 | /man/kernelFactory.Rd | da9aad984ec2226e97929e6b9bef4131d8a9c853 | [] | no_license | wrathematics/kernelFactory | 539c3ae50949a6e42ecb595c029055e125b5ed83 | 425303ac7de92ddbc6270c2fa88150bc7aa5b28d | refs/heads/master | 2021-01-10T01:54:09.944169 | 2015-11-11T17:14:00 | 2015-11-11T17:14:00 | 45,994,174 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,700 | rd | kernelFactory.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/kernelFactory.R
\name{kernelFactory}
\alias{kernelFactory}
\title{Binary classification with Kernel Factory}
\usage{
kernelFactory(x = NULL, y = NULL, cp = 1, rp = round(log(nrow(x), 10)),
method = "burn", ntree = 500, filter = 0.01... |
0a4834d5a769493ba053acbd44c185f22aa7d662 | 338cfd3efe0cc943d2e6b58becf7432ced163ab2 | /01R language in action/ch6Data_IO/i0inner_dataset.R | 5f2d9c997b904a61d3da4d24fe452be05f79c0ce | [] | no_license | greatabel/RStudy | e1b82574f1a2f1c3b00b12d21f2a50b65386b0db | 47646c73a51ec9642ade8774c60f5b1b950e2521 | refs/heads/master | 2023-08-20T17:07:34.952572 | 2023-08-07T13:22:04 | 2023-08-07T13:22:04 | 112,172,144 | 6 | 4 | null | null | null | null | UTF-8 | R | false | false | 331 | r | i0inner_dataset.R | data(geyser, package = "MASS")
data = read.table("i0car.txt", header=TRUE, quote="\"")
data[1:2,]
library(crayon)
cat(red$bold$bgGreen("mode(data) is "))
mode(data)
cat(blue$bold$bgGreen("names(data) is "))
names(data)
cat(yellow$bold$bgGreen("dim(data) is "))
dim(data)
cat(red$bold$bgGreen("data$lp100km is "))
d... |
ec75e9bd2e8285a60051bddaa9fad3840e350559 | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/13157_0/rinput.R | ba66e0eb16a21dfcfdce5d7503c515eef5b45130 | [] | 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 | 137 | r | rinput.R | library(ape)
testtree <- read.tree("13157_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="13157_0_unrooted.txt") |
6b680a03b6c166e09c1d04c4f76acb0c4765d4ce | 0b0e39a4f7fe32aa9a3988d286a3c3a393b218bf | /inst/script/requiredLibraries.R | 69ae273de3c0fa901d480422ec73b841e14095e4 | [] | no_license | Manuelaio/uncoverappLib | e3b95b6419f23b17c4babdfa371c697577b5cc07 | 66df2cbf2bc0637c3bcc0ce20c5dfea9ae83d495 | refs/heads/master | 2023-03-04T02:37:02.533719 | 2023-02-13T10:00:41 | 2023-02-13T10:00:41 | 254,597,958 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 535 | r | requiredLibraries.R | require(shiny)
require(shinyWidgets)
require(shinyBS)
require(shinyjs)
#required libraries
require(markdown)
require(DT)
require(dplyr)
require(Gviz)
require(Homo.sapiens)
require(OrganismDbi)
require(stringr)
require(condformat)
require(shinyjs)
require(bedr)
require(rlist)
require(Rsamtools)
require(TxDb.Hsapiens.UCS... |
2c6f2e2058934116c414d1829c7d806f297b3230 | e3c744f368446d33a0836289193c7c2badf39a81 | /plot1.R | b67e003b8e4fd665be306a3a47a1d26c2570033e | [] | no_license | mbontrager/ExData_Plotting1 | bb438676006220b19666c20721575d0622565f40 | d0a1d3a6cdd67b735484633d94a491a9da0053cb | refs/heads/master | 2021-01-22T15:12:21.274470 | 2015-06-06T14:44:09 | 2015-06-06T14:44:09 | 36,982,940 | 0 | 0 | null | 2015-06-06T14:27:58 | 2015-06-06T14:27:58 | null | UTF-8 | R | false | false | 791 | r | plot1.R | # Martin Bontrager
# Exploratory Data Analysis
# Project 1 - Plot1
library(data.table)
# Read power consumption data and subset to only two days: 1-2 Feb 2007
f <- "data/household_power_consumption.txt"
DT <- fread(f, sep = ";", na.strings = "?", stringsAsFactors = FALSE,
header = TRUE)
DT$Date <- as.Dat... |
28026a3327f13997cac42f9411cc6a29c3ab8a63 | 9cf1abc8ce339d07859eaa12d6143382bee0431a | /TPL_QUERY.R | 07ccc4f4e5db7af4eaec2cf0254b153141b69c16 | [] | no_license | ccsosa/GIS_ANALYSIS | 043e4d2d8fc76a8c50ea8e174914bf1b5612a2eb | f82a0b75ef67478d058216c84ad3ae78fc83cd59 | refs/heads/master | 2021-06-21T06:24:42.182397 | 2017-05-10T22:59:53 | 2017-05-10T22:59:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 178 | r | TPL_QUERY.R | require(tpl)
require(tpldata)
t<-read.table("clipboard",header=F,sep="\t")
tpl2<-tpl.get(t[,1])
write.table(tpl2,"D:/CWR/TPL_GBIF_9743.csv",sep="^",quote = F,row.names = F)
|
df00f9500b8e035cbfee903d8e1c278df7dc4ebf | 5ea830cfee38cc02964e2bf8d787824ab9275b26 | /ConvolutionalNeuralNetwork.R | c78a8b9319a351f91d2949968931b123b0c0a0e5 | [] | no_license | rahul494/Environment-Image-Recognition | e01268b50b8d75dd7b8261e9385c9b2981d75d87 | 4006f75a8483bfd9f0eb0bd1ac92bef967de0e33 | refs/heads/master | 2021-02-16T08:46:11.164011 | 2020-05-12T23:29:01 | 2020-05-12T23:29:01 | 244,985,789 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,648 | r | ConvolutionalNeuralNetwork.R | #######################################################################
## Project: Image Processing Methods for Environment Classification
## Script purpose: Analyze and classify photos via Convolutional Neural Network
## Date: 2020-03-28
## Author: Rahul Sharma
########################################################... |
c9e60f521c667eacb7792aa3fca31170e706f622 | 60247e886a6b94b7da90440c35faeb8bda2c55e2 | /plot1.R | 17a5c2a2b7318a0e5a3c87de0aff6991cb8906fd | [] | no_license | MridullS/exploratory_data_analysis | 2863b041d530b75900a1a5035d6ac26bf40ffd8f | 4580a95d2dd431469ea5c157dab9b6d8b6d450e3 | refs/heads/master | 2022-09-17T17:13:43.054039 | 2020-05-31T11:47:35 | 2020-05-31T11:47:35 | 268,265,502 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 671 | r | plot1.R | library(dplyr)
plot1 <- function() {
file_read <- read.csv2('household_power_consumption.txt', dec='.', na.strings='?',
stringsAsFactors=FALSE)
start <- ymd('2007-02-01')
end <- ymd('2007-02-03')
file_read <- file_read %>%
mutate(DateTime=dmy_hms(paste(Date, Time))) %>%
selec... |
69f839de824836b2d8072acd6d792b3499035480 | 7d5d8492c2d88b88bdc57e3c32db038a7e7e7924 | /SAL_BMU_Amazon/13-CRU_functions.R | df97d15fa0f28fb4a22a669bd32f422a43ad9405 | [] | no_license | CIAT-DAPA/dapa-climate-change | 80ab6318d660a010efcd4ad942664c57431c8cce | 2480332e9d61a862fe5aeacf6f82ef0a1febe8d4 | refs/heads/master | 2023-08-17T04:14:49.626909 | 2023-08-15T00:39:58 | 2023-08-15T00:39:58 | 39,960,256 | 15 | 17 | null | null | null | null | UTF-8 | R | false | false | 1,661 | r | 13-CRU_functions.R | require(raster)
require(ncdf)
require(rgdal)
# require(ncdf4)
# source("13-CRU_functions.R")
CRU_cut <- function(baseDir="T:/gcm/cmip5/isi_mip", region=extent(-80, -66, -16, 5), outDir="Z:/DATA/WP2/03_Future_data/isi_mip_ft_0_5deg") {
setwd(baseDir)
if (!file.exists(outDir)) {dir.create(outDir, recursive = TRUE)... |
8cc41aa1c48d8d5dd38a8fc9535bae2ef8c821bb | 4901ec89e81d76ea8ee197f49367e0e293e989c4 | /R/ds-functions.R | 2d44c4965236f85b9b90d170aea66780b41fb87b | [] | no_license | RomeroBarata/IN1165-MCS-EL3 | 19166f4e2bde2ec1a1755c208897317f3a94b030 | 9eef9f9f8d2b6864fd8e3132941471045714ab9f | refs/heads/master | 2021-01-10T22:38:02.196227 | 2016-10-10T23:35:25 | 2016-10-10T23:35:25 | 69,709,443 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,615 | r | ds-functions.R | predict.bagging_ola <- function(object, newdata, valdata, ...){
names(valdata)[ncol(valdata)] <- "Class"
val_preds <- predict.bagging(object, valdata[, -ncol(valdata)])
knn_idx <- kknn::kknn(Class ~ .,
train = valdata,
test = newdata,
k... |
71d540386c42caf3193bedaa473c6d528d95887f | 3f17ed44ae94cc7570aecd38fe075626e5df84ff | /app2020/LakeAssessmentApp_v1/buildAppModules/2018IRversions/buildStationTable.R | 62ab46f26a5ba824c4094b438c53eae1870e9b8d | [] | no_license | EmmaVJones/LakesAssessment2020 | 24c57a343ec7df5b18eada630cc2e11e01c8c83c | 72f4b6e9721c947e8b7348c9d06e3baf8f2da715 | refs/heads/master | 2020-05-18T02:41:57.259296 | 2019-06-20T13:38:55 | 2019-06-20T13:38:55 | 184,124,597 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,797 | r | buildStationTable.R | lake_filter <- filter(lakeStations, SIGLAKENAME == 'Claytor Lake')
conventionals_Lake <- filter(conventionals, FDT_STA_ID %in% unique(lake_filter$FDT_STA_ID)) %>%
left_join(dplyr::select(lakeStations, FDT_STA_ID, SEC, CLASS, SPSTDS,PWS, ID305B_1, ID305B_2, ID305B_3,
STATION_TYPE_1, STATION... |
92530d620d756c72623efced711c2646917e070b | 95989f087d37032cc39739fc2b42449268fc4d69 | /tests/testthat/tests.R | 22478b09f24b8c333a01c9b6050e300865ebb87e | [] | no_license | CharlesNaylor/walker | c3e4986ce3783165222788a25b60c147398ee732 | 95f44692673f32430bd3ee19fbbb90cf5488a52b | refs/heads/master | 2021-06-18T14:44:48.825576 | 2017-06-26T01:02:37 | 2017-06-26T01:02:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,285 | r | tests.R | context("Test walker")
test_that("arguments work as intended", {
library(walker)
expect_error(walker("aa"))
expect_error(walker(rnorm(2) ~ 1:4))
expect_error(walker(rnorm(10) ~ 1))
expect_error(walker(y ~ 1))
expect_error(walker(rnorm(10) ~ 1, beta_prior = 0))
x <- 1:3
expect_identical(c(1,1,1,1:3),... |
d1012ea065920eb749df91e58c8d45b7ee8d43a2 | 326da72853050febce950f5aabe89c97d896b7b2 | /man/ByState.Rd | 97dcff48cab01350ff78a932bfb1ce699d7dd041 | [] | no_license | lvjensen/PhysicsEdCoalition | f12f37ebe4b05790d7967ed23734b1b9808226cf | d74897d4945be3ed2ea4b27e6aff150646e1ac90 | refs/heads/master | 2021-06-25T16:41:11.482797 | 2021-03-17T17:14:33 | 2021-03-17T17:14:33 | 213,503,179 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 462 | rd | ByState.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ByState.R
\name{ByState}
\alias{ByState}
\title{ByState}
\usage{
ByState(Year, with_summary = TRUE)
}
\arguments{
\item{Year}{is digit numeric number of the year}
\item{with_summary}{defaults to TRUE. Adds a summary (total) row and the botto... |
77c4bf90ccab8c984b5e124f9064aed49f73768c | d804682583257b6fd029f4b9728e4407624560c8 | /15-RStudio-essentials/2-Debugging/palindrome.R | b589e4ccc71ed40f92524f4cf05a3872766de468 | [
"CC-BY-4.0"
] | permissive | garrettgman/webinars | 2f099b06779d73c65d2515660aca3b5af27e84e8 | a34336aa7ba411695f57c845bd0b58b1c64bd4e7 | refs/heads/master | 2022-02-14T21:54:27.447675 | 2019-09-04T19:40:34 | 2019-09-04T19:40:34 | 103,402,448 | 9 | 6 | null | 2017-09-13T13:20:48 | 2017-09-13T13:20:48 | null | UTF-8 | R | false | false | 760 | r | palindrome.R | # Extract nth (from left) digit of a number
get_digit <- function(num, n) {
# remove numbers on left, then numbers on right
(num %% (10 ^ n)) %/% (10 ^ n)
}
# Indicate whether a positive number is a palindrome
palindrome <- function(num) {
digits <- floor(log(num, 10)) + 1
for (x in 1:((digits %/% 2))) {
d... |
43b788bea9d9bd7b42b9e05c42553a0302b7d863 | 3dfc04ba341ba3f6a17c5f424735ca6d54c982b8 | /Scripts/MMM2019_Finalists.R | 269cbc34589d1cfc440fdb90694ea5c1967ca27d | [] | no_license | trcsmallwood/MMM2019 | f77fcf7d39d83dd786746d3fb9757b7d8d2bb340 | 53b87dbb5a8bf8ca56ed65eff4e4bd227c08ea49 | refs/heads/master | 2021-06-14T11:27:39.260076 | 2021-03-25T14:50:59 | 2021-03-25T14:50:59 | 173,454,411 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,323 | r | MMM2019_Finalists.R | ##MMM2019_Finalists
##Calculate the proportion of brackets that pick each species as finalists and champions
#Load in packages
library(stringr)
library(ggplot2)
#Read in dataframe of predictions and results
MMM_df <- read.csv("../Submissions/MMM2019_PredictionsSummary.csv", stringsAsFactors = F)
#Extract finalists
f... |
9f400c3542e4af62526b7733d730bf0e130457cf | ce26f322943418a1d729f52ba84726bdbd567f81 | /plot3.R | 23cd0cb61db9cfe4ceca44900c009fa6f85d2423 | [] | no_license | Sarpwus/ExData_Plotting1 | 35c24e428c25b2f5601320488687cb8380e7671a | b828d89627236238ed649210f8bea26add4111f4 | refs/heads/master | 2021-01-18T10:03:49.766010 | 2014-12-08T00:32:55 | 2014-12-08T00:32:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 670 | r | plot3.R | ## Load the dataset from the source directory
## assume we are the location of the file in the working directory
library(sqldf) # I use sqldf package for reading the data
source("load_hsepwr.R")
png(file = "plot3.png", width = 480, height = 480, units = "px", bg = "transparent")
with(hsepwr, plot(DateTime, Sub_mete... |
5320fb06d883df8bda37b8ece2b4f6e24f12375a | 7c1f0f97a327331c9a09b5440880b354d94431b9 | /man/write_mallet_state.Rd | e2dcdd08c9ba0807ea2d3580658875a1e0c25c87 | [
"MIT",
"GPL-1.0-or-later"
] | permissive | agoldst/dfrtopics | e9cae65bb98283b227187a1ec7b81f8de71458ca | b547081f5159d38e24309c439192f48bfd0a2357 | refs/heads/master | 2022-07-27T09:34:38.664365 | 2022-07-15T13:37:22 | 2022-07-15T13:37:22 | 18,853,085 | 41 | 13 | MIT | 2021-01-25T10:10:58 | 2014-04-16T19:38:02 | R | UTF-8 | R | false | true | 529 | rd | write_mallet_state.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sampling_state.R
\name{write_mallet_state}
\alias{write_mallet_state}
\title{Save the Gibbs sampling state to a file}
\usage{
write_mallet_state(m, outfile = "state.gz")
}
\arguments{
\item{m}{the \code{mallet_model} model object}
\item{outf... |
2f5124a9db7bfeb8408b6171fb23d52f288a0d13 | 0a906cf8b1b7da2aea87de958e3662870df49727 | /bravo/inst/testfiles/colSumSq_matrix/libFuzzer_colSumSq_matrix/colSumSq_matrix_valgrind_files/1609959260-test.R | 71a216fec0fd2a8c2f37af85d53ac80a9c8f5a15 | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 168 | r | 1609959260-test.R | testlist <- list(x = structure(c(1.61100627174858e+126, NaN, 1.44202388027275e+135 ), .Dim = c(3L, 1L)))
result <- do.call(bravo:::colSumSq_matrix,testlist)
str(result) |
f1d5b0825fe0ed160306fda1c9e6346692082ac9 | bcb329b2dfaa867fbf7a39b7366cfbb80552ad97 | /CollectDealerInfor/ChevroletDealersLinks.R | 013b227aa4aee46026800e2945f324f78b8e0803 | [] | no_license | jpzhangvincent/Dealership-Scraping | 3c8ecfa72e7692f0f709afcbac840899781d27e2 | 13634892a8098cca260ddf1c4017946b76f0deca | refs/heads/master | 2021-01-12T14:25:19.400164 | 2015-10-06T21:55:17 | 2015-10-06T21:55:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,435 | r | ChevroletDealersLinks.R | #Collect all the nation-wide Chevrolet dealerships with information about name, address, website, inventory link and geo location
install.packages("XML", repos = "http://cran.cnr.Berkeley.edu/")
library(XML)
install.packages("plyr", repos = "http://cran.cnr.Berkeley.edu/")
library(plyr)
load("zipdata.rdata")
cities = ... |
498c60c4f4db0582e970937ce118e9548a4a43bd | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/libamtrack/examples/AT.SPC.spectrum.at.depth.step.Rd.R | 3f379618ae614cb4deabc6e63e99f834908306c5 | [] | 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 | 179 | r | AT.SPC.spectrum.at.depth.step.Rd.R | library(libamtrack)
### Name: AT.SPC.spectrum.at.depth.step
### Title: AT.SPC.spectrum.at.depth.step
### Aliases: AT.SPC.spectrum.at.depth.step
### ** Examples
# None yet.
|
88218dc79671a33dfc71ed2aa27acb818eef3e27 | b827162c6a43fe46b313e8b7736018a34f28e76e | /man/Specmodule.Rd | 743269515dccfc94219d83c290e8ad734e176a22 | [] | no_license | JasonBason/Mosaic | f6fffa851155796d3f3b6838aba11b836130567c | 5d8e487efbce2e0e95a58ef9b9c4766b45960c57 | refs/heads/master | 2020-03-22T20:38:07.682386 | 2018-06-01T03:35:12 | 2018-06-01T03:35:12 | 136,076,296 | 0 | 0 | null | 2018-07-10T19:49:57 | 2018-06-04T20:01:38 | R | UTF-8 | R | false | true | 690 | rd | Specmodule.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/interactive_plotting_modules.R
\name{Specmodule}
\alias{Specmodule}
\title{Specmodule}
\usage{
Specmodule(input, output, session, tag, set = list(spec = list(xrange = NULL,
yrange = NULL, maxxrange = NULL, maxyrange = NULL, sel = NULL, mz =... |
3f3d962aebcd359fbf1b50b37d81d8a3057e3024 | 19a851f0a04b8fbced83254a0a0589060e9b2035 | /analyses/snv-callers/scripts/01-calculate_vaf_tmb.R | 4100f330d0d68991d21e90edeb174e9ecbc72fd1 | [] | no_license | gonzolgarcia/OpenPBTA-analysis | da0118d5edfba585786e297d4d312245ab3643f1 | 7a7b40aadff351599f7dbbdeca85d6bebaafe696 | refs/heads/master | 2020-08-07T12:38:00.508549 | 2019-10-07T15:51:30 | 2019-10-07T15:51:30 | 213,451,868 | 0 | 0 | null | 2019-10-07T18:01:55 | 2019-10-07T18:01:55 | null | UTF-8 | R | false | false | 11,090 | r | 01-calculate_vaf_tmb.R | # Run variant caller evaluation for a given MAF file.
#
# C. Savonen for ALSF - CCDL
#
# 2019
#
# Option descriptions
#
# -label : Label to be used for folder and all output. eg. 'strelka2'. Default is 'maf'.
# -output : File path that specifies the folder where the output should go.
# New folder will be crea... |
55f159a2242cce2ec41eedec31b8943c1fa32246 | 0c61299c0bfab751bfb5b5eac3f58ee2eae2e4b0 | /Nitrogen_Algae/old_code/sim_ode.R | 2c9522192ab224b0c689982bfd9e3c35b794806f | [] | no_license | jwerba14/Species-Traits | aa2b383ce0494bc6081dff0be879fc68ed24e9c2 | 242673c2ec6166d4537e8994d00a09477fea3f79 | refs/heads/master | 2022-10-13T10:57:54.711688 | 2020-06-12T01:57:21 | 2020-06-12T01:57:21 | 105,941,598 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,219 | r | sim_ode.R | ## simulation for ode
library(rstan)
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())
sim_mod <- stan_model(file = "sim_mod.stan", model_name = "sim_mod", verbose = T)
sim_out <- sampling(sim_mod, seed = 2, data = list(N = nrow(dat_27),
y =... |
63b90b55b8fb13c127d88f774fba9ee8a47108a6 | a82713f80e7481cc189255e87a5a9425379045ff | /hidrantesmanizales.R | 3ed52805ebac171b0bc51ef4b43cd40f759cdb91 | [] | no_license | dechontaduro/DataScienceExamples | 38714d74a359e8a7bbc1422a35794acf67b447cf | 0c3cbd6f5f28184670a4a67847a55e1b5b31a43d | refs/heads/master | 2021-01-12T11:03:02.230015 | 2016-11-04T01:21:02 | 2016-11-04T01:21:02 | 72,801,972 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,275 | r | hidrantesmanizales.R | getData <- function(variableName, url, filename, ...){
if(!exists(variableName)){
if (!file.exists(filename)) {
download.file(url, filename)
}
read.csv(filename, ...)
}
}
geocodeAdddress <- function(address) {
require(RJSONIO)
url <- "http://maps.google.com/maps/api/geocode/json?address="
... |
6e2e556f4e4496c85a514f961a56626e08fc481f | 8f2e62d1cb1e323639fd30b457331fc9082babc2 | /BootSU2C-TCGA-HeatmapApp.R | 0caf1d6796fdd36784d21e4508965691a05cacc8 | [] | no_license | NateDee/YuLabSU2CHeat | b3513c34a2a509d43ce4fe4205e70caf206e4e55 | 6c313d94fe143a361f10f78deca96bf282279345 | refs/heads/master | 2021-05-01T20:00:51.485785 | 2018-03-23T15:26:40 | 2018-03-23T15:26:40 | 120,956,212 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 667 | r | BootSU2C-TCGA-HeatmapApp.R | #Set working directory to SU2C_script
setwd("R:/Medicine/Hematology-Oncology/Yu_Lab/Nate/scripts_and_tools/YuLab-SU2C-TCGA")
#Create function to test for packages that are needed to run app
installPkges <- function(pkg){
if(!pkg %in% installed.packages()) install.packages(pkg, repos = "https://mirror.las.iastate... |
33929cb3477708d54a2e5736723fd2e7e22e38fb | 5d72e421cdf578655997ff1ad1f06ce59d1240db | /man/hx_timeline.Rd | 1e22843ba0834fc8303e5f9760243e9ddf9350da | [
"MIT"
] | permissive | news-r/hoaxy | 104b41014c0a5109c1ca090eac42c2ceae9af5a0 | dc127acbd78881f72b5c208ec580ff8e37526cb7 | refs/heads/master | 2020-06-04T22:18:52.114066 | 2019-06-25T07:48:28 | 2019-06-25T07:48:28 | 192,213,055 | 8 | 1 | null | null | null | null | UTF-8 | R | false | true | 575 | rd | hx_timeline.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/call.R
\name{hx_timeline}
\alias{hx_timeline}
\title{Timeline}
\usage{
hx_timeline(ids, resolution = c("D", "M", "W", "H"))
}
\arguments{
\item{ids}{A list or vector of article ids to query, see \code{\link{hx_articles}}.}
\item{resolution}{... |
8755e909a8f27f7e539694cf82dc806a20857a7a | 5099820fe4e5a0d72ea9da02d7a65d0056b41ee8 | /R/calc-mean.R | c9803656a7bdc41e558f5e024a9a9d84e5e1ec2f | [] | no_license | milanwiedemann/psychdata | a10a5b9ed58a90472333ad16a06796b729ccec2e | cb1ed4c3e45103d93b4a9307dfa43108b787d128 | refs/heads/master | 2021-07-11T08:46:35.646129 | 2020-06-05T08:36:55 | 2020-06-05T08:36:55 | 143,755,559 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,564 | r | calc-mean.R | #' Calculate mean of variables
#' @description Calculate mean addressing item-level missing data using proration
#' @param data Wide dataframe.
#' @param id_str String of identifier variable.
#' @param var_str String of variable to calculate mean for.
#' @param session_str String of session number.
#' @param n_min Mini... |
57efa1e73ab1a3a827e2e095494dcba1f6350261 | 41395c8fbe6fd5c6a5752599b49cb81dd4c70819 | /man/fit_gamlss1.Rd | 817510dd658c03446eaf6487e8e1c6620457771e | [] | no_license | cran/childsds | 7d022ff8b4551c735060a1d922d7fdb4e7dbdedf | 84799b3f488438f4e451722304c2b5e24f2a8c7b | refs/heads/master | 2022-02-21T05:33:26.726171 | 2022-02-10T15:40:02 | 2022-02-10T15:40:02 | 23,198,825 | 0 | 2 | null | 2021-07-15T02:27:17 | 2014-08-21T18:44:07 | R | UTF-8 | R | false | true | 1,391 | rd | fit_gamlss1.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/createlms.R
\name{fit_gamlss1}
\alias{fit_gamlss1}
\title{fit_gamlss1}
\usage{
fit_gamlss1(
data,
age.min = 0,
age.max = 80,
age.int = 1/12,
keep.models = F,
dist = "BCCGo",
formula = NULL,
sigma.formula = ~1,
nu.formula = ~... |
1417660752a2ed7595bb42463dc2a62996ac101d | bc57437c2c1493388add2435693f2d41ad4ca6d7 | /tests/testthat.R | 3e59fcb5ee70a990877db342405fec278b625282 | [
"MIT"
] | permissive | MilesMcBain/capsule | 374ade3e25f014a1526cbb1722c03b4fe79c1813 | 401d0c98adc329c17d0bb129069c9ec220a26646 | refs/heads/master | 2023-06-25T09:00:42.944246 | 2022-08-04T01:11:26 | 2022-08-04T01:11:26 | 215,474,942 | 136 | 8 | NOASSERTION | 2023-06-09T15:45:36 | 2019-10-16T06:36:59 | R | UTF-8 | R | false | false | 58 | r | testthat.R | library(testthat)
library(capsule)
test_check("capsule")
|
85cafcd6dafa5792e0a7704f938eccf29257a727 | fe0429febc0409adcd5ae19797907c47810779c5 | /Plot3.R | 032195c87414bc1a3d23133f864bf94229ab2db9 | [] | no_license | nikotbg/FourPlots | 124acaa72cb175e0aa543f582d2bb61a2bfe7c1c | dec0b0d86f4cbe2c5d725e16de9f1d563cd27e1b | refs/heads/master | 2021-01-10T06:26:55.785626 | 2015-11-08T20:06:43 | 2015-11-08T20:06:43 | 45,796,408 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,323 | r | Plot3.R | #ensure the file household_power_consumption is downloaded into your working directory
#read the file inot R
powerful<-read.table("household_power_consumption.txt",header=TRUE, sep=";")
#combine Date and Time columns
library(dplyr)
verypowerful<-mutate(powerful,datetime=paste(Date,Time, sep=" "))
#convert datetime colu... |
cfc1e93fb2e9dbffb3fe27aa35eb9cc5847ee9b3 | edd192f33044e894f01091014d481fcb3de64449 | /transcriptomics_scripts/DESeq2.R | 3349df4adc6e1bf11a54e5a353407b3d455d3839 | [] | no_license | karinlag/BioinfTraining | d2379e5f387c0e73cb8e86f6969ab8d9534f3d75 | 94e16a7f1f190b132199fbb798affd656c1687af | refs/heads/master | 2022-03-01T13:32:36.435699 | 2019-10-28T10:31:28 | 2019-10-28T10:31:28 | 111,698,637 | 1 | 2 | null | 2017-11-22T15:06:06 | 2017-11-22T15:06:06 | null | UTF-8 | R | false | false | 848 | r | DESeq2.R | ## To install cummeRbund and DESeq2 (do it once)
# source("https://bioconductor.org/biocLite.R")
# biocLite("DESeq2")
getwd()
setwd('../Desktop/course_data/DESeq2/')
library('DESeq2')
data <- read.delim('../featureCounts/count_gene', skip=1, sep="\t")
dim(data)
head(data)
colnames(data)
count <- data[7:12]
colname... |
5dc782f7223a69c57d1a6be3f6fc324f405ddf69 | 23cad221b4fd1656e27038880f500eed6695fde0 | /man/celdaCGMod.Rd | 83eecb51e4f76ce6fa7d7079f1defa1a8cb12c18 | [
"GPL-2.0-only",
"MIT"
] | permissive | campbio/celda | 91f8c64424fe24a74a1359b6dde371ab8ff2aea1 | 92905bda2833c9beda48c6a9404a86a102cd0553 | refs/heads/master | 2023-02-17T09:41:27.551599 | 2023-02-15T19:01:52 | 2023-02-15T19:01:52 | 158,611,235 | 134 | 32 | MIT | 2023-02-17T01:39:55 | 2018-11-21T22:01:57 | R | UTF-8 | R | false | true | 333 | rd | celdaCGMod.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{celdaCGMod}
\alias{celdaCGMod}
\title{celdaCGmod}
\format{
A celda_CG object
}
\usage{
celdaCGMod
}
\description{
celda_CG model object generated from \code{celdaCGSim} using
old \code{celda_CG} function.
}
\keywo... |
84e4c0882d9f6110232d12c67fbdd33f91f9453d | fd0622e97276bba2c04d3c2fcba902cdfb65e214 | /packages/nimble/inst/classic-bugs/vol1/litters/test1.R | 9a568e418a048e237a9ad82acfcb3cb8574650d3 | [
"GPL-2.0-only",
"BSD-3-Clause",
"CC-BY-4.0",
"GPL-1.0-or-later",
"MPL-2.0",
"GPL-2.0-or-later"
] | permissive | nimble-dev/nimble | 7942cccd73815611e348d4c674a73b2bc113967d | 29f46eb3e7c7091f49b104277502d5c40ce98bf1 | refs/heads/devel | 2023-09-01T06:54:39.252714 | 2023-08-21T00:51:40 | 2023-08-21T00:51:40 | 20,771,527 | 147 | 31 | BSD-3-Clause | 2023-08-12T13:04:54 | 2014-06-12T14:58:42 | C++ | UTF-8 | R | false | false | 272 | r | test1.R | source("../../R/Rcheck.R")
d <- read.jagsdata("litters-data.R")
inits <- read.jagsdata("litters-init.R")
m <- jags.model("litters.bug", d, inits, n.chains=2)
update(m, 5000)
x <- coda.samples(m, c("mu","theta"), n.iter=50000, thin=50)
source("bench-test1.R")
check.fun()
|
9cb2408444473efcb5a0b4fdcf7e2eb987c2b954 | 0997c835d2706cebf0419cdf50ea8899395f7226 | /Arcelor/R/Shiny_code/ui.R | 910caa31f7391d138784f44c3210eee99867b3a4 | [] | no_license | tdekelver-bd/DRBChack | d4a2e54d5fde45cdcc0f5213095bdc7e4eeb7eb0 | 54ef00402513552b1eabcacc006c24c95f5f51ec | refs/heads/master | 2021-03-12T00:15:15.101777 | 2020-03-12T14:12:11 | 2020-03-12T14:12:11 | 246,571,438 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,677 | r | ui.R | #
# This is the user-interface definition of a Shiny web application. You can
# run the application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(shinydashboard)
library(shinydashboardPlus)
setwd('C:/Users/rbelhocin... |
9646c523d647e2f1f1a2ecf8a06009bca4172c1f | 51af5871f74d13198b8fa8e3e679f173c8c6ca14 | /category.R | d26350adb63433686ab8c60fe1198836afe1c341 | [] | no_license | Mira0507/maternity_leave | 0f96b2d31f441cff99e616b49effcd475b3b0623 | a5f14587830fd445d3b16533b6cb7e2abc4212f8 | refs/heads/master | 2022-06-12T20:17:07.641914 | 2020-05-07T19:17:09 | 2020-05-07T19:17:09 | 261,359,648 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,571 | r | category.R | library(tidyverse)
library(ggplot2)
h <- head
s <- summary
g <- glimpse
t <- tail
gen <- read.csv("Gender_StatsData.csv", stringsAsFactors = FALSE)
indicator <- unique(gen$Indicator.Name)
country <- unique(gen$?..Country.Name)
gen1 <- gen %>%
rename(Country = ?..Country.Name)
topics_education <- c(
... |
303d0b906b26c9428345861e6c57d5c10b928ef9 | 14032d4d0a7e0ad6ce1df0fcd72117272e66a0ba | /R/parse_dataset.R | 23f699db733577632d10ad0d4e3d386705494b33 | [] | no_license | 5l1v3r1/dyncli | 67f5e223dfc7759a9d8e86dcc0321155b129a52d | 9fbadd904b58ca0f34b1be01bcdc2308a4f41430 | refs/heads/master | 2023-04-23T18:52:12.810151 | 2019-09-18T15:32:23 | 2019-09-18T15:32:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,716 | r | parse_dataset.R | #' @importFrom fs is_file
#' @importFrom hdf5r H5File h5attr_names
#' @importFrom Matrix sparseMatrix Matrix t
parse_dataset <- function(x, loom_expression_layer = NULL) {
assert_that(
is.character(x),
length(x) == 1,
fs::is_file(x) || fs::is_link(x),
msg = "--dataset should contain a pathname of a .l... |
5505cb1ebc16e377894d24013696975f2b6a054c | 9531c36e90445e884c0834d10f3f741263cc54e8 | /Data exploration/ui.R | c956e7898a06d753a32ca92880536d210148fc2b | [] | no_license | Ghaith701/Exploring-top-song-charts-in-each-decade | 880b0cc0f3ff2f6ffa3ea54a6af217c9b0bd3e6b | 69c5305f64ac1f5bda66dab150f7090c95c1f6fc | refs/heads/main | 2023-02-19T19:09:46.790883 | 2021-01-20T03:46:45 | 2021-01-20T03:46:45 | 331,147,830 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,466 | r | ui.R | # ui.R
library(plotly)
library(shiny)
library(shinythemes)
shinyUI(fluidPage(
theme = shinytheme("cosmo"),
# Application title
titlePanel("Top 50 Songs Each Decade"),
navbarPage(title = "My project",
tabPanel("About",
titlePanel("My app"), sidebarLayout(
... |
2eff7d8b1fa932955db220ece5f1cc5b400c0e05 | c3063b1798acc6ac01e74f4b4dcec11826da0ea5 | /code/random_forest.building_energy.grid2.R | 163743626f94392a7803def9e452c30155a8d098 | [
"LicenseRef-scancode-free-unknown",
"LicenseRef-scancode-public-domain",
"MIT"
] | permissive | DarrenCook/h2o | 1aa14b97c60bcd7c50bf07d7582953f35c8477c3 | c077af549ab539b5f71218155f11e5fe3c25042c | refs/heads/bk | 2021-05-04T06:29:50.348447 | 2017-12-22T09:43:01 | 2017-12-22T09:43:01 | 70,473,775 | 95 | 117 | MIT | 2018-02-02T01:55:35 | 2016-10-10T09:38:13 | R | UTF-8 | R | false | false | 575 | r | random_forest.building_energy.grid2.R | g <- h2o.grid("randomForest",
search_criteria = list(
strategy = "RandomDiscrete",
stopping_metric = "mse",
stopping_tolerance = 0.001,
stopping_rounds = 10,
max_runtime_secs = 120
),
hyper_params = list(
ntrees = c(50, 100, 150, 200, 250),
mtries = c(2, 3, 4, 5),
sample_rate = c(0.5, ... |
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