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b2bb914fa8398bfac1bab4e3425d200e663c90cd | a19663c25470f4b4165fea8313f880f44481e887 | /edu_stats_report2021/Params_reports56/render.R | 125b4f289d5eaaeb65a256e0e79f07fdd74ac22f | [] | no_license | perlatex/tidy_easyuse | e9322e75969c3f5802236afa6159aa355c6a42d2 | 00a90fcd68b6d121b184a507fd457e83e477185d | refs/heads/master | 2022-01-03T09:25:53.661346 | 2021-12-28T10:01:55 | 2021-12-28T10:01:55 | 231,519,749 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,207 | r | render.R | library(tidyverse)
load("../data/myData_df5.Rdata")
df <- df5_start %>%
distinct(school) %>%
mutate(title = row_number()) %>%
mutate_at(vars(title), ~str_pad(., 2, pad = "0") )
if (fs::dir_exists("output_56")) {
fs::dir_delete("output_56")
}
fs::dir_create("output_56")
# "草小子美分校" 只有5年级，没有6年级的数据
df_a <-... |
3c91db970ec3ba6842b88ca3943bfde44ffce568 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/caret/examples/twoClassSim.Rd.R | 40b28916b690d4fbf5204d865c10a4b76b9c1c70 | [] | 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 | 259 | r | twoClassSim.Rd.R | library(caret)
### Name: SLC14_1
### Title: Simulation Functions
### Aliases: SLC14_1 SLC14_2 LPH07_1 LPH07_2 twoClassSim
### Keywords: models
### ** Examples
example <- twoClassSim(100, linearVars = 1)
splom(~example[, 1:6], groups = example$Class)
|
03d4000184182c25d00e87fab384be0abb0f3165 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/rhli/examples/cfmini.Rd.R | b32542fc269f620d15fb532400c37d104ec0ed1d | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 133 | r | cfmini.Rd.R | library(rhli)
### Name: cfmini
### Title: 'cfmini'
### Aliases: cfmini
### ** Examples
status <- Integer(-1L)
cfmini(status)
|
be62c96da7f3cded2016e6514ebbb6fa24570ba3 | 3ae1cafa31be5daef34d0793ec55811ae78c3785 | /plot4.R | 141b25e1b7f5f6766a3028c18b3cfb08ddc4ec71 | [] | no_license | Leandres/ExData_Plotting1 | 41bfff0d8303f257faf8de9de4662db4b0f12d94 | b398b1d1cd40f1ea446db658c5ee19f1bc86cf55 | refs/heads/master | 2021-01-17T11:20:51.684986 | 2015-01-11T20:49:40 | 2015-01-11T20:49:40 | 29,100,850 | 0 | 0 | null | 2015-01-11T18:14:29 | 2015-01-11T18:14:29 | null | UTF-8 | R | false | false | 1,043 | r | plot4.R | setwd("C:/.")
# For code on loading data and creating new data set see plot1.R
# Create plot 4
png("plot4.png", width=480, height=480)
par(mfrow = c(2,2))
# Plot 4.1
plot(NewHHpowcon$Time, NewHHpowcon$Global_active_power, type="l",
xlab="", ylab="Global Active Power")
# Plot 4.2
plot(NewHHpowcon... |
8d5a8f2822051265877fcc0f15dc1bc2fd2925ef | 9b5dedcbde7f4d0a5ec40439198e60551e68d6dc | /code.R | fe4297dbbed14470f82ee16e72e6fc70e9169f5a | [] | no_license | conjell/gitTEM | 4e5e995c47a4cc085174ac868efcfa2412490c65 | aba48fe345629ad9de199a0bef9822bd98ffbb0e | refs/heads/master | 2020-08-29T04:39:55.948382 | 2019-10-28T23:42:59 | 2019-10-28T23:42:59 | 217,930,244 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 26,565 | r | code.R | setwd("~/Desktop/")
#install.packages('brnn')
library('brnn')
raw_data <- read.csv("samplew.csv", header=T)
raw_data <- read.csv("samplec.csv", header=T)
raw_data <- read.csv("samplewx.csv", header=T)
raw_data <- read.csv("samplecx.csv", header=T)
###temperature prediction for all sensors in future 72 hours
##without h... |
2f25ab8b60a6ad8c556dbc69db64926a0c02c1e9 | fe17217bf85ed660a1fa3173f6078133c5bc49e0 | /man/p.s.strat.Rd | 24116b310c884dbe66463382651d374081dc8ecc | [] | no_license | rgcstats/ODS | 5a4ba2107328175174b4874e10d8e47733c33397 | 0290071546fdd8dff1c8e9e6d8bc5920d1c04491 | refs/heads/master | 2020-12-10T10:30:36.232624 | 2020-01-13T10:20:29 | 2020-01-13T10:20:29 | 77,803,517 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 960 | rd | p.s.strat.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/p_s_strat.R
\name{p.s.strat}
\alias{p.s.strat}
\title{Calculates p(s) for the design stratified by y}
\usage{
p.s.strat(ys, yr, log = F, specs)
}
\arguments{
\item{ys}{vector of the sample values of the dependent variable}
\item{yr}{vector o... |
6c27610b1e4e53b855597c09a18a151b385e99ea | f9d80df2d3108c24255c4c2ba0fbce25a3acfad5 | /code/fisher_exact_test.R | f73b77a500f88253cbe2eae49cd1e033917c1ea7 | [] | no_license | certifiedwaif/phd | 12ed3c3113a5f662cf14538776f02acecd11338b | 0f6f7b48917961f6821ff7aaf27357c1be1c6d5a | refs/heads/master | 2021-10-24T22:07:44.383587 | 2019-03-29T03:12:55 | 2019-03-29T03:12:55 | 11,830,694 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 354 | r | fisher_exact_test.R | # fisher_exact_test.R
pvalue <- function(n) {
p_non_effect <- 0.05
p_effect <- 0.1
p_group <- 0.5
mat <- matrix(c(n * p_non_effect * p_group, n * (1 - p_non_effect) * (1 - p_group),
n * p_effect * p_group, n * (1 - p_effect) * p_group), ncol = 2)
fisher.test(mat)$p.value
}
for (n in 1:1000... |
78ebb8a4dc9c826cda1933584d6ae39f82421666 | ea805d721a3cdc2db7a75e38a9b212e4e1885778 | /ribiosUtils/R/factor.R | 045ac0fc72bb884724dc533de6bf0e27fca2a3f1 | [] | no_license | grst/ribios | 28c02c1f89180f79f71f21a00ba8ad8c22be3251 | 430056c85f3365e1bcb5e565153a68489c1dc7b3 | refs/heads/master | 2023-06-01T04:48:20.792749 | 2017-04-10T14:28:23 | 2017-04-10T14:28:23 | 68,606,477 | 0 | 0 | null | 2016-09-19T13:04:00 | 2016-09-19T13:04:00 | null | UTF-8 | R | false | false | 2,758 | r | factor.R | refactor <- function(factor, levels) {
if(!is.factor(factor))
stop("'factor' must be factor\n")
if(!nlevels(factor)==length(levels))
stop("Level number of factor' must be of the same length of 'levels'\n")
if(is.null(names(levels)))
stop("'levels' must be a named vector: names are (ordered) old levels... |
7f31a98f723c597a69dd692e035a0d476c733b4d | 85d27a2aa01ea5cd3259547605474eeee3e32d2f | /man/b2_authorize_account.Rd | a8e6c9fefbc900980cbe2aa5654e00677b8c7db9 | [] | no_license | mvanhala/B2R | cff1228bc231c837e7bbf199a4ae82cd6b963b2d | f7d99652d00cfeef971eeb7c2da73251df556f2f | refs/heads/master | 2020-12-13T04:28:34.337097 | 2017-07-11T06:56:29 | 2017-07-11T06:56:29 | 95,411,737 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 605 | rd | b2_authorize_account.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/b2_authorize_account.R
\name{b2_authorize_account}
\alias{b2_authorize_account}
\title{Authorize Backblaze B2}
\usage{
b2_authorize_account(account_id = Sys.getenv("B2_ACCOUNT_ID"),
application_key = Sys.getenv("B2_APPLICATION_KEY"))
}
\arg... |
3794e2ddc80ef4591a84bd408ab94d6b2f27fa9b | c203822ecd4c4c41a82bf2a768e74dd9b4b1af98 | /run_analysis.R | 6c4baba3922c2e52de838fa3625d63fee5db6d6c | [] | no_license | amagoo/Gettingcleaningdata_courseproject | e7dd3f0e7a5482f64507d74ed71ede7417025520 | c12050e1ce79b31fd7e3d9ed0a147b36de1183e6 | refs/heads/master | 2021-01-10T19:59:13.834011 | 2015-05-20T16:41:34 | 2015-05-20T16:41:34 | 35,958,756 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,769 | r | run_analysis.R |
setwd("C:/Users/andrew/Documents/Gettingcleaningdata_coursera/project_data/UCI HAR Dataset")
###submit code after here:
X_train<-read.table("X_train.txt")
y_train<-read.table("y_train.txt")
X_test<-read.table("X_test.txt")
y_test<-read.table("y_test.txt")
subject_test<-read.table("subject_test.txt")
subject_train<-re... |
220bd9a5269e7896024d3cfff6a48fa0f3ae5e1c | ee816a81ef6fbb360e679bb7b3930010c165610a | /man/reset_method.Rd | d0c0bacb52a226ed5979684d007aa66e40992a76 | [] | no_license | stephens999/dscr | 266c215e969bd1bdf27ee51b9d2e30d49baa87bc | 9bcecf5e0848b384a9e58331c890316ea9335df2 | refs/heads/master | 2021-01-19T02:10:48.104730 | 2018-06-29T19:26:07 | 2018-06-29T19:26:07 | 26,604,817 | 13 | 14 | null | 2018-06-29T17:12:24 | 2014-11-13T19:46:43 | R | UTF-8 | R | false | true | 619 | rd | reset_method.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dsc.R
\name{reset_method}
\alias{reset_method}
\title{Removes all output and scoress for a method.}
\usage{
reset_method(dsc, methodname, force = FALSE)
}
\arguments{
\item{dsc}{A dsc object.}
\item{methodname}{String indicating name of meth... |
914860bfb7b5d1ecfdfacda52a95a4e5d33e2f99 | 2bf497fe5f5d1e5baaba86d7934885106c6c6554 | /Scripts/CategoryEnrichment.R | 612647bac6e0e808a4b23246dee85fe3ef2dbdbf | [] | no_license | ericfournier2/EMAP | 25b33cc001f6f5cb96e5009c879729d295ee7251 | 3bcfe1adbf1c7f618332856016362c89d4b72210 | refs/heads/master | 2022-09-30T20:14:33.518223 | 2020-06-04T18:42:35 | 2020-06-04T18:42:35 | 269,433,749 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 39,397 | r | CategoryEnrichment.R | library(ggplot2)
library(reshape2)
library(png)
library(proto)
library(gridExtra)
# Add a "horizontal bar" type to ggplots, based on geom_bar. This is needed for the
# combined graph. Briefly, you cannot combined facets with space="free" and scale="free"
# with the coord_flip() object. However, geom_bar only has verti... |
cc76ec88c746847ae5c9b20474a29edc9cc3d0ac | a5aad7fcca1e37d64f2b8af9e6eec1c66e5ab969 | /man/totData.Rd | 874d237041f97e2b4f567442e82ce9fd93b44300 | [] | no_license | slepape/EasyqpcR | cb4780e00b3cc65fcd12ddf9af8a6d2c52d8ff51 | ebee75cef19b1ae8c7d8f818c413a8c53800a58c | refs/heads/master | 2020-03-10T03:05:04.987845 | 2018-06-01T11:22:34 | 2018-06-01T11:22:34 | 129,154,329 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,326 | rd | totData.Rd | \name{totData}
\alias{totData}
\title{
Aggregation of qPCR biological replicates and data transformation
}
\description{
This function aggregates qPCR biological replicates and calculates the main
parameters : mean (arithmetic or geometric), the standard deviation and the
standard error from your biological replicat... |
9902eb5124d8508bcf40097963ac645955ea5e60 | 25e8d08c92a5fcd67f7f36a11002ec90b9bb75b3 | /R/datasets.R | f47646d8aa4b15cc3820d06177ef3ec3fd0f8128 | [] | no_license | FBartos/RoBMA | 335f5dda8b3ebf263addda409ad01f8a66a70b99 | 8de6009b50670276efcaf65f4a9dd69b5f432fa3 | refs/heads/master | 2023-07-23T12:49:05.073261 | 2023-07-19T21:12:13 | 2023-07-19T21:12:13 | 259,885,114 | 7 | 3 | null | 2023-07-19T21:12:15 | 2020-04-29T09:43:18 | R | UTF-8 | R | false | false | 3,525 | r | datasets.R | #' @title 27 experimental studies from
#' \insertCite{anderson2010violent;textual}{RoBMA} that meet the best practice criteria
#'
#' @description The data set contains correlation coefficients, sample
#' sizes, and labels for 27 experimental studies focusing on the effect of
#' violent video games on aggressive behavio... |
803ddb3e79b93e610cb530c3bf2819a117512d0e | ce8d13de6aa47617809c5fc4d83ccd961b310104 | /man/xgb.max_sensitivity.Rd | d86a2bb3d5e462cb6f2d6f20a9271677d9cec97a | [] | no_license | BruceZhaoR/Laurae | 2c701c1ac4812406f09b50e1d80dd33a3ff35327 | 460ae3ad637f53fbde6d87b7b9b04ac05719a169 | refs/heads/master | 2021-01-22T12:24:50.084103 | 2017-03-24T19:35:47 | 2017-03-24T19:35:47 | 92,722,642 | 0 | 1 | null | 2017-05-29T08:51:26 | 2017-05-29T08:51:26 | null | UTF-8 | R | false | true | 715 | rd | xgb.max_sensitivity.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xgb.max_sensitivity.R
\name{xgb.max_sensitivity}
\alias{xgb.max_sensitivity}
\title{xgboost evaluation metric for maximum Sensitivity (True Positive Rate)}
\usage{
xgb.max_sensitivity(pred, dtrain)
}
\arguments{
\item{pred}{Type: numeric. The... |
71000425f57ea3c2e2c391a0ba733a14f0440516 | 04bd0b46324eb01410c9323d55fec99772127ea4 | /plot1.R | 24c43f0a5aaf1460715b903d225cb456e10ccfc7 | [] | no_license | cybernurl/ExData_Plotting1 | 55770fe7e2083f659ecf3377b67a4dbc2edc943b | 3c1c0c110029bf85c7049c53d8c3d4d6f05f6c49 | refs/heads/master | 2021-01-24T14:32:51.947702 | 2015-04-11T01:50:45 | 2015-04-11T01:50:45 | 33,520,194 | 0 | 0 | null | 2015-04-07T03:37:03 | 2015-04-07T03:37:02 | null | UTF-8 | R | false | false | 382 | r | plot1.R | ##Plot1
## Extracting data
fulldata<-read.csv("household_power_consumption.txt", header=TRUE, sep=";",na.strings="?")
data <- fulldata[fulldata$Date %in% c("1/2/2007","2/2/2007") ,]
##plot graph
png("plot1.png", width=480, height=480)
hist(data$Global_active_power, main="Global Active Power",
xlab="Global Acti... |
d3957f07f243b3b41d14e69cbacd8a63f4d3974a | 9b9344923dad577a25eab4354d03b8a406137b32 | /scripts/structural/structural_table.R | b0985da73938920e2225ee3beb429d13559f9272 | [] | no_license | mnvandyke/water_competition | 5572d3bbace8f6c4bc474202ce4d06bbc61be93b | 82ce2c965609f25ea22dce0c7abd6005316861d4 | refs/heads/main | 2023-04-14T15:38:14.983700 | 2022-09-15T19:00:24 | 2022-09-15T19:00:24 | 519,023,916 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,996 | r | structural_table.R | ### Analysis for "Small rainfall changes drive substantial changes in plant coexistence"
### Mary Van Dyke, mnvandyke@ucla.edu
### Last edit: 25 July 2022
### this script makes the structural analysis tables (Extended data tables 5,6,7)
library(gt)
library(tidyverse)
library(dplyr)
struc <- read.csv("./output/struc... |
d3a541aa04c948ec2f05266e656d53c74b25a127 | c2333607c201f51f0af4f57a780c4e200c47c523 | /Ejemplos Treemap.R | 0baca07e0366774c7284fb0ddd09d82d25628be9 | [] | no_license | ramirocadavid/reportes_contenidos | 6ba4f587923f63a6a6e9745502140ccb050ce089 | bb28463608eceff35df035ce5a89413144669265 | refs/heads/master | 2022-09-23T11:07:08.314222 | 2022-04-22T14:23:24 | 2022-04-22T14:23:24 | 94,578,086 | 0 | 1 | null | 2017-08-08T22:00:06 | 2017-06-16T20:16:59 | R | UTF-8 | R | false | false | 680 | r | Ejemplos Treemap.R | library(treemap)
# itreemap: Interactive user interface for treemap
data(business)
itreemap(business)
# treecolors: Interactive tool to experiment with Tree Colors
treecolors()
# treegraph Create a tree graph
treegraph(business, index=c("NACE1", "NACE2", "NACE3", "NACE4"), show.labels=FALSE)
treegraph(business[busi... |
c1e0b553e7e861911d060aaa2823991cf51bd671 | 0ca5af9a178b368386e495f62bc3616e8debc27c | /credentials.r | e797a684a245354a3b641fa33a67b3fe4a987d39 | [] | no_license | mlshipman/dataProdProj | ae4418e7a03ef266467ee94e4e0cf314e2f3b054 | 3e105d36d1def548135be273d795b1d3f4c6843a | refs/heads/master | 2020-12-24T11:25:26.889144 | 2014-08-24T07:21:26 | 2014-08-24T07:21:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 186 | r | credentials.r | consumer_key<-""
consumer_secret<-""
access_token<-""
access_secret<-""
save(list=c("consumer_key", "consumer_secret", "access_token", "access_secret"), file="credentials", ascii=TRUE)
|
df4e780c6d77369d585314de356e64e4b13e338f | 839de41c2aa412e9f9c8b8b1fcb9313caa72efe2 | /man/date2day.Rd | 2705f6f5179f2ff66344df9b4c900231d68ae73b | [] | no_license | Zhoushu/ETAS-model-for-location-transformed- | b98a93158edb96fa6bf595d4931ab642fe590002 | 88347245c210dc2bdc52377349fbc96ff7eb6a39 | refs/heads/master | 2021-01-23T07:03:17.328820 | 2014-10-14T13:30:07 | 2014-10-14T13:30:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,402 | rd | date2day.Rd | \name{date2day}
\alias{date2day}
\title{
Convert date-time data to numeric data in days
}
\description{
A function to convert date-time data to days with respect to a date-time origin.
}
\usage{
date2day(dates, start, tz = "", ...)
}
\arguments{
\item{dates}{A date-time or date object. Typically, it is a character ... |
c235ac6d596164f8085b41558caf607f5771d427 | 411eba8cfd9fc5f5753e92be1f72672fc286fbe5 | /02_gmail.automation.R | 13b012ed667be7d02c2a2bdc9d16442cdbd0f9e5 | [] | no_license | Remxy/Google-Trend- | 2a5d76f2acfe962ee4adaaf2b5fc7c3752211222 | 086fa9c916d6e8f22f2521ce2ce96000207f7152 | refs/heads/master | 2021-02-14T10:36:08.338504 | 2020-03-04T03:23:32 | 2020-03-04T03:23:32 | 244,797,295 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,411 | r | 02_gmail.automation.R | ##-GOOGLE TRENDS AUTOMATION--
#CRON JOB
#1.0 LIBRARIES
#GMAIL API
install.packages("gmailr")
library(gmailr)
#Report Automation
install.packages("rmarkdown")
library(rmarkdown)
#Core
library(tidyverse)
library(lubridate)
#File System
library(fs)
# 2.0 KEY PARAMETERS
# 2.1 Report Paramet... |
44f6919ce54f8f637bc5f0f1b5ed20375a524af2 | b81fb3d7e665ab4795491b4825f38ee79cee9f06 | /R/plot_factor_returns_cum.R | 4efcaf516a9ffcfb84e6d0e5a14983bc6e8d4594 | [] | no_license | olaoritsland/FaktorModell | 8d03a2b55c94c4b83e6e6f4cb42fe0f35a80706a | 06f84a6764ac1b3898797a8a0a523d604a0afedb | refs/heads/master | 2020-09-30T05:17:32.985320 | 2019-12-21T21:52:33 | 2019-12-21T21:52:33 | 227,213,746 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 208 | r | plot_factor_returns_cum.R | plot_factor_returns_cum <- function(.data) {
.data %>%
mutate_if(is.numeric, cumsum) %>%
pivot_longer(-date) %>%
ggplot(aes(x = date, y = value, color = name)) +
geom_line()
}
|
416d0028ac0c90b1b5d623eb4ca387a9f8fde538 | f09ad286fcd020a8219d0803ddbef7e5817ee2a2 | /PUCR-Assignment-9/4.R | 992c45eaf3173e339d1bd7c733e2549385952f64 | [] | no_license | isabellafischer/Brasil_2019 | 17874ebfc4dbf3a77c98b1defb4575e1a1a3ed96 | 544b71a52a976b2f743977b754029a891e6525ee | refs/heads/master | 2020-07-07T04:17:00.787358 | 2019-10-31T16:34:59 | 2019-10-31T16:34:59 | 203,246,322 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 576 | r | 4.R | # Isabella Fischer
library(tidyverse)
set.seed(3)
coord = as_tibble(read.csv("http://vincentarelbundock.github.io/Rdatasets/csv/boot/brambles.csv"))
coordinates = as.matrix(select(coord, x, y))
coordinates = kmeans(coordinates, centers = 3, algorithm = "Forgy")
cluster2 = fitted(coordinates, method = "classes... |
979f295cd6bb753623ee344de4e6698f7ea80e6f | 3ab4931dd17eef3f0ba3153e86e06a00e147809a | /R/hess.lo.R | 1d53e006bdc7f6bdbd328a0d68fd648e1382bec9 | [] | no_license | cran/gim | a5a8ffddbb5b59ed1bb152ec24845227bf9ff390 | 2182d9a6cee5dd3455f36ad743018ec5f840fbc5 | refs/heads/master | 2022-11-11T15:16:17.130648 | 2020-06-12T08:10:24 | 2020-06-12T08:10:24 | 125,206,702 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 260 | r | hess.lo.R |
hess.lo <- function(para, map, data, ref, inv.V, bet0, outcome){
h <- numDeriv::jacobian(score.lo, para, map = map, data = data, ref = ref, inv.V = inv.V, bet0 = bet0, outcome = outcome)
colnames(h) <- names(para)
rownames(h) <- names(para)
h
}
|
cd7894be16ccbddc3e8c26c1343f60fde000d928 | fead3b0586a4c258cb2c4964cf4a3ad7691cd8b7 | /inst/unitTests/test_SimpleCompoundDb.R | 732ea281097f609680a768769c7d4d474da0fb55 | [
"MIT"
] | permissive | jorainer/xcmsExtensions | 518ba5445a44a8989fb0c6129ff1c1468b55da0a | 6e7cb0d04e6547d6295eb1b54d324b82d141c641 | refs/heads/master | 2021-05-31T07:04:56.889256 | 2016-05-03T06:30:58 | 2016-05-03T06:30:58 | 54,638,373 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,098 | r | test_SimpleCompoundDb.R | ####============================================================
## Testing some of the (basic) functionality of the SimpleCompoundDb.
##
####------------------------------------------------------------
detach("package:xcmsExtensions", unload=TRUE)
library(xcmsExtensions)
test_SimpleCompoundDb <- function(){
## J... |
528ef8b10f56c15f4ea57d56847a2d5209a5fd20 | 54c36870b35b7504fdfb1d4c7f9fb48258485fdb | /R/SimulateData.R | 4699d33736c863ad8e1d714e2785174ceb8299a3 | [] | no_license | jackiemauro/OptSortCausal | 0e48bd1bdcc4a87bb334621d978d3266bb63084c | 134c538a7a767f255c7fec6b7c7fc592504b6f93 | refs/heads/master | 2020-03-16T17:32:25.952535 | 2019-02-21T01:51:39 | 2019-02-21T01:51:39 | 132,836,797 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,203 | r | SimulateData.R | #' Simulate a dataset
#'
#' @param N number of observations
#' @param k optional parameter setting degree to which prison/x is deterministic
simsort <- function(N,k=.75){
X = runif(N, 0, 3)
A = sample(c(1:3), size = N, replace = T)
mu = k - (1-k)*as.numeric( (A==1 & X < 1) | (A==2 & X >=1 & X < 2) | (A==3 & X >... |
25cd9a84181db9b2065606902b0e0dc45e865792 | b3a5c21adf890f0b66790f23332f0082e7f1b40a | /man/format_inline.Rd | 673f82d1dccd3940dcdc48b87b98bb9b081abb8b | [
"MIT",
"Apache-2.0",
"LicenseRef-scancode-public-domain"
] | permissive | r-lib/cli | 96886f849fe69f8435f2d22fccf5d00dee7a5ce4 | c36066ca6a208edbeb37ab13467a4dc6f5b5bbe2 | refs/heads/main | 2023-08-29T14:19:41.629395 | 2023-08-18T13:18:33 | 2023-08-18T13:18:33 | 89,723,016 | 560 | 69 | NOASSERTION | 2023-09-13T11:46:10 | 2017-04-28T16:10:28 | R | UTF-8 | R | false | true | 1,780 | rd | format_inline.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cli.R
\name{format_inline}
\alias{format_inline}
\title{Format and returns a line of text}
\usage{
format_inline(
...,
.envir = parent.frame(),
collapse = TRUE,
keep_whitespace = TRUE
)
}
\arguments{
\item{...}{Passed to \code{\link[=... |
ca15f4f823c9f6bfcac1765eda21b248160638c8 | 430e757a1e6dae14ddd80ec37268adc410ba5793 | /man/fp_save_inspection_plot.Rd | 2f466bce9b78b069520265f11d2c6d6f32ef2fb2 | [] | no_license | alb202/rfret | bf6496460320886f445f40f4c18e27e77a399ac8 | 9b444e238cd89f829e278e6d9505c11d7e4d450c | refs/heads/master | 2021-01-21T18:53:37.742723 | 2017-11-30T22:05:20 | 2017-11-30T22:05:20 | 92,093,777 | 0 | 0 | null | 2017-05-22T19:54:13 | 2017-05-22T19:54:13 | null | UTF-8 | R | false | true | 892 | rd | fp_save_inspection_plot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fp_inspect_raw_data.R
\name{fp_save_inspection_plot}
\alias{fp_save_inspection_plot}
\title{Save raw data plots from a single FP binding dataset}
\usage{
fp_save_inspection_plot(input_plot, dataset_name, output_directory, plot_format)
}
\argu... |
df8e696158f2bc500f60c01a4fa1181eeac67061 | fe1887fbc9bc4ed192c24109ad05f1575935dc1c | /Chapter_4/tables_4.5_B3_cumulative_gas_emissions_lab.R | 08e472e9a5ca68d19bc996e0be47304801ea3c9d | [] | no_license | marenwestermann/PhD-thesis | 6efb3cb9b68b56d380920dc27daa0de77ddadefc | 3942cc2099eb5edaccbef08f8866dc33da28bf9a | refs/heads/master | 2020-03-20T20:43:06.078371 | 2018-06-18T07:52:08 | 2018-06-18T07:52:08 | 137,702,409 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 826 | r | tables_4.5_B3_cumulative_gas_emissions_lab.R | gases_total <- read.csv("C:/Users/Maren/Dropbox/UQ PhD/PhD work/experiments/(4) GHG monitoring experiment Toowoomba/gases_total.csv")
##N2O
anova_N2O_kg <- aov(gases_total$N2O_N_kg ~ gases_total$treatment)
summary(anova_N2O_kg)
pairwise.t.test(gases_total$N2O_N_kg, gases_total$treatment, p.adjust = "none")
describeBy(... |
b096741275a43291dfa56d08b70eeccfc73dfa57 | 629e5242206d8e9f62fb7b1ca6a158789dc9f03f | /data/make_plots.R | 28704428c3dc331f5bcb1720f974ed319d6ac2e9 | [] | no_license | davidwhogg/CensoredData | e4c63805a3bef615b0ba25aac9522addb4d1e14d | 2fcdfbdba74a5b901eaff0d40902280794380400 | refs/heads/master | 2016-09-05T10:25:35.249568 | 2013-12-06T23:52:37 | 2013-12-06T23:52:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,494 | r | make_plots.R |
path = '/Users/jwrichar/Documents/CDI/CensoredData/'
miras = read.table(paste(path,"data/new_periods.dat",sep=""))
sum( abs((miras[,2] * 2 - miras[,4]) / miras[,4]) < 0.05) / dim(miras)[1]
# 0.1264775 of Miras are 'double' the LS period
sum( abs((miras[,2] * 3 - miras[,4]) / miras[,4]) < 0.05) / dim(miras)[1]
# 0.0... |
665c4377c325ae99521babf3925df0b4d85a68da | 016dbf82abbca9deda0d2f9a3504f526cfde9050 | /R/mlregressionrandomforest.R | eda6e62620e9693f23b618806d2c9457b3284046 | [] | no_license | JorisGoosen/JASP-Machine-Learning | d96ad7df3fe529807582d27b81b850df1955b6bf | b9b24d2f158fe8d83f78f161c183ea02db90e5f5 | refs/heads/master | 2020-05-26T23:59:26.924187 | 2019-07-23T10:07:09 | 2019-07-23T10:07:09 | 188,417,261 | 0 | 0 | null | 2019-05-24T12:21:33 | 2019-05-24T12:21:32 | null | UTF-8 | R | false | false | 12,121 | r | mlregressionrandomforest.R | #
# Copyright (C) 2019 University of Amsterdam
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# This program is dis... |
5ab991de920f104b497cb10e90c13dc0ce10b68c | 73d4b03a270781f7db515c129fb5d01244385541 | /functions.R | 7bc428445114e19a52cf86fe7a9d85b8b5661d9f | [] | no_license | fentontaylor/DataScienceCapstone | 480bdc31f8dde6721069b8fa2bfc3439f5bf0160 | 7caaedb52c5c35b015fa6f98bc602a4f11c63067 | refs/heads/master | 2021-01-23T02:10:12.581668 | 2019-11-25T20:49:03 | 2019-11-25T20:49:03 | 85,970,058 | 1 | 4 | null | null | null | null | UTF-8 | R | false | false | 16,792 | r | functions.R | downloadTextDataset <- function(){
fileURL <- "https://d396qusza40orc.cloudfront.net/dsscapstone/dataset/Coursera-SwiftKey.zip"
if(!file.exists(basename(fileURL))){
download.file(fileURL, basename(fileURL))
unzip(basename(fileURL))
}
}
subsetTextLines <- function(x, percent, destfile=NULL, ... |
5995cbd94e3817403a70a702c854696b840af285 | babb638e58b5b5937f0e85751e15c63d23e1bcd9 | /R/LambsWeight.R | dc8c4a0987c8fa5a88a911e0ee80a0ef06b05862 | [
"MIT"
] | permissive | EfratVil/MultiNav | dd982b4a8805707cbb25fc3bd3c1c43b5ea55a5a | 53f7729f0d62c6a83ace0772da7e3c01a115d7e4 | refs/heads/master | 2021-06-04T10:54:07.111510 | 2020-04-11T15:23:48 | 2020-04-11T15:23:48 | 95,772,643 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 544 | r | LambsWeight.R | #' LambsWeight
#'
#' Daily weight estimates of individual lambs in a small herd.
#' The dataset contains data of 80 daily weight estimates for 39 lambs.
#'
#' @source Volcani center, ARO PLF lab. Data was collected from a novel system which contains electronic scales and drinking behavior sensor, designed for automati... |
063d790691c29da6c466864e4dca9909ae8b3958 | a27b79fc527614f1ae9ab192bec123f7ad55ff36 | /R/utils-io.R | b40d977c23c1eb187612656f52cb767eb9a3e93a | [
"MIT"
] | permissive | r-lib/pkgdown | 59528c00deab7466f678c48ed6e26227eecf1e6c | c9206802f2888992de92aa41f517ba7812f05331 | refs/heads/main | 2023-08-29T05:25:38.049588 | 2023-07-19T14:26:10 | 2023-07-19T14:26:10 | 3,723,845 | 443 | 330 | NOASSERTION | 2023-09-06T09:08:11 | 2012-03-15T00:36:24 | R | UTF-8 | R | false | false | 888 | r | utils-io.R | # Reading -----------------------------------------------------------------
read_file <- function(path) {
lines <- read_lines(path)
paste0(lines, "\n", collapse = "")
}
# Inspired by roxygen2 utils-io.R (https://github.com/klutometis/roxygen/) -----------
readLines <- function(...) stop("Use read_lines!")
writeL... |
6ebf6b5c71181ad96c2851b4bb9a8995e720b3f3 | 3dce7cc8a4a3d729d4794211b545b14baa325419 | /R/nd_centrality.R | 6edcaa19be1a3ea5998bfb3b850d30cdc045471c | [] | no_license | cran/NetworkDistance | 674e58e0b9001ee37a651f2ade7346af86c5b481 | 76ac6589bc954845dc7ab3bbf2f1a925e0dcc70d | refs/heads/master | 2023-07-12T02:38:46.365762 | 2021-08-21T14:00:08 | 2021-08-21T14:00:08 | 113,559,722 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,806 | r | nd_centrality.R | #' Centrality Distance
#'
#' Centrality is a core concept in studying the topological structure of
#' complex networks, which can be either defined for each node or edge.
#' \code{nd.centrality} offers 3 distance measures on node-defined centralities.
#' See this \href{https://en.wikipedia.org/wiki/Centrality}{Wikipedi... |
bc921399358eb801e31cf5a0a9bd8b06f33574c8 | ab52fc262fdbfc834469e001a654f7aba23d4cb0 | /employment_rates/NV/compile_NV.R | 19b841735c9975a5b4855d6b5544f30991e2bbb0 | [] | no_license | andyzwang/coronavirus-unemployment | 89f8eb9f91b033c0e3bb97b5cea0126a343056db | be5f8f2583dbe61f79ff6032f4e8beb9bf3f210c | refs/heads/master | 2022-11-19T22:33:12.782097 | 2020-07-22T21:37:02 | 2020-07-22T21:37:02 | 271,094,644 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 938 | r | compile_NV.R | # imports
library(tidyverse)
library(janitor)
library(lubridate)
library(maps)
library(stringr)
# import
raw <- read_csv("NV_data.csv")
output <- raw %>%
clean_names("snake") %>%
mutate(
state_fips = "32",
state_short = "NV",
state = "Nevada",
area_type = case_when(
area == "Montana" ~ "st... |
e3efba5f396e87ab7b42ca238dbdbb0954c86728 | 8fa4a8b0292469cde0f5308e1c774ea8c10d4341 | /Codes/A0.R | f048cc56077c5ce04a71cb561279a619c2d87c8b | [] | no_license | Minam7/DA_Project | 255bdb04d8d48d3b8a96a60d8df8b4783d4c432b | 22a16dd9cc6881522a5a2a95246c64ef52d07bbf | refs/heads/master | 2020-07-31T19:06:05.449683 | 2020-07-07T19:30:45 | 2020-07-07T19:30:45 | 210,721,040 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 588 | r | A0.R | library(dplyr)
library(readr)
library(stringr)
casn <- read_csv("Downloads/R/DA_Project/Data/asn_c.csv") %>% as.data.frame(stringsAsFactors = F) %>%
mutate(Total_occupants = ifelse(Total_occupants == 0 & Total_fatalities != 0, Total_fatalities, Total_occupants),
Total_survivors = abs(Total_occupants - Tota... |
19f4c0097f24a72d036e5da4a0a8662b8dced1ce | 0a906cf8b1b7da2aea87de958e3662870df49727 | /gjam/inst/testfiles/tnormRcpp/libFuzzer_tnormRcpp/tnormRcpp_valgrind_files/1610046809-test.R | d7dcf27a7f63cd0b9b1ce1753d16185f035020f9 | [] | 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 | 193 | r | 1610046809-test.R | testlist <- list(hi = -3.1638862116397e+134, lo = -3.16388621163964e+134, mu = -3.16388619810127e+134, sig = 9.00092879516474e-316)
result <- do.call(gjam:::tnormRcpp,testlist)
str(result) |
4f89adac8bd7d8e952f5bdbdc05bfc850815a331 | 7b0529c5616c586602f1c060a563b1d3503593b7 | /cachematrix.R | 55b2791abb5a30e1c4da55a55293131824e2cd17 | [] | no_license | ckghosted/ProgrammingAssignment2 | e13916defcd89b49ab1c14af461ee9e8e69388f8 | 0494f23e4d1a06aca3fea68399b6d7f25b975188 | refs/heads/master | 2021-01-14T11:19:51.438105 | 2015-05-24T09:35:58 | 2015-05-24T09:35:58 | 36,113,223 | 0 | 0 | null | 2015-05-23T08:00:46 | 2015-05-23T08:00:45 | null | UTF-8 | R | false | false | 1,805 | r | cachematrix.R | ## The following two functions makeCacheMatrix() and cacheSolve() can be
## used to cache the inverse of a matrix. As long as the concerned matrix
## is not changed, its inverse is computed only once and can be retrived
## for later usage.
## The makeCacheMatrix() function takes a matrix as its argument, returns
## a ... |
bf3360fe925e409dceff6897476e812a1676b60b | d936b0cc6a234e67d2022ee2565874402f5ae09b | /sandbox/create_geojson.R | e5effa6777ccff6ddab85744e4a232ba429bd1f5 | [
"BSD-2-Clause",
"MIT"
] | permissive | crazycapivara/openlayers | b70de41605e5c60132fe879d41b95189f1824cb4 | 5d8af098d4b0e44c07e9a9da3db9325ee7995142 | refs/heads/master | 2021-01-22T12:36:24.506413 | 2019-05-20T06:52:27 | 2019-05-20T06:52:27 | 82,209,616 | 18 | 2 | NOASSERTION | 2019-07-06T13:35:49 | 2017-02-16T17:56:20 | R | UTF-8 | R | false | false | 2,262 | r | create_geojson.R | seattle_geojson = list(
type = "Feature",
geometry = list(
type = "MultiPolygon",
coordinates = list(list(list(
c(-122.36075812146, 47.6759920119894),
c(-122.360781646764, 47.6668890126755),
c(-122.360782108665, 47.6614990696722),
c(-122.366199035722, 47.6614990696722),
c(-12... |
dcf007f75ceeb5388152cae0a66d35ab668e70c2 | 62a5ff47bc82332003d28a2c17bc8e94d17f4b69 | /Documents/MA684/Work/hw10/hw10.R | a6e5bfb79111a8f5a1f652bc2057b2eb983784a1 | [] | no_license | jiayuans/Applied-Multiple-Regression-and-Multivariable-Methods | 13ce5039c174ae3a4c0228b0a1b345d95e8c42c1 | dae2fe6d2a1ccb2a7c529bb210b330ea69399efe | refs/heads/master | 2021-01-10T09:08:43.260841 | 2016-03-29T18:40:38 | 2016-03-29T18:40:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 885 | r | hw10.R | library(psych)
library(GPArotation)
dm <- read.csv("drinkingmotives2007.csv",header=T)
attach(dm)
head(dm)
compout <- princomp(~D1+D2+D3+D4+D5+D6+D7+D8+D9+D10+
D11+D12+D13+D14+D15+D16+D17+D18+D19+D20,
cor=TRUE)
summary(compout)
vars <- data.frame(D1, D2, D3, D4, D5, D6, D7, D8,... |
4de8b0b82ab560134f0b8e97fbd8cbc7578e7628 | 3b62ffa02efef29b8bbaa9041d74a1ee72b4807a | /inst/examples/ex-rhrOU.R | f58f876a9f79a74f7770b16c8526b6cbfa70b5f6 | [] | no_license | jmsigner/rhr | 52bdb94af6a02c7b10408a1dce549aff4d100709 | 7b8d1b2dbf984082aa543fe54b1fef31a7853995 | refs/heads/master | 2021-01-17T09:42:32.243262 | 2020-06-22T14:24:00 | 2020-06-22T14:24:00 | 24,332,931 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 544 | r | ex-rhrOU.R | set.seed(123)
## Standard Walk
walk <- rhrOU(n = 5000)
plot(walk)
## Adjust pull back
walk <- rhrOU(n = 5000, A = matrix(c(0.01, 0, 0, 0.01), 2))
plot(walk)
## Effect of sigma: not only the scale of x and y changes
set.seed(123)
walk <- rhrOU(n = 5000, A = matrix(c(0.01, 0, 0, 0.01), 2), sigma = 1)
plot(walk)
set.s... |
bc736ca193c9476ccb48f8dcc248ba29c4138946 | e75696bb83eaef340a5643e15688e9ad75a22b1f | /R/keras_classification.R | d0ca9c1c075a594d116f70876b49049112e3ec21 | [
"MIT"
] | permissive | RJ333/phyloseq2ML | e18981dcbc6adc057cfe41e9fcbd1a66b04458ed | 391ce779d9cb816ca109ab6f89bd7246c27c784c | refs/heads/master | 2022-08-23T10:25:52.853275 | 2020-05-28T16:03:24 | 2020-05-28T16:03:24 | 264,425,119 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,940 | r | keras_classification.R | #' Build and compile keras sequential models with 2 hidden layers
#'
#' This function can setup and compile sequential models. Please have a look at
#' [keras_model_sequential](https://keras.rstudio.com/reference/keras_model_sequential.html),
#' [layer_dense](https://keras.rstudio.com/reference/layer_dense.html) and
... |
03fcf03149ccc1bf9b300a54e43bf7596db342a5 | ec3933d1684eab22d34452eb6991aa9d8e8021ab | /algorithm/SVM.R | 3241aa4ff97883b3ee41642b8e68374d1c614db7 | [] | no_license | ssh352/R_PractiseCode | 55e992f4734dcc08253d1316986976d977acd700 | 3762a2d37bd237a3f1e25659c08ced7d01d3a8f1 | refs/heads/master | 2021-06-01T00:58:54.448799 | 2016-06-15T08:52:41 | 2016-06-15T08:52:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,933 | r | SVM.R | #支持向量机是现在被广泛用于解决多类非线性分类问题和回归问题。
#传递给函数svm()的关键参数是kernel、cost和gamma。
#Kernel指的是支持向量机的类型，它可能是线性SVM、多项式SVM、径向SVM或Sigmoid SVM。
#Cost是违反约束时的成本函数，gamma是除线性SVM外其余所有SVM都使用的一个参数。
#还有一个类型参数，用于指定该模型是用于回归、分类还是异常检测。
#但是这个参数不需要显式地设置，因为支持向量机会基于响应变量的类别自动检测这个参数，响应变量的类别可能是一个因子或一个连续变量。所以对于分类问题，一定要把你的响应变量作为一个因子。
#例子一参考：http://blog.jobbol... |
deb9570d843bd5aa376f808ccb910bd350a4147b | 57cc9fbbc9a9d34c1e3719b36fd14ad0198078b8 | /R/plots.R | 6880ab5fc753705de2a4cbf02740904087508aad | [] | no_license | cihga39871/iteremoval | d1a20eb34ecc96dbe9cc71674d627062093c45e5 | 0995da54f51d050c3e83c7e7b933f6a8ef5d4139 | refs/heads/master | 2021-05-16T15:00:40.317409 | 2018-06-27T10:28:33 | 2018-06-27T10:28:33 | 118,550,994 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,928 | r | plots.R |
#' @title Iteration trace of removed scores
#' @description plot the score of removed feature in each iteration.
#' @family plot
#' @return ggplot2 object.
#' @param li the list result of \code{feature_removal}.
#' @import utils
#' @import ggplot2
#' @export
#' @examples
#' g1 <- SWRG1; g0 <- SWRG0
#'
#' r... |
fda1b0e6ea4b7da9a994d40c8ff6078d612e065e | a34687bd7f646ed793ef65b94136f1e52e1e0abc | /scripts/DGE_analysis/lib/modOptparse.r | f45d30cdf26d2f67a8fd94d9acabb50d37fb7a28 | [] | no_license | almaan/breast_dge | a1722cd7b660a843e313f922d441cd67f24ed13b | 30582543435733ac3e0ddaecdd25c80cde1f49b9 | refs/heads/master | 2020-04-06T12:35:02.979021 | 2019-04-04T09:37:36 | 2019-04-04T09:37:36 | 157,461,458 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,382 | r | modOptparse.r | allowMultipleArgs <- function(){
#' Modify trailing arguments passed such that space
#' separated arguments to same flag becomes joined by
#' commas; a format supported by optparse, and which later
#' easily can be split into separate parts again
oriArgs <- commandArgs(trailingOnly = TRUE)
flags.p... |
85c87fda550b99bc3eb6539d100ae1c97476bff7 | 7944b44a3e0b62fc2e66a2f7687b8d947dd900b4 | /man/yadirGetCampaign.Rd | 48e5e0337c78a5b204d08df179f3f9ad77c37434 | [] | no_license | selesnow/ryandexdirect | 3316d3ba092b299b411d69b935d37c4313550482 | 04f8b84d5513d430e2f01cf5ce61c2810264f928 | refs/heads/master | 2023-04-03T08:38:45.572545 | 2023-03-17T14:57:18 | 2023-03-17T14:57:18 | 64,333,780 | 59 | 40 | null | 2019-05-02T05:56:51 | 2016-07-27T18:50:53 | R | UTF-8 | R | false | false | 2,976 | rd | yadirGetCampaign.Rd | \name{yadirGetCampaign}
\alias{yadirGetCampaignList}
\alias{yadirGetCampaign}
\title{Get Campaigns List}
\description{Returns the parameters of campaigns that meet the specified criteria.}
\usage{
yadirGetCampaignList(Logins = getOption("ryandexdirect.user"),
States = c("OFF", "ON", "SUSPENDE... |
3f477114c174ea3a5421624c88a1d6fd89e1ba7b | 27b1e9936a96dd1b9f8cd0a399f6bbfb1019de47 | /live_tweet_code.R | ddba4365b3cb53b39826f71491c71ba4cb865ddc | [] | no_license | muralidatascience/Rcode | 4c7d5a98e56106a7f0b0b19588962677c7155b9c | 43676264a899f7c366f66f0b4f408c89400927ab | refs/heads/master | 2020-04-06T06:47:24.324611 | 2016-08-26T12:33:30 | 2016-08-26T12:33:30 | 55,692,889 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,344 | r | live_tweet_code.R | install.packages("ROAuth",dependencies = TRUE)
library(streamR)
library(ROAuth)
library(RCurl)
library(bitops)
library(rjson)
library(tm)
requestURL <- "https://api.twitter.com/oauth/request_token"
accessURL <- "https://api.twitter.com/oauth/access_token"
authURL <- "https://api.twitter.com/oauth/authorize"
consumerKe... |
7978fde1dcda0c827566bfdd5afb75133136867a | 9efcf1ceb614f121cb4eea7e0e8f03e57279b2f7 | /man/setXtrain.VB1FitObj.Rd | a0a797a84dbbf990384ad4ef25df1a56059d5fb0 | [] | no_license | cran/survivalsvm | 51600fd51c86fb30330d6ad909eb25c2f384f521 | c06c917e5c26c33f88236f48750a4a0c1d389cf3 | refs/heads/master | 2021-03-19T07:22:44.380026 | 2018-02-05T06:01:13 | 2018-02-05T06:01:13 | 94,378,235 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 555 | rd | setXtrain.VB1FitObj.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/vanbelle1.R
\name{setXtrain.VB1FitObj}
\alias{setXtrain.VB1FitObj}
\title{\code{VB1FitObj} (ranking approach)}
\usage{
\method{setXtrain}{VB1FitObj}(vb1o, sv)
}
\arguments{
\item{vb1o}{[\code{VB1FitObj}]\cr
Object of class \code{RegFitObj} ta... |
d7528060da93a1927d7d2611636f063ce9f35dc2 | a4fc7101c68b3acf8528367d498781172529b5c3 | /man/makeContent.labelrepelgrob.Rd | 4e31f0f32817b7f103789a60c638c3f91367ec39 | [] | no_license | Gofer51/ggrepel | 5d01805192145df3e12ac33e0e6c54bfe94ab540 | 9805a334d841bc369d18db3fc3baabb985f4697c | refs/heads/master | 2020-12-11T03:56:45.269256 | 2016-01-07T21:45:50 | 2016-01-07T21:45:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 325 | rd | makeContent.labelrepelgrob.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geom-label-repel.R
\name{makeContent.labelrepelgrob}
\alias{makeContent.labelrepelgrob}
\title{grid::makeContent function for labelRepelGrob.}
\usage{
makeContent.labelrepelgrob(x)
}
\description{
grid::makeContent function for labelRepelGrob... |
26777b8f71046a85947bb41b6910899a3a68ac28 | 413a1c3aef6dcc562405e6afee97abf6a441bdec | /lvr_bernoulli/resi-data.R | 21311cc233712a32a3e24e58c2bb4b89e9dc45e7 | [] | no_license | danielechermisi/mcmc-jags-samples | 68d98a8b5d10888e2cc58db5d1d2eedc67559b41 | 594f6d27f4f37d3c45b5083f04f4772adc385e59 | refs/heads/master | 2021-05-06T00:04:47.042835 | 2018-01-19T14:14:01 | 2018-01-19T14:14:01 | 116,985,841 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 183 | r | resi-data.R | "n" <- 10
"Y" <- c(NA,NA,1,1,1,1,0,0,0,0)
"fatturato" <- c(100,230,123,120,1231,100,230,1230,1200,123010)
"missing" <- rep(0, n)
missing[which(is.na(Y))] <- fatturato[which(is.na(Y))] |
ea40d4b513246d9258bde24068bb4c98dceec399 | 2341115e5bdcd85530538ce53a52efc7ff506af9 | /코퍼스언어학_2차과제.R | a9965d471988f5038fd732bc15af933de717cd06 | [] | no_license | heewonham/DataAnalysis-Basic | 54cbfa5178caebe2875bb62c6c3f6447dcf94a32 | 09c82d8bf107a8ae0b5004f692eb603ee8a119c7 | refs/heads/master | 2023-03-04T18:38:08.021502 | 2021-02-11T08:15:49 | 2021-02-11T08:15:49 | 337,966,132 | 0 | 0 | null | null | null | null | UHC | R | false | false | 7,647 | r | 코퍼스언어학_2차과제.R | # 1900년대 이후 공화당, 민주당 대통령 연설문
repub <- vector()
democ <- vector()
re <- c("1909","1921","1925","1929","1953","1957","1969","1973","1981","1985","1989","2001","2005")
de <- c("1913","1917","1933","1937","1941","1945","1949","1961","1965","1977","1993","1997","2009")
for(i in list.files(path='.',pattern='[.]txt$')){
text... |
c92c5ab6c1a623390713f7a8acc34be3361d0baa | ee622c585242ce9133d0a085ee9d953e2f173654 | /R/app.R | e235e3e360cec27c1d165f82b60890f0ad161c19 | [
"MIT"
] | permissive | ctesta01/covid19_icu | 75aa0d686653cdee320a7bb56d571cdaf64913b1 | 171c95bace1c7f69ca4168328895590e9bab3e4c | refs/heads/master | 2021-05-18T04:36:17.850884 | 2020-03-29T19:31:03 | 2020-03-29T19:31:03 | 251,110,472 | 1 | 0 | null | 2020-03-29T19:02:49 | 2020-03-29T19:02:49 | null | UTF-8 | R | false | false | 5,481 | r | app.R |
####################
server <- function(input, output, session) {
observe({
updateSliderInput(session, "floorcapramp", max=input$time)
updateSliderInput(session, "icucapramp", max=input$time)
if(input$floorcaptarget < input$floorcap) {
updateSliderInput(session, "floorcaptarge... |
9e20b9eaa5157d9c5ef59b8f67e1d8a78e147fc9 | 4344aa4529953e5261e834af33fdf17d229cc844 | /input/gcamdata/man/add_title.Rd | a80a6eebaed5bb4fed0a8661979e2a1a5c59b5f7 | [
"ECL-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | JGCRI/gcam-core | a20c01106fd40847ed0a803969633861795c00b7 | 912f1b00086be6c18224e2777f1b4bf1c8a1dc5d | refs/heads/master | 2023-08-07T18:28:19.251044 | 2023-06-05T20:22:04 | 2023-06-05T20:22:04 | 50,672,978 | 238 | 145 | NOASSERTION | 2023-07-31T16:39:21 | 2016-01-29T15:57:28 | R | UTF-8 | R | false | true | 509 | rd | add_title.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils-data.R
\name{add_title}
\alias{add_title}
\title{add_title}
\usage{
add_title(x, title, overwrite = FALSE)
}
\arguments{
\item{x}{An object}
\item{title}{Title of object (character)}
\item{overwrite}{Allow overwrite of title? Logical}... |
e02586b9d3dab7ff34b8206d5665758f158ec4b7 | 7b8478fa05b32da12634bbbe313ef78173a4004f | /man/sm_desc_update.Rd | a35faa9160e333c1f30c9122b36338409f5ebe96 | [] | no_license | jeblundell/multiplyr | 92d41b3679184cf1c3a637014846a92b2db5b8e2 | 079ece826fcb94425330f3bfb1edce125f7ee7d1 | refs/heads/develop | 2020-12-25T18:02:10.156393 | 2017-11-07T12:48:41 | 2017-11-07T12:48:41 | 58,939,162 | 4 | 1 | null | 2017-11-07T12:01:35 | 2016-05-16T14:30:38 | R | UTF-8 | R | false | true | 705 | rd | sm_desc_update.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/internal.R
\name{sm_desc_update}
\alias{sm_desc_update}
\title{Update description of a big.matrix after a row subset (internal)}
\usage{
sm_desc_update(desc, first, last)
}
\arguments{
\item{desc}{Existing big.matrix.descriptor}
\item{first}... |
eb41438677f90b2991985f257071fd9f2869a056 | c53e367a5a155cfb1ee3a41e8b0351aeaa8d331d | /RcppEigen/doc/RcppEigen-Introduction.R | fc4e77892367f7154d70bd45a9ce908157562355 | [
"MIT",
"GPL-2.0-or-later"
] | permissive | solgenomics/R_libs | bcf34e00bf2edef54894f6295c4f38f1e480b3fc | e8cdf30fd5f32babf39c76a01df5f5544062224e | refs/heads/master | 2023-07-08T10:06:04.304775 | 2022-05-09T15:41:26 | 2022-05-09T15:41:26 | 186,859,606 | 0 | 2 | MIT | 2023-03-07T08:59:16 | 2019-05-15T15:57:13 | C++ | UTF-8 | R | false | false | 471 | r | RcppEigen-Introduction.R | ### R code from vignette source 'RcppEigen-Introduction.Rnw'
###################################################
### code chunk number 1: RcppEigen-Introduction.Rnw:8-13
###################################################
pkgVersion <- packageDescription("RcppEigen")$Version
pkgDate <- packageDescription("RcppEigen")$... |
06ea8cd49d91151b072732ef6fdcb5173923ef71 | b0868dac45ab597d13436209103e90540e9746a3 | /plot2.R | 082e14facc0d399e19ab8fa03abc0538762e74fe | [] | no_license | xtrios/ExData_Plotting1 | d24a17626049477f49d30e3f22a7b191f4ad12fe | ae17287b0a3c315b71a26c7a6168b6b639606202 | refs/heads/master | 2021-01-12T20:32:48.520628 | 2014-11-08T18:57:22 | 2014-11-08T18:57:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 711 | r | plot2.R | # Read data set
tabAll <- read.table("household_power_consumption.txt", sep = ";", header = TRUE)
# Convert date and time format
tabAll[,1]<-as.Date(strptime(tabAll[,1], format="%d/%m/%Y"))
tabAll[,2]<-strftime(strptime(tabAll[,2],"%H:%M:%S"), "%H:%M:%S")
# Select relevant data
tabSel <- subset(tabAll, tabAll$Date=="2... |
e91fbd1870b9178c1561b6c1dc78c1ae2607337d | d99c9f5e8bb94460ca1aad9a1aeb8c5379e76c5a | /02_Single_Factor/Intervals.R | 40ea47dc5dcebced523fceeceddeb3e5da5d2732 | [] | no_license | tdeswaef/AquaCrop-Identifiability | d24534c031257413023da8ed3688e71ed7fdd2d2 | 199f79990859a3e79c482a4876dcbe338e691947 | refs/heads/main | 2023-08-16T09:28:06.483134 | 2021-10-06T09:05:52 | 2021-10-06T09:05:52 | 352,927,804 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,368 | r | Intervals.R | graphics.off()
rm(list = ls())
options(error=stop)
library(FME)
library(plyr)
#######
#PATHS
#######
output.collin.path <- "C:/Doctoraat/Irrigatie2.0/collin/Morris/Capture_Variation/Measurement_Int95MAX/Collin_18pars/" #location where collinearity results are saved
scaled_path <- "C:/Doctoraat/Irrigatie2.0... |
87c71e269e87f6319ab0a27399c3664280482150 | cd628e1c910d766d54b4fa8417a82890e16cdca3 | /R/pbp.R | 98133dd11ca21bd47c07056506a8426e67e2f2df | [] | no_license | dashee87/deuce | 33f34ddaef4a8943adf5d704225eaaf06ec3a1ba | de7015ec55e5470ca497472171e50b6c822487dd | refs/heads/master | 2021-01-13T10:16:44.665211 | 2016-06-28T01:03:35 | 2016-06-28T01:03:35 | 69,028,710 | 0 | 0 | null | 2016-09-23T13:44:27 | 2016-09-23T13:44:26 | null | UTF-8 | R | false | false | 4,958 | r | pbp.R | #' Create Point-by-point Data
#'
#' Creates an expanded point-level version from the flat representation in \code{point_by_point}
#'
#' @param obj a row of the \code{point_by_point} data frame
#'
#' @export
pbp <- function(obj){
set_score <- function(x){
games <- strsplit(x, split = ";")[[1]]
ngames <- sapply(games... |
a8391e99a37cb20d95fd560058cfef5e4a00ccdf | b32f8bbf08fc23033cfc046922a681db312d8580 | /c4_exploratory_data_analysis/week2/swirl_ggplot2_extras.R | 7262874cace57506e8772d38b4d5b6c3e7417553 | [] | no_license | hieudtrinh/datascience | 6926afd134b6bbd948cd756e94dae87cb92a010f | 5bb7af11355a168ab326332031d6205b64b90164 | refs/heads/master | 2022-11-21T13:37:28.448172 | 2020-07-22T21:03:06 | 2020-07-22T21:03:06 | 279,670,268 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,650 | r | swirl_ggplot2_extras.R | # | Run the R command str with the argument diamonds to see what the data looks
# | like.
#
str(diamonds)
# tibble [53,940 × 10] (S3: tbl_df/tbl/data.frame)
# $ carat : num [1:53940] 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
# $ cut : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
# $... |
c542e7a164ccaed76c5fbbee5ea8b27e02087f21 | 9dcb9c2fa85c2f10dab34b1de1baabad8f957c78 | /Original/Graphs.R | dc138ef210fd304ef67d6f9db00ac226e8032c46 | [] | no_license | mattgonnerman/WinterSelection | 26aefdb14831f9b59ae99b4a4e591ccdc27281cb | 0afc68b3d80dd9ce4e81d761ff2f95c00f7ae64e | refs/heads/master | 2023-04-14T01:11:29.375417 | 2022-07-15T14:27:25 | 2022-07-15T14:27:25 | 334,658,994 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,987 | r | Graphs.R | require(dplyr)
require(ggplot2)
require(forcats)
setwd("E:/Maine Drive/Analysis/Kaj Thesis")
### Comparison of Magnitude of Interactions
interactions.raw <- read.csv("InteractionResults.csv")
interactions.raw$Beh_State <- factor(interactions.raw$Beh_State,
levels = c("Roost", "Sta... |
2b205b879dcbfcb00555f4e54bf59b115923a381 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/RSurveillance/examples/pfree.calc.Rd.R | 41720f977894d905a704acee361d25f3a3873e2f | [] | 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 | 350 | r | pfree.calc.Rd.R | library(RSurveillance)
### Name: pfree.calc
### Title: Probability of freedom over time
### Aliases: pfree.calc
### Keywords: methods
### ** Examples
# examples for pfree.calc
pfree.calc(0.8, 0.01, 0.5)
pfree.calc(rep(0.6,24), 0.01, 0.5)
pfree.calc(runif(10, 0.4, 0.6), 0.01, 0.5)
pfree.calc(runif(10, 0.4, 0.6), run... |
00ce39b9b88b1b148e82ac13454f91850f95d82d | 431860954259d02f7768dd02e6554badbf6faacc | /man/wcPCA.Rd | 72994cf7478557d6f51848ed14028ae78f3c1863 | [] | no_license | nicolas-robette/GDAtools | 5e6a7d4454d5edac3fab9bfa202f96ceddadfc66 | 4708925717cb4d0cd957faa46fd813dfcd860c41 | refs/heads/master | 2023-07-07T02:54:15.110104 | 2023-06-29T18:58:32 | 2023-06-29T18:58:32 | 214,293,710 | 5 | 3 | null | 2021-06-11T08:41:34 | 2019-10-10T22:04:12 | R | UTF-8 | R | false | false | 2,560 | rd | wcPCA.Rd | \name{wcPCA}
\alias{wcPCA}
\title{Within-class Principal Component Analysis}
\description{
Within-class Principal Component Analysis
}
\usage{
wcPCA(X, class, scale.unit = TRUE, ncp = 5, ind.sup = NULL, quanti.sup = NULL,
quali.sup = NULL, row.w = NULL, col.w = NULL, graph = FALSE,
axes = c(1, ... |
8b4c8721e7e0f2b1db95297e16be924afb6fa614 | 8839c2feadff327fcbc8573bbbb42c008a4f4406 | /assignment.R | 32ad6b44491332372ede16207cd9b8a8199fc757 | [] | no_license | lachyrussell/datascience | d69366d6708fb79292823aa487dd98ba0a6db4c1 | fa81f5a4e8f0669ce1726defa5993ebbe96fdb58 | refs/heads/master | 2021-01-21T13:49:01.530672 | 2016-05-18T09:08:32 | 2016-05-18T09:08:32 | 54,311,997 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 69 | r | assignment.R | x <- 1
print(x)
x
msg <- "hello"
msg
x <- ## Incomplete expression
x |
0e5d4af39e5da09180fab9f0430002ee21e30741 | e08714d390500a2d701adbade1e297f8138454d4 | /Regresión y correlación/matcor1.R | 1161f3e1963831a488242723af445f7c2707f826 | [] | no_license | carlosardon/Mis-ejercicios-en-R | e2c0525903d7f3950cb66cc1f8fe59bcd2f4c567 | 2d321c74087d1a8dd25f6eb3759462f716d49167 | refs/heads/master | 2020-04-29T10:46:01.374403 | 2019-03-17T08:21:39 | 2019-03-17T08:21:39 | 176,073,827 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 5,804 | r | matcor1.R | #Script R para correlación lineal de Pearson y correlación de distancias
#Primero instalar los siguientes paquetes
library(energy)
library(ggplot2)
library(magrittr)
library(ggpubr)
library(dplyr)
library(Hmisc)
library(corrplot)
library("PerformanceAnalytics")
library(psych)
library(ppcor)
#Luego deb... |
50bb850be2cea5ac96dcb56bb0386388f8b11839 | ee0689132c92cf0ea3e82c65b20f85a2d6127bb8 | /trg/d2a-fmssrcc.R | 883077ff2c9fdcb6c5f154a58bda6b4ac7449e0c | [] | no_license | DUanalytics/rAnalytics | f98d34d324e1611c8c0924fbd499a5fdac0e0911 | 07242250a702631c0d6a31d3ad8568daf9256099 | refs/heads/master | 2023-08-08T14:48:13.210501 | 2023-07-30T12:27:26 | 2023-07-30T12:27:26 | 201,704,509 | 203 | 29 | null | null | null | null | UTF-8 | R | false | false | 1,837 | r | d2a-fmssrcc.R | # Read Data into R Environment
#CSV Files----
#Read from CSV file in PC
head(iris)
write.csv(iris, "./data/iris.csv", row.names=F)
read1 = read.csv(file="./data/iris.csv", header = TRUE,sep = ",")
read1
read1 = read.csv(file="./data/dhiraj.csv", header = TRUE,sep = ",")
head(read1)
str(read1)
class(read1)
head(read1)
... |
4249ce5507b773ea78ada67efe8a476678b3284f | 39834735c33714c011da3c2ab438db4525f4ac9d | /man/wahlperioden.Rd | 6e7ad73fc56d23ff8aa2a031a7c9c61d15dad284 | [] | no_license | petermeissner/dip21 | 2e37ab5ede145d11b92bfff78202273424b2df34 | f26206409af7c89fdda80fcb3bb3f46862e435c0 | refs/heads/master | 2020-12-24T20:33:12.438958 | 2016-05-03T22:15:25 | 2016-05-03T22:15:25 | 57,194,506 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 387 | rd | wahlperioden.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dip21_search.R
\name{wahlperioden}
\alias{wahlperioden}
\title{function returning options for legislative terms}
\usage{
wahlperioden(regex = NULL)
}
\arguments{
\item{wp}{a regular expression used to look up labels and return their values}
}... |
c53ab2deff27f2318a02909db0e85ffe55d4ad96 | 941ef2fdda9fdfa5085db448ce4728fb3a8c629c | /run_analysis.R | d615aa796dbdd7640b6168a2a93e2af1235ac5b7 | [] | no_license | PepijnDG/GetCleanProject | 5f100167239483a84c6fcde495c151713ea52b3a | fe26e5109baf22339dcce1afd155f1aca457e785 | refs/heads/master | 2021-04-09T16:40:21.857010 | 2015-02-22T11:35:46 | 2015-02-22T11:35:46 | 31,124,216 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,433 | r | run_analysis.R | # load packages and save main directory
library (plyr)
library (reshape2)
wd <- getwd()
# load training data
setwd("train")
xtrain <- read.table("x_train.txt")
ytrain <- read.table("y_train.txt")
strain <- read.table("subject_train.txt")
# load test data
setwd(wd)
setwd("test")
xtest <- read.table("x_test.txt")
ytest... |
bf712959c52eac6ef3ce20b4fda9ab68ea31075a | fe4ff59b4a915bf021abb4d976045b7cd97b11ab | /man/compareTemporal.Rd | 47df285eb7cf8f445c601e33bbd75de8ee570f6e | [] | no_license | cran/crimelinkage | 4908a8b35d27e03e4b44f0c2f902834cec7f863f | 872ad1c7ec9c6445063a56a753657e1c8ab9fd4d | refs/heads/master | 2021-01-19T06:46:19.974002 | 2015-09-19T19:50:30 | 2015-09-19T19:50:30 | 34,173,828 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 928 | rd | compareTemporal.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/compareCrimes.R
\name{compareTemporal}
\alias{compareTemporal}
\title{Make temporal evidence variable from (possibly uncertain) temporal info}
\usage{
compareTemporal(DT1, DT2, show.pb = FALSE, ...)
}
\arguments{
\item{DT1}{(... |
8f0b7fb9c8470a376415c9348b87d486db3a5b30 | 1491e182e255ac4f3b2bb2cc8da24f4619da5138 | /plot6.R | fc6c56625f60ec85514bdf1b9ca02f925c29205c | [] | no_license | lenazun/ExDataProject | c9696dfb645a3666b7149e3552254d34b698a83c | 6608273a06dca182987190a7f1f1fe3c4a83d13f | refs/heads/master | 2020-06-06T16:19:59.933101 | 2014-05-23T22:39:11 | 2014-05-23T22:39:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,269 | r | plot6.R | #NEI <- readRDS("summarySCC_PM25.rds")
#SCC <- readRDS("Source_Classification_Code.rds")
#convert year and type to factors
NEI$year <- as.factor(NEI$year)
NEI$type <- as.factor(NEI$type)
#subset motor vehicles for Baltimore City, MD and Los Angeles, CA
balmotor <- subset(NEI, NEI$type == "ON-ROAD" & NEI$fips == "245... |
639b3508565bda6ffaee0566fd24025abc3e5981 | 6df0d7054556eee6127ee99378a623303ba56ecc | /R/ggstance.R | 5782c87379adc73b0db87f9bb7802d6f1252423e | [] | no_license | miguelyad26/ggstance | c9088c2735836d4171b08f78f99e4f81d20d8cd3 | 6eca6d13ea9078f63e0de439bdc473cd9977f9a1 | refs/heads/master | 2021-01-24T09:57:06.629959 | 2016-08-12T07:39:34 | 2016-08-12T07:55:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 230 | r | ggstance.R | #' @importFrom purrr map map_if walk2 keep map_at splice invoke some
#' walk partial set_names %||%
NULL
#' Base ggproto classes for ggstance
#'
#' @seealso ggplot2::ggproto
#' @keywords internal
#' @name ggstance-ggproto
NULL
|
8df142ceba5565fb4ee7f86d619ab6521602dc99 | a44b2e10db0aa9b7f3fde27d9840ab474d10ab57 | /modal/glm_modal.R | 04b3c269afb00c911b6605cfcd54cb662b26e0a3 | [] | no_license | felipeamorimbr/inla-interface | 2ebae4821b37609bdb72bc92c99434079a6c2937 | cc87a7e173d86014c15fac063b4ab2c40967f102 | refs/heads/master | 2021-09-20T08:00:06.291441 | 2021-09-06T20:37:04 | 2021-09-06T20:37:04 | 201,983,807 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,187 | r | glm_modal.R | # GLM Data
observeEvent(data_input(), {
glm_data <<- list()
glm_data$formula <<- list(
resp_var = reactive({
data_input()$covariates[1]
}),
cov_var = reactive({
NULL
}),
not_selected = reactive({
data_input()$covariates
}),
intercept = reactive({
TRUE
}),
... |
7a1cb89684265e3d02edd53f29fa6d6303c9c1e4 | 1d4e4acf0bd82f6f65bfcc0fe4588a0aabec0e73 | /assigkcluster.R | 3115daecf1fd147170869a78c63a282c2a2650e0 | [] | no_license | rosetk/datascience-course | 68516a011c3496dbb8779843819cee5a3c5d3179 | 12561c2ea6702245bcd8fe8941cbe6f6041914d1 | refs/heads/main | 2022-12-18T20:37:58.041575 | 2020-10-02T05:12:32 | 2020-10-02T05:12:32 | 300,483,862 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,960 | r | assigkcluster.R | install.packages("plyr")
library(plyr)
x<-runif(50)
x
y<-runif(50)
y
data<-cbind(x,y)
data
plot(data)
plot (data, type ="n")
text(data, rownames(data))
km<-kmeans(data,4)
str(km)
install.packages("animation")
library(animation)
km1<-kmeans.ani(data,4)
str(km1)
km$cluster
km$centers
... |
ba791002a4a562dbb3a311d1f51031b9a82abfa4 | 0d0f71b0b9eca2896cf24a6dac799a1637f5fee5 | /code/helpers/plotting_helpers.R | 593e14592f3dad392eab194f80648f444d610326 | [] | no_license | mkiang/beiwe_missing_data | eb1a037d238d2e6be7615eb96078315a7c86d38b | ef65f4379ced4009427814a5f6aaa7882bb80207 | refs/heads/master | 2023-06-20T12:57:43.452236 | 2021-07-21T15:47:11 | 2021-07-21T15:47:11 | 325,392,811 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,881 | r | plotting_helpers.R | ## Plotting helpers
## Misc helpers ----
mkdir_p <- function(dir_name) {
## Mimics mkdir -p
dir.create(dir_name, showWarnings = FALSE, recursive = TRUE)
}
## Themes ----
mk_classic <- function(...) {
## Just a shortcut for serif fonts and classic theme with legend in upper
## left by default.
t... |
d207a5c84b1f3e6ef5513561673343c32cbee42d | a176626eb55b6525d5a41e2079537f2ef51d4dc7 | /Uni/Projects/code/$Rsnips/generate_exposures.r | ba9067a1c82f96e6cea35b1ef2191c3dbad8db91 | [] | no_license | zeltak/org | 82d696b30c7013e95262ad55f839998d0280b72b | d279a80198a1dbf7758c9dd56339e8a5b5555ff2 | refs/heads/master | 2021-01-21T04:27:34.752197 | 2016-04-16T04:27:57 | 2016-04-16T04:27:57 | 18,008,592 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,092 | r | generate_exposures.r | # script to join geolocated BI lat/lon/dates with Itai's PM and temperature predictions
# started 3/24/2016
library(data.table)
library(pryr) # keep track of memory usage
library(ggmap) # to plot participant locations
#### participant info
load("data/FromHeather/BIdeID_2016-03-22.RData")
bideid <- BIdeID
rm(BIdeID)
... |
16ce57e7311c2b8af96473dc248cab9127cbc8ab | 5d9a9aeba6dd447f04709a03a1d1584f8328a6a8 | /9_FinalCode.R | 77a539a8caaaed629be12ffb4443371fc928fd64 | [] | no_license | edrake07/Machine-Learning-Project | bad70ddadc4ce1ea1f6889830763356eea308341 | bbdf78aea8c9476b92e1f139336b805fbcdff64d | refs/heads/master | 2022-04-26T19:58:09.727451 | 2020-04-14T16:41:18 | 2020-04-14T16:41:18 | 255,450,525 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,283 | r | 9_FinalCode.R | #### Final Project Code
# load packages
library(dplyr)
library(caret)
library(randomForest)
library(rattle)
library(parallel)
library(doParallel)
library(ggplot2)
# set seed for replicability
set.seed(1234567)
## Basic Data Load and Cleaning
# read in data from csv files
training_full <- read.csv("train.csv")
va... |
1caca0e2cba041456155841927c886ca850fd10a | 86d44314862fc8f10e0c8d590ef54089b932b69c | /packrat/lib/x86_64-apple-darwin19.4.0/4.0.4/effectsize/doc/convert.R | 119b7e3f30f6f4553e8b2d7dff69a49cb5c24ea4 | [] | no_license | marilotte/Pregancy_Relapse_Count_Simulation | 6f50670198174e01705ad3bf107cabad03c81b23 | c4ccd9419cf1e63520caddd1e6c89f42013b24a2 | refs/heads/main | 2023-04-06T07:20:39.965543 | 2021-03-31T21:18:29 | 2021-03-31T21:18:29 | 349,435,570 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,250 | r | convert.R | ## ----message=FALSE, warning=FALSE, include=FALSE------------------------------
library(knitr)
options(knitr.kable.NA = "")
knitr::opts_chunk$set(comment = ">")
options(digits = 3)
pkgs <- c("effectsize", "ggplot2", "correlation", "parameters", "bayestestR")
if (!all(sapply(pkgs, require, quietly = TRUE, chara... |
badd94ee5ff1ed5a6ff011b8206bad1c6e48b864 | 552d910aabda4755d6d831e1063e503015037603 | /R/net.edges.R | 9d305c6d0daa2b04310ef0ad5576556e60faf607 | [] | no_license | cran/JGL | da88bdca245751169d574e27c0fd846b70594c4c | bca5ba36a8070f7b391bbd7a9d69c5de6e9d6007 | refs/heads/master | 2021-05-16T03:11:20.349474 | 2018-11-30T22:40:15 | 2018-11-30T22:40:15 | 17,680,067 | 3 | 5 | null | null | null | null | UTF-8 | R | false | false | 256 | r | net.edges.R | net.edges <-
function(theta)
{
adj = make.adj.matrix(theta,separate=TRUE)
K = length(theta)
edges = list()
for(k in 1:K)
{
diag(adj[[k]])=0
gadj = graph.adjacency(adj[[k]],mode="upper")
edges[[k]] = E(gadj)
}
return(edges)
}
|
44f6811fca8b95bc63e64358e438c7ab8bf8648d | 4b89672c6ff0667897a3fea3819980b7868d64f7 | /man/dot.Rd | c5627052d382e238bb940f75e80491180adc695d | [] | no_license | cran/RWeka | 04abd774e061bb54dcb2dd5269d4cd48ebdb8d4f | cadb071065d055b127d265c1eae16c0f6c217a96 | refs/heads/master | 2023-03-15T23:22:07.324627 | 2023-03-07T13:18:59 | 2023-03-07T13:18:59 | 17,693,014 | 5 | 8 | null | null | null | null | UTF-8 | R | false | false | 1,297 | rd | dot.Rd | \name{dot}
\alias{write_to_dot}
\alias{write_to_dot.Weka_classifier}
\title{Create DOT Representations}
\description{
Write a DOT language representation of an object for processing via
Graphviz.
}
\usage{
write_to_dot(x, con = stdout(), \dots)
\method{write_to_dot}{Weka_classifier}(x, con = stdout(), \dots)
}
\arg... |
36ab6c478cb66f8b39aa08787672152957731947 | 2fe4c16e0377a99e198ab04d5c378ca247ae4329 | /Rscript/Library/mzkit_app/pipeline/mrm_quantify.R | 9a1889e3a8d26d1d452e8d63b23f6654e7d5ece7 | [
"MIT"
] | permissive | xieguigang/mzkit | 1964d28b0fad5f6d44950fdccdd4a70877f75c29 | 6391304b550f7e4b8bb6097a6fb1c0d3b6785ef1 | refs/heads/master | 2023-08-31T06:51:55.354166 | 2023-08-30T08:56:32 | 2023-08-30T08:56:32 | 86,005,665 | 37 | 11 | MIT | 2023-03-14T14:18:44 | 2017-03-23T23:03:07 | Visual Basic .NET | UTF-8 | R | false | false | 15,341 | r | mrm_quantify.R | #' title: MRM quantification
#' author: xieguigang <gg.xie@bionovogene.com>
#'
#' description: do LC-MS/MS targeted metabolomics quantitative analysis
#' base on the MRM ion pairs data. This script will take a set of
#' *.mzML raw data files, and then create linear models based on the
#' linear reference raw d... |
b8ac10af17a5024e54ba28d18b40156df05391c9 | 994555fa87be0fd25686cf1faa17f0675f6b3a97 | /plot1.R | 9e46f3763f50d3dfb0a6535d283fb7fe5735cac2 | [] | no_license | hcschneider30/ExData_Plotting1 | 261989352d441646a71bcfd818784c2c4b67d7c7 | 34bab8ae515ad236bb83069afc64b6dfcf60d277 | refs/heads/master | 2020-12-11T04:19:00.585114 | 2014-05-10T08:22:38 | 2014-05-10T08:22:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 934 | r | plot1.R |
# read data.frame from a ';' seperated file
# define column classes
# NA symbol in this data file is '?'
df <- read.table("household_power_consumption.txt",
sep=";",
header=TRUE,
colClasses = c("character", "character", "numeric", "numeric",
... |
a3b77b3ce8b6805dbfec90817e785509bb74b539 | 946ee68030584a153c39c6c99c5101b8d2cae2dc | /ModelConstruction/FAO56_recycle.R | 99e35cc653411a716b60d70e3d258bb73cfbe381 | [] | no_license | dsidavis/GreenWater | 613a645fe52619185477659a0c4c8af208692f98 | 0be339d86a29af676a08e148ccfd94f61d454f75 | refs/heads/master | 2021-07-18T02:07:02.672488 | 2017-10-26T20:32:50 | 2017-10-26T20:32:50 | 107,836,376 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,996 | r | FAO56_recycle.R | #alternative approach using means for different periods of the Kc curve. This was used in the FAO56 spreadsheet program.
#calculate U2 and minRH by year for mid to late period for each cell of interest; this is used by Kcb function. Should just use average of years for 2017
U2_mid <- function(U2.df, col_index, Jmid, ... |
384203baacc886120e671e42b2e05e7bb8d2642c | 64a1e9180b778b767c49666c353e57063d96b3cb | /Script_Aunts.R | cc31bf6d255a02407645cc683e3fb80800081453 | [] | no_license | JuanPablo-RamirezLoza/Wolf-Kinship-Demography | 4f9336f56a48794f103410584792cbc296e157af | bba6ab478a665b4b0031b1884a7914fd2033111b | refs/heads/main | 2023-04-27T10:49:39.235088 | 2021-05-13T08:55:16 | 2021-05-13T08:55:16 | 362,403,593 | 0 | 0 | null | 2021-04-28T09:06:58 | 2021-04-28T09:01:55 | null | UTF-8 | R | false | false | 18,588 | r | Script_Aunts.R |
# kinship relationships at equilibrium
# Packages used to build the sub-models
library(NetLogoR)
library(testthat)
library(kinship2)
library(SciViews)
library(DescTools)
load(file="popSim.Rdata")
# all indiv ever lived in the pop
# calculate relatedness for all indiv alive at the end, and for females alive only
#w... |
cb17a7a4110dd4b66c85dcae8dec66d39e84a785 | 2a747f76dca45608ddfea0348230e30555bb865c | /ese/WS1920/demo.R | 38a847e21d2311a3a3c5b5a82cad9223074e2d6c | [] | no_license | feigensp/lehre | a8efffa20be7f3b93378c7a8c51ab0102b81b903 | 8fbc86e2dcce32b427a56c6d3ffbfc34da97bd2a | refs/heads/master | 2020-08-03T18:42:47.228666 | 2020-01-20T08:13:04 | 2020-01-20T08:13:04 | 211,848,563 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,145 | r | demo.R | #Read file
input <- read.csv("C:/../rt.csv", sep=";", dec=",")
rt <- input[,'time..task']
#Minimum/Maximum
minValue <- min(rt)
maxValue <- max(rt)
#Arithmetic mean
meanValue <- mean(rt)
medianValue <- median(rt)
#Histogram
rtNum <- as.numeric(unlist(rt))
hist(rtNum)
#Boxplots
boxplot(rtNum)
#Violinplots; install a... |
1a0505e8d8953b966a6df5e7fb43529afdcf7121 | 589bf3773cd27c8d5f022747a7344596e7bd8067 | /R/convert_dates.R | 637b65177f4380a433b924a268d96e6d2895cb2f | [] | no_license | MikkoVihtakari/MarineDatabase | 974b67991ee2636028f5060e93eae875bd79e800 | 73879eecbedb92e126ce469504b8b18f635ff952 | refs/heads/master | 2022-11-06T18:17:31.395171 | 2020-06-29T14:23:09 | 2020-06-29T14:23:09 | 112,598,897 | 1 | 1 | null | 2017-12-08T09:22:11 | 2017-11-30T10:36:22 | R | UTF-8 | R | false | false | 6,870 | r | convert_dates.R | #' @title Convert Excel dates to consistent date formatting
#' @description Converts messy nonstandarized Excel dates to consistent date formatting.
#' @param dt Data.frame for which the date conversions should be made. The date column has to be named as \code{date}.
#' @param date_col Not implemented yet. Name of t... |
8f2f0a690bc06a2e225eab4ba64aee0616d29a47 | a7c4723087d16f75add07061726058060008c74e | /filepath.r | cb977a41bad24d32102604873f8f87cca89223c7 | [] | no_license | c-zhong/TriageBehaviorGraphAnalysis | 29c37aff35af2b9518c53e384c0c1188d58f6f4f | 25a492e5ab6253e1559946835f4a9f03255ba60c | refs/heads/master | 2021-05-28T21:01:06.651241 | 2015-02-25T19:16:21 | 2015-02-25T19:16:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,752 | r | filepath.r | #get the whole path of a subject
getFilename <-function(subID){
filename <- "output//"
result <- paste(filename,subID,sep="")
result <- paste(result,".csv",sep="")
return(result)
}
getNodeFilename <-function(subID){
filename <- "data//"
result <- paste(filename,subID,sep="")
result <- paste(result,".csv"... |
8bd3af430bbd8869d153bdd886338c615158678d | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/A1/Database/Letombe/Abduction/aim-50-3_4-yes1-4-50/aim-50-3_4-yes1-4-50.R | 88063bbf65b85db4d03c5aa209b0298b12575040 | [] | 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 | 69 | r | aim-50-3_4-yes1-4-50.R | 2762f1584c7536654c135a7099cf4d4c aim-50-3_4-yes1-4-50.qdimacs 320 754 |
5c27b70b9bd445d9088757b79ffff75bb7563951 | 7164d4515036f3ebce26f3a0b7f0b3031683aaa1 | /man/table.contvar.Rd | 5d8859cc4e568bd83f18c8d3fe3e12c2df3d7cb4 | [] | no_license | kcha193/simarioV2 | 0a96193cf09f32acea2287d76687a9e1ee2eb237 | 66c7bfbb3dfd3dbd7d9b95d7d9b84632e6aa5aca | refs/heads/master | 2023-03-15T14:34:46.222056 | 2023-02-26T01:59:06 | 2023-02-26T01:59:06 | 57,259,491 | 5 | 3 | null | null | null | null | UTF-8 | R | false | true | 834 | rd | table.contvar.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/simulate.r
\name{table.contvar}
\alias{table.contvar}
\title{Display a vector of continuous values in a table using the
breaks supplied.
Attachs a meta attribute with varname}
\usage{
table.contvar(x, breaks, varname)
}
\arguments{
... |
6e9ce3c0d35ab0ec9b5b4306478f2cd409619422 | 7a6be82ecc13a6deafa51b207915b08336486d91 | /gganimate.R | 4dd4c585f87eb62385d2d9e0c8668faec184499c | [] | no_license | anerigarg/MSc_data | e60ab5293b382a3280b7688bddddbb399bd578d3 | 37b52f80204d8a02d605b3380003e2fc40cee6ab | refs/heads/master | 2023-01-24T11:16:27.387328 | 2020-12-08T19:37:44 | 2020-12-08T19:37:44 | 285,697,250 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,288 | r | gganimate.R | # Trophic Group playing with animation ------------------------------------
install.packages("gganimate")
library(gganimate)
install.packages("hrbrthemes")
library(hrbrthemes)
library(ggplot2)
ARD_func_sum3 <- ARD_3 %>%
mutate(treatment = factor(treatment, levels = c("0%", "30%", "50%", "70%", "100%", "co... |
b664ced9677928e2545867a08803b7d3e8031610 | 89f8118060345bd9ed48d42f75bfcf9ba5c7be46 | /r_scripts/mysql_read.R | 956ea0d521310c091faadd4b6967bdbed305d020 | [] | no_license | DerrickStuckey/gwu-cloud-workshop | bcbd52f2a784d5d08751949467c41457acbc6fe7 | a64cc4f5b0e509c6fa2143f64a0db269fce5e714 | refs/heads/master | 2021-01-10T18:04:54.551639 | 2020-11-20T18:10:50 | 2020-11-20T18:10:50 | 44,842,127 | 1 | 8 | null | 2019-09-28T00:32:30 | 2015-10-23T22:37:57 | Python | UTF-8 | R | false | false | 862 | r | mysql_read.R | ## Read some data from an AWS MySQL Instance ##
library(RMySQL)
mysql_host <- "gwu-workshop-mysql.cegeieiv8hqw.us-west-2.rds.amazonaws.com"
mysql_user <- "derrick"
mysql_pass <- ""
mysql_dbname <- "mydb"
mysql_port <- 3306
mydb = dbConnect(MySQL(),
user=mysql_user,
password=mysql_... |
635343988a61118d0d2fd1aad5ab2bea09925792 | 94f841e4275860a5b9cf45b4f081888e21659498 | /tests/temporal_discounting.R | 4150febafcc4fa458e0c25ffe982f7c2d3a462bd | [] | no_license | SVA-SE/freedom | 33d170c45a9a5639ecbc0ea5714a6ab9b1024f88 | f6145fe5aa3b1b3db42b07d42ea41a897030a0a2 | refs/heads/master | 2023-02-06T10:13:48.392626 | 2023-01-24T10:28:48 | 2023-01-24T10:28:48 | 69,856,790 | 1 | 3 | null | 2022-09-01T09:30:20 | 2016-10-03T09:23:39 | R | UTF-8 | R | false | false | 1,261 | r | temporal_discounting.R | library(freedom)
ob <- post_fr(0.5, 0.5)
stopifnot(ob > 0.5)
ob1 <- tools::assertError(post_fr(-0.1, 0.5))[[1]]$message
ob2 <- tools::assertError(post_fr(1.1, 0.5))[[1]]$message
ex <- "The prior probability of freedom cannot be greater than 1 or less than 0"
stopifnot(length(grep(ex, ob1)) == 1L)
stopifnot(length(gre... |
ada9bbc84100726983768512e533e38fa2951c1b | da3b39f21e00611f3ef44ed0d4009bc0299e934d | /HW1.R | 117cc434c0d2ac4e1ba7a1120e07aa0b16903ff4 | [] | no_license | ky2171/ML-HW1 | c510e0c96388b3c51cf2c14ba67af09834984ea3 | 71280613df1094eef4d4f14120d0a3ba5d483269 | refs/heads/master | 2022-11-29T11:43:20.499781 | 2020-08-13T14:48:58 | 2020-08-13T14:48:58 | 287,303,032 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,334 | r | HW1.R | # Question 1
recursion <- function (n) {
if (n == 1) {
return(1)
}
else if (n == 2) {
return(1)
}
else {
return (recursion (n-1)+recursion (n-2)+ 2*(n-2))
}
}
print(recursion(36))
# Question 2
binomial <- function (n,m) {
if (m == 0 | m == n) {
return (1)
}
else {
result <- bi... |
2fcb2d34f9e667c2dacc60d1fb08bc9ea9ddcf4e | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/maxLik/examples/bread.maxLik.Rd.R | ad6e11f31796037fe51d758af95d6110bc034c74 | [] | 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 | 442 | r | bread.maxLik.Rd.R | library(maxLik)
### Name: bread.maxLik
### Title: Bread for Sandwich Estimator
### Aliases: bread.maxLik
### Keywords: methods
### ** Examples
## ML estimation of exponential duration model:
t <- rexp(100, 2)
loglik <- function(theta) log(theta) - theta*t
## Estimate with numeric gradient and hessian
a <- maxLik(l... |
4c335fb433404d7c9ff22ea3051f9ccd07da419e | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/5785_0/rinput.R | 29e945cda610e29ab9dfea586ba08a00d3ec571a | [] | 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("5785_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="5785_0_unrooted.txt") |
925e41e29dfc661132079c0731655f29d2f9e5c8 | 8af125a9516b1b783da83e7e34eb1a6eda71e0b7 | /Association Rules/Solution (Movies).R | f6e9cec704bc695ddca02045ab1e6f9eda33d830 | [] | no_license | Abhishek012345/Data-Science-Assignment | 0b2fa3f1173331703c1f27cd50f880159b121b17 | 2c0dc7f772db4f8868b4801e5cdcaf1388582a08 | refs/heads/master | 2022-12-31T15:43:52.865230 | 2020-10-18T06:11:22 | 2020-10-18T06:11:22 | 259,623,325 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 939 | r | Solution (Movies).R | library("arules")
library("readxl")
movie_data <- read.csv(file.choose())
View(movie_data)
library("arulesViz")
arule <- apriori(as.matrix(movie_data[6:15]), parameter = list(support = 0.2, confidence = 0.7))
arule1 <- apriori(as.matrix(movie_data[6:15]), parameter = list(support = 0.06, confidence = 0.8))
... |
420c1d55a4573d889ebcfcddcf9f3699b8cea292 | 1cb4d4f9f11ab04500a006d7dfe81530d07c578d | /PIBIC 2018-2019-20200413T151926Z-001/PIBIC 2018-2019/Mapa interativo/Rascunho.R | 67c5df4b45dc242bec059283589b27f73d5059ed | [] | no_license | rodolfo-oliveira/tcc | 86ee2a48f9759713608d0e80039bc6e2fae866f1 | 0340cfa20021438be7e1d4557007bba5aa4e29e4 | refs/heads/master | 2023-05-13T18:33:30.288043 | 2021-05-20T22:26:33 | 2021-05-20T22:26:33 | 256,174,124 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 313 | r | Rascunho.R | library(tidyverse)
library(ggplot2)
a=read.csv(file = "distritos_dummy.csv")
expand.grid(x =as.factor(1:3),y= as.factor(1:3), id = 1:9) ->df
df$tempo<-round(a$Tempo.de.viagem,2)
df %>%
ggplot(aes(x,y))+
geom_tile(col = "white")+
facet_wrap(~id,ncol= 3,scales = "free")+
geom_text(aes(label = tempo))
|
7cf37d3915d1e07b420f81d2b7987857af7c0d0f | 536ffbc0b339513609bbe5c5187ac0a693b7b326 | /R/get_species_docs.R | f85f9e982558163d8ba9e3184a1a7acbeeb9576a | [] | no_license | jacob-ogre/nmfsscraper | 69633e69fb0725a6b6b8e7f81e0b2c22fb75b3d1 | 6dc44f5a41e1d9dfc2553555b803670860f6323b | refs/heads/master | 2020-01-23T21:42:45.786260 | 2016-11-26T13:57:06 | 2016-11-26T13:57:06 | 74,686,806 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,580 | r | get_species_docs.R | #' Get all PDF URLs from query URL
#'
#' Function description; defaults to title if left blank
#'
#' @param url The URL to be queried for PDF links
#' @importFrom xml2 read_html
#' @importFrom rvest html_nodes html_attr
#' @export
#' @examples
#' \dontrun{
#' url <- "http://www.nmfs.noaa.gov/pr/species/turtles/gre... |
359e3b84de243ff25851952073c64f80d5253d03 | 3b2dab7452682a8ebf0be7b81494dae3e83a8872 | /man/gemeindegebiet.Rd | c3fff82a68a79bf1ac55abb10f777c838014df13 | [] | no_license | swissgeodata4r/swissvector4r | 0ac63a0e58a56d246306716c7b39a5fec48e6551 | bbf78218f39c2f4742e9b16bd1899b8b98cda766 | refs/heads/master | 2020-04-14T21:23:50.542502 | 2019-01-26T00:04:27 | 2019-01-26T00:04:27 | 164,127,052 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,046 | rd | gemeindegebiet.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data_documentation.R
\docType{data}
\name{gemeindegebiet}
\alias{gemeindegebiet}
\title{Municipality border (Polygons)}
\format{An \code{sf} object containing polygon data with four features.
\describe{
\item{NAME}{Name of the country}
\i... |
07f411fde1f64a17261ff576a6fa5469144bcf80 | ea181fde79f86982f610a6e44cc528fe7bb9d220 | /exercise.R | 9ad9a3a8d03b214907cef67737523a06945c881f | [] | no_license | jenjong/Accounting | 74f3ce129f7f7408d246503a1ed341350a4a92ae | a3690ba862317b4a2d35ee597ece7b2593b3e7c4 | refs/heads/master | 2021-01-01T15:19:46.489129 | 2017-07-18T13:13:11 | 2017-07-18T13:13:11 | 97,592,280 | 1 | 0 | null | null | null | null | UHC | R | false | false | 16,083 | r | exercise.R | rm(list = ls())
gc()
#####################################################
# 제 1절 회계등식
#####################################################
자산 <- 300000
부채 <- 200000
자본 <- 100000
자산
부채 + 자본
자산 == 부채 + 자본
print(자산)
options('scipen' = 10)
print(자산)
부채 + 자본
자산 == 부채 + 자본
###########################################... |
fb520cfd6e8e3fb1b54c629fd409d884a60e5aa8 | 214216cefc96120cd1fbd484f43e4c1c86904ecd | /R/blr-plots.R | 8e520a0303219410bd1d97994febaedba28a26a2 | [
"MIT"
] | permissive | rsquaredacademy/blorr | 9fa8a0fc18b4bbbe91d04124aa4a64f3a47051b3 | 073f672bb830080dd666c7cac4ff5d342b3ce0ac | refs/heads/master | 2023-08-30T17:01:49.220745 | 2021-07-08T11:25:36 | 2021-07-08T11:25:36 | 91,309,738 | 18 | 3 | NOASSERTION | 2021-07-08T11:25:37 | 2017-05-15T07:49:49 | R | UTF-8 | R | false | false | 18,323 | r | blr-plots.R | #' Residual vs fitted values plot
#'
#' Residual vs fitted values plot.
#'
#' @inheritParams blr_plot_pearson_residual
#' @param line_color Color of the horizontal line.
#'
#' @examples
#' model <- glm(honcomp ~ female + read + science, data = hsb2,
#' family = binomial(link = 'logit'))
#'
#' blr_plot_residual_fitted(m... |
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