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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
59304bf3035203bdd2f8ce874faf6c7891d5fd8d | bc735be6bb9db9b04e423ff365b9b73dda73dfad | /hkhousing.r | 8fe5ae14eb1454d32e54898a51565a3db1ca958b | [] | no_license | fzhang612/r_utility_code | ada2d51624e40859b9cc9b7a1608beb4cb3dbd4f | 18c48499b882e18cb946da7de1cae34f1c3cd1d0 | refs/heads/master | 2021-01-15T16:16:01.351377 | 2011-09-13T14:26:02 | 2011-09-13T14:26:02 | 2,378,644 | 2 | 5 | null | 2018-02-02T01:25:46 | 2011-09-13T14:06:11 | R | UTF-8 | R | false | false | 4,441 | r | hkhousing.r | setwd('d:\\temp')
hkh <- read.csv('hkhousing.csv')
summary(hkh)
library(arm)
corrplot(hkh[,3:8])
pairs(hkh[,3:10])
pca <- princomp(hkh[,3:10], cor = T)
# pca <- prcomp(hkh[,3:8], scale. = T, retx = T)
# summary(pca)
print(pca)
plot(pca)
screeplot(pca)
biplot(pca)
loadings(pca)
pca$scores[,1] <- pca$scores[,1] * (-1)
... |
25634c4fd185e1ecfd40a83601a6ccb9f3ae8695 | e00befe0f92d42dd1f97e9304973f4b22da03af5 | /BCS_hist1/BCS_hist1.r | 28869962f5fe5d6dc1a04ff91c3b465d091f83da | [] | no_license | QuantLet/BCS | a706ffdc3cf8777b5443b2c66ff601c3bc517ee0 | 4a5d9fc2c058e5e02534ccb37898d9e9cf2edd9e | refs/heads/master | 2023-04-03T23:59:31.647499 | 2023-03-27T22:14:39 | 2023-03-27T22:14:39 | 51,316,067 | 4 | 10 | null | null | null | null | UTF-8 | R | false | false | 264 | r | BCS_hist1.r | # setting up the axis label size and margin (bottom, left, top, right)
par(cex.lab = 1.5, mai = c(b = 1, l = 1, t = 0.7, r = 0.5))
# histogram with relative frequencies
hist(nhtemp, freq = F, main = "", ylab = expression(hat(f)(x)), xlab = expression(x %in% K[i])) |
b56a5fb19e8a05fe283ce07e3403a3814151e2d2 | 7879e0b7476cfefe1d83b8760932a2978296103f | /tests/testthat/test_basic_calls.R | 01180c37f32a1cb9eb9c3cf4eb5b8cf2928c5792 | [
"MIT"
] | permissive | go-yohan/sppcredit | 75914cb30f37c67e12190c9d2f8f51f542510626 | 19597394d0a8c489857476f50c00f9c2c39e02e6 | refs/heads/master | 2020-12-30T16:45:32.897205 | 2017-09-21T20:35:49 | 2017-09-21T20:35:49 | 91,020,921 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,985 | r | test_basic_calls.R | LocalDriveDaPrice <- "//fs2/world/Analytics/Apps/Qlikview/FTR/SPP_DATA/Markets/DA/LMP_By_SETTLEMENT_LOC"
LocalDriveRefPrice <- "//fs2/world/SMD/Staff/SPP_Auction_Revenue_Rights/PY_2017_2018_ARR_Registration/TCR_REFERENCE_PRICES"
TestListPaths <- list(list(Source = 'NPPD_NPPD', Sink = 'SPRM_SPRM'),
lis... |
03efa51e297732e7fbbc6891baf4c27c22450c06 | a53c5982b99271edf657f293647bc62f2475a391 | /R/R source/lapply.R | f47504fd6d640745cca17c552ac687facee7aec7 | [] | no_license | iwannab1/kodb | 0206f1031c315b07712314dca9528cc766b13c1e | cbeabb44eaf6f486a095edc55a5d7cf1735d608b | refs/heads/master | 2021-01-10T10:27:40.103655 | 2017-06-14T10:56:55 | 2017-06-14T10:56:55 | 45,444,156 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,984 | r | lapply.R | x = runif(10)
la = lapply(x, sqrt);la
sa = sapply(x, sqrt);sa
va = vapply(x, sqrt);va
is.list(la);is.list(sa)
is.vector(la);is.vector(sa)
va = vapply(x, sqrt, double(1));va
is.list(va);is.vector(va)
# for
xs = runif(1e5)
res <- c()
system.time(for (x in xs) {
res <- c(res, sqrt(x))
})
res <... |
2dd6d8d73dd574d57371aeb357cf05b854f30a1c | 90855c181b6cda9f32065a41e4b2e2845359ae44 | /man/print.NCM.Rd | 3b85fef3b2ac05368572d68601a5e840892fe456 | [] | no_license | jfq3/NeutralModel | fbbb4696bea17b94852216b61b483b73be6c45c2 | 33a3a3267c8bc43ef28c6f0fd76dd5142a52bfeb | refs/heads/master | 2020-09-25T21:16:35.675170 | 2020-01-29T03:16:21 | 2020-01-29T03:16:21 | 226,090,222 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 467 | rd | print.NCM.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/print.NCM.R
\name{print.NCM}
\alias{print.NCM}
\title{Print method for class NCM}
\usage{
\method{print}{NCM}(x, ...)
}
\arguments{
\item{x}{Output of the function neutral_model.}
\item{...}{Other parameters passed to the generic print funct... |
5e30527810d3a2b2b4fa255a4862da60d697781c | 629d8c6ef6c86d475ac3730623dd943e872e2b90 | /Functions/predictplot.R | 0fcd40c5c40338cad8604c659bd87c61cdbb68df | [
"MIT"
] | permissive | djhocking/Small_Mammal_Synchrony | 773b895b98ded209ca67156b53aa330739063d33 | e999a31d01ff924489397d72afe094a08349d195 | refs/heads/master | 2021-01-13T00:15:58.384006 | 2016-02-15T15:10:51 | 2016-02-15T15:10:51 | 51,762,643 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,088 | r | predictplot.R | predict.plot.data.frame <- function(x,given,given.lab,layout,partial,
type=c("prob","logit","probit"),
identify.pred=F,scol="red",...) {
type <- match.arg(type)
resp <- response.var(x)
pred <- predictor.vars(x)
if(!missing(given) && !is.fac... |
c0e9b140697816548408241d6958e5a191d06700 | 7f17e160c10eaccfe433fb72ca39a9dfadb8ab9d | /SourceCode.R | 9eb097398094609b35c55ac684ecee52e3210aa9 | [] | no_license | lmilam/Information-Retrieval-Final | c954410c0f289da7bfdd889d5974cf2462f64ff2 | 5453aec97b75585ccbf08092c31460d0cc40fe50 | refs/heads/master | 2022-06-20T04:52:13.153824 | 2020-05-06T21:22:45 | 2020-05-06T21:22:45 | 261,602,384 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,249 | r | SourceCode.R | ## Created by: Lora Milam
## Group: 12
## Course: CS 4331 003: Information Retrieval
##
##
## Import dataset from download directory
winequality.white <- read.csv("C:/Users/default.LAPTOP-QPG8VPNC/Downloads/winequality-white.csv", sep=";")
##Use Regression Modeling to find relevant predictor variables
model01<-lm(fo... |
2daea9ddcff242e9724468c0c2db1927c7430eaf | a5d7a45cdc3bc85aacd36cf229a4d743bdfbcf50 | /func_write_nls_latex.R | 748afc37da498780e84da165b7b4930b9144d95b | [
"MIT"
] | permissive | calvinwhealton/R_tables_to_LaTeX | 3e8b8cffb44591db6b5b84837955ad304a8ad946 | 4933d1a60de2dbfe9248cea4cdb6511063158ffe | refs/heads/master | 2020-04-11T03:38:10.460577 | 2016-02-05T16:24:12 | 2016-02-05T16:24:12 | 40,672,919 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,037 | r | func_write_nls_latex.R | # funciton for writing NLS ANOVA tables into LaTeX tables----
# output can include more regression results as defined by user
write_latex_nls <- function(reg_obj # regression object
, name_tex # name for latex table
, name_var=NULL # names of variabl... |
1b8aeb6aba2e3c37b8d49dec693e7ee868980a5b | 9c85a0765978a98a46e617360af75417de5a1f14 | /cachematrix.R | 44ed8c85f08a33d8ceb4dc7801fd352be070b3e6 | [] | no_license | emmanuelt/ProgrammingAssignment2 | f2039fc2311238a9904d1d361ef9850ebc8056b7 | 34775e4c68d3c0aa78dd754f5691620e9b68c71e | refs/heads/master | 2020-12-25T03:50:34.171140 | 2015-03-20T10:29:58 | 2015-03-20T10:29:58 | 32,550,868 | 0 | 0 | null | 2015-03-19T23:04:28 | 2015-03-19T23:04:28 | null | UTF-8 | R | false | false | 1,275 | r | cachematrix.R | # The two functions are used to cache the inverse of a matrix,
# because matrix inversion can be too long to compute.
# makeCacheMatrix creates a list containing 4 functions:
# 1. One function setting the value of the matrix.
# 2. One function getting the value of the matrix.
# 3. One function setting the value of in... |
0a52a7b25f7a58ae15356994760e60810b155699 | b4177d4f2ed8400edc436b79b1594c804c7a8a52 | /code/functions.R | 7c3e1e9cf8124805f9deb538d824c8e7c18ce07a | [
"CC-BY-4.0",
"MIT"
] | permissive | effectsizer/stabilitycondcoop | 5d7e9a63ef6fe7df7df6fee5c2d7ded4d8e20f6d | 979abf87b5d3cfb7aa8be13b81ef233076c3bb75 | refs/heads/master | 2023-03-16T17:03:40.283719 | 2020-08-31T18:12:45 | 2020-08-31T18:12:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,140 | r | functions.R | # =================== DETERMINING TYPE GROUP========================================== #
cat("Loading functions...")
cat("\n")
# Types Classification
# Type number refers to the 10th base conversion of a strategy
# For instance LLL -> 0 LLM -> 1
class_table <- bind_rows(
list(type = 0, wide = 'selfish', narrow = ... |
7701cb3a9e4eae73ba4729b8931c501aad6a026c | 62cb50797a68620eb6ff32f86d1d47205bed2dac | /plot1.R | c18f212e9f575100d91eb944f1543a9f4f55f020 | [] | no_license | ellenkoenig/ExData_Plotting1 | 7c66852c92b636fc387cbdf00b5eb54bb42d29de | 0deed712f22e9efc6b0bd36b6c05b6db90638d41 | refs/heads/master | 2021-01-13T06:55:14.743562 | 2015-05-09T23:28:25 | 2015-05-09T23:28:25 | 35,269,714 | 0 | 0 | null | 2015-05-08T09:06:28 | 2015-05-08T09:06:28 | null | UTF-8 | R | false | false | 1,049 | r | plot1.R | # This file constructs a barplot from data on "Global Active Power"
# It assumes a file called household_power_consumption.txt with the data in the source folder
# Imports
library("plyr")
# 1. Read the data
data <- read.csv("household_power_consumption.txt", sep = ";", colClasses = c("character", "character", "numeri... |
9caa39305dc64d8de34b880638b6e49bb1b0fb08 | e3a93fff69205b76be167006e085dc36b0bf2f39 | /R/reverse_ip.R | 32c327d5b2074dfe6f1430ca6a9137abf64728e4 | [] | no_license | cneskey/domaintools | 78c0797c982f59e1472a97bb1a334a16686b4cb4 | 2d8a1199529adf3717022f9637f3f83b7c778958 | refs/heads/master | 2020-04-19T14:40:33.438229 | 2015-08-26T06:28:14 | 2015-08-26T06:28:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,131 | r | reverse_ip.R | #' Reverse IP
#'
#' The Reverse IP API provides a list of domain names that share the same
#' Internet host (i.e. the same IP address).
#'
#' @param ip IP address to perform the reverse IP query
#' @param limit Limits the size of the domain list than can appear in a
#' response. The limit is applied per-IP addre... |
39e0990be31c56443a8621370ff88d8b9d907c12 | 284c7b66d6db034a5ccfd34486eaeba8bc2ccaf6 | /man/helpr_render_json.Rd | 0b8c8a7f43b65b847a9d518fdc98e3021c9ea206 | [] | no_license | hadley/helpr | c9967cfabe6d510212a32d83643136b3b85d5507 | 2eeb174b09493f8d5c4c072772285c100f410a29 | refs/heads/master | 2021-01-11T04:57:20.128449 | 2012-04-09T14:34:21 | 2012-04-09T14:34:21 | 377,173 | 23 | 4 | null | null | null | null | UTF-8 | R | false | false | 275 | rd | helpr_render_json.Rd | \name{helpr_render_json}
\alias{helpr_render_json}
\title{Render JSON}
\usage{
helpr_render_json(obj)
}
\arguments{
\item{obj}{object to be rendered}
}
\description{
Remove the warnings from the rendering of the JSON
}
\author{
Barret Schloerke
}
\keyword{internal}
|
177139d4a385a4f6778dcfc2e0bca196cdc2c3a2 | 2a6acc51c0fa6941abca4ac9b8bbe307dac1a85b | /M-Tech Final Year Project/R Code/twittwr_data_extrac.R | bd18d3fc0dc22dd2dd047d6449caa6994c97df48 | [] | no_license | LEOKA037/AcademicProjects | 507a74f9c6b73a103f8fb33042e22b6e2ba867de | 2fd5166dbae7d6864d2ab61442d6096162d6d140 | refs/heads/main | 2023-06-06T10:10:49.883727 | 2021-06-24T18:35:22 | 2021-06-24T18:35:22 | 372,004,267 | 10 | 2 | null | null | null | null | UTF-8 | R | false | false | 425 | r | twittwr_data_extrac.R | library(twitteR)
api_key<- "dcc4QaXU4zYQ4r9Iw9RGkmJAA"
api_secret <- "42Bwmwv0Mtl5Zw50Cp1spVtDEnylfDI11gITDVswQ0LwIb2WT7"
access_token <- "372578663-2Am0nkaYZcsaKtdkVdRM6y7v3ho5jj6ZAKHKJ3H6"
access_token_secret <- "QyQvTldRa2d5k3dMZV4NsSfjWsyadnjdJAPAXAVvDgdsm"
setup_twitter_oauth(api_key,api_secret,access_token,a... |
7c0986b1bb6dbd1b92a72ad83c7b032c442c89fa | 5edb4b41956ab38660f8af2276722ce6fea3bba0 | /man/bsmds.Rd | 53f9869f87a810f801407d2aba602ecfe49af8f6 | [] | no_license | davidaarmstrong/bsmds | 4c88465c1b191db00e830f15b2feb72d11a982d6 | 4b313de19228f2a56c71b7104b815b78ddf6bfac | refs/heads/master | 2021-08-31T13:26:11.509552 | 2017-12-21T13:17:28 | 2017-12-21T13:17:28 | 115,007,902 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,177 | rd | bsmds.Rd | \name{bsmds}
\alias{bsmds}
\alias{bsfun}
\title{Bootstrap Confidence Regions for Multidimensional Scaling Solutions}
\description{This function implements a bootstrapping solution that identifies and optionally removes potentially degenerate solutions.}
\usage{
bsmds(data, dist.fun, dist.data.arg = "x", dist.args = NUL... |
19c72bf56665084cbbed20aec38f9eae12bc1514 | 7917fc0a7108a994bf39359385fb5728d189c182 | /cran/paws.analytics/man/quicksight_describe_group.Rd | 7fb0f3f44ae256f589c8561f642b88c197aeda54 | [
"Apache-2.0"
] | permissive | TWarczak/paws | b59300a5c41e374542a80aba223f84e1e2538bec | e70532e3e245286452e97e3286b5decce5c4eb90 | refs/heads/main | 2023-07-06T21:51:31.572720 | 2021-08-06T02:08:53 | 2021-08-06T02:08:53 | 396,131,582 | 1 | 0 | NOASSERTION | 2021-08-14T21:11:04 | 2021-08-14T21:11:04 | null | UTF-8 | R | false | true | 1,175 | rd | quicksight_describe_group.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/quicksight_operations.R
\name{quicksight_describe_group}
\alias{quicksight_describe_group}
\title{Returns an Amazon QuickSight group's description and Amazon Resource
Name (ARN)}
\usage{
quicksight_describe_group(GroupName, AwsAccountId, Name... |
4e76df55da662687d00cea55865af8e0fc122f92 | feb27e905198606cff9145e3e9a97ca58c3136cf | /app/server.R | 78f641784dec81b1e584e2c985425e58a21b2ab9 | [] | no_license | TZstatsADS/Spr2017-proj2-grp13 | 9acc8125de77d3866da35f7cd71210f95802dcee | 7edb4a1da27482bc7d1b4aaa8a815ef9eb484d36 | refs/heads/master | 2021-01-18T19:10:08.074138 | 2019-04-18T21:00:20 | 2019-04-18T21:00:20 | 80,873,358 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,824 | r | server.R | library(leaflet)
library(RColorBrewer)
library(scales)
library(lattice)
library(dplyr)
library(ggplot2)
collegedata <- read.csv("clean4.csv")
colnames(collegedata)[c(which(colnames(collegedata)=="LATITUDE"):which(colnames(collegedata)=="LONGITUDE"))] <- c("latitude","longitude")
collegedata$COSTT4_A <- as.num... |
0cce6228f5c51ae8ac0fac78fb60d0df0e6d5e8d | 95772ed7b2639221d1a564999c565cd6152aad87 | /DataAnalyse/Weather.R | 352c640da0f8508a9d0468854808938629f79671 | [] | no_license | tunjing998/bigdata_ca2_ang_tunjing | f46b71f8cc3295f69826ec0c69c4b2b22ba2b5d7 | 6c9632fab1185b7e23c6c3e37a43796b1ffb35f0 | refs/heads/master | 2020-10-01T22:11:09.350516 | 2019-12-20T22:20:14 | 2019-12-20T22:20:14 | 227,634,170 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,752 | r | Weather.R | library(mongolite)
library(car)
library(StatMeasures)
library(lubridate)
m <- mongo("dublin_weather",url = "mongodb://localhost:27017",db="bigdata_ca2_ang_tunjing")
#https://stackoverflow.com/questions/34454034/r-mongolite-date-query
dstart <- as.integer(as.POSIXct(strptime("2018-01-01","%Y-%m-%d")))*1000
dend <- as.in... |
dfb8e9edca87b94bf1af78e6138c10d20097cca4 | 902037115141ead7b315e7b63e437ec61c01c2c1 | /man/rowChisq2Class.Rd | eef866ac1d19dbe53b935595b52e9040ed1c8811 | [] | no_license | cran/scrime | 4bdc7e989ba9e648d004ca47cd2d10bb5e78a717 | cf0033dbfe2a6fa807593a460ef4bcb0931db96a | refs/heads/master | 2021-06-02T21:50:17.706604 | 2018-12-01T10:00:03 | 2018-12-01T10:00:03 | 17,699,500 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,302 | rd | rowChisq2Class.Rd | \name{rowChisq2Class}
\alias{rowChisq2Class}
\alias{rowChisqMultiClass}
\title{Rowwise Pearson's ChiSquare Test Based on Tables}
\description{
Given a set of matrices, each of which represents one group of subjects, and
summarizes rowwise the numbers of these observations
showing the levels of the catego... |
cd2a69a2074722f223a2bf31b09d6a1b6520c08f | 10501df54cf10edc869b457b41cf3ebef02c9240 | /man/resmerge.ij.Rd | 7e7f4e497bbb22f9296af3eff8e1baeface3a75b | [] | no_license | mattocci27/LeafArea | 6f08cc2b039b5ef171a62ee250f4604b2139bb23 | 5c5fbab817c59d68fc0203d02cfe7927aa46917e | refs/heads/master | 2023-08-25T15:47:09.664445 | 2023-08-10T05:48:29 | 2023-08-10T05:48:29 | 38,448,428 | 32 | 12 | null | 2023-08-10T05:48:30 | 2015-07-02T18:00:51 | R | UTF-8 | R | false | true | 1,473 | rd | resmerge.ij.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/resmerge.ij.R
\name{resmerge.ij}
\alias{resmerge.ij}
\title{File management}
\usage{
resmerge.ij(path, prefix = "\\\\.|-")
}
\arguments{
\item{path}{Path to the target directory}
\item{prefix}{Regular expression to manage file names}
}
\valu... |
9b38ba45b309c3585fc51943a8acff97a7ff3c35 | 4f1d5c9043a07db40001193b42fa40309ff57cc8 | /Ngram.R | 79c4b8fc1ece84f0a8e729a1bdaff80e6bbf940b | [] | no_license | jaiswalvineet/next-word-predictor | dc9136fd6aee03bb1011b2c364bc67dffa40fe54 | c11c09f09d0abd2872b25fca16f7ca14d522a5d5 | refs/heads/master | 2021-09-01T04:30:50.932849 | 2017-12-24T11:57:13 | 2017-12-24T11:57:13 | 114,632,359 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,514 | r | Ngram.R | library(tidytext)
library(stringr)
library(dplyr)
library(tidyr)
GetNGram <- function(rawFromETL, x) {
tokenData <-
rawFromETL %>% unnest_tokens(word, cleanData, token = "ngrams", n = x)
# if (x == 1)
# {
# data(stop_words)
# tokenData <- tokenData %>%
# anti_join(stop_words)
... |
9cd86b79f46becae8a8911224eab0f6e092e5b84 | d1aeb6d430dcd8b26128e5c8b1571b253ad50b93 | /jlimR/R/fm.1cv.ver_1h4.R | a05574e7bb6e2879b4321f7e3d0d5cc6dfad9d90 | [] | no_license | xtmgah/jlim | f6dafe427cc16f12b21154fa12fec9115b4c3ac1 | f239f542f6264347716be5d5d9c69526a49ff586 | refs/heads/master | 2021-01-19T22:13:37.422542 | 2016-11-18T21:36:49 | 2016-11-18T21:36:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,087 | r | fm.1cv.ver_1h4.R |
load.isLD1.flag <- function(file1, snpset.flag) {
# cohort 1
gt1 <- read.GT0(file1)
gt1 <- gt1[, snpset.flag]
ASSERT(ncol(gt1) == sum(snpset.flag))
# IN-SAMPLE LD
gt0 <- gt1
gt0 <- stdGT(gt0)
PL("mean gt0", range(apply(gt0, 2, mean)))
PL("sd gt0", range(appl... |
5109d54d4d2863391a8b060878dbf2d24de6f7e1 | 57122870e76b44ca4b85fd91a8ca2172ad28d077 | /App_directory/AirBNB_Final _project/server.R | b042d7d51adba469268f8b0f09834acfa0eb634f | [] | no_license | L-HommeSage/Airbnb_Data_Analytics_Shiny | 21bea3924b487ce288991bbc7373a1865e640879 | 817c807e8f915ba133d666056fd2037a823a5341 | refs/heads/main | 2023-01-24T14:56:48.539829 | 2020-11-25T20:17:40 | 2020-11-25T20:17:40 | 314,373,463 | 0 | 0 | null | 2020-11-21T18:10:18 | 2020-11-19T21:24:25 | R | UTF-8 | R | false | false | 8,313 | r | server.R | library(shiny)
library(dplyr)
library(ggplot2)
library(rlist)
library(googleVis)
source("getGzCsv.R")
source("utils_tab_deep_dive.R")
source("utils_tab_data.R")
source("utils_tab_comparing_cities.R")
function(input, output, session) {
# Initialization
berlin <- get_data("https://github.com/L-HommeSage/Airbnb_Data_... |
75b36bb843cf4f928a64c73c50295771b48e752f | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/3066_1/rinput.R | edab04189d1046db6d43e74b4d337444e2c39e8f | [] | 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("3066_1.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="3066_1_unrooted.txt") |
5e54dd8c23c37a13618052042a920c1e77873497 | 78d9d12918281c917925dbbfcb3525f100c27df8 | /TIDYVERSE.R | b796971f86690eb2b8b2e3898b5bcf9d7c26b1e0 | [] | no_license | JoeNoonan/tidyverse-workshops | cec3918aaef28a1a66237741725924981abc6f4b | c150a5dde0c2715c3d588ccdfefba10286c304eb | refs/heads/master | 2020-09-07T14:18:14.392723 | 2019-11-28T16:10:04 | 2019-11-28T16:10:04 | 220,807,430 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,557 | r | TIDYVERSE.R | library(tidyverse)
library(janitor)
### Read in data
### Reading Data Locally
gsodi_long <- read_csv("gsodi_long.csv")
### Reading Data from URL
gsodi_wide <- read_csv("https://www.idea.int/gsod-indices/sites/default/files/gsodi_pv_3.csv")
### NOTE final format is important. These commands open .csv (comma sepera... |
8d0bcc2e6ad0ce542f7badfca3e65ca8e5f2a7a1 | 0a38873c36cead1262eb3ac75a8ab1b93c4bef5a | /R/stdViz.R | 7271904e075e32db822f7043dcaffcec778674ed | [
"MIT"
] | permissive | jberesni/ASHviz | bbeb0a64d7f9b8b9ee108024f558bd33dd08667f | 7deff49b1d94591f2813747cdf399aa7a66e785b | refs/heads/master | 2020-04-01T03:38:53.929801 | 2019-05-16T18:42:54 | 2019-05-16T18:42:54 | 152,830,383 | 6 | 3 | MIT | 2018-10-13T03:54:39 | 2018-10-13T03:14:36 | R | UTF-8 | R | false | false | 2,244 | r | stdViz.R | ###########################################################
# AAS over time by Instance/Wait Class etc (Top Activity)
###########################################################
#
# build CPU, WAIT summary dataset of all instances over all samples
d <- ashDF %>% group_by(INSTANCE_NUMBER,SAMPLE_TIME,SAMPLE_ID,WAITCLASS... |
0eeceafa47e3e47edac4f6c4871f102ddb3799c9 | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /meteor/inst/testfiles/ET0_Makkink/AFL_ET0_Makkink/ET0_Makkink_valgrind_files/1615848534-test.R | b7e8a3cba1386d839e2a3132c1ef6afd7c4a788f | [] | no_license | akhikolla/updatedatatype-list3 | 536d4e126d14ffb84bb655b8551ed5bc9b16d2c5 | d1505cabc5bea8badb599bf1ed44efad5306636c | refs/heads/master | 2023-03-25T09:44:15.112369 | 2021-03-20T15:57:10 | 2021-03-20T15:57:10 | 349,770,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 425 | r | 1615848534-test.R | testlist <- list(Rs = numeric(0), atmp = numeric(0), relh = numeric(0), temp = c(1.03878561526028e-13, 1.23255483898152e-250, -4.20274050050721e+305, 9.32946680697566e+98, 1.22430160524861e-250, -7.14714235507037e-15, 3.29092470798389e-270, 3.55259342257796e+59, 6.38734728873395e+149, 1.2213594310705e+35, 7.2704491... |
9993aa6de72939a15c1467d450e45a0c2823435d | 57cc155e66f317cb235ff8c2df89a881aa8c5c76 | /functions/symmetrise_scale.R | 632adbaaa98dc6aaf28019928b8d9c20e05f6258 | [] | no_license | JustinCally/SexualSelection_Speciation | dfbae89b77966049671ba7416915844fad66cb7d | 42147a432ba3566398c1881fa91ceb6df043568c | refs/heads/master | 2021-06-05T14:14:26.780188 | 2021-03-02T22:08:58 | 2021-03-02T22:08:58 | 137,003,444 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,170 | r | symmetrise_scale.R | #Symmetrise Scale when using facet_wrap function
symmetrise_scale <- function(p, axis = "x") {
gb <- ggplot2::ggplot_build(p)
type <- switch(axis, "x" = "x.range", "y" = "y.range")
fname <- setdiff(names(gb$layout$panel_layout), c("PANEL", "ROW", "COL", "SCALE_X", "SCALE_Y"))
lims <- do.call(cbind, lapply(g... |
0865633bdd2cc9d04ff44abf18d13c88651cb330 | f713e7474358ca4ed3be9e73a5c389daaee5d193 | /HW1_Code and Report/HW1 Q1.R | a4b0205c8ef314fce8d1ad9fca6d04ef0d01a176 | [] | no_license | aduispace/EE232E_Graphs-and-Network-Flows | 73fee16aac4d34fdc80d844bee0e77e43924a18c | fce539c4ffaaf4c915898f161d2b46baf55b7147 | refs/heads/master | 2021-01-19T20:57:01.460757 | 2017-06-18T10:04:22 | 2017-06-18T10:04:22 | 88,581,294 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,658 | r | HW1 Q1.R | library("igraph")
#1(a)
p <- c(0.01, 0.05, 0.1)
nodes <- 1000
graph_1 <- random.graph.game(n = nodes, p = p[1],directed = FALSE)
graph_2 <- random.graph.game(n = nodes, p = p[2],directed = FALSE)
graph_3 <- random.graph.game(n = nodes, p = p[3],directed = FALSE)
degree_1 <- degree(graph = graph_1)
degre... |
a69db9b9ebaa182fd085ac308612a24acd5df301 | 146da93ef3da74b2022bfd635b42c3622774b38e | /cachematrix.R | 658e9e3a4adce1b23a5423d6c48b423ef4dd0256 | [] | no_license | dabooc/ProgrammingAssignment2 | 5ce56ca397f3b7b9c657c007aa528d64221deaa0 | d423af5994ae124d4d8349731495cf62b91e7546 | refs/heads/master | 2021-01-18T09:12:31.262206 | 2014-06-30T06:14:26 | 2014-06-30T06:14:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 984 | r | cachematrix.R | ## Allow to cache the invertion of a matrix
## Create a specific matrix object with a fied containing the inverted matrix if already computed
makeCacheMatrix <- function(x = matrix()) {
cachedInvertion = null
## retrieve the original matrix
get <- function() {
x
}
## cache the inverted matrix
se... |
a99b249e491f2aabfa37f45920c8fd0ced1edf8b | 3cc6265e82e373d377dae488831cfdb1caad1dfe | /codedepends/DTL/plot_DTL.R | 3dfc1f25abe03ba7de28ed80cc4d4886c3308ef2 | [] | no_license | clarkfitzg/phd_research | 439ecc0d650da23bfad1e1a212e490c2746a6656 | dfe46c49f6beba54389b0074e19f3c9b1ea04645 | refs/heads/master | 2020-04-15T14:02:03.890862 | 2019-09-20T02:33:07 | 2019-09-20T02:33:07 | 59,333,323 | 6 | 3 | null | null | null | null | UTF-8 | R | false | false | 323 | r | plot_DTL.R | source("../depend_graph.R")
#igraph_options(plot.layout=layout_as_tree)
s = readScript("DTLfirst.R")
# I see 5 edges coming into 24 on the graph, which corresponds to the 5
# inputs in s[[24]].
info = lapply(s, getInputs)
g = depend_graph(s, add_source = TRUE)
write_graph(g, "graph.dot", format = "dot")
# 167 edg... |
5f30a8a1bee0671df37023286e1bd69bd4c02d70 | 9bfe3914043180472c6ec1b0ab8b5c6b5b934211 | /R/partition.R | 40e12574274e0c372a02e378b47f0b17a7c591ec | [] | no_license | cran/splitTools | e7667cfe0a2c2706266acc73a7b7d9160691f21a | 701b046f2103a41bc3b7496800dd8aa48d064f43 | refs/heads/master | 2023-06-09T04:57:33.937514 | 2023-06-06T13:00:02 | 2023-06-06T13:00:02 | 236,901,439 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,503 | r | partition.R | #' Split Data into Partitions
#'
#' This function provides row indices for data splitting, e.g., to split data
#' into training, validation, and test. Different types of split strategies are
#' supported, see Details.
#' The partition indices are either returned as list with one element per partition
#' (the defa... |
26b2ba2f5cbcc0e27ef8d37f489832e7555c7da4 | 6425ad71279b9744c1fc192df9f989129aec6397 | /R/p14.R | 4e9d6a0690303925f2d78fb6f81fbf64939c0238 | [] | no_license | dxe4/project_euler | b84d0df13c060b19cfd17be6ccfe5543f2ecc1b5 | 5e9a6a62f7fa3947d0a9ac16ced666afa1e0e4cc | refs/heads/master | 2023-06-08T13:24:38.296566 | 2023-05-29T15:04:10 | 2023-05-29T15:04:10 | 91,951,924 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,451 | r | p14.R | # 14 | Longest Collatz sequence
<<<<<<< HEAD
=======
#
>>>>>>> aa005b7... Added support to linebreaks if files are executed via command line.
# https://projecteuler.net/problem=14
#
# The following iterative sequence is defined for the set of positive integers:
#
# n → n/2 (n is even)
# n → 3n + 1 (n is odd)
#
# ... |
3770ae26294ae60813390dd91ef41bddf334423d | b605d5e30be6eac950c2962dc1e0b6496f28790f | /R/compliance.R | f012cdd7e25e906850e5ebe9fce57ad8865451ce | [] | no_license | khalidharun/DBI | 8a723be4f1ecf749df7803cdc4c5f9663008df25 | 7a0ad76dea21a846cee62f67108ab8e8b7d60a49 | refs/heads/master | 2020-12-25T07:15:03.893858 | 2015-10-30T09:28:01 | 2015-10-30T09:28:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,988 | r | compliance.R | #' Check a driver for compliance with DBI.
#'
#' @param driver Driver name.
#' @param pkg Package that driver lives in - is usually "Rdriver"
#' @export
#' @examples
#' if (require("RSQLite")) {
#' dbiCheckCompliance("SQLite")
#' dbiCheckCompliance("NoDriver", "RSQLite")
#' }
dbiCheckCompliance <- function(driver, pkg... |
c1d9103a37b98d78281b2ee31d996d6af83f7360 | e8e5bc16d8c74bb95bfcbf867cf92d9983410f49 | /R/pure_soda.R | 0a88f3d54429af723641eab0a16c4fe95ce2f6df | [] | no_license | cran/sodavis | 1a0207cf615d8f0d8821a4049bb525a6eb568e06 | ca871473e9284c0c7926a1f9064fe12366d36a63 | refs/heads/master | 2021-01-10T13:15:05.917563 | 2018-05-13T20:24:03 | 2018-05-13T20:24:03 | 48,088,856 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 24,060 | r | pure_soda.R | library(nnet)
# create predictor matrix from terms
create_pmatrix_from_terms = function(xx, terms)
{
nt = length(terms);
nr = nrow(xx);
pmatrix = matrix(0, nr, 0);
if (nt > 0)
for(it in 1:nt)
{
term = terms[it];
if (grepl("*",term,fixed=T))
{
splits = strsplit(term,"*",fixed=T)[[1]];
... |
9d3d0543bab79eed9d04e3c653c06437d275fc59 | 34542f2bdf76012b3bb93d458de4ca6068477258 | /man/my.bisec.Rd | f59008bac1e36b9e15027c4386142a351b798aa6 | [] | no_license | cran/ebGenotyping | 8f3c20d8f7af36951a09a5e5af226578c6c134b6 | 994a6072f2ba4c00fc5762262c2a29353de581cc | refs/heads/master | 2021-01-18T21:13:58.068228 | 2016-04-13T09:28:12 | 2016-04-13T09:28:12 | 35,105,609 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,660 | rd | my.bisec.Rd | \name{my.bisec}
\alias{my.bisec}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Bisection method to find the root
}
\description{
This function is to apply bisection method to find the root of a function f.
}
\usage{
my.bisec(f, int.l, int.u, eps = 1e-06)
}
%- maybe also 'usage' for other object... |
511b40770ee07c6d43ff5465121264e6a33e7dd6 | e17f9b6cb4c2fc0b37fc744e8e448de834fc435e | /Lasso regressin Analysis.R | a2d99b4ea098e82501ea77b962f8eaf0bddfa893 | [] | no_license | kamleshthakur123/R-for-Data-Science | 4a1ec2175733453fc55ff4fbdbf8a1706da54b39 | d434d254bdb2c5015e92bd72ba7dd110ab383a91 | refs/heads/main | 2023-07-05T20:19:48.431862 | 2021-08-15T17:54:10 | 2021-08-15T17:54:10 | 396,436,436 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,211 | r | Lasso regressin Analysis.R | #### library Needed
library(caret)
library(glmnet)
library(mlbench)
library(psych)
data("BostonHousing")
str(BostonHousing)
pairs.panels(BostonHousing)
### scatter plot of every variables which are numeric
pairs.panels(BostonHousing[c(-4,-14)],cex=2)
### data partition
set.seed(222)
ind <- sample(2,... |
75a5a172686f0fadb630166ff8f161e61b711b21 | 723da7e475a2814b820cc9507a3d041233117221 | /R/getROI.R | f5552517ad9093ce192b098d14190931dfba9bbf | [] | no_license | tziol001/Project-Geoscripting | d0ca9995dc4d1eb5933f6c901b3d8165f0ae1449 | 2ebaca8e68b6a7cde6a182bdde644486f6e87121 | refs/heads/master | 2020-04-12T19:47:56.262904 | 2015-01-27T05:13:34 | 2015-01-27T05:13:34 | 29,669,321 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 403 | r | getROI.R | # this function...
getROI <-
function(raster, ROI)
{
# project the layer using the ndvi raster projection
ROI_proj2raster <- spTransform(ROI, CRS(proj4string(raster)))
# mask the Lelysta forest
masked_ROI <- mask(raster, ROI_proj2raster)
# get the values for a raster brick into a new data frame!
valuetable <- g... |
aff886412a1b59e1ce0a95766a5909b3131fddca | b7534c7b2ec799d74c81d5d5a42d4733ea03a8f8 | /data_src/final_analysis.R | 6c77895503933e1d163da03c3a7f282cc9045288 | [] | no_license | robbizorg/github_msd_project | 66a6fbb02e2bab9dcd3be0088569912b95cd7310 | 1a77d0ab1c5afa26723ead09c57be7380319ea56 | refs/heads/master | 2021-01-20T03:30:17.444342 | 2017-05-06T03:21:09 | 2017-05-06T03:21:09 | 89,548,153 | 3 | 6 | null | null | null | null | UTF-8 | R | false | false | 9,551 | r | final_analysis.R | #### BIO_ANALYSIS ####
# Desc: File just for the Analysis of the Profiles with Bios
library(tidyverse)
library(readr)
library(glmnet)
library(ROCR)
library(lda)
library(randomForest)
library(tm)
library(topicmodels)
library(tidytext)
set.seed(42)
final_data <- read_csv("~/Documents/MSD/github_project/data/final_data.c... |
55401144a540e79523d42cd1af680b8f4eda6490 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/TSA/examples/ma1.1.s.Rd.R | a821c1f387a69b400eea1417aa9c027ed9bf07ba | [] | 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 | 204 | r | ma1.1.s.Rd.R | library(TSA)
### Name: ma1.1.s
### Title: A simulated MA(1) series / time series
### Aliases: ma1.1.s
### Keywords: datasets
### ** Examples
data(ma1.1.s)
## maybe str(ma1.1.s) ; plot(ma1.1.s) ...
|
0694654d1240a4d22c271f0aa2b8c65fcaf1c6f0 | e40018718c0df266e207e79351c4ddc6bca187d6 | /seminar11/Seminar 11 In class Exercises.R | e9ae97f471a5770ea337758a973977650d26d329 | [
"MIT"
] | permissive | mutazag/mda | 11fbb51d5cd8cf375a25eb77b717f3861ce4278e | c12f314a288ba9b1c314c2e22f8446bae075f3cb | refs/heads/master | 2023-01-03T14:14:02.755096 | 2020-10-30T02:57:04 | 2020-10-30T02:57:04 | 284,615,927 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,377 | r | Seminar 11 In class Exercises.R | install.packages("MASS")
install.packages("klaR")
library(MASS)
library(klaR)
creditcard_data<-read.csv("C:/Documents/creditcard.csv")
test_rows<-sample(1:nrow(creditcard_data), round(nrow(creditcard_data)*0.2) ,replace=FALSE)
creditcard_data_training<-creditcard_data[-test_rows,]
creditcard_data_test<-creditcard... |
a3c8be863947f5d7a466b61e968c127c0da95399 | 243e5a608115856910a19296fffb63f44c09f0f2 | /Data Manipulation with Dplyr and Tidyr.R | eb63fb9f311def8a7970b4dacc36650b3bde8187 | [] | no_license | koseogluonur/R-for-Data-Science-and-Machine-Learning | 53759ea87409fcb2080109d9f18a933095c2d0c0 | 19fbe910ad89640b21fc5808fc646bf6183bdfa1 | refs/heads/main | 2023-04-09T21:48:11.406448 | 2021-04-25T12:33:25 | 2021-04-25T12:33:25 | 356,064,123 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,438 | r | Data Manipulation with Dplyr and Tidyr.R | install.packages("dplyr")
library(dplyr)
mtcars
library(DT)
datatable(mtcars, options = list(scrollX=T))
library(magrittr)
library(dplyr)
arrange(
summarize(
group_by(
filter(mtcars, carb > 1),
cyl
),
Avg_mpg = mean(mpg)
),
desc(Avg_mpg)
)
a <- filter(mtcar... |
ba58ed9c2b356ccf6946e0959fd9e6f4cb1bfcce | f73df032972334544e67fcd0c5782a2d85775ccf | /ProblemSets/PS5/PS5_McGuire.R | 5c3b4f5540f5b6734951b53b3feaee69d0a00fdb | [
"MIT"
] | permissive | kmmcguire/DScourseS20 | ad8d04d8a10014a01a5d123b60ee6e45d7ac23c5 | 0bc2ff3bbd2d3f8766e1bb7f09631c13b4bd9124 | refs/heads/master | 2020-12-13T11:59:08.629335 | 2020-04-14T15:03:11 | 2020-04-14T15:03:11 | 234,405,546 | 0 | 0 | MIT | 2020-01-20T22:43:47 | 2020-01-16T20:27:50 | null | UTF-8 | R | false | false | 2,573 | r | PS5_McGuire.R | library(rvest)
polling_avg <- read_html("https://en.wikipedia.org/wiki/Nationwide_opinion_polling_for_the_2020_Democratic_Party_presidential_primaries")
table <- polling_avg %>% html_nodes("#mw-content-text > div > table:nth-child(18)") %>%
html_table(fill=TRUE)
table <- table[[1]]
#Initialize... |
9a4ee0950d7a12a10ee92188a6d34926f8c66d88 | 9250a2e62021ff09a2010278d313303af11a3250 | /R/as.timeDate.R | 5fbb3c0c8b4621d12609e176ac119ef06269713e | [] | no_license | cran/fCalendar | 9d85636646e543aa2ed9f20d208bc3d33926df43 | 26c9aeef7d2d189a193242ef6bd7992b9d85512b | refs/heads/master | 2016-09-05T22:37:38.860585 | 2009-05-25T00:00:00 | 2009-05-25T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,157 | r | as.timeDate.R |
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Library General Public
# License as published by the Free Software Foundation; either
# version 2 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be use... |
6ffda2266d479b293545b08eef0c09a67176b131 | f9f7f2fb38348204e3869259b867d4fa0480cbe9 | /NB.R | c8f538baee7c922ab508fcb4e2f83a42cc0eff72 | [] | no_license | ajmals/adult-dataset-decisiontree-naivebayes | c7ca055a945d01ab138911b72a1cf7bc3b019b65 | 91d37cd60aba40396b975be2f8137d33541ff9ef | refs/heads/master | 2021-10-23T13:02:19.318578 | 2019-03-14T16:40:33 | 2019-03-14T16:40:33 | 175,661,159 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,445 | r | NB.R | library(DataExplorer)
#library(naniar)
library(caret)
#library(ggplot2)
#library(dplyr)
library(e1071)
#training Naive Bayes classifier {e1071}
NBclassifier_e1071 = naiveBayes(income ~ ., data = trainAdf)
summary(NBclassifier_e1071)
#naiveBayes(formula, data, laplace = 0, ..., subset, na.action = na.pass... |
a25d36aafe3b9e38686476b92c5a8a78e4806a5a | e9c16352f28947839a66c3a866c1b54ecec5c870 | /Modules/generate_data.R | f90e15633b1edfd953c2baebb5b0ef7f54f65f11 | [] | no_license | AsgerMorville/GPLVM | 35dac980118ca3147495adee94298b2e96e9874d | 06f59df8798f228c6354d25545d3d5fee6c3135c | refs/heads/master | 2020-07-01T18:13:11.233987 | 2019-11-05T11:03:57 | 2019-11-05T11:03:57 | 201,251,037 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 36 | r | generate_data.R | generate_data <- function(n,t,q){}
|
f33ce9640e331a15ac475591a619b806e5567f91 | 3a4c8fc4e09edb9be762949b266192fb4abbf22e | /plot_mpp_holes.R | 0c2bcceeeb90ab755512a6743d08f3fb9d68ad64 | [] | no_license | kreitmew/mpp | 4a195f0fa92f9ba9172336d80859a7edbcc8cd01 | a5e8313c559dbe91dc741d78a954a46b48121b8d | refs/heads/master | 2021-05-08T15:04:41.111065 | 2018-03-02T18:44:15 | 2018-03-02T18:44:15 | 120,103,497 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,433 | r | plot_mpp_holes.R | plot.new()
plot.window( xlim=c(0,9), ylim=c(0,9), asp = 1 )
par(lty = 2)
do.call(rect, placeCellPanel(9, 9)[[1]])
do.call(rect, placeCellPanel(9, 9)[[2]])
do.call(rect, placeCellPanel(9, 9)[[3]])
title("Plattenfronten - Porositaet massstabsgetreu")
do.call(draw.circle, do.call(placeCircles , c(list(input$phi1ui, i... |
57369dae9cc6aae85064e03745343f212c8db6c3 | 0ff91c44fcc9c644803cc513aee916fceda85fa5 | /get_10-K_filings.R | b86d56fd4b52cd147ca5aab50be7f9c0051609f9 | [] | no_license | iangow/filings | 6cfcb8845986d43b7aaa92b9241c110c766b46a5 | c9466887d0a3f621ffc53ba207f71f09ce80f412 | refs/heads/master | 2023-05-28T00:43:02.455003 | 2023-05-16T18:49:35 | 2023-05-16T18:49:50 | 50,131,907 | 18 | 11 | null | null | null | null | UTF-8 | R | false | false | 4,665 | r | get_10-K_filings.R | library(dplyr)
pg <- src_postgres()
# The name of the local directory where filings are stored. ``
raw_directory <- "/Volumes/2TB/data/"
filings <- tbl(pg, sql("SELECT * FROM filings.filings"))
extracted <- tbl(pg, sql("SELECT * FROM filings.extracted"))
# Pull together a list of all proxy filings on EDGAR
file.lis... |
cecc26e666a53ef6d7488fc28714ac31dc85e8d8 | 8c1333fb9fbaac299285dfdad34236ffdac6f839 | /equity-valuation/ch3/placeholder-02b.R | 804fd6ff49080bc4b1039e0e5f0deaf5da396277 | [
"MIT"
] | permissive | cassiopagnoncelli/datacamp-courses | 86b4c2a6d19918fc7c6bbf12c51966ad6aa40b07 | d05b74a1e42b119efbbf74da3dfcf71569c8ec85 | refs/heads/master | 2021-07-15T03:24:50.629181 | 2020-06-07T04:44:58 | 2020-06-07T04:44:58 | 138,947,757 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 70 | r | placeholder-02b.R | # Calculate the Mylan Unlevered Beta
myl_unl_beta <- ___
myl_unl_beta
|
1f9cd94c315a51260a22e97227f9fe08a95433ab | 1a0a23794b5b1cedc885db7ff200b5c9fbc29463 | /scripts/2.1_prepare_data.R | 43c9ea94bc5432f09084bd94981898189d8327fd | [] | no_license | mreddoh/brownlow | da6a2aacd19d39ac290660fcc01303e7c1fbec8b | 62ee22c17768851e880da2e772f0a2fd41ea6b9b | refs/heads/main | 2023-08-07T05:18:51.603489 | 2021-09-15T14:27:17 | 2021-09-15T14:27:17 | 405,284,552 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,077 | r | 2.1_prepare_data.R |
# Load packages ----
library(tidyverse)
library(here)
# Load data ----
load(file = here("data","player_data_2021.Rdata"))
load(file = here("data","player_data_full.Rdata"))
load(file = here("data","player_data_partial.Rdata"))
# Wrangle data into model-able dataset with normalised and cleaned variables ----
# No... |
522019836050c63bc34d0c60edd514ebc01bf118 | 25ba484e7a24eb8b6966c489b05ac828481f5807 | /script.R | 656122f3e0ea1e5200148d35161e9a21695d0ddb | [] | no_license | yemeth/RepData_PeerAssessment1 | e279e21b311bf57374c6b3896ae80c019ce218ce | c73d8911a5e76ffde48029b84cb5e0da41e8f289 | refs/heads/master | 2021-01-18T07:32:49.415471 | 2015-02-14T21:33:28 | 2015-02-14T21:33:28 | 30,795,455 | 0 | 0 | null | 2015-02-14T12:03:43 | 2015-02-14T12:03:43 | null | UTF-8 | R | false | false | 4,197 | r | script.R | ## Read Data
url<- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip"
fileName <- "activity.csv"
zipFile <- "activity.zip"
if (!file.exists(fileName)) {
if (!file.exists(zipFile)) { download.file(url,destfile=zipFile) }
unzip(zipFile)
}
activityData <- read.csv(fileName, colClasses = c("integ... |
359c6ccefe2d556f7b5dd932977a1477d831a93d | a249beeec2598922dc69817a68d5bc7e6b1586ab | /vignettes/dobtools-vignette.R | f0bfdaa7d3e7d29711379347cbec365184ffab52 | [] | no_license | aedobbyn/dobtools | 9c9b56241c65d37d318923bd546a03ce5963b43f | f63664430648e48f6ded8dade3afe55699c025bf | refs/heads/master | 2021-01-19T21:24:33.469420 | 2019-05-03T21:13:28 | 2019-05-03T21:13:28 | 101,250,864 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 298 | r | dobtools-vignette.R | ## ----setup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----eval=FALSE----------------------------------------------------------
# # install.packages("devtools")
# devtools::install_github("aedobbyn/dobtools")
|
9cea3de0474744d0dba97c6ce89edb5ab820b48c | caad1dd61c8683f3e71f4a41d155cd39093994bf | /NMR_WORKFLOW.R | f5d5eb4e15784818401de1884e431a99c98d6821 | [] | no_license | talbenhorin/NMR_Sophie | c755a64b74ed7ab91db2a67683effe48e5015fb4 | 110b13262c98a2486e21456582d15466a195c87c | refs/heads/master | 2023-01-01T19:01:07.069409 | 2020-10-27T14:04:32 | 2020-10-27T14:04:32 | 307,721,161 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 214 | r | NMR_WORKFLOW.R | library(Rnmr1D)
data_dir <- system.file("extra", package = "Rnmr1D")
RAWDIR <- file.path(data_dir, "CD_BBI_16P02")
CMDFILE <- file.path(data_dir, "NP_macro_cmd.txt")
SAMPLEFILE <- file.path(data_dir, "Samples.txt") |
d2ad67d47c5ca51a5da47777200856a19adae4a6 | 6c897e166c1e717c5fd2495e9d9cf14e0d674eca | /PerStoreLM.R | 47b2febc4bc8d3305f6808020be5324105b18c62 | [] | no_license | tsuresh83/KaggleRossmanSalesPrediction | 388a4a704bee87b5c6a3a5c06462b78448bf75ea | 456bd99dbde455df44ae2c22e47d4afb6b875b85 | refs/heads/master | 2021-05-03T13:14:08.524947 | 2017-08-17T21:20:56 | 2017-08-17T21:20:56 | 72,159,181 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,940 | r | PerStoreLM.R | rm(list=ls())
library(xgboost)
set.seed(13)
startTime <- Sys.time()
scriptName<-"PerStoreLM"
set.seed(13)
os <- Sys.info()[["sysname"]]
nodename <- Sys.info()[["nodename"]]
trainFile <- ifelse(nodename=="bigtumor",("/home/tumor/MLExperimental/rossman/data/train.csv"),
ifelse(os=="Darwin",
... |
dbad557689f027d9e7170d62454ffe7f5ee3a0cf | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/4536_10/rinput.R | 64470c2a1b4251de289fc22e611ad6bceb070f13 | [] | 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("4536_10.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="4536_10_unrooted.txt") |
5a1d5b335e03d605846cd384144760ac00e5bcdb | da3112d28186f4000ab3aa285b9be9879da69595 | /man/surv_table.Rd | 0edca5dfb9ba49dafc70ec6bd3dfe5c091e11dac | [] | no_license | Huaichao2018/rawr | 08c0456d9822ae9654a11891bc47bf3c7a49b466 | 95857be33dd7128ab5ad48875a66c249191a2bd7 | refs/heads/master | 2023-01-19T02:07:17.790103 | 2020-11-26T04:50:44 | 2020-11-26T04:50:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,574 | rd | surv_table.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/surv.R
\name{surv_table}
\alias{surv_table}
\title{Summary table}
\usage{
surv_table(
s,
digits = ifelse(percent, 0L, 3L),
times = pretty(s$time),
maxtime = FALSE,
percent = FALSE,
...
)
}
\arguments{
\item{s}{a \code{\link[surviv... |
05e9f720cb58203d7534a4d813016e2252da1f13 | acf901961c3cebee84734b0dc19699f57786f05d | /man/nat_palette.Rd | 0c514c9fdd2536b99b14b939031e4fd52969af33 | [] | no_license | thomased/natpalette | e7040f0dfb93d22340f24f442892dbbbd3824d01 | a401faaabaa699662ac5c6e60384928a758d6043 | refs/heads/master | 2021-01-21T10:04:41.507440 | 2017-10-17T20:25:24 | 2017-10-17T20:25:24 | 83,362,706 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,239 | rd | nat_palette.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cols.R
\name{nat_palette}
\alias{nat_palette}
\title{A 'natural' colour palette generator}
\usage{
nat_palette(name, n)
}
\arguments{
\item{name}{The desired palette. One of:
\itemize{
\item \code{Acripeza}: The mountain katydid \emph{Acrip... |
10a28b5728545a3ff41a7c42e4feebba24f2a56e | af1d99a02986da336132c449b43e85473a783391 | /07_junk/script_old.R | 5f4d84b10308c00de3370db8b224b95052eaaf02 | [] | no_license | DeAngelisA/RRRRsandbox | 46a502dcbb6a339ed1204552ac86f2d8afd2cf23 | 829f34606ae885384ba416a0489990016d3a09fd | refs/heads/master | 2023-04-18T03:40:10.164062 | 2021-04-23T13:54:57 | 2021-04-23T13:54:57 | 266,096,185 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 825 | r | script_old.R | # Messy Data Project
library('tidyverse')
setwd("C:\Users\andre\Dropbox\Lavoro\Teaching\1-templates-lessons\messy-tidy-projects\Messy Data Project")
load("DSP_original.Rdata")
DSP_rec <- DSP_Dataset_v1 %>%
select(country_name, country_text_id, country_id, year, v2smgovdom, v2smregcon, v2smhargr_0, v2smpolsoc) %>%
... |
0297f63a2ae95a371af1b2bce59a580d1c382969 | d81a869717f6ac0c70799463025118ce11ca8951 | /knapsack/man/knapsack-package.Rd | 7f48558094ea0d152b7c0a78560b855648d69e25 | [] | no_license | zoepatton/Lab6 | 5e14e6abd4186c56478b096cea077a40a7172daa | 1e52ff2064002c5ab3acf798c18300482072072e | refs/heads/main | 2022-12-31T08:17:47.421984 | 2020-10-26T15:52:35 | 2020-10-26T15:52:35 | 301,396,766 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 802 | rd | knapsack-package.Rd | \name{knapsack-package}
\alias{knapsack-package}
\alias{knapsack}
\docType{package}
\title{
\packageTitle{knapsack}
}
\description{
\packageDescription{knapsack}
}
\details{
The DESCRIPTION file:
\packageDESCRIPTION{knapsack}
\packageIndices{knapsack}
~~ An overview of how to use the package, including the most import... |
65d863e52b8a98f00ea29e44850a1e7badac6db1 | f5feacda6bcf986bf61cdfa57f5387ed7e651918 | /man/amean_byelt.Rd | c855a7f0d1f2e34e9f026453f0119c48ab32206a | [] | no_license | cran/functClust | 3386c3179bdf9e255bfec00ed8f39b6c3c696da1 | f7415612fbc0fd749a1da01e822b6217e2b8bb0e | refs/heads/master | 2023-01-20T01:30:18.270906 | 2020-12-02T09:30:02 | 2020-12-02T09:30:02 | 318,755,202 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,655 | rd | amean_byelt.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/calibrating.R
\name{amean_byelt}
\alias{amean_byelt}
\title{Arithmetic mean
by components occurring within an assembly motif}
\usage{
amean_byelt(fobs, mOccur)
}
\arguments{
\item{fobs}{a numeric vector. The vector \code{fobs} cont... |
e33bccd1caee0d724d4b1043687286bca6f6bb71 | 63621c988789745da5d51c70c52330a28be21b92 | /GWAS/plots.r | 1ed58b875154082f7c1fdee0f34c5e2983e20c90 | [] | no_license | meyer-lab-cshl/Genetic-association-studies | 90514bc218c0d75b0697d812afa078c4c3d342bd | 73edc9d464184075908424565b393b5956d8dc0e | refs/heads/master | 2022-08-02T09:56:44.711666 | 2020-05-22T18:46:53 | 2020-05-22T18:46:53 | 116,718,777 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,461 | r | plots.r | #' Manhattan plot for genome-wide association studies
#'
#' The results of a genome-wide association study are visualised the genomic
#' location of the tested genetic variant on the x-axis versus its -log10(p)
#' of the association on the y-axis. Optionally, lines for suggestive and
#' genome-wide significance can be... |
89ac82827af0153079853c8a2d0164805221e1b0 | 76c3a6c9158747b5d64f0f7aec2a52d036b37ea4 | /Code/4. Clean WCQ data.R | a6bec9ffa8e828e0dface03fa83b0fee4758c0fb | [] | no_license | JulianEGerez/WCQ | a3106f009e6b80a9128487734b2ffede4f937d3b | c2ebab63327cd1993d38a1e7cd4494b74368f858 | refs/heads/master | 2020-03-10T23:49:18.699002 | 2019-03-01T22:42:35 | 2019-03-01T22:42:35 | 129,648,858 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,894 | r | 4. Clean WCQ data.R | # This file cleans WCQ data using regular expressions
# Created by Julian Gerez
# Convert from factors to character
wcq_data[,2] <- as.character(wcq_data[,2])
# Read in match dates
# There are three types: DD.MM.YY, DD- M-YY, and DD-MM-YY
wcq_data$date1 <- str_match(wcq_data$combinedtext, "[0-9]{2}.[0-9]{1,2}.[0-... |
cf397cf1f83ebec8883ffc956e97cd0f89680dba | a57ff02873a753f8cb3c21c3cbbbe4c642aa633e | /R/aula_6.R | 98b1b322118e0ecaf146e70d49dc2eaff4ea3cd1 | [] | no_license | luhne/segundaa | 0ee30be0cdd0213606f3c53d73c3b1c0d4d2c658 | d450276d21a130672a6de143854fdc1b6b28235c | refs/heads/master | 2020-06-20T08:07:12.658353 | 2019-09-05T14:28:06 | 2019-09-05T14:28:06 | 197,053,850 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,522 | r | aula_6.R | install.packages('modleR')#soon!
install.packages('rJava')#soon!
install.packages('raster')#soon!
install.packages('rlang')#soon!
install.packages('dplyr')#soon!
install.packages('rgdall')
library(rlang)
library(modleR)
library(rJava)
library(raster)
library(rgdal)
# === aula 6 Análise Exploratória de Dados ===
data... |
ca98ab2baa881337f962cb207fe54546d3910d0d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/rqPen/examples/groupMultLambda.Rd.R | f702cc6965e9bf8461f6785ce8bd7b6538a0e753 | [] | 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 | 358 | r | groupMultLambda.Rd.R | library(rqPen)
### Name: groupMultLambda
### Title: Quantile Regression with Group Penalty for multiple lambdas
### Aliases: groupMultLambda
### ** Examples
## Not run:
##D x <- matrix(rnorm(400),nrow=100)
##D y <- 1 + x[,1] - 3*x[,3] + rnorm(100)
##D cv_model <- groupMultLambda(x,y,groups=c(rep(1,2),rep(2,2)),lam... |
0d045a3578644be10fd44f68771271e4929d4267 | 85f6d81be7c6f4f3168939888bc4f7f6e59ee758 | /factors.r | be55194a71e654946580d79154b90c23f2ae92ba | [] | no_license | davidgrenier/artOfR | 65ac328c247fa6f1f5176ee1d318dbb3a4203ee8 | 200e5a56c1e74d7c710ffee701f8ee249e7afcb8 | refs/heads/master | 2021-01-01T19:58:55.377827 | 2018-04-13T11:30:40 | 2018-04-13T11:30:40 | 98,737,908 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,034 | r | factors.r | x <- c(5,12,13,12)
xf <- factor(x)
# xf
# str(xf)
# unclass(xf)
# ?unclass
# attr(xf,"levels")
# length(xf)
# xff <- factor(x,levels=c(5,12,13,88))
xff <- factor(x,c(5,12,13,88))
# xff
# xff[2] <- 88
# xff
xff[2] <- 28
ages <- c(25,26,55,37,21,42)
affils <- c("R","D","D","R","U","D")
tapply(ages,affils,mean)
d <- dat... |
a5e761105cca16df09f7bcd4bb19083b9a0d82c9 | 7590a2ceba0efdc130c5d7631617e4d829016d5c | /R/pubmedQuery.R | d626289fb0e9a2ef8e9aab8d3f7fc2ba7dc1f72d | [] | no_license | andymckenzie/bayesbio | e52b8bfb46d32d04373a3161f6a9722b47af8e32 | 1389283ba9ac8e1778dd7930af35e719a3baf540 | refs/heads/master | 2021-01-17T12:44:10.735034 | 2019-06-11T16:24:17 | 2019-06-11T16:24:17 | 59,575,223 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,866 | r | pubmedQuery.R | #' @title Perform PubMed queries on 2x2 combinations of term vectors.
#' @description Perform PubMed queries on the intersections of two character vectors. This function is a wrapper to RISmed::EUtilsSummary with type = 'esearch', db = 'pubmed'.
#' @param rowTerms Character vector of terms that should make up the rows ... |
1510e4f5c61abe6e2f4f65b65fb67dd46518f0a1 | 0c96f7bf162980f76c31c9b8d498de9652bcb903 | /1_code/06_logit_v3_mkt_check.R | f0cbe5cc087d51fffc564080bf3fbf85a528ec73 | [] | no_license | Vidogreg/payer_model | 7c582c3d4a8bd7ae3e147d9a24e426fe1c82ae8e | ce85158666ff0febc258bc1080d9ee3f39d678d6 | refs/heads/master | 2020-04-11T07:33:36.838323 | 2019-03-14T11:22:06 | 2019-03-14T11:22:06 | 161,610,941 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,973 | r | 06_logit_v3_mkt_check.R | # ------------
# Introduction
# ------------
## This file trains logistic model with payment&session data for several register months.
NAME <- '06_logit_v3_mkt_check'
# ------------
# Preamble
# ------------
## Imports
source('1_code/00_utils.R')
# packageTest('DBI')
# packageTest('odbc')
packageTest('data.table')
i... |
b70b53a7778e3a7ce840f2b0a519d4a51e54dc93 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/SCRT/examples/graph1.Rd.R | c3a160e6d0a357225d28e510545fcfe7bca0ad20 | [] | 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 | 216 | r | graph1.Rd.R | library(SCRT)
### Name: graph1
### Title: Graphical representation of single-case data
### Aliases: graph1
### Keywords: Single-case design Graph
### ** Examples
data(ABAB)
graph1(design = "ABAB", data = ABAB)
|
b401e0ae050a51cc20ec93b407b778c98691c643 | 31f4fb31a14842e9f92208e9984628cbc9118416 | /server-database/README.rd | b8584b1e7803bd153331cd85dc71903e21922b12 | [] | no_license | siyand6777/6 | 5afbfab308be98461cecff61817f878d879d6eca | 10afd0f1bf67e6cd5c4429a0188b739a98e944f1 | refs/heads/master | 2023-03-14T16:05:50.958351 | 2021-03-09T06:16:23 | 2021-03-09T06:16:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,514 | rd | README.rd | Backend of MyStory:
Methods:
* server.py: server code that implement python server middleware that
connects UI with tensorflow server and database
* server_call_tensorflow.py: part of code that calls tensorflow server
which generates captions given images
- image_processing.py: pre-process the images. ex. re... |
a13850fcec0962b0686a8b22ac7af2ac2f350ce8 | fc67b3b2035770e894893708a7d9382303a6607f | /hist_exp_obs.R | 5a71bf7edc82a1543bd987f6de34cb4312ea55a4 | [
"MIT"
] | permissive | ilBegonia666/Order-Book-Modelling | 72668f0ffa73b52614fce2a3aa405e8362249d1c | e93ecf6178591837ff0d6847e754da2fa5a185a9 | refs/heads/master | 2023-03-17T12:32:19.337393 | 2019-08-11T16:52:27 | 2019-08-11T16:52:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,129 | r | hist_exp_obs.R | # Plots histogram of exponential next to histogram of observed limit order interarrival times
# Simulate from exponential distribution with parameter the inverse of mean limit order interarrival time
# (number of simulations) = (number of limit orders)
simulated_exponential <- rexp(length(limit_interarrival_times... |
8469d8e1a3183bdaa5a4378cb61a95f7c33ed410 | 0841838ba8723e94b37a1514409a5a9767cbf181 | /MESA_project/code/haplotype_inference/MESA_haplotypeoverlap.R | b4bc9f306ab306032a0dc5f6eb69e8e15b0fe9c4 | [] | no_license | kelseysumner/taylorlab | cfa2358b5c552e7853b111de12940983d081de6a | 8801f5d32b7f81f2a66b3efd763cc18c5d35f42b | refs/heads/master | 2021-08-07T03:55:06.004801 | 2021-06-20T21:29:08 | 2021-06-20T21:29:08 | 150,612,627 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 14,012 | r | MESA_haplotypeoverlap.R | # ----------------------------------------- #
# MESA Batch 1 Create First Step #
# of Haplotype Tracking Pipeline Sample #
# Summary #
# 21AUG2018 #
# K. Sumner #
# ----------------------------------------- ... |
b88cd289393b0c484e05f0a410f46989080037ee | 451d76b27e3da30c7e5a6295e0cc289735972eb2 | /02_05082020/starter_project.R | 686dfcda0f2045184733d3ba76b7281b985bc681 | [] | no_license | seluccaajay/R_programming | f9f16f5822d14b3d07b121c94fbe41920393b251 | 2410c797bb9585e177e23b0b0ad595b758f1de27 | refs/heads/master | 2022-11-28T05:26:06.157863 | 2020-08-10T17:52:42 | 2020-08-10T17:52:42 | 284,708,900 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 280 | r | starter_project.R | ## Starter Project on Loading our own dataset
library(readxl)
data <- read_excel("C:\\Users\\Ajay Sen Riti\\Desktop\\Files and Shortcuts\\DataSet\\tamil_nadu_covid19.xls")
head(data)
tail(data)
str(data)
names(data)
data$day_tested
head(data$day_confirmed) |
a897241fb21a672b0885bae841375cefeffa172d | 2d945f0e9167a3c9ba0ebe9071c4db38f2a6141c | /tests/testthat/test-function-to_subdir_matrix.R | 79ae8bf0026e7ca8a17e766e2e2649d7d18899a9 | [
"MIT"
] | permissive | KWB-R/kwb.file | af12a91038d3f2aa81774d1856714adda0c56b8d | 87a026edc457445dd3a0e5f03d6072bd845e86c3 | refs/heads/master | 2022-06-17T14:00:33.014904 | 2021-12-16T12:26:10 | 2021-12-16T12:26:10 | 160,693,673 | 0 | 0 | MIT | 2021-12-16T12:26:11 | 2018-12-06T15:24:43 | R | UTF-8 | R | false | false | 322 | r | test-function-to_subdir_matrix.R | #library(testthat)
test_that("to_subdir_matrix() works", {
f <- kwb.file:::to_subdir_matrix
expect_error(f())
paths <- c("a", "a/b", "a/b/c")
expect_identical(dim(f(paths)), c(3L, 3L))
expect_identical(f(paths, method = 1), f(paths, method = 2))
expect_length(f(paths, result_type = "list"), 3L)
}... |
aa152e7998b3a639b84d7ba499e5c5ed80f5b0a0 | fefbb6395288d873f8d733d2a8b4575aaf411c15 | /cachematrix.R | 2fe00e04db7ee336eaff0ee3a95588e7faa02ab8 | [] | no_license | soshim/ProgrammingAssignment2 | 05f9c10807a02446b83de771229c2a162ab9ae07 | c45cd8beecb82f699d7546aea674ce82ca2740bb | refs/heads/master | 2020-12-25T10:50:02.715518 | 2015-03-17T17:21:47 | 2015-03-17T17:21:47 | 32,341,572 | 0 | 0 | null | 2015-03-16T17:30:55 | 2015-03-16T17:30:54 | null | UTF-8 | R | false | false | 2,008 | r | cachematrix.R | ###########################################################################
# In this file, two R functions, makeCacheMatrix and cacheSolve are defined.
# The former creates matrix data structure and the latter calculates
# its inverse matrix.
# As the inverse calculation is time-consuming, once the inverse is calculat... |
7b88fe43563db82dd6443895eba6fd02fc77f0f7 | 451583f87000130c407048e48e9d8367fa16bb5b | /sourceFiles/simulationCodes.R | e70bc061d51c99107352fd3fa7b087bff00b2dd7 | [] | no_license | yangxhcaf/meanVarianceCausality | d802dfc83fe06e9300e4264552790728f30f9b3c | c603d9d061d0d4598ff1250ab5114e2fa074676e | refs/heads/master | 2022-04-21T16:22:18.022999 | 2020-03-06T00:40:31 | 2020-03-06T00:40:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,033 | r | simulationCodes.R | ####################
## This is the main call function that performs the simulation for the paper
##
## simEmpiricalSize() performs the simulations for the ARMA(1,1)+GARCH(1,1) simulations
## in the paper. If we specify alternative != 0, it is technically computing power
##
##
## The second function is the call functio... |
dbc6117c0d7a1abc87a9d5d57bee51c0cedaa143 | 69b49ce61413bc8190227621b0aa8dfaf951a048 | /src/Concerto/TestBundle/Resources/R/concerto5/man/concerto-package.Rd | 0628d7d48ee37cf2d004e2027b4adcd6e3e39fb4 | [
"Apache-2.0"
] | permissive | campsych/concerto-platform | de926ae820f2a3cf6985598f3824dee8f4615232 | 988b67e8d52acbf25fdc9078e7592cc07d2dd9a3 | refs/heads/master | 2023-08-31T08:09:05.570628 | 2023-08-23T16:43:03 | 2023-08-23T16:43:03 | 55,242,761 | 164 | 109 | Apache-2.0 | 2023-07-26T15:10:48 | 2016-04-01T15:34:25 | PHP | UTF-8 | R | false | false | 518 | rd | concerto-package.Rd | \name{concerto5-package}
\alias{concerto5-package}
\docType{package}
\title{
Package for Concerto platform test logic R code.
}
\description{
Contains all required functions by Concerto platform.
}
\details{
\tabular{ll}{
Package: \tab concerto5\cr
Type: \tab Package\cr
Version: \tab 0.32.0\cr
D... |
0dc422e069dacedc84005cd67c77c5cdd5912f14 | 6911c5385656f2a310ba5f44c8867f45ad02119d | /Mid-5.R | 51c1047568690b4d53cf50b88f666ed5a9efecbe | [] | no_license | SuperLouV/CS513 | ee4dee226a074e635b301dd545c29f3d138015e5 | 31d048fc254961ddf8f9c99d59ecafb9ceb494b8 | refs/heads/master | 2022-05-20T23:52:05.757226 | 2020-04-30T22:39:03 | 2020-04-30T22:39:03 | 260,332,614 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,237 | r | Mid-5.R | # Course : Data Mining
# First Name : Yilin
# Last Name : Lou
# Id : 10445676
# Project : Midterm O5
## remove all objects
rm(list=ls())
#load a file
data<-read.csv("COVID19_v3.csv",header = TRUE,na.strings = "?",colClasses = c("MaritalStatus"="factor"))
# Remove the miss... |
8d1a44bad97a8b0a5eefba782b29177d3274c0b8 | 77c74ad76727bf22c4d2d678706d8e6cc3164d2f | /rankhospital.R | 2629eebd6c89bb579335127aa89489072f986add | [] | no_license | aryan-shrivastava09/datasciencecoursera | f940a7f8047a823eeacbcef3ce41fac4cda2344b | 3c660abcc62d2f0b6bb960ed5804b40665d68af2 | refs/heads/master | 2022-10-24T01:04:57.736892 | 2020-06-17T14:17:10 | 2020-06-17T14:17:10 | 261,231,116 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 637 | r | rankhospital.R | rankhospital <- function(state, outcome, rank) {
data<-read.csv("outcome-of-care-measures.csv")
datastate <- subset(data,subset = State == state)
if(outcome== "heart attack"){
outcome = "Heart.Attack"
} else if(outcome == "heart failure") {
outcome = "Heart.Failure"
} else if(outcome == "pneumonia") {... |
290ab280b738b879b16af10a87807343d3312586 | 25ae4c2d7fbbeb4e14372d380cf01065139bf90a | /analyses_figures_of_space_time_dataset__part1.R | 7280dfb445e7bb06d4dc05a83377c37727f2e43d | [] | no_license | bparment1/MEOT_analyses | 46dfc46ccf6c4a9c3a88e40915963ff259fdec2f | c19cc04478f6e35f0b7a8b1237d76fdc496f530a | refs/heads/master | 2020-12-25T21:01:22.175138 | 2016-12-31T15:45:00 | 2017-08-25T17:45:42 | 15,113,303 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 23,028 | r | analyses_figures_of_space_time_dataset__part1.R | ######################################## Generation of space-time dataset #######################################
########################################### For Testing MSSA-MEOT and S-T mode PCA #####################################
#This script performs analyses and produces figures for synthetic space t... |
aea88096f5b7ec8a055acfa4d6e87dfb3bda98cd | 297ab7b92af3ffc6d497358525c52a9edd5f0a5b | /cachematrix.R | 031929510fe44ba5d194dc95ebabd2c552eec606 | [] | no_license | alohr/ProgrammingAssignment2 | ee4118bb3c43e3d011e58ff04872e339af2cc2fe | 7d7bad6aa9ab8b816d637deffa53c7c97d479dbd | refs/heads/master | 2021-01-23T22:52:50.022300 | 2014-06-07T15:59:19 | 2014-06-07T15:59:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,583 | r | cachematrix.R | ## The functions makeCacheMatrix() and cacheSolve() implement a caching scheme for the
## inverse of a matrix.
##
## Example usage:
##
## > m <- makeCacheMatrix(matrix(c(1, 2, 3, 4), nrow=2, ncol=2))
##
## > m$get()
## [,1] [,2]
## [1,] 1 3
## [2,] 2 4
##
## Get the inverse for the first time:
##
## > cache... |
0f612faa23bfcf67339106c462be734058e6f53c | 654b7d883c73e2dd3d3fffdd816315677e95cc50 | /man/SpatialExperiment.Rd | ee264b74bfdeb0e33d6198fdcf73e3130565c70f | [
"MIT"
] | permissive | genesofeve/astRal | c3147fe7ab9f967fc87061c08992a103aaf8bd08 | f22f749017e954e80ddda1c5baacfc7bdb070158 | refs/heads/master | 2023-01-01T10:21:26.700219 | 2020-10-16T16:31:57 | 2020-10-16T16:31:57 | 430,408,999 | 1 | 0 | NOASSERTION | 2021-11-21T15:46:02 | 2021-11-21T15:46:01 | null | UTF-8 | R | false | true | 482 | rd | SpatialExperiment.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AllClasses.R
\docType{class}
\name{SpatialExperiment}
\alias{SpatialExperiment}
\alias{SpatialExperiment-class}
\title{The SpatialExperiment class}
\description{
The SpatialExperiment class
}
\section{Fields}{
\describe{
\item{\code{images}}... |
36b40aa50293eafc68072331f258972670990920 | 71baaddb70a95c682b579a00ef006c1526bbb872 | /plot2.r | 7d8845548909f122eaa676cb1776ba38cb76e877 | [] | no_license | mgan2014/ExData_Plotting1 | fedaf1fe077cd392bcaaf13bd1b0e2aa317deb80 | 63432631f01e3f4a9d656c90b6be37e77dc1d6b9 | refs/heads/master | 2021-01-17T17:12:05.353052 | 2014-09-06T00:46:12 | 2014-09-06T00:46:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 713 | r | plot2.r | plot2<-function (){
#import the data from the txt file
x<-read.table("household_power_consumption.txt",header=TRUE,sep=";")
#Convert classes of date
x[,1]<-as.Date(x[,1],"%d/%m/%Y")
#Extract only 2 days data
y<-x[(x$Date=="2007-02-01"|x$Date=="2007-02-02"),]
dt<-strptime(p... |
b5bf044ee51f80d6780c5a8bcc890f511b330780 | 3050849fdeb7b54d22f5b72ec004fefeb0af86a6 | /man/plotKmeansLabelstSNE.Rd | 25d141af5942b6e338de86da5f570e51efac225e | [
"MIT"
] | permissive | dami82/DIscBIO | 4e8fd3591b2c1d92c9deb83596713f076a12ac1c | 8de0522099697a9364ee01befdb13f5b36b16970 | refs/heads/master | 2021-04-22T02:19:49.309902 | 2020-04-08T11:37:15 | 2020-04-08T11:37:15 | 259,098,496 | 0 | 1 | MIT | 2020-04-26T18:01:52 | 2020-04-26T18:01:51 | null | UTF-8 | R | false | true | 797 | rd | plotKmeansLabelstSNE.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DIscBIO-generic-plotKmeansLabelstSNE.R
\name{plotKmeansLabelstSNE}
\alias{plotKmeansLabelstSNE}
\alias{plotKmeansLabelstSNE,DISCBIO-method}
\title{tSNE map for K-means clustering with labels}
\usage{
plotKmeansLabelstSNE(object)
\S4method{pl... |
0ac1f6b5f3fd9090502a338fd28b0b69cca4c963 | 34541609e2877bde7ca96a139d73d9e69f63709c | /R/guide_dendrogram.R | 778605b537fed9ef288d6128e476081f1f12ea95 | [
"MIT"
] | permissive | dimbage/ggh4x | 3e6a12a9543c511b922639ca1e4751c785b8739a | ae1ac5b085f9e487ce7438e9e321bf368596aed6 | refs/heads/master | 2023-06-24T22:57:24.376292 | 2021-07-24T17:07:34 | 2021-07-24T18:32:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,575 | r | guide_dendrogram.R | # Constructor -------------------------------------------------------------
#' Dendrogram guide
#'
#' Visual representation of a discrete variable with hierarchical relationships
#' between members, like those detailed in
#' \code{\link[=scale_x_dendrogram]{scale_(x|y)_dendrogram)}}.
#'
#' @inheritParams guide_axis_tr... |
d185850cbe991085b69cfe8ffc8dca788ba82d74 | e75a40843a8738b84bd529a549c45776d09e70d9 | /samples/client/petstore/R-httr2/man/Tag.Rd | 2aea0440ef9cb78765050b15ecae898fc47b91b2 | [
"Apache-2.0"
] | permissive | OpenAPITools/openapi-generator | 3478dbf8e8319977269e2e84e0bf9960233146e3 | 8c2de11ac2f268836ac9bf0906b8bb6b4013c92d | refs/heads/master | 2023-09-02T11:26:28.189499 | 2023-09-02T02:21:04 | 2023-09-02T02:21:04 | 133,134,007 | 17,729 | 6,577 | Apache-2.0 | 2023-09-14T19:45:32 | 2018-05-12T09:57:56 | Java | UTF-8 | R | false | true | 4,767 | rd | Tag.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tag.R
\docType{class}
\name{Tag}
\alias{Tag}
\title{Tag}
\format{
An \code{R6Class} generator object
}
\description{
Tag Class
}
\details{
OpenAPI Petstore
This is a sample server Petstore server. For this sample, you can use the api key `sp... |
4137e0ee7e0f45c871b54d9b6c726a5f399d3201 | a859ae6c8dc7e916266f38066ae8e35860cb9c6b | /R script/learn.R | 75ffc30f242cdab5764b1246b2080753947ebcbd | [] | no_license | dongwoLee/LectureData | a6d530529b7825229556cb9ab54e979c590b73c4 | 3c3e1f5d9dd6d998eb472d3787606890b0d94429 | refs/heads/master | 2021-01-18T22:09:59.462793 | 2017-10-30T07:37:51 | 2017-10-30T07:37:51 | 100,555,528 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,253 | r | learn.R | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
mosq.data <- read.csv(args[1])
inputData <- mosq.data
library(randomForest)
print("Loading 'randomForest'")
library(party)
print("Loading 'party'")
print("Importance Value Selection")
rffile1 <- paste(args[1],".rflog.Rdata",sep="")
print(rffile1)
#cf1 <- ra... |
f522e18d443bdf63a931fbc1e199cc9c1dfc75ff | caa38c536aa036bcd8d080af1ea6f42751d7d8b6 | /hils regresja/regresja_hills.R | 42764a17557b4132ef6520c4738e259d32d0e69b | [] | no_license | coprobo/R-models | 76fc1388ecede70c8aa2b3814fecf213ad84f6fe | bcf71a3ef10e0e180b3e032bc1a4a2df04af3810 | refs/heads/master | 2021-05-11T03:00:36.681034 | 2018-01-17T23:18:54 | 2018-01-17T23:18:54 | 117,903,175 | 0 | 0 | null | null | null | null | WINDOWS-1250 | R | false | false | 740 | r | regresja_hills.R | ###############################################
# Model Regresji Liniowej #
###############################################
#zaczytanie danych (tutaj skorzystam z wbudowanego zestawu "hills")
install.packages("MASS")
library("MASS")
hills = hills
attach(hills)
#konstruujemy prosty model regresji l... |
32f01cf3f23aacc0c8736ab6fa8739f2b6d79933 | 884677dd48325c8314489ce0cfe0770b0d97e5c0 | /man/cost_error.Rd | 940e6461582ee818948770a7c65f02250d41d2e9 | [] | no_license | dtrfgv/dtrfgv | 1ff5657c39933ae14f5057dbdd570c6235648461 | a736537579ecddaa124898c3683eb209aad7537e | refs/heads/master | 2020-03-28T12:36:31.603482 | 2019-04-26T20:37:56 | 2019-04-26T20:37:56 | 148,315,133 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 757 | rd | cost_error.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Functions_Cost_Complexity_Pruning.R
\name{cost_error}
\alias{cost_error}
\title{cost_error}
\usage{
cost_error(node, tree, alpha)
}
\arguments{
\item{node}{the number of the node in the tree \code{tree}}
\item{tree}{a tree. A data frame retu... |
486af586e6869908edf4db6693eddcf117eac531 | 6f1510f8efd00fa734c1e01e15b8bce20d059c86 | /R/Functions.R | c7061d2150f8e9d56167401b08ba30f4182dc59c | [] | no_license | T-Engel/CValternatives | fcf8a10412e41acba3d7ef7427ee140d2f819998 | 64656c8d5fef8800da60e8432c47b884394b5b00 | refs/heads/master | 2020-08-06T04:25:53.204355 | 2019-10-04T14:48:20 | 2019-10-04T14:48:20 | 212,830,044 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 780 | r | Functions.R | #' Proportional Variability index (PV)
#'
#' Calculate PV of the variable Z
#'
#' @param Z a numeric vector
#'
#' @return a numeric value
#' @export
#'
#' @examples
#' \donttest{
#' Z = c(2,3,4,5)
#' PV(Z)
#' }
PV <- function (Z){
n = length(Z)
pairs = combn(Z,2)
min_z = apply(pairs,2, min)
max_z = appl... |
557bd97df125c32d3d69d50ba0ace8c614111dd8 | 88ef0324c59dd7f073c5dca09e535da9c591de81 | /plot3.R | 5fa8a8dd091234a9d8885f04b5e73289f5f7ae22 | [] | no_license | alklar/ExData_Plotting1 | 00b153a2240badfce8e2c69071f2813fa85d4a81 | e6487ea1d7ae747ae24fb507717f4bb231dc2247 | refs/heads/master | 2021-01-18T21:48:13.501654 | 2016-08-23T20:12:20 | 2016-08-23T20:12:20 | 66,393,353 | 0 | 0 | null | 2016-08-23T18:37:36 | 2016-08-23T18:37:35 | null | UTF-8 | R | false | false | 1,602 | r | plot3.R | # this is how to download and unzip the zip file
# uncomment the following lines if needed
#zipURL <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
#zipFile <- "exdata.zip"
#download.file(zipURL, destfile= "exdata.zip")
#unzip(zipFile)
#create Data Frame from unzipped text fil... |
e9679bfd0c2cf3737f074fe62543fbe3d2f85835 | 5d17dde663d995fd348ad610bec94d01a6b3ca8a | /helpers.R | 8254be688b03947b4f15bfc7a4b074edcb6002bc | [] | no_license | jealie/SFM_Workflow_Comparison | b760bdaadc2eb09c731c12040f25c13d1493ab1d | ebbb4d09f289265047186445662433c84ade2a18 | refs/heads/master | 2021-03-16T08:57:25.980092 | 2018-07-31T16:00:19 | 2018-07-31T16:00:19 | 66,972,493 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,285 | r | helpers.R | mod_ashape3d = function (x, alpha, pert = FALSE, eps = 1e-09)
{
flag <- 1
flag2 <- 0
alphaux <- alpha
if (any(alphaux < 0)) {
stop("Parameter alpha must be greater or equal to zero",
call. = TRUE)
}
if (inherits(x, "ashape3d")) {
inh <- 1
tc.def <- x$tetra
tri.def <- x$triang
... |
a6951fb8d5eb4c9a29bbb9f1e9b519a5d090ab19 | ff4335cd97bd4bfda812240029024f0c0f856c3a | /R/mod_formcomplete.R | f2e385c5aa646b84c6d98c70464f3ba3fc134619 | [
"MIT"
] | permissive | SwissClinicalTrialOrganisation/secuTrialRshiny | 82cfe99c58c3fa81d4b8d6276dc6293c3a637185 | ef622ac456fa894593013dc514ea4ebd1dfe5168 | refs/heads/master | 2021-02-18T09:36:20.716973 | 2020-04-01T08:46:13 | 2020-04-01T08:46:13 | 245,182,231 | 1 | 2 | MIT | 2020-04-27T12:15:51 | 2020-03-05T14:19:36 | R | UTF-8 | R | false | false | 3,420 | r | mod_formcomplete.R | #' Shiny module UI function for form completeness monitoring
#'
#' This function represents a shiny dashboard UI module that allows users to
#' view a form completeness table.
#'
#' @param id string containing a namespace identifier
#' @param label string to be used as sidebar tab label
#' @return shiny.tag list object... |
0d15df7abdb5168dbf04c5667288b2a83b5243a9 | 492f49a78bea9ab16fc99d159653722113afa125 | /man/headers_flextable_at_bkm.Rd | 8f24873735b3afe218222f08f89bf89ab0ba9c5e | [] | no_license | davidgohel/flextable | 48c34514420e435ca70f65354e94aa69786777bc | fc62aaf29c01bbac26fe34ef85240afe4eb201ab | refs/heads/master | 2023-08-23T06:49:13.945566 | 2023-08-20T22:53:39 | 2023-08-20T22:53:39 | 62,127,938 | 502 | 83 | null | 2023-08-20T19:03:11 | 2016-06-28T09:25:11 | R | UTF-8 | R | false | true | 755 | rd | headers_flextable_at_bkm.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/body_add_flextable.R
\name{headers_flextable_at_bkm}
\alias{headers_flextable_at_bkm}
\title{Add flextable at a bookmark location in document's header}
\usage{
headers_flextable_at_bkm(x, bookmark, value)
}
\arguments{
\item{x}{an rdocx objec... |
4b2983fb2035a33f66460da68c32f3abb3047ef9 | 1390e0a0714de7dfc5314dc10afdbac1549e61c3 | /time_series_forecasting_correlate_analysis.R | b6626a7196d41e9bad723fadcb3f07c2c2d693c5 | [
"MIT"
] | permissive | hanhanwu/Hanhan_Data_Science_Practice | 1a97efa544aefca1335a1fd7b44f73636d2ddb8e | 5de73e6df2dcfc623cf06601e6e3ada5a4aaac34 | refs/heads/master | 2023-06-22T06:46:58.022975 | 2023-06-14T23:26:24 | 2023-06-14T23:26:24 | 56,539,777 | 25 | 18 | MIT | 2022-04-03T22:07:09 | 2016-04-18T20:28:02 | Jupyter Notebook | UTF-8 | R | false | false | 3,781 | r | time_series_forecasting_correlate_analysis.R | # WITH TIME SERIES OBJECT & CORRELATE ANALYSIS
cadairydata <- maml.mapInputPort(1)
# Create a new column as a POSIXct object
Sys.setenv(TZ = "PST8PDT")
cadairydata$Time <- as.POSIXct(strptime(paste(as.character(cadairydata$Year), "-", as.character(cadairydata$Month.Number), "-01 00:00:00", sep = ""), "%Y-%m-%d %H:%M:... |
726e7d4fd34a0ed2c7a8f8e74d18d9cf5e7b9e25 | 0f62a3d2021951b862c74e4cf01c292fce60a9bf | /SVM.R | fb2ebaf9340edf165564a8ae84bb4d7c106f80f8 | [] | no_license | saniya-k/bank-marketing | 9e7c3437c48121788b9fcc0273f79c5628425c46 | 0a3224ee603d458e317cff26c5be76fb5aa19fa7 | refs/heads/master | 2023-02-28T12:53:08.416989 | 2021-02-06T22:25:04 | 2021-02-06T22:25:04 | 271,338,985 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,751 | r | SVM.R | library(ggplot2)
library(caret)
library(dplyr)
library(DMwR)
library(e1071)
library(factoextra)
library(pROC)
library(kableExtra)
##### Read the pre-processed file #####
new_df= read.csv("data/Imputed_data.csv",header=TRUE)
# Subscribed vs Not Subscribed
barplot(table(new_df$y))
dim(new_df)
str(new_df)
# Remove firs... |
a2ab25905c2b1b81d994173a94945b2b9a5eef28 | 2fa33aeef712fa0a1b8043b40261d218a37cafa2 | /man/gibbs2.Rd | 080eb3ede91ff8ed01d53ee645004a8420883aad | [] | no_license | cran/bayess | 778e3cd961acecec0ccbf0de66048543af82c98c | 30208f8c4b61bc73e5885875b8134f05a963719c | refs/heads/master | 2022-09-03T00:53:47.483683 | 2022-08-11T09:30:08 | 2022-08-11T09:30:08 | 17,694,647 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,053 | rd | gibbs2.Rd | \name{gibbscap1}
\alias{gibbscap1}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Gibbs sampler for the two-stage open population capture-recapture
model%% ~~function to do ... ~~
}
\description{
This function implements a regular Gibbs sampler associated with Chapter 5 for a two-stage
capture ... |
90afe885c2683379670c1eb3ea2da3107b502a08 | c6ece4fd16b7f8d5811e161052857a0f3e26f914 | /global.R | adc65316b809e87e21f69ff75e485062cb9794a9 | [
"MIT"
] | permissive | ToshihiroIguchi/ezdis | 4caeaa2bc12f15b4ab11575a66fd337d5a409230 | a08ffe2a74da8ea4ec8be7f33334073c86362ac9 | refs/heads/master | 2023-05-06T04:55:30.346543 | 2021-06-01T14:00:14 | 2021-06-01T14:00:14 | 270,997,926 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 48,363 | r | global.R | #ライブラリ読み込み
library(ggplot2)
library(tibble)
library(readr)
library(readxl)
library(R.utils)
library(fitdistrplus)
library(ismev)
library(FAdist) #actuar,evd, EnvStarsより先に読み込ませる
library(extraDistr)
library(evd) #evdはactuarより先に読み込ませて、evd::dgumbelなどをマスクする
library(actuar)
library(EnvStats)
library(mixtools)
library(RcppFad... |
ab4bce4cb9f4fa36dc767d4bb5456f5ec93d7194 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.application.integration/man/sqs_untag_queue.Rd | d8f1295f7c90bd94a63119761aed19807ddd56e9 | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | true | 789 | rd | sqs_untag_queue.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sqs_operations.R
\name{sqs_untag_queue}
\alias{sqs_untag_queue}
\title{Remove cost allocation tags from the specified Amazon SQS queue}
\usage{
sqs_untag_queue(QueueUrl, TagKeys)
}
\arguments{
\item{QueueUrl}{[required] The URL of the queue.}... |
ffa793f22f8dfe8584df6e3a9476a056f141799c | f890ebe54e12c534de49e2160afca2e78065d770 | /R/mortalityTable.period.R | b33b150b15fc64097de63bc407f93110a0770477 | [] | no_license | Algorios/r-mortality-tables | c4dca0898b80e17e33443a2bd8469c8104fcb8a0 | 95cd06a0298b65829ab8ca2eb939a1a2b6d425e4 | refs/heads/master | 2021-06-04T16:12:04.194171 | 2016-09-07T17:12:31 | 2016-09-07T17:12:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 755 | r | mortalityTable.period.R | #' @include mortalityTable.R
NULL
#' Class mortalityTable.period - Period life tables
#'
#' A period life table, giving death probabilities for each age, up to
#' maximum age \code{omega}. The \code{baseYear} slot can be used to hold
#' information about the period.
#'
#' @slot ages The ages corresponding to the... |
72973768dc983f4719384ce822c67b03feba143a | 0b8ff928c22d87ee5d7a8a19f5ce8ff51bd7155c | /ThesisWorkThirdPaper.R | e5eb826f150fc043f238d487efa8607f163e5146 | [] | no_license | Gtmille2/seamlessTrial | d9104369cfda1987c374d8b3535d925df7211105 | 7dcd49f4c09486ee3d383eb1df8bc673d77a658e | refs/heads/master | 2020-07-31T17:24:08.170740 | 2019-11-17T18:35:21 | 2019-11-17T18:35:21 | 210,692,243 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 29,396 | r | ThesisWorkThirdPaper.R | ##### This script is to test different parameter settings for the third paper to determine how they affect the outcome of the type I error #####
library(seamlessTrials)
#Running multiple simulations:
save.boundary = save.boundary.values()
n.trt = 3
nsim = 1000
n1 = 20
N1 = 100
N=200
sigma0 = 1
sigma = 1
... |
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