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c2718e56e9720f9dbdbb9be1d5331d701825a1b0 | f64fea318bda54ddf7a18aab6ea6683d2b2c94e1 | /car_data/car_data_4_glm.R | fefd33c3e858479f8ac6748856dc41a05cb2d24f | [] | no_license | SportsTribution/doing_data | 75faedc24fe467120cbb2e46892e98db219d2e54 | c728afee4d3cb4fdf7d25cf319cf220497e9eb87 | refs/heads/master | 2018-01-08T08:10:22.206196 | 2016-02-24T16:19:00 | 2016-02-24T16:19:00 | 52,455,390 | 3 | 0 | null | null | null | null | ISO-8859-16 | R | false | false | 5,192 | r | car_data_4_glm.R | ### REGRESSION MODEL
## Let us rename ur data frame, so that we have the "original" one still handy
carDataGLM <- carData
## one important Problem: We have to take away one Factor from each Class, otherwise the glm will complain
## the following turns all characterŽinformation into factors by turning the data fr... |
710eef7c26b2cbe67cdd642f52bc40a2c854aec2 | 8e6e55fe43bc3ed64f01fec4ed07c027b29f96a6 | /man/make_bulk_get_job_url.Rd | 842ed76e2c6ecc3b3fa9e1163c9267e970e6673c | [
"MIT"
] | permissive | carlganz/salesforcer | a3ec51c556b79b4734b5c8d844f000c2573fadbc | 2078627bc988e5d58f90d16bf42c603507ab16db | refs/heads/main | 2023-04-14T23:50:26.698773 | 2021-04-27T15:44:55 | 2021-04-27T15:44:55 | 362,164,928 | 1 | 0 | NOASSERTION | 2021-04-27T15:38:47 | 2021-04-27T15:38:46 | null | UTF-8 | R | false | true | 457 | rd | make_bulk_get_job_url.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/endpoints-bulk.R
\name{make_bulk_get_job_url}
\alias{make_bulk_get_job_url}
\title{Bulk Get Job Generic URL Generator}
\usage{
make_bulk_get_job_url(
job_id,
api_type = c("Bulk 1.0", "Bulk 2.0"),
query_operation = NULL
)
}
\description{... |
4a854c9edcfdde9333d730d38f43be2412740438 | 41c8197a6586d2a4bede4e80f4d87952944e6f4f | /plot4.R | cc467d8169e169f074f110a8e77ca956dab95614 | [] | no_license | jesper4711-personal/ExData_Plotting1 | 2b47576b985434a58dd8201cfa802608c53903e6 | 12ecc522a6c5de78db036f241fe440c9ea1de2f0 | refs/heads/master | 2020-12-13T18:25:02.698292 | 2015-01-11T20:39:08 | 2015-01-11T20:39:08 | 29,090,099 | 0 | 0 | null | 2015-01-11T11:56:03 | 2015-01-11T11:56:01 | null | UTF-8 | R | false | false | 1,444 | r | plot4.R | ## format of the data
classes=c("character", "character", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric")
## read in the data
data <- read.table("household_power_consumption.txt", header=TRUE, sep=";",colClasses = classes, na="?")
## need the times converted for the plotting... |
43810a3e10596cf022d9ca4f8f54b558cb61bd59 | 6242962bfa0e8022cebc8822def9c611eea02132 | /2021/2021_01.R | 0f2588bbe9236ccede02e64782bc278c6a793bac | [] | no_license | nickopotamus/preppin_data | d2c12800252792a96e5c6d3ec311eab40064a058 | fc19d5ed55e659bed65ecb2da580039846345032 | refs/heads/main | 2023-09-01T04:54:07.096793 | 2021-10-07T05:19:22 | 2021-10-07T05:19:22 | 414,340,873 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,146 | r | 2021_01.R | # 2021, week 1
# Bike sales
# https://preppindata.blogspot.com/2021/01/2021-week-1.html
library(tidyverse)
raw_data <- googlesheets4::read_sheet("1GYv4573GnJa-C21NYeDj-OhFSTwrK0SnQNF2IQFqa50")
# Function to convert factors (called by fct_relevel())
bike_regex <- function(x = NULL) {
x[grepl("^Grav", x)] <- "Gravel... |
1ee575ac8070ed08ffbe3d43e3c94260f30689e4 | a47ce30f5112b01d5ab3e790a1b51c910f3cf1c3 | /A_github/sources/authors/2009/RNeXML/taxize_nexml.R | 1d7157549884c46df4c87e5a05c97df262afd6ac | [] | no_license | Irbis3/crantasticScrapper | 6b6d7596344115343cfd934d3902b85fbfdd7295 | 7ec91721565ae7c9e2d0e098598ed86e29375567 | refs/heads/master | 2020-03-09T04:03:51.955742 | 2018-04-16T09:41:39 | 2018-04-16T09:41:39 | 128,578,890 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,243 | r | taxize_nexml.R |
#' taxize nexml
#'
#' Check taxanomic names against the specified service and
#' add appropriate semantic metadata to the nexml OTU unit
#' containing the corresponding identifier.
#' @param nexml a nexml object
#' @param type the name of the identifier to use
#' @param ... additional arguments (not implemented yet... |
5e38e58d0b76193ffef72a3e4d4bcc267e37b879 | a0585ca647461121f67f91069809cecf7f5a7e5f | /app/ui.R | 95e73804fbcf6cdd09b37d450e5fbbb6943a6506 | [] | no_license | tyz910/dsscapstone | 9e8f1ec60d83cb97a0bab6282d07fc62d741d905 | 07a33d1c16eaaf39ad169781ad6820a396b52db4 | refs/heads/master | 2020-05-21T12:50:37.492601 | 2015-08-15T05:55:34 | 2015-08-15T05:55:34 | 39,577,614 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 214 | r | ui.R | library(shiny)
fluidPage(
titlePanel("Word prediction"),
fluidRow(
column(4, div(br(), wellPanel(textInput('sentence', 'Input text:')))),
column(8, h3("Suggestions:"), uiOutput('prediction'))
)
)
|
65892cbda379a9d506d460a237a7ece9ae4c441a | 2b7696de761986e7c295da36201f06fca701f059 | /man/hs3_hs1.Rd | 352d76e36a7c77a33ef1625ad843c0c8e4489fbd | [] | no_license | cran/concordance | 130b5cadccfce9cc5ef98432fc2f938c75eebd93 | b8d1e592399f05941ce24a4afd96007b8dae0ec5 | refs/heads/master | 2021-05-04T11:23:30.586684 | 2020-04-24T15:10:08 | 2020-04-24T15:10:08 | 49,413,285 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 648 | rd | hs3_hs1.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{hs3_hs1}
\alias{hs3_hs1}
\title{HS3-HS1 Concordance}
\format{
A data frame with 5052 rows and 6 variables:
\describe{
\item{HS3_6d}{6-digit HS3 Code}
\item{HS3_4d}{4-digit HS3 Code}
\item{HS3_2d}{2-digit HS3 ... |
315fd09bfdd95591f95af11619a73f5804fd8800 | 3449a99c56cf3120aa02ab22c58edbd3d6286074 | /Plots/ECDF_plot2.R | b01bc565ded995a9758844a1f3cb5c40e46a082a | [] | no_license | martin-vasilev/Bmeta | f124f73e8d2b53ecddb41ef50ecde699b35c84e6 | 39d587df5f816c24510093a44d8d2f100729292c | refs/heads/master | 2021-01-18T02:20:32.482029 | 2016-08-02T10:53:23 | 2016-08-02T10:53:23 | 58,971,665 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,360 | r | ECDF_plot2.R | # Martin Vasilev, 2015
ECDF_plot <- function(JAGS_model1=S1, JAGS_model2=S6, JAGS_model3=S7, type="FFD"){
# Generate samples for all models:
Samples1<-c(JAGS_model1$mu[1,,1],JAGS_model1$mu[1,,2],JAGS_model1$mu[1,,3])
ECDF1<- ecdf(Samples1);
Samples2<-c(JAGS_model2$mu[1,,1],JAGS_model2$mu[1,,2],JAGS_mo... |
91ddeb2f4e35b4694814dd89d399655f20d6cef1 | c0e66d1ebacdbf10375a818ec6b24c35e5c9073e | /R/plot_dat_in_map.R | fcb3eae701badc4dd10033af6f1930398492a451 | [] | no_license | Climandes/ClimIndVis | b1045a5cdce425e6dbf42d3032bb9ac2a0933786 | 47627ea250c1bb1d7416341645ffe1a7f5ab5b48 | refs/heads/master | 2021-11-07T05:24:44.418529 | 2021-10-22T09:17:22 | 2021-10-22T09:17:22 | 140,861,804 | 9 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,093 | r | plot_dat_in_map.R | #' function to plot data points on map of gridded data
#' @param g_lon,g_lat: arrays of longitudes and latidudes of dimension nx,ny ( or matrix of dim nx x ny if grid is not regular).
#' @param g_dat: matrix of gridded data of dimension nx x ny
#' @param g_col: color scale for plotting gridded data as character of fun... |
30b7fbd5d99afe1aa10e2a2438d5106db3a2acc6 | ac38f9ec3c1054d98660a128e6574e6c93694e4c | /cachematrix.R | 147335995d7aaa7e22e73e4252fa6b0c4edb2b59 | [] | no_license | Xcodingdata/ProgrammingAssignment2 | 1419583c59519189ac7f94043368bbec06fea46f | f7960cb6842c48ebc4c0a9c2747553b4ce0840c5 | refs/heads/master | 2021-01-11T15:01:38.874292 | 2017-01-29T18:33:27 | 2017-01-29T18:33:27 | 80,283,083 | 0 | 0 | null | 2017-01-28T12:25:42 | 2017-01-28T12:25:41 | null | UTF-8 | R | false | false | 1,284 | r | cachematrix.R | # Caching the Inverse of a Matrix
makeCacheMatrix <- function (x = matrix()) {
# Make sure the inverse is empty
inv <- NULL
# Set the matrix
set <- function (y) {
x <<- y
inv <<- NULL
}
# Get the matrix
get <- function () x
# Calculate the inverse ... |
4b0ef43510bc27de6a5963e050dcaba13b8817e2 | ec6a31fa07969e903b92fcc5aeffe816ffa5ce27 | /Multi_Linear_Regression.R | 702d7b7fbf8bcda10e19009a3bb2e024d476974a | [] | no_license | cparrett300/Data-Science | 44ff2af7db60ee1fc35af17e816c639378827a7c | e357cc447f10c289b3ec373db5827d1eaf266758 | refs/heads/master | 2023-02-08T01:50:29.699352 | 2023-02-06T20:51:21 | 2023-02-06T20:51:21 | 220,693,882 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,556 | r | Multi_Linear_Regression.R | require(R6)
OLS <- function(y, y_hat) 1/(2*length(y))*sum((y-y_hat)^2)
randn <- function(rows, cols){
return(matrix(data = rnorm(rows*cols), nrow = rows, ncol = cols))
}
MultiLinearRegression <- R6Class("MultiLinearRegression",
list(
... |
a08cd5c701e476d5f7adcb9f49ca57448db9e1cd | 43c0c2fa7cfa01633f87f1ef7e51437f5fcd448e | /pecuD3_final/pecuD3_shiny_final/server.R | b3c287853147af5e4f027c87e31e6b49483f9e0f | [
"MIT"
] | permissive | OOmegaPPanDDa/pecuD3 | a038e8a72bc06e3dfa0e1e040ca8a15884e5cabf | dab23996659c30d4afec47f96945a5a62c51b2ed | refs/heads/master | 2021-06-15T13:40:07.705691 | 2017-03-16T07:08:47 | 2017-03-16T07:08:47 | 78,158,467 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,196 | r | server.R | library(shiny)
library(dplyr)
library(googleVis)
library(ggplot2)
source('read_data.R')
shinyServer(function(input, output) {
output$map <- renderGvis({
date_data <<- train_flow %>%
filter(year==input$year & month == input$month & day == input$day)
date_flow_sum <- sum(date_data$... |
b92db002fa758ed9fd1c3f67ffcf521916b4fa02 | fbe57536cc2d84e69a5bf799c88fcb784e853558 | /R/sample.mode.R | 1386c3da5705f088d62cc7290d9b8e17b6e1255c | [
"MIT"
] | permissive | burrm/lolcat | 78edf19886fffc02e922b061ce346fdf0ee2c80f | abd3915791d7e63f3827ccb10b1b0895aafd1e38 | refs/heads/master | 2023-04-02T11:27:58.636616 | 2023-03-24T02:33:34 | 2023-03-24T02:33:34 | 49,685,593 | 5 | 2 | null | 2016-10-21T05:14:49 | 2016-01-15T00:56:55 | R | UTF-8 | R | false | false | 896 | r | sample.mode.R | #' Calculate Sample Mode
#'
#' Calculate the mode of a data set, defined as the value(s) that occur most often.
#'
#' @param x Vector - The object to remove names from
#'
#' @return Calculated mode value
sample.mode <- function(x) {
m <- NA
if (length(x) > 1) {
if (is.character(x[1])) {
m.out <- ... |
9797a278683035cb4ede006e56757bc524b6507b | 082bbd3b3e173d802cdc8f4ee090aad0fb821760 | /HW3/hw3p-p2_Xichen.R | be45519fd94110eec729b8bfbb28f7a48732159f | [] | no_license | JohnnyBarber/Investment | 2357b9004e1efdd771aabd931eda6b810369e106 | 67043859e6d61ed6d07570d9fff394977aa964f2 | refs/heads/master | 2020-04-27T19:25:22.307239 | 2019-03-08T22:09:49 | 2019-03-08T22:09:49 | 174,617,142 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 109 | r | hw3p-p2_Xichen.R |
# 2
# put a and c in pdf
# Forward rate
b=round(sqrt((1+0.07)^3/(1+0.06))-1,2)
mysoln[["Q2"]] = list(b=b)
|
c59584ad2ddc3f1a904708d76ab60f8a134759e0 | d05a635d55dd1ca2df01865f9c7da2f1fa8eaa05 | /inst/demo/demo_treeandleaf.R | 13b39ae0e54ff006cd9b094529fc0c0b43e4328e | [] | no_license | daniloimparato/easylayout | 9de520b6e6c0914e6cb2b1f0e78cf4a7b602ff40 | d200c2dbea3f56253850b9f4a52d731e4d06b971 | refs/heads/master | 2021-06-25T00:37:45.390921 | 2020-06-04T21:13:39 | 2020-06-04T21:13:39 | 210,895,795 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 374 | r | demo_treeandleaf.R | #devtools::install_github("daniloimparato/easylayout", force=T)
library(easylayout)
library(igraph)
library(ggraph)
load()
g <- graph_from_data_frame(string_interactions, directed=F)
V(g)$degree <- degree(g)
layout <- easylayout(g)
ggraph(g, layout = layout) +
geom_edge_link(color="#999999") +
geom_node_point... |
7ff775daf08f2e2fc49064352aa1a1f6569ca4b7 | 462980a11e4b73ee26290f78ba028537224eba78 | /3. Getting and Cleaning Data/Qiuz 1.R | d5a02761406cba450b8762eb0e4cab4f33f57824 | [] | no_license | gravialex/Coursera-Data-Scientist | 1673f37845320d5c376da1d2c62149a6e0f11987 | c72d20b58d8f5d5d7ff70e24ed7fce832f0fd7ca | refs/heads/master | 2020-07-22T23:13:48.130566 | 2017-03-01T11:17:38 | 2017-03-01T11:17:38 | 73,820,520 | 0 | 0 | null | 2017-03-01T11:01:28 | 2016-11-15T14:18:46 | HTML | UTF-8 | R | false | false | 1,195 | r | Qiuz 1.R |
URL <- "https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2Fss06hid.csv"
download.file(URL, destfile = "./data/data.csv")
# 1
data<-read.csv("./data/data.csv")
v24 <- subset(data, VAL==24)
nrow(v24)
# 2
fes <- data[["FES"]]
unique(fes)
summary(fes)
table(fes)
# 3
install.packages("xlsx")
library(xlsx)
URL <... |
9ec3b0ad99b36493328f6494cdc2278faad4ec15 | 6d443800445592a4bcdc3531a850d5152942e2fd | /server.R | ef0c385448c5075563330a90b1aeb92a4882e6a6 | [] | no_license | angy89/InsideNano | 35f2004414bd1065df4db686ceefdb2096b789da | 0b5ee4502106740acc3daec100cac37f015791d3 | refs/heads/master | 2021-01-18T21:11:38.811196 | 2016-01-10T20:23:47 | 2016-01-10T20:23:47 | 45,189,493 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,691 | r | server.R | shinyServer(function(input, output,session){
check_login(output,FALSE) #plot the login control
set_observer(input,output,session) #set the observe event
observeEvent(input$login, {
validate_login(input,output)
if(input$username != username | input$password != password){
login_failed(input,outp... |
0e3cea52fb9108b7f6865cd07204341047154b1d | 169a6494a475f42d0452d3ade4622bde1eb939cc | /tests/testthat/test-lowest_common.R | 720acd28f65da0449a24e57e2e107dda94cc1432 | [
"MIT"
] | permissive | ropensci/taxize | d205379bc0369d9dcdb48a8e42f3f34e7c546b9b | 269095008f4d07bfdb76c51b0601be55d4941597 | refs/heads/master | 2023-05-25T04:00:46.760165 | 2023-05-02T20:02:50 | 2023-05-02T20:02:50 | 1,771,790 | 224 | 75 | NOASSERTION | 2023-05-02T20:02:51 | 2011-05-19T15:05:33 | R | UTF-8 | R | false | false | 1,673 | r | test-lowest_common.R | context("lowest_common")
force_http1_1 <- list(http_version = 2L)
test_that("lowest_common works with ncbi, passing in classifications and doing internally", {
skip_on_cran()
skip_on_travis()
id <- c("9031", "9823", "9606", "9470")
idc <- classification(id, db = 'ncbi', callopts = force_http1_1)
aa <- lowe... |
8d1774a4503699459561e82f78b6622ba0a82b23 | e91c5f8da9291cb2dfb9436bd9934054df6940cf | /Min_Rank_Val_Bet.R | fe956eee4c43ec1d3bfc0026c4d768c455907844 | [] | no_license | melgj/UKHR | 671ee8307f2146f44c753aef8ade03eda68e4021 | 5653ff75aeddc6657503bfe171b9e6a3ce493b0a | refs/heads/master | 2020-03-27T14:34:17.238033 | 2019-04-29T18:38:28 | 2019-04-29T18:38:28 | 105,633,141 | 0 | 0 | null | 2017-10-03T09:27:40 | 2017-10-03T09:12:01 | null | UTF-8 | R | false | false | 5,047 | r | Min_Rank_Val_Bet.R | #setwd("~/git_projects/UKHR_Project")
#
# top5 <- ukhr_master_BF %>%
# drop_na(Rating_Rank, BFSP_PL, ValueOdds_BetfairFormat, BetFairSPForecastWinPrice, VOR_Range, Value_Odds_Range,
# Speed_Rank_Range) %>%
# filter(Rating_Rank <= 5, ValueOdds_BetfairFormat <= 21, Actual.Runners >= 5) %>%
# group_by(UKHR... |
bcdb5253beb145261aea92dbcb0a9ae4bcd9078b | 1603c15605353094a3807180a2645654639138bb | /chapter-06-exercises/exercise-3/exercise.R | ac12029b3baf1a39b9d21e431dc7a15817e5be93 | [
"MIT"
] | permissive | sugarbagels/2020S_INFO201_BookExercises | 254d4cefd2c6280ccc84e88045e4d75a63678040 | 658e808c2fce2994bc7b4e3132ab1a3da8d53935 | refs/heads/master | 2021-05-19T16:23:02.031454 | 2020-04-22T04:04:42 | 2020-04-22T04:04:42 | 252,024,913 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 750 | r | exercise.R | # Exercise 3: writing and executing functions
# Define a function `add_three` that takes a single argument and
# returns a value 3 greater than the input
add_three <- function(a){
sum(a+3)
}
add_three(23)
# Create a variable `ten` that is the result of passing 7 to your `add_three`
# function
ten <- add_three(7)... |
68e2e79fa441d67d7daa916a3b98c458cfe18648 | e09fa7173766215ad0284d6c2608179d6e95121c | /man/find_hrefs.Rd | a0e2e18151c6e816fbe8bf55b6ba8eba1b2fa8b9 | [] | no_license | TuQmano/indek | a20ae12c8e46ad323410345f4c6429dc16558d7f | 3bad9aff914504cfceb72a0cc67d0d209f520ae4 | refs/heads/master | 2020-04-18T03:24:06.613694 | 2019-01-07T22:41:37 | 2019-01-07T22:41:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 431 | rd | find_hrefs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/indek-page.R
\name{find_hrefs}
\alias{find_hrefs}
\title{'find_hrefs' search for resources whithin the links of a web page}
\usage{
find_hrefs(page, regex)
}
\arguments{
\item{regex}{a character vector with the regexs for matching.}
\item{x}... |
89e0b0784f085c99c618016ce326f14ca2fe155e | 49cbd9a3389a95715c578e42a81e93f59994037f | /man/cumall.Rd | 4693cfd1be4d2bbe5be6ba7bade9df5c8812f93f | [
"MIT"
] | permissive | s-fleck/lest | fec697bff4ea542d0b380e7386fb376878e33350 | 353e80e532b8a2742da6c154aefee0b64d8024c7 | refs/heads/master | 2020-03-25T09:56:58.754740 | 2019-11-28T14:58:53 | 2019-11-28T15:00:32 | 143,680,243 | 29 | 0 | null | null | null | null | UTF-8 | R | false | true | 408 | rd | cumall.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cumall.R
\name{cumall}
\alias{cumall}
\alias{cumany}
\title{Cumulative all and any}
\usage{
cumall(x)
cumany(x)
}
\arguments{
\item{x}{a \code{logical} vector.}
}
\value{
a \code{logical} vector
}
\description{
Cumulative all and any
}
\exam... |
372d06082231117d6efa88fe732303f3dde7ad58 | 09f19d2460871a24f38cfe5c71a4aefe3bb901eb | /tests/tst_mtl_preds_10data190709.R | f4dafc6704c18ed94b78924d5684422e80e1ece4 | [] | no_license | iaolier/mtl-qsar | 080b7518e5a3b98bf5da68042bdf3e4ef8354b23 | 0217155b051e9b87a5be9527127dc784c59ee041 | refs/heads/master | 2020-05-22T17:00:37.647504 | 2019-08-08T15:47:17 | 2019-08-08T15:47:17 | 186,443,092 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,094 | r | tst_mtl_preds_10data190709.R | #!/usr/bin/Rscript --vanilla
# nohup ./tests/tst_mtl_preds_10data.R > ~/logs/tst_mtl_preds_10data.log &
#' Script to compare stl and mtl through RF model performance.
library(devtools)
load_all(".")
#paths
dsets_prim_path <- "/shared/mtl-qsar/datasets/originals/"
dsets_splits_path <- "/shared/mtl-qsar/data_splits/"... |
a9c319dceddbd42de76ab4a3694c5e65a74e61eb | 823a0d1c34ac3bdccb51596fbfb0a65bbd5a77bd | /RawGeno/R/SMPLDIAGVAL.R | c0be7f1fa7984cb102c4f868b21ca13529b53799 | [] | no_license | arrigon/RawGeno | ca66e64ce5fc1ac9c39da25345c8826093a4cfd5 | 9898e0b525c54fc7afe58727fac354dfc846ac65 | refs/heads/master | 2021-01-11T00:14:19.789351 | 2016-10-11T14:18:19 | 2016-10-11T14:18:19 | 70,573,310 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 765 | r | SMPLDIAGVAL.R | SMPLDIAGVAL <-
function(path=getwd()){
if(exists("mergedTable")){
data.bin=mergedTable
} else {
data.bin=t(data.binary$data.binary)
}
### Prepare diagnostics
nbbin=rowSums(data.bin)
binFreq=colMeans(data.bin,na.rm=T)
binFR=t(t(data.bin)*binFreq)
binFR[binFR==0]=NA
binFR=rowMeans(binFR,n... |
f9fe021705b42caa2ff6b4e659caba42b2258842 | 5b153389e67e59a30aebf6a0d84b69fd89f805d4 | /quantutils/R/date.busDiff.r | a8378424a5d2c2e01b0aabc1e827e1de6df8046e | [] | no_license | dengyishuo/dengyishuo.github.com | 480d9b5911851e56eca89c347b7dc5d83ea7e07d | 86e88bbe0dc11b3acc2470206613bf6d579f5442 | refs/heads/master | 2021-03-12T23:17:10.808381 | 2019-08-01T08:13:15 | 2019-08-01T08:13:15 | 8,969,857 | 41 | 35 | null | null | null | null | UTF-8 | R | false | false | 1,143 | r | date.busDiff.r | ##' Unconfirmed
##'
##' Unconfirmed
##' @title Diff between date and BusDate
##' @param fromDate start Date
##' @param toDate end Date
##' @param region region of stocks or bonds
##' @return out Diff between date and busDate
##' @export
##' @author Weilin Lin
date.busDiff <- function(fromDate, toDate, region)... |
c242988f2934765d0e14894152a4396e15133484 | 6c0e36ea67a5f7a50051a6eef635283a589d020e | /inst/examples/PiecesDevice/pieceDevice.R | 2736a22cf785257bf722585e18c580fd7d70a627 | [] | no_license | omegahat/RGraphicsDevice | 1f07432c7a1e0400b5d955cc24754f0916e807ac | fb223a7cbb21c8e513dc377ed52f28e8f4241e9e | refs/heads/master | 2022-01-25T13:22:36.464954 | 2022-01-15T18:04:58 | 2022-01-15T18:04:58 | 4,004,745 | 4 | 4 | null | null | null | null | UTF-8 | R | false | false | 6,411 | r | pieceDevice.R | #
# This graphics device tries to figure out what is being
# drawn as higher-level constructs, e.g. axes, titles,
# box for the entire plot, ...
#
# Basically, we collect up the calls to the graphical primitive
# functions and all the arguments of relevance. We also store
# the call stack (sys.calls()) and the names o... |
7de66270af175fadf18d844af2b6ede19209f093 | 8cb0c44a74f7a61f06d41e18ff8c222cc5f28826 | /man/NamedResourceDTOExperimentModel.Rd | cadea438f9479679e9986a18b2bda62daeabd15a | [] | no_license | OpenSILEX/opensilexClientToolsR | cb33ddbb69c7596d944dcf1585a840b2018ee66c | 856a6a1d5be49437997a41587d0c87594b0c6a36 | refs/heads/master | 2023-05-31T14:26:19.983758 | 2022-01-26T17:51:51 | 2022-01-26T17:51:51 | 360,246,589 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 566 | rd | NamedResourceDTOExperimentModel.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/NamedResourceDTOExperimentModel.r
\docType{data}
\name{NamedResourceDTOExperimentModel}
\alias{NamedResourceDTOExperimentModel}
\title{NamedResourceDTOExperimentModel Class}
\format{An object of class \code{R6ClassGenerator} of length 24.}
\u... |
3f3040f94ec66b871055c3ceda1ef8de2ef58d28 | 8c6691d164054aa116a9285f1ad2e30c41c61bf5 | /R/justify.R | 24e238bd9069f3640ecab56407f57b9ec080bd16 | [
"MIT"
] | permissive | nacnudus/unpivotr | 7d0ee014e96cddae82de7df60400a5914d4d098d | f7eb82b0e5f9ea67357402f7973a49254312d15c | refs/heads/main | 2023-08-16T12:42:21.082390 | 2023-01-22T21:05:00 | 2023-01-22T21:05:00 | 66,308,149 | 179 | 21 | NOASSERTION | 2023-08-07T14:15:01 | 2016-08-22T21:04:24 | R | UTF-8 | R | false | false | 1,368 | r | justify.R | #' Align one set of cells with another set
#'
#' @description
#' If the header cells of a table aren't aligned to the left, right, top or
#' bottom of the data cells that they describe, then use [justify()] to re-align
#' them, using a second set of cells as a guide.
#'
#' @param header_cells Data frame of data cells w... |
855100fab2ae9cc868e7e646a96c84b32c0ad238 | 7cd817e9b83a5710e3b1d610dae7128436a13304 | /love.R | 8abc2614bb9ab2ae74c004e255ab909a1be69045 | [] | no_license | donlelek/Rsnippets | b6fa02827eda93cf8f8c309c621c065c962b7510 | 3315a34924c5034921bd0d0e585fb28d6ec4e825 | refs/heads/master | 2020-04-03T22:36:37.121140 | 2019-01-14T22:43:29 | 2019-01-14T22:43:29 | 24,967,402 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,665 | r | love.R | # this will be a great t-shirt one day
# source:
# http://stackoverflow.com/questions/39870405/plotting-equations-in-r
library(ggplot2)
library(dplyr)
L <- data_frame(x = 1:100,
y = 1 / x)
O <- data_frame(t = seq(-pi, pi, l = 100),
x = 3 * cos(t),
y = 3 * sin(t))
V <- d... |
fc1349c4cb73229bd916ec269087db6e05897532 | 5fffd8bd76010b3bc6407069230c7acf5518d72d | /getandclean_question5.R | f2ff1ca722a47155fc84d63b42fb9a6add7082cc | [] | no_license | cmohite/GettingAndCleaningData-Quiz2 | 8a3059c6e4a214d88425f2c2cc98ac5444a9528d | 83c1bf091d2a70ae84540694a54574192e535ca9 | refs/heads/master | 2020-04-16T12:15:17.459555 | 2015-08-16T03:50:22 | 2015-08-16T03:50:22 | 40,708,913 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 253 | r | getandclean_question5.R | fileUrl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fwksst8110.for"
download.file(fileUrl, destfile="./Fwksst8110.for")
data <- read.fwf(file="./Fwksst8110.for", widths=c(-1,9,-5,4,4,-5,4,4,-5,4,4,-5,4,4),
skip=4)
sum(data[,4])
|
2689b561f6da041d50963627dd8ce760d6ffccb0 | 3a36591cd6483b6bd36a9277e7b42af710c87b39 | /run_analysis.r | 7d180a0b66acd6ee31cb2915dc66810352203c5e | [] | no_license | tmregh/Getting-and-Cleaning-Data-Project | 457e414c7431cb374ff8f4439dab8c9bdb38dff0 | 348e13592f987e99600be9d468e2702727cf2d9f | refs/heads/master | 2021-01-20T05:04:59.161282 | 2014-08-24T23:00:17 | 2014-08-24T23:00:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,332 | r | run_analysis.r | ##############
##############
##############
#Read in the necessary Samsung data
#Set the working directory to a folder containing only the following 8 Samsung datasets:
# 1.activity_labels
# 2.features
# 3.subject_train
# 4.subject_test
# 5.X_train
# 6.y_train
# 7.X_test
# 8.y_test
##############
##############
###... |
166344e616ed0019632d54d2c900bc6318614f5a | efffa5e84284216ee1a4a3b633948eb80121c596 | /R/runMLMethod.R | 79e88693bfdf3320d1fca4734c434dacffc9f611 | [] | no_license | chethanjjj/LireNLPSystem | f178aa3969a179601cb5262db6d46ec7f9fab22f | 408da4cdf8907ed0e2a0fdc650967bf31c9181f3 | refs/heads/master | 2021-06-20T22:09:43.098365 | 2021-06-19T14:07:07 | 2021-06-19T14:07:07 | 318,691,894 | 0 | 1 | null | 2020-12-05T03:22:24 | 2020-12-05T03:22:24 | null | UTF-8 | R | false | false | 9,516 | r | runMLMethod.R | #' This function elastic net logistic regression for a given finding and feature matrix
#' @param finding string indicating finding of interest
#' @param featureMatrix feature matrix
#' @param outcome dataframe indicating the labels for each report
#' @param trainID vector indicating the imageid's for the training data... |
163c1f62fae5c24b371daef2fca73d8592fe3c11 | 335eb7d0a695b9a7bc5e1f5ed9dc676272776703 | /man/objective.keepSKL.Rd | f86e26aaa12a3e3807fb470637e8ffdc95955faa | [] | no_license | ayazagan/amalgam | 750298c89709814696642304b52b24d0fab9b4a7 | 00ca3804f031cc67ff104ce35c8522f82b444ec9 | refs/heads/master | 2022-04-16T11:59:22.642445 | 2020-02-25T03:40:22 | 2020-02-25T03:40:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 548 | rd | objective.keepSKL.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/8-entropy.R
\name{objective.keepSKL}
\alias{objective.keepSKL}
\title{Calculate Fitness of Binary Vector}
\usage{
objective.keepSKL(codon, ARGS)
}
\arguments{
\item{codon}{A binary vector.}
\item{ARGS}{Handled by \code{\link{prepareArgs}}.}
... |
8c9e60094c6471f1e8f3f333e2e27904db2bb303 | b8fa00b408af080b5e25363c7fdde05e5d869be1 | /Project_0867117/Project_11.r | ea09b2124a5cb695d379345aad1ec6d5e2679b32 | [] | no_license | anurag199/r-studio | bc89f0c18a8d44164cb4ede8df79321ea965bc77 | e42909505fbe709f476081be97f89cc945a2745d | refs/heads/master | 2020-04-26T23:04:25.061780 | 2019-03-05T06:56:26 | 2019-03-05T06:56:26 | 173,891,322 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 238 | r | Project_11.r | library("ggplot2")
library("plyr")
# plotting graph for birth_sort (Ascending order birth_data)
ggplot(birth_sort,aes(x=births, y=year, color=factor(month))) + geom_point() +
geom_smooth(method = 'loess') + ggtitle("births vs year") |
2e55f763cf697036dcc79a253fa4f388d862fccc | 37f2792ebd7f95a652cdb5f0256a63a42262fa62 | /lesson3_Shinydashboard/app1/flexdashboard_script.R | f65e70acd9d04cb0f2777219e35e4de5a0b68e57 | [] | no_license | lili013/shiny_dashboard_tutorial | ccbbc2258a1b85e9686da2663e2e8276729afacc | 22a6aa8c94b1cc99f7ead93384e3dd157961cf95 | refs/heads/master | 2022-12-11T01:41:44.639339 | 2020-09-18T22:31:51 | 2020-09-18T22:31:51 | 296,443,579 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,021 | r | flexdashboard_script.R | library(ggplot2)
library(dplyr)
# Shiny economics module
# UI function
economicUI <- fluidPage(
titlePanel("Widget"),
fillCol(height = 400, flex = c(1,1),
inputPanel(
selectInput(inputId = "variable",
label = "Select a column to display",
... |
f4b8b9d46bd7c8964955f0d976d8683e8fb65722 | dd3a4cebe8b03dfce00c930c5cc2b844fbb1c015 | /R code/plots.R | add7e6197a078c74c1088403f2aef7d6d2023409 | [] | no_license | DiabbZegpi/ggsave | c043ed9149280f12194f3a8fbcc26b8cbd1e9c8c | efd4bd3a598a79e72d6552ab986520b5adc6cec7 | refs/heads/master | 2022-04-17T09:07:10.023992 | 2020-04-19T22:58:06 | 2020-04-19T22:58:06 | 256,554,935 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 876 | r | plots.R | library(tidyverse)
library(patchwork)
library(ggforce)
data(iris)
theme_set(theme_ligth())
descrp <- "The smallest specie among virginica and versicolor"
p <-
ggplot(iris,
aes(x = Petal.Width, y = Petal.Length)) +
geom_point() +
geom_mark_ellipse(show.legend = FALSE,
aes(fill = Spec... |
b5abb9f2e131e73857352443e7a2e5a44e192d14 | 86cc55c6a11ac25a08bf7cf05e93a7620440560a | /R_code_point_pattern_analysis.r | 6b513d87f5fa455ee52d6a30e7cb77e624089a65 | [] | no_license | AmandaVecchi/Monitoring-Unibo | eebb07edce5b6a7f25cd39b2292ce4017e98ea95 | 32581e557aef3f5ba55b4ccc7b3cf8363a396754 | refs/heads/master | 2021-09-08T16:52:20.380310 | 2021-09-08T16:33:20 | 2021-09-08T16:33:20 | 250,028,031 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,544 | r | R_code_point_pattern_analysis.r | ## Point pattern analysis: density maps
install.packages("spatstat")
library(spatstat)
#attach our dataset, the one we are going to use
attach(covid)
#what are the coordinates of the datases and what are the extensions of these cooridnates
head(covid)
covids <- ppp(lon, lat, c(-180,180), c(-90,90)) #ppp= planar poi... |
cb6e53f7490c9854580de7d43d0e8b4af448632b | 6ed58d0c61899aeb5e4870621dc7412b3eaa9d6f | /GettingCleaningData/Semana3/mergingData.R | 4173384b9e32958400c8434bfdc1c89d44bc1b7d | [] | no_license | jspaz/DataScience | 3be2c9497bf11af41168acdef83c764188cf68e2 | b8bd27c4cc967c4127ef421585864f0f9de17b68 | refs/heads/master | 2020-04-06T06:19:28.585471 | 2017-12-25T03:58:48 | 2017-12-25T03:58:48 | 55,121,880 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,431 | r | mergingData.R | # Descargar archivo
library(bitops)
library(RCurl)
if(!file.exists("./data")){dir.create("./data")}
fileUrl1 = "https://dl.dropboxusercontent.com/u/7710864/data/reviews-apr29.csv"
fileUrl2 = "https://dl.dropboxusercontent.com/u/7710864/data/solutions-apr29.csv"
download.file(fileUrl1, destfile = "./data/reviews.csv", m... |
3d16766952518790d947f661084b5cdfe3374b23 | 33083fb27268d8e1198697526c7bcd13a1bf3e41 | /shiny_app/demoGraphDistance/tests/testthat/test_isNonBlankSingleString.R | 6043fc701cae4d16be32e1faa48cd9256edf3246 | [
"MIT"
] | permissive | johndrummond/demo_shiny_graph_distance | 5fedc9e4eb40e40867d55ff80bb48115c196d36b | 49dd94a2d10be6a4543a276576bedc06db0e6145 | refs/heads/master | 2020-06-16T10:36:45.808336 | 2019-07-10T10:44:15 | 2019-07-10T10:44:15 | 195,542,773 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 891 | r | test_isNonBlankSingleString.R | context("Util functions blank string")
library(demoGraphDistance)
test_that("isNonBlankSingleString false for Non character values", {
expect_equal(isNonBlankSingleString(NA), FALSE)
expect_equal(isNonBlankSingleString(NULL), FALSE)
expect_equal(isNonBlankSingleString(1), FALSE)
expect_equal(isNonBlankS... |
ed9688b87bcef3970f7f8901aba58f562f67202f | ec6dca004307b39d5629c5dc153d60a16e10c824 | /NaiveBayes.R | f0943a9b914f4ae5ee9173f7962bcd125b249da2 | [] | no_license | ragnar-lothbrok/data-analytics | df6e9b912ac180db6d188afa4f6d5a63d71b738d | 13a1c3c4824836d80b1f5a4dd28cd025f53e778c | refs/heads/master | 2020-05-21T16:44:47.255629 | 2017-02-12T10:14:58 | 2017-02-12T10:14:58 | 65,620,376 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,568 | r | NaiveBayes.R | #NaiveBayes
install.packages("e1071")
library(e1071)
CTR_SD_Data <- read.csv("/home/raghunandangupta/Downloads/splits/splitaa")
#View(CTR_SD_Data)
#Split the data in training and test data
rows = seq(from=1,to=nrow(CTR_SD_Data), by = 1)
head(rows)
train_rows = sample(x=rows, size=(0.7 * nrow(CTR_SD_Data))) #selecting... |
2eaf2f3d735539150ebefd7ded2efbaf107a62f5 | f057a79b8c1cb6ea00ec6ff1c06852b2d660537a | /man/sp2df.Rd | bdaf81c2ea4f73148d96632dca81d68711b4b53b | [] | no_license | kcucchi/myUtils | 985bed13a1378e4ee5bdbf97664c098ad4e1440f | 14418d7acf4f0c77643b1f3391180871f07b9a04 | refs/heads/master | 2021-04-12T10:05:45.741265 | 2018-08-08T22:35:06 | 2018-08-08T22:35:06 | 126,282,738 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 423 | rd | sp2df.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/map_utils.R
\name{sp2df}
\alias{sp2df}
\title{Transforms a sp object into a dataframe to plot into ggplot}
\usage{
sp2df(x_sp)
}
\arguments{
\item{x_sp}{the sp object to transform into dataframe}
}
\value{
the corresponding dataframe to be pl... |
6a4df4dfda694143909cbb7dd6703e42ab3945a9 | d4bbec7817b1704c40de6aca499625cf9fa2cb04 | /src/lib/distributions/signrank/__test__/fixture-generation/qsign1a.R | a160108ee5379367167705c91ae73835f0423b11 | [
"MIT"
] | permissive | R-js/libRmath.js | ac9f21c0a255271814bdc161b378aa07d14b2736 | 9462e581da4968938bf4bcea2c716eb372016450 | refs/heads/main | 2023-07-24T15:00:08.372576 | 2023-07-16T16:59:32 | 2023-07-16T16:59:32 | 79,675,609 | 108 | 15 | MIT | 2023-02-08T15:23:17 | 2017-01-21T22:01:44 | TypeScript | UTF-8 | R | false | false | 470 | r | qsign1a.R | # W+ values that approx map to probabilities = [0,1]
# W+
1 0
2 258
3 280
4 294
5 305
6 314
7 322
8 329
9 335
10 341
11 347
12 352
13 357
14 362
15 366
16 371
17 375
18 379
19 383
20 387
21 391
22 395
23 399
24 402
25 406
26 410
27 414
28 418
29 421
30 425
31 429
32 433
... |
0a50751d7dd0ee6c1de36507af88838ae4b68166 | 6590eea4dd7a55c10b29a4a8c3286cb7023100a3 | /cachematrix.R | 4a75ba1588d968f6604e9ec21f8a4b2da0ad05bf | [] | no_license | Swisshenri/ProgrammingAssignment2 | 8ccf87c6d14ee79cb88eef4f02c526133dfe3348 | 83c44220bb36bb5c18e9984db0ec094b66e35aa4 | refs/heads/master | 2020-05-30T08:28:36.058170 | 2019-06-01T18:49:00 | 2019-06-01T18:49:00 | 189,622,836 | 0 | 0 | null | 2019-05-31T16:06:32 | 2019-05-31T16:06:31 | null | UTF-8 | R | false | false | 1,134 | r | cachematrix.R | ## This function allows for creating a matrix
## to use it with another function cacheSolve that will invert
## the matrix / uses "<<-" for global variable (to do cache)
makeCacheMatrix <- function(x = matrix()) {
minv <- NULL
set <- function(y) {
x <<- y
minv <... |
d413b830f97653af3abf8381d057e04cfad54701 | 9d3e3c3950c4101bc863a90e69606d7c7d03a4e9 | /chilling/04_make_figures/frost_bloom/crossOvers/post_processing_1_corssovers.R | c8baefadabb4b7c4e38f0959d9a665b16d9f97c0 | [
"MIT"
] | permissive | HNoorazar/Ag | ca6eb5a72ac7ea74e4fe982e70e148d5ad6c6fee | 24fea71e9740de7eb01782fa102ad79491257b58 | refs/heads/main | 2023-09-03T18:14:12.241300 | 2023-08-23T00:03:40 | 2023-08-23T00:03:40 | 146,382,473 | 3 | 6 | null | 2019-09-23T16:45:37 | 2018-08-28T02:44:37 | R | UTF-8 | R | false | false | 4,212 | r | post_processing_1_corssovers.R | rm(list=ls())
library(data.table)
library(dplyr)
options(digits=9)
options(digit=9)
source_path_1 = "/Users/hn/Documents/GitHub/Ag/chilling/chill_core.R"
source_path_2 = "/Users/hn/Documents/GitHub/Ag/chilling/chill_plot_core.R"
source(source_path_1)
source(source_path_2)
#############################################... |
4a960ceea54ee6d4eb22e8ba1eb082ca9ec2bc5c | b8bb5f8022c0def70493d747648342dd43d5000f | /man/detBiallHitType.Rd | 84cd49b6acf295f420a854a5bf7b4c112dd9c3dd | [] | no_license | UMCUGenetics/hmfGeneAnnotation | a76321e2d01efcbd19467621fb4e5f4977c6c23f | 67227c56232184de9b673cdcc814e9884e454943 | refs/heads/master | 2021-07-25T22:16:07.883012 | 2020-05-04T10:47:56 | 2020-05-04T10:47:56 | 167,984,352 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 484 | rd | detBiallHitType.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/postProcessDiplotypes.R
\name{detBiallHitType}
\alias{detBiallHitType}
\title{Determine biallelic hit type}
\usage{
detBiallHitType(diplotypes, min.a1.score = 1, min.a2.score = 1)
}
\arguments{
\item{diplotypes}{Diplotypes dataframe}
\item{m... |
2d54d39a08f8e1ffd4c2aebd64e8ff2633ed2a74 | bb2f9a5339badd495d41334c8df70bf5c3b29a28 | /R/epicurve.R | f4ed6899101537bedfbc5cf56d4780180cd9df71 | [] | no_license | thlytras/Rivets | 7eeee206e49e462cf1a7eb06b3b372b7bddc29e7 | d438d5f5c8c01b5ebd09be9d17e3700eaa14e3a6 | refs/heads/master | 2023-07-22T04:25:04.911329 | 2023-07-13T11:20:33 | 2023-07-13T11:20:33 | 95,921,893 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,832 | r | epicurve.R | #' Draw a classic style epidemic curve (with rectangles)
#'
#' epikml() takes a set of coordinates and other associated info as input, and creates
#' a KML (Keyhole Markup Language) file that can be opened with Google Earth or other
#' similar programs. It's original intention was to plot disease cases, but can find w... |
c8be3a02e736a5bcee4eaf8d9b2d810c6c774aa9 | 4bba0777dc4516afc16af4693e22b7685e3cc77a | /scripts/02_make_RNDsp_plots.R | d6822f8bb2e1ecb6bd2e4a983215af23e4d264b4 | [] | no_license | CMPG/vole_genome_analysis | 27d9f89f25022212358357c752e0dab2d41573b3 | 424431f6c62079e1bff77382b1a49dbc988a9ee7 | refs/heads/master | 2023-04-09T13:49:13.436480 | 2021-04-20T21:29:08 | 2021-04-20T21:29:08 | 274,372,809 | 2 | 1 | null | 2021-04-20T21:29:08 | 2020-06-23T10:07:48 | R | UTF-8 | R | false | false | 2,865 | r | 02_make_RNDsp_plots.R | ### RNDsp plots
source("./scripts/Resequencing-data-analysis-functions.R")
require(GenomicRanges)
load("./data/annotation.rda")
load("./data/pi_rndsp.rda")
output.dir <- "./plots"
if(!dir.exists(output.dir)) dir.create(output.dir)
stamp <- format(Sys.time(), "/%Y%m%d_%H%M%S")
span.loess <- 0.01
vole.col <- c(rep("#... |
b4804e1fad5ab85f6546aa29429b7f40393b4dae | 9ae81d9cf8c1912e2a1ec9294596b798307e953b | /test.R | eedc310859bf54cf19b6f2e0af9e44b25ea8ddcb | [] | no_license | 15210280436/bizforecast-exercise | 62256d20317abdd1e6edf6c927575b487ade8953 | 8cd3cf8ff6c04170de3f7e2316b49a0c2a035a05 | refs/heads/master | 2020-05-07T22:32:59.472591 | 2019-04-18T06:52:05 | 2019-04-18T06:52:05 | 180,949,569 | 0 | 0 | null | 2019-04-12T07:00:25 | 2019-04-12T07:00:25 | null | UTF-8 | R | false | false | 2,582 | r | test.R | library(magrittr)
library(tidyverse)
library(purrr)
library(dplyr)
library(infer)
set.seed(1212)
income=350:400 #销售价格
leads_cost=8:10 #leads成本
constant_cost=20000 #固定成本
leads_cnt=3000:4000 #每天leads数量
leads_conversion <- matrix(rnorm(30, mean = 0.04, sd = 0.5/100), # 转化率
ncol = 2, byrow = FALSE)
i... |
3aaf92ed76b816d0f4f5f4ddc7e4e043f8091b4f | 81a14c15f1f686b71fb97c3b1b34e336125577dc | /send_errors_to_server.R | f86f9cad5518b2ed075fc7201078abbf375fa3c4 | [] | no_license | ccssaude/farmac-sync | a86924a3d4d619f626e990023370927fbf294f6c | e81747445574b9d9c969e7bb9310c8880139e0f1 | refs/heads/master | 2022-12-25T06:51:57.787440 | 2020-10-07T20:40:36 | 2020-10-07T20:40:36 | 256,339,191 | 0 | 0 | null | 2020-10-07T20:40:37 | 2020-04-16T21:58:09 | R | UTF-8 | R | false | false | 2,681 | r | send_errors_to_server.R |
###########################################################
# con_farmac = FALSE para casos de conexao nao estabelecida
# if (!is.logical(con_farmac)) {
# # Get local connections
# # con_local = FALSE para casos de conexao nao estabelecida
# if (!is.logical(con_local)) {
if (is.farmac) {
log_error_s... |
b9076323a3eb90fa847b74d043fe3c852f56df50 | 64ceb4e31d673a06f8e4c7a0d6c2ae68a926aa3e | /gamma.R | b93cc0a721f6a8b7252f84b681605dd803e3ca83 | [] | no_license | jessehamner/ResearchMethodsRScripts | b6fb207c62f8e0cb314a4cc133b3571446b1465d | c6a95686cfd5c73ec8798ed09ec328e743d21ff8 | refs/heads/master | 2021-01-11T08:30:13.094982 | 2020-10-26T15:09:00 | 2020-10-26T15:09:00 | 72,244,076 | 0 | 0 | null | 2020-10-23T14:54:21 | 2016-10-28T21:35:41 | R | UTF-8 | R | false | false | 2,221 | r | gamma.R | # Draw the gamma distribution.
library(ggplot2)
x <- seq(0, 100, 0.001)
#a <- 0.5
a <- 1.5
s <- 2
i<- seq(0,100,0.001)
y=dgamma(i, shape = a, scale = s)
y[length(y)] <-0
# graphics margins:
margins<-c(4.5,4.5,1,2)
# C<- data.frame(x,y)
B <- matrix(c(i,y), nrow=length(i),ncol=2)
C <-data.frame(B)
png(filename="gam... |
c4467823284f21c9eda9128505cc6a0be20a8b78 | 5c6f1f6ca431f60d06ad2c09f1f031885be27e29 | /Simple CTA Analysis/cta.R | 8919f28698dbeabb31a35f8c17da1869c3b4ca0e | [] | no_license | ajayjjain/nonpolitical-statistical-analysis | dcc029a2dc71494a380b3321a59c74e500532e8b | 109cbda368e859291a60bbcf3cb89921d1eed8a9 | refs/heads/master | 2021-01-24T16:56:49.695019 | 2018-02-28T21:08:31 | 2018-02-28T21:08:31 | 123,219,095 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,013 | r | cta.R | library(ggmap)
cta = read.csv("/Users/Ajay/Downloads/Webservice-CTA-master/share/cta_L_stops.csv")
chicago <- get_map(location = 'chicago', zoom=10)
colors = c()
line = c()
reds = c()
blues = c()
greens = c()
pinks = c()
purples = c()
browns = c()
yellows = c()
oranges = c()
for (row in 1:nrow(cta)){
if (!(grepl("&"... |
2dc380a4f9991cf8d07372305b3e70df6cdb8ecf | a3bf051cc61a4b28acf455d8b29ea9ca84fe7538 | /code/008_Unemployment initial claims.R | ace33c9218178cdc542e3595809c8c9b237cdd77 | [] | no_license | pgsmith2000/dailyecon | bca27c432007c59283b0adcb6081001d48fc1c4d | 1d7077a2718508b846c03395d98178af3f06376c | refs/heads/master | 2020-09-01T18:15:29.328782 | 2019-11-12T19:34:45 | 2019-11-12T19:34:45 | 219,024,281 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,029 | r | 008_Unemployment initial claims.R | # set start and end dates
end_date <- Sys.Date() - 1
if (wday(end_date) == 1){
end_date <- end_date - 3
} else if (wday(end_date) == 7){
end_date <- end_date - 2
}
start_date <- end_date - (3*(13*7))
# last 45 days of SLVPRUSD
fr_IC4WSA <- makeFREDtable("IC4WSA", start.date = start_date, end.date = end... |
763980e2e26f87fc040a84b13fd59e8897d3e82d | ef3c8b0609b38ab0388babe411eb4eccaa4d94b4 | /preprocessAffy.R | d4d9fcb917488abbebf2778b6b45ba098e0b8f99 | [] | no_license | DannyArends/ludeCode | d540c1a1d3e9373fe2beaf69a80390c483eb01c0 | ff109c9fd5fa4204805e3ea672c749189d6ed670 | refs/heads/master | 2021-01-10T19:36:54.675900 | 2013-07-19T13:07:05 | 2013-07-19T13:07:05 | 10,618,682 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,882 | r | preprocessAffy.R | source("http://bioconductor.org/biocLite.R") # Connect to bioConductor
#install.packages(c("preprocessCore", "biomaRt")
#biocLite(c("affy", "gcrma", "lumi", "gcrma","AnnotationDbi")) # Install the affy package
library(affy) # Load the affy package
library(gcrma) ... |
599caa6c533ce71b0bd75ec7a5de6937b3412420 | 0ff5853af9fd557f6591979a834d9fe82708d234 | /R/drmEMstandard.R | c2fce16dd6347d800fdbf90515ec9b671136f4eb | [] | no_license | csetraynor/drc | 4c6deed9b783802852cfd3ed60c87dec6afc0ce5 | 8719d43a09711250cd791d7d2bc965558d3e62a6 | refs/heads/master | 2023-04-11T03:15:00.881506 | 2021-05-04T02:42:06 | 2021-05-04T02:42:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,933 | r | drmEMstandard.R | "drmEMstandard" <-
function(dose, resp, multCurves, doseScaling = 1)
{
## Defining a helper function for calculating the variance-covariance matrix
# vcFct <- function(beta0, beta, sigma2, len0)
# {
# vc <- (sigma2 / len0) * (beta %o% beta) / (beta0^4)
# diag(vc) <- diag(vc) + sigma2 /... |
7bdbfcf928582b622b12f820adaeb516e231f0d6 | 04606f837c84fe0614b494054aa8e644680669df | /man/covid_world_map.Rd | e9eb84663c4f904c1535dcb0badbd3e7b8df844c | [] | no_license | Ashkan-nmt/Rcovid19 | da678ce59401bc568f8193d5b4739d7e6c59ee1a | 22c32872e309e91466b9cf985072d1310efd961f | refs/heads/master | 2023-01-10T17:37:26.374943 | 2020-11-08T13:17:34 | 2020-11-08T13:17:34 | 309,087,034 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 502 | rd | covid_world_map.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/covid_world_map.R
\name{covid_world_map}
\alias{covid_world_map}
\title{covid_world_map}
\usage{
covid_world_map(date, type)
}
\arguments{
\item{date}{a date string like "yyyy-mm-dd"}
\item{type}{should be equal to "death" or "confirmed"}
}
... |
e5d6abfd3c662fa30fe34806eb11ffa1e7113949 | bfe31790682c4ab4fd7e30bc91f43b989750a1fd | /R/scatter_libdepth.R | 411155cdac05f2c6084fd798468cbb55fe9e6532 | [] | no_license | millersan/rimmi.rnaseq | d1d5342c512854cb3e7f379cfaa0f2813fc68fc9 | 433f0fbb4392c1a47a013f4170749c79db793834 | refs/heads/master | 2022-12-28T07:33:23.053283 | 2020-10-13T12:41:26 | 2020-10-13T12:41:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,319 | r | scatter_libdepth.R | #' Plot clusters in 2 umaps with the point size corresponting to the library size
#'
#' This function allows you to see if the library size influences in your clustering
#'
#' @param Seurat_obj your Seurat object
#'
#' @return scatter plot with the library size correponding to the point size
#'
#' @keywords Seurat, sin... |
fb8cf552c77cf6c902576e8ea9cfe1aca7969869 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/reportROC/examples/reportROC.Rd.R | 3f949678eb6e75dc157b04e5de38a4a3acd08911 | [] | 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 | 351 | r | reportROC.Rd.R | library(reportROC)
### Name: reportROC
### Title: An Easy Way to Report ROC Analysis
### Aliases: reportROC
### Keywords: ROC analysis
### ** Examples
data(aSAH)
reportROC(gold=aSAH$outcome,predictor=aSAH$s100b,important="se",plot=TRUE)
binary=rep(0,nrow(aSAH))
binary[aSAH$s100b>=0.205]=1
reportROC(gold=aSAH$outco... |
f51d36495f17923c45af99b7b11c38f76e45ed6a | 331b6212e64e8c703f423d4d1b2edb857ab7396a | /inst/examples/cmm-dist.R | 665a2685b76b3f436a49fc31a6b7f0701839b834 | [] | no_license | FranciscoOlego/COMMultReg | b6376b6e32add5e22c601b76f6c5b078533cbf58 | 1f1b61204acef86209bdf6dede49ea458c41ea44 | refs/heads/master | 2023-04-05T12:04:30.013210 | 2021-04-19T12:03:56 | 2021-04-19T12:03:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 121 | r | cmm-dist.R | library(COMMultReg)
p = c(1,2,3) / 6
y = r_cmm(n = 100, m = 10, p = p, nu = 0.8, burn = 1000, thin = 10)
head(y)
d_cmm
|
b2b15e677ea71923e4cc992643358daab9834ab6 | 31ea8595b1b023988c18875d71ce2a5202c5f3ea | /rprog/Quizzes/w3_1-2.R | 0b3903332f1fdc53a0ac4f9d34b1c4537a4a46c5 | [] | no_license | datawrecker/datasciencecoursera | 3fef8322c062442e2a8222e36bdf187462c295b3 | ce1d0940fec6c0f4123d48b51a30598c24bbf074 | refs/heads/master | 2020-04-05T15:20:08.066152 | 2015-03-21T15:10:58 | 2015-03-21T15:10:58 | 31,636,947 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 127 | r | w3_1-2.R | library(datasets)
data(iris)
print(mean(iris$Sepal.Length[iris$Species == "virginica"]))
print(apply(iris[, 1:4], 2, mean))
|
f666f78911e5a7cd8a50c9eec6161f929ccd6598 | 9616c625f27b79378589972989858d789dd1a08a | /time_evol_hgT_mehgT_2050.R | 6593917486be246c94b7f9d72a33e9b47b76d868 | [] | no_license | ginevrar/BS | 46fc78121e8e22f4235d381757e4431335d6f1dd | 0de8aa416bdc95d25604f0d3b8f13122ac671baf | refs/heads/master | 2021-01-11T21:52:40.756912 | 2019-07-22T16:19:26 | 2019-07-22T16:19:26 | 78,849,166 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,304 | r | time_evol_hgT_mehgT_2050.R | getwd()
#wd('C:/Users/gi/Dropbox/29_Luglio/Anoxic3c/ore17')
setwd("C:/Users/gi/Dropbox/BlackSea2/implementazione/new_sim0/_met/Wh1")
hg<-read.csv("Dissolved_Divalent_Hg.csv", header=FALSE, skip = 1,sep = ",", dec=".")
names(hg)<-c("Time", "Oxic1","Oxic2", "CIL", "Oxycline","Suboxic1","Suboxic2", "Anoxic","Anoxic2","An... |
3f62dfbec7b9773894ff0c0a6fdb3918b6da531f | d73d6f10c28e6ecca2ff3597eac3238da3d05d42 | /R/n.tables.R | e23f52f7b63c88a3ee67de5744f7d7dae03abe91 | [
"Apache-2.0"
] | permissive | hms-dbmi/dbGaP2x | dec021b70db3684e19752b5da242086b35caa268 | 2024714c2793cc2707f41a4320b27b8da1e421ce | refs/heads/master | 2020-04-05T09:20:34.458287 | 2018-11-28T17:37:03 | 2018-11-28T17:37:03 | 156,751,577 | 2 | 0 | Apache-2.0 | 2019-04-20T21:50:46 | 2018-11-08T18:32:07 | Jupyter Notebook | UTF-8 | R | false | false | 1,340 | r | n.tables.R | #' @title Gets the number of phenotypic datatables in the study
#'
#' @param phs dbGap study ID (phs00xxxx, or 00xxxx, or xxx)
#'
#' @return Return the number of phenotypic datatables in the study
#'
#' @description This function extracts informations from data.dict.xml files from the dbgap ftp server to get the study ... |
e99ece860e3b36f4d74cb0c36e8028f743fc747e | 2cf5744042a9802bc019c0557848db8fbfda0d39 | /inst/sfMM-plotCv_fMM.Rd | f9ca19fb9fee7fd8237ffaa91f36c07d13f6927b | [] | no_license | cran/MRIaggr | bcc874f1253ab7b168e4a6d68bc66e8556b7d330 | 099c3227ac60fdad71aa5c1b79bf53b91a92e177 | refs/heads/master | 2021-01-21T21:47:16.132229 | 2015-12-23T23:44:19 | 2015-12-23T23:44:19 | 31,946,742 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,327 | rd | sfMM-plotCv_fMM.Rd | \name{plotCv_fMM}
\title{Graphical display of the convergence criteria}
\alias{plotCv_fMM}
\description{
Graphical display of the convergence criteria using the result of \code{\link{fMMalgo}} algorithm.
}
\usage{
plotCv_fMM(res.EM, window=FALSE,
filename="EM.traceLv", width=480, height=480, path=NULL, unit... |
4cb30483609f0e75a1e338875c1b2dec18276335 | 75254b62ce23b3f4c4be7b5c3b8a2a9de0a9f7ce | /app/server.R | d1f47f48f5ef5bb6ef1cc6ea10aa163f2ade0342 | [] | no_license | NlIceD/asa-shiny-app | f4589591cd440f55a583bafc9b480ea690ccbec4 | d3d78f74fc83908ce54b25a2316013e2500a88f9 | refs/heads/master | 2022-11-07T01:57:41.246957 | 2020-06-30T02:14:53 | 2020-06-30T02:14:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,352 | r | server.R | server <- function(input, output, session) {
# -----------------------------------------------
# GLOBAL SETTINGS -------------------------------
# -----------------------------------------------
# Source data -----------------------------------
source("../app/utils/retrieve_data.R")
source("..... |
cf7f72d33d3da6ecf7673c750ba2a4ce422d80c7 | efeba9f5aff2e7afbf96a57e0baf62a8fb1a3b94 | /Part2/WordCloud_Practice/test.R | a26840732c0f30a40029d7f86f85668468f0257c | [] | no_license | psm9619/R_Data_Analysis | b1db04295607b5b0811eb2151ce5378a812b2aa3 | b6b8186a582174533ab41a68aeab77bdcf0ea854 | refs/heads/master | 2020-05-29T13:27:26.350660 | 2019-10-10T01:07:53 | 2019-10-10T01:07:53 | 189,161,472 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 520 | r | test.R |
library(dplyr)
library(KoNLP)
library(wordcloud)
library(stringr)
library(RColorBrewer)
txt <- readLines("test.txt")
test <- sapply(txt, extractNoun, USE.NAMES = F)
test <- unlist(test) ;
# for some reason, the below order does not remove non-alphabet/한글 data compeletely. After this step, dataset still contains s... |
b4fecbdf596499f76829e7b975a3b153c8e0f94f | 3be358db53d6093e48c22e77a19abc1a9ca369c5 | /partitionnementSpectral.R | fbf9c58af03b03028d142e3bee92a39b7b4cb689 | [] | no_license | lucasRemera/Clustering | f6cf925505fc5537498029d3f84fa157e2495a0f | 7187a2ca949ffa3cd13e41123adce65111191df7 | refs/heads/master | 2020-03-09T17:41:06.009902 | 2018-08-21T08:44:20 | 2018-08-21T08:44:20 | 128,913,950 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,602 | r | partitionnementSpectral.R | ##détails sur le wiki
#construit une matrice symétrique de similarité, basée sur la p-value du test de Fisher
#library(deldir)
getGNeighbourVoronoiMatrix=function(x,y,cas,tem,fisher=TRUE){
m=matrix(0,ncol=length(x),nrow=length(x))
ddir=deldir(x,y)
#print(ddir)
v1=ddir$dirsgs$ind1
v2=ddir$dirsgs$ind2
for(i i... |
fa0a4f7f0e953e207efba55355dc2c3e86969eb3 | 1599eb68922f4f722b9920fd4f5b1d7be6b99947 | /Assignments/Assignment 2/Machine learning/Weather Forecast/R/MLR.R | a7dd19eb86044b1797a70f0d329cd2fd06a8bc3d | [] | no_license | chrisatrotter/INF5870-Energy-Informatics | 782ce3d47dc70652b05cca9908c84d1bbc38f6f8 | a8e1c69f263686c5516029b98a8842678c57d35c | refs/heads/master | 2021-09-14T20:44:02.019179 | 2018-05-18T21:48:02 | 2018-05-18T21:48:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,974 | r | MLR.R | # Installation of packages
packages <- c("caret")
if (length(setdiff(packages, rownames(installed.packages())))){
install.packages(setdiff(packages, rownames(installed.packages())))
}
# Loading of packages
library(caret)
# Read in data set with applliances
setwd(getwd())
directory <- './data/'
forecast <- 'predict... |
33bf69d94abce328c25fda429521780021e1fcc0 | 3fb499152f285794975e3b296d44171f3a3f510c | /man/exist_package_useragent.Rd | 986acde3d36a17b537ff4e6342e4d0e0d5901b2c | [
"MIT"
] | permissive | ebbertd/existR | fed64de590e7a89dfe57854bef8033fe3172a4c2 | e5d9bfb15fb21f0f269c5bc1ded748c6a8f04b54 | refs/heads/master | 2020-12-18T11:13:27.698242 | 2020-03-01T15:15:35 | 2020-03-01T15:15:35 | 235,359,399 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 469 | rd | exist_package_useragent.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/exist_package_useragent.R
\name{exist_package_useragent}
\alias{exist_package_useragent}
\title{existR user agent}
\usage{
exist_package_useragent()
}
\value{
A character string containing the user agent used in API calls generate with the ex... |
c3314cfca7de2c19ed3bd18cd10f166fd340881f | 187842c58b7690395eb7405842ac28bc4cafd718 | /R/logLik.nlstac.R | ff502a466d8a032c5a27179b0b90f0d61d7cb6d4 | [] | no_license | cran/nlstac | d5e38b819795e2862e1b8c7e3e94d0a9af8fbc2f | 298e5206c29e091929eb76091ba2cb67a22e8316 | refs/heads/master | 2023-04-13T16:09:33.706071 | 2023-04-11T14:20:02 | 2023-04-11T14:20:02 | 310,516,792 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,249 | r | logLik.nlstac.R | #' @title Extract Log-Likelihood from a nlstac Model
#' @description Returns the log-likelihood value from an object returned by a nlstac model fit.
#'
#' @param object An object of class \code{"nlstac"} obtained by the \code{nls_tac} function.
#' @param ... Ignored, for compatibility issues.
#'
#'
#' @return A ... |
0f0d242e172e03ad1456f77e0b4295e4df6f64b0 | 3ce6ad4a9778e0b36472ffc1b969d86279089e1a | /general_utilities/userPrompt_IF.R | fc26c5af2dd264153dafe9a2e7561472a328a2ad | [] | no_license | FlicAnderson/Scriptbox | 25e7dc097b61fea18d384319cc77a838ea251ddf | 147322a52cdd77e8fd43409de37dc696b0bae2aa | refs/heads/master | 2020-05-21T20:06:46.132456 | 2019-07-02T13:41:51 | 2019-07-02T13:41:51 | 21,389,537 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 844 | r | userPrompt_IF.R | # Padme Data:: Databasin:: userPrompt_IF.R
# ========================================================
# (2nd June 2014)
# standalone script
# AIM: require information from the user and carry out other functions dependent on result
# require information (example adds 2 numbers)
fun <- function(){
print("This funct... |
69676ba736727dca3d30a9e08695402d729325f3 | 0ab0221f9d99796cf31247e18085bc5e39fb37a7 | /data-raw/1_organize_data.R | f9f4d7fccbc7386447c878de8e27e620790f585a | [] | no_license | stamnosslin/alexis106 | 730dd81d739cec32b00c964fdf6a509b5d9a0d53 | d38532a0acf0fe11bb00074e0657640672dbc684 | refs/heads/master | 2021-01-19T07:29:52.451839 | 2017-04-11T16:08:08 | 2017-04-11T16:08:08 | 87,546,417 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,806 | r | 1_organize_data.R | # ALEXIS 106 Psychoacoustic experiment with blind and sighted listeners
# This script reads data from the raw data file and calculates thresholds
# 'alexis106_data.txt'. This script imports background data and combines part of
# it with the threshold data. It also defines experimental groups.
#
# MN: 2016-12-30
... |
540d31354a916dfdd82df52da4399a21fbab899c | f89c50f72976d4dea4e068bdfa3b61e2b4d1e5f1 | /R_analyses/03_2019_data_angels_rental/code/LA City-from USC Socrata Data.R | 0227b172fb2939696ab2475f5147f2716965e356 | [
"MIT"
] | permissive | ahla-brfoley/abundanthousingla | 5e48ef959531b3347dec1964b62699055fb9239c | d4f6d7c44a8633a83251af80a156a07654b78609 | refs/heads/master | 2021-03-10T09:08:00.356810 | 2020-08-12T18:52:13 | 2020-08-12T18:52:13 | 246,441,489 | 0 | 0 | MIT | 2020-05-15T18:56:50 | 2020-03-11T00:56:14 | Rebol | UTF-8 | R | false | false | 19,247 | r | LA City-from USC Socrata Data.R | #LA City Data
library(tidycensus)
library(tidyverse)
library(dplyr)
library(ggplot2)
library(viridisLite)
library(viridis)
library(tigris)
library(sp)
library(crosstalk)
library(leaflet)
library(leaflet.extras)
library(shiny)
library(shinyWidgets)
library(rsconnect)
setwd("/Users/alfonsoberumen/Desktop/City Files/SOCR... |
5b3a5554e67486c2843ea51723c3e6ccb82710db | 1ef15b94cd32444ac5640ecdd08d6667638aca8c | /R/plot_map4.R | e28524fb38dbba64b2f3e85d0cb72582ce896e98 | [] | no_license | martinabuck/rbeni | c27dc67391ba9d72dd75ddf1d567be3537ae662d | 41165bbeb2cef826b92e4b2815e8ce70a123d440 | refs/heads/master | 2023-08-11T16:19:31.164750 | 2021-10-11T16:46:13 | 2021-10-11T16:46:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 22,364 | r | plot_map4.R | #' Plot a nice map with ggplot.
#'
#' Returns a cowplot object for a global map plot.
#'
#' @param obj An object, either a \code{RasterBrick} (returned from a \code{raster::brick()} function call),
#' or a list returned from a \code{rbeni::read_nc_onefile()} function call.
#' @param nbin An integer specifying the numbe... |
3a094af05684e3f51fd5cdbf9cb75c233e56df0d | 293de535b0fe8cc4f9c145caebd91abc0686ddcc | /man/get_rollit_source.Rd | dd44bb2898f220b3f7cddc870fc8b850882f07a0 | [] | no_license | tyler-roberts/RcppRoll | dad4f3b2141681efd827d5f1d6120791d8593a24 | d9eeb5c7f5ff09f470317f2143d687fcdc851bfb | refs/heads/master | 2021-01-23T23:19:34.808769 | 2015-04-05T11:23:08 | 2015-04-05T11:23:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 729 | rd | get_rollit_source.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/get_rollit_source.R
\name{get_rollit_source}
\alias{get_rollit_source}
\title{Get and Edit Source File Associated with User-Generated 'rollit' Function}
\usage{
get_rollit_source(fun, edit = TRUE, RStudio = FALSE, ...)
}
\arguments{
\... |
d1e51e36a9037663045e0339ca34bb9452a0ccfe | c91d990800aacd643c49da7422b6d2c0502ab8c5 | /R/raster.R | f3aa9d8a6574f925118824814b281051a5114d74 | [] | no_license | cran/quadmesh | 2c04217d1fff117f722fe334a7a47c492f1a1e65 | 3bab1089f9a277837c78a0527f51ec3d6a36989a | refs/heads/master | 2022-09-16T22:04:58.384139 | 2022-08-31T05:50:02 | 2022-08-31T05:50:02 | 60,974,821 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,165 | r | raster.R | #' Quadmesh to raster
#'
#' Approximate re-creation of a raster from a quadmesh.
#'
#' The raster is populated with the mean of the values at each corner, which is
#' closest to the interpretation use to create mesh3d from rasters. This can be over ridden
#' by setting 'index' to 1, 2, 3, or 4.
#' @param x 'mesh3d' obj... |
d2739f7b64c46ff4ec120937afb24b201422759f | b4307e64b13191cdb9261c92332c72ee3e126de9 | /Image_dimension_from_image_url_Radhika.R | a9d0d43e1026ae30159b19a67eba19aa86f39dc3 | [] | no_license | MukeshGangwar333/R-Queries | 5653e55cc6020c5a0932db664288af4dc617f85e | 945025dc32dec0963441d7a7d401d70d18f3d545 | refs/heads/master | 2020-06-27T09:22:13.470908 | 2019-07-31T18:50:11 | 2019-07-31T18:50:11 | 199,911,530 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 879 | r | Image_dimension_from_image_url_Radhika.R | library(RSelenium)
library(readxl)
library(stringr)
#dvr <- remoteDriver(browser=c("chrome"), extraCapabilities = eCaps )
driver <- rsDriver(browser=c("chrome"), chromever = "74.0.3729.6") #starting selenium server for chrome browser
remDr <- driver$client
file_select <- choose.files()
Image_File ... |
a78ea1ea1fcd0489f5168e0e5308494fa2e44956 | 0bd7b839a46bb12f1c2319094842123e094cd921 | /R/theme_redd.R | 6b9231d6c95e4e076bc4c039687df152899f0cb9 | [] | no_license | cognack/kimchicentromadia | 7dafecec5f2269c797c6de658f1bd753ce77c77c | 8826edc439ffd3c210bb4ac14d25df5fa77f2cb9 | refs/heads/main | 2023-07-12T08:28:26.192090 | 2021-08-20T16:35:24 | 2021-08-20T16:35:24 | 395,761,805 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 389 | r | theme_redd.R |
#' theme_red
#'
#' creates an ugly red theme for use in ggplot that no one should actually use.
#'
#' @return
#' @export
#'
#' @examples
theme_redd <- function() {
theme(
plot.background = element_rect(fill = "cornsilk1"),
panel.background = element_rect(fill = "coral1"),
axis.title = element_text(colo... |
07a6d8ebd447a0d290b67d5078f32513be6aa664 | 62e00b1a6c6c6c378108643274c437ad14df5df9 | /run_analysis.R | 86ef0b8b1ec276755b84720a2390a63e2b6462aa | [] | no_license | hahendri/TidyDataCourseProject | 1a4f14d9977a0bd4810137221311b165c3f768bb | 5aefd3c249e2b90f76398cbe5120e4c1fd81d756 | refs/heads/master | 2020-03-10T09:31:28.915457 | 2018-04-13T00:36:59 | 2018-04-13T00:36:59 | 129,311,331 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,457 | r | run_analysis.R | ##Set your Working Directory
##Install dplyr library
library(dplyr)
##Create data directory and download the data
fileurl = "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
if (!file.exists("data")) {
dir.create("data")
}
download.file(fileurl, des... |
dd5f49a162819206d60041cb82845c23cd17be33 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/move/tests/test.getDuplicatedTimestamps.R | c2c25f18e4320f4c593ad185dc456350213cca37 | [] | 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 | 3,663 | r | test.getDuplicatedTimestamps.R | context('getDuplicatedTimestamps')
test_that("basic functionality", {
expect_null(getDuplicatedTimestamps(system.file("extdata", "leroy.csv.gz", package = "move")))
expect_null(getDuplicatedTimestamps(df<-data.frame(individual.local.identifier=letters, timestamps=as.POSIXct(1:26, origin="1970-1-1", tz="UTC"), ... |
6b54793409d0ab3d965fb8e0f9b5e734a00b7b2d | b8756cf7e224eed7291700aa98c4a4afe05381b3 | /man/allWorld.Rd | e3ce0d15feba8e8856f18f82f830391c1b944695 | [] | no_license | jonasbhend/NRMgraphics | 879e8cff100d1a570453cc294c504db2d4dc7a7c | 503f11fe95d562d3ce5c157b0d377ded1a8c499c | refs/heads/master | 2016-08-04T20:58:24.326426 | 2014-11-05T10:20:02 | 2014-11-05T10:20:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,111 | rd | allWorld.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{allWorld}
\alias{allWorld}
\alias{allWorldKey}
\title{Giorgi regions overview plots}
\usage{
allWorld(fun, regnames = NULL, inset.bg = "#FFFFFFCC", ...)
allWorldKey(fun, ...)
}
\arguments{
\item{fun}{function to be executed at the respective spots for insets}
... |
db5b79a06d28ea77490a8ed1cbbd327493b3b9d3 | cf6cae1a30e3b1f600b2c0b7990207a25cadb619 | /creating-git-ignore/creating-git-ignore.R | 523759d4a66545fe984a8a894f55b64980ab5a99 | [] | no_license | ARPeters/Utilities | 778f983d5ada00c643ba7ddc285d9232bf630cb9 | 675ce52bc9661fe90ff4c8b257a3cf794c0030ac | refs/heads/master | 2023-02-10T19:09:37.978078 | 2023-02-02T17:53:19 | 2023-02-02T17:53:19 | 30,270,227 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,041 | r | creating-git-ignore.R | # Following instructions for creating a gitignore file here:
# https://docs.ropensci.org/gitignore/
# library(devtools)
# devtools::install_github("ropensci/gitignore")
library(gitignore)
head(gi_available_templates(), 25)
length(gi_available_templates())
gi_fetch_templates("R")
git_ignore_text <- gi_fetch_templa... |
7db43b583ec2d6387f77853a152981bf21a50d92 | 58e919cebf3a3b23aae8b40ba48c3d68c0dfac0c | /code/data_analysis/R/qpcr_analysis.R | 7ac8829b1a078feea2b512f9f96f9a951a346b03 | [
"MIT"
] | permissive | bdwilliamson/paramedic_supplementary | 3a511aa6124a867dfefa284a7cc58f951be62ff6 | 52fcf88e4045596ad3675c246561af14d77496e4 | refs/heads/master | 2021-06-14T02:51:43.002324 | 2021-05-06T00:02:02 | 2021-05-06T00:02:02 | 192,587,146 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,762 | r | qpcr_analysis.R | ######################################################################################
##
## FILE: qpcr_analysis.R
##
## CREATED: 15 October 2018 by Brian Williamson
##
## PURPOSE: data analysis of the qPCR + br16s data from Hutch collaborators
## to showcase qPCR estimation method
##
## INPUTS: ../../data/p2_... |
87a188e4b0ccb163a6f75407b93b573cdc62c0a7 | b761234cdc3b07e81dbc05da5ec1f726650ee7bd | /R/officer-utils.R | f7aa23278067509a040457ea0dbb359b1965aba3 | [
"MIT"
] | permissive | elipousson/officerExtras | 1d76ee389f2d649cf397199d00fb6894fd42eaa0 | f491277b69e659bb65f65f258878516b2c997e78 | refs/heads/main | 2023-08-27T01:32:07.879195 | 2023-08-26T16:51:15 | 2023-08-26T16:51:15 | 606,570,447 | 8 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,305 | r | officer-utils.R | #' Convert officer object class into equivalent file extension
#'
#' @keywords internal
#' @noRd
officer_fileext <- function(x, prefix = "") {
paste0(prefix, str_remove(class(x), "^r"))
}
#' Subset officer object summary by content_type
#'
#' @keywords internal
#' @noRd
#' @importFrom rlang has_name
subset_type <- f... |
3d630ce3c1128326ecd421d0006c697c91c2e3d4 | f96af69ed2cd74a7fcf70f0f63c40f7725fe5090 | /MonteShaffer/humanVerseWSU/compiling/_stuff_/functions-latlong.R | e5cc63d4f206c975156c95bbf988882400213a26 | [
"MIT"
] | permissive | sronchet/WSU_STATS419_2021 | 80aa40978698305123af917ed68b90f0ed5fff18 | e1def6982879596a93b2a88f8ddd319357aeee3e | refs/heads/main | 2023-03-25T09:20:26.697560 | 2021-03-15T17:28:06 | 2021-03-15T17:28:06 | 333,239,117 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,685 | r | functions-latlong.R |
#' parseDMSfromFormat
#'
#'
#' @family LatLong
#'
#'
#' @param str a string form of lat/long
#' @param format
#'
#' @return a list of $degrees , $minutes , $seconds
#' @export
#'
#' @examples
#' parseDMSfromFormat();
#' parseDMSfromFormat("3 8 29.7335529232441", "MeAsUr");
#' parseDMSfromFormat("-3 8 29.733552923... |
924d2e88621ba44b859e83fdb100aab1a9d7a4fa | d01ffddc33db49131b8fe75e7257df524f8871db | /server.R | f43d9cf33a391abcc68d7d5c9e5a913495d95b28 | [] | no_license | juandratto/ShinyAppAndReprodPitch | 96d8c655e076e4f111829e6ce29af406ae544479 | fa513043951dc233a3c68eddf8c406d2b5373a71 | refs/heads/master | 2021-02-13T05:04:16.023549 | 2020-03-03T21:09:59 | 2020-03-03T21:09:59 | 244,664,214 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,210 | r | server.R | #
# This is the server logic of a Shiny web application. You can run the
# application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(caret)
library(rpart)
library(rpart.plot)
# Define server logic required to draw ... |
22ef14ba64c0c65ff25983e6f41ed29329482d10 | 62bda029e5d083b1c4c58568914bfc2251c1e5e2 | /bin/core_function.R | 20811622ae11238e490b73481a9431b6f7895cc2 | [] | no_license | franzx5/Core_Microb_Krona | 35529de55e363d0257a42cf72263355d5bb677a2 | be295647d00be6113b171785ce4ada877cf4a89b | refs/heads/master | 2020-04-26T17:37:31.929540 | 2019-03-08T09:38:03 | 2019-03-08T09:38:03 | 173,719,551 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,314 | r | core_function.R | #!/usr/bin/env Rscript
#Author: AKE Franz-Arnold
#EDF Lab r&D
#Date: 27/02/2019
#function
#libraries
#get core database based on specified conditions
#by default take all samples
#return core microbiome database
get_core_dbase = function(otu_db_input, otu_targets_input, otu_annot_input){
#'''
#get core database... |
fa7687f93bad8626e88f721247c04728dae889ad | bb8885fa4eeea616240e82af05a8827bf4855991 | /man/percentilePlot.Rd | b815fc9e88cb5aa5f7a45fa951e8196881627a7b | [] | no_license | amd112/rseiAnalysis | 95f9ef10fb43cfd349b391a08bc1414866a105e3 | 54b8c2899ca248dc1a6c12d003aefdf1fa3b65b2 | refs/heads/master | 2021-09-16T04:21:31.073052 | 2018-06-16T10:05:02 | 2018-06-16T10:05:02 | 105,723,664 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,221 | rd | percentilePlot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/percentilePlot.R
\name{percentilePlot}
\alias{percentilePlot}
\title{Create a plot of the cdf's of the log toxicity of various groups.}
\usage{
percentilePlot(data, value, log = "l", title = NA)
}
\arguments{
\item{data}{is a list of datafram... |
cdd52c24700ca25441bc89d8a4e85f7fd2a3fb96 | fa9c3976dc1a0f2cc66f2008af3331de7885ada9 | /R/RcppExports.R | f018a83fd2a23baa363b83c35eff5a73f2d55be9 | [] | no_license | adolgert/mashproto | 4cdde15337e2e9d457dfb651a7d9ad61f2c08a42 | 567f933c1da83dab886a5456bf91bf0efec90b07 | refs/heads/master | 2020-08-24T22:04:29.780216 | 2019-10-31T23:22:37 | 2019-10-31T23:22:37 | 216,915,513 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 667 | r | RcppExports.R | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
rcpp_hello_world <- function() {
.Call(`_mashproto_rcpp_hello_world`)
}
movement_init <- function(parameters) {
.Call(`_mashproto_movement_init`, parameters)
}
movement_step <- functi... |
5f7c730ca0a90084072e20396bc734809c763f6f | 88d110a9b898a15c7d27f7bc4308549525212253 | /R/plotOverlapMinimal.R | 4663dbd712364aeeed8b8e705f7bde7caf8e8daa | [] | no_license | frhl/pRoteomics | 3bbabf7c6b083e37461b8ff26e4f9db6d5b25ab2 | fc41a617b79032e3353f26c7b860bb5a7aafa791 | refs/heads/master | 2020-09-17T01:43:22.452221 | 2020-02-15T18:48:52 | 2020-02-15T18:48:52 | 223,950,481 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,360 | r | plotOverlapMinimal.R | #' @title Draw Overlap Plot
#' @description ne list overlap enrichment test + volcano plot
#' @param df something
#' @author April Kim
#' @family genoppi
#' @export
### --------------------------------------------------
### gene list overlap enrichment test + volcano plot
plotOverlapMinimal <- function(df, bait, refe... |
523d7345cdc62952a9fdfbff5568d6dafab3c496 | 5d17b85808e9dac3dcad3d0caa223cf6c3ae1221 | /man/cloPredTraj.Rd | 55e8ded1a7c7bf450f16255cf2f0e1ddc6559495 | [] | no_license | ebmtnprof/qid | 3c818f3ece373d61562bd0b4c428fa04643a9181 | 648fa6eeaf3d7ebb9e7fb94f41507d63b84ff685 | refs/heads/master | 2020-04-16T09:02:31.772340 | 2019-01-14T00:13:14 | 2019-01-14T00:13:14 | 151,756,604 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,707 | rd | cloPredTraj.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/clo.R
\name{cloPredTraj}
\alias{cloPredTraj}
\title{Plots the state variable's clo model predicted temporal trajectory}
\usage{
cloPredTraj(origdata, paramData, paramM1, paramM2, dist0name, dist1name,
obsName, m1Name, m2Name)
}
\arguments{
... |
388a8d13e3d218e474a4c7e33ce39516d38f8c54 | dafdbdfa5ea119afb77aef9030488e9a5ceb9ba9 | /R/Day6/Programs/cor.R | 13081faa031de4c6e8a264eefb079eef4bc28d19 | [] | no_license | shibinmak/advance-diploma-ai-nielit | 2f33eb5c125353032c1d23e01db3174bfce744ea | d2458f571c68f81ebf5211665dfa368fb3210c82 | refs/heads/master | 2020-04-03T10:08:17.820106 | 2018-12-17T09:44:00 | 2018-12-17T09:44:00 | 155,184,686 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 193 | r | cor.R | X<-c(7,6,8,5,6,9)
Y<-c(12,8,12,10,11,13)
cor(X,Y)
cor(X, Y , method = "spearman")
cor(X, Y , method = "kendall")
?cor
X<-c(10,8,2,1,5,6)
Y<-c(2,3,9,7,6,5)
cor(X,Y)
|
1b0bef6586d63aaceb4684c22463051e4aa4f970 | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /flying/R/lookup_table2.R | a544106d0cfbc6556c433013732ae498c16d496a | [] | no_license | akhikolla/InformationHouse | 4e45b11df18dee47519e917fcf0a869a77661fce | c0daab1e3f2827fd08aa5c31127fadae3f001948 | refs/heads/master | 2023-02-12T19:00:20.752555 | 2020-12-31T20:59:23 | 2020-12-31T20:59:23 | 325,589,503 | 9 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,971 | r | lookup_table2.R | # load table 2 generate. Description in Pennycuick earlier version
# @name .gen.table2
# @author Brian Masinde
# @return table2
# @description Pennycuick's table II aids in calculation of D factor for finding
# lift:drag ratio. C factor for finding power required at maximum
# range speed and B... |
807662c571ea1446b0f51de4ba0ba68b96a4b7c0 | 4798cb29678fb3e54a317ef28ff1ddaec260cb89 | /HD_RGB_Flight_Height_Tool/old_scripts/TestCode.R | 8886dadafcf2373ddac5c71e8fed4b1aad73b930 | [] | no_license | HiDef-Aerial-Surveying/RBG_Flight_Height_Analysis | 5dd481b3542edb662d75b67a020e24b06f1b97e8 | 167076025cc73526ae586e794bfcc4b7516fff78 | refs/heads/master | 2023-06-22T09:36:37.840795 | 2021-07-23T15:11:06 | 2021-07-23T15:11:06 | 320,638,015 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 776 | r | TestCode.R | ## Bootstrap test for skewed data to get 95% or 2SD values
require(readxl)
X <- readxl::read_xlsx("./reflected_data/HiDef GANNET reflection height data ALL projects - CLEANED.xlsx")
out <- foreach(k=1:nrow(X),.combine='c') %do% {
y <- unlist(X[k,3:10])
return(y)
}
values <- as.numeric(out)
values <- values[!is.n... |
792bc73a7bced2b46b51db9875085eb4eb9c84f7 | a5ab0f9e3a46a5f2298fa49b28058978eb310ecf | /inst/preprocessing/GSE29172/03.writeWholeGenomeData.R | 49fe269b637623f71a116dfd5d72bc16d54ce525 | [] | no_license | mpierrejean/acnr | 64e960c559867723bf79de85eefa5c65875fb5de | d65dbfa70315c6f8a2caa6f7077c4c19ce24190a | refs/heads/master | 2021-12-14T23:19:09.016536 | 2021-11-18T07:46:06 | 2021-11-18T07:46:06 | 59,839,608 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,464 | r | 03.writeWholeGenomeData.R | ## Adapted from http://aroma-project.org/vignettes/PairedPSCBS-lowlevel
dataSet <- "GSE29172"
dataSetN <- "GSE26302"
chipType <- "GenomeWideSNP_6"
tags <- "ACC,ra,-XY,BPN,-XY,AVG,FLN,-XY"
dsT <- AromaUnitTotalCnBinarySet$byName(dataSet, tags=tags, chipType = chipType)
length(dsT)
dsN <- AromaUnitTotalCnBinarySet$byN... |
aa33f6a8e031f70f8098dc387ce8b708eebd7aaa | cfd39938d7912462f83b5e4bfe6e976ada293495 | /hw/Learning_Word.R | e59b25b0743d09a36eef156828f792f4c72f5d86 | [] | no_license | jonferrin/Econometrics---Group | dfa3ed84151dd78a61f7026e98a2f276b138f5a6 | 4ea229b46d425890911515a189ce57794b9accb5 | refs/heads/master | 2022-11-19T04:10:06.851971 | 2020-07-09T15:03:04 | 2020-07-09T15:03:04 | 259,477,488 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,305 | r | Learning_Word.R | library(magrittr)
library(officer)
library(ggplot2)
gg1 <- ggplot(data = iris, aes(Sepal.Length, Petal.Length)) +
geom_point()
gg2 <- ggplot(data = iris, aes(Sepal.Length, Petal.Length, color = Species)) +
geom_point()
my_doc <- read_docx()
styles_info(my_doc)
src <- tempfile(fileext = ".png")
png(filename = ... |
267f642b78a935653bb34eb2e970dbff2fc4c3e2 | 9ad4cbe64051e36224fe652a2cca4347c478b37b | /7_GBM_build_final.r | 63de70c284fcbf4c0c04d99baf2cb78e5f1a70ef | [] | no_license | shikharsgit/SolarEnergy-Pred | b5ecac9deea4888a9a992bf93f7fa4806ef5da32 | c581543860bc53cd8d831a10a965758bee6eb28f | refs/heads/master | 2021-09-24T16:26:08.370744 | 2018-10-11T22:13:12 | 2018-10-11T22:13:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,913 | r | 7_GBM_build_final.r | ##### Building final GBM model
rmse <- function(error)
{
sqrt(mean(error^2))
}
##Load libraries
library(doSNOW)
library(foreach)
library(gbm)
start.time <- Sys.time()
home=paste0("/home/shikhar/")
setwd(home)
grnd_data = read.csv("ground_data_correctelev_eu.csv",as.is=T)
country_parent_dir = "Europe"
setwd(c... |
c16526de73cf9395bb45024385d79a93ca269ce1 | d061d08c38167fd944b27f2ded545947050c0ae5 | /Movielens.R | 5046830a39a2c93d0a6cc6fb3bf89be1a95102c8 | [] | no_license | livcaten/Movielens | 385444941b7ff9e319ae811ca9e66626944f0241 | db8bc06feb0f978cbff1fbb338283192e941bcb6 | refs/heads/master | 2020-06-05T10:59:36.077038 | 2019-06-17T20:55:57 | 2019-06-17T20:55:57 | 192,416,818 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,430 | r | Movielens.R | ###################################
# 1. Create edx set and validation set
###################################
# Note: this process could take a couple of minutes
library(kableExtra)
if(!require(tidyverse)) install.packages("tidyverse", repos = "http://cran.us.r-project.org")
if(!require(caret)) install.packages("care... |
0f4268261b0b668978486ffb56b21ec77c3c218e | d8677cdff6f9f1bc36d7d3f47460db4417b721e4 | /Counterbalancing.R | 6187f769ba662045cf5de54fd1a828853a07b6ea | [] | no_license | cphaneuf/bc_data_science_presentation | c59c8952ac6aaf73847bc9d8ab5b4142398d9c1c | 49d61a8db036564c300aea9c6565c0813e786e8e | refs/heads/master | 2020-03-23T06:15:01.435079 | 2018-07-23T18:52:10 | 2018-07-23T18:52:10 | 141,198,659 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,539 | r | Counterbalancing.R | # Counterbalancing for sample data set
# Before running this script, delete 'Remaining_Master.csv' from your bc_data_science_presentation directory
# Set working directory
# HOW TO: open terminal > change to your local datascienceworkflow directory > type 'pwd' > copy and paste output setwd("<here>")
setwd("/Users/ca... |
b29c64efe636a860000034c1f36b70c1e88c8835 | 3162c50b248d9dbb3c210db68d209a916cdc5a56 | /scripts/analyses/gyrb_visualize_all.R | 4c5d622ce22cba7fd4dc79d67c37b563219f317c | [
"MIT"
] | permissive | ahnishida/captive_ape_microbiome | b68f818c20df396180e960b1a23e6b0c3daef0ab | afa2310dfd53aa94526aedf3d3bc00f9852e7921 | refs/heads/master | 2023-04-17T17:20:47.683032 | 2021-06-08T21:21:50 | 2021-06-08T21:21:50 | 287,132,087 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 20,686 | r | gyrb_visualize_all.R | library(ape)
library(ggtree)
library(tidyverse)
library(phytools)
library(cowplot)
library(ggplot2)
set.seed(3113)
#summarize 16S and gyrB ASV distrbutions
HR_16S <- read.table('results/16s/analyses/tables/16S_ASVs_summary.txt',sep='\t',header=T)
HR_16S <- HR_16S %>% filter(Order == 'Bacteroidales')
All_16S <- HR_16S... |
61434040914746374b9f97c07f1000cf06f0b108 | 682d21975c4e622fd59c55107aa3b6105e28c8c4 | /discriminant_analysis_r/DA.R | 4d8ad54fa6632e547649221456f57dd99f9b3dbe | [] | no_license | stacymiller/dataanalysis | d4ef3def0d04e7150467af027c5a4316cb80f0a1 | a6cfe261d521c0f9bd310ef67af0eda785b4dd16 | refs/heads/master | 2021-01-01T20:05:20.565571 | 2017-10-31T19:53:24 | 2017-10-31T19:53:24 | 33,039,431 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,120 | r | DA.R | library(foreign)
library(MASS)
library(candisc)
library(car)
library(psych)
library(nortest)
library(stats)
# library(qualityTools)
# library(ppcor)
# library(mixlm)
# library(QuantPsyc)
library(klaR)
# library(biotools)
df <- read.spss("wuschiz.sav",to.data.frame=TRUE)
df$Schizo==1
df <- subset(df, Schizo!=1)
#df <- r... |
57f3d8c82b40817013903abc363bcdd28c2f7845 | d104eaae49776e9f1bf929b2a4bc8d54f305e212 | /tests/testthat/test-list_output.R | 05e6781a262b9cd7aee281047085862c2e64ab49 | [] | no_license | forestgeo/fgeo.misc | 96d1a65360511bba7ef147bca74e507218c5c654 | 3a9386cebc7512df0d4923c364585f9c7a113c2b | refs/heads/master | 2020-04-10T19:14:48.565370 | 2019-06-21T22:41:11 | 2019-06-21T22:41:11 | 161,228,174 | 2 | 2 | null | 2019-02-04T22:50:11 | 2018-12-10T19:48:01 | R | UTF-8 | R | false | false | 1,036 | r | test-list_output.R | context("list_csv")
output <- tempdir()
test_that("errs with wrong input", {
expect_error(list_csv(1, output))
expect_error(list_csv(list(1), output))
})
test_that("works as expected", {
lst <- list(df1 = data.frame(x = 1), df2 = data.frame(x = 2))
output <- tempdir()
list_csv(lst, output, prefix = "myfile-... |
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