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78386948bfffa6181d642904648c750af5816f65 | 9710c61f0387afbd3bdbd2c93db5f681649b08e5 | /ui.R | 7fdc234a6b1cbdebecd47f52ae153c9136c180d6 | [] | no_license | TMBish/HHTL | 530b9bd9dae6ddc64f0911b870682b87447e3873 | 92b4b9e1074bdb61e80eec340cd5fc1bdca89d0f | refs/heads/master | 2021-03-30T17:56:37.230344 | 2018-10-06T07:34:43 | 2018-10-06T07:34:43 | 104,702,993 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,839 | r | ui.R | shinyUI(
semanticPage(
title = "Hannah's History Timeline",
useShinyjs(),
useSweetAlert(),
extendShinyjs(text = jsCode),
# Add custom css styles
includeCSS(file.path('www', 'custom-style.css')),
div(class="app-header",
h1(class = "ui header", ": HANNAH'S HISTORY ... |
238e3d5f0d1faed2d9fb05a85efe2ee00fb0d76f | e0ad7ef4247279fad496c3c23891457c5fa0bbe1 | /analysis/09_combined_analysis.R | d85af28873cdf5657b6c39d66679682c19c51767 | [] | no_license | wiscstatman/immunostat-prostate | 8c34672d5f3266566f7a46d028360279934e6c67 | bdf6db6257ad6c6770a8fdb163067244a0f6e43b | refs/heads/master | 2022-12-23T20:53:57.428843 | 2020-10-06T04:42:01 | 2020-10-06T04:42:01 | 283,579,630 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 80,557 | r | 09_combined_analysis.R | library(xlsx)
library(allez)
library(gridExtra)
library(ggplot2)
library(matrixStats) # rowMedians
library(lme4) # linear mixed effects model
library(lmerTest)
library(fdrtool)
library(heatmap3)
library(tidyverse) # make sure you have the latest tidyverse !
#############################################################... |
36e48b8850de472cfcb95ae115eda07edc4d7607 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/palettesForR/examples/showPalette.Rd.R | c2b0c688d170c5cd30c383e1b69c2c7471bf6fb4 | [] | 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 | 170 | r | showPalette.Rd.R | library(palettesForR)
### Name: showPalette
### Title: Show a palette.
### Aliases: showPalette
### ** Examples
data(Caramel_gpl)
showPalette(myPal = Caramel_gpl)
|
97eb5d96ae287c300c6690b02226653452d85783 | b821cea9ea90e31bfe72c8ed718a68a5e4e2dd6d | /Random Forest.R | 39002562729503a6db575260ca454edad3d96b5a | [] | no_license | christyChenYa/GoogleSearchQueries | 942db1d5ad47c377e33dd8456172f81db2f0c682 | c29ce211b0c2a51fdc2e5ad3536c9003947d7c2b | refs/heads/master | 2020-04-12T05:49:16.738076 | 2019-01-11T00:32:31 | 2019-01-11T00:32:31 | 162,332,809 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,541 | r | Random Forest.R |
library(randomForest)
getwd()
setwd("/Users/Christy/Desktop/Data Mining ")
training<-read.csv("training.csv")
test=read.csv("test.csv")
training<-data.table(training)
test<-data.table(test)
######################### check data ################################################
head(training,n = 20)
str(training)
###... |
6f4e0bd4bf23de439e025c3b540d9985349df16b | 549ed4f668cb5dd1d9bc8aa40ff4c128fc5158f9 | /run_analysis.R | 842797a0ec3193385816568d9fb153d69d65f5c5 | [] | no_license | michaelgiessing/GetNClean | 08984e9235b188c0390f1967065dbb3850d9895b | aca5e6b0ba0b4e72174d9cf8f760fac83d1cc686 | refs/heads/master | 2021-01-20T09:36:52.900357 | 2017-03-04T14:44:47 | 2017-03-04T14:44:47 | 83,924,321 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,801 | r | run_analysis.R | ## Create one R script called run_analysis.R that does the following:
## 1. Merges the training and the test sets to create one data set.
## 2. Extracts only the measurements on the mean and standard deviation for each measurement.
## 3. Uses descriptive activity names to name the activities in the data set
## 4. Appro... |
a858a76c172db06978a87c49cbfdbf76b50288d3 | 9c1772919e81b898b2476e1cd7a998089d8aa103 | /myfunc/omega.coef.R | b103536a3dc8494d5d167b14e76a71d1cff2921a | [] | no_license | KeitaSugiyama/menhera | 6ec2eb0ae7593fe4327475159e3fde9350efae81 | 51dba45f78b29eaa447e9002aecaf6806d5e8908 | refs/heads/master | 2020-04-05T06:29:41.554699 | 2018-11-08T02:49:07 | 2018-11-08T02:49:07 | 156,640,232 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 442 | r | omega.coef.R | #Ω係数を計算する(１因子でも多因子でも計算可能)
#引数
# x: 生データ行列（受検者×項目）
# nfactors: 因子数
#戻り値
# Ω係数
#(独自性に観測変数の分散をかけて、誤差分散を求める)
# 関数faを使用
#
omega.coef<-function(x,nfactors=1,fm="ml",...){
1 - ( sum(fa(x,nfactors=nfactors,fm=fm,...)$uniquenesses*diag(var(x))) /
sum(var(x)) )
}
|
0b83829a6a9d30fe960e9e263f363505859e296b | 2aeb930b3ff3079be7694c77f5926838a874370b | /FWI_extract_mod_samples.R | 54dc6b0fc91c7285c3af2f7e83ffc82bd48bcc1c | [] | no_license | sm-potter/AK_CA_Combustion | ea5363689c5d37dee90e6c7f5fb58830a8730872 | f265b8110d8ae4ead8e80aadcd1e57d468b21d9c | refs/heads/master | 2021-02-09T04:31:09.134580 | 2020-03-23T19:21:33 | 2020-03-23T19:21:33 | 244,239,547 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,007 | r | FWI_extract_mod_samples.R | # rm(list=ls())
library(tidyverse)
library(rgdal)
library(raster)
library(sf)
library(lubridate)
# rm(list=ls())
#---------------------------------extract fwi information for the modis samples
pts <- read_sf("/mnt/data1/boreal/spotter/combustion/burned_area/scaling/modis_pixels/predictors/DOB.shp")
# pts <- pts %>% ... |
72cacb8de55e29ec8604fd9dbc310cd851761198 | ef52ade6d4a97cd15416c40869583f05cdef8350 | /R/ggdotchart.R | 78a92314edcf7771d01ab735b6e8a2aa56bed317 | [] | no_license | inventionate/ggpubr | 9f9c6d55614cd662104e6a4377703f111c758559 | b03d7d219342748b388503f87e6aaa8557f3718b | refs/heads/master | 2020-05-24T05:56:01.594632 | 2017-03-13T07:26:54 | 2017-03-13T07:26:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,964 | r | ggdotchart.R | #' @include utilities.R ggpar.R stat_chull.R stat_conf_ellipse.R
NULL
#' Cleveland's Dot Plots
#' @description Draw a Cleveland dot plot.
#' @inheritParams ggpar
#' @param data a data frame
#' @param x x variable for drawing.
#' @param color,size points color and size.
#' @param shape point shape. See \code{\link{show_... |
b0d320b0926089241e69cd6a34bd3786d5016e23 | f45a2fb54815b95f44e414cd9b99980e44a9770a | /Normal GLM Assignment.R | 27c62598e713e4c94744d9aa1aefa846f96b1079 | [] | no_license | kali237/assignments | e3d797f824db3624cef38824a0cf1c95e18ea15c | de898cfed9b1b3053e04d8f83a8fe12eb7160388 | refs/heads/master | 2020-04-05T19:52:58.603439 | 2018-11-12T05:47:01 | 2018-11-12T05:47:01 | 157,154,872 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,156 | r | Normal GLM Assignment.R | #Normal GLM assignment
library(carData)
library(car)
#setwd(dir="C:\\Users\\kwickens\\Documents\\Regression and Non Parametric\\Normal GLM")
setwd(dir = "C:\\Users\\Kali\\Documents\\7880 Assingments\\Assignment 2 - Normal GLM")
load("Normal_GLM.Rdata")
############### functions ###############
lik=functio... |
70639e02a4ac143c7d820f237728230a7267b944 | b7e09d76858c0e98aa32302fc70bb0eebf3ee24d | /R/fars_functions.R | 73231b3b560c7f18ea80881cbabc3756af5c3863 | [] | no_license | barnabe/building_an_r_package | df98f7015a4ee40ff7277a877e646b384c485d57 | 365afe3d3dbfa80bfd3387afcfd6b8c1648a9265 | refs/heads/master | 2021-01-11T15:04:18.341394 | 2017-01-31T14:14:00 | 2017-01-31T14:14:00 | 80,290,435 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,788 | r | fars_functions.R | utils::globalVariables(c("STATE", "MONTH","year"))
#' Read source data file
#'
#' @details This function looks for a CSV file called \code{filename} and checks whether it exists or not,
#' if found it loads the data using \code{readr::read.csv} and converts it to a dyplr dataframe using \code{dyplr::tbl_df}.
#' If no ... |
e7fd0f4e4f2607cd0cbd386d87f5cf4f1a0628a0 | 2cb2bc953975540de8dfe3aee256fb3daa852bfb | /00misc/tyama_codeiq119-codeiq167.R | 33abe88d6859e7311f416b8b41735effe84a822f | [] | no_license | cielavenir/codeiq_solutions | db0c2001f9a837716aee1effbd92071e4033d7e0 | 750a22c937db0a5d94bfa5b6ee5ae7f1a2c06d57 | refs/heads/master | 2023-04-27T14:20:09.251817 | 2023-04-17T03:22:57 | 2023-04-17T03:22:57 | 19,687,315 | 2 | 4 | null | null | null | null | UTF-8 | R | false | false | 441 | r | tyama_codeiq119-codeiq167.R | #!/usr/bin/Rscript
#cf: http://d.hatena.ne.jp/isseing333/20121224/1356279424
#問1
t=read.table('DataScience_ML1.csv',header=T,sep=',')
l=lm(y~x1+x2,t)
#問2
#(1)
pdf("167_1.pdf")
plot(t)
dev.off()
#(2)
cat(summary(l)$adj.r.squared)
cat("\n")
#(3)
pdf("167_2.pdf")
plot(l$fitted,l$resid)
dev.off()
#(4)
pdf("167_3.pdf")
plot... |
58a301e40bb514c2593028f253e63586ec0593cd | b8be9f57f05c5e279ff38ff1d0bb7efeb26b4308 | /R/leak_detection_MEU.R | 922e160640b22d30859749256036b25420cff15e | [] | no_license | Ozeidi/water | aa96f8e451b008f0224da000e8e389a6df492273 | 9aa62d17b217d00346fac920e2d02c66798c2786 | refs/heads/master | 2020-06-17T08:02:26.012796 | 2019-07-08T16:36:39 | 2019-07-08T16:36:39 | 195,854,634 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,293 | r | leak_detection_MEU.R | library(xts)
library(tidyverse)
library(dplyr)
leak_detection_MEU <- function(timeseries, model_params = NA, id = NA){
# if timseries sent is a single xts
if (class(timeseries) %in% c("xts", "zoo")){
if(is.na(id))id <- gsub("-|:|\\s","",paste0(Sys.time()))
# wrap the outcome in a list to avoid redunde... |
99254bacb8f1134cfa0300916bae6d702e4bfaa0 | a83d1e348011b202c22d411e861a7e400072131f | /man/SymKL.z.Rd | a7826a089b2c8a76d4cf291ce0157d57650e5eda | [] | no_license | cran/EntropyEstimation | b240598b4256edbeb4c03c19c852f231f264e309 | de5307012cf0dbdbd63c2c08aab1748896fa46ac | refs/heads/master | 2020-04-08T06:53:52.893493 | 2015-01-04T00:00:00 | 2015-01-04T00:00:00 | 18,805,115 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 809 | rd | SymKL.z.Rd | \name{SymKL.z}
\alias{SymKL.z}
\title{SymKL.z}
\description{
Returns the Z estimator of Symetrized Kullback-Leibler Divergence, which has exponentialy decaying bias. See Zhang and Grabchak (2014b) for details.}
\usage{
SymKL.z(x, y)
}
\arguments{
\item{x}{
Vector of counts from first distribution. Must be integer va... |
7b8476a1c34435e92b75229b92d6b88f33eb38bc | 1e9a88114a1ec903a55f925f3344ae38065009a1 | /03_Analysis/fesibility_analysis.R | 4af68cd5575b6da8c5bb06c4750ac0cc20f59675 | [] | no_license | GauravKBaruah/Individual_variation_and_tipping_points | f5bf9bb5d7a8a7febb595a92f88efc5615eb13af | 3ac128e7316eeac8b8aa7d196c479ef3fd0b38ce | refs/heads/main | 2023-07-29T05:04:19.024213 | 2021-09-09T03:40:06 | 2021-09-09T03:40:06 | 391,273,878 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,040 | r | fesibility_analysis.R | #code for Ecology Letters paper:
#" The impact of individual variation on abrupt collapses in mutualistic networks" 2021. Gaurav Baruah
#email: gbaruahecoevo@gmail.com
rm(list=ls())
source('~/02_functions_tipping_point.R', echo=F)#converting mutualism matrix to ones and zeros
require(deSolve) ## for integrating ord... |
30134f0c4cc94e4901c72cc64aa70fec0d1d83c2 | 0b72c2e836e7ba8590dd9cfd3f8dd67798c5eedc | /dev/gene.set/MapBiosystems.r | 5b042f80c9c2639623b306565a0439d294d99843 | [] | no_license | leipzig/rchive | 1fdc5b2b56009e93778556c5b11442f3dbece42c | 8814456b218137eafe57bfb19adda19c0d0d625b | refs/heads/master | 2020-12-24T12:06:15.051730 | 2015-08-06T11:59:08 | 2015-08-06T11:59:08 | 40,312,197 | 2 | 0 | null | 2015-08-06T15:24:31 | 2015-08-06T15:24:31 | null | UTF-8 | R | false | false | 10,554 | r | MapBiosystems.r | ### Functions that process BioSystems annotation info and map BioSystems IDs to other IDs
##########################################################################################################################
##########################################################################################################... |
90df3440aff6eaa0332efe8a0ef57e88f0695cbb | cc7bd76a7bfc0d65246ea92ac962b2ec665c200c | /R/irf.varshrinkest.R | 0b0b2050cd9488f539c2529d42fa481f7783b172 | [] | no_license | Allisterh/VARshrink | a8d309cac5b251b1674ea534b8b4894b29e1874e | 404b4cb254e546137df927ea93afcbb8fe6482c2 | refs/heads/master | 2022-04-07T03:46:51.261116 | 2020-03-09T16:47:57 | 2020-03-09T16:47:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,177 | r | irf.varshrinkest.R | #' Impulse response function
#'
#' Computes the impulse response coefficients of a VAR(p)
#' (or transformed VECM to VAR(p)) for n.ahead steps.
#' This is a modification of vars::irf() for the class "varshrinkest".
#'
#' @param x Object of class 'varshrinkest';
#' generated by \code{VARshrink()}
#' @param impulse A cha... |
9010e01dc444b37e537ae8aa1956eb1f1a6af45c | b0f1f080bb508c682e7f4d9f1b4ff793939af5d6 | /R/to.long.R | dd2b4467dfe45aeeb786c7f439387c5db3329bf0 | [] | no_license | CaoMengqi/sgmm | fd4989c60533d3b18df46e9d4334390f0f19b332 | a3285a9a818a0dd16e6e5ea075cc42268943f9ee | refs/heads/master | 2022-01-22T07:37:42.450967 | 2019-08-23T13:14:09 | 2019-08-23T13:14:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,330 | r | to.long.R | #' A Wide to Long Data Set Function
#'
#' This function helps you convert a wide time-series data set (with one row for each patient and multiple columns for each different time points), to a long time-series data set (with t rows for each patient where t=number of time poitns).
#' @param data enter data set contain... |
e4e121eff6deaffcaa6797e89516d51e25688d68 | 86bcacc47430c92c6d9a08c57babd0a5ca020541 | /StatisticalComputing/HW3/mix.R | 79a90ceab859dc5119b53a6d4c898e16c1edb0d2 | [] | no_license | xohyun/University | b5dc7ac522415f59080c2d54982a672c258a437e | ec5eb3dfab953140b6b61e650d8e9ec65188486d | refs/heads/master | 2023-02-19T03:59:54.882670 | 2021-01-22T15:06:34 | 2021-01-22T15:06:34 | 331,948,930 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 642 | r | mix.R | n <- 30
m <- 100000
mse <- matrix(0, 1, 3)
colnames(mse) <-c("sample mean", "sample median", "the 1st trimmed mean")
calculate_mse <- function(n, m) {
tmean <- numeric(m)
x_mean <- numeric(m)
x_median <- numeric(m)
for (i in 1:m) {
sigma <- sample(c(1,10), size = n, replace = TRUE, prob = c(0.9, 0.1))
... |
97bd1c3ce9a258f5f586c206f3eb67950ff96229 | 802683d96a176be260f8de0ff1332a382a5f90c5 | /man/attachContext.Rd | 80c679ff4de28d93cca5952a38ee152244e05fdd | [] | no_license | dami82/mutSignatures_dev | a14fbbc7ac32a288b784e6d77517ea8268d72704 | 7065d66ed896c1684f945c004524df148122cc71 | refs/heads/master | 2021-04-15T03:57:15.490229 | 2018-09-24T17:37:50 | 2018-09-24T17:37:50 | 126,903,439 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 554 | rd | attachContext.Rd | \name{attachContext}
\alias{attachContext}
\title{
Attach Context
}
\description{
Attach Context
}
\usage{
attachContext(mutData, BSGenomeDb, chr_colName = "chr", start_colName = "start_position", end_colName = "end_position", nucl_contextN = 3, context_colName = "context")
}
%- maybe also 'usage' for other objects doc... |
eb4c6eb01da5d1af1cab0d628d05025da9915f6e | 7f2cf6dbcc98b1e50aaa4d3c82bbd0291affceaf | /man/simc.model.Rd | fb45175225a5e85ab6e1e7c4f39bab78f900bd1a | [
"Apache-2.0"
] | permissive | guhjy/RsimCity | 25d9ebf226cf413f2c4228bb295a1c8647a56d4f | d21488ffaf019830cfbbf2b2558b1b2584add394 | refs/heads/master | 2020-03-27T04:27:53.572645 | 2014-02-04T18:46:08 | 2014-02-04T18:46:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,611 | rd | simc.model.Rd | \name{simc.model}
\title{Define a set of linear models}
\alias{simCity.model}
\concept{ simCity model }
\description{
Specify a data generating model for simCity simulations
}
\usage{
simc.model(file="",exdist=NULL,excov=NULL)
\cr
simc.model(file="",exdist=NULL,excov=NULL)
x~1;edist=rnorm(0,1)
y~x;edist=r... |
b52445cf712a5429434fa6418d1a104b9173c482 | e17c6aec7115cb53939b784a87f5909be5fff032 | /GISS temperature anomaly.R | 397d5ad8611caafcf95a5ed7e582b8894eba85dc | [] | no_license | fawnshao/rexamples | 801ca734159a46ac67ed03b578001529563d3142 | 8e61d423237da5cb409f032dd896903fe8ac68c4 | refs/heads/master | 2021-01-18T05:46:15.505501 | 2013-06-11T01:26:21 | 2013-06-11T01:26:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,190 | r | GISS temperature anomaly.R | #######################################################################################
## R Script to read NASA GISS monthly global (land & SST) temperatue anomnaly file #
## Summarize recent trends and generate plot #
## D Kelly O'Day, http://processtrends.com & http://ch... |
b85807e14ef852fe0227b6bea4d58a0ea394d011 | d2088d962b87482688cfe98c0c73fe978bc38c5f | /src/function_load_gene_list_data_individual_gene_plots.R | bab5f57fcfb0c067a77263dce689f97a16e681bb | [] | no_license | pascaltimshel/temporal-brain-expression-scz | e7360890243de5df04cd3e3a71cd522315a67ed0 | 8a5bed33d1c3b689cbba18e0a6ec54c5eb05f2e8 | refs/heads/master | 2021-06-29T18:47:25.020356 | 2017-09-15T08:33:49 | 2017-09-15T08:33:49 | 26,609,253 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,699 | r | function_load_gene_list_data_individual_gene_plots.R |
############################# FUNCTIONS #################################
######### Adding x-tickmarks for stage
do_stage_converter <- function (p) {
stage_converter <- c("s1"="Embryonic",
"s2a"="Early prenatal",
"s2b"="Early prenatal",
"s3a"="Ear... |
f0f7a6446437a2f2485a08ff234d38dd48a670aa | 941bcfc6469da42eec98fd10ad1f3da4236ec697 | /R/track_bearing.R | c0216bdf419298ecfbc2a12cd123daf53f04e98e | [] | no_license | cran/traipse | 29c3fd65e98f65049da98b1d878512bfdd93940f | 01635fd40512f2144e1ce712e0f5912143214e49 | refs/heads/master | 2022-10-22T02:59:19.828085 | 2022-10-10T06:40:02 | 2022-10-10T06:40:02 | 236,953,410 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,047 | r | track_bearing.R | #' Track bearing
#'
#' Calculate sequential bearing on longitude, latitude input vectors. The unit of bearing is degrees.
#'
#' By convention the last value is set to `NA` missing value, because the bearing
#' applies to the segment extending from the current location.
#'
#' To use this on multiple track ids, use a gro... |
87179534c10e810d8baf9eb74ccbdb33d3b3f466 | 03259c6f6d5814362d967eadd415835b55446fd4 | /xcms_code.R | cacc91ba2fd32d13198919a4d93e3e09689298f4 | [] | no_license | alenzhao/BatchEffects | 82663af89e74539a52cd1e7c67cb4471158a78f3 | 6542bf1139ccd55b654e0bb31eef9f324f9e57df | refs/heads/master | 2021-01-11T16:02:12.502695 | 2015-10-01T04:34:11 | 2015-10-01T04:34:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 434 | r | xcms_code.R | num_cores<-8; # The number of CPUs available, be aware of memory issue
library(xcms)
set1<-xcmsSet(nSlaves=44,method='centWave',ppm=30,peakwidth=c(5,60), prefilter=c(0,0),snthresh=6)
set2 <- group(set1,bw=5,mzwid=0.015,minsamp=1,minfrac=0)
set3 <- retcor(set2,method="obiwarp",plottype="none")
set4 <- group(set3,bw=... |
4283bee2ef52ec37c693b92264b7adea3ab7a085 | df2efbcc44a1d8046cbab35021f47b208a391c41 | /scripts/imp_packages_to_install.R | b5a00277a6c85648553ea0a5410db0f83a1ac073 | [] | no_license | csngh/machine-learning-cyber-sec | eea96bf0c4e3bca25f2021fee69bf99fc33fd191 | b82ac267631f34fd57946afec025f4435ad7f5c5 | refs/heads/master | 2020-04-16T22:07:55.946370 | 2019-01-19T13:15:27 | 2019-01-19T13:15:27 | 165,954,251 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 216 | r | imp_packages_to_install.R | #Run the below code to install all essential packages
install.packages("pacman")
pacman::p_load(char = c(
"caTools",
"caret",
"mlbench",
"randomForest",
"arules",
"arulesViz",
"ggplot2",
"plotly",
))
|
3f06b47da093c6d1dcd7ccd46ed6d664c1772cf7 | e25af04a06ef87eb9fc0c3c8a580b8ca4e663c9b | /man/r_unif.Rd | 568f90b18d1e291cb0ef45f2e4ce9a2fa297093b | [] | no_license | cran/sphunif | c049569cf09115bb9d4a47333b85c5b7522e7fd8 | 4dafb9d08e3ac8843e8e961defcf11abe2efa534 | refs/heads/master | 2023-07-16T01:12:47.852866 | 2021-09-02T06:40:02 | 2021-09-02T06:40:02 | 402,474,585 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,343 | rd | r_unif.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{r_unif}
\alias{r_unif}
\alias{r_unif_cir}
\alias{r_unif_sph}
\title{Sample uniformly distributed circular and spherical data}
\usage{
r_unif_cir(n, M = 1L, sorted = FALSE)
r_unif_sph(n, p, M = 1L)
}
\arguments{
\item{n}{s... |
0d2b1c16528a963d7ffc88d55a1a7aaa48b9c640 | a68ddef61b62475e2cb9c9a81f7ddf3b819d4db9 | /SFEI_chl/modelBuilding/addCovariateSpatialMod.R | fe49d8dfb491dd9d17d8249e2576f549b3d87b89 | [] | no_license | sastoudt/DS421_summerProject | 8f21d04c94a8ae97c6a6b70d0af31f50c9fdcd0c | 69996a5662db3564fb44a79a3f55ee0514b30824 | refs/heads/master | 2020-05-22T03:59:54.549579 | 2019-12-20T20:53:01 | 2019-12-20T20:53:01 | 60,031,870 | 1 | 2 | null | 2016-06-02T23:21:52 | 2016-05-30T18:48:18 | R | UTF-8 | R | false | false | 3,236 | r | addCovariateSpatialMod.R | ## spatial models with bam
setwd("~/Desktop/sfei")
require(mgcv)
load("perStation.Rda")
names(perStation[[1]])
wholeSeries<-c(1, 2, 5, 7, 11, 13, 15, 16, 17, 18, 21, 22, 23, 29, 40)
allData<- do.call("rbind", perStation[wholeSeries])
names(allData)
class(allData$Station) ## Need to make a factor
ctrl <- list(nthre... |
f4c4b869a0c8de3f360691b884f7dff9ea92c1e3 | 09da2ae43b503129139904784ed901eca961258f | /plot4.R | 336a95f28a6f9c7de259d73defd14b9fad416f28 | [] | no_license | sanori/ExData_Plotting1 | 451e2970e7e907ff86f479d47c3d86f797412c6a | f850a037f1c91574e584a69eda3cb4f771b425a8 | refs/heads/master | 2020-12-25T17:38:11.898809 | 2015-03-08T20:15:10 | 2015-03-08T20:15:10 | 31,612,724 | 0 | 0 | null | 2015-03-03T17:57:04 | 2015-03-03T17:57:04 | null | UTF-8 | R | false | false | 1,163 | r | plot4.R | # Draw all the trends
x <- read.table("household_power_consumption.txt", sep=";",
header=TRUE, skip=66636, nrows=2*24*60)
names(x) <- c("Date", "Time", "Global_active_power", "Global_reactive_power",
"Voltage", "Global_intensity", "Sub_metering_1", "Sub_metering_2",
"Sub_met... |
aaab2b6b7131d5c566d782cb3d18ff90242971c2 | 16d914ffe72e4efbc65a851ba3e755650a618adc | /analysis_scripts_frontiers/Statistics/stats/pairwise.t.test.with.t.and.df.R | 26e2b29d2ceb9e65d0b599c35d7a498d0249432e | [
"MIT"
] | permissive | lkorczowski/rASRMatlab | cc38a9607f9666cca08be693d0161b4f9e515a8b | 594a57bba587ed3149f11db063c9414fabd92569 | refs/heads/master | 2020-09-14T00:04:17.048642 | 2020-01-20T10:51:56 | 2020-01-20T10:51:56 | 222,947,393 | 0 | 0 | NOASSERTION | 2019-11-20T13:46:46 | 2019-11-20T13:46:46 | null | UTF-8 | R | false | false | 3,761 | r | pairwise.t.test.with.t.and.df.R | pairwise.t.test.with.t.and.df <- function (x, g, p.adjust.method = p.adjust.methods, pool.sd = !paired,
paired = FALSE, alternative = c("two.sided", "less", "greater"),
...)
{
if (paired & pool.sd)
stop("pooling o... |
37c53b5e6dec7abe93529b65eb662d68b9cf5229 | 085bb668985c3302bd1f6c6a43cb50c49bf582c4 | /Data Matcher/temp.R | 38e57b29f69ff5683ee00a07e869a030058b002a | [] | no_license | zmyao88/Nigeria_Codes | 00cf9eac7403522a1f80945bb7676ac481447736 | bddb363eb4a66ffdc68787764a48d94fe786aee9 | refs/heads/master | 2016-09-06T14:38:31.442986 | 2013-06-13T16:23:33 | 2013-06-13T16:23:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,238 | r | temp.R | l <- letters
set.seed(1)
x1 <- l[sample(1:26, 81877, replace=T)]
x2 <- l[sample(1:26, 81877, replace=T)]
x3 <- l[sample(1:26, 81877, replace=T)]
x4 <- l[sample(1:26, 81877, replace=T)]
x5 <- l[sample(1:26, 81877, replace=T)]
facility_list$random_id <- paste0(x1, x2, x3, x4, x5)
t2 <- arrange(facility_list, lga_id, en... |
53a59754a23ab1c66d7e1473dbaeca4e81941298 | 609e12295b740d904c45896444e71c7c40215c8e | /R/assignRefugiaFromAbundanceRaster.r | 01e30746a5224d3f9a24932d7c4530ed8e88dcdc | [] | no_license | stranda/holoSimCell | a7f68cec17f0c9f599b94fb08344819de40ecd2e | f1570c73345c1369ed3bf3aad01a404c1c5ab3a2 | refs/heads/master | 2023-08-20T09:08:13.503681 | 2023-07-20T19:09:41 | 2023-07-20T19:09:41 | 189,275,434 | 1 | 2 | null | 2021-03-19T19:32:01 | 2019-05-29T18:08:16 | R | UTF-8 | R | false | false | 12,969 | r | assignRefugiaFromAbundanceRaster.r | #' Rescale carrying capacity rasters and assign refugia
#'
#' This function takes an "abundance" raster (i.e., from an ENM or from a pollen surface) and identifies refugia and starting (relative) abundances for each refugium. It rescales this to the extent and resolution of a "simulation" raster which typically has coa... |
1dbb3b66a7dfcf2eccba4819fa79c8da9654cc9c | 508bae0c1b6e8b9bc6817eebfa135667b58db1d4 | /Code File.R | c46675e38dc5d7da4d294ce0479da27dfbb92a36 | [] | no_license | dcohron/RepData_PeerAssessment1 | 4588f91e8f06ddef99e4114b6b3e1a4512773909 | 99c46cacaaef8b5fb6c1da56f334d9388750168b | refs/heads/master | 2020-12-11T07:32:33.973541 | 2016-01-15T13:09:12 | 2016-01-15T13:09:12 | 49,222,540 | 0 | 0 | null | 2016-01-07T18:29:57 | 2016-01-07T18:29:56 | null | UTF-8 | R | false | false | 5,321 | r | Code File.R | # Course Project #1
# Reproducible Research
# globallye suppress warnings
oldwarn<- getOption("warn")
options(warn = -1)
# install libraries/packages
library(plyr)
library(dplyr)
library(tidyr)
library(data.table)
library(lubridate)
# read file into data frame for manipulation
file<- "activity.csv"
main<- read.csv(... |
74d4e855c62dd898c74af1ca64759ecfecb33790 | 9fecce6f3ef41202cdcc855f4b0baff36131eacc | /Analysis/new_analysis/catch_shares/Analysis/01_create_vessel_df.R | 7cbd7f68d19d1a37214cc9ba3d40f49d647e456b | [] | no_license | emfuller/cnh | 0487e9647837d8fc999850b5951ff6331f9a5159 | 8b36faf8c73607d92e59e392fff3c0094b389d26 | refs/heads/master | 2021-05-01T08:02:52.200343 | 2019-04-06T18:25:48 | 2019-04-06T18:25:48 | 28,717,834 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,415 | r | 01_create_vessel_df.R | # create vessel level landings data
create_vessel_landings <- function(minrev){
library(dplyr)
dat <- readRDS("processedData/catch/1_cleaningData/tickets.RDS")
# vessels should have less than min_rev on average across 5 years AND
# flagg landings before and after catch shares
# then drop 2011 data
# als... |
4c76339411cb4552987d516c04c141bb3cc0a909 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/letsR/examples/plot.PresenceAbsence.Rd.R | 504c007757d0523dc86f08f1fc4b36534c7e00d8 | [] | 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 | 407 | r | plot.PresenceAbsence.Rd.R | library(letsR)
### Name: plot.PresenceAbsence
### Title: Plot an object of class PresenceAbsence
### Aliases: plot.PresenceAbsence
### ** Examples
## Not run:
##D data(PAM)
##D plot(PAM)
##D plot(PAM, xlab = "Longitude", ylab = "Latitude",
##D main = "Phyllomedusa species richness")
##D plot(PAM, name = "Phyl... |
7cfaa73b396204499f05f9109376d842dbe8da9f | f079415f68017da59b404ad532a9214b86949dad | /inst/app/server.R | 72b960f1e7a3dbd7086e7680e74c08b1ef673ab2 | [] | no_license | mpeeples2008/NAA_analytical_dashboard | 8094dfcd4c1487d28ac4bdb60ccf21ceb4881004 | 15241447a45a533ebd8b7c5da05fdf47f785be25 | refs/heads/master | 2023-03-17T16:05:27.400357 | 2023-03-09T17:48:05 | 2023-03-09T17:48:05 | 137,390,525 | 6 | 1 | null | 2018-09-13T00:28:53 | 2018-06-14T17:47:20 | R | UTF-8 | R | false | false | 774 | r | server.R | #' server.R
library(ArchaeoDash)
library(shiny)
shinyServer(function(input, output, session) {
#### create reactive values ####
# rvals = reactiveValues()
## for testing
rvals <<- reactiveValues(); showNotification("warning: global variable is only for testing")
input <<- input
#### Import data ####
... |
e9e123d808793cb897a4caf57c2a032063721a61 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/alakazam/man/bulk.Rd | 4d7d5b46a3467adbd1eed01c235ff78903a3a114 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,445 | rd | bulk.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AminoAcids.R
\name{bulk}
\alias{bulk}
\title{Calculates the average bulkiness of amino acid sequences}
\usage{
bulk(seq, bulkiness = NULL)
}
\arguments{
\item{seq}{vector of strings containing amino acid sequences.}
\item{bulkiness}{named nu... |
b447a047fad764db2df5c8e39d3b0be2670d42e6 | a841b5ffb7caaee2ddfbc05ec6dd4953d4f8f29c | /newIS_dootika.R | 0a7dcaf85a236825b7e301ce2f61ab0ea08fdab0 | [] | no_license | uttiyamaji/SVE | 921c625ea145b34f07aaa2dac410fa1ec7e68aa4 | 45102a3e983f622d2bd75e532570e3b9b9fca1c9 | refs/heads/master | 2021-03-20T16:23:58.018375 | 2020-05-08T15:03:56 | 2020-05-08T15:03:56 | 247,218,669 | 0 | 0 | null | 2020-03-14T12:35:45 | 2020-03-14T05:36:59 | C++ | UTF-8 | R | false | false | 1,793 | r | newIS_dootika.R | library(mcmc)
HAC1 <- function (mcond , m )
{
require ( fftwtools )
dimmcond <- dim( mcond )
Nlen <- dimmcond [1]
qlen <- dimmcond [2]
ww <- weight(m,Nlen)
ww <- c(1, ww [1:( Nlen -1)], 0, ww [( Nlen -1) :1])
ww <- Re( fftw (ww))
FF <- rbind (mcond , matrix (0, Nlen , qlen ))
FF <- (mvfftw (FF))
FF... |
8edfca92318c25d97bf6752e4b5e834da903baf5 | b228e595fb3bf49e550329d20c0fdc18e5ebd476 | /R/docMatrix2.R | e4886b53e430915377315670b8ca6b1e0347ef56 | [] | no_license | IshidaMotohiro/RMeCab | ab0e8a6c3be2b0be1057f8a5eb4410c63bc62075 | 365af330092b77c61d7395320af0df205e2901ad | refs/heads/master | 2022-11-06T16:20:34.109663 | 2022-10-18T08:07:54 | 2022-10-18T08:07:54 | 28,012,861 | 29 | 15 | null | 2022-10-15T06:46:30 | 2014-12-15T00:03:22 | C++ | UTF-8 | R | false | false | 3,991 | r | docMatrix2.R | ## sym = 0, pos = c("名詞", "形容詞"),kigo = "記号",
docMatrix2 <-
function( directory, pos = "Default" , minFreq = 1, weight = "no", kigo = 0, co = 0, dic = "" , mecabrc = "", etc = "" ) {
# posN <- length(pos)
# gc()
if(any(suppressWarnings(dir(directory) ) > 0)){
... |
d1161b164e6da7ba5e9bb6b6b40e3d7a9d341b24 | dc1bdfbf8da66baa07b1e201021fdc687005dcd3 | /Sandbox/test_file.R | 6378135e4ebe6c0adeca397c336ff06cd07e7f86 | [] | no_license | Kyle-J-Sun/CMEE_HPC | ca6144facf3fed4756fa51b3e0f151567f5690e9 | 078c341a70586aa7797aac4491823eed39980439 | refs/heads/main | 2023-03-14T18:13:33.105659 | 2021-03-07T13:01:18 | 2021-03-07T13:01:18 | 345,346,200 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,347 | r | test_file.R | rm(list = ls())
library(ggplot2)
# Question 15
sum_vect <- function(x = c(1, 3, 5, 8, 9, 8), y = c(1, 0, 5, 2)) {
if (length(x) > length(y)){
y <- c(y, rep(0, length(x) - length(y)))
} else if(length(x) < length(y)){
x <- c(x, rep(0, length(y) - length(x)))
}
return(x + y)
}
#
sum_abundance <- c( )
... |
34bf0edb0729218b7b36e69d5567487f7011368f | 48c5a905e1ca9350c8a10e76e8facb3f02558459 | /cap15_AnaliseDeDadosEMachineLearning/packages/DMwR/man/bestScores.Rd | 8a6bcf767085f265f7cb159c6ae1504091dc8bea | [] | no_license | brunotrindademachado/DSA-PBIparaDS-v2 | 01a953a4db9c2f286b8e92f6886446ff42235f1f | 9a9ca73cf018b63b4a344ba3c8bd2d104745e7d2 | refs/heads/main | 2023-06-26T05:11:31.871661 | 2021-07-29T14:46:31 | 2021-07-29T14:46:31 | 377,211,843 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,159 | rd | bestScores.Rd | \name{bestScores}
\alias{bestScores}
\title{
Obtain the best scores from an experimental comparison
}
\description{
This function can be used to obtain the learning systems that obtained
the best scores on an experimental comparison. This information will
be shown for each of the evaluation statistics involved i... |
0e32f6fd4b55d2a8d65d2a00aed5a8205b272ca7 | 8d7ffc7c9043a7fb1b590a69154b77d9cead1c88 | /cachematrix.R | ef5c640beb0c7f7eba6f6692c8ec68532e442d8a | [] | no_license | karenshoop/ProgrammingAssignment2 | 529a83fa8e94102b3cee4121e6a54aafa23ae39e | e644dfb4931aa2835d5237c4fedaec4c3d7e81d1 | refs/heads/master | 2021-01-18T06:03:30.761172 | 2014-06-18T13:37:41 | 2014-06-18T13:37:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 903 | r | cachematrix.R | ## These functions store a matrix and cache its inverse
## to ensure for subsequent calls that the inverse
## does not have to be calculated each time
## Function creates a vector to get/set the value of
## a matrix and get/set the value of its inverse
makeCacheMatrix <- function(x = matrix()) {
i<-NULL
set<-func... |
18a45e8a17cba78a368fc963b7eeee5d9384661e | f24edb31e7cbdf4e08cbe50b4a228e98d2ff5d13 | /R/lattes_to_list.R | f5cb870ef8ce400939536c69c1177c19b29f34bf | [] | no_license | fcampelo/ChocoLattes | b870f2edbb55d2742ef9f946882f727fd65a185b | 14df29beb5d32668d209c3d378d0a7e3898567d3 | refs/heads/master | 2021-09-09T13:54:10.879361 | 2018-03-09T13:14:55 | 2018-03-09T13:14:55 | 57,807,314 | 9 | 5 | null | 2018-03-09T13:14:56 | 2016-05-01T20:33:18 | R | UTF-8 | R | false | false | 4,274 | r | lattes_to_list.R | #' Convert a set of Lattes CV XML files to a list object
#'
#' Extract information from a set of Lattes XML files and convert it to a list
#' vector
#'
#' This function extracts relevant information from a set of Lattes CV XML files
#' and outputs a list object containing specific information on the following
#' aspect... |
b1b9efc9193a8d38ec7a3c0753095e069a6cea12 | 85a9566b5760703872ad1d43af649110a5ac9928 | /Code/Lakes_hu12id_forgeo.R | 1bd71ac1a70e2862e5e8e1bb9b8dd78087525001 | [] | no_license | limnoliver/CSI-Nutrient-Time-Series | 32862f6b69b92ef189c01c6766d5a80a9db374cf | bee5acaca08f98302ebe964e89b54580be7c6970 | refs/heads/master | 2020-05-21T12:28:11.117885 | 2017-08-23T01:39:22 | 2017-08-23T01:39:22 | 47,137,691 | 2 | 2 | null | 2017-10-17T18:23:19 | 2015-11-30T18:27:28 | R | UTF-8 | R | false | false | 782 | r | Lakes_hu12id_forgeo.R | setwd("~/Dropbox/CSI/CSI-LIMNO_DATA/LAGOSData/Version1.054.1")
data.lake.specific = read.table("lagos_lakes_10541.txt",
header = TRUE,
sep = "\t",
quote = "",
dec = ".",
... |
1658415615fa45d7b8d02e0106cb9d733ebb954a | a2374ebbed3a48790cb6f0b84b99083d5e1bc974 | /no longer in use/2017_even_older/practice_piwa_170817.R | 1abe83c617b320c73f7ba330ad69950c26306bd1 | [] | no_license | woodjessem/Songbird-pine-project | 9eacaedeabf1d34a34b2965fcff30932b69da4a0 | 55b4a29b616acfbe267b82c8baaf3dc07c28cd80 | refs/heads/master | 2021-01-18T18:47:04.464375 | 2018-12-04T14:12:35 | 2018-12-04T14:12:35 | 100,524,860 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,096 | r | practice_piwa_170817.R |
library("unmarked")
setwd("~/GRAD SCHOOL - CLEMSON/Project-Specific/R_Work")
test <-read.csv("piwa_abund.csv")
summary(test)
str(test) #y.4 and Noise.4 and Wind.4 and Sky.4 JDate.4 are factors and shouldn't be
#stringsAsFactors=FALSE, but this only works for reading, not for below.
piwa.abund<- csvToUMF("piwa_abund.... |
1b08ef4ab653e849f3faee230f10b98a751de5c6 | a6ac32e43c91a3e4594685a585455ebe89c9a04e | /R/taxon.R | 9f51756af0fba592c477d75cea1d04141fe898c1 | [] | no_license | heibl/megaptera | 6aeb20bc83126c98603d9271a3f1ae87311eedc1 | 5e8b548b01c40b767bd3bb3eb73d89c33b0bc379 | refs/heads/master | 2021-07-08T16:44:30.106073 | 2021-01-11T13:58:04 | 2021-01-11T13:58:04 | 55,764,237 | 5 | 0 | null | 2019-02-13T13:50:43 | 2016-04-08T08:44:44 | R | UTF-8 | R | false | false | 2,771 | r | taxon.R | ## This code is part of the megaptera package
## © C. Heibl 2014 (last update 2019-11-24)
#' @include taxon-class.R
#' @importFrom methods new
#' @export
## USER LEVEL CONSTRUCTOR
## ----------------------
"taxon" <- function(ingroup, extend.ingroup = FALSE,
outgroup, extend.outgroup = FALSE,
... |
ad11c759a0ebf852c042c5a26b955484d8baed64 | ee8492dc3bc1f00b85332e63ca5e1da0ec8c9bb7 | /man/outputMicroFusion.Rd | 096949648d8534a6dd9636eed7fb0e84e3e9077a | [] | no_license | NHPatterson/aimsMSRC | ee151b0f007e44dd8e25257ccac8a252c0613eec | 7f257af1e4366cf41be8f587b9ca9ae9c9601b7b | refs/heads/master | 2020-03-08T17:24:26.814750 | 2019-04-27T23:16:52 | 2019-04-27T23:16:52 | 128,267,435 | 0 | 2 | null | null | null | null | UTF-8 | R | false | true | 939 | rd | outputMicroFusion.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/outputMicroFusion.R
\name{outputMicroFusion}
\alias{outputMicroFusion}
\title{Output microscopy fusion files}
\usage{
outputMicroFusion(micro_image_array, filename = "myfusionmicrodata",
mask_image_array = NULL, data_label = "microscopy dat... |
a38e5adb3e9d540dad7400cd26684a3312866854 | aa901107b9501f99c5f835881d3b42131ef581e3 | /server.R | 90cf421e81e25f2ed9cda5d114163712e7982b89 | [
"MIT"
] | permissive | fcrawford/indiana-hiv | 75a9d76810275365484d3ead70f7cfed4a7c9e65 | 116e38bf93d8a2f264f0c150ee4b990dbfe7a67e | refs/heads/master | 2021-03-24T10:09:51.226739 | 2018-10-09T18:38:06 | 2018-10-09T18:38:06 | 110,284,836 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,838 | r | server.R |
library(shiny)
source("indiana-hiv-util.R")
shinyServer(function(input, output, session) {
observe({ # observe the intvxday variable; this is to correct the width if an impossible combination is entered.
if(input$intvxday[1]>first_dx_date) {
updateSliderInput(session, "intvxday", value=c(first_dx_date,... |
a6c95a86dab731323b889a479a5001938279a2c1 | ae093209e6bfab6f3d4b9afc6b96f0fe0ea0d383 | /man/repo_manager_init.Rd | 98fcbcfe590e5876aba82f025d03ddb678bf8cff | [] | no_license | cran/RSuite | 76eb522aa0cc4f48ffad9e2641a678b4b0a35baa | e0c72e5b38b49144794220f165c23a538ebc5cbf | refs/heads/master | 2020-03-27T03:57:08.441251 | 2019-06-10T13:20:02 | 2019-06-10T13:20:02 | 145,900,970 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,831 | rd | repo_manager_init.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/27_repo_manager.R
\name{repo_manager_init}
\alias{repo_manager_init}
\title{Initializes managed repository structure.}
\usage{
repo_manager_init(repo_manager, types)
}
\arguments{
\item{repo_manager}{repo manager object}
\item{typ... |
ad1ff25cc2196d7afd2aecf71d3b05aea6c99e87 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/model4you/examples/binomial_glm_plot.Rd.R | 3bfd17d4066fd5ba5ffe384b84243c81e27764d8 | [] | 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 | 1,248 | r | binomial_glm_plot.Rd.R | library(model4you)
### Name: binomial_glm_plot
### Title: Plot for a given logistic regression model (glm with binomial
### family) with one binary covariate.
### Aliases: binomial_glm_plot
### ** Examples
set.seed(2017)
# number of observations
n <- 1000
# balanced binary treatment
# trt <- factor(rep(c("C", "... |
d10be6ee86d4028e23a2a60045e2e6ae00f8cdff | b8778aaa0785b9ad3b591b366f5abc780b3a1825 | /1.PrepareDataBCR6.R | c82ef53b528201cfb9f9808c7aadda05e7aba406 | [] | no_license | tati-micheletti/NationalModel | 9371d218db5171721cc116521f782ec220218edc | 12460f9f7575f376ab5d0470e43f14e807d5bd58 | refs/heads/master | 2020-04-17T05:28:27.237798 | 2019-01-22T16:42:17 | 2019-01-22T16:42:17 | 166,280,590 | 0 | 0 | null | 2019-01-17T19:05:45 | 2019-01-17T19:05:45 | null | UTF-8 | R | false | false | 8,644 | r | 1.PrepareDataBCR6.R | library(raster)
library(maptools)
library(dplyr)
library(data.table)
library(reshape2)
LCC <- CRS("+proj=lcc +lat_1=49 +lat_2=77 +lat_0=0 +lon_0=-95 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs")
w <-"G:/Boreal/NationalModelsV2/BCR6/"
bcr6 <- raster("G:/Boreal/NationalModelsV2/BCR6/bcr6.tif")
offl <- read.csv("G:/Bor... |
c62a2b57233ab6d0b0817a23348fa5b050fab810 | d5ea152b86f0a35b94f759f3163e4f8030b609b6 | /R/create_cells.R | b18a9c977644514500b25098057376190356d5f2 | [] | no_license | R-ramljak/MNOanalyze | 04c32a2c648fc44c8b282fa4b5c2a653f0463960 | 0e0237ab5f51d7f94904ece21b954e9eb9a21cb8 | refs/heads/master | 2023-06-03T05:53:25.787276 | 2021-06-18T21:23:47 | 2021-06-18T21:23:47 | 378,082,885 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,041 | r | create_cells.R | #' @title create_cells
#'
#' @description Create a specific tower layer with attached directional cells
#'
#' @param area.sf sf dataframe, focus area with geometry column (tile polygons)
#' @param tower.dist numeric value, distance between towers (e.g., in meters, however in general dimensionless)
#' @param rotation.de... |
cd80e6897629295023490d6b0eb2f2c809ead528 | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /metacoder/inst/testfiles/centroid/AFL_centroid/centroid_valgrind_files/1615765860-test.R | a0a239c59309fe899f6e9a0fb000d7c0c089e7b5 | [
"MIT"
] | permissive | 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 | 187 | r | 1615765860-test.R | testlist <- list(b = c(NaN, NaN, -4.91790800486089e-166, 6.47517494783079e-319, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0))
result <- do.call(metacoder:::centroid,testlist)
str(result) |
8a1cf932fac2c19fbb31c0378d5af49c1fd97d2b | cacf9d286229e3cd8b352f45f5c665469613c836 | /tests/testthat.R | bc52a94777d4ca3922b105c5f8026d4dbcc4cff2 | [
"MIT"
] | permissive | alan-turing-institute/network-comparison | e42a102c84874b54aff337bcd6a76a1089b9eab7 | ee67bd42320a587adae49bafea6a59bfb50aafc6 | refs/heads/master | 2022-07-03T04:30:06.450656 | 2022-06-06T20:14:30 | 2022-06-06T20:14:30 | 75,952,713 | 13 | 1 | MIT | 2022-06-10T12:58:41 | 2016-12-08T15:57:35 | R | UTF-8 | R | false | false | 58 | r | testthat.R | library(testthat)
library(netdist)
test_check("netdist")
|
ed6f7d080fd12e7103642ba55a920730c6358a9f | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/rstanarm/man/kfold.stanreg.Rd | ef903462f6bb6c1f63e37ec304e0a8b16da2ee90 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | true | 5,414 | rd | kfold.stanreg.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/loo-kfold.R
\name{kfold.stanreg}
\alias{kfold.stanreg}
\alias{kfold}
\title{K-fold cross-validation}
\usage{
\method{kfold}{stanreg}(
x,
K = 10,
...,
folds = NULL,
save_fits = FALSE,
cores = getOption("mc.cores", 1)
)
}
\arguments... |
d37c9390b6198d85f68a203f371bdce9dd3ed6c2 | 12e614cf86cf2ebbd977d5822d7fad99c939a8d5 | /OurBiCopSelect.R | beb45957695910bea5ab448ffae10897a1481fba | [] | no_license | sghosh89/BIVAN | 201aebd7474557b3c97265c9885cd5a08261d2f4 | 502c367a6431574e6605673b0a905e71a2cb9b1d | refs/heads/master | 2021-03-30T16:41:25.419643 | 2020-03-16T12:07:17 | 2020-03-16T12:07:17 | 120,802,555 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 9,518 | r | OurBiCopSelect.R | library(VineCopula)
source("MyBiCopGofTest.R")
#This function takes a sample from a bivariate distribution
#consisting of values between 0 and 1 and:
#a) tests for independence and for positive correlation
#b) if independence can be rejected and correlation is positive, it
# fit a series of bivariate copula models ... |
34d98b3416d129e60a6a546cf467465eb6ca7c5b | 40962c524801fb9738e3b450dbb8129bb54924e1 | /DAY - 3/Assignment/Q1 - Convert.R | 13ca9e6acc6163781329101681b8c58c1f8aa759 | [] | no_license | klmsathish/R_Programming | 628febe334d5d388c3dc51560d53f223585a0843 | 93450028134d4a9834740922ff55737276f62961 | refs/heads/master | 2023-01-14T12:08:59.068741 | 2020-11-15T13:23:31 | 2020-11-15T13:23:31 | 309,288,498 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 265 | r | Q1 - Convert.R | #collapse function
convert=function(x) {
#Using collapse for cancatenating into one string
y=paste(x,collapse=",")
print(y)
}
#Checking for 3 values
convert(c("sathish","kumar","good"))
convert(c("sathish","kumar"))
#Checking for 1 value
convert(c("sathish")) |
9b23b34b1bd18d696eddafd481a03e2e6de5304c | 034acc4479cbdcd31e08730a45b908be55032692 | /R/square_usa.R | b45a7e75a9914c8e58d207dc95b2a4711b8d14ee | [] | no_license | EmilHvitfeldt/tilemapr | 97ac78c112f9e4ba8ebac4a8a144489a637226fb | 23edc1ab6495892e8040a866995fe34c8ab47d78 | refs/heads/master | 2021-01-21T07:13:36.930060 | 2017-12-22T17:39:12 | 2017-12-22T17:39:12 | 91,603,168 | 8 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,265 | r | square_usa.R | #' Creates square tile map for the states of USA.
#'
#' @param d Numeric. Number between 0 and 1. Procentwise Diameter (length from
#' center to corner) of the tiles.
#' @param center Logical. When TRUE returns the center coordinates of the tile
#' map.
#' @param style Character. Selets the layout style of the tile... |
739daa466d69d524512077137b8b9fbc5a409d80 | 02d2c444204db4fa4f85d3f53591c10fec6a813c | /R/helpers.R | 8f2a54cc4ea644c10444f31e8303ac908e231948 | [
"MIT"
] | permissive | berdaniera/preparer | ac199b075f031709985a432bd4ae27b0768ce6ad | 201f5fb40fe6f1b2aed4a8e274933628972a8f8a | refs/heads/master | 2021-01-21T21:14:47.900087 | 2017-06-22T20:05:03 | 2017-06-22T20:05:03 | 94,791,103 | 0 | 1 | null | 2017-06-22T17:40:53 | 2017-06-19T15:19:08 | R | UTF-8 | R | false | false | 2,054 | r | helpers.R | na_fill = function(x, tol){
# x is a vector of data
# tol is max number of steps missing (if greater, it retains NA)
ina = is.na(x)
csum = cumsum(!ina)
wg = as.numeric(names(which(table(csum) > tol))) # which gaps are too long
x[ina] = approx(x, xout=which(ina))$y
x[which(csum%in%wg)[-1]] = NA
return(x)... |
3ed98e277c6ad80555a1d24810788bea45ea2a8e | a4c6332c239ea98e1e5cef105ba2c3befcfcf310 | /WangzhenTools/R/WangzhenTools-package.r | b5c39a15dfe4ad5c13e0af3f03a35e58d083553d | [] | no_license | maxx0290/myfirstpackage | 8f75f15159e15c7be70393b955349a4d1ece1cb4 | 168b76faedd741f89e3c7b7ab66b8a2f0f28d5c9 | refs/heads/master | 2021-04-28T02:27:08.931955 | 2018-03-10T01:22:58 | 2018-03-10T01:22:58 | 122,114,159 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 69 | r | WangzhenTools-package.r | #' WangzhenTools.
#'
#' @name WangzhenTools
#' @docType package
NULL
|
267f65148b6498044d81ef54a646e946d7c6a5c1 | db2b46e4e013ad57459645ff45d62e8a2afe9365 | /R/scatter_libdepth.R | f92ac6bce545a4878acda39369b647b87ea7bcfd | [] | no_license | ahadkhalilnezhad/rimmi.rnaseq | b22aeab379094b7ce1e1b88820d19315b23bcc34 | 27e1ff9e9093203897abe168f26cb952bdfef606 | refs/heads/master | 2020-09-07T13:19:29.184587 | 2019-11-08T10:51:54 | 2019-11-08T10:51:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,165 | 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... |
ea7ccdfbf3c115e86afaa95f857ffeccde7bb87e | c4685427cf56a2fdaf436c481d4e7ee9a391d081 | /ui.R | 281250396110101140598a412c2a75de7edf7ef7 | [] | no_license | makleks/newrepo | 180c24eabf069088e976590fc5152958125508fb | 2c974f2a51f05e0ccf6bb2c45dc1a0ca5547f4c7 | refs/heads/master | 2021-01-10T07:51:07.292760 | 2015-12-27T11:33:05 | 2015-12-27T11:33:05 | 48,642,553 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,195 | r | ui.R | shinyUI(
pageWithSidebar(
headerPanel("Volume of Cuboid Calculator"),
sidebarPanel(
numericInput('width', 'length of the base of the cuboid in cm', 90, min = 50, max = 200, step = 5),
#submitButton('Submit'),
sliderInput('height', 'height of the cuboid in cm', 90, min = 50, max = 200, step =... |
6e65a947c75ae8670661a162fa602d1fa09bc427 | e51a9c634d3a2fcf5b2a3b66d258b81c00ce10aa | /14a-impact-functions.R | fc8a912de8bc9eca8bc4bcc60963adf496fe2395 | [] | no_license | bennysalo/MI | 48aef59d7db4fb96120a9feb94aa8f7b95c7422a | ba07fe422d06fd9c92799e27d12799d2296f6f34 | refs/heads/master | 2020-05-22T23:27:41.118765 | 2018-02-17T10:22:42 | 2018-02-17T10:22:42 | 84,733,582 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,581 | r | 14a-impact-functions.R | # rm(list = ls())
# Define a model with the drug factor regressed on group
get_impact_model <- function(base_model) {
pt <- lavaanify(base_model)
# Identify factors
factors <- subset(pt, op == "=~")
factors <- unique(factors$lhs)
# Define regression paths
reg_paths <- paste(factors, ... |
e149d00a75bc8240e23df103950f39736c4f7bdc | 2b15d68409bf3983b1f3108cf2172117d9f9a39e | /scripts/Education-MR-MOE.R | d4c148b7311da5284fff46484c37071801c0e89e | [] | no_license | jahnvipatel1/ARDC-MR-MoE-main | 2b90e27456912bcff1078c928df19637a9e46500 | e6238b37009be9d9b4989246b07763185e8e648e | refs/heads/main | 2023-06-17T09:00:19.509770 | 2021-07-20T18:55:24 | 2021-07-20T18:55:24 | 383,582,116 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,762 | r | Education-MR-MOE.R | library(devtools)
library(tidyverse)
library(TwoSampleMR)
library(ggnewscale)
library(ggplot2)
library(glue)
library(ggpubr)
#EXPOSURE DATA ---------------------------------------------
education.file <- "~/Desktop/Lee2018educ.chrall.CPRA_b37.tsv"
education_dat <- read_exposure_data(
filename = education.file,
... |
3d892f5b09de9cd7f535141fe12f645c92b561e8 | 2a7e19a09c4c2b2a4495b8d55b7cc3792b756b82 | /Exercise_1.R | 634efc8d83c48211b3a8ce968f72f97e6065dd88 | [] | no_license | rohitturankar/R_Projects | 7b558dbc32088417ebe627e278fa15c13e761b22 | e2d06d168c5eb1df8972503427aa9aa8541d0a47 | refs/heads/master | 2022-10-14T09:16:09.949255 | 2020-06-12T18:14:53 | 2020-06-12T18:14:53 | 271,861,227 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 826 | r | Exercise_1.R |
rm(list=ls())
getwd()
setwd("C:/Users/user/Documents/R/Practical_1")
dat08<-read.table("Sight2008.txt",sep = "\t",header=TRUE)
dat09<-read.table("sight2009.txt",sep = "\t", header=TRUE)
names(dat08)
dim(dat08)
str(dat08)
summary(dat08)
dat08[6,3]
#If data frames is having different columns and rows add them using mer... |
03e4ece14ef140941ad56730cb2fdc628b76be79 | b7dba66d68ffa4f5b42038f48ec53ac0e91cf0c6 | /code/chapter4/covariates_ran.R | 5e4c3ed351456135a357beb48485b52b4a59d14a | [] | no_license | aarizvi/dissertation | 46118d37e5e6d4923b6f1201836ddbb0475ba56b | de9fd7a8d092e8a7b1063212492e0487ee96a4cc | refs/heads/master | 2020-04-05T19:18:48.746222 | 2019-03-24T18:05:25 | 2019-03-24T18:05:25 | 157,128,463 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 994 | r | covariates_ran.R | outcomes <- c("TRM", "DRM", "OS",
"PFS", "REL", "GVHD",
"OF", "INF")
time_var <- c("time to death", "time to death", "time to death",
"time to relapse", "time to relapse", "time to death",
"time to death", "time to death")
covariates <- c("recipient age, BMI, ... |
62c3a0a4a537df01a2c08d938662ad100ede720c | c00f3e0d8e09fa678391ea36b0af7019a3e61592 | /lib/tests/HSV.R | 9605fd772350831d3d02d2d2b45ef4ae229438e4 | [] | no_license | jpchamps/Project_CatsVsDogs | e12c919210180647669be02da4df6dbb70d24ea2 | 4b4404e8ef6f2c5789cb387d6173869ac9f494d3 | refs/heads/master | 2021-01-16T01:07:51.881420 | 2016-03-12T18:49:37 | 2016-03-12T18:49:37 | 52,730,463 | 0 | 0 | null | 2016-02-28T16:19:43 | 2016-02-28T16:19:42 | null | UTF-8 | R | false | false | 1,254 | r | HSV.R | library(grDevices)
library(e1071)
# Convert 3d array of RGB to 2d matrix
setwd("images")
extract.features <- function(img){
mat <- imageData(img)
mat_rgb <- mat
dim(mat_rgb) <- c(nrow(mat)*ncol(mat), 3)
mat_hsv <- rgb2hsv(t(mat_rgb))
nH <- 10
nS <- 6
nV <- 6
# Caution: determine the bins using all imag... |
581155b3ef49605464e35e07fa89094a11a9349a | e2d4208757b01fd9afc23b37b2e249d224bb9a09 | /previous_work/downsampling/LDA_analysis.R | b9dfdab7edc96c858bbeaf2cc7d322d88d2b132e | [] | no_license | ethanwhite/LDATS | 221eec07a3f75a2f9f75cd80726dba89ddc3751f | e938d3ce0ac9fec9d8362fcf9136b1cb8e2cc512 | refs/heads/master | 2020-03-11T19:38:31.051741 | 2018-04-19T01:38:45 | 2018-04-19T01:38:45 | 130,213,339 | 0 | 0 | null | 2018-04-19T12:37:58 | 2018-04-19T12:37:58 | null | UTF-8 | R | false | false | 2,272 | r | LDA_analysis.R | # ============================================================================
#' Run through whole pipeline of LDA analysis, VEM method
#'
#'
#'
#' @param dat Table of integer data (species counts by time step)
#' @param SEED set seed to keep LDA model runs consistent (default 2010)
#' @param test_topics numbers of t... |
c7b81408cb8afbeca0dac3d33d83bdbf362c2697 | d35be146b06ff6d133f8041f8667dfa9fff8c27c | /man/RunRobPCA.Rd | d89c791f45995219df9342c6eeb2969fce6c836d | [] | no_license | gmstanle/scRobustPCA | 91d4b80b6a3da3276434bd79dfc84582cc8d8204 | bc5be0e4f0cadecbe6f146cfc2cb78d96043d79c | refs/heads/master | 2021-09-12T18:21:08.866688 | 2018-04-19T21:50:30 | 2018-04-19T21:50:30 | 125,469,525 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,220 | rd | RunRobPCA.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rpca.R
\name{RunRobPCA}
\alias{RunRobPCA}
\title{Perform robust PCA (Hubert PCA) with modified PC scores on a Seurat object}
\usage{
RunRobPCA(object, npcs = 10, pc.genes = NULL,
use.modified.pcscores = TRUE)
}
\arguments{
\item{object}{Seu... |
3296801d10e9a0b1bbaf1b7bd80c92dcfb01d50c | 29585dff702209dd446c0ab52ceea046c58e384e | /mGSZ/R/toTable.R | 424e37ba99331c21da7117c0b831b8cb6612e8f7 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 878 | r | toTable.R | toTable <-
function(mGSZobj, sample = FALSE, m = c("mGSZ","mGSA","mAllez","WRS","SS","SUM","KS","wKS"), n = 5){
if(!mGSZobj$gene.perm.log & !sample & !mGSZobj$other.methods){
table <- mGSZobj$mGSZ[1:n,]
}
else if(mGSZobj$gene.perm.log & !sample & !mGSZobj$other.methods){
table <- mGSZobj$mGSZ.gene.per... |
3da37d320bb9bd27093cebe199f7348bebb16cfd | a2c4c7b961cecf08c3e88ac9571d8ca60b0b1ce4 | /futures_data.R | eb8b36b4e64303fae2144fbe43ddf74623beb1fc | [] | no_license | darobertson/r-commodity-index | d6a159245fc535512c7ce181d5473ae89d4e4c49 | 41844fe4a36b68924728d0bd5914a569ae158cc3 | refs/heads/master | 2021-01-20T01:35:59.171429 | 2016-05-16T09:04:44 | 2016-05-16T09:04:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,637 | r | futures_data.R | # TODO: Add comment
##############################################################################################################
rm(list=ls(all=TRUE))
options(width = 438L)
library(WindR)
w.start()
#w.menu()
#w.isconnected()
#---------------- parameters ----------------
#can only roll within next 12 m... |
2c31b44ae2045eae018ed10abbdae3cb774ba8a2 | 83855fae27ecebcdb8fa96d5607af6682948e98b | /Projects/DataCamp/visualizing_covid19.r | d7959287e6f65a372da67d6935b851aa2739dd49 | [] | no_license | cgalmeida/machine-learning | 94357827a312139b23410711e19e26a5bbd6b9ae | c9e90d9ae4714293a43c4d1e18d25db5b07d9391 | refs/heads/main | 2023-01-24T00:45:11.450453 | 2020-11-29T16:15:37 | 2020-11-29T16:15:37 | 305,099,220 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 316 | r | visualizing_covid19.r | # Load the readr, ggplot2, and dplyr packages
library(readr)
library(ggplot2)
library(dplyr)
# Read datasets/confirmed_cases_worldwide.csv into confirmed_cases_worldwide
confirmed_cases_worldwide <- read_csv("datasets/confirmed_cases_worldwide.csv")
# Print out confirmed_cases_worldwide
confirmed_cases_worldwide
|
81eb2e8d4e6d5b1158a31833d60920a9a11bd79c | 8a733106605304b1326d1d46955a80a85764a7ee | /tests/testthat/test-viz_fitted_line.R | c21abac7701ca873472f8cd8f09bdeea5c77d060 | [
"MIT"
] | permissive | EmilHvitfeldt/horus | 2a7500335301d84f5c33de5da169c37a3d9584c4 | 86a9b9367d29be768e7573062cd2792fe2275027 | refs/heads/master | 2021-06-14T17:56:44.778717 | 2021-06-11T03:34:53 | 2021-06-11T03:34:53 | 146,670,308 | 15 | 0 | null | null | null | null | UTF-8 | R | false | false | 857 | r | test-viz_fitted_line.R | library(testthat)
library(parsnip)
library(workflows)
set.seed(1234)
knn_spec <- nearest_neighbor() %>%
set_mode("regression") %>%
set_engine("kknn")
knn_fit <- workflow() %>%
add_formula(mpg ~ disp) %>%
add_model(knn_spec) %>%
fit(mtcars)
test_that("viz_fitted_line works", {
vdiffr::expect_doppelgange... |
1f5c7a85857b3b34b56ae9fd105e0d556b74c96d | 9722196e4d8b6cad9458e9fca12dc2d310384569 | /R-Scripts/twitter.R | 6e49852e3def856b294b2eb121d80f9fc8810dd3 | [] | no_license | siyafrica/Agenda_Setter | 5ad77ae961adae806aea9844300f9f3116ca4c2c | fab67f768258237846e15097675aee4ad6e4c8f6 | refs/heads/master | 2021-01-10T20:21:07.873184 | 2014-10-30T19:56:19 | 2014-10-30T19:56:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,635 | r | twitter.R | #I used this website for the main recipe to get my Twitter authorization: http://www.datablog.sytpp.net/2014/04/scraping-twitter-with-r-a-how-to/
rm(list=ls())
library(twitteR)
library(RCurl)
library(ROAuth)
consumerKey <- "5yViSuSbNLBBDaUZA03wQ"
consumerSecret <- "EuLTI61ncd8O7mmdpiStR86UtoXPgo6LMiFph... |
5f7c76d5b2b82968e91501477b1076bd736243ad | ed84e202329a582d370c29daf5bd6fbb360fe2b3 | /R/praise.R | 788d44343926a65b480b485c46bf34fd642c3961 | [
"MIT"
] | permissive | kristyrobledo/praiseMe | a01658e9718daa0d9cc7a01307ab723cbf332f01 | b4c6cbd5b8d24b8413a3bae6b5ddb7f24aaab5cb | refs/heads/master | 2020-09-30T10:57:50.435756 | 2019-12-11T06:56:02 | 2019-12-11T06:56:02 | 227,273,894 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 890 | r | praise.R | #' Delivers praise
#'
#' @description This function is useful when you are
#' feeling sad, and would like to recieve some praise.
#' @param name Text string of the name I would like to praise
#' @param punctuation This is our punctionation as a text input
#' @return Returns text string with praise
#' @export
#'
#' @exa... |
a1e092b33d483dc46f5ac080ef4ae9f61a4704c7 | b05784abd2d22efa58aca731e276ab4b722ddeb6 | /plot2.R | f9d5c273463dffc7aaf61adf62df521073e6f96e | [] | no_license | luisepultura/ExData_Plotting1 | 08162d297350befe006242b9b084690100c878da | f289842078c2c03197d919f019959afcb81d8f07 | refs/heads/master | 2021-01-23T16:40:28.818390 | 2014-11-07T20:30:33 | 2014-11-07T20:30:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 563 | r | plot2.R | #load libraries
library(sqldf)
fileName<-"household_power_consumption.txt"
#load data
data<-read.csv.sql(fileName,
sql = "select * from file where Date = '1/2/2007' or Date = '2/2/2007' ",
header=TRUE,sep=";")
##create a datetime variable
data$DateTime<-strptime(paste(data$Date,... |
de4a2e72335909c98e9e990e4a181870146d099e | e715b2171a8ffca7214101f21bbaedf6dfd77a2d | /RStudio/SEM II/Lab 8/8.R | 81cec01ff88070a1a7596d31c5a10dffdeb6b8f6 | [] | no_license | r-harini/Code | 17de2f9d671593cd86d023b053e5b13a82d72620 | 3e2572b34a7dc10863efad9a84646f47d20082ac | refs/heads/master | 2021-06-18T01:03:09.808004 | 2021-04-06T05:52:41 | 2021-04-06T05:52:41 | 194,418,227 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 837 | r | 8.R | rm(list=ls())
setwd("C:/Code/RStudio/SEM II/Lab 8")
data <- read.csv("iris.csv",row.names=1)
#as it is distance based metric, we scale to obtain better accuracy.
df <- scale(data)
#computing dis-similarity matrix
dissim <- dist(df, method = 'euclidean')
hierClust <- hclust(dissim, method = 'complete')
plot(hierClu... |
bee45af31655bb676168afbf78380609706c47a3 | 20fb140c414c9d20b12643f074f336f6d22d1432 | /man/NISTpascalSecTOslugPerFtSec.Rd | 49db86c1af6706e27a43ffca454b9654698bb855 | [] | no_license | cran/NISTunits | cb9dda97bafb8a1a6a198f41016eb36a30dda046 | 4a4f4fa5b39546f5af5dd123c09377d3053d27cf | refs/heads/master | 2021-03-13T00:01:12.221467 | 2016-08-11T13:47:23 | 2016-08-11T13:47:23 | 27,615,133 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 878 | rd | NISTpascalSecTOslugPerFtSec.Rd | \name{NISTpascalSecTOslugPerFtSec}
\alias{NISTpascalSecTOslugPerFtSec}
\title{Convert pascal second to slug per foot second }
\usage{NISTpascalSecTOslugPerFtSec(pascalSec)}
\description{\code{NISTpascalSecTOslugPerFtSec} converts from pascal second (Pa * s) to slug per foot second [slug/(ft * s)] }
\arguments{
\item... |
4dc3cfb53e86e3e3a536fbfaf38986a4760aa4a9 | 1c021e92bc426479ec85455835ba10ca7c9615fc | /2.scrap_youtube.R | fefd212ff0c6f39b19e1979d2377262f266ce601 | [
"MIT"
] | permissive | KeunhoLee/dtcu4_first_ep | 38188e53528fe6bb1fa77c7138f148036dd79232 | a9e05af10bb7f1dde38fec8208db584990f677ab | refs/heads/main | 2023-06-18T22:33:14.536846 | 2021-07-23T08:09:53 | 2021-07-23T08:09:53 | 386,939,182 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,038 | r | 2.scrap_youtube.R | library('rvest')
library('RSelenium')
library('stringr')
library('dplyr')
library('lubridate')
DATA_ROOT <- "./data"
SRC_ROOT <- "."
SOURCE_NAME <- "youtube"
timestamp <- strftime(now(), format="%Y%m%d%H%M%S")
file_name <- str_interp("${DATA_ROOT}/${SOURCE_NAME}_${timestamp}.rds")
# source functions ----------------... |
4acc482b021c54727abbe453d0320bdbcc64a6da | efca2158ca2f34f4ccdb99ada1099595c3877862 | /src/titanic_deck_plot.R | 435eb5f945028fcbab4c17d85867695917889309 | [
"MIT"
] | permissive | UBC-MDS/DSCI_532_GROUP_109_R | 50d4142ca97f66add3b6692a19233a4e694efcff | ffdd32b11c69e17fac848ab75d12ea114b241d7b | refs/heads/master | 2020-09-26T08:49:27.843773 | 2019-12-14T19:21:44 | 2019-12-14T19:21:44 | 226,220,727 | 0 | 3 | MIT | 2019-12-14T19:21:45 | 2019-12-06T01:28:09 | R | UTF-8 | R | false | false | 1,033 | r | titanic_deck_plot.R | library(tidyverse)
library(ggplot2)
# plot survival rate by deck level
make_deck_plot <- function() {
titanic_deck_df = read.csv("data/wrangled_titanic_df.csv")
titanic_deck_df$deck <- factor(titanic_deck_df$deck,
levels = c( "G", "F", "E", "D", "C", "B", "A"))
... |
c457a67850fceb6c38ce4bca78b16bb19af39e22 | 7a7375245bc738fae50df9e8a950ee28e0e6ec00 | /man/LGA__Year_labourForceParticipation.Rd | 827573b8f93ee4ecdece49ec97255b565b710045 | [] | no_license | HughParsonage/Census2016.DataPack.TimeSeries | 63e6d35c15c20b881d5b337da2f756a86a0153b5 | 171d9911e405b914987a1ebe4ed5bd5e5422481f | refs/heads/master | 2021-09-02T11:42:27.015587 | 2018-01-02T09:01:39 | 2018-01-02T09:02:17 | 112,477,214 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 459 | rd | LGA__Year_labourForceParticipation.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/LGA__Year_labourForceParticipation.R
\docType{data}
\name{LGA__Year_labourForceParticipation}
\alias{LGA__Year_labourForceParticipation}
\title{by LGA, Year}
\format{1,689 observations and 3 variables.}
\usage{
LGA__Year_labourForceParticipat... |
2c98fe2ae0006bd43a0906274f4823cd6787d1a9 | a9a9af4f010a883720f70391d2af66f437cb15c3 | /man/retrieve_validation_set_from_db_for_emulator.Rd | 726ec412d958cd73efcf363c4784db5a462fb80f | [] | no_license | kalden/spartanDB | ad4162c78ef54170c21c08a8a7a822fafc457636 | bc698715cdce55f593e806ac0c537c3f2d59ac7a | refs/heads/master | 2020-03-26T23:32:14.724243 | 2019-02-20T11:05:17 | 2019-02-20T11:05:17 | 145,549,860 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 995 | rd | retrieve_validation_set_from_db_for_emulator.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/emulation_generation.R
\name{retrieve_validation_set_from_db_for_emulator}
\alias{retrieve_validation_set_from_db_for_emulator}
\title{To demonstrate use of emulator, we return validation set from the database to show how the emulator can mak... |
4166e35062082f3edd3e1c62f745fa1603bbeb90 | 0142ea48c3ca7a18da74e9e67afc9b6a2e830588 | /hwscript.R | 5720d0a63fcf0b17e2010d96682a5bb5d190c584 | [] | no_license | yechenghao/RepData_PeerAssessment1 | 5f0b9c7516df4eb40e6ae65858404ff29aba8377 | 17b0ae4bc4a9940fbd547933462fa805754a6602 | refs/heads/master | 2020-03-22T16:07:40.663436 | 2018-07-10T02:04:12 | 2018-07-10T02:04:12 | 140,304,623 | 0 | 0 | null | 2018-07-09T15:22:59 | 2018-07-09T15:22:59 | null | UTF-8 | R | false | false | 3,046 | r | hwscript.R | # wk 2 assignment from Reproducible Research
#required libraries for this exercise
library(lubridate)
library(dplyr)
library(ggplot2)
# Loading and processing the data
activity<-read.table("activity.csv", sep=",", header=TRUE)
activity$date <- ymd(activity$date)
# what is the mean total number of steps ta... |
c6bcfd5a2b836bc0669163e637b05610efc7a7a1 | 4e302606dc7bb42178651bdddb28b5d1f14d51d1 | /apps/ui/ui_06.R | e1d0f28e979f12a52cb1a82fd71720efff3f78a7 | [] | no_license | jcheng5/shiny-training-rstudioconf-2018 | 5bf2092d661ae89c56ec88eddb8310fe044bcae4 | 2966e76519e8c12eefacd036979d49146b25bd6f | refs/heads/master | 2021-09-06T02:17:46.639529 | 2018-02-01T16:24:07 | 2018-02-01T16:24:07 | 118,834,801 | 47 | 23 | null | 2018-01-29T07:41:02 | 2018-01-24T23:22:32 | R | UTF-8 | R | false | false | 1,000 | r | ui_06.R | library(shiny)
# Define function that takes a YouTube URL,title, and description ---
# and returns a thumbnail frame, implemented using a template
videoThumbnail <- function(videoUrl, title, description) {
htmltools::htmlTemplate("youtube_thumbnail_template.html",
videoUrl = videoUrl,
title = title,
desc... |
1f536095e1729eb01886eb54fd8b48a8a8e51e6d | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/dtwclust/R/DISTANCES-lb-improved.R | bea7dae06a620deae3b9fb472b5c8e1737ba38c3 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,927 | r | DISTANCES-lb-improved.R | #' Lemire's improved DTW lower bound
#'
#' This function calculates an improved lower bound (LB) on the Dynamic Time Warp (DTW) distance
#' between two time series. It uses a Sakoe-Chiba constraint.
#'
#' @export
#'
#' @param x A time series (reference).
#' @param y A time series with the same length as `x` (query).
#'... |
cdf8f3870eec237e68d9f7e4a79e59328ddce3a3 | 40313f5eec63c4a2e28961181b72881f748fc02c | /man/asset_data.Rd | f98411cb00a25e00c3cf5e9ad56661e1a7a51099 | [] | no_license | snowvil/HenryQuant | 00e6050a2905e37a9cfa248a02c43ff08114dccf | e6492fd82ca9eafd34662b43fc30c76c5cd548f3 | refs/heads/master | 2021-06-09T21:39:47.510151 | 2021-04-06T07:42:55 | 2021-04-06T07:42:55 | 143,132,372 | 0 | 0 | null | 2018-08-01T09:13:22 | 2018-08-01T09:13:22 | null | UTF-8 | R | false | false | 558 | rd | asset_data.Rd | \name{asset_data}
\alias{asset_data}
\docType{data}
\title{Gloal Asset Return Data}
\description{Daily data of ten global asset return data beginning on
1993-01-01 and ending 2017-12-31. The ETF price was used first, and public funds and index prices were used before listing. All returns include dividends.
}
\format{xt... |
3d989ba403723879fbfad44260ac70f448901921 | 6a7538e4d3b6850800178315064f7e9213112b78 | /ERP_measurements_analysis/LOTP_4AFC_stat_analysis.R | e0e132cb0cd003a09ebaf22745a8ba6596544f5e | [] | no_license | kylefrankovich/ERP_analysis_scripts | 07816fe48f421cd1eea432808cd172ce4196e465 | 53640cf06f6d79658803cc12014c08bdf59fbed0 | refs/heads/master | 2020-05-29T16:09:16.973872 | 2016-09-06T00:44:57 | 2016-09-06T00:44:57 | 59,140,299 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,937 | r | LOTP_4AFC_stat_analysis.R | # statistical analysis for LOTP_4AFC_ERP
library(ez)
library(dplyr)
library(ggplot2)
setwd("~/Desktop/ERP_analysis_scripts/ERP_measurements_analysis")
## load data:
# files:
# blocked_correct_resp_N2pc.txt
# blocked_correct_resp_SPCN.txt
# mixed_correct_resp_N2pc.txt
# mixed_correct_resp_SPCN.txt
# blocked_all_res... |
401ca6aae24bd1af908d7314a786bd42a0dcdefd | 5df2b9430e84caf4775a4d632dda4e352f13a1f9 | /data_clean_file.R | 5799e4c0037ca90912174d05eb8e5af4a76f8c69 | [] | no_license | BouranDS/Project1Exploratory | aa36c8cd3973c6a71b2a505620a41f4ff10db274 | c2e0977fa64752b5a3c7702816cd40cc280af656 | refs/heads/main | 2023-01-12T19:02:22.208150 | 2020-10-31T09:30:22 | 2020-10-31T09:30:22 | 308,840,233 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,095 | r | data_clean_file.R | ## Project 1 for Week 1
# author : ibrahima dit bouran sidibe
# require(magrittr)
data_in <- read.csv2(file = "D:/MyR/exdata_data_household_power_consumption/household_power_consumption.txt",
header = TRUE, sep = ";", stringsAsFactors = FALSE)
# head(data_in)
# tail(data_in)
# str(data_in)
data_in... |
27023058b7ca082ac40d21e7e1ab994e29050563 | bded5e8f66fa9edd65ea4d9f44e2b15351856293 | /other/sqf_files/prob_any_force_used_black_vs_white.R | 2e0bfd37a1bf5b028cd1bfdee7f860054fa1a2ae | [] | no_license | msr-ds3/stop-question-frisk | 79a3112395c9d09413dbcd5c73c30ca777489c65 | 86b367093c3bb5c6c0ba927184b7fbb1bc5b35fa | refs/heads/master | 2022-11-06T01:46:55.393468 | 2020-05-11T20:21:50 | 2020-05-11T20:21:50 | 196,596,341 | 3 | 4 | null | 2020-06-18T19:27:45 | 2019-07-12T14:47:23 | HTML | UTF-8 | R | false | false | 5,712 | r | prob_any_force_used_black_vs_white.R | library(tidyverse)
library(tidycensus)
library(totalcensus)
library(sf)
library(tmap)
library(tmaptools)
library(tigris)
library(leaflet)
library(sp)
library(ggmap)
library(maptools)
library(broom)
library(httr)
library(rgdal)
library(htmlwidgets)
library(webshot)
########## LOAD AND CREATE/CLEAN DATAFRAMES ##########... |
edf2079540a3454795f51c1623b6520b9546181f | 16d9103d6d54fac9937389187ca3ffaa27089c24 | /R/channel_analytic.r | f411e57907e3f9904864f9dd2e528f30debb3c15 | [] | no_license | CodifiedHashtagsCoding/rTwChannel | 7ca17456d5d1eaac0899bae90fe3abb10294cb22 | 08b71e359bed4b855fc073a45c1e644fe3c561e7 | refs/heads/master | 2020-12-11T05:46:00.013455 | 2015-11-20T16:48:15 | 2015-11-20T16:48:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 36,237 | r | channel_analytic.r | #' channel_analytic
#'
#' @description Extract many informative stats and object from a set of tweet messages parsed as channel
#'
#' @param channel_obj Data.frame Dataset of tweets
#' @param use_channel_dates Logical Use temporal indication of channel
#' @param start_date Character Date of analisys starting.
#'... |
dde78b071315b313212fdd3e43c261ecddd35b53 | 37b51ada441c3679a42b82754d0e2f24c3ce70a2 | /man/isAngleBetweenEdgesAlwaysSuperiorToMinAngle.Rd | ac70eb2abccfd618e307436f5ab135129a0371c1 | [] | no_license | cran/AFM | a01d77751de195ca8a701cdf44ee3134ebaa00b4 | 98e8b5222e078af4d2840a20a2b58ec2196d684d | refs/heads/master | 2021-05-04T11:23:09.648739 | 2020-10-07T07:00:06 | 2020-10-07T07:00:06 | 48,076,498 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 837 | rd | isAngleBetweenEdgesAlwaysSuperiorToMinAngle.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AFMNetworksAnalyser.R
\name{isAngleBetweenEdgesAlwaysSuperiorToMinAngle}
\alias{isAngleBetweenEdgesAlwaysSuperiorToMinAngle}
\title{check if all the angles between one edge and a list of edges is superior to a specified value.}
\usage{
... |
b1981bcc3d3081fc6af18ff6fcecc3e013ed3586 | 787f2e5c1ec651cc69ea0a053eb0fe09be80cf65 | /HW1.R | 1fe3b9a4c44149117201c9898ee98d2cb0b091e5 | [] | no_license | rstieger/CS1156x | 7b9a4164aae05eff912c0c47bc3f46e36d2f03a6 | 016a272a35313be7f92ae80cdc94691b8f560f9b | refs/heads/master | 2021-01-19T07:46:04.512092 | 2014-11-20T01:08:58 | 2014-11-20T01:08:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,509 | r | HW1.R | makeTarget <- function() {
p1 <- runif(2, min=-1, max=1)
p2 <- runif(2, min=-1, max=1)
b <- (p1[2]-p2[2])/(p1[1]-p2[1])
a <- p1[2]-b*p1[1]
function(x) { sign(c(a,b,-1) %*% rbind(1,matrix(x,nrow=2)))}
}
makeHypothesis <- function(w) {
function(x) { sign(t(w) %*% rbind(1,matrix(x,nrow=2)))}
}
ma... |
af555156d6293dd280e4e14a1a1830e5d11d3686 | 9d515dfeb17d7ff8a3f00d960c2c799dfccc12b3 | /Getting and Cleaning Data/Assighnment W2/Q2.R | 9aab4ddfa1d7c13c5e9127e6edbd78f96459d79f | [] | no_license | rokbohinc86/2019.3.28.Data_Science-Specialization | af8e1c106f4ad6b6cb38a9c6aec2c1f09f0bd45c | 066d2a4ec5b61edbf38ae643eac3c286ca9e0298 | refs/heads/master | 2020-08-05T14:17:39.837996 | 2019-10-03T12:34:17 | 2019-10-03T12:34:17 | 212,575,890 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 548 | r | Q2.R | # library(RMySQL) do not read otherwise you will get an error
library(sqldf)
# set working directory and create dir named data where I download data
setwd("/home/rok/Edjucation/2019.3.28. Data_Science-Specialization/Getting and Cleaning Data/Assighnment W2")
if (!file.exists("data")){
dir.create("data")
}
fileURL... |
1ae9582506183a26162df6d9d6eb164821b41995 | 0595e02cc1e9f24d0abc59b30b3054469613980f | /R/utils.R | bdafcf46756bca582f20341965528ef2ab962a7a | [] | no_license | arturochian/ProteinVis | 37cf342f47206a12c5cf2e2c44133966d46411b6 | 8f775d0246382dfa81045aa61134de82c82fa60b | refs/heads/master | 2021-01-16T22:46:49.509149 | 2012-04-10T18:04:30 | 2012-04-10T18:04:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,187 | r | utils.R | combineTMDomains = function(tmdf, poscols = c("start", "end"))
{
tmmat = matrix(NA, ncol = 2, nrow = nrow(tmdf))
curnumrow = 1
for (i in 1:nrow(tmdf))
{
inserted = 0
for(j in 1:curnumrow)
{
if(!is.na(tmmat[j, 1]))
{
if (any(tmdf... |
dabd16b2a892fc97abefa93cc4fb259e6dcee412 | 5f160e0117368a4864f0784ba163067ae705d5dc | /myR/R/dif.plot.R | f99e12e9680489d7b6119c091165ef70edbd52f0 | [] | no_license | brunnothadeu/myR_package | e1a006a78c30f52970531decf824ff3d53ac3b3c | f56fb21c11a541bf6402bccd52760e9464e7c835 | refs/heads/master | 2020-04-04T06:38:54.029864 | 2018-11-20T19:19:43 | 2018-11-20T19:19:43 | 155,751,907 | 0 | 0 | null | 2018-11-01T18:02:06 | 2018-11-01T17:32:46 | null | UTF-8 | R | false | false | 5,392 | r | dif.plot.R | #' @title Gera os graficos de DIF
#' @name dif.plot
#'
#' @description Cria um diretorio chamado 'DIF' e gera os graficos de Funcionamento Diferenciado Do Item (DIF).
#'
#' @param EXP Objeto resultante da funcao calcDIF.
#' @param SCO Uma lista nomeada de acordo com os indices dos grupos em analise, contendo os... |
c99ee1d0eb27cca43f110900df5a5ff4a36fb655 | bb1fc4854812f2efe4931ca3c0d791317309e425 | /scripts/older_scripts/tiger_one_at_a_time.R | a64c0ee0296c326d4a80fea0d281d610351a10fa | [
"Apache-2.0"
] | permissive | dlab-berkeley/Geocoding-in-R | 890e491d84808e29d07897508dc44f2bd9a3f646 | 40a0369f3b29a5874394ffafd793edc7012144ea | refs/heads/master | 2023-03-06T07:54:41.997542 | 2021-02-18T17:42:22 | 2021-02-18T17:42:22 | 47,520,653 | 5 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,471 | r | tiger_one_at_a_time.R | #library(httr)
library(RJSONIO)
gurl <- "http://geocoding.geo.census.gov/geocoder/geographies/address?street=912+Kingston+Ave&city=Piedmont&state=CA&benchmark=Public_AR_Census2010&vintage=Census2010_Census2010&format=json"
bad_gurl <-"http://geocoding.geo.census.gov/geocoder/geographies/address?street=912+Kingston+Ave... |
ad1bb28122d7a556f6efd46677b6cc346ca72fe9 | 4f77ba4fdc074fdd2b119c293653011af6c6dcda | /R/haplo-match-cif.r | fb00c59fc33a9b77a1a1df601876eedc14999100 | [] | no_license | karthy257/HaploSurvival | 526125cd792efb91b88db1ad2b9a445393f5e3a0 | 4661eab94edd62e79dcdaefbe2dc05fb01840c5b | refs/heads/master | 2021-03-06T14:54:05.583732 | 2019-05-16T11:21:43 | 2019-05-16T11:21:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,106 | r | haplo-match-cif.r | haplomatch.cif<-function(formula,data=sys.parent(),
cause,times,designfuncX,designfuncZ,Nit=50,
clusters=NULL,gamma=0,n.sim=500,weighted=0,model="additive",
causeS=1,cens.code=0,detail=0,interval=0.01,resample.iid=1,
cens.model="KM",time.pow=0,fix.haplofreq=0,haplo.freq=NULL,alpha.iid=NULL,
geno.setup=NULL,fit.haplofre... |
905da2c370ff1e2d49a15d3a450d5b274fa48f20 | 0a906cf8b1b7da2aea87de958e3662870df49727 | /grattan/inst/testfiles/anyOutside/libFuzzer_anyOutside/anyOutside_valgrind_files/1610129556-test.R | 55bdf735643771f41cb65dd4eb9623ff48279347 | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 371 | r | 1610129556-test.R | testlist <- list(a = -1L, b = -230L, x = c(8388608L, 536873984L, -1L, 1560281088L, 0L, 0L, 0L, 0L, 0L, -163L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 255L, -193L, -14155813L, -250L, 50331647L, -193L, -14155904L, 8192L, 201523195L, -16814507L, -14024705L, -37L, -1L, -12583129L, -56833L, -8650752L, 14869218L, 41L))
result <-... |
cbc724f66c331f314ea96ee7977df8e7761ac986 | 352056ed20f0739afde982c5115417582ed1f92d | /run_analysis.R | cdef09cd9267325abfb204e6d097f0f32e12084c | [] | no_license | arunchaudharee/Getting-and-Cleaning-Data-Project | ba8c6a7068fa740415544f56ceaba9485cb97fc4 | f7a0edd85cc9c275f67f32a730c458feef762c48 | refs/heads/master | 2021-04-06T08:16:16.452139 | 2018-03-13T01:08:30 | 2018-03-13T01:08:30 | 124,890,112 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,332 | r | run_analysis.R | ## Getting and Cleaning Data Course Project
## This R script called run_analysis.R downloads the data from the following link and does the following:
## download link - https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
## 1. Merges the training and the test sets to create one data ... |
e2079bfbf755538e1feb173eb22babfa184c10cf | dde087158294465134dee9ddaba934b73f42e6ef | /get_weather.R | 277bc02c956f6510bd7524e41e0108462be9f0d3 | [] | no_license | joebrew/tbd | ef332510b4cd43fddf9311ba146e0404d3932885 | fb22c30a922c99c538474b818234cb06125dec13 | refs/heads/master | 2021-08-22T23:48:17.213421 | 2017-12-01T18:52:02 | 2017-12-01T18:52:02 | 112,350,548 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,840 | r | get_weather.R | library(tidyverse)
library(RCurl)
# Define function to replace NAs (coded in noaa as 99.9 by NOAA)
detect_noaa_na <- function(x){
x <- as.character(x)
y <- gsub('.', '', x, fixed = TRUE)
oks <- y == x
out <- unlist(lapply(strsplit(y, ''), function(x){all(x == '9')}))
out[oks] <- FALSE
out
}
# Define funct... |
819aeb80a39f515621f34d2b36a18d67316c8e0a | adecabf2b7801f7be4ef7dce51dece192406ec45 | /Misc. Charts/SpC & NOx Violin Plots_POR 20180914 to 20190709.R | 34c12289faf049ac412c2306b31c364c9c8bb780 | [] | no_license | ETaylor21/gnv_streams | ae6933c46f384a0d75644c114840a3f3d6d69789 | 6a54e58738ff2beae9afb36e04dae855a1a1b935 | refs/heads/master | 2021-11-04T04:33:19.212474 | 2021-11-01T05:38:47 | 2021-11-01T05:38:47 | 171,563,273 | 0 | 0 | null | 2019-03-14T14:05:21 | 2019-02-19T22:55:41 | R | UTF-8 | R | false | false | 1,683 | r | SpC & NOx Violin Plots_POR 20180914 to 20190709.R | ##### Violin Plots of NOx and SpC For AJ NSF Proposal 10/16/19####
####NOx, O_P, NH4 all filtered samples###
#NOx - Nitrate(NO3)-Nitrite(NO2)-N (mg/L)
#SpCond - specific conductivity (uS/cm)
####Sample Replications (col = Rep)
#a = 1; b = 2; c = 3; d = not a rep just a reading
########################Call in and ... |
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