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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
8c13c8431f874381d5b944b606c00859829ff91b | 6de59664997ab124f8b99fc956bfcdf603b6fd52 | /4_Graphs_R/a_amm_heatmaps.R | 78f346cb96827b0deb1cb52a0be53e7d9045b12e | [] | no_license | nstrasser/MasterThesisProject | 716ce243ec69a7126216b5b0c43cc6fc57579e4d | 43c521bd3044ba4c9a149b49061861191b66f25a | refs/heads/master | 2022-12-08T14:05:08.067094 | 2020-08-31T16:18:05 | 2020-08-31T16:18:05 | 291,654,879 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,328 | r | a_amm_heatmaps.R | library(ggplot2) # (Wickham, 2016)
library(tidyr) # (Wickham and Henry, 2020)
library(dplyr) # (Wickham et al., 2020)
library(cowplot) # (Wilke, 2019)
library(plotly)
library(grid)
library(gridExtra)
plot.heatmap <- function(data_var, config_var) {
current_mutation_rate <- unique(data_var$mutation_rate_a)
... |
ed07d9bd682d448618f743a042602f514e49aa36 | 54b4976030ae6a42e10282c8f41609ef266721c9 | /R/lamp-laplace-distribution-method.R | 5a28078c0004d039b082bd5044d06b264083fd0a | [] | no_license | cran/ecd | b1be437b407e20c34d65bcf7dbee467a9556b4c1 | 18f3650d6dff442ee46ed7fed108f35c4a4199b9 | refs/heads/master | 2022-05-18T20:24:56.375378 | 2022-05-09T20:10:02 | 2022-05-09T20:10:02 | 48,670,406 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,109 | r | lamp-laplace-distribution-method.R | #' Laplace distribution
#'
#' Implements some aspects of Laplace distribution (based on stats package) for
#' stable random walk simulation.
#'
#' @param n numeric, number of observations.
#' @param x numeric, vector of responses.
#' @param b numeric, the scale parameter, where the variance is 2*b^2.
#'
#' @return n... |
6f5bd7279cde0e0d224d7451e541292cb9a0b21d | 2a1d00ab9ac6fe11bff9557513f1f4a876a2afd4 | /Post_Mid_term.R | 0ff55fef955afacc39507855eb9fa3f1b67f32af | [] | no_license | mohith10/R_for_Data_Science | 9d9be3407a4f301ad3b4f41171197867fcf5945f | 685477f55b4ceb9dfd247696ea066a6c63080b32 | refs/heads/master | 2020-04-17T17:34:18.150899 | 2019-05-17T16:00:08 | 2019-05-17T16:00:08 | 166,787,771 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 323 | r | Post_Mid_term.R | #Ensemble Model - Powerful way of participating in challenges and contests
#methods-Bagging and Boosting
#sTEPS- gET-> cLEAN DATA ->tRAIN mODEL -> Test Data -> Improve
#We partition the data to avoid overfitting - Basic Question
#In unsupervised we dont have any target variable.
#Bagging - Bootstrap Aggregating
... |
666e61fc275e991f076adb3f26bef08584dfe398 | 6fe61e61e61f70c223c12a46a58846591d8494c2 | /exploratory_analyses/02_reddit_scaled_up/scripts/04_topic_modeling_comments.R | 76aea8b150eb140e0b02366095044f31fa2561b9 | [] | no_license | mllewis/LANGSCALES | a723eccc5ebdb20187b0ceaedeb324a282f5070f | ebe0749ba1f46bf628f4ae4820580d420d479cad | refs/heads/master | 2020-07-24T08:10:20.422821 | 2020-02-20T19:55:00 | 2020-02-20T19:55:00 | 207,859,174 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,666 | r | 04_topic_modeling_comments.R | # train topic models for comments using mallet package
library(here)
library(mallet)
library(tidyverse)
library(tidytext)
library(glue)
LOCAL_PATH <- here("/exploratory_analyses/02_reddit_scaled_up/data/")
MIN_WORDS_PER_POST <- 100 # excluding stop words
NTOPICS <- 50
get_topic_model_subreddit <- function(subreddit... |
3866654b59981236e5dd6bd98f956eb6578a2588 | 71181535e485db80d21f2e447c3208547b3114a2 | /R/old/check_msats.R | 85b69fe40b5b54138e1c1ca6d501a71c019c9845 | [] | no_license | mastoffel/pinniped_bottlenecks | f93ab44d3abf3f2bf125b968555a2b042517f6a8 | 3627d6596a90497a382e65c249b7245e1dbe022a | refs/heads/master | 2021-08-27T16:24:49.777306 | 2021-08-06T09:00:23 | 2021-08-06T09:00:23 | 57,881,827 | 7 | 0 | null | null | null | null | UTF-8 | R | false | false | 568 | r | check_msats.R | # script to check all microsatellite datasets
library(readxl)
# sheet numbers to load
dataset_names <- excel_sheets("data/processed/seal_data_largest_clust_and_pop.xlsx")
load_dataset <- function(dataset_names) {
read_excel("data/processed/seal_data_largest_clust_and_pop.xlsx", sheet = dataset_names)
}
# load all... |
54c36be47e2ddc271ce5eff66a052ea80363291c | a2c6618e894166b23b3bda63e32e84a749fa9c62 | /plot5.R | 5c206275ae156ed9a0dd7c0be2e4d54d936951d7 | [] | no_license | csmahori/Exploratory-Data-Analysis-Project | 110d54db5bf8cca23d46862c133b6951b12771e5 | ee86ba1b4447cd5669c3793dce19603513ef1617 | refs/heads/master | 2020-03-17T10:20:32.549774 | 2018-06-06T10:07:30 | 2018-06-06T10:07:30 | 133,508,107 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 555 | r | plot5.R | NEI<-readRDS("summarySCC_PM25.rds")
SCC<-readRDS("Source_Classification_Code.rds")
table(SCC$EI.Sector)
sub3<-SCC[grep("[Mm]otor|[Vv]ehicles",SCC$SCC.Level.Three),]
sub31<-merge(NEI,sub3,by="SCC")
sub32<-filter(sub31,fips="24510")
sub32<-mutate(sub32,year=as.factor(year))
sub33<-group_by(sub32,year,SCC.Level.Thr... |
4d09cb2aad305ffec3b7ce5ac819a97fd60fac67 | 04d0a997364ad1bab775fb920edfe5b60cf6d740 | /man/PseudoR2.Rd | 988e42301b46bc94c031f95420d1ed1ce264fb58 | [] | no_license | mainwaringb/DescTools | a2dd23ca1f727e8bbfc0e069ba46f44567e4be24 | 004f80118d463c3cb8fc2c6b3e934534049e8619 | refs/heads/master | 2020-12-22T15:12:41.335523 | 2020-03-21T17:30:52 | 2020-03-21T17:30:52 | 236,836,652 | 0 | 0 | null | 2020-01-28T20:40:03 | 2020-01-28T20:40:02 | null | UTF-8 | R | false | false | 5,162 | rd | PseudoR2.Rd | \name{PseudoR2}
\alias{PseudoR2}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Pseudo R2 Statistics
%% ~~function to do ... ~~
}
\description{Although there's no commonly accepted agreement on how to assess the fit of a logistic regression, there are some approaches. The goodness of fit of t... |
442abb7e794bcdcbf6dab31b1dd4214c32aabb3d | fc4fba8b64c564ef49beaf1560ff01bf9bdc259f | /tools/analysis_from_radiant_not_in_use/base.R | 097fc101c2c8e38e97d0f76b610695a9f3f92a01 | [] | no_license | nimrodbusany/Radiant4GoogleReps | a86665197fd507e95aa45525c5d313832d9a0348 | 9508bf9dbb0f158c9cad341d23e9692a7e30863c | refs/heads/master | 2021-01-01T20:05:26.951890 | 2020-06-27T05:41:02 | 2020-06-27T05:41:02 | 24,627,872 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 16,118 | r | base.R | # alternative hypothesis options
base_alt <- list("Two sided" = "two.sided", "Less than" = "less", "Greater than" = "greater")
###############################
# Single mean
###############################
output$uiSm_var <- renderUI({
isNum <- "numeric" == getdata_class() | "integer" == getdata_class()
vars <- varn... |
c06f91a7806d349f73ea2026a396c9f3bfcf53b4 | c0b29712073ce54f3d75e864bdd1f770c688d236 | /script/archive/package_mng.R | 039c8f9a8989367bbc8eddf61866e819a4629d9c | [
"MIT"
] | permissive | achiral/rbioc | fb6f173430f974e68b5e7e3af6e3e464de9f4d78 | 1a0c5ab2d1eebe2161ba518853179aa7ae2c50a8 | refs/heads/main | 2023-08-31T00:43:54.542237 | 2021-10-21T03:18:10 | 2021-10-21T03:18:10 | 414,892,205 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 899 | r | package_mng.R | # 分析・開発用のコード
#
# install_packages.R でインストールしたパッケージを普通にロードして使用する。
# 例:
# library(palmerpenguins)
# head(penguins)
setwd("/home/rstudio/project")
# パッケージはhome/rstudio/project/dev/packages.Rにて管理すると良い
# 新しいパッケージをライブラリにインストールするときは `renv::install()` 関数を使用
# renv::install("tidyverse")
# ライブラリの状態を記録するには `renv::snaps... |
fef3489db80458b01486e3272fc8d8447d7aae38 | 948f76002c5f0422f906a2ff805dbfc9cf82988e | /R/00_clean_data.R | cccdb9f25bc68016fc3e5f4099b479b590d1be86 | [] | no_license | ziyint/Info_repo | f642213d94a0bf5c19b4f58f7565057d2be23f5f | d0fc4cb0c7cef7e75968a96cca223d935c068956 | refs/heads/master | 2023-01-24T20:37:49.024485 | 2020-11-24T01:16:14 | 2020-11-24T01:16:14 | 304,959,505 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 823 | r | 00_clean_data.R | #! /usr/local/bin/Rscript
# Read in dataset
met_data <- read.table('raw_data/data.txt', header = T)
met_intensity <- met_data[4:53]
# Calculate the number of samples detected each of metabolic features
total_notmissing <- apply(met_intensity, 2, function(x){return(length(which(!is.na(x))))})
# Remove metabolic featu... |
e87b145e1e0d4fc43908ed068212e00432a32368 | 80f1a3756899b0f6b36f4cdfc039883138b0abc8 | /test_jags_part2.R | d58297b7611ef48fdd7e1127b93c3adbf883f487 | [] | no_license | nushiamme/AShankar_hummers | fffde730af3e55b43cbb18e6d093375b0d29e723 | c859fddd8f71501fdcb6dee034f8034e69cb7b22 | refs/heads/master | 2023-06-22T18:01:58.306283 | 2023-06-19T20:02:04 | 2023-06-19T20:02:04 | 13,457,401 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,315 | r | test_jags_part2.R | sink("Bayesian/LogN.jags")
cat("
model {
for (i in 1:N) {
y[i] ~ dbern (pr.hat[i]) #no need to change a thing
logit(pr.hat[i]) <- a[species[i]] + b1*x1 #x1 is individual mass you give it to model
e.y[i]<- ilogit(y[i] - logit(pr.hat[i])) #data-level errors, to estimate R2, need to be rescal... |
d7f6414cb9c0406703cd58e0364a666eabb32347 | dae5069a21b7c7d2077291a2325f244245be7f10 | /modules/auth/logIn.R | 771fe0a80e814d515fea32cd07c97aae5fb6465b | [] | no_license | JulioMh/web-app-shiny | 2b4777af13a40b08560d5a7f3db7a052b173fe9d | a0ff1f987921f0a9fc8aeb01c0dca5ff5e968e67 | refs/heads/master | 2022-05-30T19:51:42.134040 | 2020-05-03T14:33:35 | 2020-05-03T14:33:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,165 | r | logIn.R | logInUI <- function(id) {
ns <- NS(id)
tagList(
div(
id = "login",
style = "width: 500px; max-width: 100%; margin: 0 auto; padding: 20px;",
wellPanel(
h2("LOG IN", class = "text-center", style = "padding-top: 0;color:#333; font-weight:600;"),
textInput(
ns("userName")... |
0952c977fb59432b2035a38db484495bc30f797e | 1b1d051d9bc90d26694a6fc76839ad1f128abede | /Machine Learning/course project/code/data_import.R | 206cb9016d0101a435657eef06c5a507516da0ff | [] | no_license | jessica-dyer/datasciencecoursera | a391a2fbd7f6355600ea754aa3e65f1c5037dc19 | 0ea3def980b5a1a1288ae216cefd202eb2a57d28 | refs/heads/master | 2023-02-28T16:33:12.037914 | 2021-02-09T20:42:20 | 2021-02-09T20:42:20 | 302,464,095 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 449 | r | data_import.R | ##################################################
## Project: Machine learning course project
## Script purpose: Data import
## Date: 2021-02-06
## Author: Jessica Dyer
##################################################
train_url <- "https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv"
test_url <-... |
bc563a92e3e7bce530e53d57448c07de2aec414c | b72fb283f3d8937d4ef56b1cd61b584910add460 | /man/metropolis.Rd | b94e6e285be886819fda57ff15b7210837545873 | [] | no_license | IQSS/binb | cda8ebbf66cfad78ec9ba325ba1e0db839a8f2ef | 25a1a71caad094c254b1e33d5cbd8b6316258bac | refs/heads/master | 2020-03-28T21:32:14.534034 | 2018-09-16T13:28:08 | 2018-09-16T13:28:08 | 149,163,946 | 0 | 2 | null | 2018-09-17T17:39:57 | 2018-09-17T17:39:56 | null | UTF-8 | R | false | true | 2,520 | rd | metropolis.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/binb.R
\name{metropolis}
\alias{metropolis}
\alias{iqss}
\title{Binb is not Beamer - Metropolis-themed PDF Presentation}
\usage{
metropolis(toc = FALSE, slide_level = 2, incremental = FALSE,
fig_width = 10, fig_height = 7, fig_crop = TRUE,
... |
9ba40607cac06848d324bf89693fc1f64cedc459 | b24e55de85c6b09921f212b6d1108647096f4d5f | /rebuttal1/get_info_rev2_point2.R | 0d48ba19f69a0ff49efbdd333f1ffe9811adb822 | [] | no_license | gui11aume/REPLAY_TRIP | cdc8ab59aec590d7b49f73b8911dcbb56a564909 | 76128e7e9840bc14e8aeeca196c4bf1457829da1 | refs/heads/master | 2021-01-12T15:30:49.960925 | 2016-11-10T12:18:35 | 2016-11-10T12:18:35 | 71,796,478 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 262 | r | get_info_rev2_point2.R | load("../Fig4/models.rda")
modenh = models[[5]]
# Start writing to file.
sink(file("info_rev2_point2.txt"))
cat("Predictive power H3K27ac/H3K4me1\n")
var(modenh$fitted.values) / (var(modenh$residuals + modenh$fitted.values))
# End writing to file.
sink(NULL)
|
a6964b7f5356262c3514969cf4849ec186f22980 | f1f749fed0ff90367f5344644c61da8583036881 | /man/rast_spec.Rd | 81a167f5e64c5679ae7c4f3b8d032fae23c70d38 | [] | no_license | ozjimbob/raaqfs | 72cc1ac9b869c66b7ba1ebf6d2ad8b4a4eaf78e0 | 2037327991484de997b82ace8fda6c8fc7c081b4 | refs/heads/master | 2020-04-06T07:09:31.808134 | 2016-09-01T01:42:08 | 2016-09-01T01:42:08 | 64,715,187 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 716 | rd | rast_spec.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rast_spec.r
\name{rast_spec}
\alias{rast_spec}
\title{Return raster grid of variable for a time period}
\usage{
rast_spec(input, spec, hour)
}
\arguments{
\item{input}{An open ncdf4 object.}
\item{spec}{Full species name (4 chars) including ... |
c77f55f687c449696091eeb59e8e8e152631a5fb | f55dabad5fc47799fd95e0761d6af9997fbabbed | /cachematrix.R | 5e1e27282f0c5bd24785011d7dcb29312be6c25c | [] | no_license | fsaavedraolmos/ProgrammingAssignment2 | eb990ca7eaf2507e5f5b6149fcee5475df882ce4 | abd4bb5e17b854e1415252f1b3235b33de0220bd | refs/heads/master | 2021-01-10T11:06:25.275318 | 2016-01-25T01:34:10 | 2016-01-25T01:34:10 | 50,317,893 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,704 | r | cachematrix.R | ## This following function takes a square invertible matrix
## and return a list of functions to:
## 1.- Set the matrix.
## 2.- Get the matrix.
## 3.- Set the inverse of matrix.
## 4.- Get the inverse of matrix.
## Finally, this is used as input in cacheSolve function.
makeCacheMatrix <- function(x = matrix()) {
m ... |
6793746b8c38f353e74e7a71f036f678dd9aa94e | c6819f8a6b00e273bcdcae567a3c9be4ca3b98b3 | /Table1Reviewed.R | fd2e763608c822b8da6ea062d12a08d4309e2d49 | [] | no_license | FelipeMonts/NAPTSoilsData | 188f67f91e2ce64c3a7a11ae3bcd2c6c1b8f9753 | bb910fe458dace64a355d29f4f1bad61aadc5f8d | refs/heads/master | 2023-07-02T13:28:44.196217 | 2019-11-22T18:48:24 | 2019-11-22T18:48:24 | 129,754,077 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,057 | r | Table1Reviewed.R | ##############################################################################################################
#
#
# Program to Plot table 1 inthe laser diffraction paper, with all the information requested by the reviewwers
#
# Felipe Montes 2018 12 13
#
###########################################################... |
a2e55a2ae90b7ddd1474a8e04d4afadb3251851e | 7c38caa385ff78efcc4c3628fc0c0552896ecb17 | /plot1.R | e36b9c5c9ce5a7aca4a3583b603f76e3284a4905 | [] | no_license | AshitoshN/Exploratory_data_Analysis | 5cbbf2c9bf408e755bfc441398b569fb098fd10d | 7cee685d4c3a2e139bb239743b99639d6593138a | refs/heads/master | 2022-10-15T23:41:21.208819 | 2020-06-11T12:24:15 | 2020-06-11T12:24:15 | 271,526,714 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 608 | r | plot1.R | #Libraries
library(data.table)
#Read the data as data frame
data <- fread("C:/Users/LENOVO/Desktop/Data_Science/Exploratory_analysis/household_power_consumption.txt")
#Subsetting the data for date 1/2/2007 & 2/2/2007
data1 <- subset(data, data$Date == "1/2/2007" | data$Date == "2/2/2007")
#now we are ready with the ... |
b4cb180aeeac55e3ec220f2d186876a9aa62058b | e161195a09e161f978e8610a345bd8320806a692 | /man/simcub.Rd | 94faf9721aba869e776adc6427c0e904874fcfd2 | [] | no_license | cran/CUB | 835be9f64528f974025d8daaff7cc1f99f2eae1a | 7c47f960512aa90db261ba9ed41006a191440c1a | refs/heads/master | 2020-04-06T21:07:58.688216 | 2020-03-31T14:30:19 | 2020-03-31T14:30:19 | 48,078,715 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 652 | rd | simcub.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/simcub.R
\name{simcub}
\alias{simcub}
\title{Simulation routine for CUB models}
\usage{
simcub(n,m,pai,csi)
}
\arguments{
\item{n}{Number of simulated observations}
\item{m}{Number of ordinal categories}
\item{pai}{Uncertainty parameter}
\... |
d88e741d77d8aaefd78aa4ae1e0c772c7137eca1 | b1d604355be03002727270fe211b299f6436d124 | /election prediction.R | 9b423b1a778d1f943687ff39191a88c1b45ccf6b | [] | no_license | ckeating/AdvAnalytics | 3837f64aaec1a7101730f28c4a4376aad4c39a0c | 599fa92600089d508bd1d7ae5b0659c253c21399 | refs/heads/master | 2021-01-11T18:58:06.762213 | 2016-12-17T15:30:36 | 2016-12-17T15:30:36 | 79,280,790 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,906 | r | election prediction.R | ##initialize session
#setwd("C:/Users/Chibot/Dropbox/edgedata")
setwd("C:/Users/Craig/Dropbox/edgedata")
polling=read.csv("pollingdata.csv")
library(caTools)
library(ROCR)
library(mice)
#randomly split data into training and test
pollingorig=polling;
str(polling)
summary(polling)
table(polling$Year)
#create data fra... |
b04115680ceba77a9e4e36a46c2a10e606d6ab8a | 8b0d26cb31f02921899309f15e8f68639d74719d | /scripts/ewma_model_2018.R | d58a3f8448a0229beefb72a15512eeca8ea7fbae | [] | no_license | algoquant/lecture_slides | 49fcd0d1e2a3202580f0b23ced788fe7a80dcae0 | 5a7dbfa36a15b4a53bbbffa986b79550c5aa6e7e | refs/heads/master | 2023-05-25T22:44:54.688380 | 2023-05-24T12:14:43 | 2023-05-24T12:14:43 | 13,590,208 | 29 | 17 | null | null | null | null | UTF-8 | R | false | false | 4,257 | r | ewma_model_2018.R | # Functions for simulating EWMA strategies
# library(HighFreq) # load package HighFreq
# simulate single EWMA model using historical ohlc data
simu_ewma <- function(ohlc, lambdav=0.01, wid_th=251, bid_offer=0.001, tre_nd=1) {
# calculate EWMA prices
weights <- exp(-lambdav*1:wid_th)
weights <- weights/sum(weig... |
80b444788f4ac747102251e6ffa4f7422ecf422a | 4d8c0ab2151fe6c9abbef558808faac46cf4271b | /nonspatial.r | 2c916b387f2df5592b8fd480d11cf94b0ace2c39 | [] | no_license | ShilpaBatthineni/Road-Traffic-Injuries | ea428c7315946a1c19eb949eff8b7e35963ed2ec | c35804a4a6f61c55b3211a0fe5224fed61522b5c | refs/heads/master | 2021-01-20T20:18:26.246725 | 2016-06-17T16:02:46 | 2016-06-17T16:02:46 | 60,674,294 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 387 | r | nonspatial.r | y<-rtia$Accident_Severity
days<-rtia$Day_of_Week
wdays<-table(days,y)
chisq.test(wdays)
month<-rtia$Month
months<-table(month,y)
chisq.test(months)
year<-rtia$Year
Years<-table(year,y)
chisq.test(Years)
date<-rtia$Date
dates<-table(date,y)
chisq.test(dates)
time<-rtia$Time
time1<-table(time,y)
chisq.test... |
fc3f2f80707b4ccbc0f6ceeb3be97b3729227935 | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /EstMix/man/calc_2d.Rd | 6f7868c96920e832d0ad1fa12ad4235571607d8a | [] | no_license | akhikolla/TestedPackages-NoIssues | be46c49c0836b3f0cf60e247087089868adf7a62 | eb8d498cc132def615c090941bc172e17fdce267 | refs/heads/master | 2023-03-01T09:10:17.227119 | 2021-01-25T19:44:44 | 2021-01-25T19:44:44 | 332,027,727 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,165 | rd | calc_2d.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/calc_2d.R
\name{calc_2d}
\alias{calc_2d}
\title{Return mixture estimation of a normal and 2 tumors
Takes BAF, LRR, chr, x, gt, seg_raw}
\usage{
calc_2d(BAF, LRR, chr, x, GT, seg_raw)
}
\arguments{
\item{BAF}{vector containing B allen frequenc... |
731c53bccc0915eea53d95f955e4a55f68b4c8b1 | a17cf22be2304c96d267fc1b68db7b7279c4a293 | /R/fastaTools.R | 1f7844da19bfaf0e69f8a5069f158d97261f0794 | [] | no_license | robertdouglasmorrison/DuffyTools | 25fea20c17b4025e204f6adf56c29b5c0bcdf58f | 35a16dfc3894f6bc69525f60647594c3028eaf93 | refs/heads/master | 2023-06-23T10:09:25.713117 | 2023-06-15T18:09:21 | 2023-06-15T18:09:21 | 156,292,164 | 6 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,386 | r | fastaTools.R | # fastaTools.R -- collection of FASTA file manipulation routines
`loadFasta` <- function( file="file.fasta", mode=c("character","BStrings"), verbose=TRUE, short.desc=TRUE) {
require( Biostrings)
file <- allowCompressedFileName( file)
if (verbose) cat( "\nLoading Fasta file: ", file, "...")
fa <- readBStringS... |
e65e67c66f6001a4a115a91e9c587780f70704ae | 2d34708b03cdf802018f17d0ba150df6772b6897 | /googletranslatev2.auto/man/translate_googleAuthR.Rd | 159024006902bb9f70c5c23ceca890d39e7292ca | [
"MIT"
] | permissive | GVersteeg/autoGoogleAPI | 8b3dda19fae2f012e11b3a18a330a4d0da474921 | f4850822230ef2f5552c9a5f42e397d9ae027a18 | refs/heads/master | 2020-09-28T20:20:58.023495 | 2017-03-05T19:50:39 | 2017-03-05T19:50:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 556 | rd | translate_googleAuthR.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/translate_functions.R
\docType{package}
\name{translate_googleAuthR}
\alias{translate_googleAuthR}
\alias{translate_googleAuthR-package}
\title{Translate API
Translates text from one language to another.}
\description{
Auto-generated code by ... |
516c50695615751fb1cfa5f60f5ac44e2a5887b6 | adf18f1a24e425b74f28badb5408bf9a5bd69789 | /server.R | 12810a6e9fe441bf115efb0229bf4fe899217cf8 | [] | no_license | ksmzn/HOXOM_card | c21de6e76003ea51571b7068ba7189da8f261d4e | a0b3424cab2418cfca104aab74ecb39146e77450 | refs/heads/master | 2021-08-23T10:22:50.290525 | 2017-12-04T14:30:12 | 2017-12-04T14:30:12 | 112,952,723 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,456 | r | server.R | library(shiny)
library(dplyr)
library(magick)
library(glue)
library(jsonlite)
library(longurl)
server <- function(input, output, session) {
observe({
session$sendCustomMessage(type = 'tw_consumer_key',
message = list(key = Sys.getenv("TWITTER_CONSUMER_KEY")))
})
jp_font <-... |
8275031fecfa9a6e3929af7ed0f5f61777579a2a | cc0de06f786eb8ee1e8f1cef3c14a32f988d460b | /plot2.R | a23b61b4429201d0628b06c814562179dddf5a4d | [] | no_license | amiedemmel/ExData_Plotting1 | c4e511ddf851d4394722d34be8700c20a40db52f | 1e24a310a8675156d912c019757439e0d378bc05 | refs/heads/master | 2021-01-20T21:48:23.905972 | 2014-06-07T02:07:40 | 2014-06-07T02:07:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,256 | r | plot2.R | #Plot2.R
#Amie Demmel 6/6/14
#Reconstructs the second plot
#Read in data from already download file, make sure your files matches
file <- "Coursera/exdata-data-household_power_consumption.zip"
#Read as a data.frame and disgard rows that do not match Feb 1 2007 and Feb 2 2007
num <- rep("numeric",7)
classing <- c("... |
15d952c3ab80361f99d8c6e0af019745a8dba6fd | 4f9a9a5ca40d4f1c04ecd303b20fe2c116d3a579 | /Chapter3_DataVisualization/Section3.9/Section3.9.R | a72e2ced2ee1f7cfb2bb61b55297e152f42fe1f4 | [] | no_license | BatmanNeuroGuy/R4DataScience | bae8edc5328376d16679b9e53560ea30aaff984e | 373b05f967ca6088608cdeae8cad9977dacd0d64 | refs/heads/master | 2021-05-17T14:04:47.356576 | 2020-03-31T00:43:13 | 2020-03-31T00:43:13 | 250,811,950 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,036 | r | Section3.9.R | library(tidyverse)
# coord_flip() switches the x and y axes. This is useful (for example),
# if you want horizontal boxplots. It’s also useful for long labels:
# it’s hard to get them to fit without overlapping on the x-axis
ggplot(data = mpg, mapping = aes(x = class, y = hwy)) +
geom_boxplot() # Labels don't fit ... |
0200073fc22ff05d1b5f921608be88f9bf51a660 | a1cd651e71990555e3180f335baa28accbf41ee4 | /Homework 1.r | b4e7f96d86727183ab0bb78e7aac964690767f67 | [] | no_license | juliemattimoe/MSDS-413 | c9d8fe4d0855e66c5f6487ecaad928cfb8173c89 | 46f7c07c91bec21225031838653ff0f5336f2286 | refs/heads/master | 2020-07-06T07:41:47.508790 | 2019-09-06T14:59:22 | 2019-09-06T14:59:22 | 202,943,349 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,690 | r | Homework 1.r |
library(fpp2)
autoplot(hsales)
ggseasonplot(hsales)
ggsubseriesplot(hsales)
gglagplot(hsales)
ggAcf(hsales, lag.max = 400)
autoplot(usdeaths)
ggseasonplot(usdeaths)
ggsubseriesplot(usdeaths)
gglagplot(usdeaths)
ggAcf(usdeaths, lag.max = 60)
autoplot(bricksq)
ggseasonplot(bricksq)
ggsubseriesplot(bricksq)
gglagplot(... |
f3b2b50cb47f8075243fafc87517721fc5ed1da5 | 42886f7b175ea5f5f7c40c5c9cf1ee8d91625598 | /Lab1/Question3.R | 72e2bd93af47d00f7db1d57fe38d2add95f5635d | [] | no_license | janish-parikh/CS-605-Data-Analytics-in-R | 7d8655ea4a08b2f696c89a832c63b659010ff91a | c17be6edf9a1da9dae80fefeb07c85e4a1021942 | refs/heads/master | 2022-12-19T11:45:54.412299 | 2020-09-05T03:25:42 | 2020-09-05T03:25:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 850 | r | Question3.R | #Question-03
dataset1<-c(19, 24, 12, 19, 18, 24, 8, 5, 9, 20, 13, 11, 1, 12, 11, 10, 22, 21, 7, 16,
15, 15, 26, 16, 1, 13, 21, 21, 20, 19)
dataset2<-c(17, 24, 21, 22, 26, 22, 19, 21, 23, 11, 19, 14, 23, 25, 26, 15, 17, 26,
21, 18, 19, 21, 24, 18, 16, 20, 21, 20, 23, 33)
dataset3<-c(56, 52, 13, 34, 33, ... |
eb4c82b2cee2ef470332c3c38872d52bd3bdc9b2 | 0a79d856c32d7f63f4f5df88ff39ab22ebdfe26a | /Global Vaccines.R | 73913d232f215031ad0618e7fa078306c3955c95 | [] | no_license | tiffanguyen/Preventable-Diseases | f4406e69c55496af8d70457b62032f0b3bd2554d | 170d58832bf6732e67a0e62893052b32e8bb3089 | refs/heads/master | 2020-09-09T01:00:28.976347 | 2019-12-12T08:05:42 | 2019-12-12T08:05:42 | 221,296,641 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 873 | r | Global Vaccines.R | setwd("~/Desktop/R Scripts/Preventable-Diseases")
vacs <- read.csv("Vacs.csv")
USA <- vacs[153,]
vacs1 <- vacs[-153,]
randomizer <- data.frame(vacs[sample(nrow(vacs1), 9), ])
newdf <- rbind(randomizer, USA)
library(ggplot2)
v <- ggplot(newdf, aes(x= Entity, y = Share.of.people.who.agree.vaccines.are.important.for.ch... |
f5a899778274bd97f4498ab185ce4efea13702b8 | d45af762e445c3ed93a96f2c52e5750114e72446 | /lecture6.R | 43f13fed9161e2ec850e56c2d781d3500264ae25 | [] | no_license | doublezz10/relearning_stats | fb6c7dbbf53d0bc71d5bcfef75bacd983e9d4f12 | 21b8ef76b97af5c2d039161647c4e25f1eafaf58 | refs/heads/master | 2023-01-07T05:17:21.209504 | 2020-11-09T22:05:07 | 2020-11-09T22:05:07 | 295,513,870 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 314 | r | lecture6.R | library(brms)
library(rstanarm)
data(radon)
formula = log_radon ~ log_uranium + (1 + floor||county)
fit <- brm(formula,radon,family="gaussian")
fit2 <- brm(formula,radon,family="gaussian",prior = c(prior(normal(0,1),class=sd)))
get_prior(formula,radon,family="gaussian",prior = c(prior(normal(0,1),class=sd)))
|
092434135f533b94428a6fa90447e1ea3ae700f0 | a1137535644d2ed3ebeb3b80f5e7dfa7f3458d89 | /inst/templates/udaf.R | 429d5830a466bc878eee45e2fffd8669bc1994df | [
"MIT"
] | permissive | clarkfitzg/RHive | 451800add848136b6ee684519c3d2a34973065ce | c41f12040de70ef5d2bca7a0361909c8c73afe2c | refs/heads/master | 2021-08-23T11:44:22.183865 | 2017-12-04T19:21:25 | 2017-12-04T19:21:25 | 111,032,531 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,252 | r | udaf.R | #!/usr/bin/env Rscript
# {{{gen_time}}}
# Automatically generated from R by RHive version {{{RHive_version}}}
# These values are specific to the analysis
verbose = {{{verbose}}}
rows_per_chunk = {{{rows_per_chunk}}}
cluster_by = {{{cluster_by}}}
sep = {{{sep}}}
input_cols = {{{input_cols}}}
input_classes = {{{input_c... |
d226846324bf28c8752c38fdb6573e8785f58151 | a151b7c4b21a25884a97039166c1b5f0f6aaf159 | /R/messy.R | 405d8dd99b120293bb6009e22682ff1a148177b6 | [
"MIT"
] | permissive | andrewheiss/faux | f6e7d5c77685d6fdaf25e0d53d093004b1f64ef8 | 4ee6b200d7a9456e3bb3e051e2d222c229208052 | refs/heads/master | 2021-01-26T03:37:17.251097 | 2020-02-26T15:41:40 | 2020-02-26T15:41:40 | 243,293,051 | 0 | 0 | MIT | 2020-02-26T15:12:48 | 2020-02-26T15:12:48 | null | UTF-8 | R | false | false | 959 | r | messy.R | #' Simulate missing data
#'
#' Insert NA or another replacement value for some proportion of specified
#' columns to simulate missing data.
#'
#' @param data the tbl
#' @param prop the proportion of data to mess up
#' @param ... the columns to mess up (as a vector of column names or numbers)
#' @param replace the re... |
e778f262155d99b157af0b38f47458ac7079d565 | fe91d12f264a0a993142bc4567f095caff38284f | /visualization.R | abb174a7b13bec3e3f022228f4c64092a9a7b69c | [] | no_license | mcandar/Agents | ca74655dd25fb59890d9c47263e6740116a40569 | 566d7d5d1f96b7d0ae24763868f250afae6f1371 | refs/heads/master | 2020-05-21T23:38:10.796313 | 2017-07-27T11:59:09 | 2017-07-27T11:59:09 | 61,151,060 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 911 | r | visualization.R | RV <- sample(10,100,replace = TRUE) # form a matrix full of random integers
RM <- matrix(0,100,10) # declare and initialize a matrix to fill later, 100 rows, 10 columns
for (i in 1:10){
RM[,i] <- RV*i*i # fill each column with random integers but increase by square at each step
}
init <- 100 # starting point... |
e59c4193c6fc07b2f0ef54d3bbb6942ae0d271f4 | 8222131f45630e4bd8ebe9e7ed4d1a4ddc6e8eb5 | /plot3.R | 13a227880cda453b52db4ba53f169add0c87c13a | [] | no_license | EmilyMazo/ExData_Plotting1 | 4b5e23df67f0f7541d7b00262b9046b50f7fd1b9 | 805546310d4ee4ce1f44d2f3bc0b34d46768e729 | refs/heads/master | 2021-01-17T12:33:19.203519 | 2014-08-10T20:51:33 | 2014-08-10T20:51:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 367 | r | plot3.R | png(file="plot3.png")
with(d, plot(Date, Sub_metering_1, col="black", ylab="Energy sub metering", type="l"))
with(d, points(Date, Sub_metering_2, col="red", type="l"))
with(d, points(Date, Sub_metering_3, col="blue", type="l"))
legend("topright", lty=c(1, 1, 1), col=c("black", "red", "blue"), legend=c("Sub_metering_1",... |
e4921e427afeb814abae4e7b68f7d87240aabb86 | 1dfb6ad8ba481a0842df7acbc181b5c3936158f2 | /man/my.shortterm.Rd | 21613accef3b51b4d0deeb937118dec80a712c04 | [] | no_license | vivienroussez/autoTS | ca1ac409fafec42e62a1cad21bcdd5e6c54e595c | b457c022154753b1b8eeb531c2d8db46ae06cd1c | refs/heads/master | 2021-08-05T20:22:15.779862 | 2020-06-05T12:31:14 | 2020-06-05T12:31:14 | 183,512,754 | 11 | 3 | null | null | null | null | UTF-8 | R | false | true | 1,244 | rd | my.shortterm.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/algos.R
\name{my.shortterm}
\alias{my.shortterm}
\title{Fit short term algorithm and make the prediction}
\usage{
my.shortterm(prepedTS, n_pred, smooth_window = 2)
}
\arguments{
\item{prepedTS}{A list created by the \code{prepare.ts()} functi... |
e414174d0c1fb6c10ab5417bef5f423a232ae1c8 | 14c2f47364f72cec737aed9a6294d2e6954ecb3e | /man/cpmFilter.Rd | aa091293f1d2eeda7648212bff1a0f8059948533 | [] | no_license | bedapub/ribiosNGS | ae7bac0e30eb0662c511cfe791e6d10b167969b0 | a6e1b12a91068f4774a125c539ea2d5ae04b6d7d | refs/heads/master | 2023-08-31T08:22:17.503110 | 2023-08-29T15:26:02 | 2023-08-29T15:26:02 | 253,536,346 | 2 | 3 | null | 2022-04-11T09:36:23 | 2020-04-06T15:18:41 | R | UTF-8 | R | false | true | 288 | rd | cpmFilter.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AllGenerics.R
\name{cpmFilter}
\alias{cpmFilter}
\title{Filter by counts per million (cpm)}
\usage{
cpmFilter(object)
}
\arguments{
\item{object}{An object}
}
\description{
Filter by counts per million (cpm)
}
|
184d3f010d2575374c32e8c35e93ab85d504e5ff | c3cbb5800875d19adffa57e14ba96f631ecc0103 | /app.R | b0ebba0de70cde6af117efe6947febc54c695841 | [] | no_license | Zhu-Daniel/RDatabasePractice | 58b95906838e48c7ad8249693ffde4da5e24b3ee | 1bf4347e6ea03b0359c04ddc2fb4a13d0cdadb33 | refs/heads/master | 2021-07-16T05:34:52.837230 | 2021-01-21T19:44:05 | 2021-01-21T19:44:05 | 232,418,801 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,556 | r | app.R | #
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(dbplyr)
library(dplyr)
library(ggplot2)
library(gridExtra)
mtg <- DBI::dbConnect(
drv ... |
f4279179390790decbc132b01489b219ee42f2ed | e1fe373fd6e8b404ef826069861e12efa88f0eac | /R_code/simulation code version 1/functions/welfare_statistic.R | e4fe1d26648fd510f5ead7933550e4cdb81acc62 | [] | no_license | Nathan-Mather/Heterogeneous-Teacher-Value-Added | e7331c07fdb45d3e1174078ac01e05b0937051ba | e5be646cfe957f74caf4a23511bc0c1626a41a61 | refs/heads/master | 2023-03-31T23:01:37.889489 | 2023-03-21T19:38:05 | 2023-03-21T19:38:05 | 244,950,847 | 0 | 0 | null | 2020-07-01T00:45:02 | 2020-03-04T16:35:05 | TeX | UTF-8 | R | false | false | 16,049 | r | welfare_statistic.R | # =========================================================================== #
# ===================== Calculate the Welfare Statistic ===================== #
# =========================================================================== #
# - Purpose of code:
# - Calculate the welfare statistic for each of our method... |
cb786467a353574725377691714d7c7215358a41 | 57f1e348f411854e949936e2e4af3848be1e98c8 | /Analysis/MATdiversity.r | d46d3507f661d6727deb0bc8d8dc58d83ffabbec | [] | no_license | selmants/bacteria_MAT | 36bfd324e5ef88fe11095b746fd530625f7dc54e | bdb222ec8f625ee7a03f6d3a06c447f5d41a49c0 | refs/heads/master | 2021-01-22T09:09:27.996495 | 2017-06-16T21:39:10 | 2017-06-16T21:39:10 | 40,689,220 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 4,395 | r | MATdiversity.r | #Paul Selmants
#March 31, 2015
##MAT bacterial diversity indices##
#load required packages
library(tidyr)
library(ggplot2)
library(RColorBrewer)
library(dplyr)
#read data into R
obs <- read.delim('observed_species.txt')
chao1 <- read.delim('chao1.txt')
pd <- read.delim('PD_whole_tree.txt')
#Use tidyr to convert OTU r... |
f0bc8f2ada31b77118e5b00f1a5191c49c316192 | cb0a99127d3d2707700206b2a1e0c4cdd8cf871c | /PaternalTransmission/PaternalTransmission.R | 81893d5ee70b257159e23b5c33c1a42aaa24dc0f | [] | no_license | ijwilson/ijwilson.github.io | fa2544cd6c32dd75e27c46ecab6f9b0f9af209dc | 650353cdf746e06edd12978140e44753de094c4a | refs/heads/master | 2021-07-08T21:43:37.383006 | 2021-05-14T10:33:12 | 2021-05-14T10:33:12 | 79,342,980 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,850 | r | PaternalTransmission.R |
sperm_count <-
c(80.295, 77.191, 169.566, 19.369, 136.779, 12.938, 12.514, 44.975,
117.89, 22.329, 22.113, 146.105, 79.137, 95.176, 36.811, 61.604,
89.553, 88.86, 35.404, 99.811, 43.441, 77.926, 62.545, 41.976,
102.642, 151.866, 20.169, 25.997, 17.705, 24.782, 37.28, 89.019,
55.345, 241.77, 198.5... |
373625f16fa77052104397ef3119f9a18ca0f859 | e6a89fb6ae0056bfdc0400219be700fa4c2d419f | /man/Rbyte.Rd | 60033c965df95f1fdab75eca73af0a3591449b93 | [
"MIT"
] | permissive | ramiromagno/c3po | b88c763c7af43897c10867799394dc2a31aea826 | 8f31de2734bf856ced4ce64d19655c01e218598c | refs/heads/master | 2023-02-08T22:23:21.600000 | 2021-01-04T02:32:42 | 2021-01-04T02:32:42 | 259,738,174 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 419 | rd | Rbyte.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Rbyte.R
\name{Rbyte}
\alias{Rbyte}
\title{Rbyte}
\description{
\Sexpr[results=rd, stage=render]{c3po:::badge('typedef-dtype')}
Rbyte is an alias to an \code{unsigned char}.
}
\section{Declaration}{
\preformatted{typedef unsigned char Rbyte;
... |
3fbc210ca07c5e1e311abdb5c97f9cb28dfb1bdb | a1d75e1fb878f2fa43218e78b1361b4f1e125e2d | /Coral-Species-Cluster-PCA.R | a0a71209d8eb2ce8c5a1ed0d902534a3ee281ec1 | [] | no_license | jesslynne73/Machine-Learning | 4075f5ade1887c12d49936071107d66734309ef6 | ed7a6bd487967c73a653bd2d0cea6fd96725b745 | refs/heads/main | 2023-07-09T02:55:52.610753 | 2021-08-05T21:08:07 | 2021-08-05T21:08:07 | 329,099,558 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,813 | r | Coral-Species-Cluster-PCA.R | # Coral Species Unsupervised Learning
# Author: Jess Strait
# Clear environment and load packages
rm(list = ls())
library(data.table)
library(Rtsne)
library(ggplot2)
library(caret)
library(ClusterR)
library(cluster)
library(mlr)
# Intake data
data <- fread("speciesdata.csv")
# Save IDs
id_vector <- ... |
7b8c0b38928fb6a5d48745403fbf1d4f364a9a5a | dd90b0c9d116be4983a732bbcd46bb11c15101e3 | /R/hello.R | e71de7d5e9190505b7a681de9f6c83cc142cdab9 | [] | no_license | gaurav6351/R-Testing | ad34f086495779ed98dc5d258f73903d743af857 | ab8309ea7d2decbb0e199ab2e5db36a5cc7d0e91 | refs/heads/master | 2020-05-25T12:48:52.042791 | 2017-03-01T17:48:35 | 2017-03-01T17:48:35 | 83,583,325 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,732 | r | hello.R | # Hello, world!
#
# This is an example function named 'hello'
# which prints 'Hello, world!'.
#
# You can learn more about package authoring with RStudio at:
#
# http://r-pkgs.had.co.nz/
#
# Some useful keyboard shortcuts for package authoring:
#
# Build and Reload Package: 'Ctrl + Shift + B'
# Check Package: ... |
1d4acaed1afdb59a0554553c2767c87bbac37b29 | 570d4141186786df5179cc4346dd3808c1c41f26 | /plots/pres/2018-08-20/protcod.R | 5de88dfbccb575d8f47d7089e1becfc3787a16d4 | [
"MIT"
] | permissive | ArtemSokolov/amp-ad | 552fee92c0ec30539386745210f5ed2292931144 | dd5038f2497698b56a09471c89bb710329d3ef42 | refs/heads/master | 2021-06-21T21:04:44.368314 | 2019-09-10T17:40:48 | 2019-09-10T17:40:48 | 114,150,614 | 0 | 4 | MIT | 2019-09-10T17:40:49 | 2017-12-13T17:39:02 | HTML | UTF-8 | R | false | false | 1,656 | r | protcod.R | ## Plots showing the effect of reducing to protein-coding regions
##
## by Artem Sokolov
source( "api.R" )
synapseLogin()
## Identifies the IDs of all relevant
idsBK <- function()
{
structure(list(Dataset = c("ROSMAPpc", "ROSMAPpc", "ROSMAPpc", "ROSMAP", "ROSMAP", "ROSMAP"),
Task = c("AB", "AC"... |
4eff3ee8450f139a96a5d97bebf2786fe265561e | 794863d2e9e26424a04079a91c3a23063bdb4f8e | /man/ElasticNetVAR.Rd | 93b7006e36bb5ed3a69a7accad539c83ccc7d81b | [] | no_license | GabauerDavid/ConnectednessApproach | ef768e64e0bc458ad180bac6b667b3fe5662f01d | 0ca4799a2f5aa68fdd2c4a3e8a2e0e687d0a9b17 | refs/heads/main | 2023-08-09T07:23:45.002713 | 2023-07-27T22:57:04 | 2023-07-27T22:57:04 | 474,462,772 | 47 | 20 | null | 2023-03-12T04:22:26 | 2022-03-26T20:47:15 | R | UTF-8 | R | false | true | 1,882 | rd | ElasticNetVAR.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ElasticNetVAR.R
\name{ElasticNetVAR}
\alias{ElasticNetVAR}
\title{Elastic Net vector autoregression}
\usage{
ElasticNetVAR(
x,
configuration = list(nlag = 1, nfolds = 10, loss = "mae", alpha = NULL, n_alpha = 10)
)
}
\arguments{
\item{x}{... |
1a3b6698e6ce16938e9e11a90e92c5fa767e5546 | 9fba17b8a66b625e5bb609e78e65a631706d43bc | /testing-section/Classification-testing/part9-knn/knn.R | aff547397608402f84c9334d34e1eca756ba7abf | [] | no_license | irekizea/machine-learning-learning | 70df74e25b19be82eae5f3f5c0ab5337ba698bd3 | 0ac486b562df445f3dc5bc0bfe12b4dce4db199e | refs/heads/master | 2020-12-18T10:16:23.076138 | 2020-01-21T07:43:00 | 2020-01-21T07:43:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,662 | r | knn.R | # Get the dataset
dataset <- read.csv('/home/felipe/Documentos/Machine Learning A-Z/Part 3 - Classification/Section 14 - Logistic Regression/Social_Network_Ads.csv')
dataset <- dataset[3:ncol(dataset)]
# Feature scaling
dataset[, 1:(ncol(dataset) - 1)] <- scale(apply(dataset[, 1:(ncol(dataset) - 1)], 2, as.numeric))
... |
87708e706c38907efdaa1fd18460f2c41699f508 | 8a97255cb66455dbef0cf01864a3b334cf20a66b | /karma_ML_Ensemble/SavingDBDump.R | 7d32865732d41c474007a3dff6e1fac586abf14a | [] | no_license | AshutoshAgrahari/R_Practice | c56bbb3c0893e101305f150c0b74045f24cf5a44 | 4c31ce94f130b363f894177a1505ccac290547e0 | refs/heads/master | 2020-03-19T17:51:05.826260 | 2020-01-25T10:34:55 | 2020-01-25T10:34:55 | 136,781,266 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 508 | r | SavingDBDump.R |
# load the library
library(data.table)
# Create DBDataDump Folder if not exist in KARMA project dir.
if(!dir.exists("DBDataDump")){
dir.create("DBDataDump")
}
# read the each cut file as MFF
MFF <- fread(file.choose(),header = T,stringsAsFactors = F,data.table = F)
# save MFF file as Country_Category.RData file i... |
54f299dbf6f97a3d541638633045daaaad78ee1d | 689fbe653cd7315d760976f4bf69ab3a8820dc3b | /man/bigtps.Rd | 35c2491c0775f999afa1688444ceedb95d4e68d0 | [] | no_license | cran/bigsplines | deb579728270353a375dc589bede1dfbd75cfb98 | 9ddb95e9af0852fa80c6b5a670b702acb1859e01 | refs/heads/master | 2020-12-24T07:42:06.129006 | 2018-05-25T05:47:54 | 2018-05-25T05:47:54 | 18,805,082 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,886 | rd | bigtps.Rd | \name{bigtps}
\alias{bigtps}
\title{
Fits Cubic Thin-Plate Splines
}
\description{
Given a real-valued response vector \eqn{\mathbf{y}=\{y_{i}\}_{n\times1}}, a thin-plate spline model has the form \deqn{y_{i}=\eta(\mathbf{x}_{i})+e_{i}} where \eqn{y_{i}} is the \eqn{i}-th observation's respone, \eqn{\mathbf{x}_{i}=(x_{... |
8fe263c57642bce779a81e82608cf0b5803e4a65 | 8b92eaf5c51a4d2bedff8ec9a13408f32d883fff | /loaddata.R | 2c3caac44a7617dfbd1107f575428a6b1673a892 | [] | no_license | rajpradhan/ExData_Plotting1 | 1c515b3325ae9ebfeb1d313e42e09a489124066e | 34956aa564903f9c0804a50f030a66634a9d758c | refs/heads/master | 2020-12-11T03:19:41.452166 | 2014-10-12T14:32:51 | 2014-10-12T14:32:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 768 | r | loaddata.R | loaddata <- function() {
txtfile <- "household_power_consumption.txt"
url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
zipfile <- "exdata-data-household_power_consumption.zip"
if(!file.exists(zipfile))
download.file(url, zipfile)
if(!file.exists(txtfile))
... |
73263e68e324768f822a6563e0c7488c4db119d1 | 683301d27b28e6ec33d8a2a5b077cd0dbe955a86 | /R/pdftool.R | eff1f81af97339334e739456e4f6368a31f429d2 | [] | no_license | ekanshbari/R-programs | 5b0e1ded02f49c108f5ef61b78c06c1c9ecfab96 | 7c267cde520ae07ded2a51e9f82c0fda557b5d5e | refs/heads/master | 2020-12-12T12:10:03.872677 | 2020-01-15T17:01:48 | 2020-01-15T17:01:48 | 234,125,146 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,327 | r | pdftool.R | # text mining by pdftool ie to read read the pdf file
# to find the frequency of words in the given dataset
#
#
#
# https://www.supremecourt.gov/opnions/slipopnion/14
#
#
#
# install all the packages of library
library(pdftools)
library(wordcloud)
library(tm) #for text mining for removing whit... |
443ac9a606bcdba879cd273959746a91e212d118 | 8474e5591c6e2564895bde0522424f7cb60c90d1 | /data-raw/create_package.R | 6ea41bdc7b752faa428ccdaf595990b13f18dc95 | [] | no_license | ajpatel2007/methylSig | 398504ffe01d51c806098ee9da2751e09d260f65 | cb469678e2e4b5c3569d0927675d698dbe0f8f01 | refs/heads/master | 2022-04-14T04:20:20.587995 | 2020-03-25T18:38:33 | 2020-03-25T18:38:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,780 | r | create_package.R | # docker run --interactive --tty --rm --volume /Users/rcavalca/Projects:/Projects rcavalcante/bioconductor_docker:RELEASE_3_10
library(devtools)
# Description fields
description = list(
Title = 'MethylSig: Differential Methylation Testing for WGBS and RRBS Data',
Version = '0.99.0',
Date = '2020-02-28',
... |
728df9ac33bce503c171f5b0ecc1015ee8d26e68 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/agridat/examples/sinclair.clover.Rd.R | 6162bc751bd35ddcdbaa6d9e13e328cec0e8396c | [] | 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 | 2,403 | r | sinclair.clover.Rd.R | library(agridat)
### Name: sinclair.clover
### Title: Clover yields in a factorial fertilizer experiment
### Aliases: sinclair.clover
### Keywords: datasets
### ** Examples
data(sinclair.clover)
dat <- sinclair.clover
require(lattice)
xyplot(yield~P|factor(S), dat, layout=c(5,1),
main="sinclair.clover - Yi... |
cd38298ddcb84c4fe75f1947733091359b23d1fc | 22ffb3e36696096af9e785ee169f78d0e09b0cdb | /server_side/Rscripts/addIndexAttributeToNetwork.R | 59cd1ed8defa5f3ac71750b29f7abfe135d1c1a2 | [
"MIT"
] | permissive | ggirelli/tema | 49c6ec0dd63e7dcfd6ac2d3be252d2869346c431 | beeb3aff3ad47a8027ab5b4a425875702b3a7c0f | refs/heads/master | 2021-01-19T07:36:20.821428 | 2015-06-25T15:25:50 | 2015-06-25T15:25:50 | 27,998,620 | 1 | 0 | null | 2015-02-26T10:37:35 | 2014-12-14T15:34:37 | PHP | UTF-8 | R | false | false | 1,528 | r | addIndexAttributeToNetwork.R | #!/usr/bin/env Rscript
options(echo=TRUE)
args <- commandArgs(trailingOnly = TRUE)
# Check parameters
if(length(args) != 4) stop('./addIndexAttributeToNetwork.R session_id graph_name attr_name attr_index')
# Load requirements
library(igraph)
library(rjson)
source('./Graph_Manager.class.R')
nm <- GraphManager()
# S... |
91e0f5293cc08ae161ec3a3c5733b4811d00d199 | b313ba13c1156ccb088c4de6327a794117adc4cc | /AlanAnalysis/DefunctScripts/AB_phasing/makeHarpPrior.R | ef72c7ae07e5d589b14951ee5fcdd3e27af969e6 | [] | no_license | kbkubow/DaphniaPulex20162017Sequencing | 50c921f3c3e8f077d49ccb3417daa76fb4dde698 | c662b0900cc87a64ec43e765246c750be0830f77 | refs/heads/master | 2021-08-20T10:09:59.483110 | 2021-06-10T20:04:31 | 2021-06-10T20:04:31 | 182,109,481 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,649 | r | makeHarpPrior.R | #ijob -c1 -p standard -A berglandlab
#module load gcc/7.1.0 openmpi/3.1.4 R/3.6.0; R
library(data.table)
library(foreach)
dat <- fread("/scratch/aob2x/daphnia_hwe_sims/trioPhase/testTrio.consensus.header.phase.csv")
setnames(dat, c(1,2), c("chr", "pos"))
dat <- dat[!(A=="1/1" & B=="1/1")][!(A=="0/0" & B=="0/0")]
... |
75c4c826286f9be9289fc2e7ce3f430898c2e4ec | b30004a400b47aa21fb202a894c9a1365def53fb | /tests/testthat/test-predict-model.R | b11987371a84821366e7fe658c7b15e4d57ab34d | [] | no_license | cran/disaggregation | cfa317ff8d46c14cde349279ce9a26129d5a7cfb | 173a822b0ae14c718fec55791b6ddad168b40523 | refs/heads/master | 2023-05-11T08:30:27.362030 | 2023-04-28T20:10:12 | 2023-04-28T20:10:12 | 236,584,779 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,885 | r | test-predict-model.R | context("Predict model")
polygons <- list()
n_polygon_per_side <- 10
n_polygons <- n_polygon_per_side * n_polygon_per_side
n_pixels_per_side <- n_polygon_per_side * 2
for(i in 1:n_polygons) {
row <- ceiling(i/n_polygon_per_side)
col <- ifelse(i %% n_polygon_per_side != 0, i %% n_polygon_per_side, n_polyg... |
fe291b015bb94aeef07206a7fe100dd55abe3bf4 | b4f6e5965646758d264a9702734ea7929c8e009b | /R/geom_image.R | 591407685b60cde7d16cb2f8c642b37d7ab4076f | [] | no_license | tonyelhabr/tonythemes | ec1fbc38007685fff66c95977c69cf192f632fa8 | ae7e190f7cecdcfe51c413d980c206ac86bd5fac | refs/heads/master | 2023-05-31T22:09:11.351206 | 2021-06-13T21:16:41 | 2021-06-13T21:16:41 | 372,654,639 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 579 | r | geom_image.R |
#' @importFrom ggimage geom_image
#' @importFrom ggplot2 annotate
annotate_img <- function(..., .name) {
path <- system.file('extdata', sprintf('%s.png', .name), package = 'tonythemes', mustWork = TRUE)
list(
ggimage::geom_image(
image = path,
...
)
)
}
#' Annotate Nick Wan
#'
#' Annotate Ni... |
8aa721fdb97907ea731252e0f707c74c307b1bc4 | fde40765438f8e1e70d8623a4ed0eb7fee6f7e8b | /R/corpus_toolbox.R | 37b91d505ad1530e75606a601debca81cd29d3c1 | [
"MIT"
] | permissive | yjunechoe/junebug | 381ed27647f0d215d01fa002a6a745c6eddb85d6 | cb4db89df273fc689d98db2327e6478361ff3e32 | refs/heads/master | 2023-06-14T21:40:21.937869 | 2021-07-08T16:56:34 | 2021-07-08T16:56:34 | 312,421,966 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 445 | r | corpus_toolbox.R | #' Reconstruct utterances from observations of tokens in tidy format
#'
#' @param data A data frame of tokens
#' @param token_col Name of column of tokens
#' @param ... Grouping variables
#'
#' @return A data frame of grouping variables and utterances
#' @export
tokens_flatten <- function(data, token_col, ...) {
data... |
755d4800b2d132b9a42b775555b5824a03dd08d2 | 33efeec39033156d7b598f8989f82fcf810db812 | /man/query_pa_dist.Rd | 8cff4bc6464e24ec2aadf81e1bcd0c2eb1357dbd | [] | no_license | johnchower/oneD7 | 76b4712de0bb89fa70246880b69d7c9a1d90a7fa | 0ffcf86db58ddbe80330ac5185a7fc14c355545e | refs/heads/master | 2021-01-11T20:39:07.943924 | 2017-03-08T23:11:30 | 2017-03-08T23:11:30 | 79,161,189 | 0 | 0 | null | 2017-02-23T19:02:14 | 2017-01-16T21:28:54 | R | UTF-8 | R | false | true | 373 | rd | query_pa_dist.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data_doc.r
\docType{data}
\name{query_pa_dist}
\alias{query_pa_dist}
\title{A string containing the platform action distribution query}
\format{A length-one character vector.}
\usage{
query_pa_dist
}
\description{
A string containing the plat... |
6a1e150ab0db78eaaa0e25027b90f83f43ec699a | e55d1f014e98ad5bab2aad1a8d1322d66a1a93d4 | /cachematrix.R | 0781f66fe688f80ac116a59d959e42906c1f1cab | [] | no_license | biffster/ProgrammingAssignment2 | c7d175b83323ede7a5fb8e565a9d0aa4e6c38b29 | cba1676c6a8a7bc0b5f631630f059e02ee6f620b | refs/heads/master | 2021-01-18T11:08:08.485668 | 2015-08-23T17:50:31 | 2015-08-23T17:50:31 | 41,227,161 | 0 | 0 | null | 2015-08-22T22:47:43 | 2015-08-22T22:47:43 | null | UTF-8 | R | false | false | 1,713 | r | cachematrix.R | ## Programming Assignment 2
## Michael Fierro
## August 23, 2015
## This assignment solution contains major sections of code from the example
## given in the assignment description examples at:
## https://class.coursera.org/rprog-031/human_grading/view/courses/975105/assessments/3/submissions
## The following two fu... |
088a139aad29c92c3fec367121e3180a9bccd465 | 271abfff6e1066408334e5e5f633620c50ee6a81 | /plot3.R | 9ebeb97494f5da33c838fa9f71cc81c547c1b509 | [] | no_license | lzyempire/ExData_Plotting1 | abf160709b261388ed3786d00cb637a9483f16e1 | f6d7aaa06f2eabd35d85eeb03b23a98297a58e97 | refs/heads/master | 2020-03-25T18:15:55.426533 | 2018-08-20T14:06:28 | 2018-08-20T14:06:28 | 144,021,176 | 0 | 0 | null | 2018-08-08T13:56:09 | 2018-08-08T13:56:08 | null | UTF-8 | R | false | false | 947 | r | plot3.R | hpc <- read.table("household_power_consumption.txt", header = TRUE, sep = ";", stringsAsFactors = FALSE)
##The file was downloaded to working direction, all variables setted to Characters rather than Factors.
hpc_day <- subset(hpc, hpc$Date == "2/2/2007"|hpc$Date == "1/2/2007")
##Set Date as character, then subset... |
c567128c6f29311978e846d995cef449c017d20e | 60d40635d000c7a7ef0b8774da34ab3c29d6502e | /misc/visu/draw_histogram_wait_fromshell.R | 7728dbc9bb74dcbe18ec696ed76cc30ab91bd904 | [] | no_license | obps/obps | 8d6ce068ab5b802937ad6b8105367703105e4ed5 | 01df6619cc3d96fe821a6650979fa9f8031e9bdb | refs/heads/master | 2020-12-31T06:47:01.030245 | 2017-03-31T07:57:56 | 2017-03-31T07:57:56 | 86,603,881 | 1 | 0 | null | 2017-03-29T16:16:07 | 2017-03-29T16:16:07 | null | UTF-8 | R | false | false | 1,539 | r | draw_histogram_wait_fromshell.R | #!/usr/bin/env Rscript
library(docopt)
library(ggplot2)
'usage: tool.R <input> ... [--mean=<output1>] [--max=<output2>]
tool.R -h | --help
options:
<input> The input data.
--mean=<output> Output file [Default: tmp1.pdf]
--max=<output> Output file [Default: tmp2.... |
202e069ad531f9e15e92ef46438e2f061a6666cc | 686388fce3a84c98f52d5b934a2309aa93b9e0fb | /cachematrix.R | c186a3098b1a48e335e68f2c36ae4a9412e1d44f | [] | no_license | jwu125/ProgrammingAssignment2 | 861c5e4d8e3a20dc67d577518e665649a164a134 | 22da0c246df1942de5a994aedfab4f40282e813f | refs/heads/master | 2023-08-31T18:48:55.616253 | 2021-09-20T12:06:36 | 2021-09-20T12:06:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 945 | r | cachematrix.R | ## These functions are designed to cache potentially time-consuming computations.
## They will use cache to give already calculated data
## rather then recalculating it.
## This function creates a special matrix object that can cache its inverse.
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- ... |
32b38e77c604797e4d2863876ccca2a7b35fdec1 | 50c137bee0fa6a4d6a1172eee9a03352f86b823f | /powers-master/man/boxcox.Rd | ffb74e7c5e4877bd7c7a5138f06f5514546ec727 | [] | no_license | STAT545-UBC-hw-2018-19/hw07-divita95 | 80ba6f5a45bc11eb08642d0187fbfd1eae2458a4 | c74adba4f1639fa8d72218a60a0201173e53b47b | refs/heads/master | 2020-04-05T22:09:13.535294 | 2018-11-15T20:54:35 | 2018-11-15T20:54:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 483 | rd | boxcox.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/newfunc.R
\name{boxcox}
\alias{boxcox}
\title{Apply Box-Cox Power Transformations}
\usage{
boxcox(x, p)
}
\arguments{
\item{x}{numeric vector to transform, p power (0 = log); if p is a vector then a matrix of transformed values with columns l... |
8d2ff39b21bc3d4f0ecabb043c96a5aea2700b9a | 5e65f58f231b331ba0cddb512398e39cda3a9a67 | /mathematical_programming_research_methods/Assignment2/codes/Exercise1/choose_centroids.R | 07c2a3cf328b69bd0b7a118b2d42be3c5fcc7668 | [] | no_license | cwkprojects/myprojects | 719644297fbf8c9269f9e3e440be9988a859df57 | 0bed4cd790cf4e4fa18d4683afadfee400ab7b33 | refs/heads/master | 2021-06-20T20:57:39.324961 | 2017-08-02T18:56:32 | 2017-08-02T18:56:32 | 98,444,604 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 922 | r | choose_centroids.R | randCentroids <- function(X, n_centroids){
# Return n_centroids amount of centroids from the Data Frame X
# initialize an empty matrix
centroids <- matrix(0, n_centroids, ncol(X))
# create as many centroids as clusters we want to have in our algorithm
# for(i in 1:n_centroids){
# # initialize the ra... |
09579c9588771105307da11753b40bb6f7c35c04 | 93912bf1f51b016e68d29d436d60c3d91314a3fc | /rProgrammingWeek2.R | 4ec558898e5fe6319d078265cda217876ea50a07 | [] | no_license | dslab123/coursera_datasciencebasic | 8d52e6232b5f112c1197c1acdf909207293ad100 | 0d45eea1ba44c6e5062d6a346b4d05cf0ca2000a | refs/heads/master | 2021-01-22T08:27:46.430858 | 2016-08-18T22:27:25 | 2016-08-18T22:27:25 | 92,615,417 | 0 | 0 | null | 2017-05-27T18:02:47 | 2017-05-27T18:02:47 | null | UTF-8 | R | false | false | 2,170 | r | rProgrammingWeek2.R | #directory = /Users/mooncalf/Dropbox/skb/coursera/datasciencecoursera/specdata/
pollutantmean <- function(directory = '/Users/mooncalf/Dropbox/skb/coursera/datasciencecoursera/specdata/', pollutant = 'nitrate', id = 1:332){
pollutantVector = c()
for (i in id){
if (i < 10){
padding <- "00"
}else... |
ebf6ad1857fdb2abc9ab555a81c38982a23a6385 | 5910d75f4cc3255195bfa5b3edb4cdbbcd982ddb | /tests/testthat/test-plots_APCsurface.R | 3fa61bcc293b560010b7af5301509c6303b987be | [
"MIT"
] | permissive | bauer-alex/APCtools | fddbdb15f20c20af07161c6c660fcf807937b4d3 | f0a1b188007a45dbe3aaba16fae61df2b6faf311 | refs/heads/main | 2023-09-01T15:49:33.593873 | 2023-08-29T07:57:07 | 2023-08-29T07:57:07 | 430,766,605 | 20 | 3 | MIT | 2022-04-26T15:02:38 | 2021-11-22T15:40:42 | R | UTF-8 | R | false | false | 3,278 | r | test-plots_APCsurface.R |
test_that("plot_APCheatmap", {
testthat::skip_if_not_installed("mgcv")
data(drug_deaths)
# plot hexamap of observed data
gg1 <- plot_APCheatmap(dat = drug_deaths, y_var = "mortality_rate")
gg2 <- plot_APCheatmap(dat = drug_deaths, y_var = "mortality_rate",
bin_heatmap = F... |
82689b6c13f7e9e27c073cb432d59e2bda3169e7 | b9cd4adc1809f1c34fb96598746ab68eb2411459 | /R/mcmc-MCMC Builder and Step Sampler.R | 89648040a8e0cd1f8066d19e29464437c3b360ad | [] | no_license | GBarnsley/BinomialEpidemicsNimble | 693c46597639147f9b4062f334ed5e65532cb194 | 2eac5b36e43a910ec6485f23808e3cec5bb3d5a6 | refs/heads/master | 2023-04-02T17:29:45.959734 | 2021-01-05T14:34:14 | 2021-01-05T14:34:14 | 292,693,073 | 0 | 0 | null | 2021-01-05T14:34:15 | 2020-09-03T22:28:19 | R | UTF-8 | R | false | false | 697 | r | mcmc-MCMC Builder and Step Sampler.R | #' Generic function that calls the model specific MCMC set-up methods.
#' The naive version of the NpmDelta Algorithm is generated here.
#' @param epiModel An object of the class one of the specific epidemic model
#' @param hyperParameters A list of lists of the hyper-parameters for the epidemic model and MCMC
#' @... |
00dd8e180db23b00b00fc05c12e657d9a3a66d2f | 80f01d7fa984d7bf1150846f4282aa036f56129b | /run_analysis.R | a3a4363acdf0e2096cefc2450d3fc08995a71503 | [] | no_license | SP10000/Getting_and_Cleaning_Data | e2ce2d43885d2255d4fa555b9b19f1e4b2ec95e7 | fd713abfd6aa53bc56e3c0eec7fe72b210630e91 | refs/heads/master | 2020-03-18T16:46:17.825892 | 2018-05-27T09:43:42 | 2018-05-27T09:43:42 | 134,985,611 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,982 | r | run_analysis.R | ### Activity 1: Merge the training and the test sets to create one data set ###
### First the datafiles should be downloaded ###
fileDirectory <- "C://Users//648700//Desktop//Coursera"
setwd(fileDirectory)
if(!file.exists("./datapacks")){dir.create("./datapacks")}
FileUrl <- "https://d396qusza40orc.cloudfront.ne... |
cd0ddd35956ccc41e45c6b6b044d4ebfa2eab05b | da0221ddcae8b085bd8a39ff50842510049d21ed | /R/coverage.r | 2e7219e5f7369b7887ac8c281accebe132fa414b | [] | no_license | psmits/cosmo_prov | 806538919d62ef10e2e06ed2360eed9a5ed39d4f | fd2cd5344a4ca8d6d22eae630acfb6896e2aa531 | refs/heads/master | 2020-04-11T08:05:33.360722 | 2016-08-15T18:31:11 | 2016-08-15T18:31:11 | 12,221,765 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 536 | r | coverage.r | #' Good's estimate of frequency coverage
#'
#' Coverage is an estimator of the amount of observed diversity of some
#' set of categorical variables. For example, given a distribution of
#' taxonomic abundances, it is possible to determine how much of the possible
#' set has been sampled.
#'
#' @param ab table of tot... |
becc301a1152c5d463b64830cbaac8c74be2509e | 7505da6d4b338f172cac1af24d692302d42be6bc | /man/NLWrapper.Run.Rd | 5e0b7044ea4087f79964140f15cb543e54959efc | [
"MIT"
] | permissive | antonio-pgarcia/evoper | 367da295fd704bbde96370c990b8be56d70879b5 | 5337eb8917ed851ffb5f916023d08de12bf281d1 | refs/heads/master | 2021-01-19T04:18:37.948801 | 2020-08-30T10:25:53 | 2020-08-30T10:25:53 | 61,146,979 | 6 | 1 | null | null | null | null | UTF-8 | R | false | true | 418 | rd | NLWrapper.Run.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/netlogo-helper.R
\name{NLWrapper.Run}
\alias{NLWrapper.Run}
\title{NLWrapper.Run}
\usage{
NLWrapper.Run(obj, r = 1, seed = c())
}
\arguments{
\item{obj}{The object retuned by \link{NLWrapper.Model}}
\item{r}{The number of replications}
\ite... |
b9c02fd14b8485e9cfecdd91bd048bd6a16c18f7 | adb379ba53e72cffe0efcd091a9e06e729717bc7 | /tests/testthat/test_track_interpolation.R | 748a01991a8424b4d73148cf472b3f72b8b28aad | [] | no_license | geanders/stormwindmodel | e7edaedeee9ecbafb7695a89e899ac423e71dea0 | 0b0a2906e729d448f3da17ef16612ea461206c0a | refs/heads/master | 2022-10-08T17:52:09.271107 | 2022-09-20T17:50:05 | 2022-09-20T17:50:05 | 56,884,201 | 22 | 13 | null | 2021-05-17T20:06:44 | 2016-04-22T20:45:21 | R | UTF-8 | R | false | false | 5,995 | r | test_track_interpolation.R | library(tidyverse)
test_that("Interpolation works with North Atlantic storm", {
# Floyd
interp_track_floyd <- create_full_track(stormwindmodel::floyd_tracks[34:40, ],
tint = 3)
# Expectations are from 3-hourly IBTrACS data (expect long -77.49, which
# IBTRaCS interpo... |
27dd65fc9cfbba76eac8ac14cd332aa6cad2da44 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/sppmix/examples/plot_MPP_probs.Rd.R | 343e0252d57fe4e6f70c52a1cacde917a60c71ba | [] | 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 | 413 | r | plot_MPP_probs.Rd.R | library(sppmix)
### Name: plot_MPP_probs
### Title: Plot the mark probabilities of a marked point pattern
### Aliases: plot_MPP_probs
### ** Examples
## No test:
newMPP=rMIPPP_cond_loc(gammas=c(.1,.2,.5))
plot(newMPP$surf,main="True IPPP intensity surface for the locations")
genMPP=newMPP$genMPP
newMPP$r
mpp_est <... |
4919ca116de28c3dacfca699e98993083a100e5a | 811399b99a474b2247fe31a29daee5a50d221077 | /general_r/parallel_ADA.R | ea62de1071c3aeb150d73e1d66f597c5ebc2d122 | [] | no_license | peterwu19881230/R_Utility | b528d152f12a81a8a7d6baa3418a8e0d22b8b45b | 1ea244ad3c711ddf71f65bb2fdb168996b68c470 | refs/heads/master | 2020-08-15T16:08:09.833905 | 2019-10-15T18:30:53 | 2019-10-15T18:30:53 | 215,368,892 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,944 | r | parallel_ADA.R | #Parallel programming tested on ADA
#ssh peterwu19881230@ada.tamu.edu
#module load R/3.5.0-iomkl-2017b-recommended-mt
##Somthing below cause error on ADA (and will run for a very long time)
##if(!require(pacman)){
## install.packages("pacman")
## library(pacman)
##}
##pacman::p_load(tidyverse,xlsx,factoextra,pheatm... |
57bc4b87e99316a7eb13d9d89b877a16bbe3d329 | e0c17401fbfb1f581e3eaab2a4cb27c6283c2dff | /plot1.R | 26885aea74e3ceb7c3cca3400b1c072fa1403e0d | [] | no_license | christopherskyi/ExData_Plotting1 | b756eb85be5973e6564d096fac13bf01e4657ab3 | a501028c7446d8edbad31db1adc2a1541e3e0134 | refs/heads/master | 2021-01-16T20:49:09.690163 | 2015-08-09T19:34:16 | 2015-08-09T19:34:16 | 40,267,850 | 0 | 0 | null | 2015-08-05T20:40:36 | 2015-08-05T20:40:36 | null | UTF-8 | R | false | false | 2,926 | r | plot1.R | # to avoid bugs when using both plyr and dplyr you should load plyr before dplyr.
library(lubridate)
library(dplyr)
library(data.table)
#############################################################################
# How to use this script:
#
# 1st) Create a folder called 'Data' in the same folder as this R script... |
4a23d64638cc7dd76a78ae7dc2078a0ecc293712 | 63e5fc70d2e6233457fc9ad407d7e4984bfc8997 | /man/get_carbon_increment.Rd | 5763edcac4d31f60cb134928f291cb4867114dd7 | [] | no_license | Boffiro/hisafer | 32f648f6aca222d01006d25da1b237846b13113e | 8773fe3d5d2aa6d307af0088a6f6e79cc9a087d0 | refs/heads/master | 2023-05-06T17:53:22.060974 | 2020-10-16T09:48:48 | 2020-10-16T09:48:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 475 | rd | get_carbon_increment.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cycles.R
\name{get_carbon_increment}
\alias{get_carbon_increment}
\title{Get tree carbon increment from a hop object}
\usage{
get_carbon_increment(hop)
}
\arguments{
\item{hop}{An object of class hop or face.}
}
\value{
A tibble with extracte... |
6fd234d01e4d765eb30dff63e0569e5e5ff0fb3c | cff7a73825a6405ecb2b667beb4c607ed3358508 | /thesis/sketchbook.R | e01298a791887d3d4e0a89211b4749fb75de202e | [] | no_license | kmatusz/mgr | cc308a362d19bf1855bd7b346f161ac9c486dec1 | 40fa62b1834ae9228e5919b953e30899dc43fad5 | refs/heads/master | 2023-07-07T01:44:45.197192 | 2021-08-12T21:07:40 | 2021-08-12T21:07:40 | 246,847,687 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,517 | r | sketchbook.R | plot_map <- function(variable, title){
print(1)
tm_shape(brazil_map) +
tm_borders()+
tm_shape(stats_per_microregion3) +
tm_polygons(col = variable,border.alpha = 0) +
tm_shape(brasil_cities_coords %>% arrange(-population) %>% head(10)) +
tm_symbols(size = 0.2,
col = "black... |
6a31526bac28acb10269fcbcb50b430dbbfede7b | 61c091c21d06b7c61f35a24d4fe3d8882e9fb254 | /man/wh_plot_proportion.Rd | 3bb28e0ccdf9fcd9acbbf28f8c4a05bca131c2a9 | [] | no_license | pfmc-assessments/nwfscSurvey | b3be76b410bdc5dae168e84d2ee1a2c64c98e098 | 423800ecb91137cba1587ac19226a3ebb8d50c2d | refs/heads/main | 2023-07-28T08:35:55.810331 | 2023-07-20T17:10:25 | 2023-07-20T18:17:33 | 26,344,817 | 4 | 2 | null | 2023-07-20T17:31:58 | 2014-11-08T00:38:17 | R | UTF-8 | R | false | true | 1,839 | rd | wh_plot_proportion.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_proportion.R
\name{wh_plot_proportion}
\alias{wh_plot_proportion}
\title{Save figures of proportions by depth and latitude using warehouse data}
\usage{
wh_plot_proportion(
data_catch,
data_bio,
dir = file.path(getwd(), "plots"),
... |
dd58774e37a5f69ff7fbaf5e4ec449744e3894d7 | 962859409041bdfede9dcb1db6baa281ba6021c1 | /functions_for_solving.R | 9555d97acac3b7235f9baef7da343c2c1bb503a9 | [] | no_license | bryla121/Efficiency-of-methods-of-solving-Rubik-s-cube-implemented-in-R | aeb7306025b61b381c3403949775f2258821f7aa | 2f346c1f438405930ff8b3dec3bde7e3774b428a | refs/heads/main | 2023-02-10T04:16:45.121501 | 2021-01-13T00:46:33 | 2021-01-13T00:46:33 | 326,741,932 | 0 | 0 | null | null | null | null | WINDOWS-1250 | R | false | false | 140,548 | r | functions_for_solving.R | library("cubing", lib.loc="~/R/win-library/3.6")
WR_cross <- function(x,c) ###WHITE-RED EDGE
{
if ( x$ep["UR"]== 5) #1
{
if (x$eo["UR"]==0)
{
x <- x
c <- c
# dobre miejsce i dobra orientacja
}
else
{
x <- move(x, ... |
12aa75f60c23c32f09e5c3d368161aa81e7c0613 | 4d4d672003cb95de01f224fc5d17b727bc6949f6 | /man/getBenchMark.Rd | 450415c967d8268ffdba3c72914b403a44ca47d3 | [] | no_license | lukas1421/chinaTrading | b2d98620cf8a1dda8df1aa79caac23446f173fec | a5b3ad12e96b45ecea62b56127024298d814d66c | refs/heads/master | 2021-01-02T15:38:38.140732 | 2019-05-12T23:34:51 | 2019-05-12T23:34:51 | 99,306,470 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 370 | rd | getBenchMark.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bench.R
\name{getBenchMark}
\alias{getBenchMark}
\title{get bench of a stock and output to folder (ticker, chn, index, correl, indexChn, indexChnCorrel)}
\usage{
getBenchMark()
}
\description{
get bench of a stock and output to folder (ticker... |
fb6989179c81e975d0f63772d0962de375b481a0 | 197c4c93cc1c5417b7845c141b58a8f781213d74 | /man/cksum.Rd | 421b61c0b5cc4e3e8d9492bcf844e164c32a486e | [] | no_license | genome-vendor/r-cran-bitops | 89864b8e292984ba5b31ba694b97ab0bf754e4fe | f312c616ebab018c7e53f92302d2c88f72c4ec5a | refs/heads/master | 2016-09-06T01:45:40.861289 | 2012-04-11T04:10:33 | 2012-04-11T04:10:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 999 | rd | cksum.Rd | \name{cksum}
\alias{cksum}
\title{Compute Check Sum}
\description{
return a cyclic redundancy checksum for each element in the argument.
}
\usage{
cksum(a)
}
\arguments{
\item{a}{coerced to character vector}
}
\details{
NA's appearing in the argument are returned as NA's.
The default calculation is identical t... |
43e5993bb8b284bbb18e943d80dc87d51b82aa8c | df87bbaf8fd9b169c9a27607fd0af1c098435256 | /man/RMS.Rd | 24b1193e608c017e36618aae1c6b0b645086afda | [] | no_license | cran/Convolutioner | 714b0168dc2e805b4a27edde1dd058cf114a633d | 187d871def5abca0293bb2be006a74065f45e326 | refs/heads/master | 2023-03-15T07:41:37.819585 | 2021-03-11T09:40:02 | 2021-03-11T09:40:02 | 346,752,530 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 756 | rd | RMS.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/filters.R
\name{RMS}
\alias{RMS}
\title{Running median smoothing.}
\usage{
RMS(raw_data, buffer_size = 5)
}
\arguments{
\item{raw_data}{Data upon which the algorithm is applied}
\item{buffer_size}{number of points the algorithm use to comput... |
2b03a561d1d16704cde5460f728c9fc6e745ca59 | dd9f94ac6181401f0ea48e78f9731f1c337b85ca | /inst/code/gen_original_results.R | 2aa13b45bb3e5bae510a4772891fb009d79f648c | [
"MIT"
] | permissive | unagpal/susieR | 67de44e321ec21599d56342df409fa2c3dc8eef1 | 46d37a49ccd680b1ff5fa9dfecf8928ca09018cf | refs/heads/master | 2023-07-02T20:50:26.853819 | 2021-07-22T21:13:29 | 2021-07-22T21:13:29 | 276,458,113 | 0 | 0 | MIT | 2020-07-01T18:52:45 | 2020-07-01T18:52:44 | null | UTF-8 | R | false | false | 2,126 | r | gen_original_results.R | ## results from original susie
devtools::install_github("stephenslab/susieR")
library(susieR)
create_sparsity_mat = function(sparsity, n, p){
nonzero = round(n*p*(1-sparsity))
nonzero.idx = sample(n*p, nonzero)
mat = numeric(n*p)
mat[nonzero.idx] = 1
mat = matrix(mat, nrow=n, ncol=p)
return(mat)
}
set.see... |
c0de03416c8f81eeaf9f49d565f5954eb7f64fd8 | 09e4ea6bf480ff5620a7ab24636ad111681b7371 | /Multiple replicates - changing sex ratio.R | cd8418ee10cc49e93175313a9cd4ff2dc74030b7 | [] | no_license | eringiglio/project_sneaker | 6e96203c11e55f5807bd9a6c179055583b279ad3 | 1b7fa646424bc7f547199fff162e37af2f8352ac | refs/heads/master | 2020-05-21T04:24:30.794870 | 2018-09-13T22:25:01 | 2018-09-13T22:25:01 | 53,076,546 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,692 | r | Multiple replicates - changing sex ratio.R | #Collect runs
MODEL_OUTPUT_S<-data.frame(NULL)
MODEL_OUTPUT_T<-data.frame(NULL)
#Number of generations for simulation to run
#Also set number of replicates
#Useful for when we go to nested for loops or similar for multiple runs, to get summaries
REPLICATES<-100
GENERATIONS<-1000
for (j in 1:REPLICATES){
#Set initi... |
ce4396fbf87299553586b8ba54e54bc28fbb360d | 6c4d4ed84895fc1fc8fad4f6470bf41d1932f573 | /define_segments.R | a9ffc20b2cc8219ac00749e727a4490521c468f3 | [] | no_license | cise-midoglu/coverage-visualization | a94a45d8d994479d615c5fee3689193cd42b4321 | 319bf2b52c884cafc155ffc886fb53fc880f61b8 | refs/heads/master | 2020-03-19T14:24:11.787408 | 2018-06-08T13:46:58 | 2018-06-08T13:46:58 | 136,620,945 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,244 | r | define_segments.R | #library(fields)
#require2(geosphere)
source("util.R")
get_total_dist <- function(path) {
dist_tot = 0
n <- nrow(path) - 1
for(i in 1:n) {
p1 <- as.numeric(path[i,])
p2 <- as.numeric(path[i+1,])
r <- haversine_dist2(p1, p2);
dist_tot = dist_tot + r
}
dist_to... |
01f405f893fc5b0ecbada9ea2cadcfcef9e932d8 | 5cc023e4961c5a6ab8a2bce04c69e01de18d5400 | /Twitter Analysis.R | 2f50089d0a45b700da301520813969ec9a471ae5 | [] | no_license | pranavsinha88/pranavsinha88-Code-Repo | 34883af0425238a9c66f7841852b4b9a9cf3cfe7 | d1fe51c1b3ea6f16ec84bd205939febf4276ed54 | refs/heads/master | 2021-01-25T06:49:14.530018 | 2017-06-07T09:06:30 | 2017-06-07T09:06:30 | 93,615,116 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,910 | r | Twitter Analysis.R | ipak <- function(pkg){
new.pkg <- pkg[!(pkg %in% installed.packages() [, "Package"])]
if (length(new.pkg))
install.packages(new.pkg, dependencies = TRUE)
sapply(pkg, require, character.only=TRUE)
}
tweet_analysis_package <- c("tm" , "NLP" , "twitterMap", "openxlsx", "xlsx", "topicmodels", "lda", "sna", "twit... |
67610d2e35bbf04bfa4b876520e5df328db3ca65 | 92a0b69e95169c89ec0af530ed43a05af7134d45 | /man/load.source.directory.Rd | 6dad5e0c739d7ec9c8413ba2d2e19365d3d1316c | [] | no_license | gelfondjal/IT2 | 55185017b1b34849ac1010ea26afb6987471e62b | ee05e227403913e11bf16651658319c70c509481 | refs/heads/master | 2021-01-10T18:46:17.062432 | 2016-01-20T17:51:29 | 2016-01-20T17:51:29 | 21,449,261 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 525 | rd | load.source.directory.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/load_source_directory.R
\name{load.source.directory}
\alias{load.source.directory}
\title{Runs all source files within the directory source.directory}
\usage{
load.source.directory(source.directory)
}
\arguments{
\item{source.director... |
8280c20d6adf2bdb4538d2df1ab634495d3da6f4 | e8aa4ec68533b288ee18c609328086430b2322e4 | /man/uv_charts.Rd | 691d9bf8ed6025a1194f0279d5da452d121a612a | [] | no_license | JohnCoene/uvcharts | 473b40bcbcdcbd1e98ad154d5bd4e88def00ec57 | bf5c7c9a2a768c59979b4a4f7a61ffb7222d679b | refs/heads/master | 2021-01-21T10:30:06.544566 | 2017-04-05T08:30:39 | 2017-04-05T08:30:39 | 83,441,711 | 2 | 1 | null | null | null | null | UTF-8 | R | false | true | 2,103 | rd | uv_charts.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/uvcharts.R
\name{uv_charts}
\alias{uv_area}
\alias{uv_bar}
\alias{uv_charts}
\alias{uv_donut}
\alias{uv_line}
\alias{uv_percentarea}
\alias{uv_percentbar}
\alias{uv_pie}
\alias{uv_polar}
\alias{uv_stackarea}
\alias{uv_stackbar}
\alias{uv_step... |
ebca8173b3826e96abd44636716c07bec7f34fde | f79cd4e052c5cbb24e7ef3e4bec1c39f9ce4e413 | /BEMTOOL-ver2.5-2018_0901/src/biol/bmtALADYM/ALADYM-ver12.3-2017_0501/gui/guicontrols/fisheryControls/deactivate_FishingEffort_unused_params.r | 1e10edbea5023fe58ea63eb44c807b72f8b33563 | [] | no_license | gresci/BEMTOOL2.5 | 4caf3dca3c67423af327a8ecb1e6ba6eacc8ae14 | 619664981b2863675bde582763c5abf1f8daf34f | refs/heads/master | 2023-01-12T15:04:09.093864 | 2020-06-23T07:00:40 | 2020-06-23T07:00:40 | 282,134,041 | 0 | 0 | null | 2020-07-24T05:47:24 | 2020-07-24T05:47:23 | null | UTF-8 | R | false | false | 5,068 | r | deactivate_FishingEffort_unused_params.r | # ALADYM Age length based dynamic model - version 12.3
# Authors: G. Lembo, I. Bitetto, M.T. Facchini, M.T. Spedicato 2018
# COISPA Tecnologia & Ricerca, Via dei Trulli 18/20 - (Bari), Italy
# In case of use of the model, the Authors should be cited.
# If you have any comments or suggestions please contact the follow... |
f2f032f660ded7c6165fe31cf1437fe28038bd13 | 6c812b8136e52e760b4064de4d6fdf24ffe1f590 | /man/f2apply.Rd | 5d6ee66ae499cf5e9dcc30add80bd15aea17ead2 | [] | no_license | cran/FuzzyNumbers.Ext.2 | 39d848d6de545fafd85f971edde282dcbee175b6 | 65239db610a722ef40ae261619b632cdf21c906e | refs/heads/master | 2021-01-21T17:28:01.400250 | 2017-09-05T06:29:09 | 2017-09-05T06:29:09 | 85,421,081 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,938 | rd | f2apply.Rd | \name{f2apply}
\alias{f2apply}
\title{
Apply a two-variable function on two fuzzy numbers
}
\description{
Suppose that we are going to put two fuzzy numbers \eqn{x} and \eqn{y} into the monotonic two-variable function \eqn{f(x,y)}. A usual approach is using Zadeh's extension Principle which has a complex computat... |
ad2a48d64953ff61640b125151d580c33f4d7cd5 | a5597207c2e2c6bff92c045b3a2e0cdbd0e2ee7a | /R_databases/LS_ROM_data/plot_bars_ROM.R | ff2d20ecd37887e62860bc303535ce1c79ef59f9 | [] | no_license | Gavinlenton/loadSharing_processing | a3a7a3dd3c5bb5fdb2f096023a1fef3b06eea369 | a16faf77df2e74316afc384be3417f55898bde5e | refs/heads/master | 2021-01-20T18:20:25.903996 | 2018-02-22T01:21:19 | 2018-02-22T01:21:19 | 60,587,136 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,313 | r | plot_bars_ROM.R | ## Plots the input data using a column chart design
## data: a data frame.
## ylimits: a row vector specifying the min and max limits of the y-axis
## xData: data to plot on x-axis
## yData: data to plot on y-axis
## fillData: labels to use in the legend
## scale_cont_breaks: vector specifying the spacing o... |
b35daec06ae559a06de0e5ef285ba2345b5ad561 | 46e8512ab84cb14900ff6ed3bfbc9c93328a657d | /Stimates.R | 970da657d3ff25ab0ec417c7a9b5f8727cbb0e98 | [] | no_license | Gabo226/R | eefaec0ac4a37e1d2f5d4fcba914fa8773e0659b | 3bca72ee7ad632c386dbdc65eacdc44e337a2013 | refs/heads/master | 2022-06-17T22:29:39.286270 | 2020-05-14T22:44:16 | 2020-05-14T22:44:16 | 255,984,712 | 0 | 0 | null | 2020-04-15T17:15:31 | 2020-04-15T17:07:54 | null | UTF-8 | R | false | false | 142 | r | Stimates.R | d = read.csv("assoctest.csv")
table(d)
tab = table(d$allele, d$case)
chisq.test(tab)
tab = table(d$allele, d$case)
fisher.test(tab)
|
4d3f94fd1b449bfea56b9da363e84ddcf316a242 | c3ee012b76453254dd76ed3d69ba8a4c97a9a90d | /TestMVST/R/install-Rpkg.R | 4ddd54be90cc3aa86cab9559e91e3ac112e2ac04 | [] | no_license | shazhe/glbm | 7cca75e150a106261bae446b128bbfc6fe6b35b6 | 567f3314b446240dd3a7c2aeba09ad31274bbe1d | refs/heads/master | 2021-01-13T15:12:30.348361 | 2018-01-11T15:15:28 | 2018-01-11T15:15:28 | 76,252,126 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,958 | r | install-Rpkg.R | #######################################################################
## Install R packages in a cluster environment with job scheduler ##
## Run this script before runing any BHM scripts. ##
## Script copied from https://orfe.princeton.edu/help/r-packages ##
## Note BlueCrystal does not al... |
759fb5b15a6f91540fd92cf337bb35aeaf750e76 | 95b9aa43e1158b082930318cc4b46d187e658a4d | /df.R | 820f3aa1de87ab958232c8785e0978b1f7ec09c8 | [] | no_license | bbolker/hmm_temphet | 8a4317bec6b940dd52685c2ab1d7507a3aef0fd8 | 1b333433608cc76c89af34c428ded2e7b32aa91f | refs/heads/master | 2021-01-20T12:06:28.396963 | 2016-12-22T06:39:49 | 2016-12-22T06:39:49 | 44,646,050 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 112 | r | df.R | default = 1
num <- nrow(cat)
iter <- 500
if(default==0){
num <- 500
iter <- 30
}
cat <- head(cat,num)
|
01763e7c03678dbf6cee9e5b22fca6cd9592792c | 49e8370414d355292412bf7f7ae03a7851506403 | /prep23.r | 37b95c90a7e4fbcd90adb8aaa2c7b65c8b148340 | [] | no_license | hamparmin/causal-inference-2018 | d3578d38741585ebe08a15c22d162ed01a472564 | 580cc509414e7a9f656b52fba3ce50d249b788a1 | refs/heads/master | 2020-04-30T08:31:05.308309 | 2019-03-20T11:25:45 | 2019-03-20T11:25:45 | 176,718,437 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 913 | r | prep23.r | fred <- read.csv("fred.csv")
#fred test
x<- fred$size
y <- fred$sweetness
z <- fred$price
#fred training -RMSE
fit_best <- lm(z~x+y, data=fred)
fit_second <-loess(z~x, span = 1, degree = 1)
summary(fit_best)
RMSE_best <- sqrt( mean( (z - predict(fit_best))^2 ) )
RMSE_best2 <- sqrt( mean( (z - predict(fit_second))^2 ) ... |
b9b9ece9d93a9d2d5f054c518c04fe05bf75efbc | ecf24c2aae9f8da0527d89963616f1a39089798c | /cachematrix.R | 87bf6f3aaf25834fed1ac8ff53822d5d3d45674a | [] | no_license | HLueckhoff/ProgrammingAssignment2 | 52dd98dbd093a92dfc66baa81f81b19c00c0a6f0 | e3553179f457e3ab307794c8994dd816c242ee17 | refs/heads/master | 2021-01-18T05:12:30.703691 | 2014-10-26T19:01:45 | 2014-10-26T19:01:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,997 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## Creates a cache object for the inverse of an invertible matrix. The cache object contains gettters and setters for the
## original matrix as well as the original matirx and its inverse (once it has been computed).
makeCacheMatrix <-... |
e0424331da8994beae360e9b13636db551f24688 | afd286a06eff008fc7dd8b4716454575647f0a57 | /R/connect_analysis.R | 71f1ed9dc38bd30744c9484b3059930aadf41be2 | [] | no_license | luiscartor/PACCproject | 5845cb86182c7492939f20aa719836f605693d7c | 58c5e3fc721a3583cc38876e6dd70cb5fc61b0f6 | refs/heads/master | 2020-04-25T12:35:39.962882 | 2019-05-10T20:04:40 | 2019-05-10T20:04:40 | 172,777,671 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,206 | r | connect_analysis.R | # connect_analysis.R
# on 3 May 2019
# by Luis
# compares connectivity (protconnbound) between 2011 and present
library(rgdal)
library(maptools)
library(rgeos)
library(sf)
library(raster)
library(ggplot2)
library(viridis)
library(plyr)
library(scales)
library(broom)
library(mapproj)
library(shades)
# 1. INPUTS
OUTco... |
f9f85a2c45ed2eb7f9576515cb0d191d31f98d75 | 08945878c824498f99548b4b0b4171b9bc6f2091 | /fig_for_e2.R | 882f83e3d8d3362b8e4185b79a7143e6fd9f2a0c | [
"MIT"
] | permissive | klaricch/TransposonFigures | 3fc3eeb0f081e31a045e49f656f2b5d1db761081 | 41396eee6ec62cad6e4d2b04dea8e86a7eb5bdf6 | refs/heads/master | 2021-01-19T04:37:36.786432 | 2017-05-20T20:03:22 | 2017-05-20T20:03:22 | 43,438,083 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 24,840 | r | fig_for_e2.R | #!/usr/bin/R
#1. Correlations of absence with insertion and reference with insertion (square 4x4 inch, 16 point fonts, no title)
#2. Numbers of TE families, numbers of TEs, etc. I can probably get them from the results section, but I don't know if it is done enough for me to copy
#3. Manhattan plots for our good trait... |
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