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735112502aea41d9730601ef0956b640c8d4b245 | e034ce0925fd1d88d51b01ed49b3ba5fbbbdf58a | /class_scripts/probset2.R | 56addef63c397f0da55e94b3722b5435bb3a15c6 | [] | no_license | datafordemocracy/lppp5540_sld | 87f9269c070ebf6e5a00b01fc9ff490add40cb1f | f1206cee70725787c6e9c36bf05baf5c2108966d | refs/heads/master | 2021-01-06T20:30:05.486236 | 2020-04-24T15:53:21 | 2020-04-24T15:53:21 | 241,480,658 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,756 | r | probset2.R | # .........................
# Saving Lives with Data II
# Problem Set 1
# Wrangling and Regression in R
# Exploring SCI Data
# .........................
# Working further with Save the Children International's current data, in this problem set
# you'll make some additional changes to the data, generate visualizations... |
da02eb87bcd9e8c2eb2191e8c37bb1a6c6ed9dae | 05a62c2797d2ab194e82498122e855c9b1537559 | /Cicero links separated.r | 3f275470cd457db80450afb21ce8598e29c52707 | [] | no_license | jdavisucd/Single-cell-multiomics-reveals-the-complexity-of-TGF-signalling-to-chromatin-in-iPSC-derived-kidney | e7a81a4b96680b11b2fca38090e05f0f085798d4 | b38f29307cd6b6a0d9af1c6c76319f638eaf10ed | refs/heads/main | 2023-04-15T22:02:18.736365 | 2022-11-03T10:50:47 | 2022-11-03T10:50:47 | 564,415,654 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,003 | r | Cicero links separated.r | library(Signac)
library(monocle3)
library(cicero)
library(Seurat)
library(EnsDb.Hsapiens.v86)
library(BSgenome.Hsapiens.UCSC.hg38)
library(GenomicRanges)
library(SeuratWrappers)
library(future)
# This script splits a Signac object by sample and calculates cis-co-accessability
# networks through Cicero and ... |
511ec96913bf11b6b291cee11b975dd9083643fd | eb8ac840ab9fae607855149c23cd2960e108cd7d | /ciangene/CNV/ExomeDepth/bin/exomeDepth_optparse.R | 89b1fb61452bf7e787d851a664a218d8c8e617d3 | [] | no_license | UCLGeneticsInstitute/DNASeq_pipeline | 29426e33f12b024afd76a32bba5a4836d60d2888 | 36b627f3ac26e6f060f7e2a612344a46e2af015c | refs/heads/master | 2020-12-26T09:27:55.693600 | 2018-12-11T12:14:23 | 2018-12-11T12:14:23 | 63,901,985 | 1 | 2 | null | 2016-07-21T20:58:42 | 2016-07-21T20:58:41 | null | UTF-8 | R | false | false | 5,786 | r | exomeDepth_optparse.R | suppressPackageStartupMessages(library(S4Vectors) )
suppressPackageStartupMessages(library(IRanges) )
suppressPackageStartupMessages(library(Rsamtools))
suppressPackageStartupMessages(library(Biostrings))
suppressPackageStartupMessages(library(XVector) )
suppressPackageStartupMessages(library(GenomicRanges))
suppre... |
6087dd85870cf5f3ddd12cee92b3987e430bc099 | 8efa4abbf80541dee202d9211bec2d71991519da | /ch_03/ch_03_2.R | 6c7259c234ce1f2847479e1212f3387d6eb80f87 | [] | no_license | kimjunho12/R_BigData | ad07009c7e9b919f0321b84655758791004cb3ab | fdff2da689a31a6bbe38d448c52f7decc0730fee | refs/heads/master | 2023-06-09T10:42:01.070830 | 2021-06-30T01:53:43 | 2021-06-30T01:53:43 | 361,130,167 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 640 | r | ch_03_2.R | # 데이터 불러오기
library(readxl)
read_excel('../data/customer_profile.xlsx')
read_excel(
"../data/customer_profile.xlsx",
sheet = NULL,
range = 'B3:E13',
col_names = T
)
read.csv(file = '../data/customer_profile.csv',
header = T,
stringsAsFactors = F)
test1 = c(1:10)
write.csv(test1, file = '../d... |
f6d0f81f8e8b1993442cb0da1d16e73c190b3a0b | 2616a72d7029dd695fc133af709bb9f1a32dc2c1 | /man/stop.identify.Rd | 3f76c064fd7df848947c3f19166ad95b9184807b | [] | no_license | cran/kineticF | 0c1e1489738b0bf404a158886db37f2496aecf25 | 06431726251f35b969262e3e119da14dba711fc4 | refs/heads/master | 2016-08-11T15:21:04.538141 | 2015-06-04T00:00:00 | 2015-06-04T00:00:00 | 36,883,150 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 613 | rd | stop.identify.Rd | \name{stop.identify}
\alias{stop.identify}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Stops the process of re-ordering a matrix of coordinates
}
\description{
Changes the order in which a matrix of coordinates is plotted to allow closure on a polygon.
This function is for internal us... |
9a0d437a09bbbcce467135b858bf85f60ead300d | ac31cc01afb79292cd70046661bfb70a8fa195fc | /source/plot4.R | c6d808b84195a6b0e540835e13d729d0ac9f81f4 | [] | no_license | Coursera00/ExData_Plotting1 | 5cb5d6146cd6bfe5c7aeb46f796eeabd4b489217 | 5c035db03424dedff9dc18f60ed02b9bb20a4e7d | refs/heads/master | 2021-01-18T18:57:00.645147 | 2014-07-12T18:02:17 | 2014-07-12T18:02:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,585 | r | plot4.R |
library(dplyr)
setwd('C:/Users/Jul/Box Sync/_PhD/_Big Data/Coursera/Exploratory Data Analysis/')
#dat <- read.table(pipe('grep "^[1-2]/2/2007" "household_power_consumption.txt"'), header=TRUE, sep=';')
newFile <- read.table("household_power_consumption.txt", header = TRUE, sep = ";", na.strings ="?")
#create new c... |
9677c7926043dad01f68bec6898a3f8ad2055453 | 424b0e776929dda8a77732288170eb8b68f5d9c8 | /plot4.R | 4e1e15252c7c3d856fd6bdabac8072ba97b1f5aa | [] | no_license | ankurkhaitan/ExData_Course_Project2 | b21ba28c0be904633a2c4289e9d5ead8d5f05108 | 0cc6d06044b79b5edf743b06032275dca7eeb945 | refs/heads/master | 2022-04-18T23:47:09.724723 | 2020-04-19T15:33:31 | 2020-04-19T15:33:31 | 257,034,966 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,952 | r | plot4.R | # ========================================================================================================================================
# Load Libraries
# ========================================================================================================================================
library('dplyr')
li... |
1cb8db771b18515ce7b843ddcb801858330a2186 | a0585ca647461121f67f91069809cecf7f5a7e5f | /app/server.R | f78741b4ffc591d624c73048329ca9ffad1b675f | [] | no_license | tyz910/dsscapstone | 9e8f1ec60d83cb97a0bab6282d07fc62d741d905 | 07a33d1c16eaaf39ad169781ad6820a396b52db4 | refs/heads/master | 2020-05-21T12:50:37.492601 | 2015-08-15T05:55:34 | 2015-08-15T05:55:34 | 39,577,614 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 255 | r | server.R | source("prediction.R")
library(shiny)
function(input, output) {
output$prediction <- renderUI({
HTML(paste("<ul>", paste(lapply(predict(input$sentence), function (word) {
paste('<li>', word, '</li>')
}), collapse = " "), "</ul>"))
})
}
|
d828fc418efbad64747ba421ecde80be8c95456f | 62954b5457c4ff4392645d54e7c214a73fb440ad | /r_work/cacheSolve.R | 76a85d1bb798e8c633d4227966639b88c608afeb | [] | no_license | huskertc/datasciencecoursera | 4ee9c44b949d6219bf021a34ff3a9390070aa771 | 8696b99a246d39dd8e04ff82e758687a6e6ac478 | refs/heads/master | 2020-04-05T23:40:56.434012 | 2015-02-22T23:24:23 | 2015-02-22T23:24:23 | 29,822,882 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 670 | r | cacheSolve.R | ## Function "cacheSolve" returns the inverse of matrix x.
## If the matrix is unaltered then the cache copy of the inverse is returned,
## else the inverse is calculated and returned.
cacheSolve <- function(x, ...) {
my_inverse <- x$get_inverse() # Call get_inverse to load inverse
if(!is.null(my_inver... |
04f3a5de6243150fd07731c88c5b5e2d04ff2ef0 | 958ebca1fab699ba605b7a38f05ff47dab9b4d69 | /global.R | 276fd1e9aad2237af9e1d72fc3007d095045ba80 | [] | no_license | BeachA89/Canoe-Sprint-Stroke-Analysis | 78ec431da8da74ea9267562f65d9d836374c000e | 81e02b189c0fd7da6858746c1d2fd078d4bb43ab | refs/heads/main | 2023-02-11T19:01:52.748274 | 2021-01-09T22:25:24 | 2021-01-09T22:25:24 | 328,259,850 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 298 | r | global.R | source("peakdet.R")
library(quantmod)
library(zoo)
library(R.utils)
library(svDialogs)
library(car)
library(ggplot2)
library(ggpmisc)
library(signal)
library(shiny)
library(shinythemes)
library(dplyr)
library(quantmod)
library(zoo)
library(R.utils)
library(svDialogs)
library(DT)
library(ggplot2)
|
33afc2c4c3459901903e56cc9acb032e1d356859 | 6dbac72ced093929c84431726d2a6df3b90fd9f6 | /lib/make_cluster_diagram.r | 65814175d92fee18dbd43f8333b0c4e1afef4928 | [] | no_license | serina-robinson/wastewater_isolates | 64ded6155129765d7d896508e6708b03ca1071b0 | ee208aea9d5f4a986300faa6d1001c59b7b7ddd4 | refs/heads/master | 2020-08-01T05:15:31.874845 | 2019-10-20T05:46:28 | 2019-10-20T05:46:28 | 210,876,407 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,424 | r | make_cluster_diagram.r | make_cluster<-function(shrt, thresh1, thresh2, bynum, namvec, pal2) { # input is an all vs. all table
# #Pull out columns to make a sequence similarity network
#First remove all comparisons between identical proteins (closed loop nodes)
noprs<-shrt[!shrt$prot1==shrt$prot2,]
noprs<-noprs[order(noprs$eval),]... |
91a231c026be0a457f1acb55291fc1d0fb7d9857 | b8f69e2a1d3d706f2d9b767b99c0df95b23ad56f | /man/pcaGuide.Rd | ed188f3bf1e7d85916ac1955d563983de15c732a | [
"MIT"
] | permissive | cran/wilson | b03932a828d284a6b8b8b29411721727c6268ec0 | e2dec1181e01d212b545a6ebfb53beee6320cf2f | refs/heads/master | 2021-06-08T21:43:25.793829 | 2021-04-19T08:40:02 | 2021-04-19T08:40:02 | 145,903,340 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 340 | rd | pcaGuide.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pca.R
\name{pcaGuide}
\alias{pcaGuide}
\title{pca module guide}
\usage{
pcaGuide(session)
}
\arguments{
\item{session}{The shiny session}
}
\value{
A shiny reactive that contains the texts for the Guide steps.
}
\description{
p... |
f331986a8d73b85ea412ffeca98524fd4e6978ea | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/MazamaSpatialUtils/examples/dissolve.Rd.R | e9cf917b8e541e023ab65fa1187cdf86928744cf | [] | 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 | 256 | r | dissolve.Rd.R | library(MazamaSpatialUtils)
### Name: dissolve
### Title: Aggregate shapes in a SpatialPolygonsDataFrame
### Aliases: dissolve
### ** Examples
regions <- dissolve(SimpleCountries, field = "UN_region", sum_fields = "area")
plot(regions)
regions@data
|
aec915421746f538009085dbad4bba5920736e5c | 83f461519bff4467a1a175ca686ad06a2a7e257b | /R/kfn.R | 004785feacde1665dd15c1f1ef5a75adbc9387c7 | [] | no_license | Yashwants19/RcppMLPACK | 3af64c6b1327e895b99637649591d1671adf53a5 | 2d256c02058aa7a183d182079acff9037a80b662 | refs/heads/master | 2022-12-04T05:06:17.578747 | 2020-07-22T12:45:42 | 2020-07-22T12:45:42 | 252,217,735 | 9 | 0 | null | 2020-08-18T06:15:14 | 2020-04-01T15:41:38 | C++ | UTF-8 | R | false | false | 5,961 | r | kfn.R | #' @title k-Furthest-Neighbors Search
#'
#' @description
#' An implementation of k-furthest-neighbor search using single-tree and
#' dual-tree algorithms. Given a set of reference points and query points, this
#' can find the k furthest neighbors in the reference set of each query point
#' using trees; trees that are ... |
7dd728304e2f2581cc9d7c56471070af91657c82 | aa11979c1b0293a817175def253b107e444e759c | /plots/plotv2.R | 000cc5d5082fd6d34c36799dde0de59f6315eb80 | [] | no_license | labrax/arduino2pi | 969c7a5ebcd48b34c05e8342cadd26c5cc373c4e | 1f15719210a659afedc347be55372527a15575ce | refs/heads/master | 2020-04-15T00:20:01.642403 | 2019-09-22T11:02:51 | 2019-09-22T11:02:51 | 164,236,226 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,763 | r | plotv2.R | library(readr)
library(ggplot2)
read_v2 <- function() {
cols <- c("year", "month", "day", "hour", "minute", "second", "amount_samples", "pir_min", "pir_max", "pir_sum", "mv_min", "mv_max", "mv_sum", "phr_min", "phr_max", "bme_temp_avg", "bme_pressure_avg")
df <- NULL
for(file in c("20190107_v2.csv", "20190108... |
065a01687af0c807b66ed23754bc5d3038faf5c2 | 1fd8b00e9265e4998e5b76ea020f2420853b5875 | /ggplot_bar_status_count.R | 6ba98db0d8bc7be9bee7dfeeeab70fa27e7add43 | [] | no_license | avrao/log_analysis_r | b705f09936c1af1f34e6e9f6802edf786b8e4ca4 | 950f15aebf63e66e5c99cd5806c52eae67743f04 | refs/heads/master | 2020-03-28T12:08:09.745261 | 2018-09-12T16:24:35 | 2018-09-12T16:24:35 | 148,271,996 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 780 | r | ggplot_bar_status_count.R | library(ggplot2)
#access.log is required to be included int eh same directory
df = read.table('access.log')
#head(df)
colnames(df) = c('ip_address', 'nameless_v2', 'nameless_v3', 'date', 'nameless_v5', 'request', 'status', 'bytes', 'url', 'browser_specs')
df$date = as.Date(df$date, "[%d/%b/%Y")
#df$time = as.Date(df$ti... |
d21277db8ff5deaab27d26e5e47cc148a43bcb00 | af9fab9a0a1d0a37009c24fc2f57a9b5adae945a | /StockSplits.R | 934fe4b3e9be28ac0fb68cd61833acfca322a858 | [] | no_license | antonnemes/WRDS-CRSP-StockSplits | fbc1a41a5e3521cd543aecd04972efeaaf751319 | 60a8774c58280ad3882f5dd5d246dde44a109f0f | refs/heads/master | 2022-11-24T21:22:41.164120 | 2020-07-18T23:06:50 | 2020-07-18T23:06:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,810 | r | StockSplits.R | ####################################
# This code produces Stock Splits
# From the CRSP DSE files
# Note specific treatment and
# selection of share codes
# adjust to your own needs
# Use of this script contains no
# warrants.
####################################
# J.T. Fluharty-Jaidee
# ... |
ddd1d33fad76b65508f51dae417e7b4e40578d33 | aac2208b8358941b1655e58a2eaddf62a81e06d6 | /code/week22_quarantinis.R | 8e6f3200f35f50ebde03e77543e0909dc8256f06 | [] | no_license | VeeBurton/tidee-tuesday | dd44b5a4460462fe128b6cd45c0ba8b759fa7e49 | 46bfc896e5b023e6ddfdc331e80e10931052a2f5 | refs/heads/master | 2023-05-14T01:41:26.492916 | 2021-06-02T14:11:05 | 2021-06-02T14:11:05 | 259,920,171 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,368 | r | week22_quarantinis.R |
library(tidyverse)
# data please
tuesdata <- tidytuesdayR::tt_load(2020, week = 22)
cocktails <- tuesdata$cocktails
# warnings
# measure column intentionally left as a string with a number + volume/unit...
# so that you can try out potential strategies to cleaning it up.
# some potential tools:
# tidyr::separate():... |
db400f3c98dd55a7526351f608c8736d23bec67a | a218c73b9c34d9140be85bfc8755344554c88a5e | /man/euc.Rd | 0b2dcfddcad458b32a84bb7ed635bd156f05db40 | [
"Apache-2.0"
] | permissive | EvanYathon/distrrr-1 | 1b9f11db5c0e524216be0fbca6e14b69b5c71c6f | b3b466ac230031104865666e49c1a4129f59f529 | refs/heads/master | 2020-04-29T05:57:25.916933 | 2019-03-09T00:11:22 | 2019-03-09T00:11:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 395 | rd | euc.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_distance.R
\name{euc}
\alias{euc}
\title{euc}
\usage{
euc(point1, point2)
}
\arguments{
\item{point1}{vector with numeric values}
\item{point2}{vector with numeric values}
}
\value{
float, the distance between point1 and point2 based on ... |
058be1373e2eff944cc6171d77e458cf287ddd6c | e6dcecf42fd5ab6bd47aaf9c9e91d05143983f14 | /R/cgibbs.R | aa62309da04e434b4d4a2abe33c5ad0db2724fea | [
"Artistic-2.0"
] | permissive | hillarykoch/CLIMB | 73c0c544443df894c473eb8d139745a4e8c103b4 | 4d28091eb1b0447907e6fe7563edfc096c0eddb4 | refs/heads/master | 2022-11-05T14:58:50.302252 | 2022-10-18T12:24:24 | 2022-10-18T12:24:24 | 197,435,666 | 7 | 2 | null | null | null | null | UTF-8 | R | false | false | 5,533 | r | cgibbs.R | # Functions to call anything from cgibbs.jl
prepare_julia <- function() {
# Find julia v1.0.2 binary
julia <- JuliaCall::julia_setup()
ver <-
as.numeric(stringr::str_split(string = julia$VERSION, pattern = "\\.")[[1]][1])
if (ver < 1) {
stop("Julia version > 1.0 required for this package to run.")
}
... |
5ae814d48f971e5c6d35d3afcdec37b57c082389 | 9719ea69f693adfddc62b27eaf948fc7b16f6ad0 | /man/map_wastd.Rd | 172b43d774a213b32eac4fc4a96f924af09e433b | [] | no_license | dbca-wa/wastdr | 49fe2fb1b8b1e518f6d38549ff12309de492a2ad | 5afb22d221d6d62f6482798d9108cca4c7736040 | refs/heads/master | 2022-11-18T01:00:41.039300 | 2022-11-16T08:32:12 | 2022-11-16T08:32:12 | 86,165,655 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 4,312 | rd | map_wastd.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/map_wastd.R
\name{map_wastd}
\alias{map_wastd}
\title{Map Marine Wildlife Incident 0.6}
\usage{
map_wastd(
x,
map_animals = TRUE,
map_tracks = TRUE,
map_dist = TRUE,
map_sites = TRUE,
wastd_url = wastdr::get_wastd_url(),
fmt = "... |
0b89aeb94f697253076a87482c30805f78305450 | 64f84f0edb31a8aac3418415828b1176b6e15c64 | /assignment4/notsouseful.R | 7732d87b034e30fcd860ca9484e2711e250bbd06 | [] | no_license | Oyelowo/Advanced-Remote-Sensing-2 | 721651ddbc05b817a63162a80787f8c5950fa664 | 4582bc0602ad34e40ebc1ba1da4246f7abba7192 | refs/heads/master | 2020-03-29T16:52:33.035112 | 2018-09-24T16:15:34 | 2018-09-24T16:15:34 | 150,131,530 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 2,988 | r | notsouseful.R | ########################################
# Feature extraction for tree segments #
########################################
rm(list=ls())
setwd("C:/HY-data/MIKNIEMI/GIS202/Exercises/Exercise 5 materials")
##################################################################################
# Read 3D point data (TEXAS_l... |
fd32900a7b0aa0b5544c93c32c349819a57895d0 | 97d2622edd598afcaed1f1c2c1be12197ee2de5f | /Breast/GREAT.R | 611c2e021e8e83faf5281cee6b9a51e1f2ab30d2 | [] | no_license | gloriali/HirstLab | 8a95bd0159b349cf993eaea7e7455f3afa39d3fe | f95e3818f39ff476227ca8fc02c032f1cbaf79e9 | refs/heads/master | 2022-11-30T23:19:32.541188 | 2020-03-05T01:55:52 | 2020-03-05T01:55:52 | 19,998,930 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,887 | r | GREAT.R | # Figure S17
setwd("~/快盘/Publications/breast/revision/sup/FigureS17")
lum.GOBP <- read.delim("shown-GOBiologicalProcess.lum.UMR.tsv", head = T, as.is = T)
lum.MSig <- read.delim("shown-MSigDBGeneSetsPerturbation.lum.UMR.tsv", head = T, as.is = T)
myo.GOBP <- read.delim("shown-GOBiologicalProcess.myo.UMR.tsv", head = T,... |
8847d34e4994b495f01adbc17df672ea211bd287 | fc239655fcb08de514c131beda0c089463a6e820 | /app/modules/welcome.R | 58518266ec0761d9ad0ee16ca1be707bd1e538fd | [] | no_license | jpolonsky/pledges | 8ddd39695edc8d9f812ac7607ca5a9a96fbd337b | 80ea696da7e194af99d2a7e590c7013806df8dc4 | refs/heads/master | 2021-01-24T09:14:26.641765 | 2017-03-17T12:17:45 | 2017-03-17T12:17:45 | 69,883,447 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 238 | r | welcome.R | Welcome <- function(input, output, session){
paste("Hey Paula's team!",
"Please upload your excel file using the Upload Data button",
img(src = "arrow.png", width = "5%"),
sep = '<br/>') %>%
HTML
}
|
8a514f8e41333f40d86e0e507b00f671edc556a4 | f416f02e2e6eb2ab304966a1feabda65295228b2 | /tests/testthat/test-attack_model.R | e446135e151284f307ce4fa474f5e23aa4e346fb | [] | no_license | nicholascarey/attackR | 5150a55ef9c7176e08178ae8b799ab959b3d770d | 287544fe96ef9eb58c33e3de1ed1755da97975ab | refs/heads/master | 2020-07-26T20:30:10.820508 | 2020-07-16T15:43:04 | 2020-07-16T15:43:04 | 208,758,145 | 0 | 0 | null | 2020-07-16T09:35:28 | 2019-09-16T09:14:23 | R | UTF-8 | R | false | false | 4,398 | r | test-attack_model.R | # library(testthat)
# Profiles ----------------------------------------------------------------
## stops if profiles contain values outside correct range
expect_error(attack_model(500, 180, 60, 1000, 250, 250,
profile_v = c(0, 0.4, 0.8, 1.2),
profile_h = c(0, 0.4, ... |
16c4f4edb366a815e92e767c5e7683e6257b4071 | 725a33f27fce430ee481a3542aae5bb81a94dfc0 | /man/MQDataReader-class.Rd | a050adddb33e0fbcb534cea30775424d889c65e6 | [
"BSD-3-Clause"
] | permissive | cbielow/PTXQC | fac47ecfa381737fa0cc36d5ffe7c772400fb24e | f4dc4627e199088c83fdc91a1f4c5d91f381da6c | refs/heads/master | 2023-07-20T00:39:45.918617 | 2023-05-17T14:23:03 | 2023-05-17T14:23:03 | 20,481,452 | 41 | 30 | NOASSERTION | 2023-05-17T14:23:04 | 2014-06-04T11:53:49 | HTML | UTF-8 | R | false | true | 6,506 | rd | MQDataReader-class.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MQDataReader.R
\docType{class}
\name{MQDataReader-class}
\alias{MQDataReader-class}
\alias{MQDataReader}
\title{S5-RefClass to read MaxQuant .txt files}
\arguments{
\item{file}{(Relative) path to a MQ txt file.}
\item{filter}{Searched for "C... |
b9f30fd9e37fb73eae8f9969d5b08ee06d26c848 | e586290da2b5444595b16044e76854205db57a48 | /man/standard_hex_points.Rd | 4003ea6a1e29cce76494b316c014d16134011c84 | [
"MIT",
"ISC"
] | permissive | mattle24/electoral.hex | 9b16c4e917ecbc5b8cc5c8766156b06ec3e60799 | 3332b41e6e6ed17dac59924889e555222c36b704 | refs/heads/master | 2020-06-19T22:36:21.766954 | 2019-07-16T21:54:35 | 2019-07-16T21:54:35 | 196,899,657 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 450 | rd | standard_hex_points.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/standardize_points.R
\name{standard_hex_points}
\alias{standard_hex_points}
\title{Standard state hexagon points}
\usage{
standard_hex_points(hex_centroids)
}
\arguments{
\item{hex_centroids}{sf. An object containing the hexagon centroids.}
}... |
25d3cf247c16443527c6b2e606b785cbdea475c3 | a47ce30f5112b01d5ab3e790a1b51c910f3cf1c3 | /A_github/sources/authors/3994/preseqR/compared_methods.R | e9c8f70f735ab0e24b2119309539707c71b4594b | [] | no_license | Irbis3/crantasticScrapper | 6b6d7596344115343cfd934d3902b85fbfdd7295 | 7ec91721565ae7c9e2d0e098598ed86e29375567 | refs/heads/master | 2020-03-09T04:03:51.955742 | 2018-04-16T09:41:39 | 2018-04-16T09:41:39 | 128,578,890 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,896 | r | compared_methods.R | # Copyright (C) 2016 University of Southern California and
# Chao Deng and Andrew D. Smith and Timothy Daley
#
# Authors: Chao Deng
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Softwa... |
283f4d7c61c857106b2aaf3eb2b55e7aa1dd3b84 | 1981c964d39a32be2c471c2106e081ed4e0270ba | /R/8.R | 8ca04747f8c0eb9d3e49341444a2fc3c3dde2449 | [] | no_license | lg8897203/Rforlg | 91b74c3c9447d881956f70d25e400a7f0b6c2789 | 2c3fb851c77d9623b353cc7b59486e11e564d3d3 | refs/heads/master | 2021-01-20T06:30:55.568506 | 2015-10-25T12:43:17 | 2015-10-25T12:43:17 | 44,785,861 | 0 | 0 | null | null | null | null | WINDOWS-1252 | R | false | false | 988 | r | 8.R | y <- seq(0,1,0.05)
x <- seq(0,1,0.05)
SPIA <- function(x, y, r=1, a=0, h=5) {
S1 <- (54+105*x+41*x^2-10*x^3)*y/(6*r*(1+x)*(27+27*x-4*x^2))
S2 <- (162+495*x+496*x^2+155*x^3-8*x^4)*y*a/(2*h*r*(1+x)*(27+27*x-4*x^2)^2-2*y^2*(162+495*x+496*x^2+155*x^3-8*x^4))
P1 <- (9+15*x)*h/(9*(1+x))
P2 <- (9+11*x)*a/(27+27*x-4*x^2)
P3 <-... |
c42ad9ae6ebbea2daed06155a2a1dfe0a924c41f | bc52b9eb2376f0e0f84da6bd8c770bc26300f4f7 | /man/fast_sca.Rd | b7b5eb0bc25cb5018b15bea02133df33b3d29063 | [] | no_license | cran/svs | 86299de68a22036cdbb59cfe1fdce38f4acd8cc7 | bac0a7e5b4e48739b4273d5b9f69a3d79987fd58 | refs/heads/master | 2021-01-10T13:18:31.673174 | 2020-11-09T20:40:02 | 2020-11-09T20:40:02 | 48,089,639 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,225 | rd | fast_sca.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/svs.r
\name{fast_sca}
\alias{fast_sca}
\title{Simple Correspondence Analysis}
\usage{
fast_sca(dat, transform = 1)
}
\arguments{
\item{dat}{Input data: can be a table or a data frame (but the data frame must have only two columns).}
\item{tr... |
15801213bb057de3a61ad11d4480b62eda8161c2 | 5c81fc04db1f4fb7a4453d9772796d278bbcad5d | /Main.R | 1911736176bb5cdaaf58809e831f49f47db5b183 | [] | no_license | Tennismylife/Tennis-R-ecord-Animation | 0fb725dedcb82485db0e3d76ec8348cfb56a8d56 | a8c0f94bcf90ff07be778a82ae574c3b3bdb6833 | refs/heads/main | 2023-04-16T00:26:58.101862 | 2021-05-03T00:55:52 | 2021-05-03T00:55:52 | 359,907,836 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 441 | r | Main.R | library(tidyverse)
source("Reader.R")
source("Animator.R")
source("Formatter.R")
source("DataRetriever.R")
#Read database from csv
db <- ParallelReader()
#Get all data from a specific category with all winners tourney-by-tourney
M1000Roll <- Retrieve()
#Format the data to create a ordered and ranked da... |
12f2acc267b554703c7cb32b9dee56ed1e97637a | cb57199836e5e5de9f597c13404e2a089285bc44 | /R/leaders.R | 31046b12f4f6c59081e008702ad183ec4c847be6 | [
"MIT"
] | permissive | kevinrue/BiocChallenges | 57971f16cc828cfc657a52ab1291983adc595d7b | 8f1a9628b7816c69876b9ffa3610fd0109bb6fa5 | refs/heads/main | 2023-08-28T09:53:56.134237 | 2021-10-31T21:29:41 | 2021-10-31T21:29:41 | 294,656,697 | 0 | 0 | MIT | 2020-09-11T15:39:06 | 2020-09-11T09:50:25 | R | UTF-8 | R | false | false | 936 | r | leaders.R | # Functions to process and display leaders.
#' Challenge Leaders
#'
#' @param params Challenge parameters as `list`.
#'
#' @return
#' `format_leaders()` returns a character value indicating the challenge leaders.
#' @export
#'
#' @examples
#' params <- list(leaders = list(kevinrue = "Kevin Rue-Albrecht"))
#' cat(forma... |
0951be754aba577bec2fdc882c93d1b9af5f2fc2 | ab642017b5d96153f17712652a7d5d5f59037187 | /elrapack/R/getLmat.R | 930a4b0c7b44845d973911eb81b322328362b2f0 | [] | no_license | maj-biostat/elra-biostats | 4cc698dfbf262b15a86edf91431d3a9afcb00d58 | 0021579c0ab022fa4909fc2c70fdba063a3e9052 | refs/heads/master | 2021-09-09T19:57:39.655242 | 2018-03-19T10:47:14 | 2018-03-19T10:47:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,005 | r | getLmat.R | #' Helper functions for creation of Lag-Lead matrices
#'
#' @rdname LagLead
#' @keywords internal
#' @export
createLmatDyn <- function(
lead = 4,
lag.c = 4,
lag.f = 2,
n.days = 11,
brks = c(0:11, seq(15, 55, by = 5), 61.1), time.shift = 4) {
time.seq <- seq_len(n.days)
if ( is.null(lag.c)... |
8c2296c62d111006a9e11a7f903ef31b728cfe34 | 4e1e5ca97aa52682d72e051e8aacd8a420aa2f41 | /tests/testthat/test-scatterplot_penguins.R | d677f11ce590025c02f20bf4504dc4095e42624e | [
"MIT"
] | permissive | 2DegreesInvesting/ds.testing | 84cffd90bb660ad7289abac0a2dacd14834fd86e | e4f0cb53be747aaa7fab90b915048602b67be03a | refs/heads/master | 2023-04-22T13:12:16.718512 | 2021-05-11T12:18:07 | 2021-05-11T12:18:07 | 352,852,572 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 203 | r | test-scatterplot_penguins.R | test_that("hasn't changed", {
skip_on_os("mac")
skip_on_os("windows")
data <- na.omit(palmerpenguins::penguins)
p <- scatterplot_penguins(data)
p$plot_env <- NULL
expect_snapshot(str(p))
})
|
245151f0e25b9bb430d11fd3537d4eab5c2db00c | 88ebbada30ec62db655b56dd641ac844223660c0 | /Exercise_7.R | 011e48b65ae5a61ce5cfa5866beb276854805f8f | [] | no_license | smasca/Biocomputing2020_Tutorial09 | 71c28aee70adfc60648a0a549965f826f21abcbf | 7faaa0d6afaa6664023eb352a0eabea12b4a668e | refs/heads/main | 2022-12-26T04:18:28.511703 | 2020-10-13T19:19:47 | 2020-10-13T19:19:47 | 302,665,132 | 0 | 0 | null | 2020-10-09T14:27:52 | 2020-10-09T14:27:51 | null | UTF-8 | R | false | false | 1,597 | r | Exercise_7.R | # Samantha Masca
# BIOS 30318
# Exercise 7
# TA: Elizabeth Brooks
#Task 1: replicating the functionality of the head function in Linux to output the top n lines
setwd ("/Users/samanthamasca/Biocomputing2020_Tutorial09/")
#variable name of the file
data <- read.table(file='wages.csv',sep=',',header=TRUE,string... |
d1d57b4113410971e09244bd05758fb6ce0fad16 | 959b8d01689825ce765ef1f783c579c43831d9a9 | /R학습파일/logi_ex2.R | fd47dd6d3617ce6d01a51454600f069411e50368 | [] | no_license | leeyouhee/R2 | 9f7117e2b99f37ad1ef9bf2e4242c21468196629 | a7f448247d81ecaea148703b4ffa2be2aaa54ea7 | refs/heads/master | 2022-12-10T20:41:48.616158 | 2020-09-01T03:37:10 | 2020-09-01T03:37:10 | 283,909,285 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 412 | r | logi_ex2.R | ########## 예제 mtcars ##########
# mtcars 데이터셋 이용 아래와 같이 작성해보세요 :)
#sqld 315 page
data(mtcars)
head(mtcars)
library(dplyr)
str(mtcars)
View(mtcars)
# 1) 데이터 준비
df <- mtcars %>%
dplyr::select(mpg,vs,am)
heda(df)
# 2) 8:2 데이터 나누기
# 3) 종속변수 vs, 독립변수 mpg + am로 모형 적합
# 4) 예측
# 5) 변수 선택
step
# 6) 결과 비교 |
06b4b7fd468c680c0b517cf3c58e5985773d7686 | 1415a07510acfe50a8bcb6d1377ea98fab20c63e | /boolType.R | c806f7038950471b9dccdb241274112cdef46ae5 | [] | no_license | 00mjk/interOp-1 | 9f8b5db42fad60e34e9bff36319acf0e9db4d8bc | 2cc1f59a9a31b3a7339f2a42a2386cdadee30233 | refs/heads/master | 2021-09-28T00:14:07.785145 | 2018-11-12T01:44:02 | 2018-11-12T01:44:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 348 | r | boolType.R | #
# Henry Samuelson
#
# Bool Data Type interpreter
processBool <- function(x){
#First check the bool type true/false
# Then convert to universal T/F
if(x == "True" || x == "true" || x == "T"){
x <- "T"
} else if( x == "False" | x == "false" || x == "F"){
x <- "F"
}
# Now that we have standardi... |
05fc04944b2850c14dcaebc42378e9924f595d5b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/RSiteCatalyst/examples/GetElements.Rd.R | 8bc73367e41d733bd584a323f045d0d7202937b1 | [] | 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 | 556 | r | GetElements.Rd.R | library(RSiteCatalyst)
### Name: GetElements
### Title: Get Valid Elements for a Report Suite
### Aliases: GetElements
### ** Examples
## Not run:
##D elements.valid <- GetElements("your_report_suite",
##D metrics=c('visitors','pageviews'),
##D elements=c... |
491fac7e6f32b1bda7944c8b0e5b165ef9631025 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/RcmdrPlugin.IPSUR/examples/birthday.ipsur.Rd.R | 7ad88694bd981eb8be84450419c77317d21ad4f9 | [] | 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 | 579 | r | birthday.ipsur.Rd.R | library(RcmdrPlugin.IPSUR)
### Name: birthday.ipsur
### Title: Probability of coincidences for the IPSUR package
### Aliases: qbirthday.ipsur pbirthday.ipsur
### Keywords: distribution
### ** Examples
## the standard version
qbirthday.ipsur()
## same 4-digit PIN number
qbirthday.ipsur(classes=10^4)
## 0.9 probab... |
0dd2c90d80b7db5da9fb12fcf266c8a173c77fbb | 6cbc6e80ae07b8fb1fff0a5cad4ddcd29c358c0a | /man/ezr.impute.Rd | ae1c0bc4a8a5850bee0c5a649bc3386fa7b3b76e | [] | no_license | lenamax2355/easyr | d99638b84fd9768774fa7ede84d257b10e0bacf6 | 37ab2fe5c28e83b9b5b3c0e3002f2df45708016b | refs/heads/master | 2022-01-09T20:43:17.801623 | 2019-05-13T02:49:48 | 2019-05-13T02:49:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 799 | rd | ezr.impute.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ezr_impute.R
\name{ezr.impute}
\alias{ezr.impute}
\title{Impute values}
\usage{
ezr.impute(dataset, use_mean = FALSE, use_median = TRUE,
only_columns = NULL, adjust_chars = FALSE, exclude_columns = NULL)
}
\arguments{
\item{dataset}{Dataset... |
74910537b52d0a9107c83a27f8d23f4b8e97d3d6 | 96c937d616a7235e2af970bea6307cfca3cc7a4e | /prob5/suramrit_server.R | fc81a1c390e304b64cbe7bbddb94f16d4285fa82 | [] | no_license | suramrit/Twitter-Analysis-using-R | 6199eb47bea6c7eaa9bbff79da9b9930f394f8ff | a666aeb7fb1b99877fbf856556fee18d0a7f755d | refs/heads/master | 2021-01-21T13:14:43.435770 | 2016-05-12T15:00:59 | 2016-05-12T15:00:59 | 55,788,942 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,032 | r | suramrit_server.R | library(shiny)
library(ggplot2)
library(ggmap)
# Define server logic for random distribution application
shinyServer(function(input, output, session) {
# Reactive expression to generate the requested distribution.
# This is called whenever the inputs change. The output
# functions defined below then all use... |
b89f44461dbe09ed9cf941edbb17a1dac0dca3c0 | 2802979852f33dc4336c0e0fbc6a601a928efc5e | /R/initialize.R | ff6a7f43b9ecdd5ff8b85708f365af812a52cd90 | [] | no_license | cran/netgwas | 05ee21591f4bc89b295b4d7d6754ec9fb5cc7225 | e661e37640b335d4fa515f03411e08bb12b795fa | refs/heads/master | 2023-08-31T22:02:45.223899 | 2023-08-07T14:40:02 | 2023-08-07T16:35:15 | 112,773,132 | 3 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,783 | r | initialize.R | #-------------------------------------------------------------------------------#
# Package: Network-Based Genome-Wide Association Studies #
# Author: Pariya Behrouzi #
# Emails: <pariya.Behrouzi@gmail.com> ... |
b698ee1781fd56884590505bfedec98ed894de52 | dbfe5ce272e204a8e1663ced35c9d48ef4870496 | /R/statistic.R | 7c1057f68a39216b944a9a4736c543793020cf5f | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | hmito/hmRLib | fac91a4e2ddfcd899283ec0b63c87c31965fb17f | f2cfd54ea491ee79d64f7dd976a94086092b8ef5 | refs/heads/master | 2023-08-31T07:21:31.825394 | 2023-08-28T10:02:07 | 2023-08-28T10:02:07 | 41,907,654 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,862 | r | statistic.R | #' Return vector of the mid points of the argument
#' @description Create the sequence of the mid points of vector, i.e., (x[-1]+x[-length(x)])/2
#' @param x terget vector
#' @return vector of mid points
#' @export
#' @examples
#' x = c(0.0,0.2,0.6,1.2)
#' ans = mids(x)
#' # ans == c(0.1,0.4,0.9)
mids = function(x){
(... |
7fbb8c80df3372fc4bbff7dd22ba6a54f57a2ee3 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/chipPCR/examples/CD75.Rd.R | 0db5d51579943179efd21e6c506dff69c8ca8283 | [] | 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 | 208 | r | CD75.Rd.R | library(chipPCR)
### Name: CD75
### Title: Helicase Dependent Amplification in the VideoScan HCU
### Aliases: CD75
### Keywords: datasets
### ** Examples
data(CD75)
## maybe str(CD75) ; plot(CD75) ...
|
6f60d2a71678bec7d58cdf104adc77158a4dfec4 | 2ea4c931d915e650fa40af46d36d8c4c1abc7f29 | /run_analysis.R | e139dd1eb645a5a9d661b689ec109dbd283c0b74 | [] | no_license | muhsalem/Coursera-Peer-graded-Assignment-Getting-and-Cleaning-Data-Course-Project | b0112ff49f45501703f75d0c476b997b811bfe00 | 1c302a9c6f9f2498d91834bd1f6010bbedf19762 | refs/heads/master | 2020-03-19T23:06:24.840177 | 2018-06-11T19:47:13 | 2018-06-11T19:47:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,321 | r | run_analysis.R | ## DATA CLEANING PROJECT
## Loads the dplyr package which will be needed in this script
library(dplyr)
## **********************************************************************************************
## Part 1. Merges the training and the test sets to create one data set.
## *****************************************... |
ed035e7c179c9d0993b6ccebae5586fec7f8d7c7 | 767beb025b7bb92ad0fba01fb66f470d3a48b5c6 | /R/GrammaticalEvolution.R | 2c4d6025d9e6c7e9f13fb307d246e75d0af7b2f7 | [] | no_license | ramcqueary/gramEvol3 | cf46ba9b88d751899b630c37ca5676e6e4a21327 | 1704cd06723402e38911f37d490da790b48b420a | refs/heads/master | 2021-09-23T18:58:03.400514 | 2021-09-12T03:09:15 | 2021-09-12T03:09:15 | 249,277,554 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,105 | r | GrammaticalEvolution.R | function (grammarDef, evalFunc, numExpr = 1, max.depth = GrammarGetDepth(grammarDef), startSymb = GrammarStartSymbol(grammarDef), seqLen = GrammarMaxSequenceLen(grammarDef, max.depth, startSymb), wrappings = 3, suggestions = NULL, optimizer = c("auto", "es", "ga"), popSize = "auto", newPerGen = "auto", elitism = 2, mut... |
69b23c05512466e7413e8d8e419e727fe31b32fe | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/moezipfR/examples/rmoezipf.Rd.R | 72d9eb4bf9a63a96ed8a438be2b0be7d912ec82e | [] | 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 | 141 | r | rmoezipf.Rd.R | library(moezipfR)
### Name: rmoezipf
### Title: Random number generator.
### Aliases: rmoezipf
### ** Examples
rmoezipf(10, 2.5, 1.3)
|
306a1c42142016f8dc4195d24755107650a6b868 | ebb1f13d0493dc91d0099eb3d7cb8182210ab157 | /EleicoesTercRepublica.R | ab5b3e17a412fb456f146724ecb02dcfde019b90 | [] | no_license | ngiachetta/work | acb6fd73fce0fe127dfb521793e4547785449373 | f34b92c333fd23c0a8c90e576342ac40f1c9980d | refs/heads/master | 2020-12-30T16:45:24.493522 | 2017-11-21T17:35:56 | 2017-11-21T17:35:56 | 91,018,635 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,397 | r | EleicoesTercRepublica.R | # Analise de dados para materia: FLP
# Pacotes
library(dplyr)
library(purrr)
library(ggplot2)
library(stringr)
library(readr)
library(ggthemes)
library(RColorBrewer)
## Eleicoes gerais: 1945, 1947, 1950, 1954, 1958 e 1962
labels.partido <- c("DATA_GERACAO", "HORA_GERACAO", "ANO_ELEICAO", "NUM_TURNO", "DESCRICAO_ELEI... |
dc857d0c8e1d9cb223170dcc0d546aa0e47c5e50 | e4901ac0d0866b2a3ad4f14474e941e79e23f3ee | /RandomForests.R | df6c0313d1133dbd6ce520f15ca1d3a1b2b6d44d | [] | no_license | MarianneLawless/Random-Forests-algorithm-R | 03383c5f3a11739b09f6144f794f6ff101383191 | 74cd9c7c0d7d20236af89ccbcfe118f7f8a288eb | refs/heads/master | 2023-03-16T15:03:46.621507 | 2020-01-18T15:01:15 | 2020-01-18T15:01:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,580 | r | RandomForests.R | #Random Forest is created by aggregating trees
#Can be used for classification or regression
#Can deal with large number of features
#Avoids overfitting
#It deals with only parameters
# Download Data
setwd("C:/Users/Alexander/OneDrive - National College of Ireland/4th Year/Semester 2/Web Mining/CA2/BS-updated")... |
7a681bb3fb4dbfdf6f77d2325665be6628094b3e | b4a07b5123b9eb6fd25ac001c24b9a84a78d5683 | /data/performance.R | 246bbbfd40b7d9e7d51a96ca6e8f8925fe442064 | [] | no_license | o19s/TREC_run_explorer_app | 889bfc37d869e58c1326c45641426a01da18fdd8 | 454b2fd67615325bd975c31a6a0c66a5310995fa | refs/heads/main | 2023-02-05T23:26:19.068849 | 2020-12-21T19:47:32 | 2020-12-21T19:47:32 | 323,372,920 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,124 | r | performance.R | library(tidyverse)
us <- dir("trec2020newstrackbackgroundlinkingresults/", "trec_eval", full.names = T) %>%
set_names(gsub(".*results\\//(.*)\\..*", "\\1", .)) %>%
map_df(~read_tsv(., col_names = F), .id = "run") %>%
filter(!is.na(X2)) %>% # overall stats
filter(X1 == "ndcg_cut_5") %>%
select(-X1) %>%
... |
a8d40e2427098072891d69148b1aa9db5ab42ee0 | e1d25753f7e5d445abd805d52181c6e920756a08 | /TFL/shinyapps/global.R | e08edf4b3d2744eddfb428d89547bb27509d3998 | [] | no_license | boriscooper/nginx-rshiny | 02af54adbc284d75dea8549c243eba8490513ced | 1c4d3f6705ddce60eed59d1adff2868a7840ca3d | refs/heads/master | 2020-05-18T11:31:35.923300 | 2019-05-01T15:18:06 | 2019-05-01T15:18:06 | 184,381,814 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,483 | r | global.R | ## Start up database
## LIBRARY PACKAGES
library(sf)
library(dplyr)
library(data.table)
library(ggplot2)
library(shiny)
library(shinydashboard)
library(leaflet)
library(geojsonsf)
library(rgdal)
##-------------------------------------------------------
base_dir <- "/srv/shiny-server"
data_dir <- file.path(base_dir, "d... |
f22fe19ba0704dd5e6ada57f3170e9559c9db2a7 | 3d9f876272743b98299b08d9b435ffc9c50cebb7 | /R/bi_open.R | f4ceb2f07a4b2e7a1dd45b8c8e0f4321dd2322c7 | [] | no_license | tyler-abbot/RBi | 4342ed0a38421821de813399afc39223ec724251 | fc71463b66235b7f44deb41628a94b211708d9bc | refs/heads/master | 2021-05-03T11:27:54.067395 | 2016-10-04T10:23:28 | 2016-10-04T10:23:28 | 69,964,424 | 0 | 0 | null | 2016-10-04T12:48:55 | 2016-10-04T12:48:55 | null | UTF-8 | R | false | false | 941 | r | bi_open.R | #' @rdname bi_open
#' @name bi_open
#' @title Bi open
#' @description
#' This function opens an NetCDF file
#' The file can be specified as a string to the filepath, in which
#' case a NetCDF connection is opened, or directly as a NetCDF connection.
#'
#' @param read either a path to a NetCDF file, or a NetCDF connect... |
4b3aaf2cd17369b0c6f26bec312498ad40fc3916 | 2d9fb03feb8626c67ba5d3f1a0815710b621c5f6 | /man/activity_specialization.Rd | 4c1fa74ceb38814601a5bdcab7fa596802256772 | [] | no_license | bbrewington/edeaR | 4c8916bad4c54521764574770ae941983363dc0a | 02b31d133b5cec68caa6e0c5fa446a6a6275d462 | refs/heads/master | 2021-01-19T18:32:49.442081 | 2016-08-27T17:31:36 | 2016-08-27T17:31:36 | 66,726,375 | 0 | 0 | null | 2016-08-27T17:17:51 | 2016-08-27T17:17:51 | null | UTF-8 | R | false | true | 552 | rd | activity_specialization.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/activity_specialization.R
\name{activity_specialization}
\alias{activity_specialization}
\title{Metric: Activity Specialization}
\usage{
activity_specialization(eventlog, level_of_analysis)
}
\arguments{
\item{eventlog}{The event log to be us... |
b7a7d470c2e74761e37067ea3742f6fd4a0a9c95 | 71bb8a2619c414a153297d2dfd2a5089c53c7f9e | /Operadores-em-R.R | 010d898b521df8947766ca25bae88978f20684b6 | [] | no_license | BrunoVollin/learning-R | b1c5d4eb998ee060177ba1ab12fbd85d9ff82108 | ecc75bd3358e9746a12335a78d0a6b683841b1f0 | refs/heads/master | 2023-03-10T09:40:42.968448 | 2021-02-26T16:29:54 | 2021-02-26T16:29:54 | 342,619,948 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 348 | r | Operadores-em-R.R | ###-----Operadores em R-----###
setwd("C:/Users/bruno/Documents/Operadores-em-R.R")
getwd()
##soma
4 + 5
##sub
7 - 4
##multi
2 * 8
##div
3 / 3
##pot
3^2
3**2
##mod
16 %% 3
### Operadores relacionais
##variaveis
x = 7
y = 5
x > 8
x < 8
x <= 8
x >= 8
## lógicos
( x == 8) & (x > y) ##and
( x == 8) | (x > y) #... |
6753e1afc201c51f54c2cf043c1965ccf4bcc485 | 8522b1f802fe496ae0c5bcc2b6ca1fee3a5e036a | /multiplot2.R | 4f90dfa008ca6e94eeb44c15bef3319a2a5dd079 | [] | no_license | Neksta1/GMean | 32084c018b926816850aebe6ea7f11dffe4edff6 | 0ac28f82bb1f1de01939b3cd67fcbad46d2d432f | refs/heads/master | 2020-04-06T03:42:21.500455 | 2015-02-27T12:20:47 | 2015-02-27T12:20:47 | 30,014,561 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,066 | r | multiplot2.R |
```{r}
adccoefs <- rnorm(100, 0.001, sd = 0.0002)
adclines <-
alply(as.matrix(adccoefs), 1, function(adccoef) {
stat_function(fun=function(x){S01*exp(-x*adccoef[1])}, colour="grey")
})
```
```{r}
p <- ggplot(data = params)
p + adclines +
layer (stat = "function",
... |
6518bd0e36975aee7c4887fb575a0c68b076fb99 | bb8b96319d963e6b788af9a54ec05c0eed14c624 | /scripts/tema1/11-binning-data.R | b8bf914f880564612fe67f2572e8694f2ef120e2 | [
"MIT"
] | permissive | diegogonda/r-course | 26c065827180dae58dabe17a8a71f0d3097703ab | 8f9d2102133bf1a6eb5d431ec06b9c408e5358b3 | refs/heads/master | 2021-08-18T02:53:55.972659 | 2018-12-02T17:08:26 | 2018-12-02T17:08:26 | 146,853,749 | 0 | 0 | MIT | 2018-08-31T06:54:24 | 2018-08-31T06:54:24 | null | UTF-8 | R | false | false | 1,815 | r | 11-binning-data.R | # #d
students <- read.csv("../data/tema1/data-conversion.csv")
# #d queremos etiquetar a las personas en torno a sus ingresos como bajos, medios y altos
# #d creamos un vector de breakpoints (puntos de separacion, método cut) con estos datos.
# #d Inf: infinito
bp <- c(-Inf, 10000, 31000, Inf)
names <- c("Low", "Av... |
44a00995cb410add1ad77bfecaa9760a151fe60f | d43aa427f215e63d54526b85dce14ab957b028e2 | /3 DATA WRANGLING/Data Wrangling Exercise 2 - Dealing with missing values/exercise.R | 697e247336b99e572ea583316ac6c7ddea628403 | [] | no_license | akoukoullis/Springboard-Foundations-of-Data-Science | 1a218ab971e1a9dd53b3a896b87e1cf49c218600 | 3a108ef187b1007eec3f0789c76f419f07dcdb0d | refs/heads/master | 2021-01-12T15:50:45.882995 | 2017-02-18T10:52:42 | 2017-02-18T10:52:42 | 71,885,881 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,079 | r | exercise.R | # Foundations of Data Science
# Data Wrangling Exercise 2: Dealing with missing values
# Author: Anthony Koukoullis
load_data <- function(filename){
result <- tryCatch({
table <- tbl_df(read.csv(filename, stringsAsFactors = FALSE))
return(table)
}, error = function(e){
print("Unfort... |
0d5ab8f358b007bf66d49635800d103f6e2bd0a9 | cfb444f0995fce5f55e784d1e832852a55d8f744 | /man/plikert.Rd | ed60891451b1e823c73ff9ba5b50afddaa73e561 | [
"MIT"
] | permissive | debruine/faux | 3a9dfc44da66e245a7b807220dd7e7d4ecfa1317 | f2be305bdc6e68658207b4ad1cdcd2d4baa1abb4 | refs/heads/master | 2023-07-19T18:28:54.258681 | 2023-07-07T16:59:24 | 2023-07-07T16:59:24 | 163,506,566 | 87 | 15 | NOASSERTION | 2023-01-30T10:09:37 | 2018-12-29T11:43:04 | R | UTF-8 | R | false | true | 860 | rd | plikert.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/distribution_convertors.R
\name{plikert}
\alias{plikert}
\title{Likert distribution function}
\usage{
plikert(q, prob, labels = names(prob))
}
\arguments{
\item{q}{the vector of quantiles}
\item{prob}{a vector of probabilities or counts; if ... |
eecf0c163fbe2882b4449a74cbb99754f74033a2 | 0785924afd7709630008f3d21aac7ebfb811a5fe | /finance_examples.R | 01614a61025888128a29126c13f4441667d34286 | [] | no_license | leeslatergv/Examples | c8de15730bf2d62de977f60a49bd6a80475ebf9e | 0c8a47f2e4d445de4df9b70ee484cb3aaebdbce5 | refs/heads/main | 2023-08-24T19:53:07.228710 | 2021-07-25T22:33:48 | 2021-07-25T22:33:48 | 388,572,703 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,887 | r | finance_examples.R | # New code
create_summary_tables <- function(df, yearly = "", monthly = "", weekly = "") {
summary_function <- function(df, ...) {
dots <- enquos(...)
df %>%
dplyr::group_by(!!!dots, category) %>%
dplyr::summarise(cost = sum(amount)) %>%
dplyr::ungroup() %>%
dplyr::ar... |
9cfdb50d29c80799d28a5f1a8f4925dc1d207bc9 | cea78c8386e4f72501280eb624b5242eb215b08c | /plot1.R | 289d36e52fc81eb3ce03d5e5918c8edbf0c911b2 | [] | no_license | bionicturtle/ExData_Plotting1 | 883ba29c20e963f4c93d8b95c2d0c00aace37a92 | 2f0bd2c35ea2f07a0fa648526c90fc0cdfe443d9 | refs/heads/master | 2021-01-17T11:43:43.480391 | 2014-09-06T18:08:40 | 2014-09-06T18:08:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,373 | r | plot1.R | plot1 <- function() {
# Due to very large file size (~130 MB), the original dataset is not pushed to my github
# Below I show the local code is here, which reads the large dataset, 2,075,259 observations (rows)
# Then writes to a much smaller file. So, plots 1 to 4 read the smaller (fewer rows) file ... |
5afe3176c629a4908e709e26df80c179a2201c3f | a2ccc1c1c2c06f9d533c8fb78234509fe61cd478 | /bin/RF_200kSnps_populations.R | 9545f3fa433f128d95ff5bcc973c90d35543ecf5 | [
"MIT"
] | permissive | lifebit-ai/siteqc | e6d8edb63e5677b444ab740075dbbf48a3fbcb0a | 69425e964fd3a758085ecf854496614a0c3121e7 | refs/heads/master | 2023-01-31T23:44:21.731094 | 2020-11-10T14:43:57 | 2020-11-10T14:43:57 | 285,573,935 | 0 | 3 | MIT | 2020-11-30T12:09:33 | 2020-08-06T13:06:13 | Shell | UTF-8 | R | false | false | 13,157 | r | RF_200kSnps_populations.R |
#module load lang/R/3.6.0-foss-2019a
lapply(c("data.table", "tidyverse", "magrittr"), library, character.only = T)
#Produce RF predicting the pops based on projected SNPs on 1000kgp3
#Read population labels
indiv_pop<-fread("/re_gecip/BRS/thanos/ethnicities_aggV2/1KGP3.sample_table")
super_pop<-fread("/re_gecip/BRS... |
2d5cca52abe61040aa4a39551ac6e65574c092b0 | a27d9b26dca0897fbe312fb672230698441cc5a6 | /getXLSXTextColour.R | 7968b0db646caa260d6292f502bb3103fd66ff75 | [] | no_license | seannyD/ConvertExcelTextCoding | 4568cd5703c9f1c13a6cdf807236ffa31c5f3330 | abd008405965f5be691c76f4660bde1a988e5b5b | refs/heads/master | 2021-01-01T04:49:15.980085 | 2017-07-14T17:26:18 | 2017-07-14T17:26:18 | 97,256,725 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,719 | r | getXLSXTextColour.R | # Load an excel file and convert a column to a category based on text colour.
library(xlsx)
# Hint: after making a CellStyle object called cellStyle, run names(cellStyle) to get a list of functions that can be run to get different properties of the font/background colour.
getCellFontColour = function(cell){
# Font... |
266eb2004c8814b3e0fc9604aff7fd5c76aed36d | ea5df7e31a71a1eb8de97680c20cdf8a0dc8fb57 | /run_analysis.R | 76c1e807e2ae33f2c9e7e4213ac9a0f9bcaaea42 | [] | no_license | uredkar/datasciencecoursera | fe80464582b74ac73384eb57f434b1901453c24a | 1d7ab122599e5b436417088fa95ef068f382799e | refs/heads/master | 2020-05-30T18:50:28.125868 | 2014-04-19T17:54:33 | 2014-04-19T17:54:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,129 | r | run_analysis.R | library(reshape2)
library(data.table)
# this is where the files are stored
setwd("C:/Sources/cousera/getdata-002/Assignment1/UCI HAR Dataset")
features = read.table("features.txt",sep= "")
activities = read.table("activity_labels.txt",sep= "")
# load test
x_test = read.table("test/X_test.txt",sep= "")
y_test = read.t... |
83de65b17335b6e94588d9b9f389766149d67de8 | 984af1093a5185c475a24bd8fcb420906d770b2c | /hw2temp/question6.R | c930f25e7498db70179d9466f2213f7c1e238586 | [] | no_license | gracewindheim/gmwDataSci | 1a4f2209394824a5276a3c81cd68f61d52fde9ff | 9bb83231b14b0e56a7718f064ab0e816e31e7e93 | refs/heads/master | 2022-07-16T16:00:05.734336 | 2020-05-14T01:33:06 | 2020-05-14T01:33:06 | 235,359,264 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 519 | r | question6.R | #question 6
#Grace Windheim
birthday = function(people) {
n = 365
perm = 1
if (people <= 0) {
print(0)
} else {
for (i in 1:people) {
perm = perm * (n - (i-1))
}
cprob = perm / (365^people)
prob = 1 - cprob
print(prob)
}
}
#create a plot of probability by num p... |
5f0610385dc04e933440ae4aae4d273e6987015b | c9d7e1396064a7f5f59557ed90dd89dc88d7dc41 | /R/scores_lnorm_discrete.R | 522083b1a7ffc93bf7695f862ca447c777fe2a7f | [] | no_license | HIDDA/forecasting | 47c20ba21a35ccd2d259a58f81fdd53f13f07684 | 5dbfcbfa616a8f097adc71d071418e026eff3beb | refs/heads/master | 2021-06-06T16:01:53.562693 | 2021-03-31T15:55:04 | 2021-03-31T15:57:06 | 103,663,438 | 4 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,106 | r | scores_lnorm_discrete.R | ################################################################################
### Proper Scoring Rules for *Discretized* Log-Normal Forecasts
###
### Copyright (C) 2019 Sebastian Meyer
###
### This file is part of the R package "HIDDA.forecasting",
### free software under the terms of the GNU General Public License,... |
587ac03637f3bb160397b1b7f9d5e29d0caea546 | a9793f1bb4803bf57c0bf93978693b9ffd1afef4 | /man/LIN3df.Rd | f533c2ce200e04b2436ce0f6e3aab4eebbcc795b | [] | no_license | gsoutinho/survrec | 632b42f857cb3429a416ea2b61642c3cfb6e7a5e | 3c7bd0ff4a7d06ebb39a93aa700ccde46b5ff839 | refs/heads/main | 2023-08-16T05:54:22.941797 | 2021-10-20T13:42:08 | 2021-10-20T13:42:08 | 418,166,755 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,611 | rd | LIN3df.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/LIN3df.R
\name{LIN3df}
\alias{LIN3df}
\title{Lin's estimator for the general case of K gap times distribution function.}
\usage{
LIN3df(object, x, y, z)
}
\arguments{
\item{object}{An object of class multidf.}
\item{x}{The first time for obt... |
800c04f39f7f681c36ded19779349f3a0c63d28b | 13895420920703501ab66c28a3927089a2de042e | /R/cluster.plot.R | 68682904cfd0e51cc41fb64ef8753e1f80a9a846 | [] | no_license | cran/psych | 3349b3d562221bb8284c45a3cdd239f54c0348a7 | ee72f0cc2aa7c85a844e3ef63c8629096f22c35d | refs/heads/master | 2023-07-06T08:33:13.414758 | 2023-06-21T15:50:02 | 2023-06-21T15:50:02 | 17,698,795 | 43 | 42 | null | 2023-06-29T05:31:57 | 2014-03-13T05:54:20 | R | UTF-8 | R | false | false | 4,279 | r | cluster.plot.R | #revised Sept 16, 2013 to give control over the positon (pos) and size (cex) of the labels
#revised June 21, 2016 to allow naming of the points
"cluster.plot" <-
function(ic.results,cluster=NULL,cut = 0.0,labels=NULL, title="Cluster plot",pch=18,pos,show.points=TRUE,choose=NULL,...) {
if (!is.matrix(ic.results) ) {i... |
3cf0ccc52b06fa9f952193f329614ea649e027e6 | f582870743276a16a61cde20a8018fdd199c764a | /NFL-ML.R | de9a99508bbd0d6b521c4374c7c341be34fca2b3 | [] | no_license | jordanodonnell138/NFL-ML | d636e1ce1b0187b0906cb3cb76defbda7669c705 | 8eb87fe95183efe9d765de3a290dc940d3aa6d0c | refs/heads/master | 2020-04-14T04:12:59.820225 | 2017-03-14T03:51:19 | 2017-03-14T03:51:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,575 | r | NFL-ML.R | #lets try to build a general model for all teams. Lets start with a middle of the road
#team. Maybe that will give better predictability for ALL teams when compared to
#training a model for only one team.
#last season, the Atlanta falcons went 8-8. They also have a points scored and
#points against that is only -... |
575771bfd7e98c4327325bf1da7aa4ea5d3d1ceb | 2f384b7e511afa94a1fe48a43ae295fea258d2fa | /plot3.R | 4c07dcc79db0b11ce3d10b08fb4e24fcaf74d118 | [] | no_license | saran00us/ExData_Plotting1 | 1df648b833e047fd26dcb1103849bdf9184fa404 | 65667e68a1fa991c7088439d4ce7944f80c13acd | refs/heads/master | 2021-01-18T05:21:28.793061 | 2015-09-11T22:33:05 | 2015-09-11T22:33:05 | 42,327,165 | 0 | 0 | null | 2015-09-11T19:05:56 | 2015-09-11T19:05:55 | null | UTF-8 | R | false | false | 1,167 | r | plot3.R | ## read the downloaded dataset
setwd("/Users/saran/explore")
fileUrl <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(fileUrl,destfile="household_power_consumption.zip",method="curl")
unzip(zipfile="./household_power_consumption.zip")
data <- read.table("./househo... |
2ba5687d37007b043fddfad1fd3937a77236dc16 | 805326933defe8e1e7bae2b9f2bc0c36c84fff8d | /108-1-exam4.R | 34b16bf6284d5f2d91e864afcf20224e966d64bc | [] | no_license | astalee812/NTPU_108_R | 39096402d2f2243ce85186a517ef4abd7786ebda | e6d8f24bac37f1f0b75c71882d4d889edc278325 | refs/heads/master | 2020-06-18T17:40:51.830590 | 2019-09-07T09:51:24 | 2019-09-07T09:51:24 | 196,385,231 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,757 | r | 108-1-exam4.R | # 1 #
head(ChickWeight)
summary(ChickWeight)
plot(ChickWeight$Time,ChickWeight$weight,xlab = 'time',ylab = 'weight')
cor(ChickWeight$weight,ChickWeight$Time)
plot(ChickWeight$weight~ChickWeight$Diet,xlab = 'diet',ylab = 'weight')
par(mfrow = c(2, 2))
hist(ChickWeight$weight[ChickWeight$Diet=='1'],breaks = 10,main = ... |
a9fcc15e0211bd5a26822155e98d58e67ff0e60b | c08b43bdb1e649fa998e1a8c48b848cf52fe27a0 | /R/summarystatscorrelationbayesianpairs.R | db7f598eea2938ba5e4aba0db658f4f13976fde3 | [] | no_license | TimKDJ/jaspSummaryStatistics | 019a57908278c95798f61803f42ec5f6f7b6fc80 | e269c303fe1134446e4087590ed4c36be74adf8b | refs/heads/master | 2023-05-27T22:19:55.580206 | 2021-06-17T12:29:46 | 2021-06-17T12:29:46 | 271,536,630 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,719 | r | summarystatscorrelationbayesianpairs.R | #
# Copyright (C) 2013-2020 University of Amsterdam
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# This program i... |
ff771fd91fc30d54b42c368c12c3d22bedc63b61 | 5ed44176b4e3716a44565d118283223c07b791a3 | /R/AAA_TSP-package.R | a0d9a76ab4cfd3b556362a5b52aead2e81a5501c | [] | no_license | mhahsler/TSP | d94ead22a9d3e3b44829477ff474ce458b857623 | f274bf7098570943674f0e7ef3a281cda78a040e | refs/heads/master | 2023-08-09T03:27:48.714780 | 2023-07-21T14:32:25 | 2023-07-21T14:32:25 | 43,990,993 | 66 | 17 | null | 2020-01-23T18:53:26 | 2015-10-10T02:54:22 | R | UTF-8 | R | false | false | 1,101 | r | AAA_TSP-package.R | #' @title `r packageDescription("TSP")$Package`: `r packageDescription("TSP")$Title`
#'
#' @description Basic infrastructure and some algorithms for the traveling salesperson problem (also traveling salesman problem; TSP). The package provides some simple algorithms and an interface to the Concorde TSP solver and its i... |
de3ea82af5692e5195d5e10e3e82f4baf255daef | 98b52689fed8aa8ecc3f1d6dc000adb7970f691f | /figures/figure-3.R | 3cb8c3c523a360bd90aefd3b68fae22ecd4bc225 | [] | no_license | rsankowski/sankowski-et-al-microglia | 73114ec0dff912ee2f9b9cb99bad4e7e62b07db2 | d6c932feb4a2278993fa471a46e85be7398e206d | refs/heads/master | 2021-08-29T14:01:00.386117 | 2021-08-16T11:26:59 | 2021-08-16T11:26:59 | 154,642,566 | 3 | 2 | null | null | null | null | UTF-8 | R | false | false | 28 | r | figure-3.R | #figure 3 plotting functions |
2ce3fb03c76ba1d107e33c7b24e2fd800a117702 | 33e75096778794d0c3d8254933211e2ed155056f | /man/marginal.rcate.Rd | ab0d894355f41240c13daf5930570520f36c5521 | [] | no_license | changwn/RCATE | 75224db7efe9d94391aec3c0822510d7495e992b | bc5d199592629b09eb65f635d024f7d04b249035 | refs/heads/master | 2022-12-05T22:01:19.306974 | 2020-08-23T13:02:08 | 2020-08-23T13:02:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 520 | rd | marginal.rcate.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/marginal.R
\name{marginal.rcate}
\alias{marginal.rcate}
\title{Marginal treatment effect plot.}
\usage{
marginal.rcate(object, variable.name = NULL, ...)
}
\arguments{
\item{object}{"rcate.ml", "rcate.rf" or "rcate.am" object.}
\item{variabl... |
4250b06bcefa4fc3b54cb38371c0b7ad0984a33a | 0f0fc5307324e209d8f039e006db6d7bcbbe7ee9 | /RFinance/man/addDataFrameToSQLiteDB.Rd | 3a3106986ad46896cb1297184d112dbd9186a936 | [] | no_license | rootfs-analytics/RFinance | 169eb6ec79778755321f1736eae5f13741771530 | ec3c90e08c8d4418c5f4f3f7d1eea08ccc619f8b | refs/heads/master | 2021-05-26T20:46:26.670261 | 2012-11-11T23:12:06 | 2012-11-11T23:12:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 536 | rd | addDataFrameToSQLiteDB.Rd | \name{addDataFrameToSQLiteDB}
\alias{addDataFrameToSQLiteDB}
\title{
Add Data Frame to DB
}
\description{
Append a data frame to an existing SQLite table
}
\usage{
addDataFrameToSQLiteDB(con, tablename, df)
}
\arguments{
\item{con}{
Connection to the db
}
\item{tablename}{
Name of the table to add ... |
6281155ef2ccb78c35c0c9e740ac0fa55d9998b3 | a0830531052bd2330932c3a2c9750326cf8304fc | /vmstools/man/overlapPolygons.Rd | 52b148702f18fdfceec5acc97cd4bcbd0ad48b22 | [] | no_license | mcruf/vmstools | 17d9c8f0c875c2a107cfd21ada94977d532c882d | 093bf8666cdab26d74da229f1412e93716173970 | refs/heads/master | 2021-05-29T20:57:18.053843 | 2015-06-11T09:49:20 | 2015-06-11T09:49:20 | 139,850,057 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,832 | rd | overlapPolygons.Rd | \name{overlapPolygons}
\alias{overlapPolygons}
\title{
Calculate the surface area that polygons have in common
}
\description{
Calculate the surface area that 2 or more polygons have in common
}
\usage{
overlapPolygons(pol1 = NULL, pol2 = NULL, projection = "LL", zone = NULL)
}
\arguments{
\item{pol1}{
P... |
f1f230919ff1ab9199d82b27a56be2b2d4dc99e3 | b96e92d86bd142159e4674c59c6fbaf730049802 | /man/vd_multiple_csv.Rd | 244c2dd63578ed320a016b2d8fbf9c75cbe9de50 | [] | no_license | trinker/valiData | 0ac536b9ed0435ff27f61973d949e9036fc8c1ac | 59caaa67acaafb2508e90281812997464766d6f1 | refs/heads/master | 2022-06-09T05:59:46.696388 | 2022-05-12T18:25:54 | 2022-05-12T18:25:54 | 74,035,459 | 0 | 1 | null | 2016-11-17T14:37:24 | 2016-11-17T14:37:24 | null | UTF-8 | R | false | true | 646 | rd | vd_multiple_csv.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/vd_multiple_csv.R
\name{vd_multiple_csv}
\alias{vd_multiple_csv}
\title{Validate Which Folders Contain Multiple CSVs}
\usage{
vd_multiple_csv(path, ...)
}
\arguments{
\item{path}{path to project directory}
\item{\dots}{ignored.}
}
\value{
Re... |
188a0ae7caf4ca9d3e87f7aa4abf310ebe55458e | e22df57d0598d9e52dccc2b2a5f2386fb99afca8 | /man/textmodel_wordfish.Rd | 2b78863169d44eb2fa298d40c5a8d9a902657e30 | [] | no_license | pjsio/quanteda | 8098b23135fe414ccd408192d2cf5bd3d6a05ef3 | 647e69abb6184057f22452329ae6daf82bc991c3 | refs/heads/master | 2020-12-30T18:30:20.116664 | 2015-10-27T09:48:57 | 2015-10-27T09:48:57 | 41,432,391 | 0 | 0 | null | 2015-08-26T15:06:39 | 2015-08-26T15:06:39 | null | UTF-8 | R | false | false | 2,973 | rd | textmodel_wordfish.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/textmodel-wordfish.R
\docType{methods}
\name{textmodel_wordfish}
\alias{print.textmodel_wordfish_fitted}
\alias{show,textmodel_wordfish_fitted-method}
\alias{show,textmodel_wordfish_predicted-method}
\alias{textmodel_wordfish}
\title{... |
765f4b61daae371a5bd7378089490caa9833a048 | 29585dff702209dd446c0ab52ceea046c58e384e | /MissMech/R/OrderMissing.R | 23ec22f2d8ccfa1261701b79def573dfa9901883 | [] | 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 | 2,878 | r | OrderMissing.R | OrderMissing <- function(data, del.lesscases = 0)
{
# case order has the order of the original data
y <- data
if (is.data.frame(y)) {
y <- as.matrix(y)
}
if(!is.matrix(y))
{
cat("Warning data is not a matrix or data frame")
stop("")
}
if(length(y)==0)
{
cat("Warning: data is e... |
ea4d7ef74a2925e5a658a040df82df65471ff37c | 8dc78f2d9755faec3452760a61e717c3f426f6c2 | /network_construction_scale_free.R | bbc040e542013690538de1c3aad9ef2114401351 | [] | no_license | Kedong/supplynetworkresilience | fdcbc28d21dc508af92d8f8d9ffb0e16167059c8 | a8f91124f43432c8d67983389420d2e4621c16c1 | refs/heads/master | 2021-08-28T09:49:05.318204 | 2017-12-11T22:35:16 | 2017-12-11T22:35:16 | 109,743,880 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,432 | r | network_construction_scale_free.R | ## This function constructs a directed supply network under preferential attachment
## Inputs: tier (focal=0), number of suppliers in each tier, starting with 1 as the focal
## Inputs: initial connection m0, if allow same tier connection (0 or 1),
## Inputs: m is # edges at each time, size of tier -- can be a vector a... |
ed3edcf40fd5864761c430e92b9ec589d8ac7271 | 6927be295792e510cb2f5e1cc500fa24dffb9755 | /2주차/문제2.R | d681cc03deeb727a496c84e150d912622db89fbf | [] | no_license | jiin124/R | 0ece0f0ae85b131800a3f1015d329d2b1a3bcc82 | 2c8d3bb3d55bda6aee9139a680b7765877e24f92 | refs/heads/main | 2023-07-18T13:56:16.077549 | 2021-08-30T17:22:41 | 2021-08-30T17:22:41 | 344,785,924 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 217 | r | 문제2.R | m<-matrix(c(1, 5, 0, 2, 5, 3, 4, 5, 2, 4,3, 0, 6, 7, 3, 6, 7, 7, 3, 5 ),4,5);m
t(m)
r1=m[1,,drop=F];r1
c3=m[,3,drop=F];c3
c4=cbind(m[,2],m[,4]);c4
m1=matrix(c(m[1,2:5]),2,2);m1
apply(m,1,mean)
apply(m,2,mean)
|
c4f52d27d2465eec88389ee8513b6fa3fc4409bf | 6a782946ca5fa43ec97f15012799c4f01ecab16c | /Module 2/Lecture:HW/bioinfo_first.R | 6704b9585c4793f634040fd560520903f1e5d913 | [] | no_license | htphan16/Bioinformatics-Spring-2019 | 5231beebffba503d79eacc9c87f575bb2b1258aa | 836c0f5202e20c21702afb7901d3411a2cb05e97 | refs/heads/master | 2020-04-29T01:19:37.068559 | 2019-06-03T15:21:08 | 2019-06-03T15:21:08 | 175,726,493 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,049 | r | bioinfo_first.R | # Get current working directory
getwd()
# Set new working directory
# setwd('~/Desktop/')
data <- as.matrix(read.table('Table1.txt', header=TRUE, sep='\t', row.names = 1))
data
dim(data)
oturichness <- rowSums(data)
oturichness
rowSums(data[1:2,])
colSums(data)
# Presence-absence of OTUs in each sample
dataPA <- (dat... |
307b55a3290a5e6154f94b2aff6826ad7bd3efc2 | e2465ed21b79e20648ff98d99d98d3546000c44f | /RCode/helper_functions/get_forecast_data.R | 0f7556c5631a5ccff8ac86fed2a56bfe1c4587ba | [] | no_license | GLEON/Bayes_forecast_WG | 7b58fa482a6e6165ee0e11cb3c53c932c93db8bb | 14f61371c22c8cdc1a00cadfa37e92273eec6e78 | refs/heads/master | 2023-05-21T22:01:08.370135 | 2020-04-09T16:07:58 | 2020-04-09T16:07:58 | 250,070,619 | 2 | 11 | null | 2023-01-24T02:58:49 | 2020-03-25T19:21:20 | R | UTF-8 | R | false | false | 571 | r | get_forecast_data.R | #Title: Reading in appropriate data files for hindcasts for each model
#Author: Mary Lofton
#Date: 03MAR20
get_forecast_data <- function(model_name){
if(model_name == "Seasonal_RandomWalk" | model_name == "Seasonal_RandomWalk_Obs_error" | model_name = "Seasonal_AR"){
y <- log(as.matrix(read_csv("./Datasets/Sun... |
7e5b443f318cfa8e6bd44e996b140f0c2793c060 | bf88ed37a6e7769a73fe2e80c95132578de04a97 | /superbowlsquares/dashboard/SuperBowlBoxes/app.R | 6e2900faf973d10f7b29043ebd5f17632928d43c | [] | no_license | mikemaieli/projects | 82785a4f8e6b50252ddb34b77ec76edb83fb6394 | a73a8092eb51480bda22868c680ddc5e8a8989d5 | refs/heads/main | 2023-07-14T19:05:48.762651 | 2023-07-03T12:53:47 | 2023-07-03T12:53:47 | 300,467,230 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,898 | r | app.R | # load packages
library(shiny)
library(tidyverse)
library(hrbrthemes)
# load and mutate data
score_data <- read_csv("https://raw.githubusercontent.com/mikemaieli/superbowlsquares/master/superbowlscores.csv")
# build UI
ui <- fluidPage(
# Application title
titlePanel("Historical Super Bowl Box Winners"),
... |
c0979f70ef95465c4f404ec0f0764eab4a28735f | cb44bd2723a9e83fcd79f319e4d5303fd3298178 | /cachematrix.R | 216d2b527e1b80879b17e11d431bbb2f91152a36 | [] | no_license | kingofharts/ProgrammingAssignment2 | 356d7118c0d6d049ace2cee2789f7cf9a84dfc2a | 98c5ec562e1f099b6ded191cb7e220ca4033cfbd | refs/heads/master | 2021-01-17T07:58:00.418939 | 2014-04-21T21:45:59 | 2014-04-21T21:45:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,831 | r | cachematrix.R | ## Reply to Peer Assessed 2nd Programming Assignment for R Programming
## nicholas.paul.hartman
## Create list of functions to set, get, invert, and retrieve the
## inversion of a Matrix
makeCacheMatrix <- function(x = matrix()) { # function name & arguments
m <- NULL ... |
865a6afd8c9f2b1db6e53914fc24aeea8fdddf7e | 3cd8f6adb931d82ceea55cd58a746f4a0d5ec1fa | /Week 4/Programming Assignment 3/rankall.R | a193a6fcab7b2b1493f9dd4b213d905b49126171 | [] | no_license | krozic/JHU-R | c1a415fd9d9fb083cc21e146c4f7810e1c36e3af | 564efc456aaea27803a4b88626fd7427079788b2 | refs/heads/master | 2022-07-03T04:04:16.161522 | 2020-05-10T22:22:05 | 2020-05-10T22:22:05 | 262,083,418 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,563 | r | rankall.R | rankall <- function(outcome, num = "best") {
outcome_data <- read.csv("outcome-of-care-measures.csv", colClasses = "character")
outcomes <- c("heart attack", "heart failure", "pneumonia")
col_vals <- c(11, 17, 23)
names(col_vals) <- outcomes
col_num = as.numeric... |
421d49b20e187969efc1d38ca31faf7f412d5538 | 0c6404d89e67b07beaf5e77dcb05d93846a237cd | /Customer Segmentation/MKTProject1-Team4.R | 02cfeae19b974a0a0f4d4ef2f1e7b4df8be61a17 | [] | no_license | nbansal2020/Marketing | a4769f0fbd36a304dcaf916b444823066a2fdb6c | 85df71375257bf1bee9a1372314087a3480c5288 | refs/heads/main | 2023-03-29T21:24:05.018353 | 2021-04-13T02:12:45 | 2021-04-13T02:12:45 | 341,675,155 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 18,372 | r | MKTProject1-Team4.R | library(readr)
library(dplyr)
library(plotly)
library(lubridate)
library(tidyverse)
library(cluster) #need for knn
library(factoextra) #need for knn
library(GGally)
library(plotly)
# IMPORTING DATA ----------------------------------------------------------
product <- read_csv("product_table.csv")
transacti... |
32569c1cdc2ee3e33bb7efb701c82d6703e33d9c | 0a5dd2ef993f265ad5058d8f8e5b9522f1439e03 | /man/createMetaData.Rd | 236788821d4c14b636549cd31c1a8933ff7b9f6e | [] | no_license | oncoclass/DoseR | 30ed6cf59761a2d3f7f3e15f8d7df5e3e8b76562 | 8cc057fe12d4f38720258cd3966ec28f22848ca3 | refs/heads/master | 2020-12-24T00:46:21.189550 | 2017-11-16T13:07:21 | 2017-11-16T13:07:21 | 17,335,054 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 10,284 | rd | createMetaData.Rd | \name{createMetaData}
\alias{createMetaData}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Create an A.data object for storing the absorbance data.
}
\description{
This function creates an A.data object. This object stores all data related the dose response experiments which is saved in an .RDa... |
a234791401246df918e25ada736efd1c77fae612 | 2d3cb59bde33733306bd9007d4cc0d03d73f319a | /JHSV3TabOne.R | 017fafe6f02d7104e02dcc57cec23092d88d2091 | [] | no_license | lizlitkowski/JHSGut | 967eef726993553c560473c4d7d23f4bcb7f8321 | a9f503a89fcaa1f08b12c6f1284fafef9dd684e8 | refs/heads/master | 2020-06-05T23:05:03.810082 | 2019-10-01T16:59:32 | 2019-10-01T16:59:32 | 192,569,681 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,680 | r | JHSV3TabOne.R | # Create Table One for Jackson Heart Study grant
rm(list=ls())
library(tableone)
library(sas7bdat)
dir_data = "C:/Users/litkowse/Desktop/Vanguard_2016/Vanguard_2016/data/AnalysisData/1-data/CSV/"
save1_dir = "C:/Users/litkowse/Desktop/Vanguard_2016/Vanguard_2016/data/Visit1/"
save2_dir = "C:/Users/litkowse/De... |
ecb79a3102613a328e81ff4648be1011f13c48ce | febaac19f141ff221137b2fc9ff830fa1673d4c7 | /man/make_genepop.Rd | 3d1b4edaa8ff1fc7eb98948ca1052f5732b09cee | [] | no_license | mastoffel/sealABC | b686bf26afe4b5a57acd10bab2d22d9be5fc5e66 | c1b2aa5b339fee3d92481b1ef0aa5604956596d9 | refs/heads/master | 2020-04-12T08:15:25.064329 | 2018-01-08T11:20:38 | 2018-01-08T11:20:38 | 62,786,078 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 686 | rd | make_genepop.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/make_genepop.R
\name{make_genepop}
\alias{make_genepop}
\title{format allelic microsats to genepop}
\usage{
make_genepop(x)
}
\arguments{
\item{x}{genotypes}
}
\description{
outputs a data.frame with genepop format
}
\details{
so far, the inp... |
4975b891e60515d5f910e4f19f3860ce04e9f7bd | d56904e67efc6cd5bcf7d1c7a37a3b083843d817 | /run_analysis.R | 63c918417111d624ade017d7b132cef6cb8729e1 | [] | no_license | henryleineweber/GettingAndCleaningDataFinal | 5ccaadde4ad756c589cdcf001c279bc5f6eb7dca | 4e85b57b621193edfcf8438632846b86be89547b | refs/heads/master | 2021-01-11T03:09:39.629753 | 2016-10-16T23:52:17 | 2016-10-16T23:52:17 | 71,084,489 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,522 | r | run_analysis.R | # Set working directory, download data, and extract files to directory
setwd("./RunAnalysis")
download.file("https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip", destfile = "getdata_dataset.zip")
unzip("getdata_dataset.zip")
# Following will merge all data files in the extracted fol... |
b6540b3c9282e83a94fef82e5a47e23bdf67f734 | 7ecdf3fe6b4ed685372e06f3b31e22c074801426 | /readData.R | 594cfa94c596d3c504742c12b6871580190f9520 | [] | no_license | gmdn/ExData_Plotting1 | b5114ce4d4dbc4d319c032ce49e033428b36b5bb | c4fb518f72d3f15b8e2268d539ae802f6dfcb874 | refs/heads/master | 2020-12-26T04:55:13.765963 | 2014-09-07T22:39:58 | 2014-09-07T22:39:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 976 | r | readData.R | ## read dataset
dat <- read.delim("~/Downloads/household_power_consumption.txt",
sep = ";",
header = T,
stringsAsFactors = F)
# build date time strings
datetime <- paste(dat[, 1], dat[, 2], sep = " ")
# convert to standart dates
dtm <- as.POSIXlt(datetime, format ... |
5071191f2ab5f96df5220e71da7bb77b2b5b8622 | 374de90d91a1d5ba11e98a4a9614d98e02de8663 | /experiments/creature_production_2/results/analysis.R | eca88fba51c9059f160f71c5dc213e6b1f694c79 | [
"MIT"
] | permissive | thegricean/modals | d2223645529b59e173d18b0b248d6ea09c6d0429 | 9bb267a64542ee30e2770d79d9cd5d9cce890be8 | refs/heads/master | 2021-03-12T20:26:09.814798 | 2016-03-01T00:14:30 | 2016-03-01T00:14:30 | 26,193,528 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,506 | r | analysis.R | library(ggplot2)
setwd("~/Documents/git/cocolab/modals/experiments/creature_production_2/Submiterator-master")
d = read.table("creature-production-trials.tsv",sep="\t",header=T)
head(d)
d$q1_correct = NA
d[!is.na(d$question1_response),]$q1_correct = 0
d[!is.na(d$question1_response)&(100*(d$question1_response/8))==d$... |
46909b3939ff661b2e1cdac392f3760d532e42e3 | 46a6ae709a45d23694a8233e9ff9318645c52cb6 | /03 - UI Design/Tabs/app.R | 4ac8ef23dea0d258cbd381d8582d9af83f606693 | [] | no_license | lecy/shiny-demo | 031f2abeb74584a84e5c8ab86ff3b81016f05cf7 | 94fc8f71cdc654d232bcce0e971df6284307a4c4 | refs/heads/master | 2021-01-10T09:16:15.048932 | 2016-04-01T11:51:57 | 2016-04-01T11:51:57 | 55,196,646 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,460 | r | app.R |
# Example of UI and Server files on one page
# One nice feature about single-file apps is that you can copy
# and paste the entire app into the R console, which makes it
# easy to quickly share code for others to experiment with. For
# example, if you copy and paste the code above into the R command
# line, it w... |
abd9dc08380a0ca4ca213d8cb87d23b2230079c3 | 2eae755d5619934c814a2aec3e8ff01a69ee727f | /04/src/04_problem_04.R | fc1fd9b8cebffd1801805da6e4afc706bfaba801 | [] | no_license | tjwhalenUVA/664-Homework | 8535877e0f2400ae3544888d52a5f052f2f8144d | 2cb524132d0906d89a65aec3f5d5562d889d6e5c | refs/heads/master | 2021-05-02T00:34:02.722399 | 2018-06-08T17:30:24 | 2018-06-08T17:30:24 | 120,946,506 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 648 | r | 04_problem_04.R | x <- seq(0, 6, by=0.001)
#Prior
p4_prior <- dgamma(x, shape = p2_alpha, scale = p2_beta)
#Posterior
p4_post <- dgamma(x, shape = p3_alpha_star, scale = p3_beta_star)
#Norm Like
p4_nl=dgamma(x, shape=sum(sum_xi)+1,scale=1/sum(occurences))
p4.plot <-
data.frame('X' = x,
'prior' = p4_prior,
... |
910879c4713c20b0574338c397360c581a43a846 | bce2e59b13e142d0e32eb5829dbc2863f2ac2ceb | /Pushover/key-api.R | 3ca91b3f9cf4c73af13ae469ad17a5a4aaac234f | [] | no_license | suraggupta/r-helper | 448096224a08e9002ece202ac2ca1ba889d4e5f7 | 8cb2c3197b93de7bb41b89357119725f9995c622 | refs/heads/master | 2021-06-13T09:28:24.203891 | 2017-04-07T04:21:43 | 2017-04-07T04:21:43 | 84,502,143 | 3 | 5 | null | 2017-04-07T16:49:56 | 2017-03-10T00:35:58 | HTML | UTF-8 | R | false | false | 106 | r | key-api.R | ##
user_key <- <Your user key here>
user_api <- <Your API key here>
user_device <- <Your deivce name here> |
4fa5721efb167fe930b7f92654b93bf819671461 | c3d0a413118cc0aa48f5e1279b1e33a66c64b386 | /Scripts/Econ580_code.R | 27a3d0d16a2e2fbfd059c3b544ec81d1ba120f85 | [] | no_license | andykang8099/American-Interstate-Migration-Pattern-Analysis | 3afeff4c57cce657796c03a63eef9c774b1f8707 | fe661e6f4c6186acf0c8f1416842c2c052e1b9c0 | refs/heads/main | 2023-02-10T21:52:07.755816 | 2021-01-08T09:49:16 | 2021-01-08T09:49:16 | 319,604,454 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,222 | r | Econ580_code.R | library(dplyr)
library("cdlTools")
library("naniar")
library(ggplot2)
library(grid)
library("fastDummies")
library(tibble)
library(interactions)
library(jtools)
fill <- "#4271AE"
lines <- "#1F3552"
data1=read.csv("usa_00005.csv")
head(data1) # 34291600 obs
#for ( i in 1: nrow(data1)) {
# data1[i,2]=fips(data1[... |
260b420f79a7cc031499f3c4d793684f986f8158 | f5d0634ca05df154301645e3f0c25b66776c6e22 | /financialdata.R | 408cd0dfd4f887371b05d7082d0d87a65e001b2c | [] | no_license | YanjingWang/Finance-Project | d1b51e6854c863d364db1b05df91495b06bad1e3 | e2b5e7fd04b4024b9916eff6aefd50a32585110a | refs/heads/main | 2023-04-16T18:34:13.390842 | 2021-04-30T03:31:53 | 2021-04-30T03:31:53 | 363,013,337 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 14,370 | r | financialdata.R | install.packages("quantmod")
install.packages("plyr")
install.packages("xts")
install.packages("zoo")
install.packages("TTR")
library(TTR)
library(zoo)
library(xts)
library(quantmod)
library(plyr)
data<-as.vector(read.csv("/Users/suyue/Desktop/fe800/data/financial/2-21-FinancialData.csv",sep = ","))
#================... |
e1c4c905ca8ba644ae41bd622dbf7e669deb9422 | b4ce46f1e6d1bafab63fb76f11686626057c5fc2 | /fish-analysis.R | eaea3010fa8ac778b21f62596637188d9585a87f | [] | no_license | course-fish274-2019/ClaudiaMateo | 6d2d33b5fb779feb9c47b285b7b08e396e244e48 | 154b93446e7a1f753f922a95086d5268e53bda37 | refs/heads/master | 2020-09-06T02:53:10.844045 | 2019-11-12T18:06:04 | 2019-11-12T18:06:04 | 220,296,114 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 430 | r | fish-analysis.R | fish_data <- read.csv("data/Gaeta_etal_CLC_data1.csv")
library(dplyr)
#Create categorical size column
fish_data_cat = fish_data %>%
mutate(length_cat = ifelse(length > 300, "big", "small"))
library(tidyverse)
ggplot(fish_data) +
geom_point(mapping = aes(x = length, y = scalelength, color = lakeid))
#try this... |
c69c7769dea120a9b19e297815a9a1014682907f | 2d3a7a709e5b783e1f17cf329dd359008ae14ab6 | /R/second_mark.R | ef11c16c95a4dc7ef76ee6c537bcf8b7eda25060 | [] | no_license | debruine/markr | 125042a837d5a0b4c5846cff076005fc8129f5fe | b037d25414bf1beb4b3f5cf7bf1354a3adaefb98 | refs/heads/master | 2021-10-11T10:12:23.391018 | 2019-01-24T15:54:59 | 2019-01-24T15:54:59 | 111,586,219 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,323 | r | second_mark.R | #' Generate Second Marking List
#'
#' \code{second_mark} determines what assessments need to be second marked
#' and bundles files into a folder with the second marking sheet
#'
#' @param marking list containing a dataframe (marks) and moodle participant directory (dir)
#' @param dir directory name to save second mark... |
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