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061c9f389d253a2d45146c411f1b1e158059695c | f971b00d50be32357c35ab530be3a6a8baf70318 | /1_simulate_phenotypes.R | 865ef0a9d635b7db940444fea3a2a81ff3927863 | [] | no_license | joegage/GWAS_AUC | 34439470dfe8739f76e1820d9778472d6f05dcdc | 5b10420143c58bf9bb3b152cdf13bca2cda6fc91 | refs/heads/master | 2020-03-12T16:49:41.264086 | 2018-04-24T16:09:59 | 2018-04-24T16:09:59 | 130,724,447 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,685 | r | 1_simulate_phenotypes.R | source("simPheno.R")
genoFile <- "~/Dropbox/papers/tassel_GWAS_paper/analyses/widiv/genos_phenos_farmCPU.rda"
load(genoFile)
rm(allPhenos); gc()
GM$chrom <- as.character(GM$chrom)
GM$pos <- as.character(GM$pos)
h2 <- seq(0.1, 1, 0.1)
nCausativeValues <- c(10,100, 1000)
nReps <- 10
GDfreqs <- colMeans(GD[,-1])
GDfre... |
2495ca5b29c1335d9d054bbed7aaf955488b6421 | 94fa6de1769db0c8bc2862c67f4e7d80674fd395 | /Part 4 - Clustering/Section 25 - Hierarchical Clustering/data_preprocessing_template.R | 4dda8560fa35c41593ba3f51e99c7544d9c2b65d | [] | no_license | samehamin/Machine-Learning-A-Z | 76d6f04f25c0c7a22f70004027d7212b794a9ac7 | d83bff72bf7da1181fd9a70725010842285ee756 | refs/heads/master | 2021-05-06T15:46:35.964520 | 2018-11-17T20:23:16 | 2018-11-17T20:23:16 | 113,660,183 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 492 | r | data_preprocessing_template.R | # Data Preprocessing Template
# Importing the dataset
dataset = read.csv('Mall_Customers.csv')
X = dataset[4:5]
# Using the dendrogram to find the optimal number of clusters
dendrogram = hclust(dist(X, method = 'euclidean'), method = 'ward.D')
plot(dendrogram,
main = paste('Dendrogram'),
xlab = 'Customers',... |
c50afb252a3dea2aa7e67e26f463f13497ed8854 | 66539bfe3ccea078550d60b55b1d20b8b484dc90 | /R/lis3.R | 949e0d2de1ed21f6d898cc51888d0910e218849d | [] | no_license | xinyue-L/PML | ee1e189f32ecbaad4f24d710d517e818d9a231b6 | 79f67a8d3443b514e4536daa4579524881096292 | refs/heads/master | 2020-07-05T17:48:39.537834 | 2020-05-07T03:59:40 | 2020-05-07T03:59:40 | 202,718,263 | 0 | 1 | null | 2020-05-07T04:01:11 | 2019-08-16T11:46:35 | R | UTF-8 | R | false | false | 645 | r | lis3.R | #' Activity data for three individuals
#'
#' A synthetic data list of three elements, each consisting of an activity matrix for one individual, and
#' each column of an activity matrix is one-day activity observation. Therefore, an activity matrix is
#' \code{nob} by \code{nday}. It is a named list, the name of which i... |
0db6f5eae5976bd339237cc6a54c9d171399f90c | 8228230ee94076f3afaa84a8cd1ad7fa6bb3e7cf | /e5_reshape_if_else.R | 955da2c308fa92c3e180f815bfa186127aabc593 | [] | no_license | agaspard98/borealis | 36f1b95889ebf71f2fba8469c6903bc42225ef23 | 5ad23bfa83fec14b8d2737322889e524a57b72fa | refs/heads/master | 2023-09-05T03:33:07.154842 | 2021-10-31T11:02:50 | 2021-10-31T11:02:50 | 411,773,526 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,943 | r | e5_reshape_if_else.R | #Exercise #5
#Practice the following skills:
# reshape data frames
# if_else statements
#Part 1: version control --------------------------------------------
# Please track changes to your script using version control. Include in your homework the URL to your completed homework assignment available on your
#GitHu... |
b6148da056da8043b030ed43c8376742449dd0b8 | 54a538a87ce7a0bf2313e96cfee6776fdc26227e | /LTLABview_collector.R | eb968086799e0313850d0708ed4e9d4f43e92482 | [] | no_license | ubbikas/LTLABcollector | 7d6e360094d2944199691f7e6b6d3501db8825c9 | 17c8027c37ad417e7e673fd780d18a9be5574a24 | refs/heads/master | 2021-01-01T05:15:42.035641 | 2016-05-14T10:33:02 | 2016-05-14T10:33:02 | 56,992,986 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,675 | r | LTLABview_collector.R | # loading required libraries
suppressPackageStartupMessages(require(dplyr))
suppressPackageStartupMessages(require(xml2))
suppressPackageStartupMessages(require(methods))
if (Sys.info()[[4]] == "ANDRIUS-PC")
setwd("D:/-=Works=-/R/GitHub/LTLABview")
if (Sys.info()[[4]] == "LTLAB-DEV")
setwd("C:/R/GitHub/LTLABview... |
e5c2485511ad136959cc3d889fc56d095fe5c73c | 5b6179f52aa5df28dc2527b0dcfa809e8e502c5d | /Codes/Map_study_sites.R | b27bce77506bc95005638ffd790597d424fcfe19 | [] | no_license | jms5151/EVP | e9de694f9e062ca149a2cac5e5d72e8f7d98f3f0 | 3ef8208d10353d6d058b0ea8085d9cc0a14ba20a | refs/heads/master | 2021-06-05T18:05:24.776867 | 2020-06-12T02:53:07 | 2020-06-12T02:53:07 | 150,645,258 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,904 | r | Map_study_sites.R | # https://eriqande.github.io/rep-res-web/lectures/making-maps-with-R.html
library(ggplot2)
library(ggmap)
library(maps)
library(mapdata)
library(ggrepel)
source("C:/Users/Jeremy/Box Sync/R_functions/Google_maps_API_token.R")
# set sites
ecuador.sites <-data.frame(longitude = c(-80.2225, -79.9554, -79.6109, -79.6109)
... |
872cf39055313085e1b092ebcefa0d2650ca2eb6 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/naniar/examples/add_any_miss.Rd.R | 950d10f5330387b9412f88993e8d4a1e4638679f | [] | 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 | 265 | r | add_any_miss.Rd.R | library(naniar)
### Name: add_any_miss
### Title: Add a column describing presence of any missing values
### Aliases: add_any_miss
### ** Examples
airquality %>% add_any_miss()
airquality %>% add_any_miss(Ozone)
airquality %>% add_any_miss(Ozone, Solar.R)
|
e7b0d352096a51d67df6dcee35d0bbb74fe67cbe | 90bfb6687a16fc6d79dd99a3dc11828ad2c0321b | /scripts/hgnc_symbol_checker.R | e2c574f3938ee93d0420658fc4a79ef712e07df4 | [] | no_license | pilarcacheiro/wol | e028960f797212d885195ae4907fc05127511382 | 2ef44e6a37ac9a938354f081bb662993a98331c5 | refs/heads/master | 2022-10-05T14:40:34.123580 | 2020-06-08T21:35:36 | 2020-06-08T21:35:36 | 270,823,761 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,002 | r | hgnc_symbol_checker.R | ###############################################################################################################################
###############################################################################################################################
### Script: hgnc_symbol_checker.R
### Purpose: check gene symbo... |
2c638fdd3b2bec551d977f39119ef02e2e27072d | 52802ff28ca37aa7d028c1411b2e3948ecbf02f0 | /text_mining/tidy_text/Ch03_tf_idf.R | 49041e9e037006d7d10c74ce83723439a25282c9 | [] | no_license | PyRPy/ML_Py_Templates | a4cd06c5e0cc54ccf544269ae4bf3a8aece15b85 | 677d29207c459bbc9e89e0f1239a1792f128a413 | refs/heads/master | 2022-02-23T06:18:51.466170 | 2022-02-16T22:52:02 | 2022-02-16T22:52:02 | 167,294,425 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,250 | r | Ch03_tf_idf.R | # word and document frequency: tf-idf
# Term frequency in Jane Austen's novels
# What are the most commonly used words in Jane Austen's novels?
library(dplyr)
library(janeaustenr)
library(tidytext)
book_words <- austen_books() %>%
unnest_tokens(word, text) %>%
count(book, word, sort = TRUE)
total_wor... |
d7d6f874f83e6f935ef8dfde3520a8a8009cd034 | 12918ea5a65ded9a9f371c7b0759a6eea51cf5ab | /ratio_over_season.R | ff72068734fe6157b1b597ee7acf9225183aa9a9 | [] | no_license | dengyishuo/BPL | 17673e1a39941deca086d1feb9b6a0027a38ce23 | 94c91d50de3350d0e1e4b2454dda2ba2abc9300c | refs/heads/master | 2021-01-20T12:00:46.224195 | 2014-04-21T16:37:17 | 2014-04-21T16:37:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,497 | r | ratio_over_season.R |
#process data- 8 season 380*8 = 3040 onservations
d1 = read.csv("PL2012-13.csv",header = T)
d2 = read.csv("PL2011-12.csv",header = T)
d3 = read.csv("PL2010-11.csv",header = T)
d4 = read.csv("PL2009-10.csv",header = T)
d5 = read.csv("PL2008-09.csv",header = T)
d6 = read.csv("PL2007-08.csv",header = T)
d7 = read.c... |
3a5269c902754910b9b08436dcf23729e37ca8e6 | 58f02ca234748a1e0214774f2cc07628fb6dbad4 | /FMBNanalyzer/FMBNanalyzer_v_2_3_1.R | ca506b1fc5bd8a2255b9bcad08f257767e3da2e0 | [
"MIT"
] | permissive | ep142/FoodMicrobionet | 0bd2bed7a2127267f2534c461633cff1165d54c4 | 599578aa801af4d87acc7673370fb588b9413281 | refs/heads/master | 2023-01-27T19:52:30.431777 | 2023-01-08T10:57:56 | 2023-01-08T10:57:56 | 229,555,940 | 4 | 1 | MIT | 2022-09-01T14:42:42 | 2019-12-22T11:14:05 | HTML | UTF-8 | R | false | false | 64,174 | r | FMBNanalyzer_v_2_3_1.R | # FMBN analyzer v2.3.1 beta
# performs graphical and numerical analysis of FMBN data generated with the
# ShinyFMBN app
# for this to work correctly
# perform a search in ShinyFMBN and export the __agg__ file: see the manual for ShinyFMBN on [Mendeley Data](https://data.mendeley.com/datasets/8fwwjpm79y/4) for furth... |
1463fe7d1f6c4c96524386eb03a50658ab326e13 | 8db74a17ef5a9218a0abd1dda219ed14ab1810ac | /plot3.R | 35266b9c4c70e5400b581e452024a268adc8c015 | [] | no_license | robertjacobson/ExData_Plotting1 | 915bf8165b8340a98ed650ff237258c5bb3c073c | a7fc240137dd1f258155345f35fe5751e115922d | refs/heads/master | 2021-08-19T17:31:34.199499 | 2017-11-27T03:13:34 | 2017-11-27T03:13:34 | 112,108,990 | 0 | 0 | null | 2017-11-26T19:08:21 | 2017-11-26T19:08:21 | null | UTF-8 | R | false | false | 948 | r | plot3.R | # If datas doesn't exist, load it into the environment.
if(length(datas) == 0){
source('downloadDataset.R')
}
friIndex <- match(as.Date('2007-02-02'), datas$Date)
count <- nrow(datas)
plot_colors <- c('grey','red','blue')
max_y <- max(datas$Sub_metering_1, datas$Sub_metering_2, datas$Sub_metering_3)
png(
file='pl... |
be4a421eb187942de64e22960daeecc6842e6dec | 217c2788d589bc0dff3993bd4a02c04b26296fb2 | /dailyCovidRestimate.R | ed6db2094a18ecf508e11b95b2bccb26a08a7e8f | [] | no_license | tjibbed/UKFprojection | d3fdecf1a5deace4f4956a0c5bb924a62a4a1f99 | 37b300cb2052eec6ab58595a82350c9df65ec7c5 | refs/heads/master | 2023-02-13T09:46:00.873526 | 2021-01-14T10:20:34 | 2021-01-14T10:20:34 | 256,415,670 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,296 | r | dailyCovidRestimate.R | library(ggplot2)
library(data.table)
library(stringr)
library(dplyr)
library(psych)
library(ggpubr)
library(animation)
library(tidyverse)
library(survival)
library(flexsurv)
library(lubridate)
library(survminer)
library(RCurl)
library(httr)
library(jsonlite)
source("EarlyIncidencePrediction.R")
source("WithinHospModel... |
b1fb50fb738343851a28b4363c79ee3ef9bb0d7b | ae6580683da835a034d2e43a2b8023c43071bd05 | /my_ann_orignal.R | c3be7fc86ffca3c9063d753ed7cf693783ec6063 | [
"MIT"
] | permissive | agrutkarsh/Driving-Stereotype-Identification-of-Heavy-Goods-Vehicle-code-only-i.e.-without-dataset- | f25c3af2b71dd3c1655b6c4d3325a0411fc8ab6d | 00035f560d42150bddf031e3eea31fd99560cc19 | refs/heads/master | 2021-04-29T14:29:07.706540 | 2018-02-16T16:31:41 | 2018-02-16T16:31:41 | 121,773,042 | 0 | 0 | null | 2018-02-16T16:30:31 | 2018-02-16T16:27:37 | null | UTF-8 | R | false | false | 2,807 | r | my_ann_orignal.R | setwd('C:/Users/psxua/Dropbox/projects/phd/codes/r/edinburgh series')
library("pracma", lib.loc="~/R/R-3.2.2/library")
library("cclust", lib.loc="~/R/R-3.2.2/library")
library("cluster", lib.loc="~/R/R-3.2.2/library")
library("clValid", lib.loc="~/R/R-3.2.2/library")
data<-read.csv("output_4cls_unnormalised_data... |
536a7d1c9d134abb27ad708f50723dd99a936078 | b5e7b204795818d53e69488b3883a746a11c11ff | /plot4.r | acacfcc633a6eb272490093a050c4986eef0751e | [] | no_license | geoffness/ExData_Plotting1 | b8a56eeafa6ae16b80b23a2b704b4e687fddceea | 91ba9b445efbd7dd5114fa766937eb6b5bf3f16a | refs/heads/master | 2021-01-18T07:37:36.626144 | 2015-09-13T11:22:58 | 2015-09-13T11:22:58 | 42,343,123 | 0 | 0 | null | 2015-09-12T03:27:20 | 2015-09-12T03:27:20 | null | UTF-8 | R | false | false | 1,369 | r | plot4.r | ## Code to read household energy consumption data then plot a group of 4 plots
## covering global active power, voltage, energy sub metering and global reactive power
## Note: assumes presence of file "household_power_consumption.txt" in working directory
# Read and format
FebData <- read.table(pipe('grep "^[1-2]/2/20... |
e0beb5d01431c0eaa05a4873a0f816b2ff56a078 | 153415943e27c98bf6bd27366ce49835050f0899 | /games.R | b47b787acadf6cdce94d0a69eaeb67f186a50b96 | [] | no_license | ptoulis/long-term-causal-effects | a8f7746618df0188e58728e6035e9538fb8aebcb | 7c955450978ca81e4cbe7e6d725d37cefb748ce0 | refs/heads/master | 2021-01-22T09:43:08.141192 | 2016-09-22T22:20:07 | 2016-09-22T22:20:07 | 68,416,870 | 5 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,156 | r | games.R | # GAME = list(payoff matrix for agent A, payoff for agent B, ...)
# mixedStrategy = (piA, piB, ...) where piX = action distribution for agent X.
#
game.mckelvey <- function(A=5, B=5) {
Payoff.A <- matrix(c(1, 0, 1,
0, 0, 0,
1, A, 1), nrow=3, byrow=T)
Payoff.... |
858ae6fdda6d94c51b690be5a7bffbff6b3f4cd2 | 721d96cf611a8ee4ee224e6dae7c92a9faf180c0 | /R/FUNCTION-trim_v5.R | 99227877a3d002334a437f1c95cc07354e892e94 | [] | no_license | cran/astrochron | 5b05df5f4f114cdf6df630cdfe0f649e5f45dbbf | c86d83297c0b75ce1ab1a026a25fe05e0e6cb3fe | refs/heads/master | 2023-09-01T06:57:37.447424 | 2023-08-26T13:40:02 | 2023-08-26T14:30:40 | 21,361,937 | 5 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,333 | r | FUNCTION-trim_v5.R | ### This function is a component of astrochron: An R Package for Astrochronology
### Copyright (C) 2016 Stephen R. Meyers
###
###########################################################################
### trim: function to remove outliers - (SRM: March 9-10, 2012; June 30, 2012;
### ... |
c719af45b4d36200e0dc9b8b55721cf3f6e24e39 | 24bf55acfd9df65714d83975ae5e2e0988d2486e | /man/fbind.Rd | 7237af1f6e578f1ecfdf66a52985e001e9bf43da | [] | no_license | pdaniele/foofactors | 7764d789e5fd33712d4c5977eb872a306863154a | 7e19df7176ba16ad0a0438e4aebe4721861c0957 | refs/heads/master | 2020-06-13T16:12:42.697155 | 2016-12-08T19:07:09 | 2016-12-08T19:07:09 | 75,359,928 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 472 | rd | fbind.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fbind.R
\name{fbind}
\alias{fbind}
\title{Bind two factors}
\usage{
fbind(a, b)
}
\arguments{
\item{a}{factor}
\item{b}{factor}
}
\value{
factor
}
\description{
Create a new factor from two existing factors, where the new fa... |
8f8907458e0386780c7d84a84a16c63173fd20ff | 1e7f61b700940811ef7757d21b3690023ec9822a | /tests/dimchecks.R | d48367250063349653543b9bd09cc5adbe333aaa | [] | no_license | jwjeong1117/pomp | b2417b9514c583fbe19c3280878fab6d60817c00 | 5ce3725cc381039e310f1999b8d37631ac359c14 | refs/heads/master | 2021-01-22T11:21:53.811139 | 2015-10-20T21:15:27 | 2015-10-20T21:15:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,652 | r | dimchecks.R | library(pomp)
set.seed(1420306530L)
pompExample(ricker)
po <- ricker
pars <- coef(po)
xstart <- init.state(po,params=pars)
rprocess(po,xstart,times=0:5,params=pars)[,1,]
rprocess(po,xstart=parmat(xstart,5),times=0:5,params=pars)[,3,]
rprocess(po,xstart=xstart,times=0:5,params=parmat(pars,3))[,3,]
try(
rproces... |
07be9713b1c3a9bae2f3c0db18bd95cb630adab3 | 26f465c3aeab5838385e7008f8fc15f97deb74b9 | /experiments/Experiment 3/algorithms/LOF.R | aaa805780d777f873e75924515bd58fd72e87e36 | [] | no_license | msandim/master-thesis | 3776038565897ccd838f5f88a06499a90f2e0e86 | db60b549446e953747764e40feccc7cf1fcbe2d9 | refs/heads/master | 2021-09-14T01:23:39.003398 | 2018-05-07T02:51:41 | 2018-05-07T02:51:41 | 84,819,866 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 867 | r | LOF.R | library(dbscan)
library(caret)
lof_apply <- function(data)
{
data_X <- data %>% select(-outlier)
returnData <- data %>% select(outlier)
# Scale the data:
scaleObject <- preProcess(data_X, method = c("center", "scale"))
data_X <- predict(scaleObject, data_X) %>% as.matrix
returnData$lof_03 <- lof(data... |
3d1419bd50b9b251dcb324310eacda3c69a6ad27 | 3f680c621d68cd817097e1a83915ceaead162e12 | /man/getAllFunnelsIncAIC.Rd | 80a9a8347fc7b2fb716123f3726f61634e676c8b | [] | no_license | rohan-shah/mpMap2 | 46273875750e7a564a17156f34439a4d93260d6c | c43bb51b348bdf6937e1b11298b9cdfe7a85e001 | refs/heads/master | 2021-05-23T20:34:59.327670 | 2020-07-19T10:24:09 | 2020-07-19T10:24:09 | 32,772,885 | 10 | 6 | null | null | null | null | UTF-8 | R | false | true | 1,779 | rd | getAllFunnelsIncAIC.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getAllFunnels.R
\name{getAllFunnelsIncAIC}
\alias{getAllFunnelsIncAIC}
\title{Get all funnels, including AIC lines}
\usage{
getAllFunnelsIncAIC(cross, standardised = FALSE)
}
\arguments{
\item{cross}{The object of class \code{mpcross} contain... |
84f64fe753f6c0e4bc6887562876d6bdb68726db | f335d68753c16e38f887e7cbaee903feeaa5d37b | /man/var_sinar.Rd | 7d3004882b1e10fb430686e44544ac6a86e112d2 | [] | no_license | cran/sinar | fd50c470ed8924d6ef03c45a8ad641c17341dde6 | bd1f88640db4bf80a5dacb3f938ce842383b80e7 | refs/heads/master | 2023-01-07T03:42:14.314920 | 2020-11-06T16:00:06 | 2020-11-06T16:00:06 | 311,435,252 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 536 | rd | var_sinar.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cls_sinar.R
\name{var_sinar}
\alias{var_sinar}
\title{Empirical estimate for the variance of innovations.}
\usage{
var_sinar(X)
}
\arguments{
\item{X}{A integer matrix where each cell is the observed value in the
regular lattice.}
}
\value{
T... |
8e285cc88595bfdc2a487f21fe86de8580539673 | 89a1c2e27d5c39aa71a6f7e7680d10f362e3f0f6 | /LawfulResidentStatusScrape.r | c3d16c53871fc9d759520fb6f76376427129dfbf | [] | no_license | azankhanyari/USImmigration_Visualization | f9e9d3b59d2ae4d944d82ea826c9562edbb4376d | 547af6b6faac3f63a77be10a2b944fb5c09518da | refs/heads/master | 2022-03-25T13:33:20.079910 | 2019-12-17T17:40:30 | 2019-12-17T17:40:30 | 228,669,965 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 517 | r | LawfulResidentStatusScrape.r | library(rvest)
url <- "https://www.dhs.gov/immigration-statistics/yearbook/2017/table2"
table3 <- url %>% html() %>% html_nodes(xpath = '//*[@id="content-area"]/div/div/article/div[1]/div[2]/div/div/table') %>% html_table(fill = TRUE)
table3_unlist <- table3[[1]]
NaturalisationByRegion <- table3_unlist[2:88,c(1,4)]
... |
d2e0f0b72eb9fa2fe7ad75159c71b9adf7f951b1 | 180dd82b3c98dd23bab21ba2566277cd3a2a3d51 | /R/read_members.R | 6922a94c7afb96d21ab4c1cbda51eca4f4580019 | [
"Apache-2.0"
] | permissive | andrew-MET/harpIO | 2e90861751a2b47ec8cdebde3de6d35414575cb7 | 976ddeca6337c31e7682d7a371552942cbb4cc66 | refs/heads/master | 2023-05-10T11:55:28.830541 | 2023-01-11T09:18:03 | 2023-01-11T09:18:03 | 275,838,990 | 0 | 3 | NOASSERTION | 2022-11-08T12:24:30 | 2020-06-29T14:27:58 | R | UTF-8 | R | false | false | 7,580 | r | read_members.R | # Read ensemble members from a forecast file
#
# \code{read_members} reads all ensemble members from a forecast file for a
# single lead time. It is possible to read from grib or netcdf files via the
# helper functions. It is assumed that grib files only contain one member and
# ntcdf files contain all members.
#
# @pa... |
1dff54020061706aec70ff0eb94fab77e1517e57 | 690c3a0b5858c6f6873ba2e01093da27f7b36e04 | /R codes/Critical Slowing Down_R Codes/Out-of-sample predictions/BTC_appendix.R | 92eb2ada8db02dd5cd41b2de7229f703c8b92cdb | [] | no_license | stancld/bachelors_thesis | ca1552ac3d8ad31de5e27b6457258776b077e03e | d218639f0e76d9240f4225ae4cc57550ac9f8232 | refs/heads/master | 2023-03-26T16:18:58.484752 | 2021-03-29T21:57:08 | 2021-03-29T21:57:08 | 352,791,769 | 3 | 3 | null | null | null | null | UTF-8 | R | false | false | 4,924 | r | BTC_appendix.R | library(quantmod)
library(ggplot2)
library(gtable)
library(grid)
library(gridExtra)
library(tidyverse)
library(lubridate)
setwd("C:/Data/Skola/Bachelor's Thesis/R Codes")
getSymbols("BTC-USD", env = .GlobalEnv, from = '2015-08-20', to = '2018-02-23', periodicity = 'daily')
data <- log(`BTC-USD`[,6])
###... |
546de51905a5e5d73e981dceb64a28586b1e84d0 | 1648d74b87220c51ab86225e438dce2ff3cc72dc | /Churn_Prediction_Ensemble_Model.r | 683155fa2f649d5161ad112d1f7bd405571068e9 | [
"MIT"
] | permissive | s0yabean/ds_portfolio | fa69d138a9761790d80db696fc4ad2b26d36f605 | 446c001ba63ffb9aee18ac9d252ff88520eba145 | refs/heads/master | 2020-06-16T20:55:33.358912 | 2019-07-07T22:44:41 | 2019-07-07T22:44:41 | 195,700,875 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,037 | r | Churn_Prediction_Ensemble_Model.r | ################################################################################################
# Churn-Prediction Machine Learning Project
# Description: A paid project for a venture-backed startup, helping to predict the likeliest customers
# about to churn for a B2C subscription-based app. Here, I am going throug... |
2a1dd8265d35f84307c76216c8a3c851da7c83a3 | 41972a07b4ff2f873dc0f786621bdf3b48279af2 | /dataType/list.R | bcefbf01da8d89886b31db3aad5627787b8f1309 | [] | no_license | juniuszhou/R_language | 4920be49e26d6bbccd54839e27504f36b4b188c6 | e4925034336b372df13b310b410c7e3d34daeef7 | refs/heads/master | 2021-01-18T22:58:19.408997 | 2016-05-12T07:36:47 | 2016-05-12T07:36:47 | 21,159,610 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,072 | r | list.R | # _____________________________________________________
# To make a list "person", actually it is a map.
# 矢量中的元素只能是基本数据类型，而列表中的元素可以是R中的各种对象。
# 一个矢量中的元素必须是同一类型，而一个列表中的元素可以是不同种类的对象。
# each item in the list could be different type, different length.
# and each item has a name.
person <- list(name="payal", x=2, y=9, yea... |
e4c9b88e89971bcc5cb9ec88c5259f08b1da451b | 74550574a2c3aacffc61e6f5bb215e25f35e430b | /R/moc.R | 4409d64f62ef3d65a8ca12fc335625b90125ec49 | [] | no_license | cran/moc | 797ebc7b51f0bdc0d6badcd2b41152221dfa47de | d9e5491d9bdf1229b033f9ac6b441e4ffe023eb9 | refs/heads/master | 2021-06-03T19:05:14.538630 | 2019-02-28T21:32:16 | 2019-02-28T21:32:16 | 17,719,038 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 69,367 | r | moc.R | .packageName <- "moc"
##
## moc : Package to fit general multivariate finite mixtures models
##
## Copyright (C) 2000-2019 Bernard Boulerice
##
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License version 3 as published by
## the ... |
47a215d5fc8222b7c726a9b5fba1b93223feab4d | d746fef241f9a0e06ae48cc3b1fe72693c43d808 | /ark_87287/d7jc7h/d7jc7h-013/rotated.r | 70d0fcb7cbd95373869c270511d1f9e4aa44da19 | [
"MIT"
] | permissive | ucd-library/wine-price-extraction | 5abed5054a6e7704dcb401d728c1be2f53e05d78 | c346e48b5cda8377335b66e4a1f57c013aa06f1f | refs/heads/master | 2021-07-06T18:24:48.311848 | 2020-10-07T01:58:32 | 2020-10-07T01:58:32 | 144,317,559 | 5 | 0 | null | 2019-10-11T18:34:32 | 2018-08-10T18:00:02 | JavaScript | UTF-8 | R | false | false | 195 | r | rotated.r | r=0.21
https://sandbox.dams.library.ucdavis.edu/fcrepo/rest/collection/sherry-lehmann/catalogs/d7jc7h/media/images/d7jc7h-013/svc:tesseract/full/full/0.21/default.jpg Accept:application/hocr+xml
|
7ed3f95070be28178a4b1dceabc82cf3cc80093f | 7e4fd49824b4ef4922fb024a227dd704ed78dfb2 | /cachematrix.R | 844f73c579d206c2895dbfcfa112ae37580cd721 | [] | no_license | shprite/coursera | 37769906b67486e62fd6eb964141913738066301 | f79bc8c8fe74ced5f988dc766a02efd9b857df71 | refs/heads/master | 2021-05-12T10:11:38.198669 | 2018-01-13T15:27:06 | 2018-01-13T15:27:06 | 117,348,903 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,142 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## Write a short comment describing this function
#this function initialises the matrix and the variable to store the inverse of the matrix - inv
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y) {
x <<- ... |
e530a6f63aa84e54dfae3440398b09b0d6fa3850 | d0a994376c57bf849f2229b6afdc3b6c515e30d5 | /pollutantmean.R | 086ee3101e90c68aaec86b0933a512790aa31a4b | [] | no_license | vttoonses/R-Week-2 | 19490c2b49903147adc634f3f977679c3df00af8 | 45c3e0212ce3580cea1b9e4e0e5f025cd7713726 | refs/heads/master | 2021-08-31T13:37:54.607859 | 2017-12-21T14:23:02 | 2017-12-21T14:23:02 | 114,809,592 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,305 | r | pollutantmean.R |
pollutantmean <- function(directory, pollutant, id = 1:332) {
# Validate the function parameters
if (length(directory) != 1) stop("A single directory must be specified for the test data.")
if (!dir.exists(directory)) stop(paste("The specified test data directory", directory, "does not exist."))
if (length(pol... |
394afbc0e217fb55198edf6d9ea84a1c8beb4f06 | 7c17d45e324d26d6ac1eb42aed7c1a7db3b0238d | /z_r_proj/02_ajusta_modelo.R | af732600134f7537adcf27c417a5fb0a7446bb11 | [] | no_license | mhnk77/dpa-project | 4e267df5649f44c41795eb19e041709669b1a96e | a2cc2b4488f002de0e206726b2f633964c2d6efb | refs/heads/main | 2023-05-14T03:57:45.993203 | 2021-05-24T15:51:48 | 2021-05-24T15:51:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,057 | r | 02_ajusta_modelo.R | # library(stringr)
library(tidymodels)
dta <- readRDS('z_r_proj/dta_clean.rds') %>%
drop_na()
rcp <- recipe(results ~ ., data = dta) %>%
update_role(inspection_id, inspection_date, new_role = 'ID') %>%
# update_role(violations, inspection_date, new_role = 'trash') %>%
step_date(inspection_date, features =... |
aeb1cfcbe4094de03e3b788875a2b40ca1fb06be | 86920ac8061f5b29b86733ec332469bf83761202 | /scripts/03_combine_kelp_temp_waves.R | db8b8a90f903fa6d042f62a441eb3b9cda04401e | [] | no_license | jebyrnes/temp_kelp_change | 64b5c54dd58213d51b356017e2512867ef156e5e | b8891d25038ffab02410e5afd4b81d0f0c4d4cea | refs/heads/master | 2021-10-11T14:11:31.283204 | 2021-10-04T19:53:09 | 2021-10-04T19:53:09 | 51,555,573 | 0 | 0 | null | 2021-10-04T19:53:10 | 2016-02-11T23:36:10 | R | UTF-8 | R | false | false | 5,723 | r | 03_combine_kelp_temp_waves.R | library(dplyr)
library(readr)
library(tidyr)
library(readxl)
filename <- "wave_data_reguero/timeseries_v2.xls"
dat_year_filter <- . %>%
filter(Year >= 1952 & Year <= 2008) #first data point in kelp, last da
#### 1) Load the different pieces of the wave timeseries
unique_lat_long_tab <- read_excel(filename, sheet... |
3a3d00afcd6e3f7f2face0a500a7cc6ad97febd9 | 299585457e6f3fd9c3e82769db4d82becc67d05a | /man/backbone.Rd | 8330a9b84be672789614e149e3022376acf9fa94 | [] | no_license | jcfisher/backbone | 39ca2d01f5e99cab28fefd456a69b02a5a6c9fa8 | 89fd0f4b0d786534d398559d8cf72db1abb65b16 | refs/heads/master | 2020-08-05T06:28:38.456269 | 2019-10-03T15:42:41 | 2019-10-03T15:42:41 | 212,430,280 | 0 | 0 | null | 2019-10-03T15:42:45 | 2019-10-02T20:01:09 | null | UTF-8 | R | false | true | 2,045 | rd | backbone.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/backbone.R
\docType{package}
\name{backbone}
\alias{backbone}
\alias{backbone-package}
\title{backbone: Extracts the Backbone from Weighted Graphs}
\description{
Provides methods for extracting from a weighted graph
a binary or signed bac... |
3571ddf3a4da571c670bf3a93ff79ec4db192605 | 41cf136f375fb670d61fb88af26bdb2bbee434c3 | /man/kill_flow.Rd | d90c5328e57894a9ec617752f5ea64a21681bb55 | [] | no_license | gitter-badger/flowr | 41503fa2e4a4c0187a9864442307ee17740374ed | 2db159f2721ee8a3ae3aaf34dc0847b842a1c891 | refs/heads/master | 2020-12-03T09:15:08.286834 | 2015-06-05T17:57:00 | 2015-06-05T17:57:00 | 36,954,410 | 0 | 0 | null | 2015-06-05T20:41:25 | 2015-06-05T20:41:25 | null | UTF-8 | R | false | false | 755 | rd | kill_flow.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/status.R
\name{kill_flow}
\alias{kill_flow}
\title{kill_flow}
\usage{
kill_flow(x, wd, fobj, kill_cmd = "bkill", jobid_col = "job_sub_id")
}
\arguments{
\item{x}{either path to flow [character] or fobj object of class \link{flow}}
\i... |
68d0e8e487f92f5efb83953800502c214c6df649 | 20fb140c414c9d20b12643f074f336f6d22d1432 | /man/NISTradTOgray.Rd | 5ebf101c9bac9ab2e0d25ab9a7ca4dd4f3e8ab1e | [] | no_license | cran/NISTunits | cb9dda97bafb8a1a6a198f41016eb36a30dda046 | 4a4f4fa5b39546f5af5dd123c09377d3053d27cf | refs/heads/master | 2021-03-13T00:01:12.221467 | 2016-08-11T13:47:23 | 2016-08-11T13:47:23 | 27,615,133 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 722 | rd | NISTradTOgray.Rd | \name{NISTradTOgray}
\alias{NISTradTOgray}
\title{Convert rad to gray }
\usage{NISTradTOgray(rad)}
\description{\code{NISTradTOgray} converts from rad (absorbed dose) (rad) to gray (Gy) }
\arguments{
\item{rad}{rad (absorbed dose) (rad) }
}
\value{gray (Gy) }
\source{
National Institute of Standards and Technology (... |
5e9519fdbfcd8020c353bb2c5c974f10ea5ab76b | deaec1ac710c5a9388317a188c6005fd39c67ec2 | /05_ShotogunMetagenome/15_VisualizeAll.R | b41ef99999fe6f208974261cae826bb584b7b417 | [
"LicenseRef-scancode-unknown-license-reference",
"MIT"
] | permissive | ong8181/interaction-capacity | 57bf4cd7a833b953a12b28f28013019e8e624f7b | 7b7f5160225468a91c7241fa78b999baf9cd7efa | refs/heads/main | 2023-04-06T17:11:39.353785 | 2022-05-02T05:05:48 | 2022-05-02T05:05:48 | 235,936,633 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,182 | r | 15_VisualizeAll.R | ####
#### Compare the resuls of phyloFlash and qMiSeq
#### Visualize all figures
####
# Load libraries
library(tidyverse); packageVersion("tidyverse") # 1.3.0, 2021.7.23
library(cowplot); packageVersion("cowplot") # 1.1.1, 2021.7.23
library(ggsci); packageVersion("ggsci") # 2.9, 2021.7.23
theme_set(theme_cowplot())
#... |
9fc57f5a1662cba4016f68ad50a812406e8187af | 99a79794582dfab5cf27cbe290410b3b06c7f4df | /00_scripts/20_GBM/GBM_13.R | 7c94c37d8ab42ea61977473eb2a714cd616260db | [] | no_license | PLStenger/Pearl_Oyster_Colour_BS_Seq | 01db29172513ec8a84689ea187eff787cdd52c87 | c80162bd330573689f929554edae26cd32038917 | refs/heads/master | 2021-07-17T14:44:47.677414 | 2020-06-22T06:20:16 | 2020-06-22T06:20:16 | 183,000,705 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 444 | r | GBM_13.R | #!/usr/bin/env Rscript
setwd("/home/datawork-ihpe/Pearl_Oyster_Colour_BS_Seq/06_bismark")
dat <- read.table("Index_7.2-V-613_R1_paired_bismark_bt2_pe.deduplicated.bam_sorted_clean.bam.bed_correspondance.bed_base.txt")
for(i in 1:length(unique(dat$V1))){
sink("Index_7.2-V-613_GBM.txt", append=TRUE)
pos_name <- as... |
505b2860874fec9b97327dab2798ba5039b6c064 | bcf64441bd84791260d509236fd45ce1e8bf86f2 | /R/draw_serve_locations.R | 14455bf8717e6fba952b023024bd6ea4a17d1a50 | [] | no_license | buddyscott/esv | 69e66d93fedb64022701ff717956764e32bedd2a | 4bf8d8328b3df99c34d974567b7c09161ba83426 | refs/heads/master | 2022-12-15T18:46:43.550957 | 2020-09-05T14:30:56 | 2020-09-05T14:30:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,532 | r | draw_serve_locations.R | library(ggplot2)
library(dplyr)
library(DT)
source('static_variables.R')
source('plotting_utilities.R')
source('sample_data_prep.R')
g <- ggplot() +
geom_rect(data = draw_tennis_court_surface_ggplot(),
mapping = aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax, fill = ID)) +
geom_path(data... |
a3b2be59e974102aa9cd8b657605ec757fd2f098 | 09024c65f99a196f63d742947ce8e3026d4b9088 | /DEG-analysis.R | b5881f1739f45bd1aa788f38b44b1846dab7fc06 | [] | no_license | Xianglaichi/MSI | f8c4527d6effca4606bf5a4fac96ea682b8453c6 | f57d5888fd4dc3cd8ca2f808b783e7757c0d55ab | refs/heads/main | 2023-02-13T18:05:28.020094 | 2021-01-06T09:04:45 | 2021-01-06T09:04:45 | 327,251,680 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,012 | r | DEG-analysis.R | for (i in 1:30){
print(i)
datalist_new1[[i]]<-datalist_new1[[i]][,match(summaryrfs[[i]]$id,colnames(datalist_new1[[i]]))]}
pan_group<-list()
for (i in 1:30){
print(i)
pan_group[[cancername1[i]]]<-summaryrfs[[i]]
pan_group[[cancername1[i]]]<-within(pan_group[[cancername1[i]]],{
High<-NA
Low<... |
92f25660751dc728a97fe4a877d641545e693ec5 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/fullfact/examples/observLmer.Rd.R | be0bf9147a40b4fb5013f3e6d59f92b771c69cc3 | [] | 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 | 287 | r | observLmer.Rd.R | library(fullfact)
### Name: observLmer
### Title: Variance components for normal data
### Aliases: observLmer
### ** Examples
data(chinook_length) #Chinook salmon offspring length
length_mod1<- observLmer(observ=chinook_length,dam="dam",sire="sire",response="length")
length_mod1
|
ed6dddd75c394998eccba121183bfe1e3a9fa203 | 160622f50fc2fe9a6aaa3095849f7a8bd2caa496 | /man/b_grid.Rd | 5581746d2cd5e8c8590e059c48edd32a69268b69 | [] | no_license | jayhesselberth/platetools | 53fd0d16deca84ec6efd0c1f838d7a9fed835a1b | 617a33fc4a3482b85fc1fd8b38dcc82a53a10176 | refs/heads/master | 2020-03-22T01:45:56.557163 | 2018-06-25T13:36:13 | 2018-06-25T13:36:13 | 139,328,253 | 1 | 0 | null | 2018-07-01T12:42:49 | 2018-07-01T12:42:48 | null | UTF-8 | R | false | true | 1,013 | rd | b_grid.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/b_grid.R
\name{b_grid}
\alias{b_grid}
\title{Plots multiple b-scored normalised platemaps}
\usage{
b_grid(data, well, plate_id, plate = 96)
}
\arguments{
\item{data}{Numerical values to be plotted}
\item{well}{Vector of well identifiers e.g ... |
75ed5fdbb72e09cc3034ad857816f9976ecdb01d | bc930a6e02dd38d046a7904b31b4d3557ef8a284 | /R/event_counter.R | 9aa6c5d23388db738237cff53bc7adbb31dff804 | [
"MIT"
] | permissive | kalimu/shinyEventLogger | ea66443f427f60d4e45b1405c7fcb12b247c977b | d6dd6d5ceec57b2e44cff8fa7a83b2e018973396 | refs/heads/master | 2021-07-25T09:08:21.928018 | 2019-02-11T06:59:48 | 2019-02-11T07:00:29 | 157,194,640 | 30 | 5 | NOASSERTION | 2020-04-28T22:08:28 | 2018-11-12T10:15:58 | R | UTF-8 | R | false | false | 629 | r | event_counter.R | get_event_counter <- function() {
settings_session <-
dynGet("log_settings_session",
minframe = 1L,
inherits = TRUE,
ifnotfound = stop(paste0(
"'log_settings_session' not found. ",
"Have you call 'set_logging_session'?"
)))
settings_session$... |
2ec697bde65f7fd94ba5bfea48788b8b55c56478 | f6c871a87f64a5ca6d8dcf6931e05e6dc0fb0c1f | /PreferencesPaper/SweepReaderFC.R | a97527ebe0f630c189584265947c5590625c5ba3 | [] | no_license | CreanzaLab/SongEvolutionModel | 073342a07eaeca061f92b6798b2ab1ff40567b58 | 449087c85280e11b97924575d2e3c618169a0f8b | refs/heads/master | 2020-06-13T18:23:13.603932 | 2019-08-15T22:57:51 | 2019-08-15T22:57:51 | 194,747,721 | 0 | 1 | null | 2019-08-15T22:57:52 | 2019-07-01T21:52:31 | R | UTF-8 | R | false | false | 5,978 | r | SweepReaderFC.R | setwd("D:/Prelim2")
rm(list=objects())
source("Source_SweepSquareReaderFC.R")
#install_github("CreanzaLab/SongEvolutionModel/R-Package", force=TRUE, ref="FemalChoiceExpansion")
#Stitcher(file.path(getwd(),"StaticFinal"))
#Stitcher(file.path(getwd(),"SkipFinal"))
Data <- LoadData(file.path(getwd(),"StaticFinal", "St... |
9ae8b9dfbca3501716ccadadea58abd2f474bd9e | 77645a8337ba03357128505872dc77cb4d49e2bf | /R/lookup-title-descr.R | b430727821237440ea6f4bb55add30617457c22e | [
"MIT"
] | permissive | jameelalsalam/naicsmatch | 0316ae97cb212d791229d04ad64789b39ac39191 | a43a3f408ca2eb3be4b10c2120a03376ad830c0f | refs/heads/main | 2021-07-11T17:02:53.204673 | 2020-04-01T10:09:47 | 2020-04-01T10:09:47 | 162,162,674 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 837 | r | lookup-title-descr.R | #' Lookup Titles and Descriptions of NAICS Codes
#'
#' Convenience function. Focus on most recent NAICS code classifications. Lookup for prior may be added in teh
#' @param naics a character vector of NAICS codes to look up
#' @param naics_listing a lookup table of naics codes and titles, defaults to most recent
#' @al... |
1b48b3d817dc03d4154deda30f0cbf731e1324fb | c0fec0769584c9bd2d2a322f948e99283253ab9e | /man/Motif-class.Rd | 3ff02f33d9ab5ceda62e6da1bf0afd79e36d73eb | [
"MIT"
] | permissive | Puriney/signac | ed88d42b8f9af470badf9fee4ff6f867a6eaea73 | cd99cf2212320785f34dfd5db58e14b59e89edc5 | refs/heads/master | 2023-01-28T05:41:10.679212 | 2020-12-04T17:13:54 | 2020-12-04T17:13:54 | 273,932,199 | 0 | 0 | NOASSERTION | 2020-09-16T15:15:39 | 2020-06-21T15:30:09 | R | UTF-8 | R | false | true | 1,028 | rd | Motif-class.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects.R
\docType{class}
\name{Motif-class}
\alias{Motif-class}
\alias{Motif}
\title{The Motif class}
\description{
The Motif class is designed to store DNA sequence motif information,
including motif PWMs or PFMs, motif positions, and metad... |
3990d20fbcd9e8247d0d078bcbe717a16f20d93f | c7add6406714aad477f5b3690914fcff0befeed9 | /R/consensusPeakList.R | 588986f7dc8bec38014519a35cc28658e50e3f86 | [] | no_license | gtluu/rspeclust | 37a006aa53e7504a004a9b8475f6226fed37b3d1 | 1a212a18cc11ffefba3ca4e46d051d7201873218 | refs/heads/master | 2022-12-05T14:21:40.634741 | 2020-08-20T21:31:46 | 2020-08-20T21:31:46 | 288,889,087 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,458 | r | consensusPeakList.R | #' Get Consensus Peak List
#'
#' Generate a consensus peak list with average m/z values from multiple peak
#' lists.
#'
#' @param peakLists \code{list} of single column \code{dataframes} with the
#' column name 'mz'.
#' @param tol \code{double} value to be used for m/z tolerance when merging
#' peak lists.
#' @param ... |
80dc39decdf534fa563f12405d9adebf070b7da7 | cf770445f090d49f1a93b706f881a32b35b6777d | /R/make_target_ls.R | b6e29069cce37fd5ed837a48714c935c672e7cf7 | [] | no_license | devincaughey/estsubpop | 70ad85b33a52353c62816fe8676082498cac2bfb | 9bf4a32f8ca04d29c8307c8923e5d3264778a830 | refs/heads/master | 2020-03-25T09:22:01.395227 | 2019-06-11T02:26:10 | 2019-06-11T02:26:10 | 143,662,471 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 857 | r | make_target_ls.R | #' @export
make_target_ls <- function (design_list, formulae_list, periods,
period_var = "YEAR") {
out <- vector("list", length(periods))
names(out) <- periods
for (y in seq_along(periods)) {
out[[y]] <- list()
for (d in seq_along(design_list)) {
for (f in seq_along(form... |
6070785668ec8d1941ce1986eb904461dc793e2f | a4c82295d300bae1b76287504d968b61836a222f | /tests/testthat.R | 2530bb5684295dd4cc54e7302e20db6611b7bcfb | [
"MIT"
] | permissive | sainathadapa/stubthat | 29ddbd4166509d9abb864a14a24eb908965d866c | 6fcfcdbba6f605ef5dd093d455822ade30643682 | refs/heads/master | 2022-05-12T09:14:02.326260 | 2022-05-07T13:31:08 | 2022-05-07T13:31:08 | 41,420,394 | 20 | 6 | NOASSERTION | 2022-05-03T12:39:37 | 2015-08-26T10:47:41 | R | UTF-8 | R | false | false | 60 | r | testthat.R | library(testthat)
library(stubthat)
test_check("stubthat")
|
16976c1289a80ce747854df571acc5a3d605fbfe | 307daa5d64a3e1e5ab2b6e4e85c83133e9d43e7b | /man/getCVLData.Rd | c582ea951b8b6e37cd957d2e460cefa771b65747 | [] | no_license | hkim207/ahri-1 | 80be69695ee74acb2f10fc9b63b74998f02e04f3 | d2a671671c1b8cf66bc97d6d7135581254ebb30e | refs/heads/master | 2022-12-13T16:51:37.618854 | 2020-09-26T20:05:22 | 2020-09-26T20:05:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 358 | rd | getCVLData.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getVLData.R
\name{getCVLData}
\alias{getCVLData}
\title{getCVLData}
\usage{
getCVLData(Args)
}
\arguments{
\item{Args}{requires Args (\code{\link{setArgs}}) for filepath and setting age}
}
\value{
data.frame
}
\description{
Pulls in Community... |
6266cd224d3f5d97f88f42a275cda6027f8141c0 | 2132efc961fd422dea6ba2f6ab205e14993556e4 | /tests/testthat/test_biodiversidad.R | e61e138e0d098dfc5fb9afc7031e36303d5993e4 | [] | no_license | datasketch/GanaderiaSostenible | 3f62e05879535520a2acc7105a1b120f1928f0af | 4e78982713bfe6b1e4720d71c8a8ac50d0f7f703 | refs/heads/master | 2020-09-09T16:00:53.379680 | 2020-03-02T15:28:00 | 2020-03-02T15:28:00 | 221,488,970 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,977 | r | test_biodiversidad.R | context("Biodiversidad")
test_that("Biodiversidad works",{
area <- 10
region <- "Eje Cafetero"
pop2 <- biodiv_area(10, "Eje Cafetero", "bosque_secundario")
pop2
pop2 <- biodiv_area(1000, region, "bosque_secundario")
pop2
area <- 10
region <- "Bajo Magdalena"
tipo_cobertura <- "bosque_secundario"
... |
52101fc949aad3da2b8c2b328cf2eb271c09ab3d | b3affc2e8d14674a9db30759047077fc0ae72301 | /Fin3380/Fin3380_Quiz4/fin3380_quiz4_mywork.R | dc0f43ff09324088a7e6bc7a58976f3630367921 | [] | no_license | HeyBrawlStar/RstudioPractice | 57e46ed61ac347dfbf229949e097882d9e77f51c | b520c658c93a38b8e1fc06421b6e4b7d641349df | refs/heads/master | 2023-01-24T02:45:09.543682 | 2020-12-06T01:56:11 | 2020-12-06T01:56:11 | 295,978,579 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,042 | r | fin3380_quiz4_mywork.R | setwd("/Users/guangyaohe/Desktop/FIN3380/Fin3380_Quiz4")
dj.rsi= read.csv("dj.csv")
dj.rsi = dj.rsi[-which(dj.rsi$dji==0),]
dj.rsi = na.omit(dj.rsi)
library(zoo)
library(dplyr)
dj.rsi = dj.rsi %>%
select(date,dji) %>%
arrange(date) %>%
mutate(date=as.Date(date),
delta=dji-lag(dji,1))
head(dj.rsi,3)
... |
4badcebdcc2412d9c23d24231a04d033d226fb53 | dae9dece43c6fe692fdf00e45bcab0129b4f3787 | /examples/api-key-authentication/1-deploy.R | 9550337c13a93b6565315e97b4f552b6226cc1d3 | [] | no_license | Sandy4321/RestRserve | 31b2f83a47b51bff1be8bc9456cf9ec5f425a291 | 1ee89a0864ea9b67ca581a0ccfe2fa4917978d7b | refs/heads/master | 2020-03-17T04:48:39.978576 | 2018-04-16T05:35:21 | 2018-04-16T05:35:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 525 | r | 1-deploy.R | #!/usr/bin/env Rscript
DIR = "run"
APP = "R/app.R"
conf = c(
"http.tls.port" = "8002",
"tls.key" = normalizePath("run/cert/server.key"),
"tls.cert" = normalizePath("run/cert/server.cert"),
# you may need also put public keys (CA certs) provided by Certificate Authority (CA)
# "tls.ca" = normalizePath("run/c... |
8223dee4b4b7a8fdd0f506714cbfbe17ccaf7c48 | 587636c9ce7053359f4e094e835b2c04a0662982 | /riveredge.R | 7565c77046f654c0e78a310f50164c2688b6be00 | [] | no_license | RomeoAlphaYankee/DataScienceR | 99678099a18ad4acc3d5ed6b43b286e933380e86 | 89fb71d38c235317c4e8c203385d76c72d64f484 | refs/heads/master | 2022-05-06T20:01:05.590064 | 2022-03-19T15:36:35 | 2022-03-19T15:36:35 | 219,862,131 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,951 | r | riveredge.R | library(dplyr)
library(readr)
library(lubridate)
library(ggplot2)
ti <- read.csv("C:/Users/angel/Downloads/multiTimeline.csv", skip = 2)
head(ti)
names(ti) <- c("week", "data")
ti %>% mutate(date = ymd(week)) %>%
mutate(year = year(date), month = month(date)) %>%
filter(month >= 6, month <= 9) %>%
... |
cd05148169b09b1dd534d7702e38366778068689 | ada4a328c22ba799276e6e827ff10fc2a17aaab4 | /vectorized.R | cad31db9e0f7435695a196eac43528a122a19637 | [] | no_license | giannelli/slotmachine | aee8fb1ac7cbfb70c3f19df6fba12f84812ba0fb | b1fb4e0af9e3f4df1a06f5eb946acc0b4c8863c3 | refs/heads/master | 2020-04-01T14:44:30.371705 | 2018-10-25T14:40:08 | 2018-10-25T14:40:08 | 153,306,124 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,477 | r | vectorized.R |
# Hands-On Programming with R
# Chapter 10 : Speed
# Repeat what we need - should package this up.
get_symbols <- function() {
wheel <- c("DD", "7", "BBB", "BB", "B", "C", "0")
sample(wheel, size = 3, replace = TRUE,
prob = c(0.03, 0.03, 0.06, 0.1, 0.25, 0.01, 0.52))
}
score <- function(symbols) {
dia... |
ef78545214e168ab79bfa992f16d007f6af6752b | bbcec89cbc999b348f012b091525af29e267c4c8 | /man/impute_missing_per_quantile.Rd | 7a99c43f18e3d1571aca2697d9fcab1042b42f46 | [] | no_license | shinyfluba/covidEnsembles | 3ec08a70205d145bd0597d75bd1c763b2907f2fb | fedbc5c8921afd221ec4584a3bd63152eb4fba8b | refs/heads/master | 2023-04-04T08:10:35.323665 | 2021-03-25T14:49:15 | 2021-03-25T14:49:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,102 | rd | impute_missing_per_quantile.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/qra_fit_convenience.R
\name{impute_missing_per_quantile}
\alias{impute_missing_per_quantile}
\title{Impute missing values for each quantile level in a quantile forecast matrix
It is assumed that in each row, all quantiles for a given model ar... |
1da95af3a5ba19aadbdbc72210070c89914e8cd5 | 7a7a325a0f01d92a41e7c35278e7f938452fed84 | /man/YPR.Rd | 25d842db1edcb43b58ad0fbf15c61f37665bc896 | [] | no_license | IMPRESSPROJECT/Rfishpop | cca94973e345a7841634b5ab4a7ba5fdebf42e24 | c4055e61b7126d9ab0b4264855f39584405a8a16 | refs/heads/master | 2022-08-30T10:35:13.002516 | 2022-08-26T16:51:16 | 2022-08-26T16:51:16 | 252,695,701 | 0 | 5 | null | 2020-11-05T10:49:10 | 2020-04-03T10:04:49 | R | UTF-8 | R | false | true | 3,069 | rd | YPR.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/YPR.R
\name{YPR}
\alias{YPR}
\title{Yield-per-Recruit}
\usage{
YPR(Pop.Mod, f.grid, Fish.years, Bio.years, plot, Method, par)
}
\arguments{
\item{Pop.Mod}{A list containing the components returned by Population.Modeling function (mai... |
5205eaec2c952a1ccc8b0c6679944da2db3ac558 | 71a051ad748a7b22f427edd81d791443f4c90dea | /man/rgt.Rd | 8739b3e6489153a2f337e91aec7428542511d9e3 | [] | no_license | knausb/GeneticDiff | d13c419503591de43979dc1016383770d5094b1e | 6058edb2bd4e68152b42e80ca2807bfc370204f3 | refs/heads/master | 2021-01-01T19:45:04.216663 | 2017-09-24T21:42:58 | 2017-09-24T21:42:58 | 98,671,915 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 4,679 | rd | rgt.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{rgt}
\alias{rgt}
\title{rgt - random genotypes}
\usage{
rgt(nsamp = 4L, nvar = 3L, pphased = as.numeric(c(0.5)),
pploid = as.numeric(c(0, 1)), pallele = as.numeric(c(0.5, 0.5)),
verbose = 0L)
}
\arguments{
\item{nsamp}... |
4ea988c024b617ee7e4eafd64de8367118d3b821 | ee79ad37d2885aac5f5cecb9eea61fafee117a11 | /simultaneous-selection/analysis/analyses_rep/model_4.R | 0fd970f658dc8c32c61792ad06ee193ef7d9609b | [] | no_license | vullab/ColorBinding | d33a3609b89f0916a026cae846742e1f065938ea | b3c6e718f1233ad9b4568a51f794cf3b5966145f | refs/heads/master | 2021-03-16T10:26:02.634673 | 2018-12-16T04:04:29 | 2018-12-16T04:04:29 | 65,759,803 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,521 | r | model_4.R | library("VGAM")
#library(R.basic)
#looking good, debatable whether or not should do the whole color or flipping - whole color should not rely on part maybe?
#need seperate colors for asymetrical bars
rotate90<- function(x) {
t(mirror.matrix(x))
}
# Rotate matrix 180 clockworks
rotate180 <- function(x) {
xx <- r... |
27d6676a6eb9f4e5567cda6d62bd83799736d1e9 | 5ec06dab1409d790496ce082dacb321392b32fe9 | /clients/r/generated/R/ComDayCqDamIdsImplIDSPoolManagerImplProperties.r | 476ec884ca2009a97902c7874aa3d646013c8e38 | [
"Apache-2.0"
] | permissive | shinesolutions/swagger-aem-osgi | e9d2385f44bee70e5bbdc0d577e99a9f2525266f | c2f6e076971d2592c1cbd3f70695c679e807396b | refs/heads/master | 2022-10-29T13:07:40.422092 | 2021-04-09T07:46:03 | 2021-04-09T07:46:03 | 190,217,155 | 3 | 3 | Apache-2.0 | 2022-10-05T03:26:20 | 2019-06-04T14:23:28 | null | UTF-8 | R | false | false | 8,237 | r | ComDayCqDamIdsImplIDSPoolManagerImplProperties.r | # Adobe Experience Manager OSGI config (AEM) API
#
# Swagger AEM OSGI is an OpenAPI specification for Adobe Experience Manager (AEM) OSGI Configurations API
#
# OpenAPI spec version: 1.0.0-pre.0
# Contact: opensource@shinesolutions.com
# Generated by: https://openapi-generator.tech
#' ComDayCqDamIdsImplIDSPoolManager... |
0cd70364ce97e4007ebc740ec25403115d0b0554 | 16e799f7242507c6168cbea9c2da9c7522d36a85 | /assignments/fishery_stats.R | 932ff2a696ab11ebe38485b8e79ee2cd787c18d3 | [] | no_license | eristig/ESM262-1 | 6fc035328563d684da502fab3d6e4c85f73ccdec | b2414d073f624424f312eeca0d69a3ff91ae4310 | refs/heads/master | 2021-02-19T03:47:50.641382 | 2020-03-05T21:45:21 | 2020-03-05T21:45:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 934 | r | fishery_stats.R | ### Assignment 2: Fishing
# create vector of possible fish
possible.fish = c("salmon","steelhead","shark","tuna","cod")
possible.locations = c("A","B","C","D","E")
fish.prices = runif(min=5, max=10, n=length(possible.fish))
# we can use sample to simulate a random recording of catch by fisherman, lets say we pick 20... |
15092a39aa8e57bd5cf78d620f36cf5116aab83e | 8114393f498302505eb893a8f3f62b5200c65fe5 | /man/predict.CauseSpecificCox.Rd | 2294479d8ab53787af0a5aa2fd5a5135bb84be22 | [] | no_license | PablitoCho/riskRegression | 838fae9645819eaa0aa5fc51f9b462c22ccc84ad | d5e995d991a2de2196ab9751b69b606fea347743 | refs/heads/master | 2020-12-13T19:57:41.632436 | 2017-06-26T12:37:23 | 2017-06-26T12:37:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 4,661 | rd | predict.CauseSpecificCox.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/predict.CauseSpecificCox.R
\name{predict.CauseSpecificCox}
\alias{predict.CauseSpecificCox}
\title{Predicting absolute risk from cause-specific Cox models}
\usage{
\method{predict}{CauseSpecificCox}(object, newdata, times, cause,
landmark =... |
4ef4c7fdf9dd047830dbd312f6c61c0b72201829 | 5d1e0788c8732862077768cf3bf27e2e1dd9affa | /plot1.R | bf500f6c7bafa03202127dea921d4eeb1159652f | [] | no_license | trier257/ExData_Plotting1 | 2fe346ca3b676353edf4fec97e0e7726c7155c6b | d86572b2a800942b9f34c75beecdca90e78c0871 | refs/heads/master | 2020-11-30T16:17:27.282069 | 2014-06-07T01:03:16 | 2014-06-07T01:03:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,373 | r | plot1.R | # Read in the data.
# This code requires the zip file be downloaded and unzipped to the current directory.
# The data is big but not so large that it won't fit in my PC's memory.
allData <- read.table("household_power_consumption.txt", sep=";", header=TRUE, stringsAsFactors=FALSE, na.strings="?")
# The assignment only... |
4e4673d5a60f39c6d5380e15e7b77c6314da205c | c4eceeca4f0ef6f9aa3cc803c50db867da22fc12 | /R/hgrm2.R | 3eb43db34adace4a8cfbc46ae997b926034b6c38 | [] | no_license | xiangzhou09/hIRT | f55191377777aa8e59d21d1c6cef6349d7e3b284 | b33d90f13287f60f7feffd0c061858340dff40a6 | refs/heads/master | 2021-12-30T05:05:26.010542 | 2021-12-14T21:14:23 | 2021-12-14T21:14:23 | 97,876,422 | 8 | 1 | null | null | null | null | UTF-8 | R | false | false | 11,011 | r | hgrm2.R | #' Hierarchical Graded Response Models with Known Item Parameters
#'
#' \code{hgrm2} fits a hierarchical graded response model where the item parameters
#' are known and supplied by the user.
#'
#' @param y A data frame or matrix of item responses.
#' @param x An optional model matrix, including the intercept term, t... |
30b2864595e7aaa5479a9e4ef4aa4779a8398705 | 00cf6a882ff6436daf8e7cb511c6334dc0c387cf | /00_build_product_metadata.R | 1f118a605fb2591590d78b37ac41b26ff2f7f810 | [] | no_license | dahcase/earth_engine | 09183f95c4fc114e45d10dbb573f9549ca01e72f | 43d8fdc500ffba0f274b5234edbe70fdefd3aca0 | refs/heads/master | 2021-06-26T12:55:39.076946 | 2019-02-10T01:40:25 | 2019-02-10T01:40:25 | 116,109,534 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,764 | r | 00_build_product_metadata.R | library('data.table')
#Land surface temperature (MOD11A2 & MYD11A2)
lst = setDT(expand.grid(product =c('MOD11A2','MYD11A2'),
version = '006',
variables = c('LST_Day_1km', 'LST_Night_1km'),
lower = 7500,
upper = 65535,
qa_na ... |
a95b6579538dafa0e870253da2a49859395e383a | a24efd391f493d356c0fe3f94ebc36e6eebd4714 | /R/days_to_numeric.r | d4d284a7aa4dc0052b3e621bf6d74f8a619d94f2 | [] | no_license | dabidou025/Lockdown-Forecast | 7eb0bfe8fedb5f2289b9796cc5c1b6bc3023848c | 400f10c18c85e53bb5851c488471bce80c957dc0 | refs/heads/main | 2023-04-01T07:40:25.668489 | 2021-04-04T11:41:24 | 2021-04-04T11:41:24 | 354,528,256 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 461 | r | days_to_numeric.r | days_to_numeric = function (data) {
data$WeekDays[data$WeekDays=='Monday'] = 1
data$WeekDays[data$WeekDays=='Tuesday'] = 2
data$WeekDays[data$WeekDays=='Wednesday'] = 3
data$WeekDays[data$WeekDays=='Thursday'] = 4
data$WeekDays[data$WeekDays=='Friday'] = 5
data$WeekDays[data$WeekDays=='... |
d673d9b9a2661dfb5a3cb4c7b4c18629c34776ee | a4bcc17bab2f4ac27c37f8f0027168211a9bc24d | /man/lookupGenes.Rd | 41d42707bb4298724c2794264127d34c0b562fde | [] | no_license | hcnh174/hlsgr | 7da6307f535038d00e9030eab61e1a0045fc745a | 23b0d0cce887eef200c90fccb5a667b4fc1f37cd | refs/heads/master | 2023-04-13T18:44:28.519716 | 2023-04-03T05:38:45 | 2023-04-03T05:38:45 | 262,727,333 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 455 | rd | lookupGenes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rnaseq.R
\name{lookupGenes}
\alias{lookupGenes}
\title{Return a vector gene names corresponding to a vector of Ensembl IDs}
\usage{
lookupGenes(ensembl_ids)
}
\arguments{
\item{ensembl_ids}{vector of ensembl IDs}
}
\value{
}
\description{
Re... |
f66993d4ba8a1afc2c1b0a134e4dd73c4c3f4718 | be2d33a5547d69d7339351366c5a1a309f4455dd | /fcd/R/plan.R | c9b4b096a7e8469c91106c1aca082d1a189a573c | [
"MIT"
] | permissive | thebioengineer/drake-examples | 3e3f1f6e0ae9942b70333238875dd65849650ee7 | e7009496a9fa8495faa32806eb191ad9ae33786d | refs/heads/master | 2020-08-04T19:49:58.862394 | 2019-10-09T16:28:03 | 2019-10-09T16:28:03 | 212,259,584 | 0 | 0 | null | 2019-10-02T05:07:05 | 2019-10-02T05:07:04 | null | UTF-8 | R | false | false | 383 | r | plan.R | #Experimental Conditions
conds <- fcdConds(nPeople = c(10000,1000,100), pEdge = c(0.75,0.5,0.25,0.1), topN = c(NA, 3, 5, 10))
plan <- drake_plan(
ddist_i = target(
fcdSim(nSims = 50, nPeople = nPeople,pEdge = pEdge, topN = topN),
transform = map(nPeople, pEdge, topN, .data = !!conds)
),
ddist = target(
... |
a05afa87352479a24c6788ecf347fc3e86be442a | ccd5803bb5b558dcd89b4818b7a42b00cf63e610 | /R/format.R | a318fc8fdb8049eec20651b5562ca5fd26fbeee6 | [
"MIT"
] | permissive | labouz/recipes | bbcdedce9116745df3bb69d88d9aacce26026fe8 | 620ebfe67d59e3e440fe203b51bdb87ceb542702 | refs/heads/master | 2021-11-15T09:57:24.236122 | 2021-04-22T18:41:14 | 2021-04-22T18:41:14 | 237,512,319 | 0 | 0 | NOASSERTION | 2021-04-23T23:39:04 | 2020-01-31T20:40:30 | HTML | UTF-8 | R | false | false | 1,043 | r | format.R | #' Helpers for printing step functions
#'
#' @param x A vector of objects.
#' @param sep A character string for separating values.
#' @param width An integer for when to split the output over lines.
#' @return A character string
#' @keywords internal
#' @export
format_ch_vec <-
function(x,
sep = ", ",
... |
75c8c10127ac91ef6081bc9db1b9392fc1292731 | 4bcffce1d7b8094e5dd462705ad73834da8aed0e | /plot4.R | ba87a636aba245a677b6c46e0cf29f0a99859458 | [] | no_license | NicoHeredia/ExData_Plotting1 | ff02715aa8aacc3c8ff344c7528459b53bd83708 | 0b47ef482913dd7488cb60806496e9205e007639 | refs/heads/master | 2022-11-14T07:26:44.862702 | 2020-07-12T20:45:12 | 2020-07-12T20:45:12 | 279,129,446 | 0 | 0 | null | 2020-07-12T19:00:08 | 2020-07-12T19:00:07 | null | UTF-8 | R | false | false | 1,211 | r | plot4.R | FileDat <- "household_power_consumption.txt"
PowerData <- read.table(FileDat, header=TRUE, sep=";", stringsAsFactors=FALSE, dec=".")
SubsetDat <- PowerData[PowerData$Date %in% c("1/2/2007","2/2/2007") ,]
#str(SubsetDat)
GlobalActPower <- as.numeric(SubsetDat$Global_active_power)
TimeDat <- strptime(paste(SubsetDat$Dat... |
38645f54fdf875f9ee7c951901b7d230bdf2409c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/taRifx/examples/munch.Rd.R | db766865e270d49851232e72b4902fc55575584f | [] | 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 | 250 | r | munch.Rd.R | library(taRifx)
### Name: munch
### Title: Recursively delete entries containing 'what' before entry
### pointed to by 'which'
### Aliases: munch
### ** Examples
x <- c("a","","b","","","","","c","d","","","","e","")
munch( x, c(3,8,9,13) )
|
15c1b669319f7f8a4937aa738f5d1fcf5211a535 | 212e49d0b5df150e4d0681451925689b9e152eba | /MESSAR_WEBSERVER/helper.r | 280bb300966644aba086178a2dbdc94c356c3439 | [] | no_license | daniellyz/MESSAR | 7e3fa9a6fbfba378a37ded366bad6db89d6a226b | ebbe4d0b849d074d36d447d5488464ac4773b991 | refs/heads/master | 2023-04-14T02:19:20.990578 | 2023-03-28T17:06:07 | 2023-03-28T17:06:07 | 153,620,833 | 1 | 0 | null | 2019-07-03T14:57:32 | 2018-10-18T12:33:50 | R | UTF-8 | R | false | false | 9,930 | r | helper.r | <<<<<<< HEAD
=======
mcc_calculator<-function(rules){
P_A_B = rules$support/rules$Py
tpr = rules$support
fpr = (1 - P_A_B)*rules$Py
fnr = (1 - rules$confidence)*rules$Px
tnr = 1 - tpr - fpr - fnr
MCC = (tpr*tnr - fpr*fnr)/sqrt((tpr+fpr)*(tpr+fnr)*(tnr+fpr)*(tnr+fnr))
return(MCC)
}
>>>>>>> 7b7522d0affa0d7... |
b1a97b33b93fb325844de4c2532143569d8348ee | 9b40acbd06b56f1df9de7438516a3230ba8c2cc4 | /server.R | 9244cd4b24caa9e992224d9b8d6e03538eec7021 | [
"MIT"
] | permissive | Huade/UN_IdealPoints | 7ca7c4ec059df758bda10c7e2677b4eefbf4290e | 9276aca27ad69dd4efacaa64217c15d7671201a3 | refs/heads/master | 2016-09-06T07:03:32.325359 | 2015-04-21T15:17:33 | 2015-04-21T15:17:33 | 31,746,396 | 2 | 3 | null | 2015-04-01T14:17:15 | 2015-03-06T01:31:12 | R | UTF-8 | R | false | false | 3,252 | r | server.R | library(shiny)
library(ggplot2)
library(plotly)
library(ggthemes)
shinyServer(function(input, output, session) {
output$trendPlot <- renderGraph({
if (length(input$name)==0) print("Please select at least one country")
else {
df_trend <- subset(Ideal_Point_Data, Name %in% input$name)
... |
36c6f23ecb6b74239c55c7672a8b68c3120c551a | a67fa37fb1ae2a624f2fc306f1b6eab457fc8197 | /Hidden Markov Models/HMM.R | cae505347c13d5caf94a66a5a522cd4520571a57 | [
"MIT"
] | permissive | PedramKasebzadeh/Machine_Learning | a350ef5a05c2ae953624a69c6bb66d8f6331d0ca | 3ac60aa38722d1a6817a8347e58954892f9ebbb0 | refs/heads/main | 2023-01-24T02:19:30.162358 | 2020-12-11T12:15:20 | 2020-12-11T12:15:20 | 320,540,864 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,006 | r | HMM.R | # Task 1 - Build a hidden Markov model (HMM)
# Defining the trasmissions and the emissions probabilites matrices.
set.seed(12345)
library("HMM")
# creating the emissons probability matrix
ep= diag(0.2,10)
ep[row(ep) == (col(ep) -1)] = 0.2
ep[row(ep) == (col(ep) -2)] = 0.2
ep[row(ep) == (col(ep) +1)] = 0.2
ep[row(e... |
bf04ac00cde577bdfd319df1c028a025b66d2209 | cbd3b0f76677e4e5c00bafced19939549182ff3f | /cachematrix.R | 3badc6ef8885fa05fa6ef54e6325e6e29fb4c342 | [] | no_license | ginoandolini/ProgrammingAssignment2 | d6dc2922c2dac18420bc270c692ed1bba8a6558f | 7b4043852fb8ed6eb773ae434c9252929139334f | refs/heads/master | 2020-04-01T19:19:37.525165 | 2014-05-20T10:41:06 | 2014-05-20T10:41:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,255 | r | cachematrix.R | ## makeCacheMatrix makes a special matrix
## Sets the value of the matrix, set
## Gets the value of the matrix , get
## Sets the value of the matrix inverse, setinverse
## Gets the value of the matrix inverse, getinverse
makeCacheMatrix <- function(x = numeric()) {
m <- NULL
set <- function(y) {
... |
c9d64ac63c1a3b7f19c451097476055a3d33a196 | ecd850028010252cd24d7d67aa3ef3b8065bf109 | /man/parallelCountNbd.Rd | 81f51188730bed3e4daa424abf88265e443dbe5c | [] | no_license | EricMarcon/SpatDiv | de98877555e9d92ec7859d4ce18bf5dbb47bf2a1 | c0346e1105130d18dc8b978a415569f653ae0cf7 | refs/heads/master | 2023-05-26T18:42:38.506723 | 2023-05-21T08:42:49 | 2023-05-21T08:42:49 | 111,210,097 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 657 | rd | parallelCountNbd.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{parallelCountNbd}
\alias{parallelCountNbd}
\title{parallelCountNbd}
\usage{
parallelCountNbd(r, NbSpecies, x, y, Type, Weight)
}
\arguments{
\item{r}{The vector of distances to take into account.}
\item{NbSpecies}{The num... |
8f37165ade6eae5855a7723931f56be58d0738a0 | f04c8b91b2e26b0c4342525533032a0d33ad1333 | /R/link_tag.R | 4035f482c2f7e7bef2e8f4ee77068794d5c24eff | [] | no_license | polymathematic/Longform | c567c9d883ba2c02a03a484546a7a62ad9a1b973 | d03557ac07d6e39e0baf30c4dfa26d070c60a0b6 | refs/heads/master | 2020-03-15T21:22:07.825613 | 2020-01-14T18:00:50 | 2020-01-14T18:00:50 | 132,353,511 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 343 | r | link_tag.R | #' Add a link tag
#'
#' Format an HTML image tag
#'
#' @param text The text of the link
#' @param url The URL the link points to
#' @export
link_tag <- function(text, url){
#Create tag
output <- sprintf("[%s](%s)",
text, url)
#Class as image tag
class(output) <- "link_tag"
#Return outp... |
2c34b013e33aca29c72663b2205888299878b947 | 31325bac0088dbc4725f01794ff72413d5856c64 | /R/I_quadModMat.R | 16589c32707e11f9a573c5e9b1385dd304376d59 | [] | no_license | mfasiolo/synlik_dev | 6b2bae4f357cbe028050917f5a264e1698fcfe76 | 2508506cffe09e752c6fa221e6431997f739168e | refs/heads/master | 2021-03-27T10:56:50.668629 | 2018-05-30T10:37:00 | 2018-05-30T10:37:00 | 17,673,353 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 506 | r | I_quadModMat.R |
# Creates model matrix for quadratic multivariate regression
.quadModMat <- function(X, center = FALSE, scale = FALSE, deriv = FALSE)
{
nPar <- ncol(X)
X <- scale(X, center = center, scale = scale)
M <- cbind(1, X, X ^ 2 / ifelse(deriv, 2, 1))
# Creating all mixed terms
if(nPar > 1){
c... |
ffa6d326540f1b20c72bd427c20368fa17f81312 | c3e2451daec7c223e6bca5e8ec5d29ea3efa5c6a | /inst/Example_AR1_AR2.R | 2c392b7119d40e116380d86e754d54aa1802a16a | [] | no_license | pierrejacob/bayeshscore | d0336efc16dd0456ffa2c3f6fbe51aabbcf3f3f8 | 8f148f4074e09de4911d5645a9781c8aa844d38d | refs/heads/master | 2021-09-22T10:56:27.652463 | 2018-09-08T18:21:28 | 2018-09-08T18:21:28 | 63,806,619 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,235 | r | Example_AR1_AR2.R | ##################################################################################################
# Example - AR(1) vs AR(2)
##################################################################################################
library(bayeshscore)
library(ggplot2)
library(gridExtra)
library(doParallel)
library(foreach)
l... |
e85c9aa76fe4fd9ec4b57c4cd1e1ac1b827bd56f | 4bef412a97da9bde0e505bcaa4fe0c91d7226b6b | /R/BB_Prior_prob_phi.R | b1bddda0d5d85b083a68314a45360f8ed2504535 | [] | no_license | cran/BEDASSLE | 3578af6a84a08fe6cbe3dc157c0139e053c4bbbd | e5fb9b7a2430614f806ff16e9465384e3600d496 | refs/heads/master | 2022-04-30T11:00:10.441717 | 2022-04-10T18:12:30 | 2022-04-10T18:12:30 | 17,677,835 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 76 | r | BB_Prior_prob_phi.R | BB_Prior_prob_phi <-
function(phi){
stats::dexp(1/phi,rate=5,log=TRUE)
}
|
9cce927a9746a1a287e08165029beab1604acdb2 | e3ce3ad557ebd51429ed7acfea936723149a8d4c | /R/makeMultiObjectiveFunction.R | b824d9c94f25530c8f686f1fb809f5ff2cf5b44c | [] | permissive | jakobbossek/smoof | 87512da9d488acfe3a7cc62aa3539a99e82d52ba | d65247258fab57d08a5a76df858329a25c0bb1b8 | refs/heads/master | 2023-03-20T02:05:12.632661 | 2023-03-08T13:59:27 | 2023-03-08T13:59:27 | 22,465,741 | 32 | 27 | BSD-2-Clause | 2022-01-21T10:02:19 | 2014-07-31T10:39:43 | R | UTF-8 | R | false | false | 1,869 | r | makeMultiObjectiveFunction.R | #' Generator for multi-objective target functions.
#'
#' @template arg_name
#' @template arg_id
#' @template arg_description
#' @template arg_fn
#' @template arg_has_simple_signature
#' @template arg_par_set
#' @param n.objectives [\code{integer(1)}]\cr
#' Number of objectives of the multi-objective function.
#' @tem... |
7376f58618041f78ca0c75c8345ce649693a5bf0 | 8ea032a59e2ef9b5d822d86b537e3e84e13d8322 | /BOOK/Doit_R/가연/07_데이터 정제 - 빠진 데이터, 이상한 데이터 제거하기.R | d008b40839df80caf8300c0e4258f7fcc7527876 | [] | no_license | KimJiSeong1994/Data-Analysis_Study_Changwon | 987e9067f3f067196d92f163f26688952919d440 | f6e221e76f7557863263dd8e7e4849eb50f75b62 | refs/heads/master | 2021-08-17T09:42:15.679370 | 2020-08-26T08:35:50 | 2020-08-26T08:35:50 | 216,145,887 | 6 | 3 | null | null | null | null | UHC | R | false | false | 4,552 | r | 07_데이터 정제 - 빠진 데이터, 이상한 데이터 제거하기.R | #####07-1 빠진 데이터를 찾아라! - 결측치 정제하기#####
#결측치 찾기
df <- data.frame(sex=c("M","F",NA,"M","F"),
score=c(5,4,3,4,NA))
is.na(df) #결측치 확인(결측치=TRUE)
table(is.na(df)) #결측치 빈도 출력
table(is.na(df$sex)) #sex 결측치 빈도 출력
table(is.na(df$score)) #score 결측치 빈도 출력
mean(df$se... |
e156792dc8f179c43edf59ec5638e6a8d342a0cd | e948abc55d3973e14fd3d2201796069213860272 | /code/NYC_HVS_Analysis.R | 32420c77189e0c986333875ba299f427b15abbae | [] | no_license | madisonvolpe/evictions_nyc | 426eba0911cd64a89fa3eea5b4eab385168a7bb0 | 7309888b3d9786879e644d242b854af75a2408c7 | refs/heads/master | 2020-06-05T10:29:02.971180 | 2019-06-17T19:51:09 | 2019-06-17T19:51:09 | 192,409,157 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 18,410 | r | NYC_HVS_Analysis.R | suppressPackageStartupMessages(library(raster))
suppressPackageStartupMessages(library(survey))
suppressPackageStartupMessages(library(plyr))
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(sf)) # for shapefiles
suppressPackageSt... |
b9468a781002cb7dbbd9dc2178424690e543468f | 618e4a8505b6a5dfa8e768b474bb2bc0352f39b3 | /server.r | a6fcf4f6e758a22cdef41d1632dcb975cf0eec5e | [] | no_license | eua89/buoyviz_r_shiny | 1c660b21279b182283a40365a21f28558c7d4895 | c1bef095c43867ff32fc81e022489de55232ea45 | refs/heads/master | 2021-01-21T18:24:59.099422 | 2017-05-05T14:49:37 | 2017-05-05T14:49:37 | 92,047,336 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,022 | r | server.r | library(quantmod)
library(ggplot2)
library(scales)
#source("helper.r")
shinyServer(function(input, output) {
buoy_data=reactive({
fn = "http://localhost:5000/WAQM3.csv"
fn = paste("http://localhost:5000/",input$select1,".csv", sep="")
f = read.csv(fn,header=T, sep=",")
f$Date <- format(as.POSIXct(... |
2aaff223b91e46bb018147d878cbb319e0091390 | d8ec0122f6b914db27fdcb9d657195c18b925aa9 | /Introduction-to-R/ggplot2.R | 9c328e9224e2bf3205ff9f99d4bba96ed9b1219a | [] | no_license | sssridha/R | 5c8576849a26e7beecc2325f5ec3ce782ebb63c4 | 9d061143286a96c5b89818538d7251e2997bc788 | refs/heads/master | 2020-07-04T02:17:24.488420 | 2016-11-19T06:05:26 | 2016-11-19T06:05:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,350 | r | ggplot2.R | ### ggplot2 is the most common used graphics package in R. It has great flexibility,
### allows for a wide variety of graphs, and has an easy to understand grammar.
### Additional resources:
### https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf
### http://docs.ggplot2.org/current/
# Install ... |
52295cfed1c55dd0b7aca5ee59cf0302f82662ab | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /BigVAR/man/show-methods.Rd | af3ed58a261ebdbbbe1ee74e14a9a1612c2fa2ef | [] | no_license | akhikolla/InformationHouse | 4e45b11df18dee47519e917fcf0a869a77661fce | c0daab1e3f2827fd08aa5c31127fadae3f001948 | refs/heads/master | 2023-02-12T19:00:20.752555 | 2020-12-31T20:59:23 | 2020-12-31T20:59:23 | 325,589,503 | 9 | 2 | null | null | null | null | UTF-8 | R | false | true | 780 | rd | show-methods.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/BigVARObjectClass.R
\docType{methods}
\name{show.BigVAR}
\alias{show.BigVAR}
\alias{show,BigVAR-method}
\title{Default show method for an object of class BigVAR}
\usage{
\S4method{show}{BigVAR}(object)
}
\arguments{
\item{object}{\code{BigVAR... |
6907bccec3416bbb22bd2836a35a12e38583d3fa | 932b7a024ad453706c4a03a9e9791c5a5512c892 | /man/getBinaryConfusionMatrix.Rd | e23775f86b43dca4ff29d0144a9022cd75610e5a | [] | no_license | dputler/AlteryxPredictive | 428eb2f836999cd44312e7a8548c5c95c5a29fb1 | e35a0795c86e19cad7b022a3044a10158dbb5511 | refs/heads/master | 2020-03-06T19:20:54.612405 | 2018-04-02T18:24:30 | 2018-04-02T18:24:30 | 127,025,821 | 0 | 0 | null | 2018-03-27T17:56:39 | 2018-03-27T17:56:38 | null | UTF-8 | R | false | true | 526 | rd | getBinaryConfusionMatrix.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/interactiveutils.R
\name{getBinaryConfusionMatrix}
\alias{getBinaryConfusionMatrix}
\title{Helpful wrapper around fitted and actual values for generating confusion matrix}
\usage{
getBinaryConfusionMatrix(fitted_values, actual_values)
}
\argu... |
af822deeb9f06017f60a81753c17ddffc534c7fd | 36c8308493104441ae3aa3606ee63e2f555f86b1 | /man/convert.stress.level.Rd | 028a3b20ee4ad91c123eabaadb02c1c74ad7d6ea | [] | no_license | cran/ALTopt | 26e07bd47875d27311aa89bf1e43b91c9bea8481 | 325843e8028f0c6e2f086c0ec04a73e05d76e9ce | refs/heads/master | 2020-04-29T09:34:57.600803 | 2019-12-12T22:10:02 | 2019-12-12T22:10:02 | 29,384,214 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,560 | rd | convert.stress.level.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ALTopt.R
\name{convert.stress.level}
\alias{convert.stress.level}
\title{Coding and decoding stress level}
\usage{
convert.stress.level(lowStLv, highStLv, actual = NULL, stand = NULL)
}
\arguments{
\item{lowStLv}{a numeric vector containing t... |
c02a0fd78a6acc1d095ad4d473efb30ca3b01d4c | 5a1bf799691965bfba48f1733b2fdbbfe2050c58 | /R/database_functions.R | fcc6c3fbf8c99c384fa49b579c45ace4ff29adb1 | [] | no_license | vedhav/shipdashboard | 33e2cc367def8db6700b22ead79fe1983f889fc9 | e41d3f8045bc2ce48e3921fe8f341129d7c07e04 | refs/heads/main | 2023-01-24T11:10:17.906535 | 2020-11-21T19:16:53 | 2020-11-21T19:16:53 | 313,908,951 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,258 | r | database_functions.R | # Kills all the unclosed db connections that occur due to exceptions or timeouts
kill_dbx_connections <- function () {
all_cons <- dbListConnections(RMySQL::MySQL())
for(con in all_cons)
dbx::dbxDisconnect(con)
print(paste(length(all_cons), " connections killed."))
print("If you have not set up ... |
3da4e0b0bb2e30c59a72ca86a7983c9efe756099 | 0f2d3dcaf08ef634b59e3d7d2a88ee41fea94596 | /ycombinator-plot/code.R | a85c7aa2b36953b454ca12427350d59bfbe69442 | [
"MIT"
] | permissive | kaustavSen/dataviz-makeover | 80b944dcd7fd1aa4f88e44913b8e66c202dac95a | 75eb709fee36945deeee686a193fb496d34f5061 | refs/heads/main | 2023-08-29T04:34:30.897398 | 2021-10-30T13:09:53 | 2021-10-30T13:09:53 | 367,793,854 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,210 | r | code.R | library(tidyverse)
library(ggthemes)
library(ggtext)
library(scales)
data <- read_csv("ycombinator-plot/data.csv")
subtitle <- "% of companies funded each year which have their main focus in the emerging markets of Africa, Latin America & Middle East"
data %>%
mutate(percent = parse_number(percent) / 100) %>%
... |
754bd186f8b1090c0455dcd04ac2358d1eecf36c | 0c3fa446b7aa8e5f48201e139fd7e19c351ae54d | /src/prepare_ecdc_data/R/corrections_02August.R | 125c062335b564784bd1f219a987253b1caa9962 | [] | no_license | mrc-ide/covid19-forecasts-orderly | 477b621129ebc93dbd4e75ae4d54fbda0ad1669c | 2210ea70dc7b38d91fc1678ab0c2bb9b83c5ddcb | refs/heads/main | 2023-02-06T17:58:18.937033 | 2022-08-02T16:39:50 | 2022-08-02T16:39:50 | 254,094,782 | 33 | 12 | null | 2022-08-02T16:39:51 | 2020-04-08T13:25:08 | R | UTF-8 | R | false | false | 2,272 | r | corrections_02August.R | #####################################################################
######################################################################
######################################################################
######################################################################
########### Corrections 02nd August #... |
17c805cb769d1a49900c584cb563f7faded7ff79 | 49e30fa0003f379dc1aa21396f5678927296e629 | /Basket/code/model/add_none.R | 4a87d5d577d87f97c71aba97329cbafee66a24cc | [] | no_license | xiaofeifei1800/Kaggle | 8ce6e74b4bf7e379ae4c08afa984dfb49311148c | ece120bdc8fb37ac6f409548abb04c1d503332e5 | refs/heads/master | 2021-09-10T17:02:19.107623 | 2018-03-29T20:40:41 | 2018-03-29T20:40:41 | 63,451,543 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 705 | r | add_none.R | test = fread("/Users/xiaofeifei/I/Kaggle/Basket/result1.csv")
none = fread("/Users/xiaofeifei/I/Kaggle/Basket/None1.csv")
none = none[-1,-3]
none = none[none$V2 == "None"]
colnames(none) = c("order_id","product_id")
none$reordered = 1
test$reordered <- (test$reordered > 0.21) * 1
test = rbind(test, none)
submission ... |
bc25203b162a7f524fa66d7269219dad188c2943 | c96d12a58c06ae3d19cd48ef60fc2051ba73d7d8 | /R/9_edge_bundling_alternative.R | 61242fe7bc83ef0f2624a8586e6f275b6701fca5 | [] | no_license | rafapereirabr/stockholm_access_travelbehavior | c5757398d532da66d8066caaedbe64e6ceeccc8f | a49c3d67dbbd2c728d34c422de91ec35d837fe50 | refs/heads/main | 2023-08-08T13:15:03.762770 | 2023-07-26T20:18:08 | 2023-07-26T20:18:08 | 380,514,032 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,266 | r | 9_edge_bundling_alternative.R |
setDT(df_plot_income)
df_plot_income[, count := 1]
df_plot_income <- df_plot_income[order(group, period, income)]
ddd <- df_plot_income[, .(count = sum(count)), by=.(group,x, y)]
unique(ddd)
df_line <- sfheaders::sf_linestring(obj = ddd,
x = 'x',
... |
19bf498b4f238572919d5d8e257517e347e6e6cb | 885b9041aa386150e9b4afa077ba8c6c157858c5 | /ui.R | beb3aee444eb8440f9f12c580cf1f169aba78739 | [] | no_license | liujiashen9307/Hoitalent-small-project | 2a5fff22bf030e2ef0ad98c1ea5299e782c34d23 | 8a204f991d842343088d6146de6dff5e5cc5ee16 | refs/heads/master | 2020-09-15T22:37:35.509443 | 2016-12-04T19:04:26 | 2016-12-04T19:04:26 | 73,515,169 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,310 | r | ui.R | library(shiny)
library(shinythemes)
library(plotly)
library(wordcloud)
dt <- read.csv('HT.csv')
bg <- read.csv('Background.csv')[c(2:16,19),1]
bg<- as.character(bg)
shinyUI(fluidPage(theme = shinytheme("sandstone"),
titlePanel(h3(strong('Find the position you may be interested in'))),
... |
93bfd9fbd7cf27fcab23076400815ff0c1a08242 | 117936196834fbda370de297d6f5a77846bf45e9 | /manuscript_functions/metacom_sim_fx.R | 5939fcb6ed63b33c5aebef29a08c201e580ed61d | [] | no_license | javirudolph/testingHMSC | a79dc2ffcdec967ed45d23e46151044d1365ab51 | 61c3e1b035b8095c45755833d2ab0ebc1179a6fb | refs/heads/master | 2021-06-16T04:27:22.878177 | 2021-03-11T18:46:51 | 2021-03-11T18:46:51 | 170,368,566 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 7,355 | r | metacom_sim_fx.R |
# These are the functions needed for the processes regarding the metacommunity simulations:
# Each individual function is explained in the Supplementary information
#
# Each of these functions describes the processes going on for the metacommunity. We use these functions later on in the main_sim_fx.R script to gener... |
663346b03d4a4a618faeadbb82fda2d9e30844f5 | 4b4294254cab67348af2f98f73cc6f5341a2b3bb | /Milestone_Report/milestone_report.R | ca6f4931c9a905ae0419b5323fb9213af22a2390 | [] | no_license | jtzingsheim1/Text-Prediction-Model-and-App | f9bddd406353379be7dc12ae4dbe0a55cf8a67ef | b70a14758c6777471acd68c12fd01b5a349c1770 | refs/heads/master | 2020-06-14T07:19:08.277047 | 2019-08-01T22:50:14 | 2019-08-01T22:50:14 | 194,945,608 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,075 | r | milestone_report.R | # Coursera Data Science Specialization Capstone Project 1 Script----------------
# Milestone report for predictive text project
# The purpose of this script is to complete the basic requirements behind the
# first capstone project from Johns Hopkins University within the Data Science
# Specialization on Coursera.
#
#... |
b62edbe782fd3147cd7124749f960f02ffbe802a | bd1609c4c212dfe7169ed071dab297d6342e1dcf | /examples/createAndUpdateCoordinates.R | d4e9ab366ff22cb988ff25f7d78fd537c852f9ec | [] | no_license | restevesd/RtweetsDb | 86a5871c13683abf7ec5a4bc0dc3053f7c94b658 | efe25474a4cdb0c020fa03ae5c956a576ec74498 | refs/heads/master | 2021-01-26T07:20:38.778298 | 2015-07-06T12:09:54 | 2015-07-06T12:09:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 451 | r | createAndUpdateCoordinates.R | ## Working directory for this script should be the root of the project
## If this file is in working directory, you can run command
## setwd('..')
source('RtweetsDb.R')
createCoordinatesModel()
lookupAndAddCoordinates(c("Barcelona", "Zaragoza"))
lookupAndAddCoordinates(c("Barcelona", "Antofagasta"))
lookupAndAdd... |
eb6b08d6e947e347c0c09252724fd0e31820a660 | 0f13169c31b56ae9769850f9d909eed4909b6874 | /tests/testthat/test-components.R | 4c452865e7bb93ad8f28d441f7c0f3d4ec1ae5c3 | [] | no_license | hathawayj/osfr | d2a5f962839954ca86618f9ae5ee040f74ae3492 | e01f61c90ecc3a44cfb2f3feb2e47a07f87fd082 | refs/heads/master | 2020-03-06T19:23:14.243628 | 2018-05-03T16:04:05 | 2018-05-03T16:04:05 | 127,026,594 | 0 | 1 | null | 2018-03-27T18:03:41 | 2018-03-27T18:03:34 | R | UTF-8 | R | false | false | 671 | r | test-components.R | context("component operations")
test_that("create, update, delete", {
x <- create_project('tmp')
expect_error(create_component())
expect_error(create_component(x))
expect_is(create_component(x, title = 'Test'), 'character')
expect_is(create_component(x, title = 'Test', description = 'Text'), 'character')
e... |
cc9f6fb9f10604e919f01aaf0f6f24f7f7dc4c44 | 1f0fac7707f85b34bea60416d27d84cfbfad9169 | /man/ruch_gracz_anty_yolo_zakup_malego_psa.Rd | 9924d5213df3164a9ae91c11424f73e0fc1cec40 | [] | no_license | nkneblewska/SuperFarmer | 65dd1e44e15d6e38c2407c790dc62fdf2b3c7619 | 3b65fa7a6fa457c6cd4f288b53ea4fe5f5d2e959 | refs/heads/master | 2020-06-19T15:50:57.439890 | 2016-12-07T07:49:34 | 2016-12-07T07:49:34 | 74,902,062 | 0 | 0 | null | null | null | null | WINDOWS-1250 | R | false | true | 473 | rd | ruch_gracz_anty_yolo_zakup_malego_psa.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ruch_gracz_anty_yolo_zakup_malego_psa.R
\name{ruch_gracz_anty_yolo_zakup_malego_psa}
\alias{ruch_gracz_anty_yolo_zakup_malego_psa}
\title{Zakup małego psa}
\usage{
ruch_gracz_anty_yolo_zakup_malego_psa(stan_gracza)
}
\arguments{
\item{stan_gr... |
7532a656e60292fdb89dbeea519188d6a48d9f39 | 20ba172691d30cb84d60c374053d5d4a546d48d5 | /man/emp_vario.Rd | 3d5a97d4b7720b63a0cff3e4fb01a9d0a07237f8 | [] | no_license | sebastian-engelke/graphicalExtremes | b597b20c8a72a09e8cccd9876eb1e2b0cba3d851 | 4ad26c9f64e0f8ff8e4a96830c36be41c0330574 | refs/heads/master | 2023-09-01T15:52:11.310214 | 2023-04-03T10:05:06 | 2023-04-03T10:05:06 | 219,372,705 | 15 | 6 | null | 2022-09-16T14:03:03 | 2019-11-03T22:13:08 | R | UTF-8 | R | false | true | 1,392 | rd | emp_vario.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/estimation_param.R
\name{emp_vario}
\alias{emp_vario}
\alias{emp_vario_pairwise}
\title{Estimation of the variogram matrix \eGamma of a Huesler--Reiss distribution}
\usage{
emp_vario(data, k = NULL, p = NULL)
emp_vario_pairwise(data, k = NUL... |
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