strengejacke

// save to windows-user directory

linters: with_defaults(object_name_linter = NULL,
                       object_length_linter(50),
                       commented_code_linter = NULL,
                       object_usage_linter = NULL,
                       line_length_linter(120),
                       cyclocomp_linter = cyclocomp_linter(50))

christophsax

library(tidyverse)
library(anytime)

# Data Repo Johns Hopkins CSSE
# https://github.com/CSSEGISandData/COVID-19
url <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
dta_raw <- read_csv(url, col_types = cols()) %>%
  select(-Lat, -Long)

jbkunst

library(tidyverse)
library(rvest)

get_uf <- function(y) {
  
  # y <- 2016
  
  message(y)
  
  seleccionador <- ifelse(y <= 2012, first, last) 

AWKruijt

# essentially same as before but now restructured into a function: getCitingPMIDs()

getCitingPMIDs <- function(PMID) {
  lines <- readLines(paste0("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&linkname=pubmed_pubmed_citedin&id=", PMID)) # construct quering URL using the PMID parameter in resTargetRecord, and fetch its contents into object lines
  
  citingPMIDs <- sub("^.*(<Id>)(.*)(</Id>)", "\\2", lines[grep("<Id>", lines)]) # filter out lines in lines that contain <Id> and then filter out the content between <Id> and </Id>.Store in object citingPMIDs
  
  citingPMIDs <- citingPMIDs[-1] # remove first entry in citingPMIDs, which is the target record
return(citingPMIDs)
}

crsh

#' Plot network of package dependencies
#'
#' @param pkg package description, can be path or package name. See \code{\link[devtools]{as.package}} for
#'    more information.
#'
#' @details The resulting plot visualizes the network of package dependencies. If you are trying to cut down
#'    on package dependencies look for big red dots that represent a lot of upstream but few downstream
#'    dependencies.
#' @import ggplot2
#' @export

richarddmorey

## This function computes the (normalized) marginal likelihood.
## You will not use this function directly.
p.y.alt = function(t.stat, N, log.prior.dens,lo=-Inf,up=Inf,...){
  normalize = integrate(function(delta,...){
    exp(log.prior.dens(delta, ...))
  }, lower = lo, upper = up, ...)[[1]]
  py = integrate(function(delta,t.stat,N,...){
    exp(
      dt(t.stat, N - 1, ncp = delta*sqrt(N), log = TRUE) +

mbarnkob

#Article Network ver 2
#============================

# This program will scrape all citing articles surrounding one main article and 
# create: 1) a list of most cited articles, 2) lists of main authors, 3) network of articles
# Mike Barnkob, December 28th 2015.
# www.mikebarnkob.dk

#Background and references
#1. https://cran.r-project.org/web/packages/rentrez/vignettes/rentrez_tutorial.html#advanced-counting