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# somewhat hackish solution to: | |
# https://twitter.com/EamonCaddigan/status/646759751242620928 | |
# based mostly on copy/pasting from ggplot2 geom_violin source: | |
# https://github.com/hadley/ggplot2/blob/master/R/geom-violin.r | |
library(ggplot2) | |
library(dplyr) | |
"%||%" <- function(a, b) { |
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# install.packages("rPython") | |
# requires pandas | |
stata13 <- function(dat, | |
convert.factors = TRUE){ | |
require("rPython") | |
# make dat known to python | |
python.assign('dat',dat) | |
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#' Simplified loading and installing of packages | |
#' | |
#' This is a wrapper to \code{\link{require}} and \code{\link{install.packages}}. | |
#' Specifically, this will first try to load the package(s) and if not found | |
#' it will install then load the packages. Additionally, if the | |
#' \code{update=TRUE} parameter is specified it will check the currently | |
#' installed package version with what is available on CRAN (or mirror) and | |
#' install the newer version. | |
#' | |
#' @param pkgs a character vector with the names of the packages to load. |
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library(XML) | |
library(plyr) | |
library(pbapply) | |
xlsxToR <- function(file, keep_sheets = NULL, header = FALSE) { | |
temp_dir <- file.path(tempdir(), "xlsxToRtemp") | |
suppressWarnings(dir.create(temp_dir)) | |
file.copy(file, temp_dir) |
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/* | |
* NAME | |
* | |
* statistics-distributions.js - JavaScript library for calculating | |
* critical values and upper probabilities of common statistical | |
* distributions | |
* | |
* SYNOPSIS | |
* | |
* |
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# Author: Joona Lehtomäki <[email protected]> | |
# Updated: 13.11.2011 | |
# Version: 0.0.1 | |
if (!require("rgdal")) { | |
install.packages("rgdal") | |
} | |
if (!require("raster")) { | |
install.packages("raster") |
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plot.nnet<-function(mod.in,nid=T,all.out=T,all.in=T,wts.only=F,rel.rsc=5,circle.cex=5,node.labs=T, | |
line.stag=NULL,cex.val=1,alpha.val=1,circle.col='lightgrey',pos.col='black',neg.col='grey',...){ | |
require(scales) | |
#gets weights for neural network, output is list | |
#if rescaled argument is true, weights are returned but rescaled based on abs value | |
nnet.vals<-function(mod.in,nid,rel.rsc){ | |
library(scales) |