RobertMyles

Old_packList <- as.list(installed.packages("/Library/Frameworks/R.framework/Versions/3.2/Resources/library"))
# here, my R version was 3.2. Change it for the R version you want the packages from!
Old <- as.character(Old_packList)
install.packages(Old)

RobertMyles

# library() or require() only load one package at a time
# but...

Packages <- c("dplyr", "ggplot2", "rstan", "readr")

lapply(Packages, library, character.only = TRUE)

# this loads as many as you put in 'Packages'. They need to be installed first, of course.

RobertMyles

library(ggplot2)
library(scales)
library(tidyr)
library(dplyr)
rm(list=ls())
df <- structure(list(Animal = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), 
                  .Label = c("Buffalo", "Goat", "Sheep"), 
                  class = "factor"), 
                  Texture = structure(c(4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 2L, 1L, 1L, 4L, 3L, 4L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L), 
                  .Label = c("Hard", "Semi-Hard", "Semi-Soft", "Soft"), 

RobertMyles

#### Make 2-page color table of all colors available in R ####
# R color convention is color, color1, color2, color3, and color4
# with shades getting progressively darker
# For example skyblue, skyblue1, skyblue2, skyblue3, skyblue4
# This table prints only the full name, and fullname4 if available
# This table does not show greyscale colors grey0:grey100

# R colors minus 100 shades of grey 
cols = colors()[c(2:151,362:657)]

RobertMyles

# packages
library("ggplot2")
# devtools::install_github("dgrtwo/gganimate")
library("gganimate")
library("dplyr")
# set distribution parameters for simulation
p = 0.5
N = 100
y_lim = 20
a_a = 2

RobertMyles

Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, when I started going through the process of forking and issuing pull requests, I had some trouble figuring out the proper method for doing so and made quite a few mistakes along the way. I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.

In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.

Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your

RobertMyles

library(rvest)
library(stringr)
library(emojifont)
library(tidyverse)

url <- "http://www.webpagefx.com/tools/emoji-cheat-sheet/"
page <- read_html(url)
nodes <- html_nodes(page, css = "#content")
text <- html_text(nodes)
txt <- data_frame(label = unlist(str_extract_all(text, ":[a-z_]*:"))) %>% 

RobertMyles

library(rgdal)
library(maptools)
library(dplyr)
library(ggplot2)
library(readr)
library(httr)
library(XML)
library(ggmap)
library(scales)

RobertMyles

# modified by R. McDonnell. Original at: https://github.com/cran/BEST/blob/master/R/plotPost.R

# Original code by John Kruschke, modified by Mike.

plotPost_2 <-
function( paramSampleVec, credMass=0.95, compVal=NULL, ROPE=NULL, 
           HDItextPlace=0.7, showMode=FALSE, showCurve=FALSE, ... ) {

  # Does a plot for a single parameter. Called by plot.BEST but also exported. 
  # Returns a histogram object invisibly.

RobertMyles

plot.BEST_2 <-
function(x, which=c("mean", "sd", "effect", "nu"), credMass=0.95,
                    ROPE=NULL, compVal=0, showCurve=FALSE, ...) {
  # This function plots the posterior distribution for one selected item. 
  # Description of arguments:
  # x is mcmc.list object of the type returned by function BESTmcmc.
  # which indicates which item should be displayed; possible values are "mean", "sd",
  #  "effect" or "nu".
  # ROPE is a two element vector, such as c(-1,1), specifying the limit
  #   of the ROPE.