MilesMcBain

#' Patch data on the fly.
#'
#' @param object  to be patched
#' @param cond    logical condition(s) to be evaluated within scope of object
#' @param \dots   name-value pairs
#' @param quiet   suppress messages
#'
#' @examples
#'   patch(mtcars, where(vs == 0, am == 1), gear = Inf, carb = carb + 10)
#'

MilesMcBain

# You may add here your
# server {
#	...
# }
# statements for each of your virtual hosts to this file
server{                                                                                                                            
        #server_name localhost;                                                                                                    
        listen 80;                                                                                                                 
                                                                                                                                   
        location / {                                                                                                               

MilesMcBain

library(ggplot2)
library(dplyr)

system.time({
nycflights13::flights %>%
  mutate(timestring = lubridate::date(time_hour))
})
#0 groups
#   user  system elapsed 
#   0.048   0.000   0.047 

MilesMcBain

---
title: "Plotly Slideshow Example"
author: "Miles McBain"
date: "6 August 2016"
output:   
  ioslides_presentation
---
```{r, include=FALSE}
library(AER)
library(ggplot2)

MilesMcBain

---
title: "Comment Example"
author: "Miles McBain"
date: "4 September 2016"
output: 
  html_document:
    css: https://cdn.rawgit.com/MilesMcBain/rmarkdown_comment/master/review.css
  
---

MilesMcBain

#Hadley Gapminder Way
nested_iris <-
  iris %>%
  mutate(Setosa = ifelse(Species == "Setosa", yes=1, no=0) ) %>%
  select(-Species) %>%
  gather(key="measure", value="value", Sepal.Length:Petal.Width) %>%
  nest(Setosa, value)
  
iris_models <- map(nested_iris$data, ~ glm(data=., 
                                           formula = Setosa ~ value, 

MilesMcBain

#Remove things in the global environment with name like a string you supply.
#Like is in the regex match sense, so if you make a slip blowing away most of your environment is possibility.
rm_like <- function(pattern = character()) {
	rm( list = grep(pattern = pattern,
			x=ls(envir = globalenv()),
			value = TRUE), 
	   envir = globalenv()
	  )
}

MilesMcBain

library(readr)
library(dplyr)
library(caret)
# from: https://archive.ics.uci.edu/ml/machine-learning-databases/00222/
bank_data <- read_delim("~/Downloads/bank-additional/bank-additional/bank-additional-full.csv", delim=";")
bank_data <- bank_data[-caret::nearZeroVar(bank_data)]

dummy_var_obj <-
bank_data %>%
  caret::dummyVars(formula = y ~ .,

MilesMcBain

tribble(
      ~Aus.City,      ~Pop,
       "Sydney", "4920970",
    "Melbourne", "4529496",
     "Brisbane", "2308720",
        "Perth", "2039193",
     "Adelaide", "1316779",
  "Gold Coast", " 624918",
    "Newcastle", " 434454",
     "Canberra", " 424666"

MilesMcBain

library(tibble)
library(ggplot2)
library(dplyr)
library(ggrepel)
library(ggthemes)

stackR <- tribble(
       ~Aus.City,      ~R, ~TotalVisits,      ~Pop,
     "Melbourne", "23176",    "1544868", "4529496",
      "Adelaide",  "7181",     "350850", "1316779",