Martin Chan martinctc

## pkg_summary.R
#' Run a summary of functions and the associated description
#' for a loaded package
#'
#' @param package_name String providing the name of the loaded package, e.g. surveytoolbox
#'
#' @examples
#' \dontrun{
#' pkg_summary("surveytoolbox")
#' pkg_summary("wpa")
#' pkg_summary("rwa")

## minimal_html_report.R
## This is a minimal example to show how you can create a
## function that generates HTML reports using RMarkdown.
##
## This uses a list-pmap workflow so you can input lists
## as arguments and dynamically produce a RMarkdown that
## is only limited by the length of your lists.
##
## The example below outputs a table and two plots from
## `iris`.

## ForceNetwork_example.R
library(tidyverse)
library(networkD3)

## Nodes data frame describing all the nodes in the network
## The first entry in nodes dataframe is node 0, the next entry is node 1 and so on.
## The nodes dataframe must be sorted according to this sequence.
## This is the only way to tie the nodes dataframe to the links dataframe.
TestNodes <- data.frame(name = c("Alpha",
                                 "Beta",
                                 "Cat",

## lolololololol.R
paste(rep("l", exp(10)), collapse = "o")

## list_functions.R
lsf.str("package:wpa") %>%
  as.list() %>%
  unlist() %>%
  as_tibble()

## card_hover_effect.html
<html>
    <head>
        <script src="https://cdnjs.cloudflare.com/ajax/libs/modernizr/2.8.3/modernizr.min.js" type="text/javascript"></script>
    </head>
<style>
    body {
  background: #000;
  font-family: arial, san-serif;
  color: #fff;
}

## rowwise_example.R
library(tidyverse)
iris %>%
  rowwise() %>%
  mutate(any = any(c(Sepal.Length, Sepal.Width) > 5))

## varimax-pca.R
library(tidyverse)
library(FactoMineR)

iris %>%
  select(-Species) %>%
  PCA(graph = FALSE) -> res

# FactoMineR does not return the loadings but the coordinates of the variables
# You can divide the results of PCA by the square root of the eigenvalue of each dimension to recover the loadings

## four-pm.bat
time 16:00:00

## tidy_rCharts.R
library(tidyverse)
library(rCharts) # https://ramnathv.github.io/rCharts/

HairEyeColor %>%
  as_tibble() %>%
  filter(Sex == "Male") %>%
  nPlot(n ~ Hair,
        group = 'Eye',
        data = .,
        type = 'multiBarChart')
	#' Run a summary of functions and the associated description
	#' for a loaded package
	#'
	#' @param package_name String providing the name of the loaded package, e.g. surveytoolbox
	#'
	#' @examples
	#' \dontrun{
	#' pkg_summary("surveytoolbox")
	#' pkg_summary("wpa")
	#' pkg_summary("rwa")
	## This is a minimal example to show how you can create a
	## function that generates HTML reports using RMarkdown.
	##
	## This uses a list-pmap workflow so you can input lists
	## as arguments and dynamically produce a RMarkdown that
	## is only limited by the length of your lists.
	##
	## The example below outputs a table and two plots from
	## `iris`.
	library(tidyverse)
	library(networkD3)

	## Nodes data frame describing all the nodes in the network
	## The first entry in nodes dataframe is node 0, the next entry is node 1 and so on.
	## The nodes dataframe must be sorted according to this sequence.
	## This is the only way to tie the nodes dataframe to the links dataframe.
	TestNodes <- data.frame(name = c("Alpha",
	"Beta",
	"Cat",
	lsf.str("package:wpa") %>%
	as.list() %>%
	unlist() %>%
	as_tibble()
	<html>
	<head>
	<script src="https://cdnjs.cloudflare.com/ajax/libs/modernizr/2.8.3/modernizr.min.js" type="text/javascript"></script>
	</head>
	<style>
	body {
	background: #000;
	font-family: arial, san-serif;
	color: #fff;
	}
	library(tidyverse)
	iris %>%
	rowwise() %>%
	mutate(any = any(c(Sepal.Length, Sepal.Width) > 5))
	library(tidyverse)
	library(FactoMineR)

	iris %>%
	select(-Species) %>%
	PCA(graph = FALSE) -> res

	# FactoMineR does not return the loadings but the coordinates of the variables
	# You can divide the results of PCA by the square root of the eigenvalue of each dimension to recover the loadings
	library(tidyverse)
	library(rCharts) # https://ramnathv.github.io/rCharts/

	HairEyeColor %>%
	as_tibble() %>%
	filter(Sex == "Male") %>%
	nPlot(n ~ Hair,
	group = 'Eye',
	data = .,
	type = 'multiBarChart')