| library(rlang) | |
| library(tidyr) | |
| library(dplyr) | |
| library(purrr) | |
| spawn_progressbar <- function(x, .name = .pb, .times = 1) { | |
| .name <- substitute(.name) | |
| n <- nrow(x) * .times | |
| eval(substitute(.name <<- dplyr::progress_estimated(n))) | |
| x |
| ########################################################## | |
| # | |
| # HANJO ODENDAAL | |
| # hanjo.oden@gmail.com | |
| # www.daeconomist.com | |
| # @UbuntR314 | |
| # https://github.com/HanjoStudy | |
| # | |
| # | |
| # ██████╗ ███████╗███████╗██╗ ███████╗███╗ ██╗██╗██╗ ██╗███╗ ███╗ |
ImageMagick-x86_64-apple-darwin16.4.0.tar.gz from download buttoncd Desktop/R-directory
mkdir ImageMagick
cd ImageMagick/
| # Generate data in the form of a sine wave | |
| set.seed(1) | |
| n <- 1e3 | |
| dat <- data.frame( | |
| x = 1:n, | |
| y = sin(seq(0, 5*pi, length.out = n)) + rnorm(n=n, mean = 0, sd=0.1) | |
| ) | |
| approxData <- data.frame( |
| # Twitter Topic Modeling Using R | |
| # Author: Bryan Goodrich | |
| # Date Created: February 13, 2015 | |
| # Last Modified: April 3, 2015 | |
| # | |
| # Use twitteR API to query Twitter, parse the search result, and | |
| # perform a series of topic models for identifying potentially | |
| # useful topics from your query content. This has applications for | |
| # social media, research, or general curiosity | |
| # |
| --- | |
| title: HTML Dependencies | |
| output: html_document | |
| --- | |
| This example explains how HTML dependencies work in **htmltools**. Sometimes an HTML fragment may have additional dependencies to work correctly, such as JavaScript and/or CSS files. In R Markdown documents, you have to let **rmarkdown** know these dependencies, so they can be added to the HTML header when the document is rendered through Pandoc. | |
| Another thing that you need to pay attention is you have to "protect" your HTML output so that Pandoc does not touch it, e.g. when you have four leading spaces, Pandoc may think this line is supposed to be a `<pre>` block whereas you only meant to indent the line for cosmetic purposes. In this case, the function `htmltools::htmlPreserve()` will be _automatically_ applied to your HTML content in R Markdown if the content is generated from `htmltools::tags` or wrapped in `htmltools::HTML()`. | |
| Now we use a random CSS file in the **knitr** package to illustrate how dependencies work. The goal here is to generate a |
| library(randomForest) | |
| library(dplyr) | |
| library(ggplot2) | |
| set.seed(42) | |
| rf_out <- randomForest(Species ~ ., data=iris) | |
| # Extracts variable importance (Mean Decrease in Gini Index) | |
| # Sorts by variable importance and relevels factors to match ordering |
| ############################################################################### | |
| # Elo rating system implementation in R # | |
| ############################################################################### | |
| # | |
| # INTRODUCTION | |
| # The ELO rating system is a method for calculating the relative skill levels | |
| # of players in Player-Versus-Player games. This rating system is used on | |
| # chess and also in a number of games, videogames, etc. | |
| # It was originally invented as an improved chess rating system in the 1960s | |
| # and established at the FIDE-congress 1970 in Siegen (Germany). |
| library(shiny) | |
| library(dplyr) | |
| library(lubridate) | |
| # Load libraries and functions needed to create SQLite databases. | |
| library(RSQLite) | |
| library(RSQLite.extfuns) | |
| saveSQLite <- function(data, name){ | |
| path <- dplyr:::db_location(filename=paste0(name, ".sqlite")) |