trinker

```r
p_load_gh("jcheng5/bubbles", "trinker/qdapTools")

x <- runif(26)
colfunc <- colorRampPalette(c("white", "red"))

cols <- colfunc(length(sort(unique(x))))


bubbles(value = x, label = LETTERS,

trinker

if (!require("pacman")) install.packages("pacman")
pacman::p_load(dplyr, tidyr, wakefield)

set.seed(10)
dat <- r_data_frame(n = 10000,
        race,
        age,
	    sex
    )

trinker

#' Transform Model Output for Use with the LDAvis Package
#'
#' Convert a \pkg{topicmodels} output into the JSON form required by the \pkg{LDAvis} package.
#'
#' @param model A \code{\link[]{topicmodel}} object.
#' @param \ldots Currently ignored.
#' @seealso \code{\link[LDAvis]{createJSON}}
#' @export
#' @examples
#' \dontrun{

trinker

#' Find Optimal Number of Topics
#' 
#' Iteratively produces models and then compares log likelihoods in a graphical output.
#' 
#' @param x A \code{\link[tm]{DocumentTermMatrix}}.
#' @param max.k Maximum number of topics to fit (start small [i.e., default of 30] and add as necessary).
#' @param \ldots Ignored.
#' @return Returns the \code{\link[base]{data.frame}} of k (number of topics) and the associated log likelihood.
#' @references \url{http://stats.stackexchange.com/a/25128/7482}
#' @keywords k topicmodel 

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#' Find Optimal Number of Topics
#' 
#' Iteratively produces models and then compares of the harmonic mean of the log likelihoods in a graphical output.
#' 
#' @param x A \code{\link[tm]{DocumentTermMatrix}}.
#' @param max.k Maximum number of topics to fit (start small [i.e., default of 30] and add as necessary).
#' @param burnin Object of class \code{"integer"}; number of omitted Gibbs iterations at beginning, by default equals 0.
#' @param iter Object of class \code{"integer"}; number of Gibbs iterations, by default equals 2000.
#' @param keep Object of class \code{"integer"}; if a positive integer, the log-likelihood is saved every keep iterations.
#' @param method The method to be used for fitting; currently \code{method = "VEM"} or \code{method= "Gibbs"} are supported.

trinker

#' Find Optimal Number of Topics
#' 
#' Iteratively produces models and then compares the harmonic mean of the log 
#' likelihoods in a graphical output.
#' 
#' @param x A \code{\link[tm]{DocumentTermMatrix}}.
#' @param max.k Maximum number of topics to fit (start small [i.e., default of 
#' 30] and add as necessary).
#' @param harmonic.mean Logical.  If \code{TRUE} the harmonic means of the 
#' log likelihoods are used to determine k (see 

trinker

#' Business Radar Plot
#' 
#' Given a \code{\link[base]{data.frame}} with \code{problems}, \code{category}, \code{importance} columns, create a radar plot of problems arranged in a military style radar sequence.
#' 
#' @param data A \code{\link[base]{data.frame}} with the following columns: 
#' \item{problems}{A character vector listing the problems}
#' \item{category}{A character vector grouping the problems}
#' \item{importance}{The time perceived importance of each item; an integer value between 1-100 with 100 being the most important and 1 being least}
#' \item{perceived}{An optional column of organization perceived importance used for point size; takes a value between 1-4;if \code{NULL} \code{importance} column will be used to generate this value}
#' @param legend.title A title for the legend.

trinker

library(idbr) 
library(ggplot2)
library(gganimate)
library(tweenr)
library(countrycode)
library(dplyr)

idb_api_key("Your API key goes here")

africa_fips <- countrycode(c('Nigeria', 'Uganda', 'Tanzania', 'Ghana'), 

trinker

G <- list(c("MD", "DT", "NN", "VB", "VBG", "TO", "POS"), c("DT", "NN", 
"JJ", "RB"), c("RB", "TO", "PRP"), c("VBZ", "PRP", "VBG", "RB", 
"TO", "NN"), c("NN", "NN"))

G <- rep(G, 1000)

library(qdapTools)

trinker

library(magick)
library(magrittr)

# Convert to 'ico' format
image_read("https://www.r-project.org/logo/Rlogo.png") %>%
	image_scale("32x32!") %>% # remove the "!" after 32x32 to scale proportionally
	image_write("favicon.ico", format = "ico")


# Favicon can also be png/gif