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{
  "embeddings": [
    {
      "tensorName": "Time-Varying Word2Vec Embeddings from 2014 South African Newspapers",
      "tensorShape": [
        30000,
        100
      ],
      "tensorPath": "https://www.dropbox.com/s/pepew73w16xmrlz/word2vec1tv-word_vectors.tsv?dl=0",
      "metadataPath": "https://www.dropbox.com/s/s5qfpzgij92ew9u/tensorboard-word_vectors.tsv?dl=0"

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write_pgfkeys <- function(x, ...) {
  UseMethod("write_pgfkeys")
}

.pgfkey <- function(key, value) {
  str_c("\\pgfkeysetvalue{", key, "}{", value, "}")
}

write_pgfkeys.default <- function(x, path=NULL, root = "/data/", ...) {
  keys <- str_c(root, names(x)) %>%

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"""
preprocess-twitter.py

python preprocess-twitter.py "Some random text with #hashtags, @mentions and http://t.co/kdjfkdjf (links). :)"

Script for preprocessing tweets by Romain Paulus
with small modifications by Jeffrey Pennington
with translation to Python by Motoki Wu

Translation of Ruby script to create features for GloVe vectors for Twitter data.

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f_filter_vars <- function(formula., ...) {
  f_filter_vars_(formula., .dots = lazyeval::lazy_dots(...))
}

f_filter_vars_ <- function(formula., ..., .dots) {
  dots <- lazyeval::all_dots(.dots, ...)
  .terms <- terms(formula.)
  .factors <- attr(.terms, "factors")
  selectx <- dplyr::select_vars_(rownames(.terms), dots)
  keepx <- as.logical(colSums(attr(ft, "factors")[selectx, , drop = FALSE]))

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reganatomy <- function(model, variable) {
    variable <- if (is.character(variable) & 1 == length(variable)) {
      variable
    } else {
      deparse(substitute(variable))
    }
    mod.mat <- model.matrix(model)
    var.names <- colnames(mod.mat)
    var <- which(variable == var.names)
    if (0 == length(var))  {

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library("R6")
library("purrr")

ConjugateDist <- R6Class("ConjugateDist",
  public = list(
    post_name = NULL,
    post_d = NULL,
    post_r = NULL,
    post_p = NULL,
    post_q = NULL,

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remote_download <- function(url, local_path = NULL, quiet = FALSE, branch = NULL,
                            credentials = NULL) {
  local_path <- local_path %||% tools::file_path_sans_ext(basename(url))
  if (!quiet) {
    message("Downloading git repo ", url, " to ", local_path)
  }
  git2r::clone(url, local_path, credentials = credentials, progress = FALSE)

  if (!is.null(x$branch)) {
    r <- git2r::repository(bundle)

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```{r}
#' A line with shaded area around it
#'
#' Plot a line with a shaded area around it. This is often used
#' to plot fitted regression function with confidence intervals.
#' This is a helper function for \code{\link[ggplot2]{geom_smooth}}
#' but defaulting to `stat = "identity"`, so it expects, `y`, `ymin` and `ymax` from the data instead of generating them with a model.
#' 
#' @inheritParams geom_pointrange

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# Put both labels and values of the types of ESS missing values in a
# list to re-use later.
ESS_MISS <- list(
  not_applicable = list(pattern = "Not +.p+lic+able", value = 6),
  refused = list(pattern = "Refus..", value = 7),
  not_available = list(pattern = "(No .nswer|Not .vailable)",
                       value = 8),
  dont_know = list(pattern = "Don.?t .now", value = 9)
)

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#' geom_ghazi. Annotate any ggplot2 plot with Benghazi, because it's
#' always about Benghazi
library("ggplot2")
geom_ghazi <- function(gp = grid::gpar(fontsize = 75, fontface = "bold"),
                       ...) {
  annotation_custom(grid::textGrob("BENGHAZI", gp = gp, ...),
                    xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf)
}
ggplot(mtcars, aes(x = disp, y = mpg)) + geom_point() + geom_ghazi()