rentrop

require(tidyverse)

tibble(
  label = c("alt", "bridge1", "bridge2", "bridge3", "bridge4"),
  value = c(10, 5, -2, 3, 7)
) %>% 
  mutate(
    end = cumsum(value),
    start = dplyr::lag(end, default = 0),
    col = c("neg", "pos", "total")[c(3, (value[-1]>0)+1)],

rentrop

# By default print.summary.lmrob shows all outlier-rowindices.
# This sometimes is way too much information,
# especially data sets with lots of outliers.
# This function just removes the observations from the print output

compact_print_lmrob_summary <- function(...){
  out <- capture.output(...)
  observations_ind <- grep("observations c\\(", out)
  # Remove numbers of observations
  out[observations_ind] <- sub("observations c\\(.*", "observations", out[observations_ind])

rentrop

# Assuming the following GraphQL: https://github.com/calebmer/postgraphql/tree/master/examples/forum
# Deploy to AWS via https://github.com/rentrop/serverless-postgraphql

require(httr)
require(jsonlite)

# Define helper function --------------------------------------------------

url <- "https://your-endpoint/graphql"
GQL <- function(query, 

rentrop

parse_nested <- function(x, prefix = ''){
  kids = x %>% xml_children()
  ind = which(sapply(kids, xml_length) != 0)
  if(!length(ind)){
    return(setNames(kids %>% xml_text(), 
                    paste0(prefix,kids %>% xml_name())))
  }
  nested = parse_nested(kids[ind], 
                        prefix = paste0(prefix, kids[ind] %>% xml_name(), "_"))
  unnested = setNames(kids[-ind] %>% xml_text(), 

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gg_qq <- function(x, distribution = "norm", ..., line.estimate = NULL, conf = 0.95,
                  labels = names(x)){
  q.function <- eval(parse(text = paste0("q", distribution)))
  d.function <- eval(parse(text = paste0("d", distribution)))
  x <- na.omit(x)
  ord <- order(x)
  n <- length(x)
  P <- ppoints(length(x))
  df <- data.frame(ord.x = x[ord], z = q.function(P, ...))