Kcnarf

license: mit

john-guerra

license: mit

romainfrancois

library(purrr)
library(dplyr)

row_count <- function(.x, .f){ 
  .f <- as_mapper(.f)
  reduce(.x, function(x, y) x + .f(y), .init = 0 ) 
}

data <- tribble( 
  ~x, ~y, ~z, 

timelyportfolio

license: mit
height: 100

klmr

A little experiment using restarts.

(And while we’re at it, let’s torture R’s syntax a little.)

![screenshot][]

In the following we will be using R’s “restarts” feature to implement the state machine that drives generators in languages such as Python. Generators allow lazily generating values on demand: a consumer invokes a generator, and consumes values as they are produced. A new value is only produced once the previous one has been consumed.

benmarwick

#'  Bagplot
#' 
#' The bag geom is useful for graphical summaries of scatterplots. It 
#' is effective at showing the  location, spread, skewness, and 
#' outliers of a data set.
#'  
#' A bagplot is a bivariate generalization of the well known boxplot. It
#' was proposed by Rousseeuw, Ruts, and Tukey. This geom plots bagplots that 
#' are very similar to the one described in Rousseeuw et al. and
#' uses code from their bagplot functions in the aplpack pacakge. 

gluc

library(shiny)
library(shinyjs)



# Get table metadata. For now, just the fields
# Further development: also define field types
# and create inputs generically
GetTableMetadata <- function() {
  fields <- c(id = "Id", 

trestletech

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"))