hrbrmstr

library(rgdal)
library(dplyr)
library(readr)
library(stringi)
library(stringr)
library(tidyr)
library(grid)
library(scales)
library(ggplot2)
library(ggthemes)

pteetor

#
# This is a toy example of splitting an R application
# into three distinct parts:
#
#   - Loading the data
#   - Calculating the analytics
#   - Rendering an RMarkdown document
#
# Note: The RMarkdown doc appears in a companion file, toyDoc.Rmd.
#

fredbenenson

layout	title	description	tags
default	SQL Style Guide	A guide to writing clean, clear, and consistent SQL.	data process

Purpose

stefanwalther

// Change the amount here to create more records
SET vAmountTransactions=10000;

Characters:
Load Chr(RecNo()+Ord('A')-1) as Alpha, RecNo() as Num autogenerate 26;

ASCII:
Load 
    if(RecNo()>=65 and RecNo()<=90,RecNo()-64) as Num,
    Chr(RecNo()) as AsciiAlpha, 

kdkorthauer

sudo sh -c 'echo "deb http://cran.rstudio.com/bin/linux/ubuntu trusty/" >> /etc/apt/sources.list'
gpg --keyserver keyserver.ubuntu.com --recv-key E084DAB9
gpg -a --export E084DAB9 | sudo apt-key add -
sudo apt-get update
sudo apt-get -y install r-base libapparmor1 libcurl4-gnutls-dev libxml2-dev libssl-dev gdebi-core
sudo apt-get install libcairo2-dev
sudo apt-get install libxt-dev
sudo apt-get install git-core

sudo /bin/dd if=/dev/zero of=/var/swap.1 bs=1M count=1024

stephlocke

if(!require("ggseas")) install.packages("ggseas")
if(!require("forecast")) install.packages("forecast")
if(!require("data.table")) install.packages("data.table")
if(!require("knitr")) install.packages("knitr")

library(ggseas)
library(forecast)
library(data.table)

# Get data

tgh0831

require(data.table)
require(lubridate)

#create the data frames

#x is a data table with a list of events to match against the different dates in x to see if there is overlap
x = data.table(
  eventid=c(1,2,3),
  start  =mdy_hms(c('10/1/2016 04:30:00','10/1/2016 18:02:00','10/2/2016 14:21:00')),
  end    =mdy_hms(c('10/1/2016 05:43:00','10/2/2016 01:23:00','10/4/2016 08:54:00'))

jgilfillan

testvector <- c(.5, .1, .2, .9, .9, .2, .5)

# group rows based on cumsum with reset
cumsum_group <- function(x, threshold) {
  cumsum <- 0
  group <- 1
  result <- numeric()
  
  for (i in 1:length(x)) {
    cumsum <- cumsum + x[i]

nacnudus

library(data.table)

?`[.data.table`

DT <- data.table(x=rep(c("b","a","c"),each=3), y=c(1,3,6), v=1:9)
X  <- data.table(x=c("c","b"), v=8:7, foo=c(4,2))

colnames(DT)
# [1] "x" "y" "v"

JosiahParry

# Create a tibble with the artist's name & another with the album title

kdot_albums <- tibble(
  artist = "Kendrick Lamar",
  album = c("Section 80", "DAMN."))
  
# Iterate through the albums and artists using a map call
album_lyrics <- kdot_albums %>%
  mutate(tracks = map2(artist, album, genius_album))