msummersgill

## Setup

```r
library(knitr)
knitr::opts_chunk$set(echo = TRUE)
## Installing dev versions of plotly with new data.table group2NA()

library(devtools)
library(withr)
library(ggplot2,lib.loc = "/home/Matthew14786/dev/")

msummersgill

library(shiny)
library(BatchGetSymbols)

ui <- fluidPage(
  textInput('stock','stock',"GE"),
  sliderInput('length', 'length', min = 1, max = 100, value = 50),
  dataTableOutput('my_table')
)

msummersgill

library(data.table)

dta <- data.table(gr = LETTERS[1:3], 
                  a = 1,  
                  b = 1)

add <- function(x, y, group) if(group == "B") x + y else x - y

dta[,.BY, by = .(gr)]

msummersgill

library(arrow)
## Open source Apache Spark downloaded from this archive:
## https://archive.apache.org/dist/spark/spark-3.0.1/spark-3.0.1-bin-hadoop2.7.tgz
library(SparkR, lib.loc = "~/DatabricksTesting/spark-3.0.1-bin-hadoop2.7/R/lib/")

## $java -version
## openjdk version "1.8.0_212"
## OpenJDK Runtime Environment (build 1.8.0_212-8u212-b03-0ubuntu1.16.04.1-b03)
## OpenJDK 64-Bit Server VM (build 25.212-b03, mixed mode)

msummersgill

## /home/matthew14786/DatabricksTesting/Python-3.7.5/python

## installed (no explicit import) pyarrow version 3.0.0
import numpy as np             ## numpy   version 1.20.1
import pandas as pd            ## pandas  version 1.2.3
import pyspark                 ## pypark  version 7.3.9
from pyspark.sql import SQLContext
from pyspark.sql.types import IntegerType
from pyspark.sql.types import LongType

msummersgill

import numpy as np             ## numpy   version 
import pandas as pd            ## pandas  version 
import pyspark                 ## pypark  version 
from pyspark.sql import SQLContext
from pyspark.sql.types import IntegerType
from pyspark.sql.types import LongType

## Define dataframe row count
L = 5*365*86400

msummersgill

library(data.table)
library(fastmatch)

#' Add rows to a data table in a memory efficient, by-referencesque manner
#'
#' This mimics the by-reference functionality `DT[, new_col := value]`, but
#' for rows instead. The rows in question are assigned at the end of the data
#' table. If the data table is keyed it is automatically reordered after the
#' operation. If not this function will preserve order of existing rows, but
#' will not preserve sortedness.

msummersgill

library(circular)

## Helper function to generate a circular object in degrees, 0-360
circularDeg <- function(x) circular(x,template = "geographics",units = "degrees", modulo = "2pi")
theta <- circularDeg(seq(from = 0, to = 360,by = 1))

m1 <- circularDeg(180)
k1 <- 2.1
w1 <- 65
dvm1 <- dvonmises(theta, mu = m1, kappa = k1)