Revised with 1.6.0
This commit is contained in:
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e367d2ed21
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Data_Science/11__From_other_languages.ipynb
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Data_Science/11__From_other_languages.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Using other languages\n",
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"Often, I hear that the biggest challenge of moving from another language to Julia is giving up all the codes you have written in other languages or your favorite packages from other languages. **This notebook is not about data science, but it's about your next data science project** (if you're working on a data science project in Julia and you want to use functionality from other langages). Here, we will specifically cover Python, R, and C."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### ⚫Python"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"using PyCall"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can import any python package..."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.7071067811865475"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"math = pyimport(\"math\")\n",
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"math.sin(math.pi / 4) # returns ≈ 1/√2 = 0.70710678..."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Hint: you may need to do:\n",
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"```\n",
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"julia> using Conda\n",
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"julia> Conda.add(\"networkx\")\n",
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"```\n",
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"for the line below to work."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"PyObject <module 'networkx' from '/opt/conda/lib/python3.9/site-packages/networkx/__init__.py'>"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"python_networkx = pyimport(\"networkx\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can also write your own Python code as follows"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"py\"\"\"\n",
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"import numpy\n",
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"def find_best_fit_python(xvals,yvals):\n",
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" meanx = numpy.mean(xvals)\n",
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" meany = numpy.mean(yvals)\n",
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" stdx = numpy.std(xvals)\n",
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" stdy = numpy.std(yvals)\n",
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" r = numpy.corrcoef(xvals,yvals)[0][1]\n",
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" a = r*stdy/stdx\n",
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" b = meany - a*meanx\n",
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" return a,b\n",
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"\"\"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(0.9590099079443583, 3.3945823140627924)"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"xvals = repeat(1:0.5:10, inner=2)\n",
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"yvals = 3 .+ xvals .+ 2 .* rand(length(xvals)) .-1\n",
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"find_best_fit_python = py\"find_best_fit_python\"\n",
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"a,b = find_best_fit_python(xvals,yvals)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"If the above python code was in a file called `fit_linear.py`, you can call it as follows:\n",
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"```\n",
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"python_linear_fit = pyimport(\"fit_linear\") \n",
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"python_linear_fit.find_best_fit_python(xvals,yvals)```"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### ⚫R code"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"using RCall"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"`$` can switch to an `R` REPL from julia's REPL. We'll take a look..."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"RObject{RealSxp}\n",
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"[1] 11\n"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# we can use the rcall function\n",
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"r = rcall(:sum, Float64[1.0, 4.0, 6.0])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Float64"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"typeof(r[1])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The `@rput` allows you to put julia variable in the `R` context."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"z = 1\n",
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"@rput z"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"RObject{IntSxp}\n",
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"[1] 2\n"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"r = R\"z+z\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"2"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"r[1]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"10-element Vector{Float64}:\n",
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" -0.8375683420545423\n",
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" 0.7936207892163949\n",
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" -0.9384437083801473\n",
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" 1.3499189652001669\n",
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" 0.9703398099979935\n",
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" -0.6405138798150218\n",
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" -1.5560041013786536\n",
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" -0.5666575019004383\n",
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" -0.21398352496411505\n",
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" 2.059761076234788"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x = randn(10)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can apply R functions on julia variables"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"RObject{IntSxp}\n",
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"[1] 6\n"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"@rimport base as rbase\n",
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"rbase.sum([1, 2, 3])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Hint: for the code below to work, you will need to type `$` in the REPL followed by:\n",
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"```\n",
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"install.packages(\"boot\")\n",
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"```\n",
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"the `$` will enter you into the R REPL mode."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"RObject{VecSxp}\n",
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"\n",
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"\tOne Sample t-test\n",
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"\n",
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"data: `#JL`$x\n",
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"t = 0.11328, df = 9, p-value = 0.9123\n",
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"alternative hypothesis: true mean is not equal to 0\n",
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"95 percent confidence interval:\n",
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" -0.7975999 0.8816938\n",
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"sample estimates:\n",
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" mean of x \n",
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"0.04204696 \n",
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"\n"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"R\"t.test($x)\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The equivalent in Julia would be"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"One sample t-test\n",
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"-----------------\n",
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"Population details:\n",
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" parameter of interest: Mean\n",
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" value under h_0: 0\n",
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" point estimate: 0.042047\n",
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" 95% confidence interval: (-0.7976, 0.8817)\n",
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"\n",
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"Test summary:\n",
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" outcome with 95% confidence: fail to reject h_0\n",
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" two-sided p-value: 0.9123\n",
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"\n",
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"Details:\n",
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" number of observations: 10\n",
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" t-statistic: 0.11328193986719481\n",
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" degrees of freedom: 9\n",
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" empirical standard error: 0.3711708880068253\n"
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]
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},
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"using HypothesisTests\n",
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"OneSampleTTest(x)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### ⚫C code\n",
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"Calling standard libraries is easy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"57583336"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"t = ccall(:clock, Int32, ())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Can look at Python and C/C++ examples here: https://github.com/xorJane/Excelling-at-Julia-Basics-and-Beyond/blob/master/JuliaCon2019_Huda/Julia%20Wrappers.ipynb\n",
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"```\n",
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"ccall((:hello_world_repeated,\"hello_world_lib.dylib\"),\n",
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" Int64,\n",
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" (Int64,),\n",
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" 10)\n",
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" ```"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Finally**, I would say that this is the only off-topic notebook in this course, and it's a topic that can be covered on its own in a standalone tutorial... Nevertheless, the goal of this notebook is to tell you that porting your code from Python, R, and C should be easy and straight forward in Julia. "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 🥳 One cool finding\n",
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"\n",
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"You can easily call Python, R, C, and Cpp code from Julia!"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Julia 1.6.0",
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"language": "julia",
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"name": "julia-1.6"
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},
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"language_info": {
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"file_extension": ".jl",
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"mimetype": "application/julia",
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"name": "julia",
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"version": "1.6.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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