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index.xml
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<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
<channel>
<title>Teddy's online desktop</title>
<link>https://www.codelooper.com/</link>
<description>Recent content on Teddy's online desktop</description>
<generator>Hugo -- gohugo.io</generator>
<language>en</language>
<lastBuildDate>Thu, 08 Feb 2024 00:00:00 +0000</lastBuildDate>
<atom:link href="https://www.codelooper.com/index.xml" rel="self" type="application/rss+xml" />
<item>
<title>Mermaid </title>
<link>https://www.codelooper.com/post/2024-02-08-mermaid/</link>
<pubDate>Thu, 08 Feb 2024 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2024-02-08-mermaid/</guid>
<description>I was told by a JD Long at a recent conference where I brought up Code2Flow that Mermaid could be used just like Code2Flow within R/Github/etc to draw flowcharts, but it also has other functions that makes it a more generally useful program. Thanks James!
Mermaid is also accessible within R Studio so I can try it here.
Mermaid Live Editor at https://mermaid.live/edit#
Simple Mermaid diagram In R Studio, DiagrammeR would be the package to install for producing Mermaid graphics.</description>
</item>
<item>
<title>Code2Flow</title>
<link>https://www.codelooper.com/post/2024-02-01-code2flow/</link>
<pubDate>Thu, 01 Feb 2024 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2024-02-01-code2flow/</guid>
<description>Site to programmatically design flow charts. https://app.code2flow.com/
I even made a flowchart to see if it&rsquo;s right for you!</description>
</item>
<item>
<title>Perry Mehrling</title>
<link>https://www.codelooper.com/post/2024-01-16-perry-mehrling/</link>
<pubDate>Tue, 16 Jan 2024 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2024-01-16-perry-mehrling/</guid>
<description>Perry Mehrling Lectures
Bagehot: Lend freely at high rates against good security.
Mehrling: How the fed became the dealer of last resort. We do need central banks.
Banking runs are immediate problems of liquidity and not necessarily of solvency.</description>
</item>
<item>
<title>ChatGPT Shiny R app</title>
<link>https://www.codelooper.com/post/2023-12-22-chatgpt-code-in-python-in-r-markdown/</link>
<pubDate>Fri, 22 Dec 2023 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2023-12-22-chatgpt-code-in-python-in-r-markdown/</guid>
<description>This is a simple integration of ChatGPT into shiny R. At first I wanted to do this in Python but it was easier to do a web app in R Shiny. I used code from this site to get started:
https://www.listendata.com/2023/05/chatgpt-in-r.html#steps_to_run_chatgpt_in_r
The app is hosted at: https://twong.shinyapps.io/chat_bot/
The R code for this shiny app is below.
library(shiny) library(httr) ui &lt;- fluidPage( titlePanel(&quot;ChatGPT Shiny App&quot;), textInput(&quot;user_input&quot;, &quot;Enter your message:&quot;), actionButton(&quot;submit_btn&quot;, &quot;Submit&quot;), p(&quot;&quot;), textOutput(&quot;chat_output&quot;), p(&quot;&quot;), p(&quot;Code for this page is at https://codelooper.</description>
</item>
<item>
<title>Kuminga Dunks</title>
<link>https://www.codelooper.com/post/2023-12-22-kuminga-dunks/</link>
<pubDate>Fri, 22 Dec 2023 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2023-12-22-kuminga-dunks/</guid>
<description> </description>
</item>
<item>
<title>Distribution fitting</title>
<link>https://www.codelooper.com/post/distribution-fitting-r-shiny/</link>
<pubDate>Fri, 15 Dec 2023 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/distribution-fitting-r-shiny/</guid>
<description>This page is made using code from: https://github.com/semanzi/fitting_distributions_with_R_NHSR_2021/tree/main
The app is hosted at: https://twong.shinyapps.io/HSMA_distr_tool/</description>
</item>
<item>
<title>ChatGPT python code for distribution fitting</title>
<link>https://www.codelooper.com/post/2023-12-12-chatgpt-python-code-for-distribution-fitting/</link>
<pubDate>Tue, 12 Dec 2023 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2023-12-12-chatgpt-python-code-for-distribution-fitting/</guid>
<description>I asked ChatGPT for some python code to do distribution fitting and this is what it provided. I think I needed to edit it to fix some errors but it’s a nice solution.
First, I wanted to put in some R code that will allow me to display the results in blogdown, which is what I’m using to post to this site.
library(knitr) library(reticulate) knitr::knit_engines$set(python = reticulate::eng_python) The following python code was used for distribution fitting.</description>
</item>
<item>
<title>Copula Example</title>
<link>https://www.codelooper.com/post/copula-example/</link>
<pubDate>Fri, 08 Dec 2023 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/copula-example/</guid>
<description>Trying an introduction to copulas exercise, from (R-excerises), using the dataset (https://www.kaggle.com/datasets/gtouzin/samplestocksreturn)
Exercise 1 We’ll start by fitting the margin. First, do a histogram of both Apple and Microsoft returns to see the shape of both distributions.
returns &lt;- read.csv(&quot;returns_00_17.csv&quot;) hist(returns$Apple) hist(returns$Microsoft) Exercise 2 Both distributions seems symmetric and have a domain which contain positive and negative values. Knowing those facts, use the fitdist() function to see how the normal, logistic and Cauchy distribution fit the Apple returns dataset.</description>
</item>
<item>
<title>Random Number Generator</title>
<link>https://www.codelooper.com/post/random-number-generator-20231108/</link>
<pubDate>Wed, 08 Nov 2023 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/random-number-generator-20231108/</guid>
<description>I wanted to generate a set of random numbers without resorting to the rand() function in excel. I didn&rsquo;t want results to change each time I opened the file but I wanted a way to seed different values when needed. I found this paper by Michael Lampton, Space Sciences Lab, UC Berkeley:
https://research.ssl.berkeley.edu/~mlampton/RandomSpreadsheet4.pdf
The paper has a formula for producing a set of random values using a modulus function on a large number while maintaining portability of such randomly generated numbers between different software and hardware platforms.</description>
</item>
<item>
<title>Ergodicity and Insurance</title>
<link>https://www.codelooper.com/post/2023-07-19-ergodicity-and-insurance/</link>
<pubDate>Wed, 19 Jul 2023 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2023-07-19-ergodicity-and-insurance/</guid>
<description>I read this post on LinkedIn by Andreas Tsanakas that referenced a paper by Ole Peters titled Insurance as an Ergodicity Problem.
It seems intuitive that an equal chance bet that would allow you to win 50% or lose 40% of the value of the bet would have a positive expected value, but in the long run such a bet will bankrupt you if you bet it all each time.</description>
</item>
<item>
<title>Coursera - Generative AI </title>
<link>https://www.codelooper.com/post/2023-07-06-coursera-generative-ai/</link>
<pubDate>Thu, 06 Jul 2023 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2023-07-06-coursera-generative-ai/</guid>
<description>Encoder Models - Sentiment analysis
Encoder - Decoder Models
Decoder Only Models - GPT family of models
The paper “Attention is all you need” replaced recurrent neural networks (RNN) and convolutional neural networks (CNN) with transformer models (or attention-based models).
The Transformer architecture consists of an encoder and a decoder, each of which is composed of several layers. Each layer consists of two sub-layers: a multi-head self-attention mechanism and a feed-forward neural network.</description>
</item>
<item>
<title>Duck DB and Pandas Profiling</title>
<link>https://www.codelooper.com/post/2022-09-19-duck-db-pandas-profiling/</link>
<pubDate>Mon, 19 Sep 2022 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2022-09-19-duck-db-pandas-profiling/</guid>
<description>Saw a nice introduction to Duck DB:
https://shekhargulati.com/2019/12/15/the-5-minute-introduction-to-duckdb-the-sqlite-for-analytics/
Pandas Profiling for generating automatic data summary reports:
https://github.com/ydataai/pandas-profiling</description>
</item>
<item>
<title>Progress in AI - Natural Language Processing Edition</title>
<link>https://www.codelooper.com/post/progress-in-natural-language-processing/</link>
<pubDate>Mon, 29 Aug 2022 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/progress-in-natural-language-processing/</guid>
<description>Even if we are not surrounded by self-driving cars (yet), AI is advancing in many domains. It reminds me of the spread of computers and internet in the last few decades where the cumulative progress looking back on the past few decades seem like huge leaps while new technology seem so incremental.
When looking at the history of AI, there have been several paradigm shifts which have led to exponential gains in AI capabilities.</description>
</item>
<item>
<title>Coursera Guided Project - Predicting Diabetes</title>
<link>https://www.codelooper.com/post/2022-08-06-coursera-guided-project-predicting-diabetes/</link>
<pubDate>Sat, 06 Aug 2022 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2022-08-06-coursera-guided-project-predicting-diabetes/</guid>
<description>Most people have better things to do on a Saturday night after the kids are asleep. Well, this is my idea of a fun evening… Signing up for the guided project in predicting diabetes by using random forests. Here we go…
Course Objectives In this course, we are going to focus on four learning objectives:
Complete a random Training and Test set from one Data source using both an R function and using Base R.</description>
</item>
<item>
<title>Completed the Deeplearning.ai Tensorflow for AI course</title>
<link>https://www.codelooper.com/post/2022-08-04-completed--deeplearning-tensorflow-for-ai-course/</link>
<pubDate>Thu, 04 Aug 2022 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2022-08-04-completed--deeplearning-tensorflow-for-ai-course/</guid>
<description>I took Andrew Ng’s ML course on Coursera in 2015, but the landscape has changed since then. The 2015 course had us build a neural network from scratch using matrix multiplication using Octave (open-source Matlab). Now in 2022 it’s taught using python, tensorflow, and Keras API rather than using matrix multiplication. This course is better if you just want to apply machine learning or learn what’s involved. This course allows you to do more in less time, but you come away with a fuzzier idea of what’s happening in the neural network.</description>
</item>
<item>
<title>Coursera - introduction to tensorflow</title>
<link>https://www.codelooper.com/post/2022-08-03-coursera-introduction-to-tensorflow/</link>
<pubDate>Wed, 03 Aug 2022 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2022-08-03-coursera-introduction-to-tensorflow/</guid>
<description>Week 1 Assignment: Housing Prices In this exercise you’ll try to build a neural network that predicts the price of a house according to a simple formula.
Imagine that house pricing is as easy as:
A house has a base cost of 50k, and every additional bedroom adds a cost of 50k. This will make a 1 bedroom house cost 100k, a 2 bedroom house cost 150k etc.
How would you create a neural network that learns this relationship so that it would predict a 7 bedroom house as costing close to 400k etc.</description>
</item>
<item>
<title>Python - Handling Exceptions</title>
<link>https://www.codelooper.com/post/2022-08-03-python-handling-exceptions/</link>
<pubDate>Wed, 03 Aug 2022 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2022-08-03-python-handling-exceptions/</guid>
<description>From: LinkedIn course ‘Python Essential Training’ by Ryan Mitchell https://www.linkedin.com/learning/python-essential-training-14898805
Try, except, finally
import time as time def causeError(): start = time.time() #set start timer try: #delay run by 0.5 secs time.sleep(0.5) return 1/0 except Exception: print(&#39;There was some sort of error!&#39;) finally: print(f&#39;Function took {time.time() - start} seconds to execute&#39;) causeError() ## There was some sort of error! ## Function took 0.5048558712005615 seconds to execute Custom Decorators *args **kwargs are multiple arguments or string arguments.</description>
</item>
<item>
<title>Python - Multithreading/Multiprocessing</title>
<link>https://www.codelooper.com/post/2022-08-03-python-multithreading-multiprocessing/</link>
<pubDate>Wed, 03 Aug 2022 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2022-08-03-python-multithreading-multiprocessing/</guid>
<description>From: LinkedIn course ‘Python Essential Training’ by Ryan Mitchell https://www.linkedin.com/learning/python-essential-training-14898805
import threading import time Threads
def longSquare(num): time.sleep(1) return num**2 [longSquare(n) for n in range(0, 5)] ## [0, 1, 4, 9, 16] t1 = threading.Thread(target=longSquare, args=(1,)) #args is tuple t2 = threading.Thread(target=longSquare, args=(2,)) t1.start() t2.start() t1.join() t2.join() def longSquare(num, results): time.sleep(1) results[num] = num**2 results = {} t1 = threading.Thread(target=longSquare, args=(1, results)) #args are tuples t2 = threading.Thread(target=longSquare, args=(2, results)) t1.</description>
</item>
<item>
<title>Python - opening reading writing files</title>
<link>https://www.codelooper.com/post/2022-08-03-python-opening-reading-writing-files/</link>
<pubDate>Wed, 03 Aug 2022 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2022-08-03-python-opening-reading-writing-files/</guid>
<description>From: LinkedIn course ‘Python Essential Training’ by Ryan Mitchell https://www.linkedin.com/learning/python-essential-training-14898805
reading files
f = open(&#39;some_file.txt&#39;,&#39;r&#39;) print(f) #gets file type, need to read the file f.readline() f.readlines() #puts lines into list of strings for line in f.readlines(): print(line.strip()) # strips leading and trailing spaces writing files
f = open(&#39;somefiles.txt&#39;,&#39;w&#39;) # creates a file f.write(&#39;Line 1\n&#39;) f.write(&#39;Line 2\n&#39;) f.close() # python doesn&#39;t write until you close or run out of buffer and will overwrite existing text appending files</description>
</item>
<item>
<title>Python Neural Network Basics</title>
<link>https://www.codelooper.com/post/2022-08-03-python-neural-network/</link>
<pubDate>Wed, 03 Aug 2022 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2022-08-03-python-neural-network/</guid>
<description>From https://iamtrask.github.io/2015/07/12/basic-python-network/
import numpy as np sigmoid function def nonlin(x,deriv=False): if(deriv==True): return x*(1-x) return 1/(1+np.exp(-x)) input dataset X = np.array([ [0,0,1], [0,1,1], [1,0,1], [1,1,1] ]) output dataset y = np.array([[0,0,1,1]]).T seed random numbers to make calculation deterministic (just a good practice)
np.random.seed(1) initialize weights randomly with mean 0 syn0 = 2*np.random.random((3,1)) - 1 print(syn0) ## [[-0.16595599] ## [ 0.44064899] ## [-0.99977125]] variables l0 is input layer l1 is hidden layer l1_error is the loss function l1_delta is the gradient descent function for calculating the back-propagation syn0 are synapses, weights between l0 and l1, and also how the weights are updated are shown.</description>
</item>
<item>
<title>Codecademy Pandas Continued...</title>
<link>https://www.codelooper.com/post/2022-08-02-codecademy-pandas-continued/</link>
<pubDate>Tue, 02 Aug 2022 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2022-08-02-codecademy-pandas-continued/</guid>
<description></description>
</item>
<item>
<title>Codecademy - Pandas Lesson</title>
<link>https://www.codelooper.com/post/2022-07-28-codecademy-pandas-lesson/</link>
<pubDate>Thu, 28 Jul 2022 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2022-07-28-codecademy-pandas-lesson/</guid>
<description>You’re getting ready to staff the clinic for March this year. You want to know how many visits took place in March last year, to help you prepare.
Write a command that will produce a Series made up of the March data from df from all four clinic sites and save it to the variable march.
#import /;../,codecademylib3 import pandas as pd df = pd.DataFrame([ [&#39;January&#39;, 100, 100, 23, 100], [&#39;February&#39;, 51, 45, 145, 45], [&#39;March&#39;, 81, 96, 65, 96], [&#39;April&#39;, 80, 80, 54, 180], [&#39;May&#39;, 51, 54, 54, 154], [&#39;June&#39;, 112, 109, 79, 129]], columns=[&#39;month&#39;, &#39;clinic_east&#39;, &#39;clinic_north&#39;, &#39;clinic_south&#39;, &#39;clinic_west&#39;]) print(df) ## month clinic_east clinic_north clinic_south clinic_west ## 0 January 100 100 23 100 ## 1 February 51 45 145 45 ## 2 March 81 96 65 96 ## 3 April 80 80 54 180 ## 4 May 51 54 54 154 ## 5 June 112 109 79 129 # integer location within dataframe # locations are zero indexed and doesn&#39;t include the ending integer march = df.</description>
</item>
<item>
<title>Macbook air m1 power consumption</title>
<link>https://www.codelooper.com/post/macbook-m1-power-consumption/</link>
<pubDate>Sun, 24 Jul 2022 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/macbook-m1-power-consumption/</guid>
<description>I found that rsessions-arm64 was running the cpu at 100% and using almost 7 watts (it’s around 20 mW when idle), and ran my laptop battery down very quickly. A restart seem to have solved the problem.
Using command “sudo powermetrics” in terminal opens up the powermetrics program that’s included in osx and provides data on power usage as shown in the following screenshot:
powermetrics</description>
</item>
<item>
<title>Datacamp's tidyverse course using gapminder dataset</title>
<link>https://www.codelooper.com/post/2022-07-19-datacamp-s-tidyverse-course-using-gapminder-dataset/</link>
<pubDate>Tue, 19 Jul 2022 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2022-07-19-datacamp-s-tidyverse-course-using-gapminder-dataset/</guid>
<description>Datacamp’s Tidyverse course using Gapminder dataset
library(gapminder) library(dplyr) ## Warning: package &#39;dplyr&#39; was built under R version 4.2.3 ## ## Attaching package: &#39;dplyr&#39; ## The following objects are masked from &#39;package:stats&#39;: ## ## filter, lag ## The following objects are masked from &#39;package:base&#39;: ## ## intersect, setdiff, setequal, union head(gapminder) ## # A tibble: 6 × 6 ## country continent year lifeExp pop gdpPercap ## &lt;fct&gt; &lt;fct&gt; &lt;int&gt; &lt;dbl&gt; &lt;int&gt; &lt;dbl&gt; ## 1 Afghanistan Asia 1952 28.</description>
</item>
<item>
<title>Global size of agriculture insurance market in 2020 and beyond</title>
<link>https://www.codelooper.com/post/global-size-of-agriculture-insurance-market-in-2020-and-beyond/</link>
<pubDate>Mon, 18 Jul 2022 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/global-size-of-agriculture-insurance-market-in-2020-and-beyond/</guid>
<description>People often ask me how large is the world wide agriculture insurance market. U.S., Canada, China, and India have large government subsidized programs and provide reports on premiums. However, the reports do not contain privately sold products and premiums. These are mostly hail and top-up programs that complement the government subsidized crop and livestock insurance programs.
Roman Shynkarenko provided the post below on Linkedin for the agriculture insurance market in 2020 according to Allianz (32bn world wide in 2020).</description>
</item>
<item>
<title>Google Data Analytics Certificate</title>
<link>https://www.codelooper.com/post/google-data-analytics-certificate/</link>
<pubDate>Mon, 18 Jul 2022 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/google-data-analytics-certificate/</guid>
<description>I&rsquo;ve finally completed the Coursera Google Data Analytics Certificate.
I think the course is great for beginners in data analytics. For me it was a great refresher on SQL and R but the other sections were not all that useful. The course did motivate me to learn R (again) and this blog site is probably a result of that.
I think the course could use more on statistical thinking and spending more time on probability since data analysts could really use that in their work.</description>
</item>
<item>
<title>Ideas from programming</title>
<link>https://www.codelooper.com/post/ideas-from-programming/</link>
<pubDate>Mon, 18 Jul 2022 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/ideas-from-programming/</guid>
<description> Version Control and GitHub + Main/Branches, Push/Pull/Merge Functions/Modules Methods should be deep. Write the interface first Try to minimize exceptions define them away Be strategic + Invest in building the future/don&rsquo;t introduce bad code today Write the unit test before the useful code Make sure you do input validation </description>
</item>
<item>
<title>Running Shiny R within blogdown</title>
<link>https://www.codelooper.com/post/running-shiny-within-blogdown/</link>
<pubDate>Mon, 18 Jul 2022 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/running-shiny-within-blogdown/</guid>
<description>I just learned that I could embed the R Shiny app into blogdown from this post. So here’s an attempt at that.</description>
</item>
<item>
<title>Cat XL pricing using rate on line (ROL) curves</title>
<link>https://www.codelooper.com/post/cat-xl-pricing-using-rate-on-line-curves/</link>
<pubDate>Fri, 15 Jul 2022 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/cat-xl-pricing-using-rate-on-line-curves/</guid>
<description>When I was working with stop loss reinsurance contracts the usual way to perform the pricing exercise would be to gather all the loss data, fit a loss distribution to that data, make adjustments based on seasonal outlook or other variables, and apply the limits and retentions asked by the clients/brokers.
Although this is standard practice and almost second nature, another way to look at the pricing problem is from the point of view that you, as an underwriter, do not have all the information and perhaps should take into consideration market pricing.</description>
</item>
<item>
<title>Quarto</title>
<link>https://www.codelooper.com/post/2022-07-15-quarto/</link>
<pubDate>Fri, 15 Jul 2022 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2022-07-15-quarto/</guid>
<description>Checking out Quarto today. It looks like a more comprehensive tool than R Markdown though it does a lot of similar things like being able to write text and include code cells (like Jupyter), but then also able to render the output into many formats (pdf, html, word, etc) using Pandoc. I guess that makes sense since it’s published by the same folks behind R Markdown and R Studio:</description>
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<title>R blogdown/pagedown and Github</title>
<link>https://www.codelooper.com/post/2022-07-14-blogdown-github/2022-07-14-blogdown-github/</link>
<pubDate>Thu, 14 Jul 2022 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2022-07-14-blogdown-github/2022-07-14-blogdown-github/</guid>
<description>It’s taken all day to get blogdown to sync with Github. I already had a repo set up at https://tedtwong.github.io/ and I had set up a blogdown folder in R Studio, let’s call it: R/blogdown.
I wasn’t too familiar with how to set up git to sync the R/blogdown/public folder with https://tedtwong.github.io/ repo so that I can version control blogdown files and host the files on Github at the same time.</description>
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<title>Hello R Markdown</title>
<link>https://www.codelooper.com/post/2020-12-01-r-rmarkdown/</link>
<pubDate>Tue, 01 Dec 2020 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2020-12-01-r-rmarkdown/</guid>
<description>R Markdown This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.
You can embed an R code chunk like this:
summary(cars) ## speed dist ## Min. : 4.0 Min. : 2.00 ## 1st Qu.:12.0 1st Qu.: 26.00 ## Median :15.0 Median : 36.00 ## Mean :15.4 Mean : 42.98 ## 3rd Qu.</description>
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<title>Math Sample</title>
<link>https://www.codelooper.com/post/2017-03-05-math-sample/</link>
<pubDate>Sun, 05 Mar 2017 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/post/2017-03-05-math-sample/</guid>
<description><p>KaTeX can be used to generate complex math formulas server-side.</p>
<p>$$
\phi = \frac{(1+\sqrt{5})}{2} = 1.6180339887\cdots
$$</p>
<p>$$
e^{i\pi} = -1
$$</p>
<p>Additional details can be found on
<a href="https://github.com/Khan/KaTeX">GitHub</a> or on the
<a href="http://tiddlywiki.com/plugins/tiddlywiki/katex/">Wiki</a>.</p></description>
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<title>About me</title>
<link>https://www.codelooper.com/page/about/</link>
<pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
<guid>https://www.codelooper.com/page/about/</guid>
<description>A work in progress.</description>
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