# 欢迎加入 TidyFriday！

TidyFriday 是受 TidyTuesday 启发建立的，但是 TidyFriday 并不打算局限于 R，本着实用主义和分享精神， TidyFriday 致力于促进一切有用的知识的传播，致力于促进成员的学习和成长，致力于解决实际问题而非虚无的讨论代码。尽管如此，TidyFriday 目前阶段将着重于使用 R，Stata 和 Python 进行数据分析、模型构建、可视化及在社科领域的应用。为了保证 TidyFriday 的高质量发展，本社群将依托知识星球GitHub，实行付费制。同时 TidyFriday 将会为用户和成员提供超出会费的服务和产品。

# Cost of Living Ranking by Country & City

Expatistan provides two kinds of data: Cost of Living Ranking by Country and Cost of Living Ranking by City. It’s very easy to get these two data. This article introduced how to crawl these data and visualize theme on map.

# Launch and Interact with a STATA Session

Yesterday, I found a interesting R Package on GitHub - bubble. This package provides a REPL (交互解释器) between R and node. And its source codes are quiet simple, so I want to know if I can create a Stata REPL by imitating it. Then we have statarepl package which provides a REPL interface between R and Stata, it’s different from RStata, which I have introduced before.

# Mapping Quandl Macroeconomic Data

This is my notes for learning Mapping Quandl Macroeconomic Data. In this article, we are going to be working with a macroeconomic data source from the World Bank called World Development Indicators (WDI). The Quandl code for WDI is WWDI, and thus we’ll prepend WWDI/ to each data set call. For more details, you can refer to: https://docs.quandl.com/docs/r-time-series and http://datatopics.worldbank.org/world-development-indicators/

# Rsampling Fama French

This is my notes for learning Rsampling Fama French. This article introduced how to conduct k-fold cross validation in R using rsample and yardstick packages. For more details, you can read the original article.

# Regional Population Distribution of China, Just a Graph.

It’s very hard to get Chinese population at county level. So I just get this data for year 2004.

The shp data: chinamap.zip, theme.R can be found in this article: Create Complete China Maps Using GGPLOT2 and SF, Population data set: 全国分县市人口统计资料2004.xlsx

# Portfolio Backtesting

This is my note for learning Portfolio Backtesting

# Momentum Investing with R

This is my note for learning Momentum Investing with R.

In practice, momentum entails a look back into the past to determine whether an asset has exceed some benchmark, and if it has, buy and hold that asset for some benchmark, and if it has, buy and hold that asset for some time into the future. That’s completely flying in the face of the efficient market hypothesis because it’s positing that the past is somehow giving us information that has not been reflected in the current price of the asset.

# A quick tour of GA

This is my note for learning A quick tour of GA.

Genetic algorithms(GAs) are stochastic search algorithms inspired by the basic principles of biological evolution and natural selection. GAs simulate the evolution of living organisms, where the fittest individuals dominate over the weaker ones, by mimicking the biological mechanisms of evolution, such as selection, crossover and mutation.

# Introduction to Fama French

This is my note for learning Introduction to Fama French.

Today, we will be workding with our usual portfolio consisting of:

• SPY (S&P500 fund) weighted 25%
• EFA (a small-cap value fund) weighted 25%
• IJS (a small-cap value fund) weighted 20%
• EEM (a emerging-mkts fund) weighted 20%
• AGG (a bond fund) weighted 10%

# Themes for base plotting system in R

This is my note for learning Themes for base plotting system in R.

basetheme package is a magic package, which let you love R’s base plotting system again!

# Visualizing Natural Disaster Cost

This is my note for learning Visualizing Natural Disaster Cost.

If you cannot download data-2.tsv from the provided URL, you can download it from data-2.tsv.

# Can I do that? Inspiration from a Pudding data visualization.

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