Unix is turning 50

Wow!

NetLogo

I enjoyed reading the book Agent-Based Modeling & Geographical Information Systems. I first got interested in agent-based modeling around 1999. At that time, the only software platform for this kind of modeling is Mathematica. Now with free and open source software like NetLogo, it is much easier to translate ideas into executable code and see the results.

A large number of agent-based models have been developed. OpenABM is an online repository for such models, the majority of which were developed in NetLogo.

The red-blue divide in the US

1. Diverging economics
2. Diverging life expectancy
• Collapse of traditional values

Interactive computing using C++ within the Atom editor

With the xeus-cling project, it is possible to do interactive computing using C++ inside a Jupyter notebook. With the Hydrogen project, I would think that the same can be done inside the Atom editor. I gave it a try with no luck. The problem was that while xeus-cling created a number of C++ kernels such as "C++ 11", "C++14", and "C++17", it did not create a kernel named "C++", which is what Hydrogen looks for. Follow the suggestion mentioned in this post, I was able to get rid of the error message and get the system to work. Wow!

Spatial Data Analysis with INLA

A post on the use of INLA. Worth reading.

‘Metrics Monday: Using the Front-Door Criterion to Estimate Causal Effects in a Regression Context (Updated)

Useful information here.

How to use math symbols with ggdag

The wonderful package ggdag can easily make DAG like this:

However, what we really want to include in publications is something like this:

The second one can include subscript and superscript, among many others. After some tweaking, I found a solution, not perfect but usable for now.

-----------------------------------------------------------------
library(dagitty)
library(ggdag)
library(ggraph)
library(cowplot)
library(dplyr)

```{r, echo=FALSE}
dag <- dagify(Y1 ~ X + Z1 + Z0 + U + P,
              Y0 ~ Z0 + U,
              X ~ Y0 + Z1 + Z0 + P,
              Z1 ~ Z0,
              P ~ Y0 + Z1 + Z0,
              exposure = "X",
              outcome = "Y1")


dag %>% 
  tidy_dagitty(layout = "auto", seed = 12345) %>%
  arrange(name) %>% 
  ggplot(aes(x = x, y = y, xend = xend, yend = yend)) +
  geom_dag_point() +
  geom_dag_edges() +
  geom_dag_text(parse = TRUE, label = c("P", "U", "X", expression(Y[0]), expression(Y[1]), expression(Z[0]), expression(Z[1]))) +
  theme_dag()
-----------------------------------------------------------------

Here the trick is to sort the tidy version of the DAG data by "name", then we can assign labels by the order of the name of the nodes. I hope a more automated approach can be developed in the future.

By the way, with the package latex2exp, it is straightforward to use LaTeX instead of plotmath commands.

A useful disk.frame tutorial

Useful package and useful tutorial here.

Best table making package so far

My vote is for huxtable for now.

Using AWK and R to parse 25tb

Awsome post!

V8 on Arch/Manjaro

There is finally an V8 package for Arch/Manjaro Linux that supports the V8 R package.

Forget China – it's America's own economic system that's broken

Some good points by Robert Reich here.

R vs. Python for Data Science

Here is something from somebody who understands what he is talking about.

An A-Z list of scholarly publishing and open science platforms (Updated 6 November 2018)

Useful resources here.

Meet The Blockchain Companies Hoping to Disrupt the Publishing Industry

A collection of interesting blockchain-based projects here. Some more here.

Top IDE index

Here is a ranking of IDE. They also provide a ranking for programming language.

Will Blockchain Revolutionize Scholarly Journal Publishing?

Orvium is experimenting with novel ideas!

Meet The Blockchain Companies Hoping to Disrupt the Publishing Industry

A list of existing efforts here.

Reinvent scientific publishing with blockchain technology

This looks very exciting.

Understanding Blockchains by Coding One in R

Very helpful post.

Antergos is dead

Very unfortunate news. It was one of the useful efforts to bring Arch Linux to the masses. I tried it a few times but, in the end, I always found myself crawling back to Manjaro.

Bibliographies in RStudio Markdown are difficult – here’s how to make it easy

Informative post here.

Practical Introduction to Web Scraping in R

Useful introduction.

Ten random useful things in R that you might not know about

Useful tips.

Play with the cyphr package

The cyphr package seems to provide a good choice for small research group that shares sensitive data over internet (e.g., DropBox). I did some simple experiment myself and made sure it can actually serve my purpose.

I did my experiment on two computers (using openssl): I created the test data on my Linux workstation running Manjaro then I tried to access the data on a Windows 7 laptop.

For creating the data (Linux workstation):

library(cyphr)

# Create the test data
data_dir <- file.path("~/Dropbox/temp_files", "data")
dir.create(data_dir)
dir(data_dir)

# Encrypt the test data
cyphr::data_admin_init(data_dir)

key <- cyphr::data_key(data_dir)

filename <- file.path(data_dir, "iris.rds")
cyphr::encrypt(saveRDS(iris, filename), key)
dir(data_dir)

# Cannot read the data without decrypting it first
readRDS(filename)

# Read the decrypted version of the data
head(cyphr::decrypt(readRDS(filename), key))

For accessing the data (Windows laptop):

library(cyphr)

key <- data_key("C:/Users/Ssong/Dropbox/temp_files/data", path_user = "C:/Users/Ssong/.ssh")

# Make data access request
data_request_access("C:/Users/Ssong/Dropbox/temp_files/data", 
path_user = "C:/Users/Ssong/.ssh")

On Windows 7,  the system cannot locate the public located in "~/.ssh", which is pretty dumb.

Going back to the Linux workstation to approve the data access request:

# Review the request and approve (to share with other users)
req <- data_admin_list_requests(data_dir)
data_admin_authorise(data_dir, yes = TRUE)
data_admin_list_keys(data_dir)

Now I can access the data on my Windows laptop:

key <- data_key("C:/Users/Ssong/Dropbox/temp_files/data", path_user = "C:/Users/Ssong/.ssh")

d <- decrypt( readRDS( "C:/Users/Ssong/Dropbox/temp_files/data/iris.rds"), key)

‘Meta’ machine learning packages in R

Instead of using multiple packages for different machine learning tasks, it is now possible to use one of those meta packages to do (almost) all of them.

Finally, an IDE that does both Python and R well

Useful information here.

How GPL makes me leave R for Python

Informative post, especially David's response.