Posts

I have been a Mac user for more than 6 years now, yet occasionally I have been attracted by Linux. Compared with Mac, I like the complete openess of Linux systems and the communications in the Linux user community. I started with Ubuntu, and after some time of usage I did not like unity, which was then the default GUI. Being a newbie, I did not realize I could change the GUI, but went on to try Debian. I do not remember how that went. Trying Arch Linux was quite challenging, as the installation itself was rather complicated and took me more than an hour, doing the partitions, configurations…I must admit there is a steep learning curve for Arch Linux - to know AUR, learn to use pacman instead of apt-get, etc. I still think it is a good distribution (that has so many die hard fans), and it is very flexible in the sense that you can tweak everything and make it really ‘yours’. It just did not appeal to me. Therefore I finally chose Manjaro - the distribution that uses rolling release, but is easier to use than Arch Linux.

Another important type of data is the time-to-event structured survival data, which I worked on during my entire PhD study. Different from what we usually see, there may be censoring in survival data, which adds one more layer of complication to modeling.

Docker has been a really convenient tool for me to play with, and it has helped me both in terms of doing research and having fun. Recently I have been advertising Docker to my friends but my explanations are sometimes vague and not well-defined. Therefore I decide to refresh my own understanding by writing this post, and hopefully it can be useful to others as well.

Some good practices on R, Python and bash programming, as well as a little note for myself.

R

• Use .rds when saving only one object instead of .rdata, as readRDS() will allow you to assign a name for the object, while loading the .rdata will maintain its own name, which can be inconvenient when you are running many replicates of simulation using one set of core code.

Good tools can often boost productivity. I enjoy using the macOS a lot because of its Unix nature, delicate GUI, and its abundance of apps. It has been a important teammate for me on my academic path. In this post I would like to summarize the apps that I use the most often, and why they are recommended.

Recently I was asked by a friend for help. The background is that people in large cities, such as Shanghai, sometimes want to “escape from the city” and go to a resort in the suburban, or countryside areas. Some villages surrounding Shanghai have recognized such great opportunity, and started to build such resorts. Being a urban and rural planning major, my friend wanted to see among 9000 villages, how many have developed such resorts.

The three Zazzi babies have grown bigger! The photos are taken on May 24th.

It is only until recently that I come to realize the importance of professional academic writing. After revising one of my own manuscripts over and over again with my advisors, and reviewing two papers for journals, I feel it is better if I write down some thoughts.

There are many packages in R that facilitate parallel computing. In this post, I intend to summarize some of the most popular functions/packages for this purpose.

1. The built-in parallel package

The package parallel is included in R.