Publications by Kun Ren
Welcome to my homepage
Finally I set up my blog website hosted by GitHub Pages. It is quite amazing to create a website totally for free. After some trial and error, now I come up with a stable workflow to publish my work, introductory materials, and random thoughts online. The contents of my postings will be related to data science, statistical programming, quantitat...
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R: Getting Started
R rocks in both academia and industry nowadays. A rapidly increasing number of researchers choose R to be one of their productive tools for data analysis and data visualization. It is partially because the software is totally free and open-source but also because the community behind the stage who contributes to nearly 5000 packages remains growi...
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Useful packages for Sublime Text
Sublime Text is an extremely powerful text editor. Currently I use Sublime Text 3 and quite enjoy its simplicity and extensibility. In this blog, I would like to introduce some of my favorite packages that leverage my productivity. Package Control Sublime Text is by default equipped with its package manager: Package Control. If you need to extend...
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R: Essentials
It is quite easy to get started with R. The very first step is to download R from the official website , and install it. I suggest that you install both 32-bit and 64-bit versions for greater compatibility if you are running a 64-bit operating system. For typical statistical programming, if your dataset is not huge, it does not matter which one...
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Difference between assignment operators in R
For R beginners, the first operator they use is probably the assignment operator <-. Google's R Style Guide suggests the usage of <- rather than = even though the equal sign is also allowed in R to do exactly the same thing when we assign a value to a variable. However, you might feel inconvenient because you need to type two characters to repres...
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Introduction to parallel computing in R
For R beginners, for loop is an elementary flow-control device that simplifies repeatedly calling functions with different parameters. A possible block of code is like this: run <- function(i) { return((i+1)/(i^2+1)) } for(i in 1:100) { run(i) } In this code, we first define a function that calculates something, and then run the function fro...
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Use SQL to operate R data frames
In both research and application, we need to manipulate data frames by selecting desired columns, filtering records, transforming and aggregating data. R provides built-in functions for data frame manipulation. Suppose df is the data frame we are dealing with. We use df[1:100,] to select the first 100 rows, df[,c("price","volume")] to select pri...
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A principle of writing robust R program
Writing R code can be very easy. It depends on how much you want to achieve with your code and what features you want your code to support. To test a random thought that needs some statistical evidence, you only need to casually import data, slightly transform the data to a necessary form, and perform some statistical tests and see the conclusion...
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Extract information from texts with regular expressions in R
People love dealing with well-structured data. It costs much less efforts than working with disorganized raw texts. In economic and financial research, we typically download data from open-access websites or authentication-required databases. These sources may provide data in multiple formats. For example, almost all databases are able to provide...
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Reshape R data frame from long to wide format
Oftentimes, we obtain a long or a wide table from a certain data source, and it may be the only format we can get. For example, some financial databases provide daily tick data for all stocks in a financial market. The data table may be arranged in a long format like this: Code Date Open High Low Close 1 A01 2014-03-10 10.0 13.0 9.0 1...
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