In data-driven statistical computing and data analysis, applying a chain of commands step by step is a common situation. However, it is neither straightforward nor flexible to write a group of deeply nested functions. It is because the function that comes later must be written first.继续阅读 »
(This post is rewritten to adapt to the latest release of pipeR)
Pipeline is receiving increasing attention in R community these days. It is hard to tell when it begins but more people start to use it since the easy-and-fast dplyr package imports the magic operator %>% from magrittr, the pioneer package of pipeline op继续阅读 »
Here is a quiz on magrittr and you may check if you are really good at using it. Since the CRAN version currently does not support nested ., it won't be interesting to make a quiz on that version. All the following examples are using the latest development version on GitHub. You can do the same test with the CRAN versi继续阅读 »
rlist 0.3 is released! This package now provides a wide range of functions for dealing with list objects. It can be especially useful when they are used to store non-tabular data.继续阅读 »
pipeR 0.4 is released! Check it out at the project page. In this new version, two things happen. First, %>>% handles everything. Second, the introduction of Pipe object.继续阅读 »
(This article is adapted to the latest version of rvest package.)
In my previous post, I demonstrated how we can scrape online data using existing packages.继续阅读 »
In pipeR 0.4 version, one of the new features is Pipe() function. The function basically creates a Pipe object that allows command chaining with $, and thus makes it easier to perform operations in pipeline without any external operator.继续阅读 »
The motivation of pipeline operator is to make code more readable. In many cases, it indeed better organizes code so that the logic is presented in human-readable fluent style. In other cases, however, such operators can make things worse.继续阅读 »