My "Top 5 R Functions"(转)】的更多相关文章

In preparation for a R Workgroup meeting, I started thinking about what would be my "Top 5 R Functions". I ruled out the functions for basic mechanics - save, load, mean, etc. - they're obviously critical, but every programming language has them…
As with many aspects of the tidyverse, its non-standard evaluation (NSE) implementation is not something entirely new, but built on top of base R. What makes this one so challenging to get your mind around, is that the Honorable Doctor Sir Lord Gener…
For common process management tasks, top is so great because it gives an overview of the most active processes currently running (hence the name top). This enables you to easily find processes that might need attention. From top, you can also perform…
前面的话 r.js(下载)是requireJS的优化(Optimizer)工具,可以实现前端文件的压缩与合并,在requireJS异步按需加载的基础上进一步提供前端优化,减小前端文件大小.减少对服务器的文件请求.本文将详细介绍r.js 简单打包 [项目结构] 以一个简单的例子来说明r.js的使用.该项目名称为'demo',在js目录下包含s1.js和s2.js两个文件,使用requirejs进行模块化,内容如下 //s1.js define(function (){ return 1; }) /…
sklearn实战-乳腺癌细胞数据挖掘(博客主亲自录制视频教程) https://study.163.com/course/introduction.htm?courseId=1005269003&utm_campaign=commission&utm_source=cp-400000000398149&utm_medium=share http://blog.cloudera.com/blog/2013/12/how-to-do-statistical-analysis-with…
时间序列: (或称动态数列)是指将同一统计指标的数值按其发生的时间先后顺序排列而成的数列.时间序列分析的主要目的是根据已有的历史数据对未来进行预测.(百度百科) 主要考虑的因素: 1.长期趋势(Long-term trend) : 时间序列可能相当稳定或随时间呈现某种趋势. 时间序列趋势一般为线性的(linear),二次方程式的 (quadratic)或指数函数(exponential function). 2.季节性变动(Seasonal variation) 按时间变动,呈现重复性行为的序列…
A IMA模型是一种著名的时间序列预测方法,主要是指将非平稳时间序列转化为平稳时间序列,然后将因变量仅对它的滞后值以及随机误差项的现值和滞后值进行回归所建立的模型.ARIMA模型根据原序列是否平稳以及回归中所含部分的不同,包括移动平均过程(MA).自回归过程(AR).自回归移动平均过程(ARMA)以及ARIMA过程.其中ARIMA(p,d,q)称为差分自回归移动平均模型,AR是自回归, p为自回归项: MA为移动平均,q为移动平均项数,d为时间序列成为平稳时所做的差分次数. 通常的建立ARIMA…
http://www.sthda.com/english/wiki/create-and-format-word-documents-using-r-software-and-reporters-package Install and load the ReporteRs R package Create a simple Word document Add texts : title and paragraphs of texts Format the text of a Word docum…
You should use either indexing or the subset function. For example : R> df <- data.frame(x=1:5, y=2:6, z=3:7, u=4:8) R> df x y z u 1 1 2 3 4 2 2 3 4 5 3 3 4 5 6 4 4 5 6 7 5 5 6 7 8 Then you can use the which function and the - operator in column…
Functionals “To become significantly more reliable, code must become more transparent. In particular, nested conditions and loops must be viewed with great suspicion. Complicated control flows confuse programmers. Messy code often hides bugs.” — Bjar…