Treat it as a data.frame: set.seed(1)x <- sample(30, 100, TRUE) summary(x)#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. #    1.00   10.00   15.00   16.03   23.25   30.00 summary(data.frame(x))#        x        #  Min.   : 1.00  #  1st Qu.:10.00  …

# Goals: To write functions # To write functions that send back multiple objects. # FIRST LEARN ABOUT LISTS -- X = list(height=5.4, weight=54) print("Use default printing --") print(X) print("Accessing individual elements --") cat(&quo…

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…

在用R语言做各种事物时,用户自定义函数是不可或缺的.这期来讲讲如何自定义R的function.首先要介绍的是function的基本框架: myfunction <- function(arg1, arg2, ... ){ statements return(object) } 1 2 3 4 函数名称为myfunction arg1,arg2 为参数 statements 为函数语句 return(object)返回结果 两个例子 例子一:随机数产生,画图 function1 <- funct…

以eval(function(p,a,c,k,e,r){e=function(c)开头的js文件是经过加密的 使用下面方法可以对js文件进行加密.解密 步骤:1.新建html页面,内容如下列代码 2.把加密的代码粘进[原脚本]文本域,点击[解密]按钮: 3.把需要加密的原代码粘进[原脚本]文本域,点击[加密]按钮: HTML代码如下: <script> a=62; function encode() { var code = document.getElementById('code').va…

const log = function(x){ console.log(x); return x; } const get = R.curry(function(prop, obj){ return obj[prop]; }) var people = [ {name: "Wan"}, {name: "Zhentian"} ]; var res = R.compose( get('name'), log, R.head )(people); console.log…

首先看看什么是Lambda 表达式 Lambda是一个匿名函数,我们可以把Lambda表达式理解为一段可以传递的代码(将代码像数据一样传递):最简单的Lambda表达式可由逗号分隔的参数列表.->符号和语句块组成,例如: Arrays.asList( "a", "b", "d" ).forEach( e -> System.out.println( e ) ); 如果 ->后面的语句块有多句就使用 { } 例如: Arrays.…

建议:如果只是处理(小)数据的,用R.结果更可靠,速度可以接受,上手方便,多有现成的命令.程序可以用.要自己搞个算法.处理大数据.计算量大的,用python.开发效率高,一切尽在掌握. 概述 在真实的数据科学世界里,我们会有两个极端,一个是业务,一个是工程.偏向业务的数据科学被称为数据分析(Data Analysis),也就是A型数据科学.偏向工程的数据科学被称为数据构建(Data Building),也就是B型数据科学. 从工具上来看,按由业务到工程的顺序,这个两条是:EXCEL >> R…

Data manipulation primitives in R and Python Both R and Python are incredibly good tools to manipulate your data and their integration is becoming increasingly important1. The latest tool for data manipulation in R is Dplyr2 whilst Python relies onPa…

In two previous blog posts I discussed some techniques for visualizing relationships involving two or three variables and a large number of cases. In this tutorial I will extend that discussion to show some techniques that can be used on large datase…