如何在R中找到两个字符串向量之间的常见单词数？
要找到两个字符串向量之间的常见单词数，我们首先需要使用unlist和strsplit函数拆分两个向量，然后我们可以应用length函数和intersect函数。
查看以下示例以了解如何完成。
示例1
以下代码段创建了一个向量-
x1<-"深度学习是机器学习的一个子领域，涉及受大脑结构和功能启发的算法，称为人工神经网络。" y1<-"Deep learning is an artificial intelligence (AI) function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network." x1_split <- unlist(strsplit(x1,split=" ")) x1_split
创建以下向量-
[1] "Deep" "Learning" "is" "a" "subfield" [6] "of" "machine" "learning" "concerned" "with" [11] "algorithms" "inspired" "by" "the" "structure" [16] "and" "function" "of" "the" "brain" [21] "called" "artificial" "neural" "networks."
要查找两个字符串向量之间的常见单词数，请将以下代码添加到上面的代码段中-
x1<-"深度学习是机器学习的一个子领域，涉及受大脑结构和功能启发的算法，称为人工神经网络。" y1<-"Deep learning is an artificial intelligence (AI) function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network." y1_split <- unlist(strsplit(y1,split=" ")) y1_split输出结果
如果您将上述所有给定的片段作为单个程序执行，它会生成以下输出-
[1] "Deep" "learning" "is" "an" "artificial" [6] "intelligence" "(AI)" "function" "that" "imitates" [11] "the" "workings" "of" "the" "human" [16] "brain" "in" "processing" "data" "and" [21] "creating" "patterns" "for" "use" "in" [26] "decision" "making." "Deep" "learning" "is" [31] "a" "subset" "of" "machine" "learning" [36] "in" "artificial" "intelligence" "that" "has" [41] "networks" "capable" "of" "learning" "unsupervised" [46] "from" "data" "that" "is" "unstructured" [51] "or" "unlabeled." "Also" "known" "as" [56] "deep" "neural" "learning" "or" "deep" [61] "neural" "network."
要查找两个字符串向量之间的常见单词数，请将以下代码添加到上面的代码段中-
length(intersect(x1_split,y1_split))输出结果
如果您将上述所有给定的片段作为单个程序执行，它会生成以下输出-
[1] 12
示例2
以下代码段创建了一个向量-
x2<-"Digital marketing is the act of promoting and selling products and services by leveraging online marketing tactics such as social media marketing, search marketing, and email marketing." y2<-"Basically, digital marketing refers to any online marketing efforts or assets. Email marketing, pay-per-click advertising, social media marketing and even blogging are all great examples of digital marketing—they help introduce people to your company and convince them to buy." x2_split<-unlist(strsplit(x2,split=" ")) x2_split
创建以下向量-
[1] "Digital" "marketing" "is" "the" "act" [6] "of" "promoting" "and" "selling" "products" [11] "and" "services" "by" "leveraging" "online" [16] "marketing" "tactics" "such" "as" "social" [21] "media" "marketing," "search" "marketing," "and" [26] "email" "marketing."
要查找两个字符串向量之间的常见单词数，请将以下代码添加到上面的代码段中-
x2<-"Digital marketing is the act of promoting and selling products and services by leveraging online marketing tactics such as social media marketing, search marketing, and email marketing." y2<-"Basically, digital marketing refers to any online marketing efforts or assets. Email marketing, pay-per-click advertising, social media marketing and even blogging are all great examples of digital marketing—they help introduce people to your company and convince them to buy." y2_split<-unlist(strsplit(y2,split=" ")) y2_split输出结果
如果您将上述所有给定的片段作为单个程序执行，它会生成以下输出-
[1] "Basically," "digital" "marketing" "refers" [5] "to" "any" "online" "marketing" [9] "efforts" "or" "assets." "Email" [13] "marketing," "pay-per-click" "advertising," "social" [17] "media" "marketing" "and" "even" [21] "blogging" "are" "all" "great" [25] "examples" "of" "digital" "marketing—they" [29] "help" "introduce" "people" "to" [33] "your" "company" "and" "convince" [37] "them" "to" "buy."
要查找两个字符串向量之间的常见单词数，请将以下代码添加到上面的代码段中-
length(intersect(x2_split,y2_split))输出结果
如果您将上述所有给定的片段作为单个程序执行，它会生成以下输出-
[1] 7
示例3
以下代码段创建了一个向量-
x3<-"Data science is an essential part of any industry today, given the massive amounts of data that are produced. Data science is one of the most debated topics in the industries these days. Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In this article, we’ll learn what data science is, and how you can become a data scientist." y3<-"As the world entered the era of big data, the need for its storage also grew. It was the main challenge and concern for the enterprise industries until 2010. The main focus was on building a framework and solutions to store data. Now when Hadoop and other frameworks have successfully solved the problem of storage, the focus has shifted to the processing of this data. Data Science is the secret sauce here. All the ideas which you see in Hollywood sci-fi movies can actually turn into reality by Data Science. Data Science is the future of Artificial Intelligence. Therefore, it is very important to understand what is Data Science and how can it add value to your business." x3_split<-unlist(strsplit(x3,split=" ")) x3_split
创建以下向量-
[1] "Data" "science" "is" "an" [5] "essential" "part" "of" "any" [9] "industry" "today," "given" "the" [13] "massive" "amounts" "of" "data" [17] "that" "are" "produced." "Data" [21] "science" "is" "one" "of" [25] "the" "most" "debated" "topics" [29] "in" "the" "industries" "these" [33] "days." "Its" "popularity" "has" [37] "grown" "over" "the" "years," [41] "and" "companies" "have" "started" [45] "implementing" "data" "science" "techniques" [49] "to" "grow" "their" "business" [53] "and" "increase" "customer" "satisfaction." [57] "In" "this" "article," "we’ll" [61] "learn" "what" "data" "science" [65] "is," "and" "how" "you" [69] "can" "become" "a" "data" [73] "scientist."
要查找两个字符串向量之间的常见单词数，请将以下代码添加到上面的代码段中
x3<-"Data science is an essential part of any industry today, given the massive amounts of data that are produced. Data science is one of the most debated topics in the industries these days. Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In this article, we’ll learn what data science is, and how you can become a data scientist." y3<-"As the world entered the era of big data, the need for its storage also grew. It was the main challenge and concern for the enterprise industries until 2010. The main focus was on building a framework and solutions to store data. Now when Hadoop and other frameworks have successfully solved the problem of storage, the focus has shifted to the processing of this data. Data Science is the secret sauce here. All the ideas which you see in Hollywood sci-fi movies can actually turn into reality by Data Science. Data Science is the future of Artificial Intelligence. Therefore, it is very important to understand what is Data Science and how can it add value to your business." y3_split<-unlist(strsplit(y3,split=" ")) y3_split输出结果
如果您将上述所有给定的片段作为单个程序执行，它会生成以下输出-
[1] "As" "the" "world" "entered" [5] "the" "era" "of" "big" [9] "data," "the" "need" "for" [13] "its" "storage" "also" "grew." [17] "It" "was" "the" "main" [21] "challenge" "and" "concern" "for" [25] "the" "enterprise" "industries" "until" [29] "2010." "The" "main" "focus" [33] "was" "on" "building" "a" [37] "framework" "and" "solutions" "to" [41] "store" "data." "Now" "when" [45] "Hadoop" "and" "other" "frameworks" [49] "have" "successfully" "solved" "the" [53] "problem" "of" "storage," "the" [57] "focus" "has" "shifted" "to" [61] "the" "processing" "of" "this" [65] "data." "Data" "Science" "is" [69] "the" "secret" "sauce" "here." [73] "All" "the" "ideas" "which" [77] "you" "see" "in" "Hollywood" [81] "sci-fi" "movies" "can" "actually" [85] "turn" "into" "reality" "by" [89] "Data" "Science." "Data" "Science" [93] "is" "the" "future" "of" [97] "Artificial" "Intelligence." "Therefore," "it" [101] "is" "very" "important" "to" [105] "understand" "what" "is" "Data" [109] "Science" "and" "how" "can" [113] "it" "add" "value" "to" [117] "your" "business."
要查找两个字符串向量之间的常见单词数，请将以下代码添加到上面的代码段中-
length(intersect(x3_split,y3_split))输出结果
如果您将上述所有给定的片段作为单个程序执行，它会生成以下输出-
[1] 16