如何在 R 中查找两个字符串向量之间共同单词的数量？

要查找两个字符串向量之间共同单词的数量，我们首先需要使用 unlist 和 strsplit 函数分割这两个向量，然后我们可以结合使用 length 函数和 intersect 函数。

查看以下示例以了解如何操作。

示例 1

以下代码片段创建了一个向量 -

x1<-"Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks."
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

创建了以下向量 -

[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共同单词的数量，请将以下代码添加到上述代码片段中 -

x1<-"Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks."
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

如果您将以上所有代码片段作为单个程序执行，则会生成以下输出 -

[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共同单词的数量，请将以下代码添加到上述代码片段中 -

length(intersect(x1_split,y1_split))

输出

如果您将以上所有代码片段作为单个程序执行，则会生成以下输出 -

[1] 12

示例 2

以下代码片段创建了一个向量 -

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

创建了以下向量 -

[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共同单词的数量，请将以下代码添加到上述代码片段中 -

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

如果您将以上所有代码片段作为单个程序执行，则会生成以下输出 -

[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共同单词的数量，请将以下代码添加到上述代码片段中 -

length(intersect(x2_split,y2_split))

输出

如果您将以上所有代码片段作为单个程序执行，则会生成以下输出 -

[1] 7

示例 3

以下代码片段创建了一个向量 -

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

创建了以下向量 -

[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共同单词的数量，请将以下代码添加到上述代码片段中

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

如果您将以上所有代码片段作为单个程序执行，则会生成以下输出 -

[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共同单词的数量，请将以下代码添加到上述代码片段中 -

length(intersect(x3_split,y3_split))

输出

如果您将以上所有代码片段作为单个程序执行，则会生成以下输出 -

[1] 16

更新于: 2021年11月11日

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