使用查找表中没有for循环的值替换文本

Question

我在写一个拼写修正函数。我从维基百科上抓取了拼写变体页面，并把它转换成了一个表格。现在，我希望将它用作查找表(拼写)，并替换文档中的值(skills.db)。注意:下面的技能数据框架只是一个例子。忽略第二列。我将在简历处理过程中更早地执行拼写更正。简历很大，所以我想我应该和大家分享一下。

--我可以使用下面的for循环来实现这一点，但是我想知道是否有更好的解决方案

spellings = structure(list(preferred_spellings = c("organisation", "acknowledgement", 
"cypher", "anaesthesia", "analyse"), other_spellings = c(" organization", 
" acknowledgment", " cipher", " anesthesia", " analyze")), row.names = c(NA, 
5L), class = "data.frame")

skills.db = structure(list(skills = c("variance analysis static", "analyze kpi", 
"financial analysis", "variance analysis", "organizational", 
"analysis", "organize", "result analysis", "analytic", "datum analysis", 
"analytics", "business analysis", "organized", "quantitative analysis", 
"train need analysis", "analytic think", "analysis trial preparation", 
"analyze statue", "google analytics", "service analysis", "organize individual", 
"account analysis", "analyze department work", "pareto analysis train", 
"organization", "ratio analysis", "statistical analysis", "project organization", 
"organize client's file", "with good analytic", "nielsen analytics", 
"datum analytics", "textual analytics", "social analytics", "business intelligence analytics", 
"market analysis", "analyse", "analytic skill", "superb analytic", 
"financial statement analysis", "credit analysis", "quick analysis", 
"organizational development", "outstanding financial analytic", 
"organization design", "organize conference", "business analytics", 
"industry analysis", "fs analysis", "analyze", "cash flow analysis", 
"investment analysis", "technical analysis bloomberg", "community organize", 
"monthly financial analysis", "expense variance analysis", "stock analysis"
), level1 = c("variance analysis static", "analyze kpi", "financial analysis", 
"variance analysis", "organizational", "analysis", "organize", 
"result analysis", "analytic", "datum analysis", "analytics", 
"business analysis", "organized", "quantitative analysis", "train need analysis", 
"analytic think", "analysis trial preparation", "analyze statue", 
"google analytics", "service analysis", "organize individual", 
"account analysis", "analyze department work", "pareto analysis train", 
"organization", "ratio analysis", "statistical analysis", "project organization", 
"organize client's file", "with good analytic", "nielsen analytics", 
"datum analytics", "textual analytics", "social analytics", "business intelligence analytics", 
"market analysis", "analyse", "analytic skill", "superb analytic", 
"financial statement analysis", "credit analysis", "quick analysis", 
"organizational development", "outstanding financial analytic", 
"organization design", "organize conference", "business analytics", 
"industry analysis", "fs analysis", "analyze", "cash flow analysis", 
"investment analysis", "technical analysis bloomberg", "community organize", 
"monthly financial analysis", "expense variance analysis", "stock analysis"
)), row.names = c(49L, 65L, 77L, 82L, 155L, 190L, 215L, 244L, 
246L, 260L, 287L, 300L, 311L, 323L, 349L, 356L, 378L, 386L, 447L, 
607L, 622L, 664L, 686L, 766L, 824L, 832L, 895L, 922L, 928L, 949L, 
1020L, 1054L, 1079L, 1080L, 1081L, 1088L, 1146L, 1158L, 1228L, 
1248L, 1319L, 1366L, 1385L, 1440L, 1468L, 1475L, 1509L, 1554L, 
1584L, 1606L, 1635L, 1658L, 1660L, 1696L, 1760L, 1762L, 1798L
), class = "data.frame")

for(i in 1:nrow(spellings)){
    skills.db = skills.db %>% mutate(TEST = gsub(spellings$other_spellings[i], spellings$preferred_spellings[i], skills))
  } 

Answer 1

这里有一种方法，使用Reduce (很容易成为purrr::reduce)来迭代每个拼写并更正它们。

spellings_list <- asplit(spellings, 1)
skills.db %>%
  mutate(TEST = Reduce(function(txt, spl) gsub(spl[2], spl[1], txt), spellings_list, init = skills), changed = (skills != TEST))
#                             skills                          level1                            TEST changed
# 1         variance analysis static        variance analysis static        variance analysis static   FALSE
# 2                      analyze kpi                     analyze kpi                     analyse kpi    TRUE
# 3               financial analysis              financial analysis              financial analysis   FALSE
# 4                variance analysis               variance analysis               variance analysis   FALSE
# 5                   organizational                  organizational                  organisational    TRUE
# 6                         analysis                        analysis                        analysis   FALSE
# 7                         organize                        organize                        organize   FALSE
# 8                  result analysis                 result analysis                 result analysis   FALSE
# 9                         analytic                        analytic                        analytic   FALSE
# 10                  datum analysis                  datum analysis                  datum analysis   FALSE
# 11                       analytics                       analytics                       analytics   FALSE
# 12               business analysis               business analysis               business analysis   FALSE
# 13                       organized                       organized                       organized   FALSE
# 14           quantitative analysis           quantitative analysis           quantitative analysis   FALSE
# 15             train need analysis             train need analysis             train need analysis   FALSE
# 16                  analytic think                  analytic think                  analytic think   FALSE
# 17      analysis trial preparation      analysis trial preparation      analysis trial preparation   FALSE
# 18                  analyze statue                  analyze statue                  analyse statue    TRUE
# 19                google analytics                google analytics                google analytics   FALSE
# 20                service analysis                service analysis                service analysis   FALSE
# 21             organize individual             organize individual             organize individual   FALSE
# 22                account analysis                account analysis                account analysis   FALSE
# 23         analyze department work         analyze department work         analyse department work    TRUE
# 24           pareto analysis train           pareto analysis train           pareto analysis train   FALSE
# 25                    organization                    organization                    organisation    TRUE
# 26                  ratio analysis                  ratio analysis                  ratio analysis   FALSE
# 27            statistical analysis            statistical analysis            statistical analysis   FALSE
# 28            project organization            project organization            project organisation    TRUE
# 29          organize client's file          organize client's file          organize client's file   FALSE
# 30              with good analytic              with good analytic              with good analytic   FALSE
# 31               nielsen analytics               nielsen analytics               nielsen analytics   FALSE
# 32                 datum analytics                 datum analytics                 datum analytics   FALSE
# 33               textual analytics               textual analytics               textual analytics   FALSE
# 34                social analytics                social analytics                social analytics   FALSE
# 35 business intelligence analytics business intelligence analytics business intelligence analytics   FALSE
# 36                 market analysis                 market analysis                 market analysis   FALSE
# 37                         analyse                         analyse                         analyse   FALSE
# 38                  analytic skill                  analytic skill                  analytic skill   FALSE
# 39                 superb analytic                 superb analytic                 superb analytic   FALSE
# 40    financial statement analysis    financial statement analysis    financial statement analysis   FALSE
# 41                 credit analysis                 credit analysis                 credit analysis   FALSE
# 42                  quick analysis                  quick analysis                  quick analysis   FALSE
# 43      organizational development      organizational development      organisational development    TRUE
# 44  outstanding financial analytic  outstanding financial analytic  outstanding financial analytic   FALSE
# 45             organization design             organization design             organisation design    TRUE
# 46             organize conference             organize conference             organize conference   FALSE
# 47              business analytics              business analytics              business analytics   FALSE
# 48               industry analysis               industry analysis               industry analysis   FALSE
# 49                     fs analysis                     fs analysis                     fs analysis   FALSE
# 50                         analyze                         analyze                         analyse    TRUE
# 51              cash flow analysis              cash flow analysis              cash flow analysis   FALSE
# 52             investment analysis             investment analysis             investment analysis   FALSE
# 53    technical analysis bloomberg    technical analysis bloomberg    technical analysis bloomberg   FALSE
# 54              community organize              community organize              community organize   FALSE
# 55      monthly financial analysis      monthly financial analysis      monthly financial analysis   FALSE
# 56       expense variance analysis       expense variance analysis       expense variance analysis   FALSE
# 57                  stock analysis                  stock analysis                  stock analysis   FALSE

我添加changed只是为了一个石蕊，假设您知道哪些输入应该是不同的。

演练：

1. Reduce将对每一项拼写更正遍历整个skills列。其函数的一个迭代的输入将是前一个迭代的输出，这是一个必要的属性，因此我们可以保留更改。
2. 不幸的是，我们在这里不能很容易地使用Vectorize，而且Reduce通常喜欢简单的2-参数函数(不是很容易的Map-able)，所以我将spellings帧分解为长度-2向量的列表： spellings_list <- asplit(拼写，1) spellings_list # $1 # preferred_spellings other_spellings #“preferred_spellings”组织#$ $2 # preferred_spellings other_spellings #“确认”确认#$ $3 # preferred_spellings other_spellings #“密码”密码# $4 #preferred_spellings other_spellings #“麻醉剂”麻醉“# $5 # preferred_spellings other_spellings #”分析“分析” 这使我们能够更容易地使用gsub(spl[1], spl[2], ...)。
3. Reduce的艺术在于知道在何处使用哪个参数，以及何时使用init=。这是一门艺术。当我把自己放在一个位置上，我怀疑是在哪里喂食，我插入了一个browser()开始的anon，并运行了几次迭代的缩减。
4. 建议:您可能需要将您的other_spellings与其字符串的任何一侧的\\b混合起来，以防止部分匹配的替换。例如，您的spellings也将取代organizational，即使它实际上并不存在。虽然这可能是需要的，但取决于你的大名单，很容易出现假阳性。(例如，color/colour和Colorado.)

(编辑:我最初在spl[1]和spl[2]中交换了gsub。显然，艺术中也有“逻辑”:-)