我具有一个函数，该函数获取一些自由文本，然后根据单词列表将文本分为列。它运行良好，但有人建议我，如果它被矢量化，它将更好地工作。
该功能称为Extractor：
Extractor <- function(x, y, stra, strb, t) {
x <- data.frame(x)
t <- gsub("[^[:alnum:],]", " ", t)
t <- gsub(" ", "", t, fixed = TRUE)
x[, t] <-
stringr::str_extract(x[, y], stringr::regex(paste(stra,
"(.*)", strb, sep = ""),
dotall = TRUE))
x[, t] <- gsub("\\.*", "", x[, t])
names(x[, t]) <- gsub(".", "", names(x[, t]), fixed = TRUE)
x[, t] <- gsub(" ", "", x[, t])
x[, t] <- gsub(stra, "", x[, t], fixed = TRUE)
if (strb != "") {
x[, t] <- gsub(strb, "", x[, t], fixed = TRUE)
}
x[, t] <- gsub(" ", "", x[, t])
x[, t] <- ColumnCleanUp(x, t)
return(x)
}
ColumnCleanUp <- function(x, y) {
x <- (data.frame(x))
x[, y] <- gsub("^\.n", "", x[, y])
x[, y] <- gsub("^:", "", x[, y])
x[, y] <- gsub(".", "n", x[, y], fixed = TRUE)
x[, y] <- gsub("\s{5}", "", x[, y])
x[, y] <- gsub("^\.", "", x[, y])
x[, y] <- gsub("$\.", "", x[, y])
return(x[, y])
}
我使用如下：
HistolTree<-list("Hospital Number","Patient Name","DOB:","General Practitioner:",
"Date of Procedure:","Clinical Details:","Macroscopic description:","Histology:","Diagnosis:","")
for(i in 1:(length(HistolTree)-1)) {
Mypath<-Extractor(Mypath,"PathReportWhole",as.character(HistolTree[i]),
as.character(HistolTree[i+1]),as.character(HistolTree[i]))
}
一个示例输入文本是：
Mypath<-"Hospital Number 233456 Patient Name: Jonny Begood
DOB: 13/01/77 General Practitioner: Dr De'ath Date of Procedure: 13/01/99 Clinical Details: Dyaphagia and reflux Macroscopic description: 3 pieces of oesophagus, all good biopsies. Histology: These show chronic reflux and other bits n bobs. Diagnosis: Acid reflux likely"
Mypath<-data.frame(Mypath)
names(Mypath)<-"PathReportWhole"
预期输出为：
structure(list(PathReportWhole = structure(1L, .Label = "Hospital Number 233456 Patient Name: Jonny Begoodn DOB: 13/01/77 General Practitioner: Dr De'ath Date of Procedure: 13/01/99 Clinical Details: Dyaphagia and reflux Macroscopic description: 3 pieces of oesophagus, all good biopsies. Histology: These show chronic reflux and other bits n bobs. Diagnosis: Acid reflux likely", class = "factor"),
HospitalNumber = " 233456 ", PatientName = " Jonny Begood",
DOB = " 13/01/77 ", GeneralPractitioner = NA_character_,
Dateofprocedure = NA_character_, ClinicalDetails = " Dyaphagia and reflux ",
Macroscopicdescription = " 3 pieces of oesophagus, all good biopsiesn ",
Histology = " These show chronic reflux and other bits n bobsn ",
Diagnosis = " Acid reflux likely"), row.names = c(NA, -1L
), .Names = c("PathReportWhole", "HospitalNumber", "PatientName",
"DOB", "GeneralPractitioner", "Dateofprocedure", "ClinicalDetails",
"Macroscopicdescription", "Histology", "Diagnosis"), class = "data.frame")
基本上，我通过循环反复调用该函数（尽管这里只有一个示例，实际数据框架具有> 2000行）。
apply()是一种以矢量化的方式应用该功能的方式吗？如果没有，我可以关于如何对其进行矢量化，以便避免使用循环？我了解矢量化函数的想法意味着将函数应用于整个向量而不是循环，我需要将输入列表转换为字符向量，但我从那里陷入困境。
而不是矢量化您的现有函数，我认为我会尝试简化您的各种正则表达式。我可以看到您在做什么，您有一个data.frame带有令人讨厌的单个字符串中的原始病理数据，例如：
医院编号233456患者姓名：乔尼·乞求DOB：13/01/77 全科医生：De'Ath博士程序日期：13/01/99临床 详细信息：dyaphagia和回流宏观描述：3片 食道，所有好的活检。组织学：这些节目慢性回流 和其他bobs。诊断：胃酸反流可能
您相信您正在使用一种好方法，即使用标题（"医院号"，"患者名称："，...）来提取数据（" 233456"，" Jonny Begood"，...）。但是，我认为，有一种更简单的方法可以使用正则表达式来执行此操作，即将标题用作LookBehind和LookAhead令牌。因此，在上面的字符串中，我们看到医院编号的数据 是"医院编号"one_answers"患者名称"之间的所有内容："，删除了空格，即" 233456"。可以应用相同的原理来提取每个后续数据。另外几行代码将在数据中将单独的数据列入其自己的列。
。这是您创建测试数据的代码。Frame：
Mypath<-"Hospital Number 233456 Patient Name: Jonny Begood DOB: 13/01/77 General Practitioner: Dr De'ath Date of Procedure: 13/01/99 Clinical Details: Dyaphagia and reflux Macroscopic description: 3 pieces of oesophagus, all good biopsies. Histology: These show chronic reflux and other bits n bobs. Diagnosis: Acid reflux likely"
Mypath<-data.frame(Mypath)
names(Mypath)<-"PathReportWhole"
然后，我们创建标头的字符向量：
x <- c("Hospital Number", "Patient Name:", "DOB:", "General Practitioner:", "Date of Procedure:", "Clinical Details:", "Macroscopic description:", "Histology:", "Diagnosis:")
请注意，这些必须与数据中包含的标头完全匹配。另外，我们不需要一个空字符串作为最后一个条目，就像您上面的那样。
然后，我们可以编写一个函数，该函数以参数为data.frame df，data.frame列包含原始数据 colName的名称（以使该函数尽可能一般）和标头的向量headers。
extractPath <- function(df, colName, headers) {
# df: data.frame containing raw path data
# colName: name of column containing data
# headers: character vector of headers (delimiters in raw path data)
for (i in seq_len(length(headers))) {
# left delimiter
delimLeft <- headers[i]
# right delimiter, not relevant if at end of headers
if (i < length(headers)) {
delimRight <- headers[i+1]
# regex to match everything between delimiting headers
regex <- paste0("(?<=", delimLeft, ").*(?=", delimRight, ")")
} else {
# regex to match everything to right of final delimiting header
regex <- paste0("(?<=", delimLeft, ").*$")
}
# generate column name for new column
# use alpha characters only (i.e. ignore colon), and remove spaces
columnName <- str_extract(delimLeft, "[[:alpha:] ]*") %>% str_replace_all(" ", "")
# create new column of data, and trim whitespace
df[[columnName]] <- str_extract(df[[colName]], regex) %>% str_trim()
}
# return output data.frame
df
}
在这里，我正在使用tidverse软件包生态系统，即dplyr和stringr。函数通过每个标头循环，生成适当的正则表达式，然后应用它们创建新的数据列。
这样调用该功能：
out <- extractPath(Mypath, "PathReportWhole", x)
这是单行测试数据的输出。帧：
> glimpse(out)
Observations: 1
Variables: 10
$ PathReportWhole <fctr> Hospital Number 233456 Patient Name: Jonny Begood DOB: 13/01/77 General Practitioner: Dr De'ath Date of Procedure: 13/01/99 Clinical Details: Dyaphagia and re...
$ HospitalNumber <chr> "233456"
$ PatientName <chr> "Jonny Begood"
$ DOB <chr> "13/01/77"
$ GeneralPractitioner <chr> "Dr De'ath"
$ DateofProcedure <chr> "13/01/99"
$ ClinicalDetails <chr> "Dyaphagia and reflux"
$ Macroscopicdescription <chr> "3 pieces of oesophagus, all good biopsies."
$ Histology <chr> "These show chronic reflux and other bits n bobs."
$ Diagnosis <chr> "Acid reflux likely"
（您可能需要进一步整理数据，转换字符日期等。）
我还用数据进行了测试。框架的数千行，它在一秒钟左右的时间内运行。