根据标题将文本转换为data.frame [英] Converting text to a data.frame based on headers

问题描述

我将.txt文件上传到R中，如下所示:Election_Parties <- readr::read_lines("Election_Parties.txt").假设文件中包含以下文本:

I uploaded a .txt file in to R as follows: Election_Parties <- readr::read_lines("Election_Parties.txt"). Let's say the following text was in the file:

BOLIVIA
P17-Nationalist Revolutionary Movement-Free Bolivia Movement (Movimiento Nacionalista Revolucionario 
P19-Liberty and Justice (Libertad y Justicia [LJ])
P20-Tupak Katari Revolutionary Movement (Movimiento Revolucionario Tupak Katari [MRTK])

COLOMBIA
P1-Democratic Aliance M-19 (Alianza Democratica M-19 [AD-M19])
P2-National Popular Alliance (Alianza Nacional Popular [ANAPO])
P3-Indigenous Authorities of Colombia (Autoridades Indígenas de Colombia)

换句话说:每行空白之后，就会有一个新的国家/地区开始.我想将这个文本文件转换成一个数据框，在该数据框中，国家名称变成一个向量，而参与者列表变成一个向量.

In words: After every empty line, a new country starts. I would like to convert this text file into a dataframe where the country name becomes a vector and the list of parties becomes a vector.

所需的输出:

Bolivia     P1-Nationalist Revolutionary Movement-Free Bolivia Movement (Movimiento Nacionalista 
Bolivia     P19-Liberty and Justice (Libertad y Justicia [LJ])
Bolivia     P20-Tupak Katari Revolutionary Movement (Movimiento Revolucionario Tupak Katari [MRTK])
Colombia    P1-Democratic Aliance M-19 (Alianza Democratica M-19 [AD-M19])
Colombia    P2-National Popular Alliance (Alianza Nacional Popular [ANAPO])
Colombia    P3-Indigenous Authorities of Colombia (Autoridades Indígenas de Colombia)

如果可能的话，我希望解决方案基于标题.

I would if possible like the solution to be based on the header.

我刚刚意识到每个新的国家/地区都以P1开头，因此也可以基于此为解决方案.

I just realised that every new country starts with P1, so a solution could also be based on that.

推荐答案

如果分隔符始终为"，那么一旦将文本放在矢量中，就可以使用分隔符.用它作为分界符，并累加起来将它们分成几组.

If your separator is always "", then once you have your text in a vector; use that as a demarcator and do cumsum to separate them into groups.

TXT = readr::read_lines("Election_Parties.txt")
#we add a separator for your first country
TXT = c("",TXT)
idx <- cumsum(TXT=="")
# use idx <- cumsum(!grepl("^[A-Z]",TXT)) if weird newline

您可以看到玻利维亚进入1，哥伦比亚进入2

You can see BOLIVIA goes into 1, COLOMBIA goes into 2

tibble::tibble(TXT,idx)
# A tibble: 10 x 2
   TXT                                                                       idx
   <chr>                                                                   <int>
 1 ""                                                                          1
 2 BOLIVIA                                                                     1
 3 "P17-Nationalist Revolutionary Movement-Free Bolivia Movement (Movimie…     1
 4 P19-Liberty and Justice (Libertad y Justicia [LJ])                          1
 5 P20-Tupak Katari Revolutionary Movement (Movimiento Revolucionario Tup…     1
 6 ""                                                                          2
 7 COLOMBIA                                                                    2
 8 P1-Democratic Aliance M-19 (Alianza Democratica M-19 [AD-M19])              2
 9 P2-National Popular Alliance (Alianza Nacional Popular [ANAPO])             2
10 P3-Indigenous Authorities of Colombia (Autoridades Indígenas de Colomb…     2

我们只对每个组应用一个函数并制作一个数据框

We just apply a function to each group and make a dataframe

func = function(x){
  data.frame(Country=x[2],Parties=x[3:length(x)])
}
do.call(rbind,by(TXT,idx,func))

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