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尝试创建描述性表格，但面临对齐困难

tag-icon • tag-icon tables longtable
尝试创建描述性表格，但面临对齐困难 
Here is the code written for the same, I've also attached the image of the output. Please suggest the necessary changes.



\documentclass[landscape, 12pt]{article}
    \usepackage[table, svgnames, dvipsnames]{xcolor}
    \usepackage{longtable}
    \usepackage[a4paper, landscape, margin=1cm,]{geometry}
    
    \usepackage{makecell, cellspace, caption}
    \setlength\cellspacetoplimit{2pt}
    \setlength\cellspacebottomlimit{3pt}
    \usepackage{array}
    \newcolumntype{L}[1]{>{\raggedright\let\newline\\\arraybackslash\hspace{0pt}}m{#1}}
    \newcolumntype{C}[1]{>{\centering\let\newline\\\arraybackslash\hspace{0pt}}m{#1}}
    \newcolumntype{R}[1]{>{\raggedleft\let\newline\\\arraybackslash\hspace{0pt}}m{#1}}
    \begin{document}
    
           
            \begin{longtable}{| L {5 cm} | L{4cm} | L{5cm} | L{2.5cm} | L{1.5cm} | L{2cm} |}
                \hline
                \rowcolor{Gainsboro!60}
                \makecell{Source of \\ information \\ (reference) 
      } & \makecell{Method of  \\ identification}  & \makecell{The consumer’s strengths \\ to address these issues.} & \makecell{Consumer \\ and Nursing \\ Interventions} & \makecell{Person/s \\ Responsible} & \makecell{Timeframe}\\
    \hline
    \endhead
    \\
    \\
    \\
    \\
      “A Novel Event Detection \\ Method Using PMU Data \\ with High Precision”\\ by Mingjian Cui,\\ Jianhui Wang, Jin Tan,\\ Anthony R. Florita, and \\ Yingchen Zhang, published \\ in IEEE TRANSACTIONS ON \\POWER SYSTEMS, 2018
      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
     & This paper develops a new method of event detection using dynamic programming-based SDT (DPSDT) and compares it with the Wavelet-based event detection (WED) technique. It gives the complete algorithm of the DPSDT technique and compares various events it could detect with sufficient graphs and figures.
     %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
     & a.To reduce dimensionality of original streaming PMU data, islanding detection,
     b.compress critical disturbance info of PMU 
    c. locate power system event, 
    d. to select the best set of features of the disturbance types in the time-frequency domain, 
    e. discrete samples of collected PMU data, 
    f. assess the power system disturbance by using wide-area post disturbance records
    to identify events and characterize their features.
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
     & 1.PMU
    
       2.PMU 
     and smart meter
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
      30-120 times per sec
       \\
               \hline
               
            \end{longtable}
         
    \end{landscape}
    \end{document}

参考输出

答案1

您绝对应该删除\\第一列中的所有内容（双反斜杠）。

在此处输入图片描述

\documentclass[12pt]{article}
\usepackage[table, svgnames, dvipsnames]{xcolor}
\usepackage{longtable}
\usepackage[a4paper, landscape, margin=1cm,]{geometry}

\usepackage{makecell, cellspace, caption}
\setlength\cellspacetoplimit{2pt}
\setlength\cellspacebottomlimit{3pt}
\usepackage{array}
\newcolumntype{L}[1]{>{\RaggedRight\arraybackslash\hspace{0pt}}m{#1}}
\newcolumntype{C}[1]{>{\Centering\arraybackslash\hspace{0pt}}m{#1}}
\newcolumntype{R}[1]{>{\RaggedLeft\arraybackslash\hspace{0pt}}m{#1}}

\usepackage{enumitem,ragged2e}
\newlist{myenumerate}{enumerate}{1}
\setlist[myenumerate,1]{label=\alph*.,nosep,wide=0pt,
          before={\begin{minipage}{\hsize}\RaggedRight},
          after={\end{minipage}}}

\begin{document}
       
\begin{longtable}{| L{5cm} | L{4cm} | L{5cm} | L{2.5cm} | L{2cm} | L{2cm} |}
\hline
\rowcolor{Gainsboro!60}
\makecell{Source of \\ information \\ (reference) } & 
\makecell{Method of  \\ identification} & 
\makecell{The consumer’s strengths \\ to address these issues.} & 
\makecell{Consumer \\ and Nursing \\ Interventions} & 
\makecell{Person/s \\ Responsible} & 
\makecell{Timeframe}\\
\hline
\endhead

“A Novel Event Detection Method Using PMU Data with High Precision,” by Mingjian Cui, Jianhui Wang, Jin Tan, Anthony R. Florita, and Yingchen Zhang, published in IEEE Transactions on Power Systems, 2018

& This paper develops a new method of event detection using dynamic programming-based SDT (DPSDT) and compares it with the Wavelet-based event detection (WED) technique. It gives the complete algorithm of the DPSDT technique and compares various events it could detect with sufficient graphs and figures.

& \begin{myenumerate}
\item \strut To reduce dimensionality of original streaming PMU data, islanding detection,
\item compress critical disturbance info of PMU 
\item locate power system event, 
\item to select the best set of features of the disturbance types in the time-frequency domain, 
\item discrete samples of collected PMU data, 
\item assess the power system disturbance by using wide-area post disturbance records to identify events and characterize their features.\strut
\end{myenumerate}

 & 1.~PMU

   2.~PMU and smart meter, 30--120 times per sec
 & &
\\
\hline
\end{longtable}

\end{document}

答案2

对于 OP 来说可能有点晚了，但对其他人来说可能会有帮助......

通过使用tabularray包及其longtblr表格，使用booktabs包规则并删除垂直的，使用包enumerate中定义的列表enumitem，您的表格可以如下所示：

在此处输入图片描述

\documentclass[12pt]{article}
\usepackage[showframe,
            a4paper, landscape, margin=1cm,]{geometry}

\usepackage{microtype}
\usepackage[svgnames, dvipsnames]{xcolor}
\usepackage{tabularray}
\UseTblrLibrary{booktabs, varwidth}
\usepackage{enumitem}
\usepackage{etoolbox}
\AtBeginEnvironment{longtblr}{\setlist[enumerate]{leftmargin=*, nosep, itemsep=1pt}}

\begin{document}
\begingroup
\DefTblrTemplate{capcont}{default}{}
\begin{longtblr}[
    entry=none,     % <---
    label=none,     % <---
                ]{colsep = 4pt,
                 colspec = {*{3}{X[2.5,j]}
                                 X[1.5,l]
                            *{2}{X[1,l]}
                            },
                 cells   = {font=\small\linespread{0.84}\selectfont},
                 row{3-Z}= {abovesep=9pt},
                 row{1}  = {c, m, bg=Gainsboro!60},
                 stretch = -1,  %<--- remove extra space above and below lists
                                % with nosep option; doc p.51 tabularray
                 measure = vbox,
                 rowhead = 1
                 }
    \toprule
Source of information (reference)
    &   Method of identification   
        &   The consumer’s strengths to address these issues. 
            &   Consumer and Nursing Interventions 
                &   Person/s Responsible 
                    &   Timeframe       \\
    \midrule
“A Novel Event Detection Method Using PMU Data with High Precision” by Mingjian Cui, Jianhui Wang, Jin Tan, Anthony R. Florita, and  Yingchen Zhang, published in IEEE TRANSACTIONS ON POWER SYSTEMS, 2018
    &   This paper develops a new method of event detection using dynamic programming-based SDT (DPSDT) and compares it with the Wavelet-based event detection (WED) technique. It gives the complete algorithm of the DPSDT technique and compares various events it could detect with sufficient graphs and figures.
        &   \begin{enumerate}[label=\alph*.] 
        \item   To reduce dimensionality of original streaming PMU data, islanding detection,
        \item   compress critical disturbance info of PMU
        \item   locate power system event,
        \item   to select the best set of features of the disturbance types in the time-frequency domain,
        \item   discrete samples of collected PMU data,
        \item   assess the power system disturbance by using wide-area post disturbance records
        to identify events and characterize their features.
            \end{enumerate}
            &   \begin{enumerate}[label=\arabic*.]
            \item   PMU
            \item   PMU and smart meter 30-120 times per sec
                \end{enumerate}
                &   &   \\
“A Novel Event Detection Method Using PMU Data with High Precision” by Mingjian Cui, Jianhui Wang, Jin Tan, Anthony R. Florita, and  Yingchen Zhang, published in IEEE TRANSACTIONS ON POWER SYSTEMS, 2018
    &   This paper develops a new method of event detection using dynamic programming-based SDT (DPSDT) and compares it with the Wavelet-based event detection (WED) technique. It gives the complete algorithm of the DPSDT technique and compares various events it could detect with sufficient graphs and figures.
        &   \begin{enumerate}[label=\alph*.]
        \item   To reduce dimensionality of original streaming PMU data, islanding detection,
        \item   compress critical disturbance info of PMU
        \item   locate power system event,
        \item   to select the best set of features of the disturbance types in the time-frequency domain,
        \item   discrete samples of collected PMU data,
        \item   assess the power system disturbance by using wide-area post disturbance records
        to identify events and characterize their features.
            \end{enumerate}
            &   \begin{enumerate}[label=\arabic*.]
            \item   PMU
            \item   PMU and smart meter 30-120 times per sec
                \end{enumerate}
                &   &   \\
“A Novel Event Detection Method Using PMU Data with High Precision” by Mingjian Cui, Jianhui Wang, Jin Tan, Anthony R. Florita, and  Yingchen Zhang, published in IEEE TRANSACTIONS ON POWER SYSTEMS, 2018
    &   This paper develops a new method of event detection using dynamic programming-based SDT (DPSDT) and compares it with the Wavelet-based event detection (WED) technique. It gives the complete algorithm of the DPSDT technique and compares various events it could detect with sufficient graphs and figures.
        &   \begin{enumerate}[label=\alph*.]
        \item   To reduce dimensionality of original streaming PMU data, islanding detection,
        \item   compress critical disturbance info of PMU
        \item   locate power system event,
        \item   to select the best set of features of the disturbance types in the time-frequency domain,
        \item   discrete samples of collected PMU data,
        \item   assess the power system disturbance by using wide-area post disturbance records
        to identify events and characterize their features.
            \end{enumerate}
            &   \begin{enumerate}[label=\arabic*.]
            \item   PMU
            \item   PMU and smart meter 30-120 times per sec
                \end{enumerate}
                &   &   \\
“A Novel Event Detection Method Using PMU Data with High Precision” by Mingjian Cui, Jianhui Wang, Jin Tan, Anthony R. Florita, and  Yingchen Zhang, published in IEEE TRANSACTIONS ON POWER SYSTEMS, 2018
    &   This paper develops a new method of event detection using dynamic programming-based SDT (DPSDT) and compares it with the Wavelet-based event detection (WED) technique. It gives the complete algorithm of the DPSDT technique and compares various events it could detect with sufficient graphs and figures.
        &   \begin{enumerate}[label=\alph*.]
        \item   To reduce dimensionality of original streaming PMU data, islanding detection,
        \item   compress critical disturbance info of PMU
        \item   locate power system event,
        \item   to select the best set of features of the disturbance types in the time-frequency domain,
        \item   discrete samples of collected PMU data,
        \item   assess the power system disturbance by using wide-area post disturbance records
        to identify events and characterize their features.
            \end{enumerate}
            &   \begin{enumerate}[label=\arabic*.]
            \item   PMU
            \item   PMU and smart meter 30-120 times per sec
                \end{enumerate}
                &   &   \\
    \bottomrule
\end{longtblr}
\endgroup
\end{document}
  • 第一行的内容重复了四次，由此可以看出表格是如何分布在两页上的。
  • 如您所见，表格中使用的\small字体大小使单元格内容填充得更好。使用此字体大小，表格仍然易于阅读，但是，您可以通过\small从代码行中删除选项来保留正常字体大小（12pt）
 cells   = {font=\small\linespread{0.84}\selectfont}

在这种情况下，表格将如下所示：

在此处输入图片描述

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