我需要一张有 7 列的长表格，所以我选择了横向格式。除了最后一列的文本没有放在正确的位置外，其他一切都运行正常。我不明白为什么文本不尊重页面的边距（我在表格后面写的文本是尊重的）。图片中有一个例子。
以下几行代码可以重现该问题：
\documentclass{report}
\usepackage{array}
\usepackage{pdflscape}
\usepackage{colortbl}
\usepackage{longtable}
\begin{document}
\begin{landscape}
\footnotesize
\begin{longtable}
{>{\centering\hspace{0pt}}m{0.055\linewidth}>
{\centering\hspace{0pt}}m{0.06\linewidth}>
{\centering\hspace{0pt}}m{0.22\linewidth}>
{\centering\hspace{0pt}}m{0.04\linewidth}>
{\centering\hspace{0pt}}m{0.15\linewidth}>
{\centering\hspace{0pt}}m{0.05\linewidth}>
{\centering\arraybackslash\hspace{0pt}}m{0.3\linewidth}}
\caption{EEG table} \\
\label{tab:EEG}
\textbf{} & \textbf{Cohort} & \textbf{MI Task} & \textbf{NC} &
\textbf{Network Nodes} & \textbf{Metrics} & \textbf{Main Findings}
\endfirsthead
\multicolumn{7}{c}%
{{\bfseries \tablename\ \thetable{} -- continued from previous page}} \\
\\
\textbf{} & \textbf{Cohort} & \textbf{MI Task} & \textbf{NC}
& \textbf{Network Nodes} & \textbf{Metrics} & \textbf{Main Findings}\\ \hline
\endhead
\hline
\multicolumn{7}{>{\centering\arraybackslash\hspace{0pt}}m{1.089\linewidth}}
{{\cellcolor[rgb]{0.714,0.714,0.714}}\textbf{Effective Connectivity}}
\\
\hline
\rowcolor[rgb]{0.871,0.871,0.871} Vukelić, Mathias et al. (2014) & 16 HS & kinesthetic imagination of opening the right hand; each trial consisted of a preparation (2s), MI (6s), and rest (8s) phase & 32 & 24 electrodes as nodes & PSI & activation of an ipsilateral sensorimotor–parietal EC network is an indicator of a low ability for regional sensorimotor $\beta$-modulation \\
Yi, Weibo et al. (2014) & 10 HS & imagine left hand, right hand, feet, both hands and hand combined with foot movements; each trial consisted of a preparation cue (1s), MI (4s) and rest (1s) phase & 64 & 21 electrodes as nodes & \textcolor{red}{PLV}, SDTF & findings imply that there exist apparent intrinsic distinctions of neural mechanism between simple and compound limb MI, which presents a more complex EC network and may involve a more complex cognitive process during information processing \\
\rowcolor[rgb]{0.871,0.871,0.871} Liang, Shuang et al. (2016) & 9 HS & imagination of movement of the left hand, right hand, both feet and tongue; each trial consisted of a cue (1.25s), MI (3s, no feedback was provided) and rest (1.5s) phase & 22 & 9 electrodes used as nodes & PDC & significant EC exists in the bilateral hemisphere during the tasks, regardless of the left or right-hand MI tasks. Furthermore, the out-in rate results of the information flow reveal the existence of contralateral lateralisation; \textcolor{red}{using PDC to compute EC provides efficient features for the detection of MI tasks and has great potential to be applied in BCIs}
\end{longtable}
\textbf{Abbreviations:}
\textbf{ADTF} Adaptive Directed Transfer Function,
\textbf{BCI} Brain Computer Interface,
\textbf{CCorr} Cross Correlation,
\textbf{CMA} Cingulate Motor Areas,
\textbf{Coh} Coherence,
\textbf{Corr} Correlation,
\textbf{DCM} Dynamic Causal Modelling
\end{landscape}
\end{document}
答案1
使用包而不是包longtblr，您可以编写更短的代码，从而提供格式更好的表格。使用列，表格宽度等于（= ），还可以确定除第二列和第四列之外的列宽之间的比率，其中保留了由单元格内容确定的自然宽度。第二列和最后一列有对齐的文本，在我看来，这可以使表格看起来更好。tabularraylongtalbleX\linewidth\textheight
\documentclass{report}
\usepackage{pdflscape}
\usepackage{xcolor}
\usepackage{tabularray}
\UseTblrLibrary{booktabs}
\begin{document}
\begin{landscape}
\footnotesize
\begin{longtblr}[
caption = {EEG table},
label = {tab:EEG}
]{colsep=4pt,
colspec = {X[0.5,l,m] Q[c,m]
X[1.8,j,m] Q[c,m] X[0.7,l,m] X[0.3,c,m]
X[2.2,j,m]
},
cell{2}{1} = {c=7}{c},
row{1,2} = {c, font=\bfseries},
row{2} = {font=\bfseries, bg=gray!45},
row{odd[3]} = {bg=gray!15},
rowhead = 1}
\toprule
& Cohort
& MI Task
& NC
& Network Nodes
& Met\-rics
& Main Findings \\
\midrule
Effective Connectivity
& & & & & & \\
Vukelić, Mathias et al. (2014)
& 16 HS
& kinesthetic imagination of opening the right hand; each trial consisted of a preparation (2s), MI (6s), and rest (8s) phase
& 32
& 24 electrodes as nodes
& PSI
& activation of an ipsilateral sensorimotor–parietal EC network is an indicator of a low ability for regional sensorimotor $\beta$-modulation
\\
Yi, Weibo et al. (2014)
& 10 HS
& imagine left hand, right hand, feet, both hands and hand combined with foot movements; each trial consisted of a preparation cue (1s), MI (4s) and rest (1s) phase
& 64
& 21 electrodes as nodes
& \textcolor{red}{PLV}, SDTF
& findings imply that there exist apparent intrinsic distinctions of neural mechanism between simple and compound limb MI, which presents a more complex EC network and may involve a more complex cognitive process during information processing \\
Liang, Shuang et al. (2016)
& 9 HS
& imagination of movement of the left hand, right hand, both feet and tongue; each trial consisted of a cue (1.25s), MI (3s, no feedback was provided) and rest (1.5s) phase
& 22
& 9 electrodes used as nodes
& PDC
& significant EC exists in the bilateral hemisphere during the tasks, regardless of the left or right-hand MI tasks. Furthermore, the out-in rate results of the information flow reveal the existence of contralateral lateralisation; \textcolor{red}{using PDC to compute EC provides efficient features for the detection of MI tasks and has great potential to be applied in BCIs}
\\
\bottomrule
\end{longtblr}
\textbf{Abbreviations:}
\textbf{ADTF} Adaptive Directed Transfer Function,
\textbf{BCI} Brain Computer Interface,
\textbf{CCorr} Cross Correlation,
\textbf{CMA} Cingulate Motor Areas,
\textbf{Coh} Coherence,
\textbf{Corr} Correlation,
\textbf{DCM} Dynamic Causal Modelling
\end{landscape}
\end{document}
答案2
您强制表格比带有多列显式的文本块更宽1.089\linewidth，并且列宽和间距的总和\tabcolsep大于\linewidth，我\tabcolsep在这里减少了。
\documentclass{report}
\usepackage{array}
\usepackage{pdflscape}
\usepackage{colortbl}
\usepackage{longtable}
\begin{document}
\begin{landscape}
\noindent X\dotfill X
\footnotesize
\setlength\tabcolsep{4.9pt}
\begin{longtable}
{>{\centering\hspace{0pt}}m{0.055\linewidth}>
{\centering\hspace{0pt}}m{0.06\linewidth}>
{\centering\hspace{0pt}}m{0.22\linewidth}>
{\centering\hspace{0pt}}m{0.04\linewidth}>
{\centering\hspace{0pt}}m{0.15\linewidth}>
{\centering\hspace{0pt}}m{0.05\linewidth}>
{\centering\arraybackslash\hspace{0pt}}m{0.3\linewidth}}
\caption{EEG table} \\
\label{tab:EEG}
\textbf{} & \textbf{Cohort} & \textbf{MI Task} & \textbf{NC} &
\textbf{Network Nodes} & \textbf{Metrics} & \textbf{Main Findings}
\endfirsthead
\multicolumn{7}{c}%
{{\bfseries \tablename\ \thetable{} -- continued from previous page}} \\
\\
\textbf{} & \textbf{Cohort} & \textbf{MI Task} & \textbf{NC}
& \textbf{Network Nodes} & \textbf{Metrics} & \textbf{Main Findings}\\ \hline
\endhead
\hline
\multicolumn{7}{c}
{{\cellcolor[rgb]{0.714,0.714,0.714}}\textbf{Effective Connectivity}}
\\
\hline
\rowcolor[rgb]{0.871,0.871,0.871} Vukelić, Mathias et al. (2014) & 16 HS & kinesthetic imagination of opening the right hand; each trial consisted of a preparation (2s), MI (6s), and rest (8s) phase & 32 & 24 electrodes as nodes & PSI & activation of an ipsilateral sensorimotor–parietal EC network is an indicator of a low ability for regional sensorimotor $\beta$-modulation \\
Yi, Weibo et al. (2014) & 10 HS & imagine left hand, right hand, feet, both hands and hand combined with foot movements; each trial consisted of a preparation cue (1s), MI (4s) and rest (1s) phase & 64 & 21 electrodes as nodes & \textcolor{red}{PLV}, SDTF & findings imply that there exist apparent intrinsic distinctions of neural mechanism between simple and compound limb MI, which presents a more complex EC network and may involve a more complex cognitive process during information processing \\
\rowcolor[rgb]{0.871,0.871,0.871} Liang, Shuang et al. (2016) & 9 HS & imagination of movement of the left hand, right hand, both feet and tongue; each trial consisted of a cue (1.25s), MI (3s, no feedback was provided) and rest (1.5s) phase & 22 & 9 electrodes used as nodes & PDC & significant EC exists in the bilateral hemisphere during the tasks, regardless of the left or right-hand MI tasks. Furthermore, the out-in rate results of the information flow reveal the existence of contralateral lateralisation; \textcolor{red}{using PDC to compute EC provides efficient features for the detection of MI tasks and has great potential to be applied in BCIs}
\end{longtable}
\textbf{Abbreviations:}
\textbf{ADTF} Adaptive Directed Transfer Function,
\textbf{BCI} Brain Computer Interface,
\textbf{CCorr} Cross Correlation,
\textbf{CMA} Cingulate Motor Areas,
\textbf{Coh} Coherence,
\textbf{Corr} Correlation,
\textbf{DCM} Dynamic Causal Modelling
\end{landscape}
\end{document}