7개의 열이 있는 긴 테이블이 필요하므로 가로 형식을 선택했습니다. 올바른 위치에 있지 않은 마지막 열의 텍스트를 제외하고 모든 것이 잘 작동합니다. 텍스트가 페이지의 여백을 준수하지 않는 이유를 이해할 수 없습니다(표 뒤에 쓴 텍스트는 다음과 같습니다). 그림에는 이에 대한 예가 나와 있습니다.
다음은 문제를 재현하는 몇 줄의 코드입니다.
\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}
답변1
대신 패키지를 longtblr사용 하면 더 짧은 코드를 작성하여 더 나은 형식의 테이블을 제공할 수 있습니다. 열을 사용하면 테이블 너비가 (= )와 같고 두 번째 및 네 번째 열을 제외하고 열 너비 간의 비율을 결정할 수도 있습니다. 이 열에서는 셀 내용에 따라 결정된 자연 너비가 유지됩니다. 두 번째와 마지막 열에는 정렬된 텍스트가 있는데, 제 생각에는 표가 더 보기 좋게 보입니다.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}
답변2
1.089\linewidth여러 열에 명시적 텍스트 블록을 사용하여 표를 텍스트 블록보다 넓게 강제로 설정했는데 열 너비와 \tabcolsep간격의 합이 가 넘었기 때문에 여기서 \linewidth줄였습니다 .\tabcolsep
\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}