Longitudinal (panel) data provide the opportunity to examine temporal patterns of individuals, because measurements are collected on the same person at different, and often irregular, time points. The data is typically visualised using a "spaghetti plot", where a line plot is drawn for each individual. When overlaid in one plot, it can have the appearance of a bowl of spaghetti. With even a small number of subjects, these plots are too overloaded to be read easily. The interesting aspects of individual differences are lost in the noise. Longitudinal data is often modelled with a hierarchical linear model to capture the overall trends, and variation among individuals, while accounting for various levels of dependence. However, these models can be difficult to fit, and can miss unusual individual patterns. Better visual tools can help to diagnose longitudinal models, and better capture the individual experiences. This paper introduces the R package, brolgar (BRowse over Longitudinal data Graphically and Analytically in R), which provides tools to identify and summarise interesting individual patterns in longitudinal data.

翻译：纵向( 面板) 数据提供了检查个人时间模式的机会, 因为测量是在不同的时间点收集的, 并且往往是不定期的。 数据通常使用“ spaghetti 地块” 来视觉化, 每个人绘制线条地块。 当覆盖在一块地块时, 它会看到一碗意大利面条的外观。 这些地块即使有少数几个主题, 也太过负荷, 难以阅读。 噪音中会丢失个人差异的有趣方面 。 纵向数据往往以等级线性模型为模型, 以捕捉总体趋势和个人之间的差异, 同时又考虑到不同程度的依赖性。 然而, 这些模型可能很难适应, 并且可能错过不寻常的个人模式 。 更好的视觉工具可以帮助诊断长度模型, 更好地捕捉个人的经验 。 本文介绍了 R 包 、 broolgar ( 浏览长度数据时用图形和分析方式), 它提供了识别和总结长度数据中有趣的个人模式的工具 。