快速入门Tworavens：20项关键功能详解
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更新于2024-09-10
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"Tworavens使用指南是一份详尽的文档，专为理解和利用Tworavens这款数据探索工具而设计。该指南旨在帮助用户快速入门并理解其丰富的功能和特性。作者Vito D'Orazio和James Honaker在2016年2月20日更新了这份指南，以确保用户能够直观地操作这款界面。
当用户首次启动Tworavens时，他们会立即注意到20个核心功能，这些功能包括但不限于数据加载、数据预览、可视化分析、统计计算以及交互式数据探索。图解展示了这20项功能的概览，每个功能旁边都有简短的一句话解释，便于用户快速了解其用途。
然而，这份指南并不仅仅停留在概述层面。它深入剖析了每项功能背后的原理和设计理念，以帮助用户更好地理解和利用它们。为了方便查找，每个功能都对应着文档中的详细章节，用户可以通过图中的编号或下方表格中的链接直接跳转到相应部分。
在详细介绍各项功能之前，指南首先提供了一个整体的框架，概述了Tworavens的设计目标和如何优化用户体验。这包括了如何通过用户友好的界面设计简化数据分析过程，以及如何结合智能算法和技术来提升数据处理的效率。
因此，阅读这份Tworavens使用指南不仅有助于新手快速上手，对于有经验的用户来说，也能提供深入理解Tworavens工作原理和优化使用策略的机会。无论是初次接触还是进一步深化理解，这份指南都是不可或缺的资源。"
2 The Layout
The features are grouped together by the five regions of the interface: the header and footer, and
the three panels. We typically anticipate that TwoRavens works in a left-to-right workflow, with
exploration of the variables in the left panel, followed by construction of relationships between
variables in the center panel, and estimation and interpretation of statistical models in the right
panel. While real workflows will inevitably cycle between these phases as the data is explored
in increasing depth, that underlying model might help you remember where different features are
located, or find additional functions you are looking for.
The header and footer act like parentheses to the whole analysis, with the header containing
meta-information about the dataset that will never be adjusted by the user, such as the citation, and
the footer containing information useful after the analysis is complete, such as links to a replication
file. More details on the design of the interface can be found in Honaker and D’Orazio (2014).
3 Gesture Controls Across Devices
A goal of TwoRavens is to remove the infrastructure barriers to immersion in data. TwoRavens
does not require any installed software that has to be maintained or kept up to date. It does
not require any local storage for datasets, or any local processor power for running even the most
complicated statistical models. Its intuitive gesture and visual approach allows users to quickly
engage with data, on any available device, including not just computers, but smart boards, tablets
and phones. All that is needed is a web browser and an internet connection. If used in a lab,
no additional software needs to be installed or maintained. If used in a classroom students can
use their own variety of devices. In this guide, we occasionally mention “mouseover” as a possible
action, while on touch devices, this generally means click.
4 Data Storage and Manipulation
In addition to being visual and gesture based, a key part of the capabilities of TwoRavens is
the fundamental change in architecture that it uses for data storage and statistical computation.
Although this architecture is sophisticated, it should be seamless for the user, indeed easier than
current statistical software platforms. All data is remotely stored in repositories. All statistical
processing occurs remotely on servers. The TwoRavens interface allows statistical exploration of
datasets, but never actually touches the data, or performs statistical computation itself. TwoRavens
only understands meta-data, that is, high-level information about the data, and while the user
should feel they are immersed in an exploration of the dataset, they are actually communicating with
the data remotely through this meta-data, which is more interpretable, meaningful and informative.
What is key to understand is that the data never actually comes to the user, only information
about the data. This makes TwoRavens very powerful for exploring big data, where the data itself
is too large to transfer to the user, and too computationally demanding for arbitrary user devices
to process. It also allows TwoRavens to interact with data that requires privacy preservation, for
example where summary statistics are allowed to be made public, but the raw data contains private
information that can not be viewed.
At present we connect to data archived on an instance of Dataverse (King, 2007) (Crosas, 2011),
but are working to use many other storage modes. Statistical computation is all done in R (R Core
Team, 2015), primarily using the Zelig statistical library (Choirat et al., 2015).
3
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