track.future(track)
track.future()所属R语言包：track
Potential future features of the track package
潜在的未来的轨迹包
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Potential future features of the track package, in some vague order of feasibility and priority ('easy', 'medium' and 'hard' are an estimate of design and coding difficulty):
潜在的未来的轨迹包的功能，在一些模糊的可行性和优先顺序（“容易”，“中等”和“硬”的设计和编码困难的估计）：
objects:</dt> (medium) with cachePolicy="tltPurge", changed objects are only written to file at the end of a top level task. However, with cachePolicy="none", objects are written to file on each change – is
对象：</ DT>（中）与cachePolicy的“tltPurge”，改变对象的顶层任务结束时写入文件。然而，随着cachePolicy =“无”对象被写入到文件上的每一个变化 -
untracked variables in the summary: (easy) would this be useful? wouldn't need to cache these, mark with an asterisk in a special
摘要：不露痕迹的变量（容易）这是有用的？就不需要缓存这些，在一个特殊的星号标记
option writeToDisk: is this redundant option with cache and cachePolicy?
选项writeToDisk：是这样的冗余选项缓存和cachePolicy的吗？
other default tracked environment: (easy) would it be useful to allow an environment other than the global environment to be the default tracking environment? This could be implemented by using options("tracked.environment") as the default environment for all the
其他默认跟踪的环境：（容易）会是有益的，允许全球环境是默认的跟踪环境以外的环境吗？这可以使用options("tracked.environment")所有的预设环境中实现
DONE better cleanup: (easy) provide an integrated quiting function that saves all tracked vars and history before quitting (and maybe also
做更好的清理：（容易）提供一个的集成quiting功能，在退出之前（也许还有保存所有跟踪的变量和历史
caching rules: (hard) allow rule-based decisions for caching, e.g., only cache objects under a certain size, or only cache objects of certain classes, or enforce a limit on memory for caching tracked
缓存规则：（硬）允许缓存，例如，只有具有一定规模下的缓存对象，或只有某些类别的缓存对象，以规则为基础的决策或执行限制的内存高速缓存追踪
record file read/writes: (easy) record each time a file is read
记录文件的读/写：（容易）记录每一个文件被读取时，
auto-trust in rebuild: (easy) when rebuilding an active tracking environment, base decision whether to use summary row from file or environment on which has more recent dates in it. (whole dataframe,
自动重建信任：（容易）重建时，主动跟踪环境，碱基决定是否使用汇总行从文件或环境上有更近的日期。 （全数据框，
smarter reading of filemap.txt: (medium) check the mod time on filemap.txt when getting the filemap obj, and if the file on disk appears to have changed, reread it instead of just getting it from memory. This would allow working together better with other sessions that are simultaneously using this tracking dir. Don't know how much it would slow things down – do some timings. Note that to make
聪明的阅读的filemap.txt：（中）检查模时间filemap.txt时，得到的filemap OBJ，如果磁盘上的文件发生了变化，重读，而不是仅仅得到它从内存中。这将允许一起工作更好地与其他会话，同时使用这种跟踪目录。不知道多少，它会慢下来的东西 - 做一些计时。需要注意的是，使
investigate double-get: doing subset-replacement (e.g., X[2] <- ...) retrieves X twice
调查一倍：做子集替换（例如，X[2] <- ...）检索X两次
DONE readonly mode: (hard) to allow linking tracking dirs that might be in use by other R processes – would require not recording gets – this would require adding a new env on the
只读模式：（硬），允许连接可能会在的其他研发过程中使用的跟踪显示目录 - 要求不记录得 - 这将需要添加一个新的包膜上的
DONE autoflush: (hard) automatic flushing of variables that haven't been used frequently (triggered automaticall when memory runs low?) – this
DONE自动刷新：（硬）自动冲洗的变量没有被使用的频率（触发会自动在内存不足时？） -
safer restart: (medium) check that we will be able to restart before
更安全的重新启动（中）检查，我们将能够重新启动之前
safe saves: (hard) write files in a safe way so that the original file is not removed until the new file is written – not sure if this is necessary, because objects are in memory, and can be
安全保存：（硬盘）写入文件以安全的方式，使原来的文件不会被删除，直到写入新文件 - 不知道这是必要的，因为对象在内存中，并且可以
DONE autotrack: (hard) automatically track new variables? (would require hooks in
DONE自动跟踪：（硬），自动跟踪新的变数？ （这需要在挂钩
Example of the "double-get" when assigning a subset (using the example from the help page for makeActiveBinding). Note that it works correctly, but retrieving the object twice seems unneccessary and could be slow with very large objects.
例如，“双”时，分配一个子集（例如，从makeActiveBinding）的帮助页面。需要注意的是它能够正常工作，但两次检索的对象似乎是不必要的和非常大的对象可能是缓慢的。
参见----------See Also----------
Overview and design of the track package.
track包的概述和设计。
实例----------Examples----------
# Example (transcript shown above) of how subset-assignment[如上图所示的实例（副本）的子集分配]
# results in two retrievals when the object is an active binding.[结果在两个检索时，该对象是一个活性结合。]
f <- local( {
x <- 1
function(v) {
if (missing(v)) {
cat("get\n")
} else {
cat("set\n")
x <<- v
}
x
}
})
makeActiveBinding("X", f, .GlobalEnv)
bindingIsActive("X", .GlobalEnv)
X
X <- 2
X
X[1]
X[2] <- 1 # 'X' is fetched twice[X取两次]
X
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注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
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发表于 2012-10-1 11:19:18
