英文:
How to handle large datasets in R without running out of memory?
问题
我在R中处理大型数据集，需要找到有效的策略来处理它们，避免内存耗尽。随着数据集的增大，我希望确保我的R脚本和计算能够高效处理数据。
我尝试过使用read.csv()或data.table::fread()等函数将整个数据集加载到内存中，但经常会导致内存分配错误。我还尝试过分块处理或使用数据库连接等技术，但不确定它们是否是我特定情况下最优的方法。
英文:
I'm working with large datasets in R and I need to find effective strategies to handle them without running out of memory. As the datasets grow in size, I want to ensure that my R scripts and computations can handle the data efficiently.
I have attempted loading the entire dataset into memory using functions like read.csv() or data.table::fread(), but it often leads to memory allocation errors. I have also explored techniques such as chunk processing or using database connections, but I'm not sure if they are the most optimal approaches for my specific scenario.
答案1
得分: -1
以下是要翻译的内容：
- 获取更多的内存 / 使用具有更多内存的计算机 / 使用具有更多内存的计算机（理所当然）
- 使用云计算平台（专门针对大数据的平台将是理想的选择）。它们会提供可以使用的计算机，很可能拥有比您拥有的内存多几倍的量
- 对数据集进行抽样
- 将数值转换为它们最节省空间的版本（例如，如果一个双精度列只包含整数，切换到
整数数据类型。如果日期列被编码为日期时间或字符串，将其转换为日期类型即可（感谢：Gregor Thomas）
英文:
The best option will depend on your particular use case, but here are some other ideas (in addition to using a remote database, or chunk processing, which you mentioned):
- get more RAM / get a computer with more RAM / use a computer with more RAM (goes without saying)
- use a cloud computing platform (one which specialises in big data would be ideal). They will have computers you can use which would most likely have several times the amount of memory you have
- sample the dataset
- convert values into the most space efficient versions of themselves (e.g. if a double column only includes whole numbers, switch to the
Integerdata type. If a date column is encoded as a datetime, or as a string, convert it to aDatetype instead (credit: Gregor Thomas)
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