Just like individual datasets, all frames are kept entirely in memory. For a good introduction on frames, see help frames intro. Note that commands related to frames and framesets work exactly the same way whether you type frame or frames they are synonymous. You can do more with frames: copy data variables and observations with frame put, add new observations with frame post, etc. That will reset Stata to a state where a single, empty frame is in memory. You can delete a frame (if it’s not the current one) with frame drop. For instance, you can create a one-to-one link (by specifying 1:1) between current frame auto and frame cars by matching the observations on variable make (that keeps makes of cars). You can also use frlink to create a link between the current frame and another frame. ![]() For example, you can generate a new variable, say, newvar (with random values here), in frame cars. That said, the frame prefix capability allows you to run a command on a frame other than the current one. You always work with the current frame, by default. You can identify the current frame with pwf (print working frame). Also, even if there are multiple frames in memory, you can interactively work with one frame (the current frame) at a time. Names of datasets and frames that hold them can be different. You can make a copy of a frame and rename a frame. For example, here is how you can create a frame with frame create, make that frame the current (working) frame with frame change, and load a dataset into it. Multiple datasets can be kept in memory in multiple frames. In Stata 16 (2019), a new framework for handling multiple datasets was introduced: frames. But those require some careful coding and entail a time penalty for saving and restoring datasets to and from disk. There are Stata commands, like preserve and restore, that enable you to switch from one dataset to another. Or you may be working with a set of related datasets and want to consolidate statistics across them. You may want to multitask and work with various datasets for various projects. With large and complex data, there is often need to work with multiple, and potentially huge, datasets concurrently. In an appendix at the end of this blog, I provide an overview of how Stata’s data management capabilities have grown over time.įrames: A framework for multiple datasets I describe these features in detail in the next three sections. In this blog, I discuss new features for handling large datasets, namely, frames, framesets, and alias variables. Nonetheless, leveraging on the phenomenal growth of affordable memory, Stata’s data management capabilities kept getting bigger, stronger, and faster. However, holding entire datasets in memory is restrictive with very large datasets. This makes Stata fast and allows billions of observations to be processed in milliseconds. This underlying framework has remained the bedrock for the 17 versions of Stata that followed: datasets are still kept as tables entirely in memory, with strongly typed languages to process the data. Most of the maiden 44 commands were for data management, including the still inescapable generate, replace, and list. Data types, like integers, real numbers, and especially strings, were frugally managed. Datasets were kept entirely in memory (then measured in kilobytes) and saved on disk as. When Stata 1.0 was released in 1985, data were organized in a tabular form as observations (rows) and variables (columns) and were called a dataset. Alias variables allow you to access variables in other frames as if they were part of the current frame, with very little memory overhead. Framesets allow you to bundle, save on file, and load in memory a set of related frames that hold datasets. These features enable Stata to deal with a multiplicity of potentially very large datasets efficiently and conveniently. ![]() The aim of this blog is to describe two novel features introduced in Stata 18 (released in 2023): 1) framesets and 2) alias variables across frames.
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