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. I would like to remove this.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. *A second, minor question is that I do not understand why the x-axis gets an elipsis (.) when I add the scatteri plot.
I have tried adding axis(2) to the text box as an additional argument, but this option is apparently not allowed. It seems better to be able to attach the text to the line using the second y-axis gridpoints. Of course, I could manually determine this value for the histogram (as I did in this example), but I plan to create a set of these graphs via a loop, such that there are a dozen or so panels. The corresponding lines would instead be: text(1 `median' `"median = $`=string(`median',"%6.2f")'"', /// Instead of "0.08" which gets my text into the correct location, I would like to use "1", as this is the maximum value of the second y-axis. This is the reason I used the second y-axis in the first place. This produces the desired graph (for the most part*):Īt issue is that I do not know the height of the histogram beforehand. Scatteri 0 `median' 1 `median', recast(line) yaxis(2) /// Twoway hist mpg, start(10) width(5) || ///
Here is an example of my issue: sysuse auto However, I cannot seem to add text to this line using the second y-axis coordinates (it instead uses the first y-axis coordinates, even though the object it is labeling is on the second y-axis). To fix this, I am using scatteri with coordinates.īecause I do not know the height of the histogram beforehand, I set the scatteri plot to use a separate y-axis, which I have hidden. I cannot use xline() because the line falls behind the histogram, rather than on top of it.
I have a plot of a histogram, to which I would like to add the median.
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