![]() ![]() When string columns are append too numeric columns, Stata returns an error (and with the option force, it replaces the using dataset to missing values). Wen there are multiple matches both in the master and using datasets, these functions create all combinations of rows, similarly to Stata joinby (and contrary to Stata merge m:m). The following table gives the correspondence between the Stata commands merge and joinby with the dplyr functions: Stata frames resetįrame `y'_`x': import sasxport5 "$`y'.The packages dplyr uses the SQL concepts of left, right, full, inner, semi, anti and cross joins. NHANES uses different letters for files from different years, the 2009-2010 one uses “F”. Use analytical.dta, clear Bonus: Here’s steps 1-4 in a single loop using global macrosįor advanced users: Here’s a loop and some global macros that is adaptable to downloading several years. now reopen your previously saved analytical dataset. Be careful! This will drop all data from memory!! Drop all other frames so you don't get an annoying pop-up But in this example, we are done with other frames. Stata's "save" command won't save other frames as FYI, just the pwf. You might opt to drop the new linking variables prior to saving dataset now if you are done manipulating the other frames. Step 5 (optional): If you are satisfied with your analytical dataset and no longer need the other frames, you can now drop the “linking” variables from the analytical dataset, save your analytical dataset, clear your frames, and reopen your analytical dataset // You can save this analytical frame as a Stata ![]() Just for kicks, we'll also grab self-įrget wtint2yr wtmec2yr sdmvpsu sdmvstra, from(DEMO_F) from my NHANES post, we need to grab weighting variables from DEMO_F This is how you merge individual variables from multiple frames. Step 4: “Get” the specific variables you want from each frame. linked IDs in those other frames, so Stata knows where to look for are now new DEMO_F, BPQ_F, and KIQ_U_F variables. if you look at your dataset with the -browse- command, you'll see there You are still within the "analytical" frame, but now your frames are all remember that your cwf should be analytical right now. use the "frlink" command to link frames. Step 3: Now that you are are in the new analytical frame, link all of your frames using the “linking” variable (“seqn” here). You can change frames with "cwf" for "change working frame". Now change from the default frame to the new analytical one. see current list of frames and present working frame: variable from DEMO_F to the new analytical frame in one line.įrame DEMO_F: frame put seqn, into(analytical) This creates a new frame called "analytical" and also moves the "seqn" (Your file might have a linking variable called "id".) we will copy ("put") from the DEMO_F frame. The "linking" variable in this dataset is called "seqn", which Also switch to that new analytical frame. Step 2: Create an “analytical” frame that will contain the data you need to complete your analysis, and copy the variable that links all of your data to this frame. pwf is present working frame, or the current one in use. ditto for the "BPQ_F" and "KIQ_U_F" datasets.įrame KIQ_U_F: import sasxport5 "", clear actually changing to it using the "frame :" prefix we can tell Stata to run the command from that frame without Since this command needs to be run from within the DEMO_F frame, Then run the sas import command within it, grabbing it from the CDC website. you could type "frame create" or the brief synonym "mkf" for This will delete all unsaved data so be careful!! Step 1: Drop (reset) all frames, create new ones to import the new datasets, and run commands within each new frame to import NHANES/SAS datasets. This is a modification of the code on this post. ![]() Here’s my approach to building a single analytical dataset from multiple imported datasets, with frames. With Frames, you just import each dataset into its own Frame, and “merge” them directly, skipping the intermediate “save as Stata dta file” step. Before using Frames, I had previously imported non-Stata datasets, saved them locally, then merged them 1 by 1. One handy application of Frames is to import non-Stata datasets as separate frames and combine them (really, merge) into a single analytical dataset/Frame. So, Stata users will probably be interested in building a single dataset/Frame for a specific analysis that is built from variables taken from multiple datasets/Frames. Stata is still simplest to use when manipulating a single dataset (or, frame). This allows for dynamic manipulation of multiple datasets across multiple Frames. Stata 16 introduced the new Frames functionality, which allows multiple datasets to be stored in memory, with each dataset stored in its own “Frame”. ![]()
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