Thanks to Nick's advice, I merged with the triples: merge 1:1 country year Gender cusing "Documents\LMI. However, the merge worked with a m:m as in below, merge m:m country year using "Documents\LMI.dta" "variables country year do not uniquely identify observations in the master data" n is not a variable name it is Stata syntax for observation number. Therefore, I tried to merge the two datasets with 1:1 and 1:m merge, and for both I get: Suppose you wish to merge two files microdata.dta and macrodata.dta, both including the. The second is structured as below, but it comes from an annual survey, so each country has three entries per year (total, male, female). In Stata linking two datasets is done with the merge command. In addition, each observation is measured at one year only, for Egypt it is 2005. It has over 30 variables but I am only listing this as an example. When showing example data, always use the dataex command.I am trying to the merge two datasets that have unemployment rates from different sources, and the first is structured as below: ECONOMETRICS title merge merge datasets syntax remarks and examples menu references description also see options syntax merge on specified key. When asking for help with code, always show example data. It also makes it possible for those who want to help you to create a faithful representation of your example to try out their code, which in turn makes it more likely that their answer will actually work in your data. It includes complete information about aspects of the data that are often critical to answering your question but cannot be seen from tabular displays or screenshots. So, the downside of an m:m merge appears to be that it is quite wrong. SharkScope is the most complete database of poker tournament results available and covers virtually all. SeeU 23 Combiningdatasetsfor a comparison ofappend,merge, andjoinby. 1 Answer Sorted by: 1 On the face of it your datasets are identified by triples of country year Gender and so qualify for merge 1:1 with those variables. Track your poker statistics and avoid the sharks. Stata has commands for dropping duplicates, but it is also important to understand why there a duplicates, because there might be something else wrong with. A common problem with merging occurs when there are duplicate observations, which prevent the software from matching. If the merge succeeds, we shall save the merged data separately. Stata won't let you merge another dataset if merge is already there. Stata can also join observations from two datasets into one seeDmerge. we start with extracting the first n- number of characters from both the key variables in the two datasets and merge using the extracted (truncated) variables. If anylenameis specied without an extension.dtais assumed. dataex will save you time it is easier and quicker than typing out tables. Description appendappends Stata-format datasets stored on disk to the end of the dataset in memory. Either way, run help dataex and read the simple instructions for using it. If the variables in both the 20 data had simply been called age, inc and mstat, then when you attempted to merge them Stata would not return an. If not, run ssc install dataex to get it. If you are running version 17, 16 or a fully updated version 15.1 or 14.2, dataex is already part of your official Stata installation. nolabel prevents Stata from copying the value-label denitions from the dataset on disk. To preserve compatibility with earlier versions of joinby, merge is generated only if unmatched is specied. All are user written and can be installed using ssc install command : reclink Instead of a single unique ID, you specify one or more variables that reclink assess for similarity. merge(varname) species the name of the variable that will mark the source of the resulting observation. In order to get a helpful response, you need to show some example data.īe sure to use the dataex command to do this. There are a few commands that can help with fuzzy mergeing in Stata. There are many ways your data might be organized that are consistent with your description, and each would require a somewhat different approach. Even the best descriptions of data are no substitute for an actual example of the data. Appending data files When you have two data files, you may want to combine them by stacking them one on top of the other. Examples will include appending files, one to one match merging, and one to many match merging. Descriptions of data are well-meant but insufficient to help those who want to help you. This module will illustrate how you can combine files in Stata.
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