#Merge stata code

This code only keeps individuals who took part in BHPS and drops those who joined as part of Understanding Society. The pidp will work as the unique cross-wave identifier across both sets of files. To match individual level files from the harmonised BHPS and Understanding Society in long format, you need to remove the wave prefixes in the two sets of files and generate a wave identifier that works across both sets of files. Merging individual files from harmonised bhps and ukhls in long format The resulting file can be merged with any individual level file. In this example we will create a variable that measures the number of siblings in the household using the egoalt file. Using the egoalt file to create household composition variables In this example we will match the information of respondents living with partners/spouses with that of their partners/spouses. In this example we will summarise individual level information within a household (number of 18-24-year-olds in the household) from an individual level file and then add it to the household level file. Summarising individual level information at the household level We can do this by merging household level file (such as w_hhresp) with an individual level file (such as w_indresp) within the same Wave. In this example we will distribute household level information to individuals in those households. To match individual level files across two waves into a long format, do the following (for more waves add wave specific prefix in the foreach statement)ĭistributing household level information to the individual level Merging individual files across waves into long format To match individual level files across two waves into a wide format, do the following (for more waves add wave specific prefix in the foreach statement) Merging individual files across waves into wide format Understanding Society Working Paper SeriesĬommonly-used commands to work with data.But you can also set them to be kept in different ways by the unmatched(both/master/using) option. Joinby forms all pairwise combinations within groups.įor the unmatched observations from the datasets, the default is to be ignored. Tab _merge to find out how your merge went. The _merge variable must be dropped or renamed before we perform the next merge. _merge = 3, observation appeared in both files _merge = 2, observation appeared in the using file only _merge = 1, observation appeared in the master file only The _merge variable, in the simple cases, has three values:

It tells us how the observations have been matched. We can merge files using more than one key variable when a single variable is not adequate to match the two datasets.Īfter the merge the _merge variable will be automatically created. The fourth type, many-to-many merges, can yield random results and be problematic. | id year family role rating area SES _merge | One-to-many or many-to-one merges are most frequently met when dealing with hierarchical data. If the master dataset has many observations to match with the single observation in the using dataset, we use m:1 or we use 1:m if it is the using dataset that has many to match. In one-to-many and many-to-one merges, one observation from one dataset is matched to many observations in the other dataset. In both files there should be only one unique value for the key variable. In one-to-one merge, we use a unique key variable in both the master and the using datasets to merge files. There are three types of match merges that we frequently use. If one dataset contains more observations than the other, missing values will be generated where not matched. The key variables must have the same names and data type (numeric or string), otherwise we will receive an error message. The datasets are combined by one or more key variables. Merge combines datasets horizontally by adding variables to the existing dataset. We can also create an id for each source file by ourselves that are meaningful to the datasets before appending the datasets.Īppends file1 and file2 to the master file. append using file1 file2, generate ( filenum)Īppends file1 and file2 to the master file and assigns a number to the observations to indicate the source file. append using file, keep ( var1 var2 var3)Īppends the file file and keeps only the three specified variables in the combined dataset. append applies, for instance, when we want to combine datasets of different times or regions with mostly the same variables.Īdds the observations from file to the master file. The datasets should contain mostly the same variables absent variables from one dataset will be given missing values in the combined file. Binding Datasets append, merge and joinby bind the datasets by attaching the “using dataset” stored on disk to the “master dataset” stored in memory.Īppend binds datasets vertically by adding observations from the using dataset to the master dataset.

Merge stata