Merging datasets in Stata is a common task for data analysts and researchers as it allows them to combine multiple sources of information into a single and comprehensive data file. Sometimes, we have our required data in two or three files, and we want to merge them in a single file to perform statistical analysis. In this blog, I will explore how to merge two datasets in Stata. We will cover the basics of the merge command and its options, as well as advanced techniques for dealing with duplicates and conflicts. By the end of this article, you will have the knowledge and skills needed to effectively merge datasets in Stata and streamline your data management processes. This is another blog in data preparation series in Stata.
I have used three different datasets to explain all three primary types of merge in Stata. Their description is provided under each respective heading.
Prepare Data files before merging them
It is important to understand that we can merge two datasets of .dta format only. We cannot merge csv or excel files directly in Stata. Therefore, we first need to convert our CSV or excel files into .dta format, then merge command will work and merge two datasets.
CSV file handling
To import a CSV file in Stata, use following command:
import delimited "E:\Stata_Analysis\TSLA.csv"
Note: Use the path of your computer in above code. The code in commas shows the path of my computer.
Now data will be in Stata memory. You have to save this into .dta format. To do this use following code:
Now, the .dta file will be prepared and saved in your computer. Do this same process for second dataset. I did this same for my both files and they are ready to be merged.
Excel .xlsx file handling
To import data from an Excel .xlsx file, the import code is bit different. Use the following command for importing data into Stata:
import excel "E:\Stata_Analysis\AMZN.xlsx", sheet("AMZN") firstrow clear
Import is command name. Excel shows that file is of excel. Next part is file path. After comma, sheet name is provided. The firstrow shows that Stata should consider the first row of data as variable names, not data. The clear command clears the data in memory of Stata if already held in memory.
Now, excel data is loaded, you just simply need to save it in .dta format using following code:
Understanding the Merge Command in Stata
The merge command is the key tool in Stata for combining datasets. It allows you to match and merge observations based on a common variable between two or more datasets. The merge command in Stata has several options, such as “one-to-one”, “one-to-many”, and “many-to-one”. These options determine how the observations are matched and merged. To use the merge command, you need to specify the datasets you want to merge and the common variable that you want to use as the basis for the merge. In following section, we will delve into each merge type and discuss the different options available to help you achieve your desired outcome.
Merging Two Datasets Based on a Common Variable [One-to-One Merge]
The most common scenario in merging datasets is when you have two datasets and want to merge them based on a common variable. The common variable should be named same in both datasets. This is known as a “one-to-one” merge. In this type of merge, each observation in one dataset is matched with exactly one observation in the other dataset based on the common variable. The result is a new dataset that combines information from both datasets for each matched observation.
Here’s an example of a one-to-one merge in Stata using code. Suppose we have two datasets; one has details about GDP data [called dataset1 (gdp.dta)] and other has details about population [called dataset2 (cities.dta)]. Following image shows both datasets in Browse window of Stata before merge:
Now use following code:
use "gdp.dta", clear
merge 1:1 country_id using "cities.dta"
In this example, we start by opening the first dataset “gdp.dta” using the use command. The clear option clears any previously loaded datasets from memory.
Next, we use the merge command to combine the two datasets. The 1:1 option specifies that this is a one-to-one merge. The country_id after the : symbol is the common variable (available in both datasets) that is used to match observations between the two datasets. The using option is followed by the name of the second dataset, “cities.dta”.
Once the merge is complete, the resulting merged dataset will contain information from both “gdp.dta” and “cities.dta” for each observation matched based on the common variable “country_id”. The data after merge will look like following:
We can see that new dataset has observations of both datasets: gdp and population observations.
One-to-Many Merging in Stata
In some cases, you may need to merge a dataset where one observation in one dataset matches multiple observations in the other dataset. This type of merge is called a one-to-many merge.
For one-to-many merge, we use the merge m:n syntax, which allows us to merge datasets where one observation in one dataset matches multiple observations in the other dataset. This syntax generates a new dataset that contains all the observations from both datasets, and repeats the observation from the one-to-many dataset as necessary.
Suppose we have two datasets, population dataset and cities dataset. The population dataset contains information about countries, including their name, country_id and population, while cities dataset contains information about cities in those countries, including the city name, population, and country name.
Remember we have country_id as common variable in both datasets.
Before performing merge, our both datasets looks like following:
To perform a one-to-many merge, we can use the merge m:n syntax as follows:
merge m:n country_id using cities.dta
In this example, we first used the single identifier dataset and loaded it into Stata memory. The m:n option indicates that we are performing a one-to-many merge, where one observation in population dataset matches multiple observations in cities dataset. The using clause specifies the second dataset cities.dta, and thecountry_id indicates that the merge will be performed based on the common variable country_id.
The resulting merged dataset will contain all the observations from both population.dta and cities.dta, and will repeat the observation from population.dta as necessary to match the multiple observations in cities.dta dataset. The resulting dataset will contain information about countries and their cities, including the country name, population, and city name.
Sort the dataset by country id. It will be easy to understand data after merger:
The new dataset after one-to-many merge will look like following:
The highlighted portion in red indicates new data after one-to-many merge.
Many to One Merge
Many-to-one merge in Stata is opposite to one-to-many merge. It is performed when multiple observations in one dataset correspond to one observation in the other dataset. To illustrate this, I will use the datasets used in one-to-many merge here too. I suppose that we have two datasets: cities and population. The cities dataset has multiple observations to identify, while population dataset has one identifier in it. Both datasets look like following:
Use the following code to merge many to one dataset:
merge m:1 country_id using population.dta
In this example, we first used the countries dataset which has multiple identifier keys. The cities.dta is a one-to-one dataset.
The m:1 option in the merge command indicates that the merge is a many-to-one merge. The country_id variable is the unique identifier in both datasets and is used to merge the datasets. The new dataset will look like following:
Merging Multiple Datasets in Stata
In some cases, you may need to merge more than two datasets. This can be accomplished in Stata using multiple merge commands. The process is similar to merging two datasets, but with the added step of specifying which dataset is being merged into which. When merging multiple datasets, it’s important to keep track of the order in which the datasets are merged and the common variables used, as this can affect the final result. Here’s an example of merging multiple datasets in Stata using code:
use "dataset1.dta", clear
merge 1:1 id using "dataset2.dta"
merge 1:1 id using "dataset3.dta"
In this example, we start by opening the first dataset “dataset1.dta” using the use command and clearing any previously loaded datasets from memory.
Next, we use the merge command twice to merge “dataset2.dta” and “dataset3.dta” into “dataset1.dta”. The first merge command combines “dataset1.dta” and “dataset2.dta” based on the common variable “id”. The second merge command then combines the resulting dataset from the first merge with “dataset3.dta” also based on the common variable “id”.
The resulting merged dataset will contain information from all three datasets “dataset1.dta”, “dataset2.dta”, and “dataset3.dta” for each observation matched based on the common variable “id”. It’s important to note that the order in which the datasets are merged can affect the final result, so it’s essential to carefully consider the order when merging multiple datasets.
Dealing with Duplicates and Conflicts in Stata Merges
Merging datasets in Stata can sometimes encounter problems, such as missing values, duplicates, or conflicting values for the same observation. These issues can lead to errors or incorrect results, so it’s important to be aware of them and know how to troubleshoot. In this section, we will cover some of the most common issues that arise when merging datasets and provide tips and techniques for resolving them. With these skills, you’ll be well-equipped to merge datasets in Stata with confidence and achieve accurate results.
Here are some common issues that may arise when merging datasets in Stata, along with tips for resolving them:
- Missing Values. When merging datasets, it’s possible for one observation in one dataset to not have a match in the other dataset. This results in a missing value in the merged dataset. To resolve this issue, you can use the fillin option when merging to fill in missing values with a default value. Example below:
use "dataset1.dta", clear
merge 1:1 id using "dataset2.dta", fillin(0)
- Duplicates. If one observation in one dataset has more than one match in the other dataset, this results in duplicates in the merged dataset. To resolve this issue, you can use the unique option when merging to keep only one observation in the merged dataset. Example is shown below
use "dataset1.dta", clear
merge 1:1 id using "dataset2.dta", unique
- Conflicting Values. If an observation in one dataset has different values for the same variable in the other dataset, this results in conflicting values in the merged dataset. To resolve this issue, you can use the update option when merging to keep the value from the first dataset, or the replace option to keep the value from the second dataset.
Example (keeping value from first dataset):
use "dataset1.dta", clear
merge 1:1 id using "dataset2.dta", update
Example (keeping value from second dataset):
use "dataset1.dta", clear
merge 1:1 id using "dataset2.dta", replace
These are just a few examples of common issues that may arise when merging datasets in Stata, and the tips for resolving them. By understanding these issues and knowing how to troubleshoot, you’ll be able to merge datasets with confidence and achieve accurate results.
Merging Datasets with Different Variable Names in Stata
When merging datasets in Stata, it’s possible for the datasets to have different variable names for the same information. This can create challenges when trying to merge the datasets based on a common variable. However, there are several techniques that you can use to successfully merge datasets with different variable names.
One technique is to rename the variables in one of the datasets to match the variable names in the other dataset using the rename command. Another technique is to use a variable label to identify the common variable, even if the variable names are different. You can also create a new variable in one of the datasets with the same name as the common variable in the other dataset and use this variable to perform the merge.
Here’s an example of merging datasets with different variable names in Stata using the rename command:
use "dataset1.dta", clear
rename id _id
merge 1:1 _id using "dataset2.dta"
In this example, we start by opening the first dataset “dataset1.dta” and clearing any previously loaded datasets from memory. Next, we use the rename command to rename the variable “id” to “_id”. Finally, we use the merge command to merge “dataset1.dta” and “dataset2.dta” based on the common variable “_id”.
Advanced Techniques for Merging Datasets in Stata
When merging datasets in Stata, it’s important to take a few steps to ensure that the merging process is efficient and effective. Here are some tips to keep in mind:
- Use a Common Variable. Make sure that you have a common variable that is present in both datasets and can be used to perform the merge. This is typically a unique identifier such as an ID number or name.
- Be Prepared for Missing Values. Be prepared for the possibility of missing values in the merged dataset if an observation in one dataset doesn’t have a match in the other dataset.
- Save Time with Keyboard Shortcuts. Stata provides several keyboard shortcuts that can save you time when merging datasets. For example, the “use” command can be abbreviated as “u”.
- Monitor Memory Usage. When merging large datasets, it’s important to monitor memory usage to ensure that the merging process doesn’t consume too much memory and cause Stata to crash.
- Keep a Backup. Always keep a backup of your datasets before performing any data manipulation, including merging datasets.
Merging datasets in Stata can be a powerful tool for combining and analyzing data from multiple sources. By following the steps and tips outlined in this article, you can effectively and efficiently merge datasets in Stata and gain insights into your data. Whether you’re a beginner or an advanced user, these techniques will help you achieve accurate and effective results when merging datasets in Stata.