05 - Opening Data Sets

This notebook explains how to load, view, and clean data. We go over importing and previewing our data, as well as preparing the data for analysis.

Prerequisites

1. Understand how to effectively use Stata do-files and know how to generate log-files.
2. Run basic Stata commands such as help, describe, summarize, for and while.
3. Know how to use macros in writing Stata commands.

Learning Outcomes

1. Understand how to use clear at the beginning of do-files.
2. Know how to change directories so that Stata can find relevant files.
3. Import data sets in .csv and Excel formats.
4. Import data sets in .dta format.
5. Save data files.

5.0 Intro

In this repository, there is a folder named “data”, with a sub-folder named “raw”. In that sub-folder, there are two different versions of the same data set: “fake_data.csv” and “fake_data.dta”. The data sets simulate information from workers in the years 1982-2012 in a fake country where, in 2003, a policy was enacted that allowed some workers to enter a training program with the purpose of boosting their earnings. We will be using this data set to learn how to explore and manipulate real-world data sets.

5.1 Clearing the Workspace

Do-files should begin with a command that clears the previous work that has been open in Stata. This makes sure that:

1. We do not waste computer memory on things other than the current project.
2. Whatever result we obtain in the current session truly belongs to that session.

We can clear the workspace of many different things (see help clear if needed). For the purpose of this lecture, the most comprehensive thing to do is to run the following:

clear *

Note that * selects everything.

5.2 Changing Directories

Before we get started on importing data into Stata, it is useful to know how to change the folder that Stata accesses whenever we run a command that either opens or saves a file. Once we instruct Stata to change the directory to a specific folder, from that point onward it will open files from that folder and save all files to that folder, including data files, do-files, and log-files. Stata will continue to do this until either the program is closed or we change to another directory. This means that every time we open Stata, we need to change the directory to the one we want to use.

Having a main directory with sub-directories is extremely important for research. When we are doing large research projects that use many different data sets from different sources as well as do-files and log-files that perform different tasks, it can be easy to lose track of what is where. For that reason, we will want to create different folders for many of our different steps. We will go into more detail about this in Module 18, but it is a good idea to get into the habit of doing this as soon as you start a project.

We will begin by using the pwd command to view the current working directory.

pwd

Note: We write the directory path within quotation marks to make sure Stata interprets this as a single string of words. If we don’t do this, we may encounter issues with folders that include blank spaces.

Now let’s change the directory to the specific location where we saved the fake_data file using the command below. We can change our workspace to a directory named “some_folder/some_sub_folder” by writing cd "some_folder/some_sub_folder".

Use the space below to do this on your own computer.

cd " " 
* type your file path to the folder containing the data between the quotation marks in the line above

Notice that once we change directories, Stata outputs the full name of the directory where we are currently working.

One trick to using cd is that we can use periods (.) to move back folders: two period to move back one folder, three periods to move back two folders, etc. Try the command below to compare the folder Stata is now directed to with the command above. You can repeat this using two periods.

cd ..

An easier way to change the directory is by typing the cd command followed by the folder you want to set as your working directory. In this method, quotation marks are not necessary.

cd myfolder

* Use myfolder as a placeholder for the folder you want to set as your working directory

In addition, we can use the command cd on its own to go back to the home directory.

The process for changing directories in Stata varies depending on the type of computer being used. If one approach does not work, it is possible that the method is not suitable to your computer. Please see the Stata manual for instructions on how to change directories according to the type of computer you are using: https://www.stata.com/manuals/dcd.pdf.

5.3 Opening Data Sets

5.3.1 Excel and CSV Files

When looking for the data for your research, you will realize that many data sets are not formatted for Stata (.dta files). In some cases, data sets are formatted as Excel or CSV files. Not surprisingly, the command to load in data is called import. It comes in two main forms: import excel and import delimited.

Let’s import the data set called fake_data.csv. We need to use import delimited to import this data into Stata. The syntax for this command is import delimited [using] filename [, import_delimited_options].

We always include the option clear when we use import to make sure we’re clearing any previous data set that was opened before in our Stata session. Recall that to use an option, we include a comma (,) after the command line and write the option name. Feel free to read the documentation of these commands by writing help import delimited.

Note that the command below will not import the data unless you have changed your directory (above) to the folder which contains this file.

Ignore the following block of code that will create a csv file to be used as an example

use fake_data, clear
export delimited using "fake_data.csv", replace

To load a .csv data set, we write:

import delimited using "fake_data.csv", clear

When we run this command, Stata will print a message saying that there are 9 variables and almost 3 million observations. When we open data sets that are not in Stata format, it is very important to check whether the first row of the data includes the variable names.

We can use the command list to look at our data. It is better to limit the observations we see since we don’t want to see all 3 million! Thus, we use in to constrain the list to the first 3 observations below.

list in 1/3 

By default, the first row of data is interpreted as the variable names of the data set, which in this case is correct. If that’s not the case, we need to include the import delimited option varnames(#|nonames), where we replace # by the observation number that includes the variable names. If the data has no names, the option is varnames(nonames). Don’t forget that we can always check the documentation by writing help import delimited.

5.3.2 Stata Files

To open data sets in the Stata format, we use the command use. As we can observe from the example below, we can recognize a data set is stored in Stata format because the file’s name will end with .dta.

use fake_data.dta, clear

list in 1/3 

5.3.3 Data from Online Sources

It is also possible to download data sets directly from other sources, such as websites. This allows us to import data without saving it as a .csv or Excel file.

For example, it is possible to import data from the World Bank using the command wbopendata. This requires us to install some packages first, but once it is done, it simplifies the process of importing only what we need. More information is available here.

Another useful command is sdmxuse. This allows us to import data from all sorts of statistical agencies, such as the IMF and the OECD. More information is available here.

The types of tools are widely available and often the best approach for getting data from statistical agencies.

5.3.4 Other Files

We can open a number of different data files in Stata with no issues. If you are struggling, one option at UBC is to use the program StatTransfer to convert your file to .dta format. This program is available in the library on the UBC Vancouver Campus at one of the Digital Scholarship workstations. Once your data is in .dta format, it can be imported with the use command seen above.

Note: UBC has research support available for any student who needs help with data, including anyone who needs help getting data into a format that can be imported into Stata. You can find the contact information for the Economics Librarian on the UBC Library ECON 490 Research Guide.

5.4 Commands to Explore the Dataset

5.4.1 describe

The first command we are going to use describes the basic characteristics of the variables in the loaded data set.

describe

5.4.2 browse

In addition to the describe command, in the Stata interface we can also open the data editor and see the raw data as if it were an Excel file. To do this, we type browse. This command will open a new Stata window. If we want to do this from within Jupyter, we use the command with % before browse. Either approach gives us a “spreadsheet-like” view of the data set.

%browse

Opening the data editor has many benefits. Most importantly, we get to see the data as a whole, allowing us to have a clearer perspective of the information the data set is providing us. For example, here we observe that we have unique worker codes, the year where they are observed, worker characteristics (sex, age, and earnings), and whether or not they participated in the specified 2003 training program.

5.4.3 codebook

We can further analyze any variable by using the codebook command. Let’s do this here to learn more about the variable earnings.

codebook earnings

The codebook command gives us important information about this variable such as the type (i.e. string or numeric), how many missing observations it has (very useful to know!) and all unique values. If the variable is numeric, it will also provide some summary statistics. If the variable is a string, it will provided examples of some of the entries.

Try changing the variable name in the cell above to see the codebook entries for different variables in the data set.

5.4.4 tabulate

We can also learn more about the frequency of the different measures of one variable by using the command tabulate.

tabulate region

Here we can see that there are five regions indicated in this data set. We can see that the majority of people surveyed came from region 1.

We can actually include two variables in the tabulate command if we want more information. When we do this, below we see that there were 234,355 female identified and 425,698 male identified persons surveyed in region 1.

tabulate region sex

5.4.5 lookfor

What if there’s a gazillion variables and we’re looking for a particular one? Thankfully, Stata provides a nice command called lookfor which helps us search for variables based on keywords. Suppose we want to look for a variable that is related to year.

lookfor year

Stata found three variables that include the word year either in the variable name or in the variable label. This is super useful when we are getting to know a data set!

5.5 Saving Data Sets

We can save any opened data set in Stata format by writing save using "some_directory/dataset_name.dta", replace. The replace option overwrites a previous version of the file to keep our save current.

Try saving the data we have been working on in a new folder named “cleaned_data”.

save using "insert_directory/dataset_name.dta", replace

We can also save files in different formats with the export excel and export delimited commands. Look at the help documentation for more details.

5.6 Errors

The tabulate command may be used in conjunction with conditional statements. When specifying the condition, ensure that you use quotation marks; otherwise, Stata will return an error code. Uncomment each line of code below to see it in action.

*tabulate sex if sex==F          //incorrect
*tabulate sex if sex=="F"        //correct

5.7 Wrap Up

Now that we are able to import data into Stata, we can start doing analysis! Try finding a data set that interests you and practice some of the commands that you have already learned in the first few modules. In the next module, we will look at commands for working with data in greater depth.

5.8 Wrap-up Table

Command	Function
clear	used to clear the workspace
cd	used to change the working directory
pwd	used to view the current working directory
use	used to open a Stata dataset
import delimited	used to load a csv dataset
import excel	used to load an excel dataset
list	used to look at the data
describe	used to describe the basic characteristics of the variables in the loaded dataset
browse	used to open up the data editor and view the observations of the dataset
codebook	used to describe data contents
tabulate	used to summarize the frequency of the different measures of a variable
lookfor	used to search for the variables of a dataset based on keywords
export excel	used to save a dataset in excel format
export delimited	used to save a dataset in csv format

References

Import data from excel
Import delimited data