A1.2.1a Introduction to Stata

General syntax 

There are a number of ways to ‘get to know’ your dataset. As you proceed through this section, note that the general syntax used for the commands in Stata are as follows:

command {space} variable_name(s) {space}  [if expression], {space} options

Note that after the command and the variable name, there is a comma before any options are listed. Some commands can be abbreviated as well. Conditional statements involving the word “if” come before the comma. Keep this general syntax in mind as you work with the commands below.

Trouble shooting – how to find help

The ‘help’ command in Stata can be very useful to understand more about the commands in Stata. Simply type help and the command you want more information on and you will open a help window. For example, if you want more information on the tabulate command, you can type:

help tabulate

You can also search for a command. For example, if you wanted to look for general help for histograms, you can type:

search histogram

Exploring your data

The command ‘ describe ‘ will give provide the you the variable names and their labels. You can look at the whole dataset or specific variables. Try:

describe

describe bmi_grp4

The command ‘ codebook ‘  provides a little more information about the variables in the dataset, such as the minimum and maximum values and information on missing data. Missing values in Stata are generally coded as . but missing values can also be coded as 99 or 0 so you need to be clear about how missing values are coded prior to exploring your dataset. Try:

codebook bmi_grp4

You can also get a feel for the dataset by using the ‘ list ‘ and ‘ tabulate ‘ commands.  Try looking at the variables for ‘currsmoker’ and ‘frailty’:

tabulate currsmoker

tab currsmoker, missing

tab currsmoker frailty, row

tab currsmoker frailty, col

You can use the ‘ if ‘ condition to view specific observations. Try browsing the data for current smokers, aged between 60-70 years old:

browse if currsmoker==1 & age_grp==1

Now try to tabulate frailty level among the current smokers aged 60-70 using the if condition:

tab frailty if currsmoker==1 & age_grp==1

Notice that in Stata, if you want to specify that a variable is “equal to” some value, then you need to hit two = signs, like this “==”.

Question A1.2.1b:  Look at the variable bmi_grp4.

• • What information is collected in this variable?
  • How are the responses coded?
  • Is there any missing data?

Labelling variables

If you look at the ‘Variables’ window, you will notice that some of the variables do not have labels, or the given name of the variable is not very clear.  The data might be easier to work with if you have a short description (or label) of the variables. You can label variables using the label command.

General syntax:

 label variable variable_name “label”

When adding a label to a variable, the command is ‘ label variable ‘, and simply typing label is incorrect. For example:

label variable prior_cvd “Prior CVD” 

If you look at the ‘Variables’ window, you should see that there now is a label next to ‘prior_cvd’. Now look at this variable using either the codebook or tab command:

 codebook prior_cvd      … (or tab prior_cvd)

You can see that ‘currsmoker’ is a binary variable and it takes the value either 0 or 1.  This is not very meaningful, in fact, 0 =no and 1=yes. Therefore, we need to relabel the numeric values within the variable as ‘yes’ or ‘no’. Relabelling a variable is a two-step process. First, you must define the label and then assign the label to the variable. The general syntax is presented below:

label define label_name 0 “label1” 1 “label2” …[, add modify replace]

label values variable_name(s)  label_name

For the currsmoker variable, first we have to use the ‘label define’ command to create value label called ‘smoke_lab’, which defines 0 with “no” and 1 with “yes”. For example:

 label define smoke_lab 0 “no” 1 “yes”

Next, we need to apply the new label (smoke_lab) to the currsmoker variable. For example:

label values currsmoker smoke_lab

Now look at currsmoker (either tab or codebook) and you should see that the values 0 and 1 are now labelled as “no” and “yes”.  You can also look at the labels of a variable by typing:

label list label_name

For example, type:  label list smoke_lab

• Can you follow the commands detailed here to label the variable ‘currsmoker’ in your own dataset and version of Stata?

Creating new variables – generate

The generate command allows you to create new variables. The general syntax is:

generate new_variable=expression [if ]

Try the following:

generate var1 = 1

generate var2 = 5

browse

You can also copy existing variables or generate new variables based on existing data. For example:

generate age4 = age_grp

You can also use mathematical operations and functions such as + (add),  – (subtract),  * (multiply),   / (divide), ^ (to the power of), sqrt square root, ln (natural log), exp (exponential) For example:

 generate var3 = var1+var2

 gen var4 = var3*var2

These calculations can be used to calculate different variables. For example, you could compute the total number of prior diseases a participant has been diagnosed with:

gen prior_disease = prior_cvd + prior_t2dm + prior_cancer

tab prior_disease, m

There is also an extension to the generate command, called egen, which can be useful (see help egen for more information).

Replace and recode 

You can edit variables using ‘replace’ and ‘recode’ commands. PLEASE BE AWARE THERE ARE MULTIPLE (CORRECT) WAYS TO RECODE AND GENERATE NEW VARIABLES. You will see some examples in this practical, and more examples in future sessions. Choosing the method to replace or recode variables is generally a matter of personal preference, and it will rarely matter which method you use when recoding variables as several different commands will produce the same result.

Try changing the ‘bmi’ variable so that you create to a binary variable, which indicates those who have obesity and those that do not. But never recode the original variable (in case you change your mind)! Duplicate a variable first, and then recode it. For example:

gen bmi2=bmi 

recode bmi2 min/29=0 30/max=1

Now compare ‘bmi’ and ‘bmi2’:

 browse bmi bmi2

Here is another way to recode BMI:

gen bmi_bin = 1 if bmi_grp4<=2

replace bmi_bin = 0 if bmi_grp4>2

It is good practice to cross-tabulate your binary and categorical variables to check your coding:

tab bmi_bin bmi_grp4, miss

Stata considers missing values to be the highest numerical values, so notice where the missing values went using this code.

Question A1.2.1c:

• • Can you generate a new variable that is the same as the ‘ldlc’ variable? (call it ldl2)
  • Can you recode your new variable to indicate those with LDL-C that is 4 or below, and those with LDL-C that is above 4 mmol/L? (hint: use ‘replace’ as we did above, with ‘<=’ signs)
  • Optional: look at the recode syntax (type help recode) to figure out how you can define the value labels within the recode command.

Dropping variables 

You can drop variables from the dataset if you no longer want to use them. But once you do this, you cannot undo it, so be careful when using this command. A variable can be dropped from the dataset by typing the following command:

 drop var1

Editing data – making changes in data editor

 Stata has an editing browser, where you can see the data in your dataset and make changes to the dataset. To access the edit window, you can either type edit or you can open it from the drop down menu (Data>Data Editor> Data Editor (Edit)). You can then click on the appropriate cell in the Edit Window and change the values of the dataset.  HOWEVER… when data cleaning it is strongly recommended that you save the relevant commands in a .do-file and then run that for each session. This ensures your original dataset is kept intact in case you make a mistake while editing- or you need to remember something you edited a long time ago- and you have a permanent record of the data cleaning process. “Data cleaning” is the process whereby you get all the variables you received in your raw dataset ready to be used in your analysis.