Part 3: Introduction to Stata

Stata is a popular commercial statistical software package that was first released 30+ years ago. It has some really nice features, loads of top-rate documentation, a very active community, and approachable syntax. For beginners, I think it’s the simplest to learn.

Learning how to use Stata

Stata has really, really, really good documentation.

The documentation is outstanding. Let’s say that you want to learn how to use the –destring– command. In the command line (1a under “Stata’s Interface” below), type:

help destring

…and up will pop a focused help file. There’s the “View complete PDF manual entry” option that has EXTENSIVE documentation of the command. (Note: This file seems to only work well with Adobe PDF reader, not alternative PDF readers like Sumatra). If the focused help file isn’t sufficient to answer your questions, try the complete PDF manual.

The focused help file has multiple parts, but the syntax example is gold. Further down you’ll see example uses of the command.

Web searches will find even more answers

Odds are that someone has already hit the same problem you have in using Stata. Queries in your favorite search engine are likely to find answers on the Statalist archive or UCLA’s excellent website.

You can install Stata programs that other users have written

There are MANY MANY MANY user-written programs out there that can be installed and used in your code. You only need to install them once. Most are on BU’s repository called SSC. I use the table1_mc program extensively (it makes pretty table 1s, you can read about it here). To install table1_mc from SSC, you type:

ssc install table1_mc

…and Stata will download it and install it for you. It’s ready to use when it finishes installing. And, there’s no need to re-install it, it will load each time you start Stata.

Quirks of Stata

Stata only works with rectangular datasets

Think of a rectangular dataset as a single spreadsheet in Excel. It has vertical columns (like a y axis) and horizontal rows (like an x axis). There’s no data on a Z axis coming out of the computer at your face.

A rectangular dataset is the only type that Stata works with. Other statistical software like R or Python can handle many more complex data structures. For learners, forcing data to fit within a rectangular dataset is a huge advantage in my mind since that structure is intuitive, and you can always browse your data with the built-in data browser (see 3c under Stata’s Interface, below).

Stata only works with one dataset at a time*

One dataset in Stata is akin to one spreadsheet in a workbook in Excel. In Excel, you can have multiple spreadsheets in one .xlsx file, with each spreadsheet appearing on a different tab at the bottom. All spreadsheets are in the memory at the same time. You can do math across spreadsheets in a workbook in excel, summarize costs in one column in spreadsheet A and have the result appear in one cell on spreadsheet B. In Stata, you can only have one spreadsheet (here, dataset) open at a time.* Because of this, Stata users spend a good deal of time merging and appending multiple datasets to make a single dataset that has all of the necessary variables in the best format from the get-go.

A big problem historically with Stata was that datasets are loaded in the RAM, and big datasets would be too big for conventional computers. That’s not an issue anymore since even cheap computers have several gigabytes of RAM.

*This isn’t true anymore. Starting in version 16, Stata can actually now have multiple datasets in memory, each stored in its own frame. These frames can be very useful in certain scenarios, but for our purposes, we are going to pretend that you can have just one dataset open at a time.

Data are either string or numeric. Their color changes in the data browser

Strings are basically text that are thought to be words and not numbers. But sometimes a dataset will be imported wrong and things that are actually numbers (“1.5”, “2.5” in different rows of the same column) will be imported and considered to be strings and not numbers. This might be because they were imported incorrectly. This might be that later down in the list there is a word in a different cell (“1.5”, “2.5”, “Specimen error”). If any row of a variable contains something that isn’t a number, Stata makes the entire column, and with it the variable, a string.

IMPORTANT: When viewing strings in the data browser (3c under “Stata’s Interface” below), they appear in RED text. When specifying strings in commands, you need to enclose them in quotations (eg count if name==”Old”). Missing strings are two quotes with nothing in between them (eg count if name==””).

In order to do math, you need to have things be numbers. There are several different numerical formats that you can read about here. If something is an integer (nothing after the decimal), it can be byte, int, and long. If something has a decimal point, it’s float or double. Stata does a nice job selecting which numerical format your data should be in, so you probably don’t need to think much about the difference between byte, int, long, float, or double again.

IMPORTANT: When viewing numeric variables in the data browser, they appear in BLACK text (or BLUE if they have a label applied). When specifying strings in commands, no quotations are needed (eg count if quartile==1). Missing strings are periods (eg count if quartile==.), and a period is positive infinity (a missing value is bigger than a value of one billion).

To convert from a string to a numerical value (change the “1” to a 1), you use the –destring– command. You might need to include the force and replace options, but read up on those by typing –help destring–.

To convert from a numerical value to a string (change the “1” to a 1), use the –tostring– command. Note that missing numerical values will go from a dot to a dot in quotations (. becomes “.”), which is not the same as a missing value for a string, which is just empty quotations (“”). It’s a good idea to follow up a –tostring– command with a command that replaces “.” values with “” values.

Stata’s output is only 255 characters wide, max

The output window of Stata will print (“display”) the inputted command and results from that command. It will clip the output at up to 255 characters, and insert a line break to the next row. You can specify:

set linesize 255

…so that the output is always 255 characters wide. Otherwise, it’ll adjust the output to match how wide your output window is.

The working directory is your “documents folder” unless you manually set the working directory with the cd command or open up Stata by double clicking on a .do file in Windows explorer

The working directory is where Stata is working from. If you save a dataset with the –save– command, it’ll save it in the working directory unless you specify all of the files from the C: drive on. If you double click on the Stata icon to open it up in Windows and type the present working directory command to see where it’s working from (that’s –pwd–), it’ll print out:

. pwd 

So, if you type:

save "dataset.dta", replace

…it’ll save dataset.dta in C:\Users\USERNAME\Documents

Let’s say that you really want to be working in your OneDrive folder because that’s secure and backed up and your Documents folder isn’t. The directory for your desired folder is:

C:\Users\USERNAME\OneDrive\Research project\Analysis

In order to save your file there, you’d type:

save "C:\Users\USERNAME\OneDrive\Research project\Analysis\dataset.dta", replace

Note that there’s a space in the Research project folder name so the directory needs to be in quotations. If there was no space anywhere in the directory, you could omit the quotations. I’m including quotations everywhere here because it’s good practice.

One option is to change your working directory to the OneDrive folder. You use the –cd– command to do that then any save command will automatically save in that folder:

cd "C:\Users\USERNAME\OneDrive\Research project\Analysis\"
save "dataset.dta", replace

Alternatively, you can save your project’s Do file in the “C:\Users\USERNAME\OneDrive\Research project\Analysis\” folder. Rather than opening Stata by clicking on the icon, find the Do file in your OneDrive folder in Windows Explorer and double click on it. It’ll open Stata AND set that folder as the working directory!! For a new project, this means opening Stata by clicking on its icon, opening a blank do file, saving that do file in your OneDrive folder, closing the Do File Editor and Stata, then reopening stata by double clicking on your blank do file in Windows Explorer.

Stata is most effectively used with with command-line input, specifically through the Do File Editor. There is a graphical user interface that can be handy.

I think that everything in Stata should be completed through Do files. These are text files with sequential lines of codes that make Stata perform commands in order.

There is a graphical user interface (GUI) with clickable menus. You can click through commands and it’ll generate the code and run the command of interest, and these can be handy for stealing syntax to run an annoying command. The command from the GUI will appear in the Command History (1c below) and you can right click and copy/paste it into your do file.

I find –import excel– to be frustrating and use the GUI probably 90% of the time to generate that command then copy/paste the syntax into my do file.

Stata won’t let you close a dataset in the memory or overwrite an existing dataset without some effort

The –use– command will open up a dataset in the memory. If you don’t have a dataset opened yet, this will open one:

use dataset.dta

Remember that Stata can only have one dataset opened at a time, so any time you open one when you already have a different dataset opened in memory, Stata will need to drop the open dataset. If you spent a lot of time on the open dataset creating new variables or merging with other datasets, closing it will make you lose all of your work unless you have also saved it. Stata doesn’t want you to make this mistake so if you already have a dataset opened and you type in the above command, Stata will say “No” and you won’t be opening the new dataset.

Instead, you need to put “–, clear–” at the end of the command, like this:

use dataset.dta, clear

And now Stata will drop whatever you have open. It’s really just a nice check to keep you from discarding your work accidentally.

Similarly, if you are trying to save a dataset with the –save– command into an empty folder, you just need to type:

save newdataset.dta

…and Stata will save it no problem. HOWEVER if you are trying to overwrite an existing dataset with that same name, Stata will say “No” and you won’t be saving your dataset today. This is another check. instead, you just need to use “–, replace–” to overwrite. Example:

save newdataset.dta, replace

Stata’s interface

Here’s a quick overview of the Stata interface in Windows. Note: the Mac interface looks a bit different. There’s some way to make the Mac interface look like the Windows interface, but I don’t know how to do that. I’ll try to remember to update this page when I help a Mac user in the future.

  1. Ways to input and interact with commands:
    1a. Command line – This is where you type command by command. Unless you are just poking around in your data, you should avoid using this. Anything that you want to reproduce in your analysis should be done in the Do file editor.
    1b. Open Do file editor button – The Do File Editor is the most important part of Stata in my opinion. A do file is a long text file saving command after command. This is where you should do all of your analytical work.
    1c. Command history – If you use the command line or GUI to make a command, it’ll be saved here. You can right click on old commands and copy/paste them into your do file.
  2. Output window – Your command will appear here with a preceding dot (“. sysuse auto” means that I had previously typed in “sysuse auto”). The output from your do file or command will appear immediately below.
  3. Ways to interact with data
    3a. Variable list – This is a list of variables in the open dataset. You can double click on them and the variable name will be copied to the command line. You can ctrl+click and select multiple and then copy them to the clipboard. This is quite handy.
    3b. Variable and dataset properties – This will let you see details about a selected variable in the variable list and the current dataset in memory.
    3c. Data browser – You can also pop this open with the –bro– command. this views all data in a spreadsheet format that looks like Excel.

Table 1 with pweights in Stata

The very excellent table1_mc program will automate generation of your Table 1 for nearly all needs (read about it here), except for datasets using pweight.

I’ve been toying around with automating Excel table generation using Stata v16’s new Frames features. I recently started working on a database that requires pweighting for analyses, and opted to use this to as an opportunity to use Frames to generate the automation of a pweight adjusted Table 1.

The V1.1 of my code to automate this lives here: You can just put:


…in your code and it’ll open it up! At this point, it doesn’t produce P-values, and I might add that down the road. Although I don’t like P-values in Table 1s.

How to use this do file

// Step 1a: close all open frames, drop all macros, 
//          and open your dataset
frames reset
macro drop _all
webuse multistage, clear
// Step 1b: Figure out where your present working directory is, 
//          this is where the excel spreadsheet will be saved. 
//          Change the working directory with the "cd"
//          command as needed. 
// Step 2: Declare your data to be pweighted
svyset county [pweight=sampwgt], strata(state) fpc(ncounties) || school, fpc(nschools)
// Step 3: If your columns require the generation of pweighted 
//         tertiles, quartiles, or whatnot, do that now. 
//         For this example, we'll do by quartile of weight. 
// note: per this website:
//       ...Only the pweight needs to be specified when making 
//        weighted quartiles. 
xtile weightquart=weight [pweight=sampwgt], n(4) 
// Step 4: Recode binary variables so they are 0 and 1 (if needed)
// Note: in this dataset, it's 1 and 2 for male and female, 
//       respectively. 
gen female = 1 if sex==2 // recode sex to female, where 1 is female
replace female=0 if sex==1 // male is now 0
// Step 5: Name your variables and options for multiple options
// Note: The variables are already labeled but we are doing it 
//       again for completeness' sake. 
// Continuous variables
label variable weight "Weight in lbs"
label variable height "Height in in" // I don't know why people are 400 in tall. that's 33 ft.
// Nominal variables (same process would happen for ordinal or continuous varibles)
label variable race "Race" // Race is nominal so need to also define values of race
label define racelabels 1 "White" 2 "Black" 3 "Other"
label values race racelabels // Apply the labels!!!
// Binary variables, no need to apply labels
label variable female "Female sex"
// Step 6: Call the do file
// Note: Instructions on this program's use will show right 
//       after it's called. Look at the Stata output window. 
// Step 7: Now follow the instructions! That are in the stata 
//         output window!
table1pweight_start table1 1 4 weightquart weight 1
table1pweight_contn  table1 1 4 weightquart height 1
table1pweight_bin  table1 1 4 weightquart female 1
table1pweight_cat  table1 1 4 weightquart race 1
table1pweight_end table1 1 4 weightquart weight 1
// Step 8: Look at the excel output! Here, it's a file called 
//         table1.xlsx that's
 sitting in  your pwd (see step 
//         1b above). You might notice blanks for the 2nd and 
//         3rd columns, but that's because of a strata with a single
//         sampling unit. You can confirm numbers using the survey
//         tools.
// Example: manually check the height by quartile of weight
svy: mean height if weightquart==1
svy: mean height if weightquart==2
svy: mean height if weightquart==3
svy: mean height if weightquart==4
// now check % female sex by quartile
svy: proportion female if weightquart==1
svy: proportion female if weightquart==2
svy: proportion female if weightquart==3
svy: proportion female if weightquart==4

Getting Python and Jupyter to work with Stata in Windows

Stata 16 now integrates with Python. I’m pretty stoked about using some of the Python figure packages. Getting it up and running has been a bit of a challenge. Here’s how I got it to work.

Of note, since I started this post, Stata’s blog has started a series on using Python, which you should check out here.

Installing Anaconda (free for individuals, not for institutions)

Anaconda comes with many built-in statistical packages. The (free) individual version of Anaconda from here. Just make sure to check the “set as path” button during the Anaconda install!

Installing the (universally free) traditional Python distribution

As of July 2020, Python apparently has two versions that are commonly used, the 2.x version and the 3.x version. The end-of-life of 2.x versions is this year, so I wouldn’t recommend using it (current highest version is 2.7). Instead, use the 3.x version, currently the 3.8 version. You can find it at the Windows Python Download Page.

Make sure to install the version matching your Stata install! Stata comes as 32 bit or 64 bit. In Stata, type –about– to see what version you have. You’ll see that mine is running the 64-bit version of Stata. If you have a relatively modern computer, you are probably running the 64-bit version of Stata. Windows can actually run either 32-bit or 64-bit versions if you have a 64-bit processor, so do yourself a favor and just check. Type -about- in Stata to confirm your version.

Make sure that you install the corresponding version of Python. The highlighted one (x86-64) is the 64 bit. The other one (x86) is the 32-bit version. For this example, since I have the 64-bit version of stata, I installed the x86-64, 64-bit version of Python.

I had originally installed the 32-bit version of Python and Stata couldn’t load it. Installing the 64-bit version of Python solved that. There’s actually a big “download now” button on the main Python webpage that will give you the 32-bit version. Make sure to select the specific stable release in the picture above.

For the love of Pete, check this PATH box when you install it.

PATH is a list of commands that can be run from the Windows command line, and where their relative program exists.

See this check box right here? Select it. If you don’t, you’ll have a heck of a time getting anything to run from the command line. This should be checked on default, I have no idea why it’s not. If you forgot to check this box, uninstall Python and reinstall it after checking this box.

Also, notice that it says “64 bit” on the installer screen above. If it says “32 bit”, you probably downloaded the wrong version. Go back and try again!

What the heck did I just install?

There are two Python shell apps/programs that came along with the default Python setup. IDLE is a more user-friendly Python shell. It resembles the command line in Stata, but it has the syntax highlighting of the Do file editor.

The app called “Python 3.8 (64-bit)” is the shell without any markup. If you want to play around with Python, I recommend using IDLE.

Making your first program in IDLE

Anything you run in Python should be from a script, or a *.py file. Pop one open from within IDLE by hitting Ctrl+N. Enter the following:

print("Hello world!")

Then save it and run it (by pressing F5) and you’ll get the hello world!

How does that look in Stata?

Let’s do the same thing in a Stata do file. In order to open up the Python shell within Stata, you have to type –python– on its own line, your intended python code, then –end– on its own line. Here, I have entered:

print("hello world!")

Then just hit ctrl+d or the run button to get it to work in Stata!

How do I get the Pandas, Matplotlib, SciPy, Sklearn, and NumPy libraries installed?

Note: Anaconda comes with all of these except sklearn. For below, just complete the sklearn step.

Python by itself can do some stuff, but the heavy lifting for stats and visualization is from add-in libraries that aren’t included with the default Python and must be added in before doing much of anything else. (Note: Anaconda does come with those and is a Python installation geared towards science, but we’re doing the classic install here.) Installing these additional libraries can be done with the included pip library, which automates all downloads and installations. BUT pip it has to be called from the Windows command line, not in a Python shell (i.e., not in IDLE). You’ll know you’re in the shell if the line starts with this:


So if you type “pip install pandas” in the shell (after the “>>>”), you’ll get an error and you will not be getting pandas.

To pop up the Windows command line, hit the Start button then type “cmd” to open it up. Or hit windows key+r and type “cmd” to open it up. If you correctly checked the PATH checkbox in the install, you should get the version reported if you type the following in:

python --version

If you get some sort of error, it’s probably because you didn’t check the PATH box during the install. Uninstall Python then reinstall it and make for sure you check that stupid PATH box.

A note about the Windows 10 command line: If you type “Python” and hit enter, Windows pops up the Windows store and tries to get you to install the version of Python that they host. This is by far the dumbest Windows feature ever, and I have seen BOB. So, avoid ever typing the word “python” in the command line. Instead, use the handy “py” command, which does everything you’ll need it to do. Py is the python launcher.

To call pip, you want to type in “py” then “-m” then “pip” and its commands. the “-m” allows you to run library commands as a script. So, to install pandas, just type the following in to the command line:

py -m pip install pandas

You’ll see a screen like this:

…and ditto for the others (though it seems that NumPy installs along with pandas, it’s included here for completeness). When you are all done, you should have typed the following 5 lines individually:

py -m pip install pandas
py -m pip install matplotlib
py -m pip install numpy
py -m pip install scipy
py -m pip install sklearn

You only need to do this installation step once.

How do I use Pandas, Matplotlib, NumPy, Scikit-learn (sklearn), and SciPy in Stata?

Once the libraries are installed, you can then integrate them into your scripts. Each time you want to use them, you need to import them so you can call them. The convention is to import these using common so you don’t have to type “pandas” over and over again, you can just use “pd”. Ditto for other libraries:

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import scipy as sp
import sklearn as sk

How do I install Jupyter Lab and get it to work with Stata? (This is the updated version of Jupyter Notebook)

Note: Jupyter lab comes installed with Anaconda, but node.js, npm, and stata_kernel still must be installed.

Jupyter is a super popular way to cleanly complete analyses. Its origins were in Python, but it now works in R and Stata. Details on installing Jupyter are here. Specific instructions for getting it to interface with Stata are here. Here’s how I got it installed:

First, install node.js. Download here. I checked the box in the node.js install to also install additional software. At the end of the install, it pops open Windows powershell window and installed a bunch of stuff, including Python, which is already installed. Perhaps it updated my Python version. It also installs Chocolatey and a few other things. Then, type the following in the Windows command line (if any of below doesn’t work, give your computer a reboot and try again):

pip install npm
pip install jupyterlab
pip install stata_kernel
py -m stata_kernel.install
jupyter labextension install jupyterlab-stata-highlight

Finally, you need to do a last step described here to get this to work in Windows. You can delete the new Stata desktop shortcut once you run it as an administrator one time.

Now you need to configure Jupyter to work with your Stata install. Details are here. I found the configuration file named .stata_kernel.conf sitting in this folder: C:\Users\MYUSERNAME\.stata_kernel.conf

In reviewing the configuration file, it seems to have correctly identified my Stata SE 16 setup. I changed the graph format from svg to png, but left the rest unchanged.

Now that I have Jupyter Lab installed, how do I open and use it?

In Anaconda Navigator, just click the “Jupyter lab” button. For a traditional Python install, open the Windows command line, type:

jupyter lab

It should open up your web browser to a Jupyter page. Keep the Windows Command line terminal open in the background. If you close it, Jupyter will cease to work. (Note: This isn’t true for anaconda if you open Jupyter from the GUI.) Click the “Stata” notebook button to start.

It’ll open up a scripting page for your code. At the bottom it should say “stata idle”. That’s how you know you set it up correctly.

Now you can use traditional Stata code!

There are additional programs called “Magics” detailed here that help Stata integrate more seamlessly with Jupyter. Each of these commands begins with a % symbol. There are specific ways to modify these commands.

  • %browse – lists the first 200 rows
  • %head – first 10 rows
  • %tail – last 10 rows
  • %set – can change the graph_format, graph_scale, graph_width, and graph_height

How do I use SFI to interface Stata and Python?

Stata and Python talk to each other using the Stata function interface, or SFI. MORE TO COME ON THIS.

Making Restricted Cubic Splines in Stata

I love restricted cubic splines, made famous by Frank Harrell (see his approach starting on page 58 here). Dr. Harrell made a package for automating these in R. I’m not aware of an equivalent package for Stata. Here’s my approach to making this specific restricted cubic spline in Stata.

The model here is modified Poisson regression using the Zou 2004 method since the outcome is binary. Since it’s coded as a GLM, it’ll be relatively easy to swap out this one specific model for other models, like logistic regression using the appropriate link & family. It’s good habit to have the probability density of the outcome across the continuum of exposure, so that is plopped on the bottom here. It wouldn’t take much work to replace with a histogram.

This is for two groups (group1=blue, group2=red). It wouldn’t be too hard to make this for just one group, deleting everything having to do with group 2 or having the number 2 in it. Or, duplicate lines for a group 3. Also, sometimes folks like to present a kernel density plot for each outcome, so you’d just duplicate the kdensity lines and add some code to specify that one of each is for folks with outcome==0 then outcome==1.

One quirk is that the xbrcspline code depends on a list of all of the possible options for the exposure, which is brought in from listof to xbrcspline as a numlist. As you can see in –help limits–, numlists are capped at 2,500 numbers. If you get an error saying “values() invalid — invalid numlist has too many elements”, then you have too many individual options for your exposure. This might be because your exposure is an integer with a huge amount of digits after the decimal place, so your >2,500 observations all come with their own unique value for the exposure. Rounding can help reduce this count, if it analytically and clinically makes sense. (This is okay for BMI, for example, because there isn’t clinically relevant difference between a bmi of 27.0400558235 and 27.04). To generate a rounded value, plop —gen bmi_round=(bmi, 0.01)– in the first few lines, right after opening your data. Then use “bmi_round” as your exposure variable rather than “bmi”.

This code is an essentially entirely rewritten version of one that is used at the Welch Center at Hopkins, where it has been handed down through generations of doctoral students and post-docs.

I’ve recently needed to make these figures using pweights, so I put some comments throughout to simplify that process.


Code to make the figure above

***************load data!************************
****************and drop any open graphs*********
webuse fvex, clear
graph drop _all
macro drop _all
// if weighted, declare weighting here
**************define variables here**************
// Define the OUTCOME, = to 0 or 1, after the (first) word "outcome"
global outcome outcome // this database's outcome is called outcome...
// Define the EXPOSURE following the word "exposure"
global exposure age // this has to be a continuous variable
// Define the COVARIATES following word "covariate"
global covariates sex distance
// Define what makes GROUP 1 here after "thing1" (blue)
global thing1 if arm==1 
// Define what makes GROUP 2 here after "thing2" (red)
global thing2 if arm==2

************auto-generate subgroups and**********
*****************local macros needed*************
********************for spline*******************
// need to make the exposure by group for the kernel density plots. 
gen exposuresubgroup1 = ${exposure} ${thing1}
gen exposuresubgroup2 = ${exposure} ${thing2}

// The xbrcspine command below needs the middle value of the exposure 
// for each group. This is often times the median, but if there are 
// an even number of values for the exposure, the median will be an 
// average of the two middle values of the exposure variable. The following 
// code defines the median as a local macro (median1temp) then grabs the
// actual value for the exposure closest to that number (median_1). 
// _pctile can be used with pweight, if needed.
_pctile ${exposure} ${thing1}, p(50)
local median1temp = r(r1)
gen mediandiff1=abs(exposuresubgroup1-`median1temp')
gsort mediandiff1
local median_1 = ${exposure}[_n==1]
_pctile ${exposure} ${thing2}, p(50)
local median2temp = r(r1)
gen mediandiff2=abs(exposuresubgroup2-`median2temp')
gsort mediandiff2
local median_2 = ${exposure}[_n==1]
// get rid of these variables, since they are not needed anymore
drop mediandiff1 mediandiff2

*************make splines and view knots!********
// NOTE: look at the stata output here, the knots will be displayed
// you'll need to list these in the footer
// Harrell's method recommends using the # of knots ('k') as 3, 4 or 5
// with n >=100 as 5 and n<30 as 3. It's just a guideline. 
// Details are on page 62 here: 
mkspline ${exposure}_spline1=${exposure} ${thing1}, nknots(4) cubic displayknots
mat knots1=r(knots)
mkspline ${exposure}_spline2=${exposure} ${thing2}, nknots(4) cubic displayknots
mat knots2=r(knots)
// above won't work for pweight weighted data. instead, you need 
// to specifically define the percentiles from the table on pg 5 here:
// But basically, 
// 3 knots, percentiles are at: 10 50 90
// 4 knots, percentiles are at: 5 35 65 95
// 5 knots, percentiles are at: 5 27.5 50 72.5 95
// 6 knots, percentiles are at: 5 23 41 59 77 95
// 7 knots, percentiles are at: 2.5 18.33 34.17 50 65.83 81.67 97.5
// the code for 4 knots follows in hidden code:
//_pctile ${exposure} ${thing1} [pweight=samplingweight], p(5 35 65 95) 
//return list
//local gr1knot1 = r(r1)
//local gr1knot2 = r(r2)
//local gr1knot3 = r(r3)
//local gr1knot4 = r(r4)
//di "Knots for group 1 are at " `gr1knot1' ", " `gr1knot2' ", " ///
//`gr1knot3' ", " `gr1knot4' "."
//mkspline ${exposure}_spline1=${exposure} ${thing1},  ///
//knots(`gr1knot1' `gr1knot2' `gr1knot3' `gr1knot4' ) ///
//cubic displayknots
//mat knots1=r(knots)
//_pctile ${exposure} ${thing2} [pweight=samplingweight], p(5 35 65 95) 
//return list
//local gr2knot1 = r(r1)
//local gr2knot2 = r(r2)
//local gr2knot3 = r(r3)
//local gr2knot4 = r(r4)
//di "Knots for group 2 are at " `gr2knot1' ", " `gr2knot2' ", " ///
// `gr2knot3' ", " `gr2knot4' "."
//mkspline ${exposure}_spline2=${exposure} ${thing2},  ///
//knots(`gr2knot1' `gr2knot2' `gr2knot3' `gr2knot4') ///
//cubic displayknots
//mat knots2=r(knots)
********Generate models to use in splines********
// model time!
// the models here are modified poisson regressions using sandwich variance estimators
// which is the Zou method from this classic paper:
// GLMs can be used with pweight, if needed.
// First group 
glm ${outcome} c.${exposure}_spline1* ${covariates}  ${thing1},  ///
fam(poisson) link(log) nolog robust
// robust is required for modified poisson regression by zou's method 
// (i.e., modified poisson regression with sandwich variance estimators)
levelsof(${exposure})  ${thing1}  // generates r(levels) for next line
xbrcspline ${exposure}_spline1, values(`r(levels)') ///
ref(`median_1') matknots(knots1) /// 
eform gen(lpnt1 hr1 lb1 ub1)

// Second group
glm ${outcome} c.${exposure}_spline2* ${covariates} ${thing2}, ///
fam(poisson) link(log) nolog robust
levelsof(${exposure}) ${thing2} // generates r(levels) for next line
xbrcspline ${exposure}_spline2, values(`r(levels)') ///
ref(`median_2') matknots(knots2) /// 
eform gen(lpnt2 hr2 lb2 ub2)

***********generate extremes to drop*************
// should drop the extremes, here the 0.5 and 99.5th percentiles
// but need to define these as local macros.
// _pctile can be used with pweight, if needed.
_pctile ${exposure} ${thing1}, p(0.05 99.5)
return list
local cut_a1 = r(r1)
local cut_b1 = r(r2)

_pctile ${exposure} ${thing2}, p(0.05 99.5)
return list
local cut_a2 = r(r1)
local cut_b2 = r(r2)
***************make the figure*******************
set scheme s1mono // my favorite scheme
twoway ///
/// spline for one group
(line  hr1 lpnt1 if lpnt1 > `cut_a1' & ///
lpnt1 < `cut_b1' , yaxis(1) lp(solid) lc(blue) lwidth(medthick) ) ///
(rarea lb1 ub1 lpnt1 if lpnt1 > `cut_a1' & ///
lpnt1 < `cut_b1' , yaxis(1) color(blue%5)) ///
(line  lb1 lpnt1 if lpnt1 > `cut_a1' & ///
lpnt1 < `cut_b1', yaxis(1)  lp(dash) lc(blue) lwidth(thin) ) ///
(line  ub1 lpnt1 if lpnt1 > `cut_a1' & ///
lpnt1 < `cut_b1' , yaxis(1) lp(dash) lc(blue) lwidth(thin) ) ///
/// spline for other group
(line  hr2 lpnt2 if lpnt2 > `cut_a2' & ///
lpnt2 < `cut_b2' , yaxis(1) lp(solid) lc(red) lwidth(medthick) ) ///
(rarea lb2 ub2 lpnt2 if lpnt2 > `cut_a2' & ///
lpnt2 < `cut_b2' , yaxis(1) color(red%5)) ///
(line  lb2 lpnt2 if lpnt2 > `cut_a2' & ///
lpnt2 < `cut_b2' , yaxis(1)  lp(dash) lc(red) lwidth(thin) ) ///
(line  ub2 lpnt2 if lpnt2 > `cut_a2' & ///
lpnt2 < `cut_b2' , yaxis(1) lp(dash) lc(red) lwidth(thin) ) ///
(line  ub2 lpnt2 if lpnt2 > `cut_a2' & ///
lpnt2 < `cut_b2' , yaxis(1) lp(dash) lc(red) lwidth(thin) ) ///
/// kernel density for one group
(kdensity exposuresubgroup1 if exposuresubgroup1 > `cut_a1' & ///
exposuresubgroup1 < `cut_b1' , yaxis(2) lp(shortdash) lcolor(blue)) ///
/// kernel density for other group
(kdensity exposuresubgroup2 if exposuresubgroup2 > `cut_a2' & ///
exposuresubgroup2 < `cut_b2' , yaxis(2) lp(shortdash) lcolor(red)) ///
, ///
yline(1, lpattern(solid) lcolor(black) axis(1)) ///
/// labels for the left axis, hide the following line if you aren't sure 
/// of what the range should be:
ylabel(0.25 "0.25" 0.5 "0.5" 1 "1" 2.5 "2.5" 5 "5", axis(1) angle(0)) ///
/// range for the left axis. the BOTTOM of this range has to be WAAAY lower 
/// than the bottom ylabel in the line above in order for it to sit 
/// on the way top of the figure. 
yscale(r(0.001 2) log axis(1)) /// log scale here
/// ditto for the right axis, but the TOP of the range on the yscale needs 
/// to be WAAAY higher than the top ylabel
ylabel(0 "0" 0.01 "0.01" 0.02 "0.02" 0.03 "0.03" 0.04 "0.04" 0.05 "0.05", ///
axis(2) labsize(vsmall) angle(0)) ///
yscale(r(0.0 .2) axis(2)) /// not log scale here
/// you'll notice that the x label doesn't span the entire 
/// range of the exposure because the local macro cuts above.
xlabel(20(5)60) ///
/// for the titles, need to put a bunch of spaces so things align
ytitle("                {bf:Risk Ratio (95% CI)}", axis(1)) ///
ytitle("{bf:Probability Density}                                          ", ///
 justification(left) axis(2)) ///
xtitle("{bf:Exposure Here}") ///
title("{bf:The Title}") ///
legend(order(1 "Group 1" 5 "Group 2")) ///
*****************Save figure!********************
// save as png, change to tif if needed for submission			
graph export "mygraph1.png", replace width(1000)

Use Stata to download the NY Times COVID-19 database and render a Twitter-compatible US mortality figure

Here’s the figure!

Code follows

Comments are in-line below. Some unique strategies in this code:

  • This will automatically download the latest NY Times dataset, but the date of “last day of follow-up” needs to be specifically defined. I find that the label locations need to be tweaked every day, and this process isn’t simple to automate.
  • The colors are defined by global macros once and are applied multiple times by calling those macros.
  • Text blocks are rendered next to the last day of follow-up with a translucent white background and non-translucent colored border that matches the dotted line.
  • Twitter figures should be output at 1100 x 628, per this blog. This script does that. Twitter clips images that aren’t this size.
// step 1: download  and save NY times database
version 15.1 // my version of Stata when this was written

import delimited using ///
"", ///
varn(1) clear

// Now make the date a stata date. Load this handy date-fixing program 
// I wrote. The syntax is 'fixdate [variable name] [mdy, ymd, etc]
fixdate date ymd
// rename state to state_fullname
rename state state_fullname
// step 2: keep 50 states+DC, apply abbreviations
gen state=" " 
replace state="AL" if state_fullname=="Alabama"
replace state="AK" if state_fullname=="Alaska"
replace state="AZ" if state_fullname=="Arizona"
replace state="AR" if state_fullname=="Arkansas"
replace state="CA" if state_fullname=="California"
replace state="CO" if state_fullname=="Colorado"
replace state="CT" if state_fullname=="Connecticut"
replace state="DE" if state_fullname=="Delaware"
replace state="FL" if state_fullname=="Florida"
replace state="GA" if state_fullname=="Georgia"
replace state="HI" if state_fullname=="Hawaii"
replace state="ID" if state_fullname=="Idaho"
replace state="IL" if state_fullname=="Illinois"
replace state="IN" if state_fullname=="Indiana"
replace state="IA" if state_fullname=="Iowa"
replace state="KS" if state_fullname=="Kansas"
replace state="KY" if state_fullname=="Kentucky"
replace state="LA" if state_fullname=="Louisiana"
replace state="ME" if state_fullname=="Maine"
replace state="MD" if state_fullname=="Maryland"
replace state="MA" if state_fullname=="Massachusetts"
replace state="MI" if state_fullname=="Michigan"
replace state="MN" if state_fullname=="Minnesota"
replace state="MS" if state_fullname=="Mississippi"
replace state="MO" if state_fullname=="Missouri"
replace state="MT" if state_fullname=="Montana"
replace state="NE" if state_fullname=="Nebraska"
replace state="NV" if state_fullname=="Nevada"
replace state="NH" if state_fullname=="New Hampshire"
replace state="NJ" if state_fullname=="New Jersey"
replace state="NM" if state_fullname=="New Mexico"
replace state="NY" if state_fullname=="New York"
replace state="NC" if state_fullname=="North Carolina"
replace state="ND" if state_fullname=="North Dakota"
replace state="OH" if state_fullname=="Ohio"
replace state="OK" if state_fullname=="Oklahoma"
replace state="OR" if state_fullname=="Oregon"
replace state="PA" if state_fullname=="Pennsylvania"
replace state="RI" if state_fullname=="Rhode Island"
replace state="SC" if state_fullname=="South Carolina"
replace state="SD" if state_fullname=="South Dakota"
replace state="TN" if state_fullname=="Tennessee"
replace state="TX" if state_fullname=="Texas"
replace state="UT" if state_fullname=="Utah"
replace state="VT" if state_fullname=="Vermont"
replace state="VA" if state_fullname=="Virginia"
replace state="WA" if state_fullname=="Washington"
replace state="WV" if state_fullname=="West Virginia"
replace state="WI" if state_fullname=="Wisconsin"
replace state="WY" if state_fullname=="Wyoming"

replace state="DC" if state_fullname=="District of Columbia"

drop if state==" " // drop guam, VI, PR. would be reasonable to add them back
// would need to get their populations for the list below. 
// step 3: apply population by state
// ref: 
gen statepop=.
replace statepop=4903185 if state=="AL"
replace statepop=731545 if state=="AK"
replace statepop=7278717 if state=="AZ"
replace statepop=3017804 if state=="AR"
replace statepop=39512223 if state=="CA"
replace statepop=5758736 if state=="CO"
replace statepop=3565287 if state=="CT"
replace statepop=973764 if state=="DE"
replace statepop=705749 if state=="DC"
replace statepop=21477737 if state=="FL"
replace statepop=10617423 if state=="GA"
replace statepop=1415872 if state=="HI"
replace statepop=1787065 if state=="ID"
replace statepop=12671821 if state=="IL"
replace statepop=6732219 if state=="IN"
replace statepop=3155070 if state=="IA"
replace statepop=2913314 if state=="KS"
replace statepop=4467673 if state=="KY"
replace statepop=4648794 if state=="LA"
replace statepop=1344212 if state=="ME"
replace statepop=6045680 if state=="MD"
replace statepop=6892503 if state=="MA"
replace statepop=9986857 if state=="MI"
replace statepop=5639632 if state=="MN"
replace statepop=2976149 if state=="MS"
replace statepop=6137428 if state=="MO"
replace statepop=1068778 if state=="MT"
replace statepop=1934408 if state=="NE"
replace statepop=3080156 if state=="NV"
replace statepop=1359711 if state=="NH"
replace statepop=8882190 if state=="NJ"
replace statepop=2096829 if state=="NM"
replace statepop=19453561 if state=="NY"
replace statepop=10488084 if state=="NC"
replace statepop=762062 if state=="ND"
replace statepop=11689100 if state=="OH"
replace statepop=3956971 if state=="OK"
replace statepop=4217737 if state=="OR"
replace statepop=12801989 if state=="PA"
replace statepop=1059361 if state=="RI"
replace statepop=5148714 if state=="SC"
replace statepop=884659 if state=="SD"
replace statepop=6829174 if state=="TN"
replace statepop=28995881 if state=="TX"
replace statepop=3205958 if state=="UT"
replace statepop=623989 if state=="VT"
replace statepop=8535519 if state=="VA"
replace statepop=7614893 if state=="WA"
replace statepop=1792147 if state=="WV"
replace statepop=5822434 if state=="WI"
replace statepop=578759 if state=="WY"

// step 4: make daily death count per capita
// now make variables for cases and deaths per capita in each state (per million persons)
gen statepopave_deaths = (deaths/statepop) *1000000

// step 5: make a variable for when the death rate is 
// >=1/1,000,000 people in each state, and count days
// following that
sort state date
gen days_1_death=.
replace days_1_death=0 if statepopave_deaths < 1 
replace days_1_death=1 if (statepopave_deaths >= 1 & statepopave_deaths[_n-1] <1 ) ///
& (state==state[_n-1])
replace days_1_death = days_1_death[_n-1]+1 if state==state[_n-1] ///
& days_1_death[_n-1]!=0
// step 6: save database

save nytimes_state_fu.dta, replace

// step 7: specify last day of follow-up and 
// get rank of states and location
// to put state names in x,y location for the 
// last day of follow-up
// reload
use nytimes_state_fu.dta, clear

// Set the final date of follow-up. 
// as of today (3/29/2020), 3/27/2020 is the most
// recent day of data in the NY times database.
// This is intentionally not automated because I want to manually adjust
// labels and range each time. 
global month Mar // needs to be in 3 letter abbreviation for month
global date 27 // 2 number day in month

// drop any day beyond the specified date
drop if date>date("${date}${month}2020", "DMY")

// this global will make the x axis 1 day longer than the current follow-up
sum days_1_death
global maxdate = r(max)+1

// actually determine the order of states on the last day of follow-up,
// which is how the labels and colors are applied.
// need to drop all but the last date of follow-up
keep if date==date("${date}${month}2020", "DMY")
gsort -statepopave_deaths // sort in reverse order
gen n=_n // make variable that contains order based upon sort
drop if n >10 // drop those not in the top 10

// need to figure out where to put the labels of state names
// this loop plucks out the state name and x&y coordinates for the last
// day of follow-up. 
// it also prints the order of the states. 
foreach x in 1 2 3 4 5 6 7 8 9 10 {
global statename`x'=state[`x'] // pull state name
global datecount`x' = days_1_death[`x'] + 0.2 // x axis, need to offset by 0.2 
//                                      so the label isn't on top of the dot
global statedeath`x' = statepopave_deaths[`x'] // yaxis

di "State rank #`x': ${statename`x'}"
di "(x axis) # of days: ${datecount`x'}" 
di "(y axis) deaths/million: ${statedeath`x'}"
di " "
// The labels might overlap each other. This you can manually readjust the 
// location on the y axis following here. This won't alter data in the 
// figure, just the location of the labels. 

global statedeath1 = ${statedeath1} // don't need to move label
global statedeath2 = ${statedeath2} // don't need to move label 
global statedeath3 = ${statedeath3}  // don't need to move label
global statedeath4 = ${statedeath4}  // don't need to move label
global statedeath5 = ${statedeath5}  // don't need to move label
global statedeath6 = ${statedeath6}  // don't need to move label
global statedeath7 = ${statedeath7}  // don't need to move label
global statedeath8 = ${statedeath8}+1.5 // move GA up on y axis
global statedeath9 = ${statedeath8}-2 // move DC down on y axis
global statedeath10 = ${statedeath10} // don't need to move label
// step 8: specify colors, make figure, save figure
// in size compatible with twitter

// reload the full dataset
use nytimes_state_fu.dta, replace
// drop any day beyond the specified date. 
drop if date>date("${date}${month}2020", "DMY")

// I like the s1mono scheme. Default stata theme is ugly. 
set scheme s1mono
// colors for these states, taken from colorbrewer website
// ref:
// these are RGB triads
global color1 165 0 38
global color2 215 48 39
global color3 244 109 67
global color4 253 174 97
global color5 254 224 144
global color6 224 243 248
global color7 171 217 233
global color8 116 173 209
global color9 69 117 180
global color10 49 54 149

// the actual graphic!
// note: you need to put 'sort' after the 'twoway scatter' command so the line doesn't loop back around. 
twoway ///
(scatter statepopave_deaths days_1_death if state=="${statename1}" & days_1_death>=1 & date>=1, ///
mcolor("${color1}") msymbol(O) lpattern(solid) lcolor("${color1}") connect(L) sort) ///
(scatter statepopave_deaths days_1_death if state=="${statename2}" & days_1_death>=1 & date>=1, ///
mcolor("${color2}") msymbol(O) lpattern(solid) lcolor("${color2}") connect(L) sort) ///
(scatter statepopave_deaths days_1_death if state=="${statename3}" & days_1_death>=1 & date>=1, ///
mcolor("${color3}") msymbol(O) lpattern(solid) lcolor("${color3}") connect(L) sort) ///
(scatter statepopave_deaths days_1_death if state=="${statename4}" & days_1_death>=1 & date>=1, ///
mcolor("${color4}") msymbol(O) lpattern(solid) lcolor("${color4}") connect(L) sort) ///
(scatter statepopave_deaths days_1_death if state=="${statename5}" & days_1_death>=1 & date>=1, ///
mcolor("${color5}") msymbol(O) lpattern(solid) lcolor("${color5}") connect(L) sort) ///
(scatter statepopave_deaths days_1_death if state=="${statename6}" & days_1_death>=1 & date>=1, ///
mcolor("${color6}") msymbol(O) lpattern(solid) lcolor("${color6}") connect(L) sort) ///
(scatter statepopave_deaths days_1_death if state=="${statename7}" & days_1_death>=1 & date>=1, ///
mcolor("${color7}") msymbol(O) lpattern(solid) lcolor("${color7}") connect(L) sort) ///
(scatter statepopave_deaths days_1_death if state=="${statename8}" & days_1_death>=1 & date>=1, ///
mcolor("${color8}") msymbol(O) lpattern(solid) lcolor("${color8}") connect(L) sort) ///
(scatter statepopave_deaths days_1_death if state=="${statename9}" & days_1_death>=1 & date>=1, ///
mcolor("${color9}") msymbol(O) lpattern(solid) lcolor("${color9}") connect(L) sort) ///
(scatter statepopave_deaths days_1_death if state=="${statename10}" & days_1_death>=1 & date>=1, ///
mcolor("${color10}") msymbol(O) lpattern(solid) lcolor("${color10}") connect(L) sort) ///
, ///
yline(30, lcolor(gs14)) ///will need at add additional horizontal lines as figure grows
yline(20, lcolor(gs14)) ///
yline(10, lcolor(gs14)) ///
title("COVID-19 Cumulative Mortality by US State") ///
t1title("Top 10 states, through $month $date, 2020") ///
xla(1(2)$maxdate) ///
yla(0(5)40) ///
yti("# COVID19 Deaths/Million Persons") ///
xti("Day Since ≥1 Death/Million Persons") ///
legend(off) ///
/// the following will render each label with a surrounding box that's the same color as the line. 
text(${statedeath1} ${datecount1} "${statename1}", ///
size(small) place(e) just(left) box bcolor(white%40) lcolor("${color1}%100") lstyle(solid) lwidth(thin)) ///
text(${statedeath2} ${datecount2} "${statename2}", ///
size(small) place(e) just(left) box bcolor(white%40) lcolor("${color2}%100") lstyle(solid) lwidth(thin)) ///
text(${statedeath3} ${datecount3} "${statename3}", ///
size(small) place(e) just(left) box bcolor(white%40) lcolor("${color3}%100") lstyle(solid) lwidth(thin)) ///
text(${statedeath4} ${datecount4} "${statename4}", ///
size(small) place(e) just(left) box bcolor(white%40) lcolor("${color4}%100") lstyle(solid) lwidth(thin)) ///
text(${statedeath5} ${datecount5} "${statename5}", ///
size(small) place(e) just(left) box bcolor(white%40) lcolor("${color5}%100") lstyle(solid) lwidth(thin)) ///
text(${statedeath6} ${datecount6} "${statename6}", ///
size(small) place(e) just(left) box bcolor(white%40) lcolor("${color6}%100") lstyle(solid) lwidth(thin)) ///
text(${statedeath7} ${datecount7} "${statename7}", ///
size(small) place(e) just(left) box bcolor(white%40) lcolor("${color7}%100") lstyle(solid) lwidth(thin)) ///
text(${statedeath8} ${datecount8} "${statename8}", ///
size(small) place(e) just(left) box bcolor(white%40) lcolor("${color8}%100") lstyle(solid) lwidth(thin)) ///
text(${statedeath9} ${datecount9} "${statename9}", ///
size(small) place(e) just(left) box bcolor(white%40) lcolor("${color9}%100") lstyle(solid) lwidth(thin)) ///
text(${statedeath10} ${datecount10} "${statename10}", ///
size(small) place(e) just(left) box bcolor(white%40) lcolor("${color10}%100") lstyle(solid) lwidth(thin)) ///
caption("Using NY Times COVID19 database"  ///
"",  ///
size(small)) ///
xsize(15.3) ysize(9.0) 
// twitter default width & height is 1100x628 pixels. 
//This last line sets the corresponding height and width in inches using 72 dpi. 

graph export "COVID_mortality_2020_${month}_${date}_continuous.png", replace width(1100) 
// width(1100) sets the output to be default width on twitter, or 1100 dpi. 

Output a Stata graph that won’t be clipped in Twitter

Twitter sizing

Twitter does this weird thing where it clips figures that aren’t the correct proportion. I came across this blog post that argues that 1100×628 px is the ‘optimal’ Twitter image size.

So, how do you output Stata figures to be 1100×628?

Output a Stata figure in Twitter size in 2 steps

Step 1: Force the width and height to be 15.3 x 9.0 inches

Stata allows you to use ‘xsize(##)’ and ‘ysize(##)’ to force the height and width of a figure. Assuming a 72 dpi resolution (the default resolution for monitors), that means that your width and height should be:

twoway ///
(scatter thing otherthing) ///
, ///
xsize(15.3) ysize(9.0)

…place above behind the comma of your graph

Step 2: Set the graph output to be 1100 pixels wide

In your graph export command, after the comma, place ‘width(1100)’. Or

graph export "figurename.png", replace width(1100)

That’s it!

Figure to show the distribution of quartiles plus their median in Stata

Buried in the supplement of a recent paper is a variant of this figure that I’m rather proud of:

It shows the distribution of quartiles of BNP and NT proBNP at baseline on a log scale, by use of beta blockers (BB) at baseline. It also shows the midway point of the medians. It’s a nice figure that shows the increase of BNP with beta blocker administration. My colleagues jokingly called it the “Plante Plot” since I have had it included in several drafts of manuscripts, this is just the first one in which it was published.

The code for it is pretty complex and follows. Steps 1-3 pluck out the ranges of the quartiles and their midpoints for each group and saves them as a CSV file. Steps 4-8 render the figure. You may find it more simple to skip Steps 1-3 and manually enter the ranges of the quartiles and their medians into an Excel file and just open up that excel file in Step 4.

Good luck!

// Step 1: load the database
use "029b1b analytic datset.dta", clear

// Step 2: You need quartiles plus the intermediate points of
// each quartile, which is really 8-iles.
// Step 2a: 
// You need the maximum and minimum variables to draw the bounds
// of the bottom and top quartile
// this is by group, here's the first group:
sum baseline_bnp if efstratus50==1 & baselinebb0n1y==0, d
return list // see that r(min) and r(max) are the points needed
// save min and max as macros for 0th bound and 0th bound
local bnp8ile_nobb_0=r(min) // beginning of q1
local bnp8ile_nobb_8=r(max) // end of q4
// Step 2b: 
// now get intermediate bounds for the 8-iles
_pctile baseline_bnp  if efstratus50==1  & baselinebb0n1y==0, percentiles(12.5(12.5)87.5) // this gets bounds by 12.5iles
return list // there they are!
local bnp8ile_nobb_1=r(r1) // middle of q1
local bnp8ile_nobb_2=r(r2) // end of q1/beginning of q2
local bnp8ile_nobb_3=r(r3) // middle of q2
local bnp8ile_nobb_4=r(r4) // end of q2/beginning of q3
local bnp8ile_nobb_5=r(r5) // middle of q3
local bnp8ile_nobb_6=r(r6) // end of q3/beginning of q4
local bnp8ile_nobb_7=r(r7) // middle of q4
// now come up with a label to eventually apply to the figure
// don't use commas in this label since we'll save this
// output as a CSV file and commas will screw up the cell
// structure of a CSV (C=comma)
// step 2c: 
local label_bnp_nobb="Baseline BNP; -BB"

// now repeat for the other groups
sum baseline_bnp if efstratus50==1 & baselinebb0n1y==1, d
return list
local bnp8ile_bb_0=r(min)
local bnp8ile_bb_8=r(max)
_pctile baseline_bnp  if efstratus50==1  & baselinebb0n1y==1, percentiles(12.5(12.5)87.5)
return list
local bnp8ile_bb_1=r(r1)
local bnp8ile_bb_2=r(r2)
local bnp8ile_bb_3=r(r3)
local bnp8ile_bb_4=r(r4)
local bnp8ile_bb_5=r(r5)
local bnp8ile_bb_6=r(r6)
local bnp8ile_bb_7=r(r7)
local label_bnp_bb="Baseline BNP; +BB"

sum baseline_ntprobnp if efstratus50==1 & baselinebb0n1y==0, d
return list
local ntprobnp8ile_nobb_0=r(min)
local ntprobnp8ile_nobb_8=r(max)
_pctile baseline_ntprobnp  if efstratus50==1  & baselinebb0n1y==0, percentiles(12.5(12.5)87.5)
return list
local ntprobnp8ile_nobb_1=r(r1)
local ntprobnp8ile_nobb_2=r(r2)
local ntprobnp8ile_nobb_3=r(r3)
local ntprobnp8ile_nobb_4=r(r4)
local ntprobnp8ile_nobb_5=r(r5)
local ntprobnp8ile_nobb_6=r(r6)
local ntprobnp8ile_nobb_7=r(r7)
local label_ntprobnp_nobb="Baseline NT proBNP; +BB"

sum baseline_ntprobnp if efstratus50==1 & baselinebb0n1y==1, d
return list
local ntprobnp8ile_bb_0=r(min)
local ntprobnp8ile_bb_8=r(max)
_pctile baseline_ntprobnp  if efstratus50==1  & baselinebb0n1y==1, percentiles(12.5(12.5)87.5)
return list
local ntprobnp8ile_bb_1=r(r1)
local ntprobnp8ile_bb_2=r(r2)
local ntprobnp8ile_bb_3=r(r3)
local ntprobnp8ile_bb_4=r(r4)
local ntprobnp8ile_bb_5=r(r5)
local ntprobnp8ile_bb_6=r(r6)
local ntprobnp8ile_bb_7=r(r7)
local label_ntprobnp_bb="Baseline NT proBNP; -BB"

// Step 3: save this to a csv file that we'll open up right away.
// Note: This code goes out of frame on my blog. copy and paste it 
// into a .do file and it'll all appear. 
quietly {
capture log close bnp 
log using "bnprangefigure.csv", replace text name(bnp)
// this is the row of headers:
noisily di "row,label,eight0,eight1,eight2,eight3,eight4,eight5,eight6,eight7,eight8" 
// row 1:
noisily di "1,`label_bnp_nobb',`bnp8ile_nobb_0',`bnp8ile_nobb_1',`bnp8ile_nobb_2',`bnp8ile_nobb_3',`bnp8ile_nobb_4',`bnp8ile_nobb_5',`bnp8ile_nobb_6',`bnp8ile_nobb_7',`bnp8ile_nobb_8'"
// row 2
noisily di "2,`label_bnp_bb',`bnp8ile_bb_0',`bnp8ile_bb_1',`bnp8ile_bb_2',`bnp8ile_bb_3',`bnp8ile_bb_4',`bnp8ile_bb_5',`bnp8ile_bb_6',`bnp8ile_bb_7',`bnp8ile_bb_8'"
// blank row 3:
noisily di "3"
// row 4:
noisily di "4,`label_ntprobnp_nobb',`ntprobnp8ile_nobb_0',`ntprobnp8ile_nobb_1',`ntprobnp8ile_nobb_2',`ntprobnp8ile_nobb_3',`ntprobnp8ile_nobb_4',`ntprobnp8ile_nobb_5',`ntprobnp8ile_nobb_6',`ntprobnp8ile_nobb_7',`ntprobnp8ile_nobb_8'"
// row 5:
noisily di "5,`label_ntprobnp_bb',`ntprobnp8ile_bb_0',`ntprobnp8ile_bb_1',`ntprobnp8ile_bb_2',`ntprobnp8ile_bb_3',`ntprobnp8ile_bb_4',`ntprobnp8ile_bb_5',`ntprobnp8ile_bb_6',`ntprobnp8ile_bb_7',`ntprobnp8ile_bb_8'"
log close bnp

// step 4: open CSV file as active database:
import delim using "bnprangefigure.csv", clear
// note, you may opt to skip steps 1-3 and manually compile the 
// ranges of each quartile and their median into an excel file.
// Use the -import excel- function to open that file up instead.
// IF YOU SKIP OVER STEPS 1-3, your excel file will need the 
/// following columns:
// row - each group, with a blank row 3 to match the figure
// label - title to go to the left of the figure
// eight0 through eight8 - the even numbers are ranges of the
//    quartiles and the odd numbers are the mid-ranges.
//    See my approach in steps 2a-2b on how to get these numbers.

// step 5: steal the labels. note skipping row 3 since it's blank
local label1=label[1]
local label2=label[2] 
local label4=label[4] // NO ROW 3!!
local label5=label[5]

// step 6: pluck the intermediate points of each quartile
// which are 8-iles 1, 3, 5 and 7
// and repeat for each row
local bar1row1=eight1[1]
local bar2row1=eight3[1]
local bar3row1=eight5[1]
local bar4row1=eight7[1]

local bar1row2=eight1[2]
local bar2row2=eight3[2]
local bar3row2=eight5[2]
local bar4row2=eight7[2]

// no row 3 in this figure

local bar1row4=eight1[4]
local bar2row4=eight3[4]
local bar3row4=eight5[4]
local bar4row4=eight7[4]

local bar1row5=eight1[5]
local bar2row5=eight3[5]
local bar3row5=eight5[5]
local bar4row5=eight7[5]

// step 7: pick a different scheme than the default stata one
// I like s1mono or s1color
set scheme s1mono

// step 8: complex graph.
// stata likes to drop the macros ("local" commands) and 
// the things inside of the ticks will be missing if you 
// just run starting at the "graph twoway" below
// step 8a: rbar the ends of the quartiles, which is:
// 0 to 2, 2 to 4, 4 to 6, and 6 to 8
// step 8b: apply the labels
// step 8c: place a vertical bar at the midpoints of the
// quartiles, which are at: 1, 3, 5, and 7. A bug in Stata
// is that a centered label (placement(c)) is actually a smidge
// south still, so the rows are offset by 0.13. You'll notice
// the Y label in the text box is row value minus 0.13 (0.87, etc.)
// to account for that.
// step 8d: adjust the aspect ratio to get the bar character ("|") 
// to fit within the width of the the bar itself.

graph twoway /// step 8a:
(rbar eight0 eight2 row , horizontal) /// 
(rbar eight2 eight4 row , horizontal) ///
(rbar eight4 eight6 row , horizontal) ///
(rbar eight6 eight8 row , horizontal) ///
, ///
yscale(reverse) ///
xscale(log) ///
t2title("Quartiles of BNP or NT-proBNP, EF ≥50%", justification(center)) /// 
xla(1 "1" 10 "10" 100 "100" 1000 "1000" 10000 "10000" 20000 "20000", angle(45)) /// step 8b:
yla(1 "`label1'" 2 "`label2'" 4 "`label4'" 5 "`label5'", angle(horizontal)) ///
ytitle(" ") ///
xtitle("BNP/NTproBNP Range (Log Scale)") ///
legend(off) ///
/// step 8c:
text(0.87 `bar1row1' "|", color(white) placement(c)) ///
text(0.87 `bar2row1' "|", color(white) placement(c)) ///
text(0.87 `bar3row1' "|", color(white) placement(c)) ///
text(0.87 `bar4row1' "|", color(white) placement(c)) ///
text(1.87 `bar1row2' "|", color(white) placement(c)) ///
text(1.87 `bar2row2' "|", color(white) placement(c)) ///
text(1.87 `bar3row2' "|", color(white) placement(c)) ///
text(1.87 `bar4row2' "|", color(white) placement(c)) ///
text(3.87 `bar1row4' "|", color(white) placement(c)) ///
text(3.87 `bar2row4' "|", color(white) placement(c)) ///
text(3.87 `bar3row4' "|", color(white) placement(c)) ///
text(3.87 `bar4row4' "|", color(white) placement(c)) ///
text(4.87 `bar1row5' "|", color(white) placement(c)) ///
text(4.87 `bar2row5' "|", color(white) placement(c)) ///
text(4.87 `bar3row5' "|", color(white) placement(c)) ///
text(4.87 `bar4row5' "|", color(white) placement(c)) ///
/// step 8d:

Making a publication-ready Kaplan-Meier plot in Stata

In the early Winter of 2019, we had a paper published in JAMA: Network Open using the TOPCAT trial dataset looking at association between beta-blocker use at baseline and incident heart failure admissions. We obtained the data from the NIH/NHLBI BioLINCC repository. This is an incredible resource for datasets. With a quick application, IRB, and DUA, you can get world-class datasets from landmark clinical trials like ACCORD, SPRINT, TOPCAT, etc..

In this analysis we needed to put together a Kaplan-Meier plot for Figure 2 (sometimes called a survival plot). Well, technically it’s a cumulative incidence plot since the line starts a 0% and creeps up as events happen rather than starting at 100% and dropping down as events happen.

Features of this plot that are somewhat unique:

  • Lines are not only different colors, one is dashed and one is solid. This is key to avoiding color publication costs in some journals as it can be made greyscale and folks can still figure out which line is which. Even if there wasn’t an issue with color costs, including differing line patterns helps folks who may be colorblind. This is done with the -plot1opts- and -plot2opts- commands.
  • Text label for Log-Rank!
  • Bonus example of how to use italics in text. It’s {it:YOUR TEXT}. This could also have been bold using {bf:YOUR TEXT} instead.
  • Survival table that lines up with the years on the x axis!
  • Pretty legend on 2 rows with custom labels.

Of course, the JAMA folks remade my figure for the actual publication. Womp, womp. I still like how it came out.

What the figure looks like

Code to make this figure

// STEP 1: tell stata that it's time to event data. 
// Use the -stset- command. Syntax:
// stset [time], failure([thing that is 0 or 1 for the event of outcome]) ...
// id([participant id, useful if doing time varying covariates]) ...
// scale([time scale, here 365.25 days=1 year)

stset days_hfhosp, f(hfhosp) id(id) scale(365.25)

// STEP 2: Set the color scheme.
// I don't like the default stata color scheme.
// Try s1color or s1mono

set scheme s1color 

// STEP 3: Here's the actual figure!

sts graph if efstratus50==1 ///
, /// this was a subset of patients with EF >=50%
fail /// this makes the line starts at the bottom of the Y axis instead of the top
by(baselinebb0n1y) /// this is the variable that defines the two groups, bb use at baseline or none
title("Cumulative Heart Failure Hospitalizations") /// Title!
t1title("Among TOPCAT Participants with EF ≥50%") /// subtitle!
xtitle("Year of Follow-Up") /// x label!
ytitle("Cumulative Incidence") /// y label!
yla(0 "0%" .25 "25%" .5 "50%" .75 "75%" 1 "100%", angle(0)) /// Y-label values! Angle(0) rotates them.
xla(0(1)6) /// X-label values! From 0 to 6 years with 1 year intervals in between
text(0.63 3 "Log-Rank {it:p}<0.001", placement(e) size(medium)) /// floating label with italics!
legend(order(1 "No Beta-Blocker" 2 "Beta-Blocker") rows(2)) /// Legend, forced on two rows
plot1opts(lpattern(dash) lcolor(red)) /// this forces the first line to be dashed and red
plot2opts(lpattern(solid) lcolor(blue)) /// this forces the second line to be solid and blue
risktable(0(1)6 , size(small) order(1 "No Beta-Blocker" 2 "Beta-Blocker")) // the numbers under the X axis

// STEP 4: Export the graph as a tiny PNG file for the draft and 
// tif file to upload with the manuscript submission. 
graph export "Figure 2.png", replace width(2000)
graph export "Figure 2.tif", replace width(2000)

Working with Stata regression results: Matrix/matrices, macros, oh my!

If you make your own Stata programs and loops, you have discovered the wonders of automating output of analyses to tables. Extracting the results from regressions in Stata can be a bit cumbersome. Here’s one step-by-step approach that you might find helpful.

The set-up

Let’s use the classic 1978 auto dataset that comes with Stata. We want to regress MPG (Y) on weight (x) overall and by strata of domestic vs. foreign to complete the following table:

Weight Beta (95% CI) P-value

In Stata you’ll run three regressions to fill out the three rows:

sysuse auto, clear
regress mpg weight
regress mpg weight if foreign==0
regress mpg weight if foreign==1

You can either copy the output manually, or automate it! Let’s learn how to automate this process.

Let’s get familiar with the ‘guts’ and ‘brains’ behind Stata’s regression functions.

When you run a regression, Stata saves relevant bits of these regressions in scalars and matrices saved in different r() and e() levels, which can be viewed by -return list- and -ereturn list- commands, respectively. These have different uses. You can view the r() ‘guts’ with -return list- and e() ‘brains’ with -ereturn list-. These have different uses.

  • return list – This will give the ‘guts’ of the regression, namely the r() level bits, as you see it in the Stata output. Importantly, the r() level contains the r(table) matrix, which holds all of the raw numbers used to generate the output of your regression as you see it in Stata’s output. These are what you will use to fill out the above blank table.
    • Type -matrix list r(table)- to see the structured output of this matrix.
  • ereturn list – this will let you see the ‘brains’ behind the regression, namely the e() level bits, which are useful in post-estimation commands. We actually don’t need this to fill out the above table. Just pointing out that they’re here.

Let’s take a look at the regression output below and how they exist in the r() level r(table), I have bolded/underlined the output of interest. Our goal is to:

  1. Load the sysuse auto dataset.
  2. Run the regression.
  3. Take a look at the -return list- to see that the r(table) is hiding there (without actually viewing the contents of r(table))
  4. Actually view the r(table) matrix in order to verify that all of the data points of interest are hiding there.
. sysuse auto, clear
. regress mpg weight

Source |       SS           df       MS      Number of obs   =        74
-------+----------------------------------   F(1, 72)        =    134.62
 Model |   1591.9902         1   1591.9902   Prob > F        =    0.0000
Residu |  851.469256        72  11.8259619   R-squared       =    0.6515
-------+----------------------------------   Adj R-squared   =    0.6467
 Total |  2443.45946        73  33.4720474   Root MSE        =    3.4389

   mpg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
weight |  -.0060087   .0005179   -11.60   0.000    -.0070411   -.0049763
 _cons |   39.44028   1.614003    24.44   0.000     36.22283    42.65774

. return list

              r(level) =  95
              r(table) :  9 x 2

. matrix list r(table)

            weight       _cons
     b  -.00600869   39.440284
    se   .00051788   1.6140031
     t   -11.60251   24.436312
pvalue   3.798e-18   1.385e-36
    ll  -.00704106   36.222827
    ul  -.00497632    42.65774
    df          72          72
  crit   1.9934636   1.9934636
 eform           0           0

r(table) is a matrix, but an atypical matrix. Copy it to a custom ‘typical’ matrix before doing anything else!

Matrices are basically small spreadsheets saved in the memory that can be accessed by referencing a [row,column] cell reference. These exist separate from the dataset, which is also basically a big spreadsheet. You’ll note above (after the -matrix list r(table)- command) that Stata tells you that the r(table) matrix has 9 rows and 2 columns, or [9,2].

For various reasons that you can read about here, r(table) is not a usual matrix and Stata will do funny things if you try to run matrix commands on it. Make sure to save the r(table) matrix as custom matrix before going any further. Since we actually need to save 3 separate r(table) matrices to fill out the blank table (one for each row), you should do this anyway to help facilitate completing the table. Use the -matrix- command to copy the contents of the r(table) to a custom matrix. Here we’ll:

  1. Load the sysuse auto dataset
  2. Run three regressions, one for each row, and
  3. Save the r(table) matrix for each regression to a custom named matrix. We’ll specifically call them “row1”, “row2”, and “row3”.
  4. Then, we will confirm that each row is saved by plopping the command to view the matrices at the end.
sysuse auto, clear 
* first row
regress mpg weight
* save as a custom matrix
matrix row1=r(table)
* second row
regress mpg weight if foreign==0
* save as a custom matrix
matrix row2=r(table)
* third row
regress mpg weight if foreign==1
* save as a custom matrix
matrix row3=r(table)
* prove that all three saved
matrix list row1
matrix list row2
matrix list row3

The stata output for the last three lines should look like the output below. Note that the beta coefficient is at [1,1], the 95% confidence interval bounds are at [5,1] and [6,1], and the p-value is at 4,1]. I bolded/underlined the first to highlight this.

. matrix list row1

            weight       _cons
     b  -.00600869   39.440284
    se   .00051788   1.6140031
     t   -11.60251   24.436312
pvalue   3.798e-18   1.385e-36
    ll  -.00704106   36.222827
    ul  -.00497632    42.65774
    df          72          72
  crit   1.9934636   1.9934636
 eform           0           0

. matrix list row2

            weight       _cons
     b  -.00597508   39.646965
    se   .00046538   1.5766224
     t  -12.839251   25.146772
pvalue   1.890e-17   4.898e-30
    ll  -.00690982   36.480225
    ul  -.00504035   42.813704
    df          50          50
  crit   2.0085591   2.0085591
 eform           0           0

. matrix list row3

            weight       _cons
     b  -.01042596   48.918297
    se   .00249417   5.8718507
     t  -4.1801326   8.3309845
pvalue   .00046167   6.196e-08
    ll   -.0156287   36.669831
    ul  -.00522321   61.166763
    df          20          20
  crit   2.0859634   2.0859634
 eform           0           0

Let’s extract the matrix components of interest as macros!

Macros are little ‘codewords’ that represent another variable or string. You can pluck a cell of a matrix and store it as a macro. Later on, use can use that ‘codeword’ associated with the macro to make Stata blurt out the stored cell result. We just need to point the macro at the right matrix cell in order to extract the cell’s results. It sounds confusing but it’s not. Remember the [row,column] numbers from above? We’ll use those numbers to extract the matrix cell results into macros. Next steps:

  1. Load the sysuse auto dataset
  2. Run a regression for the first three rows of our table, saving the r(table) matrix for each regression as our custom matrix (row1-3)
  3. Use macros to extract the [1,1] as beta coefficient, [5,1] and [6,1] as the 95% confidence intervals, and [4,1] as the p-value for each row.
  4. View each macro with the -display- opening tick (`), to the left of the number 1 on your keyboard, the macro name, and a closing apostrophe (‘).
  5. You can use number formatting like %3.2f (e.g., 0.56) or %4.3f (0.558) to limit the number of digits following the decimal. This will also round.

Code to do this:

sysuse auto, clear 
* first row
regress mpg weight
* save as a custom matrix
matrix row1=r(table)
* second row
regress mpg weight if foreign==0
* save as a custom matrix
matrix row2=r(table)
* third row
regress mpg weight if foreign==1
* save as a custom matrix
matrix row3=r(table)
*now save the beta, 95% ci, and P as macros
*row 1
local beta1=row1[1,1]
local low951=row1[5,1]
local high951=row1[6,1]
local pval1=row1[4,1]
*row 2
local beta2=row2[1,1]
local low952=row2[5,1]
local high952=row2[6,1]
local pval2=row2[4,1]
*row 3
local beta3=row3[1,1]
local low953=row3[5,1]
local high953=row3[6,1]
local pval3=row3[4,1]
*now view these
*row 1
di "row1 beta is " %3.2f `beta1'
di "row1 95% CI is " %3.2f `low951' " to " %3.2f `high951'
di "row1 P-val is " %4.3f `pval1'
*row 2
di "row2 beta is " %3.2f `beta2'
di "row2 95% CI is " %3.2f `low952' " to " %3.2f `high952'
di "row2 P-val is " %4.3f `pval2'
*row 3
di "row3 beta is " %3.2f `beta3'
di "row3 95% CI is " %3.2f `low953' " to " %3.2f `high953'
di "row3 P-val is " %4.3f `pval3'

Hide in -quietly- curly brackets and output a CSV file using -noisily- commands and a log!

Here’s my code to run the three regression, store the r(table) matrices, extract the data of interest, and output as a .csv file! Run this from a .do file as it includes the -quietly- command, which confuses Stata if it’s run from the command line.

sysuse auto, clear 
* first row
regress mpg weight
* save as a custom matrix
matrix row1=r(table)
* second row
regress mpg weight if foreign==0
* save as a custom matrix
matrix row2=r(table)
* third row
regress mpg weight if foreign==1
* save as a custom matrix
matrix row3=r(table)
*view the custom matrices to prove they worked
matrix list row1
matrix list row2
matrix list row3
*now save the beta, 95% ci, and P as macros
*here's a loop that automates this a bit
foreach x in 1 2 3 {
local beta`x'=row`x'[1,1]
local low95`x'=row`x'[5,1]
local high95`x'=row`x'[6,1]
local pval`x'=row`x'[4,1]

quietly {
capture log close table // always good to close any open logs
log using "regressiontable.csv", replace text name(table)
noisily di ",Weight beta (95% CI),P-value"
*row 1
noisily di "All," %3.2f `beta1' " ("  %3.2f `low951' " to " %3.2f `high951' "),"  %4.3f `pval1'
*row 2
noisily di "Domestic," %3.2f `beta2' " ("  %3.2f `low952' " to " %3.2f `high952' "),"  %4.3f `pval2'
*row 3
noisily di "Foreign," %3.2f `beta3' " ("  %3.2f `low953' " to " %3.2f `high953' "),"  %4.3f `pval3'
log close table

Boom! you’ll get a CSV file that looks like this, which should be simple to import in Excel!

,Weight beta (95% CI),P-value
All,-0.01 (-0.01 to -0.00),0.000
Domestic,-0.01 (-0.01 to -0.01),0.000
Foreign,-0.01 (-0.02 to -0.01),0.000

You’ll notice that these numbers are small, so you may want to use %4.3f instead of %3.2f to get 3 digits past the decimal place for the beta and 95% CIs.

Make a Table 1 in Stata in no time with table1_mc

What’s in a Table 1?

Baseline demographic tables (colloquially known as ‘Table 1’ given their common location) are a core feature of nearly all epidemiologic manuscripts. The columns represent the exposure you are studying. The rows are characteristics of your population that are relevant to your research project. In placebo-controlled RCTs, the columns are drug and placebo. In observational studies, the column is your exposure of interest. Say you are curious about the relationship between smoking and development of breast cancer in a cohort. Here, the columns would be smoking and no smoking.

Wait, I’m looking at a Table 1 has more than just a column for each exposure!

There are certain variations that you’ll see in Table 1s:

  • A row for the entire population – This always seems overkill to me.
  • A row with P-values – These are of no value in RCTs in my opinion. They are only occasionally helpful in observational studies.

The ultimate design of the Table 1 will be dictated by the target journal. This creates challenges for authors, who may need to rework Table 1s in the submission (and resubmission) process.

Why have Table 1s historically been such a pain in the butt to make in Stata?

Well, Stata doesn’t natively pop out Table 1s. Formulating one either requires manually running –sum– commands over and over again or writing custom code to help automate this for you.

Enter table1_mc

The Stata program table1_mc was released by Mark Chatfield, a biostatistician at the University of Queensland. It’s a derivation of the original table1 program by Phil Clayton. It’s a work of wonder. It automates the generation of a Table 1 with a few simple codes. Need to reformat for a new target journal? Make minor changes and hit re-run and — ”POOF”’ — out pops an updated and compliant Table 1.

Step 1: Install the program


ssc install table1_mc

Step 2: Label your variables

Pluck out the variables you’ll include as the exposure and outcome. The table1_mc code will apply your bizarre, space-less variable name to the output unless you are using labels. Use real capitalization and formatting like you’d want to appear.

Step 2a: Labeling the variable itself

Let’s say you want to label your systolic blood pressure variable ‘sbp’ to be ‘Systolic blood pressure, mm Hg’. Type:

label variable sbp "Systolic blood pressure, mm Hg" 

Step 2b: Labeling the categories within variables

My suggestion is to generate a numerical ordinal variable and apply the labels to a number. The table1_mc program will put things in alphabetical or numerical order. Applying labels to numbers makes it easy to control the order. In this example, I have labels for income that I’ll make into a numerical ordinal variable first. In the raw dataset, the variables are defined using strings like “$20k-$34k”.

gen income1234=.
replace income1234=1 if income_4cat=="less than $20k"
replace income1234=2 if income_4cat=="$20k-$34k"
replace income1234=3 if income_4cat=="$35k-$74k"
replace income1234=4 if income_4cat=="$75k and above"
replace income1234=99 if income_4cat=="Refused"


1. Define the labels that you want to apply to income1234’s values of 1, 2, 3, 4, or 99, and

2. Apply the stupid labels. I always forget to apply the labels to the categorical values.

label define income_labels 1 "<$20K" 2 "$20k-$34k" 3 "$35k-$74k" 4 "$75k and above" 99 "Refused" // define the labels
label values income1234 income_labels // apply the labels!!

And, while you’re at it, don’t forget to apply a label to the overall ‘income1234’ variable that you made.

label variable income1234 "Annual household income"

Step 3: Make a table 1

The help document (type ‘help table1_mc’) is a must read. Please look at it.

First: Start with ‘table1_mc,’ then the exposure expressed as ‘by(EXPOSURE VARIABLE NAME)’. Then just list out the variables you want in each row one by one. Each variable should have an indicator for the specific data types:

  • Binary:
    • bin – binary with P-value from Pearson’s chi2
    • bine – binary with P-value from Fisher’s exact
  • Continuous:
    • contn – normally distributed, continuous variable, which will give mean and SD
    • contln – log-normally distributed, continuous variable, which will give geometric mean and GSD
    • conts – other continuous variable, which will give median and IQR.
  • Categorical:
    • cat – categorical with P-value from Pearson’s chi2
    • cate – categorical with P-value from Fisher’s exact

After the code telling Stata which format you are using, you tell it what output format you want it to report the variables. Stata defaults to a lot of decimals. If you don’t specify, mean age may be presented as ‘42.818742022’. What a mess.

You can probably do 99% of your formatting with two codes:

  • %4.0f – four leading digits, nothing after the decimal (e.g., 43)
  • %4.1f – four leading digits, one digits after the decimal (e.g., 42.8)

Next, separate each variable with a backslash (‘\’). I like to break each line using the three forward slashes after (‘///’) so that I don’t have one ungodly line of text.

FINALLY, tell it some key options at the end:

  • Ones I recommend including every time:
    • onecol – categorical variables will have a header that’s an extra leading row before they are presented, rather than a whole separate column.
    • missing – this keeps missing variables included. Helpful to show missingness of categorical variables.
    • nospace – this will drop dead spaces before single digit numbers. E.g., it’ll present ‘(3%)’ instead of ‘( 3%)’.
    • saving – output the Table 1 to Excel. Make sure that the Excel file output is not open in an Excel window when trying to overwrite a table. Otherwise, Stata will not run and you will be sad.
  • Simple things to help reformatting for journals:
    • [nothing] – presents n (%)
    • percent – presents a % alone without including the n
    • percent_n – % (n)
    • slashN – n/N instead of just n
    • total(before) – leading row with overall baseline demographics.

Some actual code to run table1_mc!

// install it!
ssc install table1_mc

// now specify things by "myexposure"
table1_mc, by(myexposure) ///
vars( ///
age contn %4.0f \ ///
sex0m1f bin %4.0f \ ///
race0w1b bin %4.0f \ ///
region123 cat %4.0f \ ///
educ1234 cat %4.0f \ ///
income1234 cat %4.0f \ ///
sbp contn %4.0f \ ///
dbp contn %4.0f \ ///
smoke7_ideal bin %4.0f \ ///
pa7_ideal bin %4.0f \ ///
diet7_ideal bin %4.0f \ ///
chol7_ideal bin %4.0f \ ///
fpg7_ideal bin %4.0f \ ///
bmi7_ideal bin %4.0f \ ///
bp7_ideal bin %4.0f \ ///
) ///
nospace percent onecol missing total(before) ///
saving("table 1.xlsx", replace)

…And here’s the (fake) result!

I’m working on an actual analysis right now so replaced all of the data from the actual output above with fake numbers. But you get the idea!

The example table!