Have you downloaded a delimited data file, or want to know how to reshape World Bank data and structure it into panel format using xtset in Stata? Want to make high quality data visualizations in Stata using xtline? Let’s do this.

It’s definitely not great yet, but do you see all the changes? The lines are thicker, the plotregion and background are all white now, the legend is moved inside, into the clock position we set, the numbers on the vertical axis are no longer sideways! The title has spacing set, the plotregion gridlines are gone, and if you are running this on your own system, you will see the graph produced is larger.

The nice thing about grstyle, is that you can set it once, in the top of your do-file, and it will apply to all graphs in your file, until you run “grstyle clear” or change one or more grstyle settings, so this can really save you time in a multi-graph project. Just get to know your own preferences, save them, and load them up whenever you start a new do-file.

Alright, let’s do more with this than grstyle. Next let’s adjust the range of the x axis, and add text labels inside the graph so we can get rid of the legend which is taking up precious space, yeah? Check out Figure 3…

*Figure 3
xtline healthex if inlist(id, 16, 78, 116, 133) & year > 2008, overlay legend(off)  ///
addplot  /// we are adding a scatter plot with no symbols and only labels for country
(scatter healthex year if inlist(id, 16, 78, 116, 133) & year == 2018, ///
msymbol(none) mlabv(pos3) mlabgap(2.5) mlabel(country) mlabcolor(black) mlabsize(medium)) ///
xtitle("") ytitle("Percent of GDP") ///
xlabel(2009(1)2018, labsize(small)) ///
xscale(range(2009 2020)) /// this is to make room for the addplot scatter mlabels on the right
title("Here We've Fixed the X Axis Range and Added" ///
"Labels to the Line Plots so We can Ditch the Legend", linegap(2.0) margin(medlarge) size(*1.1) span) ///
caption("{it:Note.} Source is World Bank data. 2018 is the most recent available year as of late 2021." ///
"World Bank indicators are available at: https://data.worldbank.org/indicator." ///
, span size (*.9)) ///
note("{&hearts} {stSerif:Erika Sanborne made this graph entirely in} {stMono:Stata} {&bullet} {it:https://geterika.com}", ///
color(maroon) size(*0.8) span) ///
name(figure3, replace)
graph export figure3.svg, replace

Now I’m liking how this looks. That’s a nice effect, right? Let’s use addplot once more, again no markers only the marker label, and this time we’ll have it show the rounded percentages plus the “%” sign, the string variable we created earlier when panelizing. Check this out now in Figure 4.

*Figure 4
xtline healthex if inlist(id, 16, 78, 116, 133) & year > 2008, overlay legend(off) ///
plot1opts(lcolor(maroon)) ///
plot2opts(lcolor(orange)) ///
plot3opts(lp(dash) lcolor(navy)) ///
plot4opts(lcolor(green)) ///
addplot ( ///
(scatter healthex year if inlist(id, 16, 78, 116, 133) & year == 2018, ///
msymbol(none) mlabv(pos3) mlabgap(2.5) mlabel(country) mlabcolor(black) mlabsize(medium)) ///
(scatter healthexr year if inlist(id, 16, 78, 116, 133) & year ==2018, ///
msymbol(none) mlabel(healthexrlab) mlabsize(vsmall) mlabcolor(black) mlabv(pos12) mlabgap(1)) ///
) /// We've got two scatters in addplot now, this one adds the string var we created 
xtitle("") ytitle("Percent of GDP") ///
xlabel(2009(1)2018, labsize(small)) ///
xscale(range(2009 2020)) /// this is to make room for the addplot scatter mlabels on the right
title("(∩°‿°)⊃━☆ﾟ.*･｡ﾟ" /// sorry, having a little ascii fun
"This is Now a Nice-looking Line Plot", linegap(3.0) margin(medlarge) size(*1.1) span) ///
caption("{it:Note.} Source is World Bank data. 2018 is the most recent available year as of late 2021." ///
"World Bank indicators are available at: https://data.worldbank.org/indicator." ///
, span size (*.9)) ///
note("{&hearts} {stSerif:Erika Sanborne made this graph entirely in} {stMono:Stata} {&bullet} {it:https://geterika.com}", ///
color(maroon) size(*0.8) span) ///
name(figure4, replace)
graph export figure4.svg, replace

If your graphics are going on a poster presentation, make sure all of your fonts meet any specifications provided. Right now, those percentages are too small for a poster, yet fine for a website or manuscript graphic, right? You might make a few versions of your data visualizations too, based on their intended uses. Pay attention to font size and contrast for accessibility.

Making all backgrounds white enhances contrast, which helps make sure your graphics are accessible. And you can always check your color contrast ratios too; there are many online color contrast checkers, and you should use them if you’re not sure. Have fun graphing!

What do you think? I hope this tutorial working with World Bank data was a useful exercise. Go grab some other indicators (type help wbopendata) and make some top notch graphics!