How To Clean World Bank Data In Stata
Have you downloaded a delimited data file, or want to know how to reshape World Bank data and structure information technology into console format using xtset in Stata ? Want to brand high quality information visualizations in Stata using xtline ? Allow'southward do this.
With Data from Hither
This tutorial uses the Earth Bank Current Health Expenditure (% of Gdp) indicator download.
The data used in this tutorial are redistributed here in accordance with their Creative Commons Attribution 4.0 (CC-By 4.0) International license, which "allows users to copy, modify and distribute information in any format for any purpose, including commercial use. Users are only obligated to give appropriate credit (attribution) and bespeak if they take made any changes, including translations."
You tin either download your own CSV file from WorldBank, or you can download my HealthEx-From-WorldBank.dta, which I created every bit noted hither.
We Will Make This Graph in Stata
We will clean the csv data download, learn how to reshape World Bank data using xtset in Stata, and then plot some observations using xtline for panel datasets.
The HealthEx-From-WorldBank.dta file used in this tutorial was constructed by offset downloading the CSV file from the World Bank website.
Then, in Stata, I did the post-obit:
import delimited "API_SH.XPD.CHEX.GD.ZS_DS2_en_csv_v2_3054013.csv" //I deleted the top cases or rows that were empty. //I deleted variables with missing observations for this indicator. //I deleted unused variables. //I renamed variables. label information "Healthcare Expenditure per Gdp (World Banking company Estimate)" saveold "HealthEx-From-WorldBank.dta", replace Starting with my HealthEx-From-WorldBank.dta, let's run into how to reshape World Depository financial institution data for this indicator!
You tin follow along with me or download World-Banking concern-demo.do, which is what I'm walking through side by side, step by step. You tin download it and run it, since I host the data file used in that do file.
// Loading data articulate all utilise https://geterika.com/downloads/HealthEx-From-WorldBank, clear /*If using an older version of Stata, you might encounter a Java runtime fault [r(5100)]. If you do, merely <correct-click> + <save-equally> that information file. Download it to your arrangement and run from in that location every bit a workaround. */ *Step 1: Let's create a numeric "unique identifier" var for each countryCode egen id = grouping(countryCode), characterization tab id in 1/5 //btw it looks like you withal don't have a numeric id var, right? //Yous exercise tho! That'southward just a label you're seeing. tab id in 1/5, nolabel //see? Then we've got our unique numeric id variable set. *Step 2: format longitudinal //okay. Look at the data before long in Wide format... go to Data Editor, or list in one/xx /* "healthex" is the start office of the indicator or variable names in the series to exist reshaped, aka the "stem" "id" is the unique identifier nosotros generated from on countryCode "twelvemonth" is the proper noun of the new variable where the end parts of the original variable names will be stored */ reshape long healthex, i(id) j(year) //and allow's clean things upwardly real quick... characterization variable healthex "hateful annual health expenditure (% of GDP)" characterization values healthex healthex drop countryCode indicator //don't need these label variable year "year" label values twelvemonth year label variable country "country proper name" //and at present look at the information in LONG format... Data Editor, or: list in x/xxx *Step 3: Make information technology a console data set //Set up? Let's make information technology official and XTSET our data xtset id yr * Step 4: Washed! Or "turn a profit!" every bit nosotros said in my day. codebook, compact xtsum //we tin now do stuff with this panel data set //generating a few variables that will be used in graphs //some starting position options for labels gen pos1 = ane gen pos2 = 2 gen pos3 = iii gen pos4 = 4 gen pos5 = 5 gen pos10 = ten gen pos11 = xi gen pos12 = 12 generate healthexr = round(healthex,0.01) //creating a rounded version gen healthexrlab = string(healthexr, "%iv.2f") + "%" label data "Healthcare Expenditures Console Information Earth Bank - Clean Console Dataset" saveold "HealthEx-Clean-Console.dta", supervene upon And that'due south really it. You tin can run the above code and end up with a cleaned panelized dataset using Globe Depository financial institution data for the healthcare expenditure indicator. You tin also download directly from Earth Banking concern using wbopendata (SSC), but this tutorial is walking through steps for the learning experience. Note the comments in lawmaking that explain each pace to reshape and panelize the data. We now have our dataset in the correct format for united states of america to analyze and graphically draw equally panel data. And if you wanna skip this part and just try the data visualization part of the tutorial, you can download my HealthEx-Clean-Panel.dta file.
Starting with HealthEx-Make clean-Panel.dta, permit's graph some of this World Bank indicator information!
Okay! Let'southward start with a bones linear plot for panel information ( xtline ) before styling it, and call it Figure one.
// Loading data clear all utilise https://geterika.com/downloads/HealthEx-Clean-Console, clear /*BTW you tin and should install the World Bank open up data user written Stata module to admission World Banking company databases, Statistical Software Components S457234, by Joao Pedro Azevedo. We're doing things "the long way" panelizing here for tutorial purposes, because Give-and-take Banking concern data is accessible equally a starting point. */ ssc install wbopendata //aid wbopendata *Effigy 1 //I chose 2009-2018, and 4 countries, for illustration xtline healthex if inlist(id, xvi, 78, 116, 133) & year > 2008, overlay /// xtitle("") ytitle("Pct of GDP") /// championship("Basic Linear Plot - Earlier Styling") /// explanation("{it:Note.} Source is World Depository financial institution data. 2018 is the near recent available year." /// "World Bank indicators are available at: https://data.worldbank.org/indicator.") /// note("{&hearts} {stSerif:Erika Sanborne fabricated this graph entirely in} {stMono:Stata} {&bullet} {it:https://geterika.com}", /// color(maroon) size(*0.viii) span) /// name(figure1, replace) graph export figure1.svg, supersede 
I don't love looking at that equally-is, but that's what Stata volition give you before you even actually try. So now let'south endeavor and see how we can amend this information visualization of our World Banking company console data. This hither will set things up differently:
*for styling graphs, install grstyle /* Reference: Ben Jann, 2017. "GRSTYLE: Stata module to customize the overall wait of graphs, "Statistical Software Components S458414, Boston College Department of Economic science, revised 19 Sep 2020. */ net install grstyle, replace from("https://raw.githubusercontent.com/benjann/grstyle/chief/") //at that place are so many grstyle settings. Here are some I'thou using in this demo. grstyle articulate //resets any previous grstyle in the file set scheme s2color grstyle init //initializes grstyle to get ready to run grstyle set horizontal //sets y axis tick labels horizontal/readable yay! grstyle set ci //makes shading of CIs transparent grstyle set legend ten, inside //clock position grstyle set graphsize 10in 14in //h 10 west grstyle set symbolsize small grstyle set size 36pt: heading grstyle set size 24pt: subheading axis_title grstyle color background white //goodbye default teal background! grstyle color plotregion none //goodbye whatever default plotregion colors! grstyle linestyle plotregion none grstyle yesno draw_major_hgrid no grstyle yesno draw_major_ygrid no grstyle yesno draw_major_vgrid no grstyle linewidth plineplot thick grstyle anglestyle vertical_tick horizontal grstyle symbolsize p small grstyle gsize axis_title_gap minor //adds infinite between ticks and axis titles grstyle color major_grid blackness grstyle linewidth major_grid vthin Gear up for Effigy 2? Nosotros're literally making the "same graph" equally Figure A, except nosotros're running the code after setting up some grstyle settings. Cheque this out now.
*Effigy 2 xtline healthex if inlist(id, sixteen, 78, 116, 133) & yr > 2008, overlay /// xtitle("") ytitle("Percentage of GDP") /// title("Same Basic Linear Plot -" /// "With Some GRSTYLE Settings", linegap(2.0) margin(medlarge) size(*one.i) span) /// caption("{information technology:Note.} Source is World Bank data. 2018 is the well-nigh recent available twelvemonth." /// "World Depository financial institution indicators are available at: https://data.worldbank.org/indicator." /// , span size (*.9)) /// note("{&hearts} {stSerif:Erika Sanborne fabricated this graph entirely in} {stMono:Stata} {&bullet} {it:https://geterika.com}", /// color(maroon) size(*0.eight) span) /// name(figure2, replace) graph consign figure2.svg, replace 
It's definitely not great withal, simply do you see all the changes? The lines are thicker, the plotregion and background are all white now, the fable is moved inside, into the clock position we set, the numbers on the vertical axis are no longer sideways! The title has spacing gear up, the plotregion gridlines are gone, and if yous are running this on your own organization, you will see the graph produced is larger.
The prissy thing about grstyle, is that you can set it once, in the superlative of your do-file, and it will apply to all graphs in your file, until you run "grstyle articulate" or change ane or more than grstyle settings, so this can really save yous time in a multi-graph projection. Just get to know your own preferences, save them, and load them up whenever you lot start a new do-file.
Alright, let's do more than with this than grstyle. Next permit'southward accommodate the range of the x centrality, and add together text labels inside the graph then nosotros tin get rid of the legend which is taking up precious infinite, aye? Check out Figure three…
*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 just labels for country (scatter healthex twelvemonth if inlist(id, 16, 78, 116, 133) & year == 2018, /// msymbol(none) mlabv(pos3) mlabgap(two.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("Hither We've Stock-still the X Centrality Range and Added" /// "Labels to the Line Plots so We can Ditch the Legend", linegap(two.0) margin(medlarge) size(*one.ane) span) /// caption("{information technology:Note.} Source is World Banking concern data. 2018 is the most recent available yr as of late 2021." /// "Globe Bank indicators are available at: https://data.worldbank.org/indicator." /// , span size (*.9)) /// note("{&hearts} {stSerif:Erika Sanborne fabricated this graph entirely in} {stMono:Stata} {&bullet} {information technology:https://geterika.com}", /// color(maroon) size(*0.8) span) /// name(figure3, supersede) graph export figure3.svg, supervene upon 
Now I'm liking how this looks. That'due south a nice effect, correct? Allow's employ addplot once again, once again no markers only the marker characterization, and this time we'll have it show the rounded percentages plus the "%" sign, the cord variable we created earlier when panelizing. Check this out now in Figure 4.
*Figure 4 xtline healthex if inlist(id, 16, 78, 116, 133) & yr > 2008, overlay fable(off) /// plot1opts(lcolor(maroon)) /// plot2opts(lcolor(orange)) /// plot3opts(lp(nuance) lcolor(navy)) /// plot4opts(lcolor(green)) /// addplot ( /// (scatter healthex twelvemonth if inlist(id, xvi, 78, 116, 133) & year == 2018, /// msymbol(none) mlabv(pos3) mlabgap(two.5) mlabel(state) mlabcolor(black) mlabsize(medium)) /// (scatter healthexr year if inlist(id, xvi, 78, 116, 133) & year ==2018, /// msymbol(none) mlabel(healthexrlab) mlabsize(vsmall) mlabcolor(blackness) mlabv(pos12) mlabgap(i)) /// ) /// We've got two scatters in addplot now, this one adds the string var nosotros created xtitle("") ytitle("Percent of GDP") /// xlabel(2009(ane)2018, labsize(small)) /// xscale(range(2009 2020)) /// this is to make room for the addplot scatter mlabels on the right championship("(∩°‿°)⊃━☆ﾟ.*･｡ﾟ" /// lamentable, having a little ascii fun "This is Now a Nice-looking Line Plot", linegap(3.0) margin(medlarge) size(*1.1) span) /// explanation("{it:Note.} Source is World Banking company information. 2018 is the most recent available yr every bit of late 2021." /// "World Bank indicators are available at: https://data.worldbank.org/indicator." /// , bridge size (*.9)) /// annotation("{&hearts} {stSerif:Erika Sanborne made this graph entirely in} {stMono:Stata} {&bullet} {information technology:https://geterika.com}", /// color(maroon) size(*0.8) span) /// proper 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 affiche, yet fine for a website or manuscript graphic. Y'all might make a few versions of your information visualizations also, based on their intended use. Pay attention to font size and contrast. Making all backgrounds white enhances dissimilarity, which helps make sure your graphics are accessible. But you tin can ever cheque that too; at that place are many online color contrast checkers, and you should use them if you lot'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 assistance wbopendata) and make some top notch graphics! Leave me a annotate if this helped you. I beloved hearing from people who use my content.
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Source: https://geterika.com/2021/10/13/how-to-reshape-world-bank-data/
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