Data visualization with Stata¶
-- Hua Peng @ StataCorp
- Creating graphs in Stata is easy.
- Stata supports a wide variety of plots.
- Stata graphic commands are highly customizable and extensible.
This presentation uses PyStata: see https://www.stata.com/python/pystata18/ for details.hub:
In [1]:
import stata_setup
stata_setup.config('C:/Program Files/Stata18', 'mp')
___ ____ ____ ____ ____ ®
/__ / ____/ / ____/ StataNow 18.5
___/ / /___/ / /___/ MP—Parallel Edition
Statistics and Data Science Copyright 1985-2023 StataCorp LLC
StataCorp
4905 Lakeway Drive
College Station, Texas 77845 USA
800-782-8272 https://www.stata.com
979-696-4600 [email protected]
Stata license: 10-user 4-core network perpetual
Serial number: 1
Licensed to: Stata Developer
StataCorp LLC
Notes:
1. Unicode is supported; see help unicode_advice.
2. More than 2 billion observations are allowed; see help obs_advice.
3. Maximum number of variables is set to 5,000 but can be increased;
see help set_maxvar.
graph command history:¶
In [2]:
%%stata -qui
clear
input float version str60 notes float(date z year) byte weight
8 "new graph command {superscript:1}" 15707 0 2003 1
9 "new plot types, png export, etc." 16548 0 2005 0
10 "graph editor" 17342 0 2007 1
11 "smcl tags in text" 18091 0 2009 0
12 "marginsplot, contour plot, export pdf" 18833 0 2011 0
13 "improvements of marginsplot" 19526 0 2013 0
14 "Unicode支持" 20180 0 2015 1
15 "color transparency, new marker symbols, and svg export" 20976 0 2017 1
16 "meta forestplot, size in points, inches, or centimeters" 21726 0 2019 0
17 "more meta and bayes plots" 22390 0 2021 0
18 "new schemes, new color styles, and colorvar()" 23125 0 2023 1
end
format %td date
label var version "Stata version"
label var z "Major features"
label var year "Release year"
twoway (scatter version z if weight==0, mlabel(notes) msymbol(none)) ///
(scatter version z if weight==1, mlabel(notes) mlabsize(*1.5) msymbol(none)) ///
(scatter year z, mcolor(none) yscale(noline axis(2)) ///
ylabel(2003(2)2023, axis(2) notick nogrid) yaxis(2)) ///
, yscale(noline) ylabel(8(1)18, notick nogrid) ///
xlabel(0 2, nogrid noticks nolabels) ///
xscale(noline noextend) ///
legend(off) xtitle("") ///
title("Major graphic features in each release") ///
note("1. old graph command can be invoked with {bf:graph7}") nodraw
graph save gr_history.gph, replace
In [3]:
%stata graph use gr_history.gph
graph command overview:¶
New default scheme¶
- The graphs are 7.5 inches wide and 4.5 inches tall.
- The color palette is updated with brighter colors. ...
- The background color is white.
- The legend contains one column and is placed on the right side of the graph.
- The y-axis labels are horizontal.
- The major grid line is dashed. The marker size is small.
New default scheme(cont.)¶
- Reference lines and lines added with the xline() and yline() options are black.
- The histogram fill color is stc1 with a 90% intensity, while the outline color is stc1 with a 70% intensity.
An example of different schemes¶
In [4]:
%%stata -qui
// new default scheme stcolor
sysuse sp500, clear
twoway scatter high low date || ///
line high low date, || ///
rarea high low date, color(gray%20)
In [5]:
%%stata -qui
// old default scheme s2color
sysuse sp500, clear
twoway scatter high low date || ///
line high low date, || ///
rarea high low date, color(gray%20) ///
scheme(s2color)
In [6]:
%%stata -qui
// new grey style scheme stmono2
sysuse sp500, clear
twoway scatter high low date || ///
line high low date, || ///
rarea high low date, color(gray%20) ///
scheme(stmono2)
In [7]:
%%stata -qui
// requires user-written schemepack
// https://github.com/asjadnaqvi/stata-schemepack
sysuse sp500, clear
twoway scatter high low date || ///
line high low date, || ///
rarea high low date, color(gray%20) ///
scheme(gg_tableau)
In [8]:
%%stata -qui
// new scheme stcolor_alt
sysuse sp500, clear
twoway scatter high low date || ///
line high low date, || ///
rarea high low date, color(gray%40) ///
scheme(stcolor_alt)
More examples with stcolor scheme¶
In [9]:
%%stata -qui
// twoway scatter
sysuse auto, clear
sc price mpg
In [10]:
%%stata -qui
// overlay twoway plots
sysuse auto, clear
twoway (scatter price mpg, msymbol(D) msize(2)) ///
(scatter weight mpg, msymbol(Oh) msize(medlarge))
In [11]:
%%stata -qui
// overlay variables with different scales
sysuse auto, clear
twoway (scatter price mpg, msymbol(D) msize(2) mcolor(%20) yaxis(1)) ///
(scatter weight mpg, msymbol(Oh) msize(medlarge) yaxis(2)) ///
, legend(pos(6) rows(1))
List graph styles and schemes¶
In [12]:
%%stata
graph query
Styles used in graph options are
addedlinestyle functiontypestyle orientationstyle
alignmentstyle
anglestyle gridstyle pstyle
areastyle
arrowstyle hrulestyle ringposstyle
arrowdirstyle
justificationstyle sizetypestyle
bystyle sunflowertypestyle
legendstyle symbolstyle
clegendstyle linestyle
clockposstyle linealignmentstyle textboxstyle
colorstyle linepatternstyle textsizestyle
compassdirstyle linewidthstyle tickstyle
connectstyle
marginstyle zyx2style
fpcolstyle markerstyle zyx2rulestyle
fpcolnamestyle markerlabelstyle zyx2typestyle
fpgraphstyle markersizestyle
functionstyle
To find out more about a style, type -graph query <stylename>-; for
instance, -graph query alignmentstyle-. (You may omit the "style" on the
end.)
In [13]:
%%stata
graph query color
colorstyle may be
538axis 538rs9 gs0 navy8 stblue
538b 538t gs1 none stc1
538background 538y gs10 olive stc10
538bs1 ananas gs11 olive_teal stc11
538bs10 black gs12 orange stc12
538bs11 blue gs13 orange_red stc13
538bs2 bluishgray gs14 orangebrown stc14
538bs3 bluishgray8 gs15 pink stc15
538bs4 brown gs16 plb1 stc2
538bs5 chocolate gs2 plb2 stc3
538bs6 cranberry gs3 plb3 stc4
538bs7 cyan gs4 plg1 stc5
538bs8 dimgray gs5 plg2 stc6
538bs9 dkgreen gs6 plg3 stc7
538g dknavy gs7 pll1 stc8
538label dkorange gs8 pll2 stc9
538m ebblue gs9 pll3 stgreen
538o ebg khaki plr1 stone
538p edkbg lavender plr2 stred
538r edkblue lime ply1 styellow
538rs1 eggshell ltblue ply2 sunflowerlime
538rs10 eltblue ltbluishgray ply3 teal
538rs11 eltgreen ltbluishgray8 purple turquoise
538rs2 emerald ltkhaki red vermillion
538rs3 emidblue magenta reddish white
538rs4 erose maroon sand yellow
538rs5 forest_green midblue sandb
538rs6 gold midgreen sea
538rs7 gray mint sienna
538rs8 green navy sky
For information on colorstyle and how to use it, see help colorstyle.
In [14]:
%%stata
palette color stc1 stc2
("stc1" = "26 133 255"%100, "stc2" = "212 17 89"%100)
More examples¶
In [15]:
%%stata -qui
// twoway line
sysuse sp500, clear
twoway line low date in 1/15, lpattern(dash) lwidth(vthick)
In [16]:
%%stata -qui
// twoway tsline (use https://www.stata-press.com/data/r18/tsline1)
use ./data/tsline1.dta, clear
tsset lags
twoway tsline ar ma
In [17]:
%%stata -qui
use ./data/tsline1.dta, clear
tsset lags
// same as twoway tsline ar ma, scheme(stcolor_alt)
tsline ar ma
In [18]:
%%stata -qui
graph query, schemes
In [19]:
%%stata -qui
// twoway histogram
sysuse auto, clear
twoway histogram rep78, discrete
In [20]:
%%stata -qui
// histogram
sysuse auto, clear
histogram rep78, discrete
In [21]:
%%stata -qui
// histogram with old default scheme
sysuse auto, clear
histogram rep78, discrete scheme(s2color)
In [22]:
%%stata -qui
// graph pie
sysuse auto, clear
graph pie price, over(rep78)
In [23]:
%%stata -qui
// graph bar, default stat is percent
sysuse auto, clear
graph bar, over(rep78) blabel(bar, format(%4.0f))
In [24]:
%%stata -qui
// graph bar, stat mean, max, and p75
sysuse auto, clear
graph bar (mean) price (max) weight (p75) length, ///
over(foreign) blabel(bar, format(%4.2f)) ///
legend(order(1 "Average price" 2 "Max weight" 3 "75th percentile of length"))
In [25]:
%%stata -qui
// histogram
sysuse sp500, clear
histogram volume, freq normal ///
xaxis(1 2) ylabel(0(10)60, grid) xlabel(,nogrid) xline(5000 25000) ///
xlabel(12321 "mean" 9735 "-1 s.d." 14907 "+1 s.d." 7149 "-2 s.d." ///
17493 "+2 s.d." 20078 "+3 s.d." 22664 "+4 s.d.",axis(2) grid gmax) ///
xtitle("", axis(2)) subtitle("S&P 500, January-December 2001") ///
note("Source:Yahoo!Finance and Commodity Systems, Inc.")
In [26]:
%%stata -qui
// twoway function, Greek letter and math symbols, see 'help graph text'
twoway function y=exp(-x/6)*sin(x), ///
range(0 12.57) yline(0, lstyle(foreground)) ///
xlabel(0 3.14 "{&pi}" 6.28 "2{&pi}" 9.42 "3{&pi}" 12.57 "4{&pi}") ///
xsca(noline) title("{&function}(x)=e{superscript:-x/6}sin(x)")
In [27]:
%%stata -qui
// subplots with by
sysuse auto, clear
twoway scatter turn mpg price, by(rep78, cols(3) total)
In [28]:
%%stata -qui
// subplots with by, change legend position and subtitle background
sysuse auto, clear
twoway scatter turn mpg price, by(rep78, cols(3) total legend(pos(6))) ///
legend(rows(1)) subtitle(, bcolor(white))
In [29]:
%%stata -qui
// subplots with by, old scheme
sysuse auto, clear
twoway scatter turn mpg price, by(rep78, cols(3) total) scheme(s2color)
In [30]:
%%stata -qui
use ./data/nhanes21.dta, clear
* fit a linear regression model using the continuous outcome
* variable bpsystol, the binary predictor variable diabetes,
* and the categorical predictor variable hlthstat.
regress bpsystol i.hlthstat##i.diabetes
* estimate marginal predictions of SBP for each
* combination of the categories of hlthstat and diabetes.
margins diabetes#hlthstat
In [31]:
%%stata -qui
// profile plot shows a separate line for each category of hlthstat
marginsplot, title("Adjusted predictions of diabetes#hlthstat" "with 95% CIs")
In [32]:
%%stata -qui
margins diabetes#hlthstat
marginsplot, title("Adjusted predictions of diabetes#hlthstat" "with 95% CIs") ///
scheme(s2color)
Change color, size, pattern, legend, and other graph styles¶
In [33]:
%%stata -qui
sysuse auto, clear
generate int wgt2 = (weight / 1000) ^ 2
twoway (scatter price mpg if foreign [aw = wgt2], mcolor(%80)) ///
(scatter price mpg if !foreign [aw = wgt2], mcolor(%20*1.2)) ///
(lfit price mpg, lcolor(yellow*1.5) lwidth(*2) lpattern(dash)) ///
, legend(order(3 2 "Foreign" 1 "Domestic")) ///
subtitle("{it}with linear prediction", size(2.75)) ///
title("{bf:Price vs. MPG weighted by vehicle weight}{superscript:2}")
In [34]:
%%stata -qui
// graph combine, note the misalignment of y axes
sysuse auto, clear
scatter weight price, name(g1, replace) nodraw
scatter turn price, name(g2, replace) nodraw
scatter mpg price, name(g3, replace) nodraw
graph combine g1 g2 g3
In [35]:
%%stata -qui
// use labelminlen to improve y-axis alignment
scatter mpg price, name(g3, replace) ylabel(, nogrid labelminlen(5)) nodraw
graph combine g1 g2 g3
In [36]:
%%stata -qui
// graph combine, alternate axes and axis label length
sysuse lifeexp, clear
generate loggnp = log10(gnppc)
label var loggnp "Log{subscript:10} of GNP per capita"
scatter lexp loggnp, ysca(alt) xsca(alt) ylabel(, nogrid) ///
xlabel(, grid gmax) name(yx, replace) nodraw
twoway histogram lexp, fraction xsca(alt reverse) horiz ///
fxsize(25) name(hy, replace) nodraw
twoway histogram loggnp, fraction ysca(alt reverse) ///
xlabel(,grid gmax) fysize(25) ///
ylabel(0(.1).2, nogrid labelminlen(2)) ///
name(hx, replace) nodraw
In [37]:
%%stata -qui
graph combine hy yx hx, hole(3) ///
imargin(0 0 0 0) graphregion(margin(l=22 r=22)) ///
title("Life expectancy at birth vs. GNP per capita") ///
note("Source: 1998 data from The World Bank Group")
Use levels of a variable to control color¶
When we create a twoway plot with numeric variables y and x, it is often useful to colorcode the plot based on values of another variable. This allows us to see how the relationship between y and x differs for each level of the third variable.
In [38]:
%%stata -qui
// overlay many graphs
sysuse auto, clear
label define repair 1 "Excellent" 2 "Good" 3 "Average" 4 "Fair" 5 "Poor"
label values rep78 repair
twoway (scatter price mpg if rep78 == 1) (scatter price mpg if rep78 == 2) ///
(scatter price mpg if rep78 == 3) (scatter price mpg if rep78 == 4) ///
(scatter price mpg if rep78 == 5) (scatter price mpg if missing(rep78)) ///
, title("{bf:Price vs. MPG}", size(medlarge)) ///
subtitle("{it}with colors based on rep78", size(2.75))
In [39]:
%%stata -qui
twoway scatter price mpg, colorvar(rep78) ///
colordiscrete coloruseplegend ///
colorrule(phue) colorformissing(stc6) zlabel(, valuelabel) ///
title("{bf:Price vs. MPG}", size(medlarge)) ///
subtitle("{it}with linear prediction", size(2.75))
colorvar() and related options¶
- colorvar(colorvar): specify variable to control color
- colordiscrete: treat colorvar as discrete instead of continuous
- colorcuts(numlist): specify list of cuts for colorvar
- colorlevels(#): specify number of levels for colorvar
- colorvarminmax: include min and max of colorvar in the list of cuts created by colorcuts() or colorlevels()
- colorrule([crule] [%# [*#]]): specify rule for creating colors for levels of colorvar
- colorstart(colorstyle): specify starting color for colorrule()
- colorend(colorstyle): specify ending color for colorrule()
- colorlist(colorstylelist): specify list of colors for each level of colorvar
colorvar() and related options (cont.)¶
- colorfillonly: specify that the color for items based on colorvar not affect the outline
- colorformissing(colorstyle): specify the color for the level of missing values
- colorkeysrange: draw keys in legend as ranged bars
- coloruseplegend: use the contour-line plot legend instead of the contour plot legend
In [40]:
%%stata -qui
sysuse auto, clear
// colorvar as continuous variable
// the color of the markers is determined by the value of weight
// the weight variable is partitioned into four levels
// <=2000, (2000, 3000], (3000, 4000], and (4000, 5000]
// the markers are colored based on the level to which they belong
twoway scatter price mpg, colorvar(weight)
In [41]:
%%stata -qui
sysuse auto, clear
// 10 levels (9 cuts)
twoway scatter price mpg, colorvar(weight) colorlevels(10)
In [42]:
%%stata -qui
sysuse auto, clear
// specify cuts
twoway scatter price mpg, colorvar(weight) colorcuts(1000(1000)5000)
In [43]:
%%stata -qui
sysuse auto, clear
// colorvar as discrete variable
generate weight2 = int(weight / 1000) * 1000
// the levels are at the values equal to 1000, 2000, 3000, or 4000
twoway scatter mpg price, colorvar(weight2) colordiscrete
In [44]:
%%stata -qui
sysuse auto, clear
// plegend
generate byte weight_category = int(weight / 1000)
twoway scatter mpg price, colorvar(weight_category) ///
colordiscrete coloruseplegend
In [45]:
%%stata -qui
sysuse auto, clear
// use plegend instead of clegend
generate byte weight_category = int(weight / 1000)
twoway scatter mpg price, colorvar(weight_category) ///
colordiscrete coloruseplegend ///
plegend(label(1 "heavy") label(2 "middle") ///
label(3 "light") label(4 "feather") order(4 3 2 1))
In [46]:
%%stata -qui
// requires user-written kdens2 -capture ssc install kdens2-
clear
set obs 1000
set seed 12345
generate x = rnormal()
generate y = x* 3 + rnormal()
kdens2 y x, saving(dens) replace
In [47]:
%%stata -qui
use dens.dta, clear
sum d
local min = `r(min)'
local max = `r(max)'
twoway scatter _y _x if d > 1e - 3, colorvar(d) colorlevels(100) ///
zlabel(`min' `max', noticks format(%4.2f)) zscale(noline) ///
title("Bivariate density plot" "kernel=Gaussian")
In [48]:
%%stata -qui
// use colorrule(), colorstart(), and colorend() to control colors
twoway scatter _y _x, colorvar(d) colorlevels(100) ///
colorrule(linear) colorstart(white) colorend(red) ///
zlabel(`min' `max', noticks format(%4.2f)) zscale(noline) ///
title("Bivariate density plot" "kernel=Gaussian")
Use colorvar() to visualize Second National Health and Nutrition Examination Survey data¶
In [49]:
%%stata
// https://www.stata.com/stata-news/news38-4/color-coded-twoway-graphs/
// Second National Health and Nutrition Examination Survey
// (NHANES II) (McDowell et al. 1981)
use ./data/nhanes2, clear
describe height weight bmi
. // https://www.stata.com/stata-news/news38-4/color-coded-twoway-graphs/
. // Second National Health and Nutrition Examination Survey
. // (NHANES II) (McDowell et al. 1981)
. use ./data/nhanes2, clear
. describe height weight bmi
Variable Storage Display Value
name type format label Variable label
-------------------------------------------------------------------------------
height float %9.0g Height (cm)
weight float %9.0g Weight (kg)
bmi float %9.0g Body mass index (BMI)
.
In [50]:
%%stata -qui
// a simple scatterplot of height and weight
use ./data/nhanes2, clear
scatter height weight
In [51]:
%%stata -qui
use ./data/nhanes2, clear
//color-code the points based on the body mass index (bmi)
scatter height weight, colorvar(bmi)
In [52]:
%%stata -qui
use ./data/nhanes2, clear
generate bmicat = irecode(bmi, 18.4, 24.9, 29.9, 34.9, 39.9) + 1
label define bmicategory 1 "Underweight (<18.5)" 2 "Normal (18.5-24.9)" ///
3 "Overweight (25.0-29.9)" 4 "Obese I (30.0 - 34.9)" ///
5 "Obese II (35.0 - 39.9)" 6 "Obese III (>=40.0)"
label values bmicat bmicategory
scatter height weight, colorvar(bmicat) ///
colordiscrete coloruseplegend zlabel(, valuelabel)
In [53]:
%%stata -qui
// reverse the order of plegend keys
scatter height weight, colorvar(bmicat) ///
colordiscrete coloruseplegend ///
plegend(order(6 5 4 3 2 1)) zlabel(, valuelabel)
In [54]:
%%stata -qui
// change color for each level
// and color level 4, 5, and 6 with the same color
scatter height weight, colorvar(bmicat) ///
colordiscrete coloruseplegend ///
colorlist(stc1*0.5 stc1 stc2 stc2*1.5) ///
plegend(order(6 5 4 3 "Obese (>=30.0)")) zlabel(, valuelabel)
For the last example, we revisit Chuck Huber's Stata News article Visualizing continuous-by-continuous interactions with margins and twoway contour.
In this article, Chuck fit a logistic regression model for high blood pressure, highbp, with continuous covariates age and weight and their interaction. Then he used margins to estimate the predicted probability of hypertension for combinations of age and weight, with values of age ranging from 20 to 80 years in increments of 5 and values of weight ranging from 40 to 180 kilograms in increments of 5. Then he used twoway contour to plot the resulting predictions:
In [55]:
%%stata -qui
use ./data/nhanes2, clear
svy: logistic highbp age weight c.age#c.weight
quietly margins, at(age=(20(5)80) weight=(40(5)180)) ///
vce(unconditional) saving(predictions, replace)
use predictions, clear
twoway contour _margin _at2 _at1, xtitle("age") ytitle("weight") ztitle("Prob.")
Now with the colorvar() option, we have another way to visualize these predictions. Below, we plot the predicted probabilities for each value of weight, using different colors to represent the values of age.
In [56]:
%%stata -qui
scatter _margin _at2, colorvar(_at1) ///
colorlist(blue*0.5 blue orange red) ///
title("Probability of hypertension by weight and age")
In [57]:
%%stata -qui
// ages contain 13 different values, why not plot all of them
scatter _margin _at2, colorvar(_at1) ///
colordiscrete zlabel(#13) ///
title("Probability of hypertension by weight and age")
In [58]:
%%stata -qui
// connected
twoway connected _margin _at2, colorvar(_at1) ///
colordiscrete zlabel(#13) ///
title("Probability of hypertension by weight and age")
Community-contributed commands¶
catplot¶
In [59]:
%%stata -qui
** ssc install catplot
sysuse auto, clear
catplot rep78 foreign, percent(foreign) bar(1, bcolor(%40)) ///
blabel(bar, position(outside) format(%3.1f)) ylabel(none) yscale(r(0,60))
coefplot¶
In [60]:
%%stata -qui
** https://repec.sowi.unibe.ch/stata/coefplot ssc install coefplot
sysuse auto, clear
regress price mpg trunk if !foreign
estimates store domestic
regress price mpg trunk if foreign
estimates store foreign
In [61]:
%%stata -qui
coefplot domestic foreign, drop(_cons) xline(0)
grmap¶
In [62]:
%%stata -qui
// copy grmap sample data
capture grmap_copy
use italy-outlinedata.dta, clear
grmap, title("Provincial capitals" " ", size(*0.9)) ///
point(data("italy-capitals.dta") xcoord(xcoord) ycoord(ycoord))
geoplot : https://github.com/benjann/geoplot¶
In [63]:
%%stata -qui
** Stata version 16.1 or newer
** ssc install: geoplot palettes colrspace moremata
clear all
// get data
local url http://fmwww.bc.edu/repec/bocode/i/
geoframe create regions `url'Italy-RegionsData.dta, id(id) coord(xcoord ycoord) ///
shp(Italy-RegionsCoordinates.dta)
geoframe create country `url'Italy-OutlineCoordinates.dta
geoframe create capitals `url'Italy-Capitals.dta, coord(xcoord ycoord)
geoframe create lakes `url'Italy-Lakes.dta, feature(water)
geoframe create rivers `url'Italy-Rivers.dta, feature(water)
In [64]:
%%stata -qui
geoplot (area regions) ///
(point capitals i.size [w=pop98], color(Set1%50) mlcolor(%0)) ///
(label capitals city if pop98>250000, color(black)) ///
, legend compass sbar(length(300) units(km))
In [65]:
%%stata -qui
// https://github.com/asjadnaqvi/stata-sankey ssc install sankey, palettes, and colrspace
// https://github.com/asjadnaqvi/stata-sankey/blob/main/data/sankey_example2.xlsx?raw=true
import excel using ./data/example2.xlsx, clear first
sankey value, from(source) to(destination) by(layer)
