稿件：econometrics666@126.com所有计量经济圈方法论丛的code程序, 宏微观数据库和各种软件都放在社群里.欢迎到计量经济圈社群交流访问.
作为一名经济学家/数据分析师的巴铁好友Fahad Mirza，在世界银行和巴基斯坦经济研究中心 (CERP) 担任顾问期间，长期沉溺于Stata软件可视化操作。这不，他最近就专门整理了一份“排名前 25 位的 Stata数据图——包含完整代码”。下面，就分享一下各种图形的绘制代码和呈现结果。
国外教授整理了高效使用Stata的48个小技巧, 制作动图的代码爱了！
*代码可以被直接复制到Stata中运行，当然社群群友可以直接下载这边整理好的完整版Stata code。正文
ssc install schemepack, replace
ssc install colrspace, replace
ssc install palettes, replace
ssc install labutil, replace
* Correlation Coef with CI (Nick Cox - corrci)
net describe pr0041_4, from(http://www.stata-journal.com/software/sj21-3)
net install pr0041_4
ssc install violinplot, replace
ssc install dstat, replace
ssc install moremata, replace
twoway (scatter price mpg, mcolor(%60) mlwidth(0)) (lowess price mpg), /// title("{bf}Scatterplot", pos(11) size(2.75)) /// subtitle("Price Vs. MPG", pos(11) size(2.5)) ///**correlation plot by group levelsof foreign, local(foreign) foreach category of local foreign { local scatter `scatter' scatter price mpg if foreign == `category', /// mcolor(%60) mlwidth(0) || twoway `scatter' (lowess price mpg), /// title("{bf}Scatterplot", pos(11) size(2.75)) /// subtitle("Price Vs. MPG", pos(11) size(2.5)) /// legend(order(1 "Domestic" 2 "Foreign") size(2)) /// import delimited "https://raw.githubusercontent.com/tidyverse/ggplot2/master/data-raw/mpg.csv", cleartwoway (scatter hwy cty, mcolor(%60) mlwidth(0)) (lfit hwy cty), /// title("{bf}Scatterplot with overlapping points", pos(11) size(2.75)) /// subtitle("mpg: City vs Highway mileage", pos(11) size(2.5)) ///import delimited "https://raw.githubusercontent.com/tidyverse/ggplot2/master/data-raw/mpg.csv", clear twoway (scatter hwy cty, jitter(5) mcolor(%60) mlwidth(0)) (lfit hwy cty), /// title("{bf}Jittered points", pos(11) size(2.75)) /// subtitle("mpg: City vs Highway mileage", pos(11) size(2.5)) /// import delimited "https://raw.githubusercontent.com/tidyverse/ggplot2/master/data-raw/mpg.csv", clear egen total = group(cty hwy) bysort total: egen count = count(total) twoway (scatter hwy cty [aw = count], mcolor(%60) mlwidth(0) msize(1)) (lfit hwy cty), /// title("{bf}Counts plot", pos(11) size(2.75)) /// subtitle("mpg: City vs Highway mileage", pos(11) size(2.5)) /// import delimited "https://raw.githubusercontent.com/tidyverse/ggplot2/master/data-raw/mpg.csv", clear keep if inlist(manufacturer, "audi", "ford", "honda", "hyundai") recode hwy (15 16 17 18 19 = 1) (20 21 22 23 24 = 4) (25 26 27 28 29 = 8) /// (30 31 32 33 34 = 16) (35 36 = 32), gen(weight) levelsof manufacturer, local(options) local wordcount : word count `options' foreach option of local options { colorpalette tableau, n(`wordcount') nograph local scatter `scatter' scatter cty displ [fw = weight] if manufacturer == "`option'", /// mcolor("`r(p`i')'%60") mlwidth(0) jitter(10) || local line `line' lfit cty displ if manufacturer == "`option'", lcolor("`r(p`i')'") || twoway `scatter' `line', /// title("{bf}Bubble Chart", pos(11) size(2.75)) /// subtitle("mpg: Displacement vs City mileage", pos(11) size(2.5)) /// ytitle("City Mileage", size(2)) /// legend(order(3 "Honda" 4 "Hyundai" 1 "Audi" 2 "Ford" ) size(2)) /// import delimited "https://raw.githubusercontent.com/tidyverse/ggplot2/master/data-raw/mpg.csv", clear egen total = group(cty hwy) bysort total: egen count = count(total) * Using loop to write and store the plotting commands and syntax by class twoway (scatter hwy cty [aw = count], mcolor(%60) mlwidth(0) msize(1) legend(off)) /// (lfit hwy cty), legend(off) name(main, replace) ytitle("Highway MPG") xtitle("City MPG") /// graphregion(margin(t=-5)) twoway (histogram cty, yscale(off) xscale(off) ylabel(, nogrid) xlabel(, nogrid) bin(30)), name(cty_hist, replace) graphregion(margin(l=16)) fysize(15) twoway (histogram hwy, horizontal yscale(off) xscale(off) ylabel(, nogrid) xlabel(, nogrid) bin(30)), name(hwy_hist, replace) graphregion(margin(b=15 t=-5)) fxsize(20) graph combine cty_hist main hwy_hist, hole(2) commonscheme scheme(white_tableau) /// title("{bf}Marginal Histogram - Scatter Count plot", size(2.75) pos(11)) subtitle("mpg: Highway vs. City Mileage", size(2.5) pos(11)) import delimited "https://raw.githubusercontent.com/tidyverse/ggplot2/master/data-raw/mpg.csv", clear egen total = group(cty hwy) bysort total: egen count = count(total) * Using loop to write and store the plotting commands and syntax by class twoway (scatter hwy cty [aw = count], mcolor(%60) mlwidth(0) msize(1) legend(off)) /// (lfit hwy cty), legend(off) name(main, replace) ytitle("Highway MPG") xtitle("City MPG") /// graphregion(margin(t=-5)) foreach var of varlist hwy cty { quietly summarize `var', detail local mean_`var' = `r(mean)' local med_p_`var' = `r(p50)' local p75_`var' = `r(p75)' local p25_`var' = `r(p25)' local iqr_`var' = `p75_`var'' - `p25_`var'' generate `var'uq = `var' if `var' <= `=`p75_`var''+(1.5*`iqr_`var'')' generate `var'lq = `var' if `var' >= `=`p25_`var''-(1.5*`iqr_`var'')' quietly summarize `var'uq local max_`var'uq = `r(max)' local min_`var'lq = `r(min)' colorpalette tableau, nograph (scatteri `p75_`var'' 1 `max_`var'uq' 1, recast(line) lpattern(solid) lcolor("`r(p`j')'") lwidth(1)) || /// (scatteri `p25_`var'' 1 `min_`var'lq' 1, recast(line) lpattern(solid) lcolor("`r(p`j')'") lwidth(1)) || /// (scatteri `med_p_`var'' 1, ms(square) mcolor(background) msize(2)) || /// (scatteri `med_p_`var'' 1, ms(square) mcolor("`r(p`j')'")) || colorpalette tableau, nograph (scatteri 1 `p75_`var'' 1 `max_`var'uq', recast(line) lpattern(solid) lcolor("`r(p`j')'") lwidth(1)) || /// (scatteri 1 `p25_`var'' 1 `min_`var'lq', recast(line) lpattern(solid) lcolor("`r(p`j')'") lwidth(1)) || /// (scatteri 1 `med_p_`var'', ms(square) mcolor(background) msize(2)) || /// (scatteri 1 `med_p_`var'', ms(square) mcolor("`r(p`j')'")) || twoway `lines1', legend(off) xlabel(, nogrid) ylabel(, nogrid) yscale(off) xscale(off) name(hwy_box, replace) graphregion(margin(b=15 t=-5)) fxsize(5) twoway `lines2', legend(off) xlabel(, nogrid) ylabel(, nogrid) yscale(off) xscale(off) name(cty_box, replace) graphregion(margin(l=16)) fysize(5) graph combine cty_box main hwy_box, hole(2) commonscheme ycommon xcommon scheme(white_tableau) /// title("{bf}Marginal Box Plot - Scatter Count plot", size(2.75) pos(11)) subtitle("mpg: Highway vs. City Mileage", size(2.5) pos(11)) * Only change names of variable in local var_corr. * The code will hopefully do the rest of the work without any hitch local var_corr price mpg trunk weight length turn foreign local countn : word count `var_corr' * Use correlation command quietly correlate `var_corr' local rnames : rownames C * Now to generate a dataset from the Correlation Matrix * For no diagonal and total count local tot_rows : display `countn' * `countn' foreach name of local var_corr { forvalues i = `rowname'/`countn' { local a : word `i' of `var_corr' replace corrname1 = "`name'" in `row' replace corrname2 = "`a'" in `row' replace corr = round(C[`i',`y'], .01) in `row' replace abs_corr = abs(C[`i',`y']) in `row' local rowname = `rowname' + 1 drop if missing(corrname1) replace abs_corr = 0.1 if abs_corr < 0.1 & abs_corr > 0.04 colorpalette HCL pinkgreen, n(10) nograph intensity(0.65) *colorpalette CET CBD1, n(10) nograph //Color Blind Friendly option forvalues colrange = -1(0.2)0.8 { replace colorname = "`r(p`col')'" if corr >= `colrange' & corr < `=`colrange' + 0.2' replace colorname = "`r(p10)'" if corr == 1 * Saving the plotting code in a local local slist "`slist' (scatteri `=y[`i']' `=x[`i']' "`: display %3.2f corr[`i']'", mlabposition(0) msize(`=abs_corr[`i']*15') mcolor("`=colorname[`i']'"))" labmask y, val(corrname1) labmask x, val(corrname2) local ylab "`ylab' `l' `" "`:lab (y) `l''" "'" local xlab "`xlab' `l' `" "`:lab (x) `l''" "'" * Plot all the above saved lolcas twoway `slist', title("Correlogram of Auto Dataset Cars", size(3) pos(11)) /// note("Dataset Used: Sysuse Auto", size(2) margin(t=5)) /// xlabel(`xlab', labsize(2.5)) ylabel(`ylab', labsize(2.5)) /// xscale(range(1.75 )) yscale(range(0.75 )) /// ytitle("") xtitle("") /// egen double mpg_z = std(mpg) * Generating indicator of below and above generate above = (mpg_z >= 0) * Sorting the mpg_z and assigning rank generate rank_des = _n * 2 labmask rank_des, value(make) colorpalette tableau, nograph intensity(0.8) twoway (bar mpg_z rank_des if above == 1, horizontal lwidth(0) barwidth(1.5) bcolor("`r(p3)'")) /// (bar mpg_z rank_des if above == 0, horizontal lwidth(0) barwidth(1.5) bcolor("`r(p4)'")), /// ytitle("") xtitle("") /// ylabel(2(2)148, valuelabel labsize(1.25) nogrid) xlabel(-4(1)4, nogrid) /// title("{bf}Diverging Bars (Normalized MPG)", size(2.75) pos(11)) ///**Diverging Lollipop Graph egen double mpg_z = std(mpg) * Sorting the mpg_z and assigning rank labmask rank_des, value(make) tostring mpg_z, gen(mpg_z_lab) force format(%3.2f) twoway (rspike zero mpg_z rank_des, horizontal) /// (scatter rank_des mpg_z, msize(5.3) mlabel(mpg_z_lab) mlabsize(1.5) mlabposition(0)), /// xlabel(-2.5(1)-0.5 0 0.5(1)2.5, labsize(2)) ylabel(1(1)20, valuelabel labsize(2)) /// title("{bf}Diverging Lollipop Chart (Normalized MPG)", size(2.75) pos(11)) /// * Keeping first 20 observations as example egen double mpg_z = std(mpg) * Sorting the mpg_z and assigning rank * Assigning label onto the sorted serial number labmask rank_des, value(make) * Generating indicator of below and above generate above = (mpg_z >= 0) tostring mpg_z, gen(mpg_z_lab) force format(%3.2f) colorpalette tableau, nograph intensity(0.8) twoway (scatter rank_des mpg_z if above == 0, mcolor("`r(p4)'") msize(5) mlabel(mpg_z_lab) mlabsize(1.3) mlabposition(0)) /// (scatter rank_des mpg_z if above == 1, mcolor("`r(p3)'") msize(5) mlabel(mpg_z_lab) mlabsize(1.3) mlabposition(0)) /// xlabel(-2.5(1)-0.5 0 0.5(1)2.5, labsize(2)) ylabel(1(1)20, valuelabel labsize(2)) /// title("{bf}Diverging Dot Plot (Normalized MPG)", size(2.75) pos(11)) ///***Diverging Bars — Correlation Plot * Only change names of variable in local var_corr. * The code will hopefully do the rest of the work without any hitch local var_corr price mpg trunk weight length turn foreign local countn : word count `var_corr' * Use correlation command * https://journals.sagepub.com/doi/pdf/10.1177/1536867X0800800307 * SE = (upper limit – lower limit) / 3.92 quietly corrci `var_corr' local rnames : rownames C * Now to generate a dataset from the Correlation Matrix * This will not have the diagonal of matrix (correlation of 1) local tot_rows : display `countn' * `countn' generate double abs_corr = . foreach name of local var_corr { forvalues i = `rowname'/`countn' { local a : word `i' of `var_corr' replace corrname1 = "`name'" in `row' replace corrname2 = "`a'" in `row' replace corr = C[`i',`y'] in `row' replace abs_corr = abs(C[`i',`y']) in `row' local rowname = `rowname' + 1 drop if missing(corrname1)*Gnerating a variable that will contain color codes * colorpalette HCL pinkgreen, n(20) nograph intensity(0.75) //Not Color Blind Friendly colorpalette CET CBD1, n(20) nograph //Color Blind Friendly option forvalues colrange = -1(0.1)0.9 { replace colorname = "`r(p`col')'" if corr >= `colrange' & corr < `=`colrange' + 0.1' replace colorname = "`r(p20)'" if corr == 1 * Grouped correlation of variables generate group_corr = corrname1 + " - " + corrname2 labmask rank_corr, values(group_corr) * Run the commands ahead in one go if you have reached this point in breaks * Saving the plotting code in a local local barlist "`barlist' (scatteri `=rank_corr[`i']' 0 `=rank_corr[`i']' `=corr[`i']' , recast(line) lcolor("`=colorname[`i']'") lwidth(*6))" * Saving labels for Y-Axis in a local levelsof rank_corr, local(yl) local ylab "`ylab' `l' `" "`:lab (rank_corr) `l''" "'" legend(off) scheme(white_tableau) ylabel(`ylab', labsize(2.5)) /// ytitle("Pairs") xtitle("Correlation Coeff.") /// title("{bf}Correlation Coefficient (Diverging Bar Plot)", size(2.75) pos(11))**Diverging Bars — Correlation Plot with Confidence Intervals * Only change names of variable in local var_corr. * The code will hopefully do the rest of the work without any hitch local var_corr price mpg trunk weight length turn foreign local countn : word count `var_corr' * Use correlation command * https://journals.sagepub.com/doi/pdf/10.1177/1536867X0800800307 * SE = (upper limit – lower limit) / 3.92 quietly corrci `var_corr' local rnames : rownames C matrix Z = r(z) //matrix of z = atanh r egen miss = rowmiss(`var_corr') * Now to generate a dataset from the Correlation Matrix * This will not have the diagonal of matrix (correlation of 1) local tot_rows : display `countn' * `countn' generate double abs_corr = . foreach name of local var_corr { forvalues i = `rowname'/`countn' { local a : word `i' of `var_corr' replace corrname1 = "`name'" in `row' replace corrname2 = "`a'" in `row' replace corr = C[`i',`y'] in `row' replace lb = LB[`i',`y'] in `row' replace ub = UB[`i',`y'] in `row' replace z = Z[`i',`y'] in `row' replace abs_corr = abs(C[`i',`y']) in `row' local rowname = `rowname' + 1 drop if missing(corrname1) * Generating total non missing count and P-Values generate double p = min(2 * ttail(N - 2, abs_corr * sqrt(N - 2) / sqrt(1 - abs_corr^2)), 1) generate stars = "*" if p <= 0.1 & p > 0.05 replace stars = "**" if p <= 0.05 & p > 0.01 replace stars = "***" if p <= 0.01 * Generating a variable that will contain color codes * colorpalette HCL pinkgreen, n(20) nograph intensity(0.75) //Not Color Blind Friendly colorpalette CET CBD1, n(20) nograph //Color Blind Friendly option forvalues colrange = -1(0.1)0.9 { replace colorname = "`r(p`col')'" if corr >= `colrange' & corr < `=`colrange' + 0.1' replace colorname = "`r(p20)'" if corr == 1 * Grouped correlation of variables generate group_corr = corrname1 + " - " + corrname2 labmask rank_corr, values(group_corr) * Run the commands ahead in one go if you have reached this point in breaks * Saving the plotting code in a local local barlist "`barlist' (scatteri `=rank_corr[`i']' 0 `=rank_corr[`i']' `=corr[`i']' , recast(line) lcolor("`=colorname[`i']'") lwidth(*6))" * Saving labels for Y-Axis in a local levelsof rank_corr, local(yl) local ylab "`ylab' `l' `" "`:lab (rank_corr) `l''" "'" (rspike lb ub rank_corr, horizontal lcolor(white) lwidth(*2)) /// (rspike lb ub rank_corr, horizontal lcolor(black*.5)), /// legend(off) scheme(white_tableau) ylabel(`ylab', labsize(2.5)) /// ytitle("Pairs") xtitle("Correlation Coeff.") /// title("{bf}Correlation Coefficient with Confidence Interval (Diverging Bar Plot)", size(2.75) pos(11)) import delimited "https://github.com/tidyverse/ggplot2/raw/main/data-raw/economics.csv", clear generate yoy = (psavert[_n] - psavert[_n-1]) / psavert[_n-1] generate monthyear = ym(year(date(date, "YMD")), month(date(date, "YMD"))) twoway (area yoy monthyear if monthyear <= tm(1975m12), lwidth(0)), /// xla(84(12)185, format(%tmCY)) /// plotregion(lstyle(solid) lwidth(.1)) /// ytitle("% Returns for Personal savings", size(2.75)) /// xscale(noline) yscale(noline) /// title("{bf}Area Chart", pos(11) size(3)) /// subtitle("% Returns for Personal Savings", pos(11) size(2.5)) /// import delimited "https://raw.githubusercontent.com/tidyverse/ggplot2/master/data-raw/mpg.csv", clear * Acquiring average mileage (city) by manufacturer collapse (mean) cty, by(manufacturer) graph bar (asis) cty, over(manufacturer, sort(1) label(labsize(1.75))) scheme(white_w3d) /// title("{bf}Ordered Bar Chart", pos(11) size(2.75)) /// ytitle("City" "Mileage", orient(horizontal) size(2)) /// subtitle("Make Vs. Avg. Mileage", pos(11) size(2.5)) **Lollipop Charts (Vertical) import delimited "https://raw.githubusercontent.com/tidyverse/ggplot2/master/data-raw/mpg.csv", clear * Acquiring average mileage (city) by manufacturer collapse (mean) cty, by(manufacturer) labmask order, values(manufacturer) twoway dropline cty order, /// ytitle("City" "Mileage", orient(horizontal)) /// xlabel(`r(min)'(1)`r(max)', valuelabel labsize(1.75)) /// title("{bf}Lollipop Chart", pos(11) size(2.75)) /// subtitle("Make Vs. Avg. Mileage", pos(11) size(2.5)) /// import delimited "https://raw.githubusercontent.com/tidyverse/ggplot2/master/data-raw/mpg.csv", clear * Acquiring average mileage (city) by manufacturer collapse (mean) cty, by(manufacturer) labmask order, values(manufacturer) twoway dot cty order, horizontal /// yscale(range(`r(min)' `r(max)')) /// ylabel(`r(min)'(1)`r(max)', valuelabel labsize(1.75)) /// ytitle("Make", orient(horizontal) size(2)) /// xscale(range(`xmin')) /// xlabel(10(5)25, nogrid) /// xtitle("Mileage", size(2)) /// title("{bf}Dot Plot", pos(11) size(2.75)) /// subtitle("Make Vs. Avg. Mileage", pos(11) size(2.5)) /// import delimited "https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv", varnames(1) clear * Adding variable names to imported data rename (v2 v3) (y1952 y1957) * Checking which value is lower than previous data value generate negative = (y1957 < y1952) generate lab1952 = continent + ", " + string(round(y1952)) generate lab1957 = continent + ", " + string(round(y1957)) twoway (pcspike y1952 continent1 y1957 continent2 if negative == 0, legend(off) lcolor("`r(p11)'")) /// (pcspike y1952 continent1 y1957 continent2 if negative == 1, legend(off) lcolor("`r(p1)'")) /// (scatter y1952 continent1, ms(i) mlabposition(9) mlabel(lab1952)) /// (scatter y1957 continent2, ms(i) mlabposition(3) mlabel(lab1957)) /// (scatteri 12700 1 "{bf}Year 1952", ms(i) mlabpos(9)) /// (scatteri 12700 2 "{bf}Year 1957", ms(i) mlabpos(3)) /// ylabel(0(4000)12000, labsize(2) nogrid) /// ytitle("Avg." "GDP/Capita", size(2) orient(horizontal)) /// yscale(range(0 13000)) /// xscale(range(0.2 2.8)) /// title("{bf}Slope Chart", pos(11) size(2.75)) /// subtitle("Mean GDP per capita: 1952 Vs. 1957" " ", pos(11) size(2)) /// graphregion(margin(r=25)) ///import delimited "https://raw.githubusercontent.com/selva86/datasets/master/health.csv", varnames(1) clear labmask srno, values(area) foreach var of varlist pct* { replace `var' = `var' * 100 twoway (rspike pct_2013 pct_2014 srno, horizontal lcolor("`r(p6)'*0.4")) /// (scatter srno pct_2013, mcolor("`r(p6)'*0.4")) /// (scatter srno pct_2014, mcolor("`r(p6)'")) /// ylabel(3(3)78, valuelabel angle(horizontal) labsize(2)) /// legend(order(3 "2014" 2 "2013") pos(11) row(1) size(2)) /// title("{bf}Dumbbell Plot", pos(11) size(2.75)) /// subtitle("% Change in Health Indicators by Area: 2014 vs. 2013", pos(11) size(2)) ///**Histogram on Continuous Variable (Over Category)import delimited "https://raw.githubusercontent.com/tidyverse/ggplot2/master/data-raw/mpg.csv", clear replace class = subinstr(class, "2", "two", .) frame copy default original, replace levelsof class, local(cls) frame put displ class, into(`l') twoway__histogram_gen displ if class == "`l'", start(1) width(0.1) frequency generate(h x, replace) twoway__histogram_gen displ, start(1) width(0.1) frequency generate(h x, replace) generate tag = 1 if missing(x) replace x = _n if missing(x) merge 1:1 x using `l', nogen replace x = . if tag == 1 reshape long h_, i(x) j(type) string bysort x (type) : gen cumul_sum_ = sum(h_) if !missing(h_) reshape wide cumul_sum_, i(x) j(type) string local wcount: word count `r(varlist)' forvalues i = `wcount'(-1)1 { local a : word `i' of `r(varlist)' colorpalette tableau, nograph n(`i') local bar "`bar' (bar `a' x, fcolor("`r(p`i')'") barwidth(0.1) lwidth(0.1) lcolor(gs4))" twoway `bar', xlabel(1(1)7) scheme(white_tableau) /// legend(order(1 "2 Seater" 2 "SUV" 3 "Subcompact" 4 "Pickup" 5 "Minivan" 6 "Midsize" 7 "Compact") rowgap(0) size(2)) /// xlabel(, labsize(2)) ylabel(, labsize(2)) /// ytitle("Count", size(2)) xtitle("Displacement", size(2)) /// title("{bf}Histogram with Auto Binning", pos(11) size(2.75)) /// subtitle("Engine Displacement across Vehicle Classes", pos(11) size(2)) ***Histogram on Categorical Variableimport delimited "https://raw.githubusercontent.com/tidyverse/ggplot2/master/data-raw/mpg.csv", clear local barlwidth "`barlwidth' bar(`i', lwidth(0)) " graph bar (count), over(class) over(manufacturer, label(alternate labsize(2))) asyvars stack /// legend(order(7 "SUV" 6 "Subcompact" 5 "Pickup" 4 "Minivan" 3 "Midsize" 2 "Compact" 1 "2 Seater") rowgap(0.25) size(2)) /// title("{bf}Histogram on Categorical Variable", pos(11) size(2.75)) /// subtitle("Manufacturer across Vehicle Classes", pos(11) size(2)) **Density Plot (By Category)import delimited "https://raw.githubusercontent.com/tidyverse/ggplot2/master/data-raw/mpg.csv", clearlevelof cyl, local(cylinders) foreach cylinder of local cylinders { local kden "`kden' (kdensity cty if cyl == `cylinder', range(`r(min)' `r(max)') recast(area) fcolor(%70) lwidth(*0.25))" twoway `kden', scheme(white_w3d) /// legend(subtitle("Cylinders", size(2)) label(1 "4") label(2 "5") label(3 "6") label(4 "8") rowgap(0.25) size(2)) /// title("{bf}Density Plot", pos(11) size(2.75)) /// ytitle("Density", size(2) orient(horizontal)) /// ylabel(, nogrid labsize(2)) /// xtitle("City Mileage", size(2)) /// xlabel(, nogrid labsize(2)) /// subtitle("City Mileage over number of cylinders", pos(11) size(2)) import delimited "https://raw.githubusercontent.com/tidyverse/ggplot2/master/data-raw/mpg.csv", clear graph box cty, over(class) /// ytitle("City Mileage", size(2.25)) /// title("{bf}Box Plot", pos(11) size(2.75)) /// b1title(" " "Class of vehicle", size(2.5)) /// subtitle("City Mileage grouped by class of vehicle", pos(11) size(2)) /// **Tufte Style Box Plot (Over Category)import delimited "https://raw.githubusercontent.com/tidyverse/ggplot2/master/data-raw/mpg.csv", clear graph box cty, over(class) /// box(1, color(white%0)) /// medmarker(mcolor(black) mlwidth(0)) /// lines(lpattern(solid) lwidth(medium)) /// title("{bf}Box Plot", pos(11) size(2.75)) /// subtitle("City Mileage over number of cylinders", pos(11) size(2)) /// **Minimalistic Box Plot (Over Category & By Type) import delimited "https://raw.githubusercontent.com/tidyverse/ggplot2/master/data-raw/mpg.csv", clear levelsof cyl, local(cylinders) local catcount: word count `cylinders' forvalues i = 1/`catcount' { colorpalette tableau, nograph n(`catcount') local boxopt "`boxopt' box(`i', color("`r(p`i')'")) " graph box cty, over(cyl) /// row(1) legend(pos(3)) imargin(l=1.5 r=1.5) style(compact) /// title("{bf}Box Plot", pos(11) size(2.75)) /// subtitle("City Mileage over number of cylinders" " ", pos(11) size(2)) /// medmarker(mcolor() mlwidth(0) msize(1)) /// lines(lpattern(solid) lwidth(medium)) /// ytitle("City Mileage", size(2.25)) /// subtitle(, size(2.5)) /// //size of group headers legend(size(2.25) rowgap(0.25) subtitle("Cylinders", size(2.25))) ///import delimited "https://raw.githubusercontent.com/tidyverse/ggplot2/master/data-raw/mpg.csv", clear * This plot contains box and distribution violinplot cty, over(class) vertical scheme(white_w3d) /// ytitle("City Mileage", size(2.25)) /// title("{bf}Box Plot", pos(11) size(2.75)) /// b1title(" " "Class of vehicle", size(2.5)) /// subtitle("City Mileage grouped by class of vehicle", pos(11) size(2)) * To make a version without box we can use: violinplot cty, over(class) vertical scheme(white_w3d) nobox nomedian noline nowhiskers /// ytitle("City Mileage", size(2.25)) /// title("{bf}Box Plot (Density Only)", pos(11) size(2.75)) /// b1title(" " "Class of vehicle", size(2.5)) /// subtitle("City Mileage grouped by class of vehicle", pos(11) size(2)) ***Population Pyramid Plotimport delimited "https://raw.githubusercontent.com/selva86/datasets/master/email_campaign_funnel.csv", clear replace users = round(users) replace users = -(users) if users < 0 & gender == "Female" replace users = -(users) if users > 0 & gender == "Male" encode stage, gen(stage_n) forvalues i = -15000000(5000000)15000000 { local xlab "`xlab' `i' `"`=abs(`i')/1000000'm"'" //Use of compound quotes to work with labels with absolute (abs) values local xlab "`xlab' 0 `"0"'" twoway (bar users stage_n if gender == "Female", horizontal lwidth(0) barwidth(0.8)) /// (bar users stage_n if gender == "Male", horizontal lwidth(0) barwidth(0.8)) /// xlabel(`xlab', nogrid) /// ylabel(1(1)18, nogrid noticks valuelabel labsize(2)) /// legend(order(1 "Female" 2 "Male") size(2)) /// title("{bf}Email Campaign Funnel", size(2.75)) ///*社群群友可以直接在社群下载Stata完整版code
Source: https://medium.com/the-stata-gallery/top-25-stata-visualizations-with-full-code-668b5df114b6
关于Stata, 1.Stata16新增功能有哪些? 满满干货拿走不谢，2.Stata资料全分享，快点收藏学习，3.Stata统计功能、数据作图、学习资源，4.Stata学习的书籍和材料大放送, 以火力全开的势头，5.史上最全Stata绘图技巧, 女生的最爱，6.把Stata结果输出到word, excel的干货方案，7.编程语言中的函数什么鬼？Stata所有函数在此集结，8.世界范围内使用最多的500个Stata程序，9.6张图掌握Stata软件的方方面面, 还有谁, 还有谁? 10.LR检验、Wald检验、LM检验什么鬼？怎么在Stata实现，11.Stata15版新功能，你竟然没有想到，一睹为快，12."高级计量经济学及Stata应用"和"Stata十八讲"配套数据，13.数据管理的Stata程序功夫秘籍，14.非线性面板模型中内生性解决方案以及Stata命令，15.把动态面板命令讲清楚了，对Stata的ado详尽解，16.半参数估计思想和Stata操作示例，17.Stata最有用的points都在这里,无可替代的材料，18.PSM倾向匹配Stata操作详细步骤和代码，干货十足，19.随机前沿分析和包络数据分析 SFA,DEA 及Stata操作，20.福利大放送, Stata编程技巧和使用Tips大集成，21.使用Stata进行随机前沿分析的经典操作指南，22.Stata, 不可能后悔的10篇文章, 编程code和注解，23.用Stata学习Econometrics的小tips, 第二发礼炮，24.用Stata学习Econometrics的小tips, 第一发礼炮，25.广义合成控制法gsynth, Stata运行程序release，26.多重中介效应的估计与检验, Stata MP15可下载，27.输出变量的描述性统计的方案，28.2SLS第一阶段输出, 截面或面板数据及统计值都行，29.盈余管理指标的构建及其Stata实现程序, 对应解读和经典文献，30.Python, Stata, R软件史上最全快捷键合辑！，31.用Stata做面板数据分析, 操作代码应有尽有，32.用Stata做面板数据分析, 操作代码应有尽有，33.没有这5个Stata命令, 我真的会活不下去！，34.第一(二)卷.Stata最新且有趣的程序系列汇编，35.第三卷.Stata最新且急需的程序系列汇编，36.第四卷.Stata最新且急需的程序系列汇编，37.干货: UN和WTO推荐的最全且权威的实证研究方法及在Stata实现！必收藏！，38.再中心化影响函数RIF回归和分解的Stata操作程序，39.R和Stata软件meta分析操作详细攻略, 对研究再开展研究的利器！，40.不能安装Stata命令咋弄？这个方法一直都比较靠谱！，41.使用Stata做结构方程模型GSEM的操作指南，42.疫情期计量课程免费开放！面板数据, 因果推断, 时间序列分析与Stata应用，43.一些Stata常见操作代码和注释, 能够让年轻学人更快掌握相关命令！44.Stata语言中的常用函数及其用法解释, 在附上42篇Stata相关学习资料，45.Stata经典操作笔记和学习资源合辑! 都是些博士生导师比较推荐的! 46.Stata17版本对外发布了, 里面提供了25个让学者激动的新程序板块！47.分享9篇文章的Stata复制代码, 可以直接下载进行一一复制实证结果, 48.实证中常用Stata操作命令和代码总结,49.如何用Stata实现熵值法的指南？50.多期DID或渐进DID或交叠DID, 最新Stata执行命令整理如下供大家学习，51.推荐一份超级大礼包资源, 里面有丰富的Stata学习材料, 写文章作报告找工作的指南
关于计量方法类书籍，参看：1.一些比较常见的因果推断书籍25本汇总, 很多可以直接下载PDF，2.推荐一本专攻处理效应分析的书籍, 包括主流政策评估计量方法，3.年龄-时期-队列分析及其实现过程和经典书籍, 附code和数据！4.推荐使用Python语言做因果推断前沿方法的书籍，5.使用Stata做时间序列分析书籍, 包括模型讲解以及Stata示例操作，6.一本最新因果推断书籍, 包括了机器学习因果推断方法, 学习主流和前沿方法，7.社会经济政策的评估计量经济学, 提供书籍和数据和程序文件，8.全面且前沿的因果推断课程, 提供视频, 课件, 书籍和经典文献，9.从网页上直接复制代码的因果推断书籍出现了, 学会主流方法成效极快，10.推荐书籍"用R软件做应用因果分析", 有需要的学者可以自行下载！11.哪本因果推断书籍最好？我们给你整理好了这个书单！12.“不一样”的因果推断书籍, 很多观点让我们能恍然大悟, 涵盖了不少其他书里没有的因果推断方法！13.搞懂因果推断中内生性问题解决方法必读的书籍和文献已搜集好！14.一位“诗人”教授写了本因果推断书籍, 现在可以直接下载PDF参看！15.使用R软件学习计量经济学方法三本书籍推荐，16.数据缺失方法处理大全, 经典书籍助你修复数据，17.中介和调节效应操作指南, 经典书籍和PPT珍藏版，18.用R语言做Econometrics的书籍推荐, 值得拥有的经典，19.史上最全的因果识别经典前沿书籍, 仅此一份，20.重磅好书"环境能源计量经济学(附代码)", 该领域主流, 时髦和前沿的计量方法，21.Acemoglu又出版了两本经济学教课书！22.Stata17MP版最新使用指南全书, 包括DSGE, Lasso回归, ERM, 贝叶斯分析等，23.全新因果推断方法新书, 配套R, Stata和Python的代码, 还包括教授视频和PPT素材！24.学习计量, 统计和各种软件的必备书单，25.机器学习第一书, 数据挖掘, 推理和预测，26.计量回归中的交互项到底什么鬼? 捎一本书给你，27.荐书，计量经济学宝典，28.送书: 应用时间序列分析(经典)，29.计量经济学教科书，多门类多级别的一个都不能少，30.50本经济学书单，入门到精通分门别类，31.从入门到进阶的Python数据分析手册, 课程内容完全免费！32.2卷RDD断点回归使用手册, 含Stata和R软件操作流程，33.社会网络分析最新文献和软件学习手册，34.环境, 能源和资源经济学手册推荐, 经典著作需要反复咀嚼，35.各领域经济学手册全在这里, 不学手册只能做重复研究，36.史上最全博士论文撰写指导手册Handbook
计量经济圈公众号搜索功能及操作流程演示
下面短链接文章属于合集，可以收藏起来阅读，不然以后都找不到了。