Stata绘图:柱状图专题-T212

发布时间:2021-05-08 阅读 1688

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作者: 徐阳 (武汉大学)
E-Mail: yangx94@qq.com

Source: 世界银行 stata-visual-library

注:本推文相关数据可通过上述世界银行网址获取,也可以通过如下两个途径获取:


目录


0. 引言

本文参考世界银行 stata-visual-library 提供的数据及代码,通过八个范例来介绍如何 绘制柱状图。部分范例有较长的数据生成过程,本文重点在于介绍如何绘制柱状图,故未做详细介绍。

1. 多变量水平柱状图(Horizontal bar with multiple variables)

代码如下:

global graph_opts ///
  title(, justification(left) ///
  color(black) span position(11)) ///
  graphregion(color(white)) ///
  ylabel(,angle(0) nogrid) ///
  xtitle(,placement(left) justification(left)) ///
  yscale(noline) xscale(noline) ///
  legend(region(lcolor(none) fcolor(none)))
        
global  pct `" 0 "0%" .25 "25%" .5 "50%" .75 "75%" 1 "100%" "'

quietly do "https://gitee.com/arlionn/stata-visual-library/raw/master/Library/ado/betterbar.ado"	

use "https://gitee.com/arlionn/stata-visual-library/raw/master/Library/data/bar-better.dta" , clear

betterbar ///
  ??_correct  checklist  refer med_any  med_class_any_6 med_class_any_16 ///
  , ///
  ${graph_opts} ///
  over(facility_type) ///
  xlabel(${pct}) ///
  barlab(mean) ///
  legend(rows(1) symxsize(small) symysize(small))

部分选项解释:

global  graph_opts                             ///定义暂元
  title(, justification(left)                  ///
  color(black) span position(11))              ///前两行为图标题设置
  graphregion(color(white))                    ///图片背景颜色
  ylabel(,angle(0) nogrid)                     ///Y轴标签设置
  xtitle(,placement(left) justification(left)) ///x轴标题设置
  yscale(noline) xscale(noline)                ///X轴与Y轴刻度设置
  legend(region(lcolor(none) fcolor(none)))            //图例颜色设置
        
global  pct `" 0 "0%" .25 "25%" .5 "50%" .75 "75%" 1 "100%" "'

quietly do "https://gitee.com/arlionn/stata-visual-library/raw/master/Library/ado/betterbar.ado"	

use "https://gitee.com/arlionn/stata-visual-library/raw/master/Library/data/bar-better.dta" , clear

betterbar ///
  ??_correct  checklist  refer med_any ///
  med_class_any_6 med_class_any_16 ///前两行为变量名
  , ///
  ${graph_opts}                    ///调用暂元
  over(facility_type)              ///根据该变量分组
  xlabel(${pct})                   ///调用暂元
  barlab(mean)                     ///画出各变量均值
  legend(rows(1) symxsize(small) symysize(small)) //图例行数与图例标志长宽设置

输出图片如下:

2. 两变量柱状图(Bar plot of two variables)

代码如下:

global graph_opts1 ///
  bgcolor(white) ///
  graphregion(color(white)) ///
  legend(region(lc(none) fc(none))) ///
  ylabel(,angle(0) nogrid) ///
  title(, justification(left) color(black) span position(11)) ///
  subtitle(, justification(left) color(black))

use "https://gitee.com/arlionn/stata-visual-library/raw/master/Library/data/bar-over.dta" , clear

graph bar treat_correct ///
, ///
  over(type) ///
  asyvars ///
  bargap(20) ///
  over(study) ///
  over(case) ///
  nofill ///
  blabel(bar, format(%9.2f)) ///
  ${graph_opts1} ///
  bar(1 , lcolor(black) lwidth(thin) fintensity(100)) ///
  bar(2 , lcolor(black) lwidth(thin) fintensity(100)) ///
  legend(rows(1) ///
  order(0 "Measurement:" 1 "Standardized Patient" 2 "Clinical Vignette")) ///
  ytitle("Providers ordering correct treatment {&rarr}", ///
  placement(bottom) ///
  justification(left)) ///
  ylabel(${pct})

部分选项解释:

global graph_opts1 ///
  bgcolor(white) ///
  graphregion(color(white)) ///
  legend(region(lc(none) fc(none))) ///
  ylabel(,angle(0) nogrid) ///
  title(, justification(left) color(black) span position(11)) ///
  subtitle(, justification(left) color(black)) //子标题设置

use "https://gitee.com/arlionn/stata-visual-library/raw/master/Library/data/bar-over.dta" , clear

graph bar treat_correct ///
, ///
  over(type)  ///type包括Standardized Patient与Vignette
  asyvars     ///将第一个over()设置的变量作为Y轴变量
  bargap(20)  ///柱之间的距离
  over(study) ///study包括Bihar、China与Delhi
  over(case)  ///case包括Diarrhea (ORS)与Tuberculosis (AFB or CXR)
  nofill      ///忽略缺少的分类
  blabel(bar, format(%9.2f)) ///柱上端的数字设置
  ${graph_opts1} ///
  bar(1 , lcolor(black) lwidth(thin) fintensity(100)) ///柱格式设置
  bar(2 , lcolor(black) lwidth(thin) fintensity(100)) ///
  legend(rows(1) ///
  order(0 "Measurement:" 1 "Standardized Patient" 2 "Clinical Vignette")) ///
  ytitle("Providers ordering correct treatment {&rarr}", ///
  placement(bottom) ///
  justification(left)) ///
  ylabel(${pct})

输出图片如下:

3. 两轴组合柱状图(Combined bar plots with two axes)

代码如下:

use "https://gitee.com/arlionn/stata-visual-library/raw/master/Library/data/bar-two-axes.dta", clear

* Adjust variable for bar position
gen x = int1mo+.5

* Create individual graphs
* ------------------------
foreach foodGroup in animal fruit grain veg starch processed_sugar {

  if "`foodGroup'" == "animal"  local graphTitle Animal Sourced
  if "`foodGroup'" == "fruit"   local graphTitle Fruit
  if "`foodGroup'" == "grain"   local graphTitle Grain
  if "`foodGroup'" == "veg"     local graphTitle Vegetables
  if "`foodGroup'" == "starch"  local graphTitle Starchy Foods
  if "`foodGroup'" == "processed_sugar"  local graphTitle Processed/Sugar
  
  twoway ///
    bar number_group x if food_group=="`foodGroup'", ///
      yaxis(1) ytitle("Avg. Number of Foods from" "Group Consumed Last Month", axis(1)) ///
      barwidth(.9) fintensity(inten0) lcolor(black) /// 
      xlabel(0 "0" 3 "3" 6 "6" 9 "9" 12 "12") ///
      ylabel(0 "0" 1 "1" 2 "2" 3 "3", axis(1)) || ///
    line total_exp int1mo if food_group=="`foodGroup'", ///
      yaxis(2) ytitle("Total Value of Exp." "1000 Real Tz Sh.", axis(2)) ///
      ylabel(0 "0" 500 "500" 1000 "1000" 1500 "1500" 2000 "2000" 2500 "2500", axis(2)) ///
      xlabel(3 "3" 6 "6" 9 "9" 12 "12") lwidth(1.2) ///
  title("`graphTitle'") xtitle("Month of Interview") ///
  graphregion(color(white)) bgcolor(white) ///
  legend(off) ///
  name("`foodGroup'") 
}

* Combine graphs into one
* -----------------------
graph combine starch animal fruit grain processed_sugar veg, ///
              graphregion(color(white)) plotregion(color(white)) 

部分选项解释:

use "https://gitee.com/arlionn/stata-visual-library/raw/master/Library/data/bar-two-axes.dta", clear

* Adjust variable for bar position
gen x = int1mo+.5

* Create individual graphs
* ------------------------
foreach foodGroup in animal fruit grain veg starch processed_sugar {

  if "`foodGroup'" == "animal"  local graphTitle Animal Sourced
  if "`foodGroup'" == "fruit"   local graphTitle Fruit
  if "`foodGroup'" == "grain"   local graphTitle Grain
  if "`foodGroup'" == "veg"     local graphTitle Vegetables
  if "`foodGroup'" == "starch"  local graphTitle Starchy Foods
  if "`foodGroup'" == "processed_sugar"  local graphTitle Processed/Sugar
  
  twoway ///
    bar number_group x if food_group=="`foodGroup'",    ///设置为柱状图
      yaxis(1) ytitle("Avg. Number of Foods from" "Group Consumed Last Month", axis(1)) ///指左边Y轴
      barwidth(.9) fintensity(inten0) lcolor(black)     ///
      xlabel(0 "0" 3 "3" 6 "6" 9 "9" 12 "12")           ///
      ylabel(0 "0" 1 "1" 2 "2" 3 "3", axis(1)) ||       ///不同Y轴图形设置之间用“||”隔开
    line total_exp int1mo if food_group=="`foodGroup'", ///设置为折线图
      yaxis(2) ytitle("Total Value of Exp." "1000 Real Tz Sh.", axis(2)) ///
      ylabel(0 "0" 500 "500" 1000 "1000" 1500 "1500" 2000 "2000" 2500 "2500", axis(2)) ///指右边Y轴
      xlabel(3 "3" 6 "6" 9 "9" 12 "12") lwidth(1.2)     ///
  title("`graphTitle'") xtitle("Month of Interview")    ///整体图片设置
  graphregion(color(white)) bgcolor(white)              ///
  legend(off) ///
  name("`foodGroup'")                                   //将图片进行临时存储,方便后续组合各图
}

* Combine graphs into one
* -----------------------
graph combine starch animal fruit grain processed_sugar veg, ///需组合的图片名
              graphregion(color(white)) plotregion(color(white)) 
  • 组合图片如下:

4. 并排水平柱状图(Side by side horizontal bar plot)(基于命令betterbar)

代码如下:

global graph_opts ///
  note(, justification(left) color(black) span position(7)) ///
  title(, justification(left) color(black) span position(11)) ///
  subtitle(, justification(left) color(black) span position(11)) ///
  graphregion(color(white)) ///
  ylabel(,angle(0) nogrid) ///
  ytitle("") ///
  xtitle(,placement(left) justification(left)) ///
  yscale(noline) xscale(noline) xsize(7) ///
  legend(region(lcolor(none) fcolor(none)))
    
quietly do "https://gitee.com/arlionn/stata-visual-library/raw/master/Library/adobetterbar.ado"
quietly do "https://gitee.com/arlionn/stata-visual-library/raw/master/Library/adolabelcollapse.ado"
local n_5 = 599
local n_6 = 601

local title_5 = "Classic case of presumed TB"
local title_6 = "TB case with positive sputum report"
quietly forvalues i = 5/6 {

  local case = `i' - 4
  use "https://gitee.com/arlionn/stata-visual-library/raw/master/Library/data/bar-betterbar.dta", clear
      
  gen n = 1
  bysort med_generic: egen med_class_typ = mode(med_class), minmode // Label with  mosttypical medicine code
  label values med_class_typ med_k
          
  keep if case == `i'
      
  labelcollapse  (firstnm) n med_class_typ med_generic_encoded sp_location, ///
    by(med_generic facilitycode) ///
    vallab(med_class_typ med_generic_encoded sp_location)
      
  labelcollapse   (sum) n (firstnm) med_generic_encoded med_class_typ, ///
    by(med_generic) ///
    vallab(med_class_typ med_generic_encoded) 
      
  capture separate n, by(med_generic_encoded) shortlabel
          
  foreach var of varlist n?* {

    local theLabel : var label `var'
    local theLabel = regexr("`theLabel'","med_generic_encoded == ","")
            
    capture summarize n if med_generic == "`theLabel'"
    capture local theN = `r(mean)'
            
    label var `var' "`theLabel' [`theN']"
  }
              
  foreach var of varlist n?* {
      
    replace `var' = . if `var' < 5 // Exclude low volumes
    replace `var' = `var'/`n_`i'' // Number of interactions
    quietly summarize `var'
    if `r(N)' == 0 drop `var' 
  }
          
  drop if med_generic == "Sodium Chloride" // not an active ingredient
      
  betterbar (n?*) , ///
    stat(sum) over(med_class_typ) by(med_class_typ) nobylabel nobycolor d(1)  ///
    legend(span cols(1) position(3) ring(1) symxsize(small) symysize(small) size(small)  ///
    dropzero ///
    xlabel(0 "0%" .2 "20%" .4 "40%" .6 "60%") ///
    ysize(6) labsize(2) ///
    ${graph_opts} ///
    title("Case `case' (N=`n_`i'')") subtitle("`title_`i''") ///
    name(figure_4_`case')         
}

grc1leg figure_4_1 figure_4_2 ///
  , ///
  position(3) ///
  graphregion(color(white)) ///
  xsize(7) 

部分选项解释:

  • note(),注释设置

  • subtitle(),副标题设置

  • by()与over()搭配使用,使图形能够基于分类清楚展示

  • grc1leg,将多个图形合并为具有一个共同图例的图形

  • 组合图片如下:

5. 两变量堆积柱状图(Stack bar graph by two variables)

代码如下:

global graph_opts1 ///
  bgcolor(white) graphregion(color(white)) ///
  legend(region(lcolor(none) fcolor(none))) ///
  ylabel(,angle(0) nogrid) ///
  subtitle(, justification(left) color(black) span position(11)) ///
  title(, color(black) span)

global pct `" 0 "0%" .25 "25%" .5 "50%" .75 "75%" 1 "100%" "'

use "https://gitee.com/arlionn/stata-visual-library/raw/master/Library/data/bar-stack-by.dta", clear

graph bar med_b2_antister_cat?? ///
          if dr_3 == 1 ///
          , ///
          stack over(checkgroup) nofill ///
          ylabel(${pct}) ///
          legend(order(5 "No Medication" ///
                       4 "Antibiotic and Steroid" ///
                       3 "Antibiotic" ///
                       2 "Steroid" ///
                       1 "No Antibiotic or Steroid") ///
                cols(1) position(3) ///
                symxsize(small) symysize(small) size(small)) ///
          ${graph_opts1} ///
          bar(5, color(white) lcolor(black) lpattern(solid) lwidth(thin)) ///
          bar(1,lwidth(thin) lcolor(black)) bar(2,lwidth(thin) lcolor(black)) ///
          bar(3,lwidth(thin) lcolor(black)) bar(4,lwidth(thin) lcolor(black)) ///
          subtitle("Referral", color(black) justification(center) position(12)) ///
          name(figure_1)
        
graph bar med_b2_antister_cat?? ///
          if dr_3 == 0 ///
          , ///
          stack over(checkgroup) nofill ///
          ylabel(${pct}) ///
          legend(order(5 "No Medication" ///
                       4 "Antibiotic and Steroid" ///
                       3 "Antibiotic" ///
                       2 "Steroid" ///
                       1 "No Antibiotic or Steroid") ///
                cols(1) position(3) ///
                symxsize(small) symysize(small) size(small)) ///
          ${graph_opts1} ///
          bar(5, color(white) lcolor(black) lpattern(solid) lwidth(thin)) ///
          bar(1,lwidth(thin) lcolor(black)) ///
          bar(2,lwidth(thin) lcolor(black)) ///
          bar(3,lwidth(thin) lcolor(black)) ///
          bar(4,lwidth(thin) lcolor(black)) ///
          subtitle("No Referral", color(black) justification(center) position(12)) ///
          name(figure_2)

grc1leg figure_2 figure_1 ///
        , ///
        position(3) ///
        graphregion(color(white)) ///
        xsize(7) ///
        rows(1) ///
        legendfrom(figure_2)

部分选项解释:

  • stack,表明把变量纵着堆积

  • grc1leg,rows(),表明两个图的放置位置

  • grc1leg,legendfrom(),表明使用哪个图的图例

  • 组合图片如下:

6. 并排水平柱状图(Side by side horizontal bar plot)(基于命令weightab)

代码如下:

global graph_opts ///
  title(, justification(left) color(black) span position(11)) ///
  graphregion(color(white) lcolor(white) lwidth(med) lalign(center)) /// 
  ylabel(,angle(0) nogrid) xtitle(,placement(left) justification(left)) ///
  yscale(noline) xscale(noline) legend(region(lcolor(none) fcolor(none)))
  
local opts lwidth(thin) lcolor(white) lalign(center)

label define  case 1 "Case 1" 2 "Case 2" 3 "Case 3" 4 "Case 4" , modify

quietly do "https://gitee.com/arlionn/stata-visual-library/raw/master/Library/ado/weightab.ado"

use "https://gitee.com/arlionn/stata-visual-library/raw/master/Library/data/bar-weightab.dta" , clear

weightab ///
  correct treat_cxr re_3 re_4 treat_refer t_12 ///
  med_any med_l_any_1 med_l_any_2 med_l_any_3  med_k_any_9   ///
  if city == 2 ///
  [pweight = weight_city] ///
  , ///
  ${graph_opts} ///
  barlab ///
  barlook(1 `opts' fintensity(100)) ///
  title("Patna") ///
  over(case) ///
  graph ///
  legend(off) ///
  xlabel(${pct}) ///
  name(Fig_1_1)
        
weightab ///
  correct treat_cxr re_3 re_4 treat_refer t_12 ///
  med_any med_l_any_1 med_l_any_2 med_l_any_3  med_k_any_9  ///
  if city == 3 ///
  [pweight = weight_city] ///
  , ///
  ${graph_opts} ///
  barlab ///
  barlook(1 `opts'  fintensity(100)) ///
  title("Mumbai") ///
  over(case) ///
  graph ///
  legend(position(5) ring(0) cols(1) symxsize(small) symysize(small)) ///
  xlabel(${pct}) ///
  name(Fig_1_2)
        
graph combine Fig_1_1 Fig_1_2 ///
  , ///
  ${comb_opts} ///
  xsize(7) r(1)

部分选项解释:

  • barlook(),柱格式设置

  • graph,这个选项是命令weightab画图的必选项

  • 组合图片如下:

7. 带有变量分组、标准误差条和跨组比较的水平柱状图(Horizontal bar plot with grouping of variables, standard error bars, and cross-group comparisons)

代码如下:

global pct `" 0 "0%" .25 "25%" .5 "50%" .75 "75%" 1 "100%" "'

quietly do "https://gitee.com/arlionn/stata-visual-library/raw/master/Library/ado/betterbar.ado"

use "https://gitee.com/arlionn/stata-visual-library/raw/master/Library/data/bar-better-ci.dta", clear

betterbar ///
  (dr_3 correct_treatment)  ///
  (med_b2_any_antibiotic med_b2_any_steroid med_b2_any_antister med_l_any_2 med_b2_any_schedule_h med_b2_any_schedule_h1 med_b2_any_schedule_x med_l_any_1)   ///
  , ///
  over(city) xlabel($pct) se bin ///
  legend(position(5) ring(0) cols(1) symxsize(small) symysize(small)) ///
  ysize(7) n barlab(upper)

部分选项解释:

  • se,表示添加标准误差条
  • n,表示在图例中添加每组观测值数量
  • barlab(),表示数值的显示位置

输出图片如下:

8. 横向堆积柱状图(Horizontal stack bar plot)

代码如下:

global graph_opts1 ///
  title(, justification(left) color(black) span position(11)) ///
  graphregion(color(white) lcolor(white) lalign(center)) ///
  ylabwl(,angle(0) nogrid)  ///
  yscale(noline) legend(region(lcolor(none) fcolor(none)))
  
global pct `" 0 "0%" .25 "25%" .5 "50%" .75 "75%" 1 "100%" "'

use "https://gitee.com/arlionn/stata-visual-library/raw/master/Library/data/bar-stack-cat.dta" , clear

local x = 0
quietly foreach var of varlist ///
correct treat_cxr re_3 re_4 treat_refer ///
med_any med_l_any_1 med_l_any_2 med_l_any_3 med_k_any_9 {

  mean `var' [pweight = weight_city]
  mat a = e(b)
  local mean = a[1,1]
  local mean = string(round(100*`mean',0))
  local mean = substr("`mean'",1,strpos("`mean'",".")+1)
  
  local ++x
  local theLabel : var label `var'
  local theLabels `" `theLabels' `x' "`theLabel'" "' // [`mean'%]
  
  capture mat drop theResult
  reg `var' i.city [pweight = weight_city]
  local theR21 = `e(r2)'
  mat theResult = nullmat(theResult) , [`theR21']
  
  reg `var' i.city i.case [pweight = weight_city]
  local theR22 = `e(r2)' - `theR21'
  mat theResult = nullmat(theResult) , [`theR22']
  
  reg `var' i.city i.case i.type_formal  [pweight = weight_city]
  local theR23 = `e(r2)' - `theR21' - `theR22'
  mat theResult = nullmat(theResult) , [`theR23']
  
  reg `var' i.city i.case i.type_formal i.sp_city_id  [pweight = weight_city]
  local theR24 = `e(r2)' - `theR21' - `theR22' - `theR23'
  mat theResult = nullmat(theResult) , [`theR24']
  
  reg `var' i.city i.case i.type_formal i.sp_city_id i.sp_city_mbbs [pweight = weight_city]
  local theR25 = `e(r2)' - `theR21' - `theR22' - `theR23' - `theR24'
  mat theResult = nullmat(theResult) , [`theR25']
  
  mean `var' [pweight = weight_city]
  mat a = e(b)
  local mean = a[1,1]
  mat theResult = nullmat(theResult) , [`mean']
  
  mat theResults = nullmat(theResults) \ theResult
  matlist theResults
}

clear
svmat theResults
gen n = _n
label define n `theLabels'
label values n n

graph bar (sum) theResults1 theResults2 theResults3 theResults4 theResults5  ///
          , ///
          ylabel($pct) ///
          ${graph_opts1} ///
          horizontal stack over(n) xsize(6) ///
          bar(1, lcolor(black) lwidth(thin)) ///
          bar(2, lcolor(black) lwidth(thin)) ///
          bar(3, lcolor(black) lwidth(thin)) ///
          bar(4, fcolor(black) lcolor(black) lwidth(thin)) ///
          bar(5, fcolor(gs12) lcolor(black) lwidth(thin)) ///
          legend(position(5) ring(0) cols(1) symxsize(small) symysize(small)  ///
                 order(6 "Variance Explained By:" ///
                       1 "City Setting" ///
                       2 "Case Scenario" ///
                       3 "MBBS Degree" ///
                       4 "All SP Characteristics" ///
                       5 "Full Interaction Model"))

部分选项解释:

  • horizontal,表示图形方向设置为横向

输出图片如下:

9. 相关推文

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  • Stata连享会 由中山大学连玉君老师团队创办,定期分享实证分析经验。
  • 连享会-主页知乎专栏,400+ 推文,实证分析不再抓狂。直播间 有很多视频课程,可以随时观看。
  • 公众号关键词搜索/回复 功能已经上线。大家可以在公众号左下角点击键盘图标,输入简要关键词,以便快速呈现历史推文,获取工具软件和数据下载。常见关键词:课程, 直播, 视频, 客服, 模型设定, 研究设计, stata, plus, 绘图, 编程, 面板, 论文重现, 可视化, RDD, DID, PSM, 合成控制法

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✏ 连享会-常见问题解答:
https://gitee.com/lianxh/Course/wikis

New! lianxh 命令发布了:
随时搜索连享会推文、Stata 资源,安装命令如下:
. ssc install lianxh
使用详情参见帮助文件 (有惊喜):
. help lianxh