Displaying number of observations in a bar chart

[Aqib Yousuf]

Hello,

I am trying to display the number of observations in a bar chart for different categories and and total observations that are present for the specific category as well.

 
 * Example generated by -dataex-. For more info, type help dataex clear input byte(sex area) 1 2 1 4 1 3 1 1 1 2 1 4 1 1 1 5 1 2 0 4 end label values sex sex label def sex 0 "Female", modify label def sex 1 "Male", modify

How can I create bar graphs that display the total observations of area '5' in the ytitles or axis, and the total observations of "Females" in the area '5', as a bar label.

Thank you.

[Maarten Buis]

clear
frames reset
set scheme s1mono

input byte(sex area)
1 2
1 4
1 3
1 1
1 2
1 4
1 1
1 5
1 2
0 4
end
label values sex sex
label def sex 0 "Female", modify
label def sex 1 "Male", modify

// make a copy of the dataset
frame copy default tograph

// move to that copy
frame change tograph

// count the number of females in each area
bys area : egen nfemale = total(sex==0)

// make a dataset with the number of obs and females in each area
contract area nfemale, freq(total)

// count the number of males in each area
gen nmale = total - nfemale

// position of the numbers in the graph
gen xfemale = nfemale / 2 if nfemale != 0
gen xmale = nfemale + nmale/2 if nmale != 0

// I like these graphs to be sorted
// requires -labmask- by Nick Cox, type in Stata -search labmask- to find it
sort total area
gen Area = _n
labmask Area, values(area)

//make the graph
twoway bar total Area, horizontal xlab(0/3) xscale(range(0 3.2)) barw(0.9) ///
       legend(order(1 "male" 2 "female")) ylab(,angle(0) val) || ///
       bar nfemale Area, horizontal barw(0.9) || ///
       scatter Area xfemale, msymbol(i) mlab(nfemale) mlabpos(0) || ///
       scatter Area xmale, msymbol(i) mlab(nmale) mlabpos(0)

// go back to original data       
frame change default

[Aqib Yousuf]

clear
frames reset
set scheme s1mono

input byte(sex area)
1 2
1 4
1 3
1 1
1 2
1 4
1 1
1 5
1 2
0 4
end
label values sex sex
label def sex 0 "Female", modify
label def sex 1 "Male", modify

// make a copy of the dataset
frame copy default tograph

// move to that copy
frame change tograph

// count the number of females in each area
bys area : egen nfemale = total(sex==0)

// make a dataset with the number of obs and females in each area
contract area nfemale, freq(total)

// count the number of males in each area
gen nmale = total - nfemale

// position of the numbers in the graph
gen xfemale = nfemale / 2 if nfemale != 0
gen xmale = nfemale + nmale/2 if nmale != 0

// I like these graphs to be sorted
// requires -labmask- by Nick Cox, type in Stata -search labmask- to find it
sort total area
gen Area = _n
labmask Area, values(area)

//make the graph
twoway bar total Area, horizontal xlab(0/3) xscale(range(0 3.2)) barw(0.9) ///
legend(order(1 "male" 2 "female")) ylab(,angle(0) val) || ///
bar nfemale Area, horizontal barw(0.9) || ///
scatter Area xfemale, msymbol(i) mlab(nfemale) mlabpos(0) || ///
scatter Area xmale, msymbol(i) mlab(nmale) mlabpos(0)

// go back to original data
frame change default

[ATTACH=CONFIG]n1702671[/ATTACH]

Hello Maarten,

Thank you for the response. It appears to be working but I would like to display the percentages on y-axis, and number of observations(N) in the bar labels. Is there a way to do that?

[Nick Cox]

Not quite what you're asking, but may be of interest to anyone interested in the thread title. Naturally the colours can be tuned to choice.


See also

SJ-22-2 gr0066_3  . . . . . . . . . . . . . . . .  Software update for tabplot
        (help tabplot if installed) . . . . . . . . . . . . . . . .  N. J. Cox
        Q2/22   SJ 22(2):467
        bug fixed; help file updated to include further references

SJ-20-3 gr0066_2  . . . . . . . . . . . . . . . .  Software update for tabplot
        (help tabplot if installed) . . . . . . . . . . . . . . . .  N. J. Cox
        Q3/20   SJ 20(3):757--758
        added new options frame() and frameopts() allowing framing
        of bars and so-called thermometer plots or charts

SJ-17-3 gr0066_1  . . . . . . . . . . . . . . . .  Software update for tabplot
        (help tabplot if installed) . . . . . . . . . . . . . . . .  N. J. Cox
        Q3/17   SJ 17(3):779
        added options for reversing axis scales; improved handling of
        axis labels containing quotation marks

SJ-16-2 gr0066  . . . . . .  Speaking Stata: Multiple bar charts in table form
        (help tabplot if installed) . . . . . . . . . . . . . . . .  N. J. Cox
        Q2/16   SJ 16(2):491--510
        provides multiple bar charts in table form representing
        contingency tables for one, two, or three categorical variables

clear
set scheme s1color

input byte(sex area)
1 2
1 4
1 3
1 1
1 2
1 4
1 1
1 5
1 2
0 4
end
label values sex sex
label def sex 0 "Female", modify
label def sex 1 "Male", modify

bysort area sex : gen freq = _N
by area : gen total = _N
gen pc = 100 * freq / total
gen toshow = strofreal(freq) + " (" + strofreal(pc, "%2.1f") + "%)"

* tabplot should be installed from Stata Journal website
tabplot sex area, showval(toshow) separate(sex) bar1(color(blue)) bar2(color(orange))

[Maarten Buis]

clear
frames reset
set scheme s1mono

input byte(sex area)
1 2
1 4
1 3
1 1
1 2
1 4
1 1
1 5
1 2
0 4
end
label values sex sex
label def sex 0 "Female", modify
label def sex 1 "Male", modify

// make a copy of the dataset
frame copy default tograph

// move to that copy
frame change tograph

// count the number of females in each area
bys area : egen nfemale = total(sex==0)

// make a dataset with the number of obs and females in each area
contract area nfemale, freq(total)

// count the number of males in each area
gen nmale = total - nfemale

// position of the numbers in the graph
gen xfemale = nfemale / 2 if nfemale != 0
gen xmale = nfemale + nmale/2 if nmale != 0

// <-- begin new
// compute perentages
gen percfemale = (nfemale/total)*100
gen percmale = (nmale/total)*100

// format the percentages so they look nice in the graph
gen labfemale = strofreal(percfemale, "%5.2g") + "%"
gen labmale = strofreal(percmale, "%5.2g") + "%"
// <-- end new

// I like these graphs to be sorted
// requires -labmask- by Nick Cox, type in Stata -search labmask- to find it
sort total area
gen Area = _n
labmask Area, values(area)

//make the graph
twoway bar total Area, horizontal xlab(0/3) xscale(range(0 3.2)) barw(0.9) ///
       legend(order(1 "male" 2 "female")) ylab(,angle(0) val) || ///
       bar nfemale Area, horizontal barw(0.9) || ///
       scatter Area xfemale, msymbol(i) mlab(labfemale) mlabpos(0) || /// <-- changed
       scatter Area xmale, msymbol(i) mlab(labmale) mlabpos(0)  // <-- changed

// go back to original data       
frame change default

[Anna Rita]

Following up on this thread title, I need to produce a bar graph of two variables, political representation for women and men, by geographical area and show the total number of observation by area and I have an unbalanced sample of countries
This my data:

Geo_regions Maleyounger40percentage Femaleyounger40percentage
SSA 0 0
SSA 12.31 12.31
SSA 11.11 8.33
SSA 20.31 1.56
SSA 12.82 15.38
SSA 2.78 1.39
SSA 8.03 .8
SSA 2.75 2.75
SSA 0 7.32
SSA 5.52 5.52
SSA 15.07 1.37
SSA 5.52 5.52
SSA 0 0
SSA 2.78 1.67
SSA 3.85 0
SSA 7.76 .86
SSA 27.78 5.56
SSA 10.91 2.18
SSA 0 0
SSA 7.8 3.55
SSA 4.82 0
SSA 11.43 10
SSA 14.29 17.14
SSA 6.67 3.33
SSA 10.91 5.45
SSA 0 0
SSA 14.71 5.88
SSA 10.93 1.09
SSA 5.94 6.93
SSA 4.55 1.52
SSA 7.69 1.1
SSA 11.6 10.4
SSA 11.11 0
SSA 0 0
SSA 1.74 2.61
SSA 2.78 1.67
SSA 5 17.5
SSA 14.55 7.27
SSA 13.27 2.04
SSA 11.11 2.78
SSA 9.09 9.09
SSA 0 0
SSA 0 0
SSA 2.76 .39
SSA 9.72 6.94
SSA 7.69 2.88
SSA 9.6 7.6
SSA 6.35 1.59
SSA 4.59 6.42
SSA 8.6 1.2
SSA 2.41 0
SSA 11.21 0
SSA 7.69 2.56
SSA 0 0
SSA 14.86 1.35
SSA 10.18 2.18
SSA 11.63 10.25
SSA 8.33 2.78
SSA 6.88 .29
SSA 9.46 3.44
SSA 14.37 2.99
SSA 5.26 5.57
SSA 10.39 0
SSA 21.02 30.15
SSA 6.19 2.65
SSA 5.26 0
SSA 15.09 10.38
SSA 8 0
SSA 9.68 15.05
SSA 14.66 12.93
SSA 5.42 1.2
SSA 11.59 1.22
SSA 11.25 13.75
SSA 8.84 0
SSA 11.76 8.82
SSA 14.56 9.77
SSA 13.85 15.38
SSA 25 2.42
SSA 25 2.42
SSA 14.05 7.44
SSA 18.18 5.45
SSA 11.11 9.26
SSA 11.11 9.26
SSA 11.32 3.77
SSA 11.26 .66
SSA 9.5 6
SSA 22.1 5.52
SSA 2.38 2.38
SSA 19.72 15.65
SSA 20 5.71
SSA 12.65 11.47
SSA 15.64 3.32
SSA 29.58 7.04
SSA 37.93 3.45
SSA 31.03 5.17
NA&WA 2.5 2.5
NA&WA 16.33 0
NA&WA 26.74 0
NA&WA 20 7.5
NA&WA 0 0
NA&WA 0 0
NA&WA 6.96 10.43
NA&WA 31.76 0
NA&WA 11.18 0
NA&WA 5.4 3.35
NA&WA 18 0
NA&WA 14.29 7.56
NA&WA 12.71 5.93
NA&WA 0 0
NA&WA 8.2 5.05
NA&WA 12.31 0
NA&WA 0 0
NA&WA 0 0
NA&WA 2.5 5
NA&WA 9.17 3.33
NA&WA 5.71 0
NA&WA 6.92 4.62
NA&WA 11.67 5.83
NA&WA 5 20
NA&WA 2.65 .66
NA&WA 0 0
NA&WA 3.91 0
NA&WA 5.47 .78
NA&WA 4.17 0
NA&WA 10 3.53
NA&WA 1.79 0
NA&WA 6.78 3.39
NA&WA 17.95 5.13
NA&WA 0 0
NA&WA 4.29 0
NA&WA 14 0
NA&WA 7.83 14.75
NA&WA 0 2.5
NA&WA 2.52 1.68
NA&WA 8.7 11.3
NA&WA 10.22 6.52
NA&WA 6.5 4.17
NA&WA 12.67 8.28
NA&WA 25.33 5.33
NA&WA 10.33 4.5
NA&WA 9.08 2.69
NA&WA 6.33 6.58
NA&WA 18.32 3.05
NA&WA 7.34 10.13
NA&WA 5.65 1.21
NA&WA 8.33 3.33
NA&WA 18.1 5.71
NA&WA 6.12 3.4
NA&WA 11.89 2.84
NA&WA 7.81 1.56
NA&WA 7.14 5.36
NA&WA 19.64 3.57
NA&WA 20 5
NA&WA 10 10
NA&WA 18 6.67
NA&WA 27.27 3.44
NA&WA 17.05 9.68
NA&WA 17.05 9.68
NA&WA 28.04 24.3
NA&WA 40.91 16.67
C&SA 3.92 4.9
C&SA 14.71 1.47
C&SA 1.89 0
C&SA 6.73 2.88
C&SA 7.02 0
C&SA .73 4.36
C&SA 6 0
C&SA 2.14 .85
C&SA 5.05 3.03
C&SA 12.9 6.45
C&SA 0 .49
C&SA 0 .49
C&SA 23.33 4.44
C&SA 11.97 2.46
C&SA 0 0
C&SA 0 0
C&SA 0 0
C&SA 1.72 0
C&SA 19.15 2.13
C&SA 3.17 3.17
C&SA 0 .49
C&SA 1.67 .84
C&SA 20 4
C&SA 0 0
C&SA 4.29 1.43
C&SA 4.29 1.43
C&SA 15.92 9.8
C&SA 7.69 3.85
C&SA 7.34 3.37
C&SA 2.91 8.73
C&SA 6.94 .46
C&SA 3.77 3.77
C&SA 5.26 0
C&SA 8.87 3.7
C&SA 21.33 2.67
C&SA 25.98 2.36
C&SA 26.4 8.8
C&SA 8.49 9.43
C&SA 11.96 .48
C&SA 11.61 0
C&SA 11.4 2.92
C&SA 26.61 10.48
C&SA 22.45 3.06
C&SA 22.22 4.27
C&SA 22.22 4.27
C&SA 24.14 2.3
C&SA 30.56 5.56
C&SA 45.83 8.33
C&SA 54.55 0
C&SA 41.13 13.71
E&SEA 1.54 1.54
E&SEA 0 0
E&SEA 8.42 7.37
E&SEA 2.4 0
E&SEA 7.44 2.07
E&SEA 1.68 2.02
E&SEA 4 0
E&SEA 0 0
E&SEA 2.05 1.64
E&SEA 6.88 1.51
E&SEA 4.17 0
E&SEA 0 0
E&SEA 8.56 2.25
E&SEA 1.83 .61
E&SEA 2.94 2.94
E&SEA 9.84 1.64
E&SEA 13.04 8.7
E&SEA 11.84 2.63
E&SEA 4.17 0
E&SEA 3.72 2.48
E&SEA 19.2 2.23
E&SEA 3.27 2.45
E&SEA 1 1.33
E&SEA 1.85 0
E&SEA 0 0
E&SEA 7.69 10.77
E&SEA 1.75 0
E&SEA 4.84 1.61
E&SEA 0 0
E&SEA 1.8 6.8
E&SEA 3.41 7.01
E&SEA 5.38 .65
E&SEA 11.52 1.84
E&SEA 2.25 3.36
E&SEA 4.23 8.06
E&SEA 11.52 0
E&SEA 9.5 .71
E&SEA 10.83 1.88
E&SEA 6.76 3.6
E&SEA 10.36 2.25
E&SEA 11.3 4.45
E&SEA 11 5.6
E&SEA 12.68 5.18
E&SEA 9.74 5.22
E&SEA 15.79 0
E&SEA 13.5 6.75
E&SEA 20.8 8.4
Oceania 9.93 3.97
Oceania 0 0
Oceania 31.58 0
Oceania 7.35 2.94
Oceania 8.78 5.41
Oceania 6.25 0
Oceania 4.88 0
Oceania 13.33 0
Oceania 14 0
Oceania 11.76 0
Oceania 3.7 0
Oceania 0 0
Oceania 7.95 0
Oceania 36.84 0
Oceania 8.33 0
Oceania 8 2
Oceania 8.72 4.7
Oceania 9.84 8.2
Oceania 13.33 8.33
Oceania 12.5 15
Oceania 5.83 4.17
Oceania 7.89 6.58
Oceania 8.7 5.8
Oceania 12.17 5.22
Oceania 12.73 0
Oceania 10.14 4.05
Oceania 9.59 2.74
LAC 0 0
LAC 9.09 0
LAC 6.17 0
LAC 14.85 1.98
LAC 8.46 3.85
LAC 4.76 4.76
LAC 2 6
LAC 19.4 11.94
LAC 14.53 0
LAC 4.9 9.8
LAC 2.47 0
LAC 20.22 2.25
LAC 5.43 8.7
LAC 4.44 0
LAC 2.47 0
LAC 1.39 5.56
LAC 11.32 5.66
LAC 4.65 2.33
LAC 0 0
LAC 2.63 2.63
LAC 1.39 1.39
LAC 0 0
LAC 10.2 6.12
LAC 2.22 0
LAC 0 5.49
LAC 23.08 15.38
LAC 5.56 5.56
LAC 7.53 1.08
LAC 0 0
LAC 6.67 20
LAC 2.82 7.04
LAC 17.65 5.88
LAC 14.04 5.26
LAC 2.38 4.76
LAC 6.25 18.75
LAC 2.44 4.88
LAC 8.33 5.95
LAC 0 0
LAC 1.39 5.56
LAC 7.07 0
LAC 3.23 16.13
LAC 5.56 5.56
LAC 0 3.3
LAC 3.33 0
LAC 0 0
LAC 6.23 6.23
LAC 7.48 9.84
LAC 6.8 8.4
LAC 12.5 11.72
LAC 9.34 9.34
LAC 19 8
LAC 26.26 5.05
LAC 17.17 4.04
LAC 15.48 9.52
LAC 26.25 2.5
LAC 18.46 7.69
LAC 11.63 14.73
LAC 10.33 .58
LAC 12.6 16
LAC 12.6 16
LAC 7.75 3.79
LAC 12.28 21.05
LAC 18.06 13.55
LAC 9.8 13.73
LAC 13.87 19.71
LAC 16.57 6.24
LAC 11.9 11.9
LAC 12.8 1.22
LAC 17.5 3.75
LAC 18.44 17.23
LAC 8.18 8.35
LAC 3.23 3.23
LAC 20 6.67
LAC 3.33 10
LAC 17.57 .68
LAC 16.93 1.95
LAC 15.79 12.28
LAC 12.77 22.77
LAC 16.06 18.25
LAC 17.52 20.44
LAC 21.25 7.5
LAC 18.06 8.39
LAC 30.34 11.24
LAC 22.03 4.87
LAC 22.29 7.23
LAC 18.75 10.63
LAC 18.75 10.63
LAC 26.67 6.06
LAC 11.76 17.65
LAC 25.49 11.76
E&NA 3 0
E&NA 1.45 .29
E&NA 6.52 6.52
E&NA 4 4
E&NA 1.23 1.23
E&NA 13.33 0
E&NA 6.52 2.17
E&NA 2.5 0
E&NA 1.45 0
E&NA 14.57 7.95
E&NA 1.56 1.25
E&NA 11.11 9.52
E&NA 4 0
E&NA 1 0
E&NA 2.37 1.18
E&NA 6.67 13.33
E&NA 4 8
E&NA 5 0
E&NA 0 0
E&NA 2 0
E&NA 5.52 1.15
E&NA 0 0
E&NA 7.32 9.76
E&NA 10.17 8.47
E&NA 0 0
E&NA 1 0
E&NA 14.29 0
E&NA 3.57 1.79
E&NA 0 .25
E&NA 4.59 .92
E&NA 1.45 .29
E&NA 6.52 4.35
E&NA 8.06 3.46
E&NA 2.17 2.17
E&NA .38 .25
E&NA 13.33 0
E&NA 12.5 1.47
E&NA 0 0
E&NA 2.9 2.9
E&NA 4 0
E&NA 10.17 8.47
E&NA 0 0
E&NA 4 5.33
E&NA 0 0
E&NA 2.06 1.03
E&NA 0 0
E&NA 2 1
E&NA 10.91 1.21
E&NA 4.17 16.67
E&NA 20.83 8.33
E&NA .58 .29
E&NA 8.82 2.21
E&NA 7.85 3.69
E&NA 6.94 3.47
E&NA 4.87 1.86
E&NA 8.25 8
E&NA 2.59 .86
E&NA 7.67 4.67
E&NA 8 3.33
E&NA 7.67 4.67
E&NA 3.79 6.44
E&NA 7.76 3.81
E&NA 12.83 4.57
E&NA 8.5 10.5
E&NA 8 7
E&NA 19.55 4.95
E&NA 7.16 6.88
E&NA 16.46 1.83
E&NA 8.61 5.96
E&NA 14 2.67
E&NA 14 2.67
E&NA 10.64 1.42
E&NA 9.35 7.19
E&NA 11.19 11.19
E&NA 12.8 11.6
E&NA 12.3 5.74
E&NA 15.83 1.67
E&NA 15.83 1.67
E&NA 17.5 10.83
E&NA 11.56 2
E&NA 8.18 5.45
E&NA 19.8 3.96
E&NA 9.67 2.33
E&NA 12 3
E&NA 18 4
E&NA 8.5 10.5
E&NA 13 4
E&NA 12 7
E&NA 11.5 7.5
E&NA 15.08 3.02
E&NA 24.87 4.57
E&NA 8 7.43
E&NA 12.5 2.5
E&NA 15.9 7.11
E&NA 16.96 6.09
E&NA 14 14.67
E&NA 5.33 6.67
E&NA 20 13.33
E&NA 12 16
E&NA 4 1.33
E&NA 8.08 6.06
E&NA 17.73 11.35
E&NA 13.48 5.67
E&NA 14.5 11
E&NA 6.77 8.65
E&NA 11.38 3.35
E&NA 9.52 11.11
E&NA 6.35 14.29
E&NA 11.04 5.84
E&NA 11.67 8.33
E&NA 6.67 5
E&NA 13.33 11.67
E&NA 15.64 18.44
E&NA 12.57 10.29
E&NA 11.41 6.18
E&NA 13.69 8
E&NA 13.13 5.63
E&NA 6.82 4.55
E&NA 6.18 3.86
E&NA 13.91 5.22
E&NA 11.18 5.92
E&NA 15.89 5.96
E&NA 12.58 3.31
E&NA 18.8 12.4
E&NA 15 14.5
E&NA 12.63 7.4
E&NA 14.5 14.5
E&NA 14.5 5.5
E&NA 14.07 4.52
E&NA 12.44 6.54
E&NA 6.25 8.33
E&NA 11.48 6.56
E&NA 12.22 5.56
E&NA 14.29 4.76
E&NA 14.29 4.76
E&NA 10 5
E&NA 12.07 7.76
E&NA 15.22 10
E&NA 14.67 9.33
E&NA 17.33 14
E&NA 12.4 16.4
E&NA 12.4 16.4
E&NA 14.29 5.71
E&NA 22.86 15.71
E&NA 22.92 5.83
E&NA 25.42 7.08
E&NA 22.08 7.5
E&NA 12.87 4.95
E&NA 13.86 5.94
E&NA 16.09 10.87
E&NA 16.52 7.83
E&NA 11.48 8.2
E&NA 13.11 12.57
E&NA 35 1.67
E&NA 20 13.33
E&NA 16.67 5.56
E&NA 26.75 7.9
E&NA 15.64 18.99
E&NA 24.69 6.28
E&NA 22.35 18.99
E&NA 16.96 5.43
E&NA 21.43 14.29
E&NA 28.57 10.71
E&NA 10.71 14.29
E&NA 7.14 3.57
E&NA 12.31 10.92
E&NA 14.75 13.11
E&NA 13.97 16.76
E&NA 21 6
E&NA 22 7
E&NA 26 13.33
E&NA 24.32 4.73
E&NA 22.76 9.76
E&NA 24.44 18.25
E&NA 22.39 4.48
E&NA 25.84 9.42
E&NA 16.05 12.89
E&NA 18.62 15.76
E&NA 29.63 7.41
E&NA 17.28 4.94
E&NA 23.33 18.33
E&NA 26 8
E&NA 16 3
E&NA 18.72 8.67
E&NA 33.1 13.24
E&NA 16.17 12.77
E&NA 15.56 10
E&NA 25.74 11.88
E&NA 17 9
E&NA 17.5 18.5
E&NA 20.8 19.6
E&NA 25 11.67
E&NA 26 8.67
E&NA 20.63 13.47
E&NA 18.93 8.28
E&NA 20.71 13.61
E&NA 20.71 13.61
E&NA 20 6.67

here the graph I produce, but without total numbers rof obs per Geo_regions

set scheme s1color
gr bar Maleyounger40percentage Femaleyounger40percentage ,over(Geo_regions, sort(2) label(angle(0) labsize(tiny))) ///
title ("Member of Parliament younger than 30 years old") ///
subtitle (" 2008-2023 ") ///
note("", size(vsmall)) ///
legend(order (1 "Men" 2 "Women" ) rows(1) size(small) ) ///
ylabel(0 "0 %" 10 "10%" 20 "20%" 30 "30%" 40 "40%" 50 "50%" , angle(0)labsize(small) ) ///
bar(1, fcolor(emidblue) lcolor(emidblue) ) bar(2, fcolor(red) lcolor(red)) ///
ytitle ("Percentage")

Thank you for your help

Anna

[Nick Cox]

You've got repeated examples -- explicit in #2 #3 #5 and implicit in #4 -- that graph bar is a dead end here, or at least not very flexible for what you want. For flexibility, use twoway bar.

[Nick Cox]

This was posted at a time when I was much busier and could see that the example needed much more work before the question could be answered fully.

No one else has yet stepped into the breach.

On reading it more carefully I have to suggest that a bar chart (of means) is too reductive here, so I propose quantile plots instead.

In terms of details, I note that the variable names imply MPs (Members of Parliament, not Military Police) under 40, not 30 as the code in #6 implies. Also, there seems little point in extending the vertical axis in the first graph to 50% when means are much lower.

Here first is your data example rewritten with dataex.

I used myaxis from the Stata Journal to get the sorting you would get with graph bar.

I used stripplot from SSC to get the quantile plots with added means.

The trick for showing number of observations in the panel (facet) subtitles was written up in https://journals.sagepub.com/doi/pdf...6867X211063413

* Example generated by -dataex-. For more info, type help dataex
clear
input str7 Geo_regions double(Maleyounger40percentage Femaleyounger40percentage)
"SSA"         0     0
"SSA"     12.31 12.31
"SSA"     11.11  8.33
"SSA"     20.31  1.56
"SSA"     12.82 15.38
"SSA"      2.78  1.39
"SSA"      8.03    .8
"SSA"      2.75  2.75
"SSA"         0  7.32
"SSA"      5.52  5.52
"SSA"     15.07  1.37
"SSA"      5.52  5.52
"SSA"         0     0
"SSA"      2.78  1.67
"SSA"      3.85     0
"SSA"      7.76   .86
"SSA"     27.78  5.56
"SSA"     10.91  2.18
"SSA"         0     0
"SSA"       7.8  3.55
"SSA"      4.82     0
"SSA"     11.43    10
"SSA"     14.29 17.14
"SSA"      6.67  3.33
"SSA"     10.91  5.45
"SSA"         0     0
"SSA"     14.71  5.88
"SSA"     10.93  1.09
"SSA"      5.94  6.93
"SSA"      4.55  1.52
"SSA"      7.69   1.1
"SSA"      11.6  10.4
"SSA"     11.11     0
"SSA"         0     0
"SSA"      1.74  2.61
"SSA"      2.78  1.67
"SSA"         5  17.5
"SSA"     14.55  7.27
"SSA"     13.27  2.04
"SSA"     11.11  2.78
"SSA"      9.09  9.09
"SSA"         0     0
"SSA"         0     0
"SSA"      2.76   .39
"SSA"      9.72  6.94
"SSA"      7.69  2.88
"SSA"       9.6   7.6
"SSA"      6.35  1.59
"SSA"      4.59  6.42
"SSA"       8.6   1.2
"SSA"      2.41     0
"SSA"     11.21     0
"SSA"      7.69  2.56
"SSA"         0     0
"SSA"     14.86  1.35
"SSA"     10.18  2.18
"SSA"     11.63 10.25
"SSA"      8.33  2.78
"SSA"      6.88   .29
"SSA"      9.46  3.44
"SSA"     14.37  2.99
"SSA"      5.26  5.57
"SSA"     10.39     0
"SSA"     21.02 30.15
"SSA"      6.19  2.65
"SSA"      5.26     0
"SSA"     15.09 10.38
"SSA"         8     0
"SSA"      9.68 15.05
"SSA"     14.66 12.93
"SSA"      5.42   1.2
"SSA"     11.59  1.22
"SSA"     11.25 13.75
"SSA"      8.84     0
"SSA"     11.76  8.82
"SSA"     14.56  9.77
"SSA"     13.85 15.38
"SSA"        25  2.42
"SSA"        25  2.42
"SSA"     14.05  7.44
"SSA"     18.18  5.45
"SSA"     11.11  9.26
"SSA"     11.11  9.26
"SSA"     11.32  3.77
"SSA"     11.26   .66
"SSA"       9.5     6
"SSA"      22.1  5.52
"SSA"      2.38  2.38
"SSA"     19.72 15.65
"SSA"        20  5.71
"SSA"     12.65 11.47
"SSA"     15.64  3.32
"SSA"     29.58  7.04
"SSA"     37.93  3.45
"SSA"     31.03  5.17
"NA&WA"     2.5   2.5
"NA&WA"   16.33     0
"NA&WA"   26.74     0
"NA&WA"      20   7.5
"NA&WA"       0     0
"NA&WA"       0     0
"NA&WA"    6.96 10.43
"NA&WA"   31.76     0
"NA&WA"   11.18     0
"NA&WA"     5.4  3.35
"NA&WA"      18     0
"NA&WA"   14.29  7.56
"NA&WA"   12.71  5.93
"NA&WA"       0     0
"NA&WA"     8.2  5.05
"NA&WA"   12.31     0
"NA&WA"       0     0
"NA&WA"       0     0
"NA&WA"     2.5     5
"NA&WA"    9.17  3.33
"NA&WA"    5.71     0
"NA&WA"    6.92  4.62
"NA&WA"   11.67  5.83
"NA&WA"       5    20
"NA&WA"    2.65   .66
"NA&WA"       0     0
"NA&WA"    3.91     0
"NA&WA"    5.47   .78
"NA&WA"    4.17     0
"NA&WA"      10  3.53
"NA&WA"    1.79     0
"NA&WA"    6.78  3.39
"NA&WA"   17.95  5.13
"NA&WA"       0     0
"NA&WA"    4.29     0
"NA&WA"      14     0
"NA&WA"    7.83 14.75
"NA&WA"       0   2.5
"NA&WA"    2.52  1.68
"NA&WA"     8.7  11.3
"NA&WA"   10.22  6.52
"NA&WA"     6.5  4.17
"NA&WA"   12.67  8.28
"NA&WA"   25.33  5.33
"NA&WA"   10.33   4.5
"NA&WA"    9.08  2.69
"NA&WA"    6.33  6.58
"NA&WA"   18.32  3.05
"NA&WA"    7.34 10.13
"NA&WA"    5.65  1.21
"NA&WA"    8.33  3.33
"NA&WA"    18.1  5.71
"NA&WA"    6.12   3.4
"NA&WA"   11.89  2.84
"NA&WA"    7.81  1.56
"NA&WA"    7.14  5.36
"NA&WA"   19.64  3.57
"NA&WA"      20     5
"NA&WA"      10    10
"NA&WA"      18  6.67
"NA&WA"   27.27  3.44
"NA&WA"   17.05  9.68
"NA&WA"   17.05  9.68
"NA&WA"   28.04  24.3
"NA&WA"   40.91 16.67
"C&SA"     3.92   4.9
"C&SA"    14.71  1.47
"C&SA"     1.89     0
"C&SA"     6.73  2.88
"C&SA"     7.02     0
"C&SA"      .73  4.36
"C&SA"        6     0
"C&SA"     2.14   .85
"C&SA"     5.05  3.03
"C&SA"     12.9  6.45
"C&SA"        0   .49
"C&SA"        0   .49
"C&SA"    23.33  4.44
"C&SA"    11.97  2.46
"C&SA"        0     0
"C&SA"        0     0
"C&SA"        0     0
"C&SA"     1.72     0
"C&SA"    19.15  2.13
"C&SA"     3.17  3.17
"C&SA"        0   .49
"C&SA"     1.67   .84
"C&SA"       20     4
"C&SA"        0     0
"C&SA"     4.29  1.43
"C&SA"     4.29  1.43
"C&SA"    15.92   9.8
"C&SA"     7.69  3.85
"C&SA"     7.34  3.37
"C&SA"     2.91  8.73
"C&SA"     6.94   .46
"C&SA"     3.77  3.77
"C&SA"     5.26     0
"C&SA"     8.87   3.7
"C&SA"    21.33  2.67
"C&SA"    25.98  2.36
"C&SA"     26.4   8.8
"C&SA"     8.49  9.43
"C&SA"    11.96   .48
"C&SA"    11.61     0
"C&SA"     11.4  2.92
"C&SA"    26.61 10.48
"C&SA"    22.45  3.06
"C&SA"    22.22  4.27
"C&SA"    22.22  4.27
"C&SA"    24.14   2.3
"C&SA"    30.56  5.56
"C&SA"    45.83  8.33
"C&SA"    54.55     0
"C&SA"    41.13 13.71
"E&SEA"    1.54  1.54
"E&SEA"       0     0
"E&SEA"    8.42  7.37
"E&SEA"     2.4     0
"E&SEA"    7.44  2.07
"E&SEA"    1.68  2.02
"E&SEA"       4     0
"E&SEA"       0     0
"E&SEA"    2.05  1.64
"E&SEA"    6.88  1.51
"E&SEA"    4.17     0
"E&SEA"       0     0
"E&SEA"    8.56  2.25
"E&SEA"    1.83   .61
"E&SEA"    2.94  2.94
"E&SEA"    9.84  1.64
"E&SEA"   13.04   8.7
"E&SEA"   11.84  2.63
"E&SEA"    4.17     0
"E&SEA"    3.72  2.48
"E&SEA"    19.2  2.23
"E&SEA"    3.27  2.45
"E&SEA"       1  1.33
"E&SEA"    1.85     0
"E&SEA"       0     0
"E&SEA"    7.69 10.77
"E&SEA"    1.75     0
"E&SEA"    4.84  1.61
"E&SEA"       0     0
"E&SEA"     1.8   6.8
"E&SEA"    3.41  7.01
"E&SEA"    5.38   .65
"E&SEA"   11.52  1.84
"E&SEA"    2.25  3.36
"E&SEA"    4.23  8.06
"E&SEA"   11.52     0
"E&SEA"     9.5   .71
"E&SEA"   10.83  1.88
"E&SEA"    6.76   3.6
"E&SEA"   10.36  2.25
"E&SEA"    11.3  4.45
"E&SEA"      11   5.6
"E&SEA"   12.68  5.18
"E&SEA"    9.74  5.22
"E&SEA"   15.79     0
"E&SEA"    13.5  6.75
"E&SEA"    20.8   8.4
"Oceania"  9.93  3.97
"Oceania"     0     0
"Oceania" 31.58     0
"Oceania"  7.35  2.94
"Oceania"  8.78  5.41
"Oceania"  6.25     0
"Oceania"  4.88     0
"Oceania" 13.33     0
"Oceania"    14     0
"Oceania" 11.76     0
"Oceania"   3.7     0
"Oceania"     0     0
"Oceania"  7.95     0
"Oceania" 36.84     0
"Oceania"  8.33     0
"Oceania"     8     2
"Oceania"  8.72   4.7
"Oceania"  9.84   8.2
"Oceania" 13.33  8.33
"Oceania"  12.5    15
"Oceania"  5.83  4.17
"Oceania"  7.89  6.58
"Oceania"   8.7   5.8
"Oceania" 12.17  5.22
"Oceania" 12.73     0
"Oceania" 10.14  4.05
"Oceania"  9.59  2.74
"LAC"         0     0
"LAC"      9.09     0
"LAC"      6.17     0
"LAC"     14.85  1.98
"LAC"      8.46  3.85
"LAC"      4.76  4.76
"LAC"         2     6
"LAC"      19.4 11.94
"LAC"     14.53     0
"LAC"       4.9   9.8
"LAC"      2.47     0
"LAC"     20.22  2.25
"LAC"      5.43   8.7
"LAC"      4.44     0
"LAC"      2.47     0
"LAC"      1.39  5.56
"LAC"     11.32  5.66
"LAC"      4.65  2.33
"LAC"         0     0
"LAC"      2.63  2.63
"LAC"      1.39  1.39
"LAC"         0     0
"LAC"      10.2  6.12
"LAC"      2.22     0
"LAC"         0  5.49
"LAC"     23.08 15.38
"LAC"      5.56  5.56
"LAC"      7.53  1.08
"LAC"         0     0
"LAC"      6.67    20
"LAC"      2.82  7.04
"LAC"     17.65  5.88
"LAC"     14.04  5.26
"LAC"      2.38  4.76
"LAC"      6.25 18.75
"LAC"      2.44  4.88
"LAC"      8.33  5.95
"LAC"         0     0
"LAC"      1.39  5.56
"LAC"      7.07     0
"LAC"      3.23 16.13
"LAC"      5.56  5.56
"LAC"         0   3.3
"LAC"      3.33     0
"LAC"         0     0
"LAC"      6.23  6.23
"LAC"      7.48  9.84
"LAC"       6.8   8.4
"LAC"      12.5 11.72
"LAC"      9.34  9.34
"LAC"        19     8
"LAC"     26.26  5.05
"LAC"     17.17  4.04
"LAC"     15.48  9.52
"LAC"     26.25   2.5
"LAC"     18.46  7.69
"LAC"     11.63 14.73
"LAC"     10.33   .58
"LAC"      12.6    16
"LAC"      12.6    16
"LAC"      7.75  3.79
"LAC"     12.28 21.05
"LAC"     18.06 13.55
"LAC"       9.8 13.73
"LAC"     13.87 19.71
"LAC"     16.57  6.24
"LAC"      11.9  11.9
"LAC"      12.8  1.22
"LAC"      17.5  3.75
"LAC"     18.44 17.23
"LAC"      8.18  8.35
"LAC"      3.23  3.23
"LAC"        20  6.67
"LAC"      3.33    10
"LAC"     17.57   .68
"LAC"     16.93  1.95
"LAC"     15.79 12.28
"LAC"     12.77 22.77
"LAC"     16.06 18.25
"LAC"     17.52 20.44
"LAC"     21.25   7.5
"LAC"     18.06  8.39
"LAC"     30.34 11.24
"LAC"     22.03  4.87
"LAC"     22.29  7.23
"LAC"     18.75 10.63
"LAC"     18.75 10.63
"LAC"     26.67  6.06
"LAC"     11.76 17.65
"LAC"     25.49 11.76
"E&NA"        3     0
"E&NA"     1.45   .29
"E&NA"     6.52  6.52
"E&NA"        4     4
"E&NA"     1.23  1.23
"E&NA"    13.33     0
"E&NA"     6.52  2.17
"E&NA"      2.5     0
"E&NA"     1.45     0
"E&NA"    14.57  7.95
"E&NA"     1.56  1.25
"E&NA"    11.11  9.52
"E&NA"        4     0
"E&NA"        1     0
"E&NA"     2.37  1.18
"E&NA"     6.67 13.33
"E&NA"        4     8
"E&NA"        5     0
"E&NA"        0     0
"E&NA"        2     0
"E&NA"     5.52  1.15
"E&NA"        0     0
"E&NA"     7.32  9.76
"E&NA"    10.17  8.47
"E&NA"        0     0
"E&NA"        1     0
"E&NA"    14.29     0
"E&NA"     3.57  1.79
"E&NA"        0   .25
"E&NA"     4.59   .92
"E&NA"     1.45   .29
"E&NA"     6.52  4.35
"E&NA"     8.06  3.46
"E&NA"     2.17  2.17
"E&NA"      .38   .25
"E&NA"    13.33     0
"E&NA"     12.5  1.47
"E&NA"        0     0
"E&NA"      2.9   2.9
"E&NA"        4     0
"E&NA"    10.17  8.47
"E&NA"        0     0
"E&NA"        4  5.33
"E&NA"        0     0
"E&NA"     2.06  1.03
"E&NA"        0     0
"E&NA"        2     1
"E&NA"    10.91  1.21
"E&NA"     4.17 16.67
"E&NA"    20.83  8.33
"E&NA"      .58   .29
"E&NA"     8.82  2.21
"E&NA"     7.85  3.69
"E&NA"     6.94  3.47
"E&NA"     4.87  1.86
"E&NA"     8.25     8
"E&NA"     2.59   .86
"E&NA"     7.67  4.67
"E&NA"        8  3.33
"E&NA"     7.67  4.67
"E&NA"     3.79  6.44
"E&NA"     7.76  3.81
"E&NA"    12.83  4.57
"E&NA"      8.5  10.5
"E&NA"        8     7
"E&NA"    19.55  4.95
"E&NA"     7.16  6.88
"E&NA"    16.46  1.83
"E&NA"     8.61  5.96
"E&NA"       14  2.67
"E&NA"       14  2.67
"E&NA"    10.64  1.42
"E&NA"     9.35  7.19
"E&NA"    11.19 11.19
"E&NA"     12.8  11.6
"E&NA"     12.3  5.74
"E&NA"    15.83  1.67
"E&NA"    15.83  1.67
"E&NA"     17.5 10.83
"E&NA"    11.56     2
"E&NA"     8.18  5.45
"E&NA"     19.8  3.96
"E&NA"     9.67  2.33
"E&NA"       12     3
"E&NA"       18     4
"E&NA"      8.5  10.5
"E&NA"       13     4
"E&NA"       12     7
"E&NA"     11.5   7.5
"E&NA"    15.08  3.02
"E&NA"    24.87  4.57
"E&NA"        8  7.43
"E&NA"     12.5   2.5
"E&NA"     15.9  7.11
"E&NA"    16.96  6.09
"E&NA"       14 14.67
"E&NA"     5.33  6.67
"E&NA"       20 13.33
"E&NA"       12    16
"E&NA"        4  1.33
"E&NA"     8.08  6.06
"E&NA"    17.73 11.35
"E&NA"    13.48  5.67
"E&NA"     14.5    11
"E&NA"     6.77  8.65
"E&NA"    11.38  3.35
"E&NA"     9.52 11.11
"E&NA"     6.35 14.29
"E&NA"    11.04  5.84
"E&NA"    11.67  8.33
"E&NA"     6.67     5
"E&NA"    13.33 11.67
"E&NA"    15.64 18.44
"E&NA"    12.57 10.29
"E&NA"    11.41  6.18
"E&NA"    13.69     8
"E&NA"    13.13  5.63
"E&NA"     6.82  4.55
"E&NA"     6.18  3.86
"E&NA"    13.91  5.22
"E&NA"    11.18  5.92
"E&NA"    15.89  5.96
"E&NA"    12.58  3.31
"E&NA"     18.8  12.4
"E&NA"       15  14.5
"E&NA"    12.63   7.4
"E&NA"     14.5  14.5
"E&NA"     14.5   5.5
"E&NA"    14.07  4.52
"E&NA"    12.44  6.54
"E&NA"     6.25  8.33
"E&NA"    11.48  6.56
"E&NA"    12.22  5.56
"E&NA"    14.29  4.76
"E&NA"    14.29  4.76
"E&NA"       10     5
"E&NA"    12.07  7.76
"E&NA"    15.22    10
"E&NA"    14.67  9.33
"E&NA"    17.33    14
"E&NA"     12.4  16.4
"E&NA"     12.4  16.4
"E&NA"    14.29  5.71
"E&NA"    22.86 15.71
"E&NA"    22.92  5.83
"E&NA"    25.42  7.08
"E&NA"    22.08   7.5
"E&NA"    12.87  4.95
"E&NA"    13.86  5.94
"E&NA"    16.09 10.87
"E&NA"    16.52  7.83
"E&NA"    11.48   8.2
"E&NA"    13.11 12.57
"E&NA"       35  1.67
"E&NA"       20 13.33
"E&NA"    16.67  5.56
"E&NA"    26.75   7.9
"E&NA"    15.64 18.99
"E&NA"    24.69  6.28
"E&NA"    22.35 18.99
"E&NA"    16.96  5.43
"E&NA"    21.43 14.29
"E&NA"    28.57 10.71
"E&NA"    10.71 14.29
"E&NA"     7.14  3.57
"E&NA"    12.31 10.92
"E&NA"    14.75 13.11
"E&NA"    13.97 16.76
"E&NA"       21     6
"E&NA"       22     7
"E&NA"       26 13.33
"E&NA"    24.32  4.73
"E&NA"    22.76  9.76
"E&NA"    24.44 18.25
"E&NA"    22.39  4.48
"E&NA"    25.84  9.42
"E&NA"    16.05 12.89
"E&NA"    18.62 15.76
"E&NA"    29.63  7.41
"E&NA"    17.28  4.94
"E&NA"    23.33 18.33
"E&NA"       26     8
"E&NA"       16     3
"E&NA"    18.72  8.67
"E&NA"     33.1 13.24
"E&NA"    16.17 12.77
"E&NA"    15.56    10
"E&NA"    25.74 11.88
"E&NA"       17     9
"E&NA"     17.5  18.5
"E&NA"     20.8  19.6
"E&NA"       25 11.67
"E&NA"       26  8.67
"E&NA"    20.63 13.47
"E&NA"    18.93  8.28
"E&NA"    20.71 13.61
"E&NA"    20.71 13.61
"E&NA"       20  6.67
end


set scheme s1color
gr bar Maleyounger40percentage Femaleyounger40percentage ,over(Geo_regions, sort(2) label(angle(0) labsize(tiny))) ///
title ("Member of Parliament younger than 30 years old") ///
subtitle (" 2008-2023 ") ///
note("", size(vsmall)) ///
legend(order (1 "Men" 2 "Women" ) rows(1) size(small) ) ///
ylabel(0 "0 %" 10 "10%" 20 "20%" 30 "30%" 40 "40%" 50 "50%" , angle(0)labsize(small) ) ///
bar(1, fcolor(emidblue) lcolor(emidblue) ) bar(2, fcolor(red) lcolor(red)) ///
ytitle ("Percentage") name(MP1, replace) 

bysort Geo_regions : gen Geo_regions2 = Geo_regions + " (" + strofreal(_N) + ")" 

myaxis Geo_regions3=Geo_regions2, sort(mean Female)

stripplot Male Female, by(Geo_regions3, subtitle(% Members of Parliament younger than 40) note("") row(2)) centre refline vertical cumul cumprob xla(1 "Men" 2 "Women") ytitle("") name(MP2, replace)