how to create histogram for sub-samples by year averages?

[Ngozi ADELEYE]

Dear all,

I use Stata16 and will appreciate any help with creating comparative histogram by the 5-year averages.

I tried a couple of histogram codes that I am familiar with but none could plot the histogram by region and 5-year averages.

Please here is my data:
----------------------- copy starting from the next line -----------------------

* Example generated by -dataex-. For more info, type help dataex
clear
input byte r_id str26 region float(period year) double(gfd shr kme klf)
1 "East Asia & Pacific"        1 1  8325.703482570614 221713507.51180354 312545535391.22266  9.884943733931932
1 "East Asia & Pacific"        2 2  9962.102881752555  291994199.0371205  374296140523.1998 10.281044165113197
2 "Europe & Central Asia"      1 1   24102.8204969368   538919571.786615 538401194443.77637 19.022858084454136
2 "Europe & Central Asia"      2 2  25799.98590965781  654494471.3396385  581745338410.1671 23.309416601936224
3 "Latin America & Caribbean"  1 1  9455.280580949593  80081604.26769644  76151826017.24394  6.093767684310448
3 "Latin America & Caribbean"  2 2   9545.11053660585 106220095.91957214  98404982310.05264  7.355575546132802
4 "Middle East & North Africa" 1 1  7373.541711250463   89946551.4761219  81922279949.52919 16.905612650025958
4 "Middle East & North Africa" 2 2 7777.9616878091565  99544773.86252138 106317425321.04172 19.484252374025836
5 "North America"              1 1  49559.27810369779           83961914       2.209272e+11  7.247645902684366
5 "North America"              2 2  53454.28768786681           98022165 270455750000.00006  7.239042277091585
6 "South Asia"                 1 1 1368.5156808676866  12190005.91249332        2.40586e+10  11.19949571592668
6 "South Asia"                 2 2 1780.9751084779448 23091459.198763825        3.49224e+10 10.671254320423737
7 "Sub-Saharan Africa"         1 1  1645.103030610854 38776587.938939884 29995171741.764538 10.925456728424516
7 "Sub-Saharan Africa"         2 2 1682.6993478548713  45598841.52158082  32372205773.37823  10.55520839362721
end
label values year fiveyr
label def fiveyr 1 "2010-2014", modify
label def fiveyr 2 "2015-2019", modify

------------------ copy up to and including the previous line ------------------
Thank you.
Ngozi

[Andrew Musau]

A region and 5-year period define an observation in your dataset. If you want to compare means, consider a bar graph.

* Example generated by -dataex-. For more info, type help dataex
clear
input byte r_id str26 region float(period year) double(gfd shr kme klf)
1 "East Asia & Pacific"        1 1  8325.703482570614 221713507.51180354 312545535391.22266  9.884943733931932
1 "East Asia & Pacific"        2 2  9962.102881752555  291994199.0371205  374296140523.1998 10.281044165113197
2 "Europe & Central Asia"      1 1   24102.8204969368   538919571.786615 538401194443.77637 19.022858084454136
2 "Europe & Central Asia"      2 2  25799.98590965781  654494471.3396385  581745338410.1671 23.309416601936224
3 "Latin America & Caribbean"  1 1  9455.280580949593  80081604.26769644  76151826017.24394  6.093767684310448
3 "Latin America & Caribbean"  2 2   9545.11053660585 106220095.91957214  98404982310.05264  7.355575546132802
4 "Middle East & North Africa" 1 1  7373.541711250463   89946551.4761219  81922279949.52919 16.905612650025958
4 "Middle East & North Africa" 2 2 7777.9616878091565  99544773.86252138 106317425321.04172 19.484252374025836
5 "North America"              1 1  49559.27810369779           83961914       2.209272e+11  7.247645902684366
5 "North America"              2 2  53454.28768786681           98022165 270455750000.00006  7.239042277091585
6 "South Asia"                 1 1 1368.5156808676866  12190005.91249332        2.40586e+10  11.19949571592668
6 "South Asia"                 2 2 1780.9751084779448 23091459.198763825        3.49224e+10 10.671254320423737
7 "Sub-Saharan Africa"         1 1  1645.103030610854 38776587.938939884 29995171741.764538 10.925456728424516
7 "Sub-Saharan Africa"         2 2 1682.6993478548713  45598841.52158082  32372205773.37823  10.55520839362721
end
label values year fiveyr
label def fiveyr 1 "2010-2014", modify
label def fiveyr 2 "2015-2019", modify

gr hbar gfd, over(year) over(region) asyvars ytitle("Some description") bar(1, color(red%50)) bar(2, color(blue%50))scheme(s1color)

[Ngozi ADELEYE]

I am so very grateful, Andrew...thanks a million!!!
Please how can modify the command to plot the bar chart by variables by year and regions?
For instance show GFD for 2010-2014, and 2015-2019 by regions such that 201-2014 are clustered together and same for 2015-2019. I believe that will show the comparison distinctly.
Thanks.

[Nick Cox]

Thanks for the data example, I can't see any histogram code here and, perhaps more crucially, I don't understand why a histogram could be of use or interest for these data.

There are four outcome variables and I don't know what any of them means, but here is some technique with klf

set scheme s1color 
graph dot (asis) klf, over(year) over(region, sort(1) descending) linetype(line) lines(lc(gs12) lw(vthin)) ytitle(klf) ysc(alt)

Conventionally, time goes on the horizontal but wanting to respect the region names implies otherwise. Similarly, asyvars is available as an option, but to me it makes the graph harder to understand. The main imperative is to explain klf

[Andrew Musau]

I am so very grateful, Andrew...thanks a million!!!
Please how can modify the command to plot the bar chart by variables by year and regions?
For instance show GFD for 2010-2014, and 2015-2019 by regions such that 201-2014 are clustered together and same for 2015-2019. I believe that will show the comparison distinctly.
Thanks.

The scales are too different, so you will need to generate the graphs one by one and then combine. Below I use grc1leg by Mead Over.

net install grc1leg2.pkg, from (http://digital.cgdev.org/doc/stata/MO/Misc/)

* Example generated by -dataex-. For more info, type help dataex
clear
input byte r_id str26 region float(period year) double(gfd shr kme klf)
1 "East Asia & Pacific"        1 1  8325.703482570614 221713507.51180354 312545535391.22266  9.884943733931932
1 "East Asia & Pacific"        2 2  9962.102881752555  291994199.0371205  374296140523.1998 10.281044165113197
2 "Europe & Central Asia"      1 1   24102.8204969368   538919571.786615 538401194443.77637 19.022858084454136
2 "Europe & Central Asia"      2 2  25799.98590965781  654494471.3396385  581745338410.1671 23.309416601936224
3 "Latin America & Caribbean"  1 1  9455.280580949593  80081604.26769644  76151826017.24394  6.093767684310448
3 "Latin America & Caribbean"  2 2   9545.11053660585 106220095.91957214  98404982310.05264  7.355575546132802
4 "Middle East & North Africa" 1 1  7373.541711250463   89946551.4761219  81922279949.52919 16.905612650025958
4 "Middle East & North Africa" 2 2 7777.9616878091565  99544773.86252138 106317425321.04172 19.484252374025836
5 "North America"              1 1  49559.27810369779           83961914       2.209272e+11  7.247645902684366
5 "North America"              2 2  53454.28768786681           98022165 270455750000.00006  7.239042277091585
6 "South Asia"                 1 1 1368.5156808676866  12190005.91249332        2.40586e+10  11.19949571592668
6 "South Asia"                 2 2 1780.9751084779448 23091459.198763825        3.49224e+10 10.671254320423737
7 "Sub-Saharan Africa"         1 1  1645.103030610854 38776587.938939884 29995171741.764538 10.925456728424516
7 "Sub-Saharan Africa"         2 2 1682.6993478548713  45598841.52158082  32372205773.37823  10.55520839362721
end
label values year fiveyr
label def fiveyr 1 "2010-2014", modify
label def fiveyr 2 "2015-2019", modify

local graphs
foreach var in gfd shr kme klf{
gr hbar `var', over(year) over(region) asyvars bar(1, color(red%50)) bar(2, color(blue%50))scheme(s1color) saving(`var', replace)
local graphs "`graphs' `var'.gph"
}
grc1leg2 `graphs', scheme(s1color)

You need to adjust the labels, y-titles, etc., which I do not do. Therefore it may be easier to create each graph separately. Also, there may be benefits to sorting the graphs, but this may change the order across variables (by categories).

[Ngozi ADELEYE]

Oh I see, this is so very helpful...thanks Andrew.
I can use this.
May God bless you real good.
...gracias!!!

[Nick Cox]

This is another take, now understanding that you want to see all four measures.

I usually want to avoid "mean of", graph combine and messing with legends whenever possible.

When some values are in billions and some definitely aren't, you may benefit from some work on units.

This required some fiddling with ranks, and some other details, but it may help.

multidot is from SSC. See also https://www.statalist.org/forums/for...ailable-on-ssc

* Example generated by -dataex-. For more info, type help dataex
clear
input byte r_id str26 region float(period year) double(gfd shr kme klf)
1 "East Asia & Pacific"        1 1  8325.703482570614 221713507.51180354 312545535391.22266  9.884943733931932
1 "East Asia & Pacific"        2 2  9962.102881752555  291994199.0371205  374296140523.1998 10.281044165113197
2 "Europe & Central Asia"      1 1   24102.8204969368   538919571.786615 538401194443.77637 19.022858084454136
2 "Europe & Central Asia"      2 2  25799.98590965781  654494471.3396385  581745338410.1671 23.309416601936224
3 "Latin America & Caribbean"  1 1  9455.280580949593  80081604.26769644  76151826017.24394  6.093767684310448
3 "Latin America & Caribbean"  2 2   9545.11053660585 106220095.91957214  98404982310.05264  7.355575546132802
4 "Middle East & North Africa" 1 1  7373.541711250463   89946551.4761219  81922279949.52919 16.905612650025958
4 "Middle East & North Africa" 2 2 7777.9616878091565  99544773.86252138 106317425321.04172 19.484252374025836
5 "North America"              1 1  49559.27810369779           83961914       2.209272e+11  7.247645902684366
5 "North America"              2 2  53454.28768786681           98022165 270455750000.00006  7.239042277091585
6 "South Asia"                 1 1 1368.5156808676866  12190005.91249332        2.40586e+10  11.19949571592668
6 "South Asia"                 2 2 1780.9751084779448 23091459.198763825        3.49224e+10 10.671254320423737
7 "Sub-Saharan Africa"         1 1  1645.103030610854 38776587.938939884 29995171741.764538 10.925456728424516
7 "Sub-Saharan Africa"         2 2 1682.6993478548713  45598841.52158082  32372205773.37823  10.55520839362721
end
label values year fiveyr
label def fiveyr 1 "2010-2014", modify
label def fiveyr 2 "2015-2019", modify

gen axis = _n
gen label = region + " 2010-2014" if mod(_n, 2)
replace label = "2015-2019" if missing(label)
labmask axis, values(label)

* rank on anything interesting that's not region name
egen rank = rank(-klf) if year == 2, unique

list region year klf rank

replace rank = rank * 2
bysort region (rank) : replace rank = rank[1] - 1 if _n == 2  

list region year klf rank

gen KME = kme / 1e9
label var KME "kme (billions)"
gen SHR = shr / 1e6
label var SHR "shr (millions)"

set scheme s1color
multidot gfd SHR KME klf, over(axis) sort(rank) ytitle("") descending

[Nick Cox]

More tweaks:

Different marker symbols.

Removing the repetition of "20".

* Example generated by -dataex-. For more info, type help dataex
clear
input byte r_id str26 region float(period year) double(gfd shr kme klf)
1 "East Asia & Pacific"        1 1  8325.703482570614 221713507.51180354 312545535391.22266  9.884943733931932
1 "East Asia & Pacific"        2 2  9962.102881752555  291994199.0371205  374296140523.1998 10.281044165113197
2 "Europe & Central Asia"      1 1   24102.8204969368   538919571.786615 538401194443.77637 19.022858084454136
2 "Europe & Central Asia"      2 2  25799.98590965781  654494471.3396385  581745338410.1671 23.309416601936224
3 "Latin America & Caribbean"  1 1  9455.280580949593  80081604.26769644  76151826017.24394  6.093767684310448
3 "Latin America & Caribbean"  2 2   9545.11053660585 106220095.91957214  98404982310.05264  7.355575546132802
4 "Middle East & North Africa" 1 1  7373.541711250463   89946551.4761219  81922279949.52919 16.905612650025958
4 "Middle East & North Africa" 2 2 7777.9616878091565  99544773.86252138 106317425321.04172 19.484252374025836
5 "North America"              1 1  49559.27810369779           83961914       2.209272e+11  7.247645902684366
5 "North America"              2 2  53454.28768786681           98022165 270455750000.00006  7.239042277091585
6 "South Asia"                 1 1 1368.5156808676866  12190005.91249332        2.40586e+10  11.19949571592668
6 "South Asia"                 2 2 1780.9751084779448 23091459.198763825        3.49224e+10 10.671254320423737
7 "Sub-Saharan Africa"         1 1  1645.103030610854 38776587.938939884 29995171741.764538 10.925456728424516
7 "Sub-Saharan Africa"         2 2 1682.6993478548713  45598841.52158082  32372205773.37823  10.55520839362721
end
label values year fiveyr
label def fiveyr 1 "2010-2014", modify
label def fiveyr 2 "2015-2019", modify

gen axis = _n
gen label = region + " 10-14" if mod(_n, 2)
replace label = "15-19" if missing(label)
labmask axis, values(label)

* rank on anything interesting that's not region name
egen rank = rank(-klf) if year == 2, unique

list region year klf rank

replace rank = rank * 2
bysort region (rank) : replace rank = rank[1] - 1 if _n == 2  

list region year klf rank

gen KME = kme / 1e9
label var KME "kme (billions)"
gen SHR = shr / 1e6
label var SHR "shr (millions)"

set scheme s1color
multidot gfd SHR KME klf, over(axis) sort(rank) ytitle("") descending sepby(year) ms(X Oh) msize(medlarge medsmall)

[Ngozi ADELEYE]

Awesome!!! This is so VERY helpful.
Thank you so much, Nick!!!
You have always been coming to our rescue.
God bless you.