Percentage change graphic

[May Blake]

Hi, I am trying to model a similar graph or table like this using STATA. I have multiple variables similar to the auto data below with price mpg and rep78 being the variables of interest. Then I have a pre/post period and whether the variable is foreign/domestic. I would like to have the foreign/domestic similar to the cefepime, ciproflaxin columns with pre/post underneath like their years 2018/2021 and then indicate the percent change for price mpg and rep78 using the arrows with green showing decrease and red showing increase on a scale similar at the bottom.

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* Example generated by -dataex-. For more info, type help dataex
clear
input str18 make int(price mpg rep78) float headroom int(trunk weight length turn displacement) float gear_ratio byte foreign
"AMC Concord"        4099 22 3 2.5 11 2930 186 40 121 3.58 0
"AMC Pacer"          4749 17 3   3 11 3350 173 40 258 2.53 0
"AMC Spirit"         3799 22 .   3 12 2640 168 35 121 3.08 0
"Buick Century"      4816 20 3 4.5 16 3250 196 40 196 2.93 0
"Buick Electra"      7827 15 4   4 20 4080 222 43 350 2.41 0
"Buick LeSabre"      5788 18 3   4 21 3670 218 43 231 2.73 0
"Buick Opel"         4453 26 .   3 10 2230 170 34 304 2.87 0
"Buick Regal"        5189 20 3   2 16 3280 200 42 196 2.93 0
"Buick Riviera"     10372 16 3 3.5 17 3880 207 43 231 2.93 0
"Buick Skylark"      4082 19 3 3.5 13 3400 200 42 231 3.08 0
"Cad. Deville"      11385 14 3   4 20 4330 221 44 425 2.28 0
"Cad. Eldorado"     14500 14 2 3.5 16 3900 204 43 350 2.19 0
"Cad. Seville"      15906 21 3   3 13 4290 204 45 350 2.24 0
"Chev. Chevette"     3299 29 3 2.5  9 2110 163 34 231 2.93 0
"Chev. Impala"       5705 16 4   4 20 3690 212 43 250 2.56 0
"Chev. Malibu"       4504 22 3 3.5 17 3180 193 31 200 2.73 0
"Chev. Monte Carlo"  5104 22 2   2 16 3220 200 41 200 2.73 0
"Chev. Monza"        3667 24 2   2  7 2750 179 40 151 2.73 0
"Chev. Nova"         3955 19 3 3.5 13 3430 197 43 250 2.56 0
"Dodge Colt"         3984 30 5   2  8 2120 163 35  98 3.54 0
"Dodge Diplomat"     4010 18 2   4 17 3600 206 46 318 2.47 0
"Dodge Magnum"       5886 16 2   4 17 3600 206 46 318 2.47 0
"Dodge St. Regis"    6342 17 2 4.5 21 3740 220 46 225 2.94 0
"Ford Fiesta"        4389 28 4 1.5  9 1800 147 33  98 3.15 0
"Ford Mustang"       4187 21 3   2 10 2650 179 43 140 3.08 0
"Linc. Continental" 11497 12 3 3.5 22 4840 233 51 400 2.47 0
"Linc. Mark V"      13594 12 3 2.5 18 4720 230 48 400 2.47 0
"Linc. Versailles"  13466 14 3 3.5 15 3830 201 41 302 2.47 0
"Merc. Bobcat"       3829 22 4   3  9 2580 169 39 140 2.73 0
"Merc. Cougar"       5379 14 4 3.5 16 4060 221 48 302 2.75 0
"Merc. Marquis"      6165 15 3 3.5 23 3720 212 44 302 2.26 0
"Merc. Monarch"      4516 18 3   3 15 3370 198 41 250 2.43 0
"Merc. XR-7"         6303 14 4   3 16 4130 217 45 302 2.75 0
"Merc. Zephyr"       3291 20 3 3.5 17 2830 195 43 140 3.08 0
"Olds 98"            8814 21 4   4 20 4060 220 43 350 2.41 0
"Olds Cutl Supr"     5172 19 3   2 16 3310 198 42 231 2.93 0
"Olds Cutlass"       4733 19 3 4.5 16 3300 198 42 231 2.93 0
"Olds Delta 88"      4890 18 4   4 20 3690 218 42 231 2.73 0
"Olds Omega"         4181 19 3 4.5 14 3370 200 43 231 3.08 0
"Olds Starfire"      4195 24 1   2 10 2730 180 40 151 2.73 0
"Olds Toronado"     10371 16 3 3.5 17 4030 206 43 350 2.41 0
"Plym. Arrow"        4647 28 3   2 11 3260 170 37 156 3.05 0
"Plym. Champ"        4425 34 5 2.5 11 1800 157 37  86 2.97 0
"Plym. Horizon"      4482 25 3   4 17 2200 165 36 105 3.37 0
"Plym. Sapporo"      6486 26 . 1.5  8 2520 182 38 119 3.54 0
"Plym. Volare"       4060 18 2   5 16 3330 201 44 225 3.23 0
"Pont. Catalina"     5798 18 4   4 20 3700 214 42 231 2.73 0
"Pont. Firebird"     4934 18 1 1.5  7 3470 198 42 231 3.08 0
"Pont. Grand Prix"   5222 19 3   2 16 3210 201 45 231 2.93 0
"Pont. Le Mans"      4723 19 3 3.5 17 3200 199 40 231 2.93 0
"Pont. Phoenix"      4424 19 . 3.5 13 3420 203 43 231 3.08 0
"Pont. Sunbird"      4172 24 2   2  7 2690 179 41 151 2.73 0
"Audi 5000"          9690 17 5   3 15 2830 189 37 131  3.2 1
"Audi Fox"           6295 23 3 2.5 11 2070 174 36  97  3.7 1
"BMW 320i"           9735 25 4 2.5 12 2650 177 34 121 3.64 1
"Datsun 200"         6229 23 4 1.5  6 2370 170 35 119 3.89 1
"Datsun 210"         4589 35 5   2  8 2020 165 32  85  3.7 1
"Datsun 510"         5079 24 4 2.5  8 2280 170 34 119 3.54 1
"Datsun 810"         8129 21 4 2.5  8 2750 184 38 146 3.55 1
"Fiat Strada"        4296 21 3 2.5 16 2130 161 36 105 3.37 1
"Honda Accord"       5799 25 5   3 10 2240 172 36 107 3.05 1
"Honda Civic"        4499 28 4 2.5  5 1760 149 34  91  3.3 1
"Mazda GLC"          3995 30 4 3.5 11 1980 154 33  86 3.73 1
"Peugeot 604"       12990 14 . 3.5 14 3420 192 38 163 3.58 1
"Renault Le Car"     3895 26 3   3 10 1830 142 34  79 3.72 1
"Subaru"             3798 35 5 2.5 11 2050 164 36  97 3.81 1
"Toyota Celica"      5899 18 5 2.5 14 2410 174 36 134 3.06 1
"Toyota Corolla"     3748 31 5   3  9 2200 165 35  97 3.21 1
"Toyota Corona"      5719 18 5   2 11 2670 175 36 134 3.05 1
"VW Dasher"          7140 23 4 2.5 12 2160 172 36  97 3.74 1
"VW Diesel"          5397 41 5   3 15 2040 155 35  90 3.78 1
"VW Rabbit"          4697 25 4   3 15 1930 155 35  89 3.78 1
"VW Scirocco"        6850 25 4   2 16 1990 156 36  97 3.78 1
"Volvo 260"         11995 17 5 2.5 14 3170 193 37 163 2.98 1
end
label values foreign origin
label def origin 0 "Domestic", modify
label def origin 1 "Foreign", modify

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Listed 74 out of 74 observations

[Nick Cox]

I can't see that the auto data has any bearing on your question, which may explain why your question hasn't been answered so far.

The graphic is presented, if I understand it, for percent change (of use?) or different drugs for various medical conditions between two years.

There is nothing remotely similar in the auto data which have no data on time.

If your real data are not suitable as an example, please present a more realistic fake dataset. There is little or no point in a data example that can't be used directly, or at least adapted very easily to match the real problem.

A compelling objection to mixing red and green on a graphic is posed by many people who cannot distinguish those colours, an example of colour blindness.