Hi everyone,

I'm working with COVID-19 data in order to study the effect of inequality on coronavirus spread. The data looks like:

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input str52 Country int date str28 Code double var float gini double(pob dead recovered) float(rate n)
"Iceland"                48 "ISL"   34 26.8     352721   0   161    .09639347   1
"Seychelles"             63 "SYC"    7 46.8      96762   0     0    .07234245   2
"Montenegro"             66 "MNE"   27   39     622227   2    91    .04339252   3
"Mauritius"              67 "MUS"   42 36.8    1265303   1    44    .03319363   4
"Slovenia"               54 "SVN"   57 24.2    2073894  12    22    .02748453   5
"Uruguay"                63 "URY"   94 39.7    3449299   5    63   .027251914   6
"Malta"                  56 "MLT"   12 29.2     484630   1   147   .024761157   7
"Cyprus"                 58 "CYP"   26 31.4    1189265   2   198    .02186224   8
"Maldives"               57 "MDV"   10 31.3     515696   1     1    .01939127   9
"Sao Tome and Principe"  86 "STP"    4 56.3     211028   0     4    .01895483  10
"Albania"                58 "ALB"   42 33.2    2866376   4    85    .01465265  11
"Panama"                 59 "PAN"   55 49.2    4176873  42   305   .013167745  12
"St. Lucia"              63 "LCA"    2 51.2     181889   0     0   .010995717  13
"Cabo Verde"             69 "CPV"    4 42.4     543767   0     3   .007356092  14
"Kyrgyz Republic"        67 "KGZ"   42 27.7    6322800   0   160   .006642627  15
"West Bank and Gaza"     54 "PSE"   30 33.7    4569087  -2   -16   .006565863  16
"Norway"                 46 "NOR"   32   27    5311916  16     0   .006024192  17
"Bulgaria"               57 "BGR"   41 40.4    7025037  14    76   .005836268  18
"Fiji"                   68 "FJI"    5 36.7     883483   0     4   .005659419  19
"Switzerland"            45 "CHE"   42 32.7    8513227 188  2800   .004933499  20
"Moldova"                57 "MDA"   12 25.7    2706049  36   521  .0044345097  21
"Costa Rica"             55 "CRI"   22   48    4999441   0   142   .004400492  22
"Guinea-Bissau"          74 "GNB"    8 50.7    1874309   1    16  .0042682397  23
"China"                  11 "CHN" 5494 38.5 1392730000   1   385  .0039447704  24
"Ireland"                49 "IRL"   18 32.8    4867309 438  4153   .003698142  25
"Luxembourg"             49 "LUX"    2 34.9     607950   7  2485   .003289744  26
"Djibouti"               67 "DJI"    3 41.6     958920   0   390  .0031285195  27
"Slovak Republic"        55 "SVK"   16 25.2    5446771   8   236    .00293752  28
"Portugal"               51 "PRT"   30 33.8   10283822 169   318  .0029172034  29
"Serbia"                 55 "SRB"   19 36.2    6982604  40   276   .002721048  30
"Botswana"               79 "BWA"    6 53.3    2254126   0     5  .0026617856  31
"Kazakhstan"             62 "KAZ"   44 27.5   18272430   5   306  .0024079995  32
"Netherlands"            47 "NLD"   38 28.5   17231624 618     0  .0022052478  33
"Bosnia and Herzegovina" 54 "BIH"    7   33    3323929  15   242  .0021059415  34
"Austria"                45 "AUT"   18 29.7    8840521  62  1213   .002036079  35
"Czech Republic"         50 "CZE"   19 24.9   10629928  26  1162  .0017874063  36
"Denmark"                47 "DNK"   10 28.7    5793636  58  1162  .0017260318  37
"Croatia"                45 "HRV"    7 30.4    4087843  19   465  .0017123945  38
"Estonia"                47 "EST"    2 30.4    1321977   7    57  .0015128857  39
"Gabon"                  63 "GAB"    3   38    2119275   1    43  .0014155784  40
"El Salvador"            68 "SLV"    9 38.6    6420744   2    52  .0014017067  41
"Rwanda"                 63 "RWA"   17 43.7   12301939   0    17   .001381896  42
"Bhutan"                 55 "BTN"    1 37.4     754394   0     2  .0013255673  43
"Namibia"                63 "NAM"    3 59.1    2448255   0     1  .0012253625  44
"Iran, Islamic Rep."     39 "IRN"   95 40.8   81800269 547 10260  .0011613654  45
"Lao PDR"                73 "LAO"    8 36.4    7061507   0     4  .0011329027  46
"Latvia"                 51 "LVA"    2 35.6    1927174   4   215   .001037789  47
"Bolivia"                60 "BOL"   11 42.2   11353142  16    73  .0009688948  48
"Hungary"                53 "HUN"    9 30.6    9775564  73   191   .000920663  49
"Eswatini"               63 "SWZ"    1 54.6    1136191   0     4  .0008801337  50
"Gambia, The"            66 "GMB"    2 35.9    2280102   0     6  .0008771538  51
"Madagascar"             69 "MDG"   23 42.6   26262368   0    34  .0008757779  52
"Paraguay"               57 "PRY"    6 46.2    6956071   1    46  .0008625559  53
"Honduras"               60 "HND"    8 52.1    9587522  24    48  .0008344179  54
"Georgia"                46 "GEO"    3 36.4    3726549   1    73  .0008050344  55
"Timor-Leste"            71 "TLS"    1 28.7    1267972   0    15   .000788661  56
"Sierra Leone"           80 "SLE"    6 35.7    7650154   6    11   .000784298  57
"Ecuador"                50 "ECU"   13 45.4   17084357 340   230    .00076093  58
"Burkina Faso"           59 "BFA"   15 35.3   19751535   2    96  .0007594347  59
"Haiti"                  69 "HTI"    8 41.1   11123176   3     6   .000719219  60
"Guatemala"              63 "GTM"   12 48.3   17247807   5    36  .0006957406  61
"Greece"                 46 "GRC"    7 34.4   10731726  15   797  .0006522716  62
"Belarus"                48 "BLR"    6 25.2    9483499  29  1448  .0006326779  63
"Liberia"                65 "LBR"    3 35.3    4818977   8    25  .0006225388  64
"Poland"                 53 "POL"   22 29.7   37974750 190  1496  .0005793323  65
"Congo, Rep."            64 "COG"    3 48.9    5244363   3     3  .0005720428  66
"Turkey"                 60 "TUR"   47 41.9   82319724 683 30395 .00057094457  67
"Uganda"                 70 "UGA"   23 42.8   42723139   0     6 .00053834997  68
"Ghana"                  63 "GHA"   16 43.5   29767108   8   113   .000537506  69
"United Arab Emirates"   18 "ARE"    5 32.5    9630959  49   792  .0005191591  70
"Iraq"                   44 "IRQ"   19 29.5   38433600  10   204  .0004943591  71
"Australia"              15 "AUS"   12 34.4   24982688  18   695  .0004803326  72
"North Macedonia"        46 "MKD"    1 34.2    2082958  21   437  .0004800865  73
"Peru"                   55 "PER"   15 42.8   31989256 479  2983  .0004689074  74
"Mauritania"             63 "MRT"    2 32.6    4403319   0     0 .00045420285  75
"Niger"                  69 "NER"   10 34.3   22442948   8   196  .0004455743  76
"Malawi"                 82 "MWI"    8 44.7   18143315   0     4  .0004409337  77
"Tunisia"                53 "TUN"    5 32.8   11565204   3   115  .0004323313  78
"Chile"                  52 "CHL"    8 44.4   18729160  59  2776  .0004271414  79
"Myanmar"                76 "MMR"   20 30.7   53708395   1    18  .0003723813  80
"Lithuania"              48 "LTU"    1 37.3    2801543   5   190  .0003569461  81
"Armenia"                50 "ARM"    1 34.4    2951776   8   270  .0003387791  82
"Israel"                 41 "ISR"    3   39    8882800  30  2950 .00033773136  83
"Mongolia"               59 "MNG"    1 32.7    3170208   0     1  .0003154367  84
"Nicaragua"              68 "NIC"    2 46.2    6465513   0     0  .0003093335  85
"Lebanon"                41 "LBN"    2 31.8    6848925   2    10  .0002920166  86
"Mozambique"             71 "MOZ"    8   54   29495962   0     3 .00027122357  87
"Argentina"              52 "ARG"   12 41.4   44494502  53   337 .00026969623  88
"Burundi"                80 "BDI"    3 38.6   11175378   0     0  .0002684473  89
"Malaysia"               14 "MYS"    8   41   31528585   7   629   .000253738  90
"Senegal"                51 "SEN"    4 40.3   15854360   3    77 .00025229654  91
"South Africa"           54 "ZAF"   13   63   57779622  28   600 .00022499282  92
"Tanzania"               65 "TZA"   12 40.5   56318348   6   156 .00021307443  93
"Zimbabwe"               69 "ZWE"    3 44.3   14439018   0     3 .00020777037  94
"Thailand"               11 "THA"   14 36.4   69428524   4   254 .00020164622  95
"Cote d'Ivoire"          60 "CIV"    5 41.5   25069229   0   215  .0001994477  96
"Chad"                   68 "TCD"    3 43.3   15477751   5    25  .0001938266  97
"Dominican Republic"     50 "DOM"    2 43.7   10627165  36   720 .00018819695  98
"Finland"                18 "FIN"    1 27.4    5515525  39  1000  .0001813064  99
"Colombia"               55 "COL"    9 50.4   49648685  78   512  .0001812737 100
end
label values n pais
label def pais 1 "Iceland", modify
label def pais 2 "Seychelles", modify
label def pais 3 "Montenegro", modify
label def pais 4 "Mauritius", modify
label def pais 5 "Slovenia", modify
I created a statistic descriptive tabla using this code:

tabstat rate gini dead recovered if inrange(n,1,5) | inrange(n,135,139), by(n)

and I got this table:

Code:
n | rate gini dead recove~d
-----------------+----------------------------------------
Iceland | .0963935 26.8 0 161
Seychelles | .0723424 46.8 0 0
Montenegro | .0433925 39 2 91
Mauritius | .0331936 36.8 1 44
Slovenia | .0274845 24.2 12 22
United States | .0000153 41.4 13272 73744
Russian Federati | .0000138 37.5 518 6728
Egypt | .0000102 31.5 105 377
Brazil | 9.55e-06 53.9 2675 9362
India | 2.22e-06 37.8 433 4056
-----------------+----------------------------------------
Total | .0272858 37.57 1701.8 9458.5
----------------------------------------------------------
I just wanted to know how to export this table to latex (in .tex format). Also, I would like to turn scientific notation off in order to show both Brazil rate and India rate as they really look like.
I hope you understand my question. Thanks