From 7477a23ea35855011219f8a989dca1b273aa9843 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mario=20Gavidia-Calder=C3=B3n?= Date: Wed, 7 Aug 2024 22:17:33 -0300 Subject: [PATCH 1/6] feat: add 4 more aqs --- R/data.R | 2 +- data-raw/cetesb_aqs.R | 15 ++++++++++----- data/cetesb_aqs.rda | Bin 2506 -> 2684 bytes man/cetesb_aqs.Rd | 2 +- 4 files changed, 12 insertions(+), 7 deletions(-) diff --git a/R/data.R b/R/data.R index 5ee6a1a..b4fdf0f 100644 --- a/R/data.R +++ b/R/data.R @@ -4,7 +4,7 @@ #' Use this to check the AQS \code{aqs_code} argument in CetesbRetrieveParam() #' function. AQS names are without diacritics. #' -#' @format A data frame with 70 observations and 5 variables: + #' @format A data frame with 74 observations and 5 variables: #' \describe{ #' \item{name}{CETESB AQS name.} #' \item{code}{CETESB AQS code in QUALAR System.} diff --git a/data-raw/cetesb_aqs.R b/data-raw/cetesb_aqs.R index 02ae426..aa1b02f 100644 --- a/data-raw/cetesb_aqs.R +++ b/data-raw/cetesb_aqs.R @@ -21,11 +21,16 @@ cetesb_aqs$loc[cetesb_aqs$code %in% litoral] <- "Coast" cetesb_aqs$loc[cetesb_aqs$code %in% interior] <- "Interior" missing_aqs <- data.frame( - name = c("Lapa", "Perus"), - code = c(84, 293), - lat = c(-23.50897, -23.41321), - lon = c(-46.70122, -46.75605), - loc = c("São Paulo", "São Paulo") + name = c("Lapa", "Perus", "Americana-Vila Sta Maria", + "Paulínia-Sta Terezinha", "Rio Claro-Jd.Guanabara", + "São Sebastião" ), + code = c(84, 293, 105, 291, 292, 294), + lat = c(-23.50897, -23.41321, -22.72425, + -22.78021, -22.43906, -23.8052), + lon = c(-46.70122, -46.75605, -41.33955, + -41.113904, -41.58144, -39.40007), + loc = c("São Paulo", "São Paulo", "Interior", "Interior", + "Interior", "Litoral") ) cetesb_aqs <- rbind(cetesb_aqs, missing_aqs) diff --git a/data/cetesb_aqs.rda b/data/cetesb_aqs.rda index 5e9084b8557883eeefaca4cec73d0a0e8a6a363c..58ba8d513c46aa1801e331dfc92d37bdbd2da9e1 100644 GIT binary patch delta 2678 zcmV-+3W@c~6Z{krLRx4!F+o`-Q(2@d_(T8%<&hC1e+OOhAp5oM;l6#Qp~k(Nz4a*i zF@({m(WJoA2AMHE3Ft9Ri7=WnXf*Oisi&h;6GIW{4Lu2W&MfAoVu(V+DVfO>;XsAvEI0009Z00w{>00003L7|`k000000MKe-5iw05(s~mO6EuuN zOix03OaK!TCX5IkkYNolH8C(W27mwpWWWFff71XX)M=(h0$@e~FaQAprqw+}F$O?- znFCCQg9K<8fEs9NqfImh38t9=rXVm(k5B;GnhhSIr=nzF5Wr2Tra;gd4FCWElT1t! zDFO&n^i5A9dY`C+)Y_h=G=pjx00T^crkXSj4T&D6fM9{3(9<9WKr{duJwRj|Q$gwg ze*n`!Gyr;=PylHh%HAY4b5G2Z5 z=5luVt}fz@#bw7E%iPk|;6njSLRdDEv$9(hP(WHs0VJrDha-p*=3UmV2vHqvoJk6K zhg6Kdw*687iVa8?byli`++GIos+|XqlkRgl;7{8hVRcpxm z7yuOl0fBIHQV&ED1VVWg73nxy#|TmoS=GYGtS>ec0wM?qq*VbE6wOeM?(#wuw1rGm z8-WN>jv``>Cz+Z#Dgwo%Als5j4J47@FcQt)K}ga`bpGyk|8qbkZ2`&7h1*z$e+Ww+ z{ysAR29hX|&P?m^RyOjiIrF zZ843BrQB*~?wY9*z-HhA%`^AC&qb`y*I!4D(?HfXqLCm35P&Of-ldH|04und4y)2{ z1S>rI>~^z;ytsqdU(7Xqk5vJOe~(D!I#56?f&lWj3HscTH8?L-ma^hEh5G!}RJUn{ zn1=rqd*a3cE&u^XKF5oMX|;8dftM4X|Z9>iWnF_Aq501e^5p@OK-;@ zpePL|&cjR@B=PO>QBP~pY#GDu!e;eu$x0b}F=rc!B6;VSY7-{oHh)Jd?@*n90qGZ1gGGl_`fhO48 z!hocylGofVaXB|`u)oNw=Jo|Zm9k8~h)_)5xg%WKf^o0TJR6TRI28o!XCOu|(<-6% zx;RW7Iy2wn$R8$06n3O$7-L`?^Aq+7mz+iMoOeq}BB>B+e^&uUOAZBcxla>0N_vQ< zgM9#khaQ_tI>&%8RRjQCsH^HH@?6RlzIkc+;4z~xBYmquY?~Uzs=R0*n1vn9|OXu;Rr3zFh3A#5MQ(fKtx}G z=t5a$q2zFif02+%3Qx;1_vA!0@~aQfYwNebMG`4~&a|cngnB}aLdn6`ov?WVMwc7L zFTuoGbT$eHSYEwpB<@;}c~RMnVC~0%8Ixst$pqG89@Y2@%4p(a4;t+H2%Z z9HR5HNJxWM?3NQhU4o(HVQ0^vw`QzbK`DFFcCo6je`bFxMMgk?83^rPK^S1MIK6bB zswdG?qKYxP6^b(0l$FS_qDl$~lTnMhtFu9d#B8-;7&a)OPBJRE<%#d)5(wF9<8xsm ziOk?pm>{0D>WEjJE$lWX1~R83UPxA?Z@Cm1*uZS|DQ|hoIc4M`ok@Yq#f%h?8Me4H z&U)u>e+;IA2$fb21@lzIMA8Egb0jpP-bQQ0p-wQ;T4*M-Gfc%XGao64h?xqI10^RJ z@rg$PO^o`neJMxaV`I9sQj!6&7*K}71OWo9&P(*%=#G4hVlbhXfD3}F1uTcHOa^Nz zX9^&Iq{uOhR=C1Q(x{NJ{IJtaA|WPsLpuL|e;8q@(^M@cCNS=3R*|VtEtz<*V9gzp z8YWJNcj`}w2flMYx6U&Iqr+F(Om59m(3j3u@Y-jslgCYPBC`%3XTmSn!yD6&$W+>_8#a*^V9nWxaF-ID873pX`4jba){_JMuN{M zP*7k13}8*D$ij#OGef9p*2^C@)6!`Lf7~EIo=VSLq*U^(su!*TD3wD=R6{Pav69At zGqHb*Uhhg8@azqr^Za)(s)5ecX!>>8zhOs|&PuxIU{R)Uj8-mb@*G`?cTXNPd2fuD zZt7Ac&d!rjkU*O2!rob08u`Q4#oZ=xuHCz1tGFYhCKFtjK+sk8LSro_e|<9Ge`xI) zEGNW@*<4;dEA5OyY8H(va5UFVzL!7Bm4UC59Kz6k{e-I1>`Gv>O&f|TpuIJ0qXhCK z5OBHC$hWJOQ&W|vY@suNl;fCJw)IFPOkdPkZ6XFo!mv(Ey(suQ6Uf5?G!(p+M?u&Zqo;L-R=?qr?(}M@i~< z84qUv;8dagNi%BIj&vRqz10FIwQ+Z4<gZHtt4%+Qdy7Bvq+nv4}9eTcJ-jLTsonG?9S$| z78K&hI+)bViC>Jd)?u;UBP`a}Nk#SXyezAwgw&h_4bG22?#)0LKFTj&LLe+K>WAlk5dUs#3Gmh5xjTlVPG#;jmjRQ2)Vi`2ZZ5nz| z$a)PmGgBb)gF`hvMvbYaPbkpA45oktzyO#40MGycOaVOtVK5B9%?K=~+KgdK zf2WKbV+xX7W$rmlwccLuD|3dq!`$&znw?}Z8o-dTjU{J|Sg4?Y*QJ1jsFj0}#0g$J z4IBYPUs=Qgs4W!(HL0UK5(<{24?3>uG=N8qbflUr%yea)ii98yrbIxE%?9VVqGyyO zW(zC4Bxz9*69}Lnepi;KB>|>pSs1&?e``CWMD$Q0*Ta~o@;vkbO+x~u7?47#7s}1?xv~>7Lo%Zfgz+MXIPNAk46ecke5&ISKQPDji5N$pc?xULbrNH znP5(6p>*Wls9?S4`Well| znRw#F{E`=ubtS;?@|&)bm)cbmg! zt^+&5ZBx-LMO9YM6=jSaWGDObZ&b83xk)aU9g&8i!4zK%!v(3D0IYZp!oPdtBQTmr zQte)OnTBoJ+$ZB^5S@Koe_Qmr3Q^9^PO?#M5^5{S2n__LhU7itU$s4nQF6<2m?aO4bo{ce_Qh4g9Iqg1B6nC z&@D4KFl;y&X#%;f&Y*5+0D=K*1Ze~)Umya&0!9W56s$Ng;K@J)l0T9I!U5lMV}Wsi zN7;$R7y@H5F!FaQjw>^6kT+s4Q@tFPAq>LdFhJ(4#`;tMrt@MRQq-K)5|G>uvhUT? 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Mario Gavidia-Calderón, Daniel Schuch & Maria de Fatima Andrade

-

2023-07-25

+

2024-08-07

Source: vignettes/qualr.Rmd
qualr.Rmd
@@ -386,20 +386,20 @@

Example to down head(cetesb_aqs, 15) #> name code lat lon loc #> 1 Americana 290 -22.72425 -47.33955 Interior -#> 2 Araçatuba 107 -21.18684 -50.43932 Interior -#> 3 Araraquara 106 -21.78252 -48.18583 Interior -#> 4 Bauru 108 -22.32661 -49.09276 Interior -#> 5 Cambuci 90 -23.56771 -46.61227 <NA> -#> 6 Campinas-Centro 89 -22.90252 -47.05721 Interior -#> 7 Campinas-Taquaral 276 -22.87462 -47.05897 Interior -#> 8 Campinas-V.União 275 -22.94673 -47.11928 Interior -#> 9 Capão Redondo 269 -23.66836 -46.78004 São Paulo -#> 10 Carapicuíba 263 -23.53140 -46.83578 MASP -#> 11 Catanduva 248 -21.14194 -48.98308 Interior -#> 12 Centro 94 -23.54781 -46.64241 <NA> -#> 13 Cerqueira César 91 -23.55354 -46.67270 São Paulo -#> 14 Cid.Universitária-USP-Ipen 95 -23.56634 -46.73741 São Paulo -#> 15 Congonhas 73 -23.61632 -46.66347 São Paulo +#> 2 Americana-Vila Sta Maria 105 -22.72425 -41.33955 Interior +#> 3 Araçatuba 107 -21.18684 -50.43932 Interior +#> 4 Araraquara 106 -21.78252 -48.18583 Interior +#> 5 Bauru 108 -22.32661 -49.09276 Interior +#> 6 Cambuci 90 -23.56771 -46.61227 <NA> +#> 7 Campinas-Centro 89 -22.90252 -47.05721 Interior +#> 8 Campinas-Taquaral 276 -22.87462 -47.05897 Interior +#> 9 Campinas-V.União 275 -22.94673 -47.11928 Interior +#> 10 Capão Redondo 269 -23.66836 -46.78004 São Paulo +#> 11 Carapicuíba 263 -23.53140 -46.83578 MASP +#> 12 Catanduva 248 -21.14194 -48.98308 Interior +#> 13 Centro 94 -23.54781 -46.64241 <NA> +#> 14 Cerqueira César 91 -23.55354 -46.67270 São Paulo +#> 15 Cid.Universitária-USP-Ipen 95 -23.56634 -46.73741 São Paulo
  1. Check ozone code or abbreviation
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 8fe1869..fb12290 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -3,5 +3,5 @@ pkgdown: 2.0.6 pkgdown_sha: ~ articles: qualr: qualr.html -last_built: 2023-07-25T12:20Z +last_built: 2024-08-08T01:55Z diff --git a/docs/reference/cetesb_aqs.html b/docs/reference/cetesb_aqs.html index 1a9e128..5027291 100644 --- a/docs/reference/cetesb_aqs.html +++ b/docs/reference/cetesb_aqs.html @@ -68,7 +68,7 @@

Usage

Format

-

A data frame with 68 observations and 5 variables:

name
+

A data frame with 74 observations and 5 variables:

name

CETESB AQS name.

code
@@ -91,73 +91,79 @@

Examples
cetesb_aqs
 #>                          name code       lat       lon       loc
 #> 1                   Americana  290 -22.72425 -47.33955  Interior
-#> 2                   Araçatuba  107 -21.18684 -50.43932  Interior
-#> 3                  Araraquara  106 -21.78252 -48.18583  Interior
-#> 4                       Bauru  108 -22.32661 -49.09276  Interior
-#> 5                     Cambuci   90 -23.56771 -46.61227      <NA>
-#> 6             Campinas-Centro   89 -22.90252 -47.05721  Interior
-#> 7           Campinas-Taquaral  276 -22.87462 -47.05897  Interior
-#> 8            Campinas-V.União  275 -22.94673 -47.11928  Interior
-#> 9               Capão Redondo  269 -23.66836 -46.78004 São Paulo
-#> 10                Carapicuíba  263 -23.53140 -46.83578      MASP
-#> 11                  Catanduva  248 -21.14194 -48.98308  Interior
-#> 12                     Centro   94 -23.54781 -46.64241      <NA>
-#> 13            Cerqueira César   91 -23.55354 -46.67270 São Paulo
-#> 14 Cid.Universitária-USP-Ipen   95 -23.56634 -46.73741 São Paulo
-#> 15                  Congonhas   73 -23.61632 -46.66347 São Paulo
-#> 16             Cubatão-Centro   87 -23.87903 -46.41848     Coast
-#> 17           Cubatão-V.Parisi   66 -23.84942 -46.38868     Coast
-#> 18       Cubatão-Vale do Mogi  119 -23.83159 -46.36957     Coast
-#> 19                    Diadema   92 -23.68588 -46.61162      MASP
-#> 20         Grajaú-Parelheiros   98 -23.77627 -46.69696 São Paulo
-#> 21              Guaratinguetá  289 -22.80192 -45.19112  Interior
-#> 22                  Guarulhos  118 -23.46321 -46.49621      MASP
-#> 23   Guarulhos-Paço Municipal  264 -23.45553 -46.51853      MASP
-#> 24         Guarulhos-Pimentas  279 -23.44012 -46.40995      MASP
-#> 25                 Ibirapuera   83 -23.59184 -46.66069 São Paulo
-#> 26                 Interlagos  262 -23.68051 -46.67504 São Paulo
-#> 27             Itaim Paulista  266 -23.50155 -46.42074 São Paulo
-#> 28                   Itaquera   97 -23.58001 -46.46665 São Paulo
-#> 29                    Jacareí  259 -23.29420 -45.96823  Interior
-#> 30                        Jaú  110 -22.29862 -48.56746  Interior
-#> 31                    Jundiaí  109 -23.19200 -46.89710  Interior
-#> 32                    Limeira  281 -22.56360 -47.41431  Interior
-#> 33    Marg.Tietê-Pte Remédios  270 -23.51871 -46.74332 São Paulo
-#> 34                    Marília  111 -22.19981 -49.95997  Interior
-#> 35                       Mauá   65 -23.66855 -46.46600      MASP
-#> 36            Mogi das Cruzes  287 -23.51817 -46.18686      <NA>
-#> 37                      Mooca   85 -23.54973 -46.60042 São Paulo
-#> 38             N.Senhora do Ó   96 -23.48010 -46.69205 São Paulo
-#> 39                     Osasco  120 -23.52672 -46.79208      MASP
-#> 40          Parque D.Pedro II   72 -23.54485 -46.62768 São Paulo
-#> 41                   Paulínia  117 -22.77232 -47.15484  Interior
-#> 42               Paulínia Sul  112 -22.78681 -47.13656  Interior
-#> 43            Pico do Jaraguá  284 -23.45627 -46.76610 São Paulo
-#> 44                  Pinheiros   99 -23.56146 -46.70202 São Paulo
-#> 45                 Piracicaba  113 -22.70122 -47.64965  Interior
-#> 46            Pirassununga-EM  268 -22.00771 -47.42756      <NA>
-#> 47        Presidente Prudente  114 -22.11994 -51.40878      <NA>
-#> 48             Ribeirão Preto  288 -21.15394 -47.82848  Interior
-#> 49            S.André Capuava  100 -23.63980 -46.49164      MASP
-#> 50             S.André-Centro  101 -23.64562 -46.53633      MASP
-#> 51     S.André-Paço Municipal  254 -23.65699 -46.53092      <NA>
-#> 52          S.Bernardo-Centro  272 -23.69867 -46.54623      MASP
-#> 53       S.Bernardo-Paulicéia  102 -23.67135 -46.58467      MASP
-#> 54              S.José Campos   88 -23.18789 -45.87120  Interior
-#> 55  S.José Campos-Jd.Satélite  277 -23.22365 -45.89080  Interior
-#> 56  S.José Campos-Vista Verde  278 -23.18370 -45.83090  Interior
-#> 57          S.Miguel Paulista  236 -23.49853 -46.44480      <NA>
-#> 58            Santa Gertrudes  273 -22.45996 -47.53630  Interior
-#> 59                    Santana   63 -23.50599 -46.62896 São Paulo
-#> 60                Santo Amaro   64 -23.65498 -46.71000 São Paulo
-#> 61                     Santos  258 -23.96306 -46.32117     Coast
-#> 62      Santos-Ponta da Praia  260 -23.98130 -46.30051     Coast
-#> 63         São Caetano do Sul   86 -23.61844 -46.55635      MASP
-#> 64      São José Do Rio Preto  116 -20.78469 -49.39828  Interior
-#> 65                   Sorocaba   67 -23.50243 -47.47903  Interior
-#> 66            Taboão da Serra  103 -23.60932 -46.75829      MASP
-#> 67                      Tatuí  256 -23.36075 -47.87080  Interior
-#> 68                    Taubaté  280 -23.03235 -45.57581  Interior
+#> 2    Americana-Vila Sta Maria  105 -22.72425 -41.33955  Interior
+#> 3                   Araçatuba  107 -21.18684 -50.43932  Interior
+#> 4                  Araraquara  106 -21.78252 -48.18583  Interior
+#> 5                       Bauru  108 -22.32661 -49.09276  Interior
+#> 6                     Cambuci   90 -23.56771 -46.61227      <NA>
+#> 7             Campinas-Centro   89 -22.90252 -47.05721  Interior
+#> 8           Campinas-Taquaral  276 -22.87462 -47.05897  Interior
+#> 9            Campinas-V.União  275 -22.94673 -47.11928  Interior
+#> 10              Capão Redondo  269 -23.66836 -46.78004 São Paulo
+#> 11                Carapicuíba  263 -23.53140 -46.83578      MASP
+#> 12                  Catanduva  248 -21.14194 -48.98308  Interior
+#> 13                     Centro   94 -23.54781 -46.64241      <NA>
+#> 14            Cerqueira César   91 -23.55354 -46.67270 São Paulo
+#> 15 Cid.Universitária-USP-Ipen   95 -23.56634 -46.73741 São Paulo
+#> 16                  Congonhas   73 -23.61632 -46.66347 São Paulo
+#> 17             Cubatão-Centro   87 -23.87903 -46.41848     Coast
+#> 18           Cubatão-V.Parisi   66 -23.84942 -46.38868     Coast
+#> 19       Cubatão-Vale do Mogi  119 -23.83159 -46.36957     Coast
+#> 20                    Diadema   92 -23.68588 -46.61162      MASP
+#> 21         Grajaú-Parelheiros   98 -23.77627 -46.69696 São Paulo
+#> 22              Guaratinguetá  289 -22.80192 -45.19112  Interior
+#> 23                  Guarulhos  118 -23.46321 -46.49621      MASP
+#> 24   Guarulhos-Paço Municipal  264 -23.45553 -46.51853      MASP
+#> 25         Guarulhos-Pimentas  279 -23.44012 -46.40995      MASP
+#> 26                 Ibirapuera   83 -23.59184 -46.66069 São Paulo
+#> 27                 Interlagos  262 -23.68051 -46.67504 São Paulo
+#> 28             Itaim Paulista  266 -23.50155 -46.42074 São Paulo
+#> 29                   Itaquera   97 -23.58001 -46.46665 São Paulo
+#> 30                    Jacareí  259 -23.29420 -45.96823  Interior
+#> 31                        Jaú  110 -22.29862 -48.56746  Interior
+#> 32                    Jundiaí  109 -23.19200 -46.89710  Interior
+#> 33                       Lapa   84 -23.50897 -46.70122 São Paulo
+#> 34                    Limeira  281 -22.56360 -47.41431  Interior
+#> 35    Marg.Tietê-Pte Remédios  270 -23.51871 -46.74332 São Paulo
+#> 36                    Marília  111 -22.19981 -49.95997  Interior
+#> 37                       Mauá   65 -23.66855 -46.46600      MASP
+#> 38            Mogi das Cruzes  287 -23.51817 -46.18686      <NA>
+#> 39                      Mooca   85 -23.54973 -46.60042 São Paulo
+#> 40             N.Senhora do Ó   96 -23.48010 -46.69205 São Paulo
+#> 41                     Osasco  120 -23.52672 -46.79208      MASP
+#> 42          Parque D.Pedro II   72 -23.54485 -46.62768 São Paulo
+#> 43                   Paulínia  117 -22.77232 -47.15484  Interior
+#> 44               Paulínia Sul  112 -22.78681 -47.13656  Interior
+#> 45     Paulínia-Sta Terezinha  291 -22.78021 -41.11390  Interior
+#> 46                      Perus  293 -23.41321 -46.75605 São Paulo
+#> 47            Pico do Jaraguá  284 -23.45627 -46.76610 São Paulo
+#> 48                  Pinheiros   99 -23.56146 -46.70202 São Paulo
+#> 49                 Piracicaba  113 -22.70122 -47.64965  Interior
+#> 50            Pirassununga-EM  268 -22.00771 -47.42756      <NA>
+#> 51        Presidente Prudente  114 -22.11994 -51.40878      <NA>
+#> 52             Ribeirão Preto  288 -21.15394 -47.82848  Interior
+#> 53     Rio Claro-Jd.Guanabara  292 -22.43906 -41.58144  Interior
+#> 54            S.André Capuava  100 -23.63980 -46.49164      MASP
+#> 55             S.André-Centro  101 -23.64562 -46.53633      MASP
+#> 56     S.André-Paço Municipal  254 -23.65699 -46.53092      <NA>
+#> 57          S.Bernardo-Centro  272 -23.69867 -46.54623      MASP
+#> 58       S.Bernardo-Paulicéia  102 -23.67135 -46.58467      MASP
+#> 59              S.José Campos   88 -23.18789 -45.87120  Interior
+#> 60  S.José Campos-Jd.Satélite  277 -23.22365 -45.89080  Interior
+#> 61  S.José Campos-Vista Verde  278 -23.18370 -45.83090  Interior
+#> 62          S.Miguel Paulista  236 -23.49853 -46.44480      <NA>
+#> 63            Santa Gertrudes  273 -22.45996 -47.53630  Interior
+#> 64                    Santana   63 -23.50599 -46.62896 São Paulo
+#> 65                Santo Amaro   64 -23.65498 -46.71000 São Paulo
+#> 66                     Santos  258 -23.96306 -46.32117     Coast
+#> 67      Santos-Ponta da Praia  260 -23.98130 -46.30051     Coast
+#> 68         São Caetano do Sul   86 -23.61844 -46.55635      MASP
+#> 69      São José Do Rio Preto  116 -20.78469 -49.39828  Interior
+#> 70              São Sebastião  294 -23.80520 -39.40007   Litoral
+#> 71                   Sorocaba   67 -23.50243 -47.47903  Interior
+#> 72            Taboão da Serra  103 -23.60932 -46.75829      MASP
+#> 73                      Tatuí  256 -23.36075 -47.87080  Interior
+#> 74                    Taubaté  280 -23.03235 -45.57581  Interior