Rodrigo Espinoza
2023-04-06
Carga elementos previos
Lectura de Datos y respaldos
library(readxl)
espinoza_1_ <- read_excel("C:/Users/Rodrigo/Desktop/Clases/Estadistica/Datos/espinoza (1).xlsx")
de<-espinoza_1_
de$PM10## [1] "6" "3" "17" "6" "18" "25" "83" "102" "126"
## [10] "104" "100" "102" "115" "50" "8" "78" "143" "168"
## [19] "142" "204" "182" "181" "195" "248" "252" "265" "266"
## [28] "260" "299" "344" "282" "242" "309" "375" "414" "358"
## [37] "117" "91" "116" "92" "96" "NA" "55" "49" "65"
## [46] "144" "238" "110" "144" "120" "93" "10" "15" "11"
## [55] "56" "61" "97" "136" "184" "197" "170" "162" "184"
## [64] "200" "215" "296" "321" "238" "188" "139" "159" "212"
## [73] "175" "369" "433" "279" "92" "86" "6" "6" "6"
## [82] "60" "37" "28" "26" "15" "62" "77" "67" "43"
## [91] "45" "31" "32" "37" "68" "106" "103" "86" "142"
## [100] "179" "196" "201" "193" "150" "89" "90" "78" "51"
## [109] "88" "7" "14" "39" "87" "74" "226" "217" "239"
## [118] "138" "123" "133" "52" "90" "52" "13" "6" "6"
## [127] "24" "50" "21" "152" "142" "138" "94" "126" "84"
## [136] "78" "113" "134" "149" "100" "107" "107" "125" "143"
## [145] "144" "202" "238" "258" "240" "37" "111" "16" "6"
## [154] "122" "156" "153" "169" "186" "186" "226" "242" "60"
## [163] "91" "181" "80" "97" "75" "72" "54" "21" "6"
## [172] "9" "6" "3" "14" "17" "40" "41" "88" "80"
## [181] "150" "108" "139" "186" "48" "30" "22" "17" "43"
## [190] "35" "39" "50" "50" "36" "58" "39" "88" "86"
## [199] "85" "45" "121" "131" "216" "176" "114" "102" "73"
## [208] "27" "29" "120" "102" "107" "200" "136" "191" "134"
## [217] "61" "53" "41" "51" "93" "98" "110" "41" "18"
## [226] "21" "25" "25" "29" "28" "51" "106" "42" "19"
## [235] "47" "62" "56" "75" "56" "86" "94" "93" "122"
## [244] "120" "177" "152" "143" "144" "91" "120" "162" "222"
## [253] "194" "218" "111" "93" "101" "110" "128" "239" "267"
## [262] "243" "195" "160" "107" "69" "107" "64" "69" "36"
## [271] "75" "93" "118" "120" "175" "109" "103" "132" "137"
## [280] "123" "97" "100" "94" "107" "122" "107" "60" "38"
## [289] "168" "79" "90" "82" "203" "56" "47" "35" "116"
## [298] "53" "55" "73" "86" "89" "111" "91" "135" "38"
## [307] "40" "61" "101" "178" "81" "116" "209" "173" "179"
## [316] "206" "333" "321" "305" "179" "179" "201" "145" "107"
## [325] "243" "138" "29" "NA" "50" "22" "52" "61" "61"
## [334] "94" "128" "87" "42" "85" "44" "44" "136" "119"
## [343] "134" "118" "53" "215" "NA" "NA" "NA" "NA" "NA"
## [352] "NA" "NA" "190" "157" "195" "195" "275" "88" "109"
## [361] "139" "61" "214" "242" "122" "167" "128" "95" "142"
## [370] "116" "86" "187" "131" "120" "106" "126" "203" "180"
## [379] "168" "204" "217" "158" "807" "114" "52" "110" "140"
## [388] "211" "201" "212" "193" "238" "274" "253" "265" "272"
## [397] "190" "132" "103" "101" "105" "183" "212" "217" "82"
## [406] "193" "181" "230" "194" "53" "432" "66" "70" "82"
## [415] "172" "172" "134" "120" "126" "131" "150" "146" "163"
## [424] "45" "33" "53" "111" "47" "22" "85" "22" "19"
## [433] "48" "36" "108" "164" "108" "148" "169" "170" "47"
## [442] "33" "157" "117" "120" "263" "133" "139" "137" "138"
## [451] "153" "170" "14" "182" "171" "141" "117" "156" "251"
## [460] "163" "191" "97" "135" "184" "112" "157" "156" "154"
## [469] "137" "84" "300" "251" "158" "10" "12" "10" "11"
## [478] "9" "16" "21" "21" "18" "25" "30" "18" "14"
## [487] "54" "77" "89" "90" "104" "9" "38" "82" "59"
## [496] "89" "99" "107" "82" "139" "193" "123" "147" "130"
## [505] "133" "163" "188" "170" "140" "157" "155" "171" "115"
## [514] "142" "129" "169" "201" "195" "157" "161" "194" "178"
## [523] "186" "49" "39" "12" "8" "11" "47" "28" "79"
## [532] "96" "74" "61" "45" "62" "156" "149" "157" "132"
## [541] "40" "52" "256" "76" "94" "94" "106" "119" "56"
## [550] "54" "83" "58" "53" "18" "60" "37" "26" "70"
## [559] "98" "65" "162" "35" "37" "153" "181" "139" "101"
## [568] "121" "66" "136" "112" "237" "18" "20" "29" "25"
## [577] "213" "175" "188" "164" "171" "181" "293" "248" "108"
## [586] "128" "155" "229" "245" "23" "13" "12" "6" "25"
## [595] "63" "26" "22" "49" "93" "82" "40" "82" "81"
## [604] "53" "96" "52" "30" "82" "37" "50" "38" "78"
## [613] "58" "97" "18" "24" "24" "44" "111" "159" "165"
## [622] "77" "75" "87" "36" "40" "30" "36" "23" "30"
## [631] "54" "28" "34" "55" "71" "48" "81" "62" "113"
## [640] "102" "55" "83" "99" "51" "70" "53" "60" "13"
## [649] "31" "26" "37" "22" "71" "82" "38" "31" "69"
## [658] "80" "109" "184" "183" "181" "111" "116" "126" "181"
## [667] "181" "153" "130" "106" "117" "98" "178" "54" "10"
## [676] "83" "103" "110" "106" "95" "93" "109" "118" "50"
## [685] "93" "37" "30" "32" "55" "84" "56" "37" "NA"
## [694] "NA" "NA" "NA" "NA" "NA" "NA" "NA" "NA" "79"
## [703] "83" "113" "129" "125" "121" "110" "117" "97" "9"
## [712] "43" "35" "38" "42" "86" "90" "95" "92" "141"
## [721] "173" "128" "137" "95" "148" "198" "50" "71" "196"
## [730] "6" "130" "46" "21" "100" "92" "69" "69" "83"
## [739] "83" "6" "73" "68" "120" "86" "123" "86" "276"
## [748] "3" "38" "59" "53" "67" "65" "60" "120" "175"
## [757] "146" "101" "153" "39" "19" "34" "13" "21" "61"
## [766] "40" "67" "99" "99" "36" "22" "109" "90" "137"
## [775] "88" "89" "116" "147" "188" "73" "221" "206" "2"
## [784] "7" "20" "21" "43" "50" "41" "52" "53" "61"
## [793] "77" "91" "81" "248" "248" "64" "82" "95" "93"
## [802] "91" "144" "105" "144" "146" "196" "163" "48" "125"
## [811] "227" "221" "58" "64" "69" "63" "35" "33" "35"
## [820] "19" "58" "51" "75" "63" "41" "22" "61" "60"
## [829] "249" "55" "108" "169" "122" "97" "112" "102" "143"
## [838] "139" "128" "147" "147" "102" "123" "125" "125" "68"
## [847] "22" "15" "35" "84" "162" "82" "40" "31" "50"
## [856] "84" "150" "145" "85" "112" "60" "87" "110" "51"
## [865] "123" "129" "144" "128" "42" "28" "37" "33" "55"
## [874] "55" "45" "69" "89" "160" "86" "35" "42" "28"
## [883] "8" "17" "11" "3" "66" "100" "41" "39" "58"
## [892] "75" "98" "177" "177" "47" "24" "51" "137" "130"
## [901] "105" "49" "49" "33" "9" "4" "105" "43" "32"
## [910] "22" "52" "23" "47" "60" "125" "45" "114" "113"
## [919] "28" "71" "228" "112" "65" "50" "112" "69" "64"
## [928] "95" "204" "68" "26" "18" "6" "95" "80" "105"
## [937] "151" "141" "133" "167" "184" "178" "62" "54" "118"
## [946] "93" "110" "58" "74" "38" "17" "58" "69" "45"
## [955] "149" "143" "142" "86" "213" "181" "141" "109" "116"
## [964] "101" "104" "102" "100" "100" "119" "122" "87" "96"
## [973] "96" "97" "89" "110" "146" "30" "17" "32" "100"
## [982] "60" "92" "33" "70" "64" "79" "97" "10" "25"
## [991] "27" "15" "24" "38" "22" "73" "57" "23" "20"
## [1000] "98" "44" "54" "86" "157" "193" "215" "289" "289"
## [1009] "348" "313" "285" "311" "184" "205" "152" "145" "156"
## [1018] "131" "191" "93" "110" "179" "172" "6" "43" "18"
## [1027] "6" "11" "13" "42" "55" "55" "9" "23" "43"
## [1036] "154" "166" "55" "43" "48" "59" "71" "288" "241"
## [1045] "289" "279" "287" "319" "310" "279" "135" "180" "120"
## [1054] "89" "37" "85" "108" "155" "185" "196" "223" "220"
## [1063] "217" "25" "36" "35" "45" "35" "61" "85" "154"
## [1072] "154" "73" "79" "66" "102" "76" "139" "185" "69"
## [1081] "34" "37" "29" "66" "55" "62" "62" "86" "78"
## [1090] "73" "73" "25" "11" "42" "17" "6" "26" "32"
## [1099] "76" "93" "58" "50" "43" "40" "164" "113" "190"
## [1108] "163" "149" "174" "233" "243" "227" "235" "244" "39"
## [1117] "72" "6" "11" "51" "68" "88" "210" "99" "112"
## [1126] "82" "147" "175" "182" "159" "99" "82" "52" "62"
## [1135] "81" "115" "165" "33" "40" "34" "42" "82" "49"
## [1144] "42" "45" "33" "125" "120" "47" "43" "79" "77"
## [1153] "73" "120" "139" "171" "173" "109" "203" "232" "251"
## [1162] "251" "245" "238" "246" "289" "305" "353" "343" "4"
## [1171] "10" "68" "113" "81" "76" "152" "153" "167" "122"
## [1180] "167" "146" "220" "213" "232" "224" "186" "242" "212"
## [1189] "110" "107" "137" "184" "267" "245" "77" "19" "12"
## [1198] "7" "36" "68" "38" "40" "141" "180" "178" "199"
## [1207] "222" "270" "292" "226" "165" "190" "74" "71" "29"
## [1216] "6" "102" "128" "132" "186" "197" "192" "179" "174"
## [1225] "180" "154" "64" "82" "116" "59" "80" "23" "18"
## [1234] "46" "60" "93" "118" "125" "123" "28" "84" "97"
## [1243] "191" "179" "255" "79" "60" "72" "97" "122" "216"
## [1252] "75" "74" "106" "49" "8" "13" "13" "7" "31"
## [1261] "38" "86" "20" "17" "32" "28" "23" "22" "14"
## [1270] "16" "11" "18" "82" "63" "22" "79" "113" "101"
## [1279] "111" "68" "151" "323" "231" "181" "228" "189" "86"
## [1288] "157" "144" "29" "12" "30" "33" "68" "18" "6"
## [1297] "34" "25" "171" "88" "76" "85" "81" "58" "82"
## [1306] "27" "75" "88" "96" "102" "132" "23" "81" "69"
## [1315] "121" "117" "114" "62" "61" "32" "46" "177" "52"
## [1324] "84" "63" "125" "149" "137" "268" "137" "222" "57"
## [1333] "47" "163" "152" "154" "61" "207" "75" "81" "41"
## [1342] "34" "132" "153" "147" "269" "301" "359" "359" "325"
## [1351] "343" "271" "251" "173" "145" "85" "35" "16" "8"
## [1360] "19" "18" "53" "116" "85" "32" "19" "6" "36"
## [1369] "35" "66" "63" "41" "62" "27" "9" "13" "57"
## [1378] "71" "66" "65" "132" "62" "33" "132" "120" "63"
## [1387] "62" "38" "18" "36" "90" "82" "91" "101" "146"
## [1396] "163" "145" "79" "52" "44" "29" "14" "19" "101"
## [1405] "106" "70" "78" "127" "69" "82" "42" "35" "195"
## [1414] "223" "223" "194" "52" "77" "326" "220" "202" "150"
## [1423] "142" "302" "359" "349" "325" "263" "296" "261" "88"
## [1432] "6" "37" "22" "25" "20" "15" "14" "18" "80"
## [1441] "90" "6" "27" "15" "45" "90" "81" "145" "93"
## [1450] "133" "76" "138" "214" "231" "93" "85" "150" "151"
## [1459] "155" "91" "54" "94" "133" "194" "205" "126" "154"
## [1468] "155" "100" "87" "39" "45" "106" "117" "119" "204"
## [1477] "92" "71" "41" "21" "128" "143" "149" "155" "163"
## [1486] "177" "191" "79" "41" "30" "35" "23" "13" "16"
## [1495] "20" "41" "166" "171" "109" "95" "113" "148" "212"
## [1504] "249" "115" "226" "84" "55" "44" "32" "63" "69"
## [1513] "82" "102" "77" "110" "186" "156" "169" "162" "100"
## [1522] "109" "65" "127" "121" "109" "140" "159" "180" "76"
## [1531] "65" "320" "557" "274" "225" "43" "38" "81" "353"
## [1540] "6" "22" "26" "158" "133" "115" "140" "123" "124"
## [1549] "153" "184" "195" "394" "217" "64" "24" "29" "15"
## [1558] "9" "7" "26" "62" "75" "62" "109" "159" "152"
## [1567] "137" "103" "102" "203" "214" "284" "319" "330" "283"
## [1576] "226" "NA" "97" "36" "54" "63" "63" "36" "10"
## [1585] "31" "30" "NA" "163" "61" "51" "60" "72" "121"
## [1594] "138" "135" "164" "169" "132" "140" "166" "110" "69"
## [1603] "43" "33" "110" "149" "130" "124" "138" "115" "78"
## [1612] "101" "107" "112" "168" "218" "104" "17" "149" "121"
## [1621] "101" "89" "154" "167" "167" "166" "167" "161" "112"
## [1630] "135" "142" "170" "171" "33" "6" "6" "22" "83"
## [1639] "34" "22" "19" "8" "13" "11" "94" "101" "82"
## [1648] "104" "143" "116" "84" "156" "63" "82" "54" "63"
## [1657] "69" "60" "54" "96" "9" "19" "12" "6" "15"
## [1666] "6" "14" "15" "22" "11" "16" "43" "75" "93"
## [1675] "87" "73" "69" "114" "187" "158" "116" "104" "152"
## [1684] "127" "185" "175" "180" "185" "215" "240" "195" "229"
## [1693] "244" "600" "451" "384" "311" "421" "499" "367" "320"
## [1702] "173" "296" "241" "23" "81" "70" "61" "19" "75"
## [1711] "68" "86" "88" "115" "126" "43" "6" "6" "6"
## [1720] "38" "146" "NA" "135" "132" "174" "291" "337" "288"
## [1729] "224" "179" "250" "280" "317" "343" "273" "276" "260"
## [1738] "224" "203" "197" "182" "189" "294" "301" "475" "505"
## [1747] "358" "341" "367" "403" "395" "388" "NA" "469" "420"
## [1756] "425" "255" "35" "5" "5" "34" "36" "114" "149"
## [1765] "161" "159" "170" "257" "141" "262" "319" "281" "277"
## [1774] "281" "85" "12" "5" "14" "23" "23" "5" "5"
## [1783] "5" "5" "7" "34" "9" "13" "11" "90" "32"
## [1792] "14" "51" "100" "171" "135" "144" "166" "199" "196"
## [1801] "199" "185" "221" "129" "101" "112" "117" "73" "22"
## [1810] "42" "94" "122" "117" "176" "180" "206" "279" "385"
## [1819] "244" "120" "37" "38" "39" "49" "47" "65" "38"
## [1828] "43" "47" "51" "139" "150" "208" "175" "125" "121"
## [1837] "149" "188" "216" "106" "69" "51" "61" "139" "253"
## [1846] "232" "65" "53" "69" "100" "79" "83" "111" "194"
## [1855] "51" "23" "12" "12" "34" "34" "29" "33" "53"
## [1864] "37" "110" "112" "119" "129" "122" "144" "31" "243"
## [1873] "145" "205" "135" "174" "139" "124" "159" "218" "266"
## [1882] "190" "203" "241" "276" "319" "244" "238" "171" "332"
## [1891] "313" "396" "410" "420" "338" "210" "142" "100" "144"
## [1900] "179" "182" "5" "38" "56" "40" "37" "144" "171"
## [1909] "146" "134" "27" "5" "5" "24" "36" "72" "160"
## [1918] "220" "183" "190" "267" "49" "132" "133" "163" "145"
## [1927] "137" "199" "37" "16" "145" "137" "64" "94" "76"
## [1936] "60" "86" "49" "195" "150" "52" "133" "107" "41"
## [1945] "153" "105" "148" "179" "238" "242" "230" "271" "254"
## [1954] "287" "231" "73" "75" "69" "75" "80" "121" "122"
## [1963] "120" "116" "149" "176" "159" "104" "166" "160" "163"
## [1972] "289" "293" "252" "69" "140" "285" "101" "103" "64"
## [1981] "80" "79" "93" "140" "121" "124" "119" "123" "78"
## [1990] "46" "46" "48" "61" "32" "68" "76" "70" "108"
## [1999] "115" "95" "5" "97" "97" "106" "86" "87" "104"
## [2008] "119" "70" "67" "120" "106" "164" "127" "142" "99"
## [2017] "97" "86" "122" "135" "168" "120" "126" "149" "212"
## [2026] "242" "105" "7" "32" "24" "39" "61" "88" "74"
## [2035] "204" "171" "37" "22" "45" "97" "135" "NA" "NA"
## [2044] "19" "48" "138" "119" "153" "104" "142" "211" "239"
## [2053] "166" "182" "177" "237" "214" "83" "32" "119" "66"
## [2062] "88" "104" "231" "89" "38" "108" "NA" "41" "51"
## [2071] "66" "83" "85" "16" "70" "82" "140" "136" "161"
## [2080] "174" "182" "43" "50" "139" "92" "79.5" "74" "84"
## [2089] "90" "140" "84" "105" "97" "107" "117" "71" "86"
## [2098] "96" "105" "94" "91" "40" "21" "9" "23" "28"
## [2107] "27" "25" "42" "110" "175" "148" "121" "93" "174"
## [2116] "159" "35" "28" "44" "66" "135" "249" "254" "224"
## [2125] "22" "158" "130" "163" "103" "128" "159" "155" "193"
## [2134] "171" "248" "263" "137" "96" "108" "157" "147" "227"
## [2143] "226" "268" "248" "256" "159" "146" "221" "110" "147"
## [2152] "133" "123" "124" "142" "226" "202" "65" "81" "80"
## [2161] "89" "115" "135" "127" "276" "87" "7" "62" "84"
## [2170] "91" "100" "112" "449" "96" "88" "87" "73" "66"
## [2179] "153" "114" "152" "147" "147" "146" "143" "61" "155"
## [2188] "136" "147" "164" "102" "209" "210" "148" "180" "266"
## [2197] "293" "270" "46" "46" "46" "41" "41" "39" "16"
## [2206] "52" "44" "30" "36" "42" "94" "102" "69" "53"
## [2215] "140" "75" "103" "20" "16" "16" "8" "8" "32"
## [2224] "41" "94" "42" "18" "31" "61" "49" "41" "34"
## [2233] "22" "51" "60" "71" "80" "68" "88" "84" "91"
## [2242] "96" "101" "87" "16" "51" "69" "62" "43" "16"
## [2251] "89" "71" "144" "131" "69" "51" "53" "31" "34"
## [2260] "34" "58" "65" "116" "114" "176" "67" "99" "107"
## [2269] "181" "165" "113" "157" "145" "38" "82" "82" "74"
## [2278] "77" "102" "104" "126" "156" "61" "56" "42" "19"
## [2287] "21" "70" "104" "82" "81" "88" "25" "23" "53"
## [2296] "38" "47" "47" "71" "98" "68" "61" "49" "46"
## [2305] "92" "160" "132" "132" "139" "158" "168" "183" "171"
## [2314] "82" "87" "63" "120" "28" "20" "14" "52" "67"
## [2323] "11" "20" "20" "65" "110" "100" "47" "154" "146"
## [2332] "78" "67" "84" "106" "128" "148" "168" "22" "44"
## [2341] "45" "39" "30" "69" "94" "116" "120" "175" "220"
## [2350] "224" "224" "243" "199" "167" "247" "237" "199" "226"
## [2359] "128" "132" "146" "133" "147" "51" "144" "144" "91"
## [2368] "146" "226" "238" "144" "26" "19" "14" "42" "33"
## [2377] "22" "34" "27" "27" "52" "NA" "NA" "NA" "NA"
## [2386] "84" "9" "32" "21" "36" "23" "31" "19" "69"
## [2395] "91" "52" "29" "62" "74" "58" "38" "59" "109"
## [2404] "80" "127" "96" "69" "53" "140" "167" "164" "163"
## [2413] "190" "106" "111" "111" "57" "58" "52" "123" "67"
## [2422] "96" "13" "16" "44" "25" "22" "NA" "111" "94"
## [2431] "129" "113" "82" "87" "98" "31" "48" "89" "54"
## [2440] "85" "61" "99" "166" "182" "209" "138" "144" "159"
## [2449] "119" "169" "174" "162" "172" "155" "130" "142" "90"
## [2458] "107" "129" "141" "183" "107" "155" "92" "102" "55"
## [2467] "147" "179" "188" "198" "205" "204" "143" "133" "169"
## [2476] "151" "123" "110" "130" "120" "98" "134" "132" "125"
## [2485] "148" "184" "109" "70" "161" "68" "71" "34" "12"
## [2494] "18" "23" "28" "15" "23.8" "48" "91" "74" "66"
## [2503] "71" "139" "119" "109" "94" "124" "77" "148" "112"
## [2512] "70" "131" "91" "95" "102" "24" "36" "96" "110"
## [2521] "127" "106" "43" "62" "47" "66" "73" "56" "35"
## [2530] "45" "49" "66" "71" "77" "60" "39" "38" "25"
## [2539] "30" "16" "25.4" "33" "50" "39" "30" "43" "57"
## [2548] "25" "93" "24" "60" "115" "67" "77" "26" "56"
## [2557] "91" "52" "44" "87" "100" "97" "76" "38" "60"
## [2566] "83" "101" "81" "94" "83" "58" "108" "166" "161"
## [2575] "125" "129" "141" "199" "156" "172" "149" "49" "25"
## [2584] "32" "43" "62" "79" "123" "150" "100" "183" "121"
## [2593] "9" "17" "14" "15" "101" "33" "10" "63" "69"
## [2602] "13" "78" "97" "131" "70" "86" "137" "98" "65"
## [2611] "145" "154" "150" "189" "160" "151" "80" "79" "118"
## [2620] "114" "138" "155" "156" "130" "110" "34" "21" "20"
## [2629] "15" "15" "12" "11" "23" "11" "40" "60" "71"
## [2638] "106" "56" "48" "87" "126" "130" "76" "172" "152"
## [2647] "164" "167" "120" "97" "102" "156" "172" "213" "214"
## [2656] "123" "10" "82" "31" "55" "68" "96" "157" "50"
## [2665] "37" "100" "114" "143" "131" "112" "58" "54" "44"
## [2674] "69" "85" "120" "158" "94" "25" "24" "32" "113"
## [2683] "24" "23" "8" "14" "39" "62" "90" "96" "23"
## [2692] "38" "45" "107" "64" "83" "91" "121" "114" "78"
## [2701] "NA" "107" "87" "58" "71" "103" "105" "124" "100"
## [2710] "68" "71" "102" "121" "138" "140" "125" "116" "111"
## [2719] "164" "155" "223" "124" "78" "73" "67" "108" "92"
## [2728] "79" "132" "128" "124" "103" "95" "46" "46" "21"
## [2737] "11" "69" "82" "84" "110" "113" "78" "85" "108"
## [2746] "126" "148" "161" "181" "154" "74" "32" "26" "26"
## [2755] "46" "47" "57" "57" "78" "111" "161" "59" "89"
## [2764] "110" "134" "140" "117" "126" "114" "30" "21" "32"
## [2773] "23" "49" "37" "38" "43" "90" "94" "112" "106"
## [2782] "92" "110" "100" "69" "79" "71" "72" "58" "102"
## [2791] "77" "91" "107" "157" "163" "116" "77" "74" "73"
## [2800] "101" "104" "83" "89" "80" "78" "62" "34" "18"
## [2809] "14" "11" "22" "19" "109" "249" "213" "244" "406"
## [2818] "370" "355" "365" "361" "418" "443" "357" "321" "255"
## [2827] "323" "302" "256" "283" "40" "15" "16" "12" "11"
## [2836] "18" "39" "56" "43" "46" "77" "113" "142" "130"
## [2845] "73" "155" "184" "127" "NA" "263" "291" "153" "148"
## [2854] "163" "278" "339" "363" "304" "422" "346" "151" "146"
## [2863] "150" "151" "211" "233" "206" "171" "60" "42" "29"
## [2872] "31" "95" "149" "167" "130" "170" "139" "146" "155"
## [2881] "153" "199" "274" "327" "334" "303" "291" "318" "329"
## [2890] "393" "393" "360" "327" "7" "5" "15" "16" "9"
## [2899] "13" "36" "70" "42" "143" "138" "85" "147" "184"
## [2908] "121" "172" "207" "175" "197" "186" "170" "150" "107"
## [2917] "119" "119" "138" "147" "187" "174" "25" "28" "21"
## [2926] "16" "6" "14" "7" "25" "59" "25" "38" "NA"
## [2935] "108" "NA" "214" "179" "60" "38" "5" "7" "5"
## [2944] "7" "23" "5" "7" "77" "62" "39" "70" "33"
## [2953] "96" "83" "52" "35" "56" "55" "60" "164" "129"
## [2962] "102" "24" "37" "43" "7" "18" "26" "42" "6"
## [2971] "10" "14" "44" "29" "25" "58" "59" "58" "109"
## [2980] "60" "40" "89" "246" "252" "189" "251" "234" "238"
## [2989] "203" "24" "8" "13" "10" "48" "88" "120" "36"
## [2998] "78" "67" "60" "79" "127" "173" "320" "392" "362"
## [3007] "309" "309" "295" "321" "NA" "307" "488" "434" "362"
## [3016] "370" "348" "273" "88" "64" "67" "29" "28" "48"
## [3025] "35" "59" "71" "108" "150" "187" "134" "121" "205"
## [3034] "240" "84" "34" "33" "21" "51" "95" "90" "66"
## [3043] "84" "164" "181" "203" "291" "301" "234" "226" "227"
## [3052] "236" "376" "358" "359" "398" "95" "28" "30" "36"
## [3061] "21" "22" "35" "106" "138" "105" "232" "232" "279"
## [3070] "386" "174" "198" "459" "29" "20" "16" "12" "8"
## [3079] "9" "81" "64" "103" "10" "21" "16" "7" "9"
## [3088] "39" "25" "27" "20" "8" "8" "13" "131" "156"
## [3097] "163" "15" "20" "23" "63" "71" "98" "26" "15"
## [3106] "45" "76" "157" "123" "146" "240" "265" "215" "246"
## [3115] "332" "292" "202" "142" "108" "85" "24" "11" "73"
## [3124] "28" "25" "12" "17" "36" "31" "56" "35" "19"
## [3133] "23" "90" "113" "84" "37" "47" "120" "140" "116"
## [3142] "38" "11" "9" "39" "36" "6" "200" "219" "199"
## [3151] "125" "155" "131" "191" "NA" "21" "22" "13" "12"
## [3160] "9" "18" "5" "5" "71" "129" "73" "97" "171"
## [3169] "166" "61" "75" "63" "56" "188" "49" "15" "26"
## [3178] "18" "18" "19" "19" "25" "20" "26" "18" "17"
## [3187] "14" "10" "70" "84" "125" "74" "154" "167" "237"
## [3196] "259" "312" "346" "360" "233" "123" "63" "62" "51"
## [3205] "55" "245" "199" "230" "149" "156" "134" "173" "34"
## [3214] "24" "105" "56" "73" "30" "8" "21" "35" "9"
## [3223] "9" "20" "46" "60" "75" "52" "10" "26" "110"
## [3232] "169" "114" "184" "166" "153" "225" "224" "239" "281"
## [3241] "307" "201" "179" "109" "109" "74" "56" "9" "8"
## [3250] "10" "52" "69" "135" "146" "22" "5" "134" "145"
## [3259] "140" "177" "192" "276" "194" "123" "55" "26" "26"
## [3268] "14" "299" "294" "9" "114" "86" "97" "122" "133"
## [3277] "148" "104" "171" "143" "240" "240" "170" "145" "110"
## [3286] "106" "126" "200" "198" "200" "210" "187" "165" "187"
## [3295] "226" "224" "248" "226" "235" "250" "275" "456" "429"
## [3304] "405" "481" "455" "137" "14" "6" "12" "14" "67"
## [3313] "102" "113" "146" "106" "116" "117.6" "118" "124.4" "46"
## [3322] "70" "43" "46" "83" "75" "85" "126" "78" "86"
## [3331] "104" "94" "93" "99" "20" "5" "16" "29" "39"
## [3340] "41" "27" "27" "83" "88" "165" "206" "215" "194"
## [3349] "201" "192" "29" "100" "87" "82" "111" "122" "167"
## [3358] "191" "204" "205" "160" "14" "9" "32" "11" "15"
## [3367] "13" "39" "48" "14" "46" "50" "52" "112" "118"
## [3376] "140" "169" "115" "24" "5" "14" "16" "27" "17"
## [3385] "22" "55" "98" "137" "104" "105" "60" "34" "41"
## [3394] "64" "153" "90" "172" "175" "44" "18" "42" "13"
## [3403] "12" "12" "52" "111" "45" "26" "13" "13" "8"
## [3412] "12" "29" "64" "74" "69" "80" "109" "86" "17"
## [3421] "17" "14" "15" "18" "11" "11" "88" "90" "100"
## [3430] "77" "89" "187" "163" "140" "124" "105" "177" "156"
## [3439] "74" "41" "20" "27" "24" "25" "12" "14" "NA"
## [3448] "108" "111" "106" "125" "185" "213" "201" "250" "319"
## [3457] "332" "174" "250" "275" "219" "158" "39" "52" "22"
## [3466] "36" "28" "30" "13" "16" "245" "265" "284" "127"
## [3475] "137" "144" "123" "124" "143" "180" "170" "175" "179"
## [3484] "209" "325" "524" "79" "70" "61" "90" "110" "59"
## [3493] "74" "111" "263" "287" "269" "257" "249" "290" "17"
## [3502] "25" "9" "10" "22" "21" "32" "34" "30" "13"
## [3511] "6" "8" "36" "45" "55" "82" "107" "220" "221"
## [3520] "233" "150" "26" "57" "22" "81" "75" "124" "137"
## [3529] "55" "16" "18" "72" "48" "13" "58" "70" "116"
## [3538] "128" "122" "130" "221" "256" "168" "169" "209" "220"
## [3547] "272" "257" "229" "187" "205" "232" "15" "22" "45"
## [3556] "87" "14" "24" "26" "16" "13" "14" "47" "108"
## [3565] "113" "106" "38" "99" "96" "140" "132" "68" "144"
## [3574] "66" "119" "118" "176" "146" "139" "137" "186" "184"
## [3583] "156" "141" "207" "237" "252" "277" "216" "307" "326"
## [3592] "318" "158" "132" "28" "47" "39" "37" "140" "170"
## [3601] "164" "180" "222" "234" "215" "162" "169" "178" "175"
## [3610] "147" "40" "35" "309" "169" "122" "44" "97" "101"
## [3619] "198" "125" "127" "158" "130" "178" "104" "88" "73"
## [3628] "90" "176" "147" "144" "NA" "463" "258" "159" "97"
## [3637] "113" "492" "414" "330" "318" "289" "370" "326" "313"
## [3646] "301" "240" "NA" "66" "104" "99" "NA" "29" "44"
## [3655] "47" "26" "13" "30" "21" "36" "35" "35" "80"
## [3664] "102" "49" "118" "49" "44" "28" "23" "3" "25"
## [3673] "27" "31" "73" "46" "17" "28" "94" "101" "99"
## [3682] "143" "138" "108" "120" "118" "139" "158" "175" "194"
## [3691] "107" "148" "258" "232" "233" "236" "217" "212" "203"
## [3700] "214" "205" "195" "157" "108" "64" "235" "200" "12"
## [3709] "24" "18" "18" "36" "12" "14" "15" "16" "82"
## [3718] "51" "50" "56" "68" "110" "117" "87" "66" "83"
## [3727] "67" "153" "330" "161" "96" "76" "43" "44" "27"
## [3736] "33" "29" "NA" "104" "149" "171" "243" "230" "295"
## [3745] "302" "331" "310" "51" "110" "127" "34" "85" "406"
## [3754] "123" "148" "43" "40" "49" "132" "112" "95" "183"
## [3763] "61" "333" "130" "86" "133" "125" "134" "164" "174"
## [3772] "123" "85" "60" "245" "248" "141" "191" "198" "192"
## [3781] "185" "214" "210" "145" "137" "224" "254" "257" "237"
## [3790] "202" "194" "193" "198" "126" "49" "63" "68" "77"
## [3799] "107" "101" "140" "151" "147" "164" "150" "207" "122"
## [3808] "145" "228" "41" "47" "40" "100" "121" "74" "99"
## [3817] "110" "53" "68" "139" "245" "17" "26" "33" "69"
## [3826] "73" "119" "145" "112" "94" "147" "144" "150" "50"
## [3835] "51" "173" "151" "179" "161" "52" "162" "84" "225"
## [3844] "59" "60" "20" "59" "37" "36" "40" "44" "28"
## [3853] "41" "36" "25" "8" "5" "11" "20" "26" "48"
## [3862] "127" "97" "98" "101" "90" "138" "178" "180" "285"
## [3871] "460" "52" "33" "98" "47" "51" "30" "30" "33"
## [3880] "61" "122" "115" "62" "123" "176" "206" "233" "277"
## [3889] "231" "203" "169" "129" "118" "28" "65" "26" "42"
## [3898] "72" "35" "35" "32" "30" "22" "81" "100" "134"
## [3907] "150" "80" "NA" "90" "147" "151" "175" "117" "140"
## [3916] "126" "149" "190" "196" "267" "265" "129" "139" "164"
## [3925] "152" "262" "245" "237" "172" "149" "212" "203" "200"
## [3934] "158" "156" "174" "97" "133" "143" "212" "150" "146"
## [3943] "199" "161" "79" "78" "65" "139" "110" "90" "118"
## [3952] "86" "89" "96" "150" "134" "137" "213" "218" "222"
## [3961] "56" "293" "134" "13" "28" "36" "165" "79" "106"
## [3970] "123" "91" "60" "75" "84" "42" "34" "34" "56"
## [3979] "98" "68" "28" "129" "35" "15" "18" "13" "20"
## [3988] "26" "12" "67" "28" "76" "68" "82" "82" "138"
## [3997] "86" "60" "59" "107" "135" "124" "58" "19" "9"
## [4006] "15" "25" "7" "14" "68" "9" "23" "51" "12"
## [4015] "21" "22" "32" "17" "12" "30" "29" "25" "34"
## [4024] "12" "45" "61" "66" "107" "131" "117" "133" "138"
## [4033] "199" "190" "68" "66" "75" "56" "84" "106" "72"
## [4042] "9" "11" "60" "35" "47" "55" "70" "75" "14"
## [4051] "10" "29" "52" "90" "76" "32" "62" "58" "59"
## [4060] "107" "96" "100" "122" "134" "78" "223" "261" "283"
## [4069] "257" "256" "108" "154" "101" "91" "134" "79" "77"
## [4078] "114" "110" "89" "176" "172" "107" "121" "113" "104"
## [4087] "123" "30" "140" "127" "113" "113" "120" "148" "197"
## [4096] "182" "170" "196" "188" "208" "22" "25" "23" "8"
## [4105] "10" "16" "28" "53" "54" "49" "54" "145" "13"
## [4114] "16" "15" "52" "39" "22" "56" "75" "52" "35"
## [4123] "39" "41" "82" "91" "61" "75" "75" "65" "71"
## [4132] "80" "56" "54" "70" "107" "162" "153" "157" "172"
## [4141] "192" "115" "112" "88" "97" "109" "119" "47" "48"
## [4150] "34" "49" "145" "90" "78" "121" "120" "154" "155"
## [4159] "125" "128" "136" "152" "231" "182" "169" "116" "76"
## [4168] "62" "63" "100" "60" "37" "80" "105" "156" "156"
## [4177] "106" "45" "42" "110" "54" "94" "84" "62" "77"
## [4186] "78" "71" "51" "28" "32" "76" "31" "24" "34"
## [4195] "85" "114" "96" "78" "43" "53" "41" "93" "57"
## [4204] "67" "107" "102" "96" "102" "173" "173" "52" "55"
## [4213] "81" "102" "92" "106" "58" "31" "34" "46" "49"
## [4222] "80" "81" "73" "41" "25" "83" "101" "74" "94"
## [4231] "110" "100" "175" "141" "157" "145" "134" "82" "77"
## [4240] "73" "163" "100" "109" "62" "52" "109" "28" "47"
## [4249] "63" "62" "59" "89" "75" "119" "112" "130" "141"
## [4258] "130" "21" "18" "24" "51" "104" "29" "23" "28"
## [4267] "53" "57" "62" "64" "81" "115" "110" "111" "81"
## [4276] "150" "NA" "13" "13" "11" "12" "48" "55" "92"
## [4285] "107" "118" "139" "NA" "32" "98" "152" "161" "102"
## [4294] "110" "32" "40" "47" "48" "45" "50" "31" "80"
## [4303] "116" "110" "118" "106" "88" "102" "86" "92" "134"
## [4312] "105" "100" "92" "32" "25" "167" "154" "126" "124"
## [4321] "123" "79" "70" "96" "170" "174" "177" "167" "200"
## [4330] "138" "118" "78" "30" "49" "23" "69" "88" "56"
## [4339] "64" "117" "132" "140" "123" "145" "142" "164" "99"
## [4348] "64" "189" "163" "113" "47" "30" "33" "103" "159"
## [4357] "91" "56" "51" "44" "49" "67" "74" "72" "44"
## [4366] "29" "26" "51" "93" "23" "21" "20" "51" "82"
## [4375] "80" "81" "72" "75" "110" "75" "13" "41" "21"
## [4384] "16" "17" "18" "12" "18" "15" "43" "35" "13"
## [4393] "20" "14" "45" "48" "43" "16" "76" "35" "77"
## [4402] "56" "52" "48" "24" "38" "35" "31" "23" "14"
## [4411] "11" "16" "16" "11" "14" "17" "27" "16" "19"
## [4420] "46" "43" "89" "41" "28" "25" "27" "28" "20"
## [4429] "24" "26" "64" "64" "37" "24" "48" "95" "36"
## [4438] "111" "132" "104" "78" "76" "27" "22" "11" "6"
## [4447] "8" "16" "13" "10" "10" "8" "22" "13" "5"
## [4456] "5" "11" "34" "54" "23" "111" "109" "134" "115"
## [4465] "127" "146" "186" "151" "140" "124" "174" "177" "183"
## [4474] "177" "163" "185" "188" "41" "88" "55" "23" "30"
## [4483] "29" "47" "55" "65" "129" "110" "126" "152" "104"
## [4492] "159" "162" "164" "174" "113" "40" "46" "21" "25"
## [4501] "36" "63" "77" "223" "172" "30" "43" "18" "32"
## [4510] "32" "36" "97" "111" "134" "138" "131" "57" "29"
## [4519] "21" "77" "61" "46" "46" "47" "51" "67" "73"
## [4528] "79" "69" "72" "35" "35" "24" "22" "29" "29"
## [4537] "33" "39" "5" "5" "5" "6" "20" "17" "18"
## [4546] "46" "52" "70" "115" "22" "32" "46" "54" "102"
## [4555] "134" "107" "115" "139" "203" "159" "159" "191" "263"
## [4564] "237" "202" "284" "315" "249" "295" "336" "298" "274"
## [4573] "314" "103" "11" "33" "6" "13" "17" "30" "35"
## [4582] "14" "41" "20" "23" "14" "10" "12" "35" "33"
## [4591] "28" "18" "17" "9" "41" "56" "11" "62" "85"
## [4600] "69" "53" "63" "67" "27" "128" "147" "148" "160"
## [4609] "172" "166" "99" "129" "175" "98" "125" "216" "188"
## [4618] "185" "265" "294" "314" "318" "326" "23" "69" "61"
## [4627] "50" "56" "58" "28" "80" "123" "116" "126" "131"
## [4636] "111" "55" "54" "43" "44" "46" "61" "54" "47"
## [4645] "64" "79" "84" "90" "70" "52" "61" "49" "57"
## [4654] "46" "51" "66" "113" "24" "26" "81" "42" "41"
## [4663] "62" "59" "42" "71" "74" "15" "21" "23" "20"
## [4672] "35" "25" "25" "29" "25" "22" "58" "88" "76"
## [4681] "85" "73" "77" "87" "84" "37" "62" "139" "101"
## [4690] "137" "129" "149" "129" "165" "167" "167" "269" "239"
## [4699] "240" "240" "230" "36" "18" "18" "19" "22" "15"
## [4708] "14" "14" "20" "19" "19" "24" "34" "44" "55"
## [4717] "80" "91" "91" "84" "87" "150" "145" "117" "149"
## [4726] "140" "116" "126" "147" "63" "64" "102" "91" "90"
## [4735] "114" "201" "213" "229" "159" "161" "208" "219" "234"
## [4744] "239" "299" "307" "304" "303" "267" "210" "212" "211"
## [4753] "206" "50" "59" "76" "71" "95" "112" "135" "89"
## [4762] "72" "73" "84" "79" "90" "103" "101" "78" "86"
## [4771] "81" "51" "60" "53" "67" "37" "47" "88" "91"
## [4780] "28" "29" "43" "49" "NA" "81" "55" "57" "48"
## [4789] "28" "23" "12" "28" "42" "38" "67" "88" "95"
## [4798] "102" "99" "96" "108" "94" "25" "8" "20" "31"
## [4807] "30" "155" "173" "184" "326" "268" "244" "404" "299"
## [4816] "283" "237" "238" "233" "378" "44" "544" "647" "656"
## [4825] "640" "662" "609" "510" "518" "434" "436" "362" "110"
## [4834] "40" "23" "33" "41" "45" "26" "24" "11" "56"
## [4843] "26" "116" "118" "126" "127" "139" "214" "183" "160"
## [4852] "190" "293" "173" "262" "249" "242" "280" "307" "252"
## [4861] "208" "229" "299" "304" "271" "291" "270" "45" "32"
## [4870] "21" "21" "30" "20" "24" "32" "66" "110" "90"
## [4879] "107" "54" "69" "107" "257" "282" "366" "123" "59"
## [4888] "310" "343" "264" "232" "227" "221" "235" "196" "16"
## [4897] "12" "8" "7" "6" "8" "5" "13" "17" "11"
## [4906] "12" "16" "47" "102" "122" "138" "135" "58" "93"
## [4915] "214" "211" "209" "187" "174" "166" "166" "276" "275"
## [4924] "263" "273" "262" "247" "162" "505" "405" "410" "384"
## [4933] "268" "129" "91" "343" "100" "35" "50" "61" "319"
## [4942] "449" "556" "547" "353" "313" "180" "104" "28" "30"
## [4951] "14" "72" "101" "106" "95" "78" "106" "109" "103"
## [4960] "109" "133" "266" "230" "606" "414" "9" "14" "9"
## [4969] "27" "14" "12" "44" "99" "111" "108" "118" "221"
## [4978] "327" "324" "228" "219" "500" "534" "428" "216" "180"
## [4987] "157" "146" "140" "206" "220" "15" "6" "13" "14"
## [4996] "10" "6" "12" "12" "34" "37" "49" "65" "93"
## [5005] "23" "57" "16" "28" "35" "28" "27" "20" "29"
## [5014] "25" "29" "43" "27" "41" "89" "212" "153" "25"
## [5023] "71" "47" "24" "43" "34" "26" "20" "21" "26"
## [5032] "34" "33" "20" "69" "79" "61" "72" "129" "82"
## [5041] "124" "87" "51" "265" "116" "148" "150" "150" "91"
## [5050] "41" "21" "29" "17" "45" "33" "26" "24" "37"
## [5059] "104" "143" "51" "41" "73" "104" "123" "119" "144"
## [5068] "131" "144" "156" "159" "154" "147" "123" "136" "125"
## [5077] "146" "141" "29" "71" "22" "28" "16" "6" "64"
## [5086] "61" "70" "49" "27" "36" "33" "42" "39" "45"
## [5095] "90" "100" "43" "42" "86" "108" "49" "61" "48"
## [5104] "32" "132" "156" "164" "145" "88" "76" "65" "61"
## [5113] "68" "87" "61" "56" "96" "50" "48" "40" "90"
## [5122] "18" "16" "14" "21" "21" "13" "14" "25" "30"
## [5131] "13" "16" "15" "17" "21" "25" "18" "23" "33"
## [5140] "37" "50" "46" "3" "14" "21" "24" "14" "12"
## [5149] "20" "15" "18" "49" "33" "23" "22" "25" "42"
## [5158] "99" "42" "60" "61" "47" "47" "671" "744" "552"
## [5167] "442" "22" "33" "29" "50" "23" "41" "95" "57"
## [5176] "49" "82" "77" "70" "116" "135" "183" "173" "137"
## [5185] "145" "169" "22" "5" "10" "6" "5" "7" "10"
## [5194] "27" "22" "39" "35" "28" "31" "28" "25" "71"
## [5203] "35" "37" "41" "56" "55" "58" "22" "35" "34"
## [5212] "20" "23" "39" "56" "56" "28" "23" "25" "19"
## [5221] "19" "8" "9" "16" "17" "27" "72" "64" "130"
## [5230] "143" "154" "127" "44" "24" "13" "11" "21" "15"
## [5239] "33" "32" "69" "47" "17" "14" "18" "42" "59"
## [5248] "72" "26" "25" "9" "6" "70" "88" "109" "120"
## [5257] "8" "25" "25" "18" "17" "77" "57" "51" "13"
## [5266] "41" "37" "61" "78" "73" "68" "95" "108" "157"
## [5275] "193" "198" "151" "197" "217" "249" "275" "331" "345"
## [5284] "711" "426" "404" "309" "236" "193" "224" "205" "93"
## [5293] "60" "167" "304" "314" "245" "240" "240" "124" "45"
## [5302] "23" "56" "58" "20" "18" "19" "12" "NA" "36"
## [5311] "27" "51" "60" "98" "37" "25" "82" "92" "148"
## [5320] "197" "160" "33" "59" "140" "129" "152" "160" "159"
## [5329] "149" "126" "71" "158" "163" "163" "169" "238" "256"
## [5338] "269" "279" "269" "345" "287" "288" "330" "268" "263"
## [5347] "259" "316" "339" "337" "316" "294" "300" "393" "402"
## [5356] "392" "360" "28" "36" "97" "104" "121" "119" "160"
## [5365] "103" "145" "38" "67" "125" "187" "150" "170" "215"
## [5374] "289" "308" "229" "181" "118" "14" "15" "43" "21"
## [5383] "21" "33" "47" "20" "19" "15" "22" "76" "16"
## [5392] "39" "55" "36" "30" "25" "33" "199" "188" "NA"
## [5401] "NA" "245" "15" "55" "173" "178" "62" "168" "171"
## [5410] "179" "210" "339" "223" "289" "44" "33" "19" "19"
## [5419] "22" "24" "34" "40" "41" "NA" "88" "104" "89"
## [5428] "97" "NA" "144" "166" "163" "209" "101" "79" "79"
## [5437] "116" "124" "131" "161" "190" "271" "299" "184" "91"
## [5446] "109" "42" "31" "104" "181" "383" "206" "225" "242"
## [5455] "218" "223" "295" "185" "166" "112" "86" "38" "122"
## [5464] "125" "116" "117" "95" "94" "94" "141" "143" "225"
## [5473] "226" "117" "28" "9" "45" "81" "56" "53" "92"
## [5482] "177" "174" "106" "52" "54" "86" "102" "29" "42"
## [5491] "33" "44" "30" "22" "34" "23" "51" "46" "33"
## [5500] "139" "135" "113" "95" "87" "119" "61" "106" "125"
## [5509] "120" "128" "128" "227" "216" "NA" "87" "282" "117"
## [5518] "161" "148" "132" "109" "132" "67" "50" "49" "35"
## [5527] "79" "123" "135" "118" "146" "154" "284" "174" "123"
## [5536] "181" "122" "184" "161" "128" "139" "146" "133" "117"
## [5545] "123" "98" "128" "137" "84" "97" "102" "106" "75"
## [5554] "211" "249" "251" "261" "236" "238" "197" "189" "206"
## [5563] "205" "233" "67" "82" "75" "67" "145" "104" "28"
## [5572] "32" "128" "82" "69" "65" "76" "90" "94" "95"
## [5581] "108" "125" "468" "NA" "403" "155" "96" "54" "44"
## [5590] "37" "39" "37" "43" "62" "120" "149" "137" "157"
## [5599] "175" "174" "108" "110" "89" "82" "114" "121" "53"
## [5608] "40" "54" "142" "151" "169" "165" "182" "197" "205"
## [5617] "191" "204" "14" "20" "21" "13" "69" "83" "46"
## [5626] "56" "49" "100" "53" "42" "42" "43" "78" "75"
## [5635] "78" "82" "18" "5" "52" "52" "73" "76" "81"
## [5644] "126" "110" "142" "125" "148" "57" "101" "125" "126"
## [5653] "157" "103" "90" "86" "105" "118" "127" "129" "117"
## [5662] "154" "141" "116" "96" "89" "45" "84" "74" "157"
## [5671] "99" "115" "128" "134" "170" "131" "69" "79" "5"
## [5680] "64" "38" "29" "105" "20" "37" "26" "32" "54"
## [5689] "47" "204" "143" "139" "35" "24" "29" "92" "132"
## [5698] "128" "132" "82" "59" "72" "87" "125" "139" "185"
## [5707] "81" "111" "105" "129" "21" "24" "81" "78" "99"
## [5716] "153" "120" "140" "30" "119" "70" "73" "78" "87"
## [5725] "106" "169" "121" "281" "118" "91" "94" "80" "96"
## [5734] "100" "64" "67" "143" "170" "154" "45" "51" "110"
## [5743] "109" "91" "98" "98" "96" "116" "115" "124" "109"
## [5752] "249" "141" "73" "297" "68" "57" "107" "128" "56"
## [5761] "50" "5" "17" "70" "102" "109" "88" "55" "36"
## [5770] "241" "103" "38" "69" "64" "61" "42" "46" "NA"
## [5779] "59" "42" "27" "24" "18" "110" "50" "28" "50"
## [5788] "76" "94" "82" "99" "95" "82" "98" "92" "72"
## [5797] "31" "48" "58" "54" "219" "87" "82" "109" "155"
## [5806] "136" "27" "38" "43" "61" "47" "90" "48" "31"
## [5815] "148" "39" "78" "88" "21" "12" "30" "15" "17"
## [5824] "18" "26" "28" "39" "58" "87" "57" "67" "71"
## [5833] "61" "79" "103" "96" "136" "71" "67" "61" "86"
## [5842] "64" "23" "28" "68" "99" "149" "143" "135" "138"
## [5851] "74" "104" "92" "61" "16" "15" "14" "52" "53"
## [5860] "NA" "21" "24" "59" "64" "108" "122" "133" "113"
## [5869] "126" "141" "239" "119" "128" "118" "74" "140" "124"
## [5878] "118" "104" "96" "64" "180" "62" "171" "122" "111"
## [5887] "149" "133" "130" "61" "88" "109" "109" "109" "134"
## [5896] "169" "68" "84" "98" "55" "106" "106" "126" "126"
## [5905] "105" "57" "5" "5" "5" "49" "47" "44" "34"
## [5914] "5" "96" "15" "69" "53" "65" "98" "54" "66"
## [5923] "61" "28" "25" "31" "48" "49" "42" "29" "48"
## [5932] "69" "70" "66" "100" "124" "129" "131" "102" "125"
## [5941] "112" "58" "100" "87" "90" "128" "116" "35" "23"
## [5950] "12" "16" "11" "23" "67" "58" "81" "90" "138"
## [5959] "118" "NA" "NA" "NA" "51" "148" "121" "72" "48"
## [5968] "67" "40" "46" "52" "5" "5" "12" "9" "24"
## [5977] "27" "27" "88" "59" "75" "52" "61" "44" "119"
## [5986] "84" "126" "128" "128" "135" "NA" "71" "240" "240"
## [5995] "250" "48" "50" "82" "80" "64" "56" "89" "89"
## [6004] "NA" "18" "67" "75" "93" "85" "130" "103" "44"
## [6013] "104" "77" "110" "96" "135" "125" "139" "123" "76"
## [6022] "77" "54" "60" "102" "117" "103" "91" "125" "15"
## [6031] "18" "15" "59" "69" "60" "61" "26" "23" "35"
## [6040] "64" "57" "116" "107" "130" "90" "95" "106" "96"
## [6049] "103" "105" "33" "29" "32" "29" "78" "91" "125"
## [6058] "23" "8" "12" "26" "5" "7" "18" "36" "38"
## [6067] "33" "41" "33" "34" "137" "118" "108" "119" "61"
## [6076] "52" "40" "21" "19" "17" "58" "109" "80" "91"
## [6085] "53" "78" "72" "72" "58" "106" "67" "69" "57"
## [6094] "38" "42" "94" "90" "67" "102" "89" "89" "144"
## [6103] "87" "93" "93" "123" "11" "27" "20" "9" "10"
## [6112] "11" "24" "17" "30" "17" "60" "15" "214" "27"
## [6121] "49" "24" "30" "36" "34" "13" "15" "68" "63"
## [6130] "37" "18" "91" "94" "106" "99" "38" "23" "34"
## [6139] "32" "48" "36" "14" "31" "28" "37" "8" "49"
## [6148] "40" "19" "35" "41" "21" "79" "36" "30" "40"
## [6157] "37" "20" "24" "54" "51" "86" "99" "153" "74"
## [6166] "54" "44" "54" "86" "94" "33" "28" "50" "22"
## [6175] "12" "15" "35" "13" "23" "NA" "NA" "NA" "86"
## [6184] "102" "NA" "27" "49" "30" "166" "30" "35" "72"
## [6193] "99" "53" "79" "41" "10" "18" "79" "28" "35"
## [6202] "49" "40" "45" "67" "85" "100" "118" "89" "119"
## [6211] "114" "126" "136" "87" "83" "76" "147" "45" "67"
## [6220] "92" "45" "132" "141" "147" "152" "177" "49" "77"
## [6229] "38" "33" "57" "50" "25" "23" "25" "27" "14"
## [6238] "16" "10" "12" "7" "30" "46" "43" "21" "45"
## [6247] "14" "22" "60" "95" "110" "87" "172" "133" "104"
## [6256] "114" "118" "112" "148" "160" "150" "129" "144" "195"
## [6265] "235" "33" "234" "321" "277" "231" "191" "138" "216"
## [6274] "119" "97" "104" "101" "36" "51" "32" "28" "30"
## [6283] "30" "18" "30" "NA" "92" "97" "71" "131" "136"
## [6292] "159" "107" "166" "193" "201" "220" "242" "185" "196"
## [6301] "174" "249" "187" "203" "231" "217" "126" "87" "142"
## [6310] "132" "52" "39" "59" "70" "44" "35" "30" "126"
## [6319] "93" "106" "87" "72" "78" "80" "46" "46" "51"
## [6328] "23" "15" "10" "13" "15" "16" "24" "40" "56"
## [6337] "89" "34" "72" "94" "110" "92" "129" "187" "204"
## [6346] "206" "217" "195" "189" "157" "63" "53" "116" "112"
## [6355] "193" "128" "124" "191" "241" "232" "214" "230" "225"
## [6364] "243" "173" "204" "103" "125" "99" "119" "133" "37"
## [6373] "45" "140" "146" "163" "227" "233" "323" "284" "276"
## [6382] "236" "96" "101" "45" "48" "45" "48" "47" "51"
## [6391] "33" "34" "12" "10" "12" "24" "48" "67" "64"
## [6400] "127" "128" "198" "179" "116" "109" "101" "126" "139"
## [6409] "155" "166" "123" "163" "160" "34" "21" "114" "65"
## [6418] "33" "82" "77" "35" "27" "19" "9" "17" "24"
## [6427] "40" "49" "35" "36" "33" "47" "57" "97" "105"
## [6436] "136" "139" "155" "140" "135" "25" "21" "10" "13"
## [6445] "20" "29" "29" "32" "42" "89" "167" "145" "203"
## [6454] "406" "388" "350" "305" "267" "279" "292" "257" "280"
## [6463] "198" "175" "125" "57" "47" "64" "48" "36" "127"
## [6472] "129" "73" "53" "17" "11" "23" "31" "59" "186"
## [6481] "154" "172" "251" "147" "225" "263" "245" "87" "71"
## [6490] "104" "201" "221" "126" "133" "139" "140" "133" "133"
## [6499] "122" "73" "59" "59" "59" "159" "272" "192" "84"
## [6508] "54" "66" "28" "69" "57" "131" "167" "127" "119"
## [6517] "194" "81" "110" "267" "281" "300" "321" "418" "382"
## [6526] "149" "247" "259" "35" "38" "115" "259" "103" "102"
## [6535] "68" "80" "92" "15" "17" "14" "6" "8" "19"
## [6544] "15" "17" "21" "25" "30" "48" "24" "22" "23"
## [6553] "46" "139" "100" "131" "147" "122" "120" "160" "265"
## [6562] "398" "408" "471" "479" "25" "24" "61" "143" "162"
## [6571] "134" "143" "206" "125" "149" "160" "168" "154" "126"
## [6580] "117" "8" "38" "52" "127" "44" "47" "222" "194"
## [6589] "102" "123" "325" "222" "130" "90" "134" "111" "20"
## [6598] "64" "52" "146" "184" "258" "172" "91" "110" "298"
## [6607] "206" "221" "246" "244" "230" "237" "242" "27" "10"
## [6616] "16" "13" "26" "94" "66" "39" "26" "50" "56"
## [6625] "79" "123" "131" "144" "191" "263" "244" "253" "265"
## [6634] "254" "245" "238" "228" "218" "185" "181" "107" "19"
## [6643] "6" "12" "11" "16" "20" "20" "13" "6" "18"
## [6652] "28" "80" "100" "42" "56" "38" "56" "202" "205"
## [6661] "35" "62" "76" "318" "185" "330" "434" "392" "242"
## [6670] "246" "268" "275" "212" "294" "490" "534" "500" "228"
## [6679] "258" "434" "394" "391" "384" "392" "341" "319" "512"
## [6688] "146" "8" "17" "17" "10" "18" "16" "17" "23"
## [6697] "70" "80" "183" "189" "163" "127" "185" "200" "209"
## [6706] "187" "141" "31" "16" "16" "12" "19" "9" "20"
## [6715] "43" "122" "158" "175" "22" "15" "21" "13" "15"
## [6724] "35" "85" "416" "162" "380" "296" "338" "453" "529"
## [6733] "625" "617" "609" "556" "435" "263" "232" "173" "285"
## [6742] "282" "241" "594" "813" "787" "746" "535" "420" "267"
## [6751] "223" "173" "287" "195" "180" "226" "225" "244" "222"
## [6760] "226" "214" "196" "191" "116" "19" "12" "23" "9"
## [6769] "7" "9" "63" "97" "158" "167" "6" "6" "25"
## [6778] "8" "136" "123" "99" "73" "147" "99" "41" "40"
## [6787] "10" "39" "40" "22" "9" "59" "84" "45" "34"
## [6796] "68" "64" "121" "97" "104" "81" "86" "126" "141"
## [6805] "148" "191" "75" "31" "36" "24" "31" "171" "109"
## [6814] "27" "14" "12" "11" "18" "19" "32" "48" "85"
## [6823] "119" "75" "53" "17" "14" "18" "18" "26" "111"
## [6832] "21" "10" "16" "16" "22" "53" "109" "104" "80"
## [6841] "36" "123" "158" "157" "151" "166" "170" "148" "165"
## [6850] "204" "291" "254" "84" "21" "5" "17" "61" "370"
## [6859] "917" "653" "820" "828" "564" "285" "344" "179" "42"
## [6868] "57" "53" "68" "48" "14" "10" "13" "14" "113"
## [6877] "94" "108" "74" "74" "101" "89" "110" "28" "19"
## [6886] "29" "25" "25" "22" "15" "210" "261" "118" "64"
## [6895] "81" "107" "172" "175" "141" "96" "121" "343" "332"
## [6904] "287" "391" "18" "10" "23" "13" "6" "16" "18"
## [6913] "9" "86" "59" "131" "41" "74" "24" "17" "17"
## [6922] "9" "6" "12" "26" "29" "25" "18" "20" "20"
## [6931] "15" "10" "26" "16" "72" "270" "264" "204" "74"
## [6940] "61" "194" "169" "123" "101" "33" "208" "184" "233"
## [6949] "230" "6" "167" "258" "268" "262" "8" "12" "13"
## [6958] "6" "24" "104" "104" "95" "103" "137" "133" "93"
## [6967] "147" "173" "176" "179" "187" "25" "23" "37" "NA"
## [6976] "NA" "NA" "127" "61" "61" "72" "94" "136" "15"
## [6985] "18" "10" "38" "36" "59" "69" "87" "41" "50"
## [6994] "41" "27" "27" "21" "30" "22" "16" "15" "37"
## [7003] "42" "23" "32" "44" "76" "132" "99" "NA" "NA"
## [7012] "32" "35" "33"#realizamos cambio de variables cualitativas a variables cuantitativas, para poder graficar las variables
de$PM10<- as.numeric(de$PM10)## Warning: NAs introducidos por coerciónde$CO<- as.numeric(de$CO)## Warning: NAs introducidos por coerciónclass(de$PM10)## [1] "numeric"str(de)## tibble [7,014 × 10] (S3: tbl_df/tbl/data.frame)
## $ year : num [1:7014] 2013 2013 2013 2013 2013 ...
## $ month : num [1:7014] 3 3 3 3 3 3 3 3 3 3 ...
## $ day : num [1:7014] 1 1 1 2 2 2 2 2 3 3 ...
## $ PM10 : num [1:7014] 6 3 17 6 18 25 83 102 126 104 ...
## $ CO : num [1:7014] 400 400 500 400 800 ...
## $ O3 : chr [1:7014] "62" "61" "52" "62" ...
## $ PRES : num [1:7014] 1024 1027 1028 1031 1027 ...
## $ RAIN : num [1:7014] 0 0 0 0 0 0 0 0 0 0 ...
## $ WSPM : num [1:7014] 3.1 3.8 2.8 1.7 2.7 1.5 1.2 1.4 1.2 0.9 ...
## $ station: chr [1:7014] "Guanyuan" "Guanyuan" "Guanyuan" "Guanyuan" ...#Sacamos los valores NA para poder graficar
de<-na.omit(de)Carga de script a usar,
Calidad del Aire en Guanyuan
Evualuar la cantidad de gases Nocivos para la salud humana
Análisis de Contexto
Estamos analizando una muestra de los datos tomados en la estacion de Guanyuan , no realizamos un estudio de poblacion ya que no tenemos todos los datos y por falta de tiempo tampoco podria realizarse .
Análisis Calidad de datos
Análisis Coherencia de Indicadores
descripYG(de,de$PM10,NULL)
## n promedio mediana desv.estd.m curtosis asimetria min max p25
## 1 6654 109.730 90.000 93.118 6.997 1.980 2.000 917.000 41.000
## p75 iqr bmin bmax
## 1 149.000 108.000 -121.000 311.000como se puese apreciar. la asimetria resulta ser positiva esperando que ocurra que los datos se agrupen mas al lado izquierdo lo que se evidencia con la relacion promedio-mediana la cual dice que la asimetria es positiva cuando el promedio es> a la mediana, lo cual se cumple, tenemos una curtosis >1 por lo que la distribución seria leptocurtico
Se puede apreciar que los datos muestran cierta robustez y coherencia.Lo cual indica que los datos podrian llevarnos a un resultado
Análisis Robustez
Se detecto la presencia de valores que estan muy por sobre el comportamiento natural de los demas datos, por tanto, se decidio limpiar los datos referentes a la cantidad de material particulado mayor a 750 en referencia a la calidad de aire de Guanyuan, que esta presencia se puede deber a un error debido que pasado estos sobrepasan el limite normal de liberacion de estos gases.
# de modo en que deje todos los datos y variables de "de" que cumplen con la condicion que PM10 sea menor que 750.
de<- de[de$PM10<=750,]
descripYG(de,de$PM10,NULL)
## n promedio mediana desv.estd.m curtosis asimetria min max p25
## 1 6649 109.187 89.000 91.007 4.936 1.758 2.000 746.000 41.000
## p75 iqr bmin bmax
## 1 148.000 107.000 -119.500 308.500Podemos apreciar que la relacion de promedio-mediana es coherente con la asimetría la cual es negativa, La curtosis es negativa por lo que podemos concluir que tenemos una distribucion de datos platocurtíco, esto lo podemos confirmar al ver la distribución de los datos en el grafíco
Dado que tenemos un conjunto de datos corregidos, se hace necesario replantear brevemente el Proposito
Proposito Analisis del PM10 durante los meses del año para evaluar los meses mas contaminantes con PM10
Realizamos un analisis de todos los meses del año 2014 para identificar los meses con mayor cantidad de PM10 en el aíre
PM10_2014<-subset(de,year ==2014)
#Separamos los datos por el año 2014, evaluando los 12 meses para
descripYG(PM10_2014,PM10_2014$PM10,PM10_2014$month)
## Picking joint bandwidth of 26.1
## ni Promedio Mediana Desv.Estd Curtosis Asimetria Min Max P25
## 1 165 119.7636 110.0000 80.8439 5.1111 1.5877 6.0000 557.0000 63.0000
## 2 134 160.5522 129.5000 135.8826 0.1799 0.9226 5.0000 600.0000 54.0000
## 3 160 130.0437 123.0000 96.1715 0.0746 0.7173 5.0000 420.0000 42.7500
## 4 128 119.8125 111.5000 64.8036 0.2518 0.7372 5.0000 293.0000 74.7500
## 5 143 127.6678 117.0000 71.1627 1.8807 0.9468 7.0000 449.0000 83.5000
## 6 136 72.9265 67.0000 43.3481 -0.2212 0.7069 8.0000 183.0000 41.0000
## 7 132 108.1818 106.5000 63.5653 -0.9162 0.2891 9.0000 247.0000 52.0000
## 8 147 84.7701 79.0000 46.6231 -0.8096 0.3007 9.0000 199.0000 46.0000
## 9 160 88.0438 89.5000 47.9346 -0.4839 0.2682 8.0000 223.0000 46.7500
## 10 120 163.2417 140.5000 121.9909 -0.8666 0.5774 5.0000 443.0000 71.7500
## 11 150 126.3333 77.5000 119.5902 0.0945 1.0509 5.0000 488.0000 33.2500
## 12 144 84.2847 55.5000 83.3286 0.9968 1.2789 5.0000 360.0000 20.0000
## P75 IQR
## 1 155.0000 92.0000
## 2 240.7500 186.7500
## 3 188.5000 145.7500
## 4 149.2500 74.5000
## 5 159.0000 75.5000
## 6 98.2500 57.2500
## 7 155.0000 103.0000
## 8 122.0000 76.0000
## 9 121.0000 74.2500
## 10 265.7500 194.0000
## 11 203.0000 169.7500
## 12 129.5000 109.5000Se dejan el resto de valores atipicos ya que son datos que pueden estar correctos y se puede deber a la falta de una analisis de variable
En este grafico realizamos una comparacion de la cantidad de PM10 por los meses del año,Para analizar cuales son los meses con mayor contaminacion y cuales son los con menor contaminacion
## RESULTADOS
#mostrar PM10 por Mes del año 2014
# en tabla_promedio deja del dataset "de" agrupado por mes, el promedio la variable PM10
tabla_promedioPM10<-PM10_2014 %>%
group_by(month) %>%
summarise(Promedio=mean(PM10))
# grafica de los datos en tabla_MEDIA, en el x la variable Month, y en el eje Y los datos del promedio de CO
ggplot(tabla_promedioPM10,aes(x=month,y=Promedio))+
geom_bar(stat="identity",color = "orange") +
scale_x_continuous(breaks = c(1,2,3,4,5,6,7,8,9,10,11,12),
labels = c("e","f","m","a","m","j","j","a","s","o","n","d"))+
labs(title='Promedio PM10 x mes año 2014 ',
x='month',
y=' Promedio',
colour="identifier")
Como se puede apreciar el PM10 esta en altos niveles en la region de Guanyuan, tienen un comportamiento entre los 80 a 160 PM10 x mes app. Lo cual es dañino para la salud humana ya que segun la OMS desde 50 Micras x m3 es dañino para la salud humana por lo tanto la calidad de aire en guanyuan es mala, tambien podemos apreciar que el mes 2 y 10 (marzo y octubre) es donde hay mayor cantidad de PM10 y el mes 7 (Junio) es el con menor cantidad.
Proposito analisis de CO por estaciones del año 2014
Agregamos una variable “Estaciones” para poder graficar y analizar
estaciones <- cut(de$month, breaks = c(0,2,5,8,12), labels = c("Primavera", "Verano", "Otoño", "Invierno"))Ahora agregamos los datos a una tabla con la estaciones del año en China
datos_con_estaciones<- cbind(de, estaciones)
#sacamos los datos de los otros años
datos_estaciones2014<-subset(datos_con_estaciones,year ==2014)
#sacamos porcentajes de las estaciones
tabla_promedioPM10estaciones2014<-datos_estaciones2014 %>%
group_by(estaciones) %>%
summarise(Promedio=mean(PM10))
# Ahora sacamos los porcentajes para agregarlos al grafico
total_Promedio <- sum(tabla_promedioPM10estaciones2014$Promedio)
porcentajes <- round(tabla_promedioPM10estaciones2014$Promedio / total_Promedio * 100, 1)
#Graficamos
ggplot(tabla_promedioPM10estaciones2014, aes(x = "estaciones", y = Promedio, fill = estaciones)) +
geom_bar(width = 1, stat = "identity") +
coord_polar("y", start = 0) +
geom_text(aes(label = paste0(porcentajes, "%")), position = position_stack(vjust = 0.5)) +
labs(title = "Gráfico de torta promedio PM10 por estaciones", fill = "Estaciones") +
theme_void()
En el grafico podemos apreciar que en promedio hay una minima variabilidad en la liberacion de porcentajes de PM10 por estaciones, a excepción de otoño que es con menor porcentaje en el año 2014, aun asi podemos apreciar que la estacion con mayor porcentaje de liberacion de PM10 es en primavera, por ende la estacion con peor calidad de aire.