rainfall <- read.csv("Rain.csv")
head(rainfall)## Year DOY Rain
## 1 1996 1 0.1
## 2 1996 2 0.6
## 3 1996 3 33.8
## 4 1996 4 1.9
## 5 1996 5 9.6
## 6 1996 6 4.3tail(rainfall)## Year DOY Rain
## 3648 2005 360 0
## 3649 2005 361 0
## 3650 2005 362 0
## 3651 2005 363 0
## 3652 2005 364 0
## 3653 2005 365 0rownames(rainfall)## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9"
## [10] "10" "11" "12" "13" "14" "15" "16" "17" "18"
## [19] "19" "20" "21" "22" "23" "24" "25" "26" "27"
## [28] "28" "29" "30" "31" "32" "33" "34" "35" "36"
## [37] "37" "38" "39" "40" "41" "42" "43" "44" "45"
## [46] "46" "47" "48" "49" "50" "51" "52" "53" "54"
## [55] "55" "56" "57" "58" "59" "60" "61" "62" "63"
## [64] "64" "65" "66" "67" "68" "69" "70" "71" "72"
## [73] "73" "74" "75" "76" "77" "78" "79" "80" "81"
## [82] "82" "83" "84" "85" "86" "87" "88" "89" "90"
## [91] "91" "92" "93" "94" "95" "96" "97" "98" "99"
## [100] "100" "101" "102" "103" "104" "105" "106" "107" "108"
## [109] "109" "110" "111" "112" "113" "114" "115" "116" "117"
## [118] "118" "119" "120" "121" "122" "123" "124" "125" "126"
## [127] "127" "128" "129" "130" "131" "132" "133" "134" "135"
## [136] "136" "137" "138" "139" "140" "141" "142" "143" "144"
## [145] "145" "146" "147" "148" "149" "150" "151" "152" "153"
## [154] "154" "155" "156" "157" "158" "159" "160" "161" "162"
## [163] "163" "164" "165" "166" "167" "168" "169" "170" "171"
## [172] "172" "173" "174" "175" "176" "177" "178" "179" "180"
## [181] "181" "182" "183" "184" "185" "186" "187" "188" "189"
## [190] "190" "191" "192" "193" "194" "195" "196" "197" "198"
## [199] "199" "200" "201" "202" "203" "204" "205" "206" "207"
## [208] "208" "209" "210" "211" "212" "213" "214" "215" "216"
## [217] "217" "218" "219" "220" "221" "222" "223" "224" "225"
## [226] "226" "227" "228" "229" "230" "231" "232" "233" "234"
## [235] "235" "236" "237" "238" "239" "240" "241" "242" "243"
## [244] "244" "245" "246" "247" "248" "249" "250" "251" "252"
## [253] "253" "254" "255" "256" "257" "258" "259" "260" "261"
## [262] "262" "263" "264" "265" "266" "267" "268" "269" "270"
## [271] "271" "272" "273" "274" "275" "276" "277" "278" "279"
## [280] "280" "281" "282" "283" "284" "285" "286" "287" "288"
## [289] "289" "290" "291" "292" "293" "294" "295" "296" "297"
## [298] "298" "299" "300" "301" "302" "303" "304" "305" "306"
## [307] "307" "308" "309" "310" "311" "312" "313" "314" "315"
## [316] "316" "317" "318" "319" "320" "321" "322" "323" "324"
## [325] "325" "326" "327" "328" "329" "330" "331" "332" "333"
## [334] "334" "335" "336" "337" "338" "339" "340" "341" "342"
## [343] "343" "344" "345" "346" "347" "348" "349" "350" "351"
## [352] "352" "353" "354" "355" "356" "357" "358" "359" "360"
## [361] "361" "362" "363" "364" "365" "366" "367" "368" "369"
## [370] "370" "371" "372" "373" "374" "375" "376" "377" "378"
## [379] "379" "380" "381" "382" "383" "384" "385" "386" "387"
## [388] "388" "389" "390" "391" "392" "393" "394" "395" "396"
## [397] "397" "398" "399" "400" "401" "402" "403" "404" "405"
## [406] "406" "407" "408" "409" "410" "411" "412" "413" "414"
## [415] "415" "416" "417" "418" "419" "420" "421" "422" "423"
## [424] "424" "425" "426" "427" "428" "429" "430" "431" "432"
## [433] "433" "434" "435" "436" "437" "438" "439" "440" "441"
## [442] "442" "443" "444" "445" "446" "447" "448" "449" "450"
## [451] "451" "452" "453" "454" "455" "456" "457" "458" "459"
## [460] "460" "461" "462" "463" "464" "465" "466" "467" "468"
## [469] "469" "470" "471" "472" "473" "474" "475" "476" "477"
## [478] "478" "479" "480" "481" "482" "483" "484" "485" "486"
## [487] "487" "488" "489" "490" "491" "492" "493" "494" "495"
## [496] "496" "497" "498" "499" "500" "501" "502" "503" "504"
## [505] "505" "506" "507" "508" "509" "510" "511" "512" "513"
## [514] "514" "515" "516" "517" "518" "519" "520" "521" "522"
## [523] "523" "524" "525" "526" "527" "528" "529" "530" "531"
## [532] "532" "533" "534" "535" "536" "537" "538" "539" "540"
## [541] "541" "542" "543" "544" "545" "546" "547" "548" "549"
## [550] "550" "551" "552" "553" "554" "555" "556" "557" "558"
## [559] "559" "560" "561" "562" "563" "564" "565" "566" "567"
## [568] "568" "569" "570" "571" "572" "573" "574" "575" "576"
## [577] "577" "578" "579" "580" "581" "582" "583" "584" "585"
## [586] "586" "587" "588" "589" "590" "591" "592" "593" "594"
## [595] "595" "596" "597" "598" "599" "600" "601" "602" "603"
## [604] "604" "605" "606" "607" "608" "609" "610" "611" "612"
## [613] "613" "614" "615" "616" "617" "618" "619" "620" "621"
## [622] "622" "623" "624" "625" "626" "627" "628" "629" "630"
## [631] "631" "632" "633" "634" "635" "636" "637" "638" "639"
## [640] "640" "641" "642" "643" "644" "645" "646" "647" "648"
## [649] "649" "650" "651" "652" "653" "654" "655" "656" "657"
## [658] "658" "659" "660" "661" "662" "663" "664" "665" "666"
## [667] "667" "668" "669" "670" "671" "672" "673" "674" "675"
## [676] "676" "677" "678" "679" "680" "681" "682" "683" "684"
## [685] "685" "686" "687" "688" "689" "690" "691" "692" "693"
## [694] "694" "695" "696" "697" "698" "699" "700" "701" "702"
## [703] "703" "704" "705" "706" "707" "708" "709" "710" "711"
## [712] "712" "713" "714" "715" "716" "717" "718" "719" "720"
## [721] "721" "722" "723" "724" "725" "726" "727" "728" "729"
## [730] "730" "731" "732" "733" "734" "735" "736" "737" "738"
## [739] "739" "740" "741" "742" "743" "744" "745" "746" "747"
## [748] "748" "749" "750" "751" "752" "753" "754" "755" "756"
## [757] "757" "758" "759" "760" "761" "762" "763" "764" "765"
## [766] "766" "767" "768" "769" "770" "771" "772" "773" "774"
## [775] "775" "776" "777" "778" "779" "780" "781" "782" "783"
## [784] "784" "785" "786" "787" "788" "789" "790" "791" "792"
## [793] "793" "794" "795" "796" "797" "798" "799" "800" "801"
## [802] "802" "803" "804" "805" "806" "807" "808" "809" "810"
## [811] "811" "812" "813" "814" "815" "816" "817" "818" "819"
## [820] "820" "821" "822" "823" "824" "825" "826" "827" "828"
## [829] "829" "830" "831" "832" "833" "834" "835" "836" "837"
## [838] "838" "839" "840" "841" "842" "843" "844" "845" "846"
## [847] "847" "848" "849" "850" "851" "852" "853" "854" "855"
## [856] "856" "857" "858" "859" "860" "861" "862" "863" "864"
## [865] "865" "866" "867" "868" "869" "870" "871" "872" "873"
## [874] "874" "875" "876" "877" "878" "879" "880" "881" "882"
## [883] "883" "884" "885" "886" "887" "888" "889" "890" "891"
## [892] "892" "893" "894" "895" "896" "897" "898" "899" "900"
## [901] "901" "902" "903" "904" "905" "906" "907" "908" "909"
## [910] "910" "911" "912" "913" "914" "915" "916" "917" "918"
## [919] "919" "920" "921" "922" "923" "924" "925" "926" "927"
## [928] "928" "929" "930" "931" "932" "933" "934" "935" "936"
## [937] "937" "938" "939" "940" "941" "942" "943" "944" "945"
## [946] "946" "947" "948" "949" "950" "951" "952" "953" "954"
## [955] "955" "956" "957" "958" "959" "960" "961" "962" "963"
## [964] "964" "965" "966" "967" "968" "969" "970" "971" "972"
## [973] "973" "974" "975" "976" "977" "978" "979" "980" "981"
## [982] "982" "983" "984" "985" "986" "987" "988" "989" "990"
## [991] "991" "992" "993" "994" "995" "996" "997" "998" "999"
## [1000] "1000" "1001" "1002" "1003" "1004" "1005" "1006" "1007" "1008"
## [1009] "1009" "1010" "1011" "1012" "1013" "1014" "1015" "1016" "1017"
## [1018] "1018" "1019" "1020" "1021" "1022" "1023" "1024" "1025" "1026"
## [1027] "1027" "1028" "1029" "1030" "1031" "1032" "1033" "1034" "1035"
## [1036] "1036" "1037" "1038" "1039" "1040" "1041" "1042" "1043" "1044"
## [1045] "1045" "1046" "1047" "1048" "1049" "1050" "1051" "1052" "1053"
## [1054] "1054" "1055" "1056" "1057" "1058" "1059" "1060" "1061" "1062"
## [1063] "1063" "1064" "1065" "1066" "1067" "1068" "1069" "1070" "1071"
## [1072] "1072" "1073" "1074" "1075" "1076" "1077" "1078" "1079" "1080"
## [1081] "1081" "1082" "1083" "1084" "1085" "1086" "1087" "1088" "1089"
## [1090] "1090" "1091" "1092" "1093" "1094" "1095" "1096" "1097" "1098"
## [1099] "1099" "1100" "1101" "1102" "1103" "1104" "1105" "1106" "1107"
## [1108] "1108" "1109" "1110" "1111" "1112" "1113" "1114" "1115" "1116"
## [1117] "1117" "1118" "1119" "1120" "1121" "1122" "1123" "1124" "1125"
## [1126] "1126" "1127" "1128" "1129" "1130" "1131" "1132" "1133" "1134"
## [1135] "1135" "1136" "1137" "1138" "1139" "1140" "1141" "1142" "1143"
## [1144] "1144" "1145" "1146" "1147" "1148" "1149" "1150" "1151" "1152"
## [1153] "1153" "1154" "1155" "1156" "1157" "1158" "1159" "1160" "1161"
## [1162] "1162" "1163" "1164" "1165" "1166" "1167" "1168" "1169" "1170"
## [1171] "1171" "1172" "1173" "1174" "1175" "1176" "1177" "1178" "1179"
## [1180] "1180" "1181" "1182" "1183" "1184" "1185" "1186" "1187" "1188"
## [1189] "1189" "1190" "1191" "1192" "1193" "1194" "1195" "1196" "1197"
## [1198] "1198" "1199" "1200" "1201" "1202" "1203" "1204" "1205" "1206"
## [1207] "1207" "1208" "1209" "1210" "1211" "1212" "1213" "1214" "1215"
## [1216] "1216" "1217" "1218" "1219" "1220" "1221" "1222" "1223" "1224"
## [1225] "1225" "1226" "1227" "1228" "1229" "1230" "1231" "1232" "1233"
## [1234] "1234" "1235" "1236" "1237" "1238" "1239" "1240" "1241" "1242"
## [1243] "1243" "1244" "1245" "1246" "1247" "1248" "1249" "1250" "1251"
## [1252] "1252" "1253" "1254" "1255" "1256" "1257" "1258" "1259" "1260"
## [1261] "1261" "1262" "1263" "1264" "1265" "1266" "1267" "1268" "1269"
## [1270] "1270" "1271" "1272" "1273" "1274" "1275" "1276" "1277" "1278"
## [1279] "1279" "1280" "1281" "1282" "1283" "1284" "1285" "1286" "1287"
## [1288] "1288" "1289" "1290" "1291" "1292" "1293" "1294" "1295" "1296"
## [1297] "1297" "1298" "1299" "1300" "1301" "1302" "1303" "1304" "1305"
## [1306] "1306" "1307" "1308" "1309" "1310" "1311" "1312" "1313" "1314"
## [1315] "1315" "1316" "1317" "1318" "1319" "1320" "1321" "1322" "1323"
## [1324] "1324" "1325" "1326" "1327" "1328" "1329" "1330" "1331" "1332"
## [1333] "1333" "1334" "1335" "1336" "1337" "1338" "1339" "1340" "1341"
## [1342] "1342" "1343" "1344" "1345" "1346" "1347" "1348" "1349" "1350"
## [1351] "1351" "1352" "1353" "1354" "1355" "1356" "1357" "1358" "1359"
## [1360] "1360" "1361" "1362" "1363" "1364" "1365" "1366" "1367" "1368"
## [1369] "1369" "1370" "1371" "1372" "1373" "1374" "1375" "1376" "1377"
## [1378] "1378" "1379" "1380" "1381" "1382" "1383" "1384" "1385" "1386"
## [1387] "1387" "1388" "1389" "1390" "1391" "1392" "1393" "1394" "1395"
## [1396] "1396" "1397" "1398" "1399" "1400" "1401" "1402" "1403" "1404"
## [1405] "1405" "1406" "1407" "1408" "1409" "1410" "1411" "1412" "1413"
## [1414] "1414" "1415" "1416" "1417" "1418" "1419" "1420" "1421" "1422"
## [1423] "1423" "1424" "1425" "1426" "1427" "1428" "1429" "1430" "1431"
## [1432] "1432" "1433" "1434" "1435" "1436" "1437" "1438" "1439" "1440"
## [1441] "1441" "1442" "1443" "1444" "1445" "1446" "1447" "1448" "1449"
## [1450] "1450" "1451" "1452" "1453" "1454" "1455" "1456" "1457" "1458"
## [1459] "1459" "1460" "1461" "1462" "1463" "1464" "1465" "1466" "1467"
## [1468] "1468" "1469" "1470" "1471" "1472" "1473" "1474" "1475" "1476"
## [1477] "1477" "1478" "1479" "1480" "1481" "1482" "1483" "1484" "1485"
## [1486] "1486" "1487" "1488" "1489" "1490" "1491" "1492" "1493" "1494"
## [1495] "1495" "1496" "1497" "1498" "1499" "1500" "1501" "1502" "1503"
## [1504] "1504" "1505" "1506" "1507" "1508" "1509" "1510" "1511" "1512"
## [1513] "1513" "1514" "1515" "1516" "1517" "1518" "1519" "1520" "1521"
## [1522] "1522" "1523" "1524" "1525" "1526" "1527" "1528" "1529" "1530"
## [1531] "1531" "1532" "1533" "1534" "1535" "1536" "1537" "1538" "1539"
## [1540] "1540" "1541" "1542" "1543" "1544" "1545" "1546" "1547" "1548"
## [1549] "1549" "1550" "1551" "1552" "1553" "1554" "1555" "1556" "1557"
## [1558] "1558" "1559" "1560" "1561" "1562" "1563" "1564" "1565" "1566"
## [1567] "1567" "1568" "1569" "1570" "1571" "1572" "1573" "1574" "1575"
## [1576] "1576" "1577" "1578" "1579" "1580" "1581" "1582" "1583" "1584"
## [1585] "1585" "1586" "1587" "1588" "1589" "1590" "1591" "1592" "1593"
## [1594] "1594" "1595" "1596" "1597" "1598" "1599" "1600" "1601" "1602"
## [1603] "1603" "1604" "1605" "1606" "1607" "1608" "1609" "1610" "1611"
## [1612] "1612" "1613" "1614" "1615" "1616" "1617" "1618" "1619" "1620"
## [1621] "1621" "1622" "1623" "1624" "1625" "1626" "1627" "1628" "1629"
## [1630] "1630" "1631" "1632" "1633" "1634" "1635" "1636" "1637" "1638"
## [1639] "1639" "1640" "1641" "1642" "1643" "1644" "1645" "1646" "1647"
## [1648] "1648" "1649" "1650" "1651" "1652" "1653" "1654" "1655" "1656"
## [1657] "1657" "1658" "1659" "1660" "1661" "1662" "1663" "1664" "1665"
## [1666] "1666" "1667" "1668" "1669" "1670" "1671" "1672" "1673" "1674"
## [1675] "1675" "1676" "1677" "1678" "1679" "1680" "1681" "1682" "1683"
## [1684] "1684" "1685" "1686" "1687" "1688" "1689" "1690" "1691" "1692"
## [1693] "1693" "1694" "1695" "1696" "1697" "1698" "1699" "1700" "1701"
## [1702] "1702" "1703" "1704" "1705" "1706" "1707" "1708" "1709" "1710"
## [1711] "1711" "1712" "1713" "1714" "1715" "1716" "1717" "1718" "1719"
## [1720] "1720" "1721" "1722" "1723" "1724" "1725" "1726" "1727" "1728"
## [1729] "1729" "1730" "1731" "1732" "1733" "1734" "1735" "1736" "1737"
## [1738] "1738" "1739" "1740" "1741" "1742" "1743" "1744" "1745" "1746"
## [1747] "1747" "1748" "1749" "1750" "1751" "1752" "1753" "1754" "1755"
## [1756] "1756" "1757" "1758" "1759" "1760" "1761" "1762" "1763" "1764"
## [1765] "1765" "1766" "1767" "1768" "1769" "1770" "1771" "1772" "1773"
## [1774] "1774" "1775" "1776" "1777" "1778" "1779" "1780" "1781" "1782"
## [1783] "1783" "1784" "1785" "1786" "1787" "1788" "1789" "1790" "1791"
## [1792] "1792" "1793" "1794" "1795" "1796" "1797" "1798" "1799" "1800"
## [1801] "1801" "1802" "1803" "1804" "1805" "1806" "1807" "1808" "1809"
## [1810] "1810" "1811" "1812" "1813" "1814" "1815" "1816" "1817" "1818"
## [1819] "1819" "1820" "1821" "1822" "1823" "1824" "1825" "1826" "1827"
## [1828] "1828" "1829" "1830" "1831" "1832" "1833" "1834" "1835" "1836"
## [1837] "1837" "1838" "1839" "1840" "1841" "1842" "1843" "1844" "1845"
## [1846] "1846" "1847" "1848" "1849" "1850" "1851" "1852" "1853" "1854"
## [1855] "1855" "1856" "1857" "1858" "1859" "1860" "1861" "1862" "1863"
## [1864] "1864" "1865" "1866" "1867" "1868" "1869" "1870" "1871" "1872"
## [1873] "1873" "1874" "1875" "1876" "1877" "1878" "1879" "1880" "1881"
## [1882] "1882" "1883" "1884" "1885" "1886" "1887" "1888" "1889" "1890"
## [1891] "1891" "1892" "1893" "1894" "1895" "1896" "1897" "1898" "1899"
## [1900] "1900" "1901" "1902" "1903" "1904" "1905" "1906" "1907" "1908"
## [1909] "1909" "1910" "1911" "1912" "1913" "1914" "1915" "1916" "1917"
## [1918] "1918" "1919" "1920" "1921" "1922" "1923" "1924" "1925" "1926"
## [1927] "1927" "1928" "1929" "1930" "1931" "1932" "1933" "1934" "1935"
## [1936] "1936" "1937" "1938" "1939" "1940" "1941" "1942" "1943" "1944"
## [1945] "1945" "1946" "1947" "1948" "1949" "1950" "1951" "1952" "1953"
## [1954] "1954" "1955" "1956" "1957" "1958" "1959" "1960" "1961" "1962"
## [1963] "1963" "1964" "1965" "1966" "1967" "1968" "1969" "1970" "1971"
## [1972] "1972" "1973" "1974" "1975" "1976" "1977" "1978" "1979" "1980"
## [1981] "1981" "1982" "1983" "1984" "1985" "1986" "1987" "1988" "1989"
## [1990] "1990" "1991" "1992" "1993" "1994" "1995" "1996" "1997" "1998"
## [1999] "1999" "2000" "2001" "2002" "2003" "2004" "2005" "2006" "2007"
## [2008] "2008" "2009" "2010" "2011" "2012" "2013" "2014" "2015" "2016"
## [2017] "2017" "2018" "2019" "2020" "2021" "2022" "2023" "2024" "2025"
## [2026] "2026" "2027" "2028" "2029" "2030" "2031" "2032" "2033" "2034"
## [2035] "2035" "2036" "2037" "2038" "2039" "2040" "2041" "2042" "2043"
## [2044] "2044" "2045" "2046" "2047" "2048" "2049" "2050" "2051" "2052"
## [2053] "2053" "2054" "2055" "2056" "2057" "2058" "2059" "2060" "2061"
## [2062] "2062" "2063" "2064" "2065" "2066" "2067" "2068" "2069" "2070"
## [2071] "2071" "2072" "2073" "2074" "2075" "2076" "2077" "2078" "2079"
## [2080] "2080" "2081" "2082" "2083" "2084" "2085" "2086" "2087" "2088"
## [2089] "2089" "2090" "2091" "2092" "2093" "2094" "2095" "2096" "2097"
## [2098] "2098" "2099" "2100" "2101" "2102" "2103" "2104" "2105" "2106"
## [2107] "2107" "2108" "2109" "2110" "2111" "2112" "2113" "2114" "2115"
## [2116] "2116" "2117" "2118" "2119" "2120" "2121" "2122" "2123" "2124"
## [2125] "2125" "2126" "2127" "2128" "2129" "2130" "2131" "2132" "2133"
## [2134] "2134" "2135" "2136" "2137" "2138" "2139" "2140" "2141" "2142"
## [2143] "2143" "2144" "2145" "2146" "2147" "2148" "2149" "2150" "2151"
## [2152] "2152" "2153" "2154" "2155" "2156" "2157" "2158" "2159" "2160"
## [2161] "2161" "2162" "2163" "2164" "2165" "2166" "2167" "2168" "2169"
## [2170] "2170" "2171" "2172" "2173" "2174" "2175" "2176" "2177" "2178"
## [2179] "2179" "2180" "2181" "2182" "2183" "2184" "2185" "2186" "2187"
## [2188] "2188" "2189" "2190" "2191" "2192" "2193" "2194" "2195" "2196"
## [2197] "2197" "2198" "2199" "2200" "2201" "2202" "2203" "2204" "2205"
## [2206] "2206" "2207" "2208" "2209" "2210" "2211" "2212" "2213" "2214"
## [2215] "2215" "2216" "2217" "2218" "2219" "2220" "2221" "2222" "2223"
## [2224] "2224" "2225" "2226" "2227" "2228" "2229" "2230" "2231" "2232"
## [2233] "2233" "2234" "2235" "2236" "2237" "2238" "2239" "2240" "2241"
## [2242] "2242" "2243" "2244" "2245" "2246" "2247" "2248" "2249" "2250"
## [2251] "2251" "2252" "2253" "2254" "2255" "2256" "2257" "2258" "2259"
## [2260] "2260" "2261" "2262" "2263" "2264" "2265" "2266" "2267" "2268"
## [2269] "2269" "2270" "2271" "2272" "2273" "2274" "2275" "2276" "2277"
## [2278] "2278" "2279" "2280" "2281" "2282" "2283" "2284" "2285" "2286"
## [2287] "2287" "2288" "2289" "2290" "2291" "2292" "2293" "2294" "2295"
## [2296] "2296" "2297" "2298" "2299" "2300" "2301" "2302" "2303" "2304"
## [2305] "2305" "2306" "2307" "2308" "2309" "2310" "2311" "2312" "2313"
## [2314] "2314" "2315" "2316" "2317" "2318" "2319" "2320" "2321" "2322"
## [2323] "2323" "2324" "2325" "2326" "2327" "2328" "2329" "2330" "2331"
## [2332] "2332" "2333" "2334" "2335" "2336" "2337" "2338" "2339" "2340"
## [2341] "2341" "2342" "2343" "2344" "2345" "2346" "2347" "2348" "2349"
## [2350] "2350" "2351" "2352" "2353" "2354" "2355" "2356" "2357" "2358"
## [2359] "2359" "2360" "2361" "2362" "2363" "2364" "2365" "2366" "2367"
## [2368] "2368" "2369" "2370" "2371" "2372" "2373" "2374" "2375" "2376"
## [2377] "2377" "2378" "2379" "2380" "2381" "2382" "2383" "2384" "2385"
## [2386] "2386" "2387" "2388" "2389" "2390" "2391" "2392" "2393" "2394"
## [2395] "2395" "2396" "2397" "2398" "2399" "2400" "2401" "2402" "2403"
## [2404] "2404" "2405" "2406" "2407" "2408" "2409" "2410" "2411" "2412"
## [2413] "2413" "2414" "2415" "2416" "2417" "2418" "2419" "2420" "2421"
## [2422] "2422" "2423" "2424" "2425" "2426" "2427" "2428" "2429" "2430"
## [2431] "2431" "2432" "2433" "2434" "2435" "2436" "2437" "2438" "2439"
## [2440] "2440" "2441" "2442" "2443" "2444" "2445" "2446" "2447" "2448"
## [2449] "2449" "2450" "2451" "2452" "2453" "2454" "2455" "2456" "2457"
## [2458] "2458" "2459" "2460" "2461" "2462" "2463" "2464" "2465" "2466"
## [2467] "2467" "2468" "2469" "2470" "2471" "2472" "2473" "2474" "2475"
## [2476] "2476" "2477" "2478" "2479" "2480" "2481" "2482" "2483" "2484"
## [2485] "2485" "2486" "2487" "2488" "2489" "2490" "2491" "2492" "2493"
## [2494] "2494" "2495" "2496" "2497" "2498" "2499" "2500" "2501" "2502"
## [2503] "2503" "2504" "2505" "2506" "2507" "2508" "2509" "2510" "2511"
## [2512] "2512" "2513" "2514" "2515" "2516" "2517" "2518" "2519" "2520"
## [2521] "2521" "2522" "2523" "2524" "2525" "2526" "2527" "2528" "2529"
## [2530] "2530" "2531" "2532" "2533" "2534" "2535" "2536" "2537" "2538"
## [2539] "2539" "2540" "2541" "2542" "2543" "2544" "2545" "2546" "2547"
## [2548] "2548" "2549" "2550" "2551" "2552" "2553" "2554" "2555" "2556"
## [2557] "2557" "2558" "2559" "2560" "2561" "2562" "2563" "2564" "2565"
## [2566] "2566" "2567" "2568" "2569" "2570" "2571" "2572" "2573" "2574"
## [2575] "2575" "2576" "2577" "2578" "2579" "2580" "2581" "2582" "2583"
## [2584] "2584" "2585" "2586" "2587" "2588" "2589" "2590" "2591" "2592"
## [2593] "2593" "2594" "2595" "2596" "2597" "2598" "2599" "2600" "2601"
## [2602] "2602" "2603" "2604" "2605" "2606" "2607" "2608" "2609" "2610"
## [2611] "2611" "2612" "2613" "2614" "2615" "2616" "2617" "2618" "2619"
## [2620] "2620" "2621" "2622" "2623" "2624" "2625" "2626" "2627" "2628"
## [2629] "2629" "2630" "2631" "2632" "2633" "2634" "2635" "2636" "2637"
## [2638] "2638" "2639" "2640" "2641" "2642" "2643" "2644" "2645" "2646"
## [2647] "2647" "2648" "2649" "2650" "2651" "2652" "2653" "2654" "2655"
## [2656] "2656" "2657" "2658" "2659" "2660" "2661" "2662" "2663" "2664"
## [2665] "2665" "2666" "2667" "2668" "2669" "2670" "2671" "2672" "2673"
## [2674] "2674" "2675" "2676" "2677" "2678" "2679" "2680" "2681" "2682"
## [2683] "2683" "2684" "2685" "2686" "2687" "2688" "2689" "2690" "2691"
## [2692] "2692" "2693" "2694" "2695" "2696" "2697" "2698" "2699" "2700"
## [2701] "2701" "2702" "2703" "2704" "2705" "2706" "2707" "2708" "2709"
## [2710] "2710" "2711" "2712" "2713" "2714" "2715" "2716" "2717" "2718"
## [2719] "2719" "2720" "2721" "2722" "2723" "2724" "2725" "2726" "2727"
## [2728] "2728" "2729" "2730" "2731" "2732" "2733" "2734" "2735" "2736"
## [2737] "2737" "2738" "2739" "2740" "2741" "2742" "2743" "2744" "2745"
## [2746] "2746" "2747" "2748" "2749" "2750" "2751" "2752" "2753" "2754"
## [2755] "2755" "2756" "2757" "2758" "2759" "2760" "2761" "2762" "2763"
## [2764] "2764" "2765" "2766" "2767" "2768" "2769" "2770" "2771" "2772"
## [2773] "2773" "2774" "2775" "2776" "2777" "2778" "2779" "2780" "2781"
## [2782] "2782" "2783" "2784" "2785" "2786" "2787" "2788" "2789" "2790"
## [2791] "2791" "2792" "2793" "2794" "2795" "2796" "2797" "2798" "2799"
## [2800] "2800" "2801" "2802" "2803" "2804" "2805" "2806" "2807" "2808"
## [2809] "2809" "2810" "2811" "2812" "2813" "2814" "2815" "2816" "2817"
## [2818] "2818" "2819" "2820" "2821" "2822" "2823" "2824" "2825" "2826"
## [2827] "2827" "2828" "2829" "2830" "2831" "2832" "2833" "2834" "2835"
## [2836] "2836" "2837" "2838" "2839" "2840" "2841" "2842" "2843" "2844"
## [2845] "2845" "2846" "2847" "2848" "2849" "2850" "2851" "2852" "2853"
## [2854] "2854" "2855" "2856" "2857" "2858" "2859" "2860" "2861" "2862"
## [2863] "2863" "2864" "2865" "2866" "2867" "2868" "2869" "2870" "2871"
## [2872] "2872" "2873" "2874" "2875" "2876" "2877" "2878" "2879" "2880"
## [2881] "2881" "2882" "2883" "2884" "2885" "2886" "2887" "2888" "2889"
## [2890] "2890" "2891" "2892" "2893" "2894" "2895" "2896" "2897" "2898"
## [2899] "2899" "2900" "2901" "2902" "2903" "2904" "2905" "2906" "2907"
## [2908] "2908" "2909" "2910" "2911" "2912" "2913" "2914" "2915" "2916"
## [2917] "2917" "2918" "2919" "2920" "2921" "2922" "2923" "2924" "2925"
## [2926] "2926" "2927" "2928" "2929" "2930" "2931" "2932" "2933" "2934"
## [2935] "2935" "2936" "2937" "2938" "2939" "2940" "2941" "2942" "2943"
## [2944] "2944" "2945" "2946" "2947" "2948" "2949" "2950" "2951" "2952"
## [2953] "2953" "2954" "2955" "2956" "2957" "2958" "2959" "2960" "2961"
## [2962] "2962" "2963" "2964" "2965" "2966" "2967" "2968" "2969" "2970"
## [2971] "2971" "2972" "2973" "2974" "2975" "2976" "2977" "2978" "2979"
## [2980] "2980" "2981" "2982" "2983" "2984" "2985" "2986" "2987" "2988"
## [2989] "2989" "2990" "2991" "2992" "2993" "2994" "2995" "2996" "2997"
## [2998] "2998" "2999" "3000" "3001" "3002" "3003" "3004" "3005" "3006"
## [3007] "3007" "3008" "3009" "3010" "3011" "3012" "3013" "3014" "3015"
## [3016] "3016" "3017" "3018" "3019" "3020" "3021" "3022" "3023" "3024"
## [3025] "3025" "3026" "3027" "3028" "3029" "3030" "3031" "3032" "3033"
## [3034] "3034" "3035" "3036" "3037" "3038" "3039" "3040" "3041" "3042"
## [3043] "3043" "3044" "3045" "3046" "3047" "3048" "3049" "3050" "3051"
## [3052] "3052" "3053" "3054" "3055" "3056" "3057" "3058" "3059" "3060"
## [3061] "3061" "3062" "3063" "3064" "3065" "3066" "3067" "3068" "3069"
## [3070] "3070" "3071" "3072" "3073" "3074" "3075" "3076" "3077" "3078"
## [3079] "3079" "3080" "3081" "3082" "3083" "3084" "3085" "3086" "3087"
## [3088] "3088" "3089" "3090" "3091" "3092" "3093" "3094" "3095" "3096"
## [3097] "3097" "3098" "3099" "3100" "3101" "3102" "3103" "3104" "3105"
## [3106] "3106" "3107" "3108" "3109" "3110" "3111" "3112" "3113" "3114"
## [3115] "3115" "3116" "3117" "3118" "3119" "3120" "3121" "3122" "3123"
## [3124] "3124" "3125" "3126" "3127" "3128" "3129" "3130" "3131" "3132"
## [3133] "3133" "3134" "3135" "3136" "3137" "3138" "3139" "3140" "3141"
## [3142] "3142" "3143" "3144" "3145" "3146" "3147" "3148" "3149" "3150"
## [3151] "3151" "3152" "3153" "3154" "3155" "3156" "3157" "3158" "3159"
## [3160] "3160" "3161" "3162" "3163" "3164" "3165" "3166" "3167" "3168"
## [3169] "3169" "3170" "3171" "3172" "3173" "3174" "3175" "3176" "3177"
## [3178] "3178" "3179" "3180" "3181" "3182" "3183" "3184" "3185" "3186"
## [3187] "3187" "3188" "3189" "3190" "3191" "3192" "3193" "3194" "3195"
## [3196] "3196" "3197" "3198" "3199" "3200" "3201" "3202" "3203" "3204"
## [3205] "3205" "3206" "3207" "3208" "3209" "3210" "3211" "3212" "3213"
## [3214] "3214" "3215" "3216" "3217" "3218" "3219" "3220" "3221" "3222"
## [3223] "3223" "3224" "3225" "3226" "3227" "3228" "3229" "3230" "3231"
## [3232] "3232" "3233" "3234" "3235" "3236" "3237" "3238" "3239" "3240"
## [3241] "3241" "3242" "3243" "3244" "3245" "3246" "3247" "3248" "3249"
## [3250] "3250" "3251" "3252" "3253" "3254" "3255" "3256" "3257" "3258"
## [3259] "3259" "3260" "3261" "3262" "3263" "3264" "3265" "3266" "3267"
## [3268] "3268" "3269" "3270" "3271" "3272" "3273" "3274" "3275" "3276"
## [3277] "3277" "3278" "3279" "3280" "3281" "3282" "3283" "3284" "3285"
## [3286] "3286" "3287" "3288" "3289" "3290" "3291" "3292" "3293" "3294"
## [3295] "3295" "3296" "3297" "3298" "3299" "3300" "3301" "3302" "3303"
## [3304] "3304" "3305" "3306" "3307" "3308" "3309" "3310" "3311" "3312"
## [3313] "3313" "3314" "3315" "3316" "3317" "3318" "3319" "3320" "3321"
## [3322] "3322" "3323" "3324" "3325" "3326" "3327" "3328" "3329" "3330"
## [3331] "3331" "3332" "3333" "3334" "3335" "3336" "3337" "3338" "3339"
## [3340] "3340" "3341" "3342" "3343" "3344" "3345" "3346" "3347" "3348"
## [3349] "3349" "3350" "3351" "3352" "3353" "3354" "3355" "3356" "3357"
## [3358] "3358" "3359" "3360" "3361" "3362" "3363" "3364" "3365" "3366"
## [3367] "3367" "3368" "3369" "3370" "3371" "3372" "3373" "3374" "3375"
## [3376] "3376" "3377" "3378" "3379" "3380" "3381" "3382" "3383" "3384"
## [3385] "3385" "3386" "3387" "3388" "3389" "3390" "3391" "3392" "3393"
## [3394] "3394" "3395" "3396" "3397" "3398" "3399" "3400" "3401" "3402"
## [3403] "3403" "3404" "3405" "3406" "3407" "3408" "3409" "3410" "3411"
## [3412] "3412" "3413" "3414" "3415" "3416" "3417" "3418" "3419" "3420"
## [3421] "3421" "3422" "3423" "3424" "3425" "3426" "3427" "3428" "3429"
## [3430] "3430" "3431" "3432" "3433" "3434" "3435" "3436" "3437" "3438"
## [3439] "3439" "3440" "3441" "3442" "3443" "3444" "3445" "3446" "3447"
## [3448] "3448" "3449" "3450" "3451" "3452" "3453" "3454" "3455" "3456"
## [3457] "3457" "3458" "3459" "3460" "3461" "3462" "3463" "3464" "3465"
## [3466] "3466" "3467" "3468" "3469" "3470" "3471" "3472" "3473" "3474"
## [3475] "3475" "3476" "3477" "3478" "3479" "3480" "3481" "3482" "3483"
## [3484] "3484" "3485" "3486" "3487" "3488" "3489" "3490" "3491" "3492"
## [3493] "3493" "3494" "3495" "3496" "3497" "3498" "3499" "3500" "3501"
## [3502] "3502" "3503" "3504" "3505" "3506" "3507" "3508" "3509" "3510"
## [3511] "3511" "3512" "3513" "3514" "3515" "3516" "3517" "3518" "3519"
## [3520] "3520" "3521" "3522" "3523" "3524" "3525" "3526" "3527" "3528"
## [3529] "3529" "3530" "3531" "3532" "3533" "3534" "3535" "3536" "3537"
## [3538] "3538" "3539" "3540" "3541" "3542" "3543" "3544" "3545" "3546"
## [3547] "3547" "3548" "3549" "3550" "3551" "3552" "3553" "3554" "3555"
## [3556] "3556" "3557" "3558" "3559" "3560" "3561" "3562" "3563" "3564"
## [3565] "3565" "3566" "3567" "3568" "3569" "3570" "3571" "3572" "3573"
## [3574] "3574" "3575" "3576" "3577" "3578" "3579" "3580" "3581" "3582"
## [3583] "3583" "3584" "3585" "3586" "3587" "3588" "3589" "3590" "3591"
## [3592] "3592" "3593" "3594" "3595" "3596" "3597" "3598" "3599" "3600"
## [3601] "3601" "3602" "3603" "3604" "3605" "3606" "3607" "3608" "3609"
## [3610] "3610" "3611" "3612" "3613" "3614" "3615" "3616" "3617" "3618"
## [3619] "3619" "3620" "3621" "3622" "3623" "3624" "3625" "3626" "3627"
## [3628] "3628" "3629" "3630" "3631" "3632" "3633" "3634" "3635" "3636"
## [3637] "3637" "3638" "3639" "3640" "3641" "3642" "3643" "3644" "3645"
## [3646] "3646" "3647" "3648" "3649" "3650" "3651" "3652" "3653"summary(rainfall)## Year DOY Rain
## Min. :1996 Min. : 1.0 Min. : 0.000
## 1st Qu.:1998 1st Qu.: 92.0 1st Qu.: 0.000
## Median :2000 Median :183.0 Median : 0.000
## Mean :2000 Mean :183.2 Mean : 2.052
## 3rd Qu.:2003 3rd Qu.:274.0 3rd Qu.: 0.600
## Max. :2005 Max. :366.0 Max. :112.900#Take a subset of the rainfall data where rain is larger than 20.
#What is the mean rainfall for days where the rainfall was at least 4?
#Subset the vector where it is either exactly zero, or exactly 0.6.
rainfall2 <- subset(rainfall, Rain > 20)
summary(rainfall2)## Year DOY Rain
## Min. :1996 Min. : 3.0 Min. : 20.10
## 1st Qu.:1998 1st Qu.: 51.5 1st Qu.: 22.95
## Median :2000 Median :133.0 Median : 26.50
## Mean :2000 Mean :163.0 Mean : 32.51
## 3rd Qu.:2003 3rd Qu.:282.0 3rd Qu.: 37.30
## Max. :2005 Max. :362.0 Max. :112.90rainfall4 <- subset(rainfall, Rain > 4)
mean(rainfall4$Rain)## [1] 14.91302head(rainfall4)## Year DOY Rain
## 3 1996 3 33.8
## 5 1996 5 9.6
## 6 1996 6 4.3
## 7 1996 7 33.7
## 20 1996 20 39.6
## 22 1996 22 4.3rainfall3 <- subset(rainfall, Rain %in% c(0,0.6))
rainfall3## Year DOY Rain
## 2 1996 2 0.6
## 9 1996 9 0.0
## 12 1996 12 0.0
## 13 1996 13 0.0
## 14 1996 14 0.0
## 15 1996 15 0.0
## 18 1996 18 0.0
## 19 1996 19 0.0
## 26 1996 26 0.0
## 27 1996 27 0.0
## 28 1996 28 0.0
## 29 1996 29 0.0
## 31 1996 31 0.0
## 33 1996 33 0.0
## 37 1996 37 0.0
## 39 1996 39 0.0
## 41 1996 41 0.0
## 43 1996 43 0.0
## 45 1996 45 0.0
## 46 1996 46 0.0
## 48 1996 48 0.0
## 49 1996 49 0.0
## 50 1996 50 0.0
## 51 1996 51 0.0
## 53 1996 53 0.0
## 54 1996 54 0.0
## 57 1996 57 0.0
## 58 1996 58 0.0
## 61 1996 61 0.0
## 62 1996 62 0.0
## 66 1996 66 0.0
## 67 1996 67 0.0
## 74 1996 74 0.0
## 75 1996 75 0.6
## 76 1996 76 0.0
## 77 1996 77 0.0
## 78 1996 78 0.0
## 79 1996 79 0.0
## 80 1996 80 0.0
## 81 1996 81 0.0
## 82 1996 82 0.0
## 83 1996 83 0.0
## 84 1996 84 0.0
## 85 1996 85 0.0
## 86 1996 86 0.0
## 87 1996 87 0.0
## 88 1996 88 0.0
## 89 1996 89 0.0
## 90 1996 90 0.0
## 91 1996 91 0.0
## 92 1996 92 0.0
## 93 1996 93 0.0
## 94 1996 94 0.0
## 95 1996 95 0.0
## 96 1996 96 0.0
## 97 1996 97 0.0
## 98 1996 98 0.0
## 100 1996 100 0.0
## 104 1996 104 0.0
## 105 1996 105 0.0
## 106 1996 106 0.0
## 107 1996 107 0.0
## 108 1996 108 0.0
## 109 1996 109 0.0
## 110 1996 110 0.0
## 111 1996 111 0.0
## 112 1996 112 0.0
## 113 1996 113 0.0
## 114 1996 114 0.0
## 115 1996 115 0.0
## 116 1996 116 0.0
## 117 1996 117 0.0
## 118 1996 118 0.0
## 119 1996 119 0.0
## 120 1996 120 0.0
## 121 1996 121 0.0
## 128 1996 128 0.0
## 130 1996 130 0.0
## 131 1996 131 0.0
## 132 1996 132 0.0
## 133 1996 133 0.0
## 134 1996 134 0.0
## 136 1996 136 0.0
## 137 1996 137 0.0
## 139 1996 139 0.0
## 142 1996 142 0.0
## 144 1996 144 0.0
## 145 1996 145 0.0
## 146 1996 146 0.0
## 147 1996 147 0.0
## 151 1996 151 0.0
## 152 1996 152 0.0
## 154 1996 154 0.0
## 161 1996 161 0.0
## 162 1996 162 0.0
## 170 1996 170 0.6
## 177 1996 177 0.0
## 178 1996 178 0.6
## 180 1996 180 0.0
## 181 1996 181 0.0
## 182 1996 182 0.0
## 183 1996 183 0.0
## 184 1996 184 0.0
## 185 1996 185 0.0
## 187 1996 187 0.0
## 189 1996 189 0.0
## 190 1996 190 0.0
## 191 1996 191 0.0
## 193 1996 193 0.0
## 194 1996 194 0.0
## 195 1996 195 0.0
## 196 1996 196 0.0
## 197 1996 197 0.0
## 198 1996 198 0.0
## 199 1996 199 0.0
## 200 1996 200 0.0
## 201 1996 201 0.0
## 203 1996 203 0.0
## 205 1996 205 0.0
## 206 1996 206 0.0
## 207 1996 207 0.0
## 214 1996 214 0.0
## 215 1996 215 0.0
## 216 1996 216 0.0
## 217 1996 217 0.0
## 218 1996 218 0.0
## 219 1996 219 0.0
## 220 1996 220 0.0
## 221 1996 221 0.0
## 222 1996 222 0.0
## 223 1996 223 0.0
## 224 1996 224 0.0
## 225 1996 225 0.0
## 226 1996 226 0.0
## 227 1996 227 0.0
## 228 1996 228 0.0
## 229 1996 229 0.0
## 230 1996 230 0.0
## 231 1996 231 0.0
## 234 1996 234 0.0
## 235 1996 235 0.0
## 237 1996 237 0.0
## 238 1996 238 0.0
## 239 1996 239 0.0
## 242 1996 242 0.0
## 246 1996 246 0.0
## 247 1996 247 0.0
## 248 1996 248 0.0
## 249 1996 249 0.0
## 250 1996 250 0.0
## 251 1996 251 0.0
## 252 1996 252 0.0
## 253 1996 253 0.0
## 254 1996 254 0.0
## 255 1996 255 0.0
## 256 1996 256 0.6
## 258 1996 258 0.0
## 259 1996 259 0.0
## 260 1996 260 0.0
## 261 1996 261 0.0
## 262 1996 262 0.0
## 266 1996 266 0.0
## 267 1996 267 0.0
## 268 1996 268 0.0
## 269 1996 269 0.0
## 270 1996 270 0.0
## 271 1996 271 0.0
## 272 1996 272 0.0
## 276 1996 276 0.0
## 277 1996 277 0.0
## 278 1996 278 0.0
## 279 1996 279 0.0
## 280 1996 280 0.0
## 282 1996 282 0.0
## 283 1996 283 0.0
## 284 1996 284 0.0
## 285 1996 285 0.0
## 286 1996 286 0.0
## 287 1996 287 0.0
## 288 1996 288 0.0
## 289 1996 289 0.0
## 290 1996 290 0.0
## 296 1996 296 0.0
## 297 1996 297 0.0
## 298 1996 298 0.0
## 299 1996 299 0.0
## 300 1996 300 0.0
## 303 1996 303 0.0
## 304 1996 304 0.0
## 306 1996 306 0.0
## 307 1996 307 0.0
## 308 1996 308 0.0
## 314 1996 314 0.0
## 315 1996 315 0.0
## 316 1996 316 0.0
## 317 1996 317 0.0
## 318 1996 318 0.0
## 319 1996 319 0.0
## 320 1996 320 0.0
## 321 1996 321 0.0
## 325 1996 325 0.0
## 330 1996 330 0.0
## 332 1996 332 0.0
## 333 1996 333 0.0
## 335 1996 335 0.0
## 336 1996 336 0.0
## 337 1996 337 0.0
## 338 1996 338 0.0
## 339 1996 339 0.0
## 344 1996 344 0.0
## 345 1996 345 0.0
## 346 1996 346 0.0
## 348 1996 348 0.0
## 349 1996 349 0.0
## 350 1996 350 0.0
## 351 1996 351 0.0
## 354 1996 354 0.0
## 355 1996 355 0.0
## 356 1996 356 0.0
## 357 1996 357 0.0
## 358 1996 358 0.0
## 359 1996 359 0.0
## 361 1996 361 0.0
## 362 1996 362 0.6
## 363 1996 363 0.0
## 368 1997 2 0.0
## 369 1997 3 0.0
## 370 1997 4 0.0
## 372 1997 6 0.0
## 375 1997 9 0.0
## 376 1997 10 0.0
## 378 1997 12 0.0
## 379 1997 13 0.0
## 380 1997 14 0.0
## 384 1997 18 0.0
## 385 1997 19 0.0
## 386 1997 20 0.0
## 388 1997 22 0.0
## 389 1997 23 0.0
## 398 1997 32 0.0
## 399 1997 33 0.0
## 402 1997 36 0.0
## 403 1997 37 0.0
## 404 1997 38 0.0
## 405 1997 39 0.0
## 406 1997 40 0.0
## 413 1997 47 0.0
## 417 1997 51 0.0
## 418 1997 52 0.0
## 419 1997 53 0.0
## 420 1997 54 0.0
## 426 1997 60 0.0
## 427 1997 61 0.0
## 433 1997 67 0.0
## 437 1997 71 0.0
## 438 1997 72 0.0
## 439 1997 73 0.0
## 440 1997 74 0.0
## 441 1997 75 0.0
## 443 1997 77 0.0
## 444 1997 78 0.0
## 445 1997 79 0.0
## 446 1997 80 0.0
## 447 1997 81 0.0
## 448 1997 82 0.0
## 450 1997 84 0.0
## 451 1997 85 0.0
## 452 1997 86 0.0
## 453 1997 87 0.0
## 454 1997 88 0.0
## 456 1997 90 0.6
## 458 1997 92 0.0
## 459 1997 93 0.0
## 460 1997 94 0.0
## 461 1997 95 0.0
## 462 1997 96 0.0
## 463 1997 97 0.0
## 464 1997 98 0.0
## 465 1997 99 0.0
## 466 1997 100 0.0
## 468 1997 102 0.0
## 469 1997 103 0.0
## 470 1997 104 0.0
## 472 1997 106 0.0
## 473 1997 107 0.0
## 474 1997 108 0.0
## 475 1997 109 0.0
## 476 1997 110 0.0
## 477 1997 111 0.0
## 478 1997 112 0.0
## 480 1997 114 0.0
## 483 1997 117 0.0
## 485 1997 119 0.0
## 487 1997 121 0.0
## 488 1997 122 0.0
## 489 1997 123 0.0
## 493 1997 127 0.0
## 496 1997 130 0.0
## 498 1997 132 0.0
## 499 1997 133 0.0
## 501 1997 135 0.0
## 502 1997 136 0.0
## 509 1997 143 0.6
## 511 1997 145 0.0
## 516 1997 150 0.0
## 517 1997 151 0.0
## 518 1997 152 0.0
## 519 1997 153 0.0
## 520 1997 154 0.0
## 521 1997 155 0.0
## 525 1997 159 0.0
## 526 1997 160 0.0
## 527 1997 161 0.0
## 528 1997 162 0.0
## 530 1997 164 0.0
## 531 1997 165 0.0
## 532 1997 166 0.0
## 533 1997 167 0.0
## 534 1997 168 0.0
## 535 1997 169 0.0
## 537 1997 171 0.0
## 538 1997 172 0.0
## 541 1997 175 0.0
## 542 1997 176 0.0
## 551 1997 185 0.0
## 554 1997 188 0.0
## 556 1997 190 0.0
## 557 1997 191 0.0
## 558 1997 192 0.0
## 559 1997 193 0.0
## 560 1997 194 0.0
## 563 1997 197 0.0
## 564 1997 198 0.0
## 565 1997 199 0.0
## 566 1997 200 0.0
## 567 1997 201 0.0
## 569 1997 203 0.0
## 570 1997 204 0.0
## 573 1997 207 0.0
## 575 1997 209 0.0
## 576 1997 210 0.0
## 577 1997 211 0.0
## 578 1997 212 0.0
## 579 1997 213 0.0
## 580 1997 214 0.0
## 581 1997 215 0.0
## 582 1997 216 0.0
## 586 1997 220 0.0
## 587 1997 221 0.0
## 588 1997 222 0.0
## 589 1997 223 0.0
## 590 1997 224 0.0
## 591 1997 225 0.0
## 592 1997 226 0.0
## 593 1997 227 0.0
## 594 1997 228 0.0
## 595 1997 229 0.0
## 596 1997 230 0.0
## 597 1997 231 0.0
## 598 1997 232 0.0
## 600 1997 234 0.0
## 601 1997 235 0.0
## 602 1997 236 0.0
## 603 1997 237 0.0
## 604 1997 238 0.0
## 605 1997 239 0.0
## 606 1997 240 0.0
## 607 1997 241 0.0
## 608 1997 242 0.0
## 609 1997 243 0.0
## 613 1997 247 0.0
## 615 1997 249 0.0
## 616 1997 250 0.0
## 617 1997 251 0.0
## 618 1997 252 0.0
## 619 1997 253 0.0
## 620 1997 254 0.0
## 621 1997 255 0.0
## 622 1997 256 0.0
## 623 1997 257 0.0
## 624 1997 258 0.0
## 625 1997 259 0.0
## 627 1997 261 0.0
## 637 1997 271 0.0
## 639 1997 273 0.0
## 640 1997 274 0.0
## 641 1997 275 0.0
## 643 1997 277 0.0
## 644 1997 278 0.0
## 645 1997 279 0.0
## 648 1997 282 0.0
## 649 1997 283 0.0
## 650 1997 284 0.0
## 651 1997 285 0.0
## 652 1997 286 0.0
## 653 1997 287 0.0
## 654 1997 288 0.0
## 655 1997 289 0.0
## 656 1997 290 0.0
## 657 1997 291 0.0
## 658 1997 292 0.0
## 659 1997 293 0.0
## 660 1997 294 0.0
## 661 1997 295 0.0
## 662 1997 296 0.0
## 663 1997 297 0.0
## 664 1997 298 0.0
## 665 1997 299 0.0
## 667 1997 301 0.0
## 668 1997 302 0.0
## 670 1997 304 0.0
## 672 1997 306 0.0
## 673 1997 307 0.0
## 674 1997 308 0.0
## 676 1997 310 0.0
## 677 1997 311 0.0
## 680 1997 314 0.0
## 682 1997 316 0.0
## 683 1997 317 0.0
## 687 1997 321 0.0
## 688 1997 322 0.0
## 689 1997 323 0.0
## 690 1997 324 0.0
## 692 1997 326 0.0
## 693 1997 327 0.0
## 694 1997 328 0.0
## 695 1997 329 0.0
## 696 1997 330 0.0
## 697 1997 331 0.0
## 698 1997 332 0.0
## 702 1997 336 0.0
## 703 1997 337 0.0
## 704 1997 338 0.0
## 705 1997 339 0.0
## 706 1997 340 0.0
## 712 1997 346 0.0
## 713 1997 347 0.0
## 714 1997 348 0.0
## 716 1997 350 0.0
## 717 1997 351 0.0
## 718 1997 352 0.0
## 721 1997 355 0.0
## 722 1997 356 0.0
## 723 1997 357 0.0
## 724 1997 358 0.0
## 726 1997 360 0.0
## 729 1997 363 0.0
## 730 1997 364 0.0
## 731 1997 365 0.0
## 732 1998 1 0.0
## 734 1998 3 0.0
## 743 1998 12 0.0
## 744 1998 13 0.0
## 745 1998 14 0.0
## 748 1998 17 0.0
## 749 1998 18 0.0
## 753 1998 22 0.0
## 754 1998 23 0.0
## 755 1998 24 0.0
## 758 1998 27 0.0
## 759 1998 28 0.0
## 760 1998 29 0.0
## 761 1998 30 0.0
## 762 1998 31 0.0
## 763 1998 32 0.0
## 764 1998 33 0.0
## 765 1998 34 0.0
## 766 1998 35 0.0
## 774 1998 43 0.0
## 775 1998 44 0.0
## 776 1998 45 0.0
## 777 1998 46 0.0
## 779 1998 48 0.0
## 780 1998 49 0.0
## 781 1998 50 0.0
## 782 1998 51 0.0
## 783 1998 52 0.0
## 784 1998 53 0.0
## 785 1998 54 0.0
## 786 1998 55 0.0
## 787 1998 56 0.0
## 788 1998 57 0.0
## 789 1998 58 0.0
## 793 1998 62 0.0
## 794 1998 63 0.0
## 797 1998 66 0.0
## 798 1998 67 0.0
## 799 1998 68 0.0
## 800 1998 69 0.0
## 801 1998 70 0.0
## 802 1998 71 0.0
## 803 1998 72 0.0
## 804 1998 73 0.0
## 809 1998 78 0.0
## 811 1998 80 0.0
## 812 1998 81 0.0
## 813 1998 82 0.0
## 814 1998 83 0.0
## 819 1998 88 0.0
## 821 1998 90 0.0
## 822 1998 91 0.0
## 824 1998 93 0.0
## 825 1998 94 0.0
## 826 1998 95 0.0
## 827 1998 96 0.0
## 828 1998 97 0.0
## 834 1998 103 0.6
## 835 1998 104 0.0
## 838 1998 107 0.6
## 840 1998 109 0.0
## 846 1998 115 0.0
## 847 1998 116 0.0
## 848 1998 117 0.0
## 849 1998 118 0.0
## 850 1998 119 0.0
## 851 1998 120 0.0
## 852 1998 121 0.0
## 858 1998 127 0.0
## 859 1998 128 0.0
## 860 1998 129 0.0
## 861 1998 130 0.0
## 862 1998 131 0.0
## 874 1998 143 0.0
## 875 1998 144 0.0
## 876 1998 145 0.0
## 877 1998 146 0.0
## 878 1998 147 0.0
## 881 1998 150 0.0
## 882 1998 151 0.0
## 888 1998 157 0.0
## 891 1998 160 0.0
## 892 1998 161 0.0
## 893 1998 162 0.0
## 895 1998 164 0.0
## 896 1998 165 0.0
## 897 1998 166 0.0
## 898 1998 167 0.0
## 899 1998 168 0.0
## 900 1998 169 0.0
## 901 1998 170 0.0
## 902 1998 171 0.0
## 906 1998 175 0.0
## 907 1998 176 0.0
## 908 1998 177 0.0
## 909 1998 178 0.0
## 910 1998 179 0.0
## 917 1998 186 0.0
## 919 1998 188 0.0
## 921 1998 190 0.0
## 922 1998 191 0.0
## 924 1998 193 0.0
## 927 1998 196 0.0
## 929 1998 198 0.0
## 935 1998 204 0.0
## 942 1998 211 0.0
## 943 1998 212 0.0
## 944 1998 213 0.0
## 945 1998 214 0.0
## 972 1998 241 0.0
## 973 1998 242 0.0
## 977 1998 246 0.0
## 978 1998 247 0.0
## 984 1998 253 0.0
## 985 1998 254 0.0
## 988 1998 257 0.0
## 989 1998 258 0.0
## 991 1998 260 0.0
## 992 1998 261 0.0
## 993 1998 262 0.0
## 1001 1998 270 0.0
## 1003 1998 272 0.0
## 1004 1998 273 0.0
## 1005 1998 274 0.0
## 1006 1998 275 0.0
## 1007 1998 276 0.0
## 1008 1998 277 0.0
## 1009 1998 278 0.0
## 1010 1998 279 0.0
## 1012 1998 281 0.0
## 1013 1998 282 0.0
## 1017 1998 286 0.0
## 1018 1998 287 0.0
## 1019 1998 288 0.0
## 1021 1998 290 0.0
## 1022 1998 291 0.0
## 1025 1998 294 0.0
## 1027 1998 296 0.0
## 1028 1998 297 0.0
## 1029 1998 298 0.0
## 1030 1998 299 0.0
## 1033 1998 302 0.0
## 1034 1998 303 0.0
## 1037 1998 306 0.0
## 1038 1998 307 0.0
## 1039 1998 308 0.0
## 1040 1998 309 0.0
## 1041 1998 310 0.0
## 1046 1998 315 0.0
## 1049 1998 318 0.0
## 1055 1998 324 0.0
## 1056 1998 325 0.0
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## 1063 1998 332 0.0
## 1064 1998 333 0.0
## 1065 1998 334 0.0
## 1066 1998 335 0.0
## 1068 1998 337 0.0
## 1069 1998 338 0.0
## 1071 1998 340 0.0
## 1074 1998 343 0.0
## 1076 1998 345 0.0
## 1077 1998 346 0.0
## 1078 1998 347 0.0
## 1085 1998 354 0.0
## 1087 1998 356 0.0
## 1088 1998 357 0.0
## 1089 1998 358 0.0
## 1090 1998 359 0.0
## 1091 1998 360 0.0
## 1092 1998 361 0.0
## 1093 1998 362 0.0
## 1094 1998 363 0.0
## 1095 1998 364 0.0
## 1099 1999 3 0.0
## 1100 1999 4 0.0
## 1101 1999 5 0.0
## 1102 1999 6 0.0
## 1103 1999 7 0.0
## 1104 1999 8 0.0
## 1106 1999 10 0.0
## 1107 1999 11 0.0
## 1108 1999 12 0.0
## 1112 1999 16 0.0
## 1113 1999 17 0.0
## 1114 1999 18 0.0
## 1115 1999 19 0.0
## 1122 1999 26 0.0
## 1133 1999 37 0.0
## 1134 1999 38 0.0
## 1141 1999 45 0.0
## 1142 1999 46 0.0
## 1143 1999 47 0.0
## 1144 1999 48 0.0
## 1146 1999 50 0.0
## 1147 1999 51 0.0
## 1148 1999 52 0.0
## 1151 1999 55 0.0
## 1155 1999 59 0.0
## 1159 1999 63 0.0
## 1160 1999 64 0.0
## 1161 1999 65 0.0
## 1166 1999 70 0.0
## 1168 1999 72 0.0
## 1172 1999 76 0.0
## 1173 1999 77 0.0
## 1177 1999 81 0.0
## 1179 1999 83 0.0
## 1180 1999 84 0.0
## 1181 1999 85 0.0
## 1182 1999 86 0.0
## 1183 1999 87 0.0
## 1184 1999 88 0.0
## 1185 1999 89 0.0
## 1186 1999 90 0.0
## 1187 1999 91 0.6
## 1194 1999 98 0.0
## 1195 1999 99 0.0
## 1203 1999 107 0.0
## 1204 1999 108 0.0
## 1205 1999 109 0.0
## 1206 1999 110 0.0
## 1207 1999 111 0.0
## 1209 1999 113 0.0
## 1211 1999 115 0.0
## 1212 1999 116 0.0
## 1221 1999 125 0.0
## 1224 1999 128 0.0
## 1225 1999 129 0.6
## 1228 1999 132 0.0
## 1230 1999 134 0.0
## 1231 1999 135 0.0
## 1232 1999 136 0.0
## 1233 1999 137 0.0
## 1237 1999 141 0.0
## 1238 1999 142 0.0
## 1243 1999 147 0.0
## 1245 1999 149 0.0
## 1259 1999 163 0.0
## 1260 1999 164 0.0
## 1261 1999 165 0.0
## 1262 1999 166 0.0
## 1263 1999 167 0.0
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## 1265 1999 169 0.0
## 1266 1999 170 0.0
## 1267 1999 171 0.0
## 1268 1999 172 0.0
## 1269 1999 173 0.0
## 1280 1999 184 0.0
## 1281 1999 185 0.0
## 1287 1999 191 0.0
## 1288 1999 192 0.0
## 1295 1999 199 0.0
## 1297 1999 201 0.0
## 1298 1999 202 0.0
## 1299 1999 203 0.0
## 1300 1999 204 0.0
## 1301 1999 205 0.0
## 1302 1999 206 0.0
## 1308 1999 212 0.0
## 1309 1999 213 0.0
## 1310 1999 214 0.0
## 1313 1999 217 0.0
## 1314 1999 218 0.0
## 1316 1999 220 0.0
## 1318 1999 222 0.0
## 1319 1999 223 0.0
## 1320 1999 224 0.0
## 1322 1999 226 0.6
## 1325 1999 229 0.0
## 1326 1999 230 0.0
## 1327 1999 231 0.0
## 1328 1999 232 0.0
## 1329 1999 233 0.0
## 1340 1999 244 0.0
## 1344 1999 248 0.0
## 1346 1999 250 0.0
## 1347 1999 251 0.0
## 1348 1999 252 0.0
## 1351 1999 255 0.0
## 1353 1999 257 0.0
## 1354 1999 258 0.0
## 1355 1999 259 0.0
## 1357 1999 261 0.0
## 1358 1999 262 0.0
## 1359 1999 263 0.0
## 1362 1999 266 0.0
## 1363 1999 267 0.0
## 1370 1999 274 0.0
## 1378 1999 282 0.0
## 1379 1999 283 0.0
## 1381 1999 285 0.0
## 1382 1999 286 0.0
## 1383 1999 287 0.0
## 1384 1999 288 0.0
## 1385 1999 289 0.0
## 1386 1999 290 0.0
## 1387 1999 291 0.0
## 1390 1999 294 0.0
## 1395 1999 299 0.0
## 1396 1999 300 0.0
## 1398 1999 302 0.0
## 1399 1999 303 0.0
## 1402 1999 306 0.0
## 1407 1999 311 0.0
## 1412 1999 316 0.0
## 1413 1999 317 0.0
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## 1415 1999 319 0.0
## 1419 1999 323 0.0
## 1420 1999 324 0.0
## 1421 1999 325 0.0
## 1424 1999 328 0.0
## 1425 1999 329 0.0
## 1426 1999 330 0.0
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## 1431 1999 335 0.0
## 1432 1999 336 0.0
## 1433 1999 337 0.0
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## 1435 1999 339 0.0
## 1437 1999 341 0.0
## 1438 1999 342 0.0
## 1441 1999 345 0.0
## 1442 1999 346 0.0
## 1443 1999 347 0.0
## 1444 1999 348 0.0
## 1445 1999 349 0.0
## 1446 1999 350 0.0
## 1447 1999 351 0.0
## 1449 1999 353 0.0
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## 3653 2005 365 0.0tail(rainfall3)## Year DOY Rain
## 3648 2005 360 0
## 3649 2005 361 0
## 3650 2005 362 0
## 3651 2005 363 0
## 3652 2005 364 0
## 3653 2005 365 0The 26 letters of the Roman alphabet are conveniently accessible in R via letters and LETTERS. These are not functions, but vectors that are always loaded. What is the 18th letter of the alphabet? What is the last letter of the alphabet (don’t guess, write code)? Use ?sample to figure out how to sample with replacement. Generate a random subset of fifteen letters. Are any letters in the subset duplicated? Hint: use the any and duplicated functions. Which letters?
letters[18]## [1] "r"letters[26]## [1] "z"set.seed(123)
lettersample <- sample(letters,15, replace = TRUE)
lettersample## [1] "h" "u" "k" "w" "y" "b" "n" "x" "o" "l" "y" "l" "r" "o" "c"duplicated(lettersample)## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [12] TRUE FALSE TRUE FALSEanyDuplicated(lettersample)## [1] 11cereal <- read.csv("cereals.csv")
head(cereal)## Cereal.name Manufacturer Cold.or.Hot calories protein fat
## 1 100%_Bran N C 70 4 1
## 2 100%_Natural_Bran Q C 120 3 5
## 3 All-Bran K C 70 4 1
## 4 All-Bran_with_Extra_Fiber K C 50 4 0
## 5 Almond_Delight R C 110 2 2
## 6 Apple_Cinnamon_Cheerios G C 110 2 2
## sodium fiber carbo sugars potass vitamins rating
## 1 130 10.0 5.0 6 280 25 68.40297
## 2 15 2.0 8.0 8 135 0 33.98368
## 3 260 9.0 7.0 5 320 25 59.42551
## 4 140 14.0 8.0 0 330 25 93.70491
## 5 200 1.0 14.0 8 NA 25 34.38484
## 6 180 1.5 10.5 10 70 25 29.50954summary(cereal)## Cereal.name Manufacturer Cold.or.Hot calories
## 100%_Bran : 1 A: 1 C:74 Min. : 50.0
## 100%_Natural_Bran : 1 G:22 H: 3 1st Qu.:100.0
## All-Bran : 1 K:23 Median :110.0
## All-Bran_with_Extra_Fiber: 1 N: 6 Mean :106.9
## Almond_Delight : 1 P: 9 3rd Qu.:110.0
## Apple_Cinnamon_Cheerios : 1 Q: 8 Max. :160.0
## (Other) :71 R: 8
## protein fat sodium fiber
## Min. :1.000 Min. :0.000 Min. : 0.0 Min. : 0.000
## 1st Qu.:2.000 1st Qu.:0.000 1st Qu.:130.0 1st Qu.: 1.000
## Median :3.000 Median :1.000 Median :180.0 Median : 2.000
## Mean :2.545 Mean :1.013 Mean :159.7 Mean : 2.152
## 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:210.0 3rd Qu.: 3.000
## Max. :6.000 Max. :5.000 Max. :320.0 Max. :14.000
##
## carbo sugars potass vitamins
## Min. : 5.0 Min. : 0.000 Min. : 15.00 Min. : 0.00
## 1st Qu.:12.0 1st Qu.: 3.000 1st Qu.: 42.50 1st Qu.: 25.00
## Median :14.5 Median : 7.000 Median : 90.00 Median : 25.00
## Mean :14.8 Mean : 7.026 Mean : 98.67 Mean : 28.25
## 3rd Qu.:17.0 3rd Qu.:11.000 3rd Qu.:120.00 3rd Qu.: 25.00
## Max. :23.0 Max. :15.000 Max. :330.00 Max. :100.00
## NA's :1 NA's :1 NA's :2
## rating
## Min. :18.04
## 1st Qu.:33.17
## Median :40.40
## Mean :42.67
## 3rd Qu.:50.83
## Max. :93.70
## str(cereal)## 'data.frame': 77 obs. of 13 variables:
## $ Cereal.name : Factor w/ 77 levels "100%_Bran","100%_Natural_Bran",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Manufacturer: Factor w/ 7 levels "A","G","K","N",..: 4 6 3 3 7 2 3 2 7 5 ...
## $ Cold.or.Hot : Factor w/ 2 levels "C","H": 1 1 1 1 1 1 1 1 1 1 ...
## $ calories : int 70 120 70 50 110 110 110 130 90 90 ...
## $ protein : int 4 3 4 4 2 2 2 3 2 3 ...
## $ fat : int 1 5 1 0 2 2 0 2 1 0 ...
## $ sodium : int 130 15 260 140 200 180 125 210 200 210 ...
## $ fiber : num 10 2 9 14 1 1.5 1 2 4 5 ...
## $ carbo : num 5 8 7 8 14 10.5 11 18 15 13 ...
## $ sugars : int 6 8 5 0 8 10 14 8 6 5 ...
## $ potass : int 280 135 320 330 NA 70 30 100 125 190 ...
## $ vitamins : int 25 0 25 25 25 25 25 25 25 25 ...
## $ rating : num 68.4 34 59.4 93.7 34.4 ...#Add a new variable to the dataset called 'totalcarb', which is the sum of 'carbo' and 'sugars'
cereal$totalcarb <- with(cereal, carbo + sugars)
#How many cereals in the dataframe are 'hot' cereals? Hint: take an appropriate subset of the data,
#and then count the number of observations in the subset.
hot <- subset(cereal, Cold.or.Hot == "H")
nrow(hot)## [1] 3#How many unique manufacturers are included in the dataset?
table(cereal$Manufacturer)##
## A G K N P Q R
## 1 22 23 6 9 8 8#Take a subset of the dataframe with only the Manufacturer 'K' (Kellogg's).
cerealk <- subset(cereal, Manufacturer == "K")
#Take a subset of the dataframe of all cereals that have less than 80 calories, AND have more than 20
#units of vitamins.
cereal2 <- subset(cereal, calories < 80 & vitamins > 20)
#Take a subset of the dataframe containing cereals that contain at least 1 unit of sugar, and keep only
#the variables 'Cereal.name', 'calories' and 'vitamins'. Then inspect the 1rst few rows of the dataframe
#with head.
cereal3 <- subset(cereal, sugars > 1, select=c(Cereal.name,calories,vitamins))
head(cereal3)## Cereal.name calories vitamins
## 1 100%_Bran 70 25
## 2 100%_Natural_Bran 120 0
## 3 All-Bran 70 25
## 5 Almond_Delight 110 25
## 6 Apple_Cinnamon_Cheerios 110 25
## 7 Apple_Jacks 110 25#For one of the above subsets, write a new CSV 1le to disk using write.csv
write.csv(cereal3,"cereal3.csv", row.names=FALSE)
#Rename the column 'Manufacturer' to 'Producer'
names(cereal)[2] <- "Producer"
head(cereal)## Cereal.name Producer Cold.or.Hot calories protein fat
## 1 100%_Bran N C 70 4 1
## 2 100%_Natural_Bran Q C 120 3 5
## 3 All-Bran K C 70 4 1
## 4 All-Bran_with_Extra_Fiber K C 50 4 0
## 5 Almond_Delight R C 110 2 2
## 6 Apple_Cinnamon_Cheerios G C 110 2 2
## sodium fiber carbo sugars potass vitamins rating totalcarb
## 1 130 10.0 5.0 6 280 25 68.40297 11.0
## 2 15 2.0 8.0 8 135 0 33.98368 16.0
## 3 260 9.0 7.0 5 320 25 59.42551 12.0
## 4 140 14.0 8.0 0 330 25 93.70491 8.0
## 5 200 1.0 14.0 8 NA 25 34.38484 22.0
## 6 180 1.5 10.5 10 70 25 29.50954 20.5Read the following data into R (number of honeyeaters seen at the EucFACE site seen in a week). Give the resulting dataframe a reasonable name.
df <-read.table(header=TRUE, text="
day nr
sun 3
mon 2
tue 5
wed 0
thu 8
fri 1
sat 2
")
#Add a day number to the dataset you read in above (sunday=1, saturday=7). Recall the seq function
df$daynumber <- seq(from=1,to=7,by=1)
#Delete the 'Day' variable (to only keep the daynumber that you added above).
df$day <- NULL
#On which daynumber did you observe the most honeyeaters? Hint: use which.max, in combination
#with indexing.
df$daynumber[which.max(df$nr)]## [1] 5#Sort the dataset by number of birds seen. Hint: use the order function to find the order of the
#number of birds, then use this vector to index the dataframe.
df <- df[order(df$nr),] titanic <- read.table("titanic.txt", header=TRUE)
str(titanic)## 'data.frame': 1313 obs. of 5 variables:
## $ Name : Factor w/ 1310 levels "Abbing, Mr Anthony",..: 22 25 26 27 24 31 45 46 50 54 ...
## $ PClass : Factor w/ 3 levels "1st","2nd","3rd": 1 1 1 1 1 1 1 1 1 1 ...
## $ Age : num 29 2 30 25 0.92 47 63 39 58 71 ...
## $ Sex : Factor w/ 2 levels "female","male": 1 1 2 1 2 2 1 2 1 2 ...
## $ Survived: int 1 0 0 0 1 1 1 0 1 0 ...summary(titanic)## Name PClass Age
## Carlsson, Mr Frans Olof : 2 1st:322 Min. : 0.17
## Connolly, Miss Kate : 2 2nd:280 1st Qu.:21.00
## Kelly, Mr James : 2 3rd:711 Median :28.00
## Abbing, Mr Anthony : 1 Mean :30.40
## Abbott, Master Eugene Joseph: 1 3rd Qu.:39.00
## Abbott, Mr Rossmore Edward : 1 Max. :71.00
## (Other) :1304 NA's :557
## Sex Survived
## female:462 Min. :0.0000
## male :851 1st Qu.:0.0000
## Median :0.0000
## Mean :0.3427
## 3rd Qu.:1.0000
## Max. :1.0000
## #Make two new dataframes : a subset of male survivors, and a subset of female survivors.
titanicfemale <- subset(titanic, Sex == "female")
titanicmale <- subset(titanic, Sex == "male")
#Based on the previous question, what was the name of the oldest surviving male? In what class was
#the youngest surviving female?
which.max(titanicmale$Age)## [1] 5titanicmale$Name[5]## [1] Artagaveytia, Mr Ramon
## 1310 Levels: Abbing, Mr Anthony ... Zimmerman, Leowhich.min(titanicfemale$Age)## [1] 299titanicfemale$PClass[299]## [1] 3rd
## Levels: 1st 2nd 3rd#Take 15 random names of passengers from the Titanic, and sort them alphabetically
# sample without replacement
mysample <- titanic[sample(1:nrow(titanic), 15, replace=FALSE),]
orderedsample <- mysample[order(mysample$Name),]