# 周次：w12
# 任務：應用(資料框處理與繪圖)
# 姓名：高千琇
# 日期：2021年04月21日
### 資料框處理
# 請至台灣傳播資料庫下載「2019年調查」的sav檔
# 網址:https://www.crctaiwan.nctu.edu.tw/AnnualSurvey.asp
## 1. 將輸入的sav檔案命名為tcs2019
# install.packages("sjlabelled")
library(sjlabelled)
tcs2019 <- read_spss("tcs2019.sav")
## 2. 檢視資料框的各種函數
# 列數
#nrow(tcs2019)
# 檢視資料框內容
#View(tcs2019)
# 前六行
#head(tcs2019)
# 後六行
#tail(tcs2019)
# 欄位名稱或變數名稱
#names(tcs2019)
# 另一種寫法
#colnames(tcs2019)
# 得知每個變數的描述性統計量
#summary(tcs2019)
# 得知資料框複合式的資訊
# (含資料結構種類、觀察值個數、變數個數、前幾筆觀察值資訊等)
#str(tcs2019)
## 欄數
#ncol(tcs2019)
## 維度
#dim(tcs2019)
## 列的索引值
row.names(tcs2019)
## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"
## [11] "11" "12" "13" "14" "15" "16" "17" "18" "19" "20"
## [21] "21" "22" "23" "24" "25" "26" "27" "28" "29" "30"
## [31] "31" "32" "33" "34" "35" "36" "37" "38" "39" "40"
## [41] "41" "42" "43" "44" "45" "46" "47" "48" "49" "50"
## [51] "51" "52" "53" "54" "55" "56" "57" "58" "59" "60"
## [61] "61" "62" "63" "64" "65" "66" "67" "68" "69" "70"
## [71] "71" "72" "73" "74" "75" "76" "77" "78" "79" "80"
## [81] "81" "82" "83" "84" "85" "86" "87" "88" "89" "90"
## [91] "91" "92" "93" "94" "95" "96" "97" "98" "99" "100"
## [101] "101" "102" "103" "104" "105" "106" "107" "108" "109" "110"
## [111] "111" "112" "113" "114" "115" "116" "117" "118" "119" "120"
## [121] "121" "122" "123" "124" "125" "126" "127" "128" "129" "130"
## [131] "131" "132" "133" "134" "135" "136" "137" "138" "139" "140"
## [141] "141" "142" "143" "144" "145" "146" "147" "148" "149" "150"
## [151] "151" "152" "153" "154" "155" "156" "157" "158" "159" "160"
## [161] "161" "162" "163" "164" "165" "166" "167" "168" "169" "170"
## [171] "171" "172" "173" "174" "175" "176" "177" "178" "179" "180"
## [181] "181" "182" "183" "184" "185" "186" "187" "188" "189" "190"
## [191] "191" "192" "193" "194" "195" "196" "197" "198" "199" "200"
## [201] "201" "202" "203" "204" "205" "206" "207" "208" "209" "210"
## [211] "211" "212" "213" "214" "215" "216" "217" "218" "219" "220"
## [221] "221" "222" "223" "224" "225" "226" "227" "228" "229" "230"
## [231] "231" "232" "233" "234" "235" "236" "237" "238" "239" "240"
## [241] "241" "242" "243" "244" "245" "246" "247" "248" "249" "250"
## [251] "251" "252" "253" "254" "255" "256" "257" "258" "259" "260"
## [261] "261" "262" "263" "264" "265" "266" "267" "268" "269" "270"
## [271] "271" "272" "273" "274" "275" "276" "277" "278" "279" "280"
## [281] "281" "282" "283" "284" "285" "286" "287" "288" "289" "290"
## [291] "291" "292" "293" "294" "295" "296" "297" "298" "299" "300"
## [301] "301" "302" "303" "304" "305" "306" "307" "308" "309" "310"
## [311] "311" "312" "313" "314" "315" "316" "317" "318" "319" "320"
## [321] "321" "322" "323" "324" "325" "326" "327" "328" "329" "330"
## [331] "331" "332" "333" "334" "335" "336" "337" "338" "339" "340"
## [341] "341" "342" "343" "344" "345" "346" "347" "348" "349" "350"
## [351] "351" "352" "353" "354" "355" "356" "357" "358" "359" "360"
## [361] "361" "362" "363" "364" "365" "366" "367" "368" "369" "370"
## [371] "371" "372" "373" "374" "375" "376" "377" "378" "379" "380"
## [381] "381" "382" "383" "384" "385" "386" "387" "388" "389" "390"
## [391] "391" "392" "393" "394" "395" "396" "397" "398" "399" "400"
## [401] "401" "402" "403" "404" "405" "406" "407" "408" "409" "410"
## [411] "411" "412" "413" "414" "415" "416" "417" "418" "419" "420"
## [421] "421" "422" "423" "424" "425" "426" "427" "428" "429" "430"
## [431] "431" "432" "433" "434" "435" "436" "437" "438" "439" "440"
## [441] "441" "442" "443" "444" "445" "446" "447" "448" "449" "450"
## [451] "451" "452" "453" "454" "455" "456" "457" "458" "459" "460"
## [461] "461" "462" "463" "464" "465" "466" "467" "468" "469" "470"
## [471] "471" "472" "473" "474" "475" "476" "477" "478" "479" "480"
## [481] "481" "482" "483" "484" "485" "486" "487" "488" "489" "490"
## [491] "491" "492" "493" "494" "495" "496" "497" "498" "499" "500"
## [501] "501" "502" "503" "504" "505" "506" "507" "508" "509" "510"
## [511] "511" "512" "513" "514" "515" "516" "517" "518" "519" "520"
## [521] "521" "522" "523" "524" "525" "526" "527" "528" "529" "530"
## [531] "531" "532" "533" "534" "535" "536" "537" "538" "539" "540"
## [541] "541" "542" "543" "544" "545" "546" "547" "548" "549" "550"
## [551] "551" "552" "553" "554" "555" "556" "557" "558" "559" "560"
## [561] "561" "562" "563" "564" "565" "566" "567" "568" "569" "570"
## [571] "571" "572" "573" "574" "575" "576" "577" "578" "579" "580"
## [581] "581" "582" "583" "584" "585" "586" "587" "588" "589" "590"
## [591] "591" "592" "593" "594" "595" "596" "597" "598" "599" "600"
## [601] "601" "602" "603" "604" "605" "606" "607" "608" "609" "610"
## [611] "611" "612" "613" "614" "615" "616" "617" "618" "619" "620"
## [621] "621" "622" "623" "624" "625" "626" "627" "628" "629" "630"
## [631] "631" "632" "633" "634" "635" "636" "637" "638" "639" "640"
## [641] "641" "642" "643" "644" "645" "646" "647" "648" "649" "650"
## [651] "651" "652" "653" "654" "655" "656" "657" "658" "659" "660"
## [661] "661" "662" "663" "664" "665" "666" "667" "668" "669" "670"
## [671] "671" "672" "673" "674" "675" "676" "677" "678" "679" "680"
## [681] "681" "682" "683" "684" "685" "686" "687" "688" "689" "690"
## [691] "691" "692" "693" "694" "695" "696" "697" "698" "699" "700"
## [701] "701" "702" "703" "704" "705" "706" "707" "708" "709" "710"
## [711] "711" "712" "713" "714" "715" "716" "717" "718" "719" "720"
## [721] "721" "722" "723" "724" "725" "726" "727" "728" "729" "730"
## [731] "731" "732" "733" "734" "735" "736" "737" "738" "739" "740"
## [741] "741" "742" "743" "744" "745" "746" "747" "748" "749" "750"
## [751] "751" "752" "753" "754" "755" "756" "757" "758" "759" "760"
## [761] "761" "762" "763" "764" "765" "766" "767" "768" "769" "770"
## [771] "771" "772" "773" "774" "775" "776" "777" "778" "779" "780"
## [781] "781" "782" "783" "784" "785" "786" "787" "788" "789" "790"
## [791] "791" "792" "793" "794" "795" "796" "797" "798" "799" "800"
## [801] "801" "802" "803" "804" "805" "806" "807" "808" "809" "810"
## [811] "811" "812" "813" "814" "815" "816" "817" "818" "819" "820"
## [821] "821" "822" "823" "824" "825" "826" "827" "828" "829" "830"
## [831] "831" "832" "833" "834" "835" "836" "837" "838" "839" "840"
## [841] "841" "842" "843" "844" "845" "846" "847" "848" "849" "850"
## [851] "851" "852" "853" "854" "855" "856" "857" "858" "859" "860"
## [861] "861" "862" "863" "864" "865" "866" "867" "868" "869" "870"
## [871] "871" "872" "873" "874" "875" "876" "877" "878" "879" "880"
## [881] "881" "882" "883" "884" "885" "886" "887" "888" "889" "890"
## [891] "891" "892" "893" "894" "895" "896" "897" "898" "899" "900"
## [901] "901" "902" "903" "904" "905" "906" "907" "908" "909" "910"
## [911] "911" "912" "913" "914" "915" "916" "917" "918" "919" "920"
## [921] "921" "922" "923" "924" "925" "926" "927" "928" "929" "930"
## [931] "931" "932" "933" "934" "935" "936" "937" "938" "939" "940"
## [941] "941" "942" "943" "944" "945" "946" "947" "948" "949" "950"
## [951] "951" "952" "953" "954" "955" "956" "957" "958" "959" "960"
## [961] "961" "962" "963" "964" "965" "966" "967" "968" "969" "970"
## [971] "971" "972" "973" "974" "975" "976" "977" "978" "979" "980"
## [981] "981" "982" "983" "984" "985" "986" "987" "988" "989" "990"
## [991] "991" "992" "993" "994" "995" "996" "997" "998" "999" "1000"
## [1001] "1001" "1002" "1003" "1004" "1005" "1006" "1007" "1008" "1009" "1010"
## [1011] "1011" "1012" "1013" "1014" "1015" "1016" "1017" "1018" "1019" "1020"
## [1021] "1021" "1022" "1023" "1024" "1025" "1026" "1027" "1028" "1029" "1030"
## [1031] "1031" "1032" "1033" "1034" "1035" "1036" "1037" "1038" "1039" "1040"
## [1041] "1041" "1042" "1043" "1044" "1045" "1046" "1047" "1048" "1049" "1050"
## [1051] "1051" "1052" "1053" "1054" "1055" "1056" "1057" "1058" "1059" "1060"
## [1061] "1061" "1062" "1063" "1064" "1065" "1066" "1067" "1068" "1069" "1070"
## [1071] "1071" "1072" "1073" "1074" "1075" "1076" "1077" "1078" "1079" "1080"
## [1081] "1081" "1082" "1083" "1084" "1085" "1086" "1087" "1088" "1089" "1090"
## [1091] "1091" "1092" "1093" "1094" "1095" "1096" "1097" "1098" "1099" "1100"
## [1101] "1101" "1102" "1103" "1104" "1105" "1106" "1107" "1108" "1109" "1110"
## [1111] "1111" "1112" "1113" "1114" "1115" "1116" "1117" "1118" "1119" "1120"
## [1121] "1121" "1122" "1123" "1124" "1125" "1126" "1127" "1128" "1129" "1130"
## [1131] "1131" "1132" "1133" "1134" "1135" "1136" "1137" "1138" "1139" "1140"
## [1141] "1141" "1142" "1143" "1144" "1145" "1146" "1147" "1148" "1149" "1150"
## [1151] "1151" "1152" "1153" "1154" "1155" "1156" "1157" "1158" "1159" "1160"
## [1161] "1161" "1162" "1163" "1164" "1165" "1166" "1167" "1168" "1169" "1170"
## [1171] "1171" "1172" "1173" "1174" "1175" "1176" "1177" "1178" "1179" "1180"
## [1181] "1181" "1182" "1183" "1184" "1185" "1186" "1187" "1188" "1189" "1190"
## [1191] "1191" "1192" "1193" "1194" "1195" "1196" "1197" "1198" "1199" "1200"
## [1201] "1201" "1202" "1203" "1204" "1205" "1206" "1207" "1208" "1209" "1210"
## [1211] "1211" "1212" "1213" "1214" "1215" "1216" "1217" "1218" "1219" "1220"
## [1221] "1221" "1222" "1223" "1224" "1225" "1226" "1227" "1228" "1229" "1230"
## [1231] "1231" "1232" "1233" "1234" "1235" "1236" "1237" "1238" "1239" "1240"
## [1241] "1241" "1242" "1243" "1244" "1245" "1246" "1247" "1248" "1249" "1250"
## [1251] "1251" "1252" "1253" "1254" "1255" "1256" "1257" "1258" "1259" "1260"
## [1261] "1261" "1262" "1263" "1264" "1265" "1266" "1267" "1268" "1269" "1270"
## [1271] "1271" "1272" "1273" "1274" "1275" "1276" "1277" "1278" "1279" "1280"
## [1281] "1281" "1282" "1283" "1284" "1285" "1286" "1287" "1288" "1289" "1290"
## [1291] "1291" "1292" "1293" "1294" "1295" "1296" "1297" "1298" "1299" "1300"
## [1301] "1301" "1302" "1303" "1304" "1305" "1306" "1307" "1308" "1309" "1310"
## [1311] "1311" "1312" "1313" "1314" "1315" "1316" "1317" "1318" "1319" "1320"
## [1321] "1321" "1322" "1323" "1324" "1325" "1326" "1327" "1328" "1329" "1330"
## [1331] "1331" "1332" "1333" "1334" "1335" "1336" "1337" "1338" "1339" "1340"
## [1341] "1341" "1342" "1343" "1344" "1345" "1346" "1347" "1348" "1349" "1350"
## [1351] "1351" "1352" "1353" "1354" "1355" "1356" "1357" "1358" "1359" "1360"
## [1361] "1361" "1362" "1363" "1364" "1365" "1366" "1367" "1368" "1369" "1370"
## [1371] "1371" "1372" "1373" "1374" "1375" "1376" "1377" "1378" "1379" "1380"
## [1381] "1381" "1382" "1383" "1384" "1385" "1386" "1387" "1388" "1389" "1390"
## [1391] "1391" "1392" "1393" "1394" "1395" "1396" "1397" "1398" "1399" "1400"
## [1401] "1401" "1402" "1403" "1404" "1405" "1406" "1407" "1408" "1409" "1410"
## [1411] "1411" "1412" "1413" "1414" "1415" "1416" "1417" "1418" "1419" "1420"
## [1421] "1421" "1422" "1423" "1424" "1425" "1426" "1427" "1428" "1429" "1430"
## [1431] "1431" "1432" "1433" "1434" "1435" "1436" "1437" "1438" "1439" "1440"
## [1441] "1441" "1442" "1443" "1444" "1445" "1446" "1447" "1448" "1449" "1450"
## [1451] "1451" "1452" "1453" "1454" "1455" "1456" "1457" "1458" "1459" "1460"
## [1461] "1461" "1462" "1463" "1464" "1465" "1466" "1467" "1468" "1469" "1470"
## [1471] "1471" "1472" "1473" "1474" "1475" "1476" "1477" "1478" "1479" "1480"
## [1481] "1481" "1482" "1483" "1484" "1485" "1486" "1487" "1488" "1489" "1490"
## [1491] "1491" "1492" "1493" "1494" "1495" "1496" "1497" "1498" "1499" "1500"
## [1501] "1501" "1502" "1503" "1504" "1505" "1506" "1507" "1508" "1509" "1510"
## [1511] "1511" "1512" "1513" "1514" "1515" "1516" "1517" "1518" "1519" "1520"
## [1521] "1521" "1522" "1523" "1524" "1525" "1526" "1527" "1528" "1529" "1530"
## [1531] "1531" "1532" "1533" "1534" "1535" "1536" "1537" "1538" "1539" "1540"
## [1541] "1541" "1542" "1543" "1544" "1545" "1546" "1547" "1548" "1549" "1550"
## [1551] "1551" "1552" "1553" "1554" "1555" "1556" "1557" "1558" "1559" "1560"
## [1561] "1561" "1562" "1563" "1564" "1565" "1566" "1567" "1568" "1569" "1570"
## [1571] "1571" "1572" "1573" "1574" "1575" "1576" "1577" "1578" "1579" "1580"
## [1581] "1581" "1582" "1583" "1584" "1585" "1586" "1587" "1588" "1589" "1590"
## [1591] "1591" "1592" "1593" "1594" "1595" "1596" "1597" "1598" "1599" "1600"
## [1601] "1601" "1602" "1603" "1604" "1605" "1606" "1607" "1608" "1609" "1610"
## [1611] "1611" "1612" "1613" "1614" "1615" "1616" "1617" "1618" "1619" "1620"
## [1621] "1621" "1622" "1623" "1624" "1625" "1626" "1627" "1628" "1629" "1630"
## [1631] "1631" "1632" "1633" "1634" "1635" "1636" "1637" "1638" "1639" "1640"
## [1641] "1641" "1642" "1643" "1644" "1645" "1646" "1647" "1648" "1649" "1650"
## [1651] "1651" "1652" "1653" "1654" "1655" "1656" "1657" "1658" "1659" "1660"
## [1661] "1661" "1662" "1663" "1664" "1665" "1666" "1667" "1668" "1669" "1670"
## [1671] "1671" "1672" "1673" "1674" "1675" "1676" "1677" "1678" "1679" "1680"
## [1681] "1681" "1682" "1683" "1684" "1685" "1686" "1687" "1688" "1689" "1690"
## [1691] "1691" "1692" "1693" "1694" "1695" "1696" "1697" "1698" "1699" "1700"
## [1701] "1701" "1702" "1703" "1704" "1705" "1706" "1707" "1708" "1709" "1710"
## [1711] "1711" "1712" "1713" "1714" "1715" "1716" "1717" "1718" "1719" "1720"
## [1721] "1721" "1722" "1723" "1724" "1725" "1726" "1727" "1728" "1729" "1730"
## [1731] "1731" "1732" "1733" "1734" "1735" "1736" "1737" "1738" "1739" "1740"
## [1741] "1741" "1742" "1743" "1744" "1745" "1746" "1747" "1748" "1749" "1750"
## [1751] "1751" "1752" "1753" "1754" "1755" "1756" "1757" "1758" "1759" "1760"
## [1761] "1761" "1762" "1763" "1764" "1765" "1766" "1767" "1768" "1769" "1770"
## [1771] "1771" "1772" "1773" "1774" "1775" "1776" "1777" "1778" "1779" "1780"
## [1781] "1781" "1782" "1783" "1784" "1785" "1786" "1787" "1788" "1789" "1790"
## [1791] "1791" "1792" "1793" "1794" "1795" "1796" "1797" "1798" "1799" "1800"
## [1801] "1801" "1802" "1803" "1804" "1805" "1806" "1807" "1808" "1809" "1810"
## [1811] "1811" "1812" "1813" "1814" "1815" "1816" "1817" "1818" "1819" "1820"
## [1821] "1821" "1822" "1823" "1824" "1825" "1826" "1827" "1828" "1829" "1830"
## [1831] "1831" "1832" "1833" "1834" "1835" "1836" "1837" "1838" "1839" "1840"
## [1841] "1841" "1842" "1843" "1844" "1845" "1846" "1847" "1848" "1849" "1850"
## [1851] "1851" "1852" "1853" "1854" "1855" "1856" "1857" "1858" "1859" "1860"
## [1861] "1861" "1862" "1863" "1864" "1865" "1866" "1867" "1868" "1869" "1870"
## [1871] "1871" "1872" "1873" "1874" "1875" "1876" "1877" "1878" "1879" "1880"
## [1881] "1881" "1882" "1883" "1884" "1885" "1886" "1887" "1888" "1889" "1890"
## [1891] "1891" "1892" "1893" "1894" "1895" "1896" "1897" "1898" "1899" "1900"
## [1901] "1901" "1902" "1903" "1904" "1905" "1906" "1907" "1908" "1909" "1910"
## [1911] "1911" "1912" "1913" "1914" "1915" "1916" "1917" "1918" "1919" "1920"
## [1921] "1921" "1922" "1923" "1924" "1925" "1926" "1927" "1928" "1929" "1930"
## [1931] "1931" "1932" "1933" "1934" "1935" "1936" "1937" "1938" "1939" "1940"
## [1941] "1941" "1942" "1943" "1944" "1945" "1946" "1947" "1948" "1949" "1950"
## [1951] "1951" "1952" "1953" "1954" "1955" "1956" "1957" "1958" "1959" "1960"
## [1961] "1961" "1962" "1963" "1964" "1965" "1966" "1967" "1968" "1969" "1970"
## [1971] "1971" "1972" "1973" "1974" "1975" "1976" "1977" "1978" "1979" "1980"
## [1981] "1981" "1982" "1983" "1984" "1985" "1986" "1987" "1988" "1989" "1990"
## [1991] "1991" "1992" "1993" "1994" "1995" "1996" "1997" "1998" "1999" "2000"
#
# # 當資料較大時，建議使用sjPlot套件
# library(sjPlot)
# view_df(tcs2019,
# file="tcs2019tab.html", # 結果直接另存新檔
# show.na = T, # 顯示未重新編碼前的無效值個數
# show.frq = T, # 顯示次數
# show.prc = T, # 顯示百分比
# encoding = "big5"
# )
### 3. 應用實作
# 偵測與處理，讓65+熟齡族告別假新聞危害
# http://www.crctaiwan.nctu.edu.tw/epaper/%E7%AC%AC202%E6%9C%9F20210409.htm
# RQ1:遇到假新聞的經驗，是否有年齡層的差異存在呢？
# RQ2:對假新聞的感受，是否有年齡層的差異存在呢？
# RQ3:對假新聞的確認與處理方式等，是否有年齡層的差異存在呢？
# (1)確認欲分析的變數
# 年齡 ra2
# 是否有遇到過假新聞? i12.1
# 對假新聞的感受:
# 普遍性 i7a
# 嚴重性 i7b
# 受影響的可能性 i7c
# 確認你接觸到的新聞是不是假新聞? i11.1-i11.8
# 遇到假新聞,你會如何處理? i12.2.1-i12.2.8
# (2)變數整理
# 年齡「變數重新分類」為4類：18-35,36-49,50-64,65UP
# 備註：break的值(x,y,z)是指: group1 >x & <=y; group2 >y & <=z
tcs2019$agegroup <- cut(tcs2019$ra2, breaks=c(17,35,49,64,Inf),
labels = c("18至35歲","36至49歲","50至64歲","65歲以上"))
# 檢視各類別有多少人?
table(tcs2019$agegroup)
##
## 18至35歲 36至49歲 50至64歲 65歲以上
## 343 534 583 540
# 另一種方法：製作次數分配表
#install.packages("sjmisc")
library(sjmisc)
frq(tcs2019$agegroup, encoding = "big-5", out="v")
x <categorical>
|
val
|
label
|
frq
|
raw.prc
|
valid.prc
|
cum.prc
|
|
18至35歲
|
|
343
|
17.15
|
17.15
|
17.15
|
|
36至49歲
|
|
534
|
26.70
|
26.70
|
43.85
|
|
50至64歲
|
|
583
|
29.15
|
29.15
|
73.00
|
|
65歲以上
|
|
540
|
27.00
|
27.00
|
100.00
|
|
NA
|
NA
|
0
|
0.00
|
NA
|
NA
|
|
total N=2000 · valid N=2000 · x̄=2.66 · σ=1.05
|
### (3)回答RQ
## RQ1:遇到假新聞的經驗，是否有年齡層的差異存在呢？
## 製表
library(sjPlot)
## Learn more about sjPlot with 'browseVignettes("sjPlot")'.
sjt.xtab(tcs2019$agegroup, tcs2019$i12.1,encoding = "utf-8")
|
agegroup
|
I12-1.你過去是否有遇到過假新聞?
|
Total
|
|
有遇到過假新聞
|
從未遇過假新聞
|
不知道是否遇過假新聞
|
|
18至35歲
|
304
|
32
|
7
|
343
|
|
36至49歲
|
426
|
87
|
21
|
534
|
|
50至64歲
|
429
|
116
|
38
|
583
|
|
65歲以上
|
309
|
170
|
61
|
540
|
|
Total
|
1468
|
405
|
127
|
2000
|
χ2=126.835 · df=6 · Cramer’s V=0.178 · p=0.000
|
#sjt.xtab(tcs2019$i12.1,tcs2019$agegroup,encoding = "utf-8",show.cell.prc = T,
# show.row.prc = T,
# show.col.prc = T)
## 製圖
# 1. 變數處理
# (1) 將要繪製的變數變成類別變數或先進行排序
tcs2019$i12.1 <- as.factor(tcs2019$i12.1)
# tcs2019$agegroup <- factor(tcs2019$agegroup, ordered = TRUE,
# levels = c("65歲以上", "50至64歲","36至49歲","18至35歲"))
# 2. 安裝並載入 ggplot2
# 參考 R for Data Science書籍: https://r4ds.had.co.nz/index.html
# 參考ggplot2書籍: https://ggplot2-book.org/index.html
# https://blog.gtwang.org/r/ggplot2-tutorial-layer-by-layer-plotting/3/
# https://rpubs.com/chiahung_tsai/lecture05012018
# https://yijutseng.github.io/DataScienceRBook/vis.html
# https://bookdown.org/jefflinmd38/r4biost/dataviz.html
#install.packages("ggplot2")
#載入 ggplot2
library(ggplot2)