class: center, middle, inverse, title-slide # Intro to data processing ### Jakub Nowosad <br><a href="mailto:nowosad.jakub@gmail.com">nowosad.jakub@gmail.com</a> ### 2017-04-24 --- ## Dataset ```r library('gapminder') ``` or ```r gapminder <- readRDS('data/gapminder.rds') ``` http://www.gapminder.org/data/ http://github.com/jennybc/gapminder http://www.youtube.com/watch?v=jbkSRLYSojo --- # Vectors - indexing - empty index - `[ ]` - positive index - `[5]` - zero index - `[0]` - negative index - `[-1]` - logical index - `[x>5]` - name index - `["country"]` --- ## data.frame - indexing - A data frame is (at least) a two-dimensional object - There are many ways to subset a data frame. Some of them are: - `[]` operator - `$` operator - or `subset()` function ```r head(gapminder) ``` ``` ## country continent year lifeExp pop gdpPercap ## 1 Afghanistan Asia 1952 28.801 8425333 779.4453 ## 2 Afghanistan Asia 1957 30.332 9240934 820.8530 ## 3 Afghanistan Asia 1962 31.997 10267083 853.1007 ## 4 Afghanistan Asia 1967 34.020 11537966 836.1971 ## 5 Afghanistan Asia 1972 36.088 13079460 739.9811 ## 6 Afghanistan Asia 1977 38.438 14880372 786.1134 ``` --- ## data.frame - indexing - `$` operator can be used to retrieve a variable (column) by its name - The result is a vector ```r gapminder$country ``` ``` ## [1] Afghanistan Afghanistan ## [3] Afghanistan Afghanistan ## [5] Afghanistan Afghanistan ## [7] Afghanistan Afghanistan ## [9] Afghanistan Afghanistan ## [11] Afghanistan Afghanistan ## [13] Albania Albania ## [15] Albania Albania ## [17] Albania Albania ## [19] Albania Albania ## [21] Albania Albania ## [23] Albania Albania ## [25] Algeria Algeria ## [27] Algeria Algeria ## [29] Algeria Algeria ## [31] Algeria Algeria ## [33] Algeria Algeria ## [35] Algeria Algeria ## [37] Angola Angola ## [39] Angola Angola ## [41] Angola Angola ## [43] Angola Angola ## [45] Angola Angola ## [47] Angola Angola ## [49] Argentina Argentina ## [51] Argentina Argentina ## [53] Argentina Argentina ## [55] Argentina Argentina ## [57] Argentina Argentina ## [59] Argentina Argentina ## [61] Australia Australia ## [63] Australia Australia ## [65] Australia Australia ## [67] Australia Australia ## [69] Australia Australia ## [71] Australia Australia ## [73] Austria Austria ## [75] Austria Austria ## [77] Austria Austria ## [79] Austria Austria ## [81] Austria Austria ## [83] Austria Austria ## [85] Bahrain Bahrain ## [87] Bahrain Bahrain ## [89] Bahrain Bahrain ## [91] Bahrain Bahrain ## [93] Bahrain Bahrain ## [95] Bahrain Bahrain ## [97] Bangladesh Bangladesh ## [99] Bangladesh Bangladesh ## [101] Bangladesh Bangladesh ## [103] Bangladesh Bangladesh ## [105] Bangladesh Bangladesh ## [107] Bangladesh Bangladesh ## [109] Belgium Belgium ## [111] Belgium Belgium ## [113] Belgium Belgium ## [115] Belgium Belgium ## [117] Belgium Belgium ## [119] Belgium Belgium ## [121] Benin Benin ## [123] Benin Benin ## [125] Benin Benin ## [127] Benin Benin ## [129] Benin Benin ## [131] Benin Benin ## [133] Bolivia Bolivia ## [135] Bolivia Bolivia ## [137] Bolivia Bolivia ## [139] Bolivia Bolivia ## [141] Bolivia Bolivia ## [143] Bolivia Bolivia ## [145] Bosnia and Herzegovina Bosnia and Herzegovina ## [147] Bosnia and Herzegovina Bosnia and Herzegovina ## [149] Bosnia and Herzegovina Bosnia and Herzegovina ## [151] Bosnia and Herzegovina Bosnia and Herzegovina ## [153] Bosnia and Herzegovina Bosnia and Herzegovina ## [155] Bosnia and Herzegovina Bosnia and Herzegovina ## [157] Botswana Botswana ## [159] Botswana Botswana ## [161] Botswana Botswana ## [163] Botswana Botswana ## [165] Botswana Botswana ## [167] Botswana Botswana ## [169] Brazil Brazil ## [171] Brazil Brazil ## [173] Brazil Brazil ## [175] Brazil Brazil ## [177] Brazil Brazil ## [179] Brazil Brazil ## [181] Bulgaria Bulgaria ## [183] Bulgaria Bulgaria ## [185] Bulgaria Bulgaria ## [187] Bulgaria Bulgaria ## [189] Bulgaria Bulgaria ## [191] Bulgaria Bulgaria ## [193] Burkina Faso Burkina Faso ## [195] Burkina Faso Burkina Faso ## [197] Burkina Faso Burkina Faso ## [199] Burkina Faso Burkina Faso ## [201] Burkina Faso Burkina Faso ## [203] Burkina Faso Burkina Faso ## [205] Burundi Burundi ## [207] Burundi Burundi ## [209] Burundi Burundi ## [211] Burundi Burundi ## [213] Burundi Burundi ## [215] Burundi Burundi ## [217] Cambodia Cambodia ## [219] Cambodia Cambodia ## [221] Cambodia Cambodia ## [223] Cambodia Cambodia ## [225] Cambodia Cambodia ## [227] Cambodia Cambodia ## [229] Cameroon Cameroon ## [231] Cameroon Cameroon ## [233] Cameroon Cameroon ## [235] Cameroon Cameroon ## [237] Cameroon Cameroon ## [239] Cameroon Cameroon ## [241] Canada Canada ## [243] Canada Canada ## [245] Canada Canada ## [247] Canada Canada ## [249] Canada Canada ## [251] Canada Canada ## [253] Central African Republic Central African Republic ## [255] Central African Republic Central African Republic ## [257] Central African Republic Central African Republic ## [259] Central African Republic Central African Republic ## [261] Central African Republic Central African Republic ## [263] Central African Republic Central African Republic ## [265] Chad Chad ## [267] Chad Chad ## [269] Chad Chad ## [271] Chad Chad ## [273] Chad Chad ## [275] Chad Chad ## [277] Chile Chile ## [279] Chile Chile ## [281] Chile Chile ## [283] Chile Chile ## [285] Chile Chile ## [287] Chile Chile ## [289] China China ## [291] China China ## [293] China China ## [295] China China ## [297] China China ## [299] China China ## [301] Colombia Colombia ## [303] Colombia Colombia ## [305] Colombia Colombia ## [307] Colombia Colombia ## [309] Colombia Colombia ## [311] Colombia Colombia ## [313] Comoros Comoros ## [315] Comoros Comoros ## [317] Comoros Comoros ## [319] Comoros Comoros ## [321] Comoros Comoros ## [323] Comoros Comoros ## [325] Congo, Dem. Rep. Congo, Dem. Rep. ## [327] Congo, Dem. Rep. Congo, Dem. Rep. ## [329] Congo, Dem. Rep. Congo, Dem. Rep. ## [331] Congo, Dem. Rep. Congo, Dem. Rep. ## [333] Congo, Dem. Rep. Congo, Dem. Rep. ## [335] Congo, Dem. Rep. Congo, Dem. Rep. ## [337] Congo, Rep. Congo, Rep. ## [339] Congo, Rep. Congo, Rep. ## [341] Congo, Rep. Congo, Rep. ## [343] Congo, Rep. Congo, Rep. ## [345] Congo, Rep. Congo, Rep. ## [347] Congo, Rep. Congo, Rep. ## [349] Costa Rica Costa Rica ## [351] Costa Rica Costa Rica ## [353] Costa Rica Costa Rica ## [355] Costa Rica Costa Rica ## [357] Costa Rica Costa Rica ## [359] Costa Rica Costa Rica ## [361] Cote d'Ivoire Cote d'Ivoire ## [363] Cote d'Ivoire Cote d'Ivoire ## [365] Cote d'Ivoire Cote d'Ivoire ## [367] Cote d'Ivoire Cote d'Ivoire ## [369] Cote d'Ivoire Cote d'Ivoire ## [371] Cote d'Ivoire Cote d'Ivoire ## [373] Croatia Croatia ## [375] Croatia Croatia ## [377] Croatia Croatia ## [379] Croatia Croatia ## [381] Croatia Croatia ## [383] Croatia Croatia ## [385] Cuba Cuba ## [387] Cuba Cuba ## [389] Cuba Cuba ## [391] Cuba Cuba ## [393] Cuba Cuba ## [395] Cuba Cuba ## [397] Czech Republic Czech Republic ## [399] Czech Republic Czech Republic ## [401] Czech Republic Czech Republic ## [403] Czech Republic Czech Republic ## [405] Czech Republic Czech Republic ## [407] Czech Republic Czech Republic ## [409] Denmark Denmark ## [411] Denmark Denmark ## [413] Denmark Denmark ## [415] Denmark Denmark ## [417] Denmark Denmark ## [419] Denmark Denmark ## [421] Djibouti Djibouti ## [423] Djibouti Djibouti ## [425] Djibouti Djibouti ## [427] Djibouti Djibouti ## [429] Djibouti Djibouti ## [431] Djibouti Djibouti ## [433] Dominican Republic Dominican Republic ## [435] Dominican Republic Dominican Republic ## [437] Dominican Republic Dominican Republic ## [439] Dominican Republic Dominican Republic ## [441] Dominican Republic Dominican Republic ## [443] Dominican Republic Dominican Republic ## [445] Ecuador Ecuador ## [447] Ecuador Ecuador ## [449] Ecuador Ecuador ## [451] Ecuador Ecuador ## [453] Ecuador Ecuador ## [455] Ecuador Ecuador ## [457] Egypt Egypt ## [459] Egypt Egypt ## [461] Egypt Egypt ## [463] Egypt Egypt ## [465] Egypt Egypt ## [467] Egypt Egypt ## [469] El Salvador El Salvador ## [471] El Salvador El Salvador ## [473] El Salvador El Salvador ## [475] El Salvador El Salvador ## [477] El Salvador El Salvador ## [479] El Salvador El Salvador ## [481] Equatorial Guinea Equatorial Guinea ## [483] Equatorial Guinea Equatorial Guinea ## [485] Equatorial Guinea Equatorial Guinea ## [487] Equatorial Guinea Equatorial Guinea ## [489] Equatorial Guinea Equatorial Guinea ## [491] Equatorial Guinea Equatorial Guinea ## [493] Eritrea Eritrea ## [495] Eritrea Eritrea ## [497] Eritrea Eritrea ## [499] Eritrea Eritrea ## [501] Eritrea Eritrea ## [503] Eritrea Eritrea ## [505] Ethiopia Ethiopia ## [507] Ethiopia Ethiopia ## [509] Ethiopia Ethiopia ## [511] Ethiopia Ethiopia ## [513] Ethiopia Ethiopia ## [515] Ethiopia Ethiopia ## [517] Finland Finland ## [519] Finland Finland ## [521] Finland Finland ## [523] Finland Finland ## [525] Finland Finland ## [527] Finland Finland ## [529] France France ## [531] France France ## [533] France France ## [535] France France ## [537] France France ## [539] France France ## [541] Gabon Gabon ## [543] Gabon Gabon ## [545] Gabon Gabon ## [547] Gabon Gabon ## [549] Gabon Gabon ## [551] Gabon Gabon ## [553] Gambia Gambia ## [555] Gambia Gambia ## [557] Gambia Gambia ## [559] Gambia Gambia ## [561] Gambia Gambia ## [563] Gambia Gambia ## [565] Germany Germany ## [567] Germany Germany ## [569] Germany Germany ## [571] Germany Germany ## [573] Germany Germany ## [575] Germany Germany ## [577] Ghana Ghana ## [579] Ghana Ghana ## [581] Ghana Ghana ## [583] Ghana Ghana ## [585] Ghana Ghana ## [587] Ghana Ghana ## [589] Greece Greece ## [591] Greece Greece ## [593] Greece Greece ## [595] Greece Greece ## [597] Greece Greece ## [599] Greece Greece ## [601] Guatemala Guatemala ## [603] Guatemala Guatemala ## [605] Guatemala Guatemala ## [607] Guatemala Guatemala ## [609] Guatemala Guatemala ## [611] Guatemala Guatemala ## [613] Guinea Guinea ## [615] Guinea Guinea ## [617] Guinea Guinea ## [619] Guinea Guinea ## [621] Guinea Guinea ## [623] Guinea Guinea ## [625] Guinea-Bissau Guinea-Bissau ## [627] Guinea-Bissau Guinea-Bissau ## [629] Guinea-Bissau Guinea-Bissau ## [631] Guinea-Bissau Guinea-Bissau ## [633] Guinea-Bissau Guinea-Bissau ## [635] Guinea-Bissau Guinea-Bissau ## [637] Haiti Haiti ## [639] Haiti Haiti ## [641] Haiti Haiti ## [643] Haiti Haiti ## [645] Haiti Haiti ## [647] Haiti Haiti ## [649] Honduras Honduras ## [651] Honduras Honduras ## [653] Honduras Honduras ## [655] Honduras Honduras ## [657] Honduras Honduras ## [659] Honduras Honduras ## [661] Hong Kong, China Hong Kong, China ## [663] Hong Kong, China Hong Kong, China ## [665] Hong Kong, China Hong Kong, China ## [667] Hong Kong, China Hong Kong, China ## [669] Hong Kong, China Hong Kong, China ## [671] Hong Kong, China Hong Kong, China ## [673] Hungary Hungary ## [675] Hungary Hungary ## [677] Hungary Hungary ## [679] Hungary Hungary ## [681] Hungary Hungary ## [683] Hungary Hungary ## [685] Iceland Iceland ## [687] Iceland Iceland ## [689] Iceland Iceland ## [691] Iceland Iceland ## [693] Iceland Iceland ## [695] Iceland Iceland ## [697] India India ## [699] India India ## [701] India India ## [703] India India ## [705] India India ## [707] India India ## [709] Indonesia Indonesia ## [711] Indonesia Indonesia ## [713] Indonesia Indonesia ## [715] Indonesia Indonesia ## [717] Indonesia Indonesia ## [719] Indonesia Indonesia ## [721] Iran Iran ## [723] Iran Iran ## [725] Iran Iran ## [727] Iran Iran ## [729] Iran Iran ## [731] Iran Iran ## [733] Iraq Iraq ## [735] Iraq Iraq ## [737] Iraq Iraq ## [739] Iraq Iraq ## [741] Iraq Iraq ## [743] Iraq Iraq ## [745] Ireland Ireland ## [747] Ireland Ireland ## [749] Ireland Ireland ## [751] Ireland Ireland ## [753] Ireland Ireland ## [755] Ireland Ireland ## [757] Israel Israel ## [759] Israel Israel ## [761] Israel Israel ## [763] Israel Israel ## [765] Israel Israel ## [767] Israel Israel ## [769] Italy Italy ## [771] Italy Italy ## [773] Italy Italy ## [775] Italy Italy ## [777] Italy Italy ## [779] Italy Italy ## [781] Jamaica Jamaica ## [783] Jamaica Jamaica ## [785] Jamaica Jamaica ## [787] Jamaica Jamaica ## [789] Jamaica Jamaica ## [791] Jamaica Jamaica ## [793] Japan Japan ## [795] Japan Japan ## [797] Japan Japan ## [799] Japan Japan ## [801] Japan Japan ## [803] Japan Japan ## [805] Jordan Jordan ## [807] Jordan Jordan ## [809] Jordan Jordan ## [811] Jordan Jordan ## [813] Jordan Jordan ## [815] Jordan Jordan ## [817] Kenya Kenya ## [819] Kenya Kenya ## [821] Kenya Kenya ## [823] Kenya Kenya ## [825] Kenya Kenya ## [827] Kenya Kenya ## [829] Korea, Dem. Rep. Korea, Dem. Rep. ## [831] Korea, Dem. Rep. Korea, Dem. Rep. ## [833] Korea, Dem. Rep. Korea, Dem. Rep. ## [835] Korea, Dem. Rep. Korea, Dem. Rep. ## [837] Korea, Dem. Rep. Korea, Dem. Rep. ## [839] Korea, Dem. Rep. Korea, Dem. Rep. ## [841] Korea, Rep. Korea, Rep. ## [843] Korea, Rep. Korea, Rep. ## [845] Korea, Rep. Korea, Rep. ## [847] Korea, Rep. Korea, Rep. ## [849] Korea, Rep. Korea, Rep. ## [851] Korea, Rep. Korea, Rep. ## [853] Kuwait Kuwait ## [855] Kuwait Kuwait ## [857] Kuwait Kuwait ## [859] Kuwait Kuwait ## [861] Kuwait Kuwait ## [863] Kuwait Kuwait ## [865] Lebanon Lebanon ## [867] Lebanon Lebanon ## [869] Lebanon Lebanon ## [871] Lebanon Lebanon ## [873] Lebanon Lebanon ## [875] Lebanon Lebanon ## [877] Lesotho Lesotho ## [879] Lesotho Lesotho ## [881] Lesotho Lesotho ## [883] Lesotho Lesotho ## [885] Lesotho Lesotho ## [887] Lesotho Lesotho ## [889] Liberia Liberia ## [891] Liberia Liberia ## [893] Liberia Liberia ## [895] Liberia Liberia ## [897] Liberia Liberia ## [899] Liberia Liberia ## [901] Libya Libya ## [903] Libya Libya ## [905] Libya Libya ## [907] Libya Libya ## [909] Libya Libya ## [911] Libya Libya ## [913] Madagascar Madagascar ## [915] Madagascar Madagascar ## [917] Madagascar Madagascar ## [919] Madagascar Madagascar ## [921] Madagascar Madagascar ## [923] Madagascar Madagascar ## [925] Malawi Malawi ## [927] Malawi Malawi ## [929] Malawi Malawi ## [931] Malawi Malawi ## [933] Malawi Malawi ## [935] Malawi Malawi ## [937] Malaysia Malaysia ## [939] Malaysia Malaysia ## [941] Malaysia Malaysia ## [943] Malaysia Malaysia ## [945] Malaysia Malaysia ## [947] Malaysia Malaysia ## [949] Mali Mali ## [951] Mali Mali ## [953] Mali Mali ## [955] Mali Mali ## [957] Mali Mali ## [959] Mali Mali ## [961] Mauritania Mauritania ## [963] Mauritania Mauritania ## [965] Mauritania Mauritania ## [967] Mauritania Mauritania ## [969] Mauritania Mauritania ## [971] Mauritania Mauritania ## [973] Mauritius Mauritius ## [975] Mauritius Mauritius ## [977] Mauritius Mauritius ## [979] Mauritius Mauritius ## [981] Mauritius Mauritius ## [983] Mauritius Mauritius ## [985] Mexico Mexico ## [987] Mexico Mexico ## [989] Mexico Mexico ## [991] Mexico Mexico ## [993] Mexico Mexico ## [995] Mexico Mexico ## [997] Mongolia Mongolia ## [999] Mongolia Mongolia ## [1001] Mongolia Mongolia ## [1003] Mongolia Mongolia ## [1005] Mongolia Mongolia ## [1007] Mongolia Mongolia ## [1009] Montenegro Montenegro ## [1011] Montenegro Montenegro ## [1013] Montenegro Montenegro ## [1015] Montenegro Montenegro ## [1017] Montenegro Montenegro ## [1019] Montenegro Montenegro ## [1021] Morocco Morocco ## [1023] Morocco Morocco ## [1025] Morocco Morocco ## [1027] Morocco Morocco ## [1029] Morocco Morocco ## [1031] Morocco Morocco ## [1033] Mozambique Mozambique ## [1035] Mozambique Mozambique ## [1037] Mozambique Mozambique ## [1039] Mozambique Mozambique ## [1041] Mozambique Mozambique ## [1043] Mozambique Mozambique ## [1045] Myanmar Myanmar ## [1047] Myanmar Myanmar ## [1049] Myanmar Myanmar ## [1051] Myanmar Myanmar ## [1053] Myanmar Myanmar ## [1055] Myanmar Myanmar ## [1057] Namibia Namibia ## [1059] Namibia Namibia ## [1061] Namibia Namibia ## [1063] Namibia Namibia ## [1065] Namibia Namibia ## [1067] Namibia Namibia ## [1069] Nepal Nepal ## [1071] Nepal Nepal ## [1073] Nepal Nepal ## [1075] Nepal Nepal ## [1077] Nepal Nepal ## [1079] Nepal Nepal ## [1081] Netherlands Netherlands ## [1083] Netherlands Netherlands ## [1085] Netherlands Netherlands ## [1087] Netherlands Netherlands ## [1089] Netherlands Netherlands ## [1091] Netherlands Netherlands ## [1093] New Zealand New Zealand ## [1095] New Zealand New Zealand ## [1097] New Zealand New Zealand ## [1099] New Zealand New Zealand ## [1101] New Zealand New Zealand ## [1103] New Zealand New Zealand ## [1105] Nicaragua Nicaragua ## [1107] Nicaragua Nicaragua ## [1109] Nicaragua Nicaragua ## [1111] Nicaragua Nicaragua ## [1113] Nicaragua Nicaragua ## [1115] Nicaragua Nicaragua ## [1117] Niger Niger ## [1119] Niger Niger ## [1121] Niger Niger ## [1123] Niger Niger ## [1125] Niger Niger ## [1127] Niger Niger ## [1129] Nigeria Nigeria ## [1131] Nigeria Nigeria ## [1133] Nigeria Nigeria ## [1135] Nigeria Nigeria ## [1137] Nigeria Nigeria ## [1139] Nigeria Nigeria ## [1141] Norway Norway ## [1143] Norway Norway ## [1145] Norway Norway ## [1147] Norway Norway ## [1149] Norway Norway ## [1151] Norway Norway ## [1153] Oman Oman ## [1155] Oman Oman ## [1157] Oman Oman ## [1159] Oman Oman ## [1161] Oman Oman ## [1163] Oman Oman ## [1165] Pakistan Pakistan ## [1167] Pakistan Pakistan ## [1169] Pakistan Pakistan ## [1171] Pakistan Pakistan ## [1173] Pakistan Pakistan ## [1175] Pakistan Pakistan ## [1177] Panama Panama ## [1179] Panama Panama ## [1181] Panama Panama ## [1183] Panama Panama ## [1185] Panama Panama ## [1187] Panama Panama ## [1189] Paraguay Paraguay ## [1191] Paraguay Paraguay ## [1193] Paraguay Paraguay ## [1195] Paraguay Paraguay ## [1197] Paraguay Paraguay ## [1199] Paraguay Paraguay ## [1201] Peru Peru ## [1203] Peru Peru ## [1205] Peru Peru ## [1207] Peru Peru ## [1209] Peru Peru ## [1211] Peru Peru ## [1213] Philippines Philippines ## [1215] Philippines Philippines ## [1217] Philippines Philippines ## [1219] Philippines Philippines ## [1221] Philippines Philippines ## [1223] Philippines Philippines ## [1225] Poland Poland ## [1227] Poland Poland ## [1229] Poland Poland ## [1231] Poland Poland ## [1233] Poland Poland ## [1235] Poland Poland ## [1237] Portugal Portugal ## [1239] Portugal Portugal ## [1241] Portugal Portugal ## [1243] Portugal Portugal ## [1245] Portugal Portugal ## [1247] Portugal Portugal ## [1249] Puerto Rico Puerto Rico ## [1251] Puerto Rico Puerto Rico ## [1253] Puerto Rico Puerto Rico ## [1255] Puerto Rico Puerto Rico ## [1257] Puerto Rico Puerto Rico ## [1259] Puerto Rico Puerto Rico ## [1261] Reunion Reunion ## [1263] Reunion Reunion ## [1265] Reunion Reunion ## [1267] Reunion Reunion ## [1269] Reunion Reunion ## [1271] Reunion Reunion ## [1273] Romania Romania ## [1275] Romania Romania ## [1277] Romania Romania ## [1279] Romania Romania ## [1281] Romania Romania ## [1283] Romania Romania ## [1285] Rwanda Rwanda ## [1287] Rwanda Rwanda ## [1289] Rwanda Rwanda ## [1291] Rwanda Rwanda ## [1293] Rwanda Rwanda ## [1295] Rwanda Rwanda ## [1297] Sao Tome and Principe Sao Tome and Principe ## [1299] Sao Tome and Principe Sao Tome and Principe ## [1301] Sao Tome and Principe Sao Tome and Principe ## [1303] Sao Tome and Principe Sao Tome and Principe ## [1305] Sao Tome and Principe Sao Tome and Principe ## [1307] Sao Tome and Principe Sao Tome and Principe ## [1309] Saudi Arabia Saudi Arabia ## [1311] Saudi Arabia Saudi Arabia ## [1313] Saudi Arabia Saudi Arabia ## [1315] Saudi Arabia Saudi Arabia ## [1317] Saudi Arabia Saudi Arabia ## [1319] Saudi Arabia Saudi Arabia ## [1321] Senegal Senegal ## [1323] Senegal Senegal ## [1325] Senegal Senegal ## [1327] Senegal Senegal ## [1329] Senegal Senegal ## [1331] Senegal Senegal ## [1333] Serbia Serbia ## [1335] Serbia Serbia ## [1337] Serbia Serbia ## [1339] Serbia Serbia ## [1341] Serbia Serbia ## [1343] Serbia Serbia ## [1345] Sierra Leone Sierra Leone ## [1347] Sierra Leone Sierra Leone ## [1349] Sierra Leone Sierra Leone ## [1351] Sierra Leone Sierra Leone ## [1353] Sierra Leone Sierra Leone ## [1355] Sierra Leone Sierra Leone ## [1357] Singapore Singapore ## [1359] Singapore Singapore ## [1361] Singapore Singapore ## [1363] Singapore Singapore ## [1365] Singapore Singapore ## [1367] Singapore Singapore ## [1369] Slovak Republic Slovak Republic ## [1371] Slovak Republic Slovak Republic ## [1373] Slovak Republic Slovak Republic ## [1375] Slovak Republic Slovak Republic ## [1377] Slovak Republic Slovak Republic ## [1379] Slovak Republic Slovak Republic ## [1381] Slovenia Slovenia ## [1383] Slovenia Slovenia ## [1385] Slovenia Slovenia ## [1387] Slovenia Slovenia ## [1389] Slovenia Slovenia ## [1391] Slovenia Slovenia ## [1393] Somalia Somalia ## [1395] Somalia Somalia ## [1397] Somalia Somalia ## [1399] Somalia Somalia ## [1401] Somalia Somalia ## [1403] Somalia Somalia ## [1405] South Africa South Africa ## [1407] South Africa South Africa ## [1409] South Africa South Africa ## [1411] South Africa South Africa ## [1413] South Africa South Africa ## [1415] South Africa South Africa ## [1417] Spain Spain ## [1419] Spain Spain ## [1421] Spain Spain ## [1423] Spain Spain ## [1425] Spain Spain ## [1427] Spain Spain ## [1429] Sri Lanka Sri Lanka ## [1431] Sri Lanka Sri Lanka ## [1433] Sri Lanka Sri Lanka ## [1435] Sri Lanka Sri Lanka ## [1437] Sri Lanka Sri Lanka ## [1439] Sri Lanka Sri Lanka ## [1441] Sudan Sudan ## [1443] Sudan Sudan ## [1445] Sudan Sudan ## [1447] Sudan Sudan ## [1449] Sudan Sudan ## [1451] Sudan Sudan ## [1453] Swaziland Swaziland ## [1455] Swaziland Swaziland ## [1457] Swaziland Swaziland ## [1459] Swaziland Swaziland ## [1461] Swaziland Swaziland ## [1463] Swaziland Swaziland ## [1465] Sweden Sweden ## [1467] Sweden Sweden ## [1469] Sweden Sweden ## [1471] Sweden Sweden ## [1473] Sweden Sweden ## [1475] Sweden Sweden ## [1477] Switzerland Switzerland ## [1479] Switzerland Switzerland ## [1481] Switzerland Switzerland ## [1483] Switzerland Switzerland ## [1485] Switzerland Switzerland ## [1487] Switzerland Switzerland ## [1489] Syria Syria ## [1491] Syria Syria ## [1493] Syria Syria ## [1495] Syria Syria ## [1497] Syria Syria ## [1499] Syria Syria ## [1501] Taiwan Taiwan ## [1503] Taiwan Taiwan ## [1505] Taiwan Taiwan ## [1507] Taiwan Taiwan ## [1509] Taiwan Taiwan ## [1511] Taiwan Taiwan ## [1513] Tanzania Tanzania ## [1515] Tanzania Tanzania ## [1517] Tanzania Tanzania ## [1519] Tanzania Tanzania ## [1521] Tanzania Tanzania ## [1523] Tanzania Tanzania ## [1525] Thailand Thailand ## [1527] Thailand Thailand ## [1529] Thailand Thailand ## [1531] Thailand Thailand ## [1533] Thailand Thailand ## [1535] Thailand Thailand ## [1537] Togo Togo ## [1539] Togo Togo ## [1541] Togo Togo ## [1543] Togo Togo ## [1545] Togo Togo ## [1547] Togo Togo ## [1549] Trinidad and Tobago Trinidad and Tobago ## [1551] Trinidad and Tobago Trinidad and Tobago ## [1553] Trinidad and Tobago Trinidad and Tobago ## [1555] Trinidad and Tobago Trinidad and Tobago ## [1557] Trinidad and Tobago Trinidad and Tobago ## [1559] Trinidad and Tobago Trinidad and Tobago ## [1561] Tunisia Tunisia ## [1563] Tunisia Tunisia ## [1565] Tunisia Tunisia ## [1567] Tunisia Tunisia ## [1569] Tunisia Tunisia ## [1571] Tunisia Tunisia ## [1573] Turkey Turkey ## [1575] Turkey Turkey ## [1577] Turkey Turkey ## [1579] Turkey Turkey ## [1581] Turkey Turkey ## [1583] Turkey Turkey ## [1585] Uganda Uganda ## [1587] Uganda Uganda ## [1589] Uganda Uganda ## [1591] Uganda Uganda ## [1593] Uganda Uganda ## [1595] Uganda Uganda ## [1597] United Kingdom United Kingdom ## [1599] United Kingdom United Kingdom ## [1601] United Kingdom United Kingdom ## [1603] United Kingdom United Kingdom ## [1605] United Kingdom United Kingdom ## [1607] United Kingdom United Kingdom ## [1609] United States United States ## [1611] United States United States ## [1613] United States United States ## [1615] United States United States ## [1617] United States United States ## [1619] United States United States ## [1621] Uruguay Uruguay ## [1623] Uruguay Uruguay ## [1625] Uruguay Uruguay ## [1627] Uruguay Uruguay ## [1629] Uruguay Uruguay ## [1631] Uruguay Uruguay ## [1633] Venezuela Venezuela ## [1635] Venezuela Venezuela ## [1637] Venezuela Venezuela ## [1639] Venezuela Venezuela ## [1641] Venezuela Venezuela ## [1643] Venezuela Venezuela ## [1645] Vietnam Vietnam ## [1647] Vietnam Vietnam ## [1649] Vietnam Vietnam ## [1651] Vietnam Vietnam ## [1653] Vietnam Vietnam ## [1655] Vietnam Vietnam ## [1657] West Bank and Gaza West Bank and Gaza ## [1659] West Bank and Gaza West Bank and Gaza ## [1661] West Bank and Gaza West Bank and Gaza ## [1663] West Bank and Gaza West Bank and Gaza ## [1665] West Bank and Gaza West Bank and Gaza ## [1667] West Bank and Gaza West Bank and Gaza ## [1669] Yemen, Rep. Yemen, Rep. ## [1671] Yemen, Rep. Yemen, Rep. ## [1673] Yemen, Rep. Yemen, Rep. ## [1675] Yemen, Rep. Yemen, Rep. ## [1677] Yemen, Rep. Yemen, Rep. ## [1679] Yemen, Rep. Yemen, Rep. ## [1681] Zambia Zambia ## [1683] Zambia Zambia ## [1685] Zambia Zambia ## [1687] Zambia Zambia ## [1689] Zambia Zambia ## [1691] Zambia Zambia ## [1693] Zimbabwe Zimbabwe ## [1695] Zimbabwe Zimbabwe ## [1697] Zimbabwe Zimbabwe ## [1699] Zimbabwe Zimbabwe ## [1701] Zimbabwe Zimbabwe ## [1703] Zimbabwe Zimbabwe ## 142 Levels: Afghanistan Albania Algeria Angola Argentina ... Zimbabwe ``` --- ## data.frame - indexing - `$` operator can be used to retrieve a variable (column) by its name - The result is a vector ```r countries <- gapminder$country countries ``` ``` ## [1] Afghanistan Afghanistan ## [3] Afghanistan Afghanistan ## [5] Afghanistan Afghanistan ## [7] Afghanistan Afghanistan ## [9] Afghanistan Afghanistan ## [11] Afghanistan Afghanistan ## [13] Albania Albania ## [15] Albania Albania ## [17] Albania Albania ## [19] Albania Albania ## [21] Albania Albania ## [23] Albania Albania ## [25] Algeria Algeria ## [27] Algeria Algeria ## [29] Algeria Algeria ## [31] Algeria Algeria ## [33] Algeria Algeria ## [35] Algeria Algeria ## [37] Angola Angola ## [39] Angola Angola ## [41] Angola Angola ## [43] Angola Angola ## [45] Angola Angola ## [47] Angola Angola ## [49] Argentina Argentina ## [51] Argentina Argentina ## [53] Argentina Argentina ## [55] Argentina Argentina ## [57] Argentina Argentina ## [59] Argentina Argentina ## [61] Australia Australia ## [63] Australia Australia ## [65] Australia Australia ## [67] Australia Australia ## [69] Australia Australia ## [71] Australia Australia ## [73] Austria Austria ## [75] Austria Austria ## [77] Austria Austria ## [79] Austria Austria ## [81] Austria Austria ## [83] Austria Austria ## [85] Bahrain Bahrain ## [87] Bahrain Bahrain ## [89] Bahrain Bahrain ## [91] Bahrain Bahrain ## [93] Bahrain Bahrain ## [95] Bahrain Bahrain ## [97] Bangladesh Bangladesh ## [99] Bangladesh Bangladesh ## [101] Bangladesh Bangladesh ## [103] Bangladesh Bangladesh ## [105] Bangladesh Bangladesh ## [107] Bangladesh Bangladesh ## [109] Belgium Belgium ## [111] Belgium Belgium ## [113] Belgium Belgium ## [115] Belgium Belgium ## [117] Belgium Belgium ## [119] Belgium Belgium ## [121] Benin Benin ## [123] Benin Benin ## [125] Benin Benin ## [127] Benin Benin ## [129] Benin Benin ## [131] Benin Benin ## [133] Bolivia Bolivia ## [135] Bolivia Bolivia ## [137] Bolivia Bolivia ## [139] Bolivia Bolivia ## [141] Bolivia Bolivia ## [143] Bolivia Bolivia ## [145] Bosnia and Herzegovina Bosnia and Herzegovina ## [147] Bosnia and Herzegovina Bosnia and Herzegovina ## [149] Bosnia and Herzegovina Bosnia and Herzegovina ## [151] Bosnia and Herzegovina Bosnia and Herzegovina ## [153] Bosnia and Herzegovina Bosnia and Herzegovina ## [155] Bosnia and Herzegovina Bosnia and Herzegovina ## [157] Botswana Botswana ## [159] Botswana Botswana ## [161] Botswana Botswana ## [163] Botswana Botswana ## [165] Botswana Botswana ## [167] Botswana Botswana ## [169] Brazil Brazil ## [171] Brazil Brazil ## [173] Brazil Brazil ## [175] Brazil Brazil ## [177] Brazil Brazil ## [179] Brazil Brazil ## [181] Bulgaria Bulgaria ## [183] Bulgaria Bulgaria ## [185] Bulgaria Bulgaria ## [187] Bulgaria Bulgaria ## [189] Bulgaria Bulgaria ## [191] Bulgaria Bulgaria ## [193] Burkina Faso Burkina Faso ## [195] Burkina Faso Burkina Faso ## [197] Burkina Faso Burkina Faso ## [199] Burkina Faso Burkina Faso ## [201] Burkina Faso Burkina Faso ## [203] Burkina Faso Burkina Faso ## [205] Burundi Burundi ## [207] Burundi Burundi ## [209] Burundi Burundi ## [211] Burundi Burundi ## [213] Burundi Burundi ## [215] Burundi Burundi ## [217] Cambodia Cambodia ## [219] Cambodia Cambodia ## [221] Cambodia Cambodia ## [223] Cambodia Cambodia ## [225] Cambodia Cambodia ## [227] Cambodia Cambodia ## [229] Cameroon Cameroon ## [231] Cameroon Cameroon ## [233] Cameroon Cameroon ## [235] Cameroon Cameroon ## [237] Cameroon Cameroon ## [239] Cameroon Cameroon ## [241] Canada Canada ## [243] Canada Canada ## [245] Canada Canada ## [247] Canada Canada ## [249] Canada Canada ## [251] Canada Canada ## [253] Central African Republic Central African Republic ## [255] Central African Republic Central African Republic ## [257] Central African Republic Central African Republic ## [259] Central African Republic Central African Republic ## [261] Central African Republic Central African Republic ## [263] Central African Republic Central African Republic ## [265] Chad Chad ## [267] Chad Chad ## [269] Chad Chad ## [271] Chad Chad ## [273] Chad Chad ## [275] Chad Chad ## [277] Chile Chile ## [279] Chile Chile ## [281] Chile Chile ## [283] Chile Chile ## [285] Chile Chile ## [287] Chile Chile ## [289] China China ## [291] China China ## [293] China China ## [295] China China ## [297] China China ## [299] China China ## [301] Colombia Colombia ## [303] Colombia Colombia ## [305] Colombia Colombia ## [307] Colombia Colombia ## [309] Colombia Colombia ## [311] Colombia Colombia ## [313] Comoros Comoros ## [315] Comoros Comoros ## [317] Comoros Comoros ## [319] Comoros Comoros ## [321] Comoros Comoros ## [323] Comoros Comoros ## [325] Congo, Dem. Rep. Congo, Dem. Rep. ## [327] Congo, Dem. Rep. Congo, Dem. Rep. ## [329] Congo, Dem. Rep. Congo, Dem. Rep. ## [331] Congo, Dem. Rep. Congo, Dem. Rep. ## [333] Congo, Dem. Rep. Congo, Dem. Rep. ## [335] Congo, Dem. Rep. Congo, Dem. Rep. ## [337] Congo, Rep. Congo, Rep. ## [339] Congo, Rep. Congo, Rep. ## [341] Congo, Rep. Congo, Rep. ## [343] Congo, Rep. Congo, Rep. ## [345] Congo, Rep. Congo, Rep. ## [347] Congo, Rep. Congo, Rep. ## [349] Costa Rica Costa Rica ## [351] Costa Rica Costa Rica ## [353] Costa Rica Costa Rica ## [355] Costa Rica Costa Rica ## [357] Costa Rica Costa Rica ## [359] Costa Rica Costa Rica ## [361] Cote d'Ivoire Cote d'Ivoire ## [363] Cote d'Ivoire Cote d'Ivoire ## [365] Cote d'Ivoire Cote d'Ivoire ## [367] Cote d'Ivoire Cote d'Ivoire ## [369] Cote d'Ivoire Cote d'Ivoire ## [371] Cote d'Ivoire Cote d'Ivoire ## [373] Croatia Croatia ## [375] Croatia Croatia ## [377] Croatia Croatia ## [379] Croatia Croatia ## [381] Croatia Croatia ## [383] Croatia Croatia ## [385] Cuba Cuba ## [387] Cuba Cuba ## [389] Cuba Cuba ## [391] Cuba Cuba ## [393] Cuba Cuba ## [395] Cuba Cuba ## [397] Czech Republic Czech Republic ## [399] Czech Republic Czech Republic ## [401] Czech Republic Czech Republic ## [403] Czech Republic Czech Republic ## [405] Czech Republic Czech Republic ## [407] Czech Republic Czech Republic ## [409] Denmark Denmark ## [411] Denmark Denmark ## [413] Denmark Denmark ## [415] Denmark Denmark ## [417] Denmark Denmark ## [419] Denmark Denmark ## [421] Djibouti Djibouti ## [423] Djibouti Djibouti ## [425] Djibouti Djibouti ## [427] Djibouti Djibouti ## [429] Djibouti Djibouti ## [431] Djibouti Djibouti ## [433] Dominican Republic Dominican Republic ## [435] Dominican Republic Dominican Republic ## [437] Dominican Republic Dominican Republic ## [439] Dominican Republic Dominican Republic ## [441] Dominican Republic Dominican Republic ## [443] Dominican Republic Dominican Republic ## [445] Ecuador Ecuador ## [447] Ecuador Ecuador ## [449] Ecuador Ecuador ## [451] Ecuador Ecuador ## [453] Ecuador Ecuador ## [455] Ecuador Ecuador ## [457] Egypt Egypt ## [459] Egypt Egypt ## [461] Egypt Egypt ## [463] Egypt Egypt ## [465] Egypt Egypt ## [467] Egypt Egypt ## [469] El Salvador El Salvador ## [471] El Salvador El Salvador ## [473] El Salvador El Salvador ## [475] El Salvador El Salvador ## [477] El Salvador El Salvador ## [479] El Salvador El Salvador ## [481] Equatorial Guinea Equatorial Guinea ## [483] Equatorial Guinea Equatorial Guinea ## [485] Equatorial Guinea Equatorial Guinea ## [487] Equatorial Guinea Equatorial Guinea ## [489] Equatorial Guinea Equatorial Guinea ## [491] Equatorial Guinea Equatorial Guinea ## [493] Eritrea Eritrea ## [495] Eritrea Eritrea ## [497] Eritrea Eritrea ## [499] Eritrea Eritrea ## [501] Eritrea Eritrea ## [503] Eritrea Eritrea ## [505] Ethiopia Ethiopia ## [507] Ethiopia Ethiopia ## [509] Ethiopia Ethiopia ## [511] Ethiopia Ethiopia ## [513] Ethiopia Ethiopia ## [515] Ethiopia Ethiopia ## [517] Finland Finland ## [519] Finland Finland ## [521] Finland Finland ## [523] Finland Finland ## [525] Finland Finland ## [527] Finland Finland ## [529] France France ## [531] France France ## [533] France France ## [535] France France ## [537] France France ## [539] France France ## [541] Gabon Gabon ## [543] Gabon Gabon ## [545] Gabon Gabon ## [547] Gabon Gabon ## [549] Gabon Gabon ## [551] Gabon Gabon ## [553] Gambia Gambia ## [555] Gambia Gambia ## [557] Gambia Gambia ## [559] Gambia Gambia ## [561] Gambia Gambia ## [563] Gambia Gambia ## [565] Germany Germany ## [567] Germany Germany ## [569] Germany Germany ## [571] Germany Germany ## [573] Germany Germany ## [575] Germany Germany ## [577] Ghana Ghana ## [579] Ghana Ghana ## [581] Ghana Ghana ## [583] Ghana Ghana ## [585] Ghana Ghana ## [587] Ghana Ghana ## [589] Greece Greece ## [591] Greece Greece ## [593] Greece Greece ## [595] Greece Greece ## [597] Greece Greece ## [599] Greece Greece ## [601] Guatemala Guatemala ## [603] Guatemala Guatemala ## [605] Guatemala Guatemala ## [607] Guatemala Guatemala ## [609] Guatemala Guatemala ## [611] Guatemala Guatemala ## [613] Guinea Guinea ## [615] Guinea Guinea ## [617] Guinea Guinea ## [619] Guinea Guinea ## [621] Guinea Guinea ## [623] Guinea Guinea ## [625] Guinea-Bissau Guinea-Bissau ## [627] Guinea-Bissau Guinea-Bissau ## [629] Guinea-Bissau Guinea-Bissau ## [631] Guinea-Bissau Guinea-Bissau ## [633] Guinea-Bissau Guinea-Bissau ## [635] Guinea-Bissau Guinea-Bissau ## [637] Haiti Haiti ## [639] Haiti Haiti ## [641] Haiti Haiti ## [643] Haiti Haiti ## [645] Haiti Haiti ## [647] Haiti Haiti ## [649] Honduras Honduras ## [651] Honduras Honduras ## [653] Honduras Honduras ## [655] Honduras Honduras ## [657] Honduras Honduras ## [659] Honduras Honduras ## [661] Hong Kong, China Hong Kong, China ## [663] Hong Kong, China Hong Kong, China ## [665] Hong Kong, China Hong Kong, China ## [667] Hong Kong, China Hong Kong, China ## [669] Hong Kong, China Hong Kong, China ## [671] Hong Kong, China Hong Kong, China ## [673] Hungary Hungary ## [675] Hungary Hungary ## [677] Hungary Hungary ## [679] Hungary Hungary ## [681] Hungary Hungary ## [683] Hungary Hungary ## [685] Iceland Iceland ## [687] Iceland Iceland ## [689] Iceland Iceland ## [691] Iceland Iceland ## [693] Iceland Iceland ## [695] Iceland Iceland ## [697] India India ## [699] India India ## [701] India India ## [703] India India ## [705] India India ## [707] India India ## [709] Indonesia Indonesia ## [711] Indonesia Indonesia ## [713] Indonesia Indonesia ## [715] Indonesia Indonesia ## [717] Indonesia Indonesia ## [719] Indonesia Indonesia ## [721] Iran Iran ## [723] Iran Iran ## [725] Iran Iran ## [727] Iran Iran ## [729] Iran Iran ## [731] Iran Iran ## [733] Iraq Iraq ## [735] Iraq Iraq ## [737] Iraq Iraq ## [739] Iraq Iraq ## [741] Iraq Iraq ## [743] Iraq Iraq ## [745] Ireland Ireland ## [747] Ireland Ireland ## [749] Ireland Ireland ## [751] Ireland Ireland ## [753] Ireland Ireland ## [755] Ireland Ireland ## [757] Israel Israel ## [759] Israel Israel ## [761] Israel Israel ## [763] Israel Israel ## [765] Israel Israel ## [767] Israel Israel ## [769] Italy Italy ## [771] Italy Italy ## [773] Italy Italy ## [775] Italy Italy ## [777] Italy Italy ## [779] Italy Italy ## [781] Jamaica Jamaica ## [783] Jamaica Jamaica ## [785] Jamaica Jamaica ## [787] Jamaica Jamaica ## [789] Jamaica Jamaica ## [791] Jamaica Jamaica ## [793] Japan Japan ## [795] Japan Japan ## [797] Japan Japan ## [799] Japan Japan ## [801] Japan Japan ## [803] Japan Japan ## [805] Jordan Jordan ## [807] Jordan Jordan ## [809] Jordan Jordan ## [811] Jordan Jordan ## [813] Jordan Jordan ## [815] Jordan Jordan ## [817] Kenya Kenya ## [819] Kenya Kenya ## [821] Kenya Kenya ## [823] Kenya Kenya ## [825] Kenya Kenya ## [827] Kenya Kenya ## [829] Korea, Dem. Rep. Korea, Dem. Rep. ## [831] Korea, Dem. Rep. Korea, Dem. Rep. ## [833] Korea, Dem. Rep. Korea, Dem. Rep. ## [835] Korea, Dem. Rep. Korea, Dem. Rep. ## [837] Korea, Dem. Rep. Korea, Dem. Rep. ## [839] Korea, Dem. Rep. Korea, Dem. Rep. ## [841] Korea, Rep. Korea, Rep. ## [843] Korea, Rep. Korea, Rep. ## [845] Korea, Rep. Korea, Rep. ## [847] Korea, Rep. Korea, Rep. ## [849] Korea, Rep. Korea, Rep. ## [851] Korea, Rep. Korea, Rep. ## [853] Kuwait Kuwait ## [855] Kuwait Kuwait ## [857] Kuwait Kuwait ## [859] Kuwait Kuwait ## [861] Kuwait Kuwait ## [863] Kuwait Kuwait ## [865] Lebanon Lebanon ## [867] Lebanon Lebanon ## [869] Lebanon Lebanon ## [871] Lebanon Lebanon ## [873] Lebanon Lebanon ## [875] Lebanon Lebanon ## [877] Lesotho Lesotho ## [879] Lesotho Lesotho ## [881] Lesotho Lesotho ## [883] Lesotho Lesotho ## [885] Lesotho Lesotho ## [887] Lesotho Lesotho ## [889] Liberia Liberia ## [891] Liberia Liberia ## [893] Liberia Liberia ## [895] Liberia Liberia ## [897] Liberia Liberia ## [899] Liberia Liberia ## [901] Libya Libya ## [903] Libya Libya ## [905] Libya Libya ## [907] Libya Libya ## [909] Libya Libya ## [911] Libya Libya ## [913] Madagascar Madagascar ## [915] Madagascar Madagascar ## [917] Madagascar Madagascar ## [919] Madagascar Madagascar ## [921] Madagascar Madagascar ## [923] Madagascar Madagascar ## [925] Malawi Malawi ## [927] Malawi Malawi ## [929] Malawi Malawi ## [931] Malawi Malawi ## [933] Malawi Malawi ## [935] Malawi Malawi ## [937] Malaysia Malaysia ## [939] Malaysia Malaysia ## [941] Malaysia Malaysia ## [943] Malaysia Malaysia ## [945] Malaysia Malaysia ## [947] Malaysia Malaysia ## [949] Mali Mali ## [951] Mali Mali ## [953] Mali Mali ## [955] Mali Mali ## [957] Mali Mali ## [959] Mali Mali ## [961] Mauritania Mauritania ## [963] Mauritania Mauritania ## [965] Mauritania Mauritania ## [967] Mauritania Mauritania ## [969] Mauritania Mauritania ## [971] Mauritania Mauritania ## [973] Mauritius Mauritius ## [975] Mauritius Mauritius ## [977] Mauritius Mauritius ## [979] Mauritius Mauritius ## [981] Mauritius Mauritius ## [983] Mauritius Mauritius ## [985] Mexico Mexico ## [987] Mexico Mexico ## [989] Mexico Mexico ## [991] Mexico Mexico ## [993] Mexico Mexico ## [995] Mexico Mexico ## [997] Mongolia Mongolia ## [999] Mongolia Mongolia ## [1001] Mongolia Mongolia ## [1003] Mongolia Mongolia ## [1005] Mongolia Mongolia ## [1007] Mongolia Mongolia ## [1009] Montenegro Montenegro ## [1011] Montenegro Montenegro ## [1013] Montenegro Montenegro ## [1015] Montenegro Montenegro ## [1017] Montenegro Montenegro ## [1019] Montenegro Montenegro ## [1021] Morocco Morocco ## [1023] Morocco Morocco ## [1025] Morocco Morocco ## [1027] Morocco Morocco ## [1029] Morocco Morocco ## [1031] Morocco Morocco ## [1033] Mozambique Mozambique ## [1035] Mozambique Mozambique ## [1037] Mozambique Mozambique ## [1039] Mozambique Mozambique ## [1041] Mozambique Mozambique ## [1043] Mozambique Mozambique ## [1045] Myanmar Myanmar ## [1047] Myanmar Myanmar ## [1049] Myanmar Myanmar ## [1051] Myanmar Myanmar ## [1053] Myanmar Myanmar ## [1055] Myanmar Myanmar ## [1057] Namibia Namibia ## [1059] Namibia Namibia ## [1061] Namibia Namibia ## [1063] Namibia Namibia ## [1065] Namibia Namibia ## [1067] Namibia Namibia ## [1069] Nepal Nepal ## [1071] Nepal Nepal ## [1073] Nepal Nepal ## [1075] Nepal Nepal ## [1077] Nepal Nepal ## [1079] Nepal Nepal ## [1081] Netherlands Netherlands ## [1083] Netherlands Netherlands ## [1085] Netherlands Netherlands ## [1087] Netherlands Netherlands ## [1089] Netherlands Netherlands ## [1091] Netherlands Netherlands ## [1093] New Zealand New Zealand ## [1095] New Zealand New Zealand ## [1097] New Zealand New Zealand ## [1099] New Zealand New Zealand ## [1101] New Zealand New Zealand ## [1103] New Zealand New Zealand ## [1105] Nicaragua Nicaragua ## [1107] Nicaragua Nicaragua ## [1109] Nicaragua Nicaragua ## [1111] Nicaragua Nicaragua ## [1113] Nicaragua Nicaragua ## [1115] Nicaragua Nicaragua ## [1117] Niger Niger ## [1119] Niger Niger ## [1121] Niger Niger ## [1123] Niger Niger ## [1125] Niger Niger ## [1127] Niger Niger ## [1129] Nigeria Nigeria ## [1131] Nigeria Nigeria ## [1133] Nigeria Nigeria ## [1135] Nigeria Nigeria ## [1137] Nigeria Nigeria ## [1139] Nigeria Nigeria ## [1141] Norway Norway ## [1143] Norway Norway ## [1145] Norway Norway ## [1147] Norway Norway ## [1149] Norway Norway ## [1151] Norway Norway ## [1153] Oman Oman ## [1155] Oman Oman ## [1157] Oman Oman ## [1159] Oman Oman ## [1161] Oman Oman ## [1163] Oman Oman ## [1165] Pakistan Pakistan ## [1167] Pakistan Pakistan ## [1169] Pakistan Pakistan ## [1171] Pakistan Pakistan ## [1173] Pakistan Pakistan ## [1175] Pakistan Pakistan ## [1177] Panama Panama ## [1179] Panama Panama ## [1181] Panama Panama ## [1183] Panama Panama ## [1185] Panama Panama ## [1187] Panama Panama ## [1189] Paraguay Paraguay ## [1191] Paraguay Paraguay ## [1193] Paraguay Paraguay ## [1195] Paraguay Paraguay ## [1197] Paraguay Paraguay ## [1199] Paraguay Paraguay ## [1201] Peru Peru ## [1203] Peru Peru ## [1205] Peru Peru ## [1207] Peru Peru ## [1209] Peru Peru ## [1211] Peru Peru ## [1213] Philippines Philippines ## [1215] Philippines Philippines ## [1217] Philippines Philippines ## [1219] Philippines Philippines ## [1221] Philippines Philippines ## [1223] Philippines Philippines ## [1225] Poland Poland ## [1227] Poland Poland ## [1229] Poland Poland ## [1231] Poland Poland ## [1233] Poland Poland ## [1235] Poland Poland ## [1237] Portugal Portugal ## [1239] Portugal Portugal ## [1241] Portugal Portugal ## [1243] Portugal Portugal ## [1245] Portugal Portugal ## [1247] Portugal Portugal ## [1249] Puerto Rico Puerto Rico ## [1251] Puerto Rico Puerto Rico ## [1253] Puerto Rico Puerto Rico ## [1255] Puerto Rico Puerto Rico ## [1257] Puerto Rico Puerto Rico ## [1259] Puerto Rico Puerto Rico ## [1261] Reunion Reunion ## [1263] Reunion Reunion ## [1265] Reunion Reunion ## [1267] Reunion Reunion ## [1269] Reunion Reunion ## [1271] Reunion Reunion ## [1273] Romania Romania ## [1275] Romania Romania ## [1277] Romania Romania ## [1279] Romania Romania ## [1281] Romania Romania ## [1283] Romania Romania ## [1285] Rwanda Rwanda ## [1287] Rwanda Rwanda ## [1289] Rwanda Rwanda ## [1291] Rwanda Rwanda ## [1293] Rwanda Rwanda ## [1295] Rwanda Rwanda ## [1297] Sao Tome and Principe Sao Tome and Principe ## [1299] Sao Tome and Principe Sao Tome and Principe ## [1301] Sao Tome and Principe Sao Tome and Principe ## [1303] Sao Tome and Principe Sao Tome and Principe ## [1305] Sao Tome and Principe Sao Tome and Principe ## [1307] Sao Tome and Principe Sao Tome and Principe ## [1309] Saudi Arabia Saudi Arabia ## [1311] Saudi Arabia Saudi Arabia ## [1313] Saudi Arabia Saudi Arabia ## [1315] Saudi Arabia Saudi Arabia ## [1317] Saudi Arabia Saudi Arabia ## [1319] Saudi Arabia Saudi Arabia ## [1321] Senegal Senegal ## [1323] Senegal Senegal ## [1325] Senegal Senegal ## [1327] Senegal Senegal ## [1329] Senegal Senegal ## [1331] Senegal Senegal ## [1333] Serbia Serbia ## [1335] Serbia Serbia ## [1337] Serbia Serbia ## [1339] Serbia Serbia ## [1341] Serbia Serbia ## [1343] Serbia Serbia ## [1345] Sierra Leone Sierra Leone ## [1347] Sierra Leone Sierra Leone ## [1349] Sierra Leone Sierra Leone ## [1351] Sierra Leone Sierra Leone ## [1353] Sierra Leone Sierra Leone ## [1355] Sierra Leone Sierra Leone ## [1357] Singapore Singapore ## [1359] Singapore Singapore ## [1361] Singapore Singapore ## [1363] Singapore Singapore ## [1365] Singapore Singapore ## [1367] Singapore Singapore ## [1369] Slovak Republic Slovak Republic ## [1371] Slovak Republic Slovak Republic ## [1373] Slovak Republic Slovak Republic ## [1375] Slovak Republic Slovak Republic ## [1377] Slovak Republic Slovak Republic ## [1379] Slovak Republic Slovak Republic ## [1381] Slovenia Slovenia ## [1383] Slovenia Slovenia ## [1385] Slovenia Slovenia ## [1387] Slovenia Slovenia ## [1389] Slovenia Slovenia ## [1391] Slovenia Slovenia ## [1393] Somalia Somalia ## [1395] Somalia Somalia ## [1397] Somalia Somalia ## [1399] Somalia Somalia ## [1401] Somalia Somalia ## [1403] Somalia Somalia ## [1405] South Africa South Africa ## [1407] South Africa South Africa ## [1409] South Africa South Africa ## [1411] South Africa South Africa ## [1413] South Africa South Africa ## [1415] South Africa South Africa ## [1417] Spain Spain ## [1419] Spain Spain ## [1421] Spain Spain ## [1423] Spain Spain ## [1425] Spain Spain ## [1427] Spain Spain ## [1429] Sri Lanka Sri Lanka ## [1431] Sri Lanka Sri Lanka ## [1433] Sri Lanka Sri Lanka ## [1435] Sri Lanka Sri Lanka ## [1437] Sri Lanka Sri Lanka ## [1439] Sri Lanka Sri Lanka ## [1441] Sudan Sudan ## [1443] Sudan Sudan ## [1445] Sudan Sudan ## [1447] Sudan Sudan ## [1449] Sudan Sudan ## [1451] Sudan Sudan ## [1453] Swaziland Swaziland ## [1455] Swaziland Swaziland ## [1457] Swaziland Swaziland ## [1459] Swaziland Swaziland ## [1461] Swaziland Swaziland ## [1463] Swaziland Swaziland ## [1465] Sweden Sweden ## [1467] Sweden Sweden ## [1469] Sweden Sweden ## [1471] Sweden Sweden ## [1473] Sweden Sweden ## [1475] Sweden Sweden ## [1477] Switzerland Switzerland ## [1479] Switzerland Switzerland ## [1481] Switzerland Switzerland ## [1483] Switzerland Switzerland ## [1485] Switzerland Switzerland ## [1487] Switzerland Switzerland ## [1489] Syria Syria ## [1491] Syria Syria ## [1493] Syria Syria ## [1495] Syria Syria ## [1497] Syria Syria ## [1499] Syria Syria ## [1501] Taiwan Taiwan ## [1503] Taiwan Taiwan ## [1505] Taiwan Taiwan ## [1507] Taiwan Taiwan ## [1509] Taiwan Taiwan ## [1511] Taiwan Taiwan ## [1513] Tanzania Tanzania ## [1515] Tanzania Tanzania ## [1517] Tanzania Tanzania ## [1519] Tanzania Tanzania ## [1521] Tanzania Tanzania ## [1523] Tanzania Tanzania ## [1525] Thailand Thailand ## [1527] Thailand Thailand ## [1529] Thailand Thailand ## [1531] Thailand Thailand ## [1533] Thailand Thailand ## [1535] Thailand Thailand ## [1537] Togo Togo ## [1539] Togo Togo ## [1541] Togo Togo ## [1543] Togo Togo ## [1545] Togo Togo ## [1547] Togo Togo ## [1549] Trinidad and Tobago Trinidad and Tobago ## [1551] Trinidad and Tobago Trinidad and Tobago ## [1553] Trinidad and Tobago Trinidad and Tobago ## [1555] Trinidad and Tobago Trinidad and Tobago ## [1557] Trinidad and Tobago Trinidad and Tobago ## [1559] Trinidad and Tobago Trinidad and Tobago ## [1561] Tunisia Tunisia ## [1563] Tunisia Tunisia ## [1565] Tunisia Tunisia ## [1567] Tunisia Tunisia ## [1569] Tunisia Tunisia ## [1571] Tunisia Tunisia ## [1573] Turkey Turkey ## [1575] Turkey Turkey ## [1577] Turkey Turkey ## [1579] Turkey Turkey ## [1581] Turkey Turkey ## [1583] Turkey Turkey ## [1585] Uganda Uganda ## [1587] Uganda Uganda ## [1589] Uganda Uganda ## [1591] Uganda Uganda ## [1593] Uganda Uganda ## [1595] Uganda Uganda ## [1597] United Kingdom United Kingdom ## [1599] United Kingdom United Kingdom ## [1601] United Kingdom United Kingdom ## [1603] United Kingdom United Kingdom ## [1605] United Kingdom United Kingdom ## [1607] United Kingdom United Kingdom ## [1609] United States United States ## [1611] United States United States ## [1613] United States United States ## [1615] United States United States ## [1617] United States United States ## [1619] United States United States ## [1621] Uruguay Uruguay ## [1623] Uruguay Uruguay ## [1625] Uruguay Uruguay ## [1627] Uruguay Uruguay ## [1629] Uruguay Uruguay ## [1631] Uruguay Uruguay ## [1633] Venezuela Venezuela ## [1635] Venezuela Venezuela ## [1637] Venezuela Venezuela ## [1639] Venezuela Venezuela ## [1641] Venezuela Venezuela ## [1643] Venezuela Venezuela ## [1645] Vietnam Vietnam ## [1647] Vietnam Vietnam ## [1649] Vietnam Vietnam ## [1651] Vietnam Vietnam ## [1653] Vietnam Vietnam ## [1655] Vietnam Vietnam ## [1657] West Bank and Gaza West Bank and Gaza ## [1659] West Bank and Gaza West Bank and Gaza ## [1661] West Bank and Gaza West Bank and Gaza ## [1663] West Bank and Gaza West Bank and Gaza ## [1665] West Bank and Gaza West Bank and Gaza ## [1667] West Bank and Gaza West Bank and Gaza ## [1669] Yemen, Rep. Yemen, Rep. ## [1671] Yemen, Rep. Yemen, Rep. ## [1673] Yemen, Rep. Yemen, Rep. ## [1675] Yemen, Rep. Yemen, Rep. ## [1677] Yemen, Rep. Yemen, Rep. ## [1679] Yemen, Rep. Yemen, Rep. ## [1681] Zambia Zambia ## [1683] Zambia Zambia ## [1685] Zambia Zambia ## [1687] Zambia Zambia ## [1689] Zambia Zambia ## [1691] Zambia Zambia ## [1693] Zimbabwe Zimbabwe ## [1695] Zimbabwe Zimbabwe ## [1697] Zimbabwe Zimbabwe ## [1699] Zimbabwe Zimbabwe ## [1701] Zimbabwe Zimbabwe ## [1703] Zimbabwe Zimbabwe ## 142 Levels: Afghanistan Albania Algeria Angola Argentina ... Zimbabwe ``` --- ## data.frame - indexing - `[]` operator can be used to subset rows and columns - `[]` operator requires two arguments - one for rows (observations) and one for columns (variables) - `[rows, columns]` ```r head(gapminder) ``` ``` ## country continent year lifeExp pop gdpPercap ## 1 Afghanistan Asia 1952 28.801 8425333 779.4453 ## 2 Afghanistan Asia 1957 30.332 9240934 820.8530 ## 3 Afghanistan Asia 1962 31.997 10267083 853.1007 ## 4 Afghanistan Asia 1967 34.020 11537966 836.1971 ## 5 Afghanistan Asia 1972 36.088 13079460 739.9811 ## 6 Afghanistan Asia 1977 38.438 14880372 786.1134 ``` --- ## data.frame - indexing - `[]` operator can be used to subset rows and columns - `[]` operator requires two arguments - one for rows (observations) and one for columns (variables) - `[rows, columns]` ```r gapminder[3, ] ``` ``` ## country continent year lifeExp pop gdpPercap ## 3 Afghanistan Asia 1962 31.997 10267083 853.1007 ``` --- ## data.frame - indexing - `[]` operator can be used to subset rows and columns - `[]` operator requires two arguments - one for rows (observations) and one for columns (variables) - `[rows, columns]` ```r gapminder[2, 3] ``` ``` ## [1] 1957 ``` --- ## data.frame - indexing - `[]` operator can be used to subset rows and columns - `[]` operator requires two arguments - one for rows (observations) and one for columns (variables) - `[rows, columns]` ```r gapminder[-(1:1650), 3] ``` ``` ## [1] 1982 1987 1992 1997 2002 2007 1952 1957 1962 1967 1972 1977 1982 1987 ## [15] 1992 1997 2002 2007 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ## [29] 2002 2007 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 ## [43] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 ``` --- ## data.frame - indexing - `[]` operator can be used to subset rows and columns - `[]` operator requires two arguments - one for rows (observations) and one for columns (variables) - `[rows, columns]` ```r gapminder[c(2,4), -3] ``` ``` ## country continent lifeExp pop gdpPercap ## 2 Afghanistan Asia 30.332 9240934 820.8530 ## 4 Afghanistan Asia 34.020 11537966 836.1971 ``` --- ## data.frame - indexing - `[]` operator can be used to subset rows and columns - `[]` operator requires two arguments - one for rows (observations) and one for columns (variables) - `[rows, columns]` ```r gapminder[c(2,4), c("country", "pop")] ``` ``` ## country pop ## 2 Afghanistan 9240934 ## 4 Afghanistan 11537966 ``` --- ## data.frame - indexing - Logical operators, such as `==, !=, >, >=, <, <=, &, |` can also be used for indexing ```r gapminder[gapminder$lifeExp>82, c("country", "pop")] ``` ``` ## country pop ## 672 Hong Kong, China 6980412 ## 804 Japan 127467972 ``` ```r subset(gapminder, pop>1000000000) ``` ``` ## country continent year lifeExp pop gdpPercap ## 295 China Asia 1982 65.525 1000281000 962.4214 ## 296 China Asia 1987 67.274 1084035000 1378.9040 ## 297 China Asia 1992 68.690 1164970000 1655.7842 ## 298 China Asia 1997 70.426 1230075000 2289.2341 ## 299 China Asia 2002 72.028 1280400000 3119.2809 ## 300 China Asia 2007 72.961 1318683096 4959.1149 ## 707 India Asia 2002 62.879 1034172547 1746.7695 ## 708 India Asia 2007 64.698 1110396331 2452.2104 ``` --- ## data.frame - indexing - Logical operators, such as `==, !=, >, >=, <, <=, &, |` can also be used for indexing ```r subset(gapminder, pop>1000000000 & country=='India') ``` ``` ## country continent year lifeExp pop gdpPercap ## 707 India Asia 2002 62.879 1034172547 1746.769 ## 708 India Asia 2007 64.698 1110396331 2452.210 ``` ```r subset(gapminder, pop>1000000000 | continent=='Oceania') ``` ``` ## country continent year lifeExp pop gdpPercap ## 61 Australia Oceania 1952 69.120 8691212 10039.5956 ## 62 Australia Oceania 1957 70.330 9712569 10949.6496 ## 63 Australia Oceania 1962 70.930 10794968 12217.2269 ## 64 Australia Oceania 1967 71.100 11872264 14526.1246 ## 65 Australia Oceania 1972 71.930 13177000 16788.6295 ## 66 Australia Oceania 1977 73.490 14074100 18334.1975 ## 67 Australia Oceania 1982 74.740 15184200 19477.0093 ## 68 Australia Oceania 1987 76.320 16257249 21888.8890 ## 69 Australia Oceania 1992 77.560 17481977 23424.7668 ## 70 Australia Oceania 1997 78.830 18565243 26997.9366 ## 71 Australia Oceania 2002 80.370 19546792 30687.7547 ## 72 Australia Oceania 2007 81.235 20434176 34435.3674 ## 295 China Asia 1982 65.525 1000281000 962.4214 ## 296 China Asia 1987 67.274 1084035000 1378.9040 ## 297 China Asia 1992 68.690 1164970000 1655.7842 ## 298 China Asia 1997 70.426 1230075000 2289.2341 ## 299 China Asia 2002 72.028 1280400000 3119.2809 ## 300 China Asia 2007 72.961 1318683096 4959.1149 ## 707 India Asia 2002 62.879 1034172547 1746.7695 ## 708 India Asia 2007 64.698 1110396331 2452.2104 ## 1093 New Zealand Oceania 1952 69.390 1994794 10556.5757 ## 1094 New Zealand Oceania 1957 70.260 2229407 12247.3953 ## 1095 New Zealand Oceania 1962 71.240 2488550 13175.6780 ## 1096 New Zealand Oceania 1967 71.520 2728150 14463.9189 ## 1097 New Zealand Oceania 1972 71.890 2929100 16046.0373 ## 1098 New Zealand Oceania 1977 72.220 3164900 16233.7177 ## 1099 New Zealand Oceania 1982 73.840 3210650 17632.4104 ## 1100 New Zealand Oceania 1987 74.320 3317166 19007.1913 ## 1101 New Zealand Oceania 1992 76.330 3437674 18363.3249 ## 1102 New Zealand Oceania 1997 77.550 3676187 21050.4138 ## 1103 New Zealand Oceania 2002 79.110 3908037 23189.8014 ## 1104 New Zealand Oceania 2007 80.204 4115771 25185.0091 ``` --- ## data.frame - indexing - A new object can be created from the results of indexing ```r africa <- subset(gapminder, continent=='Africa') africa ``` ``` ## country continent year lifeExp pop gdpPercap ## 25 Algeria Africa 1952 43.077 9279525 2449.0082 ## 26 Algeria Africa 1957 45.685 10270856 3013.9760 ## 27 Algeria Africa 1962 48.303 11000948 2550.8169 ## 28 Algeria Africa 1967 51.407 12760499 3246.9918 ## 29 Algeria Africa 1972 54.518 14760787 4182.6638 ## 30 Algeria Africa 1977 58.014 17152804 4910.4168 ## 31 Algeria Africa 1982 61.368 20033753 5745.1602 ## 32 Algeria Africa 1987 65.799 23254956 5681.3585 ## 33 Algeria Africa 1992 67.744 26298373 5023.2166 ## 34 Algeria Africa 1997 69.152 29072015 4797.2951 ## 35 Algeria Africa 2002 70.994 31287142 5288.0404 ## 36 Algeria Africa 2007 72.301 33333216 6223.3675 ## 37 Angola Africa 1952 30.015 4232095 3520.6103 ## 38 Angola Africa 1957 31.999 4561361 3827.9405 ## 39 Angola Africa 1962 34.000 4826015 4269.2767 ## 40 Angola Africa 1967 35.985 5247469 5522.7764 ## 41 Angola Africa 1972 37.928 5894858 5473.2880 ## 42 Angola Africa 1977 39.483 6162675 3008.6474 ## 43 Angola Africa 1982 39.942 7016384 2756.9537 ## 44 Angola Africa 1987 39.906 7874230 2430.2083 ## 45 Angola Africa 1992 40.647 8735988 2627.8457 ## 46 Angola Africa 1997 40.963 9875024 2277.1409 ## 47 Angola Africa 2002 41.003 10866106 2773.2873 ## 48 Angola Africa 2007 42.731 12420476 4797.2313 ## 121 Benin Africa 1952 38.223 1738315 1062.7522 ## 122 Benin Africa 1957 40.358 1925173 959.6011 ## 123 Benin Africa 1962 42.618 2151895 949.4991 ## 124 Benin Africa 1967 44.885 2427334 1035.8314 ## 125 Benin Africa 1972 47.014 2761407 1085.7969 ## 126 Benin Africa 1977 49.190 3168267 1029.1613 ## 127 Benin Africa 1982 50.904 3641603 1277.8976 ## 128 Benin Africa 1987 52.337 4243788 1225.8560 ## 129 Benin Africa 1992 53.919 4981671 1191.2077 ## 130 Benin Africa 1997 54.777 6066080 1232.9753 ## 131 Benin Africa 2002 54.406 7026113 1372.8779 ## 132 Benin Africa 2007 56.728 8078314 1441.2849 ## 157 Botswana Africa 1952 47.622 442308 851.2411 ## 158 Botswana Africa 1957 49.618 474639 918.2325 ## 159 Botswana Africa 1962 51.520 512764 983.6540 ## 160 Botswana Africa 1967 53.298 553541 1214.7093 ## 161 Botswana Africa 1972 56.024 619351 2263.6111 ## 162 Botswana Africa 1977 59.319 781472 3214.8578 ## 163 Botswana Africa 1982 61.484 970347 4551.1421 ## 164 Botswana Africa 1987 63.622 1151184 6205.8839 ## 165 Botswana Africa 1992 62.745 1342614 7954.1116 ## 166 Botswana Africa 1997 52.556 1536536 8647.1423 ## 167 Botswana Africa 2002 46.634 1630347 11003.6051 ## 168 Botswana Africa 2007 50.728 1639131 12569.8518 ## 193 Burkina Faso Africa 1952 31.975 4469979 543.2552 ## 194 Burkina Faso Africa 1957 34.906 4713416 617.1835 ## 195 Burkina Faso Africa 1962 37.814 4919632 722.5120 ## 196 Burkina Faso Africa 1967 40.697 5127935 794.8266 ## 197 Burkina Faso Africa 1972 43.591 5433886 854.7360 ## 198 Burkina Faso Africa 1977 46.137 5889574 743.3870 ## 199 Burkina Faso Africa 1982 48.122 6634596 807.1986 ## 200 Burkina Faso Africa 1987 49.557 7586551 912.0631 ## 201 Burkina Faso Africa 1992 50.260 8878303 931.7528 ## 202 Burkina Faso Africa 1997 50.324 10352843 946.2950 ## 203 Burkina Faso Africa 2002 50.650 12251209 1037.6452 ## 204 Burkina Faso Africa 2007 52.295 14326203 1217.0330 ## 205 Burundi Africa 1952 39.031 2445618 339.2965 ## 206 Burundi Africa 1957 40.533 2667518 379.5646 ## 207 Burundi Africa 1962 42.045 2961915 355.2032 ## 208 Burundi Africa 1967 43.548 3330989 412.9775 ## 209 Burundi Africa 1972 44.057 3529983 464.0995 ## 210 Burundi Africa 1977 45.910 3834415 556.1033 ## 211 Burundi Africa 1982 47.471 4580410 559.6032 ## 212 Burundi Africa 1987 48.211 5126023 621.8188 ## 213 Burundi Africa 1992 44.736 5809236 631.6999 ## 214 Burundi Africa 1997 45.326 6121610 463.1151 ## 215 Burundi Africa 2002 47.360 7021078 446.4035 ## 216 Burundi Africa 2007 49.580 8390505 430.0707 ## 229 Cameroon Africa 1952 38.523 5009067 1172.6677 ## 230 Cameroon Africa 1957 40.428 5359923 1313.0481 ## 231 Cameroon Africa 1962 42.643 5793633 1399.6074 ## 232 Cameroon Africa 1967 44.799 6335506 1508.4531 ## 233 Cameroon Africa 1972 47.049 7021028 1684.1465 ## 234 Cameroon Africa 1977 49.355 7959865 1783.4329 ## 235 Cameroon Africa 1982 52.961 9250831 2367.9833 ## 236 Cameroon Africa 1987 54.985 10780667 2602.6642 ## 237 Cameroon Africa 1992 54.314 12467171 1793.1633 ## 238 Cameroon Africa 1997 52.199 14195809 1694.3375 ## 239 Cameroon Africa 2002 49.856 15929988 1934.0114 ## 240 Cameroon Africa 2007 50.430 17696293 2042.0952 ## 253 Central African Republic Africa 1952 35.463 1291695 1071.3107 ## 254 Central African Republic Africa 1957 37.464 1392284 1190.8443 ## 255 Central African Republic Africa 1962 39.475 1523478 1193.0688 ## 256 Central African Republic Africa 1967 41.478 1733638 1136.0566 ## 257 Central African Republic Africa 1972 43.457 1927260 1070.0133 ## 258 Central African Republic Africa 1977 46.775 2167533 1109.3743 ## 259 Central African Republic Africa 1982 48.295 2476971 956.7530 ## 260 Central African Republic Africa 1987 50.485 2840009 844.8764 ## 261 Central African Republic Africa 1992 49.396 3265124 747.9055 ## 262 Central African Republic Africa 1997 46.066 3696513 740.5063 ## 263 Central African Republic Africa 2002 43.308 4048013 738.6906 ## 264 Central African Republic Africa 2007 44.741 4369038 706.0165 ## 265 Chad Africa 1952 38.092 2682462 1178.6659 ## 266 Chad Africa 1957 39.881 2894855 1308.4956 ## 267 Chad Africa 1962 41.716 3150417 1389.8176 ## 268 Chad Africa 1967 43.601 3495967 1196.8106 ## 269 Chad Africa 1972 45.569 3899068 1104.1040 ## 270 Chad Africa 1977 47.383 4388260 1133.9850 ## 271 Chad Africa 1982 49.517 4875118 797.9081 ## 272 Chad Africa 1987 51.051 5498955 952.3861 ## 273 Chad Africa 1992 51.724 6429417 1058.0643 ## 274 Chad Africa 1997 51.573 7562011 1004.9614 ## 275 Chad Africa 2002 50.525 8835739 1156.1819 ## 276 Chad Africa 2007 50.651 10238807 1704.0637 ## 313 Comoros Africa 1952 40.715 153936 1102.9909 ## 314 Comoros Africa 1957 42.460 170928 1211.1485 ## 315 Comoros Africa 1962 44.467 191689 1406.6483 ## 316 Comoros Africa 1967 46.472 217378 1876.0296 ## 317 Comoros Africa 1972 48.944 250027 1937.5777 ## 318 Comoros Africa 1977 50.939 304739 1172.6030 ## 319 Comoros Africa 1982 52.933 348643 1267.1001 ## 320 Comoros Africa 1987 54.926 395114 1315.9808 ## 321 Comoros Africa 1992 57.939 454429 1246.9074 ## 322 Comoros Africa 1997 60.660 527982 1173.6182 ## 323 Comoros Africa 2002 62.974 614382 1075.8116 ## 324 Comoros Africa 2007 65.152 710960 986.1479 ## 325 Congo, Dem. Rep. Africa 1952 39.143 14100005 780.5423 ## 326 Congo, Dem. Rep. Africa 1957 40.652 15577932 905.8602 ## 327 Congo, Dem. Rep. Africa 1962 42.122 17486434 896.3146 ## 328 Congo, Dem. Rep. Africa 1967 44.056 19941073 861.5932 ## 329 Congo, Dem. Rep. Africa 1972 45.989 23007669 904.8961 ## 330 Congo, Dem. Rep. Africa 1977 47.804 26480870 795.7573 ## 331 Congo, Dem. Rep. Africa 1982 47.784 30646495 673.7478 ## 332 Congo, Dem. Rep. Africa 1987 47.412 35481645 672.7748 ## 333 Congo, Dem. Rep. Africa 1992 45.548 41672143 457.7192 ## 334 Congo, Dem. Rep. Africa 1997 42.587 47798986 312.1884 ## 335 Congo, Dem. Rep. Africa 2002 44.966 55379852 241.1659 ## 336 Congo, Dem. Rep. Africa 2007 46.462 64606759 277.5519 ## 337 Congo, Rep. Africa 1952 42.111 854885 2125.6214 ## 338 Congo, Rep. Africa 1957 45.053 940458 2315.0566 ## 339 Congo, Rep. Africa 1962 48.435 1047924 2464.7832 ## 340 Congo, Rep. Africa 1967 52.040 1179760 2677.9396 ## 341 Congo, Rep. Africa 1972 54.907 1340458 3213.1527 ## 342 Congo, Rep. Africa 1977 55.625 1536769 3259.1790 ## 343 Congo, Rep. Africa 1982 56.695 1774735 4879.5075 ## 344 Congo, Rep. Africa 1987 57.470 2064095 4201.1949 ## 345 Congo, Rep. Africa 1992 56.433 2409073 4016.2395 ## 346 Congo, Rep. Africa 1997 52.962 2800947 3484.1644 ## 347 Congo, Rep. Africa 2002 52.970 3328795 3484.0620 ## 348 Congo, Rep. Africa 2007 55.322 3800610 3632.5578 ## 361 Cote d'Ivoire Africa 1952 40.477 2977019 1388.5947 ## 362 Cote d'Ivoire Africa 1957 42.469 3300000 1500.8959 ## 363 Cote d'Ivoire Africa 1962 44.930 3832408 1728.8694 ## 364 Cote d'Ivoire Africa 1967 47.350 4744870 2052.0505 ## 365 Cote d'Ivoire Africa 1972 49.801 6071696 2378.2011 ## 366 Cote d'Ivoire Africa 1977 52.374 7459574 2517.7365 ## 367 Cote d'Ivoire Africa 1982 53.983 9025951 2602.7102 ## 368 Cote d'Ivoire Africa 1987 54.655 10761098 2156.9561 ## 369 Cote d'Ivoire Africa 1992 52.044 12772596 1648.0738 ## 370 Cote d'Ivoire Africa 1997 47.991 14625967 1786.2654 ## 371 Cote d'Ivoire Africa 2002 46.832 16252726 1648.8008 ## 372 Cote d'Ivoire Africa 2007 48.328 18013409 1544.7501 ## 421 Djibouti Africa 1952 34.812 63149 2669.5295 ## 422 Djibouti Africa 1957 37.328 71851 2864.9691 ## 423 Djibouti Africa 1962 39.693 89898 3020.9893 ## 424 Djibouti Africa 1967 42.074 127617 3020.0505 ## 425 Djibouti Africa 1972 44.366 178848 3694.2124 ## 426 Djibouti Africa 1977 46.519 228694 3081.7610 ## 427 Djibouti Africa 1982 48.812 305991 2879.4681 ## 428 Djibouti Africa 1987 50.040 311025 2880.1026 ## 429 Djibouti Africa 1992 51.604 384156 2377.1562 ## 430 Djibouti Africa 1997 53.157 417908 1895.0170 ## 431 Djibouti Africa 2002 53.373 447416 1908.2609 ## 432 Djibouti Africa 2007 54.791 496374 2082.4816 ## 457 Egypt Africa 1952 41.893 22223309 1418.8224 ## 458 Egypt Africa 1957 44.444 25009741 1458.9153 ## 459 Egypt Africa 1962 46.992 28173309 1693.3359 ## 460 Egypt Africa 1967 49.293 31681188 1814.8807 ## 461 Egypt Africa 1972 51.137 34807417 2024.0081 ## 462 Egypt Africa 1977 53.319 38783863 2785.4936 ## 463 Egypt Africa 1982 56.006 45681811 3503.7296 ## 464 Egypt Africa 1987 59.797 52799062 3885.4607 ## 465 Egypt Africa 1992 63.674 59402198 3794.7552 ## 466 Egypt Africa 1997 67.217 66134291 4173.1818 ## 467 Egypt Africa 2002 69.806 73312559 4754.6044 ## 468 Egypt Africa 2007 71.338 80264543 5581.1810 ## 481 Equatorial Guinea Africa 1952 34.482 216964 375.6431 ## 482 Equatorial Guinea Africa 1957 35.983 232922 426.0964 ## 483 Equatorial Guinea Africa 1962 37.485 249220 582.8420 ## 484 Equatorial Guinea Africa 1967 38.987 259864 915.5960 ## 485 Equatorial Guinea Africa 1972 40.516 277603 672.4123 ## 486 Equatorial Guinea Africa 1977 42.024 192675 958.5668 ## 487 Equatorial Guinea Africa 1982 43.662 285483 927.8253 ## 488 Equatorial Guinea Africa 1987 45.664 341244 966.8968 ## 489 Equatorial Guinea Africa 1992 47.545 387838 1132.0550 ## 490 Equatorial Guinea Africa 1997 48.245 439971 2814.4808 ## 491 Equatorial Guinea Africa 2002 49.348 495627 7703.4959 ## 492 Equatorial Guinea Africa 2007 51.579 551201 12154.0897 ## 493 Eritrea Africa 1952 35.928 1438760 328.9406 ## 494 Eritrea Africa 1957 38.047 1542611 344.1619 ## 495 Eritrea Africa 1962 40.158 1666618 380.9958 ## 496 Eritrea Africa 1967 42.189 1820319 468.7950 ## 497 Eritrea Africa 1972 44.142 2260187 514.3242 ## 498 Eritrea Africa 1977 44.535 2512642 505.7538 ## 499 Eritrea Africa 1982 43.890 2637297 524.8758 ## 500 Eritrea Africa 1987 46.453 2915959 521.1341 ## 501 Eritrea Africa 1992 49.991 3668440 582.8585 ## 502 Eritrea Africa 1997 53.378 4058319 913.4708 ## 503 Eritrea Africa 2002 55.240 4414865 765.3500 ## 504 Eritrea Africa 2007 58.040 4906585 641.3695 ## 505 Ethiopia Africa 1952 34.078 20860941 362.1463 ## 506 Ethiopia Africa 1957 36.667 22815614 378.9042 ## 507 Ethiopia Africa 1962 40.059 25145372 419.4564 ## 508 Ethiopia Africa 1967 42.115 27860297 516.1186 ## 509 Ethiopia Africa 1972 43.515 30770372 566.2439 ## 510 Ethiopia Africa 1977 44.510 34617799 556.8084 ## 511 Ethiopia Africa 1982 44.916 38111756 577.8607 ## 512 Ethiopia Africa 1987 46.684 42999530 573.7413 ## 513 Ethiopia Africa 1992 48.091 52088559 421.3535 ## 514 Ethiopia Africa 1997 49.402 59861301 515.8894 ## 515 Ethiopia Africa 2002 50.725 67946797 530.0535 ## 516 Ethiopia Africa 2007 52.947 76511887 690.8056 ## 541 Gabon Africa 1952 37.003 420702 4293.4765 ## 542 Gabon Africa 1957 38.999 434904 4976.1981 ## 543 Gabon Africa 1962 40.489 455661 6631.4592 ## 544 Gabon Africa 1967 44.598 489004 8358.7620 ## 545 Gabon Africa 1972 48.690 537977 11401.9484 ## 546 Gabon Africa 1977 52.790 706367 21745.5733 ## 547 Gabon Africa 1982 56.564 753874 15113.3619 ## 548 Gabon Africa 1987 60.190 880397 11864.4084 ## 549 Gabon Africa 1992 61.366 985739 13522.1575 ## 550 Gabon Africa 1997 60.461 1126189 14722.8419 ## 551 Gabon Africa 2002 56.761 1299304 12521.7139 ## 552 Gabon Africa 2007 56.735 1454867 13206.4845 ## 553 Gambia Africa 1952 30.000 284320 485.2307 ## 554 Gambia Africa 1957 32.065 323150 520.9267 ## 555 Gambia Africa 1962 33.896 374020 599.6503 ## 556 Gambia Africa 1967 35.857 439593 734.7829 ## 557 Gambia Africa 1972 38.308 517101 756.0868 ## 558 Gambia Africa 1977 41.842 608274 884.7553 ## 559 Gambia Africa 1982 45.580 715523 835.8096 ## 560 Gambia Africa 1987 49.265 848406 611.6589 ## 561 Gambia Africa 1992 52.644 1025384 665.6244 ## 562 Gambia Africa 1997 55.861 1235767 653.7302 ## 563 Gambia Africa 2002 58.041 1457766 660.5856 ## 564 Gambia Africa 2007 59.448 1688359 752.7497 ## 577 Ghana Africa 1952 43.149 5581001 911.2989 ## 578 Ghana Africa 1957 44.779 6391288 1043.5615 ## 579 Ghana Africa 1962 46.452 7355248 1190.0411 ## 580 Ghana Africa 1967 48.072 8490213 1125.6972 ## 581 Ghana Africa 1972 49.875 9354120 1178.2237 ## 582 Ghana Africa 1977 51.756 10538093 993.2240 ## 583 Ghana Africa 1982 53.744 11400338 876.0326 ## 584 Ghana Africa 1987 55.729 14168101 847.0061 ## 585 Ghana Africa 1992 57.501 16278738 925.0602 ## 586 Ghana Africa 1997 58.556 18418288 1005.2458 ## 587 Ghana Africa 2002 58.453 20550751 1111.9846 ## 588 Ghana Africa 2007 60.022 22873338 1327.6089 ## 613 Guinea Africa 1952 33.609 2664249 510.1965 ## 614 Guinea Africa 1957 34.558 2876726 576.2670 ## 615 Guinea Africa 1962 35.753 3140003 686.3737 ## 616 Guinea Africa 1967 37.197 3451418 708.7595 ## 617 Guinea Africa 1972 38.842 3811387 741.6662 ## 618 Guinea Africa 1977 40.762 4227026 874.6859 ## 619 Guinea Africa 1982 42.891 4710497 857.2504 ## 620 Guinea Africa 1987 45.552 5650262 805.5725 ## 621 Guinea Africa 1992 48.576 6990574 794.3484 ## 622 Guinea Africa 1997 51.455 8048834 869.4498 ## 623 Guinea Africa 2002 53.676 8807818 945.5836 ## 624 Guinea Africa 2007 56.007 9947814 942.6542 ## 625 Guinea-Bissau Africa 1952 32.500 580653 299.8503 ## 626 Guinea-Bissau Africa 1957 33.489 601095 431.7905 ## 627 Guinea-Bissau Africa 1962 34.488 627820 522.0344 ## 628 Guinea-Bissau Africa 1967 35.492 601287 715.5806 ## 629 Guinea-Bissau Africa 1972 36.486 625361 820.2246 ## 630 Guinea-Bissau Africa 1977 37.465 745228 764.7260 ## 631 Guinea-Bissau Africa 1982 39.327 825987 838.1240 ## 632 Guinea-Bissau Africa 1987 41.245 927524 736.4154 ## 633 Guinea-Bissau Africa 1992 43.266 1050938 745.5399 ## 634 Guinea-Bissau Africa 1997 44.873 1193708 796.6645 ## 635 Guinea-Bissau Africa 2002 45.504 1332459 575.7047 ## 636 Guinea-Bissau Africa 2007 46.388 1472041 579.2317 ## 817 Kenya Africa 1952 42.270 6464046 853.5409 ## 818 Kenya Africa 1957 44.686 7454779 944.4383 ## 819 Kenya Africa 1962 47.949 8678557 896.9664 ## 820 Kenya Africa 1967 50.654 10191512 1056.7365 ## 821 Kenya Africa 1972 53.559 12044785 1222.3600 ## 822 Kenya Africa 1977 56.155 14500404 1267.6132 ## 823 Kenya Africa 1982 58.766 17661452 1348.2258 ## 824 Kenya Africa 1987 59.339 21198082 1361.9369 ## 825 Kenya Africa 1992 59.285 25020539 1341.9217 ## 826 Kenya Africa 1997 54.407 28263827 1360.4850 ## 827 Kenya Africa 2002 50.992 31386842 1287.5147 ## 828 Kenya Africa 2007 54.110 35610177 1463.2493 ## 877 Lesotho Africa 1952 42.138 748747 298.8462 ## 878 Lesotho Africa 1957 45.047 813338 335.9971 ## 879 Lesotho Africa 1962 47.747 893143 411.8006 ## 880 Lesotho Africa 1967 48.492 996380 498.6390 ## 881 Lesotho Africa 1972 49.767 1116779 496.5816 ## 882 Lesotho Africa 1977 52.208 1251524 745.3695 ## 883 Lesotho Africa 1982 55.078 1411807 797.2631 ## 884 Lesotho Africa 1987 57.180 1599200 773.9932 ## 885 Lesotho Africa 1992 59.685 1803195 977.4863 ## 886 Lesotho Africa 1997 55.558 1982823 1186.1480 ## 887 Lesotho Africa 2002 44.593 2046772 1275.1846 ## 888 Lesotho Africa 2007 42.592 2012649 1569.3314 ## 889 Liberia Africa 1952 38.480 863308 575.5730 ## 890 Liberia Africa 1957 39.486 975950 620.9700 ## 891 Liberia Africa 1962 40.502 1112796 634.1952 ## 892 Liberia Africa 1967 41.536 1279406 713.6036 ## 893 Liberia Africa 1972 42.614 1482628 803.0055 ## 894 Liberia Africa 1977 43.764 1703617 640.3224 ## 895 Liberia Africa 1982 44.852 1956875 572.1996 ## 896 Liberia Africa 1987 46.027 2269414 506.1139 ## 897 Liberia Africa 1992 40.802 1912974 636.6229 ## 898 Liberia Africa 1997 42.221 2200725 609.1740 ## 899 Liberia Africa 2002 43.753 2814651 531.4824 ## 900 Liberia Africa 2007 45.678 3193942 414.5073 ## 901 Libya Africa 1952 42.723 1019729 2387.5481 ## 902 Libya Africa 1957 45.289 1201578 3448.2844 ## 903 Libya Africa 1962 47.808 1441863 6757.0308 ## 904 Libya Africa 1967 50.227 1759224 18772.7517 ## 905 Libya Africa 1972 52.773 2183877 21011.4972 ## 906 Libya Africa 1977 57.442 2721783 21951.2118 ## 907 Libya Africa 1982 62.155 3344074 17364.2754 ## 908 Libya Africa 1987 66.234 3799845 11770.5898 ## 909 Libya Africa 1992 68.755 4364501 9640.1385 ## 910 Libya Africa 1997 71.555 4759670 9467.4461 ## 911 Libya Africa 2002 72.737 5368585 9534.6775 ## 912 Libya Africa 2007 73.952 6036914 12057.4993 ## 913 Madagascar Africa 1952 36.681 4762912 1443.0117 ## 914 Madagascar Africa 1957 38.865 5181679 1589.2027 ## 915 Madagascar Africa 1962 40.848 5703324 1643.3871 ## 916 Madagascar Africa 1967 42.881 6334556 1634.0473 ## 917 Madagascar Africa 1972 44.851 7082430 1748.5630 ## 918 Madagascar Africa 1977 46.881 8007166 1544.2286 ## 919 Madagascar Africa 1982 48.969 9171477 1302.8787 ## 920 Madagascar Africa 1987 49.350 10568642 1155.4419 ## 921 Madagascar Africa 1992 52.214 12210395 1040.6762 ## 922 Madagascar Africa 1997 54.978 14165114 986.2959 ## 923 Madagascar Africa 2002 57.286 16473477 894.6371 ## 924 Madagascar Africa 2007 59.443 19167654 1044.7701 ## 925 Malawi Africa 1952 36.256 2917802 369.1651 ## 926 Malawi Africa 1957 37.207 3221238 416.3698 ## 927 Malawi Africa 1962 38.410 3628608 427.9011 ## 928 Malawi Africa 1967 39.487 4147252 495.5148 ## 929 Malawi Africa 1972 41.766 4730997 584.6220 ## 930 Malawi Africa 1977 43.767 5637246 663.2237 ## 931 Malawi Africa 1982 45.642 6502825 632.8039 ## 932 Malawi Africa 1987 47.457 7824747 635.5174 ## 933 Malawi Africa 1992 49.420 10014249 563.2000 ## 934 Malawi Africa 1997 47.495 10419991 692.2758 ## 935 Malawi Africa 2002 45.009 11824495 665.4231 ## 936 Malawi Africa 2007 48.303 13327079 759.3499 ## 949 Mali Africa 1952 33.685 3838168 452.3370 ## 950 Mali Africa 1957 35.307 4241884 490.3822 ## 951 Mali Africa 1962 36.936 4690372 496.1743 ## 952 Mali Africa 1967 38.487 5212416 545.0099 ## 953 Mali Africa 1972 39.977 5828158 581.3689 ## 954 Mali Africa 1977 41.714 6491649 686.3953 ## 955 Mali Africa 1982 43.916 6998256 618.0141 ## 956 Mali Africa 1987 46.364 7634008 684.1716 ## 957 Mali Africa 1992 48.388 8416215 739.0144 ## 958 Mali Africa 1997 49.903 9384984 790.2580 ## 959 Mali Africa 2002 51.818 10580176 951.4098 ## 960 Mali Africa 2007 54.467 12031795 1042.5816 ## 961 Mauritania Africa 1952 40.543 1022556 743.1159 ## 962 Mauritania Africa 1957 42.338 1076852 846.1203 ## 963 Mauritania Africa 1962 44.248 1146757 1055.8960 ## 964 Mauritania Africa 1967 46.289 1230542 1421.1452 ## 965 Mauritania Africa 1972 48.437 1332786 1586.8518 ## 966 Mauritania Africa 1977 50.852 1456688 1497.4922 ## 967 Mauritania Africa 1982 53.599 1622136 1481.1502 ## 968 Mauritania Africa 1987 56.145 1841240 1421.6036 ## 969 Mauritania Africa 1992 58.333 2119465 1361.3698 ## 970 Mauritania Africa 1997 60.430 2444741 1483.1361 ## 971 Mauritania Africa 2002 62.247 2828858 1579.0195 ## 972 Mauritania Africa 2007 64.164 3270065 1803.1515 ## 973 Mauritius Africa 1952 50.986 516556 1967.9557 ## 974 Mauritius Africa 1957 58.089 609816 2034.0380 ## 975 Mauritius Africa 1962 60.246 701016 2529.0675 ## 976 Mauritius Africa 1967 61.557 789309 2475.3876 ## 977 Mauritius Africa 1972 62.944 851334 2575.4842 ## 978 Mauritius Africa 1977 64.930 913025 3710.9830 ## 979 Mauritius Africa 1982 66.711 992040 3688.0377 ## 980 Mauritius Africa 1987 68.740 1042663 4783.5869 ## 981 Mauritius Africa 1992 69.745 1096202 6058.2538 ## 982 Mauritius Africa 1997 70.736 1149818 7425.7053 ## 983 Mauritius Africa 2002 71.954 1200206 9021.8159 ## 984 Mauritius Africa 2007 72.801 1250882 10956.9911 ## 1021 Morocco Africa 1952 42.873 9939217 1688.2036 ## 1022 Morocco Africa 1957 45.423 11406350 1642.0023 ## 1023 Morocco Africa 1962 47.924 13056604 1566.3535 ## 1024 Morocco Africa 1967 50.335 14770296 1711.0448 ## 1025 Morocco Africa 1972 52.862 16660670 1930.1950 ## 1026 Morocco Africa 1977 55.730 18396941 2370.6200 ## 1027 Morocco Africa 1982 59.650 20198730 2702.6204 ## 1028 Morocco Africa 1987 62.677 22987397 2755.0470 ## 1029 Morocco Africa 1992 65.393 25798239 2948.0473 ## 1030 Morocco Africa 1997 67.660 28529501 2982.1019 ## 1031 Morocco Africa 2002 69.615 31167783 3258.4956 ## 1032 Morocco Africa 2007 71.164 33757175 3820.1752 ## 1033 Mozambique Africa 1952 31.286 6446316 468.5260 ## 1034 Mozambique Africa 1957 33.779 7038035 495.5868 ## 1035 Mozambique Africa 1962 36.161 7788944 556.6864 ## 1036 Mozambique Africa 1967 38.113 8680909 566.6692 ## 1037 Mozambique Africa 1972 40.328 9809596 724.9178 ## 1038 Mozambique Africa 1977 42.495 11127868 502.3197 ## 1039 Mozambique Africa 1982 42.795 12587223 462.2114 ## 1040 Mozambique Africa 1987 42.861 12891952 389.8762 ## 1041 Mozambique Africa 1992 44.284 13160731 410.8968 ## 1042 Mozambique Africa 1997 46.344 16603334 472.3461 ## 1043 Mozambique Africa 2002 44.026 18473780 633.6179 ## 1044 Mozambique Africa 2007 42.082 19951656 823.6856 ## 1057 Namibia Africa 1952 41.725 485831 2423.7804 ## 1058 Namibia Africa 1957 45.226 548080 2621.4481 ## 1059 Namibia Africa 1962 48.386 621392 3173.2156 ## 1060 Namibia Africa 1967 51.159 706640 3793.6948 ## 1061 Namibia Africa 1972 53.867 821782 3746.0809 ## 1062 Namibia Africa 1977 56.437 977026 3876.4860 ## 1063 Namibia Africa 1982 58.968 1099010 4191.1005 ## 1064 Namibia Africa 1987 60.835 1278184 3693.7313 ## 1065 Namibia Africa 1992 61.999 1554253 3804.5380 ## 1066 Namibia Africa 1997 58.909 1774766 3899.5243 ## 1067 Namibia Africa 2002 51.479 1972153 4072.3248 ## 1068 Namibia Africa 2007 52.906 2055080 4811.0604 ## 1117 Niger Africa 1952 37.444 3379468 761.8794 ## 1118 Niger Africa 1957 38.598 3692184 835.5234 ## 1119 Niger Africa 1962 39.487 4076008 997.7661 ## 1120 Niger Africa 1967 40.118 4534062 1054.3849 ## 1121 Niger Africa 1972 40.546 5060262 954.2092 ## 1122 Niger Africa 1977 41.291 5682086 808.8971 ## 1123 Niger Africa 1982 42.598 6437188 909.7221 ## 1124 Niger Africa 1987 44.555 7332638 668.3000 ## 1125 Niger Africa 1992 47.391 8392818 581.1827 ## 1126 Niger Africa 1997 51.313 9666252 580.3052 ## 1127 Niger Africa 2002 54.496 11140655 601.0745 ## 1128 Niger Africa 2007 56.867 12894865 619.6769 ## 1129 Nigeria Africa 1952 36.324 33119096 1077.2819 ## 1130 Nigeria Africa 1957 37.802 37173340 1100.5926 ## 1131 Nigeria Africa 1962 39.360 41871351 1150.9275 ## 1132 Nigeria Africa 1967 41.040 47287752 1014.5141 ## 1133 Nigeria Africa 1972 42.821 53740085 1698.3888 ## 1134 Nigeria Africa 1977 44.514 62209173 1981.9518 ## 1135 Nigeria Africa 1982 45.826 73039376 1576.9738 ## 1136 Nigeria Africa 1987 46.886 81551520 1385.0296 ## 1137 Nigeria Africa 1992 47.472 93364244 1619.8482 ## 1138 Nigeria Africa 1997 47.464 106207839 1624.9413 ## 1139 Nigeria Africa 2002 46.608 119901274 1615.2864 ## 1140 Nigeria Africa 2007 46.859 135031164 2013.9773 ## 1261 Reunion Africa 1952 52.724 257700 2718.8853 ## 1262 Reunion Africa 1957 55.090 308700 2769.4518 ## 1263 Reunion Africa 1962 57.666 358900 3173.7233 ## 1264 Reunion Africa 1967 60.542 414024 4021.1757 ## 1265 Reunion Africa 1972 64.274 461633 5047.6586 ## 1266 Reunion Africa 1977 67.064 492095 4319.8041 ## 1267 Reunion Africa 1982 69.885 517810 5267.2194 ## 1268 Reunion Africa 1987 71.913 562035 5303.3775 ## 1269 Reunion Africa 1992 73.615 622191 6101.2558 ## 1270 Reunion Africa 1997 74.772 684810 6071.9414 ## 1271 Reunion Africa 2002 75.744 743981 6316.1652 ## 1272 Reunion Africa 2007 76.442 798094 7670.1226 ## 1285 Rwanda Africa 1952 40.000 2534927 493.3239 ## 1286 Rwanda Africa 1957 41.500 2822082 540.2894 ## 1287 Rwanda Africa 1962 43.000 3051242 597.4731 ## 1288 Rwanda Africa 1967 44.100 3451079 510.9637 ## 1289 Rwanda Africa 1972 44.600 3992121 590.5807 ## 1290 Rwanda Africa 1977 45.000 4657072 670.0806 ## 1291 Rwanda Africa 1982 46.218 5507565 881.5706 ## 1292 Rwanda Africa 1987 44.020 6349365 847.9912 ## 1293 Rwanda Africa 1992 23.599 7290203 737.0686 ## 1294 Rwanda Africa 1997 36.087 7212583 589.9445 ## 1295 Rwanda Africa 2002 43.413 7852401 785.6538 ## 1296 Rwanda Africa 2007 46.242 8860588 863.0885 ## 1297 Sao Tome and Principe Africa 1952 46.471 60011 879.5836 ## 1298 Sao Tome and Principe Africa 1957 48.945 61325 860.7369 ## 1299 Sao Tome and Principe Africa 1962 51.893 65345 1071.5511 ## 1300 Sao Tome and Principe Africa 1967 54.425 70787 1384.8406 ## 1301 Sao Tome and Principe Africa 1972 56.480 76595 1532.9853 ## 1302 Sao Tome and Principe Africa 1977 58.550 86796 1737.5617 ## 1303 Sao Tome and Principe Africa 1982 60.351 98593 1890.2181 ## 1304 Sao Tome and Principe Africa 1987 61.728 110812 1516.5255 ## 1305 Sao Tome and Principe Africa 1992 62.742 125911 1428.7778 ## 1306 Sao Tome and Principe Africa 1997 63.306 145608 1339.0760 ## 1307 Sao Tome and Principe Africa 2002 64.337 170372 1353.0924 ## 1308 Sao Tome and Principe Africa 2007 65.528 199579 1598.4351 ## 1321 Senegal Africa 1952 37.278 2755589 1450.3570 ## 1322 Senegal Africa 1957 39.329 3054547 1567.6530 ## 1323 Senegal Africa 1962 41.454 3430243 1654.9887 ## 1324 Senegal Africa 1967 43.563 3965841 1612.4046 ## 1325 Senegal Africa 1972 45.815 4588696 1597.7121 ## 1326 Senegal Africa 1977 48.879 5260855 1561.7691 ## 1327 Senegal Africa 1982 52.379 6147783 1518.4800 ## 1328 Senegal Africa 1987 55.769 7171347 1441.7207 ## 1329 Senegal Africa 1992 58.196 8307920 1367.8994 ## 1330 Senegal Africa 1997 60.187 9535314 1392.3683 ## 1331 Senegal Africa 2002 61.600 10870037 1519.6353 ## 1332 Senegal Africa 2007 63.062 12267493 1712.4721 ## 1345 Sierra Leone Africa 1952 30.331 2143249 879.7877 ## 1346 Sierra Leone Africa 1957 31.570 2295678 1004.4844 ## 1347 Sierra Leone Africa 1962 32.767 2467895 1116.6399 ## 1348 Sierra Leone Africa 1967 34.113 2662190 1206.0435 ## 1349 Sierra Leone Africa 1972 35.400 2879013 1353.7598 ## 1350 Sierra Leone Africa 1977 36.788 3140897 1348.2852 ## 1351 Sierra Leone Africa 1982 38.445 3464522 1465.0108 ## 1352 Sierra Leone Africa 1987 40.006 3868905 1294.4478 ## 1353 Sierra Leone Africa 1992 38.333 4260884 1068.6963 ## 1354 Sierra Leone Africa 1997 39.897 4578212 574.6482 ## 1355 Sierra Leone Africa 2002 41.012 5359092 699.4897 ## 1356 Sierra Leone Africa 2007 42.568 6144562 862.5408 ## 1393 Somalia Africa 1952 32.978 2526994 1135.7498 ## 1394 Somalia Africa 1957 34.977 2780415 1258.1474 ## 1395 Somalia Africa 1962 36.981 3080153 1369.4883 ## 1396 Somalia Africa 1967 38.977 3428839 1284.7332 ## 1397 Somalia Africa 1972 40.973 3840161 1254.5761 ## 1398 Somalia Africa 1977 41.974 4353666 1450.9925 ## 1399 Somalia Africa 1982 42.955 5828892 1176.8070 ## 1400 Somalia Africa 1987 44.501 6921858 1093.2450 ## 1401 Somalia Africa 1992 39.658 6099799 926.9603 ## 1402 Somalia Africa 1997 43.795 6633514 930.5964 ## 1403 Somalia Africa 2002 45.936 7753310 882.0818 ## 1404 Somalia Africa 2007 48.159 9118773 926.1411 ## 1405 South Africa Africa 1952 45.009 14264935 4725.2955 ## 1406 South Africa Africa 1957 47.985 16151549 5487.1042 ## 1407 South Africa Africa 1962 49.951 18356657 5768.7297 ## 1408 South Africa Africa 1967 51.927 20997321 7114.4780 ## 1409 South Africa Africa 1972 53.696 23935810 7765.9626 ## 1410 South Africa Africa 1977 55.527 27129932 8028.6514 ## 1411 South Africa Africa 1982 58.161 31140029 8568.2662 ## 1412 South Africa Africa 1987 60.834 35933379 7825.8234 ## 1413 South Africa Africa 1992 61.888 39964159 7225.0693 ## 1414 South Africa Africa 1997 60.236 42835005 7479.1882 ## 1415 South Africa Africa 2002 53.365 44433622 7710.9464 ## 1416 South Africa Africa 2007 49.339 43997828 9269.6578 ## 1441 Sudan Africa 1952 38.635 8504667 1615.9911 ## 1442 Sudan Africa 1957 39.624 9753392 1770.3371 ## 1443 Sudan Africa 1962 40.870 11183227 1959.5938 ## 1444 Sudan Africa 1967 42.858 12716129 1687.9976 ## 1445 Sudan Africa 1972 45.083 14597019 1659.6528 ## 1446 Sudan Africa 1977 47.800 17104986 2202.9884 ## 1447 Sudan Africa 1982 50.338 20367053 1895.5441 ## 1448 Sudan Africa 1987 51.744 24725960 1507.8192 ## 1449 Sudan Africa 1992 53.556 28227588 1492.1970 ## 1450 Sudan Africa 1997 55.373 32160729 1632.2108 ## 1451 Sudan Africa 2002 56.369 37090298 1993.3983 ## 1452 Sudan Africa 2007 58.556 42292929 2602.3950 ## 1453 Swaziland Africa 1952 41.407 290243 1148.3766 ## 1454 Swaziland Africa 1957 43.424 326741 1244.7084 ## 1455 Swaziland Africa 1962 44.992 370006 1856.1821 ## 1456 Swaziland Africa 1967 46.633 420690 2613.1017 ## 1457 Swaziland Africa 1972 49.552 480105 3364.8366 ## 1458 Swaziland Africa 1977 52.537 551425 3781.4106 ## 1459 Swaziland Africa 1982 55.561 649901 3895.3840 ## 1460 Swaziland Africa 1987 57.678 779348 3984.8398 ## 1461 Swaziland Africa 1992 58.474 962344 3553.0224 ## 1462 Swaziland Africa 1997 54.289 1054486 3876.7685 ## 1463 Swaziland Africa 2002 43.869 1130269 4128.1169 ## 1464 Swaziland Africa 2007 39.613 1133066 4513.4806 ## 1513 Tanzania Africa 1952 41.215 8322925 716.6501 ## 1514 Tanzania Africa 1957 42.974 9452826 698.5356 ## 1515 Tanzania Africa 1962 44.246 10863958 722.0038 ## 1516 Tanzania Africa 1967 45.757 12607312 848.2187 ## 1517 Tanzania Africa 1972 47.620 14706593 915.9851 ## 1518 Tanzania Africa 1977 49.919 17129565 962.4923 ## 1519 Tanzania Africa 1982 50.608 19844382 874.2426 ## 1520 Tanzania Africa 1987 51.535 23040630 831.8221 ## 1521 Tanzania Africa 1992 50.440 26605473 825.6825 ## 1522 Tanzania Africa 1997 48.466 30686889 789.1862 ## 1523 Tanzania Africa 2002 49.651 34593779 899.0742 ## 1524 Tanzania Africa 2007 52.517 38139640 1107.4822 ## 1537 Togo Africa 1952 38.596 1219113 859.8087 ## 1538 Togo Africa 1957 41.208 1357445 925.9083 ## 1539 Togo Africa 1962 43.922 1528098 1067.5348 ## 1540 Togo Africa 1967 46.769 1735550 1477.5968 ## 1541 Togo Africa 1972 49.759 2056351 1649.6602 ## 1542 Togo Africa 1977 52.887 2308582 1532.7770 ## 1543 Togo Africa 1982 55.471 2644765 1344.5780 ## 1544 Togo Africa 1987 56.941 3154264 1202.2014 ## 1545 Togo Africa 1992 58.061 3747553 1034.2989 ## 1546 Togo Africa 1997 58.390 4320890 982.2869 ## 1547 Togo Africa 2002 57.561 4977378 886.2206 ## 1548 Togo Africa 2007 58.420 5701579 882.9699 ## 1561 Tunisia Africa 1952 44.600 3647735 1468.4756 ## 1562 Tunisia Africa 1957 47.100 3950849 1395.2325 ## 1563 Tunisia Africa 1962 49.579 4286552 1660.3032 ## 1564 Tunisia Africa 1967 52.053 4786986 1932.3602 ## 1565 Tunisia Africa 1972 55.602 5303507 2753.2860 ## 1566 Tunisia Africa 1977 59.837 6005061 3120.8768 ## 1567 Tunisia Africa 1982 64.048 6734098 3560.2332 ## 1568 Tunisia Africa 1987 66.894 7724976 3810.4193 ## 1569 Tunisia Africa 1992 70.001 8523077 4332.7202 ## 1570 Tunisia Africa 1997 71.973 9231669 4876.7986 ## 1571 Tunisia Africa 2002 73.042 9770575 5722.8957 ## 1572 Tunisia Africa 2007 73.923 10276158 7092.9230 ## 1585 Uganda Africa 1952 39.978 5824797 734.7535 ## 1586 Uganda Africa 1957 42.571 6675501 774.3711 ## 1587 Uganda Africa 1962 45.344 7688797 767.2717 ## 1588 Uganda Africa 1967 48.051 8900294 908.9185 ## 1589 Uganda Africa 1972 51.016 10190285 950.7359 ## 1590 Uganda Africa 1977 50.350 11457758 843.7331 ## 1591 Uganda Africa 1982 49.849 12939400 682.2662 ## 1592 Uganda Africa 1987 51.509 15283050 617.7244 ## 1593 Uganda Africa 1992 48.825 18252190 644.1708 ## 1594 Uganda Africa 1997 44.578 21210254 816.5591 ## 1595 Uganda Africa 2002 47.813 24739869 927.7210 ## 1596 Uganda Africa 2007 51.542 29170398 1056.3801 ## 1681 Zambia Africa 1952 42.038 2672000 1147.3888 ## 1682 Zambia Africa 1957 44.077 3016000 1311.9568 ## 1683 Zambia Africa 1962 46.023 3421000 1452.7258 ## 1684 Zambia Africa 1967 47.768 3900000 1777.0773 ## 1685 Zambia Africa 1972 50.107 4506497 1773.4983 ## 1686 Zambia Africa 1977 51.386 5216550 1588.6883 ## 1687 Zambia Africa 1982 51.821 6100407 1408.6786 ## 1688 Zambia Africa 1987 50.821 7272406 1213.3151 ## 1689 Zambia Africa 1992 46.100 8381163 1210.8846 ## 1690 Zambia Africa 1997 40.238 9417789 1071.3538 ## 1691 Zambia Africa 2002 39.193 10595811 1071.6139 ## 1692 Zambia Africa 2007 42.384 11746035 1271.2116 ## 1693 Zimbabwe Africa 1952 48.451 3080907 406.8841 ## 1694 Zimbabwe Africa 1957 50.469 3646340 518.7643 ## 1695 Zimbabwe Africa 1962 52.358 4277736 527.2722 ## 1696 Zimbabwe Africa 1967 53.995 4995432 569.7951 ## 1697 Zimbabwe Africa 1972 55.635 5861135 799.3622 ## 1698 Zimbabwe Africa 1977 57.674 6642107 685.5877 ## 1699 Zimbabwe Africa 1982 60.363 7636524 788.8550 ## 1700 Zimbabwe Africa 1987 62.351 9216418 706.1573 ## 1701 Zimbabwe Africa 1992 60.377 10704340 693.4208 ## 1702 Zimbabwe Africa 1997 46.809 11404948 792.4500 ## 1703 Zimbabwe Africa 2002 39.989 11926563 672.0386 ## 1704 Zimbabwe Africa 2007 43.487 12311143 469.7093 ``` --- ## data.frame - indexing - A new object can be created from the results of indexing ```r year_2007 <- gapminder[gapminder$year==2007, ] year_2007 ``` ``` ## country continent year lifeExp pop gdpPercap ## 12 Afghanistan Asia 2007 43.828 31889923 974.5803 ## 24 Albania Europe 2007 76.423 3600523 5937.0295 ## 36 Algeria Africa 2007 72.301 33333216 6223.3675 ## 48 Angola Africa 2007 42.731 12420476 4797.2313 ## 60 Argentina Americas 2007 75.320 40301927 12779.3796 ## 72 Australia Oceania 2007 81.235 20434176 34435.3674 ## 84 Austria Europe 2007 79.829 8199783 36126.4927 ## 96 Bahrain Asia 2007 75.635 708573 29796.0483 ## 108 Bangladesh Asia 2007 64.062 150448339 1391.2538 ## 120 Belgium Europe 2007 79.441 10392226 33692.6051 ## 132 Benin Africa 2007 56.728 8078314 1441.2849 ## 144 Bolivia Americas 2007 65.554 9119152 3822.1371 ## 156 Bosnia and Herzegovina Europe 2007 74.852 4552198 7446.2988 ## 168 Botswana Africa 2007 50.728 1639131 12569.8518 ## 180 Brazil Americas 2007 72.390 190010647 9065.8008 ## 192 Bulgaria Europe 2007 73.005 7322858 10680.7928 ## 204 Burkina Faso Africa 2007 52.295 14326203 1217.0330 ## 216 Burundi Africa 2007 49.580 8390505 430.0707 ## 228 Cambodia Asia 2007 59.723 14131858 1713.7787 ## 240 Cameroon Africa 2007 50.430 17696293 2042.0952 ## 252 Canada Americas 2007 80.653 33390141 36319.2350 ## 264 Central African Republic Africa 2007 44.741 4369038 706.0165 ## 276 Chad Africa 2007 50.651 10238807 1704.0637 ## 288 Chile Americas 2007 78.553 16284741 13171.6388 ## 300 China Asia 2007 72.961 1318683096 4959.1149 ## 312 Colombia Americas 2007 72.889 44227550 7006.5804 ## 324 Comoros Africa 2007 65.152 710960 986.1479 ## 336 Congo, Dem. Rep. Africa 2007 46.462 64606759 277.5519 ## 348 Congo, Rep. Africa 2007 55.322 3800610 3632.5578 ## 360 Costa Rica Americas 2007 78.782 4133884 9645.0614 ## 372 Cote d'Ivoire Africa 2007 48.328 18013409 1544.7501 ## 384 Croatia Europe 2007 75.748 4493312 14619.2227 ## 396 Cuba Americas 2007 78.273 11416987 8948.1029 ## 408 Czech Republic Europe 2007 76.486 10228744 22833.3085 ## 420 Denmark Europe 2007 78.332 5468120 35278.4187 ## 432 Djibouti Africa 2007 54.791 496374 2082.4816 ## 444 Dominican Republic Americas 2007 72.235 9319622 6025.3748 ## 456 Ecuador Americas 2007 74.994 13755680 6873.2623 ## 468 Egypt Africa 2007 71.338 80264543 5581.1810 ## 480 El Salvador Americas 2007 71.878 6939688 5728.3535 ## 492 Equatorial Guinea Africa 2007 51.579 551201 12154.0897 ## 504 Eritrea Africa 2007 58.040 4906585 641.3695 ## 516 Ethiopia Africa 2007 52.947 76511887 690.8056 ## 528 Finland Europe 2007 79.313 5238460 33207.0844 ## 540 France Europe 2007 80.657 61083916 30470.0167 ## 552 Gabon Africa 2007 56.735 1454867 13206.4845 ## 564 Gambia Africa 2007 59.448 1688359 752.7497 ## 576 Germany Europe 2007 79.406 82400996 32170.3744 ## 588 Ghana Africa 2007 60.022 22873338 1327.6089 ## 600 Greece Europe 2007 79.483 10706290 27538.4119 ## 612 Guatemala Americas 2007 70.259 12572928 5186.0500 ## 624 Guinea Africa 2007 56.007 9947814 942.6542 ## 636 Guinea-Bissau Africa 2007 46.388 1472041 579.2317 ## 648 Haiti Americas 2007 60.916 8502814 1201.6372 ## 660 Honduras Americas 2007 70.198 7483763 3548.3308 ## 672 Hong Kong, China Asia 2007 82.208 6980412 39724.9787 ## 684 Hungary Europe 2007 73.338 9956108 18008.9444 ## 696 Iceland Europe 2007 81.757 301931 36180.7892 ## 708 India Asia 2007 64.698 1110396331 2452.2104 ## 720 Indonesia Asia 2007 70.650 223547000 3540.6516 ## 732 Iran Asia 2007 70.964 69453570 11605.7145 ## 744 Iraq Asia 2007 59.545 27499638 4471.0619 ## 756 Ireland Europe 2007 78.885 4109086 40675.9964 ## 768 Israel Asia 2007 80.745 6426679 25523.2771 ## 780 Italy Europe 2007 80.546 58147733 28569.7197 ## 792 Jamaica Americas 2007 72.567 2780132 7320.8803 ## 804 Japan Asia 2007 82.603 127467972 31656.0681 ## 816 Jordan Asia 2007 72.535 6053193 4519.4612 ## 828 Kenya Africa 2007 54.110 35610177 1463.2493 ## 840 Korea, Dem. Rep. Asia 2007 67.297 23301725 1593.0655 ## 852 Korea, Rep. Asia 2007 78.623 49044790 23348.1397 ## 864 Kuwait Asia 2007 77.588 2505559 47306.9898 ## 876 Lebanon Asia 2007 71.993 3921278 10461.0587 ## 888 Lesotho Africa 2007 42.592 2012649 1569.3314 ## 900 Liberia Africa 2007 45.678 3193942 414.5073 ## 912 Libya Africa 2007 73.952 6036914 12057.4993 ## 924 Madagascar Africa 2007 59.443 19167654 1044.7701 ## 936 Malawi Africa 2007 48.303 13327079 759.3499 ## 948 Malaysia Asia 2007 74.241 24821286 12451.6558 ## 960 Mali Africa 2007 54.467 12031795 1042.5816 ## 972 Mauritania Africa 2007 64.164 3270065 1803.1515 ## 984 Mauritius Africa 2007 72.801 1250882 10956.9911 ## 996 Mexico Americas 2007 76.195 108700891 11977.5750 ## 1008 Mongolia Asia 2007 66.803 2874127 3095.7723 ## 1020 Montenegro Europe 2007 74.543 684736 9253.8961 ## 1032 Morocco Africa 2007 71.164 33757175 3820.1752 ## 1044 Mozambique Africa 2007 42.082 19951656 823.6856 ## 1056 Myanmar Asia 2007 62.069 47761980 944.0000 ## 1068 Namibia Africa 2007 52.906 2055080 4811.0604 ## 1080 Nepal Asia 2007 63.785 28901790 1091.3598 ## 1092 Netherlands Europe 2007 79.762 16570613 36797.9333 ## 1104 New Zealand Oceania 2007 80.204 4115771 25185.0091 ## 1116 Nicaragua Americas 2007 72.899 5675356 2749.3210 ## 1128 Niger Africa 2007 56.867 12894865 619.6769 ## 1140 Nigeria Africa 2007 46.859 135031164 2013.9773 ## 1152 Norway Europe 2007 80.196 4627926 49357.1902 ## 1164 Oman Asia 2007 75.640 3204897 22316.1929 ## 1176 Pakistan Asia 2007 65.483 169270617 2605.9476 ## 1188 Panama Americas 2007 75.537 3242173 9809.1856 ## 1200 Paraguay Americas 2007 71.752 6667147 4172.8385 ## 1212 Peru Americas 2007 71.421 28674757 7408.9056 ## 1224 Philippines Asia 2007 71.688 91077287 3190.4810 ## 1236 Poland Europe 2007 75.563 38518241 15389.9247 ## 1248 Portugal Europe 2007 78.098 10642836 20509.6478 ## 1260 Puerto Rico Americas 2007 78.746 3942491 19328.7090 ## 1272 Reunion Africa 2007 76.442 798094 7670.1226 ## 1284 Romania Europe 2007 72.476 22276056 10808.4756 ## 1296 Rwanda Africa 2007 46.242 8860588 863.0885 ## 1308 Sao Tome and Principe Africa 2007 65.528 199579 1598.4351 ## 1320 Saudi Arabia Asia 2007 72.777 27601038 21654.8319 ## 1332 Senegal Africa 2007 63.062 12267493 1712.4721 ## 1344 Serbia Europe 2007 74.002 10150265 9786.5347 ## 1356 Sierra Leone Africa 2007 42.568 6144562 862.5408 ## 1368 Singapore Asia 2007 79.972 4553009 47143.1796 ## 1380 Slovak Republic Europe 2007 74.663 5447502 18678.3144 ## 1392 Slovenia Europe 2007 77.926 2009245 25768.2576 ## 1404 Somalia Africa 2007 48.159 9118773 926.1411 ## 1416 South Africa Africa 2007 49.339 43997828 9269.6578 ## 1428 Spain Europe 2007 80.941 40448191 28821.0637 ## 1440 Sri Lanka Asia 2007 72.396 20378239 3970.0954 ## 1452 Sudan Africa 2007 58.556 42292929 2602.3950 ## 1464 Swaziland Africa 2007 39.613 1133066 4513.4806 ## 1476 Sweden Europe 2007 80.884 9031088 33859.7484 ## 1488 Switzerland Europe 2007 81.701 7554661 37506.4191 ## 1500 Syria Asia 2007 74.143 19314747 4184.5481 ## 1512 Taiwan Asia 2007 78.400 23174294 28718.2768 ## 1524 Tanzania Africa 2007 52.517 38139640 1107.4822 ## 1536 Thailand Asia 2007 70.616 65068149 7458.3963 ## 1548 Togo Africa 2007 58.420 5701579 882.9699 ## 1560 Trinidad and Tobago Americas 2007 69.819 1056608 18008.5092 ## 1572 Tunisia Africa 2007 73.923 10276158 7092.9230 ## 1584 Turkey Europe 2007 71.777 71158647 8458.2764 ## 1596 Uganda Africa 2007 51.542 29170398 1056.3801 ## 1608 United Kingdom Europe 2007 79.425 60776238 33203.2613 ## 1620 United States Americas 2007 78.242 301139947 42951.6531 ## 1632 Uruguay Americas 2007 76.384 3447496 10611.4630 ## 1644 Venezuela Americas 2007 73.747 26084662 11415.8057 ## 1656 Vietnam Asia 2007 74.249 85262356 2441.5764 ## 1668 West Bank and Gaza Asia 2007 73.422 4018332 3025.3498 ## 1680 Yemen, Rep. Asia 2007 62.698 22211743 2280.7699 ## 1692 Zambia Africa 2007 42.384 11746035 1271.2116 ## 1704 Zimbabwe Africa 2007 43.487 12311143 469.7093 ``` --- ## data.frame - indexing - object[row,column] ```r gapminder[2, 1] gapminder[1, 3] gapminder[, 1] gapminder[1] gapminder[1, ] gapminder[0, 3] gapminder[-2, ] gapminder[, -1] gapminder[c(1,5,7), ] gapminder[, c(1,2)] gapminder[gapminder$pop<100000, ] gapminder[gapminder$year==1952, c(4)] ``` --- ## data.frame - indexing ```r gapminder$lifeExp ``` ``` ## [1] 28.801 30.332 31.997 34.020 36.088 38.438 ``` ```r gapminder$continent ``` ``` ## [1] Asia Asia Asia Asia Asia Asia ## Levels: Africa Americas Asia Europe Oceania ``` --- ## **dplyr** ```r # install.packages('dplyr') # devtools::install_github("tidyverse/dplyr") library('dplyr') ``` - **dplyr** is a package for data exploration and transformation - Basic functions of the **dplyr** package are `select()`, `arrange()`, `filter()`, `mutate()`, and `summarize()` - Another important function is `group_by()` - Additionally, **dplyr** package implements a pipe operator (`%>%`), joins and set operations, and moving window operations --- ## **dplyr** - `select()` - `select()` function picks columns by its name - Many additional features - `?dplyr::select()` <table> <thead> <tr> <th style="text-align:left;"> country </th> <th style="text-align:left;"> continent </th> <th style="text-align:right;"> year </th> <th style="text-align:right;"> lifeExp </th> <th style="text-align:right;"> pop </th> <th style="text-align:right;"> gdpPercap </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:left;"> Asia </td> <td style="text-align:right;"> 1952 </td> <td style="text-align:right;"> 28.801 </td> <td style="text-align:right;"> 8425333 </td> <td style="text-align:right;"> 779.4453 </td> </tr> <tr> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:left;"> Asia </td> <td style="text-align:right;"> 1957 </td> <td style="text-align:right;"> 30.332 </td> <td style="text-align:right;"> 9240934 </td> <td style="text-align:right;"> 820.8530 </td> </tr> <tr> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:left;"> Asia </td> <td style="text-align:right;"> 1962 </td> <td style="text-align:right;"> 31.997 </td> <td style="text-align:right;"> 10267083 </td> <td style="text-align:right;"> 853.1007 </td> </tr> <tr> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:left;"> Asia </td> <td style="text-align:right;"> 1967 </td> <td style="text-align:right;"> 34.020 </td> <td style="text-align:right;"> 11537966 </td> <td style="text-align:right;"> 836.1971 </td> </tr> <tr> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:left;"> Asia </td> <td style="text-align:right;"> 1972 </td> <td style="text-align:right;"> 36.088 </td> <td style="text-align:right;"> 13079460 </td> <td style="text-align:right;"> 739.9811 </td> </tr> <tr> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:left;"> Asia </td> <td style="text-align:right;"> 1977 </td> <td style="text-align:right;"> 38.438 </td> <td style="text-align:right;"> 14880372 </td> <td style="text-align:right;"> 786.1134 </td> </tr> </tbody> </table> --- ## **dplyr** - `select()` ```r gapminder_sel1 <- select(gapminder, country, year, pop) ``` <table> <thead> <tr> <th style="text-align:left;"> country </th> <th style="text-align:right;"> year </th> <th style="text-align:right;"> pop </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:right;"> 1952 </td> <td style="text-align:right;"> 8425333 </td> </tr> <tr> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:right;"> 1957 </td> <td style="text-align:right;"> 9240934 </td> </tr> </tbody> </table> ```r gapminder_sel2 <- select(gapminder, -continent) ``` <table> <thead> <tr> <th style="text-align:left;"> country </th> <th style="text-align:right;"> year </th> <th style="text-align:right;"> lifeExp </th> <th style="text-align:right;"> pop </th> <th style="text-align:right;"> gdpPercap </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:right;"> 1952 </td> <td style="text-align:right;"> 28.801 </td> <td style="text-align:right;"> 8425333 </td> <td style="text-align:right;"> 779.4453 </td> </tr> <tr> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:right;"> 1957 </td> <td style="text-align:right;"> 30.332 </td> <td style="text-align:right;"> 9240934 </td> <td style="text-align:right;"> 820.8530 </td> </tr> </tbody> </table> --- ## **dplyr** - `arrange()` - `arrange()` reorder rows in ascending order... ```r gapminder_arr1 <- arrange(gapminder, lifeExp) ``` <table> <thead> <tr> <th style="text-align:left;"> country </th> <th style="text-align:left;"> continent </th> <th style="text-align:right;"> year </th> <th style="text-align:right;"> lifeExp </th> <th style="text-align:right;"> pop </th> <th style="text-align:right;"> gdpPercap </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Rwanda </td> <td style="text-align:left;"> Africa </td> <td style="text-align:right;"> 1992 </td> <td style="text-align:right;"> 23.599 </td> <td style="text-align:right;"> 7290203 </td> <td style="text-align:right;"> 737.0686 </td> </tr> <tr> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:left;"> Asia </td> <td style="text-align:right;"> 1952 </td> <td style="text-align:right;"> 28.801 </td> <td style="text-align:right;"> 8425333 </td> <td style="text-align:right;"> 779.4453 </td> </tr> <tr> <td style="text-align:left;"> Gambia </td> <td style="text-align:left;"> Africa </td> <td style="text-align:right;"> 1952 </td> <td style="text-align:right;"> 30.000 </td> <td style="text-align:right;"> 284320 </td> <td style="text-align:right;"> 485.2307 </td> </tr> <tr> <td style="text-align:left;"> Angola </td> <td style="text-align:left;"> Africa </td> <td style="text-align:right;"> 1952 </td> <td style="text-align:right;"> 30.015 </td> <td style="text-align:right;"> 4232095 </td> <td style="text-align:right;"> 3520.6103 </td> </tr> <tr> <td style="text-align:left;"> Sierra Leone </td> <td style="text-align:left;"> Africa </td> <td style="text-align:right;"> 1952 </td> <td style="text-align:right;"> 30.331 </td> <td style="text-align:right;"> 2143249 </td> <td style="text-align:right;"> 879.7877 </td> </tr> </tbody> </table> --- ## **dplyr** - `arrange()` - ...or descending order ```r gapminder_arr1 <- arrange(gapminder, desc(lifeExp)) ``` <table> <thead> <tr> <th style="text-align:left;"> country </th> <th style="text-align:left;"> continent </th> <th style="text-align:right;"> year </th> <th style="text-align:right;"> lifeExp </th> <th style="text-align:right;"> pop </th> <th style="text-align:right;"> gdpPercap </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Japan </td> <td style="text-align:left;"> Asia </td> <td style="text-align:right;"> 2007 </td> <td style="text-align:right;"> 82.603 </td> <td style="text-align:right;"> 127467972 </td> <td style="text-align:right;"> 31656.07 </td> </tr> <tr> <td style="text-align:left;"> Hong Kong, China </td> <td style="text-align:left;"> Asia </td> <td style="text-align:right;"> 2007 </td> <td style="text-align:right;"> 82.208 </td> <td style="text-align:right;"> 6980412 </td> <td style="text-align:right;"> 39724.98 </td> </tr> <tr> <td style="text-align:left;"> Japan </td> <td style="text-align:left;"> Asia </td> <td style="text-align:right;"> 2002 </td> <td style="text-align:right;"> 82.000 </td> <td style="text-align:right;"> 127065841 </td> <td style="text-align:right;"> 28604.59 </td> </tr> <tr> <td style="text-align:left;"> Iceland </td> <td style="text-align:left;"> Europe </td> <td style="text-align:right;"> 2007 </td> <td style="text-align:right;"> 81.757 </td> <td style="text-align:right;"> 301931 </td> <td style="text-align:right;"> 36180.79 </td> </tr> <tr> <td style="text-align:left;"> Switzerland </td> <td style="text-align:left;"> Europe </td> <td style="text-align:right;"> 2007 </td> <td style="text-align:right;"> 81.701 </td> <td style="text-align:right;"> 7554661 </td> <td style="text-align:right;"> 37506.42 </td> </tr> </tbody> </table> --- ## **dplyr** - `filter()` - `filter()` keeps rows matching given criteria - Logical operators, such as `==, !=, >, >=, <, <=, &, |` can be used ```r gapminder_fil1 <- filter(gapminder, year == 2007) ``` <table> <thead> <tr> <th style="text-align:left;"> country </th> <th style="text-align:left;"> continent </th> <th style="text-align:right;"> year </th> <th style="text-align:right;"> lifeExp </th> <th style="text-align:right;"> pop </th> <th style="text-align:right;"> gdpPercap </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:left;"> Asia </td> <td style="text-align:right;"> 2007 </td> <td style="text-align:right;"> 43.828 </td> <td style="text-align:right;"> 31889923 </td> <td style="text-align:right;"> 974.5803 </td> </tr> <tr> <td style="text-align:left;"> Albania </td> <td style="text-align:left;"> Europe </td> <td style="text-align:right;"> 2007 </td> <td style="text-align:right;"> 76.423 </td> <td style="text-align:right;"> 3600523 </td> <td style="text-align:right;"> 5937.0295 </td> </tr> </tbody> </table> --- ## **dplyr** - `filter()` ```r gapminder_fil2 <- filter(gapminder, year == 2007 & pop <= 999999) ``` <table> <thead> <tr> <th style="text-align:left;"> country </th> <th style="text-align:left;"> continent </th> <th style="text-align:right;"> year </th> <th style="text-align:right;"> lifeExp </th> <th style="text-align:right;"> pop </th> <th style="text-align:right;"> gdpPercap </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Bahrain </td> <td style="text-align:left;"> Asia </td> <td style="text-align:right;"> 2007 </td> <td style="text-align:right;"> 75.635 </td> <td style="text-align:right;"> 708573 </td> <td style="text-align:right;"> 29796.0483 </td> </tr> <tr> <td style="text-align:left;"> Comoros </td> <td style="text-align:left;"> Africa </td> <td style="text-align:right;"> 2007 </td> <td style="text-align:right;"> 65.152 </td> <td style="text-align:right;"> 710960 </td> <td style="text-align:right;"> 986.1479 </td> </tr> </tbody> </table> ```r gapminder_fil2 <- filter(gapminder, continent != 'Asia' | lifeExp < 30) ``` <table> <thead> <tr> <th style="text-align:left;"> country </th> <th style="text-align:left;"> continent </th> <th style="text-align:right;"> year </th> <th style="text-align:right;"> lifeExp </th> <th style="text-align:right;"> pop </th> <th style="text-align:right;"> gdpPercap </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:left;"> Asia </td> <td style="text-align:right;"> 1952 </td> <td style="text-align:right;"> 28.801 </td> <td style="text-align:right;"> 8425333 </td> <td style="text-align:right;"> 779.4453 </td> </tr> <tr> <td style="text-align:left;"> Albania </td> <td style="text-align:left;"> Europe </td> <td style="text-align:right;"> 1952 </td> <td style="text-align:right;"> 55.230 </td> <td style="text-align:right;"> 1282697 </td> <td style="text-align:right;"> 1601.0561 </td> </tr> </tbody> </table> --- ## **dplyr** - `mutate()` - `mutate()` function creates new variables based on existing variables ```r gapminder_mut1 <- mutate(gapminder, gdp_mil = (gdpPercap * pop) / 1000000) ``` <table> <thead> <tr> <th style="text-align:left;"> country </th> <th style="text-align:left;"> continent </th> <th style="text-align:right;"> year </th> <th style="text-align:right;"> lifeExp </th> <th style="text-align:right;"> pop </th> <th style="text-align:right;"> gdpPercap </th> <th style="text-align:right;"> gdp_mil </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:left;"> Asia </td> <td style="text-align:right;"> 1952 </td> <td style="text-align:right;"> 28.801 </td> <td style="text-align:right;"> 8425333 </td> <td style="text-align:right;"> 779.4453 </td> <td style="text-align:right;"> 6567.086 </td> </tr> <tr> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:left;"> Asia </td> <td style="text-align:right;"> 1957 </td> <td style="text-align:right;"> 30.332 </td> <td style="text-align:right;"> 9240934 </td> <td style="text-align:right;"> 820.8530 </td> <td style="text-align:right;"> 7585.449 </td> </tr> <tr> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:left;"> Asia </td> <td style="text-align:right;"> 1962 </td> <td style="text-align:right;"> 31.997 </td> <td style="text-align:right;"> 10267083 </td> <td style="text-align:right;"> 853.1007 </td> <td style="text-align:right;"> 8758.856 </td> </tr> <tr> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:left;"> Asia </td> <td style="text-align:right;"> 1967 </td> <td style="text-align:right;"> 34.020 </td> <td style="text-align:right;"> 11537966 </td> <td style="text-align:right;"> 836.1971 </td> <td style="text-align:right;"> 9648.014 </td> </tr> <tr> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:left;"> Asia </td> <td style="text-align:right;"> 1972 </td> <td style="text-align:right;"> 36.088 </td> <td style="text-align:right;"> 13079460 </td> <td style="text-align:right;"> 739.9811 </td> <td style="text-align:right;"> 9678.553 </td> </tr> </tbody> </table> --- ## **dplyr** - `summarize()` - `summarize()` function can be used to reduce variables to values ```r gapminder_sum1 <- summarize(gapminder, mean_le = mean(lifeExp)) ``` ``` ## mean_le ## 1 59.47444 ``` ```r gapminder_sum2 <- summarize(gapminder, mean_le = mean(lifeExp), min_le = min(lifeExp), max_le = max(lifeExp)) ``` ``` ## mean_le min_le max_le ## 1 59.47444 23.599 82.603 ``` --- ## **dplyr** - `group_by()` - `group_by()` does not change data. Its role is to create the groups of variables to be used by the previous functions ```r gapminder2007 <- filter(gapminder, year == 2007) gapminder_grp1 <- group_by(gapminder2007, continent) ``` ``` ## Source: local data frame [6 x 6] ## Groups: continent [5] ## ## country continent year lifeExp pop gdpPercap ## <fctr> <fctr> <int> <dbl> <dbl> <dbl> ## 1 Afghanistan Asia 2007 43.828 31889923 974.5803 ## 2 Albania Europe 2007 76.423 3600523 5937.0295 ## 3 Algeria Africa 2007 72.301 33333216 6223.3675 ## 4 Angola Africa 2007 42.731 12420476 4797.2313 ## 5 Argentina Americas 2007 75.320 40301927 12779.3796 ## 6 Australia Oceania 2007 81.235 20434176 34435.3674 ``` --- ## **dplyr** - `group_by()` ```r gapminder_grp1 <- summarize(gapminder_grp1, mean_lifeExp = mean(lifeExp)) ``` <table> <thead> <tr> <th style="text-align:left;"> continent </th> <th style="text-align:right;"> mean_lifeExp </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Africa </td> <td style="text-align:right;"> 54.80604 </td> </tr> <tr> <td style="text-align:left;"> Americas </td> <td style="text-align:right;"> 73.60812 </td> </tr> <tr> <td style="text-align:left;"> Asia </td> <td style="text-align:right;"> 70.72848 </td> </tr> <tr> <td style="text-align:left;"> Europe </td> <td style="text-align:right;"> 77.64860 </td> </tr> <tr> <td style="text-align:left;"> Oceania </td> <td style="text-align:right;"> 80.71950 </td> </tr> </tbody> </table> --- ## **tidyr** ```r # install.packages('tidyr') library('tidyr') ``` - Many of real world datasets are messy - they are not organized in a way we (or R functions) expect. It is important to use "tidy data" framework to ease the manipulation, modeling, and visualisation of datasets. In "tidy data": - Each variable forms a column - Each observation forms a row - Each type of observational unit forms a table (data frame) - There are two data formats in "tidy data" - wide format and long format - More information about "tidy data" can be found here - http://vita.had.co.nz/papers/tidy-data.html - The **tidyr** package has two main functions - `gather()` and `spread()`, and several additional functions --- ## **tidyr** - `spread()` - `spread()` takes two columns (key & value) and spreads them into multiple columns (creates data in a "wide" format) ```r gapminder_sel <- select(gapminder, -continent, -lifeExp, -gdpPercap) gapminder_spread <- spread(gapminder_sel, country, pop) ``` <table> <thead> <tr> <th style="text-align:right;"> year </th> <th style="text-align:right;"> Afghanistan </th> <th style="text-align:right;"> Albania </th> <th style="text-align:right;"> Algeria </th> <th style="text-align:right;"> Angola </th> <th style="text-align:right;"> Argentina </th> <th style="text-align:right;"> Australia </th> <th style="text-align:right;"> Austria </th> <th style="text-align:right;"> Bahrain </th> <th style="text-align:right;"> Bangladesh </th> </tr> </thead> <tbody> <tr> <td style="text-align:right;"> 1952 </td> <td style="text-align:right;"> 8425333 </td> <td style="text-align:right;"> 1282697 </td> <td style="text-align:right;"> 9279525 </td> <td style="text-align:right;"> 4232095 </td> <td style="text-align:right;"> 17876956 </td> <td style="text-align:right;"> 8691212 </td> <td style="text-align:right;"> 6927772 </td> <td style="text-align:right;"> 120447 </td> <td style="text-align:right;"> 46886859 </td> </tr> <tr> <td style="text-align:right;"> 1957 </td> <td style="text-align:right;"> 9240934 </td> <td style="text-align:right;"> 1476505 </td> <td style="text-align:right;"> 10270856 </td> <td style="text-align:right;"> 4561361 </td> <td style="text-align:right;"> 19610538 </td> <td style="text-align:right;"> 9712569 </td> <td style="text-align:right;"> 6965860 </td> <td style="text-align:right;"> 138655 </td> <td style="text-align:right;"> 51365468 </td> </tr> <tr> <td style="text-align:right;"> 1962 </td> <td style="text-align:right;"> 10267083 </td> <td style="text-align:right;"> 1728137 </td> <td style="text-align:right;"> 11000948 </td> <td style="text-align:right;"> 4826015 </td> <td style="text-align:right;"> 21283783 </td> <td style="text-align:right;"> 10794968 </td> <td style="text-align:right;"> 7129864 </td> <td style="text-align:right;"> 171863 </td> <td style="text-align:right;"> 56839289 </td> </tr> <tr> <td style="text-align:right;"> 1967 </td> <td style="text-align:right;"> 11537966 </td> <td style="text-align:right;"> 1984060 </td> <td style="text-align:right;"> 12760499 </td> <td style="text-align:right;"> 5247469 </td> <td style="text-align:right;"> 22934225 </td> <td style="text-align:right;"> 11872264 </td> <td style="text-align:right;"> 7376998 </td> <td style="text-align:right;"> 202182 </td> <td style="text-align:right;"> 62821884 </td> </tr> <tr> <td style="text-align:right;"> 1972 </td> <td style="text-align:right;"> 13079460 </td> <td style="text-align:right;"> 2263554 </td> <td style="text-align:right;"> 14760787 </td> <td style="text-align:right;"> 5894858 </td> <td style="text-align:right;"> 24779799 </td> <td style="text-align:right;"> 13177000 </td> <td style="text-align:right;"> 7544201 </td> <td style="text-align:right;"> 230800 </td> <td style="text-align:right;"> 70759295 </td> </tr> </tbody> </table> --- ## **tidyr** - `gather()` - `gather()` reshapes data from a wide format to a long format ```r gapminder_gather <- gather(gapminder_spread, country, pop, -year) ``` <table> <thead> <tr> <th style="text-align:right;"> year </th> <th style="text-align:left;"> country </th> <th style="text-align:right;"> pop </th> </tr> </thead> <tbody> <tr> <td style="text-align:right;"> 1952 </td> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:right;"> 8425333 </td> </tr> <tr> <td style="text-align:right;"> 1957 </td> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:right;"> 9240934 </td> </tr> <tr> <td style="text-align:right;"> 1962 </td> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:right;"> 10267083 </td> </tr> <tr> <td style="text-align:right;"> 1967 </td> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:right;"> 11537966 </td> </tr> <tr> <td style="text-align:right;"> 1972 </td> <td style="text-align:left;"> Afghanistan </td> <td style="text-align:right;"> 13079460 </td> </tr> </tbody> </table> --- ## `%>%` operator The usual approach to perform numerous operations in R is either: - by creating many "subobjects": ```r gapminder_sel <- select(gapminder, country, continent, pop) gapminder_africa <- filter(gapminder_sel, continent=='Africa') ``` - ...or by nesting: ```r gapminder_africa <- filter(select(gapminder, country, continent, pop), continent=='Africa') ``` <table> <thead> <tr> <th style="text-align:left;"> country </th> <th style="text-align:left;"> continent </th> <th style="text-align:right;"> pop </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Algeria </td> <td style="text-align:left;"> Africa </td> <td style="text-align:right;"> 9279525 </td> </tr> <tr> <td style="text-align:left;"> Algeria </td> <td style="text-align:left;"> Africa </td> <td style="text-align:right;"> 10270856 </td> </tr> <tr> <td style="text-align:left;"> Algeria </td> <td style="text-align:left;"> Africa </td> <td style="text-align:right;"> 11000948 </td> </tr> <tr> <td style="text-align:left;"> Algeria </td> <td style="text-align:left;"> Africa </td> <td style="text-align:right;"> 12760499 </td> </tr> <tr> <td style="text-align:left;"> Algeria </td> <td style="text-align:left;"> Africa </td> <td style="text-align:right;"> 14760787 </td> </tr> </tbody> </table> --- ## `%>%` operator - Nowadays, it could be easier done with the use of the `%>%` ("pipe") operator (**magrittr** or **dplyr** package) - In this approach, commands are written in a (more) natural order ```r gapminder_africa <- gapminder %>% select(country, continent, pop) %>% filter(continent=='Africa') ``` <table> <thead> <tr> <th style="text-align:left;"> country </th> <th style="text-align:left;"> continent </th> <th style="text-align:right;"> pop </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Algeria </td> <td style="text-align:left;"> Africa </td> <td style="text-align:right;"> 9279525 </td> </tr> <tr> <td style="text-align:left;"> Algeria </td> <td style="text-align:left;"> Africa </td> <td style="text-align:right;"> 10270856 </td> </tr> <tr> <td style="text-align:left;"> Algeria </td> <td style="text-align:left;"> Africa </td> <td style="text-align:right;"> 11000948 </td> </tr> <tr> <td style="text-align:left;"> Algeria </td> <td style="text-align:left;"> Africa </td> <td style="text-align:right;"> 12760499 </td> </tr> <tr> <td style="text-align:left;"> Algeria </td> <td style="text-align:left;"> Africa </td> <td style="text-align:right;"> 14760787 </td> </tr> </tbody> </table> --- ## `%>%` operator ```r gapminder_proc <- gapminder %>% filter(continent=='Europe', year==2007) %>% mutate(pop_in_thousands=pop/1000) %>% select(country, gdpPercap, pop_in_thousands) %>% gather(key, value, gdpPercap, pop_in_thousands) ``` <table> <thead> <tr> <th style="text-align:left;"> country </th> <th style="text-align:left;"> key </th> <th style="text-align:right;"> value </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Albania </td> <td style="text-align:left;"> gdpPercap </td> <td style="text-align:right;"> 5937.030 </td> </tr> <tr> <td style="text-align:left;"> Austria </td> <td style="text-align:left;"> gdpPercap </td> <td style="text-align:right;"> 36126.493 </td> </tr> <tr> <td style="text-align:left;"> Belgium </td> <td style="text-align:left;"> gdpPercap </td> <td style="text-align:right;"> 33692.605 </td> </tr> <tr> <td style="text-align:left;"> Bosnia and Herzegovina </td> <td style="text-align:left;"> gdpPercap </td> <td style="text-align:right;"> 7446.299 </td> </tr> <tr> <td style="text-align:left;"> Bulgaria </td> <td style="text-align:left;"> gdpPercap </td> <td style="text-align:right;"> 10680.793 </td> </tr> </tbody> </table> --- ## Resources - [R for Data Science](http://r4ds.had.co.nz/) - a great book which contains chapters for beginners and for more advanced users - [Tidy data](https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html) - an overview of a "tidy data" approach - [dplyr](http://dplyr.tidyverse.org/) - an official website of the `dplyr` package - [tidyr](http://tidyr.tidyverse.org/) - an official website of the `tidyr` package - [Introduction to dplyr](http://stat545.com/block009_dplyr-intro.html) - a clear introdution to the `dplyr` package - [Tidy data lesson using Lord of the Rings data](https://github.com/jennybc/lotr-tidy#readme) - an introduction to a "tidy data" and the `tidyr` package using Lord of the Rings data - [Data Wrangling with dplyr and tidyr Cheat Sheet](https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf) - a two-pages summary of `dplyr` and `tidyr` functions - [Cheatsheet for dplyr join functions](http://stat545.com/bit001_dplyr-cheatsheet.html) - examples of `dplyr` join functions - [A new data processing workflow for R: dplyr, magrittr, tidyr, ggplot2](http://zevross.com/blog/2015/01/13/a-new-data-processing-workflow-for-r-dplyr-magrittr-tidyr-ggplot2/) - a data processing workflow example