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# 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
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