"Data sets contain more information than they display."
- Garrett Grolemund                    Â
August 6, 2016
"Data sets contain more information than they display."
- Garrett Grolemund                    Â
dplyr
is a package that transforms and manipulates data
select()
: select variables of concern
filter()
: filter values based on conditions
group_by()
: group data by categorical levels
summarise()
: change unit of analysis
arrange()
: order the data
dplyr
is a package that transforms and manipulates data
mutate()
: create new variables
join()
: combine separate data sets
arrange( summarize( filter(data, variable == "numeric_value"), Total = sum(variable) ), desc(Total) )
a <- filter(data, variable == "numeric_value") b <- summarise(a, Total = sum(variable)) c <- arrange(b, desc(Total))
data %>% 14 filter(variable == "value") %>% summarise(Total = sum(variable)) %>% arrange(desc(Total))
Code alternatives:
These produce the same results: storms %>% select(wind, pressure, date) storms %>% select(wind:pressure) storms %>% select(-storm)
Import the facebook.tsv
file in the data folder
Create a new data frame that includes:
userid age gender friend_count
# Import the facebook.tsv file in the data folder facebook <- read.delim("../data/facebook.tsv") facebook2 <- facebook %>% select(userid, age, gender, friend_count) facebook2
## userid age gender friend_count ## 1 2094382 14 male 0 ## 2 1192601 14 female 0 ## 3 2083884 14 male 0 ## 4 1203168 14 female 0 ## 5 1733186 14 male 0 ## 6 1524765 14 male 0 ## 7 1136133 13 male 0 ## 8 1680361 13 female 0 ## 9 1365174 13 male 0 ## 10 1712567 13 male 0 ## 11 1612453 13 male 0 ## 12 2104073 13 male 0 ## 13 1918584 13 male 0 ## 14 1704433 13 male 0 ## 15 1932519 13 female 0 ## 16 1751722 13 female 0 ## 17 1470850 13 female 0 ## 18 1001768 13 female 0 ## 19 1537661 13 female 0 ## 20 1020296 13 male 0 ## 21 1472643 13 female 0 ## 22 2041297 13 female 0 ## 23 1514978 13 male 0 ## 24 1708962 15 male 0 ## 25 1098955 15 male 0 ## 26 1001243 15 male 0 ## 27 2113084 15 male 0 ## 28 2163454 15 male 0 ## 29 1670750 15 male 0 ## 30 1041376 15 male 0 ## 31 1762274 15 male 0 ## 32 1903650 14 male 0 ## 33 2087235 14 male 0 ## 34 1871735 14 female 0 ## 35 1459785 14 male 0 ## 36 1215208 14 male 0 ## 37 1255528 14 male 0 ## 38 1390333 14 male 0 ## 39 1570689 14 male 0 ## 40 1197377 14 male 0 ## 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24983 2109438 40 male 31 ## 24984 2087460 31 female 31 ## 24985 1862175 73 male 31 ## 24986 1965397 59 male 31 ## 24987 1367544 18 male 31 ## 24988 1444860 17 female 31 ## 24989 2181411 25 female 31 ## 24990 1988731 30 male 31 ## 24991 2187969 61 female 31 ## 24992 1342967 58 female 31 ## 24993 1883367 37 female 31 ## 24994 1262169 58 male 31 ## 24995 2053638 47 female 31 ## 24996 1855710 60 <NA> 31 ## 24997 1481190 67 male 31 ## 24998 1417153 71 female 31 ## 24999 1724300 16 male 31 ## [ reached getOption("max.print") -- omitted 74004 rows ]
We can apply multiple logic rules in the filter()
function such as:
< | Less than | != | Not equal to |
> | Greater than | %in% | Group membership |
== | Equal to | is.na | is NA |
!= | Not equal to | !is.na | is not NA |
<= | Less than or equal to | &,|,! | Boolean operators |
>= | Greater than or equal to |
Additional subsetting functions:
storms %>% distinct() |
Remove duplicate rows | |
storms %>% sample_frac(0.5, replace = TRUE) |
Randomly select fraction of rows | |
storms %>% sample_n(10, replace = TRUE) |
Randomly select n rows | |
storms %>% slice(3:5) |
Select rows by position | |
storms %>% top_n(storms, 2, date) |
Select and order top n entries |
Continuing with our facebook.tsv
data…
1. Filter the facebook data for just males
2. Select the following variables:
userid age gender friend_count
…and then filter for just males
# 1. Filter the facebook data for just males fb_males <- facebook %>% filter(gender == "male") # 2. Select specified variables and then filter for males # Option 1 facebook2 <- facebook %>% select(userid, age, gender, friend_count) fb2_males <- facebook %>% filter(gender == "male") # Option 2 fb2_males <- facebook %>% select(userid, age, gender, friend_count) %>% filter(gender == "male") fb2_males
## userid age gender friend_count ## 1 2094382 14 male 0 ## 2 2083884 14 male 0 ## 3 1733186 14 male 0 ## 4 1524765 14 male 0 ## 5 1136133 13 male 0 ## 6 1365174 13 male 0 ## 7 1712567 13 male 0 ## 8 1612453 13 male 0 ## 9 2104073 13 male 0 ## 10 1918584 13 male 0 ## 11 1704433 13 male 0 ## 12 1020296 13 male 0 ## 13 1514978 13 male 0 ## 14 1708962 15 male 0 ## 15 1098955 15 male 0 ## 16 1001243 15 male 0 ## 17 2113084 15 male 0 ## 18 2163454 15 male 0 ## 19 1670750 15 male 0 ## 20 1041376 15 male 0 ## 21 1762274 15 male 0 ## 22 1903650 14 male 0 ## 23 2087235 14 male 0 ## 24 1459785 14 male 0 ## 25 1215208 14 male 0 ## 26 1255528 14 male 0 ## 27 1390333 14 male 0 ## 28 1570689 14 male 0 ## 29 1197377 14 male 0 ## 30 1549614 14 male 0 ## 31 2091823 14 male 0 ## 32 1784528 14 male 0 ## 33 1842851 14 male 0 ## 34 1443541 14 male 0 ## 35 1927282 14 male 0 ## 36 2188851 14 male 0 ## 37 1599485 14 male 0 ## 38 2157419 14 male 0 ## 39 1264260 14 male 0 ## 40 1123633 14 male 0 ## 41 2045259 14 male 0 ## 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24423 1301845 35 male 54 ## 24424 1802784 18 male 54 ## 24425 1412166 24 male 55 ## 24426 1947212 56 male 55 ## 24427 2034381 38 male 54 ## 24428 1922581 23 male 55 ## 24429 1519207 31 male 54 ## 24430 1783719 22 male 54 ## 24431 1336946 53 male 55 ## 24432 1290475 39 male 54 ## 24433 2172294 31 male 54 ## 24434 1045207 17 male 54 ## 24435 1914061 68 male 54 ## 24436 1345486 31 male 54 ## 24437 1860119 25 male 54 ## 24438 1417520 35 male 55 ## 24439 2035219 35 male 55 ## 24440 1453472 68 male 55 ## 24441 1278648 36 male 55 ## 24442 1872713 26 male 54 ## 24443 1148970 45 male 54 ## 24444 1947050 64 male 55 ## 24445 1219628 56 male 54 ## 24446 1172446 14 male 56 ## 24447 2113390 13 male 57 ## 24448 1990911 14 male 56 ## 24449 1127152 15 male 56 ## 24450 1307439 15 male 57 ## 24451 1115373 15 male 57 ## 24452 1947610 15 male 57 ## 24453 1075948 17 male 56 ## 24454 1110659 16 male 57 ## 24455 2026583 16 male 57 ## 24456 1574223 17 male 57 ## 24457 1529259 17 male 57 ## 24458 1440510 17 male 56 ## 24459 1548130 17 male 56 ## 24460 1109123 19 male 57 ## 24461 1253292 19 male 57 ## 24462 1511284 18 male 56 ## 24463 1898041 18 male 56 ## 24464 1457196 18 male 56 ## 24465 1777359 18 male 56 ## 24466 1408700 18 male 57 ## 24467 1897300 18 male 57 ## 24468 1478608 18 male 56 ## 24469 2158031 18 male 57 ## 24470 1308469 18 male 57 ## 24471 2098759 20 male 56 ## 24472 1154459 20 male 56 ## 24473 1244174 19 male 56 ## 24474 1697137 19 male 57 ## 24475 1360806 19 male 57 ## 24476 1866781 19 male 56 ## 24477 1661218 19 male 57 ## 24478 1484337 19 male 56 ## 24479 2092945 20 male 57 ## 24480 1424317 20 male 56 ## 24481 1189053 20 male 57 ## 24482 1238165 20 male 56 ## 24483 2091762 22 male 57 ## 24484 1935754 22 male 56 ## 24485 1761256 21 male 57 ## 24486 1450465 21 male 57 ## 24487 1271509 21 male 57 ## 24488 1639040 21 male 56 ## 24489 1506076 21 male 57 ## 24490 1298490 21 male 57 ## 24491 1909350 23 male 56 ## 24492 2151780 23 male 56 ## 24493 1235040 22 male 57 ## 24494 1689085 22 male 57 ## 24495 1428924 23 male 56 ## 24496 1028602 23 male 56 ## 24497 1397060 23 male 56 ## 24498 1010826 23 male 57 ## 24499 1894012 23 male 56 ## 24500 1255567 23 male 57 ## 24501 1188366 25 male 56 ## 24502 1412848 24 male 56 ## 24503 1661883 24 male 56 ## 24504 1946055 24 male 56 ## 24505 1418947 24 male 56 ## 24506 2165136 25 male 57 ## 24507 2003348 25 male 56 ## 24508 1708447 27 male 56 ## 24509 1389439 26 male 57 ## 24510 1264073 26 male 57 ## 24511 2038585 26 male 57 ## 24512 1545677 26 male 57 ## 24513 1466877 27 male 56 ## 24514 1385052 27 male 56 ## 24515 1330148 28 male 57 ## 24516 1329435 28 male 57 ## 24517 1807339 28 male 57 ## 24518 1422851 28 male 56 ## 24519 1793294 28 male 57 ## 24520 1785377 28 male 56 ## 24521 1948318 30 male 56 ## 24522 1906723 29 male 56 ## 24523 2089282 29 male 56 ## 24524 1551085 31 male 56 ## 24525 1158545 32 male 56 ## 24526 1383097 32 male 57 ## 24527 1739881 31 male 57 ## 24528 1428745 31 male 57 ## 24529 2032858 32 male 56 ## 24530 1831558 33 male 57 ## 24531 2002599 33 male 57 ## 24532 2077233 33 male 56 ## 24533 1056414 33 male 56 ## 24534 1346315 35 male 56 ## 24535 1992924 35 male 57 ## 24536 1236427 34 male 56 ## 24537 1319342 35 male 57 ## 24538 1745649 35 male 57 ## 24539 1314582 36 male 57 ## 24540 1565679 36 male 56 ## 24541 2027454 38 male 56 ## 24542 1004836 38 male 57 ## 24543 1931358 39 male 56 ## 24544 1344880 39 male 57 ## 24545 1931421 41 male 56 ## 24546 1365481 40 male 56 ## 24547 1627834 41 male 57 ## 24548 1630193 44 male 56 ## 24549 2106307 44 male 57 ## 24550 2150259 44 male 56 ## 24551 1332561 44 male 57 ## 24552 1965446 44 male 56 ## 24553 1029571 45 male 57 ## 24554 1774647 46 male 57 ## 24555 1545355 47 male 56 ## 24556 1626533 48 male 56 ## 24557 1086600 50 male 56 ## 24558 1710792 50 male 57 ## 24559 1861154 50 male 56 ## 24560 1538547 51 male 56 ## 24561 1732002 53 male 57 ## 24562 2098369 53 male 57 ## 24563 1817731 55 male 56 ## 24564 2127734 58 male 57 ## 24565 1822631 58 male 56 ## 24566 1657258 63 male 56 ## 24567 2030340 63 male 56 ## 24568 1353499 63 male 56 ## 24569 1749181 63 male 57 ## 24570 1329526 63 male 56 ## 24571 1431060 63 male 57 ## 24572 1447732 63 male 57 ## 24573 1588846 64 male 56 ## 24574 2176281 69 male 56 ## 24575 1638242 68 male 57 ## 24576 1526617 68 male 56 ## 24577 2003191 68 male 56 ## 24578 1191383 70 male 57 ## 24579 1609702 70 male 56 ## 24580 1180200 73 male 57 ## 24581 1874805 73 male 57 ## 24582 1019544 73 male 56 ## 24583 1857519 75 male 57 ## 24584 2008802 94 male 57 ## 24585 1628916 103 male 56 ## 24586 1017678 108 male 56 ## 24587 1619565 108 male 57 ## 24588 1881521 22 male 56 ## 24589 1252629 26 male 56 ## 24590 1140996 108 male 57 ## 24591 1567702 21 male 57 ## 24592 1077975 19 male 56 ## 24593 1438227 22 male 56 ## 24594 1597605 46 male 56 ## 24595 1101588 59 male 57 ## 24596 2047060 60 male 57 ## 24597 1732385 18 male 56 ## 24598 1327873 22 male 56 ## 24599 1304979 33 male 57 ## 24600 2146212 33 male 57 ## 24601 1539760 38 male 57 ## 24602 1847063 43 male 56 ## 24603 1885118 18 male 57 ## 24604 1706294 21 male 57 ## 24605 1635231 25 male 57 ## 24606 1209411 29 male 57 ## 24607 1751028 42 male 56 ## 24608 1952286 43 male 56 ## 24609 1602041 14 male 56 ## 24610 2185445 18 male 56 ## 24611 1497681 18 male 57 ## 24612 1428574 24 male 56 ## 24613 1549396 27 male 57 ## 24614 1234739 26 male 56 ## 24615 2184289 26 male 57 ## 24616 1599120 26 male 56 ## 24617 2177343 28 male 56 ## 24618 1309231 34 male 57 ## 24619 1243641 35 male 56 ## 24620 1921001 38 male 56 ## 24621 1118727 52 male 57 ## 24622 1772690 57 male 57 ## 24623 1461572 59 male 56 ## 24624 2106829 17 male 56 ## 24625 1726303 93 male 57 ## 24626 1014744 36 male 56 ## 24627 1117580 43 male 56 ## 24628 1663038 55 male 56 ## 24629 2070014 52 male 56 ## 24630 1482415 18 male 56 ## 24631 1344658 20 male 56 ## 24632 1565216 20 male 56 ## 24633 1622447 24 male 56 ## 24634 1313877 23 male 56 ## 24635 1952369 26 male 56 ## 24636 1444189 28 male 57 ## 24637 1431358 30 male 57 ## 24638 2157577 40 male 56 ## 24639 1556538 14 male 56 ## 24640 2037887 19 male 57 ## 24641 1892651 41 male 56 ## 24642 1757468 31 male 56 ## 24643 2175685 35 male 57 ## 24644 1554602 18 male 57 ## 24645 2158020 26 male 57 ## 24646 2103168 33 male 56 ## 24647 1146117 37 male 56 ## 24648 1347707 37 male 57 ## 24649 1489043 41 male 56 ## 24650 1640588 47 male 56 ## 24651 1778281 47 male 56 ## 24652 1627574 50 male 57 ## 24653 1655596 25 male 57 ## 24654 1170045 27 male 56 ## 24655 1167869 42 male 57 ## 24656 1867038 69 male 56 ## 24657 2087919 28 male 57 ## 24658 1946556 34 male 56 ## 24659 1645490 37 male 56 ## 24660 1674334 43 male 57 ## 24661 1438792 53 male 56 ## 24662 1120087 66 male 57 ## 24663 1710426 17 male 56 ## 24664 1037293 23 male 56 ## 24665 1661205 16 male 57 ## 24666 1462054 19 male 57 ## 24667 1875291 26 male 56 ## 24668 1236439 38 male 56 ## 24669 1000173 53 male 57 ## 24670 1165960 23 male 56 ## 24671 1066332 31 male 57 ## 24672 2038374 35 male 56 ## 24673 1675909 36 male 56 ## 24674 1799163 20 male 57 ## 24675 1619396 41 male 57 ## 24676 1766317 15 male 57 ## 24677 1484966 18 male 57 ## 24678 1636402 24 male 56 ## 24679 1404883 25 male 56 ## 24680 2016525 28 male 57 ## 24681 1745461 35 male 56 ## 24682 1010658 52 male 56 ## 24683 2122707 46 male 57 ## 24684 1077711 73 male 56 ## 24685 1668747 44 male 57 ## 24686 1982431 16 male 57 ## 24687 1945555 36 male 57 ## 24688 1803892 67 male 56 ## 24689 2120163 41 male 57 ## 24690 1904243 14 male 57 ## 24691 1550132 15 male 57 ## 24692 1008361 18 male 56 ## 24693 1783772 62 male 56 ## 24694 1493413 64 male 57 ## 24695 2126360 25 male 57 ## 24696 1758628 46 male 57 ## 24697 1717889 23 male 56 ## 24698 1589759 31 male 57 ## 24699 1673944 61 male 57 ## 24700 1646194 38 male 56 ## 24701 1853909 44 male 56 ## 24702 1983780 41 male 57 ## 24703 1138170 26 male 56 ## 24704 1266061 27 male 57 ## 24705 1586020 51 male 57 ## 24706 1834326 45 male 56 ## 24707 1508818 61 male 56 ## 24708 2123665 20 male 56 ## 24709 1507635 18 male 57 ## 24710 1417730 21 male 56 ## 24711 1445721 31 male 56 ## 24712 1004889 28 male 57 ## 24713 1611424 14 male 56 ## 24714 1168276 22 male 57 ## 24715 1866611 34 male 56 ## 24716 1540563 41 male 56 ## 24717 1803902 72 male 56 ## 24718 1723114 24 male 56 ## 24719 1080993 30 male 56 ## 24720 1208656 65 male 57 ## 24721 1979359 29 male 57 ## 24722 1304392 50 male 57 ## 24723 2163500 55 male 57 ## 24724 1984893 23 male 57 ## 24725 2006780 23 male 57 ## 24726 1547568 41 male 56 ## 24727 1219254 36 male 56 ## 24728 2136970 26 male 57 ## 24729 1051770 31 male 57 ## 24730 1774903 20 male 56 ## 24731 1571963 21 male 57 ## 24732 1852204 30 male 57 ## 24733 1347002 31 male 56 ## 24734 1710380 53 male 56 ## 24735 1514827 58 male 57 ## 24736 1162461 34 male 56 ## 24737 1592528 64 male 56 ## 24738 1500648 26 male 57 ## 24739 1121309 40 male 56 ## 24740 1852977 26 male 56 ## 24741 2124743 58 male 56 ## 24742 1580120 19 male 56 ## 24743 1582350 20 male 57 ## 24744 1005096 25 male 56 ## 24745 1918830 66 male 57 ## 24746 1831548 29 male 57 ## 24747 1085322 55 male 56 ## 24748 1890604 15 male 57 ## 24749 1359309 31 male 56 ## 24750 1161717 54 male 57 ## 24751 1284280 14 male 57 ## 24752 1363670 61 male 57 ## 24753 2123041 20 male 57 ## 24754 1688057 54 male 57 ## 24755 1514337 20 male 56 ## 24756 1031590 47 male 56 ## 24757 1756356 65 male 57 ## 24758 1949593 93 male 56 ## 24759 1440410 14 male 56 ## 24760 1419309 26 male 56 ## 24761 1972400 63 male 57 ## 24762 1090513 22 male 56 ## 24763 1718729 23 male 56 ## 24764 1963351 25 male 57 ## 24765 1287500 22 male 57 ## 24766 1935344 18 male 57 ## 24767 1110622 18 male 56 ## 24768 1347883 29 male 56 ## 24769 1121396 28 male 56 ## 24770 1273880 34 male 56 ## 24771 2069635 43 male 57 ## 24772 2137807 61 male 57 ## 24773 1296042 66 male 56 ## 24774 2104365 66 male 56 ## 24775 1520020 68 male 56 ## 24776 2164311 20 male 56 ## 24777 1424605 43 male 57 ## 24778 1974017 25 male 56 ## 24779 1564841 26 male 56 ## 24780 1355348 49 male 56 ## 24781 1511009 57 male 56 ## 24782 1311275 22 male 57 ## 24783 1844489 33 male 57 ## 24784 1220272 21 male 56 ## 24785 1074879 51 male 56 ## 24786 1905538 23 male 57 ## 24787 1015078 47 male 56 ## 24788 1955389 14 male 56 ## 24789 2074652 67 male 56 ## 24790 1874970 23 male 56 ## 24791 1944210 70 male 57 ## 24792 1958520 24 male 57 ## 24793 1437102 65 male 56 ## 24794 1499034 66 male 57 ## 24795 1704246 21 male 56 ## 24796 2174029 94 male 57 ## 24797 1343727 37 male 57 ## 24798 1083594 51 male 56 ## 24799 1180362 18 male 56 ## 24800 1293144 22 male 57 ## 24801 1421820 27 male 57 ## 24802 1760904 43 male 57 ## 24803 1282165 41 male 57 ## 24804 1145923 24 male 57 ## 24805 1692931 63 male 57 ## 24806 1105346 21 male 56 ## 24807 1994276 19 male 56 ## 24808 1553809 25 male 56 ## 24809 1214393 18 male 57 ## 24810 1923194 34 male 57 ## 24811 1430414 27 male 57 ## 24812 2059241 58 male 57 ## 24813 1393877 21 male 56 ## 24814 1694316 35 male 57 ## 24815 1748963 44 male 57 ## 24816 1055259 24 male 57 ## 24817 1692271 17 male 56 ## 24818 1573522 56 male 56 ## 24819 1632896 34 male 56 ## 24820 1088743 54 male 56 ## 24821 1670417 55 male 56 ## 24822 1210964 28 male 56 ## 24823 1510918 55 male 57 ## 24824 2146824 24 male 57 ## 24825 2012310 25 male 56 ## 24826 1176918 44 male 56 ## 24827 1604943 53 male 56 ## 24828 1737198 46 male 56 ## 24829 1819481 25 male 56 ## 24830 1911962 15 male 56 ## 24831 1536112 58 male 56 ## 24832 2144589 26 male 56 ## 24833 1313982 41 male 56 ## 24834 1185069 49 male 56 ## 24835 1174770 46 male 57 ## 24836 2183634 69 male 57 ## 24837 1738245 25 male 56 ## 24838 1804791 32 male 56 ## 24839 1885703 38 male 56 ## 24840 1803267 46 male 57 ## 24841 1054418 40 male 57 ## 24842 1970953 38 male 57 ## 24843 1696150 111 male 57 ## 24844 1962340 28 male 57 ## 24845 1718765 16 male 56 ## 24846 1541616 46 male 57 ## 24847 2094266 30 male 56 ## 24848 1209824 51 male 57 ## 24849 1285885 54 male 57 ## 24850 1625449 36 male 56 ## 24851 1377531 66 male 57 ## 24852 2152231 33 male 56 ## 24853 1829349 17 male 56 ## 24854 1601906 25 male 57 ## 24855 1442050 22 male 57 ## 24856 1279884 27 male 56 ## 24857 1001391 30 male 57 ## 24858 1452028 29 male 56 ## 24859 1632844 32 male 56 ## 24860 1766548 63 male 56 ## 24861 1472588 28 male 57 ## 24862 1305109 59 male 56 ## 24863 1231185 74 male 56 ## 24864 1997100 14 male 57 ## 24865 1685373 61 male 57 ## 24866 1823292 25 male 57 ## 24867 1393037 47 male 56 ## 24868 1246538 19 male 56 ## 24869 2022485 43 male 56 ## 24870 2099858 38 male 57 ## 24871 2039967 36 male 56 ## 24872 2192073 25 male 57 ## 24873 1906003 44 male 56 ## 24874 1850444 36 male 56 ## 24875 1428657 43 male 56 ## 24876 1052017 19 male 57 ## 24877 1467154 25 male 57 ## 24878 1550035 60 male 57 ## 24879 1018010 20 male 56 ## 24880 1980452 53 male 57 ## 24881 2135282 69 male 56 ## 24882 1863479 54 male 56 ## 24883 1685280 38 male 57 ## 24884 2053072 48 male 56 ## 24885 1367672 25 male 57 ## 24886 1641849 26 male 57 ## 24887 1601422 31 male 57 ## 24888 1073948 35 male 57 ## 24889 1650410 51 male 56 ## 24890 1295378 65 male 56 ## 24891 2052456 19 male 57 ## 24892 1778252 27 male 56 ## 24893 1600471 33 male 56 ## 24894 1233684 23 male 56 ## 24895 1018306 32 male 57 ## 24896 1693375 108 male 56 ## 24897 1915667 56 male 56 ## 24898 1766888 15 male 57 ## 24899 1235648 23 male 57 ## 24900 2057204 18 male 57 ## 24901 1417231 17 male 56 ## 24902 1613629 30 male 57 ## 24903 1391351 68 male 57 ## 24904 1414140 16 male 56 ## 24905 1269872 21 male 56 ## 24906 1189039 48 male 56 ## 24907 1206973 45 male 57 ## 24908 1975840 23 male 56 ## 24909 1694544 25 male 56 ## 24910 1407902 25 male 57 ## 24911 1157030 67 male 56 ## 24912 2027141 72 male 56 ## 24913 1773727 19 male 57 ## 24914 1215962 19 male 57 ## 24915 1438899 36 male 56 ## 24916 1300275 93 male 56 ## 24917 1104375 27 male 56 ## 24918 1961690 49 male 56 ## 24919 1957025 23 male 56 ## 24920 1382392 34 male 56 ## 24921 1194212 24 male 57 ## 24922 1216508 53 male 56 ## 24923 1656263 18 male 57 ## 24924 2100430 73 male 56 ## 24925 1645042 66 male 57 ## 24926 1426876 38 male 57 ## 24927 1071521 42 male 56 ## 24928 1366829 34 male 57 ## 24929 1633268 36 male 56 ## 24930 1704609 51 male 57 ## 24931 1490001 108 male 57 ## 24932 1530495 27 male 57 ## 24933 1476120 55 male 56 ## 24934 1287697 53 male 57 ## 24935 1463381 50 male 56 ## 24936 1258119 23 male 56 ## 24937 1507036 54 male 57 ## 24938 2186561 42 male 56 ## 24939 1847540 17 male 56 ## 24940 1620763 54 male 56 ## 24941 1335279 47 male 56 ## 24942 1172486 16 male 56 ## 24943 2084632 50 male 57 ## 24944 2058489 59 male 56 ## 24945 1482850 40 male 57 ## 24946 1860467 22 male 57 ## 24947 1835071 19 male 57 ## 24948 1659893 40 male 57 ## 24949 1823061 44 male 57 ## 24950 2115401 19 male 56 ## 24951 1740432 17 male 57 ## 24952 1987274 36 male 56 ## 24953 1737828 31 male 57 ## 24954 1503869 66 male 57 ## 24955 1022875 26 male 56 ## 24956 1498783 103 male 57 ## 24957 1177223 20 male 56 ## 24958 1745816 47 male 57 ## 24959 1010990 32 male 56 ## 24960 1363716 23 male 56 ## 24961 1610879 45 male 56 ## 24962 2109935 38 male 56 ## 24963 1873178 59 male 57 ## 24964 1767379 52 male 56 ## 24965 1784519 35 male 56 ## 24966 1655623 49 male 57 ## 24967 1947072 13 male 58 ## 24968 1993281 14 male 58 ## 24969 1813667 14 male 59 ## 24970 2151531 14 male 59 ## 24971 1965234 15 male 59 ## 24972 1162408 15 male 58 ## 24973 1719594 15 male 59 ## 24974 2136414 15 male 58 ## 24975 1348495 15 male 59 ## 24976 1331355 15 male 58 ## 24977 2158537 17 male 59 ## 24978 1915828 16 male 59 ## 24979 1204771 16 male 58 ## 24980 1616358 16 male 59 ## 24981 1143785 16 male 59 ## 24982 1296297 18 male 58 ## 24983 1667582 17 male 59 ## 24984 1590551 17 male 58 ## 24985 2024331 17 male 59 ## 24986 1850873 19 male 59 ## 24987 1567161 18 male 58 ## 24988 1810153 18 male 58 ## 24989 2046037 18 male 59 ## 24990 1030660 18 male 59 ## 24991 1884209 18 male 59 ## 24992 1413218 19 male 59 ## 24993 1733336 19 male 58 ## 24994 1372365 19 male 59 ## 24995 1332978 19 male 59 ## 24996 1118457 20 male 59 ## 24997 1732935 20 male 59 ## 24998 2000776 20 male 59 ## 24999 1075838 20 male 58 ## [ reached getOption("max.print") -- omitted 33575 rows ]
group_by()
function is a silent function in which no observable manipulation of the data occurspollution %>% group_by(city)
## Source: local data frame [6 x 3] ## Groups: city [3] ## ## city size amount ## <chr> <chr> <dbl> ## 1 New York large 23 ## 2 New York small 14 ## 3 London large 22 ## 4 London small 16 ## 5 Beijing large 121 ## 6 Beijing small 56
Continuing with our facebook.tsv
data…
Select the following variables:
userid age gender friend_count
…and then group by gender
# 1. Select specified variables and then group by gender facebook %>% select(userid, age, gender, friend_count) %>% group_by(gender)
## Source: local data frame [99,003 x 4] ## Groups: gender [3] ## ## userid age gender friend_count ## <int> <int> <fctr> <int> ## 1 2094382 14 male 0 ## 2 1192601 14 female 0 ## 3 2083884 14 male 0 ## 4 1203168 14 female 0 ## 5 1733186 14 male 0 ## 6 1524765 14 male 0 ## 7 1136133 13 male 0 ## 8 1680361 13 female 0 ## 9 1365174 13 male 0 ## 10 1712567 13 male 0 ## # ... with 98,993 more rows
Summary functions:
first() |
First value of a vector | min() |
Min value in vector | |
last() |
Last value of a vector | max() |
Max value in vector | |
nth() |
Nth value of a vector | mean() |
Mean value of vector | |
n() |
# of values in a vector | median() |
Median value of vector | |
n_distinct() |
# of distinct values | var() |
Variance of vector | |
IQR() |
IQR of a vector | sd() |
St. dev. of vector |
Continuing with our facebook.tsv
data…
1. Calculate the mean and median fortenure
andfriend_count
2. Select the following variables:
gender age friend_count
group by gender and calculate the mean and median forage
andfriend_count
# 1. Calculate the mean and median for tenure and friend_count facebook %>% summarise(tenure_avg = mean(tenure, na.rm = TRUE), tenure_med = median(tenure, na.rm = TRUE), fc_avg = mean(friend_count, na.rm = TRUE), fc_med = median(friend_count, na.rm = TRUE))
## tenure_avg tenure_med fc_avg fc_med ## 1 537.8874 412 196.3508 82
# 2. Select specified variables, group by gender, and calculate mean and median facebook %>% select(gender, age, friend_count) %>% group_by(gender) %>% summarise_each(funs(mean, median))
## # A tibble: 3 × 5 ## gender age_mean friend_count_mean age_median friend_count_median ## <fctr> <dbl> <dbl> <dbl> <dbl> ## 1 female 39.45990 241.9699 31 96 ## 2 male 35.67025 165.0355 27 74 ## 3 NA 74.77714 184.4114 74 81
desc(x)
to sort variable(s) in descending order
Continuing with our facebook.tsv
data…
1. Group the data by age
2. Calculate the median friend_count
for each age
3. Arrange the median calculated friend_count
variable in descending order to find the ages with the greatest median friend count
facebook %>% group_by(age) %>% summarise(friend_count = median(friend_count, na.rm = TRUE)) %>% arrange(desc(friend_count))
## # A tibble: 101 × 2 ## age friend_count ## <int> <dbl> ## 1 104 275.0 ## 2 103 268.5 ## 3 97 260.0 ## 4 95 252.0 ## 5 94 251.0 ## 6 105 249.5 ## 7 110 243.0 ## 8 102 229.0 ## 9 99 216.0 ## 10 107 215.0 ## # ... with 91 more rows
Window functions that can be applied within mutate_each(funs())
:
lead() |
ntile() |
cumsum() |
|
lag() |
between() |
cummax() |
|
dense_rank() |
cume_dist() |
cummin() |
|
min_rank() |
cumall() |
cumprod() |
|
percent_rank() |
cumany() |
pmax() |
|
row_number() |
cumean() |
pmin() |
mutate_each(funs())
Continuing with our facebook.tsv
data…
1. Create a new variable
\[\text{friend_ratio} = \frac{\text{friendships_initiated}}{\text{friend_count}}\]friend_ratio
that equals
2. Find the median friend_ratio
for males and females.
# 1. Create a new variable `friend_ratio` that equals `friendships_initiated` / `friend_count` facebook2 <- facebook %>% mutate(friend_ratio = friendships_initiated / friend_count) # 2. Find the median `friend_ratio` for males and females. facebook2 %>% group_by(gender) %>% summarise(fr_median = median(friend_ratio, na.rm = TRUE))
## # A tibble: 3 × 2 ## gender fr_median ## <fctr> <dbl> ## 1 female 0.5681818 ## 2 male 0.6666667 ## 3 NA 0.4513274
# 1 & 2 in one fell swoop facebook %>% mutate(friend_ratio = friendships_initiated / friend_count) %>% group_by(gender) %>% summarise(fr_median = median(friend_ratio, na.rm = TRUE))
## # A tibble: 3 × 2 ## gender fr_median ## <fctr> <dbl> ## 1 female 0.5681818 ## 2 male 0.6666667 ## 3 NA 0.4513274
b
to a
b
to a
b
to a
a
to b
a
that have a match in b
.
a
that do not have a match in b
.
Additional dplyr
functions for merging data sets:
intersect(y, z) |
Rows that appear in both y and z | |
union(y, z) |
Rows that appear in either or both y and z | |
setdiff(y, z) |
Rows that appear in y but not z | |
bind_rows(y, z) |
Append z to y as new rows | |
bind_cols(y, z) |
Append z to y as new columns |
bind_
functions.
Import the reddit.csv
and regions.csv
files in the data folder
Join theregions
data to the
HINT: the common variable between the two data frames is state
reddit <- read.csv("../data/reddit.csv", stringsAsFactors = FALSE) regions <- read.csv("../data/regions.csv", stringsAsFactors = FALSE) reddit <- reddit %>% left_join(regions) str(reddit) ## 'data.frame': 32754 obs. of 16 variables: ## $ id : int 1 2 3 4 5 6 7 8 9 10 ... ## $ gender : int 0 0 1 0 1 0 0 0 0 0 ... ## $ age.range : chr "25-34" "25-34" "18-24" "25-34" ... ## $ marital.status : chr NA NA NA NA ... ## $ employment.status: chr "Employed full time" "Employed full time" "Freelance" "Freelance" ... ## $ military.service : chr NA NA NA NA ... ## $ children : chr "No" "No" "No" "No" ... ## $ education : chr "Bachelor's degree" "Bachelor's degree" "Some college" "Bachelor's degree" ... ## $ country : chr "United States" "United States" "United States" "United States" ... ## $ state : chr "New York" "New York" "Virginia" "New York" ... ## $ income.range : chr "$150,000 or more" "$150,000 or more" "Under $20,000" "$150,000 or more" ... ## $ fav.reddit : chr "getmotivated" "gaming" "snackexchange" "spacedicks" ... ## $ dog.cat : chr NA NA NA NA ... ## $ cheese : chr NA NA NA NA ... ## $ state.abreviated : chr "(NY)" "(NY)" "(VA)" "(NY)" ... ## $ division : int 2 2 5 2 9 2 NA 9 2 NA ...
library(DBI) library(RPostgreSQL) con <- dbConnect(drv = "PostgreSQL", dbname = "szgdlszd", host = "horton.elephantsql.com", port = 5432, user = "szgdlszd", password = Sys.getenv("ELEPHANTSQLPW")) ## flights <- copy_to(con, nycflights13::flights, "flights") flights_db <- tbl(con, "flights") delay_db <- flights_db %>% group_by(tailnum) %>% summarise(count = n(), dist = mean(distance, na.rm = TRUE), delay = mean(arr_delay, na.rm = TRUE)) %>% filter(count > 20, dist < 2000) # delay_db %>% explain() # delay_db %>% show_query() delay <- delay_db %>% collect()
# Interestingly, the average delay is only slightly related to the # average distance flown by a plane. ggplot(delay, aes(dist, delay)) + geom_point(aes(size = count), alpha = 1/2) + geom_smooth() + scale_size_area()
friend_ratio <- function(data, nom, denom) { call <- substitute(data_new <- data %>% transmute(friend_ratio = nom / denom), list(nom = as.name(nom), denom = as.name(denom))) eval(call); return(as.vector(data_new$friend_ratio)) } facebook$friend_ratio <- friend_ratio(data = facebook, "friendships_initiated", "friend_count") facebook %>% tbl_df %>% select(userid, age, gender, "friendships_initiated", "friend_count", friend_ratio) %>% arrange(desc(friend_count), desc(friend_ratio))
## # A tibble: 99,003 x 6 ## userid age gender friendships_initiated friend_count friend_ratio ## <int> <int> <fctr> <int> <int> <dbl> ## 1 2090699 103 female 96 4923 0.019500305 ## 2 1660276 66 male 431 4917 0.087655074 ## 3 1926655 19 female 241 4863 0.049557886 ## 4 1685573 38 male 942 4845 0.194427245 ## 5 1386477 61 male 561 4844 0.115813377 ## 6 1604095 15 female 97 4826 0.020099461 ## 7 2042824 18 male 32 4817 0.006643139 ## 8 1529639 96 female 555 4803 0.115552780 ## 9 1587911 108 female 535 4797 0.111528038 ## 10 1205425 60 female 3538 4794 0.738005841 ## # ... with 98,993 more rows
# facebook %>% group_by(gender) %>% summarise(mean_fr = mean(friend_ratio, na.rm = TRUE)) ggplot(data = filter(facebook, !is.na(gender)), aes(gender, friend_ratio, fill = gender)) + geom_boxplot() + coord_flip()
group_median <- function(data, id_vars, var, na.rm) { id_vars_sym <- lapply(id_vars, as.symbol) call <- substitute(data_new <- data %>% group_by_(.dots = id_vars) %>% summarise(var_median = median(var, na.rm = na.rm)), list(id_vars = id_vars_sym, var = as.name(var))) eval(call) return(data_new) } facebook %>% mutate(minor = ifelse(age < 18, TRUE, FALSE)) %>% group_median(id_vars = c("gender", "minor"), var = "friend_ratio", na.rm = TRUE)
## # A tibble: 6 x 3 ## # Groups: gender [?] ## gender minor var_median ## <fctr> <lgl> <dbl> ## 1 female FALSE 0.5700935 ## 2 female TRUE 0.5574885 ## 3 male FALSE 0.6666667 ## 4 male TRUE 0.7058824 ## 5 <NA> FALSE 0.4514702 ## 6 <NA> TRUE 0.4479167
dplyr
is a package that transforms and manipulates data
select()
: select variables of concern
filter()
: filter values based on conditions
group_by()
: group data by categorical levels
summarise()
: change unit of analysis
arrange()
: order the data
dplyr
is a package that transforms and manipulates data
mutate()
: create new variables
join()
: combine separate data sets
Start RStudio from \\asap1\em_apps$\R
or
Log on to server and select R installation:
\\asap1\em_apps$\R\R-3.3.1
setwd("T:/DKI/rtrainings/bootcamp/code-scripts")