| Homogenous | Heterogenous | |
|---|---|---|
| One dimension | vector: c() | list: list() |
| Two dimensions | matrix: matrix() | data.frame: data.frame() |
numericcharacterlogicalNAnames()length()c()x <- c(A = 4, B = 12, C = 34)
x A B C
4 12 34 names(x)[1] "A" "B" "C"length(x)[1] 3is.numeric(x)[1] TRUEWhat’s the result of the following expressions?
x <- c(A = 1, B = 5, C = 2)
length(x) # 1
is.numeric(names(x)) # 2
length(x == 1) # 3
sum(x == 1) # 4
is.logical(x != 2) # 5x <- c(A = 1, B = 5, C = 2)
length(x) # 1[1] 3is.numeric(names(x)) # 2[1] FALSEx <- c(A = 1, B = 5, C = 2)
length(x == 1) # 3[1] 3sum(x == 1) # 4[1] 1is.logical(x != 2) # 5[1] TRUEx[y]4 possible methods to extract values from a vector x using another vector y:
y is a numeric vector with positive indices from 1 to length(x)y is a logical vector of size length(x)y is a numeric vector with negative (exclusive) indices from -1 to -length(x)y is a character vector of names corresponding to names(x))x <- c(A = 1, B = 4, C = 6, D = 2)
xA B C D
1 4 6 2 y is a numeric vector with positive indices from 1 to length(x)x[c(1, 2)]A B
1 4 x[1:length(x)]A B C D
1 4 6 2 x[c(1, 1, 1)]A A A
1 1 1 y is a numeric vector with negative (exclusive) indices from -1 to -length(x)x[c(-1, -2)]C D
6 2 x[-1]B C D
4 6 2 x[-length(x)]A B C
1 4 6 y is a logical vector of size length(x)x[c(TRUE, FALSE, FALSE, TRUE)]A D
1 2 x[x > 3]B C
4 6 x[x == 1 | x == 4]A B
1 4 y is a character vector of names corresponding to names(x)x[c("B", "C")]B C
4 6 x["D"]D
2 x[y] <- zExtracted values can be directly updated (in-place).
x <- c(1, 4, 2, NA, 9, NA, NA, -99)
x[1] 1 4 2 NA 9 NA NA -99x[x == -99] <- NA
x[1] 1 4 2 NA 9 NA NA NAx[is.na(x)] <- -1
x[1] 1 4 2 -1 9 -1 -1 -1x <- x[y]Extracted values can be assigned to a new variable or override the original variable
x <- c(1, 4, 2, NA, 9, NA, NA, NA)
x[1] 1 4 2 NA 9 NA NA NAx <- x[!is.na(x)]
x[1] 1 4 2 9names()x chooses the rows, y chooses the columns: DF[x, y]DF[x, ]: select only rowsDF[, y]: select only columnsDF$y to select only the column with the name yDF[c(1, 2), ]DF[-1, -2]DF[x > 2, ]DF[, "pclass"]titanic <- read.csv("./www/titanic.csv", stringsAsFactors = FALSE)
titanic[c(1, 2, 3), "age"][1] 29.0000 0.9167 2.0000titanic[c(1, 2, 3), 1:3] X.1 X pclass
1 1 1 1
2 2 2 1
3 3 3 1titanic[1, ] X.1 X pclass survived name sex age embarked
1 1 1 1 1 Allen, Miss. Elisabeth Walton female 29 Stitanic$name[1:3][1] "Allen, Miss. Elisabeth Walton" "Allison, Master. Hudson Trevor"
[3] "Allison, Miss. Helen Loraine" titanic[titanic$age > 10 & titanic$age < 20 & !is.na(titanic$age), ] X.1 X pclass survived
12 12 12 1 1
28 28 28 1 1
54 54 54 1 0
55 55 55 1 1
56 56 56 1 1
93 93 93 1 1
115 115 115 1 0
138 138 138 1 1
160 160 160 1 1
188 188 188 1 1
194 194 194 1 1
196 196 196 1 1
198 198 198 1 0
199 199 199 1 1
217 217 217 1 1
229 229 229 1 0
230 230 230 1 1
250 250 250 1 1
251 251 251 1 1
271 271 271 1 1
290 290 290 1 1
296 296 296 1 1
327 327 327 2 0
332 332 332 2 0
338 338 338 2 1
342 342 342 2 1
345 345 345 2 1
351 351 351 2 1
365 365 365 2 0
387 387 387 2 0
391 391 391 2 0
395 395 395 2 0
396 396 396 2 1
406 406 406 2 0
409 409 409 2 0
417 417 417 2 0
446 446 446 2 0
459 459 459 2 1
483 483 483 2 1
502 502 502 2 1
504 504 504 2 1
511 511 511 2 0
514 514 514 2 1
519 519 519 2 0
531 531 531 2 0
535 535 535 2 1
553 553 553 2 0
559 559 559 2 1
569 569 569 2 0
570 570 570 2 0
583 583 583 2 1
602 602 602 3 0
603 603 603 3 0
605 605 605 3 1
608 608 608 3 1
613 613 613 3 1
620 620 620 3 0
622 622 622 3 1
626 626 626 3 1
629 629 629 3 0
637 637 637 3 0
643 643 643 3 0
651 651 651 3 0
654 654 654 3 1
662 662 662 3 1
666 666 666 3 0
670 670 670 3 0
677 677 677 3 0
693 693 693 3 0
695 695 695 3 0
696 696 696 3 0
699 699 699 3 0
701 701 701 3 0
702 702 702 3 0
710 710 710 3 1
718 718 718 3 0
720 720 720 3 1
732 732 732 3 0
738 738 738 3 0
739 739 739 3 1
741 741 741 3 0
745 745 745 3 0
756 756 756 3 0
762 762 762 3 0
772 772 772 3 1
773 773 773 3 0
778 778 778 3 1
787 787 787 3 0
788 788 788 3 0
792 792 792 3 0
793 793 793 3 0
800 800 800 3 0
810 810 810 3 0
811 811 811 3 0
819 819 819 3 1
828 828 828 3 0
830 830 830 3 0
831 831 831 3 0
840 840 840 3 0
842 842 842 3 0
856 856 856 3 0
860 860 860 3 0
886 886 886 3 0
899 899 899 3 0
911 911 911 3 0
920 920 920 3 0
939 939 939 3 0
1008 1008 1008 3 1
1012 1012 1012 3 0
1046 1046 1046 3 0
1048 1048 1048 3 1
1051 1051 1051 3 1
1057 1057 1057 3 1
1058 1058 1058 3 1
1061 1061 1061 3 1
1094 1094 1094 3 0
1105 1105 1105 3 0
1106 1106 1106 3 0
1109 1109 1109 3 0
1128 1128 1128 3 0
1131 1131 1131 3 0
1134 1134 1134 3 0
1158 1158 1158 3 0
1162 1162 1162 3 0
1172 1172 1172 3 0
name
12 Astor, Mrs. John Jacob (Madeleine Talmadge Force)
28 Bishop, Mrs. Dickinson H (Helen Walton)
54 Carrau, Mr. Jose Pedro
55 Carter, Master. William Thornton II
56 Carter, Miss. Lucile Polk
93 Dick, Mrs. Albert Adrian (Vera Gillespie)
115 Fortune, Mr. Charles Alexander
138 Graham, Miss. Margaret Edith
160 Hippach, Miss. Jean Gertrude
188 Lines, Miss. Mary Conover
194 Madill, Miss. Georgette Alexandra
196 Maioni, Miss. Roberta
198 Marvin, Mr. Daniel Warner
199 Marvin, Mrs. Daniel Warner (Mary Graham Carmichael Farquarson)
217 Newsom, Miss. Helen Monypeny
229 Penasco y Castellana, Mr. Victor de Satode
230 Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)
250 Ryerson, Master. John Borie
251 Ryerson, Miss. Emily Borie
271 Smith, Mrs. Lucien Philip (Mary Eloise Hughes)
290 Taussig, Miss. Ruth
296 Thayer, Mr. John Borland Jr
327 Andrew, Mr. Edgardo Samuel
332 Bailey, Mr. Percy Andrew
338 Beane, Mrs. Edward (Ethel Clarke)
342 Becker, Miss. Ruth Elizabeth
345 Bentham, Miss. Lilian W
351 Brown, Miss. Edith Eileen
365 Carbines, Mr. William
387 Davies, Mr. Charles Henry
391 Deacon, Mr. Percy William
395 Dibden, Mr. William
396 Doling, Miss. Elsie
406 Fahlstrom, Mr. Arne Jonas
409 Fillbrook, Mr. Joseph Charles
417 Gaskell, Mr. Alfred
446 Hiltunen, Miss. Marta
459 Ilett, Miss. Bertha
483 Lehmann, Miss. Bertha
502 Mellinger, Miss. Madeleine Violet
504 Mellors, Mr. William John
511 Mudd, Mr. Thomas Charles
514 Nasser, Mrs. Nicholas (Adele Achem)
519 Nicholls, Mr. Joseph Charles
531 Pengelly, Mr. Frederick William
535 Phillips, Miss. Kate Florence (\\Mrs Kate Louise Phillips Marshall\\)
553 Rogers, Mr. Reginald Harry
559 Silven, Miss. Lyyli Karoliina
569 Swane, Mr. George
570 Sweet, Mr. George Frederick
583 Watt, Miss. Bertha J
602 Abbott, Master. Eugene Joseph
603 Abbott, Mr. Rossmore Edward
605 Abelseth, Miss. Karen Marie
608 Abrahim, Mrs. Joseph (Sophie Halaut Easu)
613 Aks, Mrs. Sam (Leah Rosen)
620 Allum, Mr. Owen George
622 Andersen-Jensen, Miss. Carla Christine Nielsine
626 Andersson, Miss. Erna Alexandra
629 Andersson, Miss. Sigrid Elisabeth
637 Arnold-Franchi, Mrs. Josef (Josefine Franchi)
643 Asplund, Master. Filip Oscar
651 Attalah, Miss. Malake
654 Ayoub, Miss. Banoura
662 Badman, Miss. Emily Louisa
666 Barbara, Miss. Saiide
670 Beavan, Mr. William Thomas
677 Bjorklund, Mr. Ernst Herbert
693 Buckley, Miss. Katherine
695 Burke, Mr. Jeremiah
696 Burns, Miss. Mary Delia
699 Cacic, Mr. Jego Grga
701 Calic, Mr. Jovo
702 Calic, Mr. Petar
710 Carr, Miss. Helen \\Ellen\\
718 Chronopoulos, Mr. Demetrios
720 Cohen, Mr. Gurshon \\Gus\\
732 Cor, Mr. Liudevit
738 Crease, Mr. Ernest James
739 Cribb, Miss. Laura Alice
741 Culumovic, Mr. Jeso
745 Dakic, Mr. Branko
756 Davies, Mr. Joseph
762 de Pelsmaeker, Mr. Alfons
772 Devaney, Miss. Margaret Delia
773 Dika, Mr. Mirko
778 Dorking, Mr. Edward Arthur
787 Edvardsson, Mr. Gustaf Hjalmar
788 Eklund, Mr. Hans Linus
792 Elias, Mr. Joseph Jr
793 Elias, Mr. Tannous
800 Fischer, Mr. Eberhard Thelander
810 Ford, Mr. Edward Watson
811 Ford, Mr. William Neal
819 Gilnagh, Miss. Katherine \\Katie\\
828 Goodwin, Master. William Frederick
830 Goodwin, Miss. Lillian Amy
831 Goodwin, Mr. Charles Edward
840 Gustafsson, Mr. Karl Gideon
842 Hagardon, Miss. Kate
856 Hassan, Mr. Houssein G N
860 Hegarty, Miss. Hanora \\Nora\\
886 Jensen, Mr. Svend Lauritz
899 Johnson, Mr. William Cahoone Jr
911 Kallio, Mr. Nikolai Erland
920 Katavelas, Mr. Vassilios (\\Catavelas Vassilios\\)
939 Klasen, Mr. Klas Albin
1008 McGowan, Miss. Anna \\Annie\\
1012 McNamee, Mrs. Neal (Eileen O'Leary)
1046 Myhrman, Mr. Pehr Fabian Oliver Malkolm
1048 Najib, Miss. Adele Kiamie \\Jane\\
1051 Nakid, Mrs. Said (Waika \\Mary\\ Mowad)
1057 Nicola-Yarred, Master. Elias
1058 Nicola-Yarred, Miss. Jamila
1061 Nilsson, Miss. Berta Olivia
1094 Osen, Mr. Olaf Elon
1105 Panula, Mr. Ernesti Arvid
1106 Panula, Mr. Jaako Arnold
1109 Patchett, Mr. George
1128 Petroff, Mr. Nedelio
1131 Pettersson, Miss. Ellen Natalia
1134 Pokrnic, Mr. Mate
1158 Rosblom, Mr. Viktor Richard
1162 Rush, Mr. Alfred George John
1172 Sage, Master. William Henry
sex age embarked
12 female 18.0 C
28 female 19.0 C
54 male 17.0 S
55 male 11.0 S
56 female 14.0 S
93 female 17.0 S
115 male 19.0 S
138 female 19.0 S
160 female 16.0 C
188 female 16.0 S
194 female 15.0 S
196 female 16.0 S
198 male 19.0 S
199 female 18.0 S
217 female 19.0 S
229 male 18.0 C
230 female 17.0 C
250 male 13.0 C
251 female 18.0 C
271 female 18.0 S
290 female 18.0 S
296 male 17.0 C
327 male 18.0 S
332 male 18.0 S
338 female 19.0 S
342 female 12.0 S
345 female 19.0 S
351 female 15.0 S
365 male 19.0 S
387 male 18.0 S
391 male 17.0 S
395 male 18.0 S
396 female 18.0 S
406 male 18.0 S
409 male 18.0 S
417 male 16.0 S
446 female 18.0 S
459 female 17.0 S
483 female 17.0 C
502 female 13.0 S
504 male 19.0 S
511 male 16.0 S
514 female 14.0 C
519 male 19.0 S
531 male 19.0 S
535 female 19.0 S
553 male 19.0 S
559 female 18.0 S
569 male 18.5 S
570 male 14.0 S
583 female 12.0 S
602 male 13.0 S
603 male 16.0 S
605 female 16.0 S
608 female 18.0 C
613 female 18.0 S
620 male 18.0 S
622 female 19.0 S
626 female 17.0 S
629 female 11.0 S
637 female 18.0 S
643 male 13.0 S
651 female 17.0 C
654 female 13.0 C
662 female 18.0 S
666 female 18.0 C
670 male 19.0 S
677 male 18.0 S
693 female 18.5 Q
695 male 19.0 Q
696 female 18.0 Q
699 male 18.0 S
701 male 17.0 S
702 male 17.0 S
710 female 16.0 Q
718 male 18.0 C
720 male 18.0 S
732 male 19.0 S
738 male 19.0 S
739 female 17.0 S
741 male 17.0 S
745 male 19.0 S
756 male 17.0 S
762 male 16.0 S
772 female 19.0 Q
773 male 17.0 S
778 male 19.0 S
787 male 18.0 S
788 male 16.0 S
792 male 17.0 C
793 male 15.0 C
800 male 18.0 S
810 male 18.0 S
811 male 16.0 S
819 female 16.0 Q
828 male 11.0 S
830 female 16.0 S
831 male 14.0 S
840 male 19.0 S
842 female 17.0 Q
856 male 11.0 C
860 female 18.0 Q
886 male 17.0 S
899 male 19.0 S
911 male 17.0 S
920 male 18.5 C
939 male 18.0 S
1008 female 15.0 Q
1012 female 19.0 S
1046 male 18.0 S
1048 female 15.0 C
1051 female 19.0 C
1057 male 12.0 C
1058 female 14.0 C
1061 female 18.0 S
1094 male 16.0 S
1105 male 16.0 S
1106 male 14.0 S
1109 male 19.0 S
1128 male 19.0 S
1131 female 18.0 S
1134 male 17.0 S
1158 male 18.0 S
1162 male 16.0 S
1172 male 14.5 S
[ reached getOption("max.print") -- omitted 14 rows ]titanic[titanic$sex == "male", ] X.1 X pclass survived
2 2 2 1 1
4 4 4 1 0
6 6 6 1 1
8 8 8 1 0
10 10 10 1 0
11 11 11 1 0
15 15 15 1 1
16 16 16 1 0
17 17 17 1 0
20 20 20 1 0
21 21 21 1 1
23 23 23 1 1
26 26 26 1 0
27 27 27 1 1
30 30 30 1 1
31 31 31 1 0
32 32 32 1 1
35 35 35 1 0
38 38 38 1 1
39 39 39 1 0
40 40 40 1 0
41 41 41 1 0
46 46 46 1 0
47 47 47 1 0
48 48 48 1 1
50 50 50 1 1
52 52 52 1 0
53 53 53 1 0
54 54 54 1 0
55 55 55 1 1
57 57 57 1 1
59 59 59 1 0
61 61 61 1 0
63 63 63 1 0
65 65 65 1 1
69 69 69 1 1
71 71 71 1 0
72 72 72 1 0
75 75 75 1 0
76 76 76 1 0
78 78 78 1 0
81 81 81 1 0
82 82 82 1 0
85 85 85 1 0
87 87 87 1 1
88 88 88 1 1
90 90 90 1 0
92 92 92 1 1
94 94 94 1 1
95 95 95 1 1
97 97 97 1 0
101 101 101 1 1
102 102 102 1 0
107 107 107 1 0
110 110 110 1 1
111 111 111 1 0
115 115 115 1 0
116 116 116 1 0
119 119 119 1 0
120 120 120 1 1
121 121 121 1 1
124 124 124 1 1
126 126 126 1 0
127 127 127 1 0
129 129 129 1 0
133 133 133 1 0
134 134 134 1 1
136 136 136 1 0
137 137 137 1 1
139 139 139 1 0
141 141 141 1 1
143 143 143 1 0
144 144 144 1 1
146 146 146 1 1
148 148 148 1 0
149 149 149 1 0
151 151 151 1 0
152 152 152 1 1
153 153 153 1 1
155 155 155 1 0
157 157 157 1 0
158 158 158 1 0
159 159 159 1 0
163 163 163 1 0
165 165 165 1 1
166 166 166 1 1
167 167 167 1 0
171 171 171 1 1
172 172 172 1 0
173 173 173 1 0
174 174 174 1 0
175 175 175 1 0
176 176 176 1 0
178 178 178 1 1
180 180 180 1 0
184 184 184 1 1
185 185 185 1 0
186 186 186 1 0
190 190 190 1 0
192 192 192 1 0
195 195 195 1 0
197 197 197 1 1
198 198 198 1 0
201 201 201 1 0
202 202 202 1 0
203 203 203 1 1
204 204 204 1 0
206 206 206 1 0
207 207 207 1 0
210 210 210 1 1
211 211 211 1 0
212 212 212 1 0
213 213 213 1 0
216 216 216 1 0
218 218 218 1 0
220 220 220 1 1
222 222 222 1 0
223 223 223 1 0
224 224 224 1 0
225 225 225 1 0
226 226 226 1 0
227 227 227 1 0
229 229 229 1 0
232 232 232 1 1
233 233 233 1 0
name sex age
2 Allison, Master. Hudson Trevor male 0.9167
4 Allison, Mr. Hudson Joshua Creighton male 30.0000
6 Anderson, Mr. Harry male 48.0000
8 Andrews, Mr. Thomas Jr male 39.0000
10 Artagaveytia, Mr. Ramon male 71.0000
11 Astor, Col. John Jacob male 47.0000
15 Barkworth, Mr. Algernon Henry Wilson male 80.0000
16 Baumann, Mr. John D male NA
17 Baxter, Mr. Quigg Edmond male 24.0000
20 Beattie, Mr. Thomson male 36.0000
21 Beckwith, Mr. Richard Leonard male 37.0000
23 Behr, Mr. Karl Howell male 26.0000
26 Birnbaum, Mr. Jakob male 25.0000
27 Bishop, Mr. Dickinson H male 25.0000
30 Bjornstrom-Steffansson, Mr. Mauritz Hakan male 28.0000
31 Blackwell, Mr. Stephen Weart male 45.0000
32 Blank, Mr. Henry male 40.0000
35 Borebank, Mr. John James male 42.0000
38 Bradley, Mr. George (\\George Arthur Brayton\\) male NA
39 Brady, Mr. John Bertram male 41.0000
40 Brandeis, Mr. Emil male 48.0000
41 Brewe, Dr. Arthur Jackson male NA
46 Butt, Major. Archibald Willingham male 45.0000
47 Cairns, Mr. Alexander male NA
48 Calderhead, Mr. Edward Pennington male 42.0000
50 Cardeza, Mr. Thomas Drake Martinez male 36.0000
52 Carlsson, Mr. Frans Olof male 33.0000
53 Carrau, Mr. Francisco M male 28.0000
54 Carrau, Mr. Jose Pedro male 17.0000
55 Carter, Master. William Thornton II male 11.0000
57 Carter, Mr. William Ernest male 36.0000
59 Case, Mr. Howard Brown male 49.0000
61 Cavendish, Mr. Tyrell William male 36.0000
63 Chaffee, Mr. Herbert Fuller male 46.0000
65 Chambers, Mr. Norman Campbell male 27.0000
69 Chevre, Mr. Paul Romaine male 45.0000
71 Chisholm, Mr. Roderick Robert Crispin male NA
72 Clark, Mr. Walter Miller male 27.0000
75 Clifford, Mr. George Quincy male NA
76 Colley, Mr. Edward Pomeroy male 47.0000
78 Compton, Mr. Alexander Taylor Jr male 37.0000
81 Crafton, Mr. John Bertram male NA
82 Crosby, Capt. Edward Gifford male 70.0000
85 Cumings, Mr. John Bradley male 39.0000
87 Daly, Mr. Peter Denis male 51.0000
88 Daniel, Mr. Robert Williams male 27.0000
90 Davidson, Mr. Thornton male 31.0000
92 Dick, Mr. Albert Adrian male 31.0000
94 Dodge, Dr. Washington male 53.0000
95 Dodge, Master. Washington male 4.0000
97 Douglas, Mr. Walter Donald male 50.0000
101 Duff Gordon, Sir. Cosmo Edmund (\\Mr Morgan\\) male 49.0000
102 Dulles, Mr. William Crothers male 39.0000
107 Farthing, Mr. John male NA
110 Flynn, Mr. John Irwin (\\Irving\\) male 36.0000
111 Foreman, Mr. Benjamin Laventall male 30.0000
115 Fortune, Mr. Charles Alexander male 19.0000
116 Fortune, Mr. Mark male 64.0000
119 Franklin, Mr. Thomas Parham male NA
120 Frauenthal, Dr. Henry William male 50.0000
121 Frauenthal, Mr. Isaac Gerald male 43.0000
124 Frolicher-Stehli, Mr. Maxmillian male 60.0000
126 Fry, Mr. Richard male NA
127 Futrelle, Mr. Jacques Heath male 37.0000
129 Gee, Mr. Arthur H male 47.0000
133 Giglio, Mr. Victor male 24.0000
134 Goldenberg, Mr. Samuel L male 49.0000
136 Goldschmidt, Mr. George B male 71.0000
137 Gracie, Col. Archibald IV male 53.0000
139 Graham, Mr. George Edward male 38.0000
141 Greenfield, Mr. William Bertram male 23.0000
143 Guggenheim, Mr. Benjamin male 46.0000
144 Harder, Mr. George Achilles male 25.0000
146 Harper, Mr. Henry Sleeper male 48.0000
148 Harrington, Mr. Charles H male NA
149 Harris, Mr. Henry Birkhardt male 45.0000
151 Harrison, Mr. William male 40.0000
152 Hassab, Mr. Hammad male 27.0000
153 Hawksford, Mr. Walter James male NA
155 Hays, Mr. Charles Melville male 55.0000
157 Head, Mr. Christopher male 42.0000
158 Hilliard, Mr. Herbert Henry male NA
159 Hipkins, Mr. William Edward male 55.0000
163 Holverson, Mr. Alexander Oskar male 42.0000
165 Homer, Mr. Harry (\\Mr E Haven\\) male 35.0000
166 Hoyt, Mr. Frederick Maxfield male 38.0000
167 Hoyt, Mr. William Fisher male NA
171 Ismay, Mr. Joseph Bruce male 49.0000
172 Jones, Mr. Charles Cresson male 46.0000
173 Julian, Mr. Henry Forbes male 50.0000
174 Keeping, Mr. Edwin male 32.5000
175 Kent, Mr. Edward Austin male 58.0000
176 Kenyon, Mr. Frederick R male 41.0000
178 Kimball, Mr. Edwin Nelson Jr male 42.0000
180 Klaber, Mr. Herman male NA
184 Lesurer, Mr. Gustave J male 35.0000
185 Lewy, Mr. Ervin G male NA
186 Lindeberg-Lind, Mr. Erik Gustaf (\\Mr Edward Lingrey\\) male 42.0000
190 Long, Mr. Milton Clyde male 29.0000
192 Loring, Mr. Joseph Holland male 30.0000
195 Maguire, Mr. John Edward male 30.0000
197 Marechal, Mr. Pierre male NA
198 Marvin, Mr. Daniel Warner male 19.0000
201 McCaffry, Mr. Thomas Francis male 46.0000
202 McCarthy, Mr. Timothy J male 54.0000
203 McGough, Mr. James Robert male 36.0000
204 Meyer, Mr. Edgar Joseph male 28.0000
206 Millet, Mr. Francis Davis male 65.0000
207 Minahan, Dr. William Edward male 44.0000
210 Mock, Mr. Philipp Edmund male 30.0000
211 Molson, Mr. Harry Markland male 55.0000
212 Moore, Mr. Clarence Bloomfield male 47.0000
213 Natsch, Mr. Charles H male 37.0000
216 Newell, Mr. Arthur Webster male 58.0000
218 Nicholson, Mr. Arthur Ernest male 64.0000
220 Omont, Mr. Alfred Fernand male NA
222 Ostby, Mr. Engelhart Cornelius male 65.0000
223 Ovies y Rodriguez, Mr. Servando male 28.5000
224 Parr, Mr. William Henry Marsh male NA
225 Partner, Mr. Austen male 45.5000
226 Payne, Mr. Vivian Ponsonby male 23.0000
227 Pears, Mr. Thomas Clinton male 29.0000
229 Penasco y Castellana, Mr. Victor de Satode male 18.0000
232 Peuchen, Major. Arthur Godfrey male 52.0000
233 Porter, Mr. Walter Chamberlain male 47.0000
embarked
2 S
4 S
6 S
8 S
10 C
11 C
15 S
16 S
17 C
20 C
21 S
23 C
26 C
27 C
30 S
31 S
32 C
35 S
38 S
39 S
40 C
41 C
46 S
47 S
48 S
50 C
52 S
53 S
54 S
55 S
57 S
59 S
61 S
63 S
65 S
69 C
71 S
72 C
75 S
76 S
78 C
81 S
82 S
85 C
87 S
88 S
90 S
92 S
94 S
95 S
97 C
101 C
102 C
107 S
110 S
111 C
115 S
116 S
119 S
120 S
121 C
124 C
126 S
127 S
129 S
133 C
134 C
136 C
137 C
139 S
141 C
143 C
144 C
146 C
148 S
149 S
151 S
152 C
153 S
155 S
157 S
158 S
159 S
163 S
165 C
166 S
167 C
171 S
172 S
173 S
174 C
175 C
176 S
178 S
180 S
184 C
185 C
186 S
190 S
192 S
195 S
197 C
198 S
201 C
202 S
203 S
204 C
206 S
207 Q
210 C
211 S
212 S
213 C
216 C
218 S
220 C
222 C
223 C
224 S
225 S
226 S
227 S
229 C
232 S
233 S
[ reached getOption("max.print") -- omitted 718 rows ]rowFilter <- titanic$age > 75 & !is.na(titanic$age)
colFilter <- c("name", "survived", "pclass")
titanic[rowFilter, colFilter] name survived pclass
15 Barkworth, Mr. Algernon Henry Wilson 1 1
62 Cavendish, Mrs. Tyrell William (Julia Florence Siegel) 1 1DF$x[y] <- z$ - sign is used to select the column firsttitanic$sex[1:10] # convert the column to 0, 1 values [1] "female" "male" "female" "male" "female" "male" "female"
[8] "male" "female" "male" titanic$sex[titanic$sex == "male"] <- 0
titanic$sex[titanic$sex == "female"] <- 1
titanic$sex[1:10] [1] "1" "0" "1" "0" "1" "0" "1" "0" "1" "0"titanic$sex <- as.numeric(titanic$sex)
titanic$sex[1:10] [1] 1 0 1 0 1 0 1 0 1 0sum(is.na(titanic$age)) # age contains missing values[1] 263meanAge <- mean(titanic$age, na.rm = TRUE) # caculate the average age
meanAge [1] 29.88113titanic$age[is.na(titanic$age)] <- meanAge # impute missing values with the average age
sum(is.na(titanic$age))[1] 0as with vectors, data frames can be replaced by a subset
# only female
titanic <- titanic[titanic$sex == 1, ]
# delete the third column
titanic <- titanic[, -3]
# another possibility
titanic[,3] <- NULL
head(titanic) X.1 X name sex age
1 1 1 Allen, Miss. Elisabeth Walton 1 29
3 3 3 Allison, Miss. Helen Loraine 1 2
5 5 5 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) 1 25
7 7 7 Andrews, Miss. Kornelia Theodosia 1 63
9 9 9 Appleton, Mrs. Edward Dale (Charlotte Lamson) 1 53
12 12 12 Astor, Mrs. John Jacob (Madeleine Talmadge Force) 1 18
embarked
1 S
3 S
5 S
7 S
9 S
12 Cwhich functionlogical vector is `TRUEx <- c(FALSE, TRUE, TRUE, FALSE)
which(x)[1] 2 3which(titanic$survived == 1 & titanic$pclass == 1) [1] 1 2 6 7 9 12 13 14 15 18 19 21 22 23 24 25 27
[18] 28 29 30 32 33 34 36 37 38 42 43 44 45 48 49 50 51
[35] 55 56 57 58 60 62 64 65 66 67 68 69 70 73 74 77 79
[52] 80 83 84 86 87 88 89 91 92 93 94 95 96 98 99 100 101
[69] 103 104 105 108 109 110 112 113 114 117 118 120 121 122 123 124 125
[86] 128 130 131 132 134 135 137 138 140 141 142 144 145 146 147 150 152
[103] 153 154 156 160 161 162 164 165 166 168 169 171 177 178 179 181 182
[120] 183 184 187 188 189 191 193 194 196 197 199 200 203 205 208 209 210
[137] 214 215 217 219 220 221 228 230 231 232 234 236 239 241 243 246 248
[154] 250 251 252 254 255 256 257 258 259 260 261 262 264 265 266 271 272
[171] 273 274 275 276 278 279 281 282 283 285 289 290 292 293 294 296 297
[188] 298 299 303 305 309 310 312 315 316 318 320 321 323length(which(x))[1] 2sum(x)[1] 2x <- c(FALSE, TRUE, TRUE, FALSE)
length(which(x))[1] 2sum(x)[1] 2titanic <- titanic[order(titanic$age), ]
head(titanic) X.1 X pclass survived name
764 764 764 3 1 Dean, Miss. Elizabeth Gladys \\Millvina\\
748 748 748 3 0 Danbom, Master. Gilbert Sigvard Emanuel
1241 1241 1241 3 1 Thomas, Master. Assad Alexander
428 428 428 2 1 Hamalainen, Master. Viljo
658 658 658 3 1 Baclini, Miss. Eugenie
659 659 659 3 1 Baclini, Miss. Helene Barbara
sex age embarked
764 female 0.1667 S
748 male 0.3333 S
1241 male 0.4167 C
428 male 0.6667 S
658 female 0.7500 C
659 female 0.7500 Cyoungest <- order(titanic$age)[1:5]
oldest <- order(titanic$age, decreasing = TRUE)[1:5]
titanic[youngest, -3] X.1 X survived name sex
764 764 764 1 Dean, Miss. Elizabeth Gladys \\Millvina\\ female
748 748 748 0 Danbom, Master. Gilbert Sigvard Emanuel male
1241 1241 1241 1 Thomas, Master. Assad Alexander male
428 428 428 1 Hamalainen, Master. Viljo male
658 658 658 1 Baclini, Miss. Eugenie female
age embarked
764 0.1667 S
748 0.3333 S
1241 0.4167 C
428 0.6667 S
658 0.7500 Ctitanic[oldest, -3] X.1 X survived
15 15 15 1
62 62 62 1
1236 1236 1236 0
10 10 10 0
136 136 136 0
name sex age
15 Barkworth, Mr. Algernon Henry Wilson male 80
62 Cavendish, Mrs. Tyrell William (Julia Florence Siegel) female 76
1236 Svensson, Mr. Johan male 74
10 Artagaveytia, Mr. Ramon male 71
136 Goldschmidt, Mr. George B male 71
embarked
15 S
62 S
1236 S
10 C
136 Coldest <- order(titanic$age, decreasing = TRUE)[1:5]
titanic[oldest, -3] X.1 X survived
15 15 15 1
62 62 62 1
1236 1236 1236 0
10 10 10 0
136 136 136 0
name sex age
15 Barkworth, Mr. Algernon Henry Wilson male 80
62 Cavendish, Mrs. Tyrell William (Julia Florence Siegel) female 76
1236 Svensson, Mr. Johan male 74
10 Artagaveytia, Mr. Ramon male 71
136 Goldschmidt, Mr. George B male 71
embarked
15 S
62 S
1236 S
10 C
136 Ctitanic[which.min(titanic$age), ] X.1 X pclass survived name
764 764 764 3 1 Dean, Miss. Elizabeth Gladys \\Millvina\\
sex age embarked
764 female 0.1667 Stitanic[which.max(titanic$age), ] X.1 X pclass survived name sex age
15 15 15 1 1 Barkworth, Mr. Algernon Henry Wilson male 80
embarked
15 Stitanic[which.max(titanic$age), ] X.1 X pclass survived name sex age
15 15 15 1 1 Barkworth, Mr. Algernon Henry Wilson male 80
embarked
15 S# gives the same result but harder to read
titanic[which(titanic$age == max(titanic$age)), ][1] X.1 X pclass survived name sex age embarked
<0 rows> (or 0-length row.names)