我想知道如何获得每个玩家统计数据的平均值（或平均值）（请注意每个玩家将拥有多行统计数据）。这是数据表的一小部分选择，它定义为AFL_Table。
Player No. K H D CP UP DE. CLG M CM M50 HO CLR CC ST R50 FF FA T X1PC Bo i50 G B GA G. TG. AF Opp
1 Isaac Smith 16 14 12 26 9 17 88 1 8 0 0 1 1 0 1 1 0 0 4 0 1 3 0 0 1 0 86 107 PORT
2 Taylor Duryea 8 18 2 20 2 18 70 3 10 0 0 0 0 0 0 4 3 0 2 2 1 2 0 0 0 0 80 99 PORT
3 Jack Gunston 19 13 4 17 2 15 76 0 9 0 3 0 1 1 0 2 1 0 1 4 1 3 3 1 0 60 88 98 PORT
4 Josh Gibson 6 15 9 24 7 18 95 2 9 1 0 0 0 0 0 5 0 1 2 8 0 2 0 0 1 0 100 95 PORT
5 Jordan Lewis 3 15 11 26 9 18 65 4 5 0 0 3 4 0 4 1 1 2 2 1 0 6 1 0 0 100 82 94 PORT
6 Sam Mitchell 5 15 12 27 13 14 70 5 3 0 0 1 8 3 5 3 0 2 3 2 1 4 0 0 0 0 89 85 PORT
7 Shaun Burgoyne 9 8 9 17 9 7 88 2 4 0 2 0 4 1 3 1 1 2 7 2 0 1 1 1 0 50 85 84 PORT
8 Grant Birchall 14 15 6 21 3 15 76 1 5 0 0 0 0 0 0 2 0 0 2 2 0 3 0 1 1 0 75 81 PORT
9 Jarryd Roughead 2 10 5 15 4 12 60 2 5 0 3 5 2 0 2 1 0 1 1 3 0 1 2 2 0 50 93 75 PORT
10 Luke Hodge 15 12 7 19 8 11 57 3 2 0 0 5 6 1 5 2 2 2 4 0 0 4 0 0 0 0 86 73 PORT
11 Matthew Suckling 4 9 2 11 3 8 72 3 4 0 1 0 0 0 0 1 1 0 4 3 0 3 2 1 1 50 78 73 PORT
12 Ben McEvoy 7 2 12 14 7 8 92 1 3 2 0 22 3 0 3 1 1 0 2 6 0 0 0 0 0 0 81 70 PORT
13 Ben Stratton 24 10 2 12 0 12 83 2 7 0 0 0 0 0 0 3 0 1 3 5 0 0 0 0 0 0 92 64 PORT
14 Bradley Hill 10 10 7 17 3 14 70 2 3 0 0 0 1 0 1 1 0 0 2 1 0 3 0 1 0 0 77 62 PORT
15 James Frawley 12 7 4 11 3 8 90 1 7 1 1 0 0 0 0 1 0 0 2 3 0 1 0 0 1 0 99 58 PORT
16 Liam Shiels 26 7 8 15 9 7 66 5 2 1 0 0 4 0 4 2 1 3 4 1 0 3 1 0 0 100 86 57 PORT
17 Paul Puopolo 28 4 5 9 8 3 66 1 1 0 1 0 2 1 1 0 4 0 5 0 0 3 1 0 1 100 85 55 PORT
18 Ryan Schoenmakers 25 9 2 11 7 5 63 1 5 1 0 0 1 1 0 1 2 0 1 9 0 2 0 1 0 0 85 53 PORT
19 Cyril Rioli 33 5 5 10 4 4 80 2 3 0 3 0 2 1 1 0 1 1 2 3 0 1 2 0 3 100 86 52 PORT
20 Luke Breust 22 11 1 12 6 7 66 3 3 0 0 1 2 1 1 0 2 1 1 0 1 8 0 0 0 0 74 48 PORT
21 Brian Lake 17 2 3 5 4 1 100 1 2 1 0 0 0 0 0 2 1 0 2 1 0 1 0 0 0 0 56 27 PORT
22 Billy Hartung 40 3 4 7 2 5 100 0 3 1 0 0 1 1 0 2 0 0 0 0 1 0 0 0 0 0 37 26 PORT
24 Jack Gunston 19 15 9 24 9 15 87 3 10 3 1 0 0 0 0 2 0 1 3 2 0 3 4 0 0 100 89 126 BL
25 Jarryd Roughead 2 15 15 30 8 20 73 3 6 0 3 0 5 0 5 0 3 1 1 0 0 4 3 3 2 50 93 118 BL
26 Liam Shiels 26 12 22 34 16 20 70 3 4 0 1 0 7 5 2 0 0 0 4 0 0 4 1 0 0 100 89 114 BL
27 Ben McEvoy 7 8 7 15 9 7 80 1 6 2 1 23 3 2 1 0 4 1 6 2 0 1 1 0 0 100 74 110 BL
28 Bradley Hill 10 21 9 30 2 30 90 4 4 0 0 0 0 0 0 2 0 2 4 1 0 6 1 0 2 100 87 109 BL
29 Jordan Lewis 3 15 16 31 10 21 83 2 5 0 0 2 3 1 2 1 3 1 3 0 0 5 0 0 3 0 82 106 BL
30 Shaun Burgoyne 9 8 21 29 14 16 82 5 4 0 0 0 1 1 0 1 1 2 5 2 0 2 1 0 0 100 85 99 BL
31 James Frawley 12 11 3 14 4 10 78 0 4 0 1 0 0 0 0 1 1 0 4 0 0 3 3 1 1 75 85 87 BL
32 Josh Gibson 6 18 5 23 4 19 78 4 7 1 0 0 0 0 0 2 1 1 0 4 0 3 0 0 1 0 100 83 BL
33 David Hale 20 8 7 15 8 8 60 5 4 0 1 23 3 1 2 1 3 4 3 3 1 2 1 1 1 33 74 83 BL
34 Matthew Suckling 4 10 6 16 3 13 87 2 7 0 0 0 1 0 1 0 0 0 2 0 1 3 2 0 2 100 79 83 BL
35 Cyril Rioli 33 10 9 19 9 10 84 0 3 0 0 0 5 3 2 1 1 0 4 2 1 3 1 2 1 33 70 82 BL
36 Grant Birchall 14 13 8 21 2 16 90 2 4 0 0 0 1 0 1 1 0 2 5 2 0 2 0 0 0 0 83 81 BL
37 James Sicily 21 7 9 16 5 11 81 2 6 1 4 0 2 0 2 0 2 0 2 0 0 0 2 0 1 100 82 79 BL
38 Isaac Smith 16 9 9 18 2 16 83 0 7 0 0 2 0 0 0 1 0 0 1 0 0 5 1 0 0 100 61 78 BL
39 Luke Breust 22 6 8 14 8 6 71 0 5 1 1 0 2 1 1 0 0 0 6 1 0 3 0 1 0 0 85 74 BL
40 Taylor Duryea 8 11 2 13 1 12 76 0 7 1 0 0 0 0 0 4 0 0 4 3 0 1 0 0 1 0 85 74 BL
41 Daniel Howe 41 7 8 15 2 14 93 0 4 0 0 0 1 0 1 3 0 0 5 2 0 0 0 0 0 0 84 69 BL
42 Brian Lake 17 9 2 11 6 8 90 3 6 1 0 0 0 0 0 2 3 2 5 7 0 1 0 0 0 0 100 66 BL
43 Will Langford 29 7 7 14 8 7 78 2 1 1 0 4 5 1 4 1 2 2 7 0 0 5 0 0 1 0 84 66 BL
44 Ben Stratton 24 1 5 6 2 4 66 1 0 0 0 0 0 0 0 0 0 0 5 2 0 1 0 0 1 0 96 33 BL
45 Billy Hartung 40 4 6 10 4 5 70 1 1 0 0 0 0 0 0 0 1 0 0 0 0 2 0 0 0 0 32 28 BL
此外，对于注释和按要求，dput(droplevels(head(AFL_Table, 20)))的输出为：
structure(list(Player = c("Isaac Smith", "Taylor Duryea", "Jack Gunston",
"Josh Gibson", "Jordan Lewis", "Sam Mitchell", "Shaun Burgoyne",
"Grant Birchall", "Jarryd Roughead", "Luke Hodge", "Matthew Suckling",
"Ben McEvoy", "Ben Stratton", "Bradley Hill", "James Frawley",
"Liam Shiels", "Paul Puopolo", "Ryan Schoenmakers", "Cyril Rioli",
"Luke Breust"), No. = structure(c(5L, 19L, 6L, 17L, 13L, 16L,
20L, 3L, 7L, 4L, 15L, 18L, 9L, 1L, 2L, 11L, 12L, 10L, 14L, 8L
), .Label = c("10", "12", "14", "15", "16", "19", "2", "22",
"24", "25", "26", "28", "3", "33", "4", "5", "6", "7", "8", "9"
), class = "factor"), K = structure(c(5L, 7L, 4L, 6L, 6L, 6L,
12L, 6L, 1L, 3L, 13L, 8L, 1L, 1L, 11L, 11L, 9L, 13L, 10L, 2L), .Label = c("10",
"11", "12", "13", "14", "15", "18", "2", "4", "5", "7", "8",
"9"), class = "factor"), H = structure(c(3L, 4L, 5L, 10L, 2L,
3L, 10L, 7L, 6L, 8L, 4L, 3L, 4L, 8L, 5L, 9L, 6L, 4L, 6L, 1L), .Label = c("1",
"11", "12", "2", "4", "5", "6", "7", "8", "9"), class = "factor"),
D = structure(c(11L, 8L, 6L, 10L, 11L, 12L, 6L, 9L, 5L, 7L,
2L, 4L, 3L, 6L, 2L, 5L, 13L, 2L, 1L, 3L), .Label = c("10",
"11", "12", "14", "15", "17", "19", "20", "21", "24", "26",
"27", "9"), class = "factor"), CP = structure(c(9L, 3L, 3L,
7L, 9L, 2L, 9L, 4L, 5L, 8L, 4L, 7L, 1L, 4L, 4L, 9L, 8L, 7L,
5L, 6L), .Label = c("0", "13", "2", "3", "4", "6", "7", "8",
"9"), class = "factor"), UP = structure(c(5L, 6L, 4L, 6L,
6L, 3L, 10L, 4L, 2L, 1L, 11L, 11L, 2L, 3L, 11L, 10L, 7L,
9L, 8L, 10L), .Label = c("11", "12", "14", "15", "17", "18",
"3", "4", "5", "7", "8"), class = "factor"), DE. = structure(c(11L,
6L, 8L, 14L, 4L, 6L, 11L, 8L, 2L, 1L, 7L, 13L, 10L, 6L, 12L,
5L, 5L, 3L, 9L, 5L), .Label = c("57", "60", "63", "65", "66",
"70", "72", "76", "80", "83", "88", "90", "92", "95"), class = "factor"),
CLG = structure(c(2L, 4L, 1L, 3L, 5L, 6L, 3L, 2L, 3L, 4L,
4L, 2L, 3L, 3L, 2L, 6L, 2L, 2L, 3L, 4L), .Label = c("0",
"1", "2", "3", "4", "5"), class = "factor"), M = structure(c(8L,
2L, 9L, 9L, 6L, 4L, 5L, 6L, 6L, 3L, 5L, 4L, 7L, 4L, 7L, 3L,
1L, 6L, 4L, 4L), .Label = c("1", "10", "2", "3", "4", "5",
"7", "8", "9"), class = "factor"), CM = structure(c(1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 2L, 1L,
2L, 1L, 1L), .Label = c("0", "1", "2"), class = "factor"),
M50 = structure(c(1L, 1L, 4L, 1L, 1L, 1L, 3L, 1L, 4L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 4L, 1L), .Label = c("0",
"1", "2", "3"), class = "factor"), HO = structure(c(2L, 1L,
1L, 1L, 4L, 2L, 1L, 1L, 5L, 5L, 1L, 3L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L), .Label = c("0", "1", "22", "3", "5"), class = "factor"),
CLR = structure(c(2L, 1L, 2L, 1L, 5L, 7L, 5L, 1L, 3L, 6L,
1L, 4L, 1L, 2L, 1L, 5L, 3L, 2L, 3L, 3L), .Label = c("0",
"1", "2", "3", "4", "6", "8"), class = "factor"), CC = structure(c(1L,
1L, 2L, 1L, 1L, 3L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L), .Label = c("0", "1", "3"), class = "factor"),
ST = structure(c(2L, 1L, 1L, 1L, 5L, 6L, 4L, 1L, 3L, 6L,
1L, 4L, 1L, 2L, 1L, 5L, 2L, 1L, 2L, 2L), .Label = c("0",
"1", "2", "3", "4", "5"), class = "factor"), R50 = structure(c(2L,
5L, 3L, 6L, 2L, 4L, 2L, 3L, 2L, 3L, 2L, 2L, 4L, 2L, 2L, 3L,
1L, 2L, 1L, 1L), .Label = c("0", "1", "2", "3", "4", "5"), class = "factor"),
FF = structure(c(1L, 4L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 3L,
2L, 2L, 1L, 1L, 1L, 2L, 5L, 3L, 2L, 3L), .Label = c("0",
"1", "2", "3", "4"), class = "factor"), FA = structure(c(1L,
1L, 1L, 2L, 3L, 3L, 3L, 1L, 2L, 3L, 1L, 1L, 2L, 1L, 1L, 4L,
1L, 1L, 2L, 2L), .Label = c("0", "1", "2", "3"), class = "factor"),
T = structure(c(4L, 2L, 1L, 2L, 2L, 3L, 6L, 2L, 1L, 4L, 4L,
2L, 3L, 2L, 2L, 4L, 5L, 1L, 2L, 1L), .Label = c("1", "2",
"3", "4", "5", "7"), class = "factor"), X1PC = structure(c(1L,
3L, 5L, 8L, 2L, 3L, 3L, 3L, 4L, 1L, 4L, 7L, 6L, 2L, 4L, 2L,
1L, 9L, 4L, 1L), .Label = c("0", "1", "2", "3", "4", "5",
"6", "8", "9"), class = "factor"), Bo = structure(c(2L, 2L,
2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L), .Label = c("0", "1"), class = "factor"), i50 = structure(c(4L,
3L, 4L, 3L, 6L, 5L, 2L, 4L, 2L, 5L, 4L, 1L, 1L, 4L, 2L, 4L,
4L, 3L, 2L, 7L), .Label = c("0", "1", "2", "3", "4", "6",
"8"), class = "factor"), G = structure(c(1L, 1L, 4L, 1L,
2L, 1L, 2L, 1L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 3L,
1L), .Label = c("0", "1", "2", "3"), class = "factor"), B = structure(c(1L,
1L, 2L, 1L, 1L, 1L, 2L, 2L, 3L, 1L, 2L, 1L, 1L, 2L, 1L, 1L,
1L, 2L, 1L, 1L), .Label = c("0", "1", "2"), class = "factor"),
GA = structure(c(2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 3L, 1L), .Label = c("0",
"1", "3"), class = "factor"), G. = structure(c(1L, 1L, 4L,
1L, 2L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 1L,
2L, 1L), .Label = c("0", "100", "50", "60"), class = "factor"),
TG. = structure(c(10L, 6L, 11L, 1L, 8L, 12L, 9L, 3L, 14L,
10L, 5L, 7L, 13L, 4L, 15L, 10L, 9L, 9L, 10L, 2L), .Label = c("100",
"74", "75", "77", "78", "80", "81", "82", "85", "86", "88",
"89", "92", "93", "99"), class = "factor"), AF = structure(c(1L,
19L, 18L, 17L, 16L, 15L, 14L, 13L, 12L, 11L, 11L, 10L, 9L,
8L, 7L, 6L, 5L, 4L, 3L, 2L), .Label = c("107", "48", "52",
"53", "55", "57", "58", "62", "64", "70", "73", "75", "81",
"84", "85", "94", "95", "98", "99"), class = "factor")), .Names = c("Player",
"No.", "K", "H", "D", "CP", "UP", "DE.", "CLG", "M", "CM", "M50",
"HO", "CLR", "CC", "ST", "R50", "FF", "FA", "T", "X1PC", "Bo",
"i50", "G", "B", "GA", "G.", "TG.", "AF"), row.names = c(NA,
20L), class = "data.frame")
答案 0 :(得分：0)
我们可以使用aggregate
aggregate(.~PlayerNo, AFL_Table[setdiff(names(AFL_Table), "Opp")],
FUN= mean, na.rm=TRUE, na.action=NULL)
summarise_each来自dplyr
library(dplyr)
AFL_Table %>%
group_by(PlayerNo) %>%
summarise_each(funs(mean=mean(., na.rm=TRUE)), -matches("Opp"))
根据dput输出，除了“Player”（即factor类）之外，所有列都是character类。在执行numeric
mean
AFL_Table %>%
mutate_each(funs(as.numeric(as.character(.))), K:AF) %>%
group_by(Player) %>%
summarise_each(funs(mean=mean(., na.rm=TRUE)), -matches("No."))