R 查找时间序列数据集中某列的最大值
我有一个时间序列数据集R 查找时间序列数据集中某列的最大值,r,R,我有一个时间序列数据集DF，其中第一列是timestep，第二列是cellNo.。如何删除除最大值（DF$cellno.）以外的所有行每个时间步 > head(DF, n=100) timestep cellNo. 1 1 1 2 1 2 3 1 3 4 1 4 5 1 5 6 1 6 7
DFtimestepcellNo.最大值（DF$cellno.）以外的所有行每个时间步
> head(DF, n=100)
timestep cellNo.
1 1 1
2 1 2
3 1 3
4 1 4
5 1 5
6 1 6
7 1 7
8 1 8
9 1 9
10 1 10
11 1 11
12 1 12
13 1 13
14 1 14
15 1 15
16 1 16
17 1 17
18 1 18
19 1 19
20 1 20
21 1 21
22 1 22
23 1 23
24 1 24
25 1 25
26 1 26
27 1 27
28 1 28
29 1 29
30 1 30
31 1 31
32 1 32
33 2 1
34 2 2
35 2 3
36 2 4
37 2 5
38 2 6
39 2 7
40 2 8
41 2 9
42 2 10
43 2 11
44 2 12
45 2 13
46 2 14
47 2 15
48 2 16
49 2 17
50 2 18
51 2 19
52 2 20
53 2 21
54 2 22
55 2 23
56 2 24
57 2 25
58 2 26
59 2 27
60 2 28
61 2 29
62 2 30
63 2 31
64 2 32
65 3 1
66 3 2
67 3 3
68 3 4
69 3 5
70 3 6
71 3 7
72 3 8
73 3 9
74 3 10
75 3 11
76 3 12
77 3 13
78 3 14
79 3 15
80 3 16
81 3 17
82 3 18
83 3 19
84 3 20
85 3 21
86 3 22
87 3 23
88 3 24
89 3 25
90 3 26
91 3 27
92 3 28
93 3 29
94 3 30
95 3 31
96 3 32
97 4 1
98 4 2
99 4 3
100 4 4
如果您只需要每个timestep
的max（cellno.）
，您可以执行以下操作：
aggregate(cellNo.~timestep, DF, max)
# timestep cellNo.
# 1 1 32
# 2 2 32
# 3 3 32
# 4 4 4
如果您只需要每个timestep
的max（cellno.）
，您可以执行以下操作：
aggregate(cellNo.~timestep, DF, max)
# timestep cellNo.
# 1 1 32
# 2 2 32
# 3 3 32
# 4 4 4
试试这个
# dput your data
df <- structure(list(timestep = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L), cellNo. = c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
29L, 30L, 31L, 32L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L,
32L, 1L, 2L, 3L, 4L)), .Names = c("timestep", "cellNo."), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24",
"25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35",
"36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46",
"47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57",
"58", "59", "60", "61", "62", "63", "64", "65", "66", "67", "68",
"69", "70", "71", "72", "73", "74", "75", "76", "77", "78", "79",
"80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "90",
"91", "92", "93", "94", "95", "96", "97", "98", "99", "100"))
library(dplyr)
df %>% group_by(timestep) %>% summarise(max = max(cellNo.))
#Source: local data frame [4 x 2]
#timestep max
# (int) (int)
#1 1 32
#2 2 32
#3 3 32
#4 4 4
#输出数据
df%分组依据（时间步长）%>%汇总（最大值=最大值（单元号））
#来源：本地数据帧[4 x 2]
#最大时间步长
#（内部）（内部）
#1 1 32
#2 2 32
#3 3 32
#4 4 4
试试这个
# dput your data
df <- structure(list(timestep = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L), cellNo. = c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
29L, 30L, 31L, 32L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L,
32L, 1L, 2L, 3L, 4L)), .Names = c("timestep", "cellNo."), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24",
"25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35",
"36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46",
"47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57",
"58", "59", "60", "61", "62", "63", "64", "65", "66", "67", "68",
"69", "70", "71", "72", "73", "74", "75", "76", "77", "78", "79",
"80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "90",
"91", "92", "93", "94", "95", "96", "97", "98", "99", "100"))
library(dplyr)
df %>% group_by(timestep) %>% summarise(max = max(cellNo.))
#Source: local data frame [4 x 2]
#timestep max
# (int) (int)
#1 1 32
#2 2 32
#3 3 32
#4 4 4
#输出数据
df%分组依据（时间步长）%>%汇总（最大值=最大值（单元号））
#来源：本地数据帧[4 x 2]
#最大时间步长
#（内部）（内部）
#1 1 32
#2 2 32
#3 3 32
#4 4 4
带有数据。表格
library(data.table)
setDT(df1)[, .(Max = max(cellNo.)), timestep]
带有数据。表
library(data.table)
setDT(df1)[, .(Max = max(cellNo.)), timestep]
有趣。我得去结帐了。我得去看看。我要喜欢R统计数据包。我要喜欢R统计数据包