我尝试使用此代码计算此附加数据框中value列的平均值和值
library(plyr); library(dplyr)
library(tidyverse); library(ggplot2)
Shann_in_corn <-shann%>%
select_(data=shann, Crop_Rot, Herb, Year, Rot, Rep, value, Crop)%>%
filter(Crop=="corn")%>%
ddply(.(Rot, Herb, Year), summarise,
N=length(value),
mean=mean(value),
sd=sd(value),
se=sd/sqrt(N))
structure(list(Plot = c(11L, 11L, 13L, 13L, 18L, 18L, 23L, 23L,
24L, 24L, 28L, 28L, 34L, 34L, 37L, 37L, 38L, 38L, 44L, 44L, 46L,
46L, 48L, 48L, 14L, 14L, 16L, 16L, 19L, 19L, 25L, 25L, 27L, 27L,
29L, 29L, 32L, 32L, 33L, 33L, 35L, 35L, 42L, 42L, 45L, 45L, 49L,
49L, 12L, 12L, 15L, 15L, 17L, 17L, 21L, 21L, 22L, 22L, 26L, 26L,
31L, 31L, 36L, 36L, 39L, 39L, 41L, 41L, 43L, 43L, 47L, 47L, 11L,
11L, 13L, 13L, 18L, 18L, 23L, 23L, 24L, 24L, 28L, 28L, 34L, 34L,
37L, 37L, 38L, 38L, 44L, 44L, 46L, 46L, 48L, 48L, 12L, 12L, 15L,
15L, 19L, 19L, 21L, 21L, 26L, 26L, 27L, 27L, 31L, 31L, 35L, 35L,
36L, 36L, 43L, 43L, 45L, 45L, 47L, 47L, 17L, 17L, 16L, 16L, 14L,
14L, 22L, 22L, 29L, 29L, 25L, 25L, 39L, 39L, 33L, 33L, 32L, 32L,
49L, 49L, 41L, 41L, 42L, 42L, 12L, 12L, 15L, 15L, 19L, 19L, 21L,
21L, 26L, 26L, 27L, 27L, 31L, 31L, 35L, 35L, 36L, 36L, 43L, 43L,
45L, 45L, 47L, 47L, 13L, 13L, 16L, 16L, 17L, 17L, 22L, 22L, 24L,
24L, 25L, 25L, 33L, 33L, 34L, 34L, 39L, 39L, 41L, 41L, 42L, 42L,
44L, 44L, 11L, 11L, 14L, 14L, 18L, 18L, 23L, 23L, 28L, 28L, 29L,
29L, 32L, 32L, 37L, 37L, 38L, 38L, 46L, 46L, 48L, 48L, 49L, 49L
), Side = structure(c(2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L), .Label = c("E",
"W"), class = "factor"), Crop = structure(c(2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 3L, 3L, 3L,
3L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L,
1L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 1L,
1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 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, 1L, 1L, 3L,
3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 1L, 1L), .Label = c("alfalfa", "corn", "oat", "soybean"), class = "factor"),
Crop_Rot = structure(c(3L, 3L, 2L, 2L, 4L, 4L, 3L, 3L, 2L,
2L, 4L, 4L, 2L, 2L, 4L, 4L, 3L, 3L, 2L, 2L, 4L, 4L, 3L, 3L,
9L, 9L, 8L, 8L, 7L, 7L, 8L, 8L, 7L, 7L, 9L, 9L, 9L, 9L, 8L,
8L, 7L, 7L, 8L, 8L, 7L, 7L, 9L, 9L, 1L, 1L, 5L, 5L, 6L, 6L,
1L, 1L, 6L, 6L, 5L, 5L, 1L, 1L, 5L, 5L, 6L, 6L, 6L, 6L, 1L,
1L, 5L, 5L, 8L, 8L, 7L, 7L, 9L, 9L, 8L, 8L, 7L, 7L, 9L, 9L,
7L, 7L, 9L, 9L, 8L, 8L, 7L, 7L, 9L, 9L, 8L, 8L, 4L, 4L, 3L,
3L, 2L, 2L, 4L, 4L, 3L, 3L, 2L, 2L, 4L, 4L, 2L, 2L, 3L, 3L,
4L, 4L, 2L, 2L, 3L, 3L, 1L, 1L, 5L, 5L, 6L, 6L, 1L, 1L, 6L,
6L, 5L, 5L, 1L, 1L, 5L, 5L, 6L, 6L, 6L, 6L, 1L, 1L, 5L, 5L,
9L, 9L, 8L, 8L, 7L, 7L, 9L, 9L, 8L, 8L, 7L, 7L, 9L, 9L, 7L,
7L, 8L, 8L, 9L, 9L, 7L, 7L, 8L, 8L, 2L, 2L, 3L, 3L, 4L, 4L,
4L, 4L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 4L, 4L, 4L, 4L, 3L,
3L, 2L, 2L, 5L, 5L, 1L, 1L, 6L, 6L, 5L, 5L, 6L, 6L, 1L, 1L,
1L, 1L, 6L, 6L, 5L, 5L, 6L, 6L, 5L, 5L, 1L, 1L), .Label = c("A4",
"C2", "C3", "C4", "O3", "O4", "S2", "S3", "S4"), class = "factor"),
Herb = structure(c(1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L,
1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L,
2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L,
1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L,
1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L,
1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L,
2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L,
2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L,
2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L,
2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L,
1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L,
1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L), .Label = c("conv",
"low"), class = "factor"), kg.ha = c(0.27, 160.1, 12.84,
0.05, 22.5, 0.22, 0.12, 25.05, 3.48, 1.21, 4.85, 21.97, 2.67,
0.23, 9, 0.28, 0.28, 21.04, 0.23, 45.91, 64.39, 2.51, 130.57,
54.86, 54.36, 0.96, 2.19, 177.1, 16.3, 1.86, 221.35, 0.62,
0.33, 85.59, 0.93, 60.6, 0, 0, 7.23, 0.05, 1.09, 0.03, 0.48,
21.3, 0.61, 0, 0.07, 0.25, 61, 54.25, 100.35, 75.55, 221.8,
162.75, 46.45, 84.1, 208.1, 372.35, 107, 65.35, 108.05, 58.5,
141, 67.65, 420, 379.4, 318.35, 248.8, 258.4, 102.35, 62.05,
141.05, 2.92, 1499.22, 40.77, 61.97, 57.98, 15.76, 2.94,
1991.43, 160.85, 1.14, 1.61, 79.03, 77.5, 31.12, 0.52, 0.38,
23.08, 396.81, 5.58, 192.42, 29.6, 70.43, 282.39, 40.34,
2.36, 38.78, 20.98, 156.06, 20.25, 0.81, 221.5, 39.32, 10.11,
57.23, 0.14, 37.93, 7.39, 5.22, 2.81, 0.59, 31.59, 2.14,
10.6, 110.94, 4.37, 0.73, 1.11, 40.43, 85.6, 74.5, 85.85,
84.25, 1115.45, 815.5, 123.45, 41.75, 913.15, 1021.25, 230.25,
98.1, 152.6, 274.1, 48.8, 16.9, 648.6, 766.75, 658.95, 606.35,
334.1, 199, 117.9, 60.65, 5.27, 40.09, 1.46, 143.89, 333.49,
67.11, 276.13, 59.22, 8.67, 134.32, 38.21, 128.22, 177.3,
129.8, 1.59, 107.34, 518.77, 0.4, 31.1, 14.76, 98.15, 0.01,
28.66, 63.05, 4.44, 29.81, 12.73, 282.57, 1.33, 0.2, 0.45,
55.73, 88.82, 8.27, 203.12, 1.57, 68.78, 0.78, 166.13, 2.22,
211.03, 1.59, 10.14, 17.9, 2.05, 309.57, 6.32, 19.35, 55,
144.9, 111, 110.5, 1149.15, 1256.1, 45.7, 32.05, 1227.85,
1418.1, 104.95, 132.15, 115.4, 134.85, 1369, 795.15, 50.8,
86.65, 869.1, 1121.25, 129.7, 116.5, 111.05, 176.05), Rep = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L,
4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L,
4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L), .Label = c("rep1", "rep2", "rep3", "rep4"
), class = "factor"), Rot = structure(c(2L, 2L, 1L, 1L, 3L,
3L, 2L, 2L, 1L, 1L, 3L, 3L, 1L, 1L, 3L, 3L, 2L, 2L, 1L, 1L,
3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 3L,
3L, 3L, 3L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 3L, 3L, 3L, 3L,
2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 1L, 1L, 3L, 3L, 2L, 2L,
1L, 1L, 3L, 3L, 1L, 1L, 3L, 3L, 2L, 2L, 1L, 1L, 3L, 3L, 2L,
2L, 3L, 3L, 2L, 2L, 1L, 1L, 3L, 3L, 2L, 2L, 1L, 1L, 3L, 3L,
1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 1L, 1L, 3L, 3L, 2L, 2L, 1L,
1L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 1L, 1L,
2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 3L,
3L, 3L, 3L, 2L, 2L, 1L, 1L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L,
3L), .Label = c("2-year", "3-year", "4-year"), class = "factor"),
Year = c(2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L,
2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L,
2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L,
2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L,
2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L,
2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L,
2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L,
2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L,
2014L, 2014L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L), H_R = structure(c(2L, 5L, 4L, 1L, 6L, 3L,
2L, 5L, 4L, 1L, 3L, 6L, 4L, 1L, 6L, 3L, 2L, 5L, 1L, 4L, 6L,
3L, 5L, 2L, 6L, 3L, 2L, 5L, 4L, 1L, 5L, 2L, 1L, 4L, 3L, 6L,
6L, 3L, 5L, 2L, 4L, 1L, 2L, 5L, 4L, 1L, 6L, 3L, 3L, 6L, 2L,
5L, 6L, 3L, 6L, 3L, 3L, 6L, 2L, 5L, 3L, 6L, 5L, 2L, 6L, 3L,
6L, 3L, 3L, 6L, 2L, 5L, 2L, 5L, 4L, 1L, 6L, 3L, 2L, 5L, 4L,
1L, 3L, 6L, 4L, 1L, 6L, 3L, 2L, 5L, 1L, 4L, 6L, 3L, 5L, 2L,
3L, 6L, 2L, 5L, 4L, 1L, 6L, 3L, 2L, 5L, 1L, 4L, 3L, 6L, 4L,
1L, 5L, 2L, 3L, 6L, 4L, 1L, 2L, 5L, 6L, 3L, 2L, 5L, 6L, 3L,
3L, 6L, 3L, 6L, 5L, 2L, 6L, 3L, 5L, 2L, 6L, 3L, 6L, 3L, 3L,
6L, 2L, 5L, 3L, 6L, 2L, 5L, 4L, 1L, 6L, 3L, 2L, 5L, 1L, 4L,
3L, 6L, 4L, 1L, 5L, 2L, 3L, 6L, 4L, 1L, 2L, 5L, 4L, 1L, 2L,
5L, 6L, 3L, 3L, 6L, 4L, 1L, 5L, 2L, 5L, 2L, 4L, 1L, 6L, 3L,
6L, 3L, 2L, 5L, 1L, 4L, 2L, 5L, 6L, 3L, 6L, 3L, 2L, 5L, 3L,
6L, 3L, 6L, 6L, 3L, 6L, 3L, 2L, 5L, 6L, 3L, 5L, 2L, 6L, 3L
), .Label = c("conv - 2-year", "conv - 3-year", "conv - 4-year",
"low - 2-year", "low - 3-year", "low - 4-year"), class = "factor"),
H_R_Y = structure(c(4L, 13L, 10L, 1L, 16L, 7L, 4L, 13L, 10L,
1L, 7L, 16L, 10L, 1L, 16L, 7L, 4L, 13L, 1L, 10L, 16L, 7L,
13L, 4L, 16L, 7L, 4L, 13L, 10L, 1L, 13L, 4L, 1L, 10L, 7L,
16L, 16L, 7L, 13L, 4L, 10L, 1L, 4L, 13L, 10L, 1L, 16L, 7L,
7L, 16L, 4L, 13L, 16L, 7L, 16L, 7L, 7L, 16L, 4L, 13L, 7L,
16L, 13L, 4L, 16L, 7L, 16L, 7L, 7L, 16L, 4L, 13L, 5L, 14L,
11L, 2L, 17L, 8L, 5L, 14L, 11L, 2L, 8L, 17L, 11L, 2L, 17L,
8L, 5L, 14L, 2L, 11L, 17L, 8L, 14L, 5L, 8L, 17L, 5L, 14L,
11L, 2L, 17L, 8L, 5L, 14L, 2L, 11L, 8L, 17L, 11L, 2L, 14L,
5L, 8L, 17L, 11L, 2L, 5L, 14L, 17L, 8L, 5L, 14L, 17L, 8L,
8L, 17L, 8L, 17L, 14L, 5L, 17L, 8L, 14L, 5L, 17L, 8L, 17L,
8L, 8L, 17L, 5L, 14L, 9L, 18L, 6L, 15L, 12L, 3L, 18L, 9L,
6L, 15L, 3L, 12L, 9L, 18L, 12L, 3L, 15L, 6L, 9L, 18L, 12L,
3L, 6L, 15L, 12L, 3L, 6L, 15L, 18L, 9L, 9L, 18L, 12L, 3L,
15L, 6L, 15L, 6L, 12L, 3L, 18L, 9L, 18L, 9L, 6L, 15L, 3L,
12L, 6L, 15L, 18L, 9L, 18L, 9L, 6L, 15L, 9L, 18L, 9L, 18L,
18L, 9L, 18L, 9L, 6L, 15L, 18L, 9L, 15L, 6L, 18L, 9L), .Label = c("conv - 2-year - 2014",
"conv - 2-year - 2015", "conv - 2-year - 2016", "conv - 3-year - 2014",
"conv - 3-year - 2015", "conv - 3-year - 2016", "conv - 4-year - 2014",
"conv - 4-year - 2015", "conv - 4-year - 2016", "low - 2-year - 2014",
"low - 2-year - 2015", "low - 2-year - 2016", "low - 3-year - 2014",
"low - 3-year - 2015", "low - 3-year - 2016", "low - 4-year - 2014",
"low - 4-year - 2015", "low - 4-year - 2016"), class = "factor"),
value = c(1.33217904021012, 0.819956535422326, 0.878672353634429,
0, 1.1676557909425, 0, 1.09657543425772, 0.367756349734082,
0.469982787668461, 0.782685621948506, 0.802344573460662,
0.791263673777807, 0.947133570514693, 0.876005765643174,
1.20426433468651, 0.876886231229689, 0.967488251410454, 0.889846458201103,
1.12525740546882, 0.754443002292786, 0.900825263623699, 0.892923886768289,
0.984370001654131, 0.00617142643859088, 0.614763436435568,
0.456194410390592, 0.836757269954837, 0.502339303931386,
1.3132238923146, 0.692318801445808, 0.933920405167339, 0.45351246445217,
0.813278221693497, 1.13250222818216, 0.205306663281312, 0,
0, 0, 1.23155253902969, 0, 0, 0, 0.275463905473084, 0.705372055960579,
0.122532269126764, 0, 0, 1.38185016510291, 0.650955925769806,
1.28403627835479, 1.53476589108218, 1.30710631497493, 1.18382662133399,
0.94550740378002, 0.763942021696703, 1.34997100108116, 0.925498006822258,
1.54344786550954, 1.69789271171287, 1.15927452074438, 1.66647156565305,
1.57126887627203, 1.13159072037213, 1.1981206060242, 1.1098887443971,
1.46626401787677, 0.888995736052264, 0.958143649371997, 1.04071715969811,
0.177331997763176, 1.19359793017755, 1.15816293846566, 0.660757564761445,
0.0774370521861312, 0.750130172619606, 0.747999160052096,
0.368336516079475, 0.170031685852736, 1.34901080584795, 0.137505320397941,
0.760703849217967, 0.47392376581098, 0.933924671593655, 1.1122565131489,
0.0827765852948456, 0.388096169582111, 1.13595490455717,
1.15084015515628, 0.734433609106438, 0.630832207014761, 1.01479884050622,
0.835542396388489, 0.495330682158045, 0.163500407042419,
1.41636948337545, 0.892045151226941, 0.716084081938979, 0.939628908971045,
0.906298297573505, 0.644949387369248, 0.997848789634358,
0.204704845662185, 0.752232637168957, 0.976079199103695,
1.11555585274383, 1.25058193384111, 1.06137359241618, 1.06741907524703,
0.865407811273895, 0.781079213937551, 0.444838021569131,
0.194560072364346, 1.23354961570624, 0.861153436736848, 1.30024301119404,
0.731788183353856, 0.961392748554241, 0.408370796814235,
0.758478149152187, 1.48316598811438, 1.19322505267225, 1.03915306062704,
0.844221639816559, 0.816313894600871, 1.51914393559838, 1.29824399835146,
1.46391529366185, 1.17052001372219, 1.23911462099854, 1.57773688476882,
1.18000790476928, 0.545584952266138, 0.960394676512275, 0.908928148155333,
0.891344858432891, 0.674885394662156, 1.35750771431797, 1.2394182437946,
1.44221693894728, 1.17709146848065, 0.893904973695966, 0.690859530265238,
0.905280194700945, 0.723077643196438, 1.12763579437248, 0.553661671365167,
0.996127300734851, 1.06586343226575, 1.46851526392938, 0.995660475041175,
0.743043195150989, 0.642562205054313, 0.900920573378399,
1.0017090896094, 0.358013969719735, 0.748025241066811, 0.509760219944739,
0.859630378008836, 1.29477364211756, 0.809710431130026, 0.677111916099108,
0.681941302111554, 1.06267087728613, 0.8610734335126, 0.771315681848917,
0.0313473294043128, 1.26458989555429, 0.404100340088279,
1.42168721241312, 1.03717479360106, 0.715994525966914, 0.890391761636334,
0.264235080879163, 0.733165115027097, 0.0456079066044284,
1.05008975930881, 0.371317060575211, 0.970011896311126, 0.699615296292664,
1.07007075427315, 1.2027650888541, 0.425328293373262, 1.20842703880595,
0.213554478767209, 1.00243714224353, 0.668606096680166, 1.14366520332059,
0.132179411089192, 0.0906569219857244, 0.282572181768919,
0.967232529394703, 1.29289933452463, 0.902298373878638, 1.31186714164,
1.15788467850277, 0.555727833344835, 2.15076429424205, 1.55095532718784,
0.692430589698105, 0.823320736098347, 1.84109402581388, 1.65730901322916,
0.91456410716298, 0.941366626170298, 0.513589722402605, 0.302259733773694,
1.60636378966858, 1.46488638777445, 0.916062014515128, 0.955711747961097,
1.40012264704695, 1.48718022397172, 1.02329832697661, 1.37396763939004,
1.03430312858319, 0.832535188647181)), .Names = c("Plot",
"Side", "Crop", "Crop_Rot", "Herb", "kg.ha", "Rep", "Rot", "Year",
"H_R", "H_R_Y", "value"), class = "data.frame", row.names = c(NA,
-216L))
R给了我一个错误Error in as.lazy_dots(list(...)) : object 'Crop_Rot' not found
将select()更改为select_()会产生不同的错误
Error: All select() inputs must resolve to integer column positions.
The following do not:
* shann
列value是一个字符串，所以在我合并数据框（第1列到第11列）和value之后，我使用了shann <- as.data.frame(shann)。请查看我可能会更改代码的位置。非常感谢任何帮助。
答案 0 :(得分：1)
由于您已经在使用管道，因此无需在data = shann内指定select()。
从select()内的代码中删除该部分并使用group_by()和summarise()（因此您只使用属于dplyr的函数）以下内容将返回您的预期输出
Shann_in_corn <-
shann %>%
select(Crop_Rot, Herb, Year, Rot, Rep, value, Crop) %>%
filter(Crop == "corn") %>%
group_by(Rot, Herb, Year) %>%
summarise(N = length(value),
mean = mean(value),
sd = sd(value),
se = sd / sqrt(N))