问题：
使用count()、aggregate()、data.table()或dplyr()汇总数据(平均值、标准差)
支洋
概览
我有一个名为“subset_leaf_1”的数据集（见下文），显示了气候环境如何影响一种名为“栎树”的特定橡树物种的树冠指数。
我有一个名为Urbanisation_index（即下面的数据框）的列，其中包含四个子级别（即1，2，3和4）。每个子级别（1-4）都突出了围绕“栎属植物”的城市化程度。
我还想计算城市化指数各子级别的平均冠层指数。
问题
我想在dplyr包中使用data.table（）、聚合（）或count（）按物种计算城市化指数每个子级的行数，然后计算Urbanisation_index每个子级的平均Canopy_index。
如果有人能帮忙我将不胜感激
期望的结果
R代码：
首先，我子集了栎属植物的数据
set.seed(45L)
##Subset dataframe leaf_1 by"Quercus petraea"
subset_leaf_1<-subset(leaf_1, Species == "Quercus petraea")
#Produce new dataframe for the subsetted data (observation 1)
Subset_leaf_ob_1<-data.frame(subset_leaf_1, stringsAsFactors=TRUE)
断续器（）
library(dplyr)
#sum and count of species and urbanisation index
#Mean and standard deviation for Canopy_Index, per urbansiation level, per species
Summarised_leaf_1<-Subset_leaf_ob_1 %>%
count(Species, Urbanisation_index) %>%
summarise(Subset_leaf_ob_1, mean=mean(Canopy_Index), sd=sd(Canopy_Index))
#Error message
Error in summarise_impl(.data, dots) :
Column `Subset_leaf_ob_1` must be length 1 (a summary value), not 11
聚合（）
我可以使用这两个方程来计算每行城市化指数的计数，以及每城市化指数子级别的冠层指数的平均值：
##Row count for Urbansiation_index
aggregate_subset_leaf_1<-aggregate(Obs_.no ~ Species + Urbanisation_index,
data = Subset_leaf_ob_1, FUN = length)
##Mean Canopy_Index per Urbanisation_index sublevel per speces
subset_leaf_1_canopy<-aggregate(Canopy_Index ~ Species*Urbanisation_index,
data = Subset_leaf_ob_1, FUN = mean)
为了结合城市化指数的每行计数和每个子层的平均冠层指数，我应用了下面的函数（上表）。但是，此函数将每行的计数加0，并且我无法重命名列标题以生成新的数据帧。检查R Studio的R环境分区后，未显示冠层指数的平均值和标准差。
##Function to incorporate both counts of urbanisation index and the mean and standard deviation for canopy index
Mean_sd_Count_leaf_1<-aggregate(Canopy_Index ~ Species+Urbanisation_index,
data = Subset_leaf_ob_1,
FUN = function(x) c(Counts = length(x), Mean = mean(x), Sd = sd(x)))
##Rename the columns
colnames(Mean_sd_Count_leaf_1)<-c("Species", "Urbanisation_Index", "Counts", "Mean_Canopy_Index", "SD_Canopy_Index")
##Error message
Error in names(x) <- value :
'names' attribute [5] must be the same length as the vector [3]
traceback()
1: `colnames<-`(`*tmp*`, value = c("Species", "Urbanisation_Index",
"Counts", "Mean_Canopy_Index", "SD_Canopy_Index"))
数据.表格()
library(data.table)
Data.table.leaf.1<-data.table(Subset_leaf_ob_1)
leaf.1.data.table<-Data.table.leaf.1[, .N, by = list(Species, Urbanisation_index),
mean_test=rowMeans(Canopy_Index),
sd_test=rowMeans(Canopy_Index)]
##Error Message
Error in `[.data.table`(Data.table.leaf.1, , .N, by = list(Species, Urbanisation_index), :
unused arguments (mean_test = rowMeans(Canopy_Index), sd_test = rowMeans(Canopy_Index))
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6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 6L, 6L, 5L, 5L, 1L, 1L, 1L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 6L, 5L, 1L, 1L, 1L,
5L, 5L, 5L, 5L, 6L, 6L, 6L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 5L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 2L, 2L, 2L, 6L, 6L, 6L, 6L, 3L, 3L, 3L, 3L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 1L, 1L, 1L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L,
6L, 5L, 6L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 1L,
1L, 1L, 1L, 3L, 3L, 3L, 3L, 6L, 6L, 6L, 1L, 6L, 5L, 6L, 5L, 5L,
5L, 5L, 5L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L,
5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L), .Label = c("other deciduous tree", "other oak",
"other plant", "other shrub", "Quercus petraea", "Quercus robur"
), class = "factor"), Tree_diameter = c(68.8, 10, 98.5, 97, 32.5,
45.1, 847, 817, 569, 892, 57.3, 43.5, 120, 180, 74, 67, 69, 55,
62, 71, 140, 111.4, 114.6, 167.1, 29, 46.5, 27.7, 40.1, 68, 45,
60, 54, 104, 122, 85, 71, 81, 39.8, 43.6, 44.6, 22.6, 160, 156,
20.1, 17.8, 15.6, 12.1, 37.3, 45.1, 42.8, 51.2, 48.1, 83.7, 77.9,
80.2, 84.7, 81.8, 102.5, 75.5, 57.3, 0.3, 0.2, 0.3, 0.3, 70,
36, 53, 44, 31.5, 27.1, 23.3, 22, 85, 69.4, 37.3, 82.9, 52.9,
98.4, 64.6, 81.8, 19.9, 14.6, 196, 122, 118, 180, 58.6, 54.1,
58, 61.5, 58.4, 40.6, 61, 68.6, 44.2, 45.2, 44.2, 117, 240, 210,
310, 134, 64, 52.2, 32, 25, 22, 17, 57, 73.9, 37.1, 170, 114,
127, 158, 147.4, 135.3, 122.9, 104.1, 263, 237, 322, 302, 175,
182, 141, 155, 89, 41, 70, 83, 81.5, 29.3, 43.3, 141, 86.5, 82,
114.5, 57, 42, 58, 64, 129, 127, 143, 125, 92, 68, 90, 24.5,
20.1, 63.7, 39.8, 66.2, 112.4, 41.9, 43.8, 124.5, 94.1, 68.6,
74.4, 23.6, 27.7, 22.9, 25.2, 59.2, 78, 79.3, 24.2, 54.7, 43,
33.1, 56, 67, 62, 58, 306, 274, 56, 60, 72.5, 128.5, 22, 16,
143, 103, 53, 130, 48.4, 69.8, 6.4, 18.6, 129.2, 41.7, 57.6,
14, 75, 105, 44, 41.7, 30.2, 39.5, 24.2, 320, 352, 120.9, 108.3,
53.2, 240, 274, 122, 85, 21, 52, 43, 38, 37, 219, 215, 216, 175,
124, 133, 119, 39.2, 63, 94.9, 47.1, 126.6, 86.9, 94.7, 106.2,
85.9, 49.7, 97.1, 55, 40.8, 79.3, 62.4, 62.4, 70, 115.9, 111.1,
88.9, 80.3, 90.8, 36, 31, 37.5, 42.3, 73, 54, 75, 43, 50.3, 28.7,
31.9, 159, 181.5, 149.7, 122, 143.6, 148, 145, 99, 47, 76.4,
62.7, 49, 57.9, 54.8, 53.5, 88.8, 71.3, 101.9, 28, 32, 54, 54,
169, 152, 160, 138, 90.8, 87.9, 77.4, 81.2, 91.7, 62.7, 50, 72.9,
23.7, 58, 80.7, 73.7), Urbanisation_index = c(2L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L,
4L, 4L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L,
2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 3L, 2L, 2L, 2L, 4L, 4L, 4L, 4L,
4L, 4L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 4L,
4L, 4L, 4L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 1L, 1L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 2L, 2L, 2L, 3L, 3L, 3L, 4L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 4L, 4L, 4L, 4L, 3L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L), Stand_density_index = c(3L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L, 1L,
1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 2L, 3L, 3L,
2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 3L, 1L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 4L, 4L,
3L, 3L, 3L, 3L, 4L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 4L,
4L, 3L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L,
4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L,
3L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
3L, 3L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 4L, 4L, 4L, 4L, 3L, 3L,
3L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 2L, 3L, 3L, 3L, 2L, 4L, 4L, 4L, 4L, 4L, 2L, 1L, 1L, 4L, 4L,
2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 1L, 1L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 2L,
2L), Canopy_Index = c(85L, 85L, 85L, 75L, 45L, 25L, 75L, 65L,
65L, 75L, 65L, 15L, 75L, 85L, 85L, 45L, 45L, 65L, 75L, 75L, 95L,
95L, 95L, 95L, 95L, 55L, 85L, 65L, 85L, 65L, 95L, 85L, 85L, 85L,
75L, 75L, 65L, 85L, 85L, 85L, 85L, 65L, 35L, 75L, 75L, 85L, 65L,
55L, 65L, 45L, 45L, 95L, 85L, 85L, 85L, 65L, 95L, 85L, 95L, 95L,
75L, 75L, 85L, 85L, 85L, 85L, 85L, 75L, 85L, 85L, 85L, 85L, 45L,
75L, 75L, 65L, 75L, 35L, 35L, 75L, 85L, 85L, 65L, 75L, 85L, 75L,
95L, 95L, 95L, 95L, 75L, 75L, 65L, 65L, 85L, 95L, 95L, 35L, 75L,
65L, 85L, 95L, 95L, 55L, 75L, 75L, 75L, 85L, 65L, 95L, 75L, 75L,
65L, 75L, 65L, 85L, 95L, 95L, 75L, 95L, 75L, 95L, 65L, 75L, 75L,
85L, 85L, 65L, 95L, 65L, 65L, 75L, 75L, 65L, 65L, 65L, 65L, 65L,
35L, 65L, 75L, 35L, 85L, 85L, 75L, 95L, 85L, 85L, 75L, 45L, 55L,
35L, 35L, 25L, 25L, 75L, 65L, 95L, 85L, 75L, 85L, 85L, 75L, 75L,
65L, 95L, 95L, 95L, 75L, 85L, 65L, 45L, 75L, 35L, 65L, 95L, 95L,
95L, 95L, 95L, 65L, 75L, 45L, 35L, 75L, 95L, 95L, 85L, 75L, 65L,
85L, 95L, 75L, 85L, 85L, 95L, 95L, 95L, 55L, 65L, 65L, 45L, 65L,
85L, 35L, 95L, 85L, 85L, 75L, 85L, 95L, 85L, 95L, 75L, 65L, 65L,
65L, 65L, 55L, 75L, 85L, 85L, 85L, 85L, 55L, 25L, 55L, 65L, 35L,
75L, 25L, 35L, 85L, 95L, 85L, 55L, 75L, 75L, 75L, 75L, 65L, 85L,
75L, 65L, 85L, 55L, 95L, 95L, 95L, 95L, 45L, 55L, 35L, 65L, 45L,
75L, 75L, 55L, 65L, 65L, 75L, 65L, 95L, 95L, 95L, 45L, 15L, 85L,
65L, 95L, 95L, 45L, 65L, 45L, 55L, 85L, 65L, 75L, 75L, 75L, 65L,
75L, 35L, 75L, 75L, 75L, 75L, 25L, 45L, 45L, 35L, 85L, 95L, 85L,
95L), Phenological_Index = c(2L, 4L, 2L, 2L, 4L, 4L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 4L, 3L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 4L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 2L, 2L, 2L, 2L, 3L,
1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 4L, 3L, 2L, 1L, 4L, 4L, 1L,
1L, 1L, 1L, 1L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
4L, 4L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L,
3L, 3L, 3L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 3L, 3L,
3L, 3L, 4L, 3L, 2L, 3L, 2L, 2L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 4L,
2L, 4L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 3L, 3L, 2L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 3L, 1L, 3L, 4L, 3L, 3L,
2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L,
1L, 1L, 4L, 4L, 4L, 3L, 4L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 2L,
2L, 3L, 3L, 4L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L)), class = "data.frame", row.names = c(NA,
-295L))
共有2个答案
童浩言
使用< code>data.table，我们将“data.frame”转换为“data . table”(< code > setDT )，在< code>i中指定逻辑条件，以对行进行子集化，按“Urbanisation _ index”分组，获得行数(< code >)。N)和“Canopy_Index”的< code>mean以及“Species”的< code>first值
library(data.table)
out <- setDT(subset_leaf_1)[Species == "Quercus petraea",
.(Species = first(Species),
Obs_no = .N,
Canopy_Index = mean(Canopy_Index)), by = Urbanisation_index]
setcolorder(out, c(2, 1, 3, 4))
out
# Species Urbanisation_index Obs_no Canopy_Index
#1: Quercus petraea 2 17 75.00000
#2: Quercus petraea 4 17 72.05882
#3: Quercus petraea 3 14 76.42857
#4: Quercus petraea 1 6 61.66667
这也可以在基数R中完成
tmp1 <- subset(subset_leaf_1, Species == "Quercus petraea")
by(tmp1, tmp1$Urbanisation_index, FUN = function(x)
data.frame(Obs_no = nrow(x), Canopy_Index = mean(x$Canopy_Index)))
宋丰
使用dplyr，我们可以首先过滤物种，然后对于每个Urbanisation_index我们使用n（）和Canopy_Index平均值来计算观测值的数量。
library(dplyr)
subset_leaf_1 %>%
filter(Species == "Quercus petraea") %>%
group_by(Urbanisation_index) %>%
summarise(Species = "Quercus petraea",
Obs_no = n(),
Canopy_Index = mean(Canopy_Index))
# Urbanisation_index Species Obs_no Canopy_Index
# <int> <chr> <int> <dbl>
#1 1 Quercus petraea 6 61.7
#2 2 Quercus petraea 17 75
#3 3 Quercus petraea 14 76.4
#4 4 Quercus petraea 17 72.1
我们也可以在R基进行
df1 <- do.call(data.frame, aggregate(Canopy_Index~Urbanisation_index,
subset(subset_leaf_1, Species == "Quercus petraea"),
function(x) c(Canopy_Index = mean(x), Obs_no = length(x))))
colnames(df1) <- c("Urbanisation_index", "Canopy_Index", "Obs_no")
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