Question

概述

我有一个名为“ subset_leaf_1” 的数据集（如下所示），该数据集显示了气候环境如何影响称为“ Quercus petraea的特定橡树树种的冠层指数 em>”。

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）都突出显示了“栎栎”的城市化程度。

我还想为Urbanisation_index的每个子级别计算平均值 Canopy_Index 。

问题

我想使用 data.table（）， aggregate（）或按物种对城市化指数每个子级别的行数进行计数 dplyr软件包中的count（），然后为 Urbanisation_index 的每个子级别计算平均值 Canopy_index 。

如果有人可以帮助我，我将非常感激

所需结果

R代码：

首先，我对栎木
的数据进行了子集化
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)

dplyr（）

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

aggregate（）

我可以使用这两个方程式，使用以下两个方程式来查找Urbanisation_index每行的计数以及每个Urbanisation_index子级别的Canopy_Index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)

要结合Urbanisation_index每行的计数和每个子级别的平均Canopy_index，我在下面（上表）应用了此函数。但是，此函数在每行计数中添加零，并且我无法重命名列标题以产生新的数据帧。在检查了R Studio的R环境子部分后，Canopy_Index的均值和标准差未显示。

##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"))

data.table（）

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))

数据

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-1.27795, -1.27795, -1.27795, -1.966392, -1.966392, -1.966392, -1.34506, -1.34506, -1.34506, -0.47911, -0.47911, -0.47911, -0.47911, -0.503114, -0.503114, -0.503114, -0.503114, -0.472994, -0.472994, -0.472994, -0.472994, -3.18738, -3.18738, -3.18738, -3.18738, -2.27968, -2.27968, -2.27968, -2.27968, -0.25847, -0.25847, -0.25847, -0.25847 ), Altitude = c(5L, 0L, 68L, 68L, 68L, 68L, 104L, 104L, 104L, 104L, 11L, 11L, 0L, 0L, 7L, 7L, 7L, 7L, 15L, 15L, 23L, 23L, 23L, 23L, 184L, 184L, 184L, 184L, 176L, 176L, 176L, 176L, 12L, 12L, 12L, 12L, 178L, 36L, 36L, 36L, 36L, 0L, 0L, 11L, 11L, 11L, 11L, 718L, 718L, 718L, 718L, 47L, 47L, 47L, 42L, 42L, 210L, 210L, 210L, 210L, 97L, 97L, 97L, 97L, 23L, 23L, 23L, 23L, 0L, 0L, 0L, 0L, 110L, 9L, 9L, 9L, 9L, 30L, 30L, 30L, 4L, 4L, 200L, 200L, 200L, 200L, 160L, 160L, 160L, 160L, 166L, 166L, 166L, 166L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 74L, 74L, 74L, 74L, 36L, 36L, 36L, 47L, 47L, 47L, 47L, 58L, 58L, 58L, 58L, 43L, 43L, 43L, 43L, 97L, 97L, 97L, 97L, 133L, 133L, 133L, 133L, 18L, 18L, 18L, 123L, 123L, 123L, 123L, 5L, 5L, 5L, 5L, 128L, 128L, 128L, 15L, 15L, 15L, 15L, 14L, 14L, 65L, 65L, 65L, 65L, 45L, 45L, 129L, 129L, 129L, 129L, 140L, 140L, 140L, 140L, 0L, 0L, 0L, 18L, 18L, 18L, 18L, 0L, 0L, 0L, 0L, 30L, 30L, 30L, 30L, 19L, 19L, 19L, 19L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 96L, 96L, 96L, 96L, 88L, 88L, 88L, 169L, 169L, 169L, 169L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 123L, 123L, 123L, 1436L, 1436L, 1436L, 1436L, 0L, 0L, 0L, 0L, 43L, 43L, 43L, 6L, 75L, 75L, 75L, 75L, 0L, 73L, 73L, 73L, 109L, 109L, 0L, 0L, 0L, 0L, 115L, 115L, 115L, 110L, 110L, 110L, 110L, 119L, 95L, 95L, 95L, 95L, 112L, 112L, 112L, 23L, 23L, 23L, 34L, 34L, 34L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 24L, 24L, 24L, 24L, 38L, 38L, 38L, 38L, 29L, 29L, 29L, 29L, 20L, 20L, 20L, 20L), Species = structure(c(6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 5L, 1L, 1L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 1L, 5L, 5L, 5L, 1L, 1L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 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))

Answer 1

使用dplyr，我们可以首先filter Species，然后对于每个Urbanisation_index，我们使用n()和mean来计算观察次数的Canopy_Index。

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

我们也可以在基数R中完成

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")

Answer 2

使用data.table，我们将'data.frame'转换为'data.table'（setDT），在i中指定逻辑条件以对行进行子集，并按' Urbanisation_index'，获取行数（.N和'{Canopy_Index'的mean，以及'Species'的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base 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)))

使用count（），aggregate（），data.table（）或dplyr（）汇总数据（均值，标准差）

2 个答案: