Использование count (), aggregate (), data.table () или dplyr () для суммирования данных (среднее значение, стандартное отклонение) - PullRequest
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Использование count (), aggregate (), data.table () или dplyr () для суммирования данных (среднее значение, стандартное отклонение)

0 голосов
/ 21 января 2019

Обзор

У меня есть набор данных (см. Ниже), который называется "subset_leaf_1" , показывающий, как климатическая среда влияет на индекс полога определенного вида дуба.называется " Quercus petraea ".

У меня есть столбец с именем Urbanisation_index (т. е. кадр данных ниже), содержащий четыре подуровня (т. е. 1, 2, 3 и4).Каждый подуровень (1-4) указывает на степень урбанизации вокруг "Quercus petraea".

Я также хотел бы рассчитать среднее значение Canopy_Index для каждого подуровня Urbanisation_index.

Задача

Я хочу подсчитать количество строк для каждого подуровня индекса урбанизации по видам, используя либо данные .table () , aggregate () или count () в пакете dplyr , а затем вычисление среднего значения Canopy_index для каждого подуровня Urbanisation_index .

Если кто-то может помочь, я был бы очень признателен

Желаемый результат

enter image description here

R-код:

Во-первых, я поднастроил данные для Quercus petraea 

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

агрегат ()

Я могу использовать эти двауравнения, чтобы найти количестводля каждой строки Urbanisation_index и среднего значения Canopy_Index для подуровня Urbanisation_index с использованием этих двух уравнений: 

##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)

Чтобы объединить значения в строке Urbanisation_index и среднего Canopy_index для подуровня, я применил эту функцию ниже (таблица выше).Однако эта функция добавляет нули к счетчикам в строке, и я не могу переименовать заголовки столбцов, чтобы создать новый фрейм данных.После проверки подраздела R среды R Studio среднее и стандартное отклонение Canopy_Index не отображается. 

##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))

Данные 

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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))

Ответы [ 2 ]

0 голосов
/ 21 января 2019

С помощью data.table мы преобразуем «data.frame» в «data.table» (setDT), задаем логическое условие в i для подмножества строк, сгруппированных по «Urbanisation_index», получаем числострок (.N) и mean Canopy_Index вместе со значением first вида "Виды" 

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

Это также можно сделать в base R

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)))
0 голосов
/ 21 января 2019

Используя dplyr, мы могли бы сначала filter Species, а затем для каждого Urbanisation_index подсчитать количество наблюдений, используя n() и mean из 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

Мы также можем сделать это в базе R 

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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