Arules 包：将连续向量离散化为具有特定类别的离散向量，以在 R 中产生 table 频率
Arules Package: Discretize a continuous vector into a discrete vector with specific categories to produce a table of frequencies in R
概览
我有一个名为 "Canopy_Index" 的连续向量和另一个名为 "Species" 的向量，这是一个因子。
我的目标是使用下面的 R-code 将 Canopy_Index 向量离散化为包含特定百分比类别的离散向量，间隔为 10，即 0-10、10-20、20-30、 30-40、50-60、60-70、70-80、80-90、90-100。
objective 用于显示两种橡树（Quercus petraea 和 Quercus robur 的每个树冠指数类别的频率分布) 通过关注此 R-code example at the bottom of the page
理想情况下，我想生成一个包含 3 列和 11 行的 table。每行代表百分比类别，总共有10个类别。
列标题：
- 第 1 列 = Canopy_Index
的离散百分比类别
- 第 2 列 = Q 的 Canopy_Index 类别的频率。佩特拉
- 第 3 列 = Q 的 Canopy_Index 类别的频率。抢劫
但是，我一直在生成以下错误消息。
如果有人能提供帮助，我将不胜感激。
R-code
尝试 1
Canopy_Interval1<-table(discretize(Discrete_Canopy, methods=list(
Canopy_Index=list(method="frequency", breaks=10,
labels=c("0-10", "10-20","20-30", "30-40", "50-60", "60-70", "70-80",
"80-90", "90-100"),
Species=list(method="frequency", breaks=2,
labels=c("Quercus petraea", "Quercus robur"),
default = list(method = "none"))))))
**Error message**
Error in (1 - h) * qs[i] : non-numeric argument to binary operator
尝试 2
breaks <- seq(from = 0, to = 100, by = 10)
labels <- map_chr(breaks[1:10], ~ paste(.x, .x + 10, sep="-"))
#########Compartmentalise the number of tree species per canopy interval
indices <- SpeciesLatitudeCanopy$Canopy_Index
table(indices)
########Place the percentages into table form
percents <- discretize(indices,
method = "fixed",
breaks = breaks,
labels = labels)
###Produce the table of percents
table(percents)
##Produce the data table with binned data
SpecLatCan<-SpeciesLatitudeCanopy %>%
mutate(Canopy_Index_Binned = percents)
##Structure
head(SpecLatCan)
###Produce a data frame
SpeciesLatitudeCanopy1<-data.frame(SpecLatCan)
###Check the structure of the data frame
str(SpeciesLatitudeCanopy1)
##Count Canopy/Species
SpeciesLatitudeCanopy1_Count<-count(SpeciesLatitudeCanopy1, c(Canopy_Index_Binned, Species))
##Error Message
Error in count(SpeciesLatitudeCanopy1, c(Canopy_Index_Binned, Species)) :
object 'Canopy_Index_Binned' not found
数据框的结构
数据框的头部
数据框
structure(list(Date_observed = structure(c(10L, 15L, 3L, 3L,
3L, 3L, 13L, 13L, 11L, 11L, 11L, 11L, 9L, 9L, 9L, 9L, 12L, 12L,
12L, 12L, 6L, 6L, 10L, 10L, 10L, 10L, 14L, 14L, 14L, 14L, 13L,
13L, 13L, 9L, 9L, 9L, 9L, 14L, 14L, 14L, 14L, 8L, 8L, 8L, 8L,
14L, 7L, 7L, 7L, 7L, 12L, 12L, 12L, 12L, 12L, 12L, 5L, 5L, 5L,
5L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 13L, 13L, 6L, 8L,
8L, 8L, 1L, 1L, 1L, 1L, 6L, 6L, 6L, 6L, 13L, 13L, 13L, 13L, 12L,
12L, 12L, 12L, 2L, 2L, 2L, 2L, 11L, 11L, 11L, 3L, 3L, 3L, 3L,
14L, 14L, 14L, 9L, 9L, 9L, 9L, 14L, 14L, 13L, 13L, 13L, 13L,
3L, 3L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 3L, 3L, 3L, 3L,
14L, 14L, 14L, 14L, 11L, 11L, 11L, 11L, 13L, 13L, 13L, 13L, 3L,
3L, 3L, 3L, 14L, 14L, 6L, 6L, 6L, 12L, 12L, 12L, 12L, 14L, 13L,
13L, 13L, 13L, 14L, 14L, 14L, 14L, 11L, 11L, 11L, 14L, 14L, 14L,
10L, 10L, 10L, 10L, 6L, 5L, 5L, 5L, 10L, 10L, 10L, 4L, 4L, 13L,
13L, 13L, 13L, 9L, 9L, 9L, 13L, 13L, 13L, 14L, 14L, 14L, 14L,
6L, 6L, 6L, 6L, 13L, 13L, 13L, 13L, 12L, 12L, 12L, 12L, 14L,
14L, 14L, 14L, 6L, 6L, 6L, 6L), .Label = c("10/23/18", "11/18/18",
"11/30/18", "11/6/18", "12/1/18", "12/10/18", "12/12/18", "12/2/18",
"12/3/18", "12/4/18", "12/6/18", "12/7/18", "12/8/18", "12/9/18",
"9/10/18"), class = "factor"), Latitude = c(51.41752, 52.243806,
52.947709, 52.947709, 52.947709, 52.947709, 51.491811, 51.491811,
51.60157, 51.60157, 51.60157, 51.60157, 52.68959, 52.68959, 52.68959,
52.68959, 50.697802, 50.697802, 50.697802, 50.697802, 53.62417,
53.62417, 50.446841, 50.446841, 50.446841, 50.446841, 53.959679,
53.959679, 53.959679, 53.959679, 52.01434, 52.01434, 52.01434,
51.78375, 51.78375, 51.78375, 51.78375, 51.456965, 51.456965,
51.456965, 51.456965, 52.011812, 52.011812, 52.011812, 52.011812,
55.91924, 50.121978, 50.121978, 50.121978, 50.121978, 51.43474,
51.43474, 51.10708, 51.10708, 51.10708, 51.10708, 50.435984,
50.435984, 50.435984, 50.435984, 51.78666, 51.78666, 51.78666,
51.78666, 53.38728, 53.38728, 53.38728, 53.38728, 52.441088,
52.441088, 52.552344, 53.582285, 53.582285, 53.582285, 49.259471,
49.259471, 49.259471, 49.259471, 50.462, 50.462, 50.462, 50.462,
51.746642, 51.746642, 51.746642, 51.746642, 52.2501, 52.2501,
52.2501, 52.2501, 52.42646, 52.42646, 52.42646, 52.42646, 50.79387,
50.79387, 50.79387, 53.615575, 53.615575, 53.615575, 53.615575,
51.08478, 51.08478, 51.08478, 53.19329, 53.19329, 53.19329, 53.19329,
55.968437, 55.968437, 56.52664, 56.52664, 56.52664, 56.52664,
52.04252, 52.04252, 51.8113, 51.8113, 51.8113, 51.8113, 50.52008,
50.52008, 50.52008, 50.52008, 51.48417, 51.48417, 51.48417, 51.48417,
54.58243, 54.58243, 54.58243, 54.58243, 52.58839, 52.58839, 52.58839,
52.58839, 52.717283, 52.717283, 52.717283, 52.717283, 50.740764,
50.740764, 50.740764, 50.740764, 50.733412, 50.733412, 50.79926,
50.79926, 50.79926, 53.675788, 53.675788, 53.675788, 53.675788,
55.43828, 48.35079, 48.35079, 48.35079, 48.35079, 51.36445, 51.36445,
51.36445, 51.36445, 52.36286, 52.36286, 52.36286, 52.122402,
52.122402, 52.122402, 52.16104, 52.16104, 52.16104, 52.16104,
54.7311, 51.61842, 51.61842, 51.61842, 55.920966, 55.920966,
55.920966, 57.158724, 57.158724, 51.88468, 51.88468, 51.88468,
51.88468, 52.34015, 52.34015, 52.34015, 53.37687, 53.37687, 53.37687,
54.27745, 54.27745, 54.27745, 54.27745, 52.026042, 52.026042,
52.026042, 52.026042, 51.319032, 51.319032, 51.319032, 51.319032,
51.51365, 51.51365, 51.51365, 51.51365, 53.43202, 53.43202, 53.43202,
53.43202, 51.50797, 51.50797, 51.50797, 51.50797), Longitude = c(-0.32116,
1.30786, -1.435407, -1.435407, -1.435407, -1.435407, -3.210324,
-3.210324, -3.67111, -3.67111, -3.67111, -3.67111, -3.3081, -3.3081,
-3.3081, -3.3081, -2.11692, -2.11692, -2.11692, -2.11692, -2.43155,
-2.43155, -3.706923, -3.706923, -3.706923, -3.706923, -1.061008,
-1.061008, -1.061008, -1.061008, 1.04007, 1.04007, 1.04007, -0.65046,
-0.65046, -0.65046, -0.65046, -2.624917, -2.624917, -2.624917,
-2.624917, -0.70082, -0.70082, -0.70082, -0.70082, -3.20936,
-5.555169, -5.555169, -5.555169, -5.555169, 0.45981, 0.45981,
-2.32027, -2.32027, -2.32027, -2.32027, -4.105617, -4.105617,
-4.105617, -4.105617, -0.71433, -0.71433, -0.71433, -0.71433,
-2.95811, -2.95811, -2.95811, -2.95811, -0.176158, -0.176158,
-1.337177, -2.802239, -2.802239, -2.802239, -123.107788, -123.107788,
-123.107788, -123.107788, -3.5607, -3.5607, -3.5607, -3.5607,
0.486416, 0.486416, 0.486416, 0.486416, -0.8825, -0.8825, -0.8825,
-0.8825, -1.78771, -1.78771, -1.78771, -1.78771, 0.26684, 0.26684,
0.26684, -2.432959, -2.432959, -2.432959, -2.432959, -0.73626,
-0.73626, -0.73626, -0.63793, -0.63793, -0.63793, -0.63793, -3.179732,
-3.179732, -3.40313, -3.40313, -3.40313, -3.40313, -2.43733,
-2.43733, -0.22894, -0.22894, -0.22894, -0.22894, -4.20756, -4.20756,
-4.20756, -4.20756, -0.34854, -0.34854, -0.34854, -0.34854, -5.93229,
-5.93229, -5.93229, -5.93229, -1.96843, -1.96843, -1.96843, -1.96843,
-2.410575, -2.410575, -2.410575, -2.410575, -2.361234, -2.361234,
-2.361234, -2.361234, -2.014029, -2.014029, -3.19446, -3.19446,
-3.19446, -1.272404, -1.272404, -1.272404, -1.272404, -4.64226,
10.91812, 10.91812, 10.91812, 10.91812, -0.23106, -0.23106, -0.23106,
-0.23106, -2.06327, -2.06327, -2.06327, -0.487443, -0.487443,
-0.487443, 0.18702, 0.18702, 0.18702, 0.18702, -5.8041, -0.16034,
-0.16034, -0.16034, -3.193503, -3.193503, -3.193503, -2.166099,
-2.166099, -0.17853, -0.17853, -0.17853, -0.17853, -1.27795,
-1.27795, -1.27795, -1.34506, -1.34506, -1.34506, -0.47911, -0.47911,
-0.47911, -0.47911, -0.503113, -0.503113, -0.503113, -0.503113,
-0.472994, -0.472994, -0.472994, -0.472994, -3.18722, -3.18722,
-3.18722, -3.18722, -2.27968, -2.27968, -2.27968, -2.27968, -0.25931,
-0.25931, -0.25931, -0.25931), Altitude = c(0, 0, 103.9, 103.9,
103.9, 103.9, 15, 15, 184, 184, 184, 184, 176, 176, 176, 176,
12, 12, 12, 12, 178, 178, 36, 36, 36, 36, 11, 11, 11, 11, 47,
47, 47, 210, 210, 210, 210, 97, 97, 97, 97, 0, 0, 0, 0, 100,
68, 68, 68, 68, 4, 4, 200, 200, 200, 200, 160, 160, 160, 160,
165.8, 165.8, 165.8, 165.8, 0, 0, 0, 0, 0, 0, 0, 36, 36, 36,
47, 47, 47, 47, 0, 0, 0, 0, 43, 43, 43, 43, 97, 97, 97, 97, 133,
133, 133, 133, 18, 18, 18, 123, 123, 123, 123, 127, 127, 127,
15, 15, 15, 15, 14, 14, 65, 65, 65, 65, 45, 45, 129, 129, 129,
129, 18, 18, 18, 18, 30, 30, 30, 30, 19, 19, 19, 19, 0, 0, 0,
0, 0, 0, 0, 0, 96, 96, 96, 96, 0, 0, 0, 0, 0, 49, 49, 49, 49,
0, 0, 0, 0, 0, 48, 48, 48, 48, 123, 123, 123, 43, 43, 43, 75,
75, 75, 75, 0, 73, 73, 73, 115, 115, 115, 119, 119, 94, 94, 94,
94, 112, 112, 112, 34, 34, 34, 0, 0, 0, 0, 103, 103, 103, 103,
0, 0, 0, 0, 37.5, 37.5, 37.5, 37.5, 29, 29, 29, 29, 63, 63, 63,
63), Species = structure(c(2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 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), .Label = c("Quercus petraea",
"Quercus robur"), class = "factor"), Tree_diameter = c(68.8,
300, 847, 817, 569, 892, 62, 71, 29, 46.5, 27.7, 40.1, 68, 45,
60, 54, 104, 122, 85, 71, 81, 118, 39.8, 43.6, 44.6, 22.6, 19.8,
16.6, 15.1, 11.9, 48.1, 83.7, 77.9, 81.8, 102.5, 75.5, 57.3,
0.3, 0.2, 0.3, 0.3, 99, 85, 74, 68, 60, 67.3, 36.9, 82, 51.8,
19.9, 14.6, 196, 122, 118, 180, 58.6, 54.1, 58, 61.5, 58.4, 40.6,
61, 68.6, 117, 240, 210, 310, 134, 64, 52.2, 57, 73.9, 37.1,
170, 114, 127, 158, 147.4, 135.3, 122.9, 104.1, 263, 237, 322,
302, 173, 186, 144, 155, 89, 41, 68, 83, 81.5, 29.3, 43.3, 141.6,
85.5, 82.8, 114.1, 129, 127, 143, 125, 92, 68, 90, 25, 20, 63.7,
39.8, 66.2, 112.4, 41.9, 43.8, 124.5, 94.1, 68.6, 74.4, 24.2,
54.7, 43, 33.1, 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, 320, 352,
120.9, 108.3, 53.2, 240, 274, 122, 85, 21, 52, 43, 38, 37, 219,
215, 216, 175, 124, 133, 119, 85.9, 49.7, 97.1, 40.8, 79.3, 62.4,
62.4, 70, 115.9, 111.1, 88.9, 73, 54, 75, 159, 244, 181.5, 149.7,
122, 143.6, 148, 145, 99, 49, 57.9, 54.8, 53.5, 88.8, 71.3, 101.9,
27.5, 32, 54, 54.1, 169, 152, 160, 138, 90.8, 87.9, 77.4, 81.2,
91.7, 62.7, 50, 72.9, 24.8, 61, 88.6, 80.1), Urbanisation_index = c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 4L, 4L, 4L,
3L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L,
1L, 1L, 1L, 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, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 1L, 4L, 4L, 4L,
4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L,
4L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 3L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 4L, 2L,
2L, 3L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L,
2L, 4L, 4L, 1L, 1L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 3L, 3L, 4L, 4L,
4L, 4L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L,
3L, 2L, 2L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 3L, 3L, 3L, 2L, 2L, 2L,
1L, 4L, 4L, 3L, 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, 2L, 2L, 2L, 2L, 2L, 3L, 4L, 4L, 4L, 4L, 2L,
2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 2L,
2L, 2L, 2L, 2L, 3L, 4L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L,
2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 2L, 1L, 1L, 4L, 4L, 4L,
2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 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(15L, 95L, 45L, 5L, 5L, 45L,
5L, 5L, 25L, 5L, 5L, 15L, 25L, 25L, 35L, 35L, 25L, 35L, 15L,
15L, 15L, 15L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 45L, 45L, 55L,
35L, 35L, 55L, 35L, 5L, 5L, 5L, 5L, 95L, 95L, 95L, 95L, 15L,
35L, 45L, 25L, 25L, 15L, 5L, 25L, 25L, 25L, 25L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 35L, 25L, 5L, 35L, 15L, 15L,
35L, 35L, 25L, 25L, 5L, 5L, 5L, 5L, 35L, 25L, 25L, 25L, 5L, 5L,
15L, 15L, 35L, 65L, 35L, 35L, 25L, 25L, 5L, 25L, 25L, 25L, 25L,
15L, 15L, 5L, 35L, 35L, 45L, 35L, 5L, 15L, 15L, 25L, 5L, 15L,
5L, 5L, 15L, 5L, 5L, 15L, 5L, 5L, 5L, 5L, 5L, 85L, 5L, 35L, 15L,
5L, 5L, 5L, 25L, 25L, 15L, 35L, 95L, 95L, 95L, 95L, 15L, 15L,
5L, 25L, 25L, 5L, 15L, 15L, 5L, 5L, 15L, 5L, 5L, 5L, 25L, 25L,
25L, 25L, 5L, 5L, 5L, 5L, 15L, 25L, 15L, 25L, 25L, 55L, 35L,
35L, 25L, 25L, 45L, 5L, 25L, 5L, 5L, 5L, 5L, 55L, 55L, 15L, 15L,
25L, 15L, 45L, 35L, 35L, 15L, 5L, 25L, 15L, 15L, 15L, 15L, 15L,
35L, 15L, 15L, 35L, 15L, 25L, 25L, 15L, 15L, 15L, 15L, 5L, 5L,
5L, 5L, 5L, 5L, 15L, 15L), Phenological_Index = c(4L, 4L, 3L,
4L, 4L, 3L, 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, 4L, 4L, 4L, 4L,
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, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 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, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 2L, 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, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 4L, 4L, 4L,
3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 3L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L)), class = "data.frame", row.names = c(NA, -215L
))
这看起来像您想要的 table 吗？
library("arules")
#> Loading required package: Matrix
#>
#> Attaching package: 'arules'
#> The following objects are masked from 'package:base':
#>
#> abbreviate, write
library("tidyverse")
# Smaller tibble with only the two necessary columns
data <- tibble(
Canopy_Index = c(15, 95, 45, 5),
Species = c("A", "A", "B", "B"))
breaks <- seq(from = 0, to = 100, by = 10)
labels <- map_chr(breaks[1:10], ~ paste(.x, .x + 10, sep = "-"))
indices <- data$Canopy_Index
table(indices)
#> indices
#> 5 15 45 95
#> 1 1 1 1
percents <- discretize(indices,
method = "fixed",
breaks = breaks,
labels = labels)
table(percents)
#> percents
#> 0-10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100
#> 1 1 0 0 1 0 0 0 0 1
data %>%
mutate(Canopy_Index_Binned = percents) %>%
count(Canopy_Index_Binned, Species) %>%
spread(Species, n, fill = 0)
#> # A tibble: 4 x 3
#> Canopy_Index_Binned A B
#> <fct> <dbl> <dbl>
#> 1 0-10 0 1
#> 2 10-20 1 0
#> 3 40-50 0 1
#> 4 90-100 1 0
# There is also a base function that can convert a numeric vector to a factor in the same way:
percents <- cut(indices,
breaks = breaks,
labels = labels)
由 reprex package (v0.2.1)
于 2019-03-14 创建
概览
我有一个名为 "Canopy_Index" 的连续向量和另一个名为 "Species" 的向量，这是一个因子。
我的目标是使用下面的 R-code 将 Canopy_Index 向量离散化为包含特定百分比类别的离散向量，间隔为 10，即 0-10、10-20、20-30、 30-40、50-60、60-70、70-80、80-90、90-100。
objective 用于显示两种橡树（Quercus petraea 和 Quercus robur 的每个树冠指数类别的频率分布) 通过关注此 R-code example at the bottom of the page
理想情况下，我想生成一个包含 3 列和 11 行的 table。每行代表百分比类别，总共有10个类别。
列标题：
- 第 1 列 = Canopy_Index 的离散百分比类别
- 第 2 列 = Q 的 Canopy_Index 类别的频率。佩特拉
- 第 3 列 = Q 的 Canopy_Index 类别的频率。抢劫
但是，我一直在生成以下错误消息。
如果有人能提供帮助，我将不胜感激。
R-code
尝试 1
Canopy_Interval1<-table(discretize(Discrete_Canopy, methods=list(
Canopy_Index=list(method="frequency", breaks=10,
labels=c("0-10", "10-20","20-30", "30-40", "50-60", "60-70", "70-80",
"80-90", "90-100"),
Species=list(method="frequency", breaks=2,
labels=c("Quercus petraea", "Quercus robur"),
default = list(method = "none"))))))
**Error message**
Error in (1 - h) * qs[i] : non-numeric argument to binary operator
尝试 2
breaks <- seq(from = 0, to = 100, by = 10)
labels <- map_chr(breaks[1:10], ~ paste(.x, .x + 10, sep="-"))
#########Compartmentalise the number of tree species per canopy interval
indices <- SpeciesLatitudeCanopy$Canopy_Index
table(indices)
########Place the percentages into table form
percents <- discretize(indices,
method = "fixed",
breaks = breaks,
labels = labels)
###Produce the table of percents
table(percents)
##Produce the data table with binned data
SpecLatCan<-SpeciesLatitudeCanopy %>%
mutate(Canopy_Index_Binned = percents)
##Structure
head(SpecLatCan)
###Produce a data frame
SpeciesLatitudeCanopy1<-data.frame(SpecLatCan)
###Check the structure of the data frame
str(SpeciesLatitudeCanopy1)
##Count Canopy/Species
SpeciesLatitudeCanopy1_Count<-count(SpeciesLatitudeCanopy1, c(Canopy_Index_Binned, Species))
##Error Message
Error in count(SpeciesLatitudeCanopy1, c(Canopy_Index_Binned, Species)) :
object 'Canopy_Index_Binned' not found
数据框的结构
数据框的头部
数据框
structure(list(Date_observed = structure(c(10L, 15L, 3L, 3L,
3L, 3L, 13L, 13L, 11L, 11L, 11L, 11L, 9L, 9L, 9L, 9L, 12L, 12L,
12L, 12L, 6L, 6L, 10L, 10L, 10L, 10L, 14L, 14L, 14L, 14L, 13L,
13L, 13L, 9L, 9L, 9L, 9L, 14L, 14L, 14L, 14L, 8L, 8L, 8L, 8L,
14L, 7L, 7L, 7L, 7L, 12L, 12L, 12L, 12L, 12L, 12L, 5L, 5L, 5L,
5L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 13L, 13L, 6L, 8L,
8L, 8L, 1L, 1L, 1L, 1L, 6L, 6L, 6L, 6L, 13L, 13L, 13L, 13L, 12L,
12L, 12L, 12L, 2L, 2L, 2L, 2L, 11L, 11L, 11L, 3L, 3L, 3L, 3L,
14L, 14L, 14L, 9L, 9L, 9L, 9L, 14L, 14L, 13L, 13L, 13L, 13L,
3L, 3L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 3L, 3L, 3L, 3L,
14L, 14L, 14L, 14L, 11L, 11L, 11L, 11L, 13L, 13L, 13L, 13L, 3L,
3L, 3L, 3L, 14L, 14L, 6L, 6L, 6L, 12L, 12L, 12L, 12L, 14L, 13L,
13L, 13L, 13L, 14L, 14L, 14L, 14L, 11L, 11L, 11L, 14L, 14L, 14L,
10L, 10L, 10L, 10L, 6L, 5L, 5L, 5L, 10L, 10L, 10L, 4L, 4L, 13L,
13L, 13L, 13L, 9L, 9L, 9L, 13L, 13L, 13L, 14L, 14L, 14L, 14L,
6L, 6L, 6L, 6L, 13L, 13L, 13L, 13L, 12L, 12L, 12L, 12L, 14L,
14L, 14L, 14L, 6L, 6L, 6L, 6L), .Label = c("10/23/18", "11/18/18",
"11/30/18", "11/6/18", "12/1/18", "12/10/18", "12/12/18", "12/2/18",
"12/3/18", "12/4/18", "12/6/18", "12/7/18", "12/8/18", "12/9/18",
"9/10/18"), class = "factor"), Latitude = c(51.41752, 52.243806,
52.947709, 52.947709, 52.947709, 52.947709, 51.491811, 51.491811,
51.60157, 51.60157, 51.60157, 51.60157, 52.68959, 52.68959, 52.68959,
52.68959, 50.697802, 50.697802, 50.697802, 50.697802, 53.62417,
53.62417, 50.446841, 50.446841, 50.446841, 50.446841, 53.959679,
53.959679, 53.959679, 53.959679, 52.01434, 52.01434, 52.01434,
51.78375, 51.78375, 51.78375, 51.78375, 51.456965, 51.456965,
51.456965, 51.456965, 52.011812, 52.011812, 52.011812, 52.011812,
55.91924, 50.121978, 50.121978, 50.121978, 50.121978, 51.43474,
51.43474, 51.10708, 51.10708, 51.10708, 51.10708, 50.435984,
50.435984, 50.435984, 50.435984, 51.78666, 51.78666, 51.78666,
51.78666, 53.38728, 53.38728, 53.38728, 53.38728, 52.441088,
52.441088, 52.552344, 53.582285, 53.582285, 53.582285, 49.259471,
49.259471, 49.259471, 49.259471, 50.462, 50.462, 50.462, 50.462,
51.746642, 51.746642, 51.746642, 51.746642, 52.2501, 52.2501,
52.2501, 52.2501, 52.42646, 52.42646, 52.42646, 52.42646, 50.79387,
50.79387, 50.79387, 53.615575, 53.615575, 53.615575, 53.615575,
51.08478, 51.08478, 51.08478, 53.19329, 53.19329, 53.19329, 53.19329,
55.968437, 55.968437, 56.52664, 56.52664, 56.52664, 56.52664,
52.04252, 52.04252, 51.8113, 51.8113, 51.8113, 51.8113, 50.52008,
50.52008, 50.52008, 50.52008, 51.48417, 51.48417, 51.48417, 51.48417,
54.58243, 54.58243, 54.58243, 54.58243, 52.58839, 52.58839, 52.58839,
52.58839, 52.717283, 52.717283, 52.717283, 52.717283, 50.740764,
50.740764, 50.740764, 50.740764, 50.733412, 50.733412, 50.79926,
50.79926, 50.79926, 53.675788, 53.675788, 53.675788, 53.675788,
55.43828, 48.35079, 48.35079, 48.35079, 48.35079, 51.36445, 51.36445,
51.36445, 51.36445, 52.36286, 52.36286, 52.36286, 52.122402,
52.122402, 52.122402, 52.16104, 52.16104, 52.16104, 52.16104,
54.7311, 51.61842, 51.61842, 51.61842, 55.920966, 55.920966,
55.920966, 57.158724, 57.158724, 51.88468, 51.88468, 51.88468,
51.88468, 52.34015, 52.34015, 52.34015, 53.37687, 53.37687, 53.37687,
54.27745, 54.27745, 54.27745, 54.27745, 52.026042, 52.026042,
52.026042, 52.026042, 51.319032, 51.319032, 51.319032, 51.319032,
51.51365, 51.51365, 51.51365, 51.51365, 53.43202, 53.43202, 53.43202,
53.43202, 51.50797, 51.50797, 51.50797, 51.50797), Longitude = c(-0.32116,
1.30786, -1.435407, -1.435407, -1.435407, -1.435407, -3.210324,
-3.210324, -3.67111, -3.67111, -3.67111, -3.67111, -3.3081, -3.3081,
-3.3081, -3.3081, -2.11692, -2.11692, -2.11692, -2.11692, -2.43155,
-2.43155, -3.706923, -3.706923, -3.706923, -3.706923, -1.061008,
-1.061008, -1.061008, -1.061008, 1.04007, 1.04007, 1.04007, -0.65046,
-0.65046, -0.65046, -0.65046, -2.624917, -2.624917, -2.624917,
-2.624917, -0.70082, -0.70082, -0.70082, -0.70082, -3.20936,
-5.555169, -5.555169, -5.555169, -5.555169, 0.45981, 0.45981,
-2.32027, -2.32027, -2.32027, -2.32027, -4.105617, -4.105617,
-4.105617, -4.105617, -0.71433, -0.71433, -0.71433, -0.71433,
-2.95811, -2.95811, -2.95811, -2.95811, -0.176158, -0.176158,
-1.337177, -2.802239, -2.802239, -2.802239, -123.107788, -123.107788,
-123.107788, -123.107788, -3.5607, -3.5607, -3.5607, -3.5607,
0.486416, 0.486416, 0.486416, 0.486416, -0.8825, -0.8825, -0.8825,
-0.8825, -1.78771, -1.78771, -1.78771, -1.78771, 0.26684, 0.26684,
0.26684, -2.432959, -2.432959, -2.432959, -2.432959, -0.73626,
-0.73626, -0.73626, -0.63793, -0.63793, -0.63793, -0.63793, -3.179732,
-3.179732, -3.40313, -3.40313, -3.40313, -3.40313, -2.43733,
-2.43733, -0.22894, -0.22894, -0.22894, -0.22894, -4.20756, -4.20756,
-4.20756, -4.20756, -0.34854, -0.34854, -0.34854, -0.34854, -5.93229,
-5.93229, -5.93229, -5.93229, -1.96843, -1.96843, -1.96843, -1.96843,
-2.410575, -2.410575, -2.410575, -2.410575, -2.361234, -2.361234,
-2.361234, -2.361234, -2.014029, -2.014029, -3.19446, -3.19446,
-3.19446, -1.272404, -1.272404, -1.272404, -1.272404, -4.64226,
10.91812, 10.91812, 10.91812, 10.91812, -0.23106, -0.23106, -0.23106,
-0.23106, -2.06327, -2.06327, -2.06327, -0.487443, -0.487443,
-0.487443, 0.18702, 0.18702, 0.18702, 0.18702, -5.8041, -0.16034,
-0.16034, -0.16034, -3.193503, -3.193503, -3.193503, -2.166099,
-2.166099, -0.17853, -0.17853, -0.17853, -0.17853, -1.27795,
-1.27795, -1.27795, -1.34506, -1.34506, -1.34506, -0.47911, -0.47911,
-0.47911, -0.47911, -0.503113, -0.503113, -0.503113, -0.503113,
-0.472994, -0.472994, -0.472994, -0.472994, -3.18722, -3.18722,
-3.18722, -3.18722, -2.27968, -2.27968, -2.27968, -2.27968, -0.25931,
-0.25931, -0.25931, -0.25931), Altitude = c(0, 0, 103.9, 103.9,
103.9, 103.9, 15, 15, 184, 184, 184, 184, 176, 176, 176, 176,
12, 12, 12, 12, 178, 178, 36, 36, 36, 36, 11, 11, 11, 11, 47,
47, 47, 210, 210, 210, 210, 97, 97, 97, 97, 0, 0, 0, 0, 100,
68, 68, 68, 68, 4, 4, 200, 200, 200, 200, 160, 160, 160, 160,
165.8, 165.8, 165.8, 165.8, 0, 0, 0, 0, 0, 0, 0, 36, 36, 36,
47, 47, 47, 47, 0, 0, 0, 0, 43, 43, 43, 43, 97, 97, 97, 97, 133,
133, 133, 133, 18, 18, 18, 123, 123, 123, 123, 127, 127, 127,
15, 15, 15, 15, 14, 14, 65, 65, 65, 65, 45, 45, 129, 129, 129,
129, 18, 18, 18, 18, 30, 30, 30, 30, 19, 19, 19, 19, 0, 0, 0,
0, 0, 0, 0, 0, 96, 96, 96, 96, 0, 0, 0, 0, 0, 49, 49, 49, 49,
0, 0, 0, 0, 0, 48, 48, 48, 48, 123, 123, 123, 43, 43, 43, 75,
75, 75, 75, 0, 73, 73, 73, 115, 115, 115, 119, 119, 94, 94, 94,
94, 112, 112, 112, 34, 34, 34, 0, 0, 0, 0, 103, 103, 103, 103,
0, 0, 0, 0, 37.5, 37.5, 37.5, 37.5, 29, 29, 29, 29, 63, 63, 63,
63), Species = structure(c(2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 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), .Label = c("Quercus petraea",
"Quercus robur"), class = "factor"), Tree_diameter = c(68.8,
300, 847, 817, 569, 892, 62, 71, 29, 46.5, 27.7, 40.1, 68, 45,
60, 54, 104, 122, 85, 71, 81, 118, 39.8, 43.6, 44.6, 22.6, 19.8,
16.6, 15.1, 11.9, 48.1, 83.7, 77.9, 81.8, 102.5, 75.5, 57.3,
0.3, 0.2, 0.3, 0.3, 99, 85, 74, 68, 60, 67.3, 36.9, 82, 51.8,
19.9, 14.6, 196, 122, 118, 180, 58.6, 54.1, 58, 61.5, 58.4, 40.6,
61, 68.6, 117, 240, 210, 310, 134, 64, 52.2, 57, 73.9, 37.1,
170, 114, 127, 158, 147.4, 135.3, 122.9, 104.1, 263, 237, 322,
302, 173, 186, 144, 155, 89, 41, 68, 83, 81.5, 29.3, 43.3, 141.6,
85.5, 82.8, 114.1, 129, 127, 143, 125, 92, 68, 90, 25, 20, 63.7,
39.8, 66.2, 112.4, 41.9, 43.8, 124.5, 94.1, 68.6, 74.4, 24.2,
54.7, 43, 33.1, 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, 320, 352,
120.9, 108.3, 53.2, 240, 274, 122, 85, 21, 52, 43, 38, 37, 219,
215, 216, 175, 124, 133, 119, 85.9, 49.7, 97.1, 40.8, 79.3, 62.4,
62.4, 70, 115.9, 111.1, 88.9, 73, 54, 75, 159, 244, 181.5, 149.7,
122, 143.6, 148, 145, 99, 49, 57.9, 54.8, 53.5, 88.8, 71.3, 101.9,
27.5, 32, 54, 54.1, 169, 152, 160, 138, 90.8, 87.9, 77.4, 81.2,
91.7, 62.7, 50, 72.9, 24.8, 61, 88.6, 80.1), Urbanisation_index = c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 4L, 4L, 4L,
3L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L,
1L, 1L, 1L, 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, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 1L, 4L, 4L, 4L,
4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L,
4L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 3L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 4L, 2L,
2L, 3L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L,
2L, 4L, 4L, 1L, 1L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 3L, 3L, 4L, 4L,
4L, 4L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L,
3L, 2L, 2L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 3L, 3L, 3L, 2L, 2L, 2L,
1L, 4L, 4L, 3L, 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, 2L, 2L, 2L, 2L, 2L, 3L, 4L, 4L, 4L, 4L, 2L,
2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 2L,
2L, 2L, 2L, 2L, 3L, 4L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L,
2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 2L, 1L, 1L, 4L, 4L, 4L,
2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 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(15L, 95L, 45L, 5L, 5L, 45L,
5L, 5L, 25L, 5L, 5L, 15L, 25L, 25L, 35L, 35L, 25L, 35L, 15L,
15L, 15L, 15L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 45L, 45L, 55L,
35L, 35L, 55L, 35L, 5L, 5L, 5L, 5L, 95L, 95L, 95L, 95L, 15L,
35L, 45L, 25L, 25L, 15L, 5L, 25L, 25L, 25L, 25L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 35L, 25L, 5L, 35L, 15L, 15L,
35L, 35L, 25L, 25L, 5L, 5L, 5L, 5L, 35L, 25L, 25L, 25L, 5L, 5L,
15L, 15L, 35L, 65L, 35L, 35L, 25L, 25L, 5L, 25L, 25L, 25L, 25L,
15L, 15L, 5L, 35L, 35L, 45L, 35L, 5L, 15L, 15L, 25L, 5L, 15L,
5L, 5L, 15L, 5L, 5L, 15L, 5L, 5L, 5L, 5L, 5L, 85L, 5L, 35L, 15L,
5L, 5L, 5L, 25L, 25L, 15L, 35L, 95L, 95L, 95L, 95L, 15L, 15L,
5L, 25L, 25L, 5L, 15L, 15L, 5L, 5L, 15L, 5L, 5L, 5L, 25L, 25L,
25L, 25L, 5L, 5L, 5L, 5L, 15L, 25L, 15L, 25L, 25L, 55L, 35L,
35L, 25L, 25L, 45L, 5L, 25L, 5L, 5L, 5L, 5L, 55L, 55L, 15L, 15L,
25L, 15L, 45L, 35L, 35L, 15L, 5L, 25L, 15L, 15L, 15L, 15L, 15L,
35L, 15L, 15L, 35L, 15L, 25L, 25L, 15L, 15L, 15L, 15L, 5L, 5L,
5L, 5L, 5L, 5L, 15L, 15L), Phenological_Index = c(4L, 4L, 3L,
4L, 4L, 3L, 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, 4L, 4L, 4L, 4L,
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, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 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, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 2L, 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, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 4L, 4L, 4L,
3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 3L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L)), class = "data.frame", row.names = c(NA, -215L
))
这看起来像您想要的 table 吗？
library("arules")
#> Loading required package: Matrix
#>
#> Attaching package: 'arules'
#> The following objects are masked from 'package:base':
#>
#> abbreviate, write
library("tidyverse")
# Smaller tibble with only the two necessary columns
data <- tibble(
Canopy_Index = c(15, 95, 45, 5),
Species = c("A", "A", "B", "B"))
breaks <- seq(from = 0, to = 100, by = 10)
labels <- map_chr(breaks[1:10], ~ paste(.x, .x + 10, sep = "-"))
indices <- data$Canopy_Index
table(indices)
#> indices
#> 5 15 45 95
#> 1 1 1 1
percents <- discretize(indices,
method = "fixed",
breaks = breaks,
labels = labels)
table(percents)
#> percents
#> 0-10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100
#> 1 1 0 0 1 0 0 0 0 1
data %>%
mutate(Canopy_Index_Binned = percents) %>%
count(Canopy_Index_Binned, Species) %>%
spread(Species, n, fill = 0)
#> # A tibble: 4 x 3
#> Canopy_Index_Binned A B
#> <fct> <dbl> <dbl>
#> 1 0-10 0 1
#> 2 10-20 1 0
#> 3 40-50 0 1
#> 4 90-100 1 0
# There is also a base function that can convert a numeric vector to a factor in the same way:
percents <- cut(indices,
breaks = breaks,
labels = labels)
由 reprex package (v0.2.1)
于 2019-03-14 创建