Question

问题陈述

我有一个名为'New_Blue'的数据框（请参见下文），我想通过使用'subset（）'函数'dplyr'软件包。

我知道这个问题已被问过无数次，但是在咨询Stack Overflow和其他论坛后，作为R用户没有经验，我仍然无法解决我的问题。

在整个分析过程中，除了“ 9月” 和“ 10月” 之外，我成功地划分了所有月份的数据框。

运行代码时，'9月'和 '10月'的结果在下面显示此错误：

<0 rows> (or 0-length row.names)

<0行>（或长度为0的row.names）。如检查所示，这发生在变量名末尾有空格或没有值的情况下。但是，没有列缺少值，并且数据框具有536个观测值。

我已经重新检查了数据帧是否存在不一致的错误，但是我找不到任何错误，我用零填充所有空单元格，我想知道问题是否出在行逻辑与行号与行名之间。

经过数小时的广泛研究，我仍然无法解决这个问题！

有人可以帮忙吗？

在此先感谢您，我将不胜感激！

R代码：

##Set the seed
  set.seed(45L)

##Change the format of the columns 

New_Blue$Year<-as.numeric(New_Blue$Year)
New_Blue$Month<-as.factor(New_Blue$Month)
New_Blue$Latitude<-as.numeric(New_Blue$Latitude)
New_Blue$Longitude<-as.numeric(New_Blue$Longitude)
New_Blue$Best<-as.numeric(New_Blue$Best)

##Structure of the data

str(New_Blue)

##Results 

      New_Blue$Year<-as.numeric(New_Blue$Year)
      New_Blue$Month<-as.factor(New_Blue$Month) 
      New_Blue$Latitude<-as.numeric(New_Blue$Latitude)
      New_Blue$Longitude<-as.numeric(New_Blue$Longitude)
      New_Blue$Best<-as.numeric(New_Blue$Best)

尝试解决问题

##Is the problem the row logicals vs row numbers vs row names?

   New_Blue_Data <- New_Blue[1:536, ]
   New_Blue_Data[rep(TRUE, 536), ]

#Convert NA's to zero's

   New_Blue[is.na(New_Blue)] <- 0

按月子集数据：

package(dplyr)


##Subset the data frame by the column 'Month'

January_Whales<-subset(Blue, Month == "January")
February_Whales<-subset(Blue, Month == "February")
March_Whales<-subset(Blue, Month == "March")
April_Whales<-subset(Blue, Month == "April")
May_Whales<-subset(Blue, Month == "May")
August_Whales<-subset(Blue, Month == "August")
September_Whales<-subset(Blue, Month == "September")
October_Whales<-subset(Blue, Month == "October")
November_Whales<-subset(Blue, Month == "November")
December_Whales<-subset(Blue, Month == "December")

##Error message

September_Whales
[1] Year      Month     Latitude  Longitude Best     
 <0 rows> (or 0-length row.names)   

October_Whales
[1] Year      Month     Latitude  Longitude Best     
 <0 rows> (or 0-length row.names)

数据框：

structure(list(Year = c(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, 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, 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, 2016L, 2016L, 2016L, 2016L, 
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 
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2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 
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2016L, 2016L, 2016L, 2016L, 2016L, 2017L, 2017L, 2017L, 2017L, 
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 
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2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 
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2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 
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2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L), Month = structure(c(6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 9L, 9L, 9L, 9L, 9L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 13L, 13L, 13L, 
13L, 13L, 13L, 13L, 13L, 13L, 13L, 12L, 12L, 12L, 12L, 12L, 12L, 
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10L, 10L, 10L, 10L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
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8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 13L, 13L, 13L, 13L, 
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10L, 10L, 10L, 10L, 10L, 10L, 10L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 13L, 13L, 13L, 
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 
13L, 13L, 13L, 13L, 13L, 13L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 11L, 11L, 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), .Label = c("April", "August", "August ", "December", 
"February", "January", "July ", "March", "May", "November", "November ", 
"October ", "September "), class = "factor"), Latitude = c(5.832, 
5.836, 5.83, 5.823, 5.823, 5.83, 5.803, 5.803, 5.886, 5.858, 
5.931, 5.931, 5.865, 5.761, 5.859, 5.911, 5.919, 5.82, 5.841, 
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5.836, 5.832, 5.88, 5.781, 5.83, 5.875, 5.875, 5.782, 5.825, 
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5.816, 5.82, 5.83, 5.815, 5.822, 5.808, 5.789, 5.822, 5.789, 
5.822, 5.87, 5.828, 5.824, 5.852, 5.864, 5.86, 5.845, 5.866, 
5.758, 5.805, 5.842, 5.838, 5.838, 5.783, 5.853, 5.868, 5.883, 
5.88, 5.803, 5.787, 5.811, 5.748, 5.826, 5.818, 5.783, 5.794, 
5.779, 5.818, 5.754, 5.811, 5.754, 5.846, 5.846, 5.834, 5.79, 
5.794, 5.885, 5.823, 5.787, 5.869, 5.76, 5.841, 5.783, 5.887, 
5.847, 5.82, 5.812, 5.789, 5.805, 5.797, 5.904, 5.806, 5.843, 
5.829, 5.841, 5.829, 5.839, 5.821, 5.806, 5.825, 5.836, 5.829, 
5.812, 5.825, 5.816, 5.821, 5.857, 5.799, 5.839, 5.841, 5.837, 
5.806, 5.855, 5.857, 5.849, 5.817, 5.805, 5.822, 5.853, 5.852, 
5.838, 5.873, 5.803, 5.817, 5.848, 5.826, 5.789, 5.796, 5.837, 
5.827, 5.83, 5.827, 5.823, 5.816, 5.822, 5.87, 5.863, 5.847, 
5.839, 5.774, 5.8, 5.834, 5.838, 5.826, 5.826, 5.835, 5.841, 
5.858, 5.363, 5.857, 5.856, 5.853, 5.857, 5.863, 5.835, 5.834, 
5.842, 5.846, 5.849, 5.838, 5.833, 5.828, 5.837, 5.838, 5.811, 
5.833, 5.869, 5.91, 5.863, 5.833, 5.831, 5.805, 5.826, 5.85, 
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5.816, 5.802, 5.767, 5.761, 5.727, 5.76, 5.86, 5.789, 5.894, 
5.798, 5.848, 5.821, 5.829, 5.794, 0, 5.805, 5.734, 5.776, 5.749, 
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5.753, 5.849, 5.839, 5.836, 5.801, 5.837, 5.84, 5.862, 5.843, 
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5.84, 5.876, 5.844, 5.872, 5.84, 5.836, 5.84, 5.855, 5.829, 5.807, 
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5.842, 5.822, 5.819, 5.787, 5.788, 5.797, 5.869, 5.822), Longitude = c(80.35, 
80.326, 80.275, 80.281, 80.216, 80.112, 80.287, 80.17, 80.441, 
80.453, 80.431, 80.431, 80.469, 80.452, 80.502, 80.453, 80.441, 
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80.526, 80.52, 80.509, 80.468, 80.42, 80.369, 80.344, 80.39, 
80.447, 80.491, 80.403, 80.444, 80.47, 80.439, 80.422, 80.411, 
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80.621, 80.712, 80.746, 80.556, 80.675, 80.462, 80.496, 80.497, 
80.533, 80.45, 80.508, 80.53, 80.497, 80.503, 80.52, 80.463, 
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80.567, 80.634, 80.483, 80.548, 80.548, 80.605, 80.617, 80.554, 
80.474, 80.459, 80.486, 80.486, 80.486, 80.483, 80.378, 80.376, 
80.441, 80.405, 80.393, 80.423, 80.373, 80.492, 80.727, 80.611, 
80.39, 80.286, 80.451, 80.594, 80.435, 80.471, 80.448, 80.462, 
80.522, 80.471, 80.541, 80.526, 80.471, 80.455, 80.419, 80.411, 
80.448, 80.411, 80.448, 80.436, 80.383, 80.361, 80.42, 80.45, 
80.477, 80.488, 80.476, 80.492, 80.54, 80.602, 80.601, 80.617, 
80.572, 80.511, 80.475, 80.423, 80.366, 80.404, 80.432, 80.531, 
80.539, 80.456, 80.616, 80.667, 80.68, 80.662, 80.431, 80.544, 
80.381, 80.334, 80.559, 80.579, 80.59, 80.45, 80.654, 80.5, 80.671, 
80.553, 80.397, 80.604, 80.689, 80.65, 80.62, 80.818, 80.676, 
80.737, 80.609, 80.672, 80.71, 80.537, 80.611, 80.468, 80.434, 
80.459, 80.451, 80.443, 80.351, 80.369, 80.583, 80.491, 80.697, 
80.675, 80.768, 80.707, 80.671, 80.622, 80.705, 80.436, 80.424, 
80.441, 80.437, 80.544, 80.555, 80.535, 80.564, 80.543, 80.424, 
80.475, 80.434, 80.449, 80.443, 80.518, 80.498, 80.515, 80.43, 
80.436, 80.378, 80.404, 80.373, 80.391, 80.354, 80.286, 80.246, 
80.398, 80.26, 80.391, 80.364, 80.335, 80.492, 80.51, 80.442, 
80.432, 80.46, 80.46, 80.455, 80.425, 80.422, 80.475, 80.449, 
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Answer 1

以R为基准的底线（分成12个数据框-带入全局环境）：

list2env(split(df, df$Month), .GlobalEnv)

一个R底线（在列表中存储12个数据帧）：

# Split the dataframe up into a list of dataframes by the month: 

month_list <- split(df, df$Month)

# Name the dataframes appropriately: 

names(month_list) <- paste0(trimws(unique(df$Month), "both"), "_Whales")

R：subset（）函数中的dplyr包按月对数据帧进行子集化；错误-<0行>（或长度为0的行。名称）

1 个答案: