列名和输入中的 dplyr tidyr 传播错误

Question

我正在尝试在具有目的地和出发地名称的数据集上执行来自 tidyr 的 spread 函数，以了解飞机旅程及其乘客人数。我尝试构建一个最终可用于热图的表格。因此，我希望将 Origin 变量放在行中，将 Destination 变量作为列。

我尝试使用不同的参数组合运行代码并使用spread_，但我总是以错误告终。

如果我将 spread_ 与 key_col 和 val_col 一起使用，我会得到：

匹配错误(x, table, nomatch = 0L)： 找不到对象“Destination.Region”

在我的大型数据集上，它会产生另一种类型的错误：

colnames<-(*tmp*, value = c("ASIA SUB-CONTINENT", "AUSTRALIA", 中的错误： 'dimnames' [2] 的长度不等于数组范围

这是我第一次使用tidyr 并且我正在了解这些软件包，这听起来并不复杂。但是我几个小时以来一直在解决这个问题，在任何论坛上都找不到任何答案。

感谢您的帮助，

以下是数据类型的示例：

data2<-matrix(NA, nrow = 7, ncol=3)  
colnames(data2)<-c("Origin.Destination", "Total.Passengers", "Destination.Region")
data2[,1] <- c("EAST AFRICA","SOUTHERN AFRICA","WEST AFRICA", "EAST AFRICA", "SOUTHERN AFRICA", "EAST AFRICA","EAST AFRICA")
data2[,2] <- c(100, 5000, 200, 10000, 200, 20, 4000)
data2[,3] <- c("WESTERN EUROPE", "SOUTH AMERICA", "ASIA", "SOUTH AMERICA", "ASIA", "WESTERN EUROPE", "WESTERN EUROPE")

data2

这是我的代码：

DF<- 
  data2 %>%
  spread_(key_ = "Destination.Region",
     value_ = "Total.Passengers", 
     convert = TRUE,
     drop = FALSE)

Answer 1

这里有一些可以尝试的方法：

1) 我会将data2 转换为data.frame。它使使用它变得更加容易。

data2<-matrix(NA, nrow = 7, ncol=3)  
colnames(data2)<-c("Origin.Destination", "Total.Passengers", "Destination.Region")
data2[,1] <- c("EAST AFRICA","SOUTHERN AFRICA","WEST AFRICA", "EAST AFRICA", "SOUTHERN AFRICA", "EAST AFRICA","EAST AFRICA")
data2[,2] <- c(100, 5000, 200, 10000, 200, 20, 4000)
data2[,3] <- c("WESTERN EUROPE", "SOUTH AMERICA", "ASIA", "SOUTH AMERICA", "ASIA", "WESTERN EUROPE", "WESTERN EUROPE")

data3<-data.frame(data2)

2) 新的data.frame 需要一个明确的列（通常是索引列）才能使spread_ 函数正常工作。否则：

DF<- 
  data3 %>%
  spread_(key_ = "Destination.Region",
          value_ = "Total.Passengers", 
          convert = TRUE,
          drop = FALSE)

Error: Duplicate identifiers for rows (1, 6, 7)

但如果：

data3$index<-1:nrow(data3)

DF<- 
  data3 %>%
  spread_(key_ = "Destination.Region",
          value_ = "Total.Passengers", 
          convert = TRUE,
          drop = FALSE)
DF

Origin.Destination index ASIA SOUTH AMERICA WESTERN EUROPE
1         EAST AFRICA     1   NA            NA            100
2         EAST AFRICA     2   NA            NA             NA
3         EAST AFRICA     3   NA            NA             NA
4         EAST AFRICA     4   NA         10000             NA
5         EAST AFRICA     5   NA            NA             NA
6         EAST AFRICA     6   NA            NA             20
7         EAST AFRICA     7   NA            NA           4000
8     SOUTHERN AFRICA     1   NA            NA             NA
9     SOUTHERN AFRICA     2   NA          5000             NA
10    SOUTHERN AFRICA     3   NA            NA             NA
11    SOUTHERN AFRICA     4   NA            NA             NA
12    SOUTHERN AFRICA     5  200            NA             NA
13    SOUTHERN AFRICA     6   NA            NA             NA
14    SOUTHERN AFRICA     7   NA            NA             NA
15        WEST AFRICA     1   NA            NA             NA
16        WEST AFRICA     2   NA            NA             NA
17        WEST AFRICA     3  200            NA             NA
18        WEST AFRICA     4   NA            NA             NA
19        WEST AFRICA     5   NA            NA             NA
20        WEST AFRICA     6   NA            NA             NA
21        WEST AFRICA     7   NA            NA             NA

这里可能有意义的一件事是sum 按出发地和目的地的乘客总数。这将避免使用索引并防止出现如此多的 NA：

Origin <- c("EAST AFRICA","SOUTHERN AFRICA","WEST AFRICA", "EAST AFRICA", "SOUTHERN AFRICA", "EAST AFRICA","EAST AFRICA")
Passengers <- c(100, 5000, 200, 10000, 200, 20, 4000)
Destination <- c("WESTERN EUROPE", "SOUTH AMERICA", "ASIA", "SOUTH AMERICA", "ASIA", "WESTERN EUROPE", "WESTERN EUROPE")
data3<-data.frame(Origin, Passengers, Destination)

DF<-data3 %>% group_by(Origin, Destination) %>%
  summarise(Total.Passengers = sum(Passengers)) %>%
  spread(Destination, Total.Passengers)

DF

          Origin  ASIA SOUTH AMERICA WESTERN EUROPE
          (fctr) (dbl)         (dbl)          (dbl)
1     EAST AFRICA    NA         10000           4120
2 SOUTHERN AFRICA   200          5000             NA
3     WEST AFRICA   200            NA             NA