bind_rows_(x, .id) 中的错误:列无法从因子转换为数字 [英] Error in bind_rows_(x, .id) : Column can't be converted from factor to numeric

问题描述

我有十个数据集，它们是从 Excel 文件中读取的，使用 xlsx 库，并存储在 tibbles 中.我想合并它们.

I have ten datasets that have been read from Excel files, using the xlsx library, and stored in tibbles. I want to merge them.

以下是示例数据集.不同数据集的变量数量不同，有些变量只在一个数据集中.person 变量的值永远不会重叠.

Here are example datasets. The number of variables differ between datasets, and some variables are only in one dataset. The value of the person variable will never overlap.

data1 <- tibble(person = c("A","B","C"),
    test1 = as.factor(c(1,4,5)), 
    test2 = c(14,25,10),
    test3 = c(12.5,16.0,4),
    test4 = c(16,23,21),
    test5 = as.factor(c(49,36,52)))

data2 <- tibble(person = c("D","E","F"),
    test1 = c(8,7,2), 
    test3 = c(6.5,12.0,19.5),
    test4 = as.factor(c(15,21,29)),
    test5 = as.factor(c(54,51,36)),
    test6 = c(32,32,29),
    test7 = c(13,11,10))

实际数据集通常有 ~50 行和 ~200 个变量.我试过了

The actual datasets usually have ~50 rows and ~200 variables in them. I have tried

    all_data <- dplyr::bind_rows(data1,data2)

希望得到这个结果

# A tibble: 6 x 8
  person test1 test2 test3 test4 test5 test6 test7
   <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1      A     1    14  12.5    16    49    NA    NA
2      B     4    25  16.0    23    36    NA    NA
3      C     5    10   4.0    21    52    NA    NA
4      D     8    NA   6.5    15    54    32    13
5      E     7    NA  12.0    21    51    32    11
6      F     2    NA  19.5    29    36    29    10

但我收到此错误

Error in bind_rows_(x, .id) : Column `test1` can't be converted from factor to numeric

我搜索了 Stackoverflow，发现了与此相关的问题，大多数答案都集中在尝试将变量转换为另一个类上.但我不在乎我的变量有哪些类，因为我只会将合并的数据集写入 CSV 文件或 Excel 文件.

I have searched Stackoverflow, and I found questions regarding this, and most answers center on trying to convert the variables to another class. But I don't care which classes my variables have, because I will just write the merged dataset to a CSV-file or Excel file.

难道没有一些简单的解决方法吗?

Isn't there some kind of simple workaround?

推荐答案

我认为这应该可行:

library(plyr)
all_data <- rbind.fill(data1,data2)

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