R cbind 两个不同长度的数据帧抛出错误无效的“nrow”值（< 0)

Question

我正在尝试绑定两个不同长度的数据帧。

Category = c("New", "New","New","New","Old","Old","Old","Old",
"Expired", "Expired", "Expired", "Expired")
Monthly_Dates= c ('Jan-2014', 'Feb-2014', 'Mar-2014', 'Apr-2014',
'May-2014', 'Jun-2014', 'Jul-2014', 'Aug-2014', 'Sep-2014','Oct-2014','Nov-2014','Dec-2014')
Monthly_Vals =   c(4,7,6,7,8,12,10,11,10,16,12,120)

monthly_df <- data.frame(Category, Monthly_Dates,Monthly_Vals) 

Category = c("New","Old", "Expired",  "Expired")
Quarterly_Date= c ( 'Mar-2014', 'Jun-2014', 'Sep-2014', 'Dec-2014')
Quarterly_Vals =   c(4,7,6,7)

quarterly_df <- data.frame(Category, Quarterly_Date,Quarterly_Vals) 

monthly_df = cbind(monthly_df, matrix(data = NA, ncol = ncol(monthly_df), 
nrow = nrow(quarterly_df) - nrow(monthly_df),dimnames = list(NULL, names(monthly_df))));

Combined_dfs <- data.frame(monthly_df, quarterly_df)
View (Combined_dfs)

当我尝试

cbind

每月和每季度的值时，它会抛出错误，我做错了什么？

错误

Error in matrix(data = NA, ncol = ncol(monthly_df), nrow = nrow(quarterly_df) -  : 
  invalid 'nrow' value (< 0)

预期结果

   Category Monthly_Dates Monthly_Vals  Quarterly_Date  Quarterly_Vals
1       New      Jan-2014            4    Mar-2014       4
2       New      Feb-2014            7    Jun-2014       7
3       New      Mar-2014            6    Sep-2014       6
4       New      Apr-2014            7    Dec-2014       7
5       Old      May-2014            8
6       Old      Jun-2014           12
7       Old      Jul-2014           10
8       Old      Aug-2014           11
9   Expired      Sep-2014           10
10  Expired      Oct-2014           16
11  Expired      Nov-2014           12
12  Expired      Dec-2014          120

Answer 1

你可以做这样的事情，但我几乎不愿意建议它，因为我觉得它仍然避免了潜在的问题......

library(tidyverse)

monthly_df <- monthly_df %>% mutate(Row = row_number())
quarterly_df <- quarterly_df %>% mutate(Row = row_number())

monthly_df %>% full_join(quarterly_df, by = "Row")
   Category.x Monthly_Dates Monthly_Vals Row Category.y Quarterly_Date Quarterly_Vals
1         New      Jan-2014            4   1        New       Mar-2014              4
2         New      Feb-2014            7   2        Old       Jun-2014              7
3         New      Mar-2014            6   3    Expired       Sep-2014              6
4         New      Apr-2014            7   4    Expired       Dec-2014              7
5         Old      May-2014            8   5       <NA>           <NA>             NA
6         Old      Jun-2014           12   6       <NA>           <NA>             NA
7         Old      Jul-2014           10   7       <NA>           <NA>             NA
8         Old      Aug-2014           11   8       <NA>           <NA>             NA
9     Expired      Sep-2014           10   9       <NA>           <NA>             NA
10    Expired      Oct-2014           16  10       <NA>           <NA>             NA
11    Expired      Nov-2014           12  11       <NA>           <NA>             NA
12    Expired      Dec-2014          120  12       <NA>           <NA>             NA

注意

Category

列的“重复”，因为它们出现在两个数据框中，并且您似乎不想加入它们。我向每个数据帧引入一个

Row

列，以便较短 df 的行绑定到较长 df 的相应行。

如果您只想要

Category

中的

monthly_df

，请在

quarterly_df

ing 之前将其从

join

中删除。