r - 将字符串拆分为 R 中的新行

Question

这个问题在这里已经有了答案:





Split comma-separated strings in a column into separate rows

(6 个回答)


4年前关闭。




我有一个如下所示的数据集:

Country Region    Molecule      Item Code   
    IND     NA       PB102      FR206985511 
   THAI     AP       PB103      BA-107603 / F000113361 / 107603
   LUXE     NA       PB105      1012701 / SGP-1012701 / F041701000
    IND     AP       PB106      AU206985211 / CA-F206985211
   THAI     HP       PB107      F034702000 / 1010701 / SGP-1010701
   BANG     NA       PB108      F000007970/25781/20009021

我想根据 ITEMCODE 中的字符串值进行拆分 /上的专栏并为每个条目创建一个新行。

例如，所需的输出将是:

Country Region Molecule      Item.Code
    IND     NA    PB102    FR206985511
   THAI     AP    PB103      BA-107603
   THAI     AP    PB103     F000113361
   THAI     AP    PB103         107603
   LUXE     NA    PB105        1012701
   LUXE     NA    PB105    SGP-1012701
   LUXE     NA    PB105     F041701000
    IND     AP    PB106    AU206985211
    IND     AP    PB106  CA-F206985211
   THAI     HP    PB107     F034702000
   THAI     HP    PB107        1010701
   THAI     HP    PB107    SGP-1010701
   BANG     NA    PB108     F000007970
   BANG     NA    PB108          25781
   BANG     NA    PB108       20009021

我试过下面的代码

library(splitstackshape)
df2=concat.split.multiple(df1,"Plant.Item.Code","/", direction="long")

但得到了错误

"Error: memory exhausted (limit reached?)"

当我尝试 strsplit()我收到以下错误消息。

Error in strsplit(df1$Plant.Item.Code, "/") : non-character argument

Answer 1

试试 cSplit函数(因为您已经在使用 @Anandas 包)。请注意，它会返回 data.table对象，因此请确保安装了此软件包。您可以回复到 data.frame (如果你愿意)通过做类似 setDF(df2) 的事情

library(splitstackshape)
df2 <- cSplit(df1, "Item.Code", sep = "/", direction = "long")
df2
#     Country Region Molecule      Item.Code
#  1:     IND     NA    PB102    FR206985511
#  2:    THAI     AP    PB103      BA-107603 
#  3:    THAI     AP    PB103     F000113361 
#  4:    THAI     AP    PB103         107603
#  5:    LUXE     NA    PB105        1012701 
#  6:    LUXE     NA    PB105    SGP-1012701 
#  7:    LUXE     NA    PB105     F041701000
#  8:     IND     AP    PB106    AU206985211 
#  9:     IND     AP    PB106  CA-F206985211
# 10:    THAI     HP    PB107     F034702000 
# 11:    THAI     HP    PB107        1010701 
# 12:    THAI     HP    PB107    SGP-1010701
# 13:    BANG     NA    PB108     F000007970
# 14:    BANG     NA    PB108          25781
# 15:    BANG     NA    PB108       20009021