R / dyplr：將兩行轉換為兩列

Question

我在 R 中有一個數據框，如下所示：

Word    Base    Number  Type
-       -       -       -
shoe    shoe    4834    singular
shoes   shoe    49955   plural
toy     toy     75465   singular
toys    toy     23556   plural
key     key     39485   singular
keys    key     6546    plural
jazz    jazz    58765   plural

我想改變它，使它看起來像這樣：

Word_Sg Word_Pl Base    Num_Singular    Num_Plural
--      --      --      --              --
shoe    shoes   shoe    4834            49955
toy     toys    toy     75465           23556
key     keys    key     39485           6546
NA      jazz    jazz    NA              58765

因此，與其有兩行單數和復數的值，我想要兩列，一列是單數，另一列是復數。

我已經使用dplyr::summarize嘗試了一些東西，但到目前為止，沒有任何成功。 這是我到目前為止想出的代碼：

dataframe1 <- dataframe %>% 
      mutate(Num_Singular = case_when(Type == "singular" ~ Number)) %>%
      mutate(Num_Plural = case_when(Type == "plural" ~ Number)) %>%
      dplyr::select(Word, Base, Num_Singular, Num_Plural) %>%
      group_by(Base) %>%
      dplyr::summarize(Num_Singular = paste(na.omit(Num_Singular)),
                       Num_Plural = paste(na.omit(Num_Plural))

但是，它給了我這個錯誤：

Error in summarise_impl(.data, dots) : 
  Column `Num_Singular` must be length 1 (a summary value), not 2)

我認為問題可能在於有些行不一定有單數和復數，但只有其中之一（例如“爵士”）。 大多數行都有。

那么我怎樣才能在 R 或 dplyr 中做到這一點呢？

Answer 1

如果你先看前幾列：

select(dat, Base, Word, Type)[1:2,]
#   Base  Word     Type
# 1 shoe  shoe singular
# 2 shoe shoes   plural

從這里開始，考慮它只是將其擴展為單數/復數列，有效地從“高”到“寬”。 （如果Type有兩個以上的類別會更明顯。）

select(dat, Base, Word, Type) %>%
    spread(Type, Word) %>%
    rename(Word_Pl=plural, Word_Sg=singular)
#   Base Word_Pl Word_Sg
# 1 jazz    jazz    <NA>
# 2  key    keys     key
# 3 shoe   shoes    shoe
# 4  toy    toys     toy

您也可以輕松地為Number重復此操作。 從那里開始，只需根據鍵列Base合並/加入它們即可：

full_join(
  select(dat, Base, Word, Type) %>%
    spread(Type, Word) %>%
    rename(Word_Pl=plural, Word_Sg=singular),
  select(dat, Base, Number, Type) %>%
    spread(Type, Number) %>%
    rename(Num_Pl=plural, Num_Sg=singular),
  by = "Base"
)
#   Base Word_Pl Word_Sg Num_Pl Num_Sg
# 1 jazz    jazz    <NA>  58765     NA
# 2  key    keys     key   6546  39485
# 3 shoe   shoes    shoe  49955   4834
# 4  toy    toys     toy  23556  75465

---

耗材數據：

library(dplyr)
library(tidyr)
dat <- read.table(text='Word    Base    Number  Type
shoe    shoe    4834    singular
shoes   shoe    49955   plural
toy     toy     75465   singular
toys    toy     23556   plural
key     key     39485   singular
keys    key     6546    plural
jazz    jazz    58765   plural', header=TRUE, stringsAsFactors=FALSE)

Answer 2

tidyr的新pivot_wider()函數使這變得簡單......

library(dplyr)
library(tidyr)

dat <- read.table(header = T, stringsAsFactors = F, text='
Word    Base    Number  Type
shoe    shoe    4834    singular
shoes   shoe    49955   plural
toy     toy     75465   singular
toys    toy     23556   plural
key     key     39485   singular
keys    key     6546    plural
jazz    jazz    58765   plural')

dat %>% 
  pivot_wider(id_cols = Base, names_from = Type, values_from = c(Word, Number))

# # A tibble: 4 x 5
#   Base  Word_singular Word_plural Number_singular Number_plural
#   <chr> <chr>         <chr>                 <int>         <int>
# 1 shoe  shoe          shoes                  4834         49955
# 2 toy   toy           toys                  75465         23556
# 3 key   key           keys                  39485          6546
# 4 jazz  NA            jazz                     NA         58765

Answer 3

核心思想是通過它的類型以及它是一個單詞還是一個數字來識別每個數據點......然后很容易傳播到你想要的格式。 （我不會費心重命名變量或專門對它們進行排序以匹配您的預期輸出，因為這很容易做到，而不是這里的問題的一部分）

library(dplyr)
library(tidyr)

dat <- read.table(header = T, stringsAsFactors = F, text='
Word    Base    Number  Type
shoe    shoe    4834    singular
shoes   shoe    49955   plural
toy     toy     75465   singular
toys    toy     23556   plural
key     key     39485   singular
keys    key     6546    plural
jazz    jazz    58765   plural')

dat %>% 
  gather(variable, value, Word, Number) %>%
  unite(Type, variable, Type) %>%
  spread(Type, value, convert = T) %>% 
  as_tibble()

# # A tibble: 4 x 5
#   Base  Number_plural Number_singular Word_plural Word_singular
#   <chr>         <int>           <int> <chr>       <chr>        
# 1 jazz          58765              NA jazz        NA           
# 2 key            6546           39485 keys        key          
# 3 shoe          49955            4834 shoes       shoe         
# 4 toy           23556           75465 toys        toy  

Answer 4

您可以通過Base加入數據的plural和singular子集，然后刪除Type列並重新排序其他列......

full_join(filter(dat, Type == "plural"), 
          filter(dat, Type == "singular"),
          by = "Base", 
          suffix = c("_Pl", "_Sg")) %>% 
  select(Word_Sg, Word_Pl, Base, Number_Sg, Number_Pl)

#   Word_Sg Word_Pl Base Number_Sg Number_Pl
# 1    shoe   shoes shoe      4834     49955
# 2     toy    toys  toy     75465     23556
# 3     key    keys  key     39485      6546
# 4    <NA>    jazz jazz        NA     58765