Question

我有一个数据帧（df），要分离十个变量（var1，var2，var3，...，var10）。变量的值具有以下形式：value_text。我想应用tidyr :: separate，实际上我做了十次，每个变量一次。

tidyr::separate(col=var1,into=c("value1","text1"),extra="merge")%>%
tidyr::separate(col=var2,into=c("value2","text2"),extra="merge")%>%

...

你知道吗？更优雅＆＃34;使用tidyr的方法::一次分开（不分离10次）？

Answer 1

目前我能想到的最好的事情是这样的：

library(tidyverse)
mydf %>% 
  gather(var, val, everything()) %>%            # Gather all the columns into a key-value pair 
  separate(val, into = c("value", "text")) %>%  # Separate the gathered columns
  gather(key, val, value, text) %>%             # Gather again so you have key-key-value
  unite(cn, var, key) %>%                       # Unite your keys to become the column names
  group_by(cn) %>%                              # Keys are duplicated; we need to know their origins
  mutate(rn = sequence(n())) %>%                # .. so we add row numbers
  spread(cn, val)                               # Then we convert to the wide form
# # A tibble: 5 × 21
#      rn var1_text var1_value var10_text var10_value var2_text var2_value var3_text var3_value var4_text var4_value
# * <int>     <chr>      <chr>      <chr>       <chr>     <chr>      <chr>     <chr>      <chr>     <chr>      <chr>
# 1     1      POIL        235       ZHKV         555      QVWK        479      SKCY        454      YCTY        704
# 2     2      NENB        928       CJLE         956      JXQT        379      HPCV        186      VIQY        764
# 3     3      HFHX        966       PZZE         622      PXHE        261      IUGF        717      YIGE        842
# 4     4      LUWK        021       OJRH         741      XNWE        230      NGEN        486      INYN        003
# 5     5      BISI        637       MEJS         718      TSYO        383      ODGS        755      GBKW        564
# # ... with 10 more variables: var5_text <chr>, var5_value <chr>, var6_text <chr>, var6_value <chr>,
# #   var7_text <chr>, var7_value <chr>, var8_text <chr>, var8_value <chr>, var9_text <chr>, var9_value <chr>
# Warning message:
# attributes are not identical across measure variables; they will be dropped

以下是我使用的示例数据：

library(stringi)
set.seed(1)
mydf <- data.frame(matrix(sprintf("%s_%s", stri_rand_strings(50, 3, "[0-9]"), 
                                  stri_rand_strings(50, 4, "[A-Z]")), ncol = 10,
                          dimnames = list(NULL, paste0("var", 1:10))))
mydf
#       var1     var2     var3     var4     var5     var6     var7     var8     var9    var10
# 1 235_POIL 479_QVWK 454_SKCY 704_YCTY 924_JDJQ 883_TYMP 206_BCJE 214_FDZI 944_DFVS 555_ZHKV
# 2 928_NENB 379_JXQT 186_HPCV 764_VIQY 362_KRRO 794_MCGM 877_HEGE 959_NRCD 174_GMCJ 956_CJLE
# 3 966_HFHX 261_PXHE 717_IUGF 842_YIGE 470_LLHP 733_JYNI 448_MUAN 734_BYRC 522_ZQRI 622_PZZE
# 4 021_LUWK 230_XNWE 486_NGEN 003_INYN 838_XDKF 727_HUSE 663_WJBD 107_MMJZ 550_KZWY 741_OJRH
# 5 637_BISI 383_TSYO 755_ODGS 564_GBKW 334_LDLY 121_BTQE 296_IEDF 146_EVBK 069_VUGT 718_MEJS

注意：这似乎与“tidyverse”套装的作者推荐的完全相反的数据格式。

如上所述，另一种方法是使用我的“splitstackshape”包中的cSplit。有了这个，问题很容易解决如下：

library(splitstackshape)
cSplit(mydf, names(mydf), "_", type.convert = FALSE)
##    var1_1 var1_2 var2_1 var2_2 var3_1 var3_2 var4_1 var4_2 var5_1 var5_2 var6_1 var6_2 var7_1 var7_2 var8_1 var8_2
## 1:    235   POIL    479   QVWK    454   SKCY    704   YCTY    924   JDJQ    883   TYMP    206   BCJE    214   FDZI
## 2:    928   NENB    379   JXQT    186   HPCV    764   VIQY    362   KRRO    794   MCGM    877   HEGE    959   NRCD
## 3:    966   HFHX    261   PXHE    717   IUGF    842   YIGE    470   LLHP    733   JYNI    448   MUAN    734   BYRC
## 4:    021   LUWK    230   XNWE    486   NGEN    003   INYN    838   XDKF    727   HUSE    663   WJBD    107   MMJZ
## 5:    637   BISI    383   TSYO    755   ODGS    564   GBKW    334   LDLY    121   BTQE    296   IEDF    146   EVBK
##    var9_1 var9_2 var10_1 var10_2
## 1:    944   DFVS     555    ZHKV
## 2:    174   GMCJ     956    CJLE
## 3:    522   ZQRI     622    PZZE
## 4:    550   KZWY     741    OJRH
## 5:    069   VUGT     718    MEJS

如果你想坚持使用tidyverse，另一个选择是使用for循环。

for (i in names(mydf)) mydf <- separate_(mydf, i, paste0(i, c("_text", "_value")))

使用tidyr分离多个变量

1 个答案: