r - 如何将此垂直数据集转换为水平数据集？

Question

我想改变这个数据集：

        id PTMIINDT PTMIINTM DGOTDIAG DGOTDGGB
1: ys00000001 20160101      614     R060        1
2: ys00000002 20160101      640    S0090        1
3: ys00000002 20160101      640     A090        2
4: ys00000003 20160101      959      R42        1
5: ys00000007 20160101     1111    S0600        1
6: ys00000008 20160101     1253     R558        1

到这个数据集：

         id     PTMIINDT PTMIINTM DGOTDIAG01 DGOTDGGB01 DGOTDIAG02  DGOTDGGB02
1 ys00000001      20160101     614      R060          1         NA          NA
2 ys00000002      20160101     640     S0090          1       A090           2
.     .               .         .
.     .               .         .
.     .               .         .

像这样。

我试图用 mutate 函数制作这个数据集。但效果不佳。我怎样才能改变这样的数据集？

ba<-n6 %>% group_by(id,PTMIINDT,PTMIINTM) %>% 
  mutate(DGOTDIAG01=DGOTDIAG, DGOTDIAG02=DGOTDIAG, DGOTDGGB01=DGOTDGGB,DGOTDGGB02=DGOTDGGB)


ba<-n6 %>% group_by(id,PTMIINDT,PTMIINTM) %>% 
  mutate(DGOTDIAG01=DGOTDIAG, DGOTDIAG02=DGOTDIAG, DGOTDGGB01=DGOTDGGB,DGOTDGGB02=DGOTDGGB)

         id     PTMIINDT PTMIINTM DGOTDIAG01 DGOTDGGB01 DGOTDIAG02  DGOTDGGB02
1 ys00000001      20160101     614      R060          1         NA          NA
2 ys00000002      20160101     640     S0090          1       A090           2
.     .               .         .
.     .               .         .
.     .               .         .

Answer 1

可以使用data.table::dcast()以下单线来完成此操作。

library(data.table)

样本数据

dt <- data.table::fread("id PTMIINDT PTMIINTM DGOTDIAG DGOTDGGB
ys00000001 20160101      614     R060        1
ys00000002 20160101      640    S0090        1
ys00000002 20160101      640     A090        2
ys00000003 20160101      959      R42        1
ys00000007 20160101     1111    S0600        1
ys00000008 20160101     1253     R558        1")

代码

data.table::dcast( dt, id + PTMIINDT + PTMIINTM ~ DGOTDGGB, value.var = c("DGOTDIAG", "DGOTDGGB") )

输出

#            id PTMIINDT PTMIINTM DGOTDIAG_1 DGOTDIAG_2 DGOTDGGB.1_1 DGOTDGGB.1_2
# 1: ys00000001 20160101      614       R060       <NA>            1           NA
# 2: ys00000002 20160101      640      S0090       A090            1            2
# 3: ys00000003 20160101      959        R42       <NA>            1           NA
# 4: ys00000007 20160101     1111      S0600       <NA>            1           NA
# 5: ys00000008 20160101     1253       R558       <NA>            1           NA

Answer 2

使用最新版本的tidyr( 1.0.0，已经在 CRAN 上)：

library(tidyr)
library(dplyr)
n6 %>% 
    group_by(id) %>% 
    dplyr::mutate(sbs = row_number()) %>% 
    pivot_wider(names_from = sbs, values_from = c(DGOTDIAG,DGOTDGGB))

# A tibble: 5 x 7
# Groups:   id [5]
  id         PTMIINDT PTMIINTM DGOTDIAG_1 DGOTDIAG_2 DGOTDGGB_1 DGOTDGGB_2
  <fct>         <dbl>    <dbl> <fct>      <fct>           <dbl>      <dbl>
1 ys00000001 20160101      614 R060       NA                  1         NA
2 ys00000002 20160101      640 S0090      A090                1          2
3 ys00000003 20160101      959 R42        NA                  1         NA
4 ys00000007 20160101     1111 S0600      NA                  1         NA
5 ys00000008 20160101     1253 R558       NA                  1         NA

Answer 3

的开发版本tidyr具有一个新的动词，pivot_wider更适合这项任务。

https://tidyr.tidyverse.org/dev/articles/pivot.html

同时，您可以收集、转换和传播：

n6 %>%
  gather(column, value, -c(id, PTMIINDT, PTMIINTM)) %>%
  group_by(id, PTMIINDT, PTMIINTM) %>%
  mutate(column = paste0(column, 
                         stringr::str_pad(row_number(), width = 2, pad = 0))) %>%
  spread(column, value)

# A tibble: 5 x 8
# Groups:   id, PTMIINDT, PTMIINTM [5]
  id         PTMIINDT PTMIINTM DGOTDGGB02 DGOTDGGB03 DGOTDGGB04 DGOTDIAG01 DGOTDIAG02
  <chr>         <int>    <int> <chr>      <chr>      <chr>      <chr>      <chr>     
1 ys00000001 20160101      614 1          NA         NA         R060       NA        
2 ys00000002 20160101      640 NA         1          2          S0090      A090      
3 ys00000003 20160101      959 1          NA         NA         R42        NA        
4 ys00000007 20160101     1111 1          NA         NA         S0600      NA        
5 ys00000008 20160101     1253 1          NA         NA         R558       NA