r - 在 R 中将数据从长格式改成宽格式时出现问题

Question

检查不同的线程，我从 stats 包中了解了 reshape 函数，我在“虚拟”数据集上使用它没有问题，并设法将其从长数据集转换为宽数据集。但是，我不知道为什么它不适用于我的数据，它几乎是相同的对象，数据类型相似。我很感激你们帮我弄清楚它表现的原因。

无论如何，这没有问题:

> df <- data.frame(
  +     year   = c(rep(2000, 12), rep(2001, 12)),
  +     month  = rep(1:12, 2),
  +     values = rnorm(24)
  + )

# year    month    values
1  2000     1    1.52435428
2  2000     2   -0.89394797
3  2000     3    0.75965499
4  2000     4    1.21497443

转换为宽:

df_wide <- reshape(df, idvar="year", timevar="month", v.names="values", direction="wide")

# year    values_1  values_2  values_3  values_4   values_5    values_6   values_7 values_8  values_9  values_10 values_11  values_12
1  2000 1.524354 -0.893948  0.759655 1.2149744 -1.3237634 -0.08681768  0.5208436 -0.2602807 0.6378904 -0.9852600 -1.128048 -0.1466028
2  2001 1.913969 -1.966720 -0.947688 0.8375891 -0.1015944  1.11812723 -1.5164472 -0.7089485 0.5975851  0.2514546 -1.578210 -0.9044418

但是当使用我的数据时，它看起来像这样:

my_df <- dput(head(experiment, 30))

structure(list(transcript = c("TR100743-c0_g1_i3", "TR100743-c0_g1_i3", 
"TR100743-c0_g1_i3", "TR100743-c0_g1_i3", "TR100743-c0_g1_i3", 
"TR100987-c0_g1_i2", "TR100987-c0_g1_i2", "TR100987-c0_g1_i2", 
"TR100987-c0_g1_i2", "TR100987-c0_g1_i2", "TR101301-c4_g1_i16", 
"TR101301-c4_g1_i16", "TR101301-c4_g1_i16", "TR101301-c4_g1_i16", 
"TR101301-c4_g1_i16", "TR102190-c1_g1_i1", "TR102190-c1_g1_i1", 
"TR102190-c1_g1_i1", "TR102190-c1_g1_i1", "TR102190-c1_g1_i1", 
"TR102346-c0_g2_i1", "TR102346-c0_g2_i1", "TR102346-c0_g2_i1", 
"TR102346-c0_g2_i1", "TR102346-c0_g2_i1", "TR102352-c4_g2_i5", 
"TR102352-c4_g2_i5", "TR102352-c4_g2_i5", "TR102352-c4_g2_i5", 
"TR102352-c4_g2_i5"), hours = c(0, 2, 8, 24, 48, 0, 2, 8, 24, 
48, 0, 2, 8, 24, 48, 0, 2, 8, 24, 48, 0, 2, 8, 24, 48, 0, 2, 
8, 24, 48), exp.change = c(NA, -43.1958273184645, -61.3014008509066, 
964.925115099619, -52.7060728326392, NA, -46.2563848585369, 3.29396898799807, 
-99.9994681489801, 106710484.025972, NA, -29.6341333478577, 522.224859380388, 
40.4737694947169, -1.34388206141046, NA, -18.7670826937756, 5.49472822880452, 
55.1072690537026, 33.5824607349752, NA, -99.999962131178, 789697313.24393, 
18.6337471833012, 52.4442959208125, NA, -31.3334122297108, 9.64745757892995, 
28.48552519881, 70.5808772231999), response = c("Primary", "Primary", 
"Primary", "Primary", "Primary", "Primary", "Primary", "Primary", 
"Primary", "Primary", "Primary", "Primary", "Primary", "Primary", 
"Primary", "Tertiary", "Tertiary", "Tertiary", "Tertiary", "Tertiary", 
"Primary", "Primary", "Primary", "Primary", "Primary", "Primary", 
"Primary", "Primary", "Primary", "Primary")), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -30L))


# transcript        hours   exp.change response   
 1 TR100743-c0_g1_i3     0        NA    Primary 
 2 TR100743-c0_g1_i3     2       -43.2  Primary 
 3 TR100743-c0_g1_i3     8       -61.3  Primary 
 4 TR100743-c0_g1_i3    24       965.   Primary 
 5 TR100743-c0_g1_i3    48       -52.7  Primary 
 6 TR100987-c0_g1_i2     0        NA    Primary 
 7 TR100987-c0_g1_i2     2       -46.3  Primary 
 8 TR100987-c0_g1_i2     8         3.29 Primary 
 9 TR100987-c0_g1_i2    24      -100.0  Primary 
10 TR100987-c0_g1_i2    48 106710484.   Primary 

当我尝试“ reshape ”它时给出这个:

my_df_wide <- reshape(my_df, idvar = c("transcript", "response"), timevar = "hours", v.names="exp.change", direction = "wide")

# transcript         response  `exp.change.c(0, 2, 8, 24, 48)`
 1 TR100743-c0_g1_i3  Primary                                NA
 2 TR100987-c0_g1_i2  Primary                                NA
 3 TR101301-c4_g1_i16 Primary                                NA
 4 TR102190-c1_g1_i1  Tertiary                               NA
 5 TR102346-c0_g2_i1  Primary                                NA
 6 TR102352-c4_g2_i5  Primary                                NA
 7 TR10396-c0_g1_i6   Primary                                NA
 8 TR11844-c0_g2_i1   Secondary                              NA
 9 TR12672-c1_g2_i1   Primary                                NA
10 TR12672-c1_g2_i2   Primary                                NA

是因为NA吗？老实说，我不知道为什么它会这样……非常感谢任何帮助。

Answer 1

使用 stats::reshape reshape 数据可能很乏味。哈德利·威克姆和
他的团队花了相当多的时间来创建一个全面的解决方案。
首先是 reshape2包，然后 tidyr有 spread()和 gather() ,
这些现在被替换为 pivot_wider()和 pivot_longer() .
这是您可以使用的方式 tidyr::pivot_wider()为了达到这个结果，你似乎
要去。

library(tidyr)
pivot_wider(
  my_df,
  id_cols = c(transcript, response),
  names_from = hours,
  values_from = exp.change,
  names_prefix = "exp.change_"
)
#> # A tibble: 6 x 7
#>   transcript response exp.change_0 exp.change_2 exp.change_8 exp.change_24
#>   <chr>      <chr>           <dbl>        <dbl>        <dbl>         <dbl>
#> 1 TR100743-… Primary            NA        -43.2       -61.3          965. 
#> 2 TR100987-… Primary            NA        -46.3         3.29        -100. 
#> 3 TR101301-… Primary            NA        -29.6       522.            40.5
#> 4 TR102190-… Tertiary           NA        -18.8         5.49          55.1
#> 5 TR102346-… Primary            NA       -100.  789697313.            18.6
#> 6 TR102352-… Primary            NA        -31.3         9.65          28.5
#> # … with 1 more variable: exp.change_48 <dbl>

我认为有专门的命令和两个转换(宽/长)的专用文档使得 tidyr与 stats::reshape() 相比，命令更易于使用.
编辑: stats::reshape()给出了奇怪的结果，因为它似乎在处理 my_df 作为 tibble 时遇到问题.除此之外，你的命令很好。只需添加 as.data.frame()你很高兴去。

reshape(
  as.data.frame(my_df),
  idvar = c("transcript", "response"),
  timevar   = "hours",
  v.names = "exp.change",
  direction = "wide"
)
#>            transcript response exp.change.0 exp.change.2  exp.change.8
#> 1   TR100743-c0_g1_i3  Primary           NA    -43.19583 -6.130140e+01
#> 6   TR100987-c0_g1_i2  Primary           NA    -46.25638  3.293969e+00
#> 11 TR101301-c4_g1_i16  Primary           NA    -29.63413  5.222249e+02
#> 16  TR102190-c1_g1_i1 Tertiary           NA    -18.76708  5.494728e+00
#> 21  TR102346-c0_g2_i1  Primary           NA    -99.99996  7.896973e+08
#> 26  TR102352-c4_g2_i5  Primary           NA    -31.33341  9.647458e+00
#>    exp.change.24 exp.change.48
#> 1      964.92512 -5.270607e+01
#> 6      -99.99947  1.067105e+08
#> 11      40.47377 -1.343882e+00
#> 16      55.10727  3.358246e+01
#> 21      18.63375  5.244430e+01
#> 26      28.48553  7.058088e+01

但由于您似乎已经在使用 tidyverse tidyr::pivot_wider()似乎是最合适的。