Problem when reshaping data from long to wide format in R

Question

Checking a different thread, I learnt about the function reshape from the stats package, I had no problem using it on a 'dummy' dataset, and managed to convert it from a long to a wide dataset. However, I don't know why it is not working on my data, it is pretty much the same object, the data types are similar. I'd appreciate you guys help me figure out the reason its behaving like it is.

Anyway, this gives no trouble:

> 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

Converted to wide:

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

But when using my data, which looks like this:

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 

Gives this when I attempt to 'reshape' it:

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

Is it because of the NAs? I honestly don't know why it is behaving like that... any help is heavily appreciated.

Answer 1

使用 重塑数据stats::reshape可能很乏味。Hadley Wickham 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。首先出现的是reshape2包，然后tidyr不得不spread()和gather()那些现在换下辅之以pivot_wider()和pivot_longer()。

这就是您可以tidyr::pivot_wider()用来实现结果的方式，您似乎正在寻求。

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tidyr，与stats::reshape().

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. 除此之外，你的命令很好。只需添加一个as.data.frame()，你就可以开始了。

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

但是因为您似乎已经在使用 tidyversetidyr::pivot_wider()似乎是最合适的。