在 R 中将数据从长格式转换为宽格式时出现问题
[英]Problem when reshaping data from long to wide format in R
问题描述
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。 我很感激你们帮助我找出它表现的原因。
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“重塑”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。
2 个解决方案
解决方案1
5 已采纳 2020-02-05 17:05:29
使用stats::reshape重塑数据可能很乏味。 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。 首先是reshape2包，然后tidyr有spread()和gather() ，这些现在被替换为pivot_wider()和pivot_longer() 。
这就是您如何使用tidyr::pivot_wider()来实现结果，您似乎想要。
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，与stats::reshape()相比，具有用于两个转换（宽/长）的专用命令和专用文档使tidyr命令更易于使用。
编辑： stats::reshape()是给奇怪的结果，因为它似乎有应付my_df是一个问题tibble 。 除此之外，你的命令很好。 只需添加一个as.data.frame()就可以了。
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
但由于您似乎已经在使用 tidyverse tidyr::pivot_wider()似乎是最合适的。
解决方案2
2 2020-02-05 17:21:51
使用stats::reshape
# `idvar` has 1 value here
reshape(my_df, idvar="transcript", timevar="hours", v.names="exp.change", direction="wide")
transcript response exp.change.0 exp.change.2 exp.change.8 exp.change.24 exp.change.48
1 TR100743-c0_g1_i3 Primary NA -43.19583 -6.130140e+01 964.92512 -5.270607e+01
6 TR100987-c0_g1_i2 Primary NA -46.25638 3.293969e+00 -99.99947 1.067105e+08
11 TR101301-c4_g1_i16 Primary NA -29.63413 5.222249e+02 40.47377 -1.343882e+00
16 TR102190-c1_g1_i1 Tertiary NA -18.76708 5.494728e+00 55.10727 3.358246e+01
21 TR102346-c0_g2_i1 Primary NA -99.99996 7.896973e+08 18.63375 5.244430e+01
26 TR102352-c4_g2_i5 Primary NA -31.33341 9.647458e+00 28.48553 7.058088e+01
使用数据data.table
setDT(my_df)
dcast(my_df,transcript~hours, value.var="exp.change")
transcript 0 2 8 24 48
1: TR100743-c0_g1_i3 NA -43.19583 -6.130140e+01 964.92512 -5.270607e+01
2: TR100987-c0_g1_i2 NA -46.25638 3.293969e+00 -99.99947 1.067105e+08
3: TR101301-c4_g1_i16 NA -29.63413 5.222249e+02 40.47377 -1.343882e+00
4: TR102190-c1_g1_i1 NA -18.76708 5.494728e+00 55.10727 3.358246e+01
5: TR102346-c0_g2_i1 NA -99.99996 7.896973e+08 18.63375 5.244430e+01
6: TR102352-c4_g2_i5 NA -31.33341 9.647458e+00 28.48553 7.058088e+01
dcast(my_df,transcript + response ~hours, value.var="exp.change")
transcript response 0 2 8 24 48
1: TR100743-c0_g1_i3 Primary NA -43.19583 -6.130140e+01 964.92512 -5.270607e+01
2: TR100987-c0_g1_i2 Primary NA -46.25638 3.293969e+00 -99.99947 1.067105e+08
3: TR101301-c4_g1_i16 Primary NA -29.63413 5.222249e+02 40.47377 -1.343882e+00
4: TR102190-c1_g1_i1 Tertiary NA -18.76708 5.494728e+00 55.10727 3.358246e+01
5: TR102346-c0_g2_i1 Primary NA -99.99996 7.896973e+08 18.63375 5.244430e+01
6: TR102352-c4_g2_i5 Primary NA -31.33341 9.647458e+00 28.48553 7.058088e+01
您也可以使用旧的reshape2包
reshape2::dcast(my_df,transcript + response ~hours, value.var="exp.change")
transcript response 0 2 8 24 48
1 TR100743-c0_g1_i3 Primary NA -43.19583 -6.130140e+01 964.92512 -5.270607e+01
2 TR100987-c0_g1_i2 Primary NA -46.25638 3.293969e+00 -99.99947 1.067105e+08
3 TR101301-c4_g1_i16 Primary NA -29.63413 5.222249e+02 40.47377 -1.343882e+00
4 TR102190-c1_g1_i1 Tertiary NA -18.76708 5.494728e+00 55.10727 3.358246e+01
5 TR102346-c0_g2_i1 Primary NA -99.99996 7.896973e+08 18.63375 5.244430e+01
6 TR102352-c4_g2_i5 Primary NA -31.33341 9.647458e+00 28.48553 7.058088e+01
在 R 中从长格式重塑为宽格式时出错 - 所有数据均为 NA 且变量名称不正确
[英]Error while reshaping from long format into wide format in R - all data is NA and variable names are incorrect
将HRS数据从宽格式转换为长格式并创建时间变量
[英]Reshaping the HRS data from wide to long format and creating a time variable
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