用两行变量名重塑/融化数据答案

【问题标题】：Reshape/Melt data with two rows of variable names用两行变量名重塑/融化数据
【发布时间】：2021-05-05 21:04:09
【问题描述】：

我有一个需要重塑/融化的大型数据集。我遇到的问题是第一行和第二行是变量名（即，第一行是一个人的 id，第二行列出了这个人的四个属性）。除此之外，第一列捕获收集值的日期。要查看我的数据集示例，请查看以下 google 表 (https://docs.google.com/spreadsheets/d/19b_4hB6aM9JXReG67i9EF_sJVDHkNFHf4iShH8yrfOc/edit?usp=sharing) 中的 Sheet1。我想重塑我的数据，使其看起来像上面 google 表中的 Sheet2。

有没有办法指定我的 melt 命令中有两个 ID。第一个 id 是第一行（从第二列开始），第二个 id 是第一列（从第二行开始）。如果我可以为我正在尝试做的事情编写一个伪 R 脚本，我会写这样的东西。

melt(dt, id=c("Dates from A2:A6", "Person from B1:I1")

谢谢！

【问题讨论】：

标签： r reshape2 melt

【解决方案1】：

以下方法将完成您的工作，

library(tidyverse)
name <- readr::read_csv("csvs1.csv", col_names = F, n_max = 2)

name
# A tibble: 2 x 9
  X1    X2       X3       X4       X5       X6       X7       X8       X9      
  <chr> <chr>    <chr>    <chr>    <chr>    <chr>    <chr>    <chr>    <chr>   
1 NA    person_A person_A person_A person_A person_B person_B person_B person_B
2 Dates var1     var2     var3     var4     var1     var2     var3     var4    

nm <- paste(names[1,], names[2,], sep = "-")
nm
[1] "NA-Dates"      "person_A-var1" "person_A-var2" "person_A-var3" "person_A-var4" "person_B-var1" "person_B-var2"
[8] "person_B-var3" "person_B-var4"

data <- readr::read_csv("csvs1.csv", col_names = F, skip = 2)

names(data) <- nm
data
# A tibble: 4 x 9
  `NA-Dates` `person_A-var1` `person_A-var2` `person_A-var3` `person_A-var4` `person_B-var1` `person_B-var2`
  <date>               <dbl>           <dbl>           <dbl>           <dbl>           <dbl>           <dbl>
1 2021-05-01            45.0            43.9            67.9            35.7            40.5            38.6
2 2021-05-02            50.4            52.6            67.9            41.9            41.7            43.9
3 2021-05-03            53.7            49.1            67.9            49.6            41.3            38.6
4 2021-05-04            53.7            52.6            73.2            45.7            29.8            33.3
# ... with 2 more variables: person_B-var3 <dbl>, person_B-var4 <dbl>

data %>% 
+   pivot_longer(!`NA-Dates`, names_to = c('person', '.value'), names_sep = '-', names_prefix = 'person_')

# A tibble: 8 x 6
  `NA-Dates` person  var1  var2  var3  var4
  <date>     <chr>  <dbl> <dbl> <dbl> <dbl>
1 2021-05-01 A       45.0  43.9  67.9  35.7
2 2021-05-01 B       40.5  38.6  60.7  32.6
3 2021-05-02 A       50.4  52.6  67.9  41.9
4 2021-05-02 B       41.7  43.9  42.9  40.3
5 2021-05-03 A       53.7  49.1  67.9  49.6
6 2021-05-03 B       41.3  38.6  48.2  39.5
7 2021-05-04 A       53.7  52.6  73.2  45.7
8 2021-05-04 B       29.8  33.3  57.1  16.3

【讨论】：

谢谢！你能帮我理解为什么你需要否定日期变量吗？我只是想了解您为什么需要！在NA-Dates前面
因为除NA-dates 之外的所有列都将被旋转。因此，我没有写所有要旋转的列的名称，而是简单地使用不旋转的列的名称。

【解决方案2】：

为方便起见，这是一个使用readr::type_convert 的data.table 方法：

df <- structure(list(
    person_A = c("var1", "45.0413", "50.4132", "53.719", "53.719"),
    person_A = c("var2", "43.8596", "52.6316", "49.1228", "52.6316"),
    person_A = c("var3", "67.8571", "67.8571", "67.8571", "73.2143"),
    person_A = c("var4", "35.6589", "41.8605", "49.6124", "45.7364"),
    person_B = c("var1", "40.4959", "41.7355", "41.3223", "29.7521"),
    person_B = c("var2", "38.5965", "43.8596", "38.5965", "33.3333"),
    person_B = c("var3", "60.7143", "42.8571", "48.2143", "57.1429"),
    person_B = c("var4", "32.5581", "40.3101", "39.5349", "16.2791")),
    class = "data.frame",
    row.names = c("Dates", "2021-05-01", "2021-05-02", "2021-05-03", "2021-05-04")
    )

library(data.table)

# split data.frame by person
dfl <- split.default(df, sub('\\d+', '', names(df)))

# re-define column labels and types based on first row
dfl <- lapply(dfl, function(x) {
    setnames(readr::type_convert(data.table(x[-1,], 
    keep.rownames = TRUE)), 
    as.character(data.table(x[1,], keep.rownames = TRUE)))})

# combine list elements and reorder columns
setcolorder(rbindlist(dfl, idcol = "Person"), c(2,1))[]
#>         Dates   Person    var1    var2    var3    var4
#> 1: 2021-05-01 person_A 45.0413 43.8596 67.8571 35.6589
#> 2: 2021-05-02 person_A 50.4132 52.6316 67.8571 41.8605
#> 3: 2021-05-03 person_A 53.7190 49.1228 67.8571 49.6124
#> 4: 2021-05-04 person_A 53.7190 52.6316 73.2143 45.7364
#> 5: 2021-05-01 person_B 40.4959 38.5965 60.7143 32.5581
#> 6: 2021-05-02 person_B 41.7355 43.8596 42.8571 40.3101
#> 7: 2021-05-03 person_B 41.3223 38.5965 48.2143 39.5349
#> 8: 2021-05-04 person_B 29.7521 33.3333 57.1429 16.2791

^{由reprex package (v2.0.0) 于 2021-05-05 创建}

【讨论】：

【解决方案3】：

为了使用基本整形，重要的是在一个字符串中组合级别，用一个点分隔它们并重命名变量。

使用上面的 data.frame。

# Get variables names
varA <- as.character(df[1,])
varB <- names(df)
newNames <- paste(varA, varB, sep = '.')  # don't forget the point

# Drop first line, rename variables and pass rownames to a column
df <- df[-1,] 
names(df) <- newNames
df$dates <- row.names(df)    # added after last column

# reshape
lastVar <- ncol(df)-1        # because of rownames in last column
df <- reshape(df, varying = 1:lastVar, direction = 'long')

# and beautify
row.names(df) <- NULL
names(df)[names(df) == 'time'] <- 'person'
df <- df[, -ncol(df)]
df

>        dates   person    var1    var2    var3    var4
> 1 2021-05-01 person_A 45.0413 43.8596 67.8571 35.6589
> 2 2021-05-02 person_A 50.4132 52.6316 67.8571 41.8605
> 3 2021-05-03 person_A  53.719 49.1228 67.8571 49.6124
> 4 2021-05-04 person_A  53.719 52.6316 73.2143 45.7364
> 5 2021-05-01 person_B 40.4959 38.5965 60.7143 32.5581
> 6 2021-05-02 person_B 41.7355 43.8596 42.8571 40.3101
> 7 2021-05-03 person_B 41.3223 38.5965 48.2143 39.5349
> 8 2021-05-04 person_B 29.7521 33.3333 57.1429 16.2791

【讨论】：