根据 dplyr 与另一个数据帧的匹配更改数据帧中的列名称

Question

我有一个数据框，我想通过匹配另一个数据框来更改列名称。

包含数据和列名称的示例数据框：

df <- data.frame("Gene_Symbol" = c("Gene1","Gene2","Gene3","Gene4","Gene5","Gene6","Gene7"), 
             "Sample1" = c(85657.97656,54417.78906,110949.3281,53197.45313,87156.80469,NA,23880.2832), 
             "Sample2" = c(10423.40918,41660.73047,40094.54688,49519.78125,129387.1094,NA,23903.25977), 
             "Sample3" = c(18778.68359,43655.79688,NA,57447.08984,113266.1484,44810.26172,26316.6543), 
             "Sample4" = c(23919.53125,47829.02344,NA,51478.58203,116275.3359,43110.94922,25417.45508), 
             "Sample5" = c(NA,46677.20313,63389.45313,48722.15234,NA,77135.52344,40265.6875), 
             "Sample6" = c(NA,68596.22656,56802.60938,44712.64063,NA,47744.17969,33689.62891), 
             "Sample7" = c(NA,80506.14844,48722.99219,38629.00781,NA,37885,36638.02344))

我想用来从样本交换到上面 df 中的肿瘤编号。

df2 <- data.frame("Sample_name" = c("Sample1","Sample2","Sample3","Sample4","Sample5","Sample6", "Sample7"), "Tumor_name" = c("Tumor56", "Tumor17", "Tumor99", "Tumor2", "Tumor34", "Tumor84", "Tumor51"))

我在dplyr中找到了一种方法，见下文，但感觉很精细。有更容易的方法吗？

library(tidyverse)
df %>% 
  column_to_rownames("Gene_Symbol")%>%                    # Bring Gene_Symbol to row name before transpose
  t()%>%                                                  # Transpose to be able to use join
  data.frame()%>%                                         # Transpose makes a matrix - make dataframe again
  rownames_to_column("Sample_name")%>%                    # Bring sample names to column to use join
  left_join(., df2, by = "Sample_name", copy = TRUE) %>%  # Join by Sample_name column in both data sets
  column_to_rownames("Tumor_name")%>%                     # Bring Tumor names to row name before transpose
  select(-Sample_name)%>%                                 # Drop Sample name column 
  t()%>%                                                  # Transpose 
  data.frame()%>%                                         # Transpose makes a matrix - make dataframe again 
  rownames_to_column("Gene_Symbol")                       # Transfer rownames to column again

匹配交换名称会很好，因为我可以预见我需要对列名称的子集执行此操作。查看了重命名但无法使其工作。另外，当我转置时，我得到一个矩阵，为什么呢？

感谢亨里克的帮助

Answer 1

!!! 这是使用splice 运算符的tidyverse 友好解决方案。

library(tidyverse)

# original data set up from stack overflow -------------------------------------
df <- data.frame("Gene_Symbol" = c("Gene1","Gene2","Gene3","Gene4","Gene5","Gene6","Gene7"), 
                 "Sample1" = c(85657.97656,54417.78906,110949.3281,53197.45313,87156.80469,NA,23880.2832), 
                 "Sample2" = c(10423.40918,41660.73047,40094.54688,49519.78125,129387.1094,NA,23903.25977), 
                 "Sample3" = c(18778.68359,43655.79688,NA,57447.08984,113266.1484,44810.26172,26316.6543), 
                 "Sample4" = c(23919.53125,47829.02344,NA,51478.58203,116275.3359,43110.94922,25417.45508), 
                 "Sample5" = c(NA,46677.20313,63389.45313,48722.15234,NA,77135.52344,40265.6875), 
                 "Sample6" = c(NA,68596.22656,56802.60938,44712.64063,NA,47744.17969,33689.62891), 
                 "Sample7" = c(NA,80506.14844,48722.99219,38629.00781,NA,37885,36638.02344))

df2 <- data.frame(
  "Sample_name" = c("Sample1","Sample2","Sample3","Sample4","Sample5","Sample6", "Sample7"), 
  "Tumor_name" = c("Tumor56", "Tumor17", "Tumor99", "Tumor2", "Tumor34", "Tumor84", "Tumor51")
  )


# create named vector of variable names ----------------------------------------
# values are current variable names, vector names are the new variable names
var_names <- df2 %>% 
  # new variable names, old variable names
  select(Tumor_name, Sample_name) %>% 
  deframe()

var_names
#>   Tumor56   Tumor17   Tumor99    Tumor2   Tumor34   Tumor84   Tumor51 
#> "Sample1" "Sample2" "Sample3" "Sample4" "Sample5" "Sample6" "Sample7"

# rename vaiables---------------------------------------------------------------
df_updated <-  df %>% 
  rename(!!!var_names)

df
#>   Gene_Symbol   Sample1   Sample2   Sample3   Sample4  Sample5  Sample6
#> 1       Gene1  85657.98  10423.41  18778.68  23919.53       NA       NA
#> 2       Gene2  54417.79  41660.73  43655.80  47829.02 46677.20 68596.23
#> 3       Gene3 110949.33  40094.55        NA        NA 63389.45 56802.61
#> 4       Gene4  53197.45  49519.78  57447.09  51478.58 48722.15 44712.64
#> 5       Gene5  87156.80 129387.11 113266.15 116275.34       NA       NA
#> 6       Gene6        NA        NA  44810.26  43110.95 77135.52 47744.18
#> 7       Gene7  23880.28  23903.26  26316.65  25417.46 40265.69 33689.63
#>    Sample7
#> 1       NA
#> 2 80506.15
#> 3 48722.99
#> 4 38629.01
#> 5       NA
#> 6 37885.00
#> 7 36638.02
df_updated
#>   Gene_Symbol   Tumor56   Tumor17   Tumor99    Tumor2  Tumor34  Tumor84
#> 1       Gene1  85657.98  10423.41  18778.68  23919.53       NA       NA
#> 2       Gene2  54417.79  41660.73  43655.80  47829.02 46677.20 68596.23
#> 3       Gene3 110949.33  40094.55        NA        NA 63389.45 56802.61
#> 4       Gene4  53197.45  49519.78  57447.09  51478.58 48722.15 44712.64
#> 5       Gene5  87156.80 129387.11 113266.15 116275.34       NA       NA
#> 6       Gene6        NA        NA  44810.26  43110.95 77135.52 47744.18
#> 7       Gene7  23880.28  23903.26  26316.65  25417.46 40265.69 33689.63
#>    Tumor51
#> 1       NA
#> 2 80506.15
#> 3 48722.99
#> 4 38629.01
#> 5       NA
#> 6 37885.00
#> 7 36638.02

^{由reprex 包于 2022 年 2 月 24 日创建(v2.0.1)}

Answer 2

我们可以将宽整形为长整形，合并，然后再次整形为长整形：

library(dplyr)
library(tidyr)

pivot_longer(df, cols = starts_with("S"), names_to = "Sample_name") %>% 
  left_join(df2, by = "Sample_name") %>% 
  pivot_wider(id_cols = Gene_Symbol, names_from = Tumor_name, values_from = value)

## A tibble: 7 x 8
#  Gene_Symbol Tumor56 Tumor17 Tumor99  Tumor2 Tumor34 Tumor84 Tumor51
#  <chr>         <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#1 Gene1        85658.  10423.  18779.  23920.     NA      NA      NA 
#2 Gene2        54418.  41661.  43656.  47829.  46677.  68596.  80506.
#3 Gene3       110949.  40095.     NA      NA   63389.  56803.  48723.
#4 Gene4        53197.  49520.  57447.  51479.  48722.  44713.  38629.
#5 Gene5        87157. 129387. 113266. 116275.     NA      NA      NA 
#6 Gene6           NA      NA   44810.  43111.  77136.  47744.  37885 
#7 Gene7        23880.  23903.  26317.  25417.  40266.  33690.  36638.