通过创建新列来转换数据框

Question

我有一个包含长格式 t 检验结果的数据框，并希望对其进行转换，以便将 p 值信息创建为新列。 我的数据框是

structure(list(region = c("region 1", "region 1", "region 1", 
"region 2", "region 2", "region 2", "region 3", "region 3", "region 3", 
"region 4", "region 4", "region 4", "region 5", "region 5", "region 5", 
"region 6", "region 6", "region 6"), group = c("Male", "Female", 
"p-value", "Male", "Female", "p-value", "Male", "Female", "p-value", 
"Male", "Female", "p-value", "Male", "Female", "p-value", "Male", 
"Female", "p-value"), value = c(1.1, 0.9, 0.001, 0.5, 1.2, 0.612, 
0.9, 0.1, 0.001, 0.9, 0.8, 0.7, 1.3, 0.8, 0.04, 2.3, 1.5, 0.561
)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, 
-18L))

所需的数据框是

structure(list(region = c("region 1", "region 1", "region 2", 
"region 2", "region 3", "region 3", "region 4", "region 4", "region 5", 
"region 5", "region 6", "region 6"), group = c("Male", "Female", 
"Male", "Female", "Male", "Female", "Male", "Female", "Male", 
"Female", "Male", "Female"), value = c(1.1, 0.9, 0.5, 1.2, 0.9, 
0.1, 0.9, 0.8, 1.3, 0.8, 2.3, 1.5), `p-value` = c(0.001, 0.001, 
0.612, 0.612, 0.001, 0.001, 0.7, 0.7, 0.04, 0.04, 0.561, 0.561
)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, 
-12L))

任何帮助将不胜感激。预先感谢！

Answer 1

您可以使用

tidyr

来实现此目的。首先是

pivot_wider()

group

列，然后是

pivot_longer()

。

dat |>
    tidyr::pivot_wider(names_from = group) |>
    tidyr::pivot_longer(
        !c(region, `p-value`),
        names_to = "group"
    ) |>
    # These last two steps are get the same row and column order
    dplyr::relocate(
        region, group, value, `p-value`
    ) |>
    dplyr::arrange(region, desc(group))

# # A tibble: 12 × 4
#    region   group  value `p-value`
#    <chr>    <chr>  <dbl>     <dbl>
#  1 region 1 Male     1.1     0.001
#  2 region 1 Female   0.9     0.001
#  3 region 2 Male     0.5     0.612
#  4 region 2 Female   1.2     0.612
#  5 region 3 Male     0.9     0.001
#  6 region 3 Female   0.1     0.001
#  7 region 4 Male     0.9     0.7  
#  8 region 4 Female   0.8     0.7  
#  9 region 5 Male     1.3     0.04 
# 10 region 5 Female   0.8     0.04 
# 11 region 6 Male     2.3     0.561
# 12 region 6 Female   1.5     0.561