R数据重塑：将Nest行值转为列字段以适配Wilcox检验需求

你需要完成的是长表转宽表操作，以下是可直接运行的R代码实现：

1. tidyverse 实现（推荐，代码可读性更高） #

library(tidyverse)

# 你的样本数据
df <- structure(list(BreedingSeason = c("2017-2018", "2018-2019", "2016-2017", 
"2014-2015", "2017-2018", "2017-2018"), Nest = c(0L, 0L, 1L, 1L, 
1L, 1L), HubDist = c(828.4829,3125.2952, 1433.3238, 1539.5785, 
1602.5289, 1431.7102)), row.names = c(NA, 6L), class = "data.frame")

# 转换为目标格式
df_wide <- df %>%
  # 按繁殖季、Nest分组，为同组内的多条数据分配唯一序号，避免转宽时聚合丢失数据
  group_by(BreedingSeason, Nest) %>%
  mutate(row_id = row_number()) %>%
  ungroup() %>%
  # 长转宽，Nest取值作为列名，对应值为HubDist
  pivot_wider(
    id_cols = c(BreedingSeason, row_id),
    names_from = Nest,
    values_from = HubDist
  ) %>%
  # 重命名繁殖季列，删除辅助序号列
  rename(BreedingS = BreedingSeason) %>%
  select(-row_id)

转换后的数据结构如下：

2. 纯base R实现（无需加载第三方包） #

# 你的样本数据
df <- structure(list(BreedingSeason = c("2017-2018", "2018-2019", "2016-2017", 
"2014-2015", "2017-2018", "2017-2018"), Nest = c(0L, 0L, 1L, 1L, 
1L, 1L), HubDist = c(828.4829,3125.2952, 1433.3238, 1539.5785, 
1602.5289, 1431.7102)), row.names = c(NA, 6L), class = "data.frame")

# 生成辅助序号列
df$row_id <- ave(df$HubDist, df$BreedingSeason, df$Nest, FUN = seq_along)
# 长转宽
df_wide <- reshape(df, 
                   idvar = c("BreedingSeason", "row_id"),
                   timevar = "Nest",
                   direction = "wide")
# 调整列名
names(df_wide) <- gsub("HubDist\\.", "", names(df_wide))
names(df_wide)[1] <- "BreedingS"
df_wide$row_id <- NULL

后续使用说明 #

• 做Wilcox检验时，直接提取df_wide$"0"和df_wide$"1"列，去掉NA值即可执行检验
• 需要按繁殖季筛选时，直接对应过滤BreedingS列的取值即可

内容的提问来源于stack exchange，提问作者Burton Guster