在dplyr mutate中自定义case_when函数报错：找不到对象'WK1'

问题场景 #

现有随NFL赛季推进每周新增列的数据集，尝试基于当前周数自定义average16函数，在dplyr::mutate中调用时始终返回! object 'WK1' not found错误。

示例数据（第二周） #

library(dplyr)
library(stringr)

NumCorrect <- data.frame(
  TEAM = c(str_c("team_", 1:5)),
  WK1 = c(11,12,11,12,13),
  WK2 = c(7,7,9,10,7)
)

报错代码 #

# 定义当前周数
curWEEK = 2

# 缺失数据来源的mutate调用
correct_d <- mutate(AVG_16 = average16(curWEEK))

# 自定义函数
average16 <- function(x) {case_when(x == 1 ~ WK1,
                                    x == 2 ~ round(mean((WK1:WK2), na.rm=TRUE),1),
                                    x == 3 ~ round(mean((WK1:WK3), na.rm=TRUE),1),
                                    x %in% 4:7 ~ round(mean((WK1:WK4), na.rm=TRUE),1),
                                    x %in% 8:11 ~ round(mean(c(WK1:WK4,WK8), na.rm=TRUE),1),
                                    x %in% 12:14 ~ round(mean(c(WK1:WK4,WK8,WK12), na.rm=TRUE),1),
                                    x == 15 ~ round(mean(c(WK1:WK4,WK8,WK12,WK15), na.rm=TRUE),1),
                                    x == 16 ~ round(mean(c(WK1:WK4,WK8,WK12,WK15:WK16), na.rm=TRUE),1),
                                    x == 17 ~ round(mean(c(WK1:WK4,WK8,WK12,WK15:WK17), na.rm=TRUE),1),
                                    x == 18 ~ round(mean(c(WK1:WK4,WK8,WK12,WK15:WK18), na.rm=TRUE),1)
                                    )}

错误信息 #

Error in `mutate()`:
ℹ In argument: `AVG_16 = average16(curWEEK)`.
ℹ In row 1.
Caused by error in `case_when()`:
! Failed to evaluate the right-hand side of formula 1.
Caused by error:
! object 'WK1' not found

错误原因 #

1. mutate调用缺失数据上下文：原代码中mutate未指定处理的数据框，函数无法定位WK1等列。
2. 函数未绑定数据环境：自定义函数直接引用列名，但函数自身环境中无这些列的定义，需传入数据框或利用tidyeval语法识别数据内的列。
3. 列引用逻辑错误：WK1:WK2是生成数值序列，而非选择数据框中的列，应改用列选择或合并列向量的方式。

解决方案 #

方法1：修改函数接收数据参数 #

调整函数结构，明确传入数据框和当前周数，动态生成列名并计算行均值：

average16 <- function(data, x) {
  case_when(
    x == 1 ~ data$WK1,
    x == 2 ~ round(rowMeans(data[, paste0("WK", 1:2)], na.rm = TRUE), 1),
    x == 3 ~ round(rowMeans(data[, paste0("WK", 1:3)], na.rm = TRUE), 1),
    x %in% 4:7 ~ round(rowMeans(data[, paste0("WK", 1:4)], na.rm = TRUE), 1),
    x %in% 8:11 ~ round(rowMeans(data[, c(paste0("WK", 1:4), "WK8")], na.rm = TRUE), 1),
    x %in% 12:14 ~ round(rowMeans(data[, c(paste0("WK", 1:4), "WK8", "WK12")], na.rm = TRUE), 1),
    x == 15 ~ round(rowMeans(data[, c(paste0("WK", 1:4), "WK8", "WK12", "WK15")], na.rm = TRUE), 1),
    x == 16 ~ round(rowMeans(data[, c(paste0("WK", 1:4), "WK8", "WK12", paste0("WK", 15:16))], na.rm = TRUE), 1),
    x == 17 ~ round(rowMeans(data[, c(paste0("WK", 1:4), "WK8", "WK12", paste0("WK", 15:17))], na.rm = TRUE), 1),
    x == 18 ~ round(rowMeans(data[, c(paste0("WK", 1:4), "WK8", "WK12", paste0("WK", 15:18))], na.rm = TRUE), 1)
  )
}

# 正确调用mutate，传递数据框上下文
correct_d <- NumCorrect %>% 
  mutate(AVG_16 = average16(., curWEEK))

# 查看结果
correct_d

方法2：用tidyeval语法适配动态列 #

利用dplyr的选择函数，灵活匹配赛季新增的列，避免硬编码：

average16 <- function(x) {
  case_when(
    x == 1 ~ pull(WK1),
    x == 2 ~ round(rowMeans(select(starts_with("WK") & matches(paste0("WK", 1:2))), na.rm = TRUE), 1),
    x == 3 ~ round(rowMeans(select(starts_with("WK") & matches(paste0("WK", 1:3))), na.rm = TRUE), 1),
    x %in% 4:7 ~ round(rowMeans(select(starts_with("WK") & matches(paste0("WK", 1:4))), na.rm = TRUE), 1),
    x %in% 8:11 ~ round(rowMeans(select(starts_with("WK") & matches(paste0("WK", c(1:4,8)))), na.rm = TRUE), 1),
    x %in% 12:14 ~ round(rowMeans(select(starts_with("WK") & matches(paste0("WK", c(1:4,8,12)))), na.rm = TRUE), 1),
    x == 15 ~ round(rowMeans(select(starts_with("WK") & matches(paste0("WK", c(1:4,8,12,15)))), na.rm = TRUE), 1),
    x == 16 ~ round(rowMeans(select(starts_with("WK") & matches(paste0("WK", c(1:4,8,12,15:16)))), na.rm = TRUE), 1),
    x == 17 ~ round(rowMeans(select(starts_with("WK") & matches(paste0("WK", c(1:4,8,12,15:17)))), na.rm = TRUE), 1),
    x == 18 ~ round(rowMeans(select(starts_with("WK") & matches(paste0("WK", c(1:4,8,12,15:18)))), na.rm = TRUE), 1)
  )
}

# 调用方式
correct_d <- NumCorrect %>% 
  mutate(AVG_16 = average16(curWEEK))

关键注意点 #

• 必须通过管道%>%给mutate传递数据框，确保函数能获取列的上下文。
• 使用rowMeans而非mean，因为需要对每行的多列计算平均值，mean会计算整个向量的全局均值。
• 用paste0("WK", 1:2)动态生成列名，适配赛季推进后新增的列，避免重复硬编码。

内容的提问来源于stack exchange，提问作者D_Bugli