如何基于DataFrame现有数据为每个国家新增z-x类型计算行

现有如下R数据集：

df <- data.frame(country = c("A", "A", "B", "B", "C", "C"),
                 type = c("x", "z", "x", "z", "x", "z"),
                 value = c(4, 2, 6, 10, 54, 10))

数据预览：

country type value
1       A    x     4
2       A    z     2
3       B    x     6
4       B    z    10
5       C    x    54
6       C    z    10

需要为每个国家（A、B、C）新增一行数据，该行的type为"z-x"，value为对应国家z类型与x类型的value差值，最终得到如下结果：

result <- data.frame(country = c("A", "A", "A", "B", "B", "B", "C", "C", "C"),
                     type = c("x", "z", "z-x", "x", "z", "z-x", "x", "z", "z-x"),
                     value = c(4, 2, -2, 6, 10, 4, 54, 10, -44))

结果预览：

country type value
1       A    x     4
2       A    z     2
3       A  z-x    -2
4       B    x     6
5       B    z    10
6       B  z-x     4
7       C    x    54
8       C    z    10
9       C  z-x   -44

方法1：使用dplyr包（tidyverse风格） #

先确保安装并加载dplyr包，通过分组计算差值后合并原数据：

library(dplyr)

df_result <- df %>%
  group_by(country) %>%
  summarise(
    type = "z-x",
    value = value[type == "z"] - value[type == "x"],
    .groups = "drop"
  ) %>%
  bind_rows(df) %>%
  arrange(country, factor(type, levels = c("x", "z", "z-x")))

print(df_result)

方法2：基础R实现 #

通过循环处理每个国家，计算差值并构建新行后合并：

# 获取所有唯一国家
countries <- unique(df$country)
# 初始化存储差值行的列表
diff_rows <- list()

for (cntry in countries) {
  # 筛选当前国家的数据
  cntry_data <- df[df$country == cntry, ]
  # 计算z-x的差值
  diff_val <- cntry_data$value[cntry_data$type == "z"] - cntry_data$value[cntry_data$type == "x"]
  # 构建新行
  diff_rows[[cntry]] <- data.frame(country = cntry, type = "z-x", value = diff_val)
}

# 合并原数据和差值行，再排序
df_result <- rbind(df, do.call(rbind, diff_rows))
df_result <- df_result[order(df_result$country, match(df_result$type, c("x", "z", "z-x"))), ]
# 重置行名
row.names(df_result) <- NULL

print(df_result)

内容的提问来源于stack exchange，提问作者Pedro Cardoso