R中合并频数表分组计数的报错排查及通用实现方案问询

我在R中创建了如下年度样本频数表：

1975  1976  1980   1995  2017  2018 
2      67    44    126   64    133

需要将部分年份的计数合并，目标结果如下：

Eighties   Nineties  Two Thousands
2+67+44      126        64+133

嵌套ifelse函数 #

模仿Excel逻辑写的嵌套ifelse代码：

a$decade -> ifelse(a$year=1975 | a$year=1976 |a$year=1980,"Eighties",
                   ifelse(a$year=1995,"Ninties",
                          ifelse(a$year=2017|a$year=2018,"Two Thousands","Error")))

运行后报错：

Error: unexpected '=' in "a$decade -> ifelse(a$year="E
>                    ifelse(a$year=1995,"Ninties",
Error: unexpected '=' in "                   ifelse(a$year="
>                           ifelse(a$year=2017|a$year=2018,"Two Thousands","Error")))
Error: unexpected '=' in "                          ifelse(a$year="
> 

错误的多条件ifelse写法 #

随后尝试的代码同样报错：

a$decade <- ifelse(a$year %in% (c("1975", "1976","1980"), "Eighties", (c("1995"),"Nineties"),(c("2017", "2018"),"2000s"))

dplyr的case_when分组求和 #

最后尝试用dplyr分组求和：

a %>%
  mutate(decade = case_when(
    year %in% c(1975, 1976, 1980) ~ "Eighties",
    year == 1995 ~ "Nineties",
    year %in% c(2017, 2018) ~ "Two Thousands"
  )) %>%
  group_by(decade) %>%
  summarize(count = sum(count)) %>%
  ungroup()

报错信息：

Error: Problem with `summarise()` column `count`.
i `count = sum(count)`.
x invalid 'type' (closure) of argument
i The error occurred in group 1: decade = "Eighties".

注：该逻辑需复用至其他场景，如月份分季节、日期分工作日/周末、采样点分方位等，希望得到通用方案。

各报错原因解析 #

1. 嵌套ifelse报错：R中判断相等必须用==，而非=（=在函数参数中是赋值作用）；同时存在拼写错误Ninties，应为Nineties。
2. 多条件ifelse报错：ifelse()仅接受3个参数（条件、满足值、不满足值），无法直接传入多组(条件,值)对，语法逻辑错误。
3. dplyr代码报错：数据框中没有名为count的列，R将count识别为dplyr的count()函数（类型为closure），导致求和参数类型错误。

修正后的代码及通用方案 #

首先确保数据框结构正确，示例数据：

a <- data.frame(
  year = c(1975, 1976, 1980, 1995, 2017, 2018),
  freq = c(2, 67, 44, 126, 64, 133)
)

方案1：修正嵌套ifelse（适合简单规则） #

a$decade <- ifelse(a$year == 1975 | a$year == 1976 | a$year == 1980, "Eighties",
                   ifelse(a$year == 1995, "Nineties",
                          ifelse(a$year == 2017 | a$year == 2018, "Two Thousands", "Error")))

# 分组求和
aggregate(freq ~ decade, data = a, sum)

方案2：dplyr case_when（适合直观的多条件规则） #

library(dplyr)

a %>%
  mutate(decade = case_when(
    year %in% c(1975, 1976, 1980) ~ "Eighties",
    year == 1995 ~ "Nineties",
    year %in% c(2017, 2018) ~ "Two Thousands",
    TRUE ~ "Unclassified" # 兜底处理未匹配项
  )) %>%
  group_by(decade) %>%
  summarise(total = sum(freq)) %>%
  ungroup()

方案3：映射表匹配（最通用，适合复用至其他场景） #

通过定义分组映射表，可以快速将规则复用至月份分季节、采样点分方位等场景：

# 定义年度分组映射表
group_mapping <- data.frame(
  year = c(1975,1976,1980,1995,2017,2018),
  decade = c(rep("Eighties",3), "Nineties", rep("Two Thousands",2))
)

# 示例：月份分季节的映射表（直接替换即可复用）
# season_mapping <- data.frame(
#   month = 1:12,
#   season = c(rep("Winter",2), rep("Spring",3), rep("Summer",3), rep("Autumn",3), "Winter")
# )

# 合并分组并求和
a %>%
  left_join(group_mapping, by = "year") %>%
  group_by(decade) %>%
  summarise(total = sum(freq)) %>%
  ungroup()

方案4：factor重编码（适合固定值的分组） #

a$decade <- factor(a$year,
                   levels = c(1975,1976,1980,1995,2017,2018),
                   labels = c(rep("Eighties",3), "Nineties", rep("Two Thousands",2)))

a %>%
  group_by(decade) %>%
  summarise(total = sum(freq)) %>%
  ungroup()

内容的提问来源于stack exchange，提问作者creusac