r - R 中的 If/Else 语句

Question

我在 R 中有两个数据框:

city         price    bedroom   
San Jose     2000        1          
Barstow      1000        1          
NA           1500        1          

重新创建的代码:

data = data.frame(city = c('San Jose', 'Barstow'), price = c(2000,1000, 1500), bedroom = c(1,1,1))

和:

Name       Density
San Jose    5358
Barstow      547

重新创建的代码:

population_density = data.frame(Name=c('San Jose', 'Barstow'), Density=c(5358, 547));

我想根据条件在 data 数据集中创建一个名为 city_type 的附加列，因此如果城市人口密度高于 1000，则为城市，低于1000是郊区，NA是NA。

city         price    bedroom   city_type   
San Jose     2000        1        Urban
Barstow      1000        1        Suburb
NA           1500        1          NA

我正在为条件流使用 for 循环:

for (row in 1:length(data)) {
    if (is.na(data[row,'city'])) {
        data[row, 'city_type'] = NA
    } else if (population[population$Name == data[row,'city'],]$Density>=1000) {
        data[row, 'city_type'] = 'Urban'
    } else {
        data[row, 'city_type'] = 'Suburb'
   }
}

for 循环在我有超过 20000 个观察值的原始数据集中运行没有错误；但是，它会产生很多错误的结果(大部分情况下会产生 NA)。

这里出了什么问题，我怎样才能做得更好才能达到我想要的结果？

Answer 1

对于这种类型的连接/过滤/变异工作流程，我已经成为 dplyr 管道的忠实粉丝。所以这是我的建议:

library(dplyr)

# I had to add that extra "NA" there, did you not? Hm...
data <- data.frame(city = c('San Jose', 'Barstow', NA), price = c(2000,1000, 500), bedroom = c(1,1,1))
population <- data.frame(Name=c('San Jose', 'Barstow'), Density=c(5358, 547));

data %>% 
  # join the two dataframes by matching up the city name columns
  left_join(population, by = c("city" = "Name")) %>% 
  # add your new column based on the desired condition  
  mutate(
    city_type = ifelse(Density >= 1000, "Urban", "Suburb")
  )

输出:

      city price bedroom Density city_type
1 San Jose  2000       1    5358     Urban
2  Barstow  1000       1     547    Suburb
3     <NA>   500       1      NA      <NA>