如果在/ R中的/ Else語句

Question

我在R中有兩個數據幀：

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

要重新創建的代碼：

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

要重新創建的代碼：

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

我想根據條件在data集中創建一個名為city_type的附加列，因此如果城市人口密度高於1000，則它是城市，低於1000是郊區，NA是NA。

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

我正在為條件流使用for循環：

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循環在原始數據集中運行時沒有錯誤，觀察次數超過20000; 然而，它產生了許多錯誤的結果（它在大多數情況下產生NA）。

這里出了什么問題，我怎樣才能更好地達到我想要的結果呢？

Answer 1

對於這種類型的連接/過濾/變異工作流程，我已成為dplyr管道的粉絲。 所以這是我的建議：

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")
  )

輸出：

      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>

Answer 2

使用ifelse在population_density創建city_type ，然后我們使用match

population_density$city_type=ifelse(population_density$Density>1000,'Urban','Suburb')
data$city_type=population_density$city_type[match(data$city,population_density$Name)]
data
      city price bedroom city_type
1 San Jose  2000       1     Urban
2  Barstow  1000       1    Suburb
3     <NA>  1500       1      <NA>