If/Else statement in R

Question

I have two dataframes in R:

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

Code to recreate:

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

and:

Name       Density
San Jose    5358
Barstow      547

Code to recreate:

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

I want to create an additional column named city_type in the data dataset based on condition, so if the city population density is above 1000, it's an urban, lower than 1000 is a suburb, and NA is NA.

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

I am using a for loop for conditional flow:

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'
   }
}

The for loop runs with no error in my original dataset with over 20000 observations; however, it yields a lot of wrong results (it yields NA for the most part).

What has gone wrong here and how can I do better to achieve my desired result?

Answer 1

I have become quite a fan of dplyr pipelines for this type of join/filter/mutate workflow. So here is my suggestion:

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")
  )

Output:

      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

Using ifelse create the city_type in population_density, then we using 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>