I have this series of nested statements
data$Country == 1,"Brazil",
ifelse(data$Country == 2, "Canada",ifelse(
data$Country == 3, "China",ifelse(
data$Country == 4, "Ecuador",ifelse(
data$Country == 5, "France",ifelse(
data$Country == 6, "Germany",ifelse(
data$Country == 7, "India",ifelse(
data$Country == 8, "Italy",ifelse(
data$Country == 9, "Mexico",ifelse(
data$Country == 10, "Nigeria",ifelse(
data$Country == 11, "Poland",ifelse(
data$Country == 12, "Russia",ifelse(
data$Country == 13, "South Africa",
ifelse(
data$Country == 14, "South Korea",ifelse(
data$Country == 15, "Singapore",
ifelse(
data$Country == 16, "Spain",
ifelse(
data$Country == 17, "Sweden",ifelse(
data$Country == 18, "United Kingdom",ifelse(
data$Country == 19, "United States","l"
))))))))))))))))))))
I was looking for the quickest way to convert any encoded variable into the respective Country name. Do you think is there a way to cope with this operation?
Thank you so much
There are 2 options:
1: case_when from dplyr
library(dplyr)
data.frame(info = letters[1:5],
country_id = 1:5) %>%
mutate(country_name = case_when(country_id == 1 ~ "Brazil",
country_id == 2 ~ "Canada",
country_id == 3 ~ "China",
country_id == 4 ~ "Ecuador",
country_id == 5 ~ "France",
TRUE ~ "Unknown"))
info country_id country_name
1 a 1 Brazil
2 b 2 Canada
3 c 3 China
4 d 4 Ecuador
5 e 5 France
2: merge or join the info from a country table:
# country table
countries <- data.frame(country_id = 1:5,
country_name = c("Brazil", "Canada", "China", "Ecuador", "France"))
data.frame(info = letters[1:5],
country_id = 1:5) %>%
left_join(countries, by = "country_id")
info country_id country_name
1 a 1 Brazil
2 b 2 Canada
3 c 3 China
4 d 4 Ecuador
5 e 5 France
My preference would be 2, less coding and less chance of a mistake. You can keep the country table in your database or in a file somewhere and maintain that without needing to change the code.