用 R 中的 for 循环重估许多观察结果

Question

I have a data set where I am looking at longitudinal data for countries.

master.set <- data.frame(
  Country = c(rep("Afghanistan", 3), rep("Albania", 3)),
  Country.ID = c(rep("Afghanistan", 3), rep("Albania", 3)),
  Year = c(2015, 2016, 2017, 2015, 2016, 2017),
  Happiness.Score = c(3.575, 3.360, 3.794, 4.959, 4.655, 4.644),
  GDP.PPP = c(1766.593, 1757.023, 1758.466, 10971.044, 11356.717, 11803.282), 
  GINI = NA,
  Status = 2,
  stringsAsFactors = F
)

> head(master.set)
      Country  Country.ID Year Happiness.Score   GDP.PPP GINI Status
1 Afghanistan Afghanistan 2015           3.575  1766.593   NA      2
2 Afghanistan Afghanistan 2016           3.360  1757.023   NA      2
3 Afghanistan Afghanistan 2017           3.794  1758.466   NA      2
4     Albania     Albania 2015           4.959 10971.044   NA      2
5     Albania     Albania 2016           4.655 11356.717   NA      2
6     Albania     Albania 2017           4.644 11803.282   NA      2

I created that Country.ID variable with the intent of turning them into numerical values 1:159. I am hoping to avoid doing something like this to replace the value at each individual observation: master.set$Country.ID <- master.set$Country.ID[master.set$Country.ID == "Afghanistan"] <- 1

As I implied, there are 159 countries listed in the data set. Because it' longitudinal, there are 460 observations.

Is there any way to use a for loop to save me a lot of time? Here is what I attempted. I made a couple of lists and attempted to use an ifelse command to tell R to label each country the next number. Here is what I have:

#List of country names
N.Countries <- length(unique(master.set$Country))
Country <- unique(master.set$Country) 
Country.ID <- unique(master.set$Country.ID)
CountryList <- unique(master.set$Country)

#For Loop to make Country ID numerically match Country
for (i in 1:460){
  for (j in N.Countries){
    master.set[[Country.ID[i]]] <- ifelse(master.set[[Country[i]]] == CountryList[j], j, master.set$Country)
  }
}

I received this error:

Error in `[[<-.data.frame`(`*tmp*`, Country.ID[i], value = logical(0)) : 
  replacement has 0 rows, data has 460

Does anyone know how I can accomplish this task? Or will I be stuck using the ifelse command 159 times?

Thanks!

Answer 1

Maybe something like

master.set$Country.ID <- as.numeric(as.factor(master.set$Country.ID))

Or alternatively, using dplyr

library(tidyverse)
master.set <- master.set %>% mutate(Country.ID = as.numeric(as.factor(Country.ID)))

Or this, which creates a new variable Country.ID2 based on a key-value pair between Country.ID and a 1:length(unique(Country)) .

library(tidyverse)
master.set <- left_join(master.set,
          data.frame( Country = unique(master.set$Country), 
                      Country.ID2 = 1:length(unique(master.set$Country))))
master.set
#>       Country  Country.ID Year Happiness.Score   GDP.PPP GINI Status
#> 1 Afghanistan Afghanistan 2015           3.575  1766.593   NA      2
#> 2 Afghanistan Afghanistan 2016           3.360  1757.023   NA      2
#> 3 Afghanistan Afghanistan 2017           3.794  1758.466   NA      2
#> 4     Albania     Albania 2015           4.959 10971.044   NA      2
#> 5     Albania     Albania 2016           4.655 11356.717   NA      2
#> 6     Albania     Albania 2017           4.644 11803.282   NA      2
#>   Country.ID2
#> 1           1
#> 2           1
#> 3           1
#> 4           2
#> 5           2
#> 6           2

Answer 2

library(dplyr)
df<-data.frame("Country"=c("Afghanistan","Afghanistan","Afghanistan","Albania","Albania","Albania"),
               "Year"=c(2015,2016,2017,2015,2016,2017),
               "Happiness.Score"=c(3.575,3.360,3.794,4.959,4.655,4.644),
               "GDP.PPP"=c(1766.593,1757.023,1758.466,10971.044,11356.717,11803.282),
               "GINI"=NA,
               "Status"=rep(2,6))
df1<-df %>% arrange(Country) %>% mutate(Country_id = group_indices_(., .dots="Country"))
View(df1)