How to remove rows in data frame after frequency tables in R -

i have 3 data frames have find continent less 2 countries , remove countries(rows). data frames structured in manner similar data frame called x below:

    row        country   continent   ranking     1        kenya       africa      17     2        gabon       africa      23     3        spain       europe      04     4        belgium     europe      03     5        china       asia        10     6        nigeria     africa      14     7        holland     europe      01     8        italy       europe      05     9        japan       asia        06 

first wanted know frequency of each country per continent, did

    x2<-table(x$continent)     x2     africa europe asia     3        4      2 

then wanted identify continents less 2 countries

    x3 <- x2[x2 < 10]     x3     asia     2   

my problem how remove these countries. example above 2 countries in asia , want final data set presented below:

    row       country   continent   ranking     1        kenya       africa      17     2        gabon       africa      23     3        spain       europe      04     4        belgium     europe      03     5        nigeria     africa      14     6        holland     europe      01     7        italy       europe      05 

the number of continents less 2 countries vary among different data frames need 1 universal method can apply all.

try

 library(dplyr)  x %>%     group_by(continent) %>%      filter(n()>2)  #   row country continent ranking  #1   1   kenya    africa      17  #2   2   gabon    africa      23  #3   3   spain    europe      04  #4   4 belgium    europe      03  #5   6 nigeria    africa      14  #6   7 holland    europe      01  #7   8   italy    europe      05 

or using x2

 subset(x, continent %in% names(x2)[x2>2])  #    row country continent ranking  #1   1   kenya    africa      17  #2   2   gabon    africa      23  #3   3   spain    europe      04  #4   4 belgium    europe      03  #6   6 nigeria    africa      14  #7   7 holland    europe      01  #8   8   italy    europe      05