如何随机抽取select区和R中那个区的村？

Question

I have a data set containing information on district code and name, code and name of the blocks in that district, and the code and name of the villages that come in that block.

Based on this I want to create a data set that randomly selects a block from the district and randomly takes 10 villages in that selected block.

I have tried using the sample function and the RandomizeR package but could get it to work.

I sample of the data set (df):

structure(list(district_code = c(1701L, 1701L, 1701L, 1701L, 
1701L, 1701L, 1701L, 1701L, 1701L, 1701L, 1701L, 1701L), district_name = c("morena", 
"morena", "morena", "morena", "morena", "morena", "morena", "morena", 
"morena", "morena", "morena", "morena"), block_code = c(1701001L, 
1701001L, 1701001L, 1701001L, 1701001L, 1701001L, 1701001L, 1701001L, 
1701001L, 1701001L, 1701001L, 1701001L), block_name = c("ambah", 
"ambah", "ambah", "ambah", "ambah", "ambah", "ambah", "ambah", 
"ambah", "ambah", "ambah", "ambah"), village_code = 1701001001:1701001012, 
    village_name = c("badfara", "bichola", "bhandauli", "lallubasai", 
    "kakarari", "rithona", "goonjh", "malbasai", "aroli", "khirenta", 
    "dandoli", "beelpur")), row.names = c(NA, 12L), class = "data.frame")

Second sample (df1)

structure(list(district_code = c(3424L, 3424L, 3424L, 3424L, 
3424L, 3424L, 3424L, 3424L, 3401L, 3401L, 3401L, 3401L, 3401L, 
3402L, 3402L, 3402L, 3402L, 3402L, 3402L, 3402L, 3402L), district_name = c("khunti", 
"khunti", "khunti", "khunti", "khunti", "khunti", "khunti", "khunti", 
"ranchi", "ranchi", "ranchi", "ranchi", "ranchi", "lohardaga", 
"lohardaga", "lohardaga", "lohardaga", "lohardaga", "lohardaga", 
"lohardaga", "lohardaga"), block_code = c(3401020L, 3401020L, 
3401020L, 3401020L, 3401020L, 3401020L, 3401020L, 3401020L, 3401024L, 
3401024L, 3401024L, 3401024L, 3401024L, 3402001L, 3402001L, 3402001L, 
3402001L, 3402001L, 3402001L, 3402001L, 3402001L), block_name = c("torpa", 
"torpa", "torpa", "torpa", "torpa", "torpa", "torpa", "torpa", 
"khelari", "khelari", "khelari", "khelari", "khelari", "lohardaga", 
"lohardaga", "lohardaga", "lohardaga", "lohardaga", "lohardaga", 
"lohardaga", "lohardaga"), panchayat_code = c(3401020009, 3401020010, 
3401020011, 3401020012, 3401020013, 3401020014, 3401020015, 3401020016, 
3401024001, 3401024002, 3401024003, 3401024004, 3401024005, 3402001001, 
3402001002, 3402001003, 3402001004, 3402001005, 3402001006, 3402001007, 
3402001008), panchayat_name = c("marcha", "okra", "sundari", 
"tapkara", "torpa east", "torpa west", "ukrimari", "urikela", 
"churi east", "churi middle", "churi north", "churi south", "churi west", 
"hesal", "hirhi", "manho", "jori", "nigni", "juriya", "harmu", 
"rampur")), row.names = 379:399, class = "data.frame")
> dput(jk_subset[379:409,])
structure(list(district_code = c(3424L, 3424L, 3424L, 3424L, 
3424L, 3424L, 3424L, 3424L, 3401L, 3401L, 3401L, 3401L, 3401L, 
3402L, 3402L, 3402L, 3402L, 3402L, 3402L, 3402L, 3402L, 3402L, 
3402L, 3402L, 3402L, 3402L, 3402L, 3402L, 3402L, 3402L, 3402L
), district_name = c("khunti", "khunti", "khunti", "khunti", 
"khunti", "khunti", "khunti", "khunti", "ranchi", "ranchi", "ranchi", 
"ranchi", "ranchi", "lohardaga", "lohardaga", "lohardaga", "lohardaga", 
"lohardaga", "lohardaga", "lohardaga", "lohardaga", "lohardaga", 
"lohardaga", "lohardaga", "lohardaga", "lohardaga", "lohardaga", 
"lohardaga", "lohardaga", "lohardaga", "lohardaga"), block_code = c(3401020L, 
3401020L, 3401020L, 3401020L, 3401020L, 3401020L, 3401020L, 3401020L, 
3401024L, 3401024L, 3401024L, 3401024L, 3401024L, 3402001L, 3402001L, 
3402001L, 3402001L, 3402001L, 3402001L, 3402001L, 3402001L, 3402001L, 
3402001L, 3402001L, 3402006L, 3402001L, 3402007L, 3402007L, 3402007L, 
3402002L, 3402002L), block_name = c("torpa", "torpa", "torpa", 
"torpa", "torpa", "torpa", "torpa", "torpa", "khelari", "khelari", 
"khelari", "khelari", "khelari", "lohardaga", "lohardaga", "lohardaga", 
"lohardaga", "lohardaga", "lohardaga", "lohardaga", "lohardaga", 
"lohardaga", "lohardaga", "lohardaga", "kairo", "lohardaga", 
"peshrar", "peshrar", "peshrar", "kisko", "kisko"), village_code = c(3401020009, 
3401020010, 3401020011, 3401020012, 3401020013, 3401020014, 3401020015, 
3401020016, 3401024001, 3401024002, 3401024003, 3401024004, 3401024005, 
3402001001, 3402001002, 3402001003, 3402001004, 3402001005, 3402001006, 
3402001007, 3402001008, 3402001009, 3402001010, 3402001011, 3402001012, 
3402001013, 3402002001, 3402002002, 3402002003, 3402002004, 3402002005
), village_name = c("marcha", "okra", "sundari", "tapkara", 
"torpa east", "torpa west", "ukrimari", "urikela", "churi east", 
"churi middle", "churi north", "churi south", "churi west", "hesal", 
"hirhi", "manho", "jori", "nigni", "juriya", "harmu", "rampur", 
"bagha", "arkosa", "tigra", "guri", "bhatdhijri", "siram", "peshrar", 
"rorad", "devdaria", "pakhar")), row.names = 379:409, class = "data.frame")

Example of data set after using the code:

3404L, 3405L, 3406L, 3407L, 3408L, 3409L, 3410L, 3411L), district_name = c("khunti", 
"ranchi", "lohardaga", "gumla", "simdega", "palamu", "latehar", 
"garhwa", "west singhbhum", "saraikela kharsawan", "east singhbum", 
"dumka"), block_code = c(3401009L, 3401013L, 3402005L, 3403009L, 
3404002L, 3405018L, 3406006L, 3407009L, 3408005L, 3409006L, 3410005L, 
3411009L), block_name = c("khunti", "namkum", "bhandra", "basia", 
"bolba", "tarhasi", "garu", "bhandaria", "tantnagar", "ichagarh", 
"musabani", "masaliya"), village_code = c(3401009002, 3401013020, 
3402005002, 3403009008, 3404002002, 3405006012, 3406006008, 3407009002, 
3408005001, 3409006012, 3410005011, 3411009001), village_name = c("bhandra", 
"sithiyo", "bhandra", "mamarla", "kadopani", "manjhauli 2", "ghasitola", 
"bhandaria", "angardiha", "dewaltand", "ichra (north)", "aamgachi"
)), row.names = c(NA, -12L), class = c("tbl_df", "tbl", "data.frame"
))

Answer 1

Your example is not really suited to provide a solution since it only contains one district/block.

But you can do:

df %>%
  group_by(district_code) %>%
  filter(block_code == ifelse(length(unique(block_code)) == 1, block_code, sample(unique(block_code), size = 1))) %>%
  filter(village_code %in% ifelse(length(unique(village_code)) == 1, village_code, sample(unique(village_code), size = min(10, length(unique(village_code))), replace = FALSE))) %>%
  ungroup()

Note: I wasn't entirely sure at which level you want to sample, so here I select one block per district and then 10 villages from that block. So you will end up with 10 villages from a randomly selected block for each district.